Executive summary. African Agricultural Trade Status Report 2017

Transcription

1 Executive summary African Agricultural Trade Status Report 2017

2 Executive summary To maximise the benefits of regional integration and look for new opportunities to improve competitiveness, African policymakers, the private sector and development partners need access to accurate and comprehensive data on intra and inter-regional trade with respect to agricultural goods. It is in this context that the ACP-EU Technical Centre for Agricultural and Rural Cooperation (CTA) and the International Food Policy Research Institute (IFPRI) commissioned the African Agricultural Trade Status Report, which examines the current status, trends and outlook in African trade performance, making an important contribution towards data and analysis of developments both at regional and at continental levels. The Report builds on the work by the African Growth and Development Policy Modelling Consortium (AGRODEP) and the Regional Strategic Analysis and Knowledge Support System (ReSAKSS) of CAADP and trade and also reflects the CTA s commitment to advancing knowledge and sharing of best practices relating to agricultural trade. In addition to accurate data to assist policy-makers to take informed decisions, this collaboration aims at maximising the input from the highest African analytical capacity on agricultural trade and strengthen an African pool of expertise through AGRODEP. Regional trade within Africa and between the various regions will offer the biggest opportunities in the near future for the local private sector, SMEs and producers and value chain actors. In this context, CTA and IFPRI believe that an annual African trade report is needed and that for the next editions, a broader range of partners would join this initiative.

3 Trade provides the potential for improving consumer welfare and producer incomes, boosting overall economic growth, and reducing poverty. In Africa, increased and more diversified agricultural trade on the global and regional levels could provide leverage for efforts to raise productivity at all stages of the value chain, and facilitate the transformation of agriculture into a high-productivity sector providing adequate incomes for producers and stimulating growth throughout the economy. Increasing agricultural trade also has the potential to improve food security and contribute to stabilizing local and regional food markets by making them less vulnerable to shocks. In addition to the benefits of global trade, intra-regional trade has increasingly been recognized as a key element of efforts to increase food security and agricultural development in Africa. The 18 th African Union Summit in 2012 was organized under the theme of Boosting Intra-African Trade. In 2014, African leaders committed to tripling intra-african trade in agricultural commodities and services by 2025, as one of a limited number of commitments in the Malabo Declaration on Accelerated Agricultural Growth and Transformation for Shared Prosperity and Improved Livelihoods. The trade commitment included accelerating the establishment of a Continental Free Trade Area and a continental Common External Tariff and taking measures to increase investments in trade infrastructure and enhance Africa s position in international trade negotiations. Despite longstanding recognition of the benefits of trade and the importance of improving Africa s competitiveness, the continent is performing beneath its potential in global and regional agricultural markets. Recent increases in exports have been offset by even larger growth in imports, leading to a deterioration in Africa s trade balance. Intra-regional trade in Africa is growing, but remains significantly below the levels seen in other regions. These challenges result from a host of factors, including historical trends and more recent developments inside and outside of Africa. Action on many fronts is needed to remove constraints to improving the competitiveness of Africa s producers. Highlights The African Agricultural Trade Status Report (TSR) provides detailed descriptive assessments of the current status and recent trends in Africa s trade performance and competitiveness at the continental and regional levels, as well as more in-depth investigations of the determinants of trade

4 performance and the relative importance of different drivers and constraints. The goal of the report is to provide comprehensive and timely evidence and analysis on the status of African trade in order to inform policy discussions on measures to enhance trade performance at the global and regional level. In addition to the introductory and concluding chapters, the report is divided into five chapters presenting findings on Africa s trade performance and outlook. Chapter two reviews trends and patterns in Africa s global agricultural trade since The chapter finds that although agricultural exports more than doubled between 1998 and 2013, imports increased fivefold, leading to a growing trade deficit. The main drivers of this surge in imports are rapid population growth and urbanisation, income changes due to economic growth, and changes in dietary patterns. Among the major Regional Economic Communities (RECs), only the SADC region has maintained a consistent trade surplus over the last decade. The chapter finds that despite the increase in agricultural exports, the share of agricultural exports in Africa s total exports has declined by half over the period, due to more rapidly rising exports in minerals and oil. Africa s agricultural exports show signs of moderate diversification over the period, while imports have remained fairly stable. The EU remains Africa s top trading partner, but both imports from and exports to the EU have dropped over the period, while trade with Asia has doubled; Asia is likely to take the EU s place as Africa s top trading partner if these trends continue. Recent efforts to pursue increased economic integration have resulted in significantly increased intra-regional trade during the period, although the overall level of intra-regional trade remains low. Chapter three examines patterns in intra-regional trade at the continental level and among major RECs, namely ECOWAS, ECCAS, COMESA, and SADC. The chapter finds that intra-african agricultural trade has expanded significantly since 1998, increasing at about 12 percent per year in value terms. However, the share of intra-african trade in total African trade is still very low compared to other regions or continents. For example, 20 percent of Africa s trade was intraregional in 2013, compared to around 40 percent among American countries, 63 percent among Asian countries and 75 percent among European countries. Obstacles to better performance of intra-regional trade in Africa include weak productive capacity and the lack of trade-related infrastructure and services.

5 The largest increase in intra-rec trade in the past decade and a half took place in the ECCAS region, while the slowest increase was in the SADC region. The chapter finds that ECOWAS shows the highest regional trade integration, as measured by the ratio of intra-rec trade to the REC s trade with Africa; ECCAS shows the lowest. COMESA and SADC play larger roles as destinations for and origins of African trade than do the other two RECs. Chapter four reviews the changes in competitiveness of exports of different countries and different agricultural products over the past three decades, and investigates the determinants of these changes through econometric analysis. The chapter aims to shed light on the factors behind recent improvements in trade performance in order to further accelerate gains and reduce trade deficits. The chapter finds that most RECs saw their member countries maintain or increase their competitiveness in global and regional markets, with the exception of ECCAS, whose member countries tended to lose competitiveness. Improvements in the competitiveness of COMESA, ECOWAS and SADC member countries took place primarily in intra-regional markets. The majority of African export commodities gained competitiveness in global markets, with some exceptions; however, the most competitive commodities account for a fairly small share of exports. Africa s top five most competitive commodities in global markets represent only 1.8 percent of African exports to these markets, suggesting potential for expanding exports by leveraging competitiveness gains among emerging export products. The chapter finds that determinants of competitiveness improvements include the ease of doing business, institutional quality, the size of the domestic market, and the quality of customs. Chapter five examines the factors contributing to Africa s improved agricultural export performance, using a gravity model to assess the importance of different determinants of trade and of the constraints to further improving exports. The study finds that supply side constraints, including production capacity and the cost of trade, affect trade performance to a greater extent than demand side constraints, which include trade policies and agricultural supports in importing countries. This suggests a focus on removing domestic constraints to increased trade, including by improving infrastructure and increasing agricultural productivity. For example, the study finds that a 1 percent increase in land productivity increases trade flows to the global market by about 6 percent and to the African market by 7 percent. The chapter also finds that non-tariff barriers to trade are increasing and present larger obstacles to exports than do tariffs. The chapter highlights

6 the potential of regional economic communities to promote the removal of barriers to trade at both the regional and global levels, as well as the continued importance of global cooperation to facilitate trade. Chapter six focuses on the outlook for expanding intra-regional trade within West Africa, the feature region of this report, and the potential effects of expanded trade on regional food markets. The chapter finds that the distribution of production volatility among West African countries suggests significant potential to lessen the impacts of domestic shocks through increased regional trade, while patterns in agricultural production and trade show scope for increasing regional trade levels. Analysis of a simulation model suggests that intra-regional trade will continue to increase under current trends. Intra-regional trade growth can be accelerated through even modest reductions in trading costs, modest increases in crop yields, or a reduction in trade barriers. In particular, intra-regional trade in cereals during the period is expected to increase by 23 percent over baseline trends following a 10 percent reduction in overall trading costs; by 36 percent following a removal of harassment costs; and by 33 percent following a 10 percent increase in crop yields. The increased intra-regional trade resulting from these changes would reduce food price volatility in regional markets. The TSR chapters demonstrate undeniable improvements in Africa s trade performance over the past decade and a half, in both global and regional markets, as reflected by generally increasing competitiveness for the majority of countries and commodities. However, progress has been uneven, with some regions and countries consistently underperforming others. Challenges remain in further enhancing Africa s competitiveness on the global market and in increasing intra-regional trade, which remains below its potential despite significant recent improvements. The findings of chapter four point to the importance of the institutional and business environment in improving a country s export competitiveness, while chapter five also emphasizes the role of domestic factors in increasing exports, including production capacity and trading costs. Chapter six focuses on the West Africa region, demonstrating the role of potential domestic and regional policy actions to increase intra-regional trade and enhance the stability of regional markets. The chapters suggest a series of recommendations for policymakers, including efforts at the country and regional level to increase agricultural productivity along the value chain, improve

7 market access, and improve the functioning of institutions; regional actions to enhance economic integration; and continent-wide efforts to promote trade facilitation in international negotiations. Policy actions such as these can influence the trends described in this report and accelerate improvements in Africa s trade performance, thereby increasing incomes and improving food security across the continent.

8 Chapter 1. Introduction Extracted from African Agricultural Trade Status Report 2017

9 CHAPTER 1. INTRODUCTION Trade provides the potential for improving consumer welfare and producer incomes, boosting overall economic growth, and reducing poverty. In Africa, increased and more diversified agricultural trade on the global and regional levels could provide leverage for efforts to raise productivity at all stages of the value chain, and facilitate the transformation of agriculture into a high-productivity sector providing adequate incomes for producers and stimulating growth throughout the economy. Increasing agricultural trade also has the potential to improve food security and contribute to stabilizing local and regional food markets by making them less vulnerable to shocks. In addition to the benefits of global trade, intra-regional trade has increasingly been recognized as a key element of efforts to increase food security and agricultural development in Africa. The 18 th African Union Summit in 2012 was organized under the theme of Boosting Intra-African Trade. In 2014, African leaders committed to tripling intra-african trade in agricultural commodities and services by 2025, as one of a limited number of commitments in the Malabo Declaration on Accelerated Agricultural Growth and Transformation for Shared Prosperity and Improved Livelihoods. The trade commitment included accelerating the establishment of a Continental Free Trade Area and a continental Common External Tariff and taking measures to increase investments in trade infrastructure and enhance Africa s position in international trade negotiations. Despite longstanding recognition of the benefits of trade and the importance of improving Africa s competitiveness, the continent is performing beneath its potential in global and regional agricultural markets. Recent increases in exports have been offset by even larger growth in imports, leading to a deterioration in Africa s trade balance. Intra-regional trade in Africa is growing, but remains significantly below the levels seen in other regions. These challenges result from a host of factors, including historical trends and more recent developments inside and outside of Africa. Action on many fronts is needed to remove constraints to improving the competitiveness of Africa s producers. In 2013, the Regional Strategic Analysis and Knowledge Support System (ReSAKSS), the official monitoring and evaluation body of the CAADP, published its Annual Trends and Outlook Report (ATOR) under the theme of Promoting Agricultural Trade to Enhance Resilience in Africa. 4

10 The report reviewed patterns in Africa s global and regional agricultural trade and examined the relationship between agricultural trade and the resilience of African countries and regions to shocks, including food price volatility and weather shocks. The report detailed significant progress made in improving Africa s trade performance in recent years, as well as the remaining challenges at the global and regional levels. The current African Agricultural Trade Status Report (TSR) builds on the analysis presented in the 2013 ATOR. The report provides detailed descriptive assessments of the current status and recent trends in Africa s trade performance and competitiveness at the continental and regional levels, as well as more in-depth investigations of the determinants of trade performance and the relative importance of different drivers and constraints. This report represents the first in a series of annual publications examining current status, trends and outlook in African trade performance. The goal of this and subsequent reports is to provide comprehensive and timely evidence and analysis on the status of African trade in order to inform policy discussions on measures to enhance trade performance at the global and regional level. In addition to the introductory and concluding chapters, the report is divided into five chapters presenting findings on Africa s trade performance and outlook. Chapter two examines trends and patterns in Africa s global agricultural trade over the past decade and a half. The study assesses trends in overall trade volumes and values and in trade of key agricultural commodities. The chapter then analyzes the direction of agricultural exports and imports, changes in market shares, and changes in the composition of Africa s exports and imports, to provide a comprehensive overview of Africa s agricultural trade with the rest of the world. Chapter three addresses regional trade, discussing patterns in trade among African countries at the continental level and among its regional economic communities (RECs). The chapter reviews intra-regional trade performance for the continent as a whole and for major RECs, before analyzing trade direction, examining the role of individual RECs and countries in intra-regional trade, and discussing the key commodities important in African intra-regional trade. 5

11 Chapter four presents a detailed analysis of the competitiveness of African agricultural exports in global and regional markets. The chapter aims to shed light on the factors behind recent improvements in trade performance in order to further accelerate gains and reduce trade deficits. The study ranks countries and commodities according to their competiveness in export markets at the global, continental, and REC levels. The chapter then performs econometric analysis of the drivers of changes in competiveness at different levels and presents recommendations for further improving competiveness. Chapter five provides an in-depth examination of the determinants of African agricultural trade performance. The chapter reviews broad categories of trade determinants, including production capacity, cost of trade, trade policies, domestic agricultural supports, and global market shocks. The chapter then develops a gravity model to assess the relative importance of determinants of African trade and of different constraints to trade, and discusses how these constraints have changed over time and vary across countries. Chapter six focuses on the outlook for expanding intra-regional trade within West Africa, the focus region of this issue, and the potential effects of expanded trade on regional food markets. The chapter reviews recent trends in intra-regional trade and examines the possibilities for increased regional trade to reduce food price volatility. The study then evaluates the scope for increasing trade within the region. A simulation model is used to examine the effects of alternative policy scenarios on regional trade and on the stability of regional food markets. The final chapter concludes the report by reviewing findings from the preceding chapters. The chapter synthesizes the results of previous analyses and summarizes policy implications for addressing constraints to improved trade performance. 6

12 Chapter 2. Africa global trade patterns Extracted from African Agricultural Trade Status Report 2017

13 CHAPTER 2. AFRICA GLOBAL TRADE PATTERNS Fousseini Traore IFPRI- Markets, Trade and Institutions Division, Regional Office for West and Central Africa, Dakar, Senegal Daniel Sakyi, Department of Economics, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana 2.1 Introduction The trade performance of African countries has improved in recent years, though it is still below expectations when compared to other regions of the world. This notwithstanding, and although the region is currently considered as one of the fastest growing regions in the world, Africa s trade performance continues to be dominated by the agricultural sector. Overall, Africa s competitiveness has slightly improved and the trends in its exports have undergone major diversification since This has become possible due to the region s (i) participation in multilateral and bilateral talks (WTO-DDA; EPAs, etc.), (ii) benefits received from preferential trade agreements (AGOA, EBA, etc.), and (iii) deeper regional integration (FTAs, customs unions, etc.). In addition, technological transfer from developed countries to the region has contributed significantly to transformation of the agricultural sector and trade. Although the agriculture sector still remains key with the potential to be an important player in global food markets and continues to play a significant role in terms of value-added (NEPAD, 2015) 1, the share of agricultural exports in total exports has declined since This has remained so because the sector is still characterized by low productivity, which tends to pose a major setback to Africa s economic development and structural transformation. This presents critical challenges for Africa given the continent s rich natural resource endowments and its potential to transform and export high valued agricultural products both within the continent and abroad. It is, therefore, not surprising that the need to develop and transform the agricultural sector in Africa was heavily discussed in the 2014 Malabo Declaration, as this was crucial to accelerate Africa s development campaign. Therefore, the commitment to boosting intra-african trade in agricultural commodities and services (i.e. to triple, by the year 2025, intra-african trade in agricultural commodities and services) is seen as key to growth because its expansion will trickle down to other sectors of the region s economy. 1 In fact, agriculture accounts for a significant portion of GDP in Africa (about 20% in 2015 (World Bank, 2015)), and therefore presents considerable potential for supporting broader growth and the eradication of poverty and hunger. 7

14 In recent years the trends in international trade were largely driven by the sluggish economic growth and the persisting economic and political turmoil in various parts of the world; from 2011 to 2014 world trade grew at a rate of less than 2 percent per year, due to generally lower economic growth but also because trade has been much less responsive to output growth. This was particularly the case for Africa (UNCTAD, 2015). Regarding agricultural products, while world agricultural exports grew annually at 7% between 2010 and 2014, Africa s exports grew at 5%, highlighting more resistance for agricultural trade compared to trade in manufactures which grew at 4% (WTO, 2015). African agricultural export shares in global trade have increased steadily between 1998 and 2013, with a diverging pattern among the main Regional Economic Communities (RECs). The ECCAS and SADC regions registered a relative decline, while COMESA showed stability and ECOWAS is characterised by huge short run volatility. However, the region s imports still remain higher than its exports in value terms, yielding a growing trade deficit. The main drivers of this surge in imports are rapid population growth and urbanisation, income changes due to economic growth, and changes in dietary patterns. Among the RECs, the SADC region is the only one registering a consistent trade surplus over the last decade. One noticeable feature is the direction of Africa s trade to and from the European market that has constantly showed a downward trend, while trade with regional partners and Asian countries keeps rising. Africa also registered a decrease in the concentration of its exports over the last decade. Another interesting feature is the relative decline of the share of agricultural exports in Africa s total exports, indicating that the main source of foreign earnings come now from non-agricultural products. However, overall, despite the region s attempt to integrate into the global market, there is still some work to be done in increasing diversification, in furthering integration into global value chains and in meeting international standards. This chapter examines Africa s global trade patterns from 1998 to Specifically, section II highlights the trends of Africa s agricultural trade both in values and in volumes with a focus on the evolution of some key agricultural commodities. This is followed by a discussion of trends in net agricultural exports in section III. Changes in market shares are presented under section IV; this section also analyses in detail the direction of African s exports and imports. Since the region s export and import composition changes over time, the composition of agricultural exports and 8

15 imports is also discussed under section V. We then examine under section VI the changes in unit values of agricultural exports and imports. Finally, the last section concludes the chapter. 2.2 Trends in volumes and values of global agricultural trade (exports and imports) Global patterns Fig Total agricultural trade, billion USD (nominal values) Fig Export shares in global agricultural exports (nominal values) 5.00% 4.00% 3.00% 2.00% 1.00% 0.00% Exports Imports Africa SSA Source: BACI Source: BACI Globally, agricultural exports and imports have been increasing steadily since 1998 even though imports have been generally higher than exports (Figure 2.1). After a fall in the nineties, Africa s exports have increased continuously over the last decade at 8% annually. Over the entire period ( ) exports more than doubled. From 2008 to 2013 (the post crisis period), the annual growth rate of agricultural exports was 6.6% which is much higher than total export growth (1.3%) due to sluggish economic growth in the world (UNCTAD, 2015). Although the trend looks promising, exports still lagged behind imports. The reasons behind this increase in exports include price booms of various commodities over the last decade, the improvement in infrastructure in the continent (mostly transport and telecommunication), economic growth, and more regional and global integration efforts. 2 Unless specified, all figures refer to aggregate continental trade, i.e. extra and intra Africa trade lumped together. The main source of data is the BACI database built by CEPII. Based on UN COMTRADE, BACI has developed a procedure to reconcile exporter and importer declarations using both mirror data and gravity modeling (Gaulier et al., 2010). This allows a significant increase in the number of countries with available data. See the appendix for a complete description of the database. 9

16 While export growth has not been as high as expected, in contrast, the value of agricultural imports has increased rapidly during the years since Over the entire period, imports have grown fivefold. Specifically, there was a general rise in the value of agricultural imports from $19.07 billion in 1998 to approximately $68.28 billion in 2008 with a dip in 2009 ($60.61 billion). Total trade in agricultural imports increased again between 2009 and 2011, peaking at approximately $ billion. However, since 2012, world agricultural imports have been slightly on the decline, with the total value of world agricultural imports dropping to approximately $89.18 billion in On the other hand, and as earlier indicated, exports have been rising over the period, with the 2013 value of approximately $63.85 billion being the highest for the period. The higher imports may be attributed to both demand and supply factors. On the demand side, the main elements to mention are the increasing income levels due to higher economic growth, population growth and demographic changes, and changes in consumers dietary patterns (Rakotoarisoa et al., 2011; Diao et al. 2008). The income effect due to economic growth is at play in some countries like Ghana and Mozambique with consequences for dietary patterns. For instance, with higher incomes, consumers demand more protein (such as meat, fish, milk, and peanut). The other cause of increasing imports is population growth and rapid urbanization in Africa with a concomitant increase of the population in rural areas. Africa is indeed the most dynamic region in terms of demographics. Africa s population has more than doubled in the last 30 years while the world s population has grown by 60% with now two out of every five people living in cities. The consequence of the rapid urbanization and population growth has been an increase in the consumption of more diversified and richer animal products and in the consumption of imported cereals (wheat, rice, and maize) rather than of the local cereals, roots, and tubers generally consumed in rural areas (FAO, 2015). This trend will continue in the near future as Africa s population growth rate is twice the world average. On the supply side, the huge increase in imports is mainly due to the poor performance in terms of competitiveness of African agriculture, which has been unable to meet the requirements of the growing population. Low and stagnating agricultural productivity, water constraints, the low use of fertilizers and low mechanization are the key factors at play (FAO, 2015). Export shares of Africa and SSA in global exports are given in Figure 2.2. The shares of Africa and SSA s exports in world exports have been fluctuating below 4% with a few exceptions, the lowest share being 3.77% in The export shares of SSA countries in global exports have 10

17 experienced trends similar to those of Africa as a whole, with respect to the years of peaks and troughs, meaning that North African countries do not account significantly for the region s agricultural exports. It is obvious from the trends given in Figure 2.2 that export shares of both Africa and SSA in world agricultural trade are generally low. The contrasted evolution of Africa s share in global exports is reflected by the evolution of its competitiveness in world markets. Indeed two third of the countries of the continent registered a loss in competitiveness while the remaining ones managed to expand their exports in world markets faster than their competitors (Odjo and Badiane, 2017). The low share of Africa in world agricultural trade is to be contrasted with the facts that agriculture products continue to contribute highly to GDP in most African countries and that agriculture employs a large proportion of its workforce (WDI, 2015). The situation may however be explained by the fact that compared to other countries or regions, agricultural production in Africa is largely on a peasant scale (Bryceson, 2015; Collier and Dercon, 2014), making the overall share of agricultural exports from Africa and SSA relatively lower. However the share of Africa s agricultural exports in world agricultural exports is slightly greater than the share of its merchandise exports in global merchandise exports (Figure 2.2 versus Figure 2.3), showing the relative specialization of Africa in agricultural products. Another interesting feature is the relative decline of the share of agricultural exports in Africa s total exports (Figure 2.4). Indeed the share of agricultural products has been reduced by half since 1998, indicating a symmetric increase in export earnings from other sources (mainly textiles, minerals and fossil oil). Agricultural exports represent now 10% of Africa s total exports. 11

18 Fig Share of Africa in world total trade 3 (nominal values) Fig Share of agricultural exports in total exports (nominal values) % 18.00% 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Source: UNCTAD Source: BACI Globally, the evolution of the market shares of the main RECs follow that of Africa as a whole (Figure 2.5). The evolution in some groups is however more pronounced than for others. The ECCAS group, which has the lowest share, is also characterized by a secular decline over the entire period. This particular pattern of the SADC region is confirmed by its lack of competitiveness over the last decade compared to its main competitors (Odjo and Badiane, 2017; see chapter 4). The SADC region is also an example of a relative decline over the period after an increase of its market share in the late nineties, with a decline in competitiveness. The ECOWAS region s market share is the most volatile one, with an improvement in the most recent years, while COMESA s is relatively stable over time. The divergent evolution of the market shares of the different RECs is due to their differences in terms of specialization (commodities exported; see Annex 2) and to their ability to respond to price booms and to compete with other exporters in global markets. 3 Goods and services 12

19 Figure 2.5. Exports shares of agricultural products by major RECs 1.80% 1.60% 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% ECOWAS ECCAS COMESA SADC Source: BACI Evolution of some key exported commodities This subsection focuses on some key commodities, particularly citrus, coffee, cocoa and cotton (the main commodities exported in 1998) and fish and related products that are not part of the WTO agreement on agriculture. As evident in Figure 2.6 4, although citrus was the second most exported commodity in volume terms after cocoa between 1998 and 2002, it outstripped the volume of cocoa exported from 2002 to Notwithstanding, cocoa remains the highest exported commodity in value (see Figure 2.7) from 1998 to 2013, with the value of citrus, coffee and cotton all performing below that of cocoa in the same period. Globally, the price of cocoa and coffee in US$ per kilogram have grown continually since 2000 (see Figure 2.8). However, with the exception of the period 2001 to 2004, the coffee price grew more rapidly than the cocoa price. Also cotton price (see Figure 2.9) maintained a relatively stable growth rate between 2000 and By the year 2011, the price of cotton had more than doubled from the price in 2000, though the highest price in 2011 did not last for the subsequent years. 4 Figure 2.6 illustrates the evolution of major agricultural exported commodities in millions of tons: citrus, coffee, cocoa and cotton. 13

20 What is interesting is the imperfect and even opposite correlation between the volume of exports and world prices at the end of the period with the exception of cocoa (Figures 2.6 and 2.8). Indeed despite the huge drop in the world prices of cotton and coffee, export volumes continue to rise after This may be due to an imperfect transmission of international price shocks to local producers prices (due to stabilization mechanisms at play, exchange rate movements between USD and local currencies, etc.) but also to an income effect which pushes producers to supply more when prices fall (i.e., negative supply elasticity; see Yotopoulos and Lau, 1974). Fig Export volume of key commodities (Millions of tons) Fig Export value of key commodities (Millions of USD) Source: BACI Citrus Coffee Cocoa Cotton Source: BACI Citrus Coffee Cocoa Cotton Fig Cocoa and coffee prices in US$/KG Fig Cotton (Cotlook A index cents/lb) Cocoa Coffee Source: World Bank Source: NCC 14

21 Fish and related products Fish and related products represent a huge share of agricultural (extra-regional) exports for some countries (such as Senegal) but are not part of the WTO agreement on agriculture. It is therefore important to include them in the analysis. From 1998 to 2013, fish exports represented on average 15% of total agricultural exports. Africa and SSA s exports of fish and related products have doubled between 1998 and 2013, increasing from $3.12 billion dollars and $2.29 billion dollars respectively to $7.17 billion and $4.98 billion dollars (see Figure 2.10). For both Africa and SSA, exports of fish and related products generally increased continuously from , fell between 2008 and 2010, and increased again between 2010 and Trends in the share of Africa and SSA in global fish trade have been similar for (see Figure 2.11). It is worth noting that Africa s share in global fish exports is higher than its average share in agricultural product exports, indicating a greater role and potential in that particular market. Fig Evolution of export value in USD millions Fig Share in global fish trade Africa SSA Africa SSA Source: BACI Source: BACI 15

22 2.3 Trends in net agricultural exports Since the early nineteen eighties, Africa s agricultural exports have continued to lag behind its imports. The agricultural trade deficit has therefore continued to dominate as the region recorded a negative value in its net exports between 2001 and 2013 (see Figure 2.12). This pattern is also confirmed by the normalized trade balance 5 (see Figure 2.13). The main contributor to the trade deficit is the America region (both North America and Latin America) with US$4 billion in 2001, US$7 billion in 2005 and US$18 billion in The EU and Asia regions recorded a surplus of US$3.3 billion and US$0.9 billion respectively in Net agricultural exports to the global market have worsened since, as Africa started recording deficits with both Asia and the EU in addition to the America region. The lowest ever deficit recorded occurred in 2011 (US$39.7 billion globally). In that same year, Africa recorded a negative value of US$8.3 million to Asia, US$1.6 million to the EU and US$25.3 billion to America. Although the deficit recorded in net agricultural exports reduced somewhat, evidence for 2013 shows that net agricultural exports by African countries have not been encouraging. Also, globally the deficit is mainly due to significant increases in imports rather than a decrease in exports. The main import commodities causing the deficit are sugar, maize, and wheat from the America region; wheat, milk and cream from the EU; and rice, palm oil and wheat from Asia. It appears that most of the RECs recorded a trade deficit over the period with the exception of the SADC region which recorded a surplus over the entire period (see Annex 2). The trade deficit is particularly important for North African countries, which are huge cereal importers. According to recent studies, 23 countries in Africa are highly import dependent, with normalized trade balance index values between -1 to -0.1 while 37 countries are net importers of food (FAO, 2015). The growing agricultural trade deficit suggests that it is necessary that African countries take relevant steps to improve export performance since the continent has the agrarian environment to support agricultural exports. Agriculture on the continent must gradually be transformed from being peasant-dominated to a more commercial type as doing so in addition to other measures (such as improvement in technology and skills) will greatly improve agricultural exports. 5 The normalized balance is computed as a country's exports of agricultural products minus its imports of agricultural products, normalized by dividing it by its agricultural trade (imports plus exports). The index varies between -1 and 1. 16

23 Figure Evolution of net agricultural exports in US$ million (nominal values) World EU Asia America Source: BACI Figure Normalized trade balance Source: BACI 2.4 Directions of agricultural exports and imports and changes in market shares This section assesses the direction of Africa s agricultural exports and imports as well as the changes in Africa s market shares in these regions. Africa as a region has been noted for its natural resource abundance and a significant share of its exports are agricultural products, either semi processed or in their raw state. Different types of exports are made by Africa to different regions in the world. However, the most common agricultural export commodities are cash crops. In particular, commodities such as cotton, cocoa, coffee, cassava, and sorghum are exported to other parts of the world. The direction of these exports however depends on the demand for such 17

