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Wealth inequality, democracy and economic freedom
Accepted Manuscript
Wealth Inequality, Democracy and Economic Freedom Md. Rabiul Islam PII: DOI: Reference:
S0147-5967(18)30002-7 10.1016/j.jce.2018.01.002 YJCEC 2614
To appear in:
Journal of Comparative Economics
Received date: Revised date: Accepted date:
14 May 2017 9 January 2018 12 January 2018
Please cite this article as: Md. Rabiul Islam , Wealth Inequality, Democracy and Economic Freedom, Journal of Comparative Economics (2018), doi: 10.1016/j.jce.2018.01.002
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Highlights
Wealth inequality significantly hampers economic freedom and this effect is
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reinforced at a lower level of democracy. Higher wealth inequality causes lower protection in property rights, less access to sound money, less freedom in international trade, and greater regulations.
Larger concentration of wealth among the rich leads to lower taxes and government welfare expenses.
Democratization helps countries to undertake favorable market reforms and administer
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welfare-centric redistributive policies.
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Wealth Inequality, Democracy and Economic Freedom Md. Rabiul Islam1*
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Monash University, Australia
Abstract
Using a novel panel data set from the Credit Suisse on the top wealth shares for 46 sample
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countries spanning 2000-2014, this paper empirically investigates to what extent wealth inequality influences economic freedom and whether this relationship is affected by the level of democracy. Economic freedom is measured by the Fraser Institute‟s economic freedom summary index as well as its five major sub-indices, such as government size, property rights,
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access to sound money, freedom to trade, and regulations. Wealth inequality is measured by
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the top wealth shares. Trade union density is used as an instrument for wealth inequality. Empirical results suggest that the rising wealth inequality significantly hampers overall
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economic freedom, property rights protection, freedom to trade, soundness of money and regulatory environment. Furthermore, this negative effect of wealth inequality is reinforced at
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a lower level of democracy. These findings are robust to alternative measures of wealth
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inequality, economic freedom, treatment for endogeneity, and model specification.
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Department of Economics, Monash Business School, Monash University, 900 Dandenong Road, Caulfield East, Victoria 3145, Australia. Tel: +61-03-95722448; Fax: +61-0399031128. E-mail: [email protected] *
Acknowledgements: Helpful comments and suggestions from participants at the seminars at Deakin University and RMIT University and particularly, two referees and the editor are gratefully acknowledged. The usual disclaimer applies.
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ACCEPTED MANUSCRIPT Keywords: wealth inequality, economic freedom, democracy, panel data JEL classifications: O15, O40, O50
1. Introduction The rising income and wealth inequality over the past few decades have been dividing many
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modern societies and posing a tangible threat to the growth and freedom of the global economy. Piketty (2014) warns against the danger of rising wealth inequality in the twentyfirst century and calls for a progressive annual tax on wealth. The gap between the rich and
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the poor keeps widening which not only hampers sustainable growth but also raises uncertainty in the free-market institutions that protect property rights and support voluntary exchange and freedom of choice across nations (OECD, 2015).2 In most OECD countries the richest 10 percent population earn about 9.5 times the income of the poorest 10 percent, the
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ratio was at 7:1 in the 1980s and since then it has been rising continuously (Cingano, 2014). However, wealth inequality is even more shocking than income inequality because wealth can
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generate its own income regarding rent, capital gain, interest, dividend, and can be passed on
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between generations and thereby most wealth accumulation leads to higher concentration of capital (Piketty, 2014). Davies et al. (2008) find that income inequality is on average half of
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the size of wealth inequality in both industrial and emerging economies. For example, most Gini coefficients for disposable income lie in the range of about 0.30-0.50, whereas Gini
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coefficients for wealth typically fall in the range of about 0.60-0.80. The top 10 percent of the adult population in the world owned 70.7 percent household wealth in the year 2000, and the corresponding global wealth Gini coefficient was 0.8 (Davies et al., 2011). This unbalanced distribution of wealth tilts institutions and erodes the social contract between citizens and the state. Therefore, these large and growing concentrations of wealth in the hands of fewer
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see Neves and Silva (2014) and Neves et al. (2016) for a comprehensive survey on inequality-growth nexus. 3
ACCEPTED MANUSCRIPT people present a real threat to inclusive political and economic system, especially private property and free markets that constitute economic freedom in the capitalist world (Oxfam, 2014). Democracy refers to a political regime where the population of a country chooses its government leaders to influence public policy without undue restrictions. The strength of the
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democratic institutions depends on the extent to which a country‟s civil society institutions permit these free choices (Rivera-Batiz and Rivera-Batiz, 2002). Democratization limits rentseeking by putting in place a system of checks and balances that penalize self-interested leaders, and thereby creating an atmosphere conducive to economic liberalization policies (De
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Haan and Sturm, 2003). By extending political power to the poorer segments of the society, democratization may increase the tendency for the pro-poor policy associated with redistribution, and thus reducing inequality (Acemoglu et al., 2015). Therefore, democracy
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may help countries to lessen the adverse impact of wealth inequality on economic freedom. The vast majority of empirical literature tends to focus on the effects of economic
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freedom on income inequality, while very little is known about the role of inequality in determining economic freedom. Given the fact that the wealth inequality is more extreme than
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the income inequality and prior studies exclusively focus on income inequality, this paper is
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one of the pioneer studies that fills the gap in the literature by investigating to what extent wealth inequality affects economic freedom and whether this relationship is influenced by the
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level of democracy across nations over time. Historically, countries with higher degrees of economic freedom have thrived both
economically and socially. Economic freedom is the fundamental right of every human being to control his or her own labor and property, hence governments in economically free societies allow labor, capital, and goods to move freely, and refrain from coercion of liberty beyond the extent necessary to protect and maintain freedom itself (Miller and Kim, 2017).
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ACCEPTED MANUSCRIPT Economic freedom is associated with market-oriented institutions and policies that protect property rights, provide equal opportunity, reduce transaction cost, encourage entrepreneurial activities, increase physical and human capital investment, improve technological development, and thereby lead to an efficient allocation of resources across nations (Gwartney et al., 1999, Gwartney and Lawson, 2004, Acemoglu et al., 2005, Hall and Lawson 2014, and
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Gwartney et al., 2016). Inequality may impair growth by reducing physical and human capital accumulation, property rights protection and increasing fertility, taxes, and socio-political instability (Perotti, 1996 and Easterly, 2007). A large body of literature suggests that economic freedom is conducive to growth which may be threatened by growing inequality.3
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Despite a voluminous literature, the connection between inequality and economic freedom remains inconclusive.4
There is a considerable literature on the effects of economic freedom on income
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inequality though the empirical evidence is mixed (Berggren, 1999, Scully, 2002, and Carter, 2007). However, there is a scarcity of evidence on inequality‟s effect on economic freedom
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and the handful of extant studies solely based on income inequality measures that also report mixed findings. For example, Apergis et al. (2014) find a bidirectional causality between
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income inequality and economic freedom and suggest that higher income inequality may
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cause the US states to implement redistributive policies causing economic freedom to decline, whereas Murphy (2015) obtains an overall negative impact of income inequality on economic
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freedom but mixed effects on the different areas of economic freedom across nations. The absence of reliable and comparable wealth distribution data across countries over time causes researchers to use income as a proxy for wealth (Aghion et al., 1999). Piketty and Zucman (2014) find that wealth-income ratios have doubled over the past 40 years. Several studies suggest that growing wealth inequality is primarily driven by rising wealth concentration at 3 4
see Doucouliagos and Ulubasoglu (2006) and De Haan et al. (2006) for economic freedom-growth review. see Bennett and Nikolaev (2017) for an excellent review on economic freedom-inequality nexus. 5
ACCEPTED MANUSCRIPT the top (Piketty, 2014 and Dabla-Norris et al., 2015). Therefore, this study has used top wealth shares data to examine the economic freedom effect of wealth inequality.5 This paper makes three contributions to the empirical literature on the inequalityeconomic freedom nexus. First, it investigates the effects of wealth inequality on economic freedom and its five major areas across 46 developed and developing sample countries over
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the period 2000 to 2014. To best of our knowledge, this is the first study that uses wealth distribution to examine the empirical connection between inequality and economic freedom. Second, it examines the interaction between wealth inequality and democracy to test the hypothesis that wealth inequality is potentially more harmful to economic freedom in less
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democratic economies than in countries with fully developed democratic institutions.
The third contribution is to dealing with possible endogeneity with the help of appropriate econometric strategies. The core models use trade union density as a time-varying
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suitable instrument for wealth inequality. Historically, unionization has been negatively associated with inequality (Jaumotte and Buitron, 2015 and Islam et al., 2017). Unionization
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is a measure of wage pushiness that increases labor's income share and, therefore, reduces the profit rate and inequality (Blanchard and Giavazzi, 2003). Lower union density may trigger
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higher wealth concentration by reducing wage growth, which makes it difficult for middle-
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and lower-income workers to set aside money for saving (Dabla-Norris et al., 2015). For robustness, this study also uses 5-10 years lags in wealth inequality, and applies the system
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GMM estimator that uses internal instruments for wealth inequality and other endogenous variables.
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There are three important reasons why wealth matters more than income. First, wealth provides a means of raising long-term consumption directly by dissaving or indirectly through continuous income stream from the return on invested assets. Second, most real property and financial assets can be readily bought and sold which allow wealth owners for their consumption smoothing during periods of uncertainty. Third, wealth can be used as collateral to obtain necessary fund from financial institutions and informal sectors and thereby providing a source of finance for household and entrepreneurial activities (Davies et al., 2008, Davies & Shorrocks, 2000). 6
ACCEPTED MANUSCRIPT The rest of the paper is organized as follows. Section 2 presents a comprehensive literature review on inequality and economic freedom. Section 3 discusses estimation method, identification strategy and data. Section 4 undertakes empirical estimates, and robustness tests are carried out in Section 5. The role of democracy on the economic freedom-wealth
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inequality nexus is presented in section 6. The last section concludes.
2. Literature review on inequality and economic freedom
Classical economists believe that a rise in inequality tends to increase investment because the
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rich save more, and higher savings translate into higher investment and thereby promotes long-run economic growth (Keynes, 1920 and Kaldor, 1957). Neoclassical growth models predict that the poor countries tend to grow faster than the rich nations because of diminishing returns to capital and thus historical inequality may vanish in the long run (Solow, 1956).
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Kuznets (1955) finds a non-linear relationship between income distribution and economic development, where inequality increases with development at the first stage and then
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decreases gradually in the later stage. However, modern growth theories criticize Classical
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and Neoclassical approaches and argue that inequality hampers economic growth by reducing investment in physical and human capital and increasing taxes, socio-political instability, and
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fertility (Alesina and Rodrik, 1994, Persson and Tabellini, 1994, Perotti, 1996, and Easterly, 2007). Almost all of the studies mentioned above use income distribution as a proxy for
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income as well as wealth inequality. Davies et al. (2011) argue that the global wealth-holding is more concentrated than that of income; hence wealth inequality is more pronounced than income inequality. Only a few studies find negative significant wealth inequality-growth nexus by considering either land holdings or billionaires‟ wealth as a proxy for wealth inequality (Deininger and Olinto, 2000 and Bagchi and Svejnar, 2015).
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ACCEPTED MANUSCRIPT Economic freedom constitutes the essence of the market economy that connects to the power of Smith‟s (1776) invisible hand, highlighting that under certain conditions, the presence and perfection of markets increase competition and efficiency, which in turn promotes economic growth and development across nations (Acemoglu et al., 2005 and Farhadi et al., 2015). A free economy allows individuals, goods and capital to move freely
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inside the domestic market and across the nations under the efficient regulations and welldefined law set by state which thereby increases productivity and encourages investment in both human and physical capital as well as providing greater opportunity for entrepreneurial activities (De Haan and Sturm, 2000, Justesen, 2008, Hall and Lawson, 2014, and Farhadi et
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al., 2015). Freer societies can capitalize the virtues of the free-market system by responding to the decisions and desires of individuals in constructive and efficient ways. They provide even more diverse opportunities for individuals and tend to create virtuous growth cycles
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characterized by efficient resource allocation, high value creation and technological innovation (Miller and Kim, 2016). Therefore, economic freedom is present when economic
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activity is coordinated by personal choice, voluntary exchange, open markets, and clearly defined and enforced property rights (Gwartney et al., 2016).
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Apart from growth, economic freedom is also related to inequality across nations. The
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effects of economic freedom on income inequality are extensively studied in the literature; however, the empirical evidence is at best mixed. In one hand, higher economic freedom may
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be associated with lower taxes and welfare expenditures which are expected to be detrimental to low-income earners; on the other hand, greater economic freedom may boost growth and remove legal barriers that protect politically favored groups and opens economic opportunities to less privileged and lower-income individuals (Perez-Moreno and AnguloGuerrero, 2016). De Soto (2000) argues that capital is the force that raises the productivity of labor and creates the wealth of nations in developed countries. However, poor people in
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ACCEPTED MANUSCRIPT developing countries have assets, but due to lack of well-defined property rights, those assets cannot be used to generate capital. Therefore, economic freedom may release economic opportunity to the poor and thereby reducing income inequality. Using Gini coefficients from the Deininger and Squire (1996) database, Berggren (1999) finds a positive relationship between changes in economic freedom and income equality, indicating that sustained and
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gradual increases in economic freedom lead to higher equality over time. After disaggregating aggregate economic freedom measure, Berggren (1999) observes that not all the components of economic freedom are related to equality; only trade liberalization and financial mobility are found to enhance equality. Later, Scully (2002) argues that economic freedom promotes
economic growth and income inequality.
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both growth and equity and that there is a positive but relatively small trade-off between
Using Fraser Institute‟s revised dataset of Economic Freedom of the World and new
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and expanded income inequality (Gini coefficients) dataset of the UNU/WIDER, Carter (2007) observes contradictory evidence that economic freedom increases income inequality.
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More precisely, higher economic freedom may raise income equality by widening incomeearning opportunities, and it can lower equality by reducing income redistribution toward
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poor; however empirical evidence suggests that the latter effect is dominant except at
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comparatively low levels of economic freedom. Using cross-sectional data across US states, Ashby and Sobel (2008) find that changes in economic freedom are associated with higher
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economic growth and reductions in relative income inequality; however, the level of economic freedom appears to be insignificant. Using the recent SWIID income inequality database, Bergh and Nilsson (2010) obtain robust significant positive effects of trade liberalization on income inequality across 80 countries over the period 1970-2005. Deregulation and social globalization also raise inequality. Reforms towards economic freedom seem to increase inequality mainly in rich countries, and social globalization is more
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ACCEPTED MANUSCRIPT important in less developed countries. Perez-Moreno and Angulo-Guerrero (2016) find that higher economic freedom (overall and Government size) is robustly related to greater income inequality in the EU for the 2000s. In contrast to Carter (2007), Bennett and Vedder (2013) obtain an inverted U-shaped relationship between economic freedom and income inequality for 50 US States from 1979-
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2004. It implies that economic freedom initially generates more income inequality as the upper part of the income distribution benefits; however, as economic freedom continues to improve, the lower part of the income distribution experiences larger relative income gains. However, Sturm and De Haan (2015) do not find any robust relationship between economic
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freedom and income inequality across a panel of 108 countries over the period 1971-2010. Apergis and Cooray (2017) use linear and non-linear cointegration techniques to identify a long-run equilibrium relationship between economic freedom and income inequality for both
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the aggregate and major areas of the economic freedom composite index for 138 countries. The linear long-run parameter estimates document that the association turns out to be
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negative, while the non-linear long-run parameter estimates illustrate that above a threshold point the association between economic freedom and income inequality is negative, while
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below this threshold point, the association turns out to be positive.
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Very few empirical studies investigate the effects of economic freedom and its key areas on income inequality. Apergis et al. (2014) observe that income inequality has a
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significant negative impact on economic freedom in the US States. Murphy (2015) finds that greater income inequality leads to lower economic freedom across nations. However, not all areas of economic freedom affect income distribution similarly. Income inequality appears to increase the size of government and to have an adverse effect on the rule of law, little effect on the soundness of money and international trade, and ambiguous effects on business regulation. Finally, Krieger and Meierrieks (2016) argue that higher income inequality
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ACCEPTED MANUSCRIPT strongly and significantly hampers cross-country economic freedom, property rights and business regulations. However, the size of government and the soundness of money are not significantly affected by wealth inequality. The above-mentioned empirical studies have consistently considered income distribution as a proxy for inequality and almost all of them have investigated the effects of
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economic freedom and its major areas on income inequality. It is now well established that economic freedom is conducive to economic growth (Gwartney et al., 1999, De Haan and Sturm, 2000, Carlsson and Lundstrom, 2002, and Williamson and Mathers, 2011). Again, a large body of empirical literature suggests that inequality adversely affects economic growth
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(Alesina and Rodrik, 1994, Marrero & Rodríguez, 2013, and Madsen et al., 2018). Aghion et al. (1999) argue that the underlying theory on inequality-growth nexus is that the wealth inequality determines the degree of investment in physical or human capital, which in turn
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affects long-run growth; however, the absence of reliable and comparable wealth distribution data across large number of countries force empirical researchers to use income as a proxy for
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wealth over time. The recent availability of wealth inequality data by the Credit Suisse (2014) has greatly contributed toward filling these information gap.
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To best of our knowledge, this study for the first-time attempts to empirically examine
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the impact of wealth inequality on economic freedom and its five major areas using a unique dataset of wealth distribution (top 1 and top 10 percent wealth shares) from Credit Suisse
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(2014) for 46 advanced and developing countries over the period 2000-2014. Most of the studies on inequality-economic freedom nexus does not address endogeneity and a very few of them uses generalized method of moments (GMM) dynamic panel estimators to deal with endogeneity by using internal instruments; which are subject to small sample bias, suffer from severe weak instrument problem, and fail to control for country heterogeneity. Using external instrument is the best option to deal with the possible endogeneity and heterogeneity in the
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ACCEPTED MANUSCRIPT estimations; hence this study uses trade union density as an external instrument to wealth inequality. It also considers 5-year and 10-year lagged wealth inequality as well as system GMM estimator to address endogeneity in the robustness section.
