metadata: authors: 0: author: Jaeger,William K.
count: Africa
docna: 0: docna: The impact of policy in African agriculture
            : an empirical investigation
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Macro-Fiscal Policy,Taxation & Subsidies,Economic Adjustment and Lending,Crops and Crop Management Systems,Public Sector Economics,International Trade and Trade Rules,Food Security,Public Finance Decentralization and
            Poverty Reduction,Transport Services
teratopic: Agriculture,Macroeconomics and Economic Growth,International Economics and Trade,Environment
abstracts: cdata!: The last two decades have witnessed a
            decline in Africa's agricultural exports and a sharp
            rise in the region's food imports.  With over 70
            percent of Africa's people dependent on agriculture for
            their livelihood, getting agriculture moving in Africa is
            one of the most important formidable challenges facing
            Africa in the 1990s.  There is now wide recognition that
            government policies have contributed to Africa's crisis
            and that policy reforms can help foster restored growth.
            This report examines the link between government policy and
            agricultural performance across countries and over most of
            the past two decades.  The analysis confirms that
            governments' agricultural pricing and marketing
            arrangements and their macroeconomic policies have slowed
            agricultural growth.  In particular, overvalued currencies
            have often caused the largest distortions.  Recent trends,
            however, offer evidence that policy reforms have helped
            improve agricultural growth where these reforms have
            sustained agricultural procedures.  The author also offers
            evidence that government policies have been substantially
            responsible for Africa's growing food imports, by
            encouraging urban migration and by lowering the prices for
            imported foods relative to domestically-grown foods. This
            study contributes to a better understanding of the causes of
            Africa's agricultural crisis and can help forge a
            consensus on how to restore vitality in African agriculture.
repnme: repnme: The impact of policy in African agriculture
            : an empirical investigation
display_title: The impact of policy in African
            agriculture : an empirical investigation
listing_relative_url: /research/1991/03/700019/impact-policy-african-agriculture-empirical-investigation
topicv3: Agriculture and Food Security m1326205 435,Crop Production m1326627 174,Transport m1326661 135,Trade m1326924 698,Taxation m1327615 1121
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Agriculture,Macroeconomics and Economic Growth,International Economics and Trade,Environment
origu: Policy, Research & Ect Affairs
no_of_pages: 80
author_keys: 0: author_key: 604447
model_extractions: <!-- Start of picture text --> WPS 640<br><!-- End of picture text --> This paper is a product of the Trade and Finance Division, Technical Department, Africa Regi9nal Office. Copies are available free from the World Bank, 1818 H Street NW, W'a'shingtorn,`DC 20433. Please contanq Azeb Yideru, room J3-(P80, extension 34663 (69 pages, with figures and tables).

Jaeger examines the relationship between government policy and agriculttal performance in Sub-Saharan Africa between 1970 and 1987. Using newly compiled data enabling a wider range of empirical analyses, the study assesses, the impact of policy distortions on productivity over tune and across countries. It assesses export agriculture and food production sFparately.

eThe analysis confirns that the deterioration oAfrica's agricultual exports during the 1970s snd early 1980s was associated with agriculture's high levels of direct taxation and of indirect taxation thrtugh govermnent controls and overvalued currencies. Goverment controls in the marketing and pricing of export crops have contributed to thc detetioration in export perfirmance. But the large _indirect_ distortions and disincentives caused by exchange rate policies are what have distinguished African policy environments from those in non-African developing countries. Econometrc rerylts show that the responsiveness of agricultural exports to change in incenttives is moderate in the short run for countries exporting tree crops but more elastic in countries exporting annual crops.

The author also investigates Africa's chronic food crises and questions the conventional wisdom tiha rising food imports and declining per capita production reflect primarily a production problem. Econometric results indicate that most of the rise in Africa's food imports Is associated with shifting demand toward imported foods, rather than a failure of supply, The main factors causing the shift in demand are increasing urbanizatiopi, higher import capacity, and exchange rate distortions tAat make imported food relatively cheap. When the variation of these factors has been taken into account, the remaining unexplained trend is only I percent a year, caused in part by, declining interational prices for wheat and rice.

r Jaeger establishes a link between policy reforms and the iniprovements observed in agricultural perfortance in the late 1980s. Countries with favorable policy environments have performed better in the 1980s, on average, than those with unfavorable policy e9vironments. This has been true both in agriculture and in overall economic growth. And in countries where policy reform progams resulted in sinificant and sustained improvements in 'Incentves (for example, Ghana and Togno), productivity has improved substantially. But in countries where reforis havenot led to improved incentives or where the imProvements, were short-lived (for example, Tanzania and Zaire), little response was observable.

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# TABLE OF CONTENTS

# Executive **Sunmary**

- **I.** Introduction 1 Objective of the study 1 Background I Conceptual framework 2

- I. The Impact of Policy on Export Agriculture 3 Trends in prices, policy distortions, and export 3 Export supply response 12

- m. The Impact of Policy on the Food Crop Sector 20 Cross-price effects between export prices and food 21 Food policy 22

- Real food price trends 23 Food imports and slow food production growth 25 Policy and rural-urban migration 31

- IV. Diversity within Africa: Policy Environments and Performance 33 Comparing performance in favorable versus unfavorable policy environments 33 Country diversity: policy and response 39

- V. Concluding Comments 43 Summary of empirical findings 43 Forward looking issues 45

- Annexes A. Data and methodology 46

- B. The extent of chronic hunger in Africa 48

- C. Classification of countries by policy environment 50

- D. Data tables 53

# Bibliography 66

# Text Tables

- 1 Regression Equation for Agricultural Supply in Sub-Saharan Africa 17

- 2 Regression Equations for Agricultural Supply in Sub-Saharan Africa: Crop models 18

- 3 Regression Equations for Agricultural Supply in Sub-Saharan Africa: Country models 19

- 4 Regression equations for cross-price effect on food production 22

- _5_ Comparison of real consumer food prices in Africa 25

- 6 Regression equation for food imports 30

- 7 Rural-urban migration in Ghana since policy reform 32

- 8 Comparison of performance and policy among African country groups 34
# Annex Tables

- Cl Classification of countries by policy environment 52

- Dl Average nominal protection coefficients for major export commodities 53

- D2 Nominal protection coefficient for principal export commodities _54_

- D3 Real protection coefficients for principal export commodity 55

- D4 Average real producer price for major export commodities 56

- D5 Average nominal protection coefficients for ti.ded food crops 57

- D6 Real producer prices for major food crops 58

- D7 Comparison of estimated transport costs 59

- D8 Ratio of implicit financial outflow from agriculaire 60

- D9 Weather variable (deviation from trend of cereal yields, in logarithms) 61

- D1O "Disasters" data: number of people affected by major disasters, in thousands 62 D1I Food crop price and policy effects 63

- 012 Comparison of domestic staple food prices to imported rice and wheat prices 64 D13 Various specifications of regression equation for agricultural supply 65

# Figures

- 1 Policy, incentives, and agricultural exports in Sub-Saharan Africa 4

- 2 Nominal and real protection coefficients for major agricultural exports 6

- 3 NPCs and RPCs in CFA countries for major export crops 8

- 4 Adjusted RPCs for Africa's major export crops 10

- _5_ Decomposition of changes in RPCs for African export crops 11

- 6 The impact of weather on African agriculture 14

- 7 Real producer prices for major crops in Sub-Saharan Africa 24

- 8 Ratio of export/food producer prices in Sub-Saharan Africa 24

- 9 Africa's food imports 28

- 10 Africa's import capacity 28

- 11 Africa's agricultural exports 36

- 12 Total agricultural production 36

- 13 Total food production 37

- 14 Africa's GDP 37

- 15 Real effective exchange rates 38

- 16 Weather patterns in Africa 38

- 17 Ghana's price incentives and agricultural exports 40

- 18 Zaire's price incentives and agricutural exports 40

- 19 Tanzania's price incentives 42

- 20 Togo's price incentives ad agricultural exports 42

# ACKNOWLEDGEMENTS

This paper was largely prepared while the author was an economist at the World Bank. Data for this research was compiled under UNDP project RAP 861058. The author is gratefial to Charles Humphreys, Michael Sarris, and Gilbert Uwujaren for comments on earier draft. The research assistance of Katharina Katterbach, James Teffi, and Peter Rosendorff, and the secrearial support of Azeb Yideru, are gratefuily acknowledged.
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# FOREWARD

The Lst two decades have witnessed decline in Africa's agricultural exports and a sharp rise in the region's food imporis. With over 70 percent of Africa's people dependent on agriculture for their livelihood, getting agriculture moving in Africa is one of the most important and formidable challenges fazing Africa in the 1990s. There is now wide recognition that government policies have contributed to Africa's crisis and that policy reforms can help foster restored growth.

"The Impact of Policy on African Agriculture: An Empirical Investigation' examines the link between government policy and agricultural performance across countries and over most of the past two decades. The analysis confirms that governments' agricultural pricing and marketing arrangements and their macroeconomic policies have slowed agricultural growth. In particular, overvalued currencies have often caused the largest distortions. Recent trends, however, offer evidence that policy reforms have helped improve agricultural growth where these reforms have sustained agricultural procedures.

The author also offers evidence that government policies have been substantially responsible for Africa's growing food imports, by encouraging urban migration and by lowering the prices for imported foods relative to domestically-grown foods.

This study contributes to a better understanding of the causes of Africa's agricultural crisis and can help forge a consensus on how to restore vitality in African agriculture. This work was carried out as part of the World Bank's ongoing monitoring of Africa's economic growth and policy reforms.

Ismail Serageldin Director Technical Department Africa Region
# EXECUTIVE SUMMARY

Africa's loss of world market shares for its major<sup>agricultural exports and the decline in food</sup> production per capita are the most telling signs<sup>of stagnation and decline in Africa since 1970.</sup> Africa's international market share for its 12 major<sup>agricultural commodities tell by half between</sup> 1970 and 1983. Over the same period, per capita<sup>food production fell about 1 percent per year.</sup> Since the mid-1980s, however, there have been<sup>improvements in both food and export agriculture, a</sup> reversal which has coincided with the implementation<sup>of policy reform programs by a subsiantial</sup> number of African countries.

This paper examines the impact of policy on<sup>agricultural performance in Sub-Saharan Africa in</sup> the 1970s and 1980s. Based on recently available<sup>data enabling a wider range of detailed empirical</sup> analysis, the study assesses the distorting effects of<sup>government policies on incentives, the impact of</sup> those distortions on agricultural performance, and<sup>the changes over time and across countries in both</sup> policies and agricultural performance Export<sup>agriculture and food producdon are assessed</sup> separately.

Africa's agricultural export performance<sup>deteriorated substandally from 1970-84. The pattern of</sup> decline - and partial recovery in the late-19SOs - follows closely the high levels of distortion<sup>in the</sup> agricultural sector. These distortions were the<sup>result of both direct government controls on producer</sup> prices for Africa's principal export crops and<sup>the indirect effects of exchange rate policies. As a</sup> result of these policies, real producer prices feil<sup>by one-fourth between the early 1970s and early</sup> 1980s. The share of these crops' value retained<sup>bv farmers (as measured by the real protection</sup> coefficient) dropped from about 90 percent to<sup>about 50 percent, on average, from 1971 to 1983.</sup> Government controls and involvement in the<sup>marketing and pricing of export crops has been</sup> responsible for a considerable share of the deterioration<sup>of producer incentives, although the</sup> magnitude of these distortions has not been<sup>larger, on average, than in other non-African developing</sup> countries. However, large _indirect_ distortions and<sup>disincentives have been caused in Africa by the</sup> prevalence of highly overvalued currencies,<sup>especially from the mid-1970¢ to mid-1980s. The large</sup> size and prevalence of these indirect distortions<sup>contrast with conditions iL</sup> non-African developing countries. Pegged exchange rate regimes,<sup>coupled with poor fl6. management and high</sup> domestic inflation, resulted in highly overvalued<sup>currencies. Those countries which maintained</sup> grosdly overvalued currencies over extended<sup>periods experienced severe deterioration of export</sup> agriculture. CFA-zone countries have been more<sup>successful in avoiding large exchange rate</sup> distortions over the period, and thus have been less<sup>affected by the deleterious effects on agricultural</sup> growth. In the late 1980s, however, overvaluadon<sup>of the CFA franc has become more of a problem.</sup> There has been considerable debate in the literature<sup>on the responsiveness of agriculture to price</sup> incentives in Africa. The issue is addressed<sup>here in an econometric framework combining cross-</sup> section and time-series data. In the short-run, tree crops<sup>exports ate shown to be ordy moderately</sup> responsive to changes in price incentives.<sup>Elasticities are on the order of 0.1. However, for</sup> countries exporting annual crops the short-run response<sup>is more elastic, above 0.9. Exports are also</sup> shown to be responsive to changes in the real exchange<sup>rate, reflecting the effects on price incentives</sup> and on efficiency caused by changes in exchange<sup>and trade restrictions that generally accompany</sup> overvalued currencies. Elasticities of export supply<sup>with respect to the real exchange rate varied</sup> from 0.1 to 0.3 for tree crop exporters, and 0.6 for<sup>annual crop exporters. These estimates may</sup> not, however, reflect long-run responses, which<sup>are influenced by</sup><sup>_expected_prices.</sup>
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The possibility that export agriculture may crowd out food production is examined. Growth in export agriculture does not appear to come at the expense of food production. Food producdon is positively correlated with export crop prices and real exchange rates suggesting complementarity. However, lack of data means that the impact of policy on food production in Africa cannot be analyzed in the same way as export agriculture. Food producdon data are often questionable. Market price data for food crops are unavailable for most African countries. Because many African governments fix official prices for food, only those prices are generally reported and often do not correspond to actual market prices. However, the impact of policy on Africa's food problems can be examined indirectly.

Average food supply per capita has not declined in Africa since the 1960s. Food supply per capita - according to FAO data - has risen slightly, on average, s' esting that only the composition of consumption has shifted from domesically-grown foods to imported foods (which are often preferred, especially among urban and wealthier consumers). However, Africa's chronic food crisis is commnonly assumed to be both widespread and worsening, as evidenced by declining food production per capita and rising food imports. It is generally assumed that weak domestic production creates a need for more imports. The causal direction between imports and domestic production may be the reverse: that is, a shift in demand toward imported foods may have resulted in increased consumption of imports, and therefore reduced demand for domestic foods. This weak demand then led to weak domestic output. Demand for imported foods may rise due to urbanization, import capacity, and the relative price of imported to domestic foods (which will be affected by real exchange rates). Indeed, the doubling of Africa's food imports in the late-1970s was largely the result of events in Nigeria, which accourned fur 70 percent of the increase. This occurred during Nigeria's oil boom when there were large shifts in import capacity (a quadrupling), the real exchange rate, and urban migration.

To t_st the hypothesis that both rising food imports and declining domestic production is demand driven rather than the result of a production constraint, an econometric model is estimated. The results confirm that much of the rise in Africa's food imports can be attributed to urbanization, import capacity, and exchange rate distortions. When these factors have been taken into account, the remaining trend is only 1 percent per year, and this can probably be explained by the declining international prices for wheat and rice over the period.

Rural-urban migration, which appears to be a cause of rising food imports, is a result of government policies, imciuding exchange rate policies that have altered the internal terms of trade in favor of urban areas. Evidence of the extent to which urban migration is influenced by policy - and the potential for policy reform to alter existing migratory patterns - is demonstrated with recent data from Ghana. Survey data confirm what has been observed anecdotally in several African countries: significant reverse migration is occurring from non-agricultural to agricultural occupations since the introduction of Ghana's reform program.

The improvements in aggregate patterns of production and trade in Africa since the mid-1980s coincide with the implementation of structural adjustment programs in a large number of African countries. To assess the evidence of a causal relationship, economic perfob.ce in countries where policy distortions have been relatively small, is compared to that in countries where policy distortions have resulted in an unfavorable policy environment. In aggregate, countries with favorable policy environments have performed better in the late-1980s than the countries with unfavorable policy environments in virtually all measures of economic performance, including agricultural production, agricultural exports, and overall economic growth. The experience in
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individual countries has been quite disparate, but in countries where policy reforms have resulted in significant and sustained improvements in incentives (e.g., Ghana, Togo) productivity has improved substantially. In countries where these reforms did not improve price incentives, or where the improvements were shortlived (e.g., Tanzania, Zaire), no significant response was observed.

Overall, the evidence from this study suggests that poor policies have had a major role in the decr-ne of African agriculture. Similarly, there is evidence that policy reforms in the 1980s have cor.vibuted to some modest improvement.

There has been considerable debate about the scope for policy response in Africa, at times framed in terms of whether price or nonprice factors constrain growth most. The debate may be misleading, however. First, the distinedon between price and nonprice factors is sometimes blurred; nonprice constraints on agriculture may be seen as being price related (e.g., the lack of roads implies high transport costs; the absence of extension services raises information costs of new technology). Second, the relative importance of price and nonprice factors will vary from country to country; therefore the debate of their relative importance must take account of specific country contexts. And third, the classificadon of constraints into price- and nonprice-related is to a large extent analogous to the dichotomy between short-run and long-run response to prices. For example, bad roads, lack of irrigaion, poor institutional infrastructure, and other "nonprice" constraints may be the result of neglected investments caused by policy distortions which have made socially profitable investments privately unprofitable. Investments that would remove nonprice constraints may become attractive when policy distortions altering relative prio s are lessened.
# **I.** INTRODUCTION

# Objective of the study

The responsiveness of Sub-Saharan African 1/ agriculture to changes in farm prices and macropolicies has emerged as a critical issue for understanding Africa's poor agricultural performance as well as for prescribing corrective actions. Views on the importance of pricing policies vary widely, and a debate has emerged recently about the relative importance of price and "nonprice' factors both in explaining Africa's poor performance and as the main impedimen **t** o recovery. Surprisingly little empirical work has been done, however, on aggregate agricultural supply response in Africa. This has been due at least in part to data limitations that have prevented extensive cross-sectional and time-series analys,.

Recently, however, suitable data have been compiled to enable a wider range of more detailed empirical analyses. Based on these data, the objectives of this paper are: a) to examine the trends and magnitude of policy distortions in Africa and their effects on agricultural incentives; b) to estimate the responsiveness of African agriculture to these policy changes; c) to assess the relative importance of these policies in explaining African's poor agricultural performance both fcr food and export crops since 1970; and d) to evaluate the evidence of a link between Africa's policy reform programs and recent evidence of recovery in Africa's agricultural growth.

Within the agricultural sector, exported crops and food crops have itatures that make it difficult to address them jointly. Analysis of food policy and growth gives rise to a variety of distinctive characteristics and problems due to the nature of government policies, the importance of domestic demand, and especially data limitations on producer food prices. Given these complexities, export and food crop agriculture are examined separately below.

Agriculture is Africa's most important sector, accounting for about 80 percent of employment and 50 to 90 percent of exports. The sector has performed poorly since 1970: food production has failed to keep pace with population growth and export agricuture has experenced declining market shares. This slow growth and loss of competitiveness in international markets coincided with increasingly distorted macroeconomic policies and intensified government controls and restrictions (World Bank 1989a). Furthermore, in the late 1970s, exceptionally high commodity prices for Africa's major exports (oil, tropical beverages, phosphates) led to unrealistic expectations, overextended borrowing, and an unmanageable debt burden. The substantial imbalances on external and domestic public accounts created during this period evenually compelled many African governments to adopt macroeconomic policy reform programs. Declining revenues from agricultural exports and rising food imports led to the inclusion of agriculural sector reforms in many of these reform programs.

Most of these reform programs were initiated during the first half of the 1980s. And beginning in 1985, agricultural performance and agricultural export market shares began to show signs of recovery, with - agricultural production growing at 4 percent per year from 1985-88 faster than population growth for the first extended period since 1970 (World Barl; 1989a). These patterns have heightened interest - as well as the debate - on the merits and limits of poliay reform as a basis for recovery in Africa.

_1_ Throughout the paper "Africa" is used to donate Sub-Saharan Africa.
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# Conceptual Framework

It is generally agreed that agricultural growth is lnked to farm profits - where farm profits are affected by a range of factors including government policy. Output prices, input costs, and exchange rates are central to most discussions of the responsiveness of agriculture to policy, but since farm profits are affected by wages, interest rates, market imperfections, information, etc., these factors need to be taken into account as well (Binswanger).

Govermnent polcHy can affect farm profitability through a) control over output and Input prices, b) taxation or subsidies that affect those prices, c) controls on wages and interest rutes, d) Institutional arrangements (eg., access to credit, inputs, information), and e) actions that affect profitabiity and productivity in other sectors.

Direct government poicies, including price fixing for products or inputs, or the taxation of their trade, affect the profitability of farming directly, and result i the shifting of resources between crops, or in moving resources out of agriculture into other sectors.

Macroeconomic poUcies affect farm profits in several indirect but critical ways. Nominal exchange rates set an upper bound on the price paid to farmers for exported commodities (less transport and processing costs, and net of subsidies). In the same way, exchange rates (together with import taxes and other restrictions) set prices of inputs and agricultural imports which compete with domestic production.

Overvaluation of the exchange rate can result in severe welfare and efficiency costs, both directly from the misallocation of productive resources, and indirectly as a result of the exchange and trade controls that usually accompany overvaluation. These indirect effects from trade and exchange controls can be the biggest costs associated with overvaluation (Edwards 1989a). in addition, such controls generate unproductive resource use from rent seeking activities.

An appreciation of the real exchange rate (or raising the relative prices of nontradables to tradables) raises the cost of producing tradables in terms of nontradables, and therefore reduces directly the profitability of producing these goods. Moreover, the shifting of the internal terms of trade against agriculture will encourage migration from rral to urban areas due to the loss of competitiveness of agriculture and growth in demand for nontradables, as well as from protection of urban-based manufacturing. Migration may occur as a result of other policies too, such as the provision of cheap food and other services for urban populations.

In addition to pricing policies and macroeconomic policies, government expenditure and i'vestment In the agricultural sector can have important effects on fAm profits and are critical to long-term competitiveness and agricultural growth. By and large these pollcy measures are aimed at red-.. -ng costs of production in order to raise profits and stimulate growth: construction of transjrt infrastructure will lower transport costs thus reducing input prices and raising output prices at the famgate; extension services can be seen as reducing the costs of information (that are otherwise unavailable at almost any reasonable price); rural credt institutions make credit available at a lower cost to farmers; and research strives to raise proflts by way of technological change. Factors affecting farm proflts of this type are sometimes referred to as "non-price fators!

The macoeconomic policy environment that has led to real exchange rate appreciation is generally charcterized by intemnal as well as external imbalances. High inflation, negative real interest rates, and restricted access to credit add to the harmful effects on investments in
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agricultural as well as non-agricultural sectors. ro the extent that agriculture is not a protected industry, the adverse, effec-t on investment will be even larger.

Government intervention in marketing of farm products and inputs affects farm profits when these institutional arrangements resuit in inefficiencies, delays, fragmented markets, or inflated costs that depress producer prices. And governments intervene in other areas that affect farm profits as well, such as in land tenure policies or the allocation of newly product've lands (eg. irrigated).

In addition to policy measures aimed at promoting agricvutural growth, governments intervene in the interests of consumers (price levels and stabilization), and to raise revenues (taxation of agricultural exports is an important source of revenue in most African countries). These two typW of policies have distributional consequences between rural and urban peoples, and between export crop and food crop producers.

Most African governments exercise some control in the marketing and pricing of export crops, fixing prices in most cases, although so. ' have recently liberalized these markets. Major food commodities, both domestcally produced and imported, have come under government control for reasons of price stabilization, keeping prices low for consumers, and to prohibit excess profits and control of markets by private traders. Cheap food policies, although common in Africa, have been difficult to enforce in most countries, giving rise to large parallel markets, and making officially announced prices often of little relevance to producers.