24 products. In Figures 2.14 and 2.15, we present the direction of Africa s agricultural exports from 1998 to 2013 to four regions: among African countries; Europe; Asia; and America. As shown in Figure 2.15, thanks to free trade areas and improvement in local infrastructure, the rate at which African countries export to each other has increased at a constant rate since This outcome is however still low when compared to other regions outside Africa. The direction of exports among African countries have averaged 15.70% between 1998 and 2012 in spite of the low take off rate of 11% in Exports to Europe have shown a downward trend since 1998, yet Europe remains the region s highest export destination. Consistently, Africa s exports to the EU dropped from 62% of total agricultural exports in 1998 to 37% in Some African countries started developing tropical products for export to the EU market, to take advantage of the preferences granted by the EU (EBA for instance), but EU standards and SPS dampen the level of agricultural exports (Otsuki and Sewadeh, 2001; Kareem, 2014). It is also worth noting that the EU started negotiations with some of Africa s competitors such as Asia and Latin America, the risk being the erosion of preferences for African countries for some commodities such as cocoa and bananas. Exports to Asia (and Europe) are mostly agricultural products that are high-value and low-calorie in nature. Notable among them are cotton, coffee, flowers, fruits, tea, tobacco and fish. As evident in Figure 2.15, exports of agricultural products to Asia increased at a slower rate between 1998 and 2012 while exports to America have been fairly low. Until 2012, the share of exports to America was below 9%. The highest export share to America since 1998 was recorded to be 9.69% which occurred in This reduced to 5.63% in 2013 (see Figure 2.14). Europe, on the other hand, received the highest share of Africa s exports (37.52%) in 2013 (see Figure 2.14) followed by Asia and Africa. On the import side, as shown in Figure 2.16, the region imported 12.51% in 1999 from its own area. This increased to 16% in 2003 and dropped to 12.37% in However these low figures do not account for informal cross-border trade between African countries. This consists of flows of local products and of import/re-export flows, sometimes in order to circumvent protectionist policies put in place by some countries against imports from the international market (see the Nigeria-Benin case, LARES, 2005; Golub, 2012). Since estimates of intra-regional trade volumes are based on official statistics (customs declarations), the volume of trade is largely underestimated. For instance, more than 50% of Benin s trade in red meat, cattle and cereals was informal in 2010 (ECNE, 2010). However some obstacles still remain for intra-african trade. 18

25 Among these are mentioned inadequate transport, storage and preservation infrastructure; tariffs, non-tariff barriers and export bans; technical barriers; customs procedures; lack of harmonisation of procedures and documents; lack of recognition of national certificates and standards; migratory procedures; and roadside inspections (Levard and Benkhala, 2013; Rolland and Alpha, 2011). Finally the share of intra-trade varies among commodities: cereals and live animals are the most intra-exported while coffee, cocoa, and tea are mostly exported outside the continent. The majority of Africa s imports come from Europe. It is evident from Figure 2.16 that in % of the region s imports came from EU. Though the percentage of imports from the EU has reduced since 1998, the EU still remains Africa s largest origin of imports. Currently, imports from America have been rising steadily; between 1998 and 2003, the share of imports from this region averaged 26.62%. Moreover, the highest imports to Africa in 2011 came from America. Inside America there is a sharp drop in imports from North America which benefited Latin America. The share of imports from Asia has also increased from 11.30% in 1998 to 26.42% in This, however, dropped in 2013 to 24.78%. The main feature here is the decline of Europe and the rise of Asia over the period as Africa s trade partner both for imports and exports. Figure Direction of agricultural exports and imports in 2013 Exports (nominal values) Imports (nominal values) 5.63% 5.00% 8.53% 31.71% 20.14% 24.29% 14.42% 27.98% 37.52% 24.78% Source: BACI Africa EU Asia America Others Source: BACI Africa EU Asia America Others 19

26 Fig Directions of agricultural exports (nominal values) 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Africa EU Asia America Others Source: BACI Fig Directions of agricultural imports (nominal values) 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% Africa EU Asia America Others Source: BACI 2.5 Changes in composition of agricultural exports and imports The composition of agricultural exports and imports in Africa recorded mixed features over time. It shows an increasing diversification of exports and a relative stability for imports, with slight modifications from period to period. It is widely recognized that African exports are highly concentrated (Kose and Riezman, 2001; Songwe and Winkler, 2012). However, within the agricultural sector, Africa s exports seems to have started a gradual diversification as the composition and the shares of the region s exports changed over time. We report in Figures 2.17 and 2.18 the top ten exported products from Africa. In 1998 the top 10 (HS4) products represented 57% of exports while in 2013 they represented 43%, indicating a decrease in the concentration of exports. However, 6 out of 10 products present in 1998 were also present in By the end of the year 1998, cocoa beans were the region s top exported agricultural product. This is still the case in 2013 with 14% of total agricultural exports. Coffee and cotton emerged as the second and third most exported products in that same year (1998), amounting to US$2 billion and US$1.5 billion, respectively. Among others, sugar, tobacco, tea, citrus fruits, grapes and apples were also among the top ten exported agricultural products in The region has since 1998 witnessed a drop in the export of cotton, citrus fruits and tobacco. Conversely, cigars and cigarettes, oilseeds and frozen fish, which were absent from the list of top exports in 1998, are now among the top ten products exported in

27 Exports. Fig Top ten products in 1998 Fig Top ten products in 2013 (in % of total agricultural export value) (in % of total agricultural export value) 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Source: BACI Source: BACI Unlike exports, Africa s imports have remained quite stable in terms of composition and shares. In 1998 the top 10 (HS4) products represented 52% of imports against 49% in As evident from Figures 2.19 and 2.20, 8 out of the top 10 commodities imported in 1998 are also present in In Figure 2.19, the highest share of Africa s agricultural imports is held by wheat and meslin flour, which constituted about 16% of agricultural imports in Sugar was the second most imported product, representing 8.28% of agricultural products imported by African countries. The other products that were among the top ten imported products include maize, rice, wheat and meslin flour, soya-bean oil, palm oil, sunflower-seed, and cigars and cigarettes. In 2013, wheat and meslin continued to account for the highest share of agricultural imports. Rice is the second most imported agricultural product followed by sugar, palm oil, and milk and cream. Meat and edible offal of poultry, soya-bean oil and oil-cake and other solid residues are among the products imported in The entry of meat and edible offal in the top 10 imported commodities highlights the shift towards more protein-related products mentioned earlier. 21

28 Imports. Fig Top ten imported products in 1998 Fig Top ten imported products in 2013 (in %of total agricultural imports) (in % of total agricultural imports) 18.00% 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Source: BACI Source: BACI 2.6 Changes in unit values of agricultural exports and imports A plot of trends in the evolution of agricultural imports and exports unit values is given in Figure It shows changes in unit values of agricultural imports and exports using 2000 as the base year. The evolution of unit values is related to the so-called (deterioration of) terms of trade literature which dates back to the Prebisch-Singer hypothesis (Prebisch, 1950; Singer, 1950) that argues that the price of primary commodities declines relative to the price of manufactured goods over the long run, causing the terms of trade to deteriorate for primary products exporting and manufactured goods importing countries. However recent research regarding this topic has given mixed results (Arezki et al. 2013). From Figure 2.21, it can be seen that the unit value of both agricultural imports and exports have generally increased for the period with a mixed pattern. From 2000 to 2007, the evolution of both indicators shows a significant increase, with imports rising faster than exports, yielding a slight deterioration of the agricultural terms of trade. The period between 2008 and 2013 saw the evolution of the unit value of exports outstripping the unit value of Africa s imports. This improvement was mainly due to the huge increase in commodity prices in the late 2000s and is in 22

29 line with the evolution global terms of trade for Africa (UNCTAD, 2015) though more important here than that of total trade 6. Figure Evolution of exports and imports unit values (Base 100=2000) Exports Imports Source: BACI 2.7 Conclusion Africa has experienced a significant increase in both the value of its exports and imports over the last decade, boosted by the increase in commodity prices in international markets. However, since 1998, Africa s imports have increased more rapidly both in shares and in value terms than exports, yielding a continuously deteriorating trade deficit. This growing trade deficit driven by imports is mainly due to population and economic growth, change in dietary patterns, increasing income levels and the lack of competiveness of the domestic sector. Among the main RECs of the continent, the SADC region is the only one recording a surplus over the entire period. Africa s share of global trade in agriculture has been stable around 4%, though with some small fluctuations for the last three years. The evolution of the market shares of the main RECs shows a regular decline of the shares of the ECCAS and the SADC region, a relative stability of COMESA s share and a highly volatile pattern for ECOWAS. One of the main interesting features is the secular decline of the share of agricultural exports in Africa s total exports. The share of agricultural exports in Africa s total exports has been cut by half since 1998 to the benefit of mineral and fossil oils. 6 This is due to mineral products that are not taken into account here. 23

30 The composition of agricultural exports and imports in Africa recorded mixed features, showing an increasing diversification for exports and a relative stability for imports. Indeed, within the agricultural sector, Africa s exports seem to have started a gradual diversification. Now the top ten (HS4) exported products represent 43% of exports compared to 57% in However, most of the products present in the top exported commodities in 1998 are still present, with a concentration of cocoa beans, coffee and cotton. Unlike exports, Africa s imports have remained quite stable in terms of composition and shares, with the top ten (HS4) products still representing half the imports. Imports remain dominated by cereals (wheat, rice, maize) and sugar, with a recent shift towards more protein (meat and offal and fish). In terms of directions of trade, Africa s trade (both imports and exports) with the European market has witnessed a continuous drop since 2000, though the EU still remains the first partner for the continent. At the same time, Asia has emerged as a major partner for both imports and exports. If recent trends were to continue, Asia will soon become Africa s first trade partner. It is worth noting that the ability to meet standards and SPS measures is still dampening Africa s exports, in particular to the EU and the US markets. There is also a risk of the erosion of preferences for some African countries as the EU for instance has ongoing negotiations with some of Africa s competitors such as Asia and Latin America, the main sectors at risk being those of cocoa beans and bananas. African countries have also expanded their intra-trade over the last 10 years and become less dependent on international markets. In particular, the share of agricultural imports and exports among African countries more than doubled between 2000 and Recent improvement in intratrade is attributed to the effort of Africans to integrate into the regional and international market (Bouet et al., 2013). Despite this improvement, intra-african trade is still low, hence should be strengthened. Market fragmentation (lack of infrastructure; monetary, tax and trade fragmentation; and red tape for traders) limits the development of the region s trade potential. These barriers should be tackled and given priority as they increase price instability within the region and negatively affect food security (Badiane et al., 2014; NEPAD, 2005). 24

31 References Arezki, R., Hadri, K., Loungani, P., & Rao, Y. (2013). Testing the Prebisch-Singer Hypothesis since 1650: Evidence from Panel Techniques that Allow for Multiple Breaks. IMF Working Paper 13/180. Washington, DC: International Monetary Fund. Badiane, O., Odjo, S., & Jemaneh, S. (2014). More resilient domestic food markets through regional trade. In O. Badiane, T. Makombe & G. Bahiigwa (Eds.), Promoting Agricultural Trade to Enhance Resilience in Africa. ReSAKSS Annual Trends and Outlook Report. Washington, DC: International Food Policy Research Institute. Bouet, A., Laborde, D., & Deason, L. (2013). Global trade patterns, competitiveness and growth outlook. In O. Badiane, T. Makombe & G. Bahiigwa (Eds.), Promoting Agricultural Trade to Enhance Resilience in Africa. ReSAKSS Annual Trends and Outlook Report. Washington, DC: International Food Policy Research Institute. Brulhart, M. (2008). An Account of Global Intra-industry Trade, Background Paper, World Bank 2009 World Development Report. Washington, DC: World Bank. Bryceson, D. F. (2015). Reflections on the unravelling of the Tanzanian peasantry, In M. Stahl (Ed.), Looking Back, Looking Ahead: Land, Agriculture and Society in East Africa. Uppsala: The Nordic Africa Institute. Collier, P., & Dercon, S. (2014). African agriculture in 50 years: Smallholders in a rapidly changing world? World Development, 63, FAO (Food and Agriculture Organization of the United Nations). (2003). Trade Reforms and Food Security: Conceptualizing the Linkages. Rome: Food and Agriculture Organization of the United Nations. FAO. (2015). The State of Agricultural Commodity Markets. Rome: Food and Agriculture Organization of the United Nations. Gaulier, G., & Zignago, S. (2010). BACI: International Trade Database at the Product-level: The Version. CEPII Working Paper Paris: CEPII. Golub, S. S. (2012). Entrepôt trade and smuggling in West Africa: Benin, Togo and Nigeria. The World Economy, 35(9), Kose, M. A., & Riezman, R. (2001). Trade shocks and macroeconomic fluctuations in Africa. Journal of Development Economics, 65(1), LARES (2005). Le Trafic Illicite des Produits Pétroliers entre le Benin et le Nigeria: Vice ou Vertu pour l'economie Béninoise? Technical report. Cotonou: Laboratoire d'analyse Régional et d'expertise Sociale. Levard, L., & Benkhala, A. (2013). How to Promote Intra-African Agricultural Trade? Analysis of Possibilities and Impediments Regarding the Development of Intra-African Agricultural Trade. Nogentsur-Marne, France: Les Editions du GRET. 25

32 New Partnership for Africa's Development (NEPAD). (2013). Agriculture in Africa: Transformation and Outlook. Addis Ababa: NEPAD/African Union. Odjo, S., & Badiane, O. (2017). Competitiveness of African agricultural exports. In O. Badiane, S. Odjo & J. Collins (Eds.), African Agricultural Trade Status Report. Otsuki, T., Wilson, J., & Sewadeh, M. (2001). Saving two in a billion: Quantifying the trade effect of European food safety standards on African exports. Food Policy, 26(5), Prebisch, R. (1950). The Economic development of Latin America and its principal problems. Economic Bulletin for Latin America, 7, Rakotoarisoa, M. A., Iafrate, M., & Paschali, M. (2011). Why Has Africa Become a Net Food Importer? Explaining Africa Agricultural and Food Trade Deficits. Rome: Food and Agriculture Organization of the United Nations. Rolland, J. P., & Alpha, A. (2011). Analyse de la Cohérence des Politiques Commerciales en Afrique de l Ouest. Document de Travail 114. Paris: Agence Francaise de Developpement. Kareem, O. I. (2014). The European Union Sanitary and Phytosanitary Measures and Africa s Exports. EUI Working Paper RSCAS 2014/98. Fiesole, Italy: European University Institute. Singer, H. (1950). The distribution of gains between investing and borrowing countries. American Economic Review, Papers and Proceedings, 40, Songwe, V., & Winkler, D. (2012). Exports and Export Diversification in Sub-Saharan Africa: A Strategy for Post-Crisis Growth. African Growth Initiative Working Paper 3. Washington, DC: The Brookings Institution. World Trade Organization (2015). International Trade Statistics Geneva: World Trade Organization. UNCTAD (United Nations Conference on Trade and Development). (2015). Key Statistics and Trends in International Trade 2015: The Trade Slowdown. Geneva: UNCTAD, Division on International Trade In Goods and Services, and Commodities. UNCTAD (United Nations Conference on Trade and Development). (2014). United Nations Conference on Trade and Development Database. World Bank. (2015). World Development Indicators Database. Yotopoulos, P., & Lau, L. (1974). On modelling the agrarian sector in developing countries: An integrated approach of micro and macroeconomics. Journal of Development Economics, 1,

33 Annex 2.1 The BACI global trade database BACI stands for Base pour le commerce international and is the world trade database developed by the CEPII 7. The database is defined at a high level of product disaggregation and is the main source used throughout this chapter. BACI is based on data from the UN COMTRADE database, which is the world's largest database of trade statistics, maintained by the United Nations Statistics Division (UNSD). COMTRADE is the main global source of trade statistics in goods, covering more than 95% of world trade. BACI tries to improve UN COMTRADE by addressing the main issues related to it: missing information for some African countries, reporting in different nomenclatures, no distinction between zero trade flows and missing values in raw data, etc. To address the issues, BACI has developed a procedure that reconciles exporter and importer declarations using both mirror data and gravity modeling (see Gaulier et al., 2010). This procedure allows for a significant increase in the number of countries with available data. In its standard version, BACI provides export values and quantities at the HS 6-digit level. Data are provided for over 200 countries since The database is updated every year. The retreatment of data is particularly important for countries that do not report frequently to COMTRADE (especially in Africa). Table A1 illustrates the data issue and the absence of reporting for ECOWAS countries to UN COMTRADE from 1988 to In BACI all countries are observed for imports and exports. 7 Centre d Etudes Prospectives et d Informations Internationals is a research center based in Paris and part of the Prime Minister s Office through the Centre d Analyse Strategique, now France Strategie. 27

34 Table A.1: ECOWAS countries declaration to UN COMTRADE Total Benin Y Y Y Y Y Y Y Y Y Y Y Y Y 13 Burkina Faso Y Y Y Y Y Y Y Y Y 9 Cote d'ivoire Y Y Y Y Y Y Y Y Y 9 Cape Verde Y Y Y Y Y Y Y Y Y Y Y Y Y 13 Ghana Y Y Y Y Y Y Y Y 8 Guinea Y Y Y Y Y Y Y Y Y Y 10 Gambia Y Y Y Y Y Y Y Y Y Y Y Y Y 13 Guinea-Bissau Y Y Y 3 Liberia 0 Mali Y Y Y Y Y Y Y Y Y Y Y Y 12 Niger Y Y Y Y Y Y Y Y Y Y Y Y Y 13 Nigeria Y Y Y Y Y Y Y Y Y 9 Senegal Y Y Y Y Y Y Y Y Y Y Y Y Y 13 Sierra Leone Togo Y Y Y Y Y Y Y Y Y Y Y Y 12 NB of Countries declaring Imports Note: Y stands for yes if the country declares that particular year. 28

35 Annex 2.2 Main descriptive statistics Table A2.1: Africa s top 15 exported products by destination in (nominal value in 1,000 USD and volume in tons). World Africa America HS4 Value Volume HS4 Value Volume HS4 Value Volume Source: BACI Table A2.1 ctd. Asia European Union HS4 Value Volume HS4 Value Volume See the list of products corresponding to the HS nomenclature in Table A

36 Source: BACI Table A2.2: Africa s top 15 imported products by origin in 2013 (nominal value in 1,000 USD and volume in tons). World Africa America HS4 Value Volume HS4 Value Volume HS4 Value Volume Source: BACI Table A2.2. Ctd Asia European Union HS4 Value Volume HS4 Value Volume Source: BACI 30

37 Table A2.3: Exports, imports and trade balance for main RECS in nominal value (1,000 USD) ECOWAS ECCAS COMESA Exports Imports Trade balance Exports Imports Trade balance Exports Imports Trade balance E E E Source: BACI 31

38 Table A2.3: ctd SADC AMU Exports Imports Trade balance Exports Imports Trade balance E E E E E+07 Source: BACI 32

39 Table A2.4: list of products corresponding to the HS 4 nomenclature HS4 Product Description 0102 Live bovine animals Live sheep and goats Meat of bovine animals, frozen Edible offal of bovine animals, swine, sheep, goats, horses, asses, mules or hinnies, fresh, chilled or frozen Meat and edible offal, of the poultry of heading No , fresh, chilled or frozen Fish, frozen, excluding fish fillets and other fish meat of heading No Fish fillets and other fish meat (whether or not minced), fresh, chilled or frozen Molluscs, whether in shell or not, live, fresh, chilled, frozen, dried, salted or in brine; aquatic invertebrates other than crustaceans and molluscs, live, fresh, chilled, frozen, dried, salted or in brine; flours, meals and pellets of 0402 Milk and cream, concentrated or containing added sugar or other sweetening matter Cheese and curd Cut flowers and flower buds of a kind suitable for bouquets or for ornamental purposes, fresh, dried, dyed, bleached, impregnated or otherwise prepared Tomatoes, fresh or chilled Other vegetables, fresh or chilled Dried leguminous vegetables, shelled, whether or not skinned or split Coconuts, Brazil nuts and cashew nuts, fresh or dried, whether or not shelled or peeled Other nuts, fresh or dried, whether or not shelled or peeled Bananas, including plantains, fresh or dried Citrus fruit, fresh or dried Grapes, fresh or dried Coffee, whether or not roasted or decaffeinated; coffee husks and skins; coffee substitutes containing coffee in any proportion Tea, whether or not flavoured Wheat and meslin Maize (corn) Rice Wheat or meslin flour Malt, whether or not roasted Soya beans, whether or not broken. 33

40 1207 Other oil seeds and oleaginous fruits, whether or not broken Plants and parts of plants (including seeds and fruits), of a kind used primarily in perfumery, in pharmacy or for insecticidal, fungicidal or similar purposes, fresh or dried, whether or not cut, crushed or powdered Soya-bean oil and its fractions, whether or not refined, but not chemically modified Olive oil and its fractions, whether or not refined, but not chemically modified Palm oil and its fractions, whether or not refined, but not chemically modified Sunflower-seed, safflower or cotton-seed oil and fractions thereof, whether or not refined, but not chemically modified Animal or vegetable fats and oils and their fractions, partly or wholly hydrogenated, inter-esterified, re-esterified or elaidinised, whether or not refined, but not further prepared Prepared or preserved fish; caviar and caviar substitutes prepared from fish eggs Cane or beet sugar and chemically pure sucrose, in solid form Cocoa beans, whole or broken, raw or roasted Cocoa shells, husks, skins and other cocoa waste Cocoa paste, whether or not defatted Cocoa butter, fat and oil Malt extract; food preparations of flour, meal, starch or malt extract, not containing cocoa or containing less than 40% by weight of cocoa calculated on a totally defatted basis, not elsewhere specified or including; food preparations 1902 Pasta, whether or not cooked or stuffed (with meat or other substances) or otherwise prepared, such as spaghetti, macaroni, noodles, lasagne, gnocchi, ravioli, cannelloni; couscous, whether or not prepared Bread, pastry, cakes, biscuits and other bakers' wares, whether or not containing cocoa; communion wafers, empty cachets of a kind suitable for pharmaceutical use, sealing wafers, rice paper and similar products Tomatoes prepared or preserved otherwise than by vinegar or acetic acid Fruit juices (including grape must) and vegetable juices, unfermented and not containing added spirit, whether or not containing added sugar or other sweetening matter Food preparations not elsewhere specified or included Waters, including mineral waters and aerated waters, containing added sugar or other sweetening matter or flavoured, and other non-alcoholic beverages, not including fruit or vegetable juices of heading No

41 2203 Beer made from malt Wine of fresh grapes, including fortified wines; grape must other than that of heading No Undenatured ethyl alcohol of an alcoholic strength by volume of 80 % vol or higher; ethyl alcohol and other spirits, denatured, of any strength Undenatured ethyl alcohol of an alcoholic strength by volume of less than 80 % vol; spirits, liqueurs and other spirituous beverages Residues of starch manufacture and similar residues, beetpulp, bagasse and other waste of sugar manufacture, brewing or distilling dregs and waste, whether or not in the form of pellets Oilcake and other solid residues, whether or not ground or in the form of pellets, resulting from the extraction of soyabean oil Preparations of a kind used in animal feeding Unmanufactured tobacco; tobacco refuse Cigars, cheroots, cigarillos and cigarettes, of tobacco or of tobacco substitutes Other manufactured tobacco and manufactured tobacco substitutes; homogenised or reconstituted tobacco; tobacco extracts and essences Wool, not carded or combed Cotton, not carded or combed. 35

42 Chapter 3. Regional trade patterns Extracted from African Agricultural Trade Status Report 2017

43 CHAPTER 3. REGIONAL TRADE PATTERNS Anatole Goundan, International Food Policy Research Institute, West and Central Africa office, Dakar, Senegal Cheickh Sadibou Fall, Institut Sénégalais de Recherches Agricoles, Bureau d'analyses Macro- Economiques, Dakar, Senegal 3.1 Introduction Deepening intra-regional trade among African countries, and especially Africa s main RECs, is essential for the continent s resilience against international market shocks. Aware of that, African leaders have positioned African economic integration as a central key in almost all continental roundtables or political discussions. Important efforts have been made through several regional trade agreements (RTA) such as the creation of free trade areas (FTA), customs unions (CU), and economic and monetary unions. More recently, the 2012 African Union Summit mainly focused on Boosting Intra-African Trade. Even if those agreements have generally and positively impacted intra-african trade, the share of intra-regional trade in total African trade is still very low compared to other regions or continents. For agricultural commodities, the view is similar (Figure 3.1). The share of trade in agricultural products among African countries that is intra-regional varies between 13% and 20% over the period from 2000 to 2013, while its level is around 40% among American countries, 63% among Asian countries and 75% among European countries. Figure 3.1. Share of intra-regional agricultural trade value in total trade 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Africa America Asia Europe Intra trade Extra trade Source: BACI and authors calculation,

44 Many reasons could explain that low level of intra-regional trade in Africa. Obstacles to better performance of intra-regional trade in Africa include weak productive capacity, the lack of traderelated infrastructure and services, the limited role of the private sector in regional integration initiatives, the low diversification of traded products, the small size of consumer markets, and the quality of institutions. This chapter focuses on the state of intra-african trade for agricultural commodities over recent years. It will mainly (i) analyze the current performance of intra-african and intra-regional trade, (ii) explore trade direction at the continental and REC levels, (iii) study the trading role of each REC in African trade and each country s individual share in the corresponding REC, (iv) examine the main agricultural products traded among African countries, and finally (v) present the evolution of import and export unit values. 3.2 A general perspective of regional agricultural trade and total trade Over recent years ( ), African exports have increased rapidly, with an annual growth of 12%. During the same period, trade exchange between African countries showed a significant increase (16%), with an intra-african trade share growing from about 7% in 1998 to 13% in The average intra-african trade share stood at 10%. In terms of agricultural trade, its share in total trade has decreased over the years, passing from 18% in 1998 to about 9% in Total agricultural trade has shown an annual growth of 8%. Agricultural trade between African countries has experienced a significant growth rate of about 13% over the period, especially after the recent food crisis, with an increase between 10 and 28% over the period from 2007 to At the ECOWAS level, total exports have also considerably increased over the period, with an annual growth of 14%. Trade within the region represents on average only 8% of total trade, but has displayed a large increase between 1998 and 2013 of around 15%. Agricultural trade represents about 15% of total exports, with an annual growth of 8%. Within the region, the agricultural trade share stands at 18% on average, with on average 12% annual growth. The total trade of ECCAS countries has displayed very high growth of more than 17% over However, this trade performance is not due to an increase in intra-regional trade, which represents less than 2% of total ECCAS exports. Agricultural products represent only 4% of total exports, with about 4% growth. The trade of these products inside the region represents 18% of 37

45 the total intra-regional trade. Over the period, intra-regional agricultural trade has grown significantly, with an average growth rate of 16%. For COMESA countries, total exports have shown significant growth over the period, with an annual growth rate of 12%. Trade within the region, which represents on average only 6% of total trade, has grown more rapidly than total trade (16% compared to 12%). Agricultural trade represents about 17% of total exports, with an annual growth rate of 8%. Within the region, the agricultural trade share stands at 33% on average, which is the highest share among the considered RECs. The agricultural trade share grew by an average of 12% annually. For SADC countries, total exports have shown rapid growth over the period, with an annual growth rate of 16%, increasing from $11 billion in 1998 to $105 billion in Intra-SADC trade, which represents on average only 4% of total trade, has grown rapidly, with a 19% annual growth rate. Agricultural trade represents about 16% of total exports, with an annual growth of 7%. Within the region, the agricultural trade share stands at 27% on average, which is the second highest share among the considered RECs, with 17% average annual growth. In terms of trade balance, Figure 3.2 depicts changes in the normalized trade balance over the period for agricultural and non-agricultural products for different regional economic communities. This graph shows that the evolution of the trade balance depends immensely on the product group and the region considered. Agricultural products tend to have a negative trade balance, especially after the recent food crisis. Unlike agricultural products, non-agricultural products have a positive trade balance for several RECs and years. 38

46 Figure 3.2. Evolution of the normalized trade balance by REC and product group (a) Africa ECOWAS ECCAS COMESA SADC (b) Africa ECOWAS ECCAS COMESA SADC Source: BACI and authors calculation, Note: (a) Total agricultural trade, (b) Total non-agricultural trade. 3.3 Trends in volumes and values of intra-african and intra-regional agricultural exports and imports The evolution of agricultural trade in value and volume among African countries in general and among some RECs (ECOWAS, ECCAS, COMESA and SADC) over the period from 1998 to 2013 is represented in Figures 3.3 and In the BACI trade dataset, intra-regional exports are set to exactly equate intra-regional imports. Therefore, we use intra-regional trade to mean imports or exports. In terms of trends, imports or exports are equivalent. 39

47 The value of intra-african agricultural trade has grown rapidly over recent years, rising from $2.2 billion in 1998 to $12.8 billion in 2013 (Figure 3.3). The overall annual growth during this period is around 12%. When the period is split into two sub-periods (before and after the international crisis), an increase in the growth of agricultural products trade can be noted (13.62% between 2007 and 2013) compared the period before the crisis (11.47% between 1998 and 2006). The analysis of intra-african trade in agricultural products in volume terms shows an overall growth of 15.84%, which is greater than the nominal trade growth. Therefore, in general, growth in agricultural trade between African countries over the selected periods was not driven by price increases. Figure 3.3. Intra-regional agricultural trade over by REC (a) (b) Africa ECOWAS ECCAS COMESA SADC Africa ECOWAS ECCAS COMESA SADC Source: BACI and authors calculation, Note: (a) trade value in billion US dollar, (b) trade volume in million metric tons. Intra-ECOWAS agricultural trade shows an average growth of 12%, rising from $494 million in 1998 to $2.84 billion in Despite this apparent significant growth, agricultural trade between ECOWAS countries was very erratic. In fact, seven negative growth-rates were noticed over the considered period. The year 2006 saw the biggest decrease (-23.4%) and the largest increase was reported in 2003 (95%). Over the two sub-periods, a big growth gap was noted. The sub-period before 2007 showed an average growth of 5% while an intra-regional trade increase of 21% was registered during the sub-period starting in

