Agricultural policy incentives in sub-Saharan Africa in ... · last decade (2005–2016) Monitoring...

Agricultural policy incentives in sub-Saharan Africa in the last decade (2005–2016)Monitoring and Analysing Food and Agricultural Policies (MAFAP) synthesis study

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Valentina Pernechele Economist

Jean BaliéSenior Economist

and

Léopold GhinsEconomist

Monitoring and Analysing Food and Agricultural Policies (MAFAP) Agricultural Development Economics Division (ESA), FAO

Food and Agriculture Organization of the United Nations

Rome, 2018

Agricultural policy incentives in sub-Saharan Africa in the last decade (2005–2016)Monitoring and Analysing Food and Agricultural Policies (MAFAP) synthesis study

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Citation: Pernechele, V., Balié, J. & Ghins, L. 2018. Agricultural policy incentives in sub-Saharan Africa in the last decade (2005–2016) – Monitoring and Analysing Food and Agricultural Policies (MAFAP) synthesis study, FAO Agricultural Development Economics Technical Study 3. Rome, FAO. 77 pp.

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ISBN 978-92-5-130465-5

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Cover photo: ©FAO/Giulio Napolitano

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ContentsPreface vii

Acknowledgements ix

Acronyms x

Executive summary xii

1 Introduction 1

2 Measuring policy incentives for the agriculture sector: an overview of MAFAP indicators 3

2.1 Price incentive indicators 32.2 Public expenditure indicators 52.3. Data overview 8

3 Price incentives in key agricultural value chains 12

3.1 Aggregated results 123.2 Commodity-specific analysis 18

4 Trends in public expenditures in support of food and agriculture 35

4.1 Level 354.2 Composition 384.3 Support to agricultural commodities 474.4 Role of aid 49

5 Assessing coherence between food and agricultural policies and their effects 51

6 Conclusions 55

References 57

Annexes 59

Annex 1. Nominal Rate of Protection and Market Development Gap by country and commodity 59

Annex 2. Analytical summary of factors driving price incentives indicators for selected commodities 65

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Figures

Figure 1 Components of the Nominal Rate of Protection 3

Figure 2 Simplified MAFAP public expenditure categories 6

Figure 3 Consortium country coverage (as of 2016) 7

Figure 4 Nominal Rate of Protection by income group 7

Figure 5 MAFAP country coverage 9

Figure 6 Commodities covered in MAFAP price incentives analysis 9

Figure 7 Nominal Rate of Protection at farmgate, average for 14 sub-Saharan Africa countries 13

Figure 8 Nominal Rate of Protection at farmgate, country averages for the 2005–2016 period 13

Figure 9 Market Development Gap (percentage of farmgate price), average for 14 sub-Saharan Africa countries 14

Figure 10 Market Development Gap (as percentage of farmgate price), country averages for the 2005–2016 period 15

Figure 11 Nominal Rate of Protection at farmgate for food security and cash crops, average for 14 sub-Saharan Africa countries 16

Figure 12 Nominal Rate of Protection at farmgate for export and import crops, average for 14 sub-Saharan Africa countries 17

Figure 13 Nominal Rate of Protection at farmgate averages for East Africa and West Africa countries 18

Figure 14 Nominal Rate of Protection for rice by country, average for the 2005–2016 period 21

Figure 15 Rice production trend for selected countries 22

Figure 16 Nominal Rate of Protection for rice for selected countries, 2005–2016 23

Figure 17 Nominal Rate of Protection for maize by country, average for the 2005–2016 period 26

Figure 18 Maize production trend for selected countries 26

Figure 19 Nominal Rate of Protection for maize for selected countries, 2005–2016 28

Figure 20 Nominal Rate of Protection for cotton by country, average for the 2005–2016 period 30

Figure 21 Nominal Rate of Protection for cotton for selected countries, 2005–2016 31

Figure 22 Nominal Rate of Protection for tea by country, average for the 2005–2016 period 32

Figure 23 Nominal Rate of Protection for tea for selected countries, 2005–2016 33

Figure 24 Shares of agriculture-specific and agriculture-supportive expenditures in total public budgets for 13 African countries, actual spending, national and external resources 36

Figure 25 Execution rates of public expenditure in agriculture in 13 African countries (actual expenditure, internal and external resources) 37

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Figure 26 Relative sizes of agriculture-specific and agriculture-supportive expenditures within policy transfers for agriculture in 13 African countries, average 2006–2015, actual spending, national and external resources 39

Figure 27 Composition of agriculture-specific expenditures in 13 African countries, average 2006–2015, actual spending, national and external resources 40

Figure 28 Share of input subsidies in agriculture-specific expenditures, agricultural GDP and overall GDP in 13 African countries, actual spending, national and external resources, average 2006–2015 41

Figure 29 Composition of input subsidies in 13 African countries, actual spending, internal and external resources, average 2006–2015 42

Figure 30 Average shares of input subsidies, research, knowledge dissemination and agricultural infrastructure in public expenditure in agriculture for 13 African countries, actual spending, internal and external resources 43

Figure 31 Composition and share of spending on roads and irrigation 44

Figure 32 USD spent on knowledge dissemination per USD spent on agricultural research 45

Figure 33 Composition of agriculture-supportive expenditures in 13 African countries, average 2006–2015, actual spending 46

Figure 34 Average share of spending on groups of commodities within agriculture-specific expenditures in 9 African countries 47

Figure 35 Composition of spending on groups of agricultural commodities in 10 African countries, average 2006–2015 48

Figure 36 Average composition of spending on groups of commodities in 10 African countries 48

Figure 37 Share of donor and national expenditures within public expenditure in agriculture in 13 African countries, average 2006–2015 49

Figure 38 Share of agriculture-specific expenditures allocated to private goods originating from donor and national sources, respectively, average for 13 African countries, actual spending 50

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Boxes

Box 1 The Consortium of International Organizations for Measuring the Policy Environment for Agriculture 7

Box 2 MAFAP in Bangladesh and India 8

Box 3 Trade agreements within the African Regional Economic Communities 20

Tables

Table 1 Main data sources for MAFAP price incentives indicators computation 10

Table 2 MAFAP public expenditure indicators coverage and main source 11

Table 3 Share of administrative costs within public expenditure in agriculture in 13 African countries, actual spending, national and external resources 38

Table 4 Nominal Rate of Protection by country and commodity (percentage), 2005–2016 59

Table 5 Market Development Gap by country and commodity (percentage), 2005–2016 62

Table 6 Price incentives indicators trend and main driving factors for selected commodities, 2005–2016 65

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Preface

Monitoring food and agriculture policies and their effects is crucial to support decision makers in developing countries to take better informed decisions on their agricultural and food policies. A systematic and rigorous exercise, based on internationally-recognized methodologies that generate indicators comparable across countries and time, is needed to measure the level and trend of policy support to the agriculture sector, as well as to highlight the degree of consistency of policies with stated development objectives at national and regional level.

Since 2009, FAO’s Monitoring and Analysing Food and Agricultural Policies (MAFAP) programme has been working in several developing countries, mostly in sub-Saharan Africa, to consolidate and strengthen policy monitoring systems at country level, in close partnership with local government institutions and national and international research organizations. Through its approach, MAFAP supports the development of institutional capacity and the creation of a community of practice on policy measurement and monitoring.

The MAFAP programme produces two types of indicators, focusing on price incentives and public expenditure, respectively. Price incentives indicators, including the Nominal Rate of Protection and the Market Development Gap, allow to measure the effect of trade and market policies and inefficiencies on the degree of price incentives faced by farmers and other agents (such as traders and consumers) in key commodity value chains, both food staple and cash crops.

Other organizations, such as the Inter-American Development Bank (IADB), the Organization for Economic Co-operation and Development (OECD) and the World Bank, produce similar type of indicators for additional countries worldwide. In a collective effort to provide a global tool to analyse policy environment in agriculture, these organizations, together with the International Food Policy Research Institute (IFPRI) and FAO/MAFAP, have recently established the Consortium for Measuring the Policy Environment for Agriculture. Until recently, data on the policy environment have been found to be scattered and difficult to compare. This platform intends to bridge an information gap by compiling the information for almost 60 countries and 70 products in a single online database. The dataset provides researchers and policy makers with the most recent information on how public policies affect the agriculture sector and the welfare of farmers and consumers.

MAFAP indicators of public expenditure in support of food and agriculture allow to monitor the trends in level and composition of public spending, ideally since 2006, and to analyze how resources have been channeled to the sector. The MAFAP methodological approach is broad and captures both agriculture-specific expenditures, which directly support agriculture (e.g. in input subsidies, infrastructure research and extension), and agriculture-supportive expenditure, which do not specifically target agriculture, but are likely to have a strong influence on agriculture sector development (e.g. investments in rural roads, rural education or rural health).

In recent years, the analysis of public expenditure in agriculture has also been characterized by a growing number of initiatives implemented globally, often focusing on Africa. Among others, the Regional Strategic Analysis and Knowledge Support System (ReSAKSS), the Statistics of Public Expenditure for Economic Development Database (SPEED) led by IFPRI and the World Bank’s Agriculture Public Expenditure Review (AgPER) and BOOST initiative are all aimed at supporting evidence-based planning and policy making, in particular in the framework of the Comprehensive Africa Agriculture

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Development Programme (CAADP) implementation. Given the different nature, objectives and methodology, these tools respond to specific but complementary needs at the country and regional levels.

This publication is the second of its type. The first one, produced in 2013, was an unprecedented attempt to summarize findings of a first set of price incentives and public expenditure indicators computed in the framework of MAFAP across 10 sub-Saharan Africa countries. The coverage of the MAFAP programme has expanded since then. The present study condenses the main results of the latest 2017 update of the indicators for 14 sub-Saharan Africa countries, namely Benin, Burkina Faso, Burundi, Ethiopia, Ghana, Kenya, Malawi, Mali, Mozambique, Nigeria, Rwanda, Senegal, Tanzania and Uganda. The timeframe of the analysis is also longer and now allows to review policy support to food and agriculture over the past decade.

This study has been made possible thanks to the systematization of the monitoring exercise at country level, which has been nurtured and promoted through in-country capacity development activities, joint analytical work, as well as results dissemination and peer-review events. Long-standing country level partnerships, with government, research institutions and other stakeholders, are recognized as an essential component of this policy monitoring work.

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Acknowledgements

This study has been developed in the context of the Monitoring and Analysing Food and Agricultural Policies (MAFAP) programme since July 2017. The initial draft of this analysis is the result of the work of a team coordinated by Valentina Pernechele with contributions from Léopold Ghins, Lucile Hummel, Signe Nelgen, Guillaume Pierre, Francisco Fontes and Jean Balié. The authors are grateful for the many useful comments and suggestions received from Federica Angelucci, Christian Derlagen and Emiliano Magrini.

The final version of this study was compiled by Valentina Pernechele, Léopold Ghins, and Jean Balié with inputs from Brett Shapiro on English editing and the support of Daniela Verona for the design and publishing coordination.

Data presented and analysed in this study were collected and compiled in coordination with national counterparts (government institutions, research organizations and other partners) in the framework of the country-level partnerships that have been ongoing since 2009. A validation workshop took place on 5-7 July 2017 in Nairobi, Kenya, where nationals from 12 countries gathered to present and peer review the results of the update of the MAFAP policy monitoring indicators for the period 2005-2016. The authors acknowledge with deep appreciation and gratitude the invaluable contributions of the workshop participants, who were representatives from Benin, Burkina Faso, Burundi, Ethiopia, Ghana, Kenya, Mali, Mozambique, Rwanda, Senegal, Tanzania and Uganda.

The MAFAP programme operates in close technical collaboration with the Organization for Economic Co-operation and Development (OECD) and it is financially supported by the Bill and Melinda Gates Foundation (BMGF), the United States Agency for International Development (USAID), the Government of The Netherlands and the Government of Germany.

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Acronyms

AGRA Alliance for a Green Revolution in Africa

CAADP Comprehensive Africa Agriculture Development Programme

CARD Coalition for African Rice Development

CDO Cotton Development Organization

CET Common External Tariff

CFTA Continental Free Trade Area

CIF Cost, Insurance and Freight

COMESA Common Market for Eastern and Southern Africa

DAM Department of Agricultural Marketing

EAC East African Community

ECOWAS Economic Community of West African States

ERP Effective Rate of Protection

FAO Food and Agriculture Organization of the United Nations

FOB Free on Board

FPMU Food Policy and Monitoring Unit

GDP gross domestic product

GFS Government Finance Statistics

IDB Inter-American Development Bank

IFPRI International Food Policy Research Institute

IMF International Monetary Fund

JICA Japan International Cooperation Agency

KTDA Kenya Tea Development Authority

MAFAP Monitoring and Analysing Food and Agricultural Policies

MDG Market Development Gap

NAFCO National Buffer Stock Company

NAIP National Agricultural Investment Plan

NEPAD New Partnership for African Development

NITI New India Transformation Institute

NRA Nominal Rate of Assistance

NRDS National Rice Development Strategies

NRP Nominal Rate of Protection

OECD Organisation for Economic Co-operation and Development

OTB Office du Thé du Burundi (Burundi Tea Office)

PEA Public Expenditure in Agriculture

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SADC Southern African Development Community

SAFEX South African Futures Exchange

SSA sub-Saharan Africa

UEMOA Union Economique et Monétaire Ouest Africaine (West African Economic and Monetary Union)

USAID United States Agency for International Development

VAT value-added tax

WB World Bank

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Executive summary

Since 2009, the Monitoring and Analysing Food and Agricultural Policies (MAFAP) programme of the Food and Agriculture Organization of the United Nations (FAO) has been working in a growing number of developing countries in sub-Saharan Africa and South Asia to develop sustainable systems to monitor, analyse and reform food and agricultural policies. MAFAP works with government institutions, research organizations and other partners to establish a community of practice on policy measurement, monitoring and analysis by developing institutional capacities to systematically analyse government policies and their effects.

MAFAP regularly produces a set of indicators comparable across commodities, countries and years that are commonly used to assess the extent of policy support in agriculture. They provide evidence to support informed policy dialogue at national, regional and international levels and help to understand how different food and agricultural policies work in various contexts.

The objective of this study is to synthesize the results emerging from the 2017 update of the MAFAP indicators for the 2005–2016 period for 14 sub-Saharan African countries, i.e. Benin, Burkina Faso, Burundi, Ethiopia, Ghana, Kenya, Malawi, Mali, Mozambique, Nigeria, Rwanda, Senegal, Tanzania and Uganda. These indicators make it possible to measure the effect of trade and market policies and inefficiencies on the degree of price incentives faced by farmers in key commodity value chains, as well as to assess level and composition of public expenditures in support of the agriculture sector.

Despite results being very heterogeneous across countries and commodities, aggregate figures indicate that price incentives to agriculture are overall increasing across the period. The related indicator – the Nominal Rate of Protection (NRP) – converges to zero and even becomes positive after 2011. Policies focused on supporting domestic production, such as import tariffs and price support, are likely to be the main drivers of such a trend, following the food price crises period (2007–2011) when policy-makers were mainly concerned about consumer protection. This result is driven primarily by the favourable policy environment for food security crops, which was privileged overall by policy-makers in the most recent period. Conversely, cash crops targeting the international markets, such as tea and cotton, are generally being discouraged on the price incentives side, with an aggregated NRP trend substantially negative and particularly uneven throughout the period of the analysis. The prevalence of negative Market Development Gap – a measure of price disincentives generated by market inefficiencies – reveals the persistence of major constraints on agricultural development, namely high marketing costs, including prohibitive transport costs and lack of post-harvest support.

Consistent with the price incentives results, public expenditure indicators confirm that direct budget transfers in support of producers, mainly in the form of input subsidies, continue to represent the largest part of agriculture expenditure in most countries. A producer bias for policy support in the analysed countries is evident. Other general services, storage and marketing still receive low attention from governments, indicating the lack of coordinated strategies for the promotion of integrated value chain development. Expenditures on research are declining and expenditures on knowledge dissemination are overall stagnant. Food crops continue to dominate public budgets, as 50 percent of commodity-specific expenditures were directed to this group of commodities, on average, over all countries and years. Spending on cash crops or “innovative” products remains limited. Increased investments in rural infrastructure in some countries are somehow contradictory with the very large access

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costs to markets and inefficiencies faced by farmers in some value chains, indicating that more needs to be done in terms of infrastructure development. Overall, our results indicate little policy focus on value chain integration and commercialization, in spite of the inclusion of these objectives in most national and regional agricultural investment plans framed in accordance with the Comprehensive Africa Agriculture Development Programme (CAADP) framework.

In general terms, only a few countries increased the share of agricultural expenditure within total public budgets in 2015 (Ethiopia, Mali, Rwanda, Senegal and Uganda) while most reduced them (Benin, Burkina Faso, Burundi, Ethiopia, Kenya, Senegal, Tanzania). The analysis indicates that only a few countries in certain years have shares of spending exceeding 10 percent of the total public budget, as committed through the Malabo Declaration, even considering the broader category of agriculture-supportive expenditure in addition to agriculture-specific expenditure. Initial efforts to convert resources that were previously allocated to input subsidies into investments in agricultural and rural infrastructures are seen.

The policy support pattern, in the form of both price incentives and public expenditure, appears quite volatile. This erratic policy environment is often due to the adoption of discretionary interventions on highly politically sensitive crops that are key for food security (e.g. maize and rice), such as export restrictions or marketing board interventions. These measures, often not grounded in evidence, were implemented without notice and on an ad hoc basis, generating instability and unintended effects. Stable and predictable policies are more conducive to economic growth.

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1 Introduction

1 Introduction

Most countries in the world, including developing countries, adopt policies that have an impact on their agriculture sectors. Through such policies, governments seek to influence the behaviour of farmers, traders, processors and others involved in agricultural value chains. Trade and domestic market policies are intended to affect prices farmers receive for their produce, in addition to input prices. Governments also use budgetary transfers to support specific groups, either directly or indirectly through investments in public goods, such as research and infrastructure.

Throughout past decades, with many policy changes affecting the agriculture sector at national and international level, it has been difficult to assess the effectiveness of the various policy reforms in developing countries, as no monitoring system existed to continuously measure the effects of policy interventions on the basis of comparable indicators across countries and over time (Angelucci et al., 2013). The Monitoring and Analysing Food and Agricultural Policies (MAFAP) programme of the Food and Agriculture Organization of the United Nations (FAO) started in 2009 to establish country-owned and sustainable systems to monitor, analyse and reform food and agricultural policies, thereby enabling more effective, efficient and inclusive policy frameworks in support of agricultural development in a growing number of developing and emerging economies. MAFAP intervenes in a context where there is much diversity in government policy measures, despite common objectives and cooperation frameworks at regional and international levels. This reinforces the need to closely monitor policies and their effects on agents of the agriculture sector.

This synthesis study builds on the analysis of a set of indicators that are commonly used to assess the extent of policy support in agriculture. On the one hand, a methodology based on price differentials between domestic and world markets is used to estimate so-called price incentives faced by farmers and other agents in commodity value chains. On the other hand, budgetary expenditures in support of producers and the agriculture sector as a whole are measured, using a specific classification methodology, to account for transfers of resources among taxpayers, consumers and producers. Both of these components are necessary to carry out comprehensive policy analysis, which includes assessments of policy coherence with respect to stated development objectives in the countries and in the international community.

