World Development Indicators 2012 - ISBN ... - Khazar University

WORLDDEVELOPMENTINDICATORS12

Low incomeAfghanistanBangladeshBeninBurkina FasoBurundiCambodiaCentral African RepublicChadComorosCongo, Dem. Rep.EritreaEthiopiaGambia, TheGuineaGuinea-BissauHaitiKenyaKorea, Dem. Rep.Kyrgyz RepublicLiberiaMadagascarMalawiMaliMozambiqueMyanmarNepalNigerRwandaSierra LeoneSomaliaTajikistanTanzaniaTogoUgandaZimbabweLower middle incomeAngolaArmeniaBelizeBhutanBoliviaCameroonCape VerdeCongo, Rep.Côte d'IvoireDjiboutiEgypt, Arab Rep.El SalvadorFijiGeorgiaGhanaGuatemalaGuyanaHondurasIndiaIndonesiaIraqKiribatiKosovoLao PDRLesothoMarshall IslandsMauritaniaMicronesia, Fed. Sts.MoldovaMongoliaMoroccoNicaraguaNigeriaPakistanPapua New GuineaParaguayPhilippinesSamoaSão Tomé and PríncipeSenegalSolomon IslandsSri LankaSouth SudanSudanSwazilandSyrian Arab RepublicTimor-LesteTongaTurkmenistanTuvaluUkraineUzbekistanVanuatuVietnamWest Bank and GazaYemen, Rep.ZambiaUpper middle incomeAlbaniaAlgeriaAmerican SamoaAntigua and BarbudaArgentinaAzerbaijanBelarusBosnia and HerzegovinaBotswanaBrazilBulgariaChileChinaColombiaCosta RicaCubaDominicaDominican RepublicEcuadorGabonGrenadaIran, Islamic Rep.JamaicaThe world by incomeJordanGreenlandKazakhstanGuamLatvia Hong Kong SAR, ChinaLebanonHungaryLibyaIcelandLithuaniaIrelandMacedonia, FYRIsle of ManMalaysiaIsraelMaldivesItalyMauritiusJapanMayotteKorea, Rep.MexicoKuwaitMontenegroLiechtensteinNamibiaLuxembourgPalauMacao SAR, ChinaPanamaMaltaPeruMonacoRomaniaNetherlandsRussian FederationNew CaledoniaSerbiaNew ZealandSeychelles Northern Mariana IslandsSouth AfricaNorwaySt. Kitts and NevisOmanSt. LuciaPolandSt. Vincent & GrenadinesPortugalSurinamePuerto RicoThailandQatarTunisiaSan MarinoTurkeySaudi ArabiaUruguaySingaporeVenezuela, RBSint MaartenSlovak RepublicHigh incomeSloveniaAndorraSpainArubaSt. MartinAustraliaSwedenAustriaSwitzerlandBahamas, TheTrinidad and TobagoBahrain Turks and Caicos IslandsBarbadosUnited Arab EmiratesBelgiumUnited KingdomBermudaUnited StatesBrunei DarussalamVirgin Islands (U.S.)CanadaCayman IslandsChannel IslandsCroatiaCuraçaoCyprusCzech RepublicDenmarkEquatorial GuineaEstoniaFaeroe IslandsFinlandFranceFrench PolynesiaGermanyGibraltarGreeceINCOME MAP

The world by incomeLow ($1,005 or less)Lower middle ($1,006–$3,975)Upper middle ($3,976–$12,275)Classified according toWorld Bank estimates of2010 GNI per capitaHigh ($12,276 or more)No dataGreenland(Den)FijiSamoaAmericanSamoa (US)TongaKiribatiFrench Polynesia (Fr)DominicanRepublicAruba (Neth)PuertoRico (US)U.S. VirginIslands (US)Curaçao (Neth)R.B. de VenezuelaMexicoSt. Kittsand NevisSt. Vincent andthe GrenadinesUnited StatesCayman Is.(UK)St. Martin (Fr)St. Maarten (Neth)CanadaCosta RicaAntigua and BarbudaGuadeloupe (Fr)DominicaMartinique (Fr)CubaBelize JamaicaGuatemala HondurasEl Salvador NicaraguaPanamaEcuadorSt. LuciaBarbadosGrenadaTrinidadand TobagoThe BahamasHaitiColombiaPeruBermuda(UK)Turks and Caicos Is. (UK)R.B. deVenezuelaChileBoliviaGuyanaSurinameFrench Guiana (Fr)ParaguayArgentinaBrazilUruguayIcelandGermanyPolandCzech RepublicUkraineSlovak RepublicAustriaSloveniaCroatiaHungaryRomaniaBosnia andHerzegovina SerbiaSanMarinoKosovo BulgariaItaly Montenegro FYRMacedoniaVaticanAlbaniaCityGreeceFaeroe NorwayIslands(Den) Sweden FinlandThe NetherlandsEstoniaIsle of Man (UK)Denmark Russian LatviaFed. LithuaniaUnitedIreland Kingdom Germany Poland BelarusChannel Islands (UK)BelgiumUkraineLuxembourgMoldovaLiechtensteinFrance ItalyRomaniaSwitzerlandAndorraBulgaria GeorgiaPortugalSpainArmeniaTurkeyMonacoGreeceCyprus SyrianGibraltar (UK)TunisiaMalta LebanonArabRep.MoroccoIsraelIraqWest Bank and Gaza JordanAlgeriaLibya Arab Rep.Formerof EgyptSpanishSaharaUnited ArabEmiratesMauritaniaOmanCape VerdeMaliNigerChadSenegalSudan Eritrea Rep. of YemenThe GambiaBurkinaGuinea-BissauFasoDjiboutiGuineaBeninSierra Leone Côted’Ivoire Ghana Nigeria CentralEthiopiaAfrican SouthLiberiaRepublic SudanCameroonTogoSomaliaEquatorial GuineaUgandaSão Tomé and PríncipeGabon CongoKenyaRwandaDem.Rep.of BurundiSeychellesCongoTanzaniaComorosAngolaZambiaMalawiMozambiqueZimbabweMadagascarNamibiaBotswanaSouthAfricaSwazilandLesothoAzerbaijanSaudiArabiaMayotte(Fr)Islamic Rep.of IranKuwaitBahrainQatarKazakhstanUzbekistan KyrgyzRep.TurkmenistanTajikistanRéunion (Fr)AfghanistanMauritiusRussian FederationPakistanBhutanNepalBangladeshIndiaMyanmarMaldivesSriLankaMongoliaChinaLaoP.D.R.ThailandVietnamCambodiaBrunei DarussalamMalaysiaSingaporeDem.People’sRep.of KoreaRep.ofKoreaPhilippinesIndonesiaJapanN. Mariana Islands (US)Timor-LesteAustraliaGuam (US)PalauFederated States of MicronesiaPapua New GuineaNewZealandNauruSolomonIslandsVanuatuNewCaledonia(Fr)Marshall IslandsKiribatiTuvaluFijiAntarcticaIBRD 39125 MARCH 2012Designed, edited, and produced byCommunications Development Incorporated,Washington, D.C.,with Peter Grundy Art & Design, London

2012WORLD DEVELOPMENTINDICATORS

Copyright 2012 by the International Bankfor Reconstruction and Development/THE WORLD BANK1818 H Street NW, Washington, D.C. 20433 USAAll rights reservedManufactured in the United States of AmericaFirst printing April 2012This volume is a product of the staff of the Development Data Group of the World Bank’s Development EconomicsVice Presidency, and the judgments herein do not necessarily refl ect the views of the World Bank’s Board of ExecutiveDirectors or the countries they represent.The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibilitywhatsoever for any consequence of their use. The boundaries, colors, denominations, and other informationshown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status ofany territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projectionfor maps, which represents both area and shape reasonably well for most of the earth’s surface. Nevertheless,some distortions of area, shape, distance, and direction remain.The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sentto the Offi ce of the Publisher at the address in the copyright notice above. The World Bank encourages disseminationof its work and will normally give permission promptly and, when reproduction is for noncommercial purposes,without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center,Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA.Photo credits: World Bank photo library, except page 282, David Cieslikowski/World Bank.If you have questions or comments about this product, please contact:Development Data GroupThe World Bank1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USAHotline: 800 590 1906 or 202 473 7824; fax 202 522 1498Email: data@worldbank.orgWeb site: www.worldbank.org or data.worldbank.orgISBN 978-0-8213-8985-0ECO-AUDITEnvironmental Benefi ts StatementThe World Bank is committed to preserving endangered forests and natural resources. The Offi ce of the Publisherhas chosen to print World Development Indicators 2012 on recycled paper with 50 percent postconsumer fi ber inaccordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofi t programsupporting publishers in using fi ber that is not sourced from endangered forests. For more information, visit www.greenpressinitiative.org.Saved:64 trees26 million Btu of total energy6,503 pounds of net greenhouse gases29,321 gallons of waste water1,859 pounds of solid waste

2012WORLD DEVELOPMENTINDICATORS

PREFACEWorld Development Indicators 2012 is a compilation of relevant, high-quality, and internationally comparable statisticsabout development and the quality of people’s lives. Organized around six themes—world view, people, the environment,the economy, states and markets, and global links—it aims to put data into the hands of policy makers, developmentspecialists, students, and the public. We encourage and applaud the use of the data presented here to help reducepoverty and to solve the world’s most pressing development challenges.The full dataset used to produce World Development Indicators contains more than 1,000 indicators for 216 economies,with many time series extending back to 1960. Highly visual, interactive, and multilingual presentations of thedata are available at the popular website http://data.worldbank.org and through the DataFinder application for mobiledevices. And, as a major part of the World Bank’s Open Data Initiative, the data are freely available for use and reuseunder an open license. A companion printed volume, The Little Data Book 2012, presents a selection of indicatorsfor each economy, and the biennial Statistics for Small States presents data for less-populated developing countries.This 16th edition of World Development Indicators relies heavily on statistics produced by national authorities andagencies. Since the fi rst edition in 1997, there has been a substantial increase in the availability and quality of thedata, thanks to improvements in statistical capacity in many countries. More remains to be done: the capacity to usestatistical data remains weak; demand is growing for greater disaggregation of indicators (for instance by sex, age, orgeography); and data in some key areas, such as agriculture, are often missing or outdated. A new global statisticalaction plan (www.paris21.org/busan-action-plan), endorsed in November 2011 at the highest political levels at theFourth High Level Forum on Aid Effectiveness in Busan, Republic of Korea, provides an important framework to addressremaining challenges, to integrate statistics into decision making, to promote open access to data and improve theiruse, and to increase resources for statistical systems.World Development Indicators is possible only through the excellent collaboration of many partners who provide thedata for this collection, and I would like to thank them all: the United Nations family, the International Monetary Fund,the International Telecommunication Union, the Organisation for Economic Co-operation and Development, the statisticaloffi ces of more than 200 economies, and countless others whose support and advice have made this uniqueproduct possible.As always, we welcome your ideas for making the data in World Development Indicators useful and relevant for improvingthe lives of people around the world.Shaida BadieeDirectorDevelopment Economics Data Group2012 World Development Indicators v

ACKNOWLEDGMENTSThis book was prepared by a team led by Soong Sup Lee under the management of Neil Fantom and comprisingAwatif Abuzeid, Azita Amjadi, Maja Bresslauer, David Cieslikowski, Liu Cui, Mahyar Eshragh-Tabary, Shota Hatakeyama,Masako Hiraga, Wendy Ven-dee Huang, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong,Ibrahim Levent, Hiroko Maeda, Johan Mistiaen, Maurice Nsabimana, Sulekha Patel, Beatriz Prieto-Oramas,William Prince, Premi Rathan Raj, Evis Rucaj, Emi Suzuki, Eric Swanson, Jomo Tariku, and Estela Zamora, workingclosely with other teams in the Development Economics Vice Presidency’s Development Data Group. World DevelopmentIndicators electronic products were prepared by a team led by Reza Farivari and comprising Ramvel Chandrasekaran,Ying Chi, Jean-Pierre Djomalieu, Ramgopal Erabelly, Federico Escaler, Shelley Fu, Gytis Kanchas, UgendranMakhachkala, Vilas Mandlekar, Nacer Megherbi, Shanmugam Natarajan, Parastoo Oloumi, Atsushi Shimo, MarynaTaran, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the direction of Shaida Badiee. Valuableadvice was provided by Zia M. Qureshi and David Rosenblatt.The choice of indicators and text content was shaped through close consultation with and substantial contributions fromstaff in the World Bank’s four thematic networks—Financial and Private Sector Development, Human Development,Poverty Reduction and Economic Management, and Sustainable Development—and staff of the International FinanceCorporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help,guidance, and data from external partners. For individual acknowledgments of contributions to the book’s content,please see Credits. For a listing of our key partners, see Partners.Communications Development Incorporated provided overall design direction, editing, and layout, led byMeta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott and assisted by Rob Elson. Elaine Wilson createdthe cover and graphics and typeset the book. Joseph Caponio provided production assistance. Peter Grundy, ofPeter Grundy Art & Design, designed the report. Staff from External Affairs oversaw printing and dissemination of thebook.2012 World Development Indicators vii

TABLE OF CONTENTSFRONTPrefaceAcknowledgmentsPartnersUsers guidevviixiixxii1. WORLD VIEWIntroduction 1Tables1.1 Size of the economy 201.2 Millennium Development Goals: eradicating poverty and savinglives 241.3 Millennium Development Goals: protecting our commonenvironment 281.4 Millennium Development Goals: overcoming obstacles 321.5 Women in development 341.6 Key indicators for other economies 38Text figures, tables, and boxes1a Poverty rates fell sharply in the new millennium 21b Fewer people living in extreme poverty 21c Progress toward poverty reduction 31d Progress toward reducing undernourishment 31e More and less income equality 31f Many children remain malnourished 31g The last step toward education for all 41h 64 million children out of school 41i Progress toward education for all 51j The missing enrollments 51k How much schooling 51l Increasing participation by girls at all levels of education 61m Progress toward gender equality in education 71n Women have become a larger part of the workforce 71o More women decisionmakers 71p A slim lead for girls 71q Still far to go in reducing under-fi ve mortality 81r Most deaths occur in the fi rst year of life 81s Progress toward reducing child mortality 91t Preventing childhood diseases 91u For some, better than expected improvements 91v Maternal mortality rates have been falling but large regionaldifferences persist 101w The 12 countries with highest lifetime risk of maternal death 101x Progress in reducing maternal mortality 111y Planning for motherhood 111z Fewer young women giving birth 111aa Help for mothers 111bb Bringing HIV/AIDS under control 121cc Millions of people still affl icted with HIV/AIDS 121dd Progress toward reversing the HIV epidemic 131ee Turning the tide of tuberculosis 131ff Protecting children from malaria 131gg Carbon dioxide emissions continue to rise 141hh Forest losses and gains 141ii Progress toward improved sanitation 151jj Progress toward improved water sources 151kk Many still lack access to sanitation 151ll Water demand strains supplies 151mm Most donors have maintained their aid levels 161nn But their domestic subsidies to agricultural are greater 161oo Developing countries have easier access to Organisationfor Economic Co-operation and Development markets 171pp Cellular phones are connecting developing countries 171qq Debt service burdens have been falling 171rr A more connected world 171.2a Location of indicators for Millennium Development Goals 1–4 271.3a Location of indicators for Millennium Development Goals 5–7 311.4a Location of indicators for Millennium Development Goal 8 33viii2012 World Development Indicators

2. PEOPLE 3. ENVIRONMENTIntroduction 41Introduction 137Tables2.1 Population dynamics 422.2 Labor force structure 462.3 Employment by economic activity 502.4 Decent work and productive employment 542.5 Unemployment 582.6 Children at work 622.7 Poverty rates at national poverty lines 662.8 Poverty rates at international poverty lines 722.9 Distribution of income or consumption 742.10 Assessing vulnerability and security 782.11 Education inputs 822.12 Participation in education 862.13 Education effi ciency 902.14 Education completion and outcomes 942.15 Education gaps by income and gender 982.16 Health systems 1002.17 Health information 1042.18 Disease prevention coverage and quality 1082.19 Reproductive health 1122.20 Nutrition and growth 1162.21 Nutrition intake and supplements 1202.22 Health risk factors and future challenges 1242.23 Mortality 1282.24 Health gaps by income 132Tables3.1 Rural population and land use 1383.2 Agricultural inputs 1423.3 Agricultural output and productivity 1463.4 Deforestation and biodiversity 1503.5 Freshwater 1543.6 Water pollution 1583.7 Energy production and use 1623.8 Electricity production, sources, and access 1663.9 Energy dependency and effi ciency and carbon dioxideemissions 1703.10 Trends in greenhouse gas emissions 1743.11 Carbon dioxide emissions by sector 1783.12 Climate variability, exposure to impact, and resilience 1823.13 Urbanization 1863.14 Urban housing conditions 1903.15 Traffi c and congestion 1943.16 Air pollution 1983.17 Government commitment 2003.18 Contribution of natural resources to gross domestic product 204Text figures, tables, and boxes3.1a What is rural? Urban? 141Text figures, tables, and boxes2.8a While the number of people living on less than $1.25 a dayhas fallen, the number living on $1.25–$2 a day has increased 712.8b Poverty rates are falling in all developing regions 712.8c Regional poverty estimates 722012 World Development Indicators ix

TABLE OF CONTENTS4. ECONOMYIntroduction 209Tables4.a Recent economic performance 2104.1 Growth of output 2144.2 Structure of output 2184.3 Structure of manufacturing 2224.4 Structure of merchandise exports 2264.5 Structure of merchandise imports 2304.6 Structure of service exports 2344.7 Structure of service imports 2384.8 Structure of demand 2424.9 Growth of consumption and investment 2464.10 Toward a broader measure of national income 2504.11 Toward a broader measure of savings 2544.12 Central government fi nances 2584.13 Central government expenses 2624.14 Central government revenues 2664.15 Monetary indicators 2704.16 Exchange rates and prices 2744.17 Balance of payments current account 2785. STATES AND MARKETSIntroduction 283Tables5.1 Private sector in the economy 2845.2 Business environment: enterprise surveys 2885.3 Business environment: Doing Business indicators 2925.4 Stock markets 2965.5 Financial access, stability, and effi ciency 3005.6 Tax policies 3045.7 Military expenditures and arms transfers 3085.8 Fragile situations 3125.9 Public policies and institutions 3165.10 Transport services 3205.11 Power and communications 3245.12 The information society 3285.13 Science and technology 332x2012 World Development Indicators

6. GLOBAL LINKS BACKIntroduction 337Tables6.1 Growth of merchandise trade 3386.2 Direction and growth of merchandise trade 3426.3 High-income economy trade withlow- and middle-income economies 3446.4 Direction of trade of developing economies 3466.5 Primary commodity prices 3496.6 Regional trade blocs 3526.7 Tariff barriers 3546.8 Trade facilitation 3586.9 External debt 3626.10 Global private fi nancial fl ows 3666.11 Net offi cial fi nancial fl ows 3706.12 Aid dependency 3746.13 Distribution of net aid by Development Assistance Committeemembers 3786.14 Movement of people across borders 3826.15 Travel and tourism 386Text figures, tables, and boxes6.6a Global Preferential Trade Agreement Database 3536.13a Offi cial development assistance from non-DAC donors,2006–10 381Primary data documentation 391Statistical methods 402Credits 404Bibliography 406Index of indicators 4142012 World Development Indicators xi

PARTNERSDefi ning, gathering, and disseminating international statistics is a collective effort of many people andorganizations. The indicators presented in World Development Indicators are the fruit of decades of workat many levels, from the fi eld workers who administer censuses and household surveys to the committeesand working parties of the national and international statistical agencies that develop the nomenclature,classifi cations, and standards fundamental to an international statistical system. Nongovernmental organizationsand the private sector have also made important contributions, both in gathering primary data and inorganizing and publishing their results. And academic researchers have played a crucial role in developingstatistical methods and carrying on a continuing dialogue about the quality and interpretation of statisticalindicators. All these contributors have a strong belief that available, accurate data will improve the qualityof public and private decisionmaking.The organizations listed here have made World Development Indicators possible by sharing their dataand their expertise with us. More important, their collaboration contributes to the World Bank’s efforts,and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debtand gratitude to all who have helped to build a base of comprehensive, quantitative information about theworld and its people.For easy reference, Web addresses are included for each listed organization. The addresses shown wereactive on March 1, 2012. Information about the World Bank is also provided.International and government agenciesCarbon Dioxide Information Analysis CenterThe Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and informationanalysis center of the U.S. Department of Energy. The CDIAC’s scope includes anything that wouldpotentially be of value to those concerned with the greenhouse effect and global climate change, includingconcentrations of carbon dioxide and other radiatively active gases in the atmosphere, the role of the terrestrialbiosphere and the oceans in the biogeochemical cycles of greenhouse gases, emissions of carbondioxide to the atmosphere, long-term climate trends, the effects of elevated carbon dioxide on vegetation,and the vulnerability of coastal areas to rising sea levels.For more information, see http://cdiac.ornl.gov.Centre for Research on the Epidemiology of DisastersSince 1988 the World Health Organization Collaborating Centre for Research on the Epidemiology of Disastershas maintained the Emergency Events Database, which was created with support from the Belgiangovernment. The main objective of the database is to serve the purposes of humanitarian action at thenational and international levels. It aims to rationalize decisionmaking for disaster preparedness and providean objective base for vulnerability assessment and priority setting. The database contains essential coredata—compiled from various sources, including UN agencies, nongovernmental organizations, insurancecompanies, research institutes, and press agencies—on the occurrence and effects of more than 18,000mass disasters since 1900.For more information, see www.emdat.be.xii2012 World Development Indicators

Deutsche Gesellschaft für Internationale ZusammenarbeitThe Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH is a German government-ownedcorporation for international cooperation with worldwide operations. GIZ’s aim is to positively shape political,economic, ecological, and social development in partner countries, thereby improving people’s livingconditions and prospects.For more information, see www.giz.de.Food and Agriculture OrganizationThe Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity,and to better the condition of rural populations. The organization provides direct development assistance;collects, analyzes, and disseminates information; offers policy and planning advice to governments; andserves as an international forum for debate on food and agricultural issues.For more information, see www.fao.org.Internal Displacement Monitoring CentreThe Internal Displacement Monitoring Centre was established in 1998 by the Norwegian Refugee Council andis the leading international body monitoring conflict-induced internal displacement worldwide. The center contributesto improving national and international capacities to protect and assist the millions of people aroundthe globe who have been displaced within their own country as a result of confl icts or human rights violations.For more information, see www.internal-displacement.org.International Civil Aviation OrganizationThe International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is responsiblefor establishing international standards and recommended practices and procedures for the technical,economic, and legal aspects of international civil aviation operations. ICAO’s strategic objectives includeenhancing global aviation safety and security and the effi ciency of aviation operations, minimizing theadverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations,and strengthening laws governing international civil aviation.For more information, see www.icao.int.International Energy AgencyFounded in 1974, the International Energy Agency’s (IEA) mandate is to facilitate cooperation among membercountries in order to increase energy effi ciency, promote use of clean energy and technology, and diversifyenergy sources while protecting the environment. The IEA publishes annual and quarterly statistical publicationscovering both Organisation for Economic Co-operation and Development (OECD) and non-OECDcountries’ data on oil, gas, coal, electricity, and renewable sources of energy; energy supply and consumption;and energy prices and taxes. The IEA also analyzes all aspects of sustainable development globallyand provides policy recommendations.For more information, see www.iea.org.2012 World Development Indicators xiii

PARTNERSInternational Labour OrganizationThe International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotionof social justice and internationally recognized human and labor rights. ILO helps advance the creation ofdecent jobs and the kinds of economic and working conditions that give working people and business peoplea stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensivestatistical publication program.For more information, see www.ilo.org.International Monetary FundThe International Monetary Fund (IMF) is an international organization of 187 member countries establishedto promote international monetary cooperation, a stable system of exchange rates, and the balanced expansionof international trade and to foster economic growth and high levels of employment. The IMF reviewsnational, regional, and global economic and fi nancial developments; provides policy advice to membercountries; and serves as a forum where they can discuss the national, regional, and global consequencesof their policies.The IMF also makes fi nancing temporarily available to member countries to help them address balanceof payments problems. Among the IMF’s core missions are the collection and dissemination of high-qualitymacroeconomic and fi nancial statistics as an essential prerequisite for formulating appropriate policies. TheIMF provides technical assistance and training to member countries in areas of its core expertise, includingthe development of economic and fi nancial data in accordance with international standards.For more information, see www.imf.org.International Telecommunication UnionThe International Telecommunication Union (ITU) is the leading UN agency for information and communicationtechnologies. ITU’s mission is to enable the growth and sustained development of telecommunicationsand information networks and to facilitate universal access so that people everywhere can participate in,and benefi t from, the emerging information society and global economy. A key priority lies in bridging theso-called Digital Divide by building information and communication infrastructure, promoting adequatecapacity building, and developing confi dence in the use of cyberspace through enhanced online security.ITU also concentrates on strengthening emergency communications for disaster prevention and mitigation.For more information, see www.itu.int.National Science FoundationThe National Science Foundation (NSF) is an independent U.S. government agency whose mission is topromote the progress of science; to advance the national health, prosperity, and welfare; and to secure thenational defense. NSF’s goals—discovery, learning, research infrastructure, and stewardship—provide anintegrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive scienceand engineering workforce, expand the scientifi c literacy of all citizens, build the nation’s research capabilitythrough investments in advanced instrumentation and facilities, and support excellence in science andengineering research and education through a capable and responsive organization.For more information, see www.nsf.gov.xiv2012 World Development Indicators

The Office of U.S. Foreign Disaster AssistanceOn November 3, 1961, U.S. President John F. Kennedy established the U.S. Agency for International Development(USAID), the first U.S. foreign assistance organization whose primary emphasis was long-range economicand social development assistance to foreign countries. The Offi ce of U.S. Foreign Disaster Assistance is theoffi ce within USAID responsible for providing nonfood humanitarian assistance in response to internationalcrises and disasters. The USAID administrator is designated as the president’s special coordinator for internationaldisaster assistance, which the Offi ce of U.S. Foreign Disaster Assistance assists in coordinating.For more information see www.globalcorps.com/ofda.html and www.usaid.gov/our_work/humanitarian_assistance/disaster_assistance.Organisation for Economic Co-operation and DevelopmentThe Organisation for Economic Co-operation and Development (OECD) includes 34 member countries sharinga commitment to democratic government and the market economy to support sustainable economicgrowth, boost employment, raise living standards, maintain fi nancial stability, assist other countries’ economicdevelopment, and contribute to growth in world trade. With active relationships with some 100 othercountries, it has a global reach. It is best known for its publications and statistics, which cover economicand social issues from macroeconomics to trade, education, development, and science and innovation.The Development Assistance Committee (DAC, www.oecd.org/dac) is one of the principal bodies throughwhich the OECD deals with issues related to cooperation with developing countries. The DAC is a key forumof major bilateral donors, who work together to increase the effectiveness of their common efforts to supportsustainable development. The DAC concentrates on two key areas: the contribution of internationaldevelopment to the capacity of developing countries to participate in the global economy and the capacityof people to overcome poverty and participate fully in their societies.For more information, see www.oecd.org.Stockholm International Peace Research InstituteThe Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict andcooperation of importance for international peace and security, with the aim of contributing to an understandingof the conditions for peaceful solutions to international confl icts and for a stable peace. SIPRI’s mainpublication, SIPRI Yearbook, is an authoritative and independent source on armaments and arms controland other confl ict and security issues.For more information, see www.sipri.org.Understanding Children’s WorkAs part of broader efforts to develop effective and long-term solutions to child labor, the International LabourOrganization, the United Nations Children’s Fund (UNICEF), and the World Bank initiated the joint interagencyresearch program “Understanding Children’s Work and Its Impact” in December 2000. The UnderstandingChildren’s Work (UCW) project was located at UNICEF’s Innocenti Research Centre in Florence, Italy, untilJune 2004, when it moved to the Centre for International Studies on Economic Growth in Rome.The UCW project addresses the crucial need for more and better data on child labor. UCW’s online databasecontains data by country on child labor and the status of children.For more information, see www.ucw-project.org.2012 World Development Indicators xv

PARTNERSUnited NationsThe United Nations currently has 193 member states. The purposes of the United Nations, as set forth inits charter, are to maintain international peace and security; to develop friendly relations among nations;to cooperate in solving international economic, social, cultural, and humanitarian problems and in promotingrespect for human rights and fundamental freedoms; and to be a center for harmonizing the actions ofnations in attaining these ends.For more information, see www.un.org.United Nations Centre for Human Settlements, Global Urban ObservatoryThe Urban Indicators Programme of the United Nations Human Settlements Programme was established toaddress the urgent global need to improve the urban knowledge base by helping countries and cities design,collect, and apply policy-oriented indicators related to development at the city level.With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing aworldwide information, assessment, and capacity-building network to help governments, local authorities,the private sector, and nongovernmental and other civil society organizations.For more information, see www.unhabitat.org.United Nations Children’s FundThe United Nations Children’s Fund (UNICEF) works with other UN bodies and with governments and nongovernmentalorganizations to improve children’s lives in more than 190 countries through various programs ineducation and health. UNICEF focuses primarily on fi ve areas: child survival and development, basic educationand gender equality (including girls’ education), child protection, HIV/AIDS, and policy advocacy and partnerships.For more information, see www.unicef.org.United Nations Conference on Trade and DevelopmentThe United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the UnitedNations General Assembly in the fi eld of trade and development. Its mandate is to accelerate economicgrowth and development, particularly in developing countries. UNCTAD discharges its mandate through policyanalysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation,and follow-up; and technical cooperation.For more information, see www.unctad.org.United Nations Department of Peacekeeping OperationsThe United Nations Department of Peacekeeping Operations contributes to the most important function of theUnited Nations—maintaining international peace and security. The department helps countries torn by conflict tocreate the conditions for lasting peace. The first peacekeeping mission was established in 1948 and has evolvedto meet the demands of different conflicts and a changing political landscape. Today’s peacekeepers undertakea wide variety of complex tasks, from helping build sustainable institutions of governance, to monitoring humanrights, to assisting in security sector reform, to disarming, demobilizing, and reintegrating former combatants.For more information, see www.un.org/en/peacekeeping.xvi2012 World Development Indicators

United Nations Educational, Scientific, and Cultural Organization, Institute for StatisticsThe United Nations Educational, Scientifi c, and Cultural Organization (UNESCO) is a specialized agency of theUnited Nations that promotes international cooperation among member states and associate members ineducation, science, culture, and communications. The UNESCO Institute for Statistics is the organization’sstatistical branch, established in July 1999 to meet the growing needs of UNESCO member states and theinternational community for a wider range of policy-relevant, timely, and reliable statistics on these topics.For more information, see www.uis.unesco.org.United Nations Environment ProgrammeThe mandate of the United Nations Environment Programme is to provide leadership and encourage partnershipin caring for the environment by inspiring, informing, and enabling nations and people to improve theirquality of life without compromising that of future generations.For more information, see www.unep.org.United Nations Industrial Development OrganizationThe United Nations Industrial Development Organization was established to act as the central coordinatingbody for industrial activities and to promote industrial development and cooperation at the global, regional,national, and sectoral levels. Its mandate is to help develop scientifi c and technological plans and programsfor industrialization in the public, cooperative, and private sectors.For more information, see www.unido.org.United Nations International Strategy for Disaster ReductionCreated in December 1999 as the successor to the International Decade for Natural Disaster Reduction, the mandateof the secretariat of the United Nations International Strategy for Disaster Reduction is to serve as the focalpoint in the UN system for coordination of disaster reduction and to ensure synergies among disaster relief activities.For more information, see www.unisdr.org.United Nations Office on Drugs and CrimeThe United Nations Office on Drugs and Crime was established in 1977 and is a global leader in the fight againstillicit drugs and international crime. The office assists member states in their struggle against illicit drugs, crime,and terrorism by helping build capacity, conducting research and analytical work, and assisting in the ratification andimplementation of relevant international treaties and domestic legislation related to drugs, crime, and terrorism.For more information, see www.unodc.org.United Nations Office of the High Commissioner for RefugeesThe United Nations Offi ce of the High Commissioner for Refugees (UNHCR) is mandated to lead and coordinateinternational action to protect refugees and resolve refugee problems worldwide. Its primary purpose is tosafeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees.For more information, see www.unhcr.org.2012 World Development Indicators xvii

PARTNERSUpsalla Conflict Data ProgramThe Upsalla Confl ict Data Program has collected information on armed violence since 1946 and is one ofthe most accurate and well used data sources on global armed confl icts. Its defi nition of armed confl ict isbecoming a standard in how confl icts are systematically defi ned and studied. In addition to data collectionon armed violence, its researchers conduct theoretically and empirically based analyses of the causes,escalation, spread, prevention, and resolution of armed confl ict.For more information, see www.pcr.uu.se/research/UCDP.World BankThe World Bank is a vital source of financial and technical assistance for developing countries. The World Bankis made up of two unique development institutions owned by 187 member countries—the International Bank forReconstruction and Development (IBRD) and the International Development Association (IDA). These institutionsplay different but collaborative roles to advance the vision of an inclusive and sustainable globalization. TheIBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries.Together they provide low-interest loans, interest-free credits, and grants to developing countries for a widearray of purposes, including investments in education, health, public administration, infrastructure, financial andprivate sector development, agriculture, and environmental and natural resource management. The World Bank’swork focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty.For more information, see http://data.worldbank.org.World Health OrganizationThe objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainmentby all people of the highest possible level of health. It is responsible for providing leadership on globalhealth matters, shaping the health research agenda, setting norms and standards, articulating evidencebasedpolicy options, providing technical support to countries, and monitoring and assessing health trends.For more information, see www.who.int.World Intellectual Property OrganizationThe World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicatedto developing a balanced and accessible international intellectual property (IP) system, which rewards creativity,stimulates innovation, and contributes to economic development while safeguarding the public interest.WIPO carries out a wide variety of tasks related to the protection of IP rights. These include developinginternational IP laws and standards, delivering global IP protection services, encouraging the use of IP foreconomic development, promoting better understanding of IP, and providing a forum for debate.For more information, see www.wipo.int.World Tourism OrganizationThe World Tourism Organization is an intergovernmental body entrusted by the United Nations with promotingand developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how.For more information, see www.unwto.org.xviii2012 World Development Indicators

World Trade OrganizationThe World Trade Organization (WTO) is the only international organization dealing with the global rules of tradebetween nations. Its main function is to ensure that trade fl ows as smoothly, predictably, and freely as possible.It does this by administering trade agreements, acting as a forum for trade negotiations, settling tradedisputes, reviewing national trade policies, assisting developing countries in trade policy issues—throughtechnical assistance and training programs—and cooperating with other international organizations. At theheart of the system—known as the multilateral trading system—are the WTO’s agreements, negotiated andsigned by a large majority of the world’s trading nations and ratifi ed by their parliaments.For more information, see www.wto.org.Private and nongovernmental organizationsCenter for International Earth Science Information NetworkThe Center for International Earth Science Information Network, a center within the Earth Institute at ColumbiaUniversity, works at the intersection of the social, natural, and information sciences and specializes inonline data and information management, spatial data integration and training, and interdisciplinary researchrelated to human interactions in the environment.For more information, see www.ciesin.org.Containerisation InternationalContainerisation International Yearbook is one of the most authoritative reference books on the containerindustry. The information can be accessed on the Containerisation International Web site, which also providesa comprehensive online daily business news and information service for the container industry.For more information, see www.ci-online.co.uk.DHLDHL provides shipping and customized transportation solutions for customers in more than 220 countriesand territories. It offers expertise in express, air, and ocean freight; overland transport; contract logisticssolutions; and international mail services.For more information, see www.dhl.com.International Institute for Strategic StudiesThe International Institute for Strategic Studies (IISS) provides information and analysis on strategic trendsand facilitates contacts between government leaders, business people, and analysts that could lead to betterpublic policy in international security and international relations. The IISS is a primary source of accurate,objective information on international strategic issues.For more information, see www.iiss.org.2012 World Development Indicators xix

PARTNERSInternational Road FederationThe International Road Federation (IRF) is a nongovernmental, not-for-profi t organization whose mission isto encourage and promote development and maintenance of better, safer, and more sustainable roads androad networks. Working together with its members and associates, the IRF promotes social and economicbenefi ts that fl ow from well planned and environmentally sound road transport networks. It helps put inplace technological solutions and management practices that provide maximum economic and social returnsfrom national road investments. The IRF works in all aspects of road policy and development worldwide withgovernments and fi nancial institutions, members, and the community of road professionals.For more information, see www.irfnet.org.NetcraftNetcraft provides Internet security services such as antifraud and antiphishing services, application testing,code reviews, and automated penetration testing. Netcraft also provides research data and analysis onmany aspects of the Internet and is a respected authority on the market share of web servers, operatingsystems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scriptinglanguages, and content technologies on the Internet.For more information, see http://news.netcraft.com.PwCPwC provides industry-focused services in the fi elds of assurance, tax, human resources, transactions,performance improvement, and crisis management services to help address client and stakeholder issues.For more information, see www.pwc.com.Standard & Poor’sStandard & Poor’s is the world’s foremost provider of independent credit ratings, indexes, risk evaluation,investment research, and data. S&P’s Global Stock Markets Factbook draws on data from S&P’s EmergingMarkets Database (EMDB) and other sources covering data on more than 100 markets with comprehensivemarket profi les for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor’scalculates indexes to serve as benchmarks that are consistent across national boundaries.For more information, see www.standardandpoors.com.World Conservation Monitoring CentreThe World Conservation Monitoring Centre provides information on the conservation and sustainable use ofthe world’s living resources and helps others to develop information systems of their own. It works in closecollaboration with a wide range of people and organizations to increase access to the information neededfor wise management of the world’s living resources.For more information, see www.unep-wcmc.org.xx2012 World Development Indicators

World Economic ForumThe World Economic Forum (WEF) is an independent international organization committed to improving thestate of the world by engaging leaders in partnerships to shape global, regional, and industry agendas.Economic research at the WEF—led by the Global Competitiveness Programme—focuses on identifying theimpediments to growth so that strategies to achieve sustainable economic progress, reduce poverty, andincrease prosperity can be developed. The WEF’s competitiveness reports range from global coverage, suchas Global Competitiveness Report, to regional and topical coverage, such as Africa Competitiveness Report,The Lisbon Review, and Global Information Technology Report.For more information, see: www.weforum.org.World Resources InstituteThe World Resources Institute is an independent center for policy research and technical assistance onglobal environmental and development issues. The institute provides—and helps other institutions provide—objective information and practical proposals for policy and institutional change that will foster environmentallysound, socially equitable development. The institute’s current areas of work include trade, forests,energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resourceand environmental information, and national strategies for environmental and resource management.For more information, see www.wri.org.2012 World Development Indicators xxi

USERS GUIDETablesThe tables are numbered by section and displaythe identifying icon of the section. Countries andeconomies are listed alphabetically (except for HongKong SAR, China, which appears after China). Dataare shown for 158 economies with a population ofmore than 1 million, as well as for Taiwan, China, inselected tables. Table 1.6 presents selected indicatorsfor 58 other economies—small economieswith a population between 30,000 and 1 million andsmaller economies if they are members of the InternationalBank for Reconstruction and Developmentor, as it is commonly known, the World Bank. Data forthese economies are included on the World DevelopmentIndicators CD-ROM and the World Bank’s OpenData website (http://data.worldbank.org). The termcountry, used interchangeably with economy, doesnot imply political independence but refers to anyterritory for which authorities report separate socialor economic statistics. When available, aggregatemeasures for income and regional groups appear atthe end of each table.Indicators are shown for the most recent yearor period for which data are available and, in mosttables, for an earlier year or period (usually 1990 or2000 in this edition). Time series data for all 216economies are available on the World DevelopmentIndicators CD-ROM and the World Bank’s Open Datawebsite (http://data.worldbank.org).Known deviations from standard defi nitions orbreaks in comparability over time or across countriesare either footnoted in the tables or noted in Aboutthe data. When available data are deemed to betoo weak to provide reliable measures of levels andtrends or do not adequately adhere to internationalstandards, the data are not shown.Aggregate measures for income groupsThe aggregate measures for income groups include216 economies (the economies listed in the maintables plus those in table 1.6) whenever data areavailable. To maintain consistency in the aggregatemeasures over time and between tables, missingdata are imputed where possible. The aggregatesare totals (designated by a t if the aggregates includegap-fi lled estimates for missing data and by an s,for simple totals, where they do not), median values(m), weighted averages (w), or simple averages (u).Gap fi lling of amounts not allocated to countries mayresult in discrepancies between subgroup aggregatesand overall totals. For further discussion of aggregationmethods, see Statistical methods.Aggregate measures for regionsThe aggregate measures for regions cover only lowandmiddle-income economies, including economieswith populations of less than 1 million listedin table 1.6.The country composition of regions is based onthe World Bank’s analytical regions and may differfrom common geographic usage. For regional classifications, see the map on the inside back cover andthe list on the back cover fl ap. For further discussionof aggregation methods, see Statistical methods.StatisticsData are shown for economies as they were constitutedin 2010, and historical data are revised torefl ect current political arrangements. Exceptions arenoted throughout the tables.Additional information about the data is providedin Primary data documentation, which summarizesnational and international efforts to improve basicdata collection and gives country-level informationon primary sources, census years, fi scal years,statistical methods and concepts used, and otherbackground information. Statistical methods providestechnical information on some of the general calculationsand formulas used throughout the book.Data consistency, reliability, and comparabilityConsiderable effort has been made to standardizethe data, but full comparability cannot be assured,and care must be taken in interpreting the indicators.Many factors affect data availability, comparability,and reliability: statistical systems in many developingeconomies are still weak; statistical methods,coverage, practices, and defi nitions differ widely; andcross-country and intertemporal comparisons involvecomplex technical and conceptual problems that cannotbe resolved unequivocally. Data coverage maynot be complete because of special circumstancesaffecting the collection and reporting of data, suchas problems stemming from confl icts.For these reasons, although data are drawn fromthe sources thought to be most authoritative, theyshould be construed only as indicating trends andcharacterizing major differences among economiesrather than as offering precise quantitative measuresof those differences. Discrepancies in datapresented in different editions of World DevelopmentIndicators refl ect updates by countries as well as revisionsto historical series and changes in methodology.Thus readers are advised not to compare dataseries across editions of World Development Indicatorsor across World Bank publications. Consistenttime series data for 1960–2010 are available onthe World Development Indicators CD-ROM and theWorld Bank’s Open Data website (http://data.worldbank.org).Except where otherwise noted, growth rates arein real terms. (See Statistical methods for informationon the methods used to calculate growth rates.) Datafor some economic indicators for some economiesare presented in fi scal years rather than calendaryears; see Primary data documentation. The methodsused for converting national currencies are describedin Statistical methods.Country notes• Unless otherwise noted, data for China do notinclude data for Hong Kong SAR, China; MacaoSAR, China; or Taiwan, China.• Data for Indonesia include Timor-Leste through1999 unless otherwise noted.• Montenegro declared independence from Serbiaand Montenegro on June 3, 2006. Whenavailable, data for each country are shownseparately. However, some indicators for Serbiacontinue to include data for Montenegro through2005; these data are footnoted in the tables.Moreover, data for most indicators from 1999xxii2012 World Development Indicators

onward for Serbia exclude data for Kosovo, whichin 1999 became a territory under internationaladministration pursuant to UN Security CouncilResolution 1244 (1999); any exceptions arenoted. Kosovo became a World Bank memberon June 29, 2009, and its data are shown in thetables when available.• Netherlands Antilles, for which data were listed inprevious editions, ceased to exist on October 10,2010. Data for Curaçao and Sint Maarten, whichbecame countries within the Kingdom of theNetherlands, are now listed separately. Data forBonaire, Saba, and St. Eustatius, which becamespecial municipalities of the Netherlands, areincluded in data for the Netherlands.• South Sudan declared its independence onJuly 9, 2011. When available, data are shownseparately for South Sudan; data for Sudaninclude South Sudan unless otherwise noted.Classification of economiesFor operational and analytical purposes the WorldBank’s main criterion for classifying economies isgross national income (GNI) per capita (calculatedby the World Bank Atlas method). Every economyis classifi ed as low income, middle income (subdividedinto lower middle and upper middle), or highincome. For income classifi cations see the map onthe inside front cover and the list on the front coverfl ap. Low- and middle-income economies are sometimesreferred to as developing economies. The termis used for convenience; it is not intended to implythat all economies in the group are experiencingsimilar development or that other economies havereached a preferred or fi nal stage of development.Note that classifi cation by income does not necessarilyrefl ect development status. Because GNI percapita changes over time, the country compositionof income groups may change from one edition ofWorld Development Indicators to the next. Once theclassifi cation is fi xed for an edition, based on GNIper capita in the most recent year for which data areavailable (2010 in this edition), all historical datapresented are based on the same country grouping.Low-income economies are those with a GNI percapita of $1,005 or less in 2010. Middle-incomeeconomies are those with a GNI per capita of $1,006–$12,275. Lower middle-income and upper middleincomeeconomies are separated at a GNI per capitaof $3,976. High-income economies are those with aGNI per capita of $12,276 or more. The 17 participatingmember countries of the euro area are presentedas a subgroup under high-income economies.Symbols..means that data are not available or that aggregatescannot be calculated because of missing data in theyears shown.0 or 0.0means zero or small enough that the number wouldround to zero at the displayed number of decimalplaces./in dates, as in 2009/10, means that the period oftime, usually 12 months, straddles two calendaryears and refers to a crop year, a survey year, or afi s c a l y e a r.$means current U.S. dollars unless otherwise noted.>means more than.

WORLDVIEW

We now have data to monitor the fi rst 10years of the Millennium DevelopmentGoals. Results are starting to appear,and we have a better view of where we will bein 2015. We will not achieve all the targetswe set for ourselves, but progress measuredagainst 1990 benchmarks accelerated in thelast decade, lifting millions of people out of poverty,enrolling millions of children in school, andsharply reducing loss of life from preventablecauses. We know this because we have accessto greatly improved statistics.The need for reliable and timely st atisticswas recognized long before the MillenniumDevelopment Goals were proposed in 2000,but the widespread attention given to theirquantitative targets has increased demand forregular and uniform reporting of key indicators.The International Development Goals proposedby the Organisation for Economic Co-operationand Development in 1996 (OECD DAC 1996)included 21 indicators under seven headingsthat anticipated the Millennium DevelopmentGoals. The World Bank’s 1992 Poverty ReductionHandbook (World Bank 1992) noted theneed for an overall strategy for country statisticalcapacity and institution building.Faced with large gaps in the internationaldatabase, the Partnership in Statistics forDevelopment in the 21st Century (PARIS21)was established in 1999 to coordinate efforts toincrease developing countries’ statistical capacity.In 2004 the Second Roundtable on Managingfor Development Results endorsed theMarrakech Action Plan for Statistics, creatingan international agenda for support to statisticsin developing countries. Subsequently the AccraAgenda for Action made broad commitmentson behalf of donors and developing countriesto strengthen national statistical systems; providemore data disaggregated by sex, region,and economic status; and “invest in strengtheningdeveloping countries’ national statisticalcapacity and information systems, includingthose for managing aid” (OECD 2008a). Morerecently, the 2009 Dakar Declaration on theDevelopment of Statistics reaffi rmed that “concertedand co-ordinated actions are requiredto make more effective use of statistical datato support poverty reduction policies and programsand to strengthen and sustain the capacityof statistical systems especially in developingcountries” (PARIS21 2009a).Much progress has been made. When thecurrent round of censuses concludes in 2014,98 percent of the world’s population will havebeen counted. Since donors began reportingsupport for statistical capacity development in2008, fi nancial commitments to statistics haveincreased 60 percent to $1.6 billion over 2008–10. More than 55 developing countries haveimproved the data collection, management,and dissemination of household surveys. TheUnited Nations Inter-Agency and Expert Groupon the Millennium Development Goal Indicatorshas conducted a series of regional workshopsaimed at improving the MDG monitoring andhas reported annually on progress. The qualityof statistics as measured by the World Bank’sstatistical capacity indicator has improved fromits benchmark level of 54 in 1999 to 67 in2011. The availability of data for monitoring theMillennium Development Goals has improvedcommensurately: in 2003 only 4 countrieshad two data points for at least 16 of 22 principalMillennium Development Goals indicators;by 2009 this had risen to 118 countries(PARIS21 2009b).Any assessment of the Millennium DevelopmentGoals must acknowledge that amid all thesigns of progress, there are gaps. Shortcomings.Disappointments. Some targets will not bereached in this decade or the next. Likewise thestatistical record is still incomplete, Continuingprogress will require renewed commitment andcareful monitoring.12012 World Development Indicators 1

Eradicate extreme poverty and hungerGoal 1Poverty and hunger remain, but fewer people live in extreme poverty. The proportionof people living on less than $1.25 a day fell from 43.1 percent in 1990 to 22.2 percentin 2008. While the food, fuel, and financial crises over the past four years haveworsened the situation of vulnerable populations and slowed poverty reduction in somecountries, global poverty rates have continued to fall. Between 2005 and 2008 both thepoverty rate and the number of people living in extreme poverty fell in all six developingcountry regions, the first time that has happened. Prelimin ary estimates for 2010 showthat the extreme poverty rate fell further, reaching the global target of the MillenniumDevelopment Goals of halving world poverty fi ve years early.Further progress is possible and likely before the 2015 target date of the MillenniumDevelopment Goals, if developing countries maintain the robust growth rates achievedover much of the past decade. But even then, hundreds of millions of people will remainmired in poverty, especially in Sub-Saharan Africa and South Asia and wherever poorhealth and lack of education deprive people of productive employment; environmentalresources have been depleted or spoiled; and corruption, conflict, and misgovernancewaste public resources and discourage private investment.Poverty rates fell sharply in the new millennium1aFewer people living in extreme poverty1bPoverty rate at $1.25 a day (percent)75People living on $1.25 a day or less (billions)2.0Europe & Central AsiaMiddle East & North Africa50Sub-Saharan Africa1.5Latin America & CaribbeanSouth Asia1.0East Asia & Pacific25Latin America & CaribbeanEast Asia & Pacific0.5Sub-Saharan AfricaMiddle East & North Africa0Europe & Central Asia0South Asia19901993199619992002200520081990199319961999200220052008Source: World Bank staff estimates.Source: World Bank staff estimates.The most rapid decline in poverty occurred in East Asia and thePacifi c, where extreme poverty in China fell from 60 percent in1990 to 13 percent. In developing countries outside China, thepoverty rate fell from 37 percent to 25 percent. Poverty remainswidespread in Sub-Saharan Africa and South Asia, but progresshas been substantial. In South Asia the poverty rate fell from54 percent in 1990 to 36 percent in 2008. And over 2005–2008 the poverty rate in Sub-Saharan Africa fell 4.8 percentagepoints to less than 50 percent, the largest drop in Sub-SaharanAfrica since international poverty rates have been computed.In 2008, 1.28 billion people lived on less than $1.25 a day.Since 1990 the number of people living in extreme poverty hasfallen in all regions except Sub-Saharan Africa, where the rateof population growth exceeded the rate of poverty reduction,increasing the number of extremely poor people from 290 millionin 1990 to 356 million in 2008. The largest number ofpoor people remain in South Asia, where 571 million peoplelive on less than $1.25 a day, down from a peak of 641 millionin 2002.22012 World Development Indicators

WORLD VIEWProgress toward poverty reduction1cMore and less income equality1eShare of countries making progresstoward poverty reduction (percent)100Reached target On track Off trackSeriously off track Insufficient dataGini coefficient(most recent value, 2000–09)70Low incomeUpper middle incomeLower middle incomeNo changeLess equal5060500405030100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfricaMore equal2020 30 40 50 60 70Gini coefficient (most recent value, 1990–99)Source: World Bank staff calculations.Source: World Development Indicators database.Individual country progress is assessed by comparing the rateof poverty reduction with the average rate required to achieve a50 percent reduction in 25 years. Countries that have alreadyreached the target are listed as “achieved.” Those matching therequired rate are listed as “on track.” Countries that will takelonger but could reach the target by 2040, based on past performance,are listed as “off track.” And those that would needstill longer or where poverty rates have increased are listed as“seriously off track.”Is income inequality improving or getting worse? At any level ofincome per person, the less equal the distribution of incomethe greater the poverty rate. The Gini coeffi cient is a commonmeasure of inequality. A higher value indicates greater inequality.Poor countries often have less equal distributions of incomethan do rich countries, but there are signifi cant regional differencesas well. Data for 81 countries with values measured beforeand after 2000 show Gini coeffi cients fell in 44, includingmany low-income economies.Progress toward reducing undernourishment1dMany children remain malnourished1fShare of countries makingprogress toward reducingundernourishment (percent)100Reached target On track Off trackSeriously off track Insufficient dataMalnutrition prevalence, weight for age(percent of children under age 5)50Low income Lower middle incomeUpper middle income4050300205010100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfrica019902010Source: World Bank staff calculations.Source: World Health Organization and World Development Indicators database.Undernourishment measures the availability of food to meetpeople’s basic energy needs. The Millennium DevelopmentGoals call for halving the proportion of undernourished people,but few countries will reach that target by 2015. Rising agriculturalproduction has kept ahead of population growth, butincreasing food prices and the diversion of food crops to fuelproduction have reversed the declining rate of undernourishmentsince 2004–06. The Food and Agriculture Organizationestimated that there were 739 million people without adequatedaily food intake in 2008.Malnutrition rates have dropped substantially since 1990, butmore than 100 million children under age 5 remain malnourished.Only 40 of 90 countries with adequate data to monitortrends are on track to reach the Millennium Development Goaltarget of halving the number of people who suffer from hunger.Malnutrition in children often begins at birth, when poorly nourishedmothers give birth to underweight babies. Malnourishedchildren develop more slowly, enter school later, and performless well. Programs to encourage breastfeeding and improvethe diets of mothers and children can help.2012 World Development Indicators 3

Achieve universal primary educationGoal 2The commitment to provide primary education to every child is the oldest of theMillennium Development Goals, having been set at the first Education for All conferencein Jomtien, Thailand, more than 20 years ago. Achieving this goal has oftenseemed tantalizingly near, but only Latin America and the Caribbean has reached thegoal, although East Asia and Pacifi c and Europe and Central Asia are close.Progress among the poorest countries, slow in the 1990s, has accelerated since2000, particularly in South Asia and Sub-Saharan Africa, but full enrollment remainselusive. Many children start school but drop out before completing the primary stage,discouraged by cost, distance, physical danger, and failure to progress. Even as countriesapproach the target of Millennium Development Goal 2, the education demands ofmodern economies expand. In the 21st century primary education will be of value onlyas a stepping stone toward secondary and higher education.The last step toward education for all1g64 million children out of school 1hPrimary school completion rate (percent)110East Asia & Pacific, target100Europe & Central Asia, targetEast Asia &Pacific, actualLatin America & Caribbean,actualNumber of children not attending primary school (millions)50401999 2009908070Europe & Central Asia, actualLatin America &Caribbean, targetSouth Asia, targetMiddle East &North Africa,actualSouth Asia, actualMiddle East &North Africa, target3020605019911995Sub-Saharan Africa,target2000Sub-Saharan Africa, actual200520102015100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfricaSource: United Nations Educational, Scientifi c and Cultural Organization Institute ofStatistics and World Development Indicators database.Source: United Nations Educational, Scientifi c and Cultural Organization Institute ofStatistics.In 2009, 87 percent of children in developing countries completedprimary school. In most regions school enrollment picked upafter the Millennium Development Goals were promulgated in2000, when the completion rate was 80 percent. Sub-SaharanAfrica and South Asia, which started out farthest behind, havemade substantial progress but will still fall short of the goal. TheMiddle East and North Africa has stalled at completion ratesof around 90 percent, while Europe and Central Asia and EastAsia and Pacifi c are within striking distance but have made littleprogress in the last fi ve years.Many children enroll in primary school but attend intermittentlyor drop out entirely. This is particularly the case for girls whosework is needed at home. In rural areas the work of children ofboth sexes may be needed during planting and harvest. Otherobstacles, including the lack of suitable facilities, absence ofteachers, and school fees, discourage parents from sendingtheir children to school. The problem is worst in South Asia andSub-Saharan Africa, where more than 48 million children of primaryschool age are not in school.42012 World Development Indicators

WORLD VIEWProgress toward education for all1iHow much schooling1kShare of countries makingprogress toward universalprimary education(percent)100Reached target On track Off trackSeriously off track Insufficient dataMadagascar, 2008Share of people ages 15–19 completing each year of schooling,by year and wealth quintile (percent)1005075SecondquintileRichest quintile0505025Poorest quintileThirdquintileFourth quintile100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfrica0Year 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Source: World Bank staff calculations.Sixty developing countries, half the countries for which adequatedata are available, have achieved or are on track toachieve the Millennium Development Goal target of a full courseof primary schooling for all children. Twelve more will miss the2015 deadline. At their current rate of progress they will achievefull enrollment sometime after 2015. That leaves at least 48countries seriously off track, making little or no progress, 30 ofthem in Sub-Saharan Africa.Bangladesh, 2007Share of people ages 15–19 completing each year of schooling,by sex and location (percent)100755025Urban, boysUrban, girlsRural, boysRural, girls0The missing enrollments1jYear 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Ratio of girls’ to boys’ primary school enrollment (percent)Boys underenrolled100Girls underenrolled9080Cambodia, 2005Share of people ages 15–19 completing each year of schooling,by parents’ education level (percent)100755025Incomplete primarySecondarySome higherPrimaryIncompletesecondary7030 50 70 90 100Net primary enrollment rate (percent)Source: United Nations Educational, Scientifi c and Cultural Organization Institute ofStatistics and World Development Indicators database.0Year 1 Year 2 Year 3 Year 4 Year 5Source: Demographic and Health Surveys.Year 6Year 7No educationYear 8Year 9A major obstacle to achieving universal primary education is theshortfall in girls’ enrollments. Almost all school systems withlow enrollment rates show underenrollment of girls in primaryschool. In only a few places are boys’ enrollment rates lowerthan girls’. Starting at such a disadvantage, most girls will nevercatch up. Achieving the Millennium Development Goal target toenroll and keep girls in school is essential.Many factors affect how long students stay in school. Childrenfrom poor families are less likely to attend or stay in school. Inmost countries girls and children from rural areas are also lesslikely to attend, but Bangladesh has used targeted incentives toraise girls’ attendance rates. In Cambodia and everywhere elseparents with lower levels of education are less likely to keeptheir children in school. Achieving the Millennium DevelopmentGoal target will require breaking the cycle of lack of education–poverty– low enrollment.2012 World Development Indicators 5

Promote gender equity and empower womenGoal 3Women are making progress along the three dimensions of gender equality andwomen’s empowerment that the Millennium Development Goals monitor: education,employment, and participation in public decisionmaking. These are important,but there are others. Efforts are under way to improve monitoring of women’saccess to fi nancial services, entrepreneurship, and migration and remittances as wellas of violence against women. Time-use surveys, for example, can illuminate differencesin the roles of women and men within the household and the workplace. Disaggregatingother statistical indicators by sex can also reveal patterns of disadvantage or, occasionally,advantage for women. Whatever the case, women make important contributions toeconomic and social development. Expanding opportunities for them in the public andprivate sectors is a core development strategy. And good statistics are essential fordeveloping policies that effectively promote gender equity and increase the welfare andproductivity of women.Increasing participation by girls at all levels of education1lRatio of girls’ to boys’ enrollment rate, 2009 (percent)1501251007550250Primary Secondary TertiaryPrimary Secondary TertiaryPrimary Secondary TertiaryPrimary Secondary TertiaryPrimary a SecondaryTertiary aPrimary Secondary TertiaryEast Asia & PacificEurope & Central AsiaLatin America & CaribbeanMiddle East & North AfricaSouth AsiaSub-Saharan Africaa. Data are for 2008.Source: United Nations Educational, Scientifi c and Cultural Organization Institute of Statistics and World Development Indicators database.Girls have made substantial gains in primary and secondaryschool enrollment. In many countries girls’ enrollment rates outnumberboys’, particularly in secondary school. And more girlsare staying in school. In 1991 only 73 percent of girls in developingcountries fi nished primary school; by 2010 the completionrate stood at 86 percent. But this comparison obscures theunderlying problem of under enrollment. Girls are still less likelyto enroll in primary school or to stay through the end of primaryschool. In some countries the situation changes at the secondarylevel. Girls who complete primary school may be more likelyto stay in school, while boys drop out. In Europe and CentralAsia and Latin American and the Caribbean the differences inhigher education enrollment are substantial. This is an unsatisfactorypath to equity. Rapid growth and poverty reduction trulyrequire education for all.62012 World Development Indicators

WORLD VIEWProgress toward gender equality in education1mMore women decisionmakers1oShare of countries making progresstoward gender equality in primaryand secondary education (percent)100Reached target On track Off trackSeriously off track Insufficient dataProportion of seats held by women in national parliaments (percent)25Latin America & Caribbean20East Asia &PacificHigh incomeSouth Asia5015Sub-Saharan AfricaEurope & Central Asia010Middle East & North Africa505100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfrica0199920002001200220032004200520062007200820092010Source: World Bank staff calculations.Source: Inter-Parliamentary Union and World Development Indicators database.Substantial progress has been made toward increasing the proportionof girls enrolled in primary and secondary education. Bythe end of the 2009/10 school year, 96 countries had achievedequality in enrollment rates and 7 more were on track to do soby 2015. That leaves only 27 countries off track or seriously offtrack, mostly low- and lower middle-income economies in the MiddleEast and North Africa, South Asia, and Sub-Saharan Africa.Fourteen countries lacked adequate data to assess progress.The proportion of parliamentary seats held by women hasincreased everywhere. In Latin America and the Caribbeanwomen now hold 24 percent of all parliamentary seats. Themost impressive gains have been made in South Asia, wherethe number of seats held by women tripled between 1999 and2000. In Sub-Saharan Africa Rwanda leads the way, making historyin 2008 when it elected a parliament composed 56 percentof women. The Middle East and North Africa lags far behind.Women have become a larger part of the workforce1nA slim lead for girls1pShare of women employed in the nonagricultural sector(percent of total nonagricultural employment)501990 Most recent yearUnderweight children under age 5,most recent year available, 2005–11 (percent) Female Male5040403030202010100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaNote: Insuffi cient data are available for Sub-Saharan Africa.Source: International Labour Organization and World Development Indicators database.0Timor-LesteIndiaBangladeshNigerNepalEthiopiaSomaliaSource: World Health Organization and World Bank staff calculations.SudanLao PDRDjiboutiWomen’s share in paid employment in the nonagriculturalsector has risen marginally but remains less than 20 percentin the Middle East and North Africa and South Asia. In manycountries the majority of women who work hold insecure jobsoutside the formal sector. Overall labor force participationrates of women follow a similar pattern, but they are highest inSub-Saharan Africa, where 60 percent of women ages 15 andolder are in the labor force, although many are employed asunpaid family workers.Girls are less likely to attend school, have secure jobs, or holdpublic offi ce. But by most measures, they have an advantagein one area: malnutrition. Out of 99 countries with data for2005–11, 19 had a larger proportion of underweight girls thanof underweight boys; 74 had a larger proportion of underweightboys than of underweight girls, and 6 had no difference. Thechart shows the 10 countries with the highest proportion ofunderweight children during the period.2012 World Development Indicators 7

Reduce child mortalityGoal 4In 1990, 12 million children died before their fi fth birthday. By 1999 there were fewerthan 10 million child deaths, and the number has continued to fall to just over 7.5million in 2010. That is good news, but the ambitious Millennium Development Goaltarget of a two-thirds reduction in the under-fi ve mortality rate will be met by no morethan 40 countries. Only Latin America and the Caribbean and upper middle-incomeeconomies as a whole will, on average, reach the target.Most children die from causes that are readily preventable or curable with existinginterventions, such as acute respiratory infections, diarrhea, measles, and malaria. Andmost die in the fi rst year of life. Rapid improvements prior to 1990 in a few countriesgave hope that mortality rates for infants and children could be cut further in the following25 years, but progress slowed almost everywhere after 1990, leaving most countriesfar behind the target before the goals were announced. Was the target too challenging?Perhaps. But the more important question is whether it encouraged countries to usetheir resources wisely to achieve the fastest possible progress.Still far to go in reducing under-five mortality1qMost deaths occur in the first year of life1rUnder-five mortality rate (per 1,000 live births)200Deaths, 2010 (millions)4Children ages 1–5InfantsSub-Saharan Africa1503South Asia10050Middle East &North AfricaEast Asia & PacificEurope & Central Asia21High income01990 1992Latin America & Caribbean1994 1996 1998 2000200220042006200820100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfricaSource: World Health Organization and World Development Indicators database.Source: World Health Organization and World Development Indicators database.Mortality rates have been falling everywhere. In developingcountries the mortality rate fell from an average of 98 per 1,000live births in 1990 to 63 in 2010. But rates remain much higherin many countries, especially in Sub-Saharan Africa and parts ofSouth Asia. In Sub-Saharan Africa one child in eight dies beforehis or her fi fth birthday. The odds are somewhat better in SouthAsia, where 1 child in 15 dies. But even in these regions thereare countries exhibiting rapid progress.Almost 70 percent of deaths of children under age 5 occur inthe fi rst year of life, and half in the fi rst month. Therefore, reducingchild mortality requires addressing the causes of neonataland infant deaths: inadequate care at birth and afterward, malnutrition,poor sanitation, and exposure to acute and chronicdisease. Improvements in infant and child mortality are, in turn,the largest contributors to increased life expectancy in mostcountries.82012 World Development Indicators

WORLD VIEWProgress toward reducing child mortality1sFor some, better than expected improvements1uShare of countries making progresstoward reducing child mortality (percent)10050050100East Asia& PacificEurope &Central AsiaSource: World Bank staff calculations.Latin America& Carib.Reached target On track Off trackSeriously off track Insufficient dataMiddle East& N. AfricaSouthAsiaSub-SaharanAfricaA concerted effort by academic researchers and internationalstatistical agencies has greatly improved measurement of infantand child mortality. Therefore, few countries lack estimatesof child mortality rates, although many are derived from statisticalmodels. Ten countries have already achieved a two-thirdsreduction in under-fi ve mortality rates since 1990, and 26 areon track to do so by 2015. But that leaves 105 countries, withhalf of developing countries’ population, off track or seriouslyoff track.Preventing childhood diseasesChildren ages 12–23 months immunized against measles (percent)10075Lower middle income50250199019921994High incomeLow incomeUpper middle income1996199820002002200420062008Source: World Health Organization and World Development Indicators database.2010Illnesses that could be prevented by early childhood vaccinationsstill account for many child deaths. Despite years of vaccinationcampaigns, many children in low- and lower middle-incomeeconomies remain unprotected. Measles is one example.Other recommended immunizations include diphtheria, pertussis,tetanus, and the BCG immunization for tuberculosis. To besuccessful, vaccination campaigns must reach all children andbe sustained over time.1tReduction since 1990 2010, actual 2010, target 2010, predictedSeychellesMauritiusBotswanaCape VerdeSouth AfricaNamibiaZimbabweLesothoGabonSão Tomé and PríncipeSwazilandKenyaCongo, Rep.GhanaMauritaniaSudanComorosCameroonSenegalEritreaTogoCôte d’IvoireTanzaniaMadagascarRwandaCentral African RepublicGambia, TheUgandaBeninSomaliaCongo, Dem. Rep.ZambiaBurundiEthiopiaEquatorial GuineaBurkina FasoChadGuinea-BissauNigeriaMozambiqueMalawiLiberiaGuineaAngolaMaliSierra LeoneNigerSource: World Bank staff calculations.0 100 200 300 400Under-five mortality rate (deaths per 1,000 live births)In 1990 the under-fi ve mortality rate in Niger stood at 311 per1,000 live births, the worst in the world. In the same year, Seychelles,with an under-fi ve mortality rate of 16, was the best inSub-Saharan Africa. How have they fared since? In the 20 yearsfrom the Millennium Development Goals baseline, Niger’s mortalityrate fell by 168, the greatest in the region, while Seychelles’sfell by 3. In proportional terms Niger experienced a 54 percentreduction—second greatest in the region—and Seychelles a16 percent reduction. Both are short of the Millennium DevelopmentGoal target, but Niger, having started in last place, has progressedfaster. Has this been the general rule? On average, countriesin Sub- Saharan Africa that started in worse positions havedone better. But experience has been mixed: confl ict- affectedcountries such as the Democratic Republic of the Congo andSomalia have made almost no progress, while similarly situatedcountries such as Uganda and Zambia have done much better.Two countries, Madagascar and Malawi, are on track to achievethe Millennium Development Goal target. Several others, includingEritrea, Niger, and Tanzania, are close. Only one country, Zimbabwe,moved backward from 1990 to 2010.2012 World Development Indicators 9

Reduce maternal mortalityGoal 5An estimated 358,000 maternal deaths occurred worldwide in 2008, a 34 percentdecrease since 1990. The Millennium Development Goals call for reducing the maternalmortality ratio by 75 percent between 1990 and 2015, but few countries andno developing country region on average will achieve this target. What makes maternalmortality such a compelling problem is that it strikes young women experiencing a naturallife event. They die because they are poor. Malnourished. Weakened by disease. They diebecause they lack access to trained health care workers and modern medical facilities. Andbecause women in poor countries have more children, their lifetime risk of maternal deathmay be more than 200 times greater than for women in Western Europe and North America.Reducing maternal mortality requires a comprehensive approach to women’s reproductivehealth, starting with family planning and access to contraception. Many healthproblems among pregnant women are preventable or treatable through visits withtrained health workers before childbirth. Good nutrition, vaccinations, and treatmentof infections can improve outcomes for mother and child. Skilled attendants at time ofdelivery and access to hospital treatments are essential for dealing with life-threateningemergencies such as severe bleeding and hypertensive disorders.Maternal mortality rates have been falling,but large regional differences persistMaternal mortality ratio, modeledestimate (per 100,000 live births)1,0001v1990 1995 20002005 2008The 12 countries with highest lifetime risk of maternal deathLifetime risk of maternal death, 2008 (percent)1081w7506500422500East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSource: World Health Organization and World Development Indicators database.Sub-SaharanAfrica0BurundiCongo, Dem. Rep.NigeriaTanzaniaMaliSierra LeoneLiberiaGuinea-BissauNigerSomaliaChadAfghanistanSource: World Health Organization and World Development Indicators database.About half of all maternal deaths occur in Sub-Saharan Africaand a third in South Asia, but mothers in other regions facesubstantial risks as well. The maternal mortality ratio may bemany times higher in fragile and confl ict-affl icted states thanin countries with strong institutions and well organized healthsystems.In high-fertility countries women are repeatedly exposed to therisk of maternal mortality. In Afghanistan in 2008, where thelifetime risk of maternal death was over 9 percent, one womanin 11 is expected to die in childbirth; in Burundi one woman in25 is at risk. In high-income economies the lifetime risk is lessthan 0.03 percent, or less than 1 woman in 3,500.102012 World Development Indicators

WORLD VIEWProgress in reducing maternal mortality1xFewer young women giving birth1zShare of countries makingprogress toward reducingmaternal mortality (percent)100Reached target On track Off trackSeriously off track Insufficient dataAdolescent fertility rate (births per 1,000 women ages 15–19)150Sub-Saharan Africa50100South Asia050Latin America & CaribbeanMiddle East & North Africa50Europe & Central AsiaEast Asia & Pacific100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfrica020002001200220032004200520062007200820092010Source: World Bank staff calculations.Source: World Health Organization and World Development Indicators database.Progress in reducing maternal mortality ratios has been slow,far slower than imagined by the Millennium Development Goaltarget of a 75 percent reduction from 1990 levels. Only fourcountries have achieved this target, and fi ve more are on track.Accurately measuring maternal mortality is diffi cult and requiresspecialized surveys and good reporting of vital events. Recentefforts by statisticians have improved estimates, but for manycountries the need for improved monitoring of maternal healthwill continue long past 2015.The adolescent fertility rate is highest in Sub- Saharan Africaand is declining slowly. Women who give birth at an early ageare likely to bear more children and are at greater risk of deathor serious complications from pregnancy. In many developingcountries the number of women ages 15–19 is still increasing.Preventing unintended pregnancies and delaying childbirthamong young women increase the chances of their attendingschool and eventually obtaining paid employment.Planning for motherhood1yHelp for mothers1aaContraceptive prevalence, 2010 (percent of women ages 15–49)Births attended by skilled health staff, 2010 (percent)8010060754050202500East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfricaEast Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfricaSource: World Health Organization and World Development Indicators database.Source: World Health Organization and World Development Indicators database.Contraceptive use has increased in most developing countriesfor which data are available. In almost all regions more than halfof women who are married or in union use some method of birthcontrol. More than 200 million women want to delay or ceasechildbearing, and a substantial proportion say that their lastbirth was unwanted or mistimed. Worldwide an estimated 120million women have an unmet need for family planning.In South Asia and Sub- Saharan Africa less than half of all birthsare attended by doctors, nurses, or trained midwives. Havingskilled health workers present for deliveries is key to reducingmaternal mortality. In many places women have only untrainedcaregivers or family members to attend them during childbirth.Skilled health workers are trained to give necessary care before,during, and after delivery; they can conduct deliveries ontheir own, summon additional help in emergencies, and providecare for newborns.2012 World Development Indicators 11

Combat HIV/AIDS, malaria, and other diseasesGoal 6Epidemic diseases exact a huge toll in human suffering and lost opportunities fordevelopment. Poverty, armed conflict, and natural disasters contribute to the spreadof disease and are made worse by it. In Africa the spread of HIV/AIDS has reverseddecades of improvement in life expectancy and left millions of children orphaned. It isdraining the supply of teachers and eroding the quality of education.There are 300–500 million cases of malaria each year, leading to more than 1 milliondeaths. Malaria is a disease of poverty. Nearly all the cases occur in Sub-SaharanAfrica, where the most lethal form of the malaria parasite is abundant. Most deaths frommalaria are among children under age 5, but the disease can be debilitating in adultsas well.Tuberculosis kills some 2 million people a year, most of them ages 15–45. The disease,once controlled by antibiotics, is spreading again because of the emergence ofdrug-resistant strains. People living with HIV/AIDS, which reduces resistance to tuberculosis,are particularly vulnerable, as are refugees, displaced persons, and prisonersliving in close quarters and unsanitary conditions. Well managed medical interventionusing appropriate drug therapy is the key to stopping the spread of tuberculosis.Bringing HIV/AIDS under control1bbMillions of people still afflicted with HIV/AIDS1ccPrevalence of HIV (percent of population ages 15–49)7.5Population living with HIV, 2009 (millions)65Sub-Saharan Africa5.04322.510.01990High income1992Other developing regions19941996199820002002Latin America & Caribbean20042006Source: World Health Organization, Joint United Nations Programme on HIV/AIDS, andWorld Development Indicators database.20090CameroonChinaMalawiRussian FederationZambiaUgandaZimbabweMozambiqueTanzaniaKenyaIndiaNigeriaSouth AfricaSource: World Health Organization, Joint United Nations Programme on HIV/AIDS, andWorld Development Indicators database.Sub-Saharan Africa remains the center of the HIV/AIDS epidemic,but the proportion of adults living with AIDS has begun tofall even as the survival rate of those with access to antiretroviraldrugs has increased. In Africa 58 percent of adults withHIV/AIDS are women. Among young people ages 15–24, theprevalence rate among women is more than twice that amongmen. Latin America and the Caribbean, where 0.5 percent ofadults are infected, has the next highest prevalence rate.In 2009, 31–33 million people were living with HIV/AIDS, andapproximately 1.5 million of them were under age 15. Another16.9 million children, 14.8 million of them in Sub-SaharanAfrica, have lost one or both parents to AIDS. By the end of2009, 5.25 million people were receiving antiretroviral drugs, or36 percent of the population for which the World Health Organizationrecommends treatment.122012 World Development Indicators

WORLD VIEWProgress toward reversing the HIV epidemic1ddProtecting children from malaria1ffShare of countries makingprogress against HIV/AIDS(percent)10050050100East Asia& PacificEurope &Central AsiaSource: World Bank staff calculations.Halted and reversed Halted or reversedStable low prevalence Not improving No dataLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfricaThe Millennium Development Goals call for halting and then reversingthe spread of HIV/AIDS by 2015. The progress assessmentshown here is based on prevalence rates for adults ages15–49. Countries that have a declining prevalence rate since2005 are assessed to have halted the epidemic; those that havea prevalence rate less than their earliest measured rate havereversed the epidemic. Countries that have a prevalence rate ofless than 0.2 percent are considered stable. Countries that havea prevalence rate greater than 0.2 percent and that have neitherhalted nor reversed the epidemic are shown as not improving.Turning the tide of tuberculosisTuberculosis incidence and prevalence rates (per 100,000 people)500400300200100Low income, prevalenceLower middle income, prevalenceLow income, incidenceLower middle income, incidenceUpper middle income,prevalence1eeUpper middle income, incidenceFirst observation (2000 or earlier)SwazilandMauritaniaCôte d’IvoireGuineaCongo, Rep.ComorosBurkina FasoChadSomaliaCameroonCentral African RepublicZimbabweAngolaBeninMozambiqueSudanSierra LeoneLiberiaGhanaNigeriaSenegalUgandaEthiopiaNamibiaGuinea-BissauCongo, Dem. Rep.BurundiMadagascarKenyaEritreaGambia, TheZambiaGabonSão Tomé and PríncipeMalawiTogoTanzaniaNigerRwandaMaliMost recent observation (2006 or later)01990199219941996199820002002200420062008Source: World Health Organization and World Development Indicators database.2010Tuberculosis is one of the main causes of adult deaths froma single infectious agent in developing countries. The datashown here illustrate the association of tuberculosis with poverty.The incidence rate is three times higher in low-incomeeconomies than in upper middle-income economies. The numberof new tuberculosis cases peaked in 2004, and prevalencerates are also declining, but the targets of halving the 1990prevalence and death rates by 2015 are unlikely to be met.0 25 50 75Use of insecticide-treated nets (percent of population under age 5)Source: World Health Organization and World Development Indicators database.Malaria is endemic in most tropical and subtropical regions, but90 percent of malaria deaths occur in Sub- Saharan Africa. Thosemost severely affected are children under age 5. Even those whosurvive malaria do not escape unharmed. Repeated episodes offever and anemia take a toll on mental and physical development.Insecticide-treated nets have proved to be an effective preventative,and their use has grown rapidly. Between 2008 and 2010, 290million nets were distributed in Sub- Saharan Africa. But coverageremains uneven. In some countries with large numbers of reportedcases, use of nets for children remains at less than 20 percent.2012 World Development Indicators 13

Ensure environmental sustainabilityGoal 7Sustainable development can be ensured only by protecting the environment andusing its resources wisely. Poor people, often dependent on natural resources fortheir livelihood, are the most affected by environmental degradation and naturaldisasters (fi res, storms, earthquakes), whose effects are worsened by environmentalmismanagement. Poor people also suffer from shortcomings in the built environment:whether in urban or rural areas, they are more likely to live in substandard housing, lackbasic services, and be exposed to unhealthy living conditions.The seventh goal of the Millennium Development Goals is the most wide ranging butperhaps the least well specifi ed. Among its 10 indicators, only 3 are associated withquantified and timebound targets. For others we can only monitor trends.Most countries have adopted principles of sustainable development and agreed tointernational accords on protecting the environment. But the failure to reach a comprehensiveagreement on limiting greenhouse gas emissions leaves billions of people andfuture generations vulnerable to the impacts of climate change. Growing populations putmore pressure on marginal lands and expose more people to hazardous conditions thatwill be exacerbated by global warming.Carbon dioxide emissions continue to rise1ggForest losses and gains1hhCarbon dioxide emissions (millions of metric tons)30High incomeAverage annual change in forest area, 2000–10 (thousands of square kilometers)2520010Upper middle income–250199019921994199619982000Low income20022004Lower middle income2006Source: Carbon Dioxide Information Analysis Center and World Development Indicatorsdatabase.2008–50East Asia Europe && Pacific Central AsiaLatinAmerica& Carib.MiddleEast &N. AfricaSouthAsiaSub-SaharanAfricaHighincomeSource: Food and Agriculture Organization and World Development Indicators database.Annual emissions of carbon dioxide reached 32 million metrictons in 2008 and are still rising. High-income economies remainthe largest emitters, but the rapidly growing upper middleincomeeconomies are not far behind. Measured by emissionsper capita, however, emissions by high-income economies aremore than three times higher than average emissions by lowandmiddle-income economies.Loss of forests threatens the livelihood of poor people, destroyshabitat that harbors biodiversity, and eliminates an importantcarbon sink that helps moderate climate change. Net lossessince 1990 have been substantial, especially in Latin Americanand the Caribbean and Sub-Saharan Africa, and only partiallycompensated for by net gains in Asia and high-incomeeconomies.142012 World Development Indicators

WORLD VIEWProgress toward improved sanitation1iiMany still lack access to sanitation1kkShare of countries making progresstoward universal access to improvedsanitation (percent)100Reached target On track Off trackSeriously off track Insufficient dataShare of population with access to improved sanitation, 2008 (percent)100RuralUrban75505002550100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfrica0East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfricaSource: World Bank staff calculations.Source: World Health Organization and World Development Indicators database.The Millennium Development Goals call for cutting the proportionof the population without access to improved sanitationand water sources in half by 2015. By 2010, 2.7 billion peoplestill lacked access to improved sanitation, and more than 1 billionpeople practiced open defecation, posing enormous healthrisks. At the present pace only 37 countries are likely to reachthe target—an increase of two since 2008. East Asia and Pacific and Middle East and North Africa are the only developingregions on track to reach the target by 2015.In 1990, 63 percent of the people living in low- and middleincomeeconomies lacked access to a fl ush toilet or other formof improved sanitation. By 2010 the access rate had improved19 percentage points to 44 percent. The situation is worse inrural areas, where 57 percent of the population lack access toimproved sanitation. The large urban–rural disparity, especiallyin Sub-Saharan Africa and South Asia, is the principal reasonthe sanitation target of the Millennium Development Goals willnot be reached.Progress toward improved water sources1jjWater demand strains supplies1llShare of countries making progresstoward universal access to animproved water source (percent)100Reached target On track Off trackSeriously off track Insufficient dataAnnual freshwater withdrawals, 2009 (percent of internal resources)75505002550100East Asia& PacificEurope &Central AsiaLatin America& Carib.Middle East& N. AfricaSouthAsiaSub-SaharanAfrica0East Asia Europe && Pacific Central AsiaLatinAmerica& Carib.MiddleEast &N. AfricaSouthAsiaSub-SaharanAfricaHighincomeSource: World Bank staff calculations.Source: Food and Agriculture Organization and World Development Indicators database.In 1990 more than 1 billion people lacked access to drinkingwater from a convenient, protected source, but the situation isimproving. The proportion of people in developing countries withaccess to an improved water source increased from 71 percentin 1990 to 86 percent in 2008, reaching the Millennium DevelopmentGoal target of halving the proportion of people withoutaccess to an improved water source. Seventy-three countrieshave reached or are on track to reach the target. At this rate,only Middle East and North Africa and Sub-Saharan Africa willfall short.Worldwide more than 70 percent of freshwater withdrawals goto agricultural and 20 percent to industrial uses. Only 10 percentgo to households, many of which are underserved. Thepotential impacts of global warming and increased demandfor water will require more effi cient use of available resources.As pressure grows on internal water resources, confl icts overshared, external resources may also increase.2012 World Development Indicators 15

Develop a global partnership for developmentGoal 8The eighth and final goal distinguishes the Millennium Development Goals from previoussets of resolutions and targeted programs. It recognizes the multidimensionalnature of development and the need for wealthy countries and developing countriesto work together to create an environment in which rapid, sustainable development ispossible. Following the Millennium Summit, world leaders meeting at Monterrey, Mexico,in 2002 agreed to provide fi nancing for development through a coherent process thatrecognized the need for domestic as well as international resources. Subsequent highlevelmeetings expanded on these commitments. Along with increased aid fl ows anddebt relief for the poorest, highly indebted countries, goal 8 recognizes the need toreduce barriers to trade and to share the benefi ts of new medical and communicationtechnologies. Goal 8 also reminds us that development challenges differ for large andsmall countries and for those that are landlocked or isolated by large expanses of ocean.Building and sustaining a partnership is an ongoing process that does not stop at a specific date or when a target is reached. However it is measured, a strong commitment topartnership should be the continuing legacy of the Millennium Development Goals.Most donors have maintained their aid levels1mmBut their domestic subsidies to agricultural are greater1nnOfficial development assistance (percent of GNI)Agricultural support (percent of GDP)1.259NorwayKorea, Rep.1.000.756United Kingdom0.50Germany3NorwaySwitzerland0.250.00United StatesAll DAC donorsJapanJapanCanadaUnited States0 All DAC donors (ODA as share of GNI)1990199219941996199820002002200420062008201019901992199419961998200020022004200620082010Source: Organisation for Economic Co-operation and Development.Source: Organisation for Economic Co-operation and Development.The fi nancial crisis that began in 2008 and fi scal austerity inmany high-income economies have threatened to underminecommitments to increase offi cial development assistance. Sofar leading donors have maintained their level of support. Totaldisbursements by members of the Organisation for EconomicCo-operation and Development’s Development AssistanceCommittee reached $130 billion in 2010, a real increase of 4.3percent over 2008.Organisation for Economic Co-operation and Development(OECD) members (which include some upper middle-incomeeconomies such as Chile and Mexico) spend more on supportto domestic agricultural producers than on offi cial developmentassistance. In 2010 the OECD producer support estimate was$227 billion, down about 10 percent from the previous threeyears.162012 World Development Indicators

WORLD VIEWDeveloping countries have easier access to OECD markets1ooDebt service burdens have been falling1qqShare of total imports from least developed countriesadmitted free of duty, excluding arms (percent)100Total debt service (percent of exports of goods, services, and income)507550European UnionJapanUnited States403020South AsiaMiddle East& North AfricaLatin America & CaribbeanEurope &Central Asia2510East Asia& PacificSub-Saharan Africa01996199820002002200420062009019901992199419961998200020022004200620082010Source: World Trade Organization, International Trade Center, and United NationsConference on Trade and Development.Source: World Development Indicators database.Many rich countries have pledged to admit the exports of theleast developed countries duty free. However, arcane rules oforigin and phytosanitary standards keep many countries fromqualifying for duty-free access. And uncertainty over market accessmay inhibit development of export industries. Comparedwith the European Union, the large U.S. market retains manybarriers to the exports of the poorest countries.Growing economies, better debt management, and debt relieffor the poorest countries have allowed developing countries tosubstantially reduce their debt burdens. Despite the fi nancialcrisis and a 2.3 percent contraction in the global economy in2009, debt service ratios continued to fall in most developingcountry regions. Only in Europe and Central Asia has the ratio ofdebt service to exports risen since 2008, although rising exportearnings have helped moderate the trend.Cellular phones are connecting developing countries1ppA more connected world1rrFixed telephones and mobile cellular subscriptions (per 100 people)125High income, mobile subscriptionsInternet users (per 100 people)10010075502501990High income, fixed telephonesLower middle income,fixed telephones1992Upper middle income,fixed telephones1994199619982000Low income,mobilesubscriptions2002Lower middleincome, mobilesubscriptionsUpper middleincome, mobilesubscriptionsLow income, fixed telephones2004 2006 2008 201075502501990199219941996High incomeEurope & Central AsiaLatin America & CaribbeanEast Asia & PacificMiddle East & North AfricaSub-Saharan AfricaSouth Asia1998 2000 2002 2004 2006 2008 2010Source: International Telecommunication Union and World Development Indicatorsdatabase.Source: International Telecommunication Union and World Development Indicatorsdatabase.Telecommunications is an essential tool for development, andnew technologies are creating new opportunities everywhere.The growth of fi xed-telephone systems has peaked in highincomeeconomies and will never reach the same level of use indeveloping countries, where mobile cellular subscriptions continueto grow rapidly. In high-income economies, with more thanone subscription per person, the pace of growth appears to beslowing. Gradually the world is becoming more connected.In 2000 Internet use was spreading rapidly in high-income economiesbut was barely under way in developing country regions.Now developing countries are beginning to catch up. By 2010there were an average of 34 internet users per 100 people inupper middle-income economies. Like telephones, Internet useis strongly correlated with income. In low-income economiesthere were only 5.4 users per 100 people in 2010. But growthis picking up.2012 World Development Indicators 17

Millennium Development GoalsGoals and targets from the Millennium Declaration Indicators for monitoring progressGoal 1 Eradicate extreme poverty and hungerTarget 1.A Halve, between 1990 and 2015, the proportion ofpeople whose income is less than $1 a dayTarget 1.B Achieve full and productive employment and decentwork for all, including women and young peopleTarget 1.C Halve, between 1990 and 2015, the proportion ofpeople who suffer from hungerGoal 2 Achieve universal primary educationTarget 2.A Ensure that by 2015 children everywhere, boys andgirls alike, will be able to complete a full course ofprimary schoolingGoal 3 Promote gender equality and empower womenTarget 3.A Eliminate gender disparity in primary and secondaryeducation, preferably by 2005, and in all levels ofeducation no later than 2015Goal 4 Reduce child mortalityTarget 4.A Reduce by two-thirds, between 1990 and 2015, theunder-five mortality rateGoal 5 Improve maternal healthTarget 5.A Reduce by three-quarters, between 1990 and 2015,the maternal mortality ratioTarget 5.B Achieve by 2015 universal access to reproductivehealthGoal 6 Combat HIV/AIDS, malaria, and other diseasesTarget 6.A Have halted by 2015 and begun to reverse thespread of HIV/AIDSTarget 6.B Achieve by 2010 universal access to treatment forHIV/AIDS for all those who need itTarget 6.C Have halted by 2015 and begun to reverse theincidence of malaria and other major diseases1.1 Proportion of population below $1 purchasing powerparity (PPP) a day a1.2 Poverty gap ratio [incidence × depth of poverty]1.3 Share of poorest quintile in national consumption1.4 Growth rate of GDP per person employed1.5 Employment to population ratio1.6 Proportion of employed people living below $1 (PPP) a day1.7 Proportion of own-account and contributing familyworkers in total employment1.8 Prevalence of underweight children under five years of age1.9 Proportion of population below minimum level of dietaryenergy consumption2.1 Net enrollment ratio in primary education2.2 Proportion of pupils starting grade 1 who reach lastgrade of primary education2.3 Literacy rate of 15- to 24-year-olds, women and men3.1 Ratios of girls to boys in primary, secondary, and tertiaryeducation3.2 Share of women in wage employment in thenonagricultural sector3.3 Proportion of seats held by women in national parliament4.1 Under-five mortality rate4.2 Infant mortality rate4.3 Proportion of one-year-old children immunized againstmeasles5.1 Maternal mortality ratio5.2 Proportion of births attended by skilled health personnel5.3 Contraceptive prevalence rate5.4 Adolescent birth rate5.5 Antenatal care coverage (at least one visit and at leastfour visits)5.6 Unmet need for family planning6.1 HIV prevalence among population ages 15–24 years6.2 Condom use at last high-risk sex6.3 Proportion of population ages 15–24 years withcomprehensive, correct knowledge of HIV/AIDS6.4 Ratio of school attendance of orphans to schoolattendance of nonorphans ages 10–14 years6.5 Proportion of population with advanced HIV infection withaccess to antiretroviral drugs6.6 Incidence and death rates associated with malaria6.7 Proportion of children under age five sleeping underinsecticide-treated bednets6.8 Proportion of children under age five with fever who aretreated with appropriate antimalarial drugs6.9 Incidence, prevalence, and death rates associated withtuberculosis6.10 Proportion of tuberculosis cases detected and curedunder directly observed treatment short courseThe Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www.un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring frameworkshown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDGIndicators at its 12th meeting on November 14, 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countriesand the developing countries “to create an environment—at the national and global levels alike—which is conducive to development and the elimination of poverty.” All indicators should bedisaggregated by sex and urban-rural location as far as possible.182012 World Development Indicators

Goals and targets from the Millennium Declaration Indicators for monitoring progressGoal 7 Ensure environmental sustainabilityTarget 7.A Integrate the principles of sustainable developmentinto country policies and programs and reverse theloss of environmental resourcesTarget 7.B Reduce biodiversity loss, achieving, by 2010, asignificant reduction in the rate of lossTarget 7.C Halve by 2015 the proportion of people withoutsustainable access to safe drinking water and basicsanitationTarget 7.D Achieve by 2020 a significant improvement in thelives of at least 100 million slum dwellersGoal 8 Develop a global partnership for developmentTarget 8.A Develop further an open, rule-based, predictable,nondiscriminatory trading and financial system(Includes a commitment to good governance,development, and poverty reduction—bothnationally and internationally.)Target 8.B Address the special needs of the least developedcountries(Includes tariff and quota-free access for the leastdeveloped countries’ exports; enhanced program ofdebt relief for heavily indebted poor countries (HIPC)and cancellation of official bilateral debt; and moregenerous ODA for countries committed to povertyreduction.)Target 8.C Address the special needs of landlockeddeveloping countries and small island developingstates (through the Programme of Action forthe Sustainable Development of Small IslandDeveloping States and the outcome of the 22ndspecial session of the General Assembly)Target 8.D Deal comprehensively with the debt problemsof developing countries through national andinternational measures in order to make debtsustainable in the long termTarget 8.E In cooperation with pharmaceutical companies,provide access to affordable essential drugs indeveloping countriesTarget 8.F In cooperation with the private sector, makeavailable the benefits of new technologies,especially information and communications7.1 Proportion of land area covered by forest7.2 Carbon dioxide emissions, total, per capita andper $1 GDP (PPP)7.3 Consumption of ozone-depleting substances7.4 Proportion of fish stocks within safe biological limits7.5 Proportion of total water resources used7.6 Proportion of terrestrial and marine areas protected7.7 Proportion of species threatened with extinction7.8 Proportion of population using an improved drinking watersource7.9 Proportion of population using an improved sanitationfacility7.10 Proportion of urban population living in slums bSome of the indicators listed below are monitored separatelyfor the least developed countries (LDCs), Africa, landlockeddeveloping countries, and small island developing states.Official development assistance (ODA)8.1 Net ODA, total and to the least developed countries, aspercentage of OECD/DAC donors’ gross national income8.2 Proportion of total bilateral, sector-allocable ODA ofOECD/DAC donors to basic social services (basiceducation, primary health care, nutrition, safe water, andsanitation)8.3 Proportion of bilateral official development assistance ofOECD/DAC donors that is untied8.4 ODA received in landlocked developing countries as aproportion of their gross national incomes8.5 ODA received in small island developing states as aproportion of their gross national incomesMarket access8.6 Proportion of total developed country imports (by valueand excluding arms) from developing countries and leastdeveloped countries, admitted free of duty8.7 Average tariffs imposed by developed countries onagricultural products and textiles and clothing fromdeveloping countries8.8 Agricultural support estimate for OECD countries as apercentage of their GDP8.9 Proportion of ODA provided to help build trade capacityDebt sustainability8.10 Total number of countries that have reached their HIPCdecision points and number that have reached their HIPCcompletion points (cumulative)8.11 Debt relief committed under HIPC Initiative andMultilateral Debt Relief Initiative (MDRI)8.12 Debt service as a percentage of exports of goods andservices8.13 Proportion of population with access to affordableessential drugs on a sustainable basis8.14 Telephone lines per 100 population8.15 Cellular subscribers per 100 population8.16 Internet users per 100 populationa. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends.b. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access toimproved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material.2012 World Development Indicators 19

1.1Size of the economyPopulationSurfaceareaPopulationdensityGross nationalincome,Atlas methodGross nationalincome per capita,Atlas methodPurchasing power paritygross national incomeGross domesticproductthousand peoplePer capitaPer capitamillions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10Afghanistan 34 652 53 14.3 109 410 204 36.5 a 1,060 a 199 8.2 5.2Albania 3 29 117 12.7 117 3,960 b 124 27.3 8,520 114 3.5 3.1Algeria 35 2,382 15 155.7 49 4,390 119 287.2 a 8,100 a 117 3.3 1.8Angola 19 1,247 15 75.2 62 3,940 125 103.1 5,410 135 2.3 3.0Argentina 40 2,780 15 348.4 26 8,620 85 629.3 15,570 78 9.2 8.2Armenia 3 30 109 9.9 127 3,200 135 17.5 5,660 133 2.1 1.9Australia 22 7,741 3 1,030.3 13 46,200 20 823.0 36,910 32 2.3 0.7Austria 8 84 102 394.6 25 47,030 18 333.9 39,790 24 2.3 2.0Azerbaijan 9 87 110 48.3 74 5,330 109 83.9 9,270 108 5.0 3.8Bahrain 1 1 1,661 19.7 99 18,730 62 26.0 24,710 55 6.3 –6.5Bangladesh 149 144 1,142 104.7 57 700 187 269.7 1,810 182 6.1 4.9Belarus 9 208 47 56.5 67 5,950 104 129.0 13,590 88 7.6 7.8Belgium 11 31 360 499.5 20 45,840 21 417.3 38,290 28 2.3 1.3Benin 9 113 80 7.0 139 780 184 14.0 1,590 186 3.0 0.1Bolivia 10 1,099 9 17.9 104 1,810 158 46.0 4,640 142 4.1 2.5Bosnia and Herzegovina 4 51 74 17.9 105 4,770 112 33.5 8,910 113 0.8 1.0Botswana 2 582 4 13.6 113 6,790 95 27.5 13,700 86 7.2 5.9Brazil 195 8,515 23 1,830.4 8 9,390 82 2,144.9 11,000 98 7.5 6.6Bulgaria 8 111 69 47.3 75 6,280 101 101.2 13,440 89 0.2 0.9Burkina Faso 16 274 60 9.0 131 550 193 20.7 1,250 190 9.2 6.0Burundi 8 28 326 1.4 184 170 215 3.4 400 213 3.9 1.3Cambodia 14 181 80 10.6 121 750 185 29.4 2,080 174 6.0 4.8Cameroon 20 475 41 23.2 95 1,180 168 44.4 2,270 170 2.6 1.0Canada 34 9,985 4 1,475.9 10 43,250 23 1,309.5 38,370 27 3.2 2.0Central African Republic 4 623 7 2.1 177 470 199 3.5 790 209 3.3 1.4Chad 11 1,284 9 6.9 140 620 189 13.7 1,220 193 4.3 1.6Chile 17 756 23 173.2 47 10,120 78 250.5 14,640 80 5.2 4.2China 1,338 9,600 143 5,720.8 2 4,270 121 10,221.7 7,640 120 10.4 9.8Hong Kong SAR, China 7 1 6,783 231.7 37 32,780 36 335.6 47,480 15 7.0 6.0Colombia 46 1,142 42 255.3 34 5,510 108 419.6 9,060 109 4.3 2.9Congo, Dem. Rep. 66 2,345 29 12.0 119 180 214 21.4 320 215 7.2 4.3Congo, Rep. 4 342 12 8.7 135 2,150 152 13.0 3,220 160 8.8 6.0Costa Rica 5 51 91 31.7 87 6,810 94 52.5 a 11,270 a 96 4.2 2.7Côte d’Ivoire 20 322 62 23.0 96 1,160 171 35.8 1,810 182 3.0 1.0Croatia 4 57 79 61.4 66 13,890 67 83.4 18,890 73 –1.2 –0.9Cuba 11 110 106 62.2 64 5,520 107 .. .. 4.3 4.3Cyprus 1 9 119 23.7 c 93 29,430 c 40 24.4 c 30,300 c 42 1.0 c 0.6 cCzech Republic 11 79 136 188.3 43 17,890 59 241.0 22,910 63 2.3 2.0Denmark 6 43 131 329.5 29 59,400 10 228.0 41,100 23 1.3 0.9Dominican Republic 10 49 205 49.9 72 5,030 110 89.6 a 9,030 a 111 7.8 6.3Ecuador 14 256 58 55.7 69 3,850 b 126 114.0 7,880 118 3.6 2.1Egypt, Arab Rep. 81 1,001 81 196.2 41 2,420 148 491.3 6,060 129 5.1 3.3El Salvador 6 21 299 21.0 97 3,380 131 40.6 a 6,550 a 126 1.4 0.9Eritrea 5 118 52 1.8 180 340 209 2.8 a 540 a 212 2.2 –0.8Estonia 1 45 32 19.4 101 14,460 66 26.5 19,810 68 3.1 3.1Ethiopia 83 1,104 83 32.4 85 390 207 86.1 1,040 200 10.1 7.8Finland 5 338 18 255.2 35 47,570 16 198.9 37,070 31 3.7 3.2France 65 549 118 2,749.8 5 42,370 26 2,254.9 34,750 34 1.5 0.9Gabon 2 268 6 11.7 120 7,740 89 19.8 13,180 90 5.7 3.8Gambia, The 2 11 173 0.8 198 450 201 2.2 1,300 189 5.0 2.1Georgia 4 d 70 78 d 12.0 d 118 2,690 d 145 22.2 d 4,990 d 139 6.4 d 5.4 dGermany 82 357 235 3,522.0 4 43,070 25 3,115.4 38,100 29 3.7 3.8Ghana 24 239 107 30.1 88 1,230 166 40.5 1,660 185 6.6 5.2Greece 11 132 88 305.0 31 26,950 44 312.7 27,630 51 –3.5 –3.8Guatemala 14 109 134 39.4 79 2,740 142 66.8 a 4,650 a 141 2.8 0.2Guinea 10 246 41 4.0 160 400 206 10.2 1,020 202 1.9 –0.3Guinea-Bissau 2 36 54 0.9 194 590 191 1.8 1,180 196 3.5 1.4Haiti 10 28 363 6.6 141 670 188 11.6 a 1,180 a 195 –5.1 –6.3202012 World Development Indicators

Size of the economy1.1WORLD VIEWPopulationSurfaceareaPopulationdensityGross nationalincome,Atlas methodGross nationalincome per capita,Atlas methodPurchasing power paritygross national incomeGross domesticproductthousand peoplePer capitaPer capitamillions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10Honduras 8 112 68 14.2 110 1,870 155 28.6 a 3,770 a 151 2.8 0.7Hungary 10 93 110 128.6 53 12,860 69 195.5 19,550 69 1.3 1.5India 1,225 3,287 412 1,553.9 9 1,270 164 4,159.7 3,400 157 8.8 7.3Indonesia 240 1,905 132 599.2 18 2,500 147 1,008.2 4,200 148 6.1 5.0Iran, Islamic Rep. 74 1,745 45 330.4 27 4,520 116 840.0 11,490 95 1.8 0.6Iraq 32 435 74 74.9 63 2,340 149 108.1 3,370 158 0.8 –2.1Ireland 4 70 65 187.1 44 41,820 29 150.1 33,540 37 –0.4 –0.8Israel 8 22 352 207.2 39 27,180 43 210.8 27,660 50 4.7 2.8Italy 60 301 206 2,159.3 7 35,700 33 1,923.7 31,810 39 1.5 1.1Jamaica 3 11 249 13.0 115 4,800 111 19.7 a 7,310 a 122 –0.6 –0.8Japan 127 378 350 5,334.4 3 41,850 28 4,411.7 34,610 35 4.0 4.1Jordan 6 89 68 26.3 90 4,340 120 35.1 5,800 130 3.1 0.9Kazakhstan 16 2,725 6 123.8 56 7,580 90 175.7 10,770 103 7.3 5.8Kenya 41 580 71 31.8 86 790 183 68.1 1,680 184 5.3 2.8Korea, Dem. Rep. 24 121 202 .. .. e .. .. .. ..Korea, Rep. 49 100 503 972.3 15 19,890 55 1,422.7 29,110 43 6.2 5.9Kosovo 2 11 167 6.0 147 3,290 132 .. .. 4.0 3.4Kuwait 3 18 154 .. .. f .. .. .. ..Kyrgyz Republic 5 200 28 4.5 157 830 181 11.3 2,070 175 –1.4 –2.5Lao PDR 6 237 27 6.5 144 1,050 176 15.3 2,460 168 9.4 7.9Latvia 2 65 36 26.1 91 11,640 74 36.7 16,380 76 –0.3 0.4Lebanon 4 10 413 37.5 82 8,880 84 59.5 14,090 84 7.0 6.2Lesotho 2 30 72 2.3 174 1,040 178 4.3 1,960 177 3.3 2.6Liberia 4 111 41 0.8 197 200 213 1.4 340 214 5.5 1.3Libya 6 1,760 4 77.1 61 12,320 72 105.7 a 16,880 a 75 2.1 0.3Lithuania 3 65 52 37.8 81 11,510 76 59.4 18,060 74 1.3 2.9Macedonia, FYR 2 26 82 9.4 128 4,570 115 22.5 10,920 100 1.8 1.6Madagascar 21 587 36 8.8 134 430 203 19.9 960 204 1.6 –1.3Malawi 15 118 158 4.9 156 330 211 12.7 850 207 7.1 3.8Malaysia 28 331 86 220.4 38 7,760 87 403.9 14,220 83 7.2 5.5Mali 15 1,240 13 9.2 130 600 190 15.8 1,030 201 4.5 1.4Mauritania 3 1,031 3 3.6 162 1,030 179 6.6 1,910 180 5.0 2.7Mauritius 1 2 631 9.9 126 7,750 88 17.9 13,960 85 4.0 3.7Mexico 113 1,964 58 1,008.0 14 8,890 83 1,627.0 14,340 81 5.4 4.1Moldova 4 g 34 124 g 6.5 g 145 1,810 g 158 12.0 g 3,360 g 159 6.9 g 7.1 gMongolia 3 1,564 2 5.2 153 1,870 155 10.1 3,670 152 6.4 4.7Morocco 32 447 72 92.6 h 59 2,850 h 140 149.3 h 4,600 h 143 3.7 h 2.6 hMozambique 23 799 30 10.3 123 440 202 21.7 930 205 7.2 4.8Myanmar 48 677 73 .. .. e 93.5 a 1,950 a 178 10.4 9.6Namibia 2 824 3 10.3 124 4,510 118 14.7 6,420 127 4.8 2.9Nepal 30 147 209 14.5 108 490 197 36.2 1,210 194 4.6 2.7Netherlands 17 42 493 814.8 16 49,030 14 694.7 41,810 22 1.7 1.2New Zealand 4 268 17 124.2 54 28,770 42 121.3 28,100 46 –0.5 –1.6Nicaragua 6 130 48 6.4 146 1,110 173 16.1 a 2,790 a 164 7.6 6.1Niger 16 1,267 12 5.7 149 370 208 11.2 720 210 8.8 5.0Nigeria 158 924 174 186.4 45 1,180 168 344.2 2,170 172 7.9 5.2Norway 5 324 16 427.1 24 87,350 4 286.3 58,570 6 0.7 –0.6Oman 3 310 9 49.5 71 18,260 58 68.3 25,190 56 1.1 –1.7Pakistan 174 796 225 182.8 46 1,050 176 484.4 2,790 164 4.1 2.3Panama 4 75 47 24.5 92 6,970 92 44.9 a 12,770 a 91 4.8 3.2Papua New Guinea 7 463 15 8.9 133 1,300 161 16.6 a 2,420 a 169 8.0 5.6Paraguay 6 407 16 17.6 106 2,720 144 32.8 5,080 137 15.0 13.0Peru 29 1,285 23 136.7 52 4,700 114 259.6 8,930 112 8.8 7.6Philippines 93 300 313 192.2 42 2,060 153 370.7 3,980 149 7.6 5.8Poland 38 313 126 474.9 21 12,440 71 731.5 19,160 71 3.9 3.9Portugal 11 92 116 232.7 36 21,870 48 261.6 24,590 59 1.4 1.3Puerto Rico 4 9 448 61.7 65 15,500 63 .. .. –2.1 –2.3Qatar 2 12 152 .. .. f .. .. 8.6 –5.12012 World Development Indicators 21

1.1Size of the economyPopulationSurfaceareaPopulationdensityGross nationalincome,Atlas methodGross nationalincome per capita,Atlas methodPurchasing power paritygross national incomeGross domesticproductthousand peoplePer capitaPer capitamillions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10Romania 21 238 93 168.2 48 7,850 86 306.4 14,290 82 0.9 1.1Russian Federation 142 17,098 9 1,403.9 12 9,900 79 2,726.8 19,240 70 4.0 4.1Rwanda 11 26 431 5.5 151 520 195 12.3 1,150 197 7.5 4.3Saudi Arabia 27 2,150 i 13 434.1 23 16,190 61 609.8 22,750 64 3.8 1.3Senegal 12 197 65 13.5 114 1,090 174 23.8 1,910 180 4.2 1.4Serbia 7 88 83 41.0 78 5,630 106 80.8 11,090 97 1.0 1.4Sierra Leone 6 72 82 2.0 178 340 209 4.9 830 208 4.9 2.7Singapore 5 1 7,253 203.4 40 40,070 30 283.3 55,790 7 14.5 12.5Slovak Republic 5 49 113 91.5 60 16,840 60 124.8 22,980 62 4.2 4.0Slovenia 2 20 102 49.0 73 23,900 47 54.4 26,530 54 1.4 0.9Somalia 9 638 15 .. .. e .. .. .. ..South Africa 50 1,219 41 304.6 32 6,090 102 517.9 10,360 105 2.8 1.5South Sudan .. .. .. .. .. j .. .. .. ..Spain 46 505 92 1,462.9 11 31,750 39 1,465.2 31,800 40 –0.1 –0.5Sri Lanka 21 66 333 46.7 76 2,240 150 104.6 5,010 138 8.0 7.0Sudan 44 2,506 18 55.3 70 1,270 164 88.6 2,030 176 4.5 1.9Swaziland 1 17 61 3.1 168 2,950 138 5.7 5,430 134 1.1 0.8Sweden 9 450 23 469.8 22 50,100 13 372.6 39,730 25 5.6 4.7Switzerland 8 41 196 559.7 19 71,520 7 391.0 49,960 14 2.7 1.6Syrian Arab Republic 20 185 111 56.3 68 2,750 141 104.6 5,120 136 3.2 1.1Tajikistan 7 143 49 5.5 152 800 182 14.7 2,140 173 3.8 2.4Tanzania 45 947 51 23.4 k 94 530 k 194 62.6 k 1,430 k 187 7.0 k 3.9 kThailand 69 513 135 286.6 33 4,150 123 565.8 8,190 115 7.8 7.2Timor-Leste 1 15 76 2.5 171 2,220 151 4.0 a 3,600 a 153 7.4 5.1Togo 6 57 111 3.0 169 490 197 5.4 890 206 3.4 1.2Trinidad and Tobago 1 5 261 20.6 98 15,380 64 32.3 a 24,050 a 60 0.1 –0.2Tunisia 11 164 68 43.9 77 4,160 122 95.6 9,060 109 3.7 2.6Turkey 73 784 95 719.9 17 9,890 80 1,129.9 15,530 79 9.0 7.6Turkmenistan 5 488 11 19.1 103 3,790 128 37.8 a 7,490 a 121 9.2 7.9Uganda 33 242 167 16.6 107 500 196 41.8 1,250 190 5.2 1.9Ukraine 46 604 79 137.8 51 3,000 136 303.8 6,620 125 4.2 4.6United Arab Emirates 8 84 90 290.9 30 41,930 27 351.0 50,580 11 1.4 –6.3United Kingdom 62 244 257 2,377.2 6 38,200 31 2,230.6 35,840 33 2.1 1.4United States 309 9,832 34 14,645.6 1 47,340 17 14,635.6 47,310 16 3.0 2.1Uruguay 3 176 19 34.3 84 10,230 77 45.7 13,620 87 8.5 8.1Uzbekistan 28 447 66 36.1 83 1,280 162 87.7 a 3,110 a 161 8.5 6.7Venezuela, RB 29 912 33 334.1 28 11,590 75 350.2 12,150 93 –1.5 –3.0Vietnam 87 331 280 101.1 58 1,160 171 267.0 3,070 162 6.8 5.7West Bank and Gaza 4 6 690 .. .. j .. .. .. ..Yemen, Rep. 24 528 46 28.1 89 1,170 170 60.1 2,500 166 8.0 4.8Zambia 13 753 17 13.8 111 1,070 175 17.8 1,380 188 7.6 5.9Zimbabwe 13 391 32 5.8 148 460 200 .. .. 9.0 8.2World 6,895 s 134,222 s 53 w 62,525.2 t 9,069 w 76,295.6 t 11,066 w 4.2 w 3.0 wLow income 796 15,551 53 420.2 528 1,040.5 1,307 5.9 3.7Middle income 4,971 82,896 62 18,508.7 3,723 33,538.4 6,747 7.6 6.4Lower middle income 2,519 23,568 111 4,077.7 1,619 9,147.8 3,632 6.9 5.3Upper middle income 2,452 59,328 42 14,429.0 5,884 24,447.4 9,970 7.8 7.1Low & middle income 5,767 98,447 60 18,948.9 3,286 34,577.9 5,996 7.6 6.2East Asia & Pacifi c 1,962 16,302 124 7,249.4 3,696 13,058.0 6,657 9.7 8.9Europe & Central Asia 405 23,603 18 2,946.7 7,272 5,428.2 13,396 5.7 5.3Latin America & Carib. 583 20,394 29 4,505.0 7,733 6,365.1 10,926 6.2 5.0Middle East & N. Africa 331 8,775 38 1,283.5 3,874 2,627.3 8,068 4.3 2.5South Asia 1,633 5,131 342 1,920.1 1,176 5,101.4 3,124 8.1 6.6Sub-Saharan Africa 853 24,243 36 1,003.6 1,176 1,833.4 2,148 4.8 2.3High income 1,127 35,774 33 43,682.7 38,745 42,072.5 37,317 3.1 2.5Euro area 332 2,628 130 12,794.4 38,565 11,399.6 34,360 2.0 1.6a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Included in the aggregates for upper middle-income economiesbased on earlier data. c. Refers to the area controlled by the government of the Republic of Cyprus. d. Excludes Abkhazia and South Ossetia. e. Estimated to be low income ($1,005 orless). f. Estimated to be high income ($12,276 or more). g. Excludes Transnistria. h. Includes Former Spanish Sahara. i. Provisional estimate. j. Estimated to be lower middle income($1,006–$3,975). k. Covers mainland Tanzania only.22 2012 World Development Indicators

Size of the economy1.1WORLD VIEWAbout the dataDefinitionsPopulation, land area, income, and output are basicmeasures of the size of an economy. They alsoprovide a broad indication of actual and potentialresources. Population, land area, income (as measuredby gross national income, GNI), and output(as measured by gross domestic product, GDP) aretherefore used throughout World Development Indicatorsto normalize other indicators.Population estimates are generally based onextrapolations from the most recent national census.For further discussion of the measurement ofpopulation and population growth, see About the datafor table 2.1.The surface area of an economy includes inlandbodies of water and some coastal waterways. Surfacearea thus differs from land area, which excludesbodies of water, and from gross area, which mayinclude offshore territorial waters. Land area is particularlyimportant for understanding an economy’sagricultural capacity and the environmental effectsof human activity. (For measures of land area anddata on rural population density, land use, and agriculturalproductivity, see tables 3.1–3.3.) Innovationsin satellite mapping and computer databaseshave resulted in more precise measurements of landand water areas.GNI measures total domestic and foreign valueadded claimed by residents. GNI comprises GDPplus net receipts of primary income (compensationof employees and property income) from nonresidentsources. The World Bank uses GNI per capita in U.S.dollars to classify countries for analytical purposesand to determine borrowing eligibility. For defi nitionsof the income groups in World Development Indicators,see Users guide. For discussion of the usefulnessof national income and output as measures ofproductivity or welfare, see About the data for tables4.1 and 4.2.When calculating GNI in U.S. dollars from GNIreported in national currencies, the World Bank followsthe World Bank Atlas conversion method, usinga three-year average of exchange rates to smooththe effects of transitory fl uctuations in exchangerates. (For further discussion of the World Bank Atlasmethod, see Statistical methods.)Because exchange rates do not always refl ectdifferences in price levels between countries,the table also converts GNI and GNI per capitaestimates into international dollars using purchasingpower parity (PPP) rates. PPP rates providea standard measure allowing comparison of reallevels of expenditure between countries, just asconventional price indexes allow comparison of realvalues over time.PPP rates are calculated by simultaneously comparingthe prices of similar goods and services among alarge number of countries. In the most recent roundof price surveys conducted by the International ComparisonProgram (ICP), 146 countries and territoriesparticipated in the data collection, including Chinafor the fi rst time, India for the fi rst time since 1985,and almost all African countries. The PPP conversionfactors presented in the table come from threesources. For 47 high- and upper middle-incomecountries conversion factors are provided by Eurostatand the Organisation for Economic Co-operationand Development (OECD), with PPP estimates for35 European countries incorporating new price datacollected since 2005. For the remaining 2005 ICPcountries the PPP estimates are extrapolated fromthe 2005 ICP benchmark results, which account forrelative price changes between each economy andthe United States. For countries that did not participatein the 2005 ICP round, the PPP estimates areimputed using a statistical model. More informationon the results of the 2005 ICP is available at www.worldbank.org/data/icp.All 216 economies shown in World DevelopmentIndicators are ranked by size, including those thatappear in table 1.6. The ranks are shown only intable 1.1. No rank is shown for economies for whichnumerical estimates of GNI per capita are not published.Economies with missing data are included inthe ranking at their approximate level, so that the relativeorder of other economies remains consistent.• Population is based on the de facto defi nition ofpopulation, which counts all residents regardless oflegal status or citizenship—except for refugees notpermanently settled in the country of asylum, whoare generally considered part of the population oftheir country of origin. The values shown are midyearestimates. See also table 2.1. • Surface area isa country’s total area, including areas under inlandbodies of water and some coastal waterways. • Populationdensity is midyear population divided by landarea in square kilometers. • Gross national income(GNI) is the sum of value added by all resident producersplus any product taxes (less subsidies) notincluded in the valuation of output plus net receiptsof primary income (compensation of employees andproperty income) from abroad. Data are in currentU.S. dollars converted using the World Bank Atlasmethod (see Statistical methods). • Gross nationalincome per capita is GNI divided by midyear population.GNI per capita in U.S. dollars is converted usingthe World Bank Atlas method. • Purchasing powerparity (PPP) gross national income is GNI convertedto international dollars using PPP rates. An internationaldollar has the same purchasing power over GNIthat a U.S. dollar has in the United States. • Grossdomestic product (GDP) is the sum of value addedby all resident producers plus any product taxes (lesssubsidies) not included in the valuation of output.Growth is calculated from constant price GDP data inlocal currency. • Gross domestic product per capitais GDP divided by midyear population.Data sourcesPopulation estimates are prepared by World Bankstaff from a variety of sources (see Data sourcesfor table 2.1). Data on surface and land area arefrom the Food and Agriculture Organization (seeData sources for table 3.1). GNI, GNI per capita,GDP growth, and GDP per capita growth are estimatedby World Bank staff based on nationalaccounts data collected by World Bank staff duringeconomic missions or reported by national statisticaloffi ces to other international organizationssuch as the OECD. PPP conversion factors areestimates by Eurostat/OECD and by World Bankstaff based on data collected by the ICP.2012 World Development Indicators 23

1.2Millennium Development Goals:eradicating poverty and saving livesEradicate extreme poverty and hungerAchieve universalprimary educationPromote genderequalityReducechild mortalityShare ofpoorest quintile Vulnerablein national employmentconsumption Unpaid family workers andor income own-account workers% % of total employmentPrevalence ofmalnutritionUnderweight% of childrenunder age 5Ratio of girls to boysenrollments in primaryand secondary education%Primarycompletion rate%Under-fi vemortality rateper 1,000 live births2000–11 a,b 1990 2007–10 a 1990 2005–10 a 1991 2010 1991 2010 c 1990 2010Afghanistan 9.4 .. .. .. .. 28 .. 54 64 209 149Albania 8.1 .. .. .. 6.3 .. 86 96 98 41 18Algeria .. .. .. 9.2 3.7 80 96 83 98 68 36Angola 2.0 d .. .. .. 15.6 33 47 .. 79 243 161Argentina 4.4 d 26 e 20 e .. 2.3 100 106 107 104 27 14Armenia 8.8 .. 38 .. 4.2 105 101 .. 102 55 20Australia .. 10 9 .. .. .. .. 100 98 9 5Austria 8.6 .. 9 .. .. .. 99 95 97 9 4Azerbaijan 8.0 .. 55 .. 8.4 95 90 100 99 93 46Bahrain .. .. 2 6.3 .. 97 .. 101 .. 17 10Bangladesh 8.9 .. .. 61.5 41.3 41 65 75 107 143 48Belarus 9.2 .. .. .. 1.3 94 103 .. 101 17 6Belgium 8.5 17 10 .. .. 79 90 101 98 10 4Benin 7.0 .. .. .. 20.2 22 63 .. .. 178 115Bolivia 2.1 40 e 57 9.7 4.5 71 99 .. 99 121 54Bosnia and Herzegovina 6.7 .. .. .. 1.6 .. 70 .. 102 19 8Botswana .. .. .. .. 11.2 90 94 109 100 59 48Brazil 2.9 29 e 25 .. 2.2 93 .. .. 102 59 19Bulgaria 8.5 .. 9 .. .. 90 95 99 97 22 13Burkina Faso 6.7 .. .. 29.6 26.0 20 45 .. 89 f 205 176Burundi 9.0 .. .. 30.2 35.2 46 56 82 94 183 142Cambodia 7.5 .. 83 .. 28.8 45 87 .. 94 121 51Cameroon 6.7 .. .. 18 16.6 53 79 83 85 137 136Canada 7.2 .. .. .. .. .. .. 99 99 8 6Central African Republic 3.4 .. .. .. .. 28 41 61 69 165 159Chad 6.3 94 .. .. .. 18 33 41 65 207 173Chile 4.3 .. 26 .. 0.5 .. 96 100 99 19 9China 5.0 .. .. 12.6 3.4 107 .. 86 103 48 18Hong Kong SAR, China .. 6 7 .. .. 102 96 102 102 .. ..Colombia 3.0 28 49 8.8 3.4 73 114 108 104 37 22Congo, Dem. Rep. 5.5 .. .. .. 28.2 48 59 70 79 181 170Congo, Rep. 5.0 .. .. 21.1 11.8 54 71 89 .. 116 93Costa Rica 3.9 25 20 2.5 1.1 79 96 101 102 17 10Côte d’Ivoire 5.6 .. .. .. 29.4 42 59 f .. .. 151 123Croatia 8.1 .. 18 .. 1.0 85 95 103 102 13 6Cuba .. .. .. .. .. 99 98 106 98 13 6Cyprus .. .. 14 .. .. 90 103 100 100 11 4Czech Republic .. 7 14 0.9 .. 92 101 98 101 14 4Denmark .. 7 5 .. .. 98 97 101 101 9 4Dominican Republic 4.7 39 42 8.4 3.4 61 92 .. 97 62 27Ecuador 4.3 36 e 43 .. .. 91 106 100 103 52 20Egypt, Arab Rep. 9.2 28 27 10.5 6.8 .. 98 81 .. 94 22El Salvador 3.7 35 38 11.1 6.6 65 96 101 98 62 16Eritrea .. .. .. 36.9 .. 18 40 82 80 141 61Estonia 6.8 2 5 .. .. .. 98 103 101 21 5Ethiopia 9.3 92 .. .. 34.6 23 72 68 89 184 106Finland 9.6 .. 9 .. .. 97 98 109 102 7 3France .. 11 7 .. .. 106 .. 102 100 9 4Gabon 6.2 48 .. .. .. 62 .. 96 .. 93 74Gambia, The 4.8 .. .. .. 15.8 45 71 65 99 165 98Georgia 5.3 .. 63 .. 1.1 .. 116 98 97 47 22Germany 8.5 5 7 .. 1.1 100 100 99 96 9 4Ghana 5.2 .. .. 24.1 14.3 64 94 f 78 96 f 122 74Greece 6.7 42 28 .. .. 99 101 99 97 13 4Guatemala 3.1 .. .. 27.8 13.0 .. 84 87 95 78 32Guinea 6.4 .. .. .. 20.8 17 64 45 77 229 130Guinea-Bissau 7.3 .. .. .. 17.2 5 68 55 .. 210 150Haiti 2.4 .. .. 23.7 18.9 27 .. .. .. 151 165242012 World Development Indicators

Millennium Development Goals:1.2eradicating poverty and saving livesWORLD VIEWEradicate extreme poverty and hungerAchieve universalprimary educationPromote genderequalityReducechild mortalityShare ofpoorest quintile Vulnerablein national employmentconsumption Unpaid family workers andor income own-account workers% % of total employmentPrevalence ofmalnutritionUnderweight% of childrenunder age 5Ratio of girls to boysenrollments in primaryand secondary education%Primarycompletion rate%Under-fi vemortality rateper 1,000 live births2000–11 a,b 1990 2007–10 a 1990 2005–10 a 1991 2010 1991 2010 c 1990 2010Honduras 2.0 49 e 50 15.8 8.6 64 99 104 107 58 24Hungary 8.4 7 7 2.3 .. 82 98 100 99 19 6India 8.6 .. .. 59.5 43.5 64 96 73 92 115 63Indonesia 8.3 .. 64 31.0 17.5 93 105 93 101 85 35Iran, Islamic Rep. 6.4 .. 42 .. .. 88 104 85 96 65 26Iraq 8.7 .. .. 10.4 7.1 58 65 79 81 46 39Ireland 7.4 20 12 .. .. 103 .. 104 103 9 4Israel 5.7 .. 7 .. .. .. 103 105 101 12 5Italy 6.5 27 19 .. .. 98 103 100 99 10 4Jamaica 5.4 42 37 4.0 1.9 94 73 103 99 38 24Japan .. 19 10 .. .. 102 102 101 100 6 3Jordan 7.7 .. 10 4.8 1.9 101 101 101 102 38 22Kazakhstan 9.1 .. 32 .. 4.9 103 116 f .. 98 f 57 33Kenya 4.8 .. .. 20.1 16.4 .. .. 92 95 99 85Korea, Dem. Rep. .. .. .. .. 18.8 .. .. .. .. 45 33Korea, Rep. .. .. 24 .. .. 99 101 99 99 8 5Kosovo .. .. .. .. .. .. .. .. .. .. ..Kuwait .. .. .. .. 1.7 57 112 100 105 15 11Kyrgyz Republic 6.8 .. .. .. 2.7 .. 97 102 99 72 38Lao PDR 7.6 .. .. 39.8 31.6 41 79 77 87 145 54Latvia 6.6 .. 8 .. .. .. 92 101 98 21 10Lebanon .. .. 28 .. .. .. 87 101 104 38 22Lesotho 3.0 .. .. 13.8 13.5 59 70 124 106 89 85Liberia 6.4 .. 79 .. 20.4 .. 62 .. .. 227 103Libya .. .. .. .. 5.6 .. .. .. .. 45 17Lithuania 6.6 .. 9 .. .. .. 96 96 99 17 7Macedonia, FYR 5.1 .. 23 .. 1.8 98 92 99 99 39 12Madagascar 5.4 84 .. 35.5 .. 36 72 96 97 159 62Malawi 7.0 84 .. 24.4 13.8 31 67 82 101 222 92Malaysia 4.5 29 22 22.1 12.9 91 .. 101 .. 18 6Mali 8.0 .. 83 29.0 27.9 9 55 f 58 83 f 255 178Mauritania 6.0 .. .. 43.3 15.9 33 75 71 101 124 111Mauritius .. 12 16 .. .. 115 96 102 100 24 15Mexico 4.4 26 30 13.9 3.4 88 104 97 102 49 17Moldova 7.8 .. 29 .. 3.2 .. 93 105 101 37 19Mongolia 7.1 .. 58 10.8 5.3 .. 108 109 103 107 32Morocco 6.5 .. 51 8.1 .. 48 85 70 89 86 36Mozambique 5.2 .. .. .. 18.3 26 61 71 89 219 135Myanmar .. .. .. 28.8 .. .. 104 95 102 112 66Namibia 3.2 .. .. 21.5 17.5 74 84 106 104 73 40Nepal 8.3 .. .. .. 38.8 51 .. 59 .. 141 50Netherlands .. 9 11 .. .. .. .. 97 99 8 4New Zealand .. 13 11 .. .. .. .. 100 103 11 6Nicaragua 6.2 43 e 45 9.6 5.7 42 81 119 102 68 27Niger 8.1 .. .. 41.0 39.9 17 46 f 53 78 311 143Nigeria 4.4 .. .. 35.1 26.7 .. 74 77 90 213 143Norway 9.6 .. 6 .. .. 100 100 102 99 9 3Oman .. .. .. 21.4 8.6 74 101 89 98 47 9Pakistan 9.6 .. 63 39.0 .. .. 67 48 80 124 87Panama 3.3 34 31 .. .. 86 97 99 101 33 20Papua New Guinea .. .. .. .. 18.1 46 .. 80 .. 90 61Paraguay 3.3 23 e 45 2.8 3.4 68 94 98 100 50 25Peru 3.9 36 e 40 e 8.8 4.5 .. 102 96 99 78 19Philippines 6.0 .. 44 29.8 20.7 88 92 99 101 59 29Poland 7.7 28 19 .. .. 96 95 101 99 17 6Portugal .. 24 18 .. .. .. .. 103 100 15 4Puerto Rico .. .. .. .. .. .. .. .. 104 .. ..Qatar 3.9 .. 0 .. .. 71 100 98 109 21 82012 World Development Indicators 25

1.2Millennium Development Goals:eradicating poverty and saving livesEradicate extreme poverty and hungerAchieve universalprimary educationPromote genderequalityReducechild mortalityShare ofpoorest quintile Vulnerablein national employmentconsumption Unpaid family workers andor income own-account workers% % of total employmentPrevalence ofmalnutritionUnderweight% of childrenunder age 5Ratio of girls to boysenrollments in primaryand secondary education%Primarycompletion rate%Under-fi vemortality rateper 1,000 live births2000–11 a,b 1990 2007–10 a 1990 2005–10 a 1991 2010 1991 2010 c 1990 2010Romania 8.3 27 33 5.0 .. 96 91 99 99 37 14Russian Federation 6.5 1 6 .. .. 92 98 105 98 27 12Rwanda 5.2 .. .. 24.3 18.0 50 70 95 102 163 91Saudi Arabia .. .. .. .. 5.3 .. 93 .. 97 45 18Senegal 6.2 83 .. 19.0 14.5 39 59 69 100 139 75Serbia 8.9 .. 28 .. 1.8 .. 96 .. 101 29 7Sierra Leone 6.1 .. .. 25.4 21.3 .. 74 f 64 .. 276 174Singapore .. 8 10 .. .. .. .. .. .. 8 3Slovak Republic 10.1 .. 12 .. .. 95 98 102 101 18 8Slovenia 8.2 12 14 .. .. 95 95 103 99 10 3Somalia .. .. .. .. 32.8 .. .. .. 53 180 180South Africa 2.7 .. 10 .. 8.7 76 .. 104 99 60 57South Sudan .. .. .. .. .. .. .. .. .. .. ..Spain 7.0 23 11 .. .. 104 102 104 102 11 5Sri Lanka 6.9 43 e 40 e 29.3 21.6 101 101 102 .. 32 17Sudan 6.8 .. .. 31.8 31.7 .. 58 78 90 125 103Swaziland 4.1 .. .. .. 7.3 61 77 .. 94 96 78Sweden 9.1 .. 7 .. .. 96 94 102 99 7 3Switzerland 7.6 9 9 .. .. 53 95 97 98 8 5Syrian Arab Republic 7.7 .. 33 11.5 10.1 89 104 85 99 38 16Tajikistan 8.3 .. .. .. 15.0 .. 104 .. 90 116 63Tanzania 6.8 91 e .. 25.1 16.2 55 90 97 96 155 92Thailand 6.7 70 53 16.3 7.0 .. .. 99 103 32 13Timor-Leste 9.0 .. .. .. 45.3 .. 65 .. 98 169 81Togo 7.6 .. .. 21.2 20.5 35 74 59 75 147 103Trinidad and Tobago .. 22 .. 4.7 .. 102 91 101 101 37 27Tunisia 5.9 .. .. 8.5 3.3 74 91 86 101 49 16Turkey 5.7 .. 34 8.7 .. 90 99 81 95 80 18Turkmenistan .. .. .. .. .. .. .. .. .. 98 56Uganda 5.8 .. .. 19.7 16.4 .. 57 77 99 175 99Ukraine 9.7 .. .. .. .. 92 98 102 99 21 13United Arab Emirates .. .. 1 .. .. 103 .. 104 .. 22 7United Kingdom .. 10 11 .. .. .. .. 102 101 9 5United States 5.4 .. .. .. .. .. 104 100 100 11 8Uruguay 4.9 .. 23 e 6.5 .. 94 106 107 104 23 11Uzbekistan 7.1 .. .. .. 4.4 80 93 f .. 98 f 77 52Venezuela, RB 4.3 .. 31 6.7 3.7 81 94 105 102 33 18Vietnam 7.4 .. .. 40.7 20.2 .. .. .. 102 51 23West Bank and Gaza 7.4 .. 28 .. 2.2 .. 95 .. 104 45 22Yemen, Rep. 7.2 .. .. 29.6 .. .. 63 .. 75 128 77Zambia 3.6 65 .. 21.2 14.9 .. 103 87 96 183 111Zimbabwe .. .. .. 8.0 14.0 97 .. 92 .. 78 80World .. .. w .. w .. w .. w 79 w 88 w 87 w 96 w 90 w 58 wLow income .. .. .. 40.2 23.0 44 65 80 91 165 108Middle income .. .. .. .. .. 83 92 85 97 85 51Lower middle income .. .. 73 38.1 24.6 68 88 81 93 113 69Upper middle income .. .. .. 12.5 3.0 97 98 98 103 49 20Low & middle income .. .. .. 28.7 17.9 78 87 84 96 98 63East Asia & Pacifi c .. .. .. 20.4 5.8 101 97 89 103 56 24Europe & Central Asia .. .. 19 8.5 1.9 92 95 98 97 51 23Latin America & Carib. .. 29 31 7.5 3.0 84 102 99 102 54 23Middle East & N. Africa .. .. 36 11.8 7.9 .. 88 80 93 74 34South Asia .. .. 81 52.2 32.9 62 86 69 92 120 67Sub-Saharan Africa .. .. .. 29.0 22.0 51 67 82 89 175 121High income .. .. .. .. .. .. 97 100 100 12 6Euro area .. 15 11 .. .. 101 101 .. 99 10 4a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Covers urbanareas only. e. Limited coverage. f. Data are for 2011.262012 World Development Indicators

Millennium Development Goals:1.2eradicating poverty and saving livesWORLD VIEWAbout the dataDefinitionsTables 1.2–1.4 present indicators for 17 of the 21targets specifi ed by the Millennium DevelopmentGoals. Each of the eight goals includes one or moretargets, and each target has several associatedindicators for monitoring progress toward the target.Most of the targets are set as a value of a specifi cindicator to be attained by a certain date. In somecases the target value is set relative to a level in1990. In others it is set at an absolute level. Someof the targets for goals 7 and 8 have not yet beenquantifi ed.The indicators in this table relate to goals 1–4.Goal 1 has three targets between 1990 and 2015:to halve the proportion of people whose income isless than $1.25 a day, to achieve full and productiveemployment and decent work for all, and to halve theproportion of people who suffer from hunger. Estimatesof poverty rates are in tables 2.7 and 2.8.The indicator shown here, the share of the poorestquintile in national consumption or income, is a distributionalmeasure. Countries with more unequaldistributions of consumption (or income) have ahigher rate of poverty for a given average income.Vulnerable employment measures the portion of thelabor force that receives the lowest wages and leastsecurity in employment. No single indicator capturesthe concept of suffering from hunger. Child malnutritionis a symptom of inadequate food supply, lackof essential nutrients, illnesses that deplete thesenutrients, and undernourished mothers who givebirth to underweight children.Progress toward universal primary education ismeasured by the primary completion rate. Becausemany school systems do not record school completionon a consistent basis, it is estimated from thegross enrollment rate in the fi nal grade of primaryeducation, adjusted for repetition. Offi cial enrollmentssometimes differ signifi cantly from attendance,and even school systems with high averageenrollment ratios may have poor completion rates.Eliminating gender disparities in education wouldhelp increase the status and capabilities of women.The ratio of female to male enrollments in primaryand secondary education provides an imperfect measureof the relative accessibility of schooling for girls.The targets for reducing under-fi ve mortality ratesare among the most challenging. Under-fi ve mortalityrates are harmonized estimates produced by aweighted least squares regression model and areavailable at regular intervals for most countries.Most of the 60 indicators relating to the MillenniumDevelopment Goals can be found in World DevelopmentIndicators. Table 1.2a shows where to fi nd theindicators for the fi rst four goals. For more informationabout data collection methods and limitations,see About the data for the tables listed there. Forinformation about the indicators for goals 5–8, seeAbout the data for tables 1.3 and 1.4.• Share of poorest quintile in national consumptionor income is the share of the poorest 20 percentof the population in consumption or, in somecases, income. • Vulnerable employment is the sumof unpaid family workers and own-account workersas a percentage of total employment. • Prevalenceof malnutrition is the percentage of children underage 5 whose weight for age is more than two standarddeviations below the median for the internationalreference population ages 0–59 months. Thedata are based on the new international child growthstandards for infants and young children, called theChild Growth Standards, released in 2006 by theWorld Health Organization. • Primary completionrate is the percentage of students completing thelast year of primary education. It is calculated asthe total number of students in the last grade ofprimary education, minus the number of repeatersin that grade, divided by the total number of childrenof offi cial graduation age. • Ratio of girls to boysenrollments in primary and secondary education isthe ratio of the female to male gross enrollment ratein primary and secondary education. • Under-fivemortality rate is the probability of a child born ina specifi c year dying before reaching age 5, if subjectto the age-specifi c mortality rate of that year.The probability is derived from life tables and isexpressed as a rate per 1,000 live births.Location of indicators for Millennium Development Goals 1–4 1.2aGoal 1. Eradicate extreme poverty and hungerTable1.1 Proportion of population below $1.25 a day 2.81.2 Poverty gap ratio 2.81.3 Share of poorest quintile in national consumption 1.2, 2.91.4 Growth rate of GDP per person employed 2.41.5 Employment to population ratio 2.41.6 Proportion of employed people living below $1 per day —1.7 Proportion of own-account and unpaid family workers in total employment 1.2, 2.41.8 Prevalence of underweight in children under age 5 1.2, 2.201.9 Proportion of population below minimum level of dietary energy consumption 2.20Goal 2. Achieve universal primary education2.1 Net enrollment ratio in primary education 2.122.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.132.3 Literacy rate of 15- to 24-year-olds 2.14Goal 3. Promote gender equality and empower women3.1 Ratio of girls to boys in primary, secondary, and tertiary education 1.2, 2.12*3.2 Share of women in wage employment in the nonagricultural sector 1.5, 2.3*3.3 Proportion of seats held by women in national parliament 1.5Goal 4. Reduce child mortality4.1 Under-fi ve mortality rate 1.2, 2.23, 5.84.2 Infant mortality rate 2.234.3 Proportion of one-year-old children immunized against measles 2.18— No data are available in the World Development Indicators database. * Table shows information on related indicators.Data sourcesThe indicators here and throughout this book havebeen compiled by World Bank staff from primaryand secondary sources. Efforts have been madeto harmonize the data series used to compile thistable with those published on the United NationsMillennium Development Goals Web site (www.un.org/millenniumgoals), but some differences intiming, sources, and defi nitions remain. For moreinformation see the data sources for the indicatorslisted in table 1.2a.2012 World Development Indicators 27

1.3Millennium Development Goals:protecting our common environmentMaternalmortality ratioModeledestimateper 100,000live birthsImprove maternalhealthContraceptiveprevalencerate% of married womenages 15–49Combat HIV/AIDSand other diseasesHIVprevalence Incidence% of of tuberculosispopulation per 100,000ages 15–49 peopleCarbon dioxideemissionsper capitametric tonsEnsure environmentalsustainabilityNationallyprotectedterrestrial andmarine areas% of totalland areaAccess to improvedsanitation facilities% of populationDevelopa globalpartnership fordevelopmentInternet users aper 100 people2008 1990 2005–10 b 2009 2010 1990 2008 2010 1990 2010 2010Afghanistan 1,400 .. 23 .. 189 0.1 0.0 0.4 .. 37 3.7Albania 31 .. 69 .. 14 2.3 1.3 8.4 76 94 45.0Algeria 120 51 61 0.1 90 3.1 3.2 6.2 88 95 12.5Angola 610 .. .. 2.0 304 0.4 1.4 12.1 29 58 10.0Argentina 70 .. 78 0.5 27 3.4 4.8 5.3 90 .. 36.0Armenia 29 56 55 0.1 73 .. 1.8 8.0 .. 90 44.0Australia 8 .. .. 0.1 6 16.8 18.6 12.5 100 100 75.9Austria 5 .. .. 0.3 5 7.9 8.1 22.9 100 100 72.7Azerbaijan 38 .. 51 0.1 110 .. 5.4 7.1 .. 82 46.7Bahrain 19 54 .. .. 23 24.1 21.4 0.7 .. .. 55.0Bangladesh 340 40 53

Millennium Development Goals:1.3protecting our common environmentWORLD VIEWMaternalmortality ratioModeledestimateper 100,000live birthsImprove maternalhealthContraceptiveprevalencerate% of married womenages 15–49Combat HIV/AIDSand other diseasesHIVprevalence Incidence% of of tuberculosispopulation per 100,000ages 15–49 peopleCarbon dioxideemissionsper capitametric tonsEnsure environmentalsustainabilityNationallyprotectedterrestrial andmarine areas% of totalland areaAccess to improvedsanitation facilities% of populationDevelopa globalpartnership fordevelopmentInternet users aper 100 people2008 1990 2005–10 b 2009 2010 1990 2008 2010 1990 2010 2010Honduras 110 47 65 0.8 51 0.5 1.2 13.9 50 77 11.1Hungary 13 .. ..

1.3Millennium Development Goals:protecting our common environmentMaternalmortality ratioModeledestimateper 100,000live birthsImprove maternalhealthContraceptiveprevalencerate% of married womenages 15–49Combat HIV/AIDSand other diseasesHIVprevalence Incidence% of of tuberculosispopulation per 100,000ages 15–49 peopleCarbon dioxideemissionsper capitametric tonsEnsure environmentalsustainabilityNationallyprotectedterrestrial andmarine areas% of totalland areaAccess to improvedsanitation facilities% of populationDevelopa globalpartnership fordevelopmentInternet users aper 100 people2008 1990 2005–10 b 2009 2010 1990 2008 2010 1990 2010 2010Romania 27 .. .. 0.1 116 6.8 4.4 7.8 71 .. 40.0Russian Federation 39 .. 80 1.0 106 .. 12.0 9.2 74 70 43.4Rwanda 540 21 52 2.9 106 0.1 0.1 10.0 36 55 13.0Saudi Arabia 24 .. 24 .. 18 13.3 16.6 29.9 .. .. 41.0Senegal 410 .. 12 0.9 288 0.4 0.4 23.5 38 52 16.0Serbia 8 .. 41 0.1 18 .. 6.8 6.0 .. 92 43.1Sierra Leone 970 3 8 1.6 682 0.1 0.2 4.3 11 13 0.3Singapore 9 65 .. 0.1 35 15.4 6.7 3.4 99 100 71.1Slovak Republic 6 74 ..

Millennium Development Goals:protecting our common environment1.3WORLD VIEWAbout the dataDefinitionsThe Millennium Development Goals address concernscommon to all economies. Diseases and environmentaldegradation do not respect national boundaries.Epidemic diseases, wherever they occur, posea threat to people everywhere. And environmentaldamage in one location may affect the well-being ofplants, animals, and humans far away. The indicatorsin the table relate to goals 5, 6, and 7 and the targetsof goal 8 that address access to new technologies.For the other targets of goal 8, see table 1.4.The target of achieving universal access to reproductivehealth has been added to goal 5 to addressthe importance of family planning and health servicesin improving maternal health and preventingmaternal death. Women with multiple pregnanciesare more likely to die in childbirth. Access to contraceptionis an important way to limit and space births.Measuring disease prevalence or incidence can bediffi cult. Most developing economies lack reportingsystems for monitoring diseases. Estimates are oftenderived from survey data and report data from sentinelsites, extrapolated to the general population. Trackingdiseases such as HIV/AIDS, which has a long latencybetween contraction of the virus and the appearanceof symptoms, or malaria, which has periods of dormancy,can be particularly diffi cult. The table showsthe estimated prevalence of HIV among adults ages15–49. Prevalence among older populations can beaffected by life-prolonging treatment. The incidence oftuberculosis is based on case notifi cations and estimatesof cases detected in the population.Carbon dioxide emissions are the primary source ofgreenhouse gases, which contribute to global warming,threatening human and natural habitats. In recognitionof the vulnerability of animal and plant species, a newtarget of reducing biodiversity loss has been addedto goal 7. Increasing the proportion of terrestrial andmarine areas protected helps defend vulnerable plantand animal species and safeguard biodiversity.Access to reliable supplies of safe drinking water andsanitary disposal of excreta are two of the most importantmeans of improving human health and protectingthe environment. Improved sanitation facilities preventhuman, animal, and insect contact with excreta.Internet use includes narrowband and broadbandInternet. Narrowband is often limited to basic applications;broadband is essential to promote e-business,e-learning, e-government, and e-health.Location of indicators for Millennium Development Goals 5–7 1.3aGoal 5. Improve maternal healthTable5.1 Maternal mortality ratio 1.3, 2.195.2 Proportion of births attended by skilled health personnel 2.195.3 Contraceptive prevalence rate 1.3, 2.195.4 Adolescent fertility rate 2.195.5 Antenatal care coverage 1.5, 2.195.6 Unmet need for family planning 2.19Goal 6. Combat HIV/AIDS, malaria, and other diseases6.1 HIV prevalence among pregnant women ages 15–24 1.3*, 2.22*6.2 Condom use at last high-risk sex —6.3 Proportion of population ages 15–24 with comprehensive, correct—knowledge of HIV/AIDS6.4 Ratio of school attendance of orphans to school attendance of—nonorphans ages 10–146.5 Proportion of population with advanced HIV infection with access2.22to antiretroviral drugs6.6 Incidence and death rates associated with malaria —6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 2.186.8 Proportion of children under age 5 with fever who are treated with appropriateantimalarial drugs 2.186.9 Incidence, prevalence, and death rates associated with tuberculosis 1.3, 2.226.10 Proportion of tuberculosis cases detected and cured under directly observedtreatment, short course 2.18Goal 7. Ensure environmental sustainability7.1 Proportion of land area covered by forest 3.17.2 Carbon dioxide emissions, total, per capita, and per $1 purchasing powerparity GDP 3.97.3 Consumption of ozone-depleting substances 3.10*7.4 Proportion of fi sh stocks within safe biological limits —7.5 Proportion of total water resources used 3.57.6 Proportion of terrestrial and marine areas protected 1.37.7 Proportion of species threatened with extinction —7.8 Proportion of population using an improved drinking water source 2.18, 3.57.9 Proportion of population using an improved sanitation facility 1.3, 2.18, 3.137.10 Proportion of urban population living in slums —— No data are available in the World Development Indicators database. * Table shows information on related indicators.• Maternal mortality ratio is the number of womenwho die from pregnancy-related causes during pregnancyand childbirth, per 100,000 live births. Dataare from various years and adjusted to a common2008 base year. The values are modeled estimates(see About the data for table 2.19). • Contraceptiveprevalence rate is the percentage of women ages15–49 married or in union who are practicing, orwhose sexual partners are practicing, any form ofcontraception. • HIV prevalence is the percentageof people ages 15–49 who are infected with HIV.• Incidence of tuberculosis is the estimated numberof new tuberculosis cases (pulmonary, smear positive,and extrapulmonary). • Carbon dioxide emissionsare those stemming from the burning of fossilfuels and the manufacture of cement. They includeemissions produced during consumption of solid,liquid, and gas fuels and gas fl aring (see table 3.9).• Nationally protected terrestrial and marine areasare terrestrial and marine protected areas as a percentageof total territorial area, where all nationallydesignated protected areas with known location andextent are included. All overlaps between differentdesignations and categories, buffered points, andpolygons are removed, and all the undated protectedareas are dated. • Access to improved sanitationfacilities is the percentage of the population with atleast adequate access to excreta disposal facilities(private or shared, but not public) that can effectivelyprevent human, animal, and insect contact withexcreta (facilities do not have to include treatmentto render sewage outfl ows innocuous). Improvedfacilities range from simple but protected pit latrinesto fl ush toilets with a sewerage connection. To beeffective, facilities must be correctly constructedand properly maintained. • Internet users are peoplewith access to the worldwide network.Data sourcesThe indicators here and throughout this book havebeen compiled by World Bank staff from primaryand secondary sources. Efforts have been madeto harmonize the data series used to compile thistable with those published on the United NationsMillennium Development Goals Web site (www.un.org/millenniumgoals), but some differences intiming, sources, and defi nitions remain. For moreinformation see the data sources for the indicatorslisted in tables 1.3a and 1.4a.2012 World Development Indicators 31

1.4Millennium Development Goals:overcoming obstaclesDevelopment Assistance Committee membersNet official developmentassistance (ODA)by donor% ofdonorGNIFor basicsocial services a% of totalsector-allocableODAGoods(excluding arms)admitted free of tariffs% of exports from leastdeveloped countriesLeast developed countries’ accessto high-income marketsAverage tariff on exports ofleast developed countries%Support toagricultureAgricultural products Textiles Clothing% of GDP2010 2010 2003 2009 2003 2009 2003 2009 2003 2009 2010Australia 0.32 14.6 99.9 100.0 0.2 0.0 0.1 0.0 1.2 0.0 0.12Canada 0.33 18.1 97.5 100.0 0.2 0.1 0.2 0.2 1.7 1.7 0.67European Union 96.6 98.4 1.8 0.5 0.1 0.1 1.2 1.2 0.72Austria 0.32 3.1Belgium 0.64 11.7Denmark 0.90 10.4Finland 0.55 8.4France 0.50 8.6Germany 0.38 5.9Greece 0.17 6.6Ireland 0.53 22.9Italy 0.15 12.5Luxembourg 1.09 34.5Netherlands 0.81 7.6Portugal 0.29 6.6Spain 0.43 13.6Sweden 0.97 12.4United Kingdom 0.56 12.7Japan 0.20 2.5 44.8 99.4 4.7 1.2 2.7 2.6 0.1 0.1 1.09Korea, Rep. b 0.12 4.3 25.1 c 48.2 27.6 c 50.2 10.9 c 6.4 11.3 c 6.2 2.01New Zealand b 0.26 16.6 97.7 98.8 4.0 0.0 0.3 0.0 0.4 0.0 0.23Norway 1.10 11.1 98.6 100.0 4.7 0.2 0.0 0.0 1.2 1.0 0.99Switzerland 0.41 11.0 96.3 100.0 5.4 0.0 0.0 0.0 0.0 0.0 1.11United States 0.21 31.0 67.2 75.8 6.3 5.7 6.4 5.7 12.3 11.3 0.91Heavily indebted poor countries (HIPCs)HIPCdecisionpoint dHIPCcompletionpoint dHIPCInitiativeassistanceMDRIassistanceHIPCdecisionpoint dHIPCcompletionpoint dHIPCInitiativeassistanceMDRIassistanceend-2010net present value$ millionsAfghanistan Jul. 2007 Jan. 2010 653 20Benin Jul. 2000 Mar. 2003 384 756Bolivia e Feb. 2000 Jun. 2001 1,948 1,956Burkina Faso e,f Jul. 2000 Apr. 2002 810 765Burundi Aug. 2005 Jan. 2009 1,008 91Cameroon Oct. 2000 Apr. 2006 1,856 885Central African Republic Sep. 2007 Jun. 2009 674 231Chad May 2001 Floating 240 669Comoros Jun. 2010 Floating 150 45Congo, Dem. Rep. Jul. 2003 Jul. 2010 9,474 528Congo, Rep. Mar. 2006 Jan. 2010 1,903 130Côte d’Ivoire Mar. 2009 Floating 3,243 1,095Ethiopia f Nov. 2001 Apr. 2004 2,728 1,865Gambia, The Dec. 2000 Dec. 2007 98 244Ghana Feb. 2002 Jul. 2004 3,083 2,549Guinea Dec. 2000 Floating 799 862Guinea-Bissau Dec. 2000 Dec. 2010 744 79Guyana e Nov. 2000 Dec. 2003 894 488end-2010net present value$ millionsHaiti Nov. 2006 Jun. 2009 163 674Honduras Jul. 2000 Apr. 2005 814 1,884Liberia Mar. 2008 Jun. 2010 2,957 241Madagascar Dec. 2000 Oct. 2004 1,224 1,584Malawi f Dec. 2000 Aug. 2006 1,375 914Mali e Sep. 2000 Mar. 2003 789 1,313Mauritania Feb. 2000 Jun. 2002 911 563Mozambique e Apr. 2000 Sep. 2001 3,140 1,318Nicaragua Dec. 2000 Jan. 2004 4,847 1,178Niger f Dec. 2000 Apr. 2004 944 646Rwanda f Dec. 2000 Apr. 2005 953 286São Tomé & Príncipe f Dec. 2000 Mar. 2007 171 38Senegal Jun. 2000 Apr. 2004 715 1,696Sierra Leone Mar. 2002 Dec. 2006 917 423Tanzania Apr. 2000 Nov. 2001 2,969 2,503Togo Nov. 2008 Dec. 2010 305 466Uganda e Feb. 2000 May 2000 1,505 2,263Zambia Dec. 2000 Apr. 2005 3,662 1,871a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control,health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease controlincluding HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services.b. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development’s Trade Analysis and Information Systemsdatabase. c. Data are for 2004. d. Refers to the Enhanced HIPC Initiative. e. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief.f. Assistance includes topping up at completion point.322012 World Development Indicators

Millennium Development Goals:overcoming obstacles1.4WORLD VIEWAbout the dataDefinitionsAchieving the Millennium Development Goalsrequires an open, rule-based global economy inwhich all countries, rich and poor, participate. Manypoor countries, lacking the resources to fi nancedevelopment, burdened by unsustainable debt, andunable to compete globally, need assistance fromrich countries. For goal 8—develop a global partnershipfor development—many indicators thereforemonitor the actions of members of the Organisationfor Economic Co-operation and Development’s(OECD) Development Assistance Committee (DAC).Offi cial development assistance (ODA) has risenin recent years as a share of donor countries’ grossnational income (GNI), but the poorest economiesneed additional assistance to achieve the Millen niumDevelopment Goals. In 2010 net ODA from OECDDAC members rose 3.2 percent in real terms, to$131.1 billion or 0.33 percent of OECD DAC members’combined GNI.One important action that high-income economiescan take is to reduce barriers to exports from lowandmiddle- income economies. The European Unionhas begun to eliminate tariffs on imports of “everythingbut arms” from least developed countries,and the United States offers special concessions toimports from Sub-Saharan Africa. However, theseprograms still have many restrictions.Average tariffs in the table refl ect high-incomeOECD member tariff schedules for exports of countriesdesignated least developed countries by theUnited Nations. Although average tariffs have beenfalling, averages may disguise high tariffs on specifi cgoods (see table 6.7 for each country’s share of tarifflines with “international peaks”). The averages inthe table include ad valorem duties and equivalents.Subsidies to agricultural producers and exportersin OECD countries are another barrier to developingeconomies’ exports. Agricultural subsidies in OECDeconomies are estimated at $366 billion in 2010.The Debt Initiative for Heavily Indebted Poor Countries(HIPCs), an important step in placing debt reliefwithin the framework of poverty reduction, is the fi rstcomprehensive approach to reducing the externaldebt of the world’s poorest, most heavily indebtedcountries. A 1999 review led to an enhancement ofthe framework. In 2005, to further reduce the debtof HIPCs and provide resources for meeting the MillenniumDevelopment Goals, the Multilateral DebtRelief Initiative (MDRI), proposed by the Group ofEight countries, was launched.Under the MDRI four multilateral institutions—theInternational Development Association (IDA), InternationalMonetary Fund (IMF), African DevelopmentFund (AfDF), and Inter-American Development Bank(IDB)—provide 100 percent debt relief on eligibledebts due to them from countries having completedthe HIPC Initiative process. Data in the table referto status as of September 2011 and might not showcountries that have since reached the decision orcompletion point. Debt relief under the HIPC Initiativehas reduced future debt payments by $59 billion (inend-2010 net present value terms) for 36 countriesthat have reached the decision point. And 32 countriesthat have reached the completion point havereceived additional assistance of $33 billion (in end-2010 net present value terms) under the MDRI.Location of indicators for Millennium Development Goal 8 1.4aGoal 8. Develop a global partnership for developmentTable8.1 Net ODA as a percentage of DAC donors’ gross national income 1.48.2 Proportion of ODA for basic social services 1.48.3 Proportion of ODA that is untied —8.4 Proportion of ODA received in landlocked countries as a percentage of GNI —8.5 Proportion of ODA received in small island developing states as a percentage of GNI —8.6 Proportion of total developed country imports (by value, excluding arms) from leastdeveloped countries admitted free of duty 1.48.7 Average tariffs imposed by developed countries on agricultural products andtextiles and clothing from least developed countries 1.4, 6.7*8.8 Agricultural support estimate for OECD countries as a percentage of GDP 1.48.9 Proportion of ODA provided to help build trade capacity —8.10 Number of countries reaching HIPC decision and completion points 1.48.11 Debt relief committed under new HIPC initiative 1.48.12 Debt services as a percentage of exports of goods and services 6.9*8.13 Proportion of population with access to affordable, essential drugs on asustainable basis —8.14 Fixed telephone lines per 100 people 5.118.15 Mobile cellular subscribers per 100 people 5.118.16 Internet users per 100 people 1.3, 5.12— No data are available in the World Development Indicators database. * Table shows information on related indicators.• Net official development assistance (ODA) is grantsand loans (net of repayments of principal) that meetthe DAC defi nition of ODA and are made to countrieson the DAC list of recipients. • ODA for basic socialservices is aid commitments by DAC donors for basiceducation, primary health care, nutrition, populationpolicies and programs, reproductive health, and waterand sanitation services. • Goods admitted free oftariffs are exports of goods (excluding arms) fromleast developed countries admitted without tariff.• Average tariff is the unweighted average of theeffectively applied rates for all products subject totariffs. • Agricultural products are plant and animalproducts, including tree crops but excluding timberand fi sh products. • Textiles and clothing are naturaland synthetic fi bers and fabrics and articles ofclothing made from them. • Support to agriculture isgross transfers from taxpayers and consumers arisingfrom policy measures, net of associated budgetaryreceipts, regardless of their objectives and impacts onfarm production and income or consumption of farmproducts. • HIPC decision point is when a heavilyindebted poor country with an established track recordof good performance under adjustment programs supportedby the IMF and the World Bank commits to additionalreforms and a poverty reduction strategy andstarts receiving debt relief. • HIPC completion point iswhen a country successfully completes the key structuralreforms agreed on at the decision point, includingimplementing a poverty reduction strategy. Thecountry then receives full debt relief under the HIPCInitiative without further policy conditions. • HIPC Initiativeassistance is the debt relief committed as ofthe decision point (assuming full participation of creditors).Topping-up assistance and assistance providedunder the original HIPC Initiative were committed innet present value terms as of the decision point andare converted to end-2010 terms. • MDRI assistanceis 100 percent debt relief on eligible debt from IDA,IMF, AfDF, and IDB, delivered in full to countries havingreached the HIPC completion point.Data sourcesData on ODA are from the OECD. Data on goodsadmitted free of tariffs and average tariffs are fromthe World Trade Organization in collaboration withthe United Nations Conference on Trade and Developmentand the International Trade Centre (www.mdg-trade.org). Data on support to agriculture arefrom the OECD’s Producer and Consumer SupportEstimates, OECD Database 1986–2010. Data onthe HIPC Initiative and MDRI are from the WorldBank’s Economic Policy and Debt Department.2012 World Development Indicators 33

1.5 Women in developmentFemalepopulationLifeexpectancyat birthPregnantwomenreceivingprenatalcareTeenagemothersWomen in wageemployment innonagriculturalsectorUnpaid familyworkersFemale Femalepart-time legislators,employment seniorofficials, andmanagersWomen inparliamentsyears% ofwomenages% ofnonagriculturalwageMale% of maleFemale% of female% of total Male Female % 15–19 employment employment employment % of total % of total % of total seats2010 2010 2010 2005–10 a 2005–10 a 2009 2010 2010 2005–10 a 2005–10 a 1990 2011Afghanistan 48.3 48 48 36 .. 18 .. .. .. .. 4 28Albania 49.9 74 80 97 3 .. .. .. .. .. 29 16Algeria 49.5 71 74 89 .. 15 .. .. .. 5 2 8Angola 50.5 49 52 80 29 .. .. .. .. .. 15 39Argentina 51.1 72 79 99 .. .. 0.4 b 1.2 b 65 b 23 b 6 39Armenia 53.5 71 77 99 5 40 7.6 17.1 56 22 36 9Australia 50.2 80 84 98 .. 47 0.2 0.3 70 b 37 6 25Austria 51.2 78 83 .. .. 48 1.9 2.4 80 29 12 28Azerbaijan 50.5 68 74 77 6 43 .. .. .. 7 .. 16Bahrain 37.6 74 76 100 .. 10 0.5 0.8 .. 12 .. 3Bangladesh 49.4 68 69 53 33 .. .. .. .. 10 10 19Belarus 53.5 65 77 99 .. 56 .. .. .. .. .. 32Belgium 51.0 77 83 .. .. 47 0.3 1.8 80 34 9 39Benin 50.7 54 57 84 21 .. .. .. .. .. 3 8Bolivia 50.1 64 69 86 18 38 .. .. 56 29 9 25Bosnia and Herzegovina 51.9 73 78 99 .. 36 2.6 10.4 .. .. .. 17Botswana 49.6 54 52 94 .. 45 .. .. 55 30 5 8Brazil 50.8 70 77 98 .. 42 3.4 6.3 68 36 5 9Bulgaria 51.7 70 77 .. .. 51 0.7 1.5 52 34 21 21Burkina Faso 50.4 54 56 85 .. 27 .. .. .. 31 .. 15Burundi 50.9 49 51 99 .. .. .. .. .. .. .. 32Cambodia 51.1 61 64 89 8 .. 25.2 60.8 .. 21 .. 21Cameroon 50.1 50 52 82 .. .. .. .. .. .. 14 14Canada 50.4 79 83 100 .. 51 0.1 0.2 67 b 36 b 13 25Central African Republic 50.7 46 49 69 .. .. .. .. .. .. 4 13Chad 50.3 48 51 53 .. .. .. .. .. .. .. 13Chile 50.6 76 82 .. .. 37 1.1 2.5 58 24 .. 14China 48.1 c 72 c 75 c 92 .. .. .. .. .. 17 21 21Hong Kong SAR, China 52.6 80 86 .. .. 50 0.1 1.0 .. 29 .. ..Colombia 50.8 70 77 97 20 48 3.7 7.1 60 38 5 13Congo, Dem. Rep. 50.3 47 50 86 24 .. .. .. .. .. 5 10Congo, Rep. 49.9 56 58 86 27 .. .. .. .. .. 14 7Costa Rica 49.2 77 82 90 .. 41 0.9 2.6 64 30 11 39Côte d’Ivoire 49.0 54 56 85 .. .. .. .. .. .. 6 9Croatia 51.9 74 80 100 4 46 1.1 4.6 60 27 .. 24Cuba 49.7 77 81 100 .. 43 .. .. .. 29 34 43Cyprus 49.0 77 82 99 .. 48 1.3 2.9 64 13 2 11Czech Republic 51.0 74 81 .. .. 46 0.3 1.0 69 28 .. 22Denmark 50.4 77 81 .. .. 50 0.1 0.5 63 22 31 38Dominican Republic 49.8 70 76 99 21 39 .. .. 50 31 8 21Ecuador 49.9 73 78 .. .. .. 7.4 19.9 54 28 b 5 32Egypt, Arab Rep. 49.8 71 75 74 10 18 .. .. .. 11 4 2El Salvador 52.5 67 77 94 23 48 7.1 7.2 55 25 12 19Eritrea 50.7 59 63 .. .. .. .. .. .. .. .. 22Estonia 53.9 71 81 .. .. 54 0.2 0.2 67 37 .. 20Ethiopia 50.2 57 60 28 17 .. .. .. .. 16 b .. 28Finland 50.9 77 83 .. .. 52 0.6 0.5 62 30 32 43France 51.3 78 85 .. .. 49 0.3 0.9 78 39 7 19Gabon 49.8 61 63 .. .. .. .. .. .. .. 13 15Gambia, The 50.6 57 59 98 .. .. .. .. .. .. 8 8Georgia 52.9 70 77 98 10 48 19.6 37.9 .. 34 .. 7Germany 51.0 78 83 .. .. 48 0.3 0.9 80 30 .. 33Ghana 49.1 63 65 90 13 .. .. .. .. .. .. 8Greece 50.5 78 83 .. .. 43 3.3 9.2 66 30 7 17Guatemala 51.3 67 74 93 22 .. .. .. .. .. 7 12Guinea 49.5 52 55 88 32 29 .. .. .. .. .. ..Guinea-Bissau 50.4 46 49 93 .. .. .. .. .. .. 20 10Haiti 50.4 61 63 85 14 .. .. .. .. .. .. 11342012 World Development Indicators

Women in development1.5WORLD VIEWFemalepopulationLifeexpectancyat birthPregnantwomenreceivingprenatalcareTeenagemothersWomen in wageemployment innonagriculturalsectorUnpaid familyworkersFemale Femalepart-time legislators,employment seniorofficials, andmanagersWomen inparliamentsyears% ofwomenages% ofnonagriculturalwageMale% of maleFemale% of female% of total Male Female % 15–19 employment employment employment % of total % of total % of total seats2010 2010 2010 2005–10 a 2005–10 a 2009 2010 2010 2005–10 a 2005–10 a 1990 2011Honduras 50.0 71 75 92 22 42 .. .. .. 41 10 18Hungary 52.5 71 78 .. .. 49 0.2 0.4 66 36 21 9India 48.3 64 67 75 16 .. .. .. .. .. 5 11Indonesia 50.1 67 71 95 9 32 8.1 32.4 .. 22 12 18Iran, Islamic Rep. 49.3 71 75 98 .. .. 4.8 29.7 .. 13 2 3Iraq 49.8 65 72 84 .. 12 .. .. .. .. 11 25Ireland 50.0 78 83 .. .. 52 0.7 0.7 76 33 8 15Israel 50.7 80 83 .. .. 50 0.1 0.3 74 32 7 19Italy 51.1 79 84 .. .. 44 1.2 2.3 77 33 13 21Jamaica 50.8 70 76 99 14 48 0.4 2.1 .. .. 5 13Japan 51.3 80 86 .. .. 42 1.1 6.9 70 10 1 11Jordan 48.6 72 75 99 5 16 0.5 0.5 .. .. 0 11Kazakhstan 52.0 64 73 100 7 50 0.6 0.9 .. 38 .. 18Kenya 50.1 55 58 92 18 .. .. .. .. .. 1 10Korea, Dem. Rep. 50.9 65 72 100 .. .. .. .. .. .. 21 15Korea, Rep. 50.1 77 84 .. .. 42 1.2 12.5 60 10 2 16Kosovo .. 68 72 .. .. .. .. .. .. .. .. ..Kuwait 40.3 74 76 100 .. .. .. .. .. 14 .. 8Kyrgyz Republic 50.7 65 73 97 .. 51 .. .. .. 35 .. 23Lao PDR 50.1 66 68 71 17 .. .. .. .. .. 6 25Latvia 54.0 69 78 .. .. 55 1.3 1.4 61 41 .. 20Lebanon 51.2 70 75 .. .. .. .. .. .. 8 0 3Lesotho 50.9 48 47 92 .. .. .. .. .. 52 .. 24Liberia 49.8 55 57 79 38 .. 12.5 19.7 .. .. .. 13Libya 49.3 72 77 93 .. .. .. .. .. .. .. 8Lithuania 53.5 68 79 .. .. 53 1.5 1.9 61 41 .. 19Macedonia, FYR 49.9 73 77 94 .. 42 6.3 16.6 49 28 .. 31Madagascar 50.2 65 68 86 32 .. .. .. .. 22 7 13Malawi 50.0 53 54 92 .. .. .. .. .. .. 10 21Malaysia 49.3 72 76 79 .. 39 2.6 8.1 .. 24 5 10Mali 50.0 50 52 70 36 .. .. .. .. .. .. 10Mauritania 49.7 57 60 75 .. .. .. .. .. .. .. 22Mauritius 50.6 69 77 .. .. 37 1.0 4.9 .. 23 7 19Mexico 50.7 74 79 96 .. 39 4.8 9.7 54 31 12 26Moldova 52.6 65 73 98 6 54 1.3 3.5 .. 38 .. 19Mongolia 50.6 64 72 100 .. 51 10.3 35.1 .. 47 25 4Morocco 51.0 70 74 .. .. 21 15.0 48.6 .. 13 0 11Mozambique 51.3 49 51 92 .. .. .. .. .. .. 16 39Myanmar 50.7 63 66 80 .. .. .. .. .. .. .. 4Namibia 50.3 62 63 95 15 .. .. .. .. 36 7 24Nepal 50.4 68 69 44 19 .. .. .. .. 14 6 33Netherlands 50.4 79 83 .. .. 48 0.3 1.1 75 29 21 39New Zealand 50.9 79 83 .. .. 51 0.7 1.3 72 b 40 b 14 34Nicaragua 50.5 71 77 90 25 38 .. .. 51 41 15 21Niger 49.7 54 55 46 39 36 .. .. .. .. 5 13Nigeria 49.4 51 52 58 23 .. .. .. .. .. .. 4Norway 50.0 79 83 .. .. 50 0.2 0.3 70 34 36 40Oman 41.3 71 76 99 .. 22 .. .. .. 9 .. 0Pakistan 49.2 64 66 61 9 13 19.7 65.0 .. 3 10 22Panama 49.6 73 79 96 .. 42 3.0 6.8 48 48 8 9Papua New Guinea 49.0 60 65 79 .. .. .. .. .. .. 0 1Paraguay 49.5 70 74 96 12 40 10.4 11.0 55 34 6 13Peru 49.9 71 76 95 26 38 4.5 b 8.7 b 61 b 19 b 6 22Philippines 49.8 65 72 91 10 42 9.0 17.4 .. 55 9 22Poland 51.8 72 81 .. .. 48 2.5 5.5 68 36 14 20Portugal 51.6 76 82 .. .. 49 0.7 1.2 66 32 8 27Puerto Rico 51.9 75 83 .. .. 42 0.0 0.0 .. 43 .. ..Qatar 24.3 78 78 100 .. 10 .. .. .. 7 .. 02012 World Development Indicators 35

1.5Women in developmentFemalepopulationLifeexpectancyat birthPregnantwomenreceivingprenatalcareTeenagemothersWomen in wageemployment innonagriculturalsectorRomania 51.5 70 77 .. .. 46 6.9 20.0 47 32 34 11Russian Federation 53.7 63 75 .. .. 53 0.1 0.1 62 37 .. 14Rwanda 50.9 54 56 98 6 .. .. .. .. 0 17 56Saudi Arabia 44.6 73 75 97 .. 15 .. .. .. 8 .. 0Senegal 50.4 58 60 94 18 .. .. .. .. .. 13 23Serbia 50.5 71 77 98 .. 44 4.1 15.6 .. 36 .. 22Sierra Leone 51.2 47 48 87 34 .. .. .. .. .. .. 13Singapore 49.6 79 84 .. .. 45 0.3 1.0 .. 31 5 22Slovak Republic 51.4 72 79 .. .. 48 0.1 0.2 59 35 .. 16Slovenia 51.1 76 83 .. .. 48 3.8 6.2 59 35 .. 14Somalia 50.4 49 53 26 .. .. .. .. .. .. 4 7South Africa 50.5 51 53 97 .. 45 0.4 1.4 .. 30 3 45South Sudan .. 62 61 .. .. .. .. .. .. .. .. ..Spain 50.6 79 85 .. .. 47 0.7 1.0 78 34 15 37Sri Lanka 50.6 72 78 99 .. 31 4.5 b 22.3 b .. 24 b 5 6Sudan 49.6 59 63 64 .. .. .. .. .. .. .. 26Swaziland 50.8 49 48 97 23 .. .. .. .. .. 4 14Sweden 50.2 80 84 .. .. 50 0.3 0.2 63 31 38 45Switzerland 50.9 80 85 .. .. 48 1.6 2.4 80 33 14 29Syrian Arab Republic 49.4 74 77 88 .. 15 3.3 12.4 27 10 9 12Tajikistan 50.8 64 71 80 .. .. .. .. .. .. .. 19Tanzania 50.1 57 58 88 23 .. .. .. .. 16 .. 36Thailand 50.9 71 77 99 .. 46 13.6 28.5 .. 24 3 13Timor-Leste 49.0 61 63 84 7 .. .. .. .. .. .. 29Togo 50.5 55 58 87 .. .. .. .. .. .. 5 11Trinidad and Tobago 51.5 66 73 96 .. .. .. .. .. 43 17 29Tunisia 50.0 73 77 96 .. .. .. .. .. .. 4 28Turkey 50.1 71 76 95 .. 24 5.1 35.2 58 10 1 14Turkmenistan 50.8 61 69 99 .. .. .. .. .. .. 26 17Uganda 50.0 53 54 94 25 .. .. .. .. 33 12 35Ukraine 54.0 65 76 99 4 55 0.4 0.3 .. 39 .. 8United Arab Emirates 30.5 76 78 100 .. 20 0.0 0.0 .. 10 0 23United Kingdom 50.8 79 82 .. .. 47 0.2 0.4 75 36 6 22United States 50.7 76 81 .. .. 48 0.1 0.1 67 43 7 17Uruguay 51.7 73 80 96 .. 46 2.1 b 0.8 b 64 b 40 6 15Uzbekistan 50.3 65 71 99 .. 39 .. .. .. .. .. 22Venezuela, RB 49.8 71 77 .. .. 42 0.5 1.2 64 27 10 17Vietnam 50.6 73 77 91 .. .. .. .. .. 22 18 24West Bank and Gaza 49.2 71 74 99 .. 18 5.5 19.8 .. 10 .. ..Yemen, Rep. 49.7 64 67 47 .. 6 .. .. .. 4 4 0Zambia 49.9 48 49 94 28 .. .. .. .. 19 7 14Zimbabwe 50.7 51 49 90 d 21 .. .. .. .. .. 11 15World 49.6 w 68 w 72 w 84 w .. w .. w .. w .. w .. w 13 w 19 wLow income 50.1 58 60 69 .. .. .. .. .. .. 20Middle income 49.3 67 71 86 .. .. .. .. .. 13 18Lower middle income 49.2 64 67 78 .. .. .. .. .. 11 15Upper middle income 49.4 71 75 94 .. .. .. .. .. 14 19Low & middle income 49.4 66 70 83 .. .. .. .. .. 13 18East Asia & Pacifi c 48.8 70 74 92 .. .. .. .. .. 17 18Europe & Central Asia 52.3 66 75 .. .. 48 1.9 5.3 .. .. 16Latin America & Carib. 50.6 71 77 97 .. 41 3.5 6.8 62 12 23Middle East & N. Africa 49.7 70 74 85 .. .. .. .. .. 4 9South Asia 48.6 64 67 71 16 .. .. .. .. 6 20Sub-Saharan Africa 50.0 53 55 74 .. .. .. .. .. .. 20High income 50.5 77 83 .. .. 47 0.6 2.2 71 12 23Euro area 51.0 78 84 .. .. 47 0.7 1.5 77 12 26a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2011.Unpaid familyworkersFemale Femalepart-time legislators,employment seniorofficials, andmanagersWomen inparliamentsyears% ofwomenages% ofnonagriculturalwageMale% of maleFemale% of female% of total Male Female % 15–19 employment employment employment % of total % of total % of total seats2010 2010 2010 2005–10 a 2005–10 a 2009 2010 2010 2005–10 a 2005–10 a 1990 2011362012 World Development Indicators

Women in development1.5WORLD VIEWAbout the dataDespite much progress in recent decades, genderinequalities remain pervasive in many dimensions oflife—worldwide. But while disparities exist throughoutthe world, they are most prevalent in developingcountries. Gender inequalities in the allocation ofsuch resources as education, health care, nutrition,and political voice matter because of the strong associationwith well-being, productivity, and economicgrowth. These patterns of inequality begin at an earlyage, with boys routinely receiving a larger share of educationand health spending than do girls, for example.Because of biological differences girls are expectedto experience lower infant and child mortality ratesand to have a longer life expectancy than boys. Thisbiological advantage may be overshadowed, however,by gender inequalities in nutrition and medical interventionsand by inadequate care during pregnancyand delivery, so that female rates of illness anddeath sometimes exceed male rates. Gender biascan be seen in child mortality rates (table 2.23) andlife expectancy at birth. Female child mortality ratesthat are as high as or higher than male child mortalityrates may indicate discrimination against girls.Having a child during the teenage years limits girls’opportunities for better education, jobs, and income.Pregnancy is more likely to be unintended during theteenage years, and births are more likely to be prematureand are associated with greater risks of complicationsduring delivery and of death. In many countriesmaternal mortality (tables 1.3 and 2.19) is aleading cause of death among women of reproductiveage, although most of those deaths are preventable.Data on women in wage employment in the nonagriculturalsector show the extent to which womenhave access to paid employment —which affects theirintegration into the monetary economy —and indicatethe degree to which labor markets are open to womenin industry and services —which affects not only equalemployment opportunity for women, but also economiceffi ciency through fl exibility of the labor marketand the economy’s capacity to adapt to changes overtime. In many developing countries nonagriculturalwage employment accounts for only a small portionof total employment. As a result, the contribution ofwomen to the national economy is underestimatedand therefore misrepresented. The indicator is difficult to interpret without additional information on theshare of women in total employment, which allowsan assessment to be made of whether women areunder- or over represented in nonagricultural wageemployment. The indicator does not reveal differencesin the quality of nonagricultural wage employment interms of earnings, work conditions, or legal and socialprotection. The indicator also does not refl ect whetherwomen reap the economic benefi ts of such employment.Finally, female employment and the employmentshare of the agricultural sector for both menand women tend to be underreported.Women’s wage work is important for economicgrowth and the well-being of families. But womenoften face such obstacles as restricted access tocredit markets, capital, land, and training and education;time constraints due to traditional familyresponsibilities; and labor market bias and discrimination.These obstacles force women to limit theirfull participation in paid economic activities, to beless productive, and to receive lower wages. Morewomen than men are in unpaid family employmentand part-time employment. And men and womenhave different occupational distributions. There is nooffi cial International Labour Organization defi nitionof full-time work, so the defi nition of part-time workersdiffers across countries, and thus comparisonsshould be made with caution.The female share of high-skilled occupations suchas legislators, senior offi cials, and managers indicatesgender segregation of employment. Womenare vastly underrepresented in decisionmakingpositions in government, although there is someevidence of recent improvement. Gender parity inparliamentary representation is still far from beingrealized. Without representation at this level, it isdiffi cult for women to infl uence policy.For information on other aspects of gender, seetables 1.2 (Millennium Development Goals: eradicatingpoverty and saving lives), 1.3 (Millennium DevelopmentGoals: protecting our common environment),2.3 (Employment by economic activity), 2.4 (Decentwork and productive employment), 2.5 (Unemployment),2.6 (Children at work), 2.10 (Assessing vulnerabilityand security), 2.13 (Education effi ciency), 2.14(Education completion and outcomes), 2.15 (Educationgaps by income and gender), 2.19 (Reproductivehealth), 2.22 (Health risk factors and future challenges),and 2.23 (Mortality).Definitions• Female population is the percentage of the populationthat is female. • Life expectancy at birth isthe number of years a newborn infant would live ifprevailing patterns of mortality at the time of its birthwere to stay the same throughout its life. • Pregnantwomen receiving prenatal care are women attendedat least once during pregnancy by skilled healthpersonnel for reasons related to pregnancy. • Teenagemothers are women ages 15–19 who alreadyhave children or are currently pregnant. • Womenin wage employment in nonagricultural sector arefemale wage employees in the nonagricultural sectoras a percentage of total nonagricultural wageemployment. • Unpaid family workers are thosewho work without pay in a market-oriented establishmentor activity operated by a related personliving in the same household. There is no offi cialInternational Labour Organization defi nition of fulltimework, so the defi nition of part-time workers differsacross countries and thus comparisons shouldbe made with caution. • Female part-time employmentis the percentage of part-time workers whoare female. Part-time workers are employed peoplewhose normal hours of work are less than those ofcomparable full-time workers. The defi nition of parttimevaries across countries. • Female legislators,senior officials, and managers are the percentageof legilsators, senior offi cials, and managers (InternationalStandard Classifi cation of Occupations–88category 1) who are female. • Women in parliamentsare parliamentary seats in a single or lower chamberheld by women.Data sourcesData on female population are from the UnitedNations Population Division’s World Population Prospects:The 2010 Revision, and data on life expectancyfor more than half the countries in the table(most of them developing countries) are from itsWorld Population Prospects: The 2010 Revision, withadditional data from census reports, other statisticalpublications from national statistical offi ces,Eurostat’s Demographic Statistics, the Secretariatof the Pacifi c Community’s Statistics and DemographyProgramme, and the U.S. Bureau of the CensusInternational Data Base. Data on pregnantwomen receiving prenatal care are from the UnitedNations Children’s Fund’s (UNICEF) The State of theWorld’s Children 2012 based on household surveys,including MEASURE DHS Demographic and HealthSurveys by ICF International and Multiple IndicatorCluster Surveys by UNICEF. Data on teenagemothers are from MEASURE DHS Demographic andHealth Surveys by ICF International. Data on laborforce, employment, and occupation are from theInternational Labour Organization’s Key Indicatorsof the Labour Market, 7th edition. Data on women inparliaments are from the Inter-Parliamentary Union.2012 World Development Indicators 37

1.6 Key indicators for other economiesPopulationSurfaceareaPopulationdensityGross national incomeGross domesticproductLifeexpectancyat birthAdultliteracyrateCarbondioxideemissionsPurchasingthousand people perAtlas methodPer capitapower parityPer capitaPer capita% ages 15 thousandthousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10 2010 2005–10 a 2008American Samoa 68 0.2 342 .. .. b .. .. .. .. .. .. ..Andorra 85 0.5 181 3,447 41,750 .. .. 3.6 2.1 .. .. 539Antigua and Barbuda 88 0.4 200 1,169 13,280 c 1,795 d 20,400 d –5.2 –5.4 .. 99 447Aruba 108 0.2 600 .. .. e .. .. .. .. 75 98 2,288Bahamas, The 343 13.9 34 6,973 20,610 8,392 d 24,800 d 0.9 –0.5 75 .. 2,156Barbados 274 0.4 637 3,454 12,660 5,183 19,000 –5.3 –5.5 77 .. 1,353Belize 345 23.0 15 1,313 3,810 2,139 d 6,200 d 2.9 –0.6 76 .. 425Bermuda 65 0.1 1,292 .. .. e .. .. –8.1 –8.4 79 .. 389Bhutan 726 38.4 19 1,361 1,870 3,622 4,990 7.4 5.6 67 53 733Brunei Darussalam 399 5.8 76 12,461 31,800 19,661 50,180 –1.8 –3.6 78 95 10,594Cape Verde 496 4.0 123 1,620 3,270 1,893 3,820 5.4 4.5 74 85 308Cayman Islands 56 0.3 234 .. .. e .. .. .. .. .. 99 557Channel Islands 153 0.2 807 .. .. e .. .. .. .. 80 .. ..Comoros 735 1.9 395 550 750 802 1,090 2.1 –0.6 61 74 125Curaçao 143 0.4 321 .. .. e .. .. .. .. .. .. ..Djibouti 889 23.2 38 1,105 1,270 2,149 2,460 5.0 3.0 58 .. 524Dominica 68 0.8 91 458 6,740 812 d 11,940 d 0.1 –0.1 .. .. 128Equatorial Guinea 700 28.1 25 10,182 14,550 16,635 23,760 0.9 –1.8 51 93 4,815Faeroe Islands 49 1.4 35 .. .. e .. .. .. .. 80 .. 708Fiji 860 18.3 47 3,123 3,630 3,880 4,510 0.3 –0.6 69 .. 1,254French Polynesia 271 4.0 74 .. .. e .. .. .. .. 75 .. 891Gibraltar 29 0.0 2,924 .. .. e .. .. .. .. .. .. 422Greenland 57 410.5 0 f 1,466 26,020 .. .. –5.4 –5.4 68 .. 576Grenada 104 0.3 306 724 6,960 1,033 d 9,930 d –0.8 –0.7 76 .. 246Guam 179 0.5 331 .. .. e .. .. .. .. 76 .. ..Guyana 755 215.0 4 2,164 2,870 2,606 d 3,450 d 3.6 3.3 70 .. 1,525Iceland 318 103.0 3 10,381 32,640 8,991 28,270 –4.0 –3.8 81 .. 2,230Isle of Man 83 0.6 145 .. .. e .. .. .. .. .. .. ..Kiribati 100 0.8 123 200 2,000 352 d 3,520 d 1.8 –0.2 .. .. 29About the dataDefinitionsThe table shows data for economies with a populationbetween 30,000 and 1 million and for smallereconomies if they are members of the World Bank.Where data on gross national income (GNI) per capitaare not available, the estimated range is given. Formore information on the calculation of GNI and purchasingpower parity (PPP) conversion factors, seeAbout the data for table 1.1. Additional data for theeconomies in the table are available on the WorldDevelopment Indicators CD-ROM or at http://data.worldbank.org.• Population is based on the de facto defi nition ofpopulation, which counts all residents regardless oflegal status or citizenship—except for refugees notpermanently settled in the country of asylum, whoare generally considered part of the population oftheir country of origin. The values shown are midyearestimates. For more information, see About the datafor table 2.1. • Surface area is a country’s totalarea, including areas under inland bodies of waterand some coastal waterways. • Population densityis midyear population divided by land area in squarekilometers. • Gross national income (GNI), Atlasmethod, is the sum of value added by all residentproducers plus any product taxes (less subsidies) notincluded in the valuation of output plus net receiptsof primary income (compensation of employees andproperty income) from abroad. Data are in currentU.S. dollars converted using the World Bank Atlasmethod (see Statistical methods). • Purchasingpower parity (PPP) GNI is GNI converted to internationaldollars using PPP rates. An international dollarhas the same purchasing power over GNI that a U.S.dollar has in the United States. • GNI per capita isGNI divided by midyear population. • Gross domesticproduct (GDP) is the sum of value added by allresident producers plus any product taxes (less subsidies)not included in the valuation of output. Growthis calculated from constant price GDP data in local382012 World Development Indicators

Key indicators for other economies1.6WORLD VIEWPopulationSurfaceareaPopulationdensityGross national incomeGross domesticproductLifeexpectancyat birthAdultliteracyrateCarbondioxideemissionsPurchasingthousand people perAtlas methodPer capitapower parityPer capitaPer capita% ages 15 thousandthousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10 2010 2005–10 a 2008Liechtenstein 36 0.2 225 4,903 137,070 .. .. –1.2 –1.9 .. .. ..Luxembourg 507 2.6 196 39,030 76,980 31,050 61,240 2.7 0.8 80 .. 10,502Macao SAR, China 544 0.0 19,429 18,527 34,880 24,020 45,220 26.4 23.4 81 93 1,335Maldives 316 0.3 1,053 1,818 5,750 2,563 8,110 9.9 8.4 77 98 920Malta 416 0.3 1,300 7,958 19,130 10,258 24,660 3.1 2.6 81 92 2,560Marshall Islands 54 0.2 300 197 3,640 .. .. 5.2 4.0 .. .. 99Mayotte 204 0.4 551 .. .. b .. .. .. .. 78 .. ..Micronesia, Fed. Sts. 111 0.7 159 304 2,740 388 d 3,490 d 3.1 2.8 69 .. 62Monaco 35 0.0 17,704 6,479 183,150 .. .. –2.6 –2.7 .. .. ..Montenegro 632 13.8 47 4,260 6,740 8,073 12,770 2.5 2.2 74 .. 1,951New Caledonia 247 18.6 14 .. .. e .. .. .. .. 76 96 3,150Northern Mariana Islands 61 0.5 132 .. .. e .. .. .. .. .. .. ..Palau 20 0.5 45 134 6,560 225 d 11,000 d 2.0 1.4 .. .. 213Samoa 184 2.8 65 549 2,980 782 d 4,250 d 1.7 0.8 72 99 161San Marino 32 0.1 526 1,572 50,400 .. .. 1.9 1.3 83 .. ..São Tomé and Príncipe 165 1.0 172 199 1,200 318 1,930 4.5 2.9 64 89 128Seychelles 87 0.5 189 845 9,710 1,835 d 21,090 d 6.2 6.6 73 92 682Sint Maarten 38 0.0 1,113 .. .. e .. .. .. .. .. .. ..Solomon Islands 538 28.9 19 552 1,030 1,192 d 2,220 d 7.0 4.2 67 .. 198St. Kitts and Nevis 52 0.3 200 615 11,830 831 d 15,970 d –5.0 –5.5 .. .. 249St. Lucia 174 0.6 285 1,142 6,560 1,830 d 10,520 d 3.1 2.1 74 .. 396St. Martin 30 0.1 556 .. .. e .. .. .. .. .. .. ..St. Vincent & Grenadines 109 0.4 279 688 6,320 1,184 d 10,870 d –1.3 –1.0 72 .. 202Suriname 525 163.8 3 3,077 5,920 3,991 d 7,680 d 3.1 2.1 70 95 2,439Tonga 104 0.8 144 342 3,290 477 d 4,580 d –0.5 –0.9 72 99 176Turks and Caicos Islands 38 1.0 40 .. .. e .. .. .. .. .. .. 158Tuvalu 10 0.0 328 47 4,760 g .. .. –1.9 –2.1 .. .. ..Vanuatu 240 12.2 20 633 2,640 1,035 d 4,310 d 3.0 0.4 71 82 92Virgin Islands (U.S.) 110 0.4 314 .. .. e .. .. .. .. 79 .. ..a. Data are for the most recent year available. b. Estimated to be upper middle income ($3,976–$12,275). c. Included in the aggregates for upper middle-income economies based onearlier data. d. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. e. Estimated to be high income ($12,276 or more).f. Less than 0.5. g. Included in the aggregates for lower middle-income economies based on earlier data.currency. • GDP per capita is GDP divided by midyearpopulation. • Life expectancy at birth is the numberof years a newborn infant would live if prevailing patternsof mortality at the time of its birth were to staythe same throughout its life. • Adult literacy rate isthe percentage of adults ages 15 and older who can,with understanding, read and write a short, simplestatement about their everyday life. • Carbon dioxideemissions are those stemming from the burning offossil fuels and the manufacture of cement. Theyinclude carbon dioxide produced during consumptionof solid, liquid, and gas fuels and gas fl aring.Data sourcesThe indicators here and throughout the bookare compiled by World Bank staff from primaryand secondary sources. More information aboutthe indicators and their sources can be found inthe About the data, Defi nitions, and Data sourcesentries that accompany each table in subsequentsections.2012 World Development Indicators 39

PEOPLE

The People section documents demographictrends, labor force structure, poverty incidence,income distribution, educationinputs and outcomes, and health services andstatus. Together these indicators provide amultidimensional portrait of human developmentand social welfare. For 2012 a new tablehas been added, and the contents of othershave changed. Table 2.20 now shows data bysex for three measures of the prevalence ofchild malnutrition — underweight, stunting, andwasting — and for the proportion of overweightchildren. Data on antiretroviral therapy coverageand cause of death have been added totable 2.22. And table 2.23 now includes neonatalmortality rates computed with the samemethod ology used to estimate infant andunder-fi ve mortality rates. Also new is table2.24, which presents selected health indicatorsby income quintile. These subnational datahighlight substantial within-country disparitiesbetween the poor and rich in mortality and fertilityrates and in child and reproductive healthoutcomes.Table 2.8 provides more recent data on povertyat international poverty lines for more countries,including global and regional poverty estimatesreleased by the World Bank in February2012. In 2008, 1.29 billion people, or 22 percentof developing countries’ population, livedbelow the extreme poverty line of $1.25 a day,down from 1.94 billion people, or 52 percent ofdeveloping countries’ population, in 1981. In1990, the benchmark year for the MillenniumDevelopment Goals, the extreme poverty ratewas 43.1 percent.Progress has been slower at the $2 a daypoverty line, around which many people in lowermiddle-income economies live. The number ofpeople living below $2 a day fell f rom 2.59 billionin 1981 to 2.47 billion in 2008 — a decreaseof only 120 million — and the number of peopleliving on $1.25–$2 a day almost doubledto 1.18 billion in 2008. This bunching up justabove the extreme poverty line indicates thevulnerabilities faced by a great many people inthe world.Looking at the trend from 2005 to 2008,both the absolute number and the proportionof people living in extreme poverty declined inevery developing country region. This acrossthe-boardreduction over a three-year monitoringcycle is the fi rst since the World Bankbegan monitoring extreme poverty. Since 2008food, fuel, and fi nancial crises have had sharpnegative impacts on vulnerable populations andslowed poverty reduction in some countries, butglobal poverty kept falling. In fact, a preliminarysurvey-based estimate for 2010 — based on amuch smaller sample than the global update —indicates that the global poverty rate at $1.25a day fell to less than half its 1990 value. Ifthese results are confi rmed by follow-up studies,the fi rst target of the Millennium DevelopmentGoals — cutting the extreme poverty rateto half its 1990 level — may have been achievedon a global level before the 2015 target date.The World Bank’s database of internationalpoverty measures now includes income or consumptiondata collected by national statisticaloffi ces drawn from 850 household surveys andinterviews with 1.23 million randomly sampledhouseholds in nearly 130 countries. The mostrecent year for which a reliable global estimatecan be calculated is 2008, because for manylow-income countries more recent data areeither not available or not comparable withprevious estimates. The availability, frequency,and quality of poverty monitoring data remainlow, especially in small states and in countriesand territories with fragile situations. The needto improve household survey programs for povertymonitoring in these countries is urgent. Butinstitutional, political, and fi nancial obstaclescontinue to hamper data collection, analysis,and public access.22012 World Development Indicators 41

Tables2.1 Population dynamicsPopulationAverage annualpopulation growthPopulation agecompositionDependencyratioCrudedeathrateCrudebirthrate% % of working-ageAges Ages Agespopulation per 1,000 per 1,000millions %0–14 15–64 65+ Young Old people people2000 2010 2020 2000–10 2010–20 2010 2010 2010 2010 2010 2010 2010Afghanistan 26.0 34.4 44.8 2.8 2.6 46 51 2 91 4 16 44Albania 3.1 3.2 3.3 0.4 0.2 23 68 10 34 14 6 13Algeria 30.5 35.5 40.1 1.5 1.2 27 68 5 40 7 5 20Angola 13.9 19.1 24.8 3.1 2.6 47 51 2 91 5 14 42Argentina 36.9 40.4 43.7 0.9 0.8 25 65 11 39 16 8 17Armenia 3.1 3.1 3.1 0.1 0.1 20 69 11 29 16 9 15Australia 19.2 22.3 25.2 1.5 1.2 19 68 13 28 20 6 13Austria 8.0 8.4 8.5 0.5 0.1 15 68 18 22 26 9 9Azerbaijan 8.0 9.1 10.1 1.2 1.1 21 73 7 29 9 6 19Bahrain 0.6 1.3 1.5 6.8 1.9 20 78 2 26 3 3 20Bangladesh 129.6 148.7 167.1 1.4 1.2 31 64 5 49 7 6 20Belarus 10.0 9.5 9.2 –0.5 –0.4 15 71 14 21 19 15 11Belgium 10.3 10.9 11.1 0.6 0.2 17 66 17 26 27 10 12Benin 6.5 8.9 11.5 3.1 2.6 44 53 3 82 6 12 40Bolivia 8.3 9.9 11.6 1.8 1.5 36 59 5 61 8 7 26Bosnia and Herzegovina 3.7 3.8 3.6 0.2 –0.4 15 71 14 21 20 10 9Botswana 1.8 2.0 2.2 1.3 0.9 33 63 4 51 6 13 24Brazil 174.4 194.9 209.6 1.1 0.7 25 68 7 38 10 6 15Bulgaria 8.2 7.5 7.0 –0.8 –0.7 14 69 18 20 25 15 10Burkina Faso 12.3 16.5 22.1 2.9 2.9 45 52 2 86 4 12 43Burundi 6.4 8.4 10.1 2.7 1.8 38 59 3 64 5 14 34Cambodia 12.4 14.1 15.9 1.3 1.2 32 64 4 50 6 8 22Cameroon 15.7 19.6 24.1 2.2 2.1 41 56 4 73 6 14 36Canada 30.8 34.1 37.1 1.0 0.8 16 69 14 24 20 8 11Central African Republic 3.7 4.4 5.3 1.7 1.9 40 56 4 73 7 16 35Chad 8.2 11.2 14.4 3.1 2.5 45 52 3 88 6 16 45Chile 15.4 17.1 18.4 1.0 0.8 22 69 9 32 13 6 14China 1,262.6 1,338.3 1,381.6 0.6 0.3 19 72 8 27 11 7 12Hong Kong SAR, China 6.7 7.1 7.8 0.6 0.9 12 76 13 15 17 6 13Colombia 39.8 46.3 52.1 1.5 1.2 29 66 6 44 9 5 20Congo, Dem. Rep. 49.6 66.0 85.0 2.8 2.5 46 51 3 91 5 16 43Congo, Rep. 3.1 4.0 5.0 2.5 2.1 41 56 4 73 7 11 35Costa Rica 3.9 4.7 5.3 1.7 1.2 25 69 7 36 10 4 16Côte d’Ivoire 16.6 19.7 24.5 1.7 2.2 41 55 4 74 7 12 34Croatia 4.4 4.4 4.3 0.0 –0.3 15 68 17 22 25 12 10Cuba 11.1 11.3 11.1 0.1 –0.1 17 70 12 25 18 7 10Cyprus 0.9 1.1 1.2 1.6 1.0 18 71 12 25 16 7 12Czech Republic 10.3 10.5 10.7 0.2 0.2 14 71 15 20 21 10 11Denmark 5.3 5.5 5.7 0.4 0.3 18 66 16 27 25 10 11Dominican Republic 8.6 9.9 11.1 1.4 1.1 31 63 6 49 10 6 22Ecuador 12.3 14.5 16.3 1.6 1.2 30 63 6 48 10 5 21Egypt, Arab Rep. 67.6 81.1 94.8 1.8 1.6 32 63 5 50 8 5 23El Salvador 5.9 6.2 6.6 0.4 0.6 32 61 7 52 11 7 20Eritrea 3.7 5.3 6.8 3.6 2.6 42 56 2 74 4 8 36Estonia 1.4 1.3 1.3 –0.2 –0.1 15 67 17 23 25 12 12Ethiopia 65.6 83.0 100.9 2.3 2.0 41 55 3 75 6 10 31Finland 5.2 5.4 5.5 0.4 0.2 17 66 17 25 26 10 11France 60.8 64.9 67.6 0.7 0.4 18 65 17 28 26 8 13Gabon 1.2 1.5 1.8 2.0 1.9 35 60 4 59 7 9 27Gambia, The 1.3 1.7 2.2 2.9 2.6 44 54 2 82 4 9 38Georgia 4.4 a 4.5 a 4.2 a 0.1 a –0.7 a 17 69 14 24 21 11 12Germany 82.2 81.8 79.8 –0.1 –0.2 13 66 20 20 31 11 8Ghana 19.2 24.4 30.3 2.4 2.2 39 58 4 67 7 8 32Greece 10.9 11.3 11.5 0.4 0.1 15 67 19 22 28 9 10Guatemala 11.2 14.4 18.3 2.5 2.4 41 54 4 77 8 5 32Guinea 8.3 10.0 12.8 1.8 2.4 43 54 3 80 6 13 39Guinea-Bissau 1.2 1.5 1.9 2.0 2.1 41 55 3 75 6 17 38Haiti 8.6 10.0 11.3 1.4 1.2 36 60 4 60 7 9 27422012 World Development Indicators

Population dynamics2.1PEOPLEPopulationAverage annualpopulation growthPopulation agecompositionDependencyratioCrudedeathrateCrudebirthrate% % of working-ageAges Ages Agespopulation per 1,000 per 1,000millions %0–14 15–64 65+ Young Old people people2000 2010 2020 2000–10 2010–20 2010 2010 2010 2010 2010 2010 2010Honduras 6.2 7.6 9.2 2.0 1.9 37 59 4 62 7 5 27Hungary 10.2 10.0 9.8 –0.2 –0.2 15 69 17 21 24 13 9India 1,053.9 1,224.6 1,385.2 1.5 1.2 31 64 5 47 8 8 22Indonesia 213.4 239.9 262.1 1.2 0.9 27 67 6 40 8 7 18Iran, Islamic Rep. 65.3 74.0 80.9 1.2 0.9 23 72 5 32 7 5 17Iraq 24.3 32.0 42.3 2.8 2.8 43 54 3 81 6 6 35Ireland 3.8 4.5 5.0 1.6 1.0 21 67 12 32 17 6 17Israel 6.3 7.6 8.9 1.9 1.5 27 62 10 44 17 5 22Italy 56.9 60.5 60.8 0.6 0.1 14 66 20 21 31 10 9Jamaica 2.6 2.7 2.8 0.4 0.3 29 63 8 46 12 7 16Japan 126.9 127.5 123.6 0.0 –0.3 13 64 23 21 35 10 9Jordan 4.8 6.0 7.2 2.3 1.8 38 59 4 64 7 4 25Kazakhstan 14.9 16.3 18.0 0.9 1.0 24 69 7 36 10 9 22Kenya 31.3 40.5 52.5 2.6 2.6 42 55 3 77 5 11 38Korea, Dem. Rep. 22.9 24.3 25.3 0.6 0.4 23 68 10 34 14 10 14Korea, Rep. 47.0 48.9 50.2 0.4 0.3 16 72 11 23 15 5 9Kosovo 1.7 1.8 1.9 0.7 0.7 .. .. .. .. .. 7 19Kuwait 1.9 2.7 3.4 3.4 2.2 27 71 3 38 4 3 18Kyrgyz Republic 4.9 5.4 6.1 1.1 1.2 30 66 4 46 7 7 27Lao PDR 5.3 6.2 7.0 1.5 1.3 35 62 4 56 6 6 23Latvia 2.4 2.2 2.1 –0.6 –0.4 14 68 18 20 26 13 9Lebanon 3.7 4.2 4.5 1.2 0.6 25 68 7 36 11 7 15Lesotho 2.0 2.2 2.4 1.0 1.0 37 58 4 64 7 16 28Liberia 2.8 4.0 5.2 3.4 2.6 43 54 3 81 5 11 39Libya 5.2 6.4 7.0 1.9 1.0 30 65 4 47 7 4 23Lithuania 3.5 3.3 3.1 –0.6 –0.5 15 69 16 22 23 13 11Macedonia, FYR 2.0 2.1 2.1 0.3 0.0 18 71 12 25 17 9 11Madagascar 15.4 20.7 27.3 3.0 2.8 43 54 3 80 6 6 35Malawi 11.2 14.9 20.7 2.8 3.3 46 51 3 90 6 13 44Malaysia 23.4 28.4 33.0 1.9 1.5 30 65 5 47 7 5 20Mali 11.3 15.4 20.5 3.1 2.9 47 51 2 93 4 15 46Mauritania 2.6 3.5 4.3 2.7 2.2 40 57 3 69 5 10 34Mauritius 1.2 1.3 1.3 0.8 0.4 22 71 7 31 10 7 12Mexico 100.0 113.4 125.7 1.3 1.0 29 65 6 45 10 5 20Moldova 3.6 b 3.6 b 3.3 b –0.2 b –0.6 b 17 72 11 23 15 13 12Mongolia 2.4 2.8 3.2 1.3 1.4 28 68 4 40 6 6 23Morocco 28.8 32.0 35.0 1.0 0.9 28 66 5 42 8 6 20Mozambique 18.2 23.4 29.1 2.5 2.2 44 53 3 84 6 15 38Myanmar 45.0 48.0 51.7 0.6 0.7 26 69 5 37 7 9 17Namibia 1.9 2.3 2.7 1.9 1.6 36 60 4 61 6 8 26Nepal 24.4 30.0 35.1 2.1 1.6 36 60 4 61 7 6 24Netherlands 15.9 16.6 17.0 0.4 0.2 18 67 15 26 23 8 11New Zealand 3.9 4.4 4.8 1.2 1.0 20 67 13 31 20 7 15Nicaragua 5.1 5.8 6.6 1.3 1.3 34 61 5 57 8 5 24Niger 10.9 15.5 22.0 3.5 3.5 49 49 2 100 4 13 49Nigeria 123.7 158.4 203.7 2.5 2.5 43 54 3 80 6 14 40Norway 4.5 4.9 5.2 0.8 0.7 19 67 15 28 22 9 13Oman 2.3 2.8 3.3 2.1 1.8 27 70 3 39 4 4 18Pakistan 144.5 173.6 205.2 1.8 1.7 35 60 4 59 7 7 27Panama 3.0 3.5 4.0 1.7 1.4 29 64 7 45 10 5 20Papua New Guinea 5.4 6.9 8.5 2.4 2.1 39 58 3 67 5 8 30Paraguay 5.3 6.5 7.6 1.9 1.6 34 61 5 55 8 5 24Peru 25.9 29.1 32.3 1.2 1.1 30 64 6 47 10 5 20Philippines 77.3 93.3 109.6 1.9 1.6 35 61 4 58 6 6 25Poland 38.5 38.2 38.1 –0.1 0.0 15 72 14 21 19 10 11Portugal 10.2 10.6 10.5 0.4 –0.1 15 67 18 23 27 10 10Puerto Rico 3.8 4.0 4.0 0.4 –0.1 21 66 13 32 19 8 13Qatar 0.6 1.8 2.3 10.9 c 2.6 13 85 1 16 1 2 132012 World Development Indicators 43

2.1Population dynamicsPopulationAverage annualpopulation growthPopulation agecompositionDependencyratioCrudedeathrateCrudebirthrate% % of working-ageAges Ages Agespopulation per 1,000 per 1,000millions %0–14 15–64 65+ Young Old people people2000 2010 2020 2000–10 2010–20 2010 2010 2010 2010 2010 2010 2010Romania 22.4 21.4 20.9 –0.5 –0.3 15 70 15 22 21 12 10Russian Federation 146.3 141.8 139.3 –0.3 –0.2 15 72 13 21 18 14 13Rwanda 8.1 10.6 14.0 2.7 2.8 43 55 3 78 5 12 41Saudi Arabia 20.0 27.4 33.1 3.1 1.9 30 67 3 46 4 4 22Senegal 9.5 12.4 16.0 2.7 2.5 44 54 2 81 4 9 37Serbia 7.5 7.3 7.2 –0.3 –0.2 18 d 68 d 14 d 26 d 21 d 14 9Sierra Leone 4.1 5.9 7.2 3.5 2.0 43 55 2 78 3 16 39Singapore 4.0 5.1 5.6 2.3 0.9 17 74 9 24 12 4 9Slovak Republic 5.4 5.4 5.5 0.1 0.1 15 73 12 21 17 10 11Slovenia 2.0 2.0 2.1 0.3 0.1 14 70 16 20 24 9 11Somalia 7.4 9.3 12.2 2.3 2.7 45 52 3 86 5 15 44South Africa 44.0 50.0 52.4 1.3 0.5 30 65 5 46 7 15 21South Sudan .. .. .. .. .. .. .. .. .. .. 21 30Spain 40.3 46.1 48.4 1.3 0.5 15 68 17 22 25 8 11Sri Lanka 18.7 20.9 22.3 1.1 0.7 25 67 8 37 12 7 18Sudan 34.2 e 43.6 e 54.9 e 2.4 e 2.3 e 40 e 56 e 4 e 71 e 6 e 9 e 33 eSwaziland 1.0 1.1 1.2 0.4 1.2 38 58 3 66 6 14 29Sweden 8.9 9.4 9.9 0.6 0.5 17 65 18 25 28 10 12Switzerland 7.2 7.8 8.0 0.9 0.3 15 68 17 22 25 8 10Syrian Arab Republic 16.0 20.4 24.3 2.5 1.7 37 59 4 62 7 4 23Tajikistan 6.2 6.9 7.9 1.1 1.4 37 60 3 62 6 6 28Tanzania 34.0 44.8 61.0 2.8 3.1 45 52 3 86 6 10 41Thailand 63.2 69.1 71.9 0.9 0.4 21 71 9 29 13 7 12Timor-Leste 0.8 1.1 1.5 3.0 2.8 46 51 3 91 6 8 38Togo 4.8 6.0 7.3 2.3 2.0 40 57 3 70 6 11 32Trinidad and Tobago 1.3 1.3 1.4 0.4 0.2 21 72 7 28 10 8 15Tunisia 9.6 10.5 11.6 1.0 0.9 23 70 7 34 10 6 18Turkey 63.6 72.8 80.7 1.3 1.0 26 68 6 39 9 5 18Turkmenistan 4.5 5.0 5.7 1.1 1.2 29 67 4 44 6 8 22Uganda 24.2 33.4 45.3 3.2 3.0 48 49 3 99 5 12 45Ukraine 49.2 45.9 43.3 –0.7 –0.6 14 70 15 20 22 15 11United Arab Emirates 3.0 7.5 9.2 9.1 2.0 17 83 0 21 1 1 13United Kingdom 58.9 62.2 65.7 0.6 0.5 17 66 17 26 25 9 13United States 282.2 309.3 334.9 0.9 0.8 20 67 13 30 20 8 14Uruguay 3.3 3.4 3.5 0.2 0.3 23 64 14 35 22 9 14Uzbekistan 24.7 28.2 31.6 1.4 1.1 29 66 4 44 7 5 23Venezuela, RB 24.3 28.8 33.1 1.7 1.4 29 65 6 45 9 5 21Vietnam 77.6 86.9 95.2 1.1 0.9 24 70 6 34 9 5 17West Bank and Gaza 3.0 4.2 5.5 3.2 2.7 42 55 3 78 5 4 33Yemen, Rep. 17.7 24.1 32.2 3.1 2.9 44 53 3 83 5 6 38Zambia 10.2 12.9 17.7 2.4 3.1 46 51 3 92 6 16 46Zimbabwe 12.5 12.6 15.5 0.0 2.1 39 57 4 68 7 13 29World 6,117.8 s 6,894.6 s 7,635.2 s 1.2 w 1.0 w 27 w 66 w 8 w 41 w 12 w 8 w 20 wLow income 643.7 796.3 979.1 2.1 2.1 39 57 4 69 6 11 33Middle income 4,424.5 4,970.8 5,478.1 1.2 1.0 27 67 6 40 10 8 19Lower middle income 2,146.7 2,518.7 2,901.8 1.6 1.4 32 63 5 51 8 8 24Upper middle income 2,277.9 2,452.1 2,576.2 0.7 0.5 22 70 8 31 11 7 14Low & middle income 5,068.2 5,767.2 6,457.2 1.3 1.1 29 65 6 44 9 8 21East Asia & Pacifi c 1,813.8 1,961.6 2,068.9 0.8 0.5 22 71 7 31 10 7 14Europe & Central Asia 398.5 405.2 414.4 0.2 0.2 19 70 11 28 15 11 15Latin America & Carib. 514.3 582.6 642.4 1.2 1.0 28 65 7 43 10 6 19Middle East & N. Africa 277.4 331.3 386.5 1.8 1.6 31 64 5 48 7 5 23South Asia 1,398.0 1,633.1 1,860.8 1.6 1.3 32 64 5 50 8 8 23Sub-Saharan Africa 666.3 853.4 1,084.2 2.5 2.4 42 54 3 78 6 13 37High income 1,049.6 1,127.4 1,178.1 0.7 0.4 17 67 16 26 23 8 12Euro area 315.0 331.8 336.7 0.5 0.1 15 66 18 23 28 9 10a. Excludes Abkhazia and South Ossetia. b. Excludes Transnistria. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo. e. Includes South Sudan.442012 World Development Indicators

Population dynamics2.1PEOPLEAbout the dataDefinitionsPopulation estimates are usually based on nationalpopulation censuses. Estimates for the years beforeand after the census are interpolations or extrapolationsbased on demographic models. Errors andundercounting occur even in high-income countries;in developing countries errors may be substantialbecause of limits in the transport, communications,and other resources required to conduct and analyzea full census.The quality and reliability of offi cial demographicdata are also affected by public trust in the government,government commitment to full and accurateenumeration, confi dentiality and protection againstmisuse of census data, and census agencies’ independencefrom political influence. Moreover, comparabilityof population indicators is limited by differencesin the concepts, defi nitions, collection procedures,and estimation methods used by national statisticalagencies and other organizations that collect the data.Of the 158 economies in the table and the 58economies in table 1.6, 180 (about 86 percent)conducted a census during the 2000 census round(1995–2004). As of January 2012, 141 countrieshave completed a census for the 2010 census round(2005–14). The currentness of a census and the availabilityof complementary data from surveys or registrationsystems are objective ways to judge demographicdata quality. Some European countries’ registrationsystems offer complete information on population inthe absence of a census. See table 2.17 and Primarydata documentation for the most recent census orsurvey year and for the completeness of registration.Current population estimates for developing countriesthat lack recent census data and pre- andpost-census estimates for countries with censusdata are provided by the United Nations PopulationDivision and other agencies. The cohort componentmethod—a standard method for estimating andprojecting population —requires fertility, mortality,and net migration data, often collected from samplesurveys, which can be small or limited in coverage.Population estimates are from demographic modelingand so are susceptible to biases and errors fromshortcomings in the model and in the data. Becausethe fi ve-year age group is the cohort unit and fi ve-yearperiod data are used, interpolations to obtain annualdata or single age structure may not refl ect actualevents or age composition.The growth rate of the total population concealsage-group differences in growth rates. In many developingcountries the once rapidly growing under-15population is shrinking. Previously high fertility ratesand declining mortality rates are now refl ected in thelarger share of the working-age population.Dependency ratios capture variations in the proportionsof children, elderly people, and working-agepeople in the population that imply the dependencyburden that the working-age population bears inrelation to children and the elderly. But dependencyratios show only the age composition of a population,not economic dependency. Some children andelderly people are part of the labor force, and manyworking-age people are not.Vital rates are based on data from birth and deathregistration systems, censuses, and sample surveysby national statistical offi ces and other organizations,or on demographic analysis. Data for 2010for some high-income countries are provisional estimatesbased on vital registers. The estimates formany countries are projections based on extrapolationsof levels and trends from earlier years or interpolationsof population estimates and projectionsfrom the United Nations Population Division.Vital registers are the preferred source for thesedata, but in many developing countries systems forregistering births and deaths are absent or incompletebecause of defi ciencies in the coverage of events orgeographic areas. Many developing countries carry outspecial household surveys that ask respondents aboutrecent births and deaths. Estimates derived in thisway are subject to sampling errors and recall errors.The United Nations Statistics Division monitors thecompleteness of vital registration systems. Somecountries have made progress over the last 60 years,but others still have defi ciencies in civil registrationsystems. For example, only 57 percent of countriesand areas register at least 90 percent of births, andonly 53 percent register at least 90 percent of deaths.Some of the most populous developing countries—Bangladesh, Brazil, China, India, Indonesia, Nigeria,Pakistan —lack complete vital registration systems.International migration is the only other factorbesides birth and death rates that directly determinesa country’s population growth. From 1990 to2005 the number of migrants in high-income countriesrose 40 million. About 195 million people (3percent of the world population) live outside theirhome country. Estimating migration is diffi cult. Atany time many people are located outside their homecountry as tourists, workers, or refugees or for otherreasons. Standards for the duration and purpose ofinternational moves that qualify as migration vary,and estimates require information on fl ows into andout of countries that is diffi cult to collect.• Population is based on the de facto defi nition ofpopulation, which counts all residents regardless oflegal status or citizenship—except for refugees notpermanently settled in the country of asylum, whoare generally considered part of the population oftheir country of origin. The values shown are midyearestimates for 2000 and 2010 and projectionsfor 2020. • Average annual population growth isthe exponential change for the period indicated. SeeStatistical methods for more information. • Populationage composition is the percentage of the totalpopulation that is in specifi c age groups. • Dependencyratio is the ratio of dependents—peopleyounger than 15 or older than 64—to the workingagepopulation —those ages 15–64. • Crude deathrate and crude birth rate are the number of deathsand the number of live births occurring during theyear, per 1,000 people, estimated at midyear. Subtractingthe crude death rate from the crude birthrate provides the rate of natural increase, which isequal to the population growth rate in the absenceof migration.Data sourcesThe World Bank’s population estimates are compiledand produced by its Development DataGroup in consultation with its Human DevelopmentNetwork, operational staff, and countryoffi ces. The United Nations Population Division’sWorld Population Prospects: The 2010 Revision isa source of the demographic data for more thanhalf the countries, most of them developing countries,and the source of data on age compositionand dependency ratios for all countries. Otherimportant sources are census reports and otherstatistical publications from national statisticaloffi ces; household surveys by national agencies,ICF International (for MEASURE DHS), and theU.S. Centers for Disease Control and Prevention;Eurostat’s Demographic Statistics; Secretariat ofthe Pacifi c Community, Statistics and DemographyProgramme; and U.S. Bureau of the Census,International Data Base.2012 World Development Indicators 45

2.2Labor force structureLabor force participation rateLabor forceAges 15 and older% ages 15 and olderTotalaverage annualFemaleMaleFemalemillions% growth% of labor force2000 2010 2000 2010 2000 2010 2000–10 2000 2010Afghanistan 81 80 13 16 6.5 9.1 3.3 13.3 15.2Albania 73 71 51 50 1.3 1.5 1.2 41.7 41.6Algeria 76 72 12 15 8.8 11.2 2.4 13.7 16.9Angola 75 77 67 63 5.2 7.1 3.1 48.4 45.9Argentina 74 75 43 47 15.4 18.4 1.8 38.1 40.2Armenia 73 70 58 49 1.5 1.4 –0.2 48.8 46.5Australia 72 73 55 59 9.6 11.8 2.1 43.8 45.3Austria 69 68 48 54 3.9 4.3 1.1 43.5 45.9Azerbaijan 71 68 57 61 3.5 4.6 2.6 47.0 49.1Bahrain 86 87 35 39 0.3 0.7 8.6 21.4 19.3Bangladesh 86 84 54 57 57.3 72.3 2.3 37.5 39.9Belarus 65 62 53 50 4.7 4.5 –0.6 48.9 49.0Belgium 61 61 44 48 4.4 4.9 1.0 42.9 45.3Benin 81 78 64 67 2.6 3.6 3.5 46.5 47.5Bolivia 82 81 60 64 3.5 4.6 2.6 43.2 44.8Bosnia and Herzegovina 58 59 33 35 1.3 1.5 1.1 39.1 40.0Botswana 80 82 70 72 0.8 1.0 2.4 46.9 46.3Brazil 82 81 55 59 83.7 101.6 1.9 41.2 43.7Bulgaria 57 60 48 49 3.6 3.5 –0.3 47.1 46.8Burkina Faso 91 91 77 78 5.5 7.5 3.2 48.2 47.6Burundi 84 82 86 84 2.9 4.3 3.9 53.2 52.1Cambodia 83 87 77 79 5.8 8.0 3.2 51.2 49.9Cameroon 77 77 62 64 6.2 8.2 2.8 45.1 45.7Canada 72 72 59 62 16.3 19.0 1.6 45.8 47.1Central African Republic 86 85 71 73 1.7 2.1 2.1 46.4 47.1Chad 80 80 65 65 3.2 4.4 3.2 45.5 45.3Chile 75 74 35 47 6.1 8.0 2.8 33.1 39.6China 83 80 71 68 724.5 799.8 1.0 45.0 44.6Hong Kong SAR, China 73 68 49 51 3.3 3.7 1.0 41.9 46.0Colombia 82 80 49 55 17.3 22.1 2.5 38.7 42.5Congo, Dem. Rep. 73 72 71 70 18.5 25.3 3.1 50.1 49.9Congo, Rep. 71 73 65 68 1.3 1.7 3.0 48.1 48.6Costa Rica 81 79 37 46 1.6 2.2 3.2 30.8 36.2Côte d’Ivoire 82 81 49 52 6.4 7.8 2.0 35.0 37.4Croatia 63 60 45 46 2.0 2.0 0.0 44.1 45.9Cuba 70 70 38 43 4.7 5.3 1.1 35.0 38.0Cyprus 72 71 50 57 0.4 0.6 2.7 40.8 43.5Czech Republic 70 68 52 49 5.2 5.3 0.2 44.4 43.3Denmark 72 69 60 60 2.9 2.9 0.3 46.6 47.2Dominican Republic 80 79 46 51 3.5 4.4 2.2 36.6 39.4Ecuador 84 83 50 54 5.4 6.9 2.4 37.4 39.7Egypt, Arab Rep. 73 74 20 24 20.1 27.1 3.0 21.5 24.2El Salvador 79 79 45 47 2.2 2.6 1.6 39.6 41.4Eritrea 90 90 75 80 1.7 2.6 4.5 47.3 48.6Estonia 67 68 52 57 0.7 0.7 0.6 48.7 50.4Ethiopia 91 90 73 78 29.0 40.8 3.4 45.2 47.2Finland 67 65 57 56 2.6 2.7 0.3 47.5 47.8France 63 62 48 51 27.2 29.9 0.9 45.5 47.1Gabon 67 65 55 56 0.4 0.6 2.8 45.5 46.3Gambia, The 83 83 71 72 0.5 0.8 3.2 47.0 47.9Georgia 74 74 55 56 2.2 a 2.4 a 0.8 a 46.2 47.0Germany 68 67 49 53 40.3 42.2 0.4 43.7 45.6Ghana 77 72 73 67 8.4 10.4 2.1 48.1 47.6Greece 65 65 40 45 4.9 5.3 0.8 39.1 41.5Guatemala 86 88 42 49 4.0 5.7 3.6 34.6 38.1Guinea 78 78 63 65 3.3 4.1 2.2 44.6 45.2Guinea-Bissau 79 78 63 68 0.5 0.6 2.6 45.5 47.3Haiti 69 71 57 60 3.2 4.2 2.5 46.5 47.0462012 World Development Indicators

Labor force structure 2.2PEOPLELabor force participation rateLabor forceAges 15 and older% ages 15 and olderTotalaverage annualFemaleMaleFemalemillions% growth% of labor force2000 2010 2000 2010 2000 2010 2000–10 2000 2010Honduras 88 83 44 42 2.4 3.0 2.4 34.1 34.1Hungary 58 58 41 44 4.2 4.3 0.3 44.7 46.0India 83 81 34 29 409.4 472.6 1.4 27.8 25.3Indonesia 85 84 50 51 99.6 118.0 1.7 37.7 38.2Iran, Islamic Rep. 73 72 14 16 18.5 25.3 3.1 16.0 17.9Iraq 69 69 13 14 5.5 7.5 3.1 16.0 17.5Ireland 71 68 47 52 1.8 2.1 1.9 40.6 43.7Israel 61 62 48 53 2.5 3.2 2.6 45.7 47.1Italy 61 60 35 38 23.3 25.1 0.8 38.7 40.3Jamaica 78 72 59 56 1.2 1.2 0.3 44.3 45.1Japan 76 72 49 50 67.6 66.7 –0.1 40.7 42.3Jordan 68 65 13 15 1.2 1.6 2.5 14.3 18.0Kazakhstan 76 77 65 66 7.5 8.8 1.5 49.2 49.4Kenya 73 72 63 61 11.9 15.5 2.7 46.8 46.5Korea, Dem. Rep. 88 84 74 72 13.6 14.6 0.7 47.5 47.7Korea, Rep. 73 72 49 49 22.7 24.6 0.8 40.5 41.3Kosovo .. .. .. .. .. .. .. .. ..Kuwait 82 82 44 43 1.0 1.4 3.5 25.1 23.9Kyrgyz Republic 74 78 56 55 2.1 2.5 2.0 44.6 42.7Lao PDR 81 79 79 77 2.5 3.2 2.5 49.9 49.8Latvia 65 66 49 55 1.1 1.2 0.6 48.1 50.1Lebanon 71 71 19 23 1.1 1.5 2.4 22.9 25.5Lesotho 80 73 68 59 0.8 0.9 0.5 49.1 46.0Liberia 62 64 58 58 1.0 1.4 3.6 49.0 47.7Libya 73 77 27 30 1.8 2.4 2.8 26.1 28.0Lithuania 67 63 55 54 1.7 1.6 –0.3 49.5 50.3Macedonia, FYR 66 69 41 43 0.8 0.9 1.3 38.8 38.6Madagascar 90 89 84 84 7.3 10.1 3.3 48.7 48.9Malawi 81 81 77 85 4.8 6.7 3.3 49.7 51.5Malaysia 82 77 45 44 9.9 12.0 1.9 34.7 35.8Mali 66 70 37 37 3.1 4.3 3.4 37.5 35.5Mauritania 78 79 23 28 0.8 1.1 3.8 23.3 26.5Mauritius 81 76 41 44 0.5 0.6 1.2 34.5 37.7Mexico 83 81 39 44 40.3 49.6 2.1 32.9 36.5Moldova 64 45 55 38 1.6 b 1.2 b –3.0 b 49.7 49.2Mongolia 66 65 56 54 0.9 1.2 2.2 46.8 46.4Morocco 79 75 29 26 10.2 11.4 1.1 27.9 27.1Mozambique 83 83 88 86 8.7 11.1 2.4 55.0 53.6Myanmar 81 82 74 75 24.2 28.0 1.5 48.3 48.9Namibia 65 70 49 58 0.6 0.9 3.8 44.4 46.3Nepal 90 88 82 80 12.4 16.0 2.6 48.9 49.2Netherlands 73 72 53 58 8.2 8.9 0.8 43.1 45.6New Zealand 73 74 57 62 1.9 2.4 2.0 45.2 46.7Nicaragua 83 80 38 46 1.8 2.4 2.8 32.5 37.9Niger 88 90 38 40 3.5 5.1 3.7 30.9 31.2Nigeria 67 63 45 48 39.2 50.3 2.5 40.1 42.8Norway 72 70 60 62 2.4 2.6 1.0 46.5 46.9Oman 78 80 23 28 0.8 1.2 4.4 17.1 17.9Pakistan 84 83 16 22 43.0 59.7 3.3 15.3 20.7Panama 82 83 45 49 1.3 1.6 2.5 35.4 37.3Papua New Guinea 74 74 71 71 2.3 3.0 2.7 48.5 48.3Paraguay 87 86 51 57 2.3 3.1 3.0 36.6 39.7Peru 83 85 58 67 12.0 15.5 2.6 41.2 44.6Philippines 82 79 49 50 30.9 38.7 2.2 37.5 38.8Poland 64 64 49 48 17.4 18.2 0.5 45.8 45.1Portugal 70 68 53 56 5.2 5.6 0.7 45.3 47.4Puerto Rico 60 54 35 35 1.4 1.4 0.3 39.5 42.3Qatar 92 95 39 52 0.3 1.3 13.7 15.6 12.42012 World Development Indicators 47

2.2Labor force structureLabor force participation rateLabor forceAges 15 and older% ages 15 and olderTotalaverage annualFemaleMaleFemalemillions% growth% of labor force2000 2010 2000 2010 2000 2010 2000–10 2000 2010Romania 71 65 58 48 11.8 10.2 –1.5 46.6 44.7Russian Federation 69 71 55 56 73.3 75.5 0.3 48.6 48.9Rwanda 85 85 86 86 3.8 5.2 3.2 52.3 51.8Saudi Arabia 74 74 16 17 6.0 9.6 4.7 14.7 14.8Senegal 89 88 64 66 3.9 5.4 3.1 42.9 43.9Serbia .. 67 .. 51 .. 3.5 c .. .. 43.9Sierra Leone 63 69 67 66 1.6 2.3 3.7 53.2 50.7Singapore 78 77 53 57 2.1 2.8 3.0 40.6 42.3Slovak Republic 68 68 53 51 2.6 2.7 0.5 45.6 44.6Slovenia 64 65 51 53 1.0 1.0 0.8 46.3 46.3Somalia 78 77 37 38 2.3 2.9 2.2 32.8 33.6South Africa 61 60 44 44 15.2 18.2 1.8 43.1 42.8South Sudan .. .. .. .. .. .. .. .. ..Spain 66 67 41 52 18.2 23.2 2.4 39.5 44.3Sri Lanka 77 76 37 35 7.8 8.6 0.9 33.1 32.2Sudan 76 77 29 31 10.3 14.0 3.0 27.9 28.7Swaziland 72 71 43 44 0.3 0.4 2.2 40.3 39.7Sweden 68 68 58 59 4.6 5.0 0.9 47.1 47.0Switzerland 78 75 58 61 4.0 4.5 1.2 44.3 45.8Syrian Arab Republic 80 72 20 13 4.8 5.5 1.2 20.2 15.2Tajikistan 75 75 58 57 2.4 2.8 1.8 44.1 45.2Tanzania 91 90 87 88 16.7 22.1 2.8 49.7 49.8Thailand 81 80 65 64 34.8 39.4 1.2 46.1 45.7Timor-Leste 75 74 38 38 0.2 0.3 3.6 32.6 33.3Togo 82 81 76 80 2.2 2.9 3.1 49.0 50.5Trinidad and Tobago 77 78 47 55 0.6 0.7 1.8 39.9 43.2Tunisia 72 70 24 25 3.2 3.8 1.8 24.9 26.9Turkey 74 71 27 28 21.9 26.5 1.9 26.9 28.7Turkmenistan 74 76 48 46 1.7 2.2 2.2 40.6 39.3Uganda 83 80 81 76 10.1 13.4 2.8 50.0 49.3Ukraine 65 66 52 53 23.4 23.2 –0.1 49.1 49.3United Arab Emirates 92 92 34 44 1.7 4.9 10.5 12.0 14.8United Kingdom 70 69 54 56 29.5 31.8 0.8 45.2 45.9United States 74 70 59 58 147.1 157.5 0.7 45.8 46.1Uruguay 76 77 52 55 1.6 1.7 0.8 43.2 44.5Uzbekistan 72 74 48 48 9.2 12.1 2.8 40.6 39.8Venezuela, RB 82 80 48 52 10.5 13.4 2.4 37.2 39.3Vietnam 83 81 74 73 41.3 51.1 2.1 49.1 48.5West Bank and Gaza 67 66 11 15 0.6 1.0 4.6 13.4 17.8Yemen, Rep. 71 72 22 25 4.2 6.5 4.3 24.0 25.8Zambia 85 86 75 73 4.5 5.5 2.1 47.2 46.1Zimbabwe 82 90 69 83 5.5 6.6 1.9 46.4 49.3World 79 w 77 w 52 w 51 w 2,770.2 t 3,223.0 t 1.5 w 39.9 w 39.9 wLow income 83 83 66 68 279.3 363.5 2.6 44.9 45.6Middle income 80 79 51 49 1,987.5 2,308.6 1.5 38.4 38.0Lower middle income 81 79 39 37 828.5 994.1 1.8 32.1 31.5Upper middle income 80 78 60 59 1,159.1 1,314.4 1.3 42.8 43.0Low & middle income 81 79 52 51 2,266.8 2,672.1 1.6 39.2 39.1East Asia & Pacifi c 83 81 68 65 991.2 1,118.0 1.2 44.3 44.1Europe & Central Asia 69 70 50 50 176.8 191.8 0.8 45.0 44.8Latin America & Carib. 81 80 48 53 224.6 278.2 2.1 38.3 41.2Middle East & N. Africa 74 72 18 20 80.3 104.9 2.7 20.0 21.5South Asia 83 81 35 32 536.8 638.8 1.7 28.2 27.1Sub-Saharan Africa 77 76 61 63 257.2 340.4 2.8 44.9 45.6High income 71 69 51 52 503.4 550.9 0.9 43.2 43.9Euro area 65 65 45 50 144.9 159.6 1.0 42.6 44.8a. Excludes Abkhazia and South Ossetia. b. Excludes Transnistria. c. Includes Kosovo.482012 World Development Indicators

Labor force structure 2.2PEOPLEAbout the dataDefinitionsThe labor force is the supply of labor available for producinggoods and services in an economy. It includespeople who are currently employed and people whoare unemployed but seeking work as well as fi rst-timejob-seekers. Not everyone who works is included,however. Unpaid workers, family workers, and studentsare often omitted, and some countries do notcount members of the armed forces. Labor force sizetends to vary during the year as seasonal workersenter and leave.Data on the labor force are compiled by the InternationalLabour Organization (ILO) from labor forcesurveys, censuses, establishment censuses andsurveys, and administrative records such as employmentexchange registers and unemployment insuranceschemes. For some countries a combinationof these sources is used. Labor force surveys arethe most comprehensive source for internationallycomparable labor force data. They can cover all noninstitutionalizedcivilians, all branches and sectors ofthe economy, and all categories of workers, includingpeople holding multiple jobs. By contrast, labor forcedata from population censuses are often based on alimited number of questions on the economic characteristicsof individuals, with little scope to probe.The resulting data often differ from labor force surveydata and vary considerably by country, depending onthe census scope and coverage. Establishment censusesand surveys provide data only on the employedpopulation, not unemployed workers, workers in smallestablishments, or workers in the informal sector(ILO, Key Indicators of the Labour Market 2001–2002).The reference period of a census or survey isanother important source of differences: in somecountries data refer to people’s status on the dayof the census or survey or during a specifi c periodbefore the inquiry date, while in others data arerecorded without reference to any period. In developingcountries, where the household is often thebasic unit of production and all members contributeto output, but some at low intensity or irregularly, theestimated labor force may be much smaller than thenumbers actually working.Differing defi nitions of employment age also affectcomparability. For most countries the working age is15 and older, but in some countries children youngerthan 15 work full- or part-time and are included in theestimates. Similarly, some countries have an upperage limit. As a result, calculations may systematicallyover- or underestimate actual rates. For furtherinformation on source, reference period, or defi nition,consult the original source.The labor force participation rates in the table arefrom the ILO’s Key Indicators of the Labour Market,7th edition, database. These harmonized estimatesuse strict data selection criteria and enhanced methodsto ensure comparability across countries andover time to avoid the inconsistencies mentionedabove. Estimates are based mainly on labor forcesurveys, with other sources (population censusesand nationally reported estimates) used only whenno survey data are available.The labor force estimates in the table were calculatedby applying labor force participation rates fromthe ILO database to World Bank population estimatesto create a series consistent with these populationestimates. This procedure sometimes results inlabor force estimates that differ slightly from thosein the ILO’s Yearbook of Labour Statistics and its databaseKey Indicators of the Labour Market.Estimates of women in the labor force and employmentare generally lower than those of men and arenot comparable internationally, refl ecting that demographic,social, legal, and cultural trends and normsdetermine whether women’s activities are regardedas economic. In many countries many women workon farms or in other family enterprises without pay,and others work in or near their homes, mixing workand family activities during the day.• Labor force participation rate is the proportionof the population ages 15 and older that engagesactively in the labor market, by either working orlooking for work during a reference period. • Totallabor force is people ages 15 and older who engageactively in the labor market, either by working or lookingfor work during a reference period. It includesboth the employed and the unemployed. • Averageannual percentage growth of the labor force is calculatedusing the exponential endpoint method (seeStatistical methods for more information). • Femalelabor force as a percentage of the labor force showsthe share of women active in the total labor force.Data sourcesData on labor force participation rates are fromthe ILO’s Key Indicators of the Labour Market, 7thedition, database. Labor force numbers were calculatedby World Bank staff, applying labor forceparticipation rates from the ILO database to populationestimates.2012 World Development Indicators 49

2.3 Employment by economic activityAgriculture Industry ServicesMale% of maleemploymentFemale% of femaleemploymentMale% of maleemploymentFemale% of femaleemploymentMale% of maleemploymentFemale% of femaleemployment1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 aAfghanistan .. .. .. .. .. .. .. .. .. .. .. ..Albania .. .. .. .. .. .. .. .. .. .. .. ..Algeria .. .. .. .. .. .. .. .. .. .. .. ..Angola .. .. .. .. .. .. .. .. .. .. .. ..Argentina 0 b 2 b 0 b 0 b 40 b 33 b 18 b 10 b 59 b 65 b 81 b 89 bArmenia .. 39 .. 49 .. 25 .. 8 .. 35 .. 43Australia 6 4 4 2 32 32 12 9 61 64 84 88Austria 6 5 8 5 47 37 20 12 46 58 72 84Azerbaijan .. 37 .. 40 .. 19 .. 7 .. 44 .. 53Bahrain 3 .. 0 .. 33 .. 7 .. 64 .. 92 ..Bangladesh 54 .. 85 .. 16 .. 9 .. 25 .. 2 ..Belarus .. 15 .. 9 .. 33 .. 24 .. 37 .. 64Belgium 3 2 3 1 41 34 16 10 56 64 82 89Benin .. .. .. .. .. .. .. .. .. .. .. ..Bolivia 3 b 34 1 b 38 42 b 28 17 b 9 55 b 38 83 b 53Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. ..Botswana .. .. .. .. .. .. .. .. .. .. .. ..Brazil 31 b 21 25 b 12 27 b 29 10 b 13 43 b 50 65 b 75Bulgaria .. 8 .. 5 .. 41 .. 25 .. 51 .. 70Burkina Faso .. .. .. .. .. .. .. .. .. .. .. ..Burundi .. .. .. .. .. .. .. .. .. .. .. ..Cambodia .. 69 .. 75 .. 8 .. 9 .. 23 .. 16Cameroon .. .. .. .. .. .. .. .. .. .. .. ..Canada 6 b 3 b 2 b 1 b 31 b 32 b 11 b 10 b 64 b 65 b 87 b 89 bCentral African Republic .. .. .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. .. .. ..Chile 24 15 6 6 32 31 15 11 45 54 79 83China .. .. .. .. .. .. .. .. .. .. .. ..Hong Kong SAR, China 1 0 0 0 37 19 27 4 63 80 73 96Colombia 2 b 26 1 b 5 35 b 23 25 b 16 63 b 51 74 b 79Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. ..Costa Rica 32 17 5 4 27 27 25 13 41 51 69 82Côte d’Ivoire .. .. .. .. .. .. .. .. .. .. .. ..Croatia .. 14 .. 16 .. 38 .. 15 .. 48 .. 69Cuba .. 25 .. 9 .. 22 .. 12 .. 53 .. 80Cyprus 11 5 13 3 31 30 23 9 56 65 63 88Czech Republic .. 4 .. 2 .. 49 .. 23 .. 47 .. 75Denmark 7 4 3 1 37 29 16 9 56 67 81 90Dominican Republic 26 21 3 2 23 26 21 14 52 48 76 83Ecuador 10 b 33 2 b 22 29 b 24 17 b 11 62 b 43 81 b 67Egypt, Arab Rep. 35 28 52 46 25 27 10 6 41 44 37 49El Salvador 48 b 33 15 b 5 23 b 22 23 b 18 29 b 45 63 b 77Eritrea .. .. .. .. .. .. .. .. .. .. .. ..Estonia 23 6 13 3 42 43 30 18 36 50 57 78Ethiopia .. 9 .. 10 .. 25 .. 20 .. 76 .. 64Finland 11 6 6 3 38 36 15 10 51 58 78 87France 7 4 5 2 39 33 17 10 54 63 78 88Gabon .. .. .. .. .. .. .. .. .. .. .. ..Gambia, The .. .. .. .. .. .. .. .. .. .. .. ..Georgia .. 51 .. 57 .. 17 .. 4 .. 33 .. 40Germany 4 2 4 1 50 40 24 14 46 58 73 84Ghana 66 .. 59 .. 10 .. 10 .. 23 .. 32 ..Greece 20 12 26 13 29 28 17 8 51 60 57 79Guatemala 19 b .. 3 b .. 36 b .. 27 b .. 45 b .. 70 b ..Guinea .. .. .. .. .. .. .. .. .. .. .. ..Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..Haiti 76 .. 50 .. 9 .. 9 .. 13 .. 38 ..502012 World Development Indicators

Employment by economic activity 2.3PEOPLEAgriculture Industry ServicesMale% of maleemploymentFemale% of femaleemploymentMale% of maleemploymentFemale% of femaleemploymentMale% of maleemploymentFemale% of femaleemployment1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 aHonduras 53 b 48 6 b 10 18 b 22 25 b 22 29 b 30 69 b 68Hungary 19 6 13 2 43 40 29 20 38 53 58 78India .. 46 b .. 65 b .. 24 b .. 18 b .. 30 b .. 17 bIndonesia 54 39 57 38 15 22 13 15 31 40 31 47Iran, Islamic Rep. .. 19 .. 31 .. 33 .. 27 .. 47 .. 42Iraq .. 17 .. 51 .. 22 .. 4 .. 61 .. 46Ireland 17 8 3 1 35 29 18 9 49 63 79 90Israel 5 3 2 1 38 30 15 10 57 67 83 89Italy 8 5 9 3 41 39 23 14 52 57 68 83Jamaica 36 28 16 10 25 24 12 7 39 48 72 83Japan 6 4 7 4 40 33 27 15 54 62 65 80Jordan .. 2 .. 1 .. 21 .. 9 .. 77 .. 90Kazakhstan .. 31 .. 29 .. 26 .. 12 .. 43 .. 59Kenya .. .. .. .. .. .. .. .. .. .. .. ..Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 14 6 18 7 40 20 28 13 46 73 54 81Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait .. .. .. .. .. .. .. .. .. .. .. ..Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. .. ..Lao PDR .. .. .. .. .. .. .. .. .. .. .. ..Latvia .. 12 .. 6 .. 34 .. 14 .. 53 .. 80Lebanon .. .. .. .. .. .. .. .. .. .. .. ..Lesotho .. .. .. .. .. .. .. .. .. .. .. ..Liberia .. 50 .. 48 .. 14 .. 5 .. 37 .. 47Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania .. 12 .. 7 .. 33 .. 16 .. 55 .. 77Macedonia, FYR .. 20 .. 20 .. 33 .. 28 .. 47 .. 52Madagascar .. .. .. .. .. .. .. .. .. .. .. ..Malawi .. .. .. .. .. .. .. .. .. .. .. ..Malaysia 23 17 20 9 31 32 32 23 46 51 48 68Mali .. .. .. .. .. .. .. .. .. .. .. ..Mauritania .. .. .. .. .. .. .. .. .. .. .. ..Mauritius 15 10 13 8 36 32 48 22 48 58 39 70Mexico 34 19 11 4 25 30 19 18 41 51 70 78Moldova .. 34 .. 28 .. 26 .. 14 .. 41 .. 58Mongolia .. 41 b .. 39 b .. 19 b .. 11 b .. 40 b .. 50 bMorocco 4 b 34 3 b 59 33 b 24 46 b 15 63 b 42 51 b 25Mozambique .. .. .. .. .. .. .. .. .. .. .. ..Myanmar .. .. .. .. .. .. .. .. .. .. .. ..Namibia 45 23 52 8 21 24 8 9 34 53 40 83Nepal 75 .. 91 .. 4 .. 1 .. 20 .. 8 ..Netherlands 5 4 2 2 33 24 10 6 60 61 81 84New Zealand 13 b 9 8 b 4 31 b 31 13 b 10 56 b 61 79 b 86Nicaragua .. 42 .. 8 .. 21 .. 19 .. 37 .. 72Niger .. .. .. .. .. .. .. .. .. .. .. ..Nigeria .. .. .. .. .. .. .. .. .. .. .. ..Norway 7 4 3 1 34 31 10 7 58 65 86 92Oman .. .. .. .. .. .. .. .. .. .. .. ..Pakistan 45 37 69 75 20 22 15 12 35 41 16 13Panama 36 24 3 7 19 24 11 10 45 52 86 82Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. ..Paraguay 3 b 31 0 b 19 33 b 25 19 b 10 64 b 44 80 b 71Peru 1 b 1 b 0 b 1 b 30 b 32 b 13 b 14 b 69 b 67 b 87 b 86 bPhilippines 53 42 32 24 17 18 14 10 29 40 55 66Poland .. 13 .. 13 .. 42 .. 16 .. 45 .. 71Portugal 10 11 13 11 39 38 24 16 51 51 63 73Puerto Rico 5 2 0 1 27 25 19 10 67 73 80 90Qatar .. 3 .. 0 .. 58 .. 5 .. 39 .. 952012 World Development Indicators 51

2.3 Employment by economic activityAgriculture Industry ServicesMale% of maleemploymentFemale% of femaleemploymentMale% of maleemploymentRomania 29 29 38 31 44 36 30 20 28 35 33 49Russian Federation .. 11 .. 7 .. 38 .. 19 .. 51 .. 74Rwanda .. .. .. .. .. .. .. .. .. .. .. ..Saudi Arabia .. 5 .. 0 .. 23 .. 2 .. 72 .. 98Senegal .. .. .. .. .. .. .. .. .. .. .. ..Serbia .. 25 .. 23 .. 32 .. 16 .. 43 .. 61Sierra Leone .. .. .. .. .. .. .. .. .. .. .. ..Singapore 1 2 0 1 36 26 32 17 63 73 68 83Slovak Republic .. 4 .. 2 .. 50 .. 21 .. 46 .. 77Slovenia .. 9 .. 9 .. 43 .. 21 .. 48 .. 71Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa .. 6 .. 4 .. 35 .. 13 .. 59 .. 83South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 11 6 8 3 41 34 17 10 49 60 75 88Sri Lanka .. 30 b .. 37 b .. 25 b .. 25 b .. 27 b .. 27 bSudan .. .. .. .. .. .. .. .. .. .. .. ..Swaziland .. .. .. .. .. .. .. .. .. .. .. ..Sweden 5 3 2 1 40 31 12 8 55 66 86 91Switzerland 5 4 4 2 39 30 15 10 57 61 81 82Syrian Arab Republic 23 14 54 24 28 36 8 9 49 51 38 67Tajikistan .. .. .. .. .. .. .. .. .. .. .. ..Tanzania 78 b .. 90 b .. 7 b .. 1 b .. 15 b .. 8 b ..Thailand 60 44 62 39 18 21 13 18 22 35 25 43Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo .. .. .. .. .. .. .. .. .. .. .. ..Trinidad and Tobago 15 5 6 2 34 44 14 15 51 51 80 82Tunisia .. .. .. .. .. .. .. .. .. .. .. ..Turkey 33 18 72 39 26 30 11 16 41 52 17 45Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda .. .. .. .. .. .. .. .. .. .. .. ..Ukraine .. .. .. .. .. .. .. .. .. .. .. ..United Arab Emirates .. 5 .. 0 .. 28 .. 7 .. 66 .. 93United Kingdom 3 2 1 1 41 29 16 7 55 68 82 91United States 4 2 1 1 34 25 14 7 62 72 85 92Uruguay 7 b 16 1 b 5 36 b 29 21 b 13 57 b 56 78 b 83Uzbekistan .. .. .. .. .. .. .. .. .. .. .. ..Venezuela, RB 17 13 2 2 32 31 16 11 52 57 82 87Vietnam .. .. .. .. .. .. .. .. .. .. .. ..West Bank and Gaza .. 10 .. 28 .. 29 .. 11 .. 61 .. 61Yemen, Rep. 44 .. 83 .. 14 .. 2 .. 38 .. 13 ..Zambia 47 .. 56 .. 15 .. 3 .. 22 .. 18 ..Zimbabwe .. .. .. .. .. .. .. .. .. .. .. ..World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. wLow income .. .. .. .. .. .. .. .. .. .. .. ..Middle income .. .. .. .. .. .. .. .. .. .. .. ..Lower middle income .. .. .. .. .. .. .. .. .. .. .. ..Upper middle income .. .. .. .. .. .. .. .. .. .. .. ..Low & middle income .. .. .. .. .. .. .. .. .. .. .. ..East Asia & Pacifi c .. .. .. .. .. .. .. .. .. .. .. ..Europe & Central Asia .. 16 .. 15 .. 35 .. 18 .. 49 .. 66Latin America & Carib. 21 19 15 8 30 29 14 14 49 52 71 78Middle East & N. Africa .. 23 .. 43 .. 29 .. 14 .. 48 .. 43South Asia .. 46 .. 65 .. 24 .. 18 .. 30 .. 17Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. ..High income 6 4 5 3 38 31 19 11 56 64 76 86Euro area 7 4 6 3 42 36 20 12 50 59 73 85Note: Data across sectors may not sum to 100 percent because of workers not classifi ed by sector.a. Data are for the most recent year available. b. Limited coverage.Female% of femaleemploymentMale% of maleemploymentFemale% of femaleemployment1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a522012 World Development Indicators

Employment by economic activity2.3PEOPLEAbout the dataDefinitionsThe International Labour Organization (ILO) classifi eseconomic activity using the International StandardIndustrial Classifi cation (ISIC) of All Economic Activities,revision 2 (1968), revision 3 (1990), and revision4 (2008). Because this classifi cation is basedon where work is performed (industry) rather thantype of work performed (occupation), all of an enterprise’semployees are classifi ed under the sameindustry, regardless of their trade or occupation. Thecategories should sum to 100 percent. Where theydo not, the differences are due to workers who arenot classifi ed by economic activity.Data on employment are drawn from labor forcesurveys, household surveys, offi cial estimates, censusesand administrative records of social insuranceschemes, and establishment surveys when no otherinformation is available. The concept of employmentgenerally refers to people above a certain age whoworked, or who held a job, during a reference period.Employment data include both full-time and part-timeworkers.There are many differences in how countries defi neand measure employment status, particularly membersof the armed forces, self-employed workers, andunpaid family workers. Where members of the armedforces are included, they are allocated to the servicesector, causing that sector to be somewhat overstatedrelative to the service sector in economieswhere they are excluded. Where data are obtainedfrom establishment surveys, data cover only employees;thus self-employed and unpaid family workersare excluded. In such cases the employment shareof the agricultural sector is severely underreported.Caution should be also used where the data referonly to urban areas, which record little or no agriculturalwork. Moreover, the age group and area coveredcould differ by country or change over time within acountry. For detailed information, consult the originalsource.Countries also take different approaches to thetreatment of unemployed people. In most countriesunemployed people with previous job experience areclassifi ed according to their last job. But in somecountries the unemployed and people seeking theirfi rst job are not classifi able by economic activity.Because of these differences, the size and distributionof employment by economic activity may not befully comparable across countries.The ILO reports data by major divisions of the ISICrevision 2, revision 3, or revision 4. In the table thereported divisions or categories are aggregatedinto three broad groups: agriculture, industry, andservices. Such broad classifi cation may obscure fundamentalshifts within countries’ industrial patterns.A slight majority of countries report economic activityaccording to the ISIC revision 2 instead of revision 3or revision 4. The use of one classifi cation or theother should not have a signifi cant impact on theinformation for the three broad sectors presentedin the table.The distribution of economic wealth in the worldremains strongly correlated with employment byeconomic activity. The wealthier economies arethose with the largest share of total employment inservices, whereas the poorer economies are largelyagriculture based.The distribution of economic activity by genderreveals some clear patterns. Men still make up themajority of people employed in all three sectors, butthe gender gap is biggest in industry. Employment inagriculture is also male-dominated, although not asmuch as industry. Segregating one sex in a narrowrange of occupations signifi cantly reduces economiceffi ciency by reducing labor market fl exibility and thusthe economy’s ability to adapt to change. This segregationis particularly harmful for women, who havea much narrower range of labor market choices andlower levels of pay than men. But it is also detrimentalto men when job losses are concentratedin industries dominated by men and job growth iscentered in service occupations, where women havebetter chances, as has been the recent experiencein many countries.There are several explanations for the rising importanceof service jobs for women. Many service jobs—such as nursing and social and clerical work—areconsidered “feminine” because of a perceived similarityto women’s traditional roles. Women often donot receive the training needed to take advantage ofchanging employment opportunities. And the greateravailability of part-time work in service industriesmay lure more women, although it is unclear whetherthis is a cause or an effect.• Agriculture corresponds to division 1 (ISIC revision2), tabulation categories A and B (ISIC revision3), or tabulation category A (ISIC revision 4) andincludes hunting, forestry, and fi shing. • Industry correspondsto divisions 2–5 (ISIC revision 2), tabulationcategories C–F (ISIC revision 3), or tabulationcategories B–F (ISIC revision 4) and includes miningand quarrying (including oil production), manufacturing,construction, and public utilities (electricity,gas, and water). • Services correspond to divisions6–9 (ISIC revision 2), tabulation categories G–P(ISIC revision 3), or tabulation categories G–U (ISICrevision 4) and include wholesale and retail tradeand restaurants and hotels; transport, storage, andcommunications; fi nancing, insurance, real estate,and business services; and community, social, andpersonal services.Data sourcesData on employment are from the ILO’s Key Indicatorsof the Labour Market, 7th edition, database.2012 World Development Indicators 53

