Agricultural Commodity Markets in African Agriculture - ERD

First draft. Please do not quote without permission.Comments welcomeAgricultural Food Commodity Markets and Vulnerability of African Economies 1byAlexander Sarris and George RapsomanikisFood and Agriculture Organization of the United NationsMarch 20091. IntroductionThe sudden and unpredictable large increases (spikes) of many internationally traded foodcommodity prices in late 2007 and early 2008 caught all market participants, as well asgovernments by surprise and led to many short term policy reactions that may have worsenedthe price rises. Many governments, think tanks, and individual analysts called for improvedinternational mechanisms to prevent and/or manage sudden food price rises. Similar calls forimproved disciplines of markets were made during almost all previous market price bursts,but were largely abandoned after the spikes passed. The financial crisis that started to unravelin 2008 has coincided with sharp commodity price declines, and food commodities havefollowed this general trend. The price volatility has been considerable. For instance, inFebruary 2008, international wheat, maize and rice price indices stood higher than the sameprices in November 2007, namely only three months earlier, by 48.8, 28.3, and 23.5 percentrespectively. In November 2008, the same indices stood at -31.9, -3.2, and 52.3 percent higherrespectively, compared to November 2008. In other words within one year these foodcommodity prices had increased very sharply in the first part of the year, and subsequentlydeclined (except rice) equally sharply. Clearly such volatilities of world prices creates muchuncertainly of all market participants, and makes both short and longer term planning verydifficult for all.The high food commodity prices coincided with high prices for petroleum, and many mineralproducts, but not with high prices for many agricultural products of export interest to manyAfrican countries. Hence, the recent commodity price boom may have not benefited, and infact may have hurt most African economies. Given the size of the external shock, one mayquestion how poor agriculture dependent African economies fared during the crisis, andwhether this type of external shock adds to the already vulnerable and fragile state of many ofthese economies. These are the issues that will form the basis of the inquiry in this paper.African countries have always had exports concentrated in primary commodities, and it iswell known that these commodities are characterized by volatile world prices. This impliesthat the terms of trade for most African economies have been volatile. Nevertheless the(negative) impact of this instability on economic performance has not been explored at themacro level until recently (e.g. Collier and Dehn, 2001, Guillaumont and Chauvet, 2001,Collier and Goderis, 2007, Guillaumont and Korachais, 2006). Another issue, also wellanalyzed, albeit not resolved, concerns the possible existence of persistent negative trends ofthe prices of primary commodities (for a recent review see Cashin and McDermott , 2006).1 Paper presented at the European Report of Development workshop on “Food Crisis and the developmentpotential of the agriculture and commodity sector in fragile countries”, held in Cambridge, United Kingdom,March 17-18, 2009

The combined negative effects of negative trending and unstable terms of trade for Africaneconomies is one of the reasons for their alleged negative performance.A more recent but less analyzed development has been the increasing food import dependenceof African economies, despite ample natural resources for food production. This trend in itselfdoes not necessarily imply any problems, as increased food import dependence may be anatural tendency during the transition of an agrarian economy to one based more onmanufacturing and services, and can be managed if the export income generated by the nonagriculturalsectors can pay for the increased food imports. Such trends, which have beenobserved in several now developed or middle income developing economies, have been thenatural outgrowth of their transition to more productive and diversified structures, and havebeen characterized by increased agricultural productivity. Africa, however, does not seem tohave followed this trend, as will be pointed out below, and hence its growing dependence onfood imports seems to suggest another structural development that may contribute tovulnerability.A major issue then of African economies fragility and vulnerability is what this increasedexposure to food imports implies about food security, and the impact of external food marketshocks. The issue depends considerably on the degree to which the vulnerable populations inAfrica are exposed to the international market shocks. In other words the issue is whetherfood insecure African households are exposed to international market instability. Here, theevidence, that will be presented below, appears to be that they are very weakly exposed tointernational market signals, at least in the short term. The reasons have to do with weakinfrastructures, high transactions costs, and government policies. This, however, tends toshield vulnerable agriculture dependent African households from the international markets,while makes them more vulnerable to domestic agricultural income shocks, such as those dueto unfavorable weather events. These, in fact maybe more detrimental to these householdsthan the shocks due to external market instability. Hence insulation of food insecurehousehold from international markets can shield them from external shocks but make themmore vulnerable to internal shocks. The opposite is the case for households that are wellintegrated with international markets. This, then presents a policy dilemma with respect to theoptimal degree of insulation of food insecure households from world markets. Keeping foodinsecure households insulated from world markets makes them less vulnerable to globalshocks but more vulnerable to domestic shocks, and the opposite is the case if the degree ofinsulation is smaller. The optimal degree of insulation then to two types of shocks mustdepend on the degree of exposure to domestic shocks and global, as well as the relativemagnitude of these shocks. Some thoughts on this issue will be made towards the end of thepaper.The plan of the paper is as follows. In the following section we examine the recent food pricespikes and food market instability in order to assess whether there are tendencies differentthan the past ones that may raise new concerns. In section 3 we exhibit some structuralfeatures and trends of low income African economies. In section 4 we examine vulnerabilityto commodity shocks at the household level. In section 5 we discuss international food marketprice transmission to domestic markets. In section 6 we examine the impacts of the recentfood price increases on African households. Section 7 concludes with a summary and policyissues.2. Recent commodity price developments in perspective.Figure 1 indicates the evolution of monthly nominal international prices (index form) of themain traded food commodities since 1990. It can be seen that the main commodities that havesoared in late 2007 and early 2008 were dairy, cereals and oils, while sugar and meat prices

do not appear to have spiked in any exceptional way, given the trends since 1990. Similarly(and not shown), other agricultural commodities such as the tropical beverages coffee andcocoa, have not exhibited any marked price changes in 2007 and 2008, compared to the 1990-2006 patterns. As of mid-2008 these spikes have vanished, with most indices returning tohistorical levels.While, however, the world price changes in some of the basic food commodities appearsignificant in nominal terms in relation to the trends of the past twenty years, when examinedin real terms, prices during the recent crisis appear still considerably smaller compared to thepeaks during the previous major food crisis of the mid-1970s. Figure 2 indicates the realinternational prices (deflated by the US producer price index) of the main cereals andsoybeans from 1957 to 2008. It can be readily seen that for all commodities indicated, the realprices at the height of the crisis in 2008 were considerably lower compared to the real pricesin the mid 1970s.Another salient pattern evident in the graphs of figure 2 is that the long term decline in foodcommodity prices, that appears to have been in place since the late 1950s, seems to havestopped in the late 1980s and early 1990s, with the trend lines indicating steady, albeit stillfluctuating patterns. This suggests that there may have been several slowly evolving factorsaffecting global food markets that gradually created a situation of tightly balanced supply anddemand, where a spike was almost inevitable in response to small shocks. Several of thesefactors have been discussed and analyzed by many authors and think tanks, as well as FAO.They include the following.1. Growing world demand for basic food commodities, due to growth in emergingeconomies, such as China and India. This development has been touted considerablyby many observers, but in fact it has been occurring gradually for several years, andcannot account for the sudden price spikes. Furthermore, the rate of growth of thesecountries’ demand or utilization of cereals, the most widely consumed and traded foodcommodities, for food, feed and other non-biofuel uses, has been decreasing ratherthan increasing. In fact this is compatible and predicted by conventional economicwisdom, which indicates that as incomes rise, the demand for basic foods rises by lessthan the rise in incomes.2. Demand of cereals for biofuel production. It is true that a significant amount ofproduction of maize in the USA, oilseeds in the European Union, and sugar in Brazilhave been utilized for biofuel production, often with help from a variety of supportpolicies and mandated alternative energy targets. This has also been occurring over anumber of recent years, and accounts for a significant portion of market demand forthese commodities, as well as, via substitution, for indirect demand for several othercommodities that compete for the same resources, such as land. As this has beenoccurring for some time, and helped keep prices increasing and strong overall, it isunlikely to have been a major factor for the sudden price spikes, albeit it may have hadamplifying effects in an already tight market.3. The rise in petroleum prices. Petroleum prices started rising in 2004, and continuedrising all throughout the past few years, before sharply declining in late 2008. Thereasons are largely demand by fast growing countries with energy intensiveeconomies, such as China and India. The oil price increase, apart from pushing costsof agricultural production and transport higher, induced a demand for alternative fuels,which in the context of the rising awareness about climate change created a strongdemand for biofuels. This, in turn, translated to increasing demand for agricultural rawmaterial feed stocks for biofuel production. Oil price increases accelerated starting in

late 2007 and continued increasing rapidly until August 2008 when they started arapid decline. Food commodity prices, especially those for biofuel stocks, seem tohave followed this trend quite closely, including through the spike period of late 2007-2008 and hence one might induce that there is a close link between oil prices and foodprices, that may have been one of the main contributing factors to the recent food pricespike and subsequent decline.4. Slowing rates of increases in farm productivity. During the more than thirty yearssince the last major food price crisis of 1973-75, agricultural prices in real terms havebeen declining due to fast rates of growth of agricultural productivity (both landproductivity as well as total factor productivity). In the more recent period, agriculturehas been neglected in most developing countries, as the World Bank’s 2008 WorldDevelopment Report aptly illustrated. The neglect not only involved lowerproductivity growth, via lower investments, but also the perception that agriculturalsupplies were not a problem in a world of low prices.5. The gradual decline in global food commodity stocks. The ratio of end of seasonworld cereal stocks to global utilization appears to have decreased considerablybetween 2000 and 2008. For two of the major cereal commodities (maize and rice)this decline can be accounted for by the decline in the stocks of China. Furthermore,globalization that linked markets much more and saw the proliferation of “just intime” production methods, may have had the effect of reducing the overall level ofglobal food commodity stocks. Excluding China, world cereal stock ratios for mostcereal commodities (except wheat) have not changed appreciably in the last 20 years.Nevertheless, several major cereal producing and trading countries experiencedsecular declines in end of season stocks. Irrespective of the source of the decline,however, it is a fact that when commodity markets face lower end of season stocks,they react much stronger to any negative shocks.6. Commodity speculation. This factor has been highlighted by many analysts andpoliticians, to the point of blaming the organized commodity exchanges for the pricespikes. Speculation is an ordinary fact of life in all commodity markets, and is anecessary ingredient of all commodity trade. Any agent who buys a contract forcommodity (in the physical or future markets) with the intention of selling it later for aprofit can be considered a speculator. Organized commodity exchanges are importantinstitutions for both market transparency as well as the transfer of market risk fromphysical markets to speculators, and they guarantee transactions via the underlyingclearing houses. It is no coincidence that they have evolved and grown over a periodof more that two centuries, as they have been perceived as important institutions formanaging market risks. The advent of large investments by commodity funds in recentyears has raised new issues about the utility of the organized exchanges as risk transfermechanisms, and about the role of unfettered speculation in persistent price rises.Detailed analyses of recent events (Gilbert, 2009) have suggested that there is weakevidence that such investments have contributed to the commodity price boom.7. Macroeconomic factors. While most commodity market analysts look for commodityspecific fundamental factors to explain individual commodity price spikes, there aresystemic macroeconomic factors that affect all commodities that have been veryinfluential. The recent commodity boom has involved most traded commodities andnot only agricultural ones. One of the key factors that fueled such a boom seems tohave been a period of easy money and loose regulation of financial transactions, whichresulted in a fast expansion of global financial liquidity, a weak US dollar, and low

