Afr Dev Rev. 2020;32:718–729.wileyonlinelibrary.com/journal/afdr718 | © 2020 African Development Bank DOI: 10.1111/1467-8268.12473 OR IG INAL ART I C L E Drivers of income inequality in Africa: Does institutional quality matter? Mark Edem Kunawotor1 | Godfred Alufar Bokpin2 | Charles Barnor1 1Department of Banking and Finance, University of Professional Studies, Accra, Ghana 2Department of Finance, University of Ghana, Accra, Ghana Correspondence Mark Edem Kunawotor, Department of Banking and Finance, University of Professional Studies, PO Box LG 149, Legon, Accra, Ghana. Email: mark.kunawotor@upsamail.edu.gh Abstract This paper examines the role institutional quality plays amongst the empirical drivers of income inequality in Africa. Using a dynamic two‐step difference GMM with robust standard errors over the period 1990–2017, we find no statistically significant effect of institutions in general, on income inequality. However, we find that institutional quality indicators such as control of cor- ruption and the strict enforcement of the rule of law significantly reduce in- come inequality. We also find no statistically significant effects of the other institutional quality indicators such as government effectiveness, voice and accountability, regulatory quality and political stability on income inequality in our sample. We suggest that more premium be placed on corruption control and the stringent adherence to the rule of law in ensuring equitable dis- tribution of income in Africa. Furthermore, we re‐echo suggestions that pro- mote institutional development in Africa as institutions in general remain very weak. KEYWORD S Africa, control of corruption, income inequality, institutional quality, rule of law J E L C LA S S I F I CA T I ON D31; D63; E02; O5 1 | INTRODUCTION Inequality remains a critical focus of policymakers and researchers alike as it is currently considered a defining challenge because it remains high and keeps widening (Anyanwu, 2016; Dabla‐Norris, Kochhar, Suphaphiphat, Ricka, & Tsounta, 2015). Reducing inequality within and among countries is presently goal 10 of the sustainable development goals (SDGs), to which world leaders appear committed. Also, reducing inequality is imperative as widening inequality signals persistent disadvantage for a group of people in society. High and persistent inequality has dire implications for economic growth, political stability and causes social unrest (Berg & Ostry, 2011; Carvalho & Rezai, 2014; Jauch & Watzk, 2016; Ncube, Anyanwu, & Hausken, 2014; OECD, 2015; Pickett & Wilkinson, 2015). It also reduces investment in education and infrastructure (Cingano, 2014; Cojocaru & Diagne, 2014) and leads to lower labour productivity (Stiglitz, 2012). Some studies (see Fosu, 2015; Ravallion, 2004; Shimeles & Nabassaga, 2018) have shown that no matter how much economic growth is enhanced, it may not have any meaningful impact on poverty reduction and poverty alleviation unless there is a corresponding decline in inequality. Furthermore, Dabla‐Norris et al. (2015) show mailto:mark.kunawotor@upsamail.edu.gh http://crossmark.crossref.org/dialog/?doi=10.1111%2F1467-8268.12473&domain=pdf&date_stamp=2020-12-17 that GDP growth rate reduces over the medium term if the income share held by the richest 20% increases, while GDP growth rate is seen to increase if the income share held by the bottom 20% increases. This suggests that income distribution matters for economic growth and that the poor and the middle class cannot be ignored in the sustainable growth process. This notwithstanding, inequality remains relatively high and persistent in Africa (Adeleye, Osabuohien, & Bowale, 2017; Anyanwu, Erhijakpor, & Obi, 2016; Asongu, Orim, & Ntig, 2019; Kunawotor, Bokpin, Asuming, & Amoateng, 2020; Shimeles & Nabassaga, 2018). The African continent lags just behind Latin America and the Caribbean in the global income inequality distribution (Odusola, 2017; United Nations Department of Economic and Social Affairs, 2019; World Bank, 2016). United Nations Development Programme (2017) also asserts that 10 out of the 19 most unequal countries globally are found in sub‐Saharan Africa. While a lot of empirical studies and policy debates on income inequality drivers (see Anyanwu, 2016; Anyanwu et al., 2016; Dabla‐Norris et al., 2015; Kunawotor et al., 2020) have gone forth to address these concerns, little success can be spoken of, especially in Africa, and this leaves more room for further studies than desired. Most of these empirical studies, however, paid little or no attention to institutional quality or governance in addressing income inequality. The few that did, concentrate only on corruption or control of corruption (Adams & Klobodu, 2016; Batabyal & Chowdhury, 2015; Berisha, Meszaros, & Olson, 2018; Dincer & Gunalp, 2008; Gyimah‐ Brempong, 2002; Sulemana & Kpienbaareh, 2018; Uslaner, 2007). But corruption control is just one indicator of institutional quality. Closely related to our study are recent studies by Adeleye et al. (2017) and Chu and Hoang (2020). However, while Chu and Hoang (2020) include institutional quality as a control variable in their model without recourse to its components, Adeleye et al. (2017) consider only the interactive effects of the components of institutional quality and financial development on income inequality in sub‐Saharan Africa. The strength of our study is that it deviates from these trends of studies by rather investigating more comprehensively the role institu- tional quality/governance plays amongst the key drivers of income inequality in Africa. Also, we examine the isolated effects of the components of institutional quality in our model. Introducing institutional quality is imperative as the United Nations Development Programme (2017) suggests, among a host of other recommendations, that African countries should institutionalize better governance as one of the measures of planting and nurturing the seeds of equity in Africa. It therefore appears appropriate to empirically investigate this assertion in a more comprehensive context. The rest of the paper comprises a literature review, methodology, results and discussion and ends with a conclusion and policy recommendations. 