metadata: authors: 0: author: Anderson,Kym
1: author: Croser,Johanna Louise
count: Africa
docna: 0: docna: Agricultural distortions in Sub-Saharan
            Africa : trade and welfare indicators, 1961 to 2004
docty: Policy Research Working Paper
subtopic: Macro-Fiscal Policy,Taxation & Subsidies,Economic Adjustment and Lending,Public Sector Economics,Trade Policy,International Trade and Trade Rules,Rules of Origin,Competition Policy,Food Security,Competitiveness and Competition Policy,Public Finance Decentralization and
            Poverty Reduction,Trade and Multilateral Issues
teratopic: Private Sector Development,Macroeconomics and Economic Growth,International Economics and Trade
abstracts: cdata!: For decades, agricultural price and
            trade policies in Sub-Saharan Africa have hampered farmers
            contributions to economic growth and poverty reduction.
            Although there has been much policy reform over the past two
            decades, the injections of agricultural development funding,
            together with ongoing regional and global trade
            negotiations, have brought distortionary policies under the
            spotlight once again. A key question asked of those policies
            is: How much are they still reducing national economic
            welfare and trade? Economy-wide models are able to address
            that question, but they are not available for many poor
            countries. Even where they are, typically they apply to just
            one particular previous year and so are unable to provide
            trends in effects over time. This paper provides a
            partial-equilibrium alternative to economy-wide modeling, by
            drawing on a modification of so-called trade restrictiveness
            indexes to provide theoretically precise indicators of the
            trade and welfare effects of agricultural policy distortions
            to producer and consumer prices over the past half-century.
            The authors generate time series of country level indexes,
            as well as Africa-wide aggregates. They also provide annual
            commodity market indexes for the region, and a sense of the
            relative importance of the key policy instruments used.
repnme: repnme: Agricultural distortions in Sub-Saharan
            Africa : trade and welfare indicators, 1961 to 2004
display_title: Agricultural distortions in
            Sub-Saharan Africa : trade and welfare indicators, 1961 to 2004
listing_relative_url: /research/2010/06/12459524/agricultural-distortions-sub-saharan-africa-trade-welfare-indicators-1961-2004
topicv3: Price Controls m1326085 2107,Agriculture and Food Security m1326205 435,Trade m1326924 698,Taxation m1327615 1121,Trade Policy m1327640 776
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Private Sector Development,Macroeconomics and Economic Growth,International Economics and Trade
no_of_pages: 40
author_keys: 0: author_key: 390780
1: author_key: 955528
model_extractions: WPS5344 Policy Research Working Paper 5344 Agricultural Distortions in Sub-Saharan Africa Trade and Welfare Indicators, 1961 to 2004 _Johanna Croser Kym Anderson_ The World Bank Development Research Group Trade and Integration Team June 2010
Policy Research Working Paper 5344

# **Abstract**

For decades, agricultural price and trade policies in SubSaharan Africa have hampered farmers’ contributions to economic growth and poverty reduction. Although there has been much policy reform over the past two decades, the injections of agricultural development funding, together with ongoing regional and global trade negotiations, have brought distortionary policies under the spotlight once again. A key question asked of those policies is: How much are they still reducing national economic welfare and trade? Economy-wide models are able to address that question, but they are not available for many poor countries. Even where they are, typically they apply to just one particular previous year and so are unable to provide trends in effects over time. This paper provides a partial-equilibrium alternative to economywide modeling, by drawing on a modification of so-called trade restrictiveness indexes to provide theoretically precise indicators of the trade and welfare effects of agricultural policy distortions to producer and consumer prices over the past half-century. The authors generate time series of country level indexes, as well as Africawide aggregates. They also provide annual commodity market indexes for the region, and a sense of the relative importance of the key policy instruments used.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to improve policy making through greater transparency of the extent and effects of past policy choices. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kym. anderson@adelaide.edu.au.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Agricultural Distortions in Sub-Saharan Africa: Trade and Welfare Indicators, 1961 to 2004***

Johanna Croser University of Adelaide <u>johlou@gmail.com</u> and Kym Anderson University of Adelaide <u>kym.anderson@adelaide.edu.au</u>

**Keywords:** distorted incentives; agricultural price and trade policies; trade restrictiveness index

**JEL classifications:** F13, F14, F15, N57, Q17, Q18

**Author contact:** Kym Anderson School of Economics University of Adelaide Adelaide SA 5005 Australia Phone +61 8303 4712 Fax +61 8223 1460 <u>kym.anderson@adelaide.edu.au</u>

*This is a product of a World Bank research project on Distortions to Agricultural Incentives (see www.worldbank.org/agdistortions). The authors are grateful for the distortion estimates provided by the authors of the various African country case studies, for funding from World Bank Trust Funds provided by the governments of the Netherlands (BNPP), the United Kingdom (DfID) and Ireland, as well as from the Australian Research Council. The views expressed are the authors’ alone and not necessarily those of the World Bank and its Executive Directors, nor the countries they represent, nor of the institutions providing the project research funds.
2

# **Agricultural Distortions in Sub-Saharan Africa: Trade and Welfare Indicators, 1961 to 2004**

In the 1960s and 1970s, governments of many Sub-Saharan African countries adopted macroeconomic, sector, trade and exchange rate policies that directly or indirectly taxed farm households seeking to export their way out of poverty. This antiagricultural, anti-trade, welfare-reducing policy stance, which was also prevalent in numerous other developing country regions up to the early 1980s (Krueger, Schiff and Valdes 1988), has since begun to be reformed. How far has that reform effort gone in altering the trade- and welfare-reducing characteristics of farm and food policies in Sub-Saharan Africa? This matters greatly for economic development and poverty alleviation, because 60 percent of Sub-Saharan Africa’s workforce is still employed in agriculture, nearly 40 percent of the population is earning less than $1/day, and more than 80 percent of the region’s poorest households depend directly or indirectly on farming for their livelihoods (World Bank 2007, Chen and Ravallion 2008).

There are important questions to be addressed about future agricultural policy reform in Africa because African agriculture is currently the subject of several new agricultural development assistance programs, as well as being important in ongoing multilateral and preferential trade negotiations. A first step in considering possible future policies is to examine the impacts of past policy choices, and in particular to ask by how much are the policies still reducing national economic welfare and trade?

Economy-wide models are able to address that question, but such models are not available for many of Africa’s poorer countries. Even where they are, typically they depend on myriad assumptions about parameter (for lack of econometric estimates) and they apply to just one particular previous year and so are unable to provide trends in effects over time.

This paper provides a partial-equilibrium alternative to economy-wide modeling, by drawing on a modification of so-called trade restrictiveness indexes to provide theoretically precise indicators of the trade and welfare effects of agricultural
3 policy distortions to producer and consumer prices. By drawing on a recent comprehensive database covering most of Sub-Saharan African agriculture, we generate annual country level indexes for the past half-century, as well as region-wide aggregates including for individual commodities and a sense of the relative importance of the key policy instruments used. In doing so we make a methodological advance by incorporating a number of key nontradable products in our estimates of the indexes, which turns out to be important in the African agricultural policy context.

Data for construction of the indexes come from the World Bank’s Distortions to Agricultural Incentives database (Anderson and Valenzuela 2008). The database gives consistent measures of price-distorting policies for 75 countries for the period 1955 to 2007. The data for the 21 African countries in that database is discussed comprehensively in Anderson and Masters (2009). In this paper we focus on 19 of those African countries, leaving aside Egypt and South Africa because they are both large and very different from the others. That sample comprises five countries of eastern Africa (Ethiopia, Kenya, Sudan, Tanzania, and Uganda), four in southern Africa (Madagascar, Mozambique, Zambia, and Zimbabwe), five large economies in Africa’s western coast (Cameroon, Côte d’Ivoire, Ghana, Nigeria, and Senegal), and five smaller economies of West and Central Africa for which cotton is a crucial export (Benin, Burkina Faso, Chad, Mali, and Togo). We concentrate on the period 1961 to 2004, since those are the years for which the African data are most complete.

This paper is structured as follows: the next section presents the methodology we use. This is followed by a discussion of the data in the World Bank’s Agricultural Distortions database. We then report our estimates of the series of indexes before presenting our conclusions and listing some caveats and areas for further research.

