Fiscal Policy and Income Inequality: The Critical Role of Institutional Capacity

Abstract

Rising income inequality has become a defining global challenge that hinders the achievement of the United Nations Sustainable Development Goals. The paper investigates the effect of fiscal policy and institutional capacity on income inequality among developed and developing countries. Applying the system Generalized Method of Moments (GMM) to control potential endogeneity for countries from 2000 to 2019, the following results have been established. The dynamic effect captured by the first lag of inequality suggests that the widening income gap is persistent in both developed and developing countries. We also find evidence that income tax is more progressive and may abate income inequality in developing countries and not in developed countries. However, taxes on goods and services were found not to impact income equalization globally. Furthermore, the findings reveal that government size, education expenditure, and health expenditure are negatively associated with income inequality in developed countries only. Public debt was observed not to influence income distribution across the world. We observed that corruption and government effectiveness do not significantly impact income distribution in developed and developing countries for institutional capacity. However, in most cases, the coefficients of the interactions between fiscal policy and institutional capacity bear the expected signs, albeit insignificant. Some policy recommendations have been offered.

Full text

Citation: Malla, Manwar Hossein,
and Pairote Pathranarakul. 2022.
Fiscal Policy and Income Inequality:
The Critical Role of Institutional
Capacity. Economies 10: 115.
https://doi.org/10.3390/
economies10050115
Academic Editor: Ralf Fendel
Received: 21 March 2022
Accepted: 10 May 2022
Published: 14 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
economies
Article
Fiscal Policy and Income Inequality: The Critical Role of
Institutional Capacity
Manwar Hossein Malla * and Pairote Pathranarakul
Graduate School of Public Administration, National Institute of Development Administration, Bangkok 10240,
Thailand; pairote@nida.ac.th
*Correspondence: manwar.mal@stu.nida.ac.th
Abstract:
Rising income inequality has become a defining global challenge that hinders the achieve-
ment of the United Nations Sustainable Development Goals. The paper investigates the effect of fiscal
policy and institutional capacity on income inequality among developed and developing countries.
Applying the system Generalized Method of Moments (GMM) to control potential endogeneity for
countries from 2000 to 2019, the following results have been established. The dynamic effect captured
by the first lag of inequality suggests that the widening income gap is persistent in both developed
and developing countries. We also find evidence that income tax is more progressive and may abate
income inequality in developing countries and not in developed countries. However, taxes on goods
and services were found not to impact income equalization globally. Furthermore, the findings reveal
that government size, education expenditure, and health expenditure are negatively associated with
income inequality in developed countries only. Public debt was observed not to influence income
distribution across the world. We observed that corruption and government effectiveness do not
significantly impact income distribution in developed and developing countries for institutional
capacity. However, in most cases, the coefficients of the interactions between fiscal policy and institu-
tional capacity bear the expected signs, albeit insignificant. Some policy recommendations have been
offered.
Keywords: fiscal policy; institutions; income inequality; taxes; GMM
1. Introduction
There is no doubt that growing income inequality within and among countries is a
defining challenge of the United Nations Sustainable Development Goals. Rising income
inequality has generated much scholarly and policy attention over recent decades. Though
it is a feature of low-income countries, rich countries with high economic growth have
witnessed a widening income gap among citizens (Coady and Dizioli 2018;Nolan et al.
2019). For example, recent evidence from China (Cevik and Correa-Caro 2020;Ravallion
and Chen 2021) and the United States of America (Tsui et al. 2016) attests to the worrying
trend of widening income gap in high-income countries. Income inequalities have risen for
a majority of the world’s population: The World Inequality Report 2022 reveals that the
wealthiest 10% of the global population currently controls 52% of global income, whereas
half of the global population who are deemed to be the poorest holds 8.5% of the global
income (Chancel et al. 2022, p. 10). Implicit in this widespread concern is the view that
income inequality has severe implications for crime, social cohesion, political stability,
poverty, and social justice, which subsequently undermine investment and life-improving
public policy reforms (Jenkins 2017;Law and Soon 2020;Piketty and Saez 2003).
Conversely, scholars have insinuated that not until there is a corresponding decline
in inequality between the rich and the poor there is no way poverty reduction efforts will
be achieved despite high economic growth (Datt et al. 2020;Kwasi Fosu 2018;Shimeles
and Nabassaga 2018). Consistent with this view, empirical evidence suggests that where
Economies 2022,10, 115. https://doi.org/10.3390/economies10050115 https://www.mdpi.com/journal/economies

Economies 2022,10, 115 2 of 16
the income of 20% of the population increases, GDP growth rates plummet in the medium
term. In contrast, the economy tends to achieve a high growth rate when the income
share held by the bottom 20% increases (Dabla-Norris et al. 2015). Moreover, this evidence
seems to feed into Kuznets’s (1955) long-held view that income inequality tends to widen
during the initial stages of development, but declines, as the economy grows to a certain
level (Kuznet’s inverted U-shape curve). This implies that income distribution matters for
economic growth and poverty alleviation efforts. Ensuring even distribution of the national
cake is crucial for poverty alleviation, maintenance of peace and security, and achieving
good macroeconomic stability and growth (Anyanwu et al. 2016;Kunawotor et al. 2022).
Fiscal policy is widely seen as a vital policy instrument to ensure income distribution.
Fiscal policy in the form of taxation and social spending influences the welfare of household
members through monetary payment by way of taxes and transfers as well as through the
provision of in-kind social benefits, including expenditure on free education and health care
uptake (Clements et al. 2015, p. 3; Gupta 2018). However, some scholars have expressed
that fiscal policy is ineffective in addressing income inequality because of its lower tax-
to-GDP ratio (Kunawotor et al. 2022). The net impact is that limited expenditure will be
allocated for social sectors such as education and health care services, which benefit the
have-nots. Moreover, where fiscal policy takes the form of indirect tax, its consequential
impact on income inequality is devastating (Apergis 2021). Accordingly, an active body of
the literature (Brinca et al. 2021;Caminada et al. 2019;Cevik and Correa-Caro 2020;Dotti
2020;Salotti and Trecroci 2018) has examined the redistributive impact of fiscal policy with
inconclusive results.
It should be noted that the decidedly mixed results of the nexus between fiscal policy
and income inequality could stem from the influence of other factors, including institutional
capacity. Yet, the existing publications have paid little or no attention to institutional quality
or governance as a possible window for addressing inequalities between the rich and
poor. Fiscal policy may be ineffective in achieving distributive outcomes without efficient
institutional or bureaucratic capacity. Analogous to this view, Hyden (2007) has asked
whether poor service delivery in social sectors in developing societies can be attributed
to other factors save poor institutional or governance environment. Hu and Mendoza
(2013) also argue that institutional capacity underpins whether public resources and public
reforms are effectively allocated and influence redistributive outcomes. In this sense,
institutional capacity is defined in this paper as the capacity of state institutions to promote
economic prosperity and ensure that such property is shared among the citizens.
This paper investigates the effect of fiscal policy and institutional capacity on income
inequality among developed and developing countries. The World Inequality Report 2022
reveals that the Middle East and North Africa (MENA) are the unequal regions globally,
with the top 10% of the population controlling 58% of the region’s income. In Europe,
however, the income shares of the top 10% is 36%. Asia’s 10% income share is 43%, Latin
America is 55%, North America is 48%, and Sub-Saharan Africa is 56% (Chancel et al. 2022,
p. 10). It should be noted that despite some regions appearing to be doing well concerning
income distribution, it can be observed that income inequality is still a challenge across
countries.
Nevertheless, since Europe and North American countries (developed) appear to be
doing relatively better, it is imperative to examine what is driving the difference based
on fiscal policy and institutional quality. This will provide policy implications for other
countries, particularly developing countries. Moreso, since most economies, particularly in
the developing world, are hard hit by the COVID-19 pandemic, it is expected to devastate
the recovery process. The impact of COVID-19 on these countries amplifies the need
for fiscal policy not only for post-COVID-19 recovery but also to achieve redistributive
outcomes. Therefore, accounting for the dynamics of institutional capacity within fiscal
policy and income inequality nexus is a novelty that has been glossed over in the literature.
The paper makes some important contributions: first, since governments across the globe
rely on fiscal policy not only to raise adequate revenue but also to spend in an economy so

