Government Intervention, Institutional Quality, - Ricerca sull'intelligenza artificiale specializzata al MIT

Government Intervention, Institutional Quality,
and Income Inequality: Evidence from Asia
and the Paciﬁc, 1988–2014
Bertrand Blancheton and Dina Chhorn∗

We examine the linear and nonlinear long-run relationship between public
expenditure and institutional quality, and income inequality in Asia and the
Paciﬁc. By applying panel cointegration methods using a dataset from 1988
A 2014, our main ﬁndings suggest that public expenditure and institutional
quality have negative long-run, steady-state effects on income inequality in Asia
and the Paciﬁc. The effect of institutional quality has only a one-way Granger
causality link to income inequality. The existence of a nonlinear relationship
between public expenditure and institutional factors linked to income inequality
is also found. It implies that, at the early stage of institutional development, UN
country whose economy has experienced higher public expenditure generates
rising income inequality; Poi, in the long run, when the country improves
its institutional quality, higher public expenditure results in lower income
inequality.

Keywords: Asia and the Paciﬁc, income inequality, institutional quality, public
expenditure
JEL codes: D63, E02, H53

IO. introduzione

During the last few decades, the Asia and Paciﬁc economies have achieved
impressive economic development compared to the global average; Tuttavia, IL
region is lagging with respect to rising economic inequality (Alvaredo et al. 2018,
United Nations 2018). Hollywood’s romantic dramedy, Crazy Rich Asians, and the
black comedy thriller from the Republic of Korea, Parasite, are recent pieces of

∗Bertrand Blancheton (corresponding author): University of Bordeaux, France. E-mail: bertrand.blancheton@u-
bordeaux.fr; Dina Chhorn: University of Bordeaux, France and University of Lausanne, Svizzera. E-mail:
dina.chhorn@u-bordeaux.fr and Dina.Chhorn@unil.ch. This project has received funding from the Region Nouvelle
Aquitaine and the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie
grant agreement no. 734712. We would like to express our gratitude for the comments from the participants in the
6th Regulating for Decent Work Conference: Work and Well-Being in the 21st Century, held on 8–10 July 2019
in Geneva; and the Eighth Meeting of the Society for the Study of Economic Inequality, held on 3–5 July 2019 In
Paris. We are also grateful to Olivier Bargain, Alexandru Minea, Sandrine Mesplé-Somps, Tanguy Bernard, Yannick
Bineau, Aaro Hazak, Kadri Männasoo, and Tuomas Malinen, as well as the managing editor and two anonymous
referees for helpful comments and suggestions. The Asian Development Bank (ADB) recognizes “China” as the
People’s Republic of China. The usual ADB disclaimer applies.

Asian Development Review, vol. 38, NO. 1, pag. 176–206
https://doi.org/10.1162/adev_a_00162

© 2021 Asian Development Bank and
Asian Development Bank Institute.
Pubblicato sotto Creative Commons
Attribuzione 3.0 Internazionale (CC BY 3.0) licenza.

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Government Intervention, Institutional Quality, and Income Inequality 177

social evidence that explain how far the highest income groups are from the rest of
society in some Asian countries. Piketty (2014) argued that it would be a mistake to
underestimate the importance of ﬁlm and literature, 19th century novels especially,
which are full of detailed information about the relative wealth and living standards
of different social groups, the deep structure of inequality and the way it is justiﬁed,
and its impact on individual lives. According to the last updated data from Credit
Suisse’s global wealth report and Oxfam, the number of super-rich (millionaires
and billionaires) in the Asia and Paciﬁc region—comprising Australia; Hong Kong,
China; India; Indonesia; Japan; the People’s Republic of China (PRC); the Republic
of Korea; Taipei,China; Thailand; and Viet Nam; among other economies—has
surpassed that of North America and Europe. In another sign of rising inequality,
Asia and the Paciﬁc’s income Gini coefﬁcient increased from 0.37 A 0.48 between
1990 E 2014, while the gap in wealth equality is even wider; in addition, the Asia
and Paciﬁc region is also the home of nearly two-thirds of the world’s working poor
(Costa 2018).

During the early stage of its economic development a half-century ago,
the Asia and Paciﬁc region was widely known as a place where there were
countless internal conﬂicts, political instability, civil wars, and widespread poverty.
During that period, the region’s governments focused on offering basic needs and
instituting a minimum degree of social security, as well as securing the rule of
law for their citizens (Sobrado et al. 2014). As in most advanced economies, COME
a society prospers, people’s expectations become more demanding in terms of
access to better government services—including the rule of law, accountability,
transparency—and a
and income
redistribution. As reported by the most recent ﬁndings, rising income and wealth
inequality is considered among today’s biggest challenges for governments around
the world, and the Asia and Paciﬁc economies are no exception (Stiglitz 2012,
Piketty 2014). In the long run, lessons from history show that an unequal society
can lead to global disaster, like what Europeans faced twice in the 20th century. For
that reason, this issue does matter above all.

improvement of welfare

simultaneous

In the modern era of globalization, many aspects—from an economy’s factor
endowments, trade openness, ﬁnancial deepening, geography, and institutions, A
its historical trajectory and technological changes—have been detected to explain
inequality (Vedere, Per esempio, Kanbur and Zhuang 2013 and Hartmann et al.
2017). Therefore, to address the inequality issue, it is, without doubt, a matter of
controversy and complexity. Besides a variety of redistributive policies, government
intervention through public expenditure is normally prioritized in developing
economies. This is because taxation mechanisms are viewed as less effective and
less efﬁcient, thanks to the small size of tax revenues and low quality of governance
and institutions. Nyblade and Reed (2008) suggested that public expenditure is able
to function well and promote a more equal society only if the institutional quality
in some context (per esempio., low level of corruption and high political competition) È

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178 Asian Development Review

allowed to do it. In Asia and the Paciﬁc, Tuttavia, the institutional framework has
been seen to progress slowly like a crab, moving forward then backward from time
to time. Accordingly, a closer look at this speciﬁc issue is required.

This paper aims to investigate the linear and nonlinear long-run relationship
between public expenditure and institutional quality, and inequality in Asia and
the Paciﬁc. To realize this, we use a dataset covering eight countries in Asia and
the Paciﬁc—Australia, India, Japan, Malaysia, the PRC, the Republic of Korea,
Singapore, and Thailand—from 1998 A 2014.

The contribution of this paper to the literature is as follows. First of all, Esso
gives new empirical ﬁndings on the long-run impact of public expenditure and
institutional quality on income inequality, which previously has been extensively
studied only in the short run and medium run. Combining the strength of panel
fully modiﬁed ordinary least squares (FMOLS) and panel dynamic ordinary
least squares (DOLS) with Granger causality tests, our approach can examine
simultaneously the effect
in Asia and the Paciﬁc of public expenditure and
institutional quality on income inequality, and the effect of income inequality
on public expenditure and institutional quality. Secondly, the nonlinear panel
cointegration models are designed to investigate the nonlinear relationship of
public expenditure and institutional quality on income inequality across the sample
countries. This may be one of the pioneering theses in applying the nonlinear
long-run panel models to estimate a hypothesis, particularly in the Asia and Paciﬁc
region. Thirdly, it is applied to a new measurement. We used the World Inequality
Database (WID.world), ﬁrst developed by Piketty and Zucman (2014), which we
found a more available dataset for the Asia and Paciﬁc countries in this study.
This new measurement provides further insight into the thinking of inequality. Nostro
dataset covers at least 26 years (1988–2014) for almost all countries.

Finalmente, this paper focuses on speciﬁc countries in Asia and the Paciﬁc. It
is complementary to the existing literature, which we found focused mainly on
the advanced economies in Europe and the United States (US). Tuttavia, Quando
the global economy changes, the standard model of economic thoughts should
also change. This matters for the Asia and Paciﬁc economies, which might not
follow a similar pattern of development as that of other countries. As Robert Solow
explained, there is no economic theory of everything (Todaro and Smith 2017).
In the 21st century, when the center of gravity of the world economy has shifted
decisively from the Atlantic to the Paciﬁc Ocean, everything that happens in these
two regions will attract very strong public attention. Since the Asia and Paciﬁc
region has not been empirically studied as extensively as Europe and the US, Questo
study seeks to provide some insights in light of the controversial ﬁndings from some
previous studies.

The rest of the paper is organized as follows. Section II considers the
literature review. Section III explains the empirical methodology and data, E
then provides the testing results from panel unit root tests and panel cointegration

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Government Intervention, Institutional Quality, and Income Inequality 179

tests. Section IV looks at the overall regression results and presents a discussion of
the results. Section V discusses the robustness checks. The ﬁnal section gives the
concluding remarks.

II. Literature Review

UN.

The Effect of Public Expenditure on Inequality

Besides taxes, government intervention may help to reduce inequality by
redistributing resources through public expenditure (Doerrenberga and Peichla
2014). In this perspective,
implement a wide range
the government might
of mechanisms through transfers involving education, health, social insurance,
housing, infrastructure, public investment, and other welfare programs (Gruber
2013). Progressive taxation is a common policy measure for reducing inequality,
not only individual but also corporate taxation may impact on individual income
and wealth (per esempio., Hazak 2009). Another public ﬁnance channel used to address
inequality is (progressive) government spending, and there are many theories and
pieces of evidence to suggest that certain sorts of public spending policies are likely
to promote a more equal society.