48 This could be the result of various initiatives during and after the international food crisis. As examples of initiatives during the recent food crisis, Engel et al. (2013, page 20) mention the EUled Alliance Globale pour l Initiative Résilience Sahel (AGIR), the Comité permanent Interétat de Lutte contre la Sécheresse au Sahel (CILSS) initiative, the COMESA Alliance for Commodity Trade, and the SADC Regional Indicative Strategic Development Plan, etc. In terms of agricultural trade volume, overall growth of 11% is reported compared to 12% for nominal trade. Trade increase between ECOWAS countries was then partly driven by commodity prices. Figure 3.4. Average intra-regional trade growth (value and volume) 30% 25% 20% 15% 10% 5% 0% (a) Africa ECOWAS ECCAS COMESA SADC 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% (b) Africa ECOWAS ECCAS COMESA SADC Overall Overall Source: BACI and authors calculation, Note: (a) trade value, (b) trade volume. Agricultural trade between ECCAS countries has shown the highest overall growth in value of 17%, with a nominal value which has increased from $14 million in 1998 to $147 million in A significant change in intra-eccas trade can be noted over the two sub-periods. The first period was characterised by an improving trade performance with an average annual growth of 27%, but the growth rate of intra-exchange fell to 5% in the second period. Obviously, the food crisis has dampened the dynamic of agricultural trade inside the ECCAS zone. The volume of agricultural trade between ECCAS countries showed the same dynamics as nominal trade. Moreover, the average growth of trade volume was higher than that of trade value. 41

49 In fact, the average trade volume (nominal trade value) growth was 38% (27%) over , 8% (5%) from 2007 to 2013, and 23% (17%) for the entire period. It could be concluded that on average, trade flow of agricultural products was not driven by price increases. Like other RECs, intra-regional agricultural trade in COMESA has displayed a significant increase (14%) over , rising from $379 million in 1998 to $2.87 billion in Whereas the first two RECs (ECOWAS and ECCAS) showed a major differences between our two sub-periods, in COMESA, the growth gap between the two sub-periods is very thin (less than 3 percentage points). Over the entire period ( ), the volume of intra-regional agricultural trade has shown a significant increase (22%). The value of intra-regional trade of agricultural commodities in SADC has displayed the lowest overall annual growth of 10%, with a nominal value which has increased from $871 million in 1998 to $3.82 billion in During the first sub-period, an 8% increase was reported, against 13% over the second sub-period. In value, intra-regional agricultural trade has increased after the international food crisis. However, the volume trend is totally different over the two sub-periods. A greater average increase was noted over the first sub-period (16%) compared to growth in the second sub-period (13%). Therefore, the nominal intra-regional increase observed between the sub-periods is essentially a price effect. Nevertheless, over the whole period ( ), the intra-regional trade volume increase (14%) is greater than its value increase (10%). 3.4 Direction of agricultural exports and imports in intra-african and intra-regional markets The previous section presented trends in intra-african and intra-recs trade over the period from But, no mention was made of which country or REC leads in intra-regional trade. Therefore, the target of this section is to shed light on that aspect. Before deepening the analysis of intra-african and intra-recs trade direction, Table 3.1 summarizes trading networks between various African regions, by presenting the average trade flow (exports/imports) between them over recent years ( ). Exporting regions are in rows and importing ones are in columns. Intra-regional trade is shown by the diagonal elements in bold. 42

50 Table 3.1. Value of intra- and inter-regional trade in agricultural products in Africa, average (billion US dollars) Exporters Regional market destinations AFRICA ECOWAS ECCAS COMESA SADC SSA AFRICA ECOWAS ECCAS COMESA SADC SSA Source: BACI and authors calculation, One interesting statistic is the ratio of intra-regional trade (ECOWAS, ECCAS, COMESA and SADC) to the total trade of the REC with Africa as a whole. This statistic will show how one REC s trade with the continent is concentrated in that REC; it could be seen as an indicator of regional trade integration. The results show that ECOWAS is the REC with the highest trade integration with a ratio of 0.79, followed by SADC with 0.77, COMESA with 0.65 and ECCAS with Therefore, with the exception of ECCAS countries, each REC exchanges the principal part of its trade with Africa inside its own bloc (UNCTAD, 2013). For example, ECOWAS s intraregional agricultural trade represents, on average over 2010 and 2013, around 80% of its total trade with Africa. In terms of intra-african agricultural trade, Figure 3.5 represents the weight of individual RECs in terms of origin and destination. As destinations or origins of intra-african trade, COMESA (42% of exports and 34% of imports) and SADC (37% of exports and 42% of imports) are the main regions, while ECCAS (14% of exports and 3% of imports) is last. One could note that COMESA and SADC have opposite patterns. In fact, COMESA has gained trade share (exports and imports) over the considered period while SADC countries have lost some. COMESA s export share has increased from 40% between 1998 and 2006 to 45% between 2007 and 2013, and the region s import share has risen from 32% between 1998 and 2006 to 37% between 2007 and In contrast, SADC s export share has decreased from 39% between 1998 and 2006 to 34% between 2007 and 2013, and the region s import share has fallen from 46% between 1998 and 2006 to 38% between 2007 and

51 Figure 3.5. Regional share in intra-african agricultural trade 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% (a) 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% (b) ECOWAS ECCAS COMESA SADC ECOWAS ECCAS COMESA SADC Source: BACI and authors calculation, Note: (a) export value, (b) import value. Inside any specific African REC, many efforts and political commitments exist to promote political co-operation and economic integration. As seen previously, those commitments have increased intra-regional trade. The objectives of the following subsections are to present the importance (in terms of exports and imports) of different countries inside their regional bloc. Tables 3.2 to 3.5 present individual countries export and import shares in intra-regional trade (average shares for , and ). Table 3.2. ECOWAS intra-regional trade share by country (%) Overall Exports Imports Exports Imports Exports Imports Benin Burkina Faso Cape Verde Côte d'ivoire Gambia Ghana Guinea Guinea-Bissau Liberia Mali Niger Nigeria Senegal Sierra Leone Togo Source: BACI and authors calculation,

52 Inside ECOWAS, Côte d Ivoire remains the biggest exporter of agricultural products in the region with about 26% of total intra-regional trade. Other important exporters to the region are Niger (15.5%), Senegal (11.3%) and Mali (10.1%). In terms of destination, Nigeria is the main importer of those commodities from the region with 23% of total trade, followed by Côte d Ivoire (13.5%) and Senegal (10.2%). Some countries have seen their exporting performance worsen over the two sub-periods while others became more performant. For example, Burkina Faso s export share has fallen from 14.8% to 4.2%. In contrast, Ghana s export share has increased from 3.7% to 11.1%. Table 3.3. ECCAS intra-regional trade share by country (%) Overall Exports Imports Exports Imports Exports Imports Angola Burundi Cameroon Central African Republic Chad Congo Democratic Congo Equatorial Guinea Gabon Rwanda Sao Tome and Principe Source: BACI and authors calculation, For ECCAS countries, Cameroon controlled the export market inside this REC with around 43% of the regional agricultural products market. Rwanda (18.1%), Gabon (18%) and Congo (13%) are the other main exporters of agricultural products. In terms of destination, Congo (18.5%), Democratic Republic of the Congo (DRC) (15.9%), Gabon (15.7%) and Cameroon (14.4%) are the main markets for agricultural products. It is worth noting the impressive performance of Rwanda, which has seen its export share rise from 1.2% over to 18.1% between 2007 and

53 Table 3.4. COMESA intra-regional trade share by country (%) Overall Exports Imports Exports Imports Exports Imports Burundi Comoros DRC Djibouti Egypt Eritrea Ethiopia Kenya Libya Madagascar Malawi Mauritius Rwanda Seychelles Sudan Uganda Zambia Zimbabwe Source: BACI and authors calculation, Inside COMESA, Kenya (22.9%), Egypt (17%), Uganda (15%) and Zambia (14.6%) are the leading exporters of agricultural products. In terms of imports, Egypt (16.6%), Sudan (13.7%) and Kenya (12.2%) are the main markets for those products. Showing exceptional performance, Egypt s export share in the region has been multiplied by four, passing from 5.6% between 1998 and 2006 to 21.1% over Table 3.5. SADC intra-regional trade share by country (%) Overall Exports Imports Exports Imports Exports Imports Angola Democratic Congo Madagascar Malawi Mauritius Mozambique SACU Seychelles Tanzania Zambia Zimbabwe Source: BACI and authors calculation,

54 Within SADC, SACU countries, which are composed of Botswana, Lesotho, Namibia, Swaziland and South Africa, constitute the major exporters with around 57% of intra-regional trade in agricultural commodities. But in terms of imports, they are the second biggest market (14.3%) behind Zimbabwe (21.7%). Mozambique is the third market for agricultural products in the region with 13.5% of intra-regional trade. 3.5 Changes in export and import shares in intra-african and intra-regional agricultural markets The bubble charts presented in the next subsections show primarily the changes in trade (imports and exports) for each of the two sub-periods. The average trade in value for the sub-period is represented on the X axis. The average trade in volume over the considered period is represented on the Y axis. Each bubble corresponds to a country, and the bubble size shows the country s average GDP over the sub-period. This type of graph is chosen in order to capture whether the observed changes in trade issue from a price effect or a volume effect. In addition, it provides an idea of the size of the economies within the RECs ECOWAS The changes in intra-ecowas agricultural imports are shown in Figure 3.6. It is found that in the aggregate, the total value and volume of agricultural imports has doubled in the ECOWAS zone. At the country level, we note that all countries have at least doubled the value of their agricultural purchases from ECOWAS, except Togo, for which a 14% increase in the value of agricultural imports from the ECOWAS zone is observed. 47

55 Imports vol (1000 Tons) Imports vol (1000 Tons) Figure 3.6. ECOWAS import changes GAMB 150 GUIB 100 LIB CAPV GUI MAL GHA NIGA CIV 50 NIG TOG SIER BEN BF 0 SEN Imports (million $) GUIB GUI 200 GAMB CAPV LIB MAL GHA CIV 0 SIER BENTOG NIG SENBF Imports (million $) Source: BACI and authors calculation, Note: Benin (BEN), Burkina Faso (BF), Cape Verde (CAPV), Côte d'ivoire (CIV), Gambia (GAMB), Ghana (GHA), Guinea (GUI), Guinea-Bissau (GUIB), Liberia (LIB), Mali (MAL), Niger (NIG), Nigeria (NIGA), Senegal (SEN), Sierra Leone (SIER), Togo (TOG) Over the two periods, the largest importers remain Nigeria and Côte d Ivoire, which are the two largest economies of the zone. Nigeria s agricultural imports quadrupled in value and approximately doubled in volume between the two periods. Other countries experiencing an increase in imports in value and volume include Benin, Burkina Faso, Côte d Ivoire, Guinea, NIGA 48

56 Exports vol (1000 Tons) Exports vol (1000 Tons) Guinea-Bissau, Mali, Senegal and Sierra Leone. However, it should be noted that Senegal is the country that buys the fewest agricultural products from ECOWAS in volume. This country is the fourth largest economy in the zone after Nigeria, Côte d Ivoire and Ghana. It is also in the top five in import values in the two periods, as shown in Figure 3.6. Figure 3.7. ECOWAS export changes NIG 100 TOG SEN NIGA GHA 50 BEN GUI 0CAPV GUIB SIER GAMB LIB Exports (million $) GHA 200 BEN BF TOG 100 NIGA MAL GUI 0CAPV SIER LIB GAMB GUIB SEN NIG Source: BACI and authors calculation, Note: Benin (BEN), Burkina Faso (BF), Cape Verde (CAPV), Côte d'ivoire (CIV), Gambia (GAMB), Ghana (GHA), Guinea (GUI), Guinea-Bissau (GUIB), Liberia (LIB), Mali (MAL), Niger (NIG), Nigeria (NIGA), Senegal (SEN), Sierra Leone (SIER), Togo (TOG) BF Exports (million $) MAL CIV CIV 49

57 For the rest of the ECOWAS countries (Cape Verde, The Gambia, Ghana, Liberia, Niger, Togo), an increase in the value of imports is noted, but the volumes remain almost unchanged. As a result, the growth in value of imports recorded for these countries is due to the rising prices observed over the period. On the export side (Figure 3.7), it is noted that the total value of agricultural exports has also doubled on aggregate. Aside from Burkina Faso, Mali and Sierra Leone, all other countries have at least doubled the value of their average exports to the ECOWAS area. In volume terms, it is also noted in the aggregate that intra-area agricultural sales have also doubled. However, some countries such as Burkina Faso, Cape Verde, Mali, Niger and Sierra Leone have not increased the volume of their agricultural shipments to ECOWAS destinations. At the country level, Côte d Ivoire remains in both periods the largest agricultural exporter in the area in value. However, it is observed that during the second period Ghana has become the first supplier of agricultural products for other ECOWAS countries before Côte d'ivoire. Indeed, Ghana has multiplied the volume of its agricultural exports to the region by 11. During the second period, Niger is positioned as the second largest exporter of the zone in value with a quadrupling of the value of its exports, but the volumes remain almost unchanged over the two periods. Niger has taken advantage of the rising prices of livestock products during the period. In contrast, Mali and Burkina Faso, which were the main exporters behind Côte d'ivoire in the first period, do not benefit from the increasing agricultural prices. Instead they have experienced decreases in the value of exports by 18% and 32%, respectively. As mentioned before, these two countries export volumes have remained almost unchanged compared to the period. Regarding Mali, the political crisis that occurred in late 2011 could be an explanation for this decline ECCAS Figure 3.8 illustrates the import changes in the ECCAS zone for the two periods. The total value and volume of intra-eccas agricultural imports have tripled between the two periods. All the countries in the zone, without exception, have at least doubled their imports in value. In terms of volume, this upward trend in agricultural purchases from the area is observed except for Gabon and Rwanda, where the level of import volumes remained stable over the two periods. Between the two periods, the DRC is the country that has experienced the greatest growth in agricultural purchases from its neighbours. This is due to rising prices in the second period. Actually, the DRC is only the seventh importer in the area by volume over the period

58 Imports vol (1000 Tons) Imports vol (1000 Tons) Figure 3.8. ECCAS import changes GAB 15 RWA 10 CAR BUR 5 CHA CAM ANG DRC EGUI 0 SAO CONG Imports (million $) EGUI ANG RWA BUR CAR CHA Source: BACI and authors calculation, Note: Angola (ANG), Burundi (BUR), Cameroon (CAM), Central African Republic (CAR), Chad (CHA), Congo (CONG), Democratic Republic of the Congo (DRC), Equatorial Guinea (EGUI), Gabon (GAB), Rwanda (RWA), Sao Tome and Principe (SAO) CAM GAB CONG 0 SAO Imports (million $) DRC 51

59 Exports vol (1000 Tons) In terms of exports, Cameroon remains the largest exporter of agricultural products in the ECCAS area by doubling the value of its agricultural sales and the volume of its shipments to its neighbours between the two periods. Two other major exporters of the zone, Congo and Gabon, also experienced almost identical situations. However, Rwanda and the DRC are the countries that have made the most progress in terms of exports. In fact, Rwanda has multiplied the value of its agricultural exports in the area by 49 while the DRC has multiplied the value of its exports to its neighbours in the area by 25. In volume, Rwanda and the DRC have multiplied the volume of shipments by 25 and 31, respectively (Figure 3.9). Regarding Rwanda, which became the second largest exporter of the area behind Cameroon, its performance is linked with the economic performance recorded between 2000 and 2012 after the political crisis. In addition, Rwanda has also intensified its commercial exchanges with neighbouring Kenya and DRC10. Figure 3.9. ECCAS export changes CAM CONG 10 5 GAB CHA EGUI ANGBUR DRC RWA 0 SAO CAR Exports (million $) 10 Rwanda is also part of COMESA with these two countries. We will discuss its performance further in the COMESA subsection. 52

60 Exports vol (1000 Tons) CAM 40 RWA DRC CONG 10 BUR GAB 0EGUI ANG SAO CHA CAR Source: BACI and authors calculation, Note: Angola (ANG), Burundi (BUR), Cameroon (CAM), Central African Republic (CAR), Chad (CHA), Congo (CONG), Democratic Republic of the Congo (DRC), Equatorial Guinea (EGUI), Gabon (GAB), Rwanda (RWA), Sao Tome and Principe (SAO) COMESA Figure 3.10 shows the variations in terms of agricultural imports for the COMESA countries. In the aggregate, trade has intensified in this area. Indeed, the value of imports was quadrupled while traded volumes were doubled. In general, all countries in the region have at least doubled the value of their purchases from their neighbours with the exception of Ethiopia for which the import values remained almost unchanged over the two periods. Exports (million $) Regarding the volume variations, the trend remains the same, except for Ethiopia, Malawi and Zambia. Regarding the latter, a highly significant decrease in the volume of agricultural products imported from the area is observed. Indeed, the volume of imports in the second period is about 18 times lower compared to the first period. Despite this reduction, import values are found to have doubled. Several elements of explanation could be advanced. First, import prices in this country are very high. Second, given that Zambia is also a member of another REC, it may be that this decline is offset by a sharp increase in quantities imported from the SADC area. Finally, Zambia could have launched an agricultural self-sufficiency policy. 53

61 Unlike Zambia, Madagascar has multiplied the volume of agricultural imports by 20, becoming the largest importer in volume of the area before the largest economies of the region including Egypt, Libya, Kenya, the DRC and Sudan. However, Libya has also stepped up its agricultural orders from COMESA in the second period, Indeed, they are multiplied by 225 with respect to the value of the first period and by 280 for the quantities. Possible explanations include, among others, the Libyan crisis that took place in 2011 and which has limited supplies to Libya from Tunisia by land. Consequently, it appears that Libya buys more from COMESA. In addition, three COMESA countries, Burundi, the DRC and Rwanda, are also members of the ECCAS area. Regarding Rwanda, and despite the intensification of its exchanges in the ECCAS zone, it should be noted that the values and volumes of its imports from the COMESA are significantly higher than those from the ECCAS area. In other words, Rwanda purchases mainly within COMESA. This is also true for Burundi and the DRC. 54

62 Imports vol (1000 Tons) Imports vol (1000 Tons) Figure COMESA import changes ZAM KEN DRC SUD EGY COM BUR DJI ERI MAD RWA MAU ETH MALW UGA ZIM 0 LiB SEY Imports (million $) MAD 600 KEN 500 SUD ZIM 400 DRC 300 LiB 200 EGY RWA UGA 100 COM DJI MAU ERI BUR ZAM ETH MALW 0 SEY Imports (million $) Source: BACI and authors calculation, Note: Burundi (BUR), Comoros (COM), Democratic Republic of the Congo (DRC), Djibouti (DJI), Egypt (EGY), Eritrea (ERI), Ethiopia (ETH), Kenya (KEN), Libyan Arab Jamahiriya (LIB), Madagascar (MAD), Malawi (MALW), Mauritius (MAU), Rwanda (RWA), Seychelles (SEY), Sudan (SUD), Uganda (UGA), Zambia (ZAM), Zimbabwe (ZIM) 55

63 Exports vol (1000 Tons) Exports vol (1000 Tons) Figure COMESA export changes MALW KEN ZAMUGA EGY COMDRC ERI BUR DJI SUD MAU ETH ZIM 0 LIBMAD RWA SEY Export (million $) EGY 800 UGA 600 ZAM 400 MAD KEN 200 MALW ETH MAU SEY SUD RWA 0COM ERI LIB BUR DRC DJI ZIM Export (million $) Source: BACI and authors calculation, Note: Burundi (BUR), Comoros (COM), Democratic Republic of the Congo (DRC), Djibouti (DJI), Egypt (EGY), Eritrea (ERI), Ethiopia (ETH), Kenya (KEN), Libyan Arab Jamahiriya (LIB), Madagascar (MAD), Malawi (MALW), Mauritius (MAU), Rwanda (RWA), Seychelles (SEY), Sudan (SUD), Uganda (UGA), Zambia (ZAM), Zimbabwe (ZIM) 56

64 On the export side (Figure 3.11), it is found that the total value of intra-comesa agricultural exports has quadrupled, while volumes have doubled. At the country level, it is observed that all countries in the region have at least doubled their agricultural sales (volume and value) in the area over the two periods, with the exception of Djibouti, Malawi, Sudan and Zimbabwe. In Djibouti, Malawi and Sudan, values have increased slightly, while they declined slightly for Zimbabwe. Quantities shipped remained almost stable for Sudan. However, they have dropped more than half for the other three countries. In contrast, Egypt is the country that has increased its agricultural trade with its neighbours in the COMESA region the most, becoming the leading supplier of agricultural products before Kenya, Uganda and Zambia. Concerning Rwanda, Burundi and the DRC, these countries have at least tripled their trade in volume and value with other COMESA countries. Compared to the ECCAS zone, it is noted that these countries sell more in the COMESA region than in the ECCAS area SADC Figure 3.12 shows the changes observed in imports within SADC. However, it should be noted that in the database used, BACI, South Africa, Namibia, Botswana, Swaziland and Lesotho are grouped within SACU. In fact, information is provided only for the SACU group, rather than for the individual countries. On aggregate, it is found firstly that imports doubled in value and also decreased approximately 20% in quantity. Malawi, Mozambique, Tanzania and Zambia are the countries affected by the drop in traded quantities. Regarding Zambia, also a member of COMESA, a sharp decline is also observed in the volume of its agricultural imports from its SADC neighbours. Indeed, volumes were divided by 6. It seems that the trend for Zambia within COMESA is also valid for SADC. This reinforces the hypothesis previously issued on the possible implementation of a self-sufficiency policy to reduce imports, accompanied by a protectionist policy. To a lesser extent, Malawi, also a member of COMESA, has also decreased its agricultural purchases from SADC. Nevertheless, these two countries buy more within the SADC zone than within the COMESA zone. Other countries concerned by the decline of imported quantities are Tanzania and Mozambique. In contrast, the other countries of the zone have experienced an increase in volumes purchased from neighbouring countries in SADC. Between the two periods, Zimbabwe became the first buyer of agricultural products before Mozambique and SACU. Furthermore, it is noted that Zimbabwe is a member of COMESA but buys more within SADC. 57

65 Imports vol (1000 Tons) This observation is also true for the DRC, also a member of COMESA and ECCAS. For Angola, also a member of ECCAS, the exchanges are also more intense in the SADC region. In general, all countries that are at the same time members of SADC and another REC tend to import more from the SADC area. Figure SADC import changes MOZ 1500 ZAM ZIM SACU MAD DRC MAL TAN MAUR 0 SEY ANG Imports (million $) 58

66 Imports vol (1000 Tons) ZIM MAD DRC MOZ SACU 200 ZAM MAUR ANG TAN MAL 0 SEY Imports (million $) Source: BACI and authors calculation, Note: Angola (ANG), Democratic Republic of the Congo (DRC), Madagascar (MAD), Malawi (MALW), Mauritius (MAU), Mozambique (MOZ), Southern African Customs Union (SACU), Seychelles (SEY), United Rep. of Tanzania (TAN), Zambia (ZAM), Zimbabwe (ZIM) Figure 3.13 shows the intra-sadc agricultural exports. It is observed in the aggregate that exports values have increased and at the same time export volumes have decreased. In both periods, SACU remains the top seller. Indeed, the value of exports from SACU exceeds the aggregate exports of all other members of SADC. However, it should be noted that the quantities exported by SACU have remained unchanged and are relatively low. SACU is the 10th exporter in volume over the 11 countries. 59

67 Exports vol (1000 Tons) Exports vol (1000 Tons) Figure SADC export changes MOZ MAU ANG DRC TAN 0 MADMALW SEY ZAM ZIM SACU Export (million $) MAU ANG 500 TAN DRC MOZ MADMALW 0 SEY ZIM ZAM SACU Exports (million $) Source: BACI and authors calculation, Note: Angola (ANG), Democratic Republic of the Congo (DRC), Madagascar (MAD), Malawi (MALW), Mauritius (MAU), Mozambique (MOZ), Southern African Customs Union (SACU), Seychelles (SEY), United Rep. of Tanzania (TAN), Zambia (ZAM), Zimbabwe (ZIM) 60

68 Products exported by this regional entity appear to be more expensive. Furthermore, concerning the other SADC countries which are also member of COMESA (Zambia, Zimbabwe, Seychelles, Malawi, Madagascar, and DRC), it is noted that the quantities shipped in the COMESA region are greater. Only Madagascar exports more in value to COMESA than SADC. In the next section, the changes in the composition of products traded between the different RECs will be presented. 3.6 Changes in composition of intra-african and intra-regional agricultural exports and imports Table 3.6 shows the trade variations in both periods by group of products. It is observed on aggregate that the share of cereals in trade between African countries remained relatively stable. Indeed, it was around 7% during both of the two periods. In addition, an increase in shares of dairy products and other livestock products, fruits and processed food is observed in both periods. In contrast, intra-african trade in coffee and oilseeds has slightly fallen. Table 3.6. Changes in composition of intra-african trade (commodity groups) Africa ECOWAS ECCAS COMESA SADC Cereals 6,9 6,6 3,9 4,8 0,6 4,2 7,0 8,7 11,8 9,5 Coffee 10,4 7,4 0,4 1,5 0,9 0,5 27,4 17,0 2,8 2,2 Dairy products 2,8 3,5 3,3 2,9 1,9 3,7 1,5 4,4 3,7 3,3 Fish products 7,5 8,2 6,4 7,4 1,0 1,3 3,1 2,1 5,5 7,6 Fruits 2,5 3,3 2,7 2,4 0,1 0,2 1,2 1,1 2,8 2,8 Live cattle 2,8 3,0 10,5 8,8 1,3 3,5 1,6 3,7 1,3 1,0 Meats 0,8 0,8 0,7 1,6 0,2 0,2 0,6 0,2 1,6 1,4 Oilseeds 2,7 2,5 2,2 1,9 0,8 0,2 4,5 2,9 2,8 2,8 Processed Food 38,5 41,8 27,5 46,3 75,5 66,2 30,3 37,3 45,5 46,1 Others 25,0 22,8 42,4 22,5 17,6 19,8 22,9 22,5 22,3 23,2 Total Source: BACI and authors calculation,

69 At the product level, Figure 3.14 shows the 10 most traded agricultural commodities in Africa. Between the two periods, it is not noticed a major change in the composition of intra-african trade. Indeed, only two products that were present in the first period are out of the top 10 most traded goods between African countries in the second period. These products are cotton and food preparations nes (not elsewhere specified). In contrast, vegetables and wheat flour are among the 10 most traded products in the second period but not the first. Also, it is observed that fishery products become the most traded product between African countries in the second period. In the next subsections, the changes observed in each REC will be presented. Figure Top 10 most traded commodities (Intra-Africa) Source: BACI and authors calculation, ECOWAS Regarding ECOWAS trade by group of products (Table 3.6), trade increases in cereals, coffee, fish products, dairy products, meat and processed food are noted. This latter group accounts for almost the half of the trade of the second period, with an almost 20 percentage point increase between the two periods. 62

70 Figure Top 10 most traded commodities in the ECOWAS zone Source: BACI and authors calculation, At the product level, it is found that cotton, which was the first traded product at the ECOWAS level with a 25% share of trade between 1998 and 2006, is no longer part of the top 10 traded products in the region. In contrast, trade in cigars and cheroots has intensified and the share of this product quadrupled. To a lesser extent, exchanges of palm oil and frozen fish products have also increased. In addition, it is noted that rice and pasta are among the 10 most traded food and agricultural products in the ECOWAS region during the second period (Figure 3.15). For rice, it is likely due to the rice self-sufficiency policies launched by many ECOWAS countries to cope with the food price crisis ECCAS In the ECCAS zone, it is found during both of the two periods that processed foods account for about 2/3 of the total trade share, despite a roughly 9-point decline in the trade of this group of products between the two periods. In addition, cereals and fish products are the other most traded groups (Table 3.6). 63

71 Figure Top 10 most traded commodities in the ECCAS zone Source: BACI and authors calculation, At a more detailed level, sugar is still the most traded product, although its share has declined over the second period. Generally, the composition of trade in the ECCAS zone does not change much, even if a decreasing trend is noted for each product traded in the first period and still in the top 10 during the second period. For example, trade in cigars and cheroots halved between the two periods. In terms of new products traded, it is found that wheat flour, sauces, milk and cream are among the 10 most traded products in the ECCAS zone during the second period (Figure 3.16) COMESA It is found in both periods that the group of processed food products occupies the most important position in intra-comesa trade with over a third of the total trade share. Coffee trade has decreased (-10 points), but represents a major product in intra-community trade. As in the two RECs presented above, an increase in cereal trade is noted. In addition, trade shares of dairy products and live cattle have also increased. (Table 3.6). 64

72 Figure Top 10 most traded commodities in the COMESA zone Source: BACI and authors calculation, Figure 3.17 gives an indication of the detail of the products traded. In general, the composition of traded goods has not changed much. Only cotton, other oil seeds, and vegetables are no longer among the most traded products. However, palm oil, dried leguminous vegetables and cigars and cheroots are part of the 10 most traded products in the area during the second period SADC As with the other RECs, processed food products are still the most important group, representing nearly half of the trade over the two periods. In addition, the trade shares of fruits and oilseeds have remained unchanged in both periods. Except for fish products, for which exchanges have improved, it is found that all other group of products have experienced a drop in trade compared to the first period (Table 3.6). At the product level, the composition of trade is fairly stable. Sugar is still the most traded commodity with an almost unchanged share in both periods. Maize and tobacco are the other two most traded products, even if exchanges have fallen during the second period. However, a doubling of the share of frozen fish products is found. Furthermore, it is noted that oil trade has increased during the second period. Indeed, two types of oil (cotton-seed oil and soya-bean oil) are now part of the top 10 most traded commodities, while 65

73 drinks (waters and beer made from malt) are no longer part of the 10 most traded commodities (Figure 3.18). Figure Top 10 most traded commodities (Intra-SADC) Source: BACI and authors calculation, Changes in unit values of intra-african and intra-regional agricultural exports and imports Trade unit values (TUV) are usually used as proxies for trade prices. They measure, for individual commodity classes in a particular period, the total value of shipments divided by the corresponding total quantity (IMF, 2009). To analyze the trends of this indicator for intra-african and intraregional trade, we use the Trade Unit Values dataset by Berthou and Emlinger (2011). This database contains bilateral trade unit values at Harmonized-System 6-digit commodity categories. In this database, 45 African countries are represented. Therefore, the following discussions are related to the unit values (harmonic averages computed per year) of agricultural trade between those 45 countries. 66

