51Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Anatole Goundan and Getaw Tadesse INTRA-AFRICAN AGRICULTURAL TRADE 3Chapter Photo by stock.adobe.com 52 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade INTRODUCTION For decades, Africa’s trade integration, particularly intraregional trade, has been a major devel- opment concern. The 2014 Malabo Declaration made tripling intra-African trade in agricultural products by 2025 a central objective. And in 2018, at the 10th Extraordinary Session of the Af- rican Union in Kigali, the agreement to form the African Continental Free Trade Area (AfCFTA) was signed. Increasing intracontinental trade will require policy decisions on the free movement of people and goods and the reduction or elimination of tariffs and nontariff measures affecting trade between African countries, especially between countries of different regional economic communities (RECs). Such initiatives suggest the need to monitor the dynamics of trade among African countries, particularly for agricultural products, as a measure of intra-African trade inte- gration. The Africa Agriculture Trade Monitor (AATM) was launched in 2018 as an annual report moni- toring the continent’s progress in agricultural trade development. Chapter 3, as in the previous reports, reviews the state of intra-African agricultural trade, and provides an in-depth analysis of several agricultural products that are strategic for food security. Working from the hypothesis that total agricultural trade within Africa depends on the performance of trade in specific products, this chapter looks at how trade is affected by commodity-specific trade policies, including those related to food self-sufficiency, regulatory standards, and quality requirements. For the sake of brevity, we focus on 10 priority primary products from the groups of cereals and pulses (rice, maize, wheat, and beans), vegetables (potatoes, onions, and tomatoes), and fruits (bananas and plantains, citrus fruit, and apples). These commodities were selected because they are major staples for Africa. For example, maize, rice, and wheat together made up about 40 per- cent of the daily calories consumed by Africans over the 2014–2018 period (FAO 2021). More- over, these 10 commodities represent the major share of intra-African trade within their product group (cereals, vegetables, or fruits). Future AATM editions may cover other unprocessed and processed agricultural products. Before presenting the commodity-specific analysis, this chapter highlights trends and patterns in intra-African agricultural trade over the 2003 to 2019 period at the aggregate level and for selected agricultural products. In the following sections, we use network approach tools to ana- lyze intra-African trade in those 10 products, and look at the protection that tariffs and nontariff measures (NTMs) afford these products across the continent. The final section concludes. TRENDS AND PATTERNS IN INTRA-AFRICAN AGRICULTURAL TRADE This section explores trends and patterns in total intra-African agricultural trade and in key agri- cultural products. “Unpacking” agricultural trade is necessary to understand the challenges and opportunities for specific commodities. Total agricultural trade flows within Africa Three aspects of the overall intra-African trade in agricultural products are analyzed for unpro- cessed, semiprocessed, and processed agricultural products. Figure 3.1a shows the value of these trade flows (expressed in US dollars). Figure 3.1b shows the share of intra-African trade in total agricultural exports, which is a measure of the relative importance of intra-African trade in total continental agricultural exports. Figure 3.1c shows the evolution of the share of intra-African trade in total African imports of agricultural products. This highlights the importance of Africa as an origin of agricultural imports for the continent. 53Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Data on trade values for 2019, available with this year’s AATM database, make it clear that agri- cultural trade within Africa is still struggling to recover from the sharp decline suffered from 2013 through 2016. Despite a visible recovery in 2017, the decline continued in 2018 and, slightly more slowly, in 2019 (Figure 3.1a). This decline is mainly attributed to the weakening of trade in primary products. Total intra-African agricultural trade remains below the level of 2014, and it looks unlikely that Africa will meet its goal of tripling this trade by 2025. Despite the general trend, the value of intra-African exports of processed products has been recovering since 2016, suggesting a shift in agricultural trade within Africa from primary products to semi- or fully processed products (Figure 3.1a). This is encouraging in terms of the Malabo goal of inclusive value chain development. The growth of Africa’s middle-income population may explain the increasing trade in processed products, while trade in primary products has oscillated (AfDB 2011; Ncube and Lufumpa 2014). However, fully restoring the growth in total agricultural trade will require increasing trade in processed products more rapidly. Figure 3.1a Trends in intra-African agricultural trade, 2003–2019 (US$ billions) 0 2 4 6 8 10 12 14 16 18 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 Unprocessed Semiprocessed Processed All agriculture Source: Constructed using the 2021 AATM database. Similar to the export value, the share of intra-African exports (value of intra-African exports divid- ed by the value of Africa’s total exports of agricultural products) has not yet fully recovered to the level reached in 2013 (Figure 3.1b), although it did increase in 2019 from 19.4 to 19.7 percent. The increase is larger in semiprocessed products, for which the share rose from 22.6 to 22.9 percent; and for fully processed products, for which it increased from 52.4 to 52.9 percent. This implies that the value of intra-African exports has increased more than the value of extra-African exports (see Chapter 2). Over time, the overall share of raw agricultural products decreased from 12.1 percent (2003–2007) to 9.7 percent (2015–2019). The opposite trend is observed for semi- and fully processed agricultural commodities; the intra-African share of semiprocessed exports increased from 19.1 percent (2003–2007) to 22.2 percent over the last five years, while the share of fully processed exports increased from 47.6 to 53.0 percent. The growth in the export shares of processed products compared with unprocessed products also suggests that African markets are more attractive for processed products than primary prod- ucts. The gap between the shares of intra-African exports in primary and processed products has been expanding, even during the trade downturn since 2013. 54 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade Figure 3.1b Intra-African agricultural export shares, 2003–2019 (%) 0 10 20 30 40 50 60 70 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 Unprocessed Semiprocessed Processed All agriculture Source: Constructed using the 2021 AATM database. Figure 3.1c reveals that, despite the important intra-African share in agricultural exports, Africa supplies less than 20 percent of continental demand for agricultural imports. Most surprising is the weak role played by African countries in supplying raw agricultural products. Indeed, the share of intra-African imports of raw agricultural products declined from about 20 percent in 2003 to 9.4 percent in 2019. Africa played a larger, though still limited, role in suppling semi- and fully processed agricultural commodities. About 15 percent of total continental imports of semiprocessed agricultural products, on average, originated in Africa, as well as about 20 percent of fully processed products. Between 2018 and 2019, the share of intra-African imports overall increased from 14.7 to 15.3 percent. This included increases in all three categories of pro- cessing: intra-African trade was up from 9.3 to 9.44 percent for unprocessed items, 16.5 to 17.7 percent for semiprocessed products, and 20.1 to 20.8 percent for fully processed agricultural products. These results suggest a continued need for transformation of agricultural value chains to reduce Africa’s dependence on international markets for its food security. Figure 3.1c Intra-African agricultural import shares, 2003–2019 (%) 0 5 10 15 20 25 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 Unprocessed Semiprocessed Processed All agriculture Source: Constructed using the 2021 AATM database. 55Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Intra-African trade of selected primary commodities The definition of the 10 selected products in the Harmonized System (HS6, 2012 version) is pro- vided in Table 3.1. We focus on cereals (HS2 code 10), vegetables (HS2 code 7), and edible fruit and nuts (HS2 code 8). These products are central to African agricultural trade. In terms of total African agricultural exports, fruits and nuts have accounted for about 20.2 percent on average (2015–2019), while vegetables have averaged 7.6 percent and cereals 1.8 percent. In terms of African imports of agricultural products, cereals account for 29.7 percent, vegetables for 3.0 percent, and fruits and nuts for 2.3 percent. In intra-African agricultural trade, they all together account for 17.1 percent, with cereals accounting for 6.7 percent, vegetables 5.6 percent, and fruits and nuts 4.8 percent. Table 3.1 Definitions of products analyzed Products HS6 Code Description Rice 100610 Cereals; rice in the husk (paddy or rough) 100620 Cereals; husked (brown) rice 100630 Cereals; rice, semi-milled or wholly milled, whether or not polished or glazed 100640 Cereals; rice, broken Maize 100510 Cereals; maize (corn), seed 100590 Cereals; maize (corn), other than seed Wheat 100111 Cereals; wheat and meslin, durum wheat, seed 100119 Cereals; wheat and meslin, durum wheat, other than seed 100191 Cereals; wheat and meslin, other than durum wheat, seed 100199 Cereals; wheat and meslin, other than durum wheat, other than seed Beans 071331 Vegetables, leguminous; beans of the species vigna mungo (l.) hepper or vigna radiata (l.) wilczek, shelled, whether or not skinned or split, dried 071332 Vegetables, leguminous; small red (adzuki) beans (phaseolus or vigna angu- laris), shelled, whether or not skinned or split, dried 071333 Vegetables, leguminous; kidney