Th4_Agricultural trade for food security in Africa: A Ricardian approach

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3rd Africa Rice Congress …

3rd Africa Rice Congress
Theme 4: Rice policy for food security through smallholder and agribusiness development
Mini symposium1: Trade policies to boost Africa’s rice sector
Author: Diagne

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  • 1. Agricultural trade for food security in Africa: A Ricardian approach Mandiaye Diagne1a, Steffen Abeleb, Aliou Diagnec, Papa A. Seckc bThe aAfrica 1m.diagne@cgiar.org Rice Center (AfricaRice), Saint Louis, Senegal Food Security Center, University of Hohenheim, Stuttgart, Germany cAfrica Rice Center (AfricaRice), Cotonou, Benin
  • 2. Outline      Introduction Data Methods Results and Discussions Conclusion
  • 3. INTRODUCTION  With food security becoming even more of a challenge in the recent food crises, African governments have prioritized domestic staple food production.  Food insecurity arises from harvest failure due to climate conditions, price volatility and low agricultural productivity  Beside national level policies and commitments to tackle food insecurity; and under international market uncertainty, facilitating access to African regional markets could play a major role.  Poorly integrated markets are one of the primary causes of food supply shortages and price volatility.  The study aims at showing how staple foods trade within Africa could contribute to food security and overall welfare in Africa.
  • 4. DATA  The crops and staple foods in our model are: Rice, wheat, other grains (maize, millet, sorghum), vegetables and fruits (bananas/plantains, cassava/potatoes) and soybean  Bilateral trade flows are from the GTAP 7 database and we include 19 countries/regions.  The total number of observations, considering bilateral trade flows, is 342.
  • 5. DATA Table 1: Selected countries/regions from GTAP 7 database Country/Region ( 1) Egypt ( 2) Ethiopia ( 3) Morocco ( 4) Madagascar ( 5) Mozambique ( 6) Malawi ( 7) Nigeria ( 8) Senegal ( 9) Tunisia (10) Tanzania (11) Uganda (12) Rest of South Central Africa (Angola, DR of Congo) (13) Rest of Central Africa (Central African Republic, Cameroon, Congo, Gabon, Chad etc.) (14) Rest of Eastern Africa ( Burundi, Djibouti, Kenya, Rwanda, Sudan etc.) (15) Rest of South Africa Customs Union (Lesotho, Namibia, Swaziland) (16) Rest of West Africa (Benin, Burkina Faso, Cote d'Ivoire, Ghana, Guinea, Gambia, Mali, Niger, Togo etc.) ` (17) South Africa (18) Zambia (19) Zimbabwe GTAP code EGY ETH MAR MDG MOZ MWI NGA SEN TUN TZA UGA XAC XCF XEC XSC XWF ZAF ZMB ZWE
  • 6. Methods  We use an improved Ricardian trade model with multiple goods and multiple countries specification (Eaton and Kortum 2002; Reimer and Li 2009,2010) based on technology differences and geographic barriers among countries  The practical concern is to estimate the parameters:  Country estate of technology (Ti)  Heterogeneity of technology (  )  Geographic bariers (dni)
  • 7. Methods The equilibrium variables are represented by a system of three equations:  X ni  T w   ln i   ln i   ln d ni   ln d ni  Si  S n (1) ln  X  Tn wn  nn  , the share of the destination country n expenditure devoted to staple foods from the source country i. Where - wi is land rental rate; - dni are geographic barriers; - Ti is the state of technology and -  is the parameter of technology variability - Si measures competitiveness - lndni = mn + dk + b + l + c , the geographic barriers equation. Where mn, represents the openness to imports dk, distance in miles between countries b, proximity if two countries share border l, common language c, use the same currency
  • 8. Methods  (2) P     1     n       1/1  N T ( wi d ni ) i 1 i   1/  ,the overall price paid in the purchaser country n linked to the yield distribution, geographic barriers and land rental rate; where σ the elasticity of substitution of agricultural product derived from the Utility function, Γ is the Gamma function.  1 (3) wi  Li      Ti ( wi d ni ) n1  X n  N    Ti ( wi d ni )   i 1  N      , returns to land; where Xn is total expenditure in staple food un country n.
  • 9. Results and Discussions 1. Trade flows and yield variability in Africa  Considering total imports of crops and foods, each African country imports from the others African countries 9.96 % on average.  Considering total spending on crops and foods, the share of intra-African import is only 2.29 %.
