1. 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

2. 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)

3. 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

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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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:

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

12. 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

13. 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

14. 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

15. 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***

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

17. 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
0.469
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**
-1.799***
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
0.127
-0.533
-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
0.0443
0.611
(1.00)
(1.47)
(1.27)
(1.66)
(-0.44)
(-0.99)
(-0.20)
(-0.19)
(0.23)
(0.97)

18. 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
-0.461
-0.455
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*
0.489
-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
-0.113
0.0835
0.485
(1.10)
(0.83)
(1.02)
(1.23)
(0.85)
(-0.19)
(-0.49)
(0.47)
(1.00)

19. 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

20. Implications for food security

21. 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

22. 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

23. Thank you!