Income, social, and political inequality

Ariun-Erdene Bayarjargal

ANU College of Asia and Pacific The Australian National University

Canberra, Australia Email: ariun-erdene.bayarjargal@anu.edu.au

Abstract

This paper empirically investigates the dependence of income inequality on social and political

inequality and its relationship to GDP per capita. The analysis uses a cross-country panel data set for

72 countries, both developed and developing, over the period between 1970 and 2006. Having

identified endogeneity amongst the variables, the study employs a system-GMM estimation

technique. The empirical evidence strongly supports the inverted-U curve hypothesis between GDP

per capita and inequality which is measured by the Gini coefficient. While both social and political

inequality increase income inequality, the effects have reversed when they interactively change. Other

significant determinants of income inequality are the government spending to GDP ratio, the age

dependency ratio, labour force participation rate for females, and education.

Keywords: Inequality, Gini coefficient, Kuznets curve, social inequality, political inequality

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Introduction

Recently, one of the most talked about books on the issue of inequality, Capital in the Twenty-First

Century by Thomas Piketty (2014), has brought the issue of inequality into debate again from the

viewpoint of income and wealth concentration. The analysis of century- long data on income shares of

top earners in mostly industrialized countries reveals that inequality has been increasing significantly

in recent decades. Moreover, the Occupy Wall Street movement after the Global Financial Crisis

clearly reflects the issue of inequality and its increasing interest of the public (Stiglitz, 2012). In

addition, international organisations address increasing inequality within countries and their

consequences. Various social aspects, disproportionate gains from economic growth, and inadequate

access to opportunities are generally matter for disparities among groups (OXFAM, 2014; The World

Bank, 2013; UN, 2013). Income inequality is, however, not new in the development economics

literature.

The framework that examines income inequality has a long history going back to a seminal paper by

Simon Kuznets (1955). His principal finding was the inverse U-curve relationship between inequality

and economic development where causation goes from development to income distribution. The

driving force is structural change in a dual-economy setting, in which labour has shifted from a

relatively undifferentiated traditional sector, agriculture, to a more productive and more differentiated,

modern sector.

After three decades of relative neglect, Atkinson (1997) brought the issue of inequality back into

international attention through his paper titled Bringing income distribution in from the cold1.

Atkinson offered insights into the determinants of increasing inequality and mentioned that both

economic and political economy explanations have to be taken into account in the examination of

inequality. Around the same time, there was an upsurge of interest in the theoretical modelling of

income distribution. The theoretical models (Aghion & Bolton, 1997; Banerjee & Newman 1993;

Benabou, 1996; Galor & Zeira, 1993) analyse the role of heterogeneity in the determination of

aggregate macroeconomic activity, and thus income distribution. Subsequently, the modern

perspective about the relationship between inequality and economic development has evolved,

resulting in hundreds of research papers. Many researchers have tested the Kuznets hypothesis

empirically, along with other determinants of inequality which these theories suggest. However, there

was no satisfactory dataset of inequality to analyse the issue at an international level until Deininger

and Squire (1996) provide the most comprehensive and systematically collected data. The number of

research papers based on this dataset has boomed, but no conclusive result on the Kuznets curve has

1 The paper was titled same as a Presidential Address to the Royal Economic Society, Swansea April 1996, in order to highlight the issue.

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been reached.

The debate on inequality kept growing in the public policy and political economy arena. The issues of

income inequality, its changes over time and its impact on poverty alleviation and economic

development, came to the centre of the discussion of scholars, policy-makers, and the public at large.

A book edited by Cornia (2004) made a significant contribution to the income distribution literature.

Its publication has coincided with a period of relatively rapid increase in within-country income

inequality, and thus it resulted in a greater interest in the documentation and analysis of inequality at

international and national levels.

This revival of interest in inequality research is partly due to the undisputed rise in income inequality

observed in the majority of both developed and developing countries. Consequently, over the last two

decades, there has been a vast theoretical and empirical literature explaining inequality. Theoretical

models (Bourguignon & Verdier, 2000; Matsuyama, 2000; Mookherjee & Ray 2003) formalise the

extent to which the persistence of inequality could be explained by factors such as unequal political

participation, credit market imperfection, lack of human capital accumulation, and bias in

redistributive policy. Even though cross-country analyses have been facilitated by the availability of

the comparable long-term data developed by Deininger and Squire (1996), a substantial portion of the

literature considers overall inequality, in particular the Gini coefficient, which is relatively smooth

over time. And now, the recent developments in data collection allow us to see the income

distribution in broader aspect. Many scholars suggest a number of determinants of inequality,

including financial development, global trade, new technology, natural resource abundance, economic

policy, and ethnic polarization.

“Although economic globalization has supported rapid growth, it has also produced increased

volatility in incomes and increasing income inequality. It has not only been associated with

increasing inequality of income within developing countries, but also between developing

countries and between developed and developing countries. Inequality has also increased within

developed countries.” (UN 2009, pp.25).

Commenting on the abovementioned report, Joseph Stiglitz (2012) argues that inequality manifests

itself in every public decision which then has feedback effects on economic, social and political

inequality. Increasing inequality slows down the process of poverty reduction and dampens economic

growth. It also affects political stability and increases social tensions2. Certainly, inequality has been a

critical economic, social and political issue for millennia. Data covering world income distribution

between 1820 and 2001 suggest that inequality in the richest and the poorest regions of the world has

2 The protest movements in the Middle East were clearly a result of political inequality, which in turn affects income inequality (Stiglitz (2012) and see also Cornia (2004)).

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widened considerably from a modest ratio of 3:1 in pre-industrialization period to an 18:1 ratio in

2001 (Maddison, 2001).