# **Methodology**

There is a growing literature that identifies ways to measure the trade- and welfarereducing effects of international trade-related policies in scalar index numbers. This literature serves a key purpose: it overcomes aggregation problems (across different intervention measures and across industries) by using a theoretically sound aggregation procedure to answer precise questions regarding the trade or welfare reductions imposed by each country’s trade policies.
4 These measures represent a substantial improvement on commonly used measures. The usual tools for summarizing price-distorting policy trends in a country or region (see, e.g., Anderson and Masters 2009) are measures of the unweighted or weighted mean nominal rate of assistance (NRA) and consumer tax equivalent (CTE), the standard deviation of NRAs, and in a few instances the weighted mean NRA for exportable versus import-competing covered products.<sup>1</sup> Authors often need to report more than one measure to gain an appreciation of the nature of the policy regime. For example, indicators of dispersion of NRAs give some idea of the additional welfare losses that come from greater variation of NRAs across industries within the sector (Lloyd 1974). Further, if import-competing and exportable sub-sectors have NRAs of opposite sign, they need to be reported separately because those policies would offset each other in calculating the aggregate sector NRA.

While those various indicators are useful as a set, it is often helpful to have a single indicator to capture the overall trade or welfare effect of an individual country’s regime of agricultural price distortions in place at any time, and to trace its path over time and make cross-country comparisons. To that end, the scalar index literature is very useful. The pioneering theoretical work is by Anderson and Neary (summarized in their 2005 book), with an important partial equilibrium contribution by Feenstra (1995). The theory defines an ad valorem trade tax rate which, if applied uniformly across all tradable agricultural commodities in a country will generate the same reduction in trade, or in welfare, as the actual cross-product structure of distortions.<sup>2</sup> In recent years, several empirical papers have provided series of estimates of scalar index numbers for individual countries. Irwin (2008) uses detailed tariff data to calculate the Anderson–Neary Trade Restrictiveness Index for the United States in 1859 and annually from 1867 to 1961. Kee, Nicita and Olarreaga (2009) estimate partial-equilibrium indexes for 78 developing and developed countries for a single point in time (mid-2000s). Lloyd, Croser and Anderson (2010) estimate indexes for

> 1 The OECD (2009) measures similar indicators to the NRA and CTE, called producer and consumer support estimates (PSEs and CSEs). The main difference, apart from the CSE having the opposite sign to the CTE, is that the NRA and CTE are expressed as a percentage divergence from undistorted (e.g., border) prices whereas the PSEs/CSEs relate to the divergence from actual (distorted) prices. 2 Other indexes define an ad valorem trade tax rate which, if applied uniformly across all tradable products, will generate the same government revenue (Bach and Martin 2001), or the same real national income and general equilibrium structure of the economy (Anderson 2009a), as the actual cross-product structure of distortions.
5 75 developed and developing countries in the World Bank’s recently released Distortions to Agricultural Incentives database (Anderson and Valenzuela 2008) over the period 1955 to 2007.

In addition to being useful to summarize the agricultural and food policy regime in an individual country, the Anderson-Neary scalar index measures can be usefully adapted to summarize two other aspects of agricultural policy: they can be computed for individual policy instruments, to show the relative contributions of different policy instruments to reductions in trade and welfare (Croser and Anderson 2010); and they can be computed to measure the trade- and welfare-reducing effects of policy in a single global or regional commodity market (Croser, Lloyd and Anderson 2009). In this paper we utilize the methodology to estimate all three types of indexes. In doing so, we extend the theory and analysis to include nontradables, which have not been addressed in previous studies.

# **_Country level trade- and welfare-reduction indexes_**

To capture distortions imposed by each country’s border and domestic policies on its economic welfare and its trade volume, we adopt the methodology from Lloyd, Croser and Anderson (2010). Those authors define a Welfare Reduction Index (WRI) and a Trade Reduction Index (TRI) and estimate them by considering separately the distortions to the producer and consumer sides of the agricultural sector (which can differ when there are domestic measures in place in addition to or instead of trade measures). As their names suggest, the two indexes respectively capture in a single indicator the (partial equilibrium) welfare- or trade-reducing effects of all distortions to consumer and producer prices of farm products from all agricultural and food policy measures in place. The WRI and TRI thus go somewhat closer to what a computable general equilibrium (CGE) can provide in the way of estimates of the trade and welfare (and other) effects of price distortions, while having the advantage of providing an annual time series. Fortuitously, estimates of the actual price distortions are available in the NRAs and CTEs of the World Bank’s Distortions to Agricultural Incentives database.

The derivation of the two indexes for _n_ import-competing industries (see Appendix) leads to the expressions for the TRI and WRI for the import-competing sector of a country shown in Box 1.
6 Box 1: Expressions for the TRI and WRI

|**TRI**<br>**WRI**|
|---|
|{<br>}<br>_T_<br>_Ra_<br>_Sb_<br><br><br>, with<br>2<br>2<br>1/2<br>{<br>}<br>_W_<br>_R a_<br>_S b_<br><br><br><br><br>, with|
|1<br>_n_<br>_i_<br>_i_<br>_i_<br>_R_<br>_ru_<br><br><br><br><br><br><br><br><br>and<br>1<br>_n_<br>_i i_<br>_i_<br>_S_<br>_s v_<br><br><br><br><br><br><br><br><br>1<br>2<br>2<br>1<br>[<br>~~]~~<br>_n_<br>_i_<br>_i_<br>_i_<br>_R_<br>_r u_<br><br><br>and<br>1<br>2<br>2<br>1<br>[<br>~~]~~<br>_n_<br>_i_<br>_i_<br>_i_<br>_S_<br>_s v_<br><br>|
|where<br><br><br><br><br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_C_<br>_i_<br>_i_<br>_i_<br>_C_<br>_i_<br>_i_<br>_i_<br>_i_<br>_x_<br>_p_<br>_x_<br>_p_<br>_dp_<br>_dx_<br>_p_<br>_dp_<br>_dx_<br>_p_<br>_u_<br>)<br>(<br>/)<br>(<br>/<br>/<br>/<br>*<br>*<br>2<br>*<br>2<br>*<br><br><br><br><br><br><br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_P_<br>_i_<br>_i_<br>_i_<br>_P_<br>_i_<br>_i_<br>_i_<br>_i_<br>_y_<br>_p_<br>_y_<br>_p_<br>_dp_<br>_dy_<br>_p_<br>_dp_<br>_dy_<br>_p_<br>_v_<br>)<br>(<br>/)<br>(<br>/<br>/<br>/<br>*<br>*<br>2<br>*<br>2<br>*<br><br><br>,|
|*2<br>*2<br>dx /d<br>/<br>d<br>/d<br>_C_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_a_<br>_p_<br>_p_<br>_m_<br>_p_<br>_p_<br><br><br>, and<br>*2<br>*2<br>dy /d<br>/<br>d<br>/d<br>_P_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_i_<br>_b_<br>_p_<br>_p_<br>_m_<br>_p_<br>_p_<br><br><br>.|
|Variable definitions:<br>_T_— Trade Reduction Index;_W_— Welfare Reduction Index;_R_— index of average consumer price<br>distortions; S —index of average producer price distortions;_R_— Consumer Distortion Index;_S_—<br>Producer Distortion Index;_si_— the rate of distortion of the producer price in proportional terms;_ri_—<br>rate of distortion of the consumer price in proportional terms;_ui_— weight for each commodity in_R_<br>and_R’_, which is proportional to the marginal response of domestic consumption to changes in<br>international free-trade prices and can be written as a function of the domestic price elasticity (at the<br>protected trade situation) of demand (<br>_i_<sup>);</sup><sup>_vi_ — weight for each commodity in</sup><sup>_S_ and</sup><sup>_S’_, which is</sup><br>proportional to the marginal response of domestic production to changes in international free-trade<br>prices and can be written as a function of the domestic price elasticity (at the protected trade situation)<br>of supply, (<br>_i_<br>);_pi_<br>*— border price;<br>_P_<br>_i_<sup>_p_ =</sup><sup>_pi_</sup><br>*(1 +_si_ ) — distorted domestic price;<br>_C_<br>_i_<sup>_p_</sup><br> <sup>=</sup><sup>_p_</sup>_i_<br>*(1 +_ri_ ) —<br>distorted domestic consumer price;<br>(<br>)<br>_C_<br>_i_<br>_i_<br>_i_<br>_x_<br>_x p_<br><br>— quantity of good_i_demanded (as a function of own<br>domestic price);<br>(<br>)<br>_P_<br>_i_<br>_i_<br>_i_<br>_y_<br>_y p_<br><br>— quantity of good_i_supplied (as a function of own domestic price); a (b)<br>— weight of consumption (production) in the WRI or TRI, which is proportional to the ratio of the<br>marginal response of domestic demand (supply) to a price change relative to the marginal response of<br>imports to a price change.|

Source: Lloyd, Croser and Anderson (2010).