2 | LITERATURE REVIEW This section is divided into two subsections with a focus on the empirical evidence available on the role institutions play in addressing income inequality and also the key empirical drivers of income inequality. 2.1 | Institutional quality and income inequality A considerable number of studies on the inequality–institutions nexus rather focused on the relationship between corruption and income inequality with little or almost no regard for the other indicators of institutional quality. The main distinguishing feature about these studies is the attempt to determine whether corruption causes inequality (Batabyal & Chowdhury, 2015; Berisha et al., 2018; Dincer & Gunalp, 2008; Dobson & Ramlogan‐Dobson, 2010; Gyimah‐Brempong, 2002) or inequality causes corruption (Fried, Lagunes, & Venkataramani, 2010; Policardo & Carrera, 2018; Solt, 2008; You & Khagram, 2005). Yet still, some few others establish a bi‐causality between corruption and inequality (Apergis, Dincer, & Payne, 2010; Dwiputri, Arsyad, & Pradiptyo, 2018; Policardo & Carrera, 2018; Sulemana & Kpienbaareh, 2018; Uslaner, 2007, 2011). For example, a study by Sulemana and Kpienbaareh (2018) in sub‐Saharan Africa using an unbalanced panel data from 1996 to 2016, showed that higher levels of income inequality are rather associated with lower corruption levels. Besides finding a reverse causality between corruption and income inequality, they also establish that corruption Granger causes inequality. Most of these empirical studies aver that corruption influences inequality through a reduction in economic growth and a reduction in public spending on education, health and other essential social services, a biased tax system and high levels of tax evasion. Conversely, inequality motivates corrupt behaviour to protect the interest of the affluent and their privileges. The rich are also more able to pay bribes to consolidate their positions. Recently, a study by Chu and Hoang (2020) examined the relationship KUNAWOTOR ET AL. | 719 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense between economic complexity and income inequality in 88 countries and found that economic complexity is sig- nificantly associated with higher income inequality. The study includes institutions and its square as controls among other control variables such as education level, trade openness, government expenditure, GDP per capita and its square. They measure institutional quality using an average of six components of institutions from the World Governance Indicators and found that institutions have a positive and significant impact while the squared term has a negative and significant impact. Their findings imply that, in countries with low institutional quality, initial improvement widens economic disparity while much later improvement reduces income inequality. Also, Sonora (2019) examined the relationship between the rule of law and income inequality in Latin America and found that improvement to legal systems, particularly the protection of property rights and reduction of corruption, reduces inequality. Furthermore, Adeleye et al. (2017) investigate the influence that institutional quality has on financial development in reducing income inequality in sub‐Saharan Africa. The study deploys five dimensions of institutional quality including control of corruption, government effectiveness, political stability, rule of law and political rights. They find only the interactive term of control of corruption with financial development to be statistically significant and conclude that if corruption is controlled, given an increase in credit, income inequality will decrease. 2.2 | Drivers of income inequality Literature is densely populated with the empirical determinants and drivers of income inequality. In Southern Africa for instance, Anyanwu (2016) found the first and second lags of inequality, real GDP per capita and its square, population growth, secondary school enrolment, natural resource rent, gross capital formation, political globalization and its square to significantly influence income inequality while finding no significant effect for other variables such as age dependency, government consumption expenditure, economic globalization, social globalization, personal re- mittances, net foreign aid, democracy and unemployment. Anyanwu et al. (2016) in like manner but for West Africa found the following in addition to the significant variables found in Anyanwu (2016); government consumption expenditure, FDI inflows, trade openness, personal remittances received, social globalization, democracy, civil war and unemployment. More recently, but focused on the effects of weather‐related events on income inequality in Africa, Kunawotor et al. (2020) found the first lag of inequality, political globalization and its squared term, democracy, age dependency ratio, school enrolment, gross capital formation, and natural resource rents among the significant influ- encers of income inequality in Africa. However, they did not find any statistically