Indicator trends show that the effects of policy support to agriculture are very heterogeneous across countries. Overall, average price incentives in agricultural markets are increasing. The related indicator, the Nominal Rate of Protection (NRP), aggregated across countries and commodities, converges at zero, becoming positive since 2012. After the period of food price crises, when the objective of policy-makers was to protect consumers, measures in support of farmers became widespread again in the region. Interventions focused on incentivizing production, such as import barriers and domestic price supports, are probably the main drivers of this trend. Also, budgetary transfers in support of producers continue to represent a large slice of the agriculture expenditure pie, with other agents (traders and consumers) often being penalized by the lack of coordinated efforts to promote integrated value chain development and foster agricultural transformation. Increasing investments in rural infrastructure, emerging from MAFAP public expenditure analyses, are somehow in contrast with the still prohibitive marketing costs and large inefficiencies faced by farmers in some value chains. This is particularly true for cash crops, posing serious constraints to the development of more commercialized, profitable agriculture.

Agricultural policy incentives in sub-Saharan Africa in the last decade (2005–2016)

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This study is structured as follows. Part 2 presents a brief overview of the MAFAP methodology and its main policy indicators, i.e. price incentives and public expenditure in support of food and agriculture. Part 3 presents the price incentive results, both in aggregate terms and for individual commodities. Part 4 reviews trends in public expenditure in support of the agriculture sector. Part 5 assesses overall coherence between agricultural policies and the impact they have on agents in the sector. Part 6 concludes with some brief policy recommendations.

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2 Measuring policy incentives for the agriculture sector: an overview of MAFAP indicators


2.1 Price incentive indicators

Estimates of the Nominal Rates of Protection (NRP) are used in this study as a measure of the effect of public policies and market factors on agricultural prices. In line with the approach proposed by Krueger et al. (1988, 1991), NRP has been used mainly to examine two types of policies: (i) direct support of the agriculture sector or a specific value chain through direct sector-specific price policies (or interventions); and (ii) indirect support through trade policies, exchange rates and any other macroeconomic or non-agricultural policies.

The point along the value chain where the NRP is calculated plays a key role: the border price and domestic price need to be compared at the same point in the value chain. The methodology used by MAFAP is closest to that described by Monke and Pearson (1988) and Tsakok (1990), as NRPs are estimated at the farm gate, wholesale and retail levels, which helps to locate market and policy failures along the value chain. To compare prices in a wholesale market for an imported commodity, the border price that is used for comparison needs to be modified in such a way that it accounts for the costs (such as handling costs at the border, transportation and any processing costs) incurred to take the commodity from a CIF (Cost, Insurance and Freight) position to sale in the wholesale market. The same formula is used in reverse in the case of an export. In this latter case, the border price will be an FOB (Free on Board) price (or unit value). The elements considered in the calculation of the NRP are schematically represented below.

FIGURE 1 Components of the Nominal Rate of Protection

TransportTransport

Import / exportMarket

Benchmarkprice

Wholesaleprice

Access costs

Producerprice

HandlingStorage

Fees

ProcessingHandlingStorage

Retail marketPoint of

competition Farm gate

Access costs

Source: Authors' elaboration from FAO, 2015a.


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The NRP, expressed as a percentage, can be calculated as the difference between the border price and the domestic price at the farmgate, wholesale and retail levels:

NRPx (%) = 100 * (DP–BPx) / BPx (1)

where DP stands for domestic price and BP for border price. The lower case x indicates the level at which the NRP is computed (farmgate, wholesale or retail). For this study, only NRP at farmgate will be analysed. As such, the NRP measures the percentage by which the domestic producer price is raised above (if positive) or has fallen below (if negative) the border price (adjusted for market costs, as well as quality/quantity factors), which is considered to be the undistorted reference price of a commodity. A positive NRP indicates that the policy environment and market dynamics provide price incentives to produce or commercialize the analysed product. On the contrary, a negative NRP signals that farmers and/or traders receive disincentives in terms of a specific commodity’s output prices.

In order to quantify disincentives stemming from value chain inefficiencies, MAFAP generated a methodology to compute the Market Development Gap (MDG) indicator. The MDG is an aggregate estimate of the effect of excessive market access costs within a given value chain on prices received by producers. “Excessive” access costs may result from factors such as poor infrastructure, high processing costs due to obsolete technology, government taxes and fees (excluding fees for services), high profit margins captured by various marketing agents, illegal bribes and other informal costs. All of these can impede the transmission of world prices to domestic markets. The MDG is expressed as a share of the farmgate price, as follows:

MDGfg (%) = (ACGwh– ACGfg) / Pfg

where Pfg is the price at farmgate and ACG is the access costs gap at wholesale and farmgate level. The access costs gap is computed as the difference between the observed access costs and those adjusted by removing market inefficiencies, which therefore reflects the costs that would prevail under a more efficient market structure.

MAFAP computes additional price incentives indicators that are not analysed in this study, the Nominal Rate of Assistance (NRA) and the Effective Rate of Protection (ERP). The NRA accounts for public expenditure allocated to the commodity that is added to the price gap at farmgate. It provides an estimate of price incentives, taking into account budgetary transfers allocated to a single commodity, in addition to policies affecting the output price and market performance affecting the value chain. The ERP takes into account trade and market interventions on tradable inputs, in addition to the policy impacts on the output side (covered by the NRP). The ERP provides a very precise measure of the total level of protection for a given commodity. In other words, the ERP captures the effects of policy measures on both producer prices and the cost of inputs used for agricultural production. Preliminary ERPs have been computed in some MAFAP countries, for selected commodities and years. Since these estimates require refinement before being disseminated, ERP figures are not analysed in this study.1

1 More detailed information on the methodology and calculation of price incentives indicators is provided in the MAFAP methodology working paper, available at: www.fao.org/in-action/mafap/resources/detail/en/c/386920

http://www.fao.org/in-action/mafap/resources/detail/en/c/386920

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2.2 Public expenditure indicators

Governments from developing countries often lack organized information to systematically analyse the performance of expenditures targeting the food and agriculture sector. Key actors at national level need regular information of this type in order to make rational, evidence-based policy choices. The development of accurate and reliable indicators is thus an important prerequisite for policy analysis and efficient budgetary processes.

The Public Expenditure in Agriculture (PEA) analysis proposed by MAFAP seeks to keep track of both the level and composition of public expenditures in support of the food and agriculture sector. They allow investigators to look at both aid flows and national expenditures. The MAFAP suite of PEA indicators makes it possible to see how resources are being allocated to priority areas, whether they address investment needs, and whether they are consistent with government policy objectives (i.e. demonstrating policy coherence). Moreover, MAFAP PEA indicators are highly disaggregated, which allows investigation of the incidence of PEA on agricultural growth, poverty reduction and other development variables. The indicators are therefore very useful material for research and analysis in the domain of public expenditure policies for agriculture (Hazell et al., 2010; Benin et al., 2008; Benin et al., 2009).

The MAFAP approach is to capture all public expenditures in support of food and agricultural development, ideally since 2006. Covered expenditures include expenditures from the national budget (by either the central or regional government), regardless of the ministry or agency that implements the policy, and external aid provided either through governments or through specific projects and programmes conducted by development partners. Public expenditures considered in the MAFAP PEA methodology are those in the food and agriculture sector, including forestry and fisheries. In addition, the MAFAP PEA methodology includes all public expenditures in rural areas, as they may also play an important role in agriculture sector development, even if they are not specific to the sector. These provide a glimpse of the general policy environment in a given country and whether there may be a pro- or anti-rural bias in expenditures on significant areas such as infrastructure, health and education.

As simplified in Figure 2, under the MAFAP PEA methodology the following distinctions are established:

¡ A broad distinction between expenditures that are agriculture-specific (direct support for the agriculture sector), agriculture-supportive (indirect support for the agriculture sector) and non-agriculture-related.

¡ Within the agriculture-specific category, a distinction between support for producers and other agents in the value chain (e.g. input subsidies) and general or collective support for the sector (e.g. agricultural research). The other agents in the value chain include input suppliers, processors, consumers, traders and transporters.

Agriculture-specific expenditures generate monetary transfers to agricultural agents or to the sector as a whole. Those agents (or the sector as a whole) must be the only, or the principal, recipient of the transfers generated by the expenditure measure. Agriculture-supportive measures are not strictly specific to the agriculture sector but have a strong influence on agriculture sector development. They include investments in rural infrastructure, rural education or rural health, for instance. All measures that meet these criteria are considered in the analysis, regardless of their nature, objectives or perceived economic impacts. The detailed MAFAP PEA classification follows the Organisation for Economic


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Cooperation and Development (OECD)’s principle of classifying policies according to the way they are implemented, which provides the basis for further policy analysis.2

FIGURE 2 Simplified MAFAP public expenditure categories

Overarchingcategories

Agriculture-speci�cexpenditure

Agriculture-supportiveexpenditure

Payments to agent

Payments to consumers

Payments to producers

Payments to other agents

Rural education

Rural health

Rural infrastructure

Food aid

Cash transfers

School feeding

Input subsidies

Income support

Subsidies basedon outputs

Agricultural research

Technical assistance

Training

Extension

Inspection

Agricultural infrastructure

Storage

Marketing

Feeder roads

Off-farm irrigation

Other off-farminfrastructure

Rural roads

Rural energy

Other ruralinfrastructure

Rural water and sanitation

General support

Categories Sub-categories Components

Source: FAO, 2015b.

2 More detailed information on the methodology and calculation of public expenditure indicators is provided in the MAFAP methodology working paper, available at: www.fao.org/in-action/mafap/resources/detail/en/c/386924



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BOX 1 The Consortium of International Organizations for Measuring the Policy Environment for Agriculture

The Consortium for Measuring the Policy Environment for Agriculture is a collective effort of international institutions that have assembled the Ag-Incentives database of agricultural trade and policy indicators (available at http://ag-incentives.org). The NRP is included in the database, as the core indicator on price distortions induced by agricultural policies. The database covers the period of 2005–2016 and corresponds to over 90 percent of global value of agricultural production. The partner institutions are the Food and Agriculture Organization of the United Nations (FAO), the Inter-American Development Bank (IDB), the International Food Policy Research Institute (IFPRI), the Organisation for Economic Co-operation and Development (OECD) and the World Bank Group (WB). The objective of this collective effort is to bring together the findings of all organizations that work on agricultural policy measures, in order to provide a global overview of the policy environment by means of a database that has global coverage, spans a long time period and is frequently updated.

With this extensive database, the Consortium for Measuring the Policy Environment for Agriculture provides a tool for policy-makers and researchers that helps to analyse political economy phenomena, economic welfare trends, trade patterns and poverty effects. Questions such as “What happens if policies distorting prices are being reformed?” or “What are the effects of international trade agreements?” can be answered from past observations and their analyses. The database provides a unified measurement of distortions caused by agricultural policies for a wide audience of academics and policy-makers, to support governments designing policies and measuring their outcomes.

The database is updated (at least) once a year. The purpose is to gradually increase the country, product and indicator coverage. The graphs below show the current country coverage and the average NRPs by income group over the period covered.

FIGURE 3 Consortium country coverage (as of 2016)

FIGURE 4 Nominal Rate of Protection by income group

50

40

30

20

-20

10

-10

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

High-income coutriesMiddle-income coutriesLow-income coutries

Per

cen

tage

Note: Weighted averages on country level, unweighted average by income group.

Source: Authors' elaboration from Ag-Incentives database (available at http://ag-incentives.org).

http://ag-incentives.org

http://ag-incentives.org


8

2.3. Data overview

The MAFAP price incentive indicators dataset comprises indicators for 14 countries, reported in the map below (excluding Bangladesh and India, see Box 2), for a wide range of commodities covering the period 2005-2016, except for Nigeria for which indicators are up to 2015 and a few other series that are not yet updated to 2016.3 As shown in Figure 6, NRP at farm gate and MDG indicators for maize and rice are computed in almost all the countries (12 out of 14). Other products analysed in a large number of countries are groundnuts or cashew nuts, cotton, coffee, cassava and tea. Several criteria are taken into consideration when defining the commodities analysed at country level, in particular: (i) relevance of the product for food security; (ii) relevance for trade (contribution to export revenues or food import bills; (iii) share of total agricultural production value; (iv) policy interest; and (v) data availability.4

BOX 2 MAFAP in Bangladesh and India

The MAFAP programme of the Food and Agriculture Organization (FAO) recently started working with government partners in Bangladesh and India (in the states of Haryana, Gujarat and Chhattisgarh). The first set of MAFAP outputs in India and Bangladesh is yet to be finalized and uploaded on the MAFAP website. These countries are therefore not covered in the present study.

In the case of Bangladesh, MAFAP is collaborating with the Food Policy and Monitoring Unit (FPMU) in the Ministry of Food as well as the Department of Agricultural Marketing (DAM) of the Ministry of Agriculture. With the FPMU, the work primarily focuses on classifying and analysing public expenditures towards food security and nutrition using an extended version of the MAFAP Food Security and Nutrition Public Expenditure framework. With the DAM, the MAFAP team is expected to implement the usual price incentives analyses for the value chains that were prioritized by the Ministry. Analyses of commodity price volatility and seasonality in Bangladesh are also expected in the near future as part of the collaboration with the DAM.

In India, FAO, represented by its country office and the New India Transformation Institute (NITI) Aayog have joined forces to implement an ambitious programme of work on MAFAP. This includes producing policy-monitoring dashboards that will give decision-makers regular insights on the performances of the food and agriculture sector. The dashboards will build on both price incentives and public expenditure analyses. Moreover, additional performance indicators coupled with contextual statistics will provide snapshots on critical issues and recommendations on how to achieve faster agriculture sector development as well as food security targets.

3 More detailed data and references to series not updated can be found in Annex 1.4 In principle, the commodities analysed should represent at least 70 percent of the total value of agricultural

production in the country. In some cases this target is not always achieved, due to major limitations in terms of data availability.

9


FIGURE 5 MAFAP country coverage

MAFAP COUNTRIES

Bangladesh, Benin, Burkina Faso, Burundi, Ethiopia, Ghana, Kenya, India, Malawi, Mali, Mozambique, Nigeria, Rwanda, Senegal, Tanzania, Uganda

Source: FAO Monitoring and Analysing Food and Agricultural Policies (MAFAP) website (available at www.fao.org/in-action/mafap).

FIGURE 6 Commodities covered in MAFAP price incentives analysis

Number of countries covered

Maize (12)BEN, BFA, BDI, ETH,

GHA, KEN, MWI, MLI,MOZ, NGA, TZN, UGA

Seed cotton (8)BEN, BKF, KEN, MWI, MLI, MOZ, TZN, UGA

Groundnutswith shell (5)BFA, GHA, MWI,

MLI, SEN

Cashew nutswith shell (3)MON, NGA, TZN Cashew

nutswithoutshell (1)

MOZ

Pine

appl

es (1

)BE

N

Teff

(1)

ETH

Mill

et (1

)M

LI

Coco

a (1

) N

GA

Toba

cco

(1)

MW

I

Yam

s (1

)G

HA

Palm

oil

(1)

GHA

Lent

ils (1

)ET

H

Milk [cow] (3)MLI, RWA, UGA

Sugarcane (3)

KEN, MWI, UGA

Dry beans (4)BDI, ETH,KEN, RWA

Cattle (4)BFA, ETHMLI, UGA

Onions (2)BFA, SEN

Sesameseed (2)BKF, ETH

Barley (1)ETH

Green beans(1) KEN

Potatoes (2)BFA, SEN

Wheat (4)ETH, KEN,MWI, UGA

Sorghum (5)BFA, ETH, KEN,

MLI, NGA

Tea (5)BDI, KEN, MWI,

RWA, UGA

1 2 3 4 6 75 8 9 10 11 12

Coffeegreen (6)BDI, ETH, KEN,

RWA, TZN, UGA

Cassava (5)BDI, GHA, KEN,

MOZ, UGA

Rice (12)BEN, BKF, BDI, GHA,

KEN, MLI, MOZ, NGA, RWA, SEN, TZN, UGA

Source: Authors' elaboration from MAFAP database, as of October 2017.

http://www.fao.org/in-action/mafap


10

Table 1 provides the names of the local institutions partnering with MAFAP and involved in data collection and indicator computation that are often the main source of information on prices and access costs data. Data for the yearly update of MAFAP indicators are usually collected between January and May each year and compiled, revised and validated in June/July.5

TABLE 1 Main data sources for MAFAP price incentives indicators computation

Country Institution

Benin Ministère de l'Agriculture, de l'Elevage et de la Pêche

Burkina Faso Ministère de l'Agriculture de l'Hydraulique et des Recherches Halieutiques

Burundi Ministère de l'Agriculture et de l'Elevage

Ethiopia Ethiopian Development Research Institute

Ghana Ministry of Food and Agriculture

Kenya Kenya Agricultural and Livestock Research Organization

Malawi Ministry of Agriculture and Food Security

Mali Institut d'Economie Rurale

Mozambique Ministério da Agricultura e Segurança Alimentar; Centro de Estudos de Políticas e Programas Agroalimentares

Nigeria Federal Ministry of Agriculture/National Bureau of Statistics

Rwanda Ministry of Agriculture and Animal Resources

Senegal Ministère de l'Agriculture et de l'Equipement Rural

Tanzania Ministry of Agriculture Food Security and Cooperatives

Uganda Ministry of Agriculture, Animal Industry and Fisheries; National Agriculture Research Organization

Source: FAO Monitoring and Analysing Food and Agricultural Policies (MAFAP).

The public expenditure database classified according to the MAFAP PEA categories is currently available for 13 countries, covering slightly different time frames, as specified in the table below. PEA data are usually collected by or with the support of the Ministry of Agriculture and the Ministry of Finance of each country, updated on a yearly basis (allowing two years lag) and compiled, revised and validated in July each year.6

5 Further updates and improvements to the dataset are incorporated on a regular basis. The most updated version is available at www.fao.org/in-action/mafap/data. This study presents and analyses the October 2017 version of the MAFAP price incentives dataset.

6 In the MAFAP public expenditure database analysed in this study (version of February 2018), PEA data for Burundi is provisional; PEA data for Rwanda do not cover most of agriculture-supportive expenditure and PEA data for Ethiopia only refer to the total federal capital budget and do not include recurrent expenditure.

http://www.fao.org/in-action/mafap/data/en

11


TABLE 2 MAFAP public expenditure indicators coverage and main source

Country Time coverage

Benin 2008–2015

Burkina Faso 2006–2015

Burundi 2006–2015

Ethiopia 2007–2015

Ghana 2006–2012

Kenya 2007–2016

Malawi 2006–2013

Mali 2006–2015

Mozambique 2009–2013

Rwanda 2007–2016

Senegal 2010–2015

Tanzania 2007–2015

Uganda 2007–2016

Source: Authors' calculations based on MAFAP public expenditure database, as of February 2018.


12

3 Price incentives in key agricultural value chains

This section analyses NRP and MDG estimates for the period 2005–2016 at aggregate level by type of commodity (food security, cash crop, import or export) and by subregions (West and East Africa).7 It also provides a detailed analysis of NRP trends at country level for a selected number of commodities, namely maize, rice, cotton and tea. Full data series are reported in the Annex.