Essentially the import-competing TRI and WRI are constructed from appropriately weighted averages of the level of distortions of consumer and producer prices. The TRI and WRI use the same weights, but the TRI is a mean of order one whereas the WRI a mean of order two (reflecting the fact that loss of import volume is proportional to the distortion rate whereas the loss of welfare is proportional to the square of the distortions rate). The WRI thus captures the disproportionately higher
7 welfare costs of peak levels of assistance or taxation, and is positive regardless of whether the government’s agricultural policy is favoring or hurting farmers.

The TRI and WRI can each be extended so as to add the exportable and nontradable sub-sectors of agriculture (see Appendix). Distortions to exportable industries are inputted into the TRI as negative values because a positive (negative) price distortion in an exporting industry has a trade-expanding (-reducing) effect, and thus decreases (increases) the TRI. Distortions to nontradable industries are inputted into the TRI as zero values because a domestic price distortion in a nontradable industry by definition has neither a trade-expanding nor trade-reducing effect because of assumed prohibitively high trade costs. This extension of the TRI and WRI to include nontradables is a methodological contribution of this paper,<sup>3</sup> and is of practical significance in the case of Sub-Saharan Africa where nontradables account for a non-trivial share of the gross value of agricultural production (discussed below).

The expressions for the TRI and WRI weights above show that estimates of price elasticities are required to compute the indexes. In line with Lloyd, Croser and Anderson (2010), in the absence of elasticities we adopt some simplifying assumptions in this paper. We assume that domestic price elasticities of supply (demand) are equal across commodities within a sub-sector. This powerful simplifying assumption allows us (in the empirical section below) to find appropriately weighted aggregates of distortions on the production and consumption sides simply by aggregating the change in consumer (producer) prices across commodities and using as weights the sector share of each commodity’s domestic value of consumption (production) at undistorted prices. We expect this simplifying elasticity assumption to have a very small impact on the reported indexes. This is because elasticities appear in both the numerator and denominator of the weight expressions, and therefore cancel each other out to some extent. Further, Kee, Nicita and Olarreaga (2009) show that the TRI and WRI can be decomposed into three components and the elasticity only enters into one of the three components, which in practice is a very small component relative to the other two. This transparent assumption also makes sense in the context of computing time series of indexes for

> 3 Anderson and Neary (2005), chapter 12 discusses the possibility of extending indexes to nontradable sub-sectors and including domestic distortions in their general equilibirum framework.
8 Africa, where there is a dearth of reliable and consistent elasticity estimates across time for all our focus countries and their covered agricultural products.

# **_Policy instrument trade and welfare reduction indexes_**

The above country-level TRI and WRI measures are the aggregate of the trade- and welfare-reducing effects of all the policy measures in place. The variables _si_ and _ri_ , as domestic-to-border price ratios, can theoretically encompass distortions provided by all trade tax/subsidy and trade non-tax/subsidy measures, plus domestic price support measures (positive or negative), plus direct interventions affecting farm input prices. Furthermore, where multiple exchange rates operate, the measures can encompass an estimate of the import or export tax equivalent of that distortionary regime too.

Whilst it is desirable to have such an aggregated country level indicator that is so encompassing, agricultural policy analysts are sometimes interested in the relative contribution of different policy instruments to reductions in trade and welfare. To provide this insight, it is possible to use the Anderson-Neary framework to construct indicators of policy distortions at the instrument level to facilitate this comparison.<sup>4</sup> To capture distortions imposed by each African country’s different policy instruments on its economic welfare and its trade volume, we adopt the methodology from Croser and Anderson (2010). These authors define an Instrument Welfare Reduction Index (IWRI) and an Instrument Trade Reduction Index (ITRI), which can be estimated by considering the distortion from a single policy instrument to the producer and consumer sides of the economy.

The methodology in Croser and Anderson (2010) identifies four types of border distortions (import taxes and subsidies, and export taxes and subsidies), for which individual ITRI and IWRI series can be estimated. In addition to the border measures, the series for domestic distortions in the form of production, consumption and input taxes and subsidies can be estimated. To estimate the trade-reducing effect of an individual instrument, those authors derive expressions for the change in import volume from the individual policy measures, which are used as the basis for deriving

> 4 We note that most of the series of TRI and WRI indicators in the literature are for single instruments anyway. For example, Irwin (2008) uses only import tariffs; and Kee, Nicita and Olarreaga (2009) report two series of indexes — one based on tariffs only; and the other on tariffs plus NTBs. However, we are unaware of other studies that use the Anderson-Neary framework to directly compare the distortionary effects of different instruments on trade and welfare.
9 ITRIs. To estimate the welfare-reducing effect of individual instruments, the authors make an assumption about the allocation of the total welfare loss from the combination of individual policy instruments. The authors assume that border measures are applied first, and that this may be supplemented by additional domestic distortions. Thus the domestic distortion’s welfare reduction is the residual from subtracting the border measures’ effects from the total welfare reduction of all policy measures. This allocation assumption provides a lower-bound on welfare losses from border measures and an upper-bound on welfare losses from domestic measures.

The derivation of the ITRI and IWRI follows essentially the same steps as those for the country-level indexes which encompass all forms of distortion. The difference in the algebraic methodology is to specify separate indexes for the nine different types of policy instrument. Simplifying price elasticity assumptions can be made in the absence of reliable estimates, and again these assumptions have a minimal effect on the estimates.

# **_Commodity market trade and welfare reduction indexes_**

In addition to constructing country-level and instrument-specific indexes, this paper makes use of another methodology within the Anderson-Neary framework to analyze a different aspect of agricultural policy in Africa’s poorest nations. We construct indexes that show the extent to which African markets for individual farm commodities are distorted relative to others. We employ the methodology in Croser, Lloyd and Anderson (2010) for this purpose. This methodology is novel because whereas all previous work within the trade restrictiveness indexes literature has focused on constructing index numbers of distortions from the perspective of a single country, this methodology instead takes a regional view of individual commodity markets.

The commodity TRI (WRI) is equal to the uniform trade tax that has the same effect on regional trade volume (welfare) as the existing set of distortions in the region’s national commodity markets. The measures are constructed in the same way as those for individual country indexes, except that instead of summing across distortions in different industries for a single country, the measures are constructed by summing across distortions in different countries for a single commodity.
10 The indexes are computed using data on the domestic production and consumption sides of the region’s national commodity markets, and the measures account for all forms of border and domestic price distortion in each country for the commodity market of interest, as well as incorporating import-competing and exportable countries into the measure. In the absence of elasticity estimates, we make simplifying assumptions analogous to those made for national indexes. Croser, Lloyd and Anderson (2010) demonstrate that these assumptions have a minimal impact on the estimated series when constructing indexes for global markets.

# **Distortions to Agricultural Incentives database**

This study makes use of the World Bank’s Distortions to Agricultural Incentives database (Anderson and Valenzuela 2008). The database came out of a global research project seeking to improve the understanding of agricultural policy interventions and reforms in Asia, Europe’s transition economies, Latin America and the Caribbean as well as Africa. The database contains annual estimates of nominal rates of assistance (NRA) (positive or negative) for key farm products in 75 countries that together account for between 90 and 96 percent of the world’s population, farmers, agricultural GDP, and total GDP. There are 21 African countries in the database.