3. Empirical framework, estimation methodology, identification strategy
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and data 3.1. Empirical framework
To investigate the effects of wealth inequality on economic freedom we estimate the
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following panel data regression model:
(1)
where, EFR indicates economic freedom which is measured by the Fraser Institute‟s economic freedom of the world summary index (EF) and its five major sub-indices, for
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example, government size index (GV), legal structure and security of property rights index
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(PR), access to sound money index (SM), freedom to trade internationally index (FT), and regulation of credit, labour and business index (RG). WINQ specifies wealth inequality which
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is measured by the Credit Suisse‟s top 1% and top 10% wealth shares; X stands for the vector of control variables including, economic growth, school attainment, inflation rate, population
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size, and natural resource rents; CD is country dummy, TD is time dummy, and ε is the
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random error term. This study first estimates its baseline models for 46 countries from 20002014 and then includes control variables in the robustness section. The sample is dictated by data availability on wealth inequality; the complete list of the sample countries and the data sources are provided in the Data Appendix. Economic growth is included in Eq. (1) as a control variable because it does matter for economic freedom; higher growth may generate resources necessary for policy-makers to establish, reform, or improve existing institutions and policies (La Porta et al., 1999). 12
ACCEPTED MANUSCRIPT Education has positive effects on economic freedom because educated people are more likely to resolve their differences through discussion and negotiation rather than violent conflicts and thereby the greater the human and social capital of a community, the more attractive its institutional opportunities (Djankov et al., 2003 and Glaeser et al., 2004). Poor macroeconomic management adversely affects economic freedom because high and volatile
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rates of inflation distort relative prices, alter the fundamental terms of long-term contracts, and make it virtually impossible for individuals and businesses to plan sensibly for the future (Gwartney et al., 2016). Population size may have mixed effects on economic freedom because, at the one hand, larger countries may provide better economic institutions due to
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agglomeration effects, market size and lower per capita costs for public good provision; on the other hand, highly populated countries may experience more diversity which may produce extra cost and reduce the quality of economic institutions (Rose, 2006). Natural resource rents
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deteriorate economic freedom because rent windfalls may encourage grabber friendly institutions to involve more in rent-seeking and unproductive activities (Mehlum et al., 2006
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and Papyrakis, 2017). Finally, an abundance of natural resources hinders economic freedom and investment, which may lead to an observed resource curse (Campbell and Snyder, 2012).
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3.2. Estimation methodology
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The regression model in Eq. (1) is estimated for a yearly panel of 46 sample countries for which wealth inequality data are available over the period 2000-2014. The data are pooled
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across countries to gain efficiency and to allow for fixed effects. Fixed country effect dummies are included in the regressions to cater for time-invariant unobserved heterogeneity, implying that the parameter estimates are driven entirely by the within-country variation of the data. Time dummies are included to capture most common macroeconomic shocks that might have significant impacts on economic freedom.
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ACCEPTED MANUSCRIPT This paper estimates baseline regression models first and then incorporates control variables in the robustness tests. The estimation starts with the ordinary least squares (OLS) estimates, which has three inherent problems. First, wealth inequality may be endogenous, and hence there is more likely to have a feedback effect from economic freedom to wealth inequality that might capture reverse causality. Second, wealth inequality measures are more
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likely to have measurement errors, particularly for the developing countries. Third, there are critical explanatory variables (e.g., culture, geography, etc.) that may be omitted from the regression models but affected economic freedom and are correlated with one or more explanatory variables.
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To deal with possible endogeneity, this study applies three alternative econometric strategies. First, it estimates regression models using a two-stage least squares (2SLS) instrumental variable (IV) approach (IV-2SLS) where wealth inequality is instrumented with
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trade union density, and it is our core method. Second, it considers 5-year and 10-year lagged value of the wealth inequality series in the OLS estimates to mitigate endogeneity bias. Third,
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it also uses Arellano & Bover (1995) and Blundell & Bond‟s (1998) system GMM estimator that uses difference and level equations simultaneously and controls for the potential
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endogeneity of all explanatory variables by using internal instruments. However, the system
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GMM estimator may not entirely solve the endogeneity issue due to weak instrument problem, but it is likely to give more unbiased estimates than OLS regressions. The last two
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econometric techniques are discussed in the robustness section.
3.3. Identification strategy If wealth inequality follows from economic freedom, OLS regressions will not reveal a genuine causal relationship due to the omitted variables bias and feedback effects from economic freedom to the distribution of wealth. To deal with possible endogeneity, we need to use a plausible instrument for wealth inequality which is not easy to find. To satisfy
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ACCEPTED MANUSCRIPT exclusion restrictions, a valid instrument should adequately explain cross-country wealth inequality and should not explain economic freedom other than through the wealth distribution. Based on the hypothesis of Engerman and Sokoloff (1997) and Sokoloff and Engerman (2000) which suggests that factor endowment is the central determinant of
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inequality, Easterly (2007) instrumented inequality by the abundance of land suitable for growing wheat relative to that suitable for growing sugarcane which is time-invariant and may simply proxy for whether the country is in tropics. However, time-invariant factor endowment is not likely to be a reasonable instrument for wealth inequality in this panel
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study. We address endogeneity and unobserved heterogeneity in the estimates by using trade union density as an external instrument for wealth inequality. The identification strategy is that organized trade union membership has been associated with higher bargaining power and
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increasing labor share which in turn lead to reduce wealth inequality over time. The basic idea behind this instrument is that unionization is a measure of wage
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pushiness that increases labor's income share and, therefore, reduces the profit rate and inequality. Although most empirical estimates find that unionization increases labor‟s share
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there are circumstances under which they do not. As shown by Young & Zuleta (2013) the
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income share effects of unionization depend on the type of union bargaining model and the elasticity of substitution between capital and labor. If the elasticity of substitution is low, it is
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easy to substitute to capital when labor costs increase and replace labor and, thus, labor‟s share. Again, Blanchard & Giavazzi (2003) argue that factor shares depend on both the degree of labor market regulation, which is captured by workers bargaining power and product market regulation, which is captured by the markups of imperfectly competitive firms. When more firms enter, there is greater competition in the product market and markups fall, increasing labors share. Similarly, if workers‟ bargaining power increases due to an increase
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ACCEPTED MANUSCRIPT in unionization, for example, labor‟s share increases (Blanchard and Giavazzi, 2003). A decline in trade union membership rate could reduce the relative bargaining power of labor, thereby aggravate wage inequality; hence unionization, on average, tend to improve income distribution (OECD 2011, Dabla-Norris et al., 2015, and Jaumotte and Buitron, 2015). Lags can address potential endogeneity biases resulting from simultaneity and reverse
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causation, as lagged wealth inequality is likely to be exogenous to the current economic freedom. Hence, this study re-estimates Eq. (1) using 5-year and 10-year lagged wealth inequality. Clemens et al. (2012) avoid poor-quality instrumental variables and instead address potential biases from reverse and simultaneous causation by lagging and differencing.
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Replacement of a suspected simultaneously determined explanatory variable with its lagged value has become one of the standard practices in applied economics research (Reed, 2015). Finally, this study uses the two-step dynamic generalized method of moments (GMM)
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estimator to generate internal instruments for wealth inequality and other endogenous explanatory variables. Specifically, it uses Arellano and Bover (1995) and Blundell and
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Bond‟s (1998) system GMM estimator that combines the regressions in differences and levels in a system of equations using the lagged differences instruments for the level series, and the
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lagged levels of instruments for the differenced series. The two-step system GMM provides
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more efficient estimators over the one-step system GMM; however, the associated asymptotic standard errors may be biased downwards in the finite sample (Bond et al., 2001). It,
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therefore, applies Windmeijer (2005) finite-sample correction for the variance of the linear efficient two-step system GMM estimator.
3.4. Data
The wealth inequality data used in this paper are the top 1 percent and top 10 percent wealth shares for 46 developed and developing countries over the period 2000-2014. For the first time, top wealth shares data for 46 countries since 2000 are published in the Credit Suisse
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ACCEPTED MANUSCRIPT (2014) report. This study considers top wealth shares as the preferred measure of wealth inequality for three important reasons. First, it is simple to understand and not over-sensitive to wealth changes at the bottom of the wealth distribution; second, the top wealth holders may experience faster growth of their wealth, whereas the bottom wealth holders may experience slower or even decrease in their wealth growth; third, it correlates well with Gini coefficient
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and people use to talk about top decile or top percentile wealth growth while discussing growing wealth inequality (Credit Suisse, 2014). In almost all nations, the mean wealth of the top 10 percent is about ten times more than the median wealth, whereas, for top 1 percent, the mean wealth exceeds 100 times or even 1000 times the median wealth in the most unequal
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nations (Credit Suisse, 2014). Wealth Gini data are only reported since 2010; hence they are not suitable for our panel study.
The economic freedom of the world summary index (EF) from the Fraser Institute is
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used to measure cross-country economic freedom. This is an 11-point ordinal scale, ranging from 0 to 10, where a higher value indicates greater economic freedom. It relies mainly on
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quantitative measures and is often used as a measure of market-based institutions (Gwartney et al., 2016). The EF is constructed from the combination of five different categories or sub-
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indices, namely government size index (GV), legal structure and security of property rights
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index (PR), access to sound money index (SM), freedom to trade internationally index (FT), and regulation of credit, labour and business index (RG). There are two reasons why the EF is
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preferable to other indices of economic freedom for this study. First, EF is a better measure to extensively capture the reliance of a country on free markets to allocate resources. Second, the subcomponents of EF measure the degree of economic freedom in five broad areas, so it allows us to investigate to what extent wealth inequality affects economic freedom in all the major aspects of the overall economic freedom.
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ACCEPTED MANUSCRIPT Trade union density corresponds to the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earners. Among the control variables, economic growth is measured as the growth rate of the real GDP per capita; school attainment is measured as the average years of schooling of the population aged 15 years and above; inflation rate is measured as the growth rate of the consumer price index (CPI);
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population size is measured as the number of population per square kilometers of land area; and natural resources rents are measured as the sum of oil, gas and mineral rents to GDP ratio. [Table 1 here]
The descriptive statistics of the key variables used in the regression analysis of this
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study are reported in Table 1. The average shares of total wealth owned by the top 1 percent and top 10 percent population are 32.18% and 62.92%, respectively. The mean score of the economic freedom summary index and its sub-indices ranges from 6.11 to 8.81. The average
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value of trade union density rate and democracy score are 24.78 percent and 7.36, respectively. Most of the variables demonstrate a sufficient degree of identifying variations to
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yield efficient parameter estimates in this study.
4. Empirical results
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This section presents the baseline regression results of the effects of wealth inequality on economic freedom summary index, and it‟s five sub-indices as discussed in section 3. The econometric models are estimated using ordinary least squares (OLS) and instrumental variables estimators (IV-2SLS).
4.1. Pooled OLS & fixed effects estimates [Table 2 here] 18
ACCEPTED MANUSCRIPT Table 2 reports pooled OLS results in panel A, and fixed effects results in panel B for the baseline regressions using both the top 1 percent (Top1) and top 10 percent (Top10) wealth shares. The OLS estimates start without any fixed effects in panel A, and then incorporate country and year dummies to capture country and time specific fixed effects in the regression models in panel B.
The results are qualitatively similar in both panels, however, the
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coefficients of wealth inequality in fixed effects estimates are, on average, lower than that of the pooled OLS which may reflect the upward bias in the estimates of simple pooled regressions. The results indicate that wealth inequality has a significant adverse impact on overall economic freedom (EF) and its four key areas, such as property rights (PR), sound
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money (SM), freedom to trade (FT), and regulation of business (REG). The significant positive impact of wealth inequality on government size (GV) indicates that wealth inequality may reduce government tax and welfare activities. All these coefficients are found to be
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statistically significant at the 1 percent level. However, the coefficients of wealth inequality may be biased and inconsistent in these OLS estimates due to the presence of endogeneity:
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reverse causality, omitted variables and measurement errors in the wealth distribution. To deal with this endogeneity problems, this study now applies instrumental variables two-stage least
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squares (IV-2SLS) estimator, where wealth inequality is instrumented by the trade union
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density.
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4.2. IV-2SLS estimates [Table 3 here]
Table 3 reports IV-2SLS regression results of wealth inequality and economic freedom nexus. Panel A and B consider Top1 and Top10 percent wealth shares as alternative wealth inequality measures. The upper part of each panel presents the second-stage estimates whereas the lower part reports the associated first-stage regressions results. Consider the first
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ACCEPTED MANUSCRIPT stage regressions in both panels where organized trade union density (Union) is used as an instrument for wealth inequality. As noted above, country and time fixed effects dummies are included in all regressions including the first stage regressions. The strong negative significance of the coefficient of Union and the higher values of the first-stage F-tests and the corresponding p-values on the excluded instrument indicate that Union is sufficiently
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correlated with wealth inequality to serve as a potentially good instrument. Again, the endogeneity test results reject the null hypothesis of the exogeneity of the endogenous regressor and thereby implying that wealth inequality should be treated as endogenous which gives support for using an instrumental variable approach in this study.
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Estimated results from the second-stage regressions in panel A and B reinforce the OLS estimates reported in Table 2. The coefficients of the overall economic freedom (EF) and its four major areas, such as property rights (PR), sound money (SM), freedom to trade (FT),
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and regulation of business (REG) are found to be highly negatively significant at the 1% level, whereas, the coefficients of government size (GV) appear to be positive and strongly
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significant at the 1% level. The estimated coefficients of Top1 and Top10 percent wealth shares are noticeably greater (in absolute terms) than their OLS counterparts, providing some
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evidence of possible measurement errors. Again, economic freedom effects of wealth
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inequality are more pronounced for Top10 than they are for Top1 percent wealth shares. The elasticity between EF and Top10 is -0.38, indicating that a 10 percent increase in
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the level of 10 percent wealth shares reduces overall economic freedom by 3.8 percent. In component analysis, wealth inequality appears to reduce the size of government, which is consistent with observation of Benabou (2002) and Acemoglu and Robinson (2008) who argue that the higher inequality may call for progressive taxation and fiscal redistribution which may create disincentive to work and invest; hence rich elites lobby against the tax hike and implementation of redistributive policies. Islam et al. (2017) also find that the rising
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ACCEPTED MANUSCRIPT income inequality is associated with a declining income tax ratio in the OECD over the last 150 years. Finally, wealth inequality appears to have significant negative effects on property rights protection, soundness of money, freedom to international trade and business regulations. The largest negative effects of wealth inequality arise from property rights and legal system, which are in the line with Glaeser et al. (2003) and Sonin (2003) who argue that
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the rising inequality is detrimental to the security of property rights, and therefore to growth, because it enables the rich agents to use their wealth and accumulated political power to subvert the political, regulatory, and legal institutions of society for their own benefit.
Although the empirical results of this study are similar, to some extent, to a few
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income inequalities based studies, they are different in several aspects. First, the significant negative impact of wealth inequality on aggregate economic freedom may be consistent with the findings of the two main cross-country studies (Murphy, 2015 and Krieger and
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Meierrieks, 2016) that focus solely on income inequality; however, the size of the effect of the wealth inequality is much higher than that of the income inequality. Second, this study has
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obtained significant effects of wealth inequality on all five major areas of economic freedom, whereas the income inequality based studies find different inequality effects across sub-
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components of overall economic freedom. For example, Contrary to Murphy (2015), this
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study finds that the wealth inequality significantly reduces the size of the government which may lead to fewer interventionists policies such as lower tax and social spending in response
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to greater wealth concentration. Again, Krieger and Meierrieks (2016) obtains strong negative significant effects of income inequality on property rights and business regulations; whereas this study demonstrates that the higher wealth inequality has strong and significant negative impacts on the remaining four areas of the aggregate economic freedom, e.g., property rights protection, soundness of money, freedom to international trade, and business regulations. In
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ACCEPTED MANUSCRIPT sum, compared to other income inequality based studies, the wealth inequality shows much stronger association with the economic freedom than that of the income inequality.
4.3. Potential violation of exclusion restriction in IV-2SLS estimates To examine the sensitivity of the IV-2SLS estimates to potential violations from the exclusion
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restriction, this study employs Conley et al.‟s (2012) union of confidence intervals (UCI) approach of Plausible Exogeneity test to identify upper and lower bounds of the effect of wealth inequality on economic freedom when the instrument deviates from perfect exogeneity. The exclusion restriction is satisfied when the parameter
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following structural Eq. (2):
is equal to zero in the
(2)
where, Y is the economic freedom measure, X is the endogenous variable (wealth inequality),
a precise interval,
is assumed to have
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Z is the instrument (trade union density). Similar to Conley et al. (2012),
[ -δ; +δ]. As true value is unknown, the union of intervals for β is in that interval. δ is estimated by including the direct effect of
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estimated with respect to any
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the union density rate on economic freedom in the structural Eq. (2) and observing the effect size ̂. By changing the value of δ, we can identify the threshold level at which the second
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stage coefficient on the wealth inequality becomes insignificant. Therefore, the UCI approach gives a clear indication on the extent of the exclusion restriction violation that is required to
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invalidate the results from reduced form estimates (Madsen et al., 2018). [Table 4 here]
Table 4 presents estimated results of the Conley et al.‟s (2012) UCI approach to examine the sensitivity of the IV-2SLS results in Table 3 to the presence of a direct relationship between the IV (i.e. trade union density) and the economic freedom and its major components, regardless of the mechanisms through which this association arises. The
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ACCEPTED MANUSCRIPT estimated bounds are reported for the 95% confidence intervals. The marginal effects are statistically significant when the upper and lower bounds of confidence interval are both either above or below zero. The empirical results show that none of the confidence intervals of the overall economic freedom and related key areas contain zero and thus providing substantial evidence in favor of statistically significant robust impact of wealth inequality on
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economic freedom.
5. Robustness Tests
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This section investigates the robustness of the empirical results reported in the previous section to the use of 5-year and 10-year lagged wealth inequality in OLS estimates, system GMM regressions, alternative economic freedom and wealth inequality measures, estimates in longer time span, and the inclusion of control variables in the baseline IV-2SLS estimates.