  • 10. Results and Discussions Table 1: Yield parameters of crops and foods Paddy Oth. Veg. Wheat gr.(a) Rice Ti Soybean frt.(b) (Std. error) Egypt 9.84 6.56 7.18 24.10 3.03 3.49 (0.89) Ethiopia 1.85 1.49 1.11 5.47 0.42 0.72 (0.28) Morocco 6.70 1.81 1.16 16.87 1.03 1.55 (1.08) Madagascar 2.45 2.38 1.77 5.68 2.40 0.94 (0.19) Mozambique 0.96 1.11 0.76 6.01 0.33 0.66 (0.35) Malawi 1.17 0.75 1.02 13.09 0.64 0.73 (0.51) Nigeria 1.42 1.07 1.37 8.33 0.90 0.78 (0.28) Senegal 2.48 0.00 0.85 8.42 0.00 1.05 (0.42) Tunisia 0.00 1.66 0.71 10.50 0.00 1.08 (0.65) Tanzania 1.73 1.95 1.31 6.13 0.64 0.68 (0.21) Uganda 1.30 1.67 1.48 7.09 1.01 0.80 (0.21) Rest of South Central Africa 0.76 1.39 0.63 8.70 0.48 0.55 (0.28) Rest of Central Africa 1.15 1.33 1.00 5.42 1.61 0.59 (0.10 Rest of Eastern Africa 3.33 2.17 0.81 8.27 0.79 0.82 (0.32) Rest of South African Custom Union 3.40 0.90 0.53 8.56 0.00 0.96 (0.55) Rest of West Africa 1.60 2.05 0.71 7.55 0.58 0.72 (0.23) South Africa 2.29 2.03 2.96 20.88 1.61 1.84 (1.28) Zambia 1.38 6.12 1.74 6.12 1.40 1.26 (0.84) Zimbabwe 2.41 3.50 0.99 5.55 1.38 1.05 (0.49) Average 2.64 2.30 1.53 8.71 1.08
  • 11. Results and Discussions 1. Trade flows and yield variability in Africa  In our model the yield variability parameters governing comparative advantage are 2.62 and 2.84  In the world crop sector, the yield parameter variability is between 2.52 and 4.96 (Reimer and Li, 2010)  This reflects crop and food productivity is more heterogeneous in Africa than in the world as a whole
  • 12. Results and Discussions 2. Table 2: Determinants of bilateral trade flows Source of barrier dist1 [0,375] dist2 [275,750] dist3 [750,1500] dist4 [1500,3000] dist5 [3000, max] Border Language Currency Country Egypt Ethiopia Morocco Madagascar Mozambique Malawi Nigeria Senegal Tunisia Tanzania Uganda Rest of South Central Africa Rest of Central Africa Rest of Eastern Africa Rest of Sth African Custom Union Rest of West Africa South Africa Zambia Zimbabwe Coefficient Estimate -θd1 -7.16 -θd2 -8.80 -θd3 -10.43 -θd4 -12.06 -θd5 -12.98 -θb 1.38 -θl 0.71 -θc 0.53 Destination country Coefficient Estimate p-value -θm1 2.68 0.00 -θm2 -0.40 0.54 -θm3 2.89 0.00 -θm4 -4.84 0.00 -θm5 -0.16 0.81 -θm6 -1.36 0.03 -θm7 -2.89 0.00 -θm8 0.28 0.68 -θm9 1.25 0.05 -θm10 -0.14 0.83 -θm11 -3.09 0.00 -θm12 -2.96 0.00 -θm13 -0.88 0.18 -θm14 3.30 0.00 -θm15 0.05 0.94 -θm16 1.36 0.03 -θm17 6.65 0.00 -θm18 -0.91 0.16 -θm19 -0.84 0.18 p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.47 Source country Coefficient Estimate 1.88 S1 -1.84 S2 1.77 S3 -2.10 S4 -0.44 S5 -1.64 S6 -0.09 S7 0.04 S8 0.69 S9 -0.03 S10 -2.38 S11 -0.30 S12 0.52 S13 2.39 S14 -0.32 S15 1.45 S16 3.14 S17 -1.43 S18 -1.30 S19 p-value 0.00 0.00 0.00 0.00 0.31 0.00 0.83 0.93 0.10 0.94 0.00 0.49 0.24 0.00 0.45 0.00 0.00 0.00 0.00
  • 13. Results and Discussions 2. Table 2: Determinants of bilateral trade flows Source of barrier dist1 [0,375] dist2 [275,750] dist3 [750,1500] dist4 [1500,3000] dist5 [3000, max] Border Language Currency Country Egypt Ethiopia Morocco Madagascar Mozambique Malawi Nigeria Senegal Tunisia Tanzania Uganda Rest of South Central Africa Rest of Central Africa Rest of Eastern Africa Rest of Sth African Custom Union Rest of West Africa South Africa Zambia Zimbabwe Coefficient Estimate -θd1 -7.16 -θd2 -8.80 -θd3 -10.43 -θd4 -12.06 -θd5 -12.98 -θb 1.38 -θl 0.71 -θc 