We concentrate on the sample of 19 Sub-Saharan African countries listed in the introduction, but exclude relatively affluent Egypt and South Africa which together account for between one-third and one-fifth of Africa gross value of production at undistorted prices over the period under analysis. For the 19 African focus countries, the database contains around 6000 consistent estimates of annual NRAs to the agricultural sector and the same number of CTEs between 1955 and 2005. Country coverage up to 1960 is much less than from 1961, so the series of estimates presented in this paper begins in that latter year.

The estimates of NRA and CTE in the database are at the commodity level and cover a subset of 41 agricultural products in Africa. These so-called covered products account for around 70 percent of total agricultural production over the period studied. The data identifies each year the extent to which each commodity in each country is considered an importable, exportable or nontradable, a status that
11 may change over time. In the 19 African focus countries, nontradable products account over time for between 40 and 55 percent of the gross value of production of all covered agricultural products (last column of Table 1).

The range of policy measures included in the NRA estimates in the Distortions to Agricultural Incentives database is wide. By calculating domestic-toborder price ratios, the estimates include assistance provided by all tariff and nontariff trade measures, plus any domestic price support measures (positive or negative), plus an adjustment for the output-price equivalent of direct interventions on inputs. Where multiple exchange rates operate, an estimate of the import or export tax equivalents of that distortion are included as well. The range of measures included in the CTE estimates include both domestic consumer taxes and subsidies and trade and exchange rate policies, all of which drive a wedge between the price that consumers pay for each commodity and the international price at the border.

Anderson and Masters (2009) note several patterns that emerge in the distortions to agricultural incentives in the 21 focus countries. In the 1960s and 1970s, many African governments had macroeconomic, sector and trade policies that increasingly favored the urban sector at the expense of farm households and favored production of import-competing farm goods at the expense of exportables. The policy regime was characterized as pro-urban (anti-agricultural) and pro-selfsufficiency (anti-agricultural trade). Since the 1980s, Africa has reduced its antiagricultural and anti-trade biases, but many distortions still remain.

For the 19 countries in this study, Table 1 and Figure 1 illustrate those patterns. The weighted average NRA for the 19 countries is almost always below zero, indicating that together agricultural price, trade and exchange rate policies have reduced the earnings of farmers in these countries. The average rate of direct taxation (negative NRA) of African farmers rose until the late 1970s before declining by more than half over the next 25 years. Meanwhile, assistance to nonagricultural sectors rose (thereby making farming less attractive in relative terms) and then declined slower than for agriculture, as reflected in the Relative Rate of Assistance (RRA) estimates in Table 1.

Table 2 reports the country-level NRAs for covered products for each of the 19 countries in this sample. It reveals the considerable diversity within the sample. In some countries — such as Cameroon, Ghana, Senegal, Uganda, Tanzania, and Madagascar — there was a reduction in taxing farmers since the regional peak in
12 1975–79, while in other countries — such as and Cote d’Ivoire, Zambia, and Zimbabwe — high levels of agricultural taxation persist.

The country level aggregate measures hide the degree of variation in NRA estimates within countries. Anderson and Masters (2009) report the standard deviations around the weighted mean NRA for covered products in each country, showing that the variation is significant. An indication of the extent of variation between groups of products is seen when comparing the average NRAs for importcompeting and exportable product groups, which reflects the antitrade bias (Figure 1).

Notwithstanding the valuable contribution of the measures reported in Anderson and Masters (2009), sector averages of NRAs and RRAs can be misleading as indicators of the aggregate extent of price distortion within the sector. They can also be misleading when compared across countries that have varying degrees of dispersion in their NRAs (and CTEs) for farm products. We therefore now turn to consideration of the TRI and WRI series estimated for this paper, and the additional insights these measures can provide.

# **Trade and welfare reduction index estimates**

The regional aggregate TRI for the 19 African focus countries for all covered products is positive and of a significant magnitude over the period under analysis (Figure 2). The positive TRI indicates that overall agricultural policy in African countries resulted in reduced trade. The extent of that has decreased over time, however, with the five-year TRI averages of between 20 and 25 percent in the first two decades of data falling to around 10 percent in the most recent decade. The TRI has the opposite sign to the NRA because the TRI correctly aggregates policies that reduce trade volume, regardless of whether the NRA is positive or negative. The importance of the difference in these aggregations of the trade-reducing effect of policies can be seen in the early-1960s, for example, when the average NRA was around zero but the TRI was quite high (capturing the trade-reducing effect of both import taxes and export taxes, which offset one another in the NRA estimate). Similarly in the late 1980s, the NRA trends from around -15 to -10 percent at a time when the TRI increases from 20 to 30 percent. The aggregate NRA gives the
13 impression that policies are becoming less distorted in this period but, because the upward trend in the NRA is caused by an increase in import taxes, the TRI correctly reveals that agricultural policies are in fact becoming more restrictive in this time period.

The WRI series for all covered products is necessarily positive and everywhere lies above the TRI series (Figure 2). The WRI series correctly demonstrates the negative welfare consequences that flow from both negative and positive price distortions. Furthermore, the WRI series provides a better indicator of the welfare cost of distortions than the average level of assistance or taxation, due to the inclusion in the WRI of the ‘power of two’. A weighted arithmetic mean does not fully reflect the welfare effects of agricultural distortions because the dispersion of that support or taxation across products has been ignored. By contrast, the WRI captures the higher welfare costs of high and peak levels of assistance or taxation. That is, the WRI reflects the disparity issue discussed in Lloyd (1974): the larger the variance in assistance levels, the greater the potential for resources to be used in activities which do not maximize economic welfare.

The aggregate African results mask country-level diversity in the TRI and WRI series. Some countries — such as Cote d’Ivoire, Ethiopia, Sudan, Tanzania and Zimbabwe — persistently restrict trade (in aggregate) throughout the period under analysis (Table 3). Other countries — such as Kenya, Zambia and Mozambique — have had periods in which policies in aggregate have expanded agricultural trade slightly. In terms of the WRI, there is less diversity across countries, since WRI measures are all necessarily positive (Table 4). The extent to which agricultural policy reduced aggregate welfare does differ across countries, however. Some countries have low reductions in welfare, including Uganda and most cotton-exporting countries. Figure 3 provides a snapshot for 2000–04 of the diversity in the WRI and TRI for each of the 19 countries, with the weighted African average in the middle.

A useful way of understanding the overall welfare reduction for Africa from agricultural policy is to compute the country contributions to the WRI for the 19 African focus countries as a whole. Contributions can be found by computing dollar values of the welfare reduction for each country (by multiplying the WRI percent by the average of the gross value of production and consumption at undistorted prices). Such contributions will therefore take account of the magnitude of national WRIs as well as the significance of each country in terms of its share of the gross value of
14 production and consumption at undistorted prices. Table 5 shows that Nigeria and Sudan are the two countries that dominate the region’s contributions, with Sudan becoming more important over time (as its WRI series trends upwards). Ethiopia accounts for up to 10 percent of the focus region’s welfare reduction. The last column of Table 5 reports country contributions to the decline in the regional WRI from its value of 44 percent in 1975–79 to its value of 27 percent in 2000–04. Once again, Nigeria and Sudan dominate the overall reduction, together accounting for around 80 percent of the fall in the WRI. However, Uganda, Cameroon, Senegal and Madagascar have a slightly offsetting effect on the regional fall in the WRI over that period.

It is useful to compare the TRI and WRI series reported above for all covered products, with those for just covered tradables in Africa.<sup>5</sup> In Table 6, it can be seen that the TRI and WRI for all covered products is significantly lower than that for covered tradables. This is because nontradables account for a large share of the gross value of production and consumption. The TRI estimates for all covered products are roughly half, and WRI estimates are roughly two-thirds, what there were with nontradables included.

Another point to note from Table 6 is that the country sample matters for the reporting of TRI and WRI results. For comparison, we report the results from Lloyd, Croser and Anderson (2010), which computed TRI and WRI series for an alternative sample of African countries in the Distortions to Agricultural Incentives database. Their sample is the same 19 countries in this paper, with the addition of Egypt and South Africa, and excluding the five cotton countries of Benin, Burkina Faso, Chad, Mali and Togo, because these countries only have one covered tradable product (cotton). In general, the 19 focus countries in this study have higher TRI and WRI 5-year averages. This is driven by the exclusion of Egypt and South Africa, which had low country-level TRI and WRI estimates. The exception to the general pattern is the time period 1985–89, where the Lloyd, Croser and Anderson (2010) estimates are higher, owing to very high protection in Egypt in that five-year average period (when international food prices collapsed just as Egypt raised its previously very low domestic food prices).