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5.1. OLS estimates using 5-year and 10-year lagged wealth inequality
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[Table 5 here]
Using lagged inequality may bypass the need for finding a suitable instrument that can
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address potential endogeneity biases resulting from simultaneous and reverse causation. It is more likely that the lagged wealth inequality is exogenous to economic freedom. Therefore,
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this study re-estimates the effects of wealth inequality on economic freedom using 5-year
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(panel A) and 10-year (panel B) lagged wealth inequality (Top1 and Top10) and the results are reported in Table 5, which are qualitatively similar to the baseline IV-2SLS regression results presented in Table 3. Therefore, wealth inequality remains a robust and significant determinant of the economic freedom when 5-10-year lagged wealth inequality is used in the regression estimates.
5.2. System GMM estimates
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ACCEPTED MANUSCRIPT Using external instruments is the best option to get rid of the possible problems of endogeneity and reverse causation, but pure exogenous instruments that vary across countries and over time are rarely found. Again, the use of a weak instrument that explains little variation in the endogenous explanatory variable can lead to large inconsistencies in the IV2SLS estimates which are biased in the same direction as OLS estimates in the finite sample
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(Bound et al., 1995). Therefore, this study uses system GMM dynamic panel estimator to instrument for current wealth inequality with lagged differences in wealth inequality and other regressors, and to instrument for current differences in wealth inequality with lagged levels of wealth inequality and additional regressors.
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[Table 6 here]
Table 6 reports regression results using the two-step system GMM estimator that satisfies a battery of diagnostic checks, including Hansen‟s overidentifying restrictions tests
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for instrument validity, and AR(1) and AR(2) tests for the first order and second order serial correlations, respectively. Too many instruments can severely weaken and bias the Hansen
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overidentifying restriction tests and therefore, the number of instruments should stay below the number of countries (Roodman, 2009). The system GMM estimates generate around 20
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instruments which are lower than the number of cross-sections (i.e. 46 sample countries),
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hence regression results are free from instruments proliferation. Estimated results in Table 6 remain very similar to that of the baseline IV-2SLS results reported in Table 3. The
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coefficients of wealth inequality are statistically significant; thus, further underscoring that the results are very robust to estimation technique and, therefore, that there is likely to be a strong causal relationship from wealth inequality to economic freedom.
5.3. IV-2SLS estimates using alternative measure of economic freedom [Table 7 here]
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ACCEPTED MANUSCRIPT This study has so far considered Fraser Institute‟s economic freedom summary index and its corresponding five sub-indices to examine the effects of wealth inequality on economic freedom. For robustness, it has re-estimated its regression models in Eq. (1) using corresponding economic freedom measures from the Heritage Foundation, such as overall economic freedom, government spending, property rights, monetary freedom, trade freedom
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and business freedom. Table 7 reports IV-2SLS estimates of the regression models using these alternative economic freedom measures. The regression results are largely in line with those obtained in the baseline IV-2SLS regressions in Table 3, implying that wealth inequality depresses economic freedom across sample countries. The size of the coefficients of wealth
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inequality appears to be larger than that of Table 3, which may be due to measurement issues of the Heritage Foundation data, which not only attempts to measure macroeconomic outcome variables but also qualitatively analyzes the ability of the institutions to foster Therefore, the empirical findings in this study are not significantly
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economic freedom.
affected by alternative measures of economic freedom.
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5.4. IV-2SLS estimates using Gini coefficient of income distributions
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[Table 8 here]
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Leigh (2007) finds a strong and robust relationship between top income shares and Gini coefficient and suggests that panel data on top income shares may be a useful substitute for
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other measures of inequality over periods when alternative income distribution measures are of low quality, or unavailable. Due to unavailability of wealth Gini data over the sample period, this study has used top wealth shares data (1% and 10%) as the measure of wealth inequality. However, most of the empirical studies on inequality-growth-freedom consider income inequality measure such as, Gini coefficient of income distributions as a proxy for wealth inequality due to lack of data on wealth inequality across countries over time. The correlation coefficient between top 1% wealth shares and income Gini coefficient appears to 25
ACCEPTED MANUSCRIPT be 0.65 in this study; therefore, it will be interesting to see whether the key results of this paper hold when wealth inequality is measured by income Gini coefficient. Estimated results reported in Table 8 are qualitatively similar with those obtained in the baseline IV-2SLS regressions in Table 3, suggesting that higher wealth inequality significantly deters economic freedom across sample countries. Therefore, our empirical results are not sensitive to the use
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of Gini coefficient of income distributions.
5.5. System GMM estimates using long-term Gini coefficient of income distributions [Table 9 here]
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Thus far the regressions have been carried out over the period 2000-2014 due to the availability of wealth inequality data from the Credit Suisse database since the year 2000. As the estimation period is short, there is a possibility that the estimated effects of wealth
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inequality on economic freedom may be a short-run phenomenon which may turn to be different in the long run. Since the data on Gini coefficient of income distributions are
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available for a longer time span, it would be useful to examine the long run impact of inequality on economic freedom. The earlier data on economic freedom are available in 5-
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year intervals; hence the regression models are re-estimated in 5-year intervals over the period
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1970-2014. As trade union density data are not available for earlier periods, we have focused on system GMM estimates where inequality is instrumented by internal instruments.
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Estimated results are reported in Table 9 where the coefficients of inequality remain significant with correct signs across overall as well as sub-indices of economic freedom, suggesting that the baseline IV-2SLS results for 2000-2014 in Table 3 remain robust while considering long time series data (1970-2014) on income Gini coefficient.
5.6. IV-2SLS estimates with control variables [Table 10 here] 26
ACCEPTED MANUSCRIPT The IV-2SLS regressions in Table 3 are re-estimated using control variables in Eq. (1). The economic rationale behind these controls is discussed in Empirical Framework in section 3.1. The coefficients of wealth inequality (Top1) are strongly significant at 1%-5% levels, regardless of whether control variables are included in Table 10. The estimated results remain very similar to that of the baseline IV-2SLS estimates in Table 3. Except for government
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expenditure, the coefficients of wealth inequality remain significantly negative, suggesting that the equitable distribution of wealth among the population is associated with higher level of economic freedom across nations.
Of the control variables, the growth rate of real GDP per capita and school attainment
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show significant positive impacts on economic freedom; whereas higher inflation exhibits a negative effect on economic freedom as expected. Population size adversely affects trade freedom and business regulations; whereas resource rents exert a significant negative impact
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on the soundness of money. Overall, the baseline IV-2SLS regression results in Table 3 are
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not significantly influenced by conditioning variables.
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6. Wealth inequality, democracy and economic freedom Friedman (2002) argues that the role that government should play in a society dedicated to
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freedom and rely primarily on the market to organize economic activity; hence democracy is
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strongly associated with economic freedom. Democracy may not be limited only to free and fair elections, rather it extends to whether a country has checks and balances on executive powers, constitutional processes and guarantees, freedom of the press and the absence of censorship, clear and effective judicial and legal structures, incumbent term limits, and transparency, openness and citizen input in policymaking (Rivera-Batiz and Rivera-Batiz, 2002). If political institutions place all political power in the hands of a single individual or a small group, economic institutions that provide protection of property rights and equal 27
ACCEPTED MANUSCRIPT opportunity for the rest of the population are difficult to sustain (Acemoglu et al., 2005). Democratic regimes are more likely to exhibit the legitimacy necessary to carry out liberal economic reforms (De Haan and Sturm, 2003 and Rode and Gwartney, 2012). Nondemocracies tend to be dominated by the rich either because the rich wield sufficient power to create such a regime or because those who can wield power for other reasons subsequently
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use this power to become rich (Acemoglu et al., 2015). In this section, we have examined whether the relationship between wealth inequality and economic freedom depends on the level of democracy. The underlying hypothesis is that countries with higher level of wealth inequality may experience a significant reduction in
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economic freedom and thus strengthening democracy may improve economic freedom by limiting unequal distribution of wealth across those economies. Democracy is measured by the revised combined Polity IV score (Polity2) which is computed by subtracting the
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„Institutionalized Autocracy‟ score from the „Institutionalized Democracy‟ score; the resulting unified polity scale ranges from +10 (strongly democratic) to -10 (strongly autocratic).
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[Table 11 here]
Democracy and its interaction with wealth inequality are included in the regressions in
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Table 11. The coefficients of wealth inequality are statistically significant and negative at the
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1% level of significance in all cases except the government size, reinforcing the direct negative impact of wealth inequality on economic freedom. Furthermore, the coefficients of
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the interaction term are all positive and statistically significant at least at the 5 percent level. The positive interaction effect suggests that the economic freedom and its major five areas such as property rights, government size, soundness of money, freedom to trade and business regulations are negatively affected by wealth inequality at low levels of democracy and positively affected by wealth inequality at high levels of democracy. In other words, the influence of wealth inequality on economic freedom depends on the degree of development of
28
ACCEPTED MANUSCRIPT the democratic institutions. Finally, the coefficients of the democracy are also significantly negative, which in conjunction with the positive coefficient of the interaction term suggest that the negative effect of wealth inequality on economic freedom is greater at low levels of democracy. On average, democratic development may reduce the adverse effects of wealth
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inequality on economic freedom across sample countries.
7. Conclusion and extension
Inequality has been a growing concern for free-market institutions and policies that protect
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individuals and their properties and allow freedom of choice. However, the consequences of inequality on economic freedom remain unclear, and the empirical evidence based on income inequality remains inconclusive. Provided that the wealth inequality is more devastating than the income inequality, this paper empirically investigates the impact of wealth inequality on
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economic freedom and to what extent this relationship is affected by democratization. Wealth inequality is measured by the Credit Suisse‟s top wealth shares (1% and 10%). Economic
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freedom is measured by the Fraser Institute‟s economic freedom summary index and its five
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major sub-indices, such as government size, property rights, sound money, freedom to trade and regulation. Trade union density is used as a potential instrument for wealth inequality.
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Empirical findings across 46 sample countries over the period 2000-2014 suggest that the rising wealth inequality significantly obstructs economic freedom in all the key areas
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except government size. The magnitude of the effect is economically significant. On average, a ten percent increase in the wealth inequality measure would cause a 2.7 percent decline in the economic freedom summary measure. More specifically, higher wealth inequality causes lower protection in property rights, less access to sound money, less freedom in international trade, and greater regulation of credit, labor, and business. On the contrary, larger wealth inequality reduces government size which may indicate that higher concentration of wealth
29
ACCEPTED MANUSCRIPT may lead to lower taxes and government social welfare expenses. The largest adverse effect of wealth inequality on economic freedom arises from the uncertainty in the protection of property rights. The interaction effect between wealth inequality and democracy is positive and significant, implying that the negative effects of wealth inequality on economic freedom may be greater at a lower level of democracy.
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Some degree of inequality may be tolerable to drive growth and development by rewarding people with talent, skills, and the ambition to innovate and take entrepreneurial risks; however, too much wealth concentrations may threaten to exclude hundreds of millions of people from realizing the benefits of their talents and hard work (Oxfam, 2014). This
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extreme wealth inequality may create strong incentives for individuals to pursue their interests outside the normal market activities and thus individuals in more unequal societies are likely to engage in rent-seeking activities as well as other socio-political instability manifestations,
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for example, violent protests and coups ( Perotti, 1996). It may also cause a deterioration in the security of property rights and contractual rights which may slow down economic
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freedom. The consequences even embrace the destruction of democratic governance, the pulling apart of social cohesion, and the vanishing of equal opportunities for all (Oxfam,
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2014).
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Our results have significant policy implications. First, the negative effect of wealth inequality on economic freedom is very robust; hence governments and policymakers that
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seek to promote economic freedom in a society should curb substantial wealth concentrations among the rich elites and implement necessary redistributive policies to lessen inequality. Second, wealth inequality significantly reduces the size of government which may lead to less interventionists policies such as lower tax and social spending in response to greater wealth concentration. Taking together, this study suggests that in response to the growing wealth inequality, the economic elites lobby against tax and redistributive policies and use their
30
ACCEPTED MANUSCRIPT economic and political power to serve their own interest by depressing property rights, monetary, trade, and regulatory reforms which may discourage competition and innovation and thereby deteriorate economic freedom. However, democratization may help countries to improve economic freedom by undertaking favorable market reforms and to reduce the negative effects of wealth inequality by administering welfare-centric redistributive measures.
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Oxfam (2017) suggests that governments should increase taxes on both wealth and high incomes to ensure a more level playing field, and to generate funds needed to invest in healthcare, education and job creation. Although median voter theory states that a wellfunctioning democracy reduces inequality by creating pressures for redistribution in unequal
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societies; but significant wealth inequality may produce an unequal distribution of political power and thus the rich elites may oppose democratic movement to reduce their tax burden which may further increase inequality. The main challenge to break this vicious cycle of
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inequality is to curb the influence of wealth on the political decision-making process. Piketty (2014) also calls for a progressive annual tax on capital to mitigate wealth inequality;
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however, successful implementation of this policy requires a high level of international cooperation and regional political integration which may be one of the major challenges for
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the century ahead.
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The exploding wealth of the rich elites is only one side of the story of the global inequality; technological advancement has stagnated or even shrunk earnings for much of the
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population across nations (Rotman, 2014). In this digital era, computing and networking activities are growing at an exponential rate that contributes to increasing productivity, reducing prices and raising overall economic growth. However, as technology races ahead, the demand for highly skilled workers rises, while workers with less education and expertise fall behind, and thereby inequality rises (Brynjolfsson and McAfee, 2011). World-renowned scientist Hawking (2015) warns that the great technological advances may worsen inequality
31
ACCEPTED MANUSCRIPT in future because machine-produced wealth is mostly concentrated among the machine owners who successfully lobby against wealth redistribution, thereby creating technologydriven ever-increasing inequality. Since machines gradually substitute for labor and businesses become less capital-intensive, the major economic players in future will be those who have innovative ideas to produce new products and develop successful business models
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(Rotman, 2014). WEF (2017) has recently warned that there are new threats to social cohesion from the robotics and artificial intelligence revolution; hence fundamental reforms of capitalism may be needed to tackle excessive inequality and public anger around the globe. Therefore, a useful extension to this study would be to investigate how rapid technological
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progress influences the nexus between wealth inequality and economic freedom across nations and over time.
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Sturm, J.-E., De Haan, J., 2015. Income inequality, capitalism, and ethno-linguistic fractionalization. Amer. Econ. Rev. 105, 593-597.
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WEF., 2017. The Global Risks Report 2017 (12th ed.). World Economic Forum, Geneva. Williamson, C.R., Mathers, R.L., 2011. Economic freedom, culture, and growth. Public Choice 148,
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Young, A.T., Zuleta, H., 2013. Do unions increase labor‟s shares? Evidence from us industry-level data. Mimeo. Table 1 Summary statistics of key variables (46 countries, 2000-2014). 38
ACCEPTED MANUSCRIPT Variable
Obs.
Mean
Std. Dev.
Min.
Max.
Top 1 Percent Wealth Share (Top 1)
690
32.18
9.49
16.90
66.20
Top 10 Percent Wealth Share (Top10)
690
62.92
8.30
46.80
84.80
Economic Freedom Summary Index (EF)
690
7.32
0.73
5.14
9.17
Government Size Index (GV)
690
6.11
1.34
3.09
9.41
(PR)
690
6.78
1.49
2.89
9.62
Access to Sound Money Index (SM)
690
8.81
Freedom to Trade Internationally Index (FT)
690
7.78
Regulation of Credit, Labour and Business Index (RG)
690
7.10
Trade Union Density Rate (Union)
690
24.78
CR IP T
Legal Structure and Security of Property Rights Index
3.57
9.89
0.92
3.77
9.71
0.95
4.53
9.13
16.79
1.36
79.10
-10
10
AN US
1.15
Democracy (Polity2)
690
7.36
5.09
Notes: The sample includes annual data for 46 countries over the period 2000-2014. Economic freedom indices range from 0-10 where 0 corresponds to „less economic freedom‟ and 10 to „more economic freedom‟. Polity2
M
democracy score ranges from -10 (hereditary monarchy) to +10 (consolidated democracy).
ED
Table 2
Wealth inequality and economic freedom (OLS: 46 countries, 2000-2014). lnGV
lnPR
lnSM
lnFT
lnRG
(2)
(3)
(4)
(5)
(6)
PT
lnEF (1)
CE
Panel A: Pooled OLS
Top 1% wealth share
-0.13***
0.36***
-0.38***
-0.28***
-0.19***
-0.10***
Inequality)it
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
R2
0.14
0.21
0.23
0.28
0.20
0.04
AC
ln(Wealth
Top 10% wealth share
ln(Wealth
-0.20***
0.63***
-0.60***
-0.51***
-0.34***
-0.10***
Inequality)it
(0.03)
(0.05)
(0.06)
(0.04)
(0.03)
(0.04)
R2
0.07
0.14
0.12
0.20
0.14
0.01
Panel B: Fixed effects OLS
Top 1% wealth share
39
ACCEPTED MANUSCRIPT ln(Wealth
-0.13***
0.12***
-0.24***
-0.23***
-0.17***
-0.11***
Inequality)it
(0.02)
(0.03)
(0.04)
(0.04)
(0.02)
(0.03)
R2
0.82
0.90
0.89
0.59
0.82
0.80
Top 10% wealth share -0.20***
0.25***
-0.41***
-0.37***
-0.32***
-0.23***
Inequality)it
(0.05)
(0.06)
(0.10)
(0.10)
(0.05)
(0.06)
R2
0.80
0.90
0.89
0.58
0.81
0.75
CR IP T
ln(Wealth
Notes: The Economic freedom summary index (EF) and its five major sub-indices, e.g. government size index (GV), legal structure and security of property rights index (PR), access to sound money index (SM), freedom to trade internationally index (FT), and regulation of credit, labour and business index (RG) are considered as alternative dependent variable to explore all possible channels of economic freedom. Wealth inequality is considered as the explanatory variable measured by
AN US
the top 1% as well as top 10% wealth shares. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Fixed Effects OLS estimates in Panel B include both country and time dummies; however, the fixed effects coefficients are not reported to conserve space. The constant is included in the regression models but not reported for brevity. ln indicates natural logarithm. Total number of observation is 690. See also
M
notes to Table 1.