Muller (1988, 1989) and Hughes (1997) argue that political equality which is equivalent to the level

of democracy leads to a reduction in income inequality. Verba et al. (1978) point out that engagement

in political participation is an important factor for reducing inequality under democracy. Recently,

some economists have turned to social inequality for explaining economic inequality3 in a broader

sense. Social inequality, generally, measured as access to opportunities and social relations can be

roots of widening income inequality. Explaining changes in inequality is still a challenging issue and

this paper examines the relationship between income, social and political inequality within a

framework of the Kuznets hypothesis on inequality. The main approach of this paper is empirical in

nature, but the model formulation is well informed by the existing literature. The paper is organized as

follows. The next section presents the review of the main theoretical underpinnings and prior

empirical studies. Data and model specification is discussed in the following section. Estimation

outcome is discussed in the empirical evidence section and the final section concludes.

Review of literature

The theoretical models of the relationship between inequality and growth mainly concern the direction

from initial inequality in income/wealth to economic performances. However, persistence of

inequality indicates that the determinants of inequality need to be examined in more detail. Besides,

there exist extensive empirical studies on inequality. The literature on the explanations for inequality

is based on the seminal work by Kuznets (1955) in which he develops the “inverted-U” hypothesis

that economic growth can initially lead to a rise, and then a subsequent fall in income inequality

within a country. Since then, many empirical studies have been done testing the hypothesis but have

not reached conclusive results. While some empirical studies find a strong evidence of inverted-U

curve (Alderson & Nielsen, 1995; Barro, 2008), others reject it or find no systematic relationship

(Bourguignon & Morrisson, 1998; Deininger & Squire, 1998; Fields, 2002). Fields (2002) review

shows the results on the Kuznets hypothesis are still weak. About 10% of country studies are

consistent with the inverted-U hypothesis, and another 10% supports the ordinary-U curve, and

country cases for the remaining 80% exhibit statistically insignificant results. Atkinson and Brandolini

(2005) undertake a complete review of extant studies explaining income inequality. They also say that

empirical studies still show mixed evidence. The Kuznets hypothesis is strongly supported by the

estimation for an overall measure of inequality, but it tells different stories for the lowest and highest

quintiles. 3 Centre for East European Language based Area Studies and Russell Sage Foundations in the US are the institutions doing research on social inequality.

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The mainstream of the theoretical and empirical literature of inequality shows that highly unequal

societies in terms of income, assets, and land-ownership tend to have lower growth and, consequently

an unequal distribution of income. Besides the initial unequal distribution of income, wealth, and

land, many non-income dimensions of inequality influence the economic opportunities of human

well-being. According to the capability approach developed by Sen, inequality should be assessed

based on the person’s ability to transform resources into valuable activities. Human well-being is

about people expanding their choices to live full, creative lives with freedom and dignity.

Fundamental to enlarging human choices is the building human capabilities. Not only income, but

other opportunities/capabilities influence the individual’s economic prosperity, well-being and

freedom (Sen, 1999). The UN Human Development Report (HDR) 2002 emphasizes that political and

civil freedom and participation are as important as other basic capabilities: i.e. being educated and

having a long and healthy life (UNDP, 2002). Thus this paper examines the role of social and political

capability for economic well being. Social capability can be represented by the prevalence or absence

of social violence, people’s conventions in a community or group, various social attitudes which are

part of social capital, and self-respect etc. Political capability can embody people’s participation in

decision-making and the distribution of political power. In this paper, for the reason of comparability

of data, along with Sen, social inequality is considered as capability to participate in and establish

social organizations and to enjoy freedom of speech, and political inequality is as equality of

opportunity to represent political view and to participate in political decision-making process.

The theoretical models basically try to explain social tension and political instability in the presence

of income inequality. On the contrary, socially disparate opportunity and unequal participation in the

political decision-making process could, as well, have affect people’s economic well-being. The

literature has not provided clear cut evidence on the relationship between political, social and income

inequality. Examining social and political determinants of inequality is interdisciplinary. First field is

the income inequality literature primarily related to the development economics. Second is the

concept of social inequality through the lens of social capital whose origin is in sociology. Third,

political inequality is related to politics and political sociology field and its measurement success to

some extent has brought the concept into the development literature.

In political studies, there is now a growing literature on the relationship between income inequality

and political institutions and regimes. Chong (2001) studies the link between income inequality and

democracy and tests the hypothesis of political Kuznets curve for the period 1960-1995. Employing

different econometric techniques, the author finds significant evidence for the existence of a political

Kuznets curve. This finding is consistent with the theory about political power and inequality.

Moreover, this paper is interesting in the sense that it applies a Kuznets curve hypothesis, which is

rarely used in the political study literature, though it cannot be said that the result is an absolute

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indication of the existence of the curve. Beyond this paper, over the last decade, the literature has

focused on the political determinants of inequality: how institutions and types of political regimes

influence levels of inequality. High initial inequality in political participation and freedom causes less

secure property rights, unfavourable political regime and, consequently, high income inequality

(Chong and Gradstein, 2007). Democracy is used as a main measurement of political equality in the

studies of economic inequality e.g., Muller (1988), Alderson and Nielsen (1995), Rodrik (1999) and

Reuveny and Li (2003). The majority of the literature claims that democracies with greater political

participation in decision-making tend to redistribute more towards the poor, with decreasing

inequality as the final result. Other authors, e.g., Benabou (1996) and Rodriguez (2004), conclude that

regime type cannot be considered as one of the main determinants of inequality. On the other hand,

studies such as those of Crenshaw (1993), Simpson (1990), and Weede (1993) find a net positive

relationship between the level of democracy and income inequality. In contrast, Timmons (2010)

replicates Reuveny and Li analysis using an advanced methods and additional variable and finds no

relationship between democracy and economic inequality. Overall, there is no consensus on the

relationship between income inequality and political regime.