beans, including white pea beans (phaseo- lus vulgaris), shelled, whether or not skinned or split, dried 071334 Vegetables, leguminous; bambara beans (Vigna subterranea or Voandzeia subterranea), shelled, whether or not skinned or split, dried 071335 Vegetables, leguminous; cow peas (Vigna unguiculata), shelled, whether or not skinned or split, dried 071339 Vegetables, leguminous; n.e.c. in item no. 0713.3, shelled, whether or not skinned or split, dried Potatoes 070110 Vegetables; seed potatoes, fresh or chilled 070190 Vegetables; potatoes (other than seed), fresh or chilled Onions and shallots 070310 Vegetables, alliaceous; onions and shallots, fresh or chilled 56 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade Products HS6 Code Description Tomatoes 070200 Vegetables; tomatoes, fresh or chilled Bananas and plantains 080310 Fruit, edible; plantains, fresh or dried 080390 Fruit, edible; bananas, other than plantains, fresh or dried Citrus fruit 080510 Fruit, edible; oranges, fresh or dried 080520 Fruit, edible; mandarins (including tangerines and satsumas), clementines, wilkings and similar citrus hybrids, fresh or dried 080540 Fruit, edible; grapefruit, including pomelos, fresh or dried 080550 Fruit, edible; lemons (Citrus limon, Citrus limonum), limes (Citrus aurantifolia , Citrus latifolia), fresh or dried 080590 Fruit, edible; citrus fruit n.e.c. in heading no. 0805, fresh or dried Apples 080810 Fruit, edible; apples, fresh Source: Authors’ classification. Within these three product chapters, we consider 10 primary products: rice, maize, and wheat in the group of cereals; beans, potatoes, onions and shallots, and tomatoes within vegetables; and bananas and plantains, citrus fruit, and apples in the group of edible fruit and nuts. Within cereals, the selected products constitute about 91 percent of the intra-African trade value of cereals (rice 23.6 percent, maize 56.4 percent, and wheat 10.6 percent). For vegetables, high- lighted products account for 47 percent (beans 21.5 percent, potatoes 10.7 percent, onions and shallots 10.8 percent, and tomatoes 3.8 percent), while selected edible fruit and nuts make up 46 percent of the total intra-African trade of their group (bananas and plantains 11.8 percent, citrus fruit 11.07 percent, and apples 23.6 percent). Figure 3.2 shows the average annual value of intra-African exports for the 10 products in two periods: 2003–2007, which we consider as the baseline for the Comprehensive Africa Agriculture Development Programme (CAADP), and 2015–2019, the most recent period for which data are available. Over the two periods, the nominal value of transactions increased significantly for all the commodities considered in the analysis. However, nominal values rose more rapidly for beans and fruits and vegetables than for cereals, with the value of Africa’s trade in beans, apples, and bananas up by more than 200 percent between the two periods. The annual trade in beans increased from US$41 million in 2003–2007 to $150 million in 2015–2019.1 Trade in the other commodities increased by about 100 percent, except for rice and wheat. For example, the annual value of trade in maize increased from $261 million to $477 million. However, these are nominal values, so the change may not be fully attributable to an increase in the real volume of trade. For example, the average international price of maize was about $120 per metric ton (2003–2007), but increased to about $164 per metric ton (2015–2019). Similarly, the international price of bananas increased by 88 percent between the two subperiods and the price of rice rose 44 percent, but the price of wheat rose only 3 percent. More broadly, the international food price index increased by about 27 percent between the two subperiods (IMF 2021). Given this increase, the international price increase explains some of the change in the nominal value of intra-African trade in maize and other products. Thus, the share of trade, which we discuss next, may be a better measure than the value of trade for understanding the performance of intra-African commodity trade. 1 Throughout this chapter, “$” refers to US dollars, unless otherwise indicated. 57Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Figure 3.2 Average annual value of intra-African trade for selected commodities 161 261 83 41 36 38 11 21 31 42 202 477 90 150 75 75 26 71 67 142 0 100 200 300 400 500 600 Rice Maize Wheat Beans Potato es Onions & shallots Tomato es Bananas & plan tain s Citru s fr uit Apples Ex po rt v alu e ( US $ m illi on s) 2003-2007 2015-2019 Source: Constructed using the 2021 AATM database. Figure 3.3 shows the size of intra-African trade relative to total African exports and imports of the selected commodities over the two periods. In 2003–2007, Africa was the major desti- nation for African exports of cereals and beans; however, for other commodities, intra-African exports accounted for less than 30 percent of the total exports. By 2015–2019, this situation had changed. Of the 10 commodities analyzed, 6 have declined as a share of intra-African exports and increased in exports outside the continent. For maize, beans, and bananas, a larger share is now being exported to the rest of the world, while rice, apples, and bananas are increasingly traded within Africa. Regarding the share of imports, Africa used to depend primarily on intra-African trade to meet its import demand for tomatoes and citrus fruit. For example, panel (b) of Figure 3.3 shows that about 92 percent of total continental imports of tomatoes was supplied by Africa in the 2015–2019 period. We measure import dependence — that is, the share of imports from the rest of the world — as 1 minus the share of intra-African trade in African total imports of a given product. Thus, the import dependence for tomatoes decreased from 18 percent (2003–2007) to 8 percent (2015–2019). Regarding citrus fruit, this dependence on imports from outside the continent increased slightly, from 22 to 24 percent between the two subperiods. For cereals (rice, wheat, maize), however, Africa depends heavily on the rest of the world. More- over, this dependence is increasing, not only for cereals but also for onions, citrus, and apples. The decline of intra-African trade in import shares may be attributed to several factors. First, the supply of these products in African markets may be declining relative to demand. This is likely the case for rice and apples, for which the share of imports is still declining, even as their share of exports is increasing. Second, African traders may find African markets less attractive for these products. This is the case for those products for which both export and import shares are significantly declining, like maize and onions. Third, for products including beans, tomatoes, and bananas, for which the share of imports is increasing while the export share is either increasing or decreasing, Africa is becoming better able to meet its own demand or beyond. 58 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade Figure 3.3 Share of intra-African trade in total African trade of selected commodities (a) Export share (%) (b) Import share (%) 0 10 20 30 40 50 60 70 80 90 100 Rice Maize Wheat Beans Potatoes Onions & shallots Tomatoes Bananas & plantains Citrus fruit Apples 2003-2007 2015-2019 0 10 20 30 40 50 60 70 80 90 100 Rice Maize Wheat Beans Potatoes Onions & shallots Tomatoes Bananas & plantains Citrus fruit Apples 2003-2007 2015-2019 Source: Constructed using the 2021 AATM database. Note: Export or import share is the ratio of intraregional trade to total African exports or imports of each product. Table 3.2 shows the transaction share of the top three exporters and importers, by commodity, to and from African markets. The figures in parentheses indicate the percentage share of a coun- try in the total intra-African trade value for the commodity. The higher the percentage, the more the country dominates imports/exports of this commodity. Table 3.2 Top intra-African exporters and importers of selected products and corresponding trade share Product Ranking Top 3 exporters Top 3 importers 2003–2007 2015–2019 2003–2007 2015–2019 Rice 1 EGY [43.6%] ZAF [29.8%] LBY [20.4%] COD [17.2%] 2 ZAF [26.2%] SEN [15.5%] BWA [11.6%] MLI [14.1%] 3 SEN [5.1%] UGA [10.4%] SDN [9.9%] BWA [10.1%] Maize 1 ZAF [57.2%] ZAF [45.3%] ZWE [32.4%] ZWE [22.2%] 2 ZMB [10.8%] ZMB [21.4%] SWZ [8.9%] KEN [19.9%] 3 MWI [9.2%] UGA [13.7%] BWA [7.5%] BWA [8.3%] Wheat 1 ZAF [36.2%] ZAF [58.1%] NGA [21.5%] ZWE [36.0%] 2 DZA [20.8%] TZA [13.1%] ZMB [10.4%] BWA [20.0%] 3 MOZ [9.3%] MUS [10.9%] BWA [9.7%] SYC [11.7%] Beans 1 ZAF [25.2%] UGA [32.3%] ZMB [9.8%] KEN [32.8%] 2 ETH [14.0%] EGY [26.4%] KEN [8.6%] DZA [17.1%] 3 UGA [10.7%] ETH [8.3%] ZWE [8.4%] SSD [6.7%] Potatoes 1 ZAF [50.6%] ZAF [53.0%] ZWE [30.5%] SOM [21.4%] 2 ZMB [30.5%] ETH [24.6%] AGO [15.9%] MOZ [17.3%] 3 EGY [4.8%] MAR [5.6%] BWA [14.4%] NAM [10.8%] Onions and shallots 1 NER [52.4%] ZAF [39.7%] GHA [37.6%] AGO [15.5%] 2 ZAF [25.0%] NER [13.7%] AGO [12.9%] MOZ [14.7%] 3 NAM [7.4%] SDN [11.0%] CIV [7.3%] ETH [10.6%] Tomatoes 1 ZAF [65.9%] ETH [44.5%] BWA [32.1%] SOM [38.4%] 2 BFA [11.7%] ZAF [20.9%] NAM [12.7%] LBY [9.3%] 3 ETH [6.3%] MAR [7.8%] GHA [10.1%] BWA [7.0%] Bananas and plantains 1 CIV [46.3%] CIV [32.4%] SEN [28.0%] ZAF [40.5%] 2 ZAF [24.4%] MOZ [30.4%] BWA [12.1%] SEN [17.9%] 3 CMR [6.0%] ZAF [13.7%] ZAF [9.0%] BWA [7.3%] 59Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Citrus fruit 1 ZAF [46.4%] ZAF [40.8%] ZAF [14.5%] KEN [16.4%] 2 SWZ [16.7%] EGY [19.6%] MOZ [13.0%] ZAF [9.5%] 3 EGY [11.9%] ZWE [9.1%] ZMB [10.1%] MUS [9.3%] Apples 1 ZAF [95.8%] ZAF [97.5%] BWA [11.6%] NGA [30.3%] 2 NAM [1.3%] EGY [0.5%] BEN [11.3%] KEN [7.9%] 3 EGY [0.6%] MUS [0.5%] AGO [10.5%] AGO [5.7%] Source: Constructed using the 2021 AATM database. Note: Country labels are ISO-3 country codes. These are provided in the appendix to this chapter (Table A3.1). The figures in parentheses indicate the percentage share of a country in the total intra-African trade value for each commodity. In the first period (2003–2007), South Africa alone supplied more than 50 percent of the intra-Af- rican export value of maize, potatoes, onions, tomatoes, and apples. More than 40 percent of the exports of rice, bananas, and citrus were likewise supplied by a single country (Egypt, Côte d’Ivoire, and South Africa, respectively). Almost all export markets