ED
Table 3
Wealth inequality and economic freedom (IV-2SLS: 46 countries, 2000-2014).
PT
lnEF (1)
lnGV
lnPR
lnSM
lnFT
lnRG
(2)
(3)
(4)
(5)
(6)
CE
Panel A: Top 1% wealth share
Second stage regressions
-0.16***
0.09***
-0.30***
-0.20***
-0.26***
-0.11***
Inequality)it
(0.03)
(0.03)
(0.05)
(0.07)
(0.03)
(0.03)
AC
ln(Wealth
First stage regressions
ln(Union
-0.24***
-0.24***
-0.24***
-0.24***
-0.24***
-0.24***
Density)it
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
F-statistic
191.07
191.07
191.07
191.07
191.07
191.07
F-statistic (p-value)
0.00
0.00
0.00
0.00
0.00
0.00
Endog (p-value)
0.00
0.00
0.00
0.00
0.00
0.00
40
ACCEPTED MANUSCRIPT Panel B: Top 10% wealth share
Second stage regressions
ln(Wealth
-0.38***
0.21***
-0.69***
-0.47***
-0.65***
-0.30***
Inequality)it
(0.07)
(0.08)
(0.13)
(0.17)
(0.07)
(0.10)
First stage regressions -0.10***
-0.10***
-0.10***
-0.10***
-0.10***
-0.10***
Density)it
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
F-statistic
131.47
131.47
131.47
131.47
131.47
131.47
F-statistic (p-value)
0.00
0.00
0.00
0.00
Endog (p-value)
0.00
0.00
0.00
0.00
CR IP T
ln(Union
0.00
0.00
0.00
0.00
Notes: Wealth inequality is instrumented by trade union density. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Constant, country dummies and time dummies are
AN US
included but not reported for brevity. Endog indicates endogeneity test. See also notes to Table 1 and 2.
Table 4 Plausibly exogenous bounds test.
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
-0.90
-0.43
-0.64
-0.21
0.81
-0.29
-0.14
-0.13
-0.01
M
lnGV
ED
lnEF
Panel A: Top 1% wealth share -0.29
Upper Bound
-0.02
0.32
PT
Lower Bound
CE
Panel B: Top 10% wealth share -1.13
2.34
-3.15
-1.37
-2.12
-1.22
Upper Bound
-0.21
4.95
-0.44
-0.32
-0.52
-0.19
AC
Lower Bound
Notes: Based on the IV specifications in Table 3, this Table 4 presents upper and lower bounds of a 95% confidence interval for the effects of wealth inequality on economic freedom and its major five areas. The bounds are estimated using the union of confidence intervals (UCI) methodology of Conley et al. (2012) under various priors about the direct effect that trade union density has on economic freedom.
41
ACCEPTED MANUSCRIPT
Table 5 Wealth inequality and economic freedom (OLS estimates with lagged wealth inequality: 46 countries, 2000-2014). lnGV
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
CR IP T
lnEF
Panel A: 5-year lagged wealth inequality
Top 1% wealth share
ln(Wealth
-0.13***
0.15***
-0.31***
-0.21***
-0.17***
-0.07**
Inequality)i,t-5
(0.02)
(0.04)
(0.04)
(0.04)
(0.02)
(0.03)
R2
0.90
0.93
0.94
0.78
0.85
0.86
ln(Wealth
-0.20***
0.26***
Inequality)i,t-5
(0.05)
(0.09)
R2
0.88
0.93
-0.54***
-0.33***
-0.31***
-0.17***
(0.10)
(0.10)
(0.06)
(0.07)
0.93
0.76
0.84
0.79
Top 1% wealth share
M
Panel B: 10-year lagged wealth inequality
AN US
Top 10% wealth share
-0.16***
0.19***
-0.44***
-0.22***
-0.20***
-0.11***
Inequality)i,t-10
(0.03)
(0.07)
(0.05)
(0.06)
(0.02)
(0.04)
R2
0.94
0.96
0.96
0.88
0.89
0.91
Top 10% wealth share -0.75***
Inequality)i,t-10
PT
ED
ln(Wealth
(0.08)
(0.16)
(0.17)
(0.14)
(0.07)
(0.09)
R2
0.92
0.96
0.95
0.86
0.88
0.82
-0.27***
0.31**
-0.36***
-0.38***
-0.20**
CE
ln(Wealth
AC
Notes: The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Constant, country dummies and time dummies are included but not reported for brevity. Total number of observation is 460 and 230 for Panels A and B, respectively. See also notes to Tables 1-3.
42
ACCEPTED MANUSCRIPT Table 6 Wealth inequality and economic freedom (System GMM: 46 countries, 2000-2014). lnEF
lnGV
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
-0.06**
-0.06**
Panel A: Top 1% wealth share -0.02**
0.09**
-0.14***
-0.04*
Inequality)it
(0.01)
(0.04)
(0.04)
(0.02)
(0.02)
(0.02)
Hansen (p-value)
0.47
0.19
0.23
0.13
0.86
0.88
AR (1) (p-value)
0.00
0.00
0.00
0.01
0.00
0.00
AR (2) (p-value)
0.83
0.68
0.32
0.12
0.29
0.59
-0.06**
0.31**
Inequality)it
(0.03)
(0.13)
Hansen (p-value)
0.44
0.34
AR (1) (p-value)
0.00
0.00
AR (2) (p-value)
0.84
0.65
-0.34***
-0.09*
-0.32**
-0.16**
(0.12)
(0.05)
(0.14)
(0.06)
0.23
0.16
0.28
0.48
0.00
0.00
0.04
0.00
M
ln(Wealth
AN US
Panel B: Top 10% wealth share
CR IP T
ln(Wealth
0.12
0.29
0.58
0.33
ED
Notes: Coefficients are based on the two-step system GMM estimation, using the Windmeijer (2005) finite sample correction. The 2nd and 3rd lags of the explanatory variables are taken as instruments for the differenced equation whereas 1st
PT
differences of the explanatory variables are taken as instruments for the level equation. Total number of observation is 644. The number of instruments used in the system GMM is 20. The instrument count is based on the number of „collapsed‟
CE
instruments, using the xrabond2 specification from Roodman (2009). The Hansen test checks the validity of the instruments where the null hypothesis is that the instruments are not correlated with the residuals. The null hypotheses in the AR(1) and AR(2) tests are that the error terms in the first differenced regression exhibit no first and second order serial correlations,
AC
respectively. The one period lagged dependent variable, constant, country and time dummies are included but not reported for brevity. All regressions employ orthogonal deviations. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. See also notes to Tables 1-3.
43
ACCEPTED MANUSCRIPT Table 7 Wealth inequality and economic freedom (IV-2SLS estimates with alternative measures of economic freedom from Heritage Foundation: 46 countries, 2000-2014). lnEFH
lnGVH
lnPRH
lnSMH
lnFTH
lnRGH
(1)
(2)
(3)
(4)
(5)
(6)
Second stage regressions
CR IP T
Panel A: Top 1% wealth share ln(Wealth
-0.15***
0.62***
-0.48***
-0.52***
-0.16***
-0.14***
Inequality)it
(0.03)
(0.10)
(0.09)
(0.17)
(0.04)
(0.05)
First stage regressions -0.24***
-0.24***
-0.24***
-0.24***
-0.24***
-0.24***
Density)it
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
F-stat (p-value)
0.00
0.00
0.00
0.00
0.00
0.00
AN US
ln(Union
Panel B: Top 10% wealth share
Second stage regressions
-0.34***
1.45***
Inequality)it
(0.06)
(0.26)
-1.12***
-1.19***
-0.36***
-0.58***
(0.21)
(0.40)
(0.09)
(0.16)
M
ln(Wealth
First stage regressions
Density)it
(0.01)
F-stat (p-value)
0.00
-0.10***
-0.10***
-0.10***
-0.10***
-0.10***
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
0.00
0.00
0.00
0.00
0.00
ED
-0.10***
PT
ln(Union
Notes: The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels,
CE
respectively. Wealth inequality is instrumented by trade union density. Constant, country dummies and time dummies are included but not reported for brevity. Superscript
H
indicates measures of economic freedom and its five major components
AC
from Heritage Foundation. Total number of observation is 690. See also notes to Tables 1-3.
44
ACCEPTED MANUSCRIPT Table 8 Wealth inequality and economic freedom (IV-2SLS estimates with Gini coefficient of income distributions: 46 countries, 2000-2014). lnGV
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Top 1% wealth share
CR IP T
lnEF
Second stage regressions
ln(Wealth
-0.26***
0.14**
-0.47***
-0.32***
-0.46***
-0.11***
Inequality)it
(0.04)
(0.06)
(0.08)
(0.09)
(0.04)
(0.03)
-0.15***
-0.15***
First stage regressions -0.15***
-0.15***
Density)it
(0.01)
(0.01)
F-stat (p-value)
(0.00)
(0.00)
-0.15***
-0.15***
AN US
ln(Union
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.00)
(0.00)
(0.00)
Notes: The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels,
M
respectively. Wealth inequality is measured by the post-tax, post-transfer (net) Gini coefficient of income distributions (the results are consistent while using pre-tax, pre-transfer (market) Gini coefficient but not reported for conciseness). Wealth
ED
inequality is instrumented by trade union density. Constant, country dummies and time dummies are included but not
PT
reported for brevity. Total number of observation is 690. See also notes to Tables 1-3.
Table 9
CE
Wealth inequality and economic freedom (System GMM estimates with Gini coefficient of
AC
income distributions: 46 countries, 1970-2014). lnEF
lnGV
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
ln(Wealth
-0.72***
0.84**
-0.86**
-0.41**
-0.64**
-0.26**
Inequality)it
(0.09)
(0.29)
(0.32)
(0.14)
(0.13)
(0.07)
Hansen (p-value)
0.13
0.17
0.16
0.14
0.12
0.14
AR (1) (p-value)
0.01
0.00
0.02
0.01
0.03
0.04
AR (2) (p-value)
0.20
0.24
0.78
0.51
0.42
0.80
45
ACCEPTED MANUSCRIPT Notes: Two two-step system GMM estimates are in 5-year intervals for 1970-2010, and 4-year interval for 2010-2014 across 46 sample countries.
Wealth inequality is measured by the post-tax, post-transfer (net) Gini coefficient of income
distributions (the results are consistent while using pre-tax, pre-transfer (market) Gini coefficient but not reported for succinctness). The 2nd and 3rd lags of the explanatory variables are taken as instruments for the differenced equation whereas 1st difference of the explanatory variables is taken as instruments for the level equation in the system GMM estimates. The Hansen test checks the validity of the instruments where the null hypothesis is that the instruments are not correlated with the residuals. The null hypotheses in the AR(1) and AR(2) tests are that the error terms in the first differenced regression exhibit
CR IP T
no first and second order serial correlations, respectively. Constant, country and time dummies are included but not reported for brevity. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Total number of observation ranges from 368 to 375. See also notes to Tables 1-3 and Table 6.
AN US
Table 10
Wealth inequality and economic freedom (Conditional estimates with IV-2SLS: 46 countries, 20002014). lnGV
(1)
(2)
Top 1% wealth share
Inequality)it
(0.02)
Growthit
0.14
lnFT
lnRG
(3)
(4)
(5)
(6)
0.11**
-0.26***
-0.15**
-0.30***
-0.10**
(0.05)
(0.06)
(0.06)
(0.03)
(0.06)
0.65***
0.52***
-0.62***
0.29***
-0.12
(0.09)
(0.17)
(0.16)
(0.23)
(0.11)
(0.23)
0.25***
-0.04
0.42***
0.34***
0.15***
0.23***
Attainment)it
(0.03)
(0.05)
(0.06)
(0.09)
(0.04)
(0.04)
Inflationit
-0.04***
0.01**
-0.04***
-0.15***
0.03
-0.05***
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
ln(Population
-0.02
-0.03
0.02
-0.01
-0.02*
-0.01**
Sizeit)
(0.01)
(0.02)
(0.02)
(0.03)
(0.01)
(0.00)
Resource
0.02
0.18
0.39
-0.64**
0.01
0.23
Rentsit
(0.11)
(0.22)
(0.21)
(0.32)
(0.15)
(0.25)
AC
CE
ln(School
ED
-0.14***
lnSM
Second stage regressions
PT
ln(Wealth
lnPR
M
lnEF
First stage regressions
46
ACCEPTED MANUSCRIPT ln(Union
-0.20***
-0.20***
-0.20***
-0.20***
-0.20***
-0.20***
Density)it
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
F-stat (p-value)
0.00
0.00
0.00
0.00
0.00
0.00
Notes: Estimated results are based on top 1% wealth shares. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Wealth inequality is instrumented by trade union density. Constant, country dummies and time dummies are included but not reported for brevity. Results are similar in Top 10 percent
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wealth shares, but not reported to conserve space. Total number of observation is 643. See also notes to Tables 1-3.
Table 11
Wealth inequality, democracy and economic freedom (Conditional estimates with IV-2SLS: 46
lnEF
lnGV
(1)
(2)
Top 1% wealth share
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countries, 2000-2014). lnPR
lnSM
lnFT
lnRG
(3)
(4)
(5)
(6)
-0.37***
-0.36***
-0.25***
Second stage regressions
-0.28***
0.01
Inequality)it
(0.03)
(0.08)
(0.09)
(0.06)
(0.05)
(0.08)
Polity2it
-0.11***
-0.14***
-0.16***
-0.14***
-0.10***
-0.01
× Polity2it
ED
(0.02)
(0.04)
(0.05)
(0.05)
(0.02)
(0.01)
0.03***
0.03***
0.04***
0.04***
0.03***
0.01**
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
PT
ln(Wealth Inequality)it
-0.53***
M
ln(Wealth
(0.00)
CE
Top 10% wealth share
Second stage regressions
-0.69***
-0.09
-1.28***
-1.07***
-0.89***
-0.53**
Inequality)it
(0.07)
(0.22)
(0.21)
(0.18)
(0.13)
(0.21)
Polity2it
-0.29***
-0.39***
-0.34**
-0.48**
-0.30***
-0.01
(0.05)
(0.12)
(0.14)
(0.20)
(0.05)
(0.01)
ln(Wealth Inequality)it
0.07***
0.09***
0.08**
0.12**
0.07***
0.02**
× Polity2it
(0.01)
(0.03)
(0.03)
(0.05)
(0.01)
(0.04)
AC
ln(Wealth
Notes: Democracy is measured by the Polity2 score ranging from +10 (strongly democratic) to -10 (strongly autocratic). The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1%
47
ACCEPTED MANUSCRIPT significance levels, respectively. Wealth inequality is instrumented by trade union density. Wealth inequality and its interaction with democracy are instrumented by trade union density and its interaction with democracy, respectively. Regressions are based on IV-2SLS estimates, where the first stage results are consistent with the baseline estimates in Table 3 but not reported. Control variables used in Table 8 are included but not reported to conserve space. Constant, country dummies and time dummies are included but not reported for brevity. Total
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number of observation is 643. See also notes to Tables 1-3.
Data Appendix Country List
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The total 46 sample countries consist of Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Greece, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Japan, South Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Peru, Philippines, Poland, Portugal, Russian Federation, Saudi Arabia, Singapore,
M
South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Arab Emirates, United Kingdom, United States.
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Variables’ Definitions and Data Sources
Wealth Inequality: Top 1 percent as well as top 10 percent wealth shares are used as alternative
Databook
2014
published
by
CreditSuisse
(2014)
(see
https://www.credit-
CE
Wealth
PT
measures of wealth inequality. The wealth share data are collected from the Credit Suisse Global
suisse.com/corporate/en/research/research-institute/global-wealth-report.html).
AC
Gini Coefficients: Gini coefficient is used as an indicator of income inequality and the corresponding data are collected from Solt, Frederick (2016). “The Standardized World Income Inequality Database.” Social
Science
Quarterly 97.
SWIID
Version
6.1,
October
2017,
(see
http://myweb.uiowa.edu/fsolt/swiid/swiid.html) Economic Freedom: The world economic freedom summary index as well as its five sub-indices are used as alternative indicators for economic freedom across countries. The Fraser Institute‟s economic freedom of the world summary index (EF) that ranges from 0-10 where 0 corresponds to „less
48
ACCEPTED MANUSCRIPT economic freedom‟ and 10 to „more economic freedom‟, compiled from the Fraser Institute database (see http://www.freetheworld.com/). EF has five different components, for example, (a) Government size index (GV) that ranges from 0-10 where 0 corresponds to „large general government consumption‟, „large transfer sector‟, „many government enterprises‟, and „high marginal tax rates and low income thresholds‟, and 10 to „small general government consumption‟, „small transfer sector‟,
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„few government enterprises‟, and „low marginal tax rates and high income thresholds‟, (b) Legal structure and security of property rights index (PR) that ranges from 0-10 where 0 corresponds to „no judicial independence‟, „no trusted legal framework exists‟, „no protection of intellectual property‟, „military interference in rule of law‟, and „no integrity of the legal system‟ and 10 corresponds to „high judicial independence‟, „trusted legal framework exists‟, „protection of
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intellectual property‟, „no military interference in rule of law‟, and „integrity of the legal system‟, (c) Access to sound money index (SM) that ranges from 0-10 where 0 corresponds to „high annual money growth‟, „high variation in the annual rate of inflation‟, „high inflation rate‟, and „restricted foreign currency bank accounts‟ and 10 corresponds to „low annual money growth‟, „low or no
M
variation in the annual rate of inflation‟, „low inflation rate‟, and „foreign currency bank accounts are
ED
permissible without restrictions‟, (d) Freedom to trade internationally index (FT) that ranges from 0-10 where 0 corresponds to „increasing tax rate on international trade‟, „slow import or export
PT
process‟, „small trade sectors relative to the population and geographic size‟, „exchange rate controls are present and a black-market exists‟, and „restrictions on the freedom of citizens to engage in capital
CE
market exchange with foreigners‟ and 10 corresponds to „no specific taxes on international trade‟, „swift import or export process‟, „large trade sectors relative to the population and geographic size‟,
AC
„no black-market exchange rate‟, and „no restrictions on the freedom of citizens to engage in capital market exchange with foreigners‟, and (e) Regulation of credit, labour and business index (RG) that ranges from 0-10 where 0 corresponds to „low percentage of deposits held in privately owned banks‟, „high foreign bank license denial rate‟, „private sector‟s share of credit is close to the base-yearminimum‟, „deposit and lending rates are fixed by the government and real rates are persistently negative‟, „high impact of minimum wage‟, „widespread use of price controls throughout various sectors of the economy‟, and „starting a new business is generally complicated‟ and 10 corresponds to 49
ACCEPTED MANUSCRIPT „high percentage of deposits held in privately owned banks‟, „low foreign bank license denial rate‟, „private sector‟s share of credit is close to the base-year-maximum‟, „interest rate is determined primarily by market forces and the real rate is positive‟, „low impact of minimum wage‟, „no price controls or marketing boards‟, and „starting a new business is generally easy‟. For more detailed explanations, see Gwartney et al. (2016) and the Quality of Government Online Dataset (see
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http://www.qog.pol.gu.se/data/datadownloads/qogstandarddata/). Trade Union Density: Trade union density is used as an instrument of wealth inequality. Trade union density corresponds to the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earners; and the data are extracted from OECD.Stat database (see
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https://stats.oecd.org/Index.aspx?DataSetCode=UN_DEN).