Solt (2008) examines empirically economic inequality in the presence of political engagement of

citizens. This study tests the hypothesis that inequality increases the relative power of the wealthy to

shape politics in their own favour by using the data from multiple surveys of the advanced industrial

countries. His analysis shows that economic inequality depresses political engagement. It supports the

explanation of greater economic inequality increases the relative power of richer citizens.

Consequently, declining political engagement of non-affluent citizens with rising inequality suggests

that redistribution becomes increasingly unlikely. Thus, higher levels of economic inequality yields

greater political inequality. Indeed, the concept of social capital, which is the measure of social

inequality is studied in relation with economic growth rather than income inequality. Few studies have

taken into account the relationship between income inequality and social inequality in the form of

social capital. According to Kawachi (1997), income inequality is strongly and negatively correlated

with social group membership, but positively with general trust. In addition, social inequality can

impede democratic consolidation by stimulating social conflict and political instability and, in turn,

may act as a support for the establishment of authoritarian regimes. Thus, it is also crucial to

understand the consequences of social inequality on income inequality.

All these facts show that explaining income inequality is still an important and challenging subject in

development economics. Therefore, this study examines the relationship between income, political,

and social inequality in the presence of Kuznets hypothesis using a panel dataset of 72 countries over

the period between 1970 and 2006.

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Data and model specification

The data includes 72 countries over the period between 1970 and 2006. The list of countries and data

sources can be found in Table 5 and Table 6.

Data

Income inequality data is from the World Income Inequality Database (WIID) by UNU-WIDER4,

Version 2c, Sep 2011. The advantage of this data is that we, to some extent, can avoid the problem of

definition which is common in inequality studies. In other words, the Gini coefficient used in this

study is from income-based surveys rather than consumption-based surveys5. The Gini coefficient

which represents income inequality is the main variable of interest. According to Atkinson and

Bourguignon (2000), the data on inequality should be specified “of what among whom”. The income

based Gini coefficient is obtained from the database in which unit of analysis is a household and use

of equivalence scale is the person.

Measuring social and political inequality is not straightforward. Any form is shaped by a range of

structural factors such as geographical location or citizenship status, and is often underpinned by

cultural discourses and identities defining, for example, whether the poor are deserving or

undeserving (Walker, 2009). Based on definitions available, social inequality can be measured in two

dimensions: vertical and horizontal. Due to the unavailability of the long time-series statistics on

horizontal social inequality, we use vertical dimensions of social inequality measurement in our study.

Corruption is an indirect measure of social capital and represents vertical social inequality. It

embodies both horizontal and vertical characteristics of social capital in the sense that it enables

people to use horizontal social relations for extracting gains from vertical relations. Less corruption

means more trust in the government, and thus, it captures trust aspects of social capital. Corruption

increases income inequality by reducing economic growth and the effectiveness of social spending.

Moreover, importantly, it increases unequal access to education (Gupta et al., 2002). A variety of

corruption indexes are reported by different international organizations, but most of them do not have

long time-series estimates for countries. Transparency International (TI) provides the Corruption

Perception Index (CPI) for 176 countries only since 19966.

4 United Nations University-World Institute for Development Economics Research 5 While some researchers say that a consumption-based inequality measure has no advantage over income-based measure, the data shows the fact that the Gini coefficient calculated from income-based survey could differ from that based on consumption-based survey for an individual country for an exactly same year. 6 TI is the global civil society organization leading the fight against corruption. As it is indicated the fight against corruption starts from learning what is corruption. This organisation score countries on how corrupt their public sectors are seen to be and their database is the longest available series on the perception of corruption for countries.

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Civil Liberty index published by Freedom House is another measure of vertical social relations at

macro level7. The index contains information on the extent of freedom of speech and organization that

can be used as a precondition for establishing and participating in social activities. The Civil Liberty

index is reported for 195 countries since 1973. This index takes a value between 1 and 7 and, in this

study, it is transformed into between 1 and 10 where the higher value indicates higher inequality. As

social equality is considered to be a person’s capability to expand economic opportunities, it should

have a positive effect on income equality. Income inequality changes with social inequality in the

sense that socially unequal societies tend to have socio-economic instability which hinders investment

and consequently fosters further inequality.

One of the fundamental bases of democracy is there should be equal consideration of the preferences

and interests of all citizens. This is expressed in such principles as one-person/one-vote and equality

before the law (Verba, 2001). Thus, a democratic regime is actually considered to provide equal

opportunity for each citizen and in this regard, a more democratic society tends have more equal

political rights/participation. A new polity type index from the Polity IV project8 is used as a proxy for

political vertical inequality. Polity2 score rates countries annually along a range from -10 (strongly

autocratic) to +10 (strongly democratic). The index is transformed into 10 (refers to -10 implying

politically unequal) to 1 (refers to +10 which is politically equal). The data covers the period between

1946 and 2011 for 167 countries (Marshall et al., 2012). Theoretical models suggest there would be

positive relationship, but, according to empirical evidence, there is no consensus about the effect of

political inequality on income inequality. This paper argues that income inequality increases with

political inequality under the assumption that in a democratic society people can participate in the

decision-making process more equally. Equal political participation can be affordable in a democratic

regime.

According to the inverted-U hypothesis, inequality increases as a country grows, and then eventually

declines with income. The underlying explanation for the hypothesis is that, initially, a country has a

dominant agricultural sector in which income is more equally distributed, and then the level of

inequality increases due to the development of industrial and service sectors. Taking into account the

Lewis model, more resources have shifted to the industrial sector and this affects also the agricultural

wage rate. Consequently, income levels in modern and traditional sectors will become equalized, and

then, eventually income inequality will decline. Since our purpose is to test the Kuznets hypothesis in

line with effects of social and political inequality, per capita income is included in the model. We use 7 Freedom House is an independent organization dedicated to the expansion of freedom around the world and publish a report, Freedom in the World, annually. This report is essential source for democracy and civil liberty development for countries. 8 Polity IV project is the research coding the authority characteristics of states in the world. It is initially developed by Gurr and informed by a collaborative work with Eckstein, GURR, T. R. & ECKSTEIN, H. 1975. Patterns of Authority: A Structural Basis for Political Inquiry, New York, John Wiley & Sons..