interest rates. It is notable that the previous large commodity boom of 1973-75 wasalso preceded by a period of expanding global liquidity fueled by large US externaldeficits and loose monetary policies, much like in recent years. It has been shown byresearch (Abbott, et. al. 2008, Mitchell, 2008) that US dollar depreciation hascontributed around 20 percent to increases in food prices. Frankel (2008), in turn, hasmade the argument that low interest rates, themselves induced by monetary expansion,encourages portfolio shift into commodities, and also discourages stockholding,therefore, contributing to commodity price rises. There is an additional factor inexplaining the abrupt behaviour in food commodity prices in the midst of the financialcrisis of 2008. Many researchers suggest that commodities – especially commodityfutures – have become a new ‘asset class’. First, returns to commodity futures arenegatively correlated with returns to traditional financial assets such as equities andbonds. This relationship indicates that commodity futures offer an attractive vehiclefor portfolio diversification that reduces the volatility of portfolio returns. Second,comparisons between returns of commodity futures with those of traditional financialassets, such as stocks and bonds, indicate that investment in commodity futures isprofitable. Futures and stocks have similar returns, amounting to about 5.2–5.6 percentper annum. This is twice as high as the return from investing in bonds. Theseobservations suggest that commodity futures are not only regarded as providinginsurance against price risk for farmers and food processors, but also as an asset whichgenerates returns and can be used to diversify traditional financial portfolios. Giventhat the commodity boom of early 2008 came to an abrupt stop in late 2008, followedby subsequent strong price declines, in the wake of the global financial crisis, withoutsubstantial changes in the underlying commodity market fundamentals, suggests thatmacroeconomic factors were important in the recent boom.The important point to highlight is that most of these factors were slow in developing overseveral years, but cumulatively they created a situation of tightly balanced world supply anddemand for many agricultural commodities. Furthermore, they made the demand for theagricultural commodities very price inelastic. The demand curve for agricultural (and othercommodities) is price elastic when there are ample supplies (from both production and stocks)but becomes very inelastic when the overall supplies are small, and there is low capacity ofthe market to absord or buffer exogenous shocks. As indicated above both the reduction ofglobal stocks, as well as the macro factors that fuelled demand growth, pushed the supplydemand balance of most food agricultural commodities in a territory, where small shocks orsmall changes in perceptions could have had very strong price effects. In fact the foodproduction shocks that happened were small, exemplified by the fact that global grainproduction declined by only 1.3 percent in 2006, but then increased by 4.7 percent in 2007,and a further 4.8 percent in 2008, despite the fact that some of the major exporting countriessuch as Australia experienced very sharp negative production shocks (of the order of 50-60percent in both 2005 and 2006). Such production shocks are rather normal in global foodcommodity markets, and have occurred on similar scale several times in the past, withoutcausing price spikes. It then appears that production shocks were not the main factor drivingthe commodity markets, but rather some of the other factors indicated above.A factor that seemed to have contributed considerably to the recent short term price spikes ishoarding tendencies and policies affecting the normal flow of commodities. It is well knownthat the reaction of many private agents as well as governments at the onset of price rises wasdestabilizing, in the sense that their actions fuelled the demand for current supplies, led byfears of impending basic commodity shortages. In other words when market agents realizedthat there were inadequate buffers in the global markets to ensure smooth supply flows, they

started to behave atomistically, to ensure their own smooth supply flow. This created panicbuying and hoarding, even when the underlying conditions did not justify it, thus creating theprice spikes. The case of the global rice market is a good case in point, where, despiteadequate global production and supplies, uncoordinated government actions, such as exportbans, created a short term hoarding panic and an ensuing price spike. The realization in mid-2008 that the situation was not as critical as many thought, led to the opposite effect and asharp price decline followed.In the context of the events of the last two years, it is interesting to examine the evolution ofworld market price volatility. Figure 3 plots the indices of annualized historic volatilities(estimated by normalized period to period changes of market prices) of nominal internationalprices of wheat, maize, and rice over the previous five decades. The figures also exhibit thenominal international prices on the basis of which the indices of volatility are determined. Thereason for the juxtaposition of the two types of information is to examine visually therelationship between the level of commodity prices and the market volatility. It has beenknown for along time since Samuelson’s classic article (Samuelson, 1957) that in periods ofprice spikes, overall supplies are tight, and market volatility should be higher, hence theexpectation is that during periods of price spikes the index of market volatility should exhibita rise as well. .A most notable characteristic of the plots in figure 3 is that historic volatility (as an index ofmarket instability) of most food commodities, while quite variable, appears not to have grownsecularly in the past five decades. There also appears to be no clear correlation for mostcommodities between the two major price spike periods, namely 1973-75 and 2007-8 andvolatility. During the first boom period, namely 1973-75, volatilities of wheat and maizeappear to have increased markedly relative to previous trends. However, this is not the casefor rice. During the most recent boom of 2007-8, the volatility of wheat and rice appear tohave increased markedly, but not that of maize. While these observations are just visual andneed to be corroborated with appropriate econometric analysis, they raise some questionsabout the alleged positive relationship between the level of prices and the level of volatility.A closer examination of the factors underlying price volatility reveals the following. Thereare two factors that traditionally have been considered the main ones in influencingagricultural market price instability. These are the variability of production, and the level ofend of previous period stocks. The more variable is agricultural production from year to year,the more one expects to observe large period to period price variations, namely largervolatility. In the same vein, the smaller the end of season stocks, the more any new marketdevelopments are likely to affect prices, and hence the more variable is market price. Trendsin the coefficients of variation of annual production of wheat, maize and rice, computed forfour ten year periods ending in 1999, as well as the most recent period 2000-06, and for thefive continents, as well as the world as a whole, reveal a marked decline in world productionvariability, and significant reductions in production variability of America (North and South)and Asia, which between them account for 60 percent of global production. It is only Africa,which accounts for a small share of global wheat production (only 3.3 percent), whereproduction variability seems to have increased. Similarly for maize, global production appearsalso to have become less variable, with no apparent significant positive trend in any continent.Global paddy rice production variability also appears to be declining over time.Turning to end of season stock levels, the end of season global stocks both absolutely as wellas share of total utilization for wheat, maize, and rice, reveal that global end of season stocksof cereals do not appear to have been in 2007-8 much smaller in absolute levels than in earlierperiods, notably the early-mid 1990s. Stocks increased considerably and reached a peak

around 2000-2001 and then the started declining. The decline continued until 2004-5 andthese trends occurred both with and without China. After 2005 stocks appear to haveincreased or at least not decrease in absolute terms. Stock to utilization ratios seem to followthe same patterns and turning points both with as well as without China. Also, albeit thereappears to be a negative trend in the ratio of stocks to utilization for the world, when oneexamines the whole 30 year period from 1979 onwards, there is no marked negative trend forthe ratios if China is exluded from the world total. In fact for rice, the ratios for the world aswell as without China exhibit a slight positive trend.Turning now to the newer factors affecting market volatility, the most difficult to analyze isthe influence of commodity traders in organized exchanges. The question is whether theundoubted increase in participation of non-commercial traders in the organized futures andother derivative markets, affected the market fundamentals, and in particular the level ofprices and volatility. There is very little research on this issue, but some recent empiricalanalysis by Gilbert, (2009) and a policy brief by the Conference Board of Canada (CBS,2008) seem to suggest that is price volatility that attracts non-commercial and other financialtraders, and not the other way around.A lot has been said about the influence of the unstable exchange rate of the US dollar oncommodity markets. It is a fact that in recent years the USD exchange rate vas variedconsiderably against the currencies of other major trading countries. For instance the USDdepreciated against the Euro by more than 30 percent between 2003 and 2007. It is also thecase, albeit not obvious that since the prices of most internationally traded agriculturalcommodities are quoted in USD, a USD depreciation has a considerable influence on USDprices of traded commodities. Evidence provided by the International Monetary Fundsuggests that if the US dollar had remained at its 2002 level, oil prices would have been lowerby US$25 per barrel, and the increases in non-fuel commodity prices would have been lowerby 12 percent (Lipsky, 2008). FAO has estimated that a 1 percent USD depreciation againstall currencies, ceteris paribus, can have significant upwards influence on all agriculturalcommodity prices, and for some the relevant elasticity can be as high as 0.8-0.9 (this occursmostly for livestock commodities, where developed countries are the major traders, andexchange rates most variable). Clearly then it appears that the instability of the USD exchangerates must have contributed significantly to market price volatility. Given recent globalfinancial and production developments, the huge international financial flows they imply fromagents looking for safe heavens, it is likely that this instability will continue in the future, andhence this is likely to continue affecting adversely commodity market volatilities.Apart from the instability of the USD, macroeconomic instability is likely to have contributedconsiderably to commodity markets instability. Gilbert, (2009) in his empirical analysis findsthat both money supply as well as GDP seem to Granger cause commodity prices. The currentfinancial crisis, does not bode well for monetary stability, especially given the significantmonetary expansion that is likely to follow the fiscal stimulus packages now envisioned inmost large economies. Hence it is likely that macroeconomic factors will continue addinginstability to world commodity markets.The price of petroleum was already alluded to as an important determinant of agriculturalcommodity prices, especially for those commodities which can be utilized as biofuelproduction stock. Schmidhuber (2006) has shown that when petroleum prices are in a certainprice range, then oil prices and biofuel stock prices seem to be much strongly correlated. Thishas been empirically substantiated by Balcombe and Rapsomanikis (2008) and for the sugaroil-ethanolnexus. Several analysts have attributed significant influence on agriculturalcommodity prices from petroleum prices, coupled with biofuel policies (e.g. Mitchell, 2008,