# H. TEE IMPACT OF POIYCY ON AGRICULTURAL EXPORTS

# Trends in Prices. Policy Distortione, and Exports

Africa's loss of world market shares for its major agricultural exports is the most telling evidence of its loss of competitiveness. Africa's share of those markets for its 12 major agricultural exports fell by half between 1970 and 1983. And the volume of agricultural export actually declined as well, falling from 14 million metric tons to about 11 million over the same period. _y As figure 1 confirms, the average pattern across 26 countries where complete data are available suggest that the loss of competitiveness in international markets has followed closely the pattern WL overvalued exchange rates (real effective exchange rate index is inverted for better visual presentation), and lower real producer prices. For these 26 countries, only after exchange rate distortions began to decline and real prices paid to farmers began to rise in 1984, did export volumes improve.

The decline and recent recovery of agricultural exports appears to follow closely the pattern of real effective exchange rates over the period, except for 1987. Real producer prices have generally followed a similar pattern, although declining more slowly in the mid-1970s due the boom in international coffee and cocoa prices. The decline in export volumes in 1987 is primarily the result y FAO weighted 'verage export volumes for total agricultural commodities. Much of the analysis is focused on exl. . volumes rather than agricultural production. Agricultural production data is of questionable quality given the rough estimation jrocedures relied on for most ccuntries. Since for most export crops domestic consumption is small relative to the total exported (cocoa, coffee, tea, rubber, tobacco, cotton), export volumes can be takes as a proxy for total production for most of these crops.
**4-**

Figure 1. Policy, incentives, and agricultural exports in Sub-Saharan Africa <!-- Start of picture text --> Index 1970 = 100<br>120 -<br>110 -<br>9 0<br>8 0<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>arithmetic mean for 26 countries<br>- REER agricultural exports real producer price<br><!-- End of picture text -->
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of lower output amonig major coffee growers.<sup>Exports rose in 1988 according to preliminary data available</sup> at the time of this writing.

Direct pricing policy distortions. A variety of government<sup>acdons can affect producer prices directly (fixing</sup> prices, export taxes, marketing arrangements, etc).<sup>The net effect of which is often complex. To assess the</sup> magnitude of the collective distorting effects of these policies,<sup>some kind of measure is needed to capture the</sup> net effect of these complex interventions. The most<sup>commonly used measure of the distorting effects of</sup> policy is the nominal protection coefficient (NPC)<sup>-or ratio of producer price to border price adjusted for</sup> - marketing costs which compares what a farmer<sup>receives to the maximum he could receive short of</sup> subsidies (an NPC less than one indicates a tax<sup>on producers, an NPC greater than one would reflect a</sup> subsidy). NPCs have been widely used to assess<sup>the effect of policy on agricultural incentives, as a</sup> relatively simple means of assessing the divergence<sup>of producer prices from what they would be in the</sup> absence of government policies. I/ Figure 2 indicates that NPCs for Africa's major<sup>exports have fallen and risen twice since 1970, and</sup> are now at a level of about 1.0, on average, but having<sup>fallen below 0.5 in 1976. The range of NPCs varies</sup> widely among countries and over time (see Annex<sup>D tables 1 and 2), from as low as 0.16 for Ghanaian</sup> cocoa in 1976 to as high as 2.6 for Senegalese groundnuts<sup>in 1987. In most countries the NPC varies</sup> erratically from year to year because producer prices<sup>are usualy set at the beginning of the growing season,</sup> long before the international price that wiUl be received<sup>for their production in the folowing year is</sup> known. 4/ Only in the few countries where the producer<sup>price is based on what is eventually received for</sup> their product (using a rebate system) as in Kenyan<sup>and Ethiopian coffee, and Malawian tobacco, are the</sup> NPCs more stable across years.

The degree of taxation implicit in these NPCs is in many<sup>cases substantial. However, it does not</sup> appear to be significantly higher during this period<sup>than in other developing regions when compared to those</sup> estimates (Binswanger and Scandizzo; Krueger,<sup>Schiff, Valdes).</sup> Macconomic policy distor Thons. I e NPC as a measure of policy distortion suffers, however, from<sup>at</sup> least two important drawbacks. First, the NPC does not<sup>take account of exchange rate misalignment or</sup> the implicit taxation that it can represent, and thus<sup>will understate the degree of agricultural</sup> taxation when exchange rates are overvalued. And<sup>second, changes in the NPC over time arise</sup> from three sources, but the reladve importance of<sup>any one factor relative to the net</sup> I/ A broad range of agricultural and macroeconomic<sup>data for 1970 to 1988 were used to examine policies,</sup> incentives, and performance for up to 40 African countries<sup>(see Annex A for details). The time period being</sup> considered and the number of countries involved<sup>lends itself to an analysis of cross-sectional and time-series</sup> data. NPCs for each country's major export and food<sup>crops have been computed as the ratio of producer</sup> price to border price net of all processing and marketing<sup>costs. Both numerator and denominator are</sup> adjusted to reflect the comparison of producer to border<sup>prices at the "last joint marketing point' (Westlake);</sup> the border in the case of exports, and the major<sup>consumption center for import substituting food crops. See</sup> Scandizzo and Bruce (1980) for computational<sup>details.</sup> _41/_ The wide swings in NPCs from year to year<sup>explains, in part, conflicting findings in the literature as to</sup> the degree - and direction - of policy distorting<sup>effects in a particular country. Unless such studies are</sup> comparing producer prices and international prices<sup>for the same year, significant differences are likely to</sup> occur in the conclusions. Given these wide variations<sup>over time, multi-year analyses are advisable.</sup>
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Figure 2. Nominal and real protection coefficients for major agric. exports <!-- Start of picture text -->
1.2<br>0.8<br>0.6<br>0.41<br>1970  1972  1974  1976  1978  1980  1982  1984  1986<br>-RPC  -NPC<br><!-- End of picture text -->
-7-

effects is ambiguous. Taking account of these effects is important, especiaUy because exchange rates across Africa became highly overvalued during the 1970s, and have become less overvalued in the 1980s.

Both limitations of the NPC are addressed here, first by computing "real protection coefficients" (RPC) to take into account changes in the degree of currency overvaluation using the real exchange rate, and second by "decomposing" the annual changes in these RPC's in order to reveal the source of the changes because of changes in producer price, international price, or exchange rate.

The RPC is computed by adjusting the NPC for changes in the real exchange rate, using 1970 as a base year. 5/ The RPC will diverge from the NPC **to** the extent that the implicit taxation from exchange rate policies has changed. {/ Given the extent of exchange rate misalignmen **t** hat has occurred among African countries, the RPC offers a better means for assessing how changes in policy have affected agriculture (See Annex Table 3). Figure 2 depict both the NPC and RPC from 1970 to 1987. The two are equal by definition in 1970, but diverge significantly between 1978 and 1984 indicating an increase in the degree of exchange rate misalignment. The difference is quite large, accounting on average for 20 percent of the producer's fair value of his production, and maldng clear that the rise in the NPC in 1980-81 was really a "false improvement" whereby a higher share of the border price (converted at the official exchange rate) was being received by producers, but at the same time the indirect effects of increasily overvalued domestic currencies reduced agriutural competitiveness in a less direct but potentially more distorting way.

The differences between the NPC and the RPC is quite large in those countries where exchange rate misalignment had become severe: an NPC of 2.8 compares to an RPC of 0.51 for Ghanaian cocoa m 1981; in Uganda the NMC for coffee is 1.51, while the RPC is 0.17 in 1980. For CFA coumtries which historically have kept exchange rates in better alignment, the differences are small (less than 0.1), with some divergence during 197741 and 198546 (figure 3). The divergence in 1985-86 appears to have diminished in 1987, the result of a decline in the average REER in 1988 for CFA countries due to the strengthening of the french franc vis-a-vis to U.S. dollar.

5/ Both the NPC and RPC are computed for split crop years (i.e. 1970/71), so that the producer prices for 1970 crop year are comphred to internatonl prices for the corresponding marketing year, which is 1971. For that reasonl 1971 rather than 1970 is used as a base for the real effective exchange rate in computing the RPC for 1970.

{/ The RPC will take account of changes in the degree of exchange rate misalignment, but not in the absolute level. Only if there had been no misalignment in the base year, 1971, would the RPC accurately show the level of both direct and implicit tamtion of agriculture. The average level of exchange rate distron appears to have been relaively small in 1971 based on the ratio of official to parallel market exchange rates, which averaged 1.2 for countries with available data. Choosing a base year prior to the first oil price shock and subsquent volatility has obvious advantages. Still, exchange rates were serously overvalued at that tme in several countries, reflected in high rados of parallel **to** official exchange rates: Ethiopia (1.17), Ghana (1.48), Kenya (1.35), Malawi (1.37), Sudan (1.77), Tanzania (1.62), Uganda (1.48), Zaire (1.45), and Zambia (1.49). In Nigeria the parallel market rate in 1971 was below the official rate.
Figure 3. NPCs and RPCs in CFA countries for major export crops <!-- Start of picture text -->
1.3.<br>_<br>0.9<br>0.7<br>0.5-<br>0.3<br>1970  1972  1974  1976  1978  1980  1982  1984  1986<br>NPC  -RPC<br><!-- End of picture text -->
The actual levels of direct and implicit taxation (not just relative<sup>to a base year) of agricultural</sup> producers is difficult to assess short of elaborate and computationally<sup>difficult methods such as</sup> those recently developed by Krueger, Schiff, and Valdez, but as<sup>an approximation the RPC can be</sup> adjusted proportionally to the base year ratio of parallel market exchange<sup>rate to official exchange</sup> rate, and termed "adjusted RPC". Using this rough estimation procedure,<sup>the average adjusted</sup> RPC is compared for CFA-zone countries (where no significant parallel<sup>market distortion existed in</sup> 1971) and non-CFA countries (where the average ratio of parallel market<sup>to official exchange rate</sup> was 1.46 in 1971). The result (figure 4) suggests that: a) the degree<sup>of direct and implicit taxation</sup> of agriculture has been substantially higher in non-CFA countries throughout<sup>most of the past two</sup> decades, averaging 0.5 from 1970-1985, b) the adjusted RPC has fluctuated<sup>more in CFA countries,</sup> with averages ranging from 0.4 to 1.25, and c) for both sets of countries<sup>the level of direct and</sup> implicit taxation have moved in the direction of increased incentives<sup>to producers since 1984.2/</sup> The underlying cav-ses of the large fluctuations in the RPC can be<sup>revealed by "decomposing"</sup> the annual change into its component effects. §/ The three principal<sup>effects causing changes in the</sup> RPC are the nominal producer price, the real exchange rate, and the<sup>international price. 2/ In</sup> figure 5 each set of three bars will sum to the annual change<sup>in the overall RPC. Each bar</sup> represents the influence of each of the three major factors on the overall<sup>change in the RPC from</sup> the previous year; the sum of the three bars will reflect the change in<sup>the RPC from the previous</sup> year. For example, in 1972, the large negative effect of international<sup>price changes (rising</sup> international commodity prices) outweighs the smaller positive change<sup>due to nominal producer</sup> price changes and exchange rate changes, so that the net effect<sup>is a decline in the NPC between</sup> 1971 and 1972. By contrast, in 1986 the rise in nominal producer<sup>prices outweighed the small</sup> downward influence of changes in the exchange rate, resulting in a<sup>net effect raising the average</sup> RPC in 1986 over 1985.

Figure _5_ shows that the decline in RPCs in the early 1970s was<sup>due to rising international</sup> prices unaccompanied by higher farm prices (which causes the RPC<sup>to fall), and smaller real</sup> exchange rate depreciations in 1973 and 1975. Producer prices<sup>were raised as indicated in the</sup> figure, by substantial amounts in 1973, 1975, and 1976, although only<sup>a portion of the international</sup> price rise was passed on to producers.

Beginning in 1980, large nominal devaluations in exchange rates<sup>drove the RPC down. The</sup> average RPC rose ag,ain in 1981 due to a drop in international prices<sup>(which were not fully passed</sup> on to producers), but in 1982 and 1983, the large devaluations<sup>lowered the average RPC</sup> substantially. Not until 1984, with the increased scope<sup>for raising nominal producer prices</sup> following devaluation, were the RPCs raised as a result of large<sup>increases in producer prices in</sup> 1984-87. In 1987 increased producer prices were offset by exchange<sup>rate devaluation (occurring in</sup> 1988), resulting in a small decline in the RPC.

2/ These results are reiatively close to those estimated in Krueger,<sup>Valdez, Schiff for Cote d'Ivoire</sup> and Zambia. But for Ghana, they estimate a lower level of total<sup>taxation of cocoa during the</sup> 1975-79 period than estimated here.

/ See Jaeger and Humphreys for the derivation.

2/ If only producer prices rise, the RPC will rise; if only international<sup>prices rise, the RPC will</sup> fall; if only the real exchange rate rises, the RPC will fall.
**-10**

Figure 4. Adjusted RPOs for Africa's major export crops <!-- Start of picture text -->
1.3<br>1.1<br>0.9  /<br>0.7-<br>0.3<br>1970  1972  1974  1976  1978  1980  1982  1984  1986<br>- CFA countries  - non-CFA  countries<br><!-- End of picture text -->
- **11** .

Figure 5. Decomposition<sup>of changes in</sup> RPCs for African export<sup>crops</sup> <!-- Start of picture text --> average annuol chonge averoge RPC<br>0.2 /1.28<br>-0.2 -0.8<br>-0.4 -0.6<br>-0.6- 0.4<br>1971 1973 1975 1977 1979 1981 1983 1985 1987<br>_ prod'er price effect exchange rate effect<br>FII inter'l price effect - RPC (right scale)<br><!-- End of picture text -->
- 12 -

As of 1986, the average RPC was greater than 1.0. This reflects several factors. First, several CFA countries recently began subsidizing producers (rather than taking the politically unpopular decision to lower producer prices). IQ/ In addition, several other countries now have REERs which are "undervalued" relative to 1971, making their RPCs greater than 1.0. Taking account, - however, of the exchange rate distortions at that time accomplished to a large extent by the "adjusted" RPC -- leaves the net effect as a taxation on agriculture (figure 4).

Real producer price trends. The net effect of both direct and indirect policy changes, as well as changes in international prices, are felt most directly by producer in terms the real<sup>prices received.</sup> Real producer prices for Africa's major export commodities have witnessed wide fluctuations since 1970 (figure 1). Throughout much of the 1970s these prices rose, on average, reflecting the rise in international commodity prices in the mid- to late-1970s, primarily for tropical beverages. The<sup>rise</sup> in producer prices for these commodities, however, was far less than the increase on internadonal markets where real prices for primarily commodities more than doubled, whereas real producer prices rose, on average, less than 15 percent.

Between 1977 and 1982, the real prices received by farmers for these exports fell 25 percent resuldng in part from a return to lower prices following the "boom' in tropical<sup>beverage prices in</sup> the mid 1970s, but also due **to** high domestic inflation and the reluctance of governments in Africa to respond by adjusting their exchange rates. Only after exchange rates were devalued,<sup>and real</sup> producer prices raised, did export performance begin to improve.

# Exprt supDDl resnonse

The response of agricultural production to changes in policies and incentives is a central issue with important implications for governments and donors. Views range widely on the responsiveness of agricultural production to price and this has given rise, to much debate (Chhibber, Binswanger, Cleaver 1988).

One source of these differing views is the distinction between the supply response of individual - crops when factors of production can be quickly shifted from one crop to<sup>another -</sup> and aggregate supply response which, in the long-run requires either additional resources to move from other sectors, higher investmenu, or technological change to bring about higher productivity.<sup>The fact</sup> that farmers respond strongly to changes in the relative prices of individual crops is well documented in empirical studies. This, however, tells us little about the aggregate response to changes<sup>in</sup> agriculual prices overall.

For African countries, relatively few supply response studies have tried to assess the relative importance of price, non-price factors, and exogenous shocks in explaining agricultural growdt (Wheeler is a notable exception). And none has exploited the potential strength of using pooled cross-sectional time-series analysis to explain these reladonships.

IQ/ This situation arose as the CFA franc, which is ded to the French franc, has appreciated making the US$ denominated international commodity prices appear much lower<sup>in local</sup> currency terms.
- 13 -

In addition, while much attention has been placed on the direct price incentives (generally producer prices), little work has included the indirect effects of macroeconomic policy distortions on agricultural growth, especially exports (balassa, 1988, has used the real exchange rate as a proxy for iLt.entives). Real exchange rates affect the competitiveness of agricultural exports by altering the relative prices of inputs and products. But inclusion of the real exchange rate - in addition to real prices - serves as a proxy for the costs -id efficiency losses associated with trade and exchange restrictions that generally occur wheh exchange rates are highly overvalued.

The analysis presented here utilizes time-series data for the 1970-87 period and for 21 countries to estimate the response of agriculture to changes in real producer prices, macioeconomic policies (specificaUy the real exchange rate), the effect of weather, and major shocks in the form of "disasters" (including war, civil strife, famine, floods, or other disruptions). L1/ The model is as follows:

# AGRICULTURAL EXPORTS = b0 + b, REAL PRODUCER PRICE +b2 REAL EFFECTIVE EXCHANGE RATE + b3 WEATHER + b4 DISASTERS + _e_

The analysis focuses on exports of agricultural commodities (cocoa, coffee, tea, etc.). For most of these commodities, nearly all production is exported so that exports are a reasonable approximation of production except in cases where domestic consumption is large relative to exports (as in the case of palm oil), or where smuggling results in a significant divergence of recorded exports from actual production. 12/ The food crop sector is, for the moment, left out of the analysis. To estimate similar supply equations for food crops would be problematic due to the lack of adequate producer price data. This results in part from the prevalence of officially announced prices for food crops which in many countries have little relation to what farmers actually receive. These official prices are often the only available price series. The interaction between food production and policy variables is addressed below.

Weather has an important impact on agricultural production in Africa, but quantifying that effect is problematic. Rainfall data is spotty and difficult to interpret correctly short of complex simulation models of soil moisture, daily rainfall, and evapotranspiration data, and different rainfall patterns will effect individual crops differently. An approximation for the general effect of weather on agriculture is derived here by estimating a regression trend line for cereal yields, and taking the _iL/_ Specification of variables and data sources can be found in Annex A.

12/ Several countries have been excluded from the anm1ysis because of obvious "border effects" and smuggling. These include Benin and Zambia where dramatic swings in agricultural exports appear to have resulted from policy changes and border closings in neighboring countries. Also, - Rwanda and Burundi are excluded due to apparent smuggling exports exceeded production by substantial amounts in several years. Ghana is often thought to have substantial smuggling but it is unclear that this accounts for a substantial share of total production.
> - **'4** -

Figure 6. The impact of weather on African agriculture (deviations from trend in cereal yield) <!-- Start of picture text --> 0.08 -<br>0.06<br>0.04 -<br>0.02-<br>0 -<br>-0.02-<br>-0.04-<br>-0.06<br>-0.08<br>-0.1AI I I I I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br><!-- End of picture text --> i~~~~~~~~~~~~~
- 15 -

residuals of that esimated trend to be a proxy for the effect of weather on agriculture in each year. _Il/_ The results are consistent with expectadons overall (figure 6) and for individual countries and years where drought (Ghana and Senegal, 1983) or abundant harvests (Burkina 1985, 1986) are known to have occurreJ.

Major shocks such as war, civil strife, and natural disasters such as floods or cyclones have also had devastating effects on specific African countries. The potendal impact of these phenomenon are taken into account in the model by including the percentage of the populatdon affected by 'disasters" as recorded in "Major Disasters Worldwide" (USAID).

The real effective exchange rate (REER) is included in the model (an increase in the REER indicates appreciation). As a measure of the eompetitiveness of agriculture it incorporates some of the incentives accounted for by the real producer price. However, REERs also provide a more general measure of distortions in product and faitor markets, as well as serving as a proxy for the indirect effects of the exchange and trade controls that usually accompany overvaluation.

The results of the pooled cross-section time-series model _JAI_ are presented in table 1 (and Annex table 14). They include a number of variations of lagged and moving averages in the independent variables, and two subgroups of countries; a) those exporting primarily tree crops,<sup>and</sup> b) those exporting primarily annual crops. (Crop specific and country models are presented below.) The weather variable is strongly significant in all of the equadons. The coefficient ranges from 0. _15_ to 0.46 and is highest for the annual crop producers. The coefficients can be interpreted as a 10 percent drop from trend for cereal yields associated with a 1.5 to 4.6 percent decline in exports. The "disasters" variable has the expected sign and is significant in most cases.

The estimated price elasticities of export supply for all countries range from 0.1 to 0.3, and the elasticities with respect to REE range from -0.1 to -.25. For countries exporting primarily tree crops (where longer lagged responses are expected) the REER is the most consistent explanatory variable with elasticities ranging from -0.14 to -0.25; the price elasticities for tree crop exporters are only significant when the REER is excluded (with an elasticity of 0. 115) or in one case significant with the wrong sign (an elasticity of -0.06).

Higher price elasticities can be expected from countries exporting annual crops such as tobacco and cotton, as farmers are more able to respond quickly to changes in relative price incentives by ,3/ Cereals are the most susceptible crop to moisture stress, and for most countries variation in average yields of cereals will result primarily from variations in weather. While exported crops may respond differently to specific weather patterns, the deviations from trend can be expected to be of the same sign. Year to year variations in cereal yields could arise due to fluctuations in ferdlizer avallability or other policy related factors, but with few excepdons ferdlizer is not widely used on cereals and other factors are not likely to dominate the effects of rainfall.

_J1/_ The pooling of cross-section tme-series data can be accomplished several ways depending on the characteristics of the data. Since these data are time-wise autoregressive and crosssecdonally correlated, a hree-stage procedure is required to produce consistent, unbiased estimates foliowing Kmenta (pp. 512-514), and using the SAS Park estimation procedure from SAS SUGI Supplemental software.
- 16 -

altering their cropping pattern. The estimated<sup>elasticities for these countries are indeed much higher,</sup> ranging from 0.56 to 0.94, suggesting highly<sup>responsive adjustments to changes in real prices. For</sup> this subgroup, however, the real exchange rate<sup>variable is only weakly significant in one case, and</sup> has the wrong sign. II/ Overall, these results indicate that agriculture<sup>is only moderately responsive to changes in</sup> pricing and exchange rate policies in the short-term.<sup>Producers of annual crops such as cotton or</sup> tobacco respond quickly and with an elasticity<sup>near 1.0. The estimated elasticities for tree crop</sup> exporters are low, reflecting the limitations<sup>on quick adjustments for these crops. Even a three to</sup> five year lagged price variable will be inadequate<sup>for estimating long-run supply responses in these</sup> cases, both because adjustments can take<sup>longer than that, and because farmers respond to changes in</sup> expected prices, not short-term booms or busts<sup>that will not be expected to persist. Shorter-run</sup> responses can come only from rehabilitating<sup>existing plantations.</sup> These results indicate that for tree crops the indirect<sup>relationship with respect to the exchange</sup> rate. is stronger than the relationship with producer<sup>prices. This might reflect indirect effects caused</sup> by the constraints and distorting trade and exchange<sup>controls that commonly occur when exchange</sup> rates are overvalued. Or the estimates may be<sup>biased upward as a result of the omission of</sup> unrecorded production and smuggled exports that<sup>tend to occur when exchange rates are seriously</sup> overvalued.

Crop specific models of agricultural exports were<sup>esimated for several major export</sup> commodities (table 2). The results are similar<sup>to those in table 1. The short-run responsiveness to</sup> price is about 0.23 for coffee, about the same for<sup>cocoa but not significant. For cotton, an annual</sup> crop, the responsiveness of exports to price is much<sup>higher with an elasticity of 0.67 both for the</sup> producer price and the real exchange rate.<sup>For tea the results were not significant, apparently due to</sup> the small sample.

