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Afr Dev Rev. 2020;32:718–729.wileyonlinelibrary.com/journal/afdr718
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© 2020 African Development Bank
DOI: 10.1111/1467-8268.12473
ORIGINAL ARTICLE
Drivers of income inequality in Africa: Does institutional
quality matter?
Mark Edem Kunawotor
1
|Godfred Alufar Bokpin
2
|Charles Barnor
1
1
Department of Banking and Finance,
University of Professional Studies,
Accra, Ghana
2
Department 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.
KEYWORDS
Africa, control of corruption, income inequality, institutional quality, rule of law
JEL CLASSIFICATION
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
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;WorldBank,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
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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:
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KUNAWOTOR ET AL.
Inequality ασInequality ωInstitutionalQuality βXμμε=+ + +′+++
it it it it it
i
t
,,−1,
,,(1)
where Inequality
i,t
and Inequality
i,t −1
represent the current level and one‐period lag of income inequality in country (i)
and year (t), respectively. InstitutionalQuality
i,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. X
i,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. U
i
,U
t
, and Ԑ
i,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.
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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)
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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.
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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
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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 > F0.000 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 > χ
2
) (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.
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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 > F0.000 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 > χ
2
) (0.953) (1.000) (0.975)
Note: Standard errors are in parentheses.
*p< 0.1.
**p< 0.05.
***p< 0.01.
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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 aprioriexpectations and the findings of Dincer and Gunalp (2012)fortheUSA,Anyanwuetal.
(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 5to 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.
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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
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