Abbott, et. al. 2008). Despite the rapid fall of petroleum prices in late 2008 and early 2009,the underlying demand for oil in the medium term is real and likely to increase (OECD-FAO,2008). This is likely to induce a continuing linkage between petroleum prices and biofuelstock prices, albeit not at all periods. As oil prices are likely to be quite unstable given theuncertainties in global economic growth, this most likely will induce instability of theagricultural commodity markets, both for those those products that are directly related tobiofuels, such as maize, sugar, and rapeseed, but also in commodities that are substitutes inproduction.The final factor that is likely to affect commodity market volatility is country policy actionsand reactions to external events. The commodity scare of 2007-8 and the publicity it receivedmade many governments overreact, by measures that were not always effective at achievingtheir stated objectives. A FAO survey of government actions in 77 developing countriesduring the 2007-8 period, tabulated the type of measures that were undertaken in response tothe global price rises. It was revealed that there are only a few countries whose governmentsdid nothing in response to the global commodity crisis. Perhaps surprisingly the region wherefew additional policies were adopted appears to be Africa. Nevertheless, discrete and largelyunexpected policy responses, especially through marketing boards operations, increaseuncertainty and weaken the incentive for the private sector to engage in trade. The presenceand trading activities of both marketing boards and private firms give rise to a dual marketingsystem that often increases the fragility of the market. The lack of trust and the poorcoordination between the public and the private sectors often result in food deficits and highdomestic price volatility.The overall conclusion then is that the global food commodity markets are likely to stayvolatile in the next few years, until stocks are replenished, petroleum prices stabilize, and theglobal financial crisis works itself out. An added risk is that the efforts currently made torenew emphasis on agricultural investments to boost productive efficiency, especially indeveloping agriculture dependent countries, are derailed by the probably short lived hiatus oflow global food commodity prices.3. Food dependency and food insecurity among African economiesOver the past 40 years, and despite significant developments in global trade, technology andaid, African countries have remained very dependent on agriculture. Table 1 indicates thatboth for Africa as a whole and for LDC Africa in particular the share of agriculture in GDPhas decreased only slightly since 1970. During the same period, the share of economicallyactive population employed in agriculture, while experiencing significant decline for Africaas a whole, from 76 percent in 1970 to 57 percent in 2002-4, in LDC Africa the sharedecreased from 83 percent to a still very high 71 percent. Despite this continuing dependence,Table 2 indicates that the shares of agricultural exports in total exports of merchandise as wellas total exports of goods and services have declined to about half their shares in 1970.This decline in agricultural export shares has been accompanied by growing agriculturalimports. Table 3 indicates that during the same period, the share of agricultural imports intotal imports of goods and services has declined, but the share of imports in total merchandiseimports has increased, with the exception of North Africa. More significantly, the share ofagricultural imports in total exports of goods and services, an index that can indicate theability of the country to finance food imports, while declining from 1970 to 1980 and 1990,has increased considerably from 1990 to 2002-04. This suggests that agricultural (mostlyfood) imports have necessitated a large share of the export revenues of African countries.This development has been accompanied by a decline in the income terms of trade for LDCs,which are largely African countries. Figure 4 indicates that during the period 1960-2002 the

income terms of trade, as computed by the ratio of the value of agricultural exports to anindex of import prices (the IMF Manufacturing Unit Value index), and which measures thepurchasing power of agricultural exports, seems to have evolved totally differently fordeveloped countries, LDCs and other (middle income) developing countries, with the indexfor the LDC showing a continuing decline, while that of the developed and other developingcountries an increase. The basic reason for this development, since both groups of countriesface the same international prices is the different rates of productivity growth. Figure 4exhibits the trends in average yields of cereals for the same three groups of countries. It isclear that in the last 20 years productivity increases have been strong in developed and otherdeveloping countries, while they have been very weak in LDCs.In fact within Africa yields have stagnated most for the LDC group. Figure 5 exhibits yieldsfor basic food commodities in various Africa groups and the region as a whole. It can benoted that in LDC Africa yields have been particularly stagnant. The conclusion is that theincreasing food dependency has not been accompanied by increases in food commodityagricultural productivity in Africa.Two other interesting structural developments are in order. The first concerns the fact thatdespite the fact, illustrated in table 3, that agricultural export dependence has declined formost African countries, the high commodity dependence of agricultural exports hascontinued. Table 4 exhibits the share of the top four agricultural commodities in totalagricultural exports of 32 low income African countries over the period 1982-84 to 2002-4(the well known C4 ratio). It can be seen that for most of these countries initial commoditydependence of agriculture was very high, with 20 of the 32 countries exhibiting agriculturalC4 ratio larger than 80 percent. However, over the next twenty years, for more than half ofthese countries their C4 ratio either increased or hardly changed.The second structural development concerns changes in the production structure of Africanagriculture. After 1980, almost all African countries adopted stabilization and structuraladjustment programs that intended in transforming their economic sectors towards moretradable commodities. This was particularly intended for agriculture, which had beencharacterized by many institutional and market rigidities and government monopolisticinterventions. However, two decades after the onset of such programs the share of agriculturalproduction that is accounted for by exportable and importable products in Africa does notappear to have changed very much. The average share of the value of exportable productionin the value of total agricultural production for 24 low income African countries in 2001-3was estimated by the authors to be 21.8 percent compared to 23.1 percent in 1980-82. As forthe share of import substitute products in total agricultural production over the same period,this seems to have stayed the same from 24.7 percent in 1980-82 to 25 percent in 2001-3.Turning to medium term food outlook, we present some projections of net imports of the FAOCOSIMO model that pertain to LDC Sub-Saharan Africa (SSA) 2 . Figures 6, 7 and 8 indicatethat based on current estimates, LDC SSA is projected to become an increasing food deficitregion in wheat and rice, while in the coarse grain sector, a sector that includes maize, milletand sorghum, LDC SSA is expected to continue being on balance self sufficient. Clearly this2 this group includes 36 countries of Eastern, Southern and West Africa as follows: Burundi, Comoros, Djibouti,Eritrea, Ethiopia, Madagascar, Malawi, Rwanda, Somalia, Sudan, Uganda, Angola, Central African Republic,Chad, Dem Republic of Congo, Equatorial Guinea, Lesotho, Sao Tome and Principe, Benin, Burkina Faso, CapeVerde, Gambia, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Senegal, Sierra Leone, Togo,Mozambique, Tanzania, and Zambia

suggests that as LDC Africa becomes more dependent on international markets, it willbecome more exposed to international market instability.The final issue concerns the combination of undernourishment with high import dependenceon grains and petroleum, two of the commodity groups that have experienced high pricesrecently. Table 5 exhibits the countries in the world with highest levels of undernourishmentand simultaneous high shares of imported petroleum and high shares of imported grain. It canbe seen that most of these countries are in Africa.The conclusion of this descriptive exposition is that many LDC countries in Africa, especiallythose with large degrees of undernourishment, have become more food import dependent,without becoming more productive in their own agricultural food producing sectors, orwithout expanding other export sectors to be able to counteract that import dependency. Thisimplies that they may have become more exposed to international market instability andhence more vulnerable.4. Food dependency and vulnerability to commodity shocks at the household levelRisk and especially the contribution of risk to poverty dynamics are of growing importance inthe literature on poverty. Risks contribute to poverty in a number of ways. Firstly, risks mayblunt the adoption of technologies and strategies of specialization necessary for agriculturalefficiency (Carter, 1997). For example, households with limited options for consumptionsmoothing grow lower return, but safer crops (sweet potatoes, sorghum and millet) than thericher households which usually have more options for consumption smoothing. Risks maymotivate farmers to apply less productive technologies in exchange for greater stability(Morduch, 2002, Larson and Plessman, 2002). The cost of such an income-smoothingstrategy can be high and a farmer may forgo up to 20 percent of his or her expected income toobtain a smoother income stream (Dercon, 1996). Secondly, risks may function as amechanism for economic differentiation within a population, deepening the poverty and foodinsecurity of some individuals even as aggregate food availability improves (Carter, 1997).Thus, in the absence of risk management instruments, risky events may plunge particularlyvulnerable households into poverty. The policy message emanating out of these insights arethat risks are detrimental to the welfare of (poor) households.A household facing a risky situation is subject to future loss of welfare. The likelihood ofexperiencing future loss of welfare, generally weighted by the magnitude of expected welfareloss, is called vulnerability. The degree of vulnerability depends on the characteristic of therisk and the household ability to respond to risk through the risk management strategiesdiscussed above. Households face risks namely exposure to uncertain events. To contend withrisks households make use of a number of risk management options. Risks combined withresponses lead to outcomes. Thus a household is said to be vulnerable to the outcome of anuncertain event, if it does not have sufficient resources to adequately contend with theoutcome of the event. In other words, the extent to which a household is vulnerable to anuncertain event, namely the extent to which the household can become and/or remain poor,depends on the size of the shock and how effective the household is in managing the uncertainevent both ex-ante, as well as ex-post.Households in developing agrarian economies in Africa face many risks, but among the manyrisks faced, recent research suggests that commodity price changes (both declines but alsoincreases), droughts, and health shocks are the major risk factors both in terms of thefrequency of their occurrence as well as the severity of their effects (Hoffman, Christiaensenand Sarris, 2007).

The impact of sharp increases in food prices in the short run depends very much on whetherpeople are mainly producers or consumers of food. A low income household that spends alarge proportion of its income on tradable food staples (wheat, rice, maize) is more likely tosuffer a decline in overall welfare. The extent of this decline depends on the ability of thehousehold to shift consumption towards less expensive foods. On the other hand, householdsthat derive some part of their income from the production and sale of internationally tradedstaples could benefit from higher world prices, although high fuel and fertilizer prices arelikely to offset some of the gains households could earn. In Africa, despite beliefs to thecontrary, most households, and especially rural poor households are net staple food buyers.For instance in the latest State of World Food Insecurity published by FAO in 2008 (FAO,2008) it is indicated that in Ghana about 79 percent of all households (72 percent for rural and92 percent for urban) are net staple food buyers. In Malawi the share of net food buyinghouseholds is larger at 93 percent (93 percent rural and 96 percent urban). Table 5 makes thesame point for several countries in Eastern and Southern Africa, and also indicates the bulk ofstaple food sales (maize in this case) come from a very small share of producers. Clearly thenincreases in staple food prices are more likely than not to have a negative impact on mosthouseholds.5. Price transmission between international food commodity markets and domesticAfrican marketsA fundamental issue when analysing the impact of the current food price boom is the extent towhich market prices in developing countries respond to changes in international food prices.Price transmission from the international to domestic food markets is central in understandingthe degree to which producers and consumers are affected by international price increases andin formulating policies to manage such sustained price increases.Studies on the transmission of price signals are based on the concept of competitive pricing.The classical paradigm of the Law of One Price suggests that, in the long run, pricetransmission is complete with prices of a commodity sold on competitive foreign anddomestic markets differing only by transport costs, when converted to a common currency.Such a complete price pass-through is attained through trade. Changes in supply and demandin one market will affect prices which in turn will instigate trade with other countries. Asarbitrage and trade restores the market equilibrium, prices in the domestic market equalizewith those in foreign markets – hence the term ‘Law of One Price’.Poor price transmission may arise due to a number of reasons. In general, the implementationof import tariffs, or export taxes will allow international price changes to be fully transmittedto domestic markets in proportional terms, unless tariffs or taxes are prohibitively high. Veryhigh tariffs or taxes obliterate opportunities for arbitrage and result in the domestic andinternational prices moving independently of each other, as if an import or export ban wasimplemented. In the context of the current food price hike, many governments in developingcountries have implemented short-run border measures, such as import tariff reductions orexports bans, in order to harness domestic price increases and shield consumers andvulnerable populations from increased food costs. A decrease in import tariffs results in partlyoffsetting the impact of international price increases to the domestic market, as compared tothe previous tariff level, given stable exchange rates. For a net exporting country, theimplementation of an export ban will also hinder the transmission of price signals from theinternational market, exerting downward pressure to the domestic price level.Policies that aim in stabilizing domestic prices at a certain level are often implemented inconjunction with border measures. Government intervention in the form of food procurementor sale and inventory management is commonly practiced across African countries. Such