Economies 2022,10, 115 3 of 16
as to reduce poverty and promote development, the paper leads evidence to show whether
such policies have an inclusive effect. Second, the paper further provides evidence to
show whether the institutions required for income redistribution support the effectiveness
of fiscal policy in reducing income inequality across countries. The rest of the paper is
arranged as follows: related literature is discussed, after which the methods are defined.
The findings are discussed, policy implications are supplied, and concluding remarks are
provided.
2. Literature Review
2.1. Fiscal Policy and Income Inequality
Fiscal policy is “the setting of the level of government spending and taxation by
policymakers” (Mankiw 2021, p. 793). Theoretically, fiscal policy has implications on
income distribution through the channels of taxes, public expenditure, and transfers (Salotti
and Trecroci 2018). Thus, fiscal policy enhances equity plan in two ways: first, de Freitas
(2012) argues that direct taxes are deemed progressive because they encourage income
distribution and reduce income inequality. Thus, taxes imposed on incomes, capital, wealth,
inheritance, and private properties distribute resources from the rich and super-rich to
the poor and marginalized segments of the society (Odusola 2017). People in the high-
income group would have to pay a more significant proportion of their income as tax.
However, indirect taxes, such as taxes imposed on the consumption of goods and services
are regressive since both the rich and the poor pay the same amount on goods and services
as tax. Second, the impact on redistributive outcomes tends to be far-reaching if the
revenues raised from taxes go to finance social spending to support the poor, vulnerable,
and marginalized groups.
In recent times, some scholars have conducted empirical studies on the redistributive
effect of fiscal policy on income inequality. Employing the panel data technique, Salotti and
Trecroci (2018) examined the redistributive impact of fiscal policy in OECD countries. Thus,
public debt, government size, public expenditure on education and social security, income
tax, property tax, and taxes on goods and services were used to measure fiscal policy. They
showed that a rise in public debt and expenditure encourages unequal income distribution,
albeit minimally. It is further observed that public spending on education, social spending,
and consumption promotes distributive effects. Moreover, income and property taxes were
found to have equalizing effects. Clifton et al. (2020) investigated the impact of fiscal policy
on income inequality for Latin American countries in the 2000s. The authors generally
observed that fiscal policy marginally reduces income inequality. Specifically, spending on
education, income taxes, and social security contributions were instrumental in decreasing
income inequality. A panel data analysis (Apergis 2021) found that social transfers are more
potent fiscal policy tools in abating income inequality than taxes. Odusola (2017) also estab-
lished in Africa that low levels of taxes and social spending undermine the distributional
impacts of fiscal policy. Kunawotor et al. (2022) have empirically demonstrated that fiscal
redistribution in income taxes and transfers reduces the income gap between the rich and
the poor a relatively small extent. Based on these decidedly mixed results, the following
hypothesis is proposed:
Hypothesis 1 (H1).
There is a significant relationship between fiscal policy and income inequality
in developed and developing countries.
2.2. Institutional Capacity and Income Inequality
By definition, institutions are “humanly devised constraints that shape human in-
teractions” (North 1990, p. 3). Institutions serve as a fulcrum around which everything
within an economy revolves. They are described as the “rule of the game in a society”
(North 1990, p. 3). Theoretically, Acemoglu et al. (2014) argue that institutions influ-
ence economic and redistributive outcomes by either being inclusive or extractive. While
inclusive institutions stimulate economic activities, an institutionally extractive environ-

Economies 2022,10, 115 4 of 16
ment undermines economic development. The institutionalization of good governance
practices stimulates economic growth and engenders redistribution and equalization of
incomes through efficient allocation of resources and economic freedom (Acemoglu and
Robinson 2006). An effective institutional capacity to implement and administer income
redistributive policy reforms will invigorate the efficiency of fiscal policy to reduce income
inequality. The rule of law and absence of corruption will engender social cohesion and
ensure that social spending on education, health, and social transfers benefit the poor and
marginalized groups. There is enough evidence that insufficient institutional capacity has
a devastating impact on income distribution (Albertus and Menaldo 2014). As Odusola
(2017, p. 170) argues, “when efficiency and quality of government spending are assured
(through institutional capacity), public expenditure is a potent tool to redistribute wealth
and opportunities to the lowest quintiles of the population”. Law and Soon (2020) also
argue that in an environment where institutional capacity is established, the opportunity
for inclusive economic planning to promote income distribution is enhanced.
Sonora (2019) examined the impact of the rule of law and income inequality in Latin
American countries. They found that property protection through an effective legal regime
and effective control of corruption narrows the gap between the rich and poor. By employ-
ing four measures of institutional quality: government effectiveness, corruption, political
rights, the rule of law, Adeleye et al. (2017) investigated the interactive effect of institutions
and financial development in sub-Saharan Africa and concluded that the ability to control
corruption would increase the efficiency of economic growth in the reduction of income
inequality. Carmignani (2009) observed that weak institutions widen the income inequality.
Huynh (2021) also examined how institutional quality and foreign direct investment in-
fluence income distribution in 36 Asian countries and concluded that institutional quality
abates income inequality. The authors further observed that the quality of institutions
moderates the impact of foreign direct investment on income inequality.
Similarly, Nguyen (2021) found that effective governance or institutions and expendi-
ture on education reduce income inequality in both developed and developing countries.
While the author also observed that economic growth widens the income inequality, foreign
direct investment narrows it in developing countries but widens it in developed countries.
This finding is astonishing because one would have thought that foreign direct investment
should promote income distribution in developed countries given their good governance
environment. The rationale is that under good institutional capacity in developed countries
where policies and regulations are designed, formulated, and implemented, economic
growth should be encouraged to achieve distributive outcomes. From the above discussion,
the paper proposes the following interrelated hypotheses:
Hypothesis 2 (H2).
There is a significant and negative relationship between institutional quality
and income inequality.
Hypothesis 3 (H3).
The effect of fiscal policy on income inequality is moderated by the quality of
the institutions.
The above discussions suggest that the cumulative effect of fiscal policy and institu-
tional capacity is inconclusive and deserves research attention. Therefore, this paper’s
resolve is to verify this relationship and offer empirical evidence that will guide policymak-
ers to effectively deal with the intractable problem of income inequality, which has been
described as the defining challenge of the United Nations Sustainable Development Goals.
3. Methodology and Data Description
3.1. Methodology
Panel data analysis is often beset with several constraints as omitted variable bias,
measurement error, unobserved time-invariant and country-specific characteristics, auto-
correlation, and endogeneity or the problem of reverse causality (Phillips and Sul 2007).