For instance, the human capital theory argues that investment in further
education tends to increase a person’s stock of skills and productivity (Gruber
2013). Così, education may promote a better outcome in society. In some
particular contexts, government intervention—for instance, providing subsidies to
low-income families for early-education investments to mitigate young parents’
budgetary concerns—could have a signiﬁcant role to play in providing equal
access to education, which consequently decreases income inequality and increases
intergenerational mobility (Juan and Muyuan 2016).

Empirically, although higher education has expanded signiﬁcantly on a
global scale, it is suggested that we are living in a less equal world. One important
perspective is the contribution of human capital and investments in research and
development to growth along with convergence (Männasoo, Hein, and Ruubel
2018), but this alone does not guarantee that the beneﬁts of increasing knowledge
intensity are equally or fairly distributed. In the Asia and Paciﬁc region, we observe
that participation in higher education is increasing rapidly in most countries but,
at the same time, social mobility lags behind the development of higher education
(Marginson 2018). We also ﬁnd rising wealth and income inequality in advanced
English-speaking countries even as they have many of the top universities in the
mondo (Piketty 2014). Tuttavia, given that education from primary to tertiary is
free or almost free in European welfare countries—such as France, Germany, E
Scandinavian countries like Denmark, Finland, and Sweden—we observe that they
are more equal societies in terms of wealth and income distribution. According to
Marginson (2018), many higher education systems are more vertically stratiﬁed,

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180 Asian Development Review

with a larger stretch in status and resources between top universities and other
higher education institutions. Elite universities tend to be dominated by students
from advantaged backgrounds, blocking the potential for greater social mobility,
though their social composition varies from case to case.

In this regard, the effectiveness of distributive public policies would be
necessitated to go along with a particular assumption or hypothesis. There is much
evidence to argue that public expenditures that target the lower and lower-middle
social classes, which comprise the majority of the population, would produce a
more equal distribution of outcomes. This supports the idea that public policies
need to be involved in providing basic health insurance, compulsory education
(primary and secondary), unemployment insurance, housing subsidies, and public
infrastructure (Gruber 2013). Considering policy and implementation, it becomes
not only complex but also complicated. Some studies suggest that although public
policies may be designed to target the most vulnerable or the most needy citizens
at the early stage, the beneﬁts might end up going to the middle or elite social
classes. It might be due to government failure, corruption, or low quality of good
governance and institutions. This evidence can be found in many low-income and
middle-income developing countries (Anderson et al. 2017).

Another consideration is to view things in both the short and long run. Let us
suppose we are living in a world where we have an equal degree of good governance
and institutions so that the government can function at the highest efﬁciency (lowest
rate of corruption or least possible government failure). In questo caso, even though
public expenditure tends to reduce inequality in the short run, it does not guarantee
that inequality is less likely to worsen in the long run. Bourguignon (2004) stati
that too many income transfers, as opposed to transfers of wealth, can lower the
expected return from acquiring physical and human capital. They might distort
the economy and reduce savings and investment, and therefore the rate of growth.
According to Lee (2013), greater government income transfers may reduce people’s
incentive to work for themselves, and then the whole economy becomes less
dynamic. Consequently, it could generate a possible economic recession in the long
run. If this hypothesis were right, citizens from the lower and middle classes would
ﬁnd themselves struggling more than the higher social classes during the crisis.
Così, inequality might be rising subsequently.

The Effect of Institutional Quality on Inequality

According to Zhuang, de Dios, and Lagman-Martin (2010), we can associate
institutional quality with inequality in two different ways: (io) political institutions
and democracy, E (ii) corruption. On the one hand, in relation to political factors,
it has been suggested that more equal income distribution would be better promoted
in a democratic society with more political rights. When political rights to vote are
extended to the majority of the population, the amount of redistribution is decided

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Government Intervention, Institutional Quality, and Income Inequality 181

by the median voter and this determines, directly or indirectly, the rate of growth of
the economy (Bourguignon 2004). Tuttavia, it has failed to be veriﬁed empirically
in some cases where countries with a higher score of democracy are not necessarily
reducing inequality. It is subject to the fact that the political system alone cannot
explain inequality. Per esempio, despite having a lower score in democracy or
restrictive political rights, income distribution in many countries—such as East
European countries, the Republic of Korea, and Singapore—was relatively equal
as long as their respective societies functioned with a special political ideology.
Inoltre, democracy is more likely to reduce inequality in countries with a
parliamentary rather than a presidential system (Zhuang, de Dios, and Lagman-
Martin 2010).

Corruption, on the other hand, tends to increase income inequality for the
reason that it can lead to tax evasion, less effective administration, lower progressive
taxes, less effective public expenditure, and lower investment. The problem would
potentially create political, economic, and social systems that favor only the rich
and hurt the poor (Pedauga, Pedauga, and Delgado-Márquez 2017). In contrasto,
some argue that corruption can lead to less inequality if the social beneﬁt from
corrupted activities is greater than the social damage. Another recent study has
found that corruption tends to be associated with lower inequality in less developed
countries due to the existence of the informal sector in many developing countries
(Andres and Ramlogan-Dobson 2011). In the analysis of more disaggregated data,
Nyblade and Reed (2008) have linked corruption to inequality in two contexts:
(io) political competition and (ii) voting. The ﬁrst involves corrupt actions to gain
personal beneﬁts by the elites in society, which would increase inequality. IL
second, Tuttavia, involves buying votes by using, for instance, public budgets to
reach the mass of the population. This tends to decrease inequality because at least
the money goes to the poor people.

Inoltre, there are multiple channels through which institutions may
impact inequality. Per esempio, various social norms may propagate inequality
among different population groups (per esempio., some ethnic groups, minorities, E
females), and rent-seeking opportunities may foster inequality and ﬁnancial
constraints (per esempio., Männasoo, Maripuu, and Hazak 2018) that often have an
institutional background that may affect different
types of individuals and
companies differently.

Linking together government intervention through public expenditure and
inequality in the context of diverse institutional quality, we might consequently
presume that the distributive effect of public expenditure tends to reduce inequality,
given that an economy has high-quality governance and institutions. If this
hypothesis is not completely right,
the policies would not be implemented
effettivamente. Alternatively, in cases of low institutional quality or high corruption,
public intervention tends to increase income inequality because it would lead to tax
evasion, less effective administration, lower progressive taxes, less effective public

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182 Asian Development Review

expenditure, and lower investment. Tuttavia, it is likely to promote more quality
outcomes only if the existence of social beneﬁts, provided by public intervention, È
linked to the mass of the population (cioè., the poor), such as social assistance or gift
giving during election. This hypothesis does not take into account its effects in the
medium and long run, which are complex by nature.

III. Empirical Methodology

UN.

Data

We collected data from various sources from 1988 A 2014 in the following
countries: Australia, India, Japan, Malaysia, the PRC, the Republic of Korea,
Singapore, and Thailand.

We used the pretax top 1% income share of the population to measure income
inequality. The data were taken from the World Inequality Database (WID.world),
ﬁrst developed by Piketty and Zucman (2014). Our dataset is available for at
least 26 years (1988–2014) for most countries, except for the Republic of Korea
(1995–2014), Thailand (2000–2014), and Malaysia (a total of 13 years with missing
values and another 13 years with data between 1988 E 2014). To deal with missing
values, we applied the cubic-spline interpolation methods as explained by McKinley
and Levine (1998); Fichtenbaum and Shahidi (1988); and Bishop, Chiou, E
Formby (1994). The rationale for using this indicator follows the theses of Malinen
(2016), who presented arguments linking income inequality to credit cycles, E
Leigh (2007), who argued that there is a strong and signiﬁcant relationship between
top income shares and broader inequality measures, such as the Gini coefﬁcient.
According to Malinen (2016), the top 1% income share measures the share of
national income concentrated in the hands of the highest percentile of income
earners. As gross domestic product (GDP) È, in practice, the national income
of a country, the share of total income received by the top 1% of earners can
also be presented as income of the top 1%
. Tuttavia, to avoid the bias that the top 1%
income share cannot capture the full picture of the effect of public spending and
institutional quality in promoting economic opportunity for the poor and the middle
class, we also employed version 8.2 of the Standardized World Income Inequality
Database (SWIID) of Solt (2019) for robustness checks. It is the estimate of the Gini
index of inequality in equivalized (square root scale) household disposable (posttax,
posttransfer) income, using the Luxembourg Income Study data as the standard. IL
SWIID dataset is available for nearly 100% of our eight sample countries in Asia
and the Paciﬁc.

GDP

To make our estimation comparably reasonable, we used public expenditure
(share of GDP): Public expenditure
. Public expenditure comprises cash payments for the
operating activities of the government in providing goods and services. It includes
compensation of employees (per esempio., wages and salaries); interest and subsidies; grants;

GDP

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Government Intervention, Institutional Quality, and Income Inequality 183

social beneﬁts; and other expenses such as rent and dividends (based on World Bank
deﬁnitions). To investigate the role of institutional quality, we used the average value
of the Worldwide Governance Indicators (WGI), which are found in the empirical
works of Zhuang, de Dios, and Lagman-Martin (2010); Kaufmann, Kraay, E
Mastruzzi (2010); and Wong (2017). The WGI consists of six broad dimensions
of governance: (io) voice and accountability, (ii) political stability and absence of
violence and terrorism, (iii) government effectiveness, (iv) regulatory quality, (v)
rule of law, E (vi) control of corruption. The estimate of governance performance
in standard normal units ranges from approximately −2.5 (weak) A 2.5 (strong).
The Asia and Paciﬁc countries that are deﬁned as having strong institutional quality
have an average WGI value that is “bigger or equal to zero”; otherwise, they are
deﬁned as having weak institutional quality. Therefore, Australia, Japan, Malaysia,
the Republic of Korea, and Singapore are in a group of countries with strong
institutional quality. India, the PRC, and Thailand are in a group of countries with
weak institutional quality.