Democracy: Polity2 as a proxy for the democracy (political freedom) is measured by the revised combined polity score (Polity2) and is obtained from the Polity IV project of the University of Maryland (available at http://www.systemicpeace.org/polity/polity4.htm). The Polity score combines
M
the scores on the democracy and autocracy indices to a single regime indicator. The score captures the regime authority spectrum on a 21-point scale ranging from -10 (hereditary monarchy) to +10
ED
(consolidated democracy). Polity2 is the revised combined polity score. Control Variables: Data on real GDP per capita growth rate are collected from the Conference Board Economy
Database
(see
https://www.conference-
PT
Total
board.org/data/economydatabase/index.cfm?id=27762). Data on inflation rate (consumer price index),
CE
population size, and resource rents to GDP ratio (oil, gas and mineral rents to GDP) are compiled from the World Development Indicators online database (see http://data.worldbank.org/data-catalog/world-
AC
development-indicators). Data on school attainment (measured as the average years of schooling in the population aged 15 years and over) are collected from Barro and Lee‟s Educational Attainment online database (see http://www.barrolee.com/).
50
Wealth Inequality, Democracy and Economic Freedom Md. Rabiul Islam PII: DOI: Reference:
S0147-5967(18)30002-7 10.1016/j.jce.2018.01.002 YJCEC 2614
To appear in:
Journal of Comparative Economics
Received date: Revised date: Accepted date:
14 May 2017 9 January 2018 12 January 2018
Please cite this article as: Md. Rabiul Islam , Wealth Inequality, Democracy and Economic Freedom, Journal of Comparative Economics (2018), doi: 10.1016/j.jce.2018.01.002
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Highlights
Wealth inequality significantly hampers economic freedom and this effect is
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reinforced at a lower level of democracy. Higher wealth inequality causes lower protection in property rights, less access to sound money, less freedom in international trade, and greater regulations.
Larger concentration of wealth among the rich leads to lower taxes and government welfare expenses.
Democratization helps countries to undertake favorable market reforms and administer
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CE
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ED
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welfare-centric redistributive policies.
1
ACCEPTED MANUSCRIPT
Wealth Inequality, Democracy and Economic Freedom Md. Rabiul Islam1*
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Monash University, Australia
Abstract
Using a novel panel data set from the Credit Suisse on the top wealth shares for 46 sample
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countries spanning 2000-2014, this paper empirically investigates to what extent wealth inequality influences economic freedom and whether this relationship is affected by the level of democracy. Economic freedom is measured by the Fraser Institute‟s economic freedom summary index as well as its five major sub-indices, such as government size, property rights,
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access to sound money, freedom to trade, and regulations. Wealth inequality is measured by
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the top wealth shares. Trade union density is used as an instrument for wealth inequality. Empirical results suggest that the rising wealth inequality significantly hampers overall
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economic freedom, property rights protection, freedom to trade, soundness of money and regulatory environment. Furthermore, this negative effect of wealth inequality is reinforced at
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a lower level of democracy. These findings are robust to alternative measures of wealth
AC
inequality, economic freedom, treatment for endogeneity, and model specification.
1
Department of Economics, Monash Business School, Monash University, 900 Dandenong Road, Caulfield East, Victoria 3145, Australia. Tel: +61-03-95722448; Fax: +61-0399031128. E-mail: [email protected] *
Acknowledgements: Helpful comments and suggestions from participants at the seminars at Deakin University and RMIT University and particularly, two referees and the editor are gratefully acknowledged. The usual disclaimer applies.
2
ACCEPTED MANUSCRIPT Keywords: wealth inequality, economic freedom, democracy, panel data JEL classifications: O15, O40, O50
1. Introduction The rising income and wealth inequality over the past few decades have been dividing many
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modern societies and posing a tangible threat to the growth and freedom of the global economy. Piketty (2014) warns against the danger of rising wealth inequality in the twentyfirst century and calls for a progressive annual tax on wealth. The gap between the rich and
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the poor keeps widening which not only hampers sustainable growth but also raises uncertainty in the free-market institutions that protect property rights and support voluntary exchange and freedom of choice across nations (OECD, 2015).2 In most OECD countries the richest 10 percent population earn about 9.5 times the income of the poorest 10 percent, the
M
ratio was at 7:1 in the 1980s and since then it has been rising continuously (Cingano, 2014). However, wealth inequality is even more shocking than income inequality because wealth can
ED
generate its own income regarding rent, capital gain, interest, dividend, and can be passed on
PT
between generations and thereby most wealth accumulation leads to higher concentration of capital (Piketty, 2014). Davies et al. (2008) find that income inequality is on average half of
CE
the size of wealth inequality in both industrial and emerging economies. For example, most Gini coefficients for disposable income lie in the range of about 0.30-0.50, whereas Gini
AC
coefficients for wealth typically fall in the range of about 0.60-0.80. The top 10 percent of the adult population in the world owned 70.7 percent household wealth in the year 2000, and the corresponding global wealth Gini coefficient was 0.8 (Davies et al., 2011). This unbalanced distribution of wealth tilts institutions and erodes the social contract between citizens and the state. Therefore, these large and growing concentrations of wealth in the hands of fewer
2
see Neves and Silva (2014) and Neves et al. (2016) for a comprehensive survey on inequality-growth nexus. 3
ACCEPTED MANUSCRIPT people present a real threat to inclusive political and economic system, especially private property and free markets that constitute economic freedom in the capitalist world (Oxfam, 2014). Democracy refers to a political regime where the population of a country chooses its government leaders to influence public policy without undue restrictions. The strength of the
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democratic institutions depends on the extent to which a country‟s civil society institutions permit these free choices (Rivera-Batiz and Rivera-Batiz, 2002). Democratization limits rentseeking by putting in place a system of checks and balances that penalize self-interested leaders, and thereby creating an atmosphere conducive to economic liberalization policies (De
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Haan and Sturm, 2003). By extending political power to the poorer segments of the society, democratization may increase the tendency for the pro-poor policy associated with redistribution, and thus reducing inequality (Acemoglu et al., 2015). Therefore, democracy
M
may help countries to lessen the adverse impact of wealth inequality on economic freedom. The vast majority of empirical literature tends to focus on the effects of economic
ED
freedom on income inequality, while very little is known about the role of inequality in determining economic freedom. Given the fact that the wealth inequality is more extreme than
PT
the income inequality and prior studies exclusively focus on income inequality, this paper is
CE
one of the pioneer studies that fills the gap in the literature by investigating to what extent wealth inequality affects economic freedom and whether this relationship is influenced by the
AC
level of democracy across nations over time. Historically, countries with higher degrees of economic freedom have thrived both
economically and socially. Economic freedom is the fundamental right of every human being to control his or her own labor and property, hence governments in economically free societies allow labor, capital, and goods to move freely, and refrain from coercion of liberty beyond the extent necessary to protect and maintain freedom itself (Miller and Kim, 2017).
4
ACCEPTED MANUSCRIPT Economic freedom is associated with market-oriented institutions and policies that protect property rights, provide equal opportunity, reduce transaction cost, encourage entrepreneurial activities, increase physical and human capital investment, improve technological development, and thereby lead to an efficient allocation of resources across nations (Gwartney et al., 1999, Gwartney and Lawson, 2004, Acemoglu et al., 2005, Hall and Lawson 2014, and
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Gwartney et al., 2016). Inequality may impair growth by reducing physical and human capital accumulation, property rights protection and increasing fertility, taxes, and socio-political instability (Perotti, 1996 and Easterly, 2007). A large body of literature suggests that economic freedom is conducive to growth which may be threatened by growing inequality.3
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Despite a voluminous literature, the connection between inequality and economic freedom remains inconclusive.4
There is a considerable literature on the effects of economic freedom on income
M
inequality though the empirical evidence is mixed (Berggren, 1999, Scully, 2002, and Carter, 2007). However, there is a scarcity of evidence on inequality‟s effect on economic freedom
ED
and the handful of extant studies solely based on income inequality measures that also report mixed findings. For example, Apergis et al. (2014) find a bidirectional causality between
PT
income inequality and economic freedom and suggest that higher income inequality may
CE
cause the US states to implement redistributive policies causing economic freedom to decline, whereas Murphy (2015) obtains an overall negative impact of income inequality on economic
AC
freedom but mixed effects on the different areas of economic freedom across nations. The absence of reliable and comparable wealth distribution data across countries over time causes researchers to use income as a proxy for wealth (Aghion et al., 1999). Piketty and Zucman (2014) find that wealth-income ratios have doubled over the past 40 years. Several studies suggest that growing wealth inequality is primarily driven by rising wealth concentration at 3 4
see Doucouliagos and Ulubasoglu (2006) and De Haan et al. (2006) for economic freedom-growth review. see Bennett and Nikolaev (2017) for an excellent review on economic freedom-inequality nexus. 5
ACCEPTED MANUSCRIPT the top (Piketty, 2014 and Dabla-Norris et al., 2015). Therefore, this study has used top wealth shares data to examine the economic freedom effect of wealth inequality.5 This paper makes three contributions to the empirical literature on the inequalityeconomic freedom nexus. First, it investigates the effects of wealth inequality on economic freedom and its five major areas across 46 developed and developing sample countries over
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the period 2000 to 2014. To best of our knowledge, this is the first study that uses wealth distribution to examine the empirical connection between inequality and economic freedom. Second, it examines the interaction between wealth inequality and democracy to test the hypothesis that wealth inequality is potentially more harmful to economic freedom in less
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democratic economies than in countries with fully developed democratic institutions.
The third contribution is to dealing with possible endogeneity with the help of appropriate econometric strategies. The core models use trade union density as a time-varying
M
suitable instrument for wealth inequality. Historically, unionization has been negatively associated with inequality (Jaumotte and Buitron, 2015 and Islam et al., 2017). Unionization
ED
is a measure of wage pushiness that increases labor's income share and, therefore, reduces the profit rate and inequality (Blanchard and Giavazzi, 2003). Lower union density may trigger
PT
higher wealth concentration by reducing wage growth, which makes it difficult for middle-
CE
and lower-income workers to set aside money for saving (Dabla-Norris et al., 2015). For robustness, this study also uses 5-10 years lags in wealth inequality, and applies the system
AC
GMM estimator that uses internal instruments for wealth inequality and other endogenous variables.
5
There are three important reasons why wealth matters more than income. First, wealth provides a means of raising long-term consumption directly by dissaving or indirectly through continuous income stream from the return on invested assets. Second, most real property and financial assets can be readily bought and sold which allow wealth owners for their consumption smoothing during periods of uncertainty. Third, wealth can be used as collateral to obtain necessary fund from financial institutions and informal sectors and thereby providing a source of finance for household and entrepreneurial activities (Davies et al., 2008, Davies & Shorrocks, 2000). 6
ACCEPTED MANUSCRIPT The rest of the paper is organized as follows. Section 2 presents a comprehensive literature review on inequality and economic freedom. Section 3 discusses estimation method, identification strategy and data. Section 4 undertakes empirical estimates, and robustness tests are carried out in Section 5. The role of democracy on the economic freedom-wealth
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inequality nexus is presented in section 6. The last section concludes.
2. Literature review on inequality and economic freedom
Classical economists believe that a rise in inequality tends to increase investment because the
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rich save more, and higher savings translate into higher investment and thereby promotes long-run economic growth (Keynes, 1920 and Kaldor, 1957). Neoclassical growth models predict that the poor countries tend to grow faster than the rich nations because of diminishing returns to capital and thus historical inequality may vanish in the long run (Solow, 1956).
M
Kuznets (1955) finds a non-linear relationship between income distribution and economic development, where inequality increases with development at the first stage and then
ED
decreases gradually in the later stage. However, modern growth theories criticize Classical
PT
and Neoclassical approaches and argue that inequality hampers economic growth by reducing investment in physical and human capital and increasing taxes, socio-political instability, and
CE
fertility (Alesina and Rodrik, 1994, Persson and Tabellini, 1994, Perotti, 1996, and Easterly, 2007). Almost all of the studies mentioned above use income distribution as a proxy for
AC
income as well as wealth inequality. Davies et al. (2011) argue that the global wealth-holding is more concentrated than that of income; hence wealth inequality is more pronounced than income inequality. Only a few studies find negative significant wealth inequality-growth nexus by considering either land holdings or billionaires‟ wealth as a proxy for wealth inequality (Deininger and Olinto, 2000 and Bagchi and Svejnar, 2015).
7
ACCEPTED MANUSCRIPT Economic freedom constitutes the essence of the market economy that connects to the power of Smith‟s (1776) invisible hand, highlighting that under certain conditions, the presence and perfection of markets increase competition and efficiency, which in turn promotes economic growth and development across nations (Acemoglu et al., 2005 and Farhadi et al., 2015). A free economy allows individuals, goods and capital to move freely
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inside the domestic market and across the nations under the efficient regulations and welldefined law set by state which thereby increases productivity and encourages investment in both human and physical capital as well as providing greater opportunity for entrepreneurial activities (De Haan and Sturm, 2000, Justesen, 2008, Hall and Lawson, 2014, and Farhadi et
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al., 2015). Freer societies can capitalize the virtues of the free-market system by responding to the decisions and desires of individuals in constructive and efficient ways. They provide even more diverse opportunities for individuals and tend to create virtuous growth cycles
M
characterized by efficient resource allocation, high value creation and technological innovation (Miller and Kim, 2016). Therefore, economic freedom is present when economic
ED
activity is coordinated by personal choice, voluntary exchange, open markets, and clearly defined and enforced property rights (Gwartney et al., 2016).
PT
Apart from growth, economic freedom is also related to inequality across nations. The
CE
effects of economic freedom on income inequality are extensively studied in the literature; however, the empirical evidence is at best mixed. In one hand, higher economic freedom may
AC
be associated with lower taxes and welfare expenditures which are expected to be detrimental to low-income earners; on the other hand, greater economic freedom may boost growth and remove legal barriers that protect politically favored groups and opens economic opportunities to less privileged and lower-income individuals (Perez-Moreno and AnguloGuerrero, 2016). De Soto (2000) argues that capital is the force that raises the productivity of labor and creates the wealth of nations in developed countries. However, poor people in
8
ACCEPTED MANUSCRIPT developing countries have assets, but due to lack of well-defined property rights, those assets cannot be used to generate capital. Therefore, economic freedom may release economic opportunity to the poor and thereby reducing income inequality. Using Gini coefficients from the Deininger and Squire (1996) database, Berggren (1999) finds a positive relationship between changes in economic freedom and income equality, indicating that sustained and
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gradual increases in economic freedom lead to higher equality over time. After disaggregating aggregate economic freedom measure, Berggren (1999) observes that not all the components of economic freedom are related to equality; only trade liberalization and financial mobility are found to enhance equality. Later, Scully (2002) argues that economic freedom promotes
economic growth and income inequality.
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both growth and equity and that there is a positive but relatively small trade-off between
Using Fraser Institute‟s revised dataset of Economic Freedom of the World and new
M
and expanded income inequality (Gini coefficients) dataset of the UNU/WIDER, Carter (2007) observes contradictory evidence that economic freedom increases income inequality.
ED
More precisely, higher economic freedom may raise income equality by widening incomeearning opportunities, and it can lower equality by reducing income redistribution toward
PT
poor; however empirical evidence suggests that the latter effect is dominant except at
CE
comparatively low levels of economic freedom. Using cross-sectional data across US states, Ashby and Sobel (2008) find that changes in economic freedom are associated with higher
AC
economic growth and reductions in relative income inequality; however, the level of economic freedom appears to be insignificant. Using the recent SWIID income inequality database, Bergh and Nilsson (2010) obtain robust significant positive effects of trade liberalization on income inequality across 80 countries over the period 1970-2005. Deregulation and social globalization also raise inequality. Reforms towards economic freedom seem to increase inequality mainly in rich countries, and social globalization is more
9
ACCEPTED MANUSCRIPT important in less developed countries. Perez-Moreno and Angulo-Guerrero (2016) find that higher economic freedom (overall and Government size) is robustly related to greater income inequality in the EU for the 2000s. In contrast to Carter (2007), Bennett and Vedder (2013) obtain an inverted U-shaped relationship between economic freedom and income inequality for 50 US States from 1979-
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2004. It implies that economic freedom initially generates more income inequality as the upper part of the income distribution benefits; however, as economic freedom continues to improve, the lower part of the income distribution experiences larger relative income gains. However, Sturm and De Haan (2015) do not find any robust relationship between economic
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freedom and income inequality across a panel of 108 countries over the period 1971-2010. Apergis and Cooray (2017) use linear and non-linear cointegration techniques to identify a long-run equilibrium relationship between economic freedom and income inequality for both
M
the aggregate and major areas of the economic freedom composite index for 138 countries. The linear long-run parameter estimates document that the association turns out to be
ED
negative, while the non-linear long-run parameter estimates illustrate that above a threshold point the association between economic freedom and income inequality is negative, while
PT
below this threshold point, the association turns out to be positive.