74 Figure UV changes for intra-african trade, $ per ton Export UV Import UV Source: TUV Database and authors calculations, Figure 3.19 gives the trends of intra-african agricultural trade unit values over the period The average unit values for intra-african agricultural trade have increased over the period, with 3.54% growth for exports and 2.90% for imports. Export unit values have displayed slightly greater growth over the period (3.91%) compared to the period (3.12%). In contrast, import unit values have shown a slower increase during the post-crisis period (1.29%) relative to the period before the crisis (4.81%). Figure UV changes for intra-ecowas trade, $ per ton Export UV Import UV Source: TUV Database and authors calculations, Export unit values for intra-ecowas agricultural trade have decreased over (Figure 3.20), with a decrease of -4.67%. However, imports have become more expensive, with an overall growth of 3.23%. Therefore, it is easier to export into the region than to import from the region. 67

75 Since important progress in terms of economic integration has been made, one could attribute the increase of import unit values to non-tariff measures, corruption, etc. Figure UV changes for intra-eccas trade, $ per ton Export UV Import UV Source: TUV Database and authors calculations, Inside ECCAS, a large gap is noticeable over the first sub-period compared to the second subperiod (Figure 3.21). A 25.85% increase in export unit values and a 15.46% increase in import unit values were reported over the period, while export unit values (-4.83%) and import unit values (-4.51%) have shown a decrease over the second sub-period. This may be interpreted as an improvement in regional integration over the second period. It is worth noticing that trade unit values in ECCAS are the highest among RECs. Figure UV changes for intra-comesa trade, $ per ton Export UV Import UV Source: TUV Database and authors calculations,

76 Inside COMESA, trade unit values of agricultural products are more stable over the period (Figure 3.22). Export unit values showed a 4% increase while import unit values displayed 3.43% growth. Over the two sub-periods, export unit values have registered a decrease in growth (5.89% over and 4.09% over ) but import unit values have shown increased growth (1.77% over and 3.5% over ). Figure UV changes for intra-sadc trade Intra SADC Export UV Import UV Source: TUV Database and authors calculations, Export and import unit values for intra-sadc trade have shown steady growth over the period considered (Figure 3.23). Exports displayed overall unit value growth of 7.5% and imports showed a 5.7% increase. Following the 2011 methodological note by OECD, we computed the export/import value index for agricultural and non-agricultural products using the Fisher index (see Table A2 in the Annex for the evolution of the export/import value index). Then, we derived the terms of trade for different commodity groups as displayed in Figure Before the recent food crisis, African economies sold cheaper agricultural products but bought them more expensively from outside. On the other hand, the terms of trade for non-agricultural products show that almost all RECs (with the exception of ECCAS) have good prices for those products. 69

77 Figure Evolution of the terms of trade by group of products (a) Africa ECOWAS ECCAS COMESA SADC (b) Africa ECOWAS ECCAS COMESA SADC Source: TUV Database and authors calculations, Note: (a) for agricultural products, (b) for non-agricultural products. Conclusion In this chapter, many indicators were discussed to measure the intensity of intra-regional trade from 1998 to 2013 within African and within four RECs, including ECOWAS, ECCAS, COMESA and SADC, using mainly the BACI database. The analysis of the current performance of intra- African and intra-recs trade showed that the value of intra-african agricultural trade has grown rapidly over recent years, rising from $2.2 billion in 1998 to $12.8 billion in

78 The overall annual growth over this period is around 12%. Regarding the RECs, intra-regional agricultural trade has in general displayed significant increases over the period. Intra-ECOWAS agricultural trade shows an average growth rate of 12%, rising from $494 million in 1998 to $2.84 billion in However, agricultural trade between ECOWAS countries was very erratic. Trade increases between them were partly driven by commodity prices. Agricultural trade between ECCAS countries has shown the highest overall growth of 17%, with a nominal value which has increased from $14 million in 1998 to $147 million in Intra-regional agricultural trade in COMESA has displayed a significant increase (14%) over , rising from $379 million in 1998 to $2.87 billion in In COMESA, unlike the other RECs, the growth gap between the two sub-periods is very low (less than 3 percentage points). The volume of intra-regional agricultural trade has also shown a significant increase (22%). Lastly, in the SADC area, the lowest overall growth of 10% is observed, with a nominal value which has increased from $871 million in 1998 to $3.82 billion in The regional trade integration measures results showed that ECOWAS is the REC with the highest trade integration with a ratio of 0.79, followed by SADC with 0.77, COMESA with 0.65 and ECCAS with Except for ECCAS countries, all the RECs exchange more inside their own bloc. In terms of intra-african agricultural trade, as destinations or origins of intra-african trade, COMESA and SADC are the leading regions before ECOWAS and ECCAS. However, it is noted that COMESA and SADC have opposite patterns. In fact, COMESA has gained trade share (exports and imports) over the considered period while SADC countries have lost some. Moreover, it is also observed on aggregate that all the RECs have intensified agricultural exchanges within their group. Regarding the main agricultural products traded between African countries, between the two periods, no major changes are noted in the composition of intra-african trade. 71

79 References Berthou, A., & Emlinger, C. (2011). The Trade Unit Values Database. CEPII Working Paper Paris: CEPII. Engel, J., Jouanjean, M., & Awal, A. (2013). The History, Impact and Political Economy of Barriers to Food Trade in Sub-Saharan Africa: An Analytical Review. Overseas Development Institute Report. London: Overseas Development Institute. IMF. (2009). Export and Import Price Index Manual: Theory and Practice. Washington, DC: International Monetary Fund. OECD. (2011). Mexican Export and Import Unit Value Indices. STD/TBS/WPTGS(2011)4. Paris: Organisation for Economic Co-operation and Development. UNCTAD. (2013). Economic Development in Africa Report 2013: Intra-African Trade: Unlocking Private Sector Dynamism. Geneva: United Nations. 72

80 Annex Table A1: Regional agricultural trade share (%) Africa ECOWAS ECCAS Import Export Intra regional Import Export Intra regional Import Export Intra regional Overall

81 Table A1: Regional agricultural trade share (%), contd. COMESA SADC Import Export Intra regional Import Export Intra regional Overall Source: BACI Database and authors calculations,

82 Table A2: Evolution of export/import value index and terms trade for agricultural products Africa ECOWAS ECCAS Import Export ToT Import Export ToT Import Export ToT Source: BACI Database and authors calculations,

83 Table A2: Evolution of export/import value index and terms trade for agricultural products, contd. COMESA SADC Import Export ToT Import Export ToT Table A3: Evolution of export/import value index and terms trade for non-agricultural products Africa ECOWAS ECCAS Import Export ToT Import Export ToT Import Export ToT

84 Table A3: Evolution of export/import value index and terms trade for non-agricultural products, contd. COMESA SADC Import Export ToT Import Export ToT Source: BACI Database and authors calculations,

85 Chapter 4. Competitiveness of African agricultural exports Extracted from African Agricultural Trade Status Report 2017

86 CHAPTER 4. COMPETITIVENESS OF AFRICAN AGRICULTURAL EXPORTS Sunday Pierre Odjo, International Food Policy Research Institute, West and Central Africa office, Dakar, Senegal Ousmane Badiane, International Food Policy Research Institute, Washington DC 4.1 Introduction African agricultural trade performance has been improving over the last decade. There have been substantial gains in export value concomitantly with an increase in Africa s share of world exports. However, agricultural imports by African countries have increased faster and the continent is still below the world market share it secured three decades ago. Thus, accelerating current export trends and diversifying African export commodities and destination markets appear as a crucial policy objective in an attempt to reduce foreign trade deficits across countries and help stabilize intra- African food markets. To that end, a starting point is to understand how current advances in African exports have been brought about. Of particular interest is understanding how changes in domestic production and trading conditions have enabled improvement or degradation in African export competitiveness in global as well as intra-african markets. This would provide more insight on national and regional strategies that could help exploit untapped export potential and invest in emerging markets and new export commodities. The present chapter investigates the patterns and determinants of changes in export competitiveness among African countries and products over the last three decades. It is based on the measurement of changes in competitiveness through constant market share decomposition analysis and the comparisons of derived competitive effects in alternative export destination markets and across countries and commodity groups. In the next section we present the analytical methods and data used for the derivation of country and commodity competitiveness changes. Section 4.3 discusses the country and commodity rankings on their competitiveness in global markets. Competitiveness rankings in global markets and intra-african markets are compared in Section 4.4, while Section 4.5 deals with corresponding rankings in the markets of the regional economic communities (RECs), including the Common Market for Eastern and Southern Africa (COMESA), the Economic Community of Central African States (ECCAS), the Economic Community of West African States (ECOWAS), and the Southern African Development Community (SADC). Section 4.6 proposes an econometric model of the determinants of country 78

87 competitiveness changes in alternative agricultural export markets. Section 4.7 summarizes main findings and derives some recommendations for policy actions Export share growth decomposition model and data The model Competitiveness has widely been explored through the Constant Market Share (CMS) decomposition model as a means of assessing how countries compare to their competitors with respect to their trade performance between time periods. Since its first application to trade analysis by Tyszynski (1951), the CMS methodology has been refined and expanded through alternative model formulations attempting to enrich its analytical features (Leamer and Stern, 1970; Richardson, 1971) or to deal with some issues arising with its applications (Cheptea, Gaulier and Zignago, 2005). The formulation used in this chapter was developed by Magee (1975). It explains the growth in a country or region s share of world markets by decomposing it into two major growth sources, namely structural changes in market distribution and product composition and competitiveness changes. The market share growth model starts with the following identity: S t1 m = R m S t0 m (1) where S t0 m and S t1 m denote the shares of a given country or region m in total world exports in the beginning and end periods t 0 and t 1, respectively. R m represents a relative growth factor defined as follows: R m = 1+gm (2) 1+gw where g m and g w stand for the compound annual growth rate (between the beginning and end periods) of total exports of country or region m and of the world w, respectively. Equation (2) expresses the growth of country or region m s exports relative to the world s exports and can be rewritten as m 1+g w) (X i t0 X t 0 R m = ( 1+g i m i m ) (3) where X m m t0 = i X i t0 m Expressing X t0 for the different export products i and destinations j in (3), multiplying by [(1 + g w i )X m i t0 (1 + g w m i )X i t0 ] and by[(1 + g wj i ) (1 + g wj i )], and summing over i and j yields, after rearranging and substituting the new expression for (3) in (1): 79

88 i (1+g w i ) Xm (1+g mj wj mj i )(1+gi )Xi t 0 wj t 0 (1+g )(1+gi w i )X m i t 0 S m t1 = S m t0 (1+g i w m ) X i t 0 with X m m t0 = i X i t0 j (4) X m i t0 mj = j X i t0 where i and j are indices for export products and destinations, respectively. Our objective in this chapter is to rank African countries and agricultural commodities on changes in their competitiveness in different export destination markets, including global markets (as one market entity), intra-african markets (as one market entity) and the regional markets of COMESA, ECCAS, ECOWAS and SADC (taking each REC as one market entity). Therefore, the model is applied in three different settings corresponding to different levels of exporters and products aggregations as follows. In the first setting, m represents Africa as a whole and the model decomposes the growth in Africa s share of world exports of each of 59 agricultural commodity groups i. The second setting is a variant of the first where m stands for each REC as an aggregate exporter instead of Africa as a whole. Thus, the model explains the growth in the REC s share of world exports of each of 59 agricultural commodity groups. In the third setting, m denotes each of 51 African countries and i is an aggregate agricultural good. The model decomposes the growth in a country s share of world aggregate agricultural exports. In all three settings, calculations are carried out for j representing alternatively global markets, intra-african markets and the regional markets of COMESA, ECCAS, ECOWAS and SADC. With exporters and products aggregated as defined in the three settings, Eq. (4) simplifies to S m m t1 = S t0 (1+g mj i ) wj (1+g i ) mj X i t 0 (1+g wj i ) j (1+g w i ) X i t 0 (a) (b) (c) m (5) In the case where j represents global markets, Eq. (4) further simplifies to S m m t1 = S (1+g mj i ) t0 wj (6) (1+g i ) 80

89 From Eq. (1) it is clear that whether a country or region s share in world exports increases or diminishes during the considered time period depends upon whether the growth factor is greater or less than unity. Given the reduced expression for R in Eq. (5), the contribution of a destination j to the performance of a given country or region (in terms of the change in its export share) can be decomposed into two components: a competitive effect and a market effect. The competitive effect corresponds to the first expression (a) of the right hand side of Eq. (5). It is a measure of the change in competitiveness experienced by country or region m in exporting a good i to destination j. If it is greater (smaller) than 1.0 the competitive effect translates some gain (loss) of competitiveness by the country or region compared to the group of its competitors in the export destination considered. The market effect corresponds to the product of the terms (b) and (c) in Eq. (5). It measures the portion of the country or region s export share growth which is due to faster or slower growth of world exports of good i to destination markets j as compared to global markets. It reflects the change in the importance of j as a destination for the country s exports attributable to the expansion of markets j. For instance, in the case where j denotes the regional markets of a REC, the market effect translates the change in the importance of the community markets as a destination for its members exports which is associated with the expansion of the regional markets. For an easier interpretation, the market effect MRK can be derived in value terms from the simplified expression in Eq. (5) as follows: MRK = [ (1+g wj i ) (1+g w i ) mj X i t 0 m X i t 0 X mj i t0 m m ] X X i t1 i t 0 (7) where X m i t1 stands for the considered country or region s total exports of good i to world markets in the end period. The value of MRK measures the magnitude of the positive or negative impact of the expansion of markets j on the considered country or region s export performance. As it appears in Eq. (6), it is clear that no market effect can be derived in the case where global markets are the destination under consideration Data and product and country coverage The model is applied using data on the values of bilateral exports of agricultural products at the HS4 aggregation level over the period

90 The data are obtained from the BACI database for individual African countries, except for the Southern African Customs Union (SACU) members, namely Botswana, Lesotho, Namibia, South Africa and Swaziland, for which trade data are aggregated as SACU countries in the BACI database. For this analysis, bilateral export values are first aggregated so as to construct the variables of each country s total exports to world markets, to intra-african markets and to each REC s regional markets. These are then aggregated to construct the variables of Africa s and each REC s aggregate exports to the different export destination markets under consideration. In addition, bilateral export values are aggregated from the BACI database to construct the variables of the world s total exports of the different agricultural products to the different export destinations under analysis. In order to reduce the number of HS4 product lines, the different variables are aggregated from HS4 to HS2 level, except for a few HS4 lines of interest which are kept as such. The final dataset used for the CMS model comprises 59 commodity groups (hereafter also designated as commodities or products) and 51 individual countries, including one SACU countries aggregate. It includes all 11 ECCAS members and all 15 ECOWAS members. SADC enters the dataset with 10 individual member countries while its other 5 members are aggregated as one case (SACU countries). With Swaziland among the aggregated countries, COMESA is left with 18 of its 19 members. The dataset also includes some countries that are not members of any REC, including Algeria, Mauritania, Morocco, Tunisia, Saint Helena, Somalia, Western Sahara and Tunisia. In the present chapter only competitive effect values are reported and analyzed. Furthermore, the results relative to the application of the model under the second setting where the model decomposes the export share growth for each REC as an aggregate exporter are not presented in this chapter. Thus, in the following development, the results that refer to the change in a REC s competitiveness reflect averages over the changes in competitiveness of its member countries. Such averages reveal more meaningful differences between RECs than the results obtained from modeling the RECs as aggregate exporting entities. 82

91 Equatorial Guinea Western Sahara Angola Chad Sao Tome & Principe Central African Rep. Zimbabwe Gabon Mali D.R. Congo Madagascar Eritrea Benin Libya Guinea Sudan Mauritius Senegal Congo Côte d'ivoire Burundi Seychelles Malawi Comoros Cameroon SACU countries Gambia Kenya Mauritania Saint Helena Togo Morocco Uganda Tanzania Niger Tunisia Mozambique Burkina Faso Guinea Bissau Sierra Leone Liberia Zambia Ghana Rwanda Ethiopia Nigeria Egypt Djibouti Algeria Somalia Cape Verde Change in competitiveness 4.3. Competitiveness in global markets: country and commodity rankings The values of the competitive effect derived from the share growth decomposition analysis for individual African countries are presented in Table A4.1. They reflect the changes in competitiveness experienced by African countries compared to their competitors as a group in alternative agricultural export destination markets over the period In Figure 4.1 the values of competitive effect in global markets are sorted from lowest to highest, showing corresponding countries from the least competitive to the most competitive. As it appears on the figure, the coefficients of the competitive effect are smaller than 1.0 for 32 out of 51 countries under analysis, which means that those countries have underperformed the group of their competitors in global markets. The least competitive among them include three ECCAS members, namely Equatorial Guinea, Angola and Chad, for which estimates of the competitive effect are not greater than 0.9. Between the 0.9 and 1.0 thresholds are the values of the competitive effect estimated for all other ECCAS members, with only the exception of Rwanda. Apart from Angola, almost two thirds of the other SADC members have revealed a competitive effect within the 0.9 to 1.0 interval, the three exceptions being Tanzania, Mozambique and Zambia. As many ECCAS and SADC members are also COMESA members, up to two thirds of COMESA members are found among the countries that have underperformed the group of their competitors. As for ECOWAS, half of its members are also found among underperforming countries. Figure 4.1 Change in country competitiveness in global agricultural export markets ( ) Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 83

92 However, for nineteen out of the 51 countries considered, the coefficients of the competitive effect are greater than 1.0. These countries have succeeded in raising their levels of competitiveness by expanding their exports to global markets faster than their competitors. The strongest increases in competitiveness have been achieved by Cape Verde, Somalia, Algeria and Djibouti where estimated values of the competitive effect are greater than 1.1. The other 15 countries have more modestly outperformed their competitors, with competitive effect values between the 1.0 and 1.1 thresholds. They include the other half of ECOWAS members, namely Niger, Burkina Faso, Guinea Bissau, Sierra Leone, Liberia, Ghana and Nigeria. We can also see Tunisia among the outperforming countries, as well as Tanzania, Mozambique and Zambia within SADC, and Uganda, Rwanda, Ethiopia and Egypt within COMESA. Changes in country competitiveness are plotted in Figure 4.2 against country shares in Africa s global agricultural exports as presented in Table A4.2. The figure shows that the most notable changes in competitiveness have occurred among countries that contribute very small shares of African global exports. Conversely, countries with higher export shares have not experienced a remarkable change in competitiveness. Thus, Africa s export performance has been improving mostly among small exporters like Cape Verde, Somalia, Algeria and Djibouti while stagnating among larger exporters like Côte d Ivoire, Morocco and Kenya. It is worth noticing the performance of Egypt and Ghana. Each represents at least 5% of Africa s global agricultural exports and has achieved an index of competitiveness change greater than

93 Change in country competitiveness Figure 4.2. Scatterplot of changes in country competitiveness against country shares in Africa s agricultural exports to global markets ( ) Cape Verde Somalia Algeria Djibouti Equatorial Guinea Nigeria Tanzania Zimbabwe Angola Western Sahara Egypt Ghana Kenya Morocco Côte d'ivoire SACU countries Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. In sum, ECCAS appears to be lagging behind in the fight to gain more competitiveness in global agricultural export markets, but the proportions of underperforming countries within COMESA, SADC and ECOWAS are also a concern. In order to get a clearer insight into the difference between regional country groupings, average sizes of the competitive effect are plotted in Figure 4.3 and standard deviation values are shown on top of the bars. Within-group variations in competitive effect values seem to be homogenous across groups, which justifies average effect size comparisons. SADC and more notably ECCAS members appear to have on average lost competitiveness, with ECCAS showing a bigger loss. In contrast, ECOWAS members have on average raised competitiveness, while there has been no or little change for COMESA members on average. Country share in Africa's agricultural exports to the global markets (%) 85

94 Average competitiveness change Figure 4.3. Country-group average competitiveness change in global agricultural export markets ( ) COMESA ECCAS ECOWAS SADC Africa Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. Standard deviation values are shown on top of the bars. Table 4.1. Analysis of variance of country competitiveness changes in global agricultural export markets ( ) Test Groups Sum of Squares df Mean Square F Sig. Eta Squared COMESA vs. Between Groups non-comesa Within Groups countries Total ECCAS vs. Between Groups non-eccas Within Groups countries Total ECOWAS vs. Between Groups non-ecowas Within Groups countries Total Regional Country groups SADC vs. Between Groups non-sadc Within Groups countries Total Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. An analysis of variance is carried out to statistically test the difference between each regional country grouping and the rest of Africa as summarized in Table 4.1. The results confirm that competitive effect sizes are on average significantly lower for ECCAS and higher for ECOWAS compared to the rest of African countries. However, between-group variations account for very little in the overall variations among countries. This means that the larger part of the variations in 86

95 Groundnut oil Meat & edible offal Organic chemicals Poultry Cotton, not carded or combed Coffee Cane sugar Spices Palm oil Fish & sea foods Hides & skins Other cereals Edible preps. of meat, fish & crustaceans Tea Preps. of vegs., fruits & nuts Gums & resins Cocoa beans Other animal products Groundnuts Cotton, carded or combed Edible fruits & nuts Essential oils & resinoids Sugar confectionery Olive oil Other oilseeds Other vegetable textile fibres Misc. edible preparations Rice Furskins Beverages, spirits & vinegar Milling industry products Vegetable plaiting materials Finishing agents for textiles & paper Sorghum Maize Potatoes Tobacco & substitutes Tomatoes Albuminoidal substances Residues from food industries Cocoa preparations Medicinal plants Wheat Onions & substitutes Other live trees & plants Other edible vegetables Other live animals Other oils & facts Soybeans Wool Preps. of cereals, flour, starch or milk Sheep & goats Animal fats Roots & tubers Dairy, eggs & honey Silk Cattle Soybean oil Rye, barley & oats Change in competitiveness competitiveness change between countries is not related to regional factors but to domestic factors like changes in total factor productivity and the competitiveness of most exported commodities by individual countries. Indeed, as postulated by Hausman et al. (2005), what countries export matters for their overall competitiveness. Table A4.3 presents the values of the competitive effect calculated for agricultural commodities through the decomposition of Africa s commodity-specific export share growth in alternative export destination markets between 1998 and They capture the magnitudes of changes in competitiveness that Africa has achieved compared to the group of non-african competitors in the different export destination markets over the period In Figure 4.4 commodities are sorted in increasing order of the changes in competitiveness as experienced in global markets. In addition to the threshold of 1.0 that demarcates commodities in which Africa has lost some competitiveness from those in which Africa has gained some, we will also consider the thresholds of 0.95, 1.05 and 1.10 to help differentiate between lower and higher losses or gains. Figure 4.4. Changes in commodity competitiveness in global agricultural export markets ( ) Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. 87

96 African exporters have lost competitiveness in global markets in the exports of 15 out of 59 commodities. Important food staples affected include groundnut oil, meat & edible offal, poultry, palm oil, fish & sea foods, and some cereals 11. However, the size of competitiveness loss is modest as the corresponding estimates of the competitive effect are contained within the 0.95 to 1.0 interval. For the majority of the commodities under analysis, Africa has experienced an increased competitiveness in global markets by expanding its exports of these commodities faster than the group of non-african competitors has done. Up to 44 out of 59 commodities considered show a competitive effect value higher than 1.0. The strongest increase in competitiveness is acquired for the following five commodity groups, for which competitive effect values are greater than 1.10, including rye, barley & oats; soybean oil; cattle; silk; and dairy, eggs & honey. Many food staples are found among the commodities for which competitiveness gains are higher than 1.05 though smaller than 1.1, including roots & tubers, sheep & goats, other live animals, onions & substitutes, and wheat. But a number of other staples are among commodities for which Africa has more modestly outperformed the group of its competitors, including tomatoes, potatoes, maize, sorghum, and rice, which show competitive effect values in the 1.0 to 1.05 interval. African exporters have either lost competitiveness or modestly increased competitiveness for major African traditional cash crops like coffee, cocoa beans, tea, cotton, groundnut oil, palm oil, sugar cane, groundnuts and other oilseeds. In contrast, they have been able to improve their competitiveness for new export commodities like wool, soybeans, soybean oil, live trees & plants, and cocoa preparations. Figure 4.5 below helps assess the importance of the top ranked commodities in terms of their share in the value of Africa s total agricultural exports to global markets compared to intra-african markets. For instance, it shows that the top 15 commodities account for only 10% of Africa s global agricultural exports and the top 40 commodities in the ranking hardly reach the 50% share threshold. Conversely, the bottom 19 commodities in the ranking represent up to 51.5% of African agricultural exports. This confirms our guess that competitiveness gains in global markets are not occurring only for traditional African export 11 Within the commodity group comprising buckwheat, millet and canary seed. 88

97 Cumulative average share of African agricultral exports to the different markets (%) commodities but also for emerging export products. It is indicative of the scope for further expanding Africa s global exports by exploiting increased commodity competitiveness. Figure 4.5. Relative importance of the most competitive commodities in global and intra-african markets Global markets Intra-African markets Number of top competitive commodities in the different agricultural export markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. The same conclusions are demonstrated in Figure 4.6, which shows a scatter plot of changes in commodity competitiveness against commodity shares in Africa s global agricultural exports (presented in Table A4.4). The figure indicates that changes in competitiveness generally have been achieved for commodities that account for small shares of Africa s global agricultural exports. Conversely, little or no competitiveness change has been obtained in commodities that represent higher export shares. Thus, African exporters have been improving their performance mostly in minor export products like rye, barley & oats, soybean oil, and cattle, while their performance has been stagnating in major export products like edible fruits & nuts, cocoa beans, fish & sea foods, coffee, cotton, and cane sugar. 89

98 Change in commodity competitiveness Figure 4.6. Scatterplot of changes in commodity competitiveness against commodity shares in Africa s agricultural exports to global markets ( ) Rye, barley and oats Soybean oil 1.2 Cattle Dairy, eggs and honey 1.1 Sheep & goats Other edible vegetables 1.1 Cocoa preps. Tobacco and substitutes Edible fruits and nuts 1.0 Edible preps. of meat, fish & crustaceans Spices Cocoa beans Poultry Cane sugar Cotton, not carded or Fish & sea foods 1.0 Meat and edible offal Coffee combed. Groundnut oil Commodity share in Africa's Agricultural exports to global markets (%) Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. So far we have investigated how competitiveness has changed for countries and commodities in global markets. We now turn to exploring changes in competitiveness in intra-african markets. We will see how country and commodity rankings on competitiveness change in intra-african markets compared to the above-described rankings related to global markets Competitiveness in intra-african markets: country and commodity rankings Changes in competitiveness experienced by individual African countries in global and intra- African agricultural markets are shown in Figure 4.7 below. They are measured by the coefficients of the competitive effect derived through country-level share growth decomposition and summarized in Table A4.1. In the figure, countries are sorted in increasing order of the changes in competitiveness in intra-african markets. As it appears, competitive effect values are smaller than 1.0 for only 20 countries in this ranking compared to 32 countries in the ranking relative to global markets (cf. Figure 4.1 above). This means that a smaller share of African countries have underperformed the group of their competitors in intra-african markets compared to global markets. Of those twenty, Saint Helena, Mali, Central Africa Republic and Chad have strongly underperformed, with competitive effect values smaller than

99 Saint Helena Mali Central African Rep. Chad Sao Tome & Principe Somalia Benin Zimbabwe Niger Madagascar Togo Sierra Leone Cameroon Côte d'ivoire Libya Liberia SACU countries Kenya Gabon Burkina Faso Malawi Sudan Guinea Uganda Gambia Mauritius Angola Tanzania Seychelles Mozambique Mauritania Congo Senegal Zambia D.R. Congo Equatorial Guinea Nigeria Cape Verde Morocco Guinea Bissau Ghana Eritrea Rwanda Tunisia Burundi Ethiopia Algeria Egypt Comoros Djibouti Chnage in competitiveness At the top edge of the ranking, twelve countries have strongly outperformed, with estimates of the competitive effect greater than 1.1, among which the topmost 5 countries are Djibouti, Comoros, Egypt and Algeria. It is worth recalling that only 4 countries have reached that level of increased competitiveness in global markets. More interestingly, Figure 4.7 reveals that almost all outperforming countries have in fact performed better in intra-african markets than in global markets. And conversely, almost all underperforming countries have lost competitiveness more in intra-african markets than in global markets. Figure 4.7 Change in country competitiveness in intra-african agricultural export markets compared to global markets ( ) Intra-African markets Global markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. Table 4.2. Paired-sample T Tests for equality of country competitiveness changes in pairs of African agricultural export destination markets Paired Markets Paired Samples Correlation N Correlation Sig. Mean paired Differences t df Sig. (2-tailed) COMESA & global markets ECCAS & global markets ECOWAS & global markets SADC & global markets Intra-African & global markets COMESA & intra-african markets ECCAS & intra-african markets ECOWAS & intra-african markets SADC & intra-african markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 91

100 Average competitiveness change Table 4.2 presents the results of paired-sample T tests of no difference between the competitive effect values in global versus regional and intra-african markets. It can be read from the last row of first panel of the table that changes in competitiveness in intra-african and global markets are weakly and positively correlated. In other words, competitiveness changes are overall higher in intra-african markets compared to global markets, but not consistently for all countries in the sample. It also appears that there is a significant difference in the magnitude of competitiveness changes between intra-african and global markets. On average competitiveness changes are higher by point in intra-african markets than in global markets. It is of interest to see how the member countries of the different RECs have performed on average in intra-african markets. Figure 4.8 reveals that COMESA members have generally achieved higher gains in competitiveness than the rest of African countries in intra-african markets. Indeed, we can see in Figure 4.7 that seven COMESA members have made it to the top 10 of the ranking, namely Djibouti, Comoros, Egypt, Ethiopia, Burundi, Rwanda and Eritrea, and only Kenya is found among the bottom 20 positions in the ranking. An analysis of variance of competitive effect values in intra-african markets, summarized in Table 4.3, confirms that COMESA members have on average performed significantly better than the rest of African countries. In contrast, there is no perceptibly significant difference between ECCAS, ECOWAS and SADC members in terms of changes in their competitiveness in intra-african markets. Part of the explanation may be found in exploring differences in competitiveness gains achieved for particular export commodity groups. Figure 4.8. Country-group average competitiveness change in intra-african agricultural export markets ( ) COMESA ECCAS ECOWAS SADC Africa Regional Country Groups Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. Standard deviation values are shown on top of the bars. 92