policies hinder price transmission depending on governments’ ability and budget to realizefood purchases at certain price levels and manage the food inventories continuously. Eventhen, depending on domestic market fundamentals, trade takes place and the international anddomestic prices may not be completely unrelated, with the intervention policy resulting onlyin slow international price pass-through.Apart from policies, domestic markets can also be partly insulated by large marketing marginsthat arise due to high transport costs. Especially in developing countries, poor infrastructure,transport and communication services give rise to large marketing margins due to high costsof delivering the locally produced commodity to the border for export or the importedcommodity to the domestic market for consumption. High transfer costs hinder thetransmission of price signals, as they may prohibit arbitrage. As a consequence, changes inworld market prices are not fully transmitted to domestic prices, resulting in producers andconsumers adjusting, if at all, partly to shifts in world supply and demand.Oligopolistic behaviour and collusion among domestic traders may retain price differencesbetween international and domestic prices in levels higher that those determined by transfercosts. Consumer preferences may also result in incomplete price transmission even undercompetitive and free market conditions. Domestically produced food often has differentattributes than those characterizing internationally traded food commodities. As consumersprefer the attributes and taste of domestically produced food, the possibilities of substitutionin consumption between domestic and foreign foods are limited, resulting in incomplete pricetransmission as domestic market shocks cannot be smoothed out by trade with theinternational market.Econometricians describe the data and use it to quantify economic relations which representthe structure of a market in a model. These models are used to provide policy advice andgenerate forecasts. Price transmission analysis adheres, more or less, to the above sequence oftasks and it is often based on a statistical toolkit called vector autoregressions, or VARs. 3Vector autoregressions provide the basis for capturing the rich price dynamics in both theshort- and the long-run, while in the context of price transmission, these models provide abasis for assessing the correlation between prices of different markets, understanding thenature of the relationship between them along time and, consequently examining the extent ofprice pass-through.This kind of analysis falls short from providing a description of the structure of the marketwhich gives rise to the prices. This is a formidable task, as it entails the modelling of a largenumber of economic relationships. For each country under examination, functions thatexplain total utilization, arbitrage, in terms of trade volumes and trade flows with partnercountries, as well as public and private inventory behaviour have to be estimated resulting invery high data requirements that cannot always be met when analyzing developing countries’markets.Vector autoregressions have significantly lower data requirements and in order to assess pricepass-through one needs data on domestic market and international market prices only. Inbrief, VARs and the statistical toolkit allow us to assess price transmission by:• Testing for price co-movement. Co-movement can be thought of as a long run linearequilibrium relationship between prices which is determined by arbitrage and marketforces. In fact, co-movement satisfies the transitivity property, so if the international3 VARs are simple linear models and in the context of price transmission between two markets they consist of two equations,one for each market price, in which the current price of one market is explained by its own lags, as well as the lagged pricesof the other market.

price of maize co-moves with the South African price of white maize, which, in turn isfound to co-move with the price of white maize in Mombassa, then these relationshipssuggest that the prices in Mombassa co-move with those of the international market.• Examining the short-run dynamics and the adjustment to the long-run relationship.In the short run, co-moving prices may still affect each other or may drift apart, asshocks in one market may not be rapidly transmitted to other markets. In this case,market forces will ensure that these divergences from the underlying long-runrelationship are transitory and not permanent. Assessing the rate of adjustment to thelong-run equilibrium provides information on the speed with which changes in pricesin one market are filtered to the other market. Complete adjustment to shocks in shortperiods of 1 or 2 months is not impossible to observe, but it is a characteristic of largeand efficient markets. In developing countries, with national and regional markets thatare separated by poor transport and communication infrastructure, adjustment isslower. For example, in export-orientated sectors in Africa, such as the Ethiopiancoffee market where price discovery is based on an efficient auction system, fulladjustment of the price of Ethiopian arabica coffee to the international coffee pricetakes place in 3.7 months.• Finding the direction of the flow of price information or the direction of causality.Price information is vital for the efficient functioning of a market. Large physicalmarkets, auctions and commodity exchanges react to new information in theinternational prices, but also provide information to smaller physical markets. In manycountries, trade is facilitated by using prices from futures exchanges as a benchmarkfor setting the price for physical transactions. As information flows from one principalmarket to another smaller market, any shocks will also be transmitted and the directionin which price information flows provides additional information for policyimplementation. 4Price transmission between several maize markets in South Africa, Kenya, Zambia, Malawi,Uganda and the international market has been tested for this paper. In these countries, withthe exception of Uganda, maize consists of the main food staple, while all countries areengaged in trade in the Eastern and Southern Africa regions and, at a lesser extent, with therest of the world. Apart from high transaction costs, there is a number of reasons for whichprice transmission from the international market to the markets of these countries may not becomplete.• In Eastern and Southern Africa, consumers prefer white maize rather than theinternationally traded yellow maize. Such limited possibilities in substitution inconsumption may tend to make these markets being dependent on regional supply anddemand shocks only which cannot be smooth out through trading with theinternational market. As it is the price of internationally traded yellow maize that hasbeen experiencing a sustained increase, these white maize producing and consumingcountries may experience increases that are due to regional market conditions ratherthan due to co-movement with the international price of maize.• Most of the countries under examination implement price stabilization policies. InKenya, the state-run National Cereals and Produce Board (NCPB) is involved inprocurement of domestically produced maize and inventory management. In Malawi4 As shocks in smaller markets are caused by the shocks in larger markets, the technical term characterizing this exchange ofinformation reflects Granger-causality. It is important to note that Granger-causality is related to the flow of priceinformation between markets and does not refer to economic causal relationships between variables such as the relationshipbetween the quantity demanded and the price of a good.

and Zambia, the Agricultural Development and Marketing Corporation (ADMARC)and the Food Reserve Agency respectively maintain a strong presence. Pricestabilization policies may blunt the transmission of price signals from the internationalto the domestic markets.The relationship between prices of yellow and white maize is of central interest for Easternand Southern Africa. Co-movement between the price of locally produced white maize andthat of the internationally traded yellow maize would imply that these markets are integratedand consequently, in the ling-run, the international maize price would be likely to affect pricesthese regions. On the other hand, lack of co-movement would mean that sustained increases inthe international yellow maize price would not be likely to affect the Eastern and SouthernAfrican white maize markets.South Africa produces and trades both yellow and white maize and its futures exchange, theSouth African Futures Exchange (SAFEX) offers an efficient mechanism for trade and priceinformation exchange in a competitive market with minimum government intervention.SAFEX offers futures contracts on both white and yellow maize, while traders in SouthernAfrica use it as a benchmark for pricing physical trade.Co-movement was tested between price pairs formed by the South African SAFEX spotprices for both yellow and white maize, P SA Y and P SA W respectively, and the price of the USyellow maize No. 2, Gulf, P W . The tests were performed utilizing data spanning from January1998 to July 2008. The results are shown in table 7. There is strong evidence that SouthAfrican prices of both yellow and white maize co-move with the international price in thelong run. In the short run, there is no significant evidence that international prices affect theSAFEX yellow and white maize prices either instantaneously or with some delay of one ortwo months. Price information flows from the international market to both South Africanyellow and white maize prices, suggesting that shocks in the international market are passedthroughto South Africa in the long run.Both SAFEX prices adjust to their long run equilibrium with the international price slowly,with full adjustment to international price shocks or trend taking approximately between 7and 8 months. On balance, there is strong evidence that the South African maize markets areintegrated with the international market in the long run. The relatively slow adjustment toshocks, however, suggests that this relationship cannot be readily observed, as in the short runprices may be perceived as drifting apart, in spite of sharing the same underlying long-runtrend.Kenya is an important producer and consumer of maize with a net importing position duringthe period 1998-2008. South Africa and, to a lesser extent the US account for the larger shareof imports, while other African countries in the Eastern and Southern African regions, such asMalawi, and Uganda, also export maize to Kenya. Import tariffs, the NPBC price support andhigh transaction costs have combined to result in high maize prices as compared to pricelevels of the international market but also of other African maize consuming countries.Table 8 exhibits the results of empirical analysis for Kenya. For all markets examined, theanalysis provides strong evidence that prices co-move with the international price in the longrun.In Eldoret and Kisumu, at the eastern part of the country, prices also are found to directlyco-move with the SAFEX white maize price. The tests provide moderate evidence for comovementbetween the market prices of both Nairobi and Mombasa and the South Africanwhite maize price. Nevertheless, strong evidence of co-movement with the internationalworld price ensures that prices in these markets should, in practice co-move with the SouthAfrican prices, but probably at very slow rates of adjustment.