In addition to explanatory indicators, we added several major aggregated
variables as additional control variables (Appendix Table A.1). The country’s
openness is theoretically linked to income distribution (Vedere, Per esempio, Heckscher
1919, Ohlin 1933, Samuelson 1953, and Melitz and Redding 2015). The sum
of imports and exports is used to measure trade openness (Vedere, Per esempio,
Cameron 1978, Rojas-Vallejos and Turnovsky 2017, and Wong 2017). The level
of development is also linked to inequality. The general effect of GDP per capita on
income inequality is explained by the well-known inverted-U hypothesis developed
by Simon Kuznets: an increase in GDP per capita will increase overall economic
welfare and income disparity. Following the process of economic development,
inequality will increase during the ﬁrst stage; after it arrives at the peak, inequality
will decrease (Kuznets 1955). Changes affecting labor supply and labor demand
can also shift income inequality. Changes in population, measured by the annual
percentage growth in population, affect changes in labor supply and demand, Quale
affect wages in the labor market. An increase in population is expected to increase
income inequality if the unemployment rate increases (Vedere, Per esempio, Asteriou,
Dimelis, and Moudatsou 2014; Rojas-Vallejos and Turnovsky 2017; Wong 2017).
Oil rents (as a share of GDP) are used to account for resource-rich regimes that
can afford to gain legitimacy by redistributing revenue (Ross 2001, Wong 2017).
Taxation, in addition to public spending, may also target to improve the overall
economic well-being of a whole population, especially the poor. The effect on
income distribution depends on how the government targets speciﬁc population
groups through social protection, formazione scolastica, and health, among others (Selowsky
1979, Younger 1999).

The addition of listed control variables into the model may impair the
identiﬁcation of individual coefﬁcients in the presence of high multicollinearity.
As shown in Appendix Tables A.2 and A.3, the variance inﬂation factor and the

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184 Asian Development Review

Figura 1. Average Value of Top 1% Income Share and Public Expenditure,
1988–2014 (% of GDP)

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GDP = gross domestic product.
Fonte: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3,
2018).

pairwise correlations among explanatory variables, Tuttavia, did not reveal any
severe multicollinearity. The variance inﬂation factor was 4.04, well below the
critical level of 10. The pairwise correlation estimates conﬁrmed that correlations
between variables were well below the critical levels.

The list of countries divided by regions, income status, and institutional status
along with descriptive statistics of the Asia and Paciﬁc countries are reported in
Appendix Tables A.4 and A.5. India is the only lower-middle-income country from
South Asia; the others are all from East Asia and the Paciﬁc. Malaysia is the only
country with upper-middle-income status, but it is classiﬁed in the same group as
high-income countries with strong institutional quality. Figura 1 shows that public
expenditure followed a rising trend from the end of the 1980s to 2014. The top 1%
income share, on the other hand, evolves in a stable trend then starts increasing
from the early 1990s; overall, it also shows a rising trend from 1988 A 2014. IL
results give some evidence to the extent of the “trending hypotheses,” indicating a
possible long-term correlation between the variables. Tuttavia, that can be a reverse
causality (cioè., public expenditure explains the top 1% income share and vice versa).
Figures 2 shows a different pattern in each country. Australia, India,
Malaysia, and the Republic of Korea, show both a rising trend for public expenditure
and the top 1% income share. The PRC and Singapore show only a rising trend for
the top 1% income share and a nearly stable trend of public expenditure. This is the

Government Intervention, Institutional Quality, and Income Inequality 185

Figura 2. Top 1% Income Share and Public Expenditure, 1988–2014 (% of GDP)

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GDP = gross domestic product.
Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December
3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank.worldbank.org/source/
world-development-indicators (accessed December 3, 2018).

opposite for Japan, where public expenditure is rising but the top 1% income share
is likely to become stable in the long run. Thailand shows a very different trend than
other countries, as there is a reverse trend between the variables.

To assess the long-run equilibrium association between the variables, we
performed several tests, including the panel unit root test, panel cointegration test,
and cointegration regression estimation.

Panel Unit Root Tests

We used three types of panel unit root tests. The ﬁrst follows unit root
assuming individual unit root process, including the Im–Pesaran–Shin test (2003),

186 Asian Development Review

Augmented Dickey–Fuller (ADF)–Fisher test by Maddala and Wu (1999), E
Phillips–Perron (PP)–Fisher test by Choi (2001). The second follows unit root
assuming common unit root process, including the Levin–Lin–Chu test (2002). IL
third allows for homoscedastic error processes across the panel, including the tests
of the Hadri Z-stat and heteroscedastic consistent Z-stat by Hadri (2000).

The panel unit root tests, in this paper, are based on the following regression

equation:

(cid:2)yit = ρiyi,t−1 + αi + ηit + θt + εit

(1)

where αi are individual constants; ηit are individual time effects, and θt are the
common time effects. The null hypothesis of individual unit root process is that the
panel data has unit root, H0 : ρi = 0 ∀ i, (cioè., the series in I[0] are nonstationary).

The alternative hypothesis is as follows:

