Heilig - Development

United Nations Department of Economic and Social Affairs – Population Division 

Population Estimates and 

Projections Section 

Work Program, Outputs, Challenges, Uncertainties 

Gerhard K. Heilig 

Patrick Gerland, Kirill Andreev, Nan Li, Danan Gu, Thomas Spoorenberg 

Sarada Ravinuthala, Chandra Yamarthy, Neena Koshy 

United Nations, Department of Economic and Social Affairs (DESA), Population Division 

www.unpopulation.org 

2 May 2012

Work Program 


Prepare the World Population Prospects 

Official United Nations population projections, used throughout the UN System 

Recent revisions prepared every second year 

Latest revision: 2010 (230 countries) 

22 revisions since the early 1950s 

Prepare the World Urbanization Prospects 

Estimates and projections of urban population for 230 countries 

Estimates and projections of major urban agglomerations (about 5000) 

Latest revision: 2011 

Since 1988 

Publish results, develop methodology 

Web sites, wall charts, CD-ROMs/DVDs, databases 

Model life tables, probabilistic projections (Bayesian Hierarchical Model) 

Develop specialized databases and software 

DemoBase, DemoData, DataArchive 

Estimation and projection software, probabilistic models, data checking

Preparation: World Population Prospects 

0 


Start of 

WPP 

Data Collection, Estimation 

Data Collection 

230 countries / areas 

Evaluation 

Adjustments 

Epidem. Modeling 

for AIDS Countries 

60% of Workload 

Update and development of new 

databases and software, server 

and database maintenance, backup 

1 

Projection, 

Aggregation, Checking 

Uploading to Database 

Calculation of Variants 

Aggregation of Regions 

Checking of Results 

Fixing of Errors 

Consulting / Feedback 


2 

Early Release Data 

Online Data 

Web Site 

CD-ROMs / DVDs 

Statistical Reports 

Vol. 1, 2 

Wall Chart 

Start of 

WUP 

Output Production 

Methodological Report 

(on web site) 

Responding to clients 


3

Data Sources 

0 


1. Census data + post-enumeration surveys 

(from United Nations Demographic Yearbook database and directly from 

National Statistical Offices) 

2. All available demographic and health surveys 

(DHS, DSS, MICS, WHS, etc.) for estimating fertility and mortality 

3. Estimates from population and vital registers 

(from statistical reports of National Statistical Offices or their web sites) 

4. Scientific reports and data collections 

(Human Mortality Database, child mortality estimates, etc.) 

5. Data and estimates provided by other international 

agencies (CELADE, Regional Commissions, EUROSTAT, ESCAP, 

UNICEF, UNAIDS, WHO) 

Source: United Nations, Department of Economic and Social Affairs, Population Division (2011): World Population Prospects, the 2010 Revision. New York

Uncertainty of Projections 0 


Age 

Cohort 

Minimal uncertainty 

(adult mortality) 

Some uncertainty 

(second generation fertility, 

child mortality) 

Little uncertainty 

(first generation fertility, 

child mortality) 

Period / Year 


Estimation of Total Population 1 



Estimation of population by age and sex 1 



Estimation of Population from Censuses 1 


Under-reported births 

Under-reported migrant workers 


Estimation of Fertility: ASFR (Bangladesh) 1 



Data Problems 1 


No recent census available (latest census 10 or more years ago) 

Only headcount of census was published. Population by age and sex was not 

published. 

No data on fertility and mortality were available from the census (such as 

children ever born, children born during 12 months prior to enumeration, 

deaths during 12 month prior to enumeration). 

Census diverts significantly from previous population trends. Census 

population by age and sex cannot be reproduced from the previous census 

by using inter-censal statistics or estimates on births, deaths, and 

migration. 

Examples: Turkey 2007 register-based census; Bangladesh 2001 population census; 

Equatorial Guinea, 2001 census 

Obviously manipulated census counts, such as in countries, where the 

published census count is exactly 300,000 or grossly overestimated to meet 

a particular, politically relevant threshold (such as 1 million). 

Examples: 1983 census of Equatorial Guinea; 1969 census of Bhutan. 




Divergences in fertility data between different official sources, such as 

differences between statistical data published by various ministries. 

Example: In China, fertility estimates range from 1.38 for 2006 (2006 National Sample Survey 

on Population Changes) to 1.8 for 2007 (Statement by Dr. Hao Linan, Director-General for 

International Cooperation of the National Population and Family Planning Commission of China 

at the General Debates of the 41st Session of the UN commission on Population and 

Development. New York, 8 April 2008). 

Divergences in mortality estimates (under-five mortality, infant mortality) from 

different sources, such as vital registers, surveys, censuses as documented 

by UNICEF 

Incomplete geographical coverage of censuses and surveys due to armed 

conflicts or other problems 

Example: Democratic Republic of the Congo, Sri Lanka (Tamil Region), Sudan (Darfur) 

Lack of meta-information and quality measures for census and survey data. 