Economies 2022,10, 115 5 of 16
This paper draws insight from the recent literature on income inequality (Cevik and Correa-
Caro 2020;Clifton et al. 2020;Kunawotor et al. 2020;Kunawotor et al. 2022;Odusola 2017;
Salotti and Trecroci 2018) to deal with these challenges. Due to the persistent nature of
income inequality, the empirical estimation strategy of this paper is modeled in line with
this recent literature. Therefore, the empirical model for this paper suggests that income
inequality is predicated on its first lag, fiscal policy measures, institutional capacity, and a
set of covariates used in the policy literature as shown below:
Inequalityi,t =Inequalityi,t−1+
4
∑
h=1
whWh,i,t−t+ηi+εit
where Inequality is the income inequality measured by the disposable income of the
Gini index of households’ disposable income after taxes, liabilities, and receiving benefits.
Inequalityi
t_1
is the first lag of income inequality used to capture the dynamic effect. It
is measured from 0 (indicating perfect equality) to 100 (shows perfect inequality). The
subscript i at period t are the country and time effects. Fpolicy stands for the measures
of fiscal policy variables, including public debt (pdebt), income tax (intax), government
general consumption (gsize), health expenditure (helex), education expenditure (eduex),
inflation (inf), and taxes on goods and services (gstax). Similarly, Institution denotes the
measures of institutional quality, including government effectiveness (ge), corruption (corr),
and democracy (democ). W is the vector of the control variables,
ηi
is the country-specific
effect, and εi,t is the error term.
Since the existing literature holds that previous policies influence current social, politi-
cal, and economic processes, ordinary least squares (OLS), fixed and random effects are
likely to produce misleading results (Phillips and Sul 2007). In that regard, the dynamics of
the robustness for income inequality were examined using a dynamic panel model with
a lagged dependent variable using the system GMM. The GMM takes care of the persis-
tent nature of the dependent variable, the omitted variable problem, measurement error,
endogeneity, and country-specific heterogeneity (Arellano and Bond 1991;Arellano and
Bover 1995;Blundell and Bond 1998). GMM is efficient in a condition where the number of
cross-sections is greater than the number of periods (N > T). The consistency of the system
GMM estimator is assessed through the Hansen test of overidentification restrictions for
the overall validity of the instruments and the test for the null hypothesis where the error
term is not serially correlated. In the situation of failing to reject the two hypotheses, it
provides support for the model’s validity (Blundell and Bond 1998;Roodman 2009). Hence,
a Two-step system GMM was used to estimate the model.
In this paper, system GMM is employed for several reasons. Firstly, the cross-sections
(N) are more than the time series (T). Thus, developed countries are 35, and developing
countries are 33, with a sample period of 20 years. Secondly, time-invariant omitted
variables can be well addressed using GMM since unobserved country-level heterogeneity
can be accounted for. Third, internal instruments are used to address issues related to
potential endogeneity. Since income inequality is found to be persistent both within and
across countries (Adeleye et al. 2017;Kunawotor et al. 2020;Shimeles and Nabassaga 2018),
the use of its first lag is made possible within GMM estimation to capture such persistency.
3.2. Sources of Data and Description of Variables
The paper investigates a panel of 68 developed (35) and developing (33) countries from
2000 to 2019, subject to data availability (see Appendix A). The data were sourced from
reputable intergovernmental and international organizations, including income inequality
data from the Standardized World Income Inequality Database (SWIID), institutional
capacity data from the World Governance Indicators and Transparency International, Fiscal
policy data from the International Monetary Fund (IMF) Financial Statistics, economic and
demographic data from the World Development Indicators, World Bank, and democracy
data from the Polity-V project. The detailed description of the variables and their sources is

Economies 2022,10, 115 6 of 16
shown in Table 1, and their respective statistics are shown in Table 2. The correlation matrix
is operationalized in Appendix B.
Table 1. Variable Description.
Data Definition/Measurement Source
Income inequality
The extent to which income is distributed among
individuals or households. It is disposable
income after tax. It is measured as 0 (perfect
income distribution) 100 (perfect inequality)
Swiss
Standardized
Income Inequality
Database (SWIID)
Government
Consumption Total government expenditure (% GDP) International
Monetary fund
Government Debts Total government debt (% GDP) International
Monetary Fund
Direct tax (Income Tax)
Total income tax revenues (% GDP)
Indirect tax (Taxes on
Goods and Services)
Total revenue raised from taxes imposed on the
consumption of goods and services (%GDP)
International
Monetary Fund
Government
Education Expenditure
Total expenditure on education (% GDP) World Bank
Government Health
Expenditure Total expenditure on health (%GDP) World Bank
Government
Effectiveness
World Governance
Indicators
Corruption Transparency
International
Democracy Polity 2 Index. Measure from −10 (most
autocratic) and +10 (most democratic) Polity-V Project
Population growth
rate World Bank
Foreign Direct
Investment Foreign Direct Investment (%GDP)
GDP per capita GDP per capita (constant 2010 US$) World Bank
Trade openness Total exports and imports (% GDP) World Bank
Source: authors’ construction.
Table 2. Descriptive statistics.
Variable Obs Mean Std. Dev. Min Max
Income inequality 1186 35.376 8.251 22.4 59.9
Government size 1253 16.884 4.607 4.846 27.935
Income tax 1149 24.864 12.659 −1.348 66.715
Tax on goods and services 1147 33.383 9.327 6.342 77.688
Public debt 1234 50.28 33.845 0.828 198.438
Education expenditure 958 4.911 1.389 1.496 9.51
Health expenditure 1122 6.653 2.302 1.916 13.677
Government Effectiveness 1188 64.58 24.671 0 100
Corruption 528 53.741 19.656 24 92
Democracy 1235 7.241 4.416 −10 10
GDP per capita 1251 23,257.744 17,932.356 1075.395 97,864.195
Foreign Direct Invest. 1303 6.914 25.519 −58.323 451.639
Inflation 1195 96.637 24.273 30.76 261.069
Trade openness 1254 94.339 62.549 19.798 437.327
Population growth 1254 0.731 0.971 −9.081 7.786