101 Table 4.3. Analysis of variance in country competitiveness changes in intra-african agricultural export markets ( ) Test Groups Sum of Squares df Mean Square F Sig. Eta Squared COMESA vs. Between Groups non-comesa Within Groups countries Total ECCAS vs. Between Groups non-eccas Within Groups countries Total ECOWAS vs. Between Groups non-ecowas Within Groups countries Total SADC vs. Between Groups non-sadc Within Groups countries Total Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. Figure 4.9 below is constructed from Table A4.3 and represents the changes in competitiveness that African countries have experienced in intra-african and global markets for individual agricultural commodity groups under analysis. Commodities are sorted in increasing order of changes in competiveness in intra-african markets. For 29 out of 59 commodities under analysis, Africa has underperformed the group of its competitors in intra-african markets. The corresponding number in the preceding ranking relative to global markets is 15 out of 59 commodities. Furthermore, from Figure 4.9, it looks like Africa s performance in terms of commodity competitiveness gains is generally lower in intra-african markets than in global markets, as it appears for the majority of commodities. The statistical significance of these comparisons is verified in Table 4.4, which shows the results of a test for equality of changes in commodity competitiveness in global markets compared to intra-african and regional markets. The last row of the table shows that competitiveness changes in intra-african and global markets are positively but weakly correlated. That is, changes in competitiveness tend to be greater in global markets compared to intra-african markets, but not consistently across all commodities. At the 10% significance level, competitiveness changes are indeed lower in intra- African than in global markets. However, the difference is as small as point on average. 93

102 Organic chemicals Soybeans Groundnut oil Silk Cocoa beans Finishing agents for textiles & paper Onions & substitutes Sheep & goats Meat & edible offal Cotton, not carded or combed Coffee Sorghum Other oilseeds Essential oils & resinoids Sugar confectionery Other cereals Cocoa preparations Maize Cotton, carded or combed Palm oil Beverages, spirits & vinegar Furskins Wheat Other live trees & plants Poultry Edible fruits & nuts Groundnuts Tea Albuminoidal substances Cane sugar Misc. edible preparations Preps. of cereals, flour, starch or milk Other animal products Medicinal plants Fish & sea foods Edible preps. of meat, fish & crustaceans Tomatoes Spices Preps. of vegs., fruits & nuts Other live animals Potatoes Rice Residues from food industries Dairy, eggs & honey Milling industry products Tobacco & substitutes Wool Cattle Animal fats Vegetable plaiting materials Other oils & facts Hides & skins Roots & tubers Other edible vegetables Other vegetable textile fibres Soybean oil Gums & resins Olive oil Rye, barley & oats Change in competitiveness Figure 4.9. Change in commodity competitiveness in intra-african agricultural export markets compared to global markets ( ) Intra-African markets Global markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. Many staple food products are among commodities for which Africa has underperformed, including onions & substitutes, sheep & goats, meat & edible offal, poultry, sorghum, maize, wheat, and other cereals. We have seen above that Africa has strongly or weakly outperformed the group of its competitors in global markets in exporting some of those staples, namely onions & substitutes, sheep & goats, wheat, maize, and sorghum. Similarly to its competitiveness in global markets, Africa has experienced positive changes in its competitiveness in intra-african markets for a number of other important foodstuffs, including roots & tubers, cattle, other live animals, dairy, eggs & honey, rice, potatoes, tomatoes, and fish & sea foods. In contrast and as in global markets, Africa has lost some competitiveness in intra-african markets for its traditional cash crops like coffee, cocoa beans, tea, cotton, groundnut oil, palm oil, groundnuts and other oilseeds. Among the topmost ranked commodities we can see the same products that dominate the global markets-related ranking, including rye, barley & oats (keeping the highest position), and soybean oil. It also appears that African exporters have done better in intra-african markets than in global 94

103 markets in exporting emerging export products like olive oil, soybean oil, gums & resins, other (than cotton) vegetable textile fibers, hides & skins, and spices. Figure 4.5 above shows that the top 15 commodities account for only 24.5% of intra-african agricultural exports and the top 25 commodities do not reach the 50% share threshold. However, the contributions of the same numbers of the most competitive commodities in global markets to Africa s global agricultural exports are much smaller, as we have shown earlier with Figure 4.5. That is, more commodities with relatively higher export value have gained increased competitiveness in intra-african markets compared to global markets. This is in line with the faster growth of intra-african agricultural trade in value terms over the period of this analysis. Table 4.4. Paired-sample T Test for equality of commodity competitiveness changes in pairs of African agricultural export destination markets Paired markets Paired Samples Correlation N Correlation Sig. Mean Paired Differences t df Sig. (2-tailed) COMESA & global markets ECCAS & global markets ECOWAS & global markets SADC & global markets Intra-African & global markets COMESA & intra-african markets ECCAS & intra-african markets ECOWAS & intra-african markets SADC & intra-african markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group Competitiveness in regional markets: country and commodity rankings In the preceding sections we have assessed and compared changes in country and commodity competitiveness in global and intra-african agricultural export markets. We are now interested in exploring the scope of Africa s competitiveness gains or losses in each of four regional markets, including COMESA, ECCAS, ECOWAS and SADC markets. To that end, four graphs analogous to Figures 4.1 and 4.7 are constructed and pulled together in Figure A4.1. Each graph depicts the ranking of African countries in increasing order of changes in their competitiveness in the agricultural markets of a REC. They also help to see how competiveness changes in regional markets compare to changes in global and intra-african markets. 95

104 Similarly, four other graphs equivalent to Figures 4.4 and 4.9 are assembled in Figure A4.2 and show commodity rankings with respect to competitiveness changes in regional markets. It can be seen from Figure A4.1 that 10 countries have underperformed in all four regional markets, including Cameroon, Central African Republic, Kenya, Madagascar, Mali, Niger, Sao Tome & Principe, Togo, Zimbabwe, and SACU countries as a group. Similarly, 9 other countries are found that have outperformed in all regional markets, including Algeria, Egypt, Ethiopia, Malawi, Mauritania, Morocco, Nigeria, Rwanda and Senegal. As a general pattern, country competitiveness changes in regional markets tend to be lower than their performance in the broader intra-african and global markets, in particular among the bottommost ranked countries. The results from the test for equality presented in Table 4.2 above reveal that competitiveness changes are significantly lower in ECCAS markets than in global markets by 0.03 point on average. There are no significant differences between the other regional markets and global markets as regards changes in country competitiveness. However, the test indicates that country competitiveness changes are significantly lower in all regional markets than in the broader intra- African markets, with differences ranging from to point on average. Some of the findings conveyed by Figure A4.1 are summarized in Table 4.5 below. The table presents two panels. The bottom row of the upper panel reveals that more than half of African countries countries have underperformed their competitors in ECCAS, ECOWAS and SADC markets, with a revealed competitive effect value smaller than 1.0. Relatively fewer of them 19 countries have similarly underperformed in COMESA markets. Indeed, COMESA markets appear in the lower panel to be where at least half of African countries have outperformed their competitors, with a revealed competitive effect value greater than 1.0. The table provides a clearer insight into Africa s performance in regional markets with a breakdown of underperforming and outperforming countries by regional group membership. It helps to apprehend for each REC how many of its members have underperformed or outperformed their competitors in intra-regional versus extra-regional markets. 96

105 Table 4.5. Breakdown by REC membership of the numbers of underperforming and outperforming countries in alternative agricultural export destination markets Global markets Intra-African markets COMESA markets ECCAS markets ECOWAS markets Number of underperforming countries (with competitive effect < 1.0) SADC markets COMESA members ECCAS members ECOWAS members SADC members Whole sample Number of outperforming countries (with a competitive effect > 1.0) COMESA members ECCAS members ECOWAS members SADC members Whole sample Total number of countries in sample Whole sample Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. For instance, the first row of the upper panel of the table shows that for the COMESA region only 4 of its members have underperformed in their intra-regional markets compared to 11 members in farther extra-regional markets located in the ECOWAS region. Conversely, we can read from the first row of the lower panel of the table that for the COMESA region up to 14 of its members have outperformed their competitors in their intra-regional markets compared to only 7 members in extra-regional markets within ECOWAS. Similarly, the ECOWAS region also has a smaller number of underperforming members in intra-regional markets than in remoter extra-regional markets situated in the SADC region. The same is true for the SADC region which has fewer underperforming members in intra-regional markets than in the remoter ECOWAS and ECCAS markets. However, for the ECCAS region we see more underperforming and fewer outperforming members in intra-regional than in extra-regional markets. This is surprising enough as one would expect countries to be more performant in their region than in remoter regions. Disparities between regional country groups as regards their competitiveness gains or losses in intra-regional versus extra-regional markets are more clearly revealed in Figure 4.10 below. COMESA members have achieved a positive average competitiveness change in intra-regional 97

106 Average competitiveness change markets and to a lesser extent in SADC markets, but a negative average change in the more distant ECCAS and ECOWAS markets. ECOWAS members have also on average raised their competitiveness in intra-regional markets and reduced their competitiveness in extra-regional markets, with the biggest average reduction incurred in the remotest SADC markets. SADC members have kept their average competitiveness level practically unchanged in intra-regional and COMESA markets, but they have on average lost performance in ECOWAS markets and more notably in ECCAS markets. The patterns are different for the ECCAS region, which has underperformed in all regional markets and more remarkably in intra-regional markets. Figure Country-group average competitiveness change in regional agricultural export markets ( ) COMESA markets ECCAS markets ECOWAS markets SADC markets COMESA ECCAS ECOWAS SADC Africa Regional Country Groups Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. Furthermore, Figure 4.10 shows how group average competitiveness changes in regional markets compare to corresponding Africa-wide average changes. The statistical significance of pairwise comparisons has been tested through analysis of variance of country competitiveness changes in regional markets. Major comparison test results are summarized in Tables A4.5-A4.8 in the appendices. It appears that the COMESA region has raised its competitiveness in intra-regional and SADC markets significantly more than the rest of Africa. The ECOWAS region has performed significantly better than the rest of Africa only in SADC markets. And the ECCAS region has 98

107 undergone a significantly stronger loss of competitiveness than the rest of Africa in intra-regional and COMESA markets. These patterns of disparities between regional groups of countries suggest that differences in country competitiveness should be explained by other factors than trading distance and costs. Differences in the competitiveness of most traded goods in individual countries may contribute to the explanation. As defined above, Figure A4.2 presents the rankings of commodities in increasing order of their competitiveness change in the different regional markets. For some commodities, mostly among those ranked towards the uppermost edge of the rankings, competitiveness changes are higher in regional markets than in global and or intra-african markets. However, for other commodities, mostly towards the lowermost edge of the rankings, the reverse is true. In order to assess the consistency and significance of these comparisons, paired-sample T tests of equality of competitiveness changes in regional markets compared with global and intra- African markets are carried out and the results summarized in Table 4.4. The upper panel of the table shows that commodity competitiveness changes in global markets are positively but weakly correlated with changes in COMESA, as well as ECCAS and SADC markets. There is no significant correlation between competitiveness changes in global and ECOWAS markets. On average commodity competitiveness changes are lower by point in ECCAS markets compared to global markets at the 1% significance level, versus 0.02 point in ECOWAS markets at the 10% significance level. In contrast, there is on average no significant difference in competitiveness changes in global and COMESA or SADC markets. Comparisons reported in the lower panel of the table reveal positive and weak correlations of commodity competitiveness changes in intra-african and intra-regional markets, except for COMESA and SADC, where competiveness changes are more strongly associated with changes in intra-african markets. This means that changes in intra-african markets reflect changes in COMESA and SADC significantly more than elsewhere in Africa. On average commodity competitiveness changes are lower by point in ECCAS markets than elsewhere in Africa at the 5% significance level. The distribution of commodities across different classes of competitiveness is summarized in Table 4.6 below. 99

108 The loss of competitiveness by African countries has affected a greater number of commodities in ECCAS markets compared to the other regional markets. For a total of 32 commodities, competitive effect values are smaller than 1.0, including 26 with small competitiveness losses but only 6 with high losses. Conversely, competitiveness gains achieved by African exporters have benefited a greater number of commodities in COMESA markets compared to the other regional markets. The benefit concerns up to 31 commodities with small gains and only 8 with high gains. However, the number of commodities with increased competitiveness is still greater in global markets than in regional markets. In other words, there is room for expanding Africa s share of total world agricultural exports by aligning competitiveness changes in regional markets with improvements being made outside Africa. Table 4.6. Number of commodity groups by class of competitiveness in alternative agricultural export destination markets Export destination markets Competitiveness class Global markets Intra-African markets COMESA markets ECCAS markets ECOWAS markets SADC markets Competitive effect<= <Competitive effect<= <Competitive effect<= Competitive effect > Whole sample size Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. Among the commodities that have lost competitiveness in at least three regional markets we can find cotton, wheat, sorghum, some oilseeds 12, meat & edible offal, groundnut oil and tea. They all have also been ranked among uncompetitive products in intra-african markets and, with the exception of wheat and sorghum, in global markets. Therefore these commodities could be thought of as the most uncompetitive commodities in African markets. Towards the topmost edge of the rankings, many foodstuffs are found among the commodities that have gained competitiveness in at least three regional markets, including rice, potatoes, onions & substitutes, fish and sea foods, sheep & goats, other live animals 13, and roots & tubers. 12 Not including soybeans and groundnuts 13 This group is a broad aggregate of live swine, horses, asses, mules and hinnies 100

109 They all have also shown a competitiveness gain in global markets, except for fish and sea foods, as well as in intra-african markets, except for onions & substitutes and sheep & goats, as these two commodity groups have lost competitiveness in ECOWAS markets. Therefore, ECOWAS markets may be more stringent for African exports of onions & substitutes and sheep & goats, as non-african markets may be for African exports of fish and sea foods. In an attempt to assess how important the top ranked commodities are, Figure 4.11 shows the cumulative share of Africa s total agricultural exports to alternative markets that is contributed by an increasing number of top competitive commodities in those markets. First of all, the figure recalls the finding that the topmost competitive commodities in global and intra-african markets account for small shares of African export baskets in these markets. The same is true as regards regional markets. However, as we have already noted, the most competitive commodities represent higher cumulative shares of export baskets in intra-african markets than in global markets. They also account for higher shares of Africa s exports to regional markets compared to global markets. The top 5 and 10 commodities weigh more heavily in ECOWAS markets than in other intra- African markets. For instance, the top 5 most competitive commodities in ECOWAS markets account for 15.1% of Africa s exports to that region while the corresponding shares as regards all intra-african markets and global markets are 1.3% and 1.8%, respectively. Thus, the most competitive products in the different markets are not among the most exported ones, which reveals that competitiveness gains are happening among products that can be exploited for widening the export bases of African countries. 101

110 Cumulative average share of African agricultural exports to the different markets (%) Figure Relative importance of the most competitive commodities in regional markets compared to global and intra-african markets 100 COMESA markets ECCAS markets ECOWAS markets SADC markets Global markets Intra-African markets Number of top competitive commodities in the different agricultural export markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. The scope for expansion of intra- and extra-african exports by tapping into revealed competitiveness gains appears in the fact that there is no single set of commodities gaining competitiveness at the same pace in the different export destinations. This is demonstrated in Figure 4.12 below which shows how dissimilar commodity rankings are in the different export markets. The intuition behind the construction of the figure is that commodity rankings would be considered to be similar if commodity ranks were approximately the same in the different rankings (markets). In that case, all top K most competitive commodities in the different rankings would be found in a unique set of K products as depicted by the 45 degree straight line. The more the size of the set is greater than K the more dissimilar would be the different rankings. The distance from the curved line to the straight line shows how dissimilar the rankings are. For instance, the curved line shows that a set of 16 products encompasses all top 5 commodities in all rankings. Similarly, the size of the set that includes all top 10 commodities in all rankings amounts to 32. In other words, the most competitive commodities are not exactly the same in different markets, which justifies the belief that there is scope for a diversified export expansion in the different markets under analysis. Put differently, somewhat different baskets of non-traditional export products are gaining competitiveness in the different markets and are good candidates for export diversification and expansion. 102

111 Size of the set of top K most competitive commodities in all export markets under analysis Figure Dissimilarity of commodity rankings in the different export destination markets Value of K Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group Determinants of export competitiveness in global and regional markets The preceding sections have highlighted considerable variations between African countries in terms of changes in their competitiveness as compared to the group of their non-african competitors in agricultural export markets. We have seen that the patterns of competitiveness changes differ across export markets but also according to membership in the different RECs. Trading distance and costs have appeared to affect the changes in competiveness experienced by member countries of the different RECs in intra-regional markets as compared to extra-regional markets. However, the larger part of differences between countries as regards their competitiveness gains or losses seems to have to do more with country-specific production and trade environments than with regional differences. Indeed, the analysis of commodity competitiveness changes has suggested that differences in productivity gains and domestic market conditions may play a large role in the differences of competitiveness gains or losses achieved by African countries for the different commodities. This section is devoted to exploring the factors behind the disparities among countries in terms of the changes in their competitiveness in the different markets. Potential determinants considered include agricultural total factor productivity changes from the USDA database, the World Bank s Doing Business Distance to Frontier (DB- DTF) indicator, the World Economic Forum s Global Competitiveness Index (GCI) and country 103

112 attributes related to each of its 12 pillars, the International Logistics Performance Index and its component indicators, and Transparency International s Corruption Perceptions Index (CPI). Tables 4.7 and 4.8 present the results of a linear regression analysis where the series of country competitiveness changes in the different agricultural export destination markets are pooled together as a single variable and regressed on the above-listed country-level indicators taken as potential explanatory variables while controlling for REC membership and export destination markets, as formally summarized in Eq. 8 below: COMP mrj = α + R r β r REC r J + j γ j MKT j + P p θ p IND p + ε mrj (8) where COMP mrj is the pooled variable standing for the change in competitiveness for country m, which is a member of the Regional Economic Community r, in export markets j; REC r represents dummy variables for the different Regional Economic Communities and MKT j are dummy variables for the different export destination markets; and IND p stands for the different indicators considered above as potential explanatory variables. Table 4.7. Parameter estimates for the determinants of changes in country competitiveness Coefficients Std. Error t Sig. Constant SADC region Intra-African markets Doing Business - Distance to frontier a Institutions (GCI 1st Pillar) b Country market size (GCI 10th Pillar) b LPI - Customs c LPI - International shipments c Agricultural TFP growth estimates a. Doing Business - Distance to frontier, maximum score between 2010 and 2016 b. Global Competitiveness Index, average attribute value between 2006 and 2015 c. International Logistics Performance Index (LPI 2014 score) Source: Authors calculations. 104

113 Table 4.8. ANOVA and model summary Sum of Squares df Mean Square F Sig. Regression Residual Total Number of observations 186 R Square 0.36 Adjusted R Square 0.33 Durbin-Watson 2.36 Source: Authors calculations. The subset of explanatory variables that provide the best model fit are presented in Table 4.7. As we have seen above, country competitiveness changes are higher in intra-african markets as compared to global markets. They appear to be positively affected by the Doing Business Distance to Frontier score, the quality of institutions, country market size and the quality of customs service. Surprisingly, the model reveals that changes in country competitiveness are negatively associated with the ease of international shipments and changes in agricultural total factor productivity. Table 4.8 shows that the model accounts for nearly two-fifth of the variation in changes in competitiveness Conclusions Changes in African agricultural export competitiveness have been explored in global, intra- African, and regional markets over the period Almost consistently in all export markets under consideration, ECCAS members appear to have underperformed their competitors, while SADC, COMESA and ECOWAS members have on average proved to have preserved their competitiveness or outperformed the group of their competitors. In addition, changes in country competitiveness are on average lower in ECCAS markets and generally higher in intra-african markets than in global markets. The analysis has also shown that competitiveness gains have taken place for the COMESA, ECOWAS and SADC members remarkably more in intra-regional than in extra-regional markets. But for ECCAS, rare increases in country competitiveness have been noted and they have happened in extra-regional markets and not in intra-regional markets. However, it should be retained that while ECCAS is notably lagging behind, the proportions of underperforming countries within COMESA, SADC and ECOWAS are also a concern. 105

114 Africa s competitiveness analysis at the commodity level has revealed significant losses for some important foodstuffs, though the majority of commodities have gained more competitiveness in global markets. However, the levels of commodity competitiveness are lower in intra-african than in global markets. They are even lower in regional markets, except in COMESA markets, where the commodity competitiveness level is higher than in global and intra-african markets. In other words, there is room for expanding Africa s share of total world agricultural exports by aligning competitiveness changes in regional markets with improvements being made outside Africa. The top ranked commodities contribute a small share of intra-african agricultural export value and an even smaller share of Africa s global agricultural export value. This reflects the scope for expanding African exports by exploiting increased competitiveness that arises among new and emerging export products. The results show that the set of these candidate products for export expansion varies remarkably across the different export destination markets, showing the scope for product diversification for countries in conquering African and world markets. Apart from REC membership, the Doing Business Distance to Frontier score, the quality of domestic institutions, country market size and the quality of customs service have been shown to significantly contribute to the explanation of the variability in competitiveness changes. 106

115 References Cheptea, A., Gaulier, G., & Zignago, S. (2005). World Trade Competitiveness: A Disaggregated View by Shift-Share Analysis. CEPII Working Paper Paris: CEPII. Hausman, R., Hwang, J., & Rodrik, D. (2005). What you export matters. Journal of Economic Growth, 12(1), Leamer, E., & Stern, R. (1970). Quantitative International Economics. Aldine. Magee, S. (1975). Prices, income, and foreign trade. In P. Kenen (Ed.), International Trade and Finance: Frontiers for Research. New York: Cambridge University Press. Richardson, J. D. (1971a). Constant-market-shares analysis of export growth. Journal of International Economics, 1(2), Richardson, J. D. (1971b). Some sensitivity tests for a constant market shares analysis of export growth. Review of Economics and Statistics, 53, Tyszynski, H. (1951). World trade in manufactured commodities, The Manchester School of Economic and Social Studies, 19,

116 4.9. Tables and figures Table A4.1. Change in country competitiveness in alternative agricultural export destination markets, Global markets Intra-African markets COMESA markets ECCAS markets ECOWAS markets SADC markets Algeria Angola Benin Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d'ivoire Demo. Republic of Congo Djibouti Egypt Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea Bissau Kenya Liberia Libya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Niger Nigeria Rwanda SACU countries Saint Helena

117 Sao Tome & Principe Senegal Seychelles Sierra Leone Somalia Sudan Tanzania Togo Tunisia Uganda Western Sahara Zambia Zimbabwe Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 109

118 Table A4.2. Country shares in the value of Africa s agricultural exports to alternative markets, average (%) Global Intra-African COMESA ECCAS ECOWAS SADC Exporters markets markets markets markets markets markets Algeria Angola Benin Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d'ivoire Demo. Republic of Congo Djibouti Egypt Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea Bissau Kenya Liberia Libya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Niger Nigeria Rwanda SACU countries Saint Helena Sao Tome & Principe

119 Senegal Seychelles Sierra Leone Somalia Sudan Tanzania Togo Tunisia Uganda Western Sahara Zambia Zimbabwe Africa Source: Authors calculations using the BACI database. 111

120 Table A4.3. Change in commodity competitiveness in alternative agricultural export destination markets, Intra- Global markets African markets COMESA markets ECCAS markets ECOWAS markets SADC markets Cattle Sheep & goats Poultry Other live animals Meat & edible offal Fish & sea foods Dairy, eggs & honey Other animal products Roots & tubers Other live trees & plants Potatoes Tomatoes Onions & substitutes Other edible vegetables Edible fruits & nuts Coffee Tea Spices Wheat Rye, barley & oats Maize Rice Sorghum Other cereals Milling industry products Soybeans Groundnuts Other oilseeds Medicinal plants Gums & resins Vegetable plaiting materials Animal fats Soybean oil Groundnut oil Olive oil Palm oil Other oils & facts Edible preps. of meat, fish & crustaceans Cane sugar

121 Sugar confectionery Cocoa beans Cocoa preparations Preps. of cereals, flour, starch or milk Preps. of vegs., fruits & nuts Misc. edible preparations Beverages, spirits & vinegar Residues from food industries Tobacco & substitutes Organic chemicals Essential oils & resinoids Albuminoidal substances Finishing agents for textiles & paper Hides & skins Furskins Silk Wool Cotton, not carded or combed Cotton, carded or combed Other vegetable textile fibres Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. 113

122 Table A4.4. Commodity shares in the value of Africa s agricultural exports to alternative markets, average (%) Global markets Intra- African markets COMESA markets ECCAS markets ECOWAS markets SADC markets Cattle Sheep & goats Poultry Other live animals Meat & edible offal Fish & sea foods Dairy, eggs & honey Other animal products Roots & tubers Other live trees & plants Potatoes Tomatoes Onions & substitutes Other edible vegetables Edible fruits & nuts Coffee Tea Spices Wheat Rye, barley & oats Maize Rice Sorghum Other cereals Milling industry products Soybeans Groundnuts Other oilseeds Medicinal plants Gums & resins Vegetable plaiting materials Animal fats Soybean oil Groundnut oil Olive oil Palm oil Other oils & facts Edible preps. of meat, fish & crustaceans

123 Cane sugar Sugar confectionery Cocoa beans Cocoa preparations Preps. of cereals, flour, starch or milk Preps. of vegs., fruits & nuts Misc. edible preparations Beverages, spirits & vinegar Residues from food industries Tobacco & substitutes Organic chemicals Essential oils & resinoids Albuminoidal substances Finishing agents for textiles & paper Hides & skins Furskins Silk Wool Cotton, not carded or combed Cotton, carded or combed Other vegetable textile fibres Agricultural exports Source: Authors calculations using the BACI database. 115

124 Mali Burkina Faso Sierra Leone Sudan Saint Helena Guinea Gambia Central African Rep. Cameroon Togo Cape Verde Côte d'ivoire Liberia Madagascar D.R. Congo Gabon Zimbabwe Sao Tome & Tunisia Chad Somalia Djibouti Niger SACU countries Angola Benin Ghana Kenya Malawi Eritrea Senegal Mozambique Seychelles Algeria Uganda Mauritius Tanzania Libya Equatorial Guinea Zambia Burundi Egypt Guinea Bissau Nigeria Morocco Congo Ethiopia Comoros Rwanda Mauritania Change in competitiveness Libya Comoros Somalia Mali Saint Helena Gabon Mozambique Sudan Seychelles Burundi Zimbabwe Sao Tome & Benin Sierra Leone Togo Congo Niger Madagascar Central African Rep. Kenya Uganda Cameroon Tanzania Mauritius D.R. Congo SACU countries Côte d'ivoire Malawi Angola Mauritania Rwanda Gambia Nigeria Burkina Faso Ethiopia Tunisia Guinea Chad Liberia Senegal Cape Verde Egypt Eritrea Djibouti Equatorial Guinea Morocco Algeria Ghana Zambia Guinea Bissau Change in competitiveness Chad Central African Rep. Mauritius Guinea Madagascar Angola Comoros Equatorial Guinea Zimbabwe Mali Gambia Niger Guinea Bissau Sao Tome & Benin Congo Togo Kenya SACU countries Gabon Seychelles Cameroon Côte d'ivoire Mozambique Libya Zambia Sudan Senegal D.R. Congo Malawi Mauritania Burundi Cape Verde Uganda Tunisia Algeria Ghana Rwanda Burkina Faso Morocco Liberia Ethiopia Egypt Tanzania Nigeria Sierra Leone Change in competitiveness Mali Central African Rep. Saint Helena Angola Congo Togo Niger Sao Tome & Burkina Faso Zimbabwe Cameroon Liberia Madagascar Somalia Chad Kenya SACU countries Gambia Sudan Guinea Gabon Mauritius Uganda Tanzania Côte d'ivoire Nigeria Tunisia Sierra Leone Malawi Mozambique Mauritania Algeria Seychelles D.R. Congo Burundi Zambia Morocco Senegal Cape Verde Ethiopia Benin Ghana Comoros Djibouti Eritrea Rwanda Egypt Change in competitiveness Libya Figure A4.1. Change in country competitiveness in regional exports markets compared to global and intra- African markets ( ) COMESA markets Global markets Intra-African markets ECCAS markets Global markets Intra-African markets ECOWAS markets Global markets Intra-African markets SADC markets Global markets Intra-African markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 116

125 Sorghum Tea Furskins Cotton, not carded or combed Soybeans Organic chemicals Other vegetable textile fibres Cotton, carded or combed Meat & edible offal Palm oil Coffee Groundnut oil Medicinal plants Beverages, spirits & vinegar Other live animals Poultry Residues from food industries Dairy, eggs & honey Gums & resins Fish & sea foods Other animal products Other edible vegetables Sugar confectionery Groundnuts Tomatoes Essential oils & resinoids Cattle Other live trees & plants Other oilseeds Preps. of cereals, flour, starch or milk Wheat Misc. edible preparations Wool Edible fruits & nuts Other cereals Cane sugar Sheep & goats Cocoa preparations Edible preps. of meat, fish & crustaceans Potatoes Rice Milling industry products Tobacco & substitutes Hides & skins Onions & substitutes Maize Preps. of vegs., fruits & nuts Rye, barley & oats Vegetable plaiting materials Spices Cocoa beans Olive oil Finishing agents for textiles & paper Albuminoidal substances Other oils & facts Roots & tubers Animal fats Silk Soybean oil Change in competitiveness Soybeans Organic chemicals Wheat Cotton, not carded or combed Coffee Sorghum Other oilseeds Albuminoidal substances Hides & skins Potatoes Vegetable plaiting materials Other cereals Essential oils & resinoids Edible fruits & nuts Cocoa preparations Maize Meat & edible offal Sugar confectionery Finishing agents for textiles & paper Cotton, carded or combed Cane sugar Groundnut oil Tea Tomatoes Cocoa beans Edible preps. of meat, fish & crustaceans Medicinal plants Poultry Wool Onions & substitutes Preps. of vegs., fruits & nuts Misc. edible preparations Other live trees & plants Fish & sea foods Other animal products Beverages, spirits & vinegar Other live animals Preps. of cereals, flour, starch or milk Rice Roots & tubers Tobacco & substitutes Spices Sheep & goats Milling industry products Other oils & facts Palm oil Furskins Dairy, eggs & honey Gums & resins Groundnuts Residues from food industries Other edible vegetables Cattle Rye, barley & oats Animal fats Soybean oil Other vegetable textile fibres Olive oil Silk Change in competitiveness Figure A4.2a. Change in commodity competitiveness in regional exports markets compared to global and intra- African markets ( ) COMESA markets Global markets Intra-African markets ECCAS markets Global markets Intra-African markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. 117