0.53 Destination country Coefficient Estimate p-value -θm1 2.68 0.00 -θm2 -0.40 0.54 -θm3 2.89 0.00 -θm4 -4.84 0.00 -θm5 -0.16 0.81 -θm6 -1.36 0.03 -θm7 -2.89 0.00 -θm8 0.28 0.68 -θm9 1.25 0.05 -θm10 -0.14 0.83 -θm11 -3.09 0.00 -θm12 -2.96 0.00 -θm13 -0.88 0.18 -θm14 3.30 0.00 -θm15 0.05 0.94 -θm16 1.36 0.03 -θm17 6.65 0.00 -θm18 -0.91 0.16 -θm19 -0.84 0.18 p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.47 Source country Coefficient Estimate 1.88 S1 -1.84 S2 1.77 S3 -2.10 S4 -0.44 S5 -1.64 S6 -0.09 S7 0.04 S8 0.69 S9 -0.03 S10 -2.38 S11 -0.30 S12 0.52 S13 2.39 S14 -0.32 S15 1.45 S16 3.14 S17 -1.43 S18 -1.30 S19 p-value 0.00 0.00 0.00 0.00 0.31 0.00 0.83 0.93 0.10 0.94 0.00 0.49 0.24 0.00 0.45 0.00 0.00 0.00 0.00
  • 14. Results and Discussions 2. Table 2: Determinants of bilateral trade flows Source of barrier dist1 [0,375] dist2 [275,750] dist3 [750,1500] dist4 [1500,3000] dist5 [3000, max] Border Language Currency Country Egypt Ethiopia Morocco Madagascar Mozambique Malawi Nigeria Senegal Tunisia Tanzania Uganda Rest of South Central Africa Rest of Central Africa Rest of Eastern Africa Rest of Sth African Custom Union Rest of West Africa South Africa Zambia Zimbabwe Coefficient Estimate -θd1 -7.16 -θd2 -8.80 -θd3 -10.43 -θd4 -12.06 -θd5 -12.98 -θb 1.38 -θl 0.71 -θc 0.53 Destination country Coefficient Estimate p-value -θm1 2.68 0.00 -θm2 -0.40 0.54 -θm3 2.89 0.00 -θm4 -4.84 0.00 -θm5 -0.16 0.81 -θm6 -1.36 0.03 -θm7 -2.89 0.00 -θm8 0.28 0.68 -θm9 1.25 0.05 -θm10 -0.14 0.83 -θm11 -3.09 0.00 -θm12 -2.96 0.00 -θm13 -0.88 0.18 -θm14 3.30 0.00 -θm15 0.05 0.94 -θm16 1.36 0.03 -θm17 6.65 0.00 -θm18 -0.91 0.16 -θm19 -0.84 0.18 p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.47 Source country Coefficient Estimate 1.88 S1 -1.84 S2 1.77 S3 -2.10 S4 -0.44 S5 -1.64 S6 -0.09 S7 0.04 S8 0.69 S9 -0.03 S10 -2.38 S11 -0.30 S12 0.52 S13 2.39 S14 -0.32 S15 1.45 S16 3.14 S17 -1.43 S18 -1.30 S19 p-value 0.00 0.00 0.00 0.00 0.31 0.00 0.83 0.93 0.10 0.94 0.00 0.49 0.24 0.00 0.45 0.00 0.00 0.00 0.00
  • 15. Results and Discussions 2. Table 2: Determinants of bilateral trade flows Source of barrier dist1 [0,375] dist2 [275,750] dist3 [750,1500] dist4 [1500,3000] dist5 [3000, max] Border Language Currency Country Egypt Ethiopia Morocco Madagascar Mozambique Malawi Nigeria Senegal Tunisia Tanzania Uganda Rest of South Central Africa Rest of Central Africa Rest of Eastern Africa Rest of Sth African Custom Union Rest of West Africa South Africa Zambia Zimbabwe Coefficient Estimate -θd1 -7.16 -θd2 -8.80 -θd3 -10.43 -θd4 -12.06 -θd5 -12.98 -θb 1.38 -θl 0.71 -θc 0.53 Destination country Coefficient Estimate p-value -θm1 2.68 0.00 -θm2 -0.40 0.54 -θm3 2.89 0.00 -θm4 -4.84 0.00 -θm5 -0.16 0.81 -θm6 -1.36 0.03 -θm7 -2.89 0.00 -θm8 0.28 0.68 -θm9 1.25 0.05 -θm10 -0.14 0.83 -θm11 -3.09 0.00 -θm12 -2.96 0.00 -θm13 -0.88 0.18 -θm14 3.30 0.00 -θm15 0.05 0.94 -θm16 1.36 0.03 -θm17 6.65 0.00 -θm18 -0.91 0.16 -θm19 -0.84 0.18 p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.47 Source country Coefficient Estimate 1.88 S1 -1.84 S2 1.77 S3 -2.10 S4 -0.44 S5 -1.64 S6 -0.09 S7 0.04 S8 0.69 S9 -0.03 S10 -2.38 S11 -0.30 S12 0.52 S13 2.39 S14 -0.32 S15 1.45 S16 3.14 S17 -1.43 S18 -1.30 S19 p-value 0.00 0.00 0.00 0.00 0.31 0.00 0.83 0.93 0.10 0.94 0.00 0.49 0.24 0.00 0.45 0.00 0.00 0.00 0.00