5 Note that Lloyd, Croser and Anderson (2010) report measures for covered tradables only.
15 It is also useful to compare the TRI and WRI results for the 19 focus countries to the TRI and WRI estimates for other developing country regions, which are reported in Lloyd, Croser and Anderson (2010). The 19 African focus countries had the most welfare reducing policies over time, and generally the most trade-distorting. All three regions have shown a trend towards less trade and welfare reducing agricultural policies in recent years, however (Figure 4).

# **_Policy instrument results_**

We now turn to the national decompositions of the TRI and WRI to the policy instrument level. Figure 5 provides a summary of the estimates of the contribution to the weighted average WRI series for the 19 African focus countries of four different border measures: taxes and subsidies on both imports and exports. The figure demonstrates the very substantial role that export taxes have played in the reduction of welfare in the region. On average, more than half the welfare reductions have come from anti-agricultural export taxing policies over the period studied. Notwithstanding their significant distortionary contribution, export taxes have also been the area in which there has been most reform in recent decades. The contribution of export taxes to the reduction in the WRI over the period 1985–89 to 2000–04 is 93 percent. Import taxes reduced the overall WRI by 34 percent; while there were offsetting increases in the contribution of export subsidies (13 percent) and import subsidies (15 percent) to the WRI. The contributions to TRI and WRI estimates for the 19 African countries from domestic distortions are small, never accounting for more than 2 percent of the overall regional TRI or WRI.

# **_Commodity TRI and WRI results_**

The TRI and WRI estimates for individual regional commodity markets provide a different perspective on the level of distortion in the 19 focus countries over the period under analysis. Table 7 reports the five-year average WRI estimates for the focus region for individual commodity markets. The table reveals considerable diversity in the distortions in different commodity markets. Fruit and vegetable commodity markets, which tend to have a high share of nontradable production, have low WRI estimates on average, whereas traded commodities such as tropical crops,
16 oilseeds and livestock tend to have more welfare-reducing policies in place. Grains, which comprise a mixture of tradable and nontradable products, had highlydistortionary policies in place in the 1960s on average, but these have been reduced over time.

Figure 6 gives a snapshot of the diversity across commodity markets in the regional TRI and WRI for 2000–04. Sugar and cotton markets continue to have highly distorted policies in terms of both the trade and welfare effects of policies. Soybean, by contrast, has trade-expanding policies in aggregate, but the policies are nevertheless welfare reducing.

# **Conclusions, caveats and areas for further research**

Reform of agricultural policy in Africa is topical at present. Recently announced international investment programs, domestic policy reforms, and the negotiation of international and regional trade agreements are on the agenda. To assess each of these policy initiatives, measurement of intervention levels is required. Certainly, economywide models can measure the welfare and trade (and other) effects of policy in a particular country or market. But such models require reliable data on the structure of the economy, and econometric estimates of myriad parameters, neither of which can be easily found for the poorer countries of Africa. Even where economy-wide models are available, they may be calibrated to a particular year and so incapable of providing a long time-series of estimates of the regional effect of distortional policies over time.

Scalar index measures, by contrast, can meaningfully summarize the welfare and trade effects of policy intervention in agriculture in poorer countries. As demonstrated above, these indexes can be estimated using already available price, quantity and distortions data, and so are relatively inexpensive to generate.

The estimates in the paper reveal several important findings. The national level TRIs and WRIs indicate that although there has been policy reform in African agriculture over the past 50 years, the overall trade- and welfare-reducing effects of current policies remain significant. Some individual commodity markets in Africa are more distorted than others, sugar and cotton being two of the most distorted.

The scalar index numbers reported in this paper provide a better measure of policy intervention than widely-used NRA-type measures because they correctly
17 aggregate offsetting policies and the WRI captures the higher welfare costs of more disparate policies across industries. These scalar measures have the advantage of making policies more transparent, which can facilitate further reforms.

Notwithstanding their contribution, there are a number of limitations to the indexes. Some are empirical. First, the estimates can only take account of agricultural products which have commodity level data in the World Bank’s database. The database has product level data for approximately 70 percent of the 19 African focus countries’ farm production value, and somewhat less of their consumption value. We, therefore, necessarily miss some information about distortions to production and consumption and therefore trade and welfare in Africa. Furthermore, the data in the World Bank’s database are not highly disaggregated, which is not ideal for capturing the full extent of welfare and trade distortions from African policies affecting differentiable processed as well as primary products. Finally, in the absence of reliable, consistently estimated time-series of elasticities of demand and supply, we make simplifying assumptions about those elasticities to compute the scalar index number series. The estimates would be more precise if we had access to reliable elasticity estimates, although probably not a lot different, according to sensitivity analysis conducted by Croser, Lloyd and Anderson (2010).

There are also some methodological caveats worth noting. The methodology in the paper adopts the standard approach still presented in most textbooks on trade policy or welfare economics, based on the benchmark of competitive markets. The methodology ignores the existence of divergences and governance problems, including administrative costs. Thus the trade and welfare reduction indexes reported above may be over or under-stated to the extent that such problems exist. For example, in some cases where there is market failure, we know from second-best theory that policies that increase assistance to a lightly protected industry may increase rather than decrease national economic welfare. In particular, the RRA measure reported in Table 1 suggest that distortions to non-farm tradables sectors in Africa exist. Even so, the series reported in this paper are useful aggregations of data and almost certainly give a better indication of trade and welfare effects of policy than average NRA-type measures.
18

# **References**

Anderson, J.E. (2009a), “Consistent Trade Policy Aggregation”, _International Economic Review_ 50(3): 903–27.

Anderson, J.E. and J.P. Neary (2005), _Measuring the Restrictiveness of International Trade Policy_ , Cambridge MA: MIT Press.

Anderson, K. (2009b), ‘Five Decades of Distortions to Agricultural Incentives’, chapter 1 in K. Anderson (ed.), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.

Anderson, K. and J.L. Croser (2009), _National and Global Agricultural Trade and Welfare Reduction Indexes, 1955 to 2007,_ database available at <u>www.worldbank.org/agdistortions</u> Anderson, K. and W. Masters (eds.) (2009), _Distortions to Agricultural Incentives in Africa_ , Washington DC: World Bank.

Anderson, K. and E. Valenzuela (2008), _Global Estimates of Distortions to Agricultural Incentives, 1955 to 2007_ , database available at <u>www.worldbank.org/agdistortions</u> Bach, C. and W. Martin, (2001), ‘Would the Right Tariff Aggregator for Policy Analysis Please Stand Up?’ _Journal of Policy Modeling_ 23: 62–35.

Chen, S, and M. Ravallion (2008), ‘The Developing World is Poorer Than We Thought, But No Less Successful in the Fight Against Poverty’, Policy Research Working Paper 4703, World Bank, Washington DC, August.

Croser, J. and K. Anderson (2010), ‘Changing Contributions of Different Agricultural Policy Instruments to Global Reductions in Trade and Welfare, Contributed Paper for the Annual Conference of the Australian Agricultural and Resource Economics Society, Adelaide, 10-12 February.

Croser, J.L., P.J. Lloyd and K. Anderson (2010), ‘How do Agricultural Policy Restrictions to Global Trade and Welfare Differ Across Commodities?’ _American Journal of Agricultural Economics_ Vol. 92 (forthcoming).

Feenstra, R. (1995). ‘Estimating the Effects of Trade Policy’, in G. Grossman and

- K. Rogoff (eds.), _Handbo_ o _k of International Economics, Vol. 3_ , Amsterdam: Elsevier.
19 Irwin, D. (2008), ‘Trade Restrictiveness and Deadweight Losses from U.S. Tariffs, 1859-1961’, NBER Working Paper No. W13450.

Kee, H.L., A. Nicita and M. Olerreaga (2009), ‘Estimating Trade Restrictiveness Indexes’, _Economic Journal_ 119(534): 172–99.

