Empirical Investigation into the Determinants of Public Debts in Africa: New Insights Using a Panel Bayesian Model Averaging Approach

Abstract

The determinants of public indebtedness in developing countries is still generating a lot of interest among academics and policy makers. This paper introduces model uncertainty into the empirical study on the determinants of public debt at continental level. This is done by adopting a Bayesian model averaging approach applied to data of 51 African countries spanning from 1990 to 2018. Our results suggest that, among the set of twenty-seven (27) regressors considered in the baseline model, those reflecting international financial and institutional conditions as well as internal economic prospects tend to receive high posterior inclusion probabilities. These findings are robust to changes in the model specification, the inclusion of economic crises and geographical heterogeneities of the continent.

On the robust drivers of public debt in Africa: Fresh evidence from Bayesian model averaging approach

“…The second dataset is the World Development Indicators (WDI) of the World Bank. Based on the literature review (see Bayale, 2020a;Bayale et al, 2020;Fatás et al, 2019;Forslund et al, 2011;Sadik-Zada & Gatto, 2019), we extracted some socioeconomic variables including GDP growth, real GDP per capita, gross-fixed investment, official development assistance, debt-service paid, money supply (M2), domestic credit provided by financial sector, trade (imports and exports), natural resources rents, mobile cellular subscriptions (per 1000 people), population growth, and school enrollment. Regarding data on military expenditure and arms imports, it is provided by the SIPRI database.…”

“…where p y=X ð Þ denotes the integrated likelihood which is constant over all models and is thus simply a multiplicative term (Bayale, 2020a;Okafor & Piesse, 2017;Zeugner & Feldkircher, 2015). Therefore, the posterior model probability (PMP) is proportional to the integrated likelihood p y=M γ ; X À � , which reflects the probability of the data given model M γ .…”

“…The vector X i;t contains a list of control variables which are obtained based on BMA approach and the standard literature. These include budget balance as a percentage of GDP, real interest rate, trade openness, government stability, corruption index and political system Bayale et al, 2020;Bohn & Veiga, 2019;Chiminya & Nicolaidou, 2015;Forslund et al, 2011); aid, debt service, credit to financial sector and exchange rate (Gomez-Gonzalez, 2019; Sadik-Zada & Gatto, 2019); military expenditure, arm imports, imports cover, mobile cellular subscriptions, and the highly indebted poor countries initiative (Bayale, 2020a(Bayale, , 2020bChiminya & Nicolaidou, 2015;Sadik-Zada & Gatto, 2019). We expect a positive relationship between budget balance, aid, real interest rate, military expenditure, arm imports, respectively, and public debt (Bayale et al, 2020;Bohn & Veiga, 2019).…”

“…As most of African States accumulate budget deficits, our results suggest that, African countries are obliged to borrow in order to finance their deficit. The result is then an accumulation of public debt (Bayale, 2020a;Bayale et al, 2020). With regard to institutional variables, the study employs ICRG's government stability and corruption indexes.…”

“…Even some social crises (global health crisis) such as COVID-19 could be part of drivers of public debt accumulation in developing countries (IMF, 2020;World Bank Group, 2019). On the domestic front, macroeconomic policies have been blamed such as fiscal irresponsibility, exchange rate misalignment, policies that deter saving and the institutional framework are often cited as debt determinants (Bayale, 2020a;Calderón & Zeufack, 2020;International Monetary Fund (IMF), 2019;World Bank Group, 2019).…”