significant effect of real GDP per capita, trade openness, conflict, foreign direct investment inflows, population growth rate, government expenditure, and unemployment rate when used as control variables. These findings are very similar to that of Dabla‐Norris et al. (2015) who focus on advanced economies, emerging market economies and developing countries in their study of empirical drivers of inequality. The same applies to Jaumotte, Lall, and Papageorgiou (2013) but their main focus was on globalization and technology. More recently, Furceri and Ostry (2019) investigate the robust inequality drivers in 108 countries using weighted average least squares. They find the level of development, demographics, unemployment, trade globalization and financial globalization as robust drivers of inequality within countries. Also, they find financial development and technology to significantly drive inequality in advanced economies. The missing issue about all these papers is that they did not consider institutions or any of the components of institutional quality in their studies as direct influencers of income inequality which our paper seeks to address. The choice of control variables in our study, however, is greatly informed by the empirical drivers used by these authors. 3 | METHODOLOGY 3.1 | Model specification Our study examines income inequality in a dynamic model setting. This is because income inequality is known to exhibit a great degree of inertia as evidenced in studies by Anyanwu (2016); Anyanwu et al. (2016); Asongu, Nnanna, and Acha‐Anyi (2020); Chu and Hoang (2020); Dincer and Gunalp (2012); Kunawotor et al. (2020); Mahmood and Noor (2014). Thus, the current level of income inequality depends on its past value. Our specified model, therefore, shows that inequality depends on its lag, institutional quality and a set of controls used in the inequality literature: 720 | KUNAWOTOR ET AL. 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense Inequality α σInequality ωInstitutionalQuality β X μ μ ε= + + + ′ + + +i t i t i t i t i t i t, , −1 , , , (1) where Inequalityi,t and Inequalityi,t− 1 represent the current level and one‐period lag of income inequality in country (i) and year (t), respectively. InstitutionalQualityi,t represents Kaufmann's six indicators of institutional quality (govern- ance) in addition to its average which we term ‘institutions’. These six indicators include control of corruption, rule of law, government effectiveness, voice and accountability, political stability and absence of violence and regulatory quality. Xi,t represents a vector of control variables that affect income inequality including real GDP per capita, trade openness, natural resource rent, political globalization, democracy, unemployment, population growth, gross capital formation, school enrolment, dependency ratio, government expenditure and foreign direct investment. Ui, Ut, and Ԑi,t represents country fixed effects, time fixed effects and idiosyncratic error term, respectively. How these variables are defined and measured is presented in the next section. 3.2 | Variables definition and measurement and data sources This section addresses concerns regarding variable definition and measurement, the expected signs of the explanatory variables and the sources of data. This is shown in Table 1. In addition to the variables defined in Table 1, Kaufmann, Kraay, and Mastruzzi (2011) categorize institutions (governance) into three broad headings with two governance indicators under each. The first is the process by which governments are selected, monitored and replaced. The governance indicators that fall under this are voice and accountability and political stability and absence of violence/terrorism. The second category is the capacity of the government to effectively formulate and implement sound policies. The indicators under this are government effec- tiveness and regulatory quality. The final category is the respect of citizens and the state for the institutions that govern economic and social interactions among them. The indicators are rule of law and control of corruption. 3.3 | Scope of the study and estimation technique This study employs panel data over the sample period, 1990–2017. It includes 40 African countries and the list of these countries is shown in Table 2. In terms of our estimation technique, the dynamic two‐step difference generalized method of moment (GMM) with robust standard errors is deployed. The choice of GMM is motivated by five reasons in line with recent GMM‐centred literature (see Agoba, Abor, Osei, & Sa‐Aadu, 2019; Asongu et al., 2019, 2020; Fosu & Abass, 2019; Kunawotor et al., 2020; Ogbeide and Adeboje, 2020; Tchamyou, Asongu, & Odhiambo, 2019). First, the cross‐sectional units (N) are higher than the time series (T). Thus, the number of countries is 40 while the sampled period is 28 years. Second, the data set is panel in nature and the empirical approach accounts for cross‐country differences in the estimation process. Third, endogeneity concerns are addressed in two ways: GMM controls for unobserved heterogeneity by accounting for time‐invariant omitted variables. Also, GMM generates internal instruments that account for simultaneity bias or reverse causality. Fourth, inequality is known to be persistent and depends on its lags (see Anyanwu et al., 2016; Asongu et al., 2020; Cevik & Correa, 2015; Kunawotor et al., 2020; Shimeles & Nabassaga, 2018). This is also confirmed in our result as the first period lag of the income inequality appears statistically significant in Table 4. Finally, GMM is preferred as an estimation strategy because there are general difficulties in finding external instruments. The robustness of GMM is also evidenced in several tests. The Hansen test for overidentifying restrictions tests for the validity of the moment conditions. Also, the test of the null hypothesis of no second‐order serial correlation is performed by the Arellano–Bond test for autocorrelation (AR (2)). 