CE
Very few empirical studies investigate the effects of economic freedom and its key areas on income inequality. Apergis et al. (2014) observe that income inequality has a
AC
significant negative impact on economic freedom in the US States. Murphy (2015) finds that greater income inequality leads to lower economic freedom across nations. However, not all areas of economic freedom affect income distribution similarly. Income inequality appears to increase the size of government and to have an adverse effect on the rule of law, little effect on the soundness of money and international trade, and ambiguous effects on business regulation. Finally, Krieger and Meierrieks (2016) argue that higher income inequality
10
ACCEPTED MANUSCRIPT strongly and significantly hampers cross-country economic freedom, property rights and business regulations. However, the size of government and the soundness of money are not significantly affected by wealth inequality. The above-mentioned empirical studies have consistently considered income distribution as a proxy for inequality and almost all of them have investigated the effects of
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economic freedom and its major areas on income inequality. It is now well established that economic freedom is conducive to economic growth (Gwartney et al., 1999, De Haan and Sturm, 2000, Carlsson and Lundstrom, 2002, and Williamson and Mathers, 2011). Again, a large body of empirical literature suggests that inequality adversely affects economic growth
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(Alesina and Rodrik, 1994, Marrero & Rodríguez, 2013, and Madsen et al., 2018). Aghion et al. (1999) argue that the underlying theory on inequality-growth nexus is that the wealth inequality determines the degree of investment in physical or human capital, which in turn
M
affects long-run growth; however, the absence of reliable and comparable wealth distribution data across large number of countries force empirical researchers to use income as a proxy for
ED
wealth over time. The recent availability of wealth inequality data by the Credit Suisse (2014) has greatly contributed toward filling these information gap.
PT
To best of our knowledge, this study for the first-time attempts to empirically examine
CE
the impact of wealth inequality on economic freedom and its five major areas using a unique dataset of wealth distribution (top 1 and top 10 percent wealth shares) from Credit Suisse
AC
(2014) for 46 advanced and developing countries over the period 2000-2014. Most of the studies on inequality-economic freedom nexus does not address endogeneity and a very few of them uses generalized method of moments (GMM) dynamic panel estimators to deal with endogeneity by using internal instruments; which are subject to small sample bias, suffer from severe weak instrument problem, and fail to control for country heterogeneity. Using external instrument is the best option to deal with the possible endogeneity and heterogeneity in the
11
ACCEPTED MANUSCRIPT estimations; hence this study uses trade union density as an external instrument to wealth inequality. It also considers 5-year and 10-year lagged wealth inequality as well as system GMM estimator to address endogeneity in the robustness section.
3. Empirical framework, estimation methodology, identification strategy
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and data 3.1. Empirical framework
To investigate the effects of wealth inequality on economic freedom we estimate the
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following panel data regression model:
(1)
where, EFR indicates economic freedom which is measured by the Fraser Institute‟s economic freedom of the world summary index (EF) and its five major sub-indices, for
M
example, government size index (GV), legal structure and security of property rights index
ED
(PR), access to sound money index (SM), freedom to trade internationally index (FT), and regulation of credit, labour and business index (RG). WINQ specifies wealth inequality which
PT
is measured by the Credit Suisse‟s top 1% and top 10% wealth shares; X stands for the vector of control variables including, economic growth, school attainment, inflation rate, population
CE
size, and natural resource rents; CD is country dummy, TD is time dummy, and ε is the
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random error term. This study first estimates its baseline models for 46 countries from 20002014 and then includes control variables in the robustness section. The sample is dictated by data availability on wealth inequality; the complete list of the sample countries and the data sources are provided in the Data Appendix. Economic growth is included in Eq. (1) as a control variable because it does matter for economic freedom; higher growth may generate resources necessary for policy-makers to establish, reform, or improve existing institutions and policies (La Porta et al., 1999). 12
ACCEPTED MANUSCRIPT Education has positive effects on economic freedom because educated people are more likely to resolve their differences through discussion and negotiation rather than violent conflicts and thereby the greater the human and social capital of a community, the more attractive its institutional opportunities (Djankov et al., 2003 and Glaeser et al., 2004). Poor macroeconomic management adversely affects economic freedom because high and volatile
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rates of inflation distort relative prices, alter the fundamental terms of long-term contracts, and make it virtually impossible for individuals and businesses to plan sensibly for the future (Gwartney et al., 2016). Population size may have mixed effects on economic freedom because, at the one hand, larger countries may provide better economic institutions due to
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agglomeration effects, market size and lower per capita costs for public good provision; on the other hand, highly populated countries may experience more diversity which may produce extra cost and reduce the quality of economic institutions (Rose, 2006). Natural resource rents
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deteriorate economic freedom because rent windfalls may encourage grabber friendly institutions to involve more in rent-seeking and unproductive activities (Mehlum et al., 2006
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and Papyrakis, 2017). Finally, an abundance of natural resources hinders economic freedom and investment, which may lead to an observed resource curse (Campbell and Snyder, 2012).
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3.2. Estimation methodology
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The regression model in Eq. (1) is estimated for a yearly panel of 46 sample countries for which wealth inequality data are available over the period 2000-2014. The data are pooled
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across countries to gain efficiency and to allow for fixed effects. Fixed country effect dummies are included in the regressions to cater for time-invariant unobserved heterogeneity, implying that the parameter estimates are driven entirely by the within-country variation of the data. Time dummies are included to capture most common macroeconomic shocks that might have significant impacts on economic freedom.
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ACCEPTED MANUSCRIPT This paper estimates baseline regression models first and then incorporates control variables in the robustness tests. The estimation starts with the ordinary least squares (OLS) estimates, which has three inherent problems. First, wealth inequality may be endogenous, and hence there is more likely to have a feedback effect from economic freedom to wealth inequality that might capture reverse causality. Second, wealth inequality measures are more
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likely to have measurement errors, particularly for the developing countries. Third, there are critical explanatory variables (e.g., culture, geography, etc.) that may be omitted from the regression models but affected economic freedom and are correlated with one or more explanatory variables.
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To deal with possible endogeneity, this study applies three alternative econometric strategies. First, it estimates regression models using a two-stage least squares (2SLS) instrumental variable (IV) approach (IV-2SLS) where wealth inequality is instrumented with
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trade union density, and it is our core method. Second, it considers 5-year and 10-year lagged value of the wealth inequality series in the OLS estimates to mitigate endogeneity bias. Third,
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it also uses Arellano & Bover (1995) and Blundell & Bond‟s (1998) system GMM estimator that uses difference and level equations simultaneously and controls for the potential
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endogeneity of all explanatory variables by using internal instruments. However, the system
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GMM estimator may not entirely solve the endogeneity issue due to weak instrument problem, but it is likely to give more unbiased estimates than OLS regressions. The last two
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econometric techniques are discussed in the robustness section.
3.3. Identification strategy If wealth inequality follows from economic freedom, OLS regressions will not reveal a genuine causal relationship due to the omitted variables bias and feedback effects from economic freedom to the distribution of wealth. To deal with possible endogeneity, we need to use a plausible instrument for wealth inequality which is not easy to find. To satisfy
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ACCEPTED MANUSCRIPT exclusion restrictions, a valid instrument should adequately explain cross-country wealth inequality and should not explain economic freedom other than through the wealth distribution. Based on the hypothesis of Engerman and Sokoloff (1997) and Sokoloff and Engerman (2000) which suggests that factor endowment is the central determinant of
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inequality, Easterly (2007) instrumented inequality by the abundance of land suitable for growing wheat relative to that suitable for growing sugarcane which is time-invariant and may simply proxy for whether the country is in tropics. However, time-invariant factor endowment is not likely to be a reasonable instrument for wealth inequality in this panel
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study. We address endogeneity and unobserved heterogeneity in the estimates by using trade union density as an external instrument for wealth inequality. The identification strategy is that organized trade union membership has been associated with higher bargaining power and
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increasing labor share which in turn lead to reduce wealth inequality over time. The basic idea behind this instrument is that unionization is a measure of wage
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pushiness that increases labor's income share and, therefore, reduces the profit rate and inequality. Although most empirical estimates find that unionization increases labor‟s share
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there are circumstances under which they do not. As shown by Young & Zuleta (2013) the
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income share effects of unionization depend on the type of union bargaining model and the elasticity of substitution between capital and labor. If the elasticity of substitution is low, it is
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easy to substitute to capital when labor costs increase and replace labor and, thus, labor‟s share. Again, Blanchard & Giavazzi (2003) argue that factor shares depend on both the degree of labor market regulation, which is captured by workers bargaining power and product market regulation, which is captured by the markups of imperfectly competitive firms. When more firms enter, there is greater competition in the product market and markups fall, increasing labors share. Similarly, if workers‟ bargaining power increases due to an increase
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ACCEPTED MANUSCRIPT in unionization, for example, labor‟s share increases (Blanchard and Giavazzi, 2003). A decline in trade union membership rate could reduce the relative bargaining power of labor, thereby aggravate wage inequality; hence unionization, on average, tend to improve income distribution (OECD 2011, Dabla-Norris et al., 2015, and Jaumotte and Buitron, 2015). Lags can address potential endogeneity biases resulting from simultaneity and reverse
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causation, as lagged wealth inequality is likely to be exogenous to the current economic freedom. Hence, this study re-estimates Eq. (1) using 5-year and 10-year lagged wealth inequality. Clemens et al. (2012) avoid poor-quality instrumental variables and instead address potential biases from reverse and simultaneous causation by lagging and differencing.
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Replacement of a suspected simultaneously determined explanatory variable with its lagged value has become one of the standard practices in applied economics research (Reed, 2015). Finally, this study uses the two-step dynamic generalized method of moments (GMM)
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estimator to generate internal instruments for wealth inequality and other endogenous explanatory variables. Specifically, it uses Arellano and Bover (1995) and Blundell and
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Bond‟s (1998) system GMM estimator that combines the regressions in differences and levels in a system of equations using the lagged differences instruments for the level series, and the
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lagged levels of instruments for the differenced series. The two-step system GMM provides
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more efficient estimators over the one-step system GMM; however, the associated asymptotic standard errors may be biased downwards in the finite sample (Bond et al., 2001). It,
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therefore, applies Windmeijer (2005) finite-sample correction for the variance of the linear efficient two-step system GMM estimator.
3.4. Data
The wealth inequality data used in this paper are the top 1 percent and top 10 percent wealth shares for 46 developed and developing countries over the period 2000-2014. For the first time, top wealth shares data for 46 countries since 2000 are published in the Credit Suisse
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ACCEPTED MANUSCRIPT (2014) report. This study considers top wealth shares as the preferred measure of wealth inequality for three important reasons. First, it is simple to understand and not over-sensitive to wealth changes at the bottom of the wealth distribution; second, the top wealth holders may experience faster growth of their wealth, whereas the bottom wealth holders may experience slower or even decrease in their wealth growth; third, it correlates well with Gini coefficient
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and people use to talk about top decile or top percentile wealth growth while discussing growing wealth inequality (Credit Suisse, 2014). In almost all nations, the mean wealth of the top 10 percent is about ten times more than the median wealth, whereas, for top 1 percent, the mean wealth exceeds 100 times or even 1000 times the median wealth in the most unequal
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nations (Credit Suisse, 2014). Wealth Gini data are only reported since 2010; hence they are not suitable for our panel study.
The economic freedom of the world summary index (EF) from the Fraser Institute is
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used to measure cross-country economic freedom. This is an 11-point ordinal scale, ranging from 0 to 10, where a higher value indicates greater economic freedom. It relies mainly on
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quantitative measures and is often used as a measure of market-based institutions (Gwartney et al., 2016). The EF is constructed from the combination of five different categories or sub-
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indices, namely government size index (GV), legal structure and security of property rights
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index (PR), access to sound money index (SM), freedom to trade internationally index (FT), and regulation of credit, labour and business index (RG). There are two reasons why the EF is
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preferable to other indices of economic freedom for this study. First, EF is a better measure to extensively capture the reliance of a country on free markets to allocate resources. Second, the subcomponents of EF measure the degree of economic freedom in five broad areas, so it allows us to investigate to what extent wealth inequality affects economic freedom in all the major aspects of the overall economic freedom.
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ACCEPTED MANUSCRIPT Trade union density corresponds to the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earners. Among the control variables, economic growth is measured as the growth rate of the real GDP per capita; school attainment is measured as the average years of schooling of the population aged 15 years and above; inflation rate is measured as the growth rate of the consumer price index (CPI);
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population size is measured as the number of population per square kilometers of land area; and natural resources rents are measured as the sum of oil, gas and mineral rents to GDP ratio. [Table 1 here]
The descriptive statistics of the key variables used in the regression analysis of this
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study are reported in Table 1. The average shares of total wealth owned by the top 1 percent and top 10 percent population are 32.18% and 62.92%, respectively. The mean score of the economic freedom summary index and its sub-indices ranges from 6.11 to 8.81. The average
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value of trade union density rate and democracy score are 24.78 percent and 7.36, respectively. Most of the variables demonstrate a sufficient degree of identifying variations to
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yield efficient parameter estimates in this study.
4. Empirical results
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This section presents the baseline regression results of the effects of wealth inequality on economic freedom summary index, and it‟s five sub-indices as discussed in section 3. The econometric models are estimated using ordinary least squares (OLS) and instrumental variables estimators (IV-2SLS).
4.1. Pooled OLS & fixed effects estimates [Table 2 here] 18
ACCEPTED MANUSCRIPT Table 2 reports pooled OLS results in panel A, and fixed effects results in panel B for the baseline regressions using both the top 1 percent (Top1) and top 10 percent (Top10) wealth shares. The OLS estimates start without any fixed effects in panel A, and then incorporate country and year dummies to capture country and time specific fixed effects in the regression models in panel B.
The results are qualitatively similar in both panels, however, the
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coefficients of wealth inequality in fixed effects estimates are, on average, lower than that of the pooled OLS which may reflect the upward bias in the estimates of simple pooled regressions. The results indicate that wealth inequality has a significant adverse impact on overall economic freedom (EF) and its four key areas, such as property rights (PR), sound
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money (SM), freedom to trade (FT), and regulation of business (REG). The significant positive impact of wealth inequality on government size (GV) indicates that wealth inequality may reduce government tax and welfare activities. All these coefficients are found to be
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statistically significant at the 1 percent level. However, the coefficients of wealth inequality may be biased and inconsistent in these OLS estimates due to the presence of endogeneity:
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reverse causality, omitted variables and measurement errors in the wealth distribution. To deal with this endogeneity problems, this study now applies instrumental variables two-stage least
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squares (IV-2SLS) estimator, where wealth inequality is instrumented by the trade union
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density.
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4.2. IV-2SLS estimates [Table 3 here]
Table 3 reports IV-2SLS regression results of wealth inequality and economic freedom nexus. Panel A and B consider Top1 and Top10 percent wealth shares as alternative wealth inequality measures. The upper part of each panel presents the second-stage estimates whereas the lower part reports the associated first-stage regressions results. Consider the first
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ACCEPTED MANUSCRIPT stage regressions in both panels where organized trade union density (Union) is used as an instrument for wealth inequality. As noted above, country and time fixed effects dummies are included in all regressions including the first stage regressions. The strong negative significance of the coefficient of Union and the higher values of the first-stage F-tests and the corresponding p-values on the excluded instrument indicate that Union is sufficiently
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correlated with wealth inequality to serve as a potentially good instrument. Again, the endogeneity test results reject the null hypothesis of the exogeneity of the endogenous regressor and thereby implying that wealth inequality should be treated as endogenous which gives support for using an instrumental variable approach in this study.
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Estimated results from the second-stage regressions in panel A and B reinforce the OLS estimates reported in Table 2. The coefficients of the overall economic freedom (EF) and its four major areas, such as property rights (PR), sound money (SM), freedom to trade (FT),
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and regulation of business (REG) are found to be highly negatively significant at the 1% level, whereas, the coefficients of government size (GV) appear to be positive and strongly
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significant at the 1% level. The estimated coefficients of Top1 and Top10 percent wealth shares are noticeably greater (in absolute terms) than their OLS counterparts, providing some
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evidence of possible measurement errors. Again, economic freedom effects of wealth
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inequality are more pronounced for Top10 than they are for Top1 percent wealth shares. The elasticity between EF and Top10 is -0.38, indicating that a 10 percent increase in
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the level of 10 percent wealth shares reduces overall economic freedom by 3.8 percent. In component analysis, wealth inequality appears to reduce the size of government, which is consistent with observation of Benabou (2002) and Acemoglu and Robinson (2008) who argue that the higher inequality may call for progressive taxation and fiscal redistribution which may create disincentive to work and invest; hence rich elites lobby against the tax hike and implementation of redistributive policies. Islam et al. (2017) also find that the rising
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ACCEPTED MANUSCRIPT income inequality is associated with a declining income tax ratio in the OECD over the last 150 years. Finally, wealth inequality appears to have significant negative effects on property rights protection, soundness of money, freedom to international trade and business regulations. The largest negative effects of wealth inequality arise from property rights and legal system, which are in the line with Glaeser et al. (2003) and Sonin (2003) who argue that
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the rising inequality is detrimental to the security of property rights, and therefore to growth, because it enables the rich agents to use their wealth and accumulated political power to subvert the political, regulatory, and legal institutions of society for their own benefit.
Although the empirical results of this study are similar, to some extent, to a few
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income inequalities based studies, they are different in several aspects. First, the significant negative impact of wealth inequality on aggregate economic freedom may be consistent with the findings of the two main cross-country studies (Murphy, 2015 and Krieger and
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Meierrieks, 2016) that focus solely on income inequality; however, the size of the effect of the wealth inequality is much higher than that of the income inequality. Second, this study has
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obtained significant effects of wealth inequality on all five major areas of economic freedom, whereas the income inequality based studies find different inequality effects across sub-
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components of overall economic freedom. For example, Contrary to Murphy (2015), this
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study finds that the wealth inequality significantly reduces the size of the government which may lead to fewer interventionists policies such as lower tax and social spending in response
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to greater wealth concentration. Again, Krieger and Meierrieks (2016) obtains strong negative significant effects of income inequality on property rights and business regulations; whereas this study demonstrates that the higher wealth inequality has strong and significant negative impacts on the remaining four areas of the aggregate economic freedom, e.g., property rights protection, soundness of money, freedom to international trade, and business regulations. In
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ACCEPTED MANUSCRIPT sum, compared to other income inequality based studies, the wealth inequality shows much stronger association with the economic freedom than that of the income inequality.