J1/ The significance of the overall equations has<sup>been tested using a covariance model and is</sup> highly significant at the 99% level. The three-stage<sup>pooled esdmation procedure does not permit</sup> computation of F-values.
# Table 1. Rearession<sup>EAuation for Agricultural Supplv in Sub-Saharan AfricaL</sup>

||All coun|tries<br>|Tree Crop Expo<br>|rters<br> <br>|Annual Crop Ey<br>|porters<br>6|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|_(5)_|()|
|Model|||||||
|Producer Price<br>Moving average<sup>(t and t-1)</sup>|0.202<br>(12.97)|0.240<br>(204.2)|0.017<br>(1.13)|_0.115_<br>(5.04)|0.940<br>(6.26)|0.920<br>(6.08)|
|Real effective exchange<sup>rate</sup><br>Moving average<sup>(t and t-1)</sup>|-0.104<br>(4.15)||-0.253<br>(-13.86)<br>||0330<br>(2.09)<br>|00009|
|Disasters variable|-0.0006<br>(-2.15)|-0.0010<br>(49.7)|-0.0003<br>(-0.43)<br>|-0.0016<br>(-1.69)<br>|0.0013<br>(0.92)<br>|.<br>(0.67)<br>0435|
|Weather variable|0.373<br>(21.64)|0382<br>(299)|0.153<br>(5.18)<br>|0.219<br>(5.54)<br>|0.440<br>(6.29)<br>|.<br>(5.7)<br>033|
|Intercept|4.12<br>(26.87)|3.44<br>(625)|5.62<br>(41.5)<br>|4.01<br>(36.8)<br>|-1.29<br>(-1.46)<br>|(0.47)<br>92|
|Degrees of freedom|331|332|219|220|91||

Note. Exports,<sup>producer prices, exchange rate, and the weather variable have been expressed in logarithms. Their coefricianct may be</sup> interpreted as<sup>elasticities.</sup> Figure in parentheses<sup>are t-values.</sup>
- 18 -

Table 2. RegresIo Eaudtons for Agdricutural-SuDply In Sub-Sabaran A-frca: CrM models

||Depndan|t v<br>ble:|total agrcu|ltural exports|
|---|---|---|---|---|
||Cocoa|Coffee|Cotton|Tea|
|Producer price a/|0.22*<br>(1.75)|0.23**<br>(8.07)|0.67**<br>(4.02)|-0.04<br>_(-0.50)_|
|Real effecdve exchange rate a/|4.35**<br>(-3.91)|_0.055*_<br>(1.94)|-0.68**<br>(-5.04)|<br>0.126<br>(0.65)|
|Disasters variable|-0.0067<br>(-1.49)|-0.0037*<br>(-2.86)|* 0.0026<br>(1.64)|-0.0192**<br>(-3.14)|
|Weather variable|-0.20<br>(-1.65)|-0. 163**<br>(-2.82)|0.226*<br>(2.4)|0.033<br>(0.21)|
|Intercept|5.16**<br>(5.8)|<br>3.30**<br>(15.2)|4.43**<br>(3.9)|<br>4.24**<br>(4.02)|
|Degrees of freedom|107|219|171|59|
|Countries|7|14|11|4|

Notes: Exports, producer price, exchange rate, and the rainfall variable have been expressed in logarithms. Their coefficiants may be interpreted as elasticities.

Years refer to split crop years (e.g. 1980/8 1). Since marketing occurs in the second of the two calandar years, export data for the 1981 calandar year correspond to prices, rainfal, etc. for 1980.

The pooled cross-ection time series procedure was used.

Figures in parentheses are t-values. Significance levels are * (955) and ** (99%).

a/ Two year moving average (t and t-1)
# Table 3. Regression Eguations for Agricultural SuRpIv in<sup>Sub-Saharan Africa: Country models.</sup>

|||||Dependan|t variable:|total agri|cultural ex|ports||
|---|---|---|---|---|---|---|---|---|---|
||Ethiopia|Ghana|Kenya|Malawi|Nigeria|Senegal|Tanzania|Togo|Zimbabwe|
|Producer Price a/|-0.84*<br>(-2.3)|1.17*<br>(3.8)|-0.48<br>(-2.0)|-0.07<br>(-0.14)|0.22<br>(0.56)|1.52<br>(1.83)|0.42<br>(0.58)|2.1**<br>(3.3)|0.86*<br>(2.48)|
|Real effective exchange rate a/|-1.64*<br>(-2.55)|0.32<br>(1.6)|0.35<br>(0.27)|2.13*<br>(2.99)|-1.03**<br>(-4.4)|1.65<br>(0.22)|-1.07*<br>(-2.6)|0.24<br>(0.29)|0.29<br>(1.75)|
|Disasters variable|-0.0016<br>(-0.15)|-0.019*<br>(-2.68)|-0.046<br>(-0.75)|_-0.005_<br>(-0.12)|4.18<br>(1.52)|0.0008<br>(0.126)|-0.007<br>(-0.24)|0.068*<br>(2.51)|-9.03<br>(-0.66)|
|Weather variable|-0.029<br>(-0.069)|-0.40<br>(1.84J|0.96*<br>(2.23)|-0.023<br>(-0.049)|-0.40<br>(-0.66)|1.36<br>(2.02)|-0.65<br>(-0.66)|-0.146<br>(-0.38)|0.56**<br>(4.08)|
|Intercept|15.9<br>(379)|-2.15<br>(-2.17)|5.2<br>(0.80)|-4.90<br>(-3.78)|&16**<br>(3.1)|-10.07<br>(-1.45)|7.54<br>(1.62)|-6.17<br>(-1.01)|-0.73<br>(-.41)|
|Degree of freedom|12|12|12|12|12|12|12|12|12|
|R square|0.66|0.85|0.72|0.85|0.81|0.64|0.75|0.78|0.62|

Note: Exports, producer price, exchange rate, and the rainfall variable<sup>have been expressed in logarithms. Their coefficiants may</sup> be interpreted as elasticities.

Years refer to split crop years (eg. 1980181). Since marketing<sup>occurs in the second of the two calendar years, export data</sup> for the 1981 calendar year correspond to prices, rainfall,<sup>etc for 1980.</sup>

- indicates statistically significant at the 95% leveL

- ** indicates statistically significant at the 99%O leveL

- a/ Two-year moving average.
- 20 -

Country-specific models were also estimated (table 3). There are striking differences between these models and those estimated pooling cross-section and time-series data. The estimated elasticities differ widely between countries. For a number of countries the elasticity with respect to price, or with respect to the exchange rate, is quite high (Ghana, Nigeria, Tanzania, Togo), often around 1.0, and in one case exceeding 2.0. / These wide differences in estimated elasticities may reflect the small number of observations or other data problems, or the diversity among African countries that gives rise to real differences in the responsiveness of farmers to price incentives (see Section IV below).

Estimates of aggregate supply response in the literature range from 0.1 to 0.5 (Binswanger, Bond, Chhibber). But procedures used to estimate long-run elasticities such as the Nerlove techniques (which uses lagged dependent variables to derive intertemporal adjustment coefficients) provide estimates that are generally believed to be too low and not good estimates of the response of crops to a permanent change in the price regime of agriculture (Binswanger). In addition, there are problems with the interpretation of distributed lag models, and simultaneity problems that can arise in the data.

In summary, current statistical methods appear inadequate to estimate reliably long-run supply response. Since the year-to-year price fluctuations in the data normally reflect short-lived commodity booms rather than permanent changes from a low price to a high price regimen, any estimate based on these data will reflect short-run adjustments rather than long-run responses to permanent changes in price levels (Binswanger). Farmers will respond to expcted prices, which are generally not observable. Given the preponderance of tree crop exports in Africa, it is not surprising that these short-run elasticities are low. The long-run aggregate supply response will include the effect of reallocation of productive resources, labor and capital, among sectors in the economy overall. It may also include changes in government expenditures and public capital investments for infrastructure, research, human capital and institutional support which may be more forthcoming in the context of higher incentives.

# m. THE IMPACT OF POLICY ON THE FOOD CROP SECTOR

Analysis of the food crop sector in African countries is more problematic than export crops due to poor quality and limited availability of the production and price data -- in addition to other differences. Many African countries set official food prices, and thus only the officially announced price figures are available. For a majority African countries, however, official prices are not effectively enforced, and most producers receive a market determined price for their food sales.

16/ In three cases the estimated price elasticity is negative -- Ethiopia, Kenya, and Malawi -- although only significant for Ethiopia. These three countries differ from the others in that producer prices are determined ex post at international auctions. Farmers will not respond to current or lagged prices unless they reflect changes in expected price. Because prices received by farmers in these countries fluctuate widely from year to year, very high prices paid in one year may do little to change their price expectations unless there is some perceived change in the long-term price level for their products in international markes. Hence, the lack of a signiflcant relationship is not surprising, although in the case of Ethiopia the negative sign is puzzling, and may reflect complications related to coffee quotas, or the influence of Ethiopian exports on price.
- 21 -

# Cross-Drice effects between export prices and food

The elasticities esdmated above for export crops<sup>will overstate the responsiveness of aggregate</sup> agriculture if higher exports are offset by a faU<sup>in food production (in the same way single crop</sup> esdmations tend to overstate .ggregate supply<sup>response). If the export response has come from a</sup> shift of resources away from food crops and toward<sup>export crops, then the net increase in total</sup> agricultural output will be lower.

Direct esdmation of food crop models in the above<sup>analysis is not possible due to a lack of price</sup> data. However, by replacing the dependent<sup>variable in the above formulation with food producdon,</sup> it is possible to test the hypothesis that export<sup>incentives lead to offsetting effects in food production.</sup> Similarly, the relationship between food production<sup>and the real exchange rate can be esdmated.</sup> The results of these models are shown in table 4. Three<sup>different dependent variables have been</sup> used; total agricultural production, total food producdon,<sup>and staple food production. The estimated</sup> elasticities indicate that food production responds positively<sup>to changes in export crop producer</sup> prices. Total agricultural production, total food production<sup>and staple food producdon respond</sup> positively to changes in export crop prices and real<sup>exchange rates. The result with respect to the</sup> exchange rate is expected, as both tradable food<sup>crops and export crops are affected in similar ways</sup> by exchange rate distortions. 17i There is no evidence of offsetting changes between<sup>export and food crops. Even for countries</sup> where the principal exported crops are annuals with<sup>high supply elasticities of about 0.9, the cross-</sup> price elasticities on total food or staple foods provide<sup>no statistically significant evidence of a trade-</sup> off. Indeed, the only statistically significaint result is<sup>for a positive cross-price elasticity for staple</sup> foods.

There are several possible explanations for the positive<sup>cross-price effjct of export crop price on</sup> food production: a) food and export crops may be<sup>complements in prodaction (fertilizer benefits</sup> both crops when grown together or in succession), b)<sup>returns on export production permit higher</sup> input use or investment in food crop production,<sup>or c) a third, omitted, factor-such as government</sup> policy that generally favors agrdiulture<sup>-</sup> affects<sup>both export and food crops in the same way. The</sup> results presented here are consistent with other empirical<sup>findings that have found a positive</sup> correlation between expansion of export crops and<sup>growth in food production. One such study</sup> concluded that "conditions favoring agricultural growth,<sup>including appropriate economic policies,</sup> encourage both cash crop and basic staple food<sup>crop production" (von Braun and Kennedy).</sup> 17/ To the extent that the REER may be a proxy<sup>for agricultural terms of trade overall (for both</sup> export and food crop incentives) the aggregate responsiveness<sup>implied here for total agricultural</sup> production is an elasticity of 0.045, which is very close<sup>to the 0.05 estimate for aggregate output</sup> in India by Bapna (reported in Binswanger).
_-22-_ Table 4. Regerssion equations for cross-price effect on food production

# Independent variables a/

|Dependent variable|Export crop producer<br>price<br>(2 year moving avg.!|REER<br>(2 ear moving avg.)|
|---|---|---|
|All Countries|||
|Total agricultural production|0.033**|-0.045**|
|Food production|0.046**|-0.013*0|
|Staple foods|0.065**|-0.018**|
|Annual crop Droducinif countries|||
|Total agricultural production|0.028|-0.110|
|Food production|-0.016|0.085|
|Staple foods|0.183*|0.401|

* Statistically significant at the 95 percent level. **S** * tatistically significant at the 99 percent level.

Note: "staple foods" is an aggregate of total production of cereals, roots and tubers, pulses, oil crops, bananas, and plantains in grain equivalent units converted on the basis of their relative caloric content.

a/ Full model is same as model 1, table 1.

The importance of food production and policy goes beyond its relationship with export agriculture. Food policy in Africa differs from export agricultural policy In many respects. For this reason, and due to the more limiting data, food policy and productivity are assessed separately below.

# Food Policv

In only about half the 35 African countries in the sample do governments intervene substantially in the marketing and pricing of food crops (eleven fewer than in the 1970s). In some of those that do, or did, intervene, government intervention was restricted to marketing of imported foods, mainly rice or wheat to urban consumers, having indirect effects on domestic producers.

The effectiveness of government food marketing and pricing policies is difficult to assess precisely given the paucity of information on the size and scope of parallel markets in many African countries. Among those countries that do intervene to set prices and control marketing, however, evidence suggests that few do it effectively. Where details are available significant parallel markets appear to exist. Zambia appears .o be an exception, where the very high subsidy to official marketing has prevented much private trade from developing (Harvey), although there are substantial cross-border food movements to neighboring countries. In Zimbabwe, Kenya, and Botswana, controls are at least partially successful, but parallel markets exist. Nevertheless, accounts differ on the importance of parallel markets in many countries. For example, in Mali prior to liberalization, one account reports that the existence of parallel markets implied free price
- 23 -

formation (Lecaillon and Morrisson). And in Ethiopia<sup>government confiscation and restrictions on</sup> tnsportation of food affect only certain regions.<sup>I8/</sup> Where data are available comparisons of food<sup>crop producer prices to border prices, or NPCs,</sup> show wide swings from year to year and across<sup>countries (Annex D table 11). In many countries</sup> producer prices are higher than border prices (the<sup>NPCs are greater than 1.0) because of protection</sup> - from imports due either to government policies<sup>or high transport costs which create natural</sup> barriers. While most of these countries import<sup>food, in good years they may also export food to</sup> neighboring countries. In some countries the<sup>net effect on producer prices is an implicit subsidy due</sup> to restrictive import quotas (Nigeria, Ghana) or rationing<sup>of foreign exchange.</sup> The high transport costs (annex D table 7)<sup>and year-to-year swings in domestic production makes</sup> establishing the level of protection difficult, since<sup>the food price may fluctuate within a wide range</sup> between export and import parity prices. As<sup>long as the price remains within that band, the</sup> influence of border prices on domestic market prices<sup>is ambiguous, and it is unclear whether the</sup> producer price should be compared to export or import<sup>par.ty at all for assessing distortions. In</sup> situations like this the NPC provides litte useful information.<sup>12/</sup>

# Real food price trends

Real producer prices for food crops have fluctuated<sup>much less than for export crops, except for a</sup> rise in prices foUowing the world food shortage<sup>of 1974 (figures 7 and 8), aside from which there is</sup> no perceived trend on average. The ratio of export<sup>to food crop p;ices, however, has changed</sup> significantly over the period, falling from 1970 to<sup>1984 (except during the commodity boom in 1975-</sup> 79). OveraUl the price ratio declined by about 20<sup>percent over the period, Since 1984, however, the</sup> trend has seen a large reversal, with export crops<sup>regaining their 1970 parity in just two years,</sup> followed by a small decline in 1986.

These patterns have been due to a combination<sup>of exchange rate, trade, and pricing policies.</sup> Exchange rate policies have discriminated against tradable<sup>foods, driving down the real prces of</sup> exported and import competng commodities. In<sup>some cases, however, foreign exchange constraints</sup> and import controls limit imports and thus may give<sup>rise to somewhat higher food prices. The net</sup> effect of these policies is thus ambiguous. To the<sup>extent that domestically produced foods are</sup> nontraded crops (cassava and yams) that are imperfect<sup>substitutes, the appreciation of the real</sup> exchange rate may raise their price relative to traded<sup>food and export crops.</sup> Trends in consumer food prices show that relative<sup>to border prices at official exchange rates,</sup> domestically produced food crops became more<sup>expensive during the 1970s (Annex D table 12)</sup> jJ/ Even with more detailed information on the paraUel<sup>markets in food crops, it would still be</sup> difficult to assess the impact of government efforts<sup>to restrict private trade on producers</sup> incentives. Enforcement is rare, but where it does<sup>occur, and where transporters are restricted</sup> by law from carrying food crops, low official prices<sup>will have some disincendiv</sup><sup>**e**ffects.</sup> 12/ With high transport costs from producing region<sup>to border, an NPC (computed as an import</sup> competing commodity) less than one has an inconclusive<sup>interpretation of the effect of policy. It</sup> may simply be that the price has not falen sufficiently<sup>low for the commodity to become an</sup> exported good.
**-24 -**

Figure 7. Real producer prices for major crops in Sub-Saharan Africa <!-- Start of picture text --> Index 1970-75 = 100<br>120<br>110<br>100 X<br>90<br>80 \<br>70<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>18 country average<br>- -<br>Food crops Export crops<br><!-- End of picture text --> Figure 8. Ratio of export/food producer prices in Sub-Saharan Africa <!-- Start of picture text --> Index 1970-75 = 100<br>110<br>105<br>100<br>95-<br>90 \<br>85-<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>18 country sample<br><!-- End of picture text -->
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relative to imported rice and wheat. Cheaper imports resulted<sup>from lower internatdonal prices and</sup> overvalued exchange rates. Trade restrictions and imperfect substitution<sup>between food's appear to</sup> have kept prices of domestically produced foods somewhat<sup>higher.</sup> When traded foods are distinguished from nontraded roots<sup>and tubers, the trends in the 1980s</sup> illustrate the effects of exchange rate distortions and changes<sup>in domestic pricing policies (table 5).</sup> As distortions in real exchange rates have declined -- in addition to food<sup>market liberalization in</sup> some countries - prices of traded foods have risen, while nontraded food<sup>prices have fallen. This</sup> should have the effect of shifting demand toward domestically produced<sup>foods and away from</sup> imports (discussed in more detail below).

Tale 5. Comarison of real consumer food prices in Africa

||1977-79|Indexes<br>1980-82|1985-87|
|---|---|---|---|
|Traded food crops (n= 14)|100|103|112|
|Nontraded food crops (n=9)|100|96|89|

Overall, food prices have been affected less than export crops<sup>by government policies (with a</sup> few notable exceptions where pricing and markedng are effectively enforced).<sup>This has been</sup> because: a) in many countries direct policy interventions have been ineffective,<sup>and b) exchange rate</sup> policies have effected domestic food prices less in part because of<sup>trade restrictions that have limited</sup> imports of cheap food.

Food imnort. and slow food mroductiongrow Food production in Africa has grown more slowly than population<sup>since 1970 20/ leading</sup> many observers to conclude that this 'deterioration" is evidence of the general<sup>failure of African</sup> agricultural: "Many African states have slowly lost the capacity to feed<sup>their people" (Eicher 1986).</sup> "[Africa's] ppoor performance has led to a remarkable increase<sup>in food imports.... The</sup> availability of food imports has greatly offset the impact<sup>of poor agidulture performance on food</sup> security, but not enough in aggregate to ensure an adequate diet to<sup>the average African</sup> (Serageldin)." 20/ Per capita food production has fllen at a rate of about 2 percent per<sup>year since 1970- the</sup> only region in the world with declining per capita food production. At<sup>the same tme Africa has</sup> become a net importer of food, doubling food imports during the late-1970s<sup>while food exporu</sup> stagnated. Compounding these overall trends are the periodic and transitory<sup>shortages and food</sup> insecurity that affect large mnmbers of people in Ethiopia, Sudan, Mozambique,<sup>and the Sahelian</sup> countries most prone to food shortages over the past 15 years due to<sup>drought or war or both.</sup>
- 26 -

Consumption of food, however, has not declined. Since the 1960s average caloric consumption has been relatively stable or rising slightly in Africa. There has, however, been a shift in the composition of consumption toward an higher share of imported foods, especially rice and wheat.

But while imports have clearly b-en substituted for domestic production, this leaves open the question of the casual direction of the shift between imports and domestic supply. Has deterioration of productive capacity forced Africans to import more food? Or has a shift in demand in favor of imports resulted in declining demand for domestically grown food (and thus resulting in slower growth in production)?

Demand for food imports may rise for several reasons independent of the adequacy of domestic supplies. Imported foods (generally rice and wheat) are imperfect substitutes for other, domestically-grown staples (millet, sorghum, maize, cassava, and yams). Imported rice and wheat tend to be preferred foods in Africa for reasons of tastes, social status, and ease of preparation, and they tend to exhibit a high income elasdcity of demand (Delgado). Preference for imported foods is most often manifest among urban populations in Africa.

As a result, changes in import demand should result from changes in the following variables: a) the relative price of imported foods to domestic foods, b) income, especially foreign exchange earnings, and c) demographic changes such as urbanization which would increase the share and numbers of the population with a high propensity to consume imported foods. Before testing these hypotheses econometrically, each is elaborated further below.

The relative price of imported to domestic foods will be affected by changes in international prices, exchange rate policies, changes in barriers to trade such as import restrictions, and official domestic pricing policies. Exchange rate policies wiU alter the price of domestic food relative to imported foods to the extent that domestic food prices are not fully affected by international market prices. High transportation costs, other barriers to trade, or the lack of international markets for staples such as yams, will make these products essentially nontradable goods and therefore their prces wil rise relative to imported food prices as currencies become overvalued. Additionally, when currencies are overvalued governments generaUy must ration foreign exchange and place restrictions on imports in order to avoid exhausting reserves. These restrictions may lead to inefficiencies in domestic food production and marketing to the extent that it is dependent on imported inputs such as fertilizer, fuel, or fbrm machinery. If, however, import restrictions involve food imports themselves (eg. the banning of food imports in Nigeria) then the net effect of exchange rate distortions could be to reduce food imports, but orny a few countries have implemented effective import restrictions. Domestic pricing policies which discriminate against producers will limit the extent to which domestic production is stimulated by growth in demand, giving rise to a policy-induced food gap. In addition, consumer oriented "cheap food' policies will, to the extent that they apply to imported foods such as rice and wheat, further shift demand toward imports and away from domestically produced food.

Higher incomes wiU raise consumption, including imports. A number of studies have observed a strong positive relationship between high agricultural growth and rising food imports in developing countries generaly. These studies cite the income effect of strong agricultural growth as leading to increased demand for imported foods (Bautista). While few African countries fit the category of having strong agricultural growth, a number have seen periods of rising incomes from other sources over an extended length of time (eg. oil exporters). In particular, a country's import capacity, or the total of export earnings, foreig investment, borrowing, and grant aid, wiU affect demand for imported foods overall.

Growth in urban population is likely to lead to higher food imports if the propensity to
- 27 -

consume imported food is higher among urban dwellers. This appears to be the case for reasons of higher urban incomes, status, and easier and quicker time of preparation (Delgado). Urban growth due to rural-to-urban migration is influenced by government policy. Macroeconomic policies, pricing policies, and the provision of social services all contribute to shifting the ruraVurban terms of trade in favor of urban dwellers and thereby making migration from rural to urban areas more attractive. This is likely to increase food impets due to higher demand, but also there may be a supply-side effect if urban migration depletes the rural labor force leading to a decline in domestic production.

Agerereate pattern of food imports. The aggregate patterns of Africa's food imports are presented in figure 9. Food imports varied little between 1970 and 1976, ranging between 7-8 million metric tons. Over the next four years, however, food imports doubled to more than 14 million tons, and remained at these high levels until 1986 when they began to fall gradually. Surprisingly, the period during which this sharp increase occurred was a very favorable period in terms of the weather pattern and its effect on cereal yields (except in 1979). And food aid accounts for less than 10 percent of the increase, although food aid shipments continued to grow in the 1980s raising its share in the total considerably, especially following the 1982 and 1983 droughts (imports net of food aid fell to under 10 million tons by the mid-1980s).