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GDP per capita at constant 2005 prices from World Development Indicators (WDI) 2012 by the WB.

We test that higher level of income inequality will be observed with higher level of income in the

short-run and the effect will be reversed eventually.

Table 1 is about here.

Table 1 shows the correlation matrices of main variables of interest. Pair-wise correlation table shows

that the Gini coefficient is positively related to social and political inequality while negatively related

to per capita income. However, the partial correlation of the Gini coefficient with social inequality is

positive and that with political inequality is negative. The correlation of income inequality with per

capita income is negative and insignificant.

Control variables are derived from a wide range of literature on the determinants of income

inequality. The main source of control variables is WDI, except for the openness variable. Openness

is a ratio of the sum of exports and imports to GDP and is taken from the Penn World Table (PWT)

7.1 (Heston, Summers, & Aten, 2012). According to empirical studies, openness increases income,

but its effect on inequality is doubtful. Openness, in general, tends to increase income inequality by

decreasing the competitiveness of the economy, particularly in developing countries9. However,

Hecksher-Ohlin model says that greater openness increases income inequality in developed countries,

but reduces that in developing countries. Though the existing evidence on this effect is mixed (Barro,

2000; Calderon & Chong, 2001; Dollar & Kraay, 2002) we expect the effect would be positive

implying that openness is most likely to worsen the distribution of income.

Theoretical models, generally, suggest that financial development/depth reduces income inequality

through better access to financial services (Beck, Demirguc-Kunt, & Levine, 2007; Demirgüç-Kunt &

Levine, 2009; Greenwood & Jovanovic, 1990). We use bank concentration ratio as a proxy of

financial sector development indicator. It shows the degree of concentration in the banking sector and

is measured by the fraction of total assets held by three largest commercial banks in the country. The

higher bank concentration implies the lower level of financial sector development and thus we expect

that when bank concentration increases, income inequality surges.

We include the government consumption expenditure to GDP ratio in order to account for the

government activity. However, its effect on income inequality is not straightforward. If the

government spending is used effectively for reducing poverty through social transfers, it may help to

reduce inequality. On the other hand, higher government spending may increase income inequality

due to weak institutional performance.

9 This is explained by the skill biased technical change hypothesis. Openness increases the demand for high-skilled workers, as a result, inequality increases

10

Another important control variable is education as a proxy for human capital. We use gross enrolment

ratios at primary, secondary, and tertiary levels as suggested in Barro (2008). We expect that

education has positive impacts on income distribution. In other words, inequality will decline as

human capital increases (Barro, 2008; Benabou, 1994; De Gregorio & Lee, 2002; Galor & Moav,

2004).

Moreover, in order to see the employment effect on inequality, the labour market participation rate of

females and males are used. In general, the literature suggests that the employment of females reduces

inequality, in particular, in developing countries. The labour force participation rate is obtained from

WDI and is the proportion of population aged between 15 and 64 that is economically active.

In order to control for demographic features of countries, we use the age dependency ratio. The age

dependency ratio, which is a share of dependents – people younger than 15 or older than 64 to the

working age population those aged 15-64, has, in general, positively associated with income

inequality suggesting that a higher level of age dependency increases income disparity.

The ethnic fractionalization index is from Alesina et al. (2003). They developed a more

comprehensive measure of ethnic fractionalization using up-to-date information. The ethnic

fractionalization index is used as an instrument in our analysis. The descriptive statistics of control

variables are in Table 2.

Table 2 is about here.

Model specification

To examine the relationship between income, political, and social inequality in the presence of

inverted-U hypothesis, we develop the following empirical model for our analysis:

𝐼. 𝑖𝑛𝑒𝑞!" = 𝑏! + 𝑏! ln 𝑝𝑐𝐺𝐷𝑃!" + 𝑏! ln 𝑝𝑐𝐺𝐷𝑃!" ! + 𝑏!𝑆. 𝑖𝑛𝑒𝑞.!"+

+𝑏!𝑃. 𝑖𝑛𝑒𝑞.!"+ 𝑏!𝑃. 𝑖𝑛𝑒𝑞.!"×𝑆. 𝑖𝑛𝑒𝑞.!"+ 𝛿𝑿!"! + 𝛾! + 𝜇! + 𝜀!" (1)

where 𝐼. 𝑖𝑛𝑒𝑞!" is the Gini coefficient (Top10/Bottom10 ratio); ln 𝑝𝑐𝐺𝐷𝑃!" and ln 𝑝𝑐𝐺𝐷𝑃!" ! are

logarithm of GDP per capita and GDP per capita-squared, respectively; 𝑆. 𝑖𝑛𝑒𝑞. is social inequality

variable; 𝑃. 𝑖𝑛𝑒𝑞. is political inequality variable; 𝑃. 𝑖𝑛𝑒𝑞.!"×𝑆. 𝑖𝑛𝑒𝑞.!" is an interaction variable of

social and political inequality; and 𝑿 is vector of control variables. The last three terms in Equation

(1), 𝛾!, 𝜇!, and 𝜀!", are time- and country-effects, and an idiosyncratic error term, respectively. The

interaction variable of political and social inequality is added to the model based on the argument that

social and political inequality are interdependent.

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The model is estimated using three different estimation techniques – pooled-OLS, fixed-effects and

system Generalized Method of Moments (GMM). Fixed-effects is superior to pooled-OLS, while the

system GMM addresses endogeneity issues compared to the fixed-effects estimator. The equation is

first estimated through pooled-OLS – without and with country and/or time fixed-effects. However,

the country-specific and time-invariant factors might be correlated with the explanatory variables set.