In the short-run from one month to another, Kenyan prices are not affected by shocks in eitherthe international or the South African price. On average, Kenyan maize prices tend to adjustto their relationship with the international market price relatively slow, in approximatelybetween 6 and 9 months. Adjustment to South African market prices is even slower forEldoret and Kisumu, in line with the slow response of the South African markets to theinternational prices, mentioned above. Prices in Kisumu, a border town, appear to adjust toboth international and South African prices relatively faster than the prices in other markets.Kenyan maize markets appear to be well-integrated with each other. In addition, maize pricesin Kisumu are also found to co-move with prices in Uganda, suggesting that in the long-run,price signals pass-through from one country to the other.Malawi is an important producer of maize with a net importing trade position for most of theperiod 1998-2008. South Africa is the main source of imports, however Malawi also importsfrom the US and other countries in the region, depending on the country’s market conditions.Tqable 9 reports the results of the empirical analysis for Malawi. There is strong evidence thatmost of the maize markets in Malawi are well-integrated with both the international and theSouth African maize markets. With the exception of Mziba and Mchinji, in many markets,maize prices were found to be in long-run equilibrium with the international maize price andthe SAFEX white maize price. It takes approximately 4.7 to 6.6 months for full pricetransmission. On balance, the results indicate that in the long-run prices in Malawi do followthe underlying trend of both the international and the South African prices.Short-run effects between maize prices in Zambia and the international and SAFEX prices arefound to be insignificant, suggesting that the relationship between prices in Zambia and in theinternational and the South African is determined only by their long run equilibrium, while inthe short-run prices may drift apart due to local market conditions. With the exception ofBangula, adjustment of prices in Zambia to the international price is revealed to be slow. Onaverage, it takes a period of 4.7 to 7.7 months for the prices to fully adjust to a change in theinternational price. Prices in Bangula, an area close to the border with Mozambique, adjust tointernational prices within 3.8 months.Within the country, prices of different markets, including Mziba and Mchinji, co-move witheach other, while in the short-run, price shocks in one part of the country do affect othermarkets, however not to a full extent. In general, adjustment takes place in approximately 5months, suggesting that transport costs may hinder rapid price transmission within thecountry.Zambia usually imports maize depending on the climatic conditions that determine the size ofthe harvest. South Africa consists of the main source of maize imports, while exports from thecountry are negligible. Table 10 exhibits the results of maize price transmission analysis forZambia. Prices in all markets co-move with the South African price of white maize. Marketsin Kabwe, Kasama and Ndola also appear to be well-integrated with the international markethowever, there is moderate or weak evidence that prices in Lusaka, Chipata and Choma are inequilibrium with international prices in a direct manner. Shocks in international markets areexpected to slowly affect these domestic markets through their relationship with the SAFEXprice of white maize.In the short-run, both international and South African maize prices were found not to affectmarket prices in Zambia, with the relationship between Zambian prices and international orSAFEX prices being determined only by their long-run equilibrium. Adjustment to thisequilibrium relationship is estimated to be, in general, slow. Prices of most Zambian marketsappear to adjust to their relationship with the South African prices in approximately 5 to 8months. Prices in Ndola are found to adjust to both international and South African price

shocks quite rapidly in the course of 3 months, probably due to Ndola’s position on the borderwith Congo. Within the country, domestic markets are found to be well-integrated betweenthem, indicating that transport costs and other factors do not hinder the flow of informationfrom one part of the country to another. The extent of integration between different markets inZambia, suggests that price shocks in one market are expected to be smoothed out quiterapidly in approximately 2 months.The overall conclusion of this empirical analysis is that world prices for staple products aretransmitted only imperfectly within African countries in the short term, albeit eventually theyare transmitted fully after some time. Hence the impact of any world commodity shocks arelikely to be felt with a lag.5. The impact of food price increasesThe impact of the food price rise on households is diverse. Food price increases generatebenefits for net food producers, but significantly worsen the welfare of net food consuminghouseholds, presenting a significant threat for the poor. Households react differently to priceswings depending on their ability to expand the production of food, their consumptionpatterns, their initial asset endowment and the manner they diversify income. The constraints,households face in terms of liquidity and access to markets are also important determinants oftheir behaviour.The net position of households in the market, that is whether households are net sellers or netbuyers of food, will, on average, determine the impact of price changes on income, foodsecurity and poverty. While in general, urban households that are net staple food buyers willlose, as they have to pay more to maintain adequate diets, rural households, especially thosethat are involved in the production of staple foods may benefit to a certain extent. The shortrun impact of the food price boom on poverty is of particular concern especially in lowincome developing countries. Undoubtedly, poor urban households constitute the mostvulnerable population group. Non poor urban dwellers will also be negatively affected as nonfarmwages may not be adjusted fast to the increases in the general price level, brought aboutby the food and energy price upswing.Although, poor rural households are, in general, engaged in the production of food, they mayalso be negatively affected, especially if their production does not meet their foodrequirements and additional food has to be purchased. Net food selling rural households willgain, however the size of the price windfall will depend on their ability to increase supply andmarketed surplus. Landless rural households may also benefit, as increasing prices maystimulate supply and the demand for farm labour thus increasing wages and/or employment.The ability to adjust to the new market environment will also determine household welfareand the impact on poverty and food security. Substitution possibilities in consumption mayresult in offsetting part of the negative shock, depending on the extent of staple fooddiversification and relative price changes.For rural households, the extent to which production can respond to increasing prices dependson a number of factors, such as access to markets for output and inputs, access to technologyand adequate capital. In developing countries, markets are often missing as poor infrastructuregives rise to high transaction costs, while access to credit and technology is limited. Farmersare characterized by small holdings, poor technology and lack of specialization in productionwhich hinder the response to higher prices. Access to inputs, such as fertilizers and seed isalso limited. Input marketing chains are not well developed as private sector participation isplagued due to the small size of markets and their geographical dispersion. These, in

conjunction with variable demand and high transaction costs, limit the economies of scale,necessary for the development of marketing chains.The demand for fertilizer is affected by cash constraints, uncertainty and risk and, during thepast two decades, by the persistent low profitability of food production in relation to cashcrops. These problems in input markets are aggravated by the recent rapid upsurge in theinternational price of fertilizers. As fertilizer prices increased faster than food prices,expectations on lower profitability have resulted in significant reduction in the use offertilizers, hindering supply response and affecting the livelihood of smallholders.Sustained food price increases will have a significant impact on the rate of poverty. Onaverage, a number of studies suggest that most of the poor are net consumers of food andtherefore, are adversely affected by the food price upswing (Poulton et al. 2006). Poor ruralhouseholds are often characterized by no or insignificant marketed surplus. Small land assetsand limited access to inputs due to cash constraints, as well as limited access to outputmarkets due to distance and poor infrastructure contrive to the households beingpredominantly net buyers of food.A number of researchers have attempted to measure the implications of the food priceupswing for poverty in developing and less developed countries (Ivanic and Martin, 2007;Polaski, 2008; Wodon et al. 2008; Wodon and Zaman, 2008). These analyses utilize severaldifferent methodologies and apply them to household survey data from a number ofdeveloping countries. In general, the results suggest that, on average, food price increases willresult in increased poverty. Across countries, increases in food prices will have very diverseeffects depending on the structure of the economy, the linkages of agriculture with othersectors, the households’ net position towards food markets, as well as on the distribution ofhouseholds around the poverty threshold. Nevertheless, in most cases, increases in povertyoccur more frequently than reductions.Ivanic and Martin (2008) estimate the impact of food price increases on poverty on a numberof low income countries, utilizing a Computable General Equilibrium (CGE) model and ameasure of poverty defined by the standard 2007 World Bank World Development Indicators‘dollar-a-day’ expenditure. Although the direction and magnitude of the effect varies acrosscountries, on average, their findings suggest that the overall impact of higher food prices onpoverty is negative for the majority of countries examined. For example, high food prices areexpected to increase poverty in Nicaragua, Zambia, Pakistan and Madagascar, while povertyin Peru and Vietnam may contract to some extent due to a significant number of householdswhich are net producers of rice. On the whole, apart from a few cases where food priceincreases led to a reduction of poverty in rural areas, in most cases the number of poorhouseholds, in both urban and rural areas is expected to increase.Wodon et al. (2008) analyze the impact of the food price upswing on a number of West andCentral African countries. Their results suggest that on average, a 50 percent increase in theprice of selected food items will result in an increase in the share of population in povertybetween 2.5 and 4.4 percent, as a large share of food is imported and the negative impact onfood consumers outweighs the positive effect on the net sellers of locally produced goods.Wodon and Zaman (2008) find that the impact of increasing food prices on sub-SaharanAfrican countries is significant, with a 50 percent increase in the prices of selected foodsresulting in a 3.5 percent increase the poverty headcount. This result implies that, at anaggregate level, for all sub-Saharan Africa, which has a population of 800 million, the foodprice upturn could have led to an increase in poverty of approximately 30 million.

Another study by Polaski (2008) focused on the effect of food price changes in India, thecountry with the largest number of poor in the world, where over 80 percent of the populationlive on less than US$2 per day. The study sheds light to another dimension of the impact offood price upswings on developing countries, by exploring the links between agriculturalprices and the sources of income for both rural and urban poor. The analysis suggests thatlabour markets play a largely positive role in transmitting the price effects to the economy. Asimulation of a 50 percent increase in the price of rice utilizing a CGE model suggested thatfood price increases would benefit most poor households and, especially those that identifywith vulnerable social groups and landless who supply their labour to the agricultural sector.The findings suggest that the poorest households and the most disadvantaged groups couldexperience the largest gains, up to 6 percent increase in income, while only the richest 10percent of rural households would lose from a price increase. The reason is that food priceincreases can lead to an increase in the demand for farm labour and increased income for ruralworkers, with illiterate workers and disadvantaged groups being potentially the largestgainers. The findings also suggest that the impact on urban households can be diverse, withsome poor households gaining slightly and others losing slightly. Illiterate urban workers andother vulnerable groups can experience income increases, while the results for other urbanworkers showed a mix of small gains and small losses with no consistent pattern.Additional research carried out under the direction of the authors, examined the impact of anincrease in food prices on consumption, household food expenditure and poverty in Malawi,Zambia and Uganda. The analysis was based on food demand system models that wereestimated utilizing past household survey data sets and sheds light to the importance of staplefood diversification in offsetting the negative effects of food price increases. 5 While everyeffort was made in order to analyse the most recent data, the choice of the data sets wasdetermined by their richness in terms of information on variables, such as food prices, as wellas on information that would allow the determination of total and agricultural income. For thedemand analysis in Malawi and Zambia, we used the Integrated Living StandardMeasurement Survey 2004 and the Living Conditions Monitoring Survey I 1996 respectively.For Uganda, we used the National Household Survey II 1996. A preliminary comparison ofthe consumption patterns reflected by these data sets and current national average per capitaconsumption data from FAOSTAT suggested that consumption patterns have not changedsignificantly during the last decades, with the possible exception of wheat and cereals otherthan maize, for which consumption has increased particularly by urban households.The household classes for each country are defined according to four factors, namely, foodexpenditure, location, contribution of agricultural activities in total income, land ownershipand gender. In more detail, households were classified into food secure and food insecure byutilising thresholds set by the World Bank, and further divided according to whether they arelocated in urban and rural areas. Food insecure rural households are also presented accordingto land ownership, gender and the share of income from all agricultural activities. Rural foodsecure households are divided only according to the share of income from all agriculturalactivities. This approach draws attention to the extent to which household characteristics, such5 The empirical work involved the estimation of demand functions for several food items and aggregates, in adouble-log specification with quantities of an item consumed been the function of the price of this item, as wellas the prices of all other goods. The estimation was carried in two steps, in order to take into account theinformation contained by zero observations. As for a specific food item, only a subset of households consumedpositive quantities, the Heckman two-step estimator was used in order to correct for selection bias. The first stepinvolved the estimation of a probit model that was used to predict the probability of a household consuming aparticular food item. In the second step, the demand equations were estimated, incorporating a transformation ofthe predicted individual probabilities as an additional explanatory variable.