Lloyd, P.J. (1974), ‘A More General Theory of Price Distortions in an Open Economy’, _Journal of International Economics_ 4(4): 365-86, November. Lloyd, P.J., J.L. Croser and K. Anderson (2009), ‘Welfare-Based and Trade-Based Indicators of National Agricultural Distortions’, Ch. 11 in K. Anderson (ed.), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007_ , London: Palgrave Macmillan and Washington DC: World Bank.

Lloyd, P.J., J.L. Croser and K. Anderson (2010), ‘Global Distortions to Agricultural Markets: New Indicators of Trade and Welfare Impacts, 1960 to 2007’, _Review of Development Economics_ 14(2), May (forthcoming).

OECD (2009), _Agricultural Policies in OECD Countries: Monitoring and Evaluation 2009_ , Paris: Organization for Economic Cooperation and Development. World Bank (2007), _World Development Report 2008: Agriculture for Development_ , Washington DC: World Bank.
20

# **Appendix: Derivation of Trade- and Welfare-Reduction Indexes**

Lloyd, Croser and Anderson (2010) outline a methodology for computing indexes which accurately capture the state of trade policy regime in an individual country in a theoretically meaningful way. Their methodology, which draws heavily on the Anderson and Neary (2005) methodology, defines partial equilibrium indexes which aggregate the production and consumption sides of the economy separately (instead of trade data as is more commonly done with trade restrictiveness indexes). This form of index is well-suited to agricultural distortions research, where data is available for production and consumption of individual farm commodities. This Appendix briefly outlines that theory for the import-competing sector of a small open economy.

Start by considering an individual country, assuming it has a small, open economy in which all markets are competitive. The market for an import good may be distorted by a tariff and other nontariff border measures or by behind-the-border measures such as domestic subsidies and price controls.The first measure of interest is the effect of a country’s distortions on its import volume, the TRI. This is defined as the uniform tariff rate which, if applied to all goods in the place of all actual border and behind-the-border price distortions, would result in the same reduction in the volume of imports (summed across products by valuing them at the undistorted border price) as the actual distortions.

Consider the market for one good, good _i_ , which is distorted by a combination of measures that distort its consumer and producer prices. For the producers of the good, the distorted domestic producer price,<sup>_p_</sup> _iP_<sup>, is related to the</sup> * _P_ * border price, _pi_ , by the relation,<sup>_p_</sup> _i_<sup>=</sup><sup>_pi_</sup> (1 + _si_ ) where _si_ is the rate of distortion of the producer price in proportional terms. For the consumers of the good, the distorted domestic consumer price,<sup>_p_</sup> _Ci_<sup>, is related to the border price by the relation,</sup> _C_ *<sup>_p_</sup> _i_<sup>=</sup><sup>_p_</sup> _i_ (1 + _ri_ ) where _ri_ is the rate of distortion of the consumer price in proportional terms. In general, _ri_ ≠ _si_ . Using these relations, the change in the value of imports in the market for good _i_ is given by:

* *  _M i_  _pi_  _xi_  _pi_  _yi_
21 where the quantities of good _i_ demanded and supplied,<sup>_x_</sup> _i_<sup>and</sup><sup>_y_</sup> _i_<sup>, are functions just</sup> _C P_ of their own domestic price: _xi_  _xi_ ( _pi_ ) and _yi_  _yi_ ( _pi_ ) .

Strictly speaking, this result holds only for small distortions. In reality rates of distortion may not be small. If, however, the demand and supply functions are linear over the relevant price range, the effect on imports is given by equation (1) with constant slopes of the demand and supply curves ( _dxi_ /<sup>_dp_</sup> _iC_ and _dyi_ /<sup>_dp_</sup> _iP_ , respectively). If the functions are not linear, this expression provides an approximation to the loss.

With _n_ importable goods subject to different levels of distortions, the aggregate reduction in imports, in the absence of cross-price effects in all markets, is given by: Setting the result equal to the reduction in imports from a uniform tariff, T, gives:  _n pi_ *2 _dxi_ / _d pCi ri_   _n pi_ *2 _dyi_ / _d piPsi_   _n pi_ *2 _dmi_ / _dpTi i_  1 _i_  1 _i_  1 Solving for T, give Evidently, the uniform tariff T can be written as a weighted average of the level of distortions of consumer and producer prices ( _R_ and _S_ are indexes of average consumer and producer price distortions; they are arithmetic means). An important advantage of using this decomposition of the index into producer and consumer effects is that it treats correctly the effects of NTMs and domestic distortions that affect the two sides of the market differently.

In equation 3c (equation 3b), the weights for each commodity are proportional to the marginal response of domestic production (consumption) to
22 changes in international free-trade prices. These weights can be written as, among other things, functions of the domestic price elasticities (at the protected trade situation) of supply and demand (  _i_ and<sup></sup> _i_<sup>, respectively):6</sup> The other index defined in Lloyd, Croser and Anderson (2010), the WRI, measures the effect of a country’s distortions on its economic welfare. The derivation follows the same steps as in the derivation of the TRI except that instead of starting from the loss in trade volume from a policy, one starts from a loss of consumer and producer surplus (a welfare loss,<sup>_L_</sup> _i_<sup>). With</sup><sup>_n_importable goods</sup> subject to different levels of distortions, the aggregate welfare loss, in the absence of cross-price effects in all markets, is given by: The uniform tariff rate, W, that generates an aggregate deadweight loss identical with that of the differentiated set of tariffs is determined by the following equation: W is thus the uniform tariff which, if applied to all goods in the place of all actual tariffs and NTMs and other distortions, would result in the same aggregate loss of welfare as the actual distortions. Solving for W, we have: with _ui_ , _vi_ , _a_ and _b_ as defined for equation 3 above. W is the desired Welfare Reduction Index, while _R_  and _S_  are the contributions to W from consumer and producer price distortions, respectively. They, like their appropriately weighted average W, are means of order two. As with the index T, we can deal with, and analyse, the production and consumption sides of the sector separately.

> 6 These expressions can also be written as functions of, among other things, the domestic price elasticities at the free trade points.
23

# _Extension to exportable sectors_

Lloyd, Croser and Anderson (2010) report how the indexes can each be extended to include the exportables sub-sector. This is facilitated by way of aggregating the import-competing and exportables sub-indexes where the weights for each subsector are the share of the sub-sectors’ value of production (consumption) in the total value of production (consumption). The resulting measure is the import tax/export subsidy which, if applied uniformly to all products in the sector, would give the same loss of welfare as the combination of measures distorting consumer and producer prices in the import-competing and exportable sub-sectors.

The only trick in the case of the TRI is to keep separate track of the subsets of import-competing and exportable goods because the sign of an NRA in exportable sector (positive or negative) has the opposite effect on the TRI. That is, while an export subsidy in the exportable sub-sector reduces welfare in the same way as an import tax in the import-competing sub-sector, the export subsidy will increase trade and the import tariff reduces trade.

# _Extension to nontradables sectors_

In this paper we make a further methodological extension to the theory. We extend indexes to include nontradable, as well as tradable sectors. This is important for Africa, because in many countries the share of nontradables in the gorss value of agricultural production is high. Becasue nontradables are generally free of distortions, an index that does not take into account these sectors will tend to overstate the trade- and welfare-reducing effect of agricultural policy.

To include nontradables, we keep separate track of three subsectors of the economy: import-competing, exportable and nontradable sub-sectors. We generate sub-sector specific TRI and WRI indexes (as we previously did for each of the import-competing and exportable subsectors). The three sub-sector indexes are then aggregated using as weights each sub-sectors’ share of value of production (consumption) in the total value of production (consumption).
24 For the WRI, because distortions in nontradable secotrs cause welfare distortions, we proceed as expected and _si_ and _ri_ values in equations 7b and 7c are the actual level of distortion in the nontradable sectors.

