Effects of regional trade agreements on strategic agricultural trade in Africa and its implications for food security: Evidence from gravity model estimation Fredu Nega Edris Hussein African Economic Conference Dec 5 – 7, 2016 Abuja, Nigeria

Introduction Agriculture is The sector is characterized by: dominant sector in most African countries important vehicle for economic growth The sector is characterized by: Low level of productivity due to inadequate capital formation and low level of technology Extremely fragmented agricultural markets. Food security is recognized as one of the major challenges facing African continent Undernourishment in SSA is 23.8%, the highest proportion of all developing regions (FAO, 2014)

Introduction Since the Lagos Plan of Action (1963), the problems of African agriculture has been at the forefront of debate A practical solution to the problem evolved 2004 AU Meeting in Sirte, Libya 2006 AU/NEPAD Summit in Food Security

Introduction The solution: Create common African Agricultural Market Focus on strategic agricultural commodities without prejudice to ongoing efforts at sector wide development

Introduction Beef Poultry Dairy Products Legumes Cassava Maize Rice Strategic commodities were selected based on: Weight in the African food basket Weight in the trade balance Unexploited potential 12 strategic agri. commodities were selected Beef Poultry Dairy Products Legumes Cassava Maize Rice Sorghum Groundnut Oil palm Sugar Cotton

Regional Trade Agreements (RTAs) Regional integration is embraced as the key to improve trade performance Eight RTAs (RECs) are recognized by AU as building blocks of the African Economic Community AMU CENSAD COMESA ECCAS ECOWAS EAC IGAD SADC

Are RTAs building blocks of regional integration? Two views about RTAs Building Blocks RTAs promote free trade Stumbling Blocks RTAs lower global welfare and divert import flows from lower cost suppliers

Empirical investigation Empirical investigations failed to solve the puzzle. Optimistic Conclusion (Elbadawi, 1997; Evans 1998; Flores, 1997) South-south economic integration could generate the threshold scales necessary to trigger the much-needed strategic complementarities Pessimistic conclusion (World Bank, 2000; Yeats, 1998; Schiffs, 1997) The smaller the intra-regional shares in total trade, the more likely the trading blocs would become trade diverting

Objective of the study Objective: Analyse trade creation and trade diversion effects of RTAs in Africa on trade in selected agrifood commodities All selected strategic agricultural commodities except Maize, Cassava and Cotton are used in the analysis

Methodology – Gravity model Gravity model is used for analysis The basic premise of the model is that, trade is associated with economic size (GDP) inhibited by distance Other variables included are: Other standard variables, regional trade dummies, time dummies The full form of the model in log linear form:

Methodology – Gravity model To control for Endogeniety of the RTA, we follow Baier and Bergstand (2007) and estimate the following gravity model

Methodology – Gravity model Two Problems in linearized gravity model: How to treat zero trade flows? Drop zero values – loss of useful information Retain zeros By adding small values such as 1 Estimating the model in levels Hetroskedasticity problem – inconsistent estimates Pseudo Poisson Maximum Likelihood (PPML) is used (Santo Silva and Tenreyro, 2006): Robust in the presence of hetroskedasticity Estimated in levels

Data Source Data for the period 1998 – 2010 was obtained from: CEPII Bilateral trade data for nine commodities Distance and other dummy variables (language, colonial history, landlocked, contiguity) World Development Indicators data base GDP and PC income

Results and discussion Ten different regression results – one for each of the nine strategic commodities and one for all agrifood commodities Effects are analysed for Standard gravity model variables Regional integration dummies

Standard gravity model variables Sorghum 1.667*** 1.395*** -1.381*** Beef 0.789*** 2.074*** -2.583*** Poultry 0.659*** 2.682*** -1.040*** Dairy 0.162*** 2.402*** -1.785*** Oilpalm 0.098 0.835*** -2.225*** Groundnut 1.034*** 0.564*** -1.349*** Legumes 1.258*** 1.353*** -1.506*** Rice 2.132*** 6.938*** -2.851*** Sugar 2.168*** 4.302*** -1.324*** Total 1.645*** 3.253*** -1.654***

Standard gravity model variables Sorghum + - Beef Poultry Dairy Oilpalm Groundnut Legumes Rice Sugar Tot. Agri.