This problem is captured in the fixed-effects estimation technique. The inclusion of country-specific

fixed effects in the model mitigates the possible omitted variable bias. This technique involves

estimating the equation after demeaning it to purge the 𝜇!.

While the fixed-effects estimation method eliminates the omitted variable bias, it does not address the

source of endogeneity – potential reverse causality from income inequality to the explanatory

variables. The theory suggests that income, social, and political inequality could determine each other.

Therefore, I still need to be cautious about inferring causality based on the estimated coefficients from

fixed-effects regressions. Strictly speaking, in order to establish a causal impact, the explanatory

variables need to be statistically shown as exogenous (uncorrelated with the error term). Moreover,

there is also reason to believe that income inequality is a persistent variable, with the present nature of

the income distribution dictating its future level. Taken into account these concerns, I subsequently re-

estimate the model including lagged income inequality in the specification through the system GMM

estimation technique which is developed by Arellano and Bover (1995) and Blundell and Bond

(1998)10. The system GMM estimation has an important advantage by allowing consistent estimation

of an equation that controls for the lagged dependent variable. It allows the explanatory variables to

be either endogenous or weakly exogenous, and thus deals with the problem of likely reverse

causality from income inequality to social and political inequality establishing causal impact.

Moreover, this technique provides us with a set of internal instruments, rather than having to look for

external instruments which are highly correlated with income inequality, but do not impact the

explanatory variables through any other channel. I restrict the number of lags used for instrumenting

the right-hand side variables to one. This is because a large instrument set relative to the number of

observations causes an over-fitting bias for the estimates (Roodman, 2009)11. The system GMM

provides consistent estimates for the coefficients even when the explanatory variables are correlated

with the error term. A standard Sargan or Hansen test of over-identification allows to check for the

validity of the instrument set.

10 System GMM augments the difference GMM technique suggested by Arellano and Bond (1991). 11 Moreover, using the minimum possible lag length allows the least reduction in the number of observations available for estimation given the availability of income inequality data due to gaps in household income surveys.

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Empirical evidence

This section presents the empirical outcome. Estimation outcomes are in Table 3 and Table 4. The

model is first estimated using pooled-OLS as a baseline. The last column of Table 3 shows the results

of the OLS estimation. An inverted-U curve relationship is found between income inequality and

GDP per capita. Social inequality is positively related to income inequality and the coefficient is

highly significant at the 1 per cent level. This implies that income inequality changes with social

inequality. In other words, when social inequality increases, income inequality also increases. In terms

of political inequality, it should be noted that the estimated coefficient of political inequality variable

(democracy) was negative and statistically significant in the first pooled-OLS estimation result (See

column 1 of Table 3). It is argued that that the effect of political democracy on income inequality is

spurious, and depends on the relationship with other socio-economic characteristics (Jackman, 1974;

Keefer & Knack, 2002, 2002a). Therefore, we add the interaction variable of social and political

inequality. The estimation result in Table 3 is with the interaction variable of social and political

inequality and it shows that the estimated coefficient of interaction variable is negative and

statistically significant. However, the coefficient of interaction variable is less than that of social and

political inequality – overall effect of social and political inequality on income inequality is positive.

The coefficient estimates indicate that income inequality worsens on average by 0.05 (1.014-

0.330×SI) points with one point increase in political inequality (PI) at the average level of social

inequality12. To put it differently, when political equality worsens by 10 percentage point the Gini

coefficient increases by 0.5 percentage points13. However, this magnitude of change in democracy

happens unlikely except if a country shifts from one political regime to another. During the period of

the dataset covers, only transition countries from socialist to democratic political regime experienced

a sudden change in their history of political situation.

Table 3 is about here.

The CPI which is another proxy of social inequality also yields the same result while the coefficient is

statistically insignificant (See column 3 of Table 3). Hereafter, all estimations include civil liberty as

the main variable of social inequality. The coefficient of social inequality with the effect of the

interaction term also indicates that when social inequality (SI) worsens by 1 percentage points, the

Gini coefficient increases by 0.55 (1.192-0.33×PI) percentage points implying that income inequality

will become widened at the given average of political inequality14. The estimation result, overall,

confirms the prediction of the model at least 5 per cent significance level. In other words, the 12 The average levels of the variables are based on the sample mean. The sample mean of social inequality is 2.91 for 478 observations. 13 Here and henceforth a percentage point is meant to indicate the change as a proportion of the total range of the variable. 14 The sample mean of political inequality is 1.93 for 478 observations.

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hypothesis that unequal societies tend to have more income inequality is supported by the data.

We also use another measure of income inequality which is the ratio of income share of top 10 and

bottom 10 per cent of the population and the estimation result displays the same result for social and

political inequality but not for GDP per capita. This implies that the inverted-U curve relationship

does not exist between income inequality and per capita income when inequality is based on the tails

of the income distribution rather than the average (See column 2 of Table 3).

The fixed effects estimation results are reported in Table 4. We estimated the model using both fixed

effects (FE) and random effects (RE) and the Hausman test result shows that FE estimation is

preferred.

Table 4 is about here.

The overall explanatory power of FE regression is lower than the corresponding pooled-OLS

regressions, suggesting that fixed country-specific characters explain not much of the variation in the

extent of income inequality. The inverted-U curve relationship between income inequality and per

capita income witnessed in the pooled-OLS is also found but significant at only 10 per cent level.

Comparing the coefficients, the point estimates of political inequality and interaction term indicates

that at any given levels of social and political inequality, higher political inequality is more strongly

associated with income inequality compared to the association of higher social inequality with income

inequality.