On closer examination of these data, however, one notes that the doubling of Africa's food imports during this period resulted in large part from events in one country, Nigeria (figure 9). Indeed, seventy percent of the increase in Africa's food imports -- or about 5 million tons - is accounted for by changes in Nigeria's imports during that country's oil boom. Given the events in Nigeria during that period, a number of contributing factors suggest themselves. First, oil revenues resulted in a sharp rise in the nation's income and capacity to import. Annual import capacity, defined as export earnings, net borrowing, direct foreign investment, and aid, nearly doubled, increasing by $14 billion during the same period (figure 10). Second, the disruptive economic impact and "dutch disease" effects of the oil boom on macroeconomic variables resulted in rising prices and wages, an increasingly overvalued currency, a shift of labor from traditional agriculture to industrial and service sectors, and rural-to-urban migration. All of these factors contributed to both rising demand for food imports and a decline in domestic production relative to population.

The changes which were brought about were of such magnitude that for three of Nigeria's four major agricultural exports -- peanuts, cotton, and palm oil -- exports ceased, and in all three cases Nigeria became a net importer of these commodities by the 1980s.

These very dramatic changes that occurred in Nigeria -- Africa's largest economy -- are likely to be indicative of phenomenon at work in other countries, albeit to a lesser degree. How generalizable these phenomenon are in explaining Africa's food crisis, is the question to which we now turn.

Empirical mode. To test the significance and relative importance of these variables in explaining changes in Africa's food imports an econometric model is estimated for a sample of 31 countries with data from 1971 to 1987. For a sample with these characteristics, pooled cross-section, timeseries analysis was chosed as potentially the most powerful method. Food imports are regressed on
**-28 -**

Figure 9. Africa's food imports million metric tons <!-- Start of picture text --> 16<br>14 - . .....<br>13 - . - Y<br>121<br>1 1 .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .. ..... ..... . .... .... . ... .......... ,,<br>10 ,- .......... - -- ~,, ---<br>7-<br>2 . ... . .. s; f._A ... _Y_._. _ _ _ ... ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. .<br>1 4 . ................... .. .. .... ........... ... ..... ... .. . .... . ._.. . ...-.. . .. .. .... _ _ _<br>2<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>- Total 6 Total less Nigeria -4- Food aid<br><!-- End of picture text --> Figure 10, Africa's import capacity (exports, borrowing, aid, investment) <!-- Start of picture text --> billions of $US<br>70<br>65 .. .. - ---- Total -- -- --- l Nigeria<br>6 0 . .. ... ... ... ..... .. ..... .. .. . \ .. .. .....<br>50 -\ < L_<br>4 5 . . .... -. ... ....... \. ...<br>4 0 ----- . .. .. ...<br>1970 1972 1974 1976 1978 1980 1982 19B4 1986<br>- Total °Total less Nigeria<br><!-- End of picture text -->
-29 -

urban population, the real effective exchange rate, import capacity, a "weather" variable, and trend in the following way: _21W_ FOOD IMPORTS = bo + bjURBAN POPULATION + b2REER + b3EMPORT CAPACITY + B4WEATHER + BsTREND + **_e_** Food imports are total volume figures, net of food aid. 22/ URBAN POPULATION is an index based on the total numbers of urban residents. The REER is the real effective exchange rate as defined by the IMF. It is an index which measures the evolution of a country's prices relative to those of its trading partners, adjustment for nominal exchange rate changes. The IMPORT CAPACITY is the sum of export earnings, net borrowing, foreign investment, and grant aid deflated by Africa's import price index.

The two variables, WEATHER and TREND, reflect the hypotheses for a supply side explanation of variation in food imports, which are unrelated to prices, policy variables, or urbanization. A secularly increasing TREND in food imports would support the hypothesis that there has been a deterioration in Africa's productive capacity. And poor WEATHER (rainfall as well as tornados or floods) that affect domestic production will necessitate higher imports. Clear cases of weather-related causes occurred during the period considered, including frequent severe drought between 1973 and 1984. Since 1984 Africa appears to have seen more normal rainfall patterns on average. The WEATHER variable is a proxy derived from annual data on cereal yields per hectare for each country. It is esimated as the percentage deviation from trend in cereal yields over the period.

Empirical results. Table 6 presents the results of the regression described above. All estimates of the coefficients in the equation agree in sign with a _priori_ expectations. All the explanatory variables are statistically significant at the 1 percent level, except for the REER which is significant at the _5_ percent level.

The estimated coefficient and significance for urban population indicates that urban growth is strongly associated with increased food imports. This variable held the strongest statistical relationship in terms of the addition to the r-squared when the variable was added to the equation (using ordinary least squares estimation). The addition to the r-squared was 0.237, as compared to 0.07 for import capacity, 0.05 for the weather variable, and 0.0017 for the trend.

It should be recognized with respect to the urban population variable that the estimated relationship may conceal an underlying, omitted variable which is correlated to both. In particular, government policies which alter the rural-urban terms of trade in favor of urban consumers - such as cheap food policies - will simultaneously encourage urban consumption of the subsidized foods (generally these programs include imported rice and wheat) and urban migration, which will likewise 21/ Data are from World Bank and FAO. Data for food imports, urban population, and import capacity are normalzed as indexes to abstract from country size (1970-87 average = 100).

L2/ Food aid is excluded from food imports because it is a component of grant aid and therefore it would create an endogeneity problem if included in the right-hand-side variable, "import capacity." Also, while emergency food aid shipments respond to domestic shortfalls in production, increasingly in the 1980s the relationship between "structural food aid" shipments and patterns of demand and supply in Africa were not obvious.
- 30 -

lead to rising food consumption and imports. Indeed, Zambia, where cheap food policies have been in place for 40 years, has the most urbanized populaion in Africa.

Table 6. Regression euation for Sub-Saharan Africa's food imports

|Dependent variable:<br>variable|food imports<br>Coefficient|t-value|
|---|---|---|
|Constant|13.3|(3.3)**|
|Urban population|0.29|(10.7)**<br>|
|Real exchange rate|0.06|(2. 1)*|
|Import capacity|0.40|(21.0)**|
|Weather|-59.3|(-23.7)**|
|Trend|1.09|(7.4)**|

Degree of freedom = 487

# Source: World Bank and FAO.

* indicates significance at the 95 percent level.

** indicates significance at the 99 percent level.

Note: pooled cross-section time-series GLS method was used.

An additional ambiguity iia this result is the extent to which urbanizadon has affected domestic food production as a result of a reduced agricultural labor force. Outmigration of agricultura **l** abor may result in slower growth in production relative to overall population growth. The upward pressure of prices for domestic foods that might result would further shift demand toward imports. If, however, producer food prices are controlled, or if cheap food imports or food aid keep producer prices low, the price signals necessary to stimulate growth in domestic food production for an increasingly urban population may not ensue. To the extent that this is occurring, some of the increase in food imports may be seen as a supply problem, but one whose origins lie in rural to urban migration and the policies which have encouraged it.

The estimated coefficient for the real exchange rate implies that exchange rate policies have contributed to the growth in Africa's food imports. There are several ways in which overvalued exchange rate will contribute to food imports. The most direct way is through its effect on the relative prices of imported versus domestically produced foods, many of which are nontraded or have significant natural barriers between domestic and international prices. Thus when exchange rates become overvalued imported food prices will fall relative to domestic prices leading to a shift in consumption toward imported food. In addition, overvalued currencies are usually accompanied by trade and exchange restrictions (in order to stem the excess demand for foreign exchange reserves). This may have the added effect of reducing productivity in domestic food production in cases where food production relies on imports such as fertilizer, fuel and farm machinery. In some cases, howeve , the accompanying import restrictions may also apply to some imported foods. If effective this would have the opposite effect of reducing food imports. The existence of such contradictory effects may explain the lower significance level for this variable.

The coefficient for import capacity is positive and significant indicating an income effect, oa that changes in aggregate purchasing power are positively associated with changes in food imports. As described above the vcry large increases in Nigeria's import capacity coincided with the doubling of
-31 -

Africa's food imports, 70 percent of which went to<sup>Nigeria. Many other cour. ies were affected to</sup> a lesser degree by commodity booms in the late<sup>1970s for cocoa, coffee, and phosphates, as well as</sup> the four other countries exporting oil. And growth<sup>in aid and borrowing of recycled OPEC revenues</sup> raised tae capacity to import of many African countries<sup>during the late 1970s. By contrast, the debt</sup> crisis in the 1980s reduced substantially the import<sup>capacity of many of these countries.</sup> The weather variable (lagged one year) is significant<sup>and has the expected sign: cereal yields</sup> below trend in year t-l are associated with increased<sup>food imports in year t. Despite the exclusion</sup> of food aid from the dependent variable, commercial<sup>food imports are responsive to shortfalls in</sup> domestic production caused by weather-related factors.<sup>Deteriorating weather, however, does not</sup> explain the secular rise in food imports since this<sup>variable is in terms of residuals, or deviations from</sup> trend, and the trend for most African countries,<sup>showed increasing yields for cereals over the period</sup> The trend variable is significant and has the expected sign.<sup>The value of the coefficient is small,</sup> however, suggesting a rise in food imports of only about<sup>1 percent per year. 23/ In addition, the</sup> significance of the variable in terms of its addition to<sup>the r-squared was very small as reported</sup> above. Nonetheless, a likely explanation for the significance<sup>of a slight trend is the decline in</sup> international rice and wheat prices over the period, which<sup>would be expected to raise imports.</sup> Wheat prices have falien in real terms by a third, and rice<sup>prices by half, between 1970 and 1986.</sup> Thus, in addition to the impact of exchange rate policies<sup>on the relative prices of domestic versus</sup> imported foods, border prices for imported foods have fallen<sup>over the period, encouraging additional</sup> substitution toward imports in consumption.

Overall, the regression results provide important evidence<sup>that Africa's food import growth has</sup> been, in part, demand driven rather than resulting from<sup>failed domestic production-although urban</sup> migration and trade and exchange restrictions are likely to have had<sup>an effect on supply as well.</sup> Demand has been influenced by go urnment policies, urbanization,<sup>and import capacity.</sup> Urbanization is the strongest explanatory factor. But government<sup>policies, to the extent they account</sup> for differences in rates of urban migration, under the<sup>strong relationship ateributed to urbanization.</sup> Evidence of that link is presented below.

# Policy and rural-urban migration

The link between policies and the rate of rural-urban<sup>migration has only been imprecisely</sup> identified thus far. Governments affect the rate of rural-urban<sup>migration through macroeconomic</sup> and pricing policies that alter the domestic, or rural-urban<sup>terms of trade. These include exchange</sup> rate policies and cheap food policies, both of which generally<sup>discriminate against the rural sector.</sup> In addition, govermnent programs such as the provision<sup>of schools, health service, and water</sup> supplies, may favor urban locations thereby encouraging<sup>migration. But determining the extent of</sup> these influences is problematic due to poor census data and<sup>the gradual nature of these demographic</sup> shifts. Anecdotal evidence provides some support to<sup>the importance of these policies on</sup> urbanization. For example, Zambia, which has had<sup>highly subsidized cheap food policies for more</sup> than 40 years, has the highest share of urban population<sup>in Sub-Saharan Africa.</sup> 2D Although not expressed precisely as a growth<sup>rate, the dependent variable is an index with</sup> an average value of 100 for the entire period. Thus<sup>a coefficient of 1 can be taken as</sup> approximating a 1 percent per year trend.
- 32 -

Only with large changes in policy variables would one expect observable shifts in the rate (or even the direction) or rural-urban migration. Some observers contend that in several countries such as Ghana, Nigeria, and Tanzania, where profound policy changes altered the domestic terms of trade in favor of the rural sector, urban dwellers are returning to the farm. Because of poor demograplhic data, however, these patterns have been difficult to substantiate.

In the case of Ghana, however, recent survey data from the World Bank's Ghana Living Standards Measurement Survey offers an opportunity to authenticate the pattern more concretely. In a survey of over 8,000 individuals in Ghana, information on current and prior employment was collected. From this data, respondents were broken down by principal occupation into agricultural and non-agricultural, and similarly for previous occupation. For those who changed occupations between mid-1984 and 1988 - the time of the survey -- prior and current occupation was tabulated as shown in table 7.

Table 7. Rural-urban migration in Ghana since structural adiustment (principal occupation 1984-88, in percent)

|||Current Occupation|
|---|---|---|
||Agriculture|Non-agriculture<br>Total|
|Peius<br>ocMuation|||
|Agriculture|63|2<br>65|
|Non-agriculture|4|31<br>35|
|Total|67|33<br>100|

Source: Ghana Uving Standards Survey (World Bank). Note: sample size = 5570 These data reveal a significant evre migration from urban (or nonagricultural) to rural (agricultural) occupations since the reform program was initiated in the early 1980s. Among individuals that have changed occupations during the period, those moving from nonagricultural into agriculture outnumber those moving in the opposite direction two-to-one. Given the proportions of the rural and urban populations as a whole (and assuming it is roughly the same for the agriculturalto-nonagricultural breakdown) these data indicate a two-percent net increase in the share of the population earning a living in agriculture since 1984. This contrasts with aggregate data prior to 1984 indicating migration in the other direction at about 1 percent per year. The impact of this reversal on productivity of both food and export agriculture appears **to** have been considerable: In the late 1980s Ghana substantially improved food production and dramatically increased cecoa exports (figure 17 below).

These data confirm what many observers have suspected or observed anecdotally; that government policy has a profound effect on migration from rural to urban areas. As a corollary, these policies have also had an important effect, both directly and indirectly, on the growth in Africa's food imports.
33 -

# IV. **DIVERSITY** WITHIN AFRICA: POLICY ENVIRONMENT AND AGRICULTURAL PERFORMANCE

The pattern for individual countries will vary from the<sup>average relationships described above.</sup> And the responsiveness of agriculture to changes<sup>in price and nonprice variables will certainly</sup> differ across countries. To some extent this<sup>will depend on the type of policies pursued, whether</sup> farmers anticipate policy changes to be permanent<sup>or temporary, as well as structural factors such</sup> as the state of transport and other infrastructures.<sup>Recent debate on the effectiveness of policy</sup> reforms and adjustment programs has focused both<sup>on what evidence there is to indicate that these</sup> programs are working, and on to what extent can one expect<sup>adjustment programs to be equally</sup> effective in different countries.

To try to address these two questions, and to provide<sup>evidence complementary to the patterns</sup> and results above, this section will examine the patterns<sup>for countries grouped according to their</sup> policy environment, and it will examine a number of<sup>individual countries as well. The emphasis on</sup> the comparison of country groupings is to discern evidence<sup>of divergent trends between those</sup> countries pursuing policy reform programs and those that<sup>do not.</sup>

# Comparing performance in favorable versus unfavorable policy<sup>environment</sup>

One approach to addressing the importance of the<sup>policy environrent empirically is to</sup> compare the performance of countries that have pursued<sup>policies favorable to agriculture, with that</sup> of countries whose policies have been less favorable<sup>to agriculture. Although the set of domestic</sup> policies which impinge on the agricultural sector<sup>differs by country, key policy variables can be</sup> used to create this typology. The approach taken here<sup>is to compare countries which have</sup> maintained or adopted a policy environment favorable<sup>to the agricultural sector.</sup> The classification is based primarily on two variables, exchange rate<sup>policies and producer pricing policies. Thus, by</sup> examining 1) the extent of exchange rate distortion<sup>-revealed in the real exchange rate and the</sup> ratio of parallel market exchange rate to official rates -- and<sup>2) the trends in real prices paid to</sup> produceis as well as the nominal protection coefficient,<sup>the countries in Sub-Saharan Africa have</sup> been classified as those with a Favorable Policy<sup>Environment (FPE), and those with Unfavorable</sup> Policy Environments (UPE) which hinder or discriminate<sup>against agriculture. The classification and</sup> criteria used are given in Annex C.

Differential performance between groups. The<sup>comparisons in table 8 indicate that since the early</sup> 1980s the countries with favorable policies have performed<sup>better than those with unfavorable</sup> policies. This has been true in agriculture, and<sup>in terms of overall economic growth as weLL</sup> Between 1982 and 1988, agricultural exports and agricultural<sup>value-added rose in FPE countries</sup> 4.15 percent and 3.50 percent per year, respectively,<sup>while both of these indicators declined in UPE</sup> countries. Total agricultural production grew nearly<sup>three times as fast in FPE countries, and food</sup> production grew nearly 4 times as fast, as was<sup>the case in the UPE countries. Given the</sup> importance of agriculture and its linkages to other<sup>sectors, it is not surprising that overall GDP</sup> also grew faster in FPE countries -- at 3.25 percent<sup>per year --over the 1982-88 period. Since</sup>
# Table &8 Comparison<sup>of gerformance and policy amone African county erouns.</sup>

||Countries<br>average|with . <sup>favorable</sup><br>average|<sup>policies</sup><br>growth rate|Countries wi<br>average|th unfavorable<br>average<br>|<sup>policies</sup><br>growth rate<br>|
|---|---|---|---|---|---|---|
||<br>198082|<br>198548|198248|1980482|1985488|198248|
|Economic Performance<br>||1215|415|1018|102.8|-1.80|
|Volume of agricultural<sup>exports a/cf</sup><br>|99.1<br>|.<br>1183|.<br>255|.<br>1029|<br>1091|0.89|
|Total agricultural production<sup>a/</sup><br>|104.2<br>|.<br>1165|.<br>233|.<br>1033|.<br>107.7|0.62|
|Food production<br>|103.9<br>1015|.<br>1197|.<br>350|.<br>1017|<br>99.4|-0.91|
|Agricultural Value-added<sup>a/c/</sup><br>|.<br>|.<br>1256|.<br>325|.<br>1041|118.4|2.14|
|Gross domestic product<sup>a/</sup>|103.9|.|.|.|||
|Polc Performance<br>||956|-94|1682|112.4|-5.3|
|Real effective exchange<sup>rate b/</sup><br>|133.0<br>|.<br>1049|.<br>437|.<br>958|96.5|0.85|
|Real producer prices<sup>for exports</sup><sup>_a/cl_</sup><br>|94.3|.|.|.|||
|Nominal protection<sup>coefficient</sup><br>||114||096|0.99||
|(export commodities)<sup>c/</sup><br>|0.84|.||.|||
|Real protection<sup>coefficient</sup><br>||112||075|0.97||
|(export commodities)<sup>c/</sup>|0.65|.||.|||
|Food Sector<br>||1006||104|108||
|Real producer food<sup>price cd</sup><br>|98.4|.|||||
|Nominal protection<sup>coefficient</sup><br>||_1A_||074|1.27||
|(tradeable foods) d/<br>|2.9|||.|||
|Real protection<sup>coefficient</sup><br>||197||063|1.03||
|(tadeable foods) d/<br>|1.18<br>|.<br>133|_55_|.<br>112|160|4.5|
|Food importscl|106||_._||||
|ogenous Factors<br>||871||978|89.8||
|Index of export prices<sup>a/</sup><br>|99.1<br>|.<br>005||.<br>002|-005||
|Weather effect|-0.06|.||.|.||

a/ Index, 1979-81<sup>= 100.</sup>

- b/ Index, 1970-75<sup>= 100.</sup>

c/ Period is 1985-87<sup>only, no 1988 data are available.</sup> d/ Periods are 1981-83,<sup>and 1987 only. Reduced samples are 6 and 4 for FPE and UPE, respectively.</sup>
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1984, growth in the FPE countries has exceeded population growth ior all the performance variables mentioned above. The divergent trends are illustrated in figures 11-14. _24/_ The policy variables in table 8 mirror the way the countries were classified: PPE countries have, on average, reduced real effective exchange rates rapidly since 1982, nearly twice as fast as the UPE countries, and to a level lower than during the early 1970s whereas for the UPE countries' the REER remains 12 percent higher than in the early 1970s (figure 15). 2S/ With respect to direct policies affecting agricultural incentives, the average nominal protection coefficient for major export crops in FPE countries rose to 1.14 by 1985-87 from a level of 0.84 in the early 1980s. In UPP. countries the NPC rose as well, but by a lesser amount, to 0.99. Since the NPC does not account for the indirect effects of exchange rate disturtions, the real protection coefficients (RPCs) are compared between the two groups. They indicate that in the early 1980s, producer. in FPE countries were faced higher rates of implicit taxation than did those in UPE countries. Since that time the pattern has shifted in favor of producers in FPE countries as the RPCs for that group rose 70 percent. 2AI For the FPE group, the level of RPC in 1985-87 indicates some degree of subsidization to producers. Indeed, among CFA countries, governments have been unwilling to reduce producer prices in the face of the decline in the French franc denominated prices of their major commodity exports. This has for example resulted in an RPC of 2.56 for Senegal, and 1.15 for Cote D'Ivoire.

Exogenous factors and shocks. Exogenous factors such as better weather patterns or higher demand for a country's exports could potentially explain some or all of the differential performance observed here. To assess that possibility both a weather variable, and export prices are compared for the two groups of countries. In the case of export prices, the data suggest that export prices facing both groups of countries have fallen by similar magnitudes.

To assess the impact of rainfall, the weather variable defined in Section 2 is used: deviation from trend in cereal yields. Those residuals (in logarithms) are compared in table 1 for the two groups of countries. They indicate that, on average, FPE countries did indeed receive more favorable weather during 1985-87, but faired somewhat worse in the early 1980s (figure 16).

Can the differences in performance between the two groups be explained by the better weather experienced by the FPE countries? This quesdon can be answered in a precise way. Since the relationship between the weather variable and exports was esimated in table 1, it was also esimated with respect to total production and food. Based on the esdmated coefficients of the 24/ Performance varied witin the two groups. Not all FPE countries performed better than all UPE countries, but in the case of agricultural value added, for example, of the 12 countries with growth rates above 2 percent, 9 were FPE countries; of the 11 countries with growth rates below 2 percent 9 were UPE countries. For agricultural exports the dispersion overlapped more; in fact Senegal, a FPE country, had the worst performance with -12 percent growth for 1982-87.

2JI This improvement for FPE countries occurred in spite of an increase in REERs by CFA countries of about 1 percent per year from 1982-87.

_2I_ The actual level of the "real" protection coefficient may be misleading if the real exchange rate was distorted in the base year. Attention, therefore, should be focused on the change over time and difference between the NPC and the "real" protection coefficient.
**-36 -**

Figure 11, Africa's agricultural exports Comparing countries grouped by favorable and unfavorable policy environments <!-- Start of picture text --> Index 1979-81 = 100<br>1 70<br>150<br>1 30<br>1 10<br>90<br>70 1 ' I ' l I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>-FPE -UPE<br><!-- End of picture text --> Figure 12. Total agricultural production Comparing countries grouped by favorable and unfavorable policy environments <!-- Start of picture text --> Index 1979-81 = 100<br>125<br>115-<br>105 -<br>95<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>FAO production index numbers<br>FPE -UPE<br><!-- End of picture text -->
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Figure 13. Total food production Comparing countries grouped by favorable and unfavorable policy environments <!-- Start of picture text --> Index 1979-81 D 100<br>120<br>110p<br>100<br>90<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>FAO production index numbers<br>FPE UPE<br><!-- End of picture text --> Figure 14. Africa's GDP Comparing countries grouped by favorable and unfavorable policy environments Index 1979-81 = 100 <!-- Start of picture text --> 130<br>120 -<br>110 -<br>100<br>90<br>80<br>70<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>-FPE -UPE<br><!-- End of picture text -->
**-38-**

Figure 15. Real effective exchange rates Comparing countries grouped by favorable and unfavorable policy environments Index 1970-75 100 <!-- Start of picture text --> 200<br>180<br>160<br>140-<br>120 -<br>100<br>80<br>,o II I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>- FPE -UPE<br><!-- End of picture text --> Figure 16. Weather patterns in Africa Comparing countries grouped by favorable and unfavorable policy environments <!-- Start of picture text --> 0.15 -<br>0. 1<br>0.05 F<br>-0.05 -<br>-0. 1<br>-0.15-<br>-0.2<br>-0.25- . I . .<br>1980 1981 1982 1983 1984 1985 1986 1987<br>deviations from trend in cereal yield<br>FPE 3UPE<br><!-- End of picture text -->
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regressions in table 1, only about one-third of the differences in export growth between the two groups or countries can be attributed to differences in rainfall. 27/ The comparisons in table 9 also indicate that both food and total agricultural production grew faster - since 1982 in countries with favorable agricultural policies with food production growing nearly four times as fast in FPE countries as in UPE countries. Again, based on the coefficients estimated for their response to better weather, only about one-third of the difference can be attributed to the different weather patterns.