2.4 Decent work and productive employmentEmployment topopulation ratioGross enrollmentratio, secondaryVulnerableemploymentLaborproductivityUnpaid family workersand own-account workersGDP per personemployed% growthTotalYouthMaleFemale% ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment1991 2010 1991 2010 1991 2010 a 1990 2007–10 b 1990 2007–10 b 1990–92 2008–10Afghanistan 46 45 32 31 16 46 .. .. .. .. .. ..Albania 52 52 40 36 89 89 .. .. .. .. –16.6 6.6Algeria 34 39 22 22 60 95 .. .. .. .. –4.6 0.8Angola 66 64 47 46 12 31 .. .. .. .. –5.6 0.8Argentina 55 56 48 34 74 89 25 c 22 c 27 c 17 c 9.0 0.2Armenia 46 41 26 18 .. 92 .. 36 .. 40 –24.4 –6.5Australia 57 62 58 61 132 129 12 11 9 7 3.0 0.7Austria 54 58 61 54 102 100 .. 9 .. 9 1.3 –0.9Azerbaijan 56 60 37 31 88 .. .. 47 .. 62 –12.6 5.5Bahrain 61 65 32 32 100 .. .. 2 .. 1 0.8 1.9Bangladesh 73 68 64 53 18 49 .. .. .. .. 2.3 3.4Belarus 59 50 39 31 93 .. .. .. .. .. –4.0 3.2Belgium 46 50 31 25 101 111 18 11 17 8 1.6 –0.4Benin 72 72 66 57 .. .. .. .. .. .. .. ..Bolivia 63 69 48 49 .. 80 32 c 49 50 c 67 2.6 1.3Bosnia and Herzegovina 39 35 21 17 .. 90 .. .. .. .. –14.7 –1.9Botswana 56 63 36 41 49 .. .. .. .. .. .. ..Brazil 60 65 54 53 .. 101 29 c 27 30 c 22 –0.3 1.8Bulgaria 47 49 30 24 98 88 .. 10 .. 8 3.1 1.5Burkina Faso 82 81 76 73 7 21 .. .. .. .. 1.6 0.3Burundi 83 77 70 56 5 25 .. .. .. .. .. ..Cambodia 80 81 71 70 25 46 .. 79 .. 86 4.0 –1.2Cameroon 64 68 42 43 26 42 .. .. .. .. –6.6 –0.3Canada 59 61 57 55 101 101 .. .. .. .. 0.8 0.2Central African Republic 72 73 56 54 12 13 .. .. .. .. .. ..Chad 67 67 49 49 6 26 .. .. .. .. .. ..Chile 50 55 33 31 97 88 .. 26 .. 25 6.6 0.4China 75 71 72 57 41 81 .. .. .. .. 6.8 8.8Hong Kong SAR, China 63 57 54 31 .. 83 .. 10 .. 5 5.4 1.7Colombia 46 59 35 35 53 96 30 c 48 26 c 49 –0.7 0.6Congo, Dem. Rep. 66 66 40 39 21 38 .. .. .. .. –13.0 0.4Congo, Rep. 62 66 40 39 46 .. .. .. .. .. .. ..Costa Rica 55 60 48 42 45 100 26 19 21 20 2.4 –0.7Côte d’Ivoire 63 64 50 48 .. .. .. .. .. .. –4.1 0.7Croatia 51 46 27 25 83 95 .. 17 .. 19 –7.7 –0.6Cuba 53 56 41 40 94 89 .. .. .. .. .. ..Cyprus 57 60 41 34 72 98 .. 16 .. 12 –0.9 0.2Czech Republic 60 54 49 25 91 90 .. 17 .. 10 –5.2 0.2Denmark 62 60 65 58 109 117 8 7 6 4 2.5 0.8Dominican Republic 52 56 37 37 .. 76 42 49 30 30 0.9 2.0Ecuador 57 64 45 44 55 80 33 c 37 41 c 51 –0.1 –0.9Egypt, Arab Rep. 43 44 22 25 69 .. .. 22 .. 49 –1.3 2.6El Salvador 57 57 46 42 38 65 .. 33 .. 46 .. ..Eritrea 75 78 69 67 11 32 .. .. .. .. .. ..Estonia 67 51 50 27 100 104 2 6 3 4 –8.0 1.9Ethiopia 76 80 70 71 14 36 .. .. .. .. –7.9 5.8Finland 60 55 49 40 116 108 .. 12 .. 7 1.7 –1.3France 50 51 33 31 100 113 11 8 11 6 1.4 0.1Gabon 52 50 20 15 40 .. .. .. .. .. .. ..Gambia, The 71 72 58 56 19 54 .. .. .. .. .. ..Georgia 55 54 23 20 95 86 .. 62 .. 65 –25.4 0.3Germany 56 55 58 47 98 103 5 8 6 6 3.7 –0.9Ghana 68 67 43 36 35 58 d .. .. .. .. 0.7 1.9Greece 46 48 31 21 94 .. 40 29 46 27 2.1 –1.6Guatemala 62 65 54 58 23 59 .. .. .. .. 1.0 –2.5Guinea 69 70 51 52 11 38 .. .. .. .. .. ..Guinea-Bissau 64 68 44 48 5 .. .. .. .. .. .. ..Haiti 58 60 32 29 .. .. .. .. .. .. .. ..542012 World Development Indicators

Decent work and productive employment2.4PEOPLEEmployment topopulation ratioGross enrollmentratio, secondaryVulnerableemploymentLaborproductivityUnpaid family workersand own-account workersGDP per personemployed% growthTotalYouthMaleFemale% ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment1991 2010 1991 2010 1991 2010 a 1990 2007–10 b 1990 2007–10 b 1990–92 2008–10Honduras 57 60 49 47 33 73 48 c 49 50 c 52 .. ..Hungary 49 45 38 18 86 98 8 8 7 5 0.3 –1.3India 58 54 46 34 46 60 .. .. .. .. 1.0 5.6Indonesia 63 63 46 40 46 77 .. 62 .. 67 6.2 3.2Iran, Islamic Rep. 40 40 28 24 53 84 .. 40 .. 52 5.7 –0.9Iraq 32 34 20 17 40 .. .. .. .. .. –32.8 0.3Ireland 45 52 38 31 100 117 25 18 10 5 2.6 2.1Israel 46 54 24 27 92 91 .. 9 .. 5 1.6 0.3Italy 45 44 33 20 79 99 29 21 24 15 0.6 –1.0Jamaica 63 56 43 25 70 93 46 41 37 31 0.7 –3.1Japan 63 57 43 39 97 102 15 10 26 11 0.5 0.0Jordan 32 36 17 20 82 91 .. 11 .. 3 –5.0 0.1Kazakhstan 64 67 45 44 98 97 .. 30 .. 34 –15.1 –0.9Kenya 67 60 45 33 .. 60 .. .. .. .. –3.9 0.6Korea, Dem. Rep. 79 74 73 57 .. .. .. .. .. .. .. ..Korea, Rep. 59 58 36 24 91 97 .. 23 .. 27 5.1 2.7Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 58 66 26 31 53 101 .. .. .. .. –0.2 –4.4Kyrgyz Republic 60 61 41 40 100 84 .. .. .. .. –13.1 –2.0Lao PDR 80 77 72 62 21 45 .. .. .. .. .. ..Latvia 59 49 43 27 92 95 .. 8 .. 7 –19.6 –0.2Lebanon 39 42 26 23 61 81 .. 32 .. 16 .. ..Lesotho 49 47 38 28 24 46 .. .. .. .. .. ..Liberia 57 59 34 33 .. .. .. 69 .. 89 .. ..Libya 44 49 25 29 .. .. .. .. .. .. .. ..Lithuania 56 48 35 20 92 98 .. 10 .. 8 –13.9 –0.9Macedonia, FYR 38 38 18 15 76 83 .. 24 .. 22 –6.9 –2.0Madagascar 83 84 71 71 19 31 .. .. .. .. –5.8 –6.1Malawi 72 77 47 51 17 32 .. .. .. .. –2.1 3.7Malaysia 60 59 46 35 57 68 31 23 25 20 6.0 0.3Mali 47 48 35 36 7 38 .. 77 .. 89 1.3 1.9Mauritania 32 36 15 16 13 24 .. .. .. .. .. ..Mauritius 53 55 41 31 55 89 13 17 7 14 .. ..Mexico 57 58 50 43 54 87 29 27 15 32 1.0 –1.2Moldova 59 38 38 18 90 88 .. 34 .. 28 –23.1 –2.2Mongolia 51 57 35 32 82 93 .. 60 .. 54 .. ..Morocco 46 45 39 30 36 .. .. 47 .. 65 –1.6 2.7Mozambique 77 78 64 57 7 25 .. .. .. .. –3.6 4.2Myanmar 73 76 51 53 23 54 .. .. .. .. 2.0 ..Namibia 45 40 24 11 43 .. .. .. .. .. .. ..Nepal 84 82 79 73 34 .. .. .. .. .. .. ..Netherlands 53 62 53 63 120 120 7 13 12 10 0.4 –0.3New Zealand 57 63 54 50 92 119 15 15 10 9 0.6 1.1Nicaragua 55 60 47 46 43 69 .. 45 .. 45 .. ..Niger 54 61 47 53 7 13 .. .. .. .. –5.9 –2.5Nigeria 53 51 29 32 24 44 .. .. .. .. –2.8 4.6Norway 59 64 49 52 103 110 .. 8 .. 3 3.9 –0.3Oman 52 55 28 32 45 100 .. .. .. .. 0.6 1.4Pakistan 47 51 38 41 23 34 .. 59 .. 78 6.4 –0.1Panama 49 62 34 42 62 74 44 33 19 28 .. ..Papua New Guinea 70 71 57 54 12 .. .. .. .. .. .. ..Paraguay 68 69 64 57 31 67 17 c 42 31 c 48 .. ..Peru 53 71 35 55 67 92 30 c 34 c 46 c 47 c –0.8 2.4Philippines 60 60 42 39 70 85 .. 42 .. 46 –3.3 1.4Poland 54 51 32 27 87 97 .. 20 .. 17 1.1 1.9Portugal 59 55 53 29 66 107 22 18 28 17 2.1 1.3Puerto Rico 38 37 20 18 .. 82 .. .. .. .. .. ..Qatar 79 86 51 66 84 94 .. 0 .. 0 –0.3 11.12012 World Development Indicators 55

2.4 Decent work and productive employmentEmployment topopulation ratioGross enrollmentratio, secondaryVulnerableemploymentLaborproductivityRomania 57 52 49 24 92 95 21 33 33 33 –9.3 –3.2Russian Federation 59 58 41 36 93 89 1 6 1 5 –7.9 –1.4Rwanda 88 85 80 73 18 32 .. .. .. .. .. ..Saudi Arabia 51 47 25 12 .. 101 .. .. .. .. 4.8 –0.7Senegal 68 69 59 57 15 37 77 .. 91 .. –1.1 0.0Serbia 48 e 46 e 28 e 19 e .. 91 .. 30 .. 29 .. ..Sierra Leone 64 65 39 42 16 .. .. .. .. .. .. ..Singapore 64 63 56 34 .. .. 10 12 6 7 –2.0 5.0Slovak Republic 59 51 46 21 88 89 .. 17 .. 7 –0.8 1.6Slovenia 50 55 28 34 89 97 .. 15 .. 12 –2.3 –1.6Somalia 52 53 42 39 .. .. .. .. .. .. .. ..South Africa 37 39 15 13 69 94 .. 8 .. 12 –5.3 –0.4South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 42 47 35 25 105 119 21 13 25 9 1.8 2.5Sri Lanka 49 52 28 30 72 .. .. 38 c .. 44 c 5.5 4.8Sudan 47 49 32 27 20 39 .. .. .. .. 1.4 1.7Swaziland 44 44 27 26 49 58 .. .. .. .. .. ..Sweden 64 58 58 38 90 100 .. 9 .. 5 1.9 –0.1Switzerland 67 65 69 61 98 95 8 9 11 9 –0.6 –1.1Syrian Arab Republic 46 39 38 24 48 72 .. 34 .. 25 6.3 1.5Tajikistan 58 58 38 38 102 87 .. .. .. .. –20.4 5.8Tanzania 79 79 70 69 5 27 86 c .. 96 c .. –2.3 3.3Thailand 77 71 70 46 31 77 67 50 74 55 6.8 0.7Timor-Leste 58 54 46 41 .. 56 .. .. .. .. .. ..Togo 70 75 56 57 20 .. .. .. .. .. .. ..Trinidad and Tobago 45 63 33 48 82 90 22 .. 21 .. –3.5 0.6Tunisia 41 41 29 23 45 90 .. .. .. .. 2.5 1.7Turkey 53 44 48 32 48 78 .. 28 .. 48 1.0 –1.5Turkmenistan 53 54 34 35 .. .. .. .. .. .. –13.0 4.9Uganda 79 75 61 55 10 28 .. .. .. .. –0.8 3.1Ukraine 58 54 36 34 94 96 .. .. .. .. –7.9 –4.3United Arab Emirates 71 76 44 43 68 .. .. 1 .. 0 –3.6 –5.2United Kingdom 58 57 63 48 87 102 13 15 6 8 6.1 –0.9United States 61 58 54 42 92 96 .. .. .. .. 1.7 2.3Uruguay 55 61 45 44 84 90 .. 22 c .. 24 c 5.2 3.9Uzbekistan 52 54 33 35 99 105 .. .. .. .. –7.7 5.4Venezuela, RB 55 61 38 40 56 83 .. 30 .. 32 4.5 –3.9Vietnam 78 75 73 58 35 77 .. .. .. .. 4.6 3.4West Bank and Gaza 31 31 20 16 .. 86 .. 26 .. 31 .. ..Yemen, Rep. 39 42 24 27 .. 44 .. .. .. .. 0.9 2.2Zambia 65 67 47 51 21 .. 56 .. 81 .. –2.7 3.6Zimbabwe 69 83 48 73 49 .. .. .. .. .. –4.3 5.4World 62 w 60 w 52 w 43 w 50 w 68 w .. w .. w .. w .. w 0.9 w 2.0 wLow income 72 71 58 55 26 39 .. .. .. .. –3.4 –2.3Middle income 63 60 52 41 47 69 .. .. .. .. 1.2 4.2Lower middle income 58 55 43 36 42 58 .. 70 .. 77 0.4 3.7Upper middle income 67 65 60 48 67 83 .. .. .. .. 1.5 4.4Low & middle income 64 61 53 43 44 64 .. .. .. .. 1.0 4.3East Asia & Pacifi c 73 70 67 53 41 76 .. .. .. .. 6.4 7.9Europe & Central Asia 57 54 41 34 85 89 .. 18 .. 19 –9.3 –0.7Latin America & Carib. 57 62 47 45 57 90 30 31 29 31 1.3 0.1Middle East & N. Africa 40 41 27 24 54 72 .. 33 .. 48 1.0 1.1South Asia 59 55 47 37 37 55 .. 78 .. 86 3.2 4.8Sub-Saharan Africa 63 64 46 46 22 36 .. .. .. .. –4.8 1.5High income 57 55 46 38 91 100 .. .. .. .. 2.8 0.7Euro area 50 51 42 34 .. 107 16 13 14 9 2.3 –0.1a. Provisional data. b. Data are for the most recent year available. c. Limited coverage. d. Data are for 2011. e. Includes Montenegro.Unpaid family workersand own-account workersGDP per personemployed% growthTotalYouthMaleFemale% ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment1991 2010 1991 2010 1991 2010 a 1990 2007–10 b 1990 2007–10 b 1990–92 2008–10562012 World Development Indicators

Decent work and productive employment 2.4PEOPLEAbout the dataDefinitionsFour targets were added to the UN Millennium Declarationat the 2005 World Summit High-Level PlenaryMeeting of the 60th Session of the UN GeneralAssembly. One was full and productive employmentand decent work for all, which is seen as the mainroute for people to escape poverty. The four indicatorsfor this target have an economic focus, andthree of them are presented in the table.The employment to population ratio indicateshow effi ciently an economy provides jobs for peoplewho want to work. A high ratio means that a largepropor tion of the population is employed. But a loweremployment to population ratio can be seen as a positivesign, especially for young people, if it is causedby an increase in their education. This indicator hasa gender bias because women who do not con sidertheir work employment or who are not perceived asworking tend to be undercounted. This bias has differenteffects across countries and refl ects demographic,social, legal, and cultural trends and norms.Comparability of employment ratios across countriesis also affected by variations in defi nitions ofemployment and population (see About the data fortable 2.3). The biggest difference results from theage range used to defi ne labor force activity. Thepopulation base for employment ratios can also vary(see table 2.1). Most countries use the resi dent,noninstitutionalized population of working age livingin private households, which excludes members ofthe armed forces and individuals residing in mental,penal, or other types of institutions. But somecoun tries include members of the armed forces inthe population base of their employment ratio whileexcluding them from employment data (InternationalLabour Organization, Key Indicators of the LabourMarket, 7th edition).The proportion of unpaid family workers and ownaccountworkers in total employment is derived frominformation on status in employment. Each statusgroup faces different economic risks, and unpaidfamily workers and own-account workers are themost vulnerable—and therefore the most likely tofall into poverty. They are the least likely to have formalwork arrangements, are the least likely to havesocial protection and safety nets to guard againsteconomic shocks, and often are incapable of generatingsuffi cient savings to offset these shocks. Ahigh proportion of unpaid family workers in a countryindicates weak development, little job growth, andoften a large rural economy.Data on employment by status are drawn fromlabor force surveys and household surveys,supplemented by offi cial estimates and censusesfor a small group of countries. The labor force surveyis the most comprehensive source for internationallycomparable employment, but there are still somelimitations for comparing data across countries andover time even within a country. Information fromlabor force surveys is not always consistent in whatis included in employment. For example, informationprovided by the Organisation for Economic Cooperationand Development relates only to civilianemployment, which can result in an underestimationof “employ ees” and “workers not classifi ed bystatus,” espe cially in countries with large armedforces. While the categories of unpaid family workersand self-employed workers, which include ownaccountwork ers, would not be affected, their relativeshares would be. Geographic coverage is anotherfactor that can limit cross-country comparisons. Theemployment by status data for many Latin Americancountries covers urban areas only. Similarly, insome countries in Sub-Saharan Africa, where limitedinformation is available anyway, the members of producercooperatives are usually excluded from theself-employed category. For detailed information ondefi nitions and coverage, consult the original source.Labor productivity is used to assess a country’seconomic ability to create and sustain decentemployment opportunities with fair and equitableremuneration. Productivity increases obtainedthrough investment, trade, technological progress, orchanges in work organization can increase social protectionand reduce poverty, which in turn reduce vulnerableemployment and working poverty. Productivityincreases do not guarantee these improvements,but without them—and the economic growth theybring—improvements are highly unlikely. For comparabilityof individual sectors labor productivity is estimatedaccording to national accounts conventions.However, there are still signifi cant limitations on theavailability of reliable data. Information on consistentseries of output in both national currencies andpurchasing power parity dollars is not easily available,especially in devel oping countries, because thedefi nition, coverage, and methodology are not alwaysconsistent across countries. For example, countriesemploy different methodologies for estimating themissing values for the nonmarket service sectorsand use different defi nitions of the informal sector.• Employment to population ratio is the proportion ofa country’s population that is employed. People ages15 and older are generally considered the workingagepopulation. People ages 15–24 are generallyconsidered the youth population. • Gross enrollmentratio, secondary, is the ratio of total enroll ment insecondary education, regardless of age, to the populationof the age group that offi cially corresponds tosecondary educa tion. • Vulnerable employment isunpaid family workers and own-account workers as apercentage of total employment. • Labor productivityis the growth rate of gross domestic product(GDP) divided by the number of people engaged inthe production of goods and services.Data sourcesData on employment to population ratio, vulnerableemployment, and labor productivity are fromthe International Labour Organization’s Key Indicatorsof the Labour Market, 7th edition, database.Data on gross enrollment ratios are fromthe United Nations Educational, Scientifi c, andCultural Organization Institute for Statistics.2012 World Development Indicators 57

2.5 UnemploymentUnemploymentLong-termunemploymentUnemployment byeducational attainmentTotal% of totalMale% of maleFemale% of female% of totalunemployment% of totalunemploymentlabor forcelabor forcelabor forceTotal Male Female Primary Secondary Tertiary1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 aAfghanistan .. .. .. .. .. .. .. .. .. .. .. ..Albania .. 13.8 .. 12.2 .. 15.9 .. .. .. .. .. ..Algeria 23.0 11.4 24.2 10.0 20.3 20.0 .. .. .. .. .. ..Angola .. .. .. .. .. .. .. .. .. .. .. ..Argentina 6.7 b 8.6 b 6.4 b 7.8 b 7.0 b 9.8 b .. .. .. 48.1 36.7 15.3Armenia .. 28.6 .. 21.9 .. 35.0 58.2 52.2 63.8 11.1 68.3 20.6Australia 10.8 5.2 b 11.4 5.1 b 10.0 5.4 b 18.5 b 20.3 b 16.4 b 47.4 33.6 19.0Austria 3.6 4.4 3.5 4.6 3.8 4.2 25.2 27.8 22.0 37.5 54.5 8.0Azerbaijan .. 6.0 .. 5.2 .. 6.9 .. .. .. 9.9 75.8 14.3Bahrain 6.3 .. 5.2 .. 11.8 .. .. .. .. 22.7 38.8 31.7Bangladesh 1.9 5.0 2.0 4.2 1.9 7.4 .. .. .. .. .. ..Belarus .. .. .. .. .. .. .. .. .. 10.8 38.6 50.6Belgium 6.7 8.3 4.8 8.1 9.5 8.5 48.8 49.6 47.8 33.9 40.5 19.0Benin 1.5 .. 2.2 .. 0.6 .. .. .. .. .. .. ..Bolivia 5.5 b 5.2 5.5 b 4.5 5.6 b 6.0 .. .. .. .. .. ..Bosnia and Herzegovina 17.6 27.2 15.5 25.6 21.6 30.0 .. .. .. 94.9 .. 4.8Botswana 13.8 .. 11.7 .. 17.2 .. .. .. .. .. .. ..Brazil 6.4 b 8.3 5.4 b 6.1 7.9 b 11.0 .. .. .. 49.3 35.7 4.1Bulgaria .. 10.2 .. 10.9 .. 9.5 46.4 46.3 46.5 42.5 47.4 10.1Burkina Faso .. 3.3 .. .. .. .. .. .. .. .. .. ..Burundi 0.5 .. 0.7 .. 0.3 .. .. .. .. .. .. ..Cambodia .. 1.7 .. 1.5 .. 1.8 .. .. .. .. .. ..Cameroon .. 2.9 .. 2.5 .. 3.3 .. .. .. .. .. ..Canada 11.2 b 8.0 b 12.0 b 8.7 b 10.2 b 7.2 b 12.0 b 12.7 b 11.0 b 26.3 b 41.0 b 32.7 bCentral African Republic .. .. .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. .. .. ..Chile 4.4 8.1 3.9 7.2 5.3 9.6 .. .. .. 17.8 58.5 23.5China 2.3 b 4.3 .. .. .. .. .. .. .. .. .. ..Hong Kong SAR, China 2.0 5.2 2.0 6.0 1.9 4.3 .. .. .. 38.0 43.8 17.1Colombia 9.5 b 11.6 6.8 b 9.1 13.0 b 15.0 .. .. .. 21.0 53.7 23.0Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. ..Costa Rica 4.1 7.8 3.5 6.6 5.4 9.9 .. .. .. 66.9 23.4 7.9Côte d’Ivoire 6.7 .. .. .. .. .. .. .. .. .. .. ..Croatia 11.1 11.8 11.1 11.4 11.2 12.2 44.4 41.4 47.7 16.0 70.4 11.6Cuba .. 1.6 .. 1.4 .. 2.0 .. .. .. 46.6 48.5 3.6Cyprus 2.1 6.2 2.0 6.0 2.2 6.4 20.4 21.0 19.8 26.9 b 42.1 b 29.7 bCzech Republic 2.3 7.3 2.4 6.4 2.1 8.5 43.3 43.3 43.3 29.6 64.8 5.7Denmark 9.0 7.4 8.3 8.2 9.9 6.6 19.1 20.6 16.9 35.5 37.2 20.8Dominican Republic 20.7 14.3 12.0 9.8 35.2 21.4 .. .. .. 32.0 42.2 19.5Ecuador 8.9 b 6.5 6.0 b 5.2 13.2 b 8.4 .. .. .. .. .. ..Egypt, Arab Rep. 9.0 9.4 6.4 5.2 17.0 22.9 .. .. .. .. .. ..El Salvador 7.9 b 7.3 8.4 b 9.0 7.2 b 4.9 .. .. .. .. .. ..Eritrea .. .. .. .. .. .. .. .. .. .. .. ..Estonia 3.7 16.9 3.9 19.5 3.5 14.3 27.4 26.8 28.4 25.2 58.1 18.0Ethiopia 1.3 20.5 1.1 12.1 1.6 29.9 .. .. .. .. .. ..Finland 11.6 8.4 13.3 9.0 9.6 7.7 23.6 27.0 19.3 36.6 45.3 17.4France 10.2 9.3 8.1 9.0 12.8 9.7 40.1 41.5 38.7 39.5 41.7 18.3Gabon .. .. .. .. .. .. .. .. .. .. .. ..Gambia, The .. .. .. .. .. .. .. .. .. .. .. ..Georgia .. 16.5 .. 16.8 .. 16.1 .. .. .. 4.6 56.1 39.2Germany 6.6 7.1 5.3 7.5 8.4 6.6 47.4 48.1 46.3 32.3 56.5 11.0Ghana 4.7 .. 3.7 .. 5.5 .. .. .. .. .. .. ..Greece 7.8 12.5 4.9 9.9 12.9 16.2 45.0 38.8 50.3 27.7 48.9 22.4Guatemala .. .. .. .. .. .. .. .. .. .. .. ..Guinea .. .. .. .. .. .. .. .. .. .. .. ..Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..Haiti 12.7 .. 11.9 .. 13.8 .. .. .. .. .. .. ..582012 World Development Indicators

Unemployment2.5PEOPLEUnemploymentLong-termunemploymentUnemployment byeducational attainmentTotal% of totalMale% of maleFemale% of female% of totalunemployment% of totalunemploymentlabor forcelabor forcelabor forceTotal Male Female Primary Secondary Tertiary1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 aHonduras 3.2 b 2.9 3.3 b 2.9 3.0 b 2.9 .. .. .. .. .. ..Hungary 9.9 11.2 11.0 11.6 8.7 10.7 50.6 51.2 49.9 33.8 58.4 7.8India .. .. .. .. .. .. .. .. .. .. .. ..Indonesia 2.8 7.1 2.7 6.1 3.0 8.7 .. .. .. 43.4 40.6 10.2Iran, Islamic Rep. 11.1 10.5 9.5 9.1 24.4 16.8 .. .. .. 40.4 31.0 25.5Iraq .. .. .. .. .. .. .. .. .. .. .. ..Ireland 15.0 13.5 14.9 16.7 15.3 9.5 49.0 53.9 38.2 38.3 39.0 18.5Israel 11.2 6.6 9.2 6.8 13.9 6.5 22.4 25.7 18.5 22.1 48.0 28.1Italy 9.3 8.4 6.7 7.6 13.9 9.7 48.5 47.2 49.9 47.2 40.3 11.2Jamaica 15.4 11.4 9.4 8.5 22.2 14.8 .. .. .. 12.0 4.5 3.9Japan 2.2 5.0 2.1 5.4 2.2 4.5 37.6 44.8 25.2 66.8 .. 33.2Jordan .. 12.9 .. 10.3 .. 24.1 .. .. .. .. .. ..Kazakhstan .. 6.6 .. 5.6 .. 7.5 .. .. .. 45.1 39.7 15.2Kenya .. .. .. .. .. .. .. .. .. .. .. ..Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 2.5 3.7 2.8 4.0 2.1 3.3 0.3 0.5 0.3 15.3 63.7 21.1Kosovo .. 45.4 .. 40.7 .. 56.4 81.7 82.8 79.8 64.0 46.0 15.0Kuwait .. .. .. .. .. .. .. .. .. .. .. ..Kyrgyz Republic .. 8.6 .. 7.3 .. 9.4 .. .. .. .. .. ..Lao PDR .. .. .. .. .. .. .. .. .. .. .. ..Latvia .. 18.7 .. 21.7 .. 15.7 45.0 48.2 40.6 23.3 62.2 14.5Lebanon .. 9.0 .. 8.6 .. 10.1 .. .. .. 45.5 19.7 29.7Lesotho .. 25.3 .. 23.0 .. 28.0 .. .. .. 57.2 33.5 0.4Liberia .. 3.7 .. 3.4 .. 4.1 .. .. .. .. .. ..Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania .. 17.8 .. 21.2 .. 14.4 41.4 42.3 40.2 15.0 67.7 17.3Macedonia, FYR .. 32.0 .. 31.9 .. 32.2 83.1 83.5 82.4 .. .. ..Madagascar .. .. .. .. .. .. .. .. .. .. .. ..Malawi .. .. .. .. .. .. .. .. .. .. .. ..Malaysia 3.7 3.7 3.4 3.6 4.2 3.8 .. .. .. 10.4 60.9 24.9Mali .. .. .. .. .. .. .. .. .. .. .. ..Mauritania .. .. .. .. .. .. .. .. .. .. .. ..Mauritius 3.3 7.7 3.2 4.6 3.6 12.8 .. .. .. 43.5 29.6 7.9Mexico 3.1 5.3 2.7 5.3 4.0 5.3 2.4 2.7 2.0 51.5 25.2 21.0Moldova .. 6.4 .. 7.8 .. 4.9 .. .. .. .. .. ..Mongolia .. .. .. .. .. .. .. .. .. 28.2 49.0 22.1Morocco 16.0 b 10.0 13.0 b 9.8 25.3 b 10.5 .. .. .. .. .. ..Mozambique .. .. .. .. .. .. .. .. .. .. .. ..Myanmar 6.0 .. 4.7 .. 8.8 .. .. .. .. .. .. ..Namibia 19.0 37.6 20.0 32.5 19.0 43.0 .. .. .. .. .. ..Nepal .. 2.7 .. 3.1 .. 2.4 .. .. .. .. .. ..Netherlands 5.6 4.5 4.0 4.4 7.8 4.5 27.6 27.7 27.4 42.0 36.6 18.7New Zealand 10.6 b 6.5 b 11.4 b 6.2 b 9.7 b 6.8 b 9.0 b 8.9 b 9.0 b 30.6 39.2 25.7Nicaragua 14.4 5.0 11.3 4.9 19.5 5.1 .. .. .. .. .. ..Niger .. .. .. .. .. .. .. .. .. .. .. ..Nigeria .. .. .. .. .. .. .. .. .. .. .. ..Norway 5.9 3.6 6.6 4.1 5.1 3.0 9.5 10.6 7.7 29.9 49.3 17.9Oman .. .. .. .. .. .. .. .. .. .. .. ..Pakistan 5.2 5.0 3.8 4.0 14.0 8.7 .. .. .. 14.7 10.1 28.0Panama 14.7 6.5 10.8 5.3 22.3 8.5 .. .. .. 35.8 39.8 23.8Papua New Guinea 7.7 .. 9.0 .. 5.9 .. .. .. .. .. .. ..Paraguay 5.0 b 5.6 6.0 b 4.4 3.7 b 7.5 .. .. .. 53.5 31.4 13.4Peru 9.4 b 6.3 b 7.5 b 4.4 b 12.5 b 8.8 b .. .. .. 31.5 b 30.5 b 37.3 bPhilippines 8.6 7.4 7.9 7.6 9.9 6.9 .. .. .. 13.1 45.2 41.2Poland 13.3 9.6 12.2 9.3 14.7 10.0 25.5 25.3 25.8 15.9 71.8 12.1Portugal 4.1 b 10.8 3.5 b 9.8 5.0 b 11.9 52.3 51.7 52.8 67.3 15.9 13.5Puerto Rico 16.9 13.4 19.1 14.9 13.3 11.6 .. .. .. .. .. ..Qatar .. 0.5 .. 0.2 .. 2.6 38.5 35.3 40.3 19.0 52.7 24.02012 World Development Indicators 59

2.5UnemploymentUnemploymentLong-termunemploymentUnemployment byeducational attainmentTotal% of totalMale% of maleFemale% of female% of totalunemployment% of totalunemploymentlabor forcelabor forcelabor forceTotal Male Female Primary Secondary Tertiary1990–92 a 2007–10 a 1990–92 a 2007–10 a 1990–92 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 a 2007–10 aRomania .. 7.3 .. 7.9 .. 6.5 34.9 36.9 32.0 28.2 62.7 6.7Russian Federation 5.2 7.5 5.2 8.0 5.2 6.9 35.2 32.7 38.0 13.1 52.8 34.1Rwanda 0.3 .. 0.6 .. 0.2 .. .. .. .. .. .. ..Saudi Arabia .. 5.4 .. 3.5 .. 15.9 .. .. .. 7.5 48.6 43.6Senegal .. .. .. .. .. .. .. .. .. .. .. ..Serbia .. 19.2 .. 18.4 .. 20.2 71.1 70.1 72.1 20.3 68.4 11.2Sierra Leone .. .. .. .. .. .. .. .. .. .. .. ..Singapore 2.7 5.9 2.7 5.4 2.6 6.5 .. .. .. 27.2 22.7 50.1Slovak Republic .. 14.4 .. 14.2 .. 14.6 59.3 58.3 60.5 27.8 66.2 5.9Slovenia 7.1 7.2 8.1 7.4 6.0 7.0 43.3 45.0 41.2 23.3 58.1 16.3Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa .. 23.8 .. 22.0 .. 25.9 14.4 .. .. 15.4 80.7 0.8South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 18.1 20.1 13.9 19.7 25.8 20.5 45.1 44.6 45.6 58.4 22.6 17.7Sri Lanka 14.2 b 4.9 .. 3.5 .. 7.7 .. .. .. 45.4 b 22.8 b 31.8 bSudan .. .. .. .. .. .. .. .. .. .. .. ..Swaziland .. .. .. .. .. .. .. .. .. .. .. ..Sweden 5.7 8.4 6.7 8.5 4.6 8.2 16.6 18.1 14.8 32.5 44.9 16.4Switzerland 2.8 4.2 2.3 3.8 3.5 4.8 34.3 28.3 39.8 27.9 53.7 17.7Syrian Arab Republic 6.8 8.4 5.2 5.7 14.0 22.5 .. .. .. 46.1 28.0 4.9Tajikistan .. .. .. .. .. .. .. .. .. 66.5 28.8 4.6Tanzania 3.6 b .. 2.8 b .. 4.3 b .. .. .. .. .. .. ..Thailand 1.4 1.2 1.3 1.2 1.5 1.1 .. .. .. 41.5 49.3 0.2Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo .. .. .. .. .. .. .. .. .. .. .. ..Trinidad and Tobago 19.6 5.3 17.0 3.5 23.9 6.2 .. .. .. 27.9 65.9 5.2Tunisia .. 14.2 .. .. .. .. .. .. .. .. .. ..Turkey 8.5 11.9 8.8 11.4 7.8 13.0 28.6 24.7 37.0 52.5 26.0 13.9Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda 1.0 4.2 1.3 3.1 0.6 5.1 .. .. .. .. .. ..Ukraine .. 8.8 .. 6.6 .. 6.1 .. .. .. 7.4 52.9 39.7United Arab Emirates .. 4.0 .. 2.0 .. 12.0 .. .. .. 19.7 42.6 33.2United Kingdom 9.7 7.8 11.5 8.6 7.3 6.7 32.6 37.2 26.0 37.1 46.5 14.3United States 7.5 b 9.6 b 7.9 b 10.5 b 7.0 b 8.6 b 29.0 b 29.9 b 27.7 b 17.9 35.5 46.5Uruguay 9.0 b 7.3 6.8 b 5.3 11.8 b 9.7 .. .. .. 59.1 27.0 13.8Uzbekistan .. .. .. .. .. .. .. .. .. .. .. ..Venezuela, RB 7.7 7.6 8.2 7.2 6.8 8.1 .. .. .. .. .. ..Vietnam .. 2.4 .. .. .. .. .. .. .. .. .. ..West Bank and Gaza .. 24.5 .. 17.7 .. 38.6 .. .. .. 53.8 14.3 24.5Yemen, Rep. .. 14.6 .. 11.5 .. 40.9 .. .. .. .. .. ..Zambia 18.9 .. 16.3 .. 22.4 .. .. .. .. .. .. ..Zimbabwe .. .. .. .. .. .. .. .. .. .. .. ..World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. wLow income .. .. .. .. .. .. .. .. .. .. .. ..Middle income .. .. .. .. .. .. .. .. .. .. .. ..Lower middle income .. .. .. .. .. .. .. .. .. .. .. ..Upper middle income 3.5 5.8 .. .. .. .. .. .. .. .. .. ..Low & middle income .. .. .. .. .. .. .. .. .. .. .. ..East Asia & Pacifi c 2.5 4.7 .. .. .. .. .. .. .. .. .. ..Europe & Central Asia .. 9.5 .. 10.3 .. 8.8 .. .. .. 26.1 47.8 25.6Latin America & Carib. 6.6 7.8 5.4 6.4 8.3 9.8 .. .. .. 42.3 38.1 13.0Middle East & N. Africa 12.7 10.6 10.8 8.8 21.5 18.4 .. .. .. .. .. ..South Asia .. .. .. .. .. .. .. .. .. .. .. ..Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. ..High income 7.5 8.5 7.1 8.7 8.0 8.1 32.6 34.2 31.3 33.9 42.2 26.8Euro area 9.1 10.0 7.2 9.8 11.9 10.2 44.0 44.2 43.3 42.9 41.3 14.8a. Data are for the most recent year available. b. Limited coverage.602012 World Development Indicators

Unemployment2.5PEOPLEAbout the dataDefinitionsUnemployment and total employment are the broadestindicators of economic activity as refl ected bythe labor market. The International Labour Organization(ILO) defi nes the unemployed as members of theeconomically active population who are without workbut available for and seeking work, including peoplewho have lost their jobs or who have voluntarily leftwork. Some unemployment is unavoidable. At anytime some workers are temporarily unemployed—between jobs as employers look for the right workersand workers search for better jobs. Such unemployment,often called frictional unemployment, resultsfrom the normal operation of labor markets.Changes in unemployment over time may refl ectchanges in the demand for and supply of labor; theymay also refl ect changes in reporting practices.Paradoxically, low unemployment rates can disguisesubstantial poverty in a country, while high unemploymentrates can occur in countries with a high level ofeconomic development and low rates of poverty. Incountries without unemployment or welfare benefi tspeople eke out a living in vulnerable employment. Incountries with well developed safety nets workerscan afford to wait for suitable or desirable jobs. Buthigh and sustained unemployment indicates seriousineffi ciencies in resource allocation.The ILO defi nition of unemployment notwithstanding,reference periods, the criteria for people consideredto be seeking work, and the treatment of peopletemporarily laid off or seeking work for the fi rst timevary across countries. In many developing countriesit is especially diffi cult to measure employment andunemployment in agriculture. The timing of a survey,for example, can maximize the effects of seasonalunemployment in agriculture. And informal sectoremployment is diffi cult to quantify where informalactivities are not tracked.Data on unemployment are drawn from labor forcesample surveys and general household samplesurveys, censuses, and offi cial estimates, whichare generally based on information from differentsources and can be combined in many ways. Administrativerecords, such as social insurance statisticsand employment offi ce statistics, are not includedin the table because of their limitations in coverage.Labor force surveys generally yield the mostcomprehensive data because they include groupsnot covered in other unemployment statistics, particularlypeople seeking work for the fi rst time. Thesesurveys generally use a defi nition of unemploymentthat follows the international recommendations moreclosely than that used by other sources and thereforegenerate statistics that are more comparable internationally.But the age group, geographic coverage,and collection methods could differ by country orchange over time within a country. For detailed information,consult the original source.Women tend to be excluded from the unemploymentcount for various reasons. Women suffer morefrom discrimination and from structural, social, andcultural barriers that impede them from seekingwork. Also, women are often responsible for thecare of children and the elderly and for householdaffairs. They may not be available for work duringthe short reference period, as they need to makearrangements before starting work. Furthermore,women are considered to be employed when theyare working part-time or in temporary jobs, despitethe instability of these jobs or their active search formore secure employment.Long-term unemployment is measured by thelength of time that an unemployed person has beenwithout work and looking for a job. The data in thetable are from labor force surveys. The underlyingassumption is that shorter periods of joblessnessare of less concern, especially when the unemployedare covered by unemployment benefi ts orsimilar forms of support. The length of time that aperson has been unemployed is diffi cult to measure,because the ability to recall that time diminishes asthe period of joblessness extends. Women’s longtermunemployment is likely to be lower in countrieswhere women constitute a large share of the unpaidfamily workforce.Unemployment by level of educational attainmentprovides insights into the relation between the educationalattainment of workers and unemploymentand may be used to draw inferences about changesin employment demand. Information on educationalattainment is the best available indicator of skilllevels of the labor force. Besides the limitations tocomparability raised for measuring unemployment,the different ways of classifying the education levelmay also cause inconsistency. Education level issupposed to be classifi ed according to InternationalStandard Classifi cation of Education 1997(ISCED97). For more information on ISCED97, seeAbout the data for table 2.11.• Unemployment is the share of the labor force withoutwork but available for and seeking employment.Defi nitions of labor force and unemployment maydiffer by country (see About the data). • Long-termunemployment is the number of people with continuousperiods of unemployment extending for a year orlonger, expressed as a percentage of the total unemployed.• Unemployment by educational attainmentis the unemployed by level of educational attainmentas a percentage of the total unemployed. The levelsof educational attainment accord with the ISCED97of the United Nations Educational, Scientifi c, andCultural Organization.Data sourcesData on unemployment are from the ILO’s Key Indicatorsof the Labour Market, 7th edition, database.2012 World Development Indicators 61

2.6 Children at workSurveyyearChildren in employmentEmployment byeconomic activity aStatus inemployment a% of childrenages 7–14% of children ages 7–14in employment% of children ages 7–14in employmentWork StudyTotal Male Female only and work Agriculture Manufacturing Services% of children ages 7–14in employmentSelfemployedWageUnpaidfamilyAfghanistan .. .. .. .. .. .. .. .. .. .. ..Albania 2005 25.0 18.8 22.0 6.7 93.3 .. .. .. .. 1.4 94.5Algeria .. .. .. .. .. .. .. .. .. ..Angola b 2001 30.1 30.0 30.1 26.6 73.4 .. .. .. .. 6.2 80.1Argentina 2004 12.9 15.7 9.8 4.8 95.2 .. .. .. 34.2 8.1 56.2Armenia .. .. .. .. .. .. .. .. .. .. ..Australia .. .. .. .. .. .. .. .. .. .. ..Austria .. .. .. .. .. .. .. .. .. .. ..Azerbaijan 2005 5.2 5.8 4.5 6.3 93.7 91.7 0.7 7.4 4.1 3.8 92.1Bahrain .. .. .. .. .. .. .. .. .. .. ..Bangladesh 2006 16.2 25.7 6.4 37.8 62.2 .. .. .. – 17.0 77.8Belarus 2005 11.7 12.1 11.2 0.0 100.0 .. .. .. .. 9.2 78.8Belgium .. .. .. .. .. .. .. .. .. .. ..Benin 2006 74.4 72.8 76.1 36.1 63.9 .. .. .. .. .. ..Bolivia 2008 32.1 33.0 31.1 5.2 94.8 73.2 6.1 19.2 0.9 9.2 89.9Bosnia and Herzegovina 2006 10.6 11.7 9.5 0.1 99.9 .. .. .. .. 1.6 92.1Botswana .. .. .. .. .. .. .. .. .. .. ..Brazil 2008 5.2 6.9 3.5 4.8 95.2 54..7 7.6 34.6 5.5 24.7 69.8 cBulgaria .. .. .. .. .. .. .. .. .. .. ..Burkina Faso 2006 42.1 49.0 34.5 67.7 32.3 70.9 1.4 24.9 1.9 2.2 95.8Burundi 2005 11.7 12.5 11.0 38.9 61.1 .. 25.9 68.6Cambodia d 2003–04 48.9 49.6 48.1 13.8 86.2 82.3 4.2 12.9 6.0 4.1 89.4Cameroon 2007 43.4 43.5 43.4 21.9 78.1 88.5 3.1 8.2 2.5 9.5 87.6Canada .. .. .. .. .. .. .. .. .. .. ..Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. 2.0 56.4Chad 2004 60.4 64.4 56.2 49.1 50.9 .. .. .. .. 1.8 77.2Chile 2003 4.1 5.1 3.1 3.2 96.8 24.1 6.9 66.9 .. .. ..China .. .. .. .. .. .. .. .. .. .. ..Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. ..Colombia 2007 3.9 5.3 2.3 24.8 75.2 41.2 10.8 46.1 22.7 29.1 45.6Congo, Dem. Rep. d 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. 6.6 76.7Congo, Rep 2005 30.1 29.9 30.2 9.9 90.1 .. .. .. .. 4.2 84.5Costa Rica d 2004 5.7 8.1 3.5 44.6 55.4 40.3 9.5 49.0 15.8 57.7 26.6Côte d’Ivoire 2006 45.7 47.7 43.6 46.8 53.2 .. .. .. .. 2.4 88.0Croatia .. .. .. .. .. .. .. .. .. .. ..Cuba .. .. .. .. .. .. .. .. .. .. ..Cyprus .. .. .. .. .. .. .. .. .. .. ..Czech Republic .. .. .. .. .. .. .. .. .. .. ..Denmark .. .. .. .. .. .. .. .. .. .. ..Dominican Republic d 2005 5.8 9.0 2.7 6.2 93.8 18.5 9.8 57.5 23.8 19.5 56.2 eEcuador 2006 14.3 16.9 11.6 21.0 79.0 69.3 6.3 22.8 3.6 15.2 81.2Egypt, Arab Rep. 2005 7.9 11.5 4.3 21.0 79.0 .. .. .. 11.4 87.4El Salvador 2007 7.1 10.1 3.8 24.9 75.1 50.1 13.3 35.2 2.2 23.6 74.2Eritrea .. .. .. .. .. .. .. .. .. .. ..Estonia .. .. .. .. .. .. .. .. .. .. ..Ethiopia 2005 56.0 64.3 47.1 69.4 30.6 94.6 1.5 3.7 1.7 2.4 95.8Finland .. .. .. .. .. .. .. .. .. .. ..France .. .. .. .. .. .. .. .. .. .. ..Gabon .. .. .. .. .. .. .. .. .. .. ..Gambia, The 2005 43.5 33.9 52.3 32.1 67.9 .. .. .. .. 1.1 87.3Georgia 2006 31.8 33.6 29.9 1.0 99.0 .. .. .. .. 4.3 77.0Germany .. .. .. .. .. .. .. .. .. .. ..Ghana 2006 48.9 49.9 48.0 18.7 81.3 .. .. .. .. 6.1 76.2Greece .. .. .. .. .. .. .. .. .. .. ..Guatemala 2006 18.2 24.5 11.7 28.4 71.6 63.7 9.7 24.7 2.0 18.8 79.2Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. ..Guinea-Bissau 2006 50.5 52.8 48.1 36.4 63.6 .. .. .. .. 4.0 87.7Haiti 2005 33.4 37.3 29.6 17.7 82.3 .. .. .. .. 1.8 79.4622012 World Development Indicators

Children at work2.6PEOPLESurveyyearChildren in employmentEmployment byeconomic activity aStatus inemployment a% of childrenages 7–14% of children ages 7–14in employment% of children ages 7–14in employmentWork StudyTotal Male Female only and work Agriculture Manufacturing Services% of children ages 7–14in employmentSelfemployedWageUnpaidfamilyHonduras 2007 8.7 13.3 4.1 45.1 54.9 61.6 10.4 25.1 3.5 23.0 73.5Hungary .. .. .. .. .. .. .. .. .. .. ..India 2004–05 4.2 4.2 4.2 84.9 15.2 69.4 16.0 12.4 7.1 6.8 59.3Indonesia 2000 8.9 8.8 9.1 24.9 75.1 .. .. .. .. 17.8 75.8 eIran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. ..Iraq 2006 14.7 17.9 11.3 32.4 67.6 .. .. .. .. 7.0 85.3Ireland .. .. .. .. .. .. .. .. .. .. ..Israel .. .. .. .. .. .. .. .. .. .. ..Italy .. .. .. .. .. .. .. .. .. .. ..Jamaica 2005 9.8 11.3 8.3 2.5 97.5 .. .. .. .. 16.3 74.9Japan .. .. .. .. .. .. .. .. .. .. ..Jordan .. .. .. .. .. .. .. .. .. .. ..Kazakhstan 2006 3.6 4.4 2.8 1.6 98.4 .. .. .. – 4.0 75.0Kenya 2000 37.7 40.1 35.2 14.1 85.9 .. .. .. .. .. ..Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. .. .. .. .. .. .. .. .. .. .. ..Kosovo .. .. .. .. .. .. .. .. .. .. ..Kuwait .. .. .. .. .. .. .. .. .. .. ..Kyrgyz Republic 2006 5.2 5.8 4.6 7.9 92.1 .. .. .. – 3.7 81.9Lao PDR .. .. .. .. .. .. .. .. .. .. ..Latvia .. .. .. .. .. .. .. .. .. .. ..Lebanon .. .. .. .. .. .. .. .. .. .. ..Lesotho 2002 2.6 4.0 1.3 74.4 25.6 58.0 0.0 10.4 3.7 36.6 59.7 cLiberia 2007 37.4 37.8 37.1 45.0 55.0 .. .. .. .. 1.7 79.3Libya .. .. .. .. .. .. .. .. .. .. ..Lithuania .. .. .. .. .. .. .. .. .. .. ..Macedonia, FYR 2005 11.8 14.8 8.6 2.8 97.2 .. .. .. .. 3.9 89.5Madagascar 2007 26.0 27.7 24.2 40.9 59.1 87.6 2.9 8.2 0.1 10.0 89.9Malawi 2006 40.3 41.3 39.4 10.5 89.5 .. .. .. .. 6.7 75.5Malaysia .. .. .. .. .. .. .. .. .. .. ..Mali 2006 49.5 55.0 44.1 59.5 40.5 .. .. .. .. 1.6 80.4Mauritania .. .. .. .. .. .. .. .. .. .. ..Mauritius .. .. .. .. .. .. .. .. .. .. ..Mexico f 2009 12.2 16.5 7.6 22.6 77.4 38.2 11.7 47.0 2.7 34.3 63.1Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. 2.9 82.0Mongolia 2006–07 10.1 11.4 8.6 16.4 83.6 91.3 0.3 6.3 5.1 0.1 94.7Morocco 1998–99 13.2 13.5 12.8 93.2 6.8 60.6 8.3 10.1 2.1 10.0 81.7Mozambique d 1996 1.8 1.9 1.7 100.0 0.0 .. .. .. .. .. ..Myanmar .. .. .. .. .. .. .. .. .. .. ..Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 0.4 8.0 0.1 4.5 95.0Nepal 1999 47.2 42.2 52.4 35.6 64.4 87.0 1.4 11.1 4.2 3.3 92.4Netherlands .. .. .. .. .. .. .. .. .. .. ..New Zealand .. .. .. .. .. .. .. .. .. .. ..Nicaragua 2005 10.1 16.2 3.9 30.8 69.2 70.5 9.7 19.3 1.2 13.8 85.0 cNiger 2006 47.1 49.2 45.0 66.5 33.5 .. .. .. 4.8 74.5Nigeria .. .. .. .. .. .. .. .. .. .. ..Norway .. .. .. .. .. .. .. .. .. .. ..Oman .. .. .. .. .. .. .. .. .. .. ..Pakistan .. .. .. .. .. .. .. .. .. .. ..Panama 2008 8.9 12.1 5.4 14.6 85.4 73.3 2.9 22.9 12.6 11.3 76.1 cPapua New Guinea .. .. .. .. .. .. .. .. .. .. ..Paraguay c 2005 15.3 22.6 7.7 24.2 75.7 60.8 6.2 32.1 9.3 24.8 65.8Peru 2007 42.2 44.8 39.5 4.0 96.0 62.6 5.0 31.1 3.8 7.6 88.6Philippines 2001 13.3 16.3 10.0 14.8 85.2 64.3 4.1 30.6 4.1 22.8 73.1Poland .. .. .. .. .. .. .. .. .. .. ..Portugal 2001 3.6 4.6 2.6 3.6 96.4 48.5 11.2 33.3 .. .. ..Puerto Rico .. .. .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 63

2.6Children at workSurveyyearChildren in employmentEmployment byeconomic activity aStatus inemployment a% of childrenages 7–14% of children ages 7–14in employment% of children ages 7–14in employmentWork StudyTotal Male Female only and work Agriculture Manufacturing Services% of children ages 7–14in employmentSelfemployedRomania 2000 1.4 1.7 1.1 20.7 79.3 97.1 0.0 2.3 4.5 92.9 eRussian Federation .. .. .. .. .. .. .. .. .. .. ..Rwanda 2008 7.5 8.0 7.0 18.5 81.5 85.5 0.7 10.5 14.8 12.8 72.3Saudi Arabia .. .. .. .. .. .. .. .. .. .. ..Senegal 2005 18.5 24.4 12.6 61.9 38.1 79.1 5.0 14.0 6.3 4.4 84.1Serbia 2005 6.9 7.2 6.6 2.1 97.9 .. .. .. .. 5.2 89.4Sierra Leone 2007 14.9 14.9 14.9 57.7 42.3 83.8 0.8 13.4 9.7 0.9 87.8Singapore .. .. .. .. .. .. .. .. .. .. ..Slovak Republic .. .. .. .. .. .. .. .. .. .. ..Slovenia .. .. .. .. .. .. .. .. .. .. ..Somalia 2006 43.5 45.5 41.5 53.5 46.5 .. .. .. .. 1.6 94.8South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. 7.1 7.1 85.8South Sudan .. .. .. .. .. .. .. .. .. .. ..Spain .. .. .. .. .. .. .. .. .. .. ..Sri Lanka 1999 17.0 20.4 13.4 5.4 94.6 71.2 13.1 15.0 2.9 8.3 88.0Sudan g 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. 7.3 81.3Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. 10.4 85.9Sweden .. .. .. .. .. .. .. .. .. .. ..Switzerland .. .. .. .. .. .. .. .. .. .. ..Syrian Arab Republic 2006 6.6 8.8 4.3 34.6 65.4 .. .. .. .. 21.5 68.8Tajikistan 2005 8.9 8.7 9.1 9.0 91.0 .. .. .. .. 24.2 71.3Tanzania 2005–06 31.1 35.0 27.1 28.2 71.8 85.3 0.7 14.0 56.3 h 0.9 42.8 eThailand 2005 15.1 15.7 14.4 4.2 95.8 .. .. .. .. 13.5 80.0Timor-Leste .. .. .. .. .. .. .. .. .. .. ..Togo 2006 38.7 39.8 37.4 29.8 70.2 82.9 1.3 15.1 5.0 1.6 93.4Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. 29.8 64.9Tunisia .. .. .. .. .. .. .. .. .. ..Turkey i 2006 2.6 3.3 1.8 38.8 61.2 57.1 14.3 27.1 2.1 34.1 63.8Turkmenistan .. .. .. .. .. .. .. .. .. .. ..Uganda 2005–06 38.2 39.8 36.5 7.7 92.3 95.5 1.4 3.0 1.4 1.5 97.1Ukraine 2005 17.3 18.0 16.6 0.1 99.9 .. .. .. .. 3.1 79.3United Arab Emirates .. .. .. .. .. .. .. .. .. .. ..United Kingdom .. .. .. .. .. .. .. .. .. .. ..United States .. .. .. .. .. .. .. .. .. .. ..Uruguay .. .. .. .. .. .. .. .. .. .. ..Uzbekistan 2005 5.1 5.3 4.9 1.0 99.0 .. .. .. .. 3.8 78.6Venezuela, RB d 2006 5.1 6.9 3.3 19.8 80.2 32.3 7.2 55.7 31.6 33.1 35.3Vietnam 2006 21.3 21.0 21.6 11.9 88.1 .. .. .. .. 5.9 91.2West Bank and Gaza .. .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 2006 18.3 20.7 15.9 30.9 69.1 .. .. .. .. 6.1 86.1Zambia 2008 34.4 35.4 33.3 18.6 81.4 91.9 0.7 7.0 2.9 3.9 93.1Zimbabwe 1999 14.3 15.3 13.3 12.0 88.0 .. .. .. 3.4 28.4 68.2a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Refers to unpaid workers, regardless of whetherthey are family workers. d. Covers children ages 10–14. e. Refers to family workers, regardless of whether they are paid. f. Covers children ages 12–14. g. Covers northern Sudan only.h. Covers mainly workers working on their own shamba. i. Covers children ages 6–14.WageUnpaidfamily642012 World Development Indicators

Children at work 2.6PEOPLEAbout the dataDefinitionsThe data in the table refer to children’s work in thesense of “economic activity”—that is, children inemployment, a broader concept than child labor (seeILO 2009a for details on this distinction).In line with the defi nition of economic activityadopted by the 13th International Conference ofLabour Statisticians, the threshold set by the 1993UN System of National Accounts for classifying aperson as employed is to have been engaged atleast one hour in any activity relating to the productionof goods and services during the referenceperiod. Children seeking work are thus excluded.Economic activity covers all market production andcertain nonmarket production, including productionof goods for own use. It excludes unpaid householdservices (commonly called “household chores”)—that is, the production of domestic and personalservices by household members for a household’sown consumption.Data are from household surveys by the InternationalLabor Organization (ILO), the United NationsChildren’s Fund (UNICEF), the World Bank, andnational statistical offi ces. The surveys yield dataon education, employment, health, expenditure, andconsumption indicators related to children’s work.Household survey data generally include informationon work type—for example, whether a child isworking for payment in cash or in kind or is involvedin unpaid work, working for someone who is not amember of the household, or involved in any type offamily work (on the farm or in a business). Countrysurveys defi ne the ages for child labor as 5–17. Thedata in the table have been recalculated to presentstatistics for children ages 7–14.Although efforts are made to harmonize the defi nitionof employment and the questions on employmentin survey questionnaires, signifi cant differencesremain in the survey instruments that collectdata on children in employment and in the samplingdesign underlying the surveys. Differences existnot only across different household surveys in thesame country but also across the same type of surveycarried out in different countries, so estimatesof working children are not fully comparable acrosscountries.The table aggregates the distribution of childrenin employment by the industrial categories of theInternational Standard Industrial Classifi cation(ISIC): agriculture, manufacturing, and services.A residual category—which includes mining andquarrying; electricity, gas, and water; construction;extraterritorial organization; and other inadequatelydefi ned activities —is not presented. ISIC revision 2and revision 3 are both used, depending on the country’scodifi cation for describing economic activity.This does not affect the defi nition of the groups inthe table.The table also aggregates the distribution of childrenin employment by three major categories ofstatus in employment, based on the InternationalClassifi cation of Status in Employment (1993): selfemployedworkers, wage workers (also known asemployees), and unpaid family workers. A residualcategory—which includes those not classifi able bystatus—is not presented.In most countries more boys are involved in employment,or the gender difference is small. However, girlsare often more present in hidden or underreportedforms of employment such as domestic service, andin almost all societies girls bear greater responsibilityfor household chores in their own homes, workthat lies outside the System of National Accountsproduction boundary and is thus not considered inestimates of children’s employment.• Survey year is the year in which the underlyingdata were collected. • Children in employment arechildren involved in any economic activity for at leastone hour in the reference week of the survey. • Workonly refers to children who are employed and notattending school. • Study and work refer to chil drenattending school in combination with employment.• Employment by economic activity is the distributionof children in employment by the major industrialcategories (ISIC revision 2 or revi sion 3). • Agriculturecorresponds to division 1 (ISIC revision 2) orcategories A and B (ISIC revision 3) and includesagriculture and hunting, forestry and logging, andfi s h i n g . • Manufacturing corresponds to division 3(ISIC revision 2) or category D (ISIC revision 3). • Servicescorrespond to divisions 6–9 (ISIC revision2) or categories G–P (ISIC revision 3) and includewholesale and retail trade, hotels and restaurants,transport, fi nancial intermediation, real estate, publicadministration, education, health and social work,other community services, and private householdactivity. • Self-employed workers are people whoseremuneration depends directly on the profi ts derivedfrom the goods and services they produce, with orwithout other employees, and include employers,own-account workers, and members of producerscooperatives. • Wage workers (also known asemployees) are people who hold explicit (written ororal) or implicit employment contracts that providebasic remuneration that does not depend directly onthe revenue of the unit for which they work. • Unpaidfamily workers are people who work without pay in amarket-oriented establishment operated by a relatedperson living in the same household.Data sourcesData on children at work are estimates producedby the Understanding Children’s Work projectbased on household survey data sets made availableby the ILO’s International Programme on theElimination of Child Labour under its StatisticalMonitoring Programme on Child Labour, UNICEFunder its Multiple Indicator Cluster Survey program,the World Bank under its Living StandardsMeasurement Study program, and national statisticaloffi ces. Information on how the data werecollected and some indication of their reliabilitycan be found at www.ilo.org/public/english/standards/ipec/simpoc/, www.childinfo.org, andwww.worldbank.org/lsms. Detailed country statisticscan be found at www.ucw-project.org.2012 World Development Indicators 65

2.7 Poverty rates at national poverty linesPopulation below national poverty linePoverty gap at national poverty lineSurveyyear aRural%Urban%National%Surveyyear aAfghanistan b .. .. .. 2008 c 37.5 29.0 36.0 2008 c 8.3 6.2 7.9Albania b 2005 24.2 11.2 18.5 2008 14.6 10.1 12.4 2008 2.6 1.9 2.3Angola .. .. .. 2000 c .. 62.3 .. .. .. ..Argentina 2009 d .. 13.2 .. 2010 d .. 9.9 .. .. .. ..Armenia b 2009 34.9 33.7 34.1 2010 36.0 36.0 35.8 .. .. ..Azerbaijan b 2001 42.5 55.7 49.6 2008 18.5 14.8 15.8 .. .. ..Bangladesh 2005 43.8 28.4 40.0 2010 35.2 21.3 31.5 2010 7.4 4.3 6.5Belarus 2008 .. .. 6.1 2009 .. .. 5.4 .. .. ..Benin .. .. .. 2003 c 46.0 29.0 39.0 2003 c 14.0 8.0 12.0Bhutan .. .. .. 2007 c 30.9 1.7 23.2 2007 c 8.1 0.4 6.1Bolivia 2006 d 76.5 50.3 59.9 2007 d 77.3 50.9 60.1 .. .. ..Bosnia and Herzegovina b 2004 22.0 11.3 17.7 2007 17.8 8.2 14.0 .. .. ..Botswana 1993 40.4 24.7 32.9 2003 44.8 19.4 30.6 2003 18.4 6.5 11.7Brazil 2008 d .. .. 22.6 2009 d .. .. 21.4 .. .. ..Bulgaria b 2001 .. .. 12.8 2007 .. .. 10.6 2007 .. .. 3.0Burkina Faso 2003 65.5 22.1 51.0 2009 52.6 27.9 46.7 2009 17.4 7.8 15.1Burundi .. .. .. 2006 c 68.9 34.0 66.9 2006 c 24.2 10.3 23.4Cambodia b 2004 37.8 17.6 34.7 2007 34.5 11.8 30.1 2007 8.3 2.8 7.2Cameroon .. .. .. 2007 c 55.0 12.2 39.9 2007 c 17.5 2.8 12.3Cape Verde .. .. .. 2007 c 44.3 13.2 26.6 2007 c 14.3 3.3 8.1Central African Republic .. .. .. 2008 c 69.4 49.6 62.0 2008 c 35.0 29.8 33.1Chad .. .. .. 2003 c 58.6 24.6 55.0 2003 c 23.3 7.4 21.6Chile 2006 d 12.3 13.9 13.7 2009 d 12.9 15.5 15.1 .. .. ..China 2004 d 2.8 .. .. 2005 d 2.5 .. .. .. .. ..Colombia 2009 d 54.3 35.8 40.2 2010 d 50.3 33.0 37.2 .. .. ..Comoros .. .. .. 2004 c 48.7 34.5 44.8 2004 c 17.8 12.1 16.3Congo, Dem. Rep. .. .. .. 2006 75.7 61.5 71.3 2006 34.9 26.2 32.2Congo, Rep. .. .. .. 2005 57.7 .. 50.1 2005 20.6 .. 18.9Costa Rica 2009 d 23.0 20.7 21.7 2010 d .. .. 24.2 .. .. ..Côte d’Ivoire b 2002 45.8 32.3 40.2 2008 54.2 29.4 42.7 2008 20.3 9.5 15.3Croatia b 2002 .. .. 11.2 2004 .. .. 11.1 2004 .. .. 2.6Dominican Republic 2009 d 47.0 28.6 34.6 2010 d .. .. 34.4 .. .. ..Ecuador 2009 d 57.5 25.0 36.0 2010 d 53.0 22.5 32.8 .. .. ..Egypt, Arab Rep. 2005 26.8 10.1 19.6 2008 30.0 10.6 22.0 .. .. ..El Salvador 2008 d,e 49.0 35.7 39.9 2009 d,e 46.5 33.3 37.8 .. .. ..Ethiopia 2000 45.4 36.9 44.2 2005 39.3 35.1 38.9 2005 8.5 7.7 8.3Fiji 2003 40.0 28.0 35.0 2009 43.3 18.6 31.0 2009 14.8 5.4 10.1Gabon .. .. .. 2005 44.6 29.8 32.7 2005 16.0 8.5 10.0Gambia, The b .. .. .. 2010 c 73.9 32.7 48.4 .. .. ..Georgia b 2008 27.8 17.5 22.7 2009 30.7 18.4 24.7 .. .. ..Ghana 1998 49.6 19.4 39.5 2006 39.2 10.8 28.5 2006 13.5 3.1 9.6Guatemala 2000 74.5 27.1 56.2 2006 70.5 30.0 51.0 .. .. ..Guinea .. .. .. 2007 c 63.0 30.5 53.0 2007 c 22.0 7.7 17.6Guinea-Bissau .. .. .. 2002 69.1 51.6 64.7 2002 27.8 16.9 25.0Haiti .. .. .. 2001 d 88.0 45.0 77.0 .. .. ..Honduras 2009 d,e 64.4 52.8 58.8 2010 d,e 65.4 54.3 60.0 .. .. ..India 2005 41.8 25.7 37.2 2010 33.8 20.9 29.8 2010 6.8 4.5 6.2Indonesia 2010 16.6 9.9 13.3 2011 15.7 9.2 12.5 2011 2.6 1.5 2.1Iraq .. .. .. 2007 39.3 16.1 22.9 2007 9.0 2.7 4.5Jamaica 2006 d .. .. 14.3 2007 d .. .. 9.9 .. .. ..Jordan 2006 19.0 12.0 13.0 2006 .. .. 2.8Kazakhstan b 2008 .. .. 12.1 2009 .. .. 8.2 2009 .. .. 1.3Kenya .. .. .. 2005 c 49.1 33.7 45.9 2005 c 17.5 11.4 16.3Kosovo b 2005 49.2 37.4 45.1 2009 35.3 33.1 34.5 2009 .. .. 9.6Kyrgyz Republic b 2009 .. .. 31.7 2010 .. .. 33.7 .. .. ..Lao PDR b 2002 .. .. 33.5 2008 31.7 17.4 27.6 .. .. ..Latvia b 2002 11.6 .. 7.5 2004 12.7 .. 5.9 .. .. ..Rural%Urban%National%Surveyyear aRural%Urban%National%662012 World Development Indicators

Poverty rates at national poverty lines2.7PEOPLEPopulation below national poverty linePoverty gap at national poverty lineSurveyyear aRural%Urban%National%Surveyyear aLesotho b 1994 68.9 36.7 66.6 2003 60.5 41.5 56.6 .. .. ..Liberia b .. .. .. 2007 67.7 55.1 63.8 2007 26.3 20.2 24.4Macedonia, FYR b 2005 21.2 19.8 20.4 2006 21.3 17.7 19.0 2006 7.7 6.9 7.2Madagascar 2004 77.3 53.7 72.1 2005 73.5 52.0 68.7 2005 28.9 19.3 26.8Malawi .. .. .. 2004 55.9 25.4 52.4 2004 8.6 2.8 8.0Malaysia b 2007 7.1 2.0 3.6 2009 8.4 1.7 3.8 2009 1.8 0.3 0.8Mali 2006 57.0 18.5 47.5 2010 50.6 18.8 43.6 2010 15.6 4.7 13.2Mauritania 2004 59.0 28.9 46.7 2008 59.4 20.8 42.0 2008 22.3 4.9 14.5Mexico 2008 d 60.3 40.1 47.7 2010 d 60.8 45.5 51.3 .. .. ..Moldova b 2009 36.3 12.6 26.3 2010 30.3 10.4 21.9 2010 6.5 1.8 4.5Mongolia .. .. .. 2008 c 46.6 26.9 35.2 2008 c 13.4 7.7 10.1Montenegro 2009 14.8 2.6 6.8 2010 11.3 4.0 6.6 2010 1.7 0.7 1.1Morocco 2001 25.1 7.6 15.3 2007 14.5 4.8 9.0 .. .. ..Mozambique 2003 55.3 51.5 54.1 2008 56.9 49.6 54.7 2008 22.2 19.1 21.2Namibia 1994 69.0 31.0 58.0 2004 49.0 17.0 38.0 2004 16.0 6.0 13.0Nepal .. .. .. 2010 27.4 15.5 25.2 2010 c 6.0 3.2 5.4Nicaragua 2001 67.8 30.1 45.8 2005 67.9 29.1 46.2 .. .. ..Niger .. .. .. 2007 c 63.9 36.7 59.5 2007 c 21.2 11.3 19.6Nigeria .. .. .. 2004 c 63.8 43.1 54.7 2004 c 26.6 16.2 22.8Pakistan 2005 28.1 14.9 23.9 2006 27.0 13.1 22.3 .. .. ..Panama 2003 62.7 20.0 36.8 2008 59.8 17.7 32.7 .. .. ..Paraguay 2009 d 49.8 24.7 35.1 2010 d 48.9 24.7 34.7 .. .. ..Peru 2009 60.3 21.1 34.8 2010 54.2 19.1 31.3 .. .. ..Philippines 2006 .. .. 26.4 2009 .. .. 26.5 2009 .. .. 7.2Poland b 2007 .. .. 14.6 2008 .. .. 10.6 .. .. ..Romania b 2005 23.5 8.1 15.1 2006 22.3 6.8 13.8 2006 5.3 1.4 3.2Russian Federation 2005 22.7 8.1 11.9 2006 21.2 7.4 11.1 2006 5.5 1.7 2.7Rwanda 2006 64.2 23.2 58.5 2011 48.7 22.1 44.9 2011 .. .. 14.8São Tomé and Príncipe .. .. .. 2009 c .. .. 66.2 2009 c .. .. 24.8Senegal b .. .. .. 2005 c 61.9 35.1 50.8 2005 c 21.5 9.3 16.4Serbia b 2009 9.6 4.9 6.9 2010 13.6 5.7 9.2 .. .. ..Sierra Leone .. .. .. 2003 c 78.5 47.0 66.4 2003 c 34.6 16.3 27.5South Africa 2000 .. .. 38.0 2006 .. .. 23.0 2006 .. .. 7.0South Sudan .. .. .. 2009 55.4 24.2 50.6 2009 26.5 8.8 23.7Sri Lanka 2007 15.7 6.7 15.2 2010 9.4 5.3 8.9 2010 1.8 1.2 1.7Sudan .. .. .. 2009 57.6 26.5 46.5 2009 21.3 7.1 16.2Swaziland .. .. .. 2001 c 75.0 49.0 69.2 2001 c 37.0 20.0 32.9Tajikistan b 2007 55.0 49.4 53.5 2009 .. .. 46.7 .. .. ..Tanzania 2000 38.6 23.7 35.6 2007 37.4 21.8 33.4 2007 11.0 6.5 9.9Thailand 2008 11.5 3.0 9.0 2009 10.4 3.0 8.1 .. .. ..Timor-Leste 2001 .. .. 39.7 2007 .. .. 49.9 .. .. ..Togo .. .. .. 2006 74.3 36.8 61.7 2006 29.3 10.3 22.9Turkey 2008 34.6 9.4 17.1 2009 38.7 8.9 18.1 .. .. ..Uganda 2005 34.2 13.7 31.1 2009 27.2 9.1 24.5 2009 7.6 1.8 6.8Ukraine b 2007 8.1 2.9 4.6 2008 4.7 2.0 2.9 2008 0.7 0.3 0.4Uruguay 2009 d 9.6 21.4 20.9 2010 d 6.2 18.7 18.6 .. .. ..Venezuela, RB 2008 d .. .. 32.6 2009 d .. .. 28.5 .. .. ..Vietnam 2006 20.4 3.9 16.0 2008 18.7 3.3 14.5 2008 4.6 0.5 3.5West Bank and Gaza 2007 .. .. 31.2 2009 .. .. 21.9 2009 .. .. 4.9Yemen, Rep. 1998 42.5 32.3 40.1 2005 40.1 20.7 34.8 2005 10.6 4.5 8.9Zambia 2004 77.3 29.1 58.4 2006 76.8 26.7 59.3 2006 38.8 9.4 28.5Zimbabwe .. .. .. 2003 c .. .. 72.0 .. .. ..Note: Poverty rates are based on per capita consumption estimated from household survey data, unless otherwise noted.a. Refers to the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data werecollected. b. World Bank estimates. c. Estimates based on survey data from earlier years are available but are not comparable with the most recent year reported here; these are availableat http://data.worldbank.org and http://povertydata.worldbank.org. d. Based on income per capita estimated from household survey data. e. Measured as share of households.Rural%Urban%National%Surveyyear aRural%Urban%National%2012 World Development Indicators 67

2.7 Poverty rates at national poverty linesAbout the dataDefinitionsEstimates of poverty rates and gaps at national povertylines are useful for comparing poverty acrosstime within but not across countries. Table 2.8 showspoverty indicators at international poverty lines thatallow for comparisons across countries.For countries with an active poverty monitoring program,the World Bank—in collaboration with nationalinstitutions, other development agencies, and civilsociety—periodically prepares poverty assessmentsand other analytical reports to assess the extentand causes of poverty. These reports review levelsand changes in poverty indicators over time andacross regions within countries, assess the impactof growth and public policy on poverty and inequality,review the adequacy of monitoring and evaluation,and contain detailed technical overviews ofthe underlying household survey data and povertymeasurement methods used. The reports are a keysource of comprehensive information on poverty indicatorsat national poverty lines and generally feedinto country-owned processes to reduce poverty,build in-country capacity, and support joint work.An increasing number of countries have theirown national programs to monitor and disseminateoffi cial poverty estimates at national poverty linesalong with well documented household survey datasources and estimation methodology. Estimatesfrom national poverty monitoring programs and theunderlying methods used are periodically reviewed bythe World Bank and included in the table.The complete online database of poverty estimatesat national poverty lines (http://data. worldbank.org/topic/poverty) is regularly updated and may containmore recent data or revisions not incorporated inthe table. In addition, the poverty and equity dataportal (http://povertydata.worldbank.org/poverty/home/) provides access to both the database anduser-friendly dashboards with graphs and interactivemaps that visualize trends in key poverty and inequalityindicators for different regions and countries. Thedatabase is maintained by the Global Poverty WorkingGroup, a team of poverty experts from the PovertyReduction and Equity Network, the DevelopmentResearch Group, and the Development Data Group.Data qualityPoverty estimates at national poverty lines are computedfrom household survey data collected fromnationally representative samples of households.These data must contain suffi ciently detailed informationto compute a comprehensive estimate oftotal household income or consumption (includingconsumption or income from own production), fromwhich it is possible to construct a correctly weighteddistribution of per capita consumption or income.As with any indicator measured from householdsurveys, data quality can affect the precision ofpoverty estimates and their comparability over time.These include selective survey nonresponse, seasonalityeffects, differences in the number of incomeor consumption items in the questionnaire, and thetime period over which respondents are asked torecall their expenditures.National poverty linesNational poverty lines are the benchmark for estimatingpoverty indicators that are consistent withthe country’s specifi c economic and social circumstances.National poverty lines refl ect local perceptionsof the level and composition of consumption orincome needed to be nonpoor. The perceived boundarybetween poor and nonpoor typically rises with theaverage income of a country and thus does not providea uniform measure for comparing poverty ratesacross countries. While poverty rates at nationalpoverty lines should not be used for comparing povertyrates across countries, they are appropriate forguiding and monitoring the results of country-specifi cnational poverty reduction strategies.Almost all national poverty lines are anchored tothe cost of a food bundle—based on the prevailingnational diet of the poor—that provides adequatenutrition for good health and normal activity, plusan allowance for nonfood spending. National povertylines must be adjusted for infl ation betweensurvey years to remain constant in real terms andthus allow for meaningful comparisons of povertyover time. Because diets and consumption basketschange over time, countries periodically recalculatethe poverty line based on new survey data. In suchcases the new poverty lines should be defl ated toobtain comparable poverty estimates from earlieryears. The table reports indicators based on the twomost recent years for which survey data are available.Countries for which the most recent indicatorsreported are not comparable to those basedon survey data from an earlier year are footnotedin the table.• Survey year is the year in which the underlyinghousehold survey data were collected or, when thedata collection period bridged two calendar years,the year in which most of the data were collected.• Population below national poverty line is the percentageof the rural, urban, or national population livingbelow the corresponding rural, urban, or nationalpoverty line, based on consumption estimated fromhousehold survey data, unless otherwise noted.• Poverty gap at national poverty line is the meanshortfall from the rural, urban, or national povertyline (counting the nonpoor as having zero shortfall)as a percentage of the corresponding rural, urban,or national poverty line, based on consumption estimatedfrom household survey data, unless otherwisenoted. This measure refl ects the depth of poverty aswell as its incidence.Data sourcesData on poverty rates at national poverty lines arecompiled by the Global Poverty Working Group,based on data from World Bank’s country povertyassessments and analytical reports as well ascountry Poverty Reduction Strategies and offi cialpoverty estimates. Further documentation ofthe data, measurement methods and tools, andresearch, as well as poverty assessments andanalytical reports, are available at http://data.worldbank.org/topic/poverty, www.worldbank.org/poverty, and http://povertydata.worldbank.org/poverty/home/.682012 World Development Indicators

Poverty rates at international poverty lines 2.8PEOPLEInternational povertyline in local currencyInternational poverty line a$1.25a day2005$2a day2005Surveyyear bPopulationbelow$1.25a day%Povertygap at$1.25a day%Populationbelow$2 a day%Povertygap at$2 a day%Surveyyear bPopulationbelow$1.25a day%Povertygap at$1.25a day%Populationbelow$2 a day%Povertygap at$2 a day%Albania 75.5 120.8 2005

2.8Poverty rates at international poverty linesInternational povertyline in local currencyInternational poverty line a$1.25a day2005$2a day2005Surveyyear bPopulationbelow$1.25a day%Povertygap at$1.25a day%Populationbelow$2 a day%Povertygap at$2 a day%Surveyyear bPopulationbelow$1.25a day%Povertygap at$1.25a day%Populationbelow$2 a day%Povertygap at$2 a day%Jordan 0.6 1.0 2008

Poverty rates at international poverty lines 2.8PEOPLEInternational povertyline in local currencyInternational poverty line a$1.25a day2005$2a day2005Surveyyear bPopulationbelow$1.25a day%Povertygap at$1.25a day%Populationbelow$2 a day%Povertygap at$2 a day%Surveyyear bPopulationbelow$1.25a day%Povertygap at$1.25a day%Populationbelow$2 a day%Povertygap at$2 a day%Tunisia 0.9 1.4 2000 2.6 0.5 12.8 3.0 2005

2.8 Poverty rates at international poverty linesRegional poverty estimates 2.8cRegion or country 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008People living on less than 2005 PPP $1.25 a day (millions)East Asia & Pacifi c 1,097 970 848 926 871 640 656 523 332 284China 835 720 586 683 633 443 446 363 212 173Europe & Central Asia 8 7 7 9 14 18 18 11 6 2Latin America & Caribbean 43 53 49 53 53 54 60 63 48 37Middle East & North Africa 16 15 15 13 12 12 14 12 10 9South Asia 568 574 593 617 632 631 619 640 598 571India 429 427 443 448 462 463 473 484 466 445Sub-Saharan Africa 205 239 257 290 330 349 376 390 395 386Total 1,938 1,858 1,768 1,909 1,910 1,704 1,743 1,639 1,389 1,289Share of people living on less than 2005 PPP $1.25 a day (percent)East Asia & Pacifi c 77.2 65.0 54.1 56.2 50.7 35.9 35.6 27.6 17.1 14.3China 84.0 69.4 54.0 60.2 53.7 36.4 35.6 28.4 16.3 13.1Europe & Central Asia 1.9 1.6 1.5 1.9 2.9 3.9 3.8 2.3 1.3 0.5Latin America & Caribbean 11.9 13.6 12.0 12.2 11.4 11.1 11.9 11.9 8.7 6.5Middle East & North Africa 9.6 8.0 7.1 5.8 4.8 4.8 5.0 4.2 3.5 2.7South Asia 61.1 57.4 55.3 53.8 51.7 48.6 45.1 44.3 39.4 36.0India 59.8 55.7 54.1 51.3 49.7 47.2 45.6 44.5 40.8 37.4Sub-Saharan Africa 51.5 55.2 54.4 56.5 59.4 58.1 57.9 55.7 52.3 47.5Total 52.2 47.1 42.3 43.1 41.0 34.8 34.1 30.8 25.1 22.4People living on less than 2005 PPP $2 a day (millions)East Asia & Pacifi c 1,313 1,316 1,279 1,334 1,301 1,140 1,138 984 758 659China 972 963 907 961 926 792 770 655 482 395Europe & Central Asia 36 30 29 32 43 53 57 37 22 10Latin America & Caribbean 87 104 92 98 100 102 111 118 92 71Middle East & North Africa 52 51 54 53 53 57 60 57 53 44South Asia 811 855 906 959 1,010 1,047 1,069 1,120 1,113 1,125India 621 651 689 722 760 788 818 848 857 862Sub-Saharan Africa 288 324 350 389 434 466 503 533 559 562Total 2,585 2,680 2,710 2,864 2,941 2,865 2,937 2,848 2,596 2,471Share of people living on less than 2005 PPP $2 a day (percent)East Asia & Pacifi c 92.4 88.3 81.6 81.0 75.8 64.0 61.7 51.9 39.0 33.2China 97.8 92.9 83.7 84.6 78.6 65.1 61.4 51.2 36.9 29.8Europe & Central Asia 8.3 6.7 6.3 6.9 9.2 11.2 12.1 7.9 4.6 2.2Latin America & Caribbean 23.8 26.8 22.4 22.4 21.7 21.0 22.0 22.2 16.7 12.4Middle East & North Africa 30.1 27.1 26.1 23.5 22.1 22.2 22.0 19.7 17.4 13.9South Asia 87.2 85.6 84.5 83.6 82.7 80.7 77.8 77.4 73.4 70.9India 86.6 84.9 84.1 82.6 81.9 80.2 78.9 77.9 75.0 72.4Sub-Saharan Africa 72.2 74.7 74.3 76.0 78.1 77.5 77.4 76.1 74.1 69.2Total 69.6 68.0 64.8 64.6 63.1 58.6 57.4 53.5 46.9 43.0Source: World Bank PovcalNet.722012 World Development Indicators

Poverty rates at international poverty lines 2.8PEOPLEAbout the dataThe World Bank produced its fi rst global poverty estimatesfor developing countries for World DevelopmentReport 1990: Poverty (World Bank 1990) using householdsurvey data for 22 countries (Ravallion, Datt, andvan de Walle 1991). Since then there has been considerableexpansion in the number of countries thatfi eld household income and expenditure surveys. TheWorld Bank’s Development Research Group maintainsa database that is updated annually as new surveydata become available (and thus may contain morerecent data or revisions that are not incorporatedinto the table) and conducts a major reassessmentof progress against poverty about every three years.PovcalNet (http://iresearch.worldbank.org/Povcal-Net/) is an interactive computational tool that allowsusers to replicate these internationally comparable$1.25 and $2 a day global, regional, and country-levelpoverty estimates and to compute poverty measuresfor custom country groupings and for different povertylines. The Poverty and Equity Data portal (http://povertydata.worldbank.org/poverty/home/) providesaccess to the database and user-friendly dashboardswith graphs and interactive maps that visualize trendsin key poverty and inequality indicators for differentregions and countries. The country dashboards displaytrends in poverty measures based on the nationalpoverty lines (see table 2.7) alongside the internationallycomparable estimates in the table, produced fromand consistent with PovcalNet.Data availabilityThe World Bank’s internationally comparable povertymonitoring database now draws on income ordetailed consumption data collected from interviewswith 1.23 million randomly sampled householdsthrough more than 850 household surveys collectedby national statistical offi ces in nearly 130 countries.Despite progress in the last decade, the challengesof measuring poverty remain. The timeliness,frequency, quality, and comparability of householdsurveys need to increase substantially, particularly inthe poorest countries. The availability and quality ofpoverty monitoring data remains low in small states,countries with fragile situations, and low-incomecountries and even some middle-income countries.The low frequency and lack of comparability of thedata available in some countries create uncertaintyover the magnitude of poverty reduction. The need toimprove household survey programs for monitoringpoverty is clearly urgent. But institutional, political,and fi nancial obstacles continue to limit data collection,analysis, and public access.Data qualityBesides the frequency and timeliness of survey data,other data quality issues arise in measuring householdliving standards. The surveys ask detailed questionson sources of income and how it was spent, whichmust be carefully recorded by trained personnel.Income is generally more diffi cult to measure accurately,and consumption comes closer to the notionof living standards. And income can vary over timeeven if living standards do not. But consumption dataare not always available: the latest estimates reportedhere use consumption data for about two-thirds ofcountries.However, even similar surveys may not be strictlycomparable because of differences in timing or in thequality and training of enumerators. Comparisonsof countries at different levels of development alsopose a potential problem because of differencesin the relative importance of the consumption ofnonmarket goods. The local market value of all consumptionin kind (including own production, particularlyimportant in underdeveloped rural economies)should be included in total consumption expenditurebut may not be. Most survey data now include valuationsfor consumption or income from own production,but valuation methods vary.The statistics reported here are based on consumptiondata or, when unavailable, on incomesurveys. Analysis of some 20 countries for whichincome and consumption expenditure data wereboth available from the same surveys found incometo yield a higher mean than consumption but alsohigher inequality. When poverty measures based onconsumption and income were compared, the twoeffects roughly cancelled each other out: there wasno signifi cant statistical difference.International poverty linesInternational comparisons of poverty estimatesentail both conceptual and practical problems. Countrieshave different defi nitions of poverty, and consistentcomparisons across countries can be diffi cult.Local poverty lines tend to have higher purchasingpower in rich countries, where more generous standardsare used, than in poor countries.Poverty measures based on international povertylines attempt to hold the real value of the poverty lineconstant across countries, as is done when makingcomparisons over time. Since World DevelopmentReport 1990 the World Bank has aimed to apply acommon standard in measuring extreme poverty,anchored to what poverty means in the world’s poorestcountries. The welfare of people living in differentcountries can be measured on a common scale byadjusting for differences in the purchasing power ofcurrencies. The commonly used $1 a day standard,measured in 1985 international prices and adjustedto local currency using purchasing power parities(PPPs), was chosen for World Development Report1990 because it was typical of the poverty lines inlow-income countries at the time.Early editions of World Development Indicatorsused PPPs from the Penn World Tables to convertvalues in local currency to equivalent purchasingpower measured in U.S dollars. Later editions used1993 consumption PPP estimates produced by theWorld Bank. International poverty lines were recentlyrevised using the new data on PPPs compiled inthe 2005 round of the International ComparisonProgram, along with data from an expanded set ofhousehold income and expenditure surveys. The newextreme poverty line is set at $1.25 a day in 2005PPP terms, which represents the mean of the povertylines found in the poorest 15 countries ranked by percapita consumption. The new poverty line maintainsthe same standard for extreme poverty—the povertyline typical of the poorest countries in the world—butupdates it using the latest information on the cost ofliving in developing countries.Definitions• International poverty line in local currency isthe international poverty lines of $1.25 and $2.00a day in 2005 prices, converted to local currencyusing the PPP conversion factors estimated by theInternational Comparison Program. • Survey yearis the year in which the underlying data were collectedor, when the data collection period bridged twocalendar years, the year in which most of the datawere collected. • Population below $1.25 a day andpopulation below $2 a day are the percentages ofthe population living on less than $1.25 a day and$2 a day at 2005 international prices. As a result ofrevisions in PPP exchange rates, poverty rates forindividual countries cannot be compared with povertyrates reported in earlier editions. • Poverty gap isthe mean shortfall from the poverty line (countingthe nonpoor as having zero shortfall), expressed as apercentage of the poverty line. This measure refl ectsthe depth of poverty as well as its incidence.Data sourcesThe poverty measures are prepared by the WorldBank’s Development Research Group. The internationalpoverty lines are based on nationally representativeprimary household surveys by nationalstatistical offi ces or by private agencies under thesupervision of government or international agenciesand obtained from government statisticaloffi ces and World Bank Group country departments.Detailed information on the methodologyadopted by the Socio-Economic Database for LatinAmerica and the Caribbean to process the incomedata used for countries in this region is availableat http://sedlac.econo.unlp.edu.ar/eng/methodology.php.The World Bank Group has preparedan annual review of its poverty work since 1993.For details on data sources and methods used inderiving the World Bank’s latest estimates, seehttp://iresearch.worldbank.org/povcalnet. Forfurther discussion of the results, see Ravallion,Chen, and Sangraula (2009) and Chen and Ravallion(2011).2012 World Development Indicators 73

2.9 Distribution of income or consumptionSurveyyearGiniindexPercentage share ofincome or consumption aLowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10%Afghanistan 2008 b 27.8 4.1 9.4 13.6 17.4 22.1 37.5 23.2Albania 2008 b 34.5 3.5 8.1 12.1 15.9 20.9 43.0 29.0Algeria 1995 b 35.3 2.9 7.0 11.6 16.2 22.6 42.6 26.9Angola c 2000 b 58.6 0.6 2.0 5.7 10.8 19.7 61.9 44.7Argentina c 2010 d 44.5 1.5 4.4 9.3 14.8 22.2 49.4 32.3Armenia 2008 b 30.9 3.7 8.8 12.8 16.7 21.9 39.8 25.4Australia 1994 d 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4Austria 2000 d 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0Azerbaijan 2008 b 33.7 3.4 8.0 12.1 16.2 21.7 42.1 27.4Bangladesh 2010 b 32.1 4.0 8.9 12.4 16.1 21.3 41.4 27.0Belarus 2008 b 27.2 3.8 9.2 13.8 17.8 22.9 36.4 21.9Belgium 2000 d 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1Belize 1999 d 53.1 0.9 3.3 8.6 11.2 19.4 57.5 42.2Benin 2003 b 38.6 3.0 7.0 10.8 15.0 21.0 46.1 31.2Bhutan 2007 b 38.1 2.8 6.6 10.8 15.4 22.0 45.2 29.4Bolivia 2007 d 56.3 0.5 2.1 6.8 11.9 19.9 59.3 43.3Bosnia and Herzegovina 2007 b 36.2 2.7 6.7 11.3 16.1 22.7 43.2 27.3Botswana 1994 b 61.0 1.3 3.1 5.8 9.6 16.4 65.0 51.2Brazil 2009 d 54.7 0.8 2.9 7.1 12.4 19.0 58.6 42.9Bulgaria 2007 b 28.2 3.3 8.5 13.7 17.9 23.1 36.7 22.2Burkina Faso 2009 b 39.8 2.9 6.7 10.6 14.8 20.9 47.0 32.2Burundi 2006 b 33.3 4.1 9.0 11.9 15.4 21.0 42.8 28.0Cambodia 2008 b 37.9 3.3 7.5 11.0 14.9 20.6 45.9 31.4Cameroon 2007 b 38.9 2.9 6.7 10.5 14.9 21.7 46.2 30.4Canada 2000 d 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8Cape Verde 2002 b 50.5 1.9 4.5 7.9 12.3 19.4 55.9 40.6Central African Republic 2008 b 56.3 1.2 3.4 6.9 11.1 18.0 60.6 46.1Chad 2003 b 39.8 2.6 6.3 10.4 15.0 21.8 46.6 30.8Chile 2009 d 52.1 1.5 4.3 7.9 11.7 18.4 57.7 42.8China 2005 d 42.5 1.8 5.0 9.9 15.0 22.2 47.9 32.0Hong Kong SAR, China 1996 d 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9Colombia 2010 d 55.9 0.9 3.0 6.8 11.2 18.8 60.2 44.4Comoros 2004 b 64.3 0.9 2.6 5.4 8.9 15.1 68.0 55.2Congo, Dem. Rep. 2006 b 44.4 2.3 5.5 9.2 13.8 20.9 50.6 34.7Congo, Rep. 2005 b 47.3 2.1 5.0 8.4 13.0 20.5 53.1 37.1Costa Rica 2009 d 50.7 1.2 3.9 8.0 12.4 19.9 55.9 39.5Côte d’Ivoire 2008 b 41.5 2.2 5.6 10.1 14.9 21.8 47.6 31.8Croatia 2008 b 33.7 3.3 8.1 12.2 16.2 21.6 42.0 27.5Czech Republic 1996 d 25.8 4.3 10.2 14.3 17.5 21.7 36.2 22.7Denmark 1997 d 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3Djibouti 2002 b 40.0 2.4 6.0 10.5 15.2 21.8 46.5 30.9Dominican Republic 2010 d 47.2 1.8 4.7 8.6 13.2 20.8 52.8 36.4Ecuador 2010 d 49.3 1.4 4.3 8.2 13.0 20.7 53.8 38.3Egypt, Arab Rep. 2008 b 30.8 4.0 9.2 13.0 16.4 21.0 40.3 26.6El Salvador 2009 d 48.3 1.0 3.7 8.8 13.7 20.7 53.1 37.0Estonia 2004 b 36.0 2.7 6.8 11.6 16.2 22.2 43.2 28.0Ethiopia 2005 b 29.8 4.1 9.3 13.2 16.8 21.3 39.4 25.6Finland 2000 d 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6Fiji 2009 b 42.8 2.6 6.2 9.9 14.1 20.3 49.6 34.9France 1995 d 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1Gabon 2005 b 41.5 2.6 6.2 10.1 14.5 21.0 48.2 33.0Gambia, The 2003 b 47.3 2.0 4.8 8.6 13.2 20.6 52.8 36.9Georgia 2008 b 41.3 2.0 5.3 10.3 15.2 22.1 47.2 31.3Germany 2000 d 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1Ghana 2006 b 42.8 2.0 5.2 9.8 14.7 21.6 48.6 32.8Greece 2000 d 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0Guatemala 2006 d 55.9 1.1 3.1 6.9 11.4 18.5 60.3 44.9742012 World Development Indicators

Distribution of income or consumption 2.9PEOPLESurveyyearGiniindexPercentage share ofincome or consumption aLowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10%Guinea 2007 b 39.4 2.7 6.4 10.5 15.1 21.9 46.2 30.3Guinea-Bissau 2002 b 35.5 3.1 7.3 11.6 16.0 21.9 43.2 28.1Guyana 1998 d 44.5 1.3 4.5 9.8 14.7 21.6 49.5 34.0Haiti 2001 d 59.2 0.7 2.4 6.3 10.4 17.6 63.4 47.7Honduras 2009 d 57.0 0.4 2.0 6.1 11.4 20.5 59.9 42.4Hungary 2007 b 31.2 3.5 8.4 12.9 16.9 22.0 39.9 25.4India 2005 b 33.4 3.8 8.6 12.2 15.8 21.0 42.4 28.3Indonesia 2005 b 34.0 3.7 8.3 12.0 15.8 21.0 42.8 28.5Iran, Islamic Rep. 2005 b 38.3 2.6 6.4 10.9 15.5 22.0 45.2 29.6Iraq 2007 b 30.9 3.8 8.7 12.8 16.7 22.0 39.9 25.2Ireland 2000 d 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2Israel 2001 d 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8Italy 2000 d 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8Jamaica 2004 b 45.5 2.3 5.4 9.0 13.5 20.6 51.6 35.9Japan 1993 d 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7Jordan 2010 b 35.4 3.4 7.7 11.6 15.7 21.5 43.6 28.7Kazakhstan 2009 b 29.0 4.0 9.1 13.2 17.1 22.3 38.4 23.8Kenya 2005 b 47.7 2.0 4.8 8.7 13.2 20.1 53.2 38.0Korea, Rep. 1998 d 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5Kyrgyz Republic 2009 b 36.2 2.8 6.8 11.4 16.0 22.4 43.4 27.8Lao PDR 2008 b 36.7 3.3 7.6 11.3 15.3 20.9 44.8 30.3Latvia 2008 b 36.6 2.6 6.6 11.4 16.1 22.3 43.6 28.1Lesotho 2003 b 52.5 1.0 3.0 7.2 12.5 21.0 56.4 39.4Liberia 2007 b 38.2 2.4 6.4 11.4 15.7 21.6 45.0 30.1Lithuania 2008 b 37.6 2.6 6.6 11.1 15.7 22.1 44.4 29.1Macedonia, FYR 2009 b 43.2 2.0 5.1 9.5 14.5 22.0 48.9 32.4Madagascar 2010 b 44.1 2.2 5.4 9.5 14.1 20.9 50.1 34.7Malawi 2004 b 39.0 3.0 7.0 10.8 14.9 20.8 46.5 31.9Malaysia 2009 d 46.2 1.8 4.5 8.7 13.7 21.6 51.5 34.7Mali 2010 b 33.0 3.5 8.0 12.0 16.3 22.4 41.3 25.8Mauritania 2008 b 40.5 2.4 6.0 10.4 15.1 21.5 47.0 31.6Mexico 2010 d 47.7 1.4 4.4 8.9 13.3 20.4 53.0 37.2Micronesia, Fed. Sts. c 2000 b 61.1 0.4 1.6 5.2 10.2 19.1 64.0 47.1Moldova 2010 b 33.0 3.3 7.8 12.2 16.5 22.3 41.2 26.0Mongolia 2008 b 36.5 3.0 7.1 11.2 15.6 22.1 44.0 28.4Montenegro 2008 b 30.0 3.6 8.5 13.1 17.2 22.4 38.8 24.1Morocco 2007 b 40.9 2.7 6.5 10.5 14.5 20.6 47.9 33.2Mozambique 2008 b 45.7 1.9 5.2 9.5 13.7 20.1 51.5 36.7Namibia 2004 d 63.9 1.4 3.2 5.0 8.2 15.0 68.6 54.8Nepal 2010 b 32.8 3.6 8.3 12.2 16.2 21.9 41.5 26.5Netherlands 1999 d 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9New Zealand 1997 d 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8Nicaragua 2005 b 40.5 2.6 6.2 10.2 14.8 21.5 47.2 31.5Niger 2008 b 34.6 3.6 8.1 11.8 15.8 21.3 43.1 28.5Nigeria 2010 b 48.8 1.8 4.4 8.3 13.0 20.3 54.0 38.2Norway 2000 d 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4Pakistan 2008 b 30.0 4.4 9.6 12.9 16.4 21.1 40.0 26.1Panama 2010 d 51.9 1.1 3.3 7.8 12.5 20.1 56.4 40.1Papua New Guinea 1996 b 50.9 1.9 4.5 7.7 12.1 19.3 56.4 40.9Paraguay 2010 d 52.4 1.0 3.3 7.8 12.8 19.8 56.4 41.1Peru 2010 d 48.1 1.4 3.9 8.3 13.6 21.5 52.6 36.1Philippines 2009 b 43.0 2.6 6.0 9.4 13.9 21.0 49.7 33.6Poland 2009 b 34.1 3.3 7.7 12.0 16.2 22.0 42.1 27.1Portugal 1997 d 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8Qatar 2007 b 41.1 1.3 3.9 .. .. .. 52.0 35.9Romania 2009 b 30.0 3.4 8.3 13.1 17.4 22.9 38.3 23.5Russian Federation 2009 b 40.1 2.8 6.5 10.4 14.8 21.3 47.1 31.72012 World Development Indicators 75

2.9 Distribution of income or consumptionSurveyyearGiniindexPercentage share ofincome or consumption aLowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10%Rwanda 2011 b 50.8 2.1 5.2 8.3 11.9 17.8 56.8 43.2São Tomé and Príncipe 2001 b 50.8 2.2 5.2 8.5 12.2 17.7 56.4 43.6Senegal 2005 b 39.2 2.5 6.2 10.6 15.3 22.0 45.9 30.1Serbia 2009 b 27.8 3.7 8.9 13.7 17.8 22.8 36.9 22.2Seychelles 2007 b 65.8 1.6 3.7 5.7 8.3 12.7 69.6 60.2Sierra Leone 2003 b 42.5 2.6 6.1 9.7 14.0 20.9 49.3 33.6Singapore 1998 d 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8Slovak Republic 2009 d 26.0 4.4 10.1 14.1 17.5 22.0 36.2 22.4Slovenia 2004 b 31.2 3.4 8.2 12.8 17.0 22.6 39.4 24.6South Africa 2009 b 63.1 1.2 2.7 4.6 8.2 16.3 68.2 51.7South Sudan 2009 b 45.5 .. .. .. .. .. .. ..Spain 2000 d 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6Sri Lanka 2007 b 40.3 3.1 6.9 10.4 14.4 20.5 47.8 32.9St. Lucia 1995 d 42.6 2.0 5.2 9.9 14.8 21.8 48.3 32.5Sudan 2009 b 35.3 2.7 6.8 11.7 16.4 22.7 42.4 26.7Suriname 1999 d 52.9 1.1 3.2 7.2 12.3 20.4 56.9 40.6Swaziland 2010 b 51.5 1.7 4.1 7.4 12.0 20.0 56.6 40.1Sweden 2000 d 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2Switzerland 2000 d 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9Syrian Arab Republic 2004 b 35.8 3.4 7.7 11.4 15.5 21.4 43.9 28.9Tajikistan 2009 b 30.8 3.5 8.3 12.8 17.0 22.6 39.4 24.3Tanzania 2007 b 37.6 2.8 6.8 11.1 15.6 21.7 44.8 29.6Thailand 2009 b 40.0 2.8 6.7 10.3 14.5 21.4 47.2 31.5Timor-Leste 2007 b 31.9 4.0 9.0 12.5 16.1 21.2 41.3 27.0Togo 2006 b 34.4 3.3 7.6 11.7 16.1 22.2 42.4 27.1Trinidad and Tobago 1992 d 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9Tunisia 2005 b 41.4 2.4 5.9 10.1 14.7 21.3 47.9 32.5Turkey 2008 b 39.0 2.1 5.7 10.9 15.9 22.4 45.1 29.4Turkmenistan 1998 b 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7Uganda 2009 b 44.3 2.4 5.8 9.6 13.8 20.0 50.7 36.1Ukraine 2009 b 26.4 4.2 9.7 14.0 17.7 22.4 36.3 22.0United Kingdom 1999 d 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5United States 2000 d 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9Uruguay 2010 d 45.3 1.9 4.9 9.0 13.7 21.5 50.9 34.4Uzbekistan 2003 b 36.7 2.9 7.1 11.5 15.7 21.5 44.2 29.5Venezuela, RB 2006 d 44.8 1.2 4.3 9.5 14.6 22.2 49.4 33.2Vietnam 2008 b 35.6 3.2 7.4 11.5 15.8 21.8 43.4 28.2West Bank and Gaza 2009 b 35.5 3.2 7.4 11.5 15.8 21.8 43.4 28.2Yemen, Rep. 2005 b 37.7 2.9 7.2 11.3 15.3 21.0 45.3 30.8Zambia 2006 b 54.6 1.5 3.6 6.7 11.2 19.2 59.4 43.1Zimbabwe 1995 b 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Coversurban areas only. d. Refers to income shares by percentiles of population, ranked by per capita income.762012 World Development Indicators

Distribution of income or consumption2.9PEOPLEAbout the dataDefinitionsInequality in the distribution of income is refl ectedin the percentage shares of income or consumptionaccruing to portions of the population ranked byincome or consumption levels. The portions rankedlowest by personal income receive the smallestshares of total income. The Gini index provides a convenientsummary measure of the degree of inequality.Data on the distribution of income or consumptioncome from nationally representative householdsurveys. Where the original data from the householdsurvey were available, they have been used todirectly calculate the income or consumption sharesby quintile. Otherwise, shares have been estimatedfrom the best available grouped data.The distribution data have been adjusted forhousehold size, providing a more consistent measureof per capita income or consumption. No adjustmenthas been made for spatial differences in cost of livingwithin countries, because the data needed for suchcalculations are generally unavailable. For furtherdetails on the estimation method for low- and middleincomeeconomies, see Ravallion and Chen (1996).Because the underlying household surveys differ inmethod and type of data collected, the distributiondata are not strictly comparable across countries.These problems are diminishing as survey methodsimprove and become more standardized, but achievingstrict comparability is still impossible (see Aboutthe data for tables 2.7 and 2.8).Two sources of noncomparability should be notedin particular. First, the surveys can differ in manyrespects, including whether they use income or consumptionexpenditure as the living standard indicator.The distribution of income is typically moreunequal than the distribution of consumption. Inaddition, the defi nitions of income used differ moreoften among surveys. Consumption is usually a muchbetter welfare indicator, particularly in developingcountries. Second, households differ in size (numberof members) and in the extent of income sharingamong members. And individuals differ in age andconsumption needs. Differences among countries inthese respects may bias comparisons of distribution.World Bank staff have made an effort to ensurethat the data are as comparable as possible. Whereverpossible, consumption has been used ratherthan income. Income distribution and Gini indexes forhigh-income economies are calculated directly fromthe Luxembourg Income Study database, using anestimation method consistent with that applied fordeveloping countries.• Survey year is the year in which the underlyingdata were collected. • Gini index measures theextent to which the distribution of income (or consumptionexpenditure) among individuals or householdswithin an economy deviates from a perfectlyequal distribution. A Lorenz curve plots the cumulativepercentages of total income received against thecumulative number of recipients, starting with thepoorest individual. The Gini index measures the areabetween the Lorenz curve and a hypothetical lineof absolute equality, expressed as a percentage ofthe maximum area under the line. Thus a Gini indexof 0 represents perfect equality, while an index of100 implies perfect inequality. • Percentage shareof income or consumption is the share of totalincome or consumption that accrues to subgroups ofpopulation indicated by deciles or quintiles.Data sourcesData on distribution are compiled by the WorldBank’s Development Research Group usingPovcalNet (http://iresearch.worldbank.org/PovcalNet) based on household survey dataobtained from government statistical agenciesand World Bank country departments. Detailedinformation on the methodology adopted by theSocio-Economic Database for Latin America andthe Caribbean to process the income data usedfor countries in this region is available at http://sedlac.econo.unlp.edu.ar/eng/methodology.php.Data for high-income economies are computedbased on data from the Luxembourg Income Studydatabase.2012 World Development Indicators 77

2.10 Assessing vulnerability and securityYouthunemploymentFemale-headedhouseholdsPensioncontributorsPublic expenditureon pensionsMale% of malelabor forceages 15–24Female% of femalelabor forceages 15–24% oftotal2007–10 a 2007–10 a 2007–10 aYear% of laborforce% ofworkingagepopulationYear% ofGDPYearAveragepension% ofaveragewageAfghanistan .. .. .. 2006 3.7 2.3 2005 0.5 ..Albania 26 28 16 2007 37.9 25.7 2009 6.1 ..Algeria .. .. .. 2002 36.7 22.0 2002 3.2 ..Angola .. .. 25 .. .. .. ..Argentina 19 b 25 b 34 2008 42.3 33.1 2010 7.4 2000 43.8Armenia 37 55 36 2008 32.1 24.6 2006 3.2 2007 20.3Australia 12 b 11 b .. 2005 90.7 69.7 2007 3.4 ..Austria 9 9 .. 2005 93.7 68.5 2007 12.3 ..Azerbaijan 13 16 25 2007 35.4 24.7 2007 3.8 2006 24.3Bahrain .. .. .. 2007 20.2 13.4 2004 0.9 ..Bangladesh .. .. 13 2004 2.5 1.9 2006 0.3 ..Belarus .. .. 54 2008 93.5 66.8 2008 10.2 2002 41.6Belgium 22 22 .. 2005 91.4 61.5 2007 8.9 ..Benin .. .. 23 2005 5.5 4.2 2006 1.5 ..Bolivia .. .. 23 2008 12.2 9.4 2009 1.5 ..Bosnia and Herzegovina 45 52 .. 2009 24.5 29.0 2009 9.4 ..Botswana .. .. .. 2006 9.0 7.2 2009 1.3 ..Brazil 14 23 .. 2008 55.2 45.7 2010 6.1 ..Bulgaria 24 22 .. 2008 78.7 54.7 2008 8.5 2004 42.9Burkina Faso .. .. .. 2004 1.2 1.1 2004 0.7 ..Burundi .. .. .. 2006 3.5 3.3 2006 0.7 ..Cambodia 4 3 27 .. .. 2005 0.6 ..Cameroon .. .. .. 2006 16.2 11.5 2005 0.4 ..Canada 17 b 12 b .. 2007 66.9 53.6 2007 4.2 ..Central African Republic .. .. .. 2004 1.5 1.3 2004 0.8 ..Chad .. .. .. 2005 2.7 2.0 .. ..Chile 17 22 .. 2008 59.6 39.5 2009 5.0 2006 53.5China .. .. .. 2007 26.9 27.7 2006 2.5 ..Hong Kong SAR, China 15 10 .. 2008 78.9 55.4 2006 1.6 ..Colombia 18 30 34 2008 31.5 20.2 2010 3.5 ..Congo, Dem. Rep. .. .. 21 2008 14.2 10.5 .. ..Congo, Rep. .. .. 23 2008 9.7 7.5 .. ..Costa Rica 10 13 .. 2004 55.5 40.2 2009 2.8 ..Côte d’Ivoire .. .. .. 2004 12.8 9.1 2006 0.7 ..Croatia 30 34 24 2010 82.9 50.0 2009 10.3 2005 32.4Cuba 3 4 46 .. .. ..Cyprus 16 17 .. .. .. .. ..Czech Republic 18 19 .. 2007 95.4 67.5 2007 8.5 2005 40.7Denmark 16 12 .. 2007 92.9 86.7 2007 5.6 ..Dominican Republic 21 45 35 2008 25.6 19.1 2009 0.7 ..Ecuador 12 b 18 b .. 2004 26.4 18.0 2010 1.8 ..Egypt, Arab Rep. 17 48 13 2009 55.1 27.9 2004 4.1 ..El Salvador 13 8 .. 2008 22.9 15.7 2010 1.7 ..Eritrea .. .. .. .. .. 2001 0.3 ..Estonia 35 30 .. 2004 94.5 68.7 2007 10.9 2007 35.4Ethiopia .. .. 23 .. .. 2006 0.3 ..Finland 22 19 .. 2005 89.7 67.4 2007 8.3 ..France 22 23 .. 2005 87.3 61.4 2007 12.5 ..Gabon .. .. .. .. .. .. ..Gambia, The .. .. .. 2006 2.7 2.2 2003 0.1 ..Georgia 32 41 .. 2004 29.2 22.6 2004 3.0 2003 13.0Germany 10 9 .. 2005 86.9 65.6 2007 10.7 ..Ghana .. .. 34 2004 8.1 6.4 2002 1.3 ..Greece 27 41 .. 2005 86.0 58.3 2010 13.5 ..Guatemala .. .. .. 2008 20.3 14.7 2009 1.2 ..Guinea .. .. 17 1993 12.1 10.8 .. ..Guinea-Bissau .. .. .. 2004 2.0 1.5 2005 2.1 ..Haiti .. .. 44 .. .. .. ..782012 World Development Indicators

Assessing vulnerability and security2.10PEOPLEYouthunemploymentFemale-headedhouseholdsPensioncontributorsPublic expenditureon pensionsMale% of malelabor forceages 15–24Female% of femalelabor forceages 15–24% oftotal2007–10 a 2007–10 a 2007–10 aYear% of laborforce% ofworkingagepopulationYear% ofGDPYearAveragepension% ofaveragewageHonduras .. .. 26 2008 17.3 11.1 2010 0.0 ..Hungary 28 25 .. 2008 92.0 56.7 2008 10.5 2005 39.8India .. .. 14 2006 10.3 6.4 2007 2.2 ..Indonesia 22 23 13 2008 7.1 8.0 2010 1.0 ..Iran, Islamic Rep. 20 34 .. 2001 34.2 18.9 2000 1.1 ..Iraq .. .. 11 2009 35.6 19.0 2009 3.9 ..Ireland 34 21 .. 2005 88.9 64.1 2007 3.6 ..Israel 15 13 .. 2008 .. 89.1 2007 4.8 ..Italy 27 29 .. 2005 90.1 57.1 2007 14.1 ..Jamaica 23 33 41 2004 17.2 12.7 2004 0.7 ..Japan 10 8 .. 2005 95.4 75.0 2007 8.8 ..Jordan 23 46 11 2006 38.4 19.9 2005 2.0 ..Kazakhstan 7 8 .. 2004 62.5 48.4 2009 3.2 2003 24.9Kenya .. .. 34 2006 7.5 6.4 2003 1.1 ..Korea, Dem. Rep. .. .. .. .. .. .. ..Korea, Rep. 11 9 .. 2005 49.5 34.3 2005 1.6 ..Kosovo .. .. .. .. .. 2009 2.7 c ..Kuwait .. .. .. .. .. .. ..Kyrgyz Republic .. .. 25 2006 40.4 29.6 2010 2.7 2003 27.5Lao PDR .. .. .. 2004 .. 6.0 2005 0.2 ..Latvia 35 34 .. 2003 91.7 70.6 2009 8.5 2005 33.1Lebanon 22 22 .. 2003 34.5 17.3 2003 2.1 ..Lesotho 29 42 .. 2005 4.4 3.5 .. ..Liberia 4 8 30 .. .. .. ..Libya .. .. .. 2004 68.5 37.5 2001 2.1 ..Lithuania 38 31 .. 2007 82.9 56.9 2010 8.6 2005 30.9Macedonia, FYR 35 38 8 2008 52.3 33.2 2008 9.4 2006 55.0Madagascar .. .. 22 2009 5.3 4.9 .. ..Malawi .. .. .. .. .. .. ..Malaysia 10 12 .. 2008 49.0 32.5 2004 0.3 ..Mali .. .. 12 2010 7.3 4.4 2010 1.6 ..Mauritania .. .. .. 2000 13.1 9.4 2003 0.6 ..Mauritius 19 29 .. 2000 53.4 34.5 2007 2.9 ..Mexico 9 10 .. 2008 27.4 19.0 2007 1.4 ..Moldova 16 15 34 2009 56.7 32.1 2009 9.1 2003 20.9Mongolia .. .. 29 2005 33.5 25.6 2009 4.9 ..Morocco 23 19 .. 2007 23.8 13.5 2003 1.9 ..Mozambique .. .. .. 2006 1.9 1.7 2006 0.3 ..Myanmar .. .. .. .. .. .. ..Namibia .. .. 44 2008 9.6 5.8 2004 1.3 ..Nepal .. .. 23 2008 3.4 2.6 2006 0.2 ..Netherlands 9 9 .. 2005 90.7 70.7 2007 4.7 ..New Zealand 17 b 17 b .. .. .. 2007 4.3 ..Nicaragua .. .. .. 2008 21.7 14.6 .. ..Niger .. .. 19 2006 1.9 1.3 2006 0.7 ..Nigeria .. .. 19 2004 8.1 4.8 2004 0.9 ..Norway 11 8 .. 2005 93.2 75.2 2007 4.7 ..Oman .. .. .. .. .. .. ..Pakistan 7 11 10 2008 3.9 2.2 2004 0.5 ..Panama 12 21 .. .. .. .. ..Papua New Guinea .. .. .. 2009 4.4 3.3 2005 0.2 ..Paraguay 9 17 .. 2004 12.4 9.4 2001 1.2 ..Peru 13 b 16 b 22 2008 21.7 15.6 2010 2.5 ..Philippines 17 20 17 2007 25.0 17.0 2003 1.5 ..Poland 22 25 .. 2005 81.4 52.7 2009 10.0 2007 47.1Portugal 21 24 .. 2005 92.0 71.6 2007 10.8 ..Puerto Rico 29 22 .. .. .. .. ..Qatar 1 8 .. .. .. .. ..2012 World Development Indicators 79

2.10Assessing vulnerability and securityYouthunemploymentFemale-headedhouseholdsPensioncontributorsPublic expenditureon pensionsMale% of malelabor forceages 15–24Female% of femalelabor forceages 15–24% oftotal2007–10 a 2007–10 a 2007–10 aYear% of laborforce% ofworkingagepopulationYear% ofGDPYearAveragepension% ofaveragewageRomania 22 22 .. 2007 67.9 45.0 2009 8.3 2005 41.5Russian Federation 17 18 .. 2007 66.8 49.9 2010 8.3 2003 29.2Rwanda .. .. 31 2004 4.6 4.1 2005 0.7 ..Saudi Arabia 24 46 .. .. .. .. ..Senegal .. .. 20 2003 5.1 4.1 2006 1.4 ..Serbia 31 41 29 2003 45.0 40.2 2010 14.0 ..Sierra Leone .. .. 22 2004 5.5 3.8 .. ..Singapore 10 17 .. 2008 62.1 44.6 .. ..Slovak Republic 35 32 .. 2003 78.9 55.3 2007 9.3 2005 44.7Slovenia 15 14 .. 2008 87.4 63.8 2007 12.7 2005 44.3Somalia .. .. .. .. .. .. ..South Africa 45 53 .. 2007 6.3 3.7 2010 2.2 ..South Sudan .. .. .. .. .. .. ..Spain 43 40 .. 2005 69.4 48.7 2007 8.0 2006 58.6Sri Lanka 17 28 .. 2006 24.1 14.9 2007 2.0 ..Sudan .. .. 19 2005 5.2 2.9 .. ..Swaziland .. .. 48 2009 15.4 10.3 .. ..Sweden 27 24 .. 2005 88.8 72.2 2007 7.2 ..Switzerland 7 8 .. 2005 95.4 78.7 2007 6.4 2000 40.0Syrian Arab Republic 15 40 .. 2008 26.8 14.3 2004 1.3 ..Tajikistan .. .. .. .. .. .. 2003 25.7Tanzania .. .. 24 2006 4.3 4.0 2006 0.9 ..Thailand 4 5 30 2008 22.8 18.3 2006 0.8 ..Timor-Leste .. .. 12 .. .. .. ..Togo .. .. .. 2003 7.3 5.7 2003 0.8 ..Trinidad and Tobago 9 13 .. 2008 71.1 51.2 2010 2.8 ..Tunisia .. .. .. 2004 48.6 25.5 2003 4.3 ..Turkey 21 23 .. 2007 58.6 30.5 2008 6.2 2007 61.3Turkmenistan .. .. .. .. .. .. ..Uganda .. .. 30 2004 10.3 9.2 2003 0.3 ..Ukraine .. .. 49 2010 65.3 44.7 2010 17.8 2007 48.3United Arab Emirates 8 22 .. .. .. .. ..United Kingdom 21 17 .. 2005 93.2 71.5 2007 5.4 ..United States 21 b 16 b .. 2005 92.2 71.4 2007 6.0 2006 29.2Uruguay 16 25 .. 2007 78.5 61.2 2010 8.8 ..Uzbekistan .. .. 18 2005 .. 86.3 2005 6.5 2005 40.0Venezuela, RB 12 16 .. 2008 33.9 24.2 2010 5.0 ..Vietnam .. .. .. 2008 19.3 15.2 2004 2.5 ..West Bank and Gaza 39 47 .. 2009 14.0 6.5 2009 4.0 ..Yemen, Rep. .. .. .. 2006 10.4 5.0 2004 1.5 ..Zambia .. .. 24 2006 10.9 8.0 2008 1.4 ..Zimbabwe .. .. 38 .. .. 2002 2.3 ..World .. w .. wLow income .. ..Middle income .. ..Lower middle income .. ..Upper middle income .. ..Low & middle income .. ..East Asia & Pacifi c .. ..Europe & Central Asia 17 18Latin America & Carib. 12 18Middle East & N. Africa 19 36South Asia .. ..Sub-Saharan Africa ..High income 19 17Euro area 22 22a. Data are for the most recent year available. b. Limited coverage. c. Includes expenditure on old-age and survivors benefi ts only.802012 World Development Indicators

Assessing vulnerability and security 2.10PEOPLEAbout the dataDefinitionsAs traditionally measured, poverty is a static concept,and vulnerability a dynamic one. Vulnerabilityrefl ects a household’s resilience in the face ofshocks and the likelihood that a shock will lead to adecline in well-being. Thus, it depends primarily onthe household’s assets and insurance mechanisms.Because poor people have fewer assets and lessdiversifi ed sources of income than do the better-off,fl uctuations in income affect them more.Enhancing security for poor people means reducingtheir vulnerability to such risks as ill health, providingthem the means to manage risk themselves,and strengthening market or public institutions formanaging risk. Tools include microfi nance programs,public provision of education and basic health care,and old age assistance (see tables 2.11 and 2.16).Poor households face many risks, and vulnerabilityis thus multidimensional. The indicators in the tablefocus on individual risks—youth unemployment,female-headed households, income insecurity inold age—and the extent to which publicly providedservices may be capable of mitigating some of theserisks. Poor people face labor market risks, often havingto take up precarious, low-quality jobs and toincrease their household’s labor market participationby sending their children to work (see tables2.4 and 2.6). Income security is a prime concernfor the elderly.Youth unemployment is an important policy issuefor many economies. Experiencing unemploymentmay permanently impair a young person’s productivepotential and future employment opportunities.The table presents unemployment among youth ages15–24, but the lower age limit for young people ina country could be determined by the minimumage for leaving school, so age groups could differacross countries. Also, since this age group islikely to include school leavers, the level of youthunemployment varies considerably over the year as aresult of different school opening and closing dates.The youth unemployment rate shares similar limitationson comparability as the general unemploymentrate. For further information, see About the data fortable 2.5 and the original source.The defi nition of female-headed household differsgreatly across countries, making cross-country comparisondiffi cult. In some cases it is assumed that awoman cannot be the head of any household with anadult male, because of sex-biased stereotype. Cautionshould be used in interpreting the data.Pension scheme coverage may be broad oreven universal where eligibility is determined bycitizenship, residency, or income status. In contribution-relatedschemes, however, eligibility is usuallyrestricted to individuals who have contributed for aminimum number of years. Defi nitional issues—relatingto the labor force, for example—may arise incomparing coverage by contribution-related schemesover time and across countries (for country-specifi cinformation, see Hinz and others 2011). The shareof the labor force covered by a pension scheme maybe overstated in countries that do not try to countinformal sector workers as part of the labor force.Public interventions and institutions can provideservices directly to poor people, although whetherthese interventions and institutions work well for thepoor is debated. State action is often ineffective,in part because governments can infl uence only afew of the many sources of well-being and in partbecause of diffi culties in delivering goods and services.The effectiveness of public provision is furtherconstrained by the fi scal resources at governments’disposal and the fact that state institutions may notbe responsive to the needs of poor people.The data on public pension spending cover thepension programs of the social insurance schemesfor which contributions had previously been made.In many cases noncontributory pensions or socialassistance targeted to the elderly and disabled arealso included. A country’s pattern of spending is correlatedwith its demographic structure—spendingincreases as the population ages.• Youth unemployment is the share of the laborforce ages 15–24 without work but available for andseeking employment. • Female-headed householdsare households with a female head. • Pension contributorsare members of the labor force or workingagepopulation (here defi ned as ages 15 and older)covered by a pension scheme. • Public expenditureon pensions is all government expenditures on cashtransfers to the elderly, the disabled, and survivorsand the administrative costs of these programs.• Average pension is the average pension paymentof all pensioners of the main pension schemes(including old-age, survivors, disability, military, andwork accident or disease pensions) divided by theaverage wage of all formal sector workers.Data sourcesData on youth unemployment are from the ILO’sKey Indicators of the Labour Market, 7th edition,database. Data on female-headed householdsare from MEASURE DHS Demographic and HealthSurveys by ICF International. Data on pension contributorsand public expenditure on pensions arefrom Hinz and others (2011).2012 World Development Indicators 81

2.11 Education inputsPublic expenditureper studentPublic expenditureon educationTrainedteachersin primaryeducationPrimaryschoolpupil–teacherratioPrimary% of GDP per capitaSecondary Tertiary % of GDP% of totalgovernmentexpenditure % of totalpupils perteacher1999 2010 a 1999 2010 a 1999 2010 a 2010 a 2010 a 2010 a 2010 aAfghanistan .. .. .. .. .. .. .. .. .. 44Albania .. .. .. .. .. .. .. .. .. 20Algeria 12.0 .. .. .. .. .. 4.3 20.3 .. 23Angola .. .. .. .. .. .. .. .. .. 46Argentina 12.9 16.8 18.2 27.1 17.7 19.1 6.0 14.0 .. 16Armenia .. 16.5 .. 17.8 .. 7.5 3.2 13.0 .. ..Australia 16.4 20.2 15.0 18.8 26.6 20.7 5.1 12.9 .. ..Austria 24.9 24.1 30.0 27.4 51.8 43.5 5.5 11.2 .. 11Azerbaijan 6.9 .. 17.0 .. 19.1 22.3 3.2 10.9 100.0 11Bahrain .. .. .. .. .. .. 2.9 11.7 .. ..Bangladesh .. 8.8 11.5 12.0 46.6 27.7 2.2 14.1 58.4 43Belarus .. .. .. .. .. 14.7 4.5 8.9 99.8 15Belgium 18.2 22.4 23.7 36.5 38.2 36.6 6.4 12.9 .. 11Benin 11.8 13.0 24.0 .. 208.2 .. 4.5 18.2 42.6 46Bolivia 14.2 .. 11.7 .. 44.1 .. .. .. .. ..Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. ..Botswana .. .. .. .. .. .. 7.8 16.2 .. ..Brazil 10.8 18.5 9.5 19.5 57.2 27.6 5.4 .. .. 23Bulgaria 15.2 24.4 18.4 24.2 17.6 24.8 4.4 12.3 .. 17Burkina Faso .. .. .. .. .. .. .. .. 85.7 b 48 bBurundi 14.4 19.0 67.8 64.1 1,036.1 477.4 9.2 25.1 91.2 51Cambodia 5.8 6.8 11.5 6.8 42.5 .. 2.6 .. 99.1 48Cameroon .. 6.6 .. 28.0 .. 28.0 3.5 17.9 57.1 46Canada .. .. .. .. 44.0 .. 4.8 .. .. ..Central African Republic .. 4.4 .. 14.5 .. 96.0 1.2 12.0 .. 84Chad .. 9.6 .. 19.1 .. 279.1 2.8 10.1 45.3 56Chile 14.4 17.4 14.8 17.7 19.4 13.7 4.5 .. .. 23China .. .. 11.5 .. 90.0 .. .. .. .. 17Hong Kong SAR, China 12.4 15.1 17.7 18.0 .. 26.8 3.6 20.2 95.6 15Colombia 15.2 15.7 16.1 15.3 37.7 29.4 4.8 14.9 100.0 28Congo, Dem. Rep. .. .. .. .. .. .. .. .. 91.7 37Congo, Rep. .. 11.1 .. .. .. 134.2 6.2 .. 86.8 49Costa Rica 15.5 14.6 21.4 14.4 .. .. 6.3 23.1 89.5 18Côte d’Ivoire 14.3 .. 41.3 .. 141.0 .. 4.6 24.6 100.0 b 49 bCroatia .. 21.8 .. 25.2 35.8 29.2 4.3 .. .. 15Cuba 25.0 44.2 37.1 51.9 77.6 61.1 13.4 18.3 100.0 9Cyprus 17.1 28.7 28.2 38.3 47.9 57.0 7.4 17.4 .. 14Czech Republic 11.2 13.6 21.7 22.8 33.7 25.7 4.1 9.5 .. 19Denmark 24.6 24.9 38.1 31.5 65.9 52.1 7.7 15.0 .. ..Dominican Republic .. 7.5 .. 6.7 .. .. .. .. 84.9 26Ecuador 4.4 .. 9.6 .. .. .. .. .. 82.6 17Egypt, Arab Rep. .. .. .. .. .. .. 3.8 11.9 .. 27El Salvador 8.6 8.8 7.5 9.4 8.9 11.7 3.2 .. 92.7 31Eritrea 15.0 .. 37.4 .. 430.8 .. .. .. 93.8 38Estonia 20.9 25.9 27.2 29.6 31.8 22.1 5.7 14.2 .. 12Ethiopia .. 18.2 .. 9.8 .. 31.0 4.7 25.4 39.4 54Finland 17.4 18.5 25.9 32.2 40.5 32.5 6.1 12.4 .. 14France 17.9 18.0 29.5 27.8 30.7 37.0 5.6 10.6 .. 19Gabon .. .. .. .. .. .. .. .. .. 25 bGambia, The .. 24.6 .. 16.2 .. 94.4 5.0 22.8 .. 37Georgia .. 14.8 .. 15.5 .. 11.4 3.2 7.7 94.6 8Germany .. 15.6 .. 21.8 .. .. 4.6 10.4 .. 13Ghana 17.6 11.4 40.4 27.2 .. 171.7 5.5 24.4 50.6 b 31 bGreece 11.7 .. 15.6 .. 26.5 .. .. .. .. ..Guatemala 6.7 10.4 4.3 6.2 .. .. 3.2 .. .. 28Guinea .. 7.0 .. 6.1 .. 100.0 2.4 19.2 65.2 42Guinea-Bissau .. .. .. .. .. .. .. .. 38.9 52Haiti .. .. .. .. .. .. .. .. .. ..822012 World Development Indicators

Education inputs 2.11PEOPLEPublic expenditureper studentPublic expenditureon educationTrainedteachersin primaryeducationPrimaryschoolpupil–teacherratioPrimary% of GDP per capitaSecondary Tertiary % of GDP% of totalgovernmentexpenditure % of totalpupils perteacher1999 2010 a 1999 2010 a 1999 2010 a 2010 a 2010 a 2010 a 2010 aHonduras .. 18.7 .. 279.7 .. .. .. .. 36.4 33Hungary 17.9 21.9 19.0 23.0 34.1 24.8 5.1 10.4 .. 10India 11.9 .. 24.7 .. 95.0 .. .. .. .. ..Indonesia .. 11.4 .. 12.9 .. 16.8 4.6 26.0 .. 16Iran, Islamic Rep. 9.3 15.2 10.1 21.1 35.5 19.2 4.7 19.8 98.4 20Iraq .. .. .. .. .. .. .. .. .. ..Ireland 11.0 18.6 16.8 27.5 28.6 32.7 5.7 13.4 .. 16Israel 20.5 19.5 21.9 20.4 30.9 21.3 5.9 13.7 .. 13Italy 24.0 24.5 27.7 26.7 27.6 25.0 4.6 9.4 .. ..Jamaica 13.4 19.9 21.0 22.6 70.4 50.2 6.1 11.5 .. 21Japan 21.1 21.5 20.9 22.3 15.1 20.9 3.8 9.4 .. 18Jordan 13.7 11.9 15.8 14.3 .. .. .. .. .. ..Kazakhstan .. .. .. .. .. 10.1 3.1 .. .. 16 bKenya 21.4 .. 14.4 .. 207.9 .. 6.7 17.2 96.8 47Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 18.4 19.4 15.7 23.2 8.4 10.1 4.8 15.8 .. 22Kosovo .. .. .. .. .. .. 4.3 17.4 .. ..Kuwait 17.0 10.0 .. 13.7 .. .. .. .. 100.0 8Kyrgyz Republic .. .. .. .. 24.3 17.6 6.0 18.6 68.4 24Lao PDR 2.2 .. 4.4 .. 67.7 .. 3.3 13.2 96.9 30Latvia 19.5 31.4 23.7 32.3 27.9 14.2 5.6 14.7 .. 12Lebanon .. .. .. .. 13.8 10.0 1.8 7.2 .. 14Lesotho 37.0 24.8 82.4 55.7 939.7 .. 13.1 23.7 63.4 34Liberia .. .. .. .. .. .. 2.8 12.1 40.2 24Libya .. .. .. .. 23.4 .. .. .. .. ..Lithuania .. 18.1 .. 22.6 34.2 17.0 4.9 13.1 .. 13Macedonia, FYR .. .. .. .. .. .. .. .. .. 16Madagascar 5.7 7.8 .. 11.5 .. 144.8 3.2 13.4 90.4 40Malawi 13.4 6.8 9.6 23.3 2,503.9 1,937.6 5.7 b 14.7 b 95.9 79Malaysia 12.6 14.6 21.9 20.2 81.6 60.7 5.8 18.9 .. 13Mali 15.3 15.0 59.7 37.5 256.8 135.3 4.5 22.0 50.0 48 bMauritania 11.6 13.4 36.4 31.2 80.1 193.9 4.3 15.2 100.0 37Mauritius 9.3 9.0 14.2 14.3 25.4 16.1 3.1 11.4 100.0 21Mexico 11.9 13.7 14.5 13.6 48.8 38.9 4.9 .. 95.6 28Moldova .. 41.4 .. 39.4 .. 44.8 9.1 22.3 .. 16Mongolia .. 14.6 .. .. .. 6.0 5.4 14.6 97.6 30Morocco 17.4 16.9 45.5 .. 97.1 83.3 5.4 25.7 100.0 b 26 bMozambique .. .. .. .. 1,408.9 .. .. .. 75.9 58Myanmar .. .. 6.6 .. 27.0 .. .. .. 99.9 28Namibia 22.2 17.8 36.3 16.4 157.3 .. 8.1 22.4 95.6 30Nepal 9.1 17.8 13.1 11.3 141.3 39.3 4.7 20.2 80.7 b 30 bNetherlands 15.2 17.2 22.2 25.0 47.4 41.5 5.5 11.9 .. ..New Zealand 20.0 21.9 23.7 23.6 39.5 31.4 7.2 16.1 .. 14Nicaragua .. .. .. .. .. .. .. .. 74.9 30Niger .. 21.1 .. 41.9 .. 438.8 3.8 16.9 96.4 b 39 bNigeria .. .. .. .. .. .. .. .. 66.1 36Norway 21.8 18.3 30.4 25.6 45.8 46.8 6.5 16.1 .. ..Oman 10.7 12.8 20.9 14.6 .. 41.6 4.4 .. 100.0 12Pakistan .. .. .. .. .. .. 2.4 9.9 84.2 40Panama 13.7 7.5 19.1 9.9 33.7 21.6 3.8 .. 91.6 23Papua New Guinea .. .. .. .. .. .. .. .. .. ..Paraguay 13.6 .. 18.4 .. 58.9 .. .. .. .. ..Peru 7.6 7.8 10.8 8.9 21.1 .. 2.6 16.4 .. 20Philippines 12.0 9.0 10.2 9.1 14.4 .. 2.7 16.9 .. 31Poland .. 25.3 10.9 22.9 21.1 18.4 5.1 11.8 .. 10Portugal 18.8 19.9 26.6 31.6 27.1 26.7 4.9 11.0 .. 11Puerto Rico .. .. .. .. .. .. .. .. 6.6 12Qatar .. 10.3 .. 11.0 .. 337.7 2.4 8.2 42.9 122012 World Development Indicators 83

2.11Education inputsPublic expenditureper studentPublic expenditureon educationTrainedteachersin primaryeducationPrimaryschoolpupil–teacherratioRomania .. .. .. .. 32.6 .. .. .. .. 16Russian Federation .. .. .. .. 10.9 14.2 4.1 .. .. 18Rwanda 11.2 8.2 42.7 37.1 1,228.1 186.8 4.7 b 16.9 b 91.5 65Saudi Arabia .. .. .. .. .. .. 5.6 19.3 .. 11Senegal 13.6 16.4 .. 28.0 .. 186.9 5.6 24.0 47.9 34Serbia .. 61.6 .. 14.4 .. 43.3 5.0 9.5 94.2 16Sierra Leone .. .. .. .. .. .. 4.3 18.1 48.0 b 31 bSingapore .. 11.5 .. 17.5 .. 28.7 3.3 10.3 94.3 17Slovak Republic 10.2 15.6 18.4 15.1 32.8 18.3 3.6 10.3 .. 16Slovenia 26.1 .. 25.6 .. 27.8 21.2 5.2 11.8 .. 17Somalia .. .. .. .. .. .. .. .. .. ..South Africa 14.2 17.6 20.0 19.8 .. .. 6.0 19.2 87.4 31South Sudan .. .. .. .. .. .. .. .. .. ..Spain 18.0 20.3 24.4 25.8 19.6 27.3 4.6 11.2 .. 13Sri Lanka .. 7.4 .. .. .. .. 2.1 8.1 .. 24Sudan .. .. .. .. .. .. .. .. 59.7 38Swaziland 8.3 17.7 23.2 37.1 434.9 .. 7.4 16.0 73.1 32Sweden 22.3 26.2 26.1 30.9 51.7 41.3 6.8 12.9 .. 9Switzerland 22.7 20.5 27.3 31.1 53.8 43.8 5.4 16.7 .. ..Syrian Arab Republic 10.8 16.8 21.0 14.2 .. .. .. .. .. 18Tajikistan .. .. .. .. .. 17.5 4.0 14.7 92.9 25Tanzania .. 21.5 .. 18.8 .. 873.3 6.2 18.3 94.5 51Thailand 18.1 24.4 16.2 15.4 36.5 17.6 3.8 22.3 .. 16Timor-Leste .. .. .. .. .. 83.9 14.0 11.7 .. 30Togo 7.7 10.8 27.7 .. .. .. 4.5 17.6 76.7 41Trinidad and Tobago 11.5 14.9 12.2 16.1 148.3 .. .. .. 88.0 18Tunisia 14.2 17.3 24.6 24.3 81.1 46.1 6.3 22.7 .. 17Turkey 9.8 .. 9.6 .. 35.3 .. .. .. .. ..Turkmenistan .. .. .. .. .. .. .. .. .. ..Uganda .. 7.2 .. 20.5 .. 104.3 3.2 15.0 89.4 49Ukraine .. .. .. .. 36.5 .. .. .. 99.9 16United Arab Emirates 5.4 6.2 7.2 8.6 26.2 19.9 1.0 23.4 100.0 17United Kingdom 13.9 23.3 23.8 28.9 25.6 22.2 5.4 11.1 .. 18United States 17.7 22.5 22.3 24.8 26.8 21.2 5.5 13.8 .. 14Uruguay 7.2 .. 9.9 .. .. .. .. .. .. 14Uzbekistan .. .. .. .. .. .. .. .. 100.0 b 16 bVenezuela, RB .. .. .. .. .. .. .. .. 88.4 12Vietnam .. 19.4 .. 17.0 .. 60.6 5.3 19.8 98.3 20West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 28Yemen, Rep. .. .. .. .. .. .. 5.2 16.0 .. 31Zambia 7.0 .. 19.4 .. 160.4 .. 1.3 .. .. 58Zimbabwe 12.7 .. 19.3 .. .. 75.4 2.5 8.3 .. ..World .. m 16.4 m .. m 22.7 m .. m .. m 4.6 m 15.6 m .. w 24 wLow income .. .. .. .. .. .. 3.8 18.7 82.9 45Middle income .. .. .. .. .. .. 4.4 .. .. ..Lower middle income .. .. .. .. .. .. 4.0 .. .. ..Upper middle income 13.8 15.9 .. 15.4 .. .. 4.8 .. .. 19Low & middle income .. .. .. .. .. .. 4.1 .. .. 26East Asia & Pacifi c .. .. .. .. 37.8 .. 3.8 17.2 .. 17Europe & Central Asia .. .. .. .. .. 17.3 4.4 14.0 .. 17Latin America & Carib. 12.6 12.4 12.9 13.6 .. .. 4.4 .. .. 24Middle East & N. Africa .. .. .. .. .. .. 4.8 20.0 .. 24South Asia .. 8.8 13.1 .. 95.0 .. 2.5 12.6 .. ..Sub-Saharan Africa .. .. .. .. .. .. 5.0 18.9 .. 46High income 17.9 19.7 22.3 25.1 31.8 27.3 5.1 12.7 .. 15Euro area 17.9 19.9 25.9 27.5 30.7 32.5 5.5 11.5 .. 14a. Provisional data. b. Data are for 2011.Primary% of GDP per capitaSecondary Tertiary % of GDP% of totalgovernmentexpenditure % of totalpupils perteacher1999 2010 a 1999 2010 a 1999 2010 a 2010 a 2010 a 2010 a 2010 a842012 World Development Indicators

Education inputs2.11PEOPLEAbout the dataDefinitionsData on education are collected by the UnitedNations Educational, Scientifi c, and Cultural Organization(UNESCO) Institute for Statistics from offi cialresponses to its annual education survey. The dataare used for monitoring, policymaking, and resourceallocation. While international standards ensurecomparable datasets, data collection methods mayvary by country and within countries over time.For most countries the data on education spendingin the table refer to public spending—governmentspending on education at all levels plus subsidiesprovided to households and other private entities—and generally exclude foreign aid for education that isnot included in the government budget. The data mayalso exclude spending by religious schools, whichplay a signifi cant role in many developing countries.Data are gathered from ministries of education andfrom other ministries or agencies involved in educationspending.The share of public expenditure devoted to educationallows an assessment of the priority a governmentassigns to education relative to other publicinvestments, as well as a government’s commitmentto investing in human capital development. However,returns on investment to education, especially primaryand lower secondary education, cannot beunderstood simply by comparing current educationindicators with national income. It takes a long timebefore currently enrolled children can productivelycontribute to the national economy (Hanushek 2002).High-quality data on education fi nance are scarce.Improving their quality is a priority of the UNESCOInstitute for Statistics. Additional resources arebeing allocated for technical assistance to countriesin need, especially in Sub-Saharan Africa.Interagency partnerships and collaborations withnational ministries in charge of education fi nancedata are improving, and actual expenditure data areincreasingly being collected. Tracking private educationspending is still a challenge for all countries.The share of trained teachers in primary educationreveals a country’s commitment to investing in thedevelopment of its human capital engaged in teaching,but it does not take into account differences inteachers’ experiences and status, teaching methods,teaching materials, and classroom conditions—all factors that affect the quality of teaching andlearning. Some teachers without formal training mayhave acquired equivalent pedagogical skills throughprofessional experience.The pupil–teacher ratio refl ects the average numberof pupils per teacher. It differs from the averageclass size because of the different practices countriesemploy, such as part-time teachers, schoolshifts, and multigrade classes. The comparabilityof pupil–teacher ratios across countries is furtheraffected by the defi nition of teachers and by differencesin class size by grade and in the number ofhours taught, as well as the different practices mentionedabove. Moreover, the underlying enrollmentlevels are subject to a variety of reporting errors (forfurther discussion of enrollment data, see About thedata for table 2.12).While the pupil–teacher ratiois often used to compare the quality of schoolingacross countries, it is often weakly related to studentlearning and quality of education.All education data published by the UNESCOInstitute for Statistics are mapped to the InternationalStandard Classifi cation of Education 1997(ISCED97). This classifi cation system ensures thecomparability of education programs at the internationallevel. UNESCO developed the ISCED to facilitatecomparisons of education statistics and indicatorsof different countries on the basis of uniform andinternationally agreed defi nitions. First developed inthe 1970s, the current version was formally adoptedin November 1997.The reference years in the table refl ect the schoolyear for which the data are presented. In some countriesthe school year spans two calendar years (forexample, from September 2009 to June 2010); inthese cases the reference year refers to the year inwhich the school year ended (2010 in the example).• Public expenditure per student is public currentand capital spending on education divided by thenumber of students by level as a percentage of grossdomestic product (GDP) per capita. • Public expenditureon education is current and capital expenditureson education by local (including municipalities),regional, and national governments as a percentageof GDP and as a percentage of total governmentexpenditure. • Trained teachers in primary educationare the percentage of primary school teacherswho have received the minimum organized teachertraining (pre-service or in-service) required for teachingat the specifi ed level of education in their country.• Primary school pupil–teacher ratio is the numberof pupils enrolled in primary school divided by thenumber of primary school teachers (regardless oftheir teaching assignment).Data sourcesData on education inputs are from the UNESCOInstitute for Statistics.2012 World Development Indicators 85

2.12 Participation in educationGross enrollmentratioNet enrollmentrateAdjusted netenrollment rate,primaryChildren out ofschool% of primary school–age childrenthousandprimary school–age children% of relevant age group % of relevant age groupPreprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female2010 a 2010 a 2010 a 2010 a 1991 2010 a 1999 2010 a 2010 a 2010 a 2010 a 2010 aAfghanistan .. 97 46 3 28 .. .. .. .. .. .. ..Albania 56 87 89 .. .. 80 68 .. 79 79 23 20Algeria 77 110 95 31 89 96 .. .. 98 96 28 53Angola 104 124 31 4 .. 86 .. 12 93 78 119 373Argentina 74 118 89 71 .. .. 74 82 .. .. .. ..Armenia 31 103 92 52 .. .. 86 86 78 81 13 10Australia 81 104 129 76 98 97 90 85 97 98 31 23Austria 96 100 100 60 90 .. .. .. .. .. .. ..Azerbaijan 25 94 .. 19 89 84 75 .. 85 84 40 38Bahrain .. .. .. .. 99 .. 86 .. .. .. .. ..Bangladesh 13 103 49 11 64 92 44 46 91 100 718 1Belarus 99 100 .. 83 .. 92 82 87 95 97 9 5Belgium 118 105 111 67 96 99 .. .. 99 99 4 3Benin 18 126 .. .. 51 94 18 .. .. .. .. ..Bolivia 45 105 80 .. .. .. 68 68 .. .. .. ..Bosnia and Herzegovina 17 88 90 36 .. 85 .. .. 86 88 14 11Botswana .. .. .. .. 89 .. 53 .. .. .. .. ..Brazil 65 127 101 36 .. 94 66 82 96 94 290 396Bulgaria 79 103 88 53 .. 98 85 83 99 100 1 0 bBurkina Faso 3 c 79 c 23 c 3 27 63 c 8 18 c 65 58 452 531Burundi 9 156 25 3 50 .. .. 16 98 100 10 1Cambodia 13 127 46 8 .. 96 14 .. 96 95 32 40Cameroon 28 120 42 11 69 92 .. .. 100 88 6 171Canada 71 99 101 .. 98 .. 95 .. .. .. .. ..Central African Republic 4 93 13 3 53 71 .. 11 81 61 63 135Chad 2 90 26 2 .. .. 7 .. .. .. .. ..Chile 56 106 88 59 .. 94 .. 83 94 94 46 48China 54 111 81 26 97 .. .. .. .. .. .. ..Hong Kong SAR, China .. 102 83 60 .. 94 70 75 96 98 7 4Colombia 49 115 96 39 71 88 56 74 92 91 187 188Congo, Dem. Rep. 3 94 38 6 56 .. .. .. .. .. .. ..Congo, Rep. 13 115 .. 6 .. 91 .. .. 92 89 24 32Costa Rica 71 110 100 .. 87 .. .. .. .. .. .. ..Côte d’Ivoire 4 c 88 c .. .. 46 61 19 .. 67 56 497 664Croatia 58 93 95 49 .. 87 81 92 93 93 7 6Cuba 100 103 89 95 94 99 75 86 100 100 0 b 1Cyprus 81 105 98 52 87 99 88 96 99 99 0 b 0 bCzech Republic 106 106 90 61 .. .. 81 84 .. .. .. ..Denmark 96 99 117 74 98 96 88 89 95 97 11 6Dominican Republic 38 108 76 .. .. 90 39 62 96 90 28 57Ecuador 126 114 80 40 .. 97 46 .. .. .. .. ..Egypt, Arab Rep. 24 106 .. 30 .. 96 77 .. 100 96 15 184El Salvador 64 114 65 23 .. 94 44 58 94 95 26 21Eritrea 14 45 32 2 20 33 18 29 37 33 203 215Estonia 96 99 104 63 .. 94 84 92 96 96 1 1Ethiopia 5 102 36 5 30 81 12 .. 85 80 1,023 1,367Finland 66 99 108 92 99 97 95 94 98 98 5 4France 110 111 113 55 100 99 93 98 .. .. .. ..Gabon 42 c 182 c .. .. .. .. .. .. .. .. .. ..Gambia, The 30 83 54 4 50 66 .. .. 68 71 44 41Georgia 58 109 86 28 .. 100 76 79 .. .. .. ..Germany 114 102 103 .. 84 97 .. .. .. .. .. ..Ghana 69 107 c 58 c 9 .. 84 c 34 49 c 84 c 85 c 298 c 270 cGreece .. .. .. .. 95 .. 82 .. .. .. .. ..Guatemala 71 116 59 .. .. 97 24 50 100 98 5 27Guinea 14 94 38 9 27 77 12 29 83 70 131 224Guinea-Bissau 7 123 .. .. .. 74 9 .. 77 73 26 30Haiti .. .. .. .. 21 .. .. .. .. .. .. ..862012 World Development Indicators

Participation in education 2.12PEOPLEGross enrollmentratioNet enrollmentrateAdjusted netenrollment rate,primaryChildren out ofschool% of primary school–age childrenthousandprimary school–age children% of relevant age group % of relevant age groupPreprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female2010 a 2010 a 2010 a 2010 a 1991 2010 a 1999 2010 a 2010 a 2010 a 2010 a 2010 aHonduras 44 116 73 19 88 96 .. .. 96 98 23 8Hungary 85 102 98 62 .. 92 83 91 98 98 4 3India 54 118 60 16 .. 92 .. .. .. .. .. ..Indonesia 43 118 77 23 95 96 47 67 .. .. .. ..Iran, Islamic Rep. 43 108 84 43 97 .. .. .. .. .. .. ..Iraq .. .. .. .. 76 .. 30 .. .. .. .. ..Ireland .. 108 117 61 90 95 91 98 97 99 6 3Israel 106 113 91 62 .. 97 87 88 97 97 13 10Italy 97 103 99 66 .. 98 85 93 100 99 5 11Jamaica 113 89 93 29 97 82 83 84 84 82 28 32Japan 90 103 102 59 100 100 99 99 .. .. .. ..Jordan 36 97 91 42 .. 90 77 84 93 95 30 21Kazakhstan 66 c 111 c 100 c 41 c .. 88 c 88 90 c 99 c 100 c 3 c 1 cKenya 52 113 60 4 .. 83 33 50 84 85 523 486Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 121 104 97 104 99 99 96 96 100 99 0 b 14Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 82 106 101 .. 47 92 98 89 97 100 3 0 bKyrgyz Republic 19 100 84 49 .. 87 .. 79 95 95 9 9Lao PDR 16 121 45 13 59 89 26 37 91 87 35 47Latvia 84 101 95 60 .. 95 .. 84 94 95 3 3Lebanon 81 105 81 54 .. 92 .. 75 94 93 15 15Lesotho 33 103 46 .. 72 73 17 30 72 75 53 46Liberia .. 96 .. .. .. .. 18 .. .. .. .. ..Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 75 97 98 77 .. 93 90 91 97 97 2 2Macedonia, FYR 25 89 83 40 .. 87 .. .. 93 95 5 3Madagascar 9 149 31 4 72 .. .. 24 .. .. .. ..Malawi .. 135 32 1 .. 97 30 28 .. .. .. ..Malaysia 67 .. 68 40 .. .. 66 68 .. .. .. ..Mali 3 c 82 c 39 c 6 .. 63 c .. 31 c 71 61 377 481Mauritania .. 102 24 4 .. 74 14 .. 73 76 72 61Mauritius 96 99 89 25 93 93 67 .. 92 94 5 3Mexico 103 115 87 27 98 98 56 70 99 100 39 11Moldova 76 94 88 38 .. 88 78 79 90 90 8 7Mongolia 77 100 93 53 .. 95 58 83 98 98 2 3Morocco 63 c 114 c .. 13 56 96 c 30 .. 97 c 96 c 58 c 75 cMozambique .. 115 25 .. 42 92 3 16 95 89 124 243Myanmar 10 126 54 .. .. .. 32 51 .. .. .. ..Namibia .. 107 .. 9 82 85 39 .. 84 89 31 21Nepal .. .. .. .. .. .. .. .. .. .. .. ..Netherlands 96 108 120 63 95 100 91 87 .. .. .. ..New Zealand 93 101 119 83 100 99 90 95 99 100 1 1Nicaragua 55 118 69 .. 70 92 35 46 93 95 27 21Niger 6 c 71 c 13 1 23 62 c 6 10 64 52 478 607Nigeria 14 83 44 .. .. .. .. .. .. .. .. ..Norway 98 99 110 74 100 99 95 95 99 99 3 2Oman 45 105 100 24 69 94 61 90 98 98 3 2Pakistan .. 95 34 5 .. 74 .. 34 81 67 1,884 3,241Panama 67 108 74 45 92 98 59 69 99 98 2 4Papua New Guinea .. 60 .. .. 65 .. .. .. .. .. .. ..Paraguay 35 100 67 37 94 85 45 60 86 86 62 60Peru 78 109 92 .. 86 95 63 78 97 97 54 43Philippines 51 107 82 29 96 89 50 61 88 90 799 662Poland 66 97 97 71 .. 96 91 91 96 96 47 47Portugal 82 114 107 62 98 99 80 .. 99 100 3 2Puerto Rico 96 93 82 86 .. 86 .. .. 83 88 28 18Qatar 55 103 94 10 89 92 74 83 96 97 2 12012 World Development Indicators 87

2.12Participation in educationGross enrollmentratioNet enrollmentrateAdjusted netenrollment rate,primaryChildren out ofschoolRomania 77 96 95 64 73 87 77 82 92 93 35 32Russian Federation 90 99 89 76 .. 93 .. .. 95 96 128 93Rwanda 10 143 32 5 .. 99 .. .. 89 92 84 60Saudi Arabia 11 106 101 37 .. 90 .. 81 90 89 154 164Senegal 13 87 37 8 45 75 .. .. 76 80 238 191Serbia 53 96 91 49 .. 93 .. 90 95 94 8 8Sierra Leone 7 c 125 c .. .. .. .. .. .. .. .. .. ..Singapore .. .. .. .. .. .. .. .. .. .. .. ..Slovak Republic 91 102 89 54 .. .. .. .. .. .. .. ..Slovenia 86 98 97 87 .. 97 90 92 97 97 2 2Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 65 102 94 .. 90 85 62 .. 89 91 372 326South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 126 107 119 73 100 100 88 94 100 100 2 1Sri Lanka 42 99 .. 15 .. 94 .. .. 94 94 56 51Sudan 27 73 39 .. .. .. .. .. .. .. .. ..Swaziland 23 116 58 .. 74 86 32 33 86 85 15 15Sweden 95 100 100 71 100 99 96 96 100 99 1 3Switzerland 102 102 95 51 84 94 84 83 99 99 4 2Syrian Arab Republic 10 118 72 .. 91 93 39 67 100 98 2 16Tajikistan 9 102 87 20 .. 97 63 85 99 96 2 13Tanzania 33 102 27 2 51 98 5 .. 91 93 359 290Thailand 100 c 91 79 c 48 c .. 90 .. 74 c 90 89 304 307Timor-Leste .. 117 56 17 .. 85 .. 37 86 86 14 14Togo 9 140 .. .. 65 92 21 .. .. .. .. ..Trinidad and Tobago .. 105 90 .. 90 94 69 .. 97 94 2 4Tunisia .. 109 90 34 94 98 64 .. .. .. .. ..Turkey 22 102 78 46 89 97 56 74 98 97 59 103Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda 14 121 28 4 .. 91 13 .. 90 92 357 266Ukraine 97 99 96 79 .. 91 91 86 91 91 73 64United Arab Emirates .. .. .. .. 97 .. 75 .. .. .. .. ..United Kingdom 81 106 102 59 97 100 94 96 100 100 3 6United States 69 102 96 95 97 95 87 89 96 98 498 247Uruguay 89 113 90 63 91 99 .. 70 100 99 0 b 1Uzbekistan 26 c 95 c 106 c 9 c .. 90 c .. 92 94 c 91 c 62 c 86 cVenezuela, RB 73 103 83 78 .. 93 48 72 95 95 91 80Vietnam 82 106 77 22 .. 98 58 .. .. .. .. ..West Bank and Gaza 39 91 86 50 .. 87 75 84 90 88 23 25Yemen, Rep. 1 87 44 .. .. 78 31 .. 86 70 290 568Zambia .. 115 .. .. .. 91 16 .. 91 94 108 76Zimbabwe .. .. .. 6 .. .. 40 .. .. .. .. ..World 50 w 107 w 68 w 27 w .. w 88 w 51 w 60 w 91 w 89 wLow income 12 104 39 7 .. 80 24 32 82 80Middle income 52 109 69 24 .. 89 50 60 92 90Lower middle income 50 107 58 16 .. 85 41 50 90 86Upper middle income 51 111 83 33 .. 94 63 74 95 96Low & middle income 46 108 64 21 .. 87 47 56 90 88East Asia & Pacifi c 48 111 76 25 96 .. 56 .. .. ..Europe & Central Asia 55 98 89 55 90 92 78 81 94 93Latin America & Carib. 71 117 90 37 .. 94 59 73 95 95Middle East & N. Africa .. 102 72 27 .. 90 58 64 94 89South Asia 54 110 55 11 68 86 .. .. 93 89Sub-Saharan Africa 17 100 36 6 .. 75 19 27 78 74High income 78 101 100 70 95 95 88 90 95 96Euro area 107 105 107 60 .. 98 86 92 .. ..a. Provisional data. b. Less than 0.5. c. Data are for 2011.% of primary school–age childrenthousandprimary school–age children% of relevant age group % of relevant age groupPreprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female2010 a 2010 a 2010 a 2010 a 1991 2010 a 1999 2010 a 2010 a 2010 a 2010 a 2010 a882012 World Development Indicators

Participation in education2.12PEOPLEAbout the dataDefinitionsSchool enrollment data are reported to the UnitedNations Educational, Scientifi c, and Cultural Organization(UNESCO) Institute for Statistics by nationaleducation authorities and statistical offi ces. Enrollmentindicators help monitor whether a country is ontrack to achieve the Millennium Development Goal ofuniversal primary education by 2015, and whetheran education system has the capacity to meet theneeds of universal primary education.Enrollment indicators are based on annual schoolsurveys, but do not necessarily refl ect actual attendanceor dropout rates during the year. Also, thelength of primary education differs across countriesand can infl uence enrollment rates and ratios,although the International Standard Classifi cation ofEducation (ISCED) tries to minimize the difference.A shorter duration for primary education tends toincrease the ratio; a longer one to decrease it (inpart because older children are more at risk of droppingout).Overage or underage enrollments are frequent, particularlywhen parents prefer children to start schoolat other than the offi cial age. Age at enrollment maybe inaccurately estimated or misstated, especiallyin communities where registration of births is notstrictly enforced.Population data used to calculate populationbasedindicators are drawn from the United NationsPopulation Division. Using a single source for populationdata standardizes defi nitions, estimations, andinterpolation methods, ensuring a consistent methodologyacross countries and minimizing potentialenumeration problems in national censuses.Gross enrollment ratios indicate the capacity ofeach level of the education system, but a high ratiomay refl ect a substantial number of overage childrenenrolled in each grade because of repetition or lateentry rather than a successful education system.The net enrollment rate excludes overage and underagestudents and more accurately captures the system’scoverage and internal effi ciency. Differencesbetween the gross enrollment ratio and the netenrollment rate show the incidence of overage andunderage enrollments.The adjusted net enrollment rate in primary educationcaptures primary school–age children who haveprogressed to secondary education faster than theirpeers have and who are not counted in the traditionalnet enrollment rate.Data on children out of school (primary school–age children not enrolled in primary or secondaryschool—dropouts, children never enrolled, andchildren of primary age enrolled in preprimary education)are compiled from administrative data. Largenumbers of children out of school create pressureto enroll children and provide classrooms, teachers,and educational materials, a task made diffi cult inmany countries by limited education budgets. However,getting children into school is a high priority forcountries and crucial for achieving the MillenniumDevelopment Goal of universal primary education.The reference years in the table refl ect the schoolyear for which the data are presented. In some countriesthe school year spans two calendar years (forexample, from September 2009 to June 2010); inthese cases the reference year refers to the year inwhich the school year ended (2010 in the example).• Gross enrollment ratio is the ratio of total enrollment,regardless of age, to the population of the agegroup that offi cially corresponds to the level of educationshown. • Preprimary education (ISCED O) refersto programs at the initial stage of organized instruction,designed primarily to introduce very young children,usually age 3, to a school-type environment andto provide a bridge between the home and school. Oncompleting these programs, children continue theireducation at the primary level. • Primary education(ISCED 1) refers to programs normally designed togive students a sound basic education in reading,writing, and mathematics along with an elementaryunderstanding of other subjects such as history,geography, natural science, social science, art, andmusic. Religious instruction may also be featured. Itis sometimes called elementary education. • Secondaryeducation refers to programs of lower (ISCED2) and upper (ISCED 3) secondary education. Lowersecondary education continues the basic programsof the primary level, but the teaching is typically moresubject focused, requiring more specialized teachersfor each subject area. In upper secondary educationinstruction is often organized even more along subjectlines, and teachers typically need a higher ormore subject-specifi c qualifi cation. • Tertiary educationrefers to a wide range of programs with moreadvanced educational content. The fi rst stage of tertiaryeducation (ISCED 5) refers to theoretically basedprograms intended to provide suffi cient qualifi cationsto enter advanced research programs or professionswith high-skill requirements and programs that arepractical, technical, or occupationally specifi c. Thesecond stage of tertiary education (ISCED 6) refersto programs devoted to advanced study and originalresearch and leading to an advanced research qualific a t i o n. • Net enrollment rate is the ratio of totalenrollment of children of offi cial school age to thepopulation of the age group that offi cially correspondsto the level of education shown. • Adjusted net enrollmentrate, primary, is the ratio of total enrollment ofchildren of offi cial school age for primary educationwho are enrolled in primary or secondary education tothe total primary school–age population. • Childrenout of school are the number of primary school–agechildren not enrolled in primary or secondary school.Data sourcesData on gross enrollment ratios, net enrollmentrates, and out of school children are from theUNESCO Institute for Statistics.2012 World Development Indicators 89

2.13 Education efficiencyGross intake ratioin first grade ofprimary educationCohortsurvival rateRepeaters inprimary educationTransition rate tosecondary education% of grade 1 students% of relevantReachingReaching last grade of% ofage groupgrade 5primary educationenrollment %Male Female Male Female Male Female Male Female Male Female2010 a 2010 a 1991 2009 a 1991 2009 a 2009 a 2009 a 2010 a 2010 a 2009 a 2009 aAfghanistan 124 91 89 .. 89 .. .. .. .. .. .. ..Albania 87 87 .. 95 .. 95 95 95 1 1 97 97Algeria 107 105 82 93 79 97 93 97 9 6 90 92Angola 182 148 .. 53 .. 37 37 27 10 12 26 45Argentina 114 115 .. 96 .. 95 .. .. 6 4 96 97Armenia 91 93 .. .. .. .. .. .. 0 0 100 98Australia .. .. 98 .. 99 .. .. .. .. .. .. ..Austria 103 101 .. .. .. .. 96 99 0 0 100 100Azerbaijan 91 88 .. .. .. .. 100 97 0 0 98 99Bahrain .. .. 88 98 87 98 98 98 2 2 98 99Bangladesh 110 115 .. 62 .. 71 67 66 13 12 .. ..Belarus 96 96 .. .. .. .. 96 100 0 0 98 100Belgium 94 95 87 96 90 97 86 88 3 3 99 97Benin 159 147 30 62 31 59 .. .. 13 13 .. ..Bolivia 113 111 57 86 51 85 85 82 1 1 89 91Bosnia and Herzegovina 99 100 .. 73 .. 73 73 73 0 0 .. ..Botswana .. .. 73 .. 81 .. .. .. .. .. .. ..Brazil .. .. .. .. .. .. .. .. .. .. .. ..Bulgaria 100 99 .. .. .. .. 94 94 2 1 95 96Burkina Faso 91 b 86 b 61 73 58 78 71 72 10 10 43 c 62 cBurundi 164 158 66 59 61 66 56 64 34 34 41 31Cambodia 143 144 .. 60 .. 65 52 57 10 8 80 81Cameroon 144 123 67 76 66 77 67 65 14 13 42 45Canada 99 98 .. .. .. .. .. .. 0 0 .. ..Central African Republic 118 96 52 61 39 50 53 39 21 21 43 45Chad 134 104 43 32 22 32 24 23 23 26 77 66Chile 97 96 .. .. .. .. .. .. 1 1 84 98China 95 99 .. .. .. .. .. .. 0 0 .. ..Hong Kong SAR, China 114 119 .. 100 .. 100 .. .. 1 1 100 100Colombia 113 107 53 84 59 85 .. .. 2 2 97 96Congo, Dem. Rep. 117 105 66 62 55 58 78 73 14 14 83 76Congo, Rep. 109 108 66 75 68 79 71 71 20 18 69 67Costa Rica 98 98 70 90 73 92 88 90 7 5 93 89Côte d’Ivoire 88 b 78 b 68 66 61 66 .. .. 19 19 47 45Croatia 92 92 .. .. .. .. 97 99 0 0 100 99Cuba 98 98 .. 97 .. 97 96 95 1 0 98 99Cyprus 107 106 96 94 97 97 .. .. 0 0 100 100Czech Republic 107 108 .. 99 .. 100 99 100 1 1 99 99Denmark 100 100 98 100 99 100 99 100 0 0 99 100Dominican Republic 113 101 .. .. .. .. .. .. 9 5 82 90Ecuador .. .. .. .. .. .. .. .. .. .. .. ..Egypt, Arab Rep. 105 103 .. .. .. .. .. .. 4 2 .. ..El Salvador 117 110 54 89 57 90 86 87 7 5 95 94Eritrea 44 40 .. 71 .. 67 71 67 16 13 82 82Estonia 100 100 .. 99 .. 99 98 99 1 0 99 98Ethiopia 145 129 .. 50 .. 51 47 48 4 4 91 87Finland 99 99 96 100 97 100 100 99 1 0 100 100France .. .. .. .. .. .. .. .. .. .. .. ..Gabon .. .. 47 .. 46 .. .. .. .. .. .. ..Gambia, The 88 88 59 67 53 63 .. .. 6 5 80 82Georgia 100 102 .. 94 .. 99 94 99 0 0 100 100Germany 100 99 .. .. .. .. 98 99 1 1 99 99Ghana 109 b 111 b 72 80 65 77 .. .. 2 b 3 b 91 92Greece .. .. .. .. .. .. .. .. .. .. .. ..Guatemala 131 131 .. 71 .. 70 65 64 12 10 93 90Guinea 112 96 43 74 35 62 74 56 16 18 62 51Guinea-Bissau 169 164 .. .. .. .. .. .. 14 14 .. ..Haiti .. .. 47 .. 46 .. .. .. .. .. .. ..902012 World Development Indicators

Education efficiency 2.13PEOPLEGross intake ratioin first grade ofprimary educationCohortsurvival rateRepeaters inprimary educationTransition rate tosecondary education% of grade 1 students% of relevantReachingReaching last grade of% ofage groupgrade 5primary educationenrollment %Male Female Male Female Male Female Male Female Male Female2010 a 2010 a 1991 2009 a 1991 2009 a 2009 a 2009 a 2010 a 2010 a 2009 a 2009 aHonduras 125 120 50 75 43 80 74 79 1 1 .. ..Hungary 103 102 .. .. .. .. 98 98 2 2 97 98India 129 125 .. .. .. .. .. .. .. .. 81 81Indonesia 121 118 .. 83 .. 89 .. .. 4 3 91 93Iran, Islamic Rep. 107 108 75 94 67 94 94 94 2 2 96 97Iraq .. .. 75 .. 70 .. .. .. .. .. .. ..Ireland 106 107 .. 99 .. 100 .. .. 1 1 .. ..Israel 98 101 .. 100 .. 98 100 98 2 1 70 69Italy 101 100 .. 99 .. 100 99 100 0 0 100 100Jamaica 81 78 92 96 94 96 94 96 3 2 92 91Japan 103 103 100 100 100 100 100 100 0 0 .. ..Jordan 98 98 93 .. 89 .. .. .. 1 1 99 99Kazakhstan 112 b 111 b .. .. .. .. .. .. 0 b 0 b 100 c 100 cKenya .. .. .. .. .. .. .. .. .. .. .. ..Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 100 98 92 99 92 99 99 99 0 0 100 100Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 101 103 .. 96 .. 96 96 96 1 1 99 99Kyrgyz Republic 106 104 .. .. .. .. 98 97 0 0 100 99Lao PDR 136 126 34 66 32 68 .. .. 18 16 80 77Latvia 100 102 .. 96 .. 96 95 95 3 2 94 98Lebanon 108 106 .. 90 .. 91 .. .. 9 7 84 91Lesotho 103 94 53 76 77 85 .. .. 23 17 75 73Liberia 120 112 .. 64 .. 56 49 43 6 7 64 60Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 93 94 .. .. .. .. 98 99 1 0 99 99Macedonia, FYR 97 97 .. .. .. .. .. .. 0 0 98 98Madagascar 184 184 31 34 31 35 .. .. 21 19 65 63Malawi 150 159 37 60 33 62 63 57 19 19 78 76Malaysia .. .. 86 98 87 98 97 98 .. .. 100 99Mali 82 b 76 b 48 89 c 42 87 c 81 77 13 b 13 b 74 72Mauritania 104 107 52 74 47 75 71 70 3 4 38 31Mauritius 95 99 .. 99 .. 97 94 98 4 3 65 76Mexico 117 117 81 95 82 97 93 95 4 3 95 94Moldova 98 97 .. .. .. .. 95 96 0 0 99 98Mongolia 145 138 .. 93 .. 95 .. .. 0 0 96 98Morocco 110 b 109 b 70 94 64 94 78 78 13 9 84 c 80 cMozambique 168 159 42 56 34 51 37 34 8 7 52 55Myanmar 152 151 .. 72 .. 77 .. .. 0 0 77 77Namibia 93 95 52 90 57 93 .. .. 18 14 80 83Nepal .. .. 44 60 32 64 .. .. 12 b 12 b 81 81Netherlands 100 99 .. 99 .. 100 .. .. .. .. .. ..New Zealand .. .. 96 .. 95 .. .. .. .. .. .. ..Nicaragua 146 138 39 48 48 55 .. .. 9 7 .. ..Niger 100 b 90 b 68 74 c 65 69 c 63 60 4 b 4 b 63 c 59 cNigeria 93 83 .. 84 .. 90 77 83 .. .. .. ..Norway 97 98 99 100 100 99 100 99 .. .. 100 100Oman 108 103 77 .. 78 .. .. .. 1 2 .. ..Pakistan 129 108 .. 64 .. 59 64 59 5 4 73 74Panama 103 101 .. 95 .. 94 94 94 7 4 98 96Papua New Guinea .. .. 55 .. 52 .. .. .. .. .. .. ..Paraguay 101 98 58 81 60 84 76 80 6 4 89 89Peru 103 102 .. .. .. .. 88 88 7 6 96 93Philippines 129 121 .. 75 .. 82 72 80 3 2 99 97Poland 99 99 .. 98 .. 98 97 98 1 1 99 98Portugal 103 103 .. .. .. .. .. .. .. .. .. ..Puerto Rico 97 94 .. .. .. .. .. .. .. .. .. ..Qatar 107 107 98 92 99 99 91 97 0 0 99 992012 World Development Indicators 91

2.13Education efficiencyGross intake ratioin first grade ofprimary educationCohortsurvival rateRepeaters inprimary educationTransition rate tosecondary education% of grade 1 studentsRomania 97 95 .. .. .. .. 95 96 2 1 98 97Russian Federation .. .. .. .. .. .. .. .. .. .. .. ..Rwanda 186 182 49 45 51 50 .. .. 14 14 73 72Saudi Arabia 104 106 80 97 76 91 97 90 3 3 91 97Senegal 100 106 78 73 68 75 58 61 6 6 71 66Serbia 92 92 .. .. .. .. 98 99 1 0 97 99Sierra Leone 133 b 121 b .. .. .. .. .. .. 15 b 16 b .. ..Singapore .. .. .. 99 .. 99 99 99 0 0 86 92Slovak Republic 95 95 .. .. .. .. 98 98 3 3 97 97Slovenia 99 98 .. 100 .. 99 100 99 1 0 99 98Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 94 88 61 .. 67 .. .. .. .. .. .. ..South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 102 102 .. 99 .. 100 99 100 3 2 92 95Sri Lanka 94 95 97 .. 98 .. .. .. 1 1 95 97Sudan 83 75 .. 89 .. 100 .. .. 4 4 96 92Swaziland 123 113 58 95 64 97 81 87 17 13 90 92Sweden 104 103 99 99 99 99 99 99 0 0 100 100Switzerland 92 94 72 .. 72 .. .. .. 2 1 .. ..Syrian Arab Republic 116 117 87 .. 85 .. 94 95 9 6 94 95Tajikistan 102 98 .. .. .. .. 99 99 0 0 99 98Tanzania 96 97 69 87 71 93 76 87 3 2 45 37Thailand .. .. .. .. .. .. .. .. .. .. .. ..Timor-Leste 141 141 .. 68 .. 74 63 70 19 15 84 87Togo 157 150 55 78 38 77 .. .. 22 22 73 67Trinidad and Tobago 104 101 98 90 99 94 87 92 7 5 87 89Tunisia 106 106 76 95 70 97 .. .. 8 5 81 87Turkey 99 97 93 91 92 93 .. .. 2 2 97 97Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda 153 157 .. 56 .. 58 .. .. 11 11 60 58Ukraine 103 104 .. .. .. .. .. .. 0 0 100 100United Arab Emirates .. .. 78 .. 80 .. .. .. 2 2 94 99United Kingdom .. .. .. .. .. .. .. .. 0 0 .. ..United States 101 98 .. 98 .. 89 .. .. 0 0 .. ..Uruguay 106 106 98 94 100 97 94 97 7 4 75 87Uzbekistan 97 b 94 b .. .. .. .. 98 c 98 c 0 b 0 b 100 c 98 cVenezuela, RB 100 98 69 93 80 95 90 94 5 3 96 97Vietnam .. .. .. .. .. .. .. .. .. .. .. ..West Bank and Gaza 91 91 .. .. .. .. .. .. 0 0 96 98Yemen, Rep. 109 96 .. .. .. .. .. .. 7 6 .. ..Zambia 114 117 .. 71 .. 70 55 52 6 6 65 68Zimbabwe .. .. 70 .. 72 .. .. .. .. .. .. ..World 116 w 113 w .. w .. w .. w .. w .. w .. w .. w .. wLow income 130 123 .. 62 .. 63 11 11 .. ..Middle income 114 111 .. .. .. .. .. .. .. ..Lower middle income 122 116 .. .. .. .. .. .. 82 83Upper middle income 99 101 .. .. .. .. 2 1 .. ..Low & middle income 117 114 .. .. .. .. .. .. .. ..East Asia & Pacifi c 99 102 .. .. .. .. 1 1 .. ..Europe & Central Asia .. .. .. .. .. .. .. .. .. ..Latin America & Carib. .. .. .. .. .. .. .. .. .. ..Middle East & N. Africa 105 104 .. .. .. .. 7 4 .. ..South Asia 127 121 .. .. .. .. .. .. 80 80Sub-Saharan Africa 122 114 .. 67 .. 68 .. .. .. ..High income 101 103 .. .. .. .. 0 0 .. ..Euro area 101 100 .. .. .. .. 1 1 .. ..a. Provisional data. b. Data are for 2011. c. Data are for 2010.% of relevantReachingReaching last grade of% ofage groupgrade 5primary educationenrollment %Male Female Male Female Male Female Male Female Male Female2010 a 2010 a 1991 2009 a 1991 2009 a 2009 a 2009 a 2010 a 2010 a 2009 a 2009 a922012 World Development Indicators

Education efficiency2.13PEOPLEAbout the dataDefinitionsThe United Nations Educational, Scientifi c, and CulturalOrganization (UNESCO) Institute for Statisticscalculates indicators of students’ progress throughschool. These indicators measure an education system’ssuccess in reaching students and effi cientlymoving them from one grade to the next.The gross intake ratio in the fi rst grade of primaryeducation indicates the level of access to primaryeducation and the education system’s capacity toprovide access to primary education. A low grossintake ratio in the fi rst grade of primary educationrefl ects the fact that many children do not enter primaryeducation even though school attendance, atleast through the primary level, is mandatory in mostcountries. Because the gross intake ratio includesall new entrants regardless of age, it can exceed100 percent in some situations, such as immediatelyafter fees have been abolished or when the numberof reenrolled children is large. The indicator is notcalculated when new entrants and repeaters are notcorrectly distinguished in the fi rst grade of primaryeducation.The cohort survival rate to grade 5 and to the lastgrade of primary education shows the percentage ofstudents entering primary school who are expectedto reach the specifi ed grade. It measures an educationsystem’s holding power and internal effi ciency.Cohort survival rates are calculated based on thereconstructed cohort method, which uses data onenrollment by grade for the two most recent yearsand data on repeaters by grade for the most recentof those two years to refl ect current patterns of gradetransition. Rates approaching 100 percent indicatehigh retention and low dropout levels.Data on repeaters are often used to indicate aneducation system’s internal effi ciency. Repeaters notonly increase the cost of education for the familyand the school system, but also use limited schoolresources. Country policies on repetition and promotiondiffer. In some cases the number of repeatersis controlled because of limited capacity. In othercases the number of repeaters is almost 0 becauseof automatic promotion—suggesting a system thatis highly effi cient but that may not be endowing studentswith enough cognitive skills.The transition rate from primary to secondaryeducation conveys the degree of access or transitionbetween the two levels. As completing primaryeducation is a prerequisite for participatingin lower secondary education, growing numbers ofprimary completers will inevitably create pressurefor more available places at the secondary level. Alow transition rate can signal such problems as aninadequate examination and promotion system orinsuffi cient secondary education capacity. The qualityof data on the transition rate is affected whennew entrants and repeaters are not correctly distinguishedin the fi rst grade of secondary education.Students who interrupt their studies after completingprimary education could also affect data quality.The reference years in the table refl ect the schoolyear for which the data are presented. In some countriesthe school year spans two calendar years (forexample, from September 2009 to June 2010); inthese cases the reference year refers to the year inwhich the school year ended (2010 in the example).• Gross intake ratio in first grade of primary educationis the number of new entrants in grade 1 regardlessof age as a percentage of the population ofthe offi cial primary school entrance age. • Cohortsurvival rate is the percentage of children enrolledin the fi rst grade of primary education who eventuallyreach grade 5 or the last grade of primary education.The estimate is based on the reconstructed cohortmethod (see About the data). • Repeaters in primaryeducation are the number of students enrolled in thesame grade as in the previous year as a percentageof all students enrolled in primary education. • Transitionrate to secondary education is the numberof new entrants to the fi rst grade of secondary education(general programs only) in a given year as apercentage of the number of students enrolled in thefi nal grade of primary education in the previous year.Data sourcesData on education effi ciency are from the UNESCOInstitute for Statistics.2012 World Development Indicators 93

2.14 Education completion and outcomesPrimary completionrateYouth literacyrateAdultliteracyrateStudentsat lowestproficiencyon PISAmathematics% of relevant age group % ages 15–24 % ages 15Total Male Female Male Femaleand older %1991 2010 a 1991 2010 a 1991 2010 a 1985–94 b 2005–10 b 1985–94 b 2005–10 b 2005–10 b 2009Afghanistan 28 .. 41 .. 14 .. .. .. .. .. .. ..Albania .. 86 .. 86 .. 86 .. 99 .. 99 96 40Algeria 80 96 86 96 73 96 86 94 62 89 73 ..Angola 33 47 .. 53 .. 40 .. 81 .. 66 70 ..Argentina 100 106 .. 104 .. 108 98 99 99 99 98 37Armenia 105 101 .. 100 .. 103 100 100 100 100 100 ..Australia .. .. .. .. .. .. .. .. .. .. .. 5Austria .. 99 .. 99 .. 100 .. .. .. .. .. 8Azerbaijan 95 90 96 90 94 89 .. 100 .. 100 100 11Bahrain 97 .. 96 .. 98 .. 97 100 97 100 91 ..Bangladesh 41 65 .. 62 .. 69 52 74 38 77 56 ..Belarus 94 103 95 95 95 95 100 100 100 100 100 ..Belgium 79 90 76 89 82 92 .. .. .. .. .. 8Benin 22 63 30 74 14 53 55 65 27 43 42 ..Bolivia 71 99 78 100 64 99 96 99 92 99 91 ..Bosnia and Herzegovina .. 70 .. 69 .. 71 .. 100 .. 100 98 ..Botswana 90 94 83 92 98 96 86 94 92 97 84 ..Brazil 93 .. .. .. .. .. .. 97 .. 99 90 38Bulgaria 90 95 88 95 92 96 .. 98 .. 97 98 24Burkina Faso 20 45 25 48 15 42 27 47 14 33 29 ..Burundi 46 56 49 57 43 55 59 77 48 76 67 ..Cambodia 45 87 .. 87 .. 87 .. 89 .. 86 78 ..Cameroon 53 79 57 85 49 72 .. 89 .. 77 71 ..Canada .. .. .. .. .. .. .. .. .. .. .. 3Central African Republic 28 41 37 52 20 30 63 72 35 57 55 ..Chad 18 33 29 41 7 24 26 54 9 39 34 ..Chile .. 96 .. 102 .. 89 98 99 99 99 99 22China 107 .. .. .. .. .. 97 99 91 99 94 ..Hong Kong SAR, China 102 96 .. 95 .. 96 .. .. .. .. .. 3Colombia 73 114 70 113 76 115 89 97 92 98 93 39Congo, Dem. Rep. 48 59 61 67 36 50 .. 73 .. 62 67 ..Congo, Rep. 54 71 59 73 49 69 .. 87 .. 78 .. ..Costa Rica 79 96 77 95 81 97 .. 98 .. 99 96 ..Côte d’Ivoire 42 59 c 53 65 c 32 52 c 60 72 38 61 55 ..Croatia 85 95 .. 95 .. 95 100 100 100 100 99 12Cuba 99 98 .. 98 .. 99 .. 100 .. 100 100 ..Cyprus 90 103 89 103 90 103 100 100 100 100 98 ..Czech Republic 92 101 91 101 93 101 .. .. .. .. .. 7Denmark 98 97 98 97 98 98 .. .. .. .. .. 5Dominican Republic 61 92 .. 93 .. 91 .. 95 .. 97 88 ..Ecuador 91 106 91 105 92 106 97 97 96 97 84 ..Egypt, Arab Rep. .. 98 .. 100 .. 97 71 88 54 82 66 ..El Salvador 65 96 64 96 66 96 85 95 85 95 84 ..Eritrea 18 40 21 43 15 36 .. 92 .. 86 67 ..Estonia .. 98 .. 97 .. 98 100 100 100 100 100 3Ethiopia 23 72 28 75 18 69 39 56 28 33 30 ..Finland 97 98 98 98 97 97 .. .. .. .. .. 2France 106 .. .. .. .. .. .. .. .. .. .. 9Gabon 62 .. 59 .. 65 .. 94 99 92 97 88 ..Gambia, The 45 71 56 69 34 72 .. 71 .. 60 46 ..Georgia .. 116 .. 116 .. 116 .. 100 .. 100 100 ..Germany 100 100 99 100 100 100 .. .. .. .. .. 6Ghana 64 94 c 71 97 c 56 91 c .. 81 .. 79 67 ..Greece 99 101 99 101 98 100 99 99 99 99 97 11Guatemala .. 84 .. 87 .. 81 82 89 71 84 74 ..Guinea 17 64 24 75 9 53 .. 68 .. 54 39 ..Guinea-Bissau 5 68 7 75 3 60 .. 78 .. 64 52 ..Haiti 27 .. 29 .. 26 .. .. .. .. .. 49 ..942012 World Development Indicators

Education completion and outcomes 2.14PEOPLEPrimary completionrateYouth literacyrateAdultliteracyrateStudentsat lowestproficiencyon PISAmathematics% of relevant age group % ages 15–24 % ages 15Total Male Female Male Femaleand older %1991 2010 a 1991 2010 a 1991 2010 a 1985–94 b 2005–10 b 1985–94 b 2005–10 b 2005–10 b 2009Honduras 64 99 67 96 61 102 .. 93 .. 95 84 ..Hungary 82 98 89 98 90 97 99 99 99 99 99 8India 64 96 76 96 52 95 74 88 49 74 63 ..Indonesia 93 105 .. 104 .. 105 97 100 95 99 92 44Iran, Islamic Rep. 88 104 93 104 82 104 92 99 81 99 85 ..Iraq 58 65 63 74 52 55 .. 85 .. 80 78 ..Ireland 103 .. 103 .. 103 .. .. .. .. .. .. 7Israel .. 103 .. 102 .. 105 .. .. .. .. .. 21Italy 98 103 98 103 97 103 .. 100 .. 100 99 9Jamaica 94 73 90 74 98 73 .. 92 .. 98 86 ..Japan 102 102 102 103 102 102 .. .. .. .. .. 4Jordan 101 101 101 101 101 101 .. 99 .. 99 92 35Kazakhstan 103 116 c 103 116 c 103 116 c 100 100 100 100 100 30Kenya .. .. .. .. .. .. .. 92 .. 94 87 ..Korea, Dem. Rep. .. .. .. .. .. .. .. 100 .. 100 100 ..Korea, Rep. 99 101 99 100 100 101 .. .. .. .. .. 2Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 57 112 58 110 56 114 91 99 84 99 94 ..Kyrgyz Republic .. 97 .. 96 .. 97 .. 100 .. 100 99 65Lao PDR 41 79 46 83 36 75 .. 89 .. 79 73 ..Latvia .. 92 .. 94 .. 90 100 100 100 100 100 6Lebanon .. 87 .. 85 .. 89 .. 98 .. 99 90 ..Lesotho 59 70 42 60 76 79 .. 86 .. 98 90 ..Liberia .. 62 .. 67 .. 57 66 70 54 81 59 ..Libya .. .. .. .. .. .. 99 100 96 100 89 ..Lithuania .. 96 .. 97 .. 94 100 100 100 100 100 9Macedonia, FYR 98 92 .. 92 .. 93 99 99 99 99 97 ..Madagascar 36 72 35 72 37 73 .. 66 .. 64 64 ..Malawi 31 67 35 65 27 68 70 87 49 86 74 ..Malaysia 91 .. 91 .. 91 .. 96 98 95 99 92 ..Mali 9 55 c 12 61 c 7 50 c .. 47 .. 31 26 ..Mauritania 33 75 39 74 26 76 .. 71 .. 64 57 ..Mauritius 115 96 115 96 115 96 91 96 92 98 88 ..Mexico 88 104 91 104 92 104 96 99 95 98 93 22Moldova .. 93 .. 94 .. 91 100 99 100 100 98 ..Mongolia .. 108 .. 107 .. 109 .. 95 .. 97 97 ..Morocco 48 85 57 87 39 82 71 87 46 72 56 ..Mozambique 26 61 32 66 21 55 .. 78 .. 64 55 ..Myanmar .. 104 .. 101 .. 106 .. 96 .. 95 92 ..Namibia 74 84 67 80 81 88 86 91 90 95 89 ..Nepal 51 .. 70 .. 41 .. 68 87 33 77 59 ..Netherlands .. .. .. .. .. .. .. .. .. .. .. 3New Zealand .. .. .. .. .. .. .. .. .. .. .. 5Nicaragua 42 81 43 78 53 84 .. 85 .. 89 78 ..Niger 17 46 c 21 52 c 13 40 c .. 52 .. 23 29 ..Nigeria .. 74 .. 79 .. 70 81 78 62 65 61 ..Norway 100 100 100 101 100 100 .. .. .. .. .. 6Oman 74 101 78 102 70 100 .. 98 .. 98 87 ..Pakistan .. 67 .. 75 .. 59 .. 79 .. 61 56 ..Panama 86 97 86 97 86 97 95 97 95 96 94 51Papua New Guinea 46 .. 51 .. 42 .. .. 65 .. 70 60 ..Paraguay 68 94 68 92 69 95 96 99 95 99 95 ..Peru .. 102 .. 102 .. 102 97 98 94 97 90 48Philippines 88 92 85 89 86 94 96 97 97 98 95 ..Poland 96 95 .. 95 .. 95 100 100 100 100 100 6Portugal .. .. .. .. .. .. 99 100 99 100 95 8Puerto Rico .. .. .. .. .. .. 92 87 94 88 90 ..Qatar 71 100 71 99 72 100 89 98 91 98 95 512012 World Development Indicators 95

2.14Education completion and outcomesPrimary completionrateYouth literacyrateAdultliteracyrateStudentsat lowestproficiencyon PISAmathematics% of relevant age group % ages 15–24 % ages 15Total Male Female Male Femaleand older %1991 2010 a 1991 2010 a 1991 2010 a 1985–94 b 2005–10 b 1985–94 b 2005–10 b 2005–10 b 2009Romania 96 91 96 91 96 91 99 97 99 98 98 20Russian Federation 92 98 92 .. 93 .. 100 100 100 100 100 10Rwanda 50 70 51 65 50 74 75 77 75 77 71 ..Saudi Arabia .. 93 .. 94 .. 92 94 99 81 97 86 ..Senegal 39 59 48 58 31 61 49 74 28 56 50 ..Serbia .. 96 .. 96 .. 97 .. .. .. .. .. 18Sierra Leone .. 74 c .. 78 c .. 71 c .. 68 .. 48 41 ..Singapore .. .. .. .. .. .. 99 100 99 100 95 3Slovak Republic 95 98 95 98 96 99 .. .. .. .. .. 7Slovenia 95 95 .. 95 .. 95 100 100 100 100 100 7Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 76 .. 72 .. 80 .. .. 97 .. 98 89 ..South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 104 102 104 102 103 102 100 100 100 100 98 9Sri Lanka 101 101 101 101 101 101 .. 97 .. 99 91 ..Sudan .. 58 .. 61 .. 55 .. .. .. .. .. ..Swaziland 61 77 57 76 64 78 83 92 84 95 87 ..Sweden 96 94 96 93 96 94 .. .. .. .. .. 8Switzerland 53 95 53 94 54 97 .. .. .. .. .. 4Syrian Arab Republic 89 104 94 104 84 103 .. 96 .. 93 84 ..Tajikistan .. 104 .. 106 .. 102 100 100 100 100 100 ..Tanzania 55 90 56 88 55 92 86 78 78 76 73 ..Thailand .. .. .. .. .. .. .. 98 .. 98 94 22Timor-Leste .. 65 .. 64 .. 67 .. .. .. .. 51 ..Togo 35 74 48 84 22 64 .. 85 .. 68 57 ..Trinidad and Tobago 102 91 99 91 105 91 99 100 99 100 99 30Tunisia 74 91 79 90 70 92 .. 98 .. 96 78 43Turkey 90 99 93 100 86 98 97 99 88 97 91 18Turkmenistan .. .. .. .. .. .. .. 100 .. 100 100 ..Uganda .. 57 .. 58 .. 56 77 90 63 85 73 ..Ukraine 92 98 99 97 99 98 .. 100 .. 100 100 ..United Arab Emirates 103 .. 104 .. 103 .. 81 94 85 97 90 ..United Kingdom .. .. .. .. .. .. .. .. .. .. .. 6United States .. 104 .. 103 .. 104 .. .. .. .. .. 8Uruguay 94 106 91 105 96 106 98 98 99 100 98 23Uzbekistan 80 93 c .. 94 c .. 92 c .. 100 .. 100 99 ..Venezuela, RB 81 94 76 93 86 95 95 98 96 99 95 ..Vietnam .. .. .. .. .. .. 94 97 93 96 93 ..West Bank and Gaza .. 95 .. 97 .. 93 .. 99 .. 99 95 ..Yemen, Rep. .. 63 .. 73 .. 53 83 96 35 72 62 ..Zambia .. 103 .. 98 .. 108 67 82 66 67 71 ..Zimbabwe 97 .. 99 .. 96 .. 97 98 94 99 92 ..World 79 w 88 w 86 w 90 w 75 w 87 w 88 w 92 w 79 w 87 w 84 wLow income 44 65 .. 68 .. 63 66 75 52 68 61Middle income 83 92 89 93 77 91 89 94 79 88 83Lower middle income 68 88 76 90 60 86 87 89 75 79 71Upper middle income 97 98 101 96 94 99 94 99 93 99 93Low & middle income 78 87 85 89 73 86 86 91 75 85 80East Asia & Pacifi c 101 97 105 96 97 98 97 99 92 99 94Europe & Central Asia 92 95 93 96 92 94 99 99 98 99 98Latin America & Carib. 84 102 84 101 85 102 91 97 92 97 91Middle East & N. Africa .. 88 .. 91 .. 85 84 93 67 87 74South Asia 62 86 75 87 52 84 71 85 47 72 61Sub-Saharan Africa 51 67 57 71 47 63 73 77 58 67 62High income .. 97 .. 98 .. 97 99 99 99 99 98Euro area 101 101 100 100 100 101 .. .. .. .. ..a. Provisional data. b. Data are for the most recent year available. c. Data are for 2011.962012 World Development Indicators

Education completion and outcomes2.14PEOPLEAbout the dataDefinitionsMany governments publish statistics that indicate howtheir education systems are working and developing—statistics on enrollment and such effi ciency indicatorsas repetition rates, pupil–teacher ratios, and cohortprogression. The World Bank and the United NationsEducational, Scientifi c, and Cultural Organization(UNESCO) Institute for Statistics jointly developed theprimary completion rate indicator. Increasingly usedas a core indicator of an education system’s performance,it refl ects an education system’s coverageand the educational attainment of students. The indicatoris a key measure of education outcome at theprimary level and of progress toward the MillenniumDevelopment Goals and the Education for All initiative.However, a high primary completion rate doesnot necessarily mean high levels of student learning.The primary completion rate refl ects the primarycycle as defi ned by the International Standard Classification of Education (ISCED97), ranging from threeor four years of primary education (in a very smallnumber of countries) to fi ve or six years (in mostcountries) and seven (in a small number of countries).The primary completion rate is also called the grossintake ratio to last grade of primary education. It is thenumber of new entrants in the last grade of primaryeducation, regardless of age, divided by the populationat the entrance age for the last grade of primary education.Data limitations preclude adjusting for studentswho drop out during the final year of primary education.Thus this rate is a proxy that should be taken as anupper estimate of the actual primary completion rate.There are many reasons why the primary completionrate can exceed 100 percent. The numerator mayinclude late entrants and overage children who haverepeated one or more grades of primary education aswell as children who entered school early, while thedenominator is the number of children at the entranceage for the last grade of primary education.Basic student outcomes include achievements inreading and mathematics judged against establishedstandards. The UNESCO Institute for Statistics hasestablished literacy as an outcome indicator basedon an internationally agreed defi nition.The literacy rate is the percentage of the populationwho can, with understanding, both read andwrite a short, simple statement about their everydaylife. In practice, literacy is diffi cult to measure. Toestimate literacy using such a defi nition requirescensus or survey measurements under controlledconditions. Many countries estimate the number ofliterate people from self-reported data. Some useeducational attainment data as a proxy but applydifferent lengths of school attendance or levels ofcompletion. Because defi nitions and methodologiesof data collection differ across countries, datashould be used cautiously.The reported literacy data are compiled by theUNESCO Institute for Statistics based on nationalcensuses and household surveys during 1985–2010. For countries without recent literacy data, theUNESCO Institute for Statistics estimates literacyrates with the Global Age-Specifi c Literacy ProjectionModel. For detailed information on sources, defi nitions,and methodology, see www.uis.unesco.org.Literacy statistics for most countries cover the populationages 15 and older, but some include youngerages or are confi ned to age ranges that tend to infl ateliteracy rates. The youth literacy rate for ages 15–24refl ects recent progress in education. It measuresthe accumulated outcomes of primary education overthe previous 10 years or so by indicating the proportionof the population who have passed throughthe primary education system and acquired basicliteracy and numeracy skills. Generally, literacy alsoencompasses numeracy, the ability to make simplearithmetic calculations.In many countries national assessments enableministries of education to monitor progress in learningoutcomes. Of the handful of internationally orregionally comparable assessments, one of thelargest is the Programme for International StudentAssessment (PISA). Coordinated by the Organisationfor Economic Co-operation and Development (OECD),it measures the knowledge and skills of 15-year-olds,the age at which students in most countries are nearingthe end of their compulsory time in school. Theassessment tests reading, mathematical, and scientific literacy in terms of general competencies—that is, how well students can apply the knowledgeand skills they have learned at school to real-lifechallenges. It does not test how well a student hasmastered a school’s specifi c curriculum.The table presents the percentage of studentsat the lowest level of profi ciency on the PISA mathematicsscale. Student achievement is benchmarkedin terms of levels of profi ciency, ranging from level1 (lowest) to level 6 (highest), as demonstratedthrough ability to analyze, reason, and communicateeffectively while posing, solving, and interpretingmathematical problems that involve quantitative,spatial, probabilistic, or other mathematicalconcepts. The average score is 496. Because thefi gures are derived from samples, the data refl ect asmall measure of statistical uncertainty.• Primary completion rate, or the gross intake ratioto last grade of primary education, is the number ofnew entrants in the last grade of primary education,regardless of age, divided by the population at theentrance age for the last grade of primary education.• Youth literacy rate is the percentage of the populationages 15–24 that can, with understanding, bothread and write a short simple statement about theireveryday life. • Adult literacy rate is the percentageof the population ages 15 and older that can, withunderstanding, both read and write a short simplestatement about their everyday life. • Students atlowest proficiency on PISA mathematics is the percentageof students whose mathematics score arebelow 357.77 (level 1) on the PISA.Data sourcesData on primary completion rates and literacyrates are from the UNESCO Institute for Statistics.Data on PISA mathematics results are fromthe OECD.2012 World Development Indicators 97

2.15 Education gaps by income and genderSurveyyearGross intake ratein first grade ofprimary educationGross primaryparticipation rateAverage yearsof schoolingPrimarycompletion rateChildrenout of school% of relevantage groupPoorestquintileRichestquintile% of relevantage group Ages 15–19PoorestquintileRichestquintilePoorestquintileRichestquintilePoorestquintile% of relevantage groupRichestquintileMaleFemale% of relevantage groupPoorestquintileRichestquintileArmenia 2005 93 80 106 102 9 10 119 116 113 112 2 1Azerbaijan 2006 92 118 100 108 9 11 94 109 103 105 20 11Bangladesh 2006 144 147 96 105 8 13 65 97 83 86 12 6Belize 2006 80 89 106 113 8 11 59 130 107 72 5 7Benin 2006 67 107 61 114 6 8 31 95 67 52 57 12Bolivia 2003 92 95 108 129 6 9 76 98 90 81 22 5Burundi 2005 201 191 91 144 4 7 20 70 44 39 5 3Cambodia 2005 208 151 113 134 5 8 42 121 88 85 37 13Cameroon 2006 108 75 93 116 6 14 43 111 90 74 3 2Colombia 2005 161 84 127 99 6 10 94 109 100 103 11 2Côte d’Ivoire 2006 51 77 57 110 5 8 47 127 88 71 4 3Dominican Republic 2007 130 112 113 107 7 11 69 109 88 106 12 4Egypt, Arab Rep. 2005 107 97 95 99 9 12 84 92 92 88 12 1Ethiopia 2005 86 124 47 112 3 6 14 90 46 33 74 30Georgia 2006 90 104 101 103 15 14 102 102 106 104 2 1Ghana 2006 107 121 81 117 5 8 62 88 93 86 22 12Guatemala 2000 176 124 81 114 4 8 15 80 34 36 7 3Guinea 2005 55 119 52 121 5 7 32 93 76 48 60 16Guinea-Bissau 2006 135 184 94 166 4 7 34 125 80 54 12 11Guyana 2006 74 76 105 101 10 10 109 118 91 112 2 1Haiti 2005 177 188 87 159 4 7 31 136 73 82 69 24Kazakhstan 2006 118 101 106 103 9 9 102 115 102 97 0 a 1Kenya 2003 134 125 92 106 6 9 40 76 71 72 38 11Kosovo 2000 104 119 95 104 9 11 82 94 98 83 1 4Lesotho 2004 169 111 116 124 5 8 36 122 69 85 18 3Macedonia, FYR 2005 102 190 89 97 8 10 120 119 133 78 0 a 0 aMadagascar 2003– 04 250 153 118 145 3 8 42 141 77 77 33 3Malawi 2006 234 207 133 169 5 7 30 80 49 52 0 a 0 aMali 2006 41 98 46 110 5 8 36 79 55 41 67 20Mauritania 2007 67 96 62 116 5 9 17 89 48 52 2 2Moldova 2005 96 84 99 95 9 12 97 100 96 98 2 1Mozambique 2003 128 143 75 143 3 6 13 100 57 43 46 7Namibia 2006 112 104 118 109 7 10 81 109 94 90 11 2Nepal 2001 184 141 109 139 5 8 49 96 69 62 33 6Nicaragua 2001 149 106 85 105 4 9 34 124 78 83 40 4Niger 2006 50 90 35 89 4 7 31 71 60 30 74 28Nigeria 2003 78 101 70 108 7 10 48 71 70 54 52 6Panama 2003 125 116 108 102 7 11 100 94 105 88 1 1Peru 2004 121 90 118 96 7 11 106 99 100 97 6 1Rwanda 2005 274 195 131 151 3 5 31 88 48 42 13 8Serbia 2005 90 98 98 100 9 10 86 96 94 89 1 0 aSomalia 2005 13 44 8 93 8 10 2 58 26 20 87 46Swaziland 2006 147 117 117 114 6 9 69 110 85 98 17 4Syrian Arab Republic 2006 110 149 102 107 7 8 92 93 93 92 0 a 0 aTanzania 2004 123 123 82 119 5 7 32 108 58 60 44 15Togo 2006 115 148 99 128 6 7 40 82 67 56 1 1Turkey 2003 108 111 97 97 6 7 95 85 100 81 20 5Uganda 2006 180 144 107 124 5 8 27 68 50 42 25 7Vietnam 2006 99 100 108 100 13 18 99 104 96 103 3 2Yemen, Rep. 2006 66 109 50 101 7 10 25 103 84 31 2 2Zambia 2007 135 123 105 112 5 9 50 101 88 73 22 3Zimbabwe 1999 106 111 144 144 7 10 36 80 51 57 22 8a. Less than 0.5.982012 World Development Indicators

Education gaps by income and gender2.15PEOPLEAbout the dataDefinitionsThe data in the table describe basic information onschool participation and educational attainmentby individuals in different socioeconomic groupswithin countries. The data are from Demographicand Health Surveys by Macro International with thesupport of the U.S. Agency for International Development,Multiple Indicator Cluster Surveys by theUnited Nations Children’s Fund (UNICEF), and LivingStandards Measurement Studies by the WorldBank’s Development Economics Research Group.These large-scale household sample surveys, conductedperiodically in developing countries, collectinformation on a large number of health, nutrition,and population measures as well as on respondents’social, demographic, and economic characteristicsusing detailed questionnaires. The data presentedhere draw on responses to individual and householdquestionnaires.Typically, the surveys collect basic information oneducational attainment and enrollment levels fromevery household member ages 5 and older as part ofhousehold socioeconomic characteristics. The surveysare not intended for the collection of detailededucation data; thus the education section of thesurveys is not as detailed as, for instance, the healthsection of the Demographic and Health Survey orthe Multiple Indicator Cluster Survey, and the dataobtained from them do not replace other data oneducation fl ows. Still, the education data providemicro-level information on education that cannot beobtained from administrative data, such as informationon children not attending school.Socioeconomic status as displayed in the table isbased on household assets, including ownership ofconsumer items, features of the household dwelling,and other characteristics related to wealth. Eachhousehold asset on which information was collectedwas assigned a weight generated through principalcomponentanalysis, which was used to create breakpointsdefi ning wealth quintiles, expressed as quintilesof individuals in the population.The selection of the asset index for defi ning socioeconomicstatus was based on pragmatic ratherthan conceptual considerations: Demographic andHealth Surveys do not collect consumption data butdo have detailed information on household ownershipof consumer goods and access to a variety ofgoods and services. Like income or consumption,the asset index defi nes disparities primarily in economicterms. It therefore excludes other possibilitiesof disparities among groups, such as those basedon gender, education, ethnic background, or otherfacets of social exclusion. To that extent the indexprovides only a partial view of the multidimensionalconcepts of poverty, inequality, and inequity.Creating one index that includes all asset indicatorslimits the types of analysis that can be performed.In particular, the use of a unifi ed index doesnot permit a disaggregated analysis to examinewhich asset indicators have a more or less importantassociation with education status. In addition, someasset indicators may refl ect household wealth betterin some countries than in others—or refl ect differentdegrees of wealth in different countries. Takingsuch information into account and creating countryspecific asset indexes with country-specifi c choicesof asset indicators might produce a more effectiveand accurate index for each country. The asset indexused in the table does not have this fl exibility.The analysis was carried out for about 80 countries.The table shows the most recent estimatesfor the poorest and richest quintiles by gender only;the full set of estimates for all indicators, othersubgroups, including by urban and rural location,and older data are available in the country reports(see Data sources). The data in the table differ fromdata for similar indicators in preceding tables eitherbecause the indicator refers to a period a few yearspreceding the survey date or because the indicatordefi nition or methodology is different. Findingsshould be used with caution because of measurementerror inherent in the use of survey data.• Survey year is the year in which the underlying datawere collected. • Gross intake rate in first gradeof primary education is the number of students ingrade 1 regardless of age as a percentage of thepopulation of the offi cial primary school entranceage. These data may differ from those in table 2.13.• Gross primary participation rate is the ratio oftotal students attending primary school regardlessof age to the population of the age group that offi -cially corresponds to primary education. • Averageyears of schooling are the years of formal schoolingreceived, on average, by youths and adults ages15–19. • Primary completion rate is the numberof students, regardless of age, in the last grade ofprimary school minus the number of repeaters in thatgrade, divided by the number of students of offi cialgraduation age. These data differ from those in table2.14 because the source is different. • Children outof school are children of offi cial primary school agewho are not attending primary or secondary education.Children of offi cial primary school age who areattending preprimary education are considered outof school. These data differ from those in table 2.12because the source is different.Data sourcesData on education gaps by income and genderare from an analysis using the ADePT Educationsoftware tool (http://go.worldbank.org/X385KNDXM0) of MEASURE DHS Demographicand Health Surveys by ICF International, MultipleIndicator Cluster Surveys by UNICEF, andLiving Standards Measurement Studies by theWorld Bank. Country reports and further updatesare available at www.worldbank.org/education/edstats/.2012 World Development Indicators 99

2.16 Health systemsHealthexpenditureHealth workersHospitalbedsExternalper 1,000 peopleTotal Public Out of pocket resourcesPer capitaNurses and Community per 1,000% of GDP % of total % of total % of total $ PPP $ Physicians midwives health workers people2010 2010 2010 2010 2010 2010 2005–10 a 2005–10 a 2005–10 a 2005–10 aAfghanistan 7.6 b 11.7 b 83.0 b 32.0 b 38 b 44 b 0.2 0.5 .. 0.4Albania 6.5 39.0 60.8 1.8 241 577 1.2 3.9 .. 2.8Algeria 4.2 77.9 20.9 0.0 178 330 1.2 1.9 .. ..Angola 2.9 c 82.5 c 17.5 c 2.9 c 123 c 168 c .. .. .. 0.8Argentina 8.1 54.6 29.9 0.1 742 1,287 3.2 .. .. 4.5Armenia 4.4 40.6 55.1 14.3 133 239 3.8 4.8 .. 3.7Australia 8.7 d 68.0 d 20.5 d 0.0 d 4,775 d 3,441 d 3.0 9.6 0.0 3.8Austria 11.0 77.5 14.6 0.0 4,958 4,388 4.9 7.9 .. 7.7Azerbaijan 5.9 20.3 69.5 0.8 332 579 3.8 8.3 .. 7.5Bahrain 5.0 73.3 14.5 0.0 864 1,083 1.4 3.7 .. 1.8Bangladesh 3.5 33.6 64.1 8.0 23 57 0.3 0.3 0.3 0.3Belarus 5.6 77.7 19.8 0.5 320 786 5.2 13.1 .. 11.1Belgium 10.7 74.7 20.2 0.0 4,618 4,025 3.0 0.5 .. 6.5Benin 4.1 49.5 46.8 35.9 31 65 0.1 0.8 .. 0.5Bolivia 4.8 62.8 28.7 5.3 97 233 .. .. .. 1.1Bosnia and Herzegovina 11.1 61.4 38.6 1.8 499 972 1.6 5.0 .. 3.4Botswana 8.3 72.5 8.1 18.3 615 1,145 0.3 2.8 0.5 1.8Brazil 9.0 47.0 30.6 0.0 990 1,028 1.8 6.4 .. 2.4Bulgaria 6.9 54.5 44.2 0.0 435 947 3.7 4.7 .. 6.6Burkina Faso 6.7 51.0 36.2 22.9 40 93 0.1 0.7 0.1 0.4Burundi 11.6 c 38.2 c 37.9 c 45.8 c 21 c 47 c .. .. .. 1.9 eCambodia 5.6 37.2 40.4 23.9 45 121 0.2 0.9 .. 0.8Cameroon 5.1 c 29.6 c 66.5 c 13.2 c 61 c 122 c .. .. .. 1.3Canada 11.3 70.5 14.7 0.0 5,222 4,404 2.0 10.4 .. 3.2Central African Republic 4.0 35.4 61.4 13.4 18 31 .. .. .. 1.0 eChad 4.5 25.0 72.5 7.9 31 62 .. .. .. 0.4Chile 8.0 48.2 33.3 0.0 947 1,199 1.0 0.1 .. 2.1China 5.1 53.6 36.6 0.1 221 379 1.4 1.4 0.8 4.2Hong Kong SAR, China .. .. .. .. .. .. .. .. .. ..Colombia 7.6 72.7 19.5 0.0 472 713 0.1 0.6 .. 1.0Congo, Dem. Rep. 7.9 42.5 35.9 32.7 16 27 .. .. .. 0.8Congo, Rep. 2.5 46.7 53.3 4.1 72 104 0.1 0.8 .. 1.6Costa Rica 10.9 68.1 27.8 0.6 811 1,242 .. .. .. 1.2Côte d’Ivoire 5.3 21.6 77.5 9.8 60 98 0.1 0.5 .. 0.4Croatia 7.8 84.9 14.5 0.0 1,067 1,514 2.6 5.3 .. 5.4Cuba 10.6 91.5 8.5 0.0 607 431 6.7 9.1 .. 5.9Cyprus 6.0 41.5 48.8 0.0 1,705 1,842 2.6 4.3 .. 3.8Czech Republic 7.9 83.7 14.7 0.0 1,480 2,051 3.7 8.7 .. 7.1Denmark 11.4 85.1 13.1 0.0 6,422 4,537 3.4 16.1 .. 3.5Dominican Republic 6.2 43.4 37.2 0.7 323 578 .. .. .. 1.6Ecuador 8.1 37.2 49.0 0.4 328 653 1.7 2.0 .. 1.5Egypt, Arab Rep. 4.7 37.4 61.2 0.6 123 289 2.8 3.5 .. 1.7El Salvador 6.9 61.7 33.9 1.9 237 450 1.6 0.4 .. 1.0Eritrea 2.7 c 48.2 c 51.8 c 38.0 c 12 c 16 c .. .. .. 0.7 eEstonia 6.0 78.7 19.6 60.7 853 1,226 3.3 6.6 .. 5.4Ethiopia 4.9 53.5 37.2 39.4 16 51 0.0 0.2 0.3 0.2Finland 9.0 75.1 18.8 0.0 3,984 3,281 2.9 24.0 .. 6.2France 11.9 77.8 7.3 0.0 4,691 4,021 3.4 0.3 .. 6.9Gabon 3.5 c 52.9 c 47.1 c 2.4 c 302 c 522 c .. .. .. 6.3Gambia, The 5.7 50.8 23.8 41.2 26 80 0.0 0.6 0.1 1.1 eGeorgia 10.1 23.6 68.3 2.8 272 522 4.8 3.2 .. 3.1Germany 11.6 77.1 13.0 0.0 4,668 4,332 3.6 11.1 .. 8.2Ghana 5.2 59.5 26.9 16.9 67 85 0.1 1.0 0.2 0.9 eGreece 10.2 59.4 38.4 0.0 2,729 2,853 6.2 0.2 .. 4.8Guatemala 6.9 35.8 53.9 1.7 196 325 .. .. .. 0.6Guinea 4.9 11.3 88.1 10.8 23 56 0.1 0.0 .. 0.3Guinea-Bissau 8.5 c 10.0 c 66.4 c 23.3 c 47 c 100 c 0.0 0.6 .. 1.0Haiti 6.9 21.4 40.2 38.3 46 76 .. .. .. 1.31002012 World Development Indicators

Health systems 2.16PEOPLEHealthexpenditureHealth workersHospitalbedsExternalper 1,000 peopleTotal Public Out of pocket resourcesPer capitaNurses and Community per 1,000% of GDP % of total % of total % of total $ PPP $ Physicians midwives health workers people2010 2010 2010 2010 2010 2010 2005–10 a 2005–10 a 2005–10 a 2005–10 aHonduras 6.8 65.2 31.1 6.3 137 263 .. .. .. 0.8Hungary 7.3 69.4 24.0 0.0 942 1,469 3.0 6.4 .. 7.1India 4.1 29.2 61.2 1.2 54 132 0.6 1.0 0.0 0.9Indonesia 2.6 49.1 38.3 1.3 77 112 0.3 2.0 .. 0.6Iran, Islamic Rep. 5.6 40.1 57.8 0.0 317 836 0.9 1.4 .. 1.7Iraq 8.4 c,f 81.2 c,f 18.8 c,f 0.8 c,f 247 c,f 340 c,f 0.7 1.4 .. 1.3Ireland 9.2 69.2 15.2 0.0 4,242 3,704 3.2 15.7 .. 4.9Israel 7.6 60.3 29.2 0.0 2,183 2,186 3.7 5.2 .. 3.5Italy 9.5 77.6 19.6 0.0 3,248 3,022 3.5 0.3 .. 3.6Jamaica 4.8 53.5 33.0 2.1 247 372 .. .. .. 1.9Japan 9.5 82.5 14.3 0.0 4,065 3,204 2.1 4.1 .. 13.7Jordan 8.0 67.7 25.1 3.7 357 448 2.5 4.0 .. 1.8Kazakhstan 4.3 59.4 40.1 0.6 393 541 4.1 8.3 .. 7.6Kenya 4.8 44.3 42.7 36.1 37 78 .. .. .. 1.4Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 6.9 59.0 31.4 0.0 1,439 2,023 2.0 5.3 .. 10.3Kosovo .. .. .. .. .. .. .. .. .. ..Kuwait 2.6 80.4 17.8 0.0 1,223 1,133 1.8 4.6 .. 2.0Kyrgyz Republic 6.2 56.2 37.8 12.8 53 140 2.3 5.7 .. 5.1Lao PDR 4.5 33.3 51.2 15.1 46 97 0.3 1.0 .. 0.7Latvia 6.7 61.1 37.8 0.0 718 1,093 3.0 4.8 .. 6.4Lebanon 7.0 39.2 44.7 4.7 651 980 3.5 2.2 .. 3.5Lesotho 11.1 76.2 16.4 19.5 109 170 .. .. .. 1.3Liberia 11.8 32.5 35.2 55.1 29 49 0.0 0.3 .. 0.8Libya 3.9 c 68.8 c 31.2 c 0.6 c 484 c 713 c 1.9 6.8 .. 3.7Lithuania 7.0 73.5 25.8 1.1 781 1,299 3.6 7.2 .. 6.8Macedonia, FYR 7.1 63.8 35.9 0.8 317 791 2.6 0.6 .. 4.5Madagascar 3.8 60.3 27.1 9.0 16 36 0.2 .. .. 0.2Malawi 6.6 60.2 11.1 63.8 26 65 0.0 0.3 0.7 1.3 eMalaysia 4.4 55.5 34.2 0.0 368 641 0.9 2.7 .. 1.8Mali 5.0 46.6 53.2 27.4 32 56 0.0 0.3 .. 0.1Mauritania 4.4 c 53.1 c 44.3 c 10.1 c 43 c 79 c 0.1 0.7 .. 0.4Mauritius 6.0 41.7 51.7 2.0 449 803 .. .. .. 3.4 eMexico 6.3 48.9 47.1 0.0 604 959 2.0 .. .. 1.6Moldova 11.7 g 45.8 g 44.9 g 9.6 g 190 g 360 g 2.7 6.6 .. 6.2Mongolia 5.4 55.1 41.4 3.9 120 218 2.8 3.5 0.0 5.8Morocco 5.2 38.0 53.6 0.4 148 246 0.6 0.9 .. 1.1Mozambique 5.2 71.7 13.7 24.2 21 49 0.0 0.3 .. 0.8Myanmar 2.0 12.2 81.1 8.7 17 34 0.5 0.8 0.1 0.6Namibia 6.8 58.4 7.4 19.0 361 436 0.4 2.8 .. 2.7Nepal 5.5 33.2 48.3 11.3 30 66 .. .. .. 5.0Netherlands 11.9 79.2 5.2 0.0 5,593 5,038 2.9 0.2 .. 4.7New Zealand 10.1 83.2 10.5 0.0 3,279 3,020 2.7 10.9 .. ..Nicaragua 9.1 53.3 43.3 14.6 103 253 .. .. .. 0.8Niger 5.2 50.9 41.3 29.4 18 37 0.0 0.1 .. 0.3Nigeria 5.1 c 37.9 c 59.2 c 9.2 c 63 c 121 c 0.4 1.6 0.1 ..Norway 9.5 83.9 15.3 0.0 8,091 5,426 4.2 31.9 .. 3.3Oman 2.8 80.1 12.3 0.0 574 598 1.9 4.1 .. 1.8Pakistan 2.2 38.5 50.5 4.8 22 59 0.8 0.6 0.1 0.6Panama 8.1 75.1 19.9 0.1 616 1,123 .. .. .. 2.2Papua New Guinea 3.6 71.5 15.9 24.0 49 88 0.1 0.5 0.6 ..Paraguay 5.9 36.4 57.1 2.4 163 302 .. .. .. 1.3Peru 5.1 54.0 39.5 1.7 269 481 0.9 1.3 .. 1.5Philippines 3.6 35.3 54.0 1.3 77 142 .. .. .. 0.5Poland 7.5 72.6 22.1 0.1 917 1,476 2.2 5.8 .. 6.7Portugal 11.0 68.1 24.8 0.0 2,367 2,818 3.9 5.3 .. 3.3Puerto Rico .. .. .. .. .. .. .. .. .. ..Qatar 1.8 77.5 16.0 0.0 1,489 1,622 2.8 7.4 .. 1.22012 World Development Indicators 101

2.16Health systemsHealthexpenditureHealth workersHospitalbedsExternalper 1,000 peopleTotal Public Out of pocket resourcesPer capitaNurses and Community per 1,000% of GDP % of total % of total % of total $ PPP $ Physicians midwives health workers people2010 2010 2010 2010 2010 2010 2005–10 a 2005–10 a 2005–10 a 2005–10 aRomania 5.6 78.1 21.5 0.0 428 811 2.3 5.9 .. 6.6Russian Federation 5.1 62.1 31.4 0.0 525 998 4.3 8.5 .. 9.7Rwanda 10.5 50.1 22.2 47.0 56 121 0.0 0.4 .. 1.6Saudi Arabia 4.3 62.9 18.6 0.0 680 968 0.9 2.1 .. 2.2Senegal 5.7 55.5 35.0 18.5 59 109 0.1 0.4 .. 0.3Serbia 10.4 61.9 36.4 0.8 546 1,169 2.1 4.5 .. 5.4Sierra Leone 13.1 11.3 79.4 20.6 43 107 0.0 0.2 0.0 0.4Singapore 4.0 36.3 54.0 0.0 1,733 2,273 1.8 5.9 .. 3.1Slovak Republic 8.8 65.9 30.5 0.0 1,413 2,060 3.0 0.3 .. 6.5Slovenia 9.4 73.7 12.6 0.0 2,154 2,552 2.5 8.4 .. 4.6Somalia .. .. .. .. .. .. 0.0 0.1 .. ..South Africa 8.9 44.1 16.6 2.2 649 935 .. .. .. 2.8South Sudan .. .. .. .. .. .. .. .. .. ..Spain 9.5 72.8 20.7 0.0 2,883 3,027 4.0 5.1 .. 3.2Sri Lanka 2.9 44.7 44.9 3.0 70 148 0.5 1.9 .. ..Sudan 6.3 29.8 67.2 3.3 84 141 0.3 0.8 .. 0.7Swaziland 6.6 63.7 15.4 17.2 203 333 .. .. .. 2.1 eSweden 9.6 81.1 17.0 0.0 4,710 3,757 3.8 11.9 .. 2.8Switzerland 11.5 59.0 30.9 0.0 7,812 5,394 4.1 16.5 .. 5.2Syrian Arab Republic 3.4 46.0 54.0 0.7 97 174 1.5 1.9 .. 1.5Tajikistan 6.0 26.7 66.5 6.1 49 128 2.1 5.3 .. 5.2Tanzania 6.0 67.3 13.6 48.8 31 83 0.0 0.2 .. 0.7Thailand 3.9 75.0 13.9 0.3 179 330 0.3 .. .. 2.1Timor-Leste 9.1 55.8 11.3 33.7 57 84 .. .. .. 5.9Togo 7.7 44.2 46.9 15.2 41 77 0.1 0.3 .. 0.9Trinidad and Tobago 5.7 59.9 32.8 0.1 861 1,449 1.2 3.6 .. 2.6Tunisia 6.2 54.3 39.8 0.3 238 483 1.2 3.3 .. 2.1Turkey 6.7 75.2 16.0 0.0 678 1,029 1.5 0.6 .. 2.5Turkmenistan 2.5 c 59.4 c 40.6 c 0.3 c 106 c 199 c 2.4 4.4 .. 4.0Uganda 9.0 21.7 49.8 25.9 47 124 0.1 1.3 .. 0.5Ukraine 7.7 56.6 40.5 0.4 234 519 3.2 8.6 .. 8.7United Arab Emirates 3.7 74.4 18.8 0.0 1,450 1,544 1.9 4.1 .. 1.9United Kingdom 9.6 83.9 10.0 0.0 3,503 3,480 2.7 10.1 .. 3.3United States 17.9 53.1 11.8 0.0 8,362 8,362 2.4 9.8 .. 3.0Uruguay 8.4 67.1 13.0 0.0 998 1,188 3.7 5.5 .. 1.2Uzbekistan 5.8 47.5 42.7 0.9 82 184 2.6 11.1 .. 4.6Venezuela, RB 4.9 34.9 59.0 0.0 663 589 .. .. .. 1.1Vietnam 6.8 37.8 57.6 3.4 83 215 1.2 1.0 .. 3.1West Bank and Gaza .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 5.2 24.2 74.8 4.3 63 122 0.3 .. .. 0.7Zambia 5.9 60.3 26.5 39.2 73 90 0.1 0.7 .. 2.0Zimbabwe .. .. .. .. .. .. .. .. .. 3.0World 10.5 w 62.8 w 17.7 w 0.2 w 949 w 1,023 w 1.4 w 2.8 w .. w 2.9 wLow income 5.4 38.7 48.1 25.9 27 61 0.2 0.5 .. ..Middle income 5.7 52.0 36.4 0.6 225 369 1.2 2.0 .. 2.4Lower middle income 4.2 40.2 52.3 2.8 71 149 0.8 1.5 .. 1.4Upper middle income 6.1 54.3 33.4 0.2 382 594 1.7 2.6 .. 3.7Low & middle income 5.7 51.8 36.6 1.1 199 329 1.1 1.9 .. 2.2East Asia & Pacifi c 4.7 53.4 36.7 0.4 183 316 1.2 1.5 0.8 3.9Europe & Central Asia 5.8 65.0 29.0 0.3 439 797 3.2 6.7 .. 7.4Latin America & Carib. 7.8 50.2 34.3 0.2 670 854 1.8 .. .. 1.9Middle East & N. Africa 4.7 50.1 47.0 0.7 203 425 1.4 2.3 .. 1.6South Asia 3.8 30.0 60.5 2.3 47 115 0.6 0.9 0.1 0.9Sub-Saharan Africa 6.5 45.1 31.8 10.5 84 143 0.2 0.8 .. ..High income 12.7 65.1 13.7 0.0 4,879 4,660 2.8 7.1 .. 5.7Euro area 10.8 76.2 14.3 0.1 3,969 3,685 3.6 4.7 .. 5.8a. Data are for the most recent year available. b. Nonprofi t institutions (such as nongovernmental organizations) serving households are accounted for in external resources, which arerecorded under government expenditure. GDP includes both licit and illicit activity (for example, opium production). Public expenditures include external assistance. c. Derived fromincomplete data. d. Excludes expenditure on residential facilities for care of the aged. e. Data are for 2011. f. Excludes northern Iraq. g. Excludes Transnistria.1022012 World Development Indicators

Health systems2.16PEOPLEAbout the dataDefinitionsHealth systems—the combined arrangements ofinstitutions and actions whose primary purposeis to promote, restore, or maintain health (WorldHealth Organization, World Health Report 2000)—areincreasingly being recognized as key to combatingdisease and improving the health status of populations.The World Bank’s (2007a) Healthy Development:Strategy for Health, Nutrition, and PopulationResults emphasizes the need to strengthen healthsystems, which are weak in many countries, in orderto increase the effectiveness of programs aimed atreducing specifi c diseases and further reduce morbidityand mortality. To evaluate health systems,the World Health Organization (WHO) has recommendedthat key components—such as fi nancing,service delivery, workforce, governance, and information—bemonitored using several key indicators(WHO 2008b). The data in the table are a subset ofthe fi rst four indicators. Monitoring health systemsallows the effectiveness, effi ciency, and equity ofdifferent health system models to be compared.Health system data also help identify weaknessesand strengths and areas that need investment, suchas additional health facilities, better health informationsystems, or better trained human resources.Health expenditure data are broken down intopublic and private expenditures. In general, lowincomeeconomies have a higher share of privatehealth expenditure than do middle- and high-incomecountries, and out-of-pocket expenditure (direct paymentsby households to providers) makes up thelargest proportion of private expenditures. Highout-of-pocket expenditures may discourage peoplefrom accessing preventive or curative care and canimpoverish households that cannot afford neededcare. Health fi nancing data are collected throughnational health accounts, which systematically,comprehensively, and consistently monitoring healthsystem resource fl ows. To establish a national healthaccount, countries must defi ne the boundaries of thehealth system and classify health expenditure informationalong several dimensions, including sourcesof fi nancing, providers of health services, functionaluse of health expenditures, and benefi ciaries ofexpenditures. The accounting system can then providean accurate picture of resource envelopes andfi nancial fl ows and allow analysis of the equity andeffi ciency of fi nancing to inform policy.This year’s table, like last year’s, presents out-ofpocketexpenditure as a percentage of total healthexpenditure; editions before 2011 presented out-ofpocketexpenditure as a percentage of private healthexpenditure. Thus data for this indicator from 2011onward should not be compared with data from editionsbefore 2011.External resources for health are disbursements torecipient countries as reported by donors, lagged oneyear to account for the delay between disbursementand expenditure. Except where a reliable full nationalhealth account study has been done, most data arefrom the Organisation for Economic Co-operation andDevelopment Development Assistance Committee’sCreditor Reporting System database, which compilesdata from government expenditure accounts, governmentrecords on external assistance, routine surveysof external fi nancing assistance, and specialservices. Because of the variety of sources, cautionshould be used in interpreting the data.In countries where the fi scal year spans two calendaryears, expenditure data have been allocatedto the later year (for example, 2009 data cover fi s-cal year 2008/09). Many low-income countries useDemographic and Health Surveys or Multiple IndicatorCluster Surveys funded by donors to obtain healthsystem data.Data on health worker (physicians, nurses andmidwives, and community health workers) densityshow the availability of medical personnel. The WHOestimates that at least 2.5 physicians, nurses, andmidwives per 1,000 people are needed to provideadequate coverage with primary care interventionsassociated with achieving the Millennium DevelopmentGoals (WHO, World Health Report 2006). TheWHO compiles data from household and labor forcesurveys, censuses, and administrative records. Datacomparability is limited by differences in defi nitionsand training of medical personnel varies. In addition,human resources tend to be concentrated in urbanareas, so that average densities do not provide a fullpicture of health personnel available to the entirepopulation.Availability and use of health services, suchas hospital beds per 1,000 people, refl ect bothdemand- and supply-side factors. In the absence ofa consistent defi nition this is a crude indicator ofthe extent of physical, fi nancial, and other barriersto health care.• Total health expenditure is the sum of public andprivate health expenditure. It covers the provision ofhealth services (preventive and curative), family planningand nutrition activities, and emergency aid forhealth but excludes provision of water and sani tation.• Public health expenditure is recurrent and capitalspending from central and local governments, externalborrowing and grants (including donations frominternational agencies and nongovernmental organizations),and social (or compulsory) health insurancefunds. • Out-of-pocket health expendi ture is directhousehold outlays, including gratuities and in-kindpayments, for health practitioners and pharmaceuticalsuppliers, therapeutic appliances, and other goodsand services whose primary intent is to restore orenhance health. • External resources for health arefunds or services in kind provided by entities that arenot part of the country. The resources may come frominternational organizations, other countries throughbilateral arrangements, or foreign nongovernmentalorganizations and are part of public and privatehealth expenditure. • Health expenditure per capitais total health expenditure divided by population inU.S. dollars and in international dollars convertedusing 2005 purchasing power parity (PPP) rates fromthe World Bank’s International Comparison Project.• Physicians include generalist and specialist medicalpractitioners.• Nurses and midwives include professionalnurses and midwives, auxiliary nurses andmidwives, enrolled nurses and midwives, and otherpersonnel, such as dental nurses and primary carenurses. • Community health workers include traditionalmedicine practitioners, faith healers, assistantor community health education workers, communityhealth offi cers, family health workers, lady health visitors,health extension package workers, communitymidwives, and traditional birth attendants. • Hospitalbeds are inpatient beds for both acute and chroniccare available in public, private, general, and specializedhospitals and reha bilitation centers.Data sourcesData on health expenditure are from the WHO’sNational Global Health Expenditure database (seehttp://apps.who.int/nha/database for the mostrecent updates), supplemented by country data.Data on physicians, nurses and midwives, and communityhealth workers are from the WHO’s GlobalAtlas of the Health Workforce database (http://apps.who.int/globalatlas). Data on hospital bedsare from the WHO, supplemented by country data.2012 World Development Indicators 103

2.17Health informationYear last nationalhealth accountcompletedNumber ofnational healthaccountscompletedYear of lasthealth surveyYear of lastcensusCompleteness%BirthregistrationInfant deathreportingTotal deathreporting1995–2010 2001–11 2005–10 a 2005–10 a 2005–10 aAfghanistan 2008 1 2010 .. .. ..Albania 2009 2 2008/09 2001 99 30 85Algeria 2003 3 2006 2008 99 .. 91Angola 0 2006/07 .. .. ..Argentina 1997 1 2010 .. 100 99Armenia 2010 7 2005 2001 96 43 100Australia 2008 14 2006 .. 92 96Austria 2009 15 2011 .. 100 100Azerbaijan 0 2006 2009 94 26 79Bahrain 2000 1 2010 84 79Bangladesh 2008 13 2007 2011 10 .. ..Belarus 2010 1 2005 2009 .. 62 100Belgium 2009 7 2011 .. 96 99Benin 2008 4 2006 2002 60 .. ..Bolivia 2008 14 2008 2001 .. .. 31Bosnia and Herzegovina 2010 7 2006 100 48 93Botswana 2002 3 2000 2011 72 32 43Brazil 2009 10 1996 2010 91 50 88Bulgaria 2009 9 2011 .. 98 97Burkina Faso 2009 7 2006 2006 64 31 90Burundi 2007 1 2005 2008 60 .. ..Cambodia 0 2010 2008 66 .. ..Cameroon 1995 1 2006 2005 70 .. ..Canada 2010 16 2011 .. 99 98Central African Republic 0 2006 2003 49 .. ..Chad 0 2004 2009 .. .. ..Chile 2010 8 2002 99 100 100China 2009 15 2010 .. .. 97Hong Kong SAR, China 0 2006 .. 66 94Colombia 2009 12 2010 2006 97 55 77Congo, Dem. Rep. 2009 7 2010 28 .. ..Congo, Rep. 2005 1 2009 2007 81 .. ..Costa Rica 2003 2 1993 2011 .. 96 98Côte d’Ivoire 2008 2 2006 55 .. ..Croatia 2010 2 2011 .. 94 99Cuba 0 2006 2002 100 100 100Cyprus 2008 6 2001 56 72Czech Republic 2009 15 1993 2011 .. 89 100Denmark 2009 15 2011 .. 86 97Dominican Republic 2008 8 2007 2010 78 1 54Ecuador 2008 9 2004 2010 90 54 84Egypt, Arab Rep. 2008 3 2008 2006 99 56 100El Salvador 2010 16 2008 2007 99 35 79Eritrea 0 2002 .. .. ..Estonia 2010 12 .. 76 95Ethiopia 2008 4 2005 2007 7 .. 100Finland 2010 16 2010 .. 84 99France 2010 16 2006 .. 100 98Gabon 0 2000 2003 .. .. ..Gambia, The 2004 3 2005/06 2003 55 .. ..Georgia 2010 10 2005 2002 92 49 97Germany 2009 15 2011 .. 97 100Ghana 2002 1 2008 2010 71 100 ..Greece 0 2011 .. 69 91Guatemala 2008 14 2002 2002 .. 63 92Guinea 0 2005 43 .. ..Guinea-Bissau 0 2010 2009 24 .. ..1042012 World Development Indicators

Health information2.17PEOPLEYear last nationalhealth accountcompletedNumber ofnational healthaccountscompletedYear of lasthealth surveyYear of lastcensusCompleteness%BirthregistrationInfant deathreportingTotal deathreporting1995–2010 2001–11 2005–10 a 2005–10 a 2005–10 aHaiti 2006 1 2005/06 2003 81 .. ..Honduras 2005 3 2005/06 2001 94 100 100Hungary 2009 15 2001 .. 89 98India 2004 2 2005/06 2011 41 .. ..Indonesia 2008 8 2007 2010 53 .. ..Iran, Islamic Rep. 2007 4 2000 2006 .. .. 100Iraq 2010 3 2006 95 100 100Ireland 2009 16 2011 .. 86 98Israel 2007 2 2009 .. 100 96Italy 2009 4 2012 .. 99 98Jamaica 2000 1 2005 2011 89 73 88Japan 2008 14 2010 .. 92 100Jordan 2009 6 2009 2004 .. .. 82Kazakhstan 2010 2 2006 2009 99 83 88Kenya 2010 3 2010 2009 60 43 42Korea, Dem. Rep. 0 2010 2009 100 73 93Korea, Rep. 2010 16 2010 .. 82 98Kosovo 0 .. .. ..Kuwait 0 1996 2010 .. 100 73Kyrgyz Republic 2010 6 2005/06 2009 94 76 91Lao PDR 0 2006 2005 72 .. ..Latvia 2008 5 2011 .. 65 96Lebanon 2008 11 2000 .. .. 87Lesotho 0 2009/10 2006 45 .. ..Liberia 2008 1 2009 2008 4 .. ..Libya 0 2000 2006 .. .. ..Lithuania 2009 8 2011 .. 71 93Macedonia, FYR 0 2005 2010 94 61 100Madagascar 2007 2 2008/09 80 .. ..Malawi 2006 5 2010 2008 .. .. 71Malaysia 2009 13 2010 .. 80 99Mali 2004 6 2010 2009 81 .. ..Mauritania 2008 1 2007 2000 56 .. ..Mauritius 2004 2 2011 .. 90 100Mexico 2010 16 1995 2010 .. 82 100Moldova 2010 2 2005 2004 .. 73 91Mongolia 2003 5 2010 2010 98 60 98Morocco 2006 3 2006 2004 .. .. ..Mozambique 2006 4 2009 2007 31 .. ..Myanmar 2007 10 2000 72 58 100Namibia 2008 11 2009 2001 67 .. 100Nepal 2010 11 2010 2001 35 .. ..Netherlands 2010 16 2011 .. 89 99New Zealand 2009 15 2006 .. 97 96Nicaragua 2008 14 2006/07 2005 .. 66 68Niger 2009 6 2006 2001 32 .. ..Nigeria 2005 8 2008 2006 30 .. 3Norway 2009 13 2001 .. 97 99Oman 1998 1 1995 2010 .. 100 70Pakistan 2006 1 2010 27 88 80Panama 1997 1 2003 2010 .. 68 90Papua New Guinea 2000 3 1996 .. .. ..Paraguay 2008 14 2004 2002 .. 34 71Peru 2005 11 2008 2007 93 45 59Philippines 2007 13 2008 2010 .. 38 84Poland 2009 15 2011 .. 89 97Portugal 2008 9 2011 .. 57 992012 World Development Indicators 105

2.17Health informationYear last nationalhealth accountcompletedNumber ofnational healthaccountscompletedYear of lasthealth surveyYear of lastcensusCompletenessPuerto Rico 0 1996 2010 .. 100 100Qatar 2010 2 2010 .. 77 78Romania 2009 12 1999 2011 .. 73 100Russian Federation 2008 13 1996 2010 .. 76 100Rwanda 2006 5 2007/08 2002 82 .. 100Saudi Arabia 2008 1 2007 2010 .. 100 98Senegal 2005 2 2008/09 2002 55 100 100Serbia 2010 8 2005/06 2011 99 37 89Sierra Leone 2006 3 2008 2004 51 13 ..Singapore 0 2005 2010 .. 100 74Slovak Republic 2009 13 2011 .. 100 100Slovenia 2010 16 2011 .. 79 97Somalia 0 2006 3 .. ..South Africa 1998 3 2003 2001 92 78 82South Sudan 0 2008Spain 2009 15 2001 .. 84 94Sri Lanka 2008 14 2006/07 2001 97 67 94Sudan 2010 2 2010 2008 33 .. ..Swaziland 0 2010 2010 30 .. 100Sweden 2009 9 .. 100 98Switzerland 2010 16 2010 .. 100 100Syrian Arab Republic 0 2006 2004 95 .. 100Tajikistan 2010 4 2005 2010 88 24 72Tanzania 2006 3 2010 2002 16 .. ..Thailand 2007 13 2005/06 2010 99 53 79Timor-Leste 0 2009/10 2010 55 .. ..Togo 2002 1 2010 2010 78 .. ..Trinidad and Tobago 2000 1 2006 2011 96 49 94Tunisia 2005 5 2006 2004 .. .. 99Turkey 2008 11 2003 94 60 100Turkmenistan 0 2006 96 .. ..Uganda 2007 6 2009/10 2002 21 .. ..Ukraine 2008 6 2007 2001 100 75 93United Arab Emirates 0 2010 .. 96 91United Kingdom 2009 13 2011 .. 100 96United States 2009 15 2009 2010 .. 100 98Uruguay 2008 13 2004 .. 77 100Uzbekistan 2010 1 2006 100 .. ..Venezuela, RB 0 2000 2001 .. 62 87Vietnam 2007 10 2006 2009 88 71 86West Bank and Gaza 2005 1 2006 2007 96 28 73Yemen, Rep. 2007 4 2006 2004 22 .. 16Zambia 2006 12 2007 2010 14 .. 73Zimbabwe 2001 3 2005/06 2002 38 .. ..a. Data are for the most recent year available.%BirthregistrationInfant deathreportingTotal deathreporting1995–2010 2001–11 2005–10 a 2005–10 a 2005–10 a1062012 World Development Indicators

Health information2.17PEOPLEAbout the dataDefinitionsAccording to the World Health Organization (WHO),health information systems are crucial for monitoringand evaluating health systems, which areincreasingly recognized as important for combatingdisease and improving health status. Health informationsystems underpin decisionmaking through fourdata functions: generation, compilation, analysis andsynthesis, and communication and use. The healthinformation system collects data from the health sectorand other relevant sectors; analyzes the data andensures their overall quality, relevance, and timeliness;and converts data into information for healthrelateddecisionmaking (WHO 2008b).Numerous indicators have been proposed toassess a country’s health information system.They can be grouped into two broad types: indicatorsrelated to data generation using core sourcesand methods (health surveys, civil registration, censuses,facility reporting, health system resourcetracking) and indicators related to capacity for datasynthesis, analysis, and validation. Indicators relatedto data generation refl ect a country’s capacity to collectrelevant data at suitable intervals using the mostappropriate data sources. Benchmarks include periodicity,timeliness, contents, and availability. Indicatorsrelated to capacity for synthesis, analysis,and validation measure the dimensions of the institutionalframeworks needed to ensure data quality,including independence, transparency, and access.Benchmarks include the availability of independentcoordination mechanisms and micro- and meta-data(WHO 2008a).The indicators in the table are all related to datageneration, including the years the last nationalhealth account, last health survey, and latestpopulation census were completed. Frequency ofdata collection, a benchmark of data generation, isshown as the number of years for which a nationalhealth account was completed during the specifi edyears. National health account data may be collectedusing different approaches such as Organisation forEconomic Co-operation and Development (OECD)System of Health Accounts, WHO National HealthAccount producers guide approach, local nationalhealth accounting methods, or Pan AmericanHealth Organization/WHO satellite health accountsapproach.Indicators related to data generation include completenessof birth registration, infant death reporting,and total death reporting.• Year last national health account completedis the latest year for which the health expendituredata are available using the national health accountapproach. • Number of national health accountscompleted is the number of national health accountscompleted during the specifi ed years. • Year of lasthealth survey is the latest year the national surveythat collects health information was conducted.• Year of last census is the latest year a census wasconducted in the last 10 years. • Completeness ofbirth registration is the percentage of children underage 5 whose births were registered at the time of thesurvey. The numerator of completeness of birth registrationincludes children whose birth certifi cate wasseen by the interviewer or whose mother or caretakersays the birth has been registered. • Completenessof infant death reporting is the number of infantdeaths reported by national statistical authoritiesto the United Nations Statistics Division’s DemographicYearbook divided by the number of infantdeaths estimated by the United Nations PopulationDivision. • Completeness of total death reporting isthe number of total deaths from civil registration systemsreported by national statistical authorities tothe United Nations Statistics Division’s DemographicYearbook divided by the number of total deaths estimatedby the United Nations Population Division.Data sourcesData on year last national health account completedand number of national health accounts completedwere compiled by the World Bank’s Health, Nutrition,and Population Unit using information on thehealth expenditures provided by the WHO NationalHealth Accounts staff and the OECD. Data on yearof last health survey are from ICF International andthe United Nations Children’s Fund (UNICEF). Dataon year of last census are from United Nations StatisticsDivision’s 2011 World Population and HousingCensus Program (http://unstats.un.org/unsd/demographic/sources/census/2010_PHC/default.htm). Data on completeness of birth registrationare compiled by UNICEF in State of the World’sChildren 2012 based mostly on household surveysand ministry of health data. Data used to calculatecompleteness of infant death reporting and totaldeath reporting are from the United Nations StatisticsDivision’s Population and Vital Statistics Reportand the United Nations Population Division’s WorldPopulation Prospects: The 2010 Revision.2012 World Development Indicators 107

2.18 Disease prevention coverage and qualityAccess toan improvedwater sourceAccess toimprovedsanitationfacilitiesChildimmunizationrateChildrenwith acuterespiratoryinfection (ARI)Children withdiarrhea whoreceived oralrehydrationChildrensleepingundertreatedChildrenwith feverreceivingantimalarialtaken to and continuous nets adrugsTreatmenthealth feedingsuccessrate% ofproviderchildren ages12–23 months b % of children % of childrenTuberculosisAfghanistan .. 50 .. 37 62 66 .. .. .. .. 86 47Albania 97 95 76 94 99 99 70 63 .. .. 89 97Algeria 94 83 88 95 95 95 53 24 .. .. 91 70Angola 42 51 29 58 93 91 .. .. 17.7 29.3 72 77Argentina 94 97 90 90 99 94 .. .. .. .. 46 66Armenia .. 98 .. 90 97 94 57 59 .. .. 73 62Australia 100 100 100 100 94 92 .. .. .. .. 80 84Austria 100 100 100 100 76 83 .. .. .. .. 66 84Azerbaijan 70 80 .. 82 67 72 33 31 .. .. 62 63Bahrain .. .. .. .. 99 99 .. .. .. .. 98 84Bangladesh 77 81 39 56 94 95 37 68 .. .. 92 46Belarus 100 100 93 93 99 98 90 54 .. .. 64 74Belgium 100 100 100 100 94 99 .. .. .. .. 76 87Benin 57 75 5 13 69 83 36 42 20.1 54.0 90 45Bolivia 70 88 18 27 79 80 51 29 .. .. 86 62Bosnia and Herzegovina 97 99 .. 95 93 90 91 53 .. .. 99 71Botswana 93 96 38 62 94 96 .. .. .. .. 79 70Brazil 89 98 68 79 99 98 50 .. .. .. 72 88Bulgaria 100 100 99 100 97 94 .. .. .. .. 85 79Burkina Faso 43 79 8 17 94 95 39 42 9.6 48.0 76 53Burundi 70 72 44 46 92 96 38 23 45.2 17.2 90 70Cambodia 31 64 9 31 93 92 64 50 4.2 0.2 95 65Cameroon 49 77 48 49 79 84 35 22 13.1 57.8 78 69Canada 100 100 100 100 93 80 .. .. .. .. 75 83Central African Republic 58 67 11 34 62 54 32 47 15.1 57.0 53 47Chad 39 51 8 13 46 59 26 23 9.8 35.7 76 31Chile 90 96 84 96 93 92 .. .. .. .. 72 75China 67 91 24 64 99 99 .. .. .. .. 95 87Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. 70 87Colombia 89 92 67 77 88 88 64 52 .. .. 77 72Congo, Dem. Rep. 45 45 9 24 68 63 40 37 35.7 39.1 88 53Congo, Rep. .. 71 .. 18 76 90 48 39 6.1 48.0 78 68Costa Rica 93 97 93 95 83 88 .. .. .. .. 54 78Côte d’Ivoire 76 80 20 24 70 85 35 45 3.0 36.0 79 83Croatia 99 99 99 99 95 96 .. .. .. .. 63 73Cuba 82 94 80 91 99 96 .. .. .. .. 90 79Cyprus 100 100 100 100 87 99 .. .. .. .. 29 68Czech Republic 100 100 100 98 98 99 .. .. .. .. 67 88Denmark 100 100 100 100 85 90 .. .. .. .. 53 93Dominican Republic 88 86 73 83 79 88 70 55 .. 0.6 85 59Ecuador 72 94 69 92 98 99 .. .. .. .. 75 51Egypt, Arab Rep. 93 99 72 95 96 97 73 19 .. .. 88 64El Salvador 74 88 75 87 92 92 67 .. .. .. 89 96Eritrea 43 61 9 14 99 99 .. .. 48.9 13.1 85 55Estonia 98 98 95 95 95 94 .. .. .. .. 59 85Ethiopia 14 44 3 21 81 86 19 15 33.1 9.5 84 72Finland 100 100 100 100 98 99 .. .. .. .. 68 87France 100 100 100 100 90 99 .. .. .. .. .. 47Gabon .. 87 .. 33 55 45 .. .. 55.1 .. 55 42Gambia, The 74 89 .. 68 97 98 69 38 49.0 63.0 89 44Georgia 81 98 96 95 94 91 74 37 .. .. 75 100Germany 100 100 100 100 96 93 .. .. .. .. 77 89Ghana 53 86 7 14 93 94 51 45 28.2 43.0 87 70Greece 96 100 97 98 99 99 .. .. .. .. .. 68Guatemala 81 92 62 78 93 94 .. .. .. .. 83 37Guinea 51 74 10 18 51 57 42 38 4.5 73.9 79 33Guinea-Bissau 36 64 .. 20 61 76 52 53 35.5 51.2 67 62Haiti 59 69 26 17 59 59 31 43 .. 5.1 79 62Casedetectionrate% of newestimatedcases% of % of children % of new% of% ofunder age 5 under age 5 children under age 5 registeredpopulation population Measles DTP3 with ARI with diarrhea under age 5 with fever cases1990 2010 1990 2010 2010 2010 2005–10 c 2005–10 c 2005–10 c 2005–10 c 2009 20101082012 World Development Indicators

Disease prevention coverage and quality 2.18PEOPLEAccess toan improvedwater sourceAccess toimprovedsanitationfacilitiesChildimmunizationrateChildrenwith acuterespiratoryinfection (ARI)Children withdiarrhea whoreceived oralrehydrationChildrensleepingundertreatedChildrenwith feverreceivingantimalarialtaken to and continuous nets adrugsTreatmenthealth feedingsuccessrate% ofproviderchildren ages12–23 months b % of children % of childrenTuberculosisHonduras 76 87 50 77 99 98 56 49 .. 0.5 86 74Hungary 96 100 100 100 99 99 .. .. .. .. 57 100India 69 92 18 34 74 72 69 33 .. 8.2 88 59Indonesia 70 82 32 54 89 83 66 54 3.3 0.8 91 66Iran, Islamic Rep. 90 96 79 100 99 99 .. .. .. .. 83 81Iraq 81 79 .. 73 73 65 82 64 .. .. 90 48Ireland 100 100 99 99 90 94 .. .. .. .. 67 88Israel 100 100 100 100 98 96 .. .. .. .. 86 93Italy 100 100 .. .. 90 96 .. .. .. .. .. 57Jamaica 93 93 80 80 88 99 75 39 .. .. 70 72Japan 100 100 100 100 94 98 .. .. .. .. 52 84Jordan 97 97 97 98 98 98 75 32 .. .. 75 100Kazakhstan 96 95 96 97 99 99 71 48 .. .. 62 82Kenya 44 59 25 32 86 83 56 43 46.7 23.2 86 82Korea, Dem. Rep. 100 98 .. 80 99 93 80 67 .. .. 89 100Korea, Rep. .. 98 100 100 98 94 .. .. .. .. 83 90Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 99 99 100 100 98 98 .. .. .. .. 85 86Kyrgyz Republic .. 90 .. 93 99 96 62 22 .. .. 82 66Lao PDR .. 67 .. 63 64 74 32 49 40.5 8.2 93 72Latvia 99 99 .. 78 93 89 .. .. .. .. 75 100Lebanon 100 100 .. .. 53 74 .. .. .. .. 82 71Lesotho 80 78 .. 26 85 83 66 48 .. .. 70 85Liberia .. 73 .. 18 64 64 62 47 26.4 67.2 83 56Libya 54 .. 97 97 98 98 .. .. .. .. 69 84Lithuania .. .. .. .. 96 95 .. .. .. .. 73 76Macedonia, FYR 100 100 .. 88 98 95 93 45 .. .. 90 89Madagascar 29 46 9 15 67 74 42 49 45.8 19.7 82 44Malawi 41 83 39 51 93 93 52 27 56.5 30.9 88 65Malaysia 88 100 84 96 96 94 .. .. .. .. 78 80Mali 28 64 15 22 63 76 38 38 70.2 31.7 78 51Mauritania 30 50 16 26 67 64 45 32 .. 20.7 63 21Mauritius 99 99 89 89 99 99 .. .. .. .. 88 44Mexico 85 96 64 85 95 95 .. .. .. .. 86 110Moldova .. 96 .. 85 97 90 60 48 .. .. 54 63Mongolia 54 82 .. 51 97 96 63 47 .. .. 88 72Morocco 73 83 53 70 98 99 .. .. .. .. 84 97Mozambique 36 47 11 18 70 74 65 47 22.8 36.7 85 34Myanmar 56 83 .. 76 88 90 .. .. .. .. 85 71Namibia 64 93 24 32 75 83 72 48 34.0 20.3 85 82Nepal 76 89 10 31 86 82 43 37 .. 0.1 90 72Netherlands 100 100 100 100 96 97 .. .. .. .. 80 85New Zealand 100 100 .. .. 91 93 .. .. .. .. 76 90Nicaragua 74 85 43 52 99 98 .. .. .. .. 85 100Niger 35 49 5 9 71 70 47 34 63.7 33.0 79 35Nigeria 47 58 37 31 71 69 45 25 29.1 49.1 83 40Norway 100 100 100 100 93 93 .. .. .. .. 82 93Oman 80 89 82 99 97 99 .. .. .. .. 98 85Pakistan 85 92 27 48 86 88 69 37 .. 3.3 91 65Panama 84 93 58 69 95 94 .. .. .. .. 80 89Papua New Guinea 41 40 47 45 55 56 63 .. .. .. 72 70Paraguay 52 86 37 71 94 90 .. .. .. .. 80 77Peru 75 85 54 71 94 93 68 60 .. .. 81 100Philippines 85 92 57 74 88 87 50 60 .. 0.0 89 65Poland .. 100 .. 90 98 99 .. .. .. .. 67 80Portugal 96 99 92 100 96 98 .. .. .. .. 84 79Puerto Rico .. .. .. .. .. .. .. .. .. .. 81 96Qatar 100 100 100 100 99 97 .. .. .. .. 80 87Casedetectionrate% of newestimatedcases% of % of children % of new% of% ofunder age 5 under age 5 children under age 5 registeredpopulation population Measles DTP3 with ARI with diarrhea under age 5 with fever cases1990 2010 1990 2010 2010 2010 2005–10 c 2005–10 c 2005–10 c 2005–10 c 2009 20102012 World Development Indicators 109

2.18Disease prevention coverage and qualityAccess toan improvedwater sourceAccess toimprovedsanitationfacilitiesChildimmunizationrateChildrenwith acuterespiratoryinfection (ARI)Children withdiarrhea whoreceived oralrehydrationChildrensleepingundertreatedChildrenwith feverreceivingantimalarialtaken to and continuous nets adrugsTreatmenthealth feedingsuccessrate% ofproviderchildren ages12–23 months b % of children % of childrenTuberculosisCasedetectionrate% of newestimatedcases% of % of children % of new% of% ofunder age 5 under age 5 children under age 5 registeredpopulation population Measles DTP3 with ARI with diarrhea under age 5 with fever cases1990 2010 1990 2010 2010 2010 2005–10 c 2005–10 c 2005–10 c 2005–10 c 2009 2010Romania 75 .. 71 72 95 97 .. .. .. .. 85 74Russian Federation 93 97 74 70 98 97 .. .. .. .. 55 78Rwanda 66 65 36 55 82 80 28 24 69.8 10.8 85 60Saudi Arabia 89 .. .. .. 98 98 .. .. .. .. 65 88Senegal 61 72 38 52 60 70 47 43 29.2 9.1 85 31Serbia 99 99 .. 92 95 91 93 71 .. .. 86 130Sierra Leone 38 55 11 13 82 90 46 57 25.8 30.1 79 32Singapore 100 100 99 100 95 97 .. .. .. .. 82 87Slovak Republic 100 100 100 100 98 99 .. .. .. .. 82 88Slovenia 100 99 100 100 95 96 .. .. .. .. 87 79Somalia .. 29 .. 23 46 45 13 7 11.4 7.9 85 38South Africa 83 91 71 79 65 63 .. .. .. .. 73 72South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 100 100 100 100 95 97 .. .. .. .. .. 87Sri Lanka 67 91 70 92 99 99 58 67 2.9 0.3 86 69Sudan 65 58 27 26 90 90 90 56 25.3 35.8 80 50Swaziland 39 71 48 57 94 89 73 22 0.6 0.6 69 66Sweden 100 100 100 100 96 98 .. .. .. .. 85 87Switzerland 100 100 100 100 90 96 .. .. .. .. .. 55Syrian Arab Republic 86 90 85 95 82 80 77 34 .. .. 88 90Tajikistan .. 64 .. 94 94 93 64 22 1.3 1.9 81 44Tanzania 55 53 7 10 92 91 71 50 63.6 59.1 88 77Thailand 86 96 84 96 98 99 84 46 .. .. 86 70Timor-Leste .. 69 .. 47 66 72 71 63 41.0 6.0 85 87Togo 49 61 13 13 84 92 23 24 56.9 33.8 81 10Trinidad and Tobago 88 94 93 92 92 90 74 32 .. .. 69 87Tunisia 81 94 74 85 97 98 59 62 .. .. 83 91Turkey 85 100 84 90 97 96 .. 22 .. .. 91 77Turkmenistan .. .. 98 98 99 96 83 25 .. .. 84 96Uganda 43 72 27 34 55 60 73 39 32.8 59.6 67 61Ukraine .. 98 .. 94 94 90 .. .. .. .. 60 73United Arab Emirates 100 100 97 98 94 94 .. .. .. .. 73 57United Kingdom 100 100 100 100 93 96 .. .. .. .. 82 91United States 99 99 100 100 92 95 .. .. .. .. 60 88Uruguay 96 100 94 100 95 95 .. .. .. .. 80 97Uzbekistan 90 87 84 100 98 99 68 28 .. .. 81 48Venezuela, RB 90 .. 82 .. 79 78 .. .. .. .. 84 66Vietnam 57 95 37 76 98 93 83 65 5.0 2.6 92 54West Bank and Gaza .. 85 .. 92 .. .. .. .. .. .. 82 16Yemen, Rep. 67 55 24 53 73 87 .. 48 .. .. 88 76Zambia 49 61 46 48 91 82 .. 68 56 49.9 90 90Zimbabwe 79 80 41 40 84 83 43 35 17.3 23.6 78 56World 76 w 88 w 47 w 62 w 85 w 85 w .. w .. w .. w .. w 86 w 65 wLow income 54 65 21 37 78 80 .. 39 .. 31.9 86 58Middle income 73 90 39 59 86 85 .. .. .. .. 87 67Lower middle income 70 87 29 47 80 79 67 37 .. 13.8 87 61Upper middle income 76 93 46 73 96 96 .. .. .. .. 86 81Low & middle income 71 86 37 56 84 84 .. .. .. .. 87 65East Asia & Pacifi c 68 90 30 66 95 94 .. .. .. .. 92 76Europe & Central Asia 90 96 80 84 96 95 .. .. .. .. 65 73Latin America & Carib. 86 94 68 79 93 93 .. .. .. .. 77 80Middle East & N. Africa 86 89 73 88 88 89 .. .. .. .. 87 73South Asia 71 90 22 38 77 76 67 37 .. 7.2 88 58Sub-Saharan Africa 49 61 26 31 75 77 .. 35 34.0 37.8 79 60High income 99 100 100 100 93 95 .. .. .. .. 68 85Euro area 100 100 100 100 93 96 .. .. .. .. .. ..a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or in some cases at any time before the survey (12–23 months). c. Data are for the mostrecent year available.1102012 World Development Indicators

