Abstract
This paper analyses threshold effects and transmission Channels of foreign aid on economic growth of WAEMU countries using the World Bank, the International Monetary Fund and the International Country Risk Guide databases to cover the period spanning from 1980 to 2018. After applying endogenous threshold approach to determine aid threshold above which its affects growth and estimate its long-term effects, Pagan’s Residual-Generated Regressors method was used to identify channels that can modulate effects of aid on growth. Evidences strongly support the view that the relationship between aid and economic growth is non-linear with a threshold that lies between 12.37 and 14.08% of GDP. Above these values, the marginal effect of aid is 2.1%. Moreover, results indicate that only investment seems to be a potential channel through which aid would affect growth. Thus, aid within the WAEMU countries is beneficial. Its content and use should be the main concern of donors and policy-makers.
Acknowledgments
The main author of this paper acknowledges the immense support from the West African Economic and Monetary Union (WAEMU) Commission for the financial support and opportunity as a Visiting Research Scholar at the Department of Economic Policies and Domestic Taxation of the WAEMU Commission that culminated into this study. Moreover, he would like to thank the United Nations Economic Commission for Africa (UNECA) for the use of their facilities during the completion of this paper as a Research Fellow with the Macroeconomic and Governance Division (MGD). The views expressed are those of the author and do not represent that of the WEAMU Commission or the United Nations and the BCEAO.
Disclosure statement
We wish to confirm that there are no known conflicts of interest associated with this publication.
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Notes
1 WAEMU zone includes the following countries: Benin, Burkina Faso, Côte d'Ivoire, Guinea Bissau, Mali, Niger, Senegal and Togo.
2 See Chudik et al. (Citation2017) for more details.
3 See ‘Model 4’, Section 3, (pp. 232–235) in de Pagan’s (Citation1984) paper, which includes both RGR and PGR.
4 The institutional quality index (IQI) constructed is the arithmetic mean of the four following variables values: Political Stability, Corruption, Democratic Accountability, and Law and Order, from International Country Risk Guide provided by the Political Risk Services.
5 In the appendix of his paper, Pagan (Citation1984) shows that the standard deviations estimated by OLS are not correct because they are underestimated compared to the correct standard deviations.
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Notes on contributors
Nimonka Bayale
Nimonka Bayale is an Economist at the Economic Studies and Regional Integration Directorate, Central Bank of West African States (BCEAO), Dakar, Senegal.
Fousseini Traoré
Fousseini Traoré is a Research Fellow at the International Food Policy Research Institute (IFP RI) Dakar, Senegal.
Souleymane Diarra
Souleymane Diarra is a Professional Economist at the Strategy and Studies Division, West African and Monetary Union (WAEMU) Commission, Ouagadougou, Burkina Faso.
Faustin Maniraguha
Faustin Maniraguha is an Economist at the Modelling and Forecasting Division, Central Bank of Rwanda (CBR), Kigali, Rwanda.