as income diversification and consumption preferences, result in more or less vulnerability tofood price spikes.We do not attempt to simulate an increase on all food items due to the lack of specificinformation on prices of foods that are locally produced. Instead, we hypothesize a situationwhere only the prices of grains and meat, food commodities that are internationally traded,increase by 50 and 20 percent respectively. However, anecdotal information suggests that theprices of locally produced foods also rose mainly due to the increase in transport costs, aswell as the strengthening in demand as consumers substituted grains for other local staples.For these reasons, our results may not accurately estimate the impact on consumption ofincreased food prices. A number of additional assumptions are made in order to provideestimates on changes in the level of poverty. First, we assume that households react to foodprice increases only by decreasing consumption of more expensive foods and substituting onefood for another. This overlooks that households can smooth consumption by utilizingsavings in the form of near-liquid assets, or by increasing their supply of labour both in-, aswell as off-farm. Second, it is assumed that producing households do respond to the price riseby increasing supply. For this reason, our results pertain only to the short run.Tables 11 and 12 summarize the results for Zambia and Malawi. In general, our resultsprovide some indication of the extent of the impact of food price increases on poverty.Nevertheless, the value of this particular simulation lies more on the comparisons of theimpact across countries and the importance of income and staple diet diversification. In bothMalawi and Zambia, maize is the most important staple.In Zambia and Malawi, the annual per capita consumption of maize amounts to an average of150 kg and 193 kg respectively. The food price upswing is expected to result in householdsresponding by reducing the consumption of foods of which prices increased and exploitingsubstitution possibilities according to relative prices. For Zambia and Malawi, the simulationof a multiple price shock (50 percent increase in the prices of maize and other cereals and of20 percent in the price of meat) suggests that consumers reduce the consumption of maize andother cereals by 15.6 and 8.5 percent respectively. The consumption of other cereals, whichdo not constitute staple foods in this region, contracts by approximately 20 percent in bothcountries. The hypothetical increase of 20 percent in the price of meat is expected to result ina 16.6 percent reduction in its consumption, as the demand of meat is characterized by arelatively high own-price elasticity. On average, poor and food insecure households reducethe consumption of maize, the main staple food, to a lesser extent as compared with non poorand food secure consumers. Evidently, the consumption of other foods increases, with theextent of the changes been determined by the estimated cross-price elasticities.In both Malawi and Zambia, the decrease in the consumption of maize and other cereals doesnot offset the impact on the price rise on food expenditure. On average, total food expenditurein Zambia rises by 8 percent and in Malawi by 9.7 percent. For Zambian poor households,food price rises result in total food expenditure increasing by 8.6 percent. In Malawi, wheremaize consists of a high share, approximately 40 percent, of total food expenditure, total foodexpenditure increases by 16 percent in spite of the contraction in consumption.High food prices and the corresponding increases in food expenditure result in increases in therate of poverty, as households experience a decrease in their purchasing power. In Zambia,food prices rises result in a 5.4 percent increase in the number of food insecure. Thecorresponding increase in the number of food insecure in Malawi is significantly larger andamounts to nearly 16 percent. Although the majority of producers in these countries are netfood buyers, these results reflect the maximum impact of food price increases, as the possible

positive effect on production, or the increase in the value of food stocks held by the householdare not taken under consideration.The household classifications also reveal the extent to vulnerable population groups, such asthe landless and households headed by women, are affected by the food price upswing.Nevertheless, such a simulation exercise does not reveal the overall impact of higher foodprices. This becomes more significant if the decrease in the well-being of the already poorhouseholds is considered. Although the above analyses highlight clearly that many non poorhouseholds face increased food expenditure and fall below the poverty threshold, the foodprice upswing has a significant negative impact on households that are already poor and foodinsecure and deepens poverty.Comparisons of the impact of the food price increase across countries highlight theimportance of staple diet diversification in maintaining food security during food priceepisodes. We assess the role of diet diversification in offseting part of the high food priceimpact by conducting a separate simulation of a 50 percent increase in the price of maize inZambia, Malawi and Uganda (Table 13).In Uganda, maize consumption amounts to an average of 29 kg per capita, a quantitysignificantly lower than that consumed in both Malawi and Zambia. Maize’s share in totalfood expenditure amounts to about 8 percent. In addition to maize, Ugandan householdsconsume a variety of staple foods, such as rice, millet, matooke and cassava. Both matookeand cassava are important staples, which are produced domestically and consist to about 20percent of total household food expenditure. Although prices of rice and millet also rose, theprices of matooke and cassava, foods that are not internationally traded, exhibited weakerincreases of about 35 and 20 percent respectively, as compared to an increase of 75 percent inthe price of maize (Benson, Mugarura and Wanda, 2008).The simulation results suggest that as maize prices increase, its consumption decreasessharply by approximately 30 percent. Both food secure and insecure households exhibitsimilar response to the increase in the price. Total food expenditure rises by a very smallproportion, leading to less than one percent increase in the number of food insecurehouseholds in Uganda, as compared to an increase of 13 percent in Malawi.An additional simulation of a wider increase in food prices in Uganda, including increases inthe prices of other cereals and matooke by 20 percent, results in an increase of 2.5 percent inthe number of poor households. These findings suggest that the range of domesticallyproduced staples in conjunction with the relatively large quantities consumed moderate thenegative impact of the international price boom on Ugandan households.The above simulation results are partial equilibrium in the sense that both a small number ofprice increases are considered, and also as the full adjustment within the economy is notconsidered. There is little such analysis of commodity price booms. Tables 14 and 15 presentsome results from a paper done by Conforti and Sarris in 2008. Their analysis first showedthat the commodity price changes facing a country like Tanzania amounted to a 6 percent ofGDP negative shock to the economy. The reason for this is that while the agricultural pricerises implied a positive shock, the large petroleum crises imlied an even larger negative priceshock, as Tanzania relies very much on petroleum imports. A CGE model of Tanzania tried totrace these effects through the economy and the households. The results suggest a largenegative impact on GDP and all types of households as indicated in the two tables. Clearlythen a positive food price shock does not necessarily imply a positive shock for the economyas a whole, as it may be accompanied by other simultaneous negative shocks.

7 Conclusions and policy implicationsThe above exposition indicated that African countries are increasingly exposed tointernational staple food commodity markets, while not becoming more productive in theproduction of these staples domestically. While this international instability does not appearto be growing over time, it has not diminished, and given the growing exposure, presents alooming challenge to African economies. The result is a situation where many of the poorerAfrican countries find themselves increasingly vulnerable to the vagaries of the internationalmarkets. Asian economies that have found themselves in similar situations have chosen toisolate their domestic markets, in order to provide stable domestic markets for food staples. InAfrica several countries used to have similar policies but these have been abandoned in favourof more open and liberal trade regimes, and domestically by the private sector supplantingmost of the former government food parastatals. This development has created a situationwhereby more international price variability is transmitted domestically, albeit as was seenearlier quite imperfectly.Food market instability is not uncommon for most African households, as the domestic staplecommodity markets normally experience sharp price differences between different periodswithin a marketing year, and also from year to year. In fact, given the low and slow degrees ofinternational market transmission, and the significant domestic climate induced foodproduction shocks, most African households experience higher degrees of food priceinstability than what is evident in international markets. For instance Deaton and Laroque(1992) report annual coefficients of variation (CVs) of annual international prices of cotton,maize, rice sugar and wheat, computed over 1900-1987 in the range of 0.35 to 0.6. Bycomparison Sarris and Karfakis (2008) report domestic CVs of monthly prices of variousagricultural products in two regions of Tanzania over a twenty year period (1983-2002) in therange of 0.26 to 0.81. To deal with such instability, households in Africa have alreadydeveloped a variety of consumption smoothing and other coping mechanisms. However, suchmechanisms, while effective for small and short lived negative price shocks, may not beadequate for shocks that last longer or are sharper than normal. Such shocks can createirreversible situations, where households are drawn into deeper or more permanent poverty, orassets are depleted to points that affect considerably the subsequent growth of householdincomes.It is interesting in this context to contemplate policies to deal with the recurring external andinternal food market shocks. Policies that aim to manage food price swings can be classifiedaccording to where government chooses to intervene in the food supply chain. There are traderelated policies that are implemented at the border and tax reductions that aim to lessen theimpact of high prices at the wholesale or the retail link of the chain. Domestic marketinterventions through the management of food inventories also target the wholesale level. Thekey distinguishing feature of most of these policies is that they alter the state of the market,most notably the prices of food and inputs, thus affecting all households both poor and nonpoor. The policy choices of the government focus on mitigating the impact of internationalprice shocks, thus letting producers and consumers to adjust to a price different than thatprevailing in the international markets. Food safety nets, commonly implemented indeveloping countries, as well as targeted input subsidies aim directly at consumers andproducers respectively. These policies do not directly affect market prices at the nationallevel, as they involve transfers to specific vulnerable population groups and they will not beconsidered here, as we shall concentrate on market policies.Trade policies consist of restrictions on food imports or exports through taxation, quotas orbans and result in a change in the relative prices of traded goods in the economy. These

policies affect all households with the impact being determined by the households’consumption patterns, income level and diversification. Consequently, trade policies havealso distributional implications and unintended effects in contrast to, for example, targetedmicro-level policies. Policy instruments, such as import tariffs can easily be scaled up ordown in order to manage the price swing in a counter-cyclical manner. For food importingcountries, the temporary reduction in tariffs may result in offsetting increases in internationalprices, while tariffs can return to their former level when international prices fall.In the context of the food price upswing, both import tariffs reductions, as well as exportrestrictions have been applied by many countries in Africa. In general, the reduction or theelimination of import tariffs on food products has been the most widespread policy responsefor net food importing countries. As world prices increased, many countries lowered appliedtariffs initially, eventually eliminating them in an attempt to stabilize the domestic food pricelevel. The effectiveness of such tariff reductions is determined by the level of initial appliedtariffs, which for most African countries is rather small. For example, for a sample of 60 lowincome food deficit countries (LIFDCs) surveyed in 2008 by FAO, applied tariffs on cerealsand key vegetable oils were already fairly low in 2006 in the range of 8 and 14 percentrespectively. Tariffs were much lower than these averages for a majority of these LIFDCssuggesting that these applied rates, when reduced to zero, were adequate for offsetting only asmall part of the overall rise in the international prices which were higher by at least 50percent in 2008 as compared to the 2006 level. Other policies that aim to reduce the cost offood imports include tax breaks for importers in terms of VAT reduction on selected importedintermediate of final food goods.Many developing countries have implemented some form of export restrictions in an attemptto ensure domestic food security. According to the WTO rules for export, taxes are allowedwhile export bans are prohibited. Export taxation results in the windfall been allocated to thepublic sector, where the public sector often has poor forward linkages through investment.Although, export restrictions lower domestic prices and thus support consumers, at the sametime they imply a tax to producers, lowering the incentive to respond to the international priceincrease. In the long run, export restrictions may discourage investment on agriculture inexporting countries, having negative implications for food security. In the short run, concertedimplementation of export restrictions by important exporters renders the international marketunreliable as a source of food and exacerbates international price instability harmingconsumers in other countries and leading towards import-substitution and inefficientallocation of resources.Many countries initiated cuts in taxes in a number of food products. Such reductions offsetpart of the increase in food prices and provide some relief to poorer consumers who spent alarge share of their income on food. In addition to tariffs, taxes can also be used in order tomanage the price swing in a counter-cyclical manner. In developing countries where a VATsystem is in place, governments consider applying different VAT rates to food items. Such taxrelated policies are effective in lowering food prices only if the food retail sector iscompetitive. Oligopolistic retailers may exercise their power over the market and increasetheir price margins, thus hindering the passing-through of tax reduction to the consumers.Therefore, it is important that a competition authority is present for monitoring retail foodprices and for regulating and enforcing competition and consumer protection laws. Although,as in the case of import tariff reductions, tax reductions will benefit both poor and non-poorconsumer, a degree of targeting could be achieved if taxes are reduced selectively on foodsthat are consumed primarily by poor households, such as broken grain cereals. Finally, thereduction in taxes will bring about a reduction in government revenue that may exacerbate theimpact of the price boom on budget deficit, especially for food importing countries.