Disease prevention coverage and quality2.18PEOPLEAbout the dataDefinitionsPeople’s health is infl uenced by the environmentin which they live. Lack of clean water and basicsanitation is the main reason diseases transmittedby feces are so common in developing countries.Access to drinking water from an improved sourceand access to improved sanitation do not ensuresafety or adequacy, as these characteristics arenot tested at the time of the surveys. But improveddrinking water technologies and improved sanitationfacilities are more likely than those characterizedas unimproved to provide safe drinking water andto prevent contact with human excreta. The dataare derived by the Joint Monitoring Programme ofthe World Health Organization (WHO) and UnitedNations Children’s Fund (UNICEF) based on nationalcensuses and nationally representative householdsurveys. The coverage rates for water and sanitationare based on information from service users on thefacilities their households actually use rather thanon information from service providers, which mayinclude nonfunctioning systems. While the estimatesare based on use, the Joint Monitoring Programmereports use as access, because access is the termused in the Millennium Development Goal target fordrinking water and sanitation.Governments in developing countries usuallyfi nance immunization against measles and diphtheria,pertussis (whooping cough), and tetanus (DTP)as part of the basic public health package. In manydeveloping countries lack of precise information onthe size of the cohort of one-year-old children makesimmunization coverage diffi cult to estimate fromprogram statistics. The data shown here are basedon an assessment of national immunization coveragerates by the WHO and UNICEF. The assessmentconsidered both administrative data from serviceproviders and household survey data on children’simmunization histories. Based on the data available,consideration of potential biases, and contributionsof local experts, the most likely true level of immunizationcoverage was determined for each year.Acute respiratory infection continues to be a leadingcause of death among young children, killingnearly 1.5 million children under age 5 globally eachyear. Data are drawn mostly from household healthsurveys in which mothers report on number of episodesand treatment for acute respiratory infection.Most diarrhea- related deaths are due to dehydration,and many of these deaths can be prevented withthe use of oral rehydration salts at home. However,recommendations for the use of oral rehydrationtherapy have changed over time based on scientifi cprogress, so it is diffi cult to accurately compareuse rates across countries. Until the current recommendedmethod for home management of diarrhea isadopted and applied in all countries, the data shouldbe used with caution. Also, the prevalence of diarrheamay vary by season. Since country surveys areadministered at different times, data comparabilityis further affected.Malaria is endemic to the poorest countries in theworld, mainly in tropical and subtropical regions ofAfrica, Asia, and the Americas. Insecticide-treatednets, properly used and maintained, are one of themost important malaria-preventive strategies to limithuman-mosquito contact.Prompt and effective treatment of malaria is a criticalelement of malaria control. It is vital that sufferers,especially children under age 5, start treatmentwithin 24 hours of the onset of symptoms, to preventprogression—often rapid—to severe malariaand death. Data on malaria are from national- levelsurveys, including Multiple Indicator Cluster Surveys,Demographic and Health Surveys, and Malaria IndicatorSurveys.Data on the success rate of tuberculosis treatmentare provided for countries that have submitted datato the WHO. The treatment success rate for tuberculosisprovides a useful indicator of the quality ofhealth services. A low rate suggests that infectiouspatients may not be receiving adequate treatment.An important complement to the tuberculosis treatmentsuccess rate is the case detection rate, whichindicates whether there is adequate coverage by therecommended case detection and treatment strategy.Uncertainty bounds for the case detection rate,not shown in the table, are available at http://data.worldbank.org and from the original source.The table shows the tuberculosis detection rate forall detection methods. Editions before 2010 includedthe tuberculosis detection rates by DOTS, the internationallyrecommended strategy for tuberculosiscontrol. Thus data on the case detection rate from2010 onward cannot be compared with data in previouseditions.For indicators that are from household surveys, theyear in the table refers to the survey year. For moreinformation, consult the original sources.• Access to an improved water source refers to peoplewith access to at least 20 liters of water a persona day from an improved source, such as piped waterinto a dwelling, public tap, tubewell, protected dugwell, and rainwater collection, within 1 kilometer ofthe dwelling. • Access to improved sanitation facilitiesrefers to people with at least adequate accessto excreta disposal facilities that can effectively preventhuman, animal, and insect contact with excreta.Improved facilities range from protected pit latrinesto fl ush toilets. • Child immunization rate refers tochildren ages 12–23 months who, before 12 monthsor at any time before the survey, had received onedose of measles vaccine and three doses of diphtheria,pertussis (whooping cough), and tetanus (DTP3)vaccine. • Children with acute respiratory infection(ARI) taken to health provider are children under age5 with ARI in the two weeks before the survey whowere taken to an appropriate health provider. • Childrenwith diarrhea who received oral rehydration andcontinuous feeding are children under age 5 with diarrheain the two weeks before the survey who receivedeither oral rehydration therapy or increased fl uids,with continuous feeding. • Children sleeping undertreated nets are children under age 5 who slept underan insecticide-treated net to prevent malaria the nightbefore the survey. • Children with fever receivingantimalarial drugs are children under age 5 who wereill with fever in the two weeks before the survey andreceived any appropriate (locally defi ned) antimalarialdrugs. • Tuberculosis treatment success rate is newregistered infectious tuberculosis cases that werecured or that completed a full course of treatment asa percentage of smear-positive cases registered fortreatment outcome evaluation. • Tuberculosis casedetection rate is newly identifi ed tuberculosis cases(including relapses) as a percentage of estimatedincident cases (case detection, all forms).Data sourcesData on access to water and sanitation are fromthe WHO and UNICEF’s Progress on Drinking Waterand Sanitation (2012). Data on immunization arefrom WHO and UNICEF estimates (www.who.int/immunization_monitoring). Data on children with ARI,with diarrhea, sleeping under treated nets, and receivingantimalarial drugs are from UNICEF’s State of theWorld’s Children 2012, Childinfo, and MEASURE DHSDemographic and Health Surveys by ICF International.Data on tuberculosis are from the WHO’s Global TuberculosisControl: A Short Update to the 2011 Report.2012 World Development Indicators 111

2.19 Reproductive healthTotal fertilityrateAdolescentfertility rateUnmetneed forcontraceptionContraceptiveprevalenceratePregnantwomenreceivingprenatalcareBirths attendedby skilledhealth staffMaternalmortalityratioLifetimerisk ofmaternalmortalitybirths per % of marriedAny method% of marriedper 100,000 live births Probabilitybirths perwoman1,000 womenages 15–19women ages15–49women ages15–49% % of totalNationalestimatesModeledestimates1 womanin:1990 2010 2010 2005–10 a 2005–10 a 2005–10 a 1990 2005–10 a 2005–10 a 1990 2008 2008Afghanistan 8.0 6.3 107 .. 23 36 .. 24 .. 1,700 1,400 11Albania 3.2 1.5 16 13 69 97 93 99 21 48 31 1,700Algeria 4.7 2.3 7 11 61 89 77 95 .. 250 120 340Angola 7.2 5.4 157 .. .. 80 .. 47 .. 1,000 610 29Argentina 3.0 2.2 55 .. 78 99 96 98 55 72 70 600Armenia 2.5 1.7 34 13 55 99 100 100 27 51 29 1,900Australia 1.9 1.9 14 .. .. 98 100 .. .. 10 8 7,400Austria 1.5 1.4 11 .. .. .. .. .. .. 10 5 14,300Azerbaijan 2.7 2.3 32 23 51 77 97 88 24 64 38 1,200Bahrain 3.7 2.5 15 .. .. 100 .. 97 .. 25 19 2,200Bangladesh 4.5 2.2 73 17 53 53 .. 27 190 870 340 110Belarus 1.9 1.4 21 .. 73 99 100 100 1 37 15 5,100Belgium 1.6 1.8 12 .. .. .. .. .. .. 7 5 10,900Benin 6.7 5.3 103 30 17 84 .. 74 400 790 410 43Bolivia 4.9 3.3 76 20 61 86 43 71 310 510 180 150Bosnia and Herzegovina 1.7 1.1 15 23 36 99 97 100 3 18 9 9,300Botswana 4.7 2.8 47 .. 53 94 78 95 200 83 190 180Brazil 2.8 1.8 76 .. 81 98 70 97 75 120 58 860Bulgaria 1.8 1.5 39 .. .. .. 99 100 5 24 13 5,800Burkina Faso 6.8 5.9 120 31 17 85 .. 54 310 770 560 28Burundi 6.5 4.3 20 .. 22 99 .. 60 620 1,200 970 25Cambodia 5.7 2.6 36 17 51 89 .. 71 206 690 290 110Cameroon 5.9 4.5 120 3 29 82 64 63 .. 680 600 35Canada 1.8 1.7 12 .. .. 100 .. 100 .. 6 12 5,600Central African Republic 5.8 4.6 102 .. 19 69 .. 44 540 880 850 27Chad 6.7 6.0 149 .. 5 53 .. 23 .. 1,300 1,200 14Chile 2.6 1.9 57 .. 58 .. .. 100 17 56 26 2,000China 2.3 1.6 9 .. 85 92 94 99 32 110 38 1,500Hong Kong SAR, China 1.3 1.1 4 .. 80 .. .. 100 .. .. .. ..Colombia 3.1 2.1 71 7 79 97 94 98 76 140 85 460Congo, Dem. Rep. 7.1 5.8 183 24 17 88 .. 79 550 900 670 24Congo, Rep. 5.4 4.5 115 16 44 86 .. 83 780 460 580 39Costa Rica 3.2 1.8 63 .. 80 90 98 99 21 35 44 1,100Côte d’Ivoire 6.3 4.4 115 29 13 85 .. 57 540 690 470 44Croatia 1.6 1.5 13 .. .. 100 100 100 13 8 14 5,200Cuba 1.8 1.5 44 8 78 100 .. 100 43 63 53 1,400Cyprus 2.4 1.5 6 .. .. 99 .. .. .. 17 10 6,600Czech Republic 1.9 1.5 10 .. .. .. 100 100 2 15 8 8,500Denmark 1.7 1.9 5 .. .. .. .. .. .. 7 5 10,900Dominican Republic 3.5 2.6 106 11 73 99 92 98 160 220 100 320Ecuador 3.7 2.5 81 .. .. .. .. .. 61 230 140 270Egypt, Arab Rep. 4.4 2.7 43 9 60 74 37 79 55 220 82 380El Salvador 4.0 2.3 79 .. 73 94 90 96 59 200 110 350Eritrea 6.2 4.5 59 .. .. .. .. .. .. 930 280 72Estonia 2.0 1.6 19 .. .. .. 99 100 7 48 12 5,300Ethiopia 7.1 4.2 58 34 15 28 .. 6 670 990 470 40Finland 1.8 1.9 9 .. .. .. .. .. .. 7 8 7,600France 1.8 2.0 6 .. 71 .. .. .. .. 13 8 6,600Gabon 5.2 3.3 85 .. .. .. .. .. .. 260 260 110Gambia, The 6.1 4.9 71 .. .. 98 44 57 .. 750 400 49Georgia 2.2 1.6 42 .. 53 98 97 100 52 58 48 1,300Germany 1.5 1.4 7 .. .. .. .. 100 .. 13 7 11,100Ghana 5.6 4.2 66 35 24 90 40 57 450 630 350 66Greece 1.4 1.4 10 .. .. .. .. .. .. 6 2 31,800Guatemala 5.6 4.0 104 .. 54 93 .. 51 130 140 110 210Guinea 6.7 5.2 143 21 9 88 31 46 980 1,200 680 26Guinea-Bissau 6.6 5.1 102 25 14 93 .. 44 410 1,200 1,000 18Haiti 5.4 3.3 43 38 32 85 23 26 630 670 300 931122012 World Development Indicators

Reproductive health2.19PEOPLETotal fertilityrateAdolescentfertility rateUnmetneed forcontraceptionContraceptiveprevalenceratePregnantwomenreceivingprenatalcareBirths attendedby skilledhealth staffMaternalmortalityratioLifetimerisk ofmaternalmortalitybirths per % of marriedAny method% of marriedper 100,000 live births Probabilitybirths perwoman1,000 womenages 15–19women ages15–49women ages15–49% % of totalNationalestimatesModeledestimates1 womanin:1990 2010 2010 2005–10 a 2005–10 a 2005–10 a 1990 2005–10 a 2005–10 a 1990 2008 2008Honduras 5.1 3.1 89 17 65 92 47 67 .. 210 110 240Hungary 1.8 1.3 15 .. .. .. 99 100 19 23 13 5,500India 3.9 2.6 79 13 54 75 .. 53 250 570 230 140Indonesia 3.1 2.1 43 15 56 95 41 82 230 620 240 190Iran, Islamic Rep. 4.8 1.7 27 .. 79 98 .. 97 25 150 30 1,500Iraq 6.0 4.7 91 .. 50 84 54 80 84 93 75 300Ireland 2.1 2.1 12 .. 65 .. .. .. .. 6 3 17,800Israel 2.8 3.0 14 .. .. .. .. .. .. 12 7 5,100Italy 1.3 1.4 5 .. .. .. .. .. .. 10 5 15,200Jamaica 2.9 2.3 73 .. 72 99 92 98 .. 66 89 450Japan 1.5 1.4 6 .. 54 .. 100 .. .. 12 6 12,200Jordan 5.8 3.8 25 11 59 99 87 99 19 110 59 510Kazakhstan 2.7 2.6 27 .. 51 100 99 100 37 78 45 950Kenya 6.0 4.7 99 26 46 92 50 44 488 380 530 38Korea, Dem. Rep. 2.4 2.0 1 .. .. 100 .. 100 77 270 250 230Korea, Rep. 1.6 1.2 4 .. 80 .. .. .. .. 18 18 4,700Kosovo 3.9 2.3 .. .. .. .. .. .. .. .. .. ..Kuwait 2.6 2.3 14 .. .. 100 .. 100 .. 10 9 4,500Kyrgyz Republic 3.7 2.9 33 1 48 97 99 99 64 77 81 450Lao PDR 6.2 2.7 34 .. 38 71 .. 37 410 1,200 580 49Latvia 2.0 1.2 15 .. .. .. 100 100 32 57 20 3,600Lebanon 3.1 1.8 16 .. .. .. .. .. .. 52 26 2,000Lesotho 4.9 3.2 66 .. 47 92 .. 62 1,200 370 530 62Liberia 6.5 5.2 131 36 11 79 .. 46 990 1,100 990 20Libya 4.8 2.6 3 .. .. 93 .. 100 .. 100 64 540Lithuania 2.0 1.6 18 .. .. .. 100 100 9 34 13 5,800Macedonia, FYR 2.1 1.4 19 34 14 99 89 100 4 16 9 7,300Madagascar 6.3 4.7 127 19 40 86 57 44 500 710 440 45Malawi 6.8 6.0 111 .. 41 92 55 54 810 910 510 36Malaysia 3.5 2.6 12 .. .. 79 93 99 29 56 31 1,200Mali 7.1 6.3 176 31 8 70 .. 49 460 1,200 830 22Mauritania 5.9 4.5 75 25 9 75 40 61 690 780 550 41Mauritius 2.3 1.5 33 .. .. .. 91 99 .. 72 36 1,600Mexico 3.4 2.3 68 .. 73 96 84 95 54 93 85 500Moldova 2.4 1.5 31 7 68 98 100 100 45 62 32 2,000Mongolia 4.1 2.5 20 14 55 100 .. 100 47 130 65 730Morocco 4.0 2.3 13 .. .. .. 31 .. 130 270 110 360Mozambique 6.2 4.9 134 .. 16 92 .. 55 500 1,000 550 37Myanmar 3.4 2.0 14 .. 41 80 46 64 320 420 240 180Namibia 5.2 3.2 62 21 55 95 68 81 450 180 180 160Nepal 5.2 2.7 93 25 48 44 7 19 280 870 380 80Netherlands 1.6 1.8 5 .. 69 .. .. .. .. 10 9 7,100New Zealand 2.2 2.2 24 .. .. .. .. .. .. 18 14 3,800Nicaragua 4.8 2.6 108 8 72 90 .. 74 67 190 100 300Niger 7.8 7.1 199 16 18 46 15 18 650 1,400 820 16Nigeria 6.4 5.5 114 20 15 58 31 39 550 1,100 840 23Norway 1.9 2.0 8 .. 88 .. 100 .. .. 9 7 7,600Oman 7.2 2.3 9 .. 24 99 .. 99 17 49 20 1,600Pakistan 6.0 3.4 30 25 27 61 19 39 250 490 260 93Panama 3.0 2.5 79 .. 52 96 86 89 60 86 71 520Papua New Guinea 4.8 4.0 64 .. 32 79 .. 53 730 340 250 94Paraguay 4.5 3.0 69 .. 79 96 66 82 130 130 95 310Peru 3.8 2.5 51 8 74 95 53 84 93 250 98 370Philippines 4.3 3.1 50 22 51 91 .. 62 160 180 94 320Poland 2.0 1.4 13 .. .. .. 100 100 2 17 6 13,300Portugal 1.4 1.3 14 .. 67 .. 98 .. .. 15 7 9,800Puerto Rico 2.2 1.8 52 .. .. .. .. .. .. 29 18 3,000Qatar 4.2 2.3 16 .. .. 100 .. 100 .. 15 8 4,4002012 World Development Indicators 113

2.19 Reproductive healthTotal fertilityrateAdolescentfertility rateRomania 1.8 1.4 30 .. .. .. 100 99 21 170 27 2,700Russian Federation 1.9 1.5 26 .. 80 .. 99 100 17 74 39 1,900Rwanda 7.0 5.4 37 38 52 98 26 69 .. 1,100 540 35Saudi Arabia 5.8 2.8 18 .. 24 97 .. 97 14 41 24 1,300Senegal 6.6 4.8 96 32 12 87 .. 52 400 750 410 46Serbia 1.8 1.4 20 29 41 98 .. 99 9 13 8 7,500Sierra Leone 5.7 5.0 120 28 8 87 .. 42 860 1,300 970 21Singapore 1.9 1.2 6 .. .. .. .. .. .. 6 9 10,000Slovak Republic 2.1 1.4 18 .. .. .. 100 100 10 15 6 13,300Slovenia 1.5 1.6 5 .. .. .. 100 100 10 11 18 4,100Somalia 6.6 6.3 69 26 15 26 .. 33 1,000 1,100 1,200 14South Africa 3.7 2.5 54 .. .. 97 .. .. 400 230 410 100South Sudan .. 3.9 .. .. .. 40 .. 19 .. .. .. ..Spain 1.3 1.4 12 .. 66 .. .. .. .. 7 6 11,400Sri Lanka 2.5 2.3 23 .. 68 99 .. 99 39 91 39 1,100Sudan 6.0 4.4 57 6 8 56 69 49 1,100 830 750 32Swaziland 5.7 3.4 74 24 49 97 .. 82 589 260 420 75Sweden 2.1 2.0 6 .. .. .. .. .. .. 7 5 11,400Switzerland 1.6 1.5 4 .. .. .. .. 100 .. 8 10 7,600Syrian Arab Republic 5.3 2.9 39 11 54 88 .. 96 .. 120 46 610Tajikistan 5.2 3.3 27 24 37 80 90 83 86 120 64 430Tanzania 6.2 5.5 129 25 34 88 44 49 450 880 790 23Thailand 2.1 1.6 40 .. 80 99 .. 99 12 50 48 1,200Timor-Leste 5.3 5.6 58 31 22 84 .. 29 560 650 370 44Togo 6.3 4.1 59 41 15 87 31 60 .. 650 350 67Trinidad and Tobago 2.4 1.6 33 27 43 96 .. 98 .. 86 55 1,100Tunisia 3.6 2.0 5 .. 60 96 69 95 .. 130 60 860Turkey 3.0 2.1 34 18 73 95 .. 95 29 68 23 1,900Turkmenistan 4.3 2.4 18 .. 48 99 .. 100 12 91 77 500Uganda 7.1 6.1 136 41 24 94 38 42 440 670 430 35Ukraine 1.8 1.4 28 10 67 99 100 99 16 49 26 3,000United Arab Emirates 4.4 1.7 25 .. .. 100 .. 100 0 28 10 4,200United Kingdom 1.8 1.9 30 .. 84 .. .. .. .. 10 12 4,700United States 2.1 2.1 33 .. 79 .. 99 .. 13 12 24 2,100Uruguay 2.5 2.0 60 .. 78 96 .. 100 34 39 27 1,700Uzbekistan 4.1 2.5 13 8 65 99 .. 100 21 53 30 1,400Venezuela, RB 3.4 2.5 88 .. .. .. .. .. 57 84 68 540Vietnam 3.6 1.8 24 .. 80 91 .. 88 69 170 56 850West Bank and Gaza 6.5 4.5 50 .. 50 99 .. 99 .. .. .. ..Yemen, Rep. 8.7 5.2 71 24 28 47 16 36 .. 540 210 91Zambia 6.5 6.3 142 27 41 94 51 47 590 390 470 38Zimbabwe 5.2 3.3 58 13 59 b 93 b 70 60 b 730 390 790 42World 3.2 w 2.5 w 53 w .. w 62 w 84 w 62 w 66 w 400 w 260 w 140 wLow income 5.7 4.1 94 25 34 69 .. 44 860 590 39Middle income 3.3 2.3 51 .. 65 86 60 71 350 210 190Lower middle income 4.2 2.9 68 14 50 78 38 57 540 300 100Upper middle income 2.6 1.8 29 .. 81 94 89 98 110 60 880Low & middle income 3.6 2.6 58 .. 61 83 58 65 440 290 120East Asia & Pacifi c 2.6 1.8 19 .. 78 92 82 91 200 89 580Europe & Central Asia 2.3 1.8 27 .. 69 .. 93 98 69 34 1,700Latin America & Carib. 3.2 2.2 72 .. 75 97 74 90 140 86 480Middle East & N. Africa 4.9 2.7 37 .. 62 85 47 81 210 88 380South Asia 4.2 2.7 73 15 51 71 32 48 610 290 110Sub-Saharan Africa 6.2 4.9 108 25 22 74 .. 46 870 650 31High income 1.8 1.8 18 .. .. .. .. .. 15 15 3,900Euro area 1.5 1.6 8 .. .. .. .. .. 11 7 10,100a. Data are for most recent year available. b. Data are for 2011.Unmetneed forcontraceptionContraceptiveprevalenceratePregnantwomenreceivingprenatalcareBirths attendedby skilledhealth staffMaternalmortalityratioLifetimerisk ofmaternalmortalitybirths per % of marriedAny method% of marriedper 100,000 live births Probabilitybirths perwoman1,000 womenages 15–19women ages15–49women ages15–49% % of totalNationalestimatesModeledestimates1 womanin:1990 2010 2010 2005–10 a 2005–10 a 2005–10 a 1990 2005–10 a 2005–10 a 1990 2008 20081142012 World Development Indicators

Reproductive health2.19PEOPLEAbout the dataDefinitionsReproductive health is a state of physical and mentalwell-being in relation to the reproductive systemand its functions and processes. Means of achievingreproductive health include education and servicesduring pregnancy and childbirth, safe and effectivecontraception, and prevention and treatmentof sexually transmitted diseases. Complications ofpregnancy and childbirth are the leading cause ofdeath and disability among women of reproductiveage in developing countries.Total and adolescent fertility rates are based ondata on registered live births from vital registrationsystems or, in the absence of such systems, fromcensuses or sample surveys. The estimated ratesare generally considered reliable measures of fertilityin the recent past. Where no empirical informationon age- specifi c fertility rates is available, a model isused to estimate the share of births to adolescents.For countries without vital registration systems fertilityrates are generally based on extrapolations fromtrends observed in censuses or surveys from earlieryears.More couples in developing countries want to limitor postpone childbearing but are not using effectivecontraception. These couples have an unmetneed for contraception. Common reasons are lackof knowledge about contraceptive methods andconcerns about possible side effects. This indicatorexcludes women not exposed to the risk of unintendedpregnancy because of menopause, infertility,or postpartum anovulation.Contraceptive prevalence refl ects all methods—ineffective traditional methods as well as highlyeffective modern methods. Contraceptive prevalencerates are obtained mainly from household surveys,including Demographic and Health Surveys, MultipleIndicator Cluster Surveys, and contraceptive prevalencesurveys (see Primary data documentation forthe most recent survey year). Unmarried women areoften excluded from such surveys, which may biasthe estimates.Good prenatal and postnatal care improves maternalhealth and reduces maternal and infant mortality.However, indicators on use of antenatal care servicesprovide no information on the content or qualityof the services. Data on antenatal care are obtainedmostly from household surveys, which ask womenwho have had a live birth whether and from whomthey received antenatal care.The share of births attended by skilled health staffis an indicator of a health system’s ability to provideadequate care for pregnant women.Maternal mortality ratios are generally of unknownreliability, as are many other cause-specifi c mortalityindicators. Household surveys such as Demographicand Health Surveys attempt to measure maternalmortality by asking respondents about survivorshipof sisters. The main disadvantage of this methodis that the estimates of maternal mortality that itproduces pertain to 12 years or so before the survey,making them unsuitable for monitoring recentchanges or observing the impact of interventions.In addition, measurement of maternal mortality issubject to many types of errors. Even in high-incomecountries with reliable vital registration systems,misclassifi cation of maternal deaths has been foundto lead to serious underestimation.The national estimates of maternal mortalityratios in the table are based on national surveys,vital registration records, and surveillance data orare derived from community and hospital records.The modeled estimates are based on an exercise bythe World Health Organization (WHO), United NationsChildren’s Fund (UNICEF), United Nations PopulationFund (UNFPA), and World Bank and include countryleveltime series data. For countries with completevital registration systems with good attribution ofcause of death, the data are used directly to estimatematernal mortality. For countries without completeregistration data but with other types of dataand for countries with no data, maternal mortality isestimated with a multilevel regression model usingavailable national maternal mortality data and socioeconomicinformation, including fertility, birth attendants,and GDP. The methodology differs from thatused for previous estimates, so data should not becompared across editions. For further informationon methodology, see the original source. Neither setof ratios can be assumed to provide an exact estimateof maternal mortality for any of the countriesin the table.In countries with a high risk of maternal death,many girls die before reaching reproductive age. Lifetimerisk of maternal mortality refers to the probabilitythat a 15-year-old girl will eventually die dueto a maternal cause.For the indicators that are from household surveys,the year in the table refers to the survey year. Formore information, consult the original sources.• Total fertility rate is the number of children that wouldbe born to a woman if she were to live to the end of herchildbearing years and bear children in accordance withthe age-specifi c fertility rate of the specifi ed year. • Adolescentfertility rate is the number of births per 1,000women ages 15–19. • Unmet need for contraception isthe percentage of fertile, married women of reproductiveage who do not want to become pregnant and are notusing contraception. • Contraceptive prevalence rateis the percentage of women married or in union ages15–49 who are practicing, or whose sexual partnersare practicing, any form of contraception. • Pregnantwomen receiving prenatal care are women attended atleast once during pregnancy by skilled health personnelfor pregnancy- related reasons. • Births attended byskilled health staff are live births attended by personneltrained to give women the necessary care duringpregnancy, labor, and postpartum; to conduct deliverieson their own; and to care for newborns. • Maternal mortalityratio is the number of women who die from pregnancy-relatedcauses while pregnant or within 42 daysof pregnancy termination per 100,000 live births. • Lifetimerisk of maternal death is the probability (1 in thenumber of women likely to die due to a maternal cause)that a 15-year-old girl will eventually die due to a maternalcause, if throughout her lifetime she experiencesthe maternal death risk and overall fertility and mortalityrates of the specifi ed year for a given population.Data sourcesData on total fertility are from the United NationsPopulation Division’s World Population Prospects: The2010 Revision; census reports and other statisticalpublications from national statistical offi ces; householdsurveys by national agencies, ICF International(for MEASURE DHS), and the U.S. Centers for DiseaseControl and Prevention; Eurostat’s DemographicStatistics; and the U.S. Bureau of the Census InternationalData Base. Data on adolescent fertility arefrom World Population Prospects: The 2010 Revision,with annual data linearly interpolated by the WorldBank’s Development Data Group. Data on unmet needfor contraception and contraceptive prevalence arefrom household surveys, including MEASURE DHSDemographic and Health Surveys by ICF Internationaland Multiple Indicator Cluster Surveys by UNICEF.Data on pregnant women receiving prenatal care,births attended by skilled health staff, and nationalestimates of maternal mortality are from UNICEF’sState of the World’s Children 2012 and Childinfo andMEASURE DHS Demographic and Health Surveys byICF International. Modeled estimates of maternal mortalityand lifetime risk of maternal mortality are fromWHO, UNICEF, UNFPA, and the World Bank’s Trends inMaternal Mortality: 1990–2008 (2010).2012 World Development Indicators 115

2.20 Nutrition and growthPrevalence ofundernourishmentPrevalence of child malnutritionPrevalence ofoverweight childrenUnderweight% of children under age 5Stunting Wasting% of children under age 5% of populationMale Female Male Female Male Female Male Female1990–92 2006–08 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 aAfghanistan .. .. .. .. .. .. .. .. .. ..Albania

Nutrition and growth2.20PEOPLEPrevalence ofundernourishmentPrevalence of child malnutritionPrevalence ofoverweight childrenUnderweight% of children under age 5Stunting Wasting% of children under age 5% of populationMale Female Male Female Male Female Male Female1990–92 2006–08 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 aHonduras 19 12 8.9 8.3 31.5 28.3 1.6 1.1 6.3 5.2Hungary

2.20Nutrition and growthPrevalence ofundernourishmentPrevalence of child malnutritionPrevalence ofoverweight childrenUnderweight% of children under age 5Stunting Wasting% of children under age 5% of populationMale Female Male Female Male Female Male Female1990–92 2006–08 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 a 2005–10 aRomania

Nutrition and growth 2.20PEOPLEAbout the dataDefinitionsGood nutrition is the cornerstone for survival, healthand development. Well nourished children performbetter in school, grow into healthy adults, and inturn give their children a better start in life. Wellnourished women face fewer risks during pregnancyand childbirth, and their children set off on fi rmerdevelopmental paths, both physically and mentally(United Nations Children’s Fund [UNICEF], www.childinfo.org).Data on undernourishment are from the Food andAgriculture Organization (FAO) of the United Nationsand measure food deprivation based on averagefood available for human consumption per person,the level of inequality in access to food, and theminimum calories required for an average person.From a policy and program standpoint, however,this measure has its limits. First, food insecurityexists even where food availability is not a problembecause of inad equate access of poor householdsto food. Second, food insecurity is an individualor household phe nomenon, and the average foodavailable to each person, even corrected for possibleeffects of low income, is not a good predictor of foodinsecurity among the population. And third, nutritionsecurity is determined not only by food security butalso by the quality of care of mothers and childrenand the quality of the household’s health environment(Smith and Haddad 2000).Undernourished children have lower resistance toinfection and are more likely to die from commonchildhood ailments such as diarrheal diseases andrespiratory infections. Frequent illness saps thenutritional status of those who survive, locking theminto a vicious cycle of recurring sickness and falteringgrowth (UNICEF, www.childinfo.org). Estimatesof child malnutrition, based on prevalence of underweightand stunting, are from national survey data.The proportion of underweight children is the mostcommon malnutrition indica tor. Being even mildlyunderweight increases the risk of death and inhibitscognitive development in children. And it perpetuatesthe problem across generations, as mal nourishedwomen are more likely to have low-birth weightbabies. Stunting, or being below median height forage, is often used as a proxy for multifaceted deprivationand as an indicator of long-term changes inmalnutrition.Estimates of overweight children are also fromnational survey data. Once considered only a highincomeeconomy problem, overweight children havebecome a growing concern in developing countries.Research shows an association betweenchildhood obesity and a high prevalence of diabetes,respiratory disease, high blood pressure, andpsy chosocial and orthopedic disorders (de Onis andBlössner 2003). Childhood obesity is associatedwith a higher chance of obesity, premature death,and disability in adulthood. In addition to increasedfuture risks, obese children experience breathingdiffi culties and increased risk of fractures, hypertension,early markers of cardiovascular disease,insulin resistance, and psychological effects. Childrenin low- and middle-income countries are morevulnerable to inadequate nutrition before birth andin infancy and early childhood. Many of these childrenare exposed to high-fat, high-sugar, high-salt,calorie-dense, micronutrient-poor foods, which tendto be lower in cost than more nutritious foods. Thesedietary patterns, in conjunction with low levels ofphysical activity, result in sharp increases in childhoodobesity, while undernutrition continues (WorldHealth Organization [WHO]).New international growth reference standards forinfants and young children were released in 2006by the WHO to monitor children’s nutritional status.Differ ences in growth to age 5 are infl uenced moreby nutrition, feeding practices, environment, andhealthcare than by genetics or ethnicity. The previouslyreported data were based on the U.S. NationalCenter for Health Statistics–WHO growth reference.Because of the change in standards, the data in thisedition should not be compared with data in editionsprior to 2008.For indicators from household surveys, the year inthe table refers to the survey year. For more information,consult the original sources.• Prevalence of undernourishment is the percent ageof the population whose dietary energy consumptionis continuously below a minimum requirementfor maintaining a healthy life and carrying out lightphysical activity with an acceptable minimum weightfor height. • Prevalence of child malnutrition is thepercent age of children under age 5 whose weightfor age (underweight) or height for age (stunting)is more than two standard deviations below themedian for the international reference populationages 0–59 months. Height is measured by recumbentlength for children up to two years old and bystature while standing for older children. Data arebased on the WHO child growth standards releasedin 2006. • Prevalence of over weight children is thepercentage of children under age 5 whose weight forheight is more than two stan dard deviations abovethe median for the interna tional reference populationof the corresponding age as established by the WHOchild growth stan dards released in 2006.Data sourcesData on undernourishment are from the FAO’sThe State of Food Insecurity in the World. Data onmalnutrition and overweight children are from theWHO’s Global Database on Child Growth and Malnutrition(www.who.int/nutgrowthdb).2012 World Development Indicators 119

2.21Nutrition intake and supplementsLow-birthweightbabiesExclusivebreast feedingConsumption ofiodizedsaltVitamin AsupplementationPrevalenceof anemia%% of births% of children under6 months% ofhouseholds% of children6–59 months Children under age 5 Pregnant women2005–10 a 2005–10 a 2005–10 a 2010 2005–10 a 2005–10 aAfghanistan .. 83 .. 96 .. 61Albania 7 39 76 .. 31 34Algeria 6 7 61 .. 43 43Angola .. .. 45 28 .. 57Argentina 7 .. .. .. 17 31Armenia 7 35 97 .. 37 ..Australia .. .. .. .. 8 12Austria .. .. .. .. 11 15Azerbaijan 10 12 54 89 b .. ..Bahrain .. .. .. .. 25 28Bangladesh 22 43 84 100 .. ..Belarus 4 9 94 .. 27 26Belgium .. .. .. .. 9 13Benin 15 43 67 100 78 75Bolivia 6 60 89 24 .. ..Bosnia and Herzegovina 5 18 62 .. 27 35Botswana 13 20 .. 91 .. 21Brazil 8 40 96 .. 55 29Bulgaria 9 .. 100 .. 27 30Burkina Faso 16 16 34 100 .. ..Burundi 11 69 98 73 56 47Cambodia 8 74 83 95 55 44Cameroon 11 21 49 89 .. ..Canada .. .. .. .. 8 12Central African Republic 13 23 62 0 .. ..Chad .. 3 .. 68 71 60Chile 6 85 .. .. 24 28China 3 28 97 .. .. ..Hong Kong SAR, China .. .. .. .. .. ..Colombia 6 43 .. .. 28 31Congo, Dem. Rep. 10 37 59 83 71 67Congo, Rep. 13 19 82 84 66 55Costa Rica 7 15 .. .. .. ..Côte d’Ivoire 17 4 .. 100 69 55Croatia 5 98 88 .. 23 28Cuba 5 26 88 .. 27 39Cyprus .. .. .. .. 19 25Czech Republic .. .. .. .. 18 22Denmark .. .. .. .. 9 12Dominican Republic 11 9 19 .. 35 40Ecuador 8 .. .. .. 38 38Egypt, Arab Rep. 13 53 79 68 b 49 34El Salvador .. 31 .. .. .. ..Eritrea .. .. .. 44 70 55Estonia .. .. .. .. 23 23Ethiopia 20 49 20 84 54 31Finland .. .. .. .. 11 15France .. .. .. .. 8 11Gabon .. .. .. 0 44 46Gambia, The 11 36 21 100 .. ..Georgia 5 11 100 .. 41 42Germany .. .. .. .. 8 12Ghana 13 63 32 93 .. ..Greece .. .. .. .. 12 19Guatemala 11 50 76 36 .. ..Guinea 12 48 41 97 76 ..Guinea-Bissau 11 38 12 100 75 58Haiti 25 41 3 21 .. 501202012 World Development Indicators

Nutrition intake and supplements2.21PEOPLELow-birthweightbabiesExclusivebreast feedingConsumption ofiodizedsaltVitamin AsupplementationPrevalenceof anemia%% of births% of children under6 months% ofhouseholds% of children6–59 months Children under age 5 Pregnant women2005–10 a 2005–10 a 2005–10 a 2010 2005–10 a 2005–10 aHonduras 10 30 .. .. .. 21Hungary .. .. .. .. 19 21India 28 46 51 34 74 50Indonesia 11 15 62 80 44 44Iran, Islamic Rep. 7 23 99 .. 35 ..Iraq 15 25 28 .. 56 38Ireland .. .. .. .. 10 15Israel .. .. .. .. 12 17Italy .. .. .. .. 11 15Jamaica 14 15 .. .. .. ..Japan .. .. .. .. 11 15Jordan 13 22 .. .. .. ..Kazakhstan 6 17 92 .. .. 26Kenya 8 32 98 62 .. ..Korea, Dem. Rep. 6 .. 25 99 .. ..Korea, Rep. .. .. .. .. .. 23Kosovo .. .. .. .. .. ..Kuwait .. .. .. .. .. 31Kyrgyz Republic 5 32 76 97 .. 34Lao PDR 11 26 84 83 .. 56Latvia .. .. .. .. 27 25Lebanon .. .. .. .. .. 32Lesotho .. 54 84 .. 49 25Liberia 14 34 .. 97 .. ..Libya .. .. .. .. 34 34Lithuania .. .. .. .. 24 24Macedonia, FYR 6 16 94 .. .. 32Madagascar 16 51 53 95 .. ..Malawi 13 72 50 96 73 47Malaysia 11 .. 18 .. 32 ..Mali 19 38 79 99 .. ..Mauritania 34 46 23 97 68 53Mauritius .. .. .. .. .. ..Mexico 7 .. .. .. 24 21Moldova 6 46 60 .. 41 36Mongolia 5 57 83 61 .. 37Morocco .. .. 21 .. .. ..Mozambique 16 37 25 100 .. 52Myanmar 9 24 93 94 63 50Namibia 16 24 .. 13 41 31Nepal 21 53 .. 91 48 42Netherlands .. .. .. .. 9 13New Zealand .. .. .. .. 11 18Nicaragua 9 31 .. 7 20 ..Niger 27 27 32 98 84 61Nigeria 12 13 .. 91 .. ..Norway .. .. .. .. 6 9Oman 12 .. .. .. .. ..Pakistan 32 37 .. 87 .. ..Panama .. .. .. .. .. ..Papua New Guinea 10 56 92 14 60 55Paraguay 6 24 94 .. 30 39Peru 8 68 .. .. .. ..Philippines 21 34 81 91 21 43Poland .. .. .. .. 23 25Portugal .. .. .. .. 13 17Puerto Rico .. .. .. .. .. ..Qatar .. .. .. .. .. 292012 World Development Indicators 121

2.21Nutrition intake and supplementsLow-birthweightbabiesExclusivebreast feedingConsumption ofiodizedsaltVitamin AsupplementationPrevalenceof anemia%% of births% of children under6 months% ofhouseholds% of children6–59 months Children under age 5 Pregnant women2005–10 a 2005–10 a 2005–10 a 2010 2005–10 a 2005–10 aRomania .. .. .. .. 40 30Russian Federation 6 .. .. .. 27 21Rwanda 6 85 88 92 56 ..Saudi Arabia .. .. .. .. 33 32Senegal 19 34 41 97 83 58Serbia 6 15 32 .. .. ..Sierra Leone 14 11 58 100 83 60Singapore .. .. .. .. 19 24Slovak Republic .. .. .. .. 23 25Slovenia .. .. .. .. 14 19Somalia 11 9 1 62 .. ..South Africa .. .. .. 39 .. 22South Sudan .. .. .. .. .. ..Spain .. .. .. .. 13 18Sri Lanka 17 76 92 85 .. ..Sudan .. 34 11 82 85 58Swaziland 9 44 52 38 47 24Sweden .. .. .. .. 9 13Switzerland .. .. .. .. 6 ..Syrian Arab Republic 10 43 79 33 b 41 39Tajikistan 10 25 62 95 .. 45Tanzania 10 50 59 99 72 58Thailand 7 15 47 .. .. ..Timor-Leste .. 52 60 48 .. ..Togo 11 63 32 100 52 50Trinidad and Tobago 19 13 28 .. 30 30Tunisia 5 6 .. .. .. ..Turkey 11 42 69 .. 33 40Turkmenistan 4 11 87 .. .. 30Uganda 14 60 96 64 73 64Ukraine 4 18 18 .. .. 27United Arab Emirates 6 .. .. .. 28 28United Kingdom .. .. .. .. .. 15United States .. .. .. .. .. 6Uruguay 9 57 .. .. 19 27Uzbekistan 5 26 53 94 .. ..Venezuela, RB 8 .. .. .. 33 40Vietnam 5 17 93 95 b .. ..West Bank and Gaza 7 27 86 .. .. ..Yemen, Rep. .. .. .. .. 68 58Zambia 11 61 .. 92 .. ..Zimbabwe 11 32 c 91 49 58 47World 15 w 37 w 70 w .. w .. w .. wLow income 15 44 62 88 .. ..Middle income 15 35 71 .. .. ..Lower middle income 21 37 54 56 65 48Upper middle income 5 30 91 .. .. ..Low & middle income 15 37 70 .. .. ..East Asia & Pacifi c 6 26 86 .. .. ..Europe & Central Asia 7 .. .. .. 30 30Latin America & Carib. 8 .. .. .. 36 ..Middle East & N. Africa 11 34 69 .. 48 ..South Asia 27 47 55 50 74 50Sub-Saharan Africa 13 35 50 86 .. ..High income .. .. .. .. .. 13Euro area .. .. .. .. 10 14a. Data are for the most recent year available. b. Country’s vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Data are for 2011.1222012 World Development Indicators

Nutrition intake and supplements2.21PEOPLEAbout the dataDefinitionsLow birthweight, which is associated with maternalmalnutrition, raises the risk of infant mortality andstunts growth in infancy and childhood. There is alsoemerging evidence that low-birthweight babies aremore prone to noncommunicable diseases such asdiabetes and cardiovascular diseases. Low birthweightcan arise as a result of a baby being borntoo soon or too small for gestational age. Babiesborn prematurely who are also small for their gestationalage have the worst prognosis. In low- andmiddle-income countries low birthweight stemsprimarily from poor maternal health and nutrition.Three factors have the most impact: poor maternalnutritional status before conception, mother’s shortstature (due mostly to undernutrition and infectionsduring childhood), and poor nutrition during pregnancy(United Nations Children’s Fund [UNICEF],www.childinfo.org). Estimates of low-birthweightinfants are drawn mostly from hos pital records andhousehold surveys. Many births in developing countriestake place at home and are seldom recorded. Ahospital birth may indi cate higher income and thereforebetter nutrition, or it could indicate a higher riskbirth. Caution should therefore be used in interpretingthe data.For optimal infant and young child feeding, mothersinitiate breastfeeding within one hour of birth,breastfeed exclusively for the fi rst six months, andcontinue to breastfeed for two years or more whileproviding nutritionally adequate, safe, and ageappropriatesolid, semisolid, and soft foods (UNICEF,www.childinfo.org). Optimal breastfeeding can savean esti mated 1.4 million children a year. Breast milkalone contains all the nutrients, antibodies, hormones,and antioxidants an infant needs to thrive.It protects babies from diarrhea and acute respiratoryinfections, stimulates their immune systems andresponse to vaccination, and may confer cognitivebenefi ts. The data on breastfeeding are derived fromhousehold surveys.Iodine defi ciency is the single most importantcause of preventable mental retardation, it contributessignifi cantly to the risk of stillbirth and miscarriage,and it increases infant mortality. A diet lowin iodine is the main cause of iodine defi ciency. Itusually occurs among populations living in areaswhere the soil has been depleted of iodine. If soilis defi cient in iodine, so are the plants grown in it,including the grains and vegetables that people andanimals consume. There are almost no countries inthe world where iodine defi ciency has not been apublic health problem. Every year about 40 millionnewborns in low- and middle-income countriesremain unprotected from the lifelong consequencesof brain damage associated with iodine defi ciencydisorders, which affect a child’s ability to learn andto earn a living as an adult, thereby preventing children,communities, and countries from fulfi lling theirpotential (UNICEF, www.childinfo.org). Widely usedand inexpensive, iodized salt is the best source ofiodine, and a global campaign to iodize edible saltis signifi cantly reducing the risks. The data on consumptionof iodized salt are derived from householdsurveys.Vitamin A is essential for immune system functioning.Vitamin A defi ciency, a leading cause of blindness,also causes a greater risk of dying from a rangeof childhood ail ments such as measles, malaria, anddiarrhea. In low- and middle-income countries, wherevitamin A is consumed largely in fruits and vegetables,daily per capita intake is often insuffi cient tomeet dietary requirements. Providing young childrenwith two high-dose vitamin A capsules a year is asafe, cost-effective, effi cient strategy for eliminatingvitamin A defi ciency and improving child survival.Giv ing vitamin A to new breastfeeding mothers helpsprotect their children during the fi rst months of life.Food fortifi cation with vitamin A is being intro ducedin many developing countries.Anemia is a condition in which the number of redblood cells or their oxygen-carrying capacity is insufficient to meet physiologic needs, which vary by age,sex, altitude, smoking status, and pregnancy status.In its severe form it is associated with fatigue,weakness, dizziness, and drowsiness (World HealthOrganization [WHO], www.who.int/topics/anaemia/).Children under age 5 and pregnant women have thehighest risk for anemia. Data on anemia are compiledby the WHO based mainly on nationally representativesurveys between 1993 and 2005, whichmeasured hemoglobin in the blood. WHO’s hemoglobinthresholds were then used to determine anemiastatus based on age, sex, and physiological status.Data should be used with caution because surveysdiffer in quality, coverage, age group interviewed,and treatment of missing values across countriesand over time.For indicators from household surveys, the year inthe table refers to the survey year. For more information,consult the original sources.• Low-birthweight babies are newborns weighingless than 2.5 kilograms within the fi rst hoursof life, before signifi cant postnatal weight loss hasoccurred. • Exclusive breastfeeding is the percentageof children less than six months old who werefed breast milk alone (no other liquids) in the past 24hours. • Consumption of iodized salt is the percentageof households that use edible salt fortifi ed withiodine. • Vitamin A supplementation is the percentageof children ages 6–59 months old who receivedat least two doses of vitamin A in the previous year.• Prevalence of anemia, children under age 5, is thepercentage of children under age 5 whose hemoglobinlevel is less than 110 grams per liter at sea level.• Prevalence of anemia, pregnant women, is thepercentage of pregnant women whose hemoglobinlevel is less than 110 grams per liter at sea level.Data sourcesData on low-birthweight babies, breastfeeding,consumption of iodized salt, and vitamin A supplementationare from the United Nations Children’sFund’s The State of the World’s Children 2012 andChildinfo. Data on anemia are from the WHO’sWorldwide Prevalence of Anemia 1993 –2005(2008c) and Integrated WHO Nutrition GlobalDatabases.2012 World Development Indicators 123

2.22Health risk factors and future challengesPrevalenceof smokingIncidence oftuberculosisPrevalenceof diabetesPrevalence of HIV aAntiretroviraltherapycoverageCause of death% of populationCommunicablediseases andper% ofTotalFemale% of totalYouth% of population % of populationmaternal,prenatal, Noncommunicable% of adults 100,000 population % of population population ages 15–24 with advanced and nutritionMale Female people ages 20–79 ages 15–49 with HIV Male Female HIV infection conditions diseases Injuries2009 2009 2010 2011 1990 2009 2009 2009 2009 2005–10 b 2005–10 b 2005–10 b 2005–10 bAfghanistan .. .. 189 7.8 .. .. .. .. .. .. 63 29 8Albania 60 19 14 2.9 .. .. .. .. .. .. 5 89 5Algeria .. .. 90 7.0

Health risk factors and future challenges 2.22PEOPLEPrevalenceof smokingIncidence oftuberculosisPrevalenceof diabetesPrevalence of HIV aAntiretroviraltherapycoverageCause of death% of populationCommunicablediseases andper% ofTotalFemale% of totalYouth% of population % of populationmaternal,prenatal, Noncommunicable% of adults 100,000 population % of population population ages 15–24 with advanced and nutritionMale Female people ages 20–79 ages 15–49 with HIV Male Female HIV infection conditions diseases Injuries2009 2009 2010 2011 1990 2009 2009 2009 2009 2005–10 b 2005–10 b 2005–10 b 2005–10 bHonduras .. 3 51 6.8 1.1 0.8 32 0.3 0.2 33 23 69 8Hungary 43 33 15 6.2 0.1

2.22Health risk factors and future challengesPrevalenceof smokingIncidence oftuberculosisPrevalenceof diabetesPrevalence of HIV aAntiretroviraltherapycoverageCause of death% of populationCommunicablediseases andper% ofTotalFemale% of totalYouth% of population % of populationmaternal,prenatal, Noncommunicable% of adults 100,000 population % of population population ages 15–24 with advanced and nutritionMale Female people ages 20–79 ages 15–49 with HIV Male Female HIV infection conditions diseases Injuries2009 2009 2010 2011 1990 2009 2009 2009 2009 2005–10 b 2005–10 b 2005–10 b 2005–10 bRomania 46 24 116 7.9

Health risk factors and future challenges 2.22PEOPLEAbout the dataDefinitionsThe limited availability of data on health status is amajor constraint in assessing the health situation indeveloping countries. Surveillance data are lackingfor many major public health concerns. Estimatesof prevalence and incidence are available for somediseases but are often unreliable and incomplete.National health authorities differ widely in capacityand willingness to collect or report information. Tocompensate for this and improve reliability and internationalcomparability, the World Health Organization(WHO) prepares estimates in accordance withepidemiological models and statistical standards.Smoking is the most common form of tobacco useand the prevalence of smoking is therefore a goodmeasure of the tobacco epidemic (Corrao and others2000). Tobacco use causes heart and other vasculardiseases and cancers of the lung and other organs.Given the long delay between starting to smoke andthe onset of disease, the health impact of smokingwill increase rapidly only in the next few decades.The data presented in the table are age- standardizedrates for adults ages 15 and older from the WHO.Tuberculosis is one of the main causes of adultdeaths from a single infectious agent in developingcountries. In developed countries tuberculosishas reemerged largely as a result of cases amongimmigrants. Since tuberculosis incidence cannot bedirectly measured, estimates are obtained by elicitingexpert opinion or are derived from measurementsof prevalence or mortality. These estimates includeuncertainty intervals, which are not shown in thetable but are available at http://data.worldbank.organd from the original source.Diabetes, an important cause of ill health and arisk factor for other diseases in developed countries,is spreading rapidly in developing countries. Highestamong the elderly, prevalence rates are rising amongyounger and productive populations in developingcountries. Economic development has led to thespread of Western lifestyles and diet to developingcountries, resulting in a substantial increase indiabetes. Without effective prevention and controlprograms, diabetes will likely continue to increase.Data are estimated based on sample surveys.Adult HIV prevalence rates refl ect the rate of HIVinfection in each country’s population. Low nationalprevalence rates can be misleading, however. Theyoften disguise epidemics that are initially concentratedin certain localities or population groups andthreaten to spill over into the wider population. Inmany developing countries most new infectionsoccur in young adults, with young women especiallyvulnerable.Data on HIV are from the Joint United Nations Programmeon HIV/AIDS’s (UNAIDS) Global Report: UNAIDSReport on the Global AIDS Epidemic 2010. Changes inprocedures and assumptions for estimating the dataand better coordination with countries have resultedin improved estimates of HIV and AIDS. For example,improved software was used to model the course ofHIV epidemics and their impacts, making full use ofinformation on HIV prevalence trends from surveillancedata as well as survey data. The software explicitlyincludes the effect of antiretroviral therapy when calculatingHIV incidence and models reduced infectivityamong people receiving antiretroviral therapy, which ishaving a larger impact on HIV prevalence and allowingHIV-positive people to live longer. The software alsoallows for changes in urbanization over time—importantbecause prevalence is higher in urban areas andbecause many countries have seen rapid urbanizationover the past two decades. The estimates include plausiblebounds, not shown in the table, which refl ect thecertainty associated with each of the estimates. Thebounds are available at http://data.worldbank.org andfrom the original source.Standard antiretroviral therapy consists of the useof at least three antiretroviral drugs to maximally suppressHIV and stop the progression of HIV disease. Antiretroviraltherapy has led to huge reductions in deathand suffering of people with advanced HIV infection.Data are collected through three international monitoringand reporting processes: country responses tothe WHO; research by the Interagency Task Team onPrevention of HIV Infection in Women, Mothers andtheir Children; and country report to UNAIDS throughthe United Nations General Assembly Special SessionDeclaration of Commitment on HIV/AIDS.Data on cause of death are compiled by the WHO,based mainly on data from national vital registry systems,as well as sample registration systems, populationlaboratories, and epidemiological analysis ofspecifi c conditions. Data are classifi ed based on theInternational Statistical Classifi cation of Diseases andRelated Health Problems, 10th revision. Data havebeen carefully analyzed to take into account incompletecoverage of vital registration and the likely differencesin cause of death patterns that would be expected inundercovered and often poorer subpopulations. Specialattention has also been paid to misattribution or miscodingof causes of death in cardiovascular diseases,cancer, injuries, and general ill-defined categories. Forfurther information, consult the original source.For indicators from household surveys, the year inthe table refers to the survey year. For more information,consult the original sources.• Prevalence of smoking is the percentage of thepopulation ages 15 and older who smoke any tobaccoproducts. It includes daily and nondaily smoking.Estimates are adjusted and age- standardized prevalence• Incidence of tuberculosis is the number ofnew and relapse cases of tuberculosis (all types)per 100,000 people. • Prevalence of diabetes isthe percentage of people ages 20–79 who havetype 1 or type 2 diabetes. • Prevalence of HIV isthe percentage of people who are infected withHIV. Total and youth rates are percentages of therelevant age group. Female rate is as a percentageof the total population living with HIV. • Antiretroviraltherapy coverage is the percentage of adultsand children with advanced HIV infection currentlyreceiving antiretroviral therapy according to nationallyapproved treatment protocols (or WHO/UNAIDSstandards) among the estimated number of peoplewith advanced HIV infection. • Cause of death is theshare of all deaths due to the specifi ed underlyingcause. • Communicable diseases and maternal,perinatal, and nutrition conditions are infectiousand parasitic diseases, respiratory infections, andnutritional defi ciencies such as underweight andstunting. • Noncommunicable diseases are cancer,diabetes mellitus, cardiovascular diseases, digestivediseases, skin diseases, musculoskeletal diseases,and congenital anomalies. • Injuries include unintentionaland intentional injuries.Data sourcesData on smoking are from the WHO’s Report onthe Global Tobacco Epidemic 2011. Data on tuberculosisare from the WHO’s Global TuberculosisControl Report 2011. Data on diabetes are fromthe International Diabetes Federation’s DiabetesAtlas, 5th edition. Data on HIV are from UNAIDS’sGlobal Report: UNAIDS Report on the Global AIDSEpidemic 2010. Data on antiretroviral therapycoverage are from the WHO. Data on cause ofdeath are from the WHO’s Health Statistics andHealth Information Systems database (www.who.int/healthinfo/global_burden_disease/estimates_country).2012 World Development Indicators 127

2.23 MortalityLife expectancyat birthNeonatalmortality rateInfant mortalityrateUnder-fivemortality rateChildmortality rateAdult mortalityrateper 1,000 per 1,000years per 1,000 live births per 1,000 live births per 1,000 live births Male Female Male Female1990 2010 1990 2010 1990 2010 1990 2010 2005–10 a,b 2005–10 a,b 2006–10 a 2006–10 aAfghanistan 42 48 53 45 140 103 209 149 .. .. 409 377Albania 72 77 17 9 36 16 41 18 3 1 95 46Algeria 67 73 29 18 55 31 68 36 .. .. 126 102Angola 41 51 51 41 144 98 243 161 .. .. 386 337Argentina 71 76 15 7 24 12 27 14 .. .. 160 74Armenia 68 74 26 11 46 18 55 20 8 3 162 79Australia 77 82 5 3 8 4 9 5 .. .. 82 47Austria 76 80 4 2 8 4 9 4 .. .. 99 50Azerbaijan 65 71 31 19 74 39 93 46 9 5 181 74Bahrain 72 75 6 4 15 9 17 10 .. .. 95 73Bangladesh 59 69 55 27 99 38 143 48 16 20 163 137Belarus 71 70 7 3 14 4 17 6 .. .. 334 112Belgium 76 80 4 2 9 4 10 4 .. .. 107 61Benin 49 56 40 32 107 73 178 115 64 65 332 276Bolivia 59 66 39 23 84 42 121 54 18 20 225 167Bosnia and Herzegovina 67 75 12 5 17 8 19 8 .. .. 134 69Botswana 64 53 22 19 46 36 59 48 .. .. 535 579Brazil 66 73 28 12 50 17 59 19 .. .. 218 114Bulgaria 72 74 11 7 18 11 22 13 .. .. 205 86Burkina Faso 48 55 41 38 103 93 205 176 .. .. 300 249Burundi 46 50 49 42 110 88 183 142 65 65 418 381Cambodia 55 63 38 22 87 43 121 51 20 20 262 222Cameroon 53 51 34 34 85 84 137 136 .. .. 410 378Canada 77 81 4 4 7 5 8 6 .. .. 92 55Central African Republic 49 48 43 42 110 106 165 159 74 82 469 436Chad 51 49 45 41 113 99 207 173 .. .. 372 316Chile 74 79 9 5 16 8 19 9 .. .. 123 57China 69 c 73 c 24 11 38 16 48 18 .. .. 138 88Hong Kong SAR, China 77 83 .. .. .. .. .. .. .. .. 74 37Colombia 68 73 20 12 30 18 37 22 4 3 194 89Congo, Dem. Rep. 47 48 48 46 117 112 181 170 70 64 407 354Congo, Rep. 56 57 33 29 74 61 116 93 49 43 335 301Costa Rica 76 79 10 6 15 9 17 10 .. .. 110 58Côte d’Ivoire 53 55 46 41 105 86 151 123 .. .. 377 349Croatia 72 76 8 3 11 5 13 6 1 1 140 57Cuba 74 79 7 3 11 5 13 6 .. .. 108 68Cyprus 77 79 5 2 10 3 11 4 .. .. 77 38Czech Republic 71 77 9 2 12 3 14 4 .. .. 138 63Denmark 75 79 4 2 7 3 9 4 .. .. 107 65Dominican Republic 68 73 29 15 48 22 62 27 6 4 200 131Ecuador 69 75 20 10 41 18 52 20 .. .. 161 84Egypt, Arab Rep. 62 73 28 9 68 19 94 22 5 5 140 85El Salvador 66 72 18 6 48 14 62 16 .. .. 281 119Eritrea 48 61 31 18 87 42 141 61 .. .. 345 261Estonia 69 75 13 3 17 4 21 5 .. .. 234 77Ethiopia 47 59 48 35 111 68 184 106 56 56 304 259Finland 75 80 4 2 6 2 7 3 .. .. 123 56France d 77 81 3 2 7 3 9 4 .. .. 118 55Gabon 61 62 31 26 68 54 93 74 .. .. 288 263Gambia, The 53 58 42 31 78 57 165 98 46 39 299 244Georgia 70 73 27 15 40 20 47 22 5 4 177 67Germany 75 80 4 2 7 3 9 4 .. .. 101 54Ghana 57 64 38 28 77 50 122 74 38 28 255 225Greece 77 80 9 2 11 3 13 4 .. .. 101 46Guatemala 62 71 28 15 56 25 78 32 .. .. 226 122Guinea 44 54 51 38 135 81 229 130 89 86 352 303Guinea-Bissau 43 48 48 40 125 92 210 150 110 88 410 358Haiti 55 62 38 27 104 70 151 165 33 36 264 2361282012 World Development Indicators

Mortality 2.23PEOPLELife expectancyat birthNeonatalmortality rateInfant mortalityrateUnder-fivemortality rateChildmortality rateAdult mortalityrateper 1,000 per 1,000years per 1,000 live births per 1,000 live births per 1,000 live births Male Female Male Female1990 2010 1990 2010 1990 2010 1990 2010 2005–10 a,b 2005–10 a,b 2006–10 a 2006–10 aHonduras 66 73 23 12 45 20 58 24 8 9 164 115Hungary 69 74 12 4 17 5 19 6 .. .. 229 99India 58 65 47 32 81 48 115 63 9 12 253 168Indonesia 62 69 31 17 56 27 85 35 13 12 205 169Iran, Islamic Rep. 62 73 28 14 50 22 65 26 .. .. 163 80Iraq 68 68 23 20 37 31 46 39 6 7 281 125Ireland 75 80 5 2 8 3 9 4 .. .. 97 57Israel 77 82 6 2 10 4 12 5 .. .. 79 45Italy 77 82 6 2 8 3 10 4 .. .. 78 41Jamaica 71 73 13 9 31 20 38 24 5 6 189 117Japan 79 83 3 1 5 2 6 3 .. .. 85 42Jordan 70 73 20 13 32 18 38 22 3 7 143 99Kazakhstan 68 68 26 17 48 29 57 33 5 4 366 147Kenya 59 56 31 28 64 55 99 85 27 25 380 358Korea, Dem. Rep. 70 69 22 18 23 26 45 33 .. .. 194 124Korea, Rep. 71 81 3 2 6 4 8 5 .. .. 90 41Kosovo 68 70 .. .. .. .. .. .. .. .. .. ..Kuwait 73 75 9 6 13 10 15 11 .. .. 102 62Kyrgyz Republic 68 69 30 19 59 33 72 38 8 4 304 132Lao PDR 54 67 39 21 100 42 145 54 .. .. 207 167Latvia 69 73 12 5 16 8 21 10 .. .. 247 94Lebanon 69 72 18 12 31 19 38 22 .. .. 150 101Lesotho 59 47 36 35 72 65 89 85 .. .. 578 613Liberia 42 56 53 34 151 74 227 103 62 64 349 314Libya 68 75 22 10 33 13 45 17 .. .. 137 85Lithuania 71 73 10 3 14 5 17 7 .. .. 275 95Macedonia, FYR 71 75 17 8 34 10 39 12 2 1 126 78Madagascar 51 66 40 22 97 43 159 62 30 31 215 169Malawi 47 53 44 27 131 58 222 92 52 54 409 411Malaysia 70 74 9 3 15 5 18 6 .. .. 147 75Mali 44 51 57 48 131 99 255 178 117 114 361 297Mauritania 56 58 42 39 80 75 124 111 53 44 290 220Mauritius 69 73 16 9 21 13 24 15 .. .. 206 102Mexico 71 77 17 7 38 14 49 17 .. .. 133 73Moldova 67 69 15 9 30 16 37 19 7 4 302 146Mongolia 61 68 27 12 76 26 107 32 11 10 294 141Morocco 64 72 36 19 67 30 86 36 .. .. 144 91Mozambique 43 50 51 39 146 92 219 135 .. .. 482 444Myanmar 57 65 44 32 79 50 112 66 .. .. 235 187Namibia 61 62 25 17 49 29 73 40 24 19 345 343Nepal 54 68 54 28 97 41 141 50 21 18 186 160Netherlands 77 81 5 3 7 4 8 4 .. .. 75 56New Zealand 75 81 4 3 9 5 11 6 .. .. 87 58Nicaragua 64 74 25 12 52 23 68 27 .. .. 197 111Niger 41 54 48 32 132 73 311 143 138 135 313 271Nigeria 46 51 49 40 126 88 213 143 91 93 393 365Norway 77 81 4 2 7 3 9 3 .. .. 82 50Oman 71 73 22 5 36 8 47 9 .. .. 138 76Pakistan 61 65 51 41 96 70 124 87 14 22 189 158Panama 72 76 14 9 26 17 33 20 .. .. 133 70Papua New Guinea 56 62 30 23 65 47 90 61 .. .. 315 239Paraguay 68 72 24 14 40 21 50 25 .. .. 168 121Peru 66 74 27 9 55 15 78 19 13 4 158 97Philippines 65 68 23 14 42 23 59 29 10 9 262 145Poland 71 76 11 4 15 5 17 6 .. .. 198 76Portugal 74 79 7 2 11 3 15 4 .. .. 122 53Puerto Rico 74 79 .. .. .. .. .. .. .. .. 133 51Qatar 74 78 10 4 17 7 21 8 .. .. 69 592012 World Development Indicators 129

2.23 MortalityLife expectancyat birthNeonatalmortality rateInfant mortalityrateUnder-fivemortality rateChildmortality rateAdult mortalityrateper 1,000 per 1,000years per 1,000 live births per 1,000 live births per 1,000 live births Male Female Male Female1990 2010 1990 2010 1990 2010 1990 2010 2005–10 a,b 2005–10 a,b 2006–10 a 2006–10 aRomania 70 73 15 8 29 11 37 14 .. .. 185 76Russian Federation 69 69 12 6 22 9 27 12 .. .. 372 139Rwanda 33 55 41 29 99 59 163 91 69 55 348 315Saudi Arabia 69 74 20 10 36 15 45 18 3 4 126 96Senegal 53 59 40 27 70 50 139 75 43 39 291 239Serbia 71 74 16 4 25 6 29 7 4 3 150 e 82 eSierra Leone 39 47 57 45 162 114 276 174 67 61 464 444Singapore 76 82 4 1 6 2 8 3 .. .. 77 45Slovak Republic 71 75 12 4 15 7 18 8 .. .. 184 74Slovenia 73 79 5 2 9 2 10 3 .. .. 124 54Somalia 45 51 52 52 108 108 180 180 53 54 368 312South Africa 62 52 18 18 47 41 60 57 .. .. 567 560South Sudan .. 62 .. .. .. .. .. .. .. .. .. ..Spain 77 82 6 3 9 4 11 5 .. .. 94 43Sri Lanka 70 75 18 10 26 14 32 17 .. .. 186 78Sudan 53 61 39 35 78 66 125 103 38 30 265 211Swaziland 59 48 24 21 70 55 96 78 32 30 562 580Sweden 78 81 3 2 6 2 7 3 .. .. 69 41Switzerland 77 82 4 3 7 4 8 5 .. .. 76 42Syrian Arab Republic 71 76 18 9 31 14 38 16 5 3 110 72Tajikistan 63 67 37 25 91 52 116 63 18 13 224 128Tanzania 51 57 40 26 95 60 155 92 .. .. 362 343Thailand 72 74 17 8 26 11 32 13 .. .. 205 101Timor-Leste 46 62 48 24 127 56 169 81 .. .. 259 223Togo 53 57 40 32 87 66 147 103 55 43 340 297Trinidad and Tobago 69 70 23 18 32 24 37 27 5 8 233 136Tunisia 70 75 23 9 39 14 49 16 .. .. 123 69Turkey 63 74 33 10 66 14 80 18 6 6 136 77Turkmenistan 63 65 33 23 78 47 98 56 .. .. 304 159Uganda 47 54 36 26 106 63 175 99 75 62 400 385Ukraine 70 70 9 6 18 11 21 13 4 1 385 142United Arab Emirates 72 77 12 4 18 6 22 7 .. .. 90 68United Kingdom 76 80 5 3 8 5 9 5 .. .. 95 58United States 75 78 6 4 9 7 11 8 .. .. 139 80Uruguay 72 76 11 6 20 9 23 11 .. .. 133 60Uzbekistan 67 68 30 23 63 44 77 52 11 7 243 139Venezuela, RB 71 74 17 10 28 16 33 18 .. .. 171 89Vietnam 65 75 23 12 37 19 51 23 5 4 132 89West Bank and Gaza 68 73 .. .. 36 20 45 22 3 3 142 105Yemen, Rep. 56 65 43 32 90 57 128 77 10 11 231 186Zambia 47 48 40 30 109 69 183 111 66 55 491 493Zimbabwe 61 50 27 27 52 51 78 80 21 21 543 594World 65 w 70 w 32 w 23 w 62 w 41 w 90 w 58 w .. w .. w 210 w 150 wLow income 53 59 46 33 103 70 165 108 53 51 297 260Middle income 64 69 33 22 61 38 85 51 .. .. 202 136Lower middle income 59 65 41 29 78 50 113 69 21 22 244 175Upper middle income 69 73 23 11 39 17 49 20 .. .. 161 100Low & middle income 63 68 35 25 68 45 98 63 .. .. 213 152East Asia & Pacifi c 68 72 25 13 42 20 56 24 .. .. 157 105Europe & Central Asia 68 71 21 11 42 19 51 23 .. .. 273 116Latin America & Carib. 68 74 23 11 43 18 54 23 .. .. 181 98Middle East & N. Africa 64 72 29 16 56 27 74 34 .. .. 160 95South Asia 59 65 48 33 86 52 120 67 11 15 239 166Sub-Saharan Africa 50 54 43 35 105 76 175 121 68 65 379 346High income 75 80 6 3 10 5 12 6 .. .. 117 62Euro area 76 81 5 2 8 3 10 4 .. .. 107 53a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Taiwan,China. d. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. e. Includes Kosovo.1302012 World Development Indicators

Mortality2.23PEOPLEAbout the dataDefinitionsMortality rates for different age groups (infants, children,and adults) and overall mortality indicators (lifeexpectancy at birth or survival to a given age) areimportant indicators of health status in a country.Because data on the incidence and prevalence ofdiseases are frequently unavailable, mortality ratesare often used to identify vulnerable populations.And they are among the indicators most frequentlyused to compare socioeconomic developmentacross countries.The main sources of mortality data are vital registrationsystems and direct or indirect estimatesbased on sample surveys or censuses. A “complete”vital registration system—covering at least 90 percentof vital events in the population—is the bestsource of age-specifi c mortality data. Where reliableage-specifi c mortality data are available, life expectancyat birth is directly estimated from the life tableconstructed from age-specifi c mortality data.But complete vital registration systems are fairlyuncommon in developing countries. Thus estimatesmust be obtained from sample surveys or derivedby applying indirect estimation techniques to registration,census, or survey data (see table 2.17and Primary data documentation). Survey data aresubject to recall error, and surveys estimating infantdeaths require large samples because householdsin which a birth has occurred during a given yearcannot ordinarily be preselected for sampling. Indirectestimates rely on model life tables that may beinappropriate for the population concerned. Becauselife expectancy at birth is estimated using infant mortalitydata and model life tables for many developingcountries, similar reliability issues arise for thisindicator. Extrapolations based on outdated surveysmay not be reliable for monitoring changes in healthstatus or for comparative analytical work.Estimates of neonatal, infant, and under-fi ve mortalitytend to vary by source and method for a giventime and place. Years for available estimates alsovary by country, making comparison across countriesand over time diffi cult. To make neonatal, infant, andunder-fi ve mortality estimates comparable and toensure consistency across estimates by differentagencies, the United Nations Inter-agency Groupfor Child Mortality Estimation, which comprises theUnited Nations Children’s Fund (UNICEF), the UnitedNations Population Division, the World Health Organization(WHO), the World Bank, and other universitiesand research institutes, developed and adopted astatistical method that uses all available informationto reconcile differences. The method uses a locallyweighted polynomial regression to obtain a best estimatetrend line by fi tting a set of local regressionsof mortality rates against their reference dates. (Forfurther discussion of childhood mortality estimates,see UN Inter-agency Group for Child Mortality Estimation2011; for a graphic presentation and detailedbackground data, see www. childmortality.org).Neonatal, infant, and child mortality rates arehigher for boys than for girls in countries in whichparental gender preferences are insignifi cant. Underfive and child mortality rates capture the effect ofgender discrimination better than neonatal andinfant mortality rates do, as malnutrition and medicalinterventions are more important in this age group.Where female child mortality is higher, as in somecountries in South Asia, girls probably have unequalaccess to resources. Child mortality rates in thetable are not compatible with neonatal, infant, andunder-fi ve mortality rates because of differences inmethodology and reference year. Child mortality datawere estimated directly from surveys and cover the10 years preceding the survey. In addition to estimatesfrom Demographic Health Surveys, estimatesderived from Multiple Indicator Cluster Surveys havebeen added to the table; they cover the 5 years precedingthe survey.Rates for adult mortality come from life tables.Adult mortality rates increased notably in a dozencountries in Sub-Saharan Africa in the early 2000sand in several countries in Europe and Central Asiain the fi rst half of the 1990s. In Sub-Saharan Africathe increase stems from AIDS-related mortalityand affects both sexes, though women are moreaffected. In Europe and Central Asia the causes aremore diverse (high prevalence of smoking, high-fatdiet, excessive alcohol use, stressful conditionsrelated to the economic transition) and affect menmore.Annual data series from the United Nations areinterpolated based on fi ve-year estimates and thusmay not refl ect actual events.• Life expectancy at birth is the number of yearsa newborn infant would live if prevailing patterns ofmortality at the time of its birth were to stay thesame throughout its life. • Neonatal mortality rateis the number of neonatal infants dying before reaching28 days of age, per 1,000 live births. • Infantmortality rate is the number of infants dying beforereaching one year of age, per 1,000 live births.• Under-five mortality rate is the probability of achild born in a specifi c year dying before reaching age5, if subject to the age-specifi c mortality rate of thatyear. The probability is derived from life tables andis expressed as a rate per 1,000 live births. • Childmortality rate is the probability per 1,000 of dyingbetween ages 1 and 5—that is, the probability of a1-year-old dying before reaching age 5—if subject tocurrent age-specifi c mortality rates. • Adult mortalityrate is the probability per 1,000 of dying betweenthe ages of 15 and 60—that is, the probability of a15-year-old dying before reaching age 60—if subjectto current age-specifi c mortality rates betweenthose ages.Data sourcesData on life expectancy at birth are World Bankcalculations based on male and female data fromWorld Population Prospects: The 2010 Revision (formore than half of countries, most of them developingcountries), census reports and other statisticalpublications from national statistical offi ces, Eurostat’sDemographic Statistics, and the U.S. Bureauof the Census International Data Base. Data onneonatal, infant, and under-fi ve mortality are fromthe UN Inter-agency Group for Child Mortality Estimation’sLevels and Trends in Child Mortality: Report2011 and are based mainly on household surveys,censuses, and vital registration data. Data on childmortality are from MEASURE DHS Demographicand Health Surveys by ICF International and WorldBank calculations based on infant and under-fi vemortality from Multiple Indicator Cluster Surveysby UNICEF. Most data on adult mortality are linearinterpolations of fi ve-year data from World PopulationProspects: The 2010 Revision. Remaining dataon adult mortality are from the Human MortalityDatabase by the University of California, Berkeley,and the Max Planck Institute for DemographicResearch (www.mortality.org).2012 World Development Indicators 131Text fi gures, tables,and boxes

2.24 Health gaps by incomeDemographySurveyyearInfant mortality rate Under-five mortality rate Total fertility rate Teenage mothersper 1,000 live births per 1,000 live births births per woman % of women ages 15–19PoorestquintileRichestquintilePoorestquintileAlgeria .. .. .. .. .. .. .. ..Armenia 2005 42 14 52 23 1.8 1.5 5 0Azerbaijan 2006 52 37 63 41 2.3 1.6 6 3Bangladesh 2007 66 36 86 43 3.2 2.2 42 20Bolivia 2008 89 26 116 31 6.2 1.9 31 8Bosnia and Herzegovina .. .. .. .. .. .. .. ..Burkina Faso 2006 97 78 196 111 6.6 3.6 26 12Cambodia 2005 101 34 127 43 4.9 2.4 11 5Cameroon 2006 101 51 189 88 6.5 3.2 36 14Colombia 2010 22 12 29 13 3.2 1.4 29 7Congo, Dem. Rep. 2007 113 58 184 97 7.4 4.2 29 12Congo, Rep. 2005 92 56 135 85 6.7 2.9 35 13Côte d’Ivoire 2006 147 59 229 83 7.4 2.9 52 12Dominican Republic 2007 43 26 53 28 3.8 1.7 37 8Egypt, Arab Rep. 2008 42 17 49 19 3.4 2.7 12 5Ethiopia 2005 80 60 130 92 6.6 3.2 24 8Gambia, The 2006 106 58 158 72 .. .. .. ..Ghana 2008 59 47 103 60 6.5 2.3 18 4Guinea 2005 127 68 217 113 6.5 4.2 39 20Guinea-Bissau .. .. .. .. .. .. .. ..Haiti 2006 78 45 125 55 6.6 2.0 22 7Honduras 2006 37 19 50 20 5.6 2.1 31 10India 2006 82 34 118 39 3.9 1.8 25 5Indonesia 2007 56 26 77 32 3.0 2.7 6 10Jordan 2009 32 29 36 32 4.9 2.7 5 3Kazakhstan 2006 68 42 82 45 3.4 1.2 8 5Kenya 2009 66 57 98 69 7.0 2.9 24 16Kyrgyz Republic .. .. .. .. .. .. .. ..Lao PDR .. .. .. .. .. .. .. ..Liberia 2009 121 95 176 137 8.0 3.2 53 20Madagascar 2009 61 37 106 48 6.8 2.7 51 14Malawi 2006 72 62 123 99 7.1 4.1 43 20Mali 2006 124 80 233 124 7.6 4.9 37 23Mauritania 2007 89 57 144 87 5.4 3.6 14 11Moldova 2005 20 16 29 17 2.1 1.4 8 1Namibia 2007 60 24 92 30 5.1 2.4 22 5Nepal 2006 71 40 98 47 4.7 1.9 18 14Niger 2006 91 67 206 157 7.9 6.4 40 24Nigeria 2008 100 58 219 87 7.1 4.0 46 5Pakistan 2007 94 53 121 60 5.8 3.0 16 4Philippines 2008 40 15 59 17 5.2 1.9 19 4Rwanda 2008 99 45 161 84 5.8 4.4 5 4Senegal 2005 77 43 143 56 6.7 3.3 34 8Serbia .. .. .. .. .. .. .. ..Sierra Leone 2008 148 93 211 145 6.3 3.2 49 16Somalia .. .. .. .. .. .. .. ..Swaziland 2007 84 84 118 101 5.5 2.6 33 15Syrian Arab Republic 2006 18 16 22 20 .. .. .. ..Tanzania 2010 61 63 103 84 7.0 3.2 28 13Thailand .. .. .. .. .. .. .. ..Timor-Leste 2009/10 62 38 87 52 7.3 4.2 9 3Togo 2006 92 43 150 62 7.3 2.9 36 7Ukraine 2007 19 9 23 9 1.7 1.0 8 1Uzbekistan 2006 59 36 72 42 .. .. .. ..Yemen, Rep. 2006 94 36 118 118 .. .. .. ..Zambia 2007 69 74 124 124 8.4 3.4 37 14Zimbabwe 2006 48 45 72 72 5.5 2.3 32 7RichestquintilePoorestquintileRichestquintilePoorestquintileRichestquintile1322012 World Development Indicators

Health gaps by income 2.24PEOPLEChild healthSurveyyearPrevalence% of childrenunder age 5PoorestquintileRichestquintileDiarrheaTreatment% of childrenunder age 5with diarrheaPoorestquintileRichestquintilePrevalence% of childrenunder age 5PoorestquintileAcute respiratoryinfection (ARI)RichestquintileTreatmentChildren with ARI takento health provider% of childrenunder age 5 with ARIPoorestquintileRichestquintilePrevalence ofchild malnutrition(underweight)Old standards% of childrenunder age 5PoorestquintileRichestquintileChildimmunizationAll vaccinations% of childrenages 12–23 monthsPoorestquintileRichestquintileAlgeria 2006 10 8 .. .. 7 5 38 68 5 3 81 95Armenia 2005 20 13 65 80 11 11 30 42 5 1 66 68Azerbaijan 2006 13 10 66 55 4 2 15 11 17 4 7 32Bangladesh 2007 10 8 79 88 6 3 45 73 55 31 80 88Bolivia 2008 30 20 58 75 24 17 40 70 11 2 78 81Bosnia and Herzegovina 2006 4 5 .. .. 5 3 97 96 2 4 66 71Burkina Faso 2006 19 16 57 73 6 2 45 73 44 24 64 81Cambodia 2005 22 14 66 40 12 3 41 58 43 23 56 76Cameroon 2006 30 10 46 73 10 6 20 50 35 6 42 72Colombia 2010 16 7 67 82 7 4 54 67 12 3 64 67Congo, Dem. Rep. 2007 17 14 65 55 9 6 32 48 33 19 20 50Congo, Rep. 2005 15 12 54 54 .. .. .. .. 20 5 29 73Côte d’Ivoire 2006 20 13 55 77 8 3 21 71 26 10 58 92Dominican Republic 2007 16 13 64 63 9 4 63 65 7 2 48 76Egypt, Arab Rep. 2008 10 7 39 32 10 8 70 82 9 7 89 94Ethiopia 2005 18 14 24 55 12 11 19 33 43 29 14 36Gambia, The 2006 21 15 .. .. 6 5 68 68 26 14 83 74Ghana 2008 25 10 58 69 6 3 45 87 23 11 75 84Guinea 2005 18 17 47 73 10 8 30 59 28 21 29 45Guinea-Bissau 2006 13 14 .. .. 4 7 32 82 21 10 43 64Haiti 2006 25 18 48 67 10 5 21 37 27 7 34 56Honduras 2006 19 11 64 68 14 6 46 74 21 2 77 68India 2006 9 8 25 49 6 4 61 80 61 25 24 71Indonesia 2007 18 10 60 57 8 5 48 74 .. .. 39 75Jordan 2009 18 14 56 64 5 3 66 78 4 0 82 89Kazakhstan 2006 1 2 46 71 1 2 37 53 5 1 98 96Kenya 2009 20 13 80 79 11 5 57 63 31 12 61 70Kyrgyz Republic 2006 3 4 .. .. .. .. .. .. 3 3 .. ..Lao PDR 2006 17 9 .. .. 6 3 28 12 44 18 18 45Liberia 2007 19 19 63 83 8 9 59 89 26 17 23 56Madagascar 2009 8 10 52 67 3 3 33 68 47 28 41 82Malawi 2006 26 20 65 76 9 8 51 65 25 16 66 77Mali 2006 13 8 41 68 10 11 9 50 37 22 49 56Mauritania 2007 25 19 29 29 7 7 33 64 40 13 39 25Moldova 2005 7 12 50 70 5 10 42 68 .. .. 82 59Namibia 2007 13 11 57 74 7 1 65 94 27 9 59 82Nepal 2006 13 12 27 59 6 5 36 54 54 24 68 94Niger 2006 22 18 50 66 10 11 19 59 48 30 20 48Nigeria 2008 14 5 25 61 4 1 32 66 40 13 5 53Pakistan 2007 23 20 52 59 15 13 67 92 .. .. 26 64Philippines 2008 10 7 68 82 7 3 42 64 .. .. 64 87Rwanda 2008 16 13 32 49 16 14 16 43 30 10 82 83Senegal 2005 24 23 51 52 10 15 35 61 25 7 59 65Serbia 2006 7 5 .. .. 3 2 89 .. 4 2 50 54Sierra Leone 2008 13 9 74 86 7 4 39 46 27 15 39 40Somalia 2006 26 14 .. .. 18 12 5 28 .. .. 5 22Swaziland 2007 23 9 89 85 10 8 66 75 9 3 82 79Syrian Arab Republic 2006 9 6 .. .. 4 6 72 86 13 8 51 77Tanzania 2010 15 16 56 68 4 5 18 62 25 12 69 85Thailand 2006 10 6 .. .. 7 3 85 78 15 4 92 86Timor-Leste 2009/10 13 17 81 76 2 2 53 80 .. .. 43 45Togo 2006 15 9 .. .. 7 8 17 28 37 15 39 63Ukraine .. .. .. .. .. .. .. .. .. .. .. ..Uzbekistan 2006 .. .. .. .. .. .. .. .. 6 3 90 80Yemen, Rep. 2006 35 27 .. .. .. .. .. .. .. .. 18 73Zambia 2007 14 16 71 79 5 6 78 56 21 14 71 78Zimbabwe 2006 15 15 65 79 7 2 9 51 18 6 43 642012 World Development Indicators 133

2.24 Health gaps by incomeReproductive and women’s healthSurveyyearKnowledge ofcontraceptionContraceptiveprevalenceratePregnant womenreceiving prenatal careBirths attended byskilled health staff aProblem accessinghealth careAny method% of married womenages 15–49PoorestquintileRichestquintileAny method% of married womenages 15–49 % % %PoorestquintileRichestquintilePoorestquintileRichestquintilePoorestquintileRichestquintilePoorestquintileRichestquintileAlgeria 2006 .. .. 56 65 76 98 88 98 .. ..Armenia 2005 97 100 51 60 86 99 98 100 92 67Azerbaijan 2006 96 99 55 57 56 98 76 100 94 65Bangladesh 2007 100 100 55 60 32 85 7 57 26 17Bolivia 2008 90 100 46 71 79 97 39 99 95 78Bosnia and Herzegovina 2006 .. .. 35 35 98 100 99 100 .. ..Burkina Faso 2006 85 99 9 41 79 98 56 65 88 65Cambodia 2005 98 100 31 54 58 91 22 92 93 72Cameroon 2006 72 99 11 44 48 98 19 96 92 69Colombia 2010 100 100 76 80 92 99 86 99 .. ..Congo, Dem. Rep. 2007 79 98 14 39 78 95 62 98 95 70Congo, Rep. 2005 97 100 40 46 75 98 69 99 .. ..Côte d’Ivoire 2006 75 99 9 24 69 97 29 95 .. ..Dominican Republic 2007 99 100 68 73 87 98 90 98 75 43Egypt, Arab Rep. 2008 100 100 55 65 57 93 58 97 87 45Ethiopia 2005 76 96 4 37 .. .. .. .. 98 82Gambia, The 2006 .. .. 6 13 98 98 28 89 .. ..Ghana 2008 93 100 14 31 93 100 23 96 78 48Guinea 2005 87 97 5 17 68 98 15 87 93 71Guinea-Bissau 2006 .. .. 6 23 76 89 19 79 .. ..Haiti 2006 100 100 20 38 72 97 8 71 98 75Honduras 2006 100 100 53 73 88 99 38 98 94 69India 2006 99 100 42 68 58 97 21 90 67 15Indonesia 2007 96 100 53 64 83 99 47 96 62 25Jordan 2009 100 100 54 65 97 100 99 100 81 62Kazakhstan 2006 99 100 42 59 100 100 100 100 .. ..Kenya 2009 86 99 20 55 84 97 23 82 39 27Kyrgyz Republic 2006 .. .. 50 51 94 99 93 100 .. ..Lao PDR 2006 .. .. .. .. 16 88 3 81 .. ..Liberia 2007 72 99 4 20 68 96 30 82 91 45Madagascar 2009 87 100 20 57 73 97 22 90 75 37Malawi 2006 98 100 38 46 90 95 43 77 90 53Mali 2006 70 93 4 19 20 80 9 76 74 47Mauritania 2007 53 93 2 19 53 94 21 95 .. ..Moldova 2005 99 100 67 70 97 98 99 100 85 56Namibia 2007 97 100 32 71 90 97 61 98 89 33Nepal 2006 100 100 33 61 50 92 9 64 91 63Niger 2006 68 89 11 21 37 82 5 60 87 59Nigeria 2008 41 96 3 35 24 94 9 86 86 49Pakistan 2007 92 99 16 43 38 92 18 79 .. ..Philippines 2008 96 100 41 50 91 99 98 99 88 50Rwanda 2008 99 99 28 50 95 97 49 76 89 65Senegal 2005 89 99 4 25 86 99 21 91 88 54Serbia 2006 .. .. 33 49 96 100 98 100 .. ..Sierra Leone 2008 65 89 4 20 84 97 28 72 96 69Somalia 2006 .. .. 12 19 8 51 11 77 .. ..Swaziland 2007 100 100 37 62 96 99 51 93 70 22Syrian Arab Republic 2006 .. .. 42 68 68 94 78 99 .. ..Tanzania 2010 97 100 23 51 82 95 31 88 56 16Thailand 2006 .. .. 74 69 96 100 93 100 .. ..Timor-Leste 2009/10 66 93 15 34 75 96 12 71 94 70Togo 2006 94 99 12 19 69 100 30 97 .. ..Ukraine 2007 99 100 62 71 98 99 99 100 .. ..Uzbekistan 2006 .. .. 66 63 98 99 100 100 .. ..Yemen, Rep. 2006 .. .. 15 44 32 79 17 74 .. ..Zambia 2007 98 100 41 54 90 98 26 92 77 42Zimbabwe 2006 99 100 48 72 92 98 44 97 89 461342012 World Development Indicators

Health gaps by income 2.24PEOPLEAbout the dataDefinitionsHealth survey data at the national level do not revealwithin-country inequalities associated with socioeconomicstatus. The data in the table describe the healthand demographic status as well as use of health servicesby individuals in different socioeconomic groupswithin countries. The data are from MEASURE DHSDemographic and Health Surveys by ICF Internationaland Multiple Indicator Cluster Surveys by the UnitedNations Children’s Fund.Obtaining reliable data on a household’s or individual’ssocioeconomic status is challenging, and methodshave evolved over time. Earlier measurementsrelied on indicators such as household income andconsumption, which are prone to bias and are timeand labor intensive when included in survey questionnaires.The wealth index, developed by MEASURE DHSwith partial funding from the World Bank, is calculatedusing easy-to-collect data on a household’s ownershipof selected assets, such as televisions and bicycles;materials used for housing construction; and types ofwater access and sanitation facilities. A single assetindex is developed on the basis of data from the entirecountry sample and used. Generated with a statisticalprocedure known as principal components analysis,the wealth index places individual households on acontinuous scale of relative wealth. Demographic andHealth Surveys and Multiple Indicator Cluster Surveysseparate all interviewed households into fi ve wealthquintiles to compare the influence of wealth on variouspopulation, health and nutrition indicators. The wealthindex is presented in the final reports of these surveys.Data disaggregated by wealth quintile provideinsights into health differentials by socioeconomicstatus and allow problems particular to the poor,such as unequal access to health care to be identified. If the poor have a greater disease burden thanthe rich, programs should focus on reaching thepoor. But this is rare. Health services too often failpoor people in access, quality, and affordability. Inlow-income countries the poor are particularly disadvantagedin using health care and experience worsehealth outcomes than the rich. The table shows theestimates for the poorest and richest quintiles only;the full set of estimates for up to 70 indicators isavailable at http://data.worldbank.org and http://data.worldbank.org/data-catalog/health-nutrition-population-statistics. The estimates in the table arebased on household survey data, which may refer toa period preceding the survey date or use a defi nitionor methodology different from the estimates in theother tables. Thus the estimates may differ, and cautionshould be exercised in using the data.• Survey year is the year in which the underlying datawere collected. The reference year of the data may bepreceding the survey year. • Infant mortality rate isthe number of infants dying before reaching one yearof age, per 1,000 live births. • Under-five mortalityrate is the probability that a child born in a specifi cyear will die before reaching age 5, if subject to theage-specifi c mortality rate of that year. The probabilityis derived from life tables and expressed as arate per 1,000 live births. • Total fertility rate is thenumber of children that would be born to a woman ifshe were to live to the end of her childbearing yearsand bear children in accordance with age-specifi c fertilityrates of a reference period. • Teenage mothersare women ages 15−19 who are mothers or pregnantwith their fi rst child. • Diarrhea prevalence is thepercentage of children under age 5 who had diarrheain the two weeks preceding the survey. • Diarrheatreatment is the percentage of children under age 5with diarrhea in the two weeks preceding the surveywho received oral rehydration salts, recommendedhomemade fl uids (rehydration salts or recommendedhome solution), or increased fl uids. • Acute respiratoryinfection (ARI) prevalence is the percentageof children under age 5 who were ill with a coughaccompanied by rapid breathing in the two weekspreceding the survey. • Children with ARI taken tohealth provider are children under age 5 with ARI inthe two weeks preceding the survey who were takento a health facility. • Prevalence of child malnutrition(underweight) is the percentage of childrenunder age 5 whose weight for age is more than twostandard deviations below the median for the internationalreference population. Data are based on theold standards of the U.S. Centers for Disease ControlNational Center for Health Statistics and WorldHealth Organization international reference population.• Child immunization (all vaccinations) is thepercentage of children ages 12−23 months who havereceived vaccines for Bacillus Calmette-Guérin andmeasles; three doses each of diphtheria, pertussis,and tetanus; and polio vaccine (excluding polio 0)by the time of the survey, according to the vaccinationcard or the mother’s report. • Knowledge ofcontraception is the percentage of currently marriedwomen who know at least one contraceptive method.• Contraceptive prevalence rate is the percentageof women ages 15-49 married or in union who arepracticing, or whose sexual partners are practicing,any form of contraception. • Pregnant women receivingprenatal care are women with one or more livebirths in the one, two, or three years preceding thesurvey who have received at least one antenatalcare during pregnancy before the most recent birthfrom any skilled personnel. • Births attended byskilled health staff are live births in the one, two,or three years preceding the survey attended by anyskilled personnel. • Problem accessing health careis the percentage of women who report they havea big problem accessing health care when they aresick due to inadequate knowledge of where to gofor treatment, need to get permission or money fortreatment, distance to health facility, need to taketransport, desire not to go alone, or concern that afemale provider may not be available.Data sourcesData on health gaps by income are from MEASUREDHS Demographic and Health Surveys, by ICFInternational, downloaded through STATcompiler,and fro m Multiple Indicator Cluster Surveys byUNICEF through their fi nal reports.2012 World Development Indicators 135

ENVIRONMENT

The indicators in the Environment sectionmeasure the use of resources and the wayhuman activities affect the natural and builtenvironment. They include measures of environmentalgoods (forests, water, cultivatableland) and of degradation (pollution, deforestation,loss of habitat, and loss of biodiversity).Sustainable development and poverty reductionrequire efficient use of environmental resources.These indicators show that growing populationsand expanding economies have placed greaterdemands on land, water, forests, minerals,and energy resources. But new technologies,increasing productivity, and better policies canensure that future development is environmentallyand socially sustainable.Nowhere are these risks and opportunitiesmore intertwined than in the global effort tomitigate the effects of climate change. At theDecember 2011 United Nations Conferenceon Climate Change in Durban, South Africa, all194 participating countries adopted the DurbanPlatform for Enhanced Action, which sets thedirection of climate negotiations. The platformcalls for parties to negotiate a legal agreementon climate change no later than 2015 that wouldapply to all countries and be effective by 2020.Negotiators also launched a Green Climate Fundthat will eventually channel billions of dollars ayear to developing countries for adaptation toand mitigation of climate change.As documented in World Development Report2010: Development and Climate Change (WorldBank 2009j), climate change is already erodingdevelopment gains and causing disruptions tosocial and economic systems in some countries.Continued rise in temperature, accompanied bychanges in precipitation patterns, is projectedfor this century, and more frequent, severe, andprolonged climate-related events such as fl oodsand droughts are also projected—posing risksfor agriculture, food production, and water supplies.Poor countries and the poorest peoplein all countries are the most vulnerable to theimpacts. Understanding climate change is thuskey for development policy. Because uncertaintyincreases with climate change, better climateinformation is critical for wise developmentdecisions.This year’s Environment section includes twonew tables on information related to climatechange. Table 3.11 presents data on carbondioxide emissions by economic sector, whichshows how differences in industrial structureand production technologies affect the productionof carbon dioxide and how these patternshave changed. Table 3.12 presents dataon climate variability, exposure to impact, andresilience.Other indicators in this section describe landuse, agriculture and food production, forestsand biodiversity, water resources, energy useand effi ciency, natural resource rents, urbanization,environmental impacts, government commitments,and threatened species. Table 3.8adds newly available data on the share of thepopulation with access to electricity.Where possible, the indicators come frominternational sources and are standardized tofacilitate comparison across countries. But ecosystemsspan national boundaries, and accessto natural resources may vary within countries.For example, water may be abundant in someparts of a country but scarce in others, andcountries often share water resources. Landproductivity and optimal land use may be locationspecifi c, but widely separated regions canhave common characteristics. Greenhouse gasemissions and climate change are measuredglobally, but their effects are experiencedlocally, shaping people’s lives and opportunities.Measuring environmental phenomenaand their effects at the subnational, national,and supranational levels and incorporatingthese values in national income accounts andother statistical frameworks remain major challengesfor economists, environmentalists, andstatisticians.32012 World Development Indicators 137

Tables3.1Rural population and land useRural population Land area Land useaverage% of land area Arable land% of totalannual% growththousandsq. kmForest area Permanent cropland Arable landhectares per100 people1990 2010 1990–2010 2010 1990 2010 1990 2009 1990 2009 1990 2009Afghanistan 82 75 2.3 652.2 2.1 2.1 0.2 0.2 12.1 11.9 41.6 23.3Albania 64 52 –0.9 27.4 28.8 28.3 4.6 3.2 21.1 22.3 17.6 19.2Algeria 48 34 –0.4 2,381.7 0.7 0.6 0.2 0.4 3.0 3.1 28.0 21.5Angola 63 42 0.7 1,246.7 48.9 46.9 0.4 0.2 2.3 3.2 28.1 21.6Argentina 13 8 –1.7 2,736.7 12.7 10.7 0.4 0.4 9.6 11.3 80.9 77.4Armenia 33 36 0.5 28.5 12.2 9.2 2.1 1.9 14.9 16.0 12.3 14.8Australia 15 11 0.1 7,682.3 20.1 19.4 0.0 0.0 6.2 6.1 280.7 214.8Austria 34 32 –0.4 82.5 45.8 47.1 1.0 0.8 17.3 16.6 18.6 16.4Azerbaijan 46 48 0.8 82.6 11.2 11.3 3.7 2.7 20.5 22.7 23.1 20.9Bahrain 12 11 7.2 0.8 0.3 1.3 2.9 2.6 2.9 1.3 0.4 0.1Bangladesh 80 72 0.5 130.2 11.5 11.1 2.5 7.5 72.6 58.1 9.0 5.2Belarus 34 26 –1.8 202.9 38.4 42.5 0.9 0.6 30.0 27.3 59.6 58.3Belgium 4 3 0.0 30.3 22.4 22.4 0.5 a 0.7 23.3 a 27.7 0.2 7.8Benin 66 58 2.2 110.6 52.1 41.2 0.9 2.7 14.6 22.1 33.8 28.5Bolivia 44 34 0.2 1,083.3 58.0 52.8 0.1 0.2 1.9 3.4 31.5 38.2Bosnia and Herzegovina 61 51 –1.3 51.2 43.3 42.7 2.9 2.0 16.7 19.5 21.6 26.5Botswana 58 39 –0.7 566.7 24.2 20.0 0.0 0.0 0.7 0.4 30.4 12.6Brazil 25 14 –2.5 8,459.4 68.0 61.4 0.8 0.9 6.0 7.2 33.9 31.7Bulgaria 34 28 –1.6 108.6 30.1 36.2 2.7 1.6 34.9 28.9 44.2 41.4Burkina Faso 86 80 2.5 273.6 25.0 20.6 0.2 0.2 12.9 21.6 37.8 36.9Burundi 94 89 2.2 25.7 11.3 6.7 14.0 13.6 36.2 35.0 16.6 11.0Cambodia 87 77 0.3 176.5 73.3 57.2 0.6 0.9 20.9 22.1 38.8 27.9Cameroon 59 42 0.2 472.7 51.4 42.1 2.6 3.0 12.6 12.6 48.8 31.1Canada 23 19 0.6 9,093.5 34.1 34.1 0.7 0.8 5.0 5.0 163.7 133.7Central African Republic 63 61 1.6 623.0 37.2 36.3 0.1 0.1 3.1 3.1 65.4 45.2Chad 79 72 2.0 1,259.2 10.4 9.2 0.0 0.0 2.6 3.4 54.5 39.3Chile 17 11 –1.6 743.5 20.5 21.8 0.3 0.6 3.8 1.7 21.3 7.5China 73 55 –1.1 9,327.5 16.8 22.2 0.8 1.5 13.3 11.8 10.9 8.3Hong Kong SAR, China 1 .. .. 1.0 .. .. .. .. .. .. .. ..Colombia 32 25 0.2 1,109.5 56.3 54.5 1.5 1.4 3.0 1.6 10.0 3.9Congo, Dem. Rep. 72 65 1.8 2,267.1 70.7 68.0 0.5 0.3 2.9 3.0 18.3 10.4Congo, Rep. 46 38 1.5 341.5 66.5 65.6 0.1 0.2 1.4 1.5 20.1 12.7Costa Rica 49 36 0.0 51.1 50.2 51.0 4.9 5.9 5.1 3.9 8.5 4.4Côte d’Ivoire 60 50 0.7 318.0 32.1 32.7 11.0 13.5 7.6 8.8 19.4 14.5Croatia 46 42 –0.7 56.0 33.1 34.3 2.0 1.6 21.7 15.5 27.1 19.6Cuba 27 24 –0.1 106.4 19.2 27.0 4.2 3.5 31.6 34.3 32.1 32.4Cyprus 33 30 0.5 9.2 17.4 18.7 5.5 3.6 11.5 9.4 13.8 8.0Czech Republic 25 27 0.4 77.3 34.0 34.4 3.1 1.0 41.1 41.2 32.1 30.3Denmark 15 13 –1.6 42.4 10.5 12.8 0.2 0.1 60.4 57.3 49.8 44.0Dominican Republic 45 30 –1.2 48.3 40.8 40.8 9.3 9.7 18.6 16.6 12.5 8.2Ecuador 45 33 –0.6 248.4 49.9 39.7 4.8 5.4 5.8 4.8 15.6 8.4Egypt, Arab Rep. 57 57 1.7 995.5 0.0 0.1 0.4 0.8 2.3 2.9 4.0 3.6El Salvador 51 39 –0.2 20.7 18.2 13.9 12.5 11.1 26.5 32.7 10.3 11.0Eritrea 84 78 2.4 101.0 16.0 15.2 0.0 0.0 4.9 6.8 0.1 13.5Estonia 29 31 –0.1 42.4 49.3 52.3 0.3 0.2 26.3 14.1 72.7 44.5Ethiopia 87 82 1.8 1,000.0 13.7 12.3 0.5 1.0 10.0 13.9 1.4 17.2Finland 39 36 –0.4 303.9 71.9 72.9 0.0 0.0 7.4 7.4 45.5 42.3France 26 22 –0.5 547.7 26.5 29.1 2.2 1.9 32.9 33.5 30.9 28.4Gabon 31 14 –1.5 257.7 85.4 85.4 0.6 0.6 1.1 1.3 31.8 22.0Gambia, The 62 42 0.8 10.0 44.2 48.0 0.5 0.5 18.2 40.0 18.8 23.8Georgia 45 47 0.8 69.5 40.0 39.5 4.8 1.7 11.4 6.4 16.3 10.2Germany 27 26 –0.5 348.6 30.8 31.8 1.3 0.6 34.3 34.3 15.1 14.6Ghana 64 49 0.8 227.5 32.7 21.7 6.6 12.3 11.9 19.3 18.3 18.5Greece 41 39 –0.2 128.9 25.6 30.3 8.3 8.9 22.5 19.8 28.5 22.6Guatemala 59 51 1.6 107.2 44.3 34.1 4.5 8.8 12.1 14.0 14.6 10.7Guinea 72 65 1.5 245.7 29.6 26.6 2.0 2.8 11.6 11.6 49.5 29.2Guinea-Bissau 72 70 2.0 28.1 78.8 71.9 4.2 8.9 8.9 10.7 24.6 20.2Haiti 72 50 –1.4 27.6 4.2 3.7 11.6 10.9 28.3 38.1 11.0 10.61382012 World Development Indicators

Rural population and land use3.1ENVIRONMENTRural population Land area Land useaverage% of land area Arable land% of totalannual% growththousandsq. kmForest area Permanent cropland Arable landhectares per100 people1990 2010 1990–2010 2010 1990 2010 1990 2009 1990 2009 1990 2009Honduras 60 51 1.1 111.9 72.7 46.4 3.2 3.7 13.1 9.1 29.9 13.7Hungary 34 32 –1.4 89.6 20.0 22.6 2.6 2.1 56.2 50.6 48.7 45.8India 75 70 0.9 2,973.2 21.5 23.0 2.2 3.9 54.8 53.1 18.6 13.1Indonesia 69 46 –1.4 1,811.6 65.4 52.1 6.5 10.5 11.2 13.0 11.0 9.9Iran, Islamic Rep. 44 31 –0.6 1,628.6 6.8 6.8 0.8 1.1 9.3 10.6 27.7 23.5Iraq 30 34 3.3 437.4 1.8 1.9 0.7 0.6 13.3 10.4 31.9 14.5Ireland 43 38 –0.2 68.9 6.7 10.7 0.0 0.0 15.1 15.8 29.6 24.4Israel 10 8 1.6 21.6 6.1 7.1 4.1 3.6 15.9 14.0 7.4 4.1Italy 33 32 0.0 294.1 25.8 31.1 10.1 8.9 30.6 23.4 15.9 11.4Jamaica 51 46 –0.2 10.8 31.9 31.1 9.2 9.2 11.0 11.1 5.0 4.5Japan 37 33 –0.6 364.5 68.4 68.5 1.3 0.9 13.1 11.8 3.9 3.4Jordan 28 22 2.0 88.2 1.1 1.1 0.8 0.9 2.0 2.3 5.7 3.4Kazakhstan 44 42 1.8 2,699.7 1.3 1.2 0.1 0.0 13.0 8.7 213.2 145.4Kenya 82 78 2.2 569.1 6.5 6.1 0.8 1.1 8.8 9.5 21.3 13.7Korea, Dem. Rep. 42 37 –0.5 120.4 68.1 47.1 1.5 1.7 19.0 22.0 11.4 10.9Korea, Rep. 26 18 –0.9 96.9 64.5 64.2 1.6 2.1 19.8 16.4 4.6 3.3Kosovo .. .. .. 10.9 b .. .. .. .. .. 27.6 .. 16.8Kuwait 2 2 2.1 17.8 0.2 0.3 0.1 0.2 0.2 0.6 0.2 0.4Kyrgyz Republic 62 63 0.6 191.8 4.4 5.0 0.4 0.4 6.9 6.7 29.0 24.0Lao PDR 85 67 –0.3 230.8 75.0 68.2 0.3 0.5 3.5 5.9 19.1 22.3Latvia 31 32 –0.7 62.2 51.0 53.9 0.4 0.1 27.1 18.8 64.6 51.8Lebanon 17 13 –0.2 10.2 12.8 13.4 11.9 14.0 17.9 14.2 6.2 3.5Lesotho 86 73 0.0 30.4 1.3 1.4 0.1 0.1 10.4 11.0 19.3 15.6Liberia 55 39 2.3 96.3 51.2 44.9 1.6 2.2 3.6 4.2 16.5 10.4Libya 24 22 0.7 1,759.5 0.1 0.1 0.2 0.2 1.0 1.0 41.6 27.9Lithuania 32 33 –0.9 62.7 31.0 34.5 0.7 0.4 46.0 32.8 78.0 61.5Macedonia, FYR 42 32 –1.4 25.2 35.9 39.6 2.2 1.4 23.8 16.7 31.3 20.4Madagascar 76 70 2.4 581.5 23.5 21.6 1.0 1.0 4.7 5.1 24.1 14.7Malawi 88 80 2.5 94.1 41.3 34.4 1.4 1.3 23.9 38.2 24.0 24.9Malaysia 50 28 –1.7 328.6 68.1 62.3 16.0 17.6 5.2 5.5 9.3 6.4Mali 77 67 2.2 1,220.2 11.5 10.2 0.1 0.1 1.7 5.2 23.7 42.7Mauritania 60 59 2.1 1,030.7 0.4 0.2 0.0 0.0 0.4 0.4 20.0 11.6Mauritius 56 57 0.4 2.0 19.2 17.2 3.0 2.0 49.3 42.9 9.4 6.8Mexico 29 22 –0.1 1,944.0 36.2 33.3 1.0 1.4 12.5 12.9 28.8 22.4Moldova 53 59 0.4 32.9 9.7 11.7 14.2 9.2 52.8 55.2 46.8 51.0Mongolia 43 43 1.2 1,553.6 8.1 7.0 0.0 0.0 0.9 0.6 62.5 35.4Morocco 52 43 0.2 446.3 11.3 11.5 1.6 2.2 19.5 18.0 35.1 25.5Mozambique 79 62 1.0 786.4 55.2 49.6 0.3 0.3 4.4 6.4 25.5 22.1Myanmar 75 66 –0.2 653.5 60.0 48.6 0.8 1.7 14.6 16.9 24.4 23.2Namibia 72 62 0.9 823.3 10.6 8.9 0.0 0.0 0.8 1.0 46.7 35.7Nepal 91 82 1.2 143.4 33.7 25.4 0.5 0.8 16.0 16.7 12.0 8.2Netherlands 31 17 –2.6 33.8 10.2 10.8 0.9 1.0 26.0 31.2 5.9 6.4New Zealand 15 13 0.3 263.3 29.3 31.4 0.2 0.3 10.0 1.8 76.7 10.9Nicaragua 48 43 0.7 120.3 37.5 25.9 1.6 1.9 10.8 15.8 31.6 33.3Niger 85 83 3.4 1,266.7 1.5 1.0 0.0 0.0 8.7 11.8 141.7 99.8Nigeria 65 50 1.1 910.8 18.9 9.9 2.8 3.3 32.4 37.3 30.3 22.0Norway 28 22 0.9 305.5 30.0 32.9 0.0 0.0 2.8 2.7 20.3 17.3Oman 34 28 2.4 309.5 0.0 0.0 0.1 0.1 0.1 0.3 1.9 3.7Pakistan 69 63 1.1 770.9 3.3 2.2 0.6 1.1 26.6 26.5 18.3 12.0Panama 46 25 –1.6 74.3 51.0 43.7 2.1 2.0 6.7 7.4 20.7 15.8Papua New Guinea 85 88 2.3 452.9 69.6 63.4 1.2 1.5 0.4 0.6 4.6 3.9Paraguay 51 39 0.2 397.3 53.3 44.3 0.2 0.3 5.3 9.6 49.7 59.9Peru 31 28 0.7 1,280.0 54.8 53.1 0.3 0.6 2.7 2.9 16.1 12.7Philippines 51 34 –0.5 298.2 22.0 25.7 14.8 16.9 18.4 18.1 8.9 5.9Poland 39 39 0.2 304.2 29.2 30.7 1.1 1.3 47.3 41.2 37.8 32.9Portugal 52 39 –1.5 91.5 36.4 37.8 8.5 8.5 25.6 12.3 23.5 10.6Puerto Rico 28 1 –17.9 8.9 32.4 62.2 5.6 4.5 7.3 6.8 1.8 1.5Qatar 8 4 7.7 11.6 0.0 0.0 0.1 0.3 0.9 1.0 2.1 0.82012 World Development Indicators 139

3.1Rural population and land useRural population Land area Land useaverage% of land area Arable land% of totalannual% growththousandsq. kmForest area Permanent cropland Arable landhectares per100 people1990 2010 1990–2010 2010 1990 2010 1990 2009 1990 2009 1990 2009Romania 47 45 –0.6 229.9 27.8 28.6 2.6 1.6 41.2 38.2 40.7 40.9Russian Federation 27 27 0.0 16,376.9 49.4 49.4 0.1 0.1 8.1 7.4 88.8 85.8Rwanda 95 81 2.6 24.7 12.9 17.6 12.4 11.3 35.7 52.7 12.4 12.6Saudi Arabia 23 16 –5.5 2,000.0 c 0.5 0.5 0.0 0.1 1.6 1.5 21.0 11.9Senegal 61 57 2.2 192.5 48.6 44.0 0.2 0.3 16.1 20.0 42.7 31.8Serbia 50 48 –0.8 88.4 26.4 30.7 26.2 3.4 .. 37.7 .. 45.1Sierra Leone 67 62 1.7 71.6 43.5 38.1 1.9 1.8 6.8 15.1 12.2 18.9Singapore – – .. 0.7 3.0 2.9 1.5 0.0 1.5 0.0 0.0 0.0Slovak Republic 44 43 0.0 48.1 40.0 40.2 1.0 0.5 32.5 28.7 31.0 25.5Slovenia 50 52 1.2 20.1 59.0 62.2 1.8 1.3 9.9 8.7 10.0 8.6Somalia 70 63 1.6 627.3 13.2 10.8 0.0 0.0 1.6 1.6 15.5 11.0South Africa 48 38 0.1 1,214.5 6.8 4.7 0.7 0.8 11.1 11.8 38.2 29.1South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 25 23 –0.2 499.1 27.7 36.4 9.7 9.5 30.7 25.1 39.5 27.2Sri Lanka 83 85 0.9 62.7 37.5 29.7 15.9 15.5 14.4 19.1 5.2 5.8Sudan 73 55 0.9 2,376.0 32.1 29.4 0.0 0.1 5.4 8.5 48.3 47.5Swaziland 77 75 1.1 17.2 27.4 32.7 0.7 0.9 10.5 10.2 20.9 15.0Sweden 17 15 0.3 410.3 66.5 68.7 0.0 0.0 6.9 6.4 33.2 28.3Switzerland 27 26 0.8 40.0 28.8 31.0 0.6 0.6 10.3 10.2 6.1 5.3Syrian Arab Republic 51 45 1.3 183.6 2.0 2.7 4.0 5.4 26.6 25.4 39.6 23.3Tajikistan 68 74 1.4 140.0 2.9 2.9 0.9 1.0 6.1 5.3 15.6 10.9Tanzania 81 74 2.4 885.8 46.8 37.7 1.1 1.7 10.2 11.3 35.3 23.0Thailand 71 66 0.1 510.9 38.3 37.1 6.1 7.2 34.2 29.9 30.7 22.3Timor-Leste 79 72 1.6 14.9 65.0 49.9 3.9 4.0 7.4 11.1 14.8 15.0Togo 70 57 0.9 54.4 12.6 5.3 1.7 3.3 38.6 40.4 57.3 37.3Trinidad and Tobago 92 86 0.0 5.1 47.0 44.1 6.8 4.3 7.0 4.9 3.0 1.9Tunisia 42 33 –0.2 155.4 4.1 6.5 12.5 14.3 18.7 17.4 35.7 25.9Turkey 41 30 –0.2 769.6 12.6 14.7 3.9 3.8 32.0 27.7 45.5 29.7Turkmenistan 55 51 0.4 469.9 8.8 8.8 0.1 0.1 2.9 3.9 34.8 37.2Uganda 89 87 3.0 197.1 23.8 15.2 9.3 11.3 25.0 33.0 28.3 20.4Ukraine 33 32 –0.6 579.3 16.0 16.8 1.9 1.6 57.6 56.1 64.0 70.5United Arab Emirates 21 22 7.7 83.6 2.9 3.8 0.2 2.4 0.4 0.8 1.9 0.9United Kingdom 11 10 –0.1 241.9 10.8 11.9 0.3 0.2 27.4 25.0 11.6 9.8United States 25 18 –1.0 9,147.4 32.4 33.2 0.2 0.3 20.3 17.8 74.4 53.1Uruguay 11 8 –1.0 175.0 5.3 10.0 0.3 0.2 7.2 10.7 40.5 56.2Uzbekistan 60 63 1.3 425.4 7.2 7.7 0.9 0.8 10.5 10.1 20.9 15.5Venezuela, RB 16 6 –3.9 882.1 59.0 52.5 0.9 0.7 3.2 3.1 14.3 9.7Vietnam 80 71 0.4 310.1 28.8 44.5 3.2 10.8 16.4 20.3 8.1 7.3West Bank and Gaza 32 28 2.3 6.0 1.5 1.5 19.1 19.4 18.1 16.6 5.5 2.5Yemen, Rep. 79 68 2.2 528.0 1.0 1.0 0.2 0.5 2.9 2.2 12.8 5.0Zambia 61 64 1.4 743.4 71.0 66.5 0.0 0.0 3.9 4.5 36.8 26.3Zimbabwe 71 62 0.0 386.9 57.3 40.4 0.3 0.3 7.5 10.8 27.6 33.5World 57 w 49 w 0.3 w 129,561.0 s 32.0 w 31.1 w 1.1 w 1.2 w 10.9 w 10.7 w 23.7 w 20.4 wLow income 79 72 1.5 15,043.5 31.2 27.6 0.7 0.9 7.7 9.6 23.8 18.5Middle income 62 51 0.1 80,675.5 33.9 32.8 1.4 1.4 11.1 10.9 18.8 17.8Lower middle income 68 60 0.8 22,789.9 30.7 27.9 1.9 2.7 14.8 16.4 18.7 15.1Upper middle income 56 43 –0.9 57,885.6 35.2 34.6 1.1 0.9 9.2 8.7 18.9 20.6Low & middle income 64 54 0.3 95,718.9 33.5 31.9 1.2 1.3 10.5 10.7 19.3 17.9East Asia & Pacifi c 71 54 –0.9 15,853.7 29.0 29.6 2.2 3.4 12.1 11.5 12.0 9.4Europe & Central Asia 37 36 0.1 22,748.9 38.4 38.6 0.4 0.4 11.6 10.5 68.5 59.2Latin America & Carib. 29 21 –0.6 20,116.2 51.6 47.0 0.9 1.0 6.6 7.4 30.3 25.9Middle East & N. Africa 48 42 1.1 8,643.6 2.4 2.4 0.8 1.0 5.9 5.9 22.5 15.6South Asia 75 70 1.0 4,771.2 16.6 17.1 1.8 3.1 42.7 41.4 17.8 12.3Sub-Saharan Africa 72 63 1.7 23,585.4 31.1 28.0 0.8 1.0 6.6 8.5 32.1 24.2High income 27 22 –0.5 33,842.1 27.9 28.8 0.7 0.7 11.7 10.8 42.5 33.2Euro area 29 26 –0.4 2,552.0 33.8 37.3 4.8 4.2 27.1 24.4 22.9 18.8a. Includes Luxembourg. b. Data are from national sources. c. Provisional estimate.1402012 World Development Indicators

Rural population and land use3.1ENVIRONMENTAbout the dataDefinitionsWith more than 3 billion people, including 70 percent Satellite images show land use that differs fromof the world’s poor people, living in rural areas, adequateindicators to monitor progress in rural areas tion and type of land use. Moreover, land use datathat of ground-based measures in area under cultiva-are essential. However, few indicators are disaggregatedbetween rural and urban areas (for some that on reporting systems designed for collecting tax rev-in some countries (India is an example) are basedare, see tables 2.7, 3.5, and 3.13). The table shows enue. With land taxes no longer a major source ofindicators of rural population and land use. Rural government revenue, the quality and coverage of landpopulation is approximated as the midyear nonurban use data have declined. Data on forest area may bepopulation. While a practical means of identifying the particularly unreliable because of irregular surveysrural population, it is not precise (see box 3.1a for and differences in defi nitions (see About the datafurther discussion).for table 3.4). Forest area statistics released by theThe data in the table show that land use patterns FAO between 1948 and 1963 were based mostly onare changing. They also indicate major differences data from country questionnaires. Remote sensing,in resource endowments and uses among countries. statistical monitoring, and expert analysis of countryTrue comparability of the data is limited, however, surveys have been applied since 1980 to improveby variations in defi nitions, statistical methods, and forest coverage estimates. The FAO’s Global Forestquality of data. Countries use different defi nitions of Resources Assessment 2010 covers 230 countriesrural and urban population and land use. The Food and is the most comprehensive assessment of forests,forestry, and the benefi ts of forest resourcesand Agriculture Organization of the United Nations(FAO), the primary compiler of the data, occasionallyadjusts its defi nitions of land use categories involved. It examines status and trends for about 90in both scope and number of countries and peopleand revises earlier data. Because the data refl ect variables on the extent, condition, uses, and valueschanges in reporting procedures as well as actual of forests and other wooded land.changes in land use, apparent trends should be interpretedcautiously.What is rural? Urban? 3.1aThe rural population identifi ed in table 3.1 is approximated as the difference between total population andurban population, calculated using the urban share reported by the United Nations Population Division.There is no universal standard for distinguishing rural from urban areas, and any urban-rural dichotomy isan oversimplifi cation (see About the data for table 3.13). The two distinct images—isolated farm, thrivingmetropolis—represent poles on a continuum. Life changes along a variety of dimensions, moving from themost remote forest outpost through fi elds and pastures, past tiny hamlets, through small towns with weeklyfarm markets, into intensively cultivated areas near large towns and small cities, eventually reaching thecenter of a megacity. Along the way access to infrastructure, social services, and nonfarm employmentincrease, and with them population density and income. Because rurality has many dimensions, for policypurposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.13 is inadequate.A 2005 World Bank Policy Research Paper proposes an operational defi nition of rurality based onpopulation density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report arguesthat these criteria are important gradients along which economic behavior and appropriate developmentinterventions vary substantially. Where population densities are low, markets of all kinds are thin, and theunit cost of delivering most social services and many types of infrastructure is high. Where large urbanareas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, andit will be diffi cult to recruit skilled people to public service or private enterprises. Thus, low populationdensity and remoteness together defi ne a set of rural areas that face special development challenges.Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors’ estimates ofthe rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Theirestimates range from 13 percent of the population, based on a population density of less than 20 peopleper square kilometer, to 64 percent, based on a population density of more than 500 people per squarekilometer. Taking remoteness into account, the estimated rural population would be 13–52 percent. Theestimate for Latin America and the Caribbean in table 3.1 is 21 percent.• Rural population is calculated as the differencebetween the total population and the urban population(see Defi nitions for tables 2.1 and 3.13). • Landarea is a country’s total area, excluding area underinland water bodies and national claims to the continentalshelf and to exclusive economic zones. Inmost cases the defi nition of inland water bodiesincludes major rivers and lakes. (See table 1.1 forthe total surface area of countries.) Variations fromyear to year may be due to updated or revised datarather than to change in area. • Land use can bebroken into several categories, three of which arepresented in the table (not shown are land used aspermanent pasture and land under urban development).• Forest area is land under natural or plantedstands of trees of at least 5 meters in height in situ,whether productive or not, and excludes tree standsin agricultural production systems (for example, infruit plantations and agroforestry systems) and treesin urban parks and gardens. • Permanent croplandis land cultivated with crops that occupy the land forlong periods and need not be replanted after eachharvest, such as cocoa, coffee, and rubber. Landunder fl owering shrubs, fruit trees, nut trees, andvines is included, but land under trees grown for woodor timber is not. • Arable land is land defi ned by theFAO as under temporary crops (double-cropped areasare counted once), temporary meadows for mowingor pasture, land under market or kitchen gardens,and land temporarily fallow. Land abandoned as aresult of shifting cultivation is excluded.Data sourcesData on urban population shares used to estimaterural population are from the United Nations PopulationDivision’s World Urbanization Prospects: The2009 Revision, and data on total population areWorld Bank estimates. Data on land area, permanentcropland, and arable land are from the FAO’selectronic fi les. The FAO gathers these data fromnational agencies through annual questionnairesand by analyzing the results of national agriculturalcensuses. Data on forest area are from the FAO’sGlobal Forest Resources Assessment 2010.2012 World Development Indicators 141

3.2Agricultural inputsAgriculturalland aAverageannualprecipitationLand undercereal productionFertilizerconsumptionAgriculturalemploymentAgriculturalmachinery% offertilizerproductionkilogramsper hectareof arablelandTractorsper 100 sq. kmof arable land% ofland area%irrigated millimetersthousandhectares% of totalemployment1990–92 2007–09 2007–09 2007–09 1990–92 2008–10 2007–09 2007–09 1990–92 2007–09 1990 2009Afghanistan 58 58 4.8 327 2,250.0 3,122.0 146.8 3.2 .. .. 0.2 0.1Albania 41 44 16.8 1,485 189.5 145.7 .. 45.5 .. 44.1 212.4 121.9Algeria 16 17 2.1 89 3,530.5 2,988.8 487.9 7.8 .. .. 129.1 139.6Angola 46 47 .. 1,010 1,012.0 1,670.0 .. 1.1 5.1 .. .. ..Argentina 47 51 .. 591 8,372.5 9,358.1 153.3 25.4 0.4 1.2 100.2 86.9Armenia 41 62 8.9 562 162.8 156.7 .. 29.3 .. 44.2 345.5 291.6Australia 61 53 0.4 534 13,319.8 19,476.4 151.7 29.0 5.3 3.3 .. ..Austria 42 38 1.4 1,110 837.7 994.8 .. 83.1 7.1 5.3 2,373.6 2,390.3Azerbaijan 53 58 29.5 447 627.0 954.6 .. 13.6 34.7 38.6 194.8 148.2Bahrain 13 9 .. 83 .. .. 5.9 .. 2.4 .. 45.0 75.0Bangladesh 73 70 .. 2,666 10,860.8 12,349.1 231.5 281.7 66.4 .. 2.3 1.2Belarus 46 44 0.5 618 2,603.0 2,380.3 46.3 281.1 22.3 .. 206.9 86.8Belgium 44 b 45 0.4 847 386.2 b 329.5 .. .. 2.9 1.5 1,523.3 b 1,127.0Benin 21 30 .. 1,039 667.3 1,109.3 .. .. .. .. 1.0 ..Bolivia 33 34 .. 1,146 680.2 931.6 .. 6.0 2.1 36.1 24.8 20.0Bosnia and Herzegovina 43 42 .. 1,028 304.1 286.2 .. 24.5 .. .. 235.3 ..Botswana 46 46 .. 416 77.9 111.3 .. .. .. .. 140.5 134.8Brazil 29 31 .. 1,782 20,564.1 18,674.3 245.0 124.9 28.3 17.0 143.8 129.2Bulgaria 56 46 1.4 608 2,231.6 1,917.5 115.1 167.4 21.2 7.1 135.8 172.3Burkina Faso 35 44 .. 748 2,844.0 4,290.2 .. 9.1 .. .. 2.4 ..Burundi 83 84 .. 1,274 220.0 232.7 .. 0.9 .. .. 1.8 ..Cambodia 26 31 .. 1,904 1,733.4 3,106.8 .. 7.1 .. 72.2 3.2 5.9Cameroon 19 20 .. 1,604 950.3 1,639.9 .. 7.4 .. .. 0.9 ..Canada 7 7 .. 537 20,176.4 13,013.6 22.2 46.8 4.1 2.4 164.8 162.5Central African Republic 8 8 .. 1,343 95.4 163.1 .. .. .. .. .. ..Chad 38 39 .. 322 1,337.6 2,467.1 .. .. .. .. .. ..Chile 21 21 5.6 1,522 694.3 523.1 94.7 595.8 18.0 11.2 127.6 425.9China 57 56 .. 645 92,582.2 90,131.6 96.8 488.4 58.5 39.6 66.6 81.8Hong Kong SAR, China .. .. .. .. .. .. .. .. 0.7 0.2 .. ..Colombia 41 38 .. 2,612 1,440.7 1,047.7 227.0 496.8 1.4 19.7 96.8 ..Congo, Dem. Rep. 10 10 .. 1,543 1,921.1 1,980.4 .. 0.5 .. .. .. ..Congo, Rep. 31 31 .. 1,646 9.9 31.8 .. 1.1 .. .. .. ..Costa Rica 42 35 1.5 2,926 76.3 76.0 .. 826.6 24.1 12.3 .. ..Côte d’Ivoire 60 64 .. 1,348 1,451.0 851.0 .. 15.9 .. .. 19.9 32.1Croatia 43 23 0.4 1,113 592.7 550.0 48.7 246.8 .. 13.9 35.2 49.4Cuba 63 63 .. 1,335 252.3 401.6 445.3 14.2 25.3 18.6 226.2 203.2Cyprus 17 14 20.8 498 65.1 35.7 .. 181.9 12.0 3.9 1,377.4 1,460.1Czech Republic .. 55 0.3 677 .. 1,467.2 112.4 123.3 .. 3.1 252.0 262.3Denmark 65 62 9.6 703 1,611.9 1,489.4 .. 103.2 5.1 2.5 634.7 486.3Dominican Republic 54 51 .. 1,410 153.1 225.4 .. 27.2 18.7 14.5 25.9 21.5Ecuador 29 30 12.6 2,087 918.4 875.7 .. 187.3 6.6 28.7 54.2 90.7Egypt, Arab Rep. 3 4 .. 51 2,477.1 2,967.1 48.5 502.8 38.4 31.6 249.6 390.6El Salvador 70 75 2.1 1,724 486.3 354.3 .. 107.4 35.8 20.9 .. ..Eritrea .. 75 .. 384 .. 461.7 .. 3.5 .. .. 5.0 8.3Estonia 32 22 .. 626 453.6 274.1 378.1 69.5 18.1 4.0 455.3 604.7Ethiopia .. 35 0.5 848 .. 9,340.5 .. 7.9 .. .. .. ..Finland 8 8 .. 536 914.5 954.2 75.8 108.0 8.9 4.6 916.5 784.0France 55 53 5.1 867 9,346.0 9,258.8 437.6 148.3 5.9 2.9 800.0 635.3Gabon 20 20 .. 1,831 16.1 22.9 .. 6.1 .. .. .. ..Gambia, The 61 67 .. 836 78.3 322.7 .. 6.8 .. .. 2.4 ..Georgia 46 36 4.0 1,026 248.5 173.2 14.3 43.0 .. 53.4 295.6 216.9Germany 49 48 .. 700 6,514.4 6,612.9 60.8 181.4 3.7 1.7 1,309.4 838.3Ghana 56 68 .. 1,187 1,203.5 1,602.1 .. 11.9 62.0 .. 7.1 4.5Greece 71 64 16.9 652 1,396.1 916.8 333.5 83.7 21.9 11.9 744.2 1,004.7Guatemala 40 41 .. 1,996 822.7 923.3 .. 106.8 13.6 .. .. ..Guinea 57 58 .. 1,651 819.8 2,028.7 .. 0.6 .. .. 12.7 25.1Guinea-Bissau 51 58 .. 1,577 117.2 152.5 .. .. .. .. 0.8 ..Haiti 57 67 .. 1,440 462.9 446.2 .. .. 65.6 .. 2.6 ..1422012 World Development Indicators

Agricultural inputs 3.2ENVIRONMENTAgriculturalland aAverageannualprecipitationLand undercereal productionFertilizerconsumptionAgriculturalemploymentAgriculturalmachinery% offertilizerproductionkilogramsper hectareof arablelandTractorsper 100 sq. kmof arable land% ofland area%irrigated millimetersthousandhectares% of totalemployment1990–92 2007–09 2007–09 2007–09 1990–92 2008–10 2007–09 2007–09 1990–92 2007–09 1990 2009Honduras 30 29 .. 1,976 516.8 538.5 .. 62.3 38.2 34.6 30.9 48.7Hungary 68 64 1.8 589 2,746.0 2,585.7 154.0 80.0 11.3 4.6 97.7 261.8India 61 61 35.1 1,083 99,499.5 92,610.0 166.3 167.8 .. .. 60.7 128.5Indonesia 23 30 .. 2,702 14,732.7 17,387.5 110.9 181.4 54.9 39.7 2.2 2.0Iran, Islamic Rep. 39 30 19.0 228 9,787.4 9,440.3 125.4 69.7 .. 21.2 141.5 152.8Iraq 23 20 .. 216 3,919.5 2,555.5 118.6 37.2 .. 23.4 65.8 92.2Ireland 64 61 .. 1,118 295.3 276.4 .. 477.3 11.6 5.0 1,623.4 1,476.4Israel 27 24 28.4 435 104.9 79.1 2.5 189.5 3.5 1.7 798.8 695.3Italy 54 47 18.8 832 4,225.4 3,494.3 186.5 135.5 8.0 3.7 1,586.5 2,117.1Jamaica 44 41 .. 2,051 2.9 2.0 .. 58.2 27.3 20.2 .. ..Japan 15 13 35.2 1,668 2,430.9 1,941.8 114.3 235.1 6.4 3.9 4,492.9 4,532.1Jordan 13 12 9.2 111 121.1 44.5 32.3 2,444.2 .. 3.0 340.8 301.7Kazakhstan 82 77 .. 250 22,152.4 15,068.4 34.1 2.4 .. 29.4 62.0 24.2Kenya 48 48 0.0 630 1,804.8 2,542.4 .. 32.4 .. .. 20.0 25.2Korea, Dem. Rep. 21 24 .. 1,054 1,517.6 1,268.2 .. .. .. .. 295.4 ..Korea, Rep. 22 19 51.6 1,274 1,293.2 970.4 87.8 388.8 15.8 7.0 211.0 1,115.4Kosovo .. 52 .. .. .. .. .. .. .. .. .. ..Kuwait 8 8 .. 121 0.3 1.1 0.2 54.5 .. .. 220.0 89.0Kyrgyz Republic 53 55 9.4 533 578.0 579.8 .. 21.0 38.2 34.0 189.4 188.1Lao PDR 7 10 .. 1,834 597.9 1,090.6 .. .. .. .. 8.5 ..Latvia 41 29 0.0 641 696.7 531.2 .. 64.9 .. 8.7 363.7 501.4Lebanon 59 67 19.9 661 42.1 64.9 3.1 19.9 .. .. 174.9 ..Lesotho 77 77 .. 788 153.1 190.5 .. .. .. .. 57.7 ..Liberia 26 27 .. 2,391 120.0 251.2 .. .. .. 47.6 .. ..Libya 9 9 .. 56 287.7 329.0 28.2 40.3 .. .. 184.3 218.9Lithuania 54 43 .. 656 1,134.0 1,037.6 8.5 45.4 .. 9.2 256.0 631.8Macedonia, FYR 51 40 7.3 619 235.2 161.8 .. 56.9 .. 19.7 730.2 1,243.8Madagascar 63 70 2.2 1,513 1,340.2 1,729.0 .. 2.6 .. .. 4.9 1.9Malawi 45 59 0.5 1,181 1,430.2 1,831.2 53,107.2 26.6 .. .. .. ..Malaysia 23 24 .. 2,875 693.8 680.1 126.3 769.8 21.8 13.5 152.9 ..Mali 26 34 .. 282 2,451.6 3,975.1 .. 3.2 .. .. 10.2 2.2Mauritania 38 38 .. 92 123.6 291.5 .. .. .. .. 8.4 9.8Mauritius 56 48 21.4 2,041 0.5 0.1 .. 209.4 14.3 9.0 .. ..Mexico 54 53 5.5 752 9,928.3 9,980.0 328.9 51.7 26.8 13.5 123.5 97.7Moldova 78 75 9.2 450 675.6 884.7 .. 9.4 40.0 31.1 310.1 197.6Mongolia 80 75 .. 241 592.6 259.1 .. 7.9 .. 40.0 80.3 40.0Morocco 69 67 4.6 346 5,019.6 5,059.7 12.5 20.8 3.6 40.9 45.0 ..Mozambique 61 63 .. 1,032 1,376.9 2,490.7 .. 2.9 .. .. .. ..Myanmar 16 19 24.8 2,091 5,577.5 8,732.4 185.8 5.4 69.1 .. 13.6 10.7Namibia 47 47 .. 285 195.2 309.9 .. 1.6 48.2 16.3 .. ..Nepal 29 30 27.7 1,500 2,840.0 3,383.2 .. 1.4 81.2 .. 21.9 111.7Netherlands 59 57 10.6 778 180.9 210.4 16.3 240.9 3.7 2.5 2,073.1 1,301.5New Zealand 61 44 .. 1,732 135.3 135.5 238.3 1,231.7 10.8 6.6 315.1 ..Nicaragua 34 43 .. 2,391 295.1 464.9 .. 22.2 38.9 29.5 20.0 ..Niger 28 35 .. 151 7,554.6 10,629.4 .. 0.1 .. .. 0.2 ..Nigeria 80 82 .. 1,150 16,836.0 13,808.9 .. 2.1 .. .. 4.7 6.6Norway 3 3 .. 1,414 359.2 300.6 29.3 191.3 5.5 2.7 1,779.1 1,539.1Oman 3 6 4.0 125 2.8 3.1 2.5 236.4 .. .. 41.1 58.1Pakistan 34 34 73.9 494 11,758.2 13,288.7 150.7 217.2 48.3 44.7 129.7 153.4Panama 29 30 .. 2,692 183.6 154.9 .. 46.8 26.3 17.9 102.0 147.2Papua New Guinea 2 3 .. 3,142 2.1 3.4 .. 119.8 .. .. 59.4 ..Paraguay 44 53 .. 1,130 501.0 1,439.3 .. 66.4 1.9 26.5 71.6 69.1Peru 17 17 .. 1,738 609.0 1,248.7 98,094.6 104.5 0.8 0.8 36.3 ..Philippines 37 40 .. 2,348 6,719.2 6,853.3 1,408.8 140.5 45.4 35.2 65.2 116.8Poland 62 53 0.4 600 8,321.1 8,423.6 101.4 144.6 25.0 13.3 823.6 1,257.9Portugal 42 40 11.4 854 751.9 324.7 91.8 159.1 11.5 11.2 563.1 1,397.7Puerto Rico 46 21 8.5 2,054 0.4 0.2 .. .. 3.4 1.5 438.5 525.0Qatar 6 6 .. 74 1.2 2.1 2.8 3,191.7 .. 2.3 84.0 63.12012 World Development Indicators 143

3.2Agricultural inputsAgriculturalland aAverageannualprecipitationLand undercereal productionFertilizerconsumptionAgriculturalemploymentAgriculturalmachinery% ofland area%irrigated millimetersthousandhectares% of totalemployment1990–92 2007–09 2007–09 2007–09 1990–92 2008–10 2007–09 2007–09 1990–92 2007–09 1990 2009Romania 64 59 2.2 637 5,773.9 5,016.9 69.0 48.5 33.0 29.1 140.6 201.2Russian Federation 14 13 2.0 460 59,541.3 32,331.0 13.0 15.6 15.4 9.7 97.8 27.1Rwanda 74 81 .. 1,212 249.5 386.3 .. 1.1 .. .. 1.0 0.5Saudi Arabia 58 81 .. 59 1,121.9 317.4 8.8 43.8 .. 4.1 19.2 ..Senegal 46 49 .. 686 1,087.2 1,477.5 601.1 4.9 .. .. 1.6 2.1Serbia .. 58 0.6 .. .. 1,873.4 294.0 133.8 .. 24.0 .. 17.7Sierra Leone 39 48 .. 2,526 432.9 649.1 .. .. .. .. 4.1 ..Singapore 1 0 .. 2,497 .. .. .. .. 0.3 1.1 .. ..Slovak Republic .. 40 1.0 824 .. 701.3 59.6 95.5 .. 3.6 197.5 154.6Slovenia 28 23 0.9 1,162 112.5 95.7 .. 241.9 .. 9.1 4,000.0 5,895.2Somalia 70 70 .. 282 401.6 596.3 .. .. .. .. 15.9 12.0South Africa 81 82 .. 495 5,352.0 3,540.2 168.3 49.2 .. 5.1 107.9 43.0South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 61 55 11.9 636 7,402.0 5,984.2 106.1 96.9 9.7 4.2 483.1 831.2Sri Lanka 37 42 .. 1,712 802.6 1,124.7 2,970.1 257.9 43.6 32.6 163.1 ..Sudan 52 58 1.0 416 8,258.8 7,886.4 .. 7.9 .. .. 7.2 13.8Swaziland 71 71 .. 788 61.5 56.2 .. .. .. .. 228.9 87.1Sweden 8 8 .. 624 1,119.2 959.1 264.4 69.4 3.3 2.2 601.9 592.4Switzerland 40 38 .. 1,537 201.2 151.8 .. 190.4 4.3 3.1 2,783.1 2,602.9Syrian Arab Republic 74 76 8.9 252 3,712.6 2,620.6 153.2 65.4 28.2 15.2 128.1 215.0Tajikistan 32 34 14.8 691 273.5 443.5 493.4 47.2 46.8 .. 415.4 310.2Tanzania 38 40 .. 1,071 3,253.6 5,018.9 .. 8.7 84.2 .. 8.2 23.3Thailand 42 39 .. 1,622 10,586.6 12,339.5 1,397.2 125.1 60.8 41.5 33.0 280.5Timor-Leste 22 25 .. 1,500 77.8 106.8 .. .. .. .. 8.5 ..Togo 59 62 .. 1,168 573.2 880.8 .. 3.3 .. .. 0.5 0.6Trinidad and Tobago 16 11 .. 2,200 7.1 2.2 0.7 85.2 11.5 3.8 1,238.9 1,972.7Tunisia 60 63 4.1 207 1,469.9 651.8 10.3 42.3 .. .. 82.4 142.6Turkey 52 51 13.4 593 13,731.1 12,005.1 208.5 96.5 44.7 22.9 279.8 395.3Turkmenistan 69 69 .. 161 331.3 983.6 .. .. .. .. 464.7 ..Uganda 60 70 .. 1,180 1,139.0 1,842.5 .. 2.1 .. 65.6 .. ..Ukraine 72 71 5.3 565 12,542.3 14,184.5 44.7 29.7 20.8 15.8 153.3 102.7United Arab Emirates 4 7 .. 78 1.4 0.0 30.8 1,033.0 .. 4.2 51.4 63.3United Kingdom 75 72 .. 1,220 3,488.6 3,008.4 183.2 239.2 2.2 1.1 760.6 ..United States 46 44 .. 715 65,850.8 57,488.4 103.6 109.3 2.9 1.5 238.4 271.3Uruguay 85 85 1.2 1,265 569.8 785.8 2,741.0 131.1 4.5 11.0 260.3 219.5Uzbekistan 65 63 .. 206 1,225.3 1,642.1 77.5 193.3 .. .. .. ..Venezuela, RB 24 24 .. 1,875 763.1 1,256.3 80.7 200.2 11.8 8.5 .. ..Vietnam 22 33 .. 1,821 6,953.4 8,641.7 403.5 402.3 .. .. 47.0 262.5West Bank and Gaza 61 61 4.6 402 0.0 32.5 .. .. .. 13.4 442.2 737.3Yemen, Rep. 44 44 .. 167 730.0 927.3 .. 12.0 52.6 .. 39.0 41.0Zambia 28 31 .. 1,020 797.8 1,216.2 .. 27.3 49.8 .. 21.9 ..Zimbabwe 35 42 .. 657 1,168.5 1,901.8 185.2 28.0 .. .. 60.1 ..World 38 w 38 w .. .. 699,721.0 s 681,889.9 s 94.7 w 122.1 w .. w .. w 202.4 w .. wLow income 35 38 .. .. 57,506.1 90,727.2 236.5 25.9 .. .. 8.4 ..Middle income 38 38 .. .. 486,828.8 448,296.9 101.7 142.2 .. .. 84.1 109.4Lower middle income 45 47 .. .. 208,267.4 209,263.7 135.8 118.9 .. .. 53.0 ..Upper middle income 35 34 .. .. 278,561.3 239,033.1 89.6 159.4 48.5 31.3 111.9 114.5Low & middle income 37 38 .. .. 544,334.9 539,024.0 102.9 128.6 .. .. 75.8 ..East Asia & Pacifi c 48 49 .. .. 142,379.2 150,606.8 105.4 .. 57.8 39.6 53.2 ..Europe & Central Asia 29 28 .. .. 125,257.0 92,762.3 35.7 38.8 23.2 16.3 135.9 115.6Latin America & Carib. 34 36 .. .. 48,465.9 49,996.0 212.8 92.2 18.9 14.3 120.9 ..Middle East & N. Africa 24 23 .. .. 31,097.6 27,682.0 46.8 79.5 .. 27.2 114.6 155.8South Asia 55 55 .. .. 128,100.5 125,939.2 168.5 176.0 .. .. 62.1 120.7Sub-Saharan Africa 42 45 .. .. 69,034.7 92,037.8 192.9 10.5 .. .. 19.8 ..High income 38 37 .. .. 155,386.1 142,865.9 74.3 104.3 6.2 3.3 472.1 ..Euro area 49 46 .. .. 32,498.0 30,496.6 98.7 143.4 6.7 3.6 986.1 841.4a. Includes permanent pastures, arable land, and land under permanent crops. b. Includes Luxembourg.% offertilizerproductionkilogramsper hectareof arablelandTractorsper 100 sq. kmof arable land1442012 World Development Indicators

Agricultural inputs 3.2ENVIRONMENTAbout the dataDefinitionsAgriculture is still a major sector in many economies,and agricultural activities provide developing countrieswith food and revenue. But agricultural activitiesalso can degrade natural resources. Poor farmingpractices can cause soil erosion and loss of soilfertility. Efforts to increase productivity by usingchemical fertilizers, pesticides, and intensive irrigationhave environmental costs and health impacts.Excessive use of chemical fertilizers can alter thechemistry of soil. Pesticide poisoning is common indeveloping countries. And salinization of irrigatedland diminishes soil fertility. Thus, inappropriate useof inputs for agricultural production has far-reachingeffects.The table provides indicators of major inputs toagricultural production: land, fertilizer, labor, andmachinery. There is no single correct mix of inputs:appropriate levels and application rates vary by countryand over time and depend on the type of crops,the climate and soils, and the production processused.The agriculture sector is the most water-intensivesector, and water delivery in agriculture is increasinglyimportant. The table shows irrigated agriculturalland as share of total agricultural land area and dataon average precipitation to illustrate how countriesobtain water for agricultural use.The data here and in table 3.3 are collected bythe Food and Agriculture Organization of the UnitedNations (FAO) through annual questionnaires. TheFAO tries to impose standard defi nitions and reportingmethods, but complete consistency acrosscountries and over time is not possible. Thus, dataon agricultural land in different climates may not becomparable. For example, permanent pastures arequite different in nature and intensity in African countriesand dry Middle Eastern countries. Data on agriculturalemployment, in particular, should be usedwith caution. In many countries much agriculturalemployment is informal and unrecorded, includingsubstantial work performed by women and children.To address some of these concerns, this indicator isheavily footnoted in the database in sources, defi nition,and coverage.Fertilizer consumption measures the quantity ofplant nutrients. Consumption is calculated as productionplus imports minus exports. Because somechemical compounds used for fertilizers have otherindustrial applications, the consumption data mayoverstate the quantity available for crops. Fertilizerconsumption as a share of production showsthe agriculture sector’s vulnerability to import andenergy price fl uctuation. The FAO recently revisedthe time series for fertilizer consumption and irrigationfor 2002 onward, but recent data are not availablefor all countries. FAO collects fertilizer statisticsfor production, imports, exports, and consumptionthrough the new FAO fertilizer resources questionnaire.In the previous release, the data were basedon total consumption of fertilizers, but the data inthe recent release are based on the nutrients in fertilizers.Some countries compile fertilizer data on acalendar year basis, while others do so on a cropyear basis (July–June). Previous editions of WorldDevelopment Indicators reported data on a crop yearbasis, but this edition uses the calendar year, asadopted by the FAO. Caution should thus be usedwhen comparing data over time.• Agricultural land is permanent pastures, arableland, and land under permanent crops. Permanentpasture is land used for fi ve or more years for forage,including natural and cultivated crops. Arableland includes land defi ned by the FAO as landunder temporary crops (double-cropped areas arecounted once), temporary meadows for mowing orfor pasture, land under market or kitchen gardens,and land temporarily fallow. Land abandoned as aresult of shifting cultivation is excluded. Land underpermanent crops is land cultivated with crops thatoccupy the land for long periods and need not bereplanted after each harvest, such as cocoa, coffee,and rubber. Land under fl owering shrubs, fruittrees, nut trees, and vines is included, but land undertrees grown for wood or timber is not. • Irrigatedland refers to areas purposely provided with water,including land irrigated by controlled fl ooding. • Averageannual precipitation is the long-term average indepth (over space and time) of annual precipitationin the country. Precipitation is defi ned as any kindof water that falls from clouds as a liquid or a solid.• Land under cereal production refers to harvestedareas, although some countries report only sownor cultivated area. • Fertilizer consumption is thequantity of plant nutrients applied to arable land.Fertilizer products cover nitrogen, potash, and phosphatefertilizers (including ground rock phosphate).Traditional nutrients—animal and plant manures—are not included. • Fertilizer production is fertilizerconsumption, exports, and nonfertilizer use of fertilizerproducts minus fertilizer imports. • Agriculturalemployment is employment in agriculture, forestry,hunting, and fi shing (see table 2.3). • Agriculturalmachinery refers to wheel and crawler tractors(excluding garden tractors) in use in agriculture atthe end of the calendar year specifi ed or during thefi rst quarter of the following year.Data sourcesData on agricultural inputs are from the FAO’selectronic fi les. Data on agricultural employmentare from the International Labour Organization’sKey Indicators of the Labour Market, 7th edition,database.2012 World Development Indicators 145

3.3 Agricultural output and productivityCropproduction indexFoodproduction indexLivestockproduction indexCerealyieldAgriculturalproductivityAgriculturevalue added2004–06 = 100 2004–06 = 100 2004–06 = 100kilogramsper hectareper worker2000 $1990 2009 1990 2009 1990 2009 1990 2010 1990 2010Afghanistan 67.0 125.0 68.0 114.0 70.0 101.0 1,201 1,908 .. ..Albania 85.0 121.0 69.0 110.0 58.0 99.0 2,794 4,762 764 ..Algeria 41.0 121.0 50.0 117.0 69.0 109.0 688 1,568 1,702 2,254Angola 33.0 153.0 42.0 144.0 74.0 110.0 321 617 206 340Argentina 53.0 83.0 63.0 95.0 84.0 113.0 2,232 4,937 6,683 12,957Armenia 67.0 125.0 74.0 131.0 84.0 134.0 1,843 2,074 1,607 4,723Australia 67.0 101.0 74.0 103.0 85.0 101.0 1,716 1,721 19,447 35,208Austria 89.0 103.0 93.0 101.0 95.0 99.0 5,577 5,358 13,413 25,771Azerbaijan 95.0 110.0 82.0 115.0 79.0 118.0 2,113 2,019 1,001 1,241Bahrain 61.0 116.0 90.0 117.0 137.0 117.0 .. .. .. ..Bangladesh 66.0 122.0 63.0 121.0 56.0 113.0 2,491 4,144 275 507Belarus 77.0 111.0 110.0 120.0 127.0 129.0 2,741 2,827 2,042 5,700Belgium .. 100.0 .. 97.0 .. 98.0 5,755 a 9,231 .. ..Benin 45.0 117.0 48.0 122.0 70.0 100.0 848 1,402 427 ..Bolivia 53.0 106.0 61.0 113.0 68.0 119.0 1,361 2,333 683 716Bosnia and Herzegovina 75.0 99.0 87.0 110.0 92.0 128.0 3,553 3,858 .. 15,028Botswana 83.0 122.0 95.0 116.0 98.0 114.0 266 544 761 447Brazil 59.0 117.0 51.0 116.0 44.0 113.0 1,755 4,055 1,625 4,118Bulgaria 149.0 98.0 179.0 103.0 248.0 96.0 3,954 3,665 3,983 10,923Burkina Faso 44.0 95.0 50.0 105.0 53.0 109.0 600 1,054 107 ..Burundi 98.0 61.0 98.0 65.0 103.0 136.0 1,349 1,346 118 ..Cambodia 46.0 147.0 46.0 137.0 53.0 92.0 1,362 3,108 .. 434Cameroon 58.0 113.0 61.0 114.0 79.0 103.0 1,259 1,711 420 ..Canada 82.0 109.0 73.0 107.0 68.0 95.0 2,636 3,490 28,898 44,619Central African Republic 76.0 113.0 61.0 111.0 54.0 110.0 810 1,465 321 ..Chad 51.0 105.0 56.0 112.0 74.0 110.0 559 775 170 ..Chile 60.0 101.0 58.0 101.0 55.0 101.0 3,620 6,822 3,457 6,267China 56.0 116.0 48.0 115.0 39.0 112.0 4,325 5,521 258 545Hong Kong SAR, China .. .. .. .. .. .. .. .. .. ..Colombia 80.0 108.0 70.0 112.0 68.0 117.0 2,475 3,895 3,123 2,874Congo, Dem. Rep. 125.0 98.0 120.0 99.0 99.0 102.0 800 771 213 173Congo, Rep. 66.0 112.0 63.0 116.0 53.0 126.0 624 785 .. ..Costa Rica 62.0 109.0 59.0 113.0 70.0 116.0 3,097 3,730 2,993 5,596Côte d’Ivoire 68.0 103.0 66.0 108.0 80.0 111.0 887 1,717 658 1,056Croatia 93.0 110.0 105.0 114.0 106.0 106.0 3,975 5,486 5,552 16,423Cuba 136.0 88.0 160.0 98.0 185.0 136.0 2,342 1,940 4,117 3,618Cyprus 136.0 73.0 98.0 87.0 69.0 95.0 1,886 1,595 5,808 7,927Czech Republic .. 99.0 .. 98.0 .. 95.0 .. 4,691 .. 6,415Denmark 115.0 112.0 94.0 107.0 81.0 101.0 6,118 5,889 14,588 53,407Dominican Republic 98.0 107.0 78.0 116.0 58.0 126.0 3,996 4,234 1,974 5,083Ecuador 66.0 114.0 56.0 115.0 48.0 114.0 1,724 3,117 2,109 2,040Egypt, Arab Rep. 56.0 116.0 54.0 118.0 53.0 114.0 5,703 6,541 1,767 3,265El Salvador 92.0 112.0 75.0 109.0 62.0 107.0 1,939 2,806 1,742 2,752Eritrea .. 84.0 .. 99.0 .. 111.0 .. 536 .. 66Estonia 129.0 126.0 157.0 115.0 175.0 109.0 1,304 2,444 3,288 3,156Ethiopia .. 126.0 .. 122.0 .. 113.0 .. 1,674 .. 226Finland 110.0 109.0 108.0 101.0 107.0 97.0 3,543 3,136 17,163 47,514France 96.0 100.0 99.0 98.0 98.0 96.0 6,083 7,093 21,454 57,973Gabon 78.0 118.0 91.0 115.0 86.0 102.0 1,750 1,782 1,216 1,972Gambia, The 56.0 119.0 60.0 119.0 76.0 106.0 1,004 1,127 253 282Georgia 138.0 73.0 105.0 72.0 78.0 68.0 1,998 1,271 2,359 1,817Germany 93.0 106.0 107.0 105.0 114.0 103.0 5,411 6,716 13,730 32,866Ghana 34.0 119.0 37.0 119.0 78.0 120.0 989 1,814 .. ..Greece 80.0 81.0 86.0 86.0 99.0 95.0 3,036 4,908 .. ..Guatemala 57.0 125.0 57.0 122.0 70.0 103.0 1,998 2,299 2,241 2,803Guinea 59.0 105.0 59.0 107.0 43.0 120.0 1,455 1,409 166 242Guinea-Bissau 60.0 94.0 62.0 98.0 70.0 115.0 1,531 1,555 .. ..Haiti 107.0 109.0 96.0 106.0 59.0 102.0 1,027 980 .. ..1462012 World Development Indicators

Agricultural output and productivity 3.3ENVIRONMENTCropproduction indexFoodproduction indexLivestockproduction indexCerealyieldAgriculturalproductivityAgriculturevalue added2004–06 = 100 2004–06 = 100 2004–06 = 100kilogramsper hectareper worker2000 $1990 2009 1990 2009 1990 2009 1990 2010 1990 2010Honduras 70.0 105.0 65.0 103.0 51.0 105.0 1,468 1,094 1,184 2,041Hungary 100.0 89.0 118.0 94.0 161.0 96.0 4,521 4,759 4,177 8,522India 73.0 113.0 71.0 114.0 62.0 115.0 1,891 2,537 357 489Indonesia 61.0 121.0 62.0 120.0 62.0 114.0 3,800 4,876 493 730Iran, Islamic Rep. 55.0 99.0 54.0 104.0 51.0 115.0 1,445 2,359 1,988 ..Iraq 102.0 87.0 118.0 92.0 153.0 100.0 1,061 1,687 .. ..Ireland 91.0 82.0 97.0 91.0 96.0 93.0 6,577 7,409 .. 13,931Israel 95.0 97.0 72.0 104.0 56.0 109.0 3,486 3,015 .. ..Italy 84.0 91.0 89.0 97.0 96.0 106.0 3,945 5,436 10,435 31,254Jamaica 98.0 105.0 81.0 106.0 61.0 106.0 1,116 1,172 2,224 2,758Japan 123.0 94.0 114.0 98.0 107.0 101.0 5,846 5,852 19,563 48,794Jordan 66.0 106.0 57.0 110.0 44.0 115.0 1,220 1,963 1,976 3,401Kazakhstan 144.0 131.0 142.0 126.0 147.0 115.0 1,338 804 1,781 1,782Kenya 72.0 105.0 68.0 110.0 67.0 114.0 1,562 1,613 400 337Korea, Dem. Rep. 99.0 101.0 92.0 100.0 96.0 100.0 3,916 3,582 .. ..Korea, Rep. 91.0 106.0 79.0 108.0 62.0 110.0 5,853 6,196 5,338 19,807Kosovo .. .. .. .. .. .. .. .. .. ..Kuwait 39.0 106.0 48.0 110.0 54.0 112.0 3,653 3,415 .. ..Kyrgyz Republic 64.0 106.0 76.0 101.0 107.0 102.0 2,772 2,604 685 996Lao PDR 52.0 129.0 50.0 125.0 49.0 118.0 2,268 3,751 388 532Latvia 106.0 114.0 194.0 118.0 253.0 114.0 1,641 2,667 1,896 3,837Lebanon 96.0 101.0 83.0 104.0 54.0 112.0 1,878 2,740 .. 41,013Lesotho 127.0 81.0 106.0 100.0 91.0 112.0 1,037 909 255 202Liberia 62.0 97.0 77.0 121.0 79.0 124.0 1,029 1,179 .. ..Libya 77.0 103.0 75.0 104.0 73.0 106.0 674 662 .. ..Lithuania 81.0 136.0 133.0 110.0 154.0 95.0 1,938 2,667 .. 5,996Macedonia, FYR 102.0 110.0 102.0 114.0 97.0 114.0 2,652 3,329 2,413 5,946Madagascar 79.0 110.0 80.0 112.0 99.0 113.0 1,945 2,987 213 187Malawi 50.0 131.0 47.0 127.0 64.0 151.0 992 2,206 89 169Malaysia 59.0 109.0 53.0 114.0 58.0 109.0 2,740 3,800 3,826 6,680Mali 54.0 134.0 61.0 146.0 75.0 135.0 726 1,615 406 ..Mauritania 63.0 124.0 76.0 107.0 79.0 104.0 870 946 651 404Mauritius 107.0 95.0 89.0 98.0 45.0 109.0 4,191 10,000 3,446 6,401Mexico 74.0 99.0 65.0 101.0 59.0 105.0 2,424 3,499 2,250 3,302Moldova 121.0 90.0 139.0 91.0 196.0 91.0 2,928 2,696 1,349 1,610Mongolia 230.0 216.0 134.0 146.0 132.0 142.0 1,098 1,370 1,477 1,524Morocco 68.0 116.0 68.0 117.0 71.0 116.0 1,120 1,548 1,807 3,315Mozambique 60.0 101.0 63.0 98.0 47.0 95.0 477 1,006 132 234Myanmar 42.0 122.0 41.0 126.0 25.0 138.0 2,762 3,989 .. ..Namibia 56.0 100.0 82.0 90.0 89.0 86.0 457 373 1,267 881Nepal 62.0 112.0 64.0 110.0 70.0 107.0 1,920 2,295 248 238Netherlands 91.0 106.0 103.0 108.0 106.0 109.0 6,959 8,574 23,743 47,805New Zealand 74.0 105.0 64.0 98.0 67.0 97.0 5,034 7,387 19,767 26,556Nicaragua 59.0 111.0 51.0 115.0 45.0 119.0 1,524 2,086 .. 2,779Niger 49.0 138.0 46.0 132.0 42.0 122.0 310 479 239 ..Nigeria 47.0 88.0 49.0 92.0 61.0 112.0 1,148 1,413 .. ..Norway 135.0 84.0 110.0 103.0 103.0 106.0 4,399 3,810 17,454 46,480Oman 63.0 112.0 52.0 97.0 40.0 77.0 2,160 18,987 1,029 ..Pakistan 67.0 108.0 60.0 115.0 56.0 117.0 1,766 2,592 769 947Panama 123.0 88.0 91.0 102.0 64.0 114.0 1,867 2,131 2,303 4,109Papua New Guinea 72.0 117.0 69.0 108.0 66.0 91.0 2,603 3,839 559 683Paraguay 70.0 92.0 64.0 110.0 72.0 117.0 1,979 3,457 1,660 2,710Peru 46.0 117.0 49.0 123.0 55.0 128.0 2,603 3,899 911 1,607Philippines 72.0 111.0 67.0 112.0 49.0 114.0 2,065 3,232 854 1,119Poland 133.0 109.0 110.0 104.0 114.0 102.0 3,284 3,220 1,619 2,994Portugal 110.0 91.0 98.0 98.0 84.0 106.0 1,878 3,463 4,516 7,019Puerto Rico 138.0 100.0 133.0 103.0 132.0 104.0 1,080 1,878 .. ..Qatar 56.0 138.0 66.0 168.0 73.0 192.0 2,897 4,795 .. ..2012 World Development Indicators 147

3.3Agricultural output and productivityCropproduction indexFoodproduction indexLivestockproduction indexCerealyieldAgriculturalproductivityAgriculturevalue added2004–06 = 100 2004–06 = 100 2004–06 = 100kilogramsper hectareper worker2000 $1990 2009 1990 2009 1990 2009 1990 2010 1990 2010Romania 82.0 85.0 94.0 93.0 116.0 99.0 3,011 3,331 2,351 9,700Russian Federation 100.0 107.0 117.0 106.0 143.0 97.0 1,743 1,844 1,915 2,731Rwanda 76.0 122.0 72.0 121.0 59.0 122.0 1,043 1,930 174 ..Saudi Arabia 83.0 95.0 70.0 102.0 56.0 111.0 4,245 5,621 7,863 20,233Senegal 78.0 143.0 76.0 136.0 71.0 113.0 795 1,196 262 256Serbia 78.0 b 106.0 99.0 b 99.0 109.0 b 98.0 2,926 b 4,959 .. 2,057Sierra Leone 69.0 101.0 68.0 104.0 81.0 116.0 1,202 1,554 .. ..Singapore 90.0 149.0 492.0 98.0 535.0 93.0 .. .. 21,392 29,145Slovak Republic .. 93.0 .. 92.0 .. 86.0 .. 3,754 .. 9,924Slovenia 74.0 87.0 76.0 93.0 75.0 93.0 3,279 5,973 13,217 76,633Somalia 122.0 100.0 92.0 100.0 88.0 100.0 793 432 .. ..South Africa 83.0 108.0 78.0 109.0 79.0 110.0 1,877 4,162 2,287 3,662South Sudan .. .. .. .. .. .. .. .. .. ..Spain 89.0 105.0 83.0 104.0 72.0 98.0 2,485 3,231 8,938 22,035Sri Lanka 87.0 111.0 85.0 112.0 75.0 107.0 2,965 3,974 672 966Sudan 50.0 106.0 47.0 103.0 47.0 99.0 456 452 512 929Swaziland 99.0 97.0 90.0 101.0 85.0 110.0 1,284 1,226 1,025 1,217Sweden 130.0 106.0 109.0 101.0 103.0 97.0 4,964 4,518 23,307 51,585Switzerland 118.0 106.0 104.0 103.0 102.0 104.0 5,984 6,084 20,556 27,066Syrian Arab Republic 51.0 93.0 51.0 96.0 54.0 99.0 750 1,789 2,698 ..Tajikistan 83.0 115.0 99.0 118.0 132.0 117.0 994 2,721 370 577Tanzania 61.0 110.0 62.0 106.0 61.0 101.0 1,507 1,332 219 292Thailand 64.0 111.0 69.0 112.0 73.0 112.0 2,009 2,938 443 706Timor-Leste 81.0 136.0 95.0 129.0 104.0 109.0 1,608 2,451 .. ..Togo 67.0 104.0 64.0 114.0 68.0 123.0 747 1,187 376 ..Trinidad and Tobago 141.0 113.0 84.0 107.0 50.0 104.0 3,326 2,727 1,825 1,024Tunisia 74.0 101.0 69.0 101.0 58.0 107.0 1,145 1,702 2,383 3,050Turkey 78.0 103.0 79.0 108.0 83.0 112.0 2,214 2,727 2,254 3,770Turkmenistan 73.0 92.0 51.0 113.0 49.0 126.0 2,210 3,289 1,275 ..Uganda 72.0 106.0 71.0 105.0 64.0 100.0 1,498 1,608 189 200Ukraine 98.0 118.0 128.0 106.0 166.0 95.0 2,834 2,727 1,231 2,500United Arab Emirates 28.0 100.0 29.0 113.0 43.0 143.0 2,216 2,667 .. ..United Kingdom 104.0 102.0 107.0 101.0 107.0 99.0 6,171 6,957 19,880 25,681United States 79.0 105.0 77.0 109.0 78.0 105.0 4,755 6,988 18,703 51,120Uruguay 48.0 134.0 60.0 109.0 70.0 100.0 2,183 4,251 5,648 8,682Uzbekistan 83.0 118.0 76.0 123.0 82.0 114.0 1,777 4,516 1,427 2,782Venezuela, RB 76.0 108.0 71.0 106.0 71.0 110.0 2,486 4,038 4,458 7,667Vietnam 45.0 110.0 47.0 114.0 38.0 133.0 3,073 5,161 222 367West Bank and Gaza .. 109.0 .. 101.0 .. 85.0 .. 1,163 .. ..Yemen, Rep. 61.0 121.0 56.0 126.0 50.0 134.0 908 1,092 621 749Zambia 61.0 129.0 74.0 115.0 77.0 96.0 1,352 2,547 213 214Zimbabwe 118.0 91.0 94.0 92.0 75.0 105.0 1,625 752 270 160World 70.0 c w 122.2 w 70.0 c w 123.0 w 74.0 c w 120.3 w 2,756 c w 3,568 w 781 w 992 wLow income 78.4 132.2 78.3 133.2 78.0 131.7 1,565 2,075 240 288Middle income 73.4 128.4 68.7 130.3 62.5 131.4 2,556 3,312 481 786Lower middle income 74.8 127.2 72.8 128.2 69.4 131.8 1,928 2,718 472 677Upper middle income 72.6 129.1 66.5 131.4 59.6 131.2 3,177 3,831 487 871Low & middle income 73.8 128.7 69.4 130.5 63.3 131.4 2,429 3,103 455 728East Asia & Pacifi c 69.9 133.1 62.7 135.1 48.8 135.2 3,796 4,925 305 585Europe & Central Asia 111.4 129.4 118.2 126.3 137.7 119.2 2,596 2,239 2,245 3,204Latin America & Carib. 75.8 128.1 71.2 131.2 69.7 132.6 2,089 3,919 2,221 3,663Middle East & N. Africa 74.7 127.3 72.8 131.6 69.3 134.7 1,471 2,379 1,796 ..South Asia 78.0 119.3 74.5 122.7 69.2 132.9 1,926 2,691 373 521Sub-Saharan Africa 71.1 128.7 72.9 130.0 80.1 125.1 1,033 1,335 307 322High income 90.7 103.9 90.4 106.3 90.7 104.0 4,138 5,319 14,129 24,483Euro area 90.9 96.9 95.6 97.7 98.2 100.0 4,490 5,685 12,590 25,752a. Includes Luxembourg. b. Includes Montenegro. c. Food and Agriculture Organization estimate.1482012 World Development Indicators

Agricultural output and productivity3.3ENVIRONMENTAbout the dataDefinitionsThe agricultural production indexes in the table areprepared by the Food and Agriculture Organization ofthe United Nations (FAO). The FAO obtains data fromoffi cial and semioffi cial reports of crop yields, areaunder production, and livestock numbers. If data areunavailable, the FAO makes estimates. The indexesare calculated using the Laspeyres formula: productionquantities of each commodity are weighted byaverage international commodity prices in the baseperiod and summed for each year. Because the FAO’sindexes are based on the concept of agriculture as asingle enterprise, estimates of the amounts retainedfor seed and feed are subtracted from the productiondata to avoid double counting. The aggregates arenet production available for any use except as seedand feed. The FAO’s indexes may differ from thosefrom other sources because of differences in coverage,weights, concepts, time periods, calculationmethods, and use of international prices.To facilitate cross-country comparisons, the FAOuses international commodity prices to value production.These prices, expressed in internationaldollars (equivalent in purchasing power to the U.S.dollar), are derived using a Geary-Khamis formulaapplied to agricultural outputs (see Inter- SecretariatWorking Group on National Accounts 1993, sections16.93–96). This method assigns a single price toeach commodity so that, for example, one metric tonof wheat has the same price regardless of where itwas produced. The use of international prices eliminatesfl uctuations in the value of output due to transitorymovements of nominal exchange rates unrelatedto the purchasing power of the domestic currency.Data on cereal yield may be affected by a varietyof reporting and timing differences. Millet and sorghum,which are grown as feed for livestock and poultryin Europe and North America, are used as foodin Africa, Asia, and countries of the former SovietUnion. So some cereal crops are excluded from thedata for some countries and included elsewhere,depending on their use.• Crop production index is agricultural productionfor the period specifi ed relative to the base period2004–06. It includes all crops except fodder crops.The regional and income group aggregates for theFAO’s production indexes are calculated from theunderlying values in international dollars, normalizedto the base period 2004–06. • Food productionindex covers food crops that are considerededible and that contain nutrients. Coffee and teaare excluded because, although edible, they haveno nutritive value. • Livestock production indexincludes meat and milk from all sources, dairy productssuch as cheese, and eggs, honey, raw silk, wool,and hides and skins. • Cereal yield, measured inkilograms per hectare of harvested land, includeswheat, rice, maize, barley, oats, rye, millet, sorghum,buckwheat, and mixed grains. Production data oncereals refer to crops harvested for dry grain only.Cereal crops harvested for hay or harvested green forfood, feed, or silage, and those used for grazing, areexcluded. The FAO allocates production data to thecalendar year in which the bulk of the harvest tookplace. But most of a crop harvested near the end ofa year will be used in the following year. • Agriculturalproductivity is the ratio of agricultural valueadded, measured in 2005 U.S. dollars, to the numberof workers in agriculture. Agricultural productivityis measured by value added per unit of input. (Forfurther discussion of the calculation of value addedin national accounts, see About the data for tables4.1 and 4.2.) Agricultural value added includes thatfrom forestry and fi shing. Thus interpretations of landproductivity should be made with caution.Data sourcesData on agricultural production indexes, cerealyield, and agricultural employment are from theFAO’s electronic fi les, available on the FAO website(www.fao.org). Data on agricultural value addedare from the World Bank’s national accounts fi les.2012 World Development Indicators 149

3.4 Deforestation and biodiversityForestareaAverage annualdeforestation aThreatenedspeciesGEF benefitsindex forbiodiversityNationallyprotected areasthousandsq. km % Mammals Birds FishAfghanistan 14 14 0.00 0.00 11 14 5 2 3.4 0.4 0.4 .. ..Albania 8 8 0.26 –0.10 3 5 39 0 0.2 3.4 9.8 0.2 1.6Algeria 17 15 0.54 0.57 14 9 36 12 2.9 6.3 6.3 0.2 0.3Angola 610 585 0.21 0.21 15 23 39 34 8.3 12.4 12.4 0.1 0.1Argentina 348 294 0.88 0.81 38 49 37 35 17.7 4.6 5.5 0.8 1.1Armenia 3 3 1.31 1.48 9 12 3 1 0.2 6.9 8.0 .. ..Australia 1,545 1,493 –0.03 0.37 55 52 103 27 87.7 7.5 10.6 10.9 28.3Austria 38 39 –0.16 –0.13 3 7 11 9 0.3 20.1 22.9 .. ..Azerbaijan 9 9 0.00 0.00 7 14 10 0 0.8 6.2 7.1 .. ..Bahrain 0 c 0 c –5.56 –3.55 3 3 8 0 0.0 1.3 1.3 0.0 0.7Bangladesh 15 14 0.18 0.18 34 30 18 15 1.4 1.7 1.8 0.4 0.8Belarus 78 86 –0.62 –0.43 4 4 2 0 0.0 6.5 7.2 .. ..Belgium 7 7 0.15 –0.16 3 2 11 0 0.0 3.2 13.8 0.0 0.0Benin 58 46 1.29 1.04 11 6 27 13 0.2 23.8 23.8 0.0 0.0Bolivia 628 572 0.44 0.50 20 34 0 72 12.5 8.8 18.5 .. ..Bosnia and Herzegovina 22 22 0.11 0.00 4 5 31 0 0.4 0.5 0.6 0.7 0.7Botswana 137 114 0.90 0.99 7 10 2 0 1.4 30.3 30.9 .. ..Brazil 5,748 5,195 0.51 0.50 81 122 84 389 100.0 9.0 26.3 8.2 16.5Bulgaria 33 39 –0.14 –1.53 7 11 19 5 0.8 2.0 9.2 0.2 3.2Burkina Faso 68 56 0.91 1.01 9 7 4 3 0.3 13.7 14.2 .. ..Burundi 3 2 3.71 1.40 11 11 17 2 0.3 3.8 4.8 .. ..Cambodia 129 101 1.14 1.34 37 24 42 29 3.5 0.0 25.8 0.0 0.4Cameroon 243 199 0.94 1.05 38 20 112 378 12.5 7.0 9.2 0.4 0.4Canada 3,101 3,101 0.00 0.00 12 15 35 1 21.5 4.7 7.5 0.6 1.2Central African Republic 232 226 0.13 0.13 8 9 3 17 1.5 17.5 17.7 .. ..Chad 131 115 0.62 0.66 13 9 1 2 2.2 9.4 9.4 .. ..Chile 153 162 –0.37 –0.25 20 34 20 34 15.3 16.0 16.6 3.5 3.7China 1,571 2,069 –1.20 –1.57 75 86 113 374 66.6 13.5 16.6 0.4 1.3Hong Kong SAR, China .. .. .. .. 2 20 13 6 .. 41.1 41.8 .. ..Colombia 625 605 0.16 0.17 52 94 54 215 51.5 19.3 20.9 0.9 15.5Congo, Dem. Rep. 1,604 1,541 0.20 0.20 30 35 83 80 19.9 10.0 10.0 3.8 4.4Congo, Rep. 227 224 0.08 0.07 11 2 46 37 3.6 5.4 9.4 0.0 32.8Costa Rica 26 26 0.76 –0.93 9 19 50 112 9.7 18.7 20.9 11.9 12.2Côte d’Ivoire 102 104 –0.10 –0.15 23 15 45 106 3.4 22.6 22.6 0.1 0.1Croatia 19 19 –0.19 –0.19 7 10 60 5 0.6 7.8 13.0 1.3 3.4Cuba 21 29 –1.70 –1.66 14 17 34 155 12.5 4.3 6.4 1.3 4.4Cyprus 2 2 –0.63 –0.09 5 4 19 16 0.5 7.1 10.5 0.3 0.6Czech Republic 26 27 –0.03 –0.08 2 5 2 8 0.1 13.6 15.1 .. ..Denmark 4 5 –0.89 –1.14 2 2 15 1 0.2 4.2 4.9 3.0 3.2Dominican Republic 20 20 0.00 0.00 6 14 21 27 6.0 22.2 22.2 30.4 30.4Ecuador 138 99 1.53 1.81 43 73 50 1,714 29.3 21.6 25.1 0.2 75.4Egypt, Arab Rep. 0 c 1 –2.98 –1.73 17 9 39 2 2.9 1.9 5.9 4.4 9.3El Salvador 4 3 1.26 1.45 5 5 14 24 0.9 0.4 0.8 3.1 3.1Eritrea 16 15 0.28 0.28 10 12 18 4 0.8 4.9 5.0 0.0 0.0Estonia 21 22 –0.71 0.12 1 3 5 0 0.1 17.7 20.4 25.3 26.5Ethiopia 151 123 0.97 1.08 33 24 14 24 8.4 17.7 18.4 .. ..Finland 219 222 –0.26 0.14 1 4 6 1 0.2 4.2 9.0 3.5 5.0France 145 160 –0.55 –0.39 9 6 44 27 5.3 10.2 16.5 0.3 21.3Gabon 220 220 0.00 0.00 14 4 61 120 3.0 4.6 15.1 0.2 7.3Gambia, The 4 5 –0.42 –0.41 10 8 23 4 0.1 1.5 1.5 0.1 0.1Georgia 28 27 0.04 0.09 10 10 9 0 0.6 2.8 3.7 0.2 0.4Germany 107 111 –0.31 0.00 6 5 23 13 0.6 31.9 42.4 35.7 40.3Ghana 74 49 1.99 2.08 16 13 44 117 1.9 14.6 14.7 0.0 0.0Greece 33 39 –0.88 –0.81 10 10 75 52 2.8 5.7 16.2 0.5 2.6Guatemala 47 37 1.20 1.40 16 10 25 72 8.0 25.9 30.6 0.3 12.5Guinea 73 65 0.51 0.54 22 13 65 22 2.3 6.8 6.8 0.0 0.0Guinea-Bissau 22 20 0.44 0.48 12 5 32 4 0.6 7.6 16.1 2.7 45.8Haiti 1 1 0.62 0.76 5 13 20 26 5.2 0.3 0.3 0.0 0.0Higherplants b0–100 (nobiodiversityto maximumbiodiversity)Terrestrial% of land areaMarine% of territorial waters1990 2010 1990–2000 2000–10 2011 2011 2011 2011 2008 1990 2010 1990 20101502012 World Development Indicators

Deforestation and biodiversity3.4ENVIRONMENTForestareaAverage annualdeforestation aThreatenedspeciesGEF benefitsindex forbiodiversityNationallyprotected areasthousandsq. km % Mammals Birds FishHonduras 81 52 2.38 2.06 7 9 27 107 7.2 13.6 18.2 0.0 1.9Hungary 18 20 –0.57 –0.62 2 8 9 8 0.2 4.6 5.1 .. ..India 639 684 –0.22 –0.46 94 78 212 291 39.9 4.7 5.0 1.6 1.7Indonesia 1,185 944 1.75 0.51 184 119 140 385 81.0 10.0 14.1 0.5 2.0Iran, Islamic Rep. 111 111 0.00 0.00 16 20 29 1 7.3 5.2 7.1 1.0 1.7Iraq 8 8 –0.17 –0.09 13 16 11 0 1.6 0.1 0.1 0.0 0.0Ireland 5 7 –3.16 –1.53 5 1 20 1 0.6 0.6 1.8 0.1 0.2Israel 1 2 –1.49 –0.07 15 13 36 0 0.8 16.3 17.8 0.4 0.4Italy 76 91 –0.98 –0.90 7 7 47 61 3.8 5.0 15.1 0.5 17.4Jamaica 3 3 0.11 0.11 5 10 21 206 4.4 10.2 18.9 0.2 4.2Japan 250 250 0.03 –0.05 28 39 64 6 36.0 13.4 16.5 2.0 5.5Jordan 1 1 0.00 0.00 13 10 13 1 0.4 0.7 1.9 0.0 30.0Kazakhstan 34 33 0.17 0.17 16 20 14 16 5.1 2.4 2.5 .. ..Kenya 37 35 0.35 0.33 28 31 68 126 8.8 11.6 11.8 5.2 10.5Korea, Dem. Rep. 82 57 1.67 2.00 9 24 13 5 0.7 4.3 5.9 0.1 0.1Korea, Rep. 64 62 0.13 0.11 9 29 19 3 1.7 2.2 2.4 3.5 3.9Kosovo .. .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 0 c 0 c –3.46 –2.57 6 8 11 0 0.1 1.6 1.6 0.0 0.0Kyrgyz Republic 8 10 –0.26 –1.07 6 12 3 14 1.1 6.4 6.9 .. ..Lao PDR 173 158 0.46 0.49 45 23 46 17 5.0 1.5 16.6 .. ..Latvia 32 34 –0.21 –0.34 1 4 6 0 0.0 6.5 18.0 4.6 6.7Lebanon 1 1 0.00 –0.45 10 8 22 1 0.2 0.5 0.5 0.0 0.1Lesotho 0 c 0 c –0.49 –0.47 2 7 1 4 0.3 0.5 0.5 .. ..Liberia 49 43 0.63 0.67 18 11 53 47 2.6 1.6 1.8 0.0 0.0Libya 2 2 0.00 0.00 12 3 24 2 1.6 0.1 0.1 0.0 0.1Lithuania 19 22 –0.38 –0.68 3 4 6 0 0.0 2.0 14.5 0.8 10.7Macedonia, FYR 9 10 –0.49 –0.41 5 9 13 0 0.2 4.2 4.9 .. ..Madagascar 137 126 0.42 0.45 65 35 85 273 29.2 2.2 3.1 0.0 0.1Malawi 39 32 0.88 0.97 7 15 101 12 3.5 15.0 15.0 .. ..Malaysia 224 205 0.36 0.54 70 45 64 674 13.9 17.1 18.1 1.5 2.0Mali 141 125 0.58 0.61 12 9 3 7 1.5 2.3 2.4 .. ..Mauritania 4 2 2.66 2.66 15 11 32 0 1.3 0.5 0.5 32.1 32.1Mauritius 0 c 0 c 0.03 1.00 6 11 13 88 3.3 1.7 4.5 0.3 0.3Mexico 703 648 0.52 0.30 100 56 152 191 68.7 2.2 11.1 1.1 16.7Moldova 3 4 –0.16 –1.77 4 8 8 2 0.0 0.9 1.4 .. ..Mongolia 125 109 0.67 0.73 11 20 1 0 4.2 4.1 13.4 .. ..Morocco 50 51 0.06 –0.23 18 10 47 28 3.5 1.2 1.5 0.8 1.3Mozambique 434 390 0.52 0.54 12 24 55 40 7.2 14.8 15.8 1.8 3.3Myanmar 392 318 1.17 0.93 45 43 39 37 10.0 3.1 6.3 0.3 0.3Namibia 88 73 0.87 0.97 12 25 27 25 5.2 14.4 14.9 0.5 8.2Nepal 48 36 2.09 0.70 31 31 7 2 2.1 7.7 17.0 .. ..Netherlands 3 4 –0.43 –0.14 4 2 13 0 0.2 11.2 12.4 12.8 22.1New Zealand 77 83 –0.69 –0.01 9 70 23 19 20.2 25.4 26.2 0.4 10.8Nicaragua 45 31 1.67 2.01 6 12 30 40 3.3 15.4 36.7 0.6 37.2Niger 19 12 3.74 0.98 12 7 4 2 0.9 7.1 7.1 .. ..Nigeria 172 90 2.68 3.67 26 14 59 171 6.0 11.6 12.8 0.2 0.2Norway 91 101 –0.19 –0.80 7 2 19 2 1.3 7.0 14.6 1.2 2.4Oman 0 c 0 c 0.00 0.00 9 11 26 6 3.7 0.0 10.7 0.0 1.3Pakistan 25 17 1.76 2.24 23 27 34 2 4.9 10.1 10.1 1.8 1.8Panama 38 33 1.18 0.36 15 17 41 192 10.9 17.2 18.7 3.1 4.0Papua New Guinea 315 287 0.45 0.48 39 37 42 142 25.4 1.9 3.1 0.3 0.3Paraguay 212 176 0.88 0.97 8 27 0 9 2.8 2.9 5.4 .. ..Peru 702 680 0.14 0.18 54 98 20 268 33.4 4.7 13.6 2.8 2.8Philippines 66 77 –0.80 –0.75 38 74 71 210 32.3 8.7 10.9 0.5 2.5Poland 89 93 –0.20 –0.31 5 6 7 8 0.5 15.3 22.4 3.4 4.1Portugal 33 35 –0.28 –0.11 11 8 53 68 5.5 5.8 8.3 2.1 3.1Puerto Rico 3 6 –4.92 –1.76 3 8 19 51 4.0 10.0 10.1 1.5 1.6Qatar 0 c 0 c 0.00 0.00 3 4 11 0 0.1 1.7 2.5 0.0 0.3Higherplants b0–100 (nobiodiversityto maximumbiodiversity)Terrestrial% of land areaMarine% of territorial waters1990 2010 1990–2000 2000–10 2011 2011 2011 2011 2008 1990 2010 1990 20102012 World Development Indicators 151

3.4Deforestation and biodiversityForestareaAverage annualdeforestation aThreatenedspeciesGEF benefitsindex forbiodiversityNationallyprotected areasthousandsq. km % Mammals Birds FishRomania 64 66 0.01 –0.32 7 11 19 4 0.7 2.9 7.1 1.6 33.3Russian Federation 8,090 8,091 0.00 0.00 32 49 35 8 34.1 5.0 9.1 2.2 10.8Rwanda 3 4 –0.79 –2.38 20 12 9 4 0.9 9.9 10.0 .. ..Saudi Arabia 10 10 0.00 0.00 9 15 23 3 3.2 7.6 31.3 0.6 3.4Senegal 93 85 0.49 0.49 16 10 45 9 1.0 24.1 24.1 5.8 12.4Serbia 23 27 –0.62 –0.99 6 9 11 2 0.2 5.3 6.0 .. ..Sierra Leone 31 27 0.65 0.69 17 10 47 48 1.3 4.9 4.9 0.0 0.0Singapore 0 c 0 c 0.00 0.00 11 15 25 57 0.1 5.0 5.4 0.0 1.4Slovak Republic 19 19 0.01 –0.06 3 6 5 5 0.1 19.3 23.2 .. ..Slovenia 12 13 –0.37 –0.16 4 2 29 7 0.2 7.6 13.2 0.0 0.7Somalia 83 67 0.97 1.07 15 12 27 21 6.1 0.6 0.6 0.0 0.0South Africa 82 57 0.00 0.00 24 40 87 65 20.7 6.5 6.9 0.7 6.5South Sudan .. .. .. .. .. .. .. .. .. .. .. .. ..Spain 138 182 –2.09 –0.68 16 9 71 205 6.8 7.7 8.6 0.6 3.5Sri Lanka 24 19 1.20 1.12 29 15 44 282 7.9 20.3 21.5 0.1 1.1Sudan 764 699 0.80 0.08 15 15 19 16 5.1 4.2 4.2 0.0 0.0Swaziland 5 6 –0.93 –0.84 5 11 4 3 0.1 3.0 3.0 .. ..Sweden 273 282 –0.04 –0.30 1 2 12 4 0.3 6.0 10.9 3.9 5.3Switzerland 12 12 –0.37 –0.38 2 1 9 2 0.2 14.5 24.9 .. ..Syrian Arab Republic 4 5 –1.51 –1.29 16 14 34 2 0.9 0.3 0.6 0.0 0.6Tajikistan 4 4 –0.05 0.00 8 12 5 13 0.7 1.9 4.1 .. ..Tanzania 415 334 1.02 1.13 35 42 174 290 14.8 26.6 27.5 3.7 10.0Thailand 195 190 0.28 0.02 57 46 97 86 8.0 14.7 20.1 4.0 4.4Timor-Leste 10 7 1.22 1.40 4 7 5 0 0.6 .. 6.1 0.0 6.7Togo 7 3 3.37 5.13 11 5 24 9 0.3 11.3 11.3 0.0 0.0Trinidad and Tobago 2 2 0.30 0.32 2 2 24 1 2.2 30.5 31.2 0.2 2.8Tunisia 6 10 –2.67 –1.86 13 6 35 6 0.5 1.3 1.3 1.1 1.2Turkey 97 113 –0.47 –1.11 17 14 70 5 6.2 1.7 1.9 2.4 2.4Turkmenistan 41 41 0.00 0.00 9 16 11 3 1.8 3.0 3.0 .. ..Uganda 48 30 2.03 2.56 22 21 61 36 2.8 7.9 10.3 .. ..Ukraine 93 97 –0.25 –0.21 11 11 21 16 0.5 1.8 3.5 4.1 4.9United Arab Emirates 2 3 –2.38 –0.24 7 9 13 0 0.2 0.3 5.6 0.3 2.6United Kingdom 26 29 –0.68 –0.31 5 2 43 11 3.5 22.0 26.4 4.9 5.7United States 2,963 3,040 –0.13 –0.13 37 76 183 219 94.2 12.4 12.4 21.0 28.6Uruguay 9 17 –4.38 –2.14 11 24 36 0 1.2 0.3 0.3 0.2 0.3Uzbekistan 30 33 –0.54 –0.20 10 15 7 15 1.1 2.1 2.3 .. ..Venezuela, RB 520 463 0.57 0.60 32 27 37 68 25.3 40.1 53.8 7.0 15.3Vietnam 94 138 –2.28 –1.65 54 43 68 119 12.1 4.5 6.2 0.3 1.7West Bank and Gaza 0 c 0 c 0.00 –0.10 3 9 0 0 .. 0.6 0.6 .. ..Yemen, Rep. 5 5 0.00 0.00 9 15 23 158 3.2 0.0 0.5 0.0 1.8Zambia 528 495 0.32 0.33 9 14 20 9 3.8 36.0 36.0 .. ..Zimbabwe 222 156 1.58 1.88 9 14 3 14 1.9 18.0 28.0 .. ..World 41,582 s 40,204 s 0.18 w 0.11 w 3,105 s 3,401 s 6,213 s 10,987 s .. 8.4 w 12.3 w 4.6 w 10.0 wLow income 4,721 4,155 0.63 0.61 .. .. .. .. .. 9.6 10.7 .. ..Middle income 27,387 26,420 0.22 0.08 .. .. .. .. .. 7.9 12.3 2.6 7.5Lower middle income 7,003 6,364 0.59 0.30 .. .. .. .. .. 7.3 9.4 2.7 4.4Upper middle income 20,384 20,056 0.10 0.01 .. .. .. .. .. 8.2 13.4 2.6 8.5Low & middle income 32,108 30,575 0.28 0.15 .. .. .. .. .. 8.2 12.0 2.6 7.2East Asia & Pacifi c 4,602 4,698 0.08 –0.44 .. .. .. .. .. 10.8 15.0 0.5 1.4Europe & Central Asia 8,735 8,784 –0.02 –0.04 .. .. .. .. .. 4.3 7.5 2.2 10.4Latin America & Carib. 10,389 9,460 0.47 0.45 .. .. .. .. .. 9.7 20.2 5.0 13.1Middle East & N. Africa 207 211 –0.10 –0.15 .. .. .. .. .. 3.3 3.9 0.8 2.0South Asia 795 817 –0.01 –0.29 .. .. .. .. .. 5.3 5.9 1.5 1.7Sub-Saharan Africa 7,379 6,605 0.55 0.48 .. .. .. .. .. 11.1 11.7 3.3 5.8High income 9,474 9,629 –0.14 –0.04 .. .. .. .. .. 8.9 12.9 9.3 16.5Euro area 859 952 –0.76 –0.31 .. .. .. .. .. 11.3 17.0 6.7 15.1a. Negative values indicate an increase in forest area. b. Flowering plants only. c. Less than 0.5.Higherplants b0–100 (nobiodiversityto maximumbiodiversity)Terrestrial% of land areaMarine% of territorial waters1990 2010 1990–2000 2000–10 2011 2011 2011 2011 2008 1990 2010 1990 20101522012 World Development Indicators

Deforestation and biodiversity3.4ENVIRONMENTAbout the dataDefinitionsAs threats to biodiversity mount, the internationalcommunity is increasingly focusing on conservingdiversity. Deforestation is a major cause of lossof biodiversity, and habitat conservation is vitalfor stemming this loss. Conservation efforts havefocused on protecting areas of high biodiversity.The Food and Agriculture Organization of the UnitedNations (FAO) Global Forest Resources Assessment2010 provides detailed information on forest cover in2010 and adjusted estimates of forest cover in 1990and 2000. The current survey uses a uniform defi nitionof forest. Because of space limitations, the tabledoes not break down forest cover between naturalforest and plantation, a breakdown the FAO providesfor developing countries. Thus the deforestation datain the table may underestimate the rate at whichnatural forest is disappearing in some countries.The number of threatened species is an importantmeasure of the immediate need for conservation inan area. Global analyses of the status of threatenedspecies have been carried out for few groups oforganisms. Only for mammals, birds, and amphibianshas the status of virtually all known speciesbeen assessed. Threatened species are defi nedusing the International Union for Conservation ofNature’s (IUCN) classifi cation: endangered (in dangerof extinction and unlikely to survive if causal factorscontinue operating) and vulnerable (likely to moveinto the endangered category in the near future ifcausal factors continue operating).The Global Environment Facility’s (GEF) benefi tsindex for biodiversity is a comprehensive indicatorof national biodiversity status and is used to guideits biodiversity priorities. For each country the biodiversityindicator incorporates the best availableand comparable information in four relevant dimensions:represented species, threatened species, representedecoregions, and threatened ecoregions. Tocombine these dimensions into one measure, theindicator uses dimensional weights that refl ect theconsensus of conservation scientists at the GEF,IUCN, WWF International, and other nongovernmentalorganizations.The World Conservation Monitoring Centre (WCMC)compiles data on protected areas, numbers of certainspecies, and numbers of those species underthreat from various sources. Because of differencesin defi nitions, reporting practices, and reporting periods,cross-country comparability is limited.Nationally protected areas are defi ned using thesix IUCN management categories for areas of atleast 1,000 hectares: scientifi c reserves and strictnature reserves with limited public access; nationalparks of national or international signifi cance and notmaterially affected by human activity; natural monumentsand natural landscapes with unique aspects;managed nature reserves and wildlife sanctuaries;protected landscapes (which may include culturallandscapes); and areas managed mainly for the sustainableuse of natural systems to ensure long-termprotection and maintenance of biological diversity.The data in the table cover these six categoriesas well as terrestrial protected areas that are notassigned to a category by the IUCN. Designating anarea as protected does not mean that protectionis in force. And for small countries that have onlyprotected areas smaller than 1,000 hectares, thesize limit in the defi nition leads to an underestimateof protected areas.Due to variations in consistency and methods ofcollection, data quality is highly variable across countries.Some countries update their information morefrequently than others, some have more accuratedata on extent of coverage, and many underreportthe number or extent of protected areas.• Forest area is land spanning more than 0.5 hectarewith trees higher than 5 meters and a canopycover of more than 10 percent or with trees ableto reach these thresholds in situ. It excludes landthat is predominantly under agricultural or urbanland use. • Average annual deforestation is thepermanent conversion of natural forest area to otheruses, including agriculture, ranching, settlements,and infrastructure. Deforested areas exclude areaslogged but intended for regeneration and areasdegraded by fuelwood gathering, acid precipitation,or forest fi res. • Threatened species are speciesclassifi ed by the IUCN as endangered, vulnerable,rare, indeterminate, out of danger, or insuffi cientlyknown. Mammals exclude whales and porpoises.Birds are listed for the country where their breedingor wintering ranges are located. Fish are cold-bloodedaquatic vertebrates of the superclass Pisces. Higherplants are native vascular plant species. • GEF benefitsindex for biodiversity is a composite index ofrelative biodiversity potential based on the speciesin each country and their threat status and diversityof habitat types. • Nationally protected areas aretotally or partially protected areas of at least 1,000hectares that are designated as scientifi c reserveswith limited public access, national parks, naturalmonuments, nature reserves or wildlife sanctuaries,and protected landscapes. Terrestrial protectedareas exclude marine areas, unclassifi ed areas, littoral(intertidal) areas, and sites protected under localor provincial law. Marine protected areas are areas ofintertidal or sub tidal terrain—and over lying water andassociated fl ora and fauna and historical and culturalfeatures—that have been reserved to protect part ofor the entire enclosed environment.Data sourcesData on forest area are from the FAO’s GlobalForest Resources Assessment 2010 and website.Data on threatened species are from the electronicfi les of the United Nations Environment Programme(UNEP) and WCMC and the 2011 IUCNRed List of Threatened Species. The GEF benefi tsindex for biodiversity is from Pandey and others(2006a). Data on nationally protected areas arefrom the UNEP and WCMC, based on data fromnational authorities, national legislation, and internationalagreements.2012 World Development Indicators 153

3.5 FreshwaterInternal renewablefreshwater resources aAnnual freshwaterwithdrawalsWaterproductivityAccess to an improvedwater sourceFlowsbillion cu. mPer capitacu. mbillioncu. m% of internalresources% foragriculture% forindustry% fordomesticGDP/water use2000 $ percu. m% of urbanpopulation% of ruralpopulation2009 2009 2009 b 2009 b 2009 b 2009 b 2009 b 2009 b 2010 2010Afghanistan 55 1,645 23.1 35.6 99 1 1 .. 42 78Albania 27 8,425 1.8 4.4 58 12 30 3 94 96Algeria 11 322 6.2 52.7 64 14 23 12 79 85Angola 148 7,976 0.6 0.4 33 29 38 40 38 60Argentina 276 6,889 32.6 4.0 66 12 22 12 80 98Armenia 7 2,223 2.8 36.4 66 4 30 1 97 99Australia 492 22,413 22.6 4.6 74 11 16 24 100 100Austria 55 6,575 3.7 4.7 3 79 18 60 100 100Azerbaijan 8 907 12.2 35.2 76 19 4 2 71 88Bahrain 0 c 3 0.4 219.8 45 6 50 35 .. 100Bangladesh 105 714 35.9 2.9 88 2 10 2 80 85Belarus 37 3,913 4.3 7.5 19 54 27 6 99 100Belgium 12 1,111 6.2 34.0 1 88 12 42 100 100Benin 10 1,197 0.1 0.5 45 23 32 25 68 84Bolivia 304 31,054 2.0 0.3 57 15 28 6 71 96Bosnia and Herzegovina 36 9,422 0.3 0.9 .. .. .. 24 98 100Botswana 2 1,211 0.2 1.6 41 18 41 40 92 99Brazil 5,418 28,037 58.1 0.7 55 17 28 15 85 100Bulgaria 21 2,769 6.1 28.7 16 68 16 3 100 100Burkina Faso 13 782 1.0 7.9 70 2 28 4 73 95Burundi 10 1,231 0.3 2.3 77 6 17 3 71 83Cambodia 121 8,628 2.2 0.5 94 2 4 3 58 87Cameroon 273 14,237 1.0 0.3 76 7 17 14 52 95Canada 2,850 84,495 46.0 1.6 12 69 20 18 99 100Central African Republic 141 32,653 0.1 0.0 1 16 82 15 51 92Chad 15 1,371 0.4 0.9 52 24 24 8 44 70Chile 884 52,136 11.3 1.2 70 20 9 9 75 99China 2,813 2,113 554.1 19.5 65 23 12 5 85 98Hong Kong SAR, China .. .. .. .. .. .. .. .. .. ..Colombia 2,112 46,261 12.7 0.6 39 4 57 11 72 99Congo, Dem. Rep. 900 14,018 0.6 0.0 c 18 20 63 10 27 79Congo, Rep. 222 56,324 0.0 0.0 c 9 22 70 101 32 95Costa Rica 112 24,484 2.7 2.4 53 17 29 9 91 100Côte d’Ivoire 77 3,971 1.4 1.7 43 19 38 8 68 91Croatia 38 8,512 0.6 0.6 2 14 85 45 97 100Cuba 38 3,385 7.6 19.8 75 10 15 4 89 96Cyprus 1 715 0.2 19.3 86 3 10 66 100 100Czech Republic 13 1,254 1.7 13.3 2 57 42 45 100 100Denmark 6 1,086 0.7 10.8 36 5 58 253 100 100Dominican Republic 21 2,144 3.5 16.6 64 2 34 11 84 87Ecuador 432 30,291 15.3 3.6 92 3 6 2 89 96Egypt, Arab Rep. 2 23 68.3 119.0 86 6 8 2 99 100El Salvador 18 2,881 1.4 5.5 55 17 28 11 76 94Eritrea 3 549 0.6 9.2 95 0 5 1 57 74Estonia 13 9,483 1.8 14.0 0 c 97 3 5 97 99Ethiopia 122 1,503 5.6 4.6 94 0 c 6 3 34 97Finland 107 20,042 1.6 1.5 3 72 25 86 100 100France 200 3,099 31.6 15.0 12 69 18 46 100 100Gabon 164 110,997 0.1 0.1 38 9 53 46 41 95Gambia, The 3 1,784 0.1 0.9 28 24 48 8 85 92Georgia 58 13,179 1.6 2.6 65 13 22 3 96 100Germany 107 1,306 32.3 21.0 0 c 84 16 62 100 100Ghana 30 1,272 1.0 1.8 66 10 24 8 80 91Greece 58 5,141 9.5 12.7 89 2 9 17 99 100Guatemala 109 7,781 2.9 2.6 55 30 15 9 87 98Guinea 226 23,153 1.6 0.7 84 3 13 2 65 90Guinea-Bissau 16 10,781 0.2 0.6 82 5 13 1 53 91Haiti 13 1,319 1.2 8.6 78 4 19 3 51 851542012 World Development Indicators

Freshwater3.5ENVIRONMENTInternal renewablefreshwater resources aAnnual freshwaterwithdrawalsWaterproductivityAccess to an improvedwater sourceFlowsbillion cu. mPer capitacu. mbillioncu. m% of internalresources% foragriculture% forindustry% fordomesticGDP/water use2000 $ percu. m% of urbanpopulation% of ruralpopulation2009 2009 2009 b 2009 b 2009 b 2009 b 2009 b 2009 b 2010 2010Honduras 96 12,877 1.2 1.2 58 25 17 9 79 95Hungary 6 599 5.6 5.4 6 82 12 .. 100 100India 1,446 1,197 761.0 39.8 90 2 7 1 90 97Indonesia 2,019 8,504 113.3 5.6 82 7 12 2 74 92Iran, Islamic Rep. 129 1,757 93.3 67.7 92 1 7 2 92 97Iraq 35 1,132 66.0 87.3 79 15 7 0 c 56 91Ireland 49 10,989 .. .. .. .. .. .. 100 100Israel 1 100 2.0 101.9 58 6 36 83 100 100Italy 183 3,032 45.4 23.7 44 36 20 25 100 100Jamaica 9 3,489 0.6 6.2 34 22 44 17 88 98Japan 430 3,371 90.0 20.9 63 18 19 54 100 100Jordan 1 115 0.9 99.4 65 4 31 16 92 98Kazakhstan 75 4,686 33.1 28.9 87 12 1 1 90 99Kenya 21 525 2.7 8.9 79 4 17 7 52 82Korea, Dem. Rep. 67 2,764 8.7 11.2 76 13 10 .. 97 99Korea, Rep. 65 1,330 25.5 36.5 62 12 26 30 88 100Kosovo .. .. .. .. .. .. .. .. .. ..Kuwait 0 c 0 c 0.9 .. 54 2 44 43 99 99Kyrgyz Republic 49 9,199 10.1 43.7 94 3 3 0 c 85 99Lao PDR 190 31,151 4.3 1.3 93 4 3 1 62 77Latvia 17 7,424 0.4 1.2 12 50 39 27 96 100Lebanon 5 1,144 1.3 28.1 60 11 29 20 100 100Lesotho 5 2,433 0.1 1.7 20 40 40 20 73 91Liberia 200 52,139 0.2 0.1 34 27 40 3 60 88Libya 1 96 4.3 718.0 83 3 14 11 .. ..Lithuania 16 4,659 2.4 9.6 3 90 7 7 .. 98Macedonia, FYR 5 2,625 1.0 16.1 12 67 21 4 99 100Madagascar 337 16,746 14.7 4.4 97 1 2 0 c 34 74Malawi 16 1,118 1.0 5.6 84 4 12 3 80 95Malaysia 580 20,752 13.2 2.3 34 36 30 10 99 100Mali 60 4,024 6.5 6.5 90 1 9 1 51 87Mauritania 0 c 118 1.6 14.0 94 2 5 1 48 52Mauritius 3 2,158 0.7 26.4 68 3 30 9 99 100Mexico 409 3,651 79.8 17.5 77 9 14 8 91 97Moldova 1 280 1.9 16.4 40 52 9 1 93 99Mongolia 35 12,833 0.5 1.4 44 32 24 4 53 100Morocco 29 917 12.6 43.4 87 3 10 5 61 98Mozambique 100 4,388 0.7 0.3 74 3 23 11 29 77Myanmar 1,003 21,071 33.2 2.8 89 1 10 .. 78 93Namibia 6 2,747 0.3 1.7 71 5 24 19 90 99Nepal 198 6,734 9.8 4.7 98 0 c 2 1 88 93Netherlands 11 665 10.6 11.7 1 87 12 41 100 100New Zealand 327 75,768 4.8 1.5 74 4 21 13 100 100Nicaragua 190 33,221 1.3 0.7 84 2 14 4 68 98Niger 4 234 2.4 7.0 88 1 11 1 39 100Nigeria 221 1,431 10.3 3.6 53 15 31 8 43 74Norway 382 79,110 2.9 0.8 29 43 28 66 100 100Oman 1 516 1.3 86.6 88 1 10 23 78 93Pakistan 55 323 183.5 79.5 94 1 5 1 89 96Panama 147 42,578 0.5 0.3 51 3 46 44 83 97Papua New Guinea 801 119,492 0.4 0.0 0 c 43 57 12 33 87Paraguay 94 14,822 0.5 0.1 71 8 20 19 66 99Peru 1,616 56,179 19.3 1.0 85 8 7 4 65 91Philippines 479 5,223 81.6 17.0 82 10 8 1 92 93Poland 54 1,405 12.0 19.4 10 60 31 20 100 100Portugal 38 3,574 8.5 12.3 73 19 8 15 100 99Puerto Rico 7 1,790 1.0 14.0 7 2 91 65 .. ..Qatar 0 c 35 0.4 455.2 59 2 39 122 100 1002012 World Development Indicators 155

3.5FreshwaterInternal renewablefreshwater resources aAnnual freshwaterwithdrawalsWaterproductivityAccess to an improvedwater sourceFlowsbillion cu. mPer capitacu. mbillioncu. m% of internalresources% foragriculture% forindustry% fordomesticGDP/water use2000 $ percu. m% of urbanpopulationRomania 42 1,969 6.9 3.2 17 61 22 8 .. 99Russian Federation 4,313 30,405 66.2 1.5 20 60 20 6 92 99Rwanda 10 921 0.2 1.6 68 8 24 22 63 76Saudi Arabia 2 90 23.7 943.3 88 3 9 11 .. 97Senegal 26 2,131 2.2 5.7 93 3 4 3 56 93Serbia .. .. 4.1 .. 2 82 17 2 98 99Sierra Leone 160 27,878 0.5 0.3 71 10 19 3 35 87Singapore 1 120 .. .. .. .. .. .. .. 100Slovak Republic 13 2,325 0.7 1.4 3 50 47 64 100 100Slovenia 19 9,153 0.9 3.0 0 c 82 18 27 99 100Somalia 6 658 3.3 22.4 99 0 c 0 c .. 7 66South Africa 45 908 12.5 25.0 63 6 31 15 79 99South Sudan .. .. .. .. .. .. .. .. .. ..Spain 111 2,422 32.5 29.0 61 22 18 22 100 100Sri Lanka 53 2,555 13.0 24.5 87 6 6 2 90 99Sudan 30 706 37.1 57.6 97 1 2 1 52 67Swaziland 3 2,260 1.0 23.1 97 1 2 2 65 91Sweden 171 18,390 2.6 1.5 4 59 37 110 100 100Switzerland 40 5,217 2.6 4.9 2 58 41 110 100 100Syrian Arab Republic 7 356 16.8 99.8 88 4 9 2 86 93Tajikistan 66 9,774 12.0 74.8 92 5 4 0 c 54 92Tanzania 84 1,930 5.2 5.4 89 0 10 4 44 79Thailand 225 3,268 57.3 13.1 90 5 5 3 95 97Timor-Leste 8 7,469 1.2 14.3 91 0 c 8 0 c 60 91Togo 12 1,949 0.2 1.2 45 2 53 10 40 89Trinidad and Tobago 4 2,874 0.2 6.0 9 25 66 61 93 98Tunisia 4 402 2.9 61.7 76 4 13 11 84 99Turkey 227 3,160 40.1 18.8 74 11 15 9 99 100Turkmenistan 1 273 24.9 100.8 97 1 2 0 c 72 97Uganda 39 1,205 0.3 0.5 36 18 46 36 68 95Ukraine 53 1,153 38.5 27.6 51 36 12 1 98 98United Arab Emirates 0 c 22 4.0 2,032.0 83 2 15 39 100 100United Kingdom 145 2,346 13.0 8.8 10 33 57 132 100 100United States 2,818 9,186 478.4 15.6 40 46 14 24 94 100Uruguay 59 17,639 3.7 2.6 87 2 11 8 100 100Uzbekistan 16 588 59.6 118.3 91 3 6 0 c 81 98Venezuela, RB 722 25,451 9.1 0.7 44 8 49 18 75 94Vietnam 359 4,178 82.0 9.3 95 4 1 1 93 99West Bank and Gaza 1 201 0.4 49.9 45 7 48 9 81 86Yemen, Rep. 2 90 3.6 168.6 91 2 7 4 47 72Zambia 80 6,303 1.7 1.7 76 7 17 3 46 87Zimbabwe 12 983 4.2 21.0 79 7 14 1 69 98World 42,383 s 6,258 w 3,908.3 s 7.3 w 70 w 18 w 12 w 10 w 81 w 96 wLow income 4,197 5,381 188.6 4.3 90 2 8 2 57 86Middle income 29,152 5,944 2,791.0 7.0 79 11 10 3 84 96Lower middle income 7,995 3,227 1,607.4 12.6 88 5 7 1 83 93Upper middle income 21,157 8,718 1,183.6 5.0 66 20 14 6 86 98Low & middle income 33,348 5,867 2,979.6 6.7 79 11 10 3 80 95East Asia & Pacifi c 8,773 4,506 952.0 9.9 73 16 10 4 84 97Europe & Central Asia 5,076 12,887 330.4 5.0 63 26 11 3 91 99Latin America & Carib. 13,425 23,323 269.5 1.6 68 11 21 10 81 98Middle East & N. Africa 226 695 276.5 70.0 86 6 8 2 81 94South Asia 1,990 1,236 1,026.6 33.4 91 2 7 1 88 95Sub-Saharan Africa 3,858 4,634 124.6 2.2 84 4 12 4 49 83High income 9,034 8,305 928.7 9.4 41 42 17 31 98 100Euro area 977 2,952 185.6 16.4 32 52 17 36 100 100a. Excludes river fl ows from other countries because of data unreliability. b. Data are for the most recent year available (see Primary data documentation). c. Less than 0.5.% of ruralpopulation2009 2009 2009 b 2009 b 2009 b 2009 b 2009 b 2009 b 2010 20101562012 World Development Indicators

Freshwater3.5ENVIRONMENTAbout the dataDefinitionsThe data on freshwater resources are based onestimates of runoff into rivers and recharge ofground water. These estimates are based on differentsources and refer to different years, so crosscountrycomparisons should be made with caution.Because the data are collected intermittently, theymay hide signifi cant variations in total renewablewater resources from year to year. The data alsofail to distinguish between seasonal and geographicvariations in water availability within countries. Datafor small countries and countries in arid and semiaridzones are less reliable than those for larger countriesand countries with greater rainfall.Caution should also be used in comparing dataon annual freshwater withdrawals, which are subjectto variations in collection and estimation methods.In addition, infl ows and outfl ows are estimated atdifferent times and at different levels of quality andprecision, requiring caution in interpreting the data,particularly for water-short countries, notably in theMiddle East and North Africa.Water productivity is an indication only of theeffi ciency by which each country uses its waterresources. Given the different economic structureof each country, these indicators should be usedcarefully, taking into account the countries’ sectoralactivities and natural resource endowments.The data on access to an improved water sourcemeasure the percentage of the population with readyaccess to water for domestic purposes. The dataare based on surveys and estimates provided bygovernments to the Joint Monitoring Programme ofthe World Health Organization (WHO) and the UnitedNations Children’s Fund (UNICEF). The coveragerates are based on information from service userson actual household use rather than on informationfrom service providers, which may include nonfunctioningsystems. Access to drinking water froman improved source does not ensure that the wateris safe or adequate, as these characteristics arenot tested at the time of survey. While informationon access to an improved water source is widelyused, it is extremely subjective, and such terms assafe, improved, adequate, and reasonable may havedifferent meaning in different countries despite offi -cial WHO defi nitions (see Defi nitions). Even in highincomecountries treated water may not always besafe to drink. Access to an improved water source isequated with connection to a supply system; it doesnot take into account variations in the quality andcost (broadly defi ned) of the service.• Internal renewable freshwater resources arethe average annual fl ows of rivers and groundwaterfrom rainfall in the country. Natural incoming fl owsoriginating outside a country’s borders are excluded.Overlapping water resources between surface runoffand groundwater recharge are also deducted.• Internal renewable freshwater resources percapita are calculated using the World Bank’s populationestimates (see table 2.1). • Annual freshwaterwithdrawals are total water withdrawals, not countingevaporation losses from storage basins. Withdrawalsalso include water from desalination plantsin countries where they are a signifi cant source. Withdrawalscan exceed 100 percent of total renewableresources where extraction from nonrenewable aquifersor desalination plants is considerable or wherewater reuse is signifi cant. Withdrawals for agricultureand industry are total withdrawals for irrigation andlivestock production and for direct industrial use(including for cooling thermoelectric plants). Withdrawalsfor domestic uses include drinking water,municipal use or supply, and use for public services,commercial establishments, and homes. • Waterproductivity is calculated as GDP in constant pricesdivided by annual total water withdrawal. • Accessto an improved water source is the percentage of thepopulation with reasonable access to an adequateamount of water from an improved source, such aspiped water into a dwelling, plot, or yard; public tapor standpipe; tubewell or borehole; protected dugwell or spring; and rainwater collection. Unimprovedsources include unprotected dug wells or springs,carts with small tank or drum, bottled water, andtanker trucks. Reasonable access is defi ned as theavailability of at least 20 liters a person a day froma source within 1 kilometer of the dwelling.Data sourcesData on freshwater resources and withdrawalsare from the Food and Agriculture Organization’sAQUASTAT database. The GDP estimates used tocalculate water productivity are from the WorldBank national accounts database. Data on accessto water are from WHO and UNICEF’s Progress onDrinking Water and Sanitation (2012).2012 World Development Indicators 157

3.6 Water pollutionEmissions of organicwater pollutantsIndustry shares of emissionsof organic water pollutantsthousandkilogramsper daykilogramsper dayper worker% of totalStone,ceramics,and glass Textiles Wood OtherPrimary Paper andFood andmetals pulp Chemicals beverages1990 2007 a 1990 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 aAfghanistan .. 0.2 .. 0.21 .. 19.7 27.9 14.1 11.7 23.3 .. 3.1Albania 2.4 3.6 0.25 0.25 .. .. .. 39.8 .. 60.2 .. 11.9Algeria .. .. .. .. .. .. .. .. .. .. .. ..Angola .. .. .. .. .. .. .. .. .. .. .. ..Argentina 181.4 155.5 0.21 0.23 3.8 8.4 15.8 30.5 3.5 14.3 2.1 21.6Armenia .. .. .. .. .. .. .. .. .. .. .. ..Australia .. .. .. .. .. .. .. .. .. .. .. ..Austria 90.5 84.4 0.15 0.14 5.7 7.1 9.3 12.2 5.8 4.3 6.0 49.5Azerbaijan 41.3 20.0 0.15 0.18 8.8 3.0 18.5 19.6 8.4 11.7 1.5 28.6Bahrain .. .. .. .. .. .. .. .. .. .. .. ..Bangladesh 250.8 303.0 0.15 0.14 0.7 2.3 3.0 7.6 2.6 79.3 0.5 4.2Belarus .. .. .. .. .. .. .. .. .. .. .. ..Belgium 107.8 95.9 0.17 0.17 6.4 7.9 18.6 16.4 3.1 5.5 2.2 40.0Benin .. .. .. .. .. .. .. .. .. .. .. ..Bolivia 11.3 11.5 0.24 0.25 0.9 9.8 13.1 35.4 7.7 18.4 5.3 9.5Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. ..Botswana 2.5 3.2 0.30 0.23 .. 2.4 .. 43.8 0.6 3.9 .. 50.0Brazil .. .. .. .. .. .. .. .. .. .. .. ..Bulgaria 124.3 102.1 0.17 0.17 3.7 4.3 8.0 17.7 4.8 26.8 3.0 31.7Burkina Faso .. .. .. .. .. .. .. .. .. .. .. ..Burundi .. .. .. .. .. .. .. .. .. .. .. ..Cambodia 3.8 .. 0.17 .. .. .. .. .. .. .. .. ..Cameroon .. .. .. .. .. .. .. .. .. .. .. ..Canada 300.9 306.6 0.17 0.16 4.3 8.9 10.9 14.0 2.8 7.3 6.5 45.3Central African Republic .. .. .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. .. .. ..Chile .. 92.5 .. 0.25 7.6 6.3 13.7 35.1 3.6 9.1 6.9 17.7China .. 9,428.9 .. 0.13 7.2 3.9 13.0 7.4 6.3 20.6 1.7 39.9Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. ..Colombia .. 87.0 .. 0.20 2.3 8.9 17.3 21.3 5.3 24.1 0.9 19.9Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. ..Costa Rica .. .. .. .. .. .. .. .. .. .. .. ..Côte d’Ivoire .. .. .. .. .. .. .. .. .. .. .. ..Croatia 48.5 42.9 0.17 0.17 3.1 7.2 9.5 17.6 5.9 14.5 4.9 37.2Cuba .. .. .. .. .. .. .. .. .. .. .. ..Cyprus 7.1 8.0 0.21 0.23 0.3 8.9 9.3 36.3 9.9 5.1 8.0 22.2Czech Republic 177.1 146.5 0.14 0.13 5.4 4.8 10.9 10.9 6.4 7.4 4.4 49.8Denmark 84.5 61.0 0.18 0.16 1.4 11.5 13.1 16.4 4.8 1.5 4.0 47.3Dominican Republic 88.6 .. 0.18 .. .. .. .. .. .. .. .. ..Ecuador 28.6 44.7 0.24 0.28 1.8 7.8 12.8 46.4 4.4 12.3 2.2 12.3Egypt, Arab Rep. 206.5 .. 0.19 .. .. .. .. .. .. .. .. ..El Salvador .. .. .. .. .. .. .. .. .. .. .. ..Eritrea 2.4 2.5 0.19 0.20 0.2 4.4 9.5 27.3 9.6 29.0 0.1 20.3Estonia 21.7 16.0 0.15 0.14 0.4 7.3 7.1 14.6 5.5 8.0 16.4 40.8Ethiopia 18.5 32.2 0.23 0.24 1.4 6.0 10.9 34.7 8.3 27.9 1.5 9.3Finland 72.5 55.3 0.19 0.14 1.0 15.4 8.7 9.0 4.4 2.8 7.3 51.4France 326.5 569.4 0.11 0.16 3.2 7.4 15.0 16.6 3.8 4.8 2.4 46.9Gabon .. .. .. .. .. .. .. .. .. .. .. ..Gambia, The 0.8 .. 0.27 .. .. .. .. .. .. .. .. ..Georgia .. .. .. .. .. .. .. .. .. .. .. ..Germany 806.6 936.2 0.13 0.14 3.8 7.1 12.4 11.4 3.4 2.4 1.9 57.6Ghana .. 16.0 .. 0.17 3.0 3.8 15.9 18.6 4.1 10.2 33.3 11.2Greece 50.9 60.8 0.19 0.20 3.9 9.0 10.1 23.9 7.0 14.4 2.8 28.9Guatemala .. .. .. .. .. .. .. .. .. .. .. ..Guinea .. .. .. .. .. .. .. .. .. .. .. ..Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..1582012 World Development Indicators

Water pollution3.6ENVIRONMENTEmissions of organicwater pollutantsIndustry shares of emissionsof organic water pollutantsthousandkilogramsper daykilogramsper dayper worker% of totalStone,ceramics,and glass Textiles Wood OtherPrimary Paper andFood andmetals pulp Chemicals beverages1990 2007 a 1990 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 aHaiti 5.2 .. 0.20 .. .. .. .. .. .. .. .. ..Honduras .. .. .. .. .. .. .. .. .. .. .. ..Hungary 122.1 110.6 0.18 0.15 2.7 6.4 10.6 15.2 3.7 9.1 3.3 49.0India .. .. .. .. .. .. .. .. .. .. .. ..Indonesia 721.8 883.0 0.18 0.19 1.4 4.1 12.0 23.1 4.0 29.2 6.3 19.9Iran, Islamic Rep. 131.6 160.8 0.16 0.15 7.1 2.8 12.8 16.1 13.8 11.2 0.7 35.5Iraq 7.7 7.7 0.27 0.27 13.1 25.6 29.9 16.9 5.4 9.1 .. ..Ireland 36.1 28.4 0.19 0.16 1.3 10.2 17.6 14.8 5.9 0.8 3.8 45.5Israel 54.6 52.7 0.16 0.16 1.6 8.9 13.4 16.4 2.9 7.9 1.2 47.6Italy 378.3 479.2 0.13 0.13 3.5 5.2 10.3 9.3 5.4 13.6 2.9 49.6Jamaica .. .. .. .. .. .. .. .. .. .. .. ..Japan 1,455.0 1,126.9 0.14 0.15 3.3 7.0 11.2 15.0 3.6 5.3 2.0 52.5Jordan 15.0 29.1 0.18 0.18 2.3 6.1 13.7 20.8 11.5 18.6 2.3 24.5Kazakhstan 123.5 97.4 0.23 0.24 33.3 2.3 8.9 18.7 9.3 3.9 0.6 23.0Kenya .. .. .. .. .. .. .. .. .. .. .. ..Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 366.9 319.6 0.12 0.11 4.2 5.4 12.1 6.3 3.0 9.3 0.9 58.9Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait .. .. .. .. .. .. .. .. .. .. .. ..Kyrgyz Republic 28.9 12.2 0.14 0.20 9.8 6.3 8.5 24.2 17.5 9.8 1.6 22.4Lao PDR 4.3 4.3 0.14 0.14 1.8 2.2 3.8 9.2 7.5 49.2 21.4 4.9Latvia 39.8 28.4 0.12 0.18 2.7 7.7 5.8 21.1 4.4 11.8 19.1 27.3Lebanon 14.7 14.7 0.19 0.19 0.5 7.5 6.0 25.5 12.9 16.7 4.5 26.3Lesotho .. 5.3 .. 0.13 0.9 0.5 0.3 2.6 0.8 93.5 .. 1.4Liberia .. .. .. .. .. .. .. .. .. .. .. ..Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 54.0 42.2 0.15 0.17 0.9 5.7 8.3 20.5 4.7 17.6 11.4 30.8Macedonia, FYR 27.0 20.3 0.20 0.18 5.8 4.7 6.3 15.1 3.2 44.7 2.9 17.3Madagascar .. 92.8 .. 0.14 0.3 1.6 12.4 7.6 2.8 58.9 6.3 10.0Malawi 37.2 32.7 0.40 0.39 .. 1.4 3.7 82.1 0.6 7.5 1.1 3.6Malaysia .. 208.3 .. 0.12 2.8 4.9 16.5 9.1 3.8 6.6 7.8 48.5Mali .. .. .. .. .. .. .. .. .. .. .. ..Mauritania .. .. .. .. .. .. .. .. .. .. .. ..Mauritius 16.8 15.4 0.16 0.17 0.4 3.6 5.9 14.7 .. 63.9 0.7 10.9Mexico 425.0 .. 0.18 .. .. .. .. .. .. .. .. ..Moldova 29.2 18.8 0.44 0.45 .. 3.8 .. 95.2 .. .. .. 0.9Mongolia .. 8.8 .. 0.21 3.7 5.1 3.3 27.2 9.5 41.6 5.4 4.1Morocco .. 74.0 .. 0.16 1.0 2.9 7.9 16.3 6.5 43.5 2.0 19.9Mozambique .. .. .. .. .. .. .. .. .. .. .. ..Myanmar .. .. .. .. .. .. .. .. .. .. .. ..Namibia .. .. .. .. .. .. .. .. .. .. .. ..Nepal 26.4 26.8 0.14 0.16 1.6 3.9 7.2 19.2 29.9 29.4 2.0 6.8Netherlands 142.3 128.2 0.20 0.19 3.1 13.4 14.1 18.2 4.0 2.1 2.6 42.5New Zealand 46.7 61.6 0.24 0.23 2.0 12.2 8.6 31.1 3.1 5.8 8.0 29.3Nicaragua .. .. .. .. .. .. .. .. .. .. .. ..Niger .. .. .. .. .. .. .. .. .. .. .. ..Nigeria .. .. .. .. .. .. .. .. .. .. .. ..Norway 51.8 46.9 0.20 0.18 4.9 12.1 7.5 19.1 4.3 2.0 6.0 44.2Oman 3.8 7.6 0.15 0.16 4.0 4.6 17.8 20.4 20.5 2.4 4.0 26.3Pakistan .. 153.7 .. 0.17 2.2 1.9 9.1 15.1 4.3 55.6 0.4 11.2Panama 10.3 13.7 0.30 0.32 0.9 11.6 6.9 55.2 4.0 4.7 1.6 15.0Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. ..Paraguay 15.3 10.8 0.20 0.28 3.1 9.3 16.7 42.6 5.9 11.0 4.5 6.9Peru .. .. .. .. .. .. .. .. .. .. .. ..Philippines 169.0 144.6 0.17 0.15 2.6 4.2 9.5 14.4 2.7 21.6 2.1 42.9Poland 446.7 359.7 0.16 0.16 3.3 5.1 11.3 18.1 5.5 10.3 4.9 41.5Portugal 140.6 87.7 0.14 0.17 0.2 8.1 3.4 19.8 5.2 16.3 8.5 38.52012 World Development Indicators 159

3.6 Water pollutionEmissions of organicwater pollutantsIndustry shares of emissionsof organic water pollutantsthousandkilogramsper daykilogramsper dayper worker% of totalStone,ceramics,and glass Textiles Wood OtherPrimary Paper andFood andmetals pulp Chemicals beverages1990 2007 a 1990 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 a 2007 aPuerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar .. 6.4 .. 0.12 3.7 6.7 10.5 6.5 18.1 20.7 12.5 21.3Romania 411.2 222.1 0.12 0.15 4.5 3.5 7.1 13.9 4.0 25.0 5.3 36.8Russian Federation 1,521.4 1,381.7 0.16 0.17 8.4 4.9 11.6 17.9 8.3 6.3 4.2 38.4Rwanda 8.1 8.1 0.37 0.37 .. .. 9.0 77.1 4.3 1.9 2.9 4.8Saudi Arabia .. 106.6 .. 0.18 3.2 6.9 11.6 20.0 10.7 14.4 3.3 30.0Senegal 6.1 6.6 0.30 0.29 4.9 6.3 23.8 44.6 3.9 10.5 0.8 5.3Serbia .. .. .. .. .. .. .. .. .. .. .. ..Sierra Leone .. .. .. .. .. .. .. .. .. .. .. ..Singapore 33.1 38.3 0.09 0.09 0.5 5.5 11.9 5.3 1.3 2.3 0.5 72.7Slovak Republic 72.8 47.9 0.13 0.14 7.9 5.4 9.1 10.7 6.0 5.0 4.2 51.7Slovenia 28.1 28.8 0.13 0.13 4.6 6.1 12.2 7.7 4.1 10.8 4.9 49.6Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 260.5 229.6 0.17 0.17 9.9 6.6 10.6 15.7 5.2 10.4 4.2 37.4South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 348.0 378.8 0.16 0.15 3.1 8.0 10.8 15.3 7.9 8.4 3.8 42.7Sri Lanka .. 266.1 .. 0.19 2.6 4.3 9.0 22.4 6.3 43.6 2.5 9.3Sudan .. 38.6 .. 0.29 0.6 1.9 7.0 57.5 14.2 8.0 1.7 9.1Swaziland .. .. .. .. .. .. .. .. .. .. .. ..Sweden 116.8 96.9 0.15 0.14 5.3 11.9 9.9 8.6 2.6 1.2 5.6 54.9Switzerland .. .. .. .. .. .. .. .. .. .. .. ..Syrian Arab Republic 59.7 80.4 0.16 0.16 1.6 1.9 7.3 19.9 11.3 32.0 5.2 20.9Tajikistan 29.1 12.8 0.17 0.24 28.2 2.7 2.0 18.0 8.9 38.4 0.3 1.8Tanzania .. 30.3 .. 0.34 2.6 4.8 8.6 61.2 1.9 12.7 2.9 5.3Thailand 369.4 581.4 0.15 0.15 1.9 4.2 12.4 16.4 4.7 20.5 2.8 37.2Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo .. .. .. .. .. .. .. .. .. .. .. ..Trinidad and Tobago 7.0 7.6 0.23 0.29 4.8 18.2 21.3 39.3 8.0 7.7 8.5 5.0Tunisia .. .. .. .. .. .. .. .. .. .. .. ..Turkey 175.8 346.4 0.18 0.15 3.8 3.8 8.6 12.4 6.6 32.2 1.7 30.9Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda 3.3 2.1 0.29 0.23 .. 7.8 7.3 34.8 13.3 17.2 2.3 19.6Ukraine .. 498.2 .. 0.19 13.9 4.3 11.2 19.7 6.8 5.6 2.1 36.5United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. ..United Kingdom 599.9 521.7 0.16 0.17 2.7 12.5 13.5 14.9 3.6 4.3 2.5 46.1United States 2,307.0 1,850.8 0.14 0.14 3.5 8.1 13.1 12.0 3.9 4.3 4.1 51.1Uruguay .. .. .. .. .. .. .. .. .. .. .. ..Uzbekistan .. .. .. .. .. .. .. .. .. .. .. ..Venezuela, RB .. .. .. .. .. .. .. .. .. .. .. ..Vietnam 141.0 544.8 0.16 0.14 1.4 3.5 6.8 12.7 6.4 40.2 3.3 25.8West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 12.6 46.5 0.24 0.21 .. 2.1 7.4 35.9 14.6 15.5 5.1 19.4Zambia .. .. .. .. .. .. .. .. .. .. .. ..Zimbabwe 29.3 .. 0.20 .. .. .. .. .. .. .. .. ..a. Data are derived using the United Nations Industrial Development Organization’s (UNIDO) industry database four-digit International Standard Industrial Classifi cation (ISIC). Data initalics are for the most recent year available and are derived using UNIDO’s industry database at the three-digit ISIC.1602012 World Development Indicators

Water pollution 3.6ENVIRONMENTAbout the dataDefinitionsEmissions of organic pollutants from industrialactivities are a major cause of degradation of waterquality. Water quality and pollution levels are generallymeasured as concentration or load—the rate ofoccurrence of a substance in an aqueous solution.Polluting substances include organic matter, metals,minerals, sediment, bacteria, and toxic chemicals.The table focuses on organic water pollution resultingfrom industrial activities. Because water pollutiontends to be sensitive to local conditions, thenational-level data in the table may not refl ect thequality of water in specifi c locations.The data in the table come from an internationalstudy of industrial emissions that may have beenthe fi rst to include data from developing countries(Hettige, Mani, and Wheeler 1998). These data wereupdated through 2007 by the World Bank’s DevelopmentResearch Group. Unlike estimates from earlierstudies based on engineering or economic models,these estimates are based on actual measurementsof plant-level water pollution. The focus is on organicwater pollution caused by organic waste, measured interms of biochemical oxygen demand (BOD), becausethe data for this indicator are the most plentiful andreliable for cross-country comparisons of emissions.BOD measures the strength of an organic waste bythe amount of oxygen consumed in breaking it down.A sewage overload in natural waters exhausts thewater’s dissolved oxygen content. Wastewater treatment,by contrast, reduces BOD.Data on water pollution are more readily availablethan are other emissions data because most industrialpollution control programs start by regulatingemissions of organic water pollutants. Such data arefairly reliable because sampling techniques for measuringwater pollution are more widely understoodand much less expensive than those for air pollution.Hettige, Mani, and Wheeler (1998) used plant- andsector-level information on emissions and employmentfrom 13 national environmental protectionagencies and sector-level information on outputand employment from the United Nations IndustrialDevelopment Organization (UNIDO). Their econometricanalysis found that the ratio of BOD to employmentin each industrial sector is about the sameacross countries. This fi nding allowed the authors toestimate BOD loads across countries and over time.The estimated BOD intensities per unit of employmentwere multiplied by sectoral employment numbersfrom UNIDO’s industry database for 1980–98.These estimates of sectoral emissions were thenused to calculate kilograms of emissions of organicwater pollutants per day for each country and year.The data in the table were derived by updating theseestimates through 2007.• Emissions of organic water pollutants are measuredas biochemical oxygen demand, or the amountof oxygen that bacteria in water will consume inbreaking down waste, a standard water treatmenttest for the presence of organic pollutants. Emissionsper worker are total emissions divided by thenumber of industrial workers. • Industry shares ofemissions of organic water pollutants are emissionsfrom manufacturing activities as defi ned by two-digitdivisions of the International Standard IndustrialClassifi cation revision 3.Data sourcesData on water pollutants are from Hettige, Mani,and Wheeler (1998). The data were updatedthrough 2007 by the World Bank DevelopmentResearch Group using the same methodologyas the initial study. Data on industrial sectoralemployment are from UNIDO’s industry database.2012 World Development Indicators 161

3.7 Energy production and useEnergyproductionEnergyuseAlternative andnuclear energyTotalmillionmetric tons ofoil equivalentTotalmillionmetric tons ofoil equivalentaverageannual% growthPer capitakilograms ofoil equivalentAfghanistan .. .. .. .. .. .. .. .. .. .. .. .. ..Albania 2.4 1.3 2.7 1.7 1.6 809 538 76.5 54.1 13.6 12.4 9.2 26.4Algeria 100.1 152.3 22.2 39.8 3.0 877 1,138 99.9 99.8 0.1 0.1 0.1 0.1Angola 28.7 101.0 5.9 11.9 3.8 569 641 25.5 37.6 73.5 60.1 1.1 2.3Argentina 48.4 80.8 46.1 74.2 2.5 1,411 1,853 88.7 89.4 3.7 3.1 7.5 6.8Armenia 0.1 0.8 7.7 2.6 –2.3 2,171 843 97.2 68.4 0.0 0.0 1.7 31.7Australia 157.5 310.7 86.2 131.1 2.3 5,053 5,971 93.9 94.4 4.6 4.4 1.5 1.2Austria 8.1 11.4 24.8 31.7 1.7 3,228 3,784 79.2 70.2 10.0 17.6 11.0 12.0Azerbaijan 20.6 64.6 26.2 12.0 –2.8 3,665 1,338 100.0 98.2 0.0 0.0 0.2 1.7Bahrain 13.4 17.5 4.4 9.5 4.4 8,826 8,096 100.0 99.8 0.0 0.0 0.0 0.0Bangladesh 10.8 24.8 12.7 29.6 4.6 121 201 45.5 69.8 53.9 29.8 0.6 0.5Belarus 3.3 4.0 45.5 26.8 –1.7 4,470 2,815 95.6 90.3 0.4 6.0 0.0 0.0Belgium 13.1 15.3 48.3 57.2 0.9 4,844 5,300 76.0 73.6 1.6 4.9 23.1 21.8Benin 1.8 2.0 1.7 3.5 3.7 348 404 4.8 40.4 94.1 57.4 0.0 0.0Bolivia 4.9 14.2 2.6 6.2 3.9 392 638 67.2 79.1 28.9 17.7 3.9 3.2Bosnia and Herzegovina 4.6 4.5 7.0 6.0 2.9 1,629 1,580 93.9 92.2 2.3 3.1 3.7 9.0Botswana 0.9 0.9 1.3 2.0 2.7 912 1,034 66.0 64.3 33.4 23.6 0.0 0.0Brazil 104.2 230.3 140.2 240.2 3.0 937 1,243 51.2 51.3 34.1 31.6 13.1 15.6Bulgaria 9.6 9.8 28.6 17.5 –1.3 3,277 2,305 84.3 73.1 0.6 4.3 13.9 24.9Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. ..Burundi .. .. .. .. .. .. .. .. .. .. .. .. ..Cambodia .. 3.7 .. 5.2 3.3 .. 371 .. 27.8 .. 70.7 .. 0.1Cameroon 11.0 8.8 5.0 6.9 1.8 409 361 18.7 30.9 76.7 64.1 4.6 5.0Canada 273.7 389.8 208.6 254.1 1.4 7,505 7,534 74.6 74.9 3.9 4.5 21.5 21.7Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. .. .. .. ..Chile 7.5 9.3 13.6 28.8 4.3 1,028 1,698 74.6 74.5 19.7 17.5 5.7 7.6China 886.3 2,084.9 863.0 2,257.1 5.1 760 1,695 75.5 87.4 23.2 9.0 1.3 3.7Hong Kong SAR, China 0.0 0.1 8.7 14.9 2.5 1,518 2,133 100.0 95.1 0.6 0.4 0.0 0.0Colombia 48.2 99.1 24.2 31.8 0.8 730 697 67.4 75.2 22.8 14.0 9.8 11.1Congo, Dem. Rep. 12.0 23.3 11.8 22.9 3.9 324 357 11.2 3.7 84.7 93.7 4.1 2.9Congo, Rep. 8.7 15.3 0.8 1.4 3.2 334 356 35.1 44.1 59.5 51.1 5.3 2.0Costa Rica 1.0 2.7 2.0 4.9 5.0 660 1,067 48.4 44.7 36.6 15.8 14.4 39.5Côte d’Ivoire 3.4 11.9 4.3 10.4 5.0 345 535 23.3 23.5 73.5 75.2 2.6 1.8Croatia 5.1 4.1 9.0 8.7 1.3 1,884 1,965 86.5 83.4 3.5 4.2 3.6 6.8Cuba 9.4 5.6 19.3 11.5 –1.8 1,824 1,022 55.7 84.1 44.3 15.8 0.0 0.1Cyprus 0.0 0.1 1.4 2.5 2.9 1,775 2,298 99.5 95.7 0.5 1.8 0.0 2.5Czech Republic 40.9 31.2 49.6 42.0 0.1 4,796 4,004 91.7 79.6 1.6 5.6 6.8 17.5Denmark 10.1 23.9 17.4 18.6 0.1 3,377 3,369 89.6 80.4 6.6 16.2 0.3 3.3Dominican Republic 1.0 1.9 4.1 8.1 3.6 570 826 74.9 76.6 24.4 21.8 0.7 1.6Ecuador 16.5 27.3 6.0 11.4 3.9 584 796 79.1 86.7 13.8 5.4 7.2 7.0Egypt, Arab Rep. 54.9 88.2 31.8 72.0 4.8 560 903 94.0 96.3 3.3 2.1 2.7 1.7El Salvador 1.7 3.2 2.5 5.1 3.8 463 828 31.4 37.8 48.2 33.8 20.3 28.2Eritrea 0.7 0.6 0.9 0.7 –2.0 276 142 19.4 22.6 80.6 77.4 0.0 0.0Estonia 5.4 4.2 9.9 4.7 –2.0 6,316 3,543 100.0 83.4 1.9 14.7 0.0 0.4Ethiopia 14.1 30.4 14.9 32.7 3.8 308 402 5.5 7.1 93.9 92.0 0.6 1.0Finland 12.1 16.6 28.4 33.2 1.5 5,692 6,213 55.5 48.8 16.1 20.9 20.9 21.8France 111.9 129.5 223.9 256.2 0.9 3,848 3,970 58.1 51.0 4.9 5.9 38.7 43.9Gabon 14.6 13.6 1.2 1.8 2.7 1,272 1,214 32.0 34.0 62.9 61.8 5.1 4.2Gambia, The .. .. 0.1 0.1 .. 64 84 0.0 0.0 .. .. .. ..Georgia 2.1 1.3 12.4 3.2 –6.2 2,587 723 88.9 68.0 3.7 12.0 5.2 21.4Germany 186.2 127.1 351.4 318.5 –0.2 4,424 3,889 86.8 79.5 1.4 7.8 11.8 13.0Ghana 4.4 7.0 5.3 9.2 3.2 358 388 18.2 24.3 73.7 69.8 9.3 6.4Greece 9.2 10.1 21.4 29.4 2.2 2,111 2,609 94.6 92.4 4.2 3.4 1.0 3.0Guatemala 3.4 6.1 4.4 9.8 4.0 497 701 28.1 46.1 68.5 52.1 3.4 1.8Guinea .. .. .. .. .. .. .. .. .. .. .. .. ..Guinea-Bissau .. .. 0.1 0.1 .. 73 67 0.0 0.0 .. .. .. ..Haiti 1.3 1.9 1.6 2.6 3.4 219 263 19.7 28.1 77.7 71.2 2.5 0.7% of totalFossil fuelCombustiblerenewables and waste% of totalenergy use1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2009 1990 20091622012 World Development Indicators

Energy production and use 3.7ENVIRONMENTEnergyproductionEnergyuseAlternative andnuclear energyTotalmillionmetric tons ofoil equivalentTotalmillionmetric tons ofoil equivalentaverageannual% growthPer capitakilograms ofoil equivalentHonduras 1.7 2.2 2.4 4.4 3.6 487 592 30.0 50.3 63.0 44.3 8.2 5.5Hungary 14.6 11.0 28.7 24.9 0.0 2,762 2,480 81.5 74.2 2.3 7.1 12.8 16.8India 291.8 502.5 316.7 675.8 3.8 362 560 55.4 73.0 42.1 24.5 2.5 2.3Indonesia 169.1 351.8 101.3 202.0 3.5 550 851 54.7 65.6 42.9 26.0 2.4 8.4Iran, Islamic Rep. 179.3 349.8 67.9 215.9 6.4 1,237 2,951 98.9 99.7 0.3 0.2 0.8 0.3Iraq 104.9 119.6 18.1 32.2 3.6 994 1,035 98.6 97.6 0.1 0.1 1.2 0.9Ireland 3.5 1.5 10.0 14.3 2.6 2,842 3,216 84.6 88.7 1.1 2.2 0.6 2.3Israel 0.4 3.3 11.5 21.5 3.4 2,463 2,878 97.2 96.5 0.0 0.1 3.1 4.8Italy 25.3 27.0 146.6 164.6 1.2 2,584 2,735 93.4 87.5 0.6 4.3 3.9 5.9Jamaica 0.5 0.5 2.8 3.3 2.1 1,167 1,208 82.6 83.7 17.1 15.9 0.3 0.4Japan 75.2 93.8 439.3 472.0 0.6 3,556 3,700 84.5 81.0 1.1 1.4 14.4 17.6Jordan 0.2 0.3 3.3 7.5 4.3 1,028 1,260 98.1 98.0 0.1 0.1 1.8 1.7Kazakhstan 90.5 145.8 72.7 65.8 –0.7 4,450 4,091 96.9 99.0 0.2 0.2 0.9 0.9Kenya 9.0 15.6 10.9 18.7 2.8 467 474 17.5 16.8 77.9 76.0 4.5 7.2Korea, Dem. Rep. 28.9 20.3 33.2 19.3 –2.0 1,649 795 93.1 89.0 2.9 5.4 4.0 5.6Korea, Rep. 22.6 44.3 93.1 229.2 4.6 2,171 4,701 83.8 81.7 0.8 1.3 15.4 17.0Kosovo .. .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 50.4 130.2 9.1 30.2 8.1 4,364 11,402 99.9 100.0 0.1 0.0 0.0 0.0Kyrgyz Republic 2.5 1.2 7.5 3.0 –3.4 1,693 566 93.5 72.5 0.1 0.1 11.5 28.3Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. ..Latvia 1.1 2.1 7.9 4.2 –2.1 2,949 1,871 81.8 59.4 8.4 30.0 4.9 7.1Lebanon 0.1 0.2 2.0 6.6 4.2 663 1,580 92.5 95.9 5.3 1.8 2.2 0.8Lesotho .. .. .. 0.0 .. .. 9 .. 0.0 .. .. .. ..Liberia .. .. .. .. .. .. .. .. .. .. .. .. ..Libya 73.2 87.1 11.3 20.4 2.3 2,614 3,258 98.9 99.2 1.1 0.8 0.0 0.0Lithuania 4.9 4.2 16.1 8.4 –1.9 4,357 2,512 75.8 55.7 1.8 9.8 28.2 34.9Macedonia, FYR 1.3 1.6 2.5 2.8 0.8 1,298 1,352 98.0 84.2 0.0 7.0 1.7 4.3Madagascar .. .. .. .. .. .. .. .. .. .. .. .. ..Malawi .. .. .. .. .. .. .. .. .. .. .. .. ..Malaysia 48.8 89.7 22.0 66.8 5.9 1,208 2,391 88.8 94.7 9.7 4.5 1.6 0.9Mali .. .. .. .. .. .. .. .. .. .. .. .. ..Mauritania .. .. .. .. .. .. .. .. .. .. .. .. ..Mauritius .. .. 0.5 1.2 .. 453 947 0.0 0.0 .. .. .. ..Mexico 194.7 220.0 122.5 174.6 2.0 1,453 1,559 87.2 88.9 7.0 4.8 5.9 6.3Moldova 0.1 0.1 9.9 2.4 –5.2 2,669 687 100.0 91.3 0.4 3.3 0.2 0.2Mongolia 2.7 7.7 3.4 3.2 –0.5 1,558 1,194 96.9 96.4 2.5 3.2 0.0 0.0Morocco 0.8 0.8 6.9 15.1 4.0 280 477 93.8 92.5 4.6 3.2 1.5 1.7Mozambique 5.6 11.9 5.9 9.8 2.9 437 427 5.5 7.7 93.9 81.8 0.4 14.9Myanmar 10.7 22.4 10.7 15.1 2.3 271 316 14.4 27.7 84.7 69.9 1.0 2.4Namibia 0.2 0.3 0.7 1.7 5.4 445 764 62.0 70.5 15.9 12.0 17.4 7.2Nepal 5.5 8.8 5.8 10.0 3.0 303 338 5.1 11.1 93.7 85.8 1.3 2.7Netherlands 60.5 63.0 65.7 78.2 0.9 4,393 4,729 95.9 93.1 1.5 4.4 1.4 2.0New Zealand 11.5 15.2 12.8 17.4 1.5 3,720 4,032 67.0 63.7 5.7 6.3 27.1 29.8Nicaragua 1.5 1.7 2.1 3.1 2.6 508 540 28.3 44.7 53.9 45.9 17.5 9.4Niger .. .. .. .. .. .. .. .. .. .. .. .. ..Nigeria 150.5 228.7 70.6 108.3 2.4 724 701 19.3 14.7 80.2 84.9 0.5 0.4Norway 119.1 213.6 21.0 28.2 1.6 4,952 5,849 51.9 58.8 4.9 5.1 49.6 38.7Oman 38.3 67.2 4.2 15.1 6.4 2,258 5,554 100.0 100.0 0.0 0.0 0.0 0.0Pakistan 34.2 64.9 42.8 85.5 3.7 383 502 52.6 61.8 43.8 34.5 3.6 3.7Panama 0.6 0.7 1.5 3.1 3.3 603 896 57.5 78.6 28.9 10.7 13.1 10.8Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. ..Paraguay 4.6 7.4 3.1 4.8 1.7 724 749 21.3 28.5 72.5 53.7 76.0 99.5Peru 10.6 15.1 9.7 15.8 2.5 449 550 63.3 73.5 27.5 15.4 9.2 11.1Philippines 17.2 23.5 28.9 38.8 1.7 469 424 43.4 57.0 38.5 17.9 18.1 25.0Poland 103.9 67.5 103.1 94.0 –0.5 2,705 2,464 97.8 92.8 2.2 7.1 0.1 0.3Portugal 3.4 4.9 16.7 24.1 2.4 1,677 2,266 80.4 78.0 14.8 13.6 4.8 6.6Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. ..Qatar 26.6 139.9 6.2 23.8 7.5 13,098 14,911 100.0 100.0 0.0 0.0 0.0 0.0% of totalFossil fuelCombustiblerenewables and waste% of totalenergy use1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2009 1990 20092012 World Development Indicators 163

3.7Energy production and useEnergyproductionEnergyuseAlternative andnuclear energyRomania 40.8 28.3 62.3 34.4 –1.9 2,683 1,602 96.1 76.3 1.0 11.4 1.6 12.9Russian Federation 1,293.1 1,181.6 879.2 646.9 –1.0 5,929 4,561 93.4 90.2 1.4 1.0 5.2 9.0Rwanda .. .. .. .. .. .. .. .. .. .. .. .. ..Saudi Arabia 370.2 528.4 59.8 157.9 4.9 3,703 5,888 100.0 100.0 0.0 0.0 0.0 0.0Senegal 1.0 1.3 1.7 2.9 3.4 233 243 43.3 57.8 56.7 41.1 0.0 0.7Serbia 13.4 a 9.4 19.3 a 14.4 0.4 2,550 a 1,974 90.6 a 92.4 6.0 a 2.0 4.2 a 6.4Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. ..Singapore 0.0 0.0 11.5 18.5 0.9 3,760 3,704 100.0 99.8 0.0 0.2 0.0 0.0Slovak Republic 5.3 5.9 21.3 16.7 –0.2 4,025 3,086 81.6 69.5 0.8 5.2 15.5 24.6Slovenia 3.1 3.5 5.7 7.0 1.8 2,858 3,417 71.3 69.3 4.7 7.2 25.5 27.3Somalia .. .. .. .. .. .. .. .. .. .. .. .. ..South Africa 114.5 160.6 93.9 144.0 2.3 2,667 2,921 86.6 87.8 11.1 9.8 2.4 2.4South Sudan .. .. .. .. .. .. .. .. .. .. .. .. ..Spain 34.6 29.7 90.1 126.5 2.7 2,319 2,756 77.4 79.9 4.5 4.9 18.1 15.8Sri Lanka 4.2 5.1 5.5 9.3 3.2 318 449 24.1 45.3 71.0 51.1 4.9 3.6Sudan 8.8 35.2 10.6 15.8 2.4 401 372 17.4 30.2 81.8 68.0 0.8 1.8Swaziland .. .. 0.3 0.4 .. 358 373 0.0 0.0 .. .. .. ..Sweden 29.7 30.3 47.2 45.4 0.2 5,515 4,883 37.3 32.0 11.7 22.9 50.9 42.9Switzerland 10.3 12.8 24.3 27.0 0.6 3,621 3,480 58.6 53.3 5.9 8.2 36.3 39.2Syrian Arab Republic 22.3 23.6 10.5 22.5 4.8 849 1,123 97.7 99.3 0.0 0.0 2.2 0.7Tajikistan 2.0 1.5 5.3 2.3 –2.9 1,001 342 71.3 41.2 0.0 0.0 26.7 58.6Tanzania 9.1 18.0 9.7 19.6 4.1 382 451 6.9 11.1 91.7 87.7 1.4 1.2Thailand 26.6 61.7 41.9 103.3 4.9 735 1,504 63.8 79.4 35.0 19.9 1.0 0.6Timor-Leste .. .. .. 0.1 .. .. 58 .. 0.0 .. .. .. ..Togo 1.1 2.2 1.3 2.6 4.2 345 445 15.1 14.3 82.8 83.1 0.6 0.3Trinidad and Tobago 12.6 44.0 6.0 20.3 8.0 4,912 15,158 99.2 99.9 0.8 0.1 0.0 0.0Tunisia 5.7 7.8 4.9 9.2 3.6 607 881 87.0 85.7 12.9 14.1 0.1 0.2Turkey 25.8 30.3 52.8 97.7 3.6 975 1,359 81.8 89.9 13.7 4.8 4.6 5.4Turkmenistan 74.9 40.9 19.6 19.6 2.4 5,352 3,933 100.0 100.0 0.0 0.0 0.3 0.0Uganda .. .. .. .. .. .. .. .. .. .. .. .. ..Ukraine 135.8 76.9 251.8 115.5 –3.0 4,852 2,507 91.8 80.0 0.1 0.8 8.2 19.7United Arab Emirates 110.2 168.8 20.4 59.6 5.4 11,258 8,588 100.0 100.0 0.0 0.0 0.0 0.0United Kingdom 208.0 158.9 205.9 196.8 0.0 3,597 3,183 90.7 87.3 0.3 2.7 8.5 9.8United States 1,652.5 1,686.4 1,915.0 2,162.9 1.0 7,672 7,051 86.4 84.1 3.3 3.9 10.3 11.9Uruguay 1.1 1.5 2.3 4.1 2.1 724 1,224 58.7 60.3 24.2 26.0 26.8 11.1Uzbekistan 38.6 60.7 46.4 48.8 0.5 2,261 1,758 99.2 98.4 0.0 0.0 1.2 1.6Venezuela, RB 148.8 203.5 43.5 66.9 2.0 2,205 2,357 91.5 87.7 1.2 0.8 7.3 11.6Vietnam 24.7 76.6 24.3 64.0 5.3 368 745 20.3 56.2 77.8 39.3 1.9 4.0West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 9.4 15.2 2.5 7.6 6.0 210 324 96.9 98.7 3.1 1.3 0.0 0.0Zambia 4.9 7.2 5.4 7.9 2.0 687 617 15.6 7.6 74.3 80.9 12.7 11.2Zimbabwe 8.5 8.5 9.3 9.5 –0.2 888 763 44.8 25.7 50.8 65.6 4.0 3.8World 8,815.9 t 12,241.3 t 8,574.0 t 11,787.1 t 1.9 w 1,661 w 1,788 w 80.9 w 80.7 w 10.2 w 10.0 w 8.7 w 9.2 wLow income 160.7 250.6 186.6 263.3 2.2 386 365 41.5 29.9 54.7 65.9 3.9 4.1Middle income 4,810.5 7,351.0 3,886.6 6,158.2 2.6 1,019 1,254 78.6 81.5 17.2 13.2 4.1 5.3Lower middle income 1,247.9 1,944.1 1,085.5 1,656.0 2.4 607 665 65.1 68.0 30.7 26.9 4.4 5.3Upper middle income 3,564.8 5,410.7 2,803.9 4,505.9 2.7 1,375 1,848 83.7 86.3 12.0 8.3 4.0 5.3Low & middle income 4,969.1 7,597.3 4,059.7 6,407.1 2.6 963 1,162 77.3 79.8 18.5 15.0 4.1 5.3East Asia & Pacifi c 1,224.5 2,758.4 1,138.1 2,790.9 4.8 712 1,436 71.4 84.1 26.6 11.8 1.9 4.1Europe & Central Asia 1,770.3 1,673.3 1,585.6 1,137.6 –1.0 4,067 2,831 93.0 88.9 1.5 1.9 5.3 9.3Latin America & Carib. 613.1 937.5 457.6 716.5 2.5 1,048 1,245 70.3 71.8 20.4 17.0 9.1 11.1Middle East & N. Africa 558.2 856.0 183.8 454.5 5.0 812 1,399 97.4 98.3 1.4 0.9 1.1 0.6South Asia 349.4 611.2 386.8 817.1 3.8 340 514 53.5 70.7 43.9 26.8 2.5 2.4Sub-Saharan Africa 475.6 810.5 313.9 511.2 2.6 681 689 41.7 40.1 55.9 57.3 2.2 2.5High income 3,878.6 4,694.3 4,538.5 5,420.3 1.2 4,649 4,856 84.2 81.9 2.8 4.2 12.8 13.8Euro area 481.9 450.2 1,069.7 1,169.7 0.9 3,509 3,536 80.0 74.2 3.1 6.7 16.5 18.6a. Includes Kosovo and Montenegro.Totalmillionmetric tons ofoil equivalentTotalmillionmetric tons ofoil equivalentaverageannual% growthPer capitakilograms ofoil equivalent% of totalFossil fuelCombustiblerenewables and waste% of totalenergy use1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2009 1990 20091642012 World Development Indicators