Economies 2022,10, 115 7 of 16
Our explanatory variable of interest is the fiscal policy, which is proxied by fiscal
redistribution, tax measures (direct and indirect taxes), and public or government ex-
penditure measures. First redistribution (income inequality) consists of disposable Gini
coefficient after taxes. Tax indicators include income tax (direct tax measure), government
consumption-based tax (indirect tax), and taxes on goods and services (indirect tax). More-
over, the indicators of public expenditure encompass expenditure on health and education
and public debt situation. The spending on health and education shows investment in
social sectors that may benefit the poor and marginalized groups. They (expenditure on
health and education) are expressed as a percentage of GDP. This paper expects fiscal policy
as direct taxes should lead to income distribution and reduce the income gap between rich
and poor, while indirect taxes should widen the income gap. Two variables were used to
measure Institutional capacity: government effectiveness and corruption. Government
effectiveness looks at the perception of the quality of public services, the quality of civil
service, and the degree of its independence from political manipulations. Corruption
measures the abuse of public office for personal gains. The prior expectation of this paper is
that institutional quality should have a reducing effect on income inequality. The direction
of the relationship between corruption and income distribution is uncertain.
As a standard practice in the existing income inequality literature, some control
variables were added to the model: population growth rate, trade openness, GDP per capita,
foreign direct investment (FDI), and democracy. Population growth rate affects income
distribution through age dependency ratio: thus, either youthful population between
0–15 years or old age population of 65 and above against the active working population
of 16–64 years (Kunawotor et al. 2022). Therefore, we expect that age dependency should
increase with income inequality. Gross domestic product (GDP) per capita measures the
amount of income received by the citizens of a country. As per the Kuznets-curve hypothesis
(Kuznets 1955), we expect that income inequality will increase with GDP per capita in the
short term but decrease in the long run. Trade openness measures the volume of exports
and imports. Trade openness is related to the dynamics of trade liberalization. Thus, trade
liberation can open economic opportunities for low-skill and low-income people, likely
reducing income inequality. However, trade liberalization may also open the domestic
economy to external shocks resulting in domestic volatilities. Therefore, the relationship
between trade openness and income distribution is uncertain (Dabla-Norris et al. 2015).
Moreover, the net inflow of foreign direct investment is used to measure FDI. However,
we assume FDI to exert a negative impact on income inequality. Democracy is controlled
because it is only within a democracy that citizens are offered the opportunity to exercise
a relatively high degree of control over leaders to ensure their needs and preferences are
redressed. Therefore, democracy is responsive to the poor and vulnerable groups, which
may contribute to the abatement of income inequality (Reuveny and Li 2003).
4. Empirical Results and Discussions
This paper examines the cumulative effect of fiscal policy and institutional capacity on
income inequality with evidence from both developed and developing countries. Therefore,
some steps were followed: first, the model involving fiscal policy measures was examined;
and second, the following model investigated the interactive effect of fiscal policy and
institutional capacity on inequality. It should be underscored that negative signs on the
coefficients indicate the potential of the predictor variables to reduce income inequality. In
contrast, positive coefficients show the potential of the variables to widen the income gap
between the rich and the poor.
4.1. Empirical Results of the Effect of Fiscal Policy on Income Inequality
The empirical findings of the redistributive impacts of fiscal policy encompass the
tax measures and expenditure dimensions. Tables 3and 4are the baseline models and
show the empirical results of the effects of fiscal policy on income inequality for developed
and developing countries, respectively. While tax variants (income tax and general taxes

Economies 2022,10, 115 8 of 16
on goods and services) of fiscal policy are presented in Model (1)–Model (2) that of the
expenditure dimensions (government size, expenditure on education and health, and public
debt) are shown in Model (3)–Model (6) respectively. The results (Model (1)– Model (6))
reveal that the coefficients of the dynamic effects (first lag of inequality) are more than 0.800
and statistically significant at 1% (Tables 3and 4), which imply that income inequality is
persistent in both developed and developing countries. Adeleye et al. (2017) have argued
that the lagged value greater than 0.800 is required as the rule of thumb for establishing such
persistence. It also means that the past income inequality level is a stronger determinant of
its current level. The dynamic effect further indicates that income inequality tends to be
path-dependent, as the current income inequality of a country strongly predicts her level of
income inequality in the ensuing year. This is consistent with the findings in the existing
literature (Adeleye et al. 2017;Anyanwu et al. 2016;Kunawotor et al. 2022).
Table 3. Effect of fiscal policy on income inequality (developed countries).
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
inequality-1 1.024 ***
(0.0706)
1.033 ***
(0.0830)
0.994 ***
(0.0781)
1.014 ***
(0.0536)
0.859 ***
(0.165)
0.958 ***
(0.0632)
intax −0.00185
(0.00544)
gstax 0.00424
(0.00893)
gsize 0.0348 *
(0.0192)
eduex 0.187 *
(0.105)
helex 0.0877 *
(0.0458)
pdebt −1.67 ×10−5
(0.00115)
gdpc −6.23 ×10−6**
(2.92 ×10−6)
−7.03 ×10−6**
(2.71 ×10−6)
−1.61 ×10−6
(3.90 ×10−6)
−8.20 ×10−6
(9.58 ×10−6)
−2.56 ×10−5
(2.36 ×10−5)
−1.02 ×10−5***
(3.18 ×10−6)
gdppc square 1.44 ×10−10
(1.81 ×10−10)
fdi −0.000297
(0.000865)
−0.000380
(0.000824)
−8.44 ×10−5
(0.000682)
−0.000178
(0.000328)
0.000292
(0.000415)
−0.000229
(0.000741)
to −0.000502
(0.00130)
−0.000426
(0.00138)
0.000488
(0.00156)
0.000309
(0.00147)
0.00184
(0.00212)
7.49 ×10−5
(0.00153)
popg 0.0173 0.0147 0.00980 0.0394 * 0.0514 ** 0.0256
inf (0.0268)
0.000306
(0.0263)
0.000138
(0.0204)
−0.00189
(0.0226)
−0.00264
(0.0248)
−0.00290
(0.0324)
0.000961
democ
(0.00177)
0.0560
(0.0683)
(0.00170)
0.0415
(0.0672)
(0.00179)
0.0497
(0.0713)
(0.00322)
0.0648
(0.134)
(0.00293)
0.0458
(0.0859)
(0.00265)
0.00318
(0.0489)
Observations 537 537 542 446 507 544
Number of groups 34 34 34 34 34 34
Number of instru. 26 26 25 24 25 25
AR(1) 0.008 0.010 0.009 0.019 0.066 0.014
AR(2) 0.253 0.246 0.258 0.299 0.362 0.286
Hansen J. 0.581 0.658 0.656 0.606 0.700 0.531
Standard errors in parentheses. *** p< 0.01, ** p< 0.05, * p< 0.1.
The results (Model 1) reveal that direct tax (income tax) is negative but statistically
insignificant for developed countries (Table 3), suggesting that income tax has the potential
to reduce inequality. However, concerning developing countries (Table 4), income tax is
negative and statistically significant at a 5% level. Thus, a unit increase in the level of direct
tax, mainly income tax, is likely to reduce income inequality by 0.0118. This implies that
direct taxes in the form of income tax are powerful fiscal policy tools to reduce income
inequality in developing countries. This is particularly so because income tax is classified
as a progressive tax where the tax rate increases as the income increases. With income
tax, the wealthy or high-income people bear the tax burden by paying a more significant

