1. The 7th Annual Conference on Regional Integration in Africa (ACRIA7) on the Topic
External Sector and Inclusive Development in West Africa
Jointly organized by
CREPOL WAIFEM WAMA & WAMI
Driving factors of intra-regional trade in agricultural goods: The case of West African Economic and Monetary Union
Cotonou, July 8-9, 2016
Dr. Toussaint Houeninvo ,
African Development Bank
Regional office for Dakar, Senegal

2. Presentation Outline 1-Introduction and context
2- Intra-WAEMU trade in agricultural goods still low despite trade liberalization measures since dthe 1990s
3-The research question
4-Theoretical and empirical foundations of the gravity model in the analysis of trade in regional integration area
5- Presentation of the model, data sources and sampling
6- Model specification (fixed-effects or random-effects) and results
7- Conclusion and Policy recommendations 2

3. 1 – Introduction and context
Since the 2008 financial crisis and the European debt in 2010, and their impact on African exports, strengthening regional integration appears as an alternative to build resilience to shocks and promote economic development in West Africa.
In this context, and given the fact that agricultural value added in the GDP represents between 20% and 50% of WAEMU Countries, boosting Intra regional trade especially intra-WAEMU agricultural exports can serve as a key factor for inclusive development in West Africa.
The hypothesis of the inclusive character of trade in agricultural goods is strengthened by the fact that most of the population in West Africa live in rural area and do not mainly benefit from the broad economic growth at macro level.
In such a condition, Intra-regional trade in agricultural goods could contribute to reduce, inequality and poverty 3

4. 2 – Intra-WAEMU trade in agricultural goods still low despite trade liberalization measures since the 1990s
The structure of WAEMU tariff applied on external trade include the Custom rate and two community tax namely Community Solidarity Levy (1%) and the Statistical Tax (1%). .
Trade liberalization initiated in early 1990s with the structural adjustment programs in different WAEMU Countries has been strengthened since 1996 by the Additional Act 96/04 of 16 May 1996 that has led today to the removal of quotas and other quantitative restrictions and 0% tariff on crude products originating from WAEMU including agricultural non processed goods.
Moreover, since January 2003, there is the lifting of the certificate of origin requirement for raw products including agricultural intra-WAEMU exports except seafood.
Nevertheless non harmonized tariff rates charged by different member countries could not promote the development of intra-Community trade 4

5. 2– Intra-WAEMU trade in agricultural goods still low despite trade liberalization measures since the 1990s (cont..)
Structure of tariff before the Common External tariff of WAEMU (1st January 2000)
Types of tariff
Countries
Minimal rate
Maximal rate excluding Community Solidarity Tax (1%) and Statistical tax (1%)
Benin 2%
20%
Burkina Faso 9%
33%
Côte d’Ivoire
7.6%
35%
Guinea- Bissau
10%
105%
Mali 8%
30%
Niger
Senegal
25%
60%
Togo 6% 5

6. Community Solidarity Tax (CST)
2–Intra-WAEMU trade in agricultural goods still low despite trade liberalization measures since the 1990s (cont..)
Structure of tariff after the Common External tariff of WAEMU (1st January 2000)
Tariff
Categories
Tariff rate
Statistical tax (ST)
Community Solidarity Tax (CST)
Overall tariff
Category 0 0% 1% 2%
Category1 5% 7%
Category 2
10%
12%
Category 3
20%
22%
Category 5 (ECOWAS CET since 2015)
35%
37% 6

7. 2 – Intra-WAEMU trade in agricultural goods still low despite trade liberalization measures since the 1990s (cont..)
In spite of all these measures one can notice that several years after the creation of the UEMOA in 1994 and the adoption of a Common External Tariff in 2000 (Customs Union), the intra-WAEMU trade is still low estimated at 12% between 1994 and 2014.
As compared to the expectations, at the time of the establishment of the CET, WAEMU Commission was expecting to reach 25% of intra-WAEMU trade at the end of 2005 (Coulibaly, Traore and Diarra 2015).
In terms of comparison to the other Regional integration zones, Intra-trade is about 64% for European Union, 60% pour North American Free Trade Area (NAFTA), 35% for ASEAN and 30% for MERCOSUR.
Intra-WAEMU agricultural exports represent only 4% of total WAEMU exports 7

8. 2 – Intra-WAEMU trade in agricultural goods still low despite trade liberalization measures since the 1990s (cont..)
In terms of share of intra-WAEMU agricultural exports in intra-WAEMU exports they are estimated at 29.20% in 1996 and 32.3% in 2015, hardly 3% points increase over 21 years since the signing of the agreement establishing the WAEMU zone
According to a recent statement by the President of the WAEMU this low level of intra-WAEMU trade is due to lack of competitiveness due to cost factors, the supply, the structural weakness of the physical infrastructure, lack of complementarity between the economies and obstacles to the free movement of goods in the countries 8

9. 3 – The Research question
Beside value chain development and the level of processing, one of the constraints to trade in West Africa is the quality of transport and the related cost of transportation and logistics including Non-Tariff Barriers that jeopardize competitiveness.
Most of the time, several goods imported from out of the continent especially from Asia, Europe and America have been more competitive than the intra WAEMU products.
Hence, the main research question is on the impact of distance (transport / logistical cost) and the level of development on intra-WAEMU agricultural exports.
The paper analyzes the determinants of intra-WAEMU trade in agricultural products and therefore the variables on which policymakers could act to promote intra-regional agricultural trade. 9