Energy production and use 3.7ENVIRONMENTAbout the dataDefinitionsIn developing economies growth in energy use isclosely related to growth in the modern sectors—industry, motorized transport, and urban areas—but energy use also refl ects climatic, geographic,and economic factors (such as the relative priceof energy). Energy use has been growing rapidly inlow- and middle-income economies, but high-incomeeconomies still use almost fi ve times as much energyon a per capita basis.Energy data are compiled by the InternationalEnergy Agency (IEA). IEA data for economies thatare not members of the Organisation for EconomicCo-operation and Development (OECD) are basedon national energy data adjusted to conform toannual questionnaires completed by OECD membergovernments.Total energy use refers to the use of primary energybefore transformation to other end-use fuels (suchas electricity and refi ned petroleum products). Itincludes energy from combustible renewables andwaste—solid biomass and animal products, gas andliquid from biomass, and industrial and municipalwaste. Biomass is any plant matter used directlyas fuel or converted into fuel, heat, or electricity.Data for combustible renewables and waste areoften based on small surveys or other incompleteinformation and thus give only a broad impressionof developments and are not strictly comparableacross countries. The IEA reports include countrynotes that explain some of these differences (seeData sources). All forms of energy—primary energyand primary electricity—are converted into oil equivalents.A notional thermal effi ciency of 33 percent isassumed for converting nuclear electricity into oilequivalents and 100 percent effi ciency for convertinghydroelectric power.The IEA makes these estimates in consultationwith national statistical offi ces, oil companies, electricutilities, and national energy experts. The IEAoccasionally revises its time series to refl ect politicalchanges, and energy statistics undergo continualchanges in coverage or methodology as moredetailed energy accounts become available. Breaksin series are therefore unavoidable.• Energy production refers to forms of primaryenergy—petroleum (crude oil, natural gas liquids,and oil from nonconventional sources), natural gas,solid fuels (coal, lignite, and other derived fuels),and combustible renewables and waste—and primaryelectricity, all converted into oil equivalents(see About the data). • Energy use refers to the useof primary energy before transformation to otherend-use fuels, which is equal to indigenous productionplus imports and stock changes, minus exportsand fuels supplied to ships and aircraft engaged ininternational transport (see About the data). • Fossilfuel comprises coal, oil, petroleum, and naturalgas products. • Combustible renewables and wastecomprise solid biomass, liquid biomass, biogas,industrial waste, and municipal waste. • Alternativeand nuclear energy production is noncarbohydrateenergy that does not produce carbon dioxide whengenerated. It includes hydropower and nuclear, geothermal,and solar power, among others.Data sourcesData on energy production and use are from IEAelectronic fi les and are published in IEA’s annualpublications Energy Statistics of Non-OECD Countries,Energy Balances of Non-OECD Countries,Energy Statistics of OECD Countries, and EnergyBalances of OECD Countries.2012 World Development Indicators 165

3.8 Electricity production, sources, and accessElectricityproductionSources ofelectricity aAccess toelectricity% of totalbillion kilowatthours Coal Natural gas Oil HydropowerRenewablesources b Nuclear power % of population2009 2009 2009 2009 2009 2009 2009 2009Afghanistan .. .. .. .. .. .. .. 15.6Albania 5.3 0.0 0.0 0.6 99.4 0.0 0.0 ..Algeria 42.8 0.0 97.2 2.0 0.8 0.0 0.0 99.3Angola 4.2 0.0 0.0 23.9 76.1 0.0 0.0 26.2Argentina 121.9 2.3 51.3 10.5 27.8 1.4 6.7 97.2Armenia 5.7 0.0 20.3 0.0 35.6 0.1 44.0 ..Australia 260.9 77.9 13.7 1.0 4.7 2.6 0.0 ..Austria 65.6 7.7 18.8 1.7 61.4 9.6 0.0 ..Azerbaijan 18.9 0.0 85.1 2.6 12.2 0.0 0.0 ..Bahrain 12.1 0.0 100.0 0.0 0.0 0.0 0.0 99.4Bangladesh 37.9 1.7 89.4 4.8 4.1 0.0 0.0 41.0Belarus 30.4 0.0 81.7 17.6 0.1 0.2 0.0 ..Belgium 89.8 6.8 32.6 0.3 0.4 5.7 52.6 ..Benin 0.1 0.0 0.0 100.0 0.0 0.0 0.0 24.8Bolivia 6.1 0.0 59.7 1.7 37.5 1.0 0.0 77.5Bosnia and Herzegovina 15.7 59.9 0.2 0.1 39.8 0.0 0.0 ..Botswana 0.4 100.0 0.0 0.0 0.0 0.0 0.0 45.4Brazil 466.5 2.1 2.9 3.1 83.8 5.2 2.8 98.3Bulgaria 42.4 49.8 4.6 0.8 8.2 0.6 36.0 ..Burkina Faso .. .. .. .. .. .. .. 14.6Burundi .. .. .. .. .. .. .. ..Cambodia 1.2 0.0 0.0 95.6 3.9 0.5 0.0 24.0Cameroon 5.7 0.0 7.2 22.7 70.0 0.2 0.0 48.7Canada 603.1 15.2 6.2 1.4 60.3 1.9 15.0 ..Central African Republic .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. ..Chile 60.7 24.5 6.5 20.0 41.7 7.2 0.0 98.5China 3,695.9 78.8 1.4 0.4 16.7 0.8 1.9 99.4Hong Kong SAR, China 38.7 70.8 28.9 0.4 0.0 0.0 0.0 ..Colombia 57.3 7.3 19.3 0.6 71.7 1.2 0.0 93.6Congo, Dem. Rep. 7.8 0.0 0.4 0.1 99.6 0.0 0.0 11.1Congo, Rep. 0.5 0.0 36.0 0.0 64.0 0.0 0.0 37.1Costa Rica 9.3 0.0 0.0 4.9 77.8 17.4 0.0 99.3Côte d’Ivoire 5.9 0.0 61.9 0.1 35.9 2.1 0.0 47.3Croatia 12.7 13.1 17.4 15.9 53.0 0.6 0.0 ..Cuba 17.7 0.0 13.4 82.8 0.9 2.9 0.0 97.0Cyprus 5.2 0.0 0.0 99.2 0.0 0.1 0.0 ..Czech Republic 81.7 59.6 1.2 0.2 3.0 2.7 33.3 ..Denmark 36.4 48.6 18.5 3.2 0.1 27.6 0.0 ..Dominican Republic 15.0 12.9 13.5 63.6 9.8 0.2 0.0 95.9Ecuador 17.2 0.0 6.9 37.5 53.5 2.1 0.0 92.2Egypt, Arab Rep. 139.0 0.0 68.7 21.3 9.3 0.8 0.0 99.6El Salvador 5.8 0.0 0.0 43.7 26.0 30.3 0.0 86.4Eritrea 0.3 0.0 0.0 99.3 0.0 0.7 0.0 32.0Estonia 8.8 91.4 1.2 0.5 0.4 5.8 0.0 ..Ethiopia 4.1 0.0 0.0 12.4 87.3 0.4 0.0 17.0Finland 72.1 16.0 13.6 0.7 17.6 12.5 32.6 ..France 537.4 5.3 3.9 1.1 10.6 2.4 76.2 ..Gabon 1.7 0.0 26.4 20.0 53.2 0.4 0.0 36.7Gambia, The .. .. .. .. .. .. .. ..Georgia 8.6 0.0 12.9 0.5 86.6 0.0 0.0 ..Germany 586.4 43.8 13.5 1.6 3.2 12.8 23.0 ..Ghana 9.0 0.0 0.0 23.2 76.8 0.0 0.0 60.5Greece 61.1 56.0 18.0 12.6 8.8 4.6 0.0 ..Guatemala 9.0 8.2 0.0 34.5 23.3 34.0 0.0 80.5Guinea .. .. .. .. .. .. .. ..Guinea-Bissau .. .. .. .. .. .. .. ..Haiti 0.7 0.0 0.0 71.3 28.7 0.0 0.0 38.51662012 World Development Indicators

Electricity production, sources, and access3.8ENVIRONMENTElectricityproductionSources ofelectricity aAccess toelectricity% of totalbillion kilowatthours Coal Natural gas Oil HydropowerRenewablesources b Nuclear power % of population2009 2009 2009 2009 2009 2009 2009 2009Honduras 6.6 0.0 0.0 54.9 42.5 2.6 0.0 70.3Hungary 35.9 17.9 29.0 1.8 0.6 7.4 43.0 ..India 899.4 68.6 12.4 2.9 11.9 2.2 2.1 66.3Indonesia 155.5 41.8 22.1 22.8 7.3 6.0 0.0 64.5Iran, Islamic Rep. 203.2 0.2 70.4 25.8 3.6 0.1 0.0 98.4Iraq 46.1 0.0 0.0 93.0 7.0 0.0 0.0 86.0Ireland 27.9 14.4 58.4 3.3 3.2 11.2 0.0 ..Israel 55.0 62.5 32.8 4.0 0.0 0.1 0.0 99.7Italy 288.3 15.1 51.1 9.0 17.0 7.0 0.0 ..Jamaica 5.5 0.0 0.0 96.4 2.0 1.6 0.0 92.0Japan 1,041.0 26.8 27.4 7.2 7.2 2.5 26.9 ..Jordan 14.3 0.0 88.1 11.4 0.4 0.1 0.0 99.9Kazakhstan 78.7 74.9 13.1 3.2 8.7 0.0 0.0 ..Kenya 6.9 0.0 0.0 44.1 31.6 24.4 0.0 16.1Korea, Dem. Rep. 21.1 38.1 0.0 2.8 59.1 0.0 0.0 26.0Korea, Rep. 451.7 46.2 15.6 4.4 0.6 0.4 32.7 ..Kosovo .. .. .. .. .. .. .. ..Kuwait 53.2 0.0 28.8 71.2 0.0 0.0 0.0 100.0Kyrgyz Republic 11.1 2.8 8.0 0.0 89.3 0.0 0.0 ..Lao PDR .. .. .. .. .. .. .. 55.0Latvia 5.6 0.0 36.0 0.1 62.1 1.8 0.0 ..Lebanon 13.8 0.0 1.4 94.1 4.5 0.0 0.0 99.9Lesotho .. .. .. .. .. .. .. 16.0Liberia .. .. .. .. .. .. .. ..Libya 30.4 0.0 41.0 59.0 0.0 0.0 0.0 99.8Lithuania 14.6 0.0 14.3 5.0 2.9 1.8 74.1 ..Macedonia, FYR 6.8 77.7 0.0 3.7 18.6 0.0 0.0 ..Madagascar .. .. .. .. .. .. .. 19.0Malawi .. .. .. .. .. .. .. 9.0Malaysia 105.1 30.9 60.7 2.0 6.3 0.0 0.0 99.4Mali .. .. .. .. .. .. .. ..Mauritania .. .. .. .. .. .. .. ..Mauritius .. .. .. .. .. .. .. 99.4Mexico 261.0 11.3 53.1 17.5 10.2 3.9 4.0 ..Moldova 3.6 0.0 95.0 1.3 1.5 0.0 0.0 ..Mongolia 4.2 96.4 0.0 3.6 0.0 0.0 0.0 67.0Morocco 21.4 52.4 13.3 20.3 12.1 1.8 0.0 97.0Mozambique 17.0 0.0 0.1 0.0 99.9 0.0 0.0 11.7Myanmar 5.9 0.0 19.6 8.9 71.5 0.0 0.0 13.0Namibia 1.7 17.5 0.0 0.5 82.0 0.0 0.0 34.0Nepal 3.1 0.0 0.0 0.4 99.6 0.0 0.0 43.6Netherlands 113.5 23.4 60.5 1.3 0.1 9.5 3.7 ..New Zealand 43.5 7.6 20.6 0.0 55.7 15.9 0.0 ..Nicaragua 3.5 0.0 0.0 69.1 8.6 22.3 0.0 72.1Niger .. .. .. .. .. .. .. ..Nigeria 19.8 0.0 64.3 12.8 22.9 0.0 0.0 50.6Norway 132.0 0.1 3.2 0.0 95.7 0.9 0.0 ..Oman 17.8 0.0 82.0 18.0 0.0 0.0 0.0 98.0Pakistan 95.4 0.1 29.4 38.0 29.4 0.0 3.0 62.4Panama 6.9 0.0 0.0 43.6 56.1 0.2 0.0 88.1Papua New Guinea .. .. .. .. .. .. .. ..Paraguay 55.0 0.0 0.0 0.0 100.0 0.0 0.0 96.7Peru 35.4 2.5 34.3 4.1 57.6 1.4 0.0 85.7Philippines 61.9 26.6 32.1 8.7 15.8 16.8 0.0 89.7Poland 151.1 89.1 3.2 1.8 1.6 4.2 0.0 ..Portugal 49.5 26.1 29.7 6.6 16.7 20.2 0.0 ..Puerto Rico .. .. .. .. .. .. .. ..Qatar 24.8 0.0 100.0 0.0 0.0 0.0 0.0 98.72012 World Development Indicators 167

3.8Electricity production, sources, and accessElectricityproductionSources ofelectricity aAccess toelectricity% of totalbillion kilowatthours Coal Natural gas Oil HydropowerRenewablesources b Nuclear power % of population2009 2009 2009 2009 2009 2009 2009 2009Romania 57.7 37.7 13.2 1.8 26.9 0.0 20.4 ..Russian Federation 990.0 16.5 47.4 1.6 17.6 0.1 16.5 ..Rwanda .. .. .. .. .. .. .. ..Saudi Arabia 217.1 0.0 44.8 55.2 0.0 0.0 0.0 99.0Senegal 2.9 0.0 1.7 86.4 8.4 1.9 0.0 42.0Serbia 37.4 71.9 0.5 0.3 27.4 0.0 0.0 ..Sierra Leone .. .. .. .. .. .. .. ..Singapore 41.8 0.0 81.0 18.8 0.0 0.1 0.0 100.0Slovak Republic 25.9 16.5 7.6 2.4 16.9 2.1 54.3 ..Slovenia 16.4 31.3 3.6 0.2 28.7 1.2 35.0 ..Somalia .. .. .. .. .. .. .. ..South Africa 246.8 94.1 0.0 0.0 0.6 0.1 5.2 75.0South Sudan .. .. .. .. .. .. .. ..Spain 291.0 12.8 36.9 6.5 9.0 16.2 18.1 ..Sri Lanka 9.9 0.0 0.0 60.3 39.5 0.2 0.0 76.6Sudan 6.8 0.0 0.0 52.2 47.8 0.0 0.0 35.9Swaziland .. .. .. .. .. .. .. ..Sweden 136.6 0.7 1.1 0.5 48.2 10.2 38.2 ..Switzerland 66.7 0.0 1.0 0.2 53.6 2.0 41.5 ..Syrian Arab Republic 43.3 0.0 45.7 50.0 4.3 0.0 0.0 92.7Tajikistan 16.1 0.0 2.0 0.0 98.0 0.0 0.0 ..Tanzania 4.6 2.7 36.2 0.9 60.2 0.0 0.0 13.9Thailand 148.4 19.9 70.7 0.5 4.8 4.0 0.0 99.3Timor-Leste .. .. .. .. .. .. .. 22.0Togo 0.1 0.0 0.0 24.6 73.8 1.6 0.0 20.0Trinidad and Tobago 7.7 0.0 99.4 0.3 0.0 0.2 0.0 99.0Tunisia 15.7 0.0 89.7 9.2 0.5 0.6 0.0 99.5Turkey 194.8 28.6 49.3 2.5 18.5 1.1 0.0 ..Turkmenistan 16.0 0.0 100.0 0.0 0.0 0.0 0.0 ..Uganda .. .. .. .. .. .. .. 9.0Ukraine 173.5 36.6 8.1 0.5 6.8 0.0 48.0 ..United Arab Emirates 90.6 0.0 98.2 1.8 0.0 0.0 0.0 100.0United Kingdom 372.0 28.5 44.5 1.2 1.4 5.4 18.6 ..United States 4,165.4 45.4 22.8 1.2 6.6 3.7 19.9 ..Uruguay 8.9 0.0 0.2 31.1 59.4 9.3 0.0 98.3Uzbekistan 49.9 4.1 75.1 2.1 18.7 0.0 0.0 ..Venezuela, RB 123.4 0.0 14.7 12.5 72.8 0.0 0.0 99.0Vietnam 83.2 18.0 43.4 2.5 36.0 0.0 0.0 97.6West Bank and Gaza .. .. .. .. .. .. .. ..Yemen, Rep. 6.7 0.0 0.0 100.0 0.0 0.0 0.0 39.6Zambia 10.3 0.0 0.0 0.3 99.7 0.0 0.0 18.8Zimbabwe 7.9 46.4 0.0 0.3 53.3 0.0 0.0 41.5World 20,079.3 w 40.4 w 21.4 w 4.8 w 16.1 w 3.0 13.4 w 74.1 wLow income 185.6 6.9 20.4 4.7 45.7 0.9 0.0 23.0Middle income 9,255.1 47.9 19.2 5.7 20.6 1.4 4.6 81.6Lower middle income 2,007.9 39.6 22.0 12.1 16.9 2.4 5.3 67.3Upper middle income 7,245.8 50.2 18.5 3.9 21.6 1.2 4.4 98.0Low & middle income 9,461.8 47.0 19.2 5.7 21.0 1.4 4.5 73.7East Asia & Pacifi c 4,307.5 71.6 7.2 1.5 16.2 1.3 1.6 90.8Europe & Central Asia 1,785.3 24.0 39.5 1.9 18.0 0.2 16.1 ..Latin America & Carib. 1,296.7 5.0 20.7 12.1 55.5 3.9 2.4 93.4Middle East & N. Africa 584.2 2.0 58.5 32.9 4.9 0.3 0.0 92.9South Asia 1,054.5 58.5 16.4 6.6 13.6 1.9 2.0 62.1Sub-Saharan Africa 419.3 56.5 4.6 4.1 18.2 0.5 3.1 32.5High income 10,673.5 34.3 23.2 4.1 11.6 4.5 21.3 ..Euro area 2,244.3 21.6 23.3 3.7 10.2 9.1 30.8 ..a. Shares may not sum to 100 percent because some sources of generated electricity are not shown. b. Excludes hydropower.1682012 World Development Indicators

Electricity production, sources, and access3.8ENVIRONMENTAbout the dataDefinitionsUse of energy is important in improving people’sstandard of living. But electricity generation alsocan damage the environment. Whether such damageoccurs depends largely on how electricity is generated.For example, burning coal releases twice asmuch carbon dioxide—a major contributor to globalwarming—as does burning an equivalent amount ofnatural gas (see About the data for table 3.9). Nuclearenergy does not generate carbon dioxide emissions,but it produces other dangerous waste products. Thetable provides information on electricity productionby source.The International Energy Agency (IEA) compilesdata on energy inputs used to generate electricity.IEA data for countries that are not members of theOrganisation for Economic Co-operation and Development(OECD) are based on national energy dataadjusted to conform to annual questionnaires completedby OECD member governments. In addition,estimates are sometimes made to complete majoraggregates from which key data are missing, andadjustments are made to compensate for differencesin defi nitions. The IEA makes these estimatesin consultation with national statistical offi ces, oilcompanies, electric utilities, and national energyexperts. It occasionally revises its time series torefl ect political changes. For example, the IEA hasconstructed historical energy statistics for countriesof the former Soviet Union. In addition, energy statisticsfor other countries have undergone continuouschanges in coverage or methodology in recent yearsas more detailed energy accounts have becomeavailable. Breaks in series are therefore unavoidable.Data on access to electricity are collected by theIEA from industry, national surveys, and internationalsources.• Electricity production is measured at the terminalsof all alternator sets in a station. In addition tohydropower, coal, oil, gas, and nuclear power generation,it covers generation by geothermal, solar,wind, and tide and wave energy as well as that fromcombustible renewables and waste. Productionincludes the output of electric plants designed toproduce electricity only, as well as that of combinedheat and power plants. • Sources of electricity arethe inputs used to generate electricity: coal, gas,oil, hydropower, and nuclear power. • Coal is all coaland brown coal, both primary (including hard coaland lignite-brown coal) and derived fuels (includingpatent fuel, coke oven coke, gas coke, coke ovengas, and blast furnace gas). Peat is also included inthis category. • Natural gas is natural gas but notnatural gas liquids. • Oil is crude oil and petroleumproducts. • Hydropower is electricity produced byhydroelectric power plants. • Renewable sourcesare geothermal, solar photovoltaic, solar thermal,tide, wind, industrial waste, municipal waste, primarysolid biofuels, biogases, biogasoline, biodiesels,other liquid biofuels, nonspecifi ed primary biofuelsand waste, and charcoal. • Nuclear power is electricityproduced by nuclear power plants. • Access toelectricity is the percentage of the population withaccess to electricity.Data sourcesData on electricity production and sources arefrom the IEA’s electronic fi les and its annual publicationsEnergy Statistics of Non-OECD Countries,Energy Balances of Non-OECD Countries, EnergyStatistics of OECD Countries, and Energy Balancesof OECD Countries. Data on access to electricityare from the IEA’s World Energy Outlook (2011).2012 World Development Indicators 169

3.9Energy dependency and efficiencyand carbon dioxide emissionsNet energyimports aGDP per unit ofenergy useCarbon dioxideemissions% of energy use2005 PPP $per kilogramof oil equivalentTotalmillion metric tonsCarbon intensitykilograms perkilogram of oilequivalent energy usePer capitametric tonskilograms per2005 PPP $of GDP1990 2009 1990 2009 1990 2008 1990 2008 1990 2008 1990 2008Afghanistan .. .. .. .. 2.7 0.8 .. .. 0.1 0.0 .. 0.0Albania 8 27 4.8 13.8 7.5 4.2 2.8 2.0 2.3 1.3 0.6 0.2Algeria –351 –283 7.1 6.5 78.9 111.3 3.6 3.0 3.1 3.2 0.5 0.4Angola –387 –749 5.8 8.4 4.4 24.4 0.8 2.1 0.4 1.4 0.1 0.2Argentina –5 –9 5.3 7.2 112.6 192.4 2.4 2.5 3.4 4.8 0.5 0.4Armenia 98 68 1.4 5.7 3.7 5.5 0.9 1.9 1.1 1.8 0.7 0.3Australia –83 –137 4.7 5.7 287.3 399.2 3.3 3.1 16.8 18.6 0.7 0.5Austria 67 64 7.9 9.2 61.0 67.7 2.5 2.0 7.9 8.1 0.3 0.2Azerbaijan 21 –439 1.3 6.4 44.2 47.1 2.1 3.5 6.0 5.4 1.7 0.7Bahrain –209 –85 2.0 2.7 11.9 22.5 2.7 2.4 24.1 21.4 1.3 0.9Bangladesh 16 16 6.2 7.0 15.5 46.5 1.2 1.7 0.1 0.3 0.2 0.2Belarus 93 85 1.4 4.1 87.5 62.8 2.4 2.2 8.6 6.5 1.5 0.6Belgium 73 73 5.2 6.1 108.5 104.9 2.2 1.8 10.9 9.8 0.4 0.3Benin –7 43 3.2 3.5 0.7 4.1 0.4 1.2 0.1 0.5 0.1 0.3Bolivia –89 –128 7.8 6.7 5.5 12.8 2.1 2.1 0.8 1.3 0.3 0.3Bosnia and Herzegovina 34 25 .. 4.6 3.9 31.3 0.9 5.2 1.0 8.3 .. 1.1Botswana 28 54 7.6 11.4 2.2 4.8 1.7 2.2 1.6 2.5 0.2 0.2Brazil 26 4 7.7 7.6 208.9 393.2 1.5 1.6 1.4 2.1 0.2 0.2Bulgaria 66 44 2.3 4.9 77.7 50.5 2.7 2.6 8.9 6.6 1.2 0.6Burkina Faso .. .. .. .. 0.6 1.9 .. .. 0.1 0.1 0.1 0.1Burundi .. .. .. .. 0.3 0.2 .. .. 0.1 0.0 0.1 0.1Cambodia .. 29 .. 5.1 0.5 4.6 .. 0.9 0.0 0.3 .. 0.2Cameroon –120 –28 5.1 5.6 1.7 5.3 0.3 0.8 0.1 0.3 0.1 0.1Canada –31 –53 3.6 4.6 450.1 544.1 2.2 2.0 16.2 16.3 0.6 0.5Central African Republic .. .. .. .. 0.2 0.3 .. .. 0.1 0.1 0.1 0.1Chad .. .. .. .. 0.1 0.5 .. .. 0.0 0.0 0.0 0.0Chile 45 68 6.4 7.7 34.9 73.1 2.6 2.5 2.6 4.4 0.4 0.3China –3 8 1.4 3.7 2,460.7 7,031.9 2.9 3.3 2.2 5.3 2.0 0.9Hong Kong SAR, China 99 100 15.6 18.4 27.7 38.6 3.2 2.7 4.8 5.5 0.2 0.1Colombia –99 –211 8.4 11.8 57.3 67.7 2.4 2.2 1.7 1.5 0.3 0.2Congo, Dem. Rep. –2 –2 1.9 0.8 4.1 2.8 0.3 0.1 0.1 0.0 0.2 0.2Congo, Rep. –997 –989 10.7 10.1 1.2 1.9 1.5 1.5 0.5 0.5 0.1 0.1Costa Rica 49 45 9.5 9.5 3.0 8.0 1.5 1.6 1.0 1.8 0.2 0.2Côte d’Ivoire 22 –15 5.5 3.2 5.8 7.0 1.3 0.7 0.5 0.4 0.2 0.2Croatia 43 53 7.1 8.3 16.4 23.3 2.4 2.6 3.7 5.3 0.4 0.3Cuba 51 52 .. .. 33.3 31.4 1.7 3.0 3.2 2.8 .. ..Cyprus 100 97 7.7 8.2 4.7 8.6 3.4 3.3 6.1 7.9 0.4 0.4Czech Republic 17 26 3.4 5.5 139.5 117.0 3.2 2.6 13.5 11.2 0.9 0.5Denmark 42 –29 7.5 9.5 50.4 46.0 2.9 2.4 9.8 8.4 0.4 0.2Dominican Republic 75 77 6.7 9.5 9.6 21.6 2.3 2.7 1.3 2.2 0.3 0.3Ecuador –175 –141 9.3 8.9 16.8 26.8 2.8 2.4 1.6 1.9 0.3 0.3Egypt, Arab Rep. –72 –22 5.8 5.9 75.9 210.3 2.4 3.0 1.3 2.7 0.4 0.5El Salvador 31 38 7.9 7.2 2.6 6.1 1.1 1.2 0.5 1.0 0.1 0.2Eritrea 19 23 1.8 3.5 .. 0.4 .. 0.6 .. 0.1 .. 0.2Estonia 45 12 1.6 4.5 23.0 18.3 3.5 3.4 15.0 13.6 2.0 0.7Ethiopia 5 7 1.8 2.2 3.0 7.1 0.2 0.2 0.1 0.1 0.1 0.1Finland 57 50 4.1 4.9 50.9 56.5 1.8 1.6 10.2 10.6 0.4 0.3France 50 49 6.3 7.4 399.0 377.0 1.8 1.4 6.9 5.9 0.3 0.2Gabon –1,138 –657 11.8 10.7 4.8 2.5 4.1 1.2 5.2 1.7 0.3 0.1Gambia, The .. .. 17.6 14.0 0.2 0.4 3.1 3.0 0.2 0.3 0.2 0.2Georgia 83 61 2.4 6.0 15.3 5.2 1.8 1.7 3.1 1.2 1.2 0.3Germany 47 60 5.9 8.3 961.0 786.7 2.8 2.4 12.0 9.6 0.4 0.3Ghana 17 24 2.5 3.6 3.9 8.6 0.7 0.9 0.3 0.4 0.3 0.3Greece 57 66 8.2 9.6 72.7 97.8 3.4 3.2 7.2 8.7 0.4 0.3Guatemala 24 39 6.7 6.1 5.1 11.9 1.1 1.5 0.6 0.9 0.2 0.2Guinea .. .. .. .. 1.1 1.4 .. .. 0.2 0.1 0.2 0.1Guinea-Bissau .. .. 16.5 15.4 0.3 0.3 3.4 3.0 0.2 0.2 0.2 0.2Haiti 20 28 6.4 4.0 1.0 2.4 0.6 0.9 0.1 0.3 0.1 0.21702012 World Development Indicators

Energy dependency and efficiencyand carbon dioxide emissions 3.9ENVIRONMENTNet energyimports aGDP per unit ofenergy useCarbon dioxideemissions% of energy use2005 PPP $per kilogramof oil equivalentTotalmillion metric tonsCarbon intensitykilograms perkilogram of oilequivalent energy usePer capitametric tonskilograms per2005 PPP $of GDP1990 2009 1990 2009 1990 2008 1990 2008 1990 2008 1990 2008Honduras 29 50 5.5 5.9 2.6 8.7 1.1 1.9 0.5 1.2 0.2 0.3Hungary 49 56 4.7 6.7 63.0 54.6 2.2 2.1 6.1 5.4 0.5 0.3India 8 26 3.3 5.1 690.6 1,742.7 2.2 2.8 0.8 1.5 0.7 0.5Indonesia –67 –74 3.7 4.3 149.6 406.0 1.5 2.1 0.8 1.7 0.4 0.5Iran, Islamic Rep. –164 –62 5.0 3.5 227.2 538.4 3.3 2.6 4.1 7.4 0.7 0.7Iraq –480 –272 .. 3.2 52.6 102.9 2.9 3.0 2.9 3.4 .. 1.1Ireland 65 89 6.4 11.3 30.4 43.6 3.0 2.9 8.7 9.9 0.5 0.3Israel 96 85 7.3 8.8 33.5 37.7 2.9 1.7 7.2 5.2 0.4 0.2Italy 83 84 9.2 9.8 424.2 445.1 2.9 2.5 7.5 7.4 0.3 0.3Jamaica 83 84 5.1 5.7 8.0 12.2 2.9 2.9 3.3 4.5 0.6 0.6Japan 83 80 7.3 7.9 1,094.7 1,208.2 2.5 2.4 8.9 9.5 0.3 0.3Jordan 95 96 3.2 4.1 10.4 21.4 3.2 3.0 3.3 3.7 1.0 0.7Kazakhstan –24 –121 1.6 2.5 261.3 237.0 3.3 3.4 15.9 15.1 2.7 1.4Kenya 18 17 3.0 3.0 5.8 10.4 0.5 0.6 0.2 0.3 0.2 0.2Korea, Dem. Rep. 13 –5 .. .. .. 78.4 .. 3.9 .. 3.2 .. ..Korea, Rep. 76 81 5.2 5.4 243.8 509.2 2.6 2.2 5.7 10.5 0.5 0.4Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait –453 –332 2.5 4.6 45.4 76.7 5.0 2.8 21.8 30.1 0.6 0.6Kyrgyz Republic 67 61 1.5 3.7 10.9 6.2 2.2 2.3 2.4 1.2 1.2 0.6Lao PDR .. .. .. .. 0.2 1.5 .. .. 0.1 0.3 0.1 0.1Latvia 86 50 3.4 6.9 14.1 7.6 2.3 1.7 5.4 3.3 0.9 0.2Lebanon 93 97 8.7 7.5 9.1 17.1 4.7 3.2 3.1 4.1 0.5 0.4Lesotho .. .. .. 142.1 .. .. .. .. .. .. .. ..Liberia .. .. .. .. 0.5 0.6 .. .. 0.2 0.2 0.5 0.4Libya –546 –327 .. 4.7 40.3 58.3 3.6 3.0 9.3 9.5 .. 0.6Lithuania 69 50 2.9 6.0 22.2 15.1 2.0 1.6 6.0 4.5 0.6 0.3Macedonia, FYR 49 42 6.6 6.7 10.8 11.8 4.0 3.9 5.6 5.8 0.8 0.6Madagascar .. .. .. .. 1.0 1.9 .. .. 0.1 0.1 0.1 0.1Malawi .. .. .. .. 0.6 1.2 .. .. 0.1 0.1 0.1 0.1Malaysia –122 –34 5.5 5.2 56.6 208.3 2.6 2.9 3.1 7.6 0.5 0.6Mali .. .. .. .. 0.4 0.6 .. .. 0.0 0.0 0.1 0.0Mauritania .. .. .. .. 2.7 2.0 .. .. 1.3 0.6 0.8 0.3Mauritius .. .. 13.5 11.6 1.5 4.0 3.0 3.3 1.4 3.1 0.2 0.3Mexico –59 –26 6.9 7.7 325.6 475.8 2.7 2.6 3.9 4.3 0.4 0.3Moldova 99 96 1.7 3.8 21.0 4.8 3.1 1.5 5.7 1.3 2.1 0.5Mongolia 20 –138 1.6 2.9 10.0 10.9 2.9 3.5 4.6 4.1 1.9 1.1Morocco 89 95 9.7 8.8 23.5 47.9 3.4 3.2 1.0 1.5 0.4 0.4Mozambique 5 –22 0.9 1.9 1.0 2.3 0.2 0.2 0.1 0.1 0.2 0.1Myanmar 0 –48 1.3 5.0 4.3 12.8 0.4 0.8 0.1 0.3 0.3 0.2Namibia 67 81 9.4 7.4 0.0 4.0 0.0 2.2 0.0 1.8 0.0 0.3Nepal 5 11 2.3 3.1 0.6 3.5 0.1 0.4 0.0 0.1 0.0 0.1Netherlands 8 19 6.0 7.7 164.1 173.7 2.5 2.2 11.0 10.6 0.4 0.3New Zealand 11 13 5.0 6.1 24.0 33.1 1.9 1.9 7.0 7.8 0.4 0.3Nicaragua 29 45 3.7 4.6 2.6 4.3 1.3 1.4 0.6 0.8 0.3 0.3Niger .. .. .. .. 0.8 0.9 .. .. 0.1 0.1 0.2 0.1Nigeria –113 –111 2.0 2.9 45.4 95.8 0.6 0.9 0.5 0.6 0.3 0.3Norway –467 –656 6.5 8.1 31.3 50.0 1.5 1.7 7.4 10.5 0.2 0.2Oman –808 –346 6.5 4.4 10.4 45.7 2.5 2.8 5.5 17.3 0.4 0.7Pakistan 20 24 4.2 4.7 68.6 163.2 1.6 2.0 0.6 1.0 0.4 0.4Panama 58 79 10.1 13.2 3.1 6.9 2.2 2.5 1.3 2.0 0.2 0.2Papua New Guinea .. .. .. .. 2.1 2.1 .. .. 0.5 0.3 0.3 0.2Paraguay –49 –56 5.5 5.5 2.3 4.1 0.7 0.9 0.5 0.7 0.1 0.2Peru –9 4 10.0 14.4 21.2 40.5 2.2 2.7 1.0 1.4 0.2 0.2Philippines 40 40 5.4 7.9 44.5 83.2 1.5 2.1 0.7 0.9 0.3 0.3Poland –1 28 3.0 6.8 366.8 316.1 3.6 3.2 9.6 8.3 1.2 0.5Portugal 80 80 9.6 9.4 43.7 56.3 2.6 2.3 4.4 5.3 0.3 0.2Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar –328 –487 .. 4.9 11.8 68.5 1.9 3.0 24.9 49.1 .. 0.62012 World Development Indicators 171

3.9Energy dependency and efficiencyand carbon dioxide emissionsNet energyimports aGDP per unit ofenergy useCarbon dioxideemissions% of energy use2005 PPP $per kilogramof oil equivalentTotalmillion metric tonsCarbon intensitykilograms perkilogram of oilequivalent energy usePer capitametric tonskilograms per2005 PPP $of GDP1990 2009 1990 2009 1990 2008 1990 2008 1990 2008 1990 2008Romania 34 18 2.9 6.7 158.9 94.7 2.6 2.4 6.8 4.4 0.9 0.4Russian Federation –47 –83 2.1 3.0 2,220.7 1,708.7 2.8 2.5 14.9 12.0 1.5 0.8Rwanda .. .. .. .. 0.7 0.7 .. .. 0.1 0.1 0.1 0.1Saudi Arabia –519 –235 5.2 3.4 215.1 433.6 3.6 2.8 13.3 16.6 0.7 0.8Senegal 43 57 6.3 7.1 3.2 5.0 1.9 1.7 0.4 0.4 0.3 0.2Serbia 31 35 4.6 4.8 45.3 b 49.9 3.0 b 3.0 5.9 b 6.8 0.8 b 0.7Sierra Leone .. .. .. .. 0.4 1.3 .. .. 0.1 0.2 0.1 0.3Singapore 100 100 6.7 12.5 46.9 32.3 4.1 1.9 15.4 6.7 0.6 0.1Slovak Republic 75 65 3.2 6.3 45.6 37.6 2.5 2.1 8.6 6.9 0.9 0.3Slovenia 46 49 5.8 7.3 13.0 17.2 2.5 2.2 6.5 8.5 0.5 0.3Somalia .. .. .. .. 0.0 0.6 .. .. 0.0 0.1 .. ..South Africa –22 –12 3.0 3.2 333.5 435.9 3.6 2.9 9.5 8.9 1.2 0.9South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 62 77 8.5 9.8 227.6 329.3 2.5 2.4 5.9 7.2 0.3 0.3Sri Lanka 24 45 6.3 9.5 3.8 11.8 0.7 1.3 0.2 0.6 0.1 0.1Sudan 17 –123 2.6 5.3 5.6 14.1 0.5 1.0 0.2 0.3 0.2 0.2Swaziland .. .. 10.9 12.6 0.4 1.1 1.4 2.5 0.5 1.1 0.1 0.2Sweden 37 33 4.5 6.6 51.7 49.0 1.1 1.0 6.0 5.3 0.2 0.2Switzerland 58 53 9.2 10.6 43.0 40.4 1.8 1.5 6.4 5.3 0.2 0.1Syrian Arab Republic –113 –5 3.5 4.2 37.5 71.6 3.6 2.8 3.0 3.6 1.0 0.8Tajikistan 62 35 3.0 5.5 7.2 3.1 1.7 1.3 1.3 0.5 0.7 0.3Tanzania 7 8 2.2 2.7 2.4 6.5 0.2 0.3 0.1 0.2 0.1 0.1Thailand 37 40 5.4 4.8 95.8 285.7 2.3 2.7 1.7 4.2 0.4 0.6Timor-Leste .. .. .. 11.8 .. 0.2 .. 3.0 .. 0.2 .. 0.2Togo 17 17 2.7 2.0 0.8 1.4 0.6 0.6 0.2 0.2 0.2 0.3Trinidad and Tobago –111 –117 2.2 1.5 17.0 49.8 2.8 2.6 14.0 37.4 1.3 1.6Tunisia –16 15 7.4 9.5 13.3 25.0 2.7 2.7 1.6 2.4 0.4 0.3Turkey 51 69 8.3 8.6 150.8 284.0 2.9 2.9 2.8 4.0 0.3 0.3Turkmenistan –281 –109 0.7 1.7 28.1 47.8 2.5 2.1 7.2 9.7 2.3 1.5Uganda .. .. .. .. 0.8 3.7 .. .. 0.0 0.1 0.1 0.1Ukraine 46 33 1.7 2.3 641.7 323.5 2.9 2.4 12.3 7.0 1.9 1.0United Arab Emirates –441 –183 6.5 5.3 52.0 155.1 2.6 2.7 28.8 25.0 0.4 0.5United Kingdom –1 19 6.2 10.1 570.2 522.9 2.8 2.5 10.0 8.5 0.4 0.3United States 14 22 4.2 5.9 4,879.4 5,461.0 2.5 2.4 19.5 18.0 0.6 0.4Uruguay 49 63 10.1 9.6 4.0 8.3 1.8 2.0 1.3 2.5 0.2 0.2Uzbekistan 17 –24 0.9 1.5 114.0 124.9 2.5 2.5 5.3 4.6 3.1 1.9Venezuela, RB –242 –204 4.3 4.8 122.2 169.5 2.8 2.6 6.2 6.1 0.6 0.5Vietnam –2 –20 2.5 3.7 21.4 127.4 0.9 2.2 0.3 1.5 0.4 0.6West Bank and Gaza .. .. .. .. .. 2.1 .. .. .. 0.5 .. ..Yemen, Rep. –273 –101 8.6 7.0 9.6 23.4 3.8 3.2 0.8 1.0 0.4 0.5Zambia 9 8 1.8 2.1 2.4 1.9 0.5 0.2 0.3 0.2 0.2 0.1Zimbabwe 8 10 .. .. 15.5 9.1 1.7 1.0 1.5 0.7 .. ..World –3 c w –4 c w 4.2 w 5.5 w 22,310.9 d w32,082.6 d w 2.5 d w 2.5 d w 4.2 d w 4.8 d w 0.6 d w 0.5 d wLow income 14 5 2.6 3.4 .. 219.1 0.6 1.0 .. 0.3 .. 0.3Middle income –24 –19 3.0 4.5 9,256.8 16,638.0 2.5 2.8 2.4 3.4 0.8 0.6Lower middle income –15 –17 3.0 4.6 2,001.1 3,743.9 1.8 2.4 1.1 1.5 0.6 0.5Upper middle income –27 –20 3.0 4.4 7,255.3 12,894.1 2.8 2.9 3.6 5.3 0.9 0.7Low & middle income –22 –19 3.0 4.4 9,398.8 16,856.9 2.4 2.7 2.2 3.0 0.8 0.6East Asia & Pacifi c –8 1 2.0 3.9 2,895.4 8,259.3 2.6 3.1 1.8 4.3 1.3 0.8Europe & Central Asia –12 –47 2.2 3.6 3,893.0 3,130.1 3.0 2.6 9.8 7.8 1.3 0.7Latin America & Carib. –34 –31 6.9 7.7 986.2 1,584.1 2.2 2.2 2.3 2.8 0.3 0.3Middle East & N. Africa –204 –88 5.7 4.7 579.0 1,230.3 3.2 2.8 2.6 3.8 0.6 0.6South Asia 10 25 3.6 5.2 782.0 1,970.2 2.0 2.6 0.7 1.2 0.6 0.5Sub-Saharan Africa –52 –59 2.8 3.2 464.1 684.6 1.7 1.5 0.9 0.8 0.6 0.4High income 15 13 5.3 6.7 11,572.3 13,284.7 2.5 2.4 11.8 11.9 0.5 0.4Euro area 55 62 6.6 8.3 2,718.9 2,633.3 2.5 2.1 8.9 8.0 0.4 0.3a. A negative value indicates that a country is a net exporter. b. Includes Kosovo and Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includesemissions not allocated to specifi c countries.1722012 World Development Indicators

Energy dependency and efficiencyand carbon dioxide emissions3.9ENVIRONMENTAbout the dataDefinitionsBecause commercial energy is widely traded, its productionand use need to be distinguished. Net energyimports show the extent to which an economy’s useexceeds its production. High-income economies arenet energy importers; middle-income economies aretheir main suppliers.The ratio of gross domestic product (GDP) to energyuse indicates energy effi ciency. To produce comparableand consistent estimates of real GDP acrosseconomies relative to physical inputs to GDP—thatis, units of energy use—GDP is converted to 2005international dollars using purchasing power parity(PPP) rates. Differences in this ratio over time andacross economies refl ect structural changes in aneconomy, changes in sectoral energy effi ciency, anddifferences in fuel mixes.Carbon dioxide emissions, largely by-products ofenergy production and use (see table 3.7), accountfor the largest share of greenhouse gases, which areassociated with global warming. Anthropogenic carbondioxide emissions result primarily from fossil fuelcombustion and cement manufacturing. In combustiondifferent fossil fuels release different amountsof carbon dioxide for the same level of energy use:oil releases about 50 percent more carbon dioxidethan natural gas, and coal releases about twice asmuch. Cement manufacturing releases about half ametric ton of carbon dioxide for each metric ton ofcement produced. Carbon intensity is the ratio ofcarbon dioxide per unit of energy, or the amount ofcarbon dioxide emitted as a result of using one unitof energy in production. Kilograms per 2005 PPPdollars of GDP shows the share of carbon dioxideper unit of GDP, a measure of how clean productionprocesses are.The U.S. Department of Energy’s Carbon DioxideInformation Analysis Center (CDIAC) calculatesannual anthropogenic emissions from data on fossilfuel consumption (from the United Nations StatisticsDivision’s World Energy Data Set) and world cementmanufacturing (from the U.S. Bureau of MinesCement Manufacturing Data Set). Carbon dioxideemissions are often calculated and reported aselemental carbon. The values in the table were convertedto actual carbon dioxide mass by multiplyingthem by 3.667 (the ratio of the mass of carbon tothat of carbon dioxide). Although estimates of globalcarbon dioxide emissions are probably accuratewithin 10 percent (as calculated from global averagefuel chemistry and use), country estimates mayhave larger error bounds. Trends estimated from aconsistent time series tend to be more accurate thanindividual values. Each year the CDIAC recalculatesthe entire time series since 1949, incorporatingrecent fi ndings and corrections. Estimates excludefuels supplied to ships and aircraft in internationaltransport because of the diffi culty of apportioningthe fuels among benefi ting countries.• Net energy imports are estimated as energy useless production, both measured in oil equivalents.• GDP per unit of energy use is the ratio of grossdomestic product (GDP) per kilogram of oil equivalentof energy use, with GDP converted to 2005international dollars using purchasing power parity(PPP) rates. An international dollar has the samepurchasing power over GDP that a U.S. dollar hasin the United States. Energy use refers to the useof primary energy before transformation to otherend-use fuel, which is equal to indigenous productionplus imports and stock changes minus exportsand fuel supplied to ships and aircraft engaged ininternational transport (see About the data for table3.7). • Carbon dioxide emissions are emissions fromthe burning of fossil fuels and the manufacture ofcement and include carbon dioxide produced duringconsumption of solid, liquid, and gas fuels andgas fl aring.Data sourcesData on energy use are from the electronic fi lesof the International Energy Agency. Data on carbondioxide emissions are from the CDIAC, EnvironmentalSciences Division, Oak Ridge NationalLaboratory, Tennessee, United States.2012 World Development Indicators 173

3.10Trends in greenhouse gas emissionsCarbon dioxideemissionsMethaneemissionsNitrous oxideemissionsOther greenhousegas emissionsTotalaverageannual% growth a % change b1990–20081990–2008thousandTotalthousandTotalthousanddioxide% change b From energydioxide% change b dioxideequivalentprocesses Agricultural equivalentprocesses Agricultural equivalent1990–1990–metric tonsof carbon% of totalmetric tonsof carbonFrommetric tonsof carbon2005 2005 2005 2005 2005 2005 2005 2005 2005% change b1990–2005Afghanistan –6.9 –69.6 .. .. .. .. .. .. .. .. .. ..Albania 2.5 –44.2 2,407 –5.1 20.0 70.8 1,036 –18.7 7.1 78.4 62 ..Algeria 1.5 41.1 54,219 33.1 83.2 8.2 4,898 27.5 22.6 58.6 489 50.0Angola 10.4 450.2 45,409 –8.3 15.6 27.9 38,881 –6.7 0.4 38.4 20 ..Argentina 2.6 70.8 101,821 –8.3 18.9 70.6 49,821 29.6 3.9 89.2 785 –65.8Armenia 3.0 50.7 2,962 2.5 50.8 36.7 580 –27.6 1.2 81.6 335 ..Australia 1.7 38.9 126,488 9.7 29.7 55.1 62,966 –0.1 10.3 78.2 6,505 33.5Austria 1.1 11.1 8,515 –15.0 21.7 48.6 4,448 –13.5 31.0 52.5 2,329 46.2Azerbaijan 0.0 6.7 36,607 110.7 82.0 13.6 2,633 0.4 8.3 77.5 89 –49.4Bahrain 3.0 89.1 2,766 54.9 88.7 0.6 82 70.8 39.7 16.0 279 –89.0Bangladesh 6.7 199.5 92,414 6.5 10.0 70.5 21,386 42.1 7.5 83.1 0 ..Belarus –1.3 –28.2 11,498 –32.8 7.6 70.9 11,680 –28.3 23.1 72.9 467 ..Belgium –0.4 –3.3 10,063 –21.8 11.6 56.7 6,571 –27.6 38.1 44.3 2,106 583.8Benin 9.3 468.7 4,080 –15.8 15.6 47.8 2,902 –21.5 4.0 61.5 0 ..Bolivia 3.6 133.2 30,350 30.9 25.6 34.1 15,092 3.2 0.7 36.5 0 ..Bosnia and Herzegovina 16.6 694.1 2,741 –53.5 46.7 42.4 1,196 –40.8 24.7 57.8 571 –7.5Botswana 3.7 122.2 4,501 –22.6 8.6 84.1 3,081 –44.1 1.4 92.0 0 ..Brazil 3.3 88.2 492,160 56.4 7.6 61.1 235,987 52.6 3.4 67.0 11,816 40.5Bulgaria –1.9 –35.0 10,867 –24.8 13.0 18.9 4,227 –55.2 36.0 48.1 383 ..Burkina Faso 6.1 216.3 .. .. .. .. .. .. .. .. .. ..Burundi –4.5 –41.0 .. .. .. .. .. .. .. .. .. ..Cambodia 15.3 920.4 20,215 35.0 4.9 76.1 5,794 46.9 3.5 66.1 0 ..Cameroon 4.1 205.1 18,518 37.1 39.1 42.4 9,127 –13.3 2.6 75.9 419 –55.0Canada 1.4 20.9 89,338 30.8 32.2 29.3 40,171 –5.5 23.7 58.9 21,943 69.7Central African Republic 1.0 31.5 .. .. .. .. .. .. .. .. .. ..Chad 11.0 237.4 .. .. .. .. .. .. .. .. .. ..Chile 4.3 109.5 18,149 49.8 24.4 39.4 8,135 57.5 16.6 73.4 13 –31.6China 5.7 185.8 1,333,098 28.5 45.8 38.8 467,213 48.5 12.9 74.3 141,394 1,073.0Hong Kong SAR, China 1.9 39.5 2,820 84.1 26.7 .. 422 –0.9 38.5 0.0 119 –68.6Colombia 0.0 18.1 58,108 13.5 19.9 68.0 21,288 5.2 4.4 86.1 83 97.6Congo, Dem. Rep. –3.1 –30.8 56,445 –41.6 10.2 23.1 54,643 –37.3 2.2 31.3 0 ..Congo, Rep. –0.1 63.0 5,584 –10.4 32.2 31.9 3,566 –17.2 1.0 51.8 5 ..Costa Rica 5.0 171.2 2,580 –31.2 9.5 67.2 1,334 –26.2 4.5 85.4 62 ..Côte d’Ivoire 1.4 21.0 10,997 –2.2 16.9 17.4 7,364 –1.6 2.7 29.3 0 ..Croatia 2.6 41.7 3,864 –60.5 57.0 33.3 2,851 –24.5 36.6 52.4 59 –93.4Cuba –0.8 –5.8 9,455 –21.0 11.2 62.4 6,356 –31.8 15.1 78.7 129 ..Cyprus 3.1 83.8 616 40.0 1.8 44.0 292 19.2 13.0 65.5 190 ..Czech Republic –0.7 –16.1 11,497 –40.3 49.4 33.6 8,878 –10.2 53.0 36.9 1,121 ..Denmark –1.0 –8.7 7,935 –0.5 16.4 65.2 6,290 –21.5 18.0 73.4 1,422 457.6Dominican Republic 4.5 125.9 6,081 3.8 7.8 63.7 2,255 11.0 7.8 76.8 0 ..Ecuador 3.1 59.3 17,125 31.2 31.2 57.7 4,571 42.2 3.8 84.9 63 ..Egypt, Arab Rep. 5.9 176.9 46,996 68.8 50.7 31.7 18,996 60.7 8.3 80.0 3,181 54.5El Salvador 4.4 133.5 3,131 18.0 12.4 53.1 1,377 7.7 8.2 76.2 77 ..Eritrea 5.4 .. 2,467 30.9 11.2 73.2 1,189 15.7 3.8 90.9 0 ..Estonia –0.6 –20.6 2,108 –36.8 42.3 30.5 932 –50.7 21.5 60.5 40 1,900.0Ethiopia 4.7 135.5 52,243 32.8 14.3 72.5 30,510 19.4 5.2 88.8 10 ..Finland 1.1 10.9 9,742 –2.8 7.4 20.7 7,124 –4.1 42.8 41.7 826 726.0France –0.3 –5.5 77,252 –0.3 44.3 47.7 49,058 –30.6 24.2 66.8 15,539 57.1Gabon –3.6 –49.0 8,218 1.4 90.4 1.1 482 58.0 10.0 23.3 9 ..Gambia, The 4.4 115.4 .. .. .. .. .. .. .. .. .. ..Georgia –2.6 –66.1 4,410 –12.4 36.1 50.8 2,019 –26.8 35.5 56.9 12 ..Germany –1.1 –18.1 67,582 –44.8 32.1 43.8 56,560 –23.9 38.2 52.2 31,543 8.1Ghana 4.7 118.6 8,990 24.2 23.3 39.5 4,899 –5.6 9.3 70.5 15 –97.5Greece 2.0 34.5 7,289 2.0 26.3 50.0 5,977 –17.1 22.1 58.2 1,842 –20.8Guatemala 5.6 134.2 8,306 74.7 12.4 48.8 5,376 121.1 5.5 56.8 481 ..Guinea 1.5 31.9 .. .. .. .. .. .. .. .. .. ..Guinea-Bissau –0.2 11.6 .. .. .. .. .. .. .. .. .. ..Haiti 7.6 145.0 4,006 34.8 12.1 56.2 1,438 59.6 6.2 84.2 0 ..1742012 World Development Indicators

Trends in greenhouse gas emissions3.10ENVIRONMENTCarbon dioxideemissionsMethaneemissionsNitrous oxideemissionsOther greenhousegas emissionsTotalaverageannual% growth a % change b1990–20081990–2008thousandTotalthousandTotalthousanddioxide% change b From energydioxide% change b dioxideequivalentprocesses Agricultural equivalentprocesses Agricultural equivalent1990–1990–metric tonsof carbon% of totalmetric tonsof carbonFrommetric tonsof carbon2005 2005 2005 2005 2005 2005 2005 2005 2005% change b1990–2005Honduras 7.4 234.5 5,191 31.5 7.2 78.4 2,865 26.0 3.8 85.9 0 ..Hungary –0.6 –13.3 7,767 –22.9 29.1 33.6 6,961 –31.2 30.9 60.1 1,552 121.1India 4.8 152.4 583,978 10.5 15.9 64.4 212,927 33.3 12.8 73.4 8,433 –11.9Indonesia 4.5 171.5 208,944 18.4 25.5 46.4 123,275 43.5 3.7 71.5 1,027 –40.5Iran, Islamic Rep. 5.0 137.0 114,585 32.5 70.6 18.2 26,644 41.1 11.4 75.3 2,569 –2.9Iraq 3.7 95.9 15,937 –45.8 58.4 18.6 3,440 –9.9 9.7 63.3 86 –66.0Ireland 2.4 43.4 15,331 14.3 11.9 76.7 7,486 –8.3 4.4 90.5 1,151 3,097.2Israel 1.6 12.3 3,517 83.8 18.4 31.2 1,793 41.5 15.3 53.0 1,981 88.8Italy 0.6 4.9 40,790 –13.4 14.7 39.8 28,620 –5.4 39.1 43.7 13,968 211.2Jamaica 2.4 53.2 1,302 14.4 11.4 50.3 599 29.4 12.1 59.0 51 ..Japan 0.7 10.4 42,771 –36.5 8.1 71.2 29,785 –17.0 41.6 27.9 53,786 81.1Jordan 4.2 105.5 1,796 111.5 25.0 21.8 667 39.5 8.2 55.4 112 ..Kazakhstan 0.4 –9.3 47,119 –27.3 66.2 25.3 17,594 –46.2 12.8 62.5 339 ..Kenya 3.0 78.5 22,130 23.3 16.9 65.5 10,542 14.3 5.0 88.8 0 ..Korea, Dem. Rep. 1.2 .. 18,195 –15.0 58.6 23.5 3,422 –60.6 13.2 62.3 2,794 ..Korea, Rep. 3.7 108.8 32,069 2.4 19.9 38.6 13,548 34.7 41.3 35.9 10,221 66.0Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 7.4 69.0 14,380 119.4 93.4 1.1 650 156.9 27.7 16.9 931 254.0Kyrgyz Republic –1.7 –42.8 3,591 –38.2 6.8 72.3 1,510 –57.7 11.2 72.6 24 ..Lao PDR 13.1 553.1 .. .. .. .. .. .. .. .. .. ..Latvia –3.3 –46.0 3,108 –42.1 53.6 27.7 1,253 –58.7 11.6 77.4 890 ..Lebanon 3.0 87.9 1,003 46.6 9.7 25.5 672 79.2 12.6 58.8 0 ..Lesotho .. .. .. .. .. .. .. .. .. .. .. ..Liberia 5.0 25.8 .. .. .. .. .. .. .. .. .. ..Libya 1.8 44.7 14,682 –34.7 86.3 5.7 1,285 9.3 11.2 51.9 280 –0.7Lithuania –1.8 –31.7 5,516 –34.1 32.0 33.8 2,451 –45.7 5.0 86.0 656 ..Macedonia, FYR 0.4 9.6 1,403 –36.5 32.1 46.6 599 –33.9 15.9 63.9 120 ..Madagascar 3.4 93.7 .. .. .. .. .. .. .. .. .. ..Malawi 3.1 100.6 .. .. .. .. .. .. .. .. .. ..Malaysia 6.3 268.0 46,501 64.7 69.3 12.4 15,087 13.5 6.7 64.9 994 66.2Mali 1.9 40.9 .. .. .. .. .. .. .. .. .. ..Mauritania –3.9 –25.0 .. .. .. .. .. .. .. .. .. ..Mauritius 6.1 170.2 .. .. .. .. .. .. .. .. .. ..Mexico 1.9 46.1 128,209 26.3 40.2 42.3 42,514 8.9 10.6 75.2 4,555 53.1Moldova –8.5 –77.2 3,372 –17.5 45.2 29.4 849 –51.0 5.5 73.5 8 ..Mongolia –0.2 8.5 6,067 –25.9 2.5 92.1 3,489 –30.0 2.2 93.2 0 ..Morocco 3.7 103.5 10,573 15.8 8.0 51.7 5,814 12.2 3.0 82.6 0 ..Mozambique 5.4 131.1 12,843 18.2 22.7 44.2 9,501 –12.7 3.4 71.4 282 ..Myanmar 6.6 198.8 77,211 –7.4 12.6 69.0 30,932 –23.9 2.6 42.9 0 ..Namibia 39.5 .. 5,057 47.2 0.3 94.9 3,797 47.2 1.1 94.3 0 ..Nepal 7.6 458.4 22,142 9.7 5.9 82.9 4,516 26.0 13.0 76.8 0 ..Netherlands 0.0 5.9 21,259 –30.4 23.4 43.4 14,596 –10.7 52.5 39.5 3,750 –40.9New Zealand 2.0 37.8 27,635 3.6 3.6 90.2 12,930 23.5 3.5 94.2 973 3.4Nicaragua 4.1 63.8 6,018 26.3 6.6 74.8 3,340 10.1 3.3 91.7 0 ..Niger –0.2 2.2 .. .. .. .. .. .. .. .. .. ..Nigeria 5.4 111.0 130,317 10.9 68.9 19.8 21,565 12.6 9.1 77.3 669 176.4Norway 2.7 59.6 16,870 47.2 74.6 12.6 4,737 –3.1 46.5 39.0 5,202 –39.4Oman 8.5 341.9 17,849 194.9 94.1 3.0 561 82.7 16.0 68.0 175 ..Pakistan 4.9 138.0 137,401 50.7 23.7 63.5 26,838 46.0 14.5 74.2 819 –18.8Panama 4.0 120.5 3,219 16.5 4.0 79.2 1,204 18.0 4.9 83.7 0 ..Papua New Guinea 3.9 –1.5 .. .. .. .. .. .. .. .. .. ..Paraguay 2.7 82.0 15,388 2.0 3.9 84.1 9,067 0.6 1.7 82.6 0 ..Peru 3.7 91.5 17,187 22.7 13.5 61.3 7,560 35.4 2.9 81.9 330 ..Philippines 3.2 86.7 51,889 28.6 9.3 63.7 12,950 34.0 9.1 73.1 365 125.3Poland –1.1 –13.8 70,023 –36.6 62.0 21.9 30,198 4.7 33.5 57.7 2,451 360.7Portugal 1.8 28.8 12,173 22.4 13.8 35.4 5,958 24.3 22.0 43.8 783 605.4Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar 6.8 481.6 15,706 387.2 96.5 0.4 200 104.1 33.9 25.0 0 ..2012 World Development Indicators 175

3.10 Trends in greenhouse gas emissionsCarbon dioxideemissionsMethaneemissionsNitrous oxideemissionsOther greenhousegas emissionsTotalaverageannual% growth a % change b1990–20081990–2008thousandTotalthousandTotalthousanddioxide% change b From energydioxide% change b dioxideequivalentprocesses Agricultural equivalentprocesses Agricultural equivalent1990–1990–metric tonsof carbon% of totalmetric tonsof carbonFrommetric tonsof carbon2005 2005 2005 2005 2005 2005 2005 2005 2005% change b1990–2005Romania –2.4 –40.4 24,331 –35.1 42.7 36.0 11,537 –44.0 32.4 56.2 746 –62.8Russian Federation –1.0 –23.1 562,801 –18.3 79.3 9.1 76,121 –48.7 27.8 44.3 59,673 130.6Rwanda 0.6 3.2 .. .. .. .. .. .. .. .. .. ..Saudi Arabia 2.8 101.6 48,152 67.4 83.6 4.0 6,501 17.5 14.0 46.1 2,193 –10.6Senegal 3.0 56.3 7,129 35.1 9.9 68.3 4,083 37.2 2.7 88.5 0 ..Serbia .. .. 7,782 –58.7 41.5 43.7 4,581 –8.8 24.2 63.6 4,493 353.8Sierra Leone 7.3 243.4 .. .. .. .. .. .. .. .. .. ..Singapore –2.1 –31.2 2,237 136.7 60.1 1.3 1,068 163.1 77.6 2.8 2,532 396.5Slovak Republic –0.9 –17.7 3,911 –39.7 18.2 39.0 3,354 –37.1 52.0 37.7 395 480.9Slovenia 1.6 32.3 3,498 0.6 30.7 32.1 1,156 –12.2 13.2 70.4 473 –38.5Somalia 35.9 3,447.0 .. .. .. .. .. .. .. .. .. ..South Africa 1.4 30.7 63,785 24.6 45.4 31.4 24,048 12.9 12.6 59.8 2,552 71.2South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 2.7 44.7 36,338 11.9 10.4 56.8 26,529 6.5 18.7 62.6 9,080 47.7Sri Lanka 7.1 211.8 10,210 –11.2 5.3 65.2 2,056 18.0 12.1 65.1 0 ..Sudan 7.1 152.8 67,441 55.5 7.1 85.2 49,472 34.9 1.3 92.6 0 ..Swaziland 9.2 156.9 .. .. .. .. .. .. .. .. .. ..Sweden –0.6 –5.1 11,311 1.3 9.9 28.1 5,865 –13.1 26.8 60.2 2,078 133.7Switzerland –0.3 –6.0 4,748 –17.1 19.8 67.6 2,415 –15.5 20.8 59.3 2,109 97.3Syrian Arab Republic 3.2 91.2 12,458 –10.8 53.8 28.1 5,509 33.4 9.0 78.1 0 ..Tajikistan –2.8 –56.4 3,898 –9.3 12.8 68.6 1,378 0.2 1.4 86.9 383 –86.4Tanzania 5.3 172.5 32,024 24.0 12.6 63.2 21,647 0.8 2.5 78.8 0 ..Thailand 5.6 198.2 83,257 5.7 16.9 66.0 22,304 15.1 21.7 65.5 1,104 –22.8Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo 3.5 83.4 2,889 5.0 23.5 39.8 1,738 –21.3 5.6 67.5 0 ..Trinidad and Tobago 4.7 193.5 10,070 32.0 83.9 0.7 230 12.2 11.5 60.3 0 ..Tunisia 3.3 88.5 8,160 106.2 55.6 25.5 2,366 17.9 21.4 66.4 0 ..Turkey 3.4 88.3 64,251 46.4 16.0 33.6 32,781 12.8 22.7 66.4 5,066 96.9Turkmenistan 3.3 70.4 27,984 –5.0 75.2 21.6 4,276 93.7 16.4 78.1 73 ..Uganda 8.7 358.3 .. .. .. .. .. .. .. .. .. ..Ukraine –3.1 –49.6 70,360 –42.2 62.1 23.3 26,097 –51.3 42.8 45.6 693 209.4United Arab Emirates 6.1 198.2 23,283 58.0 93.1 2.6 1,169 78.7 18.3 43.6 1,075 27.5United Kingdom –0.6 –8.3 65,788 –44.1 24.8 38.2 30,565 –44.7 24.8 60.0 10,403 96.7United States 0.7 11.9 548,074 –14.4 41.0 34.8 317,153 1.8 30.6 56.4 239,517 158.7Uruguay 2.3 108.5 19,589 24.1 1.5 94.3 7,017 16.1 1.4 96.9 59 ..Uzbekistan 0.6 9.6 39,602 24.0 57.3 33.7 10,003 9.4 6.2 84.2 608 ..Venezuela, RB 2.6 38.8 61,183 5.9 47.4 40.0 14,935 23.4 5.0 75.2 2,468 –24.0Vietnam 11.3 495.0 82,978 40.1 22.7 63.9 23,030 98.3 6.1 83.0 0 ..West Bank and Gaza 17.3 .. .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 4.7 143.7 6,677 73.5 17.0 54.9 3,250 57.4 11.2 72.5 0 ..Zambia –1.3 –22.8 19,294 –28.4 6.7 59.3 25,068 –29.7 2.6 71.7 0 ..Zimbabwe –3.4 –41.5 9,539 –5.7 11.4 73.3 6,114 –16.1 3.7 85.2 0 ..World 2.1 w 43.8 w 7,135,850 s 6.2 w 37.3 w 42.6 w 2,852,537 s 5.8 w 15.4 w 66.2 w 724,183 s 122.4 wLow income 3.0 .. 436,333 –3.0 13.7 61.2 209,159 –15.0 4.1 62.4 .. ..Middle income 3.0 79.7 5,160,313 13.6 38.8 42.6 1,830,347 17.0 10.6 70.8 260,798 207.4Lower middle income 3.1 87.1 1,704,859 11.4 27.2 52.4 686,537 17.2 8.8 71.1 17,327 3.3Upper middle income 3.0 77.7 3,455,454 14.7 44.6 37.8 1,143,810 16.9 11.7 70.6 243,471 257.7Low & middle income 3.0 79.4 5,596,645 12.1 36.9 44.1 2,039,506 12.7 10.0 70.0 264,291 201.6East Asia & Pacifi c 5.6 185.3 1,928,355 24.5 39.2 43.6 707,496 38.0 10.5 72.2 .. ..Europe & Central Asia –0.8 –19.6 936,608 –17.3 67.5 17.5 214,402 –38.9 25.3 56.8 75,692 114.6Latin America & Carib. 2.5 60.6 1,008,557 30.3 17.3 58.6 442,132 32.9 4.6 72.4 20,972 23.5Middle East & N. Africa 4.2 112.5 287,084 20.0 64.7 20.7 73,539 36.8 10.7 74.5 6,717 20.7South Asia 4.9 151.9 846,145 14.6 16.1 65.4 267,722 34.9 12.6 74.3 9,253 –12.5Sub-Saharan Africa 2.2 47.5 589,897 5.4 30.5 44.0 334,216 –7.6 3.7 66.1 .. ..High income 0.8 14.8 1,539,204 –10.8 38.9 37.1 813,031 –8.2 29.1 56.8 459,891 93.3Euro area 0.1 –3.1 317,704 –18.2 26.0 46.8 219,189 –18.1 31.7 55.4 84,230 37.2a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emissions since1990, which is the baseline for Kyoto Protocol requirements.1762012 World Development Indicators

Trends in greenhouse gas emissions 3.10ENVIRONMENTAbout the dataDefinitionsGreenhouse gases—which include carbon dioxide,methane, nitrous oxide, hydrofl uorocarbons, perflu o r o c a r b o n s , a n d s u l f u r h e x a fl u o r i d e — c o n t r i b u t eto climate change.Carbon dioxide emissions, largely a by-product ofenergy production and use (see table 3.7), accountfor the largest share of greenhouse gases. Anthropogeniccarbon dioxide emissions result primarilyfrom fossil fuel combustion and cement manufacturing.Burning oil releases more carbon dioxide thanburning natural gas, and burning coal releases evenmore for the same level of energy use. Cement manufacturingreleases about half a metric ton of carbondioxide for each metric ton of cement produced.Methane emissions result largely from agriculturalactivities, industrial production landfi lls and wastewatertreatment, and other sources such as tropicalforest and other vegetation fi res. The emissionsare usually expressed in carbon dioxide equivalentsusing the global warming potential, which allows theeffective contributions of different gases to be compared.A kilogram of methane is 21 times as effectiveat trapping heat in the earth’s atmosphere as akilogram of carbon dioxide within 100 years.Nitrous oxide emissions are mainly from fossil fuelcombustion, fertilizers, rainforest fi res, and animalwaste. Nitrous oxide is a powerful greenhouse gas,with an estimated atmospheric lifetime of 114 years,compared with 12 years for methane. The per kilogramglobal warming potential of nitrous oxide isnearly 310 times that of carbon dioxide within 100years.Other greenhouse gases covered under the KyotoProtocol are hydrofl uorocarbons, perfl uorocarbons,and sulfur hexafl uoride. Although emissions of theseartifi cial gases are small, they are more powerfulgreenhouse gases than carbon dioxide, with muchhigher atmospheric lifetimes and high global warmingpotential.For a discussion of carbon dioxide sources andthe methodology behind emissions calculation, seeAbout the data for table 3.9.• Carbon dioxide emissions are those from the burningof fossil fuels and the manufacture of cement andinclude carbon dioxide produced during consumptionof solid, liquid, and gas fuels and gas fl aring. • Methaneemissions are those from human activities suchas agriculture and from industrial methane production.• Methane emissions from energy processesare those from the production, handling, transmission,and combustion of fossil fuels and biofuels.• Agricultural methane emissions are those fromanimals, animal waste, rice production, agriculturalwaste burning (nonenergy, on-site), and savannahburning. • Nitrous oxide emissions are those fromagricultural biomass burning, industrial activities,and livestock management. • Nitrous oxide emissionsfrom energy processes are those producedby the combustion of fossil fuels and biofuels.• Agricultural nitrous oxide emissions are thoseproduced through fertilizer use (synthetic and animalmanure), animal waste management, agriculturalwaste burning (nonenergy, on-site), and savannahburning. • Other greenhouse gas emissions arethose of hydrofl uorocarbons (used as a replacementfor chlorofl uorocarbons, mainly in refrigeration andsemiconductor manufacturing), perfl uorocarbons (aby-product of aluminum smelting and uranium enrichment,used as a replacement for chlorofl uorocarbonsin semiconductor manufacturing), and sulfur hexafl u-oride (used to insulate high-voltage electricity powerequipment), all of which are to be curbed under theKyoto Protocol.Data sourcesData on carbon dioxide emissions are from theCarbon Dioxide Information Analysis Center, EnvironmentalSciences Division, Oak Ridge NationalLaboratory, Tennessee, United States. Data onmethane, nitrous oxide, and other greenhousegas emissions are compiled by the InternationalEnergy Agency.2012 World Development Indicators 177

3.11 Carbon dioxide emissions by sectorCarbon dioxide emissions% of total fuel combustionElectricity andheat productionManufacturing industriesand constructionResidential buildingsand commercial andpublic services Transport Other sectors1990 2008 1990 2008 1990 2008 1990 2008 1990 2008Afghanistan .. .. .. .. .. .. .. .. .. ..Albania 12.0 5.2 44.2 16.1 4.8 9.8 11.2 59.6 27.8 9.1Algeria 42.9 39.2 14.1 13.6 20.3 24.9 22.6 22.4 0.0 0.0Angola 12.5 3.7 46.1 24.1 15.7 21.7 25.2 50.3 0.7 0.3Argentina 34.0 34.9 16.2 22.2 17.2 12.9 28.1 24.6 4.5 5.3Armenia 29.6 19.6 22.3 37.6 30.9 0.0 14.3 15.8 2.9 26.8Australia 54.1 62.9 17.7 12.6 3.3 3.0 23.6 20.1 1.3 1.5Austria 35.0 34.1 17.9 18.1 20.7 14.7 24.2 31.9 2.2 1.2Azerbaijan 44.3 50.3 24.4 7.4 7.2 22.1 5.4 17.9 18.7 2.4Bahrain 56.2 54.2 34.3 31.1 1.0 0.9 8.5 13.8 0.0 0.0Bangladesh 32.7 43.7 33.3 24.5 16.2 11.9 12.2 14.2 5.7 5.7Belarus 47.4 52.9 28.1 19.9 10.3 12.4 8.1 10.2 6.2 4.6Belgium 28.9 25.4 28.4 24.7 22.6 23.5 18.5 24.4 1.5 1.9Benin 12.0 3.1 12.0 5.2 12.0 30.9 64.0 61.2 0.0 0.0Bolivia 29.0 34.8 11.6 17.2 10.5 10.6 39.3 36.0 9.5 1.5Bosnia and Herzegovina 40.6 69.5 26.4 6.6 1.2 0.6 9.2 14.2 22.6 9.0Botswana 55.3 25.0 18.1 26.8 3.4 3.1 22.2 43.6 1.4 1.3Brazil 14.2 19.0 29.8 29.7 8.4 5.4 41.9 41.0 5.7 4.9Bulgaria 63.4 64.3 17.9 15.1 3.9 2.7 8.5 16.9 6.2 1.0Burkina Faso .. .. .. .. .. .. .. .. .. ..Burundi .. .. .. .. .. .. .. .. .. ..Cambodia .. 36.7 .. 3.7 .. 26.5 .. 25.0 .. 7.8Cameroon 1.1 34.3 8.2 7.0 25.5 8.4 65.2 50.6 0.0 0.0Canada 32.9 33.5 19.8 17.8 16.8 16.4 28.6 29.4 1.9 2.9Central African Republic .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. ..Chile 33.0 39.0 24.9 19.1 9.2 6.2 31.7 35.1 1.2 0.6China 32.2 51.9 40.9 33.3 16.5 6.2 5.3 7.0 5.1 1.6Hong Kong SAR, China 72.4 68.1 9.5 15.7 4.5 6.0 13.6 10.2 0.0 0.0Colombia 24.3 18.7 27.4 31.7 8.1 8.5 37.0 38.5 3.2 2.7Congo, Dem. Rep. 3.4 1.1 29.4 35.3 10.8 10.6 18.9 22.6 37.8 30.7Congo, Rep. 0.0 3.4 8.6 4.1 10.0 5.4 78.6 87.2 0.0 0.0Costa Rica 7.3 10.2 26.8 17.3 4.2 3.6 60.2 65.5 1.5 3.5Côte d’Ivoire 22.8 43.9 16.3 9.3 11.4 12.1 44.9 24.3 4.9 10.5Croatia 37.3 33.1 28.2 21.2 10.5 12.6 18.2 29.5 5.9 3.7Cuba 45.2 53.3 18.4 28.3 9.1 10.0 16.0 3.0 11.2 5.4Cyprus 45.3 51.0 20.1 15.1 4.7 4.5 29.9 26.0 0.0 3.6Czech Republic 42.7 56.9 30.0 17.7 19.0 8.8 4.6 15.3 3.6 1.3Denmark 51.7 50.1 10.9 10.0 12.5 7.7 20.3 28.3 4.6 3.9Dominican Republic 41.2 50.0 10.3 6.3 12.1 13.9 35.5 29.1 1.0 0.7Ecuador 15.2 20.7 16.9 17.5 14.5 11.4 51.7 49.2 1.8 1.2Egypt, Arab Rep. 32.5 43.0 35.8 23.4 11.9 8.1 19.8 21.9 0.0 3.6El Salvador 8.3 26.6 25.9 23.5 7.4 8.9 57.9 40.9 0.0 0.0Eritrea .. 42.2 .. 4.4 .. 28.9 .. 24.4 .. 0.0Estonia 73.2 75.8 12.4 8.2 4.1 2.3 6.6 12.9 3.8 0.9Ethiopia 13.6 6.6 27.1 24.5 5.4 12.4 41.6 56.7 12.2 0.0Finland 36.2 47.7 26.7 21.5 11.9 4.8 21.3 22.4 3.8 3.5France 18.1 18.9 22.7 19.2 24.1 22.8 31.9 33.9 3.2 5.3Gabon 34.4 28.5 15.6 38.9 13.3 9.7 35.6 21.1 2.2 1.7Gambia, The .. .. .. .. .. .. .. .. .. ..Georgia 46.3 19.5 22.4 15.5 16.5 18.5 13.1 37.6 1.7 8.9Germany 42.4 45.2 18.9 14.7 20.4 20.5 16.7 18.5 1.7 1.1Ghana 3.0 26.2 17.7 15.3 15.9 6.0 59.0 49.8 4.4 2.7Greece 52.3 53.4 14.8 9.8 7.3 10.5 21.5 23.6 4.1 2.6Guatemala 7.3 27.6 22.7 15.5 14.2 5.7 52.4 51.3 3.0 0.1Guinea .. .. .. .. .. .. .. .. .. ..Guinea-Bissau .. .. .. .. .. .. .. .. .. ..Haiti 25.5 9.8 21.3 22.6 8.5 10.3 44.7 57.3 0.0 0.01782012 World Development Indicators

Carbon dioxide emissions by sector 3.11ENVIRONMENTCarbon dioxide emissions% of total fuel combustionElectricity andheat productionManufacturing industriesand constructionResidential buildingsand commercial andpublic services Transport Other sectors1990 2008 1990 2008 1990 2008 1990 2008 1990 2008Honduras 0.9 34.4 35.2 19.9 15.5 3.3 48.4 37.7 0.0 4.9Hungary 34.8 37.6 23.2 13.2 25.2 22.7 12.5 24.2 4.4 2.2India 44.5 59.9 28.6 19.6 9.8 6.2 13.8 9.2 3.3 5.1Indonesia 37.3 37.7 23.7 34.0 13.7 6.3 22.7 19.7 2.7 2.3Iran, Islamic Rep. 22.7 29.0 26.8 22.4 22.3 24.2 21.5 21.8 6.6 2.5Iraq 28.3 35.6 27.3 24.0 9.2 9.3 35.1 31.1 0.0 0.0Ireland 36.1 33.7 15.8 11.5 29.5 22.3 16.5 30.6 2.2 1.8Israel 57.6 66.6 12.9 2.8 4.2 4.4 19.6 16.4 5.7 9.9Italy 35.9 38.3 21.1 15.8 16.7 16.8 24.0 27.2 2.2 2.0Jamaica 27.8 51.1 8.3 3.3 4.0 3.3 15.0 21.6 44.9 20.5Japan 38.4 44.6 27.0 21.5 12.9 13.3 19.7 19.7 1.9 0.9Jordan 38.6 48.0 14.2 14.1 11.3 10.9 28.9 24.8 7.1 2.2Kazakhstan 47.9 47.4 34.9 22.3 0.2 0.7 6.0 7.0 11.0 22.6Kenya 8.0 32.4 25.8 15.8 10.9 9.6 48.3 37.2 6.9 5.1Korea, Dem. Rep. 13.9 16.2 67.4 62.9 0.5 0.1 4.1 1.7 14.2 19.1Korea, Rep. 27.4 52.4 23.4 19.1 26.9 10.0 18.8 16.8 3.4 1.7Kosovo .. .. .. .. .. .. .. .. .. ..Kuwait 62.7 68.2 21.7 15.6 0.7 0.7 15.0 15.5 0.0 0.0Kyrgyz Republic 17.3 23.1 36.9 28.5 0.0 0.0 13.3 24.2 32.6 24.3Lao PDR .. .. .. .. .. .. .. .. .. ..Latvia 52.3 25.8 13.6 13.8 13.4 11.4 16.6 44.9 4.1 4.2Lebanon 43.3 49.2 5.0 11.5 22.7 10.8 28.8 28.5 0.0 0.0Lesotho .. .. .. .. .. .. .. .. .. ..Liberia .. .. .. .. .. .. .. .. .. ..Libya 59.7 62.1 13.2 9.4 4.8 5.6 22.4 22.8 0.0 0.0Lithuania 41.3 35.3 21.6 21.2 16.1 6.9 16.5 35.0 4.5 1.5Macedonia, FYR 64.8 68.1 18.8 14.2 4.9 4.0 8.9 13.3 2.6 0.6Madagascar .. .. .. .. .. .. .. .. .. ..Malawi .. .. .. .. .. .. .. .. .. ..Malaysia 35.7 49.6 30.7 24.2 4.4 2.5 29.2 23.3 0.0 0.4Mali .. .. .. .. .. .. .. .. .. ..Mauritania .. .. .. .. .. .. .. .. .. ..Mauritius .. .. .. .. .. .. .. .. .. ..Mexico 34.9 40.2 23.9 14.9 7.8 5.8 31.5 37.1 1.9 2.0Moldova 44.8 47.7 6.7 9.8 2.9 23.5 7.9 14.7 37.6 4.4Mongolia 51.3 62.6 22.1 13.2 6.4 7.3 12.1 13.6 8.1 3.2Morocco 40.0 37.1 25.7 17.0 7.3 9.5 18.7 25.7 8.2 10.7Mozambique 10.2 1.0 13.0 20.7 7.4 7.8 55.6 69.9 13.9 1.0Myanmar 39.9 20.8 28.1 25.4 0.3 2.6 31.7 28.1 0.0 23.1Namibia .. 22.6 .. 6.6 .. 0.3 .. 48.6 .. 21.9Nepal 0.0 0.3 22.7 33.9 28.4 31.5 38.6 27.9 11.4 6.6Netherlands 38.2 38.2 21.9 21.2 18.5 17.9 16.6 19.7 4.8 3.0New Zealand 21.6 32.8 28.8 18.4 6.0 4.2 39.1 41.7 4.4 2.8Nicaragua 30.6 40.1 18.0 14.7 7.1 6.5 40.4 35.3 3.8 3.1Niger .. .. .. .. .. .. .. .. .. ..Nigeria 28.5 36.5 17.3 10.1 14.3 4.8 39.9 48.6 0.0 0.0Norway 26.9 33.4 24.5 21.3 8.7 3.2 35.1 37.3 4.9 4.8Oman 54.8 56.7 21.8 23.1 1.9 1.3 16.8 15.3 4.7 3.6Pakistan 27.0 32.3 34.0 32.4 14.4 11.4 23.1 23.5 1.6 0.3Panama 22.8 27.0 21.1 18.8 9.3 5.8 46.7 47.6 0.0 0.8Papua New Guinea .. .. .. .. .. .. .. .. .. ..Paraguay 1.6 0.0 7.9 3.0 6.8 4.9 83.8 92.1 0.0 0.0Peru 20.7 26.8 22.4 26.6 18.1 6.7 35.5 37.8 3.4 2.2Philippines 35.5 44.0 21.0 18.0 6.7 5.7 34.9 31.3 2.0 1.1Poland 65.1 55.8 13.7 12.6 12.9 13.3 6.0 14.8 2.3 3.4Portugal 41.6 39.9 24.8 16.1 5.5 6.8 24.6 35.7 3.5 1.6Puerto Rico .. .. .. .. .. .. .. .. .. ..Qatar 53.3 52.1 35.9 31.5 0.6 0.4 10.1 16.0 0.0 0.02012 World Development Indicators 179

3.11 Carbon dioxide emissions by sectorCarbon dioxide emissions% of total fuel combustionElectricity andheat productionManufacturing industriesand constructionResidential buildingsand commercial andpublic services Transport Other sectors1990 2008 1990 2008 1990 2008 1990 2008 1990 2008Romania 47.3 50.4 35.8 22.3 5.2 9.1 6.9 16.6 4.8 1.6Russian Federation 55.9 59.5 13.2 14.4 13.1 9.7 13.6 15.3 4.2 1.2Rwanda .. .. .. .. .. .. .. .. .. ..Saudi Arabia 50.3 51.4 17.8 22.9 1.6 1.0 30.4 24.7 0.0 0.0Senegal 36.8 28.4 12.4 17.4 6.5 7.3 35.8 46.2 8.5 0.8Serbia 64.6 63.3 16.6 16.9 3.3 4.9 7.2 13.1 8.3 1.8Sierra Leone .. .. .. .. .. .. .. .. .. ..Singapore 78.7 71.4 6.7 11.9 0.6 0.5 14.0 16.2 0.0 0.0Slovak Republic 30.3 36.9 32.6 25.7 26.7 16.9 7.1 19.5 3.2 1.0Slovenia 43.0 37.4 20.7 14.5 15.4 10.9 20.8 35.5 0.2 1.7Somalia .. .. .. .. .. .. .. .. .. ..South Africa 56.1 64.5 26.9 13.5 4.4 7.3 11.5 13.6 1.2 1.1South Sudan .. .. .. .. .. .. .. .. .. ..Spain 37.4 37.7 22.1 17.4 7.9 8.1 30.6 34.3 2.1 2.5Sri Lanka 4.5 33.1 13.1 10.6 2.7 3.4 66.0 48.2 13.9 4.6Sudan 9.1 27.0 16.9 10.0 2.5 5.7 71.3 55.6 0.2 1.5Swaziland .. .. .. .. .. .. .. .. .. ..Sweden 18.4 22.8 24.2 21.0 17.3 3.7 37.5 50.7 2.6 1.7Switzerland 4.1 7.0 14.3 14.9 44.3 37.1 35.4 39.5 2.0 1.5Syrian Arab Republic 25.2 49.9 20.6 20.9 5.0 2.9 30.6 22.2 18.6 4.2Tajikistan 14.0 17.5 0.0 0.0 0.0 0.0 6.6 9.2 79.4 73.3Tanzania 17.5 18.5 22.2 13.3 19.9 10.4 40.4 56.8 0.0 1.2Thailand 36.5 41.1 18.8 29.2 3.1 2.8 34.5 22.3 7.1 4.6Timor-Leste .. .. .. .. .. .. .. .. .. ..Togo 12.3 1.8 3.5 7.3 10.5 12.7 73.7 78.2 0.0 0.0Trinidad and Tobago 41.6 35.9 45.6 56.6 1.2 1.4 11.7 6.1 0.0 0.0Tunisia 33.0 39.5 27.6 17.7 13.4 14.6 20.4 23.1 5.5 5.0Turkey 30.2 42.7 26.6 14.6 16.8 19.3 21.9 17.1 4.6 6.3Turkmenistan 31.6 43.4 0.0 0.0 0.0 0.0 5.2 5.9 63.3 50.8Uganda .. .. .. .. .. .. .. .. .. ..Ukraine 51.4 45.2 29.0 29.4 7.8 13.5 7.9 10.5 3.8 1.4United Arab Emirates 26.4 50.8 51.4 29.8 0.6 2.3 21.6 17.1 0.0 0.0United Kingdom 44.2 44.5 15.2 11.5 17.2 18.5 20.8 24.4 2.5 1.0United States 43.9 47.7 14.4 11.3 11.1 9.9 29.2 30.2 1.3 0.8Uruguay 13.9 39.8 19.5 11.4 15.5 7.1 40.0 34.3 11.2 7.5Uzbekistan 39.1 33.3 4.9 19.0 0.0 36.6 4.7 7.8 51.3 3.3Venezuela, RB 40.1 36.3 28.0 28.0 5.0 4.4 26.9 31.0 0.0 0.2Vietnam 27.7 29.3 32.6 35.1 11.2 9.5 24.3 24.5 4.0 1.6West Bank and Gaza .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 24.6 34.3 3.1 11.2 10.6 9.3 61.7 27.1 0.0 18.0Zambia 6.2 5.0 51.5 49.1 8.5 2.5 29.6 32.1 4.2 11.9Zimbabwe 42.8 56.8 29.4 16.1 5.9 4.0 12.9 12.5 9.0 10.6World 41.9 w 47.6 w 22.3 w 21.0 w 12.7 w 9.6 w 19.5 w 19.6 w 3.7 w 2.2 wLow income 18.1 25.5 50.4 36.7 2.8 6.9 10.2 17.5 18.4 13.5Middle income 42.5 49.1 26.5 26.0 11.7 8.0 13.7 14.1 5.6 2.7Lower middle income 42.6 48.1 25.6 23.0 8.9 8.6 14.8 16.0 8.0 4.4Upper middle income 42.4 49.4 26.7 26.8 12.6 7.9 13.3 13.6 4.9 2.3Low & middle income 41.9 48.8 27.0 26.1 11.5 8.0 13.6 14.1 5.9 2.9East Asia & Pacifi c 32.0 50.0 39.8 33.1 14.8 6.0 8.1 8.9 5.3 1.9Europe & Central Asia 51.7 53.4 19.2 16.9 10.1 11.1 11.3 14.1 7.7 4.4Latin America & Carib. 29.1 31.9 24.4 22.4 9.2 6.9 33.6 35.6 3.7 3.2Middle East & N. Africa 31.0 36.4 24.9 20.6 15.6 17.0 24.1 23.2 4.3 2.9South Asia 42.4 56.8 29.1 20.8 10.3 6.8 14.9 10.9 3.2 4.7Sub-Saharan Africa 48.1 53.3 25.8 13.8 6.0 7.6 18.0 23.6 2.0 1.7High income 41.8 46.1 18.4 15.1 13.6 11.5 24.3 25.9 1.9 1.4Euro area 36.7 37.8 21.0 16.8 18.5 17.2 21.4 25.8 2.4 2.3Note: Shares may not sum to 100 percent because of rounding.1802012 World Development Indicators

Carbon dioxide emissions by sector 3.11ENVIRONMENTAbout the dataDefinitionsCarbon dioxide emissions account for the largestshare of greenhouse gases, which are associatedwith global warming. In 2010 the International EnergyAgency (IEA) released data on carbon dioxide emissionsby sector for the fi rst time, allowing a morecomprehensive understanding of each sector’s contributionto total emissions. The sectoral approachyields data on carbon dioxide emissions from fuelcombustion (Intergovernmental Panel on ClimateChange [IPCC] source/sink category 1A) as calculatedusing the IPCC tier 1 sectoral approach. Thetable does not include all sectors.Carbon dioxide emissions from electricity and heatproduction are the sum of emissions from main activityproducers of electricity and heat, unallocated autoproducers,and other energy industries. Main activityproducers (formerly known as public supply undertakings)generate electricity or heat for sale to thirdparties as their primary activity and may be privatelyor publicly owned. Emissions from own onsite useof fuel in power plants are also included in this category.Unallocated autoproducers are undertakingsthat generate electricity or heat, wholly or partly fortheir own use as an activity that supports their primaryactivity and may be privately or publicly owned. In the1996 IPCC guidelines these emissions were allocatedamong industry, transport, and “other” sectors. Emissionsfrom other energy industries are emissions fromfuel combusted in petroleum refi neries, the manufactureof solid fuels, coal mining, oil and gas extraction,and other energy-producing industries.Carbon dioxide emissions from manufacturingindustries and construction are the emissions fromfuel combustion in industry (IPCC source/sink Category1A2). Although in the 1996 IPCC guidelines, thiscategory included emissions from industry autoproducersthat generate electricity or heat, the IEA datado not allow energy consumption to be categorized byend-use, and thus emissions from autoproducers arelisted separately under unallocated autoproducers.Emissions from manufacturing industries and constructioninclude those from coke inputs into blastfurnaces, which may be reported under the transformationsector, the industry sector, or industrialprocesses (IPCC source/sink category 2).Carbon emissions from residential buildings andcommercial and public services are the sum of emissionsfrom fuel combustion in households (IPCCsource/sink category 1A4b) and emissions from allactivities of International Standard Industrial Classification divisions 41, 50–52, 55, 63–67, 70–75,80, 85, 90–93, and 99.Carbon dioxide emissions from transport are emissionsfrom fuel combustion for all transport activity(IPCC source/sink category 1A3), including domesticaviation, domestic navigation, road, rail, and pipelinetransport but excluding international marine bunkersand international aviation. The IEA data do not allowenergy consumption to be categorized by end-use,and thus emissions from autoproducers are listedseparately under unallocated autoproducers.Carbon dioxide emissions from other sectors areemissions from commercial and institutional activitiesand from residential, agriculture and forestry,fi shing, and other processes not specifi ed elsewherethat are included in IPCC source/sink categories 1A4and 1A5. Although in the 1996 IPCC guidelines, thiscategory included emissions from autoproducers inthe commercial, residential, and agricultural sectorsthat generate electricity or heat, the IEA data do notallow energy consumption to be classifi ed by enduse,and thus emissions from autoproducers arelisted separately under unallocated autoproducers.• Carbon dioxide emissions from electricity andheat production are those from main activity producersof electricity and heat, unallocated autoproducers,and other energy industries. • Carbondioxide emissions from manufacturing industriesand construction are those from fuel combustionin industry (IPCC source/sink category 1A2). • Carbondioxide emissions from residential buildingsand commercial and public services are those fromfuel combustion in household and all activities ofISIC divisions 41, 50–52, 55, 63–67, 70–75, 80,85, 90–93, and 99. • Carbon dioxide emissionsfrom transport are those from fuel combustion for alltransport activities (IPCC source/sink category 1A3).• Carbon dioxide emissions from other sectors arethose from commercial and institutional activitiesand from residential, agriculture and forestry, fi shing,and other emissions not specifi ed elsewherethat are included in IPCC source/sink categories1A4 and 1A5.Data sourcesData on carbon dioxide emissions by sector arefrom IEA electronic fi les.2012 World Development Indicators 181

3.12Climate variability, exposureto impact, and resilienceClimate variability Exposure to impact ResilienceAverage dailyminimum/maximumtemperaturedegrees Celsiuschange in average numberof days/nightsLand areawith anelevationof 5 metersor less% of land area% of populationPopulationliving inareas withan elevationof 5 metersor lessPopulationaffected bydroughts,fl oods, andextremetemperatureDisaster riskreductionprogressscore1, worst,to 5, bestProjected annual Projected annual Projected annual Projected annualtemperaturedegrees Celsiuscool days/cold nightshot days/warm nightsprecipitationmillimeters1961–1990 2045–2065 2045–2065 2045–2065 2045–2065 2000 2000 1990–2009 2011Afghanistan 5.5 / 19.7 2.3 to 3.6 –1.4 / –1.5 3.1 / 7.0 –58 to 13 0.0 0.0 1.1 ..Albania 6.3 / 16.5 1.9 to 2.9 –1.5 / –1.4 3.2 / 6.5 –147 to –44 5.0 8.2 5.3 ..Algeria 15.4 / 29.6 2.4 to 3.0 –1.7 / –2.0 3.9 / 7.8 –39 to 4 0.4 3.5 0.0 3.5Angola 14.8 / 28.3 2.1 to 2.7 –2.2 / –2.6 7.0 / 20.2 –89 to 75 0.2 2.1 1.0 ..Argentina 8.2 / 21.4 1.4 to 2.1 –1.2 / –1.3 2.9 / 6.5 –57 to 52 1.2 4.5 0.2 3.3Armenia 1.3 / 13.0 2.1 to 3.3 –1.3 / –1.4 2.8 / 6.3 –75 to –7 0.0 0.0 0.5 3.0Australia 14.9 / 28.4 1.7 to 2.4 –1.8 / –1.9 3.6 / 9.3 –67 to 54 1.1 7.2 3.0 4.0Austria 2.3 / 10.4 1.9 to 2.9 –1.4 / –1.5 2.9 / 5.2 –70 to 27 0.0 0.0 0.0 ..Azerbaijan 6.7 / 17.2 1.9 to 3.1 –1.3 / –1.3 2.7 / 6.2 –58 to 3 20.0 29.8 1.1 ..Bahrain 20.5 / 33.8 2.1 to 3.0 –1.5 / –1.9 4.6 / 8.6 –19 to 4 39.0 66.6 .. ..Bangladesh 20.4 / 29.6 1.7 to 2.4 –1.7 / –2.1 3.4 / 11.8 –126 to 120 14.1 14.0 4.6 4.0Belarus 2.0 / 10.3 2.2 to 3.1 –1.5 / –1.7 2.1 / 4.5 8 to 70 0.0 0.0 0.0 ..Belgium 5.5 / 13.6 1.4 to 2.4 –1.4 / –1.5 2.9 / 5.5 –64 to 29 9.2 14.3 0.0 ..Benin 21.7 / 33.4 2.0 to 2.5 –2.3 / –2.6 5.5 / 19.2 –207 to 97 1.2 14.1 0.9 ..Bolivia 15.2 / 27.9 2.1 to 3.0 –2.0 / –2.1 5.1 / 16.9 –91 to 137 0.0 0.0 1.3 2.3Bosnia and Herzegovina 5.0 / 14.7 1.9 to 3.0 –1.5 / –1.5 3.1 / 6.1 –116 to –7 0.1 0.1 0.5 ..Botswana 13.6 / 29.4 2.5 to 3.3 –1.8 / –2.1 4.7 / 12.8 –106 to 24 0.0 0.0 0.7 3.0Brazil 19.6 / 30.3 1.9 to 2.6 –2.1 / –2.5 5.6 / 19.6 –95 to 136 1.2 4.9 0.5 4.5Bulgaria 5.6 / 15.5 1.9 to 3.1 –1.4 / –1.3 2.9 / 6.0 –127 to –21 0.4 1.5 0.0 3.5Burkina Faso 21.5 / 35.0 2.2 to 2.8 –2.1 / –2.6 5.3 / 16.6 –229 to 88 0.0 0.0 1.3 ..Burundi 13.8 / 25.8 2.1 to 2.4 –2.1 / –2.8 7.8 / 26.2 –21 to 206 0.0 0.0 2.4 3.3Cambodia 22.3 / 31.3 1.6 to 2.0 –1.8 / –1.7 4.5 / 18.2 –109 to 95 3.8 10.6 6.6 ..Cameroon 19.1 / 30.1 2.1 to 2.4 –2.4 / –2.4 6.2 / 22.0 –71 to 115 0.1 0.3 0.1 ..Canada –10.1 / –0.6 2.6 to 3.5 –1.6 / –1.7 1.9 / 4.4 17 to 88 2.4 4.0 0.0 4.3Central African Republic 18.3 / 31.5 2.1 to 2.4 –2.3 / –2.4 4.7 / 22.5 –73 to 100 0.0 0.0 0.2 ..Chad 18.6 / 34.5 2.3 to 2.6 –1.8 / –2.3 5.1 / 13.7 –79 to 41 0.0 0.0 2.7 ..Chile 3.5 / 13.4 1.2 to 1.9 –1.7 / –1.7 3.8 / 7.3 –118 to 24 3.1 1.6 0.3 2.8China 0.9 / 13.0 2.1 to 3.0 –1.5 / –1.6 2.9 / 5.6 –37 to 86 1.4 8.1 8.0 ..Hong Kong SAR, China 19.3 / 26.0 1.4 to 1.9 –1.2 / –1.4 7.4 / 10.3 –83 to 73 24.6 26.2 0.0 ..Colombia 19.6 / 29.4 1.8 to 2.5 –2.4 / –2.9 7.9 / 26.6 –80 to 199 0.9 2.0 0.7 3.8Congo, Dem. Rep. 18.4 / 29.6 2.1 to 2.4 –2.1 / –2.7 6.4 / 24.7 –48 to 128 0.0 0.0 0.0 ..Congo, Rep. 19.8 / 29.3 2.0 to 2.3 –2.3 / –2.7 9.1 / 26.5 –40 to 134 0.1 1.0 0.3 ..Costa Rica 19.6 / 30.0 1.6 to 2.3 –2.8 / –3.0 11.8 / 26.8 –239 to 60 2.1 0.8 0.7 4.5Côte d’Ivoire 21.1 / 31.6 1.8 to 2.4 –2.4 / –2.5 7.1 / 22.9 –125 to 73 0.2 3.2 0.0 2.5Croatia 6.2 / 15.6 1.9 to 2.9 –1.6 / –1.6 3.2 / 6.0 –112 to –2 3.0 3.4 0.0 ..Cuba 20.4 / 30.0 1.4 to 1.8 –1.9 / –1.8 10.5 / 17.6 –108 to 39 12.7 10.0 0.7 4.5Cyprus 13.1 / 23.8 1.7 to 2.3 –1.9 / –1.8 4.0 / 7.3 –98 to –44 6.4 9.7 0.0 ..Czech Republic 3.3 / 11.8 1.9 to 2.8 –1.5 / –1.6 2.5 / 4.7 –43 to 55 0.0 0.0 0.2 2.8Denmark 4.3 / 10.7 1.6 to 2.5 –1.6 / –1.7 2.6 / 5.2 21 to 87 17.7 18.5 0.0 ..Dominican Republic 19.9 / 29.2 1.5 to 1.8 –2.8 / –2.9 12.1 / 23.6 –128 to 30 4.1 3.0 0.1 3.0Ecuador 15.6 / 28.1 1.8 to 2.3 –2.6 / –2.8 9.5 / 24.4 22 to 273 2.0 7.3 0.3 4.8Egypt, Arab Rep. 14.8 / 29.4 1.9 to 2.4 –1.7 / –2.1 4.0 / 8.1 –20 to 1 4.0 25.6 0.0 ..El Salvador 18.4 / 30.5 1.7 to 2.9 –2.2 / –2.3 8.9 / 24.5 –205 to 14 2.5 1.7 0.4 3.3Eritrea 18.9 / 32.1 2.1 to 2.7 –2.0 / –2.4 6.6 / 14.4 –55 to 26 3.1 1.2 7.3 ..Estonia 1.5 / 8.7 2.2 to 3.2 –1.7 / –1.8 2.1 / 4.8 33 to 93 3.4 7.2 0.0 ..Ethiopia 15.5 / 28.9 2.1 to 2.5 –2.0 / –2.7 6.6 / 21.4 –42 to 79 0.7 0.4 3.3 ..Finland –2.4 / 5.8 2.4 to 3.7 –1.7 / –1.9 1.7 / 4.1 37 to 103 1.1 4.4 0.0 3.5France 6.5 / 14.9 1.6 to 2.6 –1.5 / –1.5 3.6 / 6.3 –112 to 2 2.1 4.0 0.0 ..Gabon 20.9 / 29.2 1.8 to 2.2 –2.6 / –2.9 13.7 / 26.9 –51 to 148 0.5 5.9 .. ..Gambia, The 20.4 / 34.6 2.1 to 2.7 –2.3 / –2.6 6.7 / 16.8 –87 to 26 16.6 33.4 0.2 ..Georgia 0.4 / 11.2 1.9 to 3.0 –1.3 / –1.3 2.8 / 5.9 –77 to –3 1.4 3.3 0.8 2.8Germany 4.6 / 12.4 1.7 to 2.6 –1.5 / –1.5 2.6 / 5.0 –38 to 57 4.9 4.4 0.0 4.3Ghana 22.1 / 32.3 1.8 to 2.4 –2.4 / –2.5 6.6 / 22.1 –159 to 83 0.8 2.3 1.0 3.3Greece 10.5 / 20.3 1.7 to 2.6 –1.7 / –1.7 3.5 / 6.5 –110 to –40 6.3 9.9 0.0 ..Guatemala 18.2 / 28.7 1.7 to 2.8 –1.7 / –1.7 7.8 / 21.6 –186 to 22 0.6 0.3 1.3 3.3Guinea 19.6 / 31.8 2.0 to 2.6 –2.4 / –2.5 6.9 / 20.2 –122 to 104 1.1 3.6 0.2 ..Guinea-Bissau 20.9 / 32.6 1.8 to 2.2 –2.7 / –2.7 9.9 / 22.2 –69 to 130 9.5 18.8 0.5 1.0Haiti 20.1 / 29.7 1.5 to 1.8 –2.8 / –2.8 12.6 / 23.1 –125 to 34 3.9 5.4 0.8 ..1822012 World Development Indicators

Climate variability, exposureto impact, and resilience3.12ENVIRONMENTClimate variability Exposure to impact ResilienceAverage dailyminimum/maximumtemperaturedegrees Celsiuschange in average numberof days/nightsLand areawith anelevationof 5 metersor less% of land area% of populationPopulationliving inareas withan elevationof 5 metersor lessPopulationaffected bydroughts,fl oods, andextremetemperatureDisaster riskreductionprogressscore1, worst,to 5, bestProjected annual Projected annual Projected annual Projected annualtemperaturedegrees Celsiuscool days/cold nightshot days/warm nightsprecipitationmillimeters1961–1990 2045–2065 2045–2065 2045–2065 2045–2065 2000 2000 1990–2009 2011Honduras 18.9 / 28.1 1.6 to 2.6 –2.2 / –2.5 10.2 / 24.5 –204 to 15 3.0 2.2 1.3 3.8Hungary 4.8 / 14.7 2.0 to 3.0 –1.5 / –1.7 3.0 / 5.3 –75 to 23 0.0 0.0 0.1 ..India 17.7 / 29.6 1.9 to 2.6 –2.0 / –2.2 4.6 / 13.3 –91 to 135 1.4 3.8 4.4 3.3Indonesia 21.1 / 30.6 1.5 to 1.8 –2.8 / –2.9 16.5 / 26.7 –160 to 234 5.5 11.2 0.2 3.3Iran, Islamic Rep. 10.1 / 24.4 2.2 to 3.3 –1.4 / –1.4 3.3 / 7.1 –51 to 6 1.6 5.0 3.1 ..Iraq 14.1 / 28.7 2.3 to 3.2 –1.5 / –1.5 3.4 / 6.8 –38 to –2 4.0 6.5 0.0 ..Ireland 5.9 / 12.7 0.9 to 1.4 –1.3 / –1.4 3.0 / 5.6 –13 to 78 4.0 6.6 0.0 ..Israel 13.4 / 25.0 1.9 to 2.7 –1.6 / –1.8 3.7 / 7.7 –41 to –11 7.8 8.3 0.0 ..Italy 9.5 / 17.4 1.8 to 2.6 –1.8 / –1.8 4.0 / 6.7 –108 to –11 5.2 7.5 0.0 3.5Jamaica 20.6 / 29.3 1.3 to 1.8 –2.9 / –2.8 14.0 / 23.7 –102 to 28 7.1 5.8 1.1 3.8Japan 7.0 / 15.3 1.8 to 2.3 –1.9 / –2.2 3.5 / 6.0 –47 to 80 5.9 16.2 0.0 4.5Jordan 11.2 / 25.4 2.1 to 2.9 –1.5 / –1.6 3.7 / 7.4 –28 to –3 2.0 4.2 0.4 ..Kazakhstan 0.6 / 12.2 2.2 to 3.1 –1.2 / –1.4 2.3 / 5.0 –1 to 37 6.7 3.9 0.2 ..Kenya 18.9 / 30.6 1.9 to 2.1 –2.0 / –2.9 8.1 / 25.0 0 to 144 0.2 1.4 6.5 4.0Korea, Dem. Rep. 0.3 / 11.1 2.1 to 2.7 –1.4 / –1.8 2.6 / 4.6 –9 to 129 2.4 5.3 2.5 ..Korea, Rep. 6.5 / 16.5 1.8 to 2.3 –1.4 / –1.8 3.2 / 5.2 0 to 181 4.3 5.0 0.1 ..Kosovo .. 2.0 to 3.1 –1.5 / –1.3 3.1 / 6.3 –148 to –34 .. .. .. ..Kuwait 18.5 / 32.2 2.2 to 3.1 –1.5 / –1.7 3.9 / 7.4 –19 to 4 8.9 22.8 0.0 ..Kyrgyz Republic –4.6 / 7.7 2.3 to 3.2 –1.2 / –1.4 3.1 / 6.6 –32 to 35 0.0 0.0 2.1 ..Lao PDR 18.2 / 27.4 1.6 to 2.4 –1.1 / –1.3 3.1 / 12.7 –95 to 70 0.0 0.0 2.7 2.3Latvia 1.9 / 9.3 2.1 to 3.1 –1.7 / –1.8 2.1 / 5.0 31 to 89 3.0 23.9 0.0 ..Lebanon 10.7 / 22.1 2.0 to 2.9 –1.7 / –1.6 3.6 / 7.5 –75 to –27 1.7 9.1 0.0 3.0Lesotho 5.2 / 18.5 2.0 to 2.5 –1.6 / –1.9 3.7 / 10.1 –66 to 89 0.0 0.0 3.4 2.5Liberia 20.0 / 30.6 1.7 to 2.2 –2.7 / –2.7 10.2 / 25.3 –115 to 81 0.4 3.3 1.9 ..Libya 14.4 / 29.2 2.0 to 2.4 –1.7 / –2.1 3.5 / 7.6 –18 to 5 0.8 4.7 0.0 ..Lithuania 2.3 / 10.1 2.0 to 3.0 –1.6 / –1.8 2.0 / 4.8 22 to 83 1.9 4.0 0.0 ..Macedonia, FYR 4.5 / 15.1 1.9 to 3.0 –1.4 / –1.3 3.2 / 6.4 –150 to –43 0.0 0.0 0.3 3.3Madagascar 18.0 / 27.3 1.7 to 2.1 –2.5 / –2.7 7.6 / 15.7 –104 to 56 1.3 2.0 0.9 3.8Malawi 16.6 / 27.2 2.0 to 2.5 –2.1 / –2.5 3.6 / 15.9 –53 to 91 0.0 0.0 8.8 1.8Malaysia 21.2 / 29.6 1.6 to 1.9 –2.5 / –2.8 13.0 / 26.3 –160 to 117 3.0 9.5 0.1 3.8Mali 20.9 / 35.6 2.5 to 3.1 –2.0 / –2.5 5.5 / 12.8 –142 to 43 0.0 0.0 0.7 ..Mauritania 20.2 / 35.1 2.4 to 3.1 –2.0 / –2.4 5.1 / 10.6 –113 to 9 1.0 20.4 3.1 ..Mauritius 18.9 / 25.9 1.3 to 1.7 –2.9 / –2.9 8.5 / 13.7 –165 to 67 7.1 5.6 0.0 3.5Mexico 13.5 / 28.5 1.7 to 2.8 –1.6 / –1.5 5.6 / 12.1 –178 to 10 2.9 2.7 0.1 4.3Moldova 5.0 / 13.9 2.0 to 3.3 –1.5 / –1.6 2.8 / 5.2 –72 to 11 1.3 0.9 0.3 ..Mongolia –7.7 / 6.3 2.3 to 3.1 –1.3 / –1.4 2.2 / 4.2 1 to 50 0.0 0.0 2.6 2.8Morocco 11.1 / 23.1 2.1 to 3.0 –1.8 / –2.0 3.6 / 7.9 –62 to –5 0.7 3.8 0.1 3.0Mozambique 18.5 / 29.1 1.9 to 2.4 –2.2 / –2.5 5.0 / 15.0 –69 to 61 1.8 6.5 3.7 4.0Myanmar 17.7 / 8.4 1.7 to 2.3 –1.7 / –1.8 3.8 / 13.9 –84 to 97 4.6 14.0 0.1 ..Namibia 12.7 / 27.2 2.2 to 2.9 –1.9 / –2.2 5.8 / 13.1 –67 to 23 0.3 2.9 3.4 ..Nepal 1.5 / 14.7 2.2 to 3.4 –2.1 / –2.1 2.5 / 8.0 –116 to 231 0.0 0.0 0.7 2.8Netherlands 5.5 / 13.0 1.4 to 2.3 –1.6 / –1.6 2.9 / 5.3 –18 to 50 58.5 61.3 0.0 ..New Zealand 6.0 / 15.1 1.1 to 1.7 –2.2 / –2.1 4.1 / 7.9 –44 to 53 2.7 12.6 0.0 3.8Nicaragua 20.3 / 29.5 1.6 to 2.4 –2.6 / –2.8 10.4 / 26.0 –220 to 19 3.6 1.5 0.8 3.8Niger 19.4 / 34.9 2.4 to 2.8 –1.8 / –2.4 5.4 / 12.2 –71 to 36 0.0 0.0 7.5 ..Nigeria 20.8 / 32.8 2.0 to 2.5 –2.2 / –2.5 5.5 / 19.0 –128 to 89 0.5 3.0 0.1 4.0Norway –2.0 / 5.0 1.8 to 3.3 –1.8 / –1.8 2.2 / 4.8 41 to 131 4.9 9.3 0.0 3.8Oman 20.8 / 30.4 2.0 to 2.4 –1.9 / –2.1 4.4 / 11.8 –16 to 31 1.1 5.5 .. ..Pakistan 13.2 / 27.2 2.4 to 3.4 –1.8 / –1.9 3.4 / 8.1 –60 to 36 1.4 1.3 1.1 3.5Panama 21.3 / 29.5 1.6 to 2.2 –2.9 / –3.0 14.0 / 26.9 –228 to 107 3.7 4.0 0.2 3.0Papua New Guinea 20.1 / 30.4 1.4 to 1.8 –2.8 / –2.9 15.5 / 25.5 –163 to 421 1.8 2.0 0.7 ..Paraguay 17.7 / 29.4 1.9 to 2.6 –0.8 / –1.2 3.5 / 11.8 –48 to 112 0.0 0.0 0.7 3.8Peru 13.5 / 25.7 2.1 to 2.7 –2.5 / –2.7 7.7 / 22.9 –57 to 181 0.4 1.7 2.0 3.0Philippines 21.6 / 30.1 1.4 to 1.8 –2.5 / –2.5 13.5 / 23.8 –136 to 140 6.0 10.5 0.8 ..Poland 3.8 / 11.9 1.9 to 2.8 –1.5 / –1.7 2.3 / 4.8 –12 to 64 1.7 2.5 0.0 3.3Portugal 10.2 / 20.1 1.3 to 2.3 –2.0 / –1.9 4.5 / 8.7 –113 to –7 2.3 5.2 0.0 ..Puerto Rico 21.2 / 29.3 1.4 to 1.8 –3.0 / –2.9 12.8 / 24.2 –133 to 37 7.7 11.3 0.0 ..Qatar 21.3 / 33.0 2.3 to 2.9 –1.6 / –1.9 4.6 / 8.8 –23 to 8 13.4 23.1 .. ..2012 World Development Indicators 183

3.12Climate variability, exposureto impact, and resilienceClimate variability Exposure to impact ResilienceAverage dailyminimum/maximumtemperaturedegrees Celsiuschange in average numberof days/nightsLand areawith anelevationof 5 metersor less% of land area% of populationPopulationliving inareas withan elevationof 5 metersor lessPopulationaffected bydroughts,fl oods, andextremetemperatureDisaster riskreductionprogressscore1, worst,to 5, bestProjected annual Projected annual Projected annual Projected annualtemperaturedegrees Celsiuscool days/cold nightshot days/warm nightsprecipitationmillimeters1961–1990 2045–2065 2045–2065 2045–2065 2045–2065 2000 2000 1990–2009 2011Romania 3.8 / 13.8 2.0 to 3.1 –1.5 / –1.6 3.0 / 5.5 –92 to 2 2.9 2.9 0.1 3.3Russian Federation –10.1 / –0.1 2.6 to 3.7 –1.6 / –1.8 1.7 / 4.0 24 to 79 1.9 2.9 0.1 ..Rwanda 11.9 / 23.8 2.1 to 2.4 –2.2 / –2.8 8.3 / 26.4 –12 to 211 0.0 0.0 1.3 ..Saudi Arabia 18.2 / 31.1 2.3 to 2.9 –1.5 / –1.7 4.6 / 9.5 –27 to 9 0.5 1.0 0.0 ..Senegal 20.8 / 34.9 2.0 to 2.7 –2.3 / –2.6 7.1 / 16.9 –124 to 37 4.5 14.8 0.6 2.8Serbia .. 2.0 to 3.1 –1.5 / –1.4 3.2 / 6.1 –125 to –15 0.2 0.1 0.0 ..Sierra Leone 21.0 / 31.1 1.8 to 2.3 –2.7 / –2.6 9.2 / 24.0 –96 to 128 3.0 5.1 0.2 3.0Singapore 22.6 / 30.3 1.5 to 1.8 –2.6 / –2.7 12.4 / 25.9 –134 to 49 8.1 12.1 .. ..Slovak Republic 2.1 / 11.5 2.0 to 2.9 –1.5 / –1.8 2.6 / 4.7 –44 to 45 0.0 0.0 0.0 ..Slovenia 4.2 / 13.6 2.0 to 3.0 –1.4 / –1.5 3.1 / 5.6 –93 to 5 0.2 1.3 0.0 ..Somalia 21.2 / 32.9 1.9 to 2.2 –2.2 / –2.8 9.5 / 21.2 –4 to 111 0.6 2.2 4.6 ..South Africa 10.6 / 24.9 1.9 to 2.7 –1.7 / –2.0 4.1 / 10.4 –78 to 33 0.1 0.5 1.8 ..South Sudan .. .. .. .. .. .. ..Spain 7.9 / 18.7 1.5 to 2.6 –1.9 / –1.8 4.0 / 8.0 –112 to –18 1.3 6.6 0.7 ..Sri Lanka 23.3 / 30.6 1.5 to 1.8 –2.8 / –2.9 7.9 / 23.9 11 to 196 3.9 5.4 2.2 3.5Sudan 19.4 / 34.4 2.1 to 2.6 –1.8 / –2.2 5.2 / 16.6 –69 to 44 0.1 0.2 2.8 ..Swaziland 15.5 / 27.3 2.0 to 2.4 –1.6 / –2.1 3.8 / 10.9 –82 to 34 0.0 0.0 9.2 ..Sweden –2.1 / 6.3 2.1 to 3.1 –1.7 / –1.8 2.0 / 4.4 35 to 107 1.5 6.3 0.0 3.8Switzerland 2.0 / 9.0 1.7 to 2.8 –1.4 / –1.5 3.1 / 5.6 –89 to 11 0.0 0.0 0.0 4.8Syrian Arab Republic 10.8 / 24.7 2.2 to 3.1 –1.6 / –1.3 3.3 / 6.7 –57 to –13 0.1 0.3 0.5 3.5Tajikistan –3.9 / 7.9 2.3 to 3.4 –1.4 / –1.5 3.2 / 7.0 –46 to 27 0.0 0.0 5.4 ..Tanzania 16.7 / 28.0 1.9 to 2.2 –2.1 / –2.8 6.2 / 21.6 –9 to 167 0.2 1.3 1.5 3.5Thailand 21.2 / 31.4 1.7 to 2.1 –1.6 / –1.6 3.6 / 17.0 –109 to 76 4.2 13.8 3.8 3.8Timor-Leste .. 1.3 to 1.7 –3.0 / –3.0 16.1 / 25.9 –123 to 206 2.9 4.4 0.0 ..Togo 21.8 / 32.5 1.9 to 2.5 –2.4 / –2.5 6.0 / 21.5 –175 to 100 0.6 6.1 0.5 ..Trinidad and Tobago 21.5 / 30.0 1.5 to 2.1 –2.9 / –3.0 14.7 / 26.5 –266 to 8 8.0 7.5 0.0 ..Tunisia 13.5 / 24.9 1.9 to 2.6 –1.9 / –2.0 3.4 / 6.8 –57 to –16 2.8 9.5 0.1 ..Turkey 5.4 / 16.8 1.9 to 2.9 –1.5 / –1.4 3.1 / 6.3 –103 to –29 1.0 2.4 0.1 ..Turkmenistan 8.7 / 21.5 1.8 to 3.0 –1.0 / –1.1 2.7 / 5.7 –32 to 12 5.3 5.6 0.0 ..Uganda 16.6 / 29.0 2.1 to 2.3 –2.1 / –2.8 6.4 / 25.6 –13 to 224 0.0 0.0 0.9 ..Ukraine 4.0 / 12.6 2.1 to 3.0 –1.4 / –1.6 2.6 / 4.8 –43 to 31 1.5 2.1 0.3 ..United Arab Emirates 21.0 / 33.0 2.2 to 2.8 –1.6 / –2.0 4.2 / 9.4 –26 to 17 4.6 7.3 .. ..United Kingdom 5.1 / 11.8 1.1 to 1.8 –1.4 / –1.5 3.0 / 5.4 –10 to 66 7.6 8.6 0.0 ..United States 2.2 / 14.9 2.0 to 3.0 –1.4 / –1.5 3.1 / 6.4 –26 to 98 1.7 4.1 0.2 3.5Uruguay 12.2 / 22.9 1.4 to 1.8 –0.8 / –1.0 2.0 / 5.7 0 to 130 1.9 4.7 0.3 ..Uzbekistan 5.8 / 18.3 2.0 to 3.0 –1.1 / –1.3 2.6 / 5.7 –26 to 17 0.1 0.0 0.1 ..Venezuela, RB 20.3 / 30.4 1.8 to 2.9 –2.5 / –2.9 9.1 / 26.5 –150 to 85 2.3 3.7 0.2 2.8Vietnam 20.6 / 28.3 1.4 to 2.1 –1.5 / –1.5 5.7 / 14.4 –107 to 61 17.5 42.8 1.6 ..West Bank and Gaza .. 2.0 to 2.6 –1.7 / –1.8 3.8 / 7.9 –40 to –13 14.8 11.2 .. ..Yemen, Rep. 18.6 / 29.1 2.0 to 2.6 –2.0 / –2.2 5.6 / 13.1 –24 to 51 0.6 1.8 0.1 2.3Zambia 14.5 / 28.3 2.1 to 2.7 –2.0 / –2.5 4.0 / 17.7 –54 to 94 0.0 0.0 4.2 3.8Zimbabwe 14.2 / 27.8 2.2 to 2.9 –1.9 / –2.3 3.8 / 13.2 –81 to 24 0.0 0.0 .. ..World 1.8 t 6.5 t .. ..Low income 0.7 5.1 .. ..Middle income 1.8 6.5 .. ..Lower middle income 1.7 6.4 .. ..Upper middle income 1.8 6.5 .. ..Low & middle income 1.6 6.3 .. ..East Asia & Pacifi c 2.5 10.3 .. ..Europe & Central Asia 2.5 3.0 .. ..Latin America & Carib. 1.5 3.8 .. ..Middle East & N. Africa 1.4 9.7 .. ..South Asia 1.5 4.4 .. ..Sub-Saharan Africa 0.4 2.0 .. ..High income 2.2 7.7 .. ..Euro area 3.6 8.5 .. ..1842012 World Development Indicators

Climate variability, exposureto impact, and resilience 3.12ENVIRONMENTAbout the dataScientists use the terms climate change and globalwarming to refer to the gradual increase in theEarth’s surface temperature that has acceleratedsince the industrial revolution and especially overthe past two decades. Most global warming has beencaused by human activities that have changed thechemical composition of the atmosphere througha buildup of greenhouse gases—primarily carbondioxide, methane, and nitrous oxide. Rising globaltemperatures will cause sea level rise and alter localclimate conditions, affecting forests, crop yields,and water supplies, and may affect human health,animals, and many types of ecosystems. The 2007Intergovernmental Panel on Climate Change’s (IPCC)assessment report concluded that global warmingis “unequivocal” and gave the strongest warning yetabout the role of human activities. The report estimatedthat sea levels would rise approximately 49centimeters over the next 100 years, with a range ofuncertainty of 20–86 centimeters. That will lead toincreased coastal fl ooding through direct inundationand a higher base for storm surges, allowing fl oodingof larger areas and higher elevations. Climate modelsimulations predict an increase in average surfaceair temperature of about 2.5°C by 2100 (Kattenbergand others 1996) and increase of “killer” heat wavesduring the warm season (Karl and others 1997).Data in the table are categorized into three climatechange topics.• Climate variability contains such indicators as averagedaily minimum and maximum temperature,projected change in annual temperature, projectedchange in annual cool days and cold nights andhot days and warm nights, and projected changein annual precipitation. These indicators are usefulfor understanding critical thresholds relatedto heat stress in such sectors as agriculture andenergy.• Exposure to impact contains indicators that measurevulnerability to the impacts of climate change,such as land area with an elevation of 5 metersor less and population living in such areas as wellas population affected by droughts, fl oods, andextreme temperature. A drought is an extendedperiod of defi ciency in a region’s water supply asa result of below average precipitation. A droughtcan lead to losses in agriculture, affect inlandnavigation and hydropower plants, reduce accessto drinking water, and cause famines. A fl ood isa signifi cant rise of water level in a stream, lake,reservoir, or coastal region. Extreme temperatureevents are either cold waves or heat waves. A coldwave can be both a prolonged period of excessivelycold weather and the sudden invasion of very coldair over a large area. Accompanied by frost, it candamage agriculture, infrastructure, and property.A heat wave is a prolonged period of excessivelyhot and sometimes humid weather. Populationaffected by these natural disasters is the numberof people injured, left homeless, or requiringimmediate assistance and can include displacedor evacuated people.• Resilience is measured by the disaster risk reductionprogress score, an average of self-assessmentscores submitted by countries under Priority1 of the Hyogo Framework National ProgressReports. The Hyogo Framework is a global blueprintfor disaster risk reduction efforts that was adoptedby 168 countries in 2005. Assessments of Priority1 include four indicators that refl ect the degree towhich countries have prioritized disaster risk reductionand the strengthening of relevant institutions.Definitions• Average daily minimum and maximum temperatureare the minimum and maximum daily temperaturesin the country averaged over the periodspecifi ed, based on gridded climatologies from theClimatic Research Unit of the University of EastAnglia. • Projected change in annual temperatureis the projected change in annual temperature duringthe period specifi ed, relative to the control period1961–2000. • Projected change in annual cooldays and cold nights are the projected change inthe annual number of cool days and cold nights duringthe period specifi ed, relative to the control period1961–2000. Cool days are those that fall below the10th percentile of maximum temperature in the controlperiod, and cold nights are those that fall belowthe 10th percentile of minimum temperature in thecontrol period. • Projected change in annual hotdays and warm nights are the projected change inthe annual number of hot days and warm nights duringthe period specifi ed, relative to the control period1961–2000. Hot days are those that exceed the90th percentile of maximum temperatures in the controlperiod, and warm nights are those that exceedthe 90th percentile of minimum temperatures in thecontrol period. • Projected change in annual precipitationis the projected change in annual precipitationduring the period specifi ed, relative to the controlperiod 1961–2000. • Land area at an elevationof 5 meters or less is the percentage of land areawith an elevation of 5 meters or less. • Populationat an elevation of 5 meters or less is the percentageof the population living in areas with an elevationof 5 meters or less. • Population affected bydroughts, floods, and extreme temperature is theaverage annual percentage of the population that isaffected by natural disasters classifi ed as droughts,fl oods, or extreme temperature events. • Disasterrisk reduction progress score is the average of selfassessmentscores, ranging from 1 to 5, where 1 isthe worst and 5 is the best, submitted by countriesunder Priority 1 of the Hyogo Framework NationalProgress Reports.Data sourcesData on average daily minimum and maximumtemperature are from Mitchell and others (2003).Data on projected change in annual temperatureare from IPCC (2007). Data on projected changein annual cool days and cold nights and projectedchange in annual hot days and warm nights arefrom University of Cape Town Climate SystemsAnalysis Group calculations based on data fromMeehl and others (2007). Data on projectedchange in annual precipitation are from Meehl andothers (2007). Data on land area with an elevationof 5 meters or less and population living in areaswith an elevation 5 meters or less are from CIESIN(2007). Data on population affected by droughts,fl oods, and extreme temperature are from theUnited States Agency for International DevelopmentOffi ce of Foreign Disaster Assistance/Centerfor Research on the Epidemiology of DisastersInternational Disaster Database (www.emdat.be),Université Catholique de Louvain, and the WorldBank. Data on disaster risk reduction progressscore are from UNISD R (2009, 2010, 2011) ProgressReports.2012 World Development Indicators 185

3.13 UrbanizationUrbanpopulationPopulationin urbanagglomerationsof more than1 millionPopulation inlargest cityAccess to improvedsanitation facilitiesmillions% of totalpopulationaverageannual% growth% of totalpopulation% of urbanpopulation% of urbanpopulation% of ruralpopulation1990 2010 1990 2010 1990–2010 1990 2010 1990 2010 1990 2010 1990 2010Afghanistan 3 9 18 25 4.3 7 11 37 44 .. 60 .. 30Albania 1 2 36 48 1.7 .. .. 21 29 94 95 66 93Algeria 13 24 52 67 2.4 7 8 14 12 99 98 77 88Angola 4 11 37 59 4.3 15 25 41 43 67 85 6 19Argentina 28 37 87 92 1.1 39 39 37 35 93 91 73 77Armenia 2 2 68 64 0.1 33 36 49 56 95 95 .. 80Australia 15 20 85 89 1.8 60 58 25 22 100 100 100 100Austria 5 6 66 68 0.6 20 20 30 30 100 100 100 100Azerbaijan 4 5 54 52 1.5 24 22 45 42 .. 86 .. 78Bahrain 0 1 88 89 7.6 .. .. 29 16 100 100 .. ..Bangladesh 21 42 20 28 2.8 9 14 32 35 58 57 34 55Belarus 7 7 66 74 0.4 16 20 24 26 91 91 96 97Belgium 10 11 96 97 0.9 17 17 17 18 100 100 100 100Benin 2 4 35 42 3.8 .. .. 31 23 14 25 0 5Bolivia 4 7 56 67 2.3 25 33 29 25 28 35 6 10Bosnia and Herzegovina 2 2 39 49 1.0 .. .. 24 22 98 99 .. 92Botswana 1 1 42 61 2.5 .. .. 21 16 61 75 22 41Brazil 112 169 75 87 1.4 35 41 13 12 80 85 33 44Bulgaria 6 5 66 72 –0.3 14 16 21 22 100 100 98 100Burkina Faso 1 3 14 20 5.1 6 12 42 57 43 50 2 6Burundi 0 1 6 11 5.3 .. .. 66 52 41 49 44 46Cambodia 1 3 13 23 3.9 6 11 51 48 36 73 5 20Cameroon 5 11 41 58 3.6 14 20 19 19 63 58 37 36Canada 21 28 77 81 1.3 40 44 18 20 100 100 99 99Central African Republic 1 2 37 39 2.3 .. .. 43 42 21 43 5 28Chad 1 3 21 28 4.3 .. .. 38 27 21 30 4 6Chile 11 15 83 89 1.2 35 35 42 39 91 98 48 83China 311 601 27 45 2.5 9 18 3 3 48 74 15 56Hong Kong SAR, China 6 7 100 100 0.9 100 105 100 100 .. .. .. ..Colombia 23 35 68 75 1.8 31 38 21 24 79 82 40 63Congo, Dem. Rep. 10 23 28 35 4.5 13 18 35 38 23 24 4 24Congo, Rep. 1 3 54 62 3.2 29 33 54 53 .. 20 .. 15Costa Rica 2 3 51 64 2.3 24 31 47 49 94 95 91 96Côte d’Ivoire 5 10 40 50 3.3 17 21 42 42 38 36 8 11Croatia 3 3 54 58 0.2 .. .. 27 27 99 99 98 98Cuba 8 9 73 76 0.0 20 19 27 25 86 94 64 81Cyprus 1 1 67 70 1.5 .. .. 33 31 100 100 100 100Czech Republic 8 8 75 74 0.3 12 11 16 15 100 99 98 97Denmark 4 5 85 87 0.7 20 21 24 25 100 100 100 100Dominican Republic 4 7 55 71 2.4 21 22 38 31 83 87 61 75Ecuador 6 10 55 67 2.4 26 31 28 28 86 96 48 84Egypt, Arab Rep. 25 35 44 43 1.8 21 19 37 32 91 97 57 93El Salvador 3 4 49 61 1.0 18 25 37 41 88 89 62 83Eritrea 0 1 16 22 5.1 .. .. 72 60 58 52 0 4Estonia 1 1 71 70 0.0 .. .. 43 43 96 96 94 94Ethiopia 6 15 13 18 3.9 4 4 29 20 20 29 1 19Finland 3 3 61 64 0.9 17 21 28 33 100 100 100 100France 43 50 74 78 0.8 22 22 22 21 100 100 100 100Gabon 1 1 69 86 2.4 .. .. 62 49 .. 33 .. 30Gambia, The 0 1 38 58 4.2 .. .. 62 45 .. 70 .. 65Georgia 3 2 55 53 1.1 25 25 46 48 97 96 95 93Germany 58 60 73 74 0.0 8 8 6 6 100 100 100 100Ghana 5 13 36 52 3.8 13 17 22 19 12 19 4 8Greece 6 7 59 61 0.6 30 29 51 47 100 99 93 97Guatemala 4 7 41 50 3.4 9 8 22 16 81 87 48 70Guinea 2 4 28 35 3.6 16 17 55 47 19 32 6 11Guinea-Bissau 0 0 28 30 2.3 .. .. 54 68 .. 44 4 9Haiti 2 5 29 50 4.1 16 21 56 43 44 24 19 101862012 World Development Indicators

Urbanization 3.13ENVIRONMENTUrbanpopulationPopulationin urbanagglomerationsof more than1 millionPopulation inlargest cityAccess to improvedsanitation facilitiesmillions% of totalpopulationaverageannual% growth% of totalpopulation% of urbanpopulation% of urbanpopulation% of ruralpopulation1990 2010 1990 2010 1990–2010 1990 2010 1990 2010 1990 2010 1990 2010Honduras 2 4 40 49 2.9 12 14 29 28 71 85 36 69Hungary 7 7 66 68 0.4 19 17 29 25 100 100 100 100India 223 369 26 30 2.3 10 12 4 6 51 58 7 23Indonesia 56 129 31 54 3.1 10 9 14 7 56 73 21 39Iran, Islamic Rep. 31 51 56 70 1.9 24 24 21 14 83 100 74 100Iraq 13 21 70 66 2.8 27 23 32 28 .. 76 .. 67Ireland 2 3 57 62 0.8 26 25 46 40 100 100 98 98Israel 4 7 90 92 1.8 56 57 48 47 100 100 100 100Italy 38 41 67 68 0.7 19 17 9 8 .. .. .. ..Jamaica 1 1 49 54 0.6 .. .. 49 40 78 78 81 82Japan 78 85 63 67 0.2 46 49 42 43 100 100 100 100Jordan 2 5 72 79 2.3 27 18 37 23 98 98 95 98Kazakhstan 9 10 56 59 1.9 7 8 12 14 96 97 97 98Kenya 4 9 18 22 4.0 6 9 32 39 27 32 25 32Korea, Dem. Rep. 12 15 58 63 1.0 13 12 21 18 .. 86 .. 71Korea, Rep. 32 40 74 82 0.5 51 48 33 24 100 100 100 100Kosovo .. .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 2 3 98 98 3.4 67 84 68 86 100 100 100 100Kyrgyz Republic 2 2 38 37 1.6 .. .. 38 43 94 94 .. 93Lao PDR 1 2 15 33 5.0 .. .. 70 40 .. 89 .. 50Latvia 2 2 69 68 –0.6 .. .. 49 46 .. 82 .. 71Lebanon 2 4 83 87 0.9 44 46 53 53 100 100 .. ..Lesotho 0 1 14 27 3.7 .. .. 49 39 .. 32 .. 24Liberia 1 2 45 62 5.2 .. .. 108 34 .. 29 .. 7Libya 3 5 76 78 1.7 20 17 26 22 97 97 96 96Lithuania 2 2 68 67 –1.4 .. .. 23 24 95 95 .. ..Macedonia, FYR 1 1 58 68 0.9 .. .. 40 35 92 92 .. 82Madagascar 3 6 24 30 4.0 8 9 36 30 15 21 7 12Malawi 1 3 12 20 5.7 .. .. 24 29 48 49 38 51Malaysia 9 21 50 72 2.9 8 9 12 7 88 96 81 95Mali 2 5 23 33 4.7 9 11 37 33 33 35 10 14Mauritania 1 1 40 41 2.9 .. .. 53 51 29 51 8 9Mauritius 0 1 44 43 0.6 .. .. 30 28 91 91 88 88Mexico 60 88 71 78 1.6 33 35 25 22 76 87 34 79Moldova 2 1 47 41 –0.8 .. .. 38 44 .. 89 .. 82Mongolia 1 2 57 58 1.9 .. .. 46 61 .. 64 .. 29Morocco 12 18 48 57 1.6 18 19 22 18 81 83 27 52Mozambique 3 9 21 38 4.4 6 7 27 18 36 38 4 5Myanmar 10 16 25 34 2.7 9 11 30 27 .. 83 .. 73Namibia 0 1 28 38 3.4 .. .. 36 41 62 57 9 17Nepal 2 5 9 18 4.4 .. .. 23 19 37 48 7 27Netherlands 10 14 69 83 1.2 13 12 9 8 100 100 100 100New Zealand 3 4 85 87 1.3 25 32 30 37 .. .. 88 ..Nicaragua 2 3 52 57 1.8 18 23 34 28 59 63 26 37Niger 1 3 15 17 4.0 6 7 36 40 19 34 2 4Nigeria 34 79 35 50 4.0 12 15 14 13 39 35 36 27Norway 3 4 72 78 1.3 .. .. 22 23 100 100 100 100Oman 1 2 66 72 2.6 .. .. 27 33 96 100 55 95Pakistan 34 64 31 37 2.9 15 18 21 20 72 72 7 34Panama 1 3 54 75 2.6 35 39 65 52 73 75 40 51Papua New Guinea 1 1 15 13 2.1 .. .. 31 37 78 71 42 41Paraguay 2 4 49 62 2.7 26 31 53 51 61 90 15 40Peru 15 21 69 72 1.2 27 31 39 43 71 81 17 37Philippines 30 62 49 66 2.8 14 14 27 19 69 79 45 69Poland 23 23 61 61 0.0 4 4 7 7 96 96 .. 80Portugal 5 6 48 61 1.1 37 39 53 44 97 100 87 100Puerto Rico 3 4 72 99 0.5 44 69 60 70 .. .. .. ..Qatar 0 2 92 96 9.7 .. .. 54 28 100 100 100 1002012 World Development Indicators 187

3.13UrbanizationUrbanpopulationPopulationin urbanagglomerationsof more than1 millionPopulation inlargest cityAccess to improvedsanitation facilitiesmillions% of totalpopulationaverageannual% growth% of totalpopulation% of urbanpopulation% of urbanpopulation% of ruralpopulation1990 2010 1990 2010 1990–2010 1990 2010 1990 2010 1990 2010 1990 2010Romania 12 12 53 55 0.1 9 9 17 17 88 88 52 54Russian Federation 109 103 73 73 –0.1 17 18 8 10 80 74 58 59Rwanda 0 2 5 19 4.5 .. .. 57 57 69 52 34 56Saudi Arabia 12 23 77 84 4.0 34 39 19 21 100 100 .. ..Senegal 3 5 39 43 3.3 19 23 50 54 62 70 22 39Serbia 4 4 50 52 –0.1 15 15 30 29 96 96 .. 88Sierra Leone 1 2 33 38 3.0 .. .. 40 40 22 23 5 6Singapore 3 5 100 100 1.8 99 95 99 95 99 100 .. ..Slovak Republic 3 3 57 57 0.4 .. .. .. .. 100 100 100 99Slovenia 1 1 50 48 –0.2 .. .. 27 26 100 100 100 100Somalia 2 3 30 37 3.5 16 16 53 43 .. 52 .. 6South Africa 18 31 52 62 2.1 28 34 10 12 82 86 60 67South Sudan .. .. .. .. .. .. .. .. .. .. .. .. ..Spain 29 36 75 77 0.5 22 24 15 16 100 100 100 100Sri Lanka 3 3 17 15 0.9 .. .. 21 22 85 88 67 93Sudan 7 20 27 45 4.5 9 12 33 26 51 44 18 14Swaziland 0 0 23 26 2.6 .. .. 22 25 62 64 44 55Sweden 7 8 83 85 0.9 12 14 15 16 100 100 100 100Switzerland 5 6 73 74 1.1 15 15 20 20 100 100 100 100Syrian Arab Republic 6 11 49 55 2.6 31 34 26 27 95 96 75 93Tajikistan 2 2 32 27 1.5 .. .. 35 39 93 95 .. 94Tanzania 5 12 19 26 4.7 5 7 27 28 10 20 6 7Thailand 17 24 29 34 1.6 10 10 35 30 94 95 80 96Timor-Leste 0 0 21 28 3.6 .. .. 79 55 .. 73 .. 37Togo 1 3 30 43 3.7 17 28 56 64 26 26 8 3Trinidad and Tobago 0 0 9 14 2.9 .. .. 45 32 93 92 93 92Tunisia 5 7 58 67 1.6 .. .. 14 11 95 96 44 64Turkey 32 51 59 70 1.9 24 29 20 21 96 97 66 75Turkmenistan 2 2 45 50 2.1 .. .. 25 26 99 99 97 97Uganda 2 4 11 13 4.4 4 5 38 36 32 34 26 34Ukraine 35 31 67 68 –0.3 12 14 7 9 97 96 .. 89United Arab Emirates 1 6 79 78 8.0 26 21 33 27 98 98 95 95United Kingdom 51 56 89 90 0.8 26 26 15 15 100 100 100 100United States 188 255 75 82 1.2 42 45 9 8 100 100 99 99Uruguay 3 3 89 93 0.5 50 49 56 53 95 100 83 99Uzbekistan 8 10 40 37 1.8 10 8 26 21 95 100 76 100Venezuela, RB 17 27 84 94 1.9 34 32 17 11 89 .. 45 ..Vietnam 13 25 20 29 2.7 9 13 25 25 63 94 30 68West Bank and Gaza 1 3 68 72 2.8 .. .. .. .. 91 92 .. 92Yemen, Rep. 2 8 21 32 4.9 5 10 26 31 70 93 12 34Zambia 3 5 39 36 2.0 10 11 24 31 61 57 37 43Zimbabwe 3 5 29 38 2.0 10 13 35 34 54 52 35 32World 2,262 s 3,482 s 43 w 51 w 2.0 w 17 w 20 w 17 w 16 w 76 w 79 w 27 w 47 wLow income 108 225 21 28 3.6 8 11 34 33 38 47 17 32Middle income 1,456 2,401 38 48 2.2 14 18 15 13 68 75 22 45Lower middle income 560 992 32 39 2.7 12 14 16 14 61 66 17 34Upper middle income 896 1,409 44 57 1.9 16 22 14 12 71 82 27 62Low & middle income 1,564 2,626 36 46 2.3 13 17 16 15 66 72 21 43East Asia & Pacifi c 463 901 29 46 2.7 .. .. 9 7 54 76 20 57Europe & Central Asia 246 259 63 64 0.5 16 18 15 16 88 87 68 80Latin America & Carib. 309 462 71 79 1.6 32 35 24 22 80 84 38 59Middle East & N. Africa 117 192 52 58 2.2 20 20 26 22 89 94 57 80South Asia 286 492 25 30 2.5 10 13 9 11 54 60 11 28Sub-Saharan Africa 144 319 28 37 3.9 11 14 27 26 42 42 19 23High income 698 856 73 78 1.0 .. .. 20 19 100 100 99 99Euro area 215 244 71 74 0.6 18 18 16 15 100 100 99 1001882012 World Development Indicators

Urbanization3.13ENVIRONMENTAbout the dataDefinitionsThere is no consistent and universally acceptedstandard for distinguishing urban from rural areas,in part because of the wide variety of situationsacross countries (see About the data for table 3.1).Most countries use an urban classifi cation relatedto the size or characteristics of settlements. Somedefi ne urban areas based on the presence of certaininfrastructure and services. And other countriesdesignate urban areas based on administrativearrangements.The population of a city or metropolitan areadepends on the boundaries chosen. For example, in1990 Beijing, China, contained 2.3 million people in87 square kilometers of “inner city” and 5.4 millionin 158 square kilometers of “core city.” The populationof “inner city and inner suburban districts” was6.3 million and that of “inner city, inner and outersuburban districts, and inner and outer counties”was 10.8 million. (Most countries use the last defi nition.)For further discussion of urban-rural issues seebox 3.1a in About the data for table 3.1.Estimates of the world’s urban population wouldchange signifi cantly if China, India, and a few otherpopulous nations were to change their defi nition ofurban centers. According to China’s State StatisticalBureau, by the end of 1996 urban residentsaccounted for about 43 percent of China’s population,more than double the 20 percent consideredurban in 1994. In addition to the continuous migrationof people from rural to urban areas, one of themain reasons for this shift was the rapid growth inthe hundreds of towns reclassifi ed as cities in recentyears.Because the estimates in the table are based onnational defi nitions of what constitutes a city or metropolitanarea, cross-country comparisons should bemade with caution. To estimate urban populations,UN ratios of urban to total population were appliedto the World Bank’s estimates of total population(see table 2.1).The table shows access to improved sanitationfacilities for both urban and rural populations toallow comparison of access. Defi nitions of accessand urban areas vary, however, so comparisonsbetween countries can be misleading.• Urban population is the midyear population ofareas defi ned as urban in each country and reportedto the United Nations (see About the data). • Populationin urban agglomerations of more than 1 millionis the percentage of a country’s population living inmetropolitan areas that in 2005 had a population ofmore than 1 million. • Population in largest city isthe percentage of a country’s urban population livingin that country’s largest metropolitan area. • Accessto improved sanitation facilities is the percentageof the urban or rural population with access to atleast adequate excreta disposal facilities (private orshared but not public) that can effectively preventhuman, animal, and insect contact with excreta.Improved facilities range from simple but protectedpit latrines to fl ush toilets with a sewerage connection.To be effective, facilities must be correctly constructedand properly maintained.Data sourcesData on urban population and the population inurban agglomerations and in the largest city arefrom the United Nations Population Division’sWorld Urbanization Prospects: The 2010 Revision.Data on total population are World Bankestimates. Data on access to sanitation are fromthe World Health Organization and United NationsChildren’s Fund’s Progress on Drinking Water andSanitation (2012).2012 World Development Indicators 189

3.14 Urban housing conditionsCensusyearHouseholdsizeOvercrowdingDurabledwellingunitsHomeownershipMultiunitdwellingsVacancyratenumber ofpeopleHouseholds livingin overcrowdeddwellings a% of totalBuildings withdurable structure% of totalPrivately owneddwellings% of total % of totalUnoccupieddwellings% of totalNational Urban National Urban National Urban National Urban National Urban National UrbanAfghanistan .. .. .. .. .. .. .. .. .. .. .. ..Albania 2001 4.2 3.9 .. .. .. .. 65 b 30 b .. .. 12 13Algeria 1998 4.9 .. .. .. .. .. 67 .. .. .. 19 ..Angola .. .. .. .. .. .. .. .. .. .. .. ..Argentina 2001 3.6 .. 19 .. 97 .. .. .. 4 .. 16 b ..Armenia 2001 4.1 4.0 4 6 93 93 95 90 1 1 .. ..Australia 2001 3.8 .. 1 .. .. .. .. .. .. .. .. ..Austria 2001 2.4 .. 2 .. .. .. 48 .. .. .. .. ..Azerbaijan 1999 4.7 4.4 .. .. .. .. 74 62 4 5 .. ..Bahrain .. .. .. .. .. .. .. .. .. .. .. ..Bangladesh 2001 4.8 4.8 .. .. 21 b 42 b 88 b 61 b .. .. .. ..Belarus 1999 .. .. .. .. .. .. .. .. .. .. .. ..Belgium 2001 2.6 .. 0 b .. .. .. 67 .. 32 b .. .. ..Benin 1992 5.9 .. .. .. 26 .. 59 .. .. .. .. ..Bolivia 2001 4.2 4.3 40 .. 43 58 70 59 3 b 5 b 6 4Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. ..Botswana 2001 4.2 3.9 27 47 88 90 b 61 47 1 .. .. ..Brazil 2000 3.8 3.7 .. .. .. .. 74 75 .. .. .. ..Bulgaria 2001 2.7 2.7 .. .. 79 89 98 98 .. .. 23 17Burkina Faso 1996 6.2 5.8 30 53 .. .. .. .. .. .. .. ..Burundi 1990 4.7 .. .. .. .. .. .. .. .. .. .. ..Cambodia 2005 5.0 4.9 35 32 79 88 58 57 27 32 .. ..Cameroon 1987 5.2 5.1 67 77 77 .. 73 48 27 42 .. ..Canada 2001 2.6 .. .. .. .. .. 64 .. 32 .. 8 ..Central African Republic 2003 5.2 5.8 32 36 b 78 92 85 74 .. .. .. ..Chad 1993 5.1 5.1 .. .. .. .. .. .. .. .. .. ..Chile 2002 3.4 3.5 .. .. 91 92 66 65 13 15 11 10China 2000 3.4 3.2 .. .. 82 .. 88 74 .. .. 1 ..Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. ..Colombia 1993 4.8 .. 27 b .. 83 b .. 68 b .. 13 .. 10 b ..Congo, Dem. Rep. 1984 5.4 .. 55 .. .. .. .. .. .. .. .. ..Congo, Rep. 1984 10.5 .. .. .. .. .. 76 .. .. .. .. ..Costa Rica 2000 4.0 .. 22 .. 88 .. 72 .. 2 3 9 6Côte d’Ivoire 1998 5.4 .. .. .. .. .. .. .. .. .. .. ..Croatia 2001 3.0 .. .. .. .. .. .. .. .. .. 12 ..Cuba 2002 3.1 .. 5 .. .. .. .. .. .. .. .. ..Cyprus .. .. .. .. .. .. .. .. .. .. .. ..Czech Republic 2001 2.4 .. .. .. .. .. 52 .. 49 .. 12 ..Denmark 2001 2.2 .. .. .. .. .. .. .. .. .. .. ..Dominican Republic 2002 3.9 .. .. .. 97 .. .. .. 8 .. 11 ..Ecuador 2001 3.5 3.7 30 .. 81 88 68 b 58 b 9 14 12 7Egypt, Arab Rep. 1996 4.7 .. .. .. .. .. .. .. 75 .. .. ..El Salvador 1992 .. .. 63 .. 67 83 70 68 3 6 11 11Eritrea .. .. .. .. .. .. .. .. .. .. .. ..Estonia 2000 2.4 2.3 3 .. .. .. .. .. 72 .. 13 ..Ethiopia 1994 4.8 4.7 .. .. .. 23 .. 54 .. .. .. ..Finland 2000 2.2 .. .. .. .. .. 64 .. 44 .. .. ..France 1999 2.5 .. .. .. .. .. 55 .. .. .. 7 ..Gabon 2003 5.2 .. .. .. .. .. .. .. .. .. .. ..Gambia, The 1993 8.9 .. .. .. 18 .. 68 .. .. .. .. ..Georgia 2002 3.5 3.5 .. .. .. .. .. .. .. .. .. ..Germany 2001 2.3 .. .. .. .. .. 43 .. .. .. 7 ..Ghana 2000 5.1 5.1 .. .. 45 .. 57 .. 53 .. 5 ..Greece 2001 3.0 .. 1 .. .. .. .. .. .. .. .. ..Guatemala 2002 4.4 4.7 .. .. 67 80 81 74 2 4 13 11Guinea 1996 6.7 .. 63 .. .. .. 76 .. .. .. .. ..Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..Haiti 1982 4.2 .. 26 .. .. .. 92 68 .. .. 9 191902012 World Development Indicators

Urban housing conditions3.14ENVIRONMENTCensusyearHouseholdsizeOvercrowdingDurabledwellingunitsHomeownershipMultiunitdwellingsVacancyratenumber ofpeopleHouseholds livingin overcrowdeddwellings a% of totalBuildings withdurable structure% of totalPrivately owneddwellings% of total % of totalUnoccupieddwellings% of totalNational Urban National Urban National Urban National Urban National Urban National UrbanHonduras 2001 4.4 .. .. .. 69 85 .. .. .. .. 14 ..Hungary 2001 2.6 .. 2 .. .. .. .. .. .. .. 4 ..India 2001 5.3 5.3 77 71 83 81 87 67 .. .. 6 9Indonesia 2000 4.0 .. .. .. .. .. .. .. .. .. .. ..Iran, Islamic Rep. 1996 4.8 4.6 33 b 26 b 72 76 73 67 .. .. .. ..Iraq 1997 7.7 7.2 .. .. 88 96 70 66 4 5 13 15Ireland 2002 3.0 .. .. .. .. .. .. .. 8 b .. .. ..Israel 1995 3.5 .. .. .. .. .. .. .. .. .. .. ..Italy 2001 2.8 .. .. .. .. .. .. .. .. .. 21 ..Jamaica 2001 3.5 .. .. .. 98 b .. 58 b .. 2 b .. .. ..Japan 2000 2.7 .. .. .. .. .. 61 .. 37 .. .. ..Jordan 2004 5.3 5.1 35 34 .. .. 64 60 72 80 .. ..Kazakhstan .. .. .. .. .. .. .. .. .. .. .. ..Kenya 1999 4.6 3.4 .. .. 35 72 72 25 .. .. 39 17Korea, Dem. Rep. 2000 3.8 .. 23 .. .. .. 50 .. 15 .. .. ..Korea, Rep. 1993 4.4 .. .. .. .. .. .. .. .. .. .. ..Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 1995 6.4 .. .. .. .. .. .. .. 9 b .. 11 ..Kyrgyz Republic 1999 4.4 3.6 .. .. .. .. .. .. .. .. .. ..Lao PDR 1995 6.1 6.1 .. .. 49 77 96 86 .. .. .. ..Latvia 2000 3.0 2.6 4 .. 88 .. 58 .. 74 .. 0 ..Lebanon .. .. .. .. .. .. .. .. .. .. .. ..Lesotho 2001 5.0 .. 10 b .. .. .. 84 .. 0 .. .. ..Liberia 1974 4.8 .. 31 .. 20 .. 1 .. .. .. .. ..Libya 6.4 .. .. .. .. .. .. .. .. .. 7 ..Lithuania 2001 2.6 .. 7 .. .. .. .. .. .. .. .. ..Macedonia, FYR 2002 3.6 3.6 b 8 b .. 95 b 95 b 48 b .. .. .. 7 b 3 bMadagascar 1993 4.9 4.8 64 57 .. .. 81 59 .. .. .. ..Malawi 1998 4.4 4.4 30 .. 48 84 86 47 .. .. .. ..Malaysia 2000 4.5 4.4 .. .. .. .. .. .. 10 b 16 b .. ..Mali 1998 5.6 .. .. .. .. .. .. .. .. .. .. ..Mauritania 1988 .. .. .. .. .. .. .. .. .. .. .. ..Mauritius 2000 3.9 3.8 6 7 91 94 87 81 .. .. 7 6Mexico 2005 4.0 3.9 24 20 .. .. .. .. .. .. 3 2Moldova 2003 .. .. .. .. .. .. .. .. .. .. .. ..Mongolia 2000 4.4 4.5 .. .. .. .. .. .. 48 56 .. ..Morocco 1982 5.9 5.3 .. .. .. .. .. .. .. .. .. ..Mozambique 1997 4.4 4.9 37 28 7 20 92 83 1 1 0 ..Myanmar .. .. .. .. .. .. .. .. .. .. .. ..Namibia 2001 5.3 .. .. .. .. .. .. .. .. .. .. ..Nepal 2001 5.4 4.9 .. .. .. .. 88 .. .. .. 0 ..Netherlands .. .. .. .. .. .. .. .. .. .. .. ..New Zealand 2001 2.8 .. 1 b .. .. .. 65 .. 17 .. 10 ..Nicaragua 1995 5.3 .. .. .. 79 87 84 86 0 0 8 ..Niger 2001 6.4 6.0 .. .. .. .. 77 40 .. .. .. ..Nigeria 1991 5.0 4.7 .. .. .. .. .. .. .. .. .. ..Norway 1980 2.7 .. 1 .. .. .. 67 .. 38 .. .. ..Oman 2003 7.1 .. .. .. .. .. .. .. .. .. .. ..Pakistan 1998 6.8 6.8 .. .. 58 86 81 .. .. .. .. ..Panama 2000 4.1 .. 28 b .. 88 98 b 80 66 b 10 b 10 b 14 ..Papua New Guinea 1990 4.5 b 6.5 .. .. .. .. .. 44 .. 8 .. ..Paraguay 2002 4.6 4.5 38 b .. 95 b 98 b 79 75 1 b 2 b 6 b 6 bPeru 2007 3.9 3.9 35 31 .. .. .. .. .. .. .. ..Philippines 2000 4.9 .. .. .. .. .. 71 .. 12Poland 1988 3.2 .. .. .. .. .. .. .. .. .. 1 ..Portugal 2001 2.8 .. .. .. .. .. 76 .. 86 .. .. ..Puerto Rico 2005 2.8 . 1 .. .. .. 75 .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 191

3.14 Urban housing conditionsCensusyearHouseholdsizeOvercrowdingDurabledwellingunitsHomeownershipMultiunitdwellingsVacancyrateRomania 2002 2.9 2.8 20 20 .. .. 84 72 .. .. .. ..Russian Federation 2002 2.8 2.7 7 5 .. .. .. .. 73 86 .. ..Rwanda 2002 4.4 3.7 43 36 13 31 79 41 36 60 .. ..Saudi Arabia 2004 5.5 .. .. .. 92 b .. 43 .. .. .. .. ..Senegal 2002 9.2 8.0 72 68 .. .. 74 54 .. .. .. ..Serbia 2001 2.9 2.2 .. .. .. .. .. .. .. .. .. ..Sierra Leone 1985 6.8 .. .. .. 34 .. 68 .. .. .. .. ..Singapore 2000 4.4 .. .. .. .. .. .. .. .. .. .. ..Slovak Republic .. .. .. .. .. .. .. .. .. .. .. ..Slovenia 2002 2.8 2.7 14 17 .. .. 91 87 33 56 .. ..Somalia 1975 .. .. .. .. .. .. .. .. .. .. .. ..South Africa 2007 3.0 2.8 16 15 .. .. 43 40 .. .. .. ..South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 2001 2.9 .. 1 .. .. .. 82 .. .. .. .. ..Sri Lanka 2001 3.8 .. .. .. 93 b 92 b 70 b 58 b 1 14 b 13 1 bSudan 1993 5.8 6.0 .. .. .. .. 86 b 58 b 0 b 1 b .. ..Swaziland 1997 5.4 3.7 .. .. .. .. .. .. .. .. .. ..Sweden 1990 2.0 .. .. .. .. .. .. .. 54 .. 1 ..Switzerland 2000 2.2 .. 1 .. .. .. 34 .. 77 .. .. ..Syrian Arab Republic 1981 6.3 6.0 .. .. .. .. .. .. .. .. .. ..Tajikistan 2000 .. .. .. .. .. .. .. .. .. .. .. ..Tanzania 2002 4.9 4.5 b 33 b 7 b .. .. 82 b 43 b .. .. .. ..Thailand 2000 3.8 .. .. .. 93 93 81 62 3 .. 3 ..Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo .. .. .. .. .. .. .. .. .. .. .. ..Trinidad and Tobago 2000 3.7 .. 9 b .. 98 b .. 74 b .. 17 b .. .. ..Tunisia 1994 8.0 .. .. .. 99 .. 71 89 b 6 10 b 15 12 bTurkey 1990 5.0 .. .. .. .. .. 70 .. .. .. .. ..Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda 2002 4.7 3.9 .. .. 19 61 76 28 37 71 .. ..Ukraine 2003 .. .. .. .. .. .. .. .. .. .. .. ..United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. ..United Kingdom 2001 .. 2.4 .. .. .. .. .. 69 .. 19 .. ..United States 2005 2.5 .. 0 .. .. .. 74 .. 26 .. .. ..Uruguay 1996 3.3 3.4 b 22 b .. .. .. 57 b 57 b .. .. 13 b 13 bUzbekistan .. .. .. .. .. .. .. .. .. .. .. ..Venezuela, RB 2001 4.4 .. .. .. .. .. 78 .. 14 .. 16 ..Vietnam 1999 4.6 4.5 .. .. 77 89 95 86 .. .. .. ..West Bank and Gaza 1997 7.1 .. .. .. .. .. 78 .. 45 .. .. ..Yemen, Rep. 1994 6.7 6.8 54 b 6 b .. .. 88 b 68 b 3 b 11 b .. ..Zambia 2000 5.3 5.9 .. .. .. .. 94 30 .. .. .. ..Zimbabwe 1992 4.8 4.2 .. .. .. .. 94 30 6 .. .. ..a. More than two people per room. b. Data are from a previous census.number ofpeopleHouseholds livingin overcrowdeddwellings a% of totalBuildings withdurable structure% of totalPrivately owneddwellings% of total % of totalUnoccupieddwellings% of totalNational Urban National Urban National Urban National Urban National Urban National Urban1922012 World Development Indicators

Urban housing conditions 3.14ENVIRONMENTAbout the dataDefinitionsUrbanization can yield important social benefi ts,improving access to public services and the job market.It also leads to signifi cant demands for services.Inadequate living quarters and demand for housingand shelter are major concerns for policymakers.The unmet demand for affordable housing, alongwith urban poverty, has led to the emergence ofslums in many poor countries. Improving the sheltersituation requires a better understanding of themechanisms governing housing markets and the processesgoverning housing availability. That requiresgood data and adequate policy-oriented analysis sothat housing policy can be formulated in a globalcomparative perspective and drawn from lessonslearned in other countries. Housing policies andoutcomes affect such broad socioeconomic conditionsas the infant mortality rate, performance inschool, household saving, productivity levels, capitalformation, and government budget defi cits. A goodunderstanding of housing conditions thus requiresan extensive set of indicators within a reasonableframework.There is a strong demand for quantitative indicatorsthat can measure housing conditions on aregular basis to monitor progress. However, datadefi ciencies and lack of rigorous quantitative analysishamper informed decisionmaking on desirablepolicies to improve housing conditions. The datain the table are from housing and population censuses,collected using similar defi nitions. The tablewill incorporate household survey data in future editions.The table focuses attention on urban areas,where housing conditions are typically most severe.Not all the compiled indicators are presented in thetable because of space limitations.• Census year is the year in which the underlyingdata were collected. • Household size is the averagenumber of people within a household, calculatedby dividing total population by the numberof households in the country and in urban areas.• Overcrowding refers to the number of householdsliving in dwellings with two or more people per roomas a percentage of total households in the countryand in urban areas. • Durable dwelling units arethe number of housing units in structures made ofdurable building materials (concrete, stone, cement,brick, asbestos, zinc, and stucco) expected to maintaintheir stability for 20 years or longer under localconditions with normal maintenance and repair, takinginto account location and environmental hazardssuch as fl oods, mudslides, and earthquakes, as apercentage of total dwellings. • Home ownershiprefers to the number of privately owned dwellings asa percentage of total dwellings. When the numberof private dwellings is not available from the censusdata, the share of households that own their housingunit is used. Privately owned and owner-occupiedunits are included, depending on the defi nition usedin the census data. State- and community-ownedunits and rented, squatted, and rent-free units areexcluded. • Multiunit dwellings are the numberof multiunit dwellings, such as apartments, fl ats,condominiums, barracks, boardinghouses, orphanages,retirement houses, hostels, hotels, and collectivedwellings, as a percentage of total dwellings.• Vacancy rate is the percentage of completed dwellingunits that are currently unoccupied. It includesall vacant units, whether on the market or not (suchas second homes).Data sourcesData on urban housing conditions are fromnational population and housing censuses.2012 World Development Indicators 193

3.15 Traffic and congestionMotorvehiclesPassengercarsRoaddensityRoad sectorenergy consumptionFuelpriceParticulate matterconcentrationper1,000peopleperkilometerof roadper1,000peoplekm. ofroad per100 sq.km. ofland areakilograms of oil equivalent per capita$ per literUrban-populationweightedPM10micrograms percubic meter% of totalSuper gradeconsumption Total Diesel fuel Gasoline fuel gasoline Diesel2009 2009 2009 2009 2009 2009 2009 2009 2010 2010 1990 2009Afghanistan 29 11 21 6 .. .. .. .. 1.15 1.00 69 32Albania 120 .. 89 63 23 123 120 15 1.46 1.40 92 37Algeria 112 35 74 5 23 265 177 67 0.32 0.19 112 75Angola 38 .. 8 4 13 85 45 36 0.65 0.43 112 56Argentina 314 .. .. 8 17 307 140 105 0.96 1.05 104 60Armenia 103 42 94 26 22 182 38 60 1.08 0.99 367 61Australia 688 18 550 11 18 1,103 433 610 1.27 1.23 22 14Austria 569 45 521 127 23 878 594 208 1.63 1.55 39 27Azerbaijan 104 13 86 61 12 155 45 109 0.75 0.56 133 29Bahrain 537 104 451 575 12 955 369 535 0.21 0.13 75 45Bangladesh 3 .. 2 166 7 14 10 3 1.09 0.63 228 121Belarus 282 .. 240 46 7 192 107 50 1.08 0.86 24 7Belgium 552 39 483 504 15 814 657 125 1.87 1.62 31 21Benin 22 .. 18 17 27 110 41 63 1.04 1.21 77 48Bolivia 68 9 18 7 32 206 98 68 0.70 0.54 105 60Bosnia and Herzegovina 138 20 121 43 15 234 148 81 1.42 1.42 36 19Botswana 133 7 69 4 31 318 131 174 0.93 0.97 92 66Brazil 209 18 178 21 24 298 149 73 1.58 1.14 39 19Bulgaria 375 72 330 36 15 336 192 81 1.51 1.58 108 45Burkina Faso 11 1 7 34 .. .. .. .. 1.44 1.28 143 63Burundi 6 .. 2 44 .. .. .. .. 1.43 1.42 60 28Cambodia 21 7 18 21 7 26 13 12 1.15 0.98 107 37Cameroon 14 .. 10 6 12 42 23 18 1.20 1.10 122 58Canada 607 15 420 14 17 1,313 405 886 1.21 1.08 25 16Central African Republic 0 .. 0 4 .. .. .. .. 1.71 1.69 60 33Chad 6 1 2 3 .. .. .. .. 1.32 1.31 209 82Chile 174 38 118 10 21 355 208 147 1.38 1.02 89 53China 47 16 34 40 5 92 56 45 1.11 1.04 115 60Hong Kong SAR, China 74 254 56 188 13 285 224 50 1.92 1.32 .. ..Colombia 71 25 53 15 21 144 77 54 1.41 0.95 38 20Congo, Dem. Rep. 5 .. .. 7 1 4 0 3 1.28 1.27 71 38Congo, Rep. 27 .. 16 5 27 98 68 26 1.27 0.84 130 60Costa Rica 166 20 130 76 30 319 156 147 1.14 0.97 43 29Côte d’Ivoire 20 5 16 25 4 21 17 6 1.68 1.30 87 29Croatia 384 58 346 52 22 435 268 153 1.59 1.49 45 25Cuba 38 .. 21 55 4 40 16 22 1.72 1.24 31 15Cyprus 675 48 529 134 30 682 294 351 1.47 1.47 60 28Czech Republic 482 39 423 166 14 557 338 186 1.75 1.69 41 17Denmark 478 36 380 170 22 727 456 292 2.00 1.79 28 16Dominican Republic 128 .. 87 26 17 144 59 78 1.23 1.03 43 16Ecuador 57 18 36 17 38 305 174 148 0.53 0.28 36 20Egypt, Arab Rep. 45 37 33 10 18 159 86 61 0.48 0.32 214 88El Salvador 54 .. 49 48 17 140 62 71 0.92 0.89 44 28Eritrea 11 .. 6 3 5 7 6 1 2.54 1.07 196 64Estonia 471 11 407 129 14 493 292 218 1.54 1.57 44 9Ethiopia 3 5 1 4 4 17 14 2 0.91 0.78 108 51Finland 532 36 459 23 12 728 409 299 1.94 1.60 22 15France 598 39 496 173 16 642 471 123 1.98 1.72 18 12Gabon .. .. .. 3 6 78 53 22 .. .. 9 5Gambia, The 8 3 5 33 .. .. .. .. .. .. 136 61Georgia 151 28 126 29 22 156 69 79 1.13 1.13 224 54Germany 564 72 510 180 16 612 323 236 1.90 1.68 27 16Ghana 30 6 18 46 16 62 37 27 0.82 0.83 38 21Greece 573 55 455 89 24 632 269 358 2.05 1.78 64 31Guatemala 98 .. 37 13 21 145 70 68 0.95 0.85 69 68Guinea .. .. .. 18 .. .. .. .. 0.95 0.95 103 54Guinea-Bissau 33 .. 27 12 .. .. .. .. .. .. 114 45Haiti .. .. .. 15 9 25 19 23 .. .. 68 331942012 World Development Indicators

Traffic and congestion 3.15ENVIRONMENTMotorvehiclesPassengercarsRoaddensityRoad sectorenergy consumptionFuelpriceParticulate matterconcentrationper1,000peopleperkilometerof roadper1,000peoplekm. ofroad per100 sq.km. ofland areakilograms of oil equivalent per capita$ per literUrban-populationweightedPM10micrograms percubic meter% of totalSuper gradeconsumption Total Diesel fuel Gasoline fuel gasoline Diesel2009 2009 2009 2009 2009 2009 2009 2009 2010 2010 1990 2009Honduras 95 .. 29 12 23 136 65 64 1.04 0.92 43 34Hungary 347 18 301 212 17 433 259 149 1.67 1.61 34 15India 18 5 12 125 7 37 26 11 1.15 0.82 110 57Indonesia 79 38 45 25 14 119 44 75 0.79 0.51 132 68Iran, Islamic Rep. 128 51 113 11 18 531 223 237 0.10 0.02 92 55Iraq 77 55 27 9 32 334 185 134 0.78 0.56 168 110Ireland 513 24 434 137 28 900 519 347 1.78 1.69 23 12Israel 316 128 265 83 27 766 347 380 1.85 1.87 66 23Italy 672 80 596 162 22 602 376 172 1.87 1.69 41 21Jamaica 120 15 84 201 16 189 65 177 0.98 0.98 56 29Japan 589 63 454 320 14 530 173 332 1.60 1.37 42 25Jordan 154 117 113 9 23 290 108 173 1.04 0.73 107 29Kazakhstan 199 33 167 4 6 235 39 205 0.71 0.51 43 17Kenya 23 14 13 11 7 31 21 11 1.33 1.27 64 30Korea, Dem. Rep. .. .. .. 21 2 13 7 5 .. .. 169 56Korea, Rep. 355 165 267 105 12 569 286 158 1.52 1.35 51 33Kosovo .. .. .. .. .. .. .. .. 1.63 1.60 .. ..Kuwait 495 212 412 37 13 1,492 491 920 0.23 0.21 113 95Kyrgyz Republic 59 9 44 17 28 160 103 51 0.85 0.79 79 35Lao PDR 20 3 2 17 .. .. .. .. 1.26 0.97 92 45Latvia 459 15 401 107 20 370 245 137 1.48 1.49 38 12Lebanon .. .. .. 67 26 418 5 386 1.13 0.77 39 28Lesotho .. .. .. 20 .. .. .. .. 0.97 1.07 108 43Liberia 3 .. 2 10 .. .. .. .. 0.98 0.96 68 31Libya 290 .. 225 5 20 640 405 206 0.17 0.13 101 81Lithuania 555 23 508 125 16 405 242 103 1.59 1.42 52 15Macedonia, FYR 155 23 138 54 15 203 116 59 1.52 1.27 45 18Madagascar 26 .. 7 8 .. .. .. .. 1.52 1.26 80 30Malawi 8 .. 4 13 .. .. .. .. 1.71 1.54 82 33Malaysia 350 69 313 30 21 492 178 295 0.59 0.56 35 19Mali 14 8 8 2 .. .. .. .. 1.42 1.25 258 106Mauritania .. .. .. 1 .. .. .. .. 1.16 0.99 145 68Mauritius 166 102 129 101 .. .. .. .. 1.55 1.23 21 14Mexico 276 81 191 19 28 439 123 295 0.81 0.72 66 33Moldova 146 41 107 38 11 75 52 23 1.21 1.08 118 33Mongolia 72 2 48 3 11 135 42 119 1.11 1.04 181 101Morocco 70 38 53 13 25 119 98 16 1.23 0.88 39 23Mozambique 12 9 9 4 5 20 17 4 1.11 0.86 112 24Myanmar 7 10 5 4 6 19 8 9 0.80 0.80 107 41Namibia 103 5 46 5 37 281 89 174 1.06 1.09 50 45Nepal 5 8 3 14 6 19 13 4 1.18 0.91 67 30Netherlands 522 63 459 329 14 673 380 240 2.13 1.71 44 30New Zealand 718 33 603 35 24 949 394 531 1.47 0.97 14 12Nicaragua 58 15 18 17 17 90 49 38 1.09 0.99 46 24Niger 8 6 6 1 .. .. .. .. 1.07 1.16 199 92Nigeria 31 .. 31 21 8 53 4 45 0.44 0.77 195 42Norway 578 30 465 29 12 704 549 258 2.12 2.01 22 15Oman 215 12 166 18 13 721 59 617 0.31 0.38 133 82Pakistan 13 8 10 32 13 63 38 11 0.86 0.92 216 101Panama 141 35 101 19 18 162 124 151 0.85 0.77 55 33Papua New Guinea 9 .. 6 4 .. .. .. .. .. .. 35 16Paraguay 91 16 39 8 27 200 150 37 1.28 1.01 107 65Peru 68 16 41 10 29 162 113 34 1.41 1.10 96 43Philippines 33 .. 8 67 19 79 47 29 1.05 0.84 56 17Poland 508 50 432 123 16 400 225 105 1.57 1.50 58 34Portugal 509 67 495 90 25 572 406 137 1.85 1.58 49 20Puerto Rico 596 92 582 301 .. .. .. .. .. .. 23 14Qatar 532 .. 335 67 9 1,331 675 591 0.19 0.19 54 312012 World Development Indicators 195

3.15Traffic and congestionMotorvehiclesPassengercarsRoaddensityRoad sectorenergy consumptionFuelpriceParticulate matterconcentrationper1,000peopleperkilometerof roadper1,000peoplekm. ofroad per100 sq.km. ofland areakilograms of oil equivalent per capita$ per literUrban-populationweightedPM10micrograms percubic meter% of totalSuper gradeconsumption Total Diesel fuel Gasoline fuel gasoline Diesel2009 2009 2009 2009 2009 2009 2009 2009 2010 2010 1990 2009Romania 230 20 198 83 14 223 147 67 1.46 1.46 42 14Russian Federation 271 39 233 6 7 330 114 221 0.84 0.72 41 16Rwanda 5 .. 2 53 .. .. .. .. 1.63 1.62 53 23Saudi Arabia 192 20 139 11 21 1,241 543 635 0.16 0.07 160 103Senegal 22 16 16 8 21 50 41 9 1.57 1.34 94 80Serbia 252 42 224 50 13 265 162 70 1.50 1.48 .. ..Sierra Leone 6 2 5 .. .. .. .. .. 0.94 0.94 87 37Singapore 156 232 121 473 15 545 347 174 1.42 1.04 108 23Slovak Republic 348 43 293 89 11 339 194 105 1.70 1.53 46 12Slovenia 566 30 522 192 24 836 514 288 1.67 1.62 39 26Somalia .. .. .. 3 .. .. .. .. .. .. 82 28South Africa 162 .. 110 30 11 320 134 176 1.19 1.14 39 26South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 596 41 478 132 24 660 528 126 1.56 1.47 41 25Sri Lanka 47 .. 19 148 18 79 54 22 1.19 0.66 93 71Sudan 27 .. 19 1 16 58 39 17 0.62 0.43 285 137Swaziland 89 25 45 21 .. .. .. .. 1.07 1.10 30 38Sweden 519 8 462 129 16 773 353 357 1.87 1.82 15 10Switzerland 563 61 519 173 21 737 286 424 1.66 1.77 34 22Syrian Arab Republic 63 20 30 37 22 248 154 83 0.96 0.45 135 71Tajikistan 38 .. 29 19 4 14 0 12 1.02 0.91 84 30Tanzania 7 .. 4 11 6 26 19 6 1.22 1.19 56 20Thailand 134 50 57 35 18 275 163 74 1.41 0.95 77 53Timor-Leste .. .. .. .. .. .. .. .. 1.40 0.90 .. ..Togo 2 1 2 21 11 51 17 31 1.18 1.17 56 28Trinidad and Tobago 353 .. .. 162 5 704 313 355 .. .. 136 98Tunisia 114 61 76 12 16 145 96 41 0.94 0.82 71 23Turkey 142 29 95 46 14 185 120 34 2.52 2.03 77 37Turkmenistan 106 .. 80 5 4 172 0 164 0.22 0.20 196 41Uganda 8 .. 3 29 .. .. .. .. 1.42 1.11 29 11Ukraine 167 45 142 28 6 153 46 102 1.01 0.92 71 17United Arab Emirates 313 271 293 5 14 1,229 606 563 0.47 0.71 273 62United Kingdom 523 77 460 172 19 616 351 255 1.92 1.98 24 13United States 802 38 439 67 23 1,640 384 1,134 0.76 0.84 30 18Uruguay 200 .. 179 44 23 281 165 99 1.49 1.44 229 142Uzbekistan .. .. .. 18 4 67 11 53 0.92 0.83 110 37Venezuela, RB 147 .. 107 11 27 626 102 459 0.02 0.01 21 9Vietnam 13 7 13 48 14 108 59 46 0.88 0.77 123 50West Bank and Gaza 30 22 25 93 .. .. .. .. 1.71 1.54 .. ..Yemen, Rep. 35 .. 16 14 24 77 2 66 0.35 0.23 133 43Zambia 21 .. 13 12 2 10 4 10 1.66 1.52 125 30Zimbabwe 114 .. 98 25 4 27 17 10 1.29 1.15 55 37World 170 w 30 w 125 w 30 w 15 w 259 w 110 w 134 w 1.21 w 1.07 w 79 w 43 wLow income 10 .. 7 .. 6 20 13 7 1.18 1.15 127 56Middle income 66 18 54 23 11 132 65 61 1.08 0.97 96 49Lower middle income 33 10 21 48 11 70 37 30 1.08 0.91 124 58Upper middle income 99 25 84 17 11 195 93 92 1.13 1.02 78 43Low & middle income 61 15 50 21 10 120 60 55 1.12 0.98 98 50East Asia & Pacifi c 54 18 38 37 7 105 58 51 1.08 0.93 112 56Europe & Central Asia 219 36 183 9 8 230 100 125 1.21 1.13 57 23Latin America & Carib. 178 23 143 18 24 299 123 137 1.04 0.98 57 30Middle East & N. Africa 87 40 67 9 20 283 142 115 0.94 0.56 125 66South Asia 17 5 11 102 7 38 26 10 1.12 0.83 130 68Sub-Saharan Africa 28 .. 23 .. 9 61 26 33 1.22 1.15 119 46High income 618 37 447 46 19 939 357 519 1.63 1.55 37 23Euro area 593 47 418 140 18 646 418 186 1.78 1.62 33 191962012 World Development Indicators

Traffic and congestion 3.15ENVIRONMENTAbout the dataDefinitionsTraffi c congestion in urban areas constrains economicproductivity, damages people’s health, anddegrades the quality of life. In recent years ownershipof passenger cars has increased, and the expansionof economic activity has led to more goods andservices being transported by road over greater distances(see table 5.10). These developments haveincreased demand for roads and vehicles, adding tourban congestion, air pollution, health hazards, andtraffi c accidents and injuries.The data in the table on motor vehicles, passengercars, and road density are compiled by the InternationalRoad Federation (IRF) through questionnairessent to national organizations. The IRF uses a hierarchyof sources to gather as much information aspossible. Primary sources are national road associations.If they lack data or do not respond, otheragencies are contacted, including road directorates,ministries of transport or public works, and centralstatistical offi ces. As a result, data quality is uneven.Coverage of each indicator may differ across countriesbecause of different defi nitions. Comparabilityis also limited when time series data are reported.The IRF is taking steps to improve the quality of thedata in its World Road Statistics. Because this effortcovers only 2003–09, time series data may not becomparable. Another reason is coverage. Road densityis a rough indicator of accessibility and does notcapture road width, type, or condition. Thus comparisonsover time and across countries should bemade with caution.Road sector energy consumption includes energyfrom petroleum products, natural gas, renewable andcombustible waste, and electricity. Biodiesel and biogasoline,forms of renewable energy, are biodegradableand emit less sulfur and carbon monoxide thanpetroleum-derived ones. They can be produced fromvegetable oils, such as soybean, corn, palm, peanut,or sunfl ower oil, and can be used directly only in amodifi ed internal combustion engine.Data on fuel prices are compiled by the GermanAgency for International Cooperation (GIZ), fromits global network and other sources, including theAllgemeiner Deutscher Automobile Club (for Europe)and the Latin American Energy Organization (for LatinAmerica). Local prices are converted to U.S. dollarsusing the exchange rate in the Financial Times internationalmonetary table on the survey date. Whenmultiple exchange rates exist, the market, parallel,or black market rate is used. Prices were compiledin mid-November 2010, based on the crude oil priceof $81 a barrel Brent.Considerable uncertainty surrounds estimatesof particulate matter concentrations, and cautionshould be used in interpreting them. They allow forcross-country comparisons of the relative risk ofparticulate matter pollution facing urban residents.Major sources of urban outdoor particulate matterpollution are traffi c and industrial emissions, butnonanthropogenic sources such as dust storms mayalso be a substantial contributor for some cities.Country technology and pollution controls are importantdeterminants of particulate matter. Data on particulatematter for selected cities are in table 3.16.• Motor vehicles include cars, buses, and freightvehicles but not two-wheelers. Population fi gures aremidyear population in the year for which data areavailable. Roads refer to motorways (a road designedand built for motor traffi c that separates the traffic fl owing in opposite directions), highways, mainor national roads, and secondary or regional roads.• Passenger cars are road motor vehicles, other thantwo-wheelers, intended for the carriage of passengersand designed to seat no more than nine people(including the driver). • Road density is the ratio ofthe length of the country’s total road network to thecountry’s land area. The road network includes allroads in the country— motorways, highways, mainor national roads, secondary or regional roads, andother urban and rural roads. • Road sector energyconsumption is the total energy used in the roadsector, including energy from petroleum products,natural gas, combustible and renewable waste,and electricity (see table 3.7). • Total energy consumptionis the country’s total energy consumptionfrom all sources (see table 3.7). • Diesel is heavyoils used in internal combustion in diesel engines.• Gasoline fuel is light hydrocarbon oil used in internalcombustion engines such as motor vehicles,excluding aircraft. • Fuel price is the pump price ofsuper grade gasoline and of diesel fuel, convertedfrom the local currency to U.S. dollars (see Aboutthe data). • Particulate matter concentration is fi nesuspended particulates of less than 10 microns indiameter (PM10) that are capable of penetratingdeep into the respiratory tract and causing severehealth damage. Data are urban-population-weightedPM10 levels in residential areas of cities with morethan 100,000 residents. The estimates representthe average annual exposure level of the averageurban resident to outdoor particulate matter.Data sourcesData on vehicles and road density are from theIRF’s electronic fi les and its annual World RoadStatistics, except where noted. Data on road sectorenergy consumption are from the IRF and theInternational Energy Agency. Data on fuel pricesare from the GIZ’s electronic fi les. Data on particulatematter concentrations are from Pandeyand others (2006b).2012 World Development Indicators 197

3.16 Air pollutionCityCitypopulationParticulate matterconcentrationSulfurdioxideNitrogendioxideAbout the dataIndoor and outdoor air pollution place a major burdenUrbanpopulation-weightedPM10 micrograms microgramsthousandsmicrograms percubic meterpercubic meterpercubic meter2010 1990 2009 2001 a 2001 aArgentina Buenos Aires 13,074 159 92 .. ..Córdoba 1,493 78 45 .. 97Australia Melbourne 3,853 17 11 .. 30Perth 1,599 16 11 5 19Sydney 4,429 27 17 28 81Austria Vienna 1,706 45 32 14 42Belgium Brussels 1,904 33 23 20 48Brazil Rio de Janeiro 11,950 50 25 129 ..São Paulo 20,262 57 28 43 83Bulgaria Sofi a 1,196 118 49 39 122Canada Montréal 3,783 24 15 10 42Toronto 5,449 29 18 17 43Vancouver 2,220 17 11 14 37Chile Santiago 5,952 100 60 29 81China Anshan 1,663 132 68 115 88Beijing 12,385 141 73 90 122Changchun 3,597 117 61 21 64Chengdu 4,961 136 71 77 74Chongqing 9,401 194 101 340 70Dalian 3,306 79 41 61 100Foshan 4,969 107 56 .. ..Guangzhou 8,884 99 52 57 136Guiyang 2,154 111 58 424 53Harbin 4,251 121 63 23 30Jinan 3,237 148 77 132 45Kunming 3,116 111 58 19 33Lanzhou 2,285 145 75 102 104Liupanshui 1,221 b 94 49 102 ..Nanchang 2,701 124 65 69 29Shanghai 16,575 115 60 53 73Shenyang 5,166 160 83 99 73Shenzhen 9,005 89 46 .. ..Tianjin 7,884 198 103 82 50Wuhan 7,681 125 65 40 43Xi’an 4,747 221 115 .. ..Zhengzhou 2,966 154 80 63 95Zibo 2,456 117 61 198 43Colombia Bogotá 8,500 51 27 .. ..Croatia Zagreb 779 b 48 26 31 ..Cuba Havana 2,130 35 17 1 5Czech Republic Prague 1,162 42 17 14 33Denmark Copenhagen 1,186 30 17 7 54Ecuador Guayaquil 2,690 33 18 15 ..Quito 1,846 44 24 22 ..Egypt, Arab Rep. Cairo 11,001 274 112 69 ..Finland Helsinki 1,117 24 17 4 35France Paris 10,485 14 10 14 57Germany Berlin 3,450 30 18 18 26Frankfurt 680 b 27 16 11 45Munich 1,349 27 16 8 53Ghana Accra 2,342 37 21 .. ..Greece Athens 3,257 69 33 34 64Hungary Budapest 1,706 35 16 39 51Iceland Reykjavik 319 b 23 16 5 42India Ahmadabad 5,717 126 66 30 21Bengaluru 7,218 68 35 .. ..on world health. More than half the world’s peoplerely on dung, wood, crop waste, or coal to meet basicenergy needs. Cooking and heating with these fuels onopen fires or stoves without chimneys lead to indoor airpollution, which is responsible for 1.6 million deaths ayear—one every 20 seconds. In many urban areas airpollution exposure is the main environmental threat tohealth. Long-term exposure to high levels of soot andsmall particles contributes to a range of health effects,including respiratory diseases, lung cancer, and heartdisease. Particulate pollution, alone or with sulfur dioxide,creates an enormous burden of ill health.Sulfur dioxide and nitrogen dioxide emissionslead to deposition of acid rain and other acidic compoundsover long distances, which can lead to theleaching of trace minerals and nutrients critical totrees and plants. Sulfur dioxide emissions can damagehuman health, particularly that of the young andold. Nitrogen dioxide is emitted by bacteria, motorvehicles, industrial activities, nitrogen fertilizers, fueland biomass combustion, and aerobic decompositionof organic matter in soils and oceans.Where coal is the primary fuel for power plantswithout effective dust controls, steel mills, industrialboilers, and domestic heating, high levels of urbanair pollution are common— especially particulatesand sulfur dioxide. Elsewhere the worst emissionsare from petroleum product combustion.Sulfur dioxide and nitrogen dioxide concentrationdata are based on average observed concentrationsat urban monitoring sites, which not all cities have.The data on particulate matter are estimated averageannual concentrations in residential areas awayfrom air pollution “hotspots,” such as industrialdistricts and transport corridors. The data are fromthe World Bank’s Development Research Group andEnvironment Department estimates of annual ambientconcentrations of particulate matter in citieswith populations exceeding 100,000 (Pandey andothers 2006b). A country’s technology and pollutioncontrols are important determinants of particulatematter concentrations.Pollutant concentrations are sensitive to local conditions,and even monitoring sites in the same citymay register different levels. Thus these data shouldbe considered only a general indication of air quality,and comparisons should be made with caution.Current World Health Organization (WHO) air qualityguidelines are annual mean concentrations of 20micrograms per cubic meter for particulate matterless than 10 microns in diameter and 40 microgramsfor nitrogen dioxide and daily mean concentrationsof 20 micrograms per cubic meter for sulfur dioxide.1982012 World Development Indicators

Air pollution 3.16ENVIRONMENTCityCitypopulationParticulate matterconcentrationSulfurdioxideNitrogendioxideDefinitionsthousands2010population-weightedmicrogramscubic1990PM10permeter2009microgramspercubic meter2001 a microgramspercubic meter2001 a • City population is the number of residents ofthe city or metropolitan area as defi ned by nationalauthorities and reported to the United Nations.• Particulate matter concentration is fi ne suspendedparticulates of less than 10 microns inUrban-India (continued) Chennai 7,547 56 29 15 17 diameter (PM10) that are capable of penetratingDelhi 22,157 227 118 24 41deep into the respiratory tract and causing severeHyderabad 6,751 62 32 12 17Kanpur 3,364 165 86 15 14health damage. Data are urban- population-weightedKolkataLucknowMumbaiNagpur15,5522,87320,0412,6071931659584101865044492633634253913PM10 levels in residential areas of cities with morethan 100,000 residents. The estimates representthe average annual exposure level of the averageurban resident to outdoor particulate matter. • SulfurPune 5,002 71 37 .. ..Indonesia Jakarta 9,210 137 70 .. ..dioxide is an air pollutant produced when fossil fuelsIran, Islamic Rep.IrelandItalyTehranDublinMilanRome7,2411,0992,9673,36292244644551324232092031......248..containing sulfur are burned. • Nitrogen dioxide isa poisonous, pungent gas formed when nitric oxidecombines with hydrocarbons and sunlight, producinga photochemical reaction. These conditions occur inTurin 1,665 66 35 .. ..Japan Osaka-Kobe 11,337 48 28 19 63both natural and anthropogenic activities.Tokyo 36,669 54 32 18 68Yokohama 3,654 b 42 25 100 13Kenya Nairobi 3,523 67 31 .. ..Korea, Rep Busan 3,425 52 33 60 51Seoul 9,773 55 35 44 60Daegu 2,458 59 38 81 62Malaysia Kuala Lumpur 1,519 36 19 24 ..Mexico Mexico City 19,460 88 43 74 130Netherlands Amsterdam 1,049 45 30 10 58New Zealand Auckland 1,404 13 11 3 20Norway Oslo 888 27 19 8 43Philippines Manila 11,628 78 24 33 ..Poland Katowice 309 b 60 35 83 79Lódz 742 b 60 34 21 43Warsaw 1,712 65 38 16 32Portugal Lisbon 2,824 44 18 8 52Romania Bucharest 1,934 47 16 10 71Russian Federation Moscow 10,550 42 16 109 ..Omsk 1,124 44 17 20 34Singapore Singapore 4,837 108 23 20 30Slovak Republic Bratislava 500 b 44 12 21 27South Africa Cape Town 3,405 24 16 21 72Durban 2,879 47 32 31 ..Johannesburg 3,670 49 33 19 31Spain Barcelona 5,083 43 27 11 43Madrid 5,851 37 23 24 66Sweden Stockholm 1,285 14 9 3 20Switzerland Zurich 1,150 32 21 11 39Thailand Bangkok 6,976 88 60 11 23 Data sourcesTurkey Ankara 3,906 75 35 55 46Istanbul 10,525 88 42 120 .. Data on city population are from the UnitedUkraine Kiev 2,805 91 21 14 51 Nations Population Division’s World UrbanizationUnited Kingdom Birmingham 2,302 33 20 9 45 Prospects: The 2010 Revision. Data on particulateLondon 8,631 27 17 25 77matter concentrations are from Pandey and othersManchester 2,253 24 12 26 49United States Chicago 9,204 33 20 14 57(2006b). Data on sulfur dioxide and nitrogen dioxideLos Angeles 12,762 46 28 9 74concentrations are from the WHO’s HealthyNew York 19,425 28 17 26 79 Cities Air Management Information System andVenezuela, RB Caracas 3,090 31 13 33 57 the World Resources Institute.a. Data are for the most recent year available. b. Data are from national sources.2012 World Development Indicators 199

3.17 Government commitmentEnvironmentalassessments,Biodiversitystrategies strategies, oror action action plansplansParticipationin treaties aClimatechange bOzonelayerAfghanistan 2002 2004 d 2004 d 2002 1985 d 1995 dAlbania 1993 1994 d 1999 d 1999 d 2003 1994 d 2005 d 2003 d 2000 d 2004Algeria 2001 1993 1992 d 1992 d 1996 1995 2005 d 1983 d 1996 2006Angola 2000 2000 d 2000 d 1990 1998 2007 1997 2006 dArgentina 1992 1994 1990 1990 1995 1994 2001 1981 1997 2005Armenia 1993 e 1999 d 1999 d 2002 1993 e 2003 d 2008 d 1997 2003Australia 1992 1994 1992 1987 d 1989 1994 1993 2007 1976 2000 2004Austria 1994 1987 1989 1995 1994 2002 1982 d 1997 d 2002Azerbaijan 1998 1995 1996 d 1996 d 2000 f 2000 d 1998 d 1998 d 2004 dBahrain 1994 1990 d 1990 d 1985 1996 2006 d 1997 d 2006Bangladesh 1991 1990 1994 1990 d 1990 d 2001 1994 2001 d 1981 1996 2007Belarus 2000 f 1986 e 1988 f 2006 1993 2005 d 1995 d 2001 d 2004 dBelgium 1996 1988 1988 1998 1996 2002 1983 1997 d 2006Benin 1993 1994 1993 d 1993 d 1997 1994 2002 d 1984 d 1996 2004Bolivia 1994 1988 1994 1994 d 1994 d 1995 1994 1999 1979 1996 2003Bosnia and Herzegovina 2000 d 1993 g 1992 g 1994 2002 e 2007 d 2009 d 2002 d 2010Botswana 1990 1991 1994 1991 d 1991 d 1990 1995 2003 d 1977 d 1996 2002 dBrazil 1988 1994 1990 d 1990 d 1988 1994 2002 1975 1997 2004Bulgaria 1994 1995 1990 d 1990 d 1996 1996 2002 1991 d 2001 d 2004Burkina Faso 1993 1993 1989 1989 2005 1993 2005 d 1989 d 1996 2004Burundi 1994 1989 1997 1997 d 1997 d 1997 2001 d 1988 d 1997 2005Cambodia 1999 1995 d 2001 d 2001 d 1995 d 2002 d 1997 1997 2006Cameroon 1989 1994 1989 d 1989 d 1985 1994 2002 d 1981 d 1997 2009Canada 1990 1994 1992 1986 1988 2003 1992 2002 1975 1995 2001Central African Republic 1995 1993 d 1993 d 1995 2008 d 1980 d 1996 2008Chad 1990 1994 1989 d 1994 2009 1994 2009 d 1989 d 1996 2004Chile 1993 1995 1990 1990 1997 1994 2002 1975 1997 2005China 1994 1994 1993 1989 d 1991 d 1996 1993 2002 f 1981 d 1997 2004Hong Kong SAR, ChinaColombia 1998 1988 1995 1990 d 1993 d 1994 2001 d 1981 1999 2008Congo, Dem. Rep. 1990 1995 1994 d 1994 d 1989 1994 2005 d 1976 d 1997 2005 dCongo, Rep. 1990 1996 1994 d 1994 2008 1996 2007 d 1983 d 1999 2007Costa Rica 1990 1992 1994 1991 d 1991 d 1992 1994 2002 1975 1998 2007Côte d’Ivoire 1994 1991 1994 1993 d 1993 d 1984 1994 2007 d 1994 d 1997 2004Croatia 2001 2000 1996 e 1992 g 1992 g 1995 1996 2007 2000 d 2000 d 2007Cuba 1994 1992 d 1992 d 1984 1994 2002 1990 d 1997 2007Cyprus 1997 1992 d 1992 d 1988 1996 1992 d 1974 2000 d 2005 dCzech Republic 1994 1993 f 1993 g 1993 g 1996 1993 f 2001 f 1993 2000 d 2002Denmark 1994 1995 1988 1988 2004 1993 2002 1977 1995 2003Dominican Republic 1995 1998 1993 d 1993 d 2009 1996 2002 d 1986 d 1997 d 2007Ecuador 1993 1995 1993 1990 d 1990 d 1993 2000 1975 1995 2004Egypt, Arab Rep. 1992 1988 1994 1988 1988 1983 1994 2005 1978 1995 2003El Salvador 1994 1988 1995 1992 1992 d 1994 1998 1987 d 1997 d 2008Eritrea 1995 1995 d 2005 d 2005 d 1996 d 2005 d 1994 d 1996 2005 dEstonia 1998 1994 1996 d 1996 d 2005 1994 2002 1992 d 2008 dEthiopia 1994 1991 1994 1994 d 1994 d 2005 d 1989 d 1997 2003Finland 1995 1994 e 1986 1988 1996 1994 e 2002 1976 d 1995 d 2002 dFrance 1990 1994 1987 f 1988 f 1996 1994 2002 f 1978 f 1997 2004 fGabon 1990 1998 1994 d 1994 d 1998 1997 2006 d 1989 d 1996 d 2007Gambia, The 1992 1989 1994 1990 d 1990 d 1984 1994 2001 d 1977 d 1996 2006Georgia 1998 1994 d 1996 d 1996 d 1996 1994 d 1999 d 1996 d 1999 2006Germany 1993 1988 1988 1994 1993 2002 1976 1996 2002Ghana 1992 1988 1995 1989 d 1992 1983 1994 2003 d 1975 1996 2003Greece 1994 1988 1988 1995 1994 2002 1992 d 1997 2006Guatemala 1994 1988 1995 1987 d 1989 d 1997 1995 1999 1979 1998 d 2008Guinea 1994 1988 1993 1992 d 1992 d 1985 1993 2000 d 1981 d 1997 2007Guinea-Bissau 1993 1991 1995 2002 d 2002 d 1986 1995 2005 d 1990 d 1995 2008Haiti 1999 1996 2000 d 2000 d 1996 1996 2005 d 1996CFCcontrolLaw ofthe Sea cBiologicaldiversity bKyotoProtocol CITES CCDStockholmConvention1992 1985 1987 1982 1992 1997 1973 1994 20012002012 World Development Indicators

Government commitment 3.17ENVIRONMENTEnvironmentalassessments,Biodiversitystrategies strategies, oror action action plansplansParticipationin treaties aClimatechange bOzonelayerHonduras 1993 1995 1993 d 1993 d 1993 1995 2000 1985 d 1997 2005Hungary 1995 1994 1988 d 1989 d 2002 1994 2002 d 1985 d 1999 d 2008India 1993 1994 1993 1991 d 1992 d 1995 1994 2002 d 1976 1996 2006Indonesia 1993 1993 1994 1992 d 1992 1986 1994 2004 1978 d 1998 2009Iran, Islamic Rep. 1996 1990 d 1990 d 1996 2005 d 1976 1997 2006Iraq 2009 d 2008 d 2008 d 1985 2009 d 2009 d 2010 dIreland 1994 1988 d 1988 1996 1996 2002 2002 1997 2010Israel 1996 1992 d 1992 1995 2004 1979 1996Italy 1994 1988 1988 1995 1994 2002 1979 1997Jamaica 1994 1995 1993 d 1993 d 1983 1995 1999 d 1997 d 1997 d 2007Japan 1993 e 1988 d 1988 e 1996 1993 e 2002 d 1980 e 1998 e 2002 dJordan 1991 1993 1989 d 1989 d 1995 1993 2003 d 1978 d 1996 2004Kazakhstan 1995 1998 d 1998 d 1994 2009 2000 d 1997 2007Kenya 1994 1992 1994 1988 d 1988 1989 1994 2005 d 1978 1997 2004Korea, Dem. Rep. 1994 f 1995 d 1995 d 1994 f 2005 d 2003 d 2002 dKorea, Rep. 1993 1992 d 1992 1996 1994 2002 1993 d 1999 2007KosovoKuwait 1994 d 1992 d 1992 d 1986 2002 2005 d 2002 1997 2006Kyrgyz Republic 1995 2000 2000 d 2000 d 1996 d 2003 d 2007 d 1997 d 2006Lao PDR 1995 1995 d 1998 d 1998 d 1998 1996 d 2003 d 2004 d 1996 d 2006Latvia 1995 1995 d 1995 d 2004 1995 2002 1997 d 2002 d 2004Lebanon 1994 1993 d 1993 d 1995 1994 2006 d 1996 2003Lesotho 1989 1995 1994 d 1994 d 2007 1995 2000 d 2003 1995 2002Liberia 2002 1996 d 1996 d 2008 2000 2002 d 1981 d 1998 d 2002 dLibya 1999 1990 d 1990 d 2001 2006 d 2003 d 1996 2005 dLithuania 1995 1995 d 1995 d 2003 1996 2003 2001 d 2003 d 2006Macedonia, FYR 1998 d 1994 g 1994 g 1994 1997 d 2004 d 2000 d 2002 d 2004Madagascar 1988 1991 1999 1996 d 1996 d 2001 2003 d 1975 1997 2005Malawi 1994 1994 1991 d 1991 d 2010 1996 2001 d 1982 d 1996 2009Malaysia 1991 1988 1994 1989 d 1989 d 1996 1994 2002 1977 d 1997Mali 1989 1995 1994 d 1994 d 1985 1995 2002 1994 d 1995 2003Mauritania 1988 1994 1994 d 1994 d 1996 1996 2005 d 1998 d 1996 2005Mauritius 1990 1992 1992 d 1992 d 1994 1992 2001 d 1975 1996 2004Mexico 1988 1993 1987 1988 e 1983 1993 2000 1991 d 1995 2003Moldova 2002 1995 1996 d 1996 d 2007 1995 2008 d 2001 d 1999 d 2004Mongolia 1995 1993 1996 d 1996 d 1996 1993 1999 d 1996 d 1996 2004Morocco 1988 1995 1995 1995 2007 1995 2002 d 1975 1996 2004Mozambique 1994 1995 1994 d 1994 d 1997 1995 2005 d 1981 d 1997 2005Myanmar 1989 1994 1993 d 1993 d 1996 1994 2003 d 1997 d 1997 d 2004 dNamibia 1992 1995 1993 d 1993 d 1983 1997 2003 d 1990 d 1997 2005 dNepal 1993 1994 1994 d 1994 d 1998 1993 2005 d 1975 d 1996 2007Netherlands 1994 1993 e 1988 e 1988 e 1996 1994 e 2002 e 1984 1995 e 2002 eNew Zealand 1994 1993 1987 1988 1996 1993 2002 1989 d 2000 d 2004Nicaragua 1994 1995 1993 d 1993 d 2000 1995 1999 1977 d 1998 2005Niger 1991 1995 1992 d 1992 d 1995 2004 1975 1996 2006Nigeria 1990 1992 1994 1988 d 1988 d 1986 1994 2004 d 1974 1997 2004Norway 1994 1993 1986 1988 1996 1993 2002 1976 1996 2002Oman 1995 1999 d 1999 d 1989 1995 2005 d 2008 d 1996 d 2005Pakistan 1994 1991 1994 1992 d 1992 d 1997 1994 2005 d 1976 d 1997 2008Panama 1990 1995 1989 d 1989 1996 1995 1999 1978 1996 2003Papua New Guinea 1992 1993 1993 1992 d 1992 d 1997 1993 2002 1975 d 2000 d 2003Paraguay 1994 1992 d 1992 d 1986 1994 1999 1976 1997 2004Peru 1988 1993 1989 1993 d 1993 2002 1975 1995 2005Philippines 1989 1989 1994 1991 d 1991 1984 1993 2003 1981 2000 2004Poland 1993 1991 1994 1990 d 1990 d 1998 1996 2002 1989 2001 d 2008Portugal 1995 1993 1988 d 1988 1997 1993 2002 f 1980 1996 2004 ePuerto RicoQatar 1996 d 1996 d 1996 d 2002 1996 2005 a 2001 d 1999 d 2004 dCFCcontrolLaw ofthe Sea cBiologicaldiversity bKyotoProtocol CITES CCDStockholmConvention1992 1985 1987 1982 1992 1997 1973 1994 20012012 World Development Indicators 201

3.17Government commitmentEnvironmentalassessments,Biodiversitystrategies strategies, oror action action plansplansParticipationin treaties aClimatechange bOzonelayerRomania 1995 1994 1993 d 1993 d 1996 1994 2001 1994 d 1998 d 2004Russian Federation 1999 1994 1994 1986 e 1988 e 1997 1995 2008 1992 h 2003 d 2011Rwanda 1991 1998 2001 d 2001 d 1996 2004 d 1980 d 1998 2002 dSaudi Arabia 1994 d 1993 d 1993 d 1996 2001 d 2005 d 1996 d 1997 dSenegal 1984 1991 1994 1993 d 1993 1984 1994 2001 d 1977 d 1995 2003Serbia 2001 a 2001 g 2001 g 2001 2002 2007 d 2006 h 2007 a 2009Sierra Leone 1994 1995 2001 d 2001 d 1994 1994 d 2006 d 1994 d 1997 2003 dSingapore 1993 1995 1997 1989 d 1989 d 1994 1995 2006 d 1986 d 1999 d 2005Slovak Republic 1994 f 1993 g 1993 g 1996 1994 f 2002 1993 g 2002 d 2002Slovenia 1994 1995 1992 g 1992 g 1995 1996 2002 2000 d 2001 d 2004Somalia 2009 d 2001 d 2001 d 1989 2009 d 2010 1985 d 2002 d 2010 dSouth Africa 1993 1997 1990 d 1990 d 1997 1995 2002 d 1975 1997 2002South SudanSpain 1993 1988 d 1988 1997 1993 2002 1986 d 1996 2004Sri Lanka 1994 1991 1993 1989 d 1989 d 1994 1994 2002 d 1979 d 1998 d 2005Sudan 1993 1993 d 1993 d 1985 1995 2004 d 1982 1995 2006Swaziland 1996 1992 d 1992 d 2009 1994 2006 d 1997 d 1996 2006 dSweden 1993 1986 1988 1996 1993 2002 1974 1995 2002Switzerland 1993 1987 1988 1994 2003 1974 1996 2003Syrian Arab Republic 1999 1996 d 1989 d 1989 d 1996 2006 d 2003 d 1997 2005Tajikistan 1998 d 1996 d 1998 d 1997 d 2008 d 1997 d 2007Tanzania 1994 1988 1996 1993 d 1993 d 1985 1996 2002 d 1979 1997 2004Thailand 1994 1989 d 1989 2011 2003 2002 1983 2001 d 2005Togo 1991 1995 e 1991 d 1991 1985 1995 e 2004 d 1978 1995 e 2004Trinidad and Tobago 1994 1989 d 1989 d 1986 1996 1999 1984 d 2000 d 2002 dTunisia 1994 1988 1993 1989 d 1989 d 1985 1993 2003 d 1974 1995 2004Turkey 1998 2004 d 1991 d 1991 d 1997 2006 d 1996 d 1998 2009Turkmenistan 1995 d 1993 d 1993 d 1996 d 1999 d 1996Uganda 1994 1988 1993 1988 d 1988 1990 1993 2002 d 1991 d 1997 2004 dUkraine 1999 1997 1986 e 1988 e 1999 1995 2004 1999 d 2002 d 2007United Arab Emirates 1995 d 1989 d 1989 d 2000 2005 d 1990 d 1998 d 2002United Kingdom 1995 1994 1993 1987 1988 1997 1994 2002 1976 1996 2005United States 1995 1995 1992 1986 1988 1974 2000Uruguay 1994 1989 d 1991 d 1992 1993 2001 1975 1999 d 2004Uzbekistan 1993 d 1993 d 1993 d 1995 d 1999 1997 d 1995Venezuela 1994 1988 d 1989 1994 2005 d 1977 1998 d 2005Vietnam 1993 1994 1994 d 1994 d 1994 1994 2002 1994 d 1998 d 2002West Bank and GazaYemen, Rep. 1996 1992 1996 1996 d 1996 d 1987 1996 2004 d 1997 d 1997 d 2004Zambia 1994 1993 1990 d 1990 d 1983 1993 2006 1980 d 1996 2006Zimbabwe 1987 1992 1992 d 1992 d 1993 1994 2009 d 1981 d 1997CFCcontrolLaw ofthe Sea cBiologicaldiversity bKyotoProtocol CITES CCDStockholmConvention1992 1985 1987 1982 1992 1997 1973 1994 2001a. Ratifi cation of the treaty. b. Year the treaty entered into force in the country. c. Convention of December 10, 1982. d. Accession. e. Acceptance. f. Approval. g. Succession. h. Continuation.2022012 World Development Indicators

Government commitment3.17ENVIRONMENTAbout the dataDefinitionsNational environmental strategies and participationin international treaties on environmental issues providesome evidence of government commitment tosound environmental management. But the signingof these treaties does not always imply ratifi cation,nor does it guarantee that governments will complywith treaty obligations.In many countries efforts to halt environmentaldegradation have failed, primarily because governmentshave neglected to make this issue a priority, arefl ection of competing claims on scarce resources.To address this problem, many countries are preparingnational environmental strategies—some focusingnarrowly on environmental issues, and othersintegrating environmental, economic, and socialconcerns. Among such initiatives are conservationstrategies and environmental action plans. Somecountries have also prepared country environmentalprofi les and biodiversity strategies and profi les.National conservation strategies—promoted bythe World Conservation Union (IUCN)—provide acomprehensive, cross-sectoral analysis of conservationand resource management issues to help integrateenvironmental concerns with the developmentprocess. Such strategies discuss current and futureneeds, institutional capabilities, prevailing technicalconditions, and the status of natural resources ina country.National environmental action plans, supported bythe World Bank and other development agencies,describe a country’s main environmental concerns,identify the principal causes of environmental problems,and formulate policies and actions to deal withthem. These plans are a continuing process in whichgovernments develop comprehensive environmentalpolicies, recommend specifi c actions, and outlinethe investment strategies, legislation, and institutionalarrangements required to implement them.Biodiversity profi les—prepared by the World ConservationMonitoring Centre and the IUCN—providebasic background on species diversity, protectedareas, major ecosystems and habitat types, andlegislative and administrative support. In an effortto establish a scientifi c baseline for measuring progressin biodiversity conservation, the United NationsEnvironment Programme (UNEP) coordinates globalbiodiversity assessments.To address global issues, many governments havealso signed international treaties and agreementslaunched in the wake of the 1972 United NationsConference on the Human Environment in Stockholmand the 1992 United Nations Conference onEnvironment and Development (the Earth Summit) inRio de Janeiro, which produced Agenda 21—an arrayof actions to address environmental challenges:• The Framework Convention on Climate Changeaims to stabilize atmospheric concentrations ofgreenhouse gases at levels that will prevent humanactivities from interfering dangerously with theglobal climate.• The Vienna Convention for the Protection of theOzone Layer aims to protect human health and theenvironment by promoting research on the effectsof changes in the ozone layer and on alternativesubstances (such as substitutes for chlorofl uorocarbon)and technologies, monitoring the ozonelayer, and taking measures to control the activitiesthat produce adverse effects.• The Montreal Protocol for Chlorofl uorocarbonControl requires that countries help protect theearth from excessive ultraviolet radiation by cuttingchlorofl uorocarbon consumption by 20 percentover their 1986 level by 1994 and by 50 percentover their 1986 level by 1999, with allowances forincreases in consumption by developing countries.• The United Nations Convention on the Law of theSea, which became effective in November 1994,establishes a comprehensive legal regime for seasand oceans, establishes rules for environmentalstandards and enforcement provisions, and developsinternational rules and national legislation toprevent and control marine pollution.• The Convention on Biological Diversity promotesconservation of biodiversity through scientifi cand technological cooperation among countries,access to fi nancial and genetic resources, andtransfer of ecologically sound technologies.But 10 years after the Earth Summit in Rio deJaneiro the World Summit on Sustainable Developmentin Johannesburg recognized that many of theproposed actions had yet to materialize. To helpdeveloping countries comply with their obligationsunder these agreements, the Global EnvironmentFacility (GEF) was created to focus on global improvementin biodiversity, climate change, internationalwaters, and ozone layer depletion. The UNEP, UnitedNations Development Programme, and World Bankmanage the GEF according to the policies of its governingbody of country representatives. The WorldBank is responsible for the GEF Trust Fund and chairsthe GEF.• Environmental strategies or action plans providea comprehensive analysis of conservation andresource management issues that integrate environmentalconcerns with development. They includenational conservation strategies, environmentalaction plans, environmental management strategies,and sustainable development strategies. The dateis the year a country adopted a strategy or actionplan. • Biodiversity assessments, strategies, oraction plans include biodiversity profi les (see Aboutthe data). • Participation in treaties covers nineinternational treaties (see About the data). • Climatechange refers to the Framework Conventionon Climate Change (signed in 1992). • Ozone layerrefers to the Vienna Convention for the Protectionof the Ozone Layer (signed in 1985). • CFC controlrefers to the Protocol on Substances That Depletethe Ozone Layer (the Montreal Protocol for Chlorofluorocarbon Control) (signed in 1987). • Law ofthe Sea refers to the United Nations Convention onthe Law of the Sea (signed in 1982). • Biologicaldiversity refers to the Convention on Biological Diversity(signed at the Earth Summit in 1992). • KyotoProtocol refers to the protocol on climate changeadopted at the third conference of the parties to theUnited Nations Framework Convention on ClimateChange in December 1997. • CITES is the Conventionon International Trade in Endangered Species ofWild Fauna and Flora, an agreement among governmentsto ensure that the survival of wild animalsand plants is not threatened by uncontrolled exploitation.Adopted in 1973, it entered into force in 1975.• CCD is the United Nations Convention to CombatDesertifi cation, an international convention addressingthe problems of land degradation in the world’sdrylands. Adopted in 1994, it entered into force in1996. • Stockholm Convention is an internationallegally binding instrument to protect human healthand the environment from persistent organic pollutants.Adopted in 2001, it entered into force in 2004.Data sourcesData on environmental strategies and participationin international environmental treaties arefrom the Secretariat of the United Nations FrameworkConvention on Climate Change, the OzoneSecretariat of the UNEP, the World ResourcesInstitute, the UNEP, the Center for InternationalEarth Science Information Network, and theUnited Nations Treaty Series.2012 World Development Indicators 203

3.18Contribution of natural resourcesto gross domestic productTotal naturalresources rentsOil rents Natural gas rents Coal rents Mineral rents Forest rents% of GDP % of GDP % of GDP % of GDP % of GDP % of GDP2010 2010 2010 2010 2010 2010Afghanistan 2.3 .. .. 0.0 0.0 2.3Albania 3.6 3.0 0.0 0.0 0.5 0.1Algeria 25.7 17.6 7.8 0.0 0.3 0.1Angola 46.3 46.1 0.1 .. 0.0 0.2Argentina 6.1 4.0 1.4 0.0 0.6 0.1Armenia 1.7 0.0 0.0 .. 1.7 0.0Australia 8.3 0.9 0.7 1.8 6.6 0.1Austria 0.3 0.1 0.1 0.0 0.0 0.1Azerbaijan 46.5 42.6 3.9 .. 0.0 0.0Bahrain 23.2 16.4 6.9 .. 0.0 0.0Bangladesh 3.3 0.0 2.8 0.1 0.0 0.5Belarus 1.9 1.3 0.0 .. 0.0 0.5Belgium 0.0 0.0 0.0 0.0 0.0 0.0Benin 1.7 0.0 0.0 .. 0.0 1.7Bolivia 18.1 4.6 9.0 .. 4.2 0.4Bosnia and Herzegovina 2.8 0.0 0.0 2.4 2.3 0.6Botswana 4.7 0.0 0.0 0.4 4.6 0.1Brazil 5.3 2.2 0.1 0.0 2.7 0.3Bulgaria 2.3 0.0 0.0 1.1 2.0 0.3Burkina Faso 7.1 .. .. .. 4.1 2.9Burundi 13.3 .. .. .. 1.3 12.0Cambodia 1.2 0.0 0.0 .. 0.0 1.2Cameroon 9.3 7.1 0.2 0.0 0.2 1.8Canada 3.8 2.3 0.3 0.1 0.8 0.5Central African Republic 5.9 .. .. .. 0.0 5.8Chad 43.3 41.2 .. .. 0.0 2.1Chile 18.9 0.0 0.1 0.0 18.3 0.5China 4.0 1.5 0.1 3.8 2.2 0.2Hong Kong SAR, China 0.0 0.0 0.0 .. 0.0 0.0Colombia 7.9 6.5 0.5 1.4 0.8 0.1Congo, Dem. Rep. 29.8 3.9 0.0 0.1 16.4 9.5Congo, Rep. 64.1 61.6 0.1 .. 0.0 2.5Costa Rica 0.4 0.0 0.0 .. 0.1 0.3Côte d’Ivoire 7.1 4.3 1.0 .. 0.7 1.1Croatia 1.2 0.5 0.5 0.0 0.0 0.2Cuba 5.1 3.0 0.6 .. 1.5 0.1Cyprus 0.0 0.0 0.0 .. 0.0 0.0Czech Republic 0.3 0.0 0.0 0.7 0.0 0.3Denmark 2.1 1.8 0.3 0.0 0.0 0.0Dominican Republic 0.3 0.0 0.0 .. 0.2 0.0Ecuador 20.6 20.2 0.1 .. 0.0 0.3Egypt, Arab Rep. 10.1 5.9 3.9 0.0 0.3 0.1El Salvador 0.5 0.0 0.0 .. 0.0 0.5Eritrea 0.6 0.0 0.0 .. 0.0 0.6Estonia 1.1 0.0 0.0 1.9 0.0 1.1Ethiopia 4.7 0.0 0.0 .. 0.3 4.4Finland 0.8 0.0 0.0 .. 0.2 0.7France 0.1 0.0 0.0 0.0 0.0 0.1Gabon 49.8 46.4 0.2 .. 0.1 3.1Gambia, The 2.5 .. .. .. 0.0 2.5Georgia 0.8 0.2 0.0 0.1 0.6 0.1Germany 0.1 0.0 0.0 0.1 0.0 0.0Ghana 10.5 0.0 0.0 .. 8.8 1.6Greece 0.2 0.0 0.0 0.3 0.1 0.0Guatemala 2.4 0.8 0.0 .. 0.8 0.8Guinea 21.2 .. .. .. 17.0 4.2Guinea-Bissau 4.6 .. .. .. 0.0 4.6Haiti 0.6 0.0 0.0 .. 0.0 0.62042012 World Development Indicators

Contribution of natural resourcesto gross domestic product 3.18ENVIRONMENTTotal naturalresources rentsOil rents Natural gas rents Coal rents Mineral rents Forest rents% of GDP % of GDP % of GDP % of GDP % of GDP % of GDP2010 2010 2010 2010 2010 2010Honduras 1.8 0.0 0.0 .. 0.7 1.1Hungary 0.6 0.3 0.2 0.1 0.0 0.1India 4.0 1.0 0.4 2.6 2.1 0.6Indonesia 6.0 2.4 0.9 3.4 1.9 0.7Iran, Islamic Rep. 31.6 23.7 6.8 0.0 1.0 0.0Iraq 69.3 69.1 0.2 .. 0.0 0.0Ireland 0.2 0.0 0.0 0.0 0.1 0.0Israel 0.3 0.0 0.2 0.0 0.1 0.0Italy 0.2 0.1 0.0 0.0 0.0 0.0Jamaica 1.1 0.0 0.0 .. 1.0 0.1Japan 0.0 0.0 0.0 0.0 0.0 0.0Jordan 1.6 0.0 0.1 .. 1.5 0.0Kazakhstan 27.6 22.4 2.7 5.5 2.5 0.0Kenya 1.3 0.0 0.0 .. 0.1 1.2Korea, Dem. Rep. .. .. .. .. .. ..Korea, Rep. 0.0 0.0 0.0 0.0 0.0 0.0Kosovo 1.5 .. .. .. 1.5 ..Kuwait 43.0 41.2 1.8 .. 0.0 0.0Kyrgyz Republic 9.2 0.7 0.0 0.4 8.5 0.0Lao PDR 14.1 .. .. 0.0 12.6 1.5Latvia 1.5 0.0 0.0 .. 0.0 1.5Lebanon 0.0 0.0 0.0 .. 0.0 0.0Lesotho 1.3 .. .. .. 0.0 1.3Liberia 14.6 .. .. .. 2.1 12.5Libya 46.1 42.3 3.8 .. 0.0 0.0Lithuania 1.8 0.1 0.0 .. 0.0 1.6Macedonia, FYR 7.4 0.0 0.0 1.5 7.3 0.1Madagascar 3.1 .. .. .. 1.3 1.8Malawi 4.1 .. .. .. 0.0 4.1Malaysia 10.9 6.3 3.9 0.1 0.1 0.6Mali 15.0 .. .. .. 14.0 1.1Mauritania 54.7 .. .. .. 54.2 0.5Mauritius 0.0 .. .. .. 0.0 0.0Mexico 7.3 6.0 0.5 0.1 0.6 0.1Moldova 0.2 0.1 0.0 .. 0.0 0.1Mongolia 23.4 2.0 0.0 22.9 21.2 0.2Morocco 2.6 0.0 0.0 0.0 2.5 0.1Mozambique 8.5 0.0 5.0 0.0 0.1 3.3Myanmar .. .. .. .. .. ..Namibia 1.0 0.0 0.0 .. 1.0 0.1Nepal 3.4 0.0 0.0 0.0 0.0 3.4Netherlands 1.2 0.1 1.1 0.0 0.0 0.0New Zealand 2.4 0.7 0.5 0.1 0.4 0.8Nicaragua 3.0 0.0 0.0 .. 1.3 1.7Niger 3.3 .. .. 0.0 1.3 2.0Nigeria 32.6 29.5 2.2 0.0 0.0 0.9Norway 13.1 10.1 2.9 0.0 0.0 0.1Oman 39.2 31.6 7.6 .. 0.0 0.0Pakistan 3.9 0.8 2.3 0.1 0.1 0.7Panama 0.1 0.0 0.0 .. 0.0 0.1Papua New Guinea 36.3 18.4 .. .. 32.2 4.1Paraguay 1.3 0.0 0.0 .. 0.0 1.3Peru 11.3 1.1 0.7 0.0 9.3 0.1Philippines 2.6 0.1 0.3 0.2 2.0 0.2Poland 1.0 0.1 0.1 1.3 0.6 0.2Portugal 0.3 0.0 0.0 0.0 0.2 0.1Puerto Rico 0.0 .. .. .. 0.0 ..Qatar 27.9 13.4 14.5 .. 0.0 ..2012 World Development Indicators 205

3.18Contribution of natural resourcesto gross domestic productTotal naturalresources rentsOil rents Natural gas rents Coal rents Mineral rents Forest rentsRomania 2.2 1.2 0.7 0.3 0.0 0.2Russian Federation 19.9 14.2 3.6 1.4 1.7 0.3Rwanda 3.4 .. .. .. 0.1 3.3Saudi Arabia 53.7 50.5 3.2 .. 0.0 0.0Senegal 2.4 0.0 0.0 .. 1.2 1.1Serbia 0.9 0.8 0.1 2.0 0.0 ..Sierra Leone 4.5 .. .. .. 0.9 3.6Singapore 0.0 0.0 0.0 .. 0.0 0.0Slovak Republic 0.4 0.0 0.0 0.0 0.0 0.3Slovenia 0.2 0.0 0.0 0.2 0.0 0.2Somalia .. .. .. .. .. ..South Africa 4.6 0.0 0.0 5.1 3.8 0.8South Sudan .. .. .. .. .. ..Spain 0.1 0.0 0.0 0.0 0.0 0.0Sri Lanka 0.5 0.0 0.0 .. 0.0 0.5Sudan 19.3 18.5 0.0 .. 0.1 0.7Swaziland 1.7 .. .. 0.0 0.0 1.7Sweden 1.2 0.0 0.0 0.0 0.7 0.5Switzerland 0.0 0.0 0.0 .. 0.0 0.0Syrian Arab Republic 16.5 14.4 1.9 .. 0.2 0.0Tajikistan 0.9 0.2 0.1 0.3 0.6 0.0Tanzania 6.8 0.0 0.5 0.0 4.2 2.1Thailand 2.7 1.1 1.2 0.1 0.1 0.3Timor-Leste 0.3 .. .. .. 0.0 0.3Togo 3.9 0.0 0.0 0.0 1.9 2.0Trinidad and Tobago 37.3 11.5 25.8 .. 0.0 0.0Tunisia 6.7 4.4 1.0 .. 1.1 0.1Turkey 0.5 0.2 0.0 0.2 0.2 0.1Turkmenistan 43.9 19.7 24.2 .. 0.0 ..Uganda 4.9 .. .. .. 0.0 4.9Ukraine 2.9 0.9 1.7 3.1 0.1 0.3United Arab Emirates 20.5 18.0 2.4 .. 0.0 ..United Kingdom 1.5 1.2 0.3 0.0 0.0 0.0United States 1.0 0.7 0.1 0.4 0.1 0.1Uruguay 1.0 0.0 0.0 .. 0.2 0.9Uzbekistan 29.4 3.3 18.1 0.2 8.1 0.0Venezuela, RB 19.6 18.0 0.9 0.1 0.7 0.0Vietnam 10.4 8.0 1.3 3.1 0.3 0.7West Bank and Gaza .. .. .. .. .. ..Yemen, Rep. 21.9 19.0 3.0 .. 0.0 0.0Zambia 28.1 0.0 0.0 0.0 26.7 1.5Zimbabwe 3.4 0.0 0.0 2.9 1.2 2.2World 4.0 w 1.8 w 0.4 w 0.8 w 0.8 w 0.2 wLow income 6.5 1.2 1.3 .. 1.8 2.3Middle income 9.8 4.6 0.9 2.0 2.0 0.3Lower middle income 11.4 5.7 1.2 2.2 1.8 0.6Upper middle income 9.3 4.3 0.8 2.0 2.1 0.2Low & middle income 9.8 4.5 0.9 2.0 2.0 0.3East Asia & Pacifi c 7.9 1.7 0.4 3.4 2.1 0.2Europe & Central Asia 14.2 8.9 2.5 1.3 1.2 0.3Latin America & Carib. 8.0 4.7 0.4 0.1 2.6 0.2Middle East & N. Africa 18.7 14.9 3.2 0.0 0.6 0.1South Asia 6.2 0.9 0.6 2.3 1.7 0.7Sub-Saharan Africa 16.7 12.0 0.5 .. 2.8 1.3High income 1.4 0.6 0.2 0.2 0.3 0.1Euro area 0.3 0.0 0.1 0.0 0.0 0.1Note: Components may not sum to 100 percent because of rounding.% of GDP % of GDP % of GDP % of GDP % of GDP % of GDP2010 2010 2010 2010 2010 20102062012 World Development Indicators

Contribution of natural resourcesto gross domestic product 3.18ENVIRONMENTAbout the dataDefinitionsAccounting for the contribution of natural resourcesto economic output is important in building an analyticalframework for sustainable development. Insome countries earnings from natural resources,especially from fossil fuels and minerals, accountfor a sizable share of GDP, and much of these earningscome in the form of economic rents—revenuesabove the cost of extracting the resources. Naturalresources give rise to economic rents because theyare not produced. For produced goods and servicescompetitive forces expand supply until economicprofi ts are driven to zero, but natural resources infi xed supply often command returns well in excessof their cost of production. Rents from nonrenewableresources—fossil fuels and minerals—as wellas rents from overharvesting of forests indicate theliquidation of a country’s capital stock. When countriesuse such rents to support current consumptionrather than to invest in new capital to replace what isbeing used up, they are, in effect, borrowing againsttheir future.The estimates of natural resources rents shown inthe table are calculated as the difference betweenthe price of a commodity and the average cost ofproducing it. This is done by estimating the worldprice of units of specifi c commodities and subtractingestimates of average unit costs of extractionor harvesting costs (including a normal return oncapital). These unit rents are then multiplied by thephysical quantities countries extract or harvest todetermine the rents for each commodity as a shareof gross domestic product (GDP).This defi nition of economic rent differs from thatused in the System of National Accounts, whererents are a form of property income, consisting ofpayments to landowners by a tenant for the use ofthe land or payments to the owners of subsoil assetsby institutional units permitting them to extract subsoildeposits.The Environment section of previous editionsof World Development Indicators included a table“Toward a broader measure of savings,” whichshowed the derivation of adjusted net savings, takinginto account consumption of fi xed and naturalcapital, pollution damage, and additions to humancapital. Adjusted net savings measures the net additionsor subtractions from a country’s stock of tangibleand intangible capital. This table is includedin the Economy section as table 4.11, along withthe closely related table 4.10, “Toward a broadermeasure of income.”• Total natural resources rents are the sum of oilrents, natural gas rents, coal rents (hard and soft),mineral rents, and forest rents. • Oil rents are thedifference between the value of crude oil productionat world prices and total costs of production.• Natural gas rents are the difference between thevalue of natural gas production at world prices andtotal costs of production. • Coal rents are the differencebetween the value of both hard and softcoal production at world prices and their total costsof production. • Mineral rents are the differencebetween the value of production for a stock of mineralsat world prices and their total costs of production.Minerals included in the calculation are tin, gold,lead, zinc, iron, copper, nickel, silver, bauxite, andphosphate. • Forest rents are roundwood harvesttimes the product of average prices and a regionspecific rental rate (based on a number of reviews;World Bank 2011c).Data sourcesData on contributions of natural resources to GDPare estimates based on sources and methodsdescribed in World Bank (2011c).2012 World Development Indicators 207

ECONOMY

The data in the Economy section provide a pictureof the global economy and the economicactivity of more than 200 countries and territoriesthat produce, trade, and consume theworld’s output. The indicators measure changesin the size and structure of the global economyand the effects of these changes on nationaleconomies. They include measures of macroeconomicperformance (gross domestic product[GDP], consumption, investment, and internationaltrade), stability (central government budgets,prices, the money supply, and the balanceof payments), and broader measures of incomeand savings adjusted for pollution, depreciation,and depletion of resources.In 2010 the world economy grew 4.2 percent,a quick rebound from 2.3 percent in 2009and well above the annual average of 2.9 percentsince 2000. Total output measured in GDPat current prices increased more than $10 trillion.Upper middle-income economies, includingChina, were affected by slowing investmentand widespread uncertainty in financial marketsbut still grew 7.8 percent. Lower middle-incomeeconomies grew 6.9 percent, and low-incomeeconomies grew 5.9 percent. High-incomeeconomies, accounting for 68 percent of theworld’s GDP, grew 3.1 percent in 2010. Developingeconomies grew faster over the lastdecade than in the previous two and faster thanhigh-income economies. World output in 2010reached $63 trillion, measured in GDP at currentprices—a nominal increase of 96 percentincrease over 2000. Developing economies’share of global output increased from 18 percentto 31 percent. The developing economiesin East Asia and Pacifi c grew the most, quadruplingtheir output and more than doublingtheir share of global output from 5 percent to12 percent.The accuracy of GDP estimates and theircomparability across countries depend ontimely revisions to data on GDP and its components.The frequency of revisions to GDP datavaries: some countries revise numbers monthly,others quarterly or annually, and others lessfrequently. Such revisions are usually smalland based on additional information receivedduring the year. However, in some cases largerrevisions are required because of new methodologies,changes to the base year, or changesin coverage. Comprehensive revisions of GDPdata usually result in upward adjustments asimproved data sources increase the coverageof the economy and as new weights for growingindustries more accurately refl ect their contributionsto the economy. Revisions to data can leadto changes in income or lending classifi cation,but such changes have been rare.Revisions to GDP data may cause breaksin series unless they are applied consistentlyto historical data. For constant price series abreak caused by rebasing can be eliminated bylinking the old series to the new using historicalgrowth rates. But for nominal GDP data a breakin the time series cannot be avoided unless thestatistics offi ce revises historical series. Otherdata series are affected by revisions to GDP.Because rebasing real GDP and its componentsleaves the pre−base year current price seriesunchanged, the GDP defl ator calculated fromthese two series is skewed for pre−base years.Other series affected by the break in GDP arefiscal indicators expressed as a percentage ofGDP. When nominal GDP is revised upward, theratio of revenue and expenditure to GDP looksmaller than previously reported. Informationon signifi cant revisions and breaks in series willbe included in the next release of the WorldDevelopment Indicators database.42012 World Development Indicators 209

4.aRecent economic performanceGross domesticproductExports of goodsand servicesImports of goodsand servicesGDP deflatorCurrent accountbalanceGross internationalreservesaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth % of GDP $ millionsmonthsof importcoverage2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2011 a 2011 aAlbania 3.5 3.0 4.2 5.0 –8.5 3.2 3.5 1.9 –11.9 –11.7 2,421 4.1Algeria 3.3 3.0 .. .. .. .. 16.2 9.6 .. .. 183,122 36.2Angola 2.3 7.0 .. .. .. .. 26.6 12.4 8.8 7.0 28,348 6.3Antigua and Barbuda –5.2 3.0 .. .. .. .. 2.0 –3.9 –13.7 –13.9 148 2.5Argentina 9.2 7.5 14.6 5.5 34.0 9.9 15.4 23.4 0.8 0.0 43,321 6.1Armenia 2.1 4.2 21.7 6.0 13.8 7.5 9.2 6.6 –14.7 –12.8 1,959 4.8Australia 2.3 1.7 5.3 5.2 5.1 9.2 0.1 4.1 –2.8 –2.2 42,921 1.7Austria 2.3 2.8 8.3 6.6 8.0 5.9 1.8 2.0 3.0 2.5 11,482 0.6Azerbaijan 5.0 0.2 24.2 1.0 1.3 2.5 11.2 17.7 29.1 26.6 10,274 9.3Bangladesh 6.1 6.5 0.9 12.3 0.7 10.5 6.5 9.9 2.1 0.7 8,533 2.9Belarus 7.6 3.8 7.1 7.2 11.9 2.0 10.2 35.2 –15.2 –15.7 6,076 1.7Belgium 2.3 2.1 9.9 4.5 8.7 5.0 1.8 1.9 1.4 –0.5 18,311 0.5Belize 2.9 2.1 .. .. .. .. 0.9 5.2 –3.3 –3.3 237 3.1Benin 3.0 3.4 .. .. .. .. 1.8 1.3 .. .. 976 4.3Bolivia 4.1 4.5 9.9 5.6 11.0 5.4 8.8 11.9 4.4 4.7 9,984 17.4Botswana 7.2 6.8 1.2 7.2 5.0 5.1 14.7 12.0 0.3 –3.3 8,337 16.7Brazil 7.5 2.9 11.5 4.4 36.2 9.7 7.3 6.9 –2.3 –2.5 350,414 14.0Bulgaria 0.2 2.0 16.2 5.0 4.5 5.0 2.9 –0.3 –1.5 1.3 15,321 5.6Burkina Faso 9.2 5.8 .. .. .. .. 4.0 9.5 .. .. 843 3.1Burundi 3.9 4.4 .. .. .. .. 7.8 4.6 –18.7 –13.4 282 5.3Cambodia 6.0 6.1 20.6 5.2 16.8 3.4 3.1 4.6 –7.8 –12.1 3,471 4.6Cameroon 2.6 3.8 –0.3 7.4 4.6 7.3 3.2 4.5 –3.8 –2.9 3,199 5.2Canada 3.2 2.3 6.4 4.2 13.1 6.0 2.9 4.1 –3.1 –2.7 65,658 1.4Cape Verde 5.4 5.8 .. .. .. .. 3.3 –4.4 –11.2 –16.7 278 2.4Central African Republic 3.3 4.0 .. .. .. .. 3.2 2.6 .. .. 155 5.6Chad 4.3 6.0 .. .. .. .. 11.6 19.9 .. .. 951 4.1Chile 5.2 6.2 –0.3 5.7 26.3 10.5 14.4 4.6 1.8 –0.4 41,932 6.0China 10.4 9.1 28.4 11.3 20.1 14.4 6.6 4.9 5.2 3.7 3,204,610 19.7Hong Kong SAR, China 7.0 4.7 16.8 4.9 17.3 5.0 0.5 12.1 5.7 –0.5 285,306 6.2Colombia 4.3 4.9 1.2 7.0 18.1 6.8 3.1 3.8 –3.1 –2.5 30,504 6.7Comoros 2.1 2.3 .. .. .. .. 3.8 5.0 .. .. 161 11.6Congo, Dem. Rep. 7.2 6.7 9.4 16.0 17.0 13.6 22.4 15.2 .. .. 1,273 2.3Congo, Rep. 8.8 5.1 .. .. .. .. 19.8 14.7 .. .. 5,641 6.2Costa Rica 4.2 3.8 5.4 6.1 15.2 7.6 7.8 4.9 –4.0 –5.2 4,756 3.2Côte d’Ivoire 3.0 –5.8 –0.5 –2.0 7.6 5.1 0.7 –6.5 .. .. 4,192 4.7Croatia –1.2 1.2 6.0 5.8 –1.3 5.5 1.0 2.0 –1.6 –2.8 14,484 7.0Czech Republic 2.3 2.1 18.0 5.0 18.0 6.8 –1.2 2.4 –3.1 –3.7 39,692 2.92102012 World Development Indicators

Recent economic performance 4.aECONOMYGross domesticproductExports of goodsand servicesImports of goodsand servicesGDP deflatorCurrent accountbalanceGross internationalreservesaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth % of GDP $ millionsmonthsof importcoverage2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2011 a 2011 aDenmark 1.3 1.3 3.2 6.6 3.5 5.4 3.9 3.2 5.5 5.8 81,794 6.1Dominica 0.1 0.9 .. .. .. .. 0.0 4.9 –15.3 –21.9 81 3.5Dominican Republic 7.8 4.9 11.6 6.8 14.4 5.1 5.1 8.4 –8.6 –8.2 3,755 2.4Ecuador 3.6 5.1 2.3 4.6 16.3 7.0 7.6 7.2 –3.1 –2.7 1,710 0.9Egypt, Arab Rep. 5.1 0.5 –3.0 –4.8 –3.2 –11.7 10.1 15.1 –2.1 1.8 15,046 3.3El Salvador 1.4 1.5 12.3 7.5 11.1 7.0 1.2 3.4 –2.3 –3.8 2,165 2.4Equatorial Guinea 0.9 2.8 .. .. .. .. 19.1 6.9 .. .. 3,054 7.8Eritrea 2.2 8.2 .. .. .. .. 11.6 14.7 .. .. 113 1.8Estonia 3.1 7.6 10.7 6.1 8.8 6.5 1.5 5.4 3.5 0.9 195 0.1Ethiopia 10.1 7.7 14.4 9.0 15.9 11.0 3.8 10.8 –1.4 –10.6 .. ..Fiji 0.3 1.3 .. .. .. .. 8.1 0.4 –12.9 –7.2 832 4.6Finland 3.7 3.1 8.6 0.0 7.4 3.5 0.4 2.2 1.9 1.0 7,942 0.9France 1.5 1.6 9.7 5.7 8.8 5.0 0.8 1.5 –1.7 –2.0 52,819 0.8Gabon 5.7 6.0 3.0 3.7 9.3 9.6 18.0 11.4 .. .. 2,157 5.0Gambia, The 5.0 5.3 4.1 4.7 7.0 4.3 8.4 5.5 6.5 1.9 223 8.2Georgia 6.4 5.4 .. .. .. .. 8.7 –1.7 –11.5 –10.4 2,818 4.6Germany 3.7 3.0 13.7 5.9 11.7 6.8 0.6 1.9 5.7 5.3 72,796 0.5Ghana 6.6 13.6 53.7 41.0 69.4 35.9 14.0 22.7 –8.6 –7.0 .. ..Greece –3.5 –5.4 4.2 –1.0 –7.2 –10.0 1.7 2.3 –10.3 –8.6 1,442 0.2Guatemala 2.8 2.8 4.4 4.5 12.4 5.5 5.0 8.7 –1.5 –2.2 5,847 3.8Guinea 1.9 4.3 1.5 1.8 0.1 5.1 19.7 10.1 –7.2 –14.2 .. ..Guinea-Bissau 3.5 4.8 .. .. .. .. 1.7 –9.2 .. .. 225 9.4Guyana 3.6 4.6 .. .. .. .. 5.9 3.9 –7.2 –10.6 798 5.4Haiti –5.1 6.7 –7.3 14.5 19.7 0.2 5.4 3.2 –2.5 –13.4 1,389 3.5Honduras 2.8 3.4 6.0 2.7 10.2 2.9 5.7 7.3 –6.2 –6.4 2,751 2.9Hungary 1.3 3.4 14.3 8.7 12.8 7.9 3.1 5.3 1.1 2.8 48,686 5.4Iceland –4.0 2.8 0.4 6.2 4.0 6.7 6.9 1.7 –8.0 –8.1 8,454 14.9India 8.8 7.0 17.9 23.0 9.2 14.0 10.5 9.1 –3.0 –3.4 272,249 5.7Indonesia 6.1 6.5 14.9 15.9 17.3 13.6 8.0 7.1 0.8 0.4 106,665 6.4Ireland –0.4 2.2 6.3 7.8 2.7 6.8 –2.4 2.0 0.5 0.6 1,410 0.1Israel 4.7 4.5 13.4 4.3 12.5 12.5 1.1 2.6 2.9 0.6 74,874 9.5Italy 1.5 0.7 12.2 6.2 12.7 4.8 0.4 2.0 –3.5 –3.9 53,421 0.9Jamaica –0.6 1.3 .. .. .. .. 10.6 7.3 –6.6 –11.2 2,273 3.8Japan 4.0 –0.2 23.9 0.5 9.8 3.5 –2.2 –1.2 3.6 2.2 1,259,494 16.0Jordan 3.1 2.5 7.6 –3.7 7.1 –0.5 6.3 1.4 –4.8 –8.5 11,489 6.8Kazakhstan 7.3 6.6 1.9 11.0 –4.0 11.0 19.5 12.9 2.0 6.3 25,316 5.7Kenya 5.3 4.3 6.1 8.9 3.0 8.6 3.9 10.1 –8.0 –9.2 4,264 3.42012 World Development Indicators 211

4.a Recent economic performanceGross domesticproductExports of goodsand servicesImports of goodsand servicesGDP deflatorCurrent accountbalanceGross internationalreservesaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth % of GDP $ millionsmonthsof importcoverage2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2011 a 2011 aKorea, Rep. 6.2 3.8 14.5 9.1 16.9 6.3 3.7 1.5 2.8 2.5 304,349 5.8Kyrgyz Republic –1.4 5.5 –4.2 6.5 1.6 5.5 6.9 17.8 –8.3 –6.4 1,707 4.2Lao PDR 9.4 7.9 29.3 6.5 4.1 5.0 9.4 7.7 0.4 –14.0 .. ..Latvia –0.3 3.8 10.3 7.0 8.6 5.0 –2.3 6.4 3.0 –0.7 6,011 4.5Lebanon 7.0 3.0 0.4 –9.1 –7.8 –4.1 4.4 2.4 –22.8 –20.6 34,236 12.7Lesotho 3.3 3.1 2.2 5.9 9.6 5.4 3.7 –0.7 –19.8 –24.5 .. ..Lithuania 1.3 5.8 16.3 5.8 17.6 9.0 2.0 10.0 1.5 –2.3 7,925 3.4Luxembourg 2.7 1.9 2.8 6.0 4.6 6.2 4.9 2.4 7.7 6.1 904 0.2Macedonia, FYR 1.8 3.8 23.4 7.2 10.9 5.8 2.2 0.7 –2.2 –5.1 2,343 4.0Madagascar 1.6 2.6 .. .. .. .. 8.1 9.2 .. .. 1,279 4.2Malawi 7.1 5.6 .. .. .. .. 7.7 9.2 .. .. 212 1.1Malaysia 7.2 4.8 9.9 4.1 15.1 5.2 5.1 4.1 11.5 9.7 131,867 7.0Mali 4.5 5.4 .. .. .. .. 3.6 2.1 .. .. 1,418 5.2Mauritania 5.0 5.1 .. .. .. .. 19.3 5.6 .. .. 485 3.3Mauritius 4.0 4.1 –4.2 1.9 –0.6 13.5 1.6 1.6 –8.2 –11.1 2,589 4.3Mexico 5.4 4.0 25.6 8.6 23.5 7.2 4.4 2.7 –0.6 –0.8 144,174 4.5Moldova 6.9 6.0 12.8 20.0 13.7 10.0 11.2 3.7 –8.3 –9.4 1,965 4.1Morocco 3.7 4.3 16.3 7.3 3.3 8.8 0.6 –0.7 –4.3 –6.7 19,572 4.7Mozambique 7.2 7.4 2.2 7.3 1.7 6.0 12.7 9.5 –11.6 –13.6 2,473 6.5Namibia 4.8 3.9 –42.3 10.9 –60.0 1.9 9.3 3.9 0.3 –0.5 1,796 3.3Nepal 4.6 4.0 –13.7 22.1 26.7 13.5 13.4 11.6 .. .. 3,567 6.1Netherlands 1.7 1.4 10.8 6.2 10.6 5.7 1.3 1.8 6.6 7.3 21,322 0.4Nicaragua 7.6 4.1 13.2 6.1 10.8 7.9 2.9 7.9 –14.7 –16.3 1,892 3.1Niger 8.8 6.0 .. .. .. .. 1.7 3.7 .. .. 659 3.3Nigeria 7.9 7.0 .. .. .. .. 7.5 23.5 1.3 14.3 35,249 4.8Norway 0.7 1.6 1.8 6.3 9.9 6.7 6.4 6.3 12.3 16.9 49,273 4.3Pakistan 4.1 3.2 15.8 11.7 4.4 8.8 12.0 11.2 –0.8 0.5 14,636 3.7Panama 4.8 8.1 6.2 7.1 21.2 7.7 3.0 5.3 –10.7 –12.3 1,792 1.0Papua New Guinea 8.0 9.0 .. .. .. .. 9.3 9.8 –6.7 –24.0 4,172 6.2Paraguay 15.0 4.9 34.3 4.0 29.3 5.1 6.7 10.6 –3.5 –3.1 4,951 4.8Peru 8.8 6.3 2.5 7.2 23.8 9.1 6.9 4.2 –1.5 –2.7 47,266 12.9Philippines 7.6 3.7 21.0 –7.7 22.5 1.6 4.2 5.0 4.5 2.1 67,565 9.7Poland 3.9 4.0 12.1 6.1 13.9 7.1 1.4 2.9 –4.7 –5.1 92,824 4.6Portugal 1.4 –1.5 8.8 6.0 5.4 –1.5 1.0 2.3 –10.0 –7.6 2,635 0.3Romania 0.9 2.4 10.5 14.7 10.5 11.9 3.6 5.4 –4.0 –4.5 43,118 6.0Russian Federation 4.0 4.1 7.1 5.0 25.6 10.0 11.4 11.7 4.7 5.1 455,474 13.92122012 World Development Indicators

Recent economic performance 4.aECONOMYGross domesticproductExports of goodsand servicesImports of goodsand servicesGDP deflatorCurrent accountbalanceGross internationalreservesaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth % of GDP $ millionsmonthsof importcoverage2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2011 a 2011 aRwanda 7.5 7.2 .. .. .. .. 2.1 3.8 –7.5 –6.1 825 4.8Saudi Arabia 3.8 5.0 .. .. .. .. 12.4 15.3 15.4 23.3 541,234 34.3Senegal 4.2 4.2 5.7 5.2 3.5 4.4 1.4 4.4 .. .. 2,536 4.2Seychelles 6.2 4.0 .. .. .. .. –0.8 2.0 –24.0 –33.8 252 2.0Sierra Leone 4.9 5.6 .. .. .. .. 14.4 9.1 –16.8 –13.6 432 7.0Singapore 14.5 4.8 19.2 4.0 16.6 5.0 –0.5 4.0 23.7 20.5 237,874 5.9Slovak Republic 4.2 3.0 16.5 8.1 16.3 8.7 0.5 4.0 –3.4 –2.3 908 0.1Slovenia 1.4 2.8 9.5 3.0 7.2 –2.6 –1.1 –3.0 –0.8 –0.6 836 0.3South Africa 2.8 3.2 16.5 5.0 5.5 11.0 8.1 6.8 –2.8 –3.0 42,811 4.1Spain –0.1 0.7 10.3 5.9 5.4 0.5 1.0 2.1 –4.6 –3.5 33,330 0.9Sri Lanka 8.0 7.7 5.8 15.0 13.0 16.5 7.3 7.0 –2.9 –3.8 6,095 3.8St. Lucia 3.1 2.7 .. .. .. .. 5.1 –3.5 –14.6 –21.4 213 3.0St. Vincent & Grenadines –1.3 –0.2 .. .. .. .. 2.1 6.2 –29.2 –29.1 90 2.6Sudan 4.5 5.3 .. .. .. .. 17.6 4.1 0.3 –7.2 220 0.2Swaziland 1.1 –2.1 –2.4 5.3 0.9 4.7 6.1 5.0 –10.7 –15.8 601 2.5Sweden 5.6 4.1 11.1 6.8 12.7 7.0 1.2 0.9 6.6 6.6 44,243 2.3Switzerland 2.7 1.7 8.4 7.4 7.3 5.9 0.1 –0.1 14.0 13.0 281,187 10.0Syrian Arab Republic 3.2 –3.0 5.7 –3.5 8.3 5.8 6.3 7.5 –0.6 –2.2 16,714 9.5Tanzania 7.0 6.4 10.7 3.9 10.9 6.7 7.7 8.3 –8.6 –9.1 3,726 4.9Thailand 7.8 2.0 14.7 8.8 21.5 13.1 3.7 1.9 4.1 0.7 167,652 7.7Togo 3.4 3.7 .. .. .. .. 1.4 3.3 .. .. 746 4.6Trinidad and Tobago 0.1 2.8 .. .. .. .. 4.4 10.3 .. .. 10,106 13.7Tunisia 3.7 –0.5 4.8 –1.2 3.8 –2.2 4.0 –5.9 –4.8 –5.8 7,652 3.5Turkey 9.0 7.9 3.4 6.0 20.7 13.0 6.3 8.9 –6.4 –9.8 78,660 3.8Uganda 5.2 6.3 5.6 6.4 7.8 16.0 9.1 4.3 –10.2 –12.1 2,617 4.0Ukraine 4.2 4.5 4.5 6.5 11.1 9.0 15.0 6.5 –2.2 –4.4 30,458 4.0United Kingdom 2.1 1.0 7.4 6.0 8.6 0.3 2.9 3.8 –3.3 –1.5 79,808 1.2United States 3.0 1.7 11.3 6.6 12.5 3.9 0.8 2.5 –3.2 –3.4 150,964 0.7Uruguay 8.5 5.5 8.5 7.1 16.2 7.1 5.1 6.8 –0.4 –2.0 10,289 10.3Uzbekistan 8.5 7.5 .. .. .. .. 18.5 19.6 .. .. .. ..Vanuatu 3.0 3.9 .. .. .. .. 2.8 5.5 .. .. 176 5.8Venezuela, RB –1.5 3.1 –12.9 6.1 –2.9 7.9 46.7 59.8 3.1 7.9 10,562 2.4Vietnam 6.8 5.6 14.7 18.2 14.1 14.5 11.9 12.0 –4.0 –4.9 .. ..Yemen, Rep. 8.0 –6.0 15.8 0.2 –6.8 –1.0 24.7 19.8 –3.9 9.6 4,519 4.4Zambia 7.6 6.8 .. .. .. .. 11.7 16.9 3.8 4.9 2,324 4.3Zimbabwe 9.0 5.0 21.5 14.1 6.6 8.0 17.5 3.3 .. .. .. ..a. Data are preliminary estimates based on World Bank staff estimates and national sources.Source: World Development Indicators data fi les, the World Bank’s Global Economic Prospects 2012, and the International Monetary Fund’s International Financial Statistics.2012 World Development Indicators 213