Economies 2022,10, 115 9 of 16
proportion of their income as tax. This result is consistent with the work of Clifton et al.
(2020); Kunawotor et al. (2022); and Salotti and Trecroci (2018).
Table 4. Effect of fiscal policy on income inequality (developing countries).
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
inequality-1 0.912 ***
(0.0384)
0.910 ***
(0.0479)
0.904 ***
(0.0608)
0.988 ***
(0.0526)
0.939 ***
(0.0347)
0.897 ***
(0.0392)
intax −0.0118 **
(0.00525)
gstax 0.00166
(0.00705)
gsize −0.00250
(0.0152)
eduex 0.00528
(0.0320)
helex −0.0195
(0.0477)
pdebt 0.00211
(0.00221)
gdppc −3.94 ×10−5***
(1.39 ×10−5)
−3.88 ×10−5*
(2.21 ×10−5)
−4.45 ×10−5*
(2.46 ×10−5)
−1.48 ×10−5
(1.34 ×10−5)
−3.61 ×10−5
(2.15 ×10−5)
−3.70 ×10−5*
(2.01 ×10−5)
fdi 0.00596
(0.00558)
0.00207
(0.00421)
0.00117
(0.00348)
0.00164
(0.00301)
0.00208
(0.00335)
0.00280
(0.00323)
to −0.00133
(0.00154)
−0.00176
(0.00225)
−0.00261
(0.00229)
−0.00214
(0.00229)
−0.00153
(0.00176)
−0.00218
(0.00169)
popg 0.0502
(0.0973)
0.0734
(0.102)
0.139
(0.114)
0.169
(0.104)
0.114
(0.0833)
0.112
(0.0784)
inf 0.000449
(0.00105)
−0.000306
(0.00109)
−0.000378
(0.000709)
−0.000897
(0.000794)
−0.00127
(0.00117)
−0.00102
(0.00127)
democ −0.000669
(0.00441)
0.000189
(0.00474)
0.00166
(0.00545)
0.000796
(0.00583)
0.00417
(0.00528)
0.00362
(0.00523)
Observations 397 395 402 308 430 454
Number of groups 30 30 30 29 30 30
Number of instru. 25 26 25 24 23 25
AR (1) OIR 0.017 0.033 0.026 0.065 0.028 0.017
AR(2) OIR 0.070 0.080 0.082 0.112 0.238 0.070
Hansen 0.545 0.329 0.233 0.411 0.618 0.545
Standard errors in parentheses. *** p< 0.01, ** p< 0.05, * p< 0.1.
However, though indirect tax (taxes on goods and services) is statistically insignificant
for both developed and developing countries, the coefficients are positive, suggesting
that indirect taxes such as taxes on the consumption of goods and services have the
potential to widen the gap between the rich and the poor in both developed and developing
countries. More interestingly, the coefficients of the expenditure dimensions of fiscal
policy (Table 3), including the size of the government, public debt, public expenditure on
education and health, are positive and statistically significant at 10% levels (Model 3, Model
4 and Model 5). This implies that government size, public investment in education, and
health tend to increase income inequality in developed countries (Table 3). Thus, a unit
increase in the size of the government will correspondingly increase income inequality by
0.0348. Moreover, the income gap between the rich and the poor in developed countries is
also likely to widen by 0.187 and 0.0877 when government expenditure on education and
health increases by a unit each, respectively. This result is astonishing in the sense that it
is not clear the condition under which government expenditure on social sectors tends to
widen the income gap between the rich and the poor in developed countries. The findings
contradict the existing literature (Apergis 2021;Gupta 2018;Odusola 2017). However, the
findings concerning developing countries show that the expenditure dimensions of fiscal
policy have no significant impact on income inequality.
It can further be observed that GDP per capita reduces income inequality in developed
and developing countries. Model (4) and Model (5) in Table 3signal that population growth
increases income inequality in developed countries but does not seem to impact developing
countries. However, foreign direct investment, inflation, trade openness, and democracy

Economies 2022,10, 115 10 of 16
do not affect income inequality in developed and developing countries. Given the choice
of one lag length, the specification of the second-order Arellano and Bond autocorrelation
test (AR(2) results reveal that the system GMM model does not suffer from second-order
serial correlation, and the Hansen J-test of overidentification restrictions shows that the
instruments used are not over-identified. Therefore, the system GMM model specification
fits and can be relied upon for subsequent discussion and policy extrapolation.
4.2. The Moderating Role of Institutional Capacity in Fiscal Policy-Income Inequality Nexus
Having accounted for the impact of fiscal policy on income inequality in our Baseline
Models (Tables 3and 4), this section examines whether institutional quality moderates the
effect of fiscal policy on income inequality. While Table 5shows the results for developed
countries, Table 6presents developing countries’ results. From Tables 5and 6, Model (1)–
Model (6) show the measures of institutional capacity and their interactions with the
variants of fiscal policy. Thus, the models show the role of institutions in fiscal policy and
the income inequality nexus captured by interacting with the measures of institutional
capacity and fiscal policy. We expect that the coefficients of the interactions between
corruption and fiscal policy measures should be positive to signal the domineering impact
of corruption in increasing income inequality. In contrast, the coefficients of the interactions
between government effectiveness and fiscal policy measures are expected to be negative
to reduce income inequality.
As shown (Tables 5and 6), the findings generally reveal no significant effect of insti-
tutions on income inequality in both developed and developing countries. Though the
interaction terms are also not statistically significant, they bear expected signs in most
cases, which suggest that institutions have an equalizing effect on income inequality if they
are significant. Nevertheless, some salient points can be deduced from these results. The
insignificance of institutional capacity in developed countries can be explained through
institutional inertia (Madni 2019), which shows that the slow changes in the institutional
environment are somewhat responsible for unequal income distribution. The institutional
inertia argues that at a particular stage of the institutional development, new institutions
need to be created to deal with unknown causes of income inequality since the exiting
institutions may act slow or may not be enough to deal with emerging causes of the un-
equal income distribution (Easton 1965;Josifidis et al. 2017). Therefore, institutional inertia
may explain why developed countries experience rising income inequality despite high
economic growth and matured institutional environment. However, the lack of statistical
strength on the part of institutional capacity concerning developing countries may be due
to the relatively weak nature of institutions and poor governance environment.
Despite the insignificance of corruption and its interactions with fiscal policy, it does
not suggest that it is irrelevant. Corruption may undermine the opportunity for income
distribution in two ways. First, since people with high income and well-connected to
political elites are the beneficiaries of corruption, the ability of the government and its allied
institutions to ensure even distribution of economic resources may be neutralized (Furceri
and Ostry 2019). Second, concerning taxation, because corruption benefits the rich and the
most connected people, induces a biased tax regime. Subsequently, tax evasion through
corruption undermines the government’s capacity to raise enough revenue talks less of
distributing it. Third, corruption denies the poor and marginalized groups access to critical
public services such as education and health. Also, the government effectiveness, which
implies invigorating the quality of public services, the effectiveness of social policies and
programs is crucial in reducing income inequality in developed and developing countries.