were characterized by a high level of concentration: the top three exporters controlled more than 65 percent of intra-African exports. South Africa was among the three top exporters for all 10 commodities, followed by Egypt for 4 commodities. This changed little in the second period (2015–2019): The share of the first exporter shrank for all products except for wheat; and for every commodity, at least one country changed among the top three exporters. However, the top three still control more than 65 percent of total intra-African exports of each commodity. African importers are more homogenous than exporters. None of the importers dominated in- tra-African imports for any commodity, and the sum of the shares of the three top importers is less than 50 percent for every product. The top three importing countries also changed signifi- cantly over the two periods. At least two new importers came to the top as new players during the second period for all commodities except bananas, for which the top importers remained the same. Two observations can be made from looking at the top importers and exporters. First, there ap- pears to be a significant regionalization of trade. Indeed, if a country is among the top exporters of a commodity, another country from the same region is found among the top importers. For example, when South Africa is the top exporter for a commodity, either Zimbabwe or Botswana is a top importer for the same commodity. Similarly, in the second period, Ethiopia is the top exporter of tomatoes, and nearby Somalia is the top importer. Second, the same country can be both a top exporter and top importer of the same commodity. For example, South Africa was among the top exporters and importers of citrus in the second period. This implies that regional trade is being used to bridge seasonal supply shortfalls, to accommodate differences in con- sumers’ preferences, or take advantage of differences in product quality. In the next section, we go beyond general trade indicators to analyze the structure of trade relations and key players. INTRA-AFRICAN TRADE NETWORK FOR SELECTED PRIMARY PRODUCTS This section explores Africa-wide trade for the selected primary products using a social network approach. In this analytic framework, two elements are important: entities (people, firms, countries, etc.) and the relationships among them. In the language of network analysis, for our purposes, each country is called a node and the trade relationship between two nodes is a link. We use the framework to look at the global organization of trade, the position of the nodes, and the quality of the links for the 10 products. The trade relationships can be studied as a weighted 60 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade or unweighted network. In the unweighted logic, trade links are represented in a binary fashion, while the weighted network analysis accounts for the strength of the trade link, such as the value of trade flows between two selected countries. De Benedictis et al. (2014) noted that weighted network analysis is not, per se, an improvement over unweighted network analysis; rather, it tackles a different dimension of the analysis. For example, the “degree” of a node, which is the number of trading partners, provides different information than the “strength” of that node, which is the total export or import value of the trade of the selected node. Here, as in the previous section, we focus on the average values for two time periods: 2003– 2007 and 2015–2019. We use average trade flows between African partners over these five-year intervals to create the period-specific network data. Then we compare indicators between the two subperiods. How many countries and trade links are there over time? Examining the components of a network is important to understand how a network has evolved over time. Table 3.3 presents six basic indicators for each product: number of countries involved in the network, number of active exporters, number of active importers, number of countries that are both active exporters and importers, number of transactions, and the network density (ratio between number of realized transactions and the total possible transactions). Table 3.3 Network properties: Counting countries and trade relationships, 2003–2007 and 2015–2019 Number of participating countries Total trade links Network densityAll Exporters Importers Both Rice 53 [53] 44 [47] 51 [52] 42 [46] 213 [246] 0.077 [0.089] Maize 50 [50] 35 [35] 50 [48] 35 [33] 240 [218] 0.098 [0.089] Wheat 39 [46] 25 [37] 34 [40] 20 [31] 74 [114] 0.050 [0.055] Beans 54 [51] 39 [37] 53 [51] 38 [37] 223 [205] 0.078 [0.08] Potatoes 50 [51] 36 [32] 46 [48] 32 [29] 164 [132] 0.067 [0.052] Onions and shallots 52 [51] 34 [36] 49 [49] 31 [34] 191 [148] 0.072 [0.058] Tomatoes 49 [47] 31 [28] 46 [39] 28 [20] 113 [96] 0.048 [0.044] Bananas and plantains 46 [44] 31 [29] 41 [41] 26 [26] 98 [82] 0.047 [0.043] Citrus fruit 54 [48] 33 [31] 53 [47] 32 [30] 197 [146] 0.069 [0.065] Apples 52 [51] 27 [34] 50 [51] 25 [34] 119 [122] 0.045 [0.048] Source: Constructed using the 2021 AATM database. Note: The first figures indicate the values over the period 2015–2019; the figures in parentheses indicate the corresponding value for 2003–2007. These networks evolved over the period 2003 to 2019, with the average number of countries active in trade of each commodity (importing or exporting) ranging from 44 to 54. This suggests that almost all African countries have at least one trade relationship with another African country for the selected products. In terms of exporting countries, results show that the rice network has the largest number of exporters, with 44 countries involved in 2015–2019 and 47 countries in 2003–2007. Intra-African wheat trade has the fewest exporters in the second period (25 countries exporting), while the tomato trade had the fewest (28 countries) in the first period. The number of exporting countries decreased between the subperiods for rice, wheat, and onions and shallots. On the import side, except for wheat in the second period, at least 40 countries have participated in intra-African trade of the selected products as importers. As shown in the standard analysis in the previous section, most African countries participate as both exporters and importers of these 61Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade products. This finding here corroborates the evidence that regional trade is being used to bridge seasonal supply shortfalls and to exploit comparative advantages in product quality. Another important metric in the network analysis is the number of trade relationships (links) among countries. Among the 10 selected products, the average number of bilateral trade links varied from 82 (for bananas and plantains) to 246 (for rice) in 2003–2007, and from 74 links (for wheat) to 240 (for maize) in 2015–2019. Except for rice, wheat, and apples, the number of trade links increased between the two periods; links increased the least for beans, up by 9 percent, and the most for citrus fruit, up by 35 percent. For apples, rice, and wheat, the average number of links decreased by 2, 13, and 35 percent, respectively. The significant increase in links for most of the products considered implies that African countries are becoming more connected. Another metric for gauging the relationships in a network is the network density (or dimensionality). This indicator, which is the ratio of the number of realized links to the number of possible links among nodes in the network, is comparable across different networks. Our results show that for 2003–2007, the intra-African network density varied between 4.3 and 8.9 percent for the products considered (Table 3.3). In the more recent period, this indicator increased slightly, ranging from 4.5 to 8.9 percent. In the first period, the least-dense trade network was that of bananas and plantains; the densest networks were for rice and maize. In 2015–2019, the apple network was the least dense; and the maize network had the highest density score. The 10 product networks are characterized by very low dimensionality, meaning that compared with the number of possible trade links, there are few actual (realized) intra-African trade relationships. For example, the rice network involved 53 countries, with only 213 links observed on average in 2015–2019, in comparison with 2,756 potential transactions (53x52, since each country has 52 potential African partners). This could mean either that very few countries are exporters or that most exporters have few African partners. In fact, only 4 rice-exporting countries had more than 10 partners within Africa in this period, including South Africa with 22 links. Thus, even if the number of links is increasing over time, the network density remains low. This implies that the trade potential among African countries is not yet sufficiently exploited, perhaps because of barriers to entry in terms of quality or competitiveness. Trade orientation In this section, we explore the types of links in the networks using several indicators. The first is the reciprocity index, which measures the share of two-way trade within each network. Estimates of this indicator show that the propensity for two-way trade between countries varies by commodity. For example, for 2015–2019, the reciprocity index was only 0.067 for apples, meaning that only about 7 percent of apple trade links between African countries were reciprocal. However, the maize and beans networks, which had the highest reciprocity scores, both scored about 0.4 over the two subperiods. In other words, about 40 percent of maize and beans trade were two-way trade flows. Results also suggested that the share of two-way trade within each network declined over time for most products considered, except rice and tomatoes. The second indicator is the clustering coefficient, which measures the frequency of trade triangles in a network (three nodes linked to each another). The analysis for Africa shows a high clustering coefficient, indicating that trade partners of a given country are also more likely to trade among themselves. However, across products and periods, results were not similar. Over the two subperiods, the most clustered network was the maize network, while the least clustered networks were for bananas and plantains and apples in the first period, and for apples in the second period. Five networks (rice, wheat, potatoes, onions and shallots, and bananas and plantains) became more clustered from the first to the second period. This suggests that these networks have expanded over time, especially as new trade links were formed between 62 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade countries. For other products, however, the clustering coefficient has decreased, meaning that a significant number of trade relationships disappeared. The third indicator is the regional homophily index, which measures the possibility (extent) of nodes from the same geographic region connecting among themselves. We looked at five regions: Central, East, North, Southern, and West Africa (see network representations in Figures 3.4 to 3.7 and those in the chapter appendix). A positive score indicates a greater chance of connection among nodes (countries) from the same region, while a negative score suggests a greater chance of connection among nodes from different regions. Our results show that African countries are likely to trade more with partners within their regions than with partners in other parts of Africa, that is, regional homophily. This is true for all 10 product networks. Over the first period, the networks showing the greatest regional homophily were tomatoes, wheat, potatoes, and onions and shallots. During the second period, wheat, bananas and plantains, and rice had the highest regional homophily scores. This means that intra-African trade of agricultural products is regionally segmented. Only a few countries have trade relationships with countries outside their geographic regions. The final indicator measures degree assortativity in a network. In assortative networks, highly connected countries tend to link to other highly connected nodes. In disassortative networks, conversely, countries with many partners tend to connect to those with very few partners. The coefficient ranges from −1 to +1, with a positive value indicating an assortative network and a negative value indicating a disassortative network. Our results for this indicator show all 10 networks are disassortative, meaning that well-connected African countries are linked to poorly connected nodes, and vice versa. For example, South Africa, the most highly connected country, exports maize to 41 African countries, while most other maize-exporting countries export to fewer than 5 countries. Table 3.4 Network properties: Reciprocity, clustering coefficient, homophily, and degree assortativity, 2003–2007 and 2015–2019 Reciprocity index Clustering coef- ficient Regional ho- mophily Degree assorta- tivity Rice 0.376 [0.317] 0.348 [0.333] 0.452 [0.354] 0.243 [-0.252] Maize 0.408 [0.459] 0.365 [0.373] 0.362 [0.373] -0.149 [-0.207] Wheat 0.270 [0.333] 0.332 [0.316] 0.504 [0.455] 0.022 [-0.166] Beans 0.404 [0.449] 0.320 [0.367] 0.317 [0.42] -0.118 [-0.131] Potatoes 0.293 [0.303] 0.284 [0.216] 0.364 [0.453] -0.174 [-0.211] Onions and shallots 0.314 [0.338] 0.328 [0.222] 0.369 [0.445] -0.140 [-0.255] Tomatoes 0.266 [0.229] 0.222 [0.239] 0.365 [0.492] -0.275 [-0.234] Bananas and plantains 0.204 [0.293] 0.283 [0.166] 0.502 [0.419] -0.244 [-0.24] Citrus fruit 0.223 [0.247] 0.234 [0.242] 0.188 [0.252] -0.336 [-0.324] Apples 0.067 [0.246] 0.150 [0.167] 0.213 [0.341] -0.406 [-0.378] Source: Constructed using the 2021 AATM database. Note: The first figures indicate the values over the period 2015–2019; the figures in parentheses indicate the corresponding value for 2003–2007. 63Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Market concentration in intra-African trade Here, we examine the extent of market concentration in intra-African trade for the selected products. Table 3.5 presents the cumulative share of the top 10 trade flows for each product, and the top three country-pairs (exporter-importer), with the corresponding trade share indicat- ed in parentheses. Table 3.5 Largest trade flows within Africa Product Period Share of top 10 flows (%) Top 3 country pairs (exporter-importer) and trade shares 1 2 3 Rice (1) 64.4 EGY-LBY (20.4) ZAF-BWA (11.6) EGY-SDN (9.4) (2) 67.2 SEN-MLI (13.3) ZAF-BWA (10) RWA-COD (9.9) Maize (1) 62.7 ZAF-ZWE (15.5) ZAF-SWZ (7.7) ZAF-BWA (7.4) (2) 59.3 ZMB-ZWE(10) UGA-KEN (8.2) ZAF-BWA (7.9) Wheat (1) 70.0 DZA-NGA (18.3) ZAF-BWA (9.6) ZAF-ZMB (9.2) (2) 88.3 ZAF-BWA (20) ZAF-ZWE (19.4) TZA-SYC (11.5) Beans (1) 43.6 ZAF-ZWE (6.2) EGY-TUN (5.8) UGA-KEN (5.2) (2) 68.6 UGA-KEN (24.9) EGY-DZA (16.9) UGA-SSD (5.3) Potatoes (1) 88.5 ZMB-ZWE (30.4) ZAF-BWA (14.4) ZAF-AGO (13.6) (2) 77.9 ETH-SOM (20.8) ZAF-MOZ (16.9) ZAF-NAM (10.8) Onions and shallots (1) 79.5 NER-GHA (36.5) ZAF-AGO (8.1) NER-BEN (6.3) (2) 68.8 ZAF-MOZ (14.7) ZAF-AGO (13.7) SDN-ETH (10.5) Tomatoes (1) 84.3 ZAF-BWA (32) ZAF-NAM (12.6) BFA-GHA (10) (2) 83.3 ETH-SOM (38.2) ZAF-BWA (7) TUN-LBY (6.5) Bananas and plan- tains (1) 79.1 CIV-SEN (28) ZAF-BWA (12) ZAF-NAM (7.8) (2) 82.7 MOZ-ZAF (29.7) CIV-SEN (15.1) ZAF-BWA (7.1) Citrus fruit (1) 68.4 SWZ-ZAF (14) ZWE-ZMB (9.8) ZAF-MOZ (9.5) (2) 51.2 ZWE-ZAF (8.6) TZA-KEN (7.7) ZAF-MUS (6.8) Apples (1) 73.4 ZAF-BWA (11.6) ZAF-BEN (11.3) ZAF-AGO (9.2) (2) 72.1 ZAF-NGA (29.7) ZAF-KEN (7.9) ZAF-AGO (5.7) Source: Constructed using the 2021 AATM database. Note: Country labels are the ISO-3 country codes (for the full list, see Table A3.1 in the appendix to this chapter). The figures in parentheses indicate the trade share for the selected country-pair. (1) is the first period (2003–2007); (2) is the second period (2015–2019). For all selected products, intra-African trade is highly concentrated in a limited number of countries and transactions. The top 10 transactions accounted for 43.6 percent of the trade in beans and 88.5 percent of the trade in potatoes in the first period. In the second period, the share of the top 10 transactions in total trade ranges from 51.2 percent for citrus fruit to 88.3 percent for wheat. For rice trade in the intra-African network, the top three transactions were exports from Egypt to Libya (20.4 percent), from South Africa to Botswana (11.6 percent), and from Egypt to Sudan (9.4 percent) in the first period. During the second period, the top bilateral trade flows of rice were from Senegal to Mali (13.3 percent), South Africa to Botswana (10 percent), and Rwanda to the Democratic Republic of Congo (9.9 percent). 64 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade For maize, South Africa was the only country exporting to the top three importing countries (Zimbabwe, Eswatini, and Botswana) in the 2003–2007 period. However, in 2015–2019, the structure of the maize trade network shifted, with Zambia and Uganda becoming important maize exporters, primarily exporting to Zimbabwe and Kenya. The maize trading relationship between South Africa and Botswana stayed strong, increasing from 7.4 to 7.9 percent of total maize trade. For the wheat network, the trade share of the top 10 transactions increased from 70 to 88 percent between the two subperiods. South Africa is also a top player in this network, primarily exporting wheat to Botswana (9.6 percent) and Zambia (9.2 percent) in the first period, and Botswana (20 percent) and Zimbabwe (19.4 percent) in the second period. Other important wheat trading partners include Algeria and Nigeria with 18.3 percent of the wheat trade in 2003–2007, and Tanzania and Seychelles, with 11.5 percent in the second period. For the beans network, the trade share of the top 10 transactions increased from 43 to 68 percent between the two subperiods. Uganda and Egypt are the top exporting countries, and Kenya and Algeria are the main importers. The role of South Africa in this value chain is very limited, unlike other products considered here. For potatoes, South Africa is one of top exporting countries; however, the largest transaction was between Zambia and Zimbabwe (30 percent of total potato trade) in the first period, and between Ethiopia and Somalia (21 percent) in the second period. For onions and shallots, the trade share of top 10 transactions dropped from almost 80 percent down to 69 percent between the two periods. At the country level, the top trade transactions for onions and shallots in the first period occurred between Niger and Ghana (36.5 percent), followed by South Africa and Angola (8.1 percent), and Niger and Benin (6.3 percent). Over the second period, the top bilateral traders in onions and shallots were South Africa and Mozambique (14.7 percent), South Africa and Angola (13.7 percent), and Sudan and Ethiopia (10.5 percent). In the first period, Niger was the leading exporter, while South Africa became the leading exporter in the more recent period. The intra-African tomato statistics show that just a few transactions dominated the network. For both periods, the top 10 transactions accounted for more than 80 percent of total intra- African tomato trade. The top transactions in the first period were between South Africa and Botswana, South Africa and Namibia, and Burkina Faso and Ghana. In the later period, the top trade partners for tomatoes were Ethiopia and Somalia (38 percent of the total trade value), followed by South Africa and Botswana (7 percent), and Tunisia and Libya (6.5 percent). East African countries dominated the tomato trade in recent years, while Southern African countries were the leaders in the first period. Bananas and plantains are staple foods for many countries in East and West Africa. For these, just 10 transactions captured 80 percent of the total trade among all African countries. Côte d'Ivoire is one of the leading exporters, with Senegal its main trade partner. This country pair accounted for 28 percent of total trade in the first period, and 15 percent in the second. Mozambique is also a leading exporter, particularly in the second period, when its exports of bananas and plantains to South Africa accounted for about 30 percent of total intra-African trade. The citrus fruit trade network has been the most dynamic, in terms of the increase in both the number of countries involved and number of transactions (Table 3.3). From an average of 48 countries involved in the first period (with 146 links), the number of countries involved increased to 54 countries (197 links) in the second period. The top 10 citrus export relationships accounted for 68.4 percent of the average total citrus fruit export value within Africa in 2003–2007, and 51.2 percent in 2015–2019. Thus, the citrus fruit trade is among the least concentrated