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The Devil is in the Details: On the Robust Determinants of Development Aid in G5 Sahel Countries

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Abstract

This paper introduces model uncertainty into the empirical study on the determinants of development aid at the regional level. This is done by adopting a panel Bayesian model averaging approach applied on the data of G5 Sahel countries, spanning the period 1980–2018. Our results suggest that among the regressors considered, those reflecting terrorist attacks, trade stakes including military expenditure, socioeconomic prospects and institutional conditions tend to receive high posterior inclusion probabilities. Then, the study explores the relationship between these regressors and foreign aid by employing the fully modified ordinary least squares (FMOLS), the continuously updated fully modified (CUP-FM), the dynamic ordinary least squares (DOLS) long-run estimators and the Dumitrescu and Hurlin’s (2012) panel causality test. Results highlight three concerns that may justify aid flows toward G5 Sahel countries: (a) peace and security purposes, (b) economic interest of donors and (c) recipient economic needs. The paper recommends that the Sahel countries should strengthen international cooperation for security and peace in compliance with the goal 16 of the 2030 Agenda for sustainable development of the United Nations (UN) and the goal 13 of the African Union’s (AU) Agenda 2063.

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Data Availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Notes

  1. The G5 Sahel is a subregional organization established in 2014 as an intergovernmental partnership between Burkina Faso, Chad, Mali, Mauritania, and Niger to foster economic cooperation and security in the Sahel and to respond to humanitarian and security challenges, including that of militant of Islamist groups (Africa Center for Strategic Studies - ACSS 2019).

  2. Econometric literature offers other alternative model priors (uniform, strong-heredity and dilution-defined tessellation priors). However, the sensitivity of BMA results to the specification of Zellner's g prior is well documented in the literature. Thus, it allows the user to carry out a serious sensitivity analysis and manage multicollinearity issue and the weighting of correlated interactions between variables (Feldkircher and Zeugner 2012; Moser and Hofmarcher 2014; Zeugner and Feldkircher 2015; Steel 2020).

  3. The reader interested in their further derivation as well as the derivation of BMA formulas might refer to one of the papers which incorporated this technique (Zeugner and Feldkircher 2015; Okafor and Piesse 2017; Sanso-Navarro and Vera-Cabello 2020; Bayale 2020, Bayale et al. 2021; Nagou et al. 2021).

  4. The lag number was chosen based on ARDL estimates and economic intuition.

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Acknowledgements

The authors are very grateful to the African Economic Research Consortium (AERC) for the financial support. Thanks to the Chair of the thematic research Group C (Finance and resource mobilization) Victor Murinde (University of London, the UK) and our Resource Persons: Issouf Soumare (Université Laval, Canada), Alessandra Guariglia (University of Birmingham, the UK), Bo Sjö (Linköping University, Sweden) Prosper Dovonon (Concordia University, Canada), etc., for their insightful comments during the conduct of the research, especially in the collection, analysis and interpretation of data. The lead author would also 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 Policy Division (MPD) of the UNECA, Addis Ababa, Ethiopia. However, the views expressed are those of the author and do not represent that of the United Nations (UN) nor the Central Bank of West African States (BCEAO) and the AERC. Finally, authors are very grateful to the anonymous reviewers and the Editor-in-Chief of the Comparative Economic Studies, whose comments have greatly improved this paper.

Funding

This work was supported by the African Economic Research Consortium (AERC) as part of its Research Proposal Writing Programme [Grant Number RT20526].

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Authors and Affiliations

  1. Central Bank of West African States (BCEAO), Economic Studies and Regional Integration Directorate, P.O. Box 3108, Dakar, Senegal

    Nimonka Bayale

  2. Graduate of the Faculty of Economics and Management Sciences, University of Kara, P.O. Box 43, Kara, Togo

    Brigitte Kanga Kouassi

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  1. Nimonka Bayale
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Correspondence to Nimonka Bayale.

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Appendix

Appendix

See Tables 9, 10, 11 and 12.

Table 9 The cross-sectional dependence (CD) test
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Table 10 The cross-sectionally augmented IPS test (CIPS) panel unit toot test
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Table 11 Westerlund (2007) panel cointegration test
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Table 12 Further sensitivity and robustness check estimates
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See Figs. 3, 4, 5 and 6.

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Size and index of models (baseline model)

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Response variable (baseline model)

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Fig. 5
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Size and index of models (robustness checks)

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Fig. 6
figure 6

Response variable (robustness checks)

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Bayale, N., Kouassi, B.K. The Devil is in the Details: On the Robust Determinants of Development Aid in G5 Sahel Countries. Comp Econ Stud 64, 646–680 (2022). https://doi.org/10.1057/s41294-021-00182-z

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