Abstract
This paper investigates the effects of taxation on income inequality in an unbalanced panel of 45 countries in Sub-Saharan Africa over the period 1980–2018. We use two-stage least squares and the instrumental variables quantile regression estimates. We find that taxation widens income inequality and that the increasing effects of taxation on income inequality are higher in the most unequal countries than in the least unequal ones. The paper provides evidence that countries in Sub-Saharan Africa should improve the progressivity of their tax systems, so that taxation policy can be used to reduce income inequality.
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Source: Authors’ construction using data from UNU-WIDER (2021a)
Source: Authors’ construction based on data from UNU-WIDER (2021b)
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Notes
South Africa, Namibia, Botswana, Central African Republic, Comoros, Zambia, Lesotho, Swaziland, Guinea Bissau, and Rwanda.
Other drivers of income inequality have attracted the attention of social scientists. These include growth (Risso et al. 2013), human capital (Li and Yu 2014), globalization (Heimberger 2020), international trade (Huang et al. 2022), foreign direct investment (Pan-Long 1995), natural resource exploitation (Kim et al.2020), political regime (Bahamonde and Trasberg 2021), urbanization (Sulemana et al. 2019), inflation ( Al-Marhubi 1997), financial development/liberalization ( Koudalo and Wub 2022), employment/unemployment ( Björklund 1991), Economic freedom ( Compton et al. 2014) and remittances (Bang et al.2016).
Inequality in the region has received limited attention historically from a research, policy, and political perspective (Cornia et al. 2017).
The list of countries in the sample are Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic (CAR), Chad, Comoros, Democratic Republic of Congo (DRC), Congo, Ivory Coast, Djibouti, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Uganda,, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Zambia, and Zimbabwe.
This decision somewhat mitigates the problem of reverse causality since post-tax and transfer Gini coefficient vary "mechanically" and "economically" with the tax system while pre-tax and transfer Gini coefficient vary only through the endogenous responses of labour supply or the equilibrium effect on factor prices (Poterba 2007). We thank one anonymous reviewer for this suggestion.
This variable is used to approximate human capital. It better reflects the quality of human capital relative to the gross rate. Also, data are more available for this variable compared to the secondary and tertiary enrollment rates. In the choice of variables, we prioritize variables which have more observations than others while considering their relevancy in the literature and the context.
Because our period of study covers a relatively a long one (1980-2018, 38 years), our estimations should rely on the assumption that explanatory variables are stationary. We use the Fisher-type unit-root test based on augmented Dickey-Fuller tests. This is because xtunitroot fisher does not require strongly balanced data, and the individual series can have gaps. It fits well with the structure and the characteristics of our data: panel data with individual series having gaps. The results are presented in Table 7 in the Appendix 1. Total revenue, Total non-resource revenue, direct taxes, indirect taxes, trade openness, foreign direct investment, total natural resources rents, urbanization rate, inflation rate, public expenditure on education, remittances are statistically and significantly stationary at level at 1%. Gini coefficient, democracy, financial development, primary education, employment rate, GDP per capita are statistically and significantly stationary at first differences at 1%. We thank two anonymous referees for suggesting this test.
This variable has been used in previous literature as an instrument of institutional quality (Acemoglu et al. 2014) or finance (Beck et al. 2000). Legal origin variable can affect income inequality through social and political institutions rather than directly: for example, the rich elites who are adopting “extractive strategies” are in most countries with French legal origin. For example, La Porta et al. (1999) link the quality of government institutions to legal origins, with French legal origin having a negative effect on institutions.
For additional robustness checks, OLS with time fixed effects, cluster for years and cluster for countries, quantile regression, and instrumental variables with internal instruments are used.
individuals with wealth of USD 1 million or more.
OLS with time fixed effects, cluster for years and cluster for countries, quantile regression and instrumental variables with internal instruments produce qualitatively similar results.
Remittances do not significantly affect income inequality in SSA over the period 1980-2018.
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Acknowledgements
We thank the two anonymous referees for their insightful comments and suggestions which have significantly improved the quality of this research paper. We are also grateful to Abel Gwaindepi, Abrams Tagem, Evgeniya Dubinina, Herman Ndoya, Ibrahim Dia, Rasmane Guigma, Rita Nikiema, Steve Hall, colleagues at the Department of Economics of the Thomas Sankara University in Burkina Faso, and participants at the United Nations University (UNU)—World Institute for Development Economics Research (WIDER) April 2022 Workshop on ‘Data for tax revenue mobilization’ for their insightful comments. The authors acknowledge financial support from UNU-WIDER Research Grant: 605UU-3384.
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Appendix
Appendix
Appendix 1: The tables
See Tables 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 and 20
Appendix 2: The figures
Trends of income inequality measured by Palma ratio in SSA, 1980–2018. Source: Authors’ construction using data from UNU-WIDER (2021a)
Source: Authors
Epanechnikov Kernel density estimate of the Gini coefficient. Notes This figure is created using the Epanechnikov kernel density estimation
Source: Authors
Epanechnikov Kernel density estimate of the Palma ratio of income. Notes This figure is created using the Epanechnikov kernel density estimation
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Ouedraogo, I., Dianda, I., Ouedraogo, P.P. et al. The Effects of Taxation on Income Inequality in Sub-Saharan Africa. Comp Econ Stud (2024). https://doi.org/10.1057/s41294-024-00235-z
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DOI: https://doi.org/10.1057/s41294-024-00235-z