4.1 Growth of outputGross domestic product Agriculture Industry Manufacturing Servicesaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10Afghanistan .. 11.3 .. 6.3 .. 14.5 .. 9.5 .. 14.2Albania 3.8 5.4 4.3 1.4 –0.5 4.7 .. .. 6.9 8.1Algeria 1.9 3.9 3.6 4.2 1.8 3.2 –2.1 2.0 1.8 5.2Angola a 1.6 12.9 –1.4 14.2 4.4 12.7 –0.3 19.3 –2.2 12.9Argentina 4.3 5.6 b 3.5 2.9 3.8 6.0 2.7 5.9 4.5 5.0Armenia –1.9 9.2 0.5 6.0 –7.4 9.2 –4.3 5.8 6.7 10.4Australia 3.7 3.2 3.4 1.7 2.8 2.8 1.7 0.9 4.0 3.6Austria 2.5 1.8 –0.1 1.3 2.5 2.0 2.5 2.6 2.7 2.0Azerbaijan –6.3 17.1 –1.7 4.7 –2.1 22.4 –15.7 8.3 –1.6 14.5Bahrain 5.0 6.6 .. .. .. .. .. .. .. ..Bangladesh 4.8 5.9 2.9 3.5 7.3 7.7 7.2 7.8 4.5 6.1Belarus –1.6 8.0 –4.0 5.5 –1.8 12.0 –0.7 10.7 –0.4 5.4Belgium 2.2 1.6 .. .. 1.8 0.6 .. .. .. ..Benin a 4.8 4.0 5.8 4.6 4.1 3.8 5.8 2.7 4.2 3.2Bolivia 4.0 4.1 2.9 2.9 4.1 5.4 3.8 4.4 4.3 3.1Bosnia and Herzegovina .. 4.6 .. 4.3 .. 6.3 .. 6.5 .. 3.9Botswana 5.7 4.1 –0.7 0.9 5.4 3.2 3.5 5.1 7.5 4.7Brazil 2.7 3.7 3.6 3.6 2.4 2.8 2.0 2.5 3.8 3.9Bulgaria –1.1 4.8 –3.9 –2.3 –19.5 5.3 .. 5.8 .. 5.6Burkina Faso 5.5 5.5 5.9 6.2 5.9 7.3 5.9 6.3 3.9 5.5Burundi –2.9 3.2 –1.9 –1.5 –4.3 –6.2 .. .. –2.8 10.4Cambodia 7.0 8.7 3.7 5.6 14.3 10.6 18.6 10.2 7.1 9.2Cameroon 1.7 3.2 5.4 3.4 –0.9 –0.4 1.4 .. 0.2 6.2Canada 3.1 2.0 1.1 1.5 3.2 0.1 4.5 –1.6 3.1 3.0Central African Republic 2.0 1.0 3.8 0.3 0.7 –0.4 –0.2 –0.1 0.2 –2.5Chad 2.2 9.0 4.9 .. 0.6 .. .. .. 0.8 ..Chile 6.6 4.0 2.2 4.2 5.6 2.3 4.4 2.7 6.9 4.4China a 10.6 10.8 4.1 4.4 13.7 11.8 12.9 11.6 11.0 11.5Hong Kong SAR, China 3.6 4.6 .. –3.9 .. –2.7 .. –3.6 .. 4.9Colombia 2.8 4.5 –2.7 2.2 1.4 4.5 –2.5 3.9 4.1 4.7Congo, Dem. Rep. –4.9 5.3 1.4 1.7 –8.0 8.7 –8.7 6.3 –13.0 11.2Congo, Rep. a 1.0 4.3 .. .. .. .. .. .. .. ..Costa Rica 5.3 4.9 4.1 3.3 6.2 4.8 6.8 4.4 4.7 5.4Côte d’Ivoire a 3.2 1.1 3.5 1.6 6.3 0.3 5.5 –1.1 2.0 1.2Croatia 0.5 3.2 –5.5 1.9 –2.2 2.8 –3.5 1.6 2.2 3.9Cuba –0.7 6.7 –3.3 –0.9 –1.0 2.3 0.8 –1.5 –0.7 8.3Cyprus 4.2 3.1 1.4 –3.3 0.6 3.0 0.2 0.1 6.5 3.9Czech Republic 1.1 3.8 0.0 0.4 0.2 5.5 4.3 6.9 1.2 3.2Denmark 2.7 0.9 4.6 –0.7 2.4 –0.8 2.2 0.2 2.7 1.2Dominican Republic a 6.3 5.6 1.9 3.4 7.1 2.6 7.0 2.8 5.9 7.1Ecuador 1.9 4.8 –1.7 4.4 2.6 5.4 1.5 5.2 2.4 2.8Egypt, Arab Rep. 4.4 5.1 3.1 3.3 5.1 5.5 6.3 4.9 4.1 5.4El Salvador 4.8 2.2 1.2 3.1 5.1 1.4 5.2 1.7 4.0 2.4Eritrea 5.7 0.2 1.5 2.7 15.0 0.6 10.6 –6.0 5.7 0.6Estonia 0.4 4.6 –6.2 –2.9 –2.4 8.6 7.3 8.9 3.2 7.1Ethiopia 3.8 8.8 2.6 7.1 4.1 9.3 3.9 7.6 5.2 10.9Finland 2.7 2.1 –0.3 2.7 3.4 3.0 6.4 3.3 2.8 1.5France 1.9 1.3 2.0 0.3 0.9 0.5 .. 0.1 2.2 1.7Gabon a 2.3 2.2 2.0 1.5 1.6 0.8 3.0 3.0 3.1 3.3Gambia, The 3.0 3.7 3.3 3.2 1.0 7.3 0.9 .. 3.7 6.2Georgia –7.1 6.9 –11.0 0.0 –8.1 9.3 .. 10.2 –0.3 8.4Germany 1.6 1.0 0.1 –0.2 –0.1 0.1 0.1 0.5 2.6 1.7Ghana 4.3 5.9 .. .. .. .. .. .. .. ..Greece 2.2 2.6 .. .. 1.0 0.8 .. .. .. ..Guatemala 4.2 3.6 2.8 2.9 4.3 2.5 2.8 2.6 4.7 4.4Guinea 4.4 2.9 4.3 5.0 4.9 4.6 4.0 3.1 3.6 –2.1Guinea-Bissau 1.2 1.5 .. .. .. .. .. .. .. ..Haiti 0.5 0.6 .. .. .. .. .. .. .. ..2142012 World Development Indicators

Growth of output4.1ECONOMYGross domestic product Agriculture Industry Manufacturing Servicesaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10Honduras 3.2 4.6 2.2 3.2 3.6 3.6 4.0 4.1 3.8 5.9Hungary 1.0 2.2 –1.9 3.4 3.7 2.1 7.9 3.4 0.5 2.1India 5.9 8.0 3.2 3.0 6.1 8.5 6.7 8.7 7.7 9.6Indonesia a 4.2 5.3 2.0 3.5 5.2 4.1 6.7 4.6 4.0 7.3Iran, Islamic Rep. 3.1 5.4 3.2 5.9 2.6 6.9 5.1 9.9 3.8 5.3Iraq .. 0.4 .. .. .. .. .. .. .. ..Ireland 7.4 2.8 0.0 –4.6 11.5 3.7 .. .. .. 3.8Israel a 5.5 3.6 .. .. .. .. .. .. .. ..Italy 1.5 0.5 2.1 –0.1 1.0 –0.8 1.6 –1.4 1.7 1.0Jamaica 1.6 1.2 –0.6 –0.2 –0.8 –0.3 –1.8 –1.5 3.8 1.7Japan 1.0 0.9 –0.4 –0.8 –0.4 0.5 0.5 1.6 1.7 1.3Jordan 5.0 6.7 –3.0 8.2 5.2 7.9 5.6 8.9 5.0 6.1Kazakhstan –4.1 8.3 –8.0 3.8 –8.6 9.0 .. 6.3 1.1 8.3Kenya 2.2 4.3 1.9 1.9 1.2 4.9 1.3 4.3 3.2 4.5Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 5.8 4.1 1.6 2.0 6.0 5.4 7.3 6.3 5.6 3.6Kosovo .. 5.3 .. .. .. .. .. .. .. ..Kuwait a 4.9 8.4 1.0 .. 0.3 .. –0.1 .. 3.5 ..Kyrgyz Republic –4.1 4.4 1.5 0.0 –10.3 1.1 –7.5 –0.6 –5.2 10.9Lao PDR 6.4 7.2 4.8 3.4 11.1 13.1 11.7 –0.2 6.6 7.3Latvia –1.5 4.8 –5.2 2.8 –8.3 3.7 –7.3 2.5 2.7 5.7Lebanon 5.3 4.9 2.9 0.8 –0.2 4.3 1.9 0.9 1.5 4.9Lesotho 4.0 3.5 2.8 –1.9 5.5 3.4 7.9 4.7 4.5 3.8Liberia 4.1 0.9 .. .. .. .. .. .. .. ..Libya .. 5.4 .. .. .. .. .. .. .. ..Lithuania –2.5 5.3 –0.4 1.9 3.3 5.8 6.6 7.1 5.8 5.3Macedonia, FYR –0.8 3.3 0.2 1.9 –2.3 3.3 –5.3 1.9 0.5 3.6Madagascar 2.0 3.4 1.8 2.4 2.4 4.2 2.0 5.1 2.3 3.6Malawi 3.7 5.2 8.6 2.9 2.0 6.2 0.5 5.7 1.6 6.5Malaysia a 7.0 5.0 0.3 3.3 8.6 3.3 9.5 4.1 7.3 6.9Mali 4.1 5.2 2.6 4.8 6.4 4.5 –1.4 5.1 3.0 6.5Mauritania 2.9 4.4 –0.2 1.3 3.4 4.4 5.8 –0.9 4.9 5.2Mauritius 5.2 3.9 0.0 0.1 5.4 1.8 5.3 0.6 6.3 5.6Mexico 3.1 2.1 1.5 1.7 3.8 1.3 4.3 1.1 2.9 2.5Moldova –9.6 5.2 –11.2 –0.9 –13.6 –1.7 –7.1 1.0 0.7 10.1Mongolia 1.0 7.2 0.3 4.6 1.5 5.8 –6.6 6.1 –0.9 8.9Morocco 2.4 4.9 –0.4 5.9 3.2 3.8 2.6 3.0 3.1 4.9Mozambique 6.1 7.8 5.2 8.3 12.3 8.5 10.2 7.1 5.0 6.9Myanmar a .. .. .. .. .. .. .. .. .. ..Namibia 4.0 5.0 3.8 –2.4 2.4 1.1 7.4 0.2 4.2 6.9Nepal 4.9 3.8 2.5 3.2 7.1 2.5 8.9 0.7 6.2 4.4Netherlands 3.2 1.6 1.8 1.6 1.7 0.8 2.6 1.1 3.5 2.1New Zealand 3.3 2.6 2.9 2.0 2.5 1.1 .. .. 3.6 3.1Nicaragua 3.7 3.6 4.7 2.9 5.5 3.4 5.3 4.7 5.0 4.3Niger a 2.4 4.2 3.0 .. 2.0 .. 2.6 .. 1.9 ..Nigeria 2.5 6.7 .. .. .. .. .. .. .. ..Norway 3.9 1.7 2.6 2.6 3.8 –0.6 1.5 2.3 3.9 3.1Oman a 4.5 4.7 5.0 .. 3.9 .. 6.0 .. 5.0 ..Pakistan 3.8 5.1 4.4 3.4 4.1 6.7 3.8 8.0 4.4 5.7Panama 4.7 6.8 3.1 2.9 6.0 6.0 2.7 1.7 4.5 7.3Papua New Guinea 3.8 3.8 4.5 2.4 5.4 4.3 4.6 3.9 –0.6 3.9Paraguay a 2.2 3.8 3.3 5.2 0.6 2.1 1.4 1.3 2.5 3.8Peru 4.7 6.1 5.5 4.1 5.4 6.6 3.8 6.2 4.0 6.2Philippines a 3.3 4.9 1.9 3.2 3.2 4.2 3.0 3.7 3.7 5.8Poland 4.7 4.3 0.5 1.1 6.7 5.8 8.1 8.7 5.2 3.6Portugal 2.9 0.7 –0.6 –0.2 3.1 –0.9 2.7 –0.4 2.4 1.6Puerto Rico a 4.3 0.0 .. .. .. .. .. .. .. ..Qatar .. 14.2 .. .. .. .. .. .. .. ..2012 World Development Indicators 215

4.1Growth of outputGross domestic product Agriculture Industry Manufacturing Servicesaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10Romania –0.6 5.0 –1.9 7.0 –1.2 5.8 .. .. –0.3 6.0Russian Federation –4.7 5.4 –4.9 1.5 –7.1 4.1 .. .. –4.7 6.5Rwanda a –0.2 7.6 2.5 .. –3.8 .. –5.8 .. –0.9 ..Saudi Arabia a 2.1 3.6 1.6 1.2 2.2 3.2 5.6 5.6 2.2 4.3Senegal 3.0 4.2 2.4 2.5 3.8 3.2 3.1 1.4 3.0 5.9Serbia –4.2 4.1 .. 1.1 .. 2.0 .. .. .. 5.4Sierra Leone –5.0 8.8 .. .. .. .. .. .. .. ..Singapore 7.2 6.0 –2.8 –3.2 7.7 5.6 7.0 6.1 7.2 6.3Slovak Republic 1.9 5.4 0.4 6.2 4.1 7.9 .. 9.7 4.4 4.2Slovenia 2.7 3.3 0.4 –0.6 1.6 3.3 1.8 3.2 3.0 3.6Somalia .. .. .. .. .. .. .. .. .. ..South Africa 2.1 3.9 1.0 1.5 1.0 2.9 1.6 3.1 3.0 4.1South Sudan .. .. .. .. .. .. .. .. .. ..Spain 2.7 2.4 3.1 –0.1 2.2 0.8 5.2 –0.2 2.7 3.2Sri Lanka a 5.3 5.6 1.9 3.1 6.9 5.7 8.1 4.6 6.0 6.2Sudan 5.5 6.7 7.4 2.6 8.5 10.6 7.5 4.7 1.9 7.9Swaziland 3.4 2.4 0.9 1.4 3.2 1.7 2.8 1.8 3.9 3.4Sweden 2.3 2.2 –0.8 3.3 4.3 2.2 8.9 2.7 2.0 2.2Switzerland 1.0 1.9 –0.9 0.5 0.2 2.0 1.0 2.3 1.2 1.9Syrian Arab Republic 5.1 5.0 6.0 .. 9.2 .. .. .. 1.5 ..Tajikistan –10.4 8.6 –6.8 7.4 –11.4 8.9 –12.6 8.2 –10.8 7.2Tanzania c 3.0 7.1 3.2 4.4 3.1 9.3 2.8 8.7 2.6 7.8Thailand a 4.2 4.5 1.0 2.2 5.7 5.4 6.9 5.6 3.7 4.0Timor-Leste a .. 3.4 .. .. .. .. .. .. .. ..Togo a 3.5 2.7 4.0 2.8 1.8 8.1 1.8 7.5 3.9 –0.7Trinidad and Tobago 3.2 6.5 2.7 –8.3 3.2 9.2 4.9 8.9 3.2 5.6Tunisia a 4.7 4.7 2.6 2.5 4.4 3.0 5.7 2.8 5.5 6.6Turkey 3.9 4.7 1.3 1.6 4.7 5.2 4.7 5.1 4.0 5.0Turkmenistan –4.9 13.6 –4.7 12.3 –2.7 20.2 .. .. –5.8 21.8Uganda 7.0 7.7 3.4 2.2 12.3 9.2 14.2 6.8 8.2 8.4Ukraine –9.3 4.8 –5.6 2.9 –12.6 3.2 –11.2 6.2 –8.1 4.8United Arab Emirates 4.8 5.1 .. .. .. .. .. .. .. ..United Kingdom 3.2 1.8 –0.3 0.2 1.5 –0.7 .. .. 4.1 2.6United States 3.6 1.8 3.8 1.9 3.7 0.3 .. 1.9 3.6 2.2Uruguay 3.9 3.6 3.9 2.1 1.3 3.2 –0.4 4.9 1.7 3.6Uzbekistan –0.2 7.1 0.5 6.4 –3.4 5.0 0.7 2.6 0.4 9.0Venezuela, RB 1.6 4.7 1.2 2.5 1.2 2.2 4.5 2.4 –0.1 6.4Vietnam a 7.9 7.5 4.3 3.7 11.9 9.3 11.2 10.9 7.5 7.5West Bank and Gaza 7.3 –0.9 .. .. .. .. .. .. .. ..Yemen, Rep. a 5.6 4.1 5.1 2.7 5.2 2.0 1.8 5.1 6.1 6.0Zambia 0.5 5.6 4.2 1.1 –4.2 9.4 0.8 5.1 2.5 5.5Zimbabwe 2.3 –6.0 4.3 –9.6 0.4 –4.9 0.4 –5.9 3.0 –4.2World 2.9 w 2.7 w 2.0 w 2.5 w 2.4 w 2.5 w .. w 3.1 w 3.1 w 2.8 wLow income 3.0 5.5 2.9 3.7 3.6 7.2 3.6 6.6 2.8 6.1Middle income 3.9 6.4 2.4 3.5 4.4 7.2 6.2 7.5 4.0 6.6Lower middle income 3.8 6.3 2.6 3.3 4.1 6.0 4.7 6.3 4.5 7.6Upper middle income 3.9 6.5 2.3 3.6 4.5 7.5 6.5 7.8 3.9 6.4Low & middle income 3.9 6.4 2.4 3.5 4.4 7.2 6.2 7.5 4.0 6.6East Asia & Pacifi c 8.5 9.4 3.4 4.1 10.9 10.2 10.9 10.1 8.4 10.0Europe & Central Asia –1.8 5.4 –2.1 2.7 –4.4 5.4 .. .. –1.2 5.9Latin America & Carib. 3.2 3.8 2.0 2.9 3.0 3.1 2.9 2.8 3.5 4.0Middle East & N. Africa 3.8 4.7 2.9 4.6 4.1 3.5 4.3 5.4 3.3 5.1South Asia 5.5 7.4 3.3 3.1 6.0 8.1 6.4 8.4 6.9 8.8Sub-Saharan Africa 2.5 5.0 3.2 3.2 1.9 4.9 2.2 3.4 2.6 4.8High income 2.7 1.8 1.5 0.7 1.9 0.7 .. 1.9 3.0 2.1Euro area 2.0 1.3 1.6 0.2 1.0 0.4 1.2 0.3 2.4 1.8a. Components are at producer prices. b. Private analysts estimate that consumer price index infl ation was considerably higher for 2007–09 and believe that GDP volume growth has beensignifi cantly lower than offi cial reports indicate since the last quarter of 2008. c. Covers mainland Tanzania only.2162012 World Development Indicators

Growth of output4.1ECONOMYAbout the dataDefinitionsAn economy’s growth is measured by the change inthe volume of its output or in the real incomes ofits residents. The 1993 United Nations System ofNational Accounts (1993 SNA) offers three plausibleindicators for calculating growth: the volume of grossdomestic product (GDP), real gross domestic income,and real gross national income. The volume of GDPis the sum of value added, measured at constantprices, by households, government, and industriesoperating in the economy.Each industry’s contribution to growth in the economy’soutput is measured by growth in the industry’svalue added. In principle, value added in constantprices can be estimated by measuring the quantityof goods and services produced in a period, valuingthem at an agreed set of base year prices, andsubtracting the cost of intermediate inputs, also inconstant prices. This double-deflation method, recommendedby the 1993 SNA and its predecessors,requires detailed information on the structure ofprices of inputs and outputs.In many industries, however, value added isextrapolated from the base year using single volumeindexes of outputs or, less commonly, inputs. Particularlyin the services industries, including most ofgovernment, value added in constant prices is oftenimputed from labor inputs, such as real wages ornumber of employees. In the absence of well definedmeasures of output, measuring the growth of servicesremains diffi cult.Moreover, technical progress can lead to improvementsin production processes and in the quality ofgoods and services that, if not properly accountedfor, can distort measures of value added and thusof growth. When inputs are used to estimate output,as for nonmarket services, unmeasured technicalprogress leads to underestimates of the volume ofoutput. Similarly, unmeasured improvements in qualitylead to underestimates of the value of output andvalue added. The result can be underestimates ofgrowth and productivity improvement and overestimatesof inflation.Informal economic activities pose a particularmeasurement problem, especially in developingcountries, where much economic activity is unrecorded.A complete picture of the economy requiresestimating household outputs produced for homeuse, sales in informal markets, barter exchanges,and illicit or deliberately unreported activities. Theconsistency and completeness of such estimatesdepend on the skill and methods of the compilingstatisticians.Rebasing national accountsWhen countries rebase their national accounts, theyupdate the weights assigned to various componentsto better reflect current patterns of production oruses of output. The new base year should representnormal operation of the economy—it should be ayear without major shocks or distortions. Somedeveloping countries have not rebased their nationalaccounts for many years. Using an old base yearcan be misleading because implicit price and volumeweights become progressively less relevantand useful.To obtain comparable series of constant price data,the World Bank rescales GDP and value added byindustrial origin to a common reference year. Thisyear’s World Development Indicators continues touse 2000 as the reference year. Because rescalingchanges the implicit weights used in forming regionaland income group aggregates, aggregate growthrates in this year’s edition are not comparable withthose from earlier editions with different base years.Rescaling may result in a discrepancy betweenthe rescaled GDP and the sum of the rescaled components.Because allocating the discrepancy wouldcause distortions in the growth rates, the discrepancyis left unallocated. As a result, the weightedaverage of the growth rates of the components generallywill not equal the GDP growth rate.Computing growth ratesGrowth rates of GDP and its components are calculatedusing the least squares method and constantprice data in the local currency. Constant price U.S.dollar series are used to calculate regional andincome group growth rates. Local currency series areconverted to constant U.S. dollars using an exchangerate in the common reference year. The growth ratesin the table are average annual compound growthrates. Methods of computing growth are describedin Statistical methods.Changes in the System of National AccountsWorld Development Indicators adopted the terminologyof the 1993 SNA in 2001. Although manycountries continue to compile their national accountsaccording to the SNA version 3 (referred to as the1968 SNA), more and more are adopting the 1993SNA. Some low-income countries still use conceptsfrom the even older 1953 SNA guidelines, includingvaluations such as factor cost, in describing majoreconomic aggregates. Countries that use the 1993SNA are identified in Primary data documentation.• Gross domestic product (GDP) at purchaser pricesis the sum of gross value added by all resident producersin the economy plus any product taxes (lesssubsidies) not included in the valuation of output. Itis calculated without deducting for depreciation offabricated capital assets or for depletion and degradationof natural resources. Value added is the netoutput of an industry after adding up all outputs andsubtracting intermediate inputs. The industrial originof value added is determined by the InternationalStandard Industrial Classifi cation (ISIC) revision3. • Agriculture is the sum of gross output lessthe value of intermediate input used in productionfor industries classifi ed in ISIC divisions 1–5 andincludes forestry and fishing. • Industry is the sumof gross output less the value of intermediate inputused in production for industries classifi ed in ISICdivisions 10–45, which cover mining, manufacturing(also reported separately), construction, electricity,water, and gas. • Manufacturing is the sum ofgross output less the value of intermediate inputused in production for industries classifi ed in ISICdivisions 15–37. • Services correspond to ISIC divisions50–99. This sector is derived as a residual(from GDP less agriculture and industry) and may notproperly reflect the sum of services output, includingbanking and financial services. For some countriesit includes product taxes (minus subsidies) and mayalso include statistical discrepancies.Data sourcesData on national accounts for most developingcountries are collected from national statisticalorganizations and central banks by visiting andresident World Bank missions. Data for highincomeeconomies are from Organisation forEconomic Co-operation and Development (OECD)data fi les. The United Nations Statistics Divisionpublishes detailed national accounts for UN membercountries in National Accounts Statistics: MainAggregates and Detailed Tables and publishesupdates in the Monthly Bulletin of Statistics.2012 World Development Indicators 217

4.2Structure of outputGross domestic product Agriculture Industry Manufacturing Services$ billions % of GDP % of GDP % of GDP % of GDP2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan 2.5 17.2 45 30 20 22 15 13 35 48Albania 3.7 11.8 29 20 19 19 11 19 52 61Algeria 54.8 162.0 9 7 59 62 7 6 33 31Angola a 9.1 84.4 6 10 72 63 3 6 22 27Argentina 284.2 368.7 5 10 28 31 18 21 67 59Armenia 1.9 9.4 26 20 39 36 19 11 35 44Australia 416.9 1,131.6 4 2 27 20 13 9 70 78Austria 192.1 379.1 2 2 31 29 20 19 67 69Azerbaijan 5.3 51.8 17 6 45 65 6 6 38 30Bahrain 8.0 20.6 .. .. .. .. .. .. .. ..Bangladesh 47.1 100.4 26 19 25 28 15 18 49 53Belarus 12.7 54.7 14 9 39 44 32 31 47 47Belgium 232.7 469.4 1 1 27 22 19 14 72 78Benin a 2.3 6.6 37 .. 14 .. 9 .. 50 ..Bolivia 8.4 19.6 15 13 30 37 15 14 55 50Bosnia and Herzegovina 5.5 16.6 11 8 23 28 10 14 66 64Botswana 5.6 14.9 3 3 53 45 5 3 45 52Brazil 644.7 2,087.9 6 6 28 27 17 16 67 67Bulgaria 12.9 47.7 14 5 26 31 18 16 61 63Burkina Faso 2.6 8.8 29 .. 24 .. 16 .. 47 ..Burundi 0.7 1.6 40 .. 19 .. 9 .. 41 ..Cambodia 3.7 11.2 38 36 23 23 17 16 39 41Cameroon 10.1 22.4 22 .. 36 .. 21 .. 42 ..Canada 724.9 1,577.0 2 .. 33 .. 19 .. 65 ..Central African Republic 1.0 2.0 53 56 16 15 7 .. 31 29Chad 1.4 7.6 42 14 11 49 9 7 46 38Chile 75.2 212.7 6 3 38 43 19 12 55 54China a 1,198.5 5,926.6 15 10 46 47 32 30 39 43Hong Kong SAR, China 169.1 224.5 0 0 12 7 3 2 88 93Colombia 100.4 288.2 9 7 29 36 15 15 62 57Congo, Dem. Rep. 4.3 13.1 50 43 20 24 5 5 30 33Congo, Rep. a 3.2 11.9 5 4 72 80 3 4 23 16Costa Rica 15.9 35.8 9 7 32 26 25 17 58 67Côte d’Ivoire a 10.4 22.8 24 23 25 27 22 19 51 50Croatia 21.5 60.9 6 6 29 27 20 18 65 67Cuba 30.6 62.7 8 5 28 20 18 10 64 75Cyprus 9.3 23.1 4 2 19 20 10 8 77 78Czech Republic 56.7 192.0 4 2 38 38 27 24 58 60Denmark 160.1 312.0 3 1 27 22 16 12 71 77Dominican Republic a 24.0 51.8 7 6 36 32 26 24 57 62Ecuador 15.9 58.0 9 7 32 38 11 10 59 55Egypt, Arab Rep. 99.8 218.9 17 14 33 38 19 16 50 48El Salvador 13.1 21.2 10 13 32 27 25 21 58 60Eritrea 0.6 2.1 15 15 23 22 11 6 62 63Estonia 5.7 19.2 5 3 28 29 18 17 68 68Ethiopia 8.2 29.7 50 48 12 14 6 5 38 38Finland 121.8 238.0 3 3 35 29 26 19 62 68France 1,326.3 2,560.0 3 2 23 19 16 11 74 79Gabon a 5.1 13.0 6 4 56 54 4 4 38 42Gambia, The 0.4 0.8 36 27 13 16 5 5 51 57Georgia 3.1 11.7 22 8 22 23 9 13 56 68Germany 1,886.4 3,280.5 1 1 30 28 23 21 68 71Ghana 5.0 31.3 39 30 28 19 10 6 32 51Greece 124.4 301.1 .. .. 21 18 .. .. .. ..Guatemala 19.3 41.2 15 13 29 19 21 19 56 68Guinea 3.1 4.5 20 13 33 47 4 5 47 40Guinea-Bissau 0.2 0.9 56 .. 13 .. 11 .. 31 ..Haiti 3.7 6.7 .. .. .. .. .. .. .. ..2182012 World Development Indicators

Structure of output 4.2ECONOMYGross domestic product Agriculture Industry Manufacturing Services$ billions % of GDP % of GDP % of GDP % of GDP2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 7.1 15.4 16 13 32 27 23 18 52 61Hungary 46.4 128.6 6 4 32 31 24 23 62 65India 460.2 1,727.1 23 19 26 26 16 14 50 55Indonesia a 165.0 706.6 16 15 46 47 28 25 38 38Iran, Islamic Rep. 101.3 331.0 14 .. 37 .. 13 .. 50 ..Iraq 25.9 82.2 5 .. 84 .. 1 .. 10 ..Ireland 97.5 206.6 3 1 42 32 33 24 55 67Israel a 124.7 217.3 .. .. .. .. .. .. .. ..Italy 1,104.0 2,061.0 3 2 28 25 21 17 69 73Jamaica 9.0 14.3 7 6 26 22 11 9 67 71Japan 4,667.4 5,458.8 2 1 32 27 22 18 66 72Jordan 8.5 27.6 2 3 26 31 16 19 72 66Kazakhstan 18.3 149.1 9 5 40 42 18 13 51 53Kenya 12.7 31.4 32 19 17 14 12 8 51 67Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 533.4 1,014.5 5 3 38 39 28 31 57 58Kosovo 1.8 5.6 .. 12 .. 20 .. 17 .. 68Kuwait a 37.7 109.5 0 .. 59 .. 3 .. 40 ..Kyrgyz Republic 1.4 4.6 37 21 31 28 19 18 32 51Lao PDR 1.7 7.3 53 33 23 30 17 8 25 37Latvia 7.8 24.0 5 4 24 22 14 12 72 74Lebanon 17.3 39.0 7 6 23 21 13 8 70 72Lesotho 0.7 2.1 12 8 32 34 14 16 56 58Liberia 0.6 1.0 72 61 12 17 9 13 16 22Libya 33.9 62.4 5 2 66 78 3 4 29 20Lithuania 11.4 36.3 6 4 30 28 19 16 64 68Macedonia, FYR 3.6 9.2 12 11 34 28 21 16 54 61Madagascar 3.9 8.7 29 29 14 16 12 14 57 55Malawi 1.7 5.1 40 31 18 16 13 10 43 53Malaysia a 93.8 237.8 9 11 48 44 31 26 43 45Mali 2.4 9.3 42 .. 21 .. 4 .. 38 ..Mauritania 1.1 3.6 28 20 30 37 9 4 43 43Mauritius 4.6 9.7 7 4 31 29 23 19 62 67Mexico 581.4 1,034.8 4 4 28 34 20 18 68 62Moldova 1.3 5.8 29 14 22 13 16 13 49 73Mongolia 1.1 6.2 31 16 25 38 8 7 44 46Morocco 37.0 90.8 15 15 29 30 17 15 56 55Mozambique 4.2 9.6 24 32 25 23 12 13 51 45Myanmar a .. .. 57 36 10 26 7 20 33 38Namibia 3.9 12.2 12 8 28 20 13 8 60 73Nepal 5.5 15.7 41 36 22 15 9 7 37 48Netherlands 385.1 779.4 3 2 25 24 16 13 72 74New Zealand 51.6 126.7 9 .. 25 .. 17 .. 66 ..Nicaragua 3.9 6.6 21 21 28 30 17 20 51 49Niger a 1.8 5.5 38 .. 18 .. 7 .. 44 ..Nigeria 46.0 193.7 49 .. 31 .. 3 .. 21 ..Norway 168.3 417.5 2 2 42 40 11 9 56 58Oman a 19.9 46.9 2 .. 57 .. 5 .. 41 ..Pakistan 74.0 176.9 26 21 23 25 15 17 51 53Panama 11.6 26.7 7 5 19 17 10 6 74 78Papua New Guinea 3.5 9.5 36 36 41 45 8 6 23 19Paraguay a 7.1 18.3 17 22 22 20 15 12 61 57Peru 53.3 157.1 8 8 30 34 16 17 62 57Philippines a 81.0 199.6 14 12 34 33 24 21 52 55Poland 171.3 469.4 5 4 32 32 19 18 63 65Portugal 117.3 228.6 4 2 28 23 18 13 68 74Puerto Rico a 61.7 96.3 1 1 42 50 39 46 57 49Qatar 17.8 98.3 .. .. .. .. .. .. .. ..2012 World Development Indicators 219

4.2Structure of outputGross domestic product Agriculture Industry Manufacturing ServicesRomania 37.1 161.6 13 7 36 26 15 22 51 67Russian Federation 259.7 1,479.8 6 4 38 37 17 16 56 59Rwanda a 1.7 5.6 37 34 14 14 7 6 49 52Saudi Arabia a 188.4 434.7 5 3 54 62 10 10 41 35Senegal 4.7 13.0 19 17 23 22 15 13 58 61Serbia 6.1 38.4 20 9 30 27 24 16 50 64Sierra Leone 0.6 1.9 58 49 28 21 4 .. 13 30Singapore 95.9 208.8 0 0 35 28 27 22 65 72Slovak Republic 28.7 87.3 4 4 36 35 25 21 59 61Slovenia 20.0 46.9 3 2 36 32 26 21 61 66Somalia .. .. .. .. .. .. .. .. .. ..South Africa 132.9 363.7 3 3 32 31 19 15 65 66South Sudan .. .. .. .. .. .. .. .. .. ..Spain 580.7 1,407.4 4 3 29 26 19 13 66 72Sri Lanka a 16.3 49.6 20 13 27 29 17 18 53 58Sudan 12.4 62.0 42 24 22 33 9 6 37 43Swaziland 1.5 3.6 12 7 45 50 39 45 43 42Sweden 247.3 458.6 2 2 29 27 22 16 69 71Switzerland 249.9 527.9 2 1 27 27 19 19 71 72Syrian Arab Republic 19.3 59.1 24 23 38 31 7 .. 38 46Tajikistan 0.9 5.6 27 21 39 22 34 10 34 57Tanzania b 10.2 23.1 33 28 19 25 9 10 47 47Thailand a 122.7 318.5 9 12 42 45 34 36 49 43Timor-Leste a 0.3 0.7 26 .. 19 .. 3 .. 56 ..Togo a 1.3 3.2 34 .. 18 .. 8 .. 48 ..Trinidad and Tobago 8.2 20.6 1 1 49 52 7 5 49 47Tunisia a 21.5 44.3 11 8 30 32 18 18 58 60Turkey 266.6 734.4 11 10 31 27 23 18 57 64Turkmenistan 2.9 20.0 24 12 44 54 11 .. 31 34Uganda 6.2 17.0 29 24 23 25 8 8 48 50Ukraine 31.3 137.9 17 8 36 31 19 17 47 61United Arab Emirates 104.3 297.6 2 1 50 54 13 10 48 46United Kingdom 1,477.2 2,261.7 1 1 27 22 17 11 72 78United States 9,898.8 14,586.7 1 1 23 20 16 13 75 79Uruguay 22.8 39.1 7 9 25 26 14 14 69 65Uzbekistan 13.8 39.0 34 20 23 35 9 9 43 45Venezuela, RB 117.1 391.8 4 .. 50 .. 20 .. 46 ..Vietnam a 31.2 106.4 25 21 37 41 19 20 39 38West Bank and Gaza 4.1 .. .. .. .. .. .. .. .. ..Yemen, Rep. a 9.6 31.3 14 8 46 29 6 6 40 63Zambia 3.2 16.2 22 9 25 37 11 9 52 54Zimbabwe 6.6 7.5 18 17 25 29 16 15 57 53World 32,248.5 w 63,242.1 w 4 w 3 w 29 w 25 w 19 w 16 w 68 w 72 wLow income 164.8 416.5 34 25 21 25 12 14 45 50Middle income 5,708.1 19,632.1 11 10 36 36 21 20 53 55Lower middle income 1,258.9 4,312.3 20 17 34 31 17 16 46 52Upper middle income 4,449.2 15,317.0 9 8 36 37 23 22 55 55Low & middle income 5,874.8 20,071.7 12 10 35 35 21 20 53 54East Asia & Pacifi c 1,727.2 7,630.5 15 11 44 45 31 29 41 43Europe & Central Asia 709.9 3,059.0 11 7 35 32 18 17 55 61Latin America & Carib. 2,054.4 4,980.8 6 6 30 31 18 17 65 63Middle East & N. Africa 433.5 1,207.0 13 .. 43 .. 13 .. 44 ..South Asia 608.2 2,090.4 24 19 26 26 15 15 50 54Sub-Saharan Africa 342.1 1,097.9 16 13 29 30 15 13 54 57High income 26,375.3 43,240.0 2 1 28 24 19 15 71 75Euro area 6,256.1 12,149.1 2 2 28 26 20 16 70 72a. Components are at producer prices. b. Covers mainland Tanzania only.$ billions % of GDP % of GDP % of GDP % of GDP2000 2010 2000 2010 2000 2010 2000 2010 2000 20102202012 World Development Indicators

Structure of output 4.2ECONOMYAbout the dataDefinitionsAn economy’s gross domestic product (GDP) representsthe sum of value added by all its producers.Value added is the value of the gross output ofproducers less the value of intermediate goods andservices consumed in production, before accountingfor consumption of fi xed capital in production. TheUnited Nations System of National Accounts callsfor value added to be valued at either basic prices(excluding net taxes on products) or producer prices(including net taxes on products paid by producersbut excluding sales or value added taxes). Both valuationsexclude transport charges that are invoicedseparately by producers. Total GDP shown in thetable and elsewhere in this volume is measured atpurchaser prices. Value added by industry is normallymeasured at basic prices. When value added is measuredat producer prices, this is noted in Primary datadocumentation and footnoted in the table.While GDP estimates based on the productionapproach are generally more reliable than estimatescompiled from the income or expenditure side, differentcountries use different defi nitions, methods,and reporting standards. World Bank staff review thequality of national accounts data and sometimesmake adjustments to improve consistency withinternational guidelines. Nevertheless, signifi cantdiscrepancies remain between international standardsand actual practice. Many statistical offi ces,especially those in developing countries, face severelimitations in the resources, time, training, and budgetsrequired to produce reliable and comprehensiveseries of national accounts statistics.Data problems in measuring outputAmong the diffi culties faced by compilers of nationalaccounts is the extent of unreported economic activityin the informal or secondary economy. In developingcountries a large share of agricultural output iseither not exchanged (because it is consumed withinthe household) or not exchanged for money.Agricultural production often must be estimatedindirectly, using a combination of methods involvingestimates of inputs, yields, and area under cultivation.This approach sometimes leads to crudeapproximations that can differ from the true valuesover time and across crops for reasons other thanclimate conditions or farming techniques. Similarly,agricultural inputs that cannot easily be allocated tospecifi c outputs are frequently “netted out” usingequally crude and ad hoc approximations. For furtherdiscussion of the measurement of agricultural production,see About the data for table 3.3.Ideally, industrial output should be measuredthrough regular censuses and surveys of fi rms.But in most developing countries such surveys areinfrequent, so earlier survey results must be extrapolatedusing an appropriate indicator. The choice ofsampling unit, which may be the enterprise (whereresponses may be based on fi nancial records) orthe establishment (where production units may berecorded separately), also affects the quality ofthe data. Moreover, much industrial production isorganized in unincorporated or owner-operated venturesthat are not captured by surveys aimed at theformal sector. Even in large industries, where regularsurveys are more likely, evasion of excise andother taxes and nondisclosure of income lower theestimates of value added. Such problems becomemore acute as countries move from state control ofindustry to private enterprise, because new firms andgrowing numbers of established firms fail to report.In accordance with the System of National Accounts,output should include all such unreported activityas well as the value of illegal activities and otherunrecorded, informal, or small-scale operations.Data on these activities need to be collected usingtechniques other than conventional surveys of fi rms.In industries dominated by large organizationsand enterprises, such as public utilities, data onoutput, employment, and wages are usually readilyavailable and reasonably reliable. But in theservices industry the many self-employed workersand one-person businesses are sometimes diffi cultto locate, and they have little incentive to respondto surveys, let alone to report their full earnings.Compounding these problems are the many formsof economic activity that go unrecorded, includingthe work that women and children do for little or nopay. For further discussion of the problems of usingnational accounts data, see Srinivasan (1994) andHeston (1994).Dollar conversionTo produce national accounts aggregates that aremeasured in the same standard monetary units,the value of output must be converted to a singlecommon currency. The World Bank conventionallyuses the U.S. dollar and applies the average offi cialexchange rate reported by the International MonetaryFund for the year shown. An alternative conversionfactor is applied if the offi cial exchange rate is judgedto diverge by an exceptionally large margin from therate effectively applied to transactions in foreign currenciesand traded products.• Gross domestic product (GDP) at purchaser pricesis the sum of gross value added by all resident producersin the economy plus any product taxes (lesssubsidies) not included in the valuation of output.It is calculated without deducting for depreciationof fabricated assets or for depletion and degradationof natural resources. Value added is the netoutput of an industry after adding up all outputs andsubtracting intermediate inputs. The industrial originof value added is determined by the InternationalStandard Industrial Classifi cation (ISIC) revision3. • Agriculture is the sum of gross output lessthe value of intermediate input used in productionfor industries classifi ed in ISIC divisions 1–5 andincludes forestry and fi shing. • Industry is the sumof gross output less the value of intermediate inputused in production for industries classifi ed in ISICdivisions 10–45, which cover mining, manufacturing(also reported separately), construction, electricity,water, and gas. • Manufacturing is the sum ofgross output less the value of intermediate inputused in production for industries classifi ed in ISICdivisions 15–37. • Services correspond to ISIC divisions50–99. This sector is derived as a residual(from GDP less agriculture and industry) and may notproperly refl ect the sum of services output, includingbanking and fi nancial services. For some countriesit includes product taxes (minus subsidies) and mayalso include statistical discrepancies.Data sourcesData on national accounts for most developingcountries are collected from national statisticalorganizations and central banks by visiting andresident World Bank missions. Data for highincomeeconomies are from Organisation forEconomic Co-operation and Development (OECD)data fi les. The United Nations Statistics Divisionpublishes detailed national accounts for UN membercountries in National Accounts Statistics: MainAggregates and Detailed Tables and publishesupdates in the Monthly Bulletin of Statistics.2012 World Development Indicators 221

4.3 Structure of manufacturingManufacturingvalue addedFood,beverages,and tobaccoTextiles andclothingMachineryand transportequipmentChemicalsOthermanufacturing a$ billions % of total % of total % of total % of total % of total2000 2010 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008Afghanistan 0.64 1.96 .. .. .. .. .. .. .. .. .. ..Albania 0.37 1.95 20 15 27 23 3 3 5 11 46 48Algeria 3.86 7.32 .. .. .. .. .. .. .. .. .. ..Angola 0.26 4.89 .. .. .. .. .. .. .. .. .. ..Argentina 46.88 69.44 29 .. 8 .. 11 .. 15 .. 37 ..Armenia 0.32 0.89 .. .. .. .. .. .. .. .. .. ..Australia 49.08 98.34 21 19 4 3 13 14 8 7 55 58Austria 35.36 65.50 10 9 4 2 25 26 7 7 54 56Azerbaijan 0.28 2.81 42 15 3 1 11 6 6 4 37 75Bahrain .. .. .. .. .. .. .. .. .. .. .. ..Bangladesh 6.92 17.36 24 .. 40 .. 3 .. 11 .. 21 ..Belarus 3.44 14.90 .. .. .. .. .. .. .. .. .. ..Belgium 39.90 59.03 12 13 5 4 19 17 20 21 44 45Benin 0.20 .. .. .. .. .. .. .. .. .. .. ..Bolivia 1.11 2.21 37 .. 5 .. 0 .. 4 .. 54 ..Bosnia and Herzegovina 0.47 1.87 .. .. .. .. .. .. .. .. .. ..Botswana 0.25 0.44 20 22 5 5 .. .. .. .. 75 73Brazil 96.17 280.65 17 18 7 6 19 21 12 11 45 44Bulgaria 1.98 6.67 20 20 14 14 14 18 11 5 41 44Burkina Faso 0.40 .. .. .. .. .. .. .. .. .. .. ..Burundi 0.05 .. .. .. .. .. .. .. .. .. .. ..Cambodia 0.59 1.65 7 .. 87 .. 0 .. 0 .. 7 ..Cameroon 1.94 .. 36 .. 19 .. 1 .. 2 .. 43 ..Canada 129.47 .. 12 1 4 2 32 26 8 8 45 64Central African Republic 0.06 .. .. .. .. .. .. .. .. .. .. ..Chad 0.12 0.38 .. .. .. .. .. .. .. .. .. ..Chile 13.25 22.65 32 14 4 2 4 2 14 14 46 69China 384.94 1,756.82 14 12 11 10 14 24 12 11 48 43Hong Kong SAR, China 5.54 3.76 7 14 20 11 12 21 4 5 58 48Colombia 14.44 40.07 30 .. 11 .. 4 .. 16 .. 39 ..Congo, Dem. Rep. 0.21 0.59 .. .. .. .. .. .. .. .. .. ..Congo, Rep. 0.11 0.46 .. .. .. .. .. .. .. .. .. ..Costa Rica 3.68 5.68 45 42 6 4 3 3 12 9 33 43Côte d’Ivoire 2.26 4.38 .. .. .. .. .. .. .. .. .. ..Croatia 3.62 9.43 .. .. .. .. .. .. .. .. .. ..Cuba 4.57 4.96 .. .. .. .. .. .. .. .. .. ..Cyprus 0.85 1.66 37 32 7 3 4 5 6 6 46 54Czech Republic 13.79 42.25 12 9 6 3 24 30 7 5 51 53Denmark 22.25 32.86 18 16 2 2 20 19 10 12 48 51Dominican Republic 5.64 11.49 .. .. .. .. .. .. .. .. .. ..Ecuador 2.17 5.41 60 40 3 3 1 2 5 4 31 51Egypt, Arab Rep. 17.97 32.98 20 18 10 12 7 6 22 14 41 50El Salvador 3.03 4.04 29 .. 28 .. 2 .. 16 .. 25 ..Eritrea 0.07 0.10 60 .. 12 31 1 1 6 11 21 56Estonia 0.90 2.39 17 14 15 8 10 18 4 6 53 55Ethiopia 0.42 1.46 54 46 12 7 7 2 5 6 22 39Finland 28.07 39.11 6 6 2 1 33 37 5 6 54 50France 190.45 253.61 13 14 4 3 26 25 12 13 45 44Gabon 0.19 0.49 .. .. .. .. .. .. .. .. .. ..Gambia, The 0.02 0.03 .. .. .. .. .. .. .. .. .. ..Georgia 0.26 1.31 37 25 1 1 12 8 7 7 43 59Germany 392.47 614.23 8 7 2 2 33 37 10 10 47 44Ghana 0.45 1.95 .. .. .. .. .. .. .. .. .. ..Greece .. .. 24 22 12 8 11 10 10 6 43 54Guatemala 2.54 7.52 .. .. .. .. .. .. .. .. .. ..Guinea 0.12 0.20 .. .. .. .. .. .. .. .. .. ..Guinea-Bissau 0.02 .. .. .. .. .. .. .. .. .. .. ..Haiti .. .. .. .. .. .. .. .. .. .. .. ..2222012 World Development Indicators

Structure of manufacturing 4.3ECONOMYManufacturingvalue addedFood,beverages,and tobaccoTextiles andclothingMachineryand transportequipmentChemicalsOthermanufacturing a$ billions % of total % of total % of total % of total % of total2000 2010 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008Honduras 1.46 2.63 .. .. .. .. .. .. .. .. .. ..Hungary 9.28 25.26 15 11 5 3 27 30 10 10 43 46India 65.75 226.79 13 9 13 8 16 16 21 14 38 54Indonesia 45.79 175.39 18 26 17 12 20 18 11 6 35 38Iran, Islamic Rep. 13.24 .. 10 .. 6 .. 20 .. 19 .. 45 ..Iraq 0.24 .. .. .. .. .. .. .. .. .. .. ..Ireland 28.22 48.71 14 17 1 1 20 14 36 36 29 33Israel .. .. 12 10 5 3 32 22 10 20 41 44Italy 205.51 308.22 9 9 12 10 23 23 8 7 48 51Jamaica 0.85 1.16 .. .. .. .. .. .. .. .. .. ..Japan 1,034.09 905.54 11 11 3 2 34 37 10 10 41 40Jordan 1.14 4.78 30 21 7 10 3 4 18 18 43 47Kazakhstan 3.02 18.54 .. .. .. .. .. .. .. .. .. ..Kenya 1.31 3.02 29 30 8 4 2 2 5 4 55 62Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 134.56 279.44 8 6 8 5 41 46 10 8 33 35Kosovo .. 0.77 .. .. .. .. .. .. .. .. .. ..Kuwait 0.97 .. 8 .. 4 .. 2 .. 3 .. 82 ..Kyrgyz Republic 0.25 0.74 33 19 2 10 1 3 1 2 63 66Lao PDR 0.29 0.52 46 .. 22 .. 8 .. 3 .. 22 ..Latvia 0.96 2.62 27 23 11 6 8 16 3 5 51 50Lebanon 1.97 2.85 26 .. 10 .. 3 .. 6 .. 55 ..Lesotho 0.10 0.30 .. .. .. .. .. .. .. .. .. ..Liberia 0.05 0.10 .. .. .. .. .. .. .. .. .. ..Libya 0.64 3.88 .. .. .. .. .. .. .. .. .. ..Lithuania 1.96 5.43 27 22 18 9 12 13 3 10 40 46Macedonia, FYR 0.62 1.25 32 18 15 17 9 4 6 6 38 55Madagascar 0.43 1.11 0 0 35 30 0 1 2 2 62 67Malawi 0.20 0.48 71 .. 5 .. 0 .. 9 .. 15 ..Malaysia 28.95 62.10 8 9 4 2 38 30 8 12 42 47Mali 0.09 .. .. .. .. .. .. .. .. .. .. ..Mauritania 0.09 0.12 .. .. .. .. .. .. .. .. .. ..Mauritius 0.94 1.60 20 28 52 31 1 1 .. .. 26 39Mexico 107.20 179.11 25 25 4 3 24 18 15 19 31 35Moldova 0.18 0.62 65 40 9 14 5 5 .. .. 22 41Mongolia 0.08 0.40 49 43 40 17 0 0 2 5 9 35Morocco 5.74 12.50 34 29 18 13 4 6 12 18 32 34Mozambique 0.45 1.09 .. .. .. .. .. .. .. .. .. ..Myanmar .. .. .. .. .. .. .. .. .. .. .. ..Namibia 0.46 0.88 .. .. .. .. .. .. .. .. .. ..Nepal 0.49 0.85 45 .. 19 .. 1 .. 10 .. 24 ..Netherlands 53.51 92.47 17 19 2 2 21 20 14 14 46 45New Zealand 7.99 .. 33 27 .. 2 .. 13 .. .. 67 58Nicaragua 0.59 1.14 .. .. .. .. .. .. .. .. .. ..Niger 0.12 .. .. .. .. .. .. .. .. .. .. ..Nigeria 1.97 .. .. .. .. .. .. .. .. .. .. ..Norway 15.70 34.12 21 20 1 1 21 25 12 9 45 45Oman 1.08 .. 12 8 5 0 1 1 5 12 76 79Pakistan 10.10 28.24 21 22 33 29 5 8 17 14 25 26Panama 1.10 1.57 56 .. 5 .. 3 .. 7 .. 32 ..Papua New Guinea 0.25 0.50 .. .. .. .. .. .. .. .. .. ..Paraguay 1.09 2.24 66 .. 6 .. 0 .. 10 .. 18 ..Peru 7.69 23.56 33 30 16 12 3 2 11 12 37 44Philippines 19.83 42.80 29 22 7 5 27 33 9 6 28 33Poland 28.21 76.44 5 17 6 4 15 19 8 8 66 53Portugal 17.99 26.97 13 14 18 11 16 7 5 6 48 61Puerto Rico 24.08 44.64 8 9 3 1 9 9 60 62 20 20Qatar .. .. 4 1 8 2 0 0 21 17 67 802012 World Development Indicators 223

4.3Structure of manufacturingManufacturingvalue addedFood,beverages,and tobaccoTextiles andclothingMachineryand transportequipmentChemicalsOthermanufacturing aRomania 4.77 32.50 32 16 12 12 13 24 5 5 38 43Russian Federation 52.13 209.23 19 15 2 2 19 10 8 10 51 63Rwanda 0.12 0.33 75 .. 2 .. .. .. 6 .. 17 ..Saudi Arabia 18.21 43.85 .. 19 .. 5 .. 6 .. 27 .. 43Senegal 0.61 1.49 22 .. 4 .. 2 .. 29 .. 43 ..Serbia 1.32 5.04 .. .. .. .. .. .. .. .. .. ..Sierra Leone 0.02 .. .. .. .. .. .. .. .. .. .. ..Singapore 24.01 43.63 3 3 1 1 57 54 14 21 25 21Slovak Republic 6.32 16.44 10 7 4 3 20 30 7 4 59 56Slovenia 4.48 8.60 10 7 10 5 18 20 11 15 52 53Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 22.93 38.85 15 19 5 3 14 13 7 6 59 58South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 97.78 172.43 14 15 7 4 19 18 10 9 51 54Sri Lanka 2.46 8.92 39 30 31 31 4 7 4 4 22 27Sudan 1.02 3.30 66 .. 4 .. 4 .. 4 .. 21 ..Swaziland 0.48 1.37 .. .. .. .. .. .. .. .. .. ..Sweden 47.75 65.64 7 7 1 1 35 35 9 11 48 47Switzerland 44.62 95.68 .. .. .. .. .. .. .. .. .. ..Syrian Arab Republic 1.29 .. .. .. .. .. .. .. .. .. .. ..Tajikistan 0.27 0.48 .. .. .. .. .. .. .. .. .. ..Tanzania b 0.89 2.05 45 62 0 8 2 1 7 .. 46 29Thailand 41.23 113.47 18 16 12 9 26 35 6 6 38 34Timor-Leste 0.01 .. .. .. .. .. .. .. .. .. .. ..Togo 0.11 .. .. .. .. .. .. .. .. .. .. ..Trinidad and Tobago 0.58 1.10 21 11 1 1 0 0 27 39 51 49Tunisia 3.50 7.27 19 17 35 24 3 .. 10 9 33 49Turkey 53.51 113.76 18 12 16 19 15 20 10 7 42 42Turkmenistan 0.29 .. .. .. .. .. .. .. .. .. .. ..Uganda 0.44 1.33 64 .. 4 .. .. .. 11 .. 21 ..Ukraine 5.10 21.06 .. .. .. .. .. .. .. .. .. ..United Arab Emirates 9.47 28.93 .. .. .. .. .. .. .. .. .. ..United Kingdom 226.97 230.62 14 15 4 2 25 24 10 12 47 48United States 1,468.08 1,814.34 13 13 3 2 30 25 12 16 42 44Uruguay 2.86 4.81 39 42 9 7 3 4 8 8 41 39Uzbekistan 1.14 3.10 .. .. .. .. .. .. .. .. .. ..Venezuela, RB 21.71 .. 22 .. 2 .. .. .. 34 .. 41 ..Vietnam 5.79 20.94 30 .. 21 .. 12 .. 6 .. 31 ..West Bank and Gaza .. .. 32 27 21 13 1 1 4 4 41 55Yemen, Rep. 0.55 1.88 43 60 4 9 1 0 5 4 48 27Zambia 0.33 1.43 .. .. .. .. .. .. .. .. .. ..Zimbabwe 0.90 0.88 .. .. .. .. .. .. .. .. .. ..World 5,737.81 t 9,989.14 t .. .. .. .. .. .. .. .. .. ..Low income 18.46 46.11 .. .. .. .. .. .. .. .. .. ..Middle income 1,188.59 3,868.14 .. .. .. .. .. .. .. .. .. ..Lower middle income 203.74 639.71 .. .. .. .. .. .. .. .. .. ..Upper middle income 985.43 3,226.81 .. .. .. .. .. .. .. .. .. ..Low & middle income 1,207.17 3,916.69 .. .. .. .. .. .. .. .. .. ..East Asia & Pacifi c 530.45 2,185.11 .. .. .. .. .. .. .. .. .. ..Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. ..Latin America & Carib. 338.85 712.08 .. .. .. .. .. .. .. .. .. ..Middle East & N. Africa 51.72 115.45 .. .. .. .. .. .. .. .. .. ..South Asia 86.21 283.82 .. .. .. .. .. .. .. .. .. ..Sub-Saharan Africa 41.20 82.72 .. .. .. .. .. .. .. .. .. ..High income 4,535.77 5,562.68 .. .. .. .. .. .. .. .. .. ..Euro area 1,115.92 1,754.91 .. .. .. .. .. .. .. .. .. ..a. Includes unallocated data. b. Covers mainland Tanzania only.$ billions % of total % of total % of total % of total % of total2000 2010 2000 2008 2000 2008 2000 2008 2000 2008 2000 20082242012 World Development Indicators

Structure of manufacturing4.3ECONOMYAbout the dataDefinitionsThe data on the distribution of manufacturing valueadded by industry are provided by the United NationsIndustrial Development Organization (UNIDO). UNIDOobtains the data from a variety of national and internationalsources, including the United Nations StatisticsDivision, the World Bank, the Organisation forEconomic Co-operation and Development, and theInternational Monetary Fund. To improve comparabilityover time and across countries, UNIDO supplementsthese data with information from industrialcensuses, statistics from national and internationalorganizations, unpublished data that it collects in thefi eld, and estimates by the UNIDO Secretariat. Nevertheless,coverage may be incomplete, particularlyfor the informal sector. When direct information oninputs and outputs is not available, estimates maybe used, which may result in errors in industry totals.Moreover, countries use different reference periods(calendar or fi scal year) and valuation methods (basicor producer prices) to estimate value added. (SeeAbout the data for table 4.2.)The data on manufacturing value added in U.S. dollarsare from the World Bank’s national accounts fi lesand may differ from those UNIDO uses to calculateshares of value added by industry, in part becauseof differences in exchange rates. Thus value addedin a particular industry estimated by applying theshares to total manufacturing value added will notmatch those from UNIDO sources. Classifi cation ofmanufacturing industries in the table accords withthe United Nations International Standard IndustrialClassifi cation (ISIC) revision 3. Editions of WorldDevelopment Indicators prior to 2008 used revision2, fi rst published in 1948. Revision 3 was completedin 1989, and many countries now use it. But revision2 is still widely used for compiling cross-countrydata. UNIDO has converted these data to accord withrevision 3. Concordances matching ISIC categoriesto national classifi cation systems and to relatedsystems such as the Standard International TradeClassifi cation are available.In establishing classifi cations systems compilersmust defi ne both the types of activities to bedescribed and the units whose activities are tobe reported. There are many possibilities, and thechoices affect how the statistics can be interpretedand how useful they are in analyzing economicbehavior. The ISIC emphasizes commonalities in theproduction process and is explicitly not intended tomeasure outputs (for which there is a newly developedCentral Product Classifi cation). Nevertheless,the ISIC views an activity as defi ned by “a processresulting in a homogeneous set of products” (UnitedNations 1990 [ISIC, series M, no. 4, rev. 3], p. 9).Firms typically use multiple processes to producea product. For example, an automobile manufacturerengages in forging, welding, and painting aswell as advertising, accounting, and other serviceactivities. Collecting data at such a detailed levelis not practical, nor is it useful to record productiondata at the highest level of a large, multiplant,multiproduct fi rm. The ISIC has therefore adopted asthe definition of an establishment “an enterprise orpart of an enterprise which independently engages inone, or predominantly one, kind of economic activityat or from one location . . . for which data are available. . .” (United Nations 1990, p. 25). By design,this definition matches the reporting unit requiredfor the production accounts of the United NationsSystem of National Accounts. The ISIC system isdescribed in the United Nations’ International StandardIndustrial Classifi cation of All Economic Activities,Third Revision (1990). The discussion of the ISICdraws on Ryten (1998).• Manufacturing value added is the sum of grossoutput less the value of intermediate inputs used inproduction for industries classifi ed in ISIC major divisionD. • Food, beverages, and tobacco correspondto ISIC divisions 15 and 16. • Textiles and clothingcorrespond to ISIC divisions 17–19. • Machinery andtransport equipment correspond to ISIC divisions29, 30, 32, 34, and 35. • Chemicals correspond toISIC division 24. • Other manufacturing, a residual,covers wood and related products (ISIC division 20),paper and related products (ISIC divisions 21 and22), petroleum and related products (ISIC division23), basic metals and mineral products (ISIC division27), fabricated metal products and professionalgoods (ISIC division 28), and other industries (ISICdivisions 25, 26, 31, 33, 36, and 37).Data sourcesData on manufacturing value added are from theWorld Bank’s national accounts fi les. Data usedto calculate shares of industry value added areprovided to the World Bank in electronic fi lesby UNIDO. The most recent published source isUNIDO’s International Yearbook of Industrial Statistics2011.2012 World Development Indicators 225

4.4 Structure of merchandise exportsMerchandiseexportsFoodAgriculturalraw materialsFuelsOres andmetalsManufactures$ millions % of total % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan 137 430 .. 40 .. 11 .. .. .. 0 .. 20Albania 258 1,550 7 4 6 2 2 18 4 13 82 62Algeria 22,031 57,053 0 1 0 0 97 97 0 0 2 2Angola 7,921 53,500 .. .. .. .. .. .. .. .. .. ..Argentina 26,341 68,133 44 51 2 1 18 8 3 4 32 33Armenia 294 1,011 14 17 5 1 11 3 27 52 43 24Australia 63,870 212,554 21 11 6 2 22 31 17 34 29 17Austria 67,710 152,313 5 7 2 2 1 3 3 4 80 80Azerbaijan 1,745 26,476 3 3 2 0 85 95 2 0 8 2Bahrain 6,195 13,647 1 7 0 0 0 0 16 70 10 22Bangladesh 6,389 19,191 8 .. 1 .. 0 .. 0 .. 91 ..Belarus 7,326 25,226 7 13 4 2 20 28 1 1 65 53Belgium 188,371 412,223 9 9 2 1 4 9 3 4 78 75Benin 392 1,200 21 .. 72 .. 0 .. 0 .. 7 ..Bolivia 1,230 6,290 30 15 3 1 13 44 25 34 29 6Bosnia and Herzegovina 1,069 4,803 .. 7 .. 6 .. 15 .. 12 .. 57Botswana 2,675 4,693 3 5 0 0 0 0 7 15 90 80Brazil 55,086 201,915 23 31 5 4 2 10 10 18 58 37Bulgaria 4,852 20,666 10 16 3 1 12 13 13 17 57 49Burkina Faso 209 1,288 19 33 59 56 3 0 0 2 18 9Burundi 50 100 91 81 8 5 .. 2 1 5 0 6Cambodia 1,389 5,030 1 1 3 2 0 0 0 0 96 96Cameroon 1,833 4,000 15 24 9 15 54 50 6 3 3 8Canada 276,635 388,019 6 10 6 4 13 26 4 8 64 49Central African Republic 161 140 11 4 13 32 0 0 8 62 68 3Chad 183 3,450 .. .. .. .. .. .. .. .. .. ..Chile 19,210 71,028 25 17 10 5 1 0 45 65 16 13China † 249,203 1,577,824 5 3 1 0 3 2 2 1 88 94Hong Kong SAR, China a 202,683 401,022 2 7 0 3 0 3 2 9 95 77Colombia 13,040 39,820 19 12 5 4 43 60 1 2 32 23Congo, Dem. Rep. 807 5,300 .. .. .. .. .. .. .. .. .. ..Congo, Rep. 2,489 8,200 .. .. .. .. .. .. .. .. .. ..Costa Rica 5,865 9,385 30 35 3 3 1 1 1 1 66 61Côte d’Ivoire 3,888 10,320 50 50 14 10 21 24 0 0 14 16Croatia 4,432 11,807 9 11 5 4 11 12 3 5 73 68Cuba 1,676 3,900 50 .. 0 .. 3 .. 37 .. 9 ..Cyprus 951 1,412 33 34 1 2 6 0 5 14 49 50Czech Republic 29,094 132,852 4 4 2 1 3 4 2 2 88 86Denmark 51,292 97,681 20 18 3 3 7 8 1 2 64 60Dominican Republic 5,737 6,598 41 27 2 1 16 2 2 4 34 65Ecuador 4,927 17,490 37 30 4 4 49 55 0 1 10 10Egypt, Arab Rep. 5,276 26,438 8 17 5 3 42 30 4 6 38 43El Salvador 2,941 4,499 19 22 0 1 3 3 1 2 21 73Eritrea 37 12 54 .. 10 .. 0 .. 8 .. 28 ..Estonia 3,830 11,605 8 10 9 5 4 16 5 3 73 62Ethiopia 486 2,238 71 79 19 9 0 0 1 1 10 9Finland 46,102 69,630 2 3 6 6 3 8 3 5 85 77France 327,611 520,661 11 12 1 1 3 4 2 2 81 78Gabon 2,598 9,371 1 1 12 9 83 83 2 3 2 4Gambia, The 15 15 81 78 1 2 0 0 0 10 17 10Georgia 323 1,583 28 22 3 1 8 6 29 21 31 50Germany 551,810 1,268,874 4 5 1 1 1 2 2 3 84 82Ghana 1,671 7,896 48 61 10 7 8 0 19 11 15 21Greece 11,751 21,409 22 24 3 3 15 11 7 9 50 50Guatemala 2,696 8,466 56 42 4 4 6 5 2 6 32 43Guinea 666 1,250 3 2 3 5 0 2 63 59 30 32Guinea-Bissau 62 125 .. .. .. .. .. .. .. .. .. ..Haiti 318 580 .. .. .. .. .. .. .. .. .. ..† Data for Taiwan, China 151,357 203,675 1 1 1 1 1 6 1 2 95 892262012 World Development Indicators

Structure of merchandise exports4.4ECONOMYMerchandiseexportsFoodAgriculturalraw materialsFuelsOres andmetalsManufactures$ millions % of total % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 3,343 5,742 72 54 5 1 0 4 6 4 17 35Hungary 28,192 95,437 7 7 1 1 2 3 2 2 86 82India 42,379 219,959 13 8 1 2 3 17 3 7 78 64Indonesia 65,403 157,818 9 16 4 7 25 30 5 10 57 37Iran, Islamic Rep. 28,739 100,524 3 6 0 0 89 71 1 3 7 16Iraq 20,603 52,800 1 0 0 0 97 99 0 0 0 0Ireland 77,413 116,801 8 9 0 1 0 1 0 1 86 85Israel 31,404 58,393 2 3 1 1 0 1 1 1 82 93Italy 240,518 447,535 6 8 1 1 2 5 1 2 89 82Jamaica 1,304 1,337 23 25 0 0 0 23 4 12 73 40Japan 479,249 769,839 0 1 0 1 0 2 1 3 94 89Jordan 1,899 7,028 16 17 0 0 0 1 15 9 69 74Kazakhstan 8,812 59,217 7 4 1 0 54 71 19 11 19 14Kenya 1,734 5,151 59 48 9 11 8 4 3 2 21 35Korea, Dem. Rep. 708 3,010 .. .. .. .. .. .. .. .. .. ..Korea, Rep. 172,267 466,384 2 1 1 1 5 7 1 2 91 89Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 19,436 67,014 0 0 0 0 94 93 0 0 4 6Kyrgyz Republic 505 1,760 17 30 14 5 27 15 11 4 31 38Lao PDR 330 1,600 .. .. .. .. .. .. .. .. .. ..Latvia 1,868 9,489 6 17 29 12 2 5 6 4 56 59Lebanon 715 5,021 20 15 2 1 0 0 7 11 71 64Lesotho 220 820 5 14 0 9 0 0 0 2 95 74Liberia 329 231 .. .. .. .. .. .. .. .. .. ..Libya 13,380 47,400 1 .. 0 .. 93 .. 0 .. 7 ..Lithuania 3,810 20,835 12 17 5 2 21 23 2 1 60 54Macedonia, FYR 1,323 3,302 15 18 2 1 5 1 9 3 69 51Madagascar 824 1,090 38 27 3 3 4 7 2 9 52 48Malawi 379 1,066 89 76 3 3 0 0 0 11 7 9Malaysia 98,229 198,801 6 12 3 3 10 16 1 2 80 67Mali 545 2,350 4 30 91 48 0 0 0 1 5 20Mauritania 355 2,033 21 58 0 0 .. 0 46 30 0 0Mauritius 1,557 2,239 18 37 1 1 0 0 0 0 81 60Mexico 166,367 298,305 5 6 1 0 10 14 1 3 84 76Moldova 472 1,582 62 72 3 1 0 0 1 4 33 23Mongolia 536 2,899 4 .. 28 .. 0 .. 41 .. 26 ..Morocco 7,432 17,579 21 19 2 2 4 1 9 12 64 66Mozambique 364 3,200 42 16 11 4 21 20 17 54 7 2Myanmar 1,646 8,749 .. .. .. .. .. .. .. .. .. ..Namibia 1,320 4,052 29 23 1 0 2 0 11 31 56 45Nepal 804 860 10 19 0 4 0 0 0 5 67 72Netherlands 233,130 573,360 13 14 3 3 8 10 2 2 59 57New Zealand 13,272 31,396 46 56 14 11 3 5 5 4 31 21Nicaragua 645 1,851 88 88 2 1 2 1 0 2 8 7Niger 283 930 44 21 3 3 2 2 41 60 9 14Nigeria 20,975 82,000 0 3 0 2 100 87 0 1 0 7Norway 60,058 131,395 6 7 1 1 64 64 6 6 18 18Oman 11,319 36,601 4 3 0 0 83 81 1 3 12 12Pakistan 9,028 21,410 11 17 3 2 1 6 0 2 85 74Panama 859 832 74 73 1 2 7 0 2 11 16 13Papua New Guinea 2,096 5,612 15 .. 2 .. 29 .. 51 .. 2 ..Paraguay 869 4,534 65 86 15 3 0 0 0 1 19 11Peru 7,028 35,565 30 20 3 1 7 12 39 52 20 14Philippines 39,783 51,496 5 7 1 1 1 2 2 4 92 86Poland 31,747 155,752 8 11 2 1 5 4 5 5 80 79Portugal 24,363 48,748 7 11 3 3 2 6 2 4 85 74Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar 11,594 62,000 0 0 0 0 91 73 0 0 9 52012 World Development Indicators 227

4.4Structure of merchandise exportsMerchandiseexportsFoodAgriculturalraw materialsFuelsOres andmetalsManufactures$ millions % of total % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Romania 10,412 49,401 3 8 5 2 7 5 7 4 77 79Russian Federation 105,565 400,132 1 2 3 2 51 64 9 6 24 15Rwanda 52 297 57 52 3 3 0 0 37 37 3 8Saudi Arabia 77,583 249,700 1 1 0 0 92 87 0 0 7 11Senegal 920 2,161 52 29 2 1 14 26 5 4 27 40Serbia .. 9,795 17 .. 6 .. 0 .. 16 .. 61 ..Sierra Leone 13 338 19 .. 1 .. .. .. 1 .. 10 ..Singapore a 137,804 351,867 2 2 0 0 7 16 1 1 86 73Slovak Republic 11,832 65,345 3 4 2 1 7 5 3 3 84 87Slovenia 8,770 29,446 4 4 2 2 1 4 4 4 90 85Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 29,983 81,821 8 9 3 2 10 10 11 33 54 47South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 115,251 245,637 14 15 1 1 4 5 2 3 78 73Sri Lanka 5,430 8,500 21 27 2 4 0 0 0 1 77 67Sudan 1,807 11,443 17 6 5 1 69 92 0 0 8 0Swaziland 910 1,550 34 .. 11 .. 1 .. 0 .. 54 ..Sweden 87,132 158,314 2 5 5 4 3 7 2 5 82 74Switzerland 80,500 195,392 3 4 1 0 0 3 6 4 91 89Syrian Arab Republic 4,634 13,500 9 22 5 1 76 39 1 4 8 33Tajikistan 785 1,195 4 .. 13 .. 14 .. 56 .. 13 ..Tanzania 734 3,687 66 32 13 7 0 3 1 34 20 24Thailand 69,057 195,319 14 13 3 5 3 5 1 1 75 75Timor-Leste .. 17 .. .. .. .. .. .. .. .. .. ..Togo 363 800 20 15 23 5 1 0 26 6 31 74Trinidad and Tobago 4,274 10,590 6 3 0 0 65 66 0 0 29 31Tunisia 5,850 16,427 9 8 1 1 12 14 2 2 77 76Turkey 27,775 113,981 13 11 1 0 1 4 3 4 81 79Turkmenistan 2,506 6,500 0 .. 10 .. 81 .. 0 .. 7 ..Uganda 403 1,612 71 67 15 7 6 1 5 2 3 23Ukraine 14,573 51,478 9 19 2 1 5 7 12 7 69 65United Arab Emirates 49,835 220,000 1 1 0 0 94 65 3 1 2 4United Kingdom 285,425 405,666 5 6 0 1 8 13 2 4 77 70United States 781,918 1,278,263 7 10 2 3 2 7 2 4 83 66Uruguay 2,295 6,733 47 64 9 8 2 1 0 0 42 26Uzbekistan 2,817 11,857 .. .. .. .. .. .. .. .. .. ..Venezuela, RB 33,529 65,786 1 0 0 0 86 93 3 2 9 4Vietnam 14,483 72,192 25 21 2 2 26 15 0 1 43 60West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 4,079 8,700 2 6 0 0 97 92 0 0 0 2Zambia 892 7,200 9 6 4 1 1 1 74 86 11 6Zimbabwe 1,925 2,500 47 20 13 7 1 2 11 35 28 36World 6,456,422 t 15,211,311 t 7 w 8 w 2 w 2 w 10 w 12 w 3 w 5 w 75 w 69 wLow income 23,852 79,667 26 .. 11 .. 3 .. 8 .. 51 ..Middle income 1,350,497 4,848,882 9 10 2 2 21 22 4 6 61 59Lower middle income 299,735 960,037 12 15 2 3 29 22 4 7 52 52Upper middle income 1,050,746 3,889,297 9 9 2 2 19 21 5 6 63 60Low & middle income 1,374,348 4,928,568 10 11 2 2 21 21 4 6 61 58East Asia & Pacifi c 544,009 2,281,768 8 8 2 2 7 8 2 3 80 79Europe & Central Asia 201,167 830,383 5 7 3 2 34 42 9 6 42 36Latin America & Carib. 356,697 862,436 16 16 2 2 17 21 6 10 58 51Middle East & N. Africa 114,670 352,565 4 .. 1 .. 77 .. 2 .. 16 ..South Asia 64,379 271,099 12 12 2 2 3 13 2 6 80 66Sub-Saharan Africa 93,392 332,645 15 15 5 4 37 32 7 18 31 31High income 5,082,101 10,278,808 6 8 2 2 8 9 2 4 78 72Euro area 1,920,244 4,007,130 8 9 1 1 3 5 2 3 80 77Note: Components may not sum to 100 percent because of unclassifi ed trade. Exports of gold are excluded.a. Includes re-exports.2282012 World Development Indicators

Structure of merchandise exports4.4ECONOMYAbout the dataDefinitionsData on merchandise trade are from customsreports of goods moving into or out of an economyor from reports of fi nancial transactions related tomerchandise trade recorded in the balance of payments.Because of differences in timing and defi -nitions, trade fl ow estimates from customs reportsand balance of payments may differ. Several internationalagencies process trade data, each correctingunreported or misreported data, leading to otherdifferences.The most detailed source of data on internationaltrade in goods is the United Nations Statistics Division’sCommodity Trade Statistics (Comtrade) database.The International Monetary Fund (IMF) also collectscustoms-based data on trade in goods. Exportsare recorded as the cost of the goods delivered to thefrontier of the exporting country for shipment—thefree on board (f.o.b.) value. Many countries reporttrade data in U.S. dollars. When countries report inlocal currency, the United Nations Statistics Divisionapplies the average offi cial exchange rate to the U.S.dollar for the period shown.Countries may report trade according to the generalor special system of trade. Under the generalsystem exports comprise outward-moving goods thatare (a) goods wholly or partly produced in the country;(b) foreign goods, neither transformed nor declaredfor domestic consumption in the country, that moveoutward from customs storage; and (c) goods previouslyincluded as imports for domestic consumptionbut subsequently exported without transformation.Under the special system exports comprise categoriesa and c. In some compilations categories b andc are classifi ed as re-exports. Because of differencesin reporting practices, data on exports may not befully comparable across economies.The data on total exports of goods (merchandise)are from the World Trade Organization (WTO), whichobtains data from national statistical offi ces and theIMF’s International Financial Statistics, supplementedby the Comtrade database and publications or databasesof regional organizations, specialized agencies,economic groups, and private sources (such asEurostat, the Food and Agriculture Organization, andcountry reports of the Economist Intelligence Unit).Country websites and email contact have improvedcollection of up-to-date statistics, reducing the proportionof estimates. The WTO database now coversmost major traders in Africa, Asia, and Latin America,which together with high-income countries accountfor nearly 95 percent of world trade. Reliability ofdata for countries in Europe and Central Asia hasalso improved.Export shares by major commodity group are fromComtrade. The values of total exports reportedhere have not been fully reconciled with the estimatesfrom the national accounts or the balanceof payments.The classifi cation of commodity groups is basedon the Standard International Trade Classifi cation(SITC) revision 3. Previous editions contained databased on the SITC revision 1. Data for earlier years inprevious editions may differ because of this changein methodology. Concordance tables are availableto convert data reported in one system to another.• Merchandise exports are the f.o.b. value of goodsprovided to the rest of the world. • Food correspondsto the commodities in SITC sections 0 (food and liveanimals), 1 (beverages and tobacco), and 4 (animaland vegetable oils and fats) and SITC division 22(oil seeds, oil nuts, and oil kernels). • Agriculturalraw materials correspond to SITC section 2 (crudematerials except fuels), excluding divisions 22, 27(crude fertilizers and minerals excluding coal, petroleum,and precious stones), and 28 (metalliferousores and scrap). • Fuels correspond to SITC section3 (mineral fuels). • Ores and metals correspond tothe commodities in SITC divisions 27, 28, and 68(nonferrous metals). • Manufactures correspond tothe commodities in SITC sections 5 (chemicals), 6(basic manufactures), 7 (machinery and transportequipment), and 8 (miscellaneous manufacturedgoods), excluding division 68.Data sourcesData on merchandise exports are from the WTO.Data on shares of exports by major commoditygroup are from the United Nations Statistics Division’sComtrade database. The WTO publishesdata on world trade in its Annual Report. The IMFpublishes estimates of total exports of goods inits International Financial Statistics and Directionof Trade Statistics, as does the United Nations StatisticsDivision in its Monthly Bulletin of Statistics.And the United Nations Conference on Trade andDevelopment publishes data on the structure ofexports in its Handbook of Statistics. Tariff linerecords of exports are compiled in the UnitedNations Statistics Division’s Comtrade database.2012 World Development Indicators 229

4.5 Structure of merchandise importsMerchandiseimportsFoodAgriculturalraw materialsFuelsOres andmetalsManufactures$ millions % of total % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan 1,176 4,400 .. 14 .. 0 .. 21 .. 0 .. 19Albania 1,090 4,601 22 18 1 1 9 14 2 4 67 64Algeria 9,171 40,212 28 16 3 2 1 2 1 2 67 78Angola 3,040 21,500 .. .. .. .. .. .. .. .. .. ..Argentina 25,154 56,503 5 3 1 1 4 7 2 3 87 85Armenia 882 3,783 25 18 1 1 21 18 1 3 52 56Australia 71,529 201,640 5 5 1 1 8 14 1 2 84 75Austria 72,394 158,752 5 7 3 2 5 11 3 5 81 74Azerbaijan 1,172 6,746 19 19 2 2 5 1 4 2 71 76Bahrain 4,633 10,000 10 12 1 1 1 2 3 19 41 65Bangladesh 8,883 27,819 16 .. 6 .. 7 .. 2 .. 68 ..Belarus 8,646 34,868 12 8 2 1 30 35 4 3 48 47Belgium 177,511 390,443 9 8 2 1 9 14 4 4 76 71Benin 613 2,200 22 .. 5 .. 19 .. 1 .. 53 ..Bolivia 1,830 5,361 14 8 2 1 5 12 1 1 79 78Bosnia and Herzegovina 3,107 9,223 .. 18 .. 2 .. 19 .. 2 .. 58Botswana 2,081 5,657 14 12 1 1 5 15 2 2 75 68Brazil 59,053 191,491 7 5 2 1 15 17 3 3 73 74Bulgaria 6,544 25,403 5 10 1 1 26 22 6 9 59 55Burkina Faso 611 2,048 13 15 1 1 25 22 1 1 61 61Burundi 148 509 23 14 2 1 12 2 2 1 60 82Cambodia 1,939 7,500 10 7 3 2 13 7 0 2 73 82Cameroon 1,489 4,850 18 18 2 2 23 27 1 1 56 52Canada 244,786 402,280 5 7 1 1 5 10 2 3 84 77Central African Republic 117 340 29 39 4 2 8 1 4 2 54 56Chad 317 2,600 .. .. .. .. .. .. .. .. .. ..Chile 18,507 58,956 7 7 1 1 18 21 1 2 71 69China † 225,094 1,395,099 4 5 5 4 9 15 6 14 75 61Hong Kong SAR, China 214,042 442,035 4 4 1 1 2 4 2 2 91 90Colombia 11,539 40,683 12 10 3 1 2 5 2 2 80 80Congo, Dem. Rep. 683 4,500 .. .. .. .. .. .. .. .. .. ..Congo, Rep. 465 2,900 .. .. .. .. .. .. .. .. .. ..Costa Rica 6,372 13,570 7 9 1 1 8 12 2 2 82 73Côte d’Ivoire 2,482 7,830 17 19 1 1 34 24 1 1 46 55Croatia 7,887 20,054 8 10 2 1 15 19 2 2 73 67Cuba 4,843 11,300 16 .. 1 .. 24 .. 1 .. 58 ..Cyprus 31,974 126,222 5 5 2 1 10 9 4 4 80 77Czech Republic 3,846 8,499 19 15 1 1 13 20 1 1 65 61Denmark 45,557 84,848 11 14 3 2 6 8 2 2 76 73Dominican Republic 9,479 15,299 12 14 2 1 23 25 1 1 62 60Ecuador 3,721 20,591 9 8 3 1 7 17 2 1 77 72Egypt, Arab Rep. 14,578 52,923 25 19 5 3 8 13 2 4 56 60El Salvador 4,947 8,498 12 17 2 2 12 16 1 1 43 64Eritrea 471 690 37 .. 1 .. 2 .. 1 .. 58 ..Estonia 5,052 12,252 10 11 3 2 7 16 3 1 76 63Ethiopia 1,260 8,552 7 11 1 0 20 19 1 1 71 69Finland 34,443 68,510 5 7 2 2 12 18 6 8 73 61France 338,940 605,706 8 8 2 1 10 14 3 3 77 73Gabon 950 2,983 18 17 1 0 4 7 1 1 76 74Gambia, The 187 276 35 36 1 1 12 20 1 1 51 41Georgia 709 5,096 23 18 1 1 20 18 1 2 55 60Germany 497,197 1,066,839 7 7 2 2 9 11 4 5 68 68Ghana 2,973 10,703 13 15 2 1 21 1 1 1 62 81Greece 33,480 63,173 11 12 2 1 13 24 3 3 70 60Guatemala 4,791 13,837 12 13 2 1 13 18 1 1 72 66Guinea 612 1,100 24 13 1 0 25 33 1 0 49 53Guinea-Bissau 59 220 .. .. .. .. .. .. .. .. .. ..Haiti 1,036 3,150 .. .. .. .. .. .. .. .. .. ..† Data for Taiwan, China 140,642 174,371 4 5 2 1 9 21 5 7 79 652302012 World Development Indicators

Structure of merchandise imports4.5ECONOMYMerchandiseimportsFoodAgriculturalraw materialsFuelsOres andmetalsManufactures$ millions % of total % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 3,988 8,550 22 19 2 1 18 19 1 1 50 60Hungary 32,172 88,120 3 5 1 1 5 11 3 3 84 72India 51,523 327,230 5 4 3 2 39 36 5 5 47 51Indonesia 43,595 131,737 10 8 7 3 19 20 3 3 61 65Iran, Islamic Rep. 13,898 65,021 19 15 3 2 2 3 2 2 73 71Iraq 13,384 42,500 1 .. 0 .. 0 .. 0 .. 8 ..Ireland 51,041 60,032 6 12 1 1 4 12 1 2 82 66Israel 37,686 61,209 5 7 1 1 10 18 2 2 81 71Italy 238,757 483,814 9 9 4 2 10 19 4 5 69 64Jamaica 3,326 5,195 15 18 2 1 18 30 1 0 61 49Japan 379,511 694,052 13 9 3 2 20 29 6 8 57 51Jordan 4,597 15,402 21 16 2 1 5 22 2 2 66 56Kazakhstan 5,040 29,760 9 9 1 1 12 10 3 1 74 80Kenya 3,105 12,090 14 12 2 2 22 22 1 2 60 63Korea, Dem. Rep. 1,686 4,420 .. .. .. .. .. .. .. .. .. ..Korea, Rep. 160,481 425,212 5 5 3 2 24 29 6 8 62 57Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 7,157 22,446 14 15 0 1 1 1 1 3 34 81Kyrgyz Republic 554 3,223 15 17 2 1 23 27 2 1 59 54Lao PDR 535 1,800 .. .. .. .. .. .. .. .. .. ..Latvia 3,202 11,593 12 15 2 1 12 15 2 2 71 59Lebanon 6,230 18,460 19 16 2 1 18 21 2 2 59 59Lesotho 809 2,200 18 20 1 2 19 11 2 1 49 53Liberia 668 700 .. .. .. .. .. .. .. .. .. ..Libya 3,732 10,500 28 .. 1 .. 0 .. 1 .. 70 ..Lithuania 5,457 23,399 10 12 3 2 22 32 2 2 61 50Macedonia, FYR 2,094 5,451 12 13 2 1 14 5 2 1 45 62Madagascar 1,097 2,650 13 14 0 1 23 15 0 0 63 70Malawi 532 1,900 10 14 2 1 16 10 1 1 72 74Malaysia 81,963 164,733 4 8 1 2 5 10 3 5 85 74Mali 806 2,850 15 12 1 0 24 26 1 1 59 61Mauritania 454 1,822 19 19 0 0 23 26 0 0 41 53Mauritius 2,093 4,402 14 21 2 2 12 19 1 1 70 56Mexico 179,464 310,618 5 6 1 1 3 8 2 3 83 80Moldova 777 3,855 13 15 2 1 32 21 1 1 51 62Mongolia 615 3,278 17 .. 1 .. 19 .. 0 .. 63 ..Morocco 11,534 35,277 14 11 3 2 18 23 3 3 63 59Mozambique 1,158 4,500 14 12 1 1 13 20 1 1 68 50Myanmar 2,401 4,807 7 .. 0 .. 19 .. 1 .. 72 ..Namibia 1,550 5,360 17 14 1 1 3 14 1 1 78 70Nepal 1,573 5,280 13 14 4 2 16 18 3 4 49 64Netherlands 218,267 516,927 9 10 2 1 10 16 2 3 65 57New Zealand 13,906 30,617 8 11 1 1 10 15 2 2 79 71Nicaragua 1,805 4,173 16 16 1 1 18 22 1 0 65 61Niger 395 2,150 39 15 4 2 15 13 2 1 41 69Nigeria 8,721 44,235 20 10 1 1 2 1 2 1 75 86Norway 34,392 77,252 6 8 2 1 4 7 5 7 81 76Oman 5,131 19,870 22 12 1 1 2 7 3 4 70 51Pakistan 10,864 39,044 14 13 3 5 33 30 2 3 47 49Panama 3,379 9,145 12 8 0 0 19 1 1 0 68 90Papua New Guinea 1,151 3,850 18 .. 1 .. 22 .. 1 .. 58 ..Paraguay 2,193 10,040 17 7 1 1 16 12 1 1 66 79Peru 7,415 30,126 12 10 2 2 16 14 1 1 70 72Philippines 37,027 58,229 7 11 1 1 11 17 2 4 78 67Poland 49,029 173,648 6 8 2 2 11 11 3 3 78 74Portugal 39,952 75,648 11 13 3 1 10 14 2 3 73 67Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar 3,252 23,240 12 8 1 0 0 1 3 4 84 842012 World Development Indicators 231

4.5 Structure of merchandise importsMerchandiseimportsFoodAgriculturalraw materialsFuelsOres andmetalsManufactures$ millions % of total % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Romania 13,148 61,995 7 8 1 1 12 10 4 3 76 75Russian Federation 44,659 248,738 20 13 2 1 4 2 3 1 59 69Rwanda 211 1,431 21 13 3 2 14 8 2 1 60 76Saudi Arabia 30,238 97,077 18 16 1 1 0 0 3 5 76 77Senegal 1,519 4,782 23 22 2 2 23 30 1 2 51 44Serbia .. 16,734 9 .. 4 .. 20 .. 4 .. 63 ..Sierra Leone 149 770 33 .. 4 .. 28 .. 1 .. 35 ..Singapore 134,545 310,791 3 3 0 0 12 26 2 2 82 66Slovak Republic 12,760 66,557 6 6 2 1 18 13 3 4 72 76Slovenia 10,147 30,037 6 8 4 3 9 13 5 6 76 70Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 29,695 94,040 5 6 1 1 14 20 2 2 69 65South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 156,143 314,320 9 10 2 1 12 18 3 4 73 65Sri Lanka 7,177 13,512 14 15 1 1 9 17 1 2 74 65Sudan 1,553 10,045 23 15 1 1 7 4 1 1 68 78Swaziland 1,046 1,700 19 .. 2 .. 13 .. 1 .. 64 ..Sweden 72,880 148,710 6 9 2 1 9 13 3 4 74 69Switzerland 82,521 175,933 6 6 1 1 5 8 6 4 83 81Syrian Arab Republic 3,815 16,900 19 14 3 3 4 31 2 4 65 47Tajikistan 675 2,900 10 .. 1 .. 37 .. 0 .. 51 ..Tanzania 1,524 7,830 15 10 3 1 19 28 1 1 63 60Thailand 61,924 182,400 4 5 3 2 12 18 3 5 77 70Timor-Leste .. 298 .. .. .. .. .. .. .. .. .. ..Togo 562 1,550 18 16 2 1 19 14 2 2 59 67Trinidad and Tobago 3,308 6,575 8 11 1 1 32 33 2 5 56 50Tunisia 8,567 22,218 8 9 3 2 11 13 2 4 76 72Turkey 54,503 185,542 4 4 4 3 14 15 4 8 70 63Turkmenistan 1,786 5,600 12 .. 0 .. 1 .. 1 .. 80 ..Uganda 1,536 4,550 14 12 2 1 17 20 2 1 65 65Ukraine 13,956 60,911 6 9 2 1 43 32 5 4 41 53United Arab Emirates 35,009 160,000 11 7 1 0 1 1 2 5 85 73United Kingdom 348,058 560,097 8 10 2 1 4 11 3 4 78 68United States 1,259,300 1,969,184 4 5 1 1 11 19 2 2 77 70Uruguay 3,466 8,622 11 10 3 2 15 24 1 1 69 62Uzbekistan 2,697 8,386 .. .. .. .. .. .. .. .. .. ..Venezuela, RB 16,213 40,800 12 15 2 1 4 1 2 1 81 81Vietnam 15,638 84,801 5 8 3 3 14 11 2 4 73 74West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 2,324 9,700 36 28 2 1 12 21 1 1 49 50Zambia 888 5,321 8 5 3 1 12 12 3 21 73 62Zimbabwe 1,863 3,800 4 19 1 3 42 11 3 14 48 52World 6,659,168 t 15,264,186 t 7 w 7 w 2 w 1 w 10 w 16 w 3 w 4 w 74 w 68 wLow income 38,879 134,286 16 .. 3 .. 16 .. 2 .. 63 ..Middle income 1,250,130 4,613,566 8 8 3 2 11 15 3 6 72 68Lower middle income 282,691 1,083,885 10 10 3 2 20 23 3 4 57 60Upper middle income 967,323 3,529,396 7 7 3 2 9 13 3 6 76 70Low & middle income 1,289,014 4,747,794 8 8 3 2 11 15 3 6 71 68East Asia & Pacifi c 475,589 2,046,331 5 6 4 3 10 15 4 10 76 66Europe & Central Asia 182,530 786,004 10 9 3 2 15 14 4 4 63 63Latin America & Carib. 375,652 872,376 7 7 2 1 7 11 2 3 79 77Middle East & N. Africa 92,037 329,532 17 15 3 2 7 13 2 3 56 66South Asia 81,760 419,140 8 7 4 2 34 32 4 5 49 52Sub-Saharan Africa 81,357 302,642 12 10 2 1 14 17 1 2 66 67High income 5,370,223 10,519,458 7 7 2 1 10 16 3 4 75 68Euro area 1,904,622 3,949,668 8 9 2 2 9 15 3 4 72 67Note: Components may not sum to 100 percent because of unclassifi ed trade.2322012 World Development Indicators

Structure of merchandise imports 4.5ECONOMYAbout the dataDefinitionsData on imports of goods are derived from thesame sources as data on exports. In principle, worldexports and imports should be identical. Similarly,exports from an economy should equal the sum ofimports by the rest of the world from that economy.But differences in timing and defi nitions result in discrepanciesin reported values at all levels. For furtherdiscussion of indicators of merchandise trade, seeAbout the data for tables 4.4 and 6.1.The value of imports is generally recorded as thecost of the goods when purchased by the importerplus the cost of transport and insurance to the frontierof the importing country—the cost, insurance,and freight (c.i.f.) value, corresponding to the landedcost at the point of entry of foreign goods into thecountry. A few countries, including Australia, Canada,and the United States, collect import data on a freeon board (f.o.b.) basis and adjust them for freight andinsurance costs. Many countries report trade data inU.S. dollars. When countries report in local currency,the United Nations Statistics Division applies theaverage offi cial exchange rate to the U.S. dollar forthe period shown.Countries may report trade according to the generalor special system of trade. Under the general systemimports include goods imported for domestic consumptionand imports into bonded warehouses andfree trade zones. Under the special system importscomprise goods imported for domestic consumption( including transformation and repair) and withdrawalsfor domestic consumption from bonded warehousesand free trade zones. Goods transported through acountry en route to another are excluded.The data on total imports of goods (merchandise)in the table come from the World Trade Organization(WTO). For further discussion of the WTO’s sourcesand methodology, see About the data for table 4.4.The import shares by major commodity group arefrom the United Nations Statistics Division’s CommodityTrade Statistics (Comtrade) database. Thevalues of total imports reported here have notbeen fully reconciled with the estimates of importsof goods and services from the national accounts(shown in table 4.8) or those from the balance ofpayments (table 4.17).The classifi cation of commodity groups is basedon the Standard International Trade Classifi cation(SITC) revision 3. Previous editions contained databased on the SITC revision 1. Data for earlier years inprevious editions may differ because of this changein methodology. Concordance tables are availableto convert data reported in one system to another.• Merchandise imports are the c.i.f. value of goodspurchased from the rest of the world valued in U.S.dollars. • Food corresponds to the commodities inSITC sections 0 (food and live animals), 1 (beveragesand tobacco), and 4 (animal and vegetable oils andfats) and SITC division 22 (oil seeds, oil nuts, and oilkernels). • Agricultural raw materials correspond toSITC section 2 (crude materials except fuels), excludingdivisions 22, 27 (crude fertilizers and mineralsexcluding coal, petroleum, and precious stones),and 28 (metalliferous ores and scrap). • Fuels correspondto SITC section 3 (mineral fuels). • Oresand metals correspond to the commodities in SITCdivisions 27, 28, and 68 (nonferrous metals). • Manufacturescorrespond to the commodities in SITCsections 5 (chemicals), 6 (basic manufactures), 7(machinery and transport equipment), and 8 (miscellaneousmanufactured goods), excluding division 68.Data sourcesData on merchandise imports are from the WTO.Data on shares of imports by major commoditygroup are from the United Nations Statistics Division’sComtrade database. The WTO publishesdata on world trade in its Annual Report. The InternationalMonetary Fund publishes estimates oftotal imports of goods in its International FinancialStatistics and Direction of Trade Statistics,as does the United Nations Statistics Division inits Monthly Bulletin of Statistics. And the UnitedNations Conference on Trade and Developmentpublishes data on the structure of imports inits Handbook of Statistics. Tariff line records ofimports are compiled in the United Nations StatisticsDivision’s Comtrade database.2012 World Development Indicators 233

4.6 Structure of service exportsCommercialservice exportsTransport Travel Insurance andfinancial servicesComputer, information,communications, andother commercialservices$ millions % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan .. .. .. .. .. .. .. .. .. ..Albania 429 2,192 4 11 91 74 0 0 5 15Algeria .. 2,794 .. 28 .. 10 .. 9 .. 54Angola 267 857 6 5 18 84 13 .. 81 11Argentina 4,775 12,931 24 16 61 38 0 0 15 46Armenia 130 750 49 21 29 54 3 3 19 22Australia 19,413 48,490 22 18 48 56 5 3 25 22Austria 22,865 54,161 18 24 43 35 8 4 31 37Azerbaijan 234 2,017 51 32 27 33 1 1 22 35Bahrain 933 4,047 30 20 61 34 .. 22 8 24Bangladesh 283 1,209 32 14 18 7 6 4 44 75Belarus 989 4,470 59 67 9 10 1 0 31 23Belgium 36,285 85,339 24 30 19 12 7 5 50 53Benin 126 204 14 9 61 64 2 2 22 24Bolivia 207 530 24 13 33 58 22 13 21 16Bosnia and Herzegovina 448 1,280 6 23 52 46 4 1 38 30Botswana 306 385 17 10 73 57 4 1 7 32Brazil 8,961 30,294 16 16 20 20 8 8 56 56Bulgaria 2,129 6,750 30 19 50 53 1 3 18 25Burkina Faso 28 142 13 26 67 47 0 2 19 26Burundi 2 7 43 10 37 24 0 22 20 44Cambodia 423 1,671 17 14 72 75 0 0 11 11Cameroon 666 1,105 21 43 9 14 3 4 68 38Canada 39,271 67,432 19 17 27 23 7 11 46 48Central African Republic .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. ..Chile 3,995 10,685 55 61 21 15 3 3 22 21China 30,146 170,249 12 20 54 27 1 2 33 51Hong Kong SAR, China 40,362 106,432 32 27 15 19 12 14 42 40Colombia 1,984 4,357 30 28 52 48 4 1 15 23Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Congo, Rep. 130 303 26 4 9 18 2 31 63 47Costa Rica 1,911 4,149 13 7 68 48 0 1 18 44Côte d’Ivoire 415 816 20 29 12 14 13 13 55 57Croatia 4,056 11,034 14 10 68 73 1 1 17 16Cuba .. .. .. .. .. .. .. .. .. ..Cyprus 6,751 20,911 21 24 44 34 6 2 30 40Czech Republic 3,798 8,044 19 24 51 27 6 15 25 34Denmark 23,721 60,405 45 .. 15 .. .. .. 39 ..Dominican Republic 3,143 4,998 2 8 91 84 0 1 7 7Ecuador 793 1,371 37 26 51 57 0 .. 13 17Egypt, Arab Rep. 9,687 23,618 27 34 45 53 1 1 27 12El Salvador 673 944 37 32 32 41 10 3 20 23Eritrea 54 .. 18 .. 64 .. 1 .. 17 ..Estonia 1,458 4,485 49 40 35 24 1 2 15 34Ethiopia 387 1,991 56 59 15 26 1 0 28 14Finland 7,669 27,729 22 11 18 10 0 2 60 76France 82,115 143,896 22 25 40 32 3 3 34 40Gabon 171 .. 56 .. 12 .. 0 .. 32 ..Gambia, The 73 88 27 42 67 36 1 0 6 22Georgia 320 1,514 48 46 44 44 3 4 5 7Germany 79,659 233,338 25 25 23 15 5 8 46 53Ghana 490 1,344 20 27 68 46 1 1 11 26Greece 19,181 37,336 41 55 48 33 1 2 9 10Guatemala 702 2,192 12 13 69 63 3 2 16 22Guinea 27 61 58 6 7 3 0 16 34 74Guinea-Bissau 4 32 2 0 77 38 2 9 21 53Haiti 158 183 2 .. 81 91 .. .. 19 92342012 World Development Indicators

Structure of service exports 4.6ECONOMYCommercialservice exportsTransport Travel Insurance andfinancial servicesComputer, information,communications, andother commercialservices$ millions % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 487 1,003 12 5 53 65 3 2 32 28Hungary 5,836 19,288 9 20 64 28 3 1 24 52India 16,031 123,277 12 11 22 11 3 6 63 71Indonesia 5,061 16,211 16 16 98 43 0 2 2 38Iran, Islamic Rep. 1,357 .. 49 .. 37 .. 11 .. 3 ..Iraq .. 1,721 .. 22 .. 0 .. 0 .. 78Ireland 18,326 97,833 8 5 14 4 17 19 61 72Israel 15,619 24,209 16 18 26 20 0 0 58 63Italy 55,998 97,368 17 15 49 39 2 6 32 40Jamaica 1,988 2,600 17 11 67 77 1 2 15 10Japan 68,303 138,875 37 28 5 10 4 4 53 59Jordan 1,602 4,782 19 18 45 71 0 .. 36 11Kazakhstan 905 3,890 51 58 39 26 2 3 8 13Kenya 727 2,920 57 54 39 27 1 5 4 14Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 30,650 81,556 45 47 22 12 3 4 31 37Kosovo .. .. .. .. .. .. .. .. .. ..Kuwait 1,571 7,137 88 45 6 3 6 2 0 50Kyrgyz Republic 57 679 29 22 27 42 3 1 42 35Lao PDR 134 489 15 10 85 78 0 2 0 9Latvia 1,131 3,657 68 50 12 17 4 7 17 26Lebanon 4,412 15,706 0 4 97 50 1 14 1 32Lesotho 20 44 3 2 88 79 0 0 9 19Liberia .. 40 .. 56 .. 31 .. .. .. 13Libya 119 410 13 64 63 15 17 18 7 3Lithuania 1,052 4,064 47 60 37 25 1 1 16 14Macedonia, FYR 290 903 42 32 13 22 2 1 44 45Madagascar 314 .. 16 .. 39 .. 1 .. 44 ..Malawi 34 126 26 18 74 55 2 0 0 26Malaysia 13,812 32,760 20 14 36 56 2 2 41 30Mali 92 335 36 6 44 57 3 2 18 35Mauritania 24 .. 3 .. 83 .. .. .. 15 ..Mauritius 1,066 2,656 21 14 51 48 6 3 22 34Mexico 13,291 14,935 8 7 62 79 14 12 16 2Moldova 155 663 54 37 25 26 2 1 19 36Mongolia 74 483 41 36 49 51 0 2 10 11Morocco 2,854 12,138 17 18 71 55 1 2 10 25Mozambique 325 576 30 28 23 34 3 1 47 36Myanmar 459 334 17 45 35 22 .. .. 48 34Namibia 163 835 23 16 68 53 0 1 8 30Nepal 410 584 15 7 38 59 0 0 47 34Netherlands 48,361 93,361 35 27 15 14 2 2 48 57New Zealand 4,352 8,908 28 20 52 55 1 1 19 24Nicaragua 187 430 16 11 69 72 2 1 13 16Niger 35 100 24 8 64 66 4 3 8 23Nigeria 1,833 2,613 12 75 6 22 1 1 82 3Norway 17,528 39,506 55 40 12 12 3 4 30 45Oman 452 1,761 43 36 49 44 3 1 5 18Pakistan 1,284 2,949 65 48 6 10 1 3 27 38Panama 1,961 5,659 59 53 23 30 9 9 9 8Papua New Guinea 243 279 5 7 3 1 2 3 90 89Paraguay 573 1,324 12 17 13 16 5 2 70 64Peru 1,445 3,816 17 22 58 60 9 6 17 12Philippines 3,377 14,358 14 9 64 18 3 1 20 71Poland 10,395 32,700 24 27 55 29 3 2 19 42Portugal 8,905 22,957 16 27 59 44 3 1 22 28Puerto Rico .. .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 235

4.6Structure of service exportsCommercialservice exportsTransport Travel Insurance andfinancial servicesComputer, information,communications, andother commercialservices$ millions % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Romania 1,720 8,728 37 29 21 13 8 2 35 56Russian Federation 9,565 44,605 37 33 36 20 1 3 26 43Rwanda 41 243 34 11 57 83 .. 0 8 6Saudi Arabia 4,779 10,346 .. 20 .. 65 .. 12 .. 3Senegal 330 909 10 5 44 51 2 2 45 42Serbia .. 3,525 .. 22 .. 23 .. 2 .. 53Sierra Leone 39 60 46 40 27 43 0 1 27 15Singapore 28,420 112,061 41 29 18 13 8 13 32 45Slovak Republic 2,218 5,817 45 31 20 38 2 1 33 30Slovenia 1,883 6,120 26 26 51 42 1 2 22 30Somalia .. .. .. .. .. .. .. .. .. ..South Africa 4,888 13,617 24 12 55 67 9 8 12 13South Sudan .. .. .. .. .. .. .. .. .. ..Spain 52,112 122,773 16 17 57 43 3 5 24 36Sri Lanka 915 2,448 44 47 27 24 4 3 25 26Sudan 24 224 63 2 22 42 8 6 15 49Swaziland 271 250 7 8 8 20 0 16 86 55Sweden 22,193 64,835 21 15 18 17 6 3 55 65Switzerland 29,443 81,649 15 7 23 18 41 25 21 49Syrian Arab Republic 1,480 7,040 17 8 73 88 2 1 10 3Tajikistan 60 182 76 27 3 2 2 5 20 65Tanzania 575 2,047 10 22 65 61 3 2 22 15Thailand 13,785 34,058 24 17 54 59 1 1 22 23Timor-Leste .. .. .. .. .. .. .. .. .. ..Togo 46 265 23 34 18 26 9 5 51 35Trinidad and Tobago 543 758 38 28 39 48 8 14 14 9Tunisia 2,680 5,471 22 28 63 48 2 3 13 21Turkey 19,267 34,247 15 28 40 61 2 4 43 8Turkmenistan 269 .. 50 .. 12 .. .. .. 38 ..Uganda 205 984 15 5 81 74 1 3 4 18Ukraine 3,800 16,466 77 47 10 23 1 3 12 27United Arab Emirates .. .. .. .. .. .. .. .. .. ..United Kingdom 118,567 253,287 16 12 18 13 22 24 43 51United States 275,881 522,510 16 14 37 26 7 16 40 45Uruguay 1,249 2,458 30 18 57 61 8 5 5 16Uzbekistan .. .. .. .. .. .. .. .. .. ..Venezuela, RB 1,057 1,626 35 35 40 45 0 0 24 19Vietnam 2,702 7,460 .. .. .. .. .. .. .. ..West Bank and Gaza 453 554 8 5 62 74 0 0 29 21Yemen, Rep. 174 1,460 12 13 42 80 .. .. 46 7Zambia 114 312 37 49 58 40 4 2 0 9Zimbabwe .. .. .. .. .. .. .. .. .. ..World 1,530,963 t 3,806,462 t 24 w 22 w 32 w 26 w 6 w 8 w 38 w 45 wLow income 6,240 20,076 30 24 35 26 4 4 33 47Middle income 223,261 773,203 21 21 48 41 4 4 28 33Lower middle income 63,035 276,733 23 23 50 32 2 3 32 42Upper middle income 161,130 504,513 21 21 48 44 4 5 27 31Low & middle income 229,067 792,143 22 22 48 41 4 4 28 33East Asia & Pacifi c 70,928 280,031 16 17 56 38 1 2 29 43Europe & Central Asia 44,988 144,429 39 36 33 27 2 3 27 34Latin America & Carib. 51,355 109,176 18 17 51 57 9 8 23 19Middle East & N. Africa .. .. .. 28 .. 29 .. 3 .. 40South Asia 19,358 131,836 24 19 20 13 3 5 52 63Sub-Saharan Africa 15,245 40,508 21 31 35 50 6 5 41 14High income 1,305,653 3,018,606 25 22 27 21 7 9 41 48Euro area 472,024 1,145,715 23 23 32 23 4 5 40 492362012 World Development Indicators

Structure of service exports 4.6ECONOMYAbout the dataDefinitionsBalance of payments statistics, the main source ofinformation on international trade in services, havemany weaknesses. Disaggregation of importantcomponents may be limited and varies considerablyacross countries. There are inconsistencies in themethods used to report items. And the recording ofmajor fl ows as net items is common (for example,insurance transactions are often recorded as premiumsless claims). These factors contribute to a downwardbias in the value of the service trade reportedin the balance of payments.Efforts are being made to improve the coverage,quality, and consistency of these data. Eurostat andthe Organisation for Economic Co-operation andDevelopment, for example, are working togetherto improve the collection of statistics on trade inservices in member countries. In addition, the InternationalMonetary Fund (IMF) has implementedthe new classifi cation of trade in services introducedin the fi fth edition of its Balance of PaymentsManual (1993).Still, diffi culties in capturing all the dimensions ofinternational trade in services mean that the recordis likely to remain incomplete. Cross-border intrafi rmservice transactions, which are usually not capturedin the balance of payments, have increased in recentyears. An example is transnational corporations’ useof mainframe computers around the clock for dataprocessing, exploiting time zone differences betweentheir home country and the host countries of theiraffi liates. Another important dimension of servicetrade not captured by conventional balance of paymentsstatistics is establishment trade—sales inthe host country by foreign affi liates. By contrast,cross-border intrafi rm transactions in merchandisemay be reported as exports or imports in the balanceof payments.The data on exports of services in the table and onimports of services in table 4.7, unlike those in editionsbefore 2000, include only commercial servicesand exclude the category “government services notincluded elsewhere.” The data are compiled by theIMF based on returns from national sources. Data ontotal trade in goods and services from the IMF’s Balanceof Payments database are shown in table 4.17.International transactions in services are defi nedby the IMF’s Balance of Payments Manual (1993) asthe economic output of intangible commodities thatmay be produced, transferred, and consumed at thesame time. Defi nitions may vary among reportingeconomies. Travel services include the goods andservices consumed by travelers, such as meals,lodging, and transport (within the economy visited),including car rental.• Commercial service exports are total serviceexports minus exports of government services notincluded elsewhere. • Transport covers all transportservices (sea, air, land, internal waterway, space,and pipeline) performed by residents of one economyfor those of another and involving the carriage ofpassengers, movement of goods (freight), rental ofcarriers with crew, and related support and auxiliaryservices. Excluded are freight insurance, which isincluded in insurance services; goods procured inports by nonresident carriers and repairs of transportequipment, which are included in goods; repairsof harbors, railway facilities, and airfi eld facilities,which are included in construction services; andrental of carriers without crew, which is includedin other services. • Travel covers goods and servicesacquired from an economy by travelers in thateconomy for their own use during visits of less thanone year for business or personal purposes. • Insuranceand financial services cover freight insuranceon goods exported and other direct insurance suchas life insurance; fi nancial intermediation servicessuch as commissions, foreign exchange transactions,and brokerage services; and auxiliary servicessuch as fi nancial market operational and regulatoryservices. • Computer, information, communications,and other commercial services cover suchactivities as international telecommunications andpostal and courier services; computer data; newsrelatedservice transactions between residents andnonresidents; construction services; royalties andlicense fees; miscellaneous business, professional,and technical services; and personal, cultural, andrecreational services.Data sourcesData on commercial service exports are from theIMF, which publishes balance of payments data inits International Financial Statistics and Balance ofPayments Statistics Yearbook.2012 World Development Indicators 237

4.7 Structure of service importsCommercialservice importsTransport Travel Insurance andfinancial servicesComputer, information,communications, andother commercialservices$ millions % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan .. .. .. .. .. .. .. .. .. ..Albania 413 1,992 23 16 66 68 8 5 3 11Algeria .. 11,203 .. 26 .. 4 .. 2 .. 67Angola 2,271 16,028 14 19 6 1 3 7 78 73Argentina 8,960 13,769 27 27 49 35 4 5 20 33Armenia 177 985 66 45 22 41 6 7 6 7Australia 18,554 51,470 34 31 34 39 5 3 27 27Austria 16,383 36,926 23 33 38 27 7 4 32 36Azerbaijan 475 3,762 30 21 28 21 2 3 42 55Bahrain 757 1,905 57 40 30 27 3 23 10 11Bangladesh 1,523 4,128 66 83 19 6 8 2 6 9Belarus 524 2,878 21 48 41 21 3 3 35 28Belgium 35,288 78,377 22 25 29 24 8 4 41 47Benin 186 488 67 59 7 11 9 5 17 25Bolivia 450 1,128 60 41 17 28 14 13 9 18Bosnia and Herzegovina 256 581 48 39 28 33 10 5 14 22Botswana 538 867 42 51 37 3 4 5 17 42Brazil 15,573 59,746 28 19 25 27 6 5 41 48Bulgaria 1,660 4,164 44 22 32 30 5 6 18 43Burkina Faso 132 545 64 56 15 12 18 14 3 18Burundi 36 156 53 71 38 13 4 3 4 14Cambodia 321 1,084 53 57 10 18 4 6 32 19Cameroon 994 1,717 22 36 21 11 4 7 53 46Canada 43,597 89,963 21 23 29 33 10 11 40 33Central African Republic .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. ..Chile 4,664 11,568 47 58 13 17 9 8 31 17China 35,858 192,174 29 33 37 29 7 9 27 30Hong Kong SAR, China 24,588 50,869 25 28 51 36 5 9 18 27Colombia 3,242 7,893 40 36 33 23 10 9 17 32Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Congo, Rep. 728 3,523 11 15 7 5 5 5 77 75Costa Rica 1,261 1,769 33 38 38 24 4 8 25 30Côte d’Ivoire 1,142 2,324 45 58 17 15 11 9 27 27Croatia 1,782 3,389 21 16 32 25 5 6 42 53Cuba .. .. .. .. .. .. .. .. .. ..Cyprus 1,563 3,114 56 41 26 37 7 8 10 14Czech Republic 5,364 16,925 13 24 24 24 9 3 54 49Denmark 21,063 51,894 44 .. 22 .. .. .. 33 ..Dominican Republic 1,340 2,044 62 59 23 19 7 9 8 13Ecuador 1,225 2,950 36 59 24 19 3 8 36 14Egypt, Arab Rep. 7,161 12,991 31 51 15 17 7 11 48 21El Salvador 912 1,024 44 45 18 21 17 13 21 21Eritrea 24 .. 28 .. 50 .. 2 .. 21 ..Estonia 870 2,770 48 35 23 23 1 2 27 40Ethiopia 479 2,534 60 65 15 6 4 4 21 25Finland 8,323 27,650 31 20 22 15 0 3 46 61France 64,400 131,391 28 27 35 29 2 4 36 40Gabon 846 .. 32 .. 10 .. 8 .. 50 ..Gambia, The 32 72 78 48 11 15 10 7 1 30Georgia 271 996 38 56 41 20 8 14 13 11Germany 135,812 262,245 19 24 39 30 2 4 40 42Ghana 514 2,444 53 46 20 24 6 5 22 25Greece 10,918 19,892 37 54 42 14 3 10 18 22Guatemala 786 2,362 54 48 23 33 8 9 15 10Guinea 183 381 61 60 5 2 4 7 30 31Guinea-Bissau 30 85 90 36 9 30 0 5 1 28Haiti 270 1,223 91 47 7 5 .. 0 3 472382012 World Development Indicators

Structure of service imports 4.7ECONOMYCommercialservice importsTransport Travel Insurance andfinancial servicesComputer, information,communications, andother commercialservices$ millions % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 688 1,312 60 45 17 24 2 8 21 23Hungary 4,708 15,376 15 21 35 16 7 2 43 61India 18,898 116,140 46 40 14 9 11 10 29 41Indonesia 15,381 25,601 26 34 21 25 2 6 51 35Iran, Islamic Rep. 1,577 .. 72 .. 13 .. 14 .. 1 ..Iraq .. 7,565 .. 53 .. 10 .. 27 .. 10Ireland 31,212 107,270 8 2 8 7 9 13 75 78Israel 11,849 17,787 36 33 24 19 3 2 38 46Italy 54,632 108,616 24 24 29 25 3 8 44 44Jamaica 1,391 1,767 42 41 15 11 8 11 35 37Japan 115,686 155,800 30 30 28 18 3 6 39 46Jordan 1,463 4,164 47 51 24 34 6 7 22 7Kazakhstan 1,831 11,142 18 17 22 11 3 5 57 67Kenya 665 1,816 51 51 20 12 10 10 19 27Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 33,128 92,936 33 31 22 19 1 2 44 48Kosovo .. .. .. .. .. .. .. .. .. ..Kuwait 4,115 12,260 37 40 61 55 1 3 1 2Kyrgyz Republic 144 915 35 46 11 30 9 3 46 21Lao PDR 13 258 38 6 62 79 –3 5 0 10Latvia 680 2,211 31 30 36 29 10 6 22 35Lebanon 3,340 13,262 14 14 80 36 4 10 2 41Lesotho 247 479 7 14 78 65 3 4 12 17Liberia .. 234 .. 54 .. 27 .. 1 .. 18Libya 815 5,251 43 45 49 39 1 12 8 4Lithuania 655 2,718 34 52 39 29 2 2 26 17Macedonia, FYR 260 816 50 39 13 11 3 6 34 44Madagascar 395 .. 48 .. 29 .. 2 .. 21 ..Malawi 167 79 53 19 30 59 0 7 17 15Malaysia 16,603 32,216 35 37 12 25 3 4 49 38Mali 324 813 74 57 12 12 4 6 10 25Mauritania 130 .. 37 .. 33 .. .. .. 30 ..Mauritius 748 1,956 35 28 24 20 7 8 34 44Mexico 16,242 21,818 38 49 34 33 14 15 14 3Moldova 190 729 32 39 38 36 2 3 28 21Mongolia 158 760 54 38 33 35 1 4 13 24Morocco 1,520 5,724 41 46 28 21 2 4 29 29Mozambique 439 1,102 38 28 25 23 6 3 32 46Myanmar 310 761 82 60 8 7 .. .. 10 33Namibia 308 697 29 32 24 21 6 7 40 40Nepal 193 846 34 33 38 48 7 4 29 16Netherlands 49,941 84,384 26 22 24 23 3 3 47 52New Zealand 4,404 9,227 32 30 33 33 3 1 32 36Nicaragua 334 660 46 50 23 31 7 11 23 8Niger 125 735 67 73 21 7 3 4 9 17Nigeria 3,144 20,163 20 43 19 28 3 3 59 26Norway 14,832 42,358 35 28 31 33 6 3 28 36Oman 1,758 6,525 37 41 27 15 6 11 30 33Pakistan 2,109 6,481 72 58 12 14 4 4 12 24Panama 1,096 2,569 55 60 17 15 8 16 20 9Papua New Guinea 772 2,737 21 22 7 4 5 13 67 61Paraguay 390 707 61 68 21 22 15 8 4 3Peru 2,165 5,843 40 42 20 22 8 9 33 27Philippines 5,175 11,188 40 44 32 31 4 3 25 21Poland 8,862 29,473 17 21 37 29 6 4 39 46Portugal 6,787 14,237 29 30 33 27 5 4 33 38Puerto Rico .. .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 239

4.7 Structure of service importsCommercialservice importsTransport Travel Insurance andfinancial servicesComputer, information,communications, andother commercialservices$ millions % of total % of total % of total % of total2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Romania 1,948 9,341 32 30 22 18 7 7 39 46Russian Federation 16,230 72,278 14 17 55 37 3 4 28 43Rwanda 113 442 73 73 19 17 .. 1 8 9Saudi Arabia 10,927 50,996 21 25 .. 41 2 5 77 28Senegal 396 1,110 61 54 12 14 10 12 18 21Serbia .. 3,477 .. 29 .. 27 .. 3 .. 41Sierra Leone 82 134 21 70 39 10 4 5 36 15Singapore 29,968 96,255 42 30 16 17 7 6 34 47Slovak Republic 1,779 6,781 24 28 17 29 5 8 54 35Slovenia 1,423 4,305 25 22 36 28 2 4 37 46Somalia .. .. .. .. .. .. .. .. .. ..South Africa 5,657 18,023 43 39 37 31 7 4 13 26South Sudan .. .. .. .. .. .. .. .. .. ..Spain 32,837 86,752 31 24 18 19 4 7 47 49Sri Lanka 1,592 3,084 62 66 15 15 6 6 17 13Sudan 632 2,195 88 46 9 51 0 1 3 2Swaziland 300 650 12 11 10 9 2 6 76 74Sweden 24,127 47,316 14 17 34 28 3 1 50 54Switzerland 14,533 39,435 36 21 37 28 7 7 20 43Syrian Arab Republic 1,468 3,377 48 47 46 45 2 4 7 4Tajikistan 103 389 79 51 2 5 6 7 13 37Tanzania 620 1,840 33 39 54 45 3 4 9 12Thailand 15,329 44,592 44 50 18 12 5 5 33 32Timor-Leste .. .. .. .. .. .. .. .. .. ..Togo 116 374 72 62 2 13 15 9 12 16Trinidad and Tobago 363 335 48 39 41 31 0 7 12 22Tunisia 1,119 3,165 49 50 23 17 7 9 20 23Turkey 7,624 18,343 32 45 22 26 13 10 32 18Turkmenistan 669 .. 23 .. 19 .. 2 .. 57 ..Uganda 459 1,809 33 55 13 14 3 13 64 18Ukraine 2,590 12,137 15 34 18 31 6 10 61 26United Arab Emirates .. .. .. .. .. .. .. .. .. ..United Kingdom 96,893 162,086 25 18 39 31 6 7 30 44United States 203,169 367,016 30 21 33 23 8 21 29 36Uruguay 842 1,365 47 45 33 31 6 4 13 20Uzbekistan .. .. .. .. .. .. .. .. .. ..Venezuela, RB 4,236 10,548 43 38 25 17 5 5 27 39Vietnam 3,252 9,921 .. .. .. .. .. .. .. ..West Bank and Gaza 459 777 12 11 66 62 2 2 20 25Yemen, Rep. 757 2,263 45 47 9 8 7 10 38 35Zambia 322 901 63 56 14 7 6 10 18 26Zimbabwe .. .. .. .. .. .. .. .. .. ..World 1,485,017 t 3,472,677 t 29 w 27 w 31 w 25 w 6 w 9 w 34 w 39 wLow income 9,090 29,773 58 61 20 14 7 5 18 21Middle income 264,736 906,395 36 39 28 26 8 8 28 27Lower middle income 82,866 296,871 40 43 20 22 6 7 34 29Upper middle income 182,790 613,085 35 38 30 27 8 9 27 27Low & middle income 273,647 935,741 36 39 28 26 8 8 28 27East Asia & Pacifi c 94,144 323,841 33 37 28 25 5 7 34 31Europe & Central Asia 39,655 160,613 26 33 33 28 7 7 33 32Latin America & Carib. 67,954 155,546 38 42 30 29 10 11 21 18Middle East & N. Africa 23,663 70,149 48 42 22 20 .. 7 23 31South Asia 24,653 132,213 53 49 15 11 9 8 23 32Sub-Saharan Africa 25,073 97,316 41 40 27 25 6 5 27 30High income 1,211,620 2,536,783 27 24 32 25 5 10 36 42Euro area 477,111 1,044,273 24 25 31 25 3 5 41 452402012 World Development Indicators

Structure of service imports4.7ECONOMYAbout the dataDefinitionsTrade in services differs from trade in goods becauseservices are produced and consumed at the sametime. Thus services to a traveler may be consumedin the producing country (for example, use of a hotelroom) but are classifi ed as imports of the traveler’scountry. In other cases services may be suppliedfrom a remote location; for example, insuranceservices may be supplied from one location andconsumed in another. For further discussion of theproblems of measuring trade in services, see Aboutthe data for table 4.6.The data on imports of services in the table and onexports of services in table 4.6, unlike those in editionsbefore 2000, include only commercial servicesand exclude the category “government services notincluded elsewhere.” The data are compiled by theInternational Monetary Fund (IMF) based on returnsfrom national sources.International transactions in services are defi nedby the IMF’s Balance of Payments Manual (1993) asthe economic output of intangible commodities thatmay be produced, transferred, and consumed at thesame time. Defi nitions may vary among reportingeconomies.Travel services include the goods and servicesconsumed by travelers, such as meals, lodging, andtransport (within the economy visited), including carrental.• Commercial service imports are total serviceimports minus imports of government services notincluded elsewhere. • Transport covers all transportservices (sea, air, land, internal waterway, space,and pipeline) performed by residents of one economyfor those of another and involving the carriage ofpassengers, movement of goods (freight), rental ofcarriers with crew, and related support and auxiliaryservices. Excluded are freight insurance, which isincluded in insurance services; goods procured inports by nonresident carriers and repairs of transportequipment, which are included in goods; repairsof harbors, railway facilities, and airfi eld facilities,which are included in construction services; andrental of carriers without crew, which is includedin other services. • Travel covers goods and servicesacquired from an economy by travelers in thateconomy for their own use during visits of less thanone year for business or personal purposes. • Insuranceand financial services cover freight insuranceon goods imported and other direct insurance suchas life insurance; fi nancial intermediation servicessuch as commissions, foreign exchange transactions,and brokerage services; and auxiliary servicessuch as fi nancial market operational and regulatoryservices. • Computer, information, communications,and other commercial services cover suchactivities as international telecommunications, andpostal and courier services; computer data; newsrelatedservice transactions between residents andnonresidents; construction services; royalties andlicense fees; miscellaneous business, professional,and technical services; and personal, cultural, andrecreational services.Data sourcesData on commercial service imports are from theIMF, which publishes balance of payments data inits International Financial Statistics and Balance ofPayments Statistics Yearbook.2012 World Development Indicators 241

4.8 Structure of demandHouseholdfinal consumptionexpenditureGeneralgovernmentfinal consumptionexpenditureGrosscapitalformationExportsof goods andservicesImportsof goods andservicesGrosssavings% of GDP % of GDP % of GDP % of GDP % of GDP % of GDP2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan 112 111 8 11 12 16 31 15 62 54 .. ..Albania 85 88 9 8 25 26 19 30 37 52 24 13Algeria 42 35 14 14 25 41 41 31 21 21 .. 54Angola .. .. .. .. 15 15 90 58 63 44 24 16Argentina 71 60 14 15 16 22 11 22 12 18 13 22Armenia 97 78 12 13 19 33 23 21 51 45 4 19Australia 58 54 18 18 26 28 19 20 21 20 21 24Austria 55 55 19 19 25 22 46 54 44 50 24 25Azerbaijan 69 37 9 11 21 17 39 55 38 20 17 46Bahrain 47 31 18 13 10 33 89 97 64 74 20 43Bangladesh 78 77 5 5 23 24 14 18 19 25 27 38Belarus 58 57 19 16 25 41 69 55 72 68 23 25Belgium 53 53 21 24 23 20 78 80 75 77 25 23Benin 82 .. 12 .. 19 26 15 14 28 28 10 13Bolivia 76 62 15 14 18 17 18 41 27 34 11 25Bosnia and Herzegovina 104 80 25 21 21 20 29 36 76 57 13 15Botswana 31 46 25 21 32 36 53 29 41 32 41 26Brazil 64 61 19 21 18 19 10 11 12 12 14 17Bulgaria 68 61 19 16 18 25 50 58 56 60 12 24Burkina Faso 79 .. 21 .. 17 .. 9 .. 25 .. 5 ..Burundi 88 .. 18 .. 6 .. 8 .. 20 .. 4 ..Cambodia 89 82 5 6 18 17 50 54 62 60 14 13Cameroon 70 .. 9 .. 17 .. 23 28 20 33 15 ..Canada 55 58 19 22 20 22 46 29 40 31 23 18Central African Republic 81 93 14 5 10 11 20 15 24 23 .. ..Chad 87 73 8 15 23 37 17 44 35 69 .. ..Chile 64 59 12 12 22 21 32 39 30 32 21 23China 47 35 16 13 35 48 23 30 21 26 37 53Hong Kong SAR, China 59 62 9 8 27 24 143 223 139 217 32 30Colombia 69 62 17 16 15 24 16 16 17 18 14 19Congo, Dem. Rep. 88 75 8 5 3 29 22 15 21 34 .. ..Congo, Rep. 29 33 12 10 23 25 80 82 44 50 31 ..Costa Rica 67 65 13 18 17 20 49 38 46 41 13 15Côte d’Ivoire 75 73 7 9 11 14 40 41 33 36 8 15Croatia 60 56 24 22 19 23 42 38 45 39 18 22Cuba 61 54 29 33 13 11 14 20 17 18 .. ..Cyprus 65 68 16 20 18 18 55 40 55 47 15 9Czech Republic 52 51 21 22 29 23 63 79 66 75 25 21Denmark 48 48 25 29 21 17 47 50 40 45 23 23Dominican Republic 78 88 8 8 23 16 37 22 46 34 18 7Ecuador 64 68 10 12 20 26 37 33 31 39 26 23Egypt, Arab Rep. 76 75 11 11 20 19 16 21 23 26 18 18El Salvador 88 93 10 11 17 13 27 26 42 44 14 11Eritrea 79 .. 64 .. 24 .. 15 5 82 20 4 ..Estonia 55 53 20 21 28 20 85 78 88 72 23 24Ethiopia 74 89 18 10 20 21 12 11 24 32 16 17Finland 49 55 21 25 21 19 44 40 34 39 29 21France 56 58 23 25 20 19 29 25 28 28 22 17Gabon 32 43 10 10 22 26 69 52 33 31 42 ..Gambia, The 78 78 14 15 17 26 48 29 57 49 .. 13Georgia 82 77 9 21 27 20 23 35 40 52 22 10Germany 58 57 19 20 22 17 33 47 33 41 20 23Ghana 84 80 10 11 24 22 49 25 67 38 15 20Greece 70 75 19 18 25 16 26 22 40 30 14 5Guatemala 84 86 7 10 18 15 20 25 29 36 12 13Guinea 78 77 7 8 20 20 24 35 28 39 15 10Guinea-Bissau 95 .. 14 .. 11 .. 32 .. 52 .. –15 ..Haiti 86 .. 8 .. 27 25 13 12 33 57 .. 232422012 World Development Indicators

Structure of demand4.8ECONOMYHouseholdfinal consumptionexpenditureGeneralgovernmentfinal consumptionexpenditureGrosscapitalformationExportsof goods andservicesImportsof goods andservicesGrosssavings% of GDP % of GDP % of GDP % of GDP % of GDP % of GDP2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 71 80 13 18 28 23 54 44 66 65 21 16Hungary 55 53 21 22 27 18 75 87 78 80 19 20India 64 57 13 12 24 35 13 22 14 25 25 34Indonesia 61 57 7 9 22 32 41 25 30 23 25 32Iran, Islamic Rep. 48 .. 14 .. 33 .. 23 .. 17 .. 39 ..Iraq .. .. .. .. .. .. .. .. .. .. .. ..Ireland 49 51 14 19 24 11 97 101 84 82 24 12Israel 53 58 26 24 21 16 37 37 37 35 17 18Italy 60 60 18 21 21 20 27 27 26 29 21 17Jamaica 74 80 14 17 .. 21 .. 26 .. 43 17 12Japan 56 59 17 20 25 20 11 15 10 14 28 24Jordan 81 85 24 21 22 15 42 45 68 66 23 9Kazakhstan 62 49 12 11 19 25 57 44 49 29 21 28Kenya 78 78 15 13 17 21 22 26 32 39 14 16Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 55 53 12 15 31 29 39 52 36 50 33 32Kosovo .. 95 .. 18 .. 30 .. 19 .. 62 .. ..Kuwait 42 28 21 21 11 14 56 56 30 28 50 59Kyrgyz Republic 66 84 20 19 20 28 42 58 48 89 15 20Lao PDR 93 69 7 9 14 26 30 36 44 41 2 20Latvia 63 63 21 17 24 21 42 53 49 54 19 24Lebanon 84 78 17 12 20 33 14 21 36 44 –4 12Lesotho 83 87 42 44 44 34 34 49 103 114 37 34Liberia 89 202 14 19 5 20 21 31 26 173 .. –2Libya 46 23 21 9 13 28 36 67 15 27 22 67Lithuania 65 65 23 20 19 17 45 68 51 70 13 19Macedonia, FYR 74 75 18 18 22 25 49 47 64 66 22 24Madagascar 83 79 9 12 15 33 31 29 38 53 9 ..Malawi 82 66 15 19 14 24 26 26 35 36 10 13Malaysia 44 48 10 13 27 21 120 97 101 79 36 33Mali 79 .. 9 .. 25 .. 27 .. 39 .. 16 ..Mauritania 83 70 26 18 19 28 46 48 74 63 21 ..Mauritius 60 76 14 14 26 22 61 45 62 58 26 16Mexico 67 65 11 12 24 25 31 30 33 32 20 24Moldova 91 91 10 24 24 24 50 40 75 78 16 16Mongolia 70 53 15 14 29 41 54 55 68 62 23 27Morocco 61 57 18 18 26 35 28 33 33 43 24 31Mozambique 81 82 9 12 31 24 16 25 37 43 10 11Myanmar .. .. .. .. 12 23 0 0 1 0 .. ..Namibia 63 53 24 22 17 23 41 39 45 38 25 34Nepal 76 82 9 11 24 35 23 10 32 37 22 37Netherlands 50 45 22 28 22 19 70 78 65 71 28 23New Zealand 60 58 17 21 21 20 35 29 33 27 18 16Nicaragua 84 91 12 10 30 28 24 41 51 70 8 13Niger 83 .. 13 .. 11 .. 18 .. 26 .. 5 ..Nigeria .. .. .. .. .. .. 54 39 32 27 .. ..Norway 43 43 19 22 20 22 47 41 29 29 35 36Oman 40 40 21 20 12 30 59 53 31 41 29 38Pakistan 75 82 9 8 17 15 13 14 15 19 20 22Panama 60 71 13 6 24 27 73 65 70 69 23 18Papua New Guinea 45 70 17 9 22 18 66 56 49 53 32 20Paraguay 79 69 13 9 19 19 38 57 49 55 11 23Peru 71 63 11 10 20 24 16 25 18 22 17 23Philippines 72 72 11 10 18 21 51 35 53 37 23 27Poland 64 61 17 19 25 21 27 42 34 43 19 17Portugal 64 66 19 22 28 20 29 31 40 38 18 10Puerto Rico 91 94 11 11 18 9 75 78 98 92 .. ..Qatar 15 21 20 25 20 39 67 47 22 31 .. ..2012 World Development Indicators 243

4.8Structure of demandHouseholdfinal consumptionexpenditureGeneralgovernmentfinal consumptionexpenditureGrosscapitalformationExportsof goods andservicesImportsof goods andservicesGrosssavingsRomania 78 60 7 15 20 31 33 23 38 30 16 26Russian Federation 46 49 15 19 19 23 44 30 24 22 36 28Rwanda 88 81 11 15 18 22 9 12 26 29 13 15Saudi Arabia 37 34 26 23 19 22 44 57 25 35 29 33Senegal 76 82 13 9 20 29 28 25 37 44 14 19Serbia 88 74 20 19 9 23 24 35 40 51 .. 16Sierra Leone 100 84 14 12 7 16 18 17 39 30 –4 13Singapore 43 37 11 11 33 24 192 211 180 183 44 46Slovak Republic 56 58 20 20 26 23 70 81 73 82 23 20Slovenia 57 56 19 21 27 23 54 65 57 65 25 22Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 63 57 18 20 16 25 28 26 25 27 16 16South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 60 58 17 21 26 23 29 26 32 28 23 19Sri Lanka 72 66 11 16 28 28 39 22 50 31 22 25Sudan 76 61 8 15 18 23 15 20 18 19 9 18Swaziland 78 77 19 25 17 17 76 58 90 77 13 3Sweden 49 49 26 27 19 18 47 50 40 44 23 25Switzerland 60 58 11 11 23 19 46 54 41 42 35 36Syrian Arab Republic 64 71 12 10 17 19 35 35 29 36 22 17Tajikistan 84 94 8 29 9 23 99 15 101 61 9 3Tanzania a 78 66 12 18 17 31 13 24 20 38 13 21Thailand 57 54 11 13 23 26 67 71 58 64 30 31Timor-Leste 112 .. 35 .. 26 .. .. .. .. .. .. ..Togo 92 .. 10 .. 18 .. 31 .. 51 .. 1 ..Trinidad and Tobago 57 51 9 10 20 11 59 65 45 38 26 35Tunisia 61 63 17 16 26 26 40 49 43 54 22 20Turkey 71 71 12 14 21 20 20 21 23 27 18 14Turkmenistan 36 33 14 11 35 59 96 52 81 55 .. ..Uganda 77 75 15 12 19 24 11 24 22 34 14 19Ukraine 54 63 21 20 20 19 62 50 57 53 24 17United Arab Emirates 61 57 9 8 22 25 49 78 41 69 .. ..United Kingdom 66 64 19 23 18 15 28 30 29 33 15 12United States 69 71 14 17 21 15 11 13 15 16 18 11Uruguay 77 67 12 13 14 19 17 27 20 26 11 17Uzbekistan 62 55 19 18 16 26 25 31 22 31 .. ..Venezuela, RB 52 57 12 11 24 21 30 29 18 17 34 31Vietnam 66 65 6 7 30 39 55 78 57 88 31 32West Bank and Gaza 95 .. 27 .. 33 .. 16 .. 71 .. 9 ..Yemen, Rep. 60 81 14 12 19 12 41 30 34 34 33 9Zambia 87 55 10 13 17 22 27 44 41 35 –1 22Zimbabwe 60 101 24 17 14 1 39 37 36 56 .. ..World 61 w 62 w 16 w 19 w 22 w 20 w 25 w 28 w 25 w 28 w 22 w 19 wLow income 79 80 10 10 19 23 17 20 25 32 19 27Middle income 60 56 14 14 24 29 27 29 26 28 25 30Lower middle income 67 64 12 11 22 28 27 28 27 31 23 28Upper middle income 58 54 15 15 24 30 27 29 25 27 25 30Low & middle income 61 56 14 14 24 29 27 28 25 28 25 30East Asia & Pacifi c 50 42 14 13 31 41 35 37 31 33 34 46Europe & Central Asia 60 60 14 17 20 23 37 31 31 31 26 22Latin America & Carib. 66 63 15 15 20 22 20 22 21 22 17 21Middle East & N. Africa 60 .. 15 .. 24 .. 27 .. 25 .. .. ..South Asia 67 62 11 11 23 32 14 20 16 25 25 33Sub-Saharan Africa 69 64 16 17 17 24 32 30 31 32 16 17High income 61 63 17 19 22 18 24 28 25 28 22 17Euro area 57 58 20 22 22 19 37 41 36 39 22 20a. Covers mainland Tanzania only.% of GDP % of GDP % of GDP % of GDP % of GDP % of GDP2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 20102442012 World Development Indicators

Structure of demand 4.8ECONOMYAbout the dataDefinitionsGross domestic product (GDP) from the expenditureside is made up of household final consumptionexpenditure, general government fi nal consumptionexpenditure, gross capital formation (private andpublic investment in fi xed assets, changes in inventories,and net acquisitions of valuables), and netexports (exports minus imports) of goods and services.Such expenditures are recorded in purchaserprices and include net taxes on products.Because policymakers have tended to focus onfostering the growth of output, and because data onproduction are easier to collect than data on spending,many countries generate their primary estimateof GDP using the production approach. Moreover,many countries do not estimate all the componentsof national expenditures but instead derive someof the main aggregates indirectly using GDP (basedon the production approach) as the control total.Household fi nal consumption expenditure (privateconsumption in the 1968 United Nations System ofNational Accounts, or SNA) is often estimated asa residual, by subtracting all other known expendituresfrom GDP. The resulting aggregate may incorporatefairly large discrepancies. When householdconsumption is calculated separately, many of theestimates are based on household surveys, whichtend to be one-year studies with limited coverage.Thus the estimates quickly become outdated andmust be supplemented by estimates using price- andquantity-based statistical procedures. Complicatingthe issue, in many developing countries the distinctionbetween cash outlays for personal businessand those for household use may be blurred. WorldDevelopment Indicators includes in household consumptionthe expenditures of nonprofit institutionsserving households.General government final consumption expenditure(general government consumption in the 1968 SNA)includes expenditures on goods and services forindividual consumption as well as those on servicesfor collective consumption. Defense expenditures,including those on capital outlays (with certain exceptions),are treated as current spending.Gross capital formation (gross domestic investmentin the 1968 SNA) consists of outlays onadditions to the economy’s fixed assets plus netchanges in the level of inventories. It is generallyobtained from industry reports of acquisitions anddistinguishes only the broad categories of capitalformation. The 1993 SNA recognizes a third categoryof capital formation: net acquisitions of valuables.Included in gross capital formation under the1993 SNA guidelines are capital outlays on defenseestablishments that may be used by the general public,such as schools, airfi elds, and hospitals, andintangibles such as computer software and mineralexploration outlays. Data on capital formation maybe estimated from direct surveys of enterprises andadministrative records or based on the commodityfl ow method using data from production, trade, andconstruction activities. The quality of data on governmentfi xed capital formation depends on the qualityof government accounting systems (which tend tobe weak in developing countries). Measures of fixedcapital formation by households and corporations—particularly capital outlays by small, unincorporatedenterprises—are usually unreliable.Estimates of changes in inventories are rarelycomplete but usually include the most importantactivities or commodities. In some countries theseestimates are derived as a composite residual alongwith household fi nal consumption expenditure.According to national accounts conventions, adjustmentsshould be made for appreciation of the valueof inventory holdings due to price changes, but thisis not always done. In highly infl ationary economiesthis element can be substantial.Data on exports and imports are compiled fromcustoms reports and balance of payments data.Although the data from the payments side providereasonably reliable records of cross-border transactions,they may not adhere strictly to the appropriatedefi nitions of valuation and timing used in the balanceof payments or correspond to the change-ofownershipcriterion. This issue has assumed greatersignifi cance with the increasing globalization of internationalbusiness. Neither customs nor balance ofpayments data usually capture the illegal transactionsthat occur in many countries. Goods carriedby travelers across borders in legal but unreportedshuttle trade may further distort trade statistics.Gross savings represent the difference betweendisposable income and consumption and replacegross domestic savings, a concept used by the WorldBank and included in World Development Indicatorseditions before 2006. The change was made to conformto SNA concepts and defi nitions. For furtherdiscussion of the problems in compiling nationalaccounts, see Srinivasan (1994), Heston (1994),and Ruggles (1994). For an analysis of the reliabilityof foreign trade and national income statistics, seeMorgenstern (1963).• Household final consumption expenditure is themarket value of all goods and services, includingdurable products (such as cars and computers),purchased by households. It excludes purchasesof dwellings but includes imputed rent for owneroccupieddwellings. It also includes government feesfor permits and licenses. Expenditures of nonprofitinstitutions serving households are included, evenwhen reported separately. Household consumptionexpenditure may include any statistical discrepancyin the use of resources relative to the supply ofresources. • General government final consumptionexpenditure is all government current expendituresfor purchases of goods and services (includingcompensation of employees). It also includes mostexpenditures on national defense and security butexcludes military expenditures with potentially widerpublic use that are part of government capital formation.• Gross capital formation is outlays on additionsto fi xed assets of the economy, net changes ininventories, and net acquisitions of valuables. Fixedassets include land improvements (fences, ditches,drains); plant, machinery, and equipment purchases;and construction (roads, railways, schools, buildings,and so on). Inventories are goods held to meet temporaryor unexpected fluctuations in production orsales, and “work in progress.” • Exports and importsof goods and services are the value of all goods andother market services provided to or received fromthe rest of the world. They include the value of merchandise,freight, insurance, transport, travel, royalties,license fees, and other services (communication,construction, financial, information, business,personal, government services, and so on). Theyexclude compensation of employees and investmentincome (factor services in the 1968 SNA) and transferpayments. • Gross savings are gross nationalincome less total consumption, plus net transfers.Data sourcesData on national accounts indicators for mostdeveloping countries are collected from nationalstatistical organizations and central banks by visitingand resident World Bank missions. Data forhigh-income economies are from Organisation forEconomic Co-operation and Development (OECD)data fi les.2012 World Development Indicators 245

4.9 Growth of consumption and investmentHousehold finalconsumptionexpenditureGeneral governmentfinal consumptionexpenditureGross capitalformationGoods andservicesaverage annual% growth average annual average annualaverage annual% growthTotal Per capita % growth% growthExports Imports1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10Afghanistan .. .. .. .. .. .. .. .. .. .. .. ..Albania 1.3 5.2 2.2 4.8 14.5 6.3 25.8 5.1 18.9 9.2 15.7 11.8Algeria –0.1 3.6 –1.9 2.1 3.6 4.8 –0.6 8.8 3.2 2.3 –1.0 7.8Angola .. .. .. .. .. .. .. .. .. .. .. ..Argentina 2.8 5.0 1.5 4.1 2.2 4.2 7.4 11.0 8.7 6.2 15.6 9.7Armenia –0.5 7.3 1.1 7.2 –1.5 8.2 –1.9 16.3 –18.4 5.3 –12.7 7.7Australia 3.3 3.7 2.1 2.1 3.0 3.1 5.3 6.9 7.8 2.5 7.6 8.4Austria 1.7 1.4 1.4 0.9 2.6 1.6 2.6 0.6 5.8 4.4 4.8 3.5Azerbaijan 2.0 12.4 1.0 11.1 7.4 22.4 41.7 13.9 13.4 22.5 15.5 17.1Bahrain .. .. .. .. .. .. .. .. .. .. .. ..Bangladesh 2.6 4.6 0.5 3.2 4.7 8.5 9.2 7.7 13.1 10.6 9.7 8.0Belarus –0.5 11.3 –0.3 11.9 –1.9 0.1 –7.5 18.1 –4.8 5.1 –8.7 10.3Belgium 1.9 1.4 1.6 0.8 1.6 1.5 2.4 2.1 5.3 2.6 5.0 2.7Benin 2.6 2.3 –0.6 –0.9 4.4 8.3 12.2 7.7 1.8 2.7 2.1 1.8Bolivia 3.6 3.4 1.3 1.6 3.6 3.5 8.5 4.8 4.5 6.9 6.0 5.4Bosnia and Herzegovina .. .. .. .. .. .. .. 5.3 .. 8.7 .. 3.4Botswana 3.2 8.5 0.8 7.1 6.1 4.8 4.6 3.1 4.4 1.6 4.3 4.8Brazil 3.7 3.9 2.2 2.8 1.0 3.3 4.2 4.7 5.9 6.4 11.6 8.5Bulgaria –2.6 5.2 –2.0 5.9 –8.0 1.4 –5.3 11.2 4.3 7.6 2.9 9.0Burkina Faso 5.7 4.5 2.8 1.5 2.9 8.7 3.1 9.0 4.4 10.9 1.9 7.2Burundi –4.9 .. .. .. –2.6 .. –0.5 .. –1.2 .. –1.6 ..Cambodia 6.0 8.0 3.5 6.7 7.2 9.9 10.3 12.6 21.7 14.2 14.8 13.8Cameroon 3.1 4.5 0.6 2.2 0.7 2.8 0.4 4.4 3.2 –0.7 5.1 3.6Canada 2.7 3.3 1.7 2.3 0.3 2.8 4.6 4.3 8.7 –0.6 7.1 3.2Central African Republic .. –0.9 .. –2.5 .. –1.3 .. –0.1 .. –3.6 .. –3.9Chad 1.5 2.7 –1.7 –0.8 –8.3 2.7 4.0 –2.4 2.3 33.6 –1.8 –3.7Chile 7.3 5.5 5.6 4.4 3.7 4.8 9.3 8.1 9.4 4.9 11.7 10.3China 8.9 7.7 7.7 7.1 9.6 9.2 10.8 13.3 15.5 19.1 16.7 16.2Hong Kong SAR, China 3.8 3.6 2.0 3.0 3.7 1.2 4.8 2.7 7.9 7.8 8.4 7.1Colombia 2.4 4.0 0.6 2.4 10.9 4.1 2.1 9.6 5.0 5.2 9.3 9.6Congo, Dem. Rep. –1.1 .. –3.7 .. –20.4 .. 2.6 .. –0.5 6.4 –2.4 15.0Congo, Rep. –1.8 .. .. .. –4.4 .. 10.4 .. 3.0 .. 2.0 ..Costa Rica 5.1 4.2 2.5 2.4 2.0 2.4 5.1 5.6 10.9 5.9 9.2 4.6Côte d’Ivoire 4.1 .. 1.2 .. 0.8 2.9 8.1 4.3 1.9 2.0 8.2 4.1Croatia 1.9 2.9 2.7 2.9 2.6 2.6 7.2 7.1 6.3 3.0 4.9 4.3Cuba 4.0 5.0 3.5 4.8 –2.9 7.6 0.7 8.8 –9.0 12.2 –2.9 10.1Cyprus 6.1 4.3 4.2 2.6 2.3 3.6 –2.6 4.7 6.3 1.4 4.6 3.7Czech Republic 3.0 3.3 3.0 3.1 –0.9 1.9 4.6 2.5 8.7 10.0 12.0 8.8Denmark 2.2 1.9 1.7 1.5 2.4 1.7 5.7 0.4 5.0 3.0 6.0 4.7Dominican Republic 6.1 6.9 4.2 5.3 7.0 4.8 11.7 2.4 8.3 1.2 9.9 2.7Ecuador 2.1 5.2 0.2 3.5 –1.5 4.3 –0.6 8.2 5.3 5.4 2.8 8.5Egypt, Arab Rep. 3.7 4.6 1.9 2.7 4.4 2.7 5.8 7.2 3.5 15.1 3.0 12.9El Salvador 5.3 2.4 4.2 2.0 2.8 1.6 7.1 0.4 13.4 3.0 11.6 2.5Eritrea –5.0 1.6 –6.7 –2.2 22.6 1.2 19.1 –1.0 –2.5 –6.3 7.5 –3.7Estonia 0.6 5.5 2.1 5.8 5.7 2.6 0.5 3.9 11.0 4.8 12.0 4.7Ethiopia 3.6 10.8 0.4 8.2 9.0 1.8 6.5 11.1 7.1 9.7 5.8 16.7Finland 1.8 2.8 1.4 2.4 0.9 1.4 3.2 1.5 10.3 3.7 6.7 4.7France 1.6 1.7 1.2 1.0 1.4 1.6 1.9 1.4 6.8 1.4 5.7 2.7Gabon –0.3 4.2 –3.1 2.2 3.7 2.7 3.0 5.5 2.1 –1.8 0.1 4.0Gambia, The 3.6 .. 0.7 .. –2.2 .. 1.9 .. 0.1 1.6 0.1 1.7Georgia .. .. .. .. .. .. .. .. .. .. .. ..Germany 1.7 0.4 1.3 0.4 2.0 1.1 1.1 0.2 5.8 5.7 5.9 4.7Ghana .. .. .. .. .. .. .. .. .. .. .. ..Greece 2.2 3.3 1.4 2.9 2.1 2.2 4.1 0.0 7.6 2.0 7.4 1.8Guatemala 4.2 3.7 1.9 1.2 5.1 3.9 6.1 –0.1 6.1 2.3 9.2 2.1Guinea 5.2 4.2 1.4 2.4 –0.5 0.9 0.1 1.8 0.3 2.4 –1.1 1.2Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..Haiti .. .. .. .. .. .. 9.0 1.2 10.1 4.1 19.4 3.12462012 World Development Indicators

Growth of consumption and investment 4.9ECONOMYHousehold finalconsumptionexpenditureGeneral governmentfinal consumptionexpenditureGross capitalformationGoods andservicesaverage annual% growth average annual average annualaverage annual% growthTotal Per capita % growth% growthExports Imports1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10Honduras 3.0 4.6 0.6 2.5 2.0 6.0 6.9 2.8 1.6 4.1 3.8 3.8Hungary 0.3 1.9 0.5 2.2 0.3 1.1 8.2 –0.6 9.3 9.5 11.0 7.9India 4.8 7.1 2.8 5.5 6.6 6.2 6.9 12.9 12.3 14.7 14.4 15.9Indonesia 6.6 4.3 5.0 3.1 0.1 8.1 –0.6 6.1 5.9 7.6 5.7 8.2Iran, Islamic Rep. 3.2 7.4 1.5 6.1 1.6 3.6 –0.1 8.3 1.2 5.0 –6.8 13.2Iraq .. .. .. .. .. .. .. .. .. .. .. ..Ireland 5.6 3.1 4.8 1.2 4.1 3.5 9.7 –1.1 15.7 3.9 14.5 3.5Israel 5.0 3.4 2.5 1.5 2.7 1.7 2.0 2.5 10.9 4.9 7.6 2.9Italy 1.6 0.6 1.6 –0.1 –0.3 1.5 1.6 0.1 5.9 1.5 4.5 2.2Jamaica .. .. .. .. .. .. .. .. .. .. .. ..Japan 1.4 0.9 1.1 0.8 2.9 1.7 –0.8 –1.6 4.3 5.1 4.3 2.2Jordan 4.9 7.6 1.1 5.2 4.7 6.7 0.3 5.1 2.6 5.0 1.5 6.3Kazakhstan –7.5 9.2 –6.4 8.2 –7.1 7.1 –19.0 15.2 –1.9 4.7 –12.7 4.3Kenya 3.6 3.9 0.6 1.3 6.9 2.5 6.1 9.1 1.0 6.1 9.4 7.9Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 4.9 2.9 3.9 2.5 4.7 4.8 3.4 2.1 16.0 10.3 10.0 8.1Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 4.5 .. 0.6 .. –2.4 .. 1.0 .. –1.6 .. 0.8 ..Kyrgyz Republic –6.4 8.1 –7.3 7.0 –8.8 1.0 –4.5 9.1 –1.6 5.9 –8.2 11.4Lao PDR .. 5.0 .. 3.4 .. 4.2 .. 16.6 .. 9.4 .. 8.8Latvia –3.9 6.1 –2.7 6.7 1.8 1.1 –3.7 3.7 4.3 6.7 7.6 6.3Lebanon –0.2 3.6 –1.9 2.3 10.9 2.4 –5.8 10.3 18.6 9.6 –1.1 6.8Lesotho 1.8 8.2 –0.1 7.1 8.1 6.3 0.2 1.6 10.3 7.9 2.7 11.3Liberia .. .. .. .. .. .. .. .. .. .. .. ..Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 5.3 6.5 6.1 7.1 1.9 3.6 11.1 5.6 4.9 8.9 7.5 9.5Macedonia, FYR 2.2 4.6 1.7 4.3 –0.4 0.5 3.6 4.8 4.2 2.6 7.5 3.8Madagascar 2.2 2.2 –0.9 –0.9 0.0 5.5 3.3 14.1 3.8 6.7 4.1 9.3Malawi 5.4 .. 3.7 .. –4.4 .. –8.4 .. 4.0 .. –1.1 ..Malaysia 5.3 7.4 2.6 5.3 4.8 7.4 5.3 3.0 12.0 4.8 10.3 5.8Mali 3.0 0.9 0.3 –2.2 3.2 .. 0.4 6.2 9.9 6.3 3.5 3.9Mauritania .. 7.4 .. 4.4 .. 3.1 .. 23.8 –1.3 –2.1 0.6 14.1Mauritius 5.1 5.6 3.9 4.7 3.6 4.0 4.8 4.7 5.6 1.6 5.1 2.0Mexico 3.9 2.8 2.1 1.6 1.8 1.1 4.7 0.4 14.6 4.6 12.3 4.7Moldova 9.9 7.2 10.1 7.5 –12.4 4.9 –15.5 7.5 0.7 8.9 5.6 10.0Mongolia .. .. .. .. .. .. .. .. .. .. .. ..Morocco 1.8 4.7 0.3 3.5 3.9 3.9 2.5 8.3 5.9 6.0 5.1 7.8Mozambique 5.8 5.8 2.6 3.2 3.2 –2.9 8.6 8.1 13.1 14.3 7.6 6.2Myanmar .. .. .. .. .. .. .. .. .. .. .. ..Namibia 4.8 4.9 1.8 3.0 3.3 4.6 7.3 8.5 3.8 2.5 5.4 5.1Nepal .. 4.2 .. .. .. 5.7 .. 10.2 .. –1.6 .. 6.1Netherlands 3.1 0.5 2.5 0.1 2.0 3.3 4.4 0.8 7.3 3.9 7.6 3.7New Zealand 3.2 3.1 2.0 1.8 2.4 3.9 6.1 3.0 5.2 2.4 6.2 4.4Nicaragua 6.1 3.7 3.9 2.4 –1.5 1.6 11.3 4.3 9.3 8.4 12.2 5.2Niger 1.8 .. .. .. 0.8 .. 4.0 .. 3.1 .. –2.1 ..Nigeria .. .. .. .. .. .. .. .. .. .. .. ..Norway 3.6 3.7 3.0 2.9 2.7 2.3 6.0 4.6 5.5 0.2 5.8 4.8Oman 5.4 .. 3.4 .. 2.4 .. 4.0 .. 6.2 .. 5.9 ..Pakistan 4.9 4.7 2.2 2.8 0.7 7.5 1.8 5.0 1.7 6.7 2.5 6.3Panama 6.4 7.2 4.2 5.4 1.7 3.6 10.4 10.6 –0.4 7.8 1.2 7.4Papua New Guinea 2.5 .. –0.1 .. 2.5 .. 1.9 .. 5.1 .. 3.4 ..Paraguay 2.6 3.3 0.3 1.4 2.5 4.2 0.7 3.6 3.1 7.6 2.9 6.7Peru 4.0 5.3 2.2 4.1 5.2 5.7 7.4 10.7 8.5 7.2 9.0 9.2Philippines 3.9 4.5 1.6 2.5 2.6 3.9 2.1 2.8 8.2 5.2 8.5 3.5Poland 5.4 3.7 5.3 3.8 3.2 4.3 10.6 5.8 11.3 8.7 16.7 7.9Portugal 3.0 1.3 2.7 0.9 3.0 1.6 5.9 –2.1 5.7 3.1 7.6 2.5Puerto Rico .. 1.9 .. .. .. 0.3 .. –3.6 1.6 1.1 4.5 1.6Qatar .. .. .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 247

4.9Growth of consumption and investmentHousehold finalconsumptionexpenditureGeneral governmentfinal consumptionexpenditureGross capitalformationGoods andservicesRomania 1.3 6.4 1.6 6.8 0.8 3.8 –5.1 10.6 8.1 9.0 6.0 12.1Russian Federation –0.9 9.3 –0.7 9.7 –2.2 2.0 –19.1 7.6 0.8 6.5 –6.1 14.5Rwanda 0.4 .. .. .. –2.6 .. 0.4 .. –6.4 .. 6.1 ..Saudi Arabia .. 5.3 .. 1.6 .. 7.6 .. 11.4 .. 6.9 .. 16.9Senegal 2.6 4.9 –0.2 2.1 0.9 –0.9 3.5 9.6 4.1 3.9 2.0 7.1Serbia .. 1.0 .. 1.3 .. 5.9 .. 18.1 .. 9.7 .. 8.6Sierra Leone –4.4 .. .. .. 10.4 .. –5.6 .. –11.2 .. –0.2 ..Singapore 5.8 3.9 2.7 1.5 9.3 4.7 6.6 5.4 11.4 9.2 11.4 8.7Slovak Republic 4.9 4.7 4.7 4.6 1.1 3.6 7.4 4.2 8.0 8.8 8.5 7.3Slovenia 4.0 2.9 4.1 2.6 2.8 3.2 10.4 3.4 1.7 6.7 5.2 6.3Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 2.9 4.1 0.6 2.8 0.3 5.6 4.7 7.7 5.8 2.6 7.1 7.0South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 2.4 2.4 2.0 0.9 2.7 4.8 3.2 1.9 10.5 2.7 9.4 3.8Sri Lanka .. .. .. .. 7.6 6.8 6.6 8.2 7.5 3.1 8.6 4.7Sudan 3.7 5.9 1.1 3.4 5.5 8.4 22.0 11.2 11.6 14.3 8.4 12.0Swaziland 7.3 2.2 5.5 1.8 7.1 6.6 –4.7 0.2 6.4 4.2 6.2 4.2Sweden 1.5 2.2 1.2 1.7 0.6 0.9 2.0 3.0 8.6 4.2 6.4 4.0Switzerland 1.1 1.5 0.5 0.6 0.5 1.4 0.7 0.2 4.1 4.5 4.3 3.5Syrian Arab Republic 3.0 .. 0.3 .. 2.0 9.1 3.3 7.9 12.0 1.9 4.4 7.5Tajikistan –11.8 6.0 –13.1 4.9 –15.7 1.6 –17.6 5.8 –5.3 9.2 –6.0 10.3Tanzania a 5.1 6.3 2.0 3.4 –8.8 12.8 –1.1 12.4 11.7 11.8 4.7 15.5Thailand 3.7 3.8 2.7 2.9 5.1 5.4 –4.0 4.5 9.5 5.6 4.5 5.4Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo 5.0 0.5 2.2 –1.9 0.0 1.3 –0.1 5.9 1.2 6.0 1.1 3.1Trinidad and Tobago 0.7 13.3 0.1 12.9 0.3 4.3 12.5 .. 6.9 5.8 9.9 9.5Tunisia 4.2 4.5 2.5 3.5 4.5 5.1 3.1 3.9 5.3 3.4 3.7 3.0Turkey 3.8 5.0 2.2 3.6 4.6 4.1 4.7 6.7 11.1 5.8 10.8 8.2Turkmenistan .. .. .. .. .. .. .. 5.7 –2.4 17.5 7.2 9.4Uganda 6.6 4.4 3.4 1.1 6.4 3.7 8.4 11.0 13.8 19.7 10.2 11.8Ukraine –6.9 11.2 –6.4 12.0 –4.1 2.0 –18.5 1.6 –3.6 0.9 –6.6 4.3United Arab Emirates .. 7.0 .. .. .. 4.7 .. 11.9 .. 12.5 .. 18.7United Kingdom 3.6 1.7 3.3 1.2 1.7 2.2 5.0 1.6 7.8 3.2 8.2 3.1United States 3.8 2.1 2.5 1.1 0.7 2.0 7.6 –0.6 7.3 4.5 9.8 3.0Uruguay 5.0 3.5 4.3 3.4 2.3 1.1 6.1 6.6 6.0 7.8 9.9 7.2Uzbekistan .. .. .. .. .. .. –2.5 4.7 2.5 4.9 –0.4 4.2Venezuela, RB 0.6 7.6 –1.5 5.7 3.7 7.7 11.0 10.7 1.0 –2.9 8.2 12.2Vietnam 5.4 7.8 3.8 6.6 3.2 7.9 19.8 12.0 19.2 11.2 19.5 13.2West Bank and Gaza 5.3 –1.5 1.1 –4.9 12.7 1.3 9.2 –3.0 8.7 –3.1 7.5 –2.3Yemen, Rep. .. .. .. .. 3.8 3.6 10.0 –0.6 22.9 1.8 11.9 5.4Zambia 2.4 0.1 –0.3 –2.2 –8.1 24.9 3.9 6.6 6.7 21.9 15.5 15.6Zimbabwe .. .. .. .. .. .. .. .. 3.9 –9.2 3.1 –3.8World 3.0 w 2.5 w 1.6 w 1.3 w 1.8 w 2.7 w 3.3 w 2.8 w 7.2 w 5.8 w 7.2 w 5.5 wLow income 2.9 5.4 0.4 3.1 –1.0 6.0 5.5 8.4 5.6 9.0 5.2 8.9Middle income 4.0 5.6 2.5 4.4 3.3 5.7 2.6 9.6 7.5 9.9 6.5 10.2Lower middle income 4.1 5.9 2.2 4.2 3.5 5.7 2.8 9.5 6.0 9.1 5.9 9.7Upper middle income 4.0 5.6 2.9 4.8 3.3 5.7 2.6 9.6 7.9 10.1 6.6 10.4Low & middle income 4.0 5.6 2.4 4.3 3.3 5.7 2.6 9.6 7.5 9.9 6.4 10.2East Asia & Pacifi c 7.4 6.9 6.1 6.0 7.9 8.7 7.7 12.0 11.8 13.9 11.0 12.1Europe & Central Asia 0.5 7.2 0.3 7.0 –0.8 3.2 –11.2 8.0 1.8 6.5 –2.3 10.6Latin America & Carib. 3.6 4.1 1.9 2.8 1.9 3.5 5.4 5.2 8.1 4.8 10.5 6.8Middle East & N. Africa 2.8 4.9 0.7 3.1 3.6 3.7 1.2 7.6 4.1 6.9 0.0 9.6South Asia 4.6 6.6 2.5 4.9 5.8 6.5 6.5 11.9 10.0 13.0 11.2 13.7Sub-Saharan Africa 3.3 4.6 0.6 2.1 0.3 5.5 4.6 7.8 .. .. 5.7 8.8High income 2.9 1.8 2.1 1.1 1.5 2.1 3.5 0.4 7.1 4.6 7.4 4.1Euro area 1.9 1.2 1.6 0.7 1.5 1.9 2.2 0.8 6.8 3.7 6.3 3.6a. Covers mainland Tanzania only.average annual% growth average annual average annualaverage annual% growthTotal Per capita % growth% growthExports Imports1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–102482012 World Development Indicators

Growth of consumption and investment4.9ECONOMYAbout the dataDefinitionsMeasures of growth in consumption and capital formationare subject to two kinds of inaccuracy. Thefi rst stems from the diffi culty of measuring expendituresat current price levels, as described in Aboutthe data for table 4.8. The second arises in defl atingcurrent price data to measure volume growth,where results depend on the relevance and reliabilityof the price indexes and weights used. Measuringprice changes is more diffi cult for investmentgoods than for consumption goods because of theone-time nature of many investments and becausethe rate of technological progress in capital goodsmakes capturing change in quality diffi cult. (Anexample is computers—prices have fallen as qualityhas improved.) Several countries estimate capitalformation from the supply side, identifying capitalgoods entering an economy directly from detailedproduction and international trade statistics. Thismeans that the price indexes used in deflating productionand international trade, refl ecting deliveredor offered prices, will determine the defl ator for capitalformation expenditures on the demand side.Growth rates of household fi nal consumptionexpenditure, household fi nal consumption expenditureper capita, general government fi nal consumptionexpenditure, gross capital formation, andexports and imports of goods and services are estimatedusing constant price data. (Consumption, capitalformation, and exports and imports of goods andservices as shares of GDP are shown in table 4.8.)To obtain government consumption in constantprices, countries may defl ate current values byapplying a wage (price) index or extrapolate fromthe change in government employment. Neithertechnique captures improvements in productivityor changes in the quality of government services.Defl ators for household consumption are usually calculatedon the basis of the consumer price index.Many countries estimate household consumptionas a residual that includes statistical discrepanciesassociated with the estimation of other expenditureitems, including changes in inventories; thus theseestimates lack detailed breakdowns of householdconsumption expenditures.• Household final consumption expenditure is themarket value of all goods and services, includingdurable products (such as cars and computers),purchased by households. It excludes purchasesof dwellings but includes imputed rent for owneroccupieddwellings. It also includes government feesfor permits and licenses. Expenditures of nonprofitinstitutions serving households are included, evenwhen reported separately. Household consumptionexpenditure may include any statistical discrepancyin the use of resources relative to the supply ofresources. • Household final consumption expenditureper capita is household fi nal consumptionexpenditure divided by midyear population. • Generalgovernment final consumption expenditure isall government current expenditures for goods andservices (including compensation of employees). Italso includes most expenditures on national defenseand security but excludes military expenditures withpotentially wider public use that are part of governmentcapital formation. • Gross capital formation isoutlays on additions to fi xed assets of the economy,net changes in inventories, and net acquisitionsof valuables. Fixed assets include land improvements(fences, ditches, drains); plant, machinery,and equipment purchases; and construction (roads,railways, schools, buildings, and so on). Inventoriesare goods held to meet temporary or unexpectedfl uctuations in production or sales, and “work in progress.”• Exports and imports of goods and servicesare the value of all goods and other market servicesprovided to or received from the rest of the world.They include the value of merchandise, freight, insurance,transport, travel, royalties, license fees, andother services (communication, construction, fi nancial,information, business, personal, governmentservices, and so on). They exclude compensation ofemployees and investment income (factor services inthe 1968 System of National Accounts) and transferpayments.Data sourcesData on national accounts indicators for mostdeveloping countries are collected from nationalstatistical organizations and central banks by visitingand resident World Bank missions. Data forhigh-income economies are from Organisation forEconomic Co-operation and Development (OECD)data fi les.2012 World Development Indicators 249

4.10Toward a broader measureof national incomeGrossdomesticproductGrossnationalincomeAdjustmentsAdjustednet nationalincomeGrossdomesticproductGrossnationalincomeAdjustednet nationalincome% of GNIConsumption of Natural resource$ billions $ billions fi xed capital depletion $ billions % growth % growth % growth2010 2010 2010 2010 2010 2000–10 2000–10 2000–10Afghanistan 17.2 15.2 8.9 2.6 13.4 .. .. ..Albania 11.8 11.7 10.6 2.5 10.1 5.4 5.8 6.6Algeria 162.0 155.5 11.3 18.1 109.8 3.9 4.4 5.0Angola 84.4 75.5 12.1 35.1 39.9 12.9 .. ..Argentina 368.7 358.6 12.1 4.9 297.6 5.6 5.3 5.8Armenia 9.4 9.7 9.9 1.0 8.6 9.2 9.2 9.6Australia 1,131.6 1,094.5 14.6 6.5 863.2 3.2 3.3 4.0Austria 379.1 377.1 14.1 0.2 323.4 1.8 1.7 1.8Azerbaijan 51.8 48.3 12.0 34.5 25.9 17.1 18.1 21.6Bahrain 20.6 21.0 6.7 30.0 13.3 6.6 .. ..Bangladesh 100.4 109.7 7.5 2.3 99.0 5.9 5.3 5.9Belarus 54.7 53.4 11.4 1.0 46.7 8.0 8.3 9.9Belgium 469.4 477.6 13.7 0.0 412.3 1.6 1.7 1.2Benin 6.6 6.6 8.4 0.3 6.1 4.0 3.9 3.6Bolivia 19.6 18.8 10.2 12.3 14.6 4.1 4.2 3.2Bosnia and Herzegovina 16.6 17.0 10.5 .. .. 4.6 5.3 ..Botswana 14.9 14.8 11.6 3.4 12.6 4.1 3.5 3.5Brazil 2,087.9 2,049.2 12.2 3.3 1,729.7 3.7 3.6 3.8Bulgaria 47.7 46.0 14.3 2.0 38.5 4.8 5.4 4.4Burkina Faso 8.8 8.8 7.9 4.3 7.7 5.5 6.0 5.2Burundi 1.6 1.6 6.6 12.7 1.3 3.2 .. ..Cambodia 11.2 10.7 8.9 0.1 9.7 8.7 8.9 9.5Cameroon 22.4 22.0 9.1 4.8 19.0 3.2 2.7 4.4Canada 1,577.0 1,549.7 14.3 2.3 1,292.3 2.0 1.8 2.6Central African Republic 2.0 2.0 7.7 0.0 1.9 1.0 –0.9 –1.2Chad 7.6 6.7 9.3 29.0 4.2 9.0 20.2 –2.5Chile 212.7 197.3 13.2 12.4 146.8 4.0 4.7 4.9China 5,926.6 5,957.0 10.8 5.1 5,013.1 10.8 10.6 9.6Hong Kong SAR, China 224.5 229.2 13.2 0.0 198.9 4.6 4.4 3.8Colombia 288.2 276.1 11.8 7.7 222.2 4.5 4.7 4.5Congo, Dem. Rep. 13.1 12.3 7.0 13.7 9.7 5.3 5.6 7.4Congo, Rep. 11.9 8.6 14.1 59.6 2.3 4.3 .. ..Costa Rica 35.8 34.9 11.9 0.1 30.7 4.9 4.5 4.0Côte d’Ivoire 22.8 21.7 9.4 3.9 18.8 1.1 0.9 1.1Croatia 60.9 58.8 12.8 0.9 50.8 3.2 3.4 4.3Cuba 62.7 61.8 11.3 3.2 52.8 6.7 6.6 6.7Cyprus 23.1 22.5 13.3 0.0 19.5 3.1 3.0 3.3Czech Republic 192.0 179.4 13.6 0.5 154.0 3.8 4.1 4.2Denmark 312.0 319.3 14.0 1.7 269.3 0.9 0.4 1.7Dominican Republic 51.8 50.0 11.4 0.2 44.2 5.6 5.5 5.4Ecuador 58.0 56.9 10.9 12.9 43.4 4.8 4.3 5.7Egypt, Arab Rep. 218.9 214.5 10.3 7.1 177.2 5.1 5.2 3.2El Salvador 21.2 20.8 10.6 0.4 18.5 2.2 2.2 1.7Eritrea 2.1 2.1 7.6 0.0 1.9 0.2 1.4 4.1Estonia 19.2 18.4 13.0 1.6 15.7 4.6 4.6 5.3Ethiopia 29.7 29.6 7.4 4.2 26.2 8.8 8.7 10.8Finland 238.0 242.0 16.0 0.1 203.1 2.1 1.9 1.6France 2,560.0 2,606.8 13.6 0.0 2,253.0 1.3 1.2 1.1Gabon 13.0 11.5 13.3 33.1 6.1 2.2 2.3 4.3Gambia, The 0.8 0.7 8.4 0.8 0.7 3.7 3.5 2.3Georgia 11.7 11.5 9.7 0.6 10.3 6.9 .. ..Germany 3,280.5 3,341.4 13.6 0.1 2,883.3 1.0 0.6 1.5Ghana 31.3 30.8 9.3 8.0 25.5 5.9 .. ..Greece 301.1 292.9 13.6 0.3 252.0 2.6 3.1 2.0Guatemala 41.2 40.0 10.5 1.7 35.1 3.6 3.8 3.1Guinea 4.5 4.2 8.2 14.3 3.3 2.9 3.7 0.3Guinea-Bissau 0.9 0.9 8.0 0.5 0.8 1.5 .. ..Haiti 6.7 6.5 8.2 0.6 5.9 0.6 .. ..2502012 World Development Indicators

Toward a broader measureof national income 4.10ECONOMYGrossdomesticproductGrossnationalincomeAdjustmentsAdjustednet nationalincomeGrossdomesticproductGrossnationalincomeAdjustednet nationalincome% of GNIConsumption of Natural resource$ billions $ billions fi xed capital depletion $ billions % growth % growth % growth2010 2010 2010 2010 2010 2000–10 2000–10 2000–10Honduras 15.4 14.8 10.1 0.5 13.2 4.6 4.6 2.5Hungary 128.6 122.4 12.9 0.5 106.0 2.2 2.2 2.4India 1,727.1 1,712.6 9.3 4.3 1,478.5 8.0 7.9 7.7Indonesia 706.6 686.6 10.5 6.6 568.9 5.3 5.1 4.7Iran, Islamic Rep. 331.0 328.6 10.9 19.9 227.5 5.4 6.2 6.7Iraq 82.2 77.8 10.6 45.7 34.0 0.4 .. ..Ireland 206.6 171.3 16.9 0.2 142.0 2.8 2.8 1.8Israel 217.3 210.4 13.8 0.2 180.9 3.6 2.8 3.6Italy 2,061.0 2,051.4 13.7 0.1 1,768.9 0.5 0.5 0.3Jamaica 14.3 13.6 11.6 0.6 11.9 1.2 .. ..Japan 5,458.8 5,601.6 13.6 0.0 4,841.4 0.9 0.7 1.2Jordan 27.6 27.8 10.8 1.0 24.6 6.7 6.5 6.9Kazakhstan 149.1 131.9 13.3 23.4 83.4 8.3 9.3 9.2Kenya 31.4 31.3 7.3 1.1 28.6 4.3 4.3 4.8Korea, Dem. Rep. .. .. .. .. .. .. .. ..Korea, Rep. 1,014.5 1,014.8 12.9 0.0 883.5 4.1 4.0 3.3Kosovo 5.6 5.7 10.0 .. .. 5.3 .. ..Kuwait 109.5 117.2 7.2 25.1 79.3 8.4 .. ..Kyrgyz Republic 4.6 4.3 9.2 6.9 3.6 4.4 4.4 2.7Lao PDR 7.3 7.0 9.4 8.3 5.7 7.2 7.1 6.2Latvia 24.0 24.1 18.0 0.5 19.6 4.8 4.6 6.0Lebanon 39.0 39.1 11.8 0.0 34.4 4.9 4.4 5.0Lesotho 2.1 2.6 7.3 1.0 2.3 3.5 13.4 12.7Liberia 1.0 0.8 8.4 6.4 0.7 0.9 .. ..Libya 62.4 62.0 12.0 29.0 36.6 5.4 .. ..Lithuania 36.3 35.7 12.3 0.6 31.1 5.3 5.2 6.5Macedonia, FYR 9.2 9.0 11.1 5.9 7.5 3.3 3.4 2.6Madagascar 8.7 8.6 7.7 1.0 7.9 3.4 3.2 2.3Malawi 5.1 5.0 7.5 1.8 4.5 5.2 .. ..Malaysia 237.8 229.6 12.1 6.9 185.9 5.0 4.5 7.0Mali 9.3 8.9 8.4 9.8 7.3 5.2 5.9 4.9Mauritania 3.6 3.7 8.8 34.3 2.1 4.4 6.3 6.9Mauritius 9.7 9.8 11.5 0.0 8.7 3.9 3.6 2.0Mexico 1,034.8 1,020.3 12.0 5.7 839.9 2.1 2.0 1.7Moldova 5.8 6.3 8.7 0.2 5.7 5.2 4.9 5.6Mongolia 6.2 5.6 10.9 32.3 3.2 7.2 .. ..Morocco 90.8 88.6 10.5 1.6 77.9 4.9 4.8 4.3Mozambique 9.6 9.4 7.7 3.3 8.4 7.8 7.4 6.5Myanmar .. .. .. .. .. .. .. ..Namibia 12.2 12.1 11.1 0.7 10.7 5.0 5.4 ..Nepal 15.7 15.8 7.8 2.5 14.2 3.8 .. ..Netherlands 779.4 772.7 14.2 0.8 656.9 1.6 2.0 1.2New Zealand 126.7 121.4 14.0 1.0 103.3 2.6 2.7 2.6Nicaragua 6.6 6.3 9.3 1.6 5.6 3.6 3.3 2.8Niger 5.5 5.5 3.2 2.4 5.2 4.2 .. ..Nigeria 193.7 176.8 9.9 22.0 120.4 6.7 .. ..Norway 417.5 427.2 14.5 10.2 321.5 1.7 1.6 3.6Oman 46.9 44.1 13.5 28.5 25.6 4.7 .. ..Pakistan 176.9 183.6 8.5 2.8 162.9 5.1 4.7 4.3Panama 26.7 25.0 12.3 0.0 22.0 6.8 7.1 6.1Papua New Guinea 9.5 9.3 9.4 22.2 6.3 3.8 .. ..Paraguay 18.3 18.0 10.4 0.0 16.1 3.8 4.0 3.9Peru 157.1 147.0 11.8 8.1 117.7 6.1 6.7 5.3Philippines 199.6 199.9 9.8 2.1 176.2 4.9 4.9 4.1Poland 469.4 452.3 12.7 1.4 388.7 4.3 4.7 4.3Portugal 228.6 221.1 18.4 0.1 180.1 0.7 0.9 0.3Puerto Rico 96.3 63.3 20.0 .. .. 0.0 0.3 ..Qatar 98.3 .. .. .. .. 14.2 .. ..2012 World Development Indicators 251

4.10Toward a broader measureof national incomeGrossdomesticproductGrossnationalincomeAdjustmentsAdjustednet nationalincomeGrossdomesticproductGrossnationalincomeAdjustednet nationalincome% of GNIConsumption of Natural resource$ billions $ billions fi xed capital depletion $ billions % growth % growth % growth2010 2010 2010 2010 2010 2000–10 2000–10 2000–10Romania 161.6 159.0 11.7 1.6 137.8 5.0 5.4 6.5Russian Federation 1,479.8 1,431.1 12.4 14.3 1,049.8 5.4 5.6 8.4Rwanda 5.6 5.6 8.0 3.1 5.0 7.6 .. ..Saudi Arabia 434.7 381.3 12.1 29.1 224.0 3.6 3.4 6.0Senegal 13.0 12.9 8.9 0.8 11.6 4.2 4.1 4.3Serbia 38.4 37.5 11.3 .. .. 4.1 4.3 ..Sierra Leone 1.9 1.9 7.2 2.1 1.7 8.8 .. ..Singapore 208.8 201.1 14.4 0.0 172.1 6.0 6.1 5.4Slovak Republic 87.3 86.1 12.7 0.4 74.8 5.4 5.5 5.6Slovenia 46.9 46.2 13.3 0.3 40.0 3.3 3.5 3.3Somalia .. .. .. .. .. .. .. ..South Africa 363.7 356.5 13.5 6.0 286.8 3.9 3.9 3.8South Sudan .. .. .. .. .. .. .. ..Spain 1,407.4 1,388.7 13.6 0.0 1,198.9 2.4 2.5 2.2Sri Lanka 49.6 48.9 10.1 0.3 43.8 5.6 5.6 5.5Sudan 62.0 55.9 10.3 12.9 43.0 6.7 6.7 5.6Swaziland 3.6 3.6 10.6 0.1 3.2 2.4 3.2 2.1Sweden 458.6 466.9 13.2 0.4 403.3 2.2 1.9 2.3Switzerland 527.9 568.6 13.5 0.0 491.9 1.9 2.3 1.8Syrian Arab Republic 59.1 57.3 10.6 11.9 44.4 5.0 4.8 7.4Tajikistan 5.6 5.6 8.6 0.8 5.0 8.6 8.1 6.2Tanzania a 23.1 23.0 7.9 3.2 20.5 7.1 6.9 6.4Thailand 318.5 304.8 11.4 2.4 263.0 4.5 4.6 4.5Timor-Leste 0.7 2.7 2.1 .. .. 3.4 .. ..Togo 3.2 2.8 8.8 3.4 2.5 2.7 .. 1.1Trinidad and Tobago 20.6 19.3 13.4 32.0 10.5 6.5 8.3 5.4Tunisia 44.3 42.0 11.3 5.1 35.1 4.7 4.9 4.1Turkey 734.4 727.1 12.1 0.4 636.8 4.7 4.6 3.9Turkmenistan 20.0 18.1 11.8 .. .. 13.6 16.1 ..Uganda 17.0 16.7 8.0 4.5 14.6 7.7 7.7 7.6Ukraine 137.9 135.9 10.4 3.7 116.7 4.8 4.7 6.8United Arab Emirates 297.6 273.5 13.6 .. .. 5.1 .. ..United Kingdom 2,261.7 2,271.6 13.6 1.3 1,931.7 1.8 1.7 1.7United States 14,586.7 14,635.6 14.0 0.9 12,453.3 1.8 1.9 1.2Uruguay 39.1 37.7 12.6 0.6 32.7 3.6 3.9 3.1Uzbekistan 39.0 39.0 9.2 19.2 27.9 7.1 5.0 –8.7Venezuela, RB 391.8 389.0 12.4 12.4 292.4 4.7 4.4 8.1Vietnam 106.4 102.0 9.4 9.4 82.8 7.5 7.8 7.3West Bank and Gaza .. .. .. .. .. –0.9 0.2 ..Yemen, Rep. 31.3 29.5 9.7 14.5 22.3 4.1 3.8 5.6Zambia 16.2 14.3 10.3 18.9 10.1 5.6 7.7 5.1Zimbabwe 7.5 7.0 8.6 2.7 6.2 –6.0 –6.0 –7.1World 63,242.1 w 63,087.6 w 13.0 w 2.6 w 53,010.4 w 2.7 w 2.6 w 2.5 wLow income 416.5 422.4 7.7 3.8 373.6 5.5 5.6 5.7Middle income 19,632.1 19,525.6 11.2 6.5 16,053.8 6.4 6.4 6.2Lower middle income 4,312.3 4,370.0 9.8 7.4 3,663.4 6.3 6.3 5.9Upper middle income 15,317.0 15,152.4 11.6 6.2 12,388.6 6.5 6.4 6.3Low & middle income 20,071.7 19,971.7 11.1 6.4 16,442.8 6.4 6.4 6.2East Asia & Pacifi c 7,630.5 7,614.8 10.7 5.2 6,401.9 9.4 9.3 8.5Europe & Central Asia 3,059.0 2,965.9 12.2 9.4 2,320.4 5.4 5.4 6.2Latin America & Carib. 4,980.8 4,867.8 12.1 5.5 4,016.0 3.8 3.7 3.9Middle East & N. Africa 1,207.0 1,334.2 10.7 12.8 1,023.9 4.7 4.9 4.9South Asia 2,090.4 2,089.3 9.2 4.0 1,814.7 7.4 7.3 7.2Sub-Saharan Africa 1,097.9 1,044.6 10.9 11.8 807.6 5.0 4.4 4.2High income 43,240.0 43,247.5 13.9 0.9 36,615.4 1.8 1.7 1.5Euro area 12,149.1 12,161.9 13.8 0.1 10,459.5 1.3 1.3 1.3a. Covers mainland Tanzania only.2522012 World Development Indicators

Toward a broader measureof national income4.10ECONOMYAbout the dataDefinitionsAn economy’s growth is typically measured by thechange in the volume of its output, as shown intable 4.1. But gross domestic product (GDP), thoughwidely tracked, may not always be the most relevantsummary of aggregated economic performance forall economies, especially when production occurs atthe expense of consuming capital stock. For countrieswith signifi cant exhaustible natural resourcesand important foreign-investor presence, adjustednet national income complements GDP in assessingeconomic progress (Hamilton and Ley 2010).The table presents three measures of economicprogress: GDP, gross national income (GNI), andadjusted net national income. GDP accounts forall domestic production, regardless of whether theincome accrues to domestic or foreign institutions.GNI accounts for the operation of foreign investors,who may be repatriating some of the incomeproduced domestically. GNI comprises GDP plusnet receipts of primary income from nonresidentsources. Adjusted net national income goes a stepfurther by subtracting from GNI a charge for the consumptionof fi xed capital (a calculation that yieldsnet national income) and for the depletion of naturalresources. The deduction for the depletion of naturalresources, which covers net forest depletion, energydepletion, and mineral depletion, refl ects the declinein asset values associated with the extraction andharvest of natural resources. For more discussionof the estimates and methodology of produced capitalconsumption and natural capital depletion, seeAbout the data for table 4.11.The United Nations System of National Accountsincludes nonproduced natural assets (such as land,mineral resources, and forests) within the assetboundary when they are under the effective controlof institutional units. The calculation of adjustednet national income, which accounts for net forest,energy, and mineral depletion, thus remains withinthe System of National Accounts boundaries. Thispoint is critical because it allows for comparisonsacross GDP, GNI, and adjusted net national income;such comparisons reveal the impact of naturalresource depletion, which is otherwise ignored bythe popular economic indicators.Adjusted net national income is particularly usefulin monitoring low-income, resource-rich economies,like many countries in Sub-Saharan Africa, becausesuch economies often see large natural resourcesdepletion as well as substantial exports of resourcerents to foreign mining companies. For recent yearsadjusted net national income gives a picture ofeconomic growth that is strikingly different from theone provided by GDP.The key to increasing future consumption andthus the standard of living lies in increasing nationalwealth—including not only the traditional measuresof capital (such as produced and human capital),but also natural capital. Natural capital comprisessuch assets as land, forests, and subsoil resources.All three types of capital are key to sustaining economicgrowth. By accounting for the consumptionof fi xed and natural capital depletion, adjusted netnational income better measures the income availablefor consumption or for investment to increasea country’s future consumption. For a measure ofhow comprehensive wealth is changing over time,see table 4.11.Methods of computing growth are described in Statisticalmethods. For a detailed note on methodology,see http://data.worldbank.org.• Gross domestic product is the sum of value addedby all resident producers plus any product taxes (lesssubsidies) not included in the valuation of output.• Gross national income is GDP plus net receiptsof primary income (compensation of employeesand property income) from abroad. • Consumptionof fixed capital is the replacement value of capitalused up in production. • Natural resource depletionis the sum of net forest depletion, energy depletion,and mineral depletion. Net forest depletion isunit resource rents times the excess of roundwoodharvest over natural growth. Energy depletion is theratio of the value of the stock of energy resources tothe remaining reserve lifetime (capped at 25 years).It covers coal, crude oil, and natural gas. Mineraldepletion is the ratio of the value of the stock ofmineral resources to the remaining reserve lifetime(capped at 25 years). It covers tin, gold, lead, zinc,iron, copper, nickel, silver, bauxite, and phosphate.• Adjusted net national income is GNI minus consumptionof fi xed capital and natural resourcesdepletion.Data sourcesGNI and GDP are estimated by World Bank staffbased on national accounts data collected byWorld Bank staff during economic missions orreported by national statistical offi ces to otherinternational organizations such as the Organisationfor Economic Co-operation and Development.Data on consumption of fi xed capital are fromthe United Nations Statistics Division’s NationalAccounts Statistics: Main Aggregates and DetailedTables, extrapolated to 2010. Data on energy, mineral,and forest depletion are estimates basedon sources and methods in World Bank (2011a).2012 World Development Indicators 253

4.11Toward a broader measure of savingsGrosssavingsConsumptionof fixedcapitalEducationexpenditureNetforestdepletionEnergydepletionMineraldepletionCarbondioxidedamageLocalpollutionAdjustednetsavings% ofGNI% ofGNI% ofGNI% ofGNI% ofGNI2010 2010 2010 2010 2010 2010 2010 2010 2010Afghanistan .. 8.9 .. 2.6 0.0 0.0 0.1 0.8 ..Albania 13.6 10.6 2.8 0.0 2.2 0.3 0.3 0.2 2.7Algeria 53.6 11.3 4.5 0.1 17.8 0.2 0.6 0.3 29.8Angola 17.9 12.1 2.3 0.0 35.1 0.0 0.3 1.8 –29.2Argentina 23.1 12.1 6.0 0.0 4.5 0.4 0.4 1.6 10.1Armenia 18.1 9.9 2.2 0.0 0.0 1.0 0.4 2.2 6.8Australia 25.1 14.6 4.5 0.0 2.2 4.3 0.3 0.0 8.2Austria 24.9 14.1 5.3 0.0 0.1 0.0 0.1 0.2 15.6Azerbaijan 49.5 12.0 3.4 0.0 34.5 0.0 0.9 0.4 5.1Bahrain 45.4 6.7 3.0 0.0 30.0 0.0 0.8 0.3 10.6Bangladesh 35.2 7.5 1.8 0.4 1.8 0.0 0.4 0.6 26.2Belarus 25.7 11.4 4.4 0.0 1.0 0.0 1.0 0.0 16.6Belgium 22.5 13.7 6.1 0.0 0.0 0.0 0.2 0.1 14.6Benin 12.8 8.4 4.3 0.3 0.0 0.0 0.5 0.5 7.4Bolivia 26.1 10.2 5.2 0.0 9.4 2.9 0.5 1.1 7.3Bosnia and Herzegovina 14.5 10.5 .. .. 1.4 1.4 1.3 0.1 ..Botswana 26.8 11.6 7.6 0.0 0.2 3.2 0.2 0.3 18.6Brazil 16.8 12.2 5.2 0.0 1.6 1.7 0.2 0.2 6.1Bulgaria 24.6 14.3 4.1 0.0 0.7 1.3 0.8 1.2 10.4Burkina Faso .. 7.9 4.3 1.5 0.0 2.8 0.2 0.8 ..Burundi .. 6.6 8.7 11.8 0.0 0.9 0.1 0.1 ..Cambodia 13.2 8.9 1.6 0.1 0.0 0.0 0.3 0.4 5.1Cameroon .. 9.1 3.1 0.0 4.6 0.1 0.2 0.7 ..Canada 18.8 14.3 4.5 0.0 1.8 0.6 0.3 0.1 6.3Central African Republic .. 7.7 1.1 0.0 0.0 0.0 0.1 0.2 ..Chad .. 9.3 2.3 0.0 29.0 0.0 0.0 1.4 ..Chile 24.9 13.2 4.6 0.0 0.1 12.3 0.3 0.6 3.0China 52.7 10.8 1.8 0.0 3.7 1.4 1.1 1.2 36.3Hong Kong SAR, China 29.3 13.2 3.1 0.0 0.0 0.0 0.1 .. ..Colombia 19.8 11.8 3.9 0.0 7.2 0.5 0.2 0.1 3.8Congo, Dem. Rep. .. 7.0 0.9 0.0 2.7 11.0 0.2 0.7 ..Congo, Rep. .. 14.1 2.5 0.0 59.6 0.0 0.2 0.9 ..Costa Rica 15.7 11.9 6.2 0.1 0.0 0.0 0.2 0.1 9.7Côte d’Ivoire 15.4 9.4 4.3 0.0 3.4 0.5 0.3 0.3 5.3Croatia 22.6 12.8 4.3 0.2 0.7 0.0 0.3 0.3 12.7Cuba .. 11.3 13.4 0.0 2.3 0.9 0.4 0.0 ..Cyprus 9.5 13.3 6.9 0.0 0.0 0.0 0.3 0.3 2.5Czech Republic 22.5 13.6 3.8 0.0 0.5 0.0 0.5 0.0 11.6Denmark 22.4 14.0 7.3 0.0 1.7 0.0 0.1 0.0 13.9Dominican Republic 7.5 11.4 1.9 0.0 0.0 0.2 0.4 0.0 –2.6Ecuador 23.1 10.9 1.4 0.0 12.9 0.0 0.4 0.1 0.2Egypt, Arab Rep. 18.2 10.3 4.4 0.1 6.8 0.2 0.8 0.7 3.6El Salvador 11.2 10.6 3.0 0.4 0.0 0.0 0.2 0.2 2.7Eritrea .. 7.6 1.7 0.0 0.0 0.0 0.2 0.4 ..Estonia 25.3 13.0 5.5 0.3 1.3 0.0 0.7 0.0 15.6Ethiopia 16.7 7.4 2.9 4.0 0.0 0.2 0.2 0.2 7.5Finland 20.3 16.0 5.7 0.0 0.0 0.1 0.2 0.0 9.7France 17.2 13.6 5.1 0.0 0.0 0.0 0.1 0.0 8.5Gabon .. 13.3 3.1 0.0 33.0 0.0 0.1 0.0 ..Gambia, The 13.7 8.4 3.1 0.8 0.0 0.0 0.4 0.5 6.7Georgia 10.0 9.7 2.8 0.0 0.2 0.4 0.4 1.5 0.7Germany 22.7 13.6 4.4 0.0 0.1 0.0 0.2 0.0 13.2Ghana 20.8 9.3 4.7 1.5 0.0 6.5 0.3 0.0 8.6Greece 4.8 13.6 3.2 0.0 0.2 0.1 0.2 0.5 –6.7Guatemala 13.3 10.5 2.9 0.7 0.5 0.6 0.3 0.2 3.4Guinea 10.5 8.2 2.3 2.3 0.0 12.0 0.3 0.6 –10.6Guinea-Bissau .. 8.0 2.3 0.5 0.0 0.0 0.3 0.8 ..Haiti 23.2 8.2 1.5 0.6 0.0 0.0 0.3 0.5 15.1% ofGNI% ofGNI% ofGNI% ofGNI2542012 World Development Indicators

Toward a broader measure of savings4.11ECONOMYGrosssavingsConsumptionof fixedcapitalEducationexpenditureNetforestdepletionEnergydepletionMineraldepletionCarbondioxidedamageLocalpollutionAdjustednetsavings% ofGNI% ofGNI% ofGNI% ofGNI% ofGNI2010 2010 2010 2010 2010 2010 2010 2010 2010Honduras 16.9 10.1 3.5 0.0 0.0 0.5 0.4 0.2 9.2Hungary 21.5 12.9 5.2 0.0 0.5 0.0 0.3 0.0 12.9India 34.0 9.3 3.1 0.5 2.5 1.3 0.9 0.7 21.8Indonesia 32.9 10.5 4.3 0.0 5.3 1.3 0.5 0.8 18.8Iran, Islamic Rep. .. 10.9 4.1 0.0 19.2 0.6 1.2 0.8 ..Iraq .. 10.6 .. 0.0 45.7 0.0 1.1 4.0 ..Ireland 14.8 16.9 5.9 0.0 0.0 0.1 0.2 0.0 3.5Israel 18.9 13.8 5.7 0.0 0.1 0.1 0.2 0.1 10.3Italy 17.1 13.7 4.4 0.0 0.1 0.0 0.2 0.1 7.5Jamaica 12.9 11.6 5.8 0.0 0.0 0.6 0.6 0.2 5.6Japan 23.2 13.6 3.2 0.0 0.0 0.0 0.2 0.3 12.3Jordan 9.1 10.8 5.6 0.0 0.1 0.9 0.7 0.2 2.0Kazakhstan 31.6 13.3 4.4 0.0 21.6 1.8 1.4 0.2 –2.3Kenya 15.6 7.3 5.9 1.1 0.0 0.1 0.3 0.1 13.1Korea, Dem. Rep. .. .. .. .. .. .. .. 0.8 ..Korea, Rep. 31.6 12.9 4.3 0.0 0.0 0.0 0.4 0.5 22.1Kosovo .. 10.0 .. .. 0.0 0.9 .. .. ..Kuwait 54.9 7.2 3.2 0.0 25.1 0.0 0.5 0.5 14.9Kyrgyz Republic 21.3 9.2 6.0 0.0 0.7 6.2 1.1 0.5 9.5Lao PDR 20.5 9.4 1.1 0.0 0.0 8.3 0.2 0.8 2.9Latvia 23.7 18.0 5.6 0.5 0.0 0.0 0.2 0.0 10.5Lebanon 12.0 11.8 1.6 0.0 0.0 0.0 0.4 0.2 1.2Lesotho 27.4 7.3 9.8 1.0 0.0 0.0 .. 0.1 ..Liberia –2.7 8.4 3.1 4.7 0.0 1.7 0.8 0.4 –14.0Libya 66.8 12.0 .. 0.0 29.0 0.0 0.7 1.8 ..Lithuania 18.8 12.3 4.7 0.5 0.1 0.0 0.3 0.1 10.4Macedonia, FYR 24.8 11.1 4.9 0.1 1.0 4.8 1.0 0.1 11.6Madagascar .. 7.7 2.7 0.2 0.0 0.8 0.2 0.1 ..Malawi 13.0 7.5 4.4 1.8 0.0 0.0 0.2 0.1 7.5Malaysia 34.1 12.1 4.1 0.0 6.9 0.1 0.7 0.0 18.5Mali .. 8.4 3.9 0.0 0.0 9.8 0.1 1.7 ..Mauritania .. 8.8 3.7 0.4 0.0 33.9 0.4 0.6 ..Mauritius 16.1 11.5 3.1 0.0 0.0 0.0 0.3 0.0 7.6Mexico 24.6 12.0 4.8 0.0 5.4 0.3 0.3 0.4 11.0Moldova 14.7 8.7 7.7 0.1 0.1 0.0 0.6 0.9 11.9Mongolia 29.8 10.9 5.1 0.0 17.5 14.8 1.6 2.8 –12.6Morocco 31.5 10.5 5.2 0.0 0.0 1.6 0.4 0.1 24.1Mozambique 11.0 7.7 4.0 0.0 3.2 0.1 0.2 0.1 3.7Myanmar .. .. 0.8 .. .. .. .. 0.5 ..Namibia 34.2 11.1 8.0 0.0 0.0 0.7 0.2 0.1 30.1Nepal 36.8 7.8 4.2 2.5 0.0 0.0 0.2 0.1 29.2Netherlands 23.6 14.2 4.7 0.0 0.8 0.0 0.2 0.4 12.8New Zealand 17.1 14.0 7.2 0.0 0.7 0.3 0.2 0.0 8.5Nicaragua 13.4 9.3 3.0 0.7 0.0 0.9 0.6 0.1 4.9Niger .. 3.2 3.5 1.5 0.0 0.9 0.1 1.4 ..Nigeria .. 9.9 0.9 0.3 21.7 0.0 0.5 0.7 ..Norway 35.2 14.5 6.0 0.0 10.2 0.0 0.1 0.0 16.4Oman 39.7 13.5 4.2 0.0 28.5 0.0 0.8 0.0 –8.5Pakistan 21.0 8.5 1.6 0.7 2.1 0.1 0.7 1.1 9.4Panama 19.5 12.3 3.5 0.0 0.0 0.0 0.2 0.2 10.3Papua New Guinea 20.8 9.4 .. 0.0 0.0 22.2 0.4 0.0 ..Paraguay 23.0 10.4 3.7 0.0 0.0 0.0 0.2 1.1 15.1Peru 24.4 11.8 2.1 0.0 1.3 6.8 0.2 0.4 5.9Philippines 27.2 9.8 2.4 0.1 0.4 1.5 0.3 0.1 17.3Poland 17.5 12.7 4.9 0.1 1.0 0.4 0.6 0.4 7.4Portugal 10.8 18.4 4.9 0.0 0.0 0.1 0.2 0.0 –3.1Puerto Rico .. 20.0 .. .. 0.0 0.0 .. .. ..Qatar .. .. 1.8 .. .. .. .. 0.1 ..% ofGNI% ofGNI% ofGNI% ofGNI2012 World Development Indicators 255

4.11Toward a broader measure of savingsGrosssavingsConsumptionof fixedcapitalEducationexpenditureNetforestdepletionEnergydepletionMineraldepletionCarbondioxidedamageLocalpollutionAdjustednetsavingsRomania 26.7 11.7 3.4 0.0 1.6 0.0 0.5 0.0 16.2Russian Federation 28.6 12.4 3.5 0.0 13.2 1.1 0.9 0.1 4.5Rwanda 15.1 8.0 4.2 3.0 0.0 0.1 0.1 0.1 7.7Saudi Arabia 32.5 12.1 7.2 0.0 29.1 0.0 0.8 1.2 –3.6Senegal 19.7 8.9 5.2 0.0 0.0 0.8 0.3 0.7 13.7Serbia 15.9 11.3 5.0 .. 1.9 0.0 1.0 .. ..Sierra Leone 13.0 7.2 3.4 1.5 0.0 0.6 0.6 1.1 5.4Singapore 47.7 14.4 3.0 0.0 0.0 0.0 0.2 0.4 35.7Slovak Republic 20.4 12.7 3.5 0.3 0.0 0.0 0.4 0.0 10.4Slovenia 22.4 13.3 4.9 0.2 0.1 0.0 0.3 0.1 13.3Somalia .. .. .. .. .. .. .. 0.5 ..South Africa 16.8 13.5 5.4 0.2 3.3 2.6 0.9 0.2 1.6South Sudan .. .. .. .. .. .. .. .. ..Spain 19.1 13.6 4.2 0.0 0.0 0.0 0.2 0.2 9.2Sri Lanka 25.1 10.1 1.7 0.3 0.0 0.0 0.2 0.3 15.9Sudan 19.7 10.3 0.9 0.0 12.9 0.0 0.2 0.9 –3.6Swaziland 2.7 10.6 6.9 0.1 0.0 0.0 0.2 0.1 –1.4Sweden 24.5 13.2 6.2 0.0 0.0 0.4 0.1 0.0 17.0Switzerland 33.4 13.5 5.3 0.0 0.0 0.0 0.1 0.1 25.0Syrian Arab Republic 17.4 10.6 2.6 0.0 11.7 0.1 1.0 1.3 –4.8Tajikistan 2.6 8.6 3.2 0.0 0.4 0.4 0.5 0.2 –4.3Tanzania a 21.1 7.9 2.4 0.0 0.3 2.9 0.2 0.1 12.1Thailand 32.3 11.4 4.1 0.1 2.2 0.1 0.8 0.3 21.5Timor-Leste .. 2.1 3.3 .. 0.0 0.0 0.1 .. ..Togo .. 8.8 4.4 2.1 0.0 1.3 0.4 0.1 ..Trinidad and Tobago 36.4 13.4 4.0 0.0 32.0 0.0 1.5 0.3 –26.0Tunisia 21.4 11.3 6.0 0.1 4.1 0.9 0.5 0.1 10.3Turkey 13.7 12.1 2.6 0.0 0.2 0.1 0.3 0.9 2.7Turkmenistan .. 11.8 .. .. 35.1 0.0 2.5 0.8 ..Uganda 19.1 8.0 3.0 4.5 0.0 0.0 0.2 0.0 9.4Ukraine 17.5 10.4 5.9 0.0 3.7 0.0 1.7 0.1 7.4United Arab Emirates .. 13.6 .. .. 10.9 0.0 0.4 0.8 ..United Kingdom 11.9 13.6 5.1 0.0 1.3 0.0 0.2 0.0 1.8United States 10.9 14.0 4.8 0.0 0.8 0.1 0.3 0.1 0.4Uruguay 17.3 12.6 2.3 0.5 0.0 0.1 0.2 1.8 4.4Uzbekistan .. 9.2 9.4 0.0 13.7 5.5 2.9 0.4 ..Venezuela, RB 31.7 12.4 3.6 0.0 12.0 0.4 0.3 0.0 10.1Vietnam 33.2 9.4 2.8 0.2 9.0 0.2 1.0 0.5 15.8West Bank and Gaza .. .. .. .. .. .. .. .. ..Yemen, Rep. 9.6 9.7 4.2 0.0 14.5 0.0 0.6 0.4 –11.5Zambia 25.4 10.3 1.3 0.0 0.0 18.9 0.1 0.3 –2.8Zimbabwe .. 8.6 2.5 0.0 1.9 0.8 1.0 0.2 ..World 22.5 w 13.0 w 4.2 w 0.0 w 2.1 w 0.5 w 0.4 w 0.3 w 6.4 wLow income 24.6 7.7 2.9 1.2 1.4 1.2 0.3 0.5 ..Middle income 33.8 11.2 3.3 0.1 5.1 1.3 0.7 0.7 14.4Lower middle income 29.2 9.8 3.3 0.3 6.0 1.2 0.7 0.7 13.3Upper middle income 35.1 11.6 3.3 0.0 4.8 1.3 0.7 0.7 14.7Low & middle income 33.7 11.1 3.3 0.1 5.0 1.3 0.7 0.7 14.5East Asia & Pacifi c 48.4 10.7 2.2 0.0 3.8 1.3 1.0 1.1 31.9Europe & Central Asia 23.9 12.2 3.6 0.0 8.6 0.8 0.8 0.4 4.5Latin America & Carib. 20.9 12.1 4.7 0.0 3.8 1.6 0.3 0.4 7.0Middle East & N. Africa 19.2 10.7 4.3 0.1 12.4 0.4 0.7 0.9 ..South Asia 32.7 9.2 2.8 0.6 2.4 1.1 0.8 0.7 20.1Sub-Saharan Africa 18.0 10.9 3.6 0.5 9.4 1.9 0.5 0.5 –1.0High income 17.6 13.9 4.6 0.0 0.7 0.2 0.2 0.1 5.3Euro area 19.4 13.8 4.7 0.0 0.1 0.0 0.2 0.1 7.3a. Covers mainland Tanzania only.% ofGNI% ofGNI% ofGNI% ofGNI% ofGNI2010 2010 2010 2010 2010 2010 2010 2010 2010% ofGNI% ofGNI% ofGNI% ofGNI2562012 World Development Indicators

Toward a broader measure of savings4.11ECONOMYAbout the dataDefinitionsAdjusted net savings measure the change in value ofa specifi ed set of assets, excluding capital gains. Ifa country’s net savings are positive and the accountingincludes a suffi ciently broad range of assets,economic theory suggests that the present valueof social welfare is increasing. Conversely, persistentlynegative adjusted net savings indicate that aneconomy is on an unsustainable path.The table shows the extent to which today’s rentsfrom a number of natural resources and changesin human capital are balanced by net savings —thatis, this generation’s bequest to future generations.Adjusted net savings are derived from standardnational accounting measures of gross savingsby making four adjustments. First, estimates offi xed capital consumption of produced assets arededucted to obtain net savings. Second, currentpublic expenditures on education are added to netsavings (in standard national accounting theseexpenditures are treated as consumption). Third,estimates of the depletion of a variety of naturalresources are deducted to refl ect the decline in assetvalues associated with their extraction and harvest.And fourth, deductions are made for damages fromcarbon dioxide emissions and local pollution.The exercise treats public education expendituresas an addition to savings. However, because of thewide variability in the effectiveness of public educationexpenditures, these fi gures cannot be construedas the value of investments in human capital.A current expenditure of $1 on education does notnecessarily yield $1 of human capital. The calculationshould also consider private education expenditure,but data are not available for a large numberof countries.While extensive, the accounting of natural resourcedepletion and pollution costs still has some gaps.Key estimates missing on the resource side includethe value of fossil water extracted from aquifers, netdepletion of fi sh stocks, and depletion and degradationof soils. Important pollutants affecting humanhealth and economic assets are excluded because nointernationally comparable data are widely availableon damage from ground-level ozone or sulfur oxides.Estimates of resource depletion are based on the“change in real wealth” method described in Hamiltonand Ruta (2008), which estimates depletion asthe ratio between the total value of the resourceand the remaining reserve lifetime. The total valueof the resource is the present value of current andfuture rents from resource extractions. An economicrent represents an excess return to a given factorof production. Natural resources give rise to rentsbecause they are not produced; in contrast, for producedgoods and services competitive forces willexpand supply until economic profi ts are driven tozero. For each type of resource and each country, unitresource rents are derived by taking the differencebetween world prices (to refl ect the social opportunitycost of resource extraction) and the averageunit extraction or harvest costs (including a “normal”return on capital). Unit rents are then multiplied bythe physical quantity extracted or harvested to arriveat total rent. To estimate the value of the resource,rents are assumed to be constant over the life of theresource (the El Serafy approach), and the presentvalue of the rent fl ow is calculated using a 4 percentsocial discount rate. For details on the estimation ofnatural wealth see World Bank (2011c).A positive net depletion fi gure for forest resourcesimplies that the harvest rate exceeds the rate ofnatural growth; this is not the same as deforestation,which represents a change in land use (seeDefi nitions for table 3.4). In principle, there shouldbe an addition to savings in countries where growthexceeds harvest, but empirical estimates suggestthat most of this net growth is in forested areas thatcannot currently be exploited economically. Becausethe depletion estimates refl ect only timber values,they ignore all the external and nontimber benefi tsassociated with standing forests.Pollution damage from emissions of carbon dioxideis calculated as the marginal social cost per unit multipliedby the increase in the stock of carbon dioxide.The unit damage fi gure represents the present valueof global damage to economic assets and to humanwelfare over the time the unit of pollution remainsin the atmosphere.Local pollution damage is estimated by valuing thehuman health effects from exposure to particulatematter pollution in urban areas. The estimates arecalculated as willingness to pay to avoid illness anddeath from cardiopulmonary disease and lung cancerin adults and acute respiratory infections in childrenthat is attributable to particulate emissions.Adjusted net savings aims to be as comprehensivea measure as possible to provide a better understandingof the rate of county wealth creation ordepletion. To do so, it treats education as investmentand accounts for pollution damages to assets andhuman welfare, which goes outside the boundariesof the United Nations System of National Accounts.For a detailed note on methodology, see http://data.worldbank.org.• Gross savings are the difference between grossnational income and public and private consumption,plus net current transfers. • Consumption offixed capital is the replacement value of capitalused up in production. • Education expenditure ispublic current operating expenditures in education,including wages and salaries and excluding capitalinvestments in buildings and equipment. • Netforest depletion is unit resource rents times theexcess of roundwood harvest over natural growth.• Energy depletion is the ratio of the value of thestock of energy resources to the remaining reservelifetime (capped at 25 years). It covers coal, crudeoil, and natural gas. • Mineral depletion is the ratioof the value of the stock of mineral resources to theremaining reserve lifetime (capped at 25 years). Itcovers tin, gold, lead, zinc, iron, copper, nickel, silver,bauxite, and phosphate. • Carbon dioxide damage isestimated at $20 per ton of carbon (the unit damagein 1995 U.S. dollars) times tons of carbon emitted.• Local pollution damage is the willingness to payto avoid illness and death attributable to particulateemissions.• Adjusted net savings are net savingsplus education expenditure minus energy depletion,mineral depletion, net forest depletion, and carbondioxide and particulate emissions damage.Data sourcesData on gross savings are from World Banknational accounts data fi les (see table 4.8).Data on consumption of fi xed capital are fromthe United Nations Statistics Division’s NationalAccounts Statistics: Main Aggregates and DetailedTables, extrapolated to 2010. Data on educationexpenditure are from the United Nations Educational,Scientifi c, and Cultural Organization Institutefor Statistics online database; missing dataare estimated by World Bank staff. Data on forest,energy, and mineral depletion are estimatesbased on sources and methods in World Bank(2011c). Data on carbon dioxide damage arefrom Fankhauser (1995). Data on local pollutiondamage are from Pandey and others (2006c). Theconceptual underpinnings of the savings measureappear in Hamilton and Clemens (1999).2012 World Development Indicators 257

4.12 Central government financesRevenue a Expense Cash surplusor deficitNet incurrenceof liabilitiesDebt and interestpayments% of GDPTotal debtInterest% of% of GDP % of GDP % of GDPDomesticForeign% of GDP revenue2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2010 2010Afghanistan b .. 10.1 .. 46.1 .. 1.4 .. 0.2 .. 0.7 .. 0.0Albania b 23.4 .. 24.3 .. –6.7 .. 3.1 .. 2.6 .. .. ..Algeria .. 37.3 .. 25.5 .. –4.5 .. 6.0 .. 0.0 .. 1.0Angola .. .. .. .. .. .. .. .. .. .. .. ..Argentina 14.1 .. 19.7 .. –5.7 .. 1.9 .. 2.0 .. .. ..Armenia b 17.7 22.4 16.4 22.7 –0.7 –4.9 0.1 0.8 2.4 2.8 .. 3.7Australia 25.8 24.6 24.0 26.6 2.0 –2.4 .. .. .. .. 24.1 3.7Austria 37.7 36.6 40.4 39.6 –2.0 –2.6 .. .. .. .. 70.5 7.0Azerbaijan b .. 25.8 .. 14.6 .. 0.3 .. 0.0 .. 0.2 .. 0.3Bahrain 32.9 28.8 22.6 18.8 8.5 4.0 4.8 .. 0.7 .. 19.2 3.2Bangladesh b 9.8 11.1 9.0 11.3 –0.7 –1.7 3.3 3.1 1.1 0.4 .. 21.7Belarus b 28.7 31.7 25.3 31.6 0.1 –1.5 0.3 –0.2 –0.5 2.8 18.7 1.8Belgium 42.8 40.7 42.9 44.1 0.0 –3.3 –8.2 1.8 8.9 1.7 91.8 7.8Benin b 16.5 18.2 11.9 15.0 0.7 –1.0 –3.3 –0.3 3.1 2.0 .. 2.7Bolivia 18.4 23.3 28.8 21.8 –8.7 1.2 2.4 –0.2 4.2 –0.1 .. 8.0Bosnia and Herzegovina 37.1 39.7 35.6 40.6 0.7 –2.3 1.0 –1.4 0.5 3.3 .. 1.1Botswana b .. .. .. .. .. .. .. .. .. .. .. ..Brazil b 19.9 23.1 21.7 25.6 –1.8 –3.5 .. 8.3 .. –0.1 61.0 20.7Bulgaria b 32.9 32.4 31.6 31.7 –0.4 –0.1 0.0 –0.4 –1.6 0.5 .. 2.2Burkina Faso 11.4 15.6 10.9 12.1 –4.5 –5.7 0.6 2.6 4.1 3.4 .. 2.4Burundi b 15.8 .. 20.6 .. –2.4 .. 3.3 .. 2.9 .. .. ..Cambodia 10.3 12.2 9.4 11.3 –3.4 –3.7 –0.2 1.1 3.6 2.1 .. 1.4Cameroon b 14.1 .. 12.0 .. 0.1 .. .. .. .. .. .. ..Canada b 20.9 17.2 18.7 19.2 2.2 –2.0 .. .. .. .. 52.6 9.8Central African Republic b .. .. .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. .. .. ..Chile 21.6 22.0 21.0 21.0 –0.7 –0.4 –0.4 1.8 –0.3 0.7 .. 2.1China b 7.1 11.9 10.7 .. –2.6 .. 4.2 2.0 0.0 0.0 .. ..Hong Kong SAR, China 14.9 20.7 21.3 19.2 –6.8 1.1 1.6 2.0 .. –0.1 34.0 0.3Colombia 15.1 18.2 19.6 18.3 –5.8 –3.5 3.5 8.2 4.3 0.0 62.9 15.3Congo, Dem. Rep. b 3.7 23.4 9.1 13.7 –4.2 3.8 4.1 –4.7 –0.1 5.5 .. 1.3Congo, Rep. b 28.6 .. 19.9 .. 1.9 .. .. .. .. .. .. ..Costa Rica .. 24.7 .. 26.0 .. –3.4 .. .. .. .. .. 8.8Côte d’Ivoire 16.9 18.9 17.4 17.8 –2.5 0.9 2.2 .. 1.1 .. .. 7.1Croatia b 35.7 32.9 39.1 36.6 –5.3 –4.3 0.5 3.9 3.9 1.3 .. 5.9Cuba .. .. .. .. .. .. .. .. .. .. .. ..Cyprus .. 39.7 61.7 42.7 .. –5.9 .. .. .. .. 97.3 7.7Czech Republic b 30.5 29.6 33.4 36.7 –3.6 –4.9 2.5 2.0 0.0 2.6 36.2 3.3Denmark 36.2 39.8 34.8 42.2 1.6 –2.1 .. .. .. .. 40.8 4.9Dominican Republic .. 14.5 .. 15.6 .. –3.6 .. 1.9 .. 2.2 .. 12.9Ecuador b .. .. .. .. .. .. .. .. .. .. .. ..Egypt, Arab Rep. b 24.3 24.8 27.2 28.9 –6.7 –7.7 14.4 9.2 1.7 0.2 85.8 20.5El Salvador 16.0 19.2 17.9 21.0 –4.7 –2.7 –2.1 –0.8 9.4 2.0 50.0 11.7Eritrea .. .. .. .. .. .. .. .. .. .. .. ..Estonia 31.0 36.8 29.5 36.5 0.2 –1.3 .. .. .. .. 9.0 0.6Ethiopia b 11.9 .. 14.6 .. –4.2 .. 0.8 .. 3.4 .. .. ..Finland 40.9 38.8 34.9 34.8 6.7 4.6 –5.2 –0.2 0.9 –0.6 36.0 3.2France 42.9 40.9 44.9 48.1 –1.7 –7.3 .. .. .. .. 83.5 5.4Gabon .. .. .. .. .. .. .. .. .. .. .. ..Gambia, The b .. .. .. .. .. .. .. .. .. .. .. ..Georgia b 10.4 23.8 11.6 26.3 –1.6 –4.4 1.9 0.6 –0.8 5.5 36.7 3.7Germany 30.6 29.7 32.0 32.0 1.4 –2.2 –0.8 3.1 0.0 –0.2 47.6 5.5Ghana b 18.1 15.4 18.7 18.0 –6.5 –5.6 –0.3 2.8 5.0 2.6 .. 15.2Greece 41.9 37.1 44.7 52.0 –3.8 –15.6 .. .. .. .. 142.0 14.3Guatemala b 10.2 10.9 10.9 12.4 –1.8 –3.1 0.5 1.4 1.2 1.3 23.0 12.6Guinea b 12.0 .. 13.5 .. –2.4 .. 0.2 .. 2.4 .. .. ..Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..Haiti .. .. .. .. .. .. .. .. .. .. .. ..2582012 World Development Indicators

Central government finances 4.12ECONOMYRevenue a Expense Cash surplusor deficitNet incurrenceof liabilitiesDebt and interestpayments% of GDPTotal debtInterest% of% of GDP % of GDP % of GDPDomesticForeign% of GDP revenue2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2010 2010Honduras 20.0 21.1 20.9 23.4 –3.0 –3.1 1.7 0.4 2.0 2.6 .. 3.9Hungary 38.6 41.3 42.0 46.2 –2.8 –4.1 –1.7 –1.9 2.8 6.0 83.2 10.6India b 11.9 11.4 15.7 15.0 –3.9 –3.7 5.1 4.2 0.4 0.3 46.1 27.1Indonesia b 18.3 15.1 16.2 14.4 –3.7 –0.6 .. 1.0 1.4 0.1 26.1 9.4Iran, Islamic Rep. b 23.4 31.9 16.9 24.7 1.8 0.6 1.2 .. 0.0 .. .. 0.6Iraq .. .. .. .. .. .. .. .. .. .. .. ..Ireland 33.0 31.0 27.6 44.2 4.9 –14.1 .. .. .. .. 70.5 6.9Israel 40.7 35.3 47.1 41.1 –3.5 –4.0 .. .. .. .. .. 12.7Italy 36.9 38.4 38.9 43.8 –0.7 –4.9 .. .. .. .. 118.4 11.1Jamaica 31.9 25.9 34.5 39.8 –2.4 –15.3 .. 7.1 .. 4.6 115.8 64.5Japan .. .. .. .. .. .. .. .. .. .. 174.4 ..Jordan b 25.1 21.8 27.1 25.9 –2.0 –5.4 1.8 3.3 –1.7 2.8 59.0 8.5Kazakhstan b 11.3 9.9 13.7 16.2 0.1 –1.1 –0.7 1.7 1.2 1.0 10.2 2.6Kenya b 19.7 20.3 16.8 22.4 2.0 –5.9 0.1 4.9 1.2 1.9 .. 10.4Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. b 22.3 22.7 16.6 19.9 4.4 1.7 –0.9 1.4 –0.1 –0.1 .. 5.0Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait b 48.3 55.5 39.4 32.6 4.9 18.7 .. .. .. .. .. 0.0Kyrgyz Republic b 14.2 20.2 15.8 22.0 –2.9 –5.0 .. –0.1 .. 3.1 .. 3.5Lao PDR .. 14.2 .. 10.8 .. –0.8 .. 0.2 .. 1.7 .. 3.3Latvia b 26.1 25.0 28.1 35.6 –2.2 –6.8 1.5 0.5 –0.2 5.9 49.9 4.5Lebanon 16.0 22.2 30.5 29.2 –18.4 –8.2 13.3 11.6 9.3 0.3 .. 48.7Lesotho b 50.7 66.2 43.7 51.9 –2.8 5.7 0.0 –0.4 9.0 1.6 .. 1.3Liberia b .. 0.4 .. 0.3 .. 0.0 .. 0.0 .. 0.0 .. 2.1Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 25.9 27.4 26.9 37.5 –2.8 –7.2 0.7 1.0 2.0 8.6 43.2 6.1Macedonia, FYR b .. 32.9 .. 30.3 .. –0.8 .. –0.6 .. 0.2 .. 1.9Madagascar 11.7 14.2 10.6 11.8 –2.0 –1.9 1.3 0.6 1.7 3.0 .. 3.9Malawi .. .. .. .. .. .. .. .. .. .. .. ..Malaysia b 17.5 20.8 16.5 19.6 –4.1 –5.4 1.6 0.5 2.1 4.8 53.1 9.8Mali 13.4 17.1 11.6 14.7 –3.4 –2.1 –1.0 –4.4 3.0 2.6 .. 1.7Mauritania .. .. .. .. .. .. .. .. .. .. .. ..Mauritius .. 22.7 .. 22.7 .. –2.4 .. –0.2 .. 1.8 37.8 10.6Mexico b 14.7 .. 15.4 .. –1.2 .. .. .. –0.7 .. .. ..Moldova b 24.5 31.6 28.9 35.0 –1.5 –2.6 1.5 0.4 –0.2 2.9 26.3 2.2Mongolia b 24.4 33.7 22.4 26.6 0.2 3.0 1.3 1.2 4.3 –0.9 45.0 1.4Morocco b 30.0 31.8 30.4 30.6 –3.1 –2.6 1.7 2.3 –2.8 2.1 50.3 3.8Mozambique .. .. .. .. .. .. .. .. .. .. .. ..Myanmar b 5.3 .. 3.1 .. –2.7 .. 2.7 .. 0.0 .. .. ..Namibia b 30.1 29.2 28.5 24.1 –1.6 2.0 1.0 –0.8 0.7 –0.1 .. 6.3Nepal b 10.6 15.2 .. .. .. .. 1.0 2.0 2.1 0.1 43.8 4.6Netherlands 40.7 40.9 39.3 45.5 2.0 –4.8 .. .. .. .. 58.2 4.6New Zealand 33.7 36.1 32.1 32.1 1.7 3.1 1.4 .. –1.0 .. 37.9 3.4Nicaragua b 15.1 19.7 16.5 20.0 –3.6 –1.0 .. .. .. .. .. 6.5Niger .. 13.5 .. 11.6 .. –0.9 .. –1.9 .. 2.4 .. 1.8Nigeria b .. 9.7 .. 7.2 .. –1.7 .. 0.1 .. .. 3.0 6.6Norway 48.4 47.7 32.6 35.7 15.7 11.7 0.0 –2.5 7.1 4.7 35.4 1.8Oman b 23.9 .. 26.2 .. –4.4 .. –1.3 .. –0.7 .. .. ..Pakistan b 13.9 13.8 17.2 17.5 –4.1 –5.0 .. .. .. .. .. 37.4Panama b 23.1 .. 22.1 .. –0.8 .. .. .. .. .. .. ..Papua New Guinea b 24.2 .. 30.0 .. –1.9 .. 1.6 .. 1.8 .. .. ..Paraguay b 17.0 18.1 17.5 15.1 –3.9 1.4 2.7 0.1 0.9 0.3 .. 2.1Peru b 17.4 18.5 17.9 16.4 –2.1 0.3 0.6 1.6 2.3 –0.6 21.6 6.0Philippines b 14.2 13.4 16.3 16.9 –3.7 –3.5 1.3 2.5 2.4 1.5 .. 25.1Poland 31.5 30.0 35.6 35.7 –2.8 –6.1 4.9 1.6 –1.7 3.6 48.1 8.1Portugal 34.6 34.5 36.7 43.0 –2.6 –8.7 –0.2 3.3 2.0 5.8 84.0 7.7Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar b .. 47.2 .. 19.3 .. 15.2 .. .. .. .. .. 3.62012 World Development Indicators 259

4.12Central government financesRevenue a Expense Cash surplusor deficitNet incurrenceof liabilitiesDebt and interestpayments% of GDPTotal debtInterest% of% of GDP % of GDP % of GDPDomesticForeign% of GDP revenue2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2010 2010Romania 25.8 30.9 25.9 33.8 –2.0 –4.6 0.4 2.4 1.7 0.9 .. 2.0Russian Federation 31.8 26.8 22.6 28.2 7.0 –1.9 –4.1 1.4 –1.9 0.2 9.4 1.6Rwanda b .. .. .. .. .. .. .. .. .. .. .. ..Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. ..Senegal b 16.9 .. 12.8 .. –0.9 .. 0.3 .. 0.5 .. .. ..Serbia b .. 37.5 .. 39.8 .. –3.9 .. 2.3 .. 1.1 38.7 2.9Sierra Leone b 11.4 11.8 28.7 22.9 –9.3 –3.2 4.8 .. .. .. .. 8.3Singapore b 26.2 18.1 16.0 13.4 11.2 8.0 7.8 10.3 .. .. 109.2 0.0Slovak Republic 35.3 28.6 39.4 37.7 –3.2 –7.3 2.9 2.9 –0.2 3.0 38.2 4.7Slovenia b 38.9 36.9 38.8 42.7 –1.1 –5.5 –0.4 3.7 1.6 –1.4 .. 4.0Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 26.3 28.4 27.9 33.2 –2.0 –4.9 1.6 7.0 0.3 1.0 .. 8.4South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 31.1 24.9 31.3 30.5 –0.5 –5.2 .. 2.4 .. 2.2 47.8 5.8Sri Lanka b 16.8 14.9 23.0 19.2 –8.4 –6.6 9.5 6.9 0.0 –0.1 85.0 31.0Sudan b 8.0 .. 7.6 .. –0.4 .. 1.0 .. .. .. .. ..Swaziland b 25.6 .. 22.1 .. –0.8 .. .. .. .. .. .. ..Sweden 42.2 34.8 .. .. .. .. .. .. .. .. 44.2 ..Switzerland b 24.3 18.3 25.5 17.0 2.2 1.3 –2.8 2.0 .. .. 28.8 3.5Syrian Arab Republic b 24.0 .. .. .. .. .. .. .. .. .. .. ..Tajikistan b 10.6 .. 9.0 .. –0.8 .. –0.5 .. 0.5 .. .. ..Tanzania .. .. .. .. .. .. .. .. .. .. .. ..Thailand 19.5 20.3 15.9 18.6 1.5 –0.6 0.9 2.6 –0.6 –0.1 28.8 6.1Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo .. 17.6 .. 14.5 .. 0.6 .. –2.7 .. 1.9 .. 4.5Trinidad and Tobago b 27.1 33.5 23.9 32.3 2.0 –4.8 .. –0.4 .. 0.3 21.4 8.4Tunisia b 26.5 29.0 25.0 27.0 –2.4 –1.3 0.5 –0.6 –0.2 –0.4 40.5 6.2Turkey b .. 24.4 .. 25.4 .. –2.2 .. 2.8 .. 0.9 50.5 18.3Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda b 10.8 12.6 15.5 13.9 –1.9 –0.9 0.6 1.6 2.0 1.9 33.1 7.7Ukraine b 26.8 34.6 26.9 40.7 –0.6 –5.6 1.5 6.8 –0.3 4.9 .. 3.1United Arab Emirates b 6.5 .. 6.2 .. 0.1 .. .. .. .. .. .. ..United Kingdom 37.1 36.0 36.2 46.4 1.6 –10.9 .. .. .. .. 73.3 5.3United States 20.1 16.9 19.6 26.8 0.5 –10.0 0.9 6.5 1.9 5.1 76.1 11.4Uruguay b 24.7 30.9 26.6 30.3 –3.0 –0.9 –7.4 –0.8 2.7 –0.4 45.6 7.9Uzbekistan .. .. .. .. .. .. .. .. .. .. .. ..Venezuela, RB b 21.2 .. 21.6 .. –1.2 .. 3.9 .. –0.5 .. .. ..Vietnam .. .. .. .. .. .. .. .. .. .. .. ..West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. 1.1Yemen, Rep. b 23.4 .. 21.6 .. –2.3 .. .. .. .. .. .. ..Zambia b 19.7 17.4 15.9 17.2 1.8 –1.5 .. .. 4.6 .. .. 9.0Zimbabwe b 29.1 .. 33.2 .. –5.2 .. –0.6 .. –0.1 .. .. ..World 25.8 m 23.8 m 26.7 m 31.1 m –0.2 m –7.0 m .. m .. m .. m .. m .. m 5.5 mLow income 10.4 .. 11.0 .. –1.7 .. .. .. .. .. .. ..Middle income 14.6 18.8 18.2 .. –2.3 .. .. 2.8 1.0 1.2 .. 7.0Lower middle income 13.8 14.9 18.7 17.6 –4.0 –3.6 .. .. .. .. .. 6.0Upper middle income 14.9 19.7 18.1 .. –1.8 .. 0.9 3.0 0.8 0.9 .. 7.0Low & middle income 14.5 18.6 18.1 .. –2.3 .. .. 0.7 .. 0.4 .. 5.8East Asia & Pacifi c 8.2 13.5 11.6 .. –2.6 .. 2.7 1.1 2.4 1.5 .. 6.1Europe & Central Asia .. 25.6 .. 27.3 .. –2.2 0.7 0.7 0.4 2.9 .. 3.2Latin America & Carib. 18.1 .. 19.2 .. –1.5 .. 1.6 2.0 2.0 1.2 .. 9.3Middle East & N. Africa .. 30.5 .. 27.2 .. –3.0 2.6 6.8 0.7 0.1 .. 6.9South Asia 12.3 11.7 16.1 15.5 –4.0 –3.8 5.1 2.0 0.4 0.3 56.5 15.9Sub-Saharan Africa .. 24.3 .. 24.2 .. –1.0 .. .. .. .. .. ..High income 27.5 24.9 27.3 32.2 0.1 –7.5 .. .. .. .. 58.2 5.4Euro area 36.3 34.7 37.5 40.0 0.1 –5.2 .. .. .. .. 70.5 6.5a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework.2602012 World Development Indicators

Central government finances4.12ECONOMYAbout the dataDefinitionsTables 4.12–4.14 present an overview of the sizeand role of central governments relative to nationaleconomies. The tables are based on the conceptsand recommendations of the second edition of theInternational Monetary Fund’s (IMF) GovernmentFinance Statistics Manual 2001. Before 2005 WorldDevelopment Indicators reported data derived on thebasis of the 1986 manual’s cash-based method. The2001 manual, harmonized with the 1993 UnitedNations System of National Accounts, recommendsan accrual accounting method, focusing on all economicevents affecting assets, liabilities, revenues,and expenses, not only those represented by cashtransactions. It takes all stocks into account, sothat stock data at the end of an accounting periodequal stock data at the beginning of the period plusfl ows over the period. The 1986 manual consideredonly the debt stock data. Further, the new manual nolonger distinguishes between current and capital revenueor expenditures, and it introduces the conceptsof nonfi nancial and fi nancial assets. Most countriesstill follow the 1986 manual, however. The IMF hasreclassifi ed historical Government Finance StatisticsYearbook data to conform to the 2001 manual’s format.Because of reporting differences, the reclassified data understate both revenue and expense.The 2001 manual describes government’s economicfunctions as the provision of goods and serviceson a nonmarket basis for collective or individualconsumption, and the redistribution of income andwealth through transfer payments. Governmentactivities are fi nanced mainly by taxation and otherincome transfers, though other fi nancing such asborrowing for temporary periods can also be used.Government excludes public corporations and quasicorporations (such as the central bank).Units of government at many levels meet this defi nition,from local administrative units to the nationalgovernment, but inadequate statistical coverage precludespresenting subnational data. Although datafor general government under the 2001 manual areavailable for a few countries, only data for the centralgovernment are shown to minimize disparities.Still, different accounting concepts of central governmentmake cross-country comparisons potentiallymisleading.Central government can refer to consolidated orbudgetary accounting. For most countries centralgovernment fi nance data have been consolidatedinto one account, but for others only budgetarycentral government accounts are available. Countriesreporting budgetary data are noted in Primarydata documentation. Because budgetary accountsmay not include all central government units (suchas social security funds), they usually provide anincomplete picture.Data on government revenue and expense are collectedby the IMF through questionnaires to membercountries and by the Organisation for EconomicCo- operation and Development. Despite IMF effortsto standardize data collection, statistics are oftenincomplete, untimely, and not comparable acrosscountries.Government fi nance statistics are reported inlocal currency. The indicators here are shown aspercentages of GDP. Many countries report governmentfi nance data by fi scal year; see Primary datadocumentation for information on fi scal year end bycountry.• Revenue is cash receipts from taxes, social contributions,and other revenues such as fines, fees,rent, and income from property or sales. Grants, usuallyconsidered revenue, are excluded. • Expense iscash payments for government operating activities inproviding goods and services. It includes compensationof employees, interest and subsidies, grants,social benefi ts, and other expenses such as rentand dividends. • Cash surplus or deficit is revenue(including grants) minus expense, minus net acquisitionof nonfi nancial assets. In editions before 2005nonfi nancial assets were included under revenueand expenditure in gross terms. This cash surplusor defi cit is close to the earlier overall budget balance(still missing is lending minus repayments, which areincluded as a fi nancing item under net acquisitionof fi nancial assets). • Net incurrence of liabilitiesis domestic fi nancing (obtained from residents) andforeign fi nancing (obtained from nonresidents), orthe means by which a government provides fi nancialresources to cover a budget defi cit or allocates fi nancialresources arising from a budget surplus. The netincurrence of liabilities should be offset by the netacquisition of fi nancial assets (a third fi nancing item).The difference between the cash surplus or defi citand the three fi nancing items is the net change inthe stock of cash. • Total debt is the entire stock ofdirect government fi xed-term contractual obligationsto others outstanding on a particular date. It includesdomestic and foreign liabilities such as currency andmoney deposits, securities other than shares, andloans. It is the gross amount of government liabilitiesreduced by the amount of equity and fi nancialderivatives held by the government. Because debtis a stock rather than a fl ow, it is measured as ofa given date, usually the last day of the fiscal year.• Interest payments are interest payments on governmentdebt—including long-term bonds, long-termloans, and other debt instruments —to domestic andforeign residents.Data sourcesData on central government fi nances are from theIMF’s Government Finance Statistics database.Each country’s accounts are reported using thesystem of common defi nitions and classifi cationsin the IMF’s Government Finance Statistics Manual2001. See these sources for complete and authoritativeexplanations of concepts, defi nitions, anddata sources.2012 World Development Indicators 261

4.13 Central government expensesGoods andservicesCompensationof employeesInterestpaymentsSubsidies andother transfersOtherexpense% of expense % of expense % of expense % of expense % of expense2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan a .. 75 .. 23 .. 0 .. 2 .. 0Albania a 15 .. 31 .. 16 .. 36 .. 3 ..Algeria .. 11 .. 34 .. 1 .. 45 .. 8Angola .. .. .. .. .. .. .. .. .. ..Argentina 4 .. 11 .. 34 .. 43 .. 7 ..Armenia a 53 13 5 24 4 4 34 39 4 20Australia 11 10 11 10 7 3 67 73 6 6Austria 5 6 13 14 9 7 70 71 5 5Azerbaijan a .. 9 .. 12 .. 1 .. 18 .. 61Bahrain 8 26 58 54 7 5 8 10 .. 5Bangladesh a 14 12 27 19 18 22 26 35 14 12Belarus a 14 10 11 12 3 2 68 69 4 7Belgium 3 3 7 7 14 7 74 82 3 3Benin a 33 16 38 48 6 4 2 30 21 2Bolivia 16 14 25 22 8 10 45 47 6 7Bosnia and Herzegovina 26 24 31 28 2 1 38 42 3 5Botswana a .. .. .. .. .. .. .. .. .. ..Brazil a 18 13 21 19 17 19 45 49 0 0Bulgaria a 24 9 9 19 12 2 53 64 2 6Burkina Faso 25 17 41 46 7 4 8 12 19 21Burundi a .. .. .. .. .. .. .. .. 14 ..Cambodia 35 29 37 39 2 2 15 19 11 11Cameroon a 23 .. 37 .. 22 .. 17 .. .. ..Canada a 8 8 11 12 22 9 59 69 1 4Central African Republic a .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. ..Chile 9 10 21 20 6 2 59 51 10 19China a .. .. .. .. 5 .. 65 .. 0 ..Hong Kong SAR, China 23 28 26 23 0 0 27 19 26 33Colombia 7 1 21 11 20 15 3 61 1 16Congo, Dem. Rep. a 56 19 27 43 6 3 1 34 16 1Congo, Rep. a 26 .. 28 .. 35 .. 11 .. 1 ..Costa Rica .. 11 .. 46 .. 8 .. 21 .. 14Côte d’Ivoire 30 29 39 38 16 9 16 16 .. 7Croatia a 24 8 26 26 4 5 43 55 3 6Cuba .. .. .. .. .. .. .. .. .. ..Cyprus 11 12 36 35 15 7 30 33 1 1Czech Republic a 7 6 10 8 3 3 74 74 7 10Denmark 9 9 14 13 10 5 24 17 2 2Dominican Republic .. 17 .. 36 .. 12 .. 28 .. 7Ecuador a .. .. .. .. .. .. .. .. .. ..Egypt, Arab Rep. a 8 8 30 25 20 18 24 42 18 8El Salvador 15 17 43 38 11 11 3 24 27 12Eritrea .. .. .. .. .. .. .. .. .. ..Estonia 19 13 26 21 0 1 42 48 3 4Ethiopia a 22 .. 23 .. 11 .. 44 .. 0 ..Finland 9 10 11 10 8 4 68 71 8 8France 7 6 23 21 6 5 60 54 6 2Gabon .. .. .. .. .. .. .. .. .. ..Gambia, The a .. .. .. .. .. .. .. .. .. ..Georgia a 17 16 11 18 24 4 48 51 .. 11Germany 4 5 6 5 7 5 81 81 4 4Ghana a 14 16 36 40 39 16 4 28 .. 12Greece 13 12 22 24 17 10 42 50 10 7Guatemala a 15 15 31 29 10 11 22 33 21 12Guinea a 15 .. 30 .. 32 .. 12 .. 1 ..Guinea-Bissau .. .. .. .. .. .. .. .. .. ..Haiti .. .. .. .. .. .. .. .. .. ..2622012 World Development Indicators

Central government expenses 4.13ECONOMYGoods andservicesCompensationof employeesInterestpaymentsSubsidies andother transfersOtherexpense% of expense % of expense % of expense % of expense % of expense2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 13 14 51 56 7 4 18 6 12 21Hungary 9 10 13 13 13 10 57 63 13 8India a 12 10 10 9 30 21 43 60 7 1Indonesia a 11 11 11 16 24 10 53 55 0 9Iran, Islamic Rep. a 18 11 57 40 0 1 24 34 1 14Iraq .. .. .. .. .. .. .. .. .. ..Ireland 6 10 14 23 7 5 29 40 1 1Israel 24 26 23 24 17 11 30 31 7 9Italy 4 4 16 15 16 10 61 66 5 6Jamaica 16 6 33 14 47 43 2 6 2 31Japan .. .. .. .. .. .. .. .. .. ..Jordan a 6 9 70 52 13 8 8 29 3 2Kazakhstan a 25 22 9 7 10 3 55 66 1 2Kenya a 21 14 55 38 18 10 3 37 2 1Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. a 14 11 11 10 7 6 53 58 15 15Kosovo .. .. .. .. .. .. .. .. .. ..Kuwait a 23 15 32 24 1 0 32 30 11 19Kyrgyz Republic a 40 25 37 28 9 4 14 42 .. 2Lao PDR .. 29 .. 43 .. 6 .. 13 .. 9Latvia a 13 9 12 12 3 4 67 72 4 3Lebanon 3 3 24 21 50 38 21 36 3 2Lesotho a 30 42 38 35 8 2 21 14 .. 6Liberia a .. 37 .. 36 .. 2 .. 24 .. ..Libya .. .. .. .. .. .. .. .. .. ..Lithuania 16 10 21 15 6 5 55 68 2 6Macedonia, FYR a .. 28 .. 17 .. 2 .. 49 .. 4Madagascar 18 15 41 40 13 7 10 25 19 14Malawi .. .. .. .. .. .. .. .. .. ..Malaysia a 15 16 28 31 15 10 48 42 3 1Mali 38 31 37 34 8 2 0 15 17 17Mauritania .. .. .. .. .. .. .. .. .. ..Mauritius .. 12 .. 35 .. 11 .. 31 .. 10Mexico a 8 .. 17 .. 13 .. .. .. .. ..Moldova a 10 20 10 14 22 2 55 59 3 5Mongolia a 23 20 13 29 7 2 56 42 2 7Morocco a 13 9 42 40 13 4 29 33 3 14Mozambique .. .. .. .. .. .. .. .. .. ..Myanmar a .. .. .. .. .. .. .. .. .. ..Namibia a 21 20 51 45 7 8 10 13 11 14Nepal a .. .. .. .. .. .. .. .. .. ..Netherlands 7 8 8 7 8 4 76 79 4 4New Zealand 31 30 25 25 6 4 37 38 4 7Nicaragua a 17 13 23 38 13 7 35 37 12 5Niger .. 30 .. 30 .. 3 .. 9 .. 28Nigeria a .. 15 .. 24 .. 9 .. 53 .. ..Norway 10 11 11 16 3 2 74 68 4 5Oman a 50 .. 34 .. 5 .. 11 .. 0 ..Pakistan a 48 22 4 4 36 31 3 25 .. 17Panama a 15 .. 37 .. 22 .. 26 .. 1 ..Papua New Guinea a 37 .. 25 .. 14 .. 24 .. .. ..Paraguay a 8 10 54 53 7 3 31 27 1 7Peru a 23 22 22 18 13 7 37 48 5 6Philippines a 26 27 31 31 24 20 19 20 2 3Poland 7 5 11 12 8 7 70 71 8 7Portugal 9 7 32 24 8 6 46 51 7 1Puerto Rico .. .. .. .. .. .. .. .. .. ..Qatar a .. 28 .. 28 .. 7 .. 16 .. 222012 World Development Indicators 263

4.13Central government expensesGoods andservicesCompensationof employeesInterestpaymentsSubsidies andother transfersOtherexpense% of expense % of expense % of expense % of expense % of expense2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Romania 22 13 16 19 8 2 43 60 12 8Russian Federation 19 13 17 16 9 2 54 67 1 9Rwanda a .. .. .. .. .. .. .. .. .. ..Saudi Arabia .. .. .. .. .. .. .. .. .. ..Senegal a 26 .. 41 .. 11 .. 19 .. .. ..Serbia a .. 13 .. 25 .. 3 .. 58 .. 1Sierra Leone a 15 24 23 28 22 7 5 23 34 18Singapore a 33 36 29 30 2 0 36 0 .. ..Slovak Republic 11 7 12 12 6 4 64 68 12 14Slovenia a 16 13 18 19 4 4 59 62 3 3Somalia .. .. .. .. .. .. .. .. .. ..South Africa 11 13 16 13 18 7 53 63 2 4South Sudan .. .. .. .. .. .. .. .. .. ..Spain 4 4 11 8 9 5 44 81 2 5Sri Lanka a 22 14 24 28 25 25 21 23 9 10Sudan a 41 .. 41 .. 10 .. 8 .. .. ..Swaziland a 26 .. 45 .. 2 .. 27 .. 21 ..Sweden .. .. .. .. .. .. .. .. .. ..Switzerland a 23 6 5 6 3 4 67 83 2 3Syrian Arab Republic a .. .. .. .. .. .. .. .. .. ..Tajikistan a 31 .. 22 .. 4 .. 43 .. 0 ..Tanzania .. .. .. .. .. .. .. .. .. ..Thailand 27 32 35 40 7 7 24 21 6 3Timor-Leste .. .. .. .. .. .. .. .. .. ..Togo .. 26 .. 36 .. 7 .. 26 .. 4Trinidad and Tobago a 15 19 37 26 18 9 29 45 1 2Tunisia a 9 6 40 36 12 7 36 39 5 12Turkey a .. 10 .. 25 .. 18 .. 44 .. 6Turkmenistan .. .. .. .. .. .. .. .. .. ..Uganda a 55 31 12 14 5 9 27 45 .. 1Ukraine a 19 12 15 13 9 3 56 70 1 2United Arab Emirates a 46 .. 36 .. .. .. .. .. .. ..United Kingdom 17 18 14 14 7 4 55 53 9 12United States 13 15 12 12 13 7 62 64 2 4Uruguay a 12 13 15 23 7 8 66 46 0 10Uzbekistan .. .. .. .. .. .. .. .. .. ..Venezuela, RB a 6 .. 21 .. 12 .. 59 .. 2 ..Vietnam .. .. .. .. .. .. .. .. .. ..West Bank and Gaza .. 12 .. 67 .. 1 .. 18 .. 1Yemen, Rep. a 17 .. 49 .. 11 .. 21 .. .. ..Zambia a 35 24 39 43 7 10 3 15 16 8Zimbabwe a 13 .. 38 .. 21 .. 28 .. .. ..World 15 m 12 m 21 m 23 m 8 m 5 m 43 m 45 m 6 m 6 mLow income .. .. .. .. .. .. .. .. .. ..Middle income 15 13 22 26 12 7 41 44 4 7Lower middle income 15 15 29 31 15 8 29 33 .. 8Upper middle income 13 11 21 22 9 7 48 46 5 6Low & middle income 17 14 27 28 10 6 34 36 .. 7East Asia & Pacifi c 23 27 31 31 10 7 44 21 0 7Europe & Central Asia 19 13 17 17 6 3 50 58 5 6Latin America & Carib. 13 13 23 29 13 9 43 33 4 12Middle East & N. Africa 8 9 41 36 12 7 .. 36 .. 9South Asia 22 22 10 9 30 21 26 25 7 1Sub-Saharan Africa .. .. .. .. .. .. .. .. .. ..High income 9 10 13 15 7 5 58 57 4 5Euro area 7 7 14 17 8 5 60 64 4 4Note: Components may not sum to 100 percent because of rounding or missing data.a. Data were reported on a cash basis and have been adjusted to the accrual framework.2642012 World Development Indicators

Central government expenses 4.13ECONOMYAbout the dataDefinitionsThe term expense has replaced expenditure in thetable since the 2005 edition of World DevelopmentIndicators in accordance with use in the InternationalMonetary Fund’s (IMF) Government Finance StatisticsManual 2001. Government expenses include allnonrepayable payments, whether current or capital,requited or unrequited. The concept of total centralgovernment expense as presented in the IMF’s GovernmentFinance Statistics Yearbook is comparable tothe concept used in the 1993 United Nations Systemof National Accounts.Expenses can be measured either by function(health, defense, education) or by economic type(interest payments, wages and salaries, purchasesof goods and services). Functional data are oftenincomplete, and coverage varies by country becausefunctional responsibilities stretch across levels ofgovernment for which no data are available. Defenseexpenses, usually the central government’s responsibility,are shown in table 5.7. For more informationon education expenses, see table 2.11; for more onhealth expenses, see table 2.16.The classifi cation of expenses by economic type inthe table shows whether the government producesgoods and services and distributes them, purchasesthe goods and services from a third party and distributesthem, or transfers cash to households tomake the purchases directly. When the governmentproduces and provides goods and services, the costis refl ected in compensation of employees, use ofgoods and services, and consumption of fi xed capital.Purchases from a third party and cash transfersto households are shown as subsidies and othertransfers, and other expenses. The economic classification can be problematic. For example, subsidiesto public corporations or banks may be disguisedas capital fi nancing or hidden in special contractualpricing for goods and services. For further discussionof government fi nance statistics, see About the datafor tables 4.12 and 4.14.• Goods and services are all government paymentsin exchange for goods and services used for theproduction of market and nonmarket goods and services.Own-account capital formation is excluded.• Compensation of employees is all payments incash, as well as in kind (such as food and housing),to employees in return for services rendered,and government contributions to social insuranceschemes such as social security and pensions thatprovide benefi ts to employees. • Interest paymentsare payments made to nonresidents, to residents,and to other general government units for the use ofborrowed money. (Repayment of principal is shownas a fi nancing item, and commission charges areshown as purchases of services.) • Subsidies andother transfers include all unrequited, nonrepayabletransfers on current account to private and publicenterprises; grants to foreign governments, internationalorganizations, and other government units;and social security, social assistance benefi ts, andemployer social benefi ts in cash and in kind. • Otherexpense is spending on dividends, rent, and othermiscellaneous expenses, including provision for consumptionof fi xed capital.Data sourcesData on central government expenses are from theIMF’s Government Finance Statistics database.Each country’s accounts are reported using thesystem of common defi nitions and classifi cationsin the IMF’s Government Finance Statistics Manual2001. See these sources for complete and authoritativeexplanations of concepts, defi nitions, anddata sources.2012 World Development Indicators 265

4.14 Central government revenuesTaxes on income,profits, andcapital gainsTaxes ongoods andservicesTaxes oninternationaltradeOthertaxesSocialcontributionsGrants andother revenue% of revenue % of revenue % of revenue % of revenue % of revenue % of revenue2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan a .. 3 .. 3 .. 5 .. 0 .. 0 .. 89Albania a 14 .. 43 .. 9 .. 1 .. 17 .. 17 ..Algeria .. 60 .. 28 .. 4 .. 1 .. .. .. 6Angola .. .. .. .. .. .. .. .. .. .. .. ..Argentina 13 .. 28 .. 14 .. 15 .. 20 .. 11 ..Armenia a 10 19 43 43 3 4 10 8 13 13 20 15Australia 67 65 18 23 2 2 2 0 .. .. 11 10Austria 24 23 24 23 0 0 4 5 41 42 7 7Azerbaijan a .. 33 .. 23 .. 4 .. 1 .. .. .. 39Bahrain 11 19 25 29 31 24 4 3 .. .. 28 24Bangladesh a 3 1 2 0 6 4 2 .. .. .. 87 95Belarus a 11 7 39 32 5 11 3 3 35 37 7 9Belgium 37 35 24 25 .. .. 2 1 34 36 2 4Benin a 20 16 35 38 21 22 7 6 .. 2 18 15Bolivia 7 10 41 43 4 3 8 9 9 7 32 28Bosnia and Herzegovina 2 6 28 45 18 0 5 0 34 39 13 10Botswana a .. .. .. .. .. .. .. .. .. .. .. ..Brazil a 25 30 34 33 4 2 8 2 25 26 5 6Bulgaria a 11 16 38 45 2 1 1 0 27 23 20 16Burkina Faso 18 15 37 36 13 11 2 2 .. .. 30 36Burundi a 18 .. 38 .. 17 .. 1 .. 6 .. 19 ..Cambodia 6 10 30 35 22 14 0 0 .. .. 42 41Cameroon a 21 .. 26 .. 28 .. 4 .. 2 .. 18 ..Canada a 54 53 16 15 3 1 .. .. 19 23 8 8Central African Republic a .. .. .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. .. .. ..Chile 22 27 47 45 6 1 2 8 7 6 16 13China a 8 25 65 59 10 4 6 1 .. .. 11 12Hong Kong SAR, China 38 36 13 9 0 0 10 17 0 0 39 38Colombia 28 21 32 32 6 6 7 5 0 0 32 36Congo, Dem. Rep. a 10 12 14 14 14 14 20 0 .. .. 41 60Congo, Rep. a 7 .. 15 .. 5 .. 0 .. 3 .. 76 ..Costa Rica .. 17 .. 32 .. 4 .. 3 .. 34 .. 10Côte d’Ivoire 18 15 18 20 46 33 3 8 8 6 7 18Croatia a 9 7 46 46 6 1 1 2 32 35 5 8Cuba .. .. .. .. .. .. .. .. .. .. .. ..Cyprus .. 26 .. 34 .. 1 .. 4 .. 22 .. ..Czech Republic a 13 15 34 28 2 0 1 1 47 44 3 12Denmark 37 45 42 36 .. .. 6 5 6 3 9 ..Dominican Republic .. 22 .. 53 .. 9 .. 4 .. 3 .. 9Ecuador a .. .. .. .. .. .. .. .. .. .. .. ..Egypt, Arab Rep. a 20 25 22 22 8 5 3 4 .. .. 47 44El Salvador 20 25 39 40 7 5 1 0 15 12 19 17Eritrea .. .. .. .. .. .. .. .. .. .. .. ..Estonia 13 8 38 39 0 .. .. .. 35 36 .. ..Ethiopia a 14 .. 10 .. 26 .. 1 .. 0 .. 49 ..Finland 26 20 32 32 0 .. 2 2 29 31 12 15France 26 22 25 23 0 0 3 4 41 45 5 ..Gabon .. .. .. .. .. .. .. .. .. .. .. ..Gambia, The a .. .. .. .. .. .. .. .. .. .. .. ..Georgia a 8 31 57 51 7 1 .. 1 21 17 7 15Germany 18 16 20 24 .. .. .. .. 57 55 4 4Ghana a 22 23 15 29 32 16 .. .. .. .. 31 32Greece 22 21 29 29 0 0 4 3 30 36 15 12Guatemala a 24 29 60 56 12 7 1 2 2 3 6 4Guinea a 6 .. 3 .. 48 .. 2 .. 1 .. 40 ..Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..Haiti .. .. .. .. .. .. .. .. .. .. .. ..2662012 World Development Indicators