4 | RESULTS AND DISCUSSION 4.1 | Descriptive statistics Income inequality is relatively high in Africa compared to the other continents as shown by the mean of market Gini (48.254) in Table 3. In terms of regional distribution, Southern Africa recorded the highest average Gini score of 59.07. KUNAWOTOR ET AL. | 721 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense TABLE 1 Variable definitions and measurements and sources of data Variables Variables definitions and measurement Data source Income inequality Inequality represents the extent of distribution of income among households or individuals within a country. It is measured by market/gross Gini (pre‐tax, pre‐transfer income) and net Gini (post‐tax, post‐transfer income). It ranges from 0 to 100, where 0 represents perfect equality while 100 represents perfect inequality. Standardized World Income Inequality Database (SWIID) from UNU‐WIDER Institutions Institutions or governance is defined as the traditions and institutions by which authority in a country is exercised. It is computed as an average of Kaufmann's six indicators of governance or institutional quality. All institutional quality variables range from −2.5 (weak institutions) to 2.5 (strong institutions). World Governance Indicators developed by Kaufmann et al. (2011). Control of corruption It captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as capture of the state by elites and private interests. World Governance Indicators Rule of law It captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police and the courts, as well as the likelihood of crime and violence. World Governance Indicators Voice and accountability This captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. World Governance Indicators Political stability and absence of violence/terrorism It captures perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically motivated violence and terrorism. World Governance Indicators Government effectiveness This captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation and the credibility of the government's commitment to such policies. World Governance Indicators Regulatory quality It captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. World Governance Indicators Real GDP per capita It is measured as the natural log of constant gross domestic product per capita (GDP). World Development Indicators from the World Bank Trade openness This captures the extent of trade liberalization in a country and measured as the sum of total exports and imports as a fraction of GDP. World Development Indicators Natural resource rent It is the extent of reliance on natural resources in a country and it is measured as total natural resources rent as a percentage of GDP. World Development Indicators Political globalization It is measured by KOF's indices of globalization and comprises the absolute number of embassies in a country, personnel contributed to UN Security Council missions (percentage of the population), number of internationally oriented non‐governmental organizations operating in a country, number of international inter‐governmental organizations in which a country is a member, KOF (2019) 722 | KUNAWOTOR ET AL. 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense This is followed by West Africa (46.040), East Africa (45.39) and North Africa (42.50). Similar results are seen in the study by Adeleye et al. (2017) and Odusola (2017) where South Africa and Namibia recorded the highest Gini scores. Institutions in general also appear very weak in Africa as depicted by the mean of −0.628, a minimum of −2.1 and a maximum of 0.88 on a scale of −2.5 (weak) to 2.5 (strong). The best indicators of institutional quality in Africa are political stability and absence of violence/terrorism (−0.506) and control of corruption (−0.603) as they have the highest mean score, albeit relatively very weak. The worst indicators are government effectiveness and regulatory quality with a mean of −0.707 and −0.667, respectively. The results of our institutional indicators are very much in line with those of Adeleye et al. (2017) and Agbloyor (2019). The summary statistics of the other variables is shown in Table 3. 4.2 | Discussion of results Our results confirm the essence of the usage of a dynamic model as the first period lag of income inequality is found to be a highly significant driver of current levels of income inequality shown in Models 1–7 in Table 4. The implication is TABLE 1 (Continued) Variables Variables definitions and measurement Data source international treaties signed and number of distinct treaty partners of a country with bilateral investment treaties. Democracy This is measured by polity 2 index and ranges from −10 representing autocracy to 10 representing democracy. Marshall's Polity IV Project Age dependency ratio It is computed as the sum of young age population and old age population as a ratio of the working age population. World Development Indicators Foreign direct investment It is measured as net inflows of foreign direct investment to GDP. World Development Indicators Gross capital formation This is defined as the extent of usage of physical capital in production and measured as gross capital formation to GDP. World Development Indicators