of the networks considered. Like all the products analyzed, the citrus fruit network is characterized by a 65Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade preference or tendency for countries to trade with others in the same region. The top three citrus transactions were between Eswatini and South Africa, Zimbabwe and Zambia, and South Africa and Mozambique in the first period; in the second period, the top trading pairs were Zimbabwe and South Africa, Tanzania and Kenya, and South Africa and Mauritius. Citrus fruit trade between African countries appears to be dominated by Southern African countries. South Africa is the main exporter of apples to African countries. Over time, its top partners have changed. In the first period, South Africa’s top partners in the apple trade were Botswana, Benin, and Angola, and in the second, Nigeria, Kenya, and Angola. Depiction of selected networks In this section, we complement the description of network statistics above with a graphical representation of the rice and maize networks for 2003–2007 and 2015–2019. Depictions of the other networks are found in the appendix to this chapter. Each country is represented by a node (or a vertex) in the network, while the directed and weighted edges indicate the trade fluxes between the two connected countries. The magnitude of a flow (edge weight) represents the total value of the annual average export value in current US dollars (millions) from a given country to the corresponding partner (see direction of arrows). To facilitate the reading of the network plots, we have created seven classes according to the values of the flow, thus the thinnest lines correspond to the average export values of less than $10,000 while the thickest lines correspond to the flows of more than $10 million. The size of a node is drawn proportional to the node’s out- degree centrality (export value). The color of a node corresponds to its geographical position within Africa (Central, Eastern, Northern, Southern, or Western). Figure 3.4 shows the intra-African rice export network for 2003–2007. The central role of South Africa and Egypt are indicated by the size of their circles and the number of links. Apart from these two countries, intra-African rice trade is dominated by West and East African countries (indicated by the color of the circles). Among West African countries, Côte d'Ivoire and Senegal have the most central role in this network. Among East African countries, Tanzania and Kenya were the leading exporters. In the second period (Figure 3.5), South Africa and Egypt remain the top rice exporters. How- ever, their dominance in the intra-African rice trade has declined. Tanzania played bigger role in this period compared to the first period, and West African rice exports also increased. However, a comparison of the network depictions for the two periods suggests that the rice trade network has become less dense. 66 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade Figure 3.4 Intra-African rice exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: Country codes are listed in the appendix to this chapter (Table A3.1). Figure 3.5 Intra-African rice exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: Country codes are listed in the appendix to this chapter (Table A3.1). Regarding intra-African maize trade, Figure 3.6 and Figure 3.7 show that this network has been dominated by South Africa and East African countries. South Africa is the leading maize exporter in the network, followed by Zambia, Tanzania, Uganda, Kenya, and Malawi. West African countries played a less significant role; Côte d'Ivoire, Burkina Faso, and Mali were the main exporting countries in this region. 67Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Figure 3.6 Intra-African maize exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: Country codes are listed in the appendix to this chapter (Table A3.1). Figure 3.7 Intra-African maize exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: Country codes are listed in the appendix to this chapter (Table A3.1). From this network-based analysis, we can see that the export networks of the selected commodities are characterized by very low density, below 10 percent. On average, 74 to 246 transactions were reported among African countries for the selected products out of more than 2,700 possible transactions per year. Agricultural trade between African countries is also found to be very concentrated, with the top 10 flows accounting for more 60 percent of the total trade value. However, despite the limited number of observed trade links between African countries, for every product about 30 percent of trade is mutual — that is, pairs of countries both import and export the product from each other. In addition, countries in the same region are more likely to trade among themselves than with countries in other African regions. 68 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade 0 5 10 15 20 25 30 Rice Maize Wheat Beans Potatoes Onions and shallots Tomatoes Bananas and plantains Citrus fruit Apples Intra-Africa World to Africa Africa to World World to World OVERVIEW OF TRADE PROTECTION WITHIN AFRICA FOR THE SELECTED PRIMARY PRODUCTS The major types of distortion in intra-African agricultural product markets are import tariffs and nontariff measures (NTMs). This section assesses the level of protection on the selected primary agricultural commodities, beginning with the applied tariff on imports at the continental level and within RECs. We also compare the rates applied to intra-African trade with rates applied on trade between African and non-African countries. Tariffs Figure 3.8 compares the weighted average tariff rates imposed by (i) Africa on intra-African trade, (ii) Africa on its imports from the world, (iii) the world on its imports from African countries, and (iv) the world on its imports from the world. All countries, non-African and African, are included in what we refer to as "world." For the products considered, except potatoes and rice, the intra-African markets are subject to lower tariffs than those faced by international commodities in Africa. However, African exports to the world, with the exceptions of rice, maize, wheat, and potatoes, face lower tariffs at the international level than among African countries. Figure 3.8 Weighted average tariff rates between Africa and the international market, 2016 (%) Source: Constructed using MAcMap-HS6 (2016). Note: “Intra-Africa” is the intracontinental weighted average tariff rate; “World to Africa” is the rate faced by imports from world markets; “Africa to World” is the average weighted tariff rate imposed on African exports by world partners; and “World to World” is the international average rate. For rice, the intra-African tariff rate in 2016 was relatively low compared to tariff rates observed between other partners; the global weighted average tariff (World to World) on rice was 28.4 percent. Moreover, intra-African rice markets were less distorted than markets for world rice imports to Africa, with a 15.6 percent tariff, while it was 10.6 percent on African rice. Both rates are below the overall world rice import tariff rate and the 22.3 percent rate applied on African rice exports to the world. 69Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade For maize, African countries applied slightly higher tariff rates on intra-African trade (5.7 percent) than on maize imported from world maize markets (5.5 percent). Conversely, in the world market, maize from Africa is subject to tariff rates above the world market average (11.3 percent compared with a world average of 8.9 percent). Similar results are observed for wheat and potatoes (Figure 3.8). Figure 3.9 compares the intra-African average tariff rates with the intra-REC rates. For the se- lected products, the continental average rate is higher than the applied rates within the RECs, except for the Economic Community of Central African States (ECCAS). This suggests that intra-REC trade is cheaper than extra-REC trade for individual countries in Africa. The applied tariffs within the ECCAS countries are the highest among those applied within RECs in Africa, particularly for maize, wheat, potatoes, onions and shallots, tomatoes, citrus fruits, and apples. Rice, maize, and wheat regional markets appear more liberal, that is, tariffs are lower, than other markets. Figure 3.9 Weighted average tariff rates within RECs in Africa, 2016 (%) Source: Constructed using MAcMap-HS6 (2016). Note: COMESA = Common Market for Eastern and Southern Africa; SADC = Southern Africa Development Community; AMU = Arab Maghreb Union; ECCAS = Economic Community of Central African States; EAC = East African Community; ECOWAS = Economic Community of West African States. This figure illustrates the level of average tariffs on intra-REC trade in Africa in 2016. Since that date, there have been changes in these customs duties. For example, there are no longer any customs duties on trade in goods within ECOWAS. Nontariff measures In addition to tariffs, trade protection is also provided through nontariff measures (NTMs). The most common NTMs include sanitary and phytosanitary measures (SPS), technical barriers to trade (TBT), price-control measures, quantity-control measures, and export-related measures. In general, these affect more trade flows than tariffs, and are more trade-prohibitive than tariffs (Gillson and Charalambides 2012). In this section, we look at the three HS chapters (edible vegetables and certain roots and tubers; edible fruit and nuts; and cereals) that include our 10 selected products. Because the NTM data were not available at the HS6 product level, results in this section are presented at the more aggregated level. Figure 3.10 reports on the frequency index (the percentage of products subject to one or more NTMs) and the coverage ratio (the percentage of import value subject 0 5 10 15 20 25 30 Rice Maize Wheat Beans Potatoes Onions and shallots Tomatoes Bananas and plantains Citrus fruit Apples Intra-Africa Intra-COMESA Intra-ECCAS Intra-ECOWAS Intra-SADC Intra-AMU 70 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade 0% 20% 40% 60% 80% 100% 120% Ethiopia Tunisia Liberia Ghana Senegal Niger Cabo Verde Mauritania Côte d'Ivoire Coverage rat io Frequency index to one or more NTMs), as of 2018. In Ethiopia and Nigeria, more than 90 percent of agricultural products (and import value) are affected by one or more NTMs. In Tunisia and in Algeria, more than 50 percent of agricultural products are subject to NTMs, and the coverage ratio is 71 percent in Tunisia and 57 percent in Algeria. For all other African countries for which data are available, the frequency index is less than 50 percent. However, most of these countries (Benin, Burkina Faso, Gambia, Ghana, Morocco, and Senegal) have a coverage ratio of more than 50 percent. This means that even though they apply NTMs on fewer than half of their imported products, the corresponding import value of these products is at least 50 percent of their total import value. Figure 3.10 Frequency and coverage of NTMs by African country, 2018 (%) Source: Nguyen, Bouët, and Traoré 2020. Figure 3.11 presents the prevalence score (the average number of NTMs that apply to a product). Nigeria, Ghana, and Algeria have the highest prevalence scores among African countries; they apply more than two different NTMs, on average, on agricultural products. Côte d’Ivoire, Cameroon, and Senegal show the lowest prevalence scores. 71Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Figure 3.11 Prevalence score of NTMs by African country, 2018 Source: Nguyen, Bouët, and Traoré 2020. Although these three indicators — frequency index, coverage ratio, and prevalence score — shed light on the use of NTMs, this information is not sufficient to assess the impact of NTMs on trade. To do so, we consider the ad valorem equivalents (AVEs)2 estimated by Nguyen, Bouët, and Traoré (2020) of SPS measures and TBTs for the three HS sections (Figure 3.12). A negative AVE suggests that NTMs facilitate trade, and a positive AVE reflects a trading-reducing effect. Figure 3.12 Ad valorem equivalents of SPS measures and TBTs in African countries, 2018 (%) -40% -20% 0% 20% 40% 60% 80% 100% 120% Algeria Benin Burkina Faso Cabo Verde Ethiopia Gambia Ghana Mali Morocco SPS measures Edible vegetables and certain roots and tubers Edible fruit and nuts Cereals -60% -40% -20% 0% 20% 40% 60% 80% 100% 120% Cabo Verde Morocco Ghana Mauritius Gambia Ethiopia Guinea Togo TBTs Edible vegetables and certain roots and tubers Edible fruit and nuts Cereals Source: Nguyen, Bouët, and Traoré 2020. Figure 3.12 shows positive AVEs of SPS measures on edible fruit and nuts for all the countries in the sample, meaning that these measures behave as an additional tariff on the imports of edible fruit and nuts to these countries. Similar results are found for cereals, except in Algeria, and for edible vegetables, except in Benin. Therefore, for almost all countries in the sample, SPS 2 An ad valorem equivalent (AVE) is an estimation the tariff equivalent (percentage) of any fixed tariff or NTM applied on a selected product or group of products. This help to compare different trade measures. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Algeria Ghana Nigeria Ethiopia Morocco Cabo Verde Tunisia Gambia Liberia Benin Mali Mauritania Togo Niger Burkina Faso Senegal Cameroon Côte d'Ivoire 72 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade measures do not facilitate trade with their partners, but rather constitute barriers to trade. In Morocco, the SPS requirements for edible vegetables have an effect equivalent to a 97 percent import tariff rate. In Burkina Faso, the estimated AVE of SPS measures on cereals is 79 percent. Regarding TBTs, except for edible fruit and nuts in Guinea, the estimated AVEs are all positive and relatively high for the countries in Figure 3.12. For edible vegetables, the estimated value stands at 82 percent in Morocco, 86 percent in Cabo Verde, 95 percent in Ghana, 96 percent in Ethiopia, and 104 percent in Togo. For edible fruit and nuts, the AVE reaches 78 percent in Morocco and 74 percent in Togo. For cereals, Cabo Verde is the country with highest AVE for TBTs. CONCLUSION We have used this chapter to examine intra-African agricultural trade at the commodity level to understand the specific challenges and opportunities for tripling intra-African trade by 2025. In addition to updating data on trends and patterns in intra-African agricultural trade flows, the chapter focused on trade networks among exporting and importing African countries for 10 primary commodities, selected because of their importance in intra-African trade and for food security across the continent. We argue that policy challenges and interventions vary by type of commodity, as they are targeted to achieving varied development goals including food security, import substitution, and export earnings, and reaching different markets such as regional and continental markets. Thus, the 10 commodities were selected to represent products that are widely traded as raw materials and fresh products, both regionally and continentally. Our examination of the intra-African export values and shares in total exports for primary, processed, and total agricultural exports from 2003 to 2019 showed that agricultural trade within Africa is struggling to recover from the sharp decline experienced in 2013. In 2019, the total value of intra-African agricultural trade ticked up from 2018, and the intra-African share in agricultural trade declined at a slower rate than in prior years. Unprocessed products continue to account for the largest share of the total value of exports within Africa; however, for processed products, the share of intra-African exports in total exports of processed products is much larger than the intra-African share of unprocessed product exports. Intra-African processed product trade is also growing much faster than intra-African trade in unprocessed products, a trend that is attributable to the growing number of urban and middle-class consumers in Africa. This clearly indicates the importance of focusing on processed products in order to achieve the goal of tripling of intra- African agricultural trade. The analysis of trade flows for the selected products that represent cereals and pulses (rice, maize, wheat, beans), vegetables (potatoes, onions, tomatoes), and fruits (bananas, citrus, apples) highlighted the significant differences among the products in African export and import markets. Over recent years (2015–2019), African cereal exports (rice, maize, wheat) have been primarily to other African countries, while African destinations are of limited importance for fruit. On the import side, the African share is very low for cereals, meaning that African countries must spend a substantial amount every year to import cereals from the rest of the world. However, tomatoes and citrus fruit are mainly imported from within Africa. It is important to note that there have been significant changes between the two periods considered. These changes differ across commodities, both in direction and magnitude, suggesting the need for targeted and differentiated trade strategies and polices for the different commodities. Further analysis of the structure of the African export and import markets for the same 10 commodities showed that African exports have been less competitive (few exporters) and more rigid than Africa’s imports. For most commodities, the top three exporters account for more than 73Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade 60 percent of the export values, whereas the top three importers account for less than 50 percent of imports. However, these shares are changing rapidly toward more diversified (competitive) exporters and importers. New exporters and importers are entering the continental markets, a positive trend that should be further encouraged through broadening the regional markets. An interesting approach included in this AATM edition is the trade network analysis that explores a variety of network indicators such as density, frequency, strength, connectedness, and concentration of transactions for the selected products over the two periods. This analysis estimated the changes in the various network indicators over time for the intra-African trade. Results show that intra-African transactions for the selected products are generally becoming denser and more interconnected and regionally clustered, and new and diversified central players are entering these markets. However, the trends vary across commodities, especially in terms of connectedness. The trade networks for some commodities, including wheat, tomatoes, and apples, are still highly centralized and less connected, while the networks for others such as rice, maize, beans, and potatoes are increasingly decentralized and broader, indicating varied levels of regional and continental integration at the commodity level. This result suggests that AfCFTA can play a significant role in harmonizing these variations through commodity-specific policies. Such harmonization would not only support expanding trade integration but also diversification of commodities traded within the continent. The analysis of import duties explains the regional preferences and clustering revealed by the network analysis. In fact, for most individual products studied and for all the RECs considered, the applied average tariff rates are very low (or zero) within each REC. This suggests that intra- REC trade is cheaper than extra-REC trade for individual countries in Africa. However, within ECCAS countries, the level of import duties is high compared to other RECs; for products such as maize, wheat, potatoes, onions and shallots, tomatoes, citrus fruits, and apples, in particular, the within–ECCAS average applied tariff rates exceed the intra-African averages. NTMs are another impediment to intra-African agricultural trade. For countries for which data are available, our results reconfirm the findings of the 2020 AATM that, in general, NTMs cause more harm to intraregional trade than tariffs. Although tariffs have been reduced, SPS rules and TBTs imposed in their wake are slowing trade with neighboring countries and others. Thus, these NTMs need to be addressed if the continent intends to meet its goal of tripling the volume of intra-African trade. The overall implication of the analysis reported in this chapter is threefold. First, policies affecting processed products should be revisited in order to accelerate expansion of intra-African agricultural trade in these products. A focus on trade of processed products can help meet the growing demand for food quality and convenience and expand employment. Unlike trade in primary products, trade in processed food products requires more harmonized regulatory policies related to food safety standards and information. Second, though African commodity markets are becoming wider, more competitive, and more connected, regional trade strategies and policies targeted to selected commodities are needed to exploit the potential and resolve the specific challenges to trade in these products. Third, African countries should redesign NTMs to promote, rather than hinder, intracontinental trade of agricultural products. 