Central government revenues 4.14ECONOMYTaxes on income,profits, andcapital gainsTaxes ongoods andservicesTaxes oninternationaltradeOthertaxesSocialcontributionsGrants andother revenue% of revenue % of revenue % of revenue % of revenue % of revenue % of revenue2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 15 20 44 40 6 4 1 2 12 13 22 21Hungary 20 23 34 32 3 0 1 1 34 32 8 12India a 27 47 29 23 19 13 0 0 0 0 25 16Indonesia a 31 36 26 29 3 2 3 4 .. .. 37 28Iran, Islamic Rep. a 13 19 7 3 6 6 1 1 11 19 61 52Iraq .. .. .. .. .. .. .. .. .. .. .. ..Ireland 39 33 .. .. 0 0 4 2 15 22 .. ..Israel 34 26 26 33 1 1 5 5 15 18 20 17Italy 35 32 23 20 .. .. 5 7 33 36 4 5Jamaica 15 25 33 37 8 7 20 10 2 3 21 18Japan .. .. .. .. .. .. .. .. .. .. .. ..Jordan a 9 13 31 43 17 6 8 2 1 0 35 36Kazakhstan a 24 24 40 20 5 10 7 0 0 .. 24 46Kenya a 29 41 41 40 15 11 0 1 0 .. 15 7Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. a 26 28 28 27 4 4 10 8 13 16 18 17Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait a 0 1 0 0 2 1 0 0 .. .. 98 98Kyrgyz Republic a 14 21 61 37 3 9 0 .. .. .. 22 34Lao PDR .. 13 .. 42 .. 7 .. 1 .. .. .. 37Latvia a 12 7 40 35 1 0 0 0 34 29 13 28Lebanon 12 15 26 44 26 6 11 10 2 1 24 23Lesotho a 17 17 12 12 41 57 0 3 .. .. 29 11Liberia a .. 28 .. 15 .. 39 .. 1 .. .. .. 18Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 11 6 44 37 1 .. 0 0 36 39 8 18Macedonia, FYR a .. 13 .. 40 .. 5 .. 0 .. 29 .. 13Madagascar 12 12 22 15 40 31 1 6 .. 4 26 32Malawi .. .. .. .. .. .. .. .. .. .. .. ..Malaysia a 43 46 25 17 7 2 3 4 .. .. 22 31Mali 12 19 42 29 11 10 5 10 .. .. 30 31Mauritania .. .. .. .. .. .. .. .. .. .. .. ..Mauritius .. 20 .. 50 .. 2 .. 7 .. 7 .. 14Mexico a 34 .. 62 .. 4 .. 1 .. 10 .. 10 ..Moldova a 3 1 45 48 5 4 0 0 23 30 23 17Mongolia a 12 27 36 29 7 6 1 2 18 13 26 23Morocco a 22 26 30 36 11 6 3 6 13 13 22 14Mozambique .. .. .. .. .. .. .. .. .. .. .. ..Myanmar a 19 .. 33 .. 4 .. .. .. .. .. 44 ..Namibia a 32 28 23 19 35 44 1 1 1 0 9 7Nepal a 15 14 30 38 23 16 3 5 .. .. 28 27Netherlands 25 26 26 27 .. .. 4 2 39 35 6 10New Zealand 54 57 29 26 3 3 0 0 0 0 14 15Nicaragua a 13 27 54 52 7 4 0 0 .. .. 27 16Niger .. 12 .. 18 .. 26 .. 3 .. .. .. 41Nigeria a .. 1 .. 2 .. .. .. .. .. .. .. 97Norway 28 30 28 24 0 0 1 1 18 20 25 24Oman a 24 .. 1 .. 3 .. 2 .. .. .. 70 ..Pakistan a 19 25 30 31 11 8 8 5 .. .. 32 32Panama a 18 .. 9 .. 9 .. 4 .. 18 .. 38 ..Papua New Guinea a 31 .. 9 .. 24 .. 2 .. 0 .. 33 ..Paraguay a 11 13 37 47 11 9 3 1 7 7 30 23Peru a 19 31 40 39 8 2 3 6 10 9 20 14Philippines a 40 41 27 29 19 21 4 .. .. .. 10 9Poland 15 14 33 37 2 0 0 1 36 37 14 10Portugal 25 23 32 31 0 0 2 2 30 33 .. ..Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar a .. 40 .. .. .. 3 .. .. .. .. .. 572012 World Development Indicators 267

4.14Central government revenuesTaxes on income,profits, andcapital gainsTaxes ongoods andservicesTaxes oninternationaltradeOthertaxesSocialcontributionsGrants andother revenue% of revenue % of revenue % of revenue % of revenue % of revenue % of revenue2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Romania 9 22 33 35 3 0 1 0 42 33 13 10Russian Federation 5 2 28 21 9 26 0 0 37 20 20 31Rwanda a .. .. .. .. .. .. .. .. .. .. .. ..Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. ..Senegal a 21 .. 34 .. 30 .. 3 .. .. .. 12 ..Serbia a .. 9 .. 45 .. 4 .. 0 .. 34 .. 8Sierra Leone a 15 17 8 25 29 14 0 .. .. .. 48 44Singapore a 29 34 18 26 1 0 9 16 .. .. 42 24Slovak Republic 17 9 31 33 1 0 1 0 39 43 13 15Slovenia a 13 10 34 34 2 0 3 0 39 42 9 14Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 52 53 33 32 3 3 3 2 2 2 7 8South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 27 23 25 20 0 .. 0 0 41 51 .. 6Sri Lanka a 13 18 57 45 11 14 4 8 2 1 14 14Sudan a 15 .. 35 .. 29 .. 1 .. .. .. 21 ..Swaziland a 24 .. 13 .. 50 .. 4 .. .. .. 9 ..Sweden 17 11 29 37 .. .. 6 13 30 25 .. ..Switzerland a 14 24 22 26 1 6 3 3 41 36 19 5Syrian Arab Republic a 30 .. 21 .. 11 .. 6 .. 0 .. 32 ..Tajikistan a 3 .. 55 .. 14 .. 1 .. 20 .. 7 ..Tanzania .. .. .. .. .. .. .. .. .. .. .. ..Thailand 29 33 40 40 10 5 0 1 4 6 17 15Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo .. 11 .. 37 .. 19 .. 6 .. .. .. 27Trinidad and Tobago a 36 47 23 15 5 4 17 11 5 6 13 16Tunisia a 20 27 37 31 11 6 4 4 17 21 10 10Turkey a .. 24 .. 51 .. 1 .. 8 .. .. .. 16Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda a 10 22 29 47 22 10 0 0 .. .. 39 22Ukraine a 12 10 33 34 4 2 2 0 30 37 19 17United Arab Emirates a .. .. 17 .. .. .. .. .. 0 .. 82 ..United Kingdom 38 36 30 28 .. .. 8 7 20 23 4 6United States 57 50 3 3 1 1 1 1 35 39 3 6Uruguay a 15 18 34 40 3 4 8 2 29 30 12 7Uzbekistan .. .. .. .. .. .. .. .. .. .. .. ..Venezuela, RB a 27 .. 25 .. 7 .. 4 .. 4 .. 34 ..Vietnam .. .. .. .. .. .. .. .. .. .. .. ..West Bank and Gaza .. 2 .. 21 .. 11 .. 0 .. 0 .. 66Yemen, Rep. a 18 .. 9 .. 10 .. 2 .. .. .. 61 ..Zambia a 36 44 45 32 13 8 0 0 0 .. 6 15Zimbabwe a 43 .. 23 .. 20 .. 2 .. 3 .. 9 ..World 20 m 23 m 30 m 32 m 6 m 4 m 2 m 2 m .. m .. m 15 m 17 mLow income .. .. .. .. .. .. .. .. .. .. .. ..Middle income 20 24 34 36 7 5 1 2 .. .. 17 17Lower middle income 18 23 32 33 13 7 1 1 .. .. 20 18Upper middle income 14 22 36 37 6 4 3 2 16 19 14 14Low & middle income 18 21 32 36 9 6 2 1 .. .. 20 18East Asia & Pacifi c 25 33 30 29 9 6 3 1 .. .. 24 28Europe & Central Asia 10 8 37 40 6 4 0 0 30 30 17 16Latin America & Carib. 18 26 40 39 6 4 4 2 9 9 16 16Middle East & N. Africa 17 27 30 31 10 6 4 3 .. 6 30 23South Asia 17 19 30 27 15 10 4 3 .. 0 26 30Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. ..High income 26 26 27 26 1 0 3 2 33 35 8 12Euro area 25 23 25 27 0 0 3 3 35 37 6 7Note: Components may not sum to 100 percent because of missing data or adjustment to tax revenue.a. Data were reported on a cash basis and have been adjusted to the accrual framework.2682012 World Development Indicators

Central government revenues4.14ECONOMYAbout the dataDefinitionsThe International Monetary Fund (IMF) classifi esgovernment revenues as taxes, grants, and propertyincome. Taxes are classifi ed by the base on whichthe tax is levied, grants by the source, and propertyincome by type (for example, interest, dividends,or rent). The most important source of revenue istaxes. Grants are unrequited, nonrepayable, noncompulsoryreceipts from other government unitsand foreign governments or from international organizations.Transactions are generally recorded on anaccrual basis.The IMF’s Government Finance Statistics Manual2001 describes taxes as compulsory, unrequitedpayments made to governments by individuals, businesses,or institutions. Taxes are classifi ed in sixmajor groups by the base on which the tax is levied:income, profi ts, and capital gains; payroll and workforce;property; goods and services; internationaltrade and transactions; and other. However, the distinctionsare not always clear. Taxes levied on theincome and profi ts of individuals and corporationsare classifi ed as direct taxes, and taxes and dutieslevied on goods and services are classifi ed as indirecttaxes. This distinction may be a useful simplifi cation,but it has no particular analytical signifi canceexcept with respect to the capacity to fi x tax rates.Direct taxes tend to be progressive, whereas indirecttaxes are proportional.Social security taxes do not refl ect compulsorypayments made by employers to provident fundsor other agencies with a like purpose. Similarly,expenditures from such funds are not refl ected ingovernment expenses (see table 4.13). For furtherdiscussion of taxes and tax policies, see About thedata for table 5.6. For further discussion of governmentrevenues and expenditures, see About the datafor tables 4.12 and 4.13.• Taxes on income, profits, and capital gains arelevied on the actual or presumptive net incomeof individuals, on the profi ts of corporations andenterprises, and on capital gains, whether realizedor not, on land, securities, and other assets.Intragovernmental payments are eliminated in consolidation.• Taxes on goods and services includegeneral sales and turnover or value added taxes,selective excises on goods, selective taxes on services,taxes on the use of goods or property, taxeson extraction and production of minerals, and profitsof fi scal monopolies. • Taxes on internationaltrade include import duties, export duties, profi tsof export or import monopolies, exchange profi ts,and exchange taxes. • Other taxes include employerpayroll or labor taxes, taxes on property, and taxesnot allocable to other categories, such as penaltiesfor late payment or nonpayment of taxes. • Socialcontributions include social security contributions byemployees, employers, and self-employed individuals,and other contributions whose source cannotbe determined. They also include actual or imputedcontributions to social insurance schemes operatedby governments. • Grants and other revenue includegrants from other foreign governments, internationalorganizations, and other government units; interest;dividends; rent; requited, nonrepayable receiptsfor public purposes (such as fi nes, administrativefees, and entrepreneurial income from governmentownership of property); and voluntary, unrequited,nonrepayable receipts other than grants.Data sourcesData on central government revenues are from theIMF’s Government Finance Statistics database.Each country’s accounts are reported using thesystem of common defi nitions and classifi cationsin the IMF’s Government Finance Statistics Manual2001. The IMF receives additional informationfrom the Organisation for Economic Co-operationand Development on the tax revenues of some ofits members. See the IMF sources for completeand authoritative explanations of concepts, defi nitions,and data sources.2012 World Development Indicators 269

4.15 Monetary indicatorsBroadmoneyClaims ondomesticeconomyClaims oncentralgovernmentInterest rateannual % growthAnnual growth% of broad moneyAnnual growth% of broad money Deposit%LendingReal2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan .. 26.9 .. 9.0 .. –6.0 .. .. .. 15.7 .. 11.6Albania 12.0 12.5 0.9 4.9 4.8 0.2 8.3 6.4 22.1 12.8 17.0 9.0Algeria 14.1 10.5 8.4 2.4 –11.6 –2.7 7.5 1.8 10.0 8.0 –11.7 –7.1Angola a 303.7 14.0 .. .. –413.7 –13.9 39.6 12.8 103.2 22.5 –60.8 –3.2Argentina a 1.5 33.1 .. .. –0.8 13.1 8.3 9.2 11.1 10.6 9.9 –4.2Armenia 38.6 10.6 0.3 24.9 –5.7 8.3 18.1 9.0 31.6 19.2 33.4 9.2Australia a 3.7 9.4 .. .. –1.8 0.2 5.1 4.2 9.3 7.3 6.6 7.2Austria b .. .. .. .. .. .. 2.2 .. 5.6 .. 5.3 ..Azerbaijan 73.4 24.3 –23.9 12.9 15.4 8.2 12.9 11.6 19.7 20.7 6.4 8.5Bahrain 10.2 10.5 .. .. –0.4 –0.5 5.8 1.2 11.6 7.2 –2.4 –3.0Bangladesh 19.3 21.1 10.7 19.7 5.6 2.7 8.6 7.1 15.5 13.0 13.4 6.1Belarus 219.3 31.9 59.9 79.2 22.2 –4.7 37.6 9.1 67.7 9.2 –41.2 –0.9Belgium b .. .. .. .. .. .. 3.6 .. 8.0 9.5 5.9 8.2Benin a 26.0 7.1 .. .. 0.6 –5.5 3.5 3.5 .. .. .. ..Bolivia 1.6 14.8 –1.3 9.6 3.1 –6.5 11.0 1.0 34.6 9.9 27.9 1.0Bosnia and Herzegovina a 11.3 7.2 19.9 3.6 –0.4 2.3 14.7 3.2 30.5 7.9 1.3 6.6Botswana 1.4 10.7 10.3 6.3 –56.2 20.2 9.4 5.6 15.5 11.5 15.4 –2.8Brazil 19.7 15.4 8.3 20.7 13.5 4.9 17.2 8.9 56.8 40.0 47.7 30.4Bulgaria 30.8 6.3 6.5 1.5 8.5 3.6 3.1 4.1 11.3 11.1 4.4 8.0Burkina Faso a 7.5 19.3 .. .. 6.6 3.4 3.5 3.5 .. .. .. ..Burundi 15.5 19.9 15.0 15.7 –22.6 5.3 .. .. 15.8 12.4 2.3 4.3Cambodia 26.9 21.3 5.4 14.8 –6.9 0.4 6.8 1.3 .. .. .. ..Cameroon a 19.1 12.8 6.6 7.1 –12.3 3.2 5.0 3.3 22.0 15.0 18.6 12.7Canada 6.6 15.1 3.6 23.3 2.4 4.7 3.5 0.1 7.3 2.6 3.0 –0.3Central African Republic a 2.4 14.2 .. .. 6.8 11.6 5.0 3.3 22.0 15.0 18.3 13.3Chad a 19.4 26.1 .. .. 15.1 8.7 5.0 3.3 22.0 15.0 15.9 9.4Chile 9.1 10.6 4.1 2.1 4.0 0.4 9.2 1.8 14.8 4.8 9.8 –8.4China a 12.3 18.9 .. .. 0.0 0.3 2.3 2.8 5.9 5.8 3.7 –0.7Hong Kong SAR, China a 9.3 7.4 .. .. 0.4 0.6 4.8 0.0 9.5 5.0 13.6 4.5Colombia 3.6 11.5 8.9 16.2 6.0 –0.7 12.1 3.7 18.8 9.4 –10.3 6.1Congo, Dem. Rep. a 51.2 30.8 .. .. –1.6 –44.7 .. 16.8 .. 56.5 .. 27.9Congo, Rep. a 58.5 37.6 .. .. –11.7 –34.4 5.0 3.3 22.0 15.0 –17.0 14.4Costa Rica 24.0 0.8 14.1 4.7 –0.2 0.2 13.4 5.3 24.9 17.1 16.7 8.6Côte d’Ivoire a –1.8 18.2 .. .. –7.6 4.7 3.5 3.5 .. .. .. ..Croatia 29.1 4.3 21.3 7.0 2.0 1.4 3.7 1.8 12.1 10.4 7.1 9.3Cuba .. .. .. .. .. .. .. .. .. .. .. ..Cyprus 10.4 19.8 11.1 35.6 0.1 3.5 6.5 3.4 8.0 6.7 4.0 2.0Czech Republic 16.0 1.9 –11.0 2.1 2.6 1.9 3.4 1.1 7.2 5.9 5.6 7.1Denmark –12.1 –3.1 26.1 5.0 3.0 0.0 3.2 .. 8.1 .. 4.9 ..Dominican Republic 16.8 12.2 13.2 13.0 2.8 –0.1 17.7 4.9 26.8 12.1 18.6 6.7Ecuador 47.0 18.6 –11.4 20.2 –28.1 –5.9 8.8 3.9 17.1 .. 26.0 ..Egypt, Arab Rep. 11.6 12.4 4.1 3.9 7.7 2.5 9.5 6.2 13.2 11.0 7.9 0.8El Salvador 1.6 –0.1 2.6 –0.4 2.3 2.0 9.3 .. 14.0 .. 10.5 ..Eritrea 17.3 15.6 3.7 1.6 25.7 12.6 .. .. .. .. .. ..Estonia 30.5 2.0 39.4 –8.6 –4.1 3.4 3.8 1.1 7.4 7.8 2.4 6.2Ethiopia a 13.1 23.4 .. .. 19.8 2.5 6.0 4.7 10.9 8.0 3.8 –17.1Finland b .. .. .. .. .. .. 1.6 .. 5.6 .. 2.9 ..France b .. .. .. .. .. .. 2.6 1.5 6.7 .. 5.0 ..Gabon a 18.3 19.2 .. .. –42.2 21.4 5.0 3.3 22.0 15.0 –4.8 9.3Gambia, The a 34.8 13.7 .. .. 2.7 15.4 12.5 14.6 24.0 27.0 19.6 17.2Georgia 39.2 34.8 18.7 24.7 19.8 –2.0 10.2 9.2 32.8 24.2 26.8 14.3Germany b .. .. .. .. .. .. 3.4 .. 9.6 .. 10.4 ..Ghana 54.2 31.9 7.5 13.0 32.9 11.7 28.6 17.1 .. .. .. ..Greece b .. .. .. .. .. .. 6.1 .. 12.3 .. 8.6 ..Guatemala 21.4 9.1 4.2 2.6 10.2 2.3 10.2 5.5 20.9 13.3 13.2 8.0Guinea a 12.9 .. .. .. 7.9 .. 7.5 .. 19.4 .. 7.4 ..Guinea-Bissau a 60.7 24.4 .. .. 16.2 9.4 3.5 3.5 .. .. .. ..2702012 World Development Indicators

Monetary indicators 4.15ECONOMYBroadmoneyClaims ondomesticeconomyClaims oncentralgovernmentInterest rateannual % growthAnnual growth% of broad moneyAnnual growth% of broad money Deposit%LendingReal2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Haiti 20.3 26.1 12.3 –0.2 13.8 –9.0 12.1 0.7 31.4 17.5 18.3 11.5Honduras 15.4 9.8 7.7 5.1 –2.6 1.4 15.9 9.8 26.8 18.9 –3.1 12.4Hungary 12.6 4.2 14.5 5.0 –2.0 1.3 9.5 4.9 12.6 7.6 2.6 4.4India a 15.2 17.8 9.9 15.8 4.7 5.3 .. .. 12.3 12.2 8.5 12.6Indonesia 16.6 15.4 7.2 17.1 17.2 –3.7 12.5 7.0 18.5 13.3 –1.7 4.8Iran, Islamic Rep. a 22.4 27.7 .. .. –7.9 2.0 11.7 11.9 .. 12.0 .. 11.3Iraq .. 31.2 .. 8.1 .. 14.4 .. 6.1 .. 14.3 .. –8.5Ireland b .. .. .. .. .. .. 0.1 .. 4.8 .. –2.2 ..Israel a 8.0 5.7 .. .. –4.8 1.3 8.6 1.6 12.9 4.5 11.1 3.4Italy b .. .. .. .. .. .. 1.8 .. 7.0 4.0 5.0 3.6Jamaica –7.0 5.8 9.1 –0.7 –2.3 –8.2 11.6 6.3 23.3 20.5 11.5 8.9Japan 1.3 2.0 –5.4 –1.4 2.6 2.8 0.1 0.5 2.1 1.6 3.9 3.8Jordan a 7.6 9.2 .. .. –1.2 0.4 7.0 3.5 11.8 9.0 12.2 2.6Kazakhstan 45.0 13.3 32.2 7.1 –3.2 1.2 .. .. .. .. .. ..Kenya 4.9 22.4 4.7 14.3 –2.1 12.8 8.1 4.6 22.3 14.4 15.3 10.1Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. a 25.4 14.9 .. .. –1.1 0.4 7.9 3.9 8.5 5.5 3.4 1.7Kosovo –12.2 13.5 12.1 10.9 –37.7 1.4 .. 3.4 .. 14.3 .. 10.9Kuwait 6.3 3.0 8.5 2.0 –7.4 –12.9 5.9 2.3 8.9 4.9 –9.7 2.5Kyrgyz Republic a 11.7 33.2 .. .. 7.8 –8.8 18.4 4.1 51.9 31.5 19.4 23.1Lao PDR a 46.0 39.1 .. .. –17.6 1.5 12.0 3.0 32.0 22.6 5.5 12.0Latvia 27.0 11.5 31.2 –18.1 7.8 2.4 4.4 1.9 11.9 9.6 7.4 12.2Lebanon a 9.8 12.1 .. .. 10.5 1.1 11.2 6.2 18.2 8.3 20.7 3.8Lesotho 1.4 14.5 6.6 5.9 14.9 13.9 4.9 3.7 17.1 11.2 14.4 7.3Liberia a 18.3 43.4 .. .. 197.0 47.7 6.2 4.1 20.5 14.2 22.1 5.9Libya a 3.1 –0.6 .. .. –10.4 –19.6 3.0 2.5 7.0 6.0 –9.6 57.8Lithuania 16.5 8.4 14.4 –6.0 0.5 –0.6 3.9 4.8 12.1 8.4 11.1 12.6Macedonia, FYR 22.2 12.1 2.7 6.3 –15.9 6.6 11.2 7.1 18.9 9.5 9.9 7.2Madagascar a 17.2 9.7 .. .. 0.1 –6.4 15.0 10.5 26.5 49.0 18.0 37.9Malawi a 45.5 17.2 .. .. 7.7 –13.1 33.3 3.6 53.1 24.6 17.3 15.7Malaysia 10.0 7.3 5.5 8.1 2.1 –0.2 3.4 2.5 7.7 5.0 –1.1 –0.1Mali a 12.5 12.2 .. .. –4.2 1.6 3.5 3.5 .. .. .. ..Mauritania a .. 15.5 .. .. .. 12.6 9.4 8.0 25.6 17.0 23.9 –2.0Mauritius 9.2 7.6 5.8 9.3 –4.7 1.0 9.6 8.4 20.8 8.9 18.3 7.2Mexico –4.5 12.8 10.1 8.7 3.5 0.8 8.3 1.2 16.9 5.3 4.3 0.9Moldova 41.7 13.4 24.4 9.3 –5.7 –4.0 24.9 7.7 33.8 16.4 5.1 4.7Mongolia 17.6 62.5 29.6 23.7 –7.1 –4.1 16.8 11.9 37.0 20.1 22.3 0.0Morocco 8.4 7.9 4.0 8.8 3.6 0.3 5.2 3.7 13.3 .. 14.0 ..Mozambique 38.3 22.8 11.9 18.3 6.9 –1.3 9.7 9.7 19.0 16.3 6.3 3.1Myanmar a 42.4 42.5 .. .. 24.9 32.6 9.8 12.0 15.3 17.0 12.5 8.1Namibia 13.2 9.6 19.4 10.1 –4.0 7.1 7.4 5.0 15.3 9.7 –9.0 0.4Nepal 18.8 9.6 –10.4 8.9 2.6 6.2 6.0 3.6 9.5 8.0 4.8 –4.8Netherlands b .. .. .. .. .. .. 2.9 2.3 4.8 1.8 0.6 0.4New Zealand a 1.5 8.4 .. .. –1.0 2.1 6.4 4.6 7.8 6.3 4.7 4.9Nicaragua 9.4 21.7 7.0 4.8 10.0 3.9 10.8 3.0 18.1 13.3 8.8 10.1Niger a 12.8 21.6 .. .. –13.8 0.8 3.5 3.5 .. .. .. ..Nigeria 48.1 9.3 5.8 –11.2 –43.0 24.0 11.7 6.5 21.3 17.6 –12.2 9.4Norway a 8.7 .. .. .. –4.7 .. 6.7 2.3 8.9 4.3 –5.8 11.4Oman 6.0 11.3 1.1 16.1 9.5 –4.1 7.6 3.4 10.1 6.8 –8.3 40.4Pakistan 12.1 15.1 2.0 2.3 2.6 10.9 .. 8.1 .. 14.0 .. 1.9Panama 9.3 11.1 –8.4 13.6 0.2 1.6 7.1 3.0 10.5 7.7 11.9 4.6Papua New Guinea 5.0 10.2 1.2 9.3 –4.6 –6.6 8.5 1.4 17.5 10.4 3.9 1.1Paraguay 2.8 19.0 1.7 27.2 4.7 –6.3 15.7 1.2 26.8 26.0 13.1 18.1Peru a –0.4 21.5 .. .. 2.3 –3.4 9.8 1.5 30.0 19.0 25.4 11.3Philippines 8.1 10.9 2.2 7.5 1.5 2.9 8.3 3.2 10.9 7.7 4.9 3.3Poland 11.6 8.7 .. 9.0 –5.8 1.2 14.2 .. 20.0 .. 11.9 ..Portugal b .. .. .. .. .. .. 2.4 .. 5.2 .. 1.8 ..2012 World Development Indicators 271

4.15Monetary indicatorsBroadmoneyClaims ondomesticeconomyClaims oncentralgovernmentInterest rateannual % growthAnnual growth% of broad moneyAnnual growth% of broad money Deposit%LendingReal2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar 10.7 23.1 –1.7 21.6 –23.1 4.2 0.0 2.9 .. 7.3 .. 31.0Romania 40.8 6.8 20.0 5.8 –1.1 8.0 33.1 7.3 53.9 14.1 6.7 10.1Russian Federation 58.5 24.6 33.1 12.4 –18.0 9.7 6.5 6.0 24.4 10.8 –9.6 –0.5Rwanda a 15.6 .. .. .. –11.4 .. 10.1 7.1 17.0 16.7 20.6 14.3Saudi Arabia a 4.5 5.2 .. .. –3.5 –5.4 .. .. .. .. .. ..Senegal a 10.8 13.7 .. .. –3.9 3.8 3.5 3.5 .. .. .. ..Serbia 160.8 13.1 –71.0 29.5 22.5 5.9 78.7 11.3 6.3 17.3 –40.1 7.6Sierra Leone a 12.1 32.7 .. .. 54.6 30.0 9.2 8.9 26.3 21.3 19.0 6.0Singapore a –2.0 8.6 .. .. –1.6 –4.9 1.7 0.2 5.8 5.4 2.1 5.9Slovak Republic b 15.2 5.0 8.2 11.7 4.1 1.1 8.5 3.8 14.9 5.8 5.0 2.8Slovenia b .. .. .. .. .. .. 10.0 1.4 15.8 5.9 10.0 2.9Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 7.2 6.9 –11.8 6.8 0.2 –0.2 9.2 6.5 14.5 9.8 5.2 1.6South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain b .. .. .. .. .. .. 3.0 .. 5.2 .. 1.7 ..Sri Lanka a 12.9 15.8 .. .. 12.5 –0.6 9.2 6.9 16.2 10.2 8.3 2.7Sudan 36.9 25.4 16.9 10.7 33.9 12.5 .. .. .. .. .. ..Swaziland –6.6 7.9 16.9 5.0 1.7 26.0 6.5 3.9 14.0 9.8 13.8 3.4Sweden 1.9 –8.5 8.5 9.5 2.4 –0.9 2.2 .. 5.8 .. 4.3 ..Switzerland a –16.9 5.5 .. .. 2.1 0.2 3.0 0.1 4.3 2.7 3.1 2.7Syrian Arab Republic 19.0 13.5 –4.1 10.3 –6.1 1.9 4.0 6.2 9.0 9.9 –0.6 3.4Tajikistan a 63.3 –3.6 .. .. 36.6 –9.8 1.3 6.1 25.6 24.1 2.4 7.6Tanzania 14.8 25.4 12.2 11.4 0.7 7.7 7.4 6.6 21.6 14.5 13.0 6.4Thailand 4.9 10.9 6.2 10.7 0.5 –1.2 3.3 1.0 7.8 5.9 6.4 2.2Timor-Leste 41.1 9.9 45.7 2.3 –36.8 –46.5 0.8 0.8 16.7 11.0 11.4 1.7Togo a 15.4 16.3 .. .. –0.3 4.3 3.5 3.5 .. .. .. ..Trinidad and Tobago a 11.7 30.5 .. .. –13.2 25.3 8.2 1.5 16.5 9.3 3.2 4.6Tunisia a 14.1 11.3 .. .. 5.6 –1.2 .. .. .. .. .. ..Turkey 40.7 18.5 16.2 27.6 26.8 4.0 47.2 15.3 .. .. .. ..Turkmenistan a 83.3 .. .. .. –53.4 .. .. .. .. .. .. ..Uganda 18.1 37.8 8.2 20.7 29.4 14.0 9.8 7.7 22.9 20.2 10.6 10.2Ukraine 44.5 22.7 30.9 2.1 –1.7 8.4 13.7 10.6 41.5 15.9 15.0 0.7United Arab Emirates a 15.3 6.2 .. .. –9.6 2.6 6.2 .. 9.7 .. –1.5 ..United Kingdom a 11.1 4.0 .. .. –2.4 2.9 4.5 .. 6.0 0.5 5.3 –2.3United States 8.1 –2.1 5.0 –1.5 0.5 0.6 .. .. 9.2 3.3 6.9 2.4Uruguay 9.5 22.1 45.1 11.3 –1.8 8.6 18.3 4.2 46.1 10.3 41.1 4.9Uzbekistan .. .. .. .. .. .. .. .. .. .. .. ..Venezuela, RB a 33.7 28.0 .. .. –6.4 –1.5 16.3 14.8 25.2 18.3 –3.3 –19.3Vietnam a 35.4 29.7 .. .. –2.4 2.3 3.7 11.2 10.6 13.1 6.9 1.1West Bank and Gaza .. .. .. .. .. .. 1.5 0.3 .. .. .. ..Yemen, Rep. a 25.3 11.8 .. .. –45.6 12.9 14.0 18.7 19.5 23.8 –3.1 –0.7Zambia 73.8 29.9 –11.4 8.1 162.0 11.5 20.2 7.4 38.8 20.9 6.7 8.2Zimbabwe a 45.7 .. .. .. 29.5 .. 50.2 121.5 68.2 579.0 67.8 605.4a. Includes claims on the private sector only. b. As members of the European Monetary Union, these countries share a single currency, the euro.2722012 World Development Indicators

Monetary indicators4.15ECONOMYAbout the dataDefinitionsMoney and the fi nancial accounts that record thesupply of money lie at the heart of a country’sfi nancial system. There are several commonly useddefi nitions of the money supply. The narrowest,M1, encompasses currency held by the public anddemand deposits with banks. M2 includes M1 plustime and savings deposits with banks that requireprior notice for withdrawal. M3 includes M2 as wellas various money market instruments, such as certificates of deposit issued by banks, bank depositsdenominated in foreign currency, and deposits withfi nancial institutions other than banks. Howeverdefi ned, money is a liability of the banking system,distinguished from other bank liabilities by the specialrole it plays as a medium of exchange, a unit ofaccount, and a store of value.The banking system’s assets include its net foreignassets and net domestic credit. Net domesticcredit includes credit extended to the private sectorand general government and credit extended to thenonfi nancial public sector in the form of investmentsin short- and long-term government securities andloans to state enterprises; liabilities to the publicand private sectors in the form of deposits with thebanking system are netted out. Net domestic creditalso includes credit to banking and nonbank fi nancialinstitutions.Domestic credit is the main vehicle through whichchanges in the money supply are regulated, with centralbank lending to the government often playing themost important role. The central bank can regulatelending to the private sector in several ways—forexample, by adjusting the cost of the refi nancingfacilities it provides to banks, by changing marketinterest rates through open market operations, or bycontrolling the availability of credit through changesin the reserve requirements imposed on banks andceilings on the credit provided by banks to the privatesector.Monetary accounts are derived from the balancesheets of fi nancial institutions—the central bank,commercial banks, and nonbank fi nancial intermediaries.Although these balance sheets are usuallyreliable, they are subject to errors of classifi cation,valuation, and timing and to differences in accountingpractices. For example, whether interest incomeis recorded on an accrual or a cash basis can makea substantial difference, as can the treatment of nonperformingassets. Valuation errors typically arisefor foreign exchange transactions, particularly incountries with fl exible exchange rates or in countriesthat have undergone currency devaluation during thereporting period. The valuation of fi nancial derivativesand the net liabilities of the banking systemcan also be diffi cult. The quality of commercial bankreporting also may be adversely affected by delays inreports from bank branches, especially in countrieswhere branch accounts are not computerized. Thusthe data in the balance sheets of commercial banksmay be based on preliminary estimates subject toconstant revision. This problem is likely to be evenmore serious for nonbank fi nancial intermediaries.Many interest rates coexist in an economy, refl ectingcompetitive conditions, the terms governingloans and deposits, and differences in the positionand status of creditors and debtors. In some economiesinterest rates are set by regulation or administrativefi at. In economies with imperfect markets, orwhere reported nominal rates are not indicative ofeffective rates, it may be diffi cult to obtain data oninterest rates that refl ect actual market transactions.Deposit and lending rates are collected by the InternationalMonetary Fund (IMF) as representative interestrates offered by banks to resident customers.The terms and conditions attached to these ratesdiffer by country, however, limiting their comparability.Real interest rates are calculated by adjustingnominal rates by an estimate of the infl ation rate inthe economy. A negative real interest rate indicatesa loss in the purchasing power of the principal. Thereal interest rates in the table are calculated as(i – P) / (1 + P), where i is the nominal lending interestrate and P is the infl ation rate (as measured bythe GDP defl ator).In 2009 the IMF began publishing a new presentationof monetary statistics for countries that reportdata in accordance with its Monetary Financial StatisticalManual 2000. The presentation for countriesthat report data in accordance with its InternationalFinancial Statistics (IFS) remains the same.• Broad money (IFS line 35L..ZK) is the sum of currencyoutside banks; demand deposits other thanthose of the central government; the time, savings,and foreign currency deposits of resident sectorsother than the central government; bank and traveler’schecks; and other securities such as certifi catesof deposit and commercial paper. Change in broadmoney is measured as the difference in end-of-yeartotals relative to the preceding year. Data for 2010for countries reporting under the old presentationof monetary statistics and data for 2000 for allcountries are based on money plus quasi money.• Claims on domestic economy (IFS line 32S..ZK)are gross credit from the fi nancial system to households,nonprofi t institutions serving households, nonfinancial corporations, state and local governments,and social security funds. Data for countries whereclaims on the domestic economy are not availableare claims on the private sector (IFS line 32D..ZKor 32D.ZF) and are footnoted as such. • Claims oncentral government (IFS line 32AN..ZK) are loansto central government institutions less deposits.• Deposit interest rate is the rate paid by commercialor similar banks for demand, time, or savingsdeposits. • Lending interest rate is the rate chargedby banks on loans to prime customers. • Real interestrate is the lending interest rate adjusted for infl a-tion as measured by the GDP defl ator.Data sourcesData on monetary and fi nancial statistics arepublished by the IMF in its monthly InternationalFinancial Statistics and annual International FinancialStatistics Yearbook. The IMF collects data onthe fi nancial systems of its member countries.The World Bank receives data from the IMF inelectronic fi les that may contain more recent revisionsthan the published sources. The discussionof monetary indicators draws from Caiola (1995).Also see the IMF’s Monetary and Financial StatisticsManual (2000) for guidelines for the presentationof monetary and fi nancial statistics. Data onreal interest rates are derived from World Bankdata on the GDP defl ator.2012 World Development Indicators 273

4.16 Exchange rates and pricesOfficialexchange ratePurchasingpower parity(PPP)conversionfactorRatio of PPPconversionfactor tomarketexchangerateRealeffectiveexchangerateGDP implicitdeflatorConsumer priceindexWholesale priceindexlocal currencyunits to $local currency unitsto international $Index2005 = 100average annual% growthaverage annual% growthaverage annual% growth2010 2011 a 2000 2010 2010 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10Afghanistan 46.45 46.58 13.7 19.3 0.4 .. .. 7.8 .. 8.9 .. ..Albania 103.94 100.51 40.6 44.5 0.4 .. 37.7 3.3 27.8 2.8 .. 4.1Algeria 74.39 72.94 25.1 40.3 0.5 102.4 18.5 8.3 17.3 3.4 .. 4.0Angola 91.91 93.74 2.8 66.9 0.7 .. 739.4 36.1 711.0 36.7 .. ..Argentina 3.90 4.11 0.8 2.2 0.6 .. 5.2 13.0 8.9 9.8 0.1 15.1Armenia 373.66 372.50 164.8 207.3 0.6 126.2 212.5 4.6 70.5 4.3 .. 2.4Australia 1.09 0.97 1.3 1.5 1.3 115.2 1.4 3.9 2.1 2.9 1.1 3.4Austria b 0.76 0.72 0.9 0.9 1.1 98.7 1.5 1.7 2.2 1.9 0.3 2.3Azerbaijan 0.80 0.79 0.3 0.5 0.6 .. 203.0 9.8 179.7 8.4 .. ..Bahrain 0.38 0.38 0.2 0.3 0.8 89.5 0.5 7.4 0.8 2.1 .. ..Bangladesh 69.65 74.15 21.3 28.1 0.4 .. 4.1 5.4 5.5 6.8 .. ..Belarus 2,978.51 4,974.63 177.3 1,232.9 0.4 .. 355.1 21.5 271.3 17.4 267.8 21.2Belgium b 0.76 0.72 0.9 0.9 1.1 100.7 1.8 2.1 1.9 2.1 1.2 2.6Benin 495.28 471.87 212.7 233.9 0.5 .. 8.7 3.3 8.7 3.2 .. ..Bolivia 7.02 6.94 2.0 2.9 0.4 122.1 8.6 6.9 8.7 5.4 .. ..Bosnia and Herzegovina 1.48 1.41 0.7 0.8 0.5 .. 4.1 3.8 .. 3.3 .. ..Botswana 6.79 6.84 1.9 3.6 0.5 .. 9.7 9.0 10.4 8.8 .. ..Brazil 1.76 1.67 1.0 1.7 1.0 .. 211.8 8.1 199.5 6.6 204.9 9.3Bulgaria 1.48 1.41 0.5 0.7 0.5 121.1 102.1 5.9 117.5 6.2 85.7 6.1Burkina Faso 495.28 471.87 196.9 211.2 0.4 .. 3.7 2.7 5.5 3.0 .. ..Burundi 1,230.75 1,261.07 255.8 535.3 0.5 113.3 13.4 10.7 16.1 9.4 .. ..Cambodia 4,184.92 4,058.50 1,232.7 1,516.7 0.4 .. 4.4 4.9 6.6 6.2 .. ..Cameroon 495.28 471.87 256.8 248.1 0.5 101.1 6.3 2.1 6.5 2.5 .. ..Canada 1.03 0.99 1.2 1.2 1.2 111.8 1.5 2.5 1.7 2.1 2.7 1.3Central African Republic 495.28 471.87 271.9 287.2 0.6 110.3 4.5 2.8 5.3 3.3 6.0 4.4Chad 495.28 471.87 180.3 244.3 0.5 .. 7.1 5.4 6.9 2.7 .. ..Chile 510.25 483.67 284.0 402.0 0.8 108.4 7.9 6.4 .. .. 7.0 6.1China 6.77 6.46 3.3 4.0 0.6 118.7 7.9 4.4 8.6 2.4 .. ..Hong Kong SAR, China 7.77 7.78 7.5 5.3 0.7 .. 4.5 –1.1 5.9 0.6 0.6 0.2Colombia 1,898.57 1,848.14 898.0 1,248.6 0.7 120.3 22.6 5.9 20.2 5.6 16.4 4.6Congo, Dem. Rep. 905.91 919.49 26.0 519.9 0.6 1,025.3 964.9 26.7 930.2 26.9 .. ..Congo, Rep. 495.28 471.87 264.4 343.3 0.7 .. 9.0 7.4 9.3 3.4 .. ..Costa Rica 525.83 505.66 173.9 349.6 0.7 121.8 15.9 10.1 15.6 10.8 14.1 12.5Côte d’Ivoire 495.28 471.87 278.7 301.0 0.6 99.6 9.2 3.2 7.2 2.9 .. ..Croatia 5.50 5.34 3.7 3.9 0.7 105.9 90.0 3.9 86.3 2.9 69.8 3.1Cuba .. .. .. .. .. .. 6.4 3.3 .. .. .. ..Cyprus b 0.76 0.72 0.7 0.7 0.9 102.3 4.3 3.1 3.7 2.7 2.8 3.8Czech Republic 19.10 17.70 14.2 14.2 0.7 122.5 12.8 2.0 7.8 2.5 8.2 2.2Denmark 5.62 5.37 8.4 7.9 1.4 102.0 1.6 2.5 2.1 2.0 1.1 2.4Dominican Republic 36.88 38.11 8.8 20.5 0.6 97.4 9.8 12.6 8.7 13.4 .. ..Ecuador .. .. 0.3 0.5 0.5 96.7 4.4 8.0 37.1 6.3 .. 7.6Egypt, Arab Rep. 5.62 5.93 1.4 2.4 0.4 .. 8.7 8.7 8.8 8.5 6.1 9.4El Salvador 8.75 8.75 0.5 0.5 0.5 .. 6.2 3.3 8.5 3.8 .. 4.4Eritrea 15.38 15.38 2.9 11.4 0.7 .. 7.9 16.7 .. .. .. ..Estonia 11.81 0.72 7.1 8.2 0.7 .. 53.7 5.4 21.6 4.3 8.1 3.4Ethiopia 14.41 16.90 2.2 4.4 0.3 .. 6.5 11.5 5.5 12.8 .. ..Finland b 0.76 0.72 1.0 0.9 1.2 98.3 1.9 1.2 1.5 1.5 0.9 2.1France b 0.76 0.72 0.9 0.9 1.2 98.8 1.3 1.9 1.6 1.8 .. 1.7Gabon 495.28 471.87 248.7 284.7 0.6 101.3 7.0 4.9 4.6 2.1 .. ..Gambia, The 28.01 29.46 4.1 9.1 0.3 101.0 4.2 10.9 4.0 7.2 .. ..Georgia 1.78 1.69 0.6 0.9 0.5 118.5 356.7 6.9 24.7 7.0 .. 6.6Germany b 0.76 0.72 1.0 0.8 1.1 97.3 1.8 1.0 2.1 1.6 0.4 2.3Ghana 1.43 1.51 0.2 1.1 0.8 97.6 26.7 26.2 28.4 15.9 .. ..Greece b 0.76 0.72 0.7 0.7 0.9 106.7 9.2 3.2 9.0 3.3 3.6 4.2Guatemala 8.06 7.79 3.8 4.8 0.6 .. 10.4 5.5 10.1 7.1 .. ..Guinea 5,726.07 6,620.84 829.0 2,480.8 0.4 .. 5.5 16.2 .. 20.5 .. ..Guinea-Bissau 495.28 471.87 121.8 230.2 0.5 .. 32.5 10.2 34.0 2.5 .. ..2742012 World Development Indicators

Exchange rates and prices 4.16ECONOMYOfficialexchange ratePurchasingpower parity(PPP)conversionfactorRatio of PPPconversionfactor tomarketexchangerateRealeffectiveexchangerateGDP implicitdeflatorConsumer priceindexWholesale priceindexlocal currencyunits to $local currency unitsto international $Index2005 = 100average annual% growthaverage annual% growthaverage annual% growth2010 2011 a 2000 2010 2010 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10Haiti 39.80 40.52 8.9 24.1 0.6 .. 18.1 14.2 21.9 15.2 .. ..Honduras 18.90 18.90 6.7 9.8 0.5 .. 19.9 6.3 18.8 7.7 .. ..Hungary 207.94 201.06 107.9 130.2 0.6 106.1 20.0 5.0 20.3 5.5 16.8 3.7India 45.73 46.67 13.2 18.8 0.4 .. 8.1 5.9 9.1 6.0 7.4 5.3Indonesia 9,090.43 8,770.43 2,801.1 6,190.8 0.7 .. 15.8 11.1 13.7 8.7 15.4 10.9Iran, Islamic Rep. 10,254.18 10,616.31 1,335.8 3,858.8 0.4 145.9 27.7 16.4 26.0 15.3 28.4 10.8Iraq 1,170.00 1,170.00 501.8 842.5 0.7 .. .. 11.1 97.6 21.0 .. ..Ireland b 0.76 0.72 1.0 0.9 1.1 100.2 3.7 1.7 2.3 2.8 1.6 –0.1Israel 3.74 3.58 3.4 3.7 1.0 115.4 11.0 1.4 9.7 2.0 8.1 4.2Italy b 0.76 0.72 0.8 0.8 1.1 99.4 3.8 2.3 3.7 2.2 2.9 2.6Jamaica 87.20 85.89 25.7 59.2 0.7 .. 24.8 11.5 23.5 11.9 .. ..Japan 87.78 79.81 154.8 111.5 1.3 102.7 0.0 –1.1 0.8 –0.1 –1.0 0.6Jordan 0.71 0.71 0.4 0.6 0.8 .. 3.2 6.5 3.5 4.6 .. 8.7Kazakhstan 147.36 146.62 36.5 109.8 0.8 .. 204.7 15.1 67.8 8.8 16.3 12.6Kenya 79.23 88.81 27.2 37.3 0.5 .. 16.6 6.1 15.6 11.2 .. ..Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 1,156.06 1,108.29 746.2 824.6 0.7 .. 5.9 2.3 5.1 3.1 3.7 2.6Kosovo .. .. .. .. .. .. .. 0.5 .. 1.7 .. ..Kuwait 0.29 0.28 0.2 .. .. .. 1.5 9.8 2.0 3.7 1.4 2.9Kyrgyz Republic 45.96 46.14 10.0 17.4 0.4 .. 110.6 8.6 23.3 7.4 35.6 11.2Lao PDR 8,258.77 8,058.40 2,155.5 3,777.2 0.5 .. 27.2 8.3 28.3 7.8 .. ..Latvia 0.53 0.50 0.3 0.4 0.7 .. 48.0 8.1 29.2 6.3 12.0 7.0Lebanon 1,507.50 1,507.50 909.2 988.7 0.7 .. 19.0 3.1 .. .. .. ..Lesotho 7.32 7.26 2.7 4.7 0.6 106.1 9.7 8.0 5.9 7.6 .. ..Liberia 71.32 72.38 19.1 42.2 0.6 .. 51.8 10.3 .. 10.6 .. ..Libya 1.27 1.22 0.3 0.7 0.6 .. .. 17.9 5.6 0.4 .. ..Lithuania 2.61 2.48 1.4 1.6 0.6 .. 75.0 3.9 32.6 3.3 24.8 4.7Macedonia, FYR 46.49 44.23 20.0 18.6 0.4 100.3 79.3 3.7 10.6 2.4 8.5 2.7Madagascar 2,089.95 2,025.12 426.5 908.3 0.4 .. 19.1 10.9 18.7 10.6 .. ..Malawi 150.49 155.78 15.6 58.6 0.4 101.1 33.6 15.8 33.8 11.7 .. ..Malaysia 3.22 3.06 1.7 1.8 0.6 108.8 4.1 3.8 3.6 2.4 3.4 4.6Mali 495.28 471.87 227.0 280.0 0.6 .. 7.0 4.5 5.2 2.5 .. ..Mauritania 275.89 281.12 71.1 148.0 0.5 .. 8.7 10.7 6.1 7.1 .. ..Mauritius 30.78 28.71 12.5 17.1 0.6 .. 6.3 6.0 6.9 6.2 .. ..Mexico 12.64 12.42 6.1 7.9 0.6 92.7 19.0 7.4 19.5 4.5 18.4 5.8Moldova 12.37 11.74 3.0 6.5 0.5 126.6 119.6 10.8 21.4 10.4 .. ..Mongolia 1,357.06 1,265.52 259.3 756.5 0.6 .. 56.3 14.7 35.7 9.1 .. ..Morocco 8.42 8.09 5.2 5.0 0.6 98.0 4.0 2.0 3.9 1.9 2.9 ..Mozambique 33.96 29.07 8.0 14.8 0.4 .. 34.1 8.2 31.8 10.6 .. ..Myanmar 5.58 5.39 107.2 433.1 .. .. 25.3 17.6 25.9 21.1 .. ..Namibia 7.32 7.26 3.6 6.0 0.8 .. 11.1 7.3 .. 6.1 .. ..Nepal 73.16 74.02 19.4 32.6 0.4 .. 8.0 7.0 8.7 6.7 .. ..Netherlands b 0.76 0.72 0.9 0.8 1.1 99.0 2.1 1.9 2.4 1.9 1.3 2.6New Zealand 1.39 1.27 1.4 1.5 1.0 94.9 1.7 3.0 1.8 2.7 1.5 3.4Nicaragua 21.36 22.42 5.2 8.3 0.4 104.3 42.4 7.8 .. 8.8 .. ..Niger 495.28 471.87 221.5 243.4 0.5 .. 6.0 3.3 6.1 2.9 .. ..Nigeria 150.30 153.90 29.0 77.2 0.5 117.9 29.5 13.7 32.5 12.4 .. ..Norway 6.04 5.60 9.1 9.0 1.5 102.8 2.7 4.6 2.2 1.9 1.6 8.4Oman 0.38 0.38 0.2 0.3 0.7 .. 0.1 8.5 .. 3.2 .. ..Pakistan 85.19 86.34 16.2 31.8 0.4 103.4 11.1 9.2 9.7 8.8 10.4 9.8Panama 1.00 1.00 0.6 0.6 0.6 .. 3.6 2.6 1.1 2.7 1.0 3.8Papua New Guinea 2.72 2.37 1.1 1.5 0.6 113.0 7.6 6.2 9.3 5.9 .. ..Paraguay 4,735.46 4,176.07 1,372.0 2,595.9 0.6 139.6 11.5 9.8 13.1 8.1 .. 9.6Peru 2.83 2.75 1.5 1.6 0.6 .. 26.7 3.4 27.3 2.5 23.7 2.8Philippines 45.11 43.31 19.4 24.3 0.5 126.8 9.4 4.7 7.7 5.4 6.3 5.9Poland 3.02 2.96 1.8 1.9 0.6 104.7 23.2 2.7 25.3 2.6 19.8 2.7Portugal b 0.76 0.72 0.7 0.6 0.9 99.9 5.2 2.5 4.5 2.5 .. 2.52012 World Development Indicators 275

4.16Exchange rates and pricesOfficialexchange ratePurchasingpower parity(PPP)conversionfactorRatio of PPPconversionfactor tomarketexchangerateRealeffectiveexchangerateGDP implicitdeflatorConsumer priceindexWholesale priceindexlocal currencyunits to $local currency unitsto international $Index2005 = 100average annual% growthaverage annual% growthaverage annual% growth2010 2011 a 2000 2010 2010 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10Puerto Rico .. .. .. .. .. .. 3.0 4.3 .. .. .. ..Qatar 3.64 3.64 1.9 2.8 0.8 .. .. 10.6 2.8 6.7 .. ..Romania 3.18 3.05 0.6 1.7 0.5 104.2 98.0 14.7 100.5 10.7 93.8 14.1Russian Federation 30.37 29.38 7.3 15.9 0.5 125.9 161.5 15.1 99.1 12.1 99.8 14.7Rwanda 583.13 600.31 142.5 265.5 0.5 .. 14.3 10.4 16.2 8.9 .. ..Saudi Arabia 3.75 3.75 2.0 2.6 0.7 104.8 1.6 7.0 1.0 2.7 1.3 2.6Senegal 495.28 471.87 259.3 264.9 0.5 .. 6.0 2.7 5.4 2.2 .. ..Serbia 77.73 73.33 8.9 36.1 0.5 .. .. 15.3 50.2 14.3 .. ..Sierra Leone 3,978.09 4,230.53 853.8 1,566.0 0.4 99.7 31.9 9.6 .. .. .. ..Singapore 1.36 1.26 1.2 1.0 0.7 111.3 1.4 1.4 1.7 1.6 –1.0 2.4Slovak Republic b 0.76 0.72 0.5 0.5 0.7 132.2 11.4 3.0 8.4 4.5 9.5 4.1Slovenia b 0.76 0.72 0.5 0.6 0.9 .. 29.4 3.7 12.0 3.9 9.1 3.7Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 7.32 7.26 3.1 5.0 0.7 101.2 9.9 7.2 8.7 5.8 7.7 6.7South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain b 0.76 0.72 0.7 0.7 1.0 103.7 3.9 3.4 3.8 2.9 2.4 3.1Sri Lanka 113.06 110.57 24.7 52.9 0.5 .. 9.1 10.6 9.9 10.9 8.1 12.2Sudan 2.31 2.67 0.8 1.6 0.6 .. 65.5 10.6 72.0 9.0 .. ..Swaziland 7.32 7.26 2.7 4.4 0.6 .. 10.5 7.8 9.5 7.0 .. ..Sweden 7.21 6.49 9.1 9.0 1.3 95.5 2.2 1.7 1.9 1.5 2.3 3.2Switzerland 1.04 0.89 1.9 1.5 1.5 107.5 1.1 1.2 1.6 0.9 –0.4 1.0Syrian Arab Republic 11.23 11.23 17.4 25.6 0.6 .. 7.9 7.3 6.4 6.3 4.7 4.5Tajikistan 4.38 4.61 0.3 1.7 0.4 .. 235.0 19.7 .. 12.3 .. ..Tanzania 1,409.27 1,572.12 320.4 518.2 0.4 .. 23.0 7.4 20.9 6.8 .. ..Thailand 31.69 30.49 16.0 17.1 0.5 .. 4.2 3.2 4.9 2.9 3.8 5.6Timor-Leste 1.00 1.00 0.5 0.7 0.7 .. .. 5.3 .. 5.2 .. ..Togo 495.28 471.87 255.9 259.6 0.5 98.1 7.0 2.4 8.5 2.9 .. ..Trinidad and Tobago 6.38 6.41 3.2 3.8 0.6 130.7 5.4 5.6 5.7 6.9 2.8 4.0Tunisia 1.43 1.41 0.6 0.6 0.4 93.5 5.7 3.3 4.4 3.4 3.6 4.6Turkey 1.50 1.67 0.3 1.0 0.6 .. 81.7 14.1 79.9 15.6 75.2 15.3Turkmenistan .. .. 0.6 1.4 0.5 .. 408.2 12.6 .. .. .. ..Uganda 2,177.56 2,522.75 564.1 811.9 0.4 111.4 12.1 5.9 8.3 6.9 .. ..Ukraine 7.94 7.97 1.1 3.6 0.5 99.1 271.0 16.6 155.7 11.4 161.6 15.2United Arab Emirates 3.67 3.67 2.1 3.1 0.8 .. 2.2 8.2 .. .. .. ..United Kingdom 0.65 0.62 0.6 0.7 1.0 83.7 2.4 2.5 2.5 2.1 2.4 2.1United States 1.00 1.00 1.0 1.0 1.0 91.4 2.0 2.5 2.7 2.6 1.2 4.0Uruguay 20.06 19.31 9.8 16.5 0.8 137.2 32.6 7.9 33.9 8.8 27.2 12.7Uzbekistan .. .. 91.3 705.2 0.4 .. 245.8 24.0 .. .. .. ..Venezuela, RB 2.58 4.29 0.4 2.9 1.1 195.3 45.3 25.1 49.0 21.9 44.1 25.7Vietnam 18,612.92 20,450.10 4,018.5 7,109.8 0.4 .. 15.2 8.7 4.1 8.2 .. ..West Bank and Gaza .. .. 2.0 .. .. .. 5.7 3.4 .. 4.0 .. ..Yemen, Rep. 219.59 213.80 46.8 107.6 0.5 .. 20.9 11.9 26.3 11.4 .. ..Zambia 4,797.14 4,860.67 1,082.7 3,847.2 0.8 126.0 52.1 15.9 57.0 15.2 101.4 ..Zimbabwe .. .. .. .. .. .. –3.9 5.1 29.0 497.7 25.9 ..Note: The differences in the growth rates of the GDP defl ator and consumer and wholesale price indexes are due mainly to differences in data availability for each of the indexes during theperiod.a. Average for December or latest monthly data available. b. As members of the euro area, these countries share a single currency, the euro.2762012 World Development Indicators

Exchange rates and prices4.16ECONOMYAbout the dataDefinitionsIn a market-based economy, household, producer,and government choices about resource allocationare infl uenced by relative prices, including the realexchange rate, real wages, real interest rates, andother prices in the economy. Relative prices alsolargely refl ect these agents’ choices. Thus relativeprices convey vital information about the interactionof economic agents in an economy and with the restof the world.The exchange rate is the price of one currencyin terms of another. Offi cial exchange rates andexchange rate arrangements are established bygovernments. Other exchange rates recognized bygovernments include market rates, which are determinedlargely by legal market forces, and for countrieswith multiple exchange arrangements, principalrates, secondary rates, and tertiary rates.Offi cial or market exchange rates are often usedto convert economic statistics in local currencies toa common currency in order to make comparisonsacross countries. Since market rates refl ect at bestthe relative prices of tradable goods, the volume ofgoods and services that a U.S. dollar buys in theUnited States may not correspond to what a U.S.dollar converted to another country’s currency atthe offi cial exchange rate would buy in that country,particularly when nontradable goods and servicesaccount for a signifi cant share of a country’s output.An alternative exchange rate—the purchasing powerparity (PPP) conversion factor—is preferred becauseit refl ects differences in price levels for both tradableand nontradable goods and services and thereforeprovides a more meaningful comparison of real output.See table 1.1 for further discussion.The ratio of the PPP conversion factor to the offi cialexchange rate—the national price level or comparativeprice level—measures differences in the pricelevel at the gross domestic product (GDP) level. Theprice level index tends to be lower in poorer countriesand to rise with income.The real effective exchange rate is a nominaleffective exchange rate index adjusted for relativemovements in national price or cost indicators ofthe home country, selected countries, and the euroarea. A nominal effective exchange rate index is theratio (expressed on the base 2005 = 100) of anindex of a currency’s period-average exchange rateto a weighted geometric average of exchange ratesfor currencies of selected countries and the euroarea. For most high-income countries weights arederived from industrial country trade in manufacturedgoods. Data are compiled from the nominaleffective exchange rate index and a cost indicatorof relative normalized unit labor costs in manufacturing.For selected other countries the nominal effectiveexchange rate index is based on manufacturedgoods and primary products trade with partner orcompetitor countries. For these countries the realeffective exchange rate index is the nominal indexadjusted for relative changes in consumer prices; anincrease represents an appreciation of the local currency.Because of conceptual and data limitations,changes in real effective exchange rates should beinterpreted with caution.Infl ation is measured by the rate of increase in aprice index, but actual price change can be negative.The index used depends on the prices beingexamined. The GDP defl ator refl ects price changesfor total GDP. The most general measure of the overallprice level, it accounts for changes in governmentconsumption, capital formation (including inventoryappreciation), international trade, and the main component,household fi nal consumption expenditure.The GDP defl ator is usually derived implicitly as theratio of current to constant price GDP—or a Paascheindex. It is defective as a general measure of infl ationfor policy use because of long lags in deriving estimatesand because it is often an annual measure.Consumer price indexes are produced more frequentlyand so are more current. They are also constructedexplicitly, based on surveys of the cost ofa defi ned basket of consumer goods and services.Nevertheless, consumer price indexes should beinterpreted with caution. The defi nition of a household,the basket of goods, and the geographic (urbanor rural) and income group coverage of consumerprice surveys can vary widely by country. In addition,weights are derived from household expendituresurveys, which, for budgetary reasons, tend tobe conducted infrequently in developing countries,impairing comparability over time. Although useful formeasuring consumer price infl ation within a country,consumer price indexes are of less value in comparingcountries.Wholesale price indexes are based on the prices atthe fi rst commercial transaction of commodities thatare important in a country’s output or consumption.Prices are farm-gate for agricultural commodities andex-factory for industrial goods. Preference is given toindexes with the broadest coverage of the economy.The least squares method is used to calculategrowth rates of the GDP implicit defl ator, consumerprice index, and wholesale price index.• Official exchange rate is the exchange rate determinedby national authorities or the rate determinedin the legally sanctioned exchange market. It is calculatedas an annual average based on monthlyaverages (local currency units relative to the U.S.dollar). • Purchasing power parity (PPP) conversionfactor is the number of units of a country’s currencyrequired to buy the same amount of goods and servicesin the domestic market that a U.S. dollar wouldbuy in the United States. • Ratio of PPP conversionfactor to market exchange rate is the resultobtained by dividing the PPP conversion factor by themarket exchange rate. • Real effective exchangerate is the nominal effective exchange rate (a measureof the value of a currency against a weightedaverage of several foreign currencies) divided bya price defl ator or index of costs. • GDP implicitdeflator measures the average annual rate of pricechange in the economy as a whole for the periodsshown. • Consumer price index refl ects changesin the cost to the average consumer of acquiring abasket of goods and services that may be fi xed ormay change at specifi ed intervals, such as yearly.The Laspeyres formula is generally used. • Wholesaleprice index refers to a mix of agricultural andindustrial goods at various stages of production anddistribution, including import duties. The Laspeyresformula is generally used.Data sourcesData on offi cial and real effective exchange ratesand consumer and wholesale price indexes arefrom the International Monetary Fund’s InternationalFinancial Statistics. PPP conversion factorsand GDP defl ators are from the World Bank’s datafi l e s .2012 World Development Indicators 277

4.17 Balance of payments current accountGoods andservicesNetincomeNet currenttransfersCurrent accountbalanceTotalreserves a$ millionsExportsImports$ millions $ millions $ millions $ millions2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan .. .. .. .. .. .. .. .. .. .. .. ..Albania 704 3,791 1,499 6,316 107 –101 533 1,223 –156 –1,403 646 2,541Algeria .. 48,171 .. 49,082 .. –1,319 .. 2,632 .. 402 13,556 170,461Angola 8,188 51,452 5,739 35,421 –1,681 –8,172 28 –438 796 7,421 1,198 19,749Argentina 31,277 81,251 33,108 67,992 –7,548 –9,938 399 –388 –8,981 2,932 25,152 52,208Armenia 447 1,937 966 4,212 53 339 188 563 –278 –1,373 314 1,866Australia 83,898 261,340 87,799 246,140 –10,814 –45,803 –47 –1,388 –14,763 –31,990 18,822 42,268Austria 87,777 202,416 85,125 189,033 –2,259 737 –1,732 –2,658 –1,339 11,461 17,650 22,242Azerbaijan 2,118 28,590 2,024 10,592 –335 –3,467 73 509 –168 15,040 680 6,409Bahrain 7,176 17,880 5,132 13,095 –224 –2,373 –990 –1,642 830 770 1,605 ..Bangladesh 7,214 21,661 9,673 29,477 –266 –1,454 2,420 11,379 –306 2,109 1,516 11,175Belarus 7,641 29,909 8,087 37,367 –47 –1,163 155 304 –338 –8,317 350 5,025Belgium 206,988 366,855 195,511 363,040 4,475 10,958 –4,341 –8,423 11,611 6,349 12,272 26,779Benin 528 1,446 708 2,234 –12 –33 111 172 –81 –649 459 1,200Bolivia 1,470 6,840 2,078 6,159 –225 –889 387 1,081 –446 874 1,184 9,731Bosnia and Herzegovina 1,580 6,221 4,157 9,819 590 331 1,591 2,258 –396 –1,008 497 4,411Botswana 3,000 5,028 2,321 5,718 –351 –243 217 979 545 46 6,318 7,885Brazil 64,584 233,736 72,444 244,360 –17,886 –39,486 1,521 2,788 –24,225 –47,323 33,015 288,575Bulgaria 7,000 27,326 7,670 28,421 –323 –1,680 290 2,038 –703 –736 3,507 17,223Burkina Faso 237 1,053 658 1,942 –20 –5 122 514 –319 –380 243 1,068Burundi 53 181 150 607 –12 –11 59 136 –50 –301 38 332Cambodia 1,826 6,887 2,263 7,879 –123 –533 425 646 –136 –879 611 3,817Cameroon 2,668 5,645 2,501 6,408 –493 –239 109 146 –218 –856 220 3,643Canada 329,252 462,349 288,093 493,120 –22,291 –15,968 754 –2,569 19,622 –49,307 32,427 57,151Central African Republic .. .. .. .. .. .. .. .. .. .. 136 181Chad .. .. .. .. .. .. .. .. .. .. 114 632Chile 23,293 81,826 21,893 66,990 –2,856 –15,424 558 4,390 –898 3,802 15,055 27,827China † 279,561 1,752,621 250,688 1,520,559 –14,666 30,380 6,311 42,932 20,518 305,374 171,763 2,913,712Hong Kong SAR. China 243,127 500,447 235,589 487,850 1,125 3,644 –1,670 –3,443 6,993 12,798 107,560 268,743Colombia 15,808 45,224 14,397 46,608 –2,289 –11,945 1,673 4,475 795 –8,855 9,006 28,076Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 83 1,300Congo, Rep. 2,628 .. 1,194 .. –805 .. 19 .. 648 .. 225 4,447Costa Rica 7,750 13,662 7,297 14,723 –1,252 –748 93 370 –707 –1,439 1,318 4,630Côte d’Ivoire 4,370 11,478 3,629 8,803 –653 –890 –330 –115 –241 1,670 674 3,624Croatia 8,645 23,105 9,592 23,409 –466 –2,080 880 1,436 –533 –947 3,524 14,133Cuba .. .. .. .. .. .. .. .. .. .. .. ..Cyprus 5,019 9,745 5,142 11,235 –542 –1,267 177 –45 –488 –2,803 1,869 1,142Czech Republic 35,858 137,665 37,550 130,932 –1,371 –13,198 373 472 –2,690 –5,992 13,142 42,483Denmark 73,805 156,176 64,506 139,022 –4,023 5,771 –3,014 –5,792 2,262 17,134 15,696 76,510Dominican Republic 8,964 11,697 10,852 17,462 –1,041 –1,788 1,902 3,118 –1,027 –4,435 632 3,501Ecuador 5,906 19,610 4,927 22,651 –1,405 –1,054 1,352 2,310 926 –1,785 1,179 2,622Egypt, Arab Rep. 16,864 48,831 22,895 59,862 888 –5,912 4,172 12,439 –971 –4,504 13,785 37,029El Salvador 3,662 5,553 5,636 9,259 –253 –381 1,797 3,599 –431 –488 1,901 2,897Eritrea 98 .. 500 .. –1 .. 299 .. –105 .. 36 114Estonia 4,784 16,169 4,965 14,771 –203 –1,067 86 341 –299 673 923 2,567Ethiopia 992 4,644 1,621 9,911 –36 –64 678 4,905 13 –425 363 1,781Finland 53,431 97,979 40,459 93,836 –1,724 2,523 –723 –2,207 10,526 4,459 8,410 9,547France 380,260 662,123 366,239 720,573 19,425 48,892 –13,771 –34,941 19,674 –44,499 63,728 165,852Gabon 3,498 .. 1,656 .. –779 .. –63 .. 1,001 .. 194 1,736Gambia, The .. 256 .. 308 .. –8 .. 113 .. 52 109 202Georgia 859 4,061 1,323 6,134 37 –363 250 1,098 –177 –1,337 116 2,264Germany 627,879 1,541,139 626,519 1,362,052 –7,662 59,648 –25,976 –50,792 –32,279 187,943 87,497 215,978Ghana 2,441 9,437 3,350 13,925 –108 –535 631 2,322 –387 –2,700 309 ..Greece 29,440 60,094 41,727 80,353 –885 –10,756 3,352 118 –9,820 –30,897 14,594 6,352Guatemala 3,862 10,827 5,567 15,188 –210 –1,211 865 4,946 –1,050 –626 1,806 5,949Guinea 734 1,534 872 1,800 –78 –77 75 17 –140 –327 168 ..Guinea-Bissau 67 155 92 289 –12 –11 26 98 –11 –48 67 156Haiti 504 799 1,369 4,084 –9 22 760 3,097 –114 –166 183 1,337† Data for Taiwan, China 171,909 314,339 164,874 285,177 4,468 13,447 –2,604 –2,710 8,899 39,899 110,464 401,1482782012 World Development Indicators

Balance of payments current account 4.17ECONOMYGoods andservicesNetincomeNet currenttransfersCurrent accountbalanceTotalreserves a$ millionsExportsImports$ millions $ millions $ millions $ millions2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 3,850 6,764 4,681 9,881 –215 –598 538 2,760 –508 –955 1,319 2,701Hungary 34,662 110,843 36,449 102,710 –2,575 –7,207 357 490 –4,004 1,417 11,217 44,988India 59,932 349,264 73,075 440,277 –4,892 –12,926 13,434 52,158 –4,601 –51,781 41,059 300,480Indonesia 70,622 174,840 56,002 153,537 –8,443 –20,291 1,816 4,630 7,992 5,643 29,353 96,211Iran, Islamic Rep. 29,727 .. 17,503 .. –200 .. 457 .. 12,481 .. .. ..Iraq .. 65,695 .. 37,731 .. 2,106 .. –2,936 .. 27,133 7,882 50,642Ireland 92,068 207,689 79,792 168,853 –13,547 –36,293 915 –1,589 –356 954 5,408 2,114Israel 46,591 80,323 46,794 76,094 –8,323 –6,312 6,470 8,426 –2,056 6,342 23,281 70,907Italy 297,030 546,949 286,526 586,410 –12,010 –11,410 –4,276 –21,144 –5,781 –72,015 47,201 158,478Jamaica 3,589 4,004 4,427 6,454 –350 –495 821 2,010 –367 –934 1,054 2,501Japan 528,751 871,533 459,660 796,674 60,401 133,291 –9,831 –12,395 119,660 195,755 361,639 1,096,069Jordan 3,539 12,189 5,796 17,949 100 507 2,184 3,941 27 –1,312 3,441 13,633Kazakhstan 10,341 65,086 8,970 43,268 –1,254 –18,325 249 –481 366 3,013 2,099 28,265Kenya 2,776 8,900 3,763 13,543 –133 –155 921 2,286 –199 –2,512 898 4,321Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 209,589 547,006 192,970 516,332 –2,383 768 566 –3,229 14,803 28,214 96,251 292,143Kosovo .. .. .. .. .. .. .. .. .. .. .. 846Kuwait 21,301 74,689 11,372 32,682 6,699 7,818 –1,956 –13,003 14,672 36,822 7,779 24,805Kyrgyz Republic 573 2,472 654 3,905 –82 –343 87 1,391 –76 –385 262 1,720Lao PDR 506 2,257 578 2,324 –52 –83 116 179 –8 29 144 1,105Latvia 3,229 12,800 3,813 13,025 17 85 195 871 –371 731 919 7,606Lebanon 5,849 21,240 9,600 31,010 –868 76 78 785 –4,541 –8,909 8,475 44,476Lesotho 269 900 1,044 2,515 467 532 237 661 –71 –421 418 ..Liberia .. 400 .. 1,800 .. 24 .. 960 .. –416 0 372Libya 12,210 49,345 5,024 30,686 –429 –30 –487 –1,828 6,270 16,801 13,730 106,144Lithuania 5,109 24,849 5,833 25,245 –194 –828 243 1,758 –675 534 1,363 6,598Macedonia, FYR 1,637 4,213 2,280 6,094 –70 –124 609 1,805 –103 –200 460 2,277Madagascar 1,188 .. 1,520 .. –42 .. 113 .. –260 .. 285 1,172Malawi 437 1,399 629 2,105 –17 –110 135 253 –73 –563 247 325Malaysia 112,370 231,714 94,350 189,499 –7,608 –8,142 –1,924 –6,783 8,488 27,290 28,651 106,528Mali 644 2,128 927 2,812 –98 –457 126 486 –255 –655 382 1,344Mauritania 393 .. 471 .. –32 .. 187 .. 77 .. 49 288Mauritius 2,622 4,957 2,707 6,141 –16 202 64 183 –37 –800 914 2,619Mexico 179,876 313,797 191,818 327,077 –13,795 –13,948 6,994 21,504 –18,743 –5,724 35,577 120,584Moldova 641 2,292 972 4,581 22 487 211 1,319 –98 –484 222 1,718Mongolia 614 3,394 771 3,869 –7 –599 94 187 –70 –887 202 2,288Morocco 10,453 30,129 12,546 40,083 –864 –1,242 2,483 7,270 –475 –3,925 5,017 23,609Mozambique 689 2,980 1,492 4,666 –192 –85 231 657 –764 –1,113 742 2,265Myanmar 2,139 8,198 2,493 5,173 –133 –1,740 276 241 –212 1,527 286 ..Namibia 1,483 4,982 1,630 5,620 –98 –564 436 1,232 192 30 260 1,696Nepal 1,282 1,574 1,790 5,887 37 94 340 4,092 –131 –128 987 2,925Netherlands 254,590 576,108 238,810 513,539 –2,297 3,570 –6,219 –14,504 7,264 51,635 17,688 46,147New Zealand 17,864 40,916 17,306 38,882 –3,202 –6,999 237 –29 –2,407 –4,994 3,952 16,723Nicaragua 1,102 3,628 2,152 5,486 –296 –278 410 1,173 –936 –963 492 1,799Niger 321 1,097 456 2,529 –16 –39 47 151 –104 –1,320 81 760Nigeria 20,965 76,774 12,017 75,768 –3,148 –18,623 1,627 20,093 7,427 2,476 10,099 35,885Norway 78,111 172,425 49,476 117,143 –2,305 872 –1,250 –4,711 25,079 51,444 27,922 52,798Oman 11,770 38,362 6,351 24,400 –838 –3,162 –1,451 –5,704 3,129 5,096 2,460 13,025Pakistan 10,119 28,062 12,148 40,021 –2,218 –3,187 4,162 13,778 –85 –1,368 2,087 17,256Panama 7,833 18,402 8,122 19,882 –560 –1,859 177 477 –673 –2,862 723 2,714Papua New Guinea 2,337 6,055 1,771 6,286 –210 –592 –5 190 351 –633 304 3,122Paraguay 2,924 9,989 3,286 10,671 22 –502 177 542 –163 –641 772 4,167Peru 8,510 39,521 9,648 34,809 –1,410 –10,053 1,001 3,026 –1,546 –2,315 8,676 44,215Philippines 40,724 65,106 48,565 73,133 –30 347 5,643 16,604 –2,228 8,924 15,074 62,326Poland 46,300 198,427 57,204 207,139 –731 –16,923 1,292 3,762 –10,343 –21,873 27,469 93,472Portugal 34,102 72,125 47,262 87,411 –2,371 –10,423 3,342 2,858 –12,189 –22,850 14,262 20,937Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. .. .. 1,163 31,1822012 World Development Indicators 279

4.17Balance of payments current accountGoods andservicesNetincomeNet currenttransfersCurrent accountbalanceTotalreserves aRomania 12,113 58,268 14,043 66,807 –285 –2,532 860 4,557 –1,355 –6,514 3,396 48,048Russian Federation 114,598 445,539 61,091 323,070 –6,736 –48,615 69 –3,600 46,839 70,255 27,656 479,222Rwanda 128 608 423 1,641 –15 –46 216 657 –94 –421 191 813Saudi Arabia 82,259 261,832 52,932 174,204 479 7,044 –15,490 –27,921 14,317 66,751 20,847 459,313Senegal 1,307 3,119 1,742 5,276 –111 –181 214 1,473 –332 –865 388 2,047Serbia .. 13,351 .. 19,689 .. –899 .. 4,417 .. –2,819 517 13,308Sierra Leone 55 423 250 879 –5 –49 88 185 –112 –320 49 409Singapore 181,346 470,793 169,223 408,190 –784 –8,230 –1,095 –4,815 10,244 49,558 80,170 225,715Slovak Republic 14,137 70,494 14,596 71,300 –355 –1,658 120 –544 –694 –3,009 4,376 2,156Slovenia 10,696 30,489 11,385 30,360 26 –662 116 146 –548 –388 3,196 1,071Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 36,995 99,703 33,075 100,318 –3,184 –7,224 –926 –2,278 –191 –10,117 7,702 43,820South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 168,221 376,601 186,027 402,450 –6,849 –28,985 1,469 –9,508 –23,185 –64,342 35,608 31,872Sri Lanka 6,378 10,776 8,105 15,282 –300 –572 983 3,660 –1,044 –1,418 1,131 7,195Sudan 1,834 11,658 2,014 11,161 –575 –2,472 237 2,131 –518 157 138 1,036Swaziland 1,240 2,063 1,438 2,625 51 –226 101 400 –46 –388 352 756Sweden 111,275 225,662 97,042 196,830 –1,231 7,781 –3,142 –6,204 9,860 30,408 16,499 48,246Switzerland 125,517 342,153 107,391 285,841 19,187 29,967 –4,483 –12,263 32,830 74,015 53,620 270,480Syrian Arab Republic 6,845 19,606 5,390 19,409 –879 –1,514 485 949 1,061 –367 355 20,632Tajikistan 768 1,512 928 3,329 –41 –79 186 1,513 –15 –383 94 ..Tanzania 1,361 6,388 2,050 8,975 –130 –216 391 824 –428 –1,978 974 3,905Thailand 81,762 227,908 71,653 206,780 –1,381 –14,061 586 6,031 9,313 13,099 32,665 172,028Timor-Leste .. .. .. .. .. .. .. .. .. .. 43 406Togo 424 1,197 602 1,690 –29 –19 68 336 –140 –177 141 715Trinidad and Tobago 4,844 9,940 3,709 7,356 –629 –997 38 27 544 1,614 1,403 9,692Tunisia 8,607 22,236 9,311 24,351 –942 –1,925 825 1,935 –821 –2,104 1,871 9,764Turkey 50,353 155,632 61,035 197,042 –4,002 –7,137 4,764 1,448 –9,920 –47,099 23,515 85,959Turkmenistan .. .. .. .. .. .. .. .. .. .. 1,513 ..Uganda 663 3,474 1,409 6,099 –112 –305 499 1,190 –359 –1,740 808 2,706Ukraine 19,522 69,255 17,947 73,239 –942 –2,009 848 2,975 1,481 –3,018 1,477 34,571United Arab Emirates .. .. .. .. .. .. .. .. .. .. 13,632 42,785United Kingdom 404,775 667,596 433,976 731,828 5,156 20,679 –14,756 –31,676 –38,800 –75,229 43,075 82,365United States 1,072,780 1,837,576 1,449,535 2,337,607 19,179 165,224 –58,767 –136,095 –416,343 –470,902 128,400 488,928Uruguay 3,660 10,555 4,193 9,743 –61 –1,093 27 122 –566 –160 2,776 7,656Uzbekistan .. .. .. .. .. .. .. .. .. .. 1,242 ..Venezuela, RB 34,711 67,603 21,300 49,661 –1,388 –5,302 –170 –568 11,853 12,072 15,899 29,665Vietnam 17,150 79,652 17,325 87,260 –451 –4,564 1,732 7,885 1,106 –4,287 3,417 12,467West Bank and Gaza 1,012 1,224 3,270 5,008 628 808 639 2,239 –990 –737 .. ..Yemen, Rep. 4,008 9,329 3,294 11,017 –777 –1,812 1,399 2,291 1,337 –1,209 2,914 5,939Zambia 872 7,725 1,312 5,650 –164 –1,893 14 432 –591 615 245 2,094Zimbabwe .. .. .. .. .. .. .. .. .. .. .. ..World 7,977,777 t 18,856,365 t 7,950,280 t 18,328,230 t .. .. .. .. .. .. .. ..Low income 32,005 105,896 45,127 156,378 .. .. .. .. .. .. .. ..Middle income 1,592,025 5,719,672 1,495,508 5,389,112 .. .. .. .. .. .. .. ..Lower middle income 369,018 1,288,993 375,652 1,374,029 .. .. .. .. .. .. .. ..Upper middle income 1,224,283 4,435,558 1,121,580 4,021,226 .. .. .. .. .. .. .. ..Low & middle income 1,623,276 5,824,525 1,540,600 5,547,163 .. .. .. .. .. .. .. ..East Asia & Pacifi c 612,105 2,566,113 550,904 2,271,625 .. .. .. .. .. .. .. ..Europe & Central Asia 251,713 993,266 223,344 944,420 .. .. .. .. .. .. .. ..Latin America & Carib. 418,299 996,199 438,522 1,009,026 .. .. .. .. .. .. .. ..Middle East & N. Africa .. .. 122,648 384,516 .. .. .. .. .. .. .. ..South Asia 85,777 414,188 106,235 537,246 .. .. .. .. .. .. .. ..Sub-Saharan Africa 111,058 374,373 102,161 394,903 .. .. .. .. .. .. .. ..High income 6,357,262 13,051,331 6,409,545 12,801,007 .. .. .. .. .. .. .. ..Euro area 2,298,517 4,964,584 2,262,865 4,779,448 .. .. .. .. .. .. .. ..a. International reserves including gold valued at London gold price.$ millionsExportsImports$ millions $ millions $ millions $ millions2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 20102802012 World Development Indicators

Balance of payments current account4.17ECONOMYAbout the dataDefinitionsThe balance of payments records an economy’stransactions with the rest of the world. Balance ofpayments accounts are divided into two groups:the current account, which records transactionsin goods, services, income, and current transfers,and the capital and fi nancial account, which recordscapital transfers, acquisition or disposal of nonproduced,nonfi nancial assets, and transactions infi nancial assets and liabilities. The table presentsdata from the current account plus gross internationalreserves.The balance of payments is a double-entryaccounting system that shows all fl ows of goods andservices into and out of an economy; all transfersthat are the counterpart of real resources or fi nancialclaims provided to or by the rest of the world withouta quid pro quo, such as donations and grants; andall changes in residents’ claims on and liabilities tononresidents that arise from economic transactions.All transactions are recorded twice—once as a creditand once as a debit. In principle the net balanceshould be zero, but in practice the accounts often donot balance, requiring inclusion of a balancing item,net errors and omissions.Discrepancies may arise in the balance of paymentsbecause there is no single source for balanceof payments data and therefore no way to ensurethat the data are fully consistent. Sources includecustoms data, monetary accounts of the bankingsystem, external debt records, information providedby enterprises, surveys to estimate service transactions,and foreign exchange records. Differences incollection methods—such as in timing, definitionsof residence and ownership, and the exchange rateused to value transactions—contribute to net errorsand omissions. In addition, smuggling and other illegalor quasi-legal transactions may be unrecorded ormisrecorded. For further discussion of issues relatingto the recording of data on trade in goods andservices, see About the data for tables 4.4–4.7.The concepts and defi nitions underlying the datain the table are based on the fi fth edition of theInternational Monetary Fund’s (IMF) Balance of PaymentsManual (1993). That edition redefi ned as capitaltransfers some transactions previously includedin the current account, such as debt forgiveness,migrants’ capital transfers, and foreign aid to acquirecapital goods. Thus the current account balance nowrefl ects more accurately net current transfer receiptsin addition to transactions in goods, services (previouslynonfactor services), and income (previouslyfactor income). Many countries maintain their datacollection systems according to the fourth editionof the Balance of Payments Manual (1977). Wherenecessary, the IMF converts such reported data toconform to the fi fth edition (see Primary data documentation).Values are in U.S. dollars converted atmarket exchange rates.• Exports and imports of goods and services areall transactions between residents of an economyand the rest of the world involving a change inownership of general merchandise, goods sent forprocessing and repairs, nonmonetary gold, and services.• Net income is receipts and payments ofemployee compensation for nonresident workers,and investment income (receipts and payments ondirect investment, portfolio investment, and otherinvestments and receipts on reserve assets). Incomederived from the use of intangible assets is recordedunder business services. • Net current transfersare recorded in the balance of payments wheneveran economy provides or receives goods, services,income, or fi nancial items without a quid pro quo.All transfers not considered to be capital are current.• Current account balance is the sum of netexports of goods and services, net income, and netcurrent transfers. • Total reserves are holdings ofmonetary gold, special drawing rights, reserves ofIMF members held by the IMF, and holdings of foreignexchange under the control of monetary authorities.The gold component of these reserves is valued atyear-end (December 31) London prices ($386.75 anounce in 1995 and $1,087.50 an ounce in 2009).Data sourcesData on the balance of payments are published inthe IMF’s Balance of Payments Statistics Yearbookand International Financial Statistics. The WorldBank exchanges data with the IMF through electronicfi les that in most cases are more timely andcover a longer period than the published sources.More information about the design and compilationof the balance of payments can be found inthe IMF’s Balance of Payments Manual, fi f t h e d i t i o n(1993), Balance of Payments Textbook (1996), andBalance of Payments Compilation Guide (1995).2012 World Development Indicators 281

STATES ANDMARKETS

States and markets includes indicators of privatesector investment and performance,the role of the public sector in nurturinginvestment and growth, and the quality andavailability of infrastructure essential for growthand development.When private fi rms make investments, createjobs, and improve productivity, they promotegrowth and expand opportunities for people.Tables 5.1–5.6 cover private investment ininfrastructure, the business environment, andthe development of fi nancial systems. Justas a vibrant private sector is essential for jobcreation and growth, so are capable governmentsand high-quality institutions essentialfor promoting growth, raising incomes, andreducing poverty. Tables 5.7–5.9 cover thesefunctions of governments, from tax policiesand public institutions to crime statistics, andmilitary expenditures. Tables 5.10–5.12 coverinfrastructure —the systems for deliveringenergy, transport, water and sanitation, andinformation and communication technology servicesto people. Table 5.13 covers innovationin science and technology.World Development Indicators 2010 introduceda new table, continued this year astable 5.8, Fragile situations, with indicators forcountries that have been identifi ed as fragileor conflict- affected. World Development Report2011: Confl ict, Security, and Development (WorldBank 2011d, p. xvi) defined fragility or fragile situationsas “periods when states or institutionslack the capacity, accountability, or legitimacyto mediate relations between citizen groups andbetween citizens and the state, making themvulnerable to violence.” The report provided aframework and practical recommendations onhow to move beyond fragility and confl ict tosecure development. Its central message wasthat strengthening legitimate institutions andgovernance to provide citizens security, justice,and jobs is crucial to breaking cycles of violenceand underdevelopment.Although civil wars and wars between statesare less common than in the past, insecurityremains a primary development challenge.Some 1.5 billion people live in areas affected byfragility, conflict, or large-scale organized criminalviolence, and no fragile or confl ict-affectedcountry has achieved a single Millennium DevelopmentGoal. People in these countries aremore than twice as likely to be undernourishedas people in other developing countries, morethan three times as likely to be unable to sendtheir children to school, twice as likely to seetheir children die before age 5, and more thantwice as likely to lack access to clean water.A major episode of violence can wipe outan entire generation of economic progress. Theaverage cost of a civil war is equivalent to morethan 30 years of GDP growth for a medium-sizedeveloping country. World Development Report2011 found that countries and areas with theweakest institutional legitimacy (both formaland informal) and poor governance are the mostvulnerable to violence and instability and theleast able to respond to internal and externalstresses.The World Bank manages the State andPeace Building Fund, a multidonor trust fundthat provides grants to scale up Bank engagementin fragile and conflict-affected countries; topromote cross-cutting, innovative approaches;and to foster strategic partnerships. As statebuildingand peace, security, and institutionbuildingbecome central objective s for developingcountries and the development community,new indicators are being developed to measureprogress. The International Dialogue on Peacebuildingand Statebuilding, with more than 40participating countries and eight internationalorganizations (including the World Bank) is contributingto this work.52012 World Development Indicators 283

Tables5.1Private sector in the economyInvestment commitments in infrastructureprojects with private participation aDomesticcredit toprivatesectorBusinessesregistered$ millionsWater andEntryTelecommunications Energy Transportsanitation % of GDP New density2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2010 2009 2009Afghanistan 466.1 1,040.4 1.6 .. .. .. .. .. 10.5 .. ..Albania 569.2 778.8 790.6 692.0 308.0 .. 8.0 0.0 38.0 2,045 0.84Algeria 3,422.5 2,162.0 962.0 2,320.0 120.9 269.0 510.0 1,572.0 15.6 10,544 0.44Angola 278.7 1,663.0 45.0 9.4 .. 53.0 .. .. 20.3 .. ..Argentina 5,836.8 5,993.9 3,826.9 3,801.9 203.6 1,402.6 791.6 .. 14.6 11,924 0.46Armenia 317.1 586.7 74.0 127.0 63.0 715.0 0.0 0.0 26.5 2,698 1.28Australia .. .. .. .. .. .. .. .. 131.1 89,960 6.38Austria .. .. .. .. .. .. .. .. 122.4 3,228 0.58Azerbaijan 355.6 1,407.8 375.2 .. .. .. 0.0 .. 18.3 5,314 0.93Bahrain .. .. .. .. .. .. .. .. 79.6 .. ..Bangladesh 1,294.3 4,250.3 501.5 340.4 0.0 0.0 .. .. 47.1 .. ..Belarus 735.4 2,638.7 .. 2,500.0 .. 4.0 .. .. 44.8 5,508 0.80Belgium .. .. .. .. .. .. .. .. 94.9 29,548 4.28Benin 116.9 793.7 590.0 .. .. .. .. .. 23.1 .. ..Bolivia 520.5 284.7 884.4 137.3 16.6 .. .. .. 40.3 2,504 0.43Bosnia and Herzegovina 0.0 1,102.5 .. 908.6 .. .. .. .. 66.0 1,896 0.58Botswana 104.0 242.9 .. .. .. .. .. .. 23.4 .. ..Brazil 41,053.8 40,063.0 26,034.6 52,825.6 3,156.5 23,527.7 1,234.4 1,581.0 57.0 315,645 2.38Bulgaria 2,179.1 2,211.3 3,253.5 2,454.1 2.1 536.2 152.0 .. 74.6 35,545 7.20Burkina Faso 41.9 979.6 .. .. .. .. .. .. 17.6 610 0.08Burundi 53.6 0.0 .. .. .. .. .. .. 25.5 .. ..Cambodia 136.1 446.7 82.1 2,452.3 125.3 40.1 .. .. 27.6 2,003 0.22Cameroon 394.4 934.4 91.8 908.0 0.0 .. .. 0.0 11.9 .. ..Canada .. .. .. .. .. .. .. .. 128.2 174,000 7.56Central African Republic 0.0 30.8 .. .. .. .. .. .. 8.7 .. ..Chad 11.0 591.4 0.0 .. .. .. .. .. 5.7 .. ..Chile 1,260.7 1,326.7 1,393.2 1,567.2 4,830.7 1,943.1 1,495.2 3.1 86.3 23,541 2.12China 8,548.0 0.0 10,970.9 7,477.5 15,454.0 15,795.0 3,505.2 4,626.9 130.0 .. ..Hong Kong SAR, China .. .. .. .. .. .. .. .. 189.0 101,023 19.19Colombia 1,570.9 6,347.9 351.6 1,080.9 1,497.4 4,461.4 314.3 305.0 43.5 31,132 1.07Congo, Dem. Rep. 473.4 1,054.0 .. .. .. .. .. .. 6.6 .. ..Congo, Rep. 61.8 407.7 .. .. .. 735.0 0.0 .. 5.5 .. ..Costa Rica .. .. 80.0 190.0 465.2 407.0 .. .. 45.9 26,765 8.78Côte d’Ivoire 134.9 1,204.4 0.0 0.0 176.4 .. .. 0.0 18.1 .. ..Croatia 1,205.7 3,035.0 7.1 85.0 451.0 492.0 298.7 .. 70.1 7,800 2.57Cuba 60.0 0.0 116.0 60.0 0.0 .. 600.0 .. .. .. ..Cyprus .. .. .. .. .. .. .. .. 283.6 16,101 20.30Czech Republic .. .. .. .. .. .. .. .. 56.2 21,717 3.00Denmark .. .. .. .. .. .. .. .. 223.5 16,519 4.57Dominican Republic 393.0 220.1 1,306.6 0.0 898.9 948.9 .. .. 22.7 12,881 2.13Ecuador 357.8 2,003.3 302.0 129.0 695.0 766.0 510.0 .. 30.8 .. ..Egypt, Arab Rep. 3,471.9 10,977.0 678.0 469.0 821.5 1,370.0 .. 475.0 33.1 6,291 0.13El Salvador 1,110.6 1,037.6 85.0 16.0 .. .. .. .. 41.0 4,400 1.19Eritrea 40.0 0.0 .. .. .. .. .. .. 16.0 .. ..Estonia .. .. .. .. .. .. .. .. 97.2 7,199 8.10Ethiopia .. 0.0 .. 4.0 .. .. .. .. 17.8 1,327 0.03Finland .. .. .. .. .. .. .. .. 95.2 11,820 3.37France .. .. .. .. .. .. .. .. 114.4 128,906 3.08Gabon 26.6 403.8 0.0 0.0 177.4 3.9 .. .. 8.2 3,490 4.27Gambia, The 6.6 35.0 .. 0.0 .. .. .. .. 19.1 .. ..Georgia 201.3 722.0 40.0 634.2 .. 573.0 .. 435.0 32.4 7,226 2.32Germany .. .. .. .. .. .. .. .. 107.8 64,840 1.19Ghana 156.5 3,206.0 590.0 100.0 10.0 .. 0.0 .. 15.2 9,606 0.72Greece .. .. .. .. .. .. .. .. 115.9 8,426 1.18Guatemala 560.1 1,724.4 110.0 1,021.8 .. .. .. 6.7 23.4 5,133 0.68Guinea 50.6 313.2 .. .. .. 159.0 .. .. .. .. ..Guinea-Bissau 9.7 107.2 .. .. .. .. .. .. 6.2 .. ..Haiti 18.0 306.0 5.5 0.0 .. .. .. 1.0 14.0 .. ..2842012 World Development Indicators

Private sector in the economyInvestment commitments in infrastructureprojects with private participation aDomesticcredit toprivatesector5.1BusinessesregisteredSTATES AND MARKETS$ millionsWater andEntryTelecommunications Energy Transportsanitation % of GDP New density2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2010 2009 2009Honduras 135.0 1,001.0 358.8 250.0 120.0 .. 207.9 .. 50.2 .. ..Hungary 5,172.8 1,523.3 851.6 1,707.0 3,297.5 1,588.0 0.0 0.0 72.6 42,951 6.26India 20,551.8 53,090.4 8,663.6 90,973.0 4,413.4 38,036.2 112.9 241.7 49.0 84,800 0.12Indonesia 6,557.2 11,009.5 1,280.5 6,079.3 159.2 1,731.5 44.8 20.2 29.1 28,998 0.18Iran, Islamic Rep. 695.0 1,992.0 650.0 .. .. .. .. .. 36.7 .. ..Iraq 984.0 4,977.0 .. 590.0 .. 500.0 .. .. 9.1 .. ..Ireland .. .. .. .. .. .. .. .. 215.0 13,188 4.67Israel .. .. .. .. .. .. .. .. 95.7 19,758 4.46Italy .. .. .. .. .. .. .. .. 122.0 68,508 1.78Jamaica 612.0 157.9 201.0 210.0 565.0 .. .. .. 24.8 2,003 1.16Japan .. .. .. .. .. .. .. .. 169.3 105,698 1.28Jordan 1,589.0 949.6 .. 989.0 0.0 1,380.0 169.0 951.0 70.3 2,737 0.74Kazakhstan 1,153.7 3,766.5 300.0 0.0 231.0 31.0 .. .. 39.3 27,978 2.59Kenya 1,434.0 3,465.8 .. 437.7 .. 404.0 .. .. 33.8 17,896 0.85Korea, Dem. Rep. .. 474.0 .. .. .. .. .. .. .. .. ..Korea, Rep. .. .. .. .. .. .. .. .. 100.8 60,039 1.72Kosovo .. 385.1 .. .. .. .. 0.0 .. 37.3 141 0.12Kuwait .. .. .. .. .. .. .. .. 82.4 .. ..Kyrgyz Republic 11.5 130.2 .. .. .. .. 0.0 .. .. 4,412 1.26Lao PDR 87.7 135.0 1,250.0 5,285.0 1.5 1.5 .. .. 20.4 .. ..Latvia 700.0 468.1 158.1 184.0 .. 135.0 .. .. 103.7 7,175 4.62Lebanon 138.1 0.0 .. .. 153.0 .. 0.0 .. 81.3 .. ..Lesotho 88.4 41.6 0.0 .. .. .. .. .. 13.6 .. ..Liberia 70.3 88.8 .. 170.0 .. 120.0 .. .. 16.0 .. ..Libya .. .. .. .. .. .. .. .. 10.9 .. ..Lithuania 993.0 548.2 514.3 464.1 .. .. .. .. 66.4 5,399 2.18Macedonia, FYR 706.6 575.4 .. 655.0 .. 295.0 .. .. 45.3 8,074 5.63Madagascar 12.6 436.8 0.0 17.8 61.0 17.5 .. .. 11.7 724 0.07Malawi 36.3 313.7 0.0 .. .. .. .. .. 16.0 619 0.08Malaysia 3,294.9 3,689.9 6,637.6 384.5 4,263.0 1,632.3 6,502.2 0.0 114.9 41,638 2.55Mali 82.6 837.0 365.9 .. 55.4 .. .. .. 18.4 .. ..Mauritania 92.1 266.1 .. .. .. .. .. .. 30.4 .. ..Mauritius 393.0 63.1 0.0 .. .. .. .. 0.0 87.8 6,626 7.33Mexico 18,758.0 16,290.6 6,749.3 2,282.7 2,970.4 12,651.5 523.7 1,096.8 24.6 44,084 0.61Moldova 46.1 426.8 227.2 68.0 0.0 60.0 .. .. 33.3 4,180 1.32Mongolia 22.1 0.0 .. .. .. .. .. .. 39.6 .. ..Morocco 6,139.5 3,673.6 1,049.0 .. 200.0 200.0 .. .. 68.7 26,166 1.28Mozambique 123.0 236.2 1,205.8 .. 334.6 0.0 .. 0.0 25.8 .. ..Myanmar .. .. .. 556.1 .. .. .. .. 4.7 .. ..Namibia 35.0 8.5 1.0 .. .. .. 0.0 .. 45.6 .. ..Nepal 109.3 26.0 15.1 34.1 .. .. .. 0.0 55.6 .. ..Netherlands .. .. .. .. .. .. .. .. 199.3 35,100 3.10New Zealand .. .. .. .. .. .. .. .. 149.0 47,897 17.08Nicaragua 218.5 512.2 126.3 510.0 104.0 .. .. .. 32.5 .. ..Niger 85.5 358.7 .. .. .. .. 3.4 .. 12.6 24 0.00Nigeria 6,949.7 14,384.1 1,920.0 280.0 2,355.4 644.1 .. .. 29.4 65,089 0.79Norway .. .. .. .. .. .. .. .. .. 13,805 4.49Oman .. .. .. .. .. .. .. .. 48.2 3,165 1.67Pakistan 6,594.9 9,006.5 375.4 4,120.5 112.8 923.7 .. .. 21.5 2,759 0.03Panama 211.4 1,262.2 449.3 877.0 51.4 0.0 .. .. 91.5 548 0.26Papua New Guinea .. 150.0 .. .. .. .. .. .. 31.8 .. ..Paraguay 199.0 636.4 .. .. .. .. .. .. 37.8 .. ..Peru 2,241.4 3,191.1 2,498.9 2,191.0 522.5 3,289.6 152.0 119.8 24.3 51,151 2.65Philippines 4,616.4 5,172.0 3,428.4 10,942.5 943.5 968.9 0.0 530.5 29.6 11,435 0.19Poland 16,800.1 7,750.0 2,620.5 2,475.4 1,672.0 3,642.3 64.3 0.8 54.8 14,434 0.52Portugal .. .. .. .. .. .. .. .. 191.0 27,759 3.92Puerto Rico .. .. .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. 51.5 .. ..2012 World Development Indicators 285

5.1Private sector in the economyInvestment commitments in infrastructureprojects with private participation aDomesticcredit toprivatesectorBusinessesregistered$ millionsWater andEntryTelecommunications Energy Transportsanitation % of GDP New density2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2010 2009 2009Romania 3,906.9 4,996.4 1,240.8 6,288.7 .. 116.8 116.0 41.0 46.1 56,698 3.66Russian Federation 22,049.4 30,982.6 1,726.0 32,401.2 109.4 4,786.9 904.7 1,241.7 45.1 261,633 2.61Rwanda 72.3 414.0 1.6 .. .. .. .. .. .. 3,028 0.51Saudi Arabia .. .. .. .. .. .. .. .. 47.6 .. ..Senegal 593.1 1,569.0 93.3 22.0 55.4 398.0 0.0 0.0 25.9 1,636 0.22Serbia 563.5 3,024.1 .. .. .. .. 0.0 .. 51.5 9,715 1.94Sierra Leone 48.8 149.2 .. 1.2 .. 130.0 .. .. 10.4 .. ..Singapore .. .. .. .. .. .. .. .. 102.1 26,416 7.40Slovak Republic .. .. .. .. .. .. .. .. 44.9 15,825 4.04Slovenia .. .. .. .. .. .. .. .. 94.4 5,836 4.16Somalia 13.4 0.0 .. .. .. .. .. .. .. .. ..South Africa 10,519.5 9,815.0 1,251.3 15.9 504.7 3,483.0 31.3 0.0 145.5 24,700 0.77South Sudan .. .. .. .. .. .. .. .. .. .. ..Spain .. .. .. .. .. .. .. .. 211.6 79,757 2.92Sri Lanka 714.6 1,283.5 270.8 .. .. .. .. .. 26.6 4,223 0.29Sudan 747.7 2,226.3 .. .. .. 30.0 .. 120.7 11.6 .. ..Swaziland 27.7 63.3 .. .. .. .. .. .. 23.0 .. ..Sweden .. .. .. .. .. .. .. .. 140.2 24,228 4.09Switzerland .. .. .. .. .. .. .. .. 174.6 25,250 4.88Syrian Arab Republic 583.0 372.7 .. .. .. 82.0 .. .. 22.5 .. ..Tajikistan 8.5 196.4 16.0 .. .. .. .. .. .. 2,171 0.48Tanzania 515.3 2,109.5 348.0 28.4 27.7 134.0 8.5 .. 16.2 .. ..Thailand 5,602.7 3,526.0 4,693.3 6,641.4 939.0 .. 524.7 18.8 116.6 27,520 0.59Timor-Leste 0.0 0.0 .. .. .. .. .. .. 15.6 .. ..Togo 0.0 67.0 657.7 190.0 .. .. .. .. 23.0 125 0.04Trinidad and Tobago .. .. .. .. .. .. .. .. 39.2 .. ..Tunisia 751.0 3,771.0 30.0 .. .. 840.0 .. 95.0 68.8 9,079 1.23Turkey 12,788.6 13,751.7 5,854.8 13,504.2 3,203.6 4,471.2 .. .. 44.0 44,472 0.87Turkmenistan 20.0 202.5 .. .. .. .. .. .. .. .. ..Uganda 387.6 1,720.0 113.9 1,000.6 .. 404.0 0.0 .. 15.8 11,152 0.72Ukraine 3,162.9 4,921.4 160.0 64.9 .. 130.0 100.0 102.0 61.7 19,300 0.60United Arab Emirates .. .. .. .. .. .. .. .. 72.5 .. ..United Kingdom .. .. .. .. .. .. .. .. 202.9 330,100 8.05United States .. .. .. .. .. .. .. .. 202.2 .. ..Uruguay 114.2 200.2 330.0 .. 251.1 .. 368.0 .. 23.0 4,664 2.08Uzbekistan 285.6 2,046.8 .. .. .. 25.0 0.0 .. .. 14,428 0.78Venezuela, RB 3,337.0 3,042.8 39.5 .. 34.0 .. 15.0 .. 18.9 .. ..Vietnam 430.0 1,593.7 2,360.6 297.0 20.0 1,120.0 266.0 .. 125.0 .. ..West Bank and Gaza 279.8 644.0 150.0 .. .. .. .. .. .. .. ..Yemen, Rep. 376.8 451.2 .. 15.8 .. 220.0 .. .. 6.2 .. ..Zambia 208.3 1,077.0 3.0 .. 15.6 .. 0.0 .. 11.5 5,509 0.88Zimbabwe 72.0 644.0 .. .. .. .. .. .. .. .. ..World .. s .. s .. s .. s .. s .. s .. s .. s 134.8 wLow income 4,646.3 21,615.6 .. .. .. .. .. .. 28.3Middle income 226,353.6 317,609.7 107,559.1 266,780.1 51,413.6 106,204.4 16,177.1 6,654.9 73.7Lower middle income 68,257.9 144,334.1 19,986.8 123,341.9 1,781.0 34,127.5 192.0 .. 40.2Upper middle income 158,095.7 34,212.5 83,156.9 143,438.2 41,607.7 72,076.9 18,427.2 9,705.8 83.3Low & middle income 232,154.6 339,225.4 85,938.8 272,012.7 5,437.5 86,673.9 2,481.0 .. 73.0East Asia & Pacifi c 29,380.1 4,662.0 30,710.4 40,115.6 21,905.4 20,538.0 10,842.9 5,196.4 116.3Europe & Central Asia 51,002.1 76,087.4 4,439.1 58,924.3 .. .. .. .. 46.2Latin America & Carib. 78,935.4 85,698.9 45,347.6 67,445.3 16,419.9 49,397.7 2,516.1 .. 41.9Middle East & N. Africa 13,435.4 29,970.1 .. .. .. .. .. .. 35.0South Asia 29,784.3 68,726.2 9,828.0 95,669.0 4,526.2 39,437.9 112.9 241.7 45.7Sub-Saharan Africa 24,622.2 52,365.4 .. .. .. .. .. .. 65.4High income .. .. .. .. .. .. .. .. 164.6Euro area .. .. .. .. .. .. .. .. 133.6a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached fi nancial closure in 1990–2010.2862012 World Development Indicators

Private sector in the economy5.1STATES AND MARKETSAbout the dataDefinitionsPrivate sector development and investment—tappingprivate sector initiative and investment for sociallyuseful purposes—are critical for poverty reduction.In parallel with public sector efforts, private investment,especially in competitive markets, has tremendouspotential to contribute to growth. Privatemarkets are the engine of productivity growth, creatingproductive jobs and higher incomes. And with governmentplaying a complementary role of regulation,funding, and service provision, private initiative andinvestment can help provide the basic services andconditions that empower poor people—by improvinghealth, education, and infrastructure.Investment in infrastructure projects with privateparticipation has made important contributions toeasing fi scal constraints, improving the effi ciencyof infrastructure services, and extending deliveryto poor people. Developing countries have been inthe forefront, pioneering better approaches to infrastructureservices and reaping the benefi ts of greatercompetition and customer focus.The data on investment in infrastructure projectswith private participation refer to all investment (publicand private) in projects in which a private companyassumes operating risk during the operatingperiod or development and operating risk during thecontract period. Investment refers to commitmentsnot disbursements. Foreign state-owned companiesare considered private entities for the purposes ofthis measure.Investments are classifi ed into two types: investmentsin physical assets—the resources a companycommits to invest in expanding and modernizingfacilities—and payments to the government toacquire state-owned enterprises or rights to provideservices in a specifi c area or to use part of the radiospectrum.The data are from the World Bank’s Private Participationin Infrastructure (PPI) Project database,which tracks infrastructure projects with private participationin developing countries. It provides informationon more than 4,800 infrastructure projectsin 139 developing economies from 1984 to 2010.The database contains more than 30 fi elds per projectrecord, including country, fi nancial closure year,infrastructure services provided, type of private participation,investment, technology, capacity, projectlocation, contract duration, private sponsors, biddingprocess, and development bank support. Data on theprojects are compiled from publicly available information.The database aims to be as comprehensiveas possible, but some projects—particularly thoseinvolving local and small-scale operators—may beomitted because they are not publicly reported.The database is a joint product of the World Bank’sFinance, Economics, and Urban DevelopmentDepartment and the Public-Private InfrastructureAdvisory Facility. Geographic and income aggregatesare calculated by the World Bank’s DevelopmentData Group. For more information, see http://ppi.worldbank.org/.Credit is an important link in money transmission;it fi nances production, consumption, and capital formation,which in turn affect economic activity. Thedata on domestic credit to the private sector aretaken from the banking survey of the InternationalMonetary Fund’s (IMF) International Financial Statisticsor, when unavailable, from its monetary survey.The monetary survey includes monetary authorities(the central bank), deposit money banks, and otherbanking institutions, such as fi nance companies,development banks, and savings and loan institutions.Credit to the private sector may sometimesinclude credit to state-owned or partially state-ownedenterprises.Entrepreneurship is essential to the dynamism ofthe modern market economy, and a greater entrydensity of new businesses can foster competitionand economic growth. The table includes data onbusiness registrations from the 2008 and 2010World Bank Group Entrepreneurship Survey, whichincludes entrepreneurial activity in more than 100countries for 2000–09. Survey data are used toanalyze fi rm creation, its relationship to economicgrowth and poverty reduction, and the impact ofregulatory and institutional reforms. The 2010 surveyimproves on earlier surveys’ methodology andcountry coverage for better cross-country comparability;the database will be updated in 2012. Data ontotal registered businesses were collected directlyfrom national registrars of companies. For crosscountrycomparability, only limited liability corporationsthat operate in the formal sector are included.For additional information on sources, methodology,calculation of entrepreneurship rates, and datalimitations see http://econ.worldbank.org/research/entrepreneurship.• Investment commitments in infrastructureprojects with private participation refers to infrastructureprojects in telecommunications, energy(electricity and natural gas transmission and distribution),transport, and water and sanitation thathave reached fi nancial closure and directly or indirectlyserve the public. Incinerators, movable assets,standalone solid waste projects, and small projectssuch as windmills are excluded. Included are operationand management contracts, concessions (operationand management contracts with major capitalexpenditure), greenfi eld projects (new facilities builtand operated by a private entity or a public-privatejoint venture), and divestitures. Investment commitmentsare the sum of investments in physical assetsand payments to the government. Investments inphysical assets are resources the project companycommits to invest during the contract period in newfacilities or in expansion and modernization of existingfacilities. Payments to the government are theresources the project company spends on acquiringgovernment assets such as state-owned enterprises,rights to provide services in a specifi c area, oruse of specifi c radio spectrums. • Domestic creditto private sector is fi nancial resources providedto the private sector—such as through loans, purchasesof nonequity securities, and trade credits andother accounts receivable—that establish a claim forrepayment. For some countries these claims includecredit to public enterprises. • New businesses registeredare the number of limited liability corporationsregistered in the calendar year. • Entry density is thenumber of newly registered limited liability corporationsper 1,000 people ages 15–64.Data sourcesData on investment commitments in infrastructureprojects with private participation arefrom the World Bank’s PPI Project database(http://ppi.worldbank.org). Data on domesticcredit are from the IMF’s International FinancialStatistics. Data on business registrationare from the World Bank’s EntrepreneurshipSnapshots (http://econ.worldbank.org/research/entrepreneurship).2012 World Development Indicators 287

5.2Business environment: enterprise surveysSurveyyearRegulationsand taxPermits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforceand licensesTimedealing withoffi cials% ofmanagementtimeAveragenumberof timesmeeting withtax offi cialsTime requiredto obtainoperatinglicensedaysInformalpaymentsto publicoffi cials% of fi rmsLosses dueto theft,robbery,vandalism,and arson% of salesFirmsformallyregisteredwhenoperationsstarted% of fi rmsFirms withfemaleparticipationin ownership% of fi rmsFirms usingbanks tofi nan c einvestment% of fi rmsValue lost dueto electricaloutages% of salesInternationallyrecognizedqualitycertifi cationownership% of fi rmsAveragetime toclear directexportsthroughcustomsdaysFirmsofferingformaltraining% of fi rmsAfghanistan 2008 6.8 1.2 13.8 41.5 1.5 88.0 2.8 1.4 6.5 8.5 14.6 14.6Albania 2007 18.7 3.9 21.2 57.7 0.5 89.4 10.8 12.4 13.7 24.6 1.9 19.9 aAlgeria 2007 25.1 2.3 19.3 66.6 0.9 98.3 15.0 8.9 4.0 5.0 14.1 17.3 aAngola 2010 12.2 2.5 34.7 48.9 1.5 62.7 56.6 13.1 12.6 21.7 6.7 23.5Argentina 2010 20.8 2.7 176.1 18.2 0.6 92.4 38.0 30.3 3.5 18.2 7.3 63.6Armenia 2009 10.3 2.1 20.0 16.0 0.6 96.2 31.8 31.9 1.8 26.9 3.3 30.4Australia .. .. .. .. .. .. .. .. .. .. .. ..Austria .. .. .. .. .. .. .. .. .. .. .. ..Azerbaijan 2009 3.0 2.1 15.8 52.2 0.3 85.1 10.8 19.0 1.8 18.2 1.9 10.5Bahrain .. .. .. .. .. .. .. .. .. .. .. ..Bangladesh 2007 3.2 1.3 6.0 85.1 0.1 .. 16.1 24.7 10.6 7.8 8.4 27.2 aBelarus 2008 13.6 1.1 38.2 26.1 0.4 98.5 52.9 35.8 0.8 13.9 2.6 44.4Belgium .. .. .. .. .. .. .. .. .. .. .. ..Benin 2009 20.7 1.2 64.3 54.5 1.9 87.9 43.9 4.2 7.5 7.3 9.6 32.4Bolivia 2010 28.5 1.6 37.3 17.6 0.8 72.4 41.3 27.8 2.5 22.4 12.4 57.1Bosnia and Herzegovina 2009 11.2 1.0 21.4 10.3 0.4 98.6 32.8 59.7 1.9 30.1 1.3 66.5Botswana 2010 10.2 1.0 27.2 7.3 1.5 93.9 55.3 32.8 3.7 21.3 6.2 51.9Brazil 2009 18.7 1.2 83.5 11.9 1.7 95.8 59.3 48.4 3.0 25.7 15.9 52.9Bulgaria 2009 10.6 2.2 20.8 22.4 0.5 98.5 33.9 34.7 1.6 19.9 4.2 30.7Burkina Faso 2009 22.2 1.5 35.8 8.5 0.3 77.7 19.2 25.6 5.8 14.4 7.4 24.8Burundi 2006 5.7 1.8 27.3 56.5 1.1 .. 34.8 12.3 10.7 7.1 .. 22.1 aCambodia 2007 5.6 1.0 .. 61.2 0.4 87.5 .. 11.3 2.4 2.8 1.5 48.4 aCameroon 2009 7.0 4.4 30.0 51.2 1.6 82.1 15.7 31.4 4.9 20.4 15.1 25.5Canada .. .. .. .. .. .. .. .. .. .. .. ..Central African Republic .. .. .. .. .. .. .. .. .. .. .. ..Chad 2009 20.8 3.4 24.3 41.8 2.5 77.1 40.1 4.2 3.3 43.3 11.9 43.4Chile 2010 9.9 2.9 68.5 0.7 0.8 96.1 29.6 44.8 1.3 22.1 10.8 57.5China 2003 18.3 14.4 11.6 72.6 0.1 .. .. 28.8 1.3 35.9 6.6 84.8Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. ..Colombia 2010 12.9 0.9 25.6 2.8 0.3 94.3 35.3 35.0 1.8 20.8 8.6 65.2Congo, Dem. Rep. 2010 29.4 8.0 40.0 65.7 1.8 61.9 38.9 6.7 22.7 8.5 18.0 24.1Congo, Rep. 2009 6.0 2.7 .. 81.8 3.3 84.3 31.8 7.7 16.4 19.6 .. 37.5Costa Rica 2010 8.4 0.8 35.6 3.7 0.4 80.8 43.5 22.2 1.7 13.3 10.0 54.7Côte d’Ivoire 2009 1.6 3.7 44.1 38.5 3.4 56.4 61.9 13.9 5.0 4.3 16.6 19.1Croatia 2007 10.9 0.7 26.5 14.5 0.2 98.1 33.5 60.0 0.8 16.5 1.3 28.0 aCuba .. .. .. .. .. .. .. .. .. .. .. ..Cyprus .. .. .. .. .. .. .. .. .. .. .. ..Czech Republic 2009 10.4 1.5 19.9 12.8 0.4 98.0 25.0 33.4 0.6 43.5 5.7 70.7Denmark .. .. .. .. .. .. .. .. .. .. .. ..Dominican Republic 2005 8.8 0.5 .. 26.3 0.7 .. .. 12.5 15.2 9.6 11.4 53.3Ecuador 2010 22.5 0.8 30.0 11.8 1.0 85.1 24.1 17.0 3.9 9.7 18.2 65.9Egypt, Arab Rep. 2008 8.8 3.4 42.7 15.2 3.0 17.9 34.0 5.6 3.2 21.1 6.2 21.7El Salvador 2010 19.7 3.0 44.4 12.7 1.6 75.7 40.2 31.7 7.0 14.5 3.7 61.0Eritrea 2009 0.5 0.2 .. 0.0 0.0 100.0 4.2 11.9 0.2 15.1 9.6 26.1Estonia 2009 5.5 0.4 8.3 3.7 0.9 97.4 36.3 41.5 0.5 21.2 1.8 69.3Ethiopia 2006 3.8 1.1 11.4 12.4 1.4 .. 30.9 11.0 0.9 4.2 4.3 38.2 aFinland .. .. .. .. .. .. .. .. .. .. .. ..France .. .. .. .. .. .. .. .. .. .. .. ..Gabon 2009 2.8 15.2 12.1 41.8 0.4 63.7 33.1 6.3 1.7 18.6 3.8 30.9Gambia, The 2006 7.3 2.5 8.4 52.4 2.7 .. 21.3 7.6 11.8 22.2 5.0 25.6 aGeorgia 2008 2.1 0.6 11.8 14.7 0.7 99.6 40.8 38.2 1.4 16.0 3.8 14.5Germany 2005 1.2 1.3 .. .. 0.5 .. 20.3 45.0 .. .. 4.7 35.4Ghana 2007 3.2 4.3 6.4 38.8 0.9 66.4 44.0 16.0 5.6 6.8 7.8 33.0 aGreece 2005 1.8 1.7 .. 21.6 0.0 .. 24.4 25.9 .. 11.7 5.5 20.0Guatemala 2010 10.2 2.7 41.1 6.3 1.3 90.0 44.2 26.6 2.8 11.9 4.7 51.9Guinea 2006 2.7 2.8 13.0 84.8 2.0 .. 25.4 0.9 14.0 5.2 4.3 21.1 aGuinea-Bissau 2006 2.9 3.4 30.4 63.1 1.1 .. 19.9 0.7 5.3 8.4 5.6 12.4 aHaiti .. .. .. .. .. .. .. .. .. .. .. ..2882012 World Development Indicators

Business environment: enterprise surveys 5.2SurveyyearRegulationsand taxPermits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforceand licensesSTATES AND MARKETSTimedealing withoffi cials% ofmanagementtimeAveragenumberof timesmeeting withtax offi cialsTime requiredto obtainoperatinglicensedaysInformalpaymentsto publicoffi cials% of fi rmsLosses dueto theft,robbery,vandalism,and arson% of salesFirmsformallyregisteredwhenoperationsstarted% of fi rmsFirms withfemaleparticipationin ownership% of fi rmsFirms usingbanks tofi nan c einvestment% of fi rmsValue lost dueto electricaloutages% of salesInternationallyrecognizedqualitycertifi cationownership% of fi rmsAveragetime toclear directexportsthroughcustomsdaysFirmsofferingformaltraining% of fi rmsHonduras 2010 17.0 2.2 28.8 6.1 2.2 81.3 43.3 17.0 9.2 16.3 10.1 35.8Hungary 2009 13.5 0.8 35.6 5.4 0.1 100.0 42.4 48.7 0.9 39.4 4.3 14.8India 2006 6.7 2.6 .. 47.5 0.1 .. 9.1 46.6 6.6 22.5 15.1 15.9 aIndonesia 2009 1.6 0.2 21.1 14.9 0.4 29.1 42.8 11.7 2.2 2.9 2.3 4.7Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. ..Iraq .. .. .. .. .. .. .. .. .. .. .. ..Ireland 2005 2.3 1.3 .. 8.3 0.3 .. 41.6 37.4 1.5 17.2 2.6 73.2Israel .. .. .. .. .. .. .. .. .. .. .. ..Italy .. .. .. .. .. .. .. .. .. .. .. ..Jamaica 2010 1.7 0.6 9.3 10.6 0.4 90.0 38.2 44.4 0.2 16.5 13.1 25.9Japan .. .. .. .. .. .. .. .. .. .. .. ..Jordan 2006 6.7 1.7 6.4 18.1 0.1 .. 13.1 8.6 1.7 15.5 3.8 23.9 aKazakhstan 2009 4.7 2.6 30.8 34.1 1.0 97.4 34.4 31.0 3.7 10.8 8.5 40.9Kenya 2007 5.1 6.7 23.4 79.2 3.9 .. 37.1 22.9 6.4 9.8 5.6 48.5 aKorea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 2005 0.1 2.2 .. 14.1 0.0 .. 19.1 39.9 .. 17.6 7.2 39.5Kosovo 2009 9.8 4.5 18.8 7.5 0.3 89.2 10.9 25.3 17.1 7.9 1.7 24.6Kuwait .. .. .. .. .. .. .. .. .. .. .. ..Kyrgyz Republic 2009 4.9 2.1 18.0 47.8 0.3 95.9 60.4 17.9 10.5 16.2 15.8 29.7Lao PDR 2009 1.2 4.4 13.6 39.8 0.3 93.5 39.4 0.0 4.3 7.2 7.5 11.1Latvia 2009 9.7 1.5 11.5 13.4 0.3 98.5 46.3 37.3 1.1 18.2 1.9 43.4Lebanon 2009 8.9 2.2 81.0 23.0 0.0 97.6 33.5 23.8 9.4 17.9 7.6 52.4Lesotho 2009 5.6 1.8 16.4 28.1 2.9 86.8 18.4 32.7 6.7 24.7 5.4 42.5Liberia 2009 7.5 6.5 16.0 55.4 2.8 73.8 53.0 10.1 2.9 2.4 .. 17.0Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 2009 9.3 0.8 65.5 10.7 0.4 97.1 38.7 47.4 0.7 15.6 2.4 46.0Macedonia, FYR 2009 14.5 3.0 33.8 16.9 0.7 99.2 36.4 47.0 5.9 21.5 2.5 19.0Madagascar 2009 17.1 0.9 41.3 21.8 1.2 97.5 50.0 12.2 7.7 8.7 14.2 27.0Malawi 2009 3.5 2.6 15.0 10.8 5.7 78.6 23.9 20.6 13.3 17.9 9.9 48.4Malaysia 2007 7.8 2.1 22.4 .. 1.0 53.0 13.1 48.6 3.0 54.1 2.7 50.1 aMali 2010 2.0 1.2 41.0 19.4 0.5 79.2 58.3 29.3 4.1 24.8 12.9 32.1Mauritania 2006 5.8 1.8 10.7 82.1 0.6 .. 17.3 3.2 1.6 5.9 3.9 25.5 aMauritius 2009 9.4 0.5 19.1 5.9 1.4 84.2 16.9 37.5 2.2 11.1 10.3 25.6Mexico 2010 13.6 1.1 54.0 11.6 1.4 84.7 25.7 16.2 3.4 24.0 7.1 50.8Moldova 2009 7.0 1.9 13.9 33.5 0.4 97.9 53.1 30.8 2.0 9.1 2.4 33.1Mongolia 2009 12.1 2.0 43.5 33.4 0.6 90.1 52.0 26.5 0.8 16.7 18.6 61.2Morocco 2007 11.4 0.9 3.4 13.4 0.0 86.0 13.1 12.3 1.3 17.3 1.8 24.7 aMozambique 2007 3.3 1.9 35.2 14.8 1.8 85.9 24.4 10.5 2.4 18.7 10.1 22.1 aMyanmar .. .. .. .. .. .. .. .. .. .. .. ..Namibia 2006 2.9 0.3 9.6 11.4 1.3 .. 33.4 8.1 0.7 17.6 1.4 44.5 aNepal 2009 6.5 1.3 14.5 15.2 0.9 94.0 27.4 17.5 27.0 3.1 5.6 8.8Netherlands .. .. .. .. .. .. .. .. .. .. .. ..New Zealand .. .. .. .. .. .. .. .. .. .. .. ..Nicaragua 2010 20.2 1.5 17.6 8.3 2.2 74.0 61.9 21.9 18.2 15.5 4.7 47.2Niger 2009 22.9 1.2 39.7 35.2 0.9 90.5 17.6 9.3 1.9 4.6 2.6 32.1Nigeria 2007 6.1 3.0 12.1 40.9 4.1 .. 20.0 2.7 8.9 8.5 7.5 25.7 aNorway .. .. .. .. .. .. .. .. .. .. .. ..Oman 4.4 11.8 33.2 .. .. .. 31.0 4.2 10.8 3.4 20.9 ..Pakistan 2007 1.9 1.5 10.2 48.0 0.4 .. 6.7 9.7 9.2 9.6 2.6 6.7 aPanama 2010 33.3 0.8 66.3 30.5 0.3 99.7 24.7 1.2 2.1 22.5 7.6 11.0Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. ..Paraguay 2010 20.7 1.0 81.3 17.5 1.3 98.7 51.6 30.1 1.4 15.0 21.7 54.9Peru 2010 14.1 1.7 46.5 21.4 0.6 82.6 28.7 45.9 3.2 14.2 16.1 60.1Philippines 2009 9.1 1.5 10.6 18.6 1.1 97.5 69.4 22.0 3.9 15.7 10.0 31.1Poland 2009 12.8 0.6 14.6 14.7 0.5 99.3 47.9 40.7 1.9 17.3 6.0 60.9Portugal 2005 1.1 1.6 .. 14.5 0.2 .. 50.8 24.4 .. 12.7 7.2 31.9Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 289

5.2Business environment: enterprise surveysSurveyyearRegulationsand taxPermits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforceand licensesTimedealing withoffi cials% ofmanagementtimeAveragenumberof timesmeeting withtax offi cialsTime requiredto obtainoperatinglicensedaysInformalpaymentsto publicoffi cials% of fi rmsLosses dueto theft,robbery,vandalism,and arson% of salesFirmsformallyregisteredwhenoperationsstarted% of fi rmsFirms withfemaleparticipationin ownership% of fi rmsFirms usingbanks tofi nan c einvestment% of fi rmsValue lost dueto electricaloutages% of salesInternationallyrecognizedqualitycertifi cationownership% of fi rmsAveragetime toclear directexportsthroughcustomsdaysFirmsofferingformaltraining% of fi rmsRomania 2009 9.2 2.3 23.7 22.2 0.3 98.7 47.9 37.3 2.2 26.1 2.0 24.9Russian Federation 2009 19.9 1.6 57.4 39.6 0.8 94.7 33.1 30.6 1.2 11.7 4.6 52.2Rwanda 2006 5.9 3.3 6.5 20.0 1.3 .. 41.0 15.9 8.7 10.8 6.7 27.6 aSaudi Arabia .. .. .. .. .. .. .. .. .. .. .. ..Senegal 2007 2.9 1.3 21.4 18.1 0.5 78.9 26.3 19.8 5.0 6.1 7.4 16.3 aSerbia 2009 12.2 1.4 28.0 21.4 0.6 95.0 28.8 42.8 1.3 21.8 1.6 36.5Sierra Leone 2009 7.4 1.9 12.6 20.4 0.8 89.2 7.9 6.9 6.6 13.8 .. 18.6Singapore .. .. .. .. .. .. .. .. .. .. .. ..Slovak Republic 2009 6.7 0.9 32.1 15.7 0.7 100.0 29.6 33.5 0.3 28.6 2.4 33.1Slovenia 2009 7.3 0.3 56.1 5.8 0.4 99.9 42.2 52.2 0.5 28.0 2.2 47.5Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 2007 6.0 0.8 36.2 15.1 1.0 91.0 22.6 34.8 1.6 26.4 4.5 36.8 aSouth Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 2005 0.8 1.5 .. 4.4 0.2 .. 34.1 32.6 3.0 21.3 4.9 51.3Sri Lanka 2004 3.5 4.9 49.5 16.3 0.5 .. .. 26.2 .. .. 7.6 32.6Sudan .. .. .. .. .. .. .. .. .. .. .. ..Swaziland 2006 4.4 1.4 24.0 40.6 1.3 .. 28.6 7.7 2.5 22.1 2.1 51.0 aSweden .. .. .. .. .. .. .. .. .. .. .. ..Switzerland .. .. .. .. .. .. .. .. .. .. .. ..Syrian Arab Republic 2009 12.2 2.3 169.2 83.8 0.8 .. 14.4 20.7 8.6 7.4 5.1 38.3Tajikistan 2008 11.7 1.4 22.6 44.6 0.3 92.7 34.4 21.4 15.1 16.7 20.4 21.1Tanzania 2006 4.0 2.7 15.9 49.5 1.2 .. 30.9 6.8 9.6 14.7 5.7 36.5 aThailand 2006 0.4 1.0 32.1 .. 0.1 .. .. 74.4 1.5 39.0 1.3 75.3 aTimor-Leste 2009 3.8 0.9 16.6 19.4 2.7 91.8 42.9 1.6 7.6 2.2 .. 49.7Togo 2009 2.7 1.2 56.4 16.7 2.4 75.8 31.8 16.9 10.5 6.6 6.7 31.0Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. ..Tunisia .. .. .. .. .. .. .. .. .. .. .. ..Turkey 2008 27.1 1.3 36.0 18.0 0.4 94.1 40.7 51.9 2.8 30.0 5.2 28.8Turkmenistan .. .. .. .. .. .. .. .. .. .. ..Uganda 2006 5.2 2.4 9.3 51.7 1.0 .. 34.7 7.7 10.2 15.5 3.2 35.0 aUkraine 2008 11.3 2.1 31.0 31.8 0.6 95.8 47.1 32.1 4.4 13.0 3.4 24.8United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. ..United Kingdom .. .. .. .. .. .. .. .. .. .. .. ..United States .. .. .. .. .. .. .. .. .. .. .. ..Uruguay 2010 11.6 1.0 108.0 8.1 0.3 94.6 23.1 13.7 0.3 10.8 6.2 48.6Uzbekistan 2008 11.1 0.7 9.1 59.5 0.7 100.0 39.8 8.2 5.4 1.3 5.1 9.6Venezuela, RB 2010 27.6 3.0 117.2 23.7 1.4 95.6 30.7 35.3 8.3 25.9 18.4 56.0Vietnam 2009 4.6 0.9 15.9 52.5 0.3 87.5 59.2 21.5 3.6 16.7 4.2 43.6West Bank and Gaza 2006 5.7 1.7 21.3 13.3 1.2 .. 18.0 4.2 4.6 18.2 6.0 26.5 aYemen, Rep. 2010 11.8 7.3 6.5 68.2 0.6 81.7 6.4 4.2 13.2 4.4 6.2 12.9Zambia 2007 4.6 1.9 48.3 14.3 1.0 96.2 37.2 10.2 3.7 17.2 2.3 26.0 aZimbabwe .. .. .. .. .. .. .. .. .. .. ..Note: Enterprise surveys are updated several times a year; see www.enterprisesurveys.org for the most recent updates.a. The sample was drawn from the manufacturing sector only.2902012 World Development Indicators

Business environment: enterprise surveys 5.2STATES AND MARKETSAbout the dataDefinitionsThe World Bank Enterprise Surveys gather fi rm-leveldata to benchmark the business environment ofeconomies and assess how business environmentconstraints affect productivity and job creation. Standardizedsurveys are conducted all over the world,and data are available on more than 130,000 fi rmsin 128 countries. The survey covers 11 dimensionsof the business environment, including regulation,corruption, crime, informality, fi nance, infrastructure,and trade. For several countries fi rm-level paneldata are also available, making it possible to trackchanges in the business environment over time.Firms evaluating investment options, governmentsinterested in improving business conditions, andeconomists seeking to explain economic performancehave all grappled with defi ning and measuringthe business environment. The fi rm-level datafrom Enterprise Surveys provide a useful tool forbenchmarking economies across a large number ofindicators measured at the fi rm level.Most countries can improve regulation and taxationwithout compromising broader social interests.Excessive regulation may harm business performanceand growth. For example, time spent withtax offi cials is a burden fi rms may face in payingtaxes. The business environment suffers when governmentsincrease uncertainty and risks or imposeunnecessary costs and unsound regulation and taxation.Time to obtain licenses and permits and theassociated red tape constrain fi rm operations.In some countries doing business requires informalpayments to “get things done” in customs, taxes,licenses, regulations, services, and the like. Suchcorruption can harm the business environment bydistorting policymaking, undermining governmentcredibility, and diverting public resources. Crime,theft, and disorder also impose costs on businessesand society.In many developing countries informal businessesoperate without formal registration. These fi rms haveless access to fi nancial and public services and canengage in fewer types of contracts and investments,constraining growth.Equal opportunities for men and women contributeto development. Female participation in fi rm ownershipand in management measures women’s integrationas decision makers.Financial markets connect fi rms to lenders andinvestors, allowing fi rms to grow their businesses:creditworthy fi rms can obtain credit from fi nancialintermediaries at competitive prices. But too oftenmarket imperfections and government-induceddistortions limit access to credit and thus restraingrowth.The reliability and availability of infrastructure benefit households and support development. Firms withaccess to modern and effi cient infrastructure—telecommunications,electricity, and transport—can bemore productive. Firm-level innovation and use ofmodern technology may help fi rms compete.Delays in clearing customs can be costly, deterringfi rms from engaging in trade or making them uncompetitiveglobally. Ill-considered labor regulations discouragefi rms from creating jobs, and while employedworkers may benefi t, unemployed, low-skilled, andinformally employed workers will not. A trained laborforce enables fi rms to thrive, compete, innovate, andadopt new technology.The data in the table are from Enterprise Surveysimplemented by the World Bank’s Financial and PrivateSector Development Enterprise Analysis Unit. Alleconomies in East Asia and Pacifi c, Europe and CentralAsia, Latin America and the Caribbean, MiddleEast and North Africa, and Sub-Saharan Africa (for2009) and Afghanistan, Bangladesh, and India drawa sample of registered nonagricultural businesses,excluding those in the fi nancial and public sectors.Samples for other economies are drawn only fromthe manufacturing sector and are footnoted in thetable. Typical Enterprise Survey sample sizes rangefrom 150 to 1,800, depending on the size of theeconomy. In each country samples are selected bystratifi ed random sampling, unless otherwise noted.Stratifi ed random sampling allows indicators to becomputed by sector, fi rm size, and location andincreases the precision of economywide indicatorscompared with alternative simple random sampling.Stratifi cation by sector of activity divides the economyinto manufacturing and retail and other servicessectors. For medium-size and large economies themanufacturing sector is further stratifi ed by industry.Firm size is stratifi ed into small (5–19 employees),medium (20–99 employees), and large (more than99 employees). Geographic stratifi cation divides thenational economy into the main centers of economicactivity.• Survey year is the year in which the underlyingdata were collected. • Time dealing with tax officialsis the average percentage of senior management’stime that is spent in a typical week dealingwith requirements imposed by government regulations.• Average number of times meeting with taxofficials is the average number of visits or requiredmeetings with tax offi cials. • Time required to obtainoperating license is the average wait to obtain anoperating license from the day applied for to the daygranted. • Informal payments to public officials arethe percentage of fi rms that answered positivelyto the question “Was a gift or informal paymentexpected or requested during a meeting with taxoffi cials?” • Losses due to theft, robbery, vandalism,and arson are the estimated losses from thosecauses that occurred on establishments’ premisesas a percentage of annual sales. • Firms formallyregistered when operations started are fi rms formallyregistered when they started operations in thecountry. Firms not formally registered (the residual)are in the informal sector of the economy. • Firmswith female participation in ownership are fi rms witha woman among the owners. • Firms using banks tofinance investment are fi rms that invested in fi xedassets during the last fi scal year that used banks tofi nance fi xed assets. • Value lost due to electricaloutages is losses that resulted from power outagesas a percentage of annual sales. • Internationallyrecognized quality certification ownership is thepercentage of fi rms that have an internationally recognizedquality certifi cation, such as InternationalOrganization for Standardization 9000, 9001, 9002,or 14000 or Hazard Analysis and Critical ControlPoints. • Average time to clear direct exportsthrough customs is the average number of days toclear direct exports through customs. • Firms offeringformal training are fi rms offering formal trainingprograms for their permanent, full-time employees.Data sourcesData on the business environment are from theWorld Bank Enterprise Surveys website (www.enterprisesurveys.org).2012 World Development Indicators 291

5.3Business environment:Doing Business indicatorsStarting abusinessRegisteringpropertyDealing withconstructionpermitsGettingelectricityEnforcingcontractsProtectinginvestorsResolvinginsolvencyNumber ofproceduresJune2011TimerequireddaysJune2011Cost% of percapitaincomeJune2011Number ofproceduresJune2011TimerequireddaysJune2011Number ofproceduresto build awarehouseJune2011Timerequiredto build awarehousedaysJune2011TimerequireddaysJune2011Number ofproceduresJune2011TimerequireddaysJune2011Disclosureindex0–10 (leastto mostdisclosure)June2011TimerequiredyearsJune2011Afghanistan 4 7 25.8 9 250 12 334 109 47 1,642 1 2.0Albania 5 5 29.0 6 33 .. .. 177 39 390 8 2.0Algeria 14 25 12.1 10 48 19 281 159 45 630 6 2.5Angola 8 68 118.9 7 184 11 321 48 46 1,011 5 6.2Argentina 14 26 11.9 7 53 25 365 67 36 590 6 2.8Armenia 3 8 2.9 3 7 18 79 242 49 440 5 1.9Australia 2 2 0.7 5 5 15 147 81 28 395 8 1.0Austria 8 28 5.2 3 21 13 194 23 25 397 3 1.1Azerbaijan 6 8 2.7 4 11 30 212 241 39 237 7 2.7Bahrain 7 9 0.7 2 31 12 43 90 48 635 8 2.5Bangladesh 7 19 30.6 8 245 11 201 372 41 1,442 6 4.0Belarus 5 5 1.3 2 10 13 140 254 29 275 7 5.8Belgium 3 4 5.2 8 64 12 169 88 26 505 8 0.9Benin 6 29 149.9 4 120 12 372 158 42 795 6 4.0Bolivia 15 50 90.4 7 92 14 249 42 40 591 1 1.8Bosnia and Herzegovina 12 40 17.0 7 33 18 181 125 37 595 3 3.3Botswana 10 61 1.8 5 16 22 145 121 28 625 7 1.7Brazil 13 119 5.4 13 39 17 469 34 45 731 6 4.0Bulgaria 4 18 1.5 8 15 23 120 130 39 564 10 3.3Burkina Faso 3 13 47.7 4 59 12 98 158 37 446 6 4.0Burundi 9 14 116.8 5 94 22 135 188 44 832 8 ..Cambodia 9 85 109.7 7 56 21 652 183 44 401 5 6.0Cameroon 5 15 45.5 5 93 11 147 67 43 800 6 3.2Canada 1 5 0.4 6 17 12 73 168 36 570 8 0.8Central African Republic 7 21 175.5 5 75 18 203 102 43 660 6 4.8Chad 11 66 208.5 6 44 13 154 67 41 743 6 4.0Chile 7 7 5.1 6 31 17 155 31 36 480 8 4.5China 14 38 3.5 4 29 33 311 145 34 406 10 1.7Hong Kong SAR, China 3 3 1.9 5 36 6 67 43 26 280 10 1.1Colombia 9 14 8.0 7 15 8 46 165 34 1,346 8 1.3Congo, Dem. Rep. 10 65 551.4 6 54 11 117 58 43 610 3 5.2Congo, Rep. 10 160 85.2 6 55 14 186 129 44 560 6 3.3Costa Rica 12 60 11.1 5 20 20 188 62 40 852 2 3.5Côte d’Ivoire 10 32 132.6 6 62 18 583 33 33 770 6 2.2Croatia 6 7 8.6 5 104 12 317 70 38 561 1 3.1Cuba .. .. .. .. .. .. .. .. .. .. .. ..Cyprus 6 8 13.1 6 42 9 677 247 43 735 8 1.5Czech Republic 9 20 8.4 4 25 33 120 279 27 611 2 3.2Denmark 4 6 0.0 3 16 5 67 38 35 410 7 1.0Dominican Republic 7 19 18.2 7 60 14 216 87 34 460 5 3.5Ecuador 13 56 28.8 9 16 16 128 89 39 588 1 5.3Egypt, Arab Rep. 6 7 5.6 7 72 22 218 54 41 1,010 8 4.2El Salvador 8 17 45.1 5 31 33 157 78 34 786 3 4.0Eritrea 13 84 62.6 11 78 .. .. 59 39 405 4 ..Estonia 5 7 1.8 3 18 13 148 111 35 425 8 3.0Ethiopia 5 9 12.8 10 41 9 128 95 37 620 4 3.0Finland 3 14 1.0 3 14 16 66 53 33 375 6 0.9France 5 7 0.9 8 59 10 184 123 29 331 10 1.9Gabon 9 58 17.3 7 39 13 201 160 38 1,070 6 5.0Gambia, The 8 27 206.1 5 66 14 143 78 33 434 2 3.0Georgia 2 2 4.3 1 2 9 74 97 36 285 9 3.3Germany 9 15 4.6 5 40 9 97 17 30 394 5 1.2Ghana 7 12 17.3 5 34 16 218 78 36 487 7 1.9Greece 10 10 20.1 11 18 14 169 77 39 819 1 2.0Guatemala 12 37 52.5 4 23 19 165 39 31 1,459 3 3.0Guinea 12 40 118.0 6 59 29 287 69 49 276 6 3.8Guinea-Bissau 9 9 49.8 8 210 12 170 455 40 1,715 6 ..Haiti 12 105 314.2 5 301 9 1,129 66 35 530 2 5.72922012 World Development Indicators

Starting abusinessBusiness environment:5.3Doing Business indicatorsRegisteringpropertyDealing withconstructionpermitsGettingelectricityEnforcingcontractsProtectinginvestorsResolvinginsolvencySTATES AND MARKETSNumber ofproceduresJune2011TimerequireddaysJune2011Cost% of percapitaincomeJune2011Number ofproceduresJune2011TimerequireddaysJune2011Number ofproceduresto build awarehouseJune2011Timerequiredto build awarehousedaysJune2011TimerequireddaysJune2011Number ofproceduresJune2011TimerequireddaysJune2011Disclosureindex0–10 (leastto mostdisclosure)June2011TimerequiredyearsJune2011Honduras 13 14 46.7 7 23 14 94 33 47 920 0 3.8Hungary 4 4 7.6 4 17 29 102 252 35 395 2 2.0India 12 29 46.8 5 44 34 227 67 46 1,420 7 7.0Indonesia 8 45 17.9 6 22 13 158 108 40 570 10 5.5Iran, Islamic Rep. 6 8 3.8 9 36 16 320 140 39 505 5 4.5Iraq 11 77 115.7 5 51 13 187 47 51 520 4 ..Ireland 4 13 0.4 5 38 10 141 205 21 650 10 0.4Israel 5 34 4.4 7 144 19 212 132 35 890 7 4.0Italy 6 6 18.2 7 27 11 258 192 41 1,210 7 1.8Jamaica 6 7 7.2 6 37 8 145 96 35 655 4 1.1Japan 8 23 7.5 6 14 14 193 117 30 360 7 0.6Jordan 7 12 13.9 7 21 17 70 43 38 689 5 4.3Kazakhstan 6 19 0.8 4 40 32 189 88 36 390 9 1.5Kenya 11 33 37.8 8 64 8 125 163 40 465 3 4.5Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 5 7 14.6 7 11 12 30 49 33 230 7 1.5Kosovo 10 58 26.7 8 33 17 301 60 53 420 3 2.0Kuwait 12 32 1.2 8 47 24 130 42 50 566 7 4.2Kyrgyz Republic 2 10 3.5 4 5 12 142 337 38 260 8 4.0Lao PDR 7 93 7.6 5 98 23 108 134 42 443 2 ..Latvia 4 16 2.6 5 18 23 205 108 27 369 5 3.0Lebanon 5 9 67.1 8 25 19 219 75 37 721 9 4.0Lesotho 7 40 24.9 6 101 12 510 140 40 785 2 2.6Liberia 4 6 68.4 10 50 23 75 586 41 1,280 4 3.0Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 6 22 2.8 3 3 15 142 148 30 275 7 1.5Macedonia, FYR 3 3 2.4 4 40 10 117 151 37 370 9 2.0Madagascar 3 8 12.1 6 74 16 172 450 38 871 5 2.0Malawi 10 39 90.9 6 69 18 200 244 42 312 4 2.6Malaysia 4 6 16.4 5 48 22 260 51 29 425 10 1.5Mali 4 8 90.5 5 29 11 179 120 36 620 6 3.6Mauritania 9 19 48.3 4 49 18 119 75 46 370 5 8.0Mauritius 5 6 3.6 4 22 16 136 91 36 645 6 1.7Mexico 6 9 11.2 7 74 10 81 114 38 415 8 1.8Moldova 7 9 9.1 5 5 27 291 140 30 352 7 2.8Mongolia 7 13 2.9 5 11 19 208 156 32 314 5 4.0Morocco 6 12 15.7 8 75 15 97 71 40 510 7 1.8Mozambique 9 13 11.7 8 42 13 370 117 30 730 5 5.0Myanmar .. .. .. .. .. .. .. .. .. .. .. ..Namibia 10 66 17.2 7 39 12 139 55 33 270 5 1.5Nepal 7 29 37.4 3 5 13 222 70 39 910 6 5.0Netherlands 6 8 5.5 5 7 15 176 143 26 514 4 1.1New Zealand 1 1 0.4 2 2 6 64 50 30 216 10 1.3Nicaragua 8 39 107.9 8 49 16 218 70 37 409 4 2.2Niger 9 17 114.4 4 35 12 326 120 39 545 6 5.0Nigeria 8 34 70.6 13 82 15 85 260 40 457 5 2.0Norway 5 7 1.8 1 3 11 250 66 34 280 7 0.9Oman 5 8 3.1 2 16 14 174 62 51 598 8 4.0Pakistan 10 21 11.2 6 50 11 222 206 46 976 6 2.8Panama 6 8 9.9 8 32 17 113 35 31 686 1 2.5Papua New Guinea 6 51 15.6 4 72 21 219 66 42 591 5 3.0Paraguay 7 35 47.2 6 46 12 137 53 38 591 6 3.9Peru 5 26 11.9 4 7 16 188 100 41 428 8 3.1Philippines 15 35 19.1 8 39 30 85 50 37 842 2 5.7Poland 6 32 17.3 6 152 30 301 143 37 830 7 3.0Portugal 5 5 2.3 1 1 14 255 64 31 547 6 2.0Puerto Rico 6 6 0.6 8 194 18 189 32 39 620 7 3.8Qatar 8 12 8.3 7 13 17 70 90 43 570 5 2.82012 World Development Indicators 293

5.3Business environment:Doing Business indicatorsStarting abusinessRegisteringpropertyDealing withconstructionpermitsGettingelectricityEnforcingcontractsProtectinginvestorsResolvinginsolvencyNumber ofproceduresJune2011TimerequireddaysJune2011Cost% of percapitaincomeJune2011Number ofproceduresJune2011TimerequireddaysJune2011Number ofproceduresto build awarehouseTimerequiredto build awarehousedaysTimerequireddaysJune2011Number ofproceduresJune2011TimerequireddaysJune2011Disclosureindex0–10 (leastto mostdisclosure)June2011TimerequiredyearsJune2011Romania 6 14 3.0 8 26 16 287 223 31 512 9 3.3Russian Federation 9 30 2.0 5 43 51 423 281 36 281 6 2.0Rwanda 2 3 4.7 5 25 12 164 30 24 230 7 3.0Saudi Arabia 3 5 5.9 2 2 9 75 71 43 635 9 1.5Senegal 3 5 68.0 6 122 13 210 125 43 780 6 3.0Serbia 7 13 7.8 6 11 19 279 131 36 635 7 2.7Sierra Leone 6 12 93.3 7 86 20 238 137 39 515 6 2.6Singapore 3 3 0.7 3 5 11 26 36 21 150 10 0.8Slovak Republic 6 18 1.8 3 17 11 286 177 32 565 3 4.0Slovenia 2 6 0.0 5 110 13 199 38 32 1,290 3 2.0Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 5 19 0.3 6 23 13 127 226 29 600 8 2.0South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 10 28 4.7 5 13 8 182 101 39 515 5 1.5Sri Lanka 4 35 4.7 8 83 18 217 132 40 1,318 6 1.7Sudan 10 36 31.4 6 9 16 270 70 53 810 0 2.0Swaziland 12 56 29.2 9 21 13 95 137 40 972 2 2.0Sweden 3 15 0.6 1 7 7 116 52 30 508 8 2.0Switzerland 6 18 2.1 4 16 13 154 39 32 390 0 3.0Syrian Arab Republic 7 13 17.1 4 19 23 104 71 55 872 7 4.1Tajikistan 5 24 33.3 6 37 26 228 238 35 430 8 1.7Tanzania 12 29 28.8 9 73 19 303 109 38 462 3 3.0Thailand 5 29 6.2 2 2 8 157 35 36 479 10 2.7Timor-Leste 10 103 4.5 .. .. 19 238 63 51 1,285 3 ..Togo 7 84 177.2 5 295 12 309 74 41 588 6 3.0Trinidad and Tobago 9 43 0.9 8 162 17 297 61 42 1,340 4 4.0Tunisia 10 11 4.2 4 39 17 88 65 39 565 5 1.3Turkey 6 6 11.2 6 6 24 189 70 36 420 9 3.3Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda 16 34 84.5 13 48 15 125 91 38 490 2 2.2Ukraine 9 24 4.4 10 117 21 375 274 30 343 5 2.9United Arab Emirates 7 13 5.6 1 2 14 46 55 49 537 4 5.1United Kingdom 6 13 0.7 6 29 9 99 109 28 399 10 1.0United States 6 6 1.4 4 12 15 26 68 32 300 7 1.5Uruguay 5 7 24.9 8 66 27 234 48 41 720 3 2.1Uzbekistan 6 14 6.4 12 78 25 243 117 42 195 4 4.0Venezuela, RB 17 141 26.1 8 38 10 381 125 30 510 3 4.0Vietnam 9 44 10.6 4 57 10 200 142 34 295 6 5.0West Bank and Gaza 11 49 96.0 7 47 18 119 63 44 540 6 ..Yemen, Rep. 6 12 83.8 6 19 12 116 35 36 520 6 3.0Zambia 6 18 27.4 5 40 14 196 117 35 471 3 2.7Zimbabwe 9 90 148.9 5 31 12 614 125 38 410 8 3.3World 7 u 31 u 36.2 u 6 u 55 u 16 u 193 u 111 u 38 u 610 u 5 u 2.9 uLow income 8 33 106.0 6 86 15 260 167 39 662 5 3.7Middle income 8 36 28.0 6 51 17 188 102 39 626 5 3.1Lower middle income 8 34 40.9 6 66 16 181 98 40 662 4 3.3Upper middle income 8 38 13.9 6 36 17 195 106 38 588 6 2.8Low & middle income 8 35 46.3 6 60 16 205 117 39 634 5 3.2East Asia & Pacifi c 8 39 26.4 5 80 17 172 98 37 548 5 3.2Europe & Central Asia 6 16 8.0 6 29 21 214 168 37 390 7 2.8Latin America & Carib. 9 57 40.8 7 56 14 221 66 40 699 4 3.1Middle East & N. Africa 8 23 50.4 7 41 17 166 84 42 692 6 3.5South Asia 7 23 21.6 6 103 16 222 145 43 1,075 5 3.4Sub-Saharan Africa 8 34 80.8 6 66 15 212 138 39 657 5 3.4High income 6 17 6.8 5 44 14 159 93 35 539 6 2.1Euro area 6 12 5.4 5 32 12 210 111 32 600 6 1.7Note: Regional aggregates differ from those reported on the Doing Business website because the regional aggregates reported on the Doing Business website include developed countries.June2011June20112942012 World Development Indicators

Business environment:Doing Business indicators5.3STATES AND MARKETSAbout the dataDefinitionsThe economic health of a country is measured not onlyin macroeconomic terms but also by other factors thatshape daily economic activity such as laws, regulations,and institutional arrangements. The Doing Businessindicators measure business regulation, gaugeregulatory outcomes, and measure the extent of legalprotection of property, the fl exibility of employmentregulation, and the tax burden on businesses.The table presents a subset of Doing Businessindicators covering 7 of the 11 sets of indicators:starting a business, registering property, dealing withconstruction permits, getting electricity, enforcingcontracts, protecting investors, and resolving insolvency.Table 5.5 includes Doing Business measuresof getting credit, and table 5.6 presents data on payingbusiness taxes.The fundamental premise of the Doing Businessproject is that economic activity requires good rulesand regulations that are effi cient, accessible to allwho need to use them, and simple to implement.Thus some Doing Business indicators give a higherscore for more regulation, such as stricter disclosurerequirements in related-party transactions, and othersgive a higher score for simplifi ed regulations,such as a one-stop shop for completing businessstartup formalities.In constructing the indicators, it is assumed thatentrepreneurs know about all regulations and complywith them; in practice, entrepreneurs may not beaware of all required procedures or may avoid legallyrequired procedures altogether. But where regulationis particularly onerous, levels of informality arehigher, which comes at a cost: fi rms in the informalsector usually grow more slowly, have less accessto credit, and employ fewer workers—and thoseworkers remain outside the protections of labor law.The indicators in the table can help policymakersunderstand the business environment in a countryand—along with information from other sources suchas the World Bank’s Enterprise Surveys—provideinsights into potential areas of reform.Doing Business data are collected with a standardizedsurvey that uses a simple business caseto ensure comparability across economies and overtime—with assumptions about the legal form of thebusiness, its size, its location, and nature of its operation.Surveys in 183 countries are administeredthrough more than 9,000 local experts, includinglawyers, business consultants, accountants, freightforwarders, government offi cials, and other professionalswho routinely administer or advise on legaland regulatory requirements.The Doing Business project encompasses twotypes of data: data from readings of laws and regulationsand data on time and motion indicators thatmeasure effi ciency in achieving a regulatory goal.Within the time and motion indicators cost estimatesare recorded from offi cial fee schedules where applicable.The data from surveys are subjected to numeroustests for robustness, which lead to revision orexpansion of the information collected.The Doing Business methodology has limitationsthat should be considered when interpreting thedata. First, the data collected refer to businessesin the economy’s largest city and may not representregulations in other locations of the economy. Toaddress this limitation, subnational indicators arebeing collected for selected economies. These subnationalstudies point to signifi cant differences inthe speed of reform and the ease of doing businessacross cities in the same economy. Second, the dataoften focus on a specifi c business form—generallya limited liability company of a specifi ed size—andmay not represent regulation for other types of businessessuch as sole proprietorships. Third, transactionsdescribed in a standardized business case referto a specifi c set of issues and may not represent thefull set of issues a business encounters. Fourth, thetime measures involve an element of judgment by theexpert respondents. When sources indicate differentestimates, the Doing Business time indicators representthe median values of several responses givenunder the assumptions of the standardized case.Fifth, the methodology assumes that a business hasfull information on what is required and does notwaste time when completing procedures.• Number of procedures for starting a business isthe number of procedures required to start a business,including interactions to obtain necessary permitsand licenses and to complete all inscriptions,verifi cations, and notifi cations to start operationsfor busi nesses with specifi c characteristics of ownership,size, and type of production. • Time requiredfor starting a business is the number of calendardays to complete the procedures for legally operatinga business using the fastest procedure, independentof cost. • Cost for starting a business is normalizedas a percentage of gross national income (GNI) percapita. It includes all offi cial fees and fees for legalor professional services if they are required by law.• Number of procedures for registering property isthe number of procedures required for a business tolegally transfer property. • Time required for registeringproperty is the number of calendar days for a businessto legally transfer property. • Number of proceduresfor deal ing with construction permits to builda warehouse is the number of interactions of a company’semployees or manag ers with external parties,including government staff, public inspectors, notaries,land registry and cadastre staff, and technicalexperts apart from architects and engineers. • Timerequired for dealing with construction permits tobuild a warehouse is the number of calendar daysto complete the required procedures for building awarehouse using the fastest procedure, independentof cost. • Time required for getting electricity is thenumber of calendar days required for a business toobtain a permanent electricity connection and supply.• Number of procedures for enforcing contracts isthe number of independent actions, mandated by lawor court regulation, that demand interaction betweenthe parties to a contract or between them and thejudge or court offi cer. • Time required for enforcingcontracts is the number of calendar days from thetime of the fi ling of a lawsuit in court to the fi nal determinationand payment. • Disclo sure index measuresthe degree to which investors are protected throughdisclosure of ownership and fi nancial information.Higher values indicate more disclosure. • Timerequired to resolve insolvency is the number of yearsfrom time of fi ling for insolvency in court until resolutionof distressed assets and payment of creditors.Data sourcesData on the business environment are fromthe World Bank’s Doing Business project (www.doingbusiness.org).2012 World Development Indicators 295

5.4 Stock marketsMarketcapitalizationMarketliquidityTurnoverratioListed domesticcompaniesS&P/GlobalEquity Indices$ millions % of GDPValue ofshares traded% of GDPValue of shares traded% of marketcapitalization number % change2005 2011 2005 2010 2005 2010 2005 2011 2005 2011 2010 2011Afghanistan .. .. .. .. .. .. .. .. .. .. .. ..Albania .. .. .. .. .. .. .. .. .. .. .. ..Algeria .. .. .. .. .. .. .. .. .. .. .. ..Angola .. .. .. .. .. .. .. .. .. .. .. ..Argentina 61,478 43,580 33.6 17.3 9.0 0.7 30.4 4.8 101 99 55.3 a –30.2 aArmenia 43 44 0.9 0.3 0.0 0.0 3.7 1.5 198 12 .. ..Australia 804,074 1,198,164 115.4 128.5 88.5 108.0 78.0 94.0 1,643 1,922 12.5 –15.7Austria 124,390 82,374 40.8 17.9 15.1 12.7 43.6 51.6 92 73 10.9 –35.8Azerbaijan .. .. .. .. .. .. .. .. .. .. .. ..Bahrain 17,364 17,152 129.0 82.2 5.3 4.2 4.6 1.5 47 44 10.0 a –14.4 aBangladesh 3,035 23,546 5.0 15.6 1.7 14.6 31.5 92.6 262 216 37.6 a –42.3 aBelarus .. .. .. .. .. .. .. .. .. .. .. ..Belgium 288,515 229,896 76.5 57.4 33.3 23.7 44.8 43.0 222 158 0.5 –15.1Benin .. .. .. .. .. .. .. .. .. .. .. ..Bolivia 2,200 4,125 23.0 17.2 0.0 0.1 0.1 0.4 36 40 .. ..Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. ..Botswana 2,437 4,107 23.8 27.4 0.4 0.9 1.8 3.6 18 23 –6.8 a –7.5 aBrazil 474,647 1,228,969 53.8 74.0 17.5 43.2 38.3 69.3 381 366 6.5 –24.4Bulgaria 5,086 8,253 17.6 15.2 4.8 0.4 35.2 3.4 331 393 –15.2 a –22.1 aBurkina Faso .. .. .. .. .. .. .. .. .. .. .. ..Burundi .. .. .. .. .. .. .. .. .. .. .. ..Cambodia .. .. .. .. .. .. .. .. .. .. .. ..Cameroon .. .. .. .. .. .. .. .. .. .. .. ..Canada 1,480,891 1,906,589 130.6 137.0 74.5 86.6 63.6 74.8 3,721 3,932 22.0 –14.7Central African Republic .. .. .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. .. .. ..Chile 136,446 270,289 115.4 160.6 16.0 25.5 14.9 18.6 245 229 47.2 –24.1China 780,763 3,389,098 34.6 80.4 26.0 135.5 82.5 188.2 1,387 2,342 6.9 –21.7Hong Kong SAR, China 693,486 889,597 390.1 481.0 165.4 711.7 43.3 157.6 1,020 1,472 21.3 –20.2Colombia 46,016 201,296 31.4 72.3 4.3 8.0 17.9 13.3 114 79 44.1 –12.0Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. ..Costa Rica 1,478 1,443 7.4 4.0 0.1 0.1 1.7 2.6 19 9 .. ..Côte d’Ivoire 2,327 6,288 14.2 31.2 0.2 0.6 1.4 1.8 39 33 19.3 a –15.2 aCroatia 12,918 21,796 28.8 40.9 1.8 1.7 6.7 4.1 145 209 –0.4 a –30.3 aCuba .. .. .. .. .. .. .. .. .. .. .. ..Cyprus 6,583 2,853 38.7 29.5 2.4 2.7 7.1 10.0 144 117 .. –71.9 aCzech Republic 38,345 38,352 30.8 22.4 33.0 7.3 118.6 38.0 36 15 0.2 –15.0Denmark 178,038 179,529 69.1 74.3 59.0 46.3 92.3 73.2 179 186 25.1 –17.3Dominican Republic .. .. .. .. .. .. .. .. .. .. .. ..Ecuador 3,214 5,779 8.7 9.1 0.4 0.2 5.0 1.9 32 41 9.7 a –9.6 aEgypt, Arab Rep. 79,672 48,683 88.8 37.7 28.3 17.0 43.0 33.5 744 231 11.5 –49.1El Salvador 3,623 5,474 21.2 19.9 0.4 0.2 2.3 1.6 35 65 .. ..Eritrea .. .. .. .. .. .. .. .. .. .. .. ..Estonia 3,495 1,611 25.1 11.8 17.8 1.7 51.1 12.6 15 15 56.0 a –23.3 aEthiopia .. .. .. .. .. .. .. .. .. .. .. ..Finland 209,504 143,081 107.0 49.6 139.7 42.8 139.1 133.5 134 121 10.7 –33.1France 1,758,721 1,568,730 82.3 75.3 71.4 57.3 92.0 84.4 885 893 –9.9 b –19.5 bGabon .. .. .. .. .. .. .. .. .. .. .. ..Gambia, The .. .. .. .. .. .. .. .. .. .. .. ..Georgia 355 796 5.5 9.1 0.6 0.0 13.6 0.2 257 135 .. ..Germany 1,221,250 1,184,459 44.1 43.6 63.7 42.8 146.0 134.5 648 670 7.4 c –16.6 cGhana 1,661 3,097 15.5 11.3 0.6 0.3 3.2 4.1 30 36 94.1 a –22.8 aGreece 145,013 33,648 60.4 24.1 27.2 14.3 48.3 46.5 307 275 –43.8 –58.3Guatemala .. .. .. .. .. .. .. .. .. .. .. ..Guinea .. .. .. .. .. .. .. .. .. .. .. ..Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..Haiti .. .. .. .. .. .. .. .. .. .. .. ..2962012 World Development Indicators

Stock markets5.4STATES AND MARKETSMarketcapitalizationMarketliquidityTurnoverratioListed domesticcompaniesS&P/GlobalEquity Indices$ millions % of GDPValue ofshares traded% of GDPValue of shares traded% of marketcapitalization number % change2005 2011 2005 2010 2005 2010 2005 2011 2005 2011 2010 2011Honduras .. .. .. .. .. .. .. .. .. .. .. ..Hungary 32,576 18,773 29.5 21.5 21.7 20.6 78.0 83.9 44 52 –10.8 –35.3India 553,074 1,015,370 66.3 93.6 52.0 61.2 92.2 56.3 4,763 5,112 18.7 –38.0Indonesia 81,428 390,107 28.5 51.0 14.7 18.3 54.2 37.2 335 440 37.9 1.1Iran, Islamic Rep. 38,724 107,249 20.2 19.1 4.3 5.2 19.1 20.7 420 347 .. ..Iraq .. .. .. .. .. .. .. .. .. .. .. ..Ireland 114,134 35,363 56.1 16.3 31.8 8.1 56.7 45.3 53 48 –7.7 –1.5Israel 120,114 144,970 89.5 100.3 44.6 61.4 55.5 64.7 572 576 7.4 –29.7Italy 798,167 431,471 44.7 15.4 62.4 26.2 140.5 236.8 275 287 –17.4 –27.6Jamaica 13,028 7,223 116.8 46.5 3.9 1.5 3.1 3.1 39 37 22.4 a 28.4 aJapan 4,736,513 3,540,685 104.0 75.1 109.8 78.4 118.8 108.9 3,279 3,961 9.6 d –12.2 dJordan 37,639 27,183 299.0 111.9 189.1 34.3 85.0 13.9 201 247 –8.6 a –16.2 aKazakhstan 10,521 43,301 18.4 40.8 1.9 1.5 14.9 2.1 62 63 –1.0 a –36.4 aKenya 6,384 10,203 34.1 46.0 2.7 3.5 9.8 7.1 47 58 33.8 a –31.6 aKorea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 718,180 994,302 85.0 107.4 142.4 160.3 209.8 195.1 1,620 1,792 25.3 –10.9Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 130,080 100,869 161.0 87.6 116.4 63.9 94.3 19.4 143 206 29.1 a –21.4 aKyrgyz Republic 42 165 1.7 1.7 0.5 0.2 34.1 2.7 8 34 .. ..Lao PDR .. .. .. .. .. .. .. .. .. .. .. ..Latvia 2,527 1,076 15.8 5.2 0.6 0.1 4.6 4.4 45 32 39.4 a –17.3 aLebanon 4,929 10,164 22.5 32.3 4.2 4.8 25.5 4.5 11 10 –8.7 a –22.2 aLesotho .. .. .. .. .. .. .. .. .. .. .. ..Liberia .. .. .. .. .. .. .. .. .. .. .. ..Libya .. .. .. .. .. .. .. .. .. .. .. ..Lithuania 8,183 4,075 31.5 15.6 2.9 0.8 10.1 5.0 43 33 44.0 a –16.1 aMacedonia, FYR 646 2,504 10.8 28.8 1.6 0.4 18.3 2.0 57 32 .. ..Madagascar .. .. .. .. .. .. .. .. .. .. .. ..Malawi 230 1,384 8.4 26.7 0.3 0.4 4.1 3.9 9 13 .. ..Malaysia 181,236 395,083 131.4 172.6 36.2 37.9 26.9 32.0 1,020 941 35.1 –1.1Mali .. .. .. .. .. .. .. .. .. .. .. ..Mauritania .. .. .. .. .. .. .. .. .. .. .. ..Mauritius 2,617 6,540 41.7 66.9 2.4 3.7 6.0 8.0 42 86 8.2 a –2.5 aMexico 239,128 408,691 28.2 43.9 6.2 10.5 25.7 26.0 151 128 26.6 –14.8Moldova .. .. .. .. 0.6 0.2 .. .. .. .. .. ..Mongolia 46 1,579 1.8 17.6 0.1 0.8 6.1 3.4 392 332 .. ..Morocco 27,220 60,088 45.7 76.2 7.0 11.8 15.9 9.8 56 75 13.1 –17.7Mozambique .. .. .. .. .. .. .. .. .. .. .. ..Myanmar .. .. .. .. .. .. .. .. .. .. .. ..Namibia 415 1,152 5.7 9.7 0.1 0.2 1.5 1.2 13 7 24.2 a 6.2 aNepal 1,344 4,529 16.5 30.8 0.6 0.6 4.4 1.7 125 181 .. ..Netherlands 592,906 594,732 92.9 84.8 130.9 76.0 147.7 88.3 237 108 1.2 –16.4New Zealand 43,409 71,657 39.1 52.9 15.7 14.2 40.0 39.6 154 144 5.2 –3.8Nicaragua .. .. .. .. .. .. .. .. .. .. .. ..Niger .. .. .. .. .. .. .. .. .. .. .. ..Nigeria 19,356 39,270 17.2 26.3 1.7 2.7 11.5 9.2 214 196 20.3 a –29.5 aNorway 190,952 219,245 62.8 60.1 64.1 52.0 117.2 88.6 191 192 13.7 –18.1Oman 15,269 19,719 49.4 36.9 10.4 12.4 29.8 12.9 96 136 12.2 a –14.1 aPakistan 45,937 32,764 41.9 21.6 128.6 7.3 376.3 28.6 661 638 15.3 a –18.8 aPanama 5,074 10,682 32.8 40.9 0.5 0.7 1.8 0.6 24 21 12.8 a 18.0 aPapua New Guinea 3,166 8,999 64.6 102.8 0.4 0.2 0.6 0.6 9 11 .. ..Paraguay 257 958 3.4 0.2 0.0 0.1 0.6 3.0 54 66 .. ..Peru 35,995 79,329 45.3 63.6 2.5 2.5 7.2 5.5 196 202 51.3 –21.3Philippines 40,153 165,380 39.0 78.8 6.7 13.4 20.1 20.4 235 251 56.7 0.2Poland 93,873 138,246 30.9 40.5 9.9 16.5 36.3 58.4 248 757 11.3 –33.4Portugal 66,981 61,688 34.9 35.9 21.7 13.7 60.7 50.3 48 46 –16.6 –31.0Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar 87,316 125,413 202.9 89.4 65.2 25.9 40.0 18.6 31 42 27.7 a 3.3 a2012 World Development Indicators 297

5.4Stock marketsMarketcapitalizationMarketliquidityTurnoverratioListed domesticcompaniesS&P/GlobalEquity Indices$ millions % of GDPValue ofshares traded% of GDPValue of shares traded% of marketcapitalization number % change2005 2011 2005 2010 2005 2010 2005 2011 2005 2011 2010 2011Romania 20,588 21,197 20.8 20.0 3.4 1.1 21.0 12.0 3,747 1,267 –6.6 a –18.2 aRussian Federation 548,579 796,376 71.8 67.9 20.9 54.0 39.0 127.3 296 327 21.7 –23.4Rwanda .. .. .. .. .. .. .. .. .. .. .. ..Saudi Arabia 646,104 338,873 204.7 81.3 349.7 46.7 231.7 84.6 77 150 9.0 e –3.9 eSenegal .. .. .. .. .. .. .. .. .. .. .. ..Serbia 5,409 8,365 21.4 25.2 2.6 0.6 15.3 3.7 864 1,322 .. ..Sierra Leone .. .. .. .. .. .. .. .. .. .. .. ..Singapore 316,658 308,320 256.4 177.3 97.0 135.1 40.4 74.8 685 462 18.4 –21.2Slovak Republic 4,393 4,736 7.2 4.8 0.1 0.2 1.6 10.2 209 81 5.4 a 3.0 aSlovenia 7,899 6,326 22.1 20.1 2.2 0.6 9.0 6.5 116 66 –20.3 a –30.7 aSomalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 565,408 855,711 228.9 278.4 81.2 93.5 39.3 39.8 388 355 32.1 –17.4South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 960,024 1,030,951 84.9 83.2 137.8 96.7 163.9 128.9 3,300 3,241 –24.5 –16.8Sri Lanka 5,720 19,437 23.4 40.2 4.7 6.7 24.3 25.1 239 253 84.6 a –23.0 aSudan .. .. .. .. .. .. .. .. .. .. .. ..Swaziland 197 .. 7.8 .. 0.0 .. 0.0 .. 6 5 .. ..Sweden 403,948 470,122 109.0 126.7 125.2 95.9 118.9 96.2 252 340 32.6 –18.5Switzerland 938,624 932,207 252.0 232.9 237.1 164.7 100.1 85.9 263 246 11.0 –9.4Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. ..Tajikistan .. .. .. .. .. .. .. .. .. .. .. ..Tanzania 588 1,539 4.2 5.5 0.1 0.1 2.3 2.5 6 17 .. ..Thailand 124,864 268,489 70.8 87.2 50.6 68.4 73.9 85.1 504 545 52.1 –4.7Timor-Leste .. .. .. .. .. .. .. .. .. .. .. ..Togo .. .. .. .. .. .. .. .. .. .. .. ..Trinidad and Tobago 16,972 14,725 106.2 59.0 3.9 0.7 3.7 1.2 37 37 0.8 a 26.7 aTunisia 2,876 9,662 8.9 24.1 1.4 3.8 16.5 11.0 46 57 11.7 a –13.4 aTurkey 161,537 201,817 33.4 41.8 41.7 57.4 154.9 162.7 302 362 21.4 –37.0Turkmenistan .. .. .. .. .. .. .. .. .. .. .. ..Uganda 103 7,727 1.1 10.5 0.0 0.1 3.1 .. 5 8 .. ..Ukraine 24,976 25,558 29.0 28.6 0.8 1.5 3.6 14.1 221 195 53.8 a –36.3 aUnited Arab Emirates 225,568 93,767 124.9 35.2 79.2 9.2 89.6 15.9 79 104 –6.8 a –16.5 aUnited Kingdom 3,058,182 1,202,031 134.1 137.4 182.7 132.9 141.9 137.9 2,759 2,001 5.2 f –6.1 fUnited States 16,970,865 15,640,707 134.9 117.5 171.0 208.8 129.2 187.6 5,143 4,171 12.8 g 0.0 gUruguay 96 175 0.6 0.4 0.0 0.0 1.1 0.4 9 6 .. ..Uzbekistan 37 .. 0.3 .. 0.3 0.1 184.7 .. 114 132 .. ..Venezuela, RB 5,017 5,143 3.4 1.0 0.2 0.0 4.5 0.9 50 36 .. ..Vietnam 461 18,316 0.9 19.2 0.2 16.2 24.8 29.5 33 301 0.5 a –26.8 aWest Bank and Gaza 4,461 2,532 111.1 .. 52.2 .. 75.4 14.7 28 45 .. ..Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. ..Zambia 989 4,009 13.8 17.4 0.2 1.6 2.0 .. 15 20 17.4 a –1.3 aZimbabwe 2,402 10,903 43.0 153.6 5.9 15.3 15.3 .. 79 75 .. ..World 43,319,352 s 45,082,821s 96.6 w 88.7 w 105.7 w 106.4 w 116.3 w 133.4 w 50,936 s 49,553 sLow income .. 59,995 .. 26.6 .. .. 15.3 48.6 .. 602Middle income 4,426,053 10,282,607 49.3 72.6 24.9 67.7 58.4 103.0 19,925 18,737Lower middle income 898,133 1,854,685 48.0 65.9 35.2 34.2 86.4 44.8 8,741 8,653Upper middle income 3,527,921 8,427,921 49.6 74.4 22.2 76.5 51.4 116.1 11,184 10,084Low & middle income 4,440,182 10,342,602 48.8 72.1 24.6 67.0 58.3 102.8 20,466 19,339East Asia & Pacifi c 1,212,704 4,638,422 40.1 79.9 25.6 113.3 68.4 154.3 3,931 5,181Europe & Central Asia 789,576 1,116,849 48.7 51.8 22.7 42.7 61.6 121.1 6,564 4,368Latin America & Carib. 1,028,157 2,274,194 40.5 57.6 9.9 22.9 28.4 46.4 1,504 1,446Middle East & N. Africa 135,018 265,561 36.8 34.6 7.2 7.5 39.3 19.4 1,531 1,012South Asia 609,110 1,095,645 58.8 81.9 55.7 52.6 111.6 55.4 6,050 6,400Sub-Saharan Africa 605,113 951,930 128.6 149.5 43.3 46.6 37.3 37.2 911 932High income 38,879,170 34,740,219 108.8 95.9 126.3 123.5 122.4 143.0 30,470 30,214Euro area 6,357,326 5,482,967 62.7 51.7 73.1 47.1 120.5 110.4 6,737 6,250a. Refers to the S&P Frontier BMI index. b. Refers to the CAC 40 index. c. Refers to the DAX index. d. Refers to the Nikkei 225 index. e. Refers to the Saudi Arabia country index. f. Refersto the FTSE 100 index. g. Refers to the S&P 500 index.2982012 World Development Indicators

Stock markets5.4STATES AND MARKETSAbout the dataDefinitionsThe development of an economy’s fi nancial markets isclosely related to its overall development. Well functioningfi nancial systems provide good and easily accessibleinformation. That lowers transaction costs, whichin turn improves resource allocation and boosts economicgrowth. Both banking systems and stock marketsenhance growth, the main factor in poverty reduction.At low levels of economic development commercialbanks tend to dominate the fi nancial system, while athigher levels domestic stock markets tend to becomemore active and effi cient relative to domestic banks.Open economies with sound macroeconomic policies,good legal systems, and shareholder protectionattract capital and therefore have larger fi nancial markets.Recent research on stock market developmentshows that modern communications technology andincreased fi nancial integration have resulted in morecross-border capital fl ows, a stronger presence offi nancial fi rms around the world, and the migration ofstock exchange activities to international exchanges.Many firms in emerging markets now cross-list on internationalexchanges, which provides them with lowercost capital and more liquidity-traded shares. However,this also means that exchanges in emerging marketsmay not have enough financial activity to sustain them,putting pressure on them to rethink their operations.The indicators in the table are from Standard &Poor’s Emerging Markets Data Base. They includemeasures of size (market capitalization, numberof listed domestic companies) and liquidity (valueof shares traded as a percentage of gross domesticproduct, value of shares traded as a percentageof market capitalization). The comparability of suchindicators across countries may be limited by conceptualand statistical weaknesses, such as inaccuratereporting and differences in accounting standards.The percentage change in stock market prices in U.S.dollars for developing economies is from Standard &Poor’s Global Equity Indices (S&P IFCI) and Standard &Poor’s Frontier Broad Market Index (BMI). The percentagechange for France, Germany, Japan, the UnitedKingdom, and the United States is from local stockmarket prices. The indicator is an important measureof overall performance. Regulatory and institutionalfactors that can affect investor confi dence, such asentry and exit restrictions, the existence of a securitiesand exchange commission, and the quality of lawsto protect investors, may infl uence the functioning ofstock markets but are not included in the table.Stock market size can be measured in variousways, and each may produce a different ranking ofcountries. Market capitalization shows the overallsize of the stock market in U.S. dollars and as apercentage of GDP. The number of listed domesticcompanies is another measure of market size. Marketsize is positively correlated with the ability tomobilize capital and diversify risk.Market liquidity, the ability to easily buy and sell securities,is measured by dividing the total value of sharestraded by GDP. The turnover ratio—the value of sharestraded as a percentage of market capitalization—isalso a measure of liquidity as well as of transactioncosts. (High turnover indicates low transaction costs.)The turnover ratio complements the ratio of valuetraded to GDP, because the turnover ratio is related tothe size of the market and the value traded ratio to thesize of the economy. A small, liquid market will havea high turnover ratio but a low value of shares tradedratio. Liquidity is an important attribute of stock marketsbecause, in theory, liquid markets improve the allocationof capital and enhance prospects for long-termeconomic growth. A more comprehensive measure ofliquidity would include trading costs and the time anduncertainty in finding a counterpart in settling trades.S&P Indices, the source for all the data in thetable, provides regular updates on 21 emerging stockmarkets and 36 frontier markets. S&P Indices maintainsa series of indexes for investors interested ininvesting in stock markets in developing countries.The S&P/IFCI index, S&P Indices’s leading emergingmarkets index, is designed to be suffi ciently investableto support index tracking portfolios in emergingmarket stocks that are legally and practically open toforeign portfolio investment. The S&P Frontier BMImeasures the performance of 36 smaller and lessliquid markets. The S&P Frontier BMI country indexesaim to include all publicly listed equities representingan aggregate of at least 80 percent of the totalmarket capitalization in each market, subject tothe securities meeting size and liquidity thresholdsdefi ned by three market size tiers. These indexes arewidely used benchmarks for international portfoliomanagement. See www.standardandpoors.com forfurther information on the indexes.Because markets included in Standard & Poor’semerging markets category vary widely in level ofdevelopment, it is best to look at the entire categoryto identify the most signifi cant market trends. And itis useful to remember that stock market trends maybe distorted by currency conversions, especially whena currency has registered a signifi cant devaluation.About the data is based on Demirgüç-Kunt andLevine (1996), Beck and Levine (2001), and Claessens,Klingebiel, and Schmukler (2002).• Market capitalization (also known as marketvalue) is the share price times the number of sharesoutstanding. • Market liquidity is the total valueof shares traded during the period divided by grossdomestic product (GDP). This indicator complementsthe market capitalization ratio by showing whethermarket size is matched by trading. • Turnover ratiois the total value of shares traded during the perioddivided by the average market capitalization for theperiod. Average market capitalization is calculated asthe average of the end-of-period values for the currentperiod and the previous period. • Listed domesticcompanies are the domestically incorporatedcompanies listed on the country’s stock exchangesat the end of the year. This indicator does not includeinvestment companies, mutual funds, or other collectiveinvestment vehicles. • S&P/Global EquityIndices measure the U.S. dollar price change in thestock markets.Data sourcesData on stock markets are from Standard & Poor’sGlobal Stock Markets Factbook 2011, which drawson the Emerging Markets Data Base, supplementedby other data from Standard & Poor’s.The fi rm collects data through an annual surveyof the world’s stock exchanges, supplemented byinformation provided by its network of correspondentsand by Reuters. Data on GDP are from theWorld Bank’s national accounts data fi les.2012 World Development Indicators 299

5.5 Financial access, stability, and efficiencyGettingcreditFinancial accessand outreachBankcapital toasset ratioRatio of banknonperformingloans to totalgross loansDomesticcreditprovided bybanking sectorInterestrate spreadRisk premiumon lendingStrength ofDepth ofcreditper 1,000 adultsLending Prime lendinglegal rights information Depositors Borrowers per 100,000 adultsrate minus rate minusindex index with from Commercial Automateddeposit rate treasury bill rate0–10 (weak 0–6 (low commercial commercial bank tellerpercentage percentageto strong) to high) banks banks branches machines % % % of GDP points pointsJune 2011 June 2011 2010 2010 2010 2010 2010 2010 2010 2010 2010Afghanistan 6 0 100 4 2.2 0.50 .. .. 2.1 .. ..Albania 9 4 .. 118 22.6 31.96 8.5 13.9 67.5 6.4 7.0Algeria 3 3 346 23 5.2 6.07 .. .. –7.4 6.3 7.7Angola 3 4 97 86 1.3 12.66 .. .. 22.9 9.8 ..Argentina 4 6 702 285 13.3 42.45 11.9 1.8 29.2 1.4 ..Armenia 6 6 589 202 17.5 32.11 20.4 3.0 25.8 10.3 8.6Australia 9 5 .. .. 31.6 162.38 5.7 2.2 147.6 3.1 2.8Austria 7 6 1,376 .. 11.4 48.16 7.5 2.8 137.5 .. ..Azerbaijan 6 5 41 256 10.4 28.43 .. .. 23.5 9.1 18.9Bahrain 4 3 .. .. .. .. .. .. 79.3 6.0 6.4Bangladesh 7 2 418 81 6.9 1.93 6.5 11.2 65.9 5.9 ..Belarus 3 5 .. .. 3.1 37.17 13.7 3.5 45.7 0.1 ..Belgium 7 4 .. .. 48.0 86.37 5.0 2.8 117.1 .. 8.9Benin 6 1 .. .. .. .. .. .. 18.1 .. ..Bolivia 1 6 .. .. .. .. 8.4 2.2 49.4 8.9 9.8Bosnia and Herzegovina 5 5 914 251 30.9 34.47 17.6 11.4 67.2 4.7 ..Botswana 7 4 496 238 8.6 .. .. .. 9.4 5.9 ..Brazil 3 5 .. 194 13.8 120.62 11.1 3.1 97.8 31.1 29.1Bulgaria 8 6 1,958 421 91.2 80.53 10.5 11.9 71.0 7.1 8.6Burkina Faso 6 1 .. .. .. .. .. .. 16.4 .. ..Burundi 3 1 .. .. .. .. .. .. 40.1 .. ..Cambodia 8 0 108 29 4.0 5.07 .. .. 22.7 .. ..Cameroon 6 2 72 17 1.4 1.40 .. .. 9.0 .. ..Canada 7 6 .. .. 24.3 220.02 4.7 1.2 177.6 2.6 2.0Central African Republic 6 2 3 1 0.6 .. .. .. 18.8 .. ..Chad 6 2 24 3 0.6 0.36 .. .. 9.3 .. ..Chile 6 5 2,134 310 17.6 62.51 8.3 2.7 90.3 3.0 ..China 6 4 .. .. .. .. 6.1 1.1 146.4 3.1 ..Hong Kong SAR, China 10 5 .. .. 23.6 .. 12.3 0.8 199.0 5.0 4.7Colombia 5 5 .. .. .. .. 14.3 2.9 65.7 5.7 ..Congo, Dem. Rep. 3 0 .. .. .. .. .. .. 1.0 39.7 ..Congo, Rep. 6 2 20 3 2.4 1.23 .. .. –16.4 .. ..Costa Rica 3 5 .. .. 19.2 .. 14.4 1.9 51.3 11.8 ..Côte d’Ivoire 6 1 .. .. .. .. .. .. 25.1 .. ..Croatia 6 5 .. .. .. 100.86 13.9 11.2 82.2 8.6 ..Cuba .. .. .. .. .. .. .. .. .. .. ..Cyprus 9 0 .. .. 151.5 87.58 5.9 5.6 315.8 .. ..Czech Republic 6 5 .. .. 22.5 41.65 6.5 6.2 64.7 4.8 5.0Denmark 9 4 .. .. 41.1 63.55 5.5 4.1 215.3 .. ..Dominican Republic 3 6 .. .. .. .. 9.3 2.9 39.6 7.3 ..Ecuador 3 6 .. .. .. .. 8.9 3.5 26.4 .. ..Egypt, Arab Rep. 3 6 .. .. .. .. 6.2 11.0 69.4 4.8 1.7El Salvador 5 6 .. .. .. .. 13.9 3.9 45.1 .. ..Eritrea 2 0 .. .. .. .. .. .. 114.4 .. ..Estonia 7 5 1,993 542 19.3 88.09 9.3 5.4 97.6 6.7 ..Ethiopia 4 2 107 2 1.8 0.31 .. .. 37.1 3.3 7.3Finland 8 4 .. .. 15.6 91.72 5.5 0.6 100.8 .. ..France 7 4 .. .. 43.1 110.07 4.4 4.2 134.4 .. ..Gabon 6 2 95 46 4.7 8.62 11.3 9.9 10.2 .. ..Gambia, The 5 0 .. .. .. .. .. .. 45.4 12.4 ..Georgia 8 6 697 377 18.6 42.34 16.9 12.5 33.8 15.0 14.7Germany 7 6 .. .. 17.6 116.80 4.3 3.3 132.0 .. ..Ghana 8 3 324 33 5.0 .. 7.9 17.6 28.3 .. ..Greece 4 5 .. .. 41.2 78.24 6.9 10.4 145.5 .. ..Guatemala 8 6 .. .. 36.5 .. 10.3 2.1 38.5 7.9 ..Guinea 6 0 .. .. .. .. .. .. .. .. ..Guinea-Bissau 6 1 .. .. .. .. .. .. 7.7 .. ..Haiti 3 2 339 12 2.8 .. .. .. 19.9 16.7 ..3002012 World Development Indicators

Financial access, stability, and efficiencyGettingcreditFinancial accessand outreachBankcapital toasset ratioRatio of banknonperformingloans to totalgross loansDomesticcreditprovided bybanking sector5.5Interestrate spreadRisk premiumon lendingStrength ofDepth ofcreditper 1,000 adultsLending Prime lendinglegal rights information Depositors Borrowers per 100,000 adultsrate minus rate minusindex index with from Commercial Automateddeposit rate treasury bill rate0–10 (weak 0–6 (low commercial commercial bank tellerpercentage percentageto strong) to high) banks banks branches machines % % % of GDP points pointsJune 2011 June 2011 2010 2010 2010 2010 2010 2010 2010 2010 2010STATES AND MARKETSHonduras 8 6 .. .. .. .. .. .. 52.3 9.0 ..Hungary 7 4 1,072 .. 16.6 56.73 9.8 9.7 81.7 2.7 2.2India 8 4 747 137 10.9 .. 7.1 2.4 71.1 .. ..Indonesia 3 4 .. 275 8.3 13.37 11.4 2.6 36.5 6.2 ..Iran, Islamic Rep. 4 4 .. .. 26.6 30.95 .. .. 37.2 0.1 ..Iraq 3 0 .. .. 4.2 1.09 .. .. –2.1 8.3 8.0Ireland 9 5 .. .. 28.6 92.47 4.4 8.6 233.2 .. ..Israel 9 5 .. 927 19.9 102.35 6.8 1.4 85.7 2.9 2.3Italy 3 5 1,307 480 66.9 98.56 9.3 7.8 154.6 .. 2.9Jamaica 8 0 .. .. 6.7 25.98 .. .. 48.1 14.1 11.2Japan 7 6 7,169 171 34.0 132.96 4.8 1.8 326.6 1.1 1.5Jordan 4 2 .. .. 18.1 .. 10.5 7.9 96.0 5.5 ..Kazakhstan 4 5 874 .. 3.3 62.75 10.9 23.8 45.4 .. ..Kenya 10 4 370 73 4.4 7.27 13.2 6.3 51.0 9.8 10.8Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 8 6 4,522 .. 18.6 250.29 7.6 1.9 103.2 1.7 ..Kosovo 8 5 770 54 17.5 22.86 .. 4.4 17.8 10.9 ..Kuwait 4 4 .. .. .. .. 12.6 8.9 84.0 2.6 4.3Kyrgyz Republic 10 4 181 26 6.2 7.45 .. .. .. 27.4 27.0Lao PDR 4 0 44 4 2.6 4.33 .. .. 26.1 19.6 14.6Latvia 10 5 1,286 273 31.7 69.98 7.3 19.0 89.6 7.7 7.1Lebanon 4 5 873 276 29.7 38.25 7.3 4.3 164.4 2.1 4.2Lesotho 6 0 291 39 3.5 7.28 8.4 3.7 –6.2 7.5 5.0Liberia 7 1 .. .. .. .. .. .. 148.9 10.1 ..Libya .. .. .. .. 10.5 3.61 .. .. –65.9 3.5 ..Lithuania 5 6 .. .. 27.3 55.29 8.9 19.7 64.6 3.6 –0.1Macedonia, FYR 7 6 .. 291 26.6 51.88 10.6 9.0 48.2 2.4 ..Madagascar 2 0 45 18 1.6 1.49 .. .. 10.5 38.5 39.7Malawi 7 0 .. .. .. .. .. .. 28.4 21.0 17.5Malaysia 10 6 1,458 280 10.4 46.38 9.1 3.4 132.2 2.5 2.4Mali 6 1 .. .. .. .. .. .. 12.5 .. ..Mauritania 3 1 .. 32 4.3 .. .. .. 52.9 9.0 8.5Mauritius 6 3 465 2,168 23.0 1,009.32 7.3 2.8 110.8 0.5 ..Mexico 6 6 1,205 344 15.2 47.28 10.4 2.0 45.0 4.1 0.9Moldova 8 4 1,197 38 10.3 143.38 16.0 13.3 37.2 8.7 9.2Mongolia 6 4 1,339 706 53.6 .. .. .. 29.9 8.2 ..Morocco 3 5 694 .. 21.0 .. 8.4 4.4 106.0 .. ..Mozambique 2 4 .. .. 3.4 5.70 8.0 1.9 24.1 6.6 4.3Myanmar .. .. .. .. .. .. .. .. 25.1 5.0 ..Namibia 8 5 624 213 7.6 30.49 8.4 2.0 48.4 4.7 3.3Nepal 7 3 .. .. .. .. .. .. 68.6 4.4 1.2Netherlands 6 5 1,769 832 23.2 58.27 4.4 2.8 212.1 –0.6 ..New Zealand 10 5 .. .. 34.7 72.75 .. .. 157.8 1.7 3.5Nicaragua 3 5 .. .. .. .. .. .. 63.4 10.3 ..Niger 6 1 .. .. .. .. .. .. 12.8 .. ..Nigeria 9 0 .. .. .. .. 3.2 17.2 36.3 11.1 13.7Norway 7 4 529 .. 7.6 56.07 6.4 1.5 .. 2.0 ..Oman 4 4 1,012 401 22.9 .. 13.5 3.3 41.3 3.5 ..Pakistan 6 4 249 28 8.8 4.40 9.8 14.7 46.3 5.9 1.5Panama 5 6 .. .. .. .. 12.5 1.1 88.0 4.7 ..Papua New Guinea 5 3 178 23 1.8 5.25 .. .. 35.7 9.1 5.8Paraguay 3 6 .. .. .. .. 9.4 1.3 32.5 24.8 ..Peru 7 6 436 139 49.5 25.21 9.5 2.6 18.0 17.4 ..Philippines 4 3 488 .. 7.7 14.88 11.7 3.8 49.2 4.5 4.2Poland 9 5 .. .. 45.8 52.10 9.1 8.8 63.6 .. ..Portugal 3 4 2,806 413 75.9 197.05 6.4 3.3 209.2 .. ..Puerto Rico 8 5 .. .. .. .. .. .. .. .. ..Qatar 4 4 770 315 23.4 67.00 .. .. 75.7 4.4 ..2012 World Development Indicators 301

5.5Financial access, stability, and efficiencyGettingcreditFinancial accessand outreachBankcapital toasset ratioRatio of banknonperformingloans to totalgross loansDomesticcreditprovided bybanking sectorInterestrate spreadRisk premiumon lendingStrength ofDepth ofcreditper 1,000 adultsLending Prime lendinglegal rights information Depositors Borrowers per 100,000 adultsrate minus rate minusindex index with from Commercial Automateddeposit rate treasury bill rate0–10 (weak 0–6 (low commercial commercial bank tellerpercentage percentageto strong) to high) banks banks branches machines % % % of GDP points pointsJune 2011 June 2011 2010 2010 2010 2010 2010 2010 2010 2010 2010Romania 9 5 .. 222 33.2 55.48 8.9 11.9 54.9 6.8 6.9Russian Federation 3 5 .. .. .. 76.58 14.0 8.2 38.6 4.8 ..Rwanda 8 6 218 29 2.2 0.81 11.4 10.8 .. 9.6 9.2Saudi Arabia 5 6 780 181 9.0 57.97 12.6 3.0 –0.2 .. ..Senegal 6 1 .. .. .. .. 10.0 20.2 29.0 .. ..Serbia 8 5 .. 186 10.2 47.43 19.7 16.9 57.8 6.0 3.1Sierra Leone 7 0 190 12 2.9 0.43 17.5 15.6 18.4 12.3 4.0Singapore 10 4 2,134 968 10.3 58.57 9.6 1.8 85.7 5.2 5.0Slovak Republic 9 4 .. .. 26.5 50.38 9.7 5.8 54.0 2.0 ..Slovenia 4 4 .. .. 39.5 103.04 8.2 3.6 97.4 4.5 4.8Somalia .. .. .. .. .. .. .. .. .. .. ..South Africa 10 6 978 415 10.1 59.58 7.0 5.8 182.2 3.4 3.4South Sudan .. .. .. .. .. .. .. .. .. .. ..Spain 6 5 775 337 38.3 153.63 6.2 4.6 231.4 .. ..Sri Lanka 4 5 .. .. .. .. .. .. 40.5 3.3 1.7Sudan 4 0 .. .. .. .. .. .. 20.5 .. ..Swaziland 6 5 455 185 5.7 21.43 12.2 8.0 17.2 5.9 3.1Sweden 7 4 .. .. .. .. 5.0 2.0 143.5 .. ..Switzerland 8 5 .. .. 52.8 97.47 5.4 0.4 191.1 2.7 2.7Syrian Arab Republic 1 2 220 67 3.8 7.35 .. .. 47.7 3.7 ..Tajikistan 2 0 .. .. .. .. .. .. .. 18.0 ..Tanzania 8 0 131 30 1.8 3.30 .. .. 21.1 8.0 10.7Thailand 5 5 1,120 237 11.2 77.69 11.3 3.9 135.5 4.9 4.5Timor-Leste 2 3 .. .. .. .. .. .. –31.6 10.2 ..Togo 6 1 181 0 3.7 .. .. .. 31.9 .. ..Trinidad and Tobago 8 4 .. .. .. .. .. .. 32.4 7.8 8.4Tunisia 3 5 .. 148 16.6 20.84 .. 12.1 73.7 .. ..Turkey 4 5 1,265 719 17.4 43.74 13.4 3.8 69.3 .. ..Turkmenistan .. .. .. .. .. .. .. .. .. .. ..Uganda 7 4 192 18 2.5 3.58 13.9 2.1 17.1 12.5 15.2Ukraine 9 4 3,220 .. 2.3 76.13 14.6 15.3 78.6 5.3 ..United Arab Emirates 4 5 .. .. 21.7 96.81 17.7 5.6 92.3 .. ..United Kingdom 10 6 .. .. 25.5 64.58 5.4 4.0 222.6 .. 0.0United States 9 6 .. .. 35.7 173.75 11.1 4.9 231.4 .. 3.1Uruguay 4 6 538 426 13.5 30.38 9.5 1.0 32.4 6.2 1.3Uzbekistan 2 3 957 37 47.5 2.52 .. .. .. .. ..Venezuela, RB 1 0 .. .. .. .. 9.9 3.4 22.6 3.5 ..Vietnam 8 5 .. .. 3.3 17.64 .. .. 135.8 1.9 2.0West Bank and Gaza 1 3 543 68 10.3 13.68 .. .. .. .. ..Yemen, Rep. 3 2 101 2 2.0 2.75 .. .. 19.3 5.2 2.9Zambia 9 5 .. .. .. .. .. .. 18.9 13.5 14.6Zimbabwe 7 0 .. .. .. .. .. .. .. .. ..World 5.9 u 3.2 u .. w .. w 13.7 w 44.00 w 9.4 m 4.0 m 167.9 w 6.2 mLow income 5.8 1.3 .. .. .. .. .. .. 37.2 11.4Middle income 5.5 3.3 .. 167 12.2 29.02 10.2 3.9 90.8 6.3Lower middle income 5.3 2.8 .. .. 5.6 9.77 .. .. 57.1 8.7Upper middle income 5.7 3.7 .. 238 16.6 47.36 10.4 3.7 100.5 5.8Low & middle income 5.6 2.8 .. .. 8.1 18.23 .. 3.9 90.0 7.3East Asia & Pacifi c 6.2 2.0 .. 221 7.4 14.09 .. .. 131.8 6.9Europe & Central Asia 6.5 4.7 894 212 18.1 44.89 13.6 12.2 50.8 6.9Latin America & Carib. 5.3 3.4 .. .. .. .. 10.1 2.4 66.7 7.3Middle East & N. Africa 2.8 3.2 443 66 10.9 6.84 .. .. 51.9 5.0South Asia 5.6 2.8 249 28 7.8 1.93 7.3 10.5 67.4 5.9Sub-Saharan Africa 5.8 1.8 167 27 2.7 .. .. .. 81.6 9.7High income 6.9 4.3 .. .. 23.9 90.08 6.8 4.1 205.6 ..Euro area 6.4 4.1 .. .. 24.9 91.72 6.2 4.4 156.1 ..3022012 World Development Indicators

Financial access, stability, and efficiency5.5STATES AND MARKETSAbout the dataAccess to fi nance can expand opportunities for allwith higher levels of access and use of banking servicesassociated with lower fi nancing obstacles forpeople and businesses. A stable fi nancial systemthat promotes effi cient savings and investment isalso crucial for a thriving democracy and marketeconomy.There are several aspects of access to fi nancialservices: availability, cost, and quality of services.The development and growth of credit marketsdepend on access to timely, reliable, and accuratedata on borrowers’ credit experiences. Access tocredit can be improved by making it easy to createand enforce collateral agreements and by increasinginformation about potential borrowers’ creditworthiness.Lenders look at a borrower’s credit historyand collateral. Where credit registries and effectivecollateral laws are absent—as in many developingcountries —banks make fewer loans. Indicators thatcover getting credit include the strength of legalrights index and the depth of credit information index.The “unbanked” have to resort to informal servicesto manage their money—saving under the mattress,borrowing from family and friends, or using moneylenders—that are usually less reliable and morecostly than formal banking institutions. The tablepresents data on fi nancial access covering depositorsand borrowers and on outreach indicators suchas the number of branches and automated tellermachines. Data for these fi nancial access and outreachindicators are from the International MonetaryFund’s (IMF) Financial Access Survey database; thissources differs from that in the 2011 edition.The size and mobility of international capitalfl ows make it increasingly important to monitor thestrength of fi nancial systems. Robust fi nancial systemscan increase economic activity and welfare, butinstability can disrupt fi nancial activity and imposewidespread costs on the economy. The ratio of bankcapital to assets, a measure of bank solvency andresiliency, shows the extent to which banks can dealwith unexpected losses. Capital includes tier 1 capital(paid-up shares and common stock), a commonfeature in all countries’ banking systems, and totalregulatory capital, which includes several types ofsubordinated debt instruments that need not berepaid if the funds are required to maintain minimumcapital levels (tier 2 and tier 3 capital). Total assetsinclude all nonfi nancial and fi nancial assets. Dataare from internally consistent fi nancial statements.The ratio of bank nonperforming loans to totalgross loans measures bank health and effi ciency byidentifying problems with asset quality in the loanportfolio. A high ratio may signal deterioration of thecredit portfolio. International guidelines recommendthat loans be classifi ed as nonperforming when paymentsof principal and interest are 90 days or morepast due or when future payments are not expectedto be received in full.Domestic credit provided by the banking sector asa share of GDP measures banking sector depth andfi nancial sector development in terms of size. In afew countries governments may hold internationalreserves as deposits in the banking system ratherthan in the central bank. Since claims on the centralgovernment are a net item (claims on the centralgovernment minus central government deposits), thefi gure may be negative, resulting in a negative fi gurefor domestic credit provided by the banking sector.The interest rate spread—the margin betweenthe cost of mobilizing liabilities and the earnings onassets—measures fi nancial sector effi ciency in intermediation.A narrow spread means low transactioncosts, which reduces the cost of funds for investment,crucial to economic growth.The risk premium on lending is the spread betweenthe lending rate to the private sector and the “riskfree”government rate. Spreads are expressed as anannual average. A small spread indicates that themarket considers its best corporate customers to below risk; a negative value indicates that the marketconsiders its best corporate clients to be lower riskthan the government.Definitions• Strength of legal rights index measures the degreeto which collateral and bankruptcy laws protect therights of borrowers and lenders and thus facilitatelending. Higher values indicate that the laws are betterdesigned to expand access to credit. • Depth ofcredit information index measures rules affectingthe scope, accessibility, and quality of informationavailable through public or private credit registries.Higher values indicate the availability of more creditinformation. • Depositors with commercial banksare deposit account holders at commercial banksand other resident banks functioning as commercialbanks that are resident nonfi nancial corporations(public and private) and households. For many countriesdata cover the total number of deposit accountsdue to lack of information on account holders. Themajor types of deposits are checking accounts, savingsaccounts, and time deposits. • Borrowers fromcommercial banks are resident customers that arenonfi nancial corporations (public and private) andhouseholds that obtained loans from commercialbanks and other banks functioning as commercialbanks. For many countries data cover the total numberof loan accounts due to lack of information onloan account holders. • Commercial bank branchesare retail locations of resident commercial banks andother resident banks that function as commercialbanks that provide fi nancial services to customersand are physically separated from the main offi cebut not legally separated subsidiaries. • Automatedteller machines are computerized telecommunicationsdevices that provide clients of a fi nancial institutionaccess to fi nancial transactions in a publicplace. • Bank capital to asset ratio is the ratio ofbank capital and reserves to total assets. Capitaland reserves include funds contributed by owners,retained earnings, general and special reserves, provisions,and valuation adjustments. • Ratio of banknonperforming loans to total gross loans is the valueof nonperforming loans (gross value of the loan asrecorded on the balance sheet) divided by the totalvalue of the loan portfolio (including nonperformingloans before the deduction of loan loss provisions).• Domestic credit provided by banking sector is allcredit to various sectors on a gross basis, except tothe central government, which is net. The bankingsector includes monetary authorities, deposit moneybanks, and other banking institutions for which dataare available. • Interest rate spread is the interestrate charged by banks on loans to prime customersminus the interest rate paid by commercial or similarbanks for demand, time, or savings deposits. • Riskpremium on lending is the interest rate charged bybanks on loans to prime private sector customersminus the “risk-free” treasury bill interest rate atwhich short-term government securities are issuedor traded in the market.Data sourcesData on getting credit are from the World Bank’sDoing Business project (www.doingbusiness.org). Data on fi nancial access and outreach arefrom the IMF’s Financial Access Survey database(http://fas.imf.org). Data on bank capital andnonperforming loans are from the IMF’s FinancialSoundness Indicators database (http://fsi.imf.org). Data on credit and interest rates are fromthe IMF’s International Financial Statistics.2012 World Development Indicators 303

5.6 Tax policiesTax revenue collectedby central governmentTaxes payableby businessesTime to prepare,% of commercial profi ts% of GDPNumberof paymentsfi le, and pay taxeshoursProfi ttaxLabor tax andcontributionsOthertaxesTotaltax rate2005 2010 June 2011 June 2011 June 2011 June 2011 June 2011 June 2011Afghanistan 6.3 8.3 8 275 0.0 0.0 36.4 36.4Albania 17.3 .. 44 371 8.7 25.0 4.8 38.5Algeria 40.7 a 34.9 a 29 451 6.6 29.7 35.7 72.0Angola .. .. 31 282 24.6 9.0 19.5 53.2Argentina 14.2 a .. 9 415 2.8 29.4 76.1 108.2Armenia 14.3 16.9 34 500 16.8 23.0 1.1 40.9Australia 24.7 a 22.1 a 11 109 26.0 20.4 1.3 47.7Austria 20.1 a 18.7 a 14 170 15.0 34.8 3.4 53.1Azerbaijan .. 15.9 18 225 12.9 24.8 2.2 40.0Bahrain 1.4 a .. 25 36 0.0 14.7 0.4 15.0Bangladesh 8.2 8.6 21 302 25.7 0.0 9.2 35.0Belarus 20.1 17.1 18 654 20.2 39.0 3.5 62.8Belgium 26.0 a 24.6 a 11 156 5.2 50.4 1.7 57.3Benin 15.5 a 16.2 a 55 270 14.8 27.3 23.9 66.0Bolivia 16.2 a .. 42 1,080 0.0 15.5 64.6 80.0Bosnia and Herzegovina 20.7 a 20.4 a 40 422 7.1 12.6 5.3 25.0Botswana .. .. 19 152 15.9 0.0 3.6 19.4Brazil 16.7 15.6 9 2,600 22.4 40.9 3.8 67.1Bulgaria 21.6 21.0 17 500 4.9 19.2 4.1 28.1Burkina Faso 11.8 a 13.0 a 46 270 14.8 22.6 6.2 43.6Burundi .. .. 24 274 37.4 7.8 1.0 46.2Cambodia 7.9 a 10.1 a 39 173 18.9 0.1 3.5 22.5Cameroon .. .. 44 654 29.9 18.3 0.9 49.1Canada 13.7 a 11.9 a 8 131 9.3 12.6 6.8 28.8Central African Republic 6.2 .. 54 504 0.0 19.8 34.8 54.6Chad .. .. 54 732 31.3 28.4 5.7 65.4Chile 18.7 a 17.8 a 9 316 18.0 3.8 3.2 25.0China 8.7 10.5 7 398 5.9 49.6 7.9 63.5Hong Kong SAR, China 12.7 a 12.8 a 3 80 17.6 5.3 0.1 23.0Colombia 12.8 a 11.5 a 9 193 18.9 28.8 27.1 74.8Congo, Dem. Rep. 10.0 13.7 32 336 58.9 7.9 272.8 339.7Congo, Rep. 6.2 .. 61 606 18.1 32.5 15.4 65.9Costa Rica .. 13.9 a 31 246 18.9 29.5 6.6 55.0Côte d’Ivoire 9.8 a 16.6 a 62 270 8.8 20.1 15.4 44.3Croatia 20.0 18.8 17 196 11.5 19.4 1.5 32.3Cuba .. .. .. .. .. .. .. ..Cyprus 45.4 a 25.8 a 27 149 9.1 11.8 2.2 23.1Czech Republic 15.6 13.9 8 557 7.5 38.4 3.2 49.1Denmark 32.6 a 34.3 a 10 135 20.1 3.6 3.8 27.5Dominican Republic 14.6 a 13.1 a 9 324 21.3 18.6 1.8 41.7Ecuador .. .. 8 654 18.4 14.2 2.7 35.3Egypt, Arab Rep. 14.1 14.1 29 433 13.0 27.1 3.6 43.6El Salvador 12.5 a 13.7 a 53 320 16.5 17.2 1.3 35.0Eritrea .. .. 18 216 8.8 0.0 75.8 84.5Estonia 16.1 a 17.4 a 8 85 8.0 39.4 11.2 58.6Ethiopia 8.8 .. 19 198 26.8 0.0 4.3 31.1Finland 22.6 a 21.2 a 8 93 13.7 24.2 1.2 39.0France 22.4 a 19.8 a 7 132 8.2 51.7 5.7 65.7Gabon .. .. 26 488 18.4 22.7 2.3 43.5Gambia, The .. .. 50 376 6.1 12.8 264.6 283.5Georgia 12.1 22.1 4 387 14.3 0.0 2.2 16.5Germany 11.1 a 12.2 a 12 221 19.0 21.8 5.9 46.7Ghana 21.3 12.6 33 224 18.4 14.7 0.5 33.6Greece 20.3 a 19.6 a 10 224 13.4 31.7 1.4 46.4Guatemala 11.2 10.3 24 344 25.9 14.3 0.7 40.9Guinea .. .. 56 416 20.9 22.8 10.6 54.3Guinea-Bissau .. .. 46 208 14.9 24.8 6.1 45.9Haiti .. .. 46 184 24.1 12.4 4.3 40.83042012 World Development Indicators

Tax revenue collectedby central governmentTax policiesTaxes payableby businesses5.6STATES AND MARKETSTime to prepare,% of commercial profi ts% of GDPNumberof paymentsfi le, and pay taxeshoursProfi ttaxLabor tax andcontributionsOthertaxesTotaltax rate2005 2010 June 2011 June 2011 June 2011 June 2011 June 2011 June 2011Honduras 14.5 a 14.8 a 47 224 24.7 10.7 8.6 44.0Hungary 20.3 a 23.9 a 13 277 14.8 34.1 3.5 52.4India 9.9 9.5 33 254 24.7 18.2 19.0 61.8Indonesia 12.5 10.9 51 266 23.7 10.6 0.1 34.5Iran, Islamic Rep. 7.9 9.3 20 344 17.8 25.9 0.4 44.1Iraq .. .. 13 312 14.9 13.5 0.0 28.4Ireland 24.9 a 21.2 a 8 76 11.9 11.6 2.7 26.3Israel 26.9 a 24.3 a 33 235 22.8 5.3 3.1 31.2Italy 21.1 a 22.9 a 15 285 22.8 43.4 2.2 68.5Jamaica 25.5 a 21.0 a 72 414 25.6 13.0 7.0 45.6Japan 10.9 a 8.7 a 14 330 27.0 16.5 5.7 49.1Jordan 24.4 15.3 25 116 13.0 12.4 2.3 27.7Kazakhstan 17.1 8.9 7 188 15.9 11.2 1.6 28.6Kenya 18.7 19.5 41 393 33.1 6.8 9.7 49.6Korea, Dem. Rep. .. .. .. .. .. .. .. ..Korea, Rep. 14.7 15.2 12 225 15.1 13.0 1.5 29.7Kosovo .. .. 33 164 9.1 5.6 0.6 15.4Kuwait 1.0 0.9 15 118 4.7 10.7 0.0 15.5Kyrgyz Republic 14.2 15.6 52 210 6.2 19.5 43.4 69.0Lao PDR 10.4 12.7 34 362 24.8 5.6 2.9 33.3Latvia 15.1 12.8 7 290 6.1 27.2 4.7 37.9Lebanon 14.9 a 17.1 a 19 180 6.1 24.1 0.0 30.2Lesotho 46.6 59.8 21 324 13.1 0.0 3.0 16.0Liberia 0.2 0.3 33 158 0.0 5.4 38.3 43.7Libya .. .. .. .. .. .. .. ..Lithuania 17.3 a 13.4 a 11 175 5.7 35.1 3.1 43.9Macedonia, FYR 19.3 19.1 28 119 6.3 0.0 3.4 9.7Madagascar 10.1 a 13.0 a 23 201 14.7 20.3 1.6 36.6Malawi .. .. 19 157 23.6 1.1 3.5 28.2Malaysia 15.4 14.3 13 133 17.0 15.6 1.4 34.0Mali 15.7 a 14.7 a 59 270 10.8 34.3 6.6 51.8Mauritania .. .. 37 696 0.0 17.6 50.7 68.3Mauritius .. 18.5 a 7 161 11.6 6.1 7.3 25.0Mexico .. .. 6 347 24.5 26.8 1.4 52.7Moldova 18.5 18.2 48 228 0.0 30.6 0.7 31.3Mongolia 28.7 22.7 41 192 10.2 12.4 2.0 24.6Morocco 22.0 a 23.4 a 17 238 25.2 22.7 1.8 49.6Mozambique .. .. 37 230 27.7 4.5 2.1 34.3Myanmar 3.9 .. .. .. .. .. .. ..Namibia 25.8 .. 37 375 4.0 1.0 4.8 9.8Nepal 9.2 13.3 34 326 17.2 11.3 3.0 31.5Netherlands 22.6 a 22.6 a 9 127 20.9 18.1 1.5 40.5New Zealand 30.8 b .. 8 172 29.9 2.9 1.7 34.4Nicaragua 16.7 18.3 42 207 24.5 20.3 22.0 66.8Niger 10.1 b .. 41 270 17.3 20.1 6.3 43.8Nigeria 0.2 0.3 35 938 22.3 9.7 0.7 32.7Norway 28.7 a 26.9 a 4 87 24.4 15.9 1.3 41.6Oman .. .. 14 62 10.0 11.8 0.1 22.0Pakistan 9.6 10.0 47 560 17.9 15.1 2.3 35.3Panama .. .. 53 482 13.7 21.7 9.7 45.2Papua New Guinea .. .. 33 194 22.0 11.7 8.6 42.3Paraguay 11.8 13.1 35 387 9.6 18.6 6.7 35.0Peru 13.5 14.5 9 309 26.6 11.0 3.1 40.7Philippines 12.4 12.1 47 195 21.0 11.3 14.2 46.5Poland 16.7 a 16.3 a 29 296 17.4 23.6 2.6 43.6Portugal 20.6 a 19.6 a 8 275 15.1 26.8 1.5 43.3Puerto Rico .. .. 16 218 28.3 14.4 20.5 63.1Qatar 21.0 19.8 3 36 0.0 11.3 0.0 11.32012 World Development Indicators 305

5.6 Tax policiesTax revenue collectedby central governmentTaxes payableby businessesTime to prepare,% of commercial profi ts% of GDPNumberof paymentsfi le, and pay taxeshoursProfi ttaxLabor tax andcontributionsOthertaxesTotaltax rate2005 2010 June 2011 June 2011 June 2011 June 2011 June 2011 June 2011Romania 12.2 a 17.9 a 113 222 10.4 31.8 2.2 44.4Russian Federation 16.6 a 13.4 a 9 290 8.9 32.1 5.8 46.9Rwanda .. .. 18 148 21.2 5.7 4.4 31.3Saudi Arabia .. .. 14 79 2.1 12.4 0.0 14.5Senegal .. .. 59 666 14.8 24.1 7.0 46.0Serbia 23.6 22.0 66 279 11.6 20.2 2.2 34.0Sierra Leone 10.8 11.0 29 357 17.6 11.3 3.3 32.1Singapore 11.8 13.8 5 84 6.5 15.9 4.7 27.1Slovak Republic 14.9 a 12.4 a 31 231 7.2 39.6 2.0 48.8Slovenia 20.5 17.1 22 260 14.1 18.2 2.4 34.7Somalia .. .. .. .. .. .. .. ..South Africa 26.9 a 25.5 a 9 200 24.4 4.1 4.6 33.1South Sudan .. .. .. .. .. .. .. ..Spain 12.9 a 11.2 a 8 187 1.2 36.7 0.7 38.7Sri Lanka 13.7 13.3 71 256 26.7 16.9 61.6 105.2Sudan .. .. 42 180 13.8 19.2 3.1 36.1Swaziland 26.1 .. 33 104 28.1 4.0 4.7 36.8Sweden 22.6 a 21.6 a 4 122 15.7 35.5 1.6 52.8Switzerland 10.3 10.9 19 63 8.9 17.5 3.6 30.1Syrian Arab Republic .. .. 19 336 20.0 19.3 0.5 39.7Tajikistan 9.8 .. 69 224 0.0 28.5 56.0 84.5Tanzania .. .. 48 172 20.1 18.0 7.3 45.5Thailand 17.2 a 16.0 a 23 264 28.8 5.7 3.0 37.5Timor-Leste .. .. 6 276 0.0 0.0 0.2 0.2Togo 13.9 a 15.8 a 53 270 9.3 26.5 13.7 49.5Trinidad and Tobago 26.4 26.2 39 210 21.6 5.8 1.8 29.1Tunisia 18.9 20.1 8 144 15.2 25.2 22.5 62.9Turkey 19.7 a 20.5 a 15 223 17.9 18.8 4.4 41.1Turkmenistan .. .. .. .. .. .. .. ..Uganda 11.8 12.2 32 213 23.3 11.3 1.1 35.7Ukraine 17.1 16.4 135 657 12.2 43.3 1.6 57.1United Arab Emirates .. .. 14 12 0.0 14.1 0.0 14.1United Kingdom 27.2 a 26.0 a 8 110 23.1 11.0 3.2 37.3United States 11.2 a 9.3 a 11 187 27.6 10.0 9.1 46.7Uruguay 17.9 19.5 53 336 23.6 15.6 2.9 42.0Uzbekistan .. .. 41 205 1.1 28.2 68.1 97.5Venezuela, RB 15.5 .. 70 864 6.9 18.0 38.5 63.5Vietnam .. .. 32 941 17.2 22.6 0.3 40.1West Bank and Gaza .. .. 27 154 16.2 0.0 0.6 16.8Yemen, Rep. .. .. 44 248 20.0 11.3 1.5 32.9Zambia 17.2 16.6 37 132 1.5 10.4 2.5 14.5Zimbabwe .. .. 49 242 20.3 5.1 10.1 35.6World 15.4 w 13.5 w 29 u 277 u 16.0 u 16.2 u 12.6 u 44.8 uLow income 10.7 11.8 38 271 18.7 13.0 36.1 67.8Middle income 12.9 13.0 32 327 16.5 15.5 9.2 41.1Lower middle income 11.1 10.9 36 324 16.0 13.7 10.7 40.4Upper middle income 13.5 13.4 26 330 17.0 17.3 7.5 41.9Low & middle income 12.8 13.0 33 314 17.0 14.9 15.5 47.4East Asia & Pacifi c 10.0 11.0 27 232 17.9 10.5 7.9 36.2Europe & Central Asia 16.5 15.5 39 314 9.2 22.2 10.2 41.6Latin America & Carib. .. .. 33 404 20.2 15.2 12.4 47.8Middle East & N. Africa 18.7 17.5 24 253 15.5 19.1 6.0 40.6South Asia 9.9 9.5 28 281 18.6 7.7 18.2 44.4Sub-Saharan Africa 17.5 .. 37 314 18.5 13.2 25.6 57.3High income 16.0 13.6 15 169 13.0 20.2 4.2 37.4Euro area 18.1 17.2 14 171 11.8 29.7 3.0 44.5Note: Regional aggregates differ from those reported on the Doing Business website because the regional aggregates reported on the Doing Business website include developed countries.a. Data were reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fund’s Government Finance Statistics Manual 2001.3062012 World Development Indicators

Tax policies 5.6STATES AND MARKETSAbout the dataDefinitionsTaxes are the main source of revenue for mostgovernments. The sources of tax revenue and theirrelative contributions are determined by governmentpolicy choices about where and how to impose taxesand by changes in the structure of the economy. Taxpolicy may refl ect concerns about distributionaleffects, economic effi ciency (including correctionsfor externalities), and the practical problems ofadministering a tax system. There is no ideal levelof taxation. But taxes infl uence incentives and thusthe behavior of economic actors and the economy’scompetitiveness.The level of taxation is typically measured by taxrevenue as a share of gross domestic product (GDP).Comparing levels of taxation across countries providesa quick overview of the fi scal obligations andincentives facing the private sector. The table showsonly central government data, which may signifi cantlyunderstate the total tax burden, particularly in countrieswhere provincial and municipal governments arelarge or have considerable tax authority.Low ratios of tax revenue to GDP may refl ect weakadministration and large-scale tax avoidance or evasion.Low ratios may also refl ect a sizable paralleleconomy with unrecorded and undisclosed incomes.Tax revenue ratios tend to rise with income, withhigher income countries relying on taxes to fi nancea much broader range of social services and socialsecurity than lower income countries are able to.The total tax rate payable by businesses providesa comprehensive measure of the cost of all the taxesa business bears. It differs from the statutory taxrate, which is the factor applied to the tax base. Incomputing business tax rates, actual tax payable isdivided by commercial profi t.The indicators covering taxes payable by businessesmeasure all taxes and contributions thatare government mandated (at any level—federal,state, or local), apply to standardized businesses,and have an impact in their income statements. Thetaxes covered go beyond the defi nition of a tax forgovernment national accounts (compulsory, unrequitedpayments to general government) and alsomeasure any imposts that affect business accounts.The main differences are in labor contributionsand value added taxes. The indicators account forgovernment-mandated contributions paid by theemployer to a requited private pension fund or workersinsurance fund but exclude value added taxesbecause they do not affect the accounting profi ts ofthe business —that is, they are not refl ected in theincome statement.To make the data comparable across countries,several assumptions are made about businesses.The main assumptions are that they are limited liabilitycompanies, they operate in the country’s mostpopulous city, they are domestically owned, they performgeneral industrial or commercial activities, andthey have certain levels of start-up capital, employees,and turnover. For details about the assumptions,see the World Bank’s Doing Business 2012.The Doing Business methodology on businesstaxes is consistent with the Total Tax Contributionframework developed by PwC, which measures thetaxes that are borne by companies and that affecttheir income statements. However, PwC bases itscalculation on data from the largest companies inthe economy, while Doing Business focuses on astandardized medium-size company.• Tax revenue collected by central governmentis compulsory transfers to the central governmentfor public purposes. Certain compulsory transferssuch as fi nes, penalties, and most social securitycontributions are excluded. Refunds and correctionsof erroneously collected tax revenue are treated asnegative revenue. The analytic framework of theInternational Monetary Fund’s (IMF) GovernmentFinance Statistics Manual 2001 (GFSM 2001) isbased on accrual accounting and balance sheets.For countries still reporting government fi nance dataon a cash basis, the IMF adjusts reported data to theGFSM 2001 accrual framework. These countries arefootnoted in the table. • Number of tax paymentsby businesses is the total number of taxes paid bybusinesses during one year. When electronic fi ling isavailable, the tax is counted as paid once a year evenif payments are more frequent. • Time to prepare,file, and pay taxes is the time, in hours per year, ittakes to prepare, fi le, and pay (or withhold) threemajor types of taxes: the corporate income tax, thevalue-added or sales tax, and labor taxes, includingpayroll taxes and social security contributions.• Profit tax is the amount of taxes on profi ts paidby businesses. • Labor tax and contributions arethe amount of taxes and mandatory contributionson labor paid by businesses. • Other taxes are theamounts paid by businesses for property taxes, turnovertaxes, and other small taxes such as municipalfees and vehicle and fuel taxes. • Total tax rate isthe amount of taxes and mandatory contributionspayable by businesses in the second year of operation,expressed as a share of commercial profi ts.Taxes withheld (such as sales or value added taxor personal income tax) but not paid by businessesare excluded. For further details on the method usedfor assessing the total tax payable, see the WorldBank’s Doing Business 2012.Data sourcesData on central government tax revenue are fromprint and electronic editions of the IMF’s GovernmentFinance Statistics Yearbook. Data on taxespayable by businesses are from Doing Business2012 (www.doingbusiness.org).2012 World Development Indicators 307

5.7 Military expenditures and arms transfersMilitary expenditures Armed forces personnel Arms transfers% of central government% ofTrend indicator values1990 $ millions% of GDPexpenditurethousandslabor forceExportsImports2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010Afghanistan 1.6 1.8 8.7 4.6 27 307 0.4 3.4 .. .. 42 407Albania 1.3 1.6 6.2 .. 23 15 1.6 1.0 .. .. 42 13Algeria 2.9 3.5 13.8 15.0 319 317 3.2 2.8 .. .. 155 791Angola 4.5 4.4 .. .. 118 117 1.9 1.6 .. .. 40 20Argentina 0.9 0.9 5.2 .. 102 104 0.6 0.6 2 .. 3 17Armenia 2.9 4.5 15.8 19.8 49 56 3.4 3.9 .. .. 120 36Australia 1.9 2.0 7.3 7.3 53 57 0.5 0.5 50 119 467 1,677Austria 0.9 0.9 2.2 2.2 40 26 1.0 0.6 3 33 22 5Azerbaijan 2.3 2.9 .. 22.5 82 82 2.0 1.8 .. .. 45 62Bahrain 3.6 3.7 17.6 .. 21 19 6.2 2.7 .. .. 63 71Bangladesh 1.2 1.2 12.4 10.2 252 221 0.4 0.3 .. .. 9 45Belarus 1.5 1.3 5.0 4.2 183 183 3.9 4.1 24 42 6 3Belgium 1.1 1.1 2.5 2.5 37 36 0.8 0.7 161 7 0 30Benin 1.1 1.0 8.1 6.8 8 7 0.3 0.2 .. .. 2 0Bolivia 1.9 1.6 7.2 .. 70 83 1.7 1.8 .. .. 1 1Bosnia and Herzegovina 1.6 1.4 4.7 3.4 12 11 0.9 0.7 .. .. .. ..Botswana 2.9 2.7 .. .. 11 11 1.2 1.0 .. .. 8 10Brazil 1.5 1.6 6.0 6.3 673 713 0.7 0.7 1 179 223 314Bulgaria 2.4 1.4 7.6 6.3 85 65 2.5 1.9 66 14 149 17Burkina Faso 1.2 1.5 10.2 12.4 11 11 0.2 0.2 .. .. 19 2Burundi 6.2 3.8 .. .. 82 51 2.3 1.2 .. .. .. 2Cambodia 1.1 1.8 14.8 16.6 191 191 2.8 2.4 .. .. 12 28Cameroon 1.3 1.6 .. .. 23 23 0.3 0.3 .. .. 5 9Canada 1.1 1.4 6.3 7.5 71 66 0.4 0.3 226 258 116 373Central African Republic 1.1 2.6 12.3 .. 3 3 0.2 0.2 .. .. 9 0Chad 1.0 3.0 .. .. 35 35 0.9 0.8 .. .. 9 17Chile 3.6 3.2 20.0 15.5 116 104 1.7 1.3 .. 133 460 434China 2.1 a 2.0 a 15.3 a 16.1 a 3,755 2,945 0.5 0.4 303 1,423 3,536 559Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. ..Colombia 3.4 3.7 12.7 20.3 336 442 1.7 2.0 .. .. 15 172Congo, Dem. Rep. 2.3 1.4 10.5 10.2 65 159 0.3 0.6 .. .. 17 25Congo, Rep. 1.3 1.1 8.8 .. 12 12 0.8 0.7 .. .. 4 1Costa Rica .. .. .. .. 10 10 0.5 0.4 .. 0 .. ..Côte d’Ivoire 1.5 1.6 9.0 8.8 19 19 0.3 0.2 .. .. 10 ..Croatia 1.6 1.7 4.7 4.6 31 22 1.6 1.1 .. .. 8 10Cuba 3.8 3.2 .. .. 76 76 1.6 1.4 .. .. .. ..Cyprus 2.2 2.2 3.1 4.7 11 13 2.1 2.2 .. .. 20 ..Czech Republic 2.0 1.3 5.4 3.6 28 29 0.5 0.5 66 3 621 73Denmark 1.3 1.4 4.1 3.3 21 19 0.7 0.6 9 11 124 16Dominican Republic 0.8 0.6 5.6 4.4 40 40 1.0 0.9 .. .. 2 33Ecuador 2.6 3.8 .. .. 47 59 0.7 0.9 .. .. 48 116Egypt, Arab Rep. 2.9 2.0 10.4 6.9 799 836 3.3 3.1 .. .. 641 681El Salvador 0.6 0.6 3.6 3.0 16 33 0.7 1.3 .. .. 9 4Eritrea 20.9 .. .. .. 202 202 9.3 7.8 .. .. 69 ..Estonia 1.9 1.7 7.2 6.2 8 6 1.2 0.8 .. .. 17 1Ethiopia 2.8 1.1 18.6 .. 183 138 0.5 0.3 .. .. 240 ..Finland 1.4 1.5 3.9 3.8 31 25 1.2 0.9 27 34 99 72France 2.5 2.3 5.4 5.3 359 342 1.2 1.1 1,724 834 60 120Gabon 1.3 1.0 .. .. 7 7 1.4 1.1 .. .. 17 17Gambia, The 0.6 .. .. .. 1 1 0.1 0.1 .. .. 5 ..Georgia 3.3 3.9 19.3 14.8 23 32 1.0 1.4 17 .. 74 34Germany 1.4 1.4 4.4 4.5 285 251 0.7 0.6 2,080 2,340 195 101Ghana 0.6 0.4 2.6 2.4 7 16 0.1 0.1 .. .. 0 6Greece 2.9 3.1 6.9 6.3 168 150 3.3 2.8 13 .. 389 703Guatemala 0.4 0.4 2.9 3.1 48 34 1.0 0.6 .. .. .. 0Guinea 2.2 .. .. .. 13 19 0.4 0.5 .. .. 1 ..Guinea-Bissau 2.1 .. .. .. 9 6 1.6 1.0 .. .. .. ..Haiti .. .. .. .. 5 0 0.1 0.0 .. .. .. ..3082012 World Development Indicators

Military expenditures and arms transfers 5.7Military expenditures Armed forces personnel Arms transfersSTATES AND MARKETS% of central government% ofTrend indicator values1990 $ millions% of GDPexpenditurethousandslabor forceExportsImports2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010Honduras 0.8 1.6 3.9 6.8 20 20 0.8 0.7 .. .. .. 0Hungary 1.4 1.0 3.4 2.5 44 35 1.0 0.8 82 .. 13 18India 2.7 2.4 18.3 16.0 3,047 2,626 0.7 0.6 19 4 1,059 3,337Indonesia 1.1 1.0 6.9 7.1 582 582 0.5 0.5 8 .. 32 198Iran, Islamic Rep. 3.5 1.9 15.3 8.6 585 563 2.4 2.2 2 5 66 88Iraq 3.6 6.0 .. .. 227 802 3.6 10.6 .. .. 165 464Ireland 0.6 0.6 1.8 1.4 10 10 0.5 0.5 .. 4 4 1Israel 7.6 6.5 16.7 15.7 176 185 6.4 5.8 368 472 1,133 43Italy 1.9 1.8 4.8 4.1 445 359 1.8 1.4 774 627 150 85Jamaica 0.5 0.8 1.7 2.3 3 3 0.2 0.2 .. .. 13 2Japan 1.0 1.0 .. .. 272 260 0.4 0.4 .. .. 375 369Jordan 4.8 5.2 13.5 20.1 111 111 8.0 7.1 17 88 35 114Kazakhstan 1.0 0.9 5.7 5.5 101 81 1.3 0.9 12 .. 42 57Kenya 1.7 1.9 9.3 8.3 29 29 0.2 0.2 .. .. 8 73Korea, Dem. Rep. .. .. .. .. 1,295 1,379 9.2 9.5 40 .. 5 1Korea, Rep. 2.6 2.7 13.3 13.7 693 660 2.9 2.7 48 95 796 1,131Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 4.3 4.0 16.7 10.2 23 23 2.0 1.7 .. .. 16 17Kyrgyz Republic 3.1 3.6 20.0 18.9 18 20 0.8 0.8 92 14 3 ..Lao PDR 0.4 0.3 3.8 2.7 129 129 4.7 4.1 .. .. 4 26Latvia 1.7 1.1 5.9 3.0 5 5 0.4 0.4 .. .. 7 15Lebanon 4.4 4.2 16.7 14.0 85 79 6.5 5.4 .. .. 1 60Lesotho 2.5 2.7 5.9 3.1 2 2 0.2 0.2 .. .. 1 ..Liberia 1.5 0.8 .. .. 15 2 1.4 0.1 .. .. .. ..Libya 1.5 1.2 .. .. 76 76 3.6 3.2 113 28 2 7Lithuania 1.6 1.3 5.7 3.4 29 25 1.8 1.5 .. .. 15 81Macedonia, FYR 2.1 1.5 7.0 5.8 19 8 2.2 0.8 .. .. 0 ..Madagascar 1.1 0.7 9.7 9.3 22 22 0.3 0.2 .. .. .. ..Malawi 1.2 .. .. .. 7 5 0.1 0.1 .. .. .. 2Malaysia 2.3 1.5 12.3 7.8 135 134 1.2 1.1 .. 0 49 411Mali 2.3 1.9 15.0 13.3 12 12 0.3 0.3 .. .. 13 9Mauritania 3.1 3.8 .. .. 21 21 2.2 1.9 .. .. 7 4Mauritius 0.2 0.2 .. .. 2 2 0.4 0.3 .. .. 6 ..Mexico 0.4 0.5 .. .. 204 332 0.5 0.7 .. .. 36 188Moldova 0.4 0.3 1.4 0.9 10 8 0.7 0.6 18 20 .. ..Mongolia 1.2 0.9 4.9 3.3 16 17 1.5 1.5 .. .. .. 13Morocco 3.4 3.5 11.4 11.4 251 246 2.3 2.2 .. .. 87 138Mozambique 0.9 0.9 .. .. 11 11 0.1 0.1 .. .. 1 0Myanmar .. .. .. .. 483 513 1.9 1.8 .. .. 65 76Namibia 2.6 3.3 10.3 .. 15 15 1.9 1.6 .. .. 72 14Nepal 1.9 1.5 12.8 .. 131 158 0.9 1.0 .. .. 5 1Netherlands 1.5 1.4 3.8 3.4 60 43 0.7 0.5 583 503 96 162New Zealand 1.0 1.1 3.1 .. 9 10 0.4 0.4 0 .. 8 71Nicaragua 0.7 0.7 4.0 3.4 14 12 0.7 0.5 .. .. .. ..Niger 1.0 0.9 10.6 .. 10 11 0.2 0.2 .. .. 14 1Nigeria 0.6 1.0 12.1 10.8 161 162 0.4 0.3 .. .. 14 189Norway 1.6 1.6 4.9 4.5 47 24 1.9 0.9 12 141 14 205Oman 11.8 9.6 .. .. 46 47 5.1 3.9 1 .. 164 36Pakistan 4.0 3.2 27.6 18.5 921 946 1.8 1.6 22 .. 406 2,580Panama .. .. .. .. 12 12 0.8 0.7 .. .. .. 5Papua New Guinea 0.6 0.5 .. .. 3 3 0.1 0.1 .. .. .. ..Paraguay 0.8 0.9 4.8 5.8 25 25 0.9 0.8 .. .. 1 3Peru 1.5 1.4 8.5 8.3 157 192 1.2 1.2 5 .. 368 60Philippines 0.8 0.8 4.9 4.8 147 166 0.4 0.4 4 .. 14 8Poland 1.9 1.9 5.3 5.1 162 121 0.9 0.7 17 8 95 142Portugal 2.1 2.2 5.1 4.8 93 90 1.7 1.6 .. 0 172 941Puerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar 2.5 2.3 12.1 15.4 12 12 2.3 0.9 6 .. 11 202012 World Development Indicators 309

5.7 Military expenditures and arms transfersMilitary expenditures Armed forces personnel Arms transfers% of central government% ofTrend indicator values1990 $ millions% of GDPexpenditurethousandslabor forceExportsImports2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010Romania 2.0 1.4 8.3 4.4 177 154 1.8 1.5 2 4 491 109Russian Federation 3.7 4.0 18.7 14.1 1,452 1,430 2.0 1.9 5,134 6,039 5 19Rwanda 1.7 1.4 .. .. 53 35 1.2 0.7 .. .. 3 13Saudi Arabia 8.0 10.4 .. .. 216 249 2.7 2.6 .. 58 150 787Senegal 1.4 1.6 .. .. 19 19 0.4 0.3 .. .. 15 4Serbia 2.5 2.2 6.5 5.5 110 29 2.8 0.8 4 5 .. 14Sierra Leone 1.9 2.3 8.3 11.2 13 11 0.7 0.5 .. .. 10 ..Singapore 4.4 3.8 33.9 28.2 167 148 7.4 5.3 3 27 537 1,078Slovak Republic 1.7 1.1 4.9 3.7 20 16 0.8 0.6 7 8 4 8Slovenia 1.4 1.6 3.6 3.8 12 12 1.2 1.2 .. .. 2 73Somalia .. .. .. .. .. 2 .. 0.1 .. .. .. ..South Africa 1.5 1.2 5.1 4.1 56 77 0.3 0.4 26 80 262 183South Sudan .. .. .. .. .. 140 .. .. .. .. 37 1Spain 1.0 1.1 4.2 3.6 220 223 1.0 1.0 108 513 339 313Sri Lanka 2.6 2.8 13.1 19.3 200 223 2.5 2.6 .. .. 33 5Sudan 4.3 .. .. .. 123 127 1.0 0.9 .. .. 104 14Swaziland 2.3 3.4 7.0 .. .. .. .. .. .. .. .. ..Sweden 1.5 1.2 .. .. 29 21 0.6 0.4 538 806 82 35Switzerland 0.9 0.9 4.9 4.8 109 25 2.6 0.6 246 137 164 34Syrian Arab Republic 5.0 3.9 .. .. 416 403 8.0 7.4 3 25 7 167Tajikistan 2.2 .. 15.8 .. 13 16 0.5 0.6 .. .. 13 ..Tanzania 1.0 1.0 .. .. 28 28 0.1 0.1 .. .. 9 9Thailand 1.1 1.5 6.7 8.2 421 420 1.1 1.1 8 .. 63 83Timor-Leste 2.5 2.8 .. .. 1 1 0.3 0.4 .. .. .. 20Togo 1.6 1.8 9.8 13.0 10 9 0.4 0.3 .. .. .. 1Trinidad and Tobago .. .. .. .. 3 4 0.4 0.6 .. .. 6 45Tunisia 1.5 1.2 5.6 4.5 47 48 1.4 1.2 .. .. 168 2Turkey 2.5 2.4 11.5 9.4 617 613 2.7 2.3 47 31 1,065 468Turkmenistan .. .. .. .. 26 22 1.3 1.0 .. .. 22 29Uganda 2.4 1.7 14.4 15.3 47 47 0.4 0.3 .. .. 9 1Ukraine 2.8 2.7 7.7 7.0 273 215 1.2 0.9 290 201 .. ..United Arab Emirates 4.2 5.4 .. .. 51 51 2.0 1.0 11 37 2,199 493United Kingdom 2.4 2.6 5.9 5.7 217 174 0.7 0.5 1,039 1,054 27 518United States 4.0 4.8 18.9 17.9 1,546 1,569 1.0 1.0 6,700 8,641 520 893Uruguay 1.3 1.5 5.0 5.0 25 25 1.6 1.5 .. .. 20 36Uzbekistan 0.5 .. .. .. 91 87 0.9 0.7 4 90 .. ..Venezuela, RB 1.4 0.9 5.6 .. 82 115 0.7 0.9 7 40 23 365Vietnam 1.9 2.2 .. .. 495 522 1.1 1.0 12 .. 328 515West Bank and Gaza .. .. .. .. 56 56 7.1 5.7 .. .. 2 4Yemen, Rep. 4.9 4.4 .. .. 138 138 2.6 2.1 .. .. 306 7Zambia 2.0 1.7 8.5 10.0 16 17 0.3 0.3 .. .. 0 ..Zimbabwe 2.3 1.3 .. .. 51 51 0.8 0.8 .. .. 25 ..World 2.5 w 2.6 w 10.8 w 10.0 w 28,539 s 27,994 s 0.9 w 0.9 w .. s .. s 20,973 s 24,960 sLow income 1.6 1.4 12.0 .. 3,330 3,714 1.1 1.0 .. .. .. ..Middle income 2.1 2.0 12.5 12.3 19,030 18,527 0.9 0.8 .. .. 10,757 13,328Lower middle income 2.4 2.1 13.8 12.7 8,635 8,858 0.9 0.9 .. .. 3,334 8,426Upper middle income 2.0 2.0 12.3 12.4 10,395 9,669 0.8 0.7 6,188 7,020 7,423 4,902Low & middle income 2.1 2.0 12.5 12.2 22,360 22,241 0.9 0.8 .. .. 11,031 14,002East Asia & Pacifi c 1.9 1.9 13.9 14.6 7,656 7,005 0.7 0.6 311 1,423 4,096 1,912Europe & Central Asia 2.9 3.0 15.2 11.9 3,398 3,169 1.9 1.6 5,585 6,380 1,152 920Latin America & Carib. 1.3 1.4 .. .. 2,072 2,439 0.8 0.9 .. .. 1,213 1,739Middle East & N. Africa 3.2 3.3 12.5 .. 3,123 3,611 3.3 3.5 .. .. 1,639 2,523South Asia 2.8 2.4 18.7 16.2 4,578 4,480 0.7 0.7 41 4 1,554 6,378Sub-Saharan Africa 1.7 1.6 .. .. 1,533 1,536 0.5 0.5 .. .. 986 530High income 2.6 2.9 10.5 10.0 6,179 5,753 1.1 1.0 14,880 16,758 9,942 10,958Euro area 1.7 1.6 4.5 4.3 1,802 1,606 1.2 1.0 5,473 4,891 1,527 2,620Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2011).a. Estimates differ from offi cial statistics of the government of China, which has published the following estimates: military expenditure as 1.4 percent of GDP in 2005 and 1.5 percent in2009 and 7.3 percent of national government expenditure in 2005 and 6.5 percent in 2009 (see National Bureau of Statistics of China, www.stats.gov.cn).3102012 World Development Indicators

Military expenditures and arms transfers5.7STATES AND MARKETSAbout the dataDefinitionsAlthough national defense is an important functionof government and security from external threatsthat contributes to economic development, highmilitary expenditures for defense or civil confl ictsburden the economy and may impede growth. Dataon military expenditures as a share of gross domesticproduct (GDP) are a rough indicator of the portionof national resources used for military activities andof the burden on the economy. As an “input” measuremilitary expenditures are not directly relatedto the “output” of military activities, capabilities, orsecurity. Comparisons of military spending amongcountries should take into account the many factorsthat infl uence perceptions of vulnerability andrisk, including historical and cultural traditions, thelength of borders that need defending, the quality ofrelations with neighbors, and the role of the armedforces in the body politic.Data on military expenditures are reported tothe United Nations Offi ce for Disarmament Affairs(UNODA) by about 60 governments. Data on militaryexpenditures are not compiled using standard defi nitionsand are often incomplete and unreliable dueto countries’ reluctance to disclose military information.Even in countries where the parliament vigilantlyreviews budgets and spending, military expendituresand arms transfers rarely receive close scrutiny orfull, public disclosure (see Ball 1984 and Happeand Wakeman-Linn 1994). However, the StockholmInternational Peace Research Institute (SIPRI) hasadopted a defi nition of military expenditure derivedfrom the North Atlantic Treaty Organization’s (NATO)former defi nition (in use until 2002; see Defi nitions).The data on military expenditures as a share of GDPare SIPRI estimates. Data on military expendituresas a share of central government expenditures usedata on central government expenditures from theInternational Monetary Fund (IMF). Therefore thedata in the table may differ from comparable datapublished by national governments.SIPRI’s primary source of military expenditure datais offi cial data provided by national governments.These data are derived from budget documents,defense white papers, and other public documentsfrom offi cial government agencies, including governmentresponses to questionnaires sent by SIPRI,the UNODA, or the Organization for Security andCo-operation in Europe. Secondary sources includeinternational statistics, such as those of NATO andthe IMF’s Government Finance Statistics Yearbook.Other secondary sources include country reports ofthe Economist Intelligence Unit, country reports byIMF staff, and specialist journals and newspapers.In the many cases where SIPRI cannot makeindependent estimates, it uses the national dataprovided. Because of the differences in defi nitionsand the diffi culty in verifying the accuracy and completenessof data, data on military expenditures arenot always comparable across countries. However,SIPRI puts a high priority on ensuring that the dataseries for each country is comparable over time.More information on SIPRI’s military expenditure projectcan be found at www.sipri.org/contents/milap/.Data on armed forces refer to military personnel onactive duty, including paramilitary forces. Becausedata exclude personnel not on active duty, theyunderestimate the share of the labor force workingfor the defense establishment. Governments rarelyreport the size of their armed forces, so such datatypically come from intelligence sources.SIPRI’s Arms Transfers Programme collects dataon arms transfers from open sources. Since publiclyavailable information is inadequate for tracking allweapons and other military equipment, SIPRI coversonly what it terms major conventional weapons. Datacover the supply of weapons through sales, aid, gifts,and manufacturing licenses; therefore the term armstransfers rather than arms trade is used. SIPRI dataalso cover weapons supplied to or from rebel forcesin an armed conflict as well as arms deliveries forwhich neither the supplier nor the recipient can beidentified with acceptable certainty; these data areavailable in SIPRI’s database.SIPRI’s estimates of arms transfers are designedas a trend-measuring device in which similar weaponshave similar values, reflecting both the quantityand quality of weapons transferred. The estimatedvalues do not reflect financial value (payments forweapons transferred) because reliable data on thevalue of the transfer are not available, and evenwhen values are known, the transfer usually includesmore than the actual conventional weapons, such asspares, support systems, and training, and details ofthe financial arrangements (such as credit and loanconditions and discounts) are usually not known.Given these measurement issues, SIPRI’s methodof estimating the transfer of military resourcesincludes an evaluation of the technical parametersof the weapons. Weapons for which a price is notknown are compared with the same weapons forwhich actual acquisition prices are available (coreweapons) or for the closest match. These weaponsare assigned a value in an index that reflects theirmilitary resource value in relation to the core weapons.These matches are based on such characteristicsas size, performance, and type of electronics,and adjustments are made for secondhand weapons.More information on SIPRI’s Arms TransfersProgramme is available at www.sipri.org/research/armaments/transfers.• Military expenditures are SIPRI data derived fromNATO’s former defi nition, (in use until 2002) whichincludes all current and capital expenditures onthe armed forces, including peacekeeping forces;defense ministries and other government agenciesengaged in defense projects; paramilitary forces, ifjudged to be trained and equipped for military operations;and military space activities. Such expendituresinclude military and civil personnel, includingretirement pensions and social services for militarypersonnel; operation and maintenance; procurement;military research and development; and military aid(in the military expenditures of the donor country).Excluded are civil defense and current expendituresfor previous military activities, such as for veteransbenefits, demobilization, and weapons conversionand destruction. This defi nition cannot be appliedfor all countries, however, since that would requiremore detailed information than is available about militarybudgets and off-budget military expenditures (forexample, whether military budgets cover civil defense,reserves and auxiliary forces, police and paramilitaryforces, and military pensions). • Armed forces personnelare active duty military personnel, includingparamilitary forces if the training, organization, equipment,and control suggest they may be used to supportor replace regular military forces. Reserve forces,which are not fully staffed or operational in peacetime, are excluded. The data also exclude civilians inthe defense establishment and so are not consistentwith the data on military expenditures on personnel.• Arms transfers cover the supply of military weaponsthrough sales, aid, gifts, and manufacturing licensesand are based on actual deliveries only. Weaponsmust be transferred voluntarily by the supplier, havea military purpose, and be destined for the armedforces, paramilitary forces, or intelligence agenciesof another country. Data cover major conventionalweapons such as aircraft, armored vehicles, artillery,radar systems and other sensors, missiles, andships designed for military use as well as some majorcomponents such as turrets for armored vehicles andengines. Excluded are other military equipment suchas most small arms and light weapons, trucks, smallartillery, ammunition, support equipment, technologytransfers, and other services.Data sourcesData on military expenditures are from SIPRI’s MilitaryExpenditure Database (www.sipri.org/databases/milex).Data on armed forces personnelare from the International Institute for StrategicStudies’ The Military Balance 2012. Data on armstransfers are from SIPRI’s Arms Transfers Database(www.sipri.org/databases/armstransfers).2012 World Development Indicators 311

5.8Fragile situationsInternationalDevelopmentAssociationResourceAllocationIndex1–6(low to high)2010Peacebuilding andpeacekeepingOperationname aDecember2011Troops, police,and militaryobserversnumberBattlerelateddeathsIntentionalhomicidesper 100,000peopleCombinedsourceestimates bMilitaryexpendituresnumber% of GDPDecember2011 2000–10 c 2008 2010SurveyyearBusiness environmentLosses due totheft, robbery,vandalism,and arson% of salesFirms formallyregistered whenoperationsstartedAfghanistan 2.6 UNAMA 15 35,460 2.4 1.8 2008 1.5 88.0Angola 2.8 .. 2,620 19.0 4.4 2010 1.5 62.7Bosnia and Herzegovina 3.7 .. .. 1.7 1.4 2009 0.4 98.6Burundi 3.1 BNUB 1 6,413 21.7 3.8 2006 1.1 ..Central African Republic 2.8 BINUCA 4 544 29.3 2.6 .. ..Chad 2.4 .. 4,238 15.8 3.0 2009 2.5 77.1Comoros 2.5 .. .. 12.2 .. .. ..Congo, Dem. Rep. 2.7 MONUSCO 18,928 6,348 21.7 1.4 2010 1.8 61.9Congo, Rep. 2.9 .. 167 30.8 1.1 2009 3.3 84.3Côte d’Ivoire 2.7 UNOCI 10,999 809 56.9 1.6 2009 3.4 56.4Eritrea 2.2 .. 25,057 17.8 .. 2009 0.0 100.0Georgia 4.4 .. 648 4.1 h 3.9 2008 0.7 99.6Guinea 2.8 .. 647 22.5 .. 2006 2.0 ..Guinea-Bissau 2.7 UNIOGBIS 17 0 20.2 .. 2006 1.1 ..Haiti 2.9 MINUSTAH 11,611 244 6.9 h .. .. ..Iraq .. UNAMI 361 24,088 2.0 6.0 .. ..Kiribati 3.0 .. .. 7.3 .. .. ..Kosovo 3.4 UNMIK 16 .. .. .. 2009 0.3 89.2Liberia 2.9 UNMIL 9,206 2,427 10.1 0.8 2009 2.8 73.8Marshall Islands .. .. .. .. .. .. ..Micronesia, Fed. Sts. .. .. .. 0.9 .. 2009 2.1 96.9Myanmar .. .. 1,590 10.2 .. .. ..Nepal 3.3 .. 9,418 2.8 i 1.5 2009 0.9 94.0Sierra Leone 3.3 .. 156 14.9 2.3 2009 0.8 89.2Solomon Islands 2.8 RAMSI 468 .. 3.7 .. .. ..Somalia .. UNPOS 6 7,574 1.5 .. .. ..Sudan 2.4 .. 17,658 24.2 .. .. ..Timor-Leste 3.0 UNMIT 1,216 .. 6.9 2.8 2009 2.7 91.8Togo 2.9 .. .. 10.9 1.8 2009 2.4 75.8West Bank and Gaza .. .. .. .. .. 2006 1.2 ..Western Sahara m .. MINURSO 228 .. .. .. .. ..Yemen, Rep. 3.2 .. 259 4.2 i 4.4 2010 0.6 81.7Zimbabwe 2.0 .. .. 14.3 1.3 .. ..Fragile situations .. 82,763 s 15.8 w 3.8 wLow income .. .. 17.5 1.4% of fi rmsNote: The countries and territories with fragile situations in the table are primarily International Development Association-eligible countries and nonmember or inactive countries andterritories that have a 3.2 or lower harmonized average of the World Bank’s Country Policy and Institutional Assessment rating and the corresponding rating by a regional developmentbank or that have had a UN or regional peacebuilding and political mission (for example by the African Union, European Union, or Organization of American States) or peacekeepingmission (for example, by the African Union, European Union, North Atlantic Treaty Organization, or Organization of American States) during the last three years. This defi nition is pursuantto an agreement among the World Bank and other multilateral development banks at the start of the International Development Association 15 round in 2007. The list of countries andterritories with fragile situations is imperfect and is used here to refl ect a complex concept. The World Bank continues to work with partners and client countries to refi ne the concept.a. UNAMA is United Nations Assistance Mission in Afghanistan, BNUB is United Nations Offi ce in Burundi, BINUCA is United Nations Integrated Peacebuilding Offi ce in the Central AfricanRepublic, MONUSCO is United Nations Organization Stabilization Mission in the Democratic Republic of the Congo, UNOCI is United Nations Operation in Côte d’Ivoire, UNIOGBIS isUnited Nations Integrated Peacebuilding Offi ce in Guinea-Bissau, MINUSTAH is United Nations Stabilization Mission in Haiti, UNAMI is United Nations Assistance Mission for Iraq, UNMIKis Interim Administration Mission in Kosovo, UNMIL is United Nations Mission in Liberia, RAMSI is Regional Assistance Mission to Solomon Islands, UNPOS is United Nations PoliticalOffi ce for Somalia, UNMIT is United Nations Integrated Mission in Timor-Leste, and MINURSO is United Nations Mission for the Referendum in Western Sahara. b. Best estimate fromthe UN Offi ce on Drug and Crime’s Intentional Homicide Statistics database. The combined source estimate uses both public health and law enforcement and criminal justice sources.c. Total over the period. d. Data are for the most recent year available. e. Average over the period. f. Covers only Angola-secured territory. g. Covers children ages 10–14. h. Data are for2010. i. Data are for 2009. j. Northern Sudan only. k. Figure represents the Internal Displacement Monitoring Centre’s (IDMC) high estimate; the low estimates is 4,500,000. l. IncludesPalestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East, who are not included in data from the Offi ce of the UnitedNations High Commissioner for Refugees. m. The designation Western Sahara is used instead of Former Spanish Sahara (the designation used on the maps on the inside front and backcover fl aps) because it is the designation used by the UN operation established there by Security Council resolution 690/1991. Neither designation expresses any World Bank view on thestatus of the territory so-identifi ed. n. Figure represents IDMC’s high estimate; the low estimate is 570,000.3122012 World Development Indicators