Trade Creation (1) Sorghum (2) Sugar (3) Rice (4) Poultry (5) Oil palm   (1) Sorghum (2) Sugar (3) Rice (4) Poultry (5) Oil palm (6) Legumes (7) Dairy (8) Beef (9) Ground Nut (10) Total AMU 0.0457 0.543 0.323 -1.256*** 0.0796 0.481 0.493 0.125 -0.285 0.216 (0.85) (1.71) (1.13) (-5.67) (0.38) (1.41) (1.59) (0.70) (-1.06) (0.53) CEN-SAD -1.323*** -3.662*** -2.417*** -0.211 -1.121 -4.587*** -5.242*** 0.049 -1.927** -3.556** (-4.64) (-3.67) (-3.37) (-0.31) (-1.75) (-4.94) (-5.99) -0.08 (-2.83) (-3.10) COMESA 0.0878 0.455 -0.0275 0.469 0.316 0.596 0.55 1.104** 0.442 1.168* (0.58) (1.25) (-0.11) (1.54) (1.17) (1.29) (1.53) (2.59) (1.34) (2.40) EAC 0.231 1.903* 1.802* 0.624 1.424* 0.251 0.692 1.132 0.595 2.370** (0.41) (2.18) (2.35) (1.42) (2.29) (0.27) (1.24) (1.70) (1.08) (3.20) ECCAS 0.0531 -1.378** -0.0462 -1.799*** -0.0007 0.397 -0.943* 0.0298 -0.337 -2.549*** -0.85 (-3.12) (-0.10) (-5.03) (-0.00) (1.02) (-2.14) (0.12) (-4.62) ECOWAS 0.0169 -0.441 -0.207 -0.313 -0.0235 0.127 -0.533 -0.0186 -0.0042 -0.109 (0.31) (-1.09) (-0.48) (-1.07) (0.36) (-1.10) (-0.08) (-0.01) (-0.23) IGAD 4.536*** 6.776*** 4.780*** -0.113 3.971*** 4.314*** 3.457*** 0.531 1.811** 6.746*** (15.13) (9.61) (8.26) (-0.22) (8.46) (5.46) (4.91) (0.91) (3.02) (7.67) SADC 0.149 0.842 0.383 0.428 -0.167 -0.301 -0.0706 -0.0405 0.0443 0.611 (1.00) (1.47) (1.27) (1.66) (-0.44) (-0.99) (-0.20) (-0.19) (0.23) (0.97)

Trade Diversion (1) Sorghum (2) Sugar (3) Rice (4) Poultry (5)   (1) Sorghum (2) Sugar (3) Rice (4) Poultry (5) Oil Palm (6) Legumes (7) Dairy (8) Beef (9) Ground Nut (10) Total AMU 0.102 -0.418 0.0858 -0.654 0.0778 0.664 -0.827 0.251 -0.252 -0.423 (0.81) (-1.05) (0.19) (-1.85) (0.29) (1.68) (-1.92) (0.95) (-0.80) (-0.85) CEN-SAD -1.413*** -4.726*** -2.507*** -1.382* -1.132 -4.661*** -5.674*** -0.16 -2.069** -5.723*** (-5.10) (-4.86) (-3.58) (-2.15) (-1.79) (-5.08) (-6.68) (-0.28) (-3.11) (-5.11) COMESA 0.18 0.349 0.0708 0.456 0.248 0.804 0.624 1.157** 0.548 1.086* (1.16) (0.99) (1.45) (0.92) (1.77) (1.72) (2.71) (1.63) (2.42) EAC -0.111 -0.102 -0.246 -0.0143 -0.0989 -0.461 -0.455 -0.0965 0.136 (-0.91) (-0.31) (-0.99) (-0.08) (-0.50) (-1.93) (-1.78) (-0.56) (0.46) (-1.13) ECCAS 0.126 -0.417 0.0859 -0.643* -0.0415 0.489 -0.598 0.122 -0.333 -0.329 (1.17) (-0.98) (-2.02) (-0.16) (1.28) (-1.40) (0.48) (-1.08) (-0.64) ECOWAS 0.0481 -0.432 -0.145 -0.131 0.459 -0.615 0.155 -0.414 -0.4 (0.84) (-1.20) (-0.35) (-1.51) (-0.57) (-1.56) (0.70) (-1.37) (-0.87) IGAD -1.371*** -3.359*** -2.427*** -0.371 -1.434** -4.440*** -5.578*** -1.053*** -2.961*** -4.512*** (-5.33) (-6.27) (-1.32) (-3.07) (-12.58) (-13.06) (-4.09) (-8.15) (-6.29) SADC 0.188 0.355 0.283 0.275 0.242 -0.266 -0.0697 -0.113 0.0835 0.485 (1.10) (0.83) (1.02) (1.23) (0.85) (-0.19) (-0.49) (0.47) (1.00)

Implications for food security Two effects of trade Allocation Effect - seems to have been small Accumulation Effect - Dynamic and can have a potentially much larger and positive effect. However, Small allocation effects likely imply accumulation effects have also been limited

Implications for food security  

Implications for food security REC No. of products considered No. of products regional I exceeds individual countries I Remark ECOWAS 10 8 These are the countries that reported Net Trade Diversion Effect CENSAD 5 IGAD 3 EAC 2 ECCAS SADC AMU 1 Countries with lower instability index than the regional index would not gain if integration is enhanced. This indicates national incentives to cooperate regional can vary widely

Conclusion RTAs in Africa have mixed results Net trade creation effects – in 4 out of 8 RTAs Net trade diversion effects – in 3 out of 8 RTAs The low level of intraregional trade and the signs that the RTAs can lead to net trade creation effect indicates the opportunities to deepen integration and expand agricultural trade Poor conditions of infrastructure forms the bottleneck e.g. DISTANCE

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