H1 : ρi < 0, i = 1, 2, . . . , N1, ρi = 0, i = N1 + 1, N2 + 2, . . . , N. The same principle is applied for the Levin–Lin–Chu test, assuming common unit root process. However, the null hypothesis of the tests of Hadri Z-stat and heteroscedastic consistent Z-stat is that panel data has no unit root (the process is stationary), and the alternative hypothesis is that the panel data has unit root (the process is nonstationary). According to the results of the panel unit root tests from Table 1, six among six tests for the top 1% income share, public expenditure (% of GDP), trade (% of GDP), per capita GDP at purchasing power parity (PPP), and tax revenue (% of GDP), and four among six tests for population growth (annual %) emphasize that the majority of tests become nonstationary at level; then, the series become stationary after ﬁrst difference, I (1). C. Panel Cointegration Testing To estimate the panel cointegration model, the series of our variables must be nonstationary at level, I (0), but become stationary at ﬁrst difference, I (1). This condition is conﬁrmed in our model. We applied, therefore, the Pedroni residual cointegration test (Pedroni 1999, 2004); Kao residual cointegration test (Kao 1999); and Fisher–Johansen cointegration test (Maddala and Wu 1999). The models for testing panel cointegration between income inequality and public expenditure are structured as follows: + ηit + θt + εit = αi + βiexpenseit inequalityit where (1, −βi) are country-speciﬁc cointegrating vectors, αi are individual constants, ηit are individual time effects, and θt are the common time effects. The null hypothesis is that H0 : βi = 1 ∀ i (i.e., there is no cointegration). (2) l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Government Intervention, Institutional Quality, and Income Inequality 187 Table 1. Panel Unit Root Tests Individual Unit Roots Common Unit Roots Heteroscedastic Series Top 1% income share Public expenses (% of GDP) Institutional quality Trade (% of GDP) Per capita GDP (PPP) IPS −0.732*** (0.232) −0.207*** (0.418) −1.704 (0.044) 0.169*** (0.567) −0.834*** (0.202) Population growth −1.212*** (annual %) Oil rents (% of GDP) Tax revenue (% of GDP) (0.113) −2.142 (0.016) −0.053*** (0.300) ADF-Fisher PP-Fisher LLC Hadri 20.845*** (0.185) 21.581*** (0.157) 32.045 (0.010) 12.266*** (0.726) 22.293*** (0.134) 32.619 (0.008) 24.299 (0.042) 22.1816*** (0.137) 0.358*** (0.640) 21.062*** (0.176) 17.752*** −0.826*** (0.339) (0.205) 24.021*** −2.189 (0.089) (0.014) 11.483*** −0.589*** (0.779) 19.556*** −1.265*** (0.241) 19.089*** (0.264) 24.906 (0.036) 16.556*** −1.3513*** (0.415) (0.103) 3.011*** (0.999) −4.178 (0.000) (0.088) (0.278) 3.807*** (0.000) 2.836*** (0.002) 2.410*** (0.008) 6.131*** (0.000) 3.514*** (0.000) 0.753 (0.226) 3.280*** (0.001) 4.030*** (0.000) Hetero Con Z-stat 3.318*** (0.001) 4.100*** (0.000) 4.792*** (0.000) 5.534*** (0.000) 2.763*** (0.003) 4.295*** (0.000) 4.670*** (0.000) 4.462*** (0.000) ADF = Augmented Dickey–Fuller, GDP = gross domestic product, IPS = Im–Pesaran–Shin, LLC = Levin–Lin– Chu, PP = Phillips–Perron, PPP = purchasing power parity. Notes: All tests are taken using automatic selection of maximum lags; automatic lag length selection based on Schwarz information criterion; Newey–West automatic bandwidth selection and Bartlett kernel; assumed asymptotic normality and individual effects; and individual linear trends, except for public expenditure (% of GDP), which we include only for individual effects. *** emphasizes that the process is nonstationary at level, then becomes stationary at level I (1) after we reject or do not reject the null hypothesis. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank.worldbank.org/source/ world-development-indicators (accessed December 3, 2018). Tables 2, 3, and 4 report the results from the Pedroni residual cointegration test, Kao residual cointegration test, and the Fisher–Johansen cointegration test with a dataset from 1988 to 2014. According to the estimated results from various panel cointegration tests, indicating that most of the statistics have a p-value of less than the 1% and 5% level of signiﬁcance, the null hypothesis of no cointegration can be rejected. Therefore, we conclude that there is a high possibility of a long-run equilibrium relationship between public expenditure and income inequality in the Asia and Paciﬁc countries. IV. Results and Discussions A. Estimating a Cointegrating Regression To obtain the long-run coefﬁcients between the variables of interest, we took into account two different but complementary estimators. First, we estimated with the FMOLS by Phillips and Hansen (1990). Second, we estimated with the DOLS by Stock and Watson (1993), and Mark and Sul (2003). l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 188 Asian Development Review Table 2. Pedroni Residual Cointegration Test Within dimension Panel v-Statistic Panel rho-Statistic Panel PP-Statistic Panel ADF-Statistic Between dimension Weighted Statistic −1.278 −3.410*** −4.870*** −5.494*** Statistic Prob 0.899 −0.562 0.000 −2.175** 0.000 −3.073*** 0.000 −3.683*** Prob 0.713 0.015 0.001 0.000 Statistic 1.120 Group rho-Statistic Group PP-Statistic 0.131 Group ADF-Statistic −0.366 Countries Observation 8 216 Prob 0.869 0.552 0.357 ADF = Augmented Dickey–Fuller, PP = Phillips–Perron. Notes: Null hypothesis = no cointegration. The tests were estimated with the following assumptions: trend assumption (deterministic intercept and trend), automatic lag length selection based on Schwarz information criterion with lags from 0 to 5, and Newey–West automatic bandwidth selection and Bartlett kernel. ***1% level of signiﬁcance, **5% level of signiﬁcance, *10% level of signiﬁcance. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank.worldbank.org/ source/world-development-indicators (accessed December 3, 2018). Table 3. Kao Residual Cointegration Test ADF Residual variance HAC variance RESID(−1) D(RESID[−1]) Observations t-Statistic 2.274** 1.414 0.792 −0.114** −0.280*** 216 Prob 0.012 0.012 0.001 ADF = Augmented Dickey–Fuller, HAC = heteroscedasticity- and autocorrelation-consistent, RESID = residual. Notes: Null hypothesis = no cointegration. The tests were estimated with the following assumptions: trend assumption (no deterministic trend), automatic lag length selection based on Schwarz information criterion with a max lag of 1, and Newey–West automatic bandwidth selection and Bartlett kernel. ***1% level of signiﬁcance, **5% level of signiﬁcance, *10% level of signiﬁcance. Sources: Authors’ Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank.worldbank.org/source/world-development-indicators (accessed December 3, 2018). calculations the World using l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Government Intervention, Institutional Quality, and Income Inequality 189 Table 4. Fisher–Johansen Cointegration Tests Hypothesized No. of CE(s) Fisher Stata (from trace test) None At most 1 Observations 62.12*** 37.45*** 216 Prob 0.000 0.001 Fisher Stata (from max-eigen test) 52.69*** 37.45*** Prob 0.000 0.001 CE = cointegrating equation. aProbabilities are computed using asymptotic chi-square distribution. Notes: Null hypothesis = each series has unit root and no cointegration. The tests were estimated with the following assumptions: trend assumption (quadratic deterministic trend) and tags interval in ﬁrst differences. ***1% level of signiﬁcance, **5% level of signiﬁcance, *10% level of signiﬁcance. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https: //databank.worldbank.org/source/world-development-indicators (accessed December 3, 2018). Besides using logarithm public expenditure (% of GDP) as the explanatory variable, we included several macroeconomic variables that may inﬂuence income inequality. We included logarithm trade (% of GDP), GDP per capita at PPP (current international US dollars), population growth (annual %), oil rents (% of GDP), and tax revenue (% of GDP). The regression is structured to estimate the following equation:1 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / inequalityit = αi + β (cid:4) + β (cid:4) 5 1expenseit (cid:2)oilit + β (cid:4) 6 + β (cid:4) 2 (cid:2)tradeit + β (cid:4) (cid:2)taxit + ηit + θt + εit 3 (cid:2)gdpit + β (cid:4) 4 (cid:2)popit (3) where αi are individual constants; ηit are individual trends; θt is the common time effect; (1, −β (cid:4) , −β (cid:4) 6) are cointegrating vectors between 5 1 logarithm public expenditure (% of GDP), trade (% of GDP), GDP per capita at PPP (current international US dollars), population growth (annual %), oil rents (% of GDP), and tax revenue (% of GDP), and εit is an idiosyncratic error. , −β (cid:4) 2 , −β (cid:4) 4 , −β (cid:4) 3 , −β (cid:4) One of the key advantages in using the FMOLS and the DOLS estimations is that we can deal with the spurious regression and draw causal effects with the nature of time series that are nonstationary at level. In this case, the standard ordinary least squares (OLS) and the generalized method of moments estimators are inconsistent. McCallum (2010) and Sollis (2011) made a huge contribution in arguing the problems of “spurious regressions.” McCallum (2010) suggested so-called spurious regression relationships, which are generally accompanied by clear signs of residual autocorrelation. In our study, the spurious relationships between the series at level I (1), or the nonintegrated variables inequalityit and expenseit, can be resolved by estimating the whole autocorrelation structure. It is 1We applied the same identiﬁcation strategy by using institutional quality (instiit ) and the interaction term between public expenditure (% of GDP) and institutional quality (expense × instiit ) as the explanatory variable in equation (3). / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 190 Asian Development Review solved in simulations, which result in test statistics closing to true values and not yielding spurious results. However, Sollis (2011) argued that the spurious regression problem can be solved by using an autocorrelation correction. It is shown that if the relevant data generation processes contain higher-order terms, this solution is not as effective as in the ﬁrst-order case. In this study, suppose we have two I (1) random vectors with panel observations, inequalityit and expenseit, with large cross section and time series dimensions. By pooling the cross section and time series observations, the strong effect of residuals is attenuated while retaining the signal of expenseit. In this regard, while the time series is spurious, applying all-time series data in cross sections reduces the limiting variance in a panel regression and provides a consistent estimate of (some) long-run regression coefﬁcient (Malinen 2016). According to Kao and Chiang (2001), the simulations of the sampling behavior show that although the FMOLS estimator provides better estimations than the standard OLS and the generalized method of moments estimators, the DOLS outperforms the other estimations. The resulting FMOLS estimator is asymptotically unbiased and has the fully efﬁcient mixture normal asymptotics, allowing for standard Wald tests using asymptotic chi-square statistical inference (Sobrado et al. 2014). Complementary to the FMOLS, the panel DOLS is estimated with ﬁxed effects; ﬁxed effects and heterogeneous trends; and ﬁxed effects, heterogeneous trends, and common time effects. The model takes into account cross-sectional dependence by introducing a common time effect, and the estimators are asymptotically normally distributed (Mark and Sul 2003). In equation (3), although the FMOLS and the DOLS estimators can provide improvements compared to the OLS estimator, we might face other statistical issues—including (i) cointegration between the explanatory variables, (ii) possible endogeneity problem of spurious correlation, and (iii) potential serial correlation—that require us to estimate with great caution. First of all, the FMOLS and the DOLS estimators do not allow for cointegration between the explanatory variables. In our estimations, we include the leads and lags of the ﬁrst differences of logarithm trade (% of GDP), GDP per capita at PPP (current international US dollars), population growth (annual %), oil rents (% of GDP), and tax revenue (% of GDP). Secondly, to address an endogeneity problem of spurious correlation between the panel DOLS inequalityit and expenseit, and other explanatory variables, estimation assumes that μit is supposed to be correlated most with ρi leads and lags of (cid:2)expenseit. The possible endogeneity can be controlled by projecting εit into these pi leads and lags (Hämäläinen and Malinen 2011): μit = pi(cid:2) s=−pi ξ (cid:4) i,s (cid:2)expensei,t−s + εit∗ = ξ (cid:4) i (cid:2)zit + ε∗ it (4) l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Government Intervention, Institutional Quality, and Income Inequality 191 is a random vector with panel observation, and ξ (cid:4) where zit projection dimensions. The projection error ε∗ (cid:2)expenseit, and therefore the estimated equation is transformed as follows: is a vector of i zit it is orthogonal to all leads and lags of inequalityit = αi + β (cid:4) + β (cid:4) 5 1expenseit (cid:2)oilit + β (cid:4) 6 (cid:2)tradeit + β (cid:4) + β (cid:4) 2 (cid:2)taxit + ηit + θt + ξ (cid:4) (cid:2)gdpit i zit + εit 3 + β (cid:4) 4 (cid:2)popit (5) Finally, to address the potential serial correlation between equilibrium error, εit, and leads and lags of other cross sections (cid:2)expense ji , j (cid:5)= i, the panel DOLS computes the same form of second-order asymptotic bias as pooled OLS. Overall, the estimation of equation (5) is consistent under the condition which T → ∞ then n → ∞. Equation (5) therefore can be feasibly estimated in a panel with small to moderate n (Mark and Sul 2003). We started our regression by estimating individually the impact of public expenditure (% of GDP) (expenseit ), institutional quality (instiit ), and interaction term between public expenditure (% of GDP) and institutional quality (expense × instiit ) on income inequality, measured by the top 1% income share. A few necessary steps were taken: ﬁrst, we estimated with the constant (level) and no trend; second, we estimated with the constant (level) and trend; ﬁnally, we introduced the control variables in our regression, including ﬁrst differences of logarithm trade (% of GDP) ((cid:2)tradeit ), GDP per capita at PPP ((cid:2)gdpit ), annual population growth ((cid:2)popit ), oil rents (% of GDP) ((cid:2)oilit ), and tax revenue (% of GDP) ((cid:2)taxit ). Only the estimations with the control variables are shown. Table 5 presents results from the FMOLS and the DOLS estimations using the dataset from 1988 to 2014. For the FMOLS estimation, the long-run variance estimates—Bartlett kernel, Newey–West ﬁxed bandwidth—were used for coefﬁcient covariances. We also used pooled estimation as panel method and ﬁxed leads and lags speciﬁcation to address the possible endogeneity and serial correlation discussed above. For the DOLS estimation, the same long-run variance estimates were used for coefﬁcient covariances. The pooled estimation as panel method and automatic leads and lags speciﬁcation were estimated. The ﬁrst, second, and third leads and lags of the ﬁrst differences of control variables were estimated as instruments for the explanatory variables. However, only the results from the ﬁrst leads and lags are shown. For control variables, trade openness (% of GDP) has positive and statistically signiﬁcant cointegrating coefﬁcients (signiﬁcant at 1%) when we estimated with public expenditure (% of GDP) for both FMOLS and DOLS. It becomes negative and statistically signiﬁcant (signiﬁcant at 10%) when we estimated with institutional quality and the interaction term between public expenditure (% of GDP) and institutional quality. Per capita GDP shows a mix of direction; yet the majority of coefﬁcients are statistically signiﬁcant at the 1% or (at least) 5% level. The estimated result of the population growth rate also shows l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 r o f d e s u s a w ) h t d i w d n a b d e x ﬁ t s e W – y e w e N , l e n r e k t t e l t r a B ( e c n a i r a v n u r - g n o L . ) * = x a m 192 Asian Development Review ) S L O D ( s e r a u q S t s a e L c i m a n y D l e n a P ) S L O M F ( s e r a u q S t s a e L d e ﬁ i d o M y l l u F l e n a P e r a h S e m o c n I % 1 p o T d n a , y t i l a u Q l a n o i t u t i t s n I , s e s n e p x E c i l b u P . 