It is noteworthy that for those countries where producer food price data are available (excluding official prices where they are not enforced) there has been very little change in the real prices paid to producers for food crops. Again the earlier results are confirmed: improved prices for export crops tend **to** benefit food production as well.

# County diversity: policy and response

While the patterns in many individual countries support the aggregate results and grouped comparisons above, performance and responsiveness varies across countries within the two groups. Aside from differentiation between annual and tree crops, two major reasons can account for differing responsiveness to price signals: first, if price changes are viewed as temporary fluctuations rather than permanent shifts in the price policy regime, farmers will not respond; second, nonprice constraints may obstruct producers from responding to incentives.

The experience of several countries provides eviderce of both responsive and nonresponsive agriculture. Ghana is a striking example of both how deleterious the effects of bad economnic policies can be, and how changing those poor policies can have a dramau^-. effect for agriculture and for the economy overall. GDP has grown by 6 percent per year since 1983, led by rhe agriculural sector. Agricultural exports (essentially cocoa), which had been falling since the 1960s, have finally rebounded, 3-4 years after real producer prices were nearly doubled (figure 17).

There are many reasons for the inpressive response of Ghana's economy. Included among these are the return of expelled workers from Nigeria at the time of the reforms bringing additional labor to the rural sector, high levels of international assistance which enabled the government to persevere in the difficult adjustment programs, and the ostensible perception that the reforms represented a permanent change in Ghana's policy regime. A survey carried out in early 1988 showed a substantial amount of new cocoa planting was taking place, an investment whose return will depend on producer prices at least four years hence. The credibility of the change in price level had been perceived (accurately thus far) to be a permanent change and thus led to revision of the prices exocte by farmers in the future, not just current prices.

In addition, though, despite a long period of deteriorating economic conditions, the physical and institutional infrastructure needed to service the cocoa sector had remained reasonably intact.

22/ Using the estimate of the export supply response to changes in the weather variable, only 4 percentage points of the 22 percent growth over the period can be explained by more favorable weather. Similarly, for the UPE group which experience poor weather during part of the period, their exports could have been 3 percentage points better than the 1 percent rise between the two periods if weather had remained favorable in the second period. Therefore, 7 of the 21 percentage point difference in growth between the two groups is accounted for by different weather. (Similar results are obtained by comparing the differences :n total production and food production.)
**-40 .**

Figure 17. Ghana's price incentives and agricultural exports <!-- Start of picture text --> Index 1970 = 100<br>130<br>110<br>90<br>70-<br>50-<br>300 I , I I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>- -<br>producer price export volume<br><!-- End of picture text --> Figure 18. Zaire's price incentives and agricultural exports <!-- Start of picture text --> Index 1970 = 100<br>170-<br>145-<br>120-<br>95<br>70-<br>45-<br>20<br>1970 1972 1974 1976 1978 1980 1982 1984 1988<br>- -<br>producer price export volume<br><!-- End of picture text -->
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Some roads are in need of repair, but for the<sup>most part the capacity existed to support the response</sup> in the sector. These nonprice factors were,<sup>in Ghana's case, not as limiting to agricultural response</sup> as may be the case elsewhere. Togo also provides<sup>an example where agriculture has been responsive</sup> to incentives (figure 20).

By contrast, Zaire is a case where policy reform<sup>led to strong policy changes, but had little effect</sup> on production. The improvement in incentives,<sup>however, was shortlived (figure 18), and to the</sup> extent that producers (correctly) viewed the<sup>change transitory rather than a credible shift in policy</sup> regime, they were unresponsive to the<sup>change. The potendal responsiveness of agriculture in Zaire</sup> is less clear for other reasons. The interior<sup>transport network, as well as support services to</sup> agriculture were never as well developed<sup>as in Ghana, and they have deteriorated considerably. The</sup> loss of this infrastructure, combined with<sup>the policies that weighed heavily on taxation of agriculture,</sup> resulted in a degeneration to essentially subsistence<sup>agriculture. Recent analysis of the situation in</sup> Zaire refers to a labor shortage in the rural areas<sup>due</sup><sup>**to**low returns</sup><sup>**to**agriculture, lack of consumer</sup> goods, and cheap food policies favoring the<sup>urban areas: quite simply agriculture will not respond</sup><sup>**to**</sup> price incentives if few people live in rural areas!

Similar to Zaire in many respects, Tanzania provides<sup>another example of disappointing response</sup> to policy reforms. While the policy distortions<sup>over the past 20 years have been very severe</sup> (Lofchie), recent policy reforms in Tanzania<sup>have resulted in little response from agriculture. There</sup> appear to be two main reasons for this. First,<sup>the reforms have yet to result in sustained</sup> improvements in the incentives facing producers.<sup>Exchange rate distortions remain, and real</sup> producer prices have not improved (figure 19).<sup>For investment to occur in agriculture, prices</sup> _mMr&k4_ in the future must be revised. If<sup>even crrnt prices have not risen, it is unlikely to change</sup> behavior. Secondly, the physical infrastructure<sup>had decayed substantially in Tanzania, leaving many</sup> producers, or potential producers, without easy<sup>access to the markets that are intended to foster</sup> incentives.

Many observers have remarked at the striking<sup>differences in performance between Kenya and</sup> Tanzania. The differences are clearly related<sup>to differences in policy. Kenya's coffee and tea</sup> growers have enjoyed high percentages<sup>of their crops international price (NPCs have averaged 0.9</sup> for both), although this leaves farmers vulnerable<sup>to wide year-to-year swings in</sup><sup>**th"**prices they</sup> receive. Nevertheless, there has been steady<sup>growth in export volumes of both crops.</sup> Land policy in Kenya, and the Ujaama policy<sup>in Tanzania are just two additional factors that</sup> enter into understanding the sources of<sup>the two countries differences. The complexities of the</sup> differences and similarities of these two countries<sup>are, however. impossible to summarize</sup> briefly. 21/ One key difference, though,<sup>is the identification of key constituencies for policy</sup> making: Kenya's farmer elites, and Tanzania's<sup>urban elite. In Kenya, the elite has financially</sup> invested in rural areas, often deriving an important<sup>portion of its income from agriculture. Their</sup> influence has undoubtedly been a factor<sup>in the consistent policy environmen</sup><sup>**t**hat has favored</sup> agriculture.

By contrast, the Tanzanian government nationalized<sup>the land, making it virtually impossible for</sup> agricultural systems based on private ownership<sup>to develop, and the government's leadership code</sup> 21/ See Michael Lofchie, 1989. The Policy<sup>Factor: Agricultml Performance in Kenya and</sup> Tanzania for a recent analysis of the economic<sup>and political issues.</sup>
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Figure 19. Tanzania's price incentives and agricultural exports <!-- Start of picture text --> Index 1970 = 100<br>120<br>100<br>80<br>40-<br>20<br>o 0 1 I I 1 I I I I I I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>-<br>producer price export volume<br><!-- End of picture text --> Figure 20. Togo's price incentives an, agricultural exports <!-- Start of picture text --> Index 1970 = 100<br>120-<br>100<br>80<br>60<br>40<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>- -<br>producer price export volume<br><!-- End of picture text -->
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"framed and implemen-e'S by politicians whose political and economic base was exclusively urban. Tanzania's agn -Itural policies thus provide one of Africa's clearest examples of a system designed to trans ;r economic resources from the countryside to the city (Lofchie, p. 191).

Indeed, many of the African countries where agriculture has been favored and as a result performed well, are countries where influential elites have vested interests in the agricultural sector: Kenya, Malawi, and Zimbabwe, are all countries with large-scale farms owned by influential citizens. By contrast, in the majority of African countries where the sector is made up almost entirely of smallholder farmers with little or no political voice, agriculture has been disadvantaged in favor of the influential urban class.

This adds a confounding factor to the arguments in favor of a broadly-based smallholder development strategy versus a "bimodal" strategy including larger, estate type farms. While the economic arguments for smallholder-based strategies are persuasive (Johnston 1982), one sees in recent experience that they have tended to be politically disadvantaged and suffered as a result. By contrast, where there is an elite with vested interest in agriculture there appears to have been a powerful voice supporting the interest of farmers. Does the importance of assuring a political lobby outweigh the economic arguments for unimodal development? Have the large-scale landholders lobbied for their interests only, at the expense of smaUholders? In some cases, there has been a discriminatory influence that has protected the interests of the large-scale farmers, sometimes at the expense of the smallholder, as in the cases of Malawi and Zimbabwe.

# V. CONCLUDING COMMENTS

# Summary of Empirical Findings

# The analysis above supports the following conclusions:

1. The decline in Africa's agricultural exports production between 1970 and the early1980s coincided with substantial and widespread macroeconomic policy distortions and deteriorating real prices paid to agricultural producers. Detailed econometric analysis indicates that agricultural exports have, overall, been depressed by direct and indirect price distortions. The effects have been substantial; aggregate patterns indicate that as both real producer prices and real exchtange rates deteriorated by about 20 percent on average during the 1970s, agricultural exports declined by a similar percentage.

2. Beginning in the early 1980s, policy reforms and macroeconomic adjustments have reduced the degree of direct and indirect policy distortion giving rise to improvements in overall agricultural export performance. The estimates of supply responsiveness confirm the relationship between improved policies and better growth. Moreover, countries that have adopted or maintained favorable policy environments since 1984 have seen higher agricultural production, exports, and overall economic growth than countries with less favorable policies.

3. The degree of policy distortion in Africa differs from other developing countries primarily with respect to exchange rate distortions rather than in direct pricing policy. The degree of direct pricing policy intervention, as measured by the rates of nominal protection, have been significant in many African countries, but they have not been significantly larger than in other developing countries. The degree of ex.change rate overvaluation, however, has been
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larger, resulting in a loss of competitiveness and leading to inefficiencies, including those arising from the exchange and trade controls that usually accompany overvaluathon.

4. The response of export agriculture to policy changes has not come by way of shifting resources out of food production. Econometric analysis indicate that food production, like exports, responds positively to improvements in real exchange rates. Moreover, food production correlates positively with higher producer prices for export crops, suggesting either that they are complements in production, that policies favorable to export agriculture also favor food producers, or that a third, omitted, variable is correlfr-<sup>4</sup> with both improved policy and output.

_5._ Urban migration has been both a symptom of policy distortions and a cause of poor agricultural performance. In many African countries, macroeconomic imbalances, declining real producer prices, "cheap food policies", and other policies favoring urban dwellers have shifted the rural-urban terms of trade significantly against the rural sector. This has led to a shift of productive resources out of agriculture and has resulted in high rates of urban migration. Policy reforms that realign the internal terms of trade can slow, or reverse, these resource flows. This has been documented in Ghana wh-re substantial reverse migration to the agricultural secto. has been occurring since the implementation of their recovery program.

6. Africa's food imports doubled during the late 1970s, making Africa a net food impolter for the first time. In the aggregate, however, this 7 million metric ton increase in annual imports is almost entirely accounted for by two factors: a) seventy percent of the increase is due to a quadrupling of Nigeria's food imports during the oil boom when its "capacity to import" tripled; and b) food aid to Africa increased from annual levels of 1 million metric tons per year to around 4 million metric tons, accounting for most of the remaining increase.

7. At the country level, rising food imports have not resulted from a failure of African agriculture (yields per hectare have, in general, been rising). Slow growth in domestic food production in Africa is primarily the result of urban migration, the availability of cheap food imports, and a tendency among urban dwellers to consume imported rice and wheat. This has resulted in a reduction in the demand for domestically produced foods, which, along with the outmigratiun of agricultural labor, has led to supply and demand for domestic production growing more slowly than population. Econometric analysis indicates that the most important factors explaining rising food imports are: a) chianges in the strcture of demand (due to demographic and income changes related to urbanization), b) changes in the ability to pay (foreign exchange earnings or "capacity to import"), and c) relative prices (due to the overvaluation of c'irrencies which have made imported foods relatively cheap). Most of the variation in food imports can be explained by these factors. The remaining trend in food imports is only about a 1 percent annual increase, which is likely the result of the substantial drop in international rice and wheat prices between 1970-86.

Taken together, these conclusions place much of the blame for the deterioration of African agriculture on government policies which have shifted the internal terms of trade strongly against agriculture and created market distortions that reduced efficiency. The result was a shift of resources -- especially labor -- out of the sector, and a decline in both private and public investment.
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# Forward Looking Issues

The prescription that follows from these findings should not be one of limiting the focus of attention to price policy. But rather it should be clear that the elimination of policy distortions sets the stage for getting on with strengthening the real sources of long-term agricultural growth: productive investment and technological change. In addition to investments by private agents, public investments in physical, human and institutional capital are essential such as transport infrastructure, support services, and agricultural research. All of these seek to raise productivity and reduce the cost of production to make agriculture more competitive.

In specific countries, prices may appear to be less important than nonprice factors; where price policies have not led to major distortions, attention should rightly be placed on promoting productive private and public investments. Where price distortions have been large and as a result agriculturai investments neglected, both price and nonprice constraints are likely to be binding, but romoving price distortiens should be seen as a prerequisite to encouraging appropriate investments in the sector.

The debate over the relative importance of price and nonprice factors is to some degree misguided, or at least misleading. There are three reasons for this. First, nonprice constraints on the responsiveness of agriculture may be interpretable as being price-related (i.e., the lack of roads is a transport cost which will reduce the farmgate price; the absence of extension services raises information costs making technology prohibitively expensive to most farmers). Second, the relative importance of these two categories clearly differs across countries. Therefore, the debate should rightly take place only within a specific country context.

And third, nonprice constraints in many cases may be a reflection of the very slow speed of adjustment for investments, maintenance, migration, and the attention of government to provide supporting services, in response to potentiaUy profitable agricultural activities. In the absence of price incentives, it will do little good to relieve nonprice constraints. In fact, it should be difficult to identify them without proper price signals (in the extreme, it does little good to build roads to rural areas if nobody lives there anymore).

In many cases constraints exist which inhibit a swift response of farmers to changes in prices. But bad roads, lack of irrigated land, and nonexistent input suppliers can be seen as the result of neglected investments in the face of unprofitable choices. The point here mirrors the dicotomy between short-run and (very) long-run supply response. In the same way that planting cocoa trees is a long-run investment decision by a farmer in response to expectations of future profitability, public funds for roads, storage facilities, or establishing institutional support reflect public sector responses to future returns on investment. In this way, the very long-run supply response includes "induced institutional change." Indeed, it should be clear that the highly developed physical and institutional infrastructure of Kenya's coffee sector would not have developed if pricing and exchange rate policies had been strongly adverse. The iasults presented here makc a case for the importance of reducing price policy distortions. Viewed in this way, it should be seen as a necessary but not sufficient condition for agricultural growth.
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# ANNEX A - DATA AND METHODOLOGY

Time-series data for official and market prices were compiled from a variety of sources including World Bank and IMF documents, consultant's reports, the FAO, the USDA, the International Federation of Agricultural Producers, and direct contacts with World Bank Resident Missions and country economists. 22/ Producer and consumer price data have been compiled for the most important agricultural commodities in each of 35 countries; for up to four majot export commodities, up to three traded food crops, and up to two nontraded staples.

Data for agricultural production and trade are from FAO. National accounts data are from the World Bank data files and IMP. Transport costs per kilometer-ton have been estimated based on the large number of existing detailed studies, and are applied to distances from the major producing and consuming region for each commodity via the most commonly used mode. Historical transport cost data, where unavailable, are extrapolated on the basis of the domestic CPL Processing costs have been estimated similarly. Ocean freight costs are estimated based on World Bank figures.

The critical distinction between official prices and open market or parallel market prices has been maintained throughout. Where the available information indicates that most farmers receive the official price for their product, that price is also assumed to ba the market price. '.Vhere data on open or parallel market prices are available these prices are used as being more representative of what producers receive. Averages across countries for export or food crops are taken as weighted averages (by production value) within countries, and simple averages across country groups. _2Q/_ For comparative analysis between country groups, group averages are simple means. Weighted averages would be inappropriate since the objective is to illuminate a central tendency among countries in the group, rather than to assess their aggregate magnitude.

The producer price data assembled are deflated by the domestic CPI for up to four major export commodities in each country. An average index of export producer prices is computed weighing each commodity's price by its relative value of total production.

The following potential data problems should be borne in mind when interpreting the results of this study:

# 1. Production data and, to a lesser extent, export data, may be inaccurate. Export data be

_29/_ In general, the producer price data reflect the average annual price received by farmers in the most important producing regions, although due to the wide regional and seasonal variations and the lack of specifics from some sources, this level of precision cannot be assured. Data series from different sources have sometimes been spliced when they seemed reasonably consistent.

L _/_ Detailed data on producer prices was compiled for 35 countries, excluding very small countries (Comoros, Seychelles, Cape Verde, Sao Tome, Lesotho, Swaziland), ones where no data were available (Angola), or where agriculture is of minor importance in the overall e.nmomy (Gabon, Djibouti).

1 iI |

l!
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biased when shifts in the share of unreported or smuggled exports changed.<sup>This could have</sup> resulted in an positive bias in the results above.

2. The producer price data have been carefully compiled to distinguish between official prices and actual producer prices where divergences were apparent. Nevertheless,<sup>'he prices</sup> used are for most countries officially reported prices and in some cases will<sup>not accurately</sup> reflect what farmers are being paid.

3. The index of producer prices computed is based on from 1 to 4 export crops per country. For countries where the composition of exports includes significant other crops, the price incentives are omitted from the analysis, while their export volumes are included.

4. Changes in input prices have been ignored. To the extent that producers are responding to changes in farm profits, changes in fertilizer and other prices should<sup>be taken</sup> into account. Typically, higher output prices and devaluation have been associated with<sup>raising</sup> the prices for imported inputs such as fertilizer. To the extent that this phenomenon<sup>has been</sup> omitted from the above analysis, the elasticities with respect to price and exchange<sup>rate have</sup> been biased downward.

_5._ There are simultaneity problems for countries where their exports account for a sizable hare of inteational nmarkets anid face less than perfectly elastic demand (Ghana, Cote d'Ivoire, Nigeria, Kenya, Cameroon). This problem could bias the results downward. For countries where the farm price reflects the highly variable international price (coffee in Kenya and Ethiopia, tobacco in Malawi), occasional sharp increases in price will not affect current production decisions, and will be viewed and temporary in nature. This may partly explain<sup>the</sup> low, or negative, results for these three countries.
-48 -

# ANNEX B - THE EXTENT OF CHRONIC HUNGER IN AFRICA

At the heart of any analysis of food policy in Africa is a concern over the welfare of African people who live in a harsh environment, where rainfall is erratic and where several regions have been prone to drought, especially in the past 15 years. Africa's famines in drought or war prone areas such as Ethiopia, Sudan, Mozambique, and the Sahel, require emergency respoases and better global preparedness for the future. But these emergency situations, now referred to as "transitory" food insecurity (World Bank 1986a) are different from the issue of chronic hunger or food insecurity, where people suffer from a continuously inadequate diet caused by inability to acquire food.

There are, however, relatively few reliable statistics with which to asses the prevalence and magnitude of _chronic_ hunger in Africa. In Africa and in other developing countries food availabilities are estimated using food balance sheet techniques which estimate availability as a residual, introducing errors which are almost invariably in the direction of understatement. In a thorough analysis of the statistics and methods quantifying nutritional situations, Poleman concluded that "With food availability estimates .hat understated set against requirement figures that overstated, the cards were so stacked that almost all LDCs could be classified as "diet deficit." Redone with truly accurate information, it is probable that few countries would he so classified. Much as the protein gap proved a statistical illusion, the list of diet-deficient countries would be whittled away." (Poleman, p.15) Given the weaknesses in the data, and their likely biases, the case being made about Africa's chronic food problem, that "average daily caloric intake was 96 percent of requirements in the 1980s" seems a remarkably weak basis on which to assert that the "inadequacy of food manifests itself both at a national level and at the household and individual lever (Serageldin).

The principal evidence that may indicate widespread undernutrition comes from dietary and anthropometric studies showing that a large share of children in Sub-Saharan Africa are stunted. Stunting, however, can result from several causes, including diseases which lead to reduced intake of food and impair the metatolism and absorptive capacity of the individual. And there is a growing consensus among nutritionists that both disease and lidernutrition can result in stunting, as weU as child morbidity and mortality. In fact, over the past 20 years the infant and child mortality rates have not dropped more in African countries where aggregate food supplies have increased, than in the countries where food supplies have declined (Svedberg).

In a systematic assessment of available evidence Svedberg (1987) concludes that "there is no firm evidence of widespread and severe undernutrition in the African population at large (p. 87).

But certain patterns observable in Africa seems at odds with tlue o onclusion that "about a quarter of Africa's population ... do not consume enough food to alowv an active working life (World Bank 1988a). The high rate and continuation of rural-urban migration, the growing importation of rice and wheat that in many countries is more costly that domestic cereals, and depressed market prices following abundant harvests suggesting market saturation, are difficult to reconcile with the assertion that in many of these countries 30 to 40 percent of the population are chronicaly "food insecure." However, even with adequate "average" food availabilities, chronic hunger may persist if poverty, income distribution, and other factor prevent certain groups from obtaining enough food. These
- 49 -

are issues that need more careful unalysis and attention. The evaluations by<sup>Poleman and Svedberg</sup> suggest that available data overstate the extent of chronic undernutrition, and<sup>that reports including</sup> strong statementt about the severity of the problem have no reliable empirical<sup>basis. Clearly, what</sup> is needed is better data and more focused analysis on the extent of the problem and its causes.
- _50_ -

# ANNEX C - CLASSIFICATION OF COUNTRIES BY POLICY ENVIRONMENT

For comparative purposes countries in Sub-Saharan Africa have been classified into two groups based on the policy environment that existed in the mid- to late-1980s. The principal considerations for classification of countries are the direct policies that affect agricultural incentives (the real producer prices, levels of direct taxation i.e. NPC), and indirect policy measures which affect the competitiveness of the agricultural sector vis-a-vis other sectors in the economy and in international markets (real exchange rate).

On the basis of criteria for these key policy variables the countries were classified as shown in the table below as having a Favorable Policy Environment (FPE) or as having an Unfavorable Policy Environment (UPE). This classification differs in several respects from that used recently in (Africa's Adjustment and Growth in the 1980s (World Bank 1989) to compare "strong reforming" and "weak reforming" countries. Policy reform programs differ among countries and, given the kinds of policy changes undertaken they may or may not be expected to have any relatively shortrun impact on economic performance overall, or agricultural performance in particular. For example, reforms in public enterprises, govemment revenue collection, or reduction of government payrolls are all measures that at least partly share the goal of alleviating fiscal imbalances, to reduce deficit financing through money creation that is inflationary, and thereby lead to lower inflation, positive real interest rates, and better allocation of investments to promote efficient growth. These measures have longer-term objectives and therefore cannot be expected to affect productivity in the economy or in the agricultural sector over a five year period.