Post-enumeration survey for census was not carried out or results are not 

available. Sampling plan for population survey is not available. No 

information about representativity of survey. 




Incompatibility of subsequent census counts due to change of enumeration 

method, such as register-based data collection. 

Examples: Turkey 2007 address-based census, Brazil 

Incompatibility of subsequent census counts due to change of definitions, 

such as the change from de-facto population to de-jure population. 

Incompatible definitions: resident population 

Examples: Hong Kong, Singapore, Qatar 

Highly disputed statistical data on mortality, fertility or migration due to war, civil 

war, natural disaster. Unlikely sex ratios at birth. 

Examples: Mortality estimates of Iraq during embargo and war; China’s “missing girls” 

Extremely volatile population changes within short periods due to (labor) 

migration, particularly in countries where a large percentage or even the 

majority of the de-fact population is foreign labor migrants. 

Examples: Qatar 


Model of fertility decline 

2 


Model: Bi-logistic function to estimate the rate of fertility decline 

P( t) 

k1 k2 

= + 

Ln(81) Ln(81) 

1+ exp[ − ( t − t )] 1+ exp[ − ( t − t )] 

∆t 

∆t 

m1 m2 

1 2 

Parameter Slow/Slow Fast/Slow Fast/Fast 

k 1 ............... -0.11 -0.16 -0.25 

∆t 1 .............. 5.03 4.34 4.01 

t m1 .............. 5.77 5.06 5.17 

k 2 ............... 0.15 0.22 0.31 

∆t 2 .............. 2.75 3.02 4.32 

t m2 .............. 3.21 3.52 3.94 

In probabilistic fertility projection: Parameters values are replaced 

by distributions. Some 100,000 trajectories of fertility decline are 

calculated by sampling from these parameter distributions. 


BHM: A fertility transition model 

2 


Phase I: Not modeled. 

Phase II: Fertility transition, modeled by 

Bayesian Hierarchical Model (BHM) 

Phase III: Modeled with a first order 

autoregressive time series model [AR1], 

with its mean fixed at the approximate 

replacement-level fertility of 2.1 


Probabilistic projection of total fertility 

2 



Uncertainty 1 

1.12 billion 

452 million 


671 million

Probabilistic Projection of Population: Japan 

2 


Total Population Population age 15-64 

Based on estimates from WPP2010; probabilistic fertility, deterministic mortality 


Probabilistic Projection of Population: Ukraine 

2 


Total Population Population age 15-64 

Based on estimates from WPP2010; probabilistic fertility, deterministic mortality 


All colored countries are below 2.1 TF (1950-2100) 

2 


1950 - 2100 

Europe NA Latin America Caribbean Asia 

Based on WPP2010 

230 Countries 

Source: United Nations, Department of Economic and Social Affairs, Population Division (2011): World Population Prospects, the 2010 Revision. New York 

Africa

World population by Total Fertility, 1950-2100 2 


Population in million 


Total Population by Variant, 1950-2100 

1 


Billions 

27 Billion 

16 Billion 

10 Billion 

6 Billion 


Population Density: Selected Countries, 1950-2100 

1 


Persons per square kilometre 

6.6 times density of Japan 


2 

Population Age 0-19 and 50+ Years 

Age 0-19 Age 50+ 

The world is ageing! 

50+ population will increase from 1.4 to 4 billion 

United Nations Department of Economic and Social Affairs – Population Division

Population Age 15-34 by Major Area, 1950-2100 

1 


Millions 


Uncertainty / Problems 


1. in the quality of empirical data on population, fertility, 

mortality and migration available 

(We are not dealing with HMD world.) 

2. in the methods to evaluate and, if necessary, adjust and 

re-construct demographic statistics 

(Inconsistencies: previous censuses; data from neighboring countries; new states) 

3. in the methods we use for projecting fertility, mortality, 

and migration 

4. Calculation errors during estimation, projection, 

aggregation, and publication of results 

(interpolation, aggregation, calculation of life tables, treatment of migration)

WPP / WUP: Web Sites 


http://esa.un.org/unpd/wpp/ 

http://esa.un.org/unpd/wup/ 


Data Checking 5 



Databases - Example: DemoData 5 


Different Data Sources 


Team 


Population Affairs 

Officer 

Kirill Andreev 


Officer 

Patrick Gerland 

Editorial Assistant 

Neena Koshy 

Chief 

Gerhard K. Heilig 


Officer 

Danan Gu 

Information 

Systems Assistant 

Sarada 

Ravinuthala 


Officer 

Nan Li 

Information 

Systems Assistant 

Chandrasekhar 

Yamarthy 


Officer 

Thomas 

Spoorenberg


Thank You ! 

www.unpopulation.org

Heilig - Development

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