5 e l b a T 6 l e d o M 5 l e d o M 4 l e d o M 3 l e d o M 2 l e d o M * * 3 4 0 1 − . ) 5 0 4 0 ( . * 2 9 6 0 − . * * * 3 2 2 4 . ) 0 2 3 0 ( . ) 2 4 8 0 ( . * 3 6 2 0 . ) 2 1 1 0 ( . * 6 1 1 0 . ) 8 4 0 0 ( . * * 8 7 6 0 . ) 9 4 2 0 ( . 3 9 6 0 . 8 * 3 1 5 . 1 − ) 4 5 6 . 0 ( * * * 8 3 6 . 3 ) 7 3 2 . 0 ( 1 7 1 . 0 − ) 9 4 4 . 0 ( * 3 4 1 . 0 ) 3 6 0 . 0 ( 0 1 0 . 0 − ) 9 3 0 . 0 ( * * * 2 2 8 . 0 ) 2 6 1 . 0 ( 3 0 8 . 0 8 * 0 0 2 . 0 − ) 9 8 0 . 0 ( * * * 4 8 3 . 0 ) 6 4 0 . 0 ( 7 3 6 . 0 − ) 7 5 5 . 0 ( * * * 1 5 0 . 0 − ) 9 0 0 . 0 ( * * 7 8 0 . 0 − ) 8 2 0 . 0 ( * * 7 2 3 . 0 − ) 6 9 0 . 0 ( 7 9 9 . 0 8 * * * 6 7 7 . 0 − ) 7 5 2 . 0 ( 2 6 2 . 0 − ) 5 7 1 . 0 ( 6 0 8 . 0 ) 9 7 6 . 0 ( 6 2 1 . 0 − ) 5 8 0 . 0 ( * * 2 7 0 . 0 − * * * 5 8 4 . 1 ) 1 3 0 . 0 ( ) 1 3 5 . 0 ( 0 9 8 . 0 8 ) 9 3 2 . 0 ( 2 0 1 . 0 − * * * 2 5 7 . 9 * 5 5 3 . 0 − ) 8 7 1 . 0 ( ) 3 9 1 . 0 ( 0 7 0 . 0 ) 0 2 2 . 0 ( 6 4 1 . 0 − ) 2 9 1 . 0 ( * * * 9 6 3 . 0 ) 3 1 1 . 0 ( 7 0 6 . 0 8 1 l e d o M * * 5 5 2 . 0 − ) 8 9 0 . 0 ( * * 6 0 2 . 0 ) 4 9 0 . 0 ( * * 4 2 0 . 6 − ) 9 8 5 . 2 ( * * 4 3 0 . 0 − ) 6 1 0 . 0 ( 5 2 0 . 0 ) 7 2 0 . 0 ( * 5 8 1 . 0 ) 2 0 1 . 0 ( 4 8 9 . 0 8 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 8 1 8 1 8 2 9 4 9 4 4 0 1 y t i l a u q l a n o i t u t i t s n I × s e s n e p x e c i l b u P ) P D G f o % ( s e s n e p x e c i l b u P y t i l a u q l a n o i t u t i t s n I ) P D G f o % ( s s e n n e p o e d a r T (cid:2) ) P P P t a a t i p a c r e p ( P D G (cid:2) ) % l a u n n a ( h t w o r g n o i t a l u p o P (cid:2) ) P D G f o % ( e u n e v e r x a T (cid:2) ) P D G f o % ( s t n e r l i O (cid:2) 2 R d e t s u j d A s e i r t n u o C s r a e Y s n o i t a v r e s b O e h t , S L O M F e h t r o F . s d o h t e m S L O D e h t d n a S L O M F e h t h t i w d e t a m i t s e e r e w s t l u s e r n o i s s e r g e r e h T . s r o r r e d r a d n a t s e t a c i d n i s e s e h t n e r a p n i a t a D : s e t o N ] l e v e l [ t n a t s n o c e h t ( s c i t s i n i m r e t e d n o i t a u q e g n i t a r g e t n i o c ; ) n o i t a m i t s e d e l o o p ( d o h t e m l e n a p : s n o i t p m u s s a g n i w o l l o f e h t h t i w d e t a m i t s e e r e w s n o i s s e r g e r e h t h t i w d e t a m i t s e e r e w s n o i s s e r g e r e h t , S L O D e h t r o F . ) h t d i w d n a b d e x ﬁ t s e W – y e w e N , l e n r e k t t e l t r a B ( s e t a m i t s e e c n a i r a v o c n u r - g n o l d n a ; ) d n e r t r o / d n a . y t i r a p r e w o p g n i s a h c r u p = P P P , t c u d o r p c i t s e m o d s s o r g = P D G s d a e l c i t a m o t u a d n a ; ) d n e r t r o / d n a ] l e v e l [ t n a t s n o c e h t ( s c i t s i n i m r e t e d n o i t a u q e g n i t a r g e t n i o c ; ) n o i t a m i t s e d e l o o p ( d o h t e m l e n a p : s n o i t p m u s s a g n i w o l l o f , n o i r e t i r c n o i t a m r o f n i z r a w h c S n o d e s a b ( n o i t a c ﬁ i c e p s s g a l d n a . 1 . 0 < p * d n a , 5 0 . 0 < p * * , 1 0 . 0 < p * * * . s e c n a i r a v o c t n e i c ﬁ f e o c . 4 1 0 2 – 8 8 9 1 . k n a B d l r o W d n a ; ) 8 1 0 2 , 3 r e b m e c e D d e s s e c c a ( d l r o w D W I . . 4 1 0 2 – 8 8 9 1 . e s a b a t a D y t i l a u q e n I d l r o W e h t g n i s u s n o i t a l u c l a c ’ s r o h t u A : s e c r u o S . ) 8 1 0 2 , 3 r e b m e c e D d e s s e c c a ( s r o t a c i d n i - t n e m p o l e v e d - d l r o w / e c r u o s / g r o . k n a b d l r o w . k n a b a t a d / / : s p t t h . s r o t a c i d n I t n e m p o l e v e D d l r o W l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Government Intervention, Institutional Quality, and Income Inequality 193 a mix of direction. It becomes negative and statistically signiﬁcant (signiﬁcant at 1%) when we estimated with public expenditure (% of GDP) for both FMOLS and DOLS; however, it becomes positive and signiﬁcant at the 5% level when we estimated with institutional quality and the interaction term between public expenditure (% of GDP) and institutional quality for the DOLS. The majority of oil rents (% of GDP) and tax revenue (% of GDP) show only one direction as there is a negative relationship for oil rents (% of GDP) and a positive relationship for tax revenue (% of GDP). Based on our ﬁndings, the globalized forces, as explained by trade (% of GDP), do increase income inequality in the Asia and Paciﬁc countries. Yet, it is not implied that the beneﬁts from trade globalization go only to the rich or the top income earners. It is possible that the living standards of poorer citizens also increase, but not as much as for the rich; therefore, we found the current discontent with globalization in the Asia and Paciﬁc countries is not as intense as in Europe and North America. In the most advanced economies (i.e., European and North American countries), there is a growing belief that globalizing forces are not all good; both ordinary citizens and policy makers think that life was better in the old days and that the fruits of globalization might go only to top earners and the rest of the world (Gray 2017, Willige 2017). According to Shanmugaratnam (2017), various social trends that have occurred in the advanced economies over the last few decades could explain this phenomenon, including stagnant wages, an overall decline in social mobility, a loss of sense of togetherness, and a growing mentality of “us against them.” The estimated results are veriﬁed by the Kuznets inverted-U hypothesis. GDP per capita is likely to increase income inequality during the ﬁrst stage of economic development but decrease it in the long run. According to Blancheton and Chhorn (2019), this result conﬁrms the fact that there is a rising number of people joining the global middle-income class, thanks to an increase in the living standards of people in Asia and the Paciﬁc, especially in India and the PRC, which together account for 36.4% of the global population. The global middle-income class is deﬁned as follows: “[T]hose households with daily expenditures between $10 and $100 per person at PPP. This excludes those who are considered poor in the poorest advanced countries and rich in the richest advanced countries” (Mahbubani 2014, 23). Because rapid demographic growth has enabled strong economic growth, especially in the Asia and Paciﬁc countries, an increase in population has not led to an increase in income inequality. Meanwhile, higher oil rents and higher tax revenue, respectively, decrease and increase income inequality. Public expenditure (% of GDP) is found to be negative and statistically signiﬁcant. The estimated value of cointegrating coefﬁcients varies between −0.2552 (signiﬁcant at 5% for FMOLS) and −0.20004 (signiﬁcant at 10% for DOLS). Institutional quality is found to be negative and statistically signiﬁcant with a coefﬁcient of 1.5132 (signiﬁcant at 10%) only if we estimated with DOLS. When we estimated with the interaction term between public expenditure (% of GDP) and institutional quality, we also found negative and signiﬁcant coefﬁcients l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 194 Asian Development Review for both FMOLS and DOLS. The results suggest that whenever the Asia and Paciﬁc countries improve their institutional quality enough, higher public expenditures are likely to reduce income inequality. Compared to countries in Europe and North America, the Asia and Paciﬁc countries have relatively weaker institutional quality. However, as discussed in the previous section, it is likely that better institutional quality does not guarantee a more equal society, or at least weaker institutional quality is not an obstacle to promote the welfare of lower-income citizens. In the modern age of a global single market, even with less effective governance and institutions, some giant or big economies are still able to attain economic growth that is sufﬁcient to allow millions of poor to become middle-income families. For instance, it has been said that the PRC grows because of its government, driven by strong public intervention, while India grows despite its government, driven by market forces even with less effective governance (Mahbubani 2014). Rising trade openness and economic growth in these economies might lead to higher inequality overall, but strong government intervention, through public spending and subsidies, as well as robustly rising incomes help to promote signiﬁcantly the poor’s living standard. In the same way, with impressive progress in higher education and research and development, along with rising social mobility, some authors argue that “The American Dream Is Alive. In China” (Hernández and Bui 2018). Considering institutional and political factors in our study, it might be relevant to review the theory of the founding father of economic reform in the PRC, Deng Xiaoping, who said the following: “It doesn’t matter whether the cat is black or white, as long as it catches mice” (Li 1977, 107). Therefore, it does not matter whether it is democracy or communism, but whether the political institutions target the majority of the people, especially the poor and the more vulnerable. B. Granger Causality Tests Many studies have emphasized that income inequality hurts economic growth, which then leads to greater demand for redistribution through public expenditure and taxes in many societies (Kennedy et al. 2017, Tanninen 1999). This may cause the reverse effect between public expenditure and income inequality. The same logical reasoning is also applied for institutional quality. For example, the interaction of political and income inequality may play a part in blocking the adoption of good institutions (Chong and Gradstein 2007). To address this issue, the Granger causality tests can be statistically applied to estimate whether public expenditure may inﬂuence income inequality or vice versa. In this paper, we used the pairwise Granger causality tests (Granger 1969). We thus estimated the bivariate regressions of the following form: yt = α0 + α1yt−1 + · · · + αlyt−l + β1xt−1 + · · · + βlxt−l + εt xt = α0 + α1xt−1 + · · · + αlxt−l + β1yt−1 + · · · + βlyt−l + εt (6) (7) l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Government Intervention, Institutional Quality, and Income Inequality 195 Table 6. Tests for Granger Noncausality between Public Expenses and Institutional Quality and the Top 1% Income Share Explanatory Variable (x) Dependent Variable (y) Obs F-Statistic Prob Public expenses (% of GDP) Top 1% income share Public expenses (% of GDP) Institutional quality Countries Years Top 1% income share Public expenses (% of GDP) Institutional quality Public expenses (% of GDP) 8 1988–2014 150 69 1.591 1.339 0.649 6.642*** 0.207 0.265 0.526 0.002 GDP = gross domestic product. Notes: The null hypothesis is that the explanatory variable (x) does not cause the dependent variable (y). ***p < 0.01, **p < 0.05, and *p < 0.1. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank. worldbank.org/source/world-development-indicators (accessed December 3, 2018). where l is a lag length, which corresponds to reasonable beliefs about the longest time over which one of the variables could help predict the other (Granger 1969). From equations (6) and (7), dependent variable y can cause explanatory variable x and, at the same time, explanatory variable x can cause dependent variable y. The joint null hypotheses of the model are as follow: “y does not Granger-cause x” and “x does not Granger-cause y.” We can reject the null hypothesis if the F-statistics, which are the Wald statistics for the joint hypothesis, have a reported p-value at the 1%, 5%, or 10% level of signiﬁcance. Table 6 presents the results of Granger noncausality tests between public expenditure (% of GDP) and institutional quality and the top 1% income share in all the Asia and Paciﬁc countries in our dataset. We have no evidence that public expenditure (% of GDP) inﬂuences the top 1% income share. It is identical that the inﬂuence of the top 1% income share cannot be used to forecast public expenditure as a share of GDP. In the case of institutional quality, we also do not have enough evidence to emphasize that the top 1% income share drives institutional quality; however, we have enough evidence at the 1% level of signiﬁcance to reject the null hypothesis (i.e., institutional quality would forecast the public expenditure as a share of GDP). V. Robustness Checks A. Nonlinearity Analysis Though government intervention and institutional factors linking to income inequality seem to be linear, the relationship may be generated by different mechanisms at different intervention and institutional factors. This can lead to thinking about a nonlinear analysis. We included the levels of government l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 196 Asian Development Review nonlinear analysis into the methodology, following the studies of Tan and Law (2012), predicting a hump or inverted U-shaped relationship between income inequality and ﬁnancial factors in line with Shahbaz et al. (2015), studying the Kuznets curve between ﬁnancial development and income inequality in line with Rojas-Vallejos and Turnovsky (2017), and exploring the nonlinear relationship between tariff reductions and income inequality. The square term of the explanatory variable is included into the equation as follows:2 inequalityit = αi + β (cid:4) + β (cid:4) 4 1expenseit + β (cid:4) (cid:2)popit 5 + β ∗ (cid:2)oilit + β (cid:4) (cid:2)tradeit + β (cid:4) 1 expense2 it 3 (cid:2)taxit + ηit + θt + εit + β (cid:4) 2 6 (cid:2)gdpit (8) From equation (8), the U-shaped nonlinear relationship between public > 0; Tuttavia, the inverted
< 0. This is also applied expenditure and inequality predicts β (cid:4) 1 U-shaped nonlinear relationship predicts β (cid:4) 1 for the institutional quality. < 0 and β ∗ 1 > 0 and β ∗
1