Population growth It is measured as the annual percentage growth in population. World Development Indicators School enrolment It is measured as the secondary school gross enrolment rate. World Development Indicators Unemployment This is measured as total unemployment as a percentage of the labour force. World Development Indicators Government expenditure It is measured as general government final consumption expenditure as a percentage of GDP. World Development Indicators Source: Authors’ construct (2020). TABLE 2 List of African countries in the study 1. Algeria 2. Benin 3. Botswana 4. Burkina Faso 5. Burundi 6. Cabo Verde 7. Cameroon 8. CAR 9. Chad 10. Comoros 11. DRC 12. Côte d'Ivoire 13. Egypt 14. Eswatini 15. Gabon 16. Gambia 17.Ghana 18. Guinea 19. Guinea Bissau 20. Kenya 21. Lesotho 22. Liberia 23. Madagascar 24. Malawi 25. Mali 26. Mauritania 27. Mauritius 28. Morocco 29. Mozambique 30. Niger 31. Nigeria 32. Rwanda 33. Senegal 34. Sierra Leone 35. South Africa 36. Tanzania 37. Togo 38. Tunisia 39. Uganda 40. Zimbabwe KUNAWOTOR ET AL. | 723 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense that the past level of income inequality drags the current level from falling. This is similar to the findings of Anyanwu (2016), Anyanwu et al. (2016). We find no significant effect of institutions in general on income inequality in our sample even though it carried a negative sign as a priori expected shown in Model 1. This may probably be due to the relatively weak nature of these institutions and hence the lack of statistical strength to cause a major impact on income inequality. However, we find control of corruption and rule of law to be statistically significant with their expected negative signs in our sample as shown in Models 2 and 3, respectively. Interestingly, these two statistically significant institutional quality or governance indicators fall under Kaufmann's third category of ‘the respect of citizens and the state for the institutions that govern economic and social interactions among them’. The policy implications we can derive from this finding is that African countries that are relatively more able to control the level of corruption in their countries have relatively reduced income inequality levels and this conforms to the findings of Gyimah‐Brempong (2002); Dincer and Gunalp (2008); Batabyal and Chowdhury (2015); and Adams and Klobodu (2016). Corruption can affect income inequality in two ways according to Ostry et al. (2019). First, corruption beneficiaries are usually well connected and have higher incomes, which undermines the capacity of the government to ensure a more equitable distribution of resources. Second, corruption tends to create a biased tax system, which favours the rich and well connected. Further, the facilitation of tax evasion through corruption affects a government's ability to collect taxes and fairly distribute wealth. Similarly, a relatively robust practice of rule of law significantly reduces income disparities in Africa as confirmed in the study of Sonora (2019) in Latin America. Intuitively and policy‐wise, as public power is not exercised for private gain and the elites and private interest groups do not capture the state, there is fairness in the distribution of national income. Also, as long as there is an improvement in the extent to which agents have confidence in and abide by the rules of society, particularly, the quality of contract enforcement, property rights, the police and the courts, there is a guaranteed reduction in income disparities and hence a fairer share of the national cake. This is consistent with the emphasis by Ostry et al. (2019) who assert that institutions that guarantee property rights are likely TABLE 3 Descriptive statistics Variable Obs. Mean Std. dev. Min. Max. Market_gini 986 48.254 7.921 33.7 70.7 Net_gini 986 43.344 7.099 30.2 62.4 Control of corruption 988 −0.603 0.6 −1.826 1.217 Rule of law 988 −0.662 0.622 −2.13 1.077 Gov't effectiveness 988 −0.707 0.599 −1.89 1.049 Political stability 988 −0.506 0.879 −2.845 1.282 Regulatory quality 988 −0.667 0.597 −2.298 1.127 Voice and accountability 988 −0.622 0.724 −2.226 1.007 Institutions 988 −0.628 0.588 −2.1 0.88 Trade openness 1,251 0.693 0.35 0.191 3.762 Natural resource rent 1,423 12.263 12.336 0 84.24 Political globalization 1,453 53.602 17.936 8.21 92.148 Democracy—polity2 1,345 0.616 5.658 −10 10 Dependency ratio 1,450 84.509 15.633 41.293 112.849 Foreign direct investment 1,388 4.036 9.132 −8.589 161.824 Real GDP per capita 1,390 2211.006 2926.692 164.337 20512.941 Gross capital formation 1,293 21.575 9.888 −2.424 85.101 Population growth rate 1,450 2.379 1.085 −6.766 8.118 School enrolment rate 862 41.225 25.644 5.221 115.957 Unemployment rate 1,377 9.299 7.593 0.285 37.94 Gov't expenditure 1,263 15.302 7.497 0.911 73.577 724 | KUNAWOTOR ET AL. 