For the TRI, however, we make an asusumption that _si_ and _ri_ values in equations 3b and 3b are zero. This assumption is such that distortions to nontradable products are assumed not to expand or reduce trade volume. The assumption recognises the high trade costs in these products. _si_ and _ri_ values). This is the case for the vast majority of non-tradable products in any case. It means that the contribution of nontradables to TRI is only through the share of nontradables in value of production (consumption) in the total value of production (consumption).
25 Figure 1: Nominal Rates of Assistance for import-competing, exportable and all covered products, 19 African countries, 1961 to 2004 <!-- Start of picture text --> (percent)<br>200<br>150<br>100<br>50<br>0<br>‐50<br>‐100<br>1961 1966 1971 1976 1981 1986 1991 1996 2001<br>Import‐competing products Exportables Nontradables All covered tradables<br><!-- End of picture text --> Source: Anderson and Valenzuela (2008)
26 Figure 2: Trade and Welfare Reduction Indexes, and Nominal Rate of Assistance for all covered products, 19 African countries, 1961 to 2004 <!-- Start of picture text --> (percent)<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>‐10<br>‐20<br>‐30<br>‐40<br>1961 1966 1971 1976 1981 1986 1991 1996 2001<br>TRI WRI NRA<br><!-- End of picture text --> Sources: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008)
27 Figure 3: Trade and Welfare Reduction Indexes, all covered products, 19 African countries and regional average<sup>a</sup> , 2000–04 (percent) <!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>TRI WRI<br><!-- End of picture text --> Source: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008) a. To get the regional average the national indexes are weighted by the average of the gross value of production and consumption at undistorted prices.
28 Figure 4: Trade- and Welfare-Reduction Indexes, 19 African focus countries, Asia and Latin America, covered tradables, 1960–64 to 2000–04<sup>a</sup> (percent) <!-- Start of picture text --> (a) TRI<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>40<br>20<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>African focus countries Asia Latin America<br>(b) WRI<br>80<br>60<br>40<br>20<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>African focus countries Asia Latin America<br><!-- End of picture text --> Source: Modified from Lloyd, Croser and Anderson (2009) using Anderson and Croser (2009), which is based on NRA and CTE data in Anderson and Valenzuela (2008) a. 1960–64 is 1961–64 for Sub-Saharan African countries.
<!-- Start of picture text -->
|<br><!-- End of picture text -->
30 Figure 6: Commodity Trade and Welfare Reduction Indexes, markets of 19 African focus countries, all covered products<sup>a</sup> , 2000–04 (percent) <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>‐20<br>‐40<br>‐60<br>WRI TRI<br><!-- End of picture text --> Source: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008) a. Products with a WRI of less than 30 percent in 2000–04 are omitted from the chart; and camel — which has the highest WRI in 2000–04 — is also omitted.
31 Table 1: Summary of Nominal Rates of Assistance for import-competing, exportable, nontradable, and all covered agricultural products, Relative Rate of Assistance and Trade Bias Index, 19 African focus countries, 1961–64 to 2000–04 (percent)

||Covered<br>exportables|NRA,<br>Covered<br>importables|agricultural p<br>All covered<br>tradables<sup>b</sup>|roducts<sup>a</sup><br>Covered<br>nontradables|All covered<br>products|Standard<br>deviation<br>of NRAs<sup>b</sup>|RRA<sup>c</sup>|Tradables<br>share (%) of<br>value of all<br>covered agric.<br>production|
|---|---|---|---|---|---|---|---|---|
|1961-64|-30|123|3|0|-1|34|5|49|
|1965-69|-39|62|-15|0|-11|33|-12|55|
|1970-74|-47|30|-27|0|-17|31|-19|55|
|1975-79|-52|22|-30|-1|-23|37|-27|54|
|1980-84|-47|4|-28|-1|-18|35|-17|46|
|1985-89|-50|49|-26|-2|-15|33|-22|46|
|1990-94|-49|5|-27|-2|-16|31|-19|41|
|1995-99|-32|3|-15|-3|-10|25|-11|39|
|2000-04|-32|7|-16|-3|-10|26|-18|43|

# Source: Anderson and Valenzuela (2008)

a. Nominal rates of assistance for the 19 African focus countries are weighted by the gross value of production at undistorted prices for the relevant sub-sector.

b. The simple average of the 19 focus countries’ standard deviation of NRA around its weighted mean.

c. The RRA is defined as 100*[(100+NRAag<sup>t</sup> )/(100+NRAnonag<sup>t</sup> )-1], where NRAag<sup>t</sup> and NRAnonag<sup>t</sup> are the percentage NRAs for the tradables parts of the agricultural and non-agricultural sectors, respectively. The regional RRA is a weighted average of national RRAs, with weights being the gross value of production at undistorted prices for all agriculture.
32 Table 2: Nominal rates of assistance, all covered products, 19 African focus countries,

# 1961–64 to 2000–04

# (percent)

||1961–64|1965–69|1970–74|1975–79|1980–84|1985–89|1990–94|1995–99|2000–04|
|---|---|---|---|---|---|---|---|---|---|
|**Africa**|**-1**|**-11**|**-17**|**-23**|**-18**|**-15**|**-16**|**-10**|**-10**|
|Benin|na|na|-3|-1|-1|-1|-4|-4|-1|
|Burkina Faso|na|na|-2|-3|-4|-1|-3|-3|0|
|Cameroon|-4|-8|-12|-25|-19|-5|-4|-4|-1|
|Chad|na|na|-12|-11|-8|-1|-3|-3|-1|
|Côte d'Ivoire|-29|-35|-33|-40|-40|-28|-22|-22|-28|
|Ethiopia|na|na|na|na|-12|-15|-17|-10|-7|
|Ghana|-15|-28|-23|-41|-32|-8|-3|-5|-2|
|Kenya|13|-2|-24|-15|-30|-8|-30|-4|4|
|Madagascar|-19|-23|-20|-38|-51|-26|-7|-4|2|
|Mali|na|na|-6|-8|-7|-3|-5|-7|0|
|Mozambique|na|na|na|-56|-42|-51|-4|5|14|
|Nigeria|21|12|7|5|8|15|4|0|-5|
|Senegal|-15|-12|-33|-34|-30|5|7|-10|-12|
|Sudan|-26|-37|-48|-28|-33|-39|-54|-29|-15|
|Tanzania|na|na|na|-50|-60|-52|-30|-29|-17|
|Togo|na|na|-1|-1|-2|-2|-4|-3|-1|
|Uganda|-3|-5|-12|-24|-12|-14|-1|1|1|
|Zambia|-24|-32|-42|-57|-26|-68|-53|-34|-36|
|Zimbabwe|-36|-36|-44|-54|-47|-43|-45|-38|-73|

Source: Anderson and Valenzuela (2008)
33 Table 3: Trade Reduction Index, all covered products<sup>a</sup> , 19 African focus countries, 1961–64 to 2000–04 (percent)

||1961–64|1965–69|1970–74|1975–79|1980–84|1985–89|1990–94|1995–99|2000–04|
|---|---|---|---|---|---|---|---|---|---|
|**Africa**|**24**|**22**|**20**|**21**|**15**|**24**|**14**|**9**|**10**|
|Benin|na|na|2|1|1|0|2|3|1|
|Burkina Faso|na|na|2|3|4|1|3|3|0|
|Cameroon|2|5|6|14|12|3|2|2|1|
|Chad|na|na|12|11|8|1|3|3|1|
|Côte d'Ivoire|13|13|24|27|19|17|12|15|22|
|Ethiopia|na|na|na|na|14|16|19|11|9|
|Ghana|6|11|10|22|20|15|7|3|7|
|Kenya|-16|-19|-4|12|21|19|-7|9|11|
|Madagascar|20|15|-13|6|-1|17|3|3|8|
|Mali|na|na|4|7|6|3|5|7|0|
|Mozambique|na|na|na|27|-6|-14|1|6|24|
|Nigeria|39|38|31|18|11|19|7|8|1|
|Senegal|14|10|30|36|28|25|26|7|12|
|Sudan|29|28|29|29|22|56|40|17|31|
|Tanzania|na|na|na|16|18|34|30|16|17|
|Togo|na|na|0|1|2|1|4|3|1|
|Uganda|2|4|8|14|9|10|2|2|1|
|Zambia|21|1|1|36|-11|-45|-27|-7|29|
|Zimbabwe|33|38|43|51|29|37|19|10|12|

Source: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008).