In addition, the categories of "strong" and "weak" reforming countries rely on formal agreements with Bretton Woods institutions as the criteria for being considered "strong reforming", rather than by having these reform programs manifested in objective policy performance indicators such as the real exchange rate or level of agricultural taxation. As a result of this different approach to classification, several differences arise in the grouping of countries.

First, the PPE category includes countries which have adopted _or mantaned_ a favorable policy environment, thereby including countries which have not undergone a stmctured reform program, but where on the basis of these policy performance measures, the need for such a program - or the detrimental effect of their policies on productivity - is less apparent (Benin, Burkina, and Cameroon fit this group).

Second, several countries considered to be "strong reformers", on the basis of the policy performance variables examined here, cannot be considered to provide a favorable policy environment, because exchange rate misalignment persists. As a result, Mauritania and Tanzania are considered to provide unfavorable policy environmtnts due to their exchange rate policy which continue to impose heavy indirect taxation on the agricultural sectors, and to make them less competitive with respect to other sectors and in international markets.

Several countries classified as "strong reforming" countries are here also considered to have favorable policy environments, but because the improvements in their policy performance variables are so recent (in 1986 or 1987), they are excluded from the comparisons of performance, since it is too soon to expect any significant response, especially in countries where agricultural exportables are tree crops where production response can take four or five years. These countries are Burundi, Ghana, Guinea, Madagascar, and Nigeria. Malawi is also a recent reformer, but because its agricultural exports are annual crops, a more immediate response can be expected.
- 51

Several countries are excluded from considerations due to lack of data, small size, or volatile swings in agricultural exports related to policies and cross-border smuggling of neighboring countries (Benin, Zambia). The resulting classification of FPE and UPE includes 12 countries in each group. 31/ _/f_ Angola, Cape Verde, Chad, Comoros, Djibouti, Gabon, Guinea-Bissau, Lesotho, Mozambique, Sao Tome, Seychelles, and Swaziland have been excluded due to their small size or lack of data. The countries excluded from comparisons of performance for other reasons are Burundi, Ghana, Guinea, Madagascar, and Nigeria.
# Annex Tabb Cl. Clelficaton of Countries by Poliy<sup>Environment.</sup>

|||Pa<br>afectlw<br>11970.73|exchange<sup>rts</sup><br>) * 100)||Ratio of<br>otffIl|pfeel<br>ma,kat to<br>hemga ta||NOmn pot<br>for majo|weon coafficmt<br>szprtad<sup>crow</sup>||Control of|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||||Crdtaron<br>|||Citwion|Averag||Criterion|markating_bv_|
||O01a<br>Caflcbatlcn|Averag<br>198486|1987|(below 120<br>Inid-l9108a|Averge<br>19848|1987|<br>tbelow1.3)|<br>198446|1987<br>|_Wio2_o.n<br>|<br>|
|BEoin|FPW|||||||_0.78d_|1.18|mPP|**W**<br>no|
|<br>Eowan|<br>UPE|NA|NA||1.49|1.29<br>|UPE<br>||092|FPE|W|
|_<br>Bunkih|WPE|82|78|WPE|1.02|1.01|WE|0.66|.|<br>||
|Euruni|FPE'|141|II|FPE'<br>|1.31<br>|1.11<br>|mE'<br>|0.69<br>090|0.79<br>138|WE<br>WE|we<br>**w"**|
|Canroon|WE|114|139<br>|mE<br>|1.02<br>|1.01<br>101|WE<br>WE|.<br>078|.<br>0.99|<br>FPE|prtl|
|CA.R.|UPS|102|107|WE|1.02|.||.||||
|Chnd|FPE\b||||1.1<br>|1.02<br>|FPE<br>FPE||||**VW**<br>-|
|Congo|WE|93|95|FWE<br>|1.02<br>|1.01<br>|<br>|079|1ie|FWE|yW|
|<br>Coadlver|P<br>WE|105|127|WE|1.02|1.01|WE|.|<br>|<br>||
|Etopi|UPE|162|11l|UPE|2.04<br>|221<br>|UPE<br>|0.70<br>111|0.77<br>192|FPE<br>WE|per<br>paW|
|<br>Gambe|FPE|102|89<br>|WE<br>|1.17<br>|1.09<br>|WE<br>UPE|.<br>096|.<br>052|<br>FWE|**VW**|
|Ghan|FWE '|112|S0|WPE'|2A1|1.37||.|.<br>|||
|uinze|FPE'||||9.26<br>|1.04<br>106|FE'<br>WE|1.79<br>0.82|NA<br>0.70|WE'<br>FPE|no<br>no|
|Klnvy|FWE|90|74|FWE|1 .08<br>|.||<br>|103|FPE|VW|
|<br>Lbera|UPE|124|10|UPE|1. Seelnota)|||_OIS_|.|<br>||
|Medagasa|FPE'|90|el<br>|FWE<br>|1.26<br>|0.79<br>|WE'<br>UPE|0.58<br>063|0.70<br>068|WE*<br>WE'|pWl<br>Pser<br>uJ|
|<br>Melawi|FWE'|96<br>|S2<br>|WE<br>|1.37<br>|1.13<br>101|<br>WE|.<br>083|.<br>_OS_|UPS<br>W<br>PE||
|Mol|UPE|122|11|UPE|1.02|.||.||||
|Munbtan|UPWE|10|92|WE<br>|2.32|2.30|UPw|||||
|Maurtls|FWE|95|94|FPE|||UPE||||-|
|Mozaq6u|UExt||||42.6|12.0||||||
|Nigt|FPE|93|78|WE<br>|1.02<br>|1.01<br>|WE<br>UPE|1.67<br>103|4.02<br>074|WE<br>FPE|no<br>no|
|Nigi|FPE'|249<br>|E0<br>|WE'<br>|3.94<br>|3.32<br>127|<br>UPE|.<br>77|.<br>0.97|<br>WE|VW|
|Rwanda|UPWE|66|155|UPE|_1.A_|.||.||||
|Snegal|FPE|10|108<br>|FPE<br>|1.02<br>|1.01<br>|FPE<br>UPE|1.12<br>114|2.62<br>2.07|FPE<br>FPE|We<br>no|
|Siera Leons|UPES"<br>|139<br>|91<br>|UPE"<br>|1.62<br>207|8.13<br>143|<br>UPE|.<br>1.0|<br>1.386|WE|pati|
|Snoml|UPE|198|95|UPW|.|.||||||
|Sudan|UPE|97<br>|E1B<br>|WE<br>'|1.97<br>|1.84<br>272|UPE<br>UPE|1J0<br>094|1.36<br>EP|FPE<br>FPE|pati<br>**VW**|
|TUnlass|UPE|187|74|FPE|4.15<br>|.<br>|<br>|.<br>062|116|FPE|pal|
|Teo|FPE|91|91|FPE|1.02|l.01|FPE|.|.<br>|<br>||
|Ugand|UPE|140|190|UPE<br>|1.99<br>|1.8<br>|UPE<br>WE|0.5i<br>_O06_|0.57<br>054|UPSE<br>UPE|V<br>**W**|
|<br>Zaid|FPE|79|66<br>|FPE<br>|1.04<br>|0.80<br>1 9|<br>UPE|<br>089|.<br>1.5|<br>WE|**we**|
|Zambia|UPEI|62<br>|37<br>|FPE<br>|1.61<br>|.<br>159|<br>UPE|.<br>1.11|<br>1.09|FWE|no|
|Zinbtbwe|UPE|72|83|FPE|1.70|.||||||
|<br>Note: FPE -<br>f|<br>avoabe poklicy<sup>nvIrn</sup>|<sup>mn</sup><br>UPE * unfavo<br>|rbl<br>Polcty evim<br> <br>|nt.<br>||||||||
|a<br>ESkdad fo<br>|m perfoman<br>ccmp<br>|aIun<br>becae<br>of v<br> <br> <br>|olatie eoxpt<br>_re_<br> <br>|_latd_to neighboFn<sup>**c**</sup>|<sup>**ommW polce.**</sup>|||||||
|%bE=kWed d|ue to lok<sup>of dta and</sup>|<sup>adve</sup><br>politica<br>d|t<br>n.|||<br>|<br> <br>|i d d<br>t|t<br>oi|ch||
|Indiotecorn|la<br>adopting fevo<br>|abl<br>polciery<br>|In<br> <br>|e theit<br> <br> <br>|pt<br>e<br> <br> <br>|, no<br>ppl<br>on<br>Ml<br>tb<br>a|<br>a<br>**be**oeo<br>in ti<br>s<br>for|n t et<br> <br>s anases|<br>py|<br>.||
|Thu.. tOa<br>'<br>Sir<br>**L-**|hey.<sup>bean</sup><sup>***luded**</sup><br>fo<br>**n**<br>st<br>tn|t pumpooe of conlPl<br>_<br>p<br>mb|ng<br>agrclra<br>in198wassus|epoot.<br>Whem<br> <br>pndd<br>in 1988.|Wnua<br>coppa<br>involved|(a<br>e<br>|<br>p<br>|.||||
_-_ .53

||~~~~~a|<br>ad<br>_<br>d<br>a<br>d<br>2-^<br>-d<br> <br> <br> <br> <br> <br> <br> <br>||d<br>|ad|
|---|---|---|---|---|
||d~~~~4<br>-<br>_d<br>**d**<br>d<br>ddf<br>d<br>d<br>d<br>d<br>d<br>d<br> <br> <br>|d|d<br>||
||d<br>d<br>d<br>d<br>d<br>d<br>d<br>d||d|l fi_l|
||d<br>d<br>d<br>d<br>d<br>d<br>d<br>d|||_-_|
||d<br>dd<br>5f<br>d_dz<br>d<br>d<br>r<br>f0_0td<br>ddd||||
||a<br>C_S6d_<br>_d_<br>_d_<br>_od_<br>_d_<br>_d_<br>_di_<br>_dddd d_|_dd_|_dd d__||
||-<br>5d<br>d<br>d<br>d<br>d<br>d<br>d<br>d<br>-<br>-<br>-<br>d||||
||-~~~~~~~~~~~~~~~~~<br>iiiI_d °iiIdd_<br>_idd Ididd_<br>_ddIid_Iii_d_|||_ii_|
||-<br>-<br>-<br>-<br>-<br>-...-<br>-<br>-<br>.<br>0<br>_-d_<br> _e _<br> <br>||-<br>|CI<br>_o_|
Annex D Table 2. NomninG pmwteon coefficienit for principal expoft commodite.,

|Oqmy<br>iuznFPso|op<br>Coro|1970<br>0.65|1971<br>0.64|1972<br>0.49|1973<br>0.39|1974<br>0.71|1975<br>0.43|1978<br>.40|1977<br>0.87|1978<br>0.84|1979<br>0.59|19s8<br>0.52|1961<br>0.55|1982<br>_O4A_|1983<br>o0.38|1984<br>059|1985<br>124|1988<br>0.88|1087<br>0.92|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|oand|Cofee|.71|0.i1|0.52|0.57|0.89|024|0.2|0.53|0.51|Qs2|0.79|0.73|0.70|0.54|Q58|0.45|098|0.79|
|Cam|Coff|1.28|129|1.29|1.13|1.34|9ss|0.39|087|0.91|1.13|1.52|1.10|0.12|0S1|0.98|098|g 1.80|1.87|
|CAR|Colon|064|0.Q8|0.51|0.3|085|0.50|0.52|0.88|0.84|0.50|0.6|0.0|0.|0.48|0.71|1.55|1.01|Os9|
|COo|Ccoa|0.73|.65|0.40|030|.Q44|<br>024|0.17|023|_027_|040|041|0.44|0.32|0.25|029|0.43|0.63|0.73|
|COW dvc8|CclIe|1.11|1.09|1.08|1.0S|1.48|0.87|0.23|0.84|0.77|1.09|1.44|1.09|0.80|0,72|0.8|0.88|1.05|1.98|
|EiD lmpl|Cdbe|0.73|0.89|0.78|0.77|a8|.79|0.40|0.81|0.57|0.59|0.73|0.71|Q05|0.59|0.5|e.7|s8|0.77|
|G381*|Gmundn|0.o8|0.90|0.88|0.49|0.4|0.89|0.79|010|0.80|1.18|0.91|1.38|08o|0.52|78|13|2.00|1.92|
|ohw|Como|0.84|0.48|0.33|022|0.42|029|0.18|022|030|0.55|01|2.81|1.98|_027_|_0.27_|33|30|0.52|
|_KW"_|co..e|Q0.7|0.98|Q93|0.91|0.01|1.03|.77|089|0.52|.89|0.78|0.79|0.38|0.82|0.88|0.77|0.8|a70|
|LUbI|Fkbbw|0.65|0.88|Q32|083|09e|0.52|0.82|053|048|0.58|0.78|0.67|0.51|0.71|080|0.74|0.03|074|
|Madag_m|Cob.|1.32|131|1.3|1.12|1.58|0.77|030|.58|0s.|0.73|Q97|Q79|as4|0.47|0.54|0.48|087|083|
|maw|Tob|1.34|1.39|1.98|1.3|0.47|12|1.17|1.45|1.17|1.02|0.82|0.89|1.12|1.02|O0.0|0.8|1.05|1.08|
|kwaD|Colon|O.|0.51|0.40|030|0.8|0.40|.3s|05|0ss|0s3|0.55|Qi38|045|0.42|a55|1.2D|0.84|ass|
|lgmi|CMoc|_0.84_|0.95|0.48|03|9.5|0.61|928|_0.49_|0R3|0.Q5|1.16|1.27|1.03|0.80|0.13|_0A7_|1.41|0.74|
|Fmatd|Corme|074|0.7|082|U54|0.73|0.3|022|0.58|0.Q4|0.82|0.78|072|0.77|0.67|0.85|0s.|1.22|0.97|
|eaul|Groundn|0.65|0.9|0.72|03|0.90|0.911|073|0.85|0.77|08|.e8|1.11|0.72|0.48|058|l.9|247|z71|
|Siam leone|Code|1.7|_1S._|0.94|1.08|1.19|045|0.31|1.19|0.98|1.34|1.70|1.15|075<br>8|1.15|1.|0.84|129|.07|
|Sanuma|u|121|1.0|1.18|1.38|77|0Q04|09|0.94|0.81|ass|074|1.01|0.2|0.95|0.75|0.7|1.08|1.0|
|_Sudan_|Co6tn|1.51|14|0.95|1.5|1.52|1.19|1.36|1.69|1.58|1.40|1.57|1.9|121|_1S8_|124|2.40|1.71|13|
|Tuanla|Cole|0.85|0.78|0.Q58|<br>0.57|048|0.41|R<br>43|0.52|3|055|0.71|0.85|.3|0.3|0.74|044|a.44||
|**Toga**|coa|0.78|0.72|Q<br>43|029|044|027|15|019|0.2s|R<br>42|_0.47_|0.49|0.35|0.32|035|0.57|0.75|a.s|
|Igd|C*|036|Q40|38|0.38|.511|038|Q0.5|Q40|0.83|_0.63_|1.51|05|0.41|28|0.0|0.23|0l.|0.8|
|z2<br>er|0d.||0S4|0.9|Qes|1.41|0.31|01|0.383|<br>0.1|054|1.3|s0.1|<br>0.42|0.22|_0A._|0.41|0.55|04|
|**Zmdha**|Tobuc|1.10|1.22|1.48|1.48|1.02|1.22|0.93|1.16|1.00|1.07|97|1.31|1.16|1.00|073|0.44|0.48|1.|
|ztnbabe|Tobaco|7|1.04|1.0|1.02|1.13|1.0|0.8|0.2|0.8|0.73|_0.65_|124|**0.983**|<br>1.90|061|1.04|1.22|0.|
# Anne D Table 3. eFa Drtcon coefft for oincoi eo ot .

# (1971-100 fo ree ecobange<sup>ratse)</sup>

|Coqa|crop|1970|1871|1972|1973|1974|1975|1978|1977|1978|1979|1990|1981|1962|1903|1984|1985|1968|1987|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Bwn<br>Fewo|Cdon|o68|068|.51|Q043|0.72|0.54|043|0.73|0.o6|0.60|0.56|063|0.49<br>|0.48<br>|0.72<br>|1.58<br>|1.16<br>|1.09<br>|
|Buwuid|Coffee|0.71|0.67|0S.8|0.o6|0.72|02B|0o.|O.6|0A7|0.58|2|0.52<br>|0.49<br>|0.38<br>|040<br>|3<br>|0.69<br>|0.65<br>|
|Cameroon|Cdbe|128|1.23|1.17|1.03|1.15|0.56|0.34|0.72|0.76|o0.9<br>|1.40<br>|1.10<br>|0.64<br>|072<br>|0.84<br>|0.75<br>|1.27<br>|121<br>|
|CAR|Colton|0.64|0.64|05.0|0.38|Q.S6|0.48|0.53|0.87|0.61|0.54|0.57|Q57|0.49<br>|0.47<br>|0.71<br>|_1.40_<br>|0.s2<br>|0.95<br>|
|CongD|Co|073|.62|0.3|0.33|.44|0.25|o.7|0.22|0.27|0.42|0.43|0A7|0.35|0.28|30<br>|.44<br>|0.64<br>|0.75<br>|
|Coatdlore|Coffee|1.11|1.13|1.07|1.04|1.37|0684|030|0.51|58|078|1.19<br>|09e<br>|0.78<br>|0.71<br>|084<br>|0.72<br>|_127_<br>|1.50<br>|
|Ebl|Coffee|0.73|97|0.84|0.95|Qe0|Q75|0.42|0.52|0.47|0.52|0.Q9|0.63|0.48<br>|.38<br>|35<br>|049<br>|0.75<br>|.e6<br>|
|Gambia|Goudnd||QS6<br>0.90|079|045|0.74|0.2|e.3|0.59|0.75|097|0.78|1.17|0.77|0.48|0.64|1.58<br>|2.19<br>|1.98<br>|
|GUim|Cocoa|0.4|0.s|0.37|0.22|038|0S|0o.|0.12|0.19|0.28|0.1<br>|Q.51<br>|0.5<br>|0.19<br>|0.25<br>|.55<br>|Q6s<br>|1.16<br>|
|Keanya|CoffC<br>e|0.3|1.11|1.11|1.09|1.|1.25|0.99|0.98|1.05|1.02|0.62|0.92<br>|1.09<br>|o09<br>|1.00<br>|1.02<br>|1.32<br>|1.15<br>|
|LUbei|Rubber|0.8|0.71|033|0.60|o0.9|Q48|0.60|0.84|047|0.54|0.68|_0s._|0.41<br>|0.55<br>|0e6<br>|0.87<br>|o.60<br>|0.71<br>|
|Madagec|Code|1.32|1.29|1.34|1.05|1.44|0.75|0.41|0.2|0.59|0.|0.7|0.67|0.53<br>|0.46<br>|0.55<br>|0.50<br>|1.09<br>|13<br>|
|Maleaw|Tbeco|1.34|1.38|223|1.87|0.62|1.38|1.33|1.5t|1.23|1.o3|0.63|1.03<br>|1.16<br>|1.07<br>|0.9a<br>|1.13<br>|1.3<br>|133<br>|
|Ma|Colon|00|0.4|0.35|0.27|0.2|0.38|34|0.40|0.47|_047_|0.42|0.51|038<br>|0.35<br>|043<br>|Qs9<br>|0.69<br>|0.62<br>|
|Nigerlia|CcNoa|0.4|_0s9_|_050_|0.37|0.57|.44|020|0.34|0.35|Q53|Q.68|0.oe|0.48<br>|0.2<br>|0.31<br>|Q46<br>|ZS9<br>|1.48<br>|
|Rands|Cathe|0.74|0.72|0.3|02|o06|029|0.17|_4S_|044|0.52|0.9|Q45<br>|045<br>|03<br>|0.37<br>|.3s<br>|0.75<br>|0.8<br>|
|Senega|Gsoundn|0.6Q|Qs3|0.o8|0.34|.es|0.80|Q64|05|0.70|G94|0.72|1.15|0.74<br>|0.48<br>|Q5s2<br>|1.68<br>|2.2<br>|2.58<br>|
|SIM'-_n|Cadte|1.07|1.07|1.03|121|1.43|0a.|0.43|1.48|128|1.73|1.90|1.04|0.55<br>|0.89<br>|0.78<br>|0.7S<br>|_IA_<br>|2.04<br>|
|Somi|AMNINr|1.21|1.18|1.30|1.38|075|0.78|074|OS.2|Qs|0.37|0.35<br>|0.QO<br>|<br>0.33<br>|0.32<br>|0.48<br>|0.O<br>|1.18<br>|0.91<br>|
|Sudan|Coaon|1.51|1.39|Q0.4|0.2|1.21|_0S8_|1.0|1.53|1.45|1.43|1.52|1.87<br>|_1As_<br>|19<br>|128<br>|2.49<br>|1.90<br>|1.37<br>|
|TaNnia|Coffee|0.65|0.7|0.63|059|_0A7_|0.42|0.48|0.83|0.42|0.52|0.52|0.41|0.35|0.34|0.35<br>|029<br>|0.61<br>|0o.<br>|
|TOP|cocoa|0.75|.e9|0.42|Q30|0.40|.25|12|0.16|0.23|038|0.43|0.47|0.34<br>|0.34<br>|040<br>|0.59<br>|0.61<br>|0.99<br>|
|Uganda|Coafe|0.36|0.45|039|025|0.35|020|a.S|0.10|0.12|0.07|0.16|023<br>|021<br>|021<br>|0.23<br>|0.18<br>|0.09<br>|0.20<br>|
|Z*de|Cdbfee||0.82|091|075|1.11|0.22|0.09|0.14|O.|0.29|0.71|0.50|020<br>|028<br>|1.15<br>|0.53<br>|0.84<br>|0.78<br>|
|Z2mbi|Tobacco|1.10|1.29|1.58|1.58|1.17|1.35|1.05|1.35|1.28|_127_|1.13|1.37|1.33|1.32<br>|1.04<br>|1.30<br>|_127_<br>|1.93<br>|
|Zbnaw|Tab.w.|087|1.04|1.18|1.68|12|1.07|0.93|1.18|1.10|0.94|0.78|1.38|1.16|1.25|1.13|1.60|1.97|075|
Annex D Wbbe<sup>4Avte</sup> mal roducer<sup>pdrceformalor exDortcmole</sup>