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense TABLE 4 Effect of institutional quality on income inequality (gross/market Gini) Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Lag of inequality 0.937*** 0.938*** 0.930*** 0.926*** 0.951*** 0.916*** 0.913*** (0.036) (0.035) (0.0312) (0.034) (0.043) (0.037) (0.039) Institutions −0.180 (0.133) Control of corruption −0.175* (0.098) Rule of law −0.225** (0.096) Gov't effectiveness −0.094 (0.094) Political stability −0.140 (0.121) Regulatory quality −0.018 (0.089) Voice and accountability 0.016 (0.109) Real GDP per capita 0.374 0.432* 0.412* 0.360 0.300 0.365 0.368 (0.230) (0.227) (0.232) (0.258) (0.237) (0.274) (0.291) Political globalization −0.004 −0.005 −0.005 −0.005 −0.003 −0.006 −0.006 (0.003) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) FDI 0.005 0.005 0.004 0.005 0.005 0.005 0.005 (0.004) (0.003) (0.003) (0.004) (0.003) (0.004) (0.004) Dependency ratio −0.008 −0.008 −0.008 −0.009 −0.009* −0.011 −0.012 (0.005) (0.005) (0.005) (0.005) (0.006) (0.007) (0.007) School enrolment −0.009* −0.009* −0.009* −0.009* −0.009** −0.009 −0.009 (0.005) (0.005) (0.005) (0.005) (0.004) (0.005) (0.006) Population growth 0.065 0.076 0.061 0.061 0.069 0.054 0.052 (0.050) (0.053) (0.046) (0.056) (0.044) (0.057) (0.064) Gov't expenditure 0.016** 0.016** 0.016** 0.017** 0.015** 0.019** 0.019** (0.007) (0.007) (0.007) (0.007) (0.006) (0.008) (0.009) Democracy—Polity2 −0.008 −0.008 −0.010 −0.010 −0.011 −0.011 −0.012 (0.008) (0.007) (0.007) (0.008) (0.008) (0.008) (0.013) Trade openness −0.109 −0.142 −0.066 −0.116 −0.096 −0.129 −0.131 (0.128) (0.128) (0.118) (0.138) (0.128) (0.146) (0.152) Unemployment rate −0.005 −0.004 −0.006 −0.005 −0.009 −0.003 −0.003 (0.005) (0.005) (0.006) (0.006) (0.007) (0.007) (0.007) Gross capital formation −0.007** −0.007** −0.007** −0.007** −0.008*** −0.007** −0.007** (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) Natural resource rent −0.011*** −0.010*** −0.011*** −0.010*** −0.010*** −0.010** −0.010** (0.004) (0.003) (0.004) (0.004) (0.004) (0.004) (0.004) Constant 1.578 1.124 1.650 2.348 1.549 2.991 3.190 (2.408) (2.426) (2.243) (2.384) (2.374) (2.623) (2.907) Observations 344 344 344 344 344 344 344 Number of instruments 16 16 16 16 16 16 16 Number of countries 40 40 40 40 40 40 40 Prob > F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AR(1):(Pr > z) (0.007) (0.008) (0.007) (0.008) (0.011) (0.007) (0.007) AR(2):(Pr > z) (0.996) (0.842) (0.912) (0.958) (0.782) (0.963) (0.964) Hansen:(Prob > χ2) (0.885) (0.842) (0.897) (0.791) (0.949) (0.697) (0.675) Note: Model 1 discusses the effect of institution on income inequality. Models 2–6 discuss the effects of the components of institution on income inequality. Standard errors are in parentheses. *p< 0.1. **p< 0.05. ***p< 0.01. KUNAWOTOR ET AL. | 725 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense TABLE 5 Effect of institutional quality on income inequality (Net Gini) Variables Model 8 Model 9 Model 10 Lag of Inequality 0.950*** 0.952*** 0.938*** (0.053) (0.056) (0.049) Institutions −0.217 (0.129) Control of corruption −0.201** (0.094) Rule of law −0.223** (0.110) Real GDP per capita 0.430* 0.496** 0.470* (0.233) (0.234) (0.246) Political globalization −0.002 −0.004 −0.004 (0.005) (0.005) (0.005) FDI 0.003 0.003 0.003 (0.003) (0.003) (0.004) Dependency ratio −0.010 −0.010* −0.011 (0.006) (0.006) (0.007) School enrolment rate −0.010* −0.011* −0.010* (0.005) (0.005) (0.006) Population growth rate 0.076 0.090 0.072 (0.054) (0.059) (0.059) Government expenditure 0.015** 0.015** 0.015** (0.007) (0.006) (0.007) Democracy—Polity2 −0.002 −0.002 −0.006 (0.012) (0.011) (0.011) Trade openness −0.076 −0.114 −0.042 (0.133) (0.135) (0.122) Unemployment rate −0.003 −0.002 −0.003 (0.006) (0.006) (0.006) Gross capital formation −0.006** −0.005** −0.006** (0.002) (0.002) (0.003) Natural resource rent −0.011** −0.010** −0.012** (0.005) (0.005) (0.005) Constant 0.347 −0.181 0.735 (3.001) (3.229) (2.764) Observations 344 344 344 Number of countries 40 40 40 Prob > F 0.000 0.000 0.000 AR(1):(Pr > z) (0.083) (0.061) (0.090) AR(2):(Pr > z) (0.361) (0.340) (0.371) Hansen:(Prob > χ2) (0.953) (1.000) (0.975) Note: Standard errors are in parentheses. *p< 0.1. **p< 0.05. ***p< 0.01. 726 | KUNAWOTOR ET AL. 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense to foster investment and growth which reduces income inequality. Also, according to Furceri and Ostry (2019), ‘institutions that guarantee civil liberties help prevent the exploitation of the poor by wealthy elites in economic bargaining. Also, institutions that deliver political rights uniformly across the public can generate pressure for redis- tributive policies’. However, we find no statistically significant effect of the other subcomponents of institutional quality such as government effectiveness, political stability, regulatory quality and voice and accountability in our sample as observed in Models 4–7. It suggests that strengthening these indicators of institutional quality may yield the needed fruits in the future. Real GDP per capita has a positive and statistically significant effect on income inequality. It implies that an increase in per capita GDP is associated with an increase in income inequality in Africa. This is very much in line with the findings of Anyanwu et al. (2016) for West Africa and Anyanwu (2016) for Southern Africa. Also, we find secondary school enrolment rate to be negatively and significantly associated with income inequality. This conforms to our a priori expectations and the findings of Dincer and Gunalp (2012) for the USA, Anyanwu et al. (2016) for West Africa and Kunawotor et al. (2020) for Africa. The policy implication is that as human capital is enhanced and empowered, it narrows the income inequality gap in Africa. This same finding applies to the usage of gross capital formation and natural resource rent as they are also statistically significant with negative signs. As more domestic investments are made into physical capital, productivity is enhanced and this translates to more job opportunities and income for the underprivileged. Similarly, the exploitation of natural resources does not only enhance the economic status of Africans but may also lead to a fairer share of income. Finally, excessive and untargeted government spending increases the income inequality gap and this may be due to high levels of corrupt spending that could not be accounted for. We find no significant effect of the other variables such as political globalization, FDI inflows, dependency ratio, population growth, democracy, trade openness and unemployment in our study. All these findings are very robust when we use the net or disposable Gini as a measure of income inequality but we show only the significant models including that of institutions in Table 5 to conserve space. These apply to institutions, control of corruption and rule of law in Models 8, 9 and 10, respectively. 