4.3. Potential violation of exclusion restriction in IV-2SLS estimates To examine the sensitivity of the IV-2SLS estimates to potential violations from the exclusion
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restriction, this study employs Conley et al.‟s (2012) union of confidence intervals (UCI) approach of Plausible Exogeneity test to identify upper and lower bounds of the effect of wealth inequality on economic freedom when the instrument deviates from perfect exogeneity. The exclusion restriction is satisfied when the parameter
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following structural Eq. (2):
is equal to zero in the
(2)
where, Y is the economic freedom measure, X is the endogenous variable (wealth inequality),
a precise interval,
is assumed to have
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Z is the instrument (trade union density). Similar to Conley et al. (2012),
[ -δ; +δ]. As true value is unknown, the union of intervals for β is in that interval. δ is estimated by including the direct effect of
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estimated with respect to any
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the union density rate on economic freedom in the structural Eq. (2) and observing the effect size ̂. By changing the value of δ, we can identify the threshold level at which the second
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stage coefficient on the wealth inequality becomes insignificant. Therefore, the UCI approach gives a clear indication on the extent of the exclusion restriction violation that is required to
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invalidate the results from reduced form estimates (Madsen et al., 2018). [Table 4 here]
Table 4 presents estimated results of the Conley et al.‟s (2012) UCI approach to examine the sensitivity of the IV-2SLS results in Table 3 to the presence of a direct relationship between the IV (i.e. trade union density) and the economic freedom and its major components, regardless of the mechanisms through which this association arises. The
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ACCEPTED MANUSCRIPT estimated bounds are reported for the 95% confidence intervals. The marginal effects are statistically significant when the upper and lower bounds of confidence interval are both either above or below zero. The empirical results show that none of the confidence intervals of the overall economic freedom and related key areas contain zero and thus providing substantial evidence in favor of statistically significant robust impact of wealth inequality on
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economic freedom.
5. Robustness Tests
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This section investigates the robustness of the empirical results reported in the previous section to the use of 5-year and 10-year lagged wealth inequality in OLS estimates, system GMM regressions, alternative economic freedom and wealth inequality measures, estimates in longer time span, and the inclusion of control variables in the baseline IV-2SLS estimates.
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5.1. OLS estimates using 5-year and 10-year lagged wealth inequality
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[Table 5 here]
Using lagged inequality may bypass the need for finding a suitable instrument that can
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address potential endogeneity biases resulting from simultaneous and reverse causation. It is more likely that the lagged wealth inequality is exogenous to economic freedom. Therefore,
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this study re-estimates the effects of wealth inequality on economic freedom using 5-year
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(panel A) and 10-year (panel B) lagged wealth inequality (Top1 and Top10) and the results are reported in Table 5, which are qualitatively similar to the baseline IV-2SLS regression results presented in Table 3. Therefore, wealth inequality remains a robust and significant determinant of the economic freedom when 5-10-year lagged wealth inequality is used in the regression estimates.
5.2. System GMM estimates
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ACCEPTED MANUSCRIPT Using external instruments is the best option to get rid of the possible problems of endogeneity and reverse causation, but pure exogenous instruments that vary across countries and over time are rarely found. Again, the use of a weak instrument that explains little variation in the endogenous explanatory variable can lead to large inconsistencies in the IV2SLS estimates which are biased in the same direction as OLS estimates in the finite sample
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(Bound et al., 1995). Therefore, this study uses system GMM dynamic panel estimator to instrument for current wealth inequality with lagged differences in wealth inequality and other regressors, and to instrument for current differences in wealth inequality with lagged levels of wealth inequality and additional regressors.
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[Table 6 here]
Table 6 reports regression results using the two-step system GMM estimator that satisfies a battery of diagnostic checks, including Hansen‟s overidentifying restrictions tests
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for instrument validity, and AR(1) and AR(2) tests for the first order and second order serial correlations, respectively. Too many instruments can severely weaken and bias the Hansen
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overidentifying restriction tests and therefore, the number of instruments should stay below the number of countries (Roodman, 2009). The system GMM estimates generate around 20
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instruments which are lower than the number of cross-sections (i.e. 46 sample countries),
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hence regression results are free from instruments proliferation. Estimated results in Table 6 remain very similar to that of the baseline IV-2SLS results reported in Table 3. The
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coefficients of wealth inequality are statistically significant; thus, further underscoring that the results are very robust to estimation technique and, therefore, that there is likely to be a strong causal relationship from wealth inequality to economic freedom.
5.3. IV-2SLS estimates using alternative measure of economic freedom [Table 7 here]
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ACCEPTED MANUSCRIPT This study has so far considered Fraser Institute‟s economic freedom summary index and its corresponding five sub-indices to examine the effects of wealth inequality on economic freedom. For robustness, it has re-estimated its regression models in Eq. (1) using corresponding economic freedom measures from the Heritage Foundation, such as overall economic freedom, government spending, property rights, monetary freedom, trade freedom
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and business freedom. Table 7 reports IV-2SLS estimates of the regression models using these alternative economic freedom measures. The regression results are largely in line with those obtained in the baseline IV-2SLS regressions in Table 3, implying that wealth inequality depresses economic freedom across sample countries. The size of the coefficients of wealth
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inequality appears to be larger than that of Table 3, which may be due to measurement issues of the Heritage Foundation data, which not only attempts to measure macroeconomic outcome variables but also qualitatively analyzes the ability of the institutions to foster Therefore, the empirical findings in this study are not significantly
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economic freedom.
affected by alternative measures of economic freedom.
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5.4. IV-2SLS estimates using Gini coefficient of income distributions
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[Table 8 here]
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Leigh (2007) finds a strong and robust relationship between top income shares and Gini coefficient and suggests that panel data on top income shares may be a useful substitute for
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other measures of inequality over periods when alternative income distribution measures are of low quality, or unavailable. Due to unavailability of wealth Gini data over the sample period, this study has used top wealth shares data (1% and 10%) as the measure of wealth inequality. However, most of the empirical studies on inequality-growth-freedom consider income inequality measure such as, Gini coefficient of income distributions as a proxy for wealth inequality due to lack of data on wealth inequality across countries over time. The correlation coefficient between top 1% wealth shares and income Gini coefficient appears to 25
ACCEPTED MANUSCRIPT be 0.65 in this study; therefore, it will be interesting to see whether the key results of this paper hold when wealth inequality is measured by income Gini coefficient. Estimated results reported in Table 8 are qualitatively similar with those obtained in the baseline IV-2SLS regressions in Table 3, suggesting that higher wealth inequality significantly deters economic freedom across sample countries. Therefore, our empirical results are not sensitive to the use
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of Gini coefficient of income distributions.
5.5. System GMM estimates using long-term Gini coefficient of income distributions [Table 9 here]
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Thus far the regressions have been carried out over the period 2000-2014 due to the availability of wealth inequality data from the Credit Suisse database since the year 2000. As the estimation period is short, there is a possibility that the estimated effects of wealth
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inequality on economic freedom may be a short-run phenomenon which may turn to be different in the long run. Since the data on Gini coefficient of income distributions are
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available for a longer time span, it would be useful to examine the long run impact of inequality on economic freedom. The earlier data on economic freedom are available in 5-
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year intervals; hence the regression models are re-estimated in 5-year intervals over the period
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1970-2014. As trade union density data are not available for earlier periods, we have focused on system GMM estimates where inequality is instrumented by internal instruments.
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Estimated results are reported in Table 9 where the coefficients of inequality remain significant with correct signs across overall as well as sub-indices of economic freedom, suggesting that the baseline IV-2SLS results for 2000-2014 in Table 3 remain robust while considering long time series data (1970-2014) on income Gini coefficient.
5.6. IV-2SLS estimates with control variables [Table 10 here] 26
ACCEPTED MANUSCRIPT The IV-2SLS regressions in Table 3 are re-estimated using control variables in Eq. (1). The economic rationale behind these controls is discussed in Empirical Framework in section 3.1. The coefficients of wealth inequality (Top1) are strongly significant at 1%-5% levels, regardless of whether control variables are included in Table 10. The estimated results remain very similar to that of the baseline IV-2SLS estimates in Table 3. Except for government
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expenditure, the coefficients of wealth inequality remain significantly negative, suggesting that the equitable distribution of wealth among the population is associated with higher level of economic freedom across nations.
Of the control variables, the growth rate of real GDP per capita and school attainment
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show significant positive impacts on economic freedom; whereas higher inflation exhibits a negative effect on economic freedom as expected. Population size adversely affects trade freedom and business regulations; whereas resource rents exert a significant negative impact
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on the soundness of money. Overall, the baseline IV-2SLS regression results in Table 3 are
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not significantly influenced by conditioning variables.
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6. Wealth inequality, democracy and economic freedom Friedman (2002) argues that the role that government should play in a society dedicated to
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freedom and rely primarily on the market to organize economic activity; hence democracy is
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strongly associated with economic freedom. Democracy may not be limited only to free and fair elections, rather it extends to whether a country has checks and balances on executive powers, constitutional processes and guarantees, freedom of the press and the absence of censorship, clear and effective judicial and legal structures, incumbent term limits, and transparency, openness and citizen input in policymaking (Rivera-Batiz and Rivera-Batiz, 2002). If political institutions place all political power in the hands of a single individual or a small group, economic institutions that provide protection of property rights and equal 27
ACCEPTED MANUSCRIPT opportunity for the rest of the population are difficult to sustain (Acemoglu et al., 2005). Democratic regimes are more likely to exhibit the legitimacy necessary to carry out liberal economic reforms (De Haan and Sturm, 2003 and Rode and Gwartney, 2012). Nondemocracies tend to be dominated by the rich either because the rich wield sufficient power to create such a regime or because those who can wield power for other reasons subsequently
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use this power to become rich (Acemoglu et al., 2015). In this section, we have examined whether the relationship between wealth inequality and economic freedom depends on the level of democracy. The underlying hypothesis is that countries with higher level of wealth inequality may experience a significant reduction in
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economic freedom and thus strengthening democracy may improve economic freedom by limiting unequal distribution of wealth across those economies. Democracy is measured by the revised combined Polity IV score (Polity2) which is computed by subtracting the
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„Institutionalized Autocracy‟ score from the „Institutionalized Democracy‟ score; the resulting unified polity scale ranges from +10 (strongly democratic) to -10 (strongly autocratic).
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[Table 11 here]
Democracy and its interaction with wealth inequality are included in the regressions in
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Table 11. The coefficients of wealth inequality are statistically significant and negative at the
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1% level of significance in all cases except the government size, reinforcing the direct negative impact of wealth inequality on economic freedom. Furthermore, the coefficients of
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the interaction term are all positive and statistically significant at least at the 5 percent level. The positive interaction effect suggests that the economic freedom and its major five areas such as property rights, government size, soundness of money, freedom to trade and business regulations are negatively affected by wealth inequality at low levels of democracy and positively affected by wealth inequality at high levels of democracy. In other words, the influence of wealth inequality on economic freedom depends on the degree of development of
28
ACCEPTED MANUSCRIPT the democratic institutions. Finally, the coefficients of the democracy are also significantly negative, which in conjunction with the positive coefficient of the interaction term suggest that the negative effect of wealth inequality on economic freedom is greater at low levels of democracy. On average, democratic development may reduce the adverse effects of wealth
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inequality on economic freedom across sample countries.
7. Conclusion and extension
Inequality has been a growing concern for free-market institutions and policies that protect
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individuals and their properties and allow freedom of choice. However, the consequences of inequality on economic freedom remain unclear, and the empirical evidence based on income inequality remains inconclusive. Provided that the wealth inequality is more devastating than the income inequality, this paper empirically investigates the impact of wealth inequality on
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economic freedom and to what extent this relationship is affected by democratization. Wealth inequality is measured by the Credit Suisse‟s top wealth shares (1% and 10%). Economic
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freedom is measured by the Fraser Institute‟s economic freedom summary index and its five
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major sub-indices, such as government size, property rights, sound money, freedom to trade and regulation. Trade union density is used as a potential instrument for wealth inequality.
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Empirical findings across 46 sample countries over the period 2000-2014 suggest that the rising wealth inequality significantly obstructs economic freedom in all the key areas
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except government size. The magnitude of the effect is economically significant. On average, a ten percent increase in the wealth inequality measure would cause a 2.7 percent decline in the economic freedom summary measure. More specifically, higher wealth inequality causes lower protection in property rights, less access to sound money, less freedom in international trade, and greater regulation of credit, labor, and business. On the contrary, larger wealth inequality reduces government size which may indicate that higher concentration of wealth
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ACCEPTED MANUSCRIPT may lead to lower taxes and government social welfare expenses. The largest adverse effect of wealth inequality on economic freedom arises from the uncertainty in the protection of property rights. The interaction effect between wealth inequality and democracy is positive and significant, implying that the negative effects of wealth inequality on economic freedom may be greater at a lower level of democracy.
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Some degree of inequality may be tolerable to drive growth and development by rewarding people with talent, skills, and the ambition to innovate and take entrepreneurial risks; however, too much wealth concentrations may threaten to exclude hundreds of millions of people from realizing the benefits of their talents and hard work (Oxfam, 2014). This
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extreme wealth inequality may create strong incentives for individuals to pursue their interests outside the normal market activities and thus individuals in more unequal societies are likely to engage in rent-seeking activities as well as other socio-political instability manifestations,
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for example, violent protests and coups ( Perotti, 1996). It may also cause a deterioration in the security of property rights and contractual rights which may slow down economic
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freedom. The consequences even embrace the destruction of democratic governance, the pulling apart of social cohesion, and the vanishing of equal opportunities for all (Oxfam,
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2014).
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Our results have significant policy implications. First, the negative effect of wealth inequality on economic freedom is very robust; hence governments and policymakers that
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seek to promote economic freedom in a society should curb substantial wealth concentrations among the rich elites and implement necessary redistributive policies to lessen inequality. Second, wealth inequality significantly reduces the size of government which may lead to less interventionists policies such as lower tax and social spending in response to greater wealth concentration. Taking together, this study suggests that in response to the growing wealth inequality, the economic elites lobby against tax and redistributive policies and use their
30
ACCEPTED MANUSCRIPT economic and political power to serve their own interest by depressing property rights, monetary, trade, and regulatory reforms which may discourage competition and innovation and thereby deteriorate economic freedom. However, democratization may help countries to improve economic freedom by undertaking favorable market reforms and to reduce the negative effects of wealth inequality by administering welfare-centric redistributive measures.
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Oxfam (2017) suggests that governments should increase taxes on both wealth and high incomes to ensure a more level playing field, and to generate funds needed to invest in healthcare, education and job creation. Although median voter theory states that a wellfunctioning democracy reduces inequality by creating pressures for redistribution in unequal
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societies; but significant wealth inequality may produce an unequal distribution of political power and thus the rich elites may oppose democratic movement to reduce their tax burden which may further increase inequality. The main challenge to break this vicious cycle of
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inequality is to curb the influence of wealth on the political decision-making process. Piketty (2014) also calls for a progressive annual tax on capital to mitigate wealth inequality;
ED
however, successful implementation of this policy requires a high level of international cooperation and regional political integration which may be one of the major challenges for
PT
the century ahead.
CE
The exploding wealth of the rich elites is only one side of the story of the global inequality; technological advancement has stagnated or even shrunk earnings for much of the
AC
population across nations (Rotman, 2014). In this digital era, computing and networking activities are growing at an exponential rate that contributes to increasing productivity, reducing prices and raising overall economic growth. However, as technology races ahead, the demand for highly skilled workers rises, while workers with less education and expertise fall behind, and thereby inequality rises (Brynjolfsson and McAfee, 2011). World-renowned scientist Hawking (2015) warns that the great technological advances may worsen inequality
31
ACCEPTED MANUSCRIPT in future because machine-produced wealth is mostly concentrated among the machine owners who successfully lobby against wealth redistribution, thereby creating technologydriven ever-increasing inequality. Since machines gradually substitute for labor and businesses become less capital-intensive, the major economic players in future will be those who have innovative ideas to produce new products and develop successful business models
CR IP T
(Rotman, 2014). WEF (2017) has recently warned that there are new threats to social cohesion from the robotics and artificial intelligence revolution; hence fundamental reforms of capitalism may be needed to tackle excessive inequality and public anger around the globe. Therefore, a useful extension to this study would be to investigate how rapid technological
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progress influences the nexus between wealth inequality and economic freedom across nations and over time.
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ACCEPTED MANUSCRIPT Variable
Obs.
Mean
Std. Dev.
Min.
Max.
Top 1 Percent Wealth Share (Top 1)
690
32.18
9.49
16.90
66.20
Top 10 Percent Wealth Share (Top10)
690
62.92
8.30
46.80
84.80
Economic Freedom Summary Index (EF)
690
7.32
0.73
5.14
9.17
Government Size Index (GV)
690
6.11
1.34
3.09
9.41
(PR)
690
6.78
1.49
2.89
9.62
Access to Sound Money Index (SM)
690
8.81
Freedom to Trade Internationally Index (FT)
690
7.78
Regulation of Credit, Labour and Business Index (RG)
690
7.10
Trade Union Density Rate (Union)
690
24.78
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Legal Structure and Security of Property Rights Index
3.57
9.89
0.92
3.77
9.71
0.95
4.53
9.13
16.79
1.36
79.10
-10
10
AN US
1.15
Democracy (Polity2)
690
7.36
5.09
Notes: The sample includes annual data for 46 countries over the period 2000-2014. Economic freedom indices range from 0-10 where 0 corresponds to „less economic freedom‟ and 10 to „more economic freedom‟. Polity2
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democracy score ranges from -10 (hereditary monarchy) to +10 (consolidated democracy).