126 Wool Organic chemicals Meat & edible offal Wheat Silk Cocoa beans Essential oils & resinoids Beverages, spirits & vinegar Other cereals Other oilseeds Dairy, eggs & honey Cane sugar Sugar confectionery Cattle Cocoa preparations Misc. edible preparations Poultry Spices Edible preps. of meat, fish & crustaceans Preps. of vegs., fruits & nuts Groundnut oil Tobacco & substitutes Tea Medicinal plants Cotton, not carded or combed Tomatoes Coffee Potatoes Residues from food industries Other live trees & plants Milling industry products Edible fruits & nuts Sorghum Finishing agents for textiles & paper Albuminoidal substances Preps. of cereals, flour, starch or milk Cotton, carded or combed Roots & tubers Other edible vegetables Other live animals Groundnuts Onions & substitutes Other vegetable textile fibres Palm oil Other animal products Maize Sheep & goats Soybeans Other oils & facts Fish & sea foods Animal fats Soybean oil Rice Gums & resins Furskins Vegetable plaiting materials Olive oil Hides & skins Rye, barley & oats Change in competitiveness Organic chemicals Rye, barley & oats Onions & substitutes Cotton, carded or combed Hides & skins Palm oil Vegetable plaiting materials Finishing agents for textiles & paper Other live trees & plants Groundnut oil Roots & tubers Residues from food industries Tea Cotton, not carded or combed Sorghum Sugar confectionery Maize Essential oils & resinoids Cane sugar Spices Medicinal plants Other edible vegetables Silk Sheep & goats Cattle Beverages, spirits & vinegar Meat & edible offal Preps. of cereals, flour, starch or milk Cocoa beans Animal fats Other live animals Groundnuts Edible fruits & nuts Other cereals Misc. edible preparations Rice Gums & resins Fish & sea foods Other animal products Poultry Albuminoidal substances Dairy, eggs & honey Coffee Other oils & facts Other oilseeds Cocoa preparations Milling industry products Furskins Soybeans Potatoes Preps. of vegs., fruits & nuts Tomatoes Wool Edible preps. of meat, fish & crustaceans Tobacco & substitutes Other vegetable textile fibres Wheat Soybean oil Olive oil Change in competitiveness Figure A4.2b. Change in commodity competitiveness in regional exports markets compared to global and intra-african markets ( ): commodity ranking ECOWAS markets Global markets Intra-African markets SADC markets Global markets Intra-African markets Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. 118

127 4.10. Statistical tests The series of competitive effect values derived for all countries and all commodities and for different destination markets are used to carry out two statistical comparison procedures. The first one is an analysis of variance (ANOVA), which is used to test the hypothesis that the means of competitiveness changes are equal across country groups. The second one is the paired-samples T test of the hypothesis that competitiveness changes in two export destination markets are equal. This is run both for country and commodity competitiveness changes. The results obtained from these procedures are presented in Tables above as well as Tables A4.5 A4.8 below and are discussed in sections Prior to running these procedures, the one-sample Kolmogorov-Smirnov test was first performed to confirm the assumption of the normality of the distribution of competitiveness change indices in each of the country groups under comparison. The same test was carried out the check the assumption that for each pair of export markets the differences in competitiveness changes in those markets follow a normal distribution. We also used the Levene's homogeneity-of-variance test to check the assumption that country groups under comparison come from populations with equal variances. In the large majority of comparisons, the Levene s test confirmed an equality of variances across groups, allowing us to perform an ANOVA procedure using the standard F statistic. However, in the rare comparisons where variances are significantly different, a robust ANOVA procedure using the Welch statistic was also performed to check whether we can trust the p value associated with the standard ANOVA F statistic. The results of the Kolmogorov- Smirnov test and the Levene's test are presented in Table A4.9 A4.12. Table A4.5. Analysis of variance of country competitiveness changes in COMESA agricultural export markets ( ) Country Groups Sum of Squares df Mean Square F Sig. Eta Squared COMESA vs. Between Groups non-comesa Within Groups countries Total ECCAS vs. Between Groups non-eccas Within Groups countries Total ECOWAS vs. Between Groups non-ecowas Within Groups countries Total SADC vs. Between Groups non-sadc Within Groups countries Total Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 119

128 Table A4.6. Analysis of variance of country competitiveness changes in ECCAS agricultural export markets ( ) Groups Sum of Squares df Mean Square F Sig. Eta Squared COMESA vs. Between Groups non-comesa Within Groups countries Total ECCAS vs. Between Groups non-eccas Within Groups countries Total ECOWAS vs. Between Groups non-ecowas Within Groups countries Total SADC vs. Between Groups non-sadc Within Groups countries Total Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. Table A4.7. Analysis of variance of country competitiveness changes in ECOWAS agricultural export markets ( ) Groups Sum of Squares df Mean Square F Sig. Eta Squared COMESA vs. Between Groups non-comesa Within Groups countries Total ECCAS vs. Between Groups non-eccas Within Groups countries Total ECOWAS vs. Between Groups non-ecowas Within Groups countries Total SADC vs. Between Groups non-sadc Within Groups countries Total Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 120

129 Table A4.8. Analysis of variance of country competitiveness changes in SADC agricultural export markets ( ) Groups Sum of Squares df Mean Square F Sig. Eta Squared COMESA vs. Between Groups non-comesa Within Groups countries Total ECCAS vs. Between Groups non-eccas Within Groups countries Total ECOWAS vs. Between Groups non-ecowas Within Groups countries Total SADC vs. Between Groups non-sadc Within Groups countries Total Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 121

130 Table A4.9. One-Sample Kolmogorov-Smirnov tests of normality of the distributions of competitiveness changes for different country groups Test groups Global markets Intra-African markets Export destination markets COMESA ECCAS markets markets ECOWAS markets SADC markets Kolmogorov- COMESA Smirnov Z countries Asymp. Sig. (2-tailed) Kolmogorov- Non Smirnov Z COMESA Asymp. Sig. countries (2-tailed) Kolmogorov- ECCAS Smirnov Z countries Asymp. Sig. (2-tailed) Kolmogorov- Non-ECCAS Smirnov Z countries Asymp. Sig. (2-tailed) Kolmogorov- ECOWAS Smirnov Z countries Asymp. Sig. (2-tailed) Kolmogorov- Non Smirnov Z ECOWAS Asymp. Sig. countries (2-tailed) Kolmogorov- SADC Smirnov Z countries Asymp. Sig. (2-tailed) Kolmogorov- Non-SADC Smirnov Z countries Asymp. Sig. (2-tailed) Note: The probability of the Z statistic is above 0.05, meaning that the normal distribution is a good fit for competitiveness changes for the different country groups tested and across all export destinations. Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 122

131 Table A4.10. One-Sample Kolmogorov-Smirnov tests of normality of the distributions of differences in country competitiveness changes in pairs of export markets Pairs of markets N Kolmogorov-Smirnov Z Asymp. Sig. (2-tailed) COMESA & global markets ECCAS & global markets ECOWAS & global markets SADC & global markets Intra-African & global markets COMESA & intra-african markets ECCAS & intra-african markets ECOWAS & intra-african markets SADC & intra-african markets Note: The probability of the Z statistic is above 0.05, meaning that the normal distribution is a good fit the differences of competitiveness changes in pairs of export destination markets. Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. Table A4.11. One-Sample Kolmogorov-Smirnov tests of normality of the distributions of differences in commodity competitiveness changes in pairs of export markets Pairs of markets N Kolmogorov-Smirnov Z Asymp. Sig. (2-tailed) COMESA and global markets ECCAS and global markets ECOWAS and global markets SADC and global markets Intra-African and global markets COMESA and intra-african markets ECCAS and intra-african markets ECOWAS and intra-african markets SADC and intra-african markets Note: The probability of the Z statistic is above 0.05, meaning that the normal distribution is a good fit the differences of competitiveness changes in pairs of export destination markets. Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from commodity-level export share decomposition analysis for African countries as a group. 123

132 Table A4.12. Levene's test for homogeneity-of-variance of country competitiveness changes for pairs of country groups Country groups COMESA vs. non-comesa countries ECCAS vs. non-eccas countries ECOWAS vs. non-ecowas countries Global markets Intra-African markets Export destination markets COMESA markets ECCAS markets ECOWAS markets SADC markets Levene Statistic Sig * Levene Statistic Sig * Levene Statistic Sig SADC vs. Levene non-sadc Statistic countries Sig * * 0.015* Note: In the large majority of tests the significance value of the Levene statistic is above 0.10, which means that we can assume an equality of variances for corresponding pairs of country-groups. The asterisk denotes a few tests resulting in significance values below 0.10, meaning that the assumption of equal variances is violated for corresponding pairs of groups. Source: Authors calculations using the BACI database. Change in competitiveness is measured by the coefficient of the competitive effect derived from export share decomposition analysis for individual countries. 124

133 Chapter 5. Determinants of African agricultural exports Extracted from African Agricultural Trade Status Report 2017

134 CHAPTER 5. DETERMINANTS OF AFRICAN AGRICULTURAL EXPORTS Getaw Tadesse, International Food Policy Research Institute (IFPRI), Eastern and Southern Africa Office, Addis Ababa, Ethiopia Ousmane Badiane, International Food Policy Research Institute, Washington DC 5.1 Introduction Trade is an important engine for economic growth, food security, reducing poverty and overall development. However, it is a complex and sensitive subject for policymaking as it involves negotiations, dialogues and agreements between partner countries residing in different sociopolitical boundaries. It becomes more complicated when linked with agriculture, which is a sector profoundly reliant on continuous social and ecological dynamism. Therefore, success in agricultural trade heavily depends on the extent of understanding of the constraints facing agriculture and its cross-broader trade. Following the 1980s trade liberalizations, a series of studies have been conducted to document agricultural trade trends, determinants and prospects both in Africa and elsewhere (Bouët, Bureau, Decreux, & Jean, 2005; Bouët, Mishra, & Roy, 2008; Bureau, Jean, & Matthews, 2006; Croser & Anderson, 2011; Moïsé, Delpeuch, Sorescu, Bottini, & Foch, 2013). These studies highlighted a wide array of constraints that are crucially important for improving African agricultural trade. More importantly they have indicated the importance of global trade policy actions and the need to address the different trade constraints in a holistic manner. According to these studies, agricultural trade determinants can be broadly classified into five major thematic areas, namely production capacity, cost of trade, trade policies, domestic agricultural supports and global market shocks. While production capacity and cost of trade are usually referred to as supply side constraints, many trade policies (except export taxes) and agricultural supports in importing countries are considered to be demand side constraints. Constraints related to global food, oil and financial crises are taken as market level trade constraints. These constraints influence imports and exports in different ways and to different extents both from the demand and supply sides. Supply-side determinants limit the competitiveness of a country in global or regional markets by increasing costs of production as well as costs of trading. These constraints include the nature and extent of resource endowments, productivity (technology), quality of infrastructure and institutions 125

135 that facilitate trade, and domestic agricultural support services provided to smallholder producers and traders in an exporting country. Demand side constraints usually emerge from trade protection measures of importing countries. Africa exports more than 75 percent of its agricultural product value outside of the continent. Many of its trade partners impose several trade protection measures which directly or indirectly limit agricultural exports. This is particularly the case for certain commodities such as tobacco, cotton, coffee, cocoa, and oilseeds, in which Africa has the comparative advantage. Therefore, close monitoring of the extent and nature of these constraints and their linkages with the flow of agricultural exports is required to guide effective evidencebased trade policymaking in Africa. The purpose of this chapter is to offer comprehensive and updated evidence to African agricultural trade policy discussions through highlighting determinants that hinder the performance and competitiveness of agricultural exports and underlining areas that should receive priority policy attention at the continental, regional and national levels. Africa aspires to triple the current level of regional agricultural trade by the year 2025, which requires a wide range of interventions in the form of policies and investments. For these interventions to be effective and achieve the intended targets, key areas of intervention have to be identified, prioritized and monitored regularly. In this chapter, we attempt to review existing evidence, identify key determinants of trade in general, and describe how these determinants are specifically important to African agricultural trade. In doing so, we provide empirical evidence that shows the relative importance of trade constraints and explains how the constraints are trending over time and varying across countries. The chapter is structured as follows. The next section briefly reviews specific factors included in each of the five major determinants of trade and their conceptual and empirical links with trade. Following this section, the empirical assessment approach used to estimate the relative importance of trade determinants is presented. This section explains the sources of data used, the variables selected, and the overall results of gravity models estimated for global-africa and intra-africa bilateral export trade. The subsequent section describes, discusses and tracks the major determinants included in the gravity models. In this section, we discuss the significance of the determinants, their magnitude and trends, and the conditions under which a factor becomes detrimental. The last section summarizes major findings and draws conclusions that would help policy dialogue and actions. 126

136 5.2 Review of trade determinants The extent of agricultural exports has been constrained by many domestic and international factors both from the demand and supply sides. Theoretical and empirical evidence suggests that these factors can be broadly classified into five major thematic areas including production capacity, cost of trade, trade policies, domestic agricultural supports and global market shocks. These constraints influence imports and exports in different ways and at different magnitudes. Production capacity refers to those factors that affect the production capacity of a country. These factors include resource endowments and other technological and institutional factors that enhance the productivity and comparative advantages of a country in global and regional markets. Both classical and neoclassical theories have exhaustively explained the importance of comparative advantage for improving performance of trade among countries. However, there has been strong contention regarding the source of this production capacity and thereby the source of comparative advantage. While the Ricardian hypothesis advocates the importance of technological (or productivity) change as the major source of comparative advantage, the Heckscher-Ohlin hypothesis argues for the importance of relative factor endowments as a prime source of trade. According to the Ricardian theory, the relative efficiency of producing goods and services determines the direction and magnitude of trade between two countries. In contrast, the Heckscher- Ohlin factor endowment theory predicts that countries with an abundance of one or more of the factors of production (land, labor and capital) will specialize in commodities that require much of the abundant resources. However, empirical studies have confirmed that differences in productivity (technology) and factor endowment explain a very small part of trade performance variations over time and across countries (Bergstrand, 1990; Bernstein & Weinstein, 2002). Moreover, recent evidence has suggested the importance of relative factor endowment over productivity or technology to explain international trade (Amoroso, Chiquiar, & Ramos-Francia, 2011). Cost of trade: factors that exacerbate costs of trade are very diverse. The two most important factors that increase the cost of trade are poor infrastructure and institutional inefficiency related to trade. Costs also include financial fees related to export and imports. The role of infrastructure in enhancing trade has been widely discussed in policy circles and in the literature (Bouët et al., 2008; Bougheas, Demetriades, & Mamuneas, 1999; Francois & Manchin, 127

137 2007; Moïsé et al., 2013). Empirical studies have generally confirmed positive and significant effects of infrastructure quality in exporting countries on trade values. However, the relative importance of infrastructural elements varies across studies. While road density has significant positive effects on trade volumes of low income countries, the effect of mobile phone density has been found to be less significant (Bouët et al., 2008). Institutional efficiency refers to the ease of doing business in relation to agricultural imports and exports. It includes procedures and delays in customs clearing, access to finance for traders, and the strength of contractual enforcement. Although customs and administrative procedures are essential for facilitating trade and implementing trade policies, they have the potential to restrict trade, particularly in less developed countries where administrative systems are less automated, capacitated and transparent. These procedures and requirements delay delivery and cause extra costs related to storage costs and losses. Empirical studies have indicated that a 10 percent reduction in the time spent to clear exports, the number of signatures required to clear exports, or the number of documents needed to cross borders increases trade by 6 to 11 percent globally (Wilson, 2007). Trade is more responsive to the number of documents than to the other metrics. Trade policies include measures aimed at protecting trade through tariffs and non-tariff barriers. The effect of tariffs on trade performance has been studied using economy-wide simulations (e.g. Bouët, Bureau, et al., 2005), gravity equations (e.g. Bouët et al., 2008), and trade restrictiveness indexes (e.g.croser & Anderson, 2011). Although the magnitudes are different, all of the studies indicated that the effect of import taxes on trade volumes is convincingly negative and significant. Bilateral, regional and international trade agreements are also part of tariff policies that either reduce tariffs through Free Trade Agreements (FTA) or facilitate cross border trade. The most important of these agreements are trade preferences, particularly the non-reciprocal ones which target opening markets to individual or sets of developing countries. This involves complete or partial lifting of import tariffs and quotas for specified products. Preferences are usually designed to offer commercial opportunities for poor countries. However, preferences are widely criticized for not being utilized due to rules of origin, their focus on commodities for which developing countries have little competitive advantage, and the presence of associated stringent standards related to sanitary and phytosanitary requirements (Brenton, 2003; Panagariya, 2003; Topp, 2003). Despite these critics, some recent studies have shown that preferences are still useful and beneficial 128

138 to less developed countries, particularly to countries in Africa south of the Sahara (Bouët, Fontagné, & Jean, 2005; Bouët, Laborde, Dienesch, & Elliott, 2012; Wainio & Gehlhar, 2004). Non-tariff measures include those trade barriers that limit the quantity and volume of imports through a variety of technical and non-technical standards. UNCTAD classifies non-tariff trade measures into sixteen broad categories, each of which constitutes several specific classifications. The major ones are sanitary and phytosanitary (SPS) requirements, technical barriers to trade (TBT) which include packing, labeling and standardizing, price controls (anti-dumping), licensing, quantitative restrictions, export subsidies and export taxes. Non-tariff barriers constrain trade through increasing the cost of inspection, certification and testing. This is particularly important for developing countries which have poor quality assurance infrastructure and technological capacity to conduct these processes and hence have to recruit third parties to access the services. Domestic agricultural supports: Both developed and developing countries provide financial and technical support to their agricultural producers for different reasons. However, the support provided by industrial countries to protect their agricultural sectors has been considered to be the most damaging for trade from developing countries. Supports in these countries take the form of border measures (import tariffs, export subsidies) and domestic measures (production and input subsidies). Domestic supports can be implemented through markets or through direct payments. Both approaches have the potential to reduce the amount of imports from foreign countries. These supports raise the price received by the producers of the supported country above the world price so that they become artificially more competitive than imports from outside of the country. Empirical studies assessing the link between domestic subsides and trade have revealed mixed results depending on the type of support (coupled or decoupled) and commodity. Many have argued that the removal of EU and US agricultural subsidies could have a significant effect on world prices of some commodities such as cotton, tobacco and soybean (Bouët, Bureau, et al., 2005; Bureau et al., 2006). However, the impact of domestic subsidies is lower than other crossborder measures (Anderson & Martin, 2005; Hoekman, Ng, & Olarreaga, 2004). Payments less related to the quantity produced (decoupled) have lesser impacts than payments directly related to production (coupled); as a result many OECD countries are moving towards payments which are less tied to the quantity of domestic production (Urban, Jensen, & Brockmeier, 2016). 129

139 Developing countries do also provide technical, financial and institutional support to smallholder producers to boost productivity and improve market efficiency, thereby enhancing agricultural exports. The extent of agricultural support provided to smallholders depends on the size, allocation and efficiency of public agricultural expenditure. Agricultural public expenditure serves to accumulate capital stock that would enhance the production as well as trading capacity of smallholder producers (Benin, Mogues, & Fan, 2012). However, the actual effect on trade depends on the focus and efficiency of public investments. Investments focused on export sectors would likely improve trade more than those investments focused on domestic food production or food security. Global market shocks: Global food, financial and oil markets are increasingly interconnected (Tadesse, Algieri, Kalkuhl, & Braun, 2014). Shocks to any of these markets would likely affect the nature and extent of agricultural trade. The 2007/2008 food price crisis, for example, has caused many countries to impose export barriers and relax import restrictions on food products, which has further aggravated the problem of price spikes and adversely affected agricultural trade (Anderson, 2014; Anderson & Nelgen, 2012; Anderson & Thennakoon, 2015; Bouet & Laborde, 2012; Yu, Tokgoz, Wailes, & Chavez, 2011). Similarly, the ongoing oil price crises may also affect the extent of agricultural exports, particularly in those countries which are oil dependent. When the oil price is declining, oil dependent countries would likely attempt to shift export dependence from oil to agricultural products, for which prices are relatively stable. 5.3 Empirical assessment 5.3.1Data and methods We used gravity-type econometric equations to examine the empirical and relative relevance of the determinants listed above in the African context. The models are used to estimate the logarithm of bilateral agricultural export values of African countries over a number of demand and supply side factors. In addition to the four 14 major thematic determinants explained above, scale variables are included to control for the size of importing and exporting economies and income differences between trading partners. Two to five specific variables were chosen to proxy each of the major thematic determinants. Total GDP of both importing and exporting countries are used to proxy the 14 Variables to represent the fifth thematic determinant, global market shocks, are not considered due to their invariability across countries. These variables can be captured in a time-series setting. 130

140 size of the economies of partnering countries. While GDP per capita in importing countries is used to capture income effects, GDP per capita in exporting countries is used as a proxy for capital endowment. Other assets such as farm machinery, irrigation facilities, etc., would have been a good indicator of capital for agriculture, but the data on these variables suffers from a large number of missing values. Quantity of land and labor are included to measure resource endowments; road density, quality of port, index of trade infrastructural quality, index of customs clearing efficiency and financial fees for exporting are used to measure costs of trade; frequency of non-tariff measures, average ad valorem equivalent tariff rates and regional trade agreements are considered to proxy external trade policy; and the ratio of the agricultural producer price index to the manufacturing producer price index of importing countries and agricultural public expenditure of exporting countries are used to measure the effect of domestic agricultural policy in importing and exporting countries respectively. The list of determinants considered in the analysis and the metrics used to estimate their magnitudes are described in Annex 1. Data used in this analysis are obtained from different sources, mainly from World Bank World Development Indicators (WDI), UN Comtrade, and World Integrated Trade Solution (WITS). While data on income, resource endowments, infrastructure and efficiency of institutions are gathered from World Bank WDI, UN Comtrade is used for trade data, and data on tariffs were extracted from WITS. Other sources such as WTO, ReSAKSS, FAOSTAT, and OECD are used for data on specific variables such as non-tariff barriers, public agricultural expenditure, producer price indices and producer support estimates (PSE) respectively. The quality of trade data in Africa has always been a big concern as sizable cross-border transactions are carried out informally and unrecorded. However, the purpose of this chapter is not to show the size of trade, but rather to look into the determinants of export flows. Thus, as long as the omitted trade transactions are random, they will have little impact on our results. All export values are for agricultural products unless and otherwise mentioned. All the regressions are estimated using cross sectional data from 2013, which is the most recent year for which adequate data are available for many of the determinants. However, one year lagged values are used for some variables (productivity and public agricultural expenditure) which are deemed to be endogenous to export values. Visualization of trade data over years indicates that there were no extraordinary events in 2013 that could bias the results. 131

141 Two groups of models are estimated. The first group is used to estimate African agricultural exports to the global market. In this models, only African countries are included as exporters ( i ). In addition to African countries, countries from all continents which had frequent transactions with Africa are included as importers ( j ). In general, a total of 49 exporters 15 and 161 trade partners are considered. The second group of models is used to estimate intra-african exports, with African countries as both exporters and importers. We also estimated African exports to the rest of the world for comparison purposes. Of all possible pairwise transactions between 49 exporting countries and 161 importing countries, about 58 percent have zero trade transactions. Excluding these transactions would likely cause selection bias, while inclusion of them would cause censoring bias. Though previous studies have excluded them and tried to control the selection bias using the Heckman approach, we choose to include them in the analysis and address the censoring bias using a Tobit model approach. We assume zero trade is an optimal outcome instead of a strategic choice of a country not to trade with a specific partner. Due to multiple data sources for different variables, the dataset is seriously affected by missing values. To overcome the problem of missing values, several specifications are considered through step-wise inclusion of explanatory variables, which have different sets of observations and represent specific sets of determinants. A total of six specifications are estimated for African global exports. The first model estimates the effect of resource endowments together with scale variables. The second model includes infrastructural and institutional variables in addition to the variables in model one. The third model adds public agricultural expenditure and hence represents a domestic trade model in which only domestic (supply side) constraints are included. The fourth model includes international (demand side) variables such as non-tariff barriers, tariffs and regional trade agreements. The fifth and sixth models are Tobit specifications without and with the agricultureto-manufacturing price ratio variable that represents domestic agricultural supports by OECD 15 Five southern Africa countries (Lesotho, South Africa, Botswana, Namibia and Swaziland) are treated as one country as they have a common customs union called SACU. Trade data in many sources is reported for the five countries together; for other variables we use the average or the sum of all or some of the countries, depending on the variable. 132

142 countries. Since the price ratio is calculated only for OECD countries, the number of observations is greatly reduced in the final specification Empirical Results Table 5.1 shows results of the six specifications for African global agricultural exports. The columns, denoted by the numbers 1 to 6, present the results of different specifications that could help to test robustness under different numbers of observations and examine the predictive power of additional variables. In general, many determinants show the theoretically expected signs, except resource endowment variables. Variables related to infrastructure and institutional efficiency are more significant than other domestic factors. These variables explain about 11 percent of the variation in agricultural export growth among African countries. Public expenditure in agriculture appears to have positive and generally significant effect on trade. Trade policy variables appear to be important determinants, next to the cost of trade, though there exists significant variation between policy instruments. Non-tariff barriers and regional trade agreements appear more important than tariffs. Resource endowment seems to be a less important factor for African agricultural trade. The effect of producer price ratios which represent domestic agricultural support in importing countries seems significant, but requires further explanation. Table 5.2 shows results of intra-africa trade determinants in comparison with African exports to the rest of the world. In this case, we used the comprehensive models (four and five), as agricultureto-manufacturing price ratios are not available for most African countries. The results indicate that many of the determinants are equally important for African exports either within Africa or outside of Africa. The level of per capita income in importing countries is more relevant for intra-african trade than for African exports to the rest of the world. Similarly, resource endowments and nontariff barriers are not as relevant for intra-african trade as they are for African trade with countries in other regions. This is consistent with the facts that resource endowments within Africa are closely similar and non-tariff barriers are not stringent as they are outside of Africa. We also learn that public expenditures in agriculture are more relevant to reach markets outside of Africa than markets within Africa. Since the determinants for intra-african and global African exports are similar, in the subsequent section we discuss why some variables are significant over the others, and track trends and 133

143 distributions of key determinants using the results of the global-africa agricultural export estimations. However, we briefly discuss the importance of a determinant for intra-africa trade whenever necessary. 134

144 Table 5.1. Response of African global agricultural export value to domestic and international factors Logarithm of value of exports from i countries to j countries Determinants OLS Tobit (1) (2) (3) (4) (5) (6) Importer s GDP (billions of US$) 1.57*** 1.65*** 2.16*** 2.23*** 3.35*** 2.70*** Exporter s GDP (billions of US$) 0.79*** 0.88*** 0.92*** 1.19*** 1.80*** 1.48*** Per capita GDP of exporters (US$) *** -2.11*** -2.30*** -3.63*** 1.14*** 2.67*** Per capita GDP of importers(us$) *** -0.12*** -0.13*** Arable land (millions of hectares) *** -0.52*** -0.47*** -0.52*** 0.52*** 0.91*** Agricultural labor (millions) *** -0.38** -0.43** -0.77*** 0.05 Road density (km per km 2 of land) *** Quality of port 4.43*** 4.26*** 4.62*** 6.94*** 8.63*** Quality of transport infrastructure 1.80*** 1.17** 1.15** Efficiency of customs clearing index 1.24*** 1.64*** 1.69*** 3.81*** 0.03 Export cost ($US per container) PAE per agricultural GDP of exporter 0.12** 0.16** 0.46*** 0.28* Incidence of importer s non-tariff *** -0.39*** barriers 0.32*** Average tariff rate of importer * *** Being in a similar REC 3.52*** 5.39*** 5.24*** The ratio of agricultural PPI to manufacturing PPI *** Constant 5.44*** -2.43* 3.30* Sigma (test for censoring) 4.32*** 3.21*** R-squared N Note: All the determinants except REC are in logarithmic form and hence the coefficients are elasticities. i countries refer to the 49 exporting African countries and j countries include importing countries all over the world. PPI denotes Producer Price Index and PAE denotes Public Agricultural Expenditure. The lagged value of PAE is used to control for possible endogeneity. 135

145 Table 5.2. Determinants of intra-africa agricultural exports Determinants Intra-Africa export African export to the rest of the world OLS Tobit OLS Tobit Importer s GDP (billions of US$) 1.91*** 2.75*** 2.31*** 3.48*** Exporter s GDP (billions of US$) 0.32** 0.44* 1.22*** 1.84*** Per capita GDP of exporters (US$) -1.39** -1.89* -2.51*** -4.03*** Per capita GDP of importers(us$) 1.24*** 2.24*** Arable land (millions of hectares) *** -0.62*** Agricultural labor (millions) ** -0.81*** Road density (km per km 2 of land) Quality of port 4.46*** 6.83*** 4.68*** 7.05*** Quality of transport infrastructure ** 1.13 Efficiency of customs clearing index 2.39* 5.45** 1.51** 3.39*** Export cost ($US per container) PAE per agricultural GDP of exporter ** 0.14** 0.41*** Incidence of importer s non-tariff barriers *** -0.39*** Average tariff rate of importer 0.53*** 0.95*** *** Being in a similar REC 3.55*** 5.68*** Constant -9.64* ** sigma 4.53*** 4.13*** R-squared N Note: All the determinants except REC are in logarithmic form and hence the coefficients are elasticities. i countries refer to the 49 exporting African countries and j countries include importing African countries for intra-african trade and importing countries outside of Africa for export to the rest of the world. PAE denotes Public Agricultural Expenditure. The lagged value of PAE is used to control for possible endogeneity. 136