Children inemploymentSurveyyear% ofchildrenages 7–14RefugeesInternallydisplacedpersonsFragile situations 5.8Accessto animprovedwatersourceAccess toimprovedsanitationfacilitiesMaternal mortalityratioUnder-fivemortalityrateDepth ofhungerPrimarygrossenrollmentratioper 100,000 live birthskilocaloriesBy country By country% of % of National Modeled per 1,000 per person % of relevantof origin of asylum number population population estimates estimates live births per day age group2010 2010 2010 2010 2010 2005–10 d 2008 2010 2006–08 e 2010STATES AND MARKETSAfghanistan .. 3,054,709 6,434 352,000 50 37 .. 1,400 149 .. 97Angola 2001 30.1 f 134,858 15,155 .. 51 58 .. 610 161 320 124Bosnia and Herzegovina 2006 10.6 63,004 7,016 113,400 99 95 3 9 8 140 88Burundi 2005 11.7 84,064 29,365 100,000 72 46 620 970 142 390 156Central African Republic 2000 67.0 164,905 21,574 192,000 67 34 540 850 159 300 93Chad 2004 60.4 53,733 347,939 171,000 51 13 .. 1,200 173 320 90Comoros .. 368 .. .. 95 36 .. 340 86 300 104Congo, Dem. Rep. 2000 39.8 g 476,693 166,336 1,700,000 45 24 550 670 170 .. 94Congo, Rep. 2005 30.1 20,679 133,112 7,800 71 18 780 580 93 230 115Côte d’Ivoire 2006 45.7 41,758 26,218 621,000 80 24 540 470 123 230 88Eritrea .. 222,460 4,809 10,000 61 14 .. 280 61 350 45Georgia 2006 31.8 10,640 639 258,000 98 95 52 48 22 160 109Guinea 1994 48.3 11,985 14,113 .. 74 18 980 680 130 260 94Guinea-Bissau 2006 50.5 1,127 7,679 .. 64 20 410 1,000 150 250 123Haiti 2005 33.4 25,892 3 .. 69 17 630 300 165 420 ..Iraq 2006 14.7 1,683,579 34,655 2,800,000 79 73 84 75 39 .. ..Kiribati .. 33 .. .. .. .. .. .. 49 180 113Kosovo .. .. .. 18,300 .. .. .. .. .. .. ..Liberia 2007 37.4 70,129 24,743 .. 73 18 990 990 103 330 96Marshall Islands .. .. .. .. 94 75 .. .. 26 .. 102Micronesia, Fed. Sts. .. .. 1 .. .. .. .. .. 42 .. ..Myanmar .. 415,670 .. 446,000 83 76 320 240 66 .. 126Nepal 1999 47.2 5,889 89,808 50,000 89 31 280 380 50 220 ..Sierra Leone 2007 14.9 11,275 8,363 .. 55 13 860 970 174 340 125Solomon Islands .. 75 .. .. .. .. .. 100 27 190 ..Somalia 2006 43.5 770,154 1,937 1,500,000 29 23 1,000 1,200 180 .. ..Sudan 2000 19.1 j 387,288 178,308 5,200,000 k 58 26 1,100 750 103 240 73Timor-Leste .. 8 1 400 69 47 560 370 81 260 117Togo 2006 38.7 18,330 14,051 .. 61 13 .. 350 103 280 140West Bank and Gaza .. 93,323 1,910,677 l 160,000 85 92 .. .. 22 190 91Western Sahara m .. .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 2006 18.3 2,076 190,092 250,000 55 53 .. 210 77 260 87Zimbabwe 1999 14.3 24,089 4,435 1,000,000 n 80 40 730 790 80 300 ..Fragile situations 7,848,793 s 3,237,459 s 14,328,500 s 65 w 41 w .. 650 w 120 w 271 w 102 wLow income 5,650,811 1,873,979 7,113,000 65 37 .. 590 108 281 104About the dataThe table focuses on countries and territories withfragile situations and highlights the links among weakinstitutions, poor development outcomes, fragility,and risk of confl ict. Many of these countries and territorieshave weak institutions that are ill-equippedto handle economic shocks, natural disasters, andillegal trade or to resist confl ict, which increasinglyspills across borders. Organized violence, includingviolent crime, interrupts economic and social developmentthrough lost human and social capital, disruptedservices, displaced populations, and reducedconfi dence for future investment. As a result, countrieswith fragile situations achieve lower developmentoutcomes and make slower progress towardthe Millennium Development Goals.According to the Geneva Declaration on Armed Violenceand Development, more than 526,000 peopledie each year because of the violence associated witharmed confl ict and large- and small-scale criminality.Recovery and rebuilding can take years, and thechallenges are numerous: infrastructure to be rebuilt,persistently high crime, widespread health problems,education systems in disrepair, and unexploded ordnanceto be cleared. Most countries emerging fromconfl ict lack the capacity to rebuild the economy.Thus, capacity building is one of the fi rst tasks forrestoring growth and is linked to building peace andcreating the conditions that lead to sustained povertyreduction. The World Bank and other internationaldevelopment agencies can help, but countries withfragile situations have to build their own institutionstailored to their own needs. Peacekeeping operationsin post-confl ict situations have been effectivein reducing the risks of reversion to confl ict.The countries and territories with fragile situationsin the table are primarily International DevelopmentAssociation– eligible countries and nonmember orinactive countries or territories of the World Bank thathave a 3.2 or lower harmonized average of the WorldBank’s Country Policy and Institutional Assessmentrating and the corresponding rating by a regionaldevelopment bank or that have had a UN or regionalpeacebuilding mission (for example, by the AfricanUnion, European Union, or Organization of AmericanStates) or peacekeeping mission (for example, by the2012 World Development Indicators 313

5.8 Fragile situationsAbout the data (continued)African Union, European Union, North Atlantic TreatyOrganization [NATO], or Organization of AmericanStates) during the last three years. Peacebuildingand peacekeeping involve many elements—military,police, and civilian—working together to lay the foundationsfor sustainable peace. The list of countriesand territories with fragile situations is imperfect andis used here to refl ect a complex concept. The WorldBank continues to work with partners and client countriesto refi ne the concept.An armed confl ict is a contested incompatibilitythat concerns a government or territory where theuse of armed force between two parties (one ofthem the government) results in at least 25 battlerelateddeaths in a calendar year. There were 30active armed confl icts in 2010. This table presentsdata from the Uppsala Confl ict Data Program (UCDP)Battle-Related Deaths Dataset (v.5 2011), whichfocuses on the incompatibility and lists the country,as well as the battle location and territory wherebattle-related deaths are reported. When more thanone country is listed in the dataset, the assignmentof battle-related deaths in the table is determined bythe battle location. See country footnotes for additionaldetails.Data on intentional homicides are from the UnitedNations Offi ce on Drugs and Crime (UNODC), whichuses a variety of national and international sourceson homicides—primarily criminal justice sourcesas well as public health data from the World HealthOrganization (WHO) and the Pan American HealthOrganization—and the United Nations Survey ofCrime Trends and Operations of Criminal JusticeSystems to present accurate and comparable statistics.The UNODC defi nes homicide as “unlawfuldeath purposefully infl icted on a person by anotherperson.” This defi nition excludes deaths arising fromarmed confl ict. For additional information, see theUNODC Homicide Statistics database at www.unodc.org/unodc/en/data-and-analysis/homicide.html.Data on military expenditures reported by governmentsare not compiled using standard defi nitionsand are often inconsistent across countries, incomplete,and unreliable. Even in countries where theparliament vigilantly reviews budgets and spending,military expenditures and arms transfers rarelyreceive close scrutiny or full public disclosure.Data are from the Stockholm International PeaceResearch Institute (SIPRI), which uses NATO’s formerdefi nition of military expenditure (in use until 2002;see Defi nitions). Therefore, the data in the table maydiffer from comparable data published by nationalgovernments. For a more detailed discussion of militaryexpenditures, see About the data for table 5.7.Along with public sector efforts, private sector developmentand investment, especially in competitive markets,has tremendous potential to contribute to growthand poverty reduction. The World Bank’s EnterpriseSurveys review the business environment, assessingconstraints to private sector growth and enterpriseperformance. Crime, theft, and disorder imposecosts on businesses and society. And in many developingcountries informal businesses operate withoutlicenses. These fi rms have less access to fi nancialand public services and can engage in fewer types ofcontracts and investments, constraining growth. Thetable presents data on the loss of sales due to theft,robbery, vandalism, and arson and on the percentageof firms operating informally. For further information onenterprise surveys, see About the data for table 5.2.As the table shows, the human toll of armed violenceacross various contexts is severe. Additionally,in countries with fragile situations weak institutionalcapacity often results in poor performance and failureto meet expectations of effective service delivery.Failure to deliver water, health, and educationservices can weaken struggling governments. Thetable includes several indicators related to living conditionsin fragile situations: children in employment,refugees, internally displaced persons, access towater and sanitation, maternal and under-fi ve mortality,depth of hunger, and primary school enrollment.For more detailed information on these indicators,see About the data for table 2.6 (children in employment),table 6.14 (refugees), table 2.18 (access toimproved water and sanitation), table 2.19 (maternalmortality), table 2.23 (under-fi ve mortality), and table2.12 (primary school enrollment).Definitions• International Development Association ResourceAllocation Index is from the Country Policy and InstitutionalAssessment rating, which is the averagescore of four clusters of indicators designed to measuremacroeconomic, governance, social, and structuraldimensions of development: economic management,structural policies, policies for social inclusionand equity, and public sector management and institutions(see table 5.9). Countries are rated on a scaleof 1 (low) to 6 (high). • Peacebuilding and peacekeepingrefer to operations that engage in peacebuilding(reducing the risk of lapsing or relapsing into confl ictby strengthening national capacities for confl ict managementand laying the foundation for sustainablepeace and development) or peacekeeping (providingessential security to preserve the peace where fi ghtinghas been halted and to assist in implementingagreements achieved by the peacemakers). UNpeacekeeping operations are authorized by the UNSecretary-General and planned, managed, directed,and supported by the United Nations Department ofPeacekeeping Operations and the Department ofField Support. The UN Charter gives the SecurityCouncil primary responsibility for maintaining internationalpeace and security, including the establishmentof a UN peacekeeping operation. • Troops, police,and military observers in peacebuilding and peacekeepingare people active in peacebuilding andpeacekeeping as part of an offi cial operation. Peacekeepersdeploy to war-torn regions where no one elseis willing or able to go to prevent confl ict from returningor escalating. • Battle-related deaths are deathsof members of warring parties in battle-related conflicts. Typically, battle-related deaths occur in warfareinvolving the armed forces of the warring parties(battlefi eld fi ghting, guerrilla activities, and all kindsof bombardments of military units, cities, and villages).The targets are usually the military and itsinstallations or state institutions and state representatives,but there is often substantial collateral damageof civilians killed in crossfi re, indiscriminatebombings, and other military activities. All deaths—civilian as well as military— incurred in such situationsare counted as battle-related deaths. • Intentionalhomicides are estimates of unlawful homicidespurposely infl icted as a result of domestic disputes,interpersonal violence, violent confl icts over landresources, intergang violence over turf or control, andpredatory violence and killing by armed groups. Intentionalhomicide does not include all intentional killing;the difference is usually in the organization of thekilling. Individuals or small groups usually commithomicide, whereas killing in armed confl ict is usuallycommitted by fairly cohesive groups of up to severalhundred members and is thus usually excluded. • Militaryexpenditures are SIPRI data derived from NATO’sformer definition (in use until 2002), which includesall current and capital expenditures on the armedforces, including peacekeeping forces; defense ministriesand other government agencies engaged indefense projects; paramilitary forces, if judged to betrained and equipped for military operations; and militaryspace activities. Such expenditures include militaryand civil personnel, including retirement pensionsand social services for military personnel; operationand maintenance; procurement; military research and3142012 World Development Indicators

Fragile situations5.8STATES AND MARKETSdevelopment; and military aid (in the military expendituresof the donor country). Excluded are civildefense and current expenditures for previous militaryactivities, such as for veterans benefits, demobilization,and weapons conversion and destruction. Thisdefi nition cannot be applied to all countries, however,since the necessary detailed information is missingin some cases for military budgets and off-budgetmilitary expenditures (for example, whether militarybudgets cover civil defense, reserves and auxiliaryforces, police and paramilitary forces, and militarypensions). • Survey year is the year in which theunderlying data were collected. • Losses due to theft,robbery, vandalism, and arson are the estimatedlosses from those causes that occurred on businessestablishment premises calculated as a percentageof annual sales. • Firms formally registered whenoperations started are fi rms formally registered whenthey started operations in the country. • Children inemployment are children involved in any economicactivity for at least one hour in the reference week ofthe survey. • Refugees are people who are recognizedas refugees under the 1951 Convention Relating tothe Status of Refugees or its 1967 Protocol, the 1969Organization of African Unity Convention Governingthe Specifi c Aspects of Refugee Problems in Africa,people recognized as refugees in accordance with theUN Refugee Agency (UNHCR) statute, people grantedrefugee-like humanitarian status, and people providedtemporary protection. Asylum seekers—peoplewho have applied for asylum or refugee status andwho have not yet received a decision, or who are registeredas asylum seekers—are excluded. Palestinianrefugees are people (and their descendants) whoseresidence was Palestine between June 1946 and May1948 and who lost their homes and means of livelihoodas a result of the 1948 Arab-Israeli confl ict.• Country of origin refers to the nationality or countryof citizenship of a claimant. • Country of asylum isthe country where an asylum claim was fi led andgranted. • Internally displaced persons are people orgroups of people who have been forced or obliged tofl ee or to leave their homes or places of habitual residence,in particular as a result of armed confl ict, or toavoid the effects of armed confl ict, situations of generalizedviolence, violations of human rights, or naturalor human-made disasters and who have notcrossed an international border. • Access to animproved water source refers to people with reasonableaccess to water from an improved source, suchas piped water into a dwelling, public tap, tubewell,protected dug well, and rainwater collection.Reasonable access is the availability of at least 20liters a person a day from a source within 1 kilometerof the dwelling. • Access to improved sanitationfacilities refers to people with at least adequateaccess to excreta disposal facilities that can effectivelyprevent human, animal, and insect contact withexcreta. Improved facilities range from protected pitlatrines to fl ush toilets. • Maternal mortality ratio isthe number of women who die from pregnancy-relatedcauses during pregnancy and childbirth per 100,000live births. National estimates are based on nationalsurveys, vital registration records, and surveillancedata or are derived from community and hospitalrecords. Modeled estimates are based on an exerciseby the WHO, United Nations Children’s Fund (UNICEF),United Nations Population Fund (UNFPA), and theWorld Bank. See About the data for table 2.19 forfurther details. • Under-five mortality rate is the probabilityof a child born in a specifi c year dying beforereaching age 5, if subject to the age-specifi c mortalityrate of that year. The probability is derived from lifetables and expressed as rate per 1,000 live births.• Depth of hunger, or the intensity of food deprivation,indicates how much people who are fooddeprivedfall short of minimum food needs in terms ofdietary energy. It is measured by comparing the averageamount of dietary energy that undernourishedpeople get from the foods they eat with the minimumamount of dietary energy they need to maintain bodyweight and undertake light activity. Depth of hungeris low when it is less than 200 kilocalories per personper day and high when it is above 300. • Primarygross enrollment ratio is the ratio of total enrollment,regardless of age, to the population of the age groupthat offi cially corresponds to the primary level of education.Primary education provides children with basicreading, writing, and mathematics skills along with anelementary understanding of such subjects as history,geography, natural science, social science, art,and music.Data sourcesData on the International Development AssociationResource Allocation Index are from the WorldBank Group’s International Development Associationdatabase (www.worldbank.org/ida). Data onpeacebuilding and peacekeeping operations arefrom the UN Department of Peacekeeping Operations.Data on battle-related deaths are from theUCDP Battle-Related Deaths Dataset (v.5-2011)1989-2010. Data on intentional homicides arefrom the UNODC’s International Homicide Statisticsdatabase (www.unodc.org/unodc/en/dataand-analysis/homicide.html).Data on militaryexpenditures are from SIPRI’s Military Expendituredatabase (www.sipri.org/databases/milex).Data on the business environment are from theWorld Bank’s Enterprise Surveys (www.enterprisesurveys.org).Data on children in employment areestimates produced by the Understanding Children’sWork project based on household surveydata sets made available by the InternationalLabour Organization’s International Programmeon the Elimination of Child Labour under its StatisticalMonitoring Programme on Child Labour,UNICEF under its Multiple Indicator Cluster Surveyprogram, the World Bank under its Living StandardsMeasurement Study program, and nationalstatistical offi ces (see table 2.6). Data on refugeesare from the UNHCR’s Statistical Yearbook2010, complemented by statistics on Palestinianrefugees under the mandate of the United NationsRelief and Works Agency for Palestine Refugees inthe Near East as published on its website (www.unrwa.org). Data on internally displaced personsare from the Internal Displacement MonitoringCentre. Data on access to water and sanitationare from the WHO and UNICEF’s Progress on DrinkingWater and Sanitation (2012). National estimatesof maternal mortality are from UNICEF’sThe State of the World’s Children 2009 and Childinfoand MEASURE DHS Demographic and HealthSurveys by ICF International. Modeled estimatesfor maternal mortality are from WHO, UNICEF,UNFPA, and the World Bank’s Trends in MaternalMortality: 1990–2008 (2010). Data on under-fi vemortality estimates by the UN Inter-agency Groupfor Child Mortality Esti mation (which comprisesUNICEF, WHO, the World Bank, United NationsPopulation Division, and other universities andresearch institutes) and are based mainly onhousehold surveys, censuses, and vital registrationdata, supplemented by the World Bank’sHuman Development Network estimates basedon vital registration and sample registration data(see table 2.23). Data on depth of hunger arefrom the Food and Agriculture Organization’s FoodSecurity Statistics database (www.fao.org/economic/ess/ess-fs/fs-data/ess-fadata/en/).Dataon primary gross enrollment are from the UnitedNations Educational, Scientifi c, and Cultural Organization’sInstitute for Statistics.2012 World Development Indicators 315

5.9 Public policies and institutionsInternationalDevelopmentAssociationResourceAllocationIndex1–6(low to high)MacroeconomicmanagementEconomic management1–6 (low to high)FiscalpolicyDebtpolicy Average TradeStructural policies1–6 (low to high)FinancialsectorBusinessregulatoryenvironmentAverage2010 2010 2010 2010 2010 2010 2010 2010 2010Afghanistan 2.6 3.5 3.0 2.5 3.0 3.0 2.0 2.5 2.5Angola 2.8 3.0 3.0 3.0 3.0 4.0 2.5 2.0 2.8Armenia 4.1 4.5 5.0 4.5 4.7 4.5 3.5 4.0 4.0Azerbaijan 3.7 4.0 4.5 4.5 4.3 3.5 3.0 4.0 3.5Bangladesh 3.5 4.0 4.0 4.0 4.0 3.0 3.5 3.5 3.3Benin 3.5 4.0 3.0 3.5 3.5 4.0 3.5 3.5 3.7Bhutan 3.9 4.5 4.5 4.5 4.5 3.0 3.0 3.5 3.2Bolivia 3.7 4.0 4.0 4.5 4.2 5.0 3.5 2.5 3.7Bosnia and Herzegovina 3.7 4.0 3.5 4.0 3.8 4.0 4.0 4.0 4.0Burkina Faso 3.8 4.5 4.5 4.0 4.3 4.0 3.0 3.5 3.5Burundi 3.1 3.5 3.5 3.0 3.3 4.0 2.5 2.5 3.0Cambodia 3.4 4.5 4.0 3.5 4.0 4.0 2.5 3.5 3.3Cameroon 3.2 4.0 3.5 3.0 3.5 3.5 3.0 3.0 3.2Cape Verde 4.1 4.5 4.5 4.0 4.3 4.0 4.0 3.5 3.8Central African Republic 2.8 3.5 3.5 3.0 3.3 3.5 2.5 2.0 2.7Chad 2.4 2.5 2.5 2.5 2.5 3.0 2.5 2.0 2.5Comoros 2.5 3.0 2.0 2.0 2.3 3.5 2.5 2.5 2.8Congo, Dem. Rep. 2.7 3.5 3.5 3.0 3.3 3.0 2.0 2.0 2.3Congo, Rep. 2.9 3.5 3.0 3.0 3.2 3.5 3.0 2.5 3.0Côte d’Ivoire 2.7 3.5 2.5 2.0 2.7 4.0 3.0 3.0 3.3Djibouti 3.2 3.5 3.0 2.5 3.0 4.0 3.0 3.5 3.5Dominica 3.8 4.0 4.5 3.0 3.8 4.0 3.5 4.5 4.0Eritrea 2.2 2.0 2.0 1.5 1.8 1.5 1.0 2.0 1.5Ethiopia 3.4 3.5 4.0 3.5 3.7 3.0 3.0 3.5 3.2Gambia, The 3.4 4.0 3.5 3.0 3.5 3.5 3.5 3.5 3.5Georgia 4.4 4.5 4.5 5.0 4.7 6.0 3.5 5.5 5.0Ghana 3.9 3.5 3.5 4.0 3.7 4.0 4.0 4.5 4.2Grenada 3.8 3.5 3.0 3.0 3.2 4.5 4.0 4.5 4.3Guinea 2.8 2.5 2.5 2.0 2.3 4.0 3.0 2.5 3.2Guinea-Bissau 2.7 3.0 2.5 2.0 2.5 4.0 2.5 2.5 3.0Guyana 3.4 3.5 3.5 4.0 3.7 4.0 3.5 3.0 3.5Haiti 2.9 4.0 3.5 2.5 3.3 4.0 3.0 2.5 3.2Honduras 3.6 3.5 3.5 4.0 3.7 4.5 3.0 3.5 3.7India 3.7 4.5 3.5 4.0 4.0 3.5 4.0 3.5 3.7Kenya 3.8 4.5 4.0 4.0 4.2 4.0 4.0 4.0 4.0Kiribati 3.0 2.5 3.0 3.5 3.0 3.0 3.0 3.0 3.0Kosovo 3.4 3.5 3.0 3.5 3.3 5.0 3.5 3.5 4.0Kyrgyz Republic 3.7 4.5 4.0 4.0 4.2 5.0 3.0 3.5 3.8About the dataThe International Development Association (IDA) is thepart of the World Bank Group that helps the poorestcountries reduce poverty by providing con cessional loansand grants for programs aimed at boosting economicgrowth and improving living con ditions. IDA funding helpsthese countries deal with the complex challenges theyface in meeting the Millennium Development Goals.The World Bank’s IDA Resource Allocation Index(IRAI), presented in the table, is based on the resultsof the annual Country Policy and Insti tutional Assessment(CPIA) exercise, which covers the IDA-eligiblecountries. The table does not include Myanmar andSomalia because they were not rated in the 2010exercise even though they are IDA eligible. Countryassessments have been carried out annually sincethe mid-1970s by World Bank staff. Over time thecriteria have been revised from a largely macroeconomicfocus to include governance aspects anda broader coverage of social and structural dimensions.Country performance is assessed against aset of 16 criteria grouped into four clusters: economicmanagement, structural policies, policiesfor social inclusion and equity, and public sectormanagement and institutions. IDA resources areallocated to a country on per capita terms basedon its IDA country performance rating and, to a limitedextent, based on its per capita gross nationalincome. This ensures that good performers receivea higher IDA allocation in per capita terms. The IRAIis a key element in the country performance rating.The CPIA exercise is intended to capture the qual ityof a country’s policies and institutional arrange ments,focusing on key elements that are within the country’scontrol, rather than on outcomes (such as economicgrowth rates) that are infl uenced by events beyondthe country’s control. More specifi cally, the CPIAmeasures the extent to which a country’s policy andinstitutional framework supports sustainable growthand poverty reduction and, consequently, the effectiveuse of development assistance.All criteria within each cluster receive equal weight,and each cluster has a 25 percent weight in the3162012 World Development Indicators

InternationalDevelopmentAssociationResourceAllocationIndex1–6(low to high)Public policies and institutions 5.9MacroeconomicmanagementEconomic management1–6 (low to high)FiscalpolicyDebtpolicy Average TradeStructural policies1–6 (low to high)FinancialsectorBusinessregulatoryenvironmentAverage2010 2010 2010 2010 2010 2010 2010 2010 2010STATES AND MARKETSLao PDR 3.3 4.0 4.0 3.0 3.7 3.5 2.0 3.0 2.8Lesotho 3.5 4.0 3.5 4.0 3.8 3.5 3.0 3.0 3.2Liberia 2.9 3.5 3.5 3.0 3.3 3.0 2.5 3.0 2.8Madagascar 3.4 3.5 3.0 4.0 3.5 4.0 3.0 3.0 3.3Malawi 3.3 3.0 3.5 3.0 3.2 3.5 3.0 3.0 3.2Maldives 3.4 2.5 2.0 2.5 2.3 4.0 3.0 4.0 3.7Mali 3.6 4.5 4.0 4.0 4.2 4.0 3.0 3.5 3.5Mauritania 3.2 3.5 3.0 3.5 3.3 4.0 2.5 3.0 3.2Moldova 3.7 3.5 3.5 4.0 3.7 4.5 3.5 3.5 3.8Mongolia 3.4 3.5 3.0 3.5 3.3 4.5 2.5 3.5 3.5Mozambique 3.7 4.5 4.5 4.5 4.5 4.5 3.5 3.0 3.7Nepal 3.3 3.5 4.0 3.0 3.5 3.5 3.0 3.0 3.2Nicaragua 3.7 4.0 4.0 4.5 4.2 4.5 3.0 3.5 3.7Niger 3.4 4.0 3.5 4.0 3.8 4.0 3.0 3.0 3.3Nigeria 3.4 4.0 4.0 4.5 4.2 3.5 3.5 3.5 3.5Pakistan 3.1 2.5 2.5 3.5 2.8 3.5 3.5 3.5 3.5Papua New Guinea 3.3 4.0 3.5 4.5 4.0 4.5 3.0 3.0 3.5Rwanda 3.8 4.0 4.0 3.5 3.8 4.0 3.5 4.0 3.8Samoa 4.1 4.0 4.0 5.0 4.3 5.0 4.0 3.5 4.2São Tomé and Príncipe 3.0 3.0 3.0 2.5 2.8 4.0 2.5 2.5 3.0Senegal 3.7 4.0 4.0 4.0 4.0 4.0 3.5 4.0 3.8Sierra Leone 3.3 4.0 3.5 3.5 3.7 3.5 3.0 3.0 3.2Solomon Islands 2.8 3.5 2.5 3.0 3.0 3.0 3.0 2.5 2.8Sri Lanka 3.5 3.0 3.0 3.5 3.2 3.5 4.0 4.0 3.8St. Lucia 3.8 4.0 3.5 3.5 3.7 4.0 3.5 4.5 4.0St. Vincent & Grenadines 3.8 4.0 3.5 3.5 3.7 4.0 3.5 4.5 4.0Sudan 2.4 3.5 3.0 1.5 2.7 2.5 2.5 2.5 2.5Tajikistan 3.3 4.0 3.5 3.5 3.7 4.0 2.5 3.0 3.2Tanzania 3.8 4.5 4.0 4.0 4.2 4.0 4.0 3.5 3.8Timor-Leste 3.0 3.0 3.5 4.0 3.5 4.5 2.5 1.5 2.8Togo 2.9 3.0 3.0 3.0 3.0 4.0 2.5 3.0 3.2Tonga 3.5 3.0 3.0 2.5 2.8 5.0 3.5 3.0 3.8Uganda 3.8 4.5 4.0 4.5 4.3 4.0 3.5 4.0 3.8Uzbekistan 3.4 4.0 4.0 4.0 4.0 2.5 3.0 3.0 2.8Vanuatu 3.4 4.0 3.5 4.5 4.0 3.0 3.0 3.5 3.2Vietnam 3.8 4.0 4.5 4.0 4.2 3.5 3.0 3.5 3.3Yemen, Rep. 3.2 3.5 3.0 3.0 3.2 4.5 2.5 3.5 3.5Zambia 3.4 4.0 3.0 3.5 3.5 4.0 3.5 3.5 3.7Zimbabwe 2.0 2.0 2.0 1.0 1.7 3.0 2.0 2.0 2.3over all score, which is obtained by averaging theaverage scores of the four clusters. For each of the16 criteria countries are rated on a scale of 1 (low)to 6 (high). The scores depend on the level of performancein a given year assessed against the criteria,rather than on changes in performance comparedwith the previous year. All 16 CPIA criteria contain adetailed description of each rating level. In assessingcountry performance, World Bank staff evaluatethe country’s performance on each of the criteriaand assign a rating. The ratings refl ect a variety ofindicators, observations, and judgments based oncountry knowl edge and on relevant publicly availableindicators. In interpreting the assessment scores, itshould be noted that the criteria are designed in adevelopmen tally neutral manner. Accordingly, higherscores can be attained by a country that, given itsstage of develop ment, has a policy and institutionalframework that more strongly fosters growth andpoverty reduction.The country teams that prepare the ratings are veryfamiliar with the country, and their assessments arebased on country diagnostic studies prepared by theWorld Bank or other development organizations andon their own professional judgment. An early consultationis conducted with country authorities tomake sure that the assessments are informed byup-to-date infor mation. To ensure that scores areconsistent across countries, the process involvestwo key phases. In the benchmarking phase a smallrepresentative sample of countries drawn from allregions is rated. Country teams prepare proposalsthat are reviewed fi rst at the regional level and thenin a Bankwide review process. A similar process isfollowed to assess the performance of the remainingcountries, using the benchmark countries’ scores asguideposts. The fi nal ratings are determined followinga Bankwide review. The overall numerical IRAI scoreand the separate criteria scores were fi rst publiclydisclosed in June 2006.See IDA’s website at www.worldbank.org/ida formore information.2012 World Development Indicators 317

5.9 Public policies and institutionsPolicies for social inclusion and equity1–6 (low to high)Public sector management and institutions1–6 (low to high)Quality ofbudgetaryandfi n a n c i a lmanagementTransparency,accountability,and corruptionEquityof public Building SocialPolicies andinstitutions forPropertyrights andEffi ciency QualityGender resource human protection environmentalrule-basedof revenue of public in the publicequality use resources and labor sustainability Average governancemobilization administration sector Average2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010Afghanistan 2.0 3.0 3.0 2.5 2.5 2.6 1.5 3.5 3.0 2.0 2.0 2.4Angola 3.5 2.5 2.5 2.5 3.0 2.8 2.0 2.5 2.5 2.5 2.5 2.4Armenia 4.5 4.5 4.0 4.5 3.0 4.1 3.5 4.5 3.5 4.0 3.0 3.7Azerbaijan 4.0 4.0 4.0 4.0 3.0 3.8 3.0 3.5 3.5 3.0 2.5 3.1Bangladesh 4.0 3.5 4.0 3.5 3.0 3.6 3.0 3.0 3.0 3.0 3.0 3.0Benin 3.5 3.5 3.5 3.0 3.5 3.4 3.0 3.5 3.5 3.0 3.5 3.3Bhutan 4.0 4.0 4.5 3.5 4.5 4.1 3.5 3.5 4.0 4.0 4.5 3.9Bolivia 4.0 4.0 4.0 3.5 3.5 3.8 2.5 3.5 4.0 3.0 3.5 3.3Bosnia and Herzegovina 4.5 3.5 3.5 3.5 3.5 3.7 3.0 3.5 4.0 3.0 3.0 3.3Burkina Faso 3.5 4.0 3.5 3.5 3.5 3.6 3.5 4.5 3.5 3.5 3.5 3.7Burundi 4.0 3.5 3.5 3.0 3.0 3.4 2.5 3.0 3.0 2.5 2.0 2.6Cambodia 4.0 3.5 3.5 3.0 3.0 3.4 2.5 3.5 3.0 2.5 2.0 2.7Cameroon 3.0 3.0 3.5 3.0 3.0 3.1 2.5 3.0 3.5 3.0 2.5 2.9Cape Verde 4.5 4.5 4.5 4.5 3.5 4.3 4.0 4.0 3.5 4.0 4.5 4.0Central African Republic 2.5 2.5 2.5 2.0 3.0 2.5 2.0 3.0 2.5 2.5 2.5 2.5Chad 2.5 2.0 2.5 2.5 2.0 2.3 2.0 2.0 2.5 2.5 2.0 2.2Comoros 3.0 2.5 3.0 2.5 2.0 2.6 2.5 2.0 2.5 2.5 2.5 2.4Congo, Dem. Rep. 2.5 3.0 3.5 2.5 2.5 2.8 2.0 2.5 2.5 2.0 2.0 2.2Congo, Rep. 3.0 2.5 3.5 2.5 2.5 2.8 2.5 2.5 3.0 2.5 2.5 2.6Côte d’Ivoire 2.5 2.0 2.5 2.5 2.5 2.4 2.0 2.5 3.5 2.0 2.0 2.4Djibouti 3.0 3.0 3.5 3.5 3.5 3.3 2.5 3.0 3.5 2.5 2.5 2.8Dominica 3.0 3.5 4.0 3.5 3.5 3.5 4.0 4.0 4.0 3.5 4.0 3.9Eritrea 3.5 2.5 3.5 2.5 2.0 2.8 2.5 2.5 3.5 3.0 2.0 2.7Ethiopia 3.0 4.5 4.0 3.5 3.0 3.6 3.0 3.5 3.5 3.5 2.5 3.2Gambia, The 3.5 4.0 3.5 2.5 3.5 3.4 3.0 3.5 3.5 3.0 2.0 3.0Georgia 4.5 4.5 4.5 4.5 3.0 4.2 3.5 4.0 4.5 4.0 3.5 3.9Ghana 4.0 4.0 4.5 4.0 3.5 4.0 3.5 3.5 4.0 3.5 4.0 3.7Grenada 4.5 3.5 4.0 3.5 4.0 3.9 3.5 4.0 3.5 3.5 4.0 3.7Guinea 3.5 3.0 3.0 3.0 2.5 3.0 2.0 3.0 3.0 3.0 2.0 2.6Guinea-Bissau 2.5 3.0 2.5 2.5 3.0 2.7 2.5 2.5 3.0 2.5 2.5 2.6Guyana 3.5 3.5 4.0 3.0 3.0 3.4 3.0 3.5 3.5 2.5 2.5 3.0Haiti 3.0 3.0 2.5 2.5 2.5 2.7 2.0 3.0 2.5 2.5 2.5 2.5Honduras 4.0 4.0 4.0 3.0 3.5 3.7 3.0 3.5 4.0 3.0 3.0 3.3India 3.5 4.0 4.0 3.5 3.5 3.7 3.5 3.5 4.0 3.5 3.5 3.6Kenya 3.5 4.0 4.0 3.5 3.5 3.7 2.5 3.5 4.0 3.5 3.0 3.3Kiribati 2.5 4.0 2.5 3.0 3.0 3.0 3.5 3.0 3.0 3.0 3.0 3.1Kosovo 3.5 3.5 2.5 3.5 3.0 3.2 3.0 4.0 3.5 2.5 3.0 3.2Kyrgyz Republic 4.5 3.5 3.5 3.5 3.0 3.6 2.5 3.5 3.5 3.0 2.5 3.0Definitions• International Development Association ResourceAllocation Index is obtained by calculating the averagescore for each cluster and then by averagingthose scores. For each of 16 cri teria countries arerated on a scale of 1 (low) to 6 (high) • Economicmanagement cluster: Macro economic managementassesses the monetary, exchange rate, andaggregate demand policy frame work. • Fiscal policyassesses the short- and medium-term sustainabilityof fi scal policy (taking into account monetary andexchange rate policy and the sustainability of thepublic debt) and its impact on growth. • Debt policyassesses whether the debt management strategy isconducive to mini mizing budgetary risks and ensuringlong-term debt sustainability. • Structural policiescluster: Trade assesses how the policy frameworkfosters trade in goods. • Financial sector assessesthe structure of the fi nancial sector and the policiesand regulations that affect it. • Business regulatoryenvironment assesses the extent to whichthe legal, regulatory, and policy environments helpor hinder private busi nesses in investing, creatingjobs, and becoming more productive. • Policies forsocial inclusion and equity cluster: Gender equalityassesses the extent to which the country hasinstalled institutions and programs to enforce lawsand policies that pro mote equal access for menand women in educa tion, health, the economy, andprotection under law. • Equity of public resource useassesses the extent to which the pattern of publicexpenditures and rev enue collection affects the poorand is consistent with national poverty reductionpriorities. • Build ing human resources assessesthe national policies and public and private sectorservice delivery that affect the access to andquality of health and edu cation services, includingprevention and treatment of HIV/AIDS, tuberculosis,and malaria. • Social protection and labor assessgovernment policies in social protection and labormarket regulations that reduce the risk of becomingpoor, assist those who are poor to better managefurther risks, and ensure a minimal level of welfare3182012 World Development Indicators

Public policies and institutions 5.9Policies for social inclusion and equity1–6 (low to high)Public sector management and institutions1–6 (low to high)STATES AND MARKETSQuality ofbudgetaryandfi n a n c i a lmanagementTransparency,accountability,and corruptionEquityof public Building SocialPolicies andinstitutions forPropertyrights andEffi ciency QualityGender resource human protection environmentalrule-basedof revenue of public in the publicequality use resources and labor sustainability Average governancemobilization administration sector Average2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010Lao PDR 3.5 4.0 3.5 2.5 4.0 3.5 3.0 3.5 3.5 3.0 2.5 3.1Lesotho 4.0 3.0 3.5 3.0 3.0 3.3 3.5 3.5 4.0 3.0 3.5 3.5Liberia 2.5 3.5 2.5 2.5 2.5 2.7 2.5 2.5 3.5 2.5 3.0 2.8Madagascar 3.5 4.0 3.5 3.5 3.5 3.6 3.0 2.5 3.5 3.5 2.5 3.0Malawi 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.0 4.0 3.5 3.0 3.4Maldives 4.0 4.0 4.0 3.5 4.0 3.9 3.5 3.0 4.5 3.5 3.0 3.5Mali 3.5 4.0 3.5 3.5 3.0 3.5 3.5 3.5 3.5 3.0 3.5 3.4Mauritania 4.0 3.5 3.0 2.5 3.0 3.2 3.0 3.0 3.5 3.0 2.5 3.0Moldova 5.0 3.5 4.0 3.5 4.0 4.0 3.5 4.0 3.5 3.0 3.0 3.4Mongolia 3.5 3.5 3.5 3.5 3.0 3.4 3.0 4.0 3.5 3.5 3.0 3.4Mozambique 3.5 3.5 3.5 3.0 3.5 3.4 3.0 4.0 4.0 3.0 3.0 3.4Nepal 4.0 4.0 4.0 3.0 3.5 3.7 2.5 2.5 3.5 3.0 2.5 2.8Nicaragua 3.5 4.0 3.5 3.5 3.5 3.6 2.5 4.0 4.0 3.0 2.5 3.2Niger 2.5 3.5 3.5 3.0 3.5 3.2 3.0 3.5 3.5 3.0 3.0 3.2Nigeria 3.0 3.5 3.0 3.5 3.0 3.2 2.5 3.0 3.0 3.0 3.0 2.9Pakistan 2.0 3.5 3.0 3.5 2.5 2.9 3.0 3.5 3.0 3.5 2.5 3.1Papua New Guinea 2.5 4.0 2.5 3.0 2.0 2.8 2.0 3.0 3.5 3.0 3.0 2.9Rwanda 4.0 4.5 4.5 3.5 3.5 4.0 3.5 4.0 3.5 4.0 3.5 3.7Samoa 3.5 4.5 4.0 3.5 4.0 3.9 4.0 3.5 4.5 4.0 4.0 4.0São Tomé and Príncipe 3.0 3.0 3.5 2.5 3.0 3.0 2.5 3.0 3.5 3.0 3.5 3.1Senegal 3.5 3.5 3.5 3.0 3.5 3.4 3.5 3.5 4.0 3.5 3.0 3.5Sierra Leone 3.0 3.5 3.5 3.5 2.5 3.2 2.5 3.5 3.0 3.0 3.0 3.0Solomon Islands 3.0 2.5 3.0 2.5 2.0 2.6 3.0 2.5 3.0 2.0 3.0 2.7Sri Lanka 4.0 3.5 4.5 3.5 3.0 3.7 3.5 4.0 3.5 3.0 3.0 3.4St. Lucia 3.5 4.0 4.0 3.5 3.5 3.7 4.0 3.5 4.5 3.5 4.5 4.0St. Vincent & Grenadines 4.0 3.5 4.0 3.5 3.5 3.7 4.0 3.5 4.0 3.5 4.0 3.8Sudan 2.5 2.5 2.5 2.5 2.0 2.4 2.0 2.0 3.0 2.5 1.5 2.2Tajikistan 4.0 3.5 3.0 3.5 3.0 3.4 2.5 3.5 3.0 3.0 2.0 2.8Tanzania 3.5 4.0 3.5 4.0 3.5 3.7 3.5 3.5 4.0 3.0 2.5 3.3Timor-Leste 3.5 3.0 3.0 2.5 2.5 2.9 2.0 3.0 3.0 2.5 3.0 2.7Togo 3.0 2.5 3.0 3.0 2.5 2.8 2.5 3.0 3.0 2.0 2.5 2.6Tonga 3.0 4.0 4.0 3.0 3.0 3.4 4.0 3.5 4.5 3.5 3.5 3.8Uganda 3.5 4.0 3.5 3.5 4.0 3.7 3.5 3.5 3.5 3.0 2.5 3.2Uzbekistan 4.0 4.0 4.0 3.5 3.5 3.8 2.5 3.5 3.5 3.0 1.5 2.8Vanuatu 3.5 3.5 2.5 2.5 3.0 3.0 3.5 4.0 3.5 3.0 3.0 3.4Vietnam 4.5 4.5 4.0 3.5 3.5 4.0 3.5 4.0 4.0 3.5 3.0 3.6Yemen, Rep. 2.0 3.5 3.0 3.5 3.5 3.1 2.5 3.5 3.0 3.0 2.5 2.9Zambia 3.5 3.5 4.0 3.0 3.5 3.5 3.0 3.5 3.5 3.0 2.5 3.1Zimbabwe 2.5 2.0 1.5 1.0 2.0 1.8 1.5 2.0 3.5 2.0 1.5 2.1to all people. • Policies and institutions for environmentalsustainability assess the extent to whichenvironmental policies foster the protection and sustainableuse of natural resources and the managementof pollution. • Public sector management andinstitutions cluster: Prop erty rights and rule-basedgovernance assess the extent to which private economicactivity is facili tated by an effective legal systemand rule-based governance structure in whichproperty and contract rights are reliably respectedand enforced. • Quality of budgetary and financialmanagement assesses the extent to which there isa comprehensive and credible budget linked to policypriorities, effective fi nancial management systems,and timely and accurate accounting and fi scal reporting,including timely and audited public accounts.• Efficiency of revenue mobilization assesses theoverall pat tern of revenue mobilization—not onlythe de facto tax structure, but also revenue fromall sources as actually collected. • Quality of publicadministration assesses the extent to which civiliancentral govern ment staff is structured to design andimplement government policy and deliver serviceseffectively. • Transparency, accountability, and corruptionin the public sector assess the extent towhich the executive can be held accountable for itsuse of funds and for the results of its actions by theelector ate, the legislature, and the judiciary and theextent to which public employees within the executiveare required to account for administrative decisions,use of resources, and results obtained. The threemain dimensions assessed are the accountabilityof the executive to oversight institutions and of publicemployees for their performance, access of civilsociety to information on public affairs, and statecapture by narrow vested interests.Data sourcesData on public policies and institutions are fromthe World Bank Group’s CPIA database (www.worldbank.org/ida).2012 World Development Indicators 319

5.10 Transport servicesRoads Railways Ports AirTotal roadnetworkkmPavedroads%PassengerscarriedmillionpassengerkmGoodshauledmillionton-kmRail linestotal routekmPassengerscarriedmillionpassengerkmAfghanistan 42,150 29.3 232 6,575 .. .. .. .. .. .. ..Albania 18,000 39.0 197 2,200 423 32 46 87 10 920 0Algeria 112,039 74.0 .. .. 3,512 1,045 1,281 266 44 3,686 6Angola 51,429 10.4 166,045 4,709 .. .. .. .. 14 1,283 91Argentina 231,374 30.0 .. .. 25,023 6,979 12,025 2,018 100 10,030 178Armenia 7,705 93.6 2,356 182 826 50 346 .. 9 757 6Australia 817,089 43.5 301,524 189,847 8,615 1,500 64,172 6,536 418 45,268 2,380Austria 106,840 100.0 69,000 16 5,066 10,306 23,104 350 184 13,869 430Azerbaijan 52,942 50.6 15,291 10,634 2,079 917 8,250 .. 10 801 8Bahrain 4,083 82.1 .. .. .. .. .. 290 58 5,339 843Bangladesh 239,226 9.5 .. .. 2,835 7,305 710 1,356 12 2,177 85Belarus 94,797 88.6 8,184 22,767 5,503 7,578 46,224 .. 13 888 2Belgium 153,872 78.2 131,470 36,174 3,578 10,493 5,439 10,985 143 7,263 1,274Benin 19,000 9.5 .. .. 758 .. 36 278 .. .. ..Bolivia 80,294 7.9 .. .. 2,866 313 1,060 .. 36 1,916 14Bosnia and Herzegovina 21,846 52.3 1,959 1,711 1,026 59 1,227 .. 2 133 0Botswana 25,798 32.6 .. .. 888 94 674 .. 8 290 0Brazil 1,751,868 5.5 .. .. 29,817 .. 267,700 8,121 885 77,255 976Bulgaria 40,231 98.4 13,839 17,742 4,098 2,100 3,061 143 31 2,243 2Burkina Faso 92,495 4.2 .. .. 622 .. .. .. 4 254 0Burundi 12,322 10.4 .. .. .. .. .. .. .. .. ..Cambodia 38,257 6.3 201 .. 650 45 92 224 5 312 18Cameroon 28,857 17.0 .. .. 977 377 978 250 10 466 23Canada 1,409,000 39.9 493,814 129,600 58,345 2,875 322,741 4,813 1,235 67,325 2,011Central African Republic 24,307 .. .. .. .. .. .. .. .. .. ..Chad 40,000 0.8 .. .. .. .. .. .. .. .. ..Chile 78,425 22.5 .. .. 5,352 840 4,032 3,172 109 10,234 1,400China 3,860,823 53.5 1,351,144 3,718,882 66,239 791,158 2,451,185 129,611 2,391 267,691 17,441Hong Kong SAR, China 2,050 100.0 .. .. .. .. .. 23,699 151 25,383 7,970Colombia 129,485 .. 157 39,726 1,672 .. 9,049 2,444 180 15,111 1,576Congo, Dem. Rep. 153,497 1.8 .. .. 3,641 37 193 .. .. .. ..Congo, Rep. 17,000 7.1 .. .. 795 211 234 297 .. .. ..Costa Rica 39,039 26.0 27 1 .. .. .. 1,013 49 1,747 20Côte d’Ivoire 81,996 7.9 .. .. 639 10 675 608 .. .. ..Croatia 29,343 90.5 3,438 9,429 2,722 1,742 2,618 137 26 1,641 2Cuba .. 49.0 6,634 2,315 5,076 1,285 1,351 228 10 862 16Cyprus 12,380 64.9 .. 944 .. .. .. 349 22 2,363 38Czech Republic 130,573 100.0 88,352 44,955 9,569 6,553 13,592 .. 94 7,103 23Denmark 73,330 100.0 68,907 10,003 2,131 7,405 2,030 709 4 b 519 b 0 bDominican Republic 12,600 49.4 .. .. .. .. .. 1,383 .. .. ..Ecuador 43,670 14.8 11,819 1,193 .. .. .. 1,222 50 4,461 115Egypt, Arab Rep. 100,472 89.4 12,793 .. 5,195 40,837 3,840 6,709 113 9,637 188El Salvador 10,029 19.8 .. .. .. .. .. 146 20 2,150 17Eritrea 4,010 21.8 .. .. .. .. .. .. .. .. ..Estonia 58,382 28.6 2,453 5,249 787 248 6,261 152 11 809 1Ethiopia 44,359 13.7 219,113 2,456 .. .. .. .. 48 3,141 672Finland 78,925 65.5 72,700 25,200 5,919 3,959 9,760 1,247 183 11,578 736France 951,260 100.0 773,000 265,000 33,608 86,853 22,840 5,323 751 65,401 5,081Gabon 9,170 12.0 .. .. 810 111 2,238 136 1 96 2Gambia, The 3,742 19.3 16 .. .. .. .. .. .. .. ..Georgia 20,329 94.1 5,724 611 1,566 655 6,228 226 6 328 15Germany 643,969 100.0 949,306 427,300 33,708 78,582 105,794 14,625 1,086 112,399 9,245Ghana 109,515 12.6 .. .. 953 85 181 514 .. .. ..Greece 116,929 91.8 .. 28,585 2,552 1,413 538 1,165 148 15,588 37Guatemala 14,095 34.5 .. .. .. .. .. 1,012 .. .. ..Guinea 44,348 9.8 .. .. .. .. .. .. .. .. ..Guinea-Bissau 3,455 27.9 .. .. .. .. .. .. .. .. ..Haiti 4,160 24.3 .. .. .. .. .. .. .. .. ..Goodshauledmillionton-kmPortcontainertraffi cthousandTEURegisteredcarrierdeparturesworldwidethousandsPassengerscarriedthousandsAir freightmillionton-km2000–09 a 2000–09 a 2000–09 a 2000–09 a 2000–10 a 2000–10 a 2000–10 a 2010 2010 2010 20103202012 World Development Indicators

Transport servicesRoads Railways Ports Air5.10STATES AND MARKETSTotal roadnetworkkmPavedroads%PassengerscarriedmillionpassengerkmGoodshauledmillionton-kmRail linestotal routekmPassengerscarriedmillionpassengerkmHonduras 13,600 20.4 .. .. .. .. .. 620 .. .. ..Hungary 197,519 38.0 20,449 35,373 7,893 5,398 1,000 .. 118 11,829 8India 4,109,592 49.5 .. .. 63,974 903,465 600,548 9,753 630 64,144 1,720Indonesia 476,337 56.9 .. .. 3,370 14,344 4,390 8,371 405 35,321 660Iran, Islamic Rep. 192,685 73.3 .. .. 6,073 16,814 20,247 2,593 152 17,585 131Iraq 40,988 84.3 .. .. 2,025 54 121 .. .. .. ..Ireland 96,424 100.0 .. 12,787 1,919 1,678 92 773 683 76,488 350Israel 18,318 100.0 .. .. 1,034 1,986 1,062 2,282 54 6,141 865Italy 487,700 100.0 97,560 192,700 18,011 44,535 12,037 9,787 364 48,748 1,164Jamaica 22,121 73.3 .. .. .. .. .. 1,892 8 974 6Japan 1,207,867 80.1 905,907 334,667 20,035 244,235 20,432 18,060 650 94,212 8,380Jordan 7,878 100.0 .. .. 294 .. 353 619 39 2,972 219Kazakhstan 96,846 88.5 110,475 66,254 14,202 15,448 213,174 .. 27 2,426 48Kenya 61,945 14.3 .. 22 1,917 226 1,399 696 36 3,284 258Korea, Dem. Rep. 25,554 2.8 .. .. .. .. .. .. 1 84 7Korea, Rep. 104,983 79.3 100,617 12,545 3,379 33,027 9,452 18,538 483 42,763 12,802Kosovo .. .. .. .. .. .. .. .. .. .. ..Kuwait 6,524 85.0 .. .. .. .. .. 888 22 2,741 274Kyrgyz Republic 34,000 91.1 6,745 912 417 99 738 .. 5 270 1Lao PDR 39,568 13.7 2,113 287 .. .. .. .. 11 645 0Latvia 69,148 20.9 14,625 8,115 1,897 79 17,164 257 66 4,466 23Lebanon 6,970 .. .. .. .. .. .. 949 17 1,600 43Lesotho 5,940 18.3 .. .. .. .. .. .. .. .. ..Liberia 10,600 6.2 .. .. .. .. .. .. .. .. ..Libya 83,200 57.2 .. .. .. .. .. 162 25 2,345 17Lithuania 81,331 29.4 20,376 17,757 1,767 373 13,431 295 5 167 120Macedonia, FYR 13,940 57.6 1,239 3,978 699 155 497 .. 2 137 0Madagascar 49,827 11.6 .. .. 854 10 12 141 12 417 22Malawi 15,451 45.0 .. .. 797 44 33 .. 3 172 4Malaysia 98,722 81.3 .. .. 1,665 1,527 1,384 18,247 240 26,255 2,451Mali 22,474 24.6 .. .. 733 196 189 .. .. .. ..Mauritania 11,066 26.8 .. .. 728 47 7,566 66 1 154 0Mauritius 2,066 98.0 .. .. .. .. .. 445 12 1,268 179Mexico 366,807 35.3 436,900 211,600 26,704 178 71,136 3,694 186 13,608 1,380Moldova 12,779 85.8 2,268 2,714 1,157 399 927 .. 6 455 1Mongolia 49,250 3.5 1,215 782 1,814 1,220 10,287 .. 8 417 3Morocco 58,216 70.3 .. 800 2,109 4,398 5,572 2,058 92 8,971 11Mozambique 30,331 20.8 .. .. 3,116 114 695 223 14 553 8Myanmar 27,000 11.9 .. .. .. 4,163 885 167 8 396 2Namibia 42,100 14.7 47 591 .. .. .. 256 9 488 0Nepal 19,875 53.9 .. .. .. .. .. .. 2 288 4Netherlands 136,827 90.0 .. 72,675 3,016 15,400 4,331 11,331 243 27,554 5,028New Zealand 94,301 66.2 .. .. .. .. 4,078 2,427 186 10,128 668Nicaragua 21,975 11.6 133 .. .. .. .. 68 .. .. ..Niger 18,948 20.7 .. .. .. .. .. .. .. .. ..Nigeria 193,200 15.0 .. .. 3,528 174 77 90 17 1,555 1Norway 93,853 80.7 64,014 16,109 4,114 2,674 2,092 331 3 b 399 b 0 bOman 56,361 46.0 .. .. .. .. .. 3,893 35 4,068 27Pakistan 258,350 65.4 263,788 129,249 7,791 24,731 6,187 2,149 50 6,012 310Panama 13,974 42.0 .. .. .. .. .. 5,906 81 7,366 2Papua New Guinea 19,600 3.5 .. .. .. .. .. 269 20 1,158 26Paraguay 31,531 50.8 .. .. .. .. .. 7 6 606 0Peru 126,500 13.9 .. .. 2,020 76 900 1,534 68 6,130 248Philippines 200,037 9.9 .. .. 479 83 1 4,947 169 18,933 472Poland 384,104 69.9 24,386 191,484 19,702 15,715 34,266 1,045 91 5,466 91Portugal 82,900 86.0 .. 35,808 2,843 3,718 1,932 1,610 171 11,124 375Puerto Rico 26,677 95.0 .. 10 .. .. .. 1,526 .. .. ..Qatar 7,790 90.0 .. .. .. .. .. 346 91 12,391 2,946Goodshauledmillionton-kmPortcontainertraffi cthousandTEURegisteredcarrierdeparturesworldwidethousandsPassengerscarriedthousandsAir freightmillionton-km2000–09 a 2000–09 a 2000–09 a 2000–09 a 2000–10 a 2000–10 a 2000–10 a 2010 2010 2010 20102012 World Development Indicators 321

5.10Transport servicesRoads Railways Ports AirTotal roadnetworkkmPavedroads%PassengerscarriedmillionpassengerkmGoodshauledmillionton-kmRail linestotal routekmPassengerscarriedmillionpassengerkmRomania 198,817 30.2 12,805 20,878 13,620 5,248 9,134 557 66 3,996 5Russian Federation 982,000 80.1 139,034 180,135 85,292 139,028 2,011,308 3,130 673 56,848 4,614Rwanda 14,008 19.0 .. .. .. .. .. .. .. .. ..Saudi Arabia 221,372 21.5 .. .. 1,020 337 1,748 5,313 163 18,998 1,336Senegal 14,825 32.0 .. .. 906 129 384 349 0 573 0Serbia 44,334 63.2 4,169 1,184 4,058 658 3,868 .. 18 1,059 2Sierra Leone 11,300 8.0 .. .. .. .. .. .. 0 24 9Singapore 3,356 100.0 5,762 .. .. .. .. 29,179 81 25,319 4,004Slovak Republic 43,879 87.1 31,093 27,484 3,587 2,291 7,669 .. 14 991 0Slovenia 38,927 100.0 777 14,762 1,228 813 3,283 477 25 1,170 2Somalia 22,100 11.8 .. .. .. .. .. .. .. .. ..South Africa 362,099 17.3 .. 434 22,051 18,865 113,342 3,806 210 16,779 1,107South Sudan .. .. .. .. .. .. .. .. .. .. ..Spain 667,064 99.0 410,192 211,891 15,317 22,304 7,844 12,608 606 58,563 1,684Sri Lanka 97,286 81.0 21,067 .. 1,463 4,767 135 4,080 17 2,800 329Sudan 11,900 36.3 .. .. 4,508 34 766 439 6 602 13Swaziland 3,594 30.0 .. .. 300 0 776 .. .. .. ..Sweden 582,950 24.4 109,100 35,000 9,957 6,774 11,500 1,391 3 b 363 b 0 bSwitzerland 71,371 100.0 95,090 16,734 3,543 17,609 8,725 99 220 21,477 1,275Syrian Arab Republic 68,157 90.3 589 .. 2,139 1,120 2,370 649 11 1,018 3Tajikistan 27,767 .. 8,591 5,013 621 33 808 .. 5 951 5Tanzania 103,706 6.7 8 7 2,600 c 475 c 728 c 427 21 749 2Thailand 180,053 98.5 .. .. 4,429 8,037 3,161 6,649 122 20,303 3,133Timor-Leste .. .. .. .. .. .. .. .. .. .. ..Togo 11,652 21.0 .. .. .. .. .. .. .. .. ..Trinidad and Tobago 8,320 51.1 .. .. .. .. .. 573 25 1,721 13Tunisia 19,371 75.2 .. 16,611 1,119 1,493 2,073 466 44 5,464 21Turkey 362,660 88.7 212,464 176,455 9,594 5,491 11,030 5,547 400 51,590 1,101Turkmenistan 24,000 81.2 .. .. 3,115 1,811 11,992 .. 3 399 6Uganda 70,746 23.0 .. .. 259 .. 218 .. 0 70 32Ukraine 169,495 97.8 54,631 33,193 21,705 50,240 218,091 660 84 5,679 598United Arab Emirates 4,080 100.0 .. .. .. .. .. 15,174 261 42,555 10,126United Kingdom 419,665 100.0 736,000 143,453 31,471 55,019 12,512 7,389 1,173 123,972 8,555United States 6,545,839 67.4 7,874,329 1,889,923 228,513 9,518 2,468,738 d 42,190 8,934 e 707,426 e 50,743 eUruguay 77,732 10.0 2,588 .. 2,993 15 284 672 19 744 0Uzbekistan 81,600 87.3 56,674 21,038 4,227 2,905 22,282 .. 24 2,167 166Venezuela, RB 96,155 33.6 .. .. 336 .. 81 1,216 125 5,881 5Vietnam 160,089 47.6 59,735 30,261 2,347 4,378 3,901 5,984 103 14,099 428West Bank and Gaza 5,588 91.7 .. .. .. .. .. .. .. .. ..Yemen, Rep. 71,300 8.7 .. .. .. .. .. 370 11 1,134 28Zambia 66,781 22.0 .. .. 1,273 183 .. .. 4 62 ..Zimbabwe 97,267 19.0 .. .. 2,583 .. 1,580 .. 5 302 12World 64.9 m .. m .. m .. s 2,134 m 4,532 m 538,284 s 28,078 s 2,595,449 s 189,325 sLow income 20.7 .. .. .. .. .. .. 182 13,444 1,142Middle income 53.5 .. .. .. 1,220 4,802 260,211 8,539 834,810 41,820Lower middle income 48.6 .. .. .. 1,120 3,910 51,323 1,973 185,587 5,190Upper middle income 57.6 .. .. .. 1,269 4,032 208,887 6,566 649,224 36,630Low & middle income 44.8 .. .. .. .. 3,127 263,722 8,721 848,254 42,962East Asia & Pacifi c 30.7 .. .. .. 4,248 3,384 174,467 3,572 388,023 24,716Europe & Central Asia 85.8 14,625 9,375 177,892 658 8,250 10,901 1,464 136,681 6,725Latin America & Carib. 22.5 .. .. .. .. .. 36,495 1,974 160,869 5,967Middle East & N. Africa 75.2 .. .. .. 1,493 2,222 15,441 547 54,412 668South Asia 53.9 .. .. .. 24,731 6,187 17,396 719 75,735 2,453Sub-Saharan Africa 18.8 .. .. .. .. .. .. 445 32,534 2,432High income 81.1 .. 28,585 .. 7,090 8,285 274,561 19,357 1,747,195 146,364Euro area 87.3 69,000 28,035 131,414 10,306 7,669 73,233 4,711 457,356 30,754a. Data are for the most recent year available in the period shown. b. Covers international nonscheduled carriers only. Total for scheduled international and domestic carriers forScandinavian countries are 621,715 registered carrier departures worldwide, 48,916,632 passengers carried, and 583,140,000 ton-kilometers of air freight. c. Includes Tazara railway.d. Refers to class 1 railways only. e. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers.Goodshauledmillionton-kmPortcontainertraffi cthousandTEURegisteredcarrierdeparturesworldwidethousandsPassengerscarriedthousandsAir freightmillionton-km2000–09 a 2000–09 a 2000–09 a 2000–09 a 2000–10 a 2000–10 a 2000–10 a 2010 2010 2010 20103222012 World Development Indicators

Transport services 5.10STATES AND MARKETSAbout the dataDefinitionsTransport infrastructure—highways, railways, portsand waterways, and airports and air traffi c controlsystems—and the services that fl ow from it arecrucial to the activities of households, producers,and governments. Because performance indicatorsvary widely by transport mode and focus (whetherphysical infrastructure or the services fl owing fromthat infrastructure), highly specialized and carefullyspecifi ed indicators are required. The table providesselected indicators of the size, extent, and productivityof roads, railways, and air transport systemsand of the volume of traffi c in these modes as wellas in ports. Indicators on traffi c and congestion arepresented in table 3.15, and indicators on logisticsperformance are presented in table 6.8.Data for transport sectors are not always internationallycomparable. Unlike for demographic statistics,national income accounts, and internationaltrade data, the collection of infrastructure data hasnot been “internationalized.” But data on roads arecollected by the International Road Federation (IRF),and data on air transport by the International CivilAviation Organization (ICAO).National road associations are the primary sourceof IRF data. In countries where a national road associationis lacking or does not respond, other agenciesare contacted, such as road directorates, ministriesof transport or public works, or central statisticaloffi ces. As a result, defi nitions and data collectionmethods and quality differ, and the compiled dataare of uneven quality. Moreover, the quality of transportservice (reliability, transit time, and condition ofgoods delivered) is rarely measured, though it may beas important as quantity in assessing an economy’stransport system.Unlike the road sector, where numerous qualifi edmotor vehicle operators can operate anywhere onthe road network, railways are a restricted transportsystem with vehicles confi ned to a fi xed guideway.Considering the cost and service characteristics,railways generally are best suited to carry—and caneffectively compete for—bulk commodities and containerizedfreight for distances of 500–5,000 kilometers,and passengers for distances of 50–1,000kilometers. Below these limits road transporttends to be more competitive, while above theselimits air transport for passengers and freight andsea transport for freight tend to be more competitive.The railways indicators in the table focus onscale and output measures: total route-kilometers,passenger- kilometers, and goods (freight) hauled inton-kilometers.Measures of port container traffi c, much of itcommodities of medium to high value added, givesome indication of economic growth in a country.But when traffi c is merely transshipment, much ofthe economic benefi t goes to the terminal operatorand ancillary services for ships and containers ratherthan to the country more broadly. In transshipmentcenters empty containers may account for as muchas 40 percent of traffi c.The air transport data represent the total (internationaland domestic) scheduled traffi c carried by theair carriers registered in a country. Countries submitair transport data to ICAO on the basis of standardinstructions and defi nitions issued by ICAO. In manycases, however, the data include estimates by ICAOfor nonreporting carriers. Where possible, these estimatesare based on previous submissions supplementedby information published by the air carriers,such as fl ight schedules.The data cover the air traffi c carried on scheduledservices, but changes in air transport regulationsin Europe have made it more diffi cult to classifytraffi c as scheduled or nonscheduled. Thus recentincreases shown for some European countries maybe due to changes in the classifi cation of air traffi crather than actual growth. For countries with few aircarriers or only one, the addition or discontinuationof a home-based air carrier may cause signifi cantchanges in air traffi c.• Total road network covers motorways, highways,main or national roads, secondary or regional roads,and all other roads in a country. • Paved roads areroads surfaced with crushed stone (macadam) andhydrocarbon binder or bituminized agents, with concrete,or with cobblestones. • Passengers carriedby road are the number of passengers transportedby road times kilometers traveled. • Goods hauledby road are the volume of goods transported by roadvehicles, measured in millions of metric tons timeskilometers traveled. • Rail lines are the length of railwayroute available for train service, irrespective ofthe number of parallel tracks. • Passengers carriedby railway are the number of passengers transportedby rail times kilometers traveled. • Goods hauledby railway are the volume of goods transported byrailway, measured in metric tons times kilometerstraveled. • Port container traffic measures the fl owof containers from land to sea transport modes andvice versa in twenty-foot-equivalent units (TEUs), astandard-size container. Data cover coastal shippingas well as international journeys. Transshipment traffic is counted as two lifts at the intermediate port(once to off-load and again as an outbound lift) andincludes empty units. • Registered carrier departuresworldwide are domestic takeoffs and takeoffsabroad of air carriers registered in the country. • Passengerscarried by air include both domestic andinternational passengers of air carriers registeredin the country. • Air freight is the volume of freight,express, and diplomatic bags carried on each fl ightstage (operation of an aircraft from takeoff to its nextlanding), measured in metric tons times kilometerstraveled.Data sourcesData on roads are from the IRF’s World RoadStatistics, supplemented by World Bank staffestimates. Data on railways are from a databasemaintained by the World Bank’s Transport, Water,and Information and Communication TechnologiesDepartment, Transport Division, based on datafrom the International Union of Railways. Dataon port container traffi c are from ContainerisationInternational’s Containerisation InternationalYearbook and the United Nations Conference onTrade and Development’s UNCTADstat database(http://unctadstat.unctad.org). Data on air transportare from the ICAO’s Civil Aviation Statistics ofthe World and ICAO staff estimates.2012 World Development Indicators 323

5.11 Power and communicationsElectric powerTelephones aConsumptionper capitakWhTransmissionanddistributionlosses% of outputSubscriptionsper 100 peopleFixedtelephoneAccess and use Quality Affordability and effi ciencyMobilecellularInternationalvoice traffi cminutes per personFixedtelephoneMobilecellularnetworkPopulationcovered bymobile cellularnetwork%Residentialfi x e d -telephonetariff$ per monthTelecommunicationsrevenue% of GDPMobile cellularand fi xedtelephonesubscriptionsper employeeAfghanistan .. .. 0 38 .. .. 75 .. .. .. ..Albania 1,747 21 10 142 46 201 98 6.4 25.8 4.7 1,151Algeria 971 21 8 92 17 36 .. 5.4 12.5 3.6 ..Angola 202 10 2 47 .. .. .. 16.5 19.2 .. ..Argentina 2,759 15 25 142 44 .. .. 3.8 30.7 3.2 ..Armenia 1,550 15 19 125 185 233 99 4.2 8.8 4.4 969Australia 11,113 7 39 101 .. .. 99 27.6 27.7 .. 315Austria 7,944 5 39 146 .. .. 99 25.3 13.9 1.8 961Azerbaijan 1,620 22 17 101 20 74 100 2.5 6.9 2.7 645Bahrain 9,214 12 18 124 .. .. 100 4.8 15.0 4.4 698Bangladesh 252 2 1 46 .. .. .. 1.3 2.0 .. ..Belarus 3,299 11 44 109 98 25 100 1.3 7.9 3.1 ..Belgium 7,903 5 43 112 .. .. 100 31.4 40.2 2.6 907Benin 91 .. 2 80 12 52 90 9.0 13.0 5.4 3,146Bolivia 558 11 9 72 84 .. .. 23.7 11.3 7.3 ..Bosnia and Herzegovina 2,867 12 27 83 190 73 100 9.3 15.4 5.3 499Botswana 1,503 79 7 118 113 .. 99 18.7 13.2 3.5 4,031Brazil 2,206 17 22 104 .. .. 100 23.0 57.2 4.9 ..Bulgaria 4,401 11 30 135 70 84 100 12.5 30.6 3.2 543Burkina Faso .. .. 1 35 .. .. .. 10.9 21.2 .. 2,306Burundi .. .. 0 14 .. .. 83 .. .. 3.1 838Cambodia 131 18 3 58 .. .. 99 7.4 6.7 5.5 1,333Cameroon 271 9 3 44 3 26 .. 15.0 20.1 3.8 2,251Canada 15,471 8 50 70 .. .. 99 19.4 34.3 2.7 ..Central African Republic .. .. 0 22 0 12 55 10.1 12.9 2.9 ..Chad .. .. 0 24 .. .. .. 16.7 15.4 .. ..Chile 3,283 11 20 116 30 9 100 25.0 23.7 .. 646China 2,631 5 22 64 9 .. 99 4.7 6.0 2.0 1,254Hong Kong SAR, China 5,925 13 62 195 1,463 504 100 8.5 1.4 3.5 1,066Colombia 1,047 15 16 96 83 .. .. 5.9 16.9 4.2 466Congo, Dem. Rep. 104 5 0 18 0 7 50 .. .. 4.9 5,538Congo, Rep. 146 73 0 94 .. .. .. .. .. .. ..Costa Rica 1,813 11 32 65 141 77 70 6.9 3.4 2.1 498Côte d’Ivoire 203 25 1 76 30 47 92 19.6 13.6 6.7 3,912Croatia 3,712 16 42 144 248 66 100 17.0 17.1 4.6 945Cuba 1,348 14 10 9 19 16 78 0.3 33.9 .. 129Cyprus 4,620 4 37 94 367 504 100 23.8 7.7 3.6 458Czech Republic 6,114 5 23 137 90 80 100 26.8 28.1 3.4 778Denmark 6,246 6 47 125 189 186 .. 27.8 9.8 2.3 616Dominican Republic 1,358 11 10 90 322 .. 81 15.5 15.6 2.2 1,394Ecuador 1,115 14 14 102 55 20 93 14.1 13.8 .. ..Egypt, Arab Rep. 1,549 11 12 87 11 22 100 2.9 7.0 3.2 1,217El Salvador 845 12 16 124 .. .. .. 10.2 9.5 3.5 2,748Eritrea 51 12 1 4 6 18 90 .. .. 3.2 149Estonia 5,950 10 36 123 85 99 100 11.5 22.3 4.9 631Ethiopia 46 10 1 8 6 4 .. 1.0 4.1 1.5 604Finland 15,242 4 23 156 .. .. 100 16.8 13.1 2.7 708France 7,488 6 54 97 288 74 99 26.6 48.6 2.3 789Gabon 922 18 2 107 26 23 .. .. .. .. ..Gambia, The .. .. 3 86 .. .. .. 2.4 6.3 .. ..Georgia 1,585 13 25 89 56 64 99 2.3 11.6 3.7 697Germany 6,779 4 56 128 .. 43 99 27.0 13.1 2.4 851Ghana 265 23 1 71 10 60 77 7.3 7.4 3.7 3,764Greece 5,540 5 46 109 185 114 100 23.8 38.5 3.0 804Guatemala 548 14 10 126 .. .. .. 5.6 7.8 .. ..Guinea .. .. 0 40 .. .. 80 1.6 4.1 .. ..Guinea-Bissau .. .. 0 39 .. .. .. .. .. .. ..Haiti 36 51 1 40 .. .. .. .. .. .. ..Mobilecellularprepaidtariff2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 20103242012 World Development Indicators

Electric powerPower and communications 5.11Telephones aSTATES AND MARKETSConsumptionper capitakWhTransmissionanddistributionlosses% of outputSubscriptionsper 100 peopleFixedtelephoneAccess and use Quality Affordability and effi ciencyMobilecellularInternationalvoice traffi cminutes per personFixedtelephoneMobilecellularnetworkPopulationcovered bymobile cellularnetwork%Residentialfi x e d -telephonetariff$ per monthTelecommunicationsrevenue% of GDPMobile cellularand fi xedtelephonesubscriptionsper employeeHonduras 678 22 9 125 39 197 .. 6.3 8.9 6.2 ..Hungary 3,773 10 30 120 .. .. 99 21.2 25.7 3.8 1,127India 571 24 3 61 .. .. 83 3.3 3.4 2.3 ..Indonesia 590 10 16 92 .. .. .. 5.0 7.8 .. ..Iran, Islamic Rep. 2,238 17 36 91 .. .. .. 0.2 3.6 .. ..Iraq 1,069 40 5 75 0 .. .. .. .. .. 1,466Ireland 6,034 8 46 105 .. .. 99 26.3 37.3 2.5 534Israel 6,608 3 43 130 413 .. 100 15.7 34.1 4.0 ..Italy 5,271 7 36 150 .. .. 99 25.6 29.8 2.1 1,341Jamaica 1,902 7 10 118 119 978 .. 11.6 12.0 5.4 ..Japan 7,819 5 32 95 .. .. 100 26.4 55.9 3.0 1,304Jordan 2,112 14 8 109 23 217 99 9.5 10.5 5.9 1,499Kazakhstan 4,448 8 25 119 23 54 95 2.4 14.4 2.2 463Kenya 147 16 1 62 1 23 89 14.2 10.8 6.3 2,546Korea, Dem. Rep. 733 16 5 2 .. .. .. .. .. .. ..Korea, Rep. 8,980 4 58 104 24 45 100 5.3 14.4 4.6 703Kosovo .. .. .. .. .. .. .. .. .. .. ..Kuwait 17,610 12 21 161 .. .. 100 8.6 7.8 .. ..Kyrgyz Republic 1,386 30 9 97 40 19 96 1.2 3.6 7.9 442Lao PDR .. .. 2 65 .. .. 80 4.0 6.3 3.8 1,283Latvia 2,875 13 24 103 .. .. .. 10.1 10.1 3.6 661Lebanon 3,130 13 21 68 .. .. 95 10.3 27.3 .. ..Lesotho .. .. 2 45 .. 26 .. 13.0 24.1 3.1 6,933Liberia .. .. 0 39 .. 52 .. .. .. .. ..Libya 4,170 14 19 172 .. .. 98 .. .. .. ..Lithuania 3,431 7 22 149 49 61 100 13.1 9.6 2.7 ..Macedonia, FYR 3,442 17 20 105 165 86 100 11.9 23.4 5.7 329Madagascar .. .. 1 37 1 6 .. 18.2 15.4 2.8 3,203Malawi .. .. 1 20 5 1 85 4.3 21.2 3.6 ..Malaysia 3,614 4 16 119 85 .. 95 5.1 7.5 .. ..Mali .. .. 1 48 2 21 .. 8.5 14.4 6.1 4,091Mauritania .. .. 2 79 4 .. 62 18.0 14.6 6.9 2,842Mauritius .. .. 30 93 105 148 99 5.1 6.8 2.4 ..Mexico 1,943 16 18 81 159 .. 100 18.9 17.4 3.0 918Moldova 1,018 40 33 89 126 133 .. 1.9 12.6 8.5 461Mongolia 1,411 12 7 91 4 48 85 0.7 3.6 5.6 372Morocco 756 12 12 100 66 63 98 21.3 33.1 4.7 2,770Mozambique 453 9 0 31 .. 9 32 12.4 17.0 3.9 2,430Myanmar 104 16 1 1 .. .. .. 0.9 12.8 .. 83Namibia 1,576 15 7 67 .. .. .. 15.1 17.0 .. ..Nepal 91 31 3 31 .. .. 35 3.1 2.7 .. 981Netherlands 6,896 4 44 115 .. 84 98 22.4 33.2 2.3 ..New Zealand 9,346 7 43 115 .. .. 97 34.1 47.2 3.2 600Nicaragua 460 24 4 65 39 .. 100 4.5 13.2 .. ..Niger .. .. 1 25 .. .. .. 11.7 20.8 .. ..Nigeria 121 6 1 55 1 30 90 14.0 13.7 3.4 ..Norway 23,550 8 34 116 .. .. .. 32.7 20.5 1.3 ..Oman 5,724 13 10 166 24 406 98 13.1 9.1 3.5 1,275Pakistan 449 20 2 57 5 33 92 3.3 2.5 2.4 1,888Panama 1,735 13 16 185 53 77 91 12.0 8.5 3.3 712Papua New Guinea .. .. 2 28 .. .. .. 4.5 23.3 .. ..Paraguay 1,056 6 6 92 41 21 94 6.8 8.6 4.0 ..Peru 1,136 8 11 100 .. .. 97 14.6 43.2 2.9 2,512Philippines 593 12 7 86 .. .. 99 15.3 10.1 .. 4,504Poland 3,591 8 20 123 .. .. 99 20.2 15.8 3.0 945Portugal 4,815 8 42 143 .. 121 99 25.3 22.9 4.5 1,507Puerto Rico .. .. 22 74 .. .. 68 .. .. .. 411Qatar 14,421 7 17 132 .. .. 100 9.1 18.4 1.6 927Mobilecellularprepaidtariff2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 20102012 World Development Indicators 325

5.11Power and communicationsElectric powerTelephones aConsumptionper capitakWhTransmissionanddistributionlosses% of outputSubscriptionsper 100 peopleFixedtelephoneAccess and use Quality Affordability and effi ciencyMobilecellularInternationalvoice traffi cminutes per personFixedtelephonePopulationcovered bymobile cellularnetwork%$ per monthTelecommunicationsrevenue% of GDPRomania 2,267 12 21 115 49 97 100 13.3 21.8 3.0 883Russian Federation 6,136 11 32 168 .. .. .. 6.2 9.2 2.8 820Rwanda .. .. 0 33 0 15 96 13.2 13.9 3.0 3,892Saudi Arabia 7,427 8 15 188 .. .. 99 9.2 14.1 3.8 2,381Senegal 196 17 3 67 12 101 90 10.3 12.7 9.9 3,710Serbia 4,224 16 43 136 125 59 97 5.4 11.6 5.1 865Sierra Leone .. .. 0 34 .. .. .. .. .. .. ..Singapore 7,949 5 39 145 .. .. 100 8.2 8.1 4.0 ..Slovak Republic 4,925 3 20 109 170 109 100 20.4 38.2 3.0 688Slovenia 6,103 5 44 104 81 117 100 17.9 20.8 3.6 552Somalia .. .. 1 7 .. .. .. .. .. .. ..South Africa 4,532 10 8 101 .. .. .. 25.0 23.3 .. ..South Sudan .. .. .. .. .. .. .. .. .. .. ..Spain 6,006 3 44 112 163 .. 100 27.0 53.2 2.7 923Sri Lanka 408 15 17 83 .. .. 98 4.9 1.9 .. 1,524Sudan 114 28 1 41 6 .. 66 3.9 3.4 3.2 2,168Swaziland .. .. 4 69 .. .. 91 5.0 24.2 4.5 2,000Sweden 14,142 7 53 116 .. .. 99 25.5 17.2 1.6 813Switzerland 8,021 6 56 122 .. .. 100 29.4 57.0 3.2 599Syrian Arab Republic 1,563 28 20 58 35 74 98 1.3 19.9 11.7 ..Tajikistan 1,985 17 5 86 .. .. .. 0.9 1.8 .. ..Tanzania 86 19 0 47 0 7 85 8.8 9.7 .. ..Thailand 2,045 6 10 104 .. .. .. 8.8 8.7 .. ..Timor-Leste .. .. 0 53 98 22 69 17.3 16.1 8.1 1,751Togo 111 53 4 41 4 42 75 11.0 19.9 9.5 1,896Trinidad and Tobago 5,662 2 22 141 .. .. 100 19.5 12.1 2.8 1,862Tunisia 1,311 13 12 105 21 52 100 2.5 10.0 3.9 1,130Turkey 2,298 15 22 85 73 29 100 16.8 43.9 2.0 2,069Turkmenistan 2,446 14 10 63 .. .. .. .. .. .. ..Uganda .. .. 1 38 .. .. 100 8.8 12.2 4.3 2,046Ukraine 3,200 12 28 118 .. .. 100 3.0 7.5 4.0 ..United Arab Emirates 11,464 12 20 145 .. .. 100 4.1 8.6 3.6 1,094United Kingdom 5,692 7 54 130 .. .. 100 21.0 31.0 4.5 ..United States 12,914 6 49 90 .. .. 100 12.8 32.7 2.0 478Uruguay 2,671 13 29 132 .. .. 100 13.3 17.8 3.1 765Uzbekistan 1,636 9 7 74 .. .. 93 1.1 3.0 .. 739Venezuela, RB 3,152 27 25 97 46 .. .. 1.7 22.3 3.3 1,490Vietnam 918 10 19 177 .. .. .. 2.4 5.4 7.1 ..West Bank and Gaza .. .. 9 45 .. .. .. .. .. .. ..Yemen, Rep. 219 24 4 46 20 61 84 1.1 8.1 3.3 1,273Zambia 635 23 1 42 .. .. 90 24.1 16.9 .. ..Zimbabwe 1,026 7 3 61 15 23 80 9.1 20.5 .. 2,369World 2,803 w 8 w 17 w 78 w .. w .. w 93 w 11.3 m 14.4 m 2.7 w 887 mLow income 229 12 1 33 .. .. .. 8.8 13.0 .. 2,306Middle income 1,675 11 14 78 .. .. 92 7.1 12.6 3.2 1,044Lower middle income 644 19 6 72 .. .. 86 5.3 10.7 3.4 ..Upper middle income 2,714 9 22 84 30 .. 99 9.8 14.9 2.4 765Low & middle income 1,525 11 12 72 .. .. 91 8.8 12.7 3.2 1,168East Asia & Pacifi c 2,095 5 19 73 11 .. 99 5.1 7.7 2.1 1,283Europe & Central Asia 3,859 12 26 124 .. .. .. 4.8 10.8 2.7 697Latin America & Carib. 1,892 16 18 98 .. .. 98 10.9 14.7 4.2 ..Middle East & N. Africa 1,497 18 16 86 17 .. .. 4.1 11.5 .. 1,131South Asia 517 23 3 59 .. .. 84 3.3 2.5 2.3 1,363Sub-Saharan Africa 511 11 1 45 .. 21 .. 12.0 15.0 .. 1,840High income 9,064 6 44 111 .. .. 100 21.2 22.3 2.6 784Euro area 6,592 5 46 122 .. .. 99 25.3 29.8 2.4 789MobilecellularnetworkResidentialfi x e d -telephonetariffMobilecellularprepaidtariffa. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators database. Please cite the ITU for third-party use of these data.Mobile cellularand fi xedtelephonesubscriptionsper employee2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 20103262012 World Development Indicators

Power and communications5.11STATES AND MARKETSAbout the dataDefinitionsThe quality of an economy’s infrastructure, includingpower and communications, is an important elementin investment decisions for both domestic andforeign investors. Government effort alone is notenough to meet the need for investments in moderninfrastructure; public-private partnerships, especiallythose involving local providers and financiers, arecritical for lowering costs and delivering value formoney. In telecommunications, competition in themarketplace, along with sound regulation, is loweringcosts, improving quality, and easing access toservices around the globe.An economy’s production and consumption of electricityare basic indicators of its size and level ofdevelopment. Although a few countries export electricpower, most production is for domestic consumption.Expanding the supply of electricity to meet thegrowing demand of increasingly urbanized and industrializedeconomies without incurring unacceptablesocial, economic, and environmental costs is oneof the great challenges facing developing countries.Data on electric power production and consumptionare collected from national energy agencies by theInternational Energy Agency (IEA) and adjusted by theIEA to meet international definitions (for data on electricityproduction, see table 3.8). Electricity consumptionis equivalent to production less power plants’ ownuse and transmission, distribution, and transformationlosses less exports plus imports. It includes consumptionby auxiliary stations, losses in transformers thatare considered integral parts of those stations, andelectricity produced by pumping installations. Wheredata are available, it covers electricity generated byprimary sources of energy—coal, oil, gas, nuclear,hydro, geothermal, wind, tide and wave, and combustiblerenewables. Neither production nor consumptiondata capture the reliability of supplies, including breakdowns,load factors, and frequency of outages.Over the past decade new financing and technology,along with privatization and market liberalization,have spurred dramatic growth in telecommunicationsin many countries. With the rapid development ofmobile telephony and the global expansion of theInternet, information and communication technologiesare increasingly recognized as essential tools ofdevelopment, contributing to global integration andenhancing public sector effectiveness, effi ciency,and transparency. The table presents telecommunicationsindicators covering access and use, quality,and affordability and effi ciency.Access to telecommunication services rose on anunprecedented scale over the past 15 years. Thisgrowth was driven primarily by wireless technologiesand liberalization of telecommunications markets,which have enabled faster and less costly networkrollout. Developing countries’ share of world mobilesubscriptions rose from 53 percent in 2005 to 73percent in 2010. And the number of short messageservice texts sent globally tripled between 2007 and2010, from 1.8 trillion to 6.1 trillion. The InternationalTelecommunication Union (ITU) estimates thatthere were 5.9 billion mobile subscriptions globally in2011. No technology has ever spread faster aroundthe world. Mobile communications have a particularlyimportant impact in rural areas. The mobility,ease of use, fl exible deployment, and relatively lowand declining rollout costs of wireless technologiesenable them to reach rural populations with low levelsof income and literacy. The next billion mobilesubscribers will consist mainly of the rural poor.Access is the key to delivering telecommunicationsservices to people. If the service is not affordableto most people, goals of universal usage will not bemet. Two indicators of telecommunications affordabilityare presented in the table: fi xed- telephoneservice tariff and prepaid mobile cellular servicetariff. Telecommunications effi ciency is measuredby total telecommunications revenue divided by GDPand by mobile cellular and fi xed- telephone subscriptionsper employee.Operators have traditionally been the main sourceof telecommunications data, so information onsubscriptions has been widely available for mostcountries. This gives a general idea of access, but amore precise measure is the penetration rate—theshare of households with access to telecommunications.During the past few years more informationon information and communication technology usehas become available from household and businesssurveys. Also important are data on actual use oftelecommunications services. Ideally, statistics ontelecommunications (and other information and communicationstechnologies) should be compiled for allthree measures: subscriptions, access, and use. Thequality of data varies among reporting countries asa result of differences in regulations covering dataprovision and availability.• Electric power consumption per capita is the productionof power plants and combined heat and powerplants less transmission, distribution, and transformationlosses and own use by heat and power plants,divided by midyear population. • Electric powertransmission and distribution losses are losses intransmission between sources of supply and pointsof distribution and in distribution to consumers,including pilferage. • Fixed-telephone subscriptionsare the sum of the active number of analog fi xedtelephonelines, voice-over-IP subscriptions, fi xedwireless local loop subscriptions, Integrated ServicesDigital Network voice-channel equivalents, andfi xe d p u b li c p ay p h o n e s . • Mobile cellular telephonesubscriptions are subscriptions to a public mobiletelephone service using cellular technology, whichprovide access to the public switched telephonenetwork. Post-paid and prepaid subscriptions areincluded. • International voice traffic is the sum ofinternational incoming and outgoing telephone traffi c(in minutes) divided by total population. • Populationcovered by mobile cellular network is the percentageof people that live in areas served by a mobile cellularsignal regardless of whether they use it. • Residentialfixed- telephone tariff is the monthly subscriptioncharge plus the cost of 30 three-minute local calls(15 peak and 15 off-peak). • Mobile cellular prepaidtariff is based on the Organisation for Economic Cooperationand Development’s low-user defi nition,which includes the cost of monthly mobile use for25 outgoing calls per month spread over the samemobile network, other mobile networks, and mobileto fi xed- telephone calls and during peak, off-peak,and weekend times as well as 30 text messages permonth. • Telecommunications revenue is the revenuefrom the provision of telecommunications servicessuch as fi xed telephone, mobile, and Internetdivided by GDP. • Mobile cellular and fixed- telephonesubscriptions per employee are telephone subscriptions(fi xed telephone plus mobile) divided by the totalnumber of telecommunications employees.Data sourcesData on electricity consumption and losses arefrom the IEA’s Energy Statistics of Non-OECDCountries 2011, Energy Balances of Non-OECDCountries 2011, and Energy Statistics of OECDCountries 2011 and from the United Nations StatisticsDivision’s Energy Statistics Yearbook. Dataon telecommunications are from the ITU’s WorldTelecommunication/ICT Indicators database andTeleGeography.2012 World Development Indicators 327

5.12 The information societyPersonal computers and the Internet Information and communicationsDaily Householdstelevision a Use Quality Affordability Applicationnewspapers withtechnology tradeFixedbroadbandInternationalInternet Fixed SecureGoods Servicespeople % users a users a people capita $ per month people exports imports exportsper 100 peopleInternet bandwidth a broadband Internet Exports Imports Exportssubscriptions a bits per Internet servers % of total % of total % of totalper 1,000Computers Internet per 100 second per access tariff a per million goods goods services2000–05 b 2010 2010 2010 2010 2010 2010 December 2011 2010 2010 2010Afghanistan .. .. .. 3.7 0.00 58 .. 1 .. 0.4 ..Albania 24 .. .. 45.0 3.29 5,304 11 14 0.8 4.1 4.8Algeria .. 98 .. 12.5 2.54 1,015 15 1 0.0 3.0 3.5Angola 2 38 .. 10.0 0.10 63 133 3 .. .. 5.4Argentina 36 .. .. 36.0 9.56 9,898 26 34 0.1 9.1 11.7Armenia 8 97 .. 44.0 2.75 3,411 32 28 0.8 4.7 16.8Australia 155 .. .. 75.9 24.15 31,392 37 2,006 1.0 10.6 4.9Austria 311 .. 77.8 72.7 23.86 53,635 26 996 3.9 5.8 6.2Azerbaijan 16 100 38.6 46.7 5.08 4,524 12 5 0.0 3.5 4.0Bahrain .. .. 72.0 55.0 5.36 7,924 27 118 0.9 4.4 ..Bangladesh .. 36 .. 3.7 0.04 103 15 1 .. .. 13.0Belarus 81 98 .. 32.1 17.55 7,060 18 12 0.5 2.4 8.8Belgium 165 .. 78.8 73.7 30.96 82,599 25 604 2.3 3.5 9.2Benin 0 25 .. 3.1 0.04 70 50 1 .. .. ..Bolivia .. 69 .. 20.0 0.97 854 35 10 0.0 3.4 11.8Bosnia and Herzegovina .. 97 .. 52.0 8.18 8,138 14 20 0.1 2.9 ..Botswana 41 .. .. 6.0 0.60 386 30 9 0.3 3.1 5.5Brazil 36 98 44.1 40.7 6.81 5,130 17 54 1.0 9.5 2.0Bulgaria 79 99 48.0 46.0 14.44 29,520 13 139 2.5 5.6 8.9Burkina Faso .. 18 .. 1.4 0.09 49 83 1 0.0 2.5 10.5Burundi .. .. .. 2.1 0.00 2 .. 0 0.4 5.8 ..Cambodia .. 60 .. 1.3 0.25 354 47 3 0.1 2.5 5.5Cameroon .. 33 .. 4.0 0.01 16 80 1 0.0 2.6 3.6Canada 175 99 .. 81.3 29.71 43,955 26 1,369 2.8 8.4 11.4Central African Republic .. .. .. 2.3 0.00 4 1,329 0 0.0 6.8 ..Chad .. .. .. 1.7 0.00 2 12 .. .. .. ..Chile 51 .. 42.8 45.0 10.45 8,613 39 67 0.4 8.2 2.2China 74 .. .. 34.4 9.44 821 18 2 29.1 20.4 6.1Hong Kong SAR, China 222 .. 70.0 71.8 29.87 557,998 19 568 44.2 42.8 1.8Colombia 23 91 43.1 36.5 5.60 3,739 35 21 0.1 9.6 6.2Congo, Dem. Rep. .. .. .. 0.7 0.01 2 .. 0 .. .. ..Congo, Rep. .. 38 .. 5.0 0.00 2 .. 1 .. .. ..Costa Rica 65 96 42.6 36.5 6.19 4,630 7 111 19.9 17.7 26.6Côte d’Ivoire .. .. .. 2.6 0.04 203 40 1 0.2 3.3 11.0Croatia .. 97 58.0 60.1 18.19 25,804 18 225 2.1 5.5 3.6Cuba 65 .. .. 15.9 0.03 35 1,753 0 .. .. ..Cyprus .. 100 57.2 53.0 17.63 27,380 21 1,121 9.1 4.8 2.3Czech Republic 183 .. 70.7 68.6 14.46 47,529 31 387 15.0 17.8 8.6Denmark 353 98 89.9 88.8 37.72 126,194 44 2,185 3.6 7.9 ..Dominican Republic 39 86 .. 39.5 3.63 4,029 19 20 2.3 4.8 4.5Ecuador 99 85 37.5 29.0 1.37 2,394 20 20 0.1 6.3 ..Egypt, Arab Rep. .. 94 21.6 26.7 1.79 1,762 8 3 0.1 3.7 4.2El Salvador 38 83 21.0 15.9 2.83 242 25 17 0.3 5.6 17.7Eritrea .. .. .. 5.4 0.00 6 .. .. .. .. ..Estonia 191 99 75.6 74.2 25.10 17,164 21 534 7.8 9.5 8.9Ethiopia 5 .. .. 0.7 0.00 40 294 0 0.2 8.4 4.5Finland 431 .. 88.7 86.9 28.57 93,214 35 1,489 6.4 8.2 24.5France 164 99 78.0 77.5 32.89 53,933 30 356 4.4 7.3 4.2Gabon .. .. .. 7.2 0.27 3,767 .. 8 0.0 3.5 ..Gambia, The .. 76 .. 9.2 0.02 98 307 3 0.4 1.5 17.8Georgia 4 94 .. 26.3 5.70 5,615 39 19 0.2 5.3 2.2Germany 267 95 85.4 82.5 31.90 61,142 39 1,026 5.1 9.2 9.1Ghana .. 47 .. 9.5 0.21 139 32 2 0.0 7.1 ..Greece .. 100 49.5 44.6 19.95 13,816 19 154 2.5 5.0 2.4Guatemala .. 71 .. 10.5 1.80 417 33 14 0.9 6.8 14.3Guinea .. .. .. 1.0 0.01 15 800 0 0.0 5.2 21.6Guinea-Bissau .. .. .. 2.5 0.00 1 .. 1 .. .. ..Haiti .. .. .. 8.4 0.00 9 .. 1 .. .. ..3282012 World Development Indicators

The information society 5.12Personal computers and the Internet Information and communicationsDaily Householdstelevision a Use Quality Affordability Applicationnewspapers withtechnology tradeFixedbroadbandInternationalInternet Fixed SecureGoods Servicespeople % users a users a people capita $ per month people exports imports exportsper 100 peopleInternet bandwidth a broadband Internet Exports Imports Exportssubscriptions a bits per Internet servers % of total % of total % of totalper 1,000Computers Internet per 100 second per access tariff a per million goods goods services2000–05 b 2010 2010 2010 2010 2010 2010 December 2011 2010 2010 2010STATES AND MARKETSHonduras .. 69 .. 11.1 1.00 658 22 8 0.1 6.3 25.2Hungary 217 99 67.1 65.2 19.56 6,500 21 220 25.6 21.3 8.5India 71 60 .. 7.5 0.90 437 5 3 2.0 6.6 47.0Indonesia .. 72 .. 9.9 0.79 292 22 3 5.1 8.6 7.4Iran, Islamic Rep. .. 97 .. 13.0 0.68 406 30 1 0.0 3.6 ..Iraq .. 98 13.5 2.5 0.00 2 .. 0 .. .. 0.6Ireland 182 .. 72.0 69.8 21.04 44,693 33 1,145 7.5 10.2 39.0Israel .. 90 65.6 65.4 24.46 5,247 8 471 12.3 9.2 33.0Italy 137 .. 55.7 53.7 21.92 33,067 26 192 2.1 7.4 8.9Jamaica .. 88 .. 26.5 4.32 5,551 25 48 0.4 3.9 6.4Japan 551 99 66.9 77.6 26.71 12,293 23 743 10.7 12.0 1.3Jordan .. 98 56.0 38.9 3.24 2,481 19 25 1.3 4.3 ..Kazakhstan .. 87 .. 33.4 8.74 2,868 13 6 0.1 2.9 2.4Kenya .. 28 .. 25.9 0.01 499 38 3 1.4 7.2 9.8Korea, Dem. Rep. .. .. .. 0.0 0.00 .. .. 0 .. .. ..Korea, Rep. .. .. 81.5 82.5 35.18 9,802 24 2,536 21.4 11.9 1.2Kosovo .. .. .. .. .. .. .. .. .. .. ..Kuwait .. .. .. 38.3 1.68 3,655 19 180 0.3 6.4 ..Kyrgyz Republic 1 .. .. 19.6 0.28 55 55 3 0.6 2.7 3.3Lao PDR 3 .. .. 7.0 0.19 161 140 1 .. .. ..Latvia 154 .. 69.7 71.5 19.42 21,438 13 205 5.8 6.4 6.2Lebanon 54 .. .. 31.0 4.73 591 23 41 7.1 2.8 3.0Lesotho .. .. .. 3.9 0.02 12 51 0 20.9 2.7 4.5Liberia .. 9 .. 7.0 0.00 14 .. 1 .. .. ..Libya .. .. .. 14.0 1.15 1,574 .. 1 .. .. ..Lithuania 108 99 64.2 62.8 20.81 28,533 10 237 2.7 4.1 3.7Macedonia, FYR 89 .. 57.5 51.9 12.47 8,738 13 29 0.4 4.7 14.1Madagascar .. 16 .. 1.7 0.03 94 91 1 0.2 3.1 ..Malawi .. .. .. 2.3 0.03 1 562 0 0.4 5.2 ..Malaysia 109 .. .. 56.3 7.32 6,443 20 54 34.0 29.8 7.0Mali .. 31 .. 2.7 0.02 50 50 1 0.1 2.6 23.2Mauritania .. 25 .. 3.0 0.19 81 24 2 .. 0.9 ..Mauritius 77 97 35.8 28.7 6.18 2,646 16 117 1.1 5.1 3.8Mexico 93 95 40.1 31.1 9.98 2,272 17 27 20.2 19.2 ..Moldova .. 93 42.4 40.1 7.55 26,390 6 20 0.7 3.9 22.7Mongolia 20 89 .. 12.9 2.60 6,237 8 14 .. .. 2.0Morocco 12 93 50.9 49.0 1.56 2,347 12 4 3.8 5.9 8.0Mozambique 3 .. .. 4.2 0.06 55 22 1 0.1 1.8 7.0Myanmar .. .. .. 0.2 0.03 21 28 0 .. .. ..Namibia 28 42 .. 6.5 0.42 287 95 20 0.5 4.0 1.7Nepal .. 36 .. 7.9 0.20 134 23 2 0.4 7.2 ..Netherlands 307 99 91.7 90.7 38.10 139,986 33 2,757 12.5 14.5 11.7New Zealand 182 97 82.7 83.0 24.93 16,026 29 1,597 1.2 8.3 4.6Nicaragua .. 66 .. 10.0 0.82 864 34 10 0.1 4.8 ..Niger 0 10 0.9 0.8 0.02 19 60 0 0.3 1.9 12.8Nigeria .. 41 .. 28.4 0.06 32 53 2 0.0 6.6 ..Norway 516 .. 93.8 93.3 35.26 102,270 49 1,822 1.4 7.5 9.5Oman .. .. .. 62.0 1.63 3,786 31 53 0.1 3.1 ..Pakistan 50 68 .. 16.8 0.31 435 14 1 0.2 3.3 6.6Panama 65 92 .. 42.7 7.84 9,096 17 143 9.6 9.6 4.8Papua New Guinea 9 .. .. 1.3 0.09 41 140 7 .. .. 0.7Paraguay .. 88 .. 19.8 0.44 1,644 19 10 0.1 27.0 1.4Peru .. 77 .. 34.3 3.14 2,911 42 19 0.1 7.4 3.1Philippines 79 74 .. 25.0 1.85 2,681 22 8 35.6 31.6 12.6Poland 114 .. 65.4 62.5 12.99 23,570 18 270 9.5 9.7 6.4Portugal .. .. 58.2 51.3 19.30 75,202 26 224 4.0 5.7 4.2Puerto Rico .. .. 61.3 42.7 13.86 57,818 .. 98 .. .. ..Qatar .. 95 84.6 81.6 8.19 13,930 55 126 0.0 4.3 ..2012 World Development Indicators 329

5.12The information societyPersonal computers and the Internet Information and communicationsDaily Householdstelevision a Use Quality Affordability Applicationnewspapers withtechnology tradeFixedbroadbandInternationalInternet Fixed SecureGoods Servicespeople % users a users a people capita $ per month people exports imports exportsper 100 peopleInternet bandwidth a broadband Internet Exports Imports Exportssubscriptions a bits per Internet servers % of total % of total % of totalper 1,000Computers Internet per 100 second per access tariff a per million goods goods services2000–05 b 2010 2010 2010 2010 2010 2010 December 2011 2010 2010 2010Romania 70 .. 45.9 40.0 13.90 20,571 5 54 8.4 9.3 18.0Russian Federation 92 99 40.3 43.4 11.08 13,346 10 27 0.2 7.9 6.0Rwanda .. 3 .. 13.0 0.02 155 86 1 0.6 11.7 4.4Saudi Arabia .. .. .. 41.0 5.45 11,584 27 22 0.1 7.2 ..Senegal 9 57 29.9 16.0 0.63 386 36 1 0.4 3.3 12.9Serbia .. .. 54.5 43.1 11.77 20,241 15 29 1.6 4.2 9.2Sierra Leone .. 10 .. 0.3 .. 29 .. 1 .. .. ..Singapore 361 .. 69.1 71.1 24.99 122,454 27 607 34.3 27.9 2.8Slovak Republic 126 .. 82.4 79.9 12.79 9,208 26 164 19.3 15.4 9.2Slovenia 173 .. 71.0 69.3 24.02 48,804 34 433 2.2 4.6 7.3Somalia .. .. .. 1.2 0.00 .. .. 0 .. .. ..South Africa 30 75 .. 12.3 1.49 211 27 74 1.0 9.4 3.7South Sudan .. .. .. .. .. .. .. .. .. .. ..Spain 144 .. 69.7 65.8 22.87 36,900 26 284 2.2 6.7 6.9Sri Lanka 26 .. .. 12.0 1.09 398 5 6 0.5 2.9 14.1Sudan .. .. .. 10.2 0.38 305 23 0 0.0 3.3 25.8Swaziland 24 .. .. 9.0 0.15 52 875 15 .. .. 9.0Sweden 481 .. 92.8 90.0 31.85 213,265 35 1,455 9.8 11.3 13.9Switzerland 420 .. .. 82.2 37.16 127,779 33 2,153 1.6 5.9 ..Syrian Arab Republic .. .. .. 20.7 0.33 280 22 0 0.0 1.1 1.9Tajikistan .. .. .. 11.5 0.07 .. 362 1 .. .. 37.0Tanzania 2 10 .. 11.0 0.01 77 21 0 0.4 3.8 2.1Thailand .. 97 30.9 21.2 4.61 2,296 19 17 18.9 14.2 ..Timor-Leste .. .. .. 0.2 0.04 28 99 3 .. .. ..Togo 2 .. .. 5.4 0.06 230 166 2 0.2 5.1 18.6Trinidad and Tobago 149 .. .. 48.5 10.81 8,656 13 85 0.2 3.0 ..Tunisia 23 .. .. 36.6 4.57 4,854 11 19 6.5 6.3 5.9Turkey .. .. 41.0 39.8 9.73 7,601 19 143 1.8 4.5 1.6Turkmenistan 9 .. .. 2.2 0.01 77 .. 0 .. .. ..Uganda .. 6 .. 12.5 0.16 108 14 2 5.7 7.4 5.2Ukraine 131 95 .. 44.6 6.44 2,616 8 18 1.1 3.2 5.5United Arab Emirates .. 96 74.0 78.0 10.47 13,991 41 180 2.0 4.5 ..United Kingdom 290 .. 86.7 84.7 31.46 112,482 25 1,594 5.9 9.3 8.8United States 193 .. .. 74.2 27.71 29,093 20 1,562 10.5 14.2 4.6Uruguay .. 94 49.1 47.9 10.95 11,004 19 70 0.1 6.4 8.2Uzbekistan .. .. .. 19.4 0.32 53 200 0 .. .. ..Venezuela, RB 93 96 .. 35.9 5.40 2,428 16 8 0.0 7.6 8.2Vietnam .. 87 .. 27.9 4.18 1,546 10 5 5.8 8.6 ..West Bank and Gaza 10 96 55.6 36.4 .. .. .. 4 0.9 3.2 6.0Yemen, Rep. 4 .. .. 12.3 0.35 133 119 0 0.0 2.0 ..Zambia 5 27 .. 10.1 0.08 39 59 2 0.0 2.3 8.0Zimbabwe .. .. .. 11.5 0.26 35 406 1 0.0 3.6 ..World 104 w .. m .. w 30.2 w 7.75 w 8,662 w 26 m 184 w 11.1 w 12.7 w 9.3 wLow income .. .. .. 5.6 0.05 93 55 1 .. .. ..Middle income 68 87 .. 23.8 4.65 1,856 20 12 14.2 14.2 13.6Lower middle income .. 72 .. 13.5 1.04 656 32 3 8.3 10.4 27.9Upper middle income 69 96 .. 34.1 8.36 3,084 18 20 15.5 15.0 5.8Low & middle income 65 84 .. 21.5 4.05 1,628 23 10 14.1 14.0 13.5East Asia & Pacifi c 74 .. .. 29.8 7.18 992 22 4 26.2 21.3 7.1Europe & Central Asia .. .. 40.9 39.3 9.12 9,777 13 48 1.2 5.5 5.9Latin America & Carib. 64 87 .. 34.0 6.59 4,062 23 36 10.3 14.0 ..Middle East & N. Africa .. 97 .. 20.9 1.36 1,139 17 3 .. 4.2 ..South Asia 68 48 .. 8.1 0.73 394 14 2 1.6 5.7 43.8Sub-Saharan Africa .. 26 .. 11.3 0.18 116 52 6 0.5 7.0 ..High income 255 .. .. 73.4 26.46 44,235 26 1,068 10.4 12.5 8.1Euro area 201 .. 73.7 71.2 27.65 53,594 26 647 5.2 8.2 10.7a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators database. Please cite the ITU for third-party use of these data. b. Data are forthe most recent year available.3302012 World Development Indicators

The information society5.12STATES AND MARKETSAbout the dataThe digital and information revolution has changedthe way the world learns, communicates, doesbusiness, and treats illnesses. New informationand communications technologies (ICT) offer vastopportunities for progress in all walks of life in allcountries —opportunities for economic growth,improved health, better service delivery, learningthrough distance education, and social and culturaladvances.Comparable statistics on access, use, quality,and affordability of ICT are needed to formulategrowth-enabling policies for the sector and to monitorand evaluate the sector’s impact on development.Although basic access data are available formany countries, in most developing countries littleis known about who uses ICT; what they are used for(school, work, business, research, government); andhow they affect people and businesses. The globalPartnership on Measuring ICT for Development ishelping to set standards, harmonize information andcommunications technology statistics, and build statisticalcapacity in developing countries. For moreinformation see www.itu.int/ITU-D/ict/partnership/.Data on daily newspaper circulation are fromUnited Nations Educational, Scientifi c, and CulturalOrganization (UNESCO) Institute for Statistics surveyson newspaper statistics.Estimates of households with television are derivedfrom household surveys. Some countries report onlythe number of households with a color television set,and so the true number may be higher than reported.Data on computer users, Internet users, andrelated indicators (broadband and bandwidth) are collectedby national statistics offi ces through householdsurveys. Since survey questions and defi nitionsdiffer, the estimates may not be strictly comparableacross countries. In particular, in the “post-PC age”what constitutes a computer is becoming harderto defi ne. Today’s smartphones and tablets havecomputer power equivalent to that of yesterday’scomputers and provide a similar range of functions.Device convergence is thus rendering the conventionaldefi nition obsolete. Countries without surveysgenerally derive their estimates by multiplying subscribercounts reported by Internet service providersby a multiplier. This method may undercount actualusers, particularly in developing countries, wheremany commercial subscribers rent out computersconnected to the Internet or prepaid cards are usedto access the Internet.Broadband refers to technologies that provideInternet speeds of at least 256 kilobits a secondof upstream and downstream capacity and includesdigital subscriber lines, cable modems, satellitebroadband Internet, fi ber-to-home Internet access,Ethernet local access networks, and wireless areanetworks. Bandwidth refers to the range of frequenciesavailable for signals. The higher the bandwidth,the more information that can be transmitted atone time. Reporting countries may have differentdefi nitions of broadband, so data are not strictlycomparable.The number of secure Internet servers, from theNetcraft Secure Server Survey, indicates how manycompanies conduct encrypted transactions over theInternet. The survey examines the use of encryptedtransactions through extensive automated exploration,tallying the number of Web sites using a securesocket layer (SSL). The country of origin of more thana third of the 1.5 million distinct valid third-partycertifi cates is unknown. Some countries, such as theRepublic of Korea, use application layers to establishthe encryption channel, which is SSL equivalent;these data are reported in the table.Information and communication technology goodsexports and imports are defi ned by the WorkingParty on Indicators for the Information Society andare reported in the Organisation for Economic Cooperationand Development’s Guide to Measuring theInformation Society (2005). Information and communicationtechnology service exports data are basedon the International Monetary Fund’s (IMF) Balanceof Payments Statistics Yearbook classifi cation.Definitions• Daily newspapers are newspapers published atleast four times a week that report mainly on eventssince the previous issue. The indicator is averagecirculation per 1,000 people. • Households withtelevision are the percentage of households with atelevision set. • Computer users are individuals whohave used a computer (in any location) in the last12 months. Computers include desktop, portable,or handheld computers (such as a personal digitalassistant) and exclude equipment with some embeddedcomputing abilities (such as mobile phones ortelevision sets). • Internet users are individuals whohave used the Internet (in any location) with a devicesuch as a computer, smartphone, or digital televisionin the last 12 months via a fi xed or mobile network.The Internet provides access to the worldwide network.• Fixed broadband Internet subscriptions arethe number of fi xed broadband subscriptions witha digital subscriber line, cable modem, or otherhigh-speed technology (excluding wireless). • InternationalInternet bandwidth is the contracted capacityof international connections between countriesfor transmitting Internet traffi c. • Fixed broadbandInternet access tariff is the lowest sampled cost per100 kilobits a second per month and are calculatedfrom low- and high-speed monthly service charges.Monthly charges do not include installation fees ormodem rentals. • Secure Internet servers are serversusing encryption technology in Internet transactions.• Information and communication technologygoods exports and imports include telecommunications,audio and video, computer and related equipment;electronic components; and other informationand communication technology goods. Software isexcluded. • Information and communication technologyservices exports include computer andcommunications services (telecommunications andpostal and courier services) and information services(databases, data processes, software design anddevelopment, maintenance and repair, and newsrelatedservice transactions).Data sourcesData on newspapers are compiled by the UNESCOInstitute for Statistics. Data on televisions, computerusers, Internet users, Internet broadbandusers and cost, and Internet bandwidth are fromthe ITU’s World Telecommunication/ICT Indicatorsdatabase and TeleGeography. Data on secureInternet servers are from Netcraft (www.netcraft.com) and offi cial government sources. Data oninformation and communication technology goodstrade are from the United Nations Conference onTrade and Development’s UNCTADstat database(http://unctadstat.unctad.org). Data on informationand communication technology servicesexports are from the IMF’s Balance of PaymentsStatistics database.2012 World Development Indicators 331

5.13 Science and technologyResearch anddevelopment(R&D)ScientificandtechnicaljournalarticlesExpendituresfor R&DHigh-technologyexportsRoyalty andlicense feesPatentapplicationsfiled a,bTrademarkapplicationsfiled a,cfull-time equivalentper million people% of manufactured$ millionsNonresidentsResearchers Technicians % of GDP $ millions exports Receipts Payments ResidentsTotal2005–09 d 2005–09 d 2009 2005–09 d 2010 2010 2010 2010 2010 2010 2010Afghanistan .. .. 12 .. .. .. .. .. .. .. ..Albania 147 38 8 0.15 9 0.9 1 12 .. 361 2,920Algeria 170 34 607 0.07 5 0.5 2 17 76 730 5,632Angola .. .. 6 .. .. .. 12 6 .. .. ..Argentina 1,046 207 3,655 0.52 1,635 7.5 119 1,541 801 4,781 69,565Armenia .. .. 164 0.27 4 1.8 .. .. 136 6 4,620Australia 4,259 1,144 18,923 2.35 3,826 11.9 703 3,026 2,409 22,478 59,459Austria 4,122 1,959 4,832 2.75 13,721 11.9 646 1,403 2,424 249 10,375Azerbaijan .. .. 151 0.26 6 1.1 0 17 222 5 3,310Bahrain .. .. 36 .. 1 0.1 .. .. .. .. 2,044Bangladesh .. .. 260 .. 134 1.2 1 18 66 276 10,231Belarus .. .. 380 0.64 408 3.0 9 101 1,759 174 10,695Belgium 3,491 1,406 7,218 1.96 32,227 10.5 2,138 1,904 620 140 25,799 eBenin .. .. 48 .. .. .. 0 3 .. .. ..Bolivia .. .. 45 .. 37 8.6 3 20 .. .. 6,081Bosnia and Herzegovina 197 71 64 0.02 71 2.6 15 5 56 9 4,730Botswana .. .. 45 0.52 15 0.4 0 11 .. .. 674Brazil 696 560 12,306 1.08 8,122 11.2 397 2,850 2,705 19,981 125,654Bulgaria 1,587 492 735 0.53 802 7.9 34 115 243 17 7,140Burkina Faso .. .. 50 0.21 3 7.8 0 1 2 .. 34Burundi .. .. 3 .. 1 8.5 .. .. .. .. ..Cambodia .. .. 27 .. 5 0.1 0 6 .. .. 2,866Cameroon .. .. 145 .. 14 4.9 0 12 .. .. ..Canada 4,335 1,740 29,017 1.95 23,966 14.1 3,813 8,665 4,550 30,899 45,220Central African Republic .. .. 4 .. .. .. .. .. .. .. ..Chad .. .. 2 .. .. .. .. .. .. .. ..Chile 355 293 1,868 0.39 483 5.5 64 496 328 748 45,104China 1,199 .. 74,019 1.47 406,090 27.5 830 13,040 293,066 98,111 1,057,480Hong Kong SAR, China 2,759 352 .. 0.79 1,106 16.1 383 1,700 133 11,569 28,872Colombia 157 .. 608 0.16 425 5.1 56 362 133 1,739 25,990Congo, Dem. Rep. .. .. 19 0.48 .. .. .. .. .. .. ..Congo, Rep. .. .. 18 .. .. .. .. .. .. .. ..Costa Rica 257 .. 98 0.40 2,193 40.0 8 64 8 1,212 11,265Côte d’Ivoire 70 .. 56 .. 126 8.2 0 21 .. .. ..Croatia 1,571 636 1,164 0.83 732 9.2 32 225 257 21 7,950Cuba .. .. 222 0.49 .. .. .. .. 59 172 1,397Cyprus 752 216 195 0.46 140 37.1 8 31 4 4 2,381Czech Republic 2,755 1,533 3,946 1.53 17,469 15.3 105 771 868 114 11,048Denmark 6,390 2,628 5,306 3.02 8,291 14.2 .. .. 1,626 142 5,788Dominican Republic .. .. 6 .. 102 2.9 .. 63 .. .. 6,453Ecuador 106 31 68 0.26 145 8.4 .. 54 4 690 16,195Egypt, Arab Rep. 420 394 2,247 0.21 96 0.9 122 226 605 1,625 3,955El Salvador .. .. 6 0.11 186 5.8 0 31 .. .. ..Eritrea .. .. 4 .. .. .. .. .. .. .. ..Estonia 3,210 627 518 1.44 721 9.1 20 60 84 13 3,140Ethiopia 21 13 175 0.17 6 3.1 0 1 12 25 719Finland 7,647 .. 4,949 3.96 5,776 10.8 2,340 1,236 1,731 102 5,504France 3,690 1,872 31,748 2.23 99,736 24.9 10,407 5,559 14,748 1,832 93,187Gabon .. .. 18 0.64 7 3.0 .. .. .. .. ..Gambia, The .. .. 20 0.02 0 1.1 .. .. .. .. 327Georgia .. .. 129 0.18 11 1.8 5 7 179 180 4,301Germany 3,780 1,329 45,003 2.82 158,507 15.3 14,384 13,051 47,047 12,198 74,339Ghana 17 15 102 0.23 8 2.0 .. .. .. .. 884Greece 1,849 756 4,881 0.58 1,090 10.2 69 627 728 16 6,559Guatemala 39 35 22 0.06 205 5.7 13 94 7 374 9,175Guinea .. .. 3 .. 0 0.1 0 1 .. .. ..Guinea-Bissau .. .. 6 .. .. .. .. 0 .. .. 6Haiti .. .. 7 .. .. .. .. 0 .. .. ..3322012 World Development Indicators

Research anddevelopment(R&D)ScientificandtechnicaljournalarticlesScience and technology 5.13Expendituresfor R&DHigh-technologyexportsRoyalty andlicense feesPatentapplicationsfiled a,bTrademarkapplicationsfiled a,cfull-time equivalentper million people% of manufactured$ millionsNonresidentsResearchers Technicians % of GDP $ millions exports Receipts Payments ResidentsTotal2005–09 d 2005–09 d 2009 2005–09 d 2010 2010 2010 2010 2010 2010 2010STATES AND MARKETSHonduras .. .. 6 .. 12 1.3 .. 30 .. .. 7,403Hungary 2,006 553 2,397 1.15 18,771 24.2 1,051 1,386 649 47 6,298India 136 93 19,917 0.76 10,087 7.2 129 2,438 7,262 27,025 141,943Indonesia 90 .. 262 0.08 6,673 11.4 60 1,616 .. .. 47,794Iran, Islamic Rep. 751 .. 6,313 0.79 584 4.5 .. .. .. .. 3,096Iraq 49 26 70 .. 0 0.1 1,312 396 .. .. ..Ireland 3,373 742 2,799 1.77 21,232 21.2 2,252 37,823 733 59 3,769Israel .. .. 6,304 4.27 7,979 14.7 849 860 1,450 5,856 8,614Italy 1,690 .. 26,755 1.27 26,366 7.2 3,603 6,986 8,814 903 4,387Jamaica .. .. 51 .. 3 0.6 5 36 .. .. 1,708Japan 5,189 597 49,627 3.45 122,047 18.0 26,680 18,769 290,081 54,517 124,726Jordan .. .. 383 0.42 122 2.9 .. .. 45 429 5,971Kazakhstan .. .. 99 0.23 2,110 29.9 .. 86 11 162 3,615Kenya 56 63 291 0.42 100 5.7 54 18 77 120 4,321Korea, Dem. Rep. .. .. 8 .. .. .. .. .. 8,018 39 1,231Korea, Rep. 4,947 825 22,271 3.36 92,856 28.7 3,146 8,965 131,805 38,296 129,486Kosovo .. .. .. .. .. .. .. .. .. .. ..Kuwait 152 35 214 0.11 16 0.5 .. .. .. .. ..Kyrgyz Republic .. .. 15 0.16 2 1.0 1 3 134 6 2,535Lao PDR .. .. 12 .. .. .. .. .. .. .. ..Latvia 1,601 561 162 0.46 396 7.6 12 33 178 7 3,589Lebanon .. .. 256 .. 279 12.8 7 13 .. .. ..Lesotho 21 22 4 0.03 0 0.2 .. 3 .. .. 566Liberia .. .. 0 .. .. .. .. .. .. .. 612Libya .. .. 34 .. .. .. .. .. .. .. ..Lithuania 2,541 445 388 0.84 1,190 10.6 1 35 108 6 4,351Macedonia, FYR 472 82 57 0.23 40 2.9 7 18 34 406 3,436Madagascar 46 25 35 0.15 5 1.0 .. .. 9 34 1,773Malawi 30 58 53 .. 1 1.3 .. 0 .. .. ..Malaysia 365 43 1,351 0.63 59,332 44.5 266 1,133 1,233 5,230 26,370Mali 38 12 25 0.25 2 2.4 0 3 .. .. ..Mauritania .. .. 3 .. .. .. .. .. .. .. ..Mauritius .. .. 22 0.37 6 0.7 1 12 2 22 2,032Mexico 347 183 4,128 0.37 37,657 16.9 .. .. 951 13,625 94,457Moldova 794 78 80 0.53 17 8.3 5 13 134 5 5,459Mongolia .. .. 42 0.24 7 7.4 0 3 110 69 2,403Morocco 661 61 391 0.64 897 7.7 4 30 152 882 11,030Mozambique 16 35 29 0.21 1 1.3 0 4 18 22 891Myanmar .. .. 10 .. .. .. .. .. .. .. ..Namibia .. .. 14 .. 19 0.9 0 8 .. .. 804Nepal .. .. 56 .. 4 0.6 .. .. .. .. 1,132Netherlands 2,818 1,131 14,866 1.84 59,510 21.3 5,491 3,707 2,575 279 ..New Zealand 4,324 886 3,188 1.17 548 9.0 183 669 1,585 5,051 17,124Nicaragua .. .. 12 .. 6 4.8 .. .. .. .. 5,975Niger 8 11 16 .. 4 6.6 0 2 .. .. ..Nigeria 39 13 462 0.22 63 1.1 .. 224 .. .. ..Norway 5,504 .. 4,440 1.80 3,830 16.1 498 536 1,117 696 13,835Oman .. .. 114 .. 18 0.6 .. .. .. .. 1,913Pakistan 162 64 1,043 0.46 262 1.7 4 123 170 1,375 15,734Panama 111 .. 73 0.21 1 0.8 .. 46 .. 468 9,629Papua New Guinea .. .. 17 .. .. .. .. .. 1 45 612Paraguay 75 .. 11 0.06 32 6.6 254 3 18 347 22,102Peru .. .. 159 .. 252 6.6 3 197 39 261 23,120Philippines 78 11 223 0.11 29,792 67.8 4 445 166 3,223 16,838Poland 1,598 189 7,355 0.68 8,378 6.6 237 2,248 3,203 227 18,251Portugal 4,308 383 4,157 1.66 1,213 3.4 41 548 499 46 19,636Puerto Rico 668 .. .. 0.49 .. .. .. .. .. .. ..Qatar .. .. 64 .. 1 0.0 .. .. .. .. ..2012 World Development Indicators 333

5.13Science and technologyResearch anddevelopment(R&D)ScientificandtechnicaljournalarticlesExpendituresfor R&DHigh-technologyexportsRoyalty andlicense feesPatentapplicationsfiled a,bTrademarkapplicationsfiled a,cfull-time equivalentper million people% of manufactured$ millionsNonresidentsResearchers Technicians % of GDP $ millions exports Receipts Payments ResidentsTotal2005–09 d 2005–09 d 2009 2005–09 d 2010 2010 2010 2010 2010 2010 2010Romania 895 185 1,367 0.48 4,249 10.9 466 453 1,382 36 12,063Russian Federation 3,091 475 14,016 1.25 5,193 8.8 625 5,066 28,722 13,778 56,856Rwanda 12 .. 12 .. 1 5.9 0 0 .. .. 238Saudi Arabia .. .. 710 0.08 201 0.7 .. .. 288 643 ..Senegal 384 53 56 0.37 9 1.2 1 12 .. .. ..Serbia 1,060 224 1,173 0.89 .. .. 39 156 290 39 7,005Sierra Leone .. .. 3 .. .. .. 1 0 .. .. 676Singapore 5,834 597 4,187 2.66 126,982 49.9 1,867 15,857 895 8,878 17,504Slovak Republic 2,438 345 1,000 0.48 3,921 7.0 45 147 234 48 5,027Slovenia 3,679 1,863 1,234 1.86 1,131 5.5 69 369 442 11 3,894Somalia .. .. 1 .. .. .. .. .. .. .. ..South Africa 396 124 2,864 0.93 1,420 4.3 59 1,941 821 5,562 30,549South Sudan .. .. .. .. .. .. .. .. .. .. ..Spain 2,932 1,148 21,543 1.38 11,290 6.4 877 2,649 3,566 213 47,120Sri Lanka 96 77 135 0.11 57 1.0 .. .. 225 235 6,244Sudan .. .. 63 0.29 10 29.4 3 11 3 13 1,026Swaziland .. .. 8 .. 1 0.1 0 16 .. .. 659Sweden 5,018 2,006 9,478 3.62 16,133 13.9 6,133 1,383 2,196 353 12,662Switzerland 3,320 2,874 9,469 3.00 42,820 24.8 .. .. 1,622 533 27,972Syrian Arab Republic .. .. 72 .. 86 1.8 1 37 .. .. 2,362Tajikistan .. .. 12 0.09 .. .. 1 0 7 3 2,293Tanzania .. .. 152 0.43 25 3.5 0 0 .. .. 556Thailand 316 140 2,033 0.21 34,156 24.0 153 3,084 1,214 723 37,656Timor-Leste .. .. .. .. .. .. .. .. .. .. ..Togo 38 18 7 .. 0 0.1 .. 5 .. .. ..Trinidad and Tobago .. .. 48 0.04 3 0.2 .. .. 1 280 ..Tunisia 1,863 43 1,022 1.10 611 4.9 25 15 .. .. ..Turkey 804 122 8,301 0.85 1,714 1.9 .. 816 2,555 177 8,241Turkmenistan .. .. 1 .. .. .. .. .. .. .. 2,245Uganda .. .. 143 0.41 6 2.4 4 4 6 1 ..Ukraine 1,353 288 1,639 0.86 1,441 4.3 132 744 2,556 2,756 28,915United Arab Emirates .. .. 265 .. 50 3.2 .. .. .. .. ..United Kingdom 3,947 871 45,649 1.87 59,447 20.9 13,822 8,499 15,490 6,439 36,484United States 4,673 .. 208,601 2.79 145,498 19.9 105,583 33,450 241,977 248,249 281,867Uruguay 346 .. 246 0.66 79 5.8 0 17 23 761 5,730Uzbekistan .. .. 139 .. .. .. .. .. 370 262 4,863Venezuela, RB 183 .. 354 .. 145 5.1 .. 340 .. .. ..Vietnam .. .. 326 .. 2,101 6.2 .. .. 306 3,276 32,289West Bank and Gaza 144 27 .. .. .. .. 0 0 .. .. ..Yemen, Rep. .. .. 25 .. 0 0.4 33 5 20 55 4,165Zambia 43 67 35 0.34 4 1.0 .. 1 .. .. 765Zimbabwe .. .. 56 .. 9 0.8 .. .. .. .. ..World 1,269 w .. w 788,333 s 2.15 w 1,572,076 s 17.5 w 210,577 s 216,352 s 1,060,313 s 621,207 s 3,023,628 sLow income .. .. 1,561 .. .. 3.1 62 56 .. .. ..Middle income 591 .. 167,803 1.07 490,375 17.9 3,612 37,290 281,357 190,556 1,826,974Lower middle income .. .. 28,049 0.61 46,413 11.0 665 6,170 11,816 44,547 352,071Upper middle income 1,197 .. 139,753 1.10 569,935 19.5 2,947 31,120 333,407 164,472 1,737,397Low & middle income 574 .. 169,364 1.07 527,339 17.8 3,674 37,346 289,428 191,053 1,850,304East Asia & Pacifi c 1,199 .. 78,373 1.47 .. 28.7 1,049 18,196 304,113 110,671 1,222,061Europe & Central Asia 2,006 329 29,089 0.96 17,622 6.7 1,352 7,678 36,143 17,415 187,119Latin America & Carib. 482 346 23,970 0.65 51,633 10.9 970 6,291 4,216 40,206 466,522Middle East & N. Africa .. .. 11,421 .. 1,572 3.2 37 326 .. .. 36,211South Asia 129 86 21,432 0.75 .. 6.7 142 2,578 7,519 27,500 172,509Sub-Saharan Africa .. .. 5,079 0.58 2,570 2.8 124 2,277 .. .. ..High income 3,982 .. 618,970 2.44 1,081,514 17.4 206,903 179,006 775,219 450,049 1,183,907Euro area 3,119 1,355 171,873 2.09 439,190 14.9 42,900 76,676 72,951 14,959 305,982a. Original information was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation ofthese data. b. Excludes applications fi led under the auspices of the European Patent Offi ce (150,961 by nonresidents) and the Eurasian Patent Organization (3,329 by nonresidents). c. Excludesapplications fi led under the auspices of the Offi ce for Harmonization in the Internal Market (98,616). d. Data are for the most recent year available. e. Includes Luxembourg and the Netherlands.3342012 World Development Indicators

Science and technology 5.13STATES AND MARKETSAbout the dataDefinitionsThe United Nations Educational, Scientifi c, andCultural Organization (UNESCO) Institute for Statisticscollects data on researchers, technicians, andexpenditure on research and development (R&D)through its biennial R&D survey and from otherinternational partners such as the Organisation forEconomic Co-operation and Development (OECD),Eurostat, and the Network for Science and TechnologyIndicators—Ibero-American and Inter-American.The OECD’s Frascati Manual 2002 (OECD 2002)defi nes research and experimental development as“creative work undertaken on a systemic basis inorder to increase the stock of knowledge, includingknowledge of man, culture and society, and the useof this stock of knowledge to devise new applications.”R&D covers basic research, applied research,and experimental development. Data on researchersand technicians in R&D are measured in bothfull-time equivalent and headcount but are shown infull-time equivalent only.Scientifi c and technical article counts are from journalsclassifi ed by the Institute for Scientifi c Information’sScience Citation Index (SCI) and Social SciencesCitation Index (SSCI). Counts are based on fractionalassignments; articles with authors from differentcountries are allocated proportionately to each country(see Defi nitions for fi elds covered). The SCI and SSCIdatabases cover the core set of scientifi c journals butmay exclude some of local importance and may refl ectsome bias toward English-language journals.R&D expenditures include expenditures from allsources for R&D performed within a country, includingcapital expenditures and current costs (wagesand associated costs of researchers, technicians,and other supporting staff and other current costs,including noncapital purchases of materials, supplies,and minor equipment to support R&D such as utilities,reference materials, subscriptions to libraries andscientifi c societies, and materials for laboratories).The method for determining high- technologyexports was developed by the Organisation for EconomicCo-operation and Development in collaborationwith Eurostat. It takes a “product approach”(rather than a “sectoral approach”) based on R&Dintensity (expenditure divided by total sales) forgroups of products from Germany, Italy, Japan,the Netherlands, Sweden, and the United States.Because industrial sectors specializing in a few hightechnologyproducts may also produce low-technologyproducts, the product approach is more appropriatefor international trade. The method takes onlyR&D intensity into account, but other characteristicsof high technology are also important, such as knowhow,scientifi c personnel, and technology embodiedin patents. Considering these characteristics wouldyield a different list (see Hatzichronoglou 1997).A patent is an exclusive right granted for a specifi edperiod (generally 20 years) for a new way of doingsomething or a new technical solution to a problem—an invention. The invention must be of practical useand display a characteristic unknown in the existingbody of knowledge in its fi eld. Most countries havesystems to protect patentable inventions. The PatentCooperation Treaty (www.wipo.int/pct) provides a twophasesystem for fi ling patent applications in 144 eligiblecountries (as of November 2011). Internationalapplications under the treaty provide for a nationalpatent grant only—there is no international patent.The national fi ling represents the applicant’s seekingof patent protection for a given territory, whereasinternational fi lings, while representing a legal right,do not accurately refl ect where patent protection issought. Resident fi lings are those from residents ofthe country concerned. Nonresident fi lings are fromapplicants abroad. For regional offi ces applicationsfrom residents of any member state of the regionalpatent convention are considered nonresident fi lings.Some offi ces (notably the U.S. Patent and TrademarkOffi ce) use the residence of the inventor rather thanthe applicant to classify fi lings.A trademark is a distinctive sign identifying goodsor services as produced or provided by a specifi c personor enterprise. A trademark protects the owner ofthe mark by ensuring exclusive right to use it to identifygoods or services or to authorize another to useit. The period of protection varies, but a trademarkcan be renewed indefi nitely for an additional fee.Detailed components of trademark fi lings, availableon the World Development Indicators CD-ROM andat http://data.worldbank.org, include applicationsfi led by direct residents (domestic applicants fi lingdirectly at a given national or regional intellectualproperty [IP] offi ce); direct nonresident (foreign applicantsfi ling directly at a given national or regional IPoffi ce); aggregate direct (applicants not identifi ed asdirect resident or direct nonresident by the nationalor regional offi ce); and Madrid (designations receivedby the national or regional IP offi ce based on internationalapplications fi led via the World IntellectualProperty Organization–administered Madrid System).Data are based on information supplied to WIPO byIP offi ces in annual surveys, supplemented by datain national IP offi ce reports. Data may be missing forsome offi ces or periods.• Researchers in research and development (R&D)are professionals engaged in conceiving of or creatingnew knowledge, products, processes, methods, andsystems and in managing the projects concerned.Postgraduate doctoral students (International StandardClassifi cation of Education 1997 level 6) engagedin R&D are considered researchers. • Technicians inR&D and equivalent staff are people whose main tasksrequire technical knowledge and experience in engineering,physical and life sciences (technicians), andsocial sciences and humanities (equivalent staff). Theyengage in R&D by performing scientifi c and technicaltasks involving the application of concepts and operationalmethods, normally under researcher supervision.• Scientific and technical journal articles are publishedarticles in physics, biology, chemistry, mathematics,clinical medicine, biomedical research, engineering andtechnology, and earth and space sciences. • Expendituresfor R&D are current and capital expenditureson creative work undertaken on a systematic basis toincrease the stock of knowledge, including knowledgeon humanity, culture, and society, and the use of knowledgeto devise new applications. • High-technologyexports are products with high R&D intensity, suchas in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery. • Royaltyand license fees are payments and receipts betweenresidents and nonresidents for authorized use of intangible,nonproduced, nonfinancial assets and proprietaryrights (such as patents, copyrights, trademarks, andindustrial processes) and for the use, through licensing,of produced originals of prototypes (such as filmsand manuscripts). • Patent applications filed are patentapplications at a national or regional patent offi ce;an international patent application (Patent CooperationTreaty filing) provides a national patent grant only.• Trademark applications filed are applications to registera trademark with a national or regional IP offi ce.Data sourcesData on R&D are provided by the UNESCO Institutefor Statistics. Data on scientifi c and technicaljournal articles are from the U.S. National ScienceBoard’s Science and Engineering Indicators 2012.Data on high-technology exports are from the UnitedNations Statistics Division’s Commodity Trade(Comtrade) database. Data on royalty and licensefees are from the International Monetary Fund’sBalance of Payments Statistics Yearbook. Data onpatents and trademarks are from the World IntellectualProperty Organization (www.wipo.int/ipstats).2012 World Development Indicators 335

GLOBALLINKS

The world economy is bound together bytrade in goods and services, fi nancial fl ows,and movements of people. As economiesdevelop, their links expand and grow more complex.The indicators in Global links measure thesize and direction of these fl ows and documentpolicy interventions, such as tariffs, trade facilitation,and aid fl ows.During economic crises, declining trade andfi nancing and changes in migration patterns inone country or region can transmit shocks toothers. An example is the fi nancial crisis thatbegan in the United States in 2007 and spreadrapidly to Europe. But the same links thattransfer shocks also allow expansion into newmarkets and access to new sources of fi nance,reducing the impacts of shocks. In an increasinglymultipolar world, developing countries aretrading and investing more with each other,reducing their dependence on high-incomeeconomies. Evidence of this shift can be foundin the tables of Global links.Capital infl ows rebounded strongly in 2010,especially in low- and middle-income economies,where combined net debt and equityinfl ows rose 37 percent, to $799 billion. Foreigndirect investment (FDI) infl ows rose3 percent—with wide disparity across incomegroups. In high-income economies FDI infl owsfell 7 percent, dragged down by a 13 percentdrop in net infl ows to the euro area. But infl owsto the United States—the world’s largest FDIrecipient—surged. And low- and middle-incomeeconomies saw net FDI infl ows jump 27 percent,to $514 billion, and their share of globalFDI infl ows increase to almost 36 percent, upfrom 29 percent in 2009. A better investmentenvironment, corporate earnings revival, andincreased developing country investment inextractive industries and infrastructure developmentin other developing countries fueled therebound. China was a key driver, with net FDIinfl ows up 62 percent in 2010 (to $185 billion).Portfolio equity fl ows recovered rapidly asthe global fi nancial crisis ebbed, particularly inemerging markets with good growth prospects.Low- and middle-income economies recordeda 19 percent increase (to $130 billion), underpinnedby a 89 percent rise in net infl ows toIndia. For high-income economies the story wasmixed—particularly in the euro area, where netinfl ows rose 13 percent.Debt-related fl ows to low- and middleincomeeconomies from private creditors rosein 2010 to $111 billion, more than twice their2009 level, underpinned by a resurgence ofbond issuance by public and private sector borrowers.Similarly, net infl ows from banks andother private creditors showed signs of recovery,rising 123 percent, to $44 billion, albeit froma low base of $20 billion in 2009. Net infl owsfrom bilateral and multilateral creditors (loansand grants) fell 11 percent, due largely to theredirection of International Monetary Fund fl owsto countries in the euro zone.Low- and middle-income economies’ combinedexternal debt rose $437 billion in 2010,to $4 trillion, but it remained within sustainablebounds, an average of 21 percent of grossnational income and 69 percent of export earnings.Short-term external debt was the fastestgrowing component, rising 34 percent in2010 compared with 6 percent for outstandinglong-term external debt. Risks associated with ahigh proportion of short-term debt—25 percentof total debt stock at end-2010—were mitigatedby international reserves equivalent to 137 percentof total outstanding external debt.62012 World Development Indicators 337

6.1 Growth of merchandise tradeExportvolumeImportvolumeExportvalueImportvalueNet barterterms of tradeindexaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth 2000 = 1002000–10 2000–10 2000–10 2000–10 2010Afghanistan 9.9 4.4 20.1 11.0 146.5Albania 15.0 11.6 20.2 17.5 95.3Algeria 0.0 12.2 14.4 17.9 177.5Angola 11.4 22.0 27.6 26.4 210.9Argentina 5.9 11.7 11.9 14.6 126.6Armenia 3.6 12.6 12.3 19.4 126.2Australia 2.3 8.3 14.0 13.1 178.9Austria 5.9 4.5 9.9 9.7 91.1Azerbaijan 22.3 14.1 38.6 20.8 160.3Bahrain 0.6 3.5 11.7 11.9 114.5Bangladesh 11.4 5.0 13.0 13.3 59.0Belarus 6.8 8.7 16.4 18.3 102.8Belgium 2.5 3.0 9.7 10.1 100.8Benin 6.6 7.1 15.5 16.2 103.5Bolivia 9.4 8.2 21.2 13.7 152.4Bosnia and Herzegovina 13.4 5.1 20.4 11.5 101.1Botswana 3.5 5.7 7.1 11.6 84.7Brazil 6.6 8.1 15.5 14.9 125.1Bulgaria 7.9 9.3 18.0 18.4 108.8Burkina Faso 11.8 7.9 19.3 14.5 120.9Burundi –3.1 9.7 7.9 15.8 153.1Cambodia 12.7 9.3 15.0 15.3 75.9Cameroon –2.5 5.2 9.8 13.9 143.8Canada –0.9 2.5 4.9 6.4 119.9Central African Republic –4.3 6.6 –0.4 13.1 85.9Chad 23.9 11.6 42.2 17.4 180.0Chile 4.3 11.2 17.1 14.9 204.0China † 20.5 15.0 22.4 20.9 77.4Hong Kong SAR, China 6.7 6.6 7.8 8.3 96.0Colombia 6.2 11.0 14.7 15.3 133.9Congo, Dem. Rep. 10.1 14.9 20.9 21.8 137.9Congo, Rep. 1.4 16.6 15.9 22.4 182.3Costa Rica 7.3 6.8 7.0 9.2 78.2Côte d’Ivoire –0.1 5.1 11.6 13.7 161.6Croatia 6.3 6.0 12.5 12.2 100.7Cuba 0.5 8.9 11.2 12.4 ..Cyprus 0.3 5.5 5.7 10.9 103.5Czech Republic 9.9 8.0 18.5 15.7 106.4Denmark 2.1 2.9 8.3 8.7 106.6Dominican Republic –0.8 3.3 2.1 6.7 98.4Ecuador 7.5 12.2 16.4 17.5 121.9Egypt, Arab Rep. 9.0 10.2 23.5 17.9 152.4El Salvador 2.6 3.4 4.8 6.6 91.3Eritrea –10.9 –3.8 –6.7 3.6 77.2Estonia 3.3 4.1 13.3 11.4 144.4Ethiopia 9.1 16.5 19.2 23.6 127.5Finland 2.6 2.9 6.7 10.0 77.1France 1.5 2.9 6.2 8.0 98.2Gabon 0.1 7.2 14.4 11.9 195.8Gambia, The –3.7 0.9 2.7 8.4 93.3Georgia 8.9 19.5 19.8 27.5 132.2Germany 5.2 4.9 10.3 9.9 103.3Ghana 5.2 9.2 17.2 16.3 175.4Greece 0.8 1.2 9.1 10.3 92.7Guatemala 7.9 5.1 12.4 10.5 92.6Guinea –0.2 2.8 8.5 9.6 110.2Guinea-Bissau 1.8 7.9 9.0 17.5 83.5Haiti 4.9 4.0 8.2 11.2 77.9† Data for Taiwan, China 7.5 2.4 8.0 8.0 66.03382012 World Development Indicators

Growth of merchandise trade 6.1GLOBAL LINKSExportvolumeImportvolumeExportvalueImportvalueNet barterterms of tradeindexaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth 2000 = 1002000–10 2000–10 2000–10 2000–10 2010Honduras 3.5 4.7 6.2 9.7 83.4Hungary 10.5 7.6 15.1 12.8 95.4India 11.4 19.5 19.9 24.2 127.2Indonesia 0.8 6.5 11.0 14.4 127.3Iran, Islamic Rep. 2.9 7.6 17.5 14.8 157.9Iraq 1.7 9.2 16.8 15.1 184.3Ireland 1.7 0.0 4.7 3.8 95.4Israel 3.3 1.5 8.1 6.4 98.1Italy –0.1 0.4 8.1 9.1 99.0Jamaica 0.0 0.0 4.0 7.7 70.7Japan 5.2 1.8 6.3 8.0 67.8Jordan 3.9 5.1 15.0 15.6 85.4Kazakhstan 8.6 14.2 25.3 20.7 192.6Kenya 5.1 8.9 12.2 17.1 91.7Korea, Dem. Rep. 7.1 –1.7 13.5 7.5 77.0Korea, Rep. 12.1 7.1 12.4 12.6 68.0Kosovo .. .. .. .. ..Kuwait 4.1 9.2 18.7 13.8 187.3Kyrgyz Republic 7.7 16.4 16.8 26.0 107.6Lao PDR 11.1 9.3 19.6 16.0 119.5Latvia 9.6 7.3 20.5 17.1 105.2Lebanon 13.4 4.4 20.6 12.1 95.5Lesotho 12.8 7.3 13.5 12.0 66.2Liberia –7.4 4.5 0.5 10.4 146.7Libya 4.2 13.5 19.4 20.8 162.5Lithuania 12.7 10.7 21.0 18.3 103.8Macedonia, FYR 6.2 6.0 13.4 14.3 89.1Madagascar 1.5 8.6 4.8 15.2 76.3Malawi 6.9 9.9 12.2 16.6 87.7Malaysia 5.5 5.1 9.2 8.6 100.2Mali 2.9 7.7 13.7 14.6 158.7Mauritania 11.2 11.3 23.6 18.0 132.7Mauritius 3.2 6.0 2.9 9.1 73.2Mexico 2.9 3.4 7.0 6.8 104.5Moldova 11.3 18.8 12.6 20.2 104.8Mongolia 3.9 12.4 21.2 20.5 215.6Morocco 0.6 8.1 10.9 15.4 134.2Mozambique 10.0 7.3 19.1 14.6 108.9Myanmar 7.4 0.3 17.3 7.6 110.2Namibia 6.2 10.6 14.5 15.4 120.3Nepal –2.9 4.5 2.5 14.0 78.3Netherlands 5.2 5.7 11.5 11.3 101.8New Zealand 3.3 5.5 9.5 9.5 124.4Nicaragua 9.5 5.6 12.9 10.7 81.7Niger 4.0 14.4 16.4 21.2 150.1Nigeria 3.0 13.9 18.4 20.3 186.9Norway –0.1 5.0 11.3 11.1 140.4Oman –0.2 11.1 14.8 17.1 193.1Pakistan 5.8 4.6 9.6 16.8 51.9Panama 0.0 9.7 1.7 13.7 87.5Papua New Guinea –1.1 6.0 14.0 15.1 150.4Paraguay 14.8 16.1 19.1 19.9 102.9Peru 7.2 9.8 20.6 17.4 152.5Philippines 2.8 –0.1 3.4 4.9 68.6Poland 10.0 7.4 19.8 16.5 101.9Portugal 5.1 3.4 9.0 9.0 88.0Puerto Rico .. .. .. .. ..Qatar 8.1 23.0 23.3 27.9 187.82012 World Development Indicators 339

6.1Growth of merchandise tradeExportvolumeImportvolumeExportvalueImportvalueNet barterterms of tradeindexaverage annual% growthaverage annual% growthaverage annual% growthaverage annual% growth 2000 = 1002000–10 2000–10 2000–10 2000–10 2010Romania 12.4 13.7 18.5 19.9 99.9Russian Federation 3.6 16.8 17.7 21.1 197.3Rwanda 1.0 17.0 18.3 23.4 234.4Saudi Arabia 0.3 10.8 16.1 16.3 222.6Senegal 1.1 5.7 9.3 14.4 98.9Serbia .. .. .. .. ..Sierra Leone 25.9 4.5 32.7 15.2 70.2Singapore 10.2 7.5 12.0 11.4 83.2Slovak Republic 17.1 13.8 21.7 19.6 90.0Slovenia 11.2 8.6 15.5 14.5 89.3Somalia –0.2 4.0 7.7 11.6 106.2South Africa 1.5 7.4 12.2 14.6 139.4South Sudan .. .. .. .. ..Spain 3.1 3.9 9.5 9.9 104.5Sri Lanka 2.5 2.1 5.9 9.2 75.7Sudan 8.0 17.0 23.5 22.1 196.8Swaziland –2.2 –0.9 5.0 5.8 110.1Sweden 2.8 3.0 8.3 9.9 88.6Switzerland 6.8 3.3 10.4 9.1 81.0Syrian Arab Republic 0.7 11.7 12.5 19.2 139.5Tajikistan –1.0 9.3 7.0 18.9 96.8Tanzania 7.0 12.3 18.2 19.9 139.2Thailand 7.2 7.5 12.6 12.7 98.0Timor-Leste .. .. .. .. ..Togo 2.0 –2.2 10.1 12.4 30.7Trinidad and Tobago 0.7 1.4 14.4 10.1 133.5Tunisia 6.6 4.8 12.4 11.2 95.2Turkey 10.4 9.2 17.4 17.0 91.8Turkmenistan –0.6 8.4 14.0 13.1 195.6Uganda 13.0 8.9 21.7 15.7 111.1Ukraine 4.7 9.9 15.1 18.7 118.9United Arab Emirates 6.5 14.6 19.3 20.2 163.3United Kingdom 0.4 2.0 4.9 6.1 103.3United States 3.9 2.5 6.6 5.9 97.1Uruguay 8.5 7.6 14.3 14.3 99.6Uzbekistan 7.6 10.6 18.9 16.0 151.6Venezuela, RB –2.5 10.8 12.2 14.3 216.3Vietnam 11.4 12.6 19.4 20.6 100.6West Bank and Gaza .. .. .. .. ..Yemen, Rep. –4.1 9.7 9.7 18.2 149.6Zambia 9.4 14.1 25.9 20.8 189.0Zimbabwe –4.9 –1.3 4.5 7.5 106.73402012 World Development Indicators

Growth of merchandise trade 6.1GLOBAL LINKSAbout the dataDefinitionsData on international trade in goods are availablefrom each country’s balance of payments andcustoms records. While the balance of paymentsfocuses on the fi nancial transactions that accompanytrade, customs data record the direction oftrade and the physical quantities and value of goodsentering or leaving the customs area. Customs datamay differ from data recorded in the balance of paymentsbecause of differences in valuation and timeof recording. The 2008 United Nations System ofNational Accounts and the fi fth edition of the InternationalMonetary Fund’s (IMF) Balance of PaymentsManual (1993) attempted to reconcile defi nitions andreporting standards for international trade statistics,but differences in sources, timing, and national practiceslimit comparability. Real growth rates derivedfrom trade volume indexes and terms of trade basedon unit price indexes may therefore differ from thosederived from national accounts aggregates.Trade in goods, or merchandise trade, includes allgoods that add to or subtract from an economy’smaterial resources. Trade data are collected on thebasis of a country’s customs area, which in mostcases is the same as its geographic area. Goodsprovided as part of foreign aid are included, butgoods destined for extraterritorial agencies (suchas embassies) are not.Collecting and tabulating trade statistics are difficult. Some developing countries lack the capacityto report timely data, especially landlocked countriesand countries whose territorial boundaries areporous. Their trade has to be estimated from the datareported by their partners. (For further discussion ofthe use of partner country reports, see About thedata for table 6.2.) Countries that belong to commoncustoms unions may need to collect data throughdirect inquiry of companies. Economic or politicalconcerns may lead some national authorities to suppressor misrepresent data on certain trade fl ows,such as oil, military equipment, or the exports of adominant producer. In other cases reported tradedata may be distorted by deliberate under- or overinvoicingto affect capital transfers or avoid taxes.And in some regions smuggling and black markettrading result in unreported trade fl ows.By international agreement customs data arereported to the United Nations Statistics Division,which maintains the Commodity Trade (Comtrade)and Monthly Bulletin of Statistics databases. TheUnited Nations Conference on Trade and Development(UNCTAD) compiles international trade statistics,including price, value, and volume indexes,from national and international sources such asthe IMF’s International Financial Statistics database,the United Nations Economic Commission forLatin America and the Caribbean, the U.S. Bureauof Labor Statistics, Japan Customs, Bank of Japan,and UNCTAD’s Commodity Price Statistics and MerchandiseTrade Matrix. The IMF also compiles dataon trade prices and volumes in its InternationalFinancial Statistics (IFS) database.The growth rates and terms of trade in the tablewere calculated from index numbers compiled byUNCTAD.The terms of trade index measures the relativeprices of a country’s exports and imports. There areseveral ways to calculate it. The most common is thenet barter (or commodity) terms of trade index, orthe ratio of the export price index to the import priceindex. When a country’s net barter terms of tradeindex increases, its exports become more expensiveor its imports become cheaper.• Export and import volumes are indexes of thequantity of goods traded. They are derived from UNC-TAD’s volume index series and are the ratio of theexport or import value indexes to the correspondingunit value indexes. Unit value indexes are based ondata reported by countries that demonstrate consistencyunder UNCTAD quality controls, supplementedby UNCTAD’s estimates using the previous year’strade values at the Standard International TradeClassifi cation three-digit level as weights. To improvedata coverage, especially for the most recent periods,UNCTAD constructs a set of average priceindexes at the three-digit product classifi cation of theStandard International Trade Classifi cation revision3 using its Commodity Price Statistics database,international and national sources, and estimatesby the UNCTAD secretariat and calculates unit valueindexes at the country level using the current year’strade values as weights. • Export and import valuesare the current value of exports (free on board, f.o.b.)or imports (cost, insurance, and freight, c.i.f.), convertedto U.S. dollars and expressed as a percentageof the average for the base period (2000). UNCTAD’sexport or import value indexes are reported for mosteconomies. • Net barter terms of trade index iscalculated as the percentage ratio of the export unitvalue indexes to the import unit value indexes, measuredrelative to the base year 2000.Data sourcesData on trade indexes are from UNCTAD’s annualHandbook of Statistics.2012 World Development Indicators 341

6.2Direction and growth of merchandise tradeDirection of tradeLow- and middle-income importersHigh-incomeimportersEast Asia& PacificEurope& CentralAsiaLatinAmerica& Caribbean% of world trade, 2010MiddleEast &N. Africa% of worldtrade, 2010Source of exportsHigh-income economies 9.2 2.6 3.5 1.4 1.6 1.0 19.3 47.6European Union 1.2 1.9 0.7 0.7 0.3 0.5 5.3 26.1Japan 1.6 0.1 0.3 0.0 0.1 0.1 2.1 3.0United States 0.9 0.1 1.9 0.1 0.2 0.1 3.4 5.2Other high-income economies 5.6 0.5 0.6 0.5 1.0 0.3 8.5 13.3Low- and middle-income economies 3.3 1.9 2.0 0.9 1.2 0.9 10.5 20.9East Asia & Pacifi c 1.9 0.5 0.7 0.3 0.6 0.4 4.4 10.8China 0.7 0.5 0.6 0.3 0.4 0.3 2.8 7.8Europe & Central Asia 0.3 1.1 0.0 0.3 0.1 0.0 1.8 2.9Russian Federation 0.2 0.4 0.0 0.1 0.0 0.0 0.7 1.5Latin America & Caribbean 0.6 0.1 1.1 0.1 0.1 0.1 2.0 3.6Brazil 0.2 0.0 0.3 0.1 0.0 0.0 0.7 0.6Middle East & N. Africa 0.2 0.1 0.0 0.2 0.2 0.1 0.8 1.4Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.3South Asia 0.2 0.1 0.1 0.1 0.1 0.1 0.6 1.2India 0.2 0.0 0.0 0.1 0.1 0.1 0.5 0.9Sub-Saharan Africa 0.1 0.0 0.1 0.0 0.2 0.2 0.9 1.1South Africa 0.1 0.0 0.0 0.0 0.0 0.1 0.2 0.3World 12.5 4.5 5.5 2.3 2.9 1.8 29.7 68.5SouthAsiaSub-SaharanAfrica Total TotalNominal growth of tradeLow- and middle-income importersHigh-incomeimportersEast Asia& PacificEurope& CentralAsiaLatinAmerica& Caribbeanaverage annual % growth, 2000–10MiddleEast &N. Africaaverage annual% growth,2000–10Source of exportsHigh-income economies 15.1 17.3 8.8 13.7 21.4 12.5 14.1 7.5European Union 15.0 16.7 9.5 11.4 15.5 11.3 13.8 8.2Japan 12.5 24.8 11.1 12.5 14.1 11.2 12.7 3.4United States 12.1 14.0 7.2 12.9 21.1 13.2 9.4 5.2Other high-income economies 16.5 21.0 13.4 20.1 25.7 15.0 17.4 8.2Low- and middle-income economies 22.3 20.8 18.1 22.7 27.0 21.5 21.7 13.9East Asia & Pacifi c 20.9 34.8 27.7 25.9 28.1 27.1 24.9 15.5China 26.1 37.2 31.5 30.3 35.0 31.0 31.0 20.4Europe & Central Asia 19.4 17.8 20.5 22.4 24.3 21.9 19.0 16.7Russian Federation 17.4 15.8 21.0 19.8 20.5 14.0 16.9 16.4Latin America & Caribbean 30.5 19.5 14.5 18.0 29.4 20.9 18.2 8.6Brazil 31.8 20.4 16.6 21.8 25.6 22.4 20.9 12.1Middle East & N. Africa 22.0 16.4 14.4 22.5 37.9 18.5 23.4 13.9Algeria 32.5 11.2 8.6 19.0 64.1 15.9 16.0 14.3South Asia 25.8 17.5 23.3 21.5 19.2 23.7 22.5 15.4India 27.2 16.0 25.2 23.8 19.8 24.7 23.8 18.2Sub-Saharan Africa 22.6 24.5 20.4 13.8 21.7 16.1 21.8 13.5South Africa 31.2 23.9 12.3 19.5 29.0 13.8 19.8 12.4World 16.5 18.6 11.4 16.6 23.5 15.8 16.3 9.1SouthAsiaSub-SaharanAfrica Total Total3422012 World Development Indicators

Direction and growth of merchandise trade 6.2GLOBAL LINKSAbout the dataDefinitionsThe table provides estimates of the fl ow of trade ingoods between groups of economies. The data arefrom the International Monetary Fund’s (IMF) Directionof Trade database. All high-income economiesand major developing economies report trade ona timely basis, covering about 85 percent of tradefor recent years. Trade by less timely reporters andby countries that do not report is estimated usingreports of trading partner countries and extrapolation.Because the largest exporting and importingcountries are reliable reporters, a large portion ofthe missing trade fl ows can be estimated from partnerreports. Partner country data may introduce discrepanciesdue to confi dentiality, different exchangerates, overreporting of transit trade, inclusion orexclusion of freight rates and insurance, and differentpoints of valuation and times of recording.Most countries report their trade data in nationalcurrencies, which are converted into U.S. dollarsusing the IMF’s published period average exchangerate (series rf or rh, monthly averages of the marketor offi cial rates) for the reporting country. Becauseimports are reported at cost, insurance, and freight(c.i.f.) valuations, and exports at free on board (f.o.b.)valuations, the IMF adjusts country reports of importvalues by dividing them by 1.10 to estimate equivalentexport values. The accuracy of this approximationdepends on the set of partners and the itemstraded. Other factors affecting the accuracy of tradedata include lags in reporting, recording differencesacross countries, and whether the country reportstrade according to the general or special system oftrade. (For further discussion of the measurement ofexports and imports, see About the data for tables4.4 and 4.5.)The regional trade fl ows in the table are calculatedfrom current price values. The growth rates are innominal terms; that is, they include the effects ofchanges in both volumes and prices.• Merchandise trade includes all trade in goods;trade in services is excluded. • High-income economiesare those classifi ed as such by the World Bank(see front cover fl ap). • European Union is defi nedas all high-income EU members: Austria, Belgium,Cyprus, Czech Republic, Denmark, Estonia, Finland,France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg,Malta, the Netherlands, Portugal, SlovakRepublic, Slovenia, Spain, Sweden, and the UnitedKingdom. • Other high-income economies includeall high-income economies (both Organisation forEconomic Co-operation and Development membersand others) except the high-income European Union,Japan, and the United States. • Low- and middleincomeregional groupings are based on World Bankclassifi cations (see back cover fl ap) and may differfrom those used by other organizations.Data sourcesData on the direction and growth of merchandisetrade were calculated using the IMF’s Directionof Trade database. Regional and income groupclassifi cations are according to the World Bankclassifi cation of economies as of July 1, 2011,and are as shown on the cover fl aps of this report.2012 World Development Indicators 343

6.3High-income economy trade withlow- and middle-income economiesHigh-income economies European Union Japan United States2000 2010 2000 2010 2000 2010 2000 2010Exports to low-income economiesTotal ($ billions) 19.5 53.9 8.8 19.3 2.3 5.3 1.9 7.3% of total exportsFood 11.8 9.8 13.8 12.0 0.5 0.3 27.5 15.1Agricultural raw materials 2.5 2.1 2.1 2.1 1.3 1.4 7.2 4.5Ores and nonferrous metals 1.0 0.9 0.6 0.7 0.5 1.1 0.4 0.5Fuels 5.6 8.7 2.9 10.2 0.4 0.2 1.3 1.7Manufactured goods 74.1 64.8 77.7 71.0 94.7 95.2 54.6 56.7Miscellaneous goods 5.1 13.7 2.9 4.0 2.6 1.8 9.0 21.5Imports from low-income economiesTotal ($ billions) 19.6 45.9 10.5 23.9 1.1 1.5 5.6 11.7% of total importsFood 20.9 14.0 24.2 17.5 42.7 26.3 8.0 5.0Agricultural raw materials 6.1 3.6 8.6 4.9 3.3 2.7 1.4 1.4Ores and nonferrous metals 5.7 11.5 5.3 11.7 21.6 12.2 2.8 1.7Fuels 2.9 7.6 0.4 2.5 1.1 0.0 3.3 22.8Manufactured goods 62.8 58.1 59.4 61.8 30.8 58.5 83.6 67.5Miscellaneous goods 1.6 5.2 2.2 1.6 0.5 0.2 0.9 1.6Simple applied tariff rates on imports from low-income economies (%) aAverage 4.2 2.5 0.8 0.9 3.1 0.5 5.2 3.3Food 5.9 4.4 3.2 0.3 10.9 1.2 2.8 1.9Agricultural raw materials 5.2 1.5 0.0 0.2 0.4 0.1 0.2 0.1Ores and nonferrous metals 1.2 0.7 0.3 0.2 1.4 0.2 0.0 0.1Fuels 3.2 1.5 0.0 1.2 0.0 0.0 0.0 0.2Manufactured goods 4.0 2.3 0.5 1.1 1.9 0.4 5.8 3.8Miscellaneous goods 0.6 0.8 0.1 0.2 0.0 0.0 0.0 0.1Exports to middle-income economiesTotal ($ billions) 737.3 2,225.7 252.8 846.5 107.1 314.4 216.6 436.2% of total exportsFood 6.1 6.3 7.0 6.1 0.3 0.4 7.8 12.0Agricultural raw materials 1.9 2.2 1.4 1.5 1.0 1.1 2.4 4.1Ores and nonferrous metals 2.1 5.1 1.8 3.0 1.9 3.4 1.6 4.2Fuels 3.9 6.9 2.2 3.8 0.5 1.5 3.1 9.6Manufactured goods 81.5 74.3 83.4 82.0 93.5 89.5 80.3 61.3Miscellaneous goods 4.3 5.3 4.3 3.5 2.8 4.1 4.9 8.8Imports from middle-income economiesTotal ($ billions) 1,231.2 3,446.5 362.3 1,229.0 136.2 318.9 453.1 1,004.2% of total importsFood 8.0 6.8 10.3 7.9 12.9 8.0 5.4 5.3Agricultural raw materials 2.1 1.3 3.2 1.7 3.6 2.1 1.0 0.9Ores and nonferrous metals 4.7 4.2 5.9 4.5 8.8 10.5 2.7 1.9Fuels 17.0 20.5 24.2 26.4 16.4 18.3 15.5 20.3Manufactured goods 65.9 64.4 53.1 56.8 56.9 59.5 72.8 69.2Miscellaneous goods 2.3 2.9 3.3 2.7 1.5 1.6 2.6 2.4Simple applied tariff rates on imports from middle-income economies (%) aAverage 5.1 3.7 2.5 1.6 2.7 2.3 3.4 2.5Food 8.8 6.0 10.4 3.2 12.9 7.1 3.7 2.9Agricultural raw materials 2.7 2.1 0.5 0.4 1.1 0.5 0.4 0.4Ores and nonferrous metals 1.8 1.4 0.9 0.8 0.1 0.1 0.3 0.4Fuels 3.2 1.7 0.0 0.3 0.8 0.2 0.6 1.3Manufactured goods 4.8 3.5 1.7 1.5 1.4 1.9 3.6 2.6Miscellaneous goods 1.9 1.0 0.9 0.5 0.0 0.0 0.5 0.3a. Includes ad valorem equivalents of specifi c rates.3442012 World Development Indicators

High-income economy trade with6.3low- and middle-income economiesGLOBAL LINKSAbout the dataDefinitionsDeveloping economies are becoming increasinglyimportant in the global trading system. Since theearly 1990s trade between high-income economiesand low- and middle-income economies has grownfaster than trade among high-income economies. Theincreased trade benefi ts consumers and producers.But as was apparent at the World Trade Organization’s(WTO) Ministerial Conferences in Doha, Qatar,in October 2001; Cancun, Mexico, in September2003; Hong Kong SAR, China, in December 2005;and Geneva, Switzerland, in December 2009 andDecember 2011, achieving a more pro-developmentoutcome from trade remains a challenge. Doing sowill require strengthening international consultation.After the Doha meetings, negotiations were launchedon services, agriculture, manufactures, WTO rules,the environment, dispute settlement, intellectualproperty rights protection, and disciplines onregional integration. At the most recent negotiationsin Geneva, Switzerland, trade ministers reaffi rmedthat development is a core element of the WTO’swork and that the WTO needs to assist in furtherintegrating developing countries into the multilateraltrading system.Trade fl ows between high-income and low- andmiddle-income economies refl ect the changing mix ofexports to and imports from developing economies.While food and primary commodities have continuedto fall as a share of high-income economies’ imports,manufactures as a share of goods imports from bothlow- and middle-income economies have grown. Andtrade between developing economies has grownsubstantially over the past decade, a result of theirincreasing share of world output and liberalization oftrade, among other infl uences.Yet trade barriers remain high. The table includesinformation about tariff rates by selected productgroups. Applied tariff rates are the tariffs in effectfor partners in preferential trade agreements suchas the North American Free Trade Agreement. Whenthese rates are unavailable, most favored nationrates are used. The difference between most favorednation and applied rates can be substantial. Simpleaverages of applied rates are shown because theyare generally a better indicator of tariff protectionthan weighted average rates are.The data on trade fl ows are from the United NationsStatistics Division’s Commodity Trade (Comtrade)database. Partner country reports by high-incomeeconomies were used for both exports and imports.Because of differences in sources of data, timing,and treatment of missing data, the numbers in thetable may not be fully comparable with those usedto calculate the direction of trade statistics in tables6.2 and 6.4 or the aggregate fl ows in tables 4.4,4.5, and 6.1. Tariff data are from the United NationsConference on Trade and Development’s (UNCTAD)Trade Analysis and Information System database. Forfurther discussion of merchandise trade statistics,see About the data for tables 4.4, 4.5, 6.1, 6.2, and6.4, and for information about tariff barriers, seetable 6.7.The product groups in the table are defi ned in accordancewith SITC revision 2: food (0, 1, 22, and 4),agricultural raw materials (2 excluding 22, 27, and28), ores and nonferrous metals (27, 28, and 68),fuels (3), manufactured goods (5–8 excluding 68),and miscellaneous goods (9). • Exports are all merchandiseexports by high-income economies to lowincomeand middle-income economies as recordedin the United Nations Statistics Division’s Comtradedatabase. Exports are recorded free on board(f.o.b.). • Imports are all merchandise imports byhigh-income economies from low-income and middleincomeeconomies as recorded in the United NationsStatistics Division’s Comtrade database. Importsinclude insurance and freight charges (c.i.f.). • High-,middle-, and low-income economies are thoseclassifi ed as such by the World Bank as of July 1,2011 (see front cover fl ap). • European Union isdefi ned as all high-income EU members: Austria, Belgium,Cyprus, Czech Republic, Denmark, Estonia,Finland, France, Germany, Greece, Hungary, Ireland,Italy, Luxembourg, Malta, the Netherlands, Poland,Portugal, Slovak Republic, Slovenia, Spain, Sweden,and the United Kingdom.Data sourcesData on trade fl ows are from United NationsStatistics Division’s Comtrade database. Dataon tariffs are from UNCTAD’s Trade Analysis andInformation System database and are calculatedby World Bank staff using the World IntegratedTrade Solution system.2012 World Development Indicators 345

6.4Direction of trade of developing economiesExportsImports% of total merchandise exports % of total merchandise importsTo developing economiesTo high-incomeFrom developing economiesFrom high-incomeWithin region Outside region economiesWithin region Outside regioneconomies2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010East Asia & Pacific 8.9 w 12.4 w 7.3 w 16.6 w 83.3 w 70.3 w 11.7 w 16.3 w 8.5 w 18.5 w 78.1 w 62.8 wCambodia 7.4 6.1 0.2 2.3 91.6 91.6 39.7 55.4 1.0 2.1 58.5 42.5China 4.9 7.0 8.9 19.0 86.2 73.7 7.8 9.6 10.4 18.0 78.4 63.4Fiji 10.9 21.1 2.0 2.2 74.5 48.2 8.3 16.7 2.6 3.2 88.1 78.1Indonesia 11.5 22.6 7.6 14.3 80.8 63.1 14.1 28.4 7.5 9.7 77.4 61.8Korea, Dem. Rep. 7.5 50.8 43.9 36.7 48.6 12.5 34.9 62.3 32.9 30.3 32.3 7.4Lao PDR .. .. 0.5 1.1 34.6 15.4 78.4 87.1 1.3 0.5 18.7 11.0Malaysia 11.1 24.6 5.9 11.6 83.1 63.8 13.7 28.3 2.8 7.1 81.6 64.0Mongolia .. .. 9.5 3.4 40.6 14.7 19.1 42.7 37.2 30.3 43.7 27.0Myanmar 22.6 59.4 10.6 20.2 56.6 14.5 48.0 70.0 2.7 4.6 49.3 25.4Papua New Guinea 9.0 12.1 0.1 1.5 58.5 48.0 11.2 19.8 1.8 1.1 85.8 77.9Philippines 9.2 19.3 1.6 2.9 88.8 77.0 11.2 27.6 4.2 4.4 83.9 68.1Thailand 15.0 29.1 6.2 11.8 78.3 58.5 15.9 26.5 7.2 7.3 74.8 64.8Vietnam 22.6 22.3 6.1 8.3 70.6 69.4 20.2 38.8 4.6 6.7 74.4 54.5Europe & Central Asia 24.7 w 20.7 w 9.6 w 14.0 w 63.0 w 56.6 w 31.3 w 25.6 w 11.8 w 14.7 w 57.9 w 52.5 wAlbania 3.9 10.7 0.0 7.4 96.0 81.9 14.0 16.1 2.7 10.6 83.3 73.3Armenia 25.0 38.7 10.5 13.1 54.2 47.1 25.2 43.0 13.6 22.6 55.8 34.3Azerbaijan 21.0 13.2 2.3 13.9 76.2 73.0 44.4 44.6 11.4 16.4 44.1 39.0Belarus 72.2 60.3 7.5 11.9 20.0 26.8 71.8 61.0 3.2 11.1 23.8 24.4Bosnia and Herzegovina 6.6 6.5 .. .. 90.3 90.3 5.4 12.6 .. .. 94.4 86.4Bulgaria 29.0 29.9 5.3 7.1 56.6 61.2 36.1 36.4 6.0 7.3 56.0 55.7Cyprus .. .. 35.1 21.1 52.9 66.9 .. .. 20.3 14.9 79.7 83.3Georgia 64.7 61.3 5.9 6.5 29.2 32.2 52.6 54.1 3.7 11.9 42.0 34.0Kazakhstan 25.2 18.8 11.3 22.8 42.3 47.1 57.6 33.2 7.0 36.3 35.2 30.5Kyrgyz Republic 45.3 80.9 .. .. 43.1 7.0 59.1 29.2 10.1 63.5 30.8 7.2Lithuania 33.2 37.6 1.2 1.6 65.5 60.8 34.1 43.2 3.9 4.4 60.5 52.4Macedonia, FYR 31.7 27.7 1.0 3.2 67.1 55.9 36.5 34.1 4.1 11.5 59.4 54.0Moldova 69.4 64.3 1.8 2.7 28.8 32.2 54.0 50.1 1.5 12.3 44.4 37.5Romania 14.3 18.3 7.6 6.1 77.6 75.5 16.4 16.6 5.5 8.9 74.4 74.4Russian Federation 21.6 14.1 10.1 12.0 68.1 60.3 36.8 13.3 10.5 26.6 52.5 58.1Serbia .. 32.5 .. 2.3 .. 56.5 .. 21.2 .. 5.3 .. 61.0Tajikistan 56.2 46.9 .. .. 41.0 4.7 91.2 65.3 .. .. 5.8 14.5Turkey 9.2 14.5 9.8 24.5 75.4 57.9 12.8 19.7 13.7 25.4 70.4 53.9Turkmenistan 60.5 26.9 .. .. 26.2 24.5 54.3 51.0 .. .. 32.2 32.8Ukraine 42.6 47.2 18.5 22.2 37.8 29.7 60.8 48.7 4.7 13.6 34.2 36.7Uzbekistan 57.0 56.5 .. .. 36.4 10.2 45.3 40.2 .. .. 51.2 42.6Latin America & Carib. 15.4 w 18.9 w 3.8 w 15.6 w 77.6 w 63.0 w 15.2 w 19.4 w 3.4 w 14.8 w 75.0 w 58.4 wArgentina 48.1 41.9 14.5 26.0 35.6 30.3 34.3 39.9 9.9 19.3 53.5 36.5Bolivia 44.5 64.4 1.0 3.7 53.4 31.5 51.2 73.3 4.5 4.4 44.1 22.2Brazil 22.9 22.7 8.6 29.8 57.3 45.5 20.8 16.6 12.5 28.5 66.4 54.5Chile 21.1 18.2 8.6 30.4 65.3 51.3 32.2 29.3 10.9 22.1 46.4 47.9Colombia 27.2 25.0 1.2 8.3 70.7 66.1 28.0 27.4 3.9 18.4 66.8 51.0Costa Rica 17.9 28.5 2.7 6.3 79.4 65.2 22.3 22.8 3.9 10.1 38.9 65.5Cuba 8.8 21.6 31.9 35.2 59.4 43.2 40.3 47.4 12.7 21.8 47.0 30.8Dominican Republic 3.9 24.9 0.7 3.0 95.3 65.3 20.7 25.2 3.0 8.5 76.2 63.8Ecuador 31.4 39.8 3.0 7.4 62.1 52.4 41.4 37.6 3.7 11.9 51.5 49.6El Salvador 27.7 42.8 0.9 1.1 70.8 56.1 30.8 40.3 2.0 7.1 64.2 52.6Guatemala 40.4 43.3 2.7 3.7 56.4 52.7 36.2 33.8 2.6 10.7 60.2 54.5Haiti 4.4 5.1 0.8 3.3 94.7 91.3 21.8 38.7 4.5 10.8 72.1 50.4Honduras 24.3 29.5 0.8 3.4 69.7 67.1 31.9 40.6 3.5 6.6 62.8 52.8Jamaica 3.5 10.1 2.5 6.8 93.5 81.8 15.3 24.8 3.4 7.4 78.3 65.3Mexico 3.1 6.9 0.3 2.1 96.1 90.2 2.5 4.2 4.3 20.2 91.1 74.2Nicaragua 30.5 20.3 0.0 1.4 64.0 77.9 45.8 52.7 0.0 9.5 42.6 37.5Panama 22.3 20.3 0.4 7.5 70.3 70.0 30.6 21.3 1.4 7.5 55.2 42.9Paraguay 74.5 67.8 1.9 13.7 20.7 17.3 49.5 46.1 12.5 37.2 38.0 16.5Peru 18.0 18.4 11.6 21.9 68.8 59.7 38.0 32.3 8.1 21.1 53.1 46.5Uruguay 53.9 43.5 9.7 24.6 35.4 29.0 51.7 48.1 12.3 23.7 35.6 28.0Venezuela, RB 19.7 12.0 0.5 16.0 74.2 52.7 21.1 34.8 2.4 11.5 57.1 52.23462012 World Development Indicators

Direction of trade of developing economies6.4GLOBAL LINKSExportsImports% of total merchandise exports % of total merchandise importsTo developing economiesTo high-incomeFrom developing economiesFrom high-incomeWithin region Outside region economiesWithin region Outside regioneconomies2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Middle East & N. Africa 3.9 w 7.4 w 15.6 w 27.0 w 76.0 w 60.4 w 5.1 w 7.9 w 18.2 w 26.5 w 71.6 w 61.3 wAlgeria 1.4 3.2 15.3 14.7 83.4 82.1 1.4 2.9 16.7 28.2 81.8 68.2Bahrain .. .. 13.6 11.4 24.4 17.1 .. .. 12.8 26.4 86.7 73.0Egypt, Arab Rep. 6.9 19.2 9.8 24.0 73.5 53.1 1.1 3.2 19.6 35.2 72.7 61.2Iran, Islamic Rep. 1.4 1.3 22.8 42.3 63.3 40.9 0.9 0.5 30.1 24.7 68.3 73.2Iraq 7.3 3.7 7.4 31.3 85.3 65.0 10.1 24.1 38.5 48.2 51.4 27.7Jordan 22.8 30.1 35.9 25.6 38.9 42.9 18.0 10.0 18.1 28.0 61.0 61.9Lebanon 17.8 39.6 13.0 14.4 68.2 45.1 7.9 15.5 20.1 26.6 70.6 56.0Libya 3.5 3.3 7.5 13.7 89.1 83.0 12.1 13.4 10.0 29.5 77.7 57.0Morocco 2.8 3.8 10.9 22.4 83.1 70.2 10.2 7.1 13.0 23.5 75.2 69.2Syrian Arab Republic 8.7 49.5 12.5 6.6 75.9 43.9 3.6 18.4 23.6 35.7 54.5 45.9Tunisia 7.5 11.3 5.6 9.5 83.2 79.2 6.6 7.4 9.4 16.6 82.4 74.7Yemen, Rep. 0.5 2.2 56.3 70.2 40.4 26.8 4.4 4.0 22.9 39.6 70.1 55.4South Asia 4.6 w 5.8 w 16.0 w 28.4 w 75.3 w 63.9 w 4.3 w 4.0 w 12.8 w 17.0 w 58.2 w 56.9 wAfghanistan 42.4 52.6 18.2 16.7 39.4 30.6 27.3 28.4 35.5 26.9 37.2 44.7Bangladesh 1.7 2.9 4.1 9.6 79.7 83.5 11.8 15.8 18.3 37.0 53.1 40.4India 4.3 5.0 18.8 30.4 73.4 63.2 0.9 0.6 20.0 39.2 55.6 59.7Nepal 42.9 65.9 .. .. 55.3 27.8 37.4 57.5 .. .. 40.5 15.1Pakistan 4.6 12.4 14.3 27.2 80.6 58.5 2.7 6.3 22.3 36.0 74.7 57.0Sri Lanka 3.5 7.4 10.5 16.4 79.9 65.5 10.6 25.7 16.7 32.3 64.2 39.8Sub-Saharan Africa 10.5 w 12.4 w 14.7 w 32.2 w 63.2 w 53.5 w 12.0 w 13.0 w 18.2 w 31.0 w 70.0 w 50.0 wAngola 0.2 3.7 25.0 54.1 74.7 42.1 19.8 7.3 13.8 31.8 66.2 60.7Benin 10.7 29.9 65.9 57.1 23.4 13.0 22.2 7.7 15.8 57.5 61.5 34.8Burkina Faso 22.0 13.7 0.0 43.0 76.5 39.2 31.0 43.4 11.7 10.8 52.6 37.3Burundi 17.0 12.0 0.0 20.4 61.9 51.3 23.8 28.6 8.1 17.4 46.7 44.4Cameroon 7.0 13.6 9.4 20.5 77.9 62.8 27.3 21.2 8.8 29.5 54.7 48.7Central African Republic 1.5 10.2 7.9 40.7 90.7 49.1 12.2 12.3 7.0 11.2 57.0 55.3Chad 6.0 0.4 .. .. 82.3 82.2 13.7 19.2 .. .. 81.7 45.4Comoros .. .. .. .. 96.2 59.3 .. .. .. .. 52.4 46.4Congo, Dem. Rep. 2.2 25.7 0.4 48.3 97.2 25.8 43.2 50.3 6.8 18.8 49.4 30.8Congo, Rep. 3.0 1.1 17.4 38.7 79.5 60.0 13.2 6.3 11.2 31.4 68.5 60.4Côte d’Ivoire 27.5 30.6 10.4 13.0 50.2 51.6 28.1 29.1 13.1 28.0 46.2 34.7Ethiopia 0.6 5.3 18.3 26.0 81.1 55.2 1.8 2.8 39.6 28.6 48.9 35.1Gabon 1.1 2.4 9.3 28.1 83.7 55.8 4.8 10.9 3.4 18.8 90.6 68.3Gambia, The 18.0 7.0 10.7 48.4 71.3 44.7 16.0 16.9 37.2 56.1 46.8 27.0Ghana 5.3 10.7 11.6 24.4 74.8 52.8 22.1 24.9 15.7 33.5 61.3 40.7Guinea 6.4 2.3 13.0 46.9 80.6 35.1 24.4 6.4 13.8 21.1 61.5 26.2Guinea-Bissau 1.2 30.5 .. .. 6.9 4.0 13.6 22.1 .. .. 48.5 38.8Kenya 40.0 37.4 18.7 15.6 39.6 38.2 8.4 12.3 16.0 38.4 75.0 48.3Liberia 1.2 32.2 14.6 9.4 84.2 58.4 1.3 0.9 5.7 29.6 93.0 69.6Madagascar 3.0 4.9 3.5 13.1 88.4 72.1 6.2 12.9 33.0 27.3 52.6 48.2Malawi 15.6 22.3 14.7 28.1 69.2 49.1 71.8 61.8 6.9 16.0 20.0 21.5Mali 6.4 6.6 40.5 41.5 50.7 43.9 26.0 30.9 7.5 12.5 32.7 28.1Mauritania 20.4 14.4 8.3 44.4 70.2 40.0 4.4 4.7 21.0 33.5 65.8 51.9Mauritius 7.6 14.6 0.9 4.6 91.5 80.8 18.3 11.5 26.8 48.6 54.9 39.8Mozambique 36.0 25.9 7.0 8.3 36.7 64.0 51.8 38.1 7.4 15.9 28.6 39.6Niger 48.5 77.6 0.5 5.6 51.0 16.7 13.2 21.1 5.8 29.8 80.5 49.1Nigeria 7.0 9.6 22.3 25.1 70.4 64.3 4.1 4.9 20.1 28.5 75.2 49.6Rwanda 7.3 58.4 16.1 21.2 42.6 19.7 33.9 50.1 5.4 12.7 37.6 36.1Senegal 27.7 50.2 15.5 14.9 48.7 26.9 19.2 15.2 17.4 32.8 58.8 51.7Sierra Leone 4.2 8.0 2.4 25.8 91.2 63.1 6.4 24.8 8.8 34.9 81.3 35.5Somalia 4.0 4.1 21.2 23.9 74.9 71.9 13.4 8.8 50.9 64.8 24.7 13.7South Africa 12.9 14.9 7.2 28.9 52.9 56.1 2.0 7.9 15.5 32.3 82.1 59.8Sudan 0.2 1.6 50.6 78.6 49.2 19.7 3.5 7.4 36.6 49.6 59.9 39.2Tanzania 18.2 19.6 17.8 33.3 63.1 38.4 20.2 16.6 21.1 42.1 58.6 37.1Togo 40.4 63.2 30.2 25.2 25.1 11.2 22.1 13.6 12.1 34.1 62.6 50.9Uganda 30.9 50.9 2.2 6.8 63.8 39.3 39.9 27.1 15.6 23.3 44.4 49.5Zambia 41.8 19.1 1.8 21.5 49.8 59.4 69.0 63.6 3.7 9.6 26.4 26.8Zimbabwe 17.4 47.3 8.9 23.1 29.4 29.4 49.6 69.2 6.3 13.0 33.0 13.6Note: Bilateral trade data are not available for Timor-Leste, Kosovo, West Bank and Gaza, Botswana, Eritrea, Lesotho, Namibia, South Sudan, and Swaziland. Components may not sum to100 percent because of trade with unspecifi ed partners or with economies not covered by World Bank classifi cation.2012 World Development Indicators 347

6.4Direction of trade of developing economiesAbout the dataDefinitionsDeveloping economies are an increasingly importantpart of the global trading system. From 2009 to2010 the volume of merchandise exports increased14.5 percent globally and 12.9 percent in developingcountries; the volume of merchandise importsincreased 13.5 percent globally and 10.7 percentin developing countries. Trade between high-incomeeconomies and low- and middle-income economieshas grown faster than trade between high-incomeeconomies. This increased trade benefi ts bothproducers and consumers in developing and highincomeeconomies.The table shows trade in goods between developingeconomies in the same region and other regionsand between developing economies and high-incomeeconomies. Data on exports and imports are fromthe International Monetary Fund’s (IMF) Direction ofTrade database and should be broadly consistentwith data from other sources, such as the UnitedNations Statistics Division’s Commodity Trade (Comtrade)database. All high-income economies andmajor developing economies report trade data to theIMF on a timely basis, covering about 85 percent oftrade for recent years. Trade data for less timelyreporters and for countries that do not report areestimated using reports of trading partner countries.Therefore, data on trade between developing andhigh-income economies shown in the table shouldbe generally complete. But trade fl ows betweenmany developing economies—particularly those inSub-Saharan Africa—are not well recorded, and thevalue of trade among developing economies may beunderstated. The table does not include some developingeconomies because data on their bilateraltrade fl ows are not available. Data on the directionof trade between selected high-income economiesare presented and discussed in tables 6.2 and 6.3.At the regional level most exports from developingeconomies are to high-income economies, but theshare of intraregional trade is increasing. Geographicpatterns of trade vary widely by country and commodity.Larger shares of exports from oil- and resourcericheconomies are to high-income economies.The relative importance of intraregional tradeis higher for both landlocked countries and smallcountries with close trade links to the largestregional economy. For most developing economies—especially smaller ones—there is a “geographicbias” favoring intraregional trade. Despite the broadtrend toward globalization and the reduction of tradebarriers, the relative share of intraregional tradeincreased for most economies between 1999 and2010. This is due partly to trade-related advantages,such as proximity, lower transport costs, increasedknowledge from repeated interaction, and culturaland historical affi nity. The direction of trade is alsoinfl uenced by preferential trade agreements that acountry has made with other economies. Thoughformal agreements on trade liberalization do notautomatically increase trade, they neverthelessaffect the direction of trade between the participatingeconomies. Table 6.6 illustrates the size of existingregional trade blocs that have formal preferentialtrade agreements.Although global integration has increased, developingeconomies still face trade barriers when accessingother markets (see table 6.7).• Exports to developing economies within regionare the sum of merchandise exports from the reportingeconomy to other developing economies in thesame World Bank region as a percentage of totalmerchandise exports by the economy. • Exports todeveloping economies outside region are the sumof merchandise exports from the reporting economyto other developing economies in other WorldBank regions as a percentage of total merchandiseexports by the economy. • Exports to high-incomeeconomies are the sum of merchandise exports fromthe reporting economy to high-income economies asa percentage of total merchandise exports by theeconomy. • Imports from developing economieswithin region are the sum of merchandise importsby the reporting economy from other developingeconomies in the same World Bank region as a percentageof total merchandise imports by the economy.• Imports from developing economies outsideregion are the sum of merchandise imports by thereporting economy from other developing economiesin other World Bank regions as a percentage of totalmerchandise imports by the economy. • Importsfrom high-income economies are the sum of merchandiseimports by the reporting economy fromhigh-income economies as a percentage of totalmerchandise imports by the economy.Data sourcesData on merchandise trade fl ows are published inthe IMF’s Direction of Trade Statistics Yearbook andDirection of Trade Statistics Quarterly; the data inthe table were calculated using the IMF’s Direc tionof Trade database. Regional and income groupclassifi cations are according to the World Bankclassifi cation of economies as of July 1, 2011,and are as shown on the cover fl aps of this report.3482012 World Development Indicators

Primary commodity prices6.5GLOBAL LINKS1970 1980 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011World Bank commodity price index(2005 = 100)Energy 11 87 45 32 60 100 115 120 156 105 128 153Nonenergy commodities 138 134 87 92 81 100 122 139 156 130 154 171Agriculture 155 157 94 104 88 100 110 124 146 136 151 170Beverages 182 207 94 113 86 100 105 114 129 144 161 169Food 165 163 94 100 86 100 108 128 159 142 150 171Fats and oils 190 158 85 105 86 100 102 145 178 151 163 181Grains 176 166 103 110 89 100 116 139 190 155 152 194Other food 124 168 97 83 83 100 110 96 106 120 131 136Raw materials 115 116 93 109 95 100 115 119 122 118 147 168Timber 94 89 85 107 102 100 112 115 117 116 116 125Other raw materials 138 144 102 111 87 100 119 123 128 121 182 215Fertilizers 61 117 68 75 75 100 102 137 341 186 166 217Metals and minerals 112 89 75 69 67 100 151 171 154 110 159 167Base metals 125 97 81 76 71 100 162 171 142 109 150 157Steel products a .. 77 76 71 61 100 95 90 133 112 110 117Commodity prices(2005 prices)EnergyCoal, Australian ($/mt) 29 53 41 37 29 48 48 61 109 66 88 98Natural gas, Europe ($/mmBtu) 2 6 3 3 4 6 8 8 11 8 7 9Natural gas, U.S. ($/mmBtu) 1 2 2 2 5 9 7 6 8 4 4 3Natural gas, liquefi ed, Japan ($/mmBtu) .. 7 4 3 5 6 7 7 11 8 10 12Petroleum, avg, spot ($/bbl) 4 48 24 16 32 53 63 66 83 56 70 85Beverages (cents/kg)Cocoa 249 342 131 133 101 154 156 180 220 264 277 242Coffee, Arabica 423 455 204 309 215 253 247 251 263 290 383 486Coffee, robusta 337 426 122 257 102 111 146 176 198 150 154 196Tea, avg., 3 auctions 308 218 213 138 210 165 183 188 207 249 255 238Tea, Colombo auctions 231 146 194 132 201 184 187 232 238 287 291 266Tea, Kolkata auctions 365 269 290 162 202 162 172 177 193 230 248 226Tea, Mombasa auctions 327 238 154 121 227 148 191 153 189 230 227 221FoodFats and oils ($/mt)Coconut oil 1,464 884 348 621 504 617 594 846 1,046 664 995 1,407Copra a 829 594 239 407 341 414 394 559 697 439 664 941Groundnut oil 1,395 1,127 997 920 799 1,060 950 1,245 1,821 1,083 1,243 1,615Palm oil 959 766 300 583 347 422 468 719 810 625 798 915Palm kernel oil a .. .. .. .. 496 627 569 818 965 640 1,049 1,341Soybeans 431 389 255 241 237 275 263 354 447 400 398 440Soybean meal 378 344 207 183 212 214 205 284 363 373 335 324Soybean oil 1,056 784 463 580 378 545 586 812 1,075 776 890 1,057Grains ($/mt)Barley .. 103 83 97 86 95 114 159 171 117 140 169Maize 215 164 113 115 99 99 119 151 191 151 165 237Rice, Thailand, 5% 466 539 280 298 227 286 298 301 555 508 433 442Rice, Thailand, 25% a .. .. 270 276 193 265 271 282 289 419 391 412Rice, Thailand, A1 a .. .. 209 269 187 218 215 251 412 299 340 373Rice, Vietnam, 5% a .. .. .. .. .. 258 255 284 485 .. 380 418Sorghum a 191 169 107 110 99 96 120 150 178 138 146 219Wheat, Canada a 231 250 162 192 165 198 212 277 388 275 277 358Wheat, U.S., soft red winter a 210 221 133 155 111 136 156 220 232 170 203 233Wheat, U.S., hard red winter 202 227 140 164 128 152 188 235 279 205 198 2572012 World Development Indicators 349

6.5 Primary commodity prices1970 1980 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011Commodity prices (continued)(2005 prices)Food (continued)Other foodBananas, U.S. ($/mt) 612 495 559 413 475 603 663 622 721 775 769 787Beef (cents/kg) 481 362 265 177 216 262 249 240 268 241 297 329Chicken meat (cents/kg) .. 99 112 114 147 163 149 159 159 173 168 157Fishmeal ($/mt) a 726 662 426 459 462 731 1,142 1,084 968 1,125 1,494 1,251Oranges ($/mt) 619 513 549 493 407 875 812 881 946 832 915 725Shrimp, Mexico (cents/kg) a .. 1,511 1,106 1,401 1,696 1,034 1,002 930 913 865 890 971Sugar, EU domestic (cents/kg) 41 64 60 64 62 67 63 63 60 48 39 37Sugar, U.S. domestic (cents/kg) 61 87 53 47 48 47 48 42 40 50 70 68Sugar, world (cents/kg) 30 83 29 27 20 22 32 20 24 37 42 47Agricultural raw materialsCotton A Index (cents/kg) 249 271 188 197 146 122 124 129 134 126 202 271Cotton, Memphis (cents/kg) .. .. 191 200 164 130 131 132 138 133 207 59Logs, Cameroon ($/cu. m) a 159 330 355 315 308 334 312 351 450 386 380 394Logs, Malaysian ($/cu. m) 159 257 183 237 213 203 234 247 250 263 246 318Rubber, Singapore (cents/kg) 150 187 89 147 75 149 203 208 221 176 324 392Rubber, TSR 20 (cents/kg) a 0 0 0 0 70 139 190 199 216 165 299 368Rubber, US (cents/kg) a 170 213 106 168 93 166 226 228 243 196 342 421Plywood (cents/sheet) a 380 359 367 542 502 509 583 590 551 517 504 494Sawnwood, Malaysian ($/cu. m) 647 520 551 687 666 659 734 743 760 737 751 764Sawnwood, Cameroon ($/cu. m) a .. .. .. 553 547 559 610 700 819 685 720 672Tobacco ($/mt) a 3,967 2,986 3,508 2,453 3,332 2,790 2,906 3,054 3,066 3,880 3,812 3,641Woodpulp ($/mt) a 654 704 842 792 744 635 684 706 701 562 768 732Fertilizers ($/mt)Diammonium phosphate 199 292 177 201 173 247 255 398 826 296 443 503Phosphate rock 41 61 42 32 49 42 43 65 295 111 109 150Potassium chloride 116 152 101 109 137 158 171 184 487 577 294 354Triple superphosphate 157 237 136 139 154 201 197 312 751 235 338 438Urea 67 252 123 174 113 219 218 285 421 228 256 342Metals and mineralsAluminum ($/mt) 2,050 1,910 1,695 1,676 1,734 1,898 2,516 2,430 2,198 1,523 1,924 1,953Copper ($/mt) 5,219 2,863 2,752 2,724 2,030 3,679 6,580 6,557 5,943 4,711 6,672 7,181Gold ($/toz) a 132 798 397 357 312 445 592 642 745 890 1,084 1,276Iron ore, spot, cfr China ($/dmt) a .. .. .. .. .. .. 68 113 133 73 129 136Lead (cents/kg) 112 119 84 59 51 98 126 238 179 157 190 195Nickel ($/mt) 10,492 8,553 9,167 7,636 9,669 14,744 23,742 34,293 18,035 13,406 19,313 18,637Silver (cents/toz) a 653 2,708 498 482 560 734 1,132 1,235 1,281 1,344 1,789 2,868Tin (cents/kg) 1,354 2,201 629 577 608 738 860 1,339 1,581 1,242 1,807 2,119Zinc (cents/kg) 109 100 157 96 126 138 321 299 160 151 191 178MUV G-15 index (2005 = 100) 27 76 97 108 89 100 102 109 117 109 113 123MUV G-5 index (2005 = 100) 26 74 93 109 91 100 102 106 114 107 110 115Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal units, mt = metric ton, toz = troy ounce.a. Series not included in the nonenergy index.3502012 World Development Indicators

Primary commodity prices6.5GLOBAL LINKSAbout the dataDefinitionsPrimary commodities—raw or partially processedmaterials that will be transformed into fi nishedgoods—are often developing countries’ most importantexports, and commodity revenues can affect livingstandards. Price data are collected from varioussources, including international commodity studygroups, government agencies, industry trade journals,and Bloomberg and Datastream. Prices arecompiled in U.S. dollars or converted to U.S. dollarswhen quoted in local currencies.The table is based on frequently updated pricereports. Prices are those received by exporters whenavailable, or the prices paid by importers or tradeunit values. Annual price series are gen erally simpleaverages based on higher frequency data. The constantprice series in the table are defl ated by themanufactures unit value (MUV) index for the Groupof Fifteen (G-15) countries (see below).Commodity price indexes are calculated asLaspeyres index numbers; the fi xed weights are the2002–04 average export values for low- and middleincomeeconomies (based on 2001 gross nationalincome) rebased to 2005. Data for exports are fromthe United Nations Statistics Division’s CommodityTrade Statistics (Comtrade) database StandardInternational Trade Classifi cation (SITC) revision 3,the Food and Agriculture Organization’s FAOSTATdatabase, the International Energy Agency database,BP’s Statistical Review of World Energy, theWorld Bureau of Metal Statistics, and World Bankstaff estimates.Each index in the table represents a fi xed basket ofprimary commodity exports over time. The nonenergycommodity price index con tains 39 price series for38 nonenergy commodities. Separate indexes arecompiled for energy and steel products, which arenot included in the nonenergy commodity price index.The MUV G-15 index is a composite index ofprices for manufactured exports from the 15 major(G-15) developed and emerging economies (Brazil,Canada, China, France, Germany, India, Italy, Japan,the Republic of Korea, Mexico, South Africa, Spain,Thailand, the United Kingdom, and the United States)to low- and middle-income economies, valued in U.S.dol lars. For the MUV G-15 index, unit value indexesin local currency for each country are converted toU.S. dollars using market exchange rates and arecombined using weights deter mined by the share ofeach country’s exports to low- and middle-incomecountries in the base year (2005). The MUV G-5index is a composite index of prices for manufacturedexports from the fi ve major (G-5) industrialeconomies (France, Germany, Japan, the UnitedKingdom, and the United States) and is included inthe table for comparison purposes.• Energy price index is the composite price index forcoal, petroleum, and natural gas, weighted by exportsof each commodity from low- and middle-incomecountries. • Nonenergy commodity price index coversthe 38 nonenergy primary commodities thatmake up the agriculture, fertilizer, and metals andminerals indexes. • Agriculture includes beverages,food, and agricultural raw materials. • Beveragesinclude cocoa, coffee, and tea. • Food includes fatsand oils, grains, and other food items. Fats and oilsinclude coconut oil, copra, groundnut oil, palm oil,palm kernel oil, soybeans, soybean meal, and soybeanoil. Grains include barley, maize, rice, sorghum,and wheat. Other food items include bananas, beef,chicken meat, fi shmeal, oranges, shrimp, and sugar.• Agricultural raw mate rials include timber andother raw materials. Timber includes tropical hardlogs and sawnwood. Other raw materials include cotton,natural rubber, and tobacco. • Fertilizers includephosphate, phosphate rock, potassium, and nitrogenousproducts. • Metals and minerals include basemetals (aluminum, copper, lead, nickel, tin, and zinc)and iron ore. • Steel products price index is thecomposite price index for eight steel products basedon quotations free on board (f.o.b.) Japan excludingshipments to the United States for all years and toChina prior to 2002, weighted by product shares ofapparent combined consumption (volume of deliveries)for Germany, Japan, and the United States.• Commodity prices—for defi nitions and sources,see “Commodity price data” (also known as the “PinkSheet”) at the World Bank Prospects for Developmentwebsite (www.worldbank.org/prospects, clickon Products). • MUV G-15 index is the manufacturesunit value index for G-15 country exports to low- andmiddle-income economies.Data sourcesData on commodity prices and the MUV G-15 indexare compiled by the World Bank’s DevelopmentProspects Group. Monthly updates of commodityprices are available at http://data.worldbank.org.2012 World Development Indicators 351

6.6 Regional trade blocsYear ofcreationYear of entryinto force of themost recentagreementType of mostrecentagreement aMerchandise exportswithin blocMerchandiseexports by bloc% of total bloc$ millionsexports % of world exports2010 2010 2010High-income and low- and middle-income economiesAPEC b 1989 None 4,868,838 67.5 47.3EEA 1994 1994 EIA 3,519,827 68.7 33.6EFTA 1960 2002 EIA 2,096 0.6 2.2European Union 1957 1958 EIA, CU 3,356,310 67.3 32.7NAFTA 1994 1994 FTA 955,598 48.7 12.9SPARTECA 1981 1981 PTA 20,717 8.1 1.7Trans-Pacifi c SEP 2006 2006 EIA, FTA 3,969 0.9 3.0East Asia and Pacific and South AsiaAPTA 1975 1976 PTA 278,451 12.1 15.1ASEAN 1967 1992 FTA 262,270 25.0 6.9MSG 1993 1994 PTA 99 0.8 0.1PICTA 2001 2003 FTA 308 2.6 0.1SAARC 1985 2006 FTA 15,702 5.8 1.8Europe, Central Asia, and Middle EastAgadir agreement 2004 NNA 2,068 3.2 0.4CEFTA 1992 1994 FTA 5,229 17.5 0.2CEZ 2003 2004 FTA 18,065 4.0 2.9CIS 1991 1994 FTA 68,596 12.9 3.5Customs Union of Belarus,Kazakhstan, and Russian Federation 2010 CU 18,065 4.0 2.9EAEC 1997 2000 CU 21,200 4.7 2.9ECO 1985 2003 PTA 27,654 8.8 2.1GCC 1981 2003 c CU 28,623 4.8 3.9PAFTA 1997 1998 FTA 81,816 9.7 5.5UMA 1989 1994 c NNA 3,926 2.9 0.9Latin America and the CaribbeanAndean Community 1969 1988 CU 7,825 8.5 0.6CACM 1961 1961 CU 6,330 22.5 0.2CARICOM 1973 1997 EIA 3,356 15.2 0.1LAIA 1980 1981 PTA 128,829 15.9 5.3MERCOSUR 1991 2005 EIA 44,239 15.7 1.9OECS 1981 1981 c NNA 132 17.5 0.0Sub-Saharan AfricaCEMAC 1994 1999 CU 383 1.2 0.2CEPGL 1976 NNA 81 1.5 0.0COMESA 1994 1994 CU 8,158 7.7 0.7EAC 1996 2000 CU 1,997 20.3 0.1ECCAS 1983 2004 c NNA 483 0.6 0.6ECOWAS 1975 1993 PTA 8,911 8.8 0.7Indian Ocean Commission 1984 2005 c NNA 184 5.3 0.0SADC 1992 2000 FTA 14,576 9.8 1.0UEMOA 1994 2000 CU 2,250 14.6 0.1Note: APEC is Asia Pacifi c Economic Cooperation, EEA is European Economic Area, EFTA is European Free Trade Association, NAFTA is North American Free Trade Agreement, SPARTECAis South Pacifi c Regional Trade and Economic Cooperation Agreement, Trans-Pacifi c SEP is Trans-Pacifi c Strategic Economic Partnership, APTA is Asia-Pacifi c Trade Agreement, ASEANis Association of South East Asian Nations, MSG is Melanesian Spearhead Group, PICTA is Pacifi c Island Countries Trade Agreement, SAARC is South Asian Association for RegionalCooperation, CEFTA is Central European Free Trade Area, CEZ is Common Economic Zone, CIS is Commonwealth of Independent States, EAEC is Eurasian Economic Community, ECOis Economic Cooperation Organization, GCC is Gulf Cooperation Council, PAFTA is Pan-Arab Free Trade Area, UMA is Arab Maghreb Union, CACM is Central American Common Market,CARICOM is Caribbean Community and Common Market, LAIA is Latin American Integration Association, MERCOSUR is Southern Common Market, OECS is Organization of EasternCaribbean States, CEMAC is Economic and Monetary Community of Central Africa, CEPGL is Economic Community of the Great Lakes Countries, COMESA is Common Market for Easternand Southern Africa, EAC is East African Community, ECCAS is Economic Community of Central African States, ECOWAS is Economic Community of West African States, SADC is SouthernAfrican Development Community, UEMOA is West African Economic and Monetary Union. For regional bloc memberships, see the World Trade Organization’s Regional Trade AgreementsInformation System (http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx).a. CU is customs union; EIA is economic integration agreement; FTA is free trade agreement; PTA is preferential trade agreement; and NNA is not notifi ed agreement, which refers topreferential trade arrangements established among member countries that are not notifi ed to the World Trade Organization (these agreements may be functionally equivalent to any of theother agreements). b. No preferential trade agreement c. Collected from the offi cial website of the trade bloc.3522012 World Development Indicators

Regional trade blocs6.6GLOBAL LINKSAbout the dataDefinitionsTrade blocs are groups of countries that have establishedpreferential arrangements governing tradebetween members. Although in some cases the preferences—suchas lower tariff duties or exemptionsfrom quantitative restrictions—may be no greaterthan those available to other trading partners, sucharrangements are intended to encourage exports bybloc members to one another—sometimes calledintraregional trade. Most countries are membersof a regional trade bloc, and the surge of countriesparticipating in such arrangements has continuedunabated since the early 1990s. While trade blocsvary in structure, they all have the same objective: toreduce trade barriers among member countries. Buteffective integration requires more than reducing tariffsand quotas. Economic gains from competition andscale may not be achieved unless other barriers thatdivide markets and impede the free fl ow of goods, services,and investments are lifted. For example, manyregional trade blocs retain contingent protections onintrabloc trade, including antidumping, countervailingduties, and “emergency protection” to address balanceof payments problems or protect an industry fromimport surges. Other barriers include differing productstandards, discrimination in public procurement, andcumbersome border formalities. In addition, becominga member of a trade bloc can result in trade diversion,where a member switches from being a relatively effi -cient, low-cost producer outside a trade bloc to a lesseffi cient, higher cost producer within a trade bloc. On aglobal scale this could lead to misallocated resources.Membership in a regional trade bloc may reducethe frictional costs of trade, increase the credibilityof reform initiatives, and strengthen security amongpartners. But making it work effectively is challenging.All economic sectors may be affected, and somemay expand while others contract, so it is importantto weigh the potential costs and benefi ts of membership.The table shows the value of intraregionalmerchandise trade (service exports are excluded) forimportant regional trade blocs and the size of intraregionaltrade relative to each bloc’s exports of goodsand the share of the bloc’s exports in world exports.Although the Asia Pacifi c Economic Cooperation hasno preferential arrangements, it is included becauseof the volume of trade between its members.The data on country exports are from the InternationalMonetary Fund’s (IMF) Direction of Trade databaseand should be broadly consistent with thosefrom sources such as the United Nations StatisticsDivision’s Commodity Trade Statistics (Comtrade) database.All high-income economies and major developingeconomies report trade to the IMF on a timely basis,covering about 85 percent of trade for recent years.Trade by less timely reporters and by countries that donot report is estimated using reports of trading partnercountries. Therefore, data on trade between developingand high-income economies shown in the table shouldbe generally complete. However, trade fl ows betweenmany developing countries—particularly those in Sub-Saharan Africa—are not well recorded, and the value oftrade among developing countries may be understated.Membership in the trade blocs shown is basedon the most recent information available (see Datasources). Other types of preferential trade agreementsmay have entered into force earlier thanthose shown in the table and may still be effective.Unless otherwise footnoted, information on the typeof agreement and date of enforcement are based onthe World Trade Organization’s (WTO) list of regionaltrade agreements. Information on trade agreementsnot notifi ed to the WTO was collected from the GlobalPreferential Trade Agreements database (box 6.6a)and from offi cial websites of the trade blocs.Some countries belong to more than one trade bloc,so shares of world exports exceed 100 percent. Exportsinclude all commodity trade, which may include itemsnot specifi ed in trade bloc agreements. Differencesfrom previously published estimates may be due tochanges in membership or revisions in underlying data.• Type of most recent agreement includes customsunion, under which members substantially eliminateall tariff and nontariff barriers among themselvesand establish a common external tariff for nonmembers;economic integration agreement, which liberalizestrade in services among members and covers asubstantial number of sectors, affects a suffi cientvolume of trade, includes substantial modes ofsupply, and is nondiscriminatory (in the sense thatsimilarly situated service suppliers are treated thesame); free trade agreement, under which memberssubstantially eliminate all tariff and nontariff barriersbut set tariffs on imports from nonmembers; preferentialtrade agreement, which is an agreementnotifi ed to the WTO that is not a free trade agreement,a customs union, or an economic integrationagreement; and not notifi ed agreement, which is apreferential trade arrangement established amongmember countries that is not notifi ed to the WorldTrade Organization (the agreement may be functionallyequivalent to any of the other agreements).• Merchandise exports within bloc are the sum ofmerchandise exports by members of a trade bloc toother members of the bloc. They are shown both inU.S. dollars and as a percentage of total merchandiseexports by the bloc. • Merchandise exports bybloc as a share of world exports are the bloc’s totalmerchandise exports (within the bloc and to the restof the world) as a share of total merchandise exportsby all economies in the world.Global Preferential Trade Agreement Database 6.6aThe Global Preferential Trade Agreement Database provides information on preferential trade agreementsaround the world, including agreements that have not been notifi ed to the World Trade Organization(WTO). It is designed to help trade policymakers, scholars, and business operators better understandand navigate the world of preferential trade agreements. Updated regularly, the database currently coversmore than 330 preferential trade agreements in their original language, which have been indexed by WTOcriteria and can be downloaded as PDFs. Users can search by provision or keyword; compare provisionsacross multiple agreements; and sort agreements by membership, date of signature, in-force status,and other criteria. The database was developed jointly by the World Bank and the Center for InternationalBusiness, Tuck School of Business at Dartmouth College. It was supported by the Multidonor Trust Fundfor Trade and Development, with fi nancing from the governments of Finland, Norway, Sweden, and theUnited Kingdom. The database is integrated with the World Integrated Trade Solution database and ispart of the World Bank’s Open Data initiative (http://wits.worldbank.org/gptad/).Data sourcesData on merchandise trade fl ows are publishedin the IMF’s Direction of Trade Statistics Yearbookand Direction of Trade Statistics Quarterly; thedata in the table were calculated using the IMF’sDirection of Trade database. Data on trade blocmembership are from World Bank (2000b), UNC-TAD’s Trade and Development Report 2007, WTO’sRegional Trade Agreements Information System(http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx), and the World Bank and the Center for InternationalBusiness at the Tuck School of Businessat Dartmouth College’s Global Preferential TradeAgreements Database.2012 World Development Indicators 353

6.7 Tariff barriersMostrecentyearBindingcoverageSimplemeanbound rateSimplemeantariffAllproducts%WeightedmeantariffShare of tarifflines withinternationalpeaksShare oftariff lineswith specifi cratesSimplemean tariffPrimaryproductsManufacturedproducts% %Weightedmean tariffSimplemean tariffWeightedmean tariffAfghanistan 2008 .. .. 6.2 6.4 4.4 0.7 7.0 6.7 6.1 6.3Albania 2009 100.0 7.1 5.7 5.1 0.0 0.0 6.8 5.4 5.6 5.0Algeria 2009 .. .. 14.2 8.6 53.2 0.0 14.5 7.8 14.2 8.9Angola 2009 100.0 59.2 7.4 7.4 23.4 0.6 11.6 13.9 6.8 5.9Antigua and Barbuda 2009 97.9 58.7 13.8 14.6 49.4 0.1 17.2 14.8 13.1 14.5Argentina 2010 100.0 31.9 11.4 6.2 24.3 0.0 7.5 1.6 11.9 7.0Armenia 2008 100.0 8.5 3.7 2.3 0.0 0.4 5.6 2.2 3.5 2.4Australia 2010 97.0 10.0 2.8 1.9 0.0 0.2 1.3 0.4 3.0 2.5Azerbaijan 2009 .. .. 8.3 3.9 46.5 2.3 9.5 3.8 8.1 4.0Bahamas, The 2010 .. .. 33.2 21.5 68.8 0.5 21.9 7.6 36.1 32.6Bahrain 2009 73.6 34.8 4.4 3.6 0.3 0.2 7.0 6.9 4.0 3.1Bangladesh 2008 15.9 169.9 13.9 13.0 38.0 0.9 16.3 8.8 13.6 14.0Barbados 2007 97.8 78.1 15.1 14.8 44.9 1.0 26.3 21.9 13.4 12.3Belarus 2010 .. .. 6.7 2.1 4.5 14.0 6.3 0.9 6.8 3.4Belize 2010 97.9 58.4 11.5 6.4 31.9 1.9 17.5 4.0 10.6 10.1Benin 2010 39.5 28.7 13.3 15.4 50.2 0.0 15.5 12.4 12.9 17.0Bermuda 2010 .. .. 18.0 26.1 64.3 2.8 10.0 14.3 19.4 27.7Bhutan 2007 .. .. 18.2 17.8 50.7 0.0 43.5 44.9 15.5 16.0Bolivia 2010 100.0 40.0 9.6 5.4 11.9 0.0 8.4 5.8 9.7 5.2Bosnia and Herzegovina 2010 .. .. 3.3 1.8 0.0 4.2 1.4 1.1 3.6 2.3Botswana 2010 96.1 19.0 8.8 5.2 20.2 1.0 6.1 0.5 9.0 6.6Brazil 2010 100.0 31.4 13.4 7.6 26.4 0.0 8.1 1.5 14.0 9.8Brunei Darussalam 2010 95.3 24.1 3.8 4.1 20.8 1.2 0.2 0.1 4.4 5.0Burkina Faso 2010 39.4 42.5 12.4 8.8 44.5 0.0 11.4 8.1 12.5 9.2Burundi 2010 22.3 67.8 9.8 5.5 29.8 0.1 15.4 9.4 9.1 4.6Cambodia 2008 100.0 19.1 12.4 9.9 19.7 0.0 13.8 11.8 12.2 9.6Cameroon 2009 13.7 79.9 18.4 15.0 52.5 0.0 20.5 12.9 18.1 15.9Canada 2010 99.7 5.2 2.9 0.9 6.5 3.4 1.7 0.3 3.2 1.1Cape Verde 2010 100.0 15.8 14.7 11.6 44.3 0.0 16.2 12.2 14.4 11.0Central African Republic 2007 62.5 36.0 17.5 13.6 47.4 0.1 18.9 13.8 17.3 13.3Chad 2009 13.9 79.9 17.6 14.7 47.4 0.3 22.5 17.2 16.8 13.8Chile 2010 100.0 25.1 4.9 4.0 0.0 0.0 4.4 2.7 4.9 4.8China † 2010 100.0 10.0 7.7 4.0 11.2 0.2 8.1 1.8 7.7 5.6Hong Kong SAR, China 2010 45.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Macao SAR, China 2010 28.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Colombia 2010 100.0 43.1 11.2 8.9 19.8 0.0 10.9 8.8 11.3 8.9Comoros 2010 .. .. 7.6 8.1 1.5 0.3 4.5 3.9 8.4 11.2Congo, Dem. Rep. 2009 100.0 96.2 12.9 11.0 42.5 0.2 14.2 10.8 12.6 11.1Congo, Rep. 2007 16.5 27.4 18.6 14.7 52.6 0.2 21.9 18.6 18.2 14.1Costa Rica 2009 100.0 43.2 4.8 2.4 0.7 0.0 6.3 3.3 4.6 2.1Côte d’Ivoire 2010 33.8 11.2 13.1 7.3 47.9 0.0 15.1 5.4 12.8 9.3Croatia 2010 100.0 6.0 2.4 1.2 4.1 5.2 4.5 1.9 2.1 1.0Cuba 2010 31.7 21.4 10.5 8.7 11.6 0.0 11.1 6.2 10.4 9.8Cyprus 2010 100.0 4.2 1.9 1.6 1.9 7.8 2.5 0.5 1.9 2.3Djibouti 2009 100.0 41.2 20.6 15.2 69.4 0.5 15.9 8.7 21.3 18.6Dominica 2007 94.7 58.7 11.9 7.9 43.3 0.0 19.2 5.7 10.6 9.3Dominican Republic 2010 100.0 34.9 8.3 6.1 30.1 0.0 11.8 4.6 7.8 6.9Ecuador 2010 100.0 21.7 9.3 6.0 20.2 0.0 9.0 4.3 9.4 6.7Egypt, Arab Rep. 2009 99.3 37.3 12.6 8.1 18.4 0.2 37.6 6.4 9.4 9.5El Salvador 2010 100.0 36.9 5.1 5.5 1.9 0.0 8.4 7.4 4.7 4.3Equatorial Guinea 2007 .. .. 18.3 15.6 52.3 0.2 21.5 21.4 17.8 14.3Eritrea 2006 .. .. 9.6 5.4 22.4 0.0 9.2 3.5 9.6 7.2Ethiopia 2010 .. .. 18.1 10.5 55.6 0.0 19.7 5.1 18.0 13.2European Union 2010 100.0 4.2 1.9 1.6 1.9 7.8 2.5 0.5 1.9 2.3Fiji 2010 51.4 40.1 11.9 11.0 23.3 1.9 13.5 9.8 11.6 12.2French Polynesia 2009 .. .. 6.8 4.2 28.0 0.0 4.1 2.7 7.3 5.2Gabon 2009 100.0 21.4 18.7 14.5 53.1 0.1 21.2 15.1 18.4 14.3Gambia, The 2009 13.7 101.8 18.7 14.8 91.2 0.1 16.9 12.8 19.2 17.0Georgia 2010 100.0 7.2 0.5 0.4 0.0 1.0 4.0 1.0 0.1 0.0Ghana 2009 14.4 92.5 13.0 8.6 40.5 1.2 16.6 8.9 12.5 8.4† Data for Taiwan, China 2010 100.0 6.0 5.3 2.5 6.0 1.7 8.4 2.0 4.8 2.93542012 World Development Indicators

Tariff barriers6.7GLOBAL LINKSMostrecentyearBindingcoverageSimplemeanbound rateSimplemeantariffAllproducts%WeightedmeantariffShare of tarifflines withinternationalpeaksShare oftariff lineswith specifi cratesSimplemean tariffPrimaryproductsManufacturedproducts% %Weightedmean tariffSimplemean tariffWeightedmean tariffGrenada 2010 100.0 56.8 8.9 7.7 36.7 0.0 10.1 7.0 8.6 8.3Guatemala 2010 100.0 42.3 4.1 2.4 16.0 0.0 4.8 1.7 4.0 2.8Guinea 2010 38.6 20.3 13.5 11.9 56.1 0.0 15.6 13.9 13.2 10.2Guinea-Bissau 2010 97.6 48.6 13.3 9.9 51.8 0.0 14.6 10.0 12.9 9.7Guyana 2010 100.0 56.8 10.1 6.9 39.2 0.0 15.5 5.9 9.4 7.7Haiti 2009 89.8 17.6 3.0 5.1 5.1 0.3 5.8 4.1 2.5 5.9Honduras 2009 100.0 32.5 6.4 6.5 0.5 0.1 9.9 8.1 6.0 5.5Iceland 2010 95.0 13.5 1.8 1.1 5.2 3.4 2.3 1.1 1.7 1.1India 2009 74.5 50.2 11.5 8.2 10.4 4.7 20.1 7.5 10.3 8.3Indonesia 2010 96.6 37.5 4.8 2.5 7.0 0.5 3.2 1.6 5.0 2.9Iran, Islamic Rep. 2008 .. .. 24.8 19.6 56.5 0.3 21.7 12.5 25.1 21.2Iraq .. .. .. .. .. .. .. .. .. ..Israel 2009 75.2 22.0 6.0 3.5 2.9 4.1 8.8 3.3 5.6 3.7Jamaica 2010 100.0 49.7 8.4 7.5 42.6 0.0 13.9 6.1 7.7 8.9Japan 2010 99.7 3.0 2.6 1.6 8.6 3.3 5.1 1.6 2.1 1.7Jordan 2009 100.0 16.3 9.7 5.2 29.5 0.6 14.2 3.9 9.0 6.1Kazakhstan 2010 .. .. 6.4 3.4 19.6 16.4 6.1 0.9 6.4 4.0Kenya 2010 15.2 95.3 12.1 9.2 36.6 0.1 16.0 12.6 11.7 6.7Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 2010 95.1 16.1 10.3 8.7 7.0 0.6 26.3 12.7 7.4 5.1Kosovo .. .. .. .. .. .. .. .. .. ..Kuwait 2009 99.9 100.0 4.1 4.1 0.0 0.3 3.4 3.0 4.2 4.4Kyrgyz Republic 2010 99.9 7.5 3.3 2.3 1.8 1.7 4.5 0.9 3.1 3.6Lao PDR 2008 .. .. 9.3 13.2 20.4 0.1 16.0 14.2 8.4 12.6Lebanon 2007 .. .. 5.6 4.8 11.6 10.9 8.2 5.0 5.2 5.1Lesotho 2010 100.0 78.9 9.5 10.5 21.6 1.4 9.2 1.6 9.5 10.9Liberia .. .. .. .. .. .. .. .. .. ..Libya 2006 .. .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Macedonia, FYR 2010 100.0 6.9 3.6 2.7 14.5 2.5 7.4 5.7 3.2 1.8Madagascar 2010 30.5 27.3 10.6 7.7 38.3 0.2 12.6 3.3 10.3 8.9Malawi 2010 32.0 75.9 11.7 6.6 42.7 0.3 13.4 6.0 11.5 6.8Malaysia 2009 83.9 14.6 6.8 4.0 16.9 0.8 10.1 5.0 6.1 3.7Maldives 2009 97.0 37.2 21.7 20.6 88.1 0.0 17.5 18.4 22.8 22.6Mali 2010 40.5 28.9 12.8 8.4 47.9 0.0 12.8 7.9 12.8 8.7Mauritania 2007 39.4 19.6 12.6 10.1 49.0 0.0 11.1 9.2 12.9 11.0Mauritius 2010 17.7 98.3 2.0 1.1 10.4 8.7 1.2 0.4 2.1 1.6Mayotte 2010 .. .. 5.1 1.8 1.4 0.0 3.7 1.5 5.3 1.9Mexico 2010 100.0 35.1 7.4 2.2 6.1 0.1 9.3 1.5 7.2 2.4Moldova 2010 99.9 6.7 4.6 2.5 8.4 2.8 7.0 2.3 4.3 2.6Mongolia 2009 100.0 17.5 4.9 5.1 0.1 0.0 5.1 5.4 4.9 4.9Montenegro 2010 .. .. 3.0 3.5 4.5 2.9 5.8 4.8 2.7 2.8Morocco 2009 100.0 41.3 9.1 7.1 23.6 0.0 18.0 8.9 8.2 5.8Mozambique 2010 14.0 97.4 7.7 4.8 23.9 0.1 8.7 4.7 7.5 4.7Myanmar 2008 17.6 83.8 4.0 3.2 4.1 0.0 5.1 2.7 3.9 3.4Namibia 2010 96.1 19.4 6.3 1.8 16.7 2.0 4.1 2.1 6.7 1.6Nepal 2010 99.4 26.2 12.6 12.1 46.3 1.2 12.9 10.3 12.6 14.4New Zealand 2010 100.0 10.0 2.5 1.6 0.0 2.9 1.4 0.4 2.6 2.1Nicaragua 2010 100.0 41.7 4.2 2.3 16.2 0.0 5.6 2.8 4.0 1.9Niger 2010 96.6 44.9 13.0 9.1 48.9 0.0 14.0 10.7 12.8 7.6Nigeria 2010 19.5 119.4 10.9 10.6 34.9 0.0 11.8 9.1 10.7 10.8Norway 2010 100.0 3.0 0.5 0.4 0.5 6.9 2.0 1.2 0.3 0.2Oman 2009 100.0 13.9 3.7 3.2 0.3 0.2 5.0 3.3 3.5 3.2Pakistan 2009 98.6 60.0 14.8 9.5 45.3 0.3 14.5 6.5 14.8 12.3Palau 2010 .. .. 2.7 1.0 1.3 6.6 0.7 0.1 3.3 3.1Panama 2009 99.9 23.5 7.6 7.6 2.8 0.0 11.5 8.4 7.1 7.3Papua New Guinea 2010 100.0 31.5 4.4 2.5 23.3 1.0 11.8 1.8 3.4 2.8Paraguay 2010 100.0 33.5 8.1 3.7 18.3 0.0 5.8 0.8 8.2 4.8Peru 2010 100.0 30.1 4.8 2.5 10.0 0.0 3.8 1.3 4.9 3.0Philippines 2010 67.2 25.8 5.3 4.8 5.4 0.0 6.8 5.1 5.1 4.7Puerto Rico .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 355

6.7Tariff barriersMostrecentyearBindingcoverageSimplemeanbound rateSimplemeantariffAllproducts%WeightedmeantariffShare of tarifflines withinternationalpeaksShare oftariff lineswith specifi cratesSimplemean tariffPrimaryproductsManufacturedproducts% %Weightedmean tariffSimplemean tariffWeightedmean tariffQatar 2009 100.0 16.0 4.2 3.8 0.2 0.2 5.1 4.0 4.1 3.8Russian Federation 2010 .. .. 6.0 3.8 17.4 15.4 5.8 3.4 6.0 3.9Rwanda 2010 100.0 89.3 9.9 6.0 31.4 0.1 11.5 6.4 9.7 5.9Saudi Arabia 2009 100.0 10.8 4.1 3.9 0.0 0.3 3.5 2.8 4.1 4.2Senegal 2010 100.0 30.0 13.4 8.9 50.5 0.0 14.1 7.7 13.3 10.2Serbia 2005 a .. .. 8.1 6.0 17.8 0.0 10.9 4.5 7.8 6.8Seychelles 2007 .. .. 6.5 28.3 12.8 2.0 14.0 50.5 4.8 6.4Sierra Leone 2004 100.0 47.4 .. .. .. .. .. .. .. ..Singapore 2010 69.6 7.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0Solomon Islands 2010 100.0 78.8 9.2 8.7 0.0 4.4 9.3 8.5 9.2 8.8Somalia .. .. .. .. .. .. .. .. .. ..South Africa 2010 96.1 19.4 7.6 4.4 17.9 1.8 5.4 1.9 7.9 5.7South Sudan .. .. .. .. .. .. .. .. .. ..Sri Lanka 2010 38.1 30.1 9.4 6.9 23.8 1.0 15.3 9.6 8.8 5.7St. Kitts and Nevis 2010 97.8 76.1 9.6 10.5 36.1 0.5 9.9 7.0 9.6 11.8St. Lucia 2007 99.6 61.9 9.6 9.0 39.9 0.0 12.7 4.9 9.1 12.2St. Vincent & Grenadines 2007 99.7 62.5 11.3 8.4 44.4 0.5 15.1 7.8 10.6 8.6Sudan 2010 .. .. 13.3 14.8 28.3 0.1 16.6 10.9 12.9 15.8Suriname 2010 27.6 18.1 11.6 11.9 36.2 0.1 18.3 15.0 10.4 10.4Swaziland 2010 96.1 19.4 10.9 10.2 26.2 3.2 9.7 1.3 11.1 16.2Switzerland 2010 99.8 0.0 0.0 0.0 0.0 26.9 0.0 0.0 0.0 0.0Syrian Arab Republic 2010 .. .. 6.7 6.1 27.6 0.0 6.5 6.1 6.7 6.1Tajikistan 2010 .. .. 4.4 5.9 8.2 0.8 3.5 1.1 4.5 7.8Tanzania 2010 13.8 120.0 12.9 8.2 39.9 0.1 17.5 8.7 12.4 8.0Thailand 2009 74.7 26.1 11.2 4.9 20.5 5.2 15.9 2.9 10.5 6.1Timor-Leste .. .. .. .. .. .. .. .. .. ..Togo 2010 14.3 80.0 12.8 14.2 47.3 0.0 14.4 12.4 12.6 14.9Tonga 2010 100.0 17.6 10.9 7.2 65.6 0.3 11.9 5.1 10.6 9.0Trinidad and Tobago 2008 100.0 55.8 8.7 10.0 43.6 0.5 16.6 3.1 7.6 17.4Tunisia 2008 58.3 58.0 21.9 16.0 57.8 0.0 26.8 12.0 21.4 18.2Turkey 2010 50.3 29.2 2.5 2.4 5.4 0.2 14.4 4.8 1.2 1.3Turkmenistan 2002 .. .. 5.4 2.9 14.8 3.2 14.7 12.6 3.8 1.1Uganda 2010 16.1 73.5 12.1 8.2 37.5 0.2 15.7 8.8 11.6 7.9Ukraine 2010 100.0 5.8 4.5 2.8 1.1 0.4 5.9 2.5 4.3 3.1United Arab Emirates 2009 100.0 14.8 4.5 3.7 0.3 0.3 6.0 2.8 4.2 4.4United States 2010 100.0 3.7 2.9 1.8 3.4 6.9 2.6 1.2 3.0 2.0Uruguay 2010 100.0 31.6 9.6 3.6 29.3 0.0 5.6 1.1 10.0 5.3Uzbekistan 2009 .. .. 11.8 6.9 20.1 10.9 12.6 3.9 11.7 7.4Vanuatu 2009 .. .. 18.2 18.5 66.6 3.4 26.7 27.8 16.5 14.6Venezuela, RB 2010 100.0 36.5 13.1 10.6 21.9 0.0 12.2 10.0 13.2 10.8Vietnam 2010 100.0 11.5 7.1 5.7 23.6 0.5 8.6 6.0 6.9 5.6West Bank and Gaza .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 2009 .. .. 5.5 4.2 1.4 0.8 7.1 3.8 5.2 4.6Zambia 2009 17.1 106.9 10.8 3.8 51.2 0.0 9.2 3.1 11.0 4.2Zimbabwe 2007 b 22.2 91.4 16.7 17.3 38.8 6.5 19.6 20.4 16.3 15.1World 78.3 w 31.2 6.9 3.1 15.0 2.0 9.0 2.7 6.6 3.3 wLow income 48.3 52.3 12.5 9.2 41.4 0.2 14.3 7.9 12.3 9.8Middle income 87.7 32.9 8.5 5.0 20.1 0.8 10.8 3.4 8.2 5.7Lower middle income 80.6 37.3 9.1 6.3 20.0 0.8 13.2 5.6 8.6 6.6Upper middle income 90.3 29.3 8.3 4.6 20.7 0.8 9.3 2.7 8.1 5.5Low & middle income 76.5 36.5 9.0 5.1 21.5 0.8 11.3 3.5 8.7 5.8East Asia & Pacifi c 84.2 22.3 8.3 4.2 15.8 0.9 10.6 2.3 7.9 5.4Europe & Central Asia 91.7 8.9 4.9 4.1 13.1 0.9 7.7 3.1 4.6 4.5Latin America & Carib. 96.1 36.6 8.1 4.9 18.1 0.4 7.9 3.1 8.1 5.5Middle East & N. Africa 99.9 30.4 10.6 7.4 25.0 0.0 17.2 6.8 9.7 7.8South Asia 81.5 41.6 13.7 8.5 39.6 2.7 17.8 7.6 13.1 8.7Sub-Saharan Africa 53.4 48.4 11.4 7.2 36.7 1.0 12.8 6.1 11.3 7.7High income 90.3 17.3 3.6 2.2 5.0 4.2 6.4 2.4 3.1 2.1OECD 89.6 7.4 3.3 2.2 5.1 5.4 7.1 2.6 2.6 2.0Non-OECD 73.1 9.1 2.9 0.8 4.9 0.0 3.2 0.8 2.8 0.8a. Includes Montenegro. b. Rates are most favored nation rates.3562012 World Development Indicators

Tariff barriers 6.7GLOBAL LINKSAbout the dataDefinitionsPoor people in developing countries work primarilyin agriculture and labor-intensive manufactures, sectorsthat confront the greatest trade barriers. Removingbarriers to merchandise trade could increasegrowth in these countries—even more if trade inservices were also liberalized.In general, tariffs in high-income countries onimports from developing countries, though low, aretwice those collected from other high-income countries.But protection is also an issue for developingcountries, which maintain high tariffs on agriculturalcommodities, labor-intensive manufactures, andother products and services.Countries use a combination of tariff and nontariffmeasures to regulate imports. The most commonform of tariff is an ad valorem duty, based on thevalue of the import, but tariffs may also be leviedon a specifi c, or per unit, basis or may combine advalorem and specifi c rates. Tariffs may be used toraise fi scal revenues or to protect domestic industriesfrom foreign competition—or both. Nontariffbarriers, which limit the quantity of imports of a particulargood, include quotas, prohibitions, licensingschemes, export restraint arrangements, and healthand quarantine measures. Because of the diffi cultyof combining nontariff barriers into an aggregate indicator,they are not included in the table.Unless specifi ed as most favored nation rates, thetariff rates used in calculating the indicators in thetable are effectively applied rates. Effectively appliedrates are those in effect for partners in preferentialtrade arrangements such as the North AmericanFree Trade Agreement. The difference betweenmost favored nation and applied rates can be substantial.Because more countries now report theirfree trade agreements, suspensions of tariffs, andother special preferences, this year’s World DevelopmentIndicators includes effectively applied rates formost countries. All estimates are calculated usingthe most recent information, which is not necessarilyrevised every year. As a result, data for the same yearmay differ from data in last year’s edition.Three measures of average tariffs are shown: simplebound rates and the simple and the weightedtariffs. Bound rates are based on all products in acountry’s tariff schedule, while the most favorednation or applied rates are calculated using all tradeditems. Weighted mean tariffs are weighted by thevalue of the country’s trade with each trading partner.Simple averages are often a better indicator oftariff protection than weighted averages, which arebiased downward because higher tariffs discouragetrade and reduce the weights applied to these tariffs.Bound rates result from trade negotiations incorporatedinto a country’s schedule of concessions andare thus enforceable.Some countries set fairly uniform tariff ratesacross all imports. Others are selective, setting hightariffs to protect favored domestic industries. Theshare of tariff lines with international peaks providesan indication of how selectively tariffs are applied.The effective rate of protection—the degree to whichthe value added in an industry is protected—mayexceed the nominal rate if the tariff system systematicallydifferentiates among imports of raw materials,intermediate products, and fi nished goods.The share of tariff lines with specifi c rates showsthe extent to which countries use tariffs based onphysical quantities or other, non–ad valorem measures.Some countries such as Switzerland applymainly specifi c duties. To the extent possible, thesespecifi c rates have been converted to their advalorem equivalent rates and have been included inthe calculation of simple and weighted tariffs.Data are classifi ed using the Harmonized Systemat the six- or eight-digit level. Tariff line data werematched to Standard International Trade Classifi -cation (SITC) revision 2 codes to defi ne commoditygroups and import weights. Import weights were calculatedusing the United Nations Statistics Division’sCommodity Trade Statistics (Comtrade) database.The table shows tariff rates for three commoditygroups: all products, primary products, and manufacturedproducts. Effectively applied rates at thesix- and eight-digit product level are averaged forproducts in each commodity group. When an effectivelyapplied rate is not available, the most favorednation rate is used instead.Data are shown only for the last year for whichcomplete data are available. EU member countriesapply a common tariff schedule that is listed underEuropean Union and are thus not listed separately.• Binding coverage is the percentage of productlines with an agreed bound rate. • Simple meanbound rate is the unweighted average of all the linesin the tariff schedule in which bound rates have beenset. • Simple mean tariff is the unweighted averageof effectively applied rates or most favored nationrates for all products subject to tariffs calculatedfor all traded goods. • Weighted mean tariff is theaverage of effectively applied rates or most favorednation rates weighted by the product import sharescorresponding to each partner country. • Share oftariff lines with international peaks is the shareof lines in the tariff schedule with tariff rates thatexceed 15 percent. • Share of tariff lines with specificrates is the share of lines in the tariff schedulethat are set on a per unit basis or that combine advalorem and per unit rates. • Primary products arecommodities classifi ed in SITC revision 2 sections0–4 plus division 68 (nonferrous metals). • Manufacturedproducts are commodities classifi ed inSITC revision 2 sections 5–8 excluding division 68.Data sourcesAll indicators in the table were calculated by WorldBank staff using the World Integrated Trade Solutionsystem (http://wits.worldbank.org). Data ontariffs are from the United Nations Conferenceon Trade and Development’s Trade Analysis andInformation System database and the World TradeOrganization’s Integrated Data Base and ConsolidatedTariff Schedules database. Data on globalimports are from the United Nations StatisticsDivision’s Comtrade database.2012 World Development Indicators 357

6.8Trade facilitationLogisticsPerformanceIndexBurden ofcustomsproceduresLead time Documents LinerShippingConnectivityIndexQuality ofportinfrastructureFreightcosts to theUnitedStatesdaysnumber1 kilogram DHLnondocument1–5(worst to best)1–7(worst to best) To export To import To export To import0–100(low to high)1–7(worst to best)air package a$2010 2010–11 b 2010 2010 June 2011 June 2011 2011 2010–11 2012Afghanistan 2.24 .. 2.0 4.0 10 10 .. .. 148.25Albania 2.46 4.2 1.7 2.0 7 8 4.5 3.9 163.10Algeria 2.36 2.8 4.6 7.1 8 9 31.1 3.0 162.00Angola 2.25 2.7 6.0 8.0 11 8 11.3 2.3 162.00Argentina 3.10 2.7 3.7 3.8 7 7 30.6 3.7 92.30Armenia 2.52 2.9 .. .. 5 8 .. 2.7 c 148.25Australia 3.84 5.1 2.6 2.8 6 5 28.3 5.1 102.10Austria 3.76 5.0 2.0 3.7 4 5 .. 4.7 c 142.10Azerbaijan 2.64 3.5 7.0 3.0 8 10 .. 4.1 c 163.10Bahrain 3.37 5.5 1.0 2.0 6 7 9.8 6.0 148.25Bangladesh 2.74 3.4 1.4 1.4 6 8 8.2 3.4 102.10Belarus 2.53 .. .. .. 9 10 .. .. 163.10Belgium 3.94 4.6 1.7 1.6 4 5 88.5 6.5 116.75Benin 2.79 3.7 3.0 7.0 7 8 12.7 3.9 162.00Bolivia 2.51 3.0 15.0 28.3 8 7 .. 3.1 c 92.30Bosnia and Herzegovina 2.66 3.4 2.0 2.0 8 9 .. 1.7 163.10Botswana 2.32 4.7 .. .. 6 8 .. 3.9 c 162.00Brazil 3.20 3.1 2.8 3.9 7 8 34.6 2.7 92.30Bulgaria 2.83 3.5 2.0 3.9 5 6 5.4 3.8 142.10Burkina Faso 2.23 4.0 4.0 14.0 10 10 .. 3.7 c 162.00Burundi 2.29 2.9 .. .. 9 10 .. 3.0 c 162.00Cambodia 2.37 3.7 1.3 4.0 9 10 5.4 4.0 111.40Cameroon 2.55 4.0 3.4 8.9 11 12 11.4 3.5 162.00Canada 3.87 4.9 2.8 3.7 3 4 38.4 5.8 76.15Central African Republic .. .. .. .. 9 17 .. .. 162.00Chad 2.49 2.8 74.0 35.0 8 11 .. 2.7 c 162.00Chile 3.09 5.5 3.5 3.0 6 6 22.8 5.2 92.30China 3.49 4.4 2.8 2.6 8 5 152.1 4.5 86.75Hong Kong SAR, China 3.88 6.2 1.7 1.6 4 4 115.3 6.6 92.75Colombia 2.77 4.0 7.0 7.0 5 6 27.3 3.4 92.30Congo, Dem. Rep. 2.68 .. 2.0 3.0 8 9 3.7 .. 162.00Congo, Rep. 2.48 .. .. .. 11 10 10.8 .. 162.00Costa Rica 2.91 3.9 2.0 2.0 6 7 10.7 2.3 92.30Côte d’Ivoire 2.53 3.9 1.0 1.0 10 9 17.4 4.9 162.00Croatia 2.77 4.1 1.0 1.0 7 8 21.8 4.0 163.10Cuba 2.07 .. .. .. .. .. 6.6 .. 80.30Cyprus 3.13 4.9 1.0 2.0 5 7 17.1 5.1 142.10Czech Republic 3.51 4.4 2.5 3.5 4 7 0.4 4.7 c 142.10Denmark 3.85 5.7 1.0 1.0 4 3 26.4 6.2 142.10Dominican Republic 2.82 4.4 2.2 3.5 6 7 22.9 4.4 80.30Ecuador 2.77 3.5 2.1 3.4 8 7 22.5 3.8 92.30Egypt, Arab Rep. 2.61 4.1 1.3 3.1 8 9 51.2 4.0 148.25El Salvador 2.67 3.9 2.0 2.0 8 8 12.0 3.8 92.30Eritrea 1.70 .. 3.0 3.0 10 12 4.0 .. 162.00Estonia 3.16 5.2 4.0 4.0 3 4 5.8 5.6 142.10Ethiopia 2.41 3.5 5.0 6.0 7 9 .. 3.9 c 162.00Finland 3.89 6.0 1.6 1.8 4 5 11.3 6.2 142.10France 3.84 4.9 3.2 4.5 2 2 71.8 5.6 116.75Gabon 2.41 .. 4.3 13.0 7 8 8.0 .. 162.00Gambia, The 2.49 5.2 4.6 3.5 6 7 5.4 4.9 162.00Georgia 2.61 4.9 .. .. 4 4 3.8 4.2 163.10Germany 4.11 4.7 3.6 2.4 4 5 93.3 6.1 116.75Ghana 2.47 3.6 2.9 6.8 6 7 18.0 4.2 162.00Greece 2.96 4.0 3.0 3.5 5 6 32.2 4.1 142.10Guatemala 2.63 4.2 2.6 3.4 10 9 20.9 4.3 92.30Guinea 2.60 .. 3.5 3.9 7 9 6.2 .. 162.00Guinea-Bissau 2.10 .. .. .. 6 6 4.1 .. 162.00Haiti 2.59 2.5 4.2 5.3 8 10 4.8 1.8 80.303582012 World Development Indicators

Trade facilitation 6.8GLOBAL LINKSLogisticsPerformanceIndexBurden ofcustomsproceduresLead time Documents LinerShippingConnectivityIndexQuality ofportinfrastructureFreightcosts to theUnitedStatesdaysnumber1 kilogram DHLnondocument1–5(worst to best)1–7(worst to best) To export To import To export To import0–100(low to high)1–7(worst to best)air package a$2010 2010–11 b 2010 2010 June 2011 June 2011 2011 2010–11 2012Honduras 2.78 4.0 2.4 3.2 6 8 9.4 5.1 92.30Hungary 2.99 4.5 3.5 5.0 6 7 .. 4.0 c 142.10India 3.12 3.8 2.3 5.3 8 9 41.5 3.9 102.10Indonesia 2.76 3.9 2.1 5.4 4 7 25.9 3.6 102.10Iran, Islamic Rep. 2.57 3.5 2.6 28.3 7 8 30.3 3.9 148.25Iraq 2.11 .. .. .. 10 10 4.2 .. 148.25Ireland 3.89 5.2 1.0 1.0 4 4 5.9 5.2 116.75Israel 3.41 4.7 2.0 2.0 5 4 28.5 4.2 148.25Italy 3.64 4.0 2.6 3.0 4 4 70.2 3.9 116.75Jamaica 2.53 3.7 10.0 10.0 6 6 28.2 5.3 80.30Japan 3.97 4.7 1.0 1.0 3 5 67.8 5.2 124.90Jordan 2.74 4.4 3.2 4.6 6 7 16.7 4.3 148.25Kazakhstan 2.83 3.5 2.8 11.5 9 12 .. 3.6 c 163.10Kenya 2.59 3.3 3.0 5.9 8 7 12.0 3.8 162.00Korea, Dem. Rep. .. .. .. .. .. .. .. .. 111.40Korea, Rep. 3.64 4.4 1.6 2.0 3 3 92.0 5.5 102.10Kosovo .. .. .. .. 8 8 .. .. 163.10Kuwait 3.28 4.1 2.0 3.0 7 10 5.6 4.2 148.25Kyrgyz Republic 2.62 2.8 2.0 .. 8 9 .. 1.5 c 163.10Lao PDR 2.46 .. .. .. 9 10 .. .. 111.40Latvia 3.25 4.1 1.3 1.6 5 6 5.5 4.7 142.10Lebanon 3.34 3.5 3.4 2.2 5 7 35.1 4.3 148.25Lesotho 2.30 3.7 .. .. 8 8 .. 3.4 c 162.00Liberia 2.38 .. 4.0 5.0 10 9 6.2 .. 162.00Libya 2.33 3.5 3.2 10.0 .. .. 6.6 3.2 162.00Lithuania 3.13 4.5 2.0 2.3 6 6 9.8 4.9 142.10Macedonia, FYR 2.77 4.2 .. .. 6 6 .. 4.1 c 163.10Madagascar 2.66 3.4 .. .. 4 9 7.7 3.3 162.00Malawi 2.42 3.8 4.2 3.7 10 9 .. 3.6 c 162.00Malaysia 3.44 5.0 2.6 2.8 6 7 91.0 5.7 102.10Mali 2.27 4.1 5.0 4.0 6 9 .. 3.7 c 162.00Mauritania 2.63 4.1 2.0 3.0 8 8 5.6 3.3 162.00Mauritius 2.72 4.6 3.0 2.4 5 6 15.4 4.7 162.00Mexico 3.05 4.1 2.1 2.5 5 4 36.1 4.0 51.15Moldova 2.57 3.5 .. .. 6 7 .. 2.9 163.10Mongolia 2.25 3.3 14.0 12.0 8 8 .. 2.8 c 111.40Morocco 2.38 4.4 2.0 3.2 6 8 55.1 4.5 162.00Mozambique 2.29 3.7 .. .. 7 10 10.1 3.4 162.00Myanmar 2.33 .. 4.6 8.4 .. .. 3.2 .. 111.40Namibia 2.02 4.1 3.0 3.0 9 7 12.0 5.5 162.00Nepal 2.20 3.4 1.8 6.3 9 9 .. 2.6 c 111.40Netherlands 4.07 5.2 1.8 1.9 4 5 92.1 6.6 116.75New Zealand 3.65 5.8 1.3 1.6 7 5 18.5 5.5 102.10Nicaragua 2.54 3.0 3.2 3.2 5 5 8.4 2.7 92.30Niger 2.54 .. .. .. 8 10 .. .. 162.00Nigeria 2.59 3.5 2.5 4.1 10 9 19.9 3.3 162.00Norway 3.93 5.2 1.0 2.0 4 4 7.3 5.5 142.10Oman 2.84 5.0 .. .. 8 8 49.3 5.4 148.25Pakistan 2.53 3.6 2.3 1.6 7 8 30.5 4.1 148.25Panama 3.02 4.2 1.4 1.4 3 4 37.5 6.4 92.30Papua New Guinea 2.41 .. .. .. 7 9 8.8 .. 111.40Paraguay 2.75 3.9 1.0 4.0 8 10 0.0 3.4 c 92.30Peru 2.80 4.4 2.0 3.8 6 8 21.2 3.5 92.30Philippines 3.14 3.0 1.8 5.0 7 8 18.6 3.0 102.10Poland 3.44 4.4 3.0 3.6 5 5 26.5 3.4 142.10Portugal 3.34 4.7 2.5 5.0 4 5 21.1 4.9 116.75Puerto Rico .. 4.6 .. .. 6 10 .. 5.3 ..Qatar 2.95 4.8 3.8 2.3 5 7 3.6 5.4 148.252012 World Development Indicators 359

6.8 Trade facilitationLogisticsPerformanceIndexBurden ofcustomsproceduresLead time Documents LinerShippingConnectivityIndexQuality ofportinfrastructureFreightcosts to theUnitedStatesdaysnumber1 kilogram DHLnondocument1–5(worst to best)1–7(worst to best) To export To import To export To import0–100(low to high)1–7(worst to best)air package a$2010 2010–11 b 2010 2010 June 2011 June 2011 2011 2010–11 2012Romania 2.84 3.3 2.0 2.0 5 6 21.4 2.8 142.10Russian Federation 2.61 2.8 4.0 2.9 8 10 20.6 3.7 163.10Rwanda 2.04 5.3 .. .. 8 8 .. 3.2 162.00Saudi Arabia 3.22 5.0 2.3 6.3 5 5 60.0 5.4 148.25Senegal 2.86 4.7 1.4 2.7 6 5 12.3 4.5 162.00Serbia 2.69 d 3.7 2.0 d 3.0 d 6 6 .. 2.7 163.10Sierra Leone 1.97 .. 2.0 32.0 7 7 5.4 .. 162.00Singapore 4.09 6.2 2.2 1.8 4 4 105.0 6.8 92.75Slovak Republic 3.24 4.3 3.0 5.0 6 7 .. 3.9 c 142.10Slovenia 2.87 5.0 1.0 2.0 6 8 21.9 5.2 142.10Somalia 1.34 .. .. .. .. .. 4.2 .. 162.00South Africa 3.46 4.2 2.3 3.3 8 8 35.7 4.7 162.00South Sudan .. .. .. .. .. .. .. .. ..Spain 3.63 4.5 4.0 7.1 6 7 76.6 5.8 116.75Sri Lanka 2.29 4.4 1.3 2.5 6 6 41.1 4.9 102.10Sudan 2.21 .. 39.0 5.0 7 7 9.3 .. 162.00Swaziland .. 3.4 .. .. 9 9 .. 4.2 162.00Sweden 4.08 5.8 1.0 2.6 3 3 30.0 6.0 142.10Switzerland 3.97 5.1 2.6 2.6 4 5 1.9 5.2 c 142.10Syrian Arab Republic 2.74 2.9 2.5 3.2 8 9 16.8 3.4 148.25Tajikistan 2.35 3.6 7.0 .. 11 9 .. 1.8 c 163.10Tanzania 2.60 3.6 3.2 7.1 6 6 11.5 3.3 162.00Thailand 3.29 3.9 1.6 2.6 5 5 36.7 4.7 102.10Timor-Leste 1.71 3.4 .. .. 6 7 .. 2.6 111.40Togo 2.60 .. .. .. 6 8 14.1 .. 162.00Trinidad and Tobago .. 3.0 .. .. 5 6 17.9 3.9 80.30Tunisia 2.84 4.6 1.7 7.0 4 7 6.3 4.6 162.00Turkey 3.22 3.7 2.2 3.8 7 8 39.4 4.2 148.25Turkmenistan 2.49 .. 3.0 .. .. .. .. .. 163.10Uganda 2.82 4.4 5.5 14.0 7 9 .. 3.7 c 162.00Ukraine 2.57 2.8 1.7 7.0 6 8 21.4 3.7 163.10United Arab Emirates 3.63 5.6 2.5 2.0 4 5 62.5 6.2 148.25United Kingdom 3.95 4.9 3.3 1.9 4 4 87.5 5.6 116.75United States 3.86 4.3 2.8 4.0 4 5 81.6 5.5 ..Uruguay 2.75 4.2 3.0 3.0 9 9 24.4 5.1 92.30Uzbekistan 2.79 .. 1.4 2.0 10 11 .. .. 163.10Venezuela, RB 2.68 2.3 9.4 12.1 8 9 20.0 2.5 92.30Vietnam 2.96 3.4 1.4 1.7 6 8 49.7 3.4 102.10West Bank and Gaza .. .. .. .. 6 6 .. .. ..Yemen, Rep. 2.58 2.9 3.1 3.6 6 9 11.9 2.9 148.25Zambia 2.28 4.1 9.2 4.0 6 8 .. 4.0 c 162.00Zimbabwe 2.29 3.8 25.0 18.0 8 9 .. 4.4 c 162.00World 2.87 e u 4.1 u 3.8 e u 4.6 e u 7 u 7 u .. 4.3 uLow income 2.38 3.7 7.3 7.8 8 9 .. 3.3Middle income 2.68 3.8 3.7 4.9 7 7 .. 3.9Lower middle income 2.54 3.6 4.8 5.3 7 8 .. 3.6Upper middle income 2.80 3.9 2.9 4.6 6 7 .. 4.1Low & middle income 2.60 3.7 4.5 5.5 7 8 .. 3.7East Asia & Pacifi c 2.73 3.8 3.6 4.9 7 7 .. 3.8Europe & Central Asia 2.71 3.7 2.8 3.0 7 8 .. 3.4Latin America & Carib. 2.74 3.7 3.9 5.5 6 7 .. 3.8Middle East & N. Africa 2.60 3.7 2.7 7.2 7 8 .. 3.9South Asia 2.49 3.7 1.9 3.3 8 9 .. 3.8Sub-Saharan Africa 2.42 3.9 8.1 7.0 8 9 .. 3.8High income 3.54 4.9 2.1 2.7 5 5 .. 5.3Euro area 3.55 4.9 2.3 3.0 4 5 .. 5.3a. Transportation charges only; excludes fuel, surcharges, duties, and taxes. b. Average of the 2010 and 2011 survey ratings. c. Landlocked country. d. Includes Montenegro. e. Aggregatesare computed according to the World Bank classifi cation of economies as of July 1, 2011, and may differ from data published in the original source.3602012 World Development Indicators

Trade facilitation 6.8GLOBAL LINKSAbout the dataDefinitionsTrade facilitation encompasses customs effi ciencyand other physical and regulatory environmentswhere trade takes place, harmonization of standardsand conformance to international regulations,and the logistics of moving goods and associateddocumentation through countries and ports. Thoughcollection of trade facilitation data has improvedover the last decade, data that allow meaningfulevaluation, especially for developing economies, arelacking. Data on trade facilitation are drawn fromresearch by private and international agencies. Mostdata are perception-based evaluations by businessexecutives and professionals. Because of differentbackgrounds, values, and personalities, those surveyedmay evaluate the same situation differently.Caution should thus be used when interpreting perception-basedindicators. Nevertheless, they conveymuch needed information on trade facilitation.The table presents data from Logistics PerformanceSurveys conducted by the World Bank in partnershipwith academic and international institutionsand private companies and individuals engaged ininternational logistics. The Logistics PerformanceIndex assesses performance across six aspects ofthe logistics environment (see Defi nitions), basedon more than 5,000 country assessments by nearly1,000 international freight forwarders. Respondentsevaluate eight markets on six core dimensions. Themarkets are chosen based on the most importantexport and import markets of the respondent’s country,random selection, and, for landlocked countries,neighboring countries that connect them with internationalmarkets. Scores for the six dimensions areaveraged across all respondents and aggregated toa single score. Details of the survey methodologyand index construction methodology are in Arvis andothers (2010).Data on the burden of customs procedures arefrom the World Economic Forum’s Executive OpinionSurvey. The 2011 round included more than 15,000respondents from 142 countries. Sampling follows adual stratifi cation based on company size and sectorof activity. Data are collected online, through inpersoninterviews, and through mail and telephoneinterviews. Responses are aggregated using sectorweightedaveraging. Data are a two-year moving average.Respondents evaluated the effi ciency of customsprocedures in their country. The lowest value (1)rates the customs procedure as extremely in effi cient,and the highest score (7) as extremely effi cient.The direct costs of cross-border trade includefreight, customs, and storage fees. Indirect costsinclude the value of time to import or export and therisk of delay or loss of shipments. Long lead timesand burdensome regulatory procedures may lowercompetitiveness. Data on lead time are from theLogistics Performance Index survey. Respondentsprovided separate values for the best case (10 percentof shipments) and the median case (50 percentof shipments). The data are exponentiated averagesof the logarithm of single value responses and of midpointvalues of range responses for the median case.Data on the number of documents needed toexport or import are from the World Bank’s DoingBusiness surveys, which compile procedural requirementsfor exporting and importing a standardizedcargo of goods by ocean transport from local freightforwarders, shipping lines, customs brokers, portoffi cials, and banks. To make the data comparableacross economies, several assumptions about thebusiness and the traded goods are used (see www.doingbusiness.org).Access to global shipping and air freight networksand the quality and accessibility of ports and roadsaffect logistics performance. The table shows twoindicators related to trade and transport serviceinfrastructure: the Liner Shipping Connectivity Indexand the quality of port infrastructure rating. The LinerShipping Connectivity Index captures how well countriesare connected to global shipping networks. Itis computed by the United Nations Conference onTrade and Development (UNCTAD) based on fi ve componentsof the maritime transport sector: number ofships, their container-carrying capacity, maximumvessel size, number of services, and number ofcompanies that deploy container ships in a country’sports. For each component a country’s value isdivided by the maximum value of each componentin 2004, the fi ve components are averaged for eachcountry, and the average is divided by the maximumaverage for 2004 and multiplied by 100. The indexgenerates a value of 100 for the country with thehighest average index in 2004.The quality of port infrastructure measures businessexecutives’ perception of their country’s portfacilities. Values range from 1 (extremely underdeveloped)to 7 (effi cient). Respondents in landlockedcountries were asked: “How accessible are portfacilities (1 = extremely inaccessible; 7 = extremelyaccessible.)”The costs of transport services are a crucial determinantof export competitiveness. The proxy indicatorin the table is the shipping rates to the UnitedStates of an international freight moving business.• Logistics Performance Index refl ects perceptionsof a country’s logistics based on effi ciency ofcustoms clearance process, quality of trade- andtransport-related infrastructure, ease of arrangingcompetitively priced shipments, quality of logisticsservices, ability to track and trace consignments, andfrequency with which shipments reach the consigneewithin the scheduled time. The index ranges from 1to 5, with a higher score representing better performance.• Burden of customs procedure measuresbusiness executives’ perceptions of their country’seffi ciency of customs procedures. Values range from1 to 7, with a higher rating indicating greater effi -ciency. • Lead time to export is the median time (thevalue for 50 percent of shipments) from shipmentpoint to port of loading. • Lead time to import is themedian time (the value for 50 percent of shipments)from port of discharge to arrival at the consignee.• Documents to export and documents to import areall documents required per shipment by governmentministries, customs authorities, port and containerterminals, health and technical control agencies, andbanks to export or import goods. Documents renewedannually and not requiring renewal per shipment areexcluded. • Liner Shipping Connectivity Index indicateshow well countries are connected to global shippingnetworks based on the status of their maritimetransport sector. The highest value in 2004 is 100.• Quality of port infrastructure measures businessexecutives’ perceptions of their country’s port facilities.Values range from 1 to 7, with a higher ratingindicating better development of port infrastructure.• Freight costs to the United States is the DHL internationalU.S. inbound worldwide priority express ratefor a 1 kilogram nondocument air package.Data sourcesData on the Logistics Performance Index andlead time to export and import are from Arvis andothers (2010). Data on the burden of customsprocedure and quality of port infrastructure ratingsare from the World Economic Forum’s GlobalCompetitiveness Report 2011–2012. Data on numberof documents to export and import are fromthe World Bank’s Doing Business project (www.doingbusiness.org). Data on the Liner ShippingConnectivity Index are from UNCTAD’s Review ofMaritime Transport (2011). Freight costs to theUnited States are based on DHL’s “DHL Rate andTransit Guide 2012” (2012).2012 World Development Indicators 361