The summary of the system GMM results are presented in the last column of Table 3 and the detailed

results are in Table 4. The causality may run both sides, from political and social inequality to income

inequality and vice versa as the literature suggest. In the system GMM estimation, in addition to

exogenous instrumental variables, we are also able to add lagged values of other endogenous variables

as potential instruments.

As mentioned above, the consistency of the GMM estimator depends on the validity of instruments.

The p-value of the Hansen test for over-identifying restriction shows that the null hypothesis that the

instrument set is valid is not rejected. At the same time, the estimation does not likely suffer from an

over-fitting bias caused by over instrumentation, as the Hansen p-value is not unrealistically high

(Roodman, 2009). Also, the p-value of the AR(2) test indicates that we cannot reject the null of no

second order serial correlation at the 10 per cent significance level, which is a necessary assumption

for consistent estimation using system GMM. The last column of Table 4 shows the estimation result

of the equation including the lag of the income inequality variable (dependent variable).

The estimation results strongly supports the hypothesis of the inverted-U curve relationship between

14

income inequality and per capita income, and this is consistent with existing evidence of Papanek and

Kyn (1986), Alderson and Nielsen (1995), Barro (2008), and Roine et al. (2009). Moreover, the

coefficient estimates of GDP per capita and GDP per capita- squared are statistically significant at the

5 per cent level.

The interesting result is that when country becomes more socially and politically unequal, the increase

in either social or political inequality reduces income inequality. In other words, the cumulative

impact of social and political inequality on income inequality depends on the coefficient of the

interaction term. The overall marginal effect of social inequality on income inequality depends on

political inequality as 2.962-1.077×PI. This effect would be positive when a political regime is more

democratic (less political inequality), and then it turns to negative when a country becomes more

autocratic, which means political participation is unequal. Put differently, income inequality increases

as social inequality increases in a more democratic society, but decreases in a more autocratic society.

However, the effect of social inequality is reversed when the political regime is relatively equal (when

PI takes a value of 2.7515, the marginal effect becomes negative). It should be noted that this result

might be related to the fact that in socialist countries which are considered as autocratic political

regimes, income was relatively equally distributed. As a result, the effect of social inequality on

income inequality reduces as political inequality widens and is sensitive to the change in political

inequality.

On the other hand, the marginal effect of political inequality on income inequality also depends on the

level of social inequality and the impact is 6.248-1.077×SI. Social inequality takes a value between 1

and 10, the effect of political inequality on income inequality is also reversed as social inequality

increases. In a socially equal country, income inequality is positively associated with political

inequality. However, it is also up to certain level of social inequality (when it takes 5.8 or more),

which indicates relatively unequal social freedom16. In other words, in a society with restriction of

religious and social freedoms, the increase in political inequality reduces income inequality. Again,

this result may represent the situation of transition countries where both social and political inequality

were high while income inequality was low. On the other hand, this result may also be related to the

fact that an autocratic countries where at the same time suppresses social freedom have a low level of

income and hence less income inequality. As we mentioned before, the literature on the relationship

between social, political and income inequality has reached no conclusive result. Therefore, our result

can be further examined as which types of democracy has affected on income inequality as did in

Rockey (2007) suggesting that constitutional rules may have different impacts on wage negotiations.

15 Converting back this value to polity score it reflects a relatively democratic regime with a score of 6 (See variable description section). 16 Converting back this value to civil liberty, this is about 4.5 which determines partly free. This implies that countries moderately protect some civil liberties while neglecting others.

15

Moreover, Acemogly and Robinson envisage de jure and de facto political power and argue that in

initially unequal society, de facto distribution of political power (through lobbying or bribery) may

differ from that where democracy promotes social stability (Acemoglu & Robinson, 2001, 2006).

As expected, government consumption expenditure has a negative and significant effect on income

inequality suggesting that the expansion in government activity may suppress private sector expansion

and subsequently increase in income inequality. The trade openness variables is expected to have a

negative association with income inequality and the coefficient is negative but insignificant for all

estimations. In the pooled-OLS estimation result, the bank concentration ratio is negatively associated

with income inequality, which is consistent with what the literature suggests. This coefficient,

however, is significant at only the 10 per cent level in the FE estimation and insignificant in the

system GMM estimation implying that financial sector development has not significantly affected

overall income inequality which is measured by the Gini coefficient.

The age dependency ratio enlarges income inequality but the coefficient is significant at the 10 per

cent level. The coefficient estimate shows however, that the increase in age dependency surges

income inequality at the relatively high magnitude.

Another interesting result is that labour participation rate of females reduces income inequality while

that of males increases it. This may be explained by their different marginal benefits from the labour

market. The literature explains this in relation to declining fertility rate. As women’s participation in

the labour market increases, fertility rate declines, and consequently, more equal income distribution

can be observed.

Contrary to previous studies, a positive relationship between education and income inequality is

found. Primary and tertiary levels of education have positively affected income inequality, but this is

not for the secondary level of education. Higher education is generally accessible for high income

groups of populations and therefore, a greater enrolment in tertiary level education implies that for

high income groups there is an increased opportunity for higher earnings, and this consequently

widens the income gap between the rich and the poor. While the impact of secondary level education

is negative, the coefficient is statistically insignificant. Thus, this result implies that the income

premium for secondary education may affect earnings but not significantly reduce income disparity.

Conclusion

Income inequality is not only an economic phenomenon but also relates to socio-political situation of

a country. Despite the existence of a rich literature to provide theoretical and empirical basis, the link

between social, political and income inequality has not been systematically investigated in a cross-

country context. Most studies focus on the causality from income inequality to social discontent and

16

political instability and they are examined separately. This paper investigates the other direction,

while incorporating the Kuznets hypothesis of inverted-U curve relationship between inequality and

income per capita. In this sense, the study empirically examines the impacts of social and political

inequality on income inequality and thus, contributes to the literature that links development

economics to political study and sociology.