Tavolo 7 shows the estimated results from the FMOLS and DOLS estimations.
We followed the same identiﬁcation strategy as a linear approach in the previous
section. We estimated public expenses (% of GDP) and its square value by
introducing the control variables in our regression. It is also applied for institutional
quality. The long-run variance estimates—Bartlett kernel, Newey–West ﬁxed
bandwidth—were used for coefﬁcient covariances for both the FMOLS and DOLS
estimations. We also took pooled estimation as panel method and ﬁxed leads and
lags speciﬁcation to address possible endogeneity and serial correlation as discussed
above for FMOLS. For the DOLS estimation, the pooled estimation as panel method
and automatic leads and lags speciﬁcation were estimated. The ﬁrst leads and lags
of the ﬁrst differences of control variables are estimated as instruments for the
explanatory variables.

According to the estimated results, public expenditure shows positive and
statistically signiﬁcant cointegrating coefﬁcients at the 1% level for FMOLS and at
IL 10% level for DOLS. Its square value shows negative and statistically signiﬁcant
cointegrating coefﬁcients at the 1% level for FMOLS and at the 10% level for
DOLS. The institutional quality also shows the same direction of coefﬁcient,
although the signiﬁcance level is different. Linking together, we obtained thus the
inverted U-shaped nonlinear relationship of public expenditure and institutional
quality on income inequality. More precisely, at the early stage of institutional
development, a country whose economy has experienced higher public expenditure
generates rising income inequality; Poi, in the long run when the country improves
its institutional quality, the higher public expenditure results in lower income
inequality.

2We also applied the same identiﬁcation strategy by using institutional quality (instiit ) and square institutional

quality (insti2

Esso ) as explanatory variables.

