Abstract
The objective of this study is to understand the process of development of Union countries by analysing real convergence. To reach this goal, it analyses absolute and conditional convergence on one hand and sigma convergence on the other. The data used comes from several sources: the World Bank, Penn World Tables, the ADB and the BCEAO.
In analyzing absolute and conditional convergence, the study uses the Bayesian estimation method to determine the speed of (absolute and conditional) convergence for each country. This study chose not to use the stacked method because it does not enable one to obtain the speed of convergence for each country. This latter method determines a single speed for all the countries. Analysis of sigma convergence is done using a graph. The idea is to represent and analyse the per capita GDP variance ratio. This ratio is the relation between per capita GDP variance at year t and at year 1994. The year 1994 was chosen because it is the year the Union was founded. The results of the study show that there is weak absolute convergence within the Union and that the educational policies, just as the openness policies, could accelerate growth and convergence in these countries.
The study also notes the presence of sigma convergence for the periods 1980–1994 and 2000–2008. Note that the first period is a “before-integration” period and the second an “after-integration” period. For the latter, one can say that the countries are in the process of economic integration. The absence of sigma convergence during the 1994–2000 period does not in any way bring into question the positive impact of integration on sigma convergence. In effect, it is possible that a policy does not produce immediate effects. Generally, there is a time-lag between when a policy is put into place and when the effects of the policy can be felt. That could be the case in this study. Countries often take time to adapt to the new rules and measures, and as a result, the date the treaty comes into effect does not coincide with the practical application of its measures.
The heterogeneity of data sources is a limitation of this study. In addition, there was no data for Guinea-Bissau for the entire study period and was therefore excluded from the analysis of absolute and conditional convergence. Other equally important variables were not integrated into the analysis. These are variables that measure the quality of institutions such as democracy, good governance, property rights protection, etc. Future research could take these aspects into account.
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Notes
- 1.
It is in the analysis of conditional convergence that in addition to the variable \(\text{log}( {y_{i,t - 1}} \)), there are other explicative variables such as degree of openness and schooling ratio.
- 2.
Convergence literature shows that the majority of studies arrive at values nearing 2%.
- 3.
One just needs to resolve the equation \( { \ln }\left( {{1}/{2}} \right) = { \ln }{{\text{e}}^{ - \beta t}} \) with t as unknown.
- 4.
UNESO Africa Department (2001).
- 5.
Cited by Gaulier and Frédéric (2001).
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Wetta, C., Yerbanga, A. (2012). Real Convergence in the WAEMU Area: A Bayesian Analysis. In: Ayuk, E., Kaboré, S. (eds) Wealth through Integration. Insight and Innovation in International Development, vol 4. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4415-2_6
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