#rfdlow0 * tG00

|COF"|1970|1s71|1972|W3|1974|ta7<br>|ts7a<br>|1s77<br>|t978<br>|1979<br>100|tsao<br>115|tost<br>10s|1a82<br>Wr|1ssa<br>107|ta94<br>109|t8e<br>Go|tsas<br>el|tsa7|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|serdn||||119|130|131|115|10S|104||||||||||
|||||||||||112|10O|105|S4|es|12D|124|12S|12s|
|BudaFaS|13a|1tn|135|137<br>|144<br>|121<br> <br>tt|132<br>|140<br>1|129<br>142|<br>108<br>too|<br> <br>as|<br> <br>S|5|7a|75|71|as|ea|
|sumr|10|5|as|93<br>S|4|71<br>|<br>|7<br>|<br>|<br> <br>t|<br> <br>10D|<br>94|89|as|85|813|83|73|
|Camro|94|S2|a7|S5|79<br>|70<br>tO5|S5<br>|eS<br>119|ioo<br>10w|os<br>tos|<br>too|<br>as|as|es|So|a7|84|102|
|CAJ|||||121||132||||||||||||
|Cads||||||||||107|sO|89|85|79|75|71|119|117|
|C4n90|so|85|7a|so|82<br>|70<br>|el<br>|ss3<br>tt5|tot<br>13|<br>115|<br>100|<br>ss|87|So|103|tos|S9|94|
|_Can_<br>_dno_|so|09|SS|10s|137|123|lie<br>|<br>|t<br>|<br>M3|<br>10o|<br>tto|n|aS|Be|IGos|79|80|
|EFd',|125|148|1as|tss|t20<br>|282<br>|2a<br>|185<br>1|aoD<br>104|<br>so|<br>10o|<br>102|so|75|eS5|14S|1aa|SO|
|G&Yb|09|tot|104|112|t3s<br>|13D<br>|123<br>|0s<br>|<br>I?|<br>15D|<br>IOD|13|113|a5|9t|157|tsoD|2ts|
|hom|221|20a|2U4|20a|2aa<br>|20a|1a2<br>|13t<br>|s<br>l|<br>10w|<br>100|91|n|72|SO|78|||
|Gut|28|28<br>|3t|33|444|as<br>|aS9<br>|0<br>|e<br>135|<br>125|<br>10D|109|109|147|125|123|7s|el|
|1eR|IC"|tOS|III|115|tos|174<br>|2B4<br>|tes<br>|<br>O|<br>ts|<br>tco|<br>e 5|oo|so|as|sD|52|e3|
|Ubzt|n7|a2|eD|to7<br>|10a<br>|65<br>|S2<br>|el<br>|e<br>114|<br>t04|<br>10D|ss|eS|ea|ao|aS9|BB|tot|
|magm|143|tas|13S|130|t2s|lie<br>|113<br>|tts<br>|<br>|<br>147|<br>tOO|t0s|119|132|161|12D|lsO|ss|
|_MWad_|tss|145|log|1sa|122|14s<br>|17a<br>|t7s<br>|ts4<br>99|<br>109|<br>100|<br>tos|<br>102|107|eS9|tOD|103|990|
|bil|177|_147_|13s|115|15a|145|134|124|||||||||||
|||||||||||t10|sOD|el|131|1s5|152|195|20t|224|
|_NW_|so|84|so|st<br>|173|1E9<br>|129<br>|117<br>|123<br>|<br>102|<br>rOD|<br>83|_7n_|67|S2|52|tss|128|
|N-<br>b|05|sa|st|9t|III|113<br>|S2<br>|119<br>|s7<br>|<br>10|<br>10D|<br>94|84|78|74|73|_n7_|74|
|_Rwnd_|121|t20|117|lag|te5|Oa<br>|of<br>|141<br>|124<br>10|w<br>102|<br>10D|<br>122|<br>104|79|7o|III|tO104|<br>a|
|8d""|1G0|121|114|112|IN8<br> <br>|120<br>|119<br>|la?<br>137|a<br>123|<br>121<br>t|<br> <br>o|el|52|73|so|e4|143|78|
|aw<br>s1|GI<br>0|2|_49_|as|72<br> <br>|sD<br>|so<br>|<br>|<br>|<br> <br>t4|<br> <br>tOO|144|1n7|123|125|172|1S3|1e4|
|8o0dlh|242|2S3|271|2Es|215|2Z7|log<br>|log<br>|ta1<br>|<br>|<br>tOO|<br>10s|so|To|?4|S3|9C|es|
|SwJ0|108|10?|67|110|1w|94|80<br>|102<br>|10a<br>|so<br>122|<br>too|<br>9t|84|el|el|104|go|So|
|Tminh|l2a|124|113|too|go|125<br>|1s3|132<br>|III<br>l|<br>104|<br>tOO|<br>Be|ea|go|103|lie|123|123|
|T"p|112|10O<br>|too|so|S2|94|so<br>|78<br>|e<br>|<br>54|<br>too|<br>1es|12s|<br>173|248|tst|1m|104|
|UpndI|135|t<br>13|a<br>1|os|as9<br>so|<br>109|78<br>|S<br>|7a<br>|<br>4|<br>100|<br>so|n2|as|235|t3s|14t|7n|
|2d|12s|122|132|122|ISO<br>|7n<br>|45<br>|34<br>|20<br>12D|e<br>III|<br>soo|<br>110|ttO|as|S5|10s|or|139|
|Zmnbh|117|III|110|141|1#2|tsX<br>|12a<br>|131<br>|<br>140|<br>t0S|<br>tOO|la?|14s|132|1|141|1us|Oss|
|ZkFbx_|107|tt3|114|13s|1aD|ISO|t34|12s|||||||||||
Arnnx 0 tabb .<sup>Aveas nominal patAclon coefficentsfortraded food o</sup>

|CWY|1970|1971|1072|1973|1974<br>|1975<br>|1970<br>|1977|1978<br>|1979<br>|1910<br>09099|1081|198Z<br>0 9s7|1983<br>06|1984<br>097|1985<br>109|1988<br>1.31|1987|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|atR<br>A|1.01|0.93|074|0.82|Q0.5|58|074|e.|0.74|0.70|.|101|g<br>075|<br>089|.<br>07|.||1.88|
|Sundn Faso||||||||||||.<br>103|.<br>102|.<br>096|||1.3|1.38|
|Cob d%alm|074|0.83|t0.|0.80|0.74|074<br>|0.9<br>|1.42<br>|2.07<br>|1.97<br>|1.64<br>|.<br>14838|.<br>908|.<br>557|z77|157|<br>1.69|2-31|
|Ghan|1.03|0.93|0.4|0.78|1.22|1.5e|97<br>|472<br>|2ee<br>|.6<br>|8.75<br>|<br>5|.<br>088|.<br>07|<br>073|.<br>09D|<br>0.95|0.93|
|Kenya|Q04|0.75|0.50|043|0.Q8|0.08<br>|084<br>|0.88<br>|070<br>|0.79<br>|0.e<br>|0.8<br>_210_|.<br>22|.<br>2Z7|.<br>274|<br>270|<br>Z15||
|UbeaB|1.07|0.94|0.57|0.50|0.75|_IAI_|1.55<br>|1.19<br>|1.29<br>|1.04<br>|1.53<br>|_._<br>|.<br>073|<br>078|.<br>085|.<br>092|108|1t2|
|Medoca|0.60|064|035|0.2|Q53|0.74|0.89<br>|0.8e<br>|0.74<br>|0.88<br>|0.52<br>|0.67<br>|.<br>047|.<br>339|.<br>442|.<br>-030|.<br>-0.Z|5.00|
|Malee|Ss|25.81|1.09|072|1.31|1.89|4.9|6.89|.74|1Z5|_12.18_|-0.32|..<br>059|.<br>085|<br>094|.<br>069|<br>048||
|Mai|||||||||||||.|.|.|.<br>170|.||
|NIge|||||||||||||203|140|&78|.<br>203||078|
|Nda&||||||||||1.85<br>|1.50<br>|228<br>069|.<br>07|.<br>055|<br>078|.<br>087|0.77|Z21|
|Renda|Q02|c04|0.51|0.37|51|080|0.88|078|0.4|O.9|077|.|.<br>119|.|.<br>147|.<br>187|||
|4newaw|||||||||||||.||.|.<br>1.50|1.13||
|smn"|||||||||||||094|108||<br>148|145|1.11|
|aand|123|1.18|0.70|0.8|1.88|1.19|1.35|1.10|0.98|0.1<br>|1.05<br>|G09<br>|<br>22|.<br>409|320|.<br>197|.<br>119||
|Tanhs||||||||||2.51<br>|9.71<br>|.44<br>|.<br>150|.<br>140|<br>121|.<br>12D|.<br>204||
|Togo|||1.29|078|1.01|1.0<br>|1.72<br>|2.30<br>|_1.40_<br>|1.S<br>|1.01<br>|2.05<br>094|.<br>073|.<br>09|<br>081|<br>052|.<br>064|0.7|
|Z7mba|0.85|0.78|08D|048|0s.|0.o<br>|0.69<br>|0.68<br>|070<br>|0.80<br>|0.Q0<br>|.<br>080|.<br>043|.<br>38|.<br>039|.<br>048|<br>Q47|0.92|
|ZbnbabWe|042|.40|0.30|Q.27|0.35|0.32|m37|Q40|0.35|0.39|045||.||.|.|||
00 Annex D lbibl<sup>Q</sup> el _Pe,=_ fo folmalw hiod on p ts=o10ol

|CA|tS7D|197t|1S2|1973|1974|1975|1s7B|1977|ts7|ts7|1sE0|1991|1962|1SEs|1SB4|t9E|tSHB|1SB7|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|<br>Bi|||||105|107|114|IC"|1or|105|IOD|93|||||||
|on||||||9t|ES|75|aSB|OD|10|109|1t<br>11|4<br>i|140|134|t2B||
|sobo|||||go|||||||||||||72|
|_<br>|||Si|s7|109|75|104|112|129|112|1oo|lie|123|122|_14?_|137|70||
|u&mFeso|so|67|||||||||||||||||
|||112|116|113|104|esB|ss|S3|83|s7|10D|bs|eas|83|17||||
|auuEw|112||||||||||||||7||||
|"|S4|03|89|91|92|102|97|104|102|10B|tco|103|102||||||
|Camswn<br>_r-AtR_|<br>_107_|<br>103|<br>so|117|la?|S2|117|119|107|eSB|1c|as|79|Es|110|so<br>|||
|Chad||||||0|9<br>|8<br>|10<br>|s<br>|0<br>100<br>|10<br>s78|0<br>to|10<br>r2<br>9|7<br>9|S<br>8<br>O?|14<br>0|a|
||||||||93|E|O|s|<br>s||<br>|7|||90|E|
|owp<br>||11|12B|122|194|203|195|209|128|115|o|7B|S|8||s|||
|Cdet dWbeM|109|7|-||||||||||||S3|4|s7||
||20B|227|172|214|221|210|175|1e4|1e7|109|100|94|102|101||e|||
|Ehbpha||||<br>||||120|123|io7|10D|104|94|85|82|104|70|57|
|Gbh|78|74|es|t0s|109|113|123||||||||||||
|sn||<br>||||102|141|148|10t|E|10D|es|103|178|S1|75|72|lie|
|GhU|78|79|so||93||||||||||||||
|<br>Gu_|<br>30|3t|30|33|119|115|114|III|10S9|n|100|9t|87|e7|79|17S|||
||10|112|10B|119|151|4|144|12S|7|1a<br>9|o<br>7<br>1|12||t15|ttSTo||1B<br>|0|
|Kea|t||||||||||||5|73|72|73|aSB||
|Ubeda|~43|43|eo|70|S9|73|Es|78|73|so|10|EOD|7||<br>|<br>|||
||||||||||||10|84|el|92|S3|esB|175|231|
|_|<br>7E|sD|7a|_n_|S~~~~<br>8|~72<br>108|103<br>|117<br>|tto<br>114|la?<br>|o<br>10|<br>89|<br>117|<br>120|<br>117|110|8|s|
|Md|10a3|S5|es|09S|10|108|104|103||lie|o|<br>|<br>|<br>|||WBs|103|
|_MO&A_<br>|107|Bes|8S|75|107|101|Oa|85|71|793|100|112|114|114|105|tos|||
|Maushd|||||||||||||||||||
|NxprM"|sB|E|a<br>|U|a||to<br>|8<br>|1<br>|1<br>Il|l<br>o<br>100|142<br>89|_128_<br>83|149<br>e7|172<br>74|10t|82||
|.|25<br>|15<br>|_11_<br>|3<br>|143<br>|113<br>93|103<br>eS9|as<br>10s|70<br>103|a<br>S4|<br>100|<br>So|<br>83|es|EB|107|a3|BOD|
|F"h|~~1|7<sup>153</sup>|141|13|109|||<br>|<br>|109|1a0|lie|1ot|too|92|91|97|10t|
|Rwnzp|127|144|137|t31171|1||n|1|119||||||||||
||||||||92|S5|102|so|too|142|112|asB|||||
|Si<br>ew|73|as|OD|70|93|121|||<br>||||79|ST|97|181|133||
|80meh|ISe|181|1sB|149|138|149|135|13E|123<br>|go<br>|100<br>|So<br>85|<br>esB|<br>B4|||5t|44|
||||||77|ss|71|w|79|El|10||||||||
|Sudn|47|as|54|as|<br>|<br>|<br>||139|128|100|95|95|s7|120|125|114|113|
|Tazf|75|83|so|121|130|131|143|147||||<br>||||1|122||
|T*|||164|U8e|137|13B|174|212|115|so|100|les|185|184|145|a|||
|Ogt<br>UgDnda|7ss|7ss|<br>77|<br>6se|6B|7t4<br>|549|307|225|t50|100<br>100|540<br>140|370<br>Ise|155|14B|127|136|la|
|||||||146|||||||||||||
|Z -||||l||95|so|77|82|s7|1ao|105|104|117|112|IGO|150|1a|
|Zambkl<br>_ZWn&_|7s<br>65|so<br>77|85<br>er|e<br>72|so<br>Be|<br>eo|<br>e4|e7|e2|78|IOo|123|113|S4|el|10B|S3|84|
**_59_ -**

||I<br>t|d<br>d|o<br>d|d<br>d d|d,d|
|---|---|---|---|---|---|
||tI<br>i<br>1<br>ll<br>~11|000<br>dd<br>dd|6<br>d<br>d<br>d<br>dd<br>ddd<br>dd|dd<br>d<br>ddd<br>d<br>ddd|dd d<br>dod|
|I|ot §<br>aJl|d<br>d<br>Ido<br>dd|d<br>d d<br>dd<br>d<br>dd<br>d<br>odd<br>d<br>dd|dd<br>d<br>d<br> <br>d<br>d<br>dd|d<br>d<br>d|
|Ils|I~1ZRifJ<br>|tlXJgIff{<br>{XZ|0<br>oi{§}Z}l||zg|||
-60-

Annex D Table 8. Ratio of implicit financial outflow from agriculture (1980-85 average, in percent)

||To total<br>government<br>expenditure|To total<br>government<br>revenue|To total<br>government<br>tax revenue|To<br>agricultural<br>GDP|
|---|---|---|---|---|
|Country|in agriculture||||
|Benin||0.55||0.18|
|Burkina Faso|49.58|2.41|2.82|0.92|
|Burundi||15.69|17.03|4.06|
|Cameroon|86.65|4.67|5.18|4.21|
|CAR|68.79 a|||4.04|
|Congo||0.32||1.37|
|Cote d' Ivoi.e|450.05|17.86|22.20|17.80|
|Ethiopia|22.68|3.36|4.41|1.65|
|Gambia|13.95|3.14|3.50|2.16|
|Ghana|420.54|47.07|55.23|7.39|
|Guinea||-0.39||-0.26|
|Kenya|15.12|1.78|2.01|1.39|
|<br>Liberia|74.19|6.29|6.56|4.06|
|Madagascar|204.05|22.38|28.63|8.80|
|<br>Malawi|25.00|4.70|_5.50_|2.90|
|Mali|37.33|3.94|4.60|0.94|
|Mozambique|||||
|Niger|-0.01|0.00||0.00|
|<br>Nigeria|2.57|0.16|0.02|0.08|
|<br>Rwanda|135.87|11.08||3.?7|
|Senegal|4.57 a|0.42|5.12|0.43|
|Sierra Leone|93.94|13.12|0.02|3.54|
|Somalia|14.89|0.89|15.12|0.13|
|Sudan|-25.05 a|-3.28|-1.28|-1.29|
|Tanzania|56.35|6.83||. 47|
|Togo|101.35 a|8.18|61.62|8.20|
|<br>Uganda|19.80|1.20|0.97|0.14|
|Zaire|124.38 a|6.41|1.84|3.02|
|Zambia|1.19|G.25|0.02|0.41|
|Zimbabwe|-0.47|-0.05|-0.09|-0.13|

- Note: Implicit financial outflows are the estimated unit transfers based on the nominal protection coefficients weighted by total export quantities.

- a. Estimated Government expenditures in agriculture were available for entire 1980-85 period.
Amnex 0 able 9. Weather valable (dewAatlon from trend of cereal vieds. In logarWnM

|CoUb|197o|1971|1972|1973|1974|1S75|1978|1977|1978|1979|1s8o|1981|1982|1983|1984|1985|1988|1987|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Senn|4-052|4.0147|-0.120|0.078|0.Q01|0.115|0.079|0.105|0.073|0.032|0.013|40.094|4Q118|.0211|0.058|0.098|0.092|4D092|
|<br>_Boft A_|<br>-1.628|0173|0.986|0.228|<br>0.736|0.342|0.750|0.346|40.0s8|4sesG|.0.116|0.099|0.154|0.103|-0.503|40084|-0.15S|0.177|
|<br>Suidna FeS|0.138|0.037|-0.025|-0.104|Q001|<br>0.092|40.044|4Q011|0.080|0.032|.0.010|-0.023|-0.082|4-.188|-0.075|0.085|0.145|0.033|
|Bunid|0.031|-0.123|0.010|0.003|0.088|4Q.004|0.008|-0.04e|MOSS|0.052|-0.030|0.093|-0.005|-.<br>o0|-0.120|.0.015|0O28|0.033|
|Cewnemon|-0.013|-0.017|-0.009|40.083|0.075|0.211|0.000|-0.015|4QOB|40104|.0.037|-0.059|4G025|0.094|a027|0.027|0.004|4019|
|Cape<br>Vede|0.385|0.304|-0221|-1.275|0.141|0.539|0.473|0.184|0.23|1.34|0.334|-0.011|40.001|-0.249|-0.088|-0.343|022|0.439|
|<br>Chad|Q179|0.079|40.1B9|-0.122|0.115|0.012|Q0.107|.0.076|40.05e|0.031|40015|0.188|0.20|40.13|-0.322|0.117|0,113|Q.007|
|Con0|0.122|0211|0.166|-0.087|40.28|4.0.9|0074|400389|-0383|0.023|0.161|4.235|-0.093|-0.070|-0.050|0.027|0.201|Q221|
|_Cow_<br>d7eore|.015|0.197|-0.004|0.010|0.058|-0.025|-0.125|40.125|-0.132|.0.150|0.083|0.037|0cas|-109|0.101|0.134|-0.042|0.022|
|CAR|Q.128|0.149|0.128|Q114|40.185|-0.119|-0.117|-0.115|0.031|-0.144|-0.07|4021|-0.024|-0.041|-0.084|0.093|0122|0.15s|
|EtIopia|<br>4.010|-0.024|-0.074|<br>-0.114|-0.095|0.107|0.080|-0.098|0.107|0.150|0.121|0.070|0.189|0.037|-240|-0.133|0.071|-0.11|
|Gabon|-0.030|-0.032|.0.001|0.013|4015|-0.084|-0.022|40.007|-0.014|0.178|0.102|0.069|0.031|0021|-0.041|-0.083|4-0.2|4.054|
|GaMbia|0.142|0.132|0.057|0.058|0.071|-0.098|-Q151|40.317|0.021|-0.159|0.053|0.100|0.038|-. 229|-22|123|0.148|0233|
|Ghana|.012|0.019|0.03 i|Q031|0.138|Q004|0.013|0.010|0.020|0.032|40.128|0.059|-0.199|.0.780|0.127|0.204|0212|as20|
|Guinea|0.082|4-.2|4.012|<br>0.005|0.013|<br>0.018|0.024|0.024|0.034|0.029|0.053|0.081|0.088|-0.124|4.105|-0.101|0L051|.049|
|Gtu<br>dnasa|0200|015|0.090|.0.012|40.097|0.094|0117|-04208|A140|_-173_|-0.140|0.04|-0.095|40125|-0.091|a167|0131|0.195|
|Kea|0014|Q.018|0.022|0.028|40.010|.089|0.050|0.023|-0.079|-0.18o|0.217|0.007|227|056|-0.183|o097|.17S|4Q122|
|Lesotho|-0217|0.012|-0.4Q2|023|0.179|-0.083|4Q181|0.671|Qs94|038|0.208|0058|4-170|4152|4203|-0.102|-. 168|40125|
|Ubeft|0.003|0.003|<br>0.003|0.094|0.001|-0.028|40.008|0.080|0o.1|0.004|-0.001|Q002|-0.037|40005|Q027|-0.008|0C.23|40017|
|Madagasow|0.040|0.009|0.014|4014|0.028|0.003|0.085|4C.027|-0o08|-0.028|4025|4058|4072|Q013|Q003|Q032|Q027|0.088|
|mawal|0.088|0.094|.110|-0.435|0.02Q|0.12|-0 '38|-0.240|0.315|0.071|40.148|0.052|.015|.085|4147|0.044|0.091|402|
|Mabu|4197|0.042|G051|<br>0.0|0.028|4oss09|4-04|0148|0.097|0.083|40.073|40o04|0.028|009|0.040|4.038|40.089|4091|
|MaWawnl|0.283|0271|4305|4.319|0.052|0281|0482|0.13|4B14|40308|0.123|0219|4144|0.139|4130|0O093|4153|4QDe2|
|MWamMu|0.088|Q323|m171|<br>Q1e2|4.010|-0.254|4047|4074|4200|4205|40.28|4M|4027|4120|0.237|0279|.183|0235|
|mamnbu|0.097|0c.62|Q128|.039|-4051|4333|Q034|0.038|4ne3|4Q129|0.010|4092|4028|4Q007|0.019|MOD0|<br>0.127|4Q020|
|NIger|4o41|Q.048|0.02s|-0217|0.015|4143|0.s1|0.083|0.081|0.128|Q153|MOO0|nc28|0O046|4401|m0ss|Q8OS1|4103|
|<br>N%p%|O.Q03|40o48|4188|4Q110|0.194|0137|41ao|0587|4L041|0.oe|0234|00.82|4048|4077|4075|0.007|0.034|4L114|
|Fhnda|Q0.05|0.016|0.08|4-020|4-140|4007|0.088|0.014|Q012|0.019|0.04|40o14|.082|0.021|4087|4088|0.034|0.018|
|Sena|4098|.202|.0390|4QD98|0258|01ls|Q164|4Q189|023|4c039|4182|Qo87|4004|4204|4189|0.109|4137|0230|
|Sene Leone|0S|4047|0.001|4010|0.012|.075|Q038|0.045|.089|4-114|4107|4109|.030|0.13s9|104|40128|0c.9|40m18|
|soma9a|m0oo|0.011|0.011|4C002|_OA023_|0.030|4s05s|Q011|0.021|Q027|os0|0.001|Q002|4369|4040|Q222|Q072|QDQ7|
|Sud-n|4018|0.056|4X14|4144|4092|.083|4.015|0.097|0.224|4071|a168o|078|498o8|4.132|4514|0.154|0.191|4153|
|Sw,d|4873|0.240|Q316|49ss|0.190|0.098|0.127|0.019|.32|4300|o053|n24s|-4380|4608|0.307|0.007|0Q057|0.075|
|Tarami|0.002|4LO42|4050|4126|413S|n144|n128|M110|QOOE|0.057|.0o0|095|Q020|4973|4013|0.008|--<br>:w|405|
|TogP|4.090|0.24|.10S|4009|MOe07|<br>Q12s|4017|0.007|4094|4092|0252|41i0S|0.077|4087|0.121|0.089|Q013|0.24|
|Uganda|0.011|4123|4003|4050|413|0.068|4.018|4029|4L004|02s9|0.178|0223|0.25|0230|4482|4-433|0.048|0.0S|
|Zabe|0.028|Q013|0209|0.009s|4001|40009|4024|4048|4072|0.011|4620|0.029|0.013|0o0s|0.008|4L013|0.004|0.027|
|Zambia|4295|4033|0.053|4183|4o|0.158|0.153|0.189|06143|Qos.1|0087|0204|4032|4988|4092|4L043|0.072|4314|
|ambeb|4.333|0.081|0231|4293|0.151|071<br>Q|0.100|I189|Mi110|4089|4048|0370|4L049|40.24|4398|0.489|0300|4301|
Annex 0 tabie 10. 1lasts data: rnumber of pople affected by major disastes, In thousands.