3.1 Aggregated results

Aggregated Nominal Rate of Protection

The aggregate NRP indicator for the 14 African countries analysed here is close to zero on average (-2 percent) but some major fluctuations are visible in 2009 and 2011 (Figure 7). These were seemingly caused by the food price crises and the policy interventions that were put in place to curb food prices in these years. The trend prior to 2012 is overall negative and quite uneven, mainly driven by low NRP for specific food crops, such as maize, rice and cassava in major producing countries such as Ghana, Nigeria and Uganda. The average NRP increased significantly in 2012 from -6 to 6 percent and remained slightly positive in the following years (except for 2015) indicating an increased support to the agriculture sector after international markets had stabilized.

A substantial heterogeneity of price incentives across countries is shown in Figure 8. Four countries exhibit a positive average NRP for the period 2005–2016, three have an average NRP of around zero, while the average indicator is negative for seven countries. Senegal and Tanzania have the highest rates, above 20 percent on average. The country with the lowest NRP is Ethiopia, with an average value of -26 percent during the period. These results are often driven by strong interventions on individual commodities which are under particular government focus, such as food staples (i.e. maize and rice). These commodities seemingly benefited from ad hoc policy measures aimed at boosting production. In particular, import tariffs on key food staples were reintroduced after the 2007/08 and 2010/11 food price crises, in a bid to protect producers. The section on commodity-specific NRP provides more insights on incentives faced by farmers for each product.

7 All the aggregated NRP and MDG figures presented in this section are weighted averages with weights based on production value (production volume*reference price at farmgate) in country and unweighted across countries, following Anderson (2009). For the estimation of weighted averages, the NRP series for cattle has been excluded for all the countries where it is computed, namely, Uganda, Mali, Burkina Faso and Ethiopia. This product is usually characterized by large production values and therefore this series would largely affect the weighted NRP values; moreover, in some cases data quality for cattle indicators has been an issue. Missing NRP and MDG (like for Nigeria in 2016), are excluded from the average, while missing production values are interpolated using their lagged value.

13


FIGURE 7 Nominal Rate of Protection at farmgate, average for 14 sub-Saharan Africa countries

10

-20

5

-10

-5

-15

0

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

NRP at farmgate Linear (NRP at farmgate)

Per

cen

tage

Source: Authors' calculations based on MAFAP price incentives database, as of October 2017.

FIGURE 8 Nominal Rate of Protection at farmgate, country averages for the 2005–2016 period

Degree of incentives

Strong incentives > 20%

Incentives [5%, 20%]

Disincentives [-20%, -5%]

Strong disincentives < -20%

NA

Neutral [-5%, 5%]SEN35.9

NGA-26.1

ETH-26.4

UGA-25.8

BDI21.9

TZA21.9

KEN1.6

BEN3.3

MLI-11.7

GHA-13.1

BFA19.3

RWA-13.2

MWI-16.8

MOZ-1.7



14

Aggregated Market Development Gap

During the 2005-2016 period, the aggregate MDG, expressed as percentage of the farmgate price, was -4 percent on average, ranging between -5.5 percent in 2005 and -3 percent in 2007 (Figure 9). This means that overall market inefficiencies reduced price incentives to farmers by about 4 percent. The trend remained stable throughout the period, with no clear improvement seen in the most recent period. High transport and processing costs, fees and bribes, as well as the apparent concentration of rents among intermediaries (e.g. excessive traders’ profit margins) remain major constraints for the development of the sector, at least on the price incentives side.

FIGURE 9 Market Development Gap (percentage of farmgate price), average for 14 sub-Saharan Africa countries

Market Development Gap

0

-6

-1

-4

-3

-5

-2

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Per

cen

tage


Looking at MDG figures by country reveals that, in East Africa, the most negative MDG is observed in Uganda, at -13 percent (Figure 10). In that country, market inefficiencies contribute highly to price disincentives faced by farmers, adding to the significantly negative NRP (-25 percent) generated by trade and market policies. In Tanzania, the average MDG was -9 percent. In this case, domestic policies – effects of which are captured by the NRP – offset the impacts of market inefficiencies – effects of which are captured by the MDG – as the NRP for Tanzania was largely positive, over 20 percent on average.

In West Africa, the largest negative MDGs are observed in Ghana and Burkina Faso (-16 and -15 percent, respectively). In the case of Ghana, disincentives generated by high access costs add to the price disincentives generated by the policy environment (-13 percent NRP). On the other hand, in Burkina Faso, policies have overall supported farmers on average (19 percent NRP), despite substantial market inefficiencies.

Burundi, Nigeria and Rwanda have slightly positive MDGs on average, which means that inefficiencies have contributed to cause price incentives at farmgate level to some extent. This is likely driven by some major imported commodities for which prohibitive access costs act somehow in favour of farmers by making the imported product less competitive against the domestic production. In such cases, inefficiencies are still present and generate

15


distortions and suboptimal value chain functioning; high access costs penalize traders of major agricultural commodities in the country, rather than farmers.

The prevalence of significantly negative MDGs in almost all countries studied highlights the damaging role played by market inefficiencies, which lead in most cases to even higher price disincentives for farmers. There is a need to relieve not only farmers but also other value chain agents from such persistent constraints, which are a burden for value chain development. Policy-makers in many countries used public expenditures to provide public goods in an attempt to reduce inefficiencies in agriculture markets. For instance, infrastructure building is expected to lower transport costs or improve market functioning. Countries like Burundi, Mali, Rwanda and Senegal are examples of such a policy orientation. How their interventions have influenced MDG trends, however, still needs to be assessed.

FIGURE 10 Market Development Gap (as percentage of farmgate price), country averages for the 2005–2016 period

Market Development Gap(% of producer price)

5

-5

-10

-15

0SEN0.4

NGA8.4

ETH-2.8

UGA-12.8

BDI4.6

TZA-9.2

KEN1.2

BEN-2.8

MLI-4.7

GHA-16.3

BFA-15.6

RWA5.6

MWI-3.5

MOZ-10.2


Aggregated Nominal Rate of Protection by type of crop

Although some progress has been made on agricultural transformation and commercialization in Africa, since the ratification of the Comprehensive Africa Agriculture Development Programme (CAADP) in 2003, most of the workforce of the continent is still engaged in smallholder subsistence agriculture (AGRA, 2016). The share of production that is explicitly grown for the market is still small for most producers. Figure 11 shows that cash crops targeting the international market are generally being discouraged through explicit as well as implicit agricultural policies. The average aggregated NRP is -6 percent, being negative


16

throughout the period analysed, except in 2005, 2008 and 2012. By contrast, the policy environment has generated incentives for food security (or subsistence) crops, which are often import substitutes, and particularly so since 2012, after the food price crises.

The average NRP for maize, rice, cassava and sorghum – which are among the main staples in most of the countries under analysis – has been following an overall upward trend, while the policy environment for cash crops (e.g. tea, cotton and coffee) has been much more volatile, especially in recent years. High variability of NRP estimates is often caused by ad hoc and discretionary measures or by the inability of policy-makers to adopt effective price stabilization policies. Such an unstable policy environment raises concerns about the sustainable development of market-oriented value chains, such as cotton, tea or coffee.

FIGURE 11 Nominal Rate of Protection at farmgate for food security and cash crops, average for 14 sub-Saharan Africa countries

30

-30

20

-10

0

-20

10

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

NRP at farmgate, cash cropsNRP at farmgate, food security crops

Per

cen

tage


NRPs for commodities grouped according to their trade status can also be analysed (Figure 12).8 Price support for imported commodities has increased between 2005 and 2016. After seven years of price disincentives (2005–2011), in conjunction with the food price crises, in 2012 the average NRP for imported products became positive, reaching 8 percent. This seems to be in line with the NRP trend for major imported food staples across the region, whose producers usually received price incentives in the most recent period. Fluctuations are mainly due to international price spikes. For instance, favourable import measures were promoted during 2008–2012 to temper the impact of high prices on poor consumers. This had a negative influence on price incentives for farmers, whose products are often not competitive with respect to the imported commodities.

8 The trade status of a commodity is defined on the basis of the net trade position of a country for the product (export minus import) and assessed every year, in order to determine the international benchmark price used in the computation of the NRP. In many developing countries, it is normal that an import commodity may become an export, or may not be traded once price incentives are removed (Anderson and Valdes, 2008; Dawe et al., 2015). Therefore, composition of the two groups is not predefined and may change across the period of analysis.

17


On the contrary, the price incentives for export products followed a slightly declining trend of price support, although the NRP is positive on average along the period (4 percent). Fluctuations in NRP levels in this case have been large. The 2007/08 food price crisis was followed by a significant decrease in the average NRP for export products, from 18 percent in 2007 to -8 percent in 2009. The decrease presumably resulted from stricter rules and policy changes implemented by policy-makers during the crisis in order to discourage exports of food crops.

FIGURE 12 Nominal Rate of Protection at farmgate for export and import crops, average for 14 sub-Saharan Africa countries

30

-30

20

-10

0

-20

10

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

NRP at farmgate, import crops NRP at farmgate, export crops

Per

cen

tage


Aggregated Nominal Rate of Protection by region

The average aggregated NRPs analysed by subregions show that the pattern of price incentives in West Africa exhibits two distinct trends: declining NRP levels from 2005 to 2009 and increasing NRP levels from 2010 onwards. The drop in the NRP for West Africa to -18 percent in 2009 was mainly driven by food staples in Ghana (maize and yams), Mali (millet and sorghum) and Nigeria (maize, rice and sorghum) that were largely penalized that year.

The price incentives dynamic in East African countries (including Malawi and Mozambique) is more consistent across time, following a clear upward trend. Negative NRPs are observed from 2005 to 2011, which is likely attributable to large price disincentives for maize in Ethiopia, milk in Uganda and Rwanda, and tea in Burundi and Malawi during those years. Conversely, the last five years are characterized by higher NRPs for the East Africa region. This trend of price incentives since 2012 is linked to high protection for some commodities, in particular food staples, in many countries of this subregion.


18

FIGURE 13 Nominal Rate of Protection at farmgate averages for East Africa and West Africa countries

30

-30

20

-10

0

-20

10

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

NRP at farmgate, East Africa NRP at farmgate, West Africa

Per

cen

tage


3.2 Commodity-specific analysis

Aggregated results are often driven by strong interventions on individual commodities in which policy-makers are particularly interested. This section discusses NRP trends for such commodities, in order to provide more insights into price (dis)incentives by product. The analysis focuses on two food security crops – i.e. rice and maize, which are analysed in almost all MAFAP countries – and two cash crops – i.e. cotton, which is a main cash crop in 8 out of the 14 countries analysed, and tea, which is equally relevant as an export commodity for 5 countries analysed by MAFAP. For these products, aggregated NRPs as well as country-specific estimates are analysed in order to highlight common and/or contrasting trends and policy factors driving these trends. Additional details on policy and markets factors driving the NRP trends here discussed are available in Annex.

Rice

MAFAP estimated NRP for rice in 12 countries throughout the 2005–2016 period.9 Although rice is mainly produced by small-scale farmers in all these countries, it is not primarily a subsistence crop consumed on-farm. In general, rice is a cash crop produced in competition with imports and consumed in urban areas, mainly by middle- and high-income consumers or on special occasions. The demand for rice is therefore expected to grow rapidly with rising incomes and urbanization.

9 Benin, Burkina Faso, Burundi, Ethiopia, Ghana, Kenya, Malawi, Mali, Mozambique, Nigeria, Tanzania and Uganda.

19


The price spike in global commodity markets in 2007/08 was greater for rice than for any other cereal (Dawe, 2010; Headey and Fan, 2010). This fact, together with import dependency and rapidly increasing domestic demand, triggered concerns about rice policies. It is this combination of factors that made rice a priority commodity for policy-makers, as illustrated in three policy areas: the National Rice Development Strategies (NRDS);10 additional budgetary resources for rice-related infrastructure; and tariff regimes that evidently protected rice more than other staple commodities. Tanzania’s objective, for instance, was to double rice production by 2018 to “develop the agricultural sector in order to attain the desired food security situation and growth for poverty reduction”. Other countries included in this study have had similarly ambitious targets.

Many of the countries studied still have parastatals that process and market major staple commodities. These have generally had a dual function: (i) maintaining a buffer stock that can be used to respond to exceptional shortfall in production resulting in a food security crisis for some segments of the population;11 and (ii) intervening when market prices for consumers rise above a ceiling level or fall below a floor level for producers. In addition, all the countries under review belong to one or more trading blocs, which discipline their tariff regimes, both between members and with the rest of the world. These blocs have also sought to limit the expected negative effect of non-tariff barriers on trade (see Box 3).

The high interest of regional policy-makers in rice can explain why positive NRPs for the commodity are observed in all countries except Ghana and Mozambique (Figure 14). A slightly different picture appears for countries in East and West Africa. Price incentives for rice have been higher in East Africa countries than in West Africa. Burundi, Kenya, Rwanda, Tanzania and Uganda had average NRPs exceeding 20 percent throughout the period. The high incentives primarily result from the protective effect of the high Common External tariff (CET) applied by the East African Community (EAC) on sensitive products since 2010 (see Box 3).

In West Africa, rice producers in Benin, Nigeria and Senegal received substantial price incentives in the years under review. However, producers in Burkina Faso and Mali did not benefit from a clearly supportive policy and market environment. By contrast, farmers in Ghana were substantially penalized and got prices which were 14 percent lower than reference prices. Under the customs union of the Economic Community of West African State (ECOWAS), rice is also subject to a CET. However, this tariff has been lowered since 2015 (10 percent). The ECOWAS rice tariff rate is much lower than the EAC rate (see Box 3).

10 All countries covered in the present study have prepared NRDSs in partnership with the Coalition for African Rice Development (CARD). CARD was established by the Japan International Cooperation Agency (JICA), the Alliance for a Green Revolution in Africa (AGRA) and the New Partnership for African Development (NEPAD). The CARD strategy focuses on strengthening the production and multiplication of certified seed, research, agricultural extension services, development of agricultural land and water resources, and improved small-scale, post-harvest rice processing equipment.

11 The use of stocks to stabilize cereal prices has been criticized for at least three reasons: they replace private stocks; they were found to be poorly managed; and they distort market signals (Jane, 2011; Tangermann, 2011; Timmer, 2011; Demeke et al., 2014).


20

BOX 3 Trade agreements within the African Regional Economic Communities

The Regional Trade Area of the East African Community (EAC), including Kenya, Tanzania and Uganda since 2005, with the addition of Burundi and Rwanda in 2009, became a zero tariff zone as of 2010. The Customs Union was adopted in 2005, committing member states to the adoption of a Common External Tariff (CET). The EAC-CET consists of a triple band structure for raw materials and capital goods (0 percent), intermediate goods (10 percent) and final goods (25 percent), as well as a Sensitive Items regime for specified commodities attracting higher rates of duty (all above 30 percent) and including 31 agricultural tariff lines. For example, in recognition of the region’s capacity for self-reliance in rice production and consumption, the EAC protects its rice producers by classifying it as a sensitive product whose imports from outside the EAC face a CET of rate of 75 percent.

The above-mentioned countries are also members of the Common Market for Eastern and Southern Africa (COMESA), the free trade area for fifteen Eastern and Southern States. COMESA, not yet a Customs Union, has the most extensive programme for trade facilitation, including a wide range of initiatives related to harmonization of coding systems, computerization of customs and international trade statistics, and creation of automated customs systems.

The Southern African Development Community (SADC) became a Free Trade Area in 2008 when, through a phased programme of tariff reductions that commenced in 2001, 85 percent of intraregional trade among the partner states attained zero duty. Additional and more recent trade facilitation measures under SADC include policies aimed at simplifying and harmonizing customs clearance procedures, granting freedom of transit to traverse member states, and harmonizing sanitary and phytosanitary measures.

The Economic Community of West African States (ECOWAS) has promoted the West African region as a Free Trade Area since 1979. A CET launched in January 2015 – after 10 years of negotiations – has replaced the West African Economic and Monetary Union (UEMOA) CET and offers relatively high levels of protection to agricultural goods producers. It is organized into five different tariff bands: 0 percent, 5 percent, 10 percent, 10 percent, 20 percent and 35 percent. After intense negotiations, 90 percent of the products in the 35 percent band are agricultural goods, while no agricultural products are in the 0 percent band. As such, agriculture is relatively more protected than other sectors. A notable exception is rice, which is in the 10 percent tariff band, implying that the interests of rice consumers (low price) prevailed over those of rice producers. ECOWAS has also developed a wide range of initiatives on trade facilitation, including the West Africa Road Transport and Transit Facilitation, the Abidjan–Lagos Transport and Trade Facilitation and the adoption of automated customs systems in some member countries (e.g. Mali).

Source: Elbeshbishi, 2013; Torres and van Seters, 2016; de Roquefeuil et al., 2014.

21


As can be seen from Figure 15, countries with largely positive average rice NRP like Rwanda, Senegal, Tanzania, Uganda have increased their production along the period. Price incentives have been a major driver behind the evolution of production levels, in line with the evidence recently provided by the economic literature (Magrini et al., 2017).

FIGURE 14 Nominal Rate of Protection for rice by country, average for the 2005–2016 period






NA

Neutral [-5%, 5%]SEN24.8

NGA25.1

UGA33.7

BDI33.6

TZA35.6

KEN65.6

BEN26.4

MLI1.2

GHA-14

BFA4

RWA85.2

MOZ-16



22

FIGURE 15 Rice production trend for selected countries

A Mali, Senegal and Tanzania

3 000 000

0

2 500 000

1 000 000

1 500 000

500 000

2 000 000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Mali(Linear)

Senegal(Linear)

Tanzania(Linear)

Ton

nes

B Burkina Faso, Burundi, Kenya and Rwanda

400 000

0

300 000

100 000

200 000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Kenya(Linear)

Burkina Faso(Linear)

Rwanda(Linear)

Burundi(Linear)

Uganda(Linear)

Ton

nes

Source: Authors' calculation from different national sources and FAOSTAT.

Looking at the NRP trend by country, as reported in Figure 16, the scenario appears more difficult to articulate as price incentives patterns are quite volatile in all countries. In many cases, the level of price protection decreased sensibly and even became negative in the years following the food price crisis. Measures adopted as a response to the food crisis in 2007/08 in view of curbing food price inflation (e.g. price controls) and increasing domestic availability of key staples (e.g. import facilitation, food stocks release) had the intended effect, and depressed prices at farmgate during the 2009–2010 period.

23


In the most recent years, the NRP trend appears less consistent across countries, as well as extremely volatile. Some countries, such as Mozambique and Mali, apparently increased price support for the rice sector and hence have positive NRP in 2015–2016. Others, like Tanzania, Burkina Faso and Ghana, show declining NRP levels since 2012 and are falling well below the zero line in some cases. However, a more specific analysis of country contexts is required to further interpret these NRP trends. Domestic market and policy dynamics must also be taken into account.

FIGURE 16 Nominal Rate of Protection for rice for selected countries, 2005–2016

Burkina Faso Burundi Ghana

Mali Mozambique Senegal

Tanzania Uganda

40

20

-40

-20

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

80

40

60

-20

0

20

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

60

30

-60

-30

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

30

15

-30

-15

0

20

05

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24

In Burundi, the rice value chain is characterized by a strong demand that cannot be matched by domestic production and is consistently supplied by imports that represent 40 percent of domestic consumption. The external tariff on imported rice, which is set at 75 percent by the EAC and the high internal demand for rice are the main drivers of the domestic policy and market environment. These drivers explain why such high positive levels of incentives are observed. The average NRP (at about 30 percent) would probably be even higher in the absence of important market failures – e.g. high transport costs and market power exercised by middle men – that push downward on producer prices. Similarly, in Uganda, where most of the commodity markets are fully liberalized, the average 34 percent NRP at farm level is mainly due to the effect of the 35 percent tariff applied by Uganda as a member of the EAC.