  • 16. Results and Discussions 3. Counterfactual 1: Yield increase effects  A yield increase of 30% in Western Africa (Nigeria, Senegal and the Rest of West Africa) would increase net welfare by 5.66 % due to prices drop of 8.59-8.75% and intra-African trade would slightly improve by 0.54%  A rice yield increase of 30% in Africa would increase net welfare by 1.23% with a price decrease of 2.03%.  The percentage change in Africa home production of all staple foods would decrease by 9.5%,  There is no significant change in Africa staple food trade even if only 2 countries/regions would record a drop in imports of all staple foods
  • 17. Results and Discussions 3. Counterfactual 2: Effects of increased yield variability  Almost African countries would have welfare decrease with a minimum of 1.5 % for Morocco and a maximum of 10.7 % for Zimbabwe, due to a crop and food price increase of 2.3 % and 58.2 %, respectively.  Only Egypt and South Africa would have a welfare increase of 5.9% and 2.2%, respectively. The highest decrease in crop and food prices would offset the decrease in land rental rate (-0.41 % for Egypt and -10.8 % for South Africa).  The intra-African crop and food trade would only increase by 2.7%.
  • 18. Results and Discussions 3. Counterfactual 3: Land increase effects  A 30% increase in cultivated land in Tanzania would rise its net welfare by at least 16 % mainly due to a drop of crop and foods prices and a respective decrease of the land rental rate of 17 %. The Rest of Eastern Africa would benefit the most from this situation with a decrease of domestic food price of around 2 %.  The intra-African trade would increase by 3% with an export rise of 67% for Tanzania.  The highest imports increase are recorded by Malawi (32%) and the Rest of Eastern Africa (25%).
  • 19. Results and Discussions 4. Food security implications  On average these crops and foods provided 1419 Kcal/capita/day in Africa in 2004.  We found a positive and significant correlation (66%) between quantities of crop and food imported and total Kcal/Pers/Day.  We found, as well, a positive and significant correlation (43%) between GDP per capita and total Kcal/Pers/Day.
  • 20. Results and Discussions 4. Food security implications From these evidences agricultural trade in Africa could play a major role for Food Security in the continent:  When the other African countries reduce their import trade costs to the level of South Africa,  African trade would increase by 1525%.  Net welfare would increase on average by 38 % Doubling intra African Trade volume:  A welfare increase of 1.3%  Decrease of crop and food price of 6%
  • 21. Conclusion  Productivity is still more heterogeneous across African countries than in the world as a whole  Distance is the main impediment for African trade and makes prohibitive barriers costs for trading partners.  Common borders and languages have a positive impact on trade in Africa  An improvement of competitiveness could highly contribute to food security by stimulating trade and increasing total income in the agricultural sector.
  • 22. Acknowledgement Many thanks to  DAAD (German Academic Exchange Service) and the Food Security Center (University of Hohenheim, Germany)  Associate Prof. Jeffrey Reimer (Oregon State University, USA)  Prof Martina Brockmeier and Beyhan Bektasoglu (Assistant of Prof. Brockmeier) (University of Hohenheim, Germany)
  • 23. Thank you! Merci!