Therefore, governments ought to consider carefully the composition of revenue, in relation tothe support tax reductions may provide. Several of these trade and tax policies were examinedby Conforti and Sarris (2008) in a CGE context. It was shown that they are very ineffective atmoderating or reversing the overall impact of foreign price shocks.Market management policies include measures such as open market operations throughimportation of food into public stocks managed by national marketing boards and progressiverelease of food kept in reserve with a view to reducing market prices. Managing the marketcan also be realized by price controls through administrative orders, anti-hoarding measuressuch as restrictions on stockholding by private traders, as well as restrictions on inter-regionalmovement of foods. Such policies aim to directly manage domestic markets in order to ensurefood supplies at pre-determined prices.Prior to the 1990s decade, the presence of national marketing boards which controlleddomestic prices by managing public food stocks and coordinating open market operations inboth food and input markets was a central characteristic of many African countries’ policiestowards agricultural development and self sufficiency. Such open market operations involvedeither direct or indirect subsidies, as in many cases, food producer prices were guaranteedabove export parity, while governments subsidized consumer prices by releasing foodquantities at low price levels. Most of these practices have been discontinued mainly due totheir high costs, as well as for not being market-friendly and conducive to private sectordevelopment. For example, pan-territorial pricing of grains although increased the share ofproduce delivered by smallholders to the marketing boards resulted in significantly increasingthe costs of policy, as operations were characterized by high transaction costs. Additionalfiscal demands accrued by food stock-piling costs, as well as by production variability and thecorresponding declining volumes of food purchased by marketing boards.Inventory management and open market operations through state controlled boards mayfunction well in securing the availability of cheaper food for consumers, while providingstrong incentives to producers. Nevertheless, the costs of state marketing operations aresignificant. Past experience suggests that state marketing channels incurred large operationallosses which were covered by governments or aid donors. Given the significantly high costsassociated with open market operations and the unintended effects, developing countries,since the 1990s, tend to rely more on trade for price stabilization and less on stock operations.Nevertheless, when governments perceive that the international market may not be a reliablesource for food at short notice, stockholding and open market operations are still carried out.State marketing board operations have continued to play a major role in many countries andespecially in Eastern and Southern Africa. Within the context of sustained price increases,such policies may be effective in lowering consumer prices through domestic purchases orimports at market price level and the subsequent release of stocks at lower prices in order toensure the availability of cheaper food. However, such practices are expected to be effectivein the very short run only, as operating costs escalate in line with the increases in theinternational market prices rendering such policy options fiscally unsustainable. In addition totheir high financing requirements, state marketing operations can also result in discouragingthe development of the private sector in food market management, as well as extrapolatingprice increases, as producers often react to price rises by hoarding.The experience of the food price upswing provides an opportunity for food importingcountries to reassess the relative reliance on trade and stocks, taking into account the role ofthe private sector. Jayne, Zulu and Nijhoff (2006) propose that effective coordination betweenthe public and the private sector would require greater consultation, as well as transparencywith regard to the governments’ intentions about market operations. Given the political nature

of issues that surround food availability and food security in some countries, it is unlikely thatgovernments will be willing to reform food markets and cease intervening. In this case, it iscritical that the government and the private sector follow an integrated approach to marketoperations. A set of clearly defined and transparent rules, such as price thresholds, fortriggering government intervention are necessary, as well as the provision of credibleinformation on export and import decisions, the quantities intended to be purchased in, andsold by the government agencies, as well as the respective prices. For example the adoption ofprice band rules, with floor and ceiling prices, outside which government intervention can betriggered may allow efficient private trading, providing the appropriate incentives for storageand improve market information. Policy makers may also consider the possibility ofmaintaining small strategic food reserves, instead of managing the market through largerinventories should also be explored. Combining small-scale open market operations, such asthe release of subsidized food, with social safety nets, effectively targets vulnerablepopulation groups without distorting the market.It is important to note that, within the context of price upswings, open market operations canresult in lowering the price of food at very high costs and hence the policy may be effectiveonly in the short run. In addition, the decisions made by marketing boards to import food mayhave negative implications on the private food marketing sector, if prior consultations withprivate traders have not taken place.There is no blueprint approach to managing food price episodes and policy choice needs toaddress the possible limited access to food or inputs, or lack of food availability. Trade andtax measures are the easiest to implement. Nevertheless, given the low level of applied tariffsin most developing countries, they may not be effective in stabilizing domestic prices. Microlevelinterventions are likely to need complementary measures at other levels. For example,food price increases alleviated by micro-level interventions can lead to rapid increases inbudgetary expenditures which may need to be offset by increased government borrowing.Nevertheless, if such safety net mechanisms are in place, they can be scaled up to effectivelysupport those in need. Most micro-level interventions can be designed as universal or targetedprograms. Although, universal food or input subsidies are characterized by high budgetarycosts and non-exclusion of the non vulnerable, targeted programs can be administrativelyexpensive and that in many cases, the costs may exceed the benefits of targeting.

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Table 1. Africa and dependence on agricultureShare of Agriculture in GDP1969-71 1979-81 1989-91 2002-04North Africa 19.1 14.7 16.0 13.6Sub-Saharan Africa: LDC 40.2 40.4 37.5 38.8Sub-Saharan Africa: Other 30.6 27.6 27.1 26.6Africa 31.9 29.6 28.7 28.4Share of economically active population in agriculture in totaleconomically active population1969-71 1979-81 1989-91 2002-04North Africa 0.54 0.43 0.30 0.23Sub-Saharan Africa: LDC 0.83 0.79 0.76 0.71Sub-Saharan Africa: Other 0.68 0.60 0.49 0.41Africa 0.76 0.70 0.63 0.57Source. Authors’calculations from FAO dataTable 2. Africa and agricultural exportsShare of agricultural exports in total exports of goods and services1969-71 1979-81 1989-91 2002-04North Africa 24.5 7.3 4.2 3.7Sub-Saharan Africa: LDC 65.5 43.4 38.6 32.4Sub-Saharan Africa: Other 37.4 25.5 20.7 23.5Africa 46.8 29.6 25.1 23.4Share of agricultural exports in total merchandise exports1969-71 1979-81 1989-91 2002-04North Africa 33.4 11.8 8.3 6.0Sub-Saharan Africa: LDC 65.6 54.4 46.0 32.5Sub-Saharan Africa: Other 52.1 34.2 26.2 19.3Africa 58.8 44.7 36.9 26.3Source. Authors’calculations from FAO data

Table 3. Developments in African agricultural import dependence 1970-2004Share of agricultural imports in total imports of goods andservices1969-711979-811989-912002-04North Africa 20.4 4.8 3.5 3.4Sub-Saharan Africa: LDC 38.4 22.2 19.6 15.1Sub-Saharan Africa: Other 33.5 20.9 21.4 15.9Africa 33.3 18.5 17.3 13.2Share of agricultural imports in total merchandise imports1969-711979-811989-912002-04North Africa 23.9 24.2 23.0 17.5Sub-Saharan Africa: LDC 21.5 22.2 25.9 27.3Sub-Saharan Africa: Other 17.4 14.8 14.2 18.1Africa 20.6 20.3 22.4 23.7Share of food imports in total exports of goods and services1969-711979-811989-912002-04North Africa 14.4 18.3 13.2 9.9Sub-Saharan Africa: LDC 37.6 28.2 30.2 34.9Sub-Saharan Africa: Other 14.1 8.7 6.8 11.1Africa 24.1 18.8 17.9 20.9Source. Authors’calculations from FAO data

Table 4. African commodity dependenceS hare of 4 m ost im portantcom m odities in agriculturalexportsS hare of 4 m ost im portantcom m odities inm erchandise exports1982/84 1992/94 2002/04 1982/84 1992/94 2002/04An gola 91.4 100.0 53.8 4.6 0.1 0.0B urundi 94.8 88.5 93.7 91.5 78.4 67.1C ape V erde 0.0 2.2 85.5 0.0 0.2 2.0C ent Afr Rep 81.8 94.8 92.8 46.7 25.1 12.0C had 98.6 93.5 95.5 na 66.2 46.6C om oros 97.7 99.5 99.9 81.7 75.4 naB enin 39.9 93.4 85.6 21.3 25.9 37.4E quatorial G uinea 100.0 100.0 100.0 97.0 6.4 0.2D jibouti 64.3 65.7 86.5 29.7 13.3 16.0G am bia 99.5 77.8 88.9 60.2 29.5 naG uinea 80.2 83.0 54.3 4.6 6.2 2.7Lesotho 55.7 79.5 87.5 32.9 7.9 1.2Liberia 97.3 97.4 99.2 23.4 4.5 37.1M adagascar 83.6 69.5 83.7 70.5 31.7 24.3M alaw i 87.5 92.0 87.8 83.8 81.9 78.4M ali 91.7 95.2 92.7 na 63.9 31.5M ozam bique 62.0 75.5 62.6 30.9 25.0 7.7E thiopia 80.8 78.7 73.9 73.6 72.8 56.1E ritrea 0.0 43.2 83.0 na 4.1 2.2N iger 50.2 52.8 48.3 11.7 7.7 10.6G uinea-B isau 50.7 97.6 99.5 34.3 70.9 82.3R w anda 95.1 90.8 92.3 58.7 64.7 48.0S ao Tom e andP rincipe 98.3 99.9 97.7 na 72.2 80.7S enegal 84.0 78.4 54.2 23.9 12.5 7.0S ierra Leone 84.5 66.2 84.8 22.9 6.7 12.6S udan 70.9 65.8 73.0 65.0 70.0 13.2Tanzania 70.4 74.9 47.7 60.8 51.3 17.0Togo 91.0 79.0 61.7 31.3 32.5 16.6U ganda na 74.6 67.3 na 64.6 32.7B urkina Faso 66.9 84.4 84.6 59.8 98.7 72.6C ongo, D em R ep of 84.4 89.9 79.1 9.3 6.1 2.5Zam bia 80.4 44.6 67.9 0.8 1.8 10.2Source. Authors’calculations from FAO data