a. Includes all import-competing, exportable and nontradable products; with nontradable sectors assumed to have a zero level of distortion on the volume of trade.
34 Table 4: Welfare Reduction Index, all covered products, 19 African focus countries, 1961–64 to 2000–04 (percent)

||1961–64|1965–69|1970–74|1975–79|1980–84|1985–89|1990–94|1995–99|2000–04|
|---|---|---|---|---|---|---|---|---|---|
|**Africa**|**49**|**46**|**45**|**44**|**39**|**45**|**40**|**28**|**27**|
|Benin|na|na|9|6|7|4|8|7|4|
|Burkina Faso|na|na|9|13|14|5|9|9|9|
|Cameroon|9|14|17|29|22|12|11|10|4|
|Chad|na|na|24|23|20|5|9|8|6|
|Côte d'Ivoire|28|36|36|40|38|30|25|25|31|
|Ethiopia|na|na|na|na|22|24|27|20|16|
|Ghana|17|30|28|44|49|36|17|11|15|
|Kenya|35|39|29|34|38|28|35|26|29|
|Madagascar|23|27|26|43|55|37|21|11|13|
|Mali|na|na|16|20|18|8|13|14|9|
|Mozambique|na|na|na|63|52|63|18|18|41|
|Nigeria|87|78|68|54|45|63|48|36|31|
|Senegal|17|15|38|41|36|50|55|11|16|
|Sudan|36|40|51|40|40|65|79|42|44|
|Tanzania|na|na|na|58|65|62|53|46|38|
|Togo|na|na|4|5|9|5|10|8|5|
|Uganda|6|9|20|35|24|24|4|4|4|
|Zambia|26|41|47|57|31|69|58|39|42|
|Zimbabwe|39|45|50|56|46|42|46|40|72|

Source: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008).
35 Table 5: Country contributions to the regional Welfare Reduction Index for 19 African focus countries, all covered products, 1961–64 to 2000–04 and country contributions to the fall in the Welfare Reduction Index for all 19 countries from 1975–79 to 2000–04

|(percent)|1961-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|Contribution to fall<br>in WRI 1975–79 to|
|---|---|---|---|---|---|---|---|---|---|---|
|**Africa WRI**|**49**|**46**|**45**|**44**|**39**|**45**|**40**|**28**|**27**|2000–04<sup>c</sup>|
|Benin|-|-|0|0|0|0|0|0|0|0|
|Burkina Faso|-|-|0|0|0|0|0|0|0|0|
|Cameroon|2|3|3|4|2|1|1|1|0|-4|
|Chad|-|-|0|0|0|0|0|0|0|0|
|Cote d’Ivoire|3|5|5|8|6|4|4|4|5|1|
|Ethiopia|-|-|-|-|10|11|10|11|9|na|
|Ghana|2|4|3|4|4|3|2|2|3|1|
|Kenya|2|2|2|3|3|2|3|2|2|1|
|Madagascar|1|2|3|3|4|2|1|1|1|-2|
|Mali|-|-|0|0|0|0|0|0|0|0|
|Mozambique|-|-|-|2|2|1|1|1|2|2|
|Nigeria|74|60|51|36|37|37|38|45|35|34|
|Senegal|1|1|2|3|2|2|2|0|1|-3|
|Sudan|10|15|21|15|18|27|30|19|27|44|
|Tanzania|-|-|-|9|7|4|3|6|6|1|
|Togo|-|-|0|0|0|0|0|0|0|0|
|Uganda|1|2|4|7|4|2|0|1|1|-8|
|Zambia|1|2|2|2|1|1|1|2|2|1|
|Zimbabwe|3|4|4|4|3|2|3|3|5|7|
|Africa<sup>b</sup>|100|100|100|102|103|100|100|100|100|100|
36 Source: Authors’ calculations from data in Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008).

- a. Country contributions are computed by converting national percentage WRIs to dollar values by multiplying by the average of the gross value

of production and consumption at undistorted prices. The country contributions therefore capture both the magnitude of the WRI and the share of a country’s gross value of production and consumption in the regional value of production and consumption.

- b. The total for all countries does not necessarily sum to exactly 100 for five-year averages, but it does sum to 100 for individual years.

- c. This column gives the country contribution to the fall in the WRI from 1975–79 to 2000–04.
37 Table 6: Trade and Welfare Reduction Indexes, all covered products and all tradables, 19 African focus countries and 16 African focus countries, 1961–64 to 2000–04 (percent)

||1961-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|
|---|---|---|---|---|---|---|---|---|---|
|**19 Africa focus countries**||||||||||
|**Trade Reduction Indexes**||||||||||
|Covered tradables|49|40|39|39|27|49|29|17|20|
|All covered products|24|22|20|21|15|24|14|9|10|
|**Welfare Reduction Indexes**||||||||||
|Covered tradables|68|61|61|58|54|68|67|45|45|
|All covered products|49|46|45|44|39|45|40|28|27|
|**16 African focus countries**<sup>**a**</sup>||||||||||
|TRI, Covered tradables|21|22|21|26|18|50|18|14|14|
|WRI, Covered tradables|51|51|52|49|50|80|52|37|36|

Source: Lloyd, Croser and Anderson (2010) and Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008). a. 1961–64 results are for 1960–64 for 16 African focus countries. The 16 African focus country results are those reported in Lloyd, Croser and Anderson (2010). The 16 countries are those in this study including Egypt and South Africa, and excluding the five cotton countries — Benin, Burkina Faso, Chad, Mali and Togo.
38 Table 7: Commodity Welfare Reduction Index, African regional market of 19 focus countries, 31 covered products, 1961–64 to 2000–04 <u>(percent)</u>

||1961-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|
|---|---|---|---|---|---|---|---|---|---|
|**Grains**|**59**|**50**|**44**|**34**|**28**|**33**|**26**|**20**|**18**|
|Cassava|0|0|1|1|3|1|1|4|3|
|Maize|114|73|63|71|54|67|40|38|35|
|Millet|18|18|11|5|10|13|16|18|8|
|Rice|31|30|40|36|48|60|38|16|18|
|Sorghum|153|144|118|95|83|95|80|52|49|
|Wheat|17|37|40|30|14|16|35|16|16|
|**Oilseeds**|**28**|**42**|**54**|**49**|**47**|**40**|**72**|**43**|**36**|
|Cashew|na|na|na|80|80|85|61|13|11|
|Groundnut|27|43|54|51|50|35|60|41|47|
|Oilseed|na|na|na|na|47|52|61|56|42|
|Palmoil|25|31|45|26|28|44|132|50|13|
|Sesame|50|60|62|65|56|44|47|45|38|
|Soybean|na|14|34|44|45|44|56|52|64|
|Sunflower|0|0|0|0|0|0|0|0|0|
|**Tropical crops**|**36**|**41**|**45**|**61**|**54**|**49**|**53**|**44**|**51**|
|Cocoa|31|51|46|62|54|41|37|37|38|
|Coffee|39|41|46|64|56|48|47|35|21|
|Cotton|42|35|44|57|59|59|71|59|64|
|Sugar|22|35|47|49|43|38|45|45|87|
|Tea|12|8|24|56|52|47|51|50|49|
|Tobacco|39|38|48|56|50|50|40|39|58|
|**Fruit & vegetables**|**0**|**0**|**0**|**4**|**5**|**5**|**2**|**5**|**5**|
|Banana|2|4|0|2|2|1|5|5|2|
|Bean|7|10|3|48|62|73|35|42|40|
|Roots & tubers|0|0|0|0|0|0|0|0|0|
|Pepper|na|42|9|39|47|80|30|62|27|
|Plantain|0|0|0|0|0|0|0|0|0|
|Potato|na|na|na|0|0|0|0|0|0|
|Sweet potato|0|0|0|0|0|0|0|0|0|
|Yam|0|0|0|1|2|1|1|4|4|
|**Livestock**|**30**|**36**|**52**|**35**|**33**|**68**|**66**|**40**|**38**|
|Beef|34|42|58|29|29|60|73|43|42|
|Camel|38|60|34|38|34|68|84|49|99|
|Milk|19|16|41|36|29|79|40|30|29|
|Sheepmeat|42|48|61|46|38|59|70|54|33|

Source: Authors’ calculations based on NRA and CTE data in Anderson and Valenzuela (2008).