10. 4 –Theoretical and empirical foundations of the gravity model in the analysis of trade in regional integration area
The underlining assumption is that the GDP, that is the economic mass, acts as the attractor of trade between two trading partners and therefore exert a positive effect on trade. In contrast, distance, a measure of the cost of transport used by most studies, serves as resistance factor and plays a negative role in trade.
From an empirical point of view, This has been applied by Tinbergen (1962); Linnemann (1966); Obstfeld and Rogoff (2000); Xubei (2001); Jakab et al. (2001); Gbetnkom and Avom (2005); Coulibaly, Traore and Diarra (2015) 10

11. 5 – Presentation of the model, data sources and sampling
With a1, a2 >0 and a3<0
Where EXPAij is the value of Exports from country i to country j
B is an intercept
t is the period
Yit is the GDP of Country i during the period
Yjt is the GDP of Country j during the period
DISTij is the distance between country i and country j (i ≠ j). In practice it is the distance between the major capitals and ports of the two countries 11 11

12. 5 – Presentation of the model, data sources and sampling (cont..)
Taking the logarithm of equation1 lead to
Where a1 and a2 are >0 and a3 <0
But external trade is not influenced only by these two factors. Therefore the basic model has been extended by adding other factors include the characteristics of partner countries(control variables) following Luo Xubei (2001), Gbetnkom and Avom (2005), Coulibaly Traore and Diarra (2015)
These control variables are Population, Foreign Direct Investment (FDI), Level of Integration(Common external tariff),Political Stability and Absence of Terrorism 12

13. 5 – Presentation of the model, data sources and sampling (cont..)
Where
Yit is the GDP of Country i during the period
Yjt is the GDP of Country j during the period
DISTij is the distance between country i and country j (i ≠ j). In practice it is the distance between the major capitals and ports of the two countries
POPit is the population of country I in the year t
FDIit-2 is the Foreign Direct Investment of Country I in the year t-2
Zit is the dummy variable for the Common External Tariff during the period t (level of integration)
PSit stands political stability and absence of terrorism (Governance variable)
ᵋ is the error term 13

14. 5 – Presentation of the model, data sources and sampling (cont..)
Expected signs of the variables
Source of data
WDI, UNCTAD, WAEMU Commission,
Sampling
126 observations of panel data covering 7 WAEMU member countries over yearly data
Variables
Intercept
GDPit
DISTij
POPit
FDIit
Zit
PSit
Coefficients b0 a1 a2 a3 a4 a5 a6
Expected signs + - 14

15. 6 – Model specification (fixed-effects, random-effects or mixed effects) and results
Several types of test have been considered to that end including Fischer test and Hausman test. Breusch and Pagan Lagrangian Multiplier test for random-effects.
While the Fischer global test measures the global significance of individual effects (fixed-effects), Breusch Pagan Lagrangian Multiplier, the Hausman test the presence of random-effects
In the Fischer test using STATA software with which we regressed exports on the other explanatory variables the “within” coefficient of determination which gives the explanatory power of the fixed-effects model has a very weak value of This suggests that the assumption of the presence of fixed-effects is not corroborated.
The random-effects has been tested and the “between” coefficient of determination which gives the explanatory power of the random-effects model has a value of 0.69 corroborating the random-effects assumptions 15

16. 6 – Model specification (fixed-effects, random-effects or mixed effects) and results
Moreover in order to deepen the specification process we run the Hausman test for which the probability is 54%. Since the probability is greater than 10%, the Hausman test corroborates the choice in favor of a random-effects model.
This is consistent with the fact that the within estimator is unable to estimate the marginal impact of variables that are invariant over time (e.g distance in our case).
Hence, estimating the model under the above assumption and after performing the required econometric testing (Wooldgride autocorrelation test, Breusch-Pagan heteroscedasticity) gives the following results 16

17. 6 – Model specification (fixed-effects, random-effects or mixed effects) and results(cont..)
Recapitulation of the results
t student in parenthesis;
** Coefficient significant at 1%
* Coefficient significant at 5%
(1)
(2)
Log (EXPA)
Intercept
2.56
(0.31) -
Log (GDP)
1.01*
(3.13)
0.92**
(7.01)
Log(DIST)
-0.97*
(-3.02)
-0.80**
(-12.18)
Log(FDI-2)
0.27*
(2.80)
0.24**
(3.34)
Log(POP)
-0.13
(-0.27) PS
0.11
(1.13) Z
0.15
(0.94)
Observations
111
Wald chi2 (6)
349.86
Prob>chi2
0.0000 17

18. 6 – Model specification (fixed-effects, random-effects or mixed effects) and results(cont..)
As shown in table 2, in the panel random effects models, all the key variables are significant and have the expected signs.
The basic variables (the two factors of gravity) are significant at 1% level with the expected signs.
Regarding the four control variables meaning population (pop), foreign direct investment (FDI-2), Political stability (PS) and the Common External Tariff (Z), all of them have the expected signs but only the FDI with 2 lag years is significant. Similar to the core variables of the model the FDI is strongly significant at1%. 18

19. 6 – Model specification (fixed-effects, random-effects or mixed effects) and results(cont..)
1% increase in distance will lead to 0.8% decrease in intra-WAEMU agricultural exports
1% increase of GDP leads to 0.9% increase in intra-WAEMU agricultural exports
1% in the FDI (with 2 lag years) will yield 0.25% increase in intra WAEMU agricultural exports. 19

20. 7– Conclusion and policy recommendations
These results call for some policy recommendations including the following:
Macroeconomic and structural reforms that can lead to an overall increase of GDP and the level of development of WAEMU which will in turn favor intra-WAEMU agricultural exports.
Construction/modernization of West African Corridors to facilitate connection among West African Countries
Reforms in agricultural sector including attracting FDI to modernize the sector and promote agricultural transformation as it appears in an agricultural transformation strategy for Africa under preparation at African Development Bank 20

21. Thank you 21