5 | CONCLUSION AND RECOMMENDATIONS Income inequality is pervasive in Africa and this has necessitated numerous studies to find the key drivers of this canker, albeit to the neglect of institutional quality indicators. This paper, therefore, sought to examine the implications of institutional quality or governance in addressing income inequality in Africa. Our findings reveal that control of corruption and the ardent practice of rule of law statistically and significantly reduce income inequality in Africa. We find no statistically significant impact of institutions in general on inequality. In the same vein, we find no significant impact of the other components of institutional quality on inequality, namely government effectiveness, regulatory quality, political stability and the absence of violence and voice and accountability, in our sample. These results are robust to the usage of an alternative measure of income inequality (Net Gini). We suggest that government efforts should be more directed at enhancing contract enforcement and property rights and also preventing the exploitation of the poor by the wealthy elites in the economic bargaining process to ensure a fairer distribution of the national cake and reduce economic disparities in Africa. Thus, while it is important to strengthen institutions as a whole in Africa, particular attention should be paid to efforts in enhancing control of corruption and the strict adherence to the rule of law. Also, human capital should be enriched and there should be an effective deployment of capital as well as effective management of natural resources to enhance the well‐being of Africans. REFERENCES Adams, S., & Klobodu, E. K. M. (2016). Financial development, control of corruption and income inequality. International Review of Applied Economics, 30(6), 790–808. Adeleye, N., Osabuohien, E., & Bowale, E. (2017). The role of institutions in the finance‐inequality nexus in Sub‐Saharan Africa. Journal of Contextual Economics, 137, 173–192. Agbloyor, E. K. (2019). Foreign direct investment, political business cycles and welfare in Africa. Journal of International Development, 31, 345–373. KUNAWOTOR ET AL. | 727 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense Agoba, A. M., Abor, J., Osei, K. A., & Sa‐Aadu, J. (2019). Do independent central banks exhibit varied behaviour in election and non‐election years? The case of fiscal policy in Africa”. Journal of African Business, 21(1), 105–125. Anyanwu, J. C. (2016). Empirical analysis of the main drivers of income inequality in Southern Africa. Annals of Economics and Finance, 17(2), 337–364. Anyanwu, J. C., Erhijakpor, A. E. O., & Obi, E. (2016). Empirical analysis of the key drivers of income inequality in West Africa. African Development Review, 28(1), 18–38. Apergis, N., Dincer, O. C., & Payne, J. E. (2010). The relationship between corruption and income inequality in US states: Evidence from a panel cointegration and error correction model. Public Choice, 145, 25–135. Asongu, S. A., Nnanna, J., & Acha‐Anyi (2020). Finance, inequality and inclusive education in Sub‐Saharan Africa. Economic Analysis and Policy, 67, 162–177. Asongu, S. A., Orim, S. I., & Ntig, R. T. (2019). Inequality, information technology and inclusive education in sub‐Saharan Africa. Technological Forecasting and Social Change, 146(C), 380–389. Batabyal, S., & Chowdhury, A. (2015). Curbing corruption, financial development and income inequality. Progress in Development Studies, 15(1), 49–72. Berg, A., & Ostry, J. D. (2011). Inequality and unsustainable growth: Two sides of the same coin? (IMF Staff Discussion Note 11/08). Washington, DC: International Monetary Fund. Berisha, E., Meszaros, J., & Olson, E. (2018). Income inequality, equities, household debt, and interest rates: Evidence from a century of data. Journal of International Money Finance, 80, 1–14. Carvalho, L., & Rezai, A. (2014). Personal income inequality and aggregate demand (Working Paper 2014‐23). São Paulo: Department of Economics, University of São Paulo. Cevik, S., & Correa‐Caro, C. (2015). ‘Growing (Un)equal: Fiscal policy and income inequality in China and BRIC+’, IMF Working Paper WP/ 15/68. Chu, L. K., & Hoang, D. P. (2020). How does economic complexity influence income inequality? New evidence from international data. Economic Analysis and Policy, 68, 44–57. Cingano, F. (2014). Trends in income inequality and its impact on economic growth (OECD Social, Employment and Migration Working Papers, No. 163). Paris, France: OECD Publishing. Cojocaru, A., & Diagne, M. F. (2014, November). Should income inequality be reduced and who should benefit? Redistributive preferences in Europe and Central Asia (World Bank Policy Research Working Paper 7097). Washington D.C, USA. Dabla‐Norris, E., Kochhar, K., Suphaphiphat, N., Ricka, F., & Tsounta, E. (2015, June). Causes and consequences of income inequality: A global perspective (IMF Staff Discussion Note 15/13). USA: International Monetary Fund. Dincer, O., & Gunalp, B. (2008). Corruption, income inequality, and poverty in the United States (Knowledge, Technology, Human Capital