74 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade REFERENCES AfDB (African Development Bank). 2011. The Middle of the Pyramid: Dynamics of the Middle Class in Africa. AfDB Market Brief, April 20. Abidjan. De Benedictis, L., S. Nenci, G. Santoni, L. Tajoli, and C. Vicarelli. 2014. “Network Analysis of World Trade Using the BACI-CEPII Dataset.” Global Economy Journal 14 (3–4): 287– 343. FAO (Food and Agriculture Organization of the United Nations). 2021. 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Primary Commodity Prices. https://www.imf.org/en/ Research/commodity-prices https://www.indexmundi.com/fr/matieres-premieres/?marchandise=feves-de-cacao&mois=180 http://www.fao.org/faostat/en/ http://www.fao.org/faostat/en/ 75Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade APPENDIX Table A3 List of African country ISO-3 codes ISO3 Country Name ISO3 Country Name DZA Algeria LBR Liberia AGO Angola LBY Libya BEN Benin MDG Madagascar BWA Botswana MWI Malawi BFA Burkina Faso MLI Mali BDI Burundi MRT Mauritania CPV Cabo Verde MUS Mauritius CMR Cameroon MAR Morocco CAF Central African Republic MOZ Mozambique TCD Chad NAM Namibia COM Comoros NER Niger COD Congo Dem. Rep. NGA Nigeria COG Congo, Rep. RWA Rwanda CIV Côte d’Ivoire STP Sao Tome and Principe DJI Djibouti SEN Senegal EGY Egypt, Arab Rep. SYC Seychelles GNQ Equatorial Guinea SLE Sierra Leone ERI Eritrea SOM Somalia SWZ Eswatini ZAF South Africa ETH Ethiopia SSD South Sudan GAB Gabon SDN Sudan GMB Gambia TZA Tanzania GHA Ghana TGO Togo GIN Guinea TUN Tunisia GNB Guinea-Bissau UGA Uganda KEN Kenya ZMB Zambia LSO Lesotho ZWE Zimbabwe 76 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade Figure A3.1 Intra-African wheat exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. Figure A3.2 Intra-African wheat exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. 77Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Figure A3.3 Intra-African beans exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. Figure A3.4 Intra-African beans exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1 78 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade Figure A3.5 Intra-African potato exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1 Figure A3.6 Intra-African potato exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. 79Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Figure A3.7 Intra-African onions and shallots exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. Figure A3.8 Intra-African onions and shallots exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. 80 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade Figure A3.9 Intra-African tomato exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. Figure A3.10 Intra-African tomato exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. 81Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Figure A3.11 Intra-African banana and plantain exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. Figure A3.12 Intra-African banana and plantain exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. 82 Chapter 1 - OverviewAfrica Agriculture Trade Monitor / 2021 Report Chapter 3 - Intra-African Agricultural Trade Chapter 3 - Intra-African Agricultural Trade Figure A3.13 Intra-African citrus exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. Figure A3.14 Intra-African citrus fruit exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. 83Chapter 1 - Overview Africa Agriculture Trade Monitor / 2021 ReportChapter 3 - Intra-African Agricultural Trade Figure A3.15 Intra-African apple exports network, 2003–2007 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. Figure A3.16 Intra-African apple exports network, 2015–2019 Source: Constructed from the 2021 AATM database. Note: For country codes, see Table A3.1. List of appendices List of TABLES List of FIGUREs Acronyms and abbreviations Acknowledgments Foreword Executive Summary Chapter1,Overview Introduction Africa and COVID-19 Implementation of the AfCFTA Issues concerning data and methodology The way forward References Chapter2, African participation in global agricultural trade Introduction An overview of African trade in agricultural goods African participation in global trade by sector Comparative advantages Economic complexity of African agricultural trade Caloric content of exports and imports Table 2.1 Distribution by sector of Africa’s agricultural exports and imports, and of global trade, 2003–2019 average Figure 2.1 Share of African exports in world exports by sector, 2003–2019 (%) Figure 2.2 Share of African imports in world imports by sector, 2003–2019 (%) Table 2.2 Number and share of African RCAs at the HS6 level for each of 8 sectors, 2017–2019 and 2003–2005 averages Table 2.3 Top 50 RCAs for agricultural products in Africa, 2017–2019 average Figure 2.3 Share of RCAs (at HS6 level) in 8 sectors by processing stage, 2003–2005 and 2017–2019 (%) Figure 2.4 Classification of countries Figure 2.5 Method of reflection: Classification of African and other countries, 2017–2019 Figure 2.6 Method of reflection: Classification of African and other countries, 2003–2005 Table 2.4. List of African countries by group, 2003–2005 and 2017–2019 Figure 2.7 Caloric trade balance, 2016 (kcal per person per day) Figure 2.8 African caloric exports, imports, and trade balance, 2016 (kcal per person per day) Figure 2.9 Geographic structure of calorie imports of African countries, 2017 (%) An overview of trade in resources Fertilizers and pesticides Labor content in trade Water content in trade Table 2.5 Fertilizer use, production, and trade by nutrient, 2003–2005 and 2016–2018 (% of total) Table 2.6 Pesticide trade participation by world region, 2003–2005 and 2016–2018 Figure 2.10 Labor content in agricultural trade in 2011, share of value added (%) Figure 2.11 Virtual water export–import balance for 1986, 1993, 2000, and 2010 (m3) What causes the weak performance of Africa in global agricultural trade? Domestic causes Trade policy causes NTMs imposed by African countries NTMs faced by African countries Table 2.7 Land use indicators by continent, 2016–2018 average Table 2.8 Agricultural productivity by region, 2003–2006 and 2016–2018 averages Table 2.9 Fertilizer consumption and cereal yields by region, 2016–2017 Figure 2.12 Share of public agricultural expenditure in agricultural value added, 2017–2019 average (%) Figure 2.13 Share of agricultural value added in total GDP, 2017–2019 average (%) Figure 2.14 Share of public agricultural expenditure in total expenditure, 2017–2019 average (%) Figure 2.15 Agricultural research spending in developing countries, total and share of agricultural GDP Figure 2.16 Number of researchers in developing countries, per farmer and per capita Table 2.10 AVEs of SPS barriers: Top 7 countries by GDP, 2018 (%) Table 2.11 Ad valorem equivalents of technical barriers to trade: Top 7 countries in terms of GDP, 2018 (%) Conclusion References chapter3, Intra-African agricultural trade Introduction Trends and patterns in intra-African agricultural trade Total agricultural trade flows within Africa Intra-African trade of selected primary commodities Figure 3.1a Trends in intra-African agricultural trade, 2003–2019 (US$ billions) Figure 3.1b Intra-African agricultural export shares, 2003–2019 (%) Figure 3.1c Intra-African agricultural import shares, 2003–2019 (%) Table 3.1 Definitions of products analyzed Figure 3.2 Average annual value of intra-African trade for selected commodities Figure 3.3 Share of intra-African trade in total African trade of selected commodities Table 3.2 Top intra-African exporters and importers of selected products and corresponding trade share Intra-African trade network for selected primary products How many countries and trade links are there over time? Trade orientation Market concentration in intra-African trade Depiction of selected networks Table 3.3 Network properties: Counting countries and trade relationships, 2003–2007 and 2015–2019 Table 3.4 Network properties: Reciprocity, clustering coefficient, homophily, and degree assortativity, 2003–2007 and 2015–2019 Table 3.5 Largest trade flows within Africa Figure 3.4 Intra-African rice exports network, 2003–2007 Figure 3.5 Intra-African rice exports network, 2015–2019 Figure 3.6 Intra-African maize exports network, 2003–2007 Figure 3.7 Intra-African maize exports network, 2015–2019 Overview of trade protection within Africa for the selected primary products Tariffs Nontariff measures Figure 3.8 Weighted average tariff rates between Africa and the international market, 2016 (%) Figure 3.9 Weighted average tariff rates within RECs in Africa, 2016 (%) Figure 3.10 Frequency and coverage of NTMs by African country, 2018 (%) Figure 3.11 Prevalence score of NTMs by African country, 2018 Figure 3.12 Ad valorem equivalents of SPS measures and TBTs in African countries, 2018 (%) Conclusion References Appendix Table A3 List of African country ISO-3 codes Figure A3.1 Intra-African wheat exports network, 2003–2007 Figure A3.2 Intra-African wheat exports network, 2015–2019 Figure A3.3 Intra-African beans exports network, 2003–2007 Figure A3.4 Intra-African beans exports network, 2015–2019 Figure A3.5 Intra-African potato exports network, 2003–2007 Figure A3.6 Intra-African potato exports network, 2015–2019 Figure A3.7 Intra-African onions and shallots exports network, 2003–2007 Figure A3.8 Intra-African onions and shallots exports network, 2015–2019 Figure A3.9 Intra-African tomato exports network, 2003–2007 Figure A3.10 Intra-African tomato exports network, 2015–2019 Figure A3.11 Intra-African banana and plantain exports network, 2003–2007 Figure A3.12 Intra-African banana and plantain exports network, 2015–2019 Figure A3.13 Intra-African citrus exports network, 2003–2007 Figure A3.14 Intra-African citrus fruit exports network, 2015–2019 Figure A3.15 Intra-African apple exports network, 2003–2007 