Table 5. Countries with high levels of undernourishment and with high dependence onimported grains and petroleumCountriesPetroleum% importedMajor grains% imported% undernourishmentEritrea 100 88 75Burundi 100 12 66Comoros 100 80 60Tajikistan 99 43 56Sierra Leone 100 53 51Liberia 100 62 50Zimbabwe 100 2 47Ethiopia 100 22 46Haiti 100 72 46Zambia 100 4 46Central African Republic 100 25 44Mozambique 100 20 44Tanzania 100 14 44Guinea-Bissau 100 55 39Madagascar 100 14 38Malawi 100 7 35Cambodia 100 5 33Korea, DPR 98 45 33Rwanda 100 29 33Botswana 100 76 32Niger 100 82 32Kenya 100 20 31Source. FAO

Table 6. Distribution of small-scale farm population according to their position in thestaple grain market, selected countries.Household category withrespect to main staple grain:Zambia(maize)Mozambique(maize)Kenya(maize)Malawi(maize)Ethiopia(maize and teff)Sellers only:Percent accounting for top50% of total salesPercent accounting forbottom 50% of total sales---------------------------- % of rural farm population --------------------------19217Buyers only 33 51 55 na 60Buy and sell (net buyers) 3 7 na 1312Buy and sell (net sellers) 612 na 12Neither buy nor sell 39 24 8 na 2Source: Jayne, Chapoto, and Govereh, 2007132111821651413211100% 100% 100% 100% 100%

Table 7. Results of maize price transmission analysis for South AfricaEvidence for comovementwith:SAFEX yellow maize price, P SA YSAFEX white maize price, P SA WP w , world price strong strongP SA Y, SAFEXyellow maize price- strongCausality P W →P SA y P W →P SA wMonths to fulladjustment to P W 7.8 7.2Source. Authors’ calculations

Table 8. Results of maize price transmission analysis for KenyaEvidence for comovementwith:Nairobi Mombasa Eldoret KisumuP w , world price strong strong strong strongP SA w, South Africanprice (white maize)moderate moderate strong strongCausality P w → P P w → P P w , P SA w→P P w , P SA w→PMonths to fulladjustment to P W 6.2 9.1 9.1 6.2Months to fulladjustment to P SA W- - 11.1 7.7Source. Authors’ calculations

Table 9. Results of maize price transmission analysis for MalawiCo-movementwithChipita Karonga Rumphi Mziba Mzuzu MchinjiP w , world price strong strong moderate weak strong weakP SA w, South Africanprice (white maize)strong strong strong weak strong weakCausality P w P SA w →P P w , P SA w →P P w , P SA w →P - P w P SA w →P -Months to fulladjustment toP W (P SA w)6.6 (5.0) 4.7 (4.8) (8.3) - 5.5 (6.1) -Salima Mitundu Liwonde Lizulu BangulaCo-movementwithP w , world price weak strong strong strong strongP SA w, South Africanprice (white maize)strong strong strong strong strongCausality P SA w →P P w , P SA w →P P w , P SA w →P P w , P SA w →P P w , P SA w →PMonths to fulladjustment toP W (P SA w)(5.3) 5.8 (5.0) 7.7 (6.4) 7.1 (5.5) 3.8 (4.7)Source. Authors’ calculations

Table 10 Results of maize price transmission analysis for ZambiaEvidence for comovementwith:Lusaka Chipata Kabwe Choma Kasama NdolaP w , world price moderate weak strong weak strong strongP SA w, South Africanprice (white maize)strong strong strong strong strong strongCausality P SA w → P - P SA w → P P SA → P P w , P SA w → P P w , P SA w → PMonths to fulladjustment to P W - - 4.8 - 5.5 3.2Months to fulladjustment to P SA W7.6 8.3 5.3 6.7 7.7 3.1Source. Authors’ calculations

Table 11 – Zambia: Impact of higher food prices (percentage changes)Quantities consumedFood Expenditure sharesUrbanMaleheadLandlessFood Insecure Food Secure Total populationRuralRuralFemaleheadLandowner>60% income 60%incomefromagriculture

Table 12 – Malawi: Impact of higher food prices (percentage changes)Quantities consumedFood Expenditure sharesFood Insecure Food Secure Total populationRuralRuralLandlessLandowner>60% 60%

Table 13 Impact of a 50 percent Increase in the Price of Maize in Malawi, Zambia andUgandaMalawiZambiaUgandaFood insecurehouseholdsFood securehouseholdsAll householdsQuantity of maize consumed, kg percapita 163 135 189Actual expenditure share of maize, % 33.00 41.5 25.2Quantity of maize consumed, % change -8.60 -4.79 -11.78Expenditure share of maize, % change 14.34 16.13 12.84Total food expenditure, % change 10.31 15.69 4.63Number of households, % change n/a 13.01 -10.47Quantity of maize consumed, kg percapita 150 150 151Actual expenditure share of maize, % 19.00 21 15Quantity of maize consumed, % change -16.97 -15.52 -18.85Expenditure share of maize, % change 12.95 15.61 9.51Total food expenditure, % change 2.96 3.11 2.63Number of households, % change n/a 4.34 -5.61Quantity of maize consumed, kg percapita 29 15 35Actual expenditure share of maize, % 8 8 8Quantity of maize consumed, % change -29.73 -30.03 -29.6Expenditure share of maize, % change 2.6 2.9 2.5Total food expenditure, % change 0.33 0.94 0.07Number of households, % change n/a 0.76 -0.33Source. Authors’calculations

Table 14. Impacts on macroeconomicaggregates in Tanzania, due to commodity pricechanges between 2002-8(million Tz shillings in BASE and percentage changesfrom BASE in the simulation)BASE SimulationGPD 7804.2 -1.3agricultural imports 24.8 -3.4total imports 209.3 -7.0agricultural exports 22.6 35.7total exports 107.8 6.0investment 1.0 -11.2government savings 92.5 30.0unskilled labour employed 2.5 -3.0nominal exchange rate 1.0 -21.1real exchange rate (trd/nontrd) 1.0 2.1Source: Conforti and Sarris, 2008

Table 15. Impacts on welfare of various groupsin Tanzania, due to commodity price changesbetween 2002-8(Percentage changes from BASE in the simulation)SimulationRural Poor -2.0Rural non-poor uneducated -3.0Rural non-poor educated -4.0Urban poor -3.1Urban non-poor uneducated -3.5Urban non-poor educated -5.0Source: Conforti and Sarris, 2008

Figure 1. Recent basic food commodity international price indices (1998-2000=100)30025020015010050020092008200720062005200420032002200120001999199819971996199519941993199219911990Source. FAOMEAT DAIRY CEREALS OILS SUGAR

Figure 2. Real bulk food commodity prices 1957-20081400Real Prices: Bulk Commodities (1957-2008)12001000WheatRiceMaizeSoybeans8006004002000195719601963196619691972197519781981198419871990199319961999200220052008Source. FAO

Figure 3. Annualized price volatilities of basic food commoditiesWh e a t6 0 05 0 04 0 03 0 02 0 0v o la t i li t yn o m i n a l p r i c e s1 0 001 9 5 71 9 6 21 9 6 71 9 7 21 9 7 71 9 8 21 9 8 71 9 9 21 9 9 72 0 0 22 0 0 7M a i z e4 0 03 5 03 0 02 5 02 0 01 5 0v o l a t i li t yn o m i n a l p r i c e s1 0 05 001 9 5 71 9 6 21 9 6 71 9 7 21 9 7 71 9 8 21 9 8 71 9 9 21 9 9 72 0 0 22 0 0 7R i c e6 0 05 0 04 0 03 0 02 0 0v o la t i l i t yn o m i n a l p r i c e s1 0 001 9 5 71 9 6 21 9 6 71 9 7 21 9 7 71 9 8 21 9 8 71 9 9 21 9 9 72 0 0 22 0 0 7Source. FAO

Figure 4. Income terms of trade for agriculture have deteriorated for LDCs during thelast forty years.Income terms of trade for agriculture92008180$U S B il - L D C s765432LDCsOther developing countriesIndustrialized countries160140120100806040$U S B il O th er d evelo p in g an d In d u strializ ed120001961 1966 1971 1976 1981 1986 1991 1996 2001Source: FAO, State of Agricultural Commodity Markets 2004

Figure 4. Average yields in Developed, LDC and other developing countries4 .54 .03 .5Cereals - weighted average yields: 1985 - 2004Dev'edLDCsODCs3 .02 .52 .01.51.00 .50 .019 8 5 19 9 0 19 9 5 2 0 0 0 2 0 0 5Source. FAO

Figure 5. African cereal yield developments in basic food commoditiesAverage Yields: Maize86420198019851990199520002005Africa North Africa SSA LDCOther SSASouth AfricaAverage Yields: Rice, Paddy1086420198019851990199520002005Africa North Africa SSA LDCOther SSASouth Africa

Average Yields: Millet & Sorghum6420198019851990199520002005Africa North Africa SSA LDCOther SSASouth AfricaAverage Yields: Roots & Tubers302520151050198019851990199520002005Africa North Africa SSA LDCOther SSASouth AfricaSource. FAO

Figure 6. Wheat production, utilization, net-trade and per-capita food use of SSA LDCcountriesMillion MT15kg per capita251020515010-55-101996 2000 2004 2008 2012 2016Production Utilization Net Trade Per-capita Consumption0Source. FAO

49Figure 7. Coarse* grain production, utilization, net-trade and per-capita food use ofSSA LDC countries.Million MT6050403020100kg per capita908070605040302010-101996 2000 2004 2008 2012 2016Production Utilization Net Trade Per-capita Consumption0*Coarse grains include maize, oats, rye, millet, sorghum, barley and other coarse grainsSource. FAO

50Figure 8. Rice production, utilization, net-trade and per-capita food use of SSA LDCcountries.Million MT20kg per capita25152010155010-55-101996 2000 2004 2008 2012 2016Production Utilization Net Trade Per-capita Consumption0Source. FAO