Economies 2022,10, 115 11 of 16
Table 5.
Moderating role of fiscal policy and institutional capacity on income inequality (developed
countries).
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
inequality-1 0.482
(0.325)
0.884 ***
(0.168)
0.731 ***
(0.191)
0.738 ***
(0.191)
0.838 ***
(0.229)
0.884 ***
(0.152)
pdebt ×corr 0.000114
(0.000571)
pdebt ×ge −0.000897
(0.000782)
helex ×corr 0.0129
(0.0117)
helex ×ge −7.03 ×10−6
(0.0109)
eduex ×corr 0.00926
(0.0496)
eduex ×ge −0.00973
(0.0347)
gsize ×corr 0.000623
(0.0156)
gsize ×ge −0.00458
(0.00967)
gstax ×corr 0.00135
(0.00408)
gstax ×ge −0.000183
(0.00297)
intax ×corr 0.00782
(0.00690)
intax ×ge −0.000281
(0.00216)
pdebt −0.0738
(0.0546)
gsize 0.259
(1.228)
helex 0.896
(1.158)
eduex −0.407
(5.584)
gstax 0.0604
(0.376)
intax −0.578
(0.605)
ge 0.0605
(0.0684)
−0.0518
(0.100)
0.0568
(0.173)
−0.0966
(0.259)
−0.0332
(0.116)
−0.121
(0.107)
corr −0.0822
(0.116)
0.0952
(0.150)
0.120
(0.352)
0.103
(0.326)
0.155
(0.113)
0.0310
(0.0598)
gdppc −2.36 ×10−5
(2.25 ×10−5)
−1.98 ×10−5
(1.29 ×10−5)
−5.91 ×10−5
(4.33 ×10−5)
−3.73 ×10−5
(9.48 ×10−5)
−2.79 ×10−5
(3.22 ×10−5)
−1.36 ×10−5
(1.98 ×10−5)
fdi 0.00146
(0.00267)
0.00221
(0.00229)
0.00273
(0.00346)
−0.00216
(0.00192)
0.00189
(0.00185)
0.000495
(0.00123)
to 0.00660
(0.00719)
−0.001000
(0.00299)
−0.00482
(0.00456)
−0.00197
(0.00693)
−0.00170
(0.00505)
−0.00154
(0.00315)
popg −0.436
(0.330)
−0.124
(0.143)
−0.274
(0.441)
−0.281
(0.302)
−0.195
(0.161)
−0.0645
(0.114)
inf −0.00388
(0.0187)
−0.00725
(0.0142)
0.0186
(0.0243)
0.00174
(0.0504)
0.0131
(0.0199)
−0.0203
(0.0184)
democ −0.245
(0.322)
−0.00588
(0.918)
0.853
(1.400)
−0.220
(0.712)
−0.0327
(0.863)
−0.312
(0.513)
Observations 170 170 170 113 134 176
Number groups 34 34 34 32 34 34
Number of instruments 25 25 25 24 25 25
AR(1) 0.048 0.081 0.072 0.008 0.053 0.043
AR(2) 0.245 0.473 0.464 0.231 0.349 0.679
Hansen 0.420 0.442 0.368 0.157 0.329 0.298
Standard errors in parentheses. *** p< 0.01.

Economies 2022,10, 115 12 of 16
Table 6.
Moderating role of fiscal policy and institutional capacity on income inequality (developing
countries).
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
inequality-1 0.705 *** 0.873 *** 0.673 *** 0.917 *** 0.783 *** 0.715 ***
(0.152) (0.107) (0.171) (0.146) (0.120) (0.101)
pdebt ×corr 0.000251
(0.000240)
pdebt ×ge −9.51 ×10−5
(0.000222)
helex ×corr 0.0192
(0.0246)
helex ×ge 0.00294
(0.00674)
eduex ×corr −0.00343
(0.0202)
eduex ×ge −0.00314
(0.0125)
gsize ×corr 0.00331
(0.00463)
gsize ×ge −0.0120
(0.0102)
gstax ×corr 0.000704
(0.00110)
gstax ×ge −8.54 ×10−5
(0.000855)
intax ×corr 0.00232
(0.00151)
intax×ge −0.000119
(0.00144)
pdebt −0.00535
(0.00878)
helex −0.773
(1.009)
eduex 0.373
(0.458)
gsize 0.599
(0.488)
gstax −0.0345
(0.0482)
intax −0.120
(0.0929)
ge −0.0122 −0.00105 0.196 0.0221 −0.0253 0.0116
(0.0360) (0.0308) (0.162) (0.0532) (0.0430) (0.0255)
corr −0.0743 −0.0361 −0.0895 0.0304 −0.126 −0.0235
(0.0695) (0.0553) (0.102) (0.0951) (0.154) (0.0302)
gdppc −4.57 ×10−5−3.39 ×10−5−6.40 ×10−6−4.59 ×10−5−7.13 ×10−5−3.89 ×10−5
(4.61 ×10−5) (3.80 ×10−5) (7.08 ×10−5) (8.87 ×10−5) (5.47 ×10−5) (3.43 ×10−5)
fdi −0.00351 −0.00175 0.00275 0.00126 −0.00967 −0.000292
(0.00631) (0.00505) (0.00739) (0.00546) (0.0114) (0.00554)
to −0.00396 −0.000241 0.00446 −0.00148 0.00406 0.00118
(0.00775) (0.00460) (0.0101) (0.00725) (0.00644) (0.00369)
popg −0.237 0.174 0.845 0.282 0.0831 0.265
(0.281) (0.312) (0.795) (0.430) (0.413) (0.184)
inf 0.00189 0.00184 −0.00222 0.00238 −0.000850 −0.000248
(0.00317) (0.00195) (0.00317) (0.00310) (0.00285) (0.00209)
democ 0.0173 −0.0166 −0.000352 −0.0285 −0.0174 −0.00218
(0.0207) (0.0157) (0.0315) (0.0684) (0.0206) (0.0150)
Observations 119 119 119 83 106 131
Number of groups 26 26 26 23 28 27
Number of instruments 25 25 25 26 23 25
AR (1) 0.335 0.123 0.258 0.231 0.112 0.134
AR(2) 0.128 0.308 0.408 0.136 0.996 0.214
Hansen 0.557 0.518 0.528 0.396 0.598 0.606
Standard errors in parentheses. *** p< 0.01.