Figure A3.16 Intra-African apple exports network, 2015–2019 Chapter4, African Trade in Livestock Products and Value Chains Introduction General overview of trade flows The role of informal livestock trade Figure 4.1 African formal exports, intra-African and extracontinental, annual average 2010–2019 Figure 4.2 African imports from rest of the world, annual average 2010–2019 Figure 4.3 Formal intra-African exports of live animals in East Africa, annual average 2010–2019 Figure 4.4 Informal intra-African exports of live animals in East Africa by country, 2010–2019 totals Figure 4.5 Total informal exports of live animals in East Africa, 2010–2019 Net African trade flows of livestock products Meat value chain Dairy value chain Poultry value chain Figure 4.6 Continent-level net exports by stage of processing of meat, dairy, and poultry value chains, averages 2010–2014 and 2015–2019 Table 4.1 Share of African livestock export and import values, 2010–2014 and 2015–2019 averages Figure 4.7 Net African exports of livestock products, 2003–2019 (US$ billions) Figure 4.8 Net exports of Africa of meat and animals by processing stage, 2003–2019 (US$ billions) Figure 4.9 Meat and animal net exports by stage of processing, country averages 2015–2019 (US$ millions) Figure 4.10 Net African dairy exports, 2003–2019 (US$ billions) Figure 4.11 Net dairy exports by African country and stage of processing, average 2015–2019 (US$ millions) Figure 4.12 Net African poultry exports by processing stage, 2003–2019 (US$ billions) Figure 4.13 Net poultry exports by processing stage, average 2015–2019 (US$ millions) Table 4.2 Average share and ranking of top 10 exporters of live animals, 2015–2019 Table 4.3 Average share and ranking of top 10 exporters of meat products, carcasses and cuts, hides and skins, 2015–2019 African livestock markets: Destinations and origins Intra-African livestock trade Meat and live animals Dairy and poultry Livestock trade between Africa and ROW Origins of African global imports Top African importers from ROW African global livestock export destinations Figure 4.14 Top intra-African importers and exporters of livestock products, annual average 2010-201 Figure 4.15 Top 10 intra-African exporters of live animals and meat, annual average 2010–2019 Figure 4.16 Top intra-African importing countries, animals and meat, annual average 2010–2019 Figure 4.17 Top 10 intra-African dairy exporters, annual average 2010–2019 Figure 4.18 Top intra-African dairy importers, annual average 2010–2019 Figure 4.19 Top 10 intra-African poultry exporters, annual average 2010–2019 Figure 4.20 Top intra-African poultry importers, annual average 2010–2019 Figure 4.21 Top African exporters of meat and live animals to ROW, annual average 2010–2019 Figure 4.22 Top African exporters of dairy to ROW, annual average 2010–2019 Figure 4.23 Top origin countries of African livestock product imports, annual average 2010–2019 Figure 4.24 Top origin countries of African meat and live animal imports, annual average 2010–2019 Figure 4.25 Top origin countries of African dairy imports, annual average 2010–2019 Figure 4.26 Top origin countries of African poultry imports, annual average 2010–2019 Figure 4.27 African countries importing more than US$10 million in livestock products, annual average 2010–2019 Figure 4.28 Top 10 destinations for African livestock product exports, annual average 2010–2019 Figure 4.29 Top global importers of African meat and animal products, annual average 2010–2019 Figure 4.30 Top global importers of African dairy products, annual average 2010–2019 Figure 4.31 Top global importers of African poultry products, annual average 2010–2019 Trade policies Tariffs Nontariff measures Figure 4.32 Tariffs on livestock products, 2016 (%) Figure 4.33 Tariffs imposed on meat products, 2016 (%) Figure 4.34 Tariffs imposed on poultry products, 2016 (%) Figure 4.35 Tariffs imposed on dairy products, 2016 (%) Figure 4.36 Intra-African tariffs imposed on meat, 2016 (%) Figure 4.37 Intra-African tariffs imposed on poultry, 2016 (%) Figure 4.38 Intra-African tariffs imposed on dairy, 2016 (%) Figure 4.39 Number of nontariff measures imposed by EU and US in the agriculture sector, 2018 Table 4.4 Number of nontariff measures imposed by EU and US, 2018 Figure 4.40 Ad valorem equivalent of SPS measures (%) Figure 4.41 Ad valorem equivalent of TBT measures (%) Table 4.5 Domestic support in the US and EU Risks to African livestock supply chains Climate risks Conflict Figure 4.42 Shared suitability for pastoralism and agriculture, with number of conflict events, 1989–2018 Conclusion References Appendix Table A4.1 List of HS codes by value chain Table A4.2 Tariffs imposed by main agriculture producers, 2016 (%) Chapter5, The impact of COVID-19 on agricultural trade, economic activity, and poverty in Africa Introduction Figure 5.1 Total COVID-19 deaths per million people vs. GDP per capita, February 2021 (logarithmic scales) Policy Responses Overall policy responses Focus on at-the-border policies Figure 5.2 Average of COVID-19 Government Response Index by continent Figure 5.3 Average of COVID-19 Containment and Health Index by continent Figure 5.4 Average of COVID-19 Economic Support Index by continent Figure 5.5 Comparison of African countries in terms of Containment and Health Index and Economic Support Index, average from March 1, 2020, to February 24, 2021 Figure 5.6 Closure of land borders in Africa, March 2020 Figure 5.7 Number of countries with food-related export restrictions in 2020 (left axis) and share of global trade in calories affected by these restrictions (right axis) Impact transmission channels Vulnerability in the African balance of payments Trade in goods Tourism Remittances International aid Effect on real exchange rates Effect on trade costs Figure 5.8 Sub-Saharan Africa current account as share of GDP, 2018 (%) Figure 5.9 Rate of variation of average commodity prices between 2017–2019 and 2020 (%) Figure 5.10 Terms of trade variation in Africa, 2020 (%) Figure 5.11 Real effective exchange rates in selected African countries and RECs (Jan. 2019 =100) Figure 5.12 Air and sea transportation costs (Jan. 2020 = 100) Figure 5.13 Kenyan cut flower exports, 2020 (‘000 metric tons) Assessment of the trade and economic impact of the crisis Impact on intra-African trade Simulated impact on trade, GDP, and poverty Simulating the impact on trade, GDP, and poverty A global CGE model Evaluating the socioeconomic impact by country from a household perspective The case of Senegal The case of Ghana The case of Uganda Figure 5.14 Volume index of total trade, seasonally adjusted (100 = 2010 Q1) Figure 5.15 Trade of CILSS products (by value) between Benin, Burkina Faso, Ghana, and Togo, 2018–2020 (US$ millions) Figure 5.16 Trade volume index of cereals in eastern Africa, 3-month moving averages (100 = January 2014) Figure 5.17 Trade value at three border posts in Uganda (US$ million) Table 5.1 Poverty and macroeconomic impact projections, MIRAGRODEP COVID-19 scenarios  Table 5.2 COVID-19 incidence and deaths in Senegal, Ghana, and Uganda in the African context Table 5.3 Summary results of estimated COVID-19 socioeconomic impacts in Senegal, Ghana, and Uganda Conclusion References Chapter6, The Arab Maghreb Union: Regionalization without Integration Historical background Trends and structure of agricultural trade Overview of AMU agriculture and trade Intraregional and extra-regional flows Intraregional vs. extra-regional trade flows Heterogeneity of AMU Members Agricultural products exported and imported by AMU members Top 10 exported and imported agricultural products Figure 6.1a Sectoral contributions to GDP, 2000–2019 average (%) Figure 6.1b Agriculture value added, 2003–2019 (% of GDP) Figure 6.2 Global Food Security Index, 2019 Figure 6.3 AMU trade flows of goods by region Figure 6.4 Evolution of AMU countries' exports of agricultural and nonagricultural products, 2003–2019 (US$ billions) Figure 6.5 Evolution of trade among the AMU countries, 2003–2019 (US$ millions) Figure 6.6 Intra–AMU trade in agricultural products, 2003–2019 (US$ millions) Figure 6.7 Agricultural exports to AMU, Africa, and EU, 2003–2019 average (US$ millions) Figure 6.8 Agricultural imports from the AMU, Africa, and EU, 2003–2019 average (US$ millions) Figure 6.9 Top 10 agricultural products traded within the AMU, average exports 2003–2019 (current US$ million) Figure 6.10 Top 10 agricultural products traded by AMU countries with extra-AMU countries Untapped potential Table 6.1 Actual and untapped export potential by destination (US$ millions) Table 6.2 Actual and untapped export potential by sector (US$ millions) Figure 6.11 Level of water stress, 2014 Why has the AMU not achieved its objective? The spaghetti bowl of North African agreements Tariff structure Nontariff measures Trade logistics Domestic institutions Table 6.3 AMU scores on the Africa Regional Integration Index, 2019 Table 6.4 Major free trade agreements of the AMU countries Figure 6.12 Bound and applied MFN Tariffs in AMU countries, 2019 (%) Table 6.5 Top 10 intra–AMU tariffs at HS2 level, 2016 (%) Figure 6.13 Tariffs faced and imposed by AMU countries, 2016 (%) Figure 6.14 Highest tariffs imposed by main trade partners on AMU countries, 2016 (%) Figure 6.15 Nontariff measures on exports and imports in AMU countries, 2017 Figure 6.16 Technical vs. nontechnical nontariff measures in AMU countries, 2017 Table 6.6 Types of nontariff measures in AMU countries, 2017 Table 6.7 Nontariff measures in agriculture sectors of AMU countries, 2017 Figure 6.17 Ad valorem equivalents of technical and nontechnical measures faced by AMU exports to the EU and US, 2016 (%) Figure 6.18 Number of nontariff measures faced by AMU countries in different destinations (%) Figure 6.19a Maritime connectivity between the AMU and other regions Figure 6.19b Maritime connectivity to the world by region Figure 6.19c Maritime connectivity to the world, by AMU country Figure 6.20a Time to export/import by AMU country (hours) Figure 6.20b Cost to export/import by AMU country (US$) Figure 6.21a Time to export/import by region in 2020 (hours) Figure 6.21b Cost to export/import by region in 2020 (US$) Figure 6.22 Quality of infrastructure in AMU countries, 2018 Figure 6.23a Corruption and property rights in AMU countries, 2018 Figure 6.23b Competition in AMU countries, 2018 Conclusion References SUMMARY and CONCLUSION References