In Tanzania, which is a major rice producer of the region, production incentives declined sensibly since 2013. Prior to 2013, import tariffs and non-tariff barriers probably acted in favour of producers, while during 2014–2016 farmgate prices were depressed by the combined effect of bumper harvests and the impossibility of exporting rice surpluses to neighbouring countries. Indeed, since 2013, Rwanda, Burundi and Uganda have applied the CET rate on rice imports from Tanzania as a response to the cheap Asian rice that is being blended with and labelled as Tanzanian rice before being traded duty-free across EAC countries.

Turning to the West Africa region, we observe that the strong production incentives in Senegal result from an import tariff, which is set at about 10 percent. In this case, the existence of an indicative producer price published by the government adds to the effect of the tariff. The indicative producer price intends to protect farmers from excessively low prices. However, these policies mask inefficiencies in the sector, such as too high access costs from farmgate to wholesale. The same drivers apply in Burkina Faso, where import restrictions and implementation of a recommended producer price on rice have played in favour of farmers in most years until 2013.

In Ghana, rice imports are subject to a 20 percent import duty (temporarily removed in 2008 and reinstated in 2009), as well as to other taxes and levies. These protection measures seem to have been poorly enforced or largely ineffective, as price disincentives for rice farmers have been significant and even increasing in recent years. This is most likely due to important market failures that impede price transmission. An important market imperfection follows from the strong influence of the National Buffer Stock Company (NAFCO), which purchases and releases rice to stabilize prices and to build emergency stocks.

In Mali, price disincentives were recorded in all years except 2009, 2010 and 2013. The government has been adjusting its policy in support of either producers or consumers depending on the perceived food security situation of the population. However, the effects of interventions have been partially offset by huge market inefficiencies constraining price transmission from international to domestic markets. Mali established price ceilings at both wholesale and retail levels in 2008, 2009 and 2012. The ceilings presumably resulted in farmgate disincentives in the subsequent cropping seasons.

In Mozambique, where rice is also imported to match domestic demand, especially in the south of the country, the level of price disincentives stood at about -16 percent on average over the period. It seems that the 7.5 percent tariff on imported rice did not produce the intended effect. High marketing costs, mainly due to poor infrastructure, further penalize farmers; the primary consumption region is around Maputo in the south while the main production region is located far away in the north. The diminution of disincentives since 2013 probably relates to macroeconomic variables; the significant depreciation of the metical probably had a positive impact on domestic prices, including rice prices at farmgate.

25


Maize

Maize is one of the most important crops in sub-Saharan Africa (SSA) by almost any measure, including in terms of area and volume of production. Two major reasons for this are: (i) that maize can be grown successfully in a wide range of African agro-ecological zones; and (ii) that maize is easy to cook. In Africa, 90 percent of total maize production is white maize. Despite the importance of this staple, maize productivity in the region remains very low, and stagnates at around 2 tonnes/hectare/year, whereas the worldwide average yield stands at about 5.5 tonnes/hectare/year (Cairns et al., 2013).

Maize is generally thinly marketed and traded in all countries analysed here. Trade volumes are very small relative to production and consumption levels. In all countries there are networks of small traders who buy directly from farmers or in local markets. Among countries covered in the MAFAP database, only Uganda is somewhat different because maize is also seen as an important export crop, especially in some districts of the country. As a result, marketing channels are generally not well developed.12 This is also due to the fact that maize is a government-controlled crop in most countries. It has often been a politically sensitive commodity in Eastern and Southern Africa. However, the disbandment of parastatals has disorganized marketing channels throughout most countries. Low-quality seed, limited use of fertilizers and mechanization, poor post-harvest management, drought and climate change are other limitations affecting the sector.

The NRP results for maize presented in Figure 17 are different for the West Africa and East Africa regions, suggesting policy interventions and market dynamics are moving in opposite directions. All countries in East Africa, with the exception of Ethiopia, depict largely positive NRPs on average – in some cases over 30 percent, such as in Burundi and Tanzania. On the other hand, rates of protection for maize in West Africa countries are negative on average, except in Burkina Faso. Once again, results vary greatly across countries and also within countries over years, as can be seen in Figure 19.

As was the case for rice, it appears that price incentives to maize farmers fostered production. This holds particularly true for Burkina Faso, Senegal, Tanzania and Uganda (Figure 18). On the other hand, declining or stagnating production levels are seen in countries with negative NRP trends, such as Ghana and Nigeria, confirming again the impact of price disincentives on supply responses (Magrini et al., 2017).

12 Maize prices in the 12 countries studied are frequently both below import parity and above export parity relative to maize benchmark prices such as US Gulf and South African Futures Exchange (SAFEX). Consequently, trade often occurs with bordering countries, suggesting an important role played by informal trade across SSA countries (Balié and Nelgen, 2016).


26

FIGURE 17 Nominal Rate of Protection for maize by country, average for the 2005–2016 period

MWI16.4






NA

Neutral [-5%, 5%]

NGA-35.1

ETH-56.8

UGA18.3

BDI49

TZA37.7

KEN26.6

BEN0.6

MLI-13.6

GHA-37.5

BFA21.9

MOZ18.2


FIGURE 18 Maize production trend for selected countries

10 000 000

0

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4 000 000

6 000 000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Ghana(Linear)

Tanzania(Linear)

Kenya(Linear)

Burkina Faso(Linear)

Nigeria(Linear)

Uganda(Linear)

Ton

nes

Source: Authors' calculation from different national sources and FAOSTAT.

27


The NRP trends for maize in selected countries are shown in Figure 19. In Mali, the NRP at farm level was negative on average (-14 percent) over the period. Price disincentives are largely explained by the poor connection between small farmers and domestic and regional markets, where prices are generally higher. Trade levels have been too low for producers to take advantage of potential market opportunities. The apparent low correlation between domestic and international prices indicates a rather limited capacity of the economy to transmit price signals (Fiamohe et al., 2015).

In Ghana, maize farmers have been penalized by low prices with an average NRP of roughly -37 percent over the 2005–2016 period. Value-added tax (VAT) and other levies were regularly charged on imports. A 20 percent tariff on maize imposed in 2005/06 and 2009/10 proved ineffective in supporting domestic producer prices. The main drivers behind the observed price disincentives are the substantial market failures; poor information and excessive market power by some agents in the value chain exacerbate farmers’ disconnection with wholesale markets and depress producer prices for maize.

In Benin, the NRP for maize was close to zero during 2010–2016, except in 2015 and 2016. In these two latter years, incentives became negative and reached -10 percent, likely due to export restrictions implemented since 2014. Slight positive values in some years could be explained by production shortages resulting from adverse climatic conditions which pushed domestic up prices.

Among East African countries, Tanzania had one of the highest average NRP for maize, although it declined sensibly from 2005 to 2009 and has been quite erratic and volatile in the following period. Uncertain government policies and unpredictable market interventions such as export bans, a bureaucratic export licensing system, public procurement, price setting and distributions of subsidized maize have been regarded as destabilizing and even ineffective in some years.

In Uganda, the price incentives pattern was positive but variable overall. However, the country is characterized by a very liberal market environment. Government interventions such as buying, selling or setting prices are rare. In Uganda, it seems the EAC free trade agreement triggered incentives to production for maize farmers. Levels of incentives to maize production in Uganda appear strongly related to the demand in the neighbouring countries, in particular Kenya, which is the main importer of Ugandan maize. For example, when demand from Kenya decreased in 2005 and 2009, due to the imposition of higher import tariffs, maize famers in Uganda suffered from price disincentives.

In Kenya, domestic prices that were higher than the declining international reference price led to a surge in NRP values over the last few years. Regular drops in maize output due to adverse weather conditions (in 2011, 2014 and 2016) and multiple interventions by the National Cereals and Produce Board led domestic prices to increase. In 2016, for example, the Board decided to increase the price at which it buys maize from farmers.

In Mozambique, border measures, namely the tariff (2.5 percent) and VAT (17 percent) applied on imported maize, together with rising demand for maize from new large-scale maize buyers (i.e. poultry processors and maize millers) contributed to generate price incentives for farmers, especially in recent years.

Ethiopia stands out as a regional exception. Farmers faced heavy price disincentives of about -55 percent on average over the period under review. Restrictive trade policies (export bans) and prohibitive marketing costs did not allow for effective price transmission from the international to the domestic level.13 Low domestic demand during years of good

13 For instance, disincentives declined when the export ban was lifted in 2010 and when the exchange rate improved following devaluation in 2011. In 2012, the export ban was reinstated and the currency was overvalued again. At the same time, a record level of domestic production coincided with a context of high international prices, and the price disincentive reached its maximum.


28

harvest, such as 2012, combined with fairly steady levels of imports between 2005 and 2011 contributed to the decline of domestic maize prices. The policy on food staples – not only maize, but also sorghum and teff – in Ethiopia seems to have privileged consumers, in line with government food security objectives.

FIGURE 19 Nominal Rate of Protection for maize for selected countries, 2005–2016

Benin Ethiopia Ghana

Kenya Mali Mozambique

Tanzania Uganda

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Cotton

Cotton is currently one of the most valuable cash crops in Africa, in spite of massive subsidies or protection policies being applied by many developed and emerging economies across the globe and world prices often being distorted as a result (Baffes, 2002; Baffes et al., 2010; de Gorter, 2012). Cotton is the second biggest agricultural commodity in Africa in terms of yearly value of production, after cocoa (IPBO, 2017). The commodity represents a crucial source of income for SSA countries, both for rural populations and for national economies. In 2010, cotton was by far the most important export crop (in terms of value) in Benin, Burkina Faso and Mali. In Tanzania, it ranked third after coffee and tobacco. Cotton was the fourth largest export crop in Malawi and Mozambique, and ranked seventh in Uganda.

In all countries studied here, one or more agencies or parastatal organizations promote the cotton sector, while a small number of companies (public or private) operate ginneries. Each ginnery deals with a large number of smallholder farmers and usually operates well below capacity. The private sector is responsible for cotton ginning in all countries, with the exception of Burkina Faso and Mali. In these countries, parastatals are still responsible for marketing cotton. The governments of these two countries have set up regional companies that provide inputs to and collect seed cotton from village-level producer associations, process seed cotton in their ginneries and then market cotton lint, cottonseed and other by-products. The regional companies play an important role in setting producer prices for seed cotton. As a consequence, largely positive NRPs have been observed in West African countries, as is shown in Figure 19.

In other countries, such as Malawi, Tanzania and Uganda, the cotton value chain is more market-oriented. Nevertheless, high levels of distortion translated into domestic prices well above the international equivalent can be observed. Free market dynamics thus supported farmers, probably at the expense of ginners and traders. Farmgate price exceeded the reference price by 50 percent in Uganda and 72 percent in Tanzania, on average, during the analysed period.


30

FIGURE 20 Nominal Rate of Protection for cotton by country, average for the 2005–2016 period






NA

Neutral [-5%, 5%]

UGA50.6

TZA72.6

KEN-11.4

BEN36.8

MLI47

BFA30.3

MOZ-0.7

MWI6.5


Looking at in-country price incentives trends as plotted in Figure 21, the effects of the cotton international price surge in 2011 is visible in most countries. NRPs decreased significantly or even became negative in some West African countries, as domestic prices remained low compared to international ones. The post-crisis period is characterized by the emergence of a common pattern for price incentives in both regions.

Burkina Faso, the first cotton producer of West Africa, provided price support amounting to 33 percent of equivalent international prices, on average throughout the period. The importance of cotton for the economy has prompted the government to subsidize cotton farmers. While producers enjoyed significant price incentives, especially before 2010, the main state-controlled ginning company (SOFITEX) was selling cotton at a price lower than the international price. The resulting cost weighed heavily on the national budget, especially when international prices were low. Benefits have been declining since 2012 and the NRP even became negative in 2016 (-15 percent). The decline in international prices after 2011 has undermined the sustainability of the price support policy, which was considered ineffective and was consequently removed.

Price incentives trends appear similar in Benin and Mali, where producers operate in a policy environment that allows them to obtain higher prices than in the absence of market interventions, regardless of price changes on international markets. In Benin, the cotton pricing mechanism used since 2000 and the related stabilization fund seemed to protect producers from declining prices in the international market (as observed in 2007 and 2015), but also prevented them from benefiting from international price hikes in 2011 and 2012.

31


Mozambique is another country that has implemented a floor price mechanism which had an alternate effect in some years, being sometimes used as a price ceiling when international prices spiked. In this case, exchange rate fluctuations also significantly affected price incentives, as sudden changes in rates were not properly reflected in the price set at the beginning of the cropping season. Moreover, the concession system presumably led ginners to pay lower prices to farmers.

Kenya exhibits the highest level of disincentives, equal to around -23 percent on average. Contrary to the other countries analysed here, Kenya is a net cotton importer. Kenya is endowed with a well-developed textile industry that requires a constant supply of cotton lint and continues to import most of its factory inputs rather than purchase them domestically. The market has been fully liberalized and is now in the hands of the private sector. Price disincentives at farmgate are primarily due to the distribution of market power along the value chain, which had a strong influence on the producer price setting systems, to the detriment of farmers.

The cotton sector in Uganda is also liberalized and some relevant export promotion policies have been implemented in the past decade. The Cotton Development Organization (CDO) has been setting an indicative farmgate price, which seemingly led farmers to receive relatively stable price incentives over the last decade. The pricing mechanism used by the CDO is based on the level of world lint prices at the moment of price announcement. The variability of the indicators is mainly caused by highly volatile world cotton prices and by the difference between announced producer prices and the realized export price for lint.

FIGURE 21 Nominal Rate of Protection for cotton for selected countries, 2005–2016

Benin Burkina Faso Kenya

Mali Mozambique Uganda

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32

Tea

Like cotton, tea is a key export crop for some SSA countries, in particular in East Africa. Tea producing countries in the continent include Kenya, Malawi, South Africa, Tanzania and Zimbabwe. These countries together supply about 32 percent of the world’s yearly exports, which represents about 430 000 tonnes of made tea.14 Tea is also a relevant and growing business in Burundi and Rwanda but, as seen in Figure 22, tea producers have been largely penalized in these two countries in the past decade. On the contrary, in Kenya and Uganda tea production seems to be promoted through price incentives at farmgate.

FIGURE 22 Nominal Rate of Protection for tea by country, average for the 2005–2016 period






NA

Neutral [-5%, 5%]

UGA17.5

KEN21.4

MWI-37

BDI-47.2

RWA-42.6


In 2015, Kenya was the third largest tea producer, after China and India, and the biggest tea exporting country in the world. The country produced over 400 000 tonnes of made tea in the most recent cropping seasons.15 About 70 000 hectares are under tea cultivation in Kenya, mainly by smallholders, who produce about 60 percent of the total output and operate under the supervision of the Kenya Tea Development Authority (KTDA). The KTDA provides planting materials, credit and advisory services to smallholder tea farmers. It also enforces strict plucking standards, arranges for leaf collection and provides payments to smallholder tea farmers.

14 See http://blog.espemporium.com/post/tea-production-in-africa.aspx15 For more statistics on tea see www.statista.com/statistics/264189/main-export-countries-for-tea-worldwide

and www.eatta.com/the-mombasa-tea-auction/kenya-tea-production-figures.

http://blog.espemporium.com/post/tea-production-in-africa.aspx

https://www.statista.com/statistics/264189/main-export-countries-for-tea-worldwide/

http://www.eatta.com/the-mombasa-tea-auction/kenya-tea-production-figures

33


The average NRP for tea in Kenya, throughout the period, was about 20 percent. The trend remained positive overall, and relatively stable (between 0 and 20 percent), with the exception of a sudden increase in 2016 (Figure 23). Tea farmers had strong negotiating power when dealing in production and sales with processors through the KTDA. In 2016, a drop in the international price, not reflected at domestic level where prices remain stable, induced a major increase of the NRP.

Uganda has the potential to be a top tea producing country in the region in terms of volume, quality and price. However, while the farmgate price of tea gradually increased during 2005–2013, it did not increase at the same pace as the international reference price. The average NRP stands at 17 percent but the trend has been particularly uneven, negative values being recorded in 2010–2011 and 2015–2016. In a fully liberalized market, Ugandan tea processors gained significantly to the detriment of farmers. Price differences between the border and the farmgate may also be due to quality and value differentiation achieved at the processing stage, not accounted for in the indicators estimation.

FIGURE 23 Nominal Rate of Protection for tea for selected countries, 2005–2016

Burundi Kenya Malawi

Rwanda Uganda

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In Malawi, tea farmers were mostly penalized during the 2005–2013 period. Negative NRP of about 40 percent on average is seen in each of the analysed years. The sector receives limited policy support in terms of direct investments and the prevailing price setting model has distorted price transmission between the border and the farmgate. Producer prices were set at too low a level, and farmers had no power to negotiate better prices. A significantly overvalued exchange rate following from the fixed exchange rate policy, which was applied up to 2012, also led farmers to face even bigger disincentives.


34

Tea cultivation occupies a key position in Rwanda’s fast-growing economy, as the main single source of foreign currency in the country and hence one of the top priority crops in the 2011 National Export Strategy. In spite of that, NRP trends indicate that tea producers did not receive price incentives between 2005 and 2016. The prices they received were on average 40 percent below the estimated reference price. The negative price gap resulted from a number of market constraints (e.g. poor market access, limited bargaining power for farmers, low level of competition at buyer level) and from a lack of effective government policies in support of the sector. The introduction of a price setting mechanism in 2012 helped and allowed farmers to get better prices, thereby reducing disincentives. But disincentives started to increase again in 2015–2016.

A similar situation is seen in Burundi, where producer prices for tea were on average 47 percent lower than reference prices. The trend remained largely negative up to 2016. In Burundi, the domestic market is controlled by the Office du Thé du Burundi (OTB), which buys the vast majority of green leaves produced in the country. Taking advantage of its monopsonistic position, the OTB can impose lower prices on farmers, possibly offsetting some heavy management costs in this way.

35

4 Trends in public expenditures in support of food and agriculture


In addition to trade and price policies, budgetary allocations in support of agriculture are often used by governments to pursue their development and growth objectives. In such a context, monitoring and analysing Public Expenditure in Agriculture (PEA) is of the utmost importance for African governments and donors alike.16

4.1 LevelIn July 2003, members of the African Union committed themselves to allocate at least 10 percent of their national budgets to agriculture in order to achieve 6 percent growth rate in the agriculture sector. The commitment was renewed in the Malabo Declaration of 2014 (AU, 2014). Figure 24 shows the evolution of agriculture-supportive and agriculture-specific spending for 13 SSA countries, namely Benin, Burkina Faso, Burundi, Ethiopia, Ghana, Kenya, Malawi, Mali, Mozambique, Rwanda, Senegal, Tanzania and Uganda.17

In most countries, identifiable agriculture-specific expenditures have been higher than agriculture-supportive expenditures. However, wide variations between countries are observed. While there is still controversy on exactly what should be considered agricultural spending in reference to the Malabo Declaration, most analysts refer to COFOG categories of agriculture, forestry and fisheries when measuring the level of PEA which are broadly equivalent to MAFAP’s agriculture-specific expenditures, and exclude expenditure categories that relate to rural development at large. Looking only at agriculture-specific expenditures shows that, out of the 13 countries sampled here, only Senegal, Malawi and Mali (since 2010) have shares of spending exceeding 10 percent of total public budget. Ethiopia was also above the 10 percent goal but followed a declining trend and is now below it, although these figures are to be taken cautiously given the partial coverage of the Ethiopia PEA dataset. Even considering both agriculture-specific and agriculture-supportive expenditures (i.e. spending on rural health, rural infrastructure and rural education), only five countries had levels of spending above 10 percent during 2012–2014 (Senegal, Malawi, Mali, Ethiopia and Burkina Faso).