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Government Intervention, Institutional Quality, and Income Inequality 197

Tavolo 7. Nonlinearity Analysis of Public Expenses, Institutional Quality,
and the Top 1% Income Share

Public expenses (% of GDP)

Public expenses (% of GDP), squared

Institutional quality

Institutional quality, squared

(cid:2)Trade openness (% of GDP)

(cid:2)GDP (per capita at PPP)

(cid:2)Population growth (annual %)

(cid:2)Oil rents (% of GDP)

(cid:2)Tax revenue (% of GDP)

Adjusted R2
Countries
Years
Observations

Panel Fully Modiﬁed
Least Squares (FMOLS)

Panel Dynamic Least
Squares (DOLS)

Model 1

Model 2

Model 3

Model 4

0.417***
(0.979)
−0.117***
(0.022)

0.053***
(0.019)
−0.673***
(0.243)
−1.497
(1.598)
−1.670**
(0.748)
0.344
(0.251)
0.469
8
1988–2014
106

0.882***
(0.181)
−0.322*
(0.182)
0.049***
(0.013)
0.825***
(0.140)
−1.958***
(0.733)
0.473
(0.328)
0.089
(0.138)
0.964
8
1988–2014
92

0.109*
(0.312)
−0.319*
(0.089)

0.672**
(0.152)
−0.227*
(0.711)
−1.963
(1.987)
−0.281**
(0.535)
0.870*
(0.235)
0.724
8
1988–2014
16

0.328
(1.196)
−0.251***
(0.652)
0.030
(0.025)
−0.138**
(0.508)
0.96***
(0.298)
−0.208
(0.244)
0.372***
(0.040)
0.997
8
1988–2014
35