Economies 2022,10, 115 13 of 16
5. Concluding Remarks and Policy Implications
In recent years, income inequality has been rising in developing countries and de-
veloped countries despite the ever-increasing level of economic growth across the globe.
Extant literature has examined the drivers of income inequality, including fiscal policy.
However, the empirical evidence on fiscal policy-income inequality nexus has not only
produced decidedly mixed results so far; however, little attention has been paid to the role
of institutional quality. Therefore, the paper examines the cumulative effect of fiscal policy
and institutional capacity on income inequality in developed and developing countries.
The fiscal policy measure employed includes direct tax (income tax), indirect tax (taxes on
goods and services), government size, public expenditure on education and health, and
public debt. Moreover, government effectiveness and corruption were used to proxy for
institutional capacity. The following results have been established by applying a more
robust econometric technique (system GMM) to control for potential endogeneity. The
dynamic effect captured by the first lag of inequality suggests that the widening income
gap is persistent in both developed and developing countries. We also find evidence that
income tax is more progressive and may abate income inequality in developing countries
and not in developed countries. However, taxes on goods and services were found not to
impact income equalization across the globe.
Furthermore, the findings reveal that government size, education expenditure, and
health expenditure are negatively associated with income inequality in developed countries
only. Public debt was observed not to influence income distribution across the world. We
observed that corruption and government effectiveness do not significantly impact income
distribution in developed and developing countries for institutional capacity. However,
in most cases, the coefficients of the interactions between fiscal policy and institutional
capacity bear the expected signs, albeit insignificant. This implies that institutions play a
minor role in influencing the effect of fiscal policy on income inequality.
Therefore, the paper offers the following policy recommendations based on the find-
ings above. The paper recommends that developed and developing countries vigorously
pursue tax reforms to broaden the tax net to make the structure of tax more progressive as a
potent avenue to reduce income inequality. This implies that efforts need to be made to roll
out more direct taxes, such as property tax. This will ensure that the rich or high-income
earners pay more of their income as tax. Moreover, governments need to strengthen tax
administration capacity to ensure that direct tax instruments are used more than indirect tax
instruments. We emphasize that revenues accrued from taxes should be invested in social
sectors such as free education and health care services to benefit the poor and marginalized
segments of the population. Thus, free health insurance programs should be implemented
to ensure unfettered access to health care services by the poor. Additionally, conscious
efforts should be put in place to use fiscal policy as an instrument to promote growth that
creates jobs ensure skills development and general improvement in human development.
Finally, since developed countries may likely have reached the stage of institutional inertia,
which makes it impossible to address emerging income inequality, it is crucial to create
new effective institutions to achieve income equalization effects. Similarly, developing
countries need to embark upon institutional renewal to ensure practical institutionalization
of administrative efficiency and good governance practices. This will provide the suitable
institutional capacity to address corruption and facilitate redistributive outcomes.
The paper is not without limitations. It should be noted that only government effec-
tiveness and control of corruption are used as a measure of institutional capacity. Further
studies should employ other indicators such as the rule of law, regulatory quality, and voice
and accountability to very this relationship. Moreover, the study adopted the methodology
that treated all countries as a single unit, likely to be glossed over individual country
heterogeneities. Therefore, further studies should provide evidence on this relationship
based on individual country realities.

Economies 2022,10, 115 14 of 16
Author Contributions:
Conceptualization, M.H.M. and P.P.; methodology, M.H.M., and P.P.; software,
M.H.M.; validation, P.P.; formal analysis, M.H.M.; investigation, M.H.M.; resources, M.H.M.; data
curation, M.H.M.; writing—original draft preparation, M.H.M.; writing—review and editing, M.H.M.
and P.P.; visualization, M.H.M.; supervision, P.P. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement:
The data used in this study are publicly available in respective organi-
zation’s websites mentioned in Table 1. Variable Description.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Appendix A.1. Developed Countries
Australia, Austria, Belgium, Chile, Croatia, Cyprus, Czech Republic, Denmark, Esto-
nia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Latvia,
Lithuania, Luxemburg, Malta, Netherlands, New Zealand, Norway, Poland, Portugal,
Singapore, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, and United Kingdom.
Appendix A.2. Developing Countries
Burkina Faso, Nepal, Bangladesh, Bhutan, Bolivia, Cote D’Ivoire, El Salvador, India,
Indonesia, Kyrgyz Republic, Moldova, Mongolia, Ukraine, Philippines, Tunisia, Brazil,
Belarus, Argentina, Costa Rica, Columbia, China, Jamaica, Georgia, Dominican Republic,
Peru, Malaysia, Mexica, Mauritius, South Africa, Romania, Russia, Sri Lanka, and Thailand.
Appendix B
Correlation Matrix
Correlations
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
(1) gini 1.000
(2) gsize −
0.521
1.000
(3) intax 0.141 −
0.034
1.000
(4) gstax 0.146 −
0.088
−
0.284
1.000
(5) pdebt 0.002 0.143 0.114 −
0.156
1.000
(6) eduex −
0.332
0.641 0.046 −
0.067
−
0.006
1.000
(7) helex −
0.444
0.654 0.029 −
0.005
0.135 0.530 1.000
(8) ge −
0.326
0.422 0.287 −
0.093
0.150 0.228 0.401 1.000
(9) cpi −
0.459
0.542 0.274 −
0.159
0.108 0.419 0.596 0.679 1.000
(10) democ −
0.152
0.282 0.145 0.082 0.071 0.275 0.474 0.397 0.376 1.000
(11) gdppc −
0.567
0.378 0.240 −
0.257
0.044 0.321 0.496 0.628 0.810 0.280 1.000
(12) fdi −
0.124
0.038 0.028 0.020 0.094 0.094 0.059 0.124 0.070 0.054 0.120
1.000
(13) inf −
0.092
0.024 0.067 −
0.040
0.056 0.148 0.178 0.011 −
0.481
0.012 0.108 −
0.013 1.000
(14) to −
0.294
0.024 −
0.007
−
0.105
0.008 0.069 −
0.062
0.230 0.327 −
0.090
0.495
0.273 0.009 1.000
(15) popg 0.375 −
0.241
0.323 −
0.218
−
0.012
−
0.016
−
0.236
−
0.096
−
0.041
−
0.171
−
0.003 0.013 0.039 0.042 1.000
References
Acemoglu, Daron, and James A. Robinson. 2006. De Facto Political Power and Institutional Persistence. American Economic Review 96:
325–30. [CrossRef]
Acemoglu, Daron, Francisco A. Gallego, and James A. Robinson. 2014. Institutions, Human Capital, and Development. Annual Review
of Economics 6: 875–912. [CrossRef]
Adeleye, Bosede Ngozi, E. S. C. Osabuohien, and Ebenezer I. K. Bowale. 2017. The Role of Institutions in the Finance-Inequality Nexus
in Sub-Saharan Africa. Journal of Contextual Economics 137: 173–92. [CrossRef]
Albertus, Michael, and Victor Menaldo. 2014. The Political Economy of Autocratic Constitutions. In Constitutions in Authoritatian
Regime. New York: Cambridge University Press, pp. 53–82. [CrossRef]
Anyanwu, John C., Andrew EO Erhijakpor, and Emeka Obi. 2016. Empirical Analysis of the Key Drivers of Income Inequality in West
Africa. African Development Review 28: 18–38. [CrossRef]
Apergis, Nicholas. 2021. The role of fiscal policy in the link between income inequality and banking crises. Applied Economic Letters 28:
1283–87. [CrossRef]