PEA was very heterogeneous across MAFAP countries in 2015. A few countries increased their shares of agricultural expenditures within total public budgets (Mali, Rwanda, Uganda), while most reduced them (Benin, Burkina Faso, Burundi, Ethiopia, Kenya, Senegal, Tanzania) (Figure 24). Such variations suggest that, despite a joint and renewed commitment at regional level, country-specific policy objectives and budgetary constraints prevent governments from achieving the Malabo target.

16 The definition of PEA used here is: (agriculture-specific expenditures) + (agriculture-supportive expenditures) + (identified administrative costs) = PEA. The sum of (agriculture-specific expenditures) and (agriculture-supportive expenditures) is referred to policy transfers (PEA without administrative costs).

17 Several data limitations apply. Public expenditure data for Burundi is provisional. PEA data for Rwanda does not cover most of agriculture-supportive expenditure. PEA data for Ethiopia only looks at the total federal capital budget and does not include recurrent expenditures. Furthermore, as highlighted in Section 2.3, data for Malawi, Mozambique and Ghana do not include 2015. The MAFAP dataset will be updated as soon as the data become available.


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FIGURE 24 Shares of agriculture-specific and agriculture-supportive expenditures in total public budgets for 13 African countries, actual spending, national and external resources

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Note: Countries are presented by decreasing order of shares of agriculture-specific expenditures within total public budgets over 2006–2015.

Execution rates, defined as the share of actual expenditures in budgeted expenditures, vary substantially across countries (Figure 25). The median rate for the whole series stands at around 83 percent. Within countries, rates often vary over time. Some countries have improved their execution rates while others have kept shifting actual expenditures away from planned targets. Rates in Burkina Faso and Tanzania largely declined in recent years, for instance. On the other hand, Benin, Ethiopia, Ghana, Kenya, Malawi, Mali, Mozambique and Uganda have been either improving or stabilizing their levels of budget execution. Rates that appear consistently close to 100 percent, as in Ghana, may trigger questions on the nature of PEA. It is a consequence of large recurrent expenditures, such as expenditures on salaries. Investment expenditures indeed often have execution rates that are below 100 percent, as disbursements for long- term projects are frequently delayed (when funding infrastructure, for example).

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FIGURE 25 Execution rates of public expenditure in agriculture in 13 African countries (actual expenditure, internal and external resources)

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MAFAP identifies administrative costs that go along with policy transfers for food and agriculture, and allocates them to a separate category. The share of administrative costs within PEA provides a measure of expenditure efficiency. Higher administrative costs imply lower transfers to agents and/or lower investments in public goods specific to the sector for a given public expenditure envelope. For the countries reviewed here, the average share of administrative costs stood at about 22 percent in 2006–2015; however, this average masks a significant dispersion across countries (Table 3). The gradual increase in the proportion of administrative costs within PEA for all but three countries is concerning. It implies that costs such as project salaries, project formulation costs, running costs or monitoring costs are increasingly clogging the budgets.


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TABLE 3 Share of administrative costs within public expenditure in agriculture in 13 African countries, actual spending, national and external resources

Country 2006–2008 2009–2011 2012–2014 2015Average yearly

share growth rate

Benin 25% 29% 44% 43% 9%

Burkina Faso 12% 9% 13% 35% 20%

Burundi 2% 5% 11% 20% 43%

Ethiopia 12% 17% 15% 17% 8%

Ghana 33% 36% NA NA 6%

Kenya 13% 31% 19% 17% 16%

Malawi 5% 5% 4% NA 41%

Mali 31% 8% 6% 4% -3%

Mozambique NA 19% 20% NA 3%

Rwanda NA NA 10% 8% -3%

Senegal NA 28% 18% 16% -9%

Tanzania 6% 14% 15% 24% 28%

Uganda 2% 3% 3% 3% 9%


Some correlation between the share of administrative costs and execution rates would be expected, given that expenditures on salaries, for instance, usually have higher execution rates. The correlation coefficient between the series of average execution rates across countries and shares of administrative costs amounts to about 0.05, showing there is no strong relationship between these two variables. Countries such as Rwanda have low shares of administrative costs but high execution rates. No robust linkage can be identified as administrative costs and execution rates will also depend on the types of projects that are funded or the structure of a country’s financial system.

4.2 Composition

The highly disaggregated classification used in the MAFAP dataset on PEA makes it possible to go beyond the discussion on the level of spending for agriculture and analyse expenditure composition. The MAFAP PEA classification differentiates between agriculture-specific expenditures and agriculture-supportive expenditures. The latter category includes rural education, rural health and rural infrastructure. Agriculture-specific expenditures can be disaggregated into transfers for the provision of private goods and transfers for the provision of public goods. Transfers providing private goods include payments to identifiable groups of agents (producers, consumers, traders, transporters, agrodealers) and transfers providing public goods include agricultural research, technical assistance, extension, public storage facilities and marketing.

39


As noted above, agriculture-specific expenditures tend to dominate PEA in the countries reviewed, as they account for 68 percent of policy transfers on average between 2006 and 2015 (Figure 26). In most countries, the shares tended to grow during these years, indicating that support for the food and agriculture sector is becoming more focused and specialized. However, this trend should be cautiously interpreted since to some extent it may reflect data collection modalities in MAFAP-participating countries. Data on agriculture-supportive expenditures are indeed harder to collect because sources typically span multiple ministries and donors. The limitation particularly applies to the cases of Rwanda, Burundi and Ethiopia. Other East African countries (Tanzania, Uganda and Kenya) exhibited high average shares of agriculture-supportive expenditures, ranging from 40 to 50 percent.

FIGURE 26 Relative sizes of agriculture-specific and agriculture-supportive expenditures within policy transfers for agriculture in 13 African countries, average 2006–2015, actual spending, national and external resources

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The composition of agriculture-specific expenditures in SSA is varied, as shown in Figure 27. There is a significant producer bias in general. Agricultural infrastructure and activities dedicated to knowledge dissemination also received a large proportion of the spending in some countries. Rwanda and, to some extent, Mali are good examples of infrastructure-oriented countries. Expenditures on knowledge dissemination mainly target producers in effect (as opposed to traders or processors), which reinforces the producer bias. Research was frequently neglected, with the exception of Kenya, Uganda and Tanzania, which seem to have sustained efforts in this domain. Consistent with the findings of the price incentives analysis, very few public investments targeted marketing or storage, partly explaining the very high marketing costs faced by producers and other commodity chain agents in the countries studied here.


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FIGURE 27 Composition of agriculture-specific expenditures in 13 African countries, average 2006–2015, actual spending, national and external resources

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The “other payments to agents” category includes payments to consumers, traders, processors and transporters. Consumers were the largest beneficiary group within this subset, absorbing about 20 percent of payments to agents on average across all countries and years. However, that average is driven up by countries like Ethiopia, Mozambique or Senegal. In these countries, payments to agents mainly consist of cash transfers and food aid.18 The other countries in fact showed little interest in supporting consumers through PEA. Interventions such as border measures or other trade, market and price policies influencing consumer prices were preferred, as was mentioned in the previous chapter.

Almost all payments to producers were input subsidies in the countries reviewed. The share of input subsidies within payments to producers exceeds 90 percent on average. The merits of input subsidies with respect to other spending categories such as research or infrastructure have been extensively discussed in the literature (Wiggins and Brooks, 2010; Ghins et al., 2017). The relative importance and composition of input subsidies within agriculture and food budgets should be carefully examined, however, as they vary greatly between countries and regions.

Computing the share of input subsidies within agriculture-specific expenditures leads to characterization of four groups of countries. Low spenders are Burundi, Ethiopia and Kenya, with respective shares of 12, 13 and 14 percent on average for the period.19

18 In Ethiopia much recurrent expenditure targeting producers could not be included in the MAFAP PEA dataset, leading to a higher share of payments to other agents.

19 Also in this case the low share measured for Ethiopia is partly attributable to some missing recurrent expenditure data in the MAFAP PEA dataset.

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Average spenders are Senegal, Benin, Uganda, Mozambique and Mali, with shares oscillating between 23 and 32 percent. High spenders are Burundi, Ghana, Rwanda, Tanzania and Burkina Faso, with shares oscillating between 35 and 42 percent. Malawi forms its own high-spending group, with a staggering average share of 70 percent (Figure 28).

A similar pattern can be observed when considering average shares of input subsidies in national gross domestic product (GDP) and agricultural GDP. Ghana and Senegal are somehow outliers in this regard. In Ghana, input subsidies represent a large proportion of agricultural expenditures, but only account for 0.2 percent of GDP and 0.6 percent of agricultural GDP. In this case, there is a substantial imbalance between public spending in agriculture and the value added it produces. By contrast, input subsidies in Senegal are about a fifth of agriculture-specific expenditures but represent more than 3 percent of agricultural GDP.

FIGURE 28 Share of input subsidies in agriculture-specific expenditures, agricultural GDP and overall GDP in 13 African countries, actual spending, national and external resources, average 2006–2015

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The composition of input subsidies should also be examined. While on average in the sample used here, 61 percent of input subsidies targeted variable inputs such as fertilizers and seeds, significant shares went to capital subsidies (29 percent) for machinery and equipment or on-farm irrigation and to on-farm services (10 percent) such as technical assistance, veterinary services and inspection. West African countries such as Senegal, Mali and Burkina Faso have been particularly keen to subsidize on-farm capital (Figure 29). This is mainly attributable to spending on on-farm irrigation, which is of crucial importance in the Sahel area.


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FIGURE 29 Composition of input subsidies in 13 African countries, actual spending, internal and external resources, average 2006–2015

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As argued by Ghins, Mas Aparisi and Balié (2017), the popular case of Malawi, which is abundantly discussed in the literature (Chisinga, 2012; Jayne et al., 2013; Jayne and Rashid, 2013; Ricker-Gilbert et al., 2013), is thus an exception rather than a representative example of how and how much input subsidies are used in SSA.

It is relevant to note that the relative size of input subsidies in PEA has remained rather stable on average during 2006–2015 (Figure 30). In recent years, the share declined. Periodic spikes of spending on input subsidies were noted in 2006, 2009–2010 and 2012–2013. SSA countries have mostly been using input subsidies as an emergency policy response for the various food price crises (2005 West African food crisis, 2007–2008 and 2010–2012 world food price crises) and not as a preferred and sustained policy instrument for boosting agricultural productivity in the long run. Gradually recognizing that input subsidies represent a burden on the budget in the medium/long run and have had questionable effects on productivity, budget- holders (although not necessarily national budget-holders) appear to have progressively moved away from input subsidies towards the benefit of investments in agricultural infrastructure, such as feeder roads and off-farm irrigation. The trend is particularly visible in 2014–2015, although this should be taken with caution as data for Ghana, Malawi and Mozambique was not available for 2015 and were thus not included in the average.

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FIGURE 30 Average shares of input subsidies, research, knowledge dissemination and agricultural infrastructure in public expenditure in agriculture for 13 African countries, actual spending, internal and external resources

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Growing spending on infrastructure has mainly gone to irrigation over the recent years, to the detriment of roads (both feeder roads and rural roads) (Figure 31). However, considering both rural and agricultural infrastructure (the former being agriculture-supportive expenditure, the latter agriculture-specific expenditure) reveals that expenditure shares for that broad construction category mostly stagnated during the period under review. It thus seems that public support for infrastructure in rural areas did not improve significantly over the last decade in the countries of the sample.


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FIGURE 31 Composition and share of spending on roads and irrigation

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Furthermore, expenditures on research are declining and expenditures on knowledge dissemination are stagnant. Support for research is crucial for agricultural development, as it has well-known multiplier effects on productivity, resilience and growth (Fan et al., 2000). It is enlightening to compute the ratio of spending on knowledge dissemination per farm to spending on research per farm. Research efforts are indeed more likely to trickle down into tangible development outcomes if they are combined with technical assistance, training or extension.

Some countries have low ratios of extension to research, as in the cases of Uganda and Tanzania. Other countries, like Ethiopia or West African countries (excluding Ghana), have significantly higher ratios (Figure 32).20 However, there is a declining average trend in the extension to research ratio. Governments need to revamp their research programmes, as well as establish initiatives aimed at bridging the gap between research and farming practices. Storage and marketing also still receive low attention from governments. Clearly, there is still much room to incorporate value chain approaches and post-harvest interventions into the design of agriculture budgets across SSA.

20 For Ethiopia, some national expenditures on research and extension are not covered in the MAFAP PEA dataset.

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FIGURE 32 USD spent on knowledge dissemination per USD spent on agricultural research

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Agriculture-supportive expenditures

Agriculture-supportive expenditures are expected to enable the rural environment to become more conducive to agricultural development and other economic activities. As described above, MAFAP countries have made uneven efforts to support rural development in general. The importance of agriculture-supportive spending within average PEA has been diminishing over time, however, to the benefit of agriculture-specific spending.

Within agriculture-supportive expenditures, a predominance of transfers in support of rural infrastructure can be observed in almost all countries, to the detriment of other categories such as rural education and rural health (Figure 33). Ethiopia, Senegal and Ghana seem to have allocated higher shares of public resources to rural health and education, especially in comparison with other countries in the sample. High shares of spending on rural health were recorded in Benin and Burkina Faso.


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FIGURE 33 Composition of agriculture-supportive expenditures in 13 African countries, average 2006–2015, actual spending

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Over time, expenditures in the “rural infrastructure” category, measured in 2011 USD per farm, grew slightly on average, going from around 21 USD in 2006 to around 27 USD in 2015. Expenditures on rural health and education did not increase significantly and fluctuated at around 10 and 5 USD, respectively, since 2010. There is a need for more integrated and cohesive approaches to agriculture development. The interactions between subsectors of the rural economy need to be properly understood to create balanced budgets that exploit complementarities between different interventions and activities.

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4.3 Support to agricultural commodities

Agriculture-specific expenditures are disaggregated across targeted groups of commodities for 10 countries in the MAFAP PEA database (Ghana, Malawi and Mozambique are excluded). Some expenditures cannot be mapped to specific groups of products (for instance, roads, large-scale extension programmes or warehouses). In this case, they are considered linked to “all commodities”. About 60 percent of agriculture-specific expenditures were mapped to groups of commodities, on average for 2006–2015, for 9 countries in our sample (Ghana, Malawi, Mozambique and Senegal excluded – for the latter country, almost all agriculture-specific expenditures were mapped to identifiable commodity groups). The average share of commodity groups has been slightly declining over time, showing that policy-makers have been gradually preferring broad-based development projects targeting multiple commodities (Figure 34).

FIGURE 34 Average share of spending on groups of commodities within agriculture-specific expenditures in 9 African countries

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The “food crops” category was the most targeted group of commodities. It absorbed 50 percent of expenditures on groups of commodities, on average over all countries and years. The second largest group was “livestock and dairy” (22 percent). Countries such as Benin, Mali, Rwanda and Tanzania significantly prioritized food crops over other groups, due to the substantial support to rice, maize and other cereals (Figure 35). Countries in East Africa, such as Ethiopia, Kenya and Uganda, had higher shares of support to livestock and dairy.


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FIGURE 35 Composition of spending on groups of agricultural commodities in 10 African countries, average 2006–2015

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Over time, the composition remained rather stable, as food crops continued to dominate. Spending on innovative items such as horticulture, fruits or honey and silk gained slight momentum (Figure 36).

FIGURE 36 Average composition of spending on groups of commodities in 10 African countries

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Overall, countries analysed here mostly continued to focus on the same set of commodities over the reviewed period. Support was provided primarily through input subsidies or training, technical assistance, extension and inspection. The magnitude of such interventions has been declining over time as budgets were shifted to other activities, such as the construction of irrigation schemes. More effort could be dedicated to smart commodity selection in view of comparative advantage and supply- and demand-side trends. A commodity such as maize is prioritized in most countries of our sample. However, it is a globally traded commodity whose market is dominated by developed countries. Budgets should create space for the promotion of other products which offer good prospects to remain competitive in the long run.

4.4 Role of aid

Finally, it is interesting and relevant, both from a policy standpoint and a sustainability perspective, to better understand the role of aid – in other words, who (donors or government) is funding what in PEA. Some countries have been extremely reliant on external resources to fund their food and agriculture sectors. The most striking cases include Burundi, Ethiopia (in this case, partly because some recurrent expenditures are not covered by the MAFAP PEA dataset), Ghana and, to a lesser extent, Burkina Faso and Mali. These countries had shares of external resources in PEA of 89, 71, 73, 50 and 47 percent on average, respectively (Figure 37). Such a situation leads to questioning the sustainability of PEA and the degree of independence that countries have in deciding which programme to fund or which policy to favour.

FIGURE 37 Share of donor and national expenditures within public expenditure in agriculture in 13 African countries, average 2006–2015

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Donors also tend to behave differently from governments when facing budget composition choices. As Figure 38 indicates, a greater proportion of national resources were allocated to the provision of private goods in comparison with donor resources. Donors therefore seem more willing to fund public goods than do their national counterparts. However, since 2012, governments started dedicating greater shares of their budgets to public goods, showing that the phenomenon may phase out in the future.

FIGURE 38 Share of agriculture-specific expenditures allocated to private goods originating from donor and national sources, respectively, average for 13 African countries, actual spending

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5 Assessing coherence between food and agricultural policies and their effects


The analysis of the main policy measures adopted by governments in the last 11 years in terms of public expenditure and price interventions makes it possible to draw the following conclusions about the coherence of agricultural policies with respect to their intended objectives.

Government interventions in agricultural markets have gained momentum over the last decade. Market price support and border protections are still widespread and often implemented in a discretionary manner. They generate important distortions and high unpredictability, leading to unintended effects.

Even after the end of the food price crises turmoil that led countries to rely on short-term policies to keep food affordable for consumers, governments still tend to use trade and market policies to control domestic prices. These interventions – which have the clear intention to insulate domestic markets – appear to be a move away from the ongoing liberalization process. In some cases, this policy stance is openly presented as a protectionist one, with the aim of attaining self-sufficiency in food production, especially for food staples (rice and maize). Emerging from the price incentives analyses, import restrictions have been commonly implemented by several SSA countries, with the objective of protecting farmers growing sensitive products, such as rice. In some cases, this move led to the expected effect of incentivizing domestic production; in others, like Ghana, Mali and Mozambique, such protectionist measures seemed to be less effective.