GDP = gross domestic product, PPP = purchasing power parity.
Notes: Data in parentheses indicate standard errors. The regression results were estimated with the FMOLS
and the DOLS methods. For the FMOLS, the regressions were estimated with the following assumptions:
panel method (pooled estimation); cointegrating equation deterministics (the constant [level] and/or trend); E
long-run covariance estimates (Bartlett kernel, Newey–West ﬁxed bandwidth). For the DOLS, the regressions
were estimated with the following assumptions: panel method (pooled estimation); cointegrating equation
deterministics (the constant [level] and/or trend); and automatic leads and lags speciﬁcation (based on Schwarz
information criterion, max = *). Long-run variance (Bartlett kernel, Newey–West ﬁxed bandwidth) was used
for coefﬁcient covariances. ***P < 0.01, **p < 0.05, and *p < 0.1. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank.worldbank. org/source/world-development-indicators (accessed December 3, 2018). B. Alternative of Measuring Inequality Although this study brings new insight into the thinking of inequality using a new measurement of the top income segment, it may face bias in that the top 1% income share cannot capture the effect of public spending and institutional quality in promoting the economic opportunity of the poor and the middle-income class. To see the complete picture, we also used the SWIID (version 8.2) as the robustness check (Solt 2019). Notice that the SWIID is the Gini index of inequality in the equalized household market (pretax, pretransfer income).3 3For details, see Fredrick Solt. “Using the SWIID in Stata.” https://osf.io/tj7ck/download. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 198 Asian Development Review We applied the same identiﬁcation strategy for both the linear and nonlinear long-run approaches for the estimations of the top 1% income share. In Table 8, we estimated separately public expenditure and institutional quality, the squares of public expenditure and institutional quality, and the interaction term between public expenditure and institutional quality. We obtained higher values for adjusted R-squared and the number of observations. This is likely due to having a more complete SWIID database compared to the top 1% income share. The SWIID dataset is available for nearly all of our eight sample countries in Asia and the Paciﬁc. We obtained nearly similar results as estimating with the top 1% income share, considering the direction and signiﬁcance level of the cointegrating coefﬁcients. We presumed therefore that public expenditure and institutional quality drive inequality reduction, and that the effects follow the inverted U-shaped nonlinear relationship in the long run. VI. Conclusion Inequality has indeed mattered not only in the past but also in the present and the future. Therefore, the legitimacy of this issue has always been in the equation. Many studies have linked inequality to government intervention and institutional quality, but most of them were not quantitatively estimated to understand the long-run equilibrium relationship. Thus, the main objective of our paper is to examine the signiﬁcance of such a long-run relationship, in both linear and nonlinear analysis, by applying the strength of FMOLS and DOLS, as well as the Granger causality tests. We used a dataset for eight countries in Asia and the Paciﬁc—Australia, India, Japan, Malaysia, the PRC, the Republic of Korea, Singapore, and Thailand—from 1998 to 2014. As reported by our estimated results, there are negative long-run, steady-state effects of government intervention (measured by public expenditure as a share of GDP) and institutional quality (measured by the WGI) on income inequality (measured by the top 1% income share in the World Inequality Database ﬁrst developed by Piketty and Zucman [2014]) in the sample countries in Asia and the Paciﬁc. The effect of institutional quality has only a one-way Granger causality link to income inequality. The existence of a nonlinear relationship between public expenditure and institutional factors linking to income inequality is also found. It implies that, at the early stage of institutional development, a country whose economy has experienced higher public expenditure generates rising income inequality; then, in the long run when a country improves its institutional quality, the higher public expenditure results in lower income inequality. The ﬁndings also suggested a nonlinear relationship in the long run when we estimated results with the Gini index of inequality of the SWIID (version 8.2). To develop a full picture of how government intervention and institutional factors inﬂuence inequality in the long run, additional studies are needed. Firstly, l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Government Intervention, Institutional Quality, and Income Inequality 199 ) 0 6 7 0 ( . 6 1 5 0 − . ) 0 5 3 0 ( . 0 4 2 0 0 . ) 2 0 1 0 ( . 7 8 9 0 . 8 ) 3 4 1 0 ( . 4 1 2 1 − . * * * 2 1 5 0 . ) 9 4 0 0 ( . * 7 2 3 0 . ) 7 6 1 0 ( . * 7 8 0 0 − . ) 6 3 7 1 ( . * 6 2 0 0 . ) 3 1 0 0 ( . * 2 2 0 3 . ) 8 4 5 1 ( . 3 0 3 0 . * * * 7 4 0 0 . * * * 0 2 9 0 . ) 2 1 0 0 ( . ) 2 7 2 0 ( . 4 7 8 1 . * * * 7 9 2 . 0 ) 7 9 1 0 0 ( . ) 6 2 0 0 ( . * * * 2 9 1 . 1 * * 4 4 0 . 0 − ) 6 8 0 0 ( . 5 0 0 . 0 − ) 6 6 5 1 ( . * 4 2 5 0 − . ) 2 2 0 0 ( . * * * 9 3 0 . 0 − ) 5 0 3 0 ( . 3 0 0 0 . ) 4 1 1 0 ( . * * * 9 2 2 . 0 ) 3 1 0 0 ( . ) 1 4 0 0 ( . * * 6 4 0 . 0 − ) 8 1 0 . 0 ( * * * 4 0 3 . 0 * * * 7 0 1 . 1 ) 2 2 0 . 0 ( ) 6 6 0 . 0 ( 3 1 0 . 0 − ) 0 2 0 . 0 ( * * * 1 4 0 . 0 − * * * 8 1 2 . 0 ) 2 1 0 . 0 ( ) 5 3 0 . 0 ( 0 5 1 . 0 ) 2 0 1 . 0 ( 6 9 6 . 0 ) 0 1 8 . 0 ( 6 5 0 . 0 ) 6 5 0 . 0 ( 6 6 2 0 . 0 − ) 0 6 1 . 0 ( 6 0 0 . 0 − ) 3 9 6 . 0 ( * * * 9 4 6 . 0 ) 3 5 1 . 0 ( * 6 7 2 . 0 − ) 6 5 1 . 0 ( * * * 0 4 0 . 0 * * 1 1 7 . 3 ) 0 1 0 . 0 ( ) 6 4 4 . 1 ( 1 4 1 . 0 ) 3 9 6 . 0 ( 5 4 2 . 0 − ) 6 1 2 . 0 ( 5 3 1 . 0 ) 8 1 1 . 0 ( * * 6 2 0 . 0 ) 1 1 0 . 0 ( * * * 0 7 6 . 0 ) 7 7 5 . 1 ( 5 4 9 . 0 ) 3 3 7 . 0 ( * * 0 9 5 . 0 − ) 2 7 2 . 0 ( 4 3 0 . 0 ) 8 3 1 . 0 ( * * * 2 1 4 . 0 − ) 6 5 0 . 0 ( * * 7 0 1 . 0 − * * * 6 1 6 . 2 ) 2 5 0 . 0 ( ) 2 7 2 . 0 ( 6 0 0 . 0 − ) 6 3 0 . 0 ( 3 1 0 . 0 ) 1 1 0 . 0 ( * * * 6 6 4 . 0 ) 1 3 1 . 0 ( * * * 7 3 0 . 2 * 6 9 0 . 0 − ) 9 4 0 . 0 ( ) 7 3 2 . 0 ( 8 3 0 . 0 − ) 3 3 0 . 0 ( * * 9 1 0 . 0 ) 0 1 0 . 0 ( * * * 2 1 3 . 0 ) 3 1 1 . 0 ( * * * 6 1 1 . 0 * * 6 7 2 . 0 ) 5 1 0 . 0 ( ) 5 0 1 . 0 ( * * * 4 2 0 . 0 ) 6 0 0 . 0 ( * * * 6 3 0 . 0 − ) 7 0 0 . 0 ( 3 3 0 . 0 ) 3 3 0 . 0 ( ) 5 4 9 . 0 ( 4 6 0 . 0 − * * * 8 0 5 . 1 − * * 2 3 0 . 0 ) 2 0 5 . 0 ( ) 3 1 0 . 0 ( * * * 4 1 4 . 0 − ) 6 5 0 . 0 ( * * * 7 8 0 . 0 − ) 7 2 0 . 0 ( 0 1 l e d o M 9 l e d o M 8 l e d o M 7 l e d o M 6 l e d o M 5 l e d o M 4 l e d o M 3 l e d o M 2 l e d o M 1 l e d o M ) S L O D ( s e r a u q S t s a e L c i m a n y D l e n a P ) S L O M F ( s e r a u q S t s a e L d e ﬁ i d o M y l l u F l e n a P 2 . 8 n o i s r e v D I I W S y b y t i l a u q e n I d n a , y t i l a u Q l a n o i t u t i t s n I , ) P D G f o % ( s e s n e p x e c i l b u P . 8 e l b a T ) P D G f o % ( s e s n e p x e c i l b u P , ) P D G f o % ( s e s n e p x e c i l b u P y t i l a u q l a n o i t u t i t s n I d e r a u q s l a n o i t u t i t s n I × s e s n e p x e c i l b u P d e r a u q s , y t i l a u q l a n o i t u t i t s n I ) P D G f o % ( s s e n n e p o y t i l a u q e d a r T (cid:2) ) % l a u n n a ( h t w o r g n o i t a l u p o P (cid:2) ) P P P t a a t i p a c r e p ( P D G (cid:2) ) P D G f o % ( e u n e v e r x a T (cid:2) ) P D G f o % ( s t n e r l i O (cid:2) d e r a u q s – R d e t s u j d A s n o i t a v r e s b O s e i r t n u o C s r a e Y 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 4 1 0 2 – 8 8 9 1 8 8 0 6 6 3 6 3 5 6 7 0 1 7 2 1 5 6 5 6 1 2 1 3 9 9 0 . 8 0 9 9 . 0 8 0 9 9 . 0 8 9 8 9 . 0 8 7 7 9 . 0 8 0 6 9 . 0 8 5 8 4 . 0 8 9 9 4 . 0 8 4 8 9 . 0 8 d e t a m i t s e e r e w s n o i s s e r g e r e h t , S L O M F e h t r o F . s d o h t e m S L O D e h t d n a S L O M F e h t h t i w d e t a m i t s e e r e w s t l u s e r n o i s s e r g e r e h T . s r o r r e d r a d n a t s e t a c i d n i s e s e h t n e r a p n i a t a D : s e t o N s e t a m i t s e e c n a i r a v o c n u r - g n o l d n a ; ) d n e r t r o / d n a ] l e v e l [ t n a t s n o c e h t ( s c i t s i n i m r e t e d n o i t a u q e g n i t a r g e t n i o c ; ) n o i t a m i t s e d e l o o p ( d o h t e m l e n a p : s n o i t p m u s s a g n i w o l l o f e h t h t i w . y t i r a p r e w o p g n i s a h c r u p = P P P ; t c u d o r p c i t s e m o d s s o r g = P D G g n i t a r g e t n i o c ; ) n o i t a m i t s e d e l o o p ( d o h t e m l e n a p : s n o i t p m u s s a g n i w o l l o f e h t h t i w d e t a m i t s e e r e w s n o i s s e r g e r e h t , S L O D e h t r o F . ) h t d i w d n a b d e x ﬁ t s e W – y e w e N , l e n r e k t t e l t r a B ( t t e l t r a B ( e c n a i r a v n u r - g n o L . ) * = x a m , n o i r e t i r c n o i t a m r o f n i z r a w h c S n o d e s a b ( n o i t a c ﬁ i c e p s s g a l d n a s d a e l c i t a m o t u a d n a ; ) d n e r t r o / d n a ] l e v e l [ t n a t s n o c e h t ( s c i t s i n i m r e t e d n o i t a u q e . 1 . 0 < p * d n a , 5 0 . 0 < p * * , 1 0 . 0 < p * * * . s e c n a i r a v o c t n e i c ﬁ f e o c r o f d e s u s a w ) h t d i w d n a b d e x ﬁ t s e W – y e w e N , l e n r e k t n e m p o l e v e D d l r o W . 4 1 0 2 – 8 8 9 1 . k n a B d l r o W d n a ; ) 8 1 0 2 , 3 r e b m e c e D d e s s e c c a ( d l r o w D W I . . 4 1 0 2 – 8 8 9 1 . e s a b a t a D y t i l a u q e n I d l r o W e h t g n i s u s n o i t a l u c l a c ’ s r o h t u A : s e c r u o S . ) 8 1 0 2 , 3 r e b m e c e D d e s s e c c a ( s r o t a c i d n i - t n e m p o l e v e d - d l r o w / e c r u o s / g r o . k n a b d l r o w . k n a b a t a d / / : s p t t h . s r o t a c i d n I l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 200 Asian Development Review it might be possible to use other tools of public intervention through government expenditure at a more disaggregated level, which are extensively studied in short- and medium-run analyses. 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GDP at purchaser’s prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars based on the 2011 ICP round. ID: NY.GDP.PCAP.PP.CD Annual population growth rate for year t is the exponential rate of growth of midyear population from year t − 1 to t, expressed as a percentage. Population is based on the de facto deﬁnition of population, which counts all residents regardless of legal status or citizenship. ID: SP.POP.GROW f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2019 Revision, (2) Census reports and other statistical publications from national statistical ofﬁces, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Report (various years), (5) US Census Bureau: International Database, and (6) Secretariat of the Paciﬁc Community: Statistics and Demography Programme. Continued. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d / . Government Intervention, Institutional Quality, and Income Inequality 205 Variable Oil rents (% of GDP) Table A.1. Continued. Description and ID Sources Oil rents are the difference between the value of crude oil production at world prices and total costs of production. ID: NY.GDP.PETR.RT.ZS Estimates based on sources and methods described in World Bank. 2011. The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium. Washington, DC. Tax revenue Tax revenue refers to compulsory International Monetary Fund, (% of GDP) transfers to the central government for public purposes. Certain compulsory transfers such as ﬁnes, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue. ID: GC.TAX.TOTL.GD.ZS Government Finance Statistics Yearbook and data ﬁles, and World Bank and OECD GDP estimates. GDP = gross domestic product, ICP = International Comparison Program, PPP = purchasing power parity, OECD = Organisation for Economic Co-operation and Development, US = United States. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank.worldbank.org/source/ world-development-indicators (accessed December 3, 2018). Table A.2. Pairwise Correlation among Control Variables trade gdp pop oil tax Trade (% of GDP) [trade] Signiﬁcance level Observation Per capita GDP (at PPP) [gdp] Signiﬁcance level Observation Population growth (annual %) [pop] Signiﬁcance level Observation Oil rents (% of GDP) [oil] Signiﬁcance level Observation Tax revenue (% of GDP) [tax] Signiﬁcance level Observation 1 216 0.4* 0.0 200 0.4 0.5 216 0.3 0.3 173 0.1 0.4 191 1 200 −0.02 0.8 200 −0.1 0.1 161 0.4* 0.0 179 1 216 0.4* 0.0 173 0.2* 0.01 191 1 173 0.1 0.3 148 1 191 GDP = gross domestic product, PPP = purchasing power parity. Note: *1% level of signiﬁcance. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https: //databank.worldbank.org/source/world-development-indicators (accessed December 3, 2018). l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 206 Asian Development Review Table A.3. Variance Inﬂation Factor among Control Variables Variable Public expenses (% of GDP) Tax revenue (% of GDP) Per capita GDP (at PPP) Population growth (annual %) Oil rents (% of GDP) Trade (% of GDP) Mean VIF VIF 5.7 4.6 4.4 3.95 3.2 2.4 4.04 1/VIF 0.2 0.2 0.2 0.3 0.3 0.4 GDP = gross domestic product, PPP = purchasing power parity, VIF = variance inﬂation factor. Note: Top 1% income share is used as dependent variable. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank.worldbank. org/source/world-development-indicators (accessed December 3, 2018). Table A.4. List of Countries Country Region Income Status Institutional Status Australia People’s Republic of China India Japan Republic of Korea Malaysia Singapore Thailand Source: Authors’ compilation. East Asia and Paciﬁc High income Strong quality East Asia and Paciﬁc Upper-middle income Weak quality Lower-middle income Weak quality South Asia Strong quality East Asia and Paciﬁc High income Strong quality East Asia and Paciﬁc High income Strong quality East Asia and Paciﬁc Upper-middle income East Asia and Paciﬁc High income Strong quality East Asia and Paciﬁc Upper-middle income Weak quality Table A.5. Eight Countries in Asia and the Paciﬁc Obs Mean Median Max Min Std Dev Skewness Kurtosis Top 1% income share 178 Public expenses (% of GDP) 205 152 Institutional quality 216 Trade openness (% of GDP) 200 GDP per capita 216 Population growth 173 Oil rents (% of GDP) 191 Tax revenue (% of GDP) 12.0 17.0 0.6 106.7 4.1 1.3 1.5 14.2 10.6 15.8 0.5 54.9 4.2 1.2 0.9 13.7 0.0 23.5 26.8 10.8 1.7 −0.6 13.3 439.7 4.9 3.0 5.3 −1.5 0.0 9.6 8.1 24.9 4.6 4.1 0.8 109.1 0.5 0.9 1.9 4.2 1.0 1.0 −0.0 1.6 −0.6 0.8 1.9 0.9 3.3 3.1 1.5 4.3 2.6 4.5 2.0 3.1 GDP = gross domestic product. Notes: Asia and the Paciﬁc comprises Australia, India, Japan, Malaysia, the People’s Republic of China, the Republic of Korea, Singapore, and Thailand. Data are from 1988 to 2014. Sources: Authors’ calculations using the World Inequality Database. 1988–2014. WID.world (accessed December 3, 2018); and World Bank. 1988–2014. World Development Indicators. https://databank.worldbank.org/source/ world-development-indicators (accessed December 3, 2018). l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 8 1 1 7 6 1 8 9 7 7 2 3 a d e v _ a _ 0 0 1 6 2 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3
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