|Angohp|1970<br>0|1971<br>0|1i72<br>0|1973<br>0|1974<br>0|1975<br>1is6|1976<br>8O|1977<br>0|1978<br>0|1979<br>0|1960<br>0|1981<br>F81|1962<br>0|1983<br>0|1984<br>0|1985<br>500|1968<br>0|1987<br>1|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|<br>Bonin|<br>0|<br>0|<br>115|<br>0|<br>0|<br>0|<br>0|<br>0|<br>4|<br>0|<br>0|<br>0|<br>500|<br>250|<br>0|<br>350|<br>375|0|
|FAwanda|8S|0|1|0|0|0|0|0|0|0|0|0|410|410|1037|s80|848|671|
|IwidnFaso|0|0|0|1300|0|0|0|0|442|2|0|4|7|0|2502|0|0|0|
|3urunm|0|0|5O0|0|0|0|0|0|2|0|0|0|0|0|0|0|0|0|
|Canewon|0|400|0|0|0|a|0|0|0|0|0|0|0|0|0|0|a|0|
|Cape Verte|0|0|0|0|0|0|0|0|0|0|0|0|2|0|0|a|0|0|
|<br>CAR|0|C|0|13|0|0|0|0|0|500|0||0|0|0|0|0|0|
|Chad|0|10|0|BOO|0|0|0|1900|0|475|1500|1500|1700|i00|1503|0|0|0|
|Conorc|0|0|0|0|0|0|0|35|0|0|0|0|0|30|0|35|0|0|
|Congo|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|<br>Cote dlvof|2|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|EquaLGuinea|0|0|0|0|0|0|0|0|0|250|0|0|0|0|0|0|0|0|
|<br>EtNopa|1700|5|0|1580|1580|0|50|am|1417|0|25|0|0|2000|500|8051|0|7330|
|Gabon|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|GmbIa|0|150|0|0|0|0|0|0|85|0|500|301|0|0|0|0|0|0|
|Ghana|0|12|0|0|0|0|0|7|0|0|0|0|0|1320O|0|2|0|0|
|GuInea|0|0|0|0|0|0|0|0|0|0|0|0|0|2D|0|0|0|0|
|Guuna-Blsa|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|4|
|K-na|0|150|0|0|0|0|18|ao|0|0|40|0|4|13|800|0|o|0|
|Lesotho|0|0|0|0|1|0|0|0|0|0|0|0|0|0|500|90|o|0|
|Ubela|0|0|0|0|0|0|0|0|1|204|2|0|0|0|0|0|0|0|
|Madaga|10|0|2500|0|0|10|500|30|0|18|0|1000|118|0|114|0|84|28|
|MalawI|0|0|0|0|0|0|0|0|0|20|0|0|0|0|0|0|0|715|
|mani|0|0|0|1900|0|0|0|0|0|0|0|5|0|0|1503|1503|0|0|
|mawIlhl|0|0|0|1300|0|0|0|710|710|27|0|0|0|80|1853|8wo0|<br>0|0|
|_MuAws_|0|0|_25_|0|0|828|0|0|0|0|100|0|32|0|0|0|0|0|
|Mo<br>Dn-bkq|0|501|0|0|13|87|0|_400_|200|so|0|6006|0|400o|5010|3068|0|6580|
|NIger|3|0|0|160o|8|8|0|0|0|0|0|0|3|67|3500|0|0|0|
|<br>Nwla|82|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|2|
|Fkrda|0|0|0|3|9o5|9O0|0|1700|1|1|0|0|0|0|0|420|0|0|
|SWoTorn&a,Ffdpe|0|0|0|0|0|0|0|0|0|0|0|0|0|0|983|<br>0|0|0|
|SeNa|0|0|0|1400|0|0|0|0|3715|0|95D|0|0|605|600|0|3|0|
|9eychel|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|<br>S8M Le"|0|0|0|0|0|5|5|0|0|0|0|0|0|0|0|0|3|0|
|Salak|30|0|0|0|124|125|0|173|20|0|0|20|0|0|0|a|8|50i|
|<br>Suda|0|0|0|60|0|75|70|0|1W|0|0|0|0|140|5202|4200|20Dr|0|
|<br>tand|<br>0|<br>0|<br>0|<br>0|<br>0|<br>0|<br>0|<br>0|<br>0|<br>0|<br>0|<br>0|<br>0|<br>1|<br>632|<br>0|<br>C|0|
||||||||||||||4|0|||0|0|
|Tanzania|0|0|0|0|0|0|0|20|16|80|0|0|0||0|1o9|||
|TogP|200|150|0|0|0|0|0|0|0|0|0|0|a|2D|0|0|0|0|
|<br>Ugan|0|0|0|0|0|0|0|0|0|400|500|0|0|15D|0|0|10o0|331|
|zaIo|0|0|0|0|0|0|1|8|6S|500|18o|0|0|0|300|0|0|0|
|Zambia|0|1|0|0|0|0|0|0|30|1|11|12|0|0|0|0|0|0|
|Zabnb|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|

N SOUR0 US0 May 196& Ue"M MW. Clbe d U.S Foreign Otrsv Assanoe
- 63

# Annex D Table 11. Food Crop Price and Policy Effects

||Real p<br>(index|roducer pric<br>1979-81 = 1|es<br>00)|Nomina|l protection|coefficients|
|---|---|---|---|---|---|---|
||Average<br>1970-72|Average<br>1975-77|Average<br>1985-87|Average<br>1970-72|Average<br>1975-77|Average<br>1985-87|
|Benin||108|||||
|Botswana||87|136|0.89|0.66|1.2|
|Burkina Paso|66|89|85||||
|Burundi|123|104|||||
|Cameroon|89|98|||||
|CAR.|107|115|||||
|Chad|||||||
|Congo||90|96||||
|Cote d'Ivoire|120|207|93|0.73|1.04||
|Ethiopia|219|181|85||||
|Gambia|71|117|74||||
|G;hana|84|138|93|0.93|3.32|1.89|
|Guinea|30|114|||||
|Guinea-Bissau|||||||
|Kenya|105|134|104|0.63|0.79|0.93|
|<br>Liberia|59|96|80|0.86|1.38|2.43|
|Madagascar|80|113|172|0.53|0.7|1.06|
|<br>Malawi|95|101|94|10.92|4.58|1.49|
|Mali|98|96|109|||0.58|
|Mauritania|||||||
|Mozambique|||||||
|Niger|71|91|||||
|Nigeria|140|101|92||||
|<br>Rwanda|140|102|92|0.59|0.66|1.27|
|Senegal|103|106|88||||
|<br>Sierra Leone|71|91|||||
|Somalia|181|142|1.59|||1.32|
|Sudan|66|88||1.03|1.24|1.34|
|Tanzania|72|130|109|||1.58|
|Togo||139|88||1.7|1.62|
|<br>Uganda|299|202|||||
|Zaire|||||||
|Zambia|83|84|139|0.68|0.61|0.68|
|Zimbabwe|76|84|94|0.37|0.36|0.62|
-64 -

# Arinex D Table 12. Comparison of domestic staple food prices to imported rice and wheat prices

|||As a f|raction of inte<br>rice price|rnational|As of|fraction of in<br>wheat pri|ternational<br>ce|
|---|---|---|---|---|---|---|---|
|Country|Crop|1970-72|1980-82|1985-87|1970-72|1980-82|1985-87|
|Burkina Faso|sorghum|0.58|0.79||1.31|2.08||
||maize|0.58|0.79||1.31|2.08||
|Cameroon|mai7e|0.99|1.08||2.23|2.86||
||cassava|0.36|0.33||0.82|0.88||
||plantains|0.53|0.69||1.19|1.83||
|Cote d'Ivoire|maize|1.25|1.29||2.80|3.43||
||cassava|0.93|1.25||2.09|3.32||
||yams|0.92|1.19||2.07|3.15||
|Ghana|maizz|1.02|6.88|1.92|2.28|18.26|3.52|
||cassava|0.42|3.43|1.10|0.95|9.09|2.01|
|Kenya|maize|0.56|0.70||1.27|1.85||
|Malawi|maize|0.32|0.27|0.41|0.72|0.71|0.75|
|Mali|sorghum|_0.75_|0.86|1.22|1.69|2.29|2.24|
|Niger|sorghum|0.75|1.32||1.69|3.50||
|Togo|maize|0.72|0.80||1.61|2.12||
||sorghum|0.80|0.64||1.79|1.69||
|Senegal|sorghum|1.00|0.74|1.54|2.25|1.97|2.83|
|Zambia|maize|0.60|0.60||1.35|1.60||
|Zimbabwe|maize|0.47|0.43|0.65|1.06|1.15|1.18|
_Am=e_ 0 Tebb 1. Vkaus Sp _eafiof _Rd_ Pebn Equelon for Aadcjftura SuPnh.

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- 66 -

# BIBILOGRAPHY

Ademola T. Oyejide (1986). "The Effects of Trade<sup>and Exchange Rate Policies on Agriculture</sup> in Nigeria." Research Report 55. Washington D.C.: International<sup>Food Policy Research</sup> Institute.

Balassa Bela (1988). "Incentive Policies and Agricultural<sup>Performance in Sub-Saharan Africa."</sup> PPR Working Paper No. 77. Washington D.C.: World Bank.

Bateman, Merrill, David M. Newbery, William Asenso<sup>Okyere and Gerald T. O'Mara (1988).</sup> "Ghana Cocoa Pricing Policy Paper." World Bank consultants<sup>report (processed).</sup> Bates, Robert H. (1981). Markets and States in Trogical<sup>Africa - The Political Basis of</sup> Aricultural Policies. Berkeley: University of California Press.

Bautista, Romeo M. (1988? undated). "Agricultural<sup>growth and food imports in developing</sup> countries: a reexamination," Processed. Washington, D.C.: International<sup>Food Policy</sup> Research Institute.

Berg, Robert J. and Jennifer Seymour Whitaker, eds. (1986).<sup>Strateeies for African</sup> Development. Berkeley: University of California Press.

Binswanger, Hans (1989). 'The Policy Response of Agriculture,"<sup>Proceedings of the World Bank</sup> Annual Conference on DeveloRment Economics 1989. Washington,<sup>D.C.: World Bank.</sup> Binswanger, Hans P. and Pasquale L. Scandizzo (1983).<sup>"Patterns of Agricultural Protection."</sup> ARU Discussion Paper No. 15. Processed. World Bank.

Bond, Marian E. (1983). "Agriculture Responses to Prices<sup>in Sub-Saharan AEican Countries.</sup> "International Monetary Fund Staff Papers, Vol. 20 No. 4 pp.<sup>703-736. Washington D.C.</sup> Chhibber, Ajay (1988). The Amereeate SUDPIY ResRonse in Agriculture. Forthcoming in S. Commander (ed) Structural Adjustment in Agriculture:<sup>Theory and Practice. London:</sup> James Curry Publishers.

Cleaver, Kevin M. (1985). "The Impact of Price and Exchange<sup>Rate Policies on Agriculture in</sup> Sub-Saharan Africa." World Bank Staff Working Papers Number<sup>728. Washington D.C.:</sup> World Bank.

Cleaver, Kevin (1988). "Agricultural Policy Reform and<sup>Structural Adjustment in Sub-Saharan</sup> Africa: Results to date. "World Bank Africa Department I.<sup>Washington, D.C. Processed.</sup> Delgado, Christopher L. (1987). "The Role of Prices in the<sup>Shift to Rice and</sup> Wheat Consumption in Francophone West Africa." Paper<sup>prepared for the Conference on</sup> the Dynamics of Cereals Consumption and Production Patterns<sup>in West Africa July 15-17,</sup> 1987, Dakar, Senegal: International Food Policy Research Institute.
|- 67-|
|---|

Devarajan, Shantayanan and Jaime de<sup>Melo (May 1987). "Adjustment with a Fixed Exchange</sup> Rate: Cameroon, Cote d'Ivoire, and Senegal."<sup>The World Bank Economic Reiew, Vol. 1, No.</sup> 3: 447487.

Edwards, Sebastian (1989a). "Exchange<sup>Rate Misalignment in Developing Countries."</sup> 'eerch Qbsrver, Vol. 4, No. 1. World Bank. Edwards, Sebastdan (1989b). "Openness,<sup>Outward Orientation, Trade Liberalization, and</sup> Economic Performance in Developing<sup>Countries". PPR Working Paper No. 191. Washington</sup> D.C.: World Bank.

Eicher, Carl K. (1986). "Strategic Issues<sup>in Combating Hunger and Poverty in Africa" in Berg</sup> and whitaker.

Gitfinger, Price J., Joanne Leslie and<sup>Caroline Hoisington (1987). Food Policy: Integrat</sup> Supply. Distribution, and Consumption.<sup>Baltimore: The Johns Hopkins University Press.</sup> Haggblade,Steven, Peter B. Hazel and<sup>James Brown (1987). "Farm/Nonfarm Linkages in Rural</sup> Sub-Saharan Africa Empirical Evidence<sup>and Policy Implications.' BGRAP Economic Discussion</sup> Paper No. 2. Washington D.C.: World Bank.

Harvey, Charles ed. (1988). Agricultural<sup>Pricing Policy in Africa. Four Country Case Studies,</sup> Hayami, Yujiro and Vernon W. Ruttan<sup>(1971). Agricultural Davelopment. Baltimore: Ihe</sup> Johns Hopkins University Press. Imran, Mudassar and Duncan Ron (1988).<sup>"Opfimal Export Taxes for Exporters of Pernnial</sup> Crops." PPR Working Paper No.<sup>10. Washingon D.C.: World Bank.</sup> Jaeger, William and Charles Humphreys<sup>(1988). "The Effect of Policy Reforms on Agricultural</sup> Incentives in Sub-Saharan Africa."<sup>American Journal of Agriculural Economics Vol. 70 No.</sup><sup>_5,_</sup> December 1988.

Jansen, Doris (1988). Trade Exchange<sup>Rate. and Agrictltural Pricing Policies in Zambia.</sup> Washington D.C.: World Bank. Johnston, Bruce F. and William C.<sup>Clark (1982). Redesining Rural Development - A Strategic</sup> Perctive. Baltimore: The Johns Hopkdns University<sup>Press.</sup> Johnston, Bruce F. (1986). "Government<sup>Strategies for Agricultural Development' in Berg and</sup> Whitaker.

Kmenta, Jan (1971). Elements<sup>of Econometrics. New York: Macmillan Publishing Co., Inc.</sup> Krueger, Anne 0. (1974) "The Political<sup>Economy of Rent-Seeking" American Economic</sup> Review 64, No. 3: 291-303.
-68 -

|Krueger, Anne O., Maurice Schiff, and Alberto Valdes (1988). "Agricultural Incentives in|
|---|
|Developing Countries: Measuring the Effect of Sectoral and Economywide Policies." Mm|
|<br>WoQd Bank Economic Revie, Vol. 2, Number 3.|

|Lecaillon, Jacques and Chdstian Morrisson (1986a). Economic Picies arnd Agicultural|
|---|
|Performance: The Case of Burldna Faso. Paris: Development Center of the Organizadon for|
|Economic Co-Operation and Development.|

|Lecaillon, Jacques and Christian Morrisson (1986b). Economic PQlicies and Agricull|
|---|
|Pejq=mance: The Case of Mali, 196041983. Paris: Development Center of the Organizadon|
|for Economic Co-Operation and Development.|

|Lele, Uma (1989). "Sources of Growth in East African Agriculture." The World Do&<br>Economic Review. Vol. 3, No. 1: 119-144.|
|---|
|Upton, Michael (1987). "Limits of Price Policy for Agriculture: Which Way for the World<br>Bank?" Development Policy Review. Vol. 5, 197-215.|
|Lofchie, Michael F. (1989). The Policy Factor: Aericultural Performance in Kenya and<br>Tanzgnia. Boulder: Lynne Rienner Publishers.|

|Mellor, John W. and Ahmed Raisuddin (1988). Agricltural Price Policy for Developing|
|---|
|Countries, Baltimore: The Johns Hopkdns University Press.|

|Mellor John W. and Bruce F. Johnston (1984). "The World Food Equation: Interrelations|
|---|
|Among Development, Employment, and Food Consumption." Journal of Economic Literature.|
|Vol. XXI (June 1984), pp. 531-574.|

|Poleman, Thomas T. (1981). "Quantifying the Nutrtion Situadon in Developing Countries."|
|---|
|Food Research InsItute Studies. Vol. XVIII, No. 1, 1981.|

|Rassas, Bechir, Charles Rabenarivo and Lawrence Meserve (1988). "Evaluation of the Food for<br>Progress Rice Program in Madagascar." Washington D.C.: International Science and<br>Technology Institute, Inc. (Processed).|
|---|
|Roberts, Coileen (1988). Trade. Aid. and Policy Reform. Proceedings of the Eighth Agriculture<br>Sector Symposium. Washington D.C.: World Bank.|
|Serageldin, I. (1989) Povert. Adiustment and Growth in Africa. Washington, D.C.: World<br>Bank.|
|Sharply, Jennifer (1986). Economic Policies and Agicultural Performance. The Case of Kenya.<br>Paris: De.velopment Center of the Organization for Economic Co-Operation and Development.|
|Scandizzo, Pasquale L. and Colh Bruce (1980). Methgdologies for MAeasuring<br>Agricultural<br>Price Intervention Effects, World Bank Staff Working Paper No. 394. Washington, D.C.:<br>World Bank.|
-69 -

# Stryker, Dick, J. (1988). "Ghana: A Comparative Study of the Political Economy of Agriaultural Pricing Policies." Processed. Washington D.C.: World Bank.

- Svedberg, Peter (1987). "Undernutrition in Sub-Saharan Africa: A critical Assessment of the Evidence." WIDER working paper 15. Helsinid: World Institute for Development Economics Research, and Institute for International Economic Studies University of Stockholm, Stockholm.

USAID. (1988). Disaster History: Significant Data on Major Disasters Worldwide. 1900pre . Washington, D.C.

- United Nations (1989). Update on the Nutrition Situation. Recent Trends in Nutrition in 33 countries. Geneva: Administrative Committee on Coordinadon - Sub-Committee on Nutridon.

- Valdes, Alberto and Ammar Siamwalia (1988). "Foreign Trade Regime, Exchange Rate Policy, and the Structure of Incentives." in Gittinger et al.

- von Braun, Joachim and Eileen Kennedy (1987). "Cash Crops versus Subsistence Crops: Income and Nutritional Effects in Developing Countries." In Gittinger et al.

- Westlake, M.J. "The Measurement of Agricultural Price Distordons in Developing Countries." Journal of Develogment Studies. 23(1987): 367-81.

- Wheeler, David (1984). "Sources of Stagnation in Sub-Saharan Africa". World Development Vol. 12, No. 1, pp. 1-23.

- World Bank (1986a). Poverty and Hunger. Issues and Opions for Food Secumity in Developing Countries. Washington D.C.: World Bank.

- World Bank (1986b). World Development Report 1986. New York: Oxford University Press.

- World Bank (1988a). The Challenge of Hunger in Africa. Washington D.C.: World Bank.

- World Bank (1988b). Cotton Deyelopment Programs in Burkina Easo. Cote d'Ivoire. and Togo. Washington D.C.: Operations Evaluadon Department.

World Bank (1988c). The Nigerian Structural Adjustment Program: Policies, Impact, and Prospects. Washington D.C.

World Bank (1989a). Africa's Adjustmnent and Growth in the 1980s. Washington D.C.: World Bank.

World Bank (1989b). Ghana: Structural Adjustnent for Growth. Washington D.C.: World Bank.

World Bank (1989c). Successfil Development in Africa. Washington D.C.: World Bank.

World Bank (1989d). World Development Report 1989. New York: Oxford University Press.
I ia
# PRE Working Paper Series

||Auftho||Contact<br>gape|
|---|---|---|---|
|WPS619 F6.ceign<br>Direct Investment in<br>Sub-Saharan Africa|Laurence Cockoroft<br>Roger C. Riddell|March 1991|S. King-Watson<br>33730|
|WPS620 Hovetommercial Banks<sup>Ignored</sup><br>History?|Sule Ozler|March 1991|S. King-Watsrn<br>33730|
|WPS621 Sensible Debt Buybacks<br>for Highly<br>Indebted Countries|Enrica Detragiache|March 1991|<br>S. King-Watson<br>33730|
|WPS622 How Factors in Creditor Countries<br>Affect Secondary Market Prices<br>for Developing<br>Country Debt|Sule Ozler<br>Harry Huizinga|March 1991|<br>S. King-Watson<br>N<br>33730|
|WPS623 World Bank-Supported<br>Adjustment<br>Programs: Country Performance 2<br>and Effectiveness<br>-.|ViftoriqCotbo<br>Patricio<sup>Rojas</sup>|March 1991|<br>A. Oropesa<br>39075|
|WPS624 Choosing Policy Intruments<sup>for</sup><br>Pollution.Control:<br>A Review<br>t|Gunnar Eskeland<br>Emmanuel<br>Jimenez|March 1991|<br>A.Bhalla<br>37699|
|WPS625 How Trade and Macroeconomic<br>Policies Affect Economic Growth<br>and Capital Accumulation<br>In.<br>Developing<br>Counfties|Ramon Lopez|March 1991|<br>K. Cabana<br>37946|
|WPS626 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of<br>Colombia|William Easterly|March 1991|<br>R. Luz<br>39059|
|WPS627 The>Role<br>of Institutions<br>In Poverty<br>Reduction: A Focus on the<br>Productive Sectors|Sharon L. Holt|March 1991|<br>WDR Office<br>31393|
|WPS28 The Indonesian<br>Family Planning<br>Program: An EconomicAwspective<br>14<br>¢<br>|Dov Chernichovsky<br> <sup>Henry Pardoko</sup><br>David De Leeuw<br>3<br>DPudjoRahardjo<br>Charles Lerman|<br>March 1,99|1<br>0. Nadora<br>31091|
|WPSO2 An Atheoretic Evaluation<br>ot SuccOs<br>in Structural<br>Adjustment|Patrick Conway|March 1991|<br>D. Ballantyne<br>,<br>37947|
|WPS630 The Spiritr't Capitnlisr and<sup>Long-ru</sup><br>Growth|<sup>n Heng-fu</sup><br>Zou|Marc<br>t<br>191|<br>, A. Shalla<br>37699|
|WPS631 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of<br>Morocco|Rlccard Falni|March 199|1<br>R. Lux,<br>34303|
# PRE Working Paper **Serieas**

||Iilli|~~~~~<br>~~Author|_12aQ_|Contact<br>for paaeer|
|---|---|---|---|---|
|WPS63|2 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of<br>Argentina|Carlos Alredo Rodrigtez|March 1991|R. Luz<br>34303|
|WPS63|3 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of<br>Thailand|Virabongse<br>Ramangkur<br>Bhanupongse<br>Nidhiprab|a March 1991<br>ha|R. Luz<br>34303|
|WPS63|4 Trends in Developing<br>Country<br>Expot's, 1963-88|Bela Balassa|March 1991|WDR Office<br>31393|
|WPS635|Exchange<br>Rates and Foreign Trade<br>in Korea|Bela Balassa|March 1991|C. Cuskelly-<br>Young<br>39413|
|WPS636|Economic Integration<br>in Eastern<br>Europe|Bela Balassa '|March 1991|C. Cuskelly-<br>Young<br>39413|
|WPS637|Poverty in Poland, 1978-88|Branko Milanovic|March 1991|WDR Office'<br>31393|
|WPS63|8 Researching<br>the Trade-Productivi<br>Link: New Directions|ty James Tybout|March 1991|D. Ballantyne<br>37947|
|WPS639|The'high Cost of Protecting<br>Uruguay's Autpmotive<br>Industry|Wendy E.Takacs<br>:|'9rch 1991|D.B3allantyne<br>37947|
|WPS64<br>C,,>|0 The Impact of Policy in African<br>Agriculture;,<br>An Empirical<br>Investigation|Wiliam Jaeger 'YMarch|<br>19|ideru<br>34663|

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