Overall, in West Africa the level of border protection has increased in absolute terms since 2015 because ECOWAS has taken over the trade policy of countries formerly operating trade under the UEMOA CET. East Africa countries have adopted a high CET for more than 30 agricultural products which are considered extremely sensitive, including rice and maize. Despite this, several countries presumably considered such protections insufficient and so national governments started to use additional policy instruments, such as minimum price policies, input subsidies programmes, other direct transfers or tax and VAT exemptions. Unfortunately, the adoption and implementation of these measures are often ineffective or unpredictable and therefore increase uncertainty for agents in agricultural value chains.

Results of the price incentives analyses support the intuition that, in some cases, ad hoc policies often taken on discretionary grounds may have ultimately penalized farmers who were expected to benefit from protection and price insulation measures. For example, minimum price systems implemented to support cotton production (e.g. in Benin and Mozambique) are often poorly implemented and end up being considered indicative prices by the market stakeholders, preventing farmers from fully benefiting from significant price increases in the international market. At the same time, export restrictions on food staples, such as maize in the cases of Tanzania and Ethiopia, have often generated indirect effects which increased domestic price volatility and harmed farmers, despite their primary objective of ensuring adequate food supplies for poor urban populations and curbing food prices. These restrictions, intended to be an exception during the food crisis period of 2007–2009, seem to have become the rule – again suggesting that governments have been moving further away from the principles of freer trade and market integration.


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Despite the increase in policy support to production, agriculture sector performance in SSA countries remains uneven, with productivity increasing overall at a moderate rate over time (IFPRI, 2016). The production trends for maize and rice over the last decade (as presented in Section 3.2) show relevant improvements in some countries, while less clear progress or even unambiguous stagnation are noted in other cases. This somehow suggests that the causality relationship between price incentives and sector performance is not straightforward, due to several sources of disturbance, a lack of transparency and the poor predictability of sector dynamics. Similarly, large-scale input subsidy schemes do not always translate into sustainable productivity increases, mainly due to implementation challenges as well as to limited complementary investments in, for example, extension services or output market linkages (Jane and Rashid, 2013; Dorward, 2009). Despite this, at least until 2013, public expenditure on input subsidies continued to increase. Governments continue to allocate substantial amounts of resources to directly support producers, instead of investing in public goods that could foster sustainable development, such as infrastructure, irrigation systems and rural roads.

Despite the alleged relevance of the value chain approach to agricultural development, the pro-producer bias of policy support is a common pattern across SSA countries. Public support to other value chain agents and the promotion of commercial crops remain very limited.

Price incentives to production have increased on average since 2011, especially for food security crops. In addition, direct payment to producers represents the largest share of public expenditure in most of the countries studied. Although the share of input subsidies in total agriculture expenditure has been diminishing over time, the subsidies are still the largest spending category in 2015, on average.

Public expenditures targeting consumers have remained limited in comparison with expenditures targeting producers, in spite of the volatile conditions which consumers faced during 2005–2010 and, more recently, in times of production shortages. In years of food price surges, ad hoc trade and price policies focused on consumers. However, the focus was rapidly reversed in favour of producers as international food markets stabilized. The trend has continued until very recently, except for a few countries that increased investments in consumer-oriented programmes, such as Senegal (cash transfers) or Mozambique (cash transfers and food aid).

Moreover, attention to commercial farming has been noted in only a few cases during the last decade. Agricultural transformation stands as a core goal of the CAADP agenda. Policies and strategies designed to promote agricultural exports and to exploit the export potential of selected value chains seem to be growing fast in most of the countries covered here (e.g. Ghana, Malawi, Uganda and Tanzania). However, the priority given to these strategies is not supported by the results of the price incentives analyses, which depict an overall volatile and often negative trend in price support for cash crops, even in recent years. Likewise, the public expenditure analysis shows that many countries significantly prioritized food security crops over cash crops. Budgets in these countries largely ignored the investments or transfers that would be needed to support the development of export-oriented agricultural value chains (e.g. market access facilitation, investments in off-farm services and technology development). Efforts to promote commercial agriculture and integrated value chain approaches by overcoming market access and trade constraints are still not tangible in SSA countries.

Policy-makers have put little emphasis on enhancing strategic linkages between farmers, processors and traders, on the assumption that conflicting interests prevail. Value chain functioning is often perceived as suboptimal and discouraging for farmers as opposed to downstream agents. There is a widespread belief that intermediaries, for example, tend to take advantage of an alleged market power position.

53


Although investments in infrastructure development are increasing, market inefficiencies remain the primary driver of price disincentives.

The analysis of agriculture-specific and agriculture-supportive expenditure denotes an increasing predominance of transfers in support of rural infrastructure in almost all countries over the analysed period. Several SSA countries have focused on rural infrastructure development and market access as key areas of intervention in numerous regional and national agricultural investment plans (NAIPs). Despite these efforts, governments have not managed to substantially reduce the excessive costs borne by farmers when reaching out to markets.

As suggested by the MDG figures discussed in Section 3.1, in many cases market inefficiencies are still adding to price disincentives generated by public policies. Poor access to markets, limited provision of post-production services and underdeveloped infrastructure are among the main constraints faced by farmers, particularly for cash crops. This suggests that even more investment, both public and private, is required to fill the gap. Rebalancing the composition of agriculture-specific expenditures is a priority; spending on private goods (i.e. input subsidies) could be reduced in favour of investments in public services, such as agricultural research or agriculture support services on marketing and extension, for example.

Regional market integration is insufficient, in spite of regional compacts assigning a fundamental role to intraregional trade and market access for agricultural products.

Regional policy has corroborated national measures in providing price support to producers. ECOWAS protects agricultural products by means of a relatively high CET. The community policy also puts great emphasis on reviving rice production and attaining self-sufficiency. This could have contributed, at least partially, to the slight increase in price incentives in some countries of the region (e.g. Burkina Faso, Senegal) even if in other countries of the West Africa region, like Ghana, this policy had less significant effect.

However, the supposedly increased role of the Regional Economic Communities to spur intraregional trade does not seem to have emerged. On the contrary, efforts from the regional blocks to reduce non-tariff barriers to trade (Box 3) have remained quite ineffective in preventing national governments from imposing additional restrictions on trade, beyond the regional CETs. Coherent strategies to support regional market integration are negligible or ineffective; even less visible are measures to support intercontinental trade, despite it being a priority of the African Union Community.21

The level of agricultural expenditure does not meet CAADP targets for most of the countries studied, despite the renewed commitment made in the Malabo Declaration.

Most of the countries analysed here do not comply with the CAADP objective of allocating at least 10 percent of their national budget to agriculture. Despite the renewed commitment made by all governments of the Africa region and the launch of the second generation of NAIPs, budgetary allocations in support of agriculture are still driven by country-specific policy objectives and budgetary constraints that hamper the achievement of the Malabo Declaration target.

For instance, while agricultural research and knowledge generation are top priorities in the Malabo Declaration and commonly reaffirmed as policy objectives in NAIPs or food security strategies, shares of public expenditure allotted to these spending categories have been limited and even declining over the last decade. Agricultural research accounted for

21 For example, the ongoing negotiations for an Africa-wide Continental Free Trade Area (CFTA) and the Programme for Infrastructure Development in Africa 2012-2020 could have offered additional opportunities to enhance regional trade and promote infrastructure investments (Torres and van Seters, 2016).


54

less than 5 percent of total PEA, on average. The trend of this share has even been declining since 2006.

Policy support, in the form of price incentives and public expenditure, appears volatile. This instability is often driven by internal political considerations and dynamics as well as external conditions, such as development aid flows.

Policy support to agriculture in the countries surveyed here, whether in the form of price incentives or public expenditure, has been highly volatile and unpredictable in the years reviewed. In some cases, erratic policies can be attributed to the instability of the sociopolitical environment of a given country or discretionary application of market interventions on highly politically sensitive crops – usually key food security products, such as maize in East African countries. As discussed in Section 3, the imposition of intermittent export bans, procurement through marketing agencies or price fixation policies were often not grounded in evidence and were implemented without notice and proper communication to the concerned agents. In this way they can further destabilize markets and discourage farmers from remaining in their own businesses. It is common wisdom that for policies to be effective at spurring economic growth, they must be stable, predictable and based on a strong rule of law (Aisen and Vega, 2011; Taylor, 2016).

External factors also play a role in determining the stability of the domestic policy environment. For example, weather conditions and climate variability seem to increasingly affect agricultural production, with the associated consequence of diverting public resources away from the sector to address acute weather-related crises. However, such a vulnerability to weather conditions arguably also indicates the limited attention paid in the past to prevention and risk mitigation measures, including the development of adequate insurance mechanisms that could protect producers against shocks and ultimately keep up the indicators of sector performance. In line with the key pillar for CAADP objectives implementation, governments are encouraged to improve their policy responses to food emergency crises through the provision of safety nets and/or through agricultural production enhancements.

Another important “external” element compromising policy predictability at national level relates to the share of aid in public expenditure for agriculture, which has remained extremely high in recent years. Many countries largely depend on external resources to support the development of their food and agriculture sectors. Donor priorities are susceptible to change. In a scenario of overreliance on development aid, the stability and sustainability of agricultural investment therefore appears to be at risk.

55

6 Conclusions

6 Conclusions

Governments use policies to influence domestic agricultural markets and budgetary transfers to support specific groups or to finance certain services in the sector. The policy support indicators computed and analysed by MAFAP provide an estimate of the impact of such policies and help to understand whether these measures are aligned with the countries’ main development objectives and generating the intended effects.

Indicators follow very heterogeneous trends across the 14 countries reviewed, but overall support to producers undoubtedly increased over time. Average levels of policy distortion stood close to zero but became slightly positive since 2012. This suggests government interventions in agricultural markets have regained emphasis and are particularly focused on supporting producers and reversing the pro-consumer trend that characterized the food security crises period (2007/08–2010/11). In the last five years, support has been particularly geared towards production of food staples, such as maize and rice, in the form of import tariffs and market price support. Moreover, budgetary transfers targeting producers accounted for the majority of public spending in the sector. Expenditures for other agents (traders and consumers) or public goods (e.g. research and knowledge dissemination) received limited attention, even though they have been found to be more effective in fostering agricultural growth. Scaling up investments in such general services for agriculture must become a priority for African governments.

Furthermore, few value chain integration measures aiming to improve commercialization and agriculture transformation are effectively being implemented. This is the case in spite of the inclusion of integrated value chain development and increased commercialization as a core target of most national and regional agricultural investment plans framed in accordance with the CAADP framework. Our analysis highlighted that support to cash crops production has been limited, in terms of both price incentives and direct monetary investments. More dialogue and strategic thinking on opportunities to rapidly develop post-farmgate activities, achieve higher shares of marketed production and promote commercial farming seem desirable. In this sense, exploring comparative advantages and identifying competitive market “niches” for commodities for which international demand is increasing would be a wise strategy. Product differentiation and marketing strategies, including branding and packaging that puts traders and other private sector actors at the heart of the system, can play a key role in attracting the benefits of a more commercialized agrifood sector.

Trade and market interventions taken on discretionary grounds often work against the stabilization of food markets. A stable and predictable policy and price environment is recognized to be crucial for economic growth. As abundantly discussed in the literature, export restrictions tend to generate negative effects on farmers and increase domestic price volatility. Moreover, reliance on parastatals for marketing food staples usually results in unsustainable management costs and increased distortions in market signals, which have destabilizing effects on price dynamics. As noted in this study, minimum price policies sometimes have harmful effects on farmers, as they prevent smooth price transmission from the international to the domestic level. Benefits following from increases in international food prices are typically captured by traders or processors in such contexts. An unstable and volatile policy and price environment discourages producers from investing. Instability also negatively affects investment decisions by other agents in the agriculture sector. The use of such policies should be limited to emergency situations, following a transparent and evidence-based approach.


56

Recent efforts to convert resources that were previously allocated to input subsidies into investments in agricultural and rural infrastructures have not yet had the effects that were hoped for. The level of market inefficiencies is such that substantially more public resources are required before the costs of such inefficiencies can be significantly downsized. The magnitude of the market development gap remained constant over the period, indicating that high transport, marketing and/or other post-harvest services costs, continue to be a source of price disincentives for farmers. This is particularly true for cash crops. More could be done at national but also at regional levels to prioritize public investments and reduce market failures and inefficiencies, such as further investment in roads development as well as storage and marketing facilities, in line with the strategies adopted by the Regional Economic Communities for agricultural trade facilitation and integration.

The lack of solid market information systems persists as a constraint on policy formulation and reform. The need for integrated systems, including real-time market data, persists in African countries. Data quality and availability were found to be a constraint on the estimation of MAFAP policy support indicators, even in countries where MAFAP has been implemented for several years. Institutionalization of policy monitoring activities has been working to varying degrees. In most cases, however, data collection remains ad hoc and vulnerable to institutional or personnel changes, as proper information systems are not in place. The so-called “virtuous cycle”, allowing enhanced data generation as a consequence of improvements and relevance of policy analyses, has not yet fully materialized.

The enhancement of country-based market information systems is key to formulating timely and nuanced policy responses to shocks, but also to planning long-term sustainable interventions grounded on reliable data and sound evidence. This would also help governments to avoid the adoption of discretionary policies.

57

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Annexes

Annexes

Annex 1. Nominal Rate of Protection and Market Development Gap by country and commodity

TABLE 4 Nominal Rate of Protection by country and commodity (percentage), 2005–2016

Country Commodity 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Benin Maize 1.73 6.64 5.63 0.75 9.72 -9.50 -11.09

Pineapples -46.5 -44.3 -46.1 -69.3 -69.8 -20.9 3.6 -26.6 -41.5 -53.7 -59.8

Rice, paddy 17.0 25.8 12.1 44.3 -4.3 28.6 61.5

Seed cotton 85.8 43.7 80.9 27.6 37.2 21.7 11.5 8.3 18.8 13.0 78.7 14.7

Burkina Faso

Groundnuts 12.3 7.7 -20.2 -55.0 -4.0 -28.4 -22.1 -21.8 46.5 -42.2 -11.6 -30.7

Maize -4.7 40.4 -6.3 40.2 0.0 31.1 32.3 25.6 -0.5 40.3 25.8 39.2

Onions -66.9 -47.2 -45.6 -47.3 -47.3 -65.2 -8.0 -67.9 -36.1 -30.2 5.0 -42.3

Rice, paddy 22.5 8.5 20.0 7.2 -4.1 2.6 8.5 9.4 17.9 -14.5 -6.0 -24.4

Seed cotton 62.0 43.2 30.3 184.1 5.0 -60.1 31.7 34.0 24.2 10.8 13.7 -15.7

Sesame seed 30.3 51.0 -11.3 -19.2 14.4 13.3 18.1 51.9 -11.0 118.7 33.1 -33.7

Sorghum 32.7 67.2 81.3 37.1 8.2 53.1 71.6 76.2 46.0 14.2 20.5 53.1

Burundi Beans -66.2 -9.1 44.1 -0.7 57.3 94.1 -41.8 -2.3 -11.2

Cassava 56.2 7.5 74.7 89.6 23.1 81.2 80.7

Coffee 62.9 34.8 147.5 21.3 95.3 86.0 32.8 -15.3 7.5 45.1 -15.9 16.0

Maize 4.8 67.3 6.8 49.5 18.6 143.0 89.4 48.7 34.9 45.3 52.3 27.6

Rice, paddy -14.8 -2.3 -6.6 66.8 4.1 58.3 44.7 62.3 20.9 32.2 71.2 66.4

Tea -3.6 -46.0 -29.8 -60.8 -59.8 -56.7 -53.9 -55.4 -47.0 -41.8 -61.9 -50.2

Ethiopia Barley -29.9 19.1 8.2 75.6 47.3 -28.1 -43.3 -29.0 -82.6 -65.3 -4.5 -6.0

Beans, dry 20.9 -14.8 -22.4 -8.7 -9.9 -8.8 23.1 2.6 44.5 83.5 31.9 43.9

Coffee -28.0 -19.8 -4.5 25.4 -9.1 39.8 -22.6 -22.2 9.6 15.9 18.5 33.9

Lentils -23.4 46.3 23.8 13.7 -20.7 -20.3 -10.6 -28.8 3.9 25.5 15.6 0.5

Maize -71.3 -58.0 -59.0 -15.3 -55.1 -50.5 -39.3 -52.7 -60.6 -65.5 -76.3 -78.6

Sesame seed 6.9 22.0 75.1 -14.4 16.8 11.5 -12.5 10.2 105.7 7.4 -3.4 27.4

Sorghum -9.9 -20.8 5.9 111.7 78.2 9.8 -51.7 6.5 -26.8 -22.0 -63.4 -61.9

Teff -31.5 -11.7 -27.9 -16.9 -31.3 -43.3 -27.8 58.3 65.5 60.9 44.4 75.3

Wheat -37.3 -18.0 -36.4 -1.9 4.0 -10.8 -2.7 7.9 13.8 19.0 18.2 -29.8


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Ghana Cassava 4.7 3.2 -18.8 29.4 -0.2 101.1 241.2 132.6 258.8 194.6 69.0 140.5

Groundnuts -20.1 -25.2 -16.7 -10.8 11.1 1.5 25.4 22.9 17.7 17.4

Maize -38.3 -61.0 -72.3 -49.7 -38.7 -67.4 -30.9 -19.1 -27.0 -55.7 -11.6 21.1

Oil, palm -4.0 -22.7 -47.1 -45.0 -44.2 -36.3 -47.2 -40.0 -23.3 0.1 96.5 175.0

Rice, paddy 36.0 12.4 -2.9 13.0 -22.3 -36.1 -19.3 10.8 -23.9 -41.5 -45.2 -48.8

Yams -39.7 -39.1 -23.6 -40.2 -41.6 0.9 -33.0 -44.5 -9.1 17.1 -76.4 -53.9

Kenya Beans, dry -55.6 -43.9 -31.6 -18.6 -25.7 -18.3 -19.7 -13.9 -28.7 7.4 3.3 -38.5

Beans, green -67.9 -57.8 -46.2 -26.1 -38.8 -42.2 -65.6 -65.9 -51.3 -53.8 -55.1

Cassava -39.9 -36.9 65.9 -24.2 36.7 -25.7 -13.4 54.2 19.8 18.7 -11.6

Coffee, green -79.3 -75.9 -76.6 -77.3 -81.6 -78.7 -79.2 -79.8 -82.9 -83.8 -86.5

Maize -26.8 -42.8 39.1 -3.0 29.5 -29.9 6.9 54.2 51.2 68.1 48.1 125.1

Potatoes -0.6 -20.7 -29.2 -15.8 -11.6 -32.3 5.5 86.3 57.9 37.2

Rice, paddy -17.1 57.2 76.3 48.0 130.8 103.1 128.2 48.9 48.4 32.6

Seed cotton -3.8 -14.5 -18.8 -32.8 1.5 1.7 -25.2 -34.5 -25.5 -10.0 2.2 23.0

Sorghum -45.8 -45.2 -0.1 -16.9 -13.9 -28.8 -37.1 -60.3 -56.1 -40.3 -41.6 -36.2

Sugar cane 69.2 63.5 43.2 64.7 83.4 22.6 -18.9 12.4 -2.0 -8.9 -3.8

Tea 19.5 2.7 19.3 -2.1 8.7 26.3 20.1 25.6 35.6 5.4 -4.3 100.2

Wheat 10.4 21.6 35.0 7.7 31.6 14.0 -22.5 0.6 0.8 13.4

Malawi Groundnuts -0.2 22.5 -6.6 -38.4 2.5 -1.7 -11.5 -37.0 -68.0 -57.2 -25.4 -47.4