working papers). Milan, Italy: Fondazione Eni Enrico Mattei. Dincer, O., & Gunalp, B. (2012). Corruption and income inequality in the United States. Contemporary Economic Policy, 30(2), 283–292. Dobson, S., & Ramlogan‐Dobson, C. (2010). Is there a trade‐off between income inequality and corruption? Evidence from Latin America. Economics Letters, 107(2), 102–104. Dwiputri, I. N., Arsyad, L., & Pradiptyo, R. (2018). The corruption‐income inequality trap: A study of Asian countries (Economics Discussion Paper, No. 2018‐81). Kiel Institute for the World Economy. Germany. Fosu, A. K. (2015). Growth, inequality and poverty in Sub‐Saharan Africa: Recent progress in a global context. Oxford Development Studies, 43(1), 44–59. Fosu, A. K., & Abass, A. F. (2019). Domestic credit and export diversification: Africa from a global perspective. Journal of African Business, 20(2), 160–179. Fried, B. J., Lagunes, P., & Venkataramani, A. (2010). Corruption and inequality at the 21 crossroad: A multimethod study of bribery and discrimination in Latin America. Latin American Research Review, 45(1), 76–97. Furceri, D., & Ostry, J. D. (2019). Robust determinants of income inequality. Oxford Review of Economic Policy, 35(3), 490–517. Gyimah‐Brempong, K. (2002). Corruption, economic growth and income inequality in Africa. Economics of Governance, 3, 183–209. Jauch, S., & Watzk, S. (2016). Financial development and income inequality: A panel data approach. Empirical Economics, 51(1), 291–314. Jaumotte, F., Lall, S., & Papageorgiou, C. (2013). Rising income inequality: Technology, or trade and financial globalization? IMF Economic Review, 61(2), 271–309. Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011). The worldwide governance indicators: Methodology and analytical issues. Hague Journal on the Rule of Law, 3(2), 220–246. Mahmood, S., & Noor, Z. M. (2014). Human capital and income inequality in developing countries: New evidence using the Gini coefficient. Journal of Entrepreneurship and Business, 2(1), 40–48. Ncube, M., Anyanwu, J. C., & Hausken, K. (2014). Inequality, economic growth and poverty in the Middle East and North Africa (MENA). African Development Review, 26(3), 435–453. Odusola, A. (2017). Fiscal space, poverty and inequality in Africa. African Development Review, 29(1), 1–14. OECD. (2015). In it together: Why less inequality benefits all. Paris: OECD Publishing. Ogbeide, F. I., & Adeboje, O. M. (2020). Effects of financial reform on business entry in sub‐Saharan African countries: Do resource dependence and institutional quality matter? African Development Review, 32(2), 188–199. 728 | KUNAWOTOR ET AL. 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense Ostry, J. D., Berg, A., & Tsangarides, C. (2014). Redistribution, inequality, and growth. IMF staff discussion Note 14/02. Washington, DC: International Monetary Fund. Pickett, K. E., & Wilkinson, R. G. (2015). Income inequality and health: A causal review. Social Science & Medicine, 128, 316–326. Policardo, L., & Carrera, E. J. S. (2018). Corruption causes inequality, or is it the other way around? An empirical investigation for a panel of countries. Economic Analysis & Policy, 59, 92–102. Ravallion, M. (2004). Pro‐poor growth: A primer (Policy Research Working Paper Series 3242). Washington, DC: World Bank. Savina, G., Haelg, F., Potrafke, N., & Sturm, J. (2019). The KOF globalisation index – revisited. Review of International Organizations, 14(3), 543–574. Shimeles, A., & Nabassaga, T. (2018). Why is inequality high in Africa? Journal of African Economies, 27(1), 108–126. Solt, F. (2008). Economic inequality and democratic political engagement. American Journal of Political Science, 52(1), 48–60. Sonora, R. (2019). Income inequality, poverty and the rule of law: Latin America vs the rest of the world (Munich Personal RePEc Archive (MPRA) Paper No. 91512). Munich, Germany. Stiglitz, J. (2012). The price of inequality: How today's divided society endangers our future. New York: W.W. Norton. Sulemana, I., & Kpienbaareh, D. (2018). An empirical examination of the relationship between income inequality and corruption in Africa. Economic Analysis and Policy, 60, 27–42. Tchamyou, V. S., Asongu, S. A., & Odhiambo, N. M. (2019). The role of ICT in modulating the effect of education and lifelong learning on income inequality and economic growth in Africa. African Development Review, 31(3), 261–274. United Nations Department of Economic and Social Affairs. (2019). Income inequality trends: The choice of indicators matters. UNDESA Social Development Brief. New York, USA. United Nations Development Programme. (2017). Income inequality trends in Sub‐Saharan Africa: Divergence, determinants and consequences. UNDP Report. New York, USA. Uslaner, E. (2007). Corruption and the inequality trap in Africa(Afrobarometer Working Paper Series, Working Paper No. 69). Uslaner, E. M. (2011). Corruption and inequality. Forum CESifo DICE Report 2/2011. World Bank. (2016). Poverty and shared prosperity, taking on inequality. Washington, DC: Author. You, J.‐S., & Khagram, S. (2005). A comparative study of inequality and corruption. American Sociological Review, 70(1), 136–157. How to cite this article: Kunawotor ME, Bokpin GA, Barnor C. Drivers of income inequality in Africa: Does institutional quality matter? Afr Dev Rev. 2020;32:718–729. https://doi.org/10.1111/1467-8268.12473 KUNAWOTOR ET AL. | 729 14678268, 2020, 4, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/1467-8268.12473 by U niversity of G hana - A ccra, W iley O nline L ibrary on [23/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://doi.org/10.1111/1467-8268.12473