6.9External debtTotal externaldebtLong-termdebtShort-termdebtTotaldebt servicePresent value of debt% of exports$ millions% of exports$ millions % of GNI guaranteed nonguaranteed $ millions % of total debt reserves income a % of GNI a income aPublic andpublicly Private% of totalof goods andservices andof goods andservices and2010 2010 2010 2010 2010 2010 2010 2010 2010 2010Afghanistan 2,297 .. 2,076 .. 102 4.4 .. .. 6.5 21.1Albania 4,736 40.5 2,972 1,133 573 12.1 22.6 11.1 29.5 87.6Algeria 5,276 3.4 2,530 968 1,778 33.7 1.0 1.0 3.0 5.4Angola 18,562 24.6 15,440 .. 2,241 12.1 11.3 4.5 22.1 22.3Argentina 127,849 36.1 67,331 25,514 35,005 27.4 67.1 16.7 37.5 150.3Armenia 6,103 64.8 2,557 2,187 618 10.1 33.1 33.4 46.5 182.6Australia .. .. .. .. .. .. .. .. .. ..Austria .. .. .. .. .. .. .. .. .. ..Azerbaijan 6,974 14.9 3,892 2,158 878 12.6 13.7 1.4 9.1 13.7Bahrain .. .. .. .. .. .. .. .. .. ..Bangladesh 24,963 22.8 21,371 .. 2,974 11.9 26.6 4.7 16.2 84.3Belarus 25,726 46.8 7,850 2,401 11,980 46.6 238.4 4.6 41.8 74.8Belgium .. .. .. .. .. .. .. .. .. ..Benin 1,221 18.4 1,134 .. 32 2.7 2.7 .. 12.8 b 70.1 bBolivia 5,267 27.8 2,806 2,358 103 2.0 1.1 9.3 17.4 b 40.9 bBosnia and Herzegovina 8,457 48.8 3,751 3,149 1,037 12.3 23.7 19.9 37.3 95.0Botswana 1,709 11.6 1,352 .. 357 20.9 4.5 1.5 8.7 18.9Brazil 346,978 16.9 96,542 184,940 65,496 18.9 22.7 19.0 18.8 146.0Bulgaria 48,077 104.8 4,466 28,238 15,373 32.0 89.3 14.2 94.9 159.1Burkina Faso 2,053 23.3 1,925 .. 0 0.0 0.0 .. 18.6 b 168.7 bBurundi 537 33.8 412 .. 16 2.9 4.7 .. 14.3 b 151.5 bCambodia 4,676 43.4 4,414 .. 262 5.6 6.9 .. 35.8 58.6Cameroon 2,964 13.5 2,185 577 31 1.0 0.8 3.6 5.3 b 19.1 bCanada .. .. .. .. .. .. .. .. .. ..Central African Republic 385 19.2 218 .. 77 19.9 42.3 .. 12.4 b 75.9 bChad 1,733 25.7 1,708 .. 8 0.5 1.3 .. 28.3 b 57.3 bChile 86,349 45.9 12,929 47,541 25,879 30.0 93.0 15.2 47.7 98.9China 548,551 9.3 90,180 110,847 347,524 63.4 11.9 3.3 10.1 31.2Hong Kong SAR, China .. .. .. .. .. .. .. .. .. ..Colombia 63,064 22.8 36,777 18,078 8,209 13.0 29.2 21.0 37.5 211.9Congo, Dem. Rep. 5,774 47.1 4,957 .. 494 8.6 38.0 3.8 27.0 b 77.8 bCongo, Rep. 3,781 43.9 3,531 .. 222 5.9 5.0 .. 19.5 b 19.2 bCosta Rica 8,849 26.8 3,725 2,693 2,431 27.5 52.5 7.7 26.8 59.1Côte d’Ivoire 11,430 52.6 10,416 280 351 3.1 9.7 .. 48.0 b 88.2 bCroatia .. .. .. .. .. .. .. .. .. ..Cuba .. .. .. .. .. .. .. .. .. ..Cyprus .. .. .. .. .. .. .. .. .. ..Czech Republic .. .. .. .. .. .. .. .. .. ..Denmark .. .. .. .. .. .. .. .. .. ..Dominican Republic 13,045 26.2 9,115 843 1,948 14.9 55.6 11.0 23.7 89.1Ecuador 14,815 23.1 8,598 5,848 369 2.5 14.1 9.4 22.1 64.3Egypt, Arab Rep. 34,844 16.2 31,641 54 3,149 9.0 8.5 6.0 15.2 46.0El Salvador 11,069 53.2 6,394 3,569 1,105 10.0 38.1 19.0 46.5 177.1Eritrea 1,010 48.2 1,003 .. 7 0.7 6.3 .. 33.8 b 731.6 bEstonia .. .. .. .. .. .. .. .. .. ..Ethiopia 7,147 24.1 6,545 .. 314 4.4 .. .. 13.3 b 106.7 bFinland .. .. .. .. .. .. .. .. .. ..France .. .. .. .. .. .. .. .. .. ..Gabon 2,331 20.3 2,158 .. 174 7.5 10.0 .. 18.7 16.2Gambia, The 470 63.3 395 .. 44 9.4 21.9 7.2 29.5 b 72.6 bGeorgia 9,238 80.4 4,081 3,143 963 10.4 42.6 18.1 65.0 183.9Germany .. .. .. .. .. .. .. .. .. ..Ghana 8,368 27.2 5,727 0 2,249 26.9 .. 3.4 17.5 b 61.0 bGreece .. .. .. .. .. .. .. .. .. ..Guatemala 14,340 35.9 5,527 7,218 1,595 11.1 26.8 14.3 31.7 117.6Guinea 2,923 69.1 2,752 .. 123 4.2 .. 5.6 53.6 b 145.2 bGuinea-Bissau 1,095 124.8 963 .. 128 11.7 81.6 .. 14.6 b 86.6 bHaiti 492 7.3 479 .. 0 0.0 0.0 15.7 4.3 b 31.7 b3622012 World Development Indicators

External debt6.9GLOBAL LINKSTotal externaldebtLong-termdebtShort-termdebtTotaldebt servicePresent value of debt% of exports$ millions% of exports$ millions % of GNI guaranteed nonguaranteed $ millions % of total debt reserves income a % of GNI a income aPublic andpublicly Private% of totalof goods andservices andof goods andservices and2010 2010 2010 2010 2010 2010 2010 2010 2010 2010Honduras 4,168 28.2 2,798 942 398 9.6 14.7 7.6 14.0 b 26.9 bHungary .. .. .. .. .. .. .. .. .. ..India 290,282 16.9 106,205 127,629 56,448 19.4 18.8 5.6 17.7 79.4Indonesia 179,064 26.1 91,024 56,785 31,255 17.5 32.5 16.6 28.2 102.0Iran, Islamic Rep. 12,570 .. 6,411 0 6,159 49.0 .. 2.0 3.3 ..Iraq .. .. .. .. .. .. .. .. .. ..Ireland .. .. .. .. .. .. .. .. .. ..Israel .. .. .. .. .. .. .. .. .. ..Italy .. .. .. .. .. .. .. .. .. ..Jamaica 13,865 104.2 7,593 4,307 1,180 8.5 47.2 27.9 102.2 221.3Japan .. .. .. .. .. .. .. .. .. ..Jordan 7,822 27.9 6,504 .. 1,310 16.7 9.6 4.9 25.9 51.2Kazakhstan 118,723 94.3 3,842 105,844 9,037 7.6 32.0 71.4 89.1 154.7Kenya 8,400 26.9 6,978 0 1,005 12.0 23.3 4.4 19.6 71.8Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..Korea, Rep. .. .. .. .. .. .. .. .. .. ..Kosovo 342 6.0 342 .. 0 0.0 0.0 1.6 4.2 24.8Kuwait .. .. .. .. .. .. .. .. .. ..Kyrgyz Republic 3,984 89.2 2,442 1,171 195 4.9 11.3 21.9 37.8 b 66.0 bLao PDR 5,559 79.0 2,939 2,610 0 0.0 0.0 .. 65.4 210.8Latvia 39,555 164.3 6,891 18,428 12,723 32.2 167.3 76.4 129.1 260.0Lebanon 24,293 60.7 20,213 500 3,482 14.3 7.8 19.1 67.1 98.4Lesotho 726 28.4 698 .. 0 0.0 .. 1.9 17.1 23.6Liberia 228 28.3 184 .. 0 0.0 .. 1.3 11.7 b 13.8 bLibya .. .. .. .. .. .. .. .. .. ..Lithuania 29,602 83.0 11,664 12,468 5,469 18.5 80.0 34.3 68.7 107.1Macedonia, FYR 5,804 65.1 1,865 1,885 2,054 35.4 90.2 15.2 57.0 118.4Madagascar 2,295 26.6 1,981 3 214 9.3 18.3 2.6 20.8 b 60.2 bMalawi 922 18.5 715 .. 61 6.6 18.7 .. 15.7 b 63.4 bMalaysia 81,497 35.4 25,795 20,626 35,076 43.0 32.9 5.2 35.7 33.1Mali 2,326 26.1 2,271 .. 6 0.3 0.5 2.5 16.5 b 57.6 bMauritania 2,461 67.0 2,174 .. 237 9.6 82.4 .. 68.4 b 126.1 bMauritius 1,076 11.0 972 100 3 0.3 0.1 2.4 8.5 15.2Mexico 200,081 19.5 111,467 49,600 39,013 19.5 32.4 9.8 18.0 60.5Moldova 4,615 73.5 817 1,906 1,564 33.9 91.1 12.8 65.5 135.9Mongolia 2,444 44.3 1,795 221 230 9.4 10.1 5.0 33.1 57.9Morocco 25,403 28.1 21,015 2,589 1,800 7.1 7.6 10.7 23.4 67.1Mozambique 4,124 43.8 2,960 .. 975 23.6 43.0 2.9 20.9 b 65.6 bMyanmar 6,352 .. 4,395 .. 1,956 30.8 .. .. .. ..Namibia .. .. .. .. .. .. .. .. .. ..Nepal 3,702 23.4 3,527 .. 61 1.6 2.1 10.5 19.9 b 150.6 bNetherlands .. .. .. .. .. .. .. .. .. ..New Zealand .. .. .. .. .. .. .. .. .. ..Nicaragua 4,786 76.9 2,668 1,255 697 14.6 38.7 14.3 36.8 b 70.7 bNiger 1,127 20.5 972 0 94 8.4 12.4 .. 10.5 b 53.2 bNigeria 7,883 4.5 4,686 .. 3,197 40.6 8.9 0.4 3.2 7.4Norway .. .. .. .. .. .. .. .. .. ..Oman .. .. .. .. .. .. .. .. .. ..Pakistan 56,773 31.3 43,202 2,544 2,291 4.0 13.3 15.2 24.1 158.8Panama 11,412 45.8 10,421 991 0 0.0 0.0 5.7 43.7 54.7Papua New Guinea 5,822 62.9 1,030 4,400 392 6.7 12.6 12.9 54.9 79.1Paraguay 4,938 25.3 2,369 1,421 1,147 23.2 27.5 4.6 25.7 47.7Peru 36,271 24.6 20,027 10,189 6,055 16.7 13.7 16.7 24.9 88.9Philippines 72,337 36.2 44,641 21,402 6,295 8.7 10.1 18.4 34.6 99.6Poland .. .. .. .. .. .. .. .. .. ..Portugal .. .. .. .. .. .. .. .. .. ..Puerto Rico .. .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 363

6.9 External debtTotal externaldebtLong-termdebtShort-termdebtTotaldebt servicePresent value of debt% of exports$ millions% of exports$ millions % of GNI guaranteed nonguaranteed $ millions % of total debt reserves income a % of GNI a income aPublic andpublicly Private% of totalof goods andservices andof goods andservices and2010 2010 2010 2010 2010 2010 2010 2010 2010 2010Romania 121,505 76.4 20,557 60,826 25,029 20.6 52.1 31.2 58.1 170.9Russian Federation 384,740 26.9 162,924 183,059 38,756 10.1 8.1 12.8 24.7 72.1Rwanda 795 14.2 766 .. 14 1.8 1.7 2.3 11.5 b 102.7 bSaudi Arabia .. .. .. .. .. .. .. .. .. ..Senegal 3,677 28.5 3,155 308 0 0.0 0.0 .. 20.0 b 67.3 bSerbia 32,222 84.3 9,477 17,912 2,798 8.7 21.0 30.9 67.1 201.2Sierra Leone 778 40.8 661 .. 4 0.5 1.0 2.6 22.8 b 115.1 bSingapore .. .. .. .. .. .. .. .. .. ..Slovak Republic .. .. .. .. .. .. .. .. .. ..Slovenia .. .. .. .. .. .. .. .. .. ..Somalia 2,942 .. 1,990 .. 780 26.5 .. .. .. ..South Africa 45,165 12.7 17,753 15,107 12,305 27.2 28.1 4.9 14.7 45.4South Sudan .. .. .. .. .. .. .. .. .. ..Spain .. .. .. .. .. .. .. .. .. ..Sri Lanka 20,452 41.8 16,449 919 1,773 8.7 24.6 13.0 36.6 156.1Sudan 21,846 39.1 14,444 0 7,012 32.1 676.7 4.2 70.2 b 339.2 bSwaziland 616 17.2 385 .. 231 37.5 30.5 .. 19.5 26.3Sweden .. .. .. .. .. .. .. .. .. ..Switzerland .. .. .. .. .. .. .. .. .. ..Syrian Arab Republic 4,729 8.2 4,171 0 558 11.8 2.7 .. 7.9 23.1Tajikistan 2,955 53.1 1,806 927 122 4.1 .. 44.8 42.4 145.9Tanzania 8,664 37.7 5,572 1,224 1,515 17.5 38.8 3.0 22.8 b 84.7 bThailand 71,263 23.4 11,357 21,434 38,471 54.0 22.4 4.8 24.2 31.2Timor-Leste .. .. .. .. .. .. .. .. .. ..Togo 1,728 61.1 1,534 .. 61 3.5 8.5 .. 13.9 b 34.9 bTrinidad and Tobago .. .. .. .. .. .. .. .. .. ..Tunisia 21,584 51.1 14,609 1,996 4,979 23.1 51.0 10.4 47.5 77.1Turkey 293,872 40.4 93,088 117,035 78,123 26.6 90.9 36.7 39.4 165.0Turkmenistan 422 2.1 359 7 55 13.0 .. .. 2.2 2.5Uganda 2,994 17.9 2,671 .. 314 10.5 11.1 1.8 7.1 b 33.1 bUkraine 116,808 85.9 16,246 59,858 26,459 22.7 76.5 40.7 75.0 144.0United Arab Emirates .. .. .. .. .. .. .. .. .. ..United Kingdom .. .. .. .. .. .. .. .. .. ..United States .. .. .. .. .. .. .. .. .. ..Uruguay 11,347 29.0 9,704 93 1,550 13.7 20.2 12.4 30.6 101.6Uzbekistan 7,404 19.0 3,426 3,741 238 3.2 .. .. 18.1 45.3Venezuela, RB 55,572 14.3 37,086 3,060 15,426 27.8 52.0 .. .. ..Vietnam 35,139 36.5 28,145 .. 6,949 19.8 55.7 1.7 28.9 37.1West Bank and Gaza .. .. .. .. .. .. .. .. .. ..Yemen, Rep. 6,324 .. 5,933 .. 313 5.0 5.3 2.8 15.2 44.4Zambia 3,689 25.8 1,309 794 1,191 32.3 56.9 1.9 12.0 b 26.6 bZimbabwe 5,016 71.8 3,686 378 843 16.8 .. .. 110.3 275.3World .. .. .. .. .. .. .. .. .. ..Low income 116,593 28.5 95,929 3,703 12,806 11.0 19.0 .. .. ..Middle income 3,959,705 20.8 1,486,657 1,388,971 1,023,572 25.8 18.3 .. .. ..Lower middle income 1,021,016 24.7 518,320 308,804 163,917 16.1 21.9 .. .. ..Upper middle income 2,938,688 19.7 968,337 1,080,167 859,655 29.3 17.7 .. .. ..Low & middle income 4,076,298 21.0 1,582,586 1,392,674 1,036,378 25.4 18.3 .. .. ..East Asia & Pacifi c 1,013,971 13.5 306,774 238,402 468,525 46.2 13.8 .. .. ..Europe & Central Asia 1,273,418 43.0 366,663 627,496 234,232 18.4 31.1 .. .. ..Latin America & Carib. 1,038,725 21.7 457,714 370,470 208,277 20.1 32.3 .. .. ..Middle East & N. Africa 143,595 14.1 113,766 6,106 23,528 16.4 5.3 .. .. ..South Asia 400,596 19.2 194,376 131,428 63,879 15.9 18.7 .. .. ..Sub-Saharan Africa 205,992 20.0 143,293 18,772 37,936 18.4 21.8 .. .. ..High income .. .. .. .. .. .. .. .. .. ..Euro area .. .. .. .. .. .. .. .. .. ..a. The numerator refers to 2010, whereas the denominator is a three-year average of 2008–10 data. b. Data are from debt sustainability analyses for low-income countries and include theeffects of traditional relief, debt relief under the Heavily Indebted Poor Country Initiative, and relief under the Multilateral Debt Relief Initiative. Present value estimates for these countriesare for public and publicly guaranteed debt only.3642012 World Development Indicators

External debt 6.9GLOBAL LINKSAbout the dataDefinitionsExternal indebtedness affects a country’s creditworthinessand investor perceptions. Data on externaldebt are gathered through the World Bank’sDebtor Reporting System (DRS). Indebtedness iscalculated using loan-by-loan reports submitted bycountries on long-term public and publicly guaranteedborrowing and information on short-term debtcollected by the countries or from creditors throughthe reporting systems of the Bank for InternationalSettlements (BIS). These data are supplemented byinformation from major multilateral banks and offi ciallending agencies in major creditor countries and byestimates by World Bank and International MonetaryFund (IMF) staff.Currently, 129 developing countries report to theDRS. Nonreporting countries might have outstandingdebt with the World Bank, other international fi nancialinstitutions, or private creditors.Debt data, normally reported in the currency ofrepayment, are converted into U.S. dollars to producesummary tables. Stock fi gures (amount ofdebt outstanding) are converted using end-of-periodexchange rates, as published in the IMF’s InternationalFinancial Statistics. Flow fi gures are convertedat annual average exchange rates. Projected debtservice is converted using end-of-period exchangerates. Debt repayable in multiple currencies, goods,or services and debt with a provision for maintenanceof the value of the currency of repayment are shownat book value.A country’s external debt burden, both debt outstandingand debt service, affects its creditworthinessand vulnerability. The table shows total externaldebt relative to a country’s size—gross nationalincome (GNI). While data related to public and publiclyguaranteed debt are reported to the DRS on aloan-by-loan basis. Aggregate data on long-term privatenonguaranteed debt are reported annually andare reported by the country or estimated by WorldBank staff for countries where this type of externaldebt is known to be signifi cant. Estimates are basedon national data from the World Bank’s QuarterlyExternal Debt Statistics.The DRS encourages debtor countries to voluntarilyprovide information on their short-term externalobligations. By its nature, short-term external debtis diffi cult to monitor: loan-by-loan registration isnormally impractical, and monitoring systems typicallyrely on information requested periodically bythe central bank from the banking sector. The WorldBank regards the debtor country as the authoritativesource of information on its short-term debt.Where such information is not available from thedebtor country, data are derived from BIS data oninternational bank lending based on time remainingto original maturity. The data are reported basedon residual maturity, but an estimate of short-termexternal liabilities by original maturity can be derivedby deducting from claims due in one year those thathave a maturity of between one and two years.However, BIS data include liabilities reported onlyby banks within the BIS reporting area. The resultsshould thus be interpreted with caution. Becauseshort-term debt poses an immediate burden and isparticularly important for monitoring vulnerability, itis compared with total debt and foreign exchangereserves, which are instrumental in providing coveragefor such obligations.Total debt service is contrasted with countries’ability to obtain foreign exchange through exports ofgoods, services, income, and workers’ remittances.The present value of external debt provides a measureof future debt service obligations. It is calculatedby discounting the debt service (interest plusamortization) due on long-term external debt overthe life of existing loans. Short-term debt is includedat face value. The discount rate on long-term debtdepends on the currency of repayment and is basedon commercial interest reference rates establishedby the Organisation for Economic Co-operation andDevelopment. Loans from the International Bankfor Reconstruction and Development (IBRD), creditsfrom the International Development Association(IDA), and obligations to the IMF are discounted usinga special drawing rights reference rate. When thediscount rate is greater than the loan interest rate,the present value is less than the nominal sum offuture debt service obligations.Debt ratios are used to assess the sustainability ofa country’s debt service obligations, but no absoluterules determine what values are too high. Empiricalanalysis of developing countries’ experience anddebt service performance shows that debt servicediffi culties become increasingly likely when the presentvalue of debt reaches 200 percent of exports.Still, what constitutes a sustainable debt burden variesby country. Countries with fast-growing economiesand exports are likely to be able to sustainhigher debt levels.• Total external debt is debt owed to nonresidentcreditors and repayable in foreign currencies, goods, orservices by public and private entities in the country. Itis the sum of long-term external debt, short-term debt,and use of IMF credit. Debt repayable in domestic currencyis excluded. • Long-term debt is debt that hasan original or extended maturity of more than one year.It has three components: public, publicly guaranteed,and private nonguaranteed debt. • Public and publiclyguaranteed debt is the long-term external obligationsof public debtors, including the national governmentand political subdivisions (or an agency of either) andautonomous public bodies, and the external obligationsof private debtors that are guaranteed for repaymentby a public entity. • Private nonguaranteed debtis the long-term external obligations of private debtorsthat are not guaranteed for repayment by a publicentity. • Short-term debt is debt owed to nonresidentshaving an original maturity of one year or less and interestin arrears on long-term debt and on the use of IMFcredit. • Total reserves are holdings of monetary gold,special drawing rights, reserves of IMF members heldby the IMF, and holdings of foreign exchange underthe control of monetary authorities. • Total debt serviceis the sum of principal repayments and interestactually paid in foreign currency, goods, or serviceson long-term debt, interest paid on short-term debt,and repayments (repurchases and charges) to theIMF. • Exports of goods and services and incomeare the total value of exports of goods and services,receipts of compensation of nonresident workers, andinvestment income from abroad. • Present value ofdebt is the sum of short-term external debt plus thediscounted sum of total debt service payments due onpublic, publicly guaranteed, and private nonguaranteedlong-term external debt over the life of existing loans.Data sourcesData on external debt are mainly from reportsto the World Bank through its DRS from membercountries that have received IBRD loans or IDAcredits, with additional information from the fi lesof the World Bank, the IMF, the African DevelopmentBank and African Development Fund, theAsian Development Bank and Asian DevelopmentFund, and the Inter-American Development Bank.Summary tables of the external debt of developingcountries are published annually in the WorldBank’s Global Development Finance, on its GlobalDevelopment Finance CD-ROM, and in its GlobalDevelopment Finance database.2012 World Development Indicators 365

6.10 Global private financial flowsEquity net flowsDebt flowsForeign direct investment$ millionsNet infl owPortfolio equityCommercial bank and$ millions% of GDP$ millions Bondsother lending2010 2010 2010 2010 2010Afghanistan 76 0.4 .. 0 0Albania 1,110 9.4 8 398 –35Algeria 2,291 1.4 .. 0 –398Angola –3,227 –3.8 0 0 –1,553Argentina 7,055 1.9 –208 –1,660 –2,477Armenia 570 6.1 0 0 703Australia 30,576 2.7 9,974 .. ..Austria –25,636 –6.8 –385 .. ..Azerbaijan 563 1.1 1 0 2,021Bahrain 156 .. 1,653 .. ..Bangladesh 917 0.9 0 0 –11Belarus 1,403 2.6 1 1,777 702Belgium 72,914 15.5 –1,837 .. ..Benin 111 1.7 .. 0 0Bolivia 622 3.2 0 0 –359Bosnia and Herzegovina 232 1.4 0 –25 –913Botswana 265 1.8 18 0 –1Brazil 48,506 2.3 37,684 24,086 19,981Bulgaria 2,355 4.9 9 0 –1,820Burkina Faso 37 0.4 .. 0 –2Burundi 1 0.1 0 0 0Cambodia 783 7.0 0 0 0Cameroon –1 0.0 0 0 –32Canada 23,587 1.5 17,775 .. ..Central African Republic 72 3.6 .. 0 0Chad 781 10.3 .. 0 –2Chile 15,095 7.1 1,748 4,867 3,813China 185,081 3.1 31,357 11,112 2,066Hong Kong SAR, China 71,066 31.7 18,534 .. ..Colombia 6,914 2.4 1,351 972 4,302Congo, Dem. Rep. 2,939 22.4 .. 0 –4Congo, Rep. 2,816 23.7 .. 0 –24Costa Rica 1,466 4.1 0 0 313Côte d’Ivoire 418 1.8 .. 0 –58Croatia 408 0.7 112 .. ..Cuba 86 .. .. .. ..Cyprus 1,886 8.2 440 .. ..Czech Republic 6,720 3.5 287 .. ..Denmark –7,697 –2.5 7,262 .. ..Dominican Republic 1,626 3.1 0 645 –85Ecuador 167 0.3 0 –6 –3Egypt, Arab Rep. 6,386 2.9 1,724 1,500 19El Salvador –6 0.0 0 0 131Eritrea 56 2.6 .. 0 0Estonia 1,539 8.0 15 .. ..Ethiopia 288 1.0 0 0 647Finland 7,072 3.0 1,980 .. ..France 33,672 1.3 –8,442 .. ..Gabon 170 1.3 .. –23 189Gambia, The 37 4.6 0 0 6Georgia 817 7.0 –20 250 100Germany 46,127 1.4 –1,991 .. ..Ghana 2,527 8.1 0 0 251Greece 430 0.1 –1,459 .. ..Guatemala 881 2.1 0 0 –88Guinea 101 2.3 0 0 4Guinea-Bissau 9 1.0 .. 0 0Haiti 150 2.2 0 0 03662012 World Development Indicators

Global private financial flows 6.10GLOBAL LINKSEquity net flowsDebt flowsForeign direct investment$ millionsNet infl owPortfolio equityCommercial bank and$ millions% of GDP$ millions Bondsother lending2010 2010 2010 2010 2010Honduras 797 5.2 0 0 30Hungary –42,283 –32.9 –143 .. ..India 24,159 1.4 39,972 10,339 12,971Indonesia 13,371 1.9 2,132 2,329 3,563Iran, Islamic Rep. 3,617 .. .. 0 –1,084Iraq 1,426 1.7 .. .. ..Ireland 27,085 13.1 152,236 .. ..Israel 5,152 2.4 –612 .. ..Italy 9,594 0.5 3,826 .. ..Jamaica 228 1.6 0 1,007 146Japan –1,359 0.0 40,328 .. ..Jordan 1,701 6.2 –20 733 2Kazakhstan 10,677 7.2 131 –1,053 7,156Kenya 186 0.6 33 0 8Korea, Dem. Rep. 38 .. .. .. ..Korea, Rep. –150 0.0 23,026 .. ..Kosovo 481 8.7 0 0 0Kuwait 81 .. –815 .. ..Kyrgyz Republic 438 9.5 –18 0 97Lao PDR 279 3.8 54 0 –14Latvia 369 1.5 9 0 –4,045Lebanon 4,280 11.0 163 –396 5Lesotho 117 5.5 0 0 0Liberia 453 45.9 0 0 0Libya 1,784 .. 0 .. ..Lithuania 748 2.1 37 2,458 –3,897Macedonia, FYR 207 2.3 –4 0 36Madagascar 860 9.9 .. 0 –1Malawi 140 2.7 .. 0 0Malaysia 9,167 3.9 .. 2,024 2,017Mali 148 1.6 .. 0 –1Mauritania 14 0.4 .. 0 –11Mauritius 431 4.4 –40 0 27Mexico 19,792 1.9 641 13,338 643Moldova 193 3.3 6 0 119Mongolia 1,455 23.5 680 –75 151Morocco 1,241 1.4 132 1,327 77Mozambique 789 8.2 0 0 71Myanmar 910 .. .. 0 –546Namibia 796 6.5 4 .. ..Nepal 88 .. .. 0 0Netherlands –15,597 –2.0 11,327 .. ..New Zealand 701 .. –298 .. ..Nicaragua 508 7.8 0 0 –63Niger 947 17.1 .. 0 –7Nigeria 6,049 3.1 2,161 0 –33Norway 11,747 2.8 1,993 .. ..Oman 2,333 .. 703 .. ..Pakistan 2,022 1.1 524 –1,200 –227Panama 2,350 8.8 0 –150 0Papua New Guinea 29 0.3 .. 0 2,418Paraguay 345 1.9 0 0 426Peru 7,328 4.7 87 4,635 971Philippines 1,713 0.9 503 2,712 3,767Poland 9,104 1.9 7,875 .. ..Portugal 1,476 0.7 –1,628 .. ..Puerto Rico .. .. .. .. ..Qatar 5,534 .. .. .. ..2012 World Development Indicators 367

6.10Global private financial flowsEquity net flowsDebt flowsRomania 2,941 1.8 –25 –929 –858Russian Federation 43,288 2.9 –4,808 14,900 –6,734Rwanda 42 0.8 21 0 0Saudi Arabia 21,560 5.0 .. .. ..Senegal 237 1.8 .. 0 –63Serbia 1,340 a 3.5 84 0 312Sierra Leone 87 4.6 0 0 0Singapore 38,638 18.5 3,559 .. ..Slovak Republic 553 0.6 25 .. ..Slovenia 366 0.8 169 .. ..Somalia 112 .. .. 0 0South Africa 1,224 0.3 5,826 1,422 795South Sudan .. .. .. .. ..Spain 24,658 1.8 –4,790 .. ..Sri Lanka 478 1.0 –1,049 1,000 72Sudan 2,064 3.3 1 0 0Swaziland 136 3.7 5 0 0Sweden 5,847 1.3 5,474 .. ..Switzerland 21,707 4.1 –7,210 .. ..Syrian Arab Republic 1,469 2.5 .. 0 0Tajikistan 16 0.3 0 0 50Tanzania 433 1.9 3 0 137Thailand 9,679 3.0 2,606 1,730 –452Timor-Leste 280 39.9 .. .. ..Togo 41 1.3 .. 0 0Trinidad and Tobago 549 2.7 .. .. ..Tunisia 1,401 3.2 –26 0 –550Turkey 9,084 1.2 3,468 5,961 –8,310Turkmenistan 2,083 10.4 .. 0 –39Uganda 817 4.8 –70 0 0Ukraine 6,495 4.7 290 3,089 6,892United Arab Emirates 3,948 1.3 .. .. ..United Kingdom 52,968 2.3 –11,488 .. ..United States 236,226 1.6 172,376 .. ..Uruguay 1,627 4.2 –12 –93 7Uzbekistan 822 2.1 .. 0 534Venezuela, RB 1,209 0.3 10 1,141 –264Vietnam 8,000 7.5 2,383 981 129West Bank and Gaza .. .. .. .. ..Yemen, Rep. 56 0.2 0 0 –1Zambia 1,041 6.4 101 0 –21Zimbabwe 105 1.4 .. 0 289World 1,430,438 s 2.3 s 779,547 s .. s .. sLow income 13,017 3.4 –31 0 733Middle income 501,236 2.6 129,690 111,383 43,393Lower middle income 90,233 2.1 49,598 22,252 29,824Upper middle income 411,003 2.8 80,092 89,130 13,569Low & middle income 514,253 2.6 129,660 111,383 44,126East Asia & Pacifi c 231,299 3.1 39,715 20,813 13,127Europe & Central Asia 86,991 2.8 –840 27,091 –7,863Latin America & Carib. 117,368 2.4 41,302 48,776 27,376Middle East & N. Africa 25,688 2.7 1,973 3,164 –1,933South Asia 27,923 1.3 39,447 10,139 12,751Sub-Saharan Africa 24,984 2.3 8,063 1,400 669High income 916,185 2.1 649,887 .. ..Euro area 395,004 3.3 359,369 .. ..a. Includes Montenegro.Foreign direct investment$ millionsNet infl owPortfolio equityCommercial bank and$ millions% of GDP$ millions Bondsother lending2010 2010 2010 2010 20103682012 World Development Indicators

Global private financial flows 6.10GLOBAL LINKSAbout the dataDefinitionsPrivate fi nancial fl ows—equity and debt—account forthe bulk of development fi nance. Equity fl ows compriseforeign direct investment (FDI) and portfolioequity. Debt fl ows are fi nancing raised through bondissuance, bank lending, and supplier credits. Dataon equity fl ows are based on balance of paymentsdata reported by the International Monetary Fund(IMF). FDI data are supplemented by staff estimatesusing data from the United Nations Conference onTrade and Development and offi cial national sources.The internationally accepted defi nition of FDI (fromthe fi fth edition of the IMF’s Balance of PaymentsManual [1993]), includes three components: equityinvestment, reinvested earnings, and short- andlong-term loans between parent fi rms and foreignaffi liates. Distinguished from other kinds of internationalinvestment, FDI is made to establish a lastinginterest in or effective management control over anenterprise in another country. A lasting interest in aninvestment enterprise typically involves establishingwarehouses, manufacturing facilities, and otherpermanent or long-term organizations abroad. Directinvestments may take the form of greenfi eld investment,where the investor starts a new venture ina foreign country by constructing new operationalfacilities; joint venture, where the investor enters intoa partnership agreement with a company abroad toestablish a new enterprise; or merger and acquisition,where the investor acquires an existing enterpriseabroad. The IMF suggests that investmentsshould account for at least 10 percent of voting stockto be counted as FDI. In practice many countriesset a higher threshold. Many countries fail to reportreinvested earnings, and the defi nition of long-termloans differs among countries.FDI data do not give a complete picture of internationalinvestment in an economy. Balance ofpayments data on FDI do not include capital raisedlocally, an important source of investment fi nancingin some developing countries. In addition, FDI dataomit nonequity cross-border transactions such asintrafi rm fl ows of goods and services. For a detaileddiscussion of the data issues, see the World Bank’sGlobal Development Finance.Statistics on bonds, bank lending, and suppliercredits are produced by aggregating transactionsof public and publicly guaranteed debt and privatenonguaranteed debt. Data on public and publiclyguaranteed debt are reported through the DebtorReporting System by World Bank member economiesthat have received loans from the InternationalBank for Reconstruction and Development or creditsfrom the International Development Association. Thereports are cross-checked with data from marketsources that include transactions data. Informationon private nonguaranteed bonds and bank lendingis collected from market sources when data are notreported to the Debtor Reporting System.Data on equity fl ows are shown for all countriesfor which data are available. Debt fl ows are shownonly for 129 developing countries that report to theDebtor Reporting System; nonreporting countriesmay also receive debt fl ows.The volume of global private fi nancial fl owsreported by the World Bank generally differs fromthat reported by other sources because of differencesin sources, classifi cation of economies, andmethod used to adjust and disaggregate reportedinformation. In addition, particularly for debt fi nancing,differences may also refl ect how some installmentsof the transactions and certain offshore issuancesare treated.• Foreign direct investment is net infl ows of investmentto acquire a lasting interest in or managementcontrol over an enterprise operating in an economyother than that of the investor. It is the sum of equitycapital, reinvested earnings, other long-term capital,and short-term capital, as shown in the balance ofpayments. Net infl ows are new investments less disinvestments.• Portfolio equity includes net infl owsfrom equity securities other than those recordedas direct investment and including shares, stocks,depository receipts, and direct purchases of sharesin local stock markets by foreign investors • Bondsare securities issued with a fi xed rate of interest for aperiod of more than one year. They include net fl owsthrough cross-border public and publicly guaranteedand private nonguaranteed bond issues. • Commercialbank and other lending includes net commercialbank lending (public and publicly guaranteed and privatenonguaranteed) and other private credits.Data sourcesData on equity and debt fl ows are compiled from avariety of public and private sources, including theWorld Bank’s Debtor Reporting System, the IMF’sInternational Financial Statistics and Balance ofPayments databases, and Dealogic. These dataare also published annually in the World Bank’sGlobal Development Finance, on its Global DevelopmentFinance CD-ROM, and in the Global DevelopmentFinance database.2012 World Development Indicators 369

6.11 Net official financial flowsTotalInternational financial institutionsWorld Bank IMF$ millions $ millionsFrombilateralsourcesFrommultilateralsources a IDA IBRD Conces sionalNonconcessionalRegional developmentConces sionalbanks aNonconcessionalOtherinstitutions2010 2010 2010 2010 2010 2010 2010 2010 2010Afghanistan 0.0 77.8 8.4 .. 8.6 .. 64.9 .. 4.5Albania –7.1 102.5 15.1 3.1 –12.1 –0.2 .. 1.9 82.3Algeria –142.4 –2.3 .. –0.5 .. .. .. .. –1.7Angola 3,372.3 1.5 –0.7 .. .. 524.2 3.0 –0.4 –0.5Argentina –249.8 788.3 .. 46.2 .. .. .. 609.9 136.7Armenia 64.9 109.5 20.3 52.4 35.5 127.4 22.3 8.7 5.9Australia .. .. .. .. .. .. .. .. ..Austria .. .. .. .. .. .. .. .. ..Azerbaijan 147.7 310.4 35.9 101.2 –14.7 .. 5.1 33.6 134.6Bahrain .. .. .. .. .. .. .. .. ..Bangladesh –112.3 352.1 83.5 .. –45.6 0.0 75.0 166.1 27.6Belarus 1,589.5 33.6 .. 35.7 .. 668.3 .. –2.1 ..Belgium .. .. .. .. .. .. .. .. ..Benin 19.2 144.2 78.3 .. 16.2 .. 40.1 .. 25.7Bolivia 45.8 297.4 44.0 0.0 .. .. 86.2 29.7 140.8Bosnia and Herzegovina 65.5 277.4 97.2 25.7 .. 237.4 .. 58.6 100.7Botswana –13.6 –24.8 –0.5 6.5 .. .. –1.8 –3.0 –26.0Brazil 3,615.0 5,069.6 .. 3,511.7 .. .. .. 951.3 589.8Bulgaria –52.6 –8.6 .. –69.9 .. .. .. –11.8 73.0Burkina Faso 16.5 228.4 66.6 .. 20.2 .. 63.4 .. 98.5Burundi .. 33.9 17.8 .. 20.1 .. 1.6 .. 14.4Cambodia 262.7 50.6 9.0 .. .. .. 35.1 .. 6.6Cameroon 34.8 119.4 80.6 –5.4 0.0 .. 50.3 –9.7 3.6Canada .. .. .. .. .. .. .. .. ..Central African Republic –0.1 5.1 5.0 .. 13.2 .. 0.0 .. 0.1Chad –4.1 17.9 –16.0 .. –11.1 .. 3.8 .. 30.2Chile 36.9 –44.8 –0.7 –25.8 .. .. .. –18.3 ..China –123.3 828.7 –348.4 195.6 .. .. .. 1,033.3 –51.8Hong Kong SAR, China .. .. .. .. .. .. .. .. ..Colombia –81.8 1,094.1 –0.7 937.2 .. .. –1.6 188.0 –28.7Congo, Dem. Rep. –23.4 –15.8 17.5 .. 18.9 .. 17.5 –38.1 –12.7Congo, Rep. –42.4 –11.2 1.5 .. 1.8 .. –0.2 –8.3 0.2Costa Rica 10.1 527.6 –0.2 512.3 .. .. –8.2 18.6 5.0Côte d’Ivoire –36.3 –81.3 –0.9 –24.9 44.4 .. –2.2 –32.5 –5.0Croatia .. .. .. .. .. .. .. .. ..Cuba .. .. .. .. .. .. .. .. ..Cyprus .. .. .. .. .. .. .. .. ..Czech Republic .. .. .. .. .. .. .. .. ..Denmark .. .. .. .. .. .. .. .. ..Dominican Republic 362.0 470.1 –0.7 116.2 .. 382.9 –21.2 359.5 16.2Ecuador 890.3 826.1 –1.1 –85.5 .. .. –26.7 64.0 455.4Egypt, Arab Rep. –1,015.2 772.7 –44.1 693.6 .. .. –6.0 140.5 –11.2El Salvador –44.4 365.8 –0.8 345.5 .. .. –23.2 26.0 18.3Eritrea –4.1 0.1 –0.5 .. .. .. 3.7 .. –3.1Estonia .. .. .. .. .. .. .. .. ..Ethiopia 510.7 472.1 384.7 .. 122.4 .. 80.8 –6.7 13.3Finland .. .. .. .. .. .. .. .. ..France .. .. .. .. .. .. .. .. ..Gabon 15.9 –7.4 .. 5.6 .. .. –0.2 2.6 –15.4Gambia, The 6.5 18.5 1.9 .. 3.0 .. 2.3 .. 14.3Georgia 36.1 294.0 52.9 72.8 –21.5 296.9 38.3 118.7 11.2Germany .. .. .. .. .. .. .. .. ..Ghana 357.9 427.0 304.8 .. 124.4 .. 130.0 0.0 –7.1Greece .. .. .. .. .. .. .. .. ..Guatemala –14.6 631.0 .. 260.0 .. .. –18.3 320.8 68.5Guinea –9.0 –11.0 0.0 .. –10.2 .. 6.3 –4.6 –12.7Guinea-Bissau .. –2.5 –1.0 .. 15.7 –8.1 –2.4 .. 0.8Haiti 121.4 42.3 –35.9 .. 124.8 .. 69.5 .. 8.83702012 World Development Indicators

Net official financial flows 6.11GLOBAL LINKSTotalInternational financial institutionsWorld Bank IMF$ millions $ millionsFrombilateralsourcesFrommultilateralsources a IDA IBRD Conces sionalNonconcessionalRegional developmentConces sionalbanks aNonconcessionalOtherinstitutions2010 2010 2010 2010 2010 2010 2010 2010 2010Honduras 2.4 388.9 108.7 .. –1.6 .. 101.0 36.1 143.2Hungary .. .. .. .. .. .. .. .. ..India 826.1 4,575.8 231.5 2,795.2 .. .. .. 1,440.1 97.2Indonesia –8.2 1,367.1 110.0 1,177.4 .. .. 66.1 13.6 0.0Iran, Islamic Rep. –289.5 73.4 .. 64.2 .. .. .. .. ..Iraq .. .. .. .. .. .. .. .. ..Ireland .. .. .. .. .. .. .. .. ..Israel .. .. .. .. .. .. .. .. ..Italy .. .. .. .. .. .. .. .. ..Jamaica 32.1 835.2 .. 180.6 .. 778.1 –4.2 559.8 99.1Japan .. .. .. .. .. .. .. .. ..Jordan 13.1 153.9 –2.6 –68.5 .. –3.9 .. .. 225.0Kazakhstan –195.0 1,436.1 .. 1,283.0 .. .. –0.2 101.9 51.3Kenya 40.4 223.3 132.4 .. –25.5 .. 83.7 –2.4 9.6Korea, Dem. Rep. .. .. .. .. .. .. .. .. ..Korea, Rep. .. .. .. .. .. .. .. .. ..Kosovo .. –16.3 .. –16.3 .. .. .. .. ..Kuwait .. .. .. .. .. .. .. .. ..Kyrgyz Republic 101.3 7.7 5.4 .. 12.4 .. –8.5 2.3 8.5Lao PDR 57.3 –31.2 –12.6 .. –5.5 .. –11.1 0.2 –7.6Latvia –17.6 1,086.1 .. 117.6 .. 409.6 .. –1.8 970.3Lebanon –71.8 –26.3 .. 3.0 .. –19.4 .. .. –29.3Lesotho 0.6 20.9 22.3 –0.7 4.4 .. –1.6 .. 0.9Liberia 3.7 –3.7 –1.7 .. 13.6 .. –2.1 0.0 ..Libya .. .. .. .. .. .. .. .. ..Lithuania .. 446.0 .. –3.1 .. .. .. –4.9 454.0Macedonia, FYR 0.0 85.4 –8.2 61.6 .. .. .. –5.8 37.9Madagascar 68.9 106.4 74.6 .. –1.7 .. 12.5 .. 19.3Malawi 22.8 44.8 33.8 .. 21.2 .. 4.9 –2.1 8.2Malaysia –599.4 –69.9 .. –38.5 .. .. .. –21.5 –10.0Mali 42.9 230.6 151.3 .. 5.9 .. 65.5 .. 13.8Mauritania 97.6 249.4 38.7 .. 33.7 .. 9.9 –8.1 209.0Mauritius 110.7 136.8 –0.6 –3.0 .. .. –0.2 153.9 –13.3Mexico 464.4 3,580.4 .. 2,255.6 .. .. .. 1,299.1 25.7Moldova –6.8 48.8 59.4 –17.8 113.6 61.0 .. –3.1 10.2Mongolia 11.6 20.2 17.3 .. –4.6 23.4 0.5 .. 2.3Morocco 504.5 625.7 –1.4 69.2 .. .. –1.1 160.3 398.7Mozambique 7.8 237.6 157.7 .. 21.4 .. 68.4 .. 11.4Myanmar –103.6 –0.8 .. .. .. .. 0.0 .. –0.8Namibia .. .. .. .. .. .. .. .. ..Nepal –4.2 –11.1 –32.6 .. 39.4 .. 15.8 .. 5.7Netherlands .. .. .. .. .. .. .. .. ..New Zealand .. .. .. .. .. .. .. .. ..Nicaragua –3.1 215.0 36.2 .. 19.5 .. 97.3 43.7 37.4Niger 14.7 64.1 14.1 .. 4.9 .. 16.4 .. 33.7Nigeria –28.7 866.5 975.4 –70.8 .. .. 21.9 –65.2 5.1Norway .. .. .. .. .. .. .. .. ..Oman .. .. .. .. .. .. .. .. ..Pakistan –8.4 439.4 204.9 –127.1 –264.6 1,633.8 87.5 148.2 126.0Panama 87.2 188.1 .. –14.8 .. .. –5.6 214.9 –6.5Papua New Guinea –21.9 –2.1 4.6 –10.6 .. .. –5.5 13.1 –3.8Paraguay –62.9 142.4 –1.5 –25.0 .. .. –11.3 140.4 43.3Peru –841.9 –527.3 .. 118.9 .. .. .. –755.5 109.3Philippines –292.0 –179.9 –6.9 58.9 .. .. –41.1 –203.2 12.5Poland .. .. .. .. .. .. .. .. ..Portugal .. .. .. .. .. .. .. .. ..Puerto Rico .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. ..2012 World Development Indicators 371

6.11Net official financial flowsTotalInternational financial institutionsWorld Bank IMF$ millions $ millionsFrombilateralsourcesFrommultilateralsources a IDA IBRD Conces sionalNonconcessionalRegional developmentConces sionalbanks aNonconcessionalOtherinstitutions2010 2010 2010 2010 2010 2010 2010 2010 2010Romania 84.4 3,374.6 .. –74.6 .. 5,664.3 4.7 –51.5 3,496.0Russian Federation –297.2 –659.5 .. –596.4 .. .. .. –61.1 –2.1Rwanda 17.1 36.0 9.4 .. –0.1 .. 14.8 .. 11.9Saudi Arabia .. .. .. .. .. .. .. .. ..Senegal 68.0 204.8 108.5 .. 48.9 .. 63.0 –12.7 46.2Serbia 142.5 708.5 16.4 172.0 .. 457.6 .. 181.4 326.2Sierra Leone 6.1 55.5 32.1 .. 40.7 .. 14.9 .. 8.6Singapore .. .. .. .. .. .. .. .. ..Slovak Republic .. .. .. .. .. .. .. .. ..Slovenia .. .. .. .. .. .. .. .. ..Somalia 0.0 0.0 0.0 .. .. .. 0.0 0.0 0.0South Africa .. 872.8 .. 363.1 .. .. .. 509.8 ..South Sudan .. .. .. .. .. .. .. .. ..Spain .. .. .. .. .. .. .. .. ..Sri Lanka 1,002.8 293.8 82.9 .. –11.7 608.5 18.0 184.6 8.3Sudan 471.9 65.9 0.0 .. .. –5.8 0.0 0.0 65.9Swaziland 0.9 –19.8 –0.3 –5.2 .. .. –1.4 –7.8 –5.1Sweden .. .. .. .. .. .. .. .. ..Switzerland .. .. .. .. .. .. .. .. ..Syrian Arab Republic –309.3 98.3 –1.5 0.0 0.0 0.0 0.0 0.0 99.8Tajikistan 83.7 129.8 10.7 .. 59.8 .. 33.0 –0.9 17.1Tanzania 143.5 817.7 650.0 .. 29.9 .. 138.6 –1.0 30.2Thailand –352.5 –21.2 –3.4 –1.7 .. .. .. –4.8 –11.3Timor-Leste .. .. .. .. .. .. .. .. ..Togo 36.7 20.9 –22.0 .. 43.6 .. –2.0 .. 34.7Trinidad and Tobago .. .. .. .. .. .. .. .. ..Tunisia 83.8 547.3 –1.9 76.4 .. .. .. 103.6 369.3Turkey 174.9 3,657.1 –5.9 2,094.3 .. –2,170.8 .. .. 1,533.7Turkmenistan –75.8 –2.9 .. –1.4 .. .. .. .. –1.5Uganda 25.5 421.2 323.0 .. –0.3 .. 92.0 0.0 6.3Ukraine –148.3 –6.1 .. –84.7 .. 3,433.3 .. –11.9 90.5United Arab Emirates .. .. .. .. .. .. .. .. ..United Kingdom .. .. .. .. .. .. .. .. ..United States .. .. .. .. .. .. .. .. ..Uruguay –20.6 –175.0 .. –42.0 .. .. –1.8 –408.4 279.2Uzbekistan 72.7 106.3 32.1 –27.6 .. .. 36.3 55.0 10.5Venezuela, RB –78.7 895.0 .. .. .. .. .. 425.5 469.4Vietnam 588.6 1,924.7 800.9 700.0 –37.9 .. 171.4 170.3 82.0West Bank and Gaza .. .. .. .. .. .. .. .. ..Yemen, Rep. –15.2 148.6 28.2 .. 26.0 .. .. .. 120.4Zambia 59.3 70.6 29.9 .. 55.3 .. 51.7 –3.2 –7.9Zimbabwe 18.7 –0.2 0.0 –0.1 –4.0 .. 0.0 –0.1 0.0World .. s .. s .. s .. s .. s .. s .. s .. s .. sLow income 1,525.0 3,790.5 2,155.6 –0.1 559.7 –8.1 1,008.3 112.5 434.0Middle income 10,366.5 41,745.3 3,190.5 17,081.8 143.2 13,125.0 986.7 8,277.1 11,723.0Lower middle income 5,826.9 14,764.8 3,365.2 5,803.1 163.2 6,702.9 1,026.0 2,747.0 1,839.5Upper middle income 4,539.6 26,980.5 –174.8 11,278.7 –20.0 6,422.2 –39.2 5,530.1 9,883.5Low & middle income 11,891.5 45,535.8 5,346.1 17,081.7 702.8 13,116.9 1,995.0 8,389.7 12,157.0East Asia & Pacifi c –498.0 3,926.3 589.6 2,081.2 –38.5 23.4 230.9 1,012.0 12.7Europe & Central Asia 1,760.8 11,516.2 334.3 3,121.8 172.9 9,184.9 130.9 408.2 7,408.4Latin America & Carib. 4,312.3 15,755.4 163.8 8,090.7 146.7 1,161.0 276.8 4,120.5 2,659.8Middle East & N. Africa –1,234.4 2,401.1 –24.0 837.3 22.7 –23.2 –5.4 404.3 1,179.7South Asia 1,841.2 5,802.0 594.6 2,668.1 –272.4 2,247.8 301.2 1,960.1 266.3Sub-Saharan Africa 5,709.6 6,134.8 3,687.8 282.7 671.5 523.1 1,060.6 484.6 630.0High income .. .. .. .. .. .. .. .. ..Euro area .. .. .. .. .. .. .. .. ..a. Aggregates include amounts for economies not specifi ed elsewhere.3722012 World Development Indicators

Net official financial flows 6.11GLOBAL LINKSAbout the dataDefinitionsThe table shows concessional and nonconcessionalfi nancial fl ows from offi cial bilateral sources and themajor international fi nancial institutions. The internationalfi nancial institutions fund nonconcessionallending operations primarily by selling low-interest,highly rated bonds backed by prudent lending andfi nancial policies and the strong fi nancial support oftheir members. Funds are then on-lent to developingcountries at slightly higher interest rates with 15- to20-year maturities. Lending terms vary with marketconditions and institutional policies.Concessional fl ows from international fi nancialinstitutions are credits provided through concessionallending facilities. Subsidies from donors orother resources reduce the cost of these loans.Grants are not included in net fl ows. The Organisationfor Economic Co-operation and Development’s(OECD) Development Assistance Committee (DAC)defi nes concessional fl ows from bilateral donors asfl ows with a grant element of at least 25 percent;they are evaluated assuming a 10 percent nominaldiscount rate.World Bank concessional lending is done by theInternational Development Association (IDA) basedon gross national income (GNI) per capita and performancestandards assessed by World Bank staff.The cutoff for IDA eligibility, set at the beginning ofthe World Bank’s fi scal year, has been $1,175 sinceJuly 1, 2011, measured in 2010 U.S. dollars usingthe World Bank Atlas method (see Users guide). Inexceptional circumstances IDA extends temporaryeligibility to countries above the cutoff that areundertaking major adjustments but are not creditworthyfor International Bank for Reconstructionand Development (IBRD) lending. Exceptions are alsomade for small island economies. The IBRD lendsto creditworthy countries at a variable base rate ofsix-month LIBOR plus a spread, either variable orfi xed, for the life of the loan. The rate is reset everysix months and applies to the interest period beginningon that date. Although some outstanding IBRDloans have a low enough interest rate to be classifi edas concessional under the DAC defi nition, all IBRDloans in the table are classifi ed as nonconcessional.Lending by the International Finance Corporation, theMultilateral Investment Guarantee Agency, and theInternational Centre for Settlement of InvestmentDisputes is excluded.The International Monetary Fund (IMF) makes concessionalfunds available through its Extended CreditFacility (which replaced the Poverty Reduction andGrowth Facility in 2010), the Standby Credit Facility,and the Rapid Credit Facility. Eligibility is based principallyon a country’s per capita income and eligibilityunder IDA. Nonconcessional lending from the IMFis provided mainly through Stand-by Arrangements,the Flexible Credit Line, and the Extended Fund Facility.The IMF’s loan instruments have changed overtime to address the specifi c circumstances of itsmembers.Regional development banks also maintain concessionalwindows. Their loans are recorded in the tableaccording to each institution’s classifi cation and notaccording to the DAC defi nition.Data for fl ows from international fi nancial institutionsare available for 129 countries that report tothe World Bank’s Debtor Reporting System. Nonreportingcountries may have net fl ows from otherinternational fi nancial institutions.• Total net official financial flows are disbursementsof public or publicly guaranteed loans and credits,less repayments of principal. • IDA is the InternationalDevelopment Association, the concessionalarm of the World Bank Group. • IBRD is the InternationalBank for Reconstruction and Development,the founding and largest member of the World BankGroup. • IMF is the International Monetary Fund,which provides concessional lending through itsExtended Credit Facility, Standby Credit Facility, andRapid Credit Facility and nonconcessional lendingthrough credit to members, mainly for balance ofpayments needs. • Regional development banks arethe African Development Bank, which serves Africa,including North Africa; the Asian Development Bank,which serves South and Central Asia and East Asiaand Pacifi c; the European Bank for Reconstructionand Development, which serves Europe and CentralAsia; and the Inter-American Development Bank,which serves the Americas. • Concessional fi n a n c i a lfl ows are disbursements through concessional lendingfacilities. • Nonconcessional fi nancial fl ows areall disbursements that are not concessional. • Otherinstitutions, a residual category, includes such institutionsas the Caribbean Development Fund, Councilof Europe, European Development Fund, IslamicDevelopment Bank, and Nordic Development Fund.Data sourcesData on net fi nancial fl ows from international fi nancialinstitutions are from the World Bank’s DebtorReporting System and published in the WorldBank’s Global Development Finance 2012, on itsGlobal Development Finance CD-ROM, and in itsGlobal Development Finance database.2012 World Development Indicators 373

6.12 Aid dependencyNet officialdevelopment assistanceAid dependencyratiosNet offi cial development assistance$ millions% of imports ofPer capita$GrantsTechnicalcooperation % of GNI% of gross capitalformationgoods, services,and income2010 2010 2010 2010 2010 2010 2010 2010Total$ millions% ofcentral governmentexpenseAfghanistan 6,374 185 5,476 982 42.0 227.0 .. 80.2Albania 357 112 160 101 2.9 11.1 5.0 ..Algeria 319 9 74 185 0.1 0.3 .. ..Angola 239 13 207 42 0.3 1.9 0.5 ..Argentina 127 3 36 69 0.0 0.2 0.2 ..Armenia 526 170 159 32 3.5 10.9 7.0 15.9AustraliaAustriaAzerbaijan 232 26 84 53 0.3 1.8 1.1 ..Bahrain .. .. .. .. .. .. .. ..Bangladesh 1,226 8 1,082 218 1.3 5.8 4.6 ..Belarus 98 10 105 37 0.3 0.6 0.4 0.8BelgiumBenin 682 79 462 92 10.4 40.4 .. 69.7Bolivia 725 74 527 133 3.6 20.2 9.5 ..Bosnia and Herzegovina 414 110 214 138 2.9 15.2 4.9 7.3Botswana 279 141 141 21 1.1 2.9 2.5 ..Brazil 337 2 413 237 0.0 0.2 0.2 ..Bulgaria .. .. .. .. .. .. .. ..Burkina Faso 1,083 68 766 125 12.1 .. .. 99.6Burundi 561 69 525 74 39.8 .. 102.2 ..Cambodia 721 52 453 186 6.9 37.7 8.7 58.1Cameroon 648 34 358 155 2.4 .. 8.0 ..CanadaCentral African Republic 242 56 241 17 13.1 .. .. ..Chad 561 51 453 40 7.3 17.6 .. ..Chile 79 5 83 65 0.1 0.4 0.2 0.4China 1,129 1 459 960 0.0 0.0 0.0 ..Hong Kong SAR, China .. .. .. .. .. .. .. ..Colombia 1,059 23 669 141 0.3 1.3 1.5 1.7Congo, Dem. Rep. 2,357 37 5,480 234 27.8 .. .. 189.1Congo, Rep. 283 72 1,574 24 15.2 44.3 .. ..Costa Rica 109 24 75 29 0.3 1.3 0.6 ..Côte d’Ivoire 2,402 124 646 54 3.9 26.9 .. ..Croatia 169 38 106 39 0.3 1.0 0.6 0.7Cuba 115 10 96 33 .. .. .. ..Cyprus .. .. .. .. .. .. .. ..Czech Republic .. .. .. .. .. .. .. ..DenmarkDominican Republic 119 12 197 41 0.4 2.1 0.9 ..Ecuador 208 15 166 81 0.3 1.0 0.6 ..Egypt, Arab Rep. 999 13 655 165 0.3 1.4 0.9 0.9El Salvador 276 45 331 59 1.4 10.1 2.9 6.4Eritrea 144 28 147 7 7.7 .. .. ..Estonia .. .. .. .. .. .. .. ..Ethiopia 3,819 47 2,628 264 11.9 55.3 35.3 ..FinlandFranceGabon 77 52 56 41 0.9 3.1 .. ..Gambia, The 127 76 94 9 16.3 57.8 36.6 ..Georgia 907 206 377 122 5.5 27.5 9.1 20.4GermanyGhana 1,582 66 920 123 5.5 24.2 11.7 ..GreeceGuatemala 376 27 297 119 1.0 6.6 2.4 ..Guinea 214 22 167 62 5.1 23.6 11.3 ..Guinea-Bissau 147 99 270 18 16.0 .. .. ..Haiti 1,120 114 3,496 269 .. 183.4 75.1 ..3742012 World Development Indicators

Aid dependency 6.12GLOBAL LINKSNet officialdevelopment assistanceAid dependencyratios$ millionsNet offi cial development assistance% of imports of % ofTotalPer capitaTechnical% of gross capital goods, services, central government$ millions$Grants cooperation % of GNI formation and income expense2010 2010 2010 2010 2010 2010 2010 2010Honduras 456 61 279 55 3.9 16.3 5.5 16.0Hungary .. .. .. .. .. .. .. ..India 2,500 2 1,007 283 0.2 0.5 0.6 1.1Indonesia 1,047 4 851 520 0.2 0.6 0.8 1.4Iran, Islamic Rep. 92 1 78 73 .. .. .. ..Iraq 2,791 90 1,881 126 2.8 .. .. ..IrelandIsrael .. .. .. .. .. .. .. ..ItalyJamaica 149 55 152 15 1.0 4.8 2.0 ..JapanJordan 740 125 762 73 3.4 22.6 5.2 13.4Kazakhstan 298 18 186 50 0.2 0.6 0.3 0.9Kenya 1,776 45 1,228 153 5.2 24.4 11.8 22.6Korea, Dem. Rep. 65 3 72 5 .. .. .. ..Korea, Rep.Kosovo 781 433 318 275 10.3 35.0 .. ..Kuwait .. .. .. .. .. .. .. ..Kyrgyz Republic 313 58 296 58 8.7 28.4 8.6 36.8Lao PDR 419 69 281 110 6.0 21.9 16.9 52.8Latvia .. .. .. .. .. .. .. ..Lebanon 580 138 343 90 1.2 3.5 1.4 ..Lesotho 122 57 219 12 10.1 35.7 9.0 ..Liberia 513 134 1,698 30 176.8 .. 78.7 ..Libya 41 7 12 9 .. .. 0.0 ..Lithuania .. .. .. .. .. .. .. ..Macedonia, FYR 192 94 97 68 2.0 7.6 2.8 ..Madagascar 444 22 307 67 5.5 .. .. ..Malawi 771 53 880 85 20.6 82.2 .. ..Malaysia 143 5 50 49 0.0 0.0 0.0 0.0Mali 984 66 741 128 12.3 .. .. ..Mauritania 373 111 120 53 10.2 36.6 .. ..Mauritius 155 122 73 20 1.3 5.7 2.0 5.7Mexico 184 2 286 112 0.0 0.2 0.1 ..Moldova 244 68 241 54 7.5 34.1 9.7 23.0Mongolia 371 137 189 96 5.4 12.0 6.7 18.4Morocco 930 29 424 291 1.1 3.1 2.4 3.6Mozambique 2,012 88 1,443 171 20.8 86.2 39.9 ..Myanmar 356 7 339 57 .. .. 5.1 ..Namibia 326 145 194 44 2.1 9.1 4.0 ..Nepal 854 29 684 125 .. .. 13.7 ..NetherlandsNew ZealandNicaragua 773 135 316 98 10.0 34.9 10.9 47.8Niger 469 31 589 105 13.6 .. .. ..Nigeria 1,657 11 827 198 1.2 .. 2.2 ..NorwayOman 154 57 18 3 .. .. –0.1 ..Pakistan 2,769 16 2,743 232 1.6 11.1 6.9 9.7Panama 65 19 27 18 0.5 1.8 0.6 ..Papua New Guinea 412 61 221 313 5.5 30.4 7.4 ..Paraguay 148 23 104 53 0.6 2.9 0.9 3.8Peru 441 15 327 173 –0.2 –0.7 –0.6 –1.0Philippines 309 3 429 212 0.3 1.3 0.7 1.6Poland .. .. .. .. .. .. .. ..PortugalPuerto Rico .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. ..2012 World Development Indicators 375

6.12Aid dependencyNet officialdevelopment assistanceAid dependencyratios$ millionsNet offi cial development assistance% of imports of % ofTotalPer capitaTechnical% of gross capital goods, services, central government$ millions$Grants cooperation % of GNI formation and income expense2010 2010 2010 2010 2010 2010 2010 2010Romania .. .. .. .. .. .. .. ..Russian Federation .. .. .. .. .. .. .. ..Rwanda 934 91 848 153 18.5 .. 60.9 ..Saudi Arabia .. .. .. .. .. .. .. ..Senegal 1,016 84 449 195 7.2 24.8 .. ..Serbia 614 84 444 127 1.7 7.4 3.1 4.3Sierra Leone 448 78 301 75 24.9 158.2 50.6 ..Singapore .. .. .. .. .. .. .. ..Slovak Republic .. .. .. .. .. .. .. ..Slovenia .. .. .. .. .. .. .. ..Somalia 662 73 470 30 .. .. .. ..South Africa 1,075 22 841 161 0.3 1.1 0.9 ..South Sudan .. .. .. .. .. .. .. ..SpainSri Lanka 703 34 332 78 1.2 4.2 3.6 ..Sudan 2,351 55 1,760 228 3.7 14.2 14.9 ..Swaziland 56 54 90 8 2.6 15.3 3.0 ..SwedenSwitzerlandSyrian Arab Republic 208 10 203 119 0.2 1.2 0.6 ..Tajikistan 408 60 267 40 7.7 33.4 12.6 ..Tanzania 2,933 67 1,814 214 12.9 42.0 31.7 ..Thailand –78 –1 167 115 0.0 0.0 0.0 0.0Timor-Leste 216 197 144 149 10.8 .. .. ..Togo 499 84 455 52 14.9 .. .. 92.1Trinidad and Tobago 7 5 3 2 0.0 .. .. ..Tunisia 503 48 142 162 1.3 4.7 2.1 4.6Turkey 1,362 19 340 154 0.1 0.7 0.5 0.6Turkmenistan 40 8 24 20 0.2 0.4 .. ..Uganda 1,785 55 1,189 105 10.3 42.8 26.9 ..Ukraine 666 14 338 239 0.5 2.3 0.8 ..United Arab Emirates .. .. .. .. .. .. .. ..United KingdomUnited StatesUruguay 50 15 30 24 0.1 0.7 0.4 0.4Uzbekistan 190 7 70 53 0.6 2.2 .. ..Venezuela, RB 66 2 36 19 0.0 0.1 0.1 ..Vietnam 3,732 43 656 378 2.9 7.1 3.2 ..West Bank and Gaza 2,817 697 2,345 169 .. .. .. ..Yemen, Rep. 558 24 523 63 2.3 18.3 5.2 ..Zambia 1,267 100 706 72 6.4 25.2 12.1 32.9Zimbabwe 736 59 669 78 10.6 1,860.4 .. ..World 126,968 s 19 w .. .. 0.2 w 0.9 w 0.6 w ..Low income 36,252 46 .. .. 9.6 40.9 24.6 ..Middle income 54,621 11 .. .. 0.3 0.8 0.9 ..Lower middle income 40,510 16 .. .. 0.9 3.0 2.6 ..Upper middle income 13,151 5 .. .. 0.1 0.2 0.3 ..Low & middle income 126,593 22 95,651 19,564 0.7 2.0 2.1 ..East Asia & Pacifi c 10,165 5 .. .. 0.1 0.3 0.4 ..Europe & Central Asia 8,087 20 .. .. 0.2 1.0 0.7 ..Latin America & Carib. 9,036 16 .. .. 0.2 1.0 0.9 ..Middle East & N. Africa 13,383 41 .. .. 0.9 .. 3.1 ..South Asia 14,591 9 .. .. 0.7 2.3 2.7 ..Sub-Saharan Africa 44,582 54 .. .. 4.3 18.8 9.9 ..High income 374 0 .. .. 0.0 0.0 0.0 ..Euro area .. .. .. .. .. .. .. ..Note: Regional aggregates include data for economies not listed in the table. World and income group totals include aid not allocated by country or region—including administrative costs,research on development issues, and aid to nongovernmental organizations. Thus regional and income group totals do not sum to the world total.3762012 World Development Indicators

Aid dependency6.12GLOBAL LINKSAbout the dataDefinitionsThe fl ows of offi cial and private fi nancial resourcesfrom the members of the Development AssistanceCommittee (DAC) of the Organisation for EconomicCo-operation and Development (OECD) to developingeconomies are compiled by DAC, based principally onreporting by DAC members using standard questionnairesissued by the DAC Secretariat.DAC exists to help its members coordinate theirdevelopment assistance and to encourage theexpansion and improve the effectiveness of theaggregate resources fl owing to recipient economies.In this capacity DAC monitors the fl ow of all fi nancialresources, but its main concern is offi cial developmentassistance (ODA). Grants or loans to countriesand territories on the DAC list of aid recipients haveto meet three criteria to be counted as ODA. Theyare provided by offi cial agencies, including state andlocal governments, or by their executive agencies.They promote economic development and welfare asthe main objective. And they are provided on concessionalfi nancial terms (loans must have a grant elementof at least 25 percent, calculated at a discountrate of 10 percent). The DAC Statistical ReportingDirectives provide the most detailed explanation ofthis defi nition and all ODA-related rules.This defi nition excludes nonconcessional fl owsfrom offi cial creditors, which are classifi ed as “otheroffi cial fl ows,” and aid for military and anti-terrorismpurposes. Transfer payments to private individuals,such as pensions, reparations, and insurance payouts,are in general not counted. In addition to fi nancialfl ows, ODA includes technical cooperation, mostexpenditures for peacekeeping under UN mandatesand assistance to refugees, contributions to multilateralinstitutions such as the United Nations andits specialized agencies, and concessional fundingto multilateral development banks.Flows are transfers of resources, either in cash orin the form of commodities or services measured ona cash basis. Short-term capital transactions (withone year or less maturity) are not counted. Repaymentsof the principal (but not interest) of ODA loansare recorded as negative fl ows. Proceeds from offi -cial equity investments in a developing country arereported as ODA, while proceeds from their later saleare recorded as negative fl ows.The table shows data on ODA for aid-receivingcountries. The data cover loans and grants fromDAC member countries, multilateral organizations,and non-DAC donors. They do not refl ect aid given byrecipient countries to other developing countries. Asa result, some countries that are net donors (such asSaudi Arabia) are shown in the table as aid recipients(see table 6.13a).The table does not distinguish types of aid (program,project, or food aid; emergency assistance; orpost-confl ict peacekeeping assistance), which mayhave different effects on the economy.Ratios of aid to gross national income (GNI), grosscapital formation, imports, and government spendingprovide measures of recipient country dependencyon aid. But care must be taken in drawing policy conclusions.For foreign policy reasons some countrieshave traditionally received large amounts of aid. Thusaid dependency ratios may reveal as much abouta donor’s interests as about a recipient’s needs.Ratios are generally much higher in Sub-SaharanAfrica than in other regions, and they increased inthe 1980s. High ratios are due only in part to aidfl ows. Many African countries saw severe erosionin their terms of trade in the 1980s, which, alongwith weak policies, contributed to falling incomes,imports, and investment. Thus the increase in aiddependency ratios refl ects events affecting both thenumerator (aid) and the denominator (GNI).Because the table relies on information fromdonors, it is not necessarily consistent with informationrecorded by recipients in the balance of payments,which often excludes all or some technicalassistance—particularly payments to expatriatesmade directly by the donor. Similarly, grant commodityaid may not always be recorded in trade data or inthe balance of payments. Moreover, DAC statisticsexclude aid for military and antiterrorism purposes.The nominal values used here may overstate thereal value of aid to recipients. Changes in internationalprices and exchange rates can reduce the purchasingpower of aid. Tying aid, still prevalent thoughdeclining in importance, also tends to reduce its purchasingpower. Tying requires recipients to purchasegoods and services from the donor country or froma specifi ed group of countries. Such arrangementsprevent a recipient from misappropriating or mismanagingaid receipts, but they may also be motivated bya desire to benefi t donor country suppliers.The aggregates refer to World Bank classifi cationsof economies and therefore may differ from thoseof the OECD.• Net official development assistance is fl ows (netof repayment of principal) that meet the DAC defi nitionof ODA and are made to countries and territorieson the DAC list of aid recipients. • Net officialdevelopment assistance per capita is net ODAdivided by midyear population. • Grants are legallybinding commitments that obligate a specifi c valueof funds available for disbursement for which thereis no payment requirement. • Technical cooperationis the provision of resources whose main aim isto augment the stock of human intellectual capital,such as the level of knowledge, skills, and technicalknow-how in the recipient country (including thecost of associated equipment). Contributions takethe form mainly of the supply of human resourcesfrom donors or action directed to human resources(such as training or advice). Also included are aid forpromoting development awareness and aid providedto refugees in the donor economy. Assistance specifically to facilitate a capital project is not included.• Aid dependency ratios are calculated using valuesin U.S. dollars converted at offi cial exchange rates.Imports of goods, services, and income refer to internationaltransactions involving a change in ownershipof general merchandise, goods sent for processingand repairs, nonmonetary gold, services, receiptsof employee compensation for nonresident workers,and investment income. For defi nitions of GNI, grosscapital formation, and central government expense,see Defi nitions for tables 1.1, 4.8, and 4.10.Data sourcesData on fi nancial fl ows are compiled by OECD DACand published in its annual statistical report, GeographicalDistribution of Financial Flows to DevelopingCountries, and in its annual Development CooperationReport. Data are available electronicallyon the OECD DAC International Development StatisticsCD-ROM and at www.oecd.org/dac/stats/idsonline. Data on population, GNI, gross capitalformation, imports of goods and services, andcentral government expense used in computingthe ratios are from World Bank and InternationalMonetary Fund databases.2012 World Development Indicators 377

6.13Distribution of net aid by DevelopmentAssistance Committee membersTen major DAC donors$ millionsTotal$ millionsUnitedStatesEUinstitutions GermanyUnitedKingdom France Japan Netherlands Spain Canada NorwayOtherDAC donors$ millions2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010Afghanistan 5,701.3 2,893.4 285.0 469.8 234.8 58.6 745.7 59.2 60.8 267.1 120.2 506.8Albania 301.6 30.1 75.0 35.4 0.9 4.3 2.4 4.0 4.8 0.0 2.5 142.2Algeria 194.7 8.6 51.8 10.2 2.2 69.8 15.5 0.2 9.5 –0.5 1.0 26.5Angola 174.0 54.8 24.4 7.1 16.7 4.1 37.6 –2.7 5.5 0.7 13.2 12.7Argentina 149.4 3.9 8.0 21.9 0.5 13.5 73.8 0.5 23.0 1.0 0.1 3.2Armenia 238.8 91.6 33.4 16.7 0.5 4.5 77.5 1.2 0.3 0.0 3.6 9.7AustraliaAustriaAzerbaijan 81.2 35.9 20.8 15.9 0.9 5.1 –11.0 0.0 0.1 0.0 3.4 10.2Bahrain .. .. .. .. .. .. .. .. .. .. .. ..Bangladesh 1,071.6 124.7 188.7 65.1 228.3 –3.0 24.2 78.0 5.5 86.1 16.9 257.2Belarus 96.0 27.2 15.2 18.0 0.4 4.1 1.4 0.0 0.3 0.0 2.2 27.2BelgiumBenin 461.8 98.9 122.8 34.7 0.0 48.8 29.1 31.3 1.1 6.4 0.1 88.7Bolivia 521.4 86.1 64.7 42.5 0.1 12.2 54.2 46.9 69.0 19.0 5.9 120.8Bosnia and Herzegovina 348.2 28.3 105.1 30.0 9.7 3.5 2.2 14.3 20.2 0.4 18.2 116.4Botswana 145.4 77.0 39.3 2.4 1.1 6.5 10.7 0.0 0.0 1.5 0.6 6.3Brazil 632.3 24.4 21.3 247.5 40.7 46.6 –62.7 0.4 26.4 7.1 245.4 35.2Bulgaria .. .. .. .. .. .. .. .. .. .. .. ..Burkina Faso 619.9 62.1 164.1 52.5 0.1 63.8 41.6 51.4 12.3 30.8 0.8 140.4Burundi 413.8 43.5 131.2 29.5 20.1 15.2 39.1 19.1 1.2 4.7 19.5 90.8Cambodia 544.8 84.7 27.3 41.3 26.0 26.6 147.5 1.3 23.1 8.2 4.7 154.3Cameroon 340.7 18.0 74.2 90.5 1.0 82.1 42.0 0.2 9.0 7.2 0.4 16.0CanadaCentral African Republic 197.6 20.5 84.8 3.1 3.0 24.5 8.1 2.0 3.3 1.4 0.0 46.9Chad 386.9 134.6 101.9 20.1 2.9 40.7 13.8 4.9 7.8 11.7 1.7 47.1Chile 173.4 13.3 16.2 71.8 0.7 10.1 15.9 0.3 11.3 2.7 13.1 18.2China 745.4 86.5 42.6 321.5 86.7 316.7 –192.7 4.0 1.4 9.0 22.6 47.1Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. ..Colombia 840.7 424.0 53.7 45.3 2.6 160.3 –26.2 26.3 56.2 22.6 14.2 61.8Congo, Dem. Rep. 2,750.9 277.9 364.3 77.1 250.8 13.5 80.0 420.5 306.2 26.5 28.3 905.9Congo, Rep. 1,247.5 21.4 32.2 9.4 78.8 909.4 6.0 0.0 0.5 21.9 0.6 167.4Costa Rica 94.2 0.7 4.2 21.9 0.8 4.8 63.7 3.4 5.2 0.7 0.5 –11.8Côte d’Ivoire 504.5 76.3 66.9 92.6 26.0 138.5 81.3 5.5 –7.6 6.9 1.7 16.5Croatia 142.2 0.2 105.4 22.5 1.1 3.8 1.9 0.2 0.4 0.0 3.5 3.3Cuba 112.4 16.4 24.9 2.5 0.4 2.7 5.2 0.1 42.8 5.7 1.1 10.6Cyprus .. .. .. .. .. .. .. .. .. .. .. ..Czech Republic .. .. .. .. .. .. .. .. .. .. .. ..DenmarkDominican Republic 172.5 35.5 80.7 0.9 0.1 1.0 –1.9 0.2 49.9 1.3 0.3 4.6Ecuador 160.8 33.0 24.7 27.8 0.0 0.5 –5.2 0.5 55.3 1.4 3.1 19.7Egypt, Arab Rep. 502.8 52.7 136.9 104.5 9.0 140.1 –17.7 11.0 7.2 8.9 0.7 49.7El Salvador 291.1 151.3 52.5 17.1 –48.8 3.3 8.8 0.1 85.5 2.0 1.1 18.2Eritrea 70.8 0.9 37.1 1.1 5.5 0.7 9.9 0.5 0.2 0.0 9.6 5.4Estonia .. .. .. .. .. .. .. .. .. .. .. ..Ethiopia 2,164.3 875.3 237.6 96.5 407.0 13.3 93.9 53.2 39.5 140.4 32.6 175.1FinlandFranceGabon 96.9 1.5 13.1 –1.3 0.2 58.1 24.8 0.0 0.2 0.1 0.0 0.3Gambia, The 56.0 6.5 22.7 0.6 2.0 0.4 17.2 0.0 4.0 0.5 0.1 2.1Georgia 504.7 202.2 154.7 82.0 3.4 6.2 6.5 3.2 0.1 0.0 10.0 36.4GermanyGhana 1,005.2 208.1 105.6 58.2 166.6 33.8 70.0 72.9 14.3 114.2 3.6 158.1GreeceGuatemala 390.3 105.0 37.4 13.5 0.2 3.3 41.2 19.6 92.9 10.3 9.4 57.6Guinea 164.1 21.7 72.4 13.3 0.0 36.0 10.8 0.0 2.1 2.2 0.0 5.6Guinea-Bissau 70.4 6.5 16.6 1.3 0.1 1.8 16.1 0.0 8.3 0.5 0.0 19.1Haiti 2,612.1 1,106.9 284.3 43.6 26.2 144.1 72.0 19.2 155.8 458.9 66.8 234.63782012 World Development Indicators

Distribution of net aid by DevelopmentAssistance Committee members6.13GLOBAL LINKSTen major DAC donors$ millionsTotal$ millionsUnitedStatesEUinstitutions GermanyUnitedKingdom France Japan Netherlands Spain Canada NorwayOtherDAC donors$ millions2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010Honduras 339.0 102.9 58.5 14.1 25.3 1.4 16.0 0.3 69.1 17.6 1.5 32.4Hungary .. .. .. .. .. .. .. .. .. .. .. ..India 2,313.5 57.4 94.3 396.9 650.3 2.8 981.1 2.5 11.4 7.9 24.0 84.8Indonesia 1,093.5 180.3 105.5 –12.6 26.9 262.5 61.1 34.1 –10.6 10.9 41.9 393.6Iran, Islamic Rep. 76.1 1.6 3.4 45.8 0.0 14.2 –7.1 3.4 0.2 0.1 7.3 7.3Iraq 2,059.3 1,622.9 54.1 36.9 31.0 9.6 144.4 2.6 0.4 6.3 7.7 143.5IrelandIsrael .. .. .. .. .. .. .. .. .. .. .. ..ItalyJamaica 104.5 –3.6 106.4 –6.2 3.9 –0.4 –2.1 –2.9 0.5 4.1 0.1 4.7JapanJordan 544.4 371.6 129.9 39.4 2.6 6.2 –50.9 1.0 9.2 7.1 0.4 27.9Kazakhstan 112.7 68.1 17.4 13.6 0.3 4.1 –1.8 0.4 0.1 0.1 4.3 6.2Kenya 1,260.5 565.9 101.6 79.8 105.2 123.4 36.7 17.6 8.3 25.9 13.4 182.6Korea, Dem. Rep. 42.6 5.4 14.8 2.5 0.4 0.7 0.0 0.1 0.0 0.0 2.5 16.2Korea, Rep.Kosovo 558.2 101.0 279.3 30.6 9.5 1.7 1.1 3.2 0.1 0.0 24.2 107.5Kuwait .. .. .. .. .. .. .. .. .. .. .. ..Kyrgyz Republic 182.9 56.0 24.4 25.3 7.3 1.5 23.2 0.6 0.9 2.0 6.3 35.6Lao PDR 301.9 12.8 16.0 24.8 0.1 15.0 121.5 0.1 0.0 0.6 2.6 108.5Latvia .. .. .. .. .. .. .. .. .. .. .. ..Lebanon 316.9 84.1 53.4 28.1 4.0 59.7 3.2 1.0 23.4 5.3 9.3 45.7Lesotho 168.4 57.5 74.3 5.0 4.8 –1.4 8.8 0.0 0.0 0.4 1.1 17.9Liberia 793.4 131.4 90.9 50.1 25.6 232.0 134.3 40.0 1.8 1.1 22.8 63.3Libya 18.4 6.6 1.1 3.5 1.6 3.8 0.1 0.1 0.2 0.0 0.0 1.5Lithuania .. .. .. .. .. .. .. .. .. .. .. ..Macedonia, FYR 150.1 20.4 55.1 14.1 1.2 2.9 23.1 2.3 0.4 0.0 7.5 23.2Madagascar 254.5 77.0 40.1 13.1 –0.3 84.0 9.6 0.0 0.3 1.9 13.0 15.7Malawi 725.5 126.3 208.3 41.9 148.0 –1.0 69.5 0.0 0.6 16.5 64.7 50.5Malaysia –13.7 18.6 1.2 11.2 –0.8 1.0 –53.2 0.2 0.1 0.0 0.8 7.2Mali 782.4 197.9 98.5 60.3 0.1 77.6 38.3 56.1 28.4 96.0 16.0 113.2Mauritania 130.9 11.4 25.3 7.7 0.0 32.2 14.6 0.0 34.7 0.5 0.6 4.0Mauritius 126.0 0.5 67.9 –0.2 5.5 54.0 –2.9 0.0 0.0 0.0 0.3 0.8Mexico 428.6 205.6 7.5 35.5 9.4 205.8 –46.7 –0.3 5.3 1.4 0.1 5.0Moldova 228.4 19.4 138.0 8.7 14.5 6.4 0.9 5.6 0.1 0.2 3.0 31.7Mongolia 232.1 47.2 13.4 29.1 0.8 5.0 53.9 8.8 –0.3 8.3 1.7 64.2Morocco 822.3 47.7 223.4 38.9 3.2 254.4 121.2 1.1 90.6 3.1 0.0 38.6Mozambique 1,549.7 277.9 192.3 76.9 104.4 38.1 62.9 81.8 43.9 82.0 73.7 515.7Myanmar 304.1 31.3 55.9 18.3 44.2 2.0 46.8 2.7 0.0 0.6 21.7 80.5Namibia 222.1 117.2 10.8 24.0 0.6 0.4 40.6 1.6 8.5 0.5 –3.0 20.9Nepal 521.9 51.9 46.2 42.0 105.2 –3.2 81.2 0.0 0.2 11.8 47.2 139.4NetherlandsNew ZealandNicaragua 416.2 54.5 21.9 27.7 7.3 1.0 34.4 26.3 106.2 12.5 18.5 106.1Niger 531.6 102.6 150.8 22.6 3.2 50.0 25.2 2.8 25.9 53.7 3.5 91.4Nigeria 909.5 445.9 60.3 39.0 264.6 8.9 23.9 9.1 0.5 12.6 14.1 30.7NorwayOman 7.6 2.9 0.0 1.0 0.9 0.8 1.5 0.4 0.0 0.0 0.0 0.0Pakistan 2,586.5 1,196.8 172.3 142.1 298.5 14.4 207.9 52.1 22.7 101.9 83.1 294.8Panama 125.3 11.9 1.5 1.4 0.0 0.1 101.8 0.0 5.9 0.5 2.4 –0.3Papua New Guinea 490.8 2.3 50.1 0.8 1.0 0.2 22.2 0.0 0.2 0.1 1.8 412.2Paraguay 97.4 28.0 29.9 5.4 0.0 0.1 –3.7 0.0 21.8 0.5 1.0 14.4Peru –288.2 130.6 25.8 51.9 1.3 10.1 –711.6 0.3 118.1 22.2 3.8 59.5Philippines 505.0 114.8 51.9 26.3 0.6 189.4 –87.7 0.4 27.0 16.7 17.6 148.1Poland .. .. .. .. .. .. .. .. .. .. .. ..PortugalPuerto Rico .. .. .. .. .. .. .. .. .. .. .. ..Qatar .. .. .. .. .. .. .. .. .. .. .. ..2012 World Development Indicators 379

6.13Distribution of net aid by DevelopmentAssistance Committee membersTen major DAC donors$ millionsTotal$ millionsUnitedStatesEUinstitutions GermanyUnitedKingdom France Japan Netherlands Spain Canada NorwayOtherDAC donors$ millions2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010Romania .. .. .. .. .. .. .. .. .. .. .. ..Russian Federation .. .. .. .. .. .. .. .. .. .. .. ..Rwanda 652.1 140.6 104.4 48.3 106.2 4.1 22.8 39.4 1.8 58.7 4.4 121.5Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. ..Senegal 618.4 101.4 84.1 23.1 0.9 157.2 55.2 30.0 45.6 56.7 0.3 64.0Serbia 603.2 57.9 290.1 126.3 5.4 14.0 5.2 3.8 0.4 0.8 20.2 79.2Sierra Leone 279.9 29.8 80.3 13.3 84.8 0.3 12.2 1.1 1.9 33.0 3.0 20.1Singapore .. .. .. .. .. .. .. .. .. .. .. ..Slovak Republic .. .. .. .. .. .. .. .. .. .. .. ..Slovenia .. .. .. .. .. .. .. .. .. .. .. ..Somalia 435.5 59.4 127.1 12.5 62.3 3.5 29.1 9.3 5.9 4.4 31.6 90.5South Africa 974.7 529.5 153.1 39.5 39.3 47.6 7.1 36.1 1.1 16.5 24.7 80.1South Sudan .. .. .. .. .. .. .. .. .. .. .. ..SpainSri Lanka 433.8 26.1 48.1 –6.4 –8.5 45.1 155.4 3.6 –0.7 11.5 29.0 130.6Sudan 1,793.4 726.4 284.2 39.2 119.1 10.1 119.1 57.6 23.0 108.3 116.7 189.9Swaziland 52.6 23.6 21.5 –0.6 0.0 0.2 4.4 0.0 0.0 0.8 1.6 1.3SwedenSwitzerlandSyrian Arab Republic 95.7 6.9 51.6 45.9 2.0 23.1 –54.7 0.1 6.1 0.1 1.3 13.4Tajikistan 201.2 45.9 36.6 34.7 12.5 0.2 43.4 0.6 0.0 0.7 3.2 23.3Tanzania 1,848.1 457.4 192.6 134.5 240.9 21.3 104.6 59.2 3.2 111.6 124.0 398.8Thailand –76.1 47.2 24.1 –23.2 7.2 –13.6 –143.5 0.3 0.6 –0.6 0.3 25.3Timor-Leste 272.7 27.3 14.6 9.4 0.0 0.1 27.7 0.0 6.0 1.1 7.8 178.7Togo 301.7 4.0 48.9 8.8 –0.1 168.0 7.5 17.6 1.6 23.1 0.1 22.1Trinidad and Tobago 4.1 1.3 0.6 0.4 0.2 0.5 0.1 0.0 0.1 0.3 0.0 0.6Tunisia 447.6 –3.3 92.3 23.9 2.5 126.8 35.9 –0.7 158.0 –2.9 0.0 15.1Turkey 1,029.7 6.4 295.2 –10.3 3.8 88.4 543.5 0.3 56.0 –2.6 0.1 49.0Turkmenistan 17.0 8.4 5.7 1.8 0.1 0.1 –0.9 0.0 0.0 0.0 0.6 1.4Uganda 1,162.0 378.1 128.9 40.9 179.3 1.8 71.2 36.7 4.3 5.7 71.5 243.6Ukraine 545.6 140.2 153.0 89.1 0.8 21.5 53.2 0.1 0.3 20.2 3.8 63.4United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. ..United KingdomUnited StatesUruguay 40.0 1.2 7.1 0.8 0.1 1.2 11.4 0.1 8.4 0.5 0.1 9.4Uzbekistan 90.8 13.4 6.5 25.0 1.2 2.3 7.0 0.0 0.2 0.0 0.3 34.9Venezuela, RB 43.4 8.6 5.9 7.0 1.1 6.9 3.1 0.1 8.2 0.4 0.1 2.0Vietnam 1,866.5 93.1 41.9 96.4 82.2 242.4 807.8 21.2 16.0 25.0 20.2 420.3West Bank and Gaza 2,069.2 720.8 441.1 104.6 97.6 69.3 78.6 33.7 97.6 65.1 109.5 251.4Yemen, Rep. 322.8 45.4 40.7 82.1 63.9 7.0 26.7 26.5 1.1 2.6 0.4 26.4Zambia 685.5 225.1 92.5 33.3 79.3 0.8 46.1 36.1 0.2 8.7 54.1 109.3Zimbabwe 630.8 175.2 109.5 33.1 108.0 3.0 18.9 11.1 3.6 9.3 24.5 134.6World103,174.7 s 26,586.4 s 12,428.0 s 8,035.5 s 8,016.8 s 7,786.7 s 7,331.1 s 4,644.2 s 3,998.9 s 3,919.6 s 3,560.9 s 16,866.6 sLow income 29,779.4 8,672.4 4,003.4 1,708.5 2,544.1 1,313.1 2,186.9 1,117.1 763.8 1,583.6 848.0 5,038.5Middle income 41,152.0 10,211.0 6,373.5 3,372.7 2,351.5 4,952.3 3,460.9 724.8 1,637.4 844.5 1,103.9 6,119.4Lower middle income 29,587.9 7,602.4 3,681.5 1,899.4 2,088.0 2,899.8 3,710.9 519.5 868.2 696.2 671.5 4,950.4Upper middle income 10,377.3 2,549.0 2,088.8 1,312.3 254.1 1,954.3 –252.0 178.7 715.0 107.2 410.5 1,059.4Low & middle income 102,918.5 26,581.7 12,301.2 8,011.3 8,014.4 7,778.5 7,326.1 4,643.7 3,978.6 3,918.5 3,557.5 16,807.2East Asia & Pacifi c 7,875.1 1,032.2 530.7 565.1 281.2 1,221.0 973.2 73.1 88.4 116.2 151.5 2,842.4Europe & Central Asia 5,556.1 979.9 1,754.1 664.7 72.5 173.7 780.3 41.4 97.7 37.0 120.8 834.1Latin America & Carib. 9,201.1 2,720.6 1,268.2 912.0 179.2 664.2 –311.4 223.2 1,369.9 807.1 434.2 933.9Middle East & N. Africa 8,009.6 3,006.8 1,441.4 622.9 229.7 859.7 335.8 80.1 462.6 123.5 150.0 697.3South Asia 12,867.1 4,383.0 850.7 1,127.7 1,510.9 115.4 2,277.8 200.0 108.8 498.1 324.5 1,470.1Sub-Saharan Africa 32,263.2 7,637.2 4,788.9 1,736.0 3,003.3 3,526.9 1,694.5 1,313.7 912.0 1,512.0 945.2 5,193.7High income 256.2 4.7 126.9 24.3 2.4 8.2 5.0 0.5 20.3 1.0 3.5 59.4Euro area .. .. .. .. .. .. .. .. .. .. ..Note: Regional aggregates include data for economies not specifi ed elsewhere. World and income group totals include aid not allocated by country or region.3802012 World Development Indicators

Distribution of net aid by DevelopmentAssistance Committee members 6.13GLOBAL LINKSAbout the dataDefinitionsThe table shows net bilateral aid to low- and middleincomeeconomies from members of the DevelopmentAssistance Committee (DAC) of the Organisation forEconomic Co-operation and Development (OECD).DAC has 24 members—23 individual economies and1 multilateral institution (European Union institutions).The table is based on donor country reports ofbilateral programs, which may differ from reportsby recipient countries. Recipients may lackaccess to information on such aid expenditures asdevelopment- oriented research, stipends and tuitioncosts for aid-fi nanced students in donor countries,and payment of experts hired by donor countries.Moreover, a full accounting would include donorcountry contributions to multilateral institutions,the fl ow of resources from multilateral institutionsto recipient countries, and fl ows from countries thatare not members of DAC.Data in the table exclude DAC members’ multilateralaid (contributions to the regular budgets of themultilateral institutions). However, projects executedby multilateral institutions or nongovernmental organizationson behalf of DAC members are classifi edas bilateral aid (since the donor country effectivelycontrols the use of the funds) and are included in thedata reported in the table.The data include aid to some countries and territoriesnot shown in the table and aid to unspecifi edeconomies recorded only at the regional or globallevel. Aid to countries and territories not shown inthe table has been assigned to regional totals basedon the World Bank’s regional classifi cation system.Aid to unspecifi ed economies is included in regionaltotals and, when possible, income group totals. Aidnot allocated by country or region—including administrativecosts, research on development, and aid tonongovernmental organizations—is included in theworld total. Thus regional and income group totalsdo not sum to the world total.Some of the aid recipients shown in the table arealso aid donors. Development cooperation activitiesby non-DAC members have increased in recent yearsand in some cases surpass those of individual DACmembers. Some non-DAC donors report their developmentcooperation activities to DAC on a voluntarybasis, but many do not yet report their aid fl ows toDAC. See table 6.13a for a summary of ODA fromnon-DAC countries.• Net aid refers to net bilateral offi cial developmentassistance that meets the DAC defi nition of offi cialdevelopment assistance and is made to countriesand territories on the DAC list of aid recipients.• Other DAC donors are Australia, Austria, Belgium,Denmark, Finland, Greece, Ireland, Italy, the Republicof Korea, Luxembourg, New Zealand, Portugal, Sweden,and Switzerland.Official development assistance from non-DAC donors, 2006–10 6.13aNet disbursements ($ millions)2006 2007 2008 2009 2010OECD members (non-DAC)Czech Republic 161 179 249 215 228Hungary 149 103 107 117 114Iceland 42 48 48 34 29Poland 297 363 372 375 378Slovak Republic 55 67 92 75 74Turkey 714 602 780 707 967Arab countriesKuwait 158 110 283 221 211Saudi Arabia 2,025 1,551 4,979 3,134 3,480United Arab Emirates 783 2,426 1,266 834 412Other donorsIsrael a 90 111 138 124 145Taiwan, China 513 514 435 411 381Thailand 74 67 178 40 10Others b 121 188 343 385 808Total 5,181 6,329 9,271 6,672 7,235Note: The above table does not refl ect aid provided by several major emerging non–Organisation for EconomicCo-operation and Development (OECD) donors, as information on their aid has not been disclosed.a. Data are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by theOECD is without prejudice to the status of the Golan Heights, East Jerusalem, and Israeli settlements in the West Bankunder the terms of international law. b. Includes Cyprus, Estonia, Latvia, Liechtenstein, Lithuania, Malta, Romania, theRussian Federation, and Slovenia.Data sourcesData on fi nancial fl ows are compiled by OECDDAC and published in its annual statistical report,Geographical Distribution of Financial Flows toAid Recipients, and its annual DevelopmentCo-operation Report. Data are available electronicallyon the DAC’s International Development StatisticsCD-ROM and at www.oecd.org/dac/stats/idsonline.2012 World Development Indicators 381

6.14 Movement of people across bordersNet migrationInternationalmigrant stockEmigration oftertiary educatedpopulation toOECD countriesRefugeesWorkers’ remittances andcompensation of employees% oftertiary educatedpopulationthousands$ millionsthousandsthousands ages 25 and older By country of origin By country of asylum Received Paid2005–10 2010 2000 2010 2010 2010 2010Afghanistan –381 91 22.6 3,054.7 6.4 .. ..Albania –48 89 17.5 14.8 0.1 1,156 24Algeria –140 242 9.5 6.7 94.1 2,044 a 46Angola 82 65 3.7 134.9 15.2 82 716Argentina –200 1,449 2.8 0.6 3.3 641 993Armenia –75 324 8.9 17.5 3.3 996 157Australia 1,125 4,711 2.7 0.0 21.8 4,840 a 3,776 aAustria 160 1,310 13.5 0.0 42.6 3,220 3,453Azerbaijan 53 264 1.8 16.8 1.9 1,432 961Bahrain 448 315 5.1 0.1 0.2 .. 1,642Bangladesh –2,908 1,085 4.4 10.0 229.3 10,852 9Belarus –50 1,090 3.2 5.7 0.6 376 105Belgium 200 975 5.5 0.1 17.9 10,178 4,040Benin 50 232 8.7 0.4 7.1 248 a 88Bolivia –165 146 5.8 0.6 0.7 1,088 104Bosnia and Herzegovina –10 28 20.3 63.0 7.0 1,905 54Botswana 19 115 5.1 0.1 3.0 100 102Brazil –500 688 2.0 1.0 4.4 4,000 1,198Bulgaria –50 107 9.6 2.6 5.5 1,387 25Burkina Faso –125 1,043 2.6 1.1 0.5 95 a 100Burundi 370 61 9.3 84.1 29.4 28 1Cambodia –255 336 21.5 16.3 0.1 369 215Cameroon –19 197 17.3 15.0 104.3 195 54Canada 1,098 7,202 4.7 0.1 165.5 .. ..Central African Republic 5 80 7.3 164.9 21.6 .. ..Chad –75 388 9.1 53.7 347.9 .. ..Chile 30 320 6.0 1.2 1.6 3 5China –1,884 b 686 b 3.8 199.7 c 301.0 53,038 a 1,754Hong Kong SAR, China 176 2,742 29.6 0.0 0.2 347 433Colombia –120 110 10.4 395.6 0.2 4,058 112Congo, Dem. Rep. –24 445 14.9 476.7 166.3 .. ..Congo, Rep. 50 143 28.2 20.7 133.1 15 a 102Costa Rica 76 489 7.1 0.4 19.5 552 271Côte d’Ivoire –360 2,407 6.2 41.8 26.2 179 754Croatia 10 700 24.6 65.9 0.9 1,315 164Cuba –190 15 28.8 7.5 0.4 .. ..Cyprus 44 154 34.2 0.0 3.4 146 404Czech Republic 240 453 8.5 0.8 2.4 1,122 1,812Denmark 90 484 7.8 0.0 17.9 633 3,184Dominican Republic –140 434 22.4 0.2 0.6 3,369 29Ecuador –120 394 9.5 0.9 121.2 2,569 81Egypt, Arab Rep. –347 245 4.7 6.9 95.1 7,725 255El Salvador –292 40 31.7 5.0 0.0 3,449 23Eritrea 55 16 35.2 222.5 4.8 .. ..Estonia 0 182 9.9 0.2 0.0 322 94Ethiopia –300 548 9.8 68.8 154.3 225 27Finland 73 226 7.2 0.0 8.7 826 437France 500 6,685 3.5 0.1 200.7 15,629 5,264Gabon 5 284 14.6 0.2 9.0 .. ..Gambia, The –14 290 67.8 2.2 8.4 116 58Georgia –150 167 2.8 10.6 0.6 806 50Germany 550 10,758 5.8 0.2 594.3 11,338 15,908Ghana –51 1,852 44.7 20.2 13.8 136 ..Greece 154 1,133 12.2 0.1 1.4 1,499 1,932Guatemala –200 59 23.9 5.7 0.1 4,229 21Guinea –300 395 4.7 12.0 14.1 60 43Guinea-Bissau –10 19 27.7 1.1 7.7 48 17 aHaiti –240 35 83.4 25.9 0.0 1,499 1353822012 World Development Indicators

Movement of people across borders 6.14GLOBAL LINKSNet migrationInternationalmigrant stockEmigration oftertiary educatedpopulation toOECD countriesRefugeesWorkers’ remittances andcompensation of employees% oftertiary educatedpopulationthousands$ millionsthousandsthousands ages 25 and older By country of origin By country of asylum Received Paid2005–10 2010 2000 2010 2010 2010 2010Honduras –100 24 24.8 1.3 0.0 2,649 12Hungary 75 368 12.8 1.4 5.4 2,265 1,265India –3,000 5,436 4.3 17.8 184.8 54,035 3,888Indonesia –1,293 123 2.9 16.9 0.8 6,916 2,840Iran, Islamic Rep. –186 2,129 14.3 68.8 1,073.4 1,181 a ..Iraq –150 83 10.9 1,683.6 34.7 71 32 aIreland 100 899 33.7 0.0 9.1 601 1,751Israel 274 2,940 7.8 1.3 25.5 1,411 3,739Italy 1,999 4,463 9.7 0.1 56.4 6,803 12,201Jamaica –100 30 84.7 1.1 0.0 2,011 314Japan 270 2,176 1.2 0.2 2.6 1,802 4,474Jordan 203 2,973 7.4 2.3 2,455.7 d 3,641 495Kazakhstan 7 3,079 1.2 3.6 4.4 291 3,021Kenya –189 818 38.5 8.6 402.9 1,777 a 61Korea, Dem. Rep. 0 37 .. 0.9 .. .. ..Korea, Rep. –30 535 7.5 0.6 0.4 8,708 11,385Kosovo .. .. .. .. .. 932 146Kuwait 278 2,098 7.1 1.0 0.2 .. 11,770Kyrgyz Republic –132 223 0.9 2.7 2.5 1,275 a 297Lao PDR –75 19 37.2 8.4 .. 41 8Latvia –10 335 8.5 0.7 0.1 614 43Lebanon –13 758 43.9 15.9 435.1 d 7,558 3,737Lesotho –20 6 4.1 0.0 .. 746 19Liberia 300 96 44.3 70.1 24.7 27 a 1Libya –20 682 4.3 2.3 7.9 17 a 1,361Lithuania –35 129 8.4 0.5 0.8 1,575 538Macedonia, FYR 2 130 29.4 7.9 1.4 388 23Madagascar –5 38 7.7 0.3 .. .. ..Malawi –20 276 20.9 0.2 5.7 .. ..Malaysia 84 2,358 10.5 0.6 81.5 1,301 6,528Mali –101 163 14.8 3.7 13.6 436 a 167Mauritania 10 99 8.6 37.7 26.7 .. ..Mauritius 0 43 56.0 0.0 .. 226 a 13Mexico –1,805 726 15.5 6.8 1.4 22,048 ..Moldova –172 408 4.1 6.2 0.1 1,370 117Mongolia –15 10 7.4 1.7 0.0 277 169Morocco –675 49 18.6 2.3 0.8 6,423 62Mozambique –20 450 22.6 0.1 4.1 132 80Myanmar –500 89 3.9 415.7 .. 133 a ..Namibia –1 139 3.4 1.0 7.3 15 16Nepal –100 946 4.0 5.9 89.8 3,468 32Netherlands 50 1,753 9.6 0.1 75.0 3,834 12,923New Zealand 65 962 21.8 0.0 2.3 843 1,167Nicaragua –200 40 30.2 1.4 0.1 823 ..Niger –28 202 5.5 0.8 0.3 88 22 aNigeria –300 1,128 10.5 15.6 8.7 10,045 a 48Norway 171 485 6.2 0.0 40.3 680 4,045Oman 153 826 0.4 0.1 0.1 39 5,704Pakistan –2,000 4,234 12.7 40.0 1,900.6 9,690 19Panama 11 121 16.7 0.1 17.1 231 248Papua New Guinea 0 25 27.8 0.1 9.7 15 323Paraguay –40 161 3.8 0.1 0.1 673 ..Peru –725 38 5.8 5.8 1.1 2,534 122Philippines –1,233 435 13.6 1.0 0.2 21,423 62Poland 56 827 14.3 1.8 15.6 7,614 1,575Portugal 150 919 19.0 0.0 0.4 3,540 1,406Puerto Rico –145 324 .. .. .. .. ..Qatar 857 1,305 2.1 0.1 0.1 .. ..2012 World Development Indicators 383

6.14Movement of people across bordersNet migrationInternationalmigrant stockEmigration oftertiary educatedpopulation toOECD countriesRefugeesWorkers’ remittances andcompensation of employeesRomania –100 133 11.3 3.9 1.0 3,883 355Russian Federation 1,136 12,270 1.4 111.9 4.9 5,264 18,796Rwanda 15 465 31.7 114.8 55.4 92 71Saudi Arabia 1,056 7,289 0.9 0.7 0.6 236 27,069Senegal –133 210 17.2 16.3 20.7 1,346 144 aSerbia 0 525 .. 183.3 73.6 3,351 a 70Sierra Leone 60 107 49.2 11.3 8.4 58 6Singapore 722 1,967 14.5 0.1 0.0 .. ..Slovak Republic 37 131 14.3 0.2 0.5 1,591 70Slovenia 22 164 11.0 0.0 0.3 309 158Somalia –300 23 34.5 770.2 1.9 .. ..South Africa 700 1,863 7.4 0.4 57.9 1,119 1,372South Sudan .. .. .. .. .. .. ..Spain 2,250 6,378 4.2 0.0 3.8 10,507 12,227Sri Lanka –250 340 28.2 141.1 0.2 4,155 545Sudan 135 753 6.8 387.3 178.3 1,974 a 1 aSwaziland –6 40 5.4 0.0 0.8 109 11Sweden 266 1,306 4.5 0.0 82.6 688 695Switzerland 183 1,763 9.6 0.0 48.8 2,619 21,668Syrian Arab Republic –56 2,206 6.2 18.5 1,483.2 d 1,646 a 214 aTajikistan –296 284 0.6 0.6 3.1 2,254 856Tanzania –300 659 12.1 1.1 109.3 25 127Thailand 492 1,157 2.2 0.4 96.7 1,764 ..Timor-Leste –50 14 16.5 0.0 0.0 .. ..Togo –5 185 16.5 18.3 14.1 333 a 72 aTrinidad and Tobago –20 34 78.9 0.3 0.0 120 a ..Tunisia –20 34 12.6 2.2 0.1 1,970 13Turkey –50 1,411 5.8 146.8 10.0 874 175Turkmenistan –54 208 0.4 0.7 0.1 .. ..Uganda –135 647 36.0 6.4 135.8 915 602Ukraine –40 5,258 4.3 25.1 3.0 5,607 24United Arab Emirates 3,077 3,293 0.7 0.4 0.5 .. ..United Kingdom 1,020 6,452 17.1 0.2 238.2 7,532 3,528United States 4,955 42,813 0.5 3.0 264.6 5,277 51,597Uruguay –50 80 9.0 0.2 0.2 103 7Uzbekistan –518 1,176 0.8 8.8 0.3 .. ..Venezuela, RB 40 1,007 3.8 6.7 201.5 143 805Vietnam –431 69 27.0 338.7 1.9 8,260 a ..West Bank and Gaza –90 1,924 12.0 93.3 1,910.7 d 1,151 a 9 aYemen, Rep. –135 518 6.0 2.1 190.1 1,240 337Zambia –85 233 16.4 0.2 47.9 44 68Zimbabwe –900 372 13.1 24.1 4.4 .. ..World .. e s 213,397 f s 5.4 w 15,369.9 d,g s 15,369.9 d s 449,197 s 303,799 sLow income –6,818 11,158 11.8 5,650.8 1,874.0 24,553 3,088Middle income –16,342 70,369 6.8 4,518.3 11,535.2 300,725 55,580Lower middle income –12,613 31,148 8.0 3,223.5 6,412.2 161,464 11,579Upper middle income –3,729 39,220 6.1 1,294.8 5,123.0 139,262 44,001Low & middle income –23,160 81,527 7.1 10,169.1 13,409.2 325,278 58,668East Asia & Pacifi c –5,221 5,434 7.0 1,002.3 492.0 93,957 11,945Europe & Central Asia –595 27,681 3.5 637.2 140.8 36,037 25,865Latin America & Carib. –5,088 6,569 10.6 470.4 373.8 57,275 4,603Middle East & N. Africa –1,628 11,957 10.5 1,905.3 7,795.9 34,700 6,566South Asia –8,622 12,175 5.3 3,344.6 2,411.2 82,209 4,687Sub-Saharan Africa –2,006 17,710 12.6 2,809.5 2,195.6 21,101 5,002High income 22,906 131,871 4.1 79.4 1,960.7 123,919 245,131Euro area 6,336 36,317 7.1 1.0 1,023.9 71,976 82,745a. World Bank estimates. b. Includes Taiwan, China. c. Includes Tibetans, who are listed separately by the UN Refugee Agency (UNHCR). d. Includes Palestinian refugees under themandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), who are not included in data from the UNHCR. e. World totals computed bythe United Nations sum to zero, but because the aggregates refer to World Bank defi nitions, regional and income group totals do not. f. World totals are computed by the World Bankand include only economies covered by World Development Indicators, so data may differ from what is published by the United Nations Population Division. g. Includes refugees withoutspecifi ed country of origin and Palestinian refugees under the mandate of the UNRWA, so regional and income group totals do not sum to the world total.3842012 World Development Indicators% oftertiary educatedpopulationthousands$ millionsthousandsthousands ages 25 and older By country of origin By country of asylum Received Paid2005–10 2010 2000 2010 2010 2010 2010

Movement of people across borders 6.14GLOBAL LINKSAbout the dataDefinitionsMovement of people, most often through migration,is a signifi cant part of global integration. Migrantscontribute to the economies of both their host countryand their country of origin. Yet reliable statisticson migration are diffi cult to collect and are oftenincomplete, making international comparisons achallenge.The United Nations Population Division providesdata on net migration and migrant stock. Becausedata on migrant stock is diffi cult for countries to collect,the United Nations Population Division takesinto account the past migration history of a countryor area, the migration policy of a country, and theinfl ux of refugees in recent periods when derivingestimates of net migration. The data to calculatethese estimates come from a variety of sources,including border statistics, administrative records,surveys, and censuses. When there is insuffi cientdata, net migration is derived through the differencebetween the overall population growth rate and therate of natural increase (the difference between thebirth rate and the death rate) during the same period.Such calculations are usually made for intercensalperiods. The estimates are also derived from thedata on foreign-born population —people who haveresidence in one country but were born in anothercountry. When data on the foreign-born populationare not available, data on foreign population —thatis, people who are citizens of a country other than thecountry in which they reside—are used as estimates.For countries with information on the internationalmigrant stock for at least two points in time,interpolation or extrapolation was used to estimatethe international migrant stock on July 1 of the referenceyears. For countries with only one observation,estimates for the reference years were derived usingrates of change in the migrant stock in the years precedingor following the single observation available.A model was used to estimate migrants for countriesthat had no data.One negative effect of migration is “brain drain”—emigration of highly educated people. The tableshows data on emigration of people with tertiaryeducation, drawn from Docquier, Marfouk, and Lowell(2007), which analyzes skilled migration usingdata from censuses and registers of Organisationfor Economic Development and Co-operation (OECD)countries and provides data disaggregated by genderfor 1990 and 2000.The table also shows data on refugees becausethey are an important part of migrant stock. Therefugee data refer to people who have crossedan international border to fi nd sanctuary and havebeen granted refugee or refugee-like status or temporaryprotection. Asylum seekers— people whohave applied for asylum or refugee status and whohave not yet received a decision or who are registeredas asylum seekers—and internally displacedpeople—who are often confused with refugees—arenot included. Unlike refugees, internally displacedpeople remain under the protection of their own government,even if their reason for fl eeing was similarto that of refugees. Palestinian refugees are people(and their descendants) whose residence was Palestinebetween June 1946 and May 1948 and wholost their homes and means of livelihood as a resultof the 1948 Arab-Israeli confl ict.Registrations, together with other sources—including estimates and surveys—are the mainsources of refugee data. There are diffi culties incollecting accurate statistics. Many refugees maynot be aware of the need to register or may choosenot to do so, and administrative records tend toover estimate the number of refugees because it iseasier to register than to de-register. The UN RefugeeAgency (UNHCR) collects and maintains data onrefugees, except for Palestinian refugees residing inareas under the mandate of the United Nations Reliefand Works Agency for Palestine Refugees in the NearEast (UNRWA). Registration is voluntary, and estimatesby the UNRWA are not an accurate count of thePalestinian refugee population. The table shows estimatesof refugees collected by the UNHCR, complementedby estimates of Palestinian refugees underthe UNRWA mandate. Thus, the aggregates differfrom those published by the UNHCR.Workers’ remittances and compensation ofemployees are from the International MonetaryFund’s (IMF) Balance of Payments Statistics Yearbook.The IMF data are supplemented by World Bank staffestimates for missing data for countries where workers’remittances are important. The data reportedhere are the sum of three items defi ned in the fi fthedition of the IMF’s Balance of Payments Manual:workers’ remittances, compensation of employees,and migrants’ transfers.• Net migration is the net total of migrants (immigrantsless emigrants, including both citizens andnoncitizens) during the period. Data are fi ve-year estimates.• International migrant stock is the numberof people, including refugees, born in a country otherthan that in which they live. • Emigration of tertiaryeducated population to OECD countries is the stockof emigrants ages 25 and older with at least one yearof tertiary education who reside in an OECD countryother than that in which they were born. • Refugeesare people recognized as refugees under the 1951Convention Relating to the Status of Refugees orits 1967 Protocol, the 1969 Organization of AfricanUnity Convention Governing the Specifi c Aspects ofRefugee Problems in Africa; recognized as refugeesin accordance with the UNHCR statute; granted refugee-likehumanitarian status; or provided temporaryprotection. Asylum seekers and internally displacedpeople are excluded. • Country of origin refers tothe nationality or country of citizenship of a claimant.• Country of asylum is the country where an asylumclaim was fi led and granted. • Workers’ remittancesand compensation of employees, received and paid,are current transfers by migrant workers and wagesand salaries earned by nonresident workers. Remittancesare classifi ed as current private transfersfrom migrant workers resident in the host countryfor more than a year, irrespective of their immigrationstatus, to recipients in their country of origin.Migrants’ transfers are defi ned as the net worth ofmigrants who are expected to remain in the hostcountry for more than one year that is transferred toanother country at the time of migration. Compensationof employees is the income of migrants whohave lived in the host country for less than a year.Data sourcesData on net migration are from the United NationsPopulation Division’s World Population Prospects:The 2010 Revision. Data on international migrationstock are from the United Nations PopulationDivision’s Trends in Total Migrant Stock: The 2008Revision. Data on migration of tertiary educatedpopulation are from Docquier, Lowell, and Marfouk(2009). Data on refugees are from the UNHCR’sStatistical Yearbook 2010, complemented by statisticson Palestinian refugees under the mandateof the UNRWA as published on its website. Dataon remittances are from the IMF’s Balance of PaymentsStatistics Yearbook supplemented by WorldBank staff estimates.2012 World Development Indicators 385

6.15Travel and tourismInternational tourists Inbound tourism expenditure Outbound tourism expenditurethousandsInbound Outbound $ millions % of exports $ millions % of imports2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Afghanistan .. .. .. .. .. .. .. .. .. .. .. ..Albania 317 a,b 2,417 a,b .. 3,443 398 1,780 56.6 47.0 290 1,454 19.3 23.0Algeria 866 a,c .. 1,006 .. 102 d .. .. .. 193 e .. .. ..Angola 51 425 .. .. 34 e 726 0.4 1.4 146 275 2.5 0.8Argentina 2,909 5,325 4,953 5,307 3,195 5,629 10.2 6.9 5,460 6,375 16.5 9.4Armenia 45 .. 111 .. 52 456 11.6 23.5 56 466 5.8 11.1Australia 4,931 a,b 5,885 a,b,e 3,498 7,111 13,016 .. 15.5 .. 8,780 .. 10.0 ..Austria 17,982 f 22,004 f 7,528 9,882 11,382 20,931 13.0 10.3 7,001 12,215 8.2 6.5Azerbaijan .. 1,280 1,326 3,176 68 792 3.2 2.8 138 856 6.8 8.1Bahrain 2,420 .. .. .. 854 2,163 11.9 12.1 425 684 8.3 5.2Bangladesh 199 .. 1,128 .. 50 d 103 d 0.7 0.5 471 835 4.9 2.8Belarus 60 119 1,289 415 188 662 2.5 2.2 247 738 3.1 2.0Belgium 6,457 f 7,186 f 7,932 10,170 6,592 d 11,431 .. 3.1 9,429 d,e 20,558 .. 5.7Benin 96 199 .. .. 77 134 14.6 .. 50 97 7.1 ..Bolivia 319 807 201 708 101 339 6.9 5.0 116 421 5.6 6.8Bosnia and Herzegovina 171 f 365 f .. .. 246 668 15.6 10.7 92 244 2.2 2.5Botswana 1,104 2,145 .. .. 227 222 7.6 4.4 209 26 9.0 0.5Brazil 5,313 5,161 3,228 5,305 1,969 6,181 3.0 2.6 4,548 19,340 6.3 7.9Bulgaria 2,785 6,047 2,337 3,676 1,364 4,035 19.5 14.8 764 1,382 10.0 4.9Burkina Faso 126 g 274 g .. .. 23 .. 9.7 .. 30 .. 4.6 ..Burundi 29 c .. 28 .. 1 2 2.6 1.2 14 d 35 9.3 5.8Cambodia .. 2,399 41 505 345 1,412 18.9 20.5 52 268 2.3 3.4Cameroon 277 g .. .. .. 132 171 4.9 3.0 241 265 9.6 4.1Canada 19,627 16,097 19,182 28,678 13,035 18,281 4.0 4.0 15,125 36,677 5.3 7.4Central African Republic 11 h .. .. .. 5 e .. .. .. 33 e .. .. ..Chad 43 g .. 27 .. 14 e .. .. .. 56 e .. .. ..Chile 1,742 2,766 1,830 3,348 1,179 2,413 5.1 2.9 904 2,339 4.1 3.5China 31,229 55,664 10,473 57,386 17,318 50,154 6.2 2.9 14,169 59,840 5.7 3.9Hong Kong SAR, China 8,814 20,085 .. 84,442 8,198 e 27,028 e 3.4 5.4 12,502 d,e 17,461 d,e 5.3 3.6Colombia 557 .. 1,235 .. 1,313 2,797 8.3 6.2 1,452 2,368 10.1 5.1Congo, Dem. Rep. 103 .. .. .. .. .. .. .. .. .. .. ..Congo, Rep. 19 g .. .. .. 12 .. 0.5 .. 59 .. 4.9 ..Costa Rica 1,088 2,100 381 662 1,477 2,189 19.1 16.0 551 534 7.6 3.6Côte d’Ivoire .. .. .. .. 53 .. 1.2 .. 291 .. 8.0 ..Croatia 5,831 f 9,111 f .. 1,873 2,871 8,209 33.2 35.5 634 853 6.6 3.6Cuba 1,741 h 2,507 h 139 251 1,948 e 2,396 e .. .. .. .. .. ..Cyprus 2,686 2,173 503 1,067 2,137 2,416 42.6 24.8 543 1,436 10.6 12.8Czech Republic .. 8,185 .. 6,429 2,973 d 8,017 8.3 5.8 1,276 d 4,166 3.4 3.2Denmark 3,535 f 8,744 f 5,011 .. 3,671 d 5,704 e 5.0 3.7 4,669 d 9,082 d 7.2 6.5Dominican Republic 2,978 c,h 4,125 c,h 360 401 2,860 d 4,209 d 31.9 36.0 440 542 4.1 3.1Ecuador 627 a,b 1,047 a,b 520 899 451 786 7.6 4.0 416 862 8.4 3.8Egypt, Arab Rep. 5,116 14,051 2,964 .. 4,657 13,633 27.6 27.9 1,206 2,696 5.3 4.5El Salvador 795 1,150 923 .. 437 646 11.9 11.6 219 280 3.9 3.0Eritrea 70 a,c 84 a,c .. .. 36 e .. 36.8 .. .. .. .. ..Estonia 1,220 2,120 1,800 955 657 1,412 13.7 8.7 253 719 5.1 4.9Ethiopia 136 c .. .. .. 205 1,434 20.7 30.9 80 143 d 4.9 1.4Finland 2,714 3,670 5,914 6,633 2,035 4,362 3.8 4.5 2,293 5,202 5.7 5.5France 77,190 77,148 19,886 21,609 38,534 56,654 10.1 8.6 26,703 46,227 7.3 6.4Gabon 155 h .. 168 .. 99 .. 2.8 .. 183 .. 11.1 ..Gambia, The 79 91 .. .. .. 38 .. 14.9 .. 11 d .. 3.6Georgia 387 a 2,033 a 315 2,089 107 738 12.5 18.2 129 328 9.8 5.3Germany 18,983 f 26,875 f 74,400 .. 24,943 49,133 4.0 3.2 57,601 91,208 9.2 6.7Ghana 399 c .. .. .. 357 706 14.6 7.5 162 882 4.8 6.3Greece 13,096 15,007 .. .. 9,262 12,579 31.5 20.9 4,564 2,874 10.9 3.6Guatemala 826 a 1,876 a 488 1,136 498 1,378 12.9 12.7 216 1,033 3.9 6.8Guinea 33 h .. .. .. 8 2 1.1 0.1 13 17 1.5 0.9Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. ..Haiti 140 h .. .. .. 128 d 167 d 25.4 20.9 173 431 12.6 10.63862012 World Development Indicators

Travel and tourism6.15GLOBAL LINKSInternational tourists Inbound tourism expenditure Outbound tourism expenditurethousandsInbound Outbound $ millions % of exports $ millions % of imports2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010Honduras 471 896 274 429 263 652 6.8 9.6 198 406 4.2 4.1Hungary .. 9,510 11,065 16,082 3,809 6,346 11.0 5.7 1,722 2,867 4.7 2.8India 2,649 b 5,776 b 4,416 12,988 3,598 14,673 6.0 4.2 3,686 13,746 5.0 3.1Indonesia 5,064 7,003 2,205 6,235 4,975 d 7,618 7.0 4.4 3,197 d 8,432 5.7 5.5Iran, Islamic Rep. 1,342 .. 2,286 .. 677 .. 2.3 .. 671 .. 3.8 ..Iraq 78 a 1,518 a .. .. 2 d .. .. .. 9 d .. .. ..Ireland 6,646 .. 3,783 .. 3,517 8,071 3.8 3.9 2,626 7,798 3.3 4.6Israel 2,417 b 2,803 b 3,530 4,269 4,611 5,474 9.9 6.8 3,733 4,433 8.0 5.8Italy 41,181 43,626 21,993 29,823 28,706 40,058 9.7 7.3 18,169 33,053 6.3 5.6Jamaica 1,323 c,h 1,922 c,h .. .. 1,577 2,095 43.9 52.3 238 235 5.4 3.6Japan 4,757 a,b 8,611 a,b 17,819 16,637 5,970 15,356 1.1 1.8 42,643 39,306 9.3 4.9Jordan 1,580 c 4,557 c 1,625 2,917 935 4,018 26.4 33.0 387 1,605 6.7 8.9Kazakhstan 1,471 3,393 1,247 .. 403 1,236 3.9 1.9 483 1,437 5.4 3.3Kenya 899 1,469 .. .. 500 1,620 18.0 18.2 156 212 d 4.1 1.6Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..Korea, Rep. 5,322 a,c 8,798 a,c 5,508 .. 8,527 13,805 4.1 2.5 7,945 19,695 4.1 3.8Kosovo .. .. .. .. .. .. .. .. .. .. .. ..Kuwait 78 g 207 g 1,236 .. 394 510 1.8 0.7 2,852 7,419 25.1 22.7Kyrgyz Republic 59 1,316 47 1,296 20 336 3.5 13.6 28 398 4.3 10.2Lao PDR 191 1,670 .. .. 114 d 385 d 22.5 17.1 8 215 d 1.4 9.3Latvia 509 1,373 2,596 3,332 172 963 5.3 7.5 281 771 7.4 5.9Lebanon 742 2,168 .. .. 742 8,174 .. 38.5 .. 5,080 .. 16.4Lesotho .. 414 .. .. 18 d 34 d 6.7 3.8 12 .. 1.1 ..Liberia .. .. .. .. .. 12 d .. 3.0 .. 134 .. 7.4Libya .. .. .. .. 84 170 0.7 0.3 495 2,184 9.9 7.1Lithuania 1,083 1,507 3,632 1,411 430 1,097 8.4 4.4 261 786 4.5 3.1Macedonia, FYR 224 f 262 f .. .. 88 209 5.4 5.0 58 141 2.5 2.3Madagascar 160 h 196 h .. .. 152 633 12.8 .. 139 110 d 9.1 ..Malawi 228 746 .. .. 29 .. 6.6 .. 53 .. 8.4 ..Malaysia 10,222 24,577 30,532 .. 5,873 18,315 5.2 7.9 2,543 7,943 d 2.7 4.2Mali 86 g,h 169 g .. .. 47 296 7.3 .. 66 235 7.1 ..Mauritania 30 .. .. .. .. .. .. .. .. .. .. ..Mauritius 656 935 163 212 732 1,585 27.9 32.0 203 423 7.5 6.9Mexico 20,641 c 22,260 c 11,079 14,395 9,133 12,417 5.1 4.0 6,365 9,075 3.3 2.8Moldova 18 8 32 117 57 233 8.9 10.2 86 329 8.8 7.2Mongolia 137 457 .. .. 43 288 7.0 8.5 54 319 7.0 8.2Morocco 4,278 c 9,288 c 1,508 .. 2,280 8,176 21.8 27.1 506 1,879 4.0 4.7Mozambique .. .. .. .. 74 d 230 10.7 7.7 122 285 8.2 6.1Myanmar 208 311 .. .. 195 92 9.1 1.1 30 d 54 1.2 1.0Namibia 656 984 .. .. 193 d 560 d 13.0 11.2 86 d 145 5.3 2.6Nepal 464 603 155 765 219 378 17.1 24.0 109 528 6.1 9.0Netherlands 10,003 f 10,883 f 13,896 18,430 11,285 18,690 4.4 3.2 13,649 19,772 5.7 3.9New Zealand 1,780 2,492 1,283 2,026 2,272 d 4,907 d 12.7 12.0 1,235 d 3,038 d 7.1 7.8Nicaragua 486 1,011 c 486 908 129 d 309 d 11.7 8.5 126 323 5.9 5.9Niger 50 .. .. .. 23 d .. 7.2 .. 32 .. 7.0 ..Nigeria 813 .. .. .. 186 738 0.9 1.0 610 8,379 5.1 11.1Norway 3,104 4,767 2,394 .. 2,521 5,083 3.2 2.9 4,893 13,971 9.9 11.9Oman 571 g 1,048 g .. .. 377 1,251 3.2 3.3 629 d 1,768 9.9 7.2Pakistan 557 .. .. .. 551 998 5.4 3.6 574 1,370 4.7 3.4Panama 484 1,324 216 392 628 2,552 8.0 13.9 241 575 3.0 2.9Papua New Guinea 58 .. 52 .. 7 d 2 d 0.3 0.0 50 d 138 2.8 2.2Paraguay 289 b 465 175 313 88 243 3.0 2.4 154 269 4.7 2.5Peru 800 2,299 730 2,058 861 2,741 10.1 6.9 641 1,646 6.6 4.7Philippines 1,992 c 3,520 c 1,670 .. 2,334 3,228 5.7 5.0 1,841 4,253 3.8 5.8Poland 17,400 12,470 56,677 42,760 6,128 9,986 13.2 5.0 3,417 9,100 6.0 4.4Portugal 5,599 f 6,756 f .. .. 6,027 12,969 17.7 18.0 2,754 4,691 5.8 5.4Puerto Rico 3,341 h 3,679 h 1,259 1,357 2,388 e 3,598 e .. .. 1,333 e 1,723 e .. ..Qatar 378 g,i 1,866 g,i .. .. 128 .. e .. .. 307 d,e .. .. ..2012 World Development Indicators 387

6.15 Travel and tourismInternational tourists Inbound tourism expenditure Outbound tourism expenditureRomania 5,264 a .. 6,388 .. 394 1,653 3.3 2.8 447 1,897 3.2 2.8Russian Federation 21,169 a 22,281 a 18,371 39,323 3,429 d 13,379 3.0 3.0 8,848 d 29,993 14.5 9.3Rwanda .. 666 .. .. 27 218 d 21.1 35.9 35 94 8.3 5.7Saudi Arabia 6,585 10,850 .. 7,233 .. 7,655 .. 2.9 .. 22,803 .. 13.1Senegal .. .. .. .. 152 .. 11.6 .. 125 .. 7.2 ..Serbia .. 683 .. .. .. 951 .. 7.1 .. 1,106 .. 5.6Sierra Leone 16 h 39 f,h 13 76 10 d 26 d 18.2 6.1 35 22 13.8 2.5Singapore 6,062 9,161 4,444 7,342 5,142 d 14,181 d 2.8 3.0 4,535 d 16,770 d 2.7 4.1Slovak Republic 1,053 f .. .. .. 441 2,335 3.1 3.3 341 2,146 2.3 3.0Slovenia 1,090 f 1,869 f 1,965 2,874 1,016 2,735 9.5 9.0 544 1,377 4.8 4.5Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 5,872 8,074 3,834 5,165 3,338 10,308 9.0 10.3 2,684 8,139 8.1 8.1South Sudan .. .. .. .. .. .. .. .. .. .. .. ..Spain 46,403 52,677 4,100 .. 32,656 58,810 19.4 15.6 7,710 22,800 4.1 5.7Sri Lanka 400 b 654 b 524 1,122 388 1,044 6.1 9.7 383 828 4.7 5.4Sudan 38 .. .. .. 5 d 94 d 0.3 0.8 55 d 1,116 d 2.7 10.0Swaziland 281 i 868 g .. 1,141 24 51 1.9 2.5 32 87 2.2 3.3Sweden 3,828 f 4,951 f 10,147 13,042 4,825 13,316 4.3 5.9 8,959 14,878 9.2 7.6Switzerland 7,821 g 8,628 g 12,240 .. 8,988 17,847 7.2 5.2 7,360 13,317 6.9 4.7Syrian Arab Republic 2,100 c,f 8,546 3,863 6,259 1,082 d 6,308 15.8 32.2 669 d 1,598 12.4 8.2Tajikistan .. .. 6 .. .. 32 .. 2.1 .. 18 d .. 0.5Tanzania 459 783 .. .. 381 1,279 28.0 20.0 369 861 18.0 9.6Thailand 9,579 c 15,936 1,909 5,451 9,935 23,407 12.2 10.3 3,218 6,582 4.5 3.2Timor-Leste .. 40 .. .. .. 21 .. .. .. 72 .. ..Togo 60 g .. .. .. 11 .. 2.6 .. 15 .. 2.5 ..Trinidad and Tobago 399 h .. .. .. 371 .. 7.7 .. 190 .. 5.1 ..Tunisia 5,058 b 6,903 b 1,632 2,250 1,977 3,477 23.0 15.6 310 611 3.3 2.5Turkey 9,586 27,000 5,284 11,002 7,636 d 24,784 15.2 15.9 1,713 d 5,451 2.8 2.8Turkmenistan 3 .. 78 .. .. .. .. .. .. .. .. ..Uganda 193 946 153 324 165 d 762 24.9 21.9 .. 389 .. 6.4Ukraine 6,431 21,203 13,422 17,180 563 4,696 2.9 6.8 561 4,134 3.1 5.6United Arab Emirates 3,907 c,i .. .. .. 1,063 e 8,577 e .. .. 3,019 e 11,818 e .. ..United Kingdom 23,212 28,295 56,837 55,562 29,978 39,945 7.4 6.0 47,009 60,291 10.8 8.2United States 51,238 59,791 61,327 .. 120,912 165,777 11.3 9.0 91,473 109,975 6.3 4.7Uruguay 1,968 2,353 667 1,027 827 1,607 22.6 15.2 381 534 9.1 5.5Uzbekistan 302 975 217 1,610 63 e 121 d,e .. .. .. .. .. ..Venezuela, RB 469 .. 954 .. 469 672 1.4 1.0 1,647 2,196 7.7 4.4Vietnam 2,140 a .. .. .. .. .. .. .. .. .. .. ..West Bank and Gaza 310 g 522 g .. .. 283 d .. 28.0 .. 316 .. 9.7 ..Yemen, Rep. 73 536 .. .. 73 d 622 d 1.8 6.7 127 252 3.9 2.3Zambia 457 815 .. .. 67 d 125 d 7.7 1.6 102 128 7.8 2.3Zimbabwe 1,967 a 2,239 a .. 650 125 e 634 e .. .. .. .. .. ..World 681,151 t 941,666 t 730,907 t .. t 572,408 t 1,111,919 t 7.4 w 5.9 w 531,843 t 996,273 t 6.8 w 5.4 wLow income 7,122 16,234 .. .. 3,304 11,469 11.0 11.2 2,837 7,086 6.0 4.3Middle income 196,996 360,007 179,453 370,724 112,754 304,678 7.2 5.5 82,078 261,770 5.6 4.8Lower middle income 39,368 98,178 45,422 86,323 26,019 77,617 7.7 6.8 17,992 65,963 5.0 4.7Upper middle income 157,416 261,779 124,379 .. 86,796 227,263 7.0 5.2 64,076 196,511 5.8 4.8Low & middle income 207,153 380,843 196,477 409,004 115,911 315,502 7.2 5.6 84,837 269,032 5.6 4.8East Asia & Pacifi c 62,673 117,829 51,581 .. 42,855 109,418 7.0 4.3 26,308 91,275 4.8 4.1Europe & Central Asia 52,830 106,138 59,247 114,685 16,274 60,832 6.5 6.1 15,983 56,988 7.2 5.9Latin America & Carib. 47,146 64,289 29,567 43,711 30,617 53,813 6.9 5.2 25,096 50,734 5.7 5.0Middle East & N. Africa 21,987 53,132 18,577 .. 12,902 48,730 12.9 20.9 5,237 23,279 5.5 6.8South Asia 4,839 9,330 7,137 20,157 5,152 18,026 6.0 4.3 5,333 17,625 5.0 3.3Sub-Saharan Africa 17,703 31,646 .. .. 8,000 24,873 7.2 7.0 7,133 32,518 6.9 6.9High income 470,103 558,474 481,718 .. 456,489 796,198 7.4 6.0 446,402 730,544 7.1 5.7Euro area 254,371 282,908 179,060 .. 181,607 307,946 8.4 6.2 155,713 275,992 7.1 5.8Note: Aggregates are based on World Bank country classifi cations and differ from those of the World Tourism Organization. Regional and income group totals include countries not shownin the table for which data are available.a. Arrivals of nonresident visitors at national borders. b. Excludes nationals residing abroad. c. Includes nationals residing abroad. d. Expenditure of travel- related items only; excludes passengertransport items. e. Data are from national sources. f. Arrivals in all types of accommodation establishments. g. Arrivals in hotels and similar establishments. h. Arrivals in hotels only. i. Arrivals by air only.3882012 World Development IndicatorsthousandsInbound Outbound $ millions % of exports $ millions % of imports2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010

Travel and tourism6.15GLOBAL LINKSAbout the dataDefinitionsTourism is defi ned as the activities of people travelingto and staying in places outside their usualenvironment for no more than one year for leisure,business, and other purposes not related to an activityremunerated from within the place visited. Thesocial and economic phenomenon of tourism hasgrown substantially over the past quarter century.Statistical information on tourism is based mainlyon data on arrivals and overnight stays along withbalance of payments information. These data do notcompletely capture the economic phenomenon oftourism or provide the information needed for effectivepublic policies and effi cient business operations.Data are needed on the scale and signifi cance oftourism. Information on the role of tourism in nationaleconomies is particularly defi cient. Although theWorld Tourism Organization reports progress in harmonizingdefi nitions and measurement, differencesin national practices still prevent full comparability.The usual environment of an individual is a keyconcept in tourism statistics and is defi ned as thegeographical area within which an individual conductsregular life routines. This concept excludestravelers who commute regularly between their placeof usual residence and place of work or study orwho frequently visit places within their current liferoutine—for instance, homes of friends or relatives;shopping centers; and religious, health care, or otherfacilities a substantial distance away or in a differentadministrative area.The data in the table are from the World TourismOrganization, a United Nations agency. The data oninbound and outbound tourists refer to the number ofarrivals and departures, not to the number of peopletraveling. Thus a person who makes several trips toa country during a given period is counted each timeas a new arrival. Unless otherwise indicated in thefootnotes, the data on inbound tourism show thearrivals of nonresident tourists (overnight visitors)at national borders. When data on international touristsare unavailable or incomplete, the table showsthe arrivals of international visitors, which includetourists, same-day visitors, cruise passengers, andcrew members.Sources and collection methods for arrivals differacross countries. In some cases data are from borderstatistics (police, immigration, and the like) andsupplemented by border surveys. In other cases dataare from tourism accommodation establishments.For some countries number of arrivals is limited toarrivals by air and for others to arrivals staying inhotels. Some countries include arrivals of nationalsresiding abroad while others do not. Caution shouldthus be used in comparing arrivals across countries.The World Tourism Organization is improving itscoverage of tourism expenditure data, using balanceof payments data from the International MonetaryFund (IMF) supplemented by data from individualcountries. These data, shown in the table, includetravel and passenger transport items as defi ned inthe IMF’s Balance of Payments Manual, 5th edition.When the IMF does not report data on passengertransport items, expenditure data for travel itemsare shown.Tourism can be either domestic or international.The table shows data relevant to international tourism,where the traveler’s country of residence differsfrom the visiting country. International tourism consistsof inbound and outbound tourism.The aggregates are calculated using the WorldBank’s weighted aggregation methodology (see Statisticalmethods) and differ from the World TourismOrganization’s aggregates.• International inbound tourists (overnight visitors)are tourists who travel to a country other than thatin which they usually reside, and outside their usualenvironment, for a period not exceeding 12 monthsand whose main purpose in visiting is other thanan activity remunerated in the country visited. Whennumber of tourists are not available, data on visitors,which include tourists, same-day visitors, cruise passengers,and crew members, are shown. • Internationaloutbound tourists are departures that peoplemake from their country of usual residence to anyother country for any purpose other than an activityremunerated in the country visited. • Inbound tourismexpenditure is expenditures by internationalinbound visitors, including payments to nationalcarriers for international transport and any other prepaymentmade for goods or services received in thedestination country. They may include receipts fromsame-day visitors, except when these are importantenough to justify separate classifi cation. For somecountries they do not include receipts for passengertransport items. Their share in exports is calculatedas a ratio to exports of goods and services (all transactionsbetween residents of a country and the restof the world involving a change of ownership fromresidents to nonresidents of general merchandise,goods sent for processing and repairs, nonmonetarygold, and services). • Outbound tourism expenditureis expenditures of international outbound visitors inother countries, including payments to foreign carriersfor international transport. These expendituresmay include those by residents traveling abroad assame-day visitors, except when these are importantenough to justify separate classifi cation. For somecountries they do not include expenditures for passengertransport items. Their share in imports is calculatedas a ratio to imports of goods and services(all transactions between residents of a country andthe rest of the world involving a change of ownershipfrom nonresidents to residents of general merchandise,goods sent for processing and repairs, nonmonetarygold, and services).Data sourcesData on visitors and tourism expenditure are fromthe World Tourism Organization’s Yearbook of TourismStatistics and Compendium of Tourism Statistics2011. Data in the table are updated from electronicfi les provided by the World Tourism Organization.Data on exports and imports are from the IMF’s Balanceof Payments Statistics Yearbook and data files.2012 World Development Indicators 389Text fi gures, tables,and boxes

PRIMARY DATA DOCUMENTATIONAs a major user of socioeconomic data, the World Bank recognizes the importanceof data documentation to inform users of differences in the methods andconventions used by primary data collectors—usually national statistical agencies,central banks, and customs services—and by international organizations,which compile the statistics that appear in the World Development Indicatorsdatabase. These differences may give rise to signifi cant discrepancies over timeboth within countries and across them. Delays in reporting data and the use ofold surveys as the base for current estimates may further compromise the qualityof data reported here.The tables in this section provide information on sources, methods, andreporting standards of the principal demographic, economic, and environmentalindicators in World Development Indicators. Additional documentation is availablefrom the World Bank’s Bulletin Board on Statistical Capacity at http://data.worldbank.org.The demand for good-quality statistical data is ever increasing. Statisticsprovide the evidence needed to improve decisionmaking, document results,and heighten public accountability. The need for improved statistics to monitorthe Millennium Development Goals and the parallel effort to support a cultureof results-based management has stimulated a decade-long effort to improvestatistics. The results has been impressive, but more needs to done.Thus a new action plan for statistics, “Statistics for Transparency, Accountability,and Results: A Busan Action Plan for Statistics,” was endorsed by theBusan Partnership for Effective Development Cooperation at the Fourth High-levelForum for Aid Effectiveness held November 29–December 1, 2011, in Busan,Republic of Korea. This new action plan builds on the progress made under thefi rst global plan to improve national and international statistics, the 2004 MarrakechAction Plan for Statistics from 2004, but goes beyond it in many ways. Themain objectives of the new plan are to integrate statistics into decisionmaking,promote open access to statistics within government and for all other uses, andincrease resources for statistical systems, both for investment in new capacityand for maintaining c urrent operations.2012 World Development Indicators 391

PRIMARY DATA DOCUMENTATIONCurrencyNationalaccountsBalance of paymentsand tradeGovernment IMF datafinance disseminationstandardBaseyearReferenceyearSystem ofNationalAccountsSNApricevaluationAlternativeconversionfactorPPPsurveyyearBalance ofPaymentsManualin useExternaldebtSystemof tradeAccountingconceptAfghanistan Afghan afghani 2002/03 VAB Actual G C GAlbania Albanian lek a 1996 b VAB 2005 BPM5 Actual G C GAlgeria Algerian dinar 1980 VAB BPM5 Actual S B GAmerican Samoa U.S. dollar SAndorra Euro SAngola Angolan kwanza 1997 VAP 1991–96 2005 BPM5 Actual S GAntigua and Barbuda East Caribbean dollar 2006 VAB BPM5 G GArgentina Argentine peso 1993 b VAB 1971–84 2005 BPM5 Actual S C SArmenia Armenian dram a 1996 b VAB 1990–95 2005 BPM5 Actual S C SAruba Aruban fl orin 1995 BPM5 SAustralia Australian dollar a 2009 b VAB 2005 BPM5 G C SAustria Euro 2005 b VAB 2005 BPM5 S C SAzerbaijan New Azeri manat a 2003 b VAB 1992–95 2005 BPM5 Actual G B GBahamas, The Bahamian dollar 2006 b VAB BPM5 G B GBahrain Bahraini dinar 1985 VAP 2005 BPM5 G B GBangladesh Bangladeshi taka 1995/96 b VAB 2005 BPM5 Actual G C GBarbados Barbados dollar 1974 VAB BPM5 G B GBelarus Belarusian rubel a 2000 b VAB 1990–95 2005 BPM5 Actual G C SBelgium Euro 2005 b VAB 2005 BPM5 S C SBelize Belize dollar 2000 b VAB BPM5 Actual G B GBenin CFA franc 1985 VAP 1992 2005 BPM5 Actual S B GBermuda Bermuda dollar 1996 VAB BPM5 GBhutan Bhutanese ngultrum 2000 b VAB 2005 BPM5 Actual G C GBolivia Bolivian Boliviano 1990 b VAB 1960–85 2005 BPM5 Actual G C GBosnia and Herzegovina Bosnia and Herzegovinaa 1996 b VAB 2005 BPM5 Actual S Cconvertible markBotswana Botswana pula 1993/94 b VAB 2005 BPM5 Actual G B GBrazil Brazilian real 2000 b VAB 2005 BPM5 Actual G C SBrunei Darussalam Brunei dollar 2000 VAP 2005 S GBulgaria Bulgarian lev a 2002 b VAB 1978–89, 2005 BPM5 Actual S C S1991–92Burkina Faso CFA franc 1999 VAB 1992–93 2005 BPM5 Actual G B GBurundi Burundi franc 1980 VAB 2005 BPM5 Preliminary S C GCambodia Cambodian riel 2000 VAB 2005 BPM5 Actual S C GCameroon CFA franc 2000 b VAB 2005 BPM5 Actual S B GCanada Canadian dollar 2005 b VAB 2005 BPM5 G C SCape Verde Cape Verde escudo 1980 VAP 2005 BPM5 Actual G C GCayman Islands Cayman Islands dollar GCentral African Republic CFA franc 2000 VAB 2005 BPM5 Preliminary S B GChad CFA franc 1995 b VAB 2005 BPM5 Actual S GChannel Islands Pound sterling 2007, 2007 b VAB2003Chile Chilean peso 2003 b VAB 2005 BPM5 Actual S C SChina Chinese yuan 2000 b VAP 1978–93 2005 BPM5 Preliminary S B GHong Kong SAR, China Hong Kong dollar 2009 b VAB 2005 BPM5 G C SMacao SAR, China Macao pataca 2009 VAB 2005 BPM5 G C GColombia Colombian peso 2005 b VAB 1992–94 2005 BPM5 Actual G B SComoros Comorian franc 1990 VAP 2005 Actual SCongo, Dem. Rep. Congolese franc 1987 VAB 1999–2001 2005 BPM4 Estimate S C GCongo, Rep. CFA franc 1978 VAP 1993 2005 BPM5 Estimate S C GCosta Rica Costa Rican colon 1991 b VAB BPM5 Actual S C SCôte d’Ivoire CFA franc 1996 VAP 2005 BPM5 Estimate S C GCroatia Croatian kuna a 2000 b VAB 2005 BPM5 G C SCuba Cuban peso 1990 VAB SCuraçaoNetherlandsAntilles guilder3922012 World Development Indicators

PRIMARY DATA DOCUMENTATIONLatestpopulationcensusLatest demographic,education, or healthhousehold surveySource of mostrecent incomeand expenditure dataVital Latestregistration agriculturalcomplete censusLatestindustrialdataLatesttradedataLatestwaterwithdrawaldataAfghanistan 1979 MICS, 2003; SpecialIHS, 2008 2010 2010 2000Survey, 2010Albania 2001 DHS, 2008/09 LSMS, 2008 Yes 1998 2010 2010 2000Algeria 2008 MICS, 2006 IHS, 1995 2001 2009 2010 2000American Samoa 2010 Yes 2007 2009Andorra 1989 Yes 2006Angola 1970 MICS, 2001; MIS,IHS, 2000 1964–65 2010 1991 20002006/07Antigua and Barbuda 2011 Yes 2007 2010 2010 1990Argentina 2010 IHS, 2010 Yes 2007 2010 2010 2000Armenia 2001 DHS, 2005 IHS, 2010 Yes 2010 2010 2006Aruba 2010 Yes 2010Australia 2006 ES/BS, 1994 Yes 2011 2010 2010 2000Austria 2011 IS, 2000 Yes 2010 2010 2010 2000Azerbaijan 2009 DHS, 2006 ES/BS, 2008 Yes 2010 2010 2005Bahamas, The 2010 2006 2010Bahrain 2010 Yes 1995 2010 2003Bangladesh 2011 DHS, 2007 IHS, 2010 2008 2010 2007 2008Barbados 2010 Yes 2005 2010 2000Belarus 2009 MICS, 2005 ES/BS, 2009 Yes 1994 2010 2010 2000Belgium 2011 IHS, 2000 Yes 1999–2000 2009 2010 2007Belize 2010 MICS, 2006 ES/BS, 1999 2008 2010 2000Benin 2002 DHS, 2006 CWIQ, 2003 2011–12 2005 2006 2001Bermuda 2010 Yes 2009Bhutan 2005 MICS, 2010 IHS, 2007 2008 2009 2010 2008Bolivia 2001 DHS, 2008 IHS, 2008 2008 2010 2010 2000Bosnia and Herzegovina 1991 MICS, 2006 LSMS, 2007 Yes 2010 2010 2009Botswana 2011 MICS, 2000 ES/BS, 2003 1993 2010 2010 2000Brazil 2010 DHS, 1996 LFS, 2009 2006 2010 2011 2006Brunei Darussalam 2001 Yes 2008 2006 1994Bulgaria 2011 ES/BS, 2007 Yes 2010 2010 2010 2009Burkina Faso 2006 MICS, 2006 CWIQ, 2009 2010 2006 2010 2000Burundi 2008 MICS, 2005 CWIQ, 2006 2005 2010 2000Cambodia 2008 DHS, 2010 IHS, 2008 2012 2010 2010 2006Cameroon 2005 MICS, 2006 PS, 2007 1984 2007 2010 2000Canada 2011 LFS, 2000 Yes 2011 2007 2010 2000Cape Verde 2010 DHS, 2005 ES/BS, 2007 Yes 2004 2009 2010Cayman Islands 2010 YesCentral African Republic 2003 MICS, 2006 PS, 2008 1985 2006 2009 2000Chad 2009 DHS, 2004 PS, 2002/03 2011 2008 1995 2000Channel Islands 2001Chile 2002 IHS, 2009 Yes 2007 2010 2010 2000China 2010 NSS, 2007 IHS, 2008 2007 2010 2010 2005Hong Kong SAR, China 2006 Yes 2010 2010Macao SAR, China 2011 Yes 2009 2010Colombia 2006 DHS, 2010 IHS, 2010 2001 2010 2010 2000Comoros 2003 MICS, 2000 IHS, 2004 2009 2007 1999Congo, Dem. Rep. 1984 MICS, 2010 1–2-3, 2005/06 1990 2009 1986 2000Congo, Rep. 2007 AIS, 2009; DHS, 2005 CWIQ/PS, 2005 1985–86 2010 2005 2002Costa Rica 2011 RHS, 1993 LFS, 2010 Yes 1973 2010 2010 2000Côte d’Ivoire 1998 MICS, 2006 IHS, 2008 2001 2010 2010 2000Croatia 2011 ES/BS, 2008 Yes 2003 2010 2011 2009Cuba 2002 MICS, 2006 Yes 2008 2006 2000Curaçao2012 World Development Indicators 393

PRIMARY DATA DOCUMENTATIONCurrencyNationalaccountsBalance of paymentsand tradeGovernment IMF datafinance disseminationstandardBaseyearReferenceyearSystem ofNationalAccountsSNApricevaluationAlternativeconversionfactorPPPsurveyyearBalance ofPaymentsManualin useExternaldebtSystemof tradeAccountingconceptCyprus Euro a 2000 VAB 2005 BPM5 G C SCzech Republic Czech koruna 2000 1995 b VAB 2005 BPM5 S C SDenmark Danish krone 2005 b VAB 2005 BPM5 S C SDjibouti Djibouti franc 1990 VAB 2005 BPM5 Actual G GDominica East Caribbean dollar 2006 b VAB BPM5 Actual S GDominican Republic Dominican peso 1991 VAB BPM5 Actual G C GEcuador U.S. dollar 2000 b VAB 2005 BPM5 Actual G B SEgypt, Arab Rep. Egyptian pound 1991/92 VAB 2005 BPM5 Actual G C SEl Salvador U.S. dollar 1990 VAB BPM5 Actual S C SEquatorial Guinea CFA franc 2000 VAB 1965–84 2005 GEritrea Eritrean nakfa 1992 VAB BPM4 ActualEstonia Estonian kroon 2000 b VAB 1987–95 2005 BPM5 S C SEthiopia Ethiopian birr 1999/2000 b VAB 2005 BPM5 Actual G B GFaeroe Islands Danish krone VAB BPM5 GFiji Fijian dollar 2005 VAB 2005 BPM5 Actual G B GFinland Euro 2005 b VAB 2005 BPM5 G C SFrance Euro a 2005 b VAB 2005 BPM5 S C SFrench Polynesia CFP franc SGabon CFA franc 1991 VAP 1993 2005 BPM5 Actual S GGambia, The Gambian dalasi 1987 VAB 2005 BPM5 Actual G C GGeorgia Georgian lari a 1996 b VAB 1990–95 2005 BPM5 Actual G C SGermany Euro 2005 b VAB 2005 BPM5 S C SGhana New Ghanaian cedi 2006 VAB 1973–87 2005 BPM5 Actual G B GGibraltarGibraltar poundGreece Euro a 2005 VAB 2005 BPM5 S C SGreenland Danish krone GGrenada East Caribbean dollar 2006 VAB BPM5 Actual S B GGuam U.S. dollar GGuatemala Guatemalan quetzal 2001 b VAB BPM5 Actual S B GGuinea Guinean franc 1996 VAB 2005 BPM5 Estimate S B GGuinea-Bissau CFA franc 2005 VAB 2005 BPM5 Estimate G GGuyana Guyana dollar 2006 VAB BPM5 Actual S GHaiti Haitian gourde 1986/87 VAB 1991 BPM5 Actual G GHonduras Honduran lempira 2000 b VAB 1988–89 BPM5 Actual S C GHungary Hungarian forint a 2005 b VAB 2005 BPM5 S C SIceland Iceland krona 2005 VAB 2005 BPM5 G C SIndia Indian rupee 2004/05 b VAB 2005 BPM5 Actual G C SIndonesia Indonesian rupiah 2000 VAP 2005 BPM5 Actual S B SIran, Islamic Rep. Iranian rial 1997/98 VAB 1980–2002 2005 BPM4 Actual S CIraq Iraqi dinar 1997 VAB 1997, 2004 2005 BPM5 GIreland Euro 2005 b VAB 2005 BPM5 G C SIsle of Man Pound sterling 2005 2003Israel Israeli new shekel 2005 b VAP 2005 BPM5 S C SItaly Euro 2005 b VAB 2005 BPM5 S C SJamaica Jamaican dollar 2003 VAB BPM5 Actual G C GJapan Japanese yen 2005 VAB 2005 BPM5 G C SJordan Jordanian dinar 1994 VAB 2005 BPM5 Actual G B SKazakhstan Kazakh tenge a 2000 b VAB 1987–95 2005 BPM5 Actual G C SKenya Kenyan shilling 2001 b VAB 2005 BPM5 Actual G B GKiribati Australian dollar 2006 VAB BPM5 G GKorea, Dem. Rep. Democratic People’sBPM4Republic of Korean wonKorea, Rep. Korean won 2005 b VAB 2005 BPM5 G C SKosovo Euro Actual GKuwait Kuwaiti dinar 1995 VAP 2005 BPM5 S B GKyrgyz Republic Kyrgyz som a 1995 b VAB 1990–95 2005 BPM5 Actual S B SLao PDR Lao kip 1990 VAB 2005 BPM5 Preliminary S BLatvia Latvian lats 2000 b VAB 1987–95 2005 BPM5 Actual S C SLebanon Lebanese pound 1997 VAB 2005 BPM5 Actual G B G3942012 World Development Indicators

PRIMARY DATA DOCUMENTATIONLatestpopulationcensusLatest demographic,education, or healthhousehold surveySource of mostrecent incomeand expenditure dataVital Latestregistration agriculturalcomplete censusLatestindustrialdataLatesttradedataLatestwaterwithdrawaldataCyprus 2001 Yes 2008 2010 2009Czech Republic 2011 RHS, 1993 IS, 1996 Yes 2010 2010 2010 2007Denmark 2011 ITR, 1997 Yes 2010 2010 2010 2009Djibouti 2009 MICS, 2006 PS, 2002 2007 2009 2000Dominica 2011 Yes 2010 2010 2004Dominican Republic 2010 DHS, 2007 IHS, 2010 1971 2010 2010 2000Ecuador 2010 RHS, 2004 LFS, 2010 2012 2010 2010 2000Egypt, Arab Rep. 2006 DHS, 2008 ES/BS, 2008 Yes 2010 2010 2010 2000El Salvador 2007 RHS, 2008 IHS, 2009 Yes 2007–08 2010 2010 2000Equatorial Guinea 2002 2009 2000Eritrea 1984 DHS, 2002 2009 2005 2004Estonia 2000 ES/BS, 2004 Yes 2010 2009 2011 2007Ethiopia 2007 DHS, 2005 ES/BS, 2005 2001–02 2010 2011 2002Faeroe Islands 2011 Yes 2009Fiji 2007 ES/BS, 2009 Yes 2009 2010 2010 2000Finland 2010 IS, 2000 Yes 2010 2010 2010 2005France 2006 c ES/BS, 1994/95 Yes 2010 2009 2010 2007French Polynesia 2007 Yes 2010Gabon 2003 DHS, 2000 CWIQ/IHS, 2005 1974–75 2010 2010 2000Gambia, The 2003 MICS, 2005/06 IHS, 2003 2001–02 2010 2010 2000Georgia 2002 MICS, 2005; RHS, 2005 IHS, 2009 Yes 2004 2010 2010 2005Germany 2011 IHS, 2000 Yes 2010 2010 2010 2007Ghana 2010 DHS, 2008 LSMS, 2006 2011 2010 2010 2000Gibraltar 2001 YesGreece 2011 IHS, 2000 Yes 2009 2009 2010 2007Greenland 2010 Yes 2007Grenada 2011 Yes 2011 2010 2009 2005Guam 2010 YesGuatemala 2002 RHS, 2002 LSMS, 2006 Yes 2008 2010 2010 2000Guinea 1996 DHS, 2005 CWIQ, 2007 2000–01 2010 2008 2000Guinea-Bissau 2009 MICS, 2010 CWIQ, 2002 1988 2002 2005 2000Guyana 2002 DHS, 2009 IHS, 1998 2010 2010 2000Haiti 2003 DHS, 2005/06 IHS, 2001 2009 1997 2000Honduras 2001 DHS, 2005/06 IHS, 2009 2013 2010 2009 2000Hungary 2001 ES/BS, 2007 Yes 2007 2010 2010 2007Iceland d Yes 2010 2009 2010 2005India 2011 DHS, 2005/06 IHS, 2010 2011 2010 2010 2010Indonesia 2010 DHS, 2007 IHS, 2011 2003 2010 2010 2000Iran, Islamic Rep. 2006 DHS, 2000 ES/BS, 2005 Yes 2003 2007 2010 2004Iraq 1997 MICS, 2006 IHS, 2007 2011 2003 2009 2000Ireland 2011 IHS, 2000 Yes 2010 2009 2010 2000Isle of Man 2006 YesIsrael 2009 ES/BS, 2001 Yes 1981 2010 2004Italy 2012 ES/BS, 2000 Yes 2010 2010 2010 2000Jamaica 2011 MICS, 2005 LSMS, 2007 2007 2010 2010 2000Japan 2010 IS, 1993 Yes 2010 2009 2011 2001Jordan 2004 DHS, 2009 ES/BS, 2010 2007 2010 2010 2005Kazakhstan 2009 MICS, 2006 ES/BS, 2009 Yes 2010 2009 2000Kenya 2009 DHS, 2008/09; MIS, 2010 IHS, 2005/06 1977–79 2010 2010 2003Kiribati 2005 2009 2009Korea, Dem. Rep. 2009 MICS, 2010 2005Korea, Rep. 2010 ES/BS, 1998 Yes 2000 2010 2010 2002Kosovo 1981 IHS, 2009 2010Kuwait 2010 FHS, 1996 Yes 1970 2003 2009 2002Kyrgyz Republic 2009 MICS, 2005/06 ES/BS, 2010 Yes 2002 2010 2010 2000Lao PDR 2005 MICS, 2006 ES/BS, 2008 2010–11 2010 1975 2005Latvia 2011 IHS, 2008 Yes 2010 2010 2010 2000Lebanon 1970 MICS, 2000 Yes 2010 2010 2010 20052012 World Development Indicators 395

PRIMARY DATA DOCUMENTATIONCurrencyNationalaccountsBalance of paymentsand tradeGovernment IMF datafinance disseminationstandardBaseyearReferenceyearSystem ofNationalAccountsSNApricevaluationAlternativeconversionfactorPPPsurveyyearBalance ofPaymentsManualin useExternaldebtSystemof tradeAccountingconceptLesotho Lesotho loti 1995 b VAB 2005 BPM5 Actual G C GLiberia Liberian dollar 1992 VAP 2005 BPM5 Actual S B GLibya Libyan dinar 1999 VAB 1986 BPM5 G GLiechtenstein Swiss franc VAB SLithuania Lithuanian litas 2000 b VAB 1990–95 2005 BPM5 Actual G C SLuxembourg Euro 2005 VAB 2005 BPM5 S C SMacedonia, FYR Macedonian denar 1997 1995 b VAB 2005 BPM5 Actual S SMadagascar Malagasy ariary 1984 VAB 2005 BPM5 Actual S C GMalawi Malawi kwacha 1994 VAB 2005 BPM5 Actual G GMalaysia Malaysian ringgit 2000 VAP 2005 BPM5 Estimate G B SMaldives Maldivian rufi yaa 2003 VAB 2005 BPM5 Actual G C GMali CFA franc 1987 VAB 2005 BPM5 Actual S B GMalta Euro 2005 VAB 2005 BPM5 G C SMarshall Islands U.S. dollar 2004 VAB GMauritania Mauritanian ouguiya 2004 VAB 2005 BPM4 Actual S GMauritius Mauritian rupee 2006 VAB 2005 BPM5 Actual G C GMayotte Euro GMexico Mexican peso 2003 b VAB 2005 BPM5 Actual G C SMicronesia, Fed. Sts. U.S. dollar 2004 VABMoldova Moldovan leu a 1996 b VAB 1990–95 2005 BPM5 Actual G C SMonaco Euro SMongolia Mongolian tugrik 2005 b VAB 2005 BPM5 Actual G C GMontenegro Euro 2000 b VAB 2005 BPM5 Actual S GMorocco Moroccan dirham 1998 VAB 2005 BPM5 Actual S C SMozambiqueNew Mozambican2003 VAB 1992–95 2005 BPM5 Actual S GmeticalMyanmar Myanmar kyat 2005/06 VAP BPM5 Estimate G CNamibia Namibian dollar 2004/05 b VAB 2005 BPM5 G B GNepal Nepalese rupee 2000/01 VAB 2005 BPM5 Actual G C GNetherlands Euro a 2005 b VAB 2005 BPM5 S C SNew Caledonia CFP franc SNew Zealand New Zealand dollar 2005/06 VAB 2005 BPM5 G CNicaraguaNicaraguan gold1994 b VAB 1965–95 BPM5 Actual G B GcordobaNiger CFA franc 1987 VAP 1993 2005 BPM5 Actual S B GNigeria Nigerian naira 2002 VAB 1971–98 2005 BPM5 Actual G B GNorthern Mariana Islands U.S. dollarNorway Norwegian krone a 2005 b VAB 2005 BPM5 G C SOman Rial Omani 1988 VAP 2005 BPM5 G B GPakistan Pakistani rupee 1999/2000 b VAB 2005 BPM5 Actual G B GPalau U.S. dollar 1995 VAB SPanama Panamanian balboa 1996 b VAB BPM5 Actual S C GPapua New Guinea Papua New Guinea kina 1998 VAB 1989 BPM5 Actual G B GParaguay Paraguayan guarani 1994 VAP 2005 BPM5 Actual S B GPeru Peruvian new sol 1994 VAB 1985–90 2005 BPM5 Actual S C SPhilippines Philippine peso 2000 VAP 2005 BPM5 Actual G B SPoland Polish zloty a 2005 b VAB 2005 BPM5 S C SPortugal Euro 2005 b VAB 2005 BPM5 S C SPuerto Rico U.S. dollar 1954 VAP GQatar Qatari riyal 2001 VAP 2005 S B GRomania New Romanian leu a 2005 b VAB 1987–89, 2005 BPM5 Actual S C S1992Russian Federation Russian ruble 2000 b VAB 1987–95 2005 BPM5 Preliminary G C SRwanda Rwandan franc 1995 VAP 1994 2005 BPM5 Actual G C GSamoa Samoan tala 2002 VAB BPM5 Actual SSan Marino Euro 1995 2000 b VAB C GSão Tomé and Príncipe São Tomé andPríncipe dobra2001 VAP 2005 BPM4 Actual S G3962012 World Development Indicators

PRIMARY DATA DOCUMENTATIONLatestpopulationcensusLatest demographic,education, or healthhousehold surveySource of mostrecent incomeand expenditure dataVital Latestregistration agriculturalcomplete censusLatestindustrialdataLatesttradedataLatestwaterwithdrawaldataLesotho 2006 DHS, 2009/10 ES/BS, 2002/03 2010 2010 2008 2000Liberia 2008 DHS, 2007; MIS, 2009 CWIQ, 2007 2008 1985 2000Libya 2006 MICS, 2000 2001 2008 2004 2000Liechtenstein 2010 YesLithuania 2011 ES/BS, 2008 Yes 2010 2009 2010 2007Luxembourg 2011 Yes 2010 2010 2010 1999Macedonia, FYR 2010 MICS, 2005 ES/BS, 2009 Yes 2007 2010 2009 2007Madagascar 1993 DHS, 2008/09 PS, 2010 2004 2009 2010 2000Malawi 2008 DHS, 2010 LSMS, 2004/05 2006–07 2010 2010 2000Malaysia 2010 ES/BS, 2009 Yes 2012 2010 2010 2005Maldives 2011 DHS, 2009 IHS, 2004 Yes 2010 2010 2008Mali 2009 DHS, 2006; Special, 2010 IHS, 2010 2007 2010 2000Malta 2005 Yes 2010 2009 2010 2002Marshall Islands 2011Mauritania 2000 MICS, 2007 IHS, 2008 2010 2010 2000Mauritius 2011 Yes 2010 2010 2003Mayotte 2007 Yes 2009Mexico 2010 ENPF, 1995 LFS, 2010 2007 2010 2010 2008Micronesia, Fed. Sts. 2000 IHS, 2000 1983Moldova 2004 DHS, 2005 ES/BS, 2010 Yes 2011 2010 2010 2000Monaco 2008 YesMongolia 2010 MICS, 2010 LSMS, 2007/08 Yes 2011 2010 2007 2005Montenegro 2011 MICS, 2005/06 ES/BS, 2008 Yes 2010 2010Morocco 2004 MICS, 2006 ES/BS, 2007 2012 2010 2010 2000Mozambique 2007 DHS, 2003; AIS, 2009 ES/BS, 2008 2009–10 2010 2010 2000Myanmar 1983 MICS, 2000 2010–11 2010 2001 2000Namibia 2001 DHS, 2006/07;ES/BS, 2004 1996–97 2010 2008 2000HIV/MCH SPA, 2009Nepal 2001 MICS, 2010 LSMS, 2010 2011–12 2010 2010 2005Netherlands 2011 IHS, 1999 Yes 2010 2010 2010 2008New Caledonia 2009 Yes 1997 2010New Zealand 2006 IS, 1997 Yes 2012 2006 2010 2002Nicaragua 2005 RHS, 2006/07 LSMS, 2005 2011 2010 2010 2000Niger 2001 DHS, 2006 CWIQ/PS, 2008 2005–07 2003 2010 2000Nigeria 2006 DHS, 2008 IHS, 2010 2007 2006 2010 2000Northern Mariana Islands 2010Norway 2001 IS, 2000 Yes 2010 2010 2010 2006Oman 2010 FHS, 1995 1978–79 2004 2010 2003Pakistan 1998 MICS, 2010 IHS, 2008 2000 2010 2010 2008Palau 2010 Yes 2007Panama 2010 LSMS, 2003 LFS, 2010 2011 2010 2010 2000Papua New Guinea 2000 DHS, 1996 IHS, 1996 2010 2005 2005Paraguay 2002 RHS, 2004 IHS, 2010 2008 2010 2011 2000Peru 2007 DHS, 2007/08 IHS, 2010 2012 2010 2010 2000Philippines 2010 DHS, 2008 ES/BS, 2009 Yes 2012 2010 2010 2009Poland 2011 ES/BS, 2009 Yes 2010 2010 2010 2009Portugal 2011 IS, 1997 Yes 2009 2010 2010 2002Puerto Rico 2010 RHS, 1995/96 Yes 2007 2010 2005Qatar 2010 Yes 2000–01 2009 2005Romania 2011 RHS, 1999 LFS, 2009 Yes 2010 2010 2010 2009Russian Federation 2010 RHS, 1996 IHS, 2009 Yes 2006 2010 2010 2001Rwanda 2002 DHS, 2007/08 IHS, 2011 2008 2009 2010 2000Samoa 2006 DHS, 2009 2009 2010 2010San Marino 2010 YesSão Tomé and Príncipe 2001 DHS, 2008/09 PS, 2000/01 2011 2005 2010 19932012 World Development Indicators 397

PRIMARY DATA DOCUMENTATIONCurrencyNationalaccountsBalance of paymentsand tradeGovernment IMF datafinance disseminationstandardBaseyearReferenceyearSystem ofNationalAccountsSNApricevaluationAlternativeconversionfactorPPPsurveyyearBalance ofPaymentsManualin useExternaldebtSystemof tradeAccountingconceptSaudi Arabia Saudi Arabian riyal 1999 VAP 2005 BPM5 S GSenegal CFA franc 1999 1987 b VAB 2005 BPM5 Actual G B GSerbia New Serbian dinar a 2002 b VAB 2005 BPM5 Actual S C GSeychelles Seychelles rupee 1986 VAP BPM5 Actual G C GSierra Leone Sierra Leonean leone 1990 b VAB 2005 BPM5 Actual S B GSingapore Singapore dollar 2005 b VAB 2005 BPM5 G C SSint MaartenNetherlandsAntilles guilderSlovak Republic Euro 2005 b VAB 2005 BPM5 S C SSlovenia Euro a 2005 b VAB 2005 BPM5 S C SSolomon Islands Solomon Islands dollar 2004 VAB BPM5 Actual S GSomalia Somali shilling 1985 VAB 1977–90 EstimateSouth Africa South African rand 2005 b VAB 2005 BPM5 Preliminary G C SSouth SudanSouth Sudanese PoundSpain Euro 2005 b VAB 2005 BPM5 S C SSri Lanka Sri Lankan rupee 2002 VAP 2005 BPM5 Actual G B GSt. Kitts and Nevis East Caribbean dollar 2006 b VAB BPM5 Actual S C GSt. Lucia East Caribbean dollar 2006 VAB BPM5 Actual S GSt. MartinEuroSt. Vincent & Grenadines East Caribbean dollar 2006 VAB BPM5 Actual S B GSudan Sudanese pound 1981/82 e 1996 VAB 2005 BPM5 Actual G B GSuriname Suriname dollar 1990 b VAB BPM5 G GSwaziland Swaziland lilangeni 2000 VAB 2005 BPM5 Actual G B GSweden Swedish krona a 2005 VAB 2005 BPM5 G C SSwitzerland Swiss franc 2005 VAB 2005 BPM5 S C SSyrian Arab Republic Syrian pound 2000 VAB 1970–2010 2005 BPM5 Actual S C GTajikistan Tajik somoni a 2000 b VAB 1990–95 2005 BPM5 Actual G C GTanzania Tanzanian shilling a 2001 VAB 2005 BPM5 Actual G GThailand Thai baht 1988 VAP 2005 BPM5 Actual S C STimor-Leste U.S. dollar 2000 VAP GTogo CFA franc 1978 VAP 2005 BPM5 Actual S B GTonga Tongan pa’anga 2000/01 VAB BPM5 Actual G GTrinidad and Tobago Trinidad and2000 b VAB BPM5 S C GTobago dollarTunisia Tunisian dinar 1990 VAB 2005 BPM5 Actual G C STurkey New Turkish lira 1998 VAB 2005 BPM5 Actual S B STurkmenistan New Turkmen manat a 2007 b VAB 1987–95,BPM4 Estimate G1997–2007Turks and Caicos Islands U.S. dollar GTuvalu Australian dollar VAP GUganda Ugandan shilling 2001/02 VAB 2005 BPM5 Actual G B GUkraine Ukrainian hryvnia a 2003 b VAB 1987–95 2005 BPM5 Actual G C SUnited Arab Emirates U.A.E. dirham 2007 VAP BPM5 G B GUnited Kingdom Pound sterling 2005 b VAB 2005 BPM5 G C SUnited States U.S. dollar a 2005 VAB 2005 BPM5 G C SUruguay Uruguayan peso 2005 VAB 2005 BPM5 Actual G C SUzbekistan Uzbek sum a 1997 b VAB 1990–95 BPM4 Actual GVanuatu Vanuatu vatu 2006 VAB BPM5 Estimate G C GVenezuela, R.B. Venezuelan bolivar fuerte 1997 VAB 2005 BPM5 Actual G C GVietnam Vietnamese dong 1994 b VAP 1991 2005 BPM5 Preliminary G GVirgin Islands (U.S.) U.S. dollar 1982 GWest Bank and Gaza Israeli new shekel 1997 VAB BPM5 S B GYemen, Rep. Yemeni rial 1990 VAP 1990–96 2005 BPM5 Actual S B GZambia Zambian kwacha 1994 VAB 1990–92 2005 BPM5 Preliminary S B GZimbabwe U.S. dollar 2009 VAB 1991, 1998 2005 BPM4 Actual G C G3982012 World Development Indicators

PRIMARY DATA DOCUMENTATIONLatestpopulationcensusLatest demographic,education, or healthhousehold surveySource of mostrecent incomeand expenditure dataVital Latestregistration agriculturalcomplete censusLatestindustrialdataLatesttradedataLatestwaterwithdrawaldataSaudi Arabia 2010 Demographic survey, 2007 2010 2010 2010 2006Senegal 2002 DHS, 2005; MIS, 2008/09 PS, 2005 2011–12 2010 2011 2002Serbia 2011 MICS, 2005/06 IHS, 2009 Yes 2012 2010 2008 2009Seychelles 2010 IHS, 2007 Yes 2011 2009 2008 2005Sierra Leone 2004 DHS, 2008 IHS, 2003 1984–85 2003 2002 2000Singapore 2010 General household, 2005 Yes 2010 2010Sint MaartenSlovak Republic 2011 IS, 2009 Yes 2001 2010 2010 2007Slovenia 2011 ES/BS, 2004 Yes 2010 2010 2010 2009Solomon Islands 2009 2012 2009 2007Somalia 1987 MICS, 2006 1990 1982 2003South Africa 2001 DHS, 2003 ES/BS, 2009 2012 2010 2010 2000South Sudan 2008Spain 2001 IHS, 2000 Yes 1999 2009 2010 2008Sri Lanka 2001 DHS, 2006/07 ES/BS, 2007 Yes 2012 2010 2010 2005St. Kitts and Nevis 2011 Yes 2010 2008St. Lucia 2010 IHS, 1995 Yes 2010 2008 2005St. MartinSt. Vincent & Grenadines 2011 Yes 2010 2010 1995Sudan 2008 MICS, 2010 ES/BS, 2009 2010 2009 2000 fSuriname 2004 MICS, 2006 ES/BS, 1999 Yes 2008 2009 2010 2000Swaziland 2007 MICS, 2010 ES/BS, 2010 2003 2010 2007 2000Sweden d IS, 2000 Yes 2010 2010 2010 2007Switzerland 2010 ES/BS, 2000 Yes 2008 2010 2010 2000Syrian Arab Republic 2004 MICS, 2006 ES/BS, 2004 2014 2002 2008 2005Tajikistan 2010 MICS, 2005 LSMS, 2009 2013 2010 2000 2000Tanzania 2002 DHS, 2010 ES/BS, 2007 2007–08 2010 2011 2002Thailand 2010 MICS, 2005/06 IHS, 2009 2013 2010 2010 2007Timor-Leste 2010 DHS, 2009/10 LSMS, 2007 2012 2000 2005 2004Togo 2010 MICS, 2010 CWIQ, 2006 2011–12 2005 2010 2002Tonga 2006 Yes 2011–12 2010 2010Trinidad and Tobago 2011 MICS, 2006 IHS, 1992 Yes 2004 2010 2010 2000Tunisia 2004 MICS, 2006 IHS, 2005/06 2004 2010 2010 2001Turkey 2000 DHS, 2003 LFS, 2008 2001 2010 2010 2003Turkmenistan 1995 MICS, 2006 LSMS, 1998 Yes 2004 2000 2000Turks and Caicos Islands 2001 Yes 2009Tuvalu 2002 2008 2008Uganda 2002 DHS, 2006; MIS, 2009/10 PS, 2009 2008 2010 2010 2002Ukraine 2001 DHS, 2007 ES/BS, 2009 Yes 2011 2010 2010 2000United Arab Emirates 2010 2012 2010 2009 2005United Kingdom 2011 IS, 1999 Yes 2010 2010 2010 2006United States 2010 CPS (monthly) LFS, 2000 Yes 2007 2010 2010 2005Uruguay 2004 IHS, 2010 Yes 2011 2010 2009 2000Uzbekistan 1989 MICS, 2006 ES/BS, 2003 Yes 2010 2000Vanuatu 2009 MICS, 2007 2007 2009 2007Venezuela, R.B. 2001 MICS, 2000 IHS, 2009 Yes 2007 2007 2010 2000Vietnam 2009 MICS, 2006 IHS, 2008 Yes 2011 2010 2009 2005Virgin Islands (U.S.) 2010 Yes 2007West Bank and Gaza 2007 PAPFAM, 2006 IHS, 2009 1971 2008Yemen, Rep. 2004 MICS, 2006 ES/BS, 2005 2002 2010 2009 2005Zambia 2010 DHS, 2007 IHS, 2006 1990 2010 2010 2000Zimbabwe 2002 DHS, 2005/06 IHS, 2003 1960 2010 2010 2002Note: For explanation of the abbreviations used in the table, see notes following the table.a. Original chained constant price data are rescaled. b. Country uses the 1993 System of National Accounts methodology. c. Register based. d. Rolling. e. Reporting period switch fromfi scal year to calendar year from 1996. Pre-1996 data converted to calendar year.2012 World Development Indicators 399

Primary data documentation notes• Base year is the base or pricing period used forconstant price calculations in the country’s nationalaccounts. Price indexes derived from nationalaccounts aggregates, such as the implicit defl atorfor gross domestic product (GDP), express the pricelevel relative to base year prices. • Reference yearis the year in which the local currency, constant priceseries of a country is valued. The reference year isusually the same as the base year used to report theconstant price series. However, when the constantprice data are chain linked, the base year is changedannually, so the data are rescaled to a specifi c referenceyear to provide a consistent time series. Whenthe country has not rescaled following a change inbase year, World Bank staff rescale the data to maintaina longer historical series. To allow for crosscountrycomparison and data aggregation, constantprice data reported in World Development Indicatorsare rescaled to a common reference year (2000) andcurrency (U.S. dollars). • System of NationalAccounts identifi es countries that use the 1993System of National Accounts (1993 SNA), the terminologyapplied in World Development Indicators since2001, to compile national accounts. Although morecountries are adopting the 1993 SNA, many still followthe 1968 SNA, and some low-income countriesuse concepts from the 1953 SNA. • SNA price valuationshows whether value added in the nationalaccounts is reported at basic prices (VAB) or producerprices (VAP). Producer prices include taxespaid by producers and thus tend to overstate theactual value added in production. However, VAB canbe higher than VAP in countries with high agriculturalsubsidies. See About the data for tables 4.1 and 4.2for further discussion of national accounts valuation.• Alternative conversion factor identifi es the countriesand years for which a World Bank–estimatedconversion factor has been used in place of the offi -cial exchange rate (line rf in the International MonetaryFund’s [IMF] International Financial Statistics).See Statistical methods for further discussion ofalternative conversion factors. • Purchasing powerparity (PPP) survey year is the latest available surveyyear for the International Comparison Program’sestimates of PPPs. See About the data for table 1.1for a more detailed description of PPPs. • Balanceof Payments Manual in use refers to the classifi cationsystem used to compile and report data on balanceof payments items in table 4.17. BPM4 refersto the 4th edition of the IMF’s Balance of PaymentsManual (1977), and BPM5 to the 5th edition (1993).• External debt shows debt reporting status for2010 data. Actual indicates that data are asreported, preliminary that data are based on reportedor collected information but include an element ofstaff estimation, and estimate that data are WorldBank staff estimates. • System of trade refers tothe United Nations general trade system (G) or specialtrade system (S). Under the general trade systemgoods entering directly for domestic consumptionand goods entered into customs storage arerecorded as imports at arrival. Under the specialtrade system goods are recorded as imports whendeclared for domestic consumption whether at timeof entry or on withdrawal from customs storage.Exports under the general system comprise outwardmovinggoods: (a) national goods wholly or partlyproduced in the country; (b) foreign goods, neithertransformed nor declared for domestic consumptionin the country, that move outward from customs storage;and (c) nationalized goods that have beendeclared for domestic consumption and move outwardwithout being transformed. Under the specialsystem of trade, exports are categories a and c. Insome compilations categories b and c are classifi edas re-exports. Direct transit trade—goods enteringor leaving for transport only—is excluded from bothimport and export statistics. See About the data fortables 4.4, 4.5, and 6.2 for further discussion.• Government finance accounting concept is theaccounting basis for reporting central governmentfi nancial data. For most countries governmentfi nance data have been consolidated (C) into one setof accounts capturing all central government fi scalactivities. Budgetary central government accounts(B) exclude some central government units. SeeAbout the data for tables 4.12, 4.13, and 4.14 forfurther details. • IMF data dissemination standardshows the countries that subscribe to the IMF’s SpecialData Dissemination Standard (SDDS) or GeneralData Dissemination System (GDDS). S refers to countriesthat subscribe to the SDDS and have posteddata on the Dissemination Standards Bulletin Boardat http://dsbb.imf.org. G refers to countries that subscribeto the GDDS. The SDDS was established formember countries that have or might seek accessto international capital markets to guide them in providingtheir economic and fi nancial data to the public.The GDDS helps countries disseminate comprehensive,timely, accessible, and reliable economic, fi nancial,and sociodemographic statistics. IMF membercountries elect to participate in either the SDDS orthe GDDS. Both standards enhance the availabilityof timely and comprehensive data and therefore contributeto the pursuit of sound macroeconomic policies.The SDDS is also expected to improve thefunctioning of fi nancial markets. • Latest populationcensus shows the most recent year in which a censuswas conducted and in which at least preliminaryresults have been released. The preliminary resultsfrom the very recent censuses could be refl ected intimely revisions if basic data are available, such aspopulation by age and sex, as well as the detaileddefi nition of counting, coverage, and completeness.Countries that hold register-based censuses producesimilar census tables every 5 or 10 years. Germany’s2001 census is a register-based test census usinga sample of 1.2 percent of the population. A rarecase, France has been conducting a rolling censusevery year since 2004; the 1999 general populationcensus was the last to cover the entire populationsimultaneously (www.insee.fr/en/recensement/page_accueil_rp.htm). • Latest demographic, education,or health household survey indicates thehousehold surveys used to compile the demographic,education, and health data in section 2. AIS is HIV/AIDS Indicator Survey, CPS is Current PopulationSurvey, DGHS is Demographic and General HealthSurvey, DHS is Demographic and Health Survey,ENPF is National Family Planning Survey (EncuestaNacional de Planifi cacion Familiar), FHS is FamilyHealth Survey, LSMS is Living Standards MeasurementSurvey, MICS is Multiple Indicator Cluster Survey,MIS is Malaria Indicator Survey, NSS is NationalSample Survey on Population Change, PAPFAM is PanArab Project for Family Health, RHS is ReproductiveHealth Survey, and SPA is Service Provision Assessments.Detailed information for AIS, DHS, MIS, andSPA are available at www.measuredhs.com/aboutsurveys; for MICS at www.childinfo.org; and forRHS at www.cdc.gov/reproductivehealth/surveys.• Source of most recent income and expendituredata shows household surveys that collect incomeand expenditure data. Names and detailed informationon household surveys can be found on thewebsite of the International Household Survey Network(www.surveynetwork.org). Core Welfare IndicatorQuestionnaire Surveys (CWIQ), developed by theWorld Bank, measure changes in key social indicatorsfor different population groups— specifi callyindicators of access, utilization, and satisfactionwith core social and economic services. Expendituresurvey/budget surveys (ES/BS) collect detailed informationon household consumption as well as ongeneral demographic, social, and economic characteristics.Integrated household surveys (IHS) collectdetailed information on a wide variety of topics,including health, education, economic activities,housing, and utilities. Income surveys (IS) collectinformation on the income and wealth of householdsas well as various social and economic characteristics.Labor force surveys (LFS) collect information onemployment, unemployment, hours of work, income,and wages. Living Standards Measurement Studies(LSMS), developed by the World Bank, provide a4002012 World Development Indicators

Primary data documentation notescomprehensive picture of household welfare and thefactors that affect it; they typically incorporate datacollection at the individual, household, and communitylevels. Priority surveys (PS) are a light monitoringsurvey, designed by the World Bank, for collectingdata from a large number of households cost-effectivelyand quickly. Income tax registers (ITR) provideinformation on a population’s income and allowance,such as gross income, taxable income, and taxes bysocioeconomic group. 1-2-3 surveys (1-2-3) are implementedin three phases and collect sociodemographicand employment data, data on the informalsector, and information on living conditions andhousehold consumption. • Vital registration completeidentifi es countries that report at least 90percent complete registries of vital (birth and death)statistics to the United Nations Statistics Divisionand reported in Population and Vital StatisticsReports. Countries with complete vital statistics registriesmay have more accurate and more timelydemographic indicators than other countries. • Latestagricultural census shows the most recent yearin which an agricultural census was conducted andreported to the Food and Agriculture Organization ofthe United Nations. • Latest industrial data showthe most recent year for which manufacturing valueadded data at the three-digit level of the InternationalStandard Industrial Classifi cation (ISIC, revision 2 or3) are available in the United Nations IndustrialDevelopment Organization database. • Latest tradedata show the most recent year for which structureof merchandise trade data from the United NationsStatistics Division’s Commodity Trade (Comtrade)database are available. • Latest water withdrawaldata show the most recent year for which data onfreshwater withdrawals have been compiled from avariety of sources. See About the data for table 3.5for more information.Exceptional reporting periodsIn most economies the fiscal year is concurrent withthe calendar year. Exceptions are shown in the tableat right. The ending date reported here is for the fi s-cal year of the central government. Fiscal years forother levels of government and reporting years forstatistical surveys may differ.The reporting period for national accounts datais designated as either calendar year basis (CY) orfi scal year basis (FY). Most economies report theirnational accounts and balance of payments datausing calendar years, but some use fi scal years. InWorld Development Indicators fi scal year data areassigned to the calendar year that contains the largershare of the fi scal year. If a country’s fi scal year endsbefore June 30, data are shown in the fi rst year ofthe fi scal period; if the fi scal year ends on or afterJune 30, data are shown in the second year of theperiod. Balance of payments data are reported inWorld Development Indicators by calendar year.Economies with exceptional reporting periodsEconomyFiscalyear endReporting periodfor nationalaccounts dataAfghanistan Mar. 20 FYAustralia Jun. 30 FYBangladesh Jun. 30 FYBotswana Jun. 30 FYCanada Mar. 31 CYEgypt, Arab Rep. Jun. 30 FYEthiopia Jul. 7 FYGambia, The Jun. 30 CYHaiti Sep. 30 FYIndia Mar. 31 FYIndonesia Mar. 31 CYIran, Islamic Rep. Mar. 20 FYJapan Mar. 31 CYKenya Jun. 30 CYKuwait Jun. 30 CYLesotho Mar. 31 CYMalawi Mar. 31 CYMyanmar Mar. 31 FYNamibia Mar. 31 CYNepal Jul. 14 FYNew Zealand Mar. 31 FYPakistan Jun. 30 FYPuerto Rico Jun. 30 FYSierra Leone Jun. 30 CYSingapore Mar. 31 CYSouth Africa Mar. 31 CYSwaziland Mar. 31 CYSweden Jun. 30 CYThailand Sep. 30 CYUganda Jun. 30 FYUnited States Sep. 30 CYZimbabwe Jun. 30 CYRevisions to national accounts dataNational accounts data are revised by national statisticaloffices when methodologies change or data sourcesimprove. National accounts data in World DevelopmentIndicators are also revised when data sourceschange. The following notes, while not comprehensive,provide information on revisions from previous data.• Antigua and Barbuda. Based on offi cial governmentstatistics, national accounts data have beenrevised for 2000 onward; the new base year is 2006.• Azerbaijan. National accounts historical expenditureseries in constant prices have been revised inline with State Statistical Committee data that werenot previously available. • Botswana. The CentralStatistical Offi ce has revised national accountsseries for 2004 onward. • Dominica. Based on offi -cial government statistics, national accounts datahave been revised for 2000 onward; the new baseyear is 2006. • Grenada. Based on offi cial governmentstatistics, national accounts data have beenrevised for 2000 onward; the new base year is 2006.• Hungary. Based on data from the Organisation forEconomic Co-operation and Development, nationalaccounts data have been revised for 1991 onward.• Macedonia. Based on offi cial statistics, nationalaccounts data have been revised for 2003 onward.• Maldives. The Department of National Planninghas revised national accounts data for 2000onward; the new base year is 2003. • Mauritania.Based on offi cial government statistics, data havebeen revised for 1991 onward; the new base yearfor constant price series is 2004. • Philippines.National accounts data have been revised for 1998onward. Because intellectual property products arenow reported as a part of gross fi xed capital formation,gross domestic product (GDP) in current pricesaverages 4 percent higher than previous estimates.• Puerto Rico. Based on data from the Instituto deEstadísticas de Puerto Rico, national accounts datahave been revised for 2001 onward. • Serbia. TheStatistical Offi ce has improved the methodology ofnational accounts data for 2003 onward. Specifi cally,the classifi cation of sectors has been revised, andGDP is now calculated using chain linked volumes in2005 prices. • Singapore. National accounts timeseries have been replaced with offi cial governmentstatistics. • St. Kitts and Nevis, St. Lucia, andSt. Vincent and the Grenadines. Based on offi cialgovernment statistics, national accounts data havebeen revised for 2000 onward; the new base yearis 2006. • Swaziland. The Central Statistical Offi cehas revised national accounts data for 1990 onward.• Syria. The Central Bureau of Statistics has revisednational accounts data for 2003 onward. • Tunisia.Based on data from the Central Bank and its StatisticalBulletin, national accounts data have beenrevised for 1997 onward. • Uganda. The Bureau ofStatistics has revised national accounts series for1998 onward; the new base year for constant priceseries is 2001/02. • United Arab Emirates. TheNational Bureau of Statistics has revised nationalaccounts data for 2001 onward; the new base yearis 2007. • Uruguay. The Central Bank has revisednational accounts data for 2006 onward. • Yemen.Based on offi cial government statistics and InternationalMonetary Fund data, national accounts datahave been revised for 1990 onward.2012 World Development Indicators 401

STATISTICAL METHODSThis section describes some of the statistical procedures used in preparing WorldDevelopment Indicators. It covers the methods employed for calculating regionaland income group aggregates and for calculating growth rates, and it describes theWorld Bank Atlas method for deriving the conversion factor used to estimate grossnational income (GNI) and GNI per capita in U.S. dollars. Other statistical proceduresand calculations are described in the About the data sections following each table.Aggregation rulesAggregates based on the World Bank’s regional and income classifi cations of economiesappear at the end of most tables. The countries included in these classificationsare shown on the flaps on the front and back covers of the book. Most tables alsoinclude the aggregate euro area. This aggregate includes the member states of theEconomic and Monetary Union (EMU) of the European Union that have adopted theeuro as their currency: Austria, Belgium, Cyprus, Estonia, Finland, France, Germany,Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic,Slovenia, and Spain. Other classifi cations, such as the European Union and regionaltrade blocs, are documented in About the data for the tables in which they appear.Because of missing data, aggregates for groups of economies should betreated as approximations of unknown totals or average values. Regional andincome group aggregates are based on the largest available set of data, includingvalues for the 158 economies shown in the main tables, other economiesshown in table 1.6, and Taiwan, China. The aggregation rules are intended toyield estimates for a consistent set of economies from one period to the next andfor all indicators. Small differences between sums of subgroup aggregates andoverall totals and averages may occur because of the approximations used. Inaddition, compilation errors and data reporting practices may cause discrepanciesin theoretically identical aggregates such as world exports and world imports.Five methods of aggregation are used in World Development Indicators:• For group and world totals denoted in the tables by a t, missing data areimputed based on the relationship of the sum of available data to the totalin the year of the previous estimate. The imputation process works forwardand backward from 2000. Missing values in 2000 are imputed using one ofseveral proxy variables for which complete data are available in that year. Theimputed value is calculated so that it (or its proxy) bears the same relationshipto the total of available data. Imputed values are usually not calculatedif missing data account for more than a third of the total in the benchmarkyear. The variables used as proxies are GNI in U.S. dollars, total population,exports and imports of goods and services in U.S. dollars, and value addedin agriculture, industry, manufacturing, and services in U.S. dollars.• Aggregates marked by an s are sums of available data. Missing values arenot imputed. Sums are not computed if more than a third of the observationsin the series or a proxy for the series are missing in a given year.• Aggregates of ratios are denoted by a w when calculated as weighted averagesof the ratios (using the value of the denominator or, in some cases, anotherindicator as a weight) and denoted by a u when calculated as unweightedaverages. The aggregate ratios are based on available data, including datafor economies not shown in the main tables. Missing values are assumedto have the same average value as the available data. No aggregate is calculatedif missing data account for more than a third of the value of weightsin the benchmark year. In a few cases the aggregate ratio may be computedas the ratio of group totals after imputing values for missing data accordingto the above rules for computing totals.• Aggregate growth rates are denoted by a w when calculated as a weightedaverage of growth rates. In a few cases growth rates may be computed fromtime series of group totals. Growth rates are not calculated if more than halfthe observations in a period are missing. For further discussion of methodsof computing growth rates see below.• Aggregates denoted by an m are medians of the values shown in the table.No value is shown if more than half the observations for countries with apopulation of more than 1 million are missing.Exceptions to the rules occur throughout the book. Depending on the judgmentof World Bank analysts, the aggregates may be based on as little as 50 percent ofthe available data. In other cases, where missing or excluded values are judged tobe small or irrelevant, aggregates are based only on the data shown in the tables.Growth ratesGrowth rates are calculated as annual averages and represented as percentages.Except where noted, growth rates of values are computed from constant priceseries. Three principal methods are used to calculate growth rates: least squares,exponential endpoint, and geometric endpoint. Rates of change from one periodto the next are calculated as proportional changes from the earlier period.Least squares growth rate. Least squares growth rates are used whereverthere is a suffi ciently long time series to permit a reliable calculation. No growthrate is calculated if more than half the observations in a period are missing.The least squares growth rate, r, is estimated by fi tting a linear regression trendline to the logarithmic annual values of the variable in the relevant period. Theregression equation takes the formln X t = a + btwhich is the logarithmic transformation of the compound growth equation,X t = X o (1 + r ) t .In this equation X is the variable, t is time, and a = ln X o and b = ln (1 + r) areparameters to be estimated. If b* is the least-squares estimate of b, then theaverage annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by100 for expression as a percentage. The calculated growth rate is an average4022012 World Development Indicators

ate that is representative of the available observations over the entire period.It does not necessarily match the actual growth rate between any two periods.Exponential growth rate. The growth rate between two points in time for certaindemographic indicators, notably labor force and population, is calculatedfrom the equationr = ln(p n /p 0 )/nwhere p n and p 0 are the last and fi rst observations in the period, n is the numberof years in the period, and ln is the natural logarithm operator. This growth rate isbased on a model of continuous, exponential growth between two points in time.It does not take into account the intermediate values of the series. Nor does itcorrespond to the annual rate of change measured at a one-year interval, whichis given by (p n – p n–1 )/p n–1 .“SDR defl ator.” (Special drawing rights, or SDRs, are the International MonetaryFund’s unit of account.) The SDR defl ator is calculated as a weighted average ofthese countries’ GDP defl ators in SDR terms, the weights being the amount ofeach country’s currency in one SDR unit. Weights vary over time because boththe composition of the SDR and the relative exchange rates for each currencychange. The SDR defl ator is calculated in SDR terms fi rst and then convertedto U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conversionfactor is then applied to a country’s GNI. The resulting GNI in U.S. dollars isdivided by the midyear population to derive GNI per capita.When offi cial exchange rates are deemed to be unreliable or unrepresentativeof the effective exchange rate during a period, an alternative estimate of theexchange rate is used in the Atlas formula (see below).The following formulas describe the calculation of the Atlas conversion factorfor year t:Geometric growth rate. The geometric growth rate is applicable to compoundgrowth over discrete periods, such as the payment and reinvestment of interestor dividends. Although continuous growth, as modeled by the exponential growthrate, may be more realistic, most economic phenomena are measured only atintervals, in which case the compound growth model is appropriate. The averagegrowth rate over n periods is calculated asand the calculation of GNI per capita in U.S. dollars for year t:Y t$ = (Y t /N t )/e t*r = exp[ln(p n /p 0 )/n] – 1.World Bank Atlas methodIn calculating GNI and GNI per capita in U.S. dollars for certain operationalpurposes, the World Bank uses the Atlas conversion factor. The purpose of theAtlas conversion factor is to reduce the impact of exchange rate fl uctuations inthe cross-country comparison of national incomes.The Atlas conversion factor for any year is the average of a country’s exchangerate (or alternative conversion factor) for that year and its exchange rates for thetwo preceding years, adjusted for the difference between the rate of infl ation inthe country and that in Japan, the United Kingdom, the United States, and theeuro area. A country’s infl ation rate is measured by the change in its GDP defl ator.The infl ation rate for Japan, the United Kingdom, the United States, and theeuro area, representing international infl ation, is measured by the change in thewhere e *t is the Atlas conversion factor (national currency to the U.S. dollar)for year t, e t is the average annual exchange rate (national currency to the U.S.dollar) for year t, p t is the GDP defl ator for year t, p S$t is the SDR defl ator in U.S.dollar terms for year t, Y $t is the Atlas GNI per capita in U.S. dollars in year t, Y t iscurrent GNI (local currency) for year t, and N t is the midyear population for year t.Alternative conversion factorsThe World Bank systematically assesses the appropriateness of offi cial exchangerates as conversion factors. An alternative conversion factor is used when theoffi cial exchange rate is judged to diverge by an exceptionally large margin fromthe rate effectively applied to domestic transactions of foreign currencies andtraded products. This applies to only a small number of countries, as shownin Primary data documentation. Alternative conversion factors are used in theAtlas methodology and elsewhere in World Development Indicators as single-yearconversion factors.2012 World Development Indicators 403

CREDITS1. World viewSection 1 was prepared by a team led by Eric Swanson. Eric Swanson wrote theintroduction with input from Neil Fantom, Shota Hatakeyama, Masako Hiraga,Wendy Huang, Hiroko Maeda, Johan Mistiaen, Sulekha Patel, William Prince, EvisRucaj, and Emi Suzuki and with valuable suggestions from Jose Alejandro Quijada,Sachin Shahria, Jos Verbeek, and participants of the Global Monitoring Report2012 seminar series. Bala Bhaskar Naidu Kalimili coordinated tables 1.1 and 1.6.Shota Hatakeyama, Masako Hiraga, Hiroko Maeda, and Johan Mistiaen preparedtables 1.2 and 1.5, and Mahyar Eshragh- Tabary, Shota Hatakeyama, MasakoHiraga, Buyant Erdene Khaltarkhuu, and Hiroko Maeda prepared table 1.3. WendyHuang prepared table 1.4 with input from Azita Amjadi. Signe Zeikate of theWorld Bank’s Economic Policy and Debt Department provided the estimates ofdebt relief for the Heavily Indebted Poor Countries Debt Initiative and MultilateralDebt Relief Initiative.2. PeopleSection 2 was prepared by Shota Hatakeyama, Masako Hiraga, Hiroko Maeda, andSulekha Patel in partnership with the World Bank’s Human Development Networkand the Development Research Group in the Development Economics Vice Presidency.Masako Hiraga, Sulekha Patel, and Johan Mistiaen wrote the introductionwith valuable input and comments from Eric Swanson. Emi Suzuki prepared thedemographic estimates and projections. The poverty estimates at national povertylines were compiled by the Global Poverty Working Group, a team of poverty expertsfrom the Poverty Reduction and Equality Network, the Development ResearchGroup, and the Development Data Group. Shaohua Chen and Prem Sangraula ofthe World Bank’s Development Research Group prepared the poverty estimates atinternational poverty lines. Lorenzo Guarcello and Furio Rosati of the UnderstandingChildren’s Work project prepared the data on children at work. Other contributionswere provided by Emi Suzuki (health and nutrition); Montserrat Pallares-Miralles andCarolina Romero Robayo (vulnerability and security); Theo door Sparreboom andAlan Wittrup of the International Labour Organization (labor force); Amelie Gagnon,Said Ould Voffal, and Weixin Lu of the United Nations Educational, Scientifi c, andCultural Organization Institute for Statistics (education and literacy); the WorldHealth Organization’s Chandika Indikadahena (health expenditure), Charu Garg(national health account), Monika Bloessner and Mercedes de Onis (malnutritionand overweight), Teena Kunjument (health workers), Jessica Ho (hospital beds),Rifat Hossain (water and sanitation), and Hazim Timimi (tuberculosis); Leonor Guariguataof the International Diabetes Federation (diabetes); and Colleen Murrayof the United Nations Children’s Fund (health). Eric Swanson provided valuablecomments and suggestions on the introduction and at all stages of production.3. EnvironmentSection 3 was prepared by Mahyar Eshragh-Tabary in partnership with the EnvironmentDepartment of the Sustainable Development Vice Presidency of the WorldBank. Mahyar Eshragh-Tabary wrote the introduction with input from Neil Fantomand Eric Swanson and with valuable suggestions from Jane Ebinger, Tim Herzog,Glenn-Marie Lange, and Soong Sup Lee. Esther G. Naikal and William Prince alsocontributed to the section. Other contributors include Kirk Hamilton; Carola Fabiand Edward Gillin of the Food and Agriculture Organization of the United Nations;Ricardo Quercioli and Karen Treanton of the International Energy Agency; LauraBattlebury of the World Conservation Monitoring Centre; Gerhard Metchies andArmin Wagner of German International Cooperation; and Craig Hilton- Taylor andCaroline Pollock of the International Union for Conservation of Nature. The WorldBank’s Environment Department devoted substantial staff resources.4. EconomySection 4 was prepared by Bala Bhaskar Naidu Kalimili in close collaborationwith the Sustainable Development and Economic Data Team of the World Bank’sDevelopment Data Group and with valuable suggestions from Liu Cui and WilliamPrince. Bala Bhaskar Naidu Kalimili wrote the introduction with valuable suggestionsfrom Eric Swanson. Azita Amjadi and Esther G. Naikal also contributed tothe section. The national accounts data for low- and middle-income economieswere gathered by the World Bank’s regional staff through the annual Unifi edSurvey. Maja Bresslauer, Mahyar Eshragh-Tabary, Bala Bhaskar Naidu Kalimili,Buyant Erdene Khaltarkhuu, and Maurice Nsabimana updated, estimated, andvalidated the databases for national accounts. The team is grateful to Eurostat,the International Monetary Fund, Organisation for Economic Co-operation andDevelopment, United Nations Industrial Development Organization, and WorldTrade Organization for access to their databases.5. States and marketsSection 5 was prepared by Buyant Erdene Khaltarkhuu in partnership with theWorld Bank’s Financial and Private Sector Development Network, Poverty Reductionand Economic Management Network, and Sustainable Development Network;the International Finance Corporation; and external partners. David Cieslikowskiand Buyant Erdene Khaltarkhuu wrote the introduction with input fromEric Swanson. Other contributors include Ada Karina Izaguirre (privatization andinfrastructure projects); Leora Klapper (business registration); Federica Saliolaand Joshua Wimpey (Enterprise Surveys); Carolin Geginat and Frederic Meunier(Doing Business); Alka Banerjee, Trisha Malinky, and Michael Orzano (Standard& Poor’s global stock market indexes); Oya Pinar Ardic Alper (fi nancial access);Gary Milante, Holly Benner, and Kenneth Anya (fragile situations); Satish Mannan(public policies and institutions); James Hackett of the International Institutefor Strategic Studies (military personnel); Sam Perlo-Freeman of the StockholmInternational Peace Research Institute (military expenditures and arms transfers);Christian Gonzalez of the International Road Federation, Narjess Teyssier andZubair Anwar of the International Civil Aviation Organization, and Hélène Stephanand Marc Juhel (transport); Jane Degerlund of Containerisation International andVincent Valentine of the United Nations Conference on Trade and Development(ports); Azita Amjadi (high-tech exports); Vanessa Grey, Esperanza Magpantay, andSusan Teltscher of the International Telecommunication Union; Torbjörn Fredrikssonand Rémi Lang of the United Nations Conference on Trade and Development(information and communication technology goods trade); Martin Schaaper ofthe United Nations Educational, Scientifi c, and Cultural Organization Institute forStatistics (research and development, researchers, and technicians); and RyanLamb of the World Intellectual Property Organization (patents and trademarks).4042012 World Development Indicators

6. Global linksSection 6 was prepared by Wendy Huang and Evis Rucaj with valuable inputfrom Uranbileg Batjargal and in partnership with the Financial Data Team ofthe World Bank’s Development Data Group, Development Research Group(trade), Development Prospects Group (commodity prices and remittances),International Trade Department (trade facilitation), and external partners.Malvina Pollock and Evis Rucaj wrote the introduction with substantial inputfrom Eric Swanson. Azita Amjadi (trade and tariffs) and Rubena Sukaj (externaldebt and fi nancial data) provided substantial input on the data and tables.Other contributors include Frédéric Docquier (emigration rates); Flavine Creppyand Yumiko Mochizuki of the United Nations Conference on Trade and Developmentand Mondher Mimouni of the International Trade Centre (trade); CristinaSavescu (commodity prices); Jeff Reynolds and Joseph Siegel of DHL(freight costs); Yasmin Ahmad and Elena Bernaldo of the Organisation forEconomic Co-operation and Development (aid); Ibrahim Levent, Hiroko Maeda,and Maryna Taran (external debt); Sanket Mohapatra and Ani Rudra Silwal(remittances); and Teresa Ciller of the World Tourism Organization (tourism).Ramgopal Erabelly, Shelley Lai Fu, and William Prince provided valuable technicalassistance.Other parts of the bookJeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of themaps on the inside covers. William Prince prepared Users guide. Eric Swansonwrote Statistical methods. Maja Bresslauer, Federico Escaler, and Buyant ErdeneKhaltarkhuu, prepared Primary data documentation. Alison Kwong prepared Partnersand Index of indicators.Database managementWilliam Prince coordinated management of the World Development Indicatorsdatabase, with assistance from Liu Cui. Operation of the database managementsystem was made possible by Ramgopal Erabelly and Shelley Fu in the Data andInformation Systems Team under the leadership of Reza Farivari.Design, production, and editingAzita Amjadi and Alison Kwong coordinated all stages of production with CommunicationsDevelopment Incorporated, which provided overall design direction,editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson,and Christopher Trott and assisted by Rob Elson. Elaine Wilson created thecover and graphics and typeset the book. Joseph Caponio provided productionassistance. Peter Grundy, of Peter Grundy Art & Design, designed thereport.Client servicesThe Development Data Group’s Client Services and Communications Team (FedericoEscaler, Alison Kwong, Beatriz Prieto-Oramas, Maryna Taran, Jomo Tariku,and Vera Wen) contributed to the design and planning and helped coordinate workwith the Offi ce of the Publisher under the leadership of Azita Amjadi.Administrative assistance, office technology, and systemsdevelopment supportAwatif Abuzeid, Elysee Kiti, Premi Ratham Raj, and Estela Zamora provided administrativeassistance. Jean-Pierre Djomalieu, Gytis Kanchas, and Nacer Megherbiprovided information technology support. Ramvel Chandrasekaran, UgendranMachakkalai, Shanmugam Natarajan, Atsushi Shimo, and Malarvizhi Veerappanprovided software support on the Development Data Platform application.Publishing and disseminationThe Offi ce of the Publisher, under the direction of Carlos Rossel, provided valuableassistance throughout the production process. Denise Bergeron, Dina Towbin,Stephen McGroarty, and Nora Ridolfi coordinated printing and supervisedmarketing and distribution. Merrell Tuck-Primdahl of the Development EconomicsVice President’s Offi ce managed the communications strategy.World Development Indicators CD-ROMSoftware preparation and testing was managed by Vilas Mandlekar with theassistance of Ramgopal Erabelly, Parastoo Oloumi, and William Prince. Systemsdevelopment was undertaken by the Data and Information Systems Team ledby Reza Farivari. William Prince coordinated user interface design and overallproduction and provided quality assurance, with assistance from Jomo Tariku.World Development Indicators mobile applicationsSoftware preparation and testing was managed by Vilas Mandlekar and ShelleyFu with assistance from Azita Amjadi, Prashant Chaudhari, Ying Chi, Liu Cui,Ghislaine Delaine, Ramgopal Erabelly, Federico Escaler, Buyant Erdene Khaltarkhuu,Maurice Nsabimana Parastoo Oloumi, Beatriz Prieto Oramas, WilliamPrince, Virginia Romand, Jomo Tariku, and Vera Wen. Systems development wasundertaken in the Data and Information Systems Team led by Reza Farivari. WilliamPrince provided data quality assurance.Open Data and online accessCoordination of the Open Data website (http://data.worldbank.org) was providedby Neil Fantom. Design, programming, and testing were carried out by RezaFarivari and his team: Azita Amjadi, Ramvel Chandrasekaran, Ying Chi, FedericoEscaler, Shelley Fu, Buyant Erdene Khaltarkhuu, Ugendran Machakkalai, ShanmugamNatarajan, Atsushi Shimo, Sun Hwa Song, Lakshmikanthan Subramanian,Maryna Taran, Jomo Tariku, Malarvizhi Veerappan, and Vera Wen. WilliamPrince coordinated production and provided quality assurance. Support fromthe Corporate Communications Unit in External Affairs was provided by a teamincluding George Gongadze and Jeffrey McC oy. The multilingual web team wasled by James Edward Rosenberg.Client feedbackThe team is grateful to the many people who have taken the time to providefeedback and suggestions, which have helped improve this year’s edition. Pleasecontact us at data@worldbank.org.2012 World Development Indicators 405

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INDEX OF INDICATORSReferences are to table numbers.AAgricultureagricultural raw materialscommodity prices 6.5exportsas share of total exports 4.4from high-income economies as share of total exports 6.3importsas share of total imports 4.5by high-income economies as share of total imports 6.3tariff rates applied by high-income countries 6.3cerealarea under production 3.2yield 3.3employment, as share of total 3.2fertilizercommodity prices 6.5consumption, per hectare of arable land 3.2foodcommodity prices 6.5exports 4.4from high-income economies as share of total exports 6.3imports 4.5by high-income economies as share of total imports 6.3tariff rates applied by high-income countries 6.3freshwater withdrawals for, as share of total 3.5landagricultural, as share of land area 3.2arableas share of land area 3.1per 100 people 3.1area under cereal production 3.2Irrigated 3.2permanent cropland, as share of land area 3.1machinerytractors per 100 square kilometers of arable land 3.2production indexescrop 3.3food 3.3livestock 3.3value addedannual growth 4.1as share of GDP 4.2Aidby recipientaid dependency ratios 6.12grants 6.12per capita 6.12technical cooperation 6.12total 6.12net disbursementsas share of donor GNI 1.4from major donors, by recipient 6.13net fl owsbilateral aid 6.13from bilateral sources 6.11from international fi nancial institutions 6.11from multilateral sources 6.11for basic social services, as share of sector-allocable bilateralODA commitments 1.4AIDS—see HIV, prevalenceAir pollution—see PollutionAir transportair freight 5.10passengers carried 5.10registered carrier departures worldwide 5.10Antiretroviral therapy coverage 2.22Asylum seekers—see Migration; RefugeesBBalance of paymentscurrent account balance 4.17as share of GDP 4.aexports and imports of goods and services 4.17net current transfers 4.17net income 4.17total reserves 4.a, 4.17See also Exports; Imports; Investment; Private fi nancial fl ows; TradeBase metalscommodity prices and price index 6.54142012 World Development Indicators

Battle-related deaths 5.8Beveragescommodity prices 6.5Biodiversity—see Biological diversityBiological diversityassessment, date prepared, by country 3.17GEF benefi ts index 3.4threatened species 3.4birds 3.4fi s h 3 .4higher plants 3.4mammals 3.4treaty 3.17Birth rate, crude 2.1See also Fertility rateBirths attended by skilled health staff 2.19Birthweight, low 2.21Bonds—see Debt fl ows; Private fi nancial fl owsBrain drain—see Emigration of people with tertiary education to OECDcountriesBreastfeeding, exclusive 2.21Broad money 4.15Business environmentbusinesses registeredentry density 5.1new 5.1corruptioninformal payments to public offi cials 5.2crimelosses due to theft, robbery, vandalism, and arson 5.2, 5.8dealing with construction permits to build a warehousenumber of procedures 5.3time required 5.3enforcing contractsnumber of procedures 5.3time required 5.3fi n a n c efi rms using banks to fi nance investment 5.2genderfemale participation in ownership 5.2getting electricitytime required 5.3informalityfi rms formally registered when operations started 5.2, 5.8infrastructurevalue lost due to electrical outages 5.2innovationinternationally recognized quality certifi cation ownership 5.2permits and licensestime required to obtain operating license 5.2protecting investorsdisclosure, index 5.3registering propertynumber of procedures 5.3time to register 5.3regulation and taxaverage number of times fi rms spend meeting with tax offi cials 5.2time dealing with offi cials 5.2resolving insolvencytime required 5.3starting a businesscost to start a business 5.3number of start-up procedures 5.3time to start a business 5.3tradeaverage time to clear direct exports 5.2workforcefi rms offering formal training 5.2CCarbon dioxidedamage, as share of GNI 4.11emissionsby sector 3.11from electricity and heat production, as share of total fuelcombustion 3.11from manufacturing industries and construction, as share oftotal fuel combustion 3.11from other sectors, as share of total fuel combustion 3.112012 World Development Indicators 415

INDEX OF INDICATORSfrom residential buildings and commercial and publicservices, as share of total fuel combustion 3.11from transport as share of total fuel combustion 3.11per capita 1.3, 3.9per unit of GDP 3.9total 1.6, 3.9intensity 3.9Cause of deathcommunicable diseases and maternal, prenatal, and nutritionalconditions 2.22injuries 2.22noncommunicable disease 2.22Children at workby economic activity 2.6male and female 2.6status in employment 2.6study and work 2.6total 2.6, 5.8work only 2.6Cities—see Urban environment;Climate variabilityaverage daily minimum/maximum temperature 3.12projected annual precipitation change 3.12projected annual temperature change 3.12projected change in annual cool days/cold nights 3.12projected change in annual hot days/warm nights 3.12Closing a business—see Business environmentCommercial bank and other lending 6.10See also Debt fl ows; Private fi nancial fl owsCommodity prices and price indexes 6.5Computer users per 100 people 5.12Consumptionas share of GDP 4.8average annual growth 4.9per capita 4.9distribution—see Income distributionfi xed capital 4.10, 4.11government, general fi nal expenditureannual growth 4.9as share of GDP 4.8See also Purchasing power parity (PPP)Contraceptivesprevalence rate 1.3, 2.19unmet need for 2.19Contract enforcementnumber of procedures 5.3time required for 5.3Corruption, informal payments to public offi cials 5.2Country Policy and Institutional Assessment (CPIA)—see Economicmanagement; Social inclusion and equity policies; Public sector managementand institutions; Structural policiesCreditgetting creditdepth of credit information index 5.5strength of legal rights index 5.5provided by banking sector 5.5to private sector 5.1Crimebusiness losses due to 5.2intentional homicide rate 5.8Communications—see Internet; Newspapers, daily; Telephones; Television,households withCurrent account balance 4.17See also Balance of paymentsCompensationof central government employees 4.13See also RemittancesCustomsaverage time to clear exports 5.2burden of procedures 6.84162012 World Development Indicators

DDAC (Development Assistance Committee)—see Aideconomic management cluster average 5.9fi s c a l po l i c y 5. 9macroeconomic management 5.9Death rate, crude 2.1See also Mortality rateDebt, externalas share of GNI 6.9debt servicetotal, as share of exports of goods and services and income 6.9long-termprivate nonguaranteed 6.9public and publicly guaranteed 6.9present valueas share of GNI 6.9as share of exports of goods and services and income 6.9short-termas share of total debt 6.9as share of total reserves 6.9total 6.9Debt fl owsbonds 6.10commercial banks and other lending 6.10See also Private fi nancial fl owsDeforestation, average annual 3.4Demand—see Consumption; Imports; Exports; SavingsDensity—see Population, densityDependency ratio—See Population, age dependency ratioDevelopment assistance—see AidDisaster risk reduction progress score—see ResilienceDisease—see Health risksDistribution of income or consumption—see Income distributionEEconomic management (Country Policy and Institutional Assessment)debt policy 5.9Educationchildren out of schoolmale and female 2.12poorest and richest wealth quintile 2.15cohort survival rateto grade 5, male and female 2.13to last grade of primary education, male and female 2.13completion rate, primarymale and female 2.14, 2.15poorest and richest wealth quintiles 2.15total 1.2, 2.14enrollment ratiogirls to boys enrollment in primary and secondary education 1.2grossby level 2.12primary 5.8secondary 2.4netby level 2.12primary, adjusted 2.12intake ratio, grossfi rst grade of primary education 2.13grade 1 2.15primary participation rate, gross 2.15public expenditure onas share of GDP 2.11as share of GNI 4.11as share of total government expenditure 2.11per student, as share of GDP per capita, by level 2.11pupil–teacher ratio, primary 2.11repeaters, primary, male and female 2.13teachers, primary, trained 2.11transition to secondary school, male and female 2.13unemployment by level of educational attainment 2.5years of schooling, average, poorest and richest wealth quintiles 2.15Electricityaccess to 3.8business lost due to outages 5.2consumption 5.11productiontotal 3.8sources 3.82012 World Development Indicators 417

INDEX OF INDICATORStime required 5.3transmission and distribution losses 5.11Emissionscarbon dioxideaverage annual growth 3.10intensity 3.9per capita 1.3, 3.9per unit of GDP 3.9total 1.6, 3.9See also Carbon dioxide, emissionsmethaneagricultural, as share of total 3.10from energy processes, as share of total 3.10total 3.10nitrous oxideagricultural, as share of total 3.10energy and industry, as share of total 3.10total 3.10other greenhouse gases 3.10GDP per unit of energy use 3.9per capita 3.7total 3.7See also Electricity; FuelsEnforcing contracts—see Business environmentEnrollment—see EducationEntry regulations for business—see Business environmentEnvironmental strategy or action plans, year adopted 3.17Equity fl owsforeign direct investment 6.10portfolio equity 6.10See also Private fi nancial fl owsEuropean Commissiondistribution of net aid from 6.13Employmentchildren in employment 2.6, 5.8in agricultureas share of total employment 3.2female 1.5, 2.3male 2.3in industry, male and female 2.3in services, male and female 2.3to population ratio 2.4vulnerable 1.2, 2.4See also Labor force; UnemploymentEndangered species—see Biological diversity; Plants, higherEnergycommodity price index 6.5consumption, road sector 3.15depletion, as share of GNI 4.11emissions—see Pollutionimports, net 3.9production 3.7usealternative and nuclear energy 3.7average annual growth 3.7combustible renewables and waste, as share of total 3.7fossil fuel consumption, as share of total 3.7Exchange ratesoffi cial, local currency units to U.S. dollar 4.16purchasing power parity conversion factor 4.16ratio of PPP conversion factor to offi cial exchange rate 4.16real effective 4.16See also Purchasing power parity (PPP)Exportsarms 5.7documents required for 6.8goods and servicesas share of GDP 4.8average annual growth 4.a, 4.9total 4.17high-technologyshare of manufactured exports 5.13total 5.13information and communications technology 5.12lead time 6.8merchandiseannual growth 6.1, 6.2by regional trade blocs 6.6direction of trade 6.2from high-income countries, by product 6.3from developing countries, by recipient 6.4structure 4.44182012 World Development Indicators

total 4.4value, average annual growth 6.1volume, average annual growth 6.1servicesstructure 4.6total 4.6travel 4.6, 6.15See also TradeForeign direct investment, net—see Investment; Private fi nancial fl owsForestareaas share of total land area 3.1total 3.4deforestation, average annual 3.4net depletion, as share of GNI 4.11Exposure to impactland area with elevation of 5 meters or less 3.12population affected by droughts, fl oods, and extreme temperature 3.12population living in areas with elevation of fi ve meters or less 3.12FFemale-headed households 2.10Female participation in ownership 5.2Fertility rateadolescent 2.19crude birth rate 2.1desired 2.19poorest and richest wealth quintile 2.24total 2.19Finance, fi rms using banks to fi nance investment 5.2Financial access, stability, and effi ciencyautomated teller machines 5.5bank capital to asset ratio 5.5bank nonperforming loans, ratio to total gross loans 5.5borrowers from commercial banks 5.5commercial bank branches 5.5depositors with commercial banks 5.5Financial fl ows, netoffi cialfrom bilateral sources 6.11from international fi nancial institutions 6.11from multilateral sources 6.11total 6.11See also AidFood—see Agriculture, production indexes; Commodity prices and priceindexesFuelsconsumptionroad sector 3.15exportsas share of total merchandise exports 4.5from high-income economies, as share of total exports 6.3importsas share of total imports 4.4by high-income economies, as share of total imports 6.3prices 3.15tariff rates applied by high-income countries 6.3GGEF benefi ts index for biodiversity 3.4Gender differencesin children in employment 2.6, 5.8in education 1.2, 2.12, 2.13, 2.14in employment 2.3by economic activity 2.3in HIV prevalence 2.22in labor force participation 2.2in life expectancy at birth 1.5in literacyadult 2.14youth 2.14in mortalityadult 2.23child 2.23in ownership of fi rms 5.2in parliaments 1.5in smoking 2.22unemploymenttotal 2.5youth 2.10nonagricultural wage employment 1.5unpaid family workers 1.52012 World Development Indicators 419

INDEX OF INDICATORSvulnerable employment 2.4Gini index 2.9Government, centralcash surplus or defi cit 4.12debtas share of GDP 4.12interest, as share of revenue 4.12expenseas share of GDP 4.12by economic type 4.13net incurrence of liabilities, as share of GDP, domestic and foreign 4.12revenueas share of GDP 4.12grants and other 4.14social contributions 4.14taxesas share of GDP 5.6by source, as share of revenue 4.14Greenhouse gases—see EmissionsGross capital formationannual growth 4.9as share of GDP 4.8Gross domestic product (GDP)annual growth 1.1, 1.6, 4.1, 4.10contribution of natural resources 3.18implicit defl ator—see Pricesper capita, annual growth 1.1, 1.6total 4.2, 4.10Gross enrollment—see EducationU.S. dollars 1.1totalPPP dollars 1.1, 1.6U.S. dollars 1.1, 1.6, 4.10HHealth careantiretroviral therapy coverage 2.22children sleeping under treated nets 2.18children with acute respiratory infection taken to health provider 2.18children with diarrhea who received oral rehydration and continuedfeeding 2.18children with fever receiving antimalarial drugs 2.18HIV, prevalence 1.3hospital beds per 1,000 people 2.16immunization rate, child 2.18nurses and midwives per 1,000 people 2.16outpatient visits per capita 2.16physicians per 1,000 people 2.16reproductiveanemia, prevalence of, pregnant women 2.21births attended by skilled health staff 2.19contraceptive prevalence rate 1.3, 2.19fertility rateadolescent 2.19total 2.19lifetime risk of maternal death 2.19low-birthweight babies 2.21maternal mortality ratio 1.3, 2.19, 5.8pregnant women receiving prenatal care 1.5, 2.19unmet need for contraception 2.19tuberculosisincidence 1.3, 2.22treatment success rate 2.18Gross national income (GNI)adjusted net national incomeannual growth 4.10total 4.10annual growth 4.10per capitaPPP dollars 1.1, 1.6rank 1.1U.S. dollars 1.1, 1.6rankPPP dollars 1.1Health expenditureas share of GDP 2.16external resources 2.16out of pocket 2.16per capita 2.16public 2.16Health informationcensus, year last completed 2.17completeness of vital registrationbirth registration 2.174202012 World Development Indicators

infant death 2.17total death 2.17health survey, year last completed 2.17national health accountnumber completed 2.17year last completed 2.17Health risksanemia, prevalence ofchildren under age 5 2.21pregnant women 2.21cause of death 2.22child malnutrition, prevalencestunting 2.20underweight 1.2, 2.20wasting 2.20diabetes, prevalence 2.22HIV, prevalence 1.3, 2.22low-birthweight babies 2.21overweight children, prevalence 2.20smoking, prevalence, male and female 2.22tuberculosis, incidence 1.3, 2.22undernourishment, prevalence 2.20Heavily indebted poor countries (HIPCs)assistance 1.4completion point 1.4decision point 1.4Multilateral Debt Relief Initiative (MDRI) assistance 1.4HIVprevalencefemale 2.22population ages 15–24, male and female 2.22total 1.3, 2.22Homicide rate, intentional 5.8Hospital beds—see Health careHousing conditions, national and urbandurable dwelling units 3.14home ownership 3.14household size 3.14multiunit dwellings 3.14overcrowding 3.14vacancy rate 3.14Hunger, depth 5.8IImmunization rate, childDPT, share of children ages 12–23 months 2.18measles, share of children ages 12–23 months 2.18Importsarms 5.7documents required for 6.8energy, net, as share of total energy use 3.9goods and servicesas share of GDP 4.8average annual growth 4.9total 4.17information and communications technology goods 5.12lead time 6.8merchandiseannual growth 6.2by developing countries, by partner 6.4by high-income countries, by product 6.3structure 4.5tariffs 6.3, 6.7total 4.5value, average annual growth 6.1volume, average annual growth 6.1servicesstructure 4.7total 4.7travel 4.7, 6.14See also TradeIncome distributionGini index 2.9percentage of 1.2, 2.9Industryannual growth 4.1as share of GDP 4.2employment, male and female 2.3freshwater withdrawals for output 3.5Infl ation—see PricesInformal economy, fi rms formally registered when operations started 5.22012 World Development Indicators 421

INDEX OF INDICATORSInformation and communications technology trade 5.11Innovation, internationally recognized certifi cation ownership 5.2Interest payments—see Government, central, debtInterest ratesdeposit 4.15lending 4.15real 4.15risk premium on lending 5.5spread 5.5LLabor forceannual growth 2.2armed forces 5.7children at work 2.6female 2.2nonagricultural 1.5part-time 1.5participation of population ages 15 and older, male and female 2.2total 2.2See also Employment; Migration; UnemploymentInternally displaced persons 5.8International Bank for Reconstruction and Development (IBRD)net fi nancial fl ows from 6.11International Development Association (IDA)net concessional fl ows from 6.11Resource Allocation Index 5.8, 5.9International Monetary Fund (IMF)net fi nancial fl ows from 6.11Internetfi xed broadbandaccess tariff 5.12subscriptions 5.12international Internet bandwidth 5.12secure servers 5.12users 5.12Investmentforeign direct, net infl owsas share of GDP 6.10total 6.10infrastructure, private participation inenergy 5.1telecommunications 5.1transport 5.1water and sanitation 5.1See also Gross capital formation; Private fi nancial fl owsLand areaarable—see Agriculture, land; Land useSee also Protected areas; Surface area; and Exposure to impactLand usearable landas share of total land 3.1per 100 people 3.1area under cereal production 3.2by type 3.1forest area, as share of total land 3.1irrigated land 3.2permanent cropland, as share of total land 3.1total area 3.1Life expectancy at birthmale and female 1.5total 1.6, 2.23Literacyadultmale and female 2.14total 1.6mathematics, students at the lowest level of profi ciency on PISA 2.14youth, male and female 2.14Logistics Performance Index 6.8MMalariaIodized salt, consumption of 2.21children sleeping under treated nets 2.18children with fever receiving antimalarial drugs 2.184222012 World Development Indicators

Malnutrition, in children under age 5 1.2, 2.20Management time dealing with offi cials 5.2Manufacturingannual growth 4.1as share of GDP 4.2value addedchemicals 4.3food, beverages, and tobacco 4.3machinery and transport equipment 4.3other 4.3structure 4.3textiles and clothing 4.3total 4.3See also MerchandiseMarket access to high-income countriesgoods admitted free of tariffs 1.4support to agriculture 1.4tariffs on exports from least developed countriesagricultural products 1.4clothing 1.4textiles 1.4Merchandiseexportsagricultural raw materials 4.4, 6.3food 4.4, 6.3from developing countries, by partner 6.4from regional trade blocs 6.6fuels 4.4, 6.3information and communications technology goods 5.12information and communications technology services 5.12manufactures 4.4ores and metals 4.4ores and nonferrous materials 6.3structure 4.4to low-income economies from high-income economies, by product 6.3to middle-income economies from high-income economies,by product 6.3total 4.4value, average annual growth 6.1volume, average annual growth 6.1within regional trade blocs 6.6importsagricultural raw materials 4.5, 6.3by developing countries, by partner 6.4food 4.5, 6.3fuels 4.5, 6.3information and communications technology goods 5.12manufactures 4.5ores and metals 4.5ores and nonferrous materials 6.3to low-income economies by high-income economies, by product 6.3to middle-income economies by high-income economies, by product 6.3total 4.5value, average annual growth 6.1volume, average annual growth 6.1tradeby developing countries, by partner 6.4direction 6.2growth 6.2regional trade blocs 6.6Metals and mineralscommodity prices and price index 6.5Methane emissionsagricultural as share of total 3.10industrial as share of total 3.10total 3.10Migrationemigration of people with tertiary education to OECD countries 6.14international migrant stock, total 6.14net 6.14See also Refugees; RemittancesMilitaryarmed forces personnelas share of labor force 5.7total 5.7arms transfersexports 5.7imports 5.7military expenditureas share of central government expenditure 5.7as share of GDP 5.7, 5.8Millennium Development Goals, indicators foraccess to improved sanitation facilities 1.3, 2.18, 3.13, 5.8access to improved water source 2.18, 3.5, 5.82012 World Development Indicators 423

INDEX OF INDICATORSaverage tariff imposed by developed countries on exports of leastdeveloped countries 1.4births attended by skilled health staff 2.19carbon dioxide emissions per capita 1.3, 3.9children sleeping under treated nets 2.18contraceptive prevalence rate 1.3, 2.19employment to population ratio 2.4enrollment ratio, net, primary 2.12female to male enrollments, primary and secondary 1.2fertility rate, adolescent 2.19goods admitted free of tariffs from least developed countries 1.4heavily indebted poor countries (HIPCs)assistance 1.4completion point 1.4decision point 1.4Multilateral Debt Relief Initiative (MDRI) assistance nominal debtservice relief committed 1.4immunization rate, childDPT 2.18measles 2.18income or consumption, national share of poorest quintile 1.2, 2.9infant mortality rate 2.22poorest and richest wealth quintile 2.24total 2.23Internet users per 100 people 1.3, 5.12labor productivity, GDP per person employed 2.4literacy rate of 15- to 24-year-olds 2.14malnutrition, prevalence 1.2, 2.20malariachildren under age 5 sleeping under treated nets 2.18children under age 5 with fever who are treated with appropriateantimalarial drugs 2.18maternal mortality ratio 1.3, 2.19, 5.8mobile cellular subscriptions per 100 people 5.11national parliament seats held by women 1.5offi cial development assistancefor basic social services as share of total sector allocable ODAcommitments 1.4net disbursements, as share of donor GNI 1.4poverty gap 2.7, 2.8pregnant women receiving prenatal care 1.5, 2.19share of cohort reaching last grade of primary education 2.13support to agriculture 1.4telephone lines, fi xed, per 100 people 5.11tuberculosiscase detection rate 2.18incidence 1.3, 2.22treatment success rate 2.18under-fi ve mortality rate 1.2, 2.22, 5.8poorest and richest wealth quintile 2.24total 1.2, 2.23, 5.8undernourishment, prevalence 2.20unmet need for contraception 2.19vulnerable employment 1.2, 2.4women in wage employment in the nonagricultural sector 1.5Minerals depletion, as share of GNI 4.11Monetary indicatorsbroad money 4.15claims central government 4.15other claims on domestic economy 4.15Mortality rateadult, male and female 2.23child, male and female 2.23children under age 5 1.2, 2.23, 5.8crude death rate 2.1infant 2.23life expectancy at birth 2.23maternal 1.3, 2.19, 5.8lifetime risk of maternal death 2.19neonatal 2.23Motor vehiclespassenger cars 3.15per 1,000 people 3.15per kilometer of road 3.15road density 3.15See also Roads; Traffi cMUV G-5 index 6.5MUV G-15 index 6.5NNatural resource depletion, as share of GNI 4.10Net enrollment—see EducationNewspapers, daily 5.124242012 World Development Indicators

Nitrous oxide emissionsagricultural as share of total 3.10Industrial as share of total 3.10total 3.10Nutritionanemia, prevalence ofchildren ages under 5 2.21pregnant women 2.21breastfeeding, exclusive 2.21iodized salt consumption 2.21malnutrition, child 1.2, 2.20overweight children, prevalence 2.20undernourishment, prevalence 2.20vitamin A supplementation 2.21OOffi cial development assistance—see AidOffi cial fl ows—see Aid; Financial fl ows, netPParticulate matterselected cities 3.16urban-population-weighted PM10 3.15Passenger cars per 1,000 people 3.15Plants, higher, threatened species 3.4Pollutioncarbon dioxidedamage, as share of GNI 4.11emissionsaverage annual growth 3.10intensity 3.9per capita 1.3, 3.9per unit of GDP 3.9total 1.6, 3.9local damage 4.11methane emissionsagricultural, as share of total 3.10from energy processes, as share of total 3.10total 3.10nitrogen dioxide, selected cities 3.16nitrous oxide emissionsagricultural, as share of total 3.10energy and industry, as share of total 3.10total 3.10organic water pollutants, emissionsby industry 3.6per day 3.6per worker 3.6particulate matter concentrationselected cities 3.16total 3.15sulfur dioxide, selected cities 3.16Patent applications fi led 5.13Peacebuilding and peacekeeping operationsoperation name 5.8troops, police, and military observers 5.8Pensionaverage, as share of per capita income 2.10contributorsas share of labor force 2.10as share of working-age population 2.10public expenditure on, as share of GDP 2.10Permits and licenses, time required to obtain operating license 5.2Physicians—see Health carePopulationage dependency ratio, young and old 2.1average annual growth 2.1by age group, as share of total0–14 2.115–64 2.165 and older 2.1density 1.1, 1.6female, as share of total 1.5ruralannual growth 3.1as share of total 3.1total 1.1, 1.6, 2.1urbanas share of total 3.13average annual growth 3.13in largest city 3.132012 World Development Indicators 425

INDEX OF INDICATORSin selected cities 3.16in urban agglomerations 3.13total 3.13See also Migration; Electricity; and Exposure to impactPortfolio—see Equity fl ows; Private fi nancial fl owsSee also InvestmentProductivityagricultural 3.3labor 2.4water 3.5Portscontainer traffi c in 5.9quality of infrastructure 6.8Povertyinternational poverty linelocal currency 2.8population living below$1.25 a day 2.8$2 a day 2.8national poverty linepopulation living below, national, rural, and urban 2.7poverty gap, national, rural, and urban 2.7Power—see Electricity, productionPrecipitationaverage annual 3.2, 3.12see also Climate variabilityPrenatal care, pregnant women receiving 1.5, 2.19Pricescommodity prices and price indexes 6.5consumer, annual growth 4.16gasoline fuel 3.15GDP implicit defl ator, annual growth 4.16net barter terms of trade 6.1wholesale, annual growth 4.16Protected areasmarineas share of total surface area 3.4total 3.4terrestrialas share of total surface area 3.4total 3.4Protecting investors disclosure index 5.3Public sector management and institutions (Country Policy and InstitutionalAssessment)effi ciency of revenue mobilization 5.9property rights and rule-based governance 5.9public sector management and institutions cluster average 5.9quality of budgetary and fi nancial management 5.9quality of public administration 5.9transparency, accountability, and corruption in the public sector 5.9Purchasing power parity (PPP)conversion factor 4.16gross national income 1.1, 1.6RRailwaysgoods hauled by 5.10lines, total 5.10passengers carried 5.10Primary education—see EducationPrivate fi nancial fl owsdebt fl owsbonds 6.10commercial bank and other lending 6.10equity fl owsforeign direct investment, net infl ows 6.10portfolio equity 6.10Refugeesby country of asylum 5.8, 6.14by country of origin 5.8, 6.14internally displaced persons 5.8Regional development banks, net fi nancial fl ows from 6.11Regional trade agreements—see Trade blocs, regional4262012 World Development Indicators

Registering propertynumber of procedures 5.3time to register 5.3Sanitation, access to improved facilities, population withtotal 1.3, 2.18, 5.8urban and rural 3.13Regulation and tax administrationmanagement time dealing with offi cials 5.2meeting with tax offi cials, number of times 5.2Relative prices (PPP)—see Purchasing power parity (PPP)Remittancesworkers’ remittances and compensation of employeespaid 6.14received 6.14Savingsadjusted net 4.11gross, as share of GDP 4.8gross, as share of GNI 4.11Schooling—see EducationScience and technologyscientifi c and technical journal articles 5.13See also Research and developmentResearch and developmentexpenditures 5.13researchers 5.13technicians 5.13Reserves, gross international—see Balance of paymentsResiliencedisaster risk reduction progress score 3.12See also Electricity, access toRoadsgoods hauled by 5.10passengers carried 5.10paved, as share of total 5.10sectoral energy consumption 3.15total network 5.10Royalty and license feespayments 5.13receipts 5.13Rural environmentaccess to improved sanitation facilities 3.13access to an improved water source 3.5populationannual growth 3.1as share of total 3.1SS&P/Global Equity Indices 5.4Secondary education—see EducationServicesemployment, male and female 2.3exportscomputer, information and communications, and othercommercial services 4.6insurance and fi nancial services 4.6structure 4.6total commercial 4.6transport 4.6travel 4.6importscomputer, information and communications, and othercommercial services 4.7insurance and fi nancial services 4.7structure 4.7total commercial 4.7transport 4.7travel 4.7value addedannual growth 4.1as share of GDP 4.2Smoking, prevalence, male and female 2.22Social inclusion and equity policies (Country Policy and InstitutionalAssessment)building human resources 5.9equity of public resource use 5.9gender equity 5.9policy and institutions for environmental sustainability 5.92012 World Development Indicators 427

INDEX OF INDICATORSsocial inclusion and equity cluster average 5.9social protection and labor 5.9Starting a business—see Business environmentStock marketslisted domestic companies 5.4market capitalizationas share of GDP 5.4total 5.4market liquidity 5.4S&P/Global Equity Indices 5.4turnover ratio 5.4Steel products, commodity prices and price index 6.5Structural policies (Country Policy and Institutional Assessment)business regulatory environment 5.9fi nancial sector 5.9structural policies cluster average 5.9trade 5.9Stunting, prevalence of 2.20Sulfur dioxide emissions—see PollutionSurface area 1.1, 1.6See also Land useSuspended particulate matter—see PollutionTTariffsall productsbinding coverage 6.7simple mean bound rate 6.7simple mean tariff 6.7weighted mean tariff 6.7applied rates on imports from low- and middle-income economies 6.3manufactured productssimple mean tariff 6.7weighted mean tariff 6.7on exports of least developed countries 1.4primary productssimple mean tariff 6.7weighted mean tariff 6.7share of tariff lines with international peaks 6.7share of tariff lines with specifi c rates 6.7Taxes and tax policiesbusiness taxesaverage number of times fi rms meet with tax offi cials 5.2labor tax, as share of commercial profi ts 5.6number of payments 5.6other taxes, as share of commercial profi ts 5.6profi t tax, as share of commercial profi ts 5.6time to prepare, fi le, and pay 5.6total tax rate, as share of commercial profi ts 5.6goods and services taxes, domestic 4.14income, profi t, and capital gains taxes 4.14international trade taxes 4.14other taxes 4.14social contributions 4.14tax revenue collected by central government, as share of GDP 5.6Technology—see Computers; Exports, high-technology; Internet; Research anddevelopment; Science and technologyTelephonesfi x e dinternational voice traffi c 5.11per 100 people 5.11residential tariff 5.11mobile cellularinternational voice traffi c 5.11population covered 5.11prepaid tariff 5.11subscriptions per 100 people 5.11mobile cellular and fi xed subscriptions per employee 5.11total revenue 5.11Television, households with 5.12Temperature—see Climate variabilityTerms of trade index, net barter 6.1Tertiary education—see EducationThreatened species—see Animal species; Biological diversity; Plants, higher4282012 World Development Indicators

Tourism, internationaltourism expenditureinboundas share of exports 6.15total 6.15outboundas share of imports 6.15total 6.15touristsinbound 6.15outbound 6.15Tradearms 5.7barriers 6.7facilitationburden of customs procedures 6.8documentsto export 6.8to import 6.8freight costs to the United States 6.8lead timeto export 6.8to import 6.8liner shipping connectivity index 6.8logistics performance index 6.8quality of port infrastructure 6.8information and communications technology 5.12merchandisedirection of, by developing countries 6.4direction of, by region 6.2high-income economy with low- and middle-income economies,by product 6.3nominal growth, by region 6.2regional trading blocs 6.6structure 4.4, 4.5total 4.4, 4.5servicesstructure 4.6, 4.7total 4.6, 4.7See also Balance of payments; Exports; Imports; Manufacturing;Merchandise; Terms of trade; Trade blocsTrade blocs, regionalexports within bloc 6.6total exports, by bloc 6.6type of agreement 6.6year of creation 6.6year of entry into force of the most recent agreement 6.6Trademark applications fi led 5.13Trade policies—see TariffsTraffi c—see Fuels; Motor vehicles; RoadsTransport—see Air transport; Ports; Railways; RoadsTravel—see Tourism, internationalTreaties, participation inbiological diversity 3.17CFC control 3.17climate change 3.17Convention on International Trade on Endangered Species (CITES) 3.17Convention to Combat Desertifi cation (CCD) 3.17Kyoto Protocol 3.17Law of the Sea 3.17Ozone layer 3.17Stockholm Convention on Persistent Organic Pollutants 3.17Tuberculosiscase detection rate 2.18incidence 1.3, 2.22treatment rate 2.18UUndernourishment, prevalence of 2.20Underweight, prevalence of 2.20Unemploymentby level of educational attainment, primary, secondary, tertiary 2.5incidence of long-term, total, male, and female 2.5total, male, and female 2.5youth, male, and female 1.3, 2.10Urban environmentaccess to improved sanitation facilities 3.13access to an improved water source 3.5emissions, selected citiesnitrogen dioxide 3.16particulate matter 3.162012 World Development Indicators 429

INDEX OF INDICATORSsulfur dioxide 3.16housing conditionsdurable dwelling units 3.14home ownership 3.14household size 3.14multiunit dwellings 3.14overcrowding 3.14vacancy rate 3.14populationas share of total 3.13average annual growth 3.13in largest city 3.13in selected cities 3.16in urban agglomerations 3.13total 3.13See also Pollution; Population; Sanitation; WaterVValue addedas share of GDPin agriculture 4.2in industry 4.2in manufacturing 4.2in services 4.1, 4.2per workerin agriculture 3.3urban and rural 3.5freshwaterannual withdrawalsas share of internal resources 3.5for agriculture 3.5for domestic use 3.5for industry 3.5total 3.5internal renewable resourcesfl ows 3.5per capita 3.5pollution—see Pollution, organic water pollutantsproductivity 3.5Women in developmentfemale-headed households 2.10female population, as share of total 1.5life expectancy at birth 1.5pregnant women receiving prenatal care 1.5, 2.19teenage mothers 1.5poorest and richest wealth quintile 2.24total 1.5unpaid family workers 1.5vulnerable employment 2.4women in nonagricultural sector 1.5women in parliaments 1.5Workforce, fi rms offering formal training 5.2WWasting, prevalence of 2.20Wateraccess to improved source of, population withtotal 2.18, 5.8World Bank, net fi nancial fl ows from 6.11See also International Bank for Reconstruction and Development;International Development Association4302012 World Development Indicators

REGION MAPThe world by regionEast Asia and Pacific ChileAmerican SamoaColombiaCambodiaCosta RicaChinaCubaFijiDominicaIndonesiaDominican RepublicKiribatiEcuadorKorea, Dem. Rep.El SalvadorLao PDRGrenadaMalaysiaGuatemalaMarshall IslandsGuyanaMicronesia, Fed. Sts.HaitiMongoliaHondurasMyanmarJamaicaPalauMexicoPapua New GuineaNicaraguaPhilippinesPanamaSamoaParaguaySolomon IslandsPeruThailandSt. Kitts and NevisTimor-LesteSt. LuciaTongaSt. Vincent & GrenadinesTuvaluSurinameVanuatuUruguayVietnamVenezuela, RBEurope andMiddle East andCentral AsiaNorth AfricaAlbaniaAlgeriaArmeniaDjiboutiAzerbaijanEgypt, Arab Rep.BelarusIran, Islamic Rep.Bosnia and Herzegovina IraqBulgariaJordanGeorgiaLebanonKazakhstanLibyaKosovoMoroccoKyrgyz RepublicSyrian Arab RepublicLatviaTunisiaLithuaniaWest Bank and GazaMacedonia, FYRYemen, Rep.MoldovaMontenegroSouth AsiaRomaniaAfghanistanRussian FederationBangladeshSerbiaBhutanTajikistanIndiaTurkeyMaldivesTurkmenistanNepalUkrainePakistanUzbekistanSri LankaLatin America andSub-Saharan Africathe CaribbeanAngolaAntigua and BarbudaBeninArgentinaBotswanaBelizeBurkina FasoBoliviaBurundiBrazilCameroonCape VerdeCentral African RepublicChadComorosCongo, Dem. Rep.Congo, Rep.Côte d'IvoireEritreaEthiopiaGabonGambia, TheGhanaGuineaGuinea-BissauKenyaLesothoLiberiaMadagascarMalawiMaliMauritaniaMauritiusMayotteMozambiqueNamibiaNigerNigeriaRwandaSão Tomé and PríncipeSenegalSeychellesSierra LeoneSomaliaSouth AfricaSouth SudanSudanSwazilandTanzaniaTogoUgandaZambiaZimbabweHigh-income OECDAustraliaAustria*Belgium*CanadaCzech RepublicDenmarkEstonia*Finland*France*Germany*Greece*HungaryIcelandIreland*IsraelItaly*JapanKorea, Rep.Luxembourg*Netherlands*New ZealandNorwayPolandPortugal*Slovak Republic*Slovenia*Spain*SwedenSwitzerlandUnited KingdomUnited StatesOther high incomeAndorraArubaBahamas, TheBahrainBarbadosBermudaBrunei DarussalamCayman IslandsChannel IslandsCroatiaCuraçaoCyprus*Equatorial GuineaFaeroe IslandsFrench PolynesiaGibraltarGreenlandGuamHong Kong SAR, ChinaIsle of ManKuwaitLiechtensteinMacao SAR, ChinaMalta*MonacoNew CaledoniaNorthern Mariana IslandsOmanPuerto RicoQatarSan MarinoSaudi ArabiaSingaporeSint MaartenSt. MartinTrinidad and TobagoTurks and Caicos IslandsUnited Arab EmiratesVirgin Islands (U.S.)*Member of the Euro area

The world by regionClassified according toWorld Bank analyticalgroupingLow- and middle-income economiesEast Asia and Pacifi cEurope and Central AsiaMiddle East and North AfricaSouth AsiaHigh-income economiesOECDLatin America and the CaribbeanSub-Saharan AfricaOther No dataGreenland(Den)FijiSamoaAmericanSamoa (US)TongaKiribatiFrench Polynesia (Fr)DominicanRepublicAruba (Neth)PuertoRico (US)U.S. VirginIslands (US)Curaçao (Neth)R.B. de VenezuelaMexicoSt. Kittsand NevisSt. Vincent andthe GrenadinesUnited StatesCayman Is.(UK)CanadaCosta RicaAntigua and BarbudaGuadeloupe (Fr)DominicaMartinique (Fr)CubaBelize JamaicaGuatemala HondurasEl Salvador NicaraguaSt. Martin (Fr)St. Maarten (Neth)PanamaEcuadorSt. LuciaBarbadosGrenadaTrinidadand TobagoThe BahamasHaitiColombiaPeruBermuda(UK)R.B. deVenezuelaChileTurks and Caicos Is. (UK)BoliviaGuyanaSurinameFrench Guiana (Fr)ParaguayArgentinaBrazilUruguayIcelandGermanyPolandCzech RepublicUkraineSlovak RepublicAustriaSloveniaCroatiaHungaryRomaniaBosnia andHerzegovina SerbiaSanMarinoKosovo BulgariaItaly Montenegro FYRMacedoniaVaticanAlbaniaCityGreeceFaeroe NorwayIslands(Den) Sweden FinlandThe NetherlandsEstoniaIsle of Man (UK)Denmark Russian LatviaFed. LithuaniaUnitedIreland Kingdom Germany Poland BelarusChannel Islands (UK)BelgiumUkraineLuxembourgMoldovaLiechtensteinFrance ItalyRomaniaSwitzerlandAndorraBulgaria GeorgiaPortugalSpainArmeniaTurkeyMonacoGreeceCyprus SyrianGibraltar (UK)TunisiaMalta LebanonArabRep.MoroccoIsraelIraqWest Bank and Gaza JordanAlgeriaLibya Arab Rep.Formerof EgyptSpanishSaharaUnited ArabEmiratesMauritaniaOmanCape VerdeMaliNigerChadSenegalSudan Eritrea Rep. of YemenThe GambiaBurkinaGuinea-BissauFasoDjiboutiGuineaBeninSierra Leone Côted’Ivoire Ghana Nigeria CentralEthiopiaAfrican SouthLiberiaRepublic SudanCameroonTogoSomaliaEquatorial GuineaUgandaSão Tomé and PríncipeGabon CongoKenyaRwandaDem.Rep.of BurundiSeychellesCongoTanzaniaComorosAngolaZambiaMalawiMozambiqueZimbabweMadagascarNamibiaBotswanaSouthAfricaSwazilandLesothoAzerbaijanSaudiArabiaMayotte(Fr)Islamic Rep.of IranKuwaitBahrainQatarKazakhstanUzbekistan KyrgyzRep.TurkmenistanTajikistanRéunion (Fr)AfghanistanMauritiusRussian FederationPakistanBhutanNepalBangladeshIndiaMyanmarMaldivesSriLankaMongoliaChinaLaoP.D.R.ThailandVietnamCambodiaBrunei DarussalamMalaysiaSingaporeDem.People’sRep.of KoreaRep.ofKoreaPhilippinesIndonesiaJapanN. Mariana Islands (US)Timor-LesteAustraliaGuam (US)PalauFederated States of MicronesiaPapua New GuineaNewZealandNauruSolomonIslandsVanuatuNewCaledonia(Fr)Marshall IslandsKiribatiTuvaluFijiAntarcticaIBRD 39126 MARCH 2012

ISBN 978-0-8213-8985-0SKU 18985The World Bank1818 H Street N.W.Washington, D.C.20433 USATelephone: 202 473 1000Fax: 202 477 6391Web site: data.worldbank.orgEmail: data@worldbank.orgThe World Development Indicators• Includes more than 1,000 indicators for 158 economies• Provides definitions, sources, and other information about the data• Organizes the data into six thematic areasWORLD VIEWLiving standardsand developmentprogressPEOPLEGender, health, andemploymentENVIRONMENTNatural resourcesand environmentalchangesECONOMYSTATES & MARKETSGLOBAL LINKSNew opportunitiesfor growthElements of a goodinvestment climateEvidence onglobalizationSaved: 64 trees26 million Btu of total energy6,503 pounds of net greenhouse gases29,321 gallons of waste water1,859 pounds of solid waste