Maize 40.0 8.7 44.6 -8.5 23.0 44.3 67.2 0.4 -26.9 6.2 -18.9

Seed cotton 21.8 -8.9 -15.1 59.6 47.5 -8.7 -48.8 -20.4 1.1 -6.3 37.4 19.2

Sugar cane -27.6 -56.8 -30.7 -42.0 -31.5 -34.0 -24.1 68.7 -32.7 -66.6

Tea -40.1 -28.8 -18.3 -22.7 -46.1 -44.1 -56.1 -24.5 -52.3

Tobacco 19.5 -9.2 39.1 21.4 -8.7 -9.9 -31.3 109.7 44.3

Mali Groundnuts 30.9 7.3 1.3 -17.9 -15.0 -28.8 -21.4 -28.9 -32.4 -30.4 3.1 0.3

Maize -8.1 35.1 -24.9 8.3 -20.3 -27.7 -11.2 0.4 -16.3 -19.8 -38.3 -40.5

Milk, cow 2.9 -4.7 -12.1 -21.5 15.9 -8.3 -21.5 -0.7 -41.6 -44.3

Millet 23.9 -34.7 -53.8 -31.0 -61.6 -11.1 -38.2 24.4 -55.6 -45.5 -5.6 5.4

Rice, paddy 17.1 3.1 3.2 2.4 -3.0 -16.2 7.6 -2.9 -21.5 2.8 1.2 21.1

Seed cotton 88.6 38.5 79.4 40.1 106.1 -0.6 -32.7 3.0 28.5 16.7 90.2 105.7

Sorghum -37.9 -41.7 -2.2 -26.5 -57.8 -13.0 -23.3 12.2 -36.6 19.7

Mozambique Cashew nuts, unshelled

6.7 28.1 26.9 63.5 20.5 -11.6 62.7 42.3 -6.6 12.6 -21.1 -34.9

Cashew nuts -13.6 6.7 2.5 -11.6 -22.6 -21.1 -22.3 -0.9 -11.8 -28.0 -41.7 -52.9

Cassava -45.0 9.6 -31.2 -12.4 0.7 -5.2 -28.5 -13.1 24.2 -17.7 -3.0 69.6

Maize 72.6 -12.1 -43.7 30.5 35.4 21.7 2.0 -20.3 18.7 18.4 29.8 64.9

Rice, paddy -16.3 -6.5 -11.4 -6.5 -19.6 -38.0 -28.5 -23.8 -26.7 -13.8 -6.1 5.1

Seed cotton 38.3 17.8 -22.6 -10.9 -14.4 -46.5 -15.9 21.5 10.3 66.6 -3.1 -49.1

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Annexes


Nigeria Cashew nuts 29.2 28.4 19.9 23.8 26.5 29.1 30.2 39.6 55.5 71.2 36.0

Cocoa, beans 2.7 8.7 -6.2 -17.2 -32.5 -21.9 -34.8 -15.5 -20.3 -40.9 -50.9

Maize -23.6 -32.3 -28.0 -46.5 -50.0 -44.3 -30.3 -24.2 -31.6 -38.7 -37.0

Rice, paddy 34.1 36.8 38.1 -43.6 -38.9 60.4 25.3 20.7 17.8 52.5 72.8

Sorghum -65.0 -38.9 -55.6 -35.1 -55.6 -24.9 -35.0 -23.9 -12.3 -33.9 -37.5

Rwanda Beans, dry -73.5 114.5 -44.7 105.0 -42.9 -17.7 -55.8 24.9 72.8 48.0 63.0 57.1

Coffee, green -23.3 1.6 50.5 97.5 118.1 70.5 5.0 15.0 -27.1 53.7 13.6 -10.6

Milk, cow -18.9 -29.3 -23.6 -26.4 -29.5 -42.3 -34.4 -39.1 -37.8 -9.7 -36.4 -54.8

Rice, paddy 48.1 43.9 67.2 97.9 116.3 71.1 78.6 109.6 71.3 107.9 91.6 119.3

Tea -31.3 -54.9 -28.8 -41.4 -54.1 -45.1 -40.6 -38.9 -37.4 -28.6 -54.1 -56.6

Wheat -30.9 -68.9 -27.0 -6.1 6.5 11.0 6.2 31.3 25.7 108.0 77.0 139.4

Senegal Groundnuts 123.6 156.3 116.0 58.4 49.0 49.8 0.8 3.5 47.5 50.5 53.9

Onions -2.8 6.4 82.9 -1.0 -0.4 14.7 10.7 -11.9 11.7 -17.5 8.8

Potatoes -33.7 -21.9 -28.3 -17.1

Rice, paddy 41.9 89.7 5.2 -14.9 19.3 8.7 7.1 18.0 30.5 27.8 39.3

Uganda Cassava -43.9 -42.3 -35.8 -21.0 -59.5 -12.1 13.9 -51.6 -30.8 12.0 -62.0 32.6

Coffee, green -26.3 -28.0 -17.9 -21.9 -21.6 5.8 16.6 -10.8 -21.5 -7.3 -37.9 -33.7

Maize -54.3 -0.7 81.1 0.9 -43.4 52.3 31.4 -6.2 13.4 -26.0 30.0 140.9

Milk, cow -54.2 -86.1 20.4 -54.2 -45.7 -68.5 -25.3 6.0 -54.9 -20.1 -14.4

Rice, paddy -44.8 -39.5 -8.8 -5.6 41.5 66.3 98.7 94.8 61.6 53.4 43.6 43.3

Seed cotton 72.9 25.8 55.1 45.0 110.7 28.6 -4.3 52.6 41.9 60.7 67.8

Sugar cane -96.4 -96.6 -96.1 -95.8 -96.8 -96.5 -98.0 -97.8 -97.4 -92.9 -92.8 -91.2

Tea 121.9 -14.1 83.1 21.0 24.3 -31.0 -36.9 -0.4 8.0 125.9 -46.5 -45.8

Tanzania Cashew nuts -15.4 -23.7 -22.3 13.4 -9.7 8.0 21.8 12.2 13.0 -23.7 -50.8 -38.7

Coffee, green 0.9 -12.1 8.3 -23.4 -21.3 -21.7 -31.8 -31.0 -9.3 23.5 -1.9 -3.3

Maize 142.1 81.3 30.9 10.9 -12.1 52.0 -26.1 -11.5 67.5 45.8 -20.3 91.8

Rice, paddy -4.7 30.1 17.3 -14.8 12.4 1.3 32.3 90.7 133.6 67.2 77.4 -15.5

Seed cotton -10.6 40.0 41.4 23.0 77.0 53.8 68.1 159.9 105.3 182.7 128.7 2.2

Wheat -50.6 -50.4 -58.3 -48.0 -5.1 -8.7 -16.9 -8.8 5.2 22.2



62

TABLE 5 Market Development Gap by country and commodity (percentage), 2005–2016


Benin Maize -2.4 -2.4 -2.4 -2.3 -2.4 -2.4 -2.3

Pineapples -7.8 -7.6 -7.9 -8.4 -7.8 -7.9 -8.0 -7.7 -7.6 -7.2 -7.7

Rice, paddy -0.2 -0.3 -0.2 -0.6 0.1 -0.2 -0.1

Seed cotton

Burkina Faso

Cattle -24.0 -25.5 -22.4 -18.8 -7.6 -7.7 -12.5 -12.2 -15.1 -17.8 -8.3

Groundnuts -17.9 -15.6 -21.3 -25.5 -15.9 -16.6 0.0 -0.5 -15.4 3.2 -18.1 -19.7

Maize -20.6 -24.9 12.7 -19.0 -21.7 -22.3 -22.3 -19.7 -22.3 -26.1 -27.4 -19.3

Onions 12.1 -26.9 -29.7 -26.1 -26.3 11.7 1.2 -36.1 -23.4 -33.8 -28.3 -41.1

Rice, paddy 51.3 32.7 31.2 32.0 62.3 40.4 47.9 42.1 34.5 63.2 58.5 84.3

Seed cotton -12.4 -12.9 -14.0 -13.0 -13.6 -14.6 -11.2 -11.6 -12.0 -12.9 -12.8 -11.3

Sesame seed -14.6 -14.7 -16.1 -14.9 -13.4 -13.3 -13.1 -14.0 -13.8 -10.7 -11.5 -14.2

Sorghum -26.1 -31.2 -34.4 -34.0 -35.8 -31.0 -33.2 -24.9 -30.2 -36.8 -38.5 -31.0

Burundi Beans, dry 4.2 2.2 2.1 2.1 1.2 0.9 1.3 0.8 0.9

Cassava 7.6 9.3 10.1 8.3 11.9 7.9 6.8

Coffee, green -19.8 -20.3 -19.3 -39.8 -39.4 -45.6 -26.8 -17.3 -19.1 -31.6 -23.3 -28.8

Maize 8.7 9.2 9.1 9.3 9.4 9.4 9.4 10.2 11.3 9.8 10.5 12.5

Rice, paddy 16.2 14.5 14.7 8.6 7.3 7.8 7.1 6.9 10.4 10.0 7.2 6.7

Tea -53.0 -49.3 -55.1 -31.0 -22.6 -22.9 -14.3 -15.0 -24.5 -28.7 -12.6 -27.2

Ethiopia Barley -5.8 -5.2 -4.5 -4.0 -4.8 1.4 1.6 1.8 -1.8 -1.8 -12.2 -11.3

Beans, dry -29.4 -31.8 -27.8 -21.1 -23.6 -24.3 -20.8 -21.3 -25.6 -24.9 -38.5 -39.3

Cattle -81.0 -76.8 -77.3 -73.7 -69.0 -30.5 -58.2 -55.4

Coffee, green -10.3 -9.0 -7.9 -12.0 -13.1 -9.7 -4.4 -5.4 -6.8 -5.7 -4.4 -4.7

Lentils -0.4 -0.7 -5.2 -2.7 -0.6 0.2 2.2 -0.8 -1.0 -1.6 -0.7 0.8

Maize 0.1 0.9 -0.3 0.3 -0.9 -10.3 -8.0 -6.7 -1.5 -1.6 -1.7 -1.5

Sesame seed -19.5 -23.0 -10.9 -11.1 -8.6 -8.6 -14.3 -16.7 -23.3 -17.4 -21.8 -25.5

Sorghum 2.0 2.1 2.2 1.0 0.7 0.0 -2.2 -1.5 5.2 4.8 7.2 7.1

Teff -7.0 -6.3 -6.6 -6.3 -6.4 -7.1 -8.7 -7.3 -7.3 -7.3 -5.6 -5.6

Wheat 1.3 1.0 0.7 0.0 -0.8 -1.2 0.2 0.0 -2.8 -2.2 -2.8 -3.6

Ghana Cassava -19.8 -19.3 -24.8 -19.6 -19.6 -16.0 -16.7 -10.6 -5.2 -5.4 -7.6 -6.8

Groundnuts -16.5 -16.6 -13.8 -12.2 -11.6 -11.8 -11.5 -11.7 -9.6 -10.7

Maize 13.7 30.6 37.7 18.7 17.2 16.1 7.2 1.9 3.3 4.1 1.7 0.9

Oil, palm -9.4 -6.9 -3.5 -15.2 -16.3 -12.1 -9.9 -14.8 -18.0 -6.2 -4.5 -5.4

Rice, paddy 14.9 18.3 20.6 12.9 19.0 17.6 12.9 9.0 14.2 11.7 9.5 19.7

Yams -31.7 -27.7 -27.9 -27.2 -22.8 -19.1 -22.4 -35.0 -35.8 -17.8 -29.8 -30.5

63

Annexes


Kenya Beans, dry -6.0 -5.8 -5.7 -6.1 -6.1 -6.1 -6.1 -6.3 -6.3 -6.5 -6.2 -6.7

Beans -23.7 -24.4 -18.7 -17.7 -13.0 -18.5 -11.3 -14.5 -10.6 -17.1 -19.8

Cassava 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Coffee -30.2 -29.6 -27.0 -26.6 -30.8 -26.5 -27.2 -31.3 -28.2 -32.0 -29.6

Maize 0.9 1.4 -0.8 -0.6 -0.1 0.1 -0.5 0.5 0.1 0.2 0.7 -0.2

Potatoes 23.5 27.2 29.4 26.1 25.3 30.3 22.6 16.5 18.0 19.5

Rice, paddy 10.2 3.4 2.7 4.0 1.1 2.3 1.1 3.5 6.7 8.2

Seed cotton 0.7 -3.7 -4.0 -3.6 -3.5 -2.7 -1.8 -2.7 -3.4 -2.4 1.0 0.8

Sorghum 3.9 2.4 3.1 2.7 0.9 4.3 3.2 10.8 12.2 4.3 4.3 3.6

Sugar cane -39.9 -33.6 -16.7 18.2 -17.0 -5.0 -36.3 -29.9 4.3 43.8 48.3 14.2

Tea -15.3 -14.1 -15.1 -15.2 -14.8 -14.0 -14.1 -14.8 -14.8 -16.8 -16.9 -13.6

Wheat 1.5 1.5 1.5 2.1 1.9 2.2 3.4 2.8 2.9 2.7

Malawi Groundnuts, with shell

-21.2 -16.6 -13.9 -15.4 -11.7 -12.1 -7.8 -7.4 -7.9 -7.4 -5.0 -6.1

Maize -3.9 -4.7 -16.2 1.7 0.4 -5.1 -0.8 4.0 -2.3 1.1 4.7

Seed cotton -15.5 -20.1 -19.3 -7.2 -15.5 -7.2 -8.3 -7.1 -2.8 -5.3 -4.4 -2.5

Sugar cane -6.2 -5.7 -4.7 -4.2 -4.1 -3.4 -2.7 -1.8 -1.7 -2.0

Tea -0.8 -0.7 -0.8 -0.6 -0.5 -0.5 -0.5 -0.3 -0.3

Tobacco 5.1 5.8 2.2 8.8 -10.7 -5.5 7.4 -11.2 -11.1

Mali Cattle -4.1 -4.0 -4.3 -4.3 -4.1 -4.1 -3.7 -3.5 -5.7 -5.6 -6.2 -6.7

Groundnuts -13.5 -14.8 -14.2 -15.3 -16.5 -17.6 -13.9 -12.2 -16.9 -16.5 -13.4 -13.4

Maize 0.1 -21.4 1.1 -0.6 -13.8 1.3 0.8 0.2 -1.6 -0.9 -11.3 -11.1

Milk, cow 16.0 17.6 19.4 22.1 13.9 18.5 31.3 20.0 30.5 32.2

Millet -12.9 -14.1 -16.3 -14.2 -14.0 -14.6 -12.1 -11.4 -16.9 -16.2 -9.9 -9.9

Rice, paddy -0.1 0.5 0.5 0.4 0.7 1.5 0.3 0.8 2.1 0.6 0.6 -0.1

Seed cotton

Sorghum -16.0 -19.0 -18.0 -16.9 -16.6 -15.7 -15.3 -14.5 -16.7 -15.6

Mozambique Cashew nuts, unshelled

-16.6 -13.5 -18.9 -11.2 -15.5 -18.2 -5.0 -11.4 -21.8 -14.3 -23.1 -27.9

Cashew nuts -18.1 -13.8 -19.9 -20.3 -23.4 -18.1 -17.9 -15.1 -19.7 -21.7 -28.7 -37.7

Cassava -6.3 -6.9 -7.1 -9.4 -12.4 -9.9 -6.7 -8.1 -4.7 -27.4 -20.1 -8.9

Maize -6.5 -8.1 -19.2 -6.4 -1.7 -4.6 -2.1 -11.0 -5.2 -6.0 -6.6 -3.6

Rice, paddy 4.5 5.0 5.2 -1.3 1.4 -3.2 -3.6 1.0 1.0 2.3 -1.4 5.2

Seed cotton -16.6 -19.6 -27.4 -23.4 -27.4 -34.3 -18.4 -16.9 -18.2 -10.8 -19.9 -33.4


64


Nigeria Cashew nuts -15.4 -15.4 -15.4 -15.4 -17.0 -15.4 -15.4 -15.4 -15.4 -15.4 -15.4

Cocoa, beans -3.7 -3.5 -3.6 -3.7 -4.7 -3.6 -3.8 -4.1 -4.6 -5.0 -5.0

Maize 15.5 16.3 14.3 15.9 17.5 19.5 12.1 12.3 13.3 18.7 19.6

Rice, paddy 3.0 2.1 1.9 2.1 2.3 1.3 1.7 1.7 2.3 2.8 2.4

Sorghum 15.3 8.4 11.5 8.4 12.0 7.6 8.4 7.1 6.2 9.2 9.2

Rwanda Beans, dry -2.8 -2.8 5.3 -8.6 10.2 12.1 30.2 16.9 15.8 15.8 14.9 14.6

Coffee, green -17.2 -28.1 -39.3 -46.1 -53.7 -47.9 -35.9 -40.9 -62.3 -39.1 -39.1 -51.0

Milk, cow 4.1 6.3 8.8 10.4 9.8 10.4 8.1 7.0 6.7 -6.9 -6.5 7.0

Rice, paddy 21.9 19.8 17.5 15.7 15.2 16.4 5.0 6.2 5.9 5.5 5.7 5.3

Tea -34.9 -46.8 -73.4 -58.2 -56.0 -49.8 -50.2 -37.1 -31.5 -26.9 -30.5 -19.6

Wheat 25.2 25.1 24.2 26.8 24.5 23.2 72.2 49.5 50.1 45.4 44.8 44.1

Senegal Groundnuts -10.0 -10.0 -10.0 -10.0 -10.0 -10.0 -9.9 -9.9 -10.0 -9.9 -10.8

Onions 23.1 26.7 12.4 28.1 25.4 25.2 20.1 26.8 21.7 30.3 11.6

Potatoes 30.6 25.2 27.9 8.5

Rice, paddy -4.3 -3.3 -3.1 -2.4 -2.0 -2.0 -2.0 -2.0 -2.1 -2.0 -2.0

Uganda Cassava -16.8 -12.7 -10.2 -10.7 -0.1 -10.3 -12.2 2.9 -5.0 -5.0 -5.0 -5.0

Cattle -21.3 -21.2 -15.7 -15.6 -13.3 -13.4 -11.4 -11.9 -12.9 -10.2 -8.8 -9.8

Coffee, green -3.1 -3.2 -3.1 -3.2 -2.9 -3.4 -3.9 -3.4 -3.1 -3.4 -2.9 -2.9

Maize 6.0 -6.6 -4.1 8.1 6.1 -5.9 -4.6 -6.2 -5.0 -5.5 -5.2 -5.3

Milk, cow -45.7 -41.8 -36.8 -33.3 -33.7 -29.3 -33.4 -33.4 -40.0 -40.7 -42.0

Rice, paddy -7.4 -3.5 -3.4 -0.2 2.0 1.9 3.4 4.2 3.4 2.3 2.6 2.9

Seed cotton -11.3 -12.4 -11.9 -10.9 -10.1 -9.9 -10.5 -11.3 -9.9 -11.5 -12.3

Sugar cane -52.4 -49.1 -49.8 -39.3 -59.1 -56.9 -66.3 -55.7 -49.0

Tea -20.6 -22.8 -21.6 -20.7 -20.2 -20.9 -19.6 -24.1 -28.2 -26.9 -22.4 -17.9

Tanzania Cashew nuts -27.3 -29.7 -38.5 -20.3 -25.8 -19.8 -11.9 -15.4 -13.4 -15.0 -10.2 -6.6

Coffee, green -7.2 -4.9 -5.8 -3.8 -5.1 -8.5 -7.5 -7.6 -9.8 -9.1 -0.6 -5.3

Maize -33.2 -35.4 -33.8 -28.4 -30.1 -21.2 -19.2 -17.7 -20.6 -25.0 -22.9 -17.7

Rice, paddy 14.8 10.5 10.1 10.5 7.3 -18.5 5.5 7.4 4.8 6.1 4.4 4.5

Seed cotton -11.9 -9.0 -9.8 -10.5 -11.0 -9.1 -7.1 -16.0 -9.6 -8.6 -8.9 -6.8

Wheat -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0


65

Annexes

Annex 2. Analytical summary of factors driving price incentives indicators for selected commodities

TABLE 6 Price incentives indicators trend and main driving factors for selected commodities, 2005–2016

Price incentives indicators, 2005–2016 Price incentives indicators, 2005–2016

Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)

Policies Market and sector features

BENIN

MA

IZE

(ex

por

ted

)

0.6 15

10

-15

-10

-5

5

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-2.4 An export ban implemented in late 2014 to curb consumers’ prices seem to have penalized farmers by decreasing incentives in the following years.