Economies 2022,10, 115 15 of 16
Arellano, Manuel, and Stephen Bond. 1991. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to
Employment Equations. The Review of Economic Studies 58: 277–97. [CrossRef]
Arellano, Manuel, and Olympia Bover. 1995. Another look at the instrumental variable estimation of error-components models. Journal
of Econometrics 68: 29–51. [CrossRef]
Blundell, Richard, and Stephen Bond. 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of
Econometrics 87: 115–43. [CrossRef]
Brinca, Pedro, Miguel H. Ferreira, Francesco Franco, Hans A. Holter, and Laurence Malafry. 2021. Fiscal consofidation programs and
income inequality. International Economic Review 61: 405–60. [CrossRef]
Caminada, Koen, Kees Goudswaard, Chen Wang, and Jinxian Wang. 2019. Income inequality and fiscal redistribution in 31 countries
after the crisis. Comparative Economic Studies 61: 119–48. [CrossRef]
Carmignani, Fabrizio. 2009. The distributive effects of institutional quality when government stability is endogenous. European Journal
of Political Economy 25: 409–21. [CrossRef]
Cevik, Mr Serhan, and Carolina Correa-Caro. 2020. Growing (un) equal: Fiscal policy and income inequality in China and BRIC+.
Journal of the Asia Pacific Economy 25: 634–53. [CrossRef]
Chancel, L., T. Piketty, E. Saez, and G. Zucman. 2022. World Inequality Report 2022. Available online: https://wir2022.wid.world/
download/ (accessed on 12 January 2022).
Clements, B., R. De Mooij, M. Francese, S. Gupta, and M. Keen. 2015. Fiscal Policy and Income Inequality: An Overview. In Fical Policy
and Income Inequality. Edited by B. Clements, R. De Mooij, S. Gupta and M. Keen. Washington, DC: International Monetary Fund,
pp. 3–20.
Clifton, J., D. Díaz-Fuentes, and J. Revuelta. 2020. Falling inequality in Latin America: The role of fiscal policy. Journal of Latin Americal
Studies 52: 317–41. [CrossRef]
Coady, David, and Allan Dizioli. 2018. Income inequality and education revisited: Persistence, endogeneity and heterogeneity. Applied
Economics 50: 2747–61. [CrossRef]
Dabla-Norris, E., K. Kochhar, N. Suphaphiphat, F. Ricka, and E. Tsounta. 2015. Causes and Consequences of Income Inequality: A
Global Perspective. In IMF Staff Discussion Notes. Washington, DC: International Monetary Fund, vol. 15. [CrossRef]
Datt, Gaurav, Martin Ravallion, and Rinku Murgai. 2020. Poverty and growth in India over six decades. American Journal of Agricultural
Economics 102: 4–27. [CrossRef]
de Freitas, Jenny. 2012. Inequality, the politics of redistribution and the tax mix. Public Choice 151: 611–30. [CrossRef]
Dotti, Valerio. 2020. Income inequality, size of government, and tax progressivity: A positive theory. European Economic Review 121:
103327. [CrossRef]
Easton, D. 1965. A Systems Analysis of Political Life. New York: John Wiley & Sons, Inc.
Furceri, Davide, and Jonathan D. Ostry. 2019. Robust determinants of income inequality. Oxford Review of Economic Policy 35: 490–517.
[CrossRef]
Gupta, S. 2018. Income Inequality and Fiscal Policy: Agenda for Reform in Developing Countries. (Growth and Reduce Income Inequality
Series Working Paper); New York: Friedrich-Ebert-Stiftung.
Hu, Bingjie, and Ronald U. Mendoza. 2013. Public health spending, governance and child health outcomes: Revisiting the links. Journal
of Human Development and Capabilities 14: 285–311. [CrossRef]
Huynh, Cong Minh. 2021. Foreign direct investment and income inequality: Does institutional quality matter? The Journal of
International Trade & Economic Development 30: 1231–43. [CrossRef]
Hyden, Goran. 2007. Governance and poverty reduction in Africa. Proceedings of the National Academy of Sciences 104: 16751–56.
[CrossRef] [PubMed]
Jenkins, S. 2017. The measurement of income inequality. In Economic Inequality and Poverty. New York: Routledge, pp. 3–38.
Josifidis, Kosta, Novica Supi´c, and Emilija Beker-Pucar. 2017. Institutional quality and income inequality in the advanced countries.
Panoeconomicus 64: 169–88. [CrossRef]
Kunawotor, Mark Edem, Godfred Alufar Bokpin, and Charles Barnor. 2020. Drivers of income inequality in Africa: Does institutional
quality matter? African Development Review 32: 718–29. [CrossRef]
Kunawotor, Mark Edem, Godfred Alufar Bokpin, Patrick O. Asuming, and Kofi A. Amoateng. 2022. The distributional effects of fiscal
and monetary policies in Africa. Journal of Social and Economic Development, 1–20. [CrossRef]
Kuznets, Simon. 1955. Economic Growth and Income Inequality. The American Economic Review 45: 1–28.
Kwasi Fosu, Augustin. 2018. Economic structure, growth, and evolution of inequality and poverty in Africa: An overview. Journal of
African Economies 27: 1–9. [CrossRef]
Law, Chee-Hong, and Siew-Voon Soon. 2020. The impact of inflation on income inequality: The role of institutional quality. Applied
Economics Letters 27: 1735–38. [CrossRef]
Madni, Ghulam Rasool. 2019. Probing Institutional Quality Through Ethnic Diversity, Income Inequality and Public Spending. Social
Indicators Research 142: 581–95. [CrossRef]
Mankiw, N. G. 2021. Principles of Economics, 9th ed. Boston: Cengage Learning.
Nguyen, Van Bon. 2021. The difference in the FDI inflows–Income inequality relationship between developed and developing countries.
The Journal of International Trade & Economic Development 30: 1123–37.

Economies 2022,10, 115 16 of 16
Nolan, Brian, Matteo G. Richiardi, and Luis Valenzuela. 2019. The drivers of income inequality in rich countries. Journal of Economic
Surveys 33: 1285–324. [CrossRef]
North, D. C. 1990. Institutions, Institutional Change and Economic Performance. Cambridge: Cambridge University Press. [CrossRef]
Odusola, A. 2017. Fiscal Policy, Redistribution and Inequality in Africa. In Income Inequality in Sub-Saharan Africa: Divergence,
Determinants, and Consequence. New York: United Nations Development Programme, pp. 155–77.
Phillips, Peter C.B., and Donggyu Sul. 2007. Transition modeling and econometric convergence tests. Econometrica 75: 1771–855.
[CrossRef]
Piketty, Thomas, and Emmanuel Saez. 2003. Income inequality in the United States, 1913–1998. Quarterly Journal of Economics 118: 1–41.
[CrossRef]
Ravallion, M., and S. Chen. 2021. Is That Really a Kuznets Curve? Turning Points for Income Inequality in China. (No. W29199; NBER
Working Paper Series). Available online: http://www.nber.org/papers/w29199.pdf (accessed on 13 January 2022).
Reuveny, Rafael, and Quan Li. 2003. Economic openness, democracy, and income inequality: An empirical analysis. Comparative
Political Studies 36: 575–601. [CrossRef]
Roodman, David. 2009. How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal 9: 86–136.
[CrossRef]
Salotti, Simone, and Carmine Trecroci. 2018. Cross-country evidence on the distributional impact of fiscal policy. Applied Economics 50:
5521–42. [CrossRef]
Shimeles, Abebe, and Tiguene Nabassaga. 2018. Why is inequality high in Africa? Journal of African Economies 27: 108–26. [CrossRef]
Sonora, R. 2019. Income Inequality, Poverty, and the Rule of Law: Latin America vs the Rest of the World1. (MPRA Paper 91512).
Available online: https://mpra.ub.uni-muenchen.de/91512/1/MPRA_paper_91512.pdf (accessed on 30 December 2021).
Tsui, Anne S., Georges Enderle, and Kaifeng Jiang. 2016. Income inequality in the United States: Reflections on the role of corporations.
Academy of Management Review 43: 159–168. [CrossRef]