The empirical evidence confirms hypotheses on the effects of social and political inequality on

income inequality and the relationship between inequality and GDP per capita. In particular, the

estimation result strongly supports the Kuznets hypothesis suggesting an inverted-U curve

relationship between inequality and GDP per capita. In other words, this implies that income

inequality increases in the beginning of economic development and then declines gradually as

economy’s per capita income increases.

The effects of social and political inequality on income inequality are determined interactively. Social

inequality is positively and significantly associated with income inequality in a highly democratic

society implying that income inequality enlarges as a social freedom worsens. The similar relationship

is found for political inequality. In other words, in a country with a high level of social inequality,

higher political inequality expands income disparity. These effects, however, reversed when social

and political inequality reaches to the certain levels. In other words, when both social and political

inequality are high, the increase in either social or political inequality reduces income inequality. This

result may related to the facts observed in transition countries that income inequality increased when

these countries shifted from an autocratic to a democratic political regime resulting at the same time

more social freedom. Another possible explanation is that a society with a high level of political and

social inequality might have the lower level of overall income and consequently less income disparity

alike a traditional economy where agricultural sector dominates.

The government spending to GDP ratio, age dependency, labour force participation rate for females

and primary and tertiary level education have detrimental impacts on income inequality.

The estimates presented here are very aggregate in nature. Further research could perhaps identify

with more clarity the exact channels through which political inequality impacts income inequality.

This result can further be examined as to which types of democracy have affected income inequality

as in the study by Rockey in 2007 which suggests that constitutional rules may have different impacts

on wage negotiations. Another possible extension is to use a horizontal measure of social inequality if

data allows and compare the effect with that of the vertical measure which is used in this study. The

World Values Surveys Database is a potential source for constructing the horizontal measure of social

inequality, but this could be a future extension of this study. The last, but not least, extension could be

to investigate the relationship between income, political and social inequality using other potential

measures of income polarization.

17

Tables

Table 1. Correlation matrices

a) Pair-wise correlation of main variables of interest

G

ini

SI (C

ivil

liber

ty)

PI

(Dem

ocra

cy)

SI (C

PI)

Ln(G

DP

per

capi

ta)

Gini 1.000

SI (Civil liberty) 0.471 1.000

PI (Democracy) 0.239 0.775 1.000

SI (CPI) -0.641 -0.652 -0.388 1.000

Ln(GDP per capita) -0.733 -0.688 -0.556 0.794 1.000

Source: Author’s estimation

b) Partial and semi-partial correlations of Gini coefficient

Partial Correlation Semi-partial Correlation Significance Value

SI (Civil liberty) 0.035 0.028 0.001

PI (Democracy) -0.264 -0.213 0.070

Ln(GDP per capita) -0.552 -0.515 0.305 Source: Author’s estimation

18

Table 2. Descriptive statistics of control variables

Variables Mean Std. Dev. Coef. of variation Min Max Obs.

Openness 76.15 43.43 0.57 9.18 308.93 955

Bank concentration ratio 61.88 20.85 0.34 14.44 100.00 665

Government consumption expenditure to GDP 16.80 5.05 0.30 2.97 34.14 915

Age dependency ratio 58.01 13.11 0.23 39.22 103.49 966

Labour force participation-female 49.51 8.47 0.17 31.3 86.9 690

Labour force participation-male 72.39 8.13 0.11 49.8 91.4 690

Primary school enrolment ratio 104.15 10.9 0.10 39.98 154.53 862

Secondary school enrolment ratio 85.29 26.18 0.31 3.08 162.35 812

Tertiary school enrolment ratio 35.12 20.66 0.59 0.26 95.21 784

Telephone lines (per 100 population) 25.93 19.15 0.74 0.13 72.17 921

Ethnic fractionalization 0.32 0.21 0.66 0.002 0.898 972

Source: World Development Indicators, April 2013 Penn World Table, version 7.1 Alesina et al. (2003) Note: The coefficient of variation is the ratio of the standard deviation to mean.

19

Table 3. Determinants of income inequality: Pooled-OLS estimation results Dependent variable: Gini coefficient+

1 2 3 4

GDP per capita 30.49*** (5.712)

4.297 (34.03)

12.58* (6.883)

21.54*** (5.991)

GDP per capita squared -1.987*** (0.308)

-0.896 (1.807)

-1.045*** (0.377)

-1.445*** (0.326)

SI (Civil liberty) 0.573** (0.256)

2.252** (1.019) 1.192***

(0.276)

SI (CPI) -0.090 (0.220)

PI (Democracy) -1.566*** (2.253)

-5.114*** (1.087)

-1.284*** (0.179)

1.014* (0.600)

SI×PI -0.330*** (0.070)

Government expenditure -0.415*** (0.072)

0.377 (0.301)

-0.408*** (0.083)

-0.366*** (0.071)

Openness -0.002 (0.006)

0.052** (0.026)

-0.019*** (0.007)

-0.013** (0.006)

Bank concentration -0.060*** (0.012)

-0.197*** (0.050)

-0.040*** (0.013)

-0.038*** (0.012)

Age dependency 0.193*** (0.038)

0.758*** (0.172)

0.153*** (0.043)

0.146*** (0.038)

Labour force parti-cipation rate-female

-0.199*** (0.038)

-0.010 (0.156)

-0.215*** (0.048)

-0.203*** (0.038)

Labour force participation rate-male

0.448*** (0.054)

0.900*** (0.228)

0.456*** (0.058)

0.481*** (0.053)

School enrolment primary 0.213*** (0.030)

0.303** (0.123)

0.221*** (0.033)

0.209*** (0.030)

School enrolment secondary

-0.016 (0.017)

-0.056 (0.082)

-0.037** (0.019)

-0.019 (0.017)

School enrolment tertiary 0.092*** (0.017)

0.185** (0.082)

0.069*** (0.019)

0.055*** (0.018)

Constant -117.2*** (27.64)

-74.75 (165.8)

-26.03 (31.97)

-87.56*** (28.14)

Adjusted R-squared 0.8157 0.5706 0.8483 0.8246 Observations 478 325 349 478 Countries 72 F-stat/ 163.36*** 34.12*** 150.73*** 161.16*** Year effects No No No Yes Note: +- Dependent variable is the Gini coefficient except column 2 where it is Top10/Bottom10 ratio. Standard errors are in parentheses. ***, **, * indicate significance levels of 1, 5, and 10 per cent, respectively.