146 5.4 Describing and tracking key determinants Resource endowment and productivity As this study exclusively considers agricultural products, we assume that agriculture is land and labor intensive in the African context but less capital intensive compared to other sectors products, expecting a negative effect of capital and positive effects of land and labor on agricultural exports. However, all three resource endowment variables, labor, land and capital (represented by exporters per capita income), show negative effects on agricultural exports (see Table 5.1). According to this result, countries with higher per capita income are less likely to export agricultural products than countries with lower per capita income. This is in line with the relative resource endowment theory which predicts that a country specializes in an industry that requires less of the scarcest resource in the country. Hence, while countries grow (accumulate capital), their export portfolio shifts from agriculture (less capital intensive) to sectors which are more capital intensive. Thus, capital endowment reduces exports of primary agricultural products. The results also suggest that countries with scarce arable land and agricultural labor export more than countries with abundant agricultural land and labor endowments. The negative effect of land on agricultural exports is due to the exclusion of land productivity from the models. When land and labor productivity are included in the model, the results become significantly different (Table 5.3). If productivity is controlled for, land positively affects the performance of agricultural exports both to the world and African markets. The elasticity is greater for intra-african trade than for global trade. The impact of labor has remained negative. Labor-abundant countries export less than labor-scarce countries, keeping productivity constant. This could be due to the fact that African agriculture is not labor intensive as we expected. Alternatively, in an area where labor is abundant with low productivity, agricultural production may serve only for household subsistence without any significant contribution to exports. Similarly, while countries with high land productivity export at a higher rate than countries with low land productivity, countries with high labor productivity export at a lower rate than countries with low labor productivity. Labor productivity negatively affects trade, probably because wherever the productivity of labor is high, the local market becomes more attractive to producers than the export market. Increased agricultural labor productivity might be good for reducing poverty, but it seems to negatively affect agricultural export performance in Africa. But the 137

147 negative effect may indicate the extent of economic transformation. Countries with higher labor productivity are countries in which economic activity is shifting to the non-agricultural sector, and hence the composition of their exports is shifting from agricultural to non-agricultural products. All these imply that while availability of arable land and increased land productivity can positively affect agricultural trade, having abundant labor alone does not necessarily lead to higher trade; rather it may retard the continent s global as well as intra-regional trade. Moreover, trade seems more elastic for land productivity than land availability, implying that investment in land productivity-enhancing technologies or institutions would help not only to increase farmers income but also to boost regional trade. A 1 percent increase in land productivity increases trade flows by about 6 percent to the global market and 7 percent to the African market. Land productivity has a stronger effect on intra-african trade than on global trade, which further explains the importance of improving land productivity to triple intra-african trade. This is because many African countries have similar resource endowments and closely similar trade facilities, so their competitiveness in regional trade mainly depends on the extent of agricultural productivity. Table 5.3. African agricultural export response to land and labor endowments and productivity (elasticity) Endowment and productivity indicators Global trade Intra-African trade (3) (7) (8) (9) (10) Arable land (millions of hectares) -0.52*** 5.82*** 7.15*** Agricultural labor (millions) -0.38** -6.00*** -6.88*** Land productivity (US$ per ha) 6.24*** 0.56*** 7.21*** 0.35*** Labor productivity (US$ per person) -6.43*** *** 0.00 R-squared N Source: Authors estimation based on international sources Note: Global trade denotes bilateral trade between African countries and selected countries globally, including other African countries. Intra-African trade denotes trade among African countries only. Estimations include additional variables for which results are not presented here. 138

148 5.4.2 Infrastructural quality and institutional efficiency Variables addressing the quality of ports and transport, road density, efficiency of customs clearing, and financial export costs have explained a significant part of the variation in agricultural export performance among African countries (Table 5.1). However, there appear to be significant differences among cost indicators in explaining trade flows. On one hand, road density and financial export costs do not have statistically significant effects on export growth. On the other hand, the quality of port infrastructure and the efficiency of customs clearing consistently and positively affect trade performance. Since the cost of trade affects not only export performance but also trade competitiveness, which is defined as the ratio of a country s exports to total African exports to the world or to the African market, further analysis is made to shed light on how cost indicators affect the competiveness of a country in global and regional markets. Table 5.4 presents the effects of trade cost indicators on global and regional competiveness. From these results, it is obvious that although road density and financial export costs have no effect on export volumes, they do have significant effects on competiveness. This is particularly significant when it comes to financial payments to clear exports. Financial export costs include all costs exporters pay for documents, administrative fees for customs clearance and technical control, customs brokers, terminal handling charges, and inland transport, and these costs are found to be very crucial for trade competiveness. The lower these fees, the more likely a country becomes competitive both in regional and global markets. Unfortunately, financial fees for exports are increasing over time in Africa South of the Sahara (SSA) (Figure 5.1). Sixteen African countries do not have their own ports. These countries incur higher per unit financial export costs than costal countries. The cost gap between these groups of countries is widening over time. Lack of port access may induce preferential fees for port services and increase inland transport costs, thereby raising export costs. It also creates business insecurity. 139

149 Table 5.4. Effect of trade costs on agricultural trade competiveness in Africa (elasticity) Share of country i s supply in total African supply to Cost indicators Global markets African markets Road density (km per km 2 of land) 0.002*** 0.003*** Quality of port 0.105*** 0.118*** Quality of transport infrastructure Efficiency of customs clearing index *** ** Financial fees for export ($US per container) *** *** Source: Authors estimation based on international sources Note: Estimations include additional variables for which results are not presented here. Figure 5.1. Trends of average financial costs for export in SSA US$ per container Figure 1. Trends of average financial costs for export in SSA SSA Landlocked countries Costal countries Source: Authors calculation based on World Bank Development Indicators Note: Land locked countries are those SSA countries which do not have their own ports. Costal countries are all SSA countries which have their own port(s). Although the effect of road density on export performance was insignificant in most specifications (Table 5.1), it appears to have a strong and positive effect on competiveness (Table 5.4). This 140

150 could be due to the fact that the African road networks are biased to connect local markets more than regional markets (Gwilliam et al., 2008). Even though domestic road networks have improved in many African countries over the past two decades, they are not well-connected to the regional roads, and hence they failed to increase export volumes but still contribute to the country s competiveness. Unlike export volumes, which depend primarily on external efficiency, competitiveness depends mainly on internal efficiency. A country might be competitive compared to other producers but its export volumes may not grow at a faster rate than others. This is exactly what the road density results demonstrate. Improved road density improves a country s internal competiveness to supply cheaper products to external markets, so that the share of that country is higher than those of countries with lower road density. However, since the roads do not adequately connect local markets with regional or global markets, their effect on absolute export volumes remains insignificant. Despite the significance of road density, Africa still remains poorly connected both internally and externally. According to the World Bank Rural Accessibility Index, only 34 percent of the rural population in Africa South of the Sahara lives within 2 kilometers of an all-weather road (Carruthers, Krishnamani, & Murray, 2010). Port quality has remained important both for absolute export volumes (Table 5.1) and trade competiveness (Table 5.4). However, Africa has the lowest port quality of all regions. Based on the quality of port infrastructure, the World Bank classifies ports into 7 groups, 1 being extremely underdeveloped and 7 being considered efficient by international standards. According to this classification Africa South of the Sahara scores 3.65, which is 13 percent below the world average and 29 percent below the average for high income countries. This indicates an urgent need for African countries to invest in port infrastructure to improve both regional and global trade. Other variables related to transport infrastructure and institutional efficiency are important for export growth but not for competiveness (Table 5.4). The negative effect of institutional efficiency on competiveness is very hard to explain. The institutional efficiency indicator is developed based on the number of documents, number of signatures and number of days required to clear customs, both for imports and exports. The mix of these requirements may explain how the institutional efficiency index is related to trade competiveness. 141

151 Figure 5.2. Number of days and documents needed to clear exports Figure 2. Number of days and documents needed to clear exports 49.5 Documents Days HIC LDCs 0 mean ECOWAS Africa SSA COMESA ECCAS SADC Source: Authors calculation based on World Bank World Development Indicators Note: HIC refers to high income countries and LDCs to least developed countries according to the UN classification. Values refer to the mean of an average country in the group. Figure 5.2 shows the number of documents and number of days required for clearing exports across different regions. In many instances, more requirements are imposed on imports than exports for all indicators. SSA has the highest requirements for all indicators compared to other regions. On average it takes more than 32 days to clear exports in Africa South of the Sahara as compared to less than 10 for high-income countries and 27 days in all least developed countries. We observe significant differences across regional economic communities, the worst being SADC member states in which an average export takes close to 50 days. The same is true for the number of documents required to clear exports. However, both indicators are declining over time (Figure 5.3). The number of documents has already declined from nine on average in 2006 to seven in 2010 and remained constant thereafter. It seems that countries progress in improving customs clearing processes has stalled. The number of days continues to decline from 36 in 2006 to below 30 days in 2014, but the rate of decline remains very slow. 142

152 Figure 5.3. Trends of export clearing efficiency in Africa South of the Sahara Figure 3. Trends of export clearing efficiency in Africa South of the Sahara Number of days 8.2 Number of documents Number of documents Number of days Source: Authors calculation based on World Bank World Development Indicators Public Agricultural Expenditure The effect of domestic agricultural support in exporting countries could be an important determinant of export growth in developing countries due to the fact that farmers and traders in these countries are poor and less commercialized, and therefore less able to facilitate production and trade by themselves. The support provided in these countries is different from the support provided in high income countries. In developing countries support is given to facilitate provision of agricultural extension, advisory, market access and financial services. Public agricultural expenditure (PAE) is used as a proxy variable to measure the significance of government support in promoting agricultural exports in Africa. The empirical results reveal that there exists a positive and statistically significant association between PAE and export growth. On average a 10 percent increase in public agricultural expenditure relative to agricultural GDP increases agricultural exports in the following year by about 2 to 4 percent. The correlation between public agricultural spending and export performance significantly varies across countries. Figure 5.4 illustrates the correlation coefficients for selected African countries calculated using time series data for the last ten years. Unexpectedly, public agricultural expenditure has no or negative correlation with exports in many countries. While Ethiopia stands 143

153 out as the country with the largest negative correlation, Rwanda takes the leading role as the most successful country on the positive end. Many factors could explain why countries experience a negative correlation. First, these countries might have focused more on domestic food security and hence, public expenditure has little or no relevance in promoting external trade. This is the case in Ethiopia, where a significant part of the public budget is allocated to mega food security projects such as the Productive Safety Net Program (PSNP) and extension personnel who primarily provide services for food crop production. The country s competitive commodities such as coffee, oilseeds, and hides and skins have been receiving very little budget allocation, relative to their importance to exports. Second, these countries investments in export commodities might be less efficient in facilitating trade and production. Third, a decline in the terms of trade could explain part of the paradox, but empirically this should have little contribution to the negative correlation. On the other end of the graph (Figure 5.4), there are many countries which are able to utilize the public budget to motivate agricultural exports. Rwanda is followed by Liberia, Ghana, and Zimbabwe, in which expenditures and exports are strongly correlated, with coefficients above 0.8. Policymakers aiming to achieve the Malabo target may consider having a preferential public expenditure allocation towards commodities in which they have competitive advantage, and should balance investments in domestic food self-sufficiency (non-tradables) and the export sector (tradables). 144

154 Figure 5.4. Correlation between public agricultural expenditure and agricultural exports Figure 4. Correlation between public agricultural expenditure and agricultural export Ethiopia Mozambique Guinea Equatorial Guinea Congo Nigeria Burundi Mauritania DRC Guinea-Bissau Eritrea Niger Tunisia Central Africa Mauritius Senegal Burkina Faso Togo Kenya Gambia Cote d'ivore Uganda Malawi Zimbabwe Ghana Liberia Rwanda Source: Authors estimation based UNCOMTRADE export data and ReSAKSS public expenditure data. Note: Correlations are calculated between current export values and previous year s public expenditure Regional trade agreements Regional trade agreements remove or reduce tariffs and facilitate joint trade for member states of Regional Economic Communities (RECs). These agreements create trade within the trade agreement zone and divert imports from the rest of the world. Empirical results have shown that the trade creation effect of African RECs such as COMESA, ECOWAS, SADC and ECCAS are stronger than their trade diversion effects (Figure 5.5). The overall trade creation effect as captured by the variable REC, which takes 1 if the importing and exporting countries are from the same RECs and zero otherwise, has a positive and statistically and economically significant effect on export growth. Being a member of any of the RECs increases a country s export value by 3 to 5 percent. This effect captures not only the effect of free trade agreements but also the effect of trade facilitations commonly targeted for cross-border trade. Countries within the same REC are geographically closer to each other, and hence this variable may also capture proximity effects as well. In any case, the trade creation effects of African RECs are convincingly large and significant. 145

155 Figure 5.5. Trade creation and diversion effects of RECs in Africa Figure 5. Trade creation and diversion effects of regional economic comunities in Africa REC COMESA ECOWAS SADC ECCAS Coefficencts Note: The values under REC indicate the trade creation effects of all communities. REC is a dummy variable that takes the value 1 if both importing and exporting countries are from the same REC and 0 otherwise. Effects denoted by each of the RECs indicate the trade diversion effects. For example, the value under COMESA indicates the effect of a variable that takes 1 if the importing country is a COMESA member and the exporting country is a non-member and 0 otherwise, and hence measures the trade diversion effect of COMESA. The same holds for the other RECs. The graph shows coefficients and 95 percent confidence intervals. If zero is included within the confidence interval, the coefficient is interpreted as statistically insignificant. The trade diversion effects of these RECs are not yet significant and uniform. The effects were captured by including dummy variables for each REC that take the value of 1 if the importing country is a member of a given REC and the exporting country is not, and zero otherwise. This variable measures openness of member states to non-member states. As shown in Figure 5.5, the variable representing ECOWAS has a significant and positive effect on exports, implying that being a member of ECOWAS makes countries open to non-member states, signifying a positive trade diversion effect. SADC has a protective effect, but it is only significant at 10 percent (90 percent confidence interval). COMESA and ECCAS have shown negative diversion effects, which may imply import protecting effects to the detriment of non-member states, but the coefficients are not statistically significant. The results are consistent with previous evidence (Makochekanwa, 2012). Since welfare depends on the extent of both trade diversion and trade creation, policymakers 146

156 should target increasing the diversion as well as the creation effects. Internal institutions and efficiency may explain the differential effects of RECs on trade diversion Tariffs and Preferences Despite declining trends in tariff rates imposed on agricultural products worldwide, tariffs are still important determinants of trade. According to our estimation (Table 5.1), a 10 percent increase in tariff rates reduces African agricultural exports by about 3 percent, which is closely similar to previous studies (Bouët et al., 2008; Moïsé et al., 2013). Luckily, Africa, particularly SSA, is increasingly receiving tariff preferences from importing countries. Figure 5.6 shows the average tariff rates imposed by selected countries on agricultural products imported from the world as a whole, least developed countries (LDCs), and SSA. Though India and Pakistan impose the largest tariff rates on agricultural imports globally, they impose lower tariff rates for imports from SSA than imports from the world. Other countries such as the US, Canada and Russia also impose lower average duties on imports from SSA. As expected, SSA countries impose lower taxes on imports from the region than imports from outside the region. Figure 5.6. Tariff rates imposed by major African trade partners on agricultural imports Figure 6. Tariff rates imposed by major African trade partners on agricultural imports India Turkey SSA Pakistan China Canada EU Russia Middle East Japan US Malaysia Australia Percent (weighted average) On all countries On LDCs On SSA Source: Authors estimation based on WITS data. Note: Tariff rates are weighted averages based on amount of imports. Each country or group of countries levies different rates for different countries for the same product. The rates are averaged for three groups: for all countries, for LDCs and for SSA. 147

157 In some countries and regions, including the EU, China and the Middle East, agricultural products from SSA are being taxed more than the world average. This could be due to the fact that preferences, especially by the EU, are given for selected products and that preference rates are exceeded by the tariff rates imposed on non-preferential products. In many countries, African products are taxed at higher rates than the average for LDCs. This indicates that although several preferences are enacted in the EU and the US, African products are still highly taxed compared to other developing countries. Most importantly, SSA countries impose import tax on other SSA countries at a higher rate than they impose on all LDCs. This implies that some African countries are providing a lower tax rate for non-african countries than they impose on African countries. Tariff rates applicable on imports of agricultural products from any part of the world are sharply declining (Figure 5.7). Average tariff rates declined from above 12 percent in 2005 to close to 8 percent in 2014, which indicates a 3 percent annual rate of decline. Multilateral negotiations through WTO and the increasing global food demand as demonstrated by the food price crisis in 2007/2008 might have contributed to this effect. The decline is proportionally similar among the rates applicable to the whole world, SSA and LDCs. Globally, African products are being taxed at lower rates than the world average since 2009 and the gap between these tax rates has widened since then. 148

158 Figure 5.7. Trends of tariff rates imposed on SSA, LDCs, and world exports Figure 7 : Trends of tariff rates imposed on SSA LDCs and World exports 6 8 %, average On all countries On LDCs On SSA countries Source: Authors estimation based on WITS data Despite clear evidence of preferences given to African products over the world average, there are a wide range of debates regarding the benefits of these preferences in enhancing African trade. One of the criticisms is that preferences are given on commodities or products on which Africa has no comparative advantage. Through this criticism applies to comparisons of manufactured and agricultural products, it can also be applicable among agricultural products. As shown in Figure 5.8, there exist significant variations in preference rates 16 given to SSA by the world, the US and the EU across different agricultural products. The US provides preferences for a wider range of products than the EU and others. However, the US does not provide preferences for tobacco and silk. In contrast, the EU provides the highest preference for tobacco. The US provides the highest preference to dairy products followed by sugar and hides and skin. Though some African countries could have comparative advantage in sugar and hides and skin, many countries may not have global comparative advantage in dairy products (Badiane, Odijo, & Jemaneh, 2014). While 16 Defined as the difference between average tariff rates on imports from the world and imports from SSA. 149

159 preference rates for cocoa are reasonably significant, preference rates for coffee and tea are minimal, confirming that preferences are given irrespective of comparative advantage. Figure 5.8. Rates of preference given to SSA exports for major products Figure 8. Rates of preferences given to SSA exports for major products FRUIT AND NUTS CEREALS COCOA COFFEE and TEA COTTON DAIRY and EGGS FISH HIDES AND SKINS LIVE ANIMALS LIVE TREES MEAT OIL SEEDS SILK SUGARS TOBACCO VEGETABLES World EU US Source: Authors estimation based on WITS data Note: Values (rates of preferences) are calculated as average tariff rates imposed by all countries (world), the EU and the US on world imports minus tariff rates imposed on SSA imports Non-tariff barriers (NTBs) There is much empirical evidence, including the findings of this paper, that indicates that trade is more responsive to non-tariff barriers than tariffs (Table 5.1). This shows the increasing importance of non-tariff barriers following the declining trends of tariffs due to bilateral and multilateral trade agreements and preferences. However, despite the growing understanding of the significance of non-tariff barriers to trade, there are certain issues that are not yet clear. These include 1) which type of non-tariff barriers cause significant impacts on trade; 2) which type of non-tariff barriers are prevalent in agricultural trade; 3) how these measures are trending; and 4) what strategic options African countries have to reduce the effect of NTBs on trade performance. Figure 5.9 shows the prevalence of different NTBs across major African trade partners, which import about 90 percent of African agricultural exports. Of all the countries, the US takes the lead 150

160 in terms of the number of measures imposed on imports of agricultural products. During the past four years, the US has imposed about 1,000 measures annually, which are counted across products and types of NTBs. Close to 50 percent of these relate to SPS measures. SPS measures followed by TBT are the dominant type of NTBs in many countries. Quantitative restrictions are widely prevalent in the EU. Unlike many other measures, SPS requirements are politically and environmentally acceptable as they relate to health, safety and hygiene. Unfortunately, these requirements impact trade more than any other measures (Figure 5.10). A ten percent increase in the number of products affected by SPS measures reduces trade by about 3 percent. This result is consistent with a previous study which shows that SPS penalizes poor countries more strongly than others (Disdier, Fontagne, & Mimouni, 2008). Export subsidies, which are prevalent in the EU, the US and Turkey, are the next type of NTB which negatively and significantly affects African agricultural trade. The involvement of state enterprises in imports and exports positively affects African exports, probably due to the discretionary preference that these enterprises may provide to African imports. The involvement of state enterprises in agricultural trade is most prevalent in China and India and in some EU member states. The number of NTBs in general are steadily increasing over time both in the US and the EU, which impose the largest number of tradereducing non-tariff barriers of all of Africa s trading partners (Figure 5.11). Figure 5.9. Frequency of non-tariff measures on agricultural products (average ) Figure 9. Frequency of non-tariff measures on agricultural products (mean ) US EU Japan China Australia Canada India Singapore Turkey Middle East Russia Malaysia SSA Pakistan ,000 SPS TBT Trade defence Quantitative restriction Export subsidy State trading Source: Authors calculation based on WTO data Note: Frequency of non-tariff barriers is measured as the sum of all types of measures for all HS6 classified products. For example, if 2 measures are imposed on one product, 3 measures on 3 products, and zero on all other products, the frequency will be 2*1+3*3=

161 The significant impact of NTBs on trade and their growth over time present significant challenges to policymakers as to how to minimize the adverse effects of these measures. Because of domestic public concerns, reducing their prevalence through international negotiation is not likely to be possible. Rather, policymakers in Africa should focus on reducing the vulnerability of their trade to these measures. The majority of the measures demand certification and labeling, which increase the cost of trading. Efficient institutional and infrastructural arrangements are required to reduce these costs. Establishing a certification and accreditation center for an individual country could be costly and in some cases impossible. Therefore, regional cooperation should be an important area of focus for African policymakers. Furthermore, there are areas in which individual countries can facilitate exports by establishing export facilitation centers that would primarily assist exporters in fulfilling the requirements imposed by importers. Figure Effects of non-tariff measures on export growth in Africa Figure 10. Effects of NTMs on export growth in Africa SPS TBT Trade_defense Quantitative_restriction Export_subsidy State_trading Elasticities Source: Authors calculation based on WTO data Note: SPS refers to sanitary and phytosanitary measures and TBT refers to technical barriers to trade based on the UNCTAD classification. The graph shows coefficients and confidence intervals. If zero is included within the confidence interval, the coefficient is interpreted as statistically insignificant. 152

162 Figure Trends of non-tariff measures in US and EU Figure 11. Trends of non - tarff measures in USA and EU Number of measures USA EU Source: Authors calculation based on WTO data Domestic agricultural supports in OECD countries The empirical link between domestic agricultural supports in OECD countries and the value of agricultural exports in African countries is assessed using a ratio of agricultural and nonagricultural producer prices. This price ratio may capture the effect of all border and domestic supports including tariffs, export subsidies, and production and input subsidies. Since tariffs and non-tariff barriers are included as explanatory variables, the price ratio should predict the effect of domestic supports. As shown in Table 5.1, the effect of this price ratio is negative and statistically significant. According to this estimation, a 1 percent increase in the price ratio reduces African exports by about 5 percent. However, the implication of this elasticity depends on the actual correlation of the price ratio with domestic support. Many economists argue that since most payments to agricultural producers are made through direct payments, the impact of agricultural subsidies on trade is very limited (Anderson & Martin, 2005; Croser & Anderson, 2011; Hoekman et al., 2004). But if we compare producer prices of agricultural and manufacturing products, in many cases we get a ratio greater than one, which implies that agriculture is treated preferentially and that this treatment restricts imports from developing countries. 153

163 Generally we conclude that although the effect of domestic support might not be as large as cross border measures such as tariffs and non-tariff barriers, it still plays a significant role. It appears, however, that the rate of agricultural support in general is declining over time in many OECD countries. Figure 5.12 shows trends in Producer Support Estimates (PSE) estimated by OECD for selected countries and groups of countries. Of all countries considered, EU countries provided the highest support throughout the last two decades. Emerging economies such as China and Russia are also increasingly supporting their producers despite the instability and unpredictability of their support. In these countries, support is said to be mainly through tariffs and non-tariff barriers instead of subsidies. Figure Trends of Producer Support Estimates (PSE) in OECD countries Figure 12. Trends of Producer Support Estimates ( PSE ) in OECD countries 0 Percent EU Russia USA China OECD Source: Authors estimation based on OECD data Both our empirical analysis and trends in the PSE suggest the importance of domestic support in high income countries for the performance of African exports. However, African countries in particular and developing countries in general have very few policy options to curb the adverse effects of this domestic policy action in foreign countries. 154

164 Although multilateral trade negotiations through the WTO are usually of limited effectiveness, they remain the most likely avenue for developing countries to compel high income countries to reduce or redesign their agricultural supports. Economic growth in many African and Asian countries and the increasing threat of climate change may create leverage for developing countries to organize themselves and enforce effective global policy actions through the WTO. 5.5 Conclusions African countries are striving to expand market opportunities for domestic producers regionally as well as globally. However, this effort is being impeded by emerging and evolving constraints. Though many of the constraints seem conventional and traditional, the nature and extent of the constraints are evolving dramatically following global and regional shocks and opportunities. This chapter aims to closely monitor these evolutions and identify key determinants of trade performance with the purpose of provoking discussions among policymakers and development partners on how to help Africa achieve the targets set by the Malabo Declaration. To do so, existing theoretical and empirical evidence is reviewed and comprehensive empirical assessments are made to supplement existing evidence. The review generally found that the existing evidence is not sufficiently comprehensive, updated and focused on African context. Realistic and updated assessments are required to feed the increasing policy momentum to improve African agriculture. We also learned that agricultural trade determinants are diverse and complex, ranging from farm level supply side constraints to global level demand side barriers. This calls for regular monitoring and prioritization of these constraints for immediate policy and development actions. The empirical analysis that aimed at identifying and tracking key determinants of trade indicated that supply side constraints, which include production capacity and cost of trade, are more important determinants than demand side global constraints. This gives the opportunity for African policymakers to focus on domestic production and trade facilitation which can easily be influenced through national and regional policies and investments. A lot can be achieved by simply focusing on domestic factors instead of assuming that international factors are the culprits for low and, in some countries, declining agricultural exports. This does not, however, rule out the importance of cooperation, both regionally and globally. 155

165 Regional cooperation is key for enhancing trade through reducing trade barriers and increasing productivity. The empirical analysis clearly confirmed that regional economic communities in Africa are significantly contributing to the growth of agricultural exports. These regional units can be further utilized to reduce regional as well as global barriers. One important function of regional bodies could be joint trade facilitation initiatives that can help to fulfil the growing non-tariff trade requirements of African trade partners. Despite a growing tendency toward import tariff reductions partly due to preferential trade, nontariff barriers are significantly increasing and impacting African exports more than tariffs. This trend demands not only regional cooperation but also global cooperation. Ensuring global cooperation has always been a challenge for developing countries. However, there are growing opportunities that can enhance the bargaining power of developing countries in general and African countries in particular. These are the growing economic importance of the continent for markets and investments and the global climate threat, in that Africa can play pivotal role in mitigating the problem. However, global cooperation should not be viewed only as an instrument to influence international trade policies; rather Africa should also seek this cooperation for facilitating trade and enhancing domestic agricultural value addition. 156

166 References Amoroso, N., Chiquiar, D., & Ramos-Francia, M. (2011). Technology and endowments as determinants of comparative advantage: Evidence from Mexico. The North American Journal of Economics and Finance, 22(2), Anderson, K. (2014). The intersection of trade policy, price volatility, and food security. Annual Review of Resource Economics, 6(1), Anderson, K., & Martin, W. (2005). Agricultural trade reform and the Doha Developemnt Agenda. The World Economy, 28(9), Anderson, K., & Nelgen, S. (2012). Agricultural trade distortions during the global financial crisis. Oxford Review of Economic Policy, 28(2), Anderson, K., & Thennakoon, J. (2015). Food price spikes and poor, small economies: What role for trade policies. African Journal of Agricultural and Resource Economics, 10(1), Badiane, O., Odjo, S., & Jemaneh, S. (2014). More resilient domestic food markets through regional trade. In O. Badiane, T. Makombe & G. Bahiigwa (Eds.), Promoting Agricutral Trade to Enhance Resilience in Africa. ReSAKSS Annual Trends and Outlook Report Washington DC: International Food Policy Research Institute. Benin, S., Mogues, T., & Fan, S. (2012). Agricultural growth and poverty reduction impacts of public investments: Assessment concepts and techniques. In T. Mogues & S. Benin (Eds.), Public Expenditures for Agricultural and Rural Development in Africa. UK: Routledge. Bergstrand, J. H. (1990). The Heckscher-Ohlin-Samuelson Model, the Linder Hypothesis and the determinants of bilateral intra-industry trade. The Economic Journal, 100(403), Bernstein, J., & Weinstein, D. (2002). Do endowments predict the location of production? Evidence from national and international data. Journal of International Economics, 56, Bouët, A., Bureau, J.-C., Decreux, Y., & Jean, S. (2005). Multilateral agricultural trade liberalisation: The contrasting fortunes of developing countries in the Doha Round. The World Economy, 28(9), Bouët, A., Fontagné, L., & Jean, S. (2005). Is Erosion of Tariff Preferences a Serious Concern? CEPII Working Paper Paris: CEPII. Bouet, A., & Laborde, D. (2012). Food crisis and export taxation: The cost of non-cooperative trade policies. Review of World Economics, 148(1), Bouët, A., Laborde, D., Dienesch, E., & Elliott, K. (2012). The costs and benefits of duty-free, quota-free market access for poor countries: Who and what matters. Journal of Globalization and Development, 3(1), Bouët, A., Mishra, S., & Roy, D. (2008). Does Africa Trade Less than It Should, and If So, Why? The Role of Market Access and Domestic Factors. IFPRI Discussion Paper 770. Washington, DC: International Food Policy Research Institute. Bougheas, S., Demetriades, P. O., & Mamuneas, T. P. (1999). Infrastrucutre, transport costs and trade. Journal of International Economics, 47(1),