Climatic hazards impacted NRP volatility: incentives in 2011 and 2012 were driven by supply shocks due to severe floods in late 2010.

The high dependency on external demand can also explain the volatility in the price incentives pattern: in 2014, drought in neighbouring countries (i.e. Niger) raised demand and thus prices for Benin maize.

Weak transport infrastructure still hinders value chain development and contribute to the negative MDG.

CO

TT

ON

(ex

por

ted

)

36.8 100

0

20

40

80

60

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

NA The cotton floor price policy offered protection to farmers and ensured price incentives along the entire period analysed, although it prevented farmers from benefiting from international price hikes in 2011 and 2012.

Since 2012, the role of government in supervising the value chain has increased: the agreement stipulated in 2005 with the private Association Interprofessionnelle du Coton (AIC) was suspended until 2016.

Relatively efficient value chain, particularly in terms of agents’ relationships with an important role played by the State since 2012 supported positive NRP trend.


66


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


BURKINA FASO

RIC

E (

imp

orte

d)

4 30

20

-30

-20

-10

10

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

48.4 Import tariff (CET) set at 13.5 percent protected farmers to some extent. Its temporary suspension since 2008 decreased price incentives.

The producer floor price set in 2009, 2012 and 2014 had a contradictory impact, as it seems it worked as price ceiling, limiting domestic price adjustments to international fluctuations;

Subsidized sale of rice and establishment of fair price shops by the Société Nationale de Gestion du Stock de Sécurité Alimentaires (SONAGESS) in support of consumers can explain negative NRP at farm gate in some years.

Unbalanced market power at wholesale level and information asymmetry could have reduced farmers bargaining power.

High access costs from the border to the point of competition offered protection to domestic production against imports and contributed to the positive average MDG.

CO

TT

ON

(ex

por

ted

)

30.3 200

150

-100

-50

0

100

50

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-12.7 Price stabilization mechanism ("smoothing fund") in place since 2007 to protect against price risks ensured stable price incentives to farmers overall, except in 2010.

Privatization of the Cotton companies and progressive disengagement of the State in the sector may have benefitted farmers who recently enjoyed moderate but stable price incentives.

Empowerment of stakeholders through increased professionalization in the organization and operation of the sector could explain the improved price transmission and stable positive NRP since 2011.

67

Annexes


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


BURUNDI

RIC

E (

imp

orte

d)

33.6 80

-20

0

20

60

40

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

9.8 Import tariff (75 percent) set in the framework of the EAC since 2009 protected local producers; the exemption of the CET in 2012 to favour consumers contributed to lower price incentives in the following years.

Weak organization/integration of the value chain and presence of market inefficiencies limit price transmission and contributed to the significant NRP volatility.

High volatility of domestic prices was often due to uneven production pattern, especially in the most recent years.

TE

A (

exp

orte

d)

-47.2 0

-80

-60

-20

-40

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-3.9 The green leaf price fixing policy implemented by the OTB (Office du Thé du Burundi) was not effective in supporting producer prices leading to a largely negative NRP.

Provision of input subsidy by the OTB as well as under the Programme National de Subvention des Engrais au Burundi (PNSEB) launched in 2012 can partially explain the trend of price disincentives.

Weak farmers’ organization and poor bargaining power, coupled with high marketing costs contributed to poor price transmission and negative MDG.

Monopsonistic structure of the value chain penalized farmers throughout the period.

Gradual liberalization of the sector with additional private buyers emerging since 2011: the increased competition favoured farmers that benefited from higher prices.


68


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


ETHIOPIA

MA

IZE

(m

ain

ly i

mp

orte

d)

-56.8 0

-80

-60

-20

-40

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-2.6 Imposition of an export ban intermittently throughout the period, except in 2010/2011 and 2015, penalized domestic producers’ prices by reducing total demand for the commodity.

High yields and increased production since 2010, coupled with low domestic demand during years of good harvest, such as 2012, contributed to lower prices.

The lack of integration with international markets and poor price transmission led to a very negative NRP, mostly due to market distortions, including asymmetry of information, unbalanced market power and high marketing costs.

TE

FF

(ex

por

ted

)

9.5 100

-50

0

50

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-6.8 Export bans imposed intermittently since 2006 in view of protecting consumers, contributed to price disincentives at least until 2011.

High marketing and transport costs contributed to the negative MDG.

The sector largely based on subsistence farming with low marketed and traded volumes does not react to international price signals.

69

Annexes


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


GHANA

RIC

E (

imp

orte

d)

-14 60

40

-60

-40

-20

20

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

15 Import tariff on rice set at 20 percent until 2014 and decreased to 10 percent in 2015 was not fully effective in ensuring higher prices for farmers.

Suspension of the import duty in 2008 and 2009 to curb consumer prices led to severe disincentives in those years.

The National Food Buffer Stock Company operations (established in 2010) aimed at ensuring market outlet for grains producers had limited effect on producer prices and was only in place in 2011 and 2012.

Input subsidy programme in place since 2008 could have made domestic rice production more competitive and supported the low prices trend.

Poor quality of domestically produced rice and lack of storage facilities penalized producers bargaining power and arbitrage opportunities to get better prices.

Market inefficiencies (e.g. poor infrastructures) from the border to the wholesale level offered some degree of protection to local producers by making imported rice more expensive and led to positive MDG.

MA

IZE

(im

por

ted

)

-37.5 20

-80

-60

-40

-20

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

12.8 Import duties and other taxes applied over the 2005–2015 period were not effective in protecting farmers and generating price incentives.

The National Food Buffer Stock Company operations (established in 2010) aimed at ensuring market outlet for grains producers operated on a very small share of domestic production and therefore had no major effects.

Increasing demand for maize for animal feed sustained prices in the latest period.

Depreciation of the exchange rate incentivized substitution from imported to domestically produced maize and pushed prices upward recently.

Volatile and low production marketed shares reduced price transmission and increase local price volatility.

Lack of market infrastructure from the border to the wholesale level benefitted to some extent local producers by making imported rice more expensive and led to positive MDG on average.


70


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


KENYA

MA

IZE

(im

por

ted

)

26.6 150

100

-50

0

50

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

0.1 The National Cereals and Produce Board (NCPB) operations (procurement from farmers at subsidized price and strategic reserves creation) were not effective in stabilizing the maize market from 2005 to 2010 (high volatility of prices and NRP). After 2010, NCPS operations seem to have resulted in an improved support to production.

The frequent suspensions (as in 2009 and 2011) of the 50 percent CET on imports outside the EAC generated contradictory effects on domestic prices.

Severe drought cycles affected maize production causing repeated episodes of production shortages, particularly from 2011 onwards. These circumstances drove prices up and added to price incentives volatility.

Civil unrest in most productive regions caused maize shortages in 2007.

TE

A (

exp

orte

d)

21.4 100

80

-20

0

20

60

40

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-15 Organization and supervision of the value chain by the Kenya Tea Development Agency (KTDA) at production, processing and marketing stages supported prices incentives to production.

Liberalized market with the value chain functioning quite efficiently under the support and supervision of the KTDA.

Lower NRP in some years appear linked to international trade dynamics which reduced demand for Kenyan tea exports.

The 2016 NRP peak is likely due to a lag in price transmission, as the drop in the international price was not reflected at domestic level where prices remain stable.

Market inefficiencies, like excessive profit margins by the KTDA, contributed to a negative average MDG of -15 percent of the producer price.

71

Annexes


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


MALAWI

MA

IZE

(im

por

ted

)

16.4 80

-40

-20

0

20

60

40

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-1.9 Intermittent import and export restrictions during the period generated high market instability and price volatility.

The large input subsidy programme (FISP) in place since 2008 contributed to make domestic maize production more competitive and could justify the low or negative NRP in certain periods.

The procurement and stocking capacity of the parastatal agency ADMARC was ineffective in stabilizing prices for farmers. Also, the price band system (minimum support price for farmers and maximum retail price) was not enforced properly.

The country is prone to extreme weather events, such as consecutive droughts and floods, which affected maize production severely in some years (e.g. 2005 and 2010), leading to inflated domestic prices.

Weak rural infrastructure and unbalanced market power (high traders margins) limited price transmission contributing to high volatility of price (dis)incentives

Lack of storage facilities impeded farmers to take advantage of better prices in the months after the harvest.

TE

A (

exp

orte

d)

-37 0

-60

-20

-40

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-0.5 Producer prices are determined through a pricing model, composed of base prices reflecting the cost of production and bonuses based on the auction price. The formula set a price lower than the international equivalent in all the years over the 2005-2013 period leading to large negative values of the NRP throughout the period.

Limited number of buyers involved in both direct and auction sales undermined competition of the sector and affected producers’ price incentives.

A significantly overvalued exchange rate following from the fixed exchange rate policy in place up to 2012, led farmers to face even bigger disincentives.


72


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


MALI

RIC

E (

imp

orte

d)

1.2 30

20

-30

-20

-10

10

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

0.7 Import tariff (CET) applied from 2005 to 2007 provided incentives to farmers. Its removal in 2008–2009, coupled with other pro-consumer measures, such as price ceilings at retail, lowered the incentives for rice farmers.

Support to production through input subsidies, in place since 2008, can partly justify the low NRPs, as farmers can charge low prices given the lower production costs.

High volatility of international prices coupled with poor price transmission due to information asymmetry and unbalanced market power contributed to volatile, and in some cases, very negative NRPs.

MA

IZE

(m

ain

ly i

mp

orte

d)

-13.6 40

-60

-40

-20

20

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-4.7 Large input subsidy scheme since 2009 decreased production costs and induced lower prices.

The value chain being poorly integrated with limited marketed shares, is characterized by poor price transmission.

Market inefficiencies, including high transport costs and illicit taxes, add to price disincentives and led to negative MDG on average.

73

Annexes


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


MOZAMBIQUE

RIC

E (

imp

orte

d)

-16 10

-40

-30

-20

-10

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

1.3 Free Trade Agreement with SADC members and application of import tariffs for other countries were often not enforced satisfactorily.

Input subsidies and investments in infrastructure.

The domestic supply is highly dependent on large volume of imports with a domestic sector characterized by low and volatile production levels that largely affected prices stability.

The weak price transmission from downstream to upstream markets, partly due to sharp geographical divide between producer and consumer regions, contributed to price disincentives.

The rapid depreciation of the local currency against the USD since 2013 made imports more expensive benefitting local producers.

CO

TT

ON

(ex

por

ted

)

-0.7 90

60

-60

-30

30

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-22.2 The floor price policy set by the Government was effective in protecting farmers from international prices drop, but not in reflecting full incentives at times of increasing prices.

The concessionary policy on domestic cotton trade hindered competition at buyers’ level and limited farmers’ bargaining power.

Cotton Development Tax (2.5 to 3.5 percent) on cotton lint trade also likely to have penalized farmers.

Volatile exchange rates with a sharp depreciation of the metical In 2015 and 2016 which created price disincentives for farmers

Supply chain inefficiencies and high market access costs produced a largely negative MDG.


74


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


NIGERIA

RIC

E (

imp

orte

d)

25.1 90

60

-60

-30

30

0

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

2.1 Self-sufficiency rice policy promoted by the Government, including inputs and post-harvest support interventions, led to more stable incentives to production since 2010.

Import tariff, levies and other restrictions, including a ban on rice imports through land borders in place from 2011 to 2015, protected domestic rice producers.

Volatile exchange rates contributed to the NRP instability.

Socio-economic instability in the Northeast grain-producing region caused supply shortages and price increases in some years.

RWANDA

RIC

E (

imp

orte

d)

85.2 150

0

25

100

50

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

11.7 The 75 percent CET applied on rice imported from non-EAC members was subsequently reduced to 45 percent in 2015. This policy was effective in protecting domestic producers.

The minimum price policy which set minimum seasonal farm gate prices for paddy rice further contributed to generate price incentives.

Domestic and international markets remain poorly connected, not only due to the trade and market policies in place, but also because of market constraints and unbalanced market power (monopsony of rice importers/wholesalers).

TE

A (

exp

orte

d)

-42.6 0

-60

-20

-40

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-42.9 Green leaf price setting mechanism, although reformed in 2012, resulted in low producer prices along the entire period, making the policy ineffective.

Extensive production support, in terms of input provision and subsidies, can partially explain the low level of the NRP.

Despite significant expansion of tea production quantities, sales and exports over the last decade, low producer prices and poor marketing facilities persist as the main constraints in the sector.

75

Annexes


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


SENEGAL

RIC

E (

imp

orte

d)

24.8 100

80

-20

0

20

60

40

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-2.5 The minimum price policy ensured prices higher than the international equivalent to farmers.

Import tariff (CET) set at 10 percent until 2015 was subsequently raised to 35 percent, which further protected domestic producers.

The suspension of the import tariff coupled with other measures in favor of consumers in 2007/2008 decreased price incentives until 2009.

Increased government support aiming to increase rice production and trade and to promote self-sufficiency since 2016 under the umbrella of the Programme National d’Autosuffisance en Riz.

Improved quality of milled rice produced locally allowed farmers to get better prices in the most recent years.

Weak transport infrastructure.


76


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


TANZANIA

RIC

E (

imp

orte

d)

35.6 150

100

-50

0

50

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

5.6 The EAC CET tariff at 75 percent until 2013 benefitted producers. After its reduction to 15 percent in 2013 and subsequently increased to 35 percent from 2014, the policy had only a moderate effect on price incentives to production.

The National Rice Sector Development Strategy (NRDS) adopted in 2009, aimed at improving market access and information, induced price incentives for farmers.

Strong national demand for domestically produced rice probably dampened the effect of the import tariff in the first years of the analysis.

Production shortfalls in 2012 and 2013 inflated domestic prices and led to higher NRP. Conversely, the bumper harvest in 2014 depressed farm gate prices compared to the previous years.

Non-tariff barriers and inefficiencies between the border the wholesale market add to price incentives by making rice imports less competitive (positive MDG).

MA

IZE

(ex

por

ted

)

37.7 150

100

-50

0

50

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-25.4 Intermittent trade restrictions (export bans imposed until 2012, subsequently replaced by a stringent export licensing system) generated market instability and price disincentives in some years.

Market interventions by the National Food Reserve Agency (i.e. public procurement and price support programme) remained quite ineffective in ensuring stable and supportive prices to farmers.

Limited competition and market information compromise price transmission and farmers bargaining power.

High transport costs due to poor infrastructure together with bribes and informal fees along the value chain contributed to the large negative MDG.

77

Annexes


Ave

rage

NR

P

at f

arm

gate

(%

)

NRP trend Ave

rage

MD

G

(% o

f fa

rmga

te

pri

ce)


UGANDA

RIC

E (

imp

orte

d)

33.7 150

100

-50

0

50

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

0.7 Adoption of the EAC Common External Tariff (CET) at 75 percent ad-valorem duty or USD 200 per tonne since 2005 was effective in providing price incentives to farmers from 2009.

The National Rice Development Strategy for the promotion of high quality rice production launched in 2008, mainly through improved technology adoption and dissemination, allowed farmers to get higher prices for their produce.

Increasing demand for domestically produced rice – despite its low quality – supported the largely positive NRP trend.

Seasonal supply shortage of rice in rural markets and subsequent price peaks during the drought cycles in 2008/09 and in 2011 contributed to increase prices.

High marketing costs hinder price transmission and protect farmers by making imported rice less competitive against the locally-produced one (MDG close to zero).

MA

IZE

(m

ain

ly e

xpor

ted

)

37.7 150

100

-50

0

50

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

16

20

15

Per

cen

tage

-2.4 Liberalized market with no significant government interventions since 1990s: traders are only required to obtain phytosanitary certificate and quality standards certificates prior to export.

International market fundamentals, such as decrease in demand of Ugandan maize from neighboring countries (Kenya) negatively affected price incentives in Uganda in some years (2005, 2009).

Significant price volatility (domestic and international) and limited market information at the producer level contributed to the unstable price incentives trend.

High transportation costs due to poor rural roads and limited storage facilities reflected in the negative MDG.

Source: Authors’ interpretation based on MAFAP price incentives database, as of October 2017.

Monitoring food and agriculture policies and their effects is crucial to support decision makers in developing countries to take better informed decisions on their agricultural and food policies.

The present publication summarizes the results emerging from the 2017 update of the Monitoring and Analysing Food and Agricultural Policies (MAFAP) indicators for the 2005-2016 period for 14 sub-Saharan African countries. These indicators are based on internationally-recognized methodologies and are commonly used to assess the extent of policy support in agriculture. They measure the effect of trade and market policies and inefficiencies on the degree of price incentives faced by farmers in key commodity value chains, as well as the level and composition of public expenditures in support of the agriculture sector.

The results here presented are relevant to shed light on the recent policy trends and constraints on agricultural development. The study also reviews the degree of consistency of policies with stated development objectives at national and regional levels, in view of informing policy processes which are coherent, sustainable and conducive to economic growth and structural transformation.

The FAO Agricultural Development Economics Technical Study series collects technical papers addressing policy-oriented assessments of economic and social aspects of food security and nutrition, sustainable agriculture and rural development.

The series is available at www.fao.org/economic/esa/technical-studies

FOR FURTHER INFORMATION

Agricultural Development Economics Division (ESA)

Food and Agriculture Organization of the United Nations (FAO)

¡ www.fao.org/economic/esa

¡ [email protected]

3

FAO

AG

RIC

ULT

UR

AL

DE

VE

LOP

ME

NT

EC

ON

OM

ICS

TE

CH

NIC

AL

ST

UD

Y

I8997EN/1/03.18

ISBN 978-92-5-130465-5

9 7 8 9 2 5 1 3 0 4 6 5 5

ISSN 2521-7240

http://www.fao.org/economic/esa/technical-studies

http://www.fao.org/economic/esa

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Agricultural policy incentives in sub-Saharan Africa in ... · last decade (2005–2016) Monitoring...

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