20

Table 4. Determinants of income inequality: FE and GMM estimation results

Dependent variable: Gini coefficient+

FE System GMM Variables 5 7 8

Gini coefficient -1 0.026 (0.065)

GDP per capita 21.00* (11.06)

23.10** (9.186)

48.81** (22.72)

GDP per capita squared -1.221* (0.633)

-1.540*** (0.472)

-2.990** (1.303)

SI (Civil liberty) 0.616** (0.294)

1.961*** (0.303)

2.962** (1.077)

PI (Democracy) 1.866*** (0.677)

3.208*** (0.539)

6.248** (2.505)

SI×PI -0.248** (0.104)

-0.604*** (0.065)

-1.077*** (0.249)

Government expenditure -0.122 (0.084)

-0.421*** (0.071)

-0.620*** (0.173)

Openness 0.006 (0.012)

-0.005 (0.011)

-0.054 (0.048)

Bank concentration 0.021* (0.011)

-0.029** (0.011)

0.044 (0.039)

Age dependency -0.059 (0.068)

0.132** (0.067)

0.526* (0.335)

Labour force participation rate-female -0.155* (0.082)

-0.277*** (0.055)

-0.576*** (0.098)

Labour force participation rate-male 0.161 (0.121)

0.679*** (0.084)

0.734 (0.253)

School enrolment primary -0.005 (0.029)

0.214*** (0.057)

0.501*** (0.133)

School secondary 0.018 (0.016)

-0.033*** (0.012)

-0.011 (0.076)

School enrolment tertiary 0.039** (0.017)

0.140*** (0.017)

0.265*** (0.058)

Constant -61.92 (51.67)

-106.8*** (47.16)

-207.8* (108.0)

Adjusted R-squared 0.4312 R-squared (within) 0.1608 R-squared (between) 0.3903 Observations 478 468 377 Countries 61 61 52 F-stat/Wald 𝜒!+ 2.47*** 12133.8*** 50337.1*** Year effects Yes Yes Yes Hausman test 242.86*** Hansen J test p-value 0.618 0.353 AR(2) test p-value 0.273 0.605 Instruments 35 30 Note: +- Fixed-effects estimation result shows F-statistic while random-effects and GMM estimation results show Wald 𝜒! statistics.

***, **, * indicate significance levels of 1, 5, and 10 per cent, respectively.

21

Table 5. List of countries

Argentina Finland Norway Armenia France Pakistan Australia Germany Panama` Austria Greece Paraguay Bangladesh Guatemala Peru Belarus Honduras Philippines Belgium Hungary Poland Bolivia Ireland Portugal Botswana Israel Russian Federation Brazil Italy Senegal Bulgaria Jamaica Serbia Canada Korea, Republic of Slovak Republic Chile Kyrgyz Republic Slovenia China Latvia South Africa Colombia Lithuania Spain Costa Rica Luxembourg Sweden Croatia Macedonia, FYR Tanzania Cyprus Malaysia Thailand Czech Republic Mexico Ukraine Denmark Moldova United Kingdom Dominican Republic Netherlands United States Ecuador New Zealand Uruguay El Salvador Nicaragua Venezuela Estonia Nigeria Zambia

22

Table 6. Data sources and definition

Variable Description and source Income inequality

Gini coefficient Overall measure of inequality based on income distribution WIID, version 2.0c, 1970-2006

Social inequality Civil liberty Vertical measure of social inequality which indicates freedoms of expression,

assembly, association, education, and religion. Freedom House, Freedom in the World, 2011

Corruption Perception Index

Perceived level of public sector’s corruption and it is also vertical measure of social inequality Transparency International-Corruption Perception Index Reports, 2012

Political inequality Democracy The score captures the regime authority characteristics on the scale from -10 to +10,

lower value indicates less democracy. Polity IV Project, (Marshall et al., 2012)

Control variables GDP per capita GDP per capita is in 2005 constant PPP ($).

WDI 2013 School enrolment ratios, at primary, secondary, and tertiary levels

Gross enrolment ratios at primary, secondary and tertiary levels of education to the population of the age group that officially corresponds to the level of relevant education. WDI 2013

Labour force participation rate

Female and male LFPRs are used in the analysis, separately. WDI 2013

Government spending/GDP

Government consumption expenditures as a percentage of GDP. WDI 2013

Openness Ratio of the sum of total exports and imports to GDP Penn World Table 7.1 (Heston et al., 2012)

Bank concentration Degree of concentration in the banking industry, calculated as the fraction of assets held by the three largest commercial banks in each country. WDI 2013

Age dependency ratio Ratio of dependents – people younger than 15 or older than 64 – to the working age population – those ages between 15 and 64. WDI 2013

Ethnic fractionalization Average value of five different indices of ethnolinguistic fractionalization. Value ranges from 0 to 1 in which higher values indicate more ethnically fractionalized. Alesina et al. (2003, pp 159-160)

Telephone lines per 100 population

The indicator is derived by dividing the number of fixed telephone lines by the population. WDI 2013

Transition dummy Identifies transition countries: Armenia, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kyrgyzstan, Latvia, Lithuania, Macedonia, Russian Federation, Slovak Republic, Slovenia, Ukraine The World Bank classification

23

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