167 Brenton, P. (2003). Integrating the Least Developed Countries into the World Trade System: The Current Impact of EU Preferences under Everything But Arms. World Bank Policy Research Working Paper Washington, DC: World Bank. Bureau, J.-C., Jean, S., & Matthews, A. (2006). The Consequences of Agricultural Trade Liberalization for Developing Countries. Paper presented at the International Association of Agricultural Economists Conference, Gold Coast, Australia, Aug , Carruthers, R., Krishnamani, R. R., & Murray, S. (2010). Africa Infrastructure Country Diagnostic: Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa. Washington, DC: World Bank. Croser, J. L., & Anderson, K. (2011). Agricultural distortions in Sub-Saharan Africa: Trade and welfare indicators, 1961 to The World Bank Economic Review, 25(2), Disdier, A.-C., Fontagne, L., & Mimouni, M. (2008). The impact of regulations on agricultural trade: Evidence from SPS and TBT agreements. American Journal of Agricultural Economics, 90(2), Francois, J., & Manchin, M. (2007). Institutions, Infrastructure and Trade. CEPR Discussion Paper London: Centre for Economic Policy Research. Gwilliam, K., Foster, V., Archondo-Callao, R., Briceño-Garmendia, C., Nogales, A., & Kavita Sethi. (2008). Africa Infrastructure Country Diagnostic: Roads in Sub-Saharan Africa. Washington, DC: World Bank. Hoekman, B., Ng, F., & Olarreaga, M. (2004). Agricultural tariffs or subsidies: Which are more important for developing economies? The World Bank Economic Review, 18(2), Makochekanwa, A. (2012). Impacts of Regional Trade Agreements on Trade on Agrifood Products: Evidence from Eastern and Southern Africa. Paper presented at the African Economic Conference 2012, Kigali, Rwanda, Oct. 30-Nov. 2, Moïsé, E., Delpeuch, C., Sorescu, S., Bottini, N., & Foch, A. (2013). Estimating the Constraints to Agricultural Trade of Developing Countries. OECD Trade Policy Paper 142. Paris: OECD Publishing. Panagariya, A. (2003). Aid through Trade: An Effective Option? Mimeo. Tadesse, G., Algieri, B., Kalkuhl, M., & Braun, J. v. (2014). Drivers and triggers of international food price spikes and volatility. Food Policy, 47, Topp, A. (2003). Are Trade Preferences Useful in Advancing Economic Development? Australian National University Working Paper Urban, K., Jensen, H. G., & Brockmeier, M. (2016). How decoupled is the Single Farm Payment and does it matter for international trade? Food Policy, 59, Wainio, J., & Gehlhar, M. (2004). MFN Tariff Cuts and U.S. Agrciutral Imports Under Nonresprocal Trade Preference Programs. Paper presented at the 7th Annual Conference on Global Economic Analysis, Washington, DC, June 17-19, Wilson, N. (2007). Examining the Trade Effect of Certain Customs and Administrative Procedures. OECD Trade Policy Paper 42. Paris: OECD Publishing. 158

168 Yu, T.-h., Tokgoz, S., Wailes, E., & Chavez, E. (2011). A quantitative analysis of trade policy responses to higher world agricultural commodity prices. Food Policy, 36,

169 Annex 1. List of determinants and their indicators used to estimate African agricultural export performance Determinants Size and income level Resource endowment & productivity Infrastructural quality: Institutional efficiency Financial cost of exports Indicators and definitions Total GDP and per capita GDP are used to control for the size of both importing and exporting economies. GDP is measured as real values deflated by 2005 constant price in billions of US$. Per capita GDP is measured in US$ per person. In both cases, the 2013 values are used. Missing values are replaced by values of the previous year. Land and labor of the exporting countries are chosen to test the role of resource endowment for trade. Land is measured as the total arable land in millions of hectares and labor is measured as total agricultural labor in millions of persons. The productivity of these resources are also included at a later stage of the analysis to test the relevance of endowment vs. technology. Land productivity is measured as agricultural value added per hectare of land; similarly labor productivity is estimated as the ratio of agricultural GDP to agricultural labor force. All the data are obtained from the ReSAKSS database ( Road density, quality of port and quality of trade transport infrastructure quality are used to measure the effect of infrastructure on trade performance. Road density is obtained from publicly available international sources 17 and measured in terms of kilometer per square kilometer. Indices of port and trade transport qualities are obtained from the World Bank survey on doing business. The indices are represented by scalar cores that ranges from 1 to 7; 1 being extremely poor/inaccessible and 7 being very efficient/accessible. Since the survey data is available in different years for different countries, the average of available data from 2010 to 2013 are used. The World Bank Logistics Performance Index specific to the efficiency of customs clearance process (1=low to 5=high) is used to proxy institutional efficiency related to trade. It aggregates the respondents ranking of the efficiency of customs clearance processes (i.e. speed, simplicity and predictability of formalities), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents. Both infrastructural quality and institutional efficiency used to proxy costs of trade do not capture all costs involved in the export of import of commodities. The cost of export estimated by the World Bank is used to control for unaccounted trade costs. The cost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with completing the procedures to export or import the goods are included. These include costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges and inland transport. The cost measure does not include tariffs or trade taxes. The average cost from 2010 to 2013 of the exporting country is used

170 Public agricultural expenditure Regional trade agreements Tariff Non-tariff measures Domestic agricultural supports This variable is included to examine the empirical link between public investment and trade performance. While it is very relevant from a policy perspective, it may cause endogeneity problems. It may also correlate with other explanatory variables. To avoid these problems, its lagged value is used for the regression analysis. The nominal value is normalized by agricultural GDP. Regional trade agreement is included as a dummy variable that takes 1 if both trading countries are members of the same regional economic community (COMESA, ECOWAS, SADC, ECCAS) and 0 otherwise. At a later stage we also included dummies for each regional block to measure trade diversion effects of each REC. In this case, for example, we include a dummy for COMESA that takes 1 if the importing country is member of COMESA and 0 otherwise. Similar dummies are used for the other RECs. Aggregation is the primary concern for measuring the effect of tariffs on trade. The use of tariff indices such as the trade restrictiveness index, ad valorum equivalent, trade reduction index and nominal rate assistance is quite common to aggregate the different tariff lines. These indices are preferred over averages because simple averages of tariff rates of the different agricultural lines will include untraded products and the weighted average based on imports will be endogenous to trade. However, an all-inclusive index for all the countries considered in this study is not available. Thus, a mix of weighted and simple averages of ad valorum rates from WITS ( is used to proxy the effect of tariffs on trade. Weighted averages are used to aggregate tariff rates on products up to the H2 level and rates imposed on different countries, and then simple averages are used to approximate a tariff rate imposed by a country on global imports. Since only exports of African countries are considered in this analysis, the weighted tariff rates of other countries are less likely to be endogenous to trade, as the share imports from Africa is relatively small. The total number of non-tariff measures (NTM) imposed by the importing country, which is the sum of all measures reported to the WTO ( is used to capture the effect of non-tariff barriers on African trade. Measures are counted across products and types of measures. Alternatively we use the frequency of six major types of NTM separately. Only measures applicable to all WTO members are considered. Non-tariff measures imposed bilaterally are not considered as they are mostly for non-african countries. Unfortunately, not all countries reported to WTO, so this variable has many missing values. Data on the extent of domestic agricultural support specifically for production and input subsides is not available for all countries. We used the ratio of the agricultural producer price index (PPI) to the manufacturing producer price index for OECD countries as a proxy to represent domestic agricultural support. The agricultural PPI is obtained from FAOSTAT and the manufacturing PPI is collected from the OECD database ( 161

171 Chapter 7. West Africa trade outlook: business as usual vs alternative options Extracted from African Agricultural Trade Status Report 2017

172 CHAPTER 7. WEST AFRICA TRADE OUTLOOK: BUSINESS AS USUAL vs ALTERNATIVE OPTIONS Sunday Pierre Odjo, International Food Policy Research Institute, West and Central Africa office, Dakar, Senegal Ousmane Badiane, International Food Policy Research Institute, Washington DC 7.1 Introduction Recent studies have indicated that Africa as a whole and a number of individual countries have exhibited relatively strong trade performance in the global market (Bouët et al. 2014) as well as in continental and major regional markets (Badiane et al. 2014). The increased competitiveness has generally translated into higher shares of regional markets as destinations for exports from African countries and regions. Faster growth in demand in continental and regional markets compared to the global market has also boosted the export performance of African countries. For instance, during the second half of the last decade, Africa s share of the global export market rose sharply, in relative terms, for all goods and agricultural products in value terms, from 0.05 % to 0.21 % and from 0.15 % to 0.34 %, respectively. This is in line with the stronger competitive position of African exporters mentioned earlier. The increase in intra-african and intra-regional trade, and the rising role of continental and regional markets as major destinations for agricultural exports by African countries, suggest that cross-border trade flows will exert greater influence on the level and stability of domestic food supplies. The more countries find ways to accelerate the pace of intra-trade growth, the larger that influence is expected to be in the future. The current chapter examines the future outlook for intra-regional trade expansion in West Africa and the implications for the volatility of regional food markets. The chapter starts with an analysis of historical trends in intra-regional trade of major staple food products as well as the positions of West African individual countries in the regional market. This is followed by an exploration of the potential of regional trade to contribute to stabilizing food markets, and by an assessment of the scope for cross-border trade expansion. A regional trade simulation model is then developed and used to simulate alternative scenarios to boost trade and reduce volatility in the regional market. 162

173 7.2. Long-term trends in intra-regional trade of staple food products Over the last two decades, the cross-border trade of staple food products has followed an increasing but unsteady trend. It appears from Figure 7.1 and Table 7.1 that fish and animal products including meat, dairy and eggs are the most traded commodities between West African countries in value terms. Intra-regional trade of these products has on average amounted to US$ million in from only US$ million approximately a decade before. They are followed by live animals and edible oils, the exchange of which has averaged US$ 95.7 million and US$ million, respectively, in At this amount, the cross-border trade of vegetable oils has grown fourfold compared to its average level in the early 2000s. Intra-West Africa trade of cereals and vegetables has generally occurred in lower amounts. For instance, the regional market of cereals and vegetables amounted on average to US$ 81.5 million and US$ 28.5 million, respectively, in The region then more than doubled the level of its cereals trade in early 2000s. However, a remarkable contraction of the regional market of cereals has occurred in In contrast, a surge of trade in vegetables happened in 2011, inflating the average market size to US$ million for the period Oilseeds are the least traded product within West Africa in value terms. Cross-border exchange of this commodity amounted to US$ 31.8 million on average in , reaching almost the double of its value in the early 2000s. Other staple food crops including edible fruits & nuts and live trees and plants like roots & tubers constitute a relatively larger regional market size. Their regional trade reached on average the value of US$ 54.8 million in , more than doubling the corresponding value in the early 2000s. 163

174 Million US dollars in log scale Figure 7.1. Trends in intra-regional exports of staple food products in West Africa, LIVE ANIMALS 100 FISH & ANIMAL PRODUCTS VEGETABLES CEREALS 10 OILSEEDS EDIBLE OILS 1 OTHER FOOD CROPS Source: Author s calculations based on HS4-level bilateral trade values from the BACI database, Note: West Africa is here extended to the ECOWAS/CILSS area, including 15 ECOWAS members and Chad and Mauritania. Table 7.1. Average value of intra-regional trade of staple food products in West Africa (million US dollars) Live animals Fish & animal products Vegetables Cereals Oilseeds Edible oils Other food crops All staple food products Source: Author s calculations based on HS4-level bilateral trade values from the BACI database, Note: West Africa is here extended to the ECOWAS/CILSS area, including 15 ECOWAS members and Chad and Mauritania. 164

175 In sum, the cross-border trade of major food products has been expanding among West African countries. It is tempting to explore which countries are the major exporters versus importers in the regional markets of the different commodity groups under analysis. Table 7.2 presents the net trade positions of each country in the regional market of each commodity group. In each cell, a negative (positive) number indicates for a net importing (net exporting) country its share in the total value of net imports (net exports) of a commodity across all countries of the region. In the bottom line of the table, the contributions of all countries add up to zero for each commodity since the regional market clears in the sense that the sums of net imports and net exports of the commodity over all countries are equal. For instance, Table 7.2 shows that Nigeria is the biggest net importer of live animals, followed by Côte-d Ivoire and Senegal with 50.4%, 20.6% and 18.4% of the regional import market, respectively. Thus, these 3 major importing countries account for 89.4% of the regional import market, the remaining 10.6% being made up by net imports of Benin, Ghana, Guinea, Mauritania and Togo. In contrast, Niger and Mali are the biggest net exporters of live animals, with 50.5% and 43.2% of the regional export market, followed by Burkina Faso with 6.2%, while other countries contribute negligible market shares. To help visualize major differences between countries in terms of their regional market positions across the different commodity markets, the results of Table 7.2 have been mapped into Figure

176 Table 7.2. Contributions to values of net imports and net exports of staple food products among West African countries, (%) Live animals Fish & animal products Live trees & plants Vegetables Edible fruits & nuts Cereals Oilseeds Edible oils Benin Burkina Faso Cape Verde Chad Cote d'ivoire Gambia Ghana Guinea Guinea-Bissau Liberia Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo Sum of contributions Source: Author s calculations based on HS4-level bilateral trade values from the BACI database, Note: i) West Africa is here extended to the ECOWAS/CILSS area, including 15 ECOWAS members and Chad and Mauritania; ii) Negative (positive) numbers indicate the shares of net importing (net exporting) countries in the sum of net import (net export) values across all countries of the region. What we have just said about country positions in the regional market of live animals appears more clearly in Figure 7.2, where major net importers and net exporters are clustered at the top and the bottom of the figure, and countries with modest market participations are spread in between. Nigeria and Côte d Ivoire are the biggest net importers of vegetables while Niger, Ghana, and Burkina Faso are net exporters. In addition, Nigeria and Côte d Ivoire are net importers of fish & animal products while net exports are supplied by Mauritania, Senegal, Cape Verde and Guinea. The regional oilseeds market is dominated by Ghana, Togo, Senegal and Nigeria as net importers and by Burkina Faso, Côte d Ivoire, Gambia and Benin as net exporters. Cereals are mostly net imported by Niger, Mali, Nigeria and Guinea Bissau and net exported by Senegal, Benin and Côte d Ivoire. Edible fruits & nuts are particularly net imported in the regional market by Senegal, Nigeria and Niger and net exported notably by Côte d Ivoire and less considerably by Ghana. 166

177 The regional market of vegetable oils is dominated by Nigeria, Senegal, Mali and Niger as major net importers and by Côte d Ivoire as the only major net exporter. Finally, Ghana and Chad dominate the market of live trees and plants as net importers while Côte d Ivoire and Nigeria are the biggest net exporters. Figure 7.2. Distribution of net exports and net imports of staple food products among West African countries, Nigeria Côte d'ivoire Senegal Ghana Liberia Sierra Leone Gambia Togo Benin Guinea Cape Verde Mauritania Burkina Faso Chad Guinea-Bissau Mali Niger LIVE ANIMALS VEGE- TABLES FISH & ANIMAL PRODUCTS OIL- SEEDS CEREALS EDIBLE FRUITS & NUTS EDIBLE OILS LIVE TREES & PLANTS LEGEND Country share in total net-imports value (%) Country share in total net-exports value (%) ]-100,-50] ]-50,-10] ]-10, 0] [0, 10[ [10, 50[ [50, 100[ Source: Author s calculations, constructed from Table 7.2 above, based on HS4-level bilateral trade values from the BACI database, Note: West Africa is here extended to the ECOWAS/CILSS area, including 15 ECOWAS members and Chad and Mauritania. Before closing this section on historical trends in intra-regional trade, it is important to analyze harassment practices that are perceived as bottlenecks to the free movement of goods and persons across the region. Figure 7.3 summarizes survey data on checkpoints, bribes paid and delays along major cross-border transport corridors in West Africa. The average numbers plotted are illustrative of the importance of abnormal trade costs to traders that operate in the regional market. 167

178 Minutes Francs CFA Every 100 km at least 2 checkpoints are encountered and a minimum of CFAF 2000 are paid in bribes across the surveyed corridors. More than 3 checkpoints are found along the corridor connecting Bamako (Mali) and Ouagadougou (Burkina Faso) and average bribes exceed CFAF Figure 7.3. Indicators of harassment practices along West African corridors, Average number of checkpoints per 100 km Average bribe taken per 100 km Delay per 100 km Source: Authors calculations based on survey results by the Improved Road Transport Governance (IRTG) Initiative. 168

179 The preceding analysis has demonstrated that cross-border trade of staple food products is increasing. We now turn to exploring the potential for expanding the current level of intra-regional trade Regional Potential for the Stabilization of Domestic Food Markets through Trade Variability of domestic production is a major contributor to local food price instability in low income countries. The causes of production variability are such that an entire region is less likely to be affected than individual countries. Moreover, fluctuations in national production levels for different countries tend to partially offset each other, so that such fluctuations are less than perfectly correlated. Food production can be expected to be more stable at the regional level than at the country level. In this case, expanding cross-border trade and allowing greater integration of domestic food markets would reduce supply volatility and price instability in these markets. Integrating regional markets through increased trade raises the capacity of domestic markets to absorb local price risks by: (1) enlarging the area of production and consumption and thus increasing the volume of demand and supply that can be adjusted to respond to and dampen the effects of shocks; (2) providing incentives to invest in marketing services and expand capacities and activities in the marketing sector, which raises the capacity of the private sector to respond to future shocks; and (3) lowering the size of needed carryover stocks, thereby reducing the cost of supplying markets during periods of shortage and hence decreasing the likely amplitude of price variation. A simple comparison of the variability of cereal production in individual countries against the regional average is carried out to illustrate the potential for trade and local market stabilization through greater market integration (Badiane, 1988). For that purpose, a trend-corrected coefficient of variation is used as a measure of production variability at the country and regional levels. Following Cuddy and Della Valle (1978), the trend-corrected coefficient of variation in cereal production is calculated for each ECOWAS member country as follows: TCV i = CV i 1 R i 2 169

180 where CV i is the coefficient of variation in the series of cereal production quantities in country i 2 from 1980 to 2010 and R i is the adjusted coefficient of determination of the linear trend model fitted to the series. Then an index of regional cereal production volatility TCV reg is derived for the ECOWAS region as a weighted average of the trend-corrected coefficients of variation of its member countries with the formula (Koester, 1986): TCV reg n i n i 2 = s 2 2 i TCV i + 2 s i s j v ij TCV i TCV j n j where TCV i and TCV j are the trend-corrected coefficients of variation in cereal production in countries i and j, n is the number of ECOWAS member countries, s i and s j are the shares of countries i and j in the region s overall cereal production, and v ij is the coefficient of correlation between the series of cereal production quantities in countries i and j. Finally, the trend-corrected coefficients of variation calculated at the country level are normalized by dividing them by the regional coefficient. In Figure 7.4, the bars represent the normalized coefficients of variation which indicate by how much individual country production levels are more (normalized coefficient greater than 1) or less (normalized coefficient less than 1) volatile than production in the ECOWAS region. The figure shows that for almost all countries, national production volatility is considerably larger than regional level volatility, with only the exception of Côte d Ivoire. Gambia, Liberia, Mali, Niger and Senegal show considerably higher volatility levels than the region. These countries would be the biggest beneficiaries of increased regional trade in terms of greater stability of domestic supplies. However, the likelihood that a given country will benefit from the trade stabilization potential suggested by the difference between its volatility level and the regional average will be greater the more the fluctuations of its production and that of the other countries in the region are weakly correlated. 170

181 Normalized coefficient of variation Figure 7.4. Cereal production instability in ECOWAS countries ( ) Benin Burkina Faso Cote Gambia Ghana Guinea Guinea d'ivoire Bissau Liberia Mali Niger Nigeria Senegal Togo Source: Authors calculations based on FAOSTAT 2014 data for the period Therefore, we plot in Figure 7.5 the distribution of production correlation coefficients between individual countries in the region. For each country, the lower segment of the bar shows the percentage of correlation coefficients that are 0.65 or less, or the share of countries with production fluctuations that we define as relatively weakly correlated with the country s own production movements. The top segment represents the share of countries with highly correlated production fluctuations, with coefficients that are higher than The middle segment is the share of moderately correlated country productions with coefficients that are between 0.65 and Country production levels tend to fluctuate together as shown by the high share of coefficients that are above 0.75 for the majority of countries. However, for some of them, including Guinea Bissau, Liberia and Senegal, the share is smaller than 30%. The division of the region into two nearly uniform sub-regions, sahelian and coastal, may be an explanation. In general, the patterns and distribution of production fluctuations across countries in the region are such that increased trade could be expected to contribute to stabilizing domestic agricultural and food markets. But that is only one condition, the other being that there is actual potential to increase cross-border trade, a question that is examined in the next section. 171

182 % share of corr. coefficients Figure 7.5. Distribution of production correlation coefficients between ECOWAS countries ( ) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Corr. coefficients < 0.65 Corr. coefficients between 0.65 and 0.75 Corr. coefficients > 0.75 Benin Burkina Faso Cote Gambia Ghana d'ivoire Guinea Guinea Bissau Liberia Mali Niger Nigeria Senegal Togo Source: Authors calculations based on FAOSTAT 2014 data for the period The scope for specialization and regional trade expansion in agriculture Despite the recent upward trends, the level of intra-african and intra-regional trade is still very low compared with other regions. Intra-African markets accounted only for an average of 34 % of the total agricultural exports from African countries between 2007 and 2011 (Badiane et al. 2014). Among the three RECs, SADC had the highest share of intra-regional trade (42 %), and ECOWAS the lowest (6 %). COMESA s share of intra-regional trade was 20 %. Although SADC is doing much better than the other two RECs, intra-regional exports still account for far less than half of the value of the region s total agricultural exports (Badiane et al. 2014). There may be a host of factors behind the low levels of intra-regional trade. These factors may not only make trading with extra-regional partners more attractive, but they may also raise the cost of supplying regional markets from intra-regional sources. The exploitation of the stabilization potential of regional trade, as pointed out above, would require measures to lower the barriers to and the bias against transborder trade so as to stimulate the expansion of regional supply capacities and of trade flows across borders. This suppose that there is sufficient scope for specialization in production and trade within the sub-regions. Often, it is assumed that neighboring developing countries would exhibit similar production and trading patterns because of the similarities in their resource bases, leaving little room for future specialization. 172

183 There are, however, several factors that may lead to different specialization patterns among such countries. These factors include (1) differences in historical technological investments and thus the level and structure of accumulated production capacities and skills; (2) the economic distance to, and opportunity to trade with, distant markets; and (3) differences in dietary patterns as well as consumer preferences that affect the structure of local production. The different patterns of specialization in Senegal compared with the rest of Sahelian West Africa and in Kenya compared with other Eastern African countries illustrate the influence of these factors. Consequently, we use a series of indicators to assess the actual degree of specialization in agricultural production and trade, and whether there is real scope for transborder trade expansion as a strategy to exploit the less-than-perfect correlation between national production levels to reduce the vulnerability of domestic food markets to shocks. The first two indicators are the production and export similarity indices, which measure and rank the relative importance of the production and trading of individual agricultural products in every country. The two indices are calculated for country pairs using the following formulas: SQ ij = 100 Min(q ik, q jk ) k SE ij = 100 Min(e ik, e jk ) k where SQ ij and SE ij are the production and export similarity indices, respectively, q ik and q jk are the shares of a product k in the total agricultural production of countries i and j, respectively, and e ik and e jk are the shares of a product k in the total agricultural exports of countries i and j, respectively. The level of importance or position of each product is then compared for all relevant pairs of countries within the region. 20 The indices have a maximum value of 100, which would reflect complete similarity of production or trade patterns between the considered pair of countries. The more the value of the indices tends towards zero, the greater the degree of specialization between the two countries. Index values of around 50 and below are interpreted as indicating patterns of specialization that are compatible with higher degrees of trade expansion possibilities. Figures 7.6 and 7.7 present the results of the calculations covering 150 products in total. Each bar represents the number of country pairs that fall within the corresponding range of index values. The vast majority of country pairs fall within the 0-50 range. 173

184 Number of Country Pairs Number of Country Pairs A value of less than 60 is conventionally interpreted as compatible with higher trade exchange between the considered pair of countries. The estimated index values therefore suggest that there exists sufficient dissimilarity in current country production and trading patterns and hence scope for trans-border trade expansion in the region. Figure 7.6. Similarity of production patterns among ECOWAS countries ( ) Production Similarity Index Source: Authors calculations based on data from FAOSTAT, Figure 7.7. Similarity of trading patterns among ECOWAS countries ( ) Export Similarity Index Source: Authors calculations based on data from FAOSTAT,

185 A third indicator, the revealed comparative advantage (RCA) index, is computed to further assess the degree of trade specialization among countries within the region. It is calculated according to the following formula (Balassa, 1965): RCA ijk = E ijk k E ijk E wjk k E wjk where E ijk is the export value of an agricultural product k from country i to destination j and E wjk = i E ijk is the world export value of the same product to the same destination. The RCA index compares the share of a given product in a given country s export basket with that of the same product in total world exports. A value greater than 1 indicates that the considered country performs better than the world average, and the higher the value is, the stronger the performance of the country in exporting the considered product. Of the nearly 450 RCA indicators estimated for various products exported by different ECOWAS countries, 73 percent have a value higher than 1. Following Laursen (2000), the RCA index is normalized through the formula NRCA ijk = (RCA ijk 1) (RCA ijk + 1). Thus, the normalized RCA (NRCA) is positive for RCA indicators that are greater than 1 and negative otherwise. For very high RCA indicators, the normalized value tends towards 1. The 20 products with the highest normalized RCA index values are presented in Table

186 Table 7.3. List of the 20 products with highest normalized revealed comparative advantage index values in ECOWAS countries, average Commodity Country Cashew nuts, with shell Guinea Bissau Cake of Groundnuts Gambia Groundnut oil Gambia Cashew nuts, with shell Benin Groundnuts Shelled Gambia Cashew nuts, with shell Gambia Groundnut oil Senegal Copra Gambia Cake of Groundnuts Senegal Cake of Cottonseed Benin Rubber Nat Dry Liberia Cottonseed oil Togo Cottonseed oil Benin Sugar beet Gambia Cashew nuts, with shell Cote d'ivoire Cotton Linter Benin Cocoa beans Cote d'ivoire Cake of Groundnuts Togo Cocoa Paste Cote d'ivoire Cocoa beans Ghana Source: Authors calculations based on FAOSTAT 2014 All the products listed in the table have normalized RCA values above The rankings reflect the degree of cross-country specialization within the ECOWAS region. For instance, a total of 12 products spread across 8 out of 15 member countries account for the highest 20 normalized RCA indicator values for the region. So far, the analysis has established the existence of dissimilar patterns of specialization in production and trade of agricultural products among countries within ECOWAS. Two final indicators, the Trade Overlap Indicator (TOI) and the Trade Expansion Indicator (TEI), are calculated to examine the potential to expand trade within the region based on current trade patterns. They measure how much of the same product a given country or region exports and imports at the same time. The TOI measures the overall degree of overlapping trade flows for a country or region as a whole, while the TEI measures the overlapping trade flows at the level of individual products for a country or region. 176

187 The TOI and TEI are calculated as follows: TOI i = 2( k Min(E ik, M ik )) k(e ik + M ik ) TEI ik = 100 [Min(E ik, M ik ) Max(E ik, M ik )] where E ik and M ik denote the values of the exports and imports of an agricultural product k by a country i. The TOI varies between 0 and 1. It will be zero if the country only exports or imports any individual products. It will be 1 in the unlikely situation in which the country both exports and imports all traded products by an equal amount. As regards the TEI, it indicates the percentage of the country s exports (imports) of a product that are matched by the country s imports (exports) of the same product. The results are presented in Figure 7.8 and Table 7.4. The Figure indicates that there is a considerable degree of overlapping trade flows; 25 percent for Africa as whole and as much as 17 percent for the ECOWAS region. Normalized TOI values obtained by dividing country TOI values by the TOI value for the region can be found in Badiane et al. (2014). In the vast majority of cases, they are significantly less than 1. The overlapping regional trade flows must therefore be from different importing and exporting countries. In other words, some countries are exporting (importing) the same products that are being imported (exported) by other ECOWAS member countries, but in both cases to and from countries outside the region. By redirecting such flows, countries should be able to expand trans-border trade within the region. The TEI indicates which products have the highest potential for increased trans-border trade based on the degree of overlapping trade flows. Table 7.4 lists the 20 products with the highest TEI value for the region. The lowest indicator value for any of the products is 0.41 and the average value is RCA values for the same products, presented in Badiane et al. (2014), are all greater than 1, except for fresh fruits. The fact that products with high TEI values also have high RCA indicator values points to a real scope for trans-border trade expansion in the region. 177

188 Trade Overlap Index Figure 7.8. Trade Overlap indicators, ECOWAS region, Africa ECOWAS Source: Authors' calculations based on FAOSTAT 2014 Table 7.4. Trade Expansion Indicators, ECOWAS region, average Commodity TEI value Tobacco products Fatty acids Groundnuts, shelled Hides, cattle, wet salted Coffee, extracts Fruit, fresh 0.62 Fruit, tropical fresh Cigarettes Tea, mate extracts Oilseeds Onions, dry Oil, cottonseed 0.51 Pepper (piper spp.) Margarine Short Roots and tubers Cereal preparations Chickpeas Vegetables fresh or dried Products Fruit, prepared Pineapple, canned Source: Authors calculations based on FAOSTAT Note: Italics designate products with RCA < 1; six products with high TEI but which are not being produced in the region are included, as they relate to re-export trade. 178