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Table 2
Wealth inequality and economic freedom (OLS: 46 countries, 2000-2014). lnGV
lnPR
lnSM
lnFT
lnRG
(2)
(3)
(4)
(5)
(6)
PT
lnEF (1)
CE
Panel A: Pooled OLS
Top 1% wealth share
-0.13***
0.36***
-0.38***
-0.28***
-0.19***
-0.10***
Inequality)it
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
R2
0.14
0.21
0.23
0.28
0.20
0.04
AC
ln(Wealth
Top 10% wealth share
ln(Wealth
-0.20***
0.63***
-0.60***
-0.51***
-0.34***
-0.10***
Inequality)it
(0.03)
(0.05)
(0.06)
(0.04)
(0.03)
(0.04)
R2
0.07
0.14
0.12
0.20
0.14
0.01
Panel B: Fixed effects OLS
Top 1% wealth share
39
ACCEPTED MANUSCRIPT ln(Wealth
-0.13***
0.12***
-0.24***
-0.23***
-0.17***
-0.11***
Inequality)it
(0.02)
(0.03)
(0.04)
(0.04)
(0.02)
(0.03)
R2
0.82
0.90
0.89
0.59
0.82
0.80
Top 10% wealth share -0.20***
0.25***
-0.41***
-0.37***
-0.32***
-0.23***
Inequality)it
(0.05)
(0.06)
(0.10)
(0.10)
(0.05)
(0.06)
R2
0.80
0.90
0.89
0.58
0.81
0.75
CR IP T
ln(Wealth
Notes: The Economic freedom summary index (EF) and its five major sub-indices, e.g. government size index (GV), legal structure and security of property rights index (PR), access to sound money index (SM), freedom to trade internationally index (FT), and regulation of credit, labour and business index (RG) are considered as alternative dependent variable to explore all possible channels of economic freedom. Wealth inequality is considered as the explanatory variable measured by
AN US
the top 1% as well as top 10% wealth shares. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Fixed Effects OLS estimates in Panel B include both country and time dummies; however, the fixed effects coefficients are not reported to conserve space. The constant is included in the regression models but not reported for brevity. ln indicates natural logarithm. Total number of observation is 690. See also
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notes to Table 1.
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Table 3
Wealth inequality and economic freedom (IV-2SLS: 46 countries, 2000-2014).
PT
lnEF (1)
lnGV
lnPR
lnSM
lnFT
lnRG
(2)
(3)
(4)
(5)
(6)
CE
Panel A: Top 1% wealth share
Second stage regressions
-0.16***
0.09***
-0.30***
-0.20***
-0.26***
-0.11***
Inequality)it
(0.03)
(0.03)
(0.05)
(0.07)
(0.03)
(0.03)
AC
ln(Wealth
First stage regressions
ln(Union
-0.24***
-0.24***
-0.24***
-0.24***
-0.24***
-0.24***
Density)it
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
F-statistic
191.07
191.07
191.07
191.07
191.07
191.07
F-statistic (p-value)
0.00
0.00
0.00
0.00
0.00
0.00
Endog (p-value)
0.00
0.00
0.00
0.00
0.00
0.00
40
ACCEPTED MANUSCRIPT Panel B: Top 10% wealth share
Second stage regressions
ln(Wealth
-0.38***
0.21***
-0.69***
-0.47***
-0.65***
-0.30***
Inequality)it
(0.07)
(0.08)
(0.13)
(0.17)
(0.07)
(0.10)
First stage regressions -0.10***
-0.10***
-0.10***
-0.10***
-0.10***
-0.10***
Density)it
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
F-statistic
131.47
131.47
131.47
131.47
131.47
131.47
F-statistic (p-value)
0.00
0.00
0.00
0.00
Endog (p-value)
0.00
0.00
0.00
0.00
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ln(Union
0.00
0.00
0.00
0.00
Notes: Wealth inequality is instrumented by trade union density. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Constant, country dummies and time dummies are
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included but not reported for brevity. Endog indicates endogeneity test. See also notes to Table 1 and 2.
Table 4 Plausibly exogenous bounds test.
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
-0.90
-0.43
-0.64
-0.21
0.81
-0.29
-0.14
-0.13
-0.01
M
lnGV
ED
lnEF
Panel A: Top 1% wealth share -0.29
Upper Bound
-0.02
0.32
PT
Lower Bound
CE
Panel B: Top 10% wealth share -1.13
2.34
-3.15
-1.37
-2.12
-1.22
Upper Bound
-0.21
4.95
-0.44
-0.32
-0.52
-0.19
AC
Lower Bound
Notes: Based on the IV specifications in Table 3, this Table 4 presents upper and lower bounds of a 95% confidence interval for the effects of wealth inequality on economic freedom and its major five areas. The bounds are estimated using the union of confidence intervals (UCI) methodology of Conley et al. (2012) under various priors about the direct effect that trade union density has on economic freedom.
41
ACCEPTED MANUSCRIPT
Table 5 Wealth inequality and economic freedom (OLS estimates with lagged wealth inequality: 46 countries, 2000-2014). lnGV
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
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lnEF
Panel A: 5-year lagged wealth inequality
Top 1% wealth share
ln(Wealth
-0.13***
0.15***
-0.31***
-0.21***
-0.17***
-0.07**
Inequality)i,t-5
(0.02)
(0.04)
(0.04)
(0.04)
(0.02)
(0.03)
R2
0.90
0.93
0.94
0.78
0.85
0.86
ln(Wealth
-0.20***
0.26***
Inequality)i,t-5
(0.05)
(0.09)
R2
0.88
0.93
-0.54***
-0.33***
-0.31***
-0.17***
(0.10)
(0.10)
(0.06)
(0.07)
0.93
0.76
0.84
0.79
Top 1% wealth share
M
Panel B: 10-year lagged wealth inequality
AN US
Top 10% wealth share
-0.16***
0.19***
-0.44***
-0.22***
-0.20***
-0.11***
Inequality)i,t-10
(0.03)
(0.07)
(0.05)
(0.06)
(0.02)
(0.04)
R2
0.94
0.96
0.96
0.88
0.89
0.91
Top 10% wealth share -0.75***
Inequality)i,t-10
PT
ED
ln(Wealth
(0.08)
(0.16)
(0.17)
(0.14)
(0.07)
(0.09)
R2
0.92
0.96
0.95
0.86
0.88
0.82
-0.27***
0.31**
-0.36***
-0.38***
-0.20**
CE
ln(Wealth
AC
Notes: The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Constant, country dummies and time dummies are included but not reported for brevity. Total number of observation is 460 and 230 for Panels A and B, respectively. See also notes to Tables 1-3.
42
ACCEPTED MANUSCRIPT Table 6 Wealth inequality and economic freedom (System GMM: 46 countries, 2000-2014). lnEF
lnGV
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
-0.06**
-0.06**
Panel A: Top 1% wealth share -0.02**
0.09**
-0.14***
-0.04*
Inequality)it
(0.01)
(0.04)
(0.04)
(0.02)
(0.02)
(0.02)
Hansen (p-value)
0.47
0.19
0.23
0.13
0.86
0.88
AR (1) (p-value)
0.00
0.00
0.00
0.01
0.00
0.00
AR (2) (p-value)
0.83
0.68
0.32
0.12
0.29
0.59
-0.06**
0.31**
Inequality)it
(0.03)
(0.13)
Hansen (p-value)
0.44
0.34
AR (1) (p-value)
0.00
0.00
AR (2) (p-value)
0.84
0.65
-0.34***
-0.09*
-0.32**
-0.16**
(0.12)
(0.05)
(0.14)
(0.06)
0.23
0.16
0.28
0.48
0.00
0.00
0.04
0.00
M
ln(Wealth
AN US
Panel B: Top 10% wealth share
CR IP T
ln(Wealth
0.12
0.29
0.58
0.33
ED
Notes: Coefficients are based on the two-step system GMM estimation, using the Windmeijer (2005) finite sample correction. The 2nd and 3rd lags of the explanatory variables are taken as instruments for the differenced equation whereas 1st
PT
differences of the explanatory variables are taken as instruments for the level equation. Total number of observation is 644. The number of instruments used in the system GMM is 20. The instrument count is based on the number of „collapsed‟
CE
instruments, using the xrabond2 specification from Roodman (2009). The Hansen test checks the validity of the instruments where the null hypothesis is that the instruments are not correlated with the residuals. The null hypotheses in the AR(1) and AR(2) tests are that the error terms in the first differenced regression exhibit no first and second order serial correlations,
AC
respectively. The one period lagged dependent variable, constant, country and time dummies are included but not reported for brevity. All regressions employ orthogonal deviations. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. See also notes to Tables 1-3.
43
ACCEPTED MANUSCRIPT Table 7 Wealth inequality and economic freedom (IV-2SLS estimates with alternative measures of economic freedom from Heritage Foundation: 46 countries, 2000-2014). lnEFH
lnGVH
lnPRH
lnSMH
lnFTH
lnRGH
(1)
(2)
(3)
(4)
(5)
(6)
Second stage regressions
CR IP T
Panel A: Top 1% wealth share ln(Wealth
-0.15***
0.62***
-0.48***
-0.52***
-0.16***
-0.14***
Inequality)it
(0.03)
(0.10)
(0.09)
(0.17)
(0.04)
(0.05)
First stage regressions -0.24***
-0.24***
-0.24***
-0.24***
-0.24***
-0.24***
Density)it
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
F-stat (p-value)
0.00
0.00
0.00
0.00
0.00
0.00
AN US
ln(Union
Panel B: Top 10% wealth share
Second stage regressions
-0.34***
1.45***
Inequality)it
(0.06)
(0.26)
-1.12***
-1.19***
-0.36***
-0.58***
(0.21)
(0.40)
(0.09)
(0.16)
M
ln(Wealth
First stage regressions
Density)it
(0.01)
F-stat (p-value)
0.00
-0.10***
-0.10***
-0.10***
-0.10***
-0.10***
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
0.00
0.00
0.00
0.00
0.00
ED
-0.10***
PT
ln(Union
Notes: The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels,
CE
respectively. Wealth inequality is instrumented by trade union density. Constant, country dummies and time dummies are included but not reported for brevity. Superscript
H
indicates measures of economic freedom and its five major components
AC
from Heritage Foundation. Total number of observation is 690. See also notes to Tables 1-3.
44
ACCEPTED MANUSCRIPT Table 8 Wealth inequality and economic freedom (IV-2SLS estimates with Gini coefficient of income distributions: 46 countries, 2000-2014). lnGV
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Top 1% wealth share
CR IP T
lnEF
Second stage regressions
ln(Wealth
-0.26***
0.14**
-0.47***
-0.32***
-0.46***
-0.11***
Inequality)it
(0.04)
(0.06)
(0.08)
(0.09)
(0.04)
(0.03)
-0.15***
-0.15***
First stage regressions -0.15***
-0.15***
Density)it
(0.01)
(0.01)
F-stat (p-value)
(0.00)
(0.00)
-0.15***
-0.15***
AN US
ln(Union
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.00)
(0.00)
(0.00)
Notes: The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels,
M
respectively. Wealth inequality is measured by the post-tax, post-transfer (net) Gini coefficient of income distributions (the results are consistent while using pre-tax, pre-transfer (market) Gini coefficient but not reported for conciseness). Wealth
ED
inequality is instrumented by trade union density. Constant, country dummies and time dummies are included but not
PT
reported for brevity. Total number of observation is 690. See also notes to Tables 1-3.
Table 9
CE
Wealth inequality and economic freedom (System GMM estimates with Gini coefficient of
AC
income distributions: 46 countries, 1970-2014). lnEF
lnGV
lnPR
lnSM
lnFT
lnRG
(1)
(2)
(3)
(4)
(5)
(6)
ln(Wealth
-0.72***
0.84**
-0.86**
-0.41**
-0.64**
-0.26**
Inequality)it
(0.09)
(0.29)
(0.32)
(0.14)
(0.13)
(0.07)
Hansen (p-value)
0.13
0.17
0.16
0.14
0.12
0.14
AR (1) (p-value)
0.01
0.00
0.02
0.01
0.03
0.04
AR (2) (p-value)
0.20
0.24
0.78
0.51
0.42
0.80
45
ACCEPTED MANUSCRIPT Notes: Two two-step system GMM estimates are in 5-year intervals for 1970-2010, and 4-year interval for 2010-2014 across 46 sample countries.
Wealth inequality is measured by the post-tax, post-transfer (net) Gini coefficient of income
distributions (the results are consistent while using pre-tax, pre-transfer (market) Gini coefficient but not reported for succinctness). The 2nd and 3rd lags of the explanatory variables are taken as instruments for the differenced equation whereas 1st difference of the explanatory variables is taken as instruments for the level equation in the system GMM estimates. The Hansen test checks the validity of the instruments where the null hypothesis is that the instruments are not correlated with the residuals. The null hypotheses in the AR(1) and AR(2) tests are that the error terms in the first differenced regression exhibit
CR IP T
no first and second order serial correlations, respectively. Constant, country and time dummies are included but not reported for brevity. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Total number of observation ranges from 368 to 375. See also notes to Tables 1-3 and Table 6.
AN US
Table 10
Wealth inequality and economic freedom (Conditional estimates with IV-2SLS: 46 countries, 20002014). lnGV
(1)
(2)
Top 1% wealth share
Inequality)it
(0.02)
Growthit
0.14
lnFT
lnRG
(3)
(4)
(5)
(6)
0.11**
-0.26***
-0.15**
-0.30***
-0.10**
(0.05)
(0.06)
(0.06)
(0.03)
(0.06)
0.65***
0.52***
-0.62***
0.29***
-0.12
(0.09)
(0.17)
(0.16)
(0.23)
(0.11)
(0.23)
0.25***
-0.04
0.42***
0.34***
0.15***
0.23***
Attainment)it
(0.03)
(0.05)
(0.06)
(0.09)
(0.04)
(0.04)
Inflationit
-0.04***
0.01**
-0.04***
-0.15***
0.03
-0.05***
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
ln(Population
-0.02
-0.03
0.02
-0.01
-0.02*
-0.01**
Sizeit)
(0.01)
(0.02)
(0.02)
(0.03)
(0.01)
(0.00)
Resource
0.02
0.18
0.39
-0.64**
0.01
0.23
Rentsit
(0.11)
(0.22)
(0.21)
(0.32)
(0.15)
(0.25)
AC
CE
ln(School
ED
-0.14***
lnSM
Second stage regressions
PT
ln(Wealth
lnPR
M
lnEF
First stage regressions
46
ACCEPTED MANUSCRIPT ln(Union
-0.20***
-0.20***
-0.20***
-0.20***
-0.20***
-0.20***
Density)it
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
F-stat (p-value)
0.00
0.00
0.00
0.00
0.00
0.00
Notes: Estimated results are based on top 1% wealth shares. The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels, respectively. Wealth inequality is instrumented by trade union density. Constant, country dummies and time dummies are included but not reported for brevity. Results are similar in Top 10 percent
CR IP T
wealth shares, but not reported to conserve space. Total number of observation is 643. See also notes to Tables 1-3.
Table 11
Wealth inequality, democracy and economic freedom (Conditional estimates with IV-2SLS: 46
lnEF
lnGV
(1)
(2)
Top 1% wealth share
AN US
countries, 2000-2014). lnPR
lnSM
lnFT
lnRG
(3)
(4)
(5)
(6)
-0.37***
-0.36***
-0.25***
Second stage regressions
-0.28***
0.01
Inequality)it
(0.03)
(0.08)
(0.09)
(0.06)
(0.05)
(0.08)
Polity2it
-0.11***
-0.14***
-0.16***
-0.14***
-0.10***
-0.01
× Polity2it
ED
(0.02)
(0.04)
(0.05)
(0.05)
(0.02)
(0.01)
0.03***
0.03***
0.04***
0.04***
0.03***
0.01**
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
PT
ln(Wealth Inequality)it
-0.53***
M
ln(Wealth
(0.00)
CE
Top 10% wealth share
Second stage regressions
-0.69***
-0.09
-1.28***
-1.07***
-0.89***
-0.53**
Inequality)it
(0.07)
(0.22)
(0.21)
(0.18)
(0.13)
(0.21)
Polity2it
-0.29***
-0.39***
-0.34**
-0.48**
-0.30***
-0.01
(0.05)
(0.12)
(0.14)
(0.20)
(0.05)
(0.01)
ln(Wealth Inequality)it
0.07***
0.09***
0.08**
0.12**
0.07***
0.02**
× Polity2it
(0.01)
(0.03)
(0.03)
(0.05)
(0.01)
(0.04)
AC
ln(Wealth
Notes: Democracy is measured by the Polity2 score ranging from +10 (strongly democratic) to -10 (strongly autocratic). The numbers in parentheses are robust standard errors. *, ** and *** denote 10%, 5% and 1%
47
ACCEPTED MANUSCRIPT significance levels, respectively. Wealth inequality is instrumented by trade union density. Wealth inequality and its interaction with democracy are instrumented by trade union density and its interaction with democracy, respectively. Regressions are based on IV-2SLS estimates, where the first stage results are consistent with the baseline estimates in Table 3 but not reported. Control variables used in Table 8 are included but not reported to conserve space. Constant, country dummies and time dummies are included but not reported for brevity. Total
CR IP T
number of observation is 643. See also notes to Tables 1-3.
Data Appendix Country List
AN US
The total 46 sample countries consist of Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Greece, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Japan, South Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Peru, Philippines, Poland, Portugal, Russian Federation, Saudi Arabia, Singapore,
M
South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Arab Emirates, United Kingdom, United States.
ED
Variables’ Definitions and Data Sources
Wealth Inequality: Top 1 percent as well as top 10 percent wealth shares are used as alternative
Databook
2014
published
by
CreditSuisse
(2014)
(see
https://www.credit-
CE
Wealth
PT
measures of wealth inequality. The wealth share data are collected from the Credit Suisse Global
suisse.com/corporate/en/research/research-institute/global-wealth-report.html).
AC
Gini Coefficients: Gini coefficient is used as an indicator of income inequality and the corresponding data are collected from Solt, Frederick (2016). “The Standardized World Income Inequality Database.” Social
Science
Quarterly 97.
SWIID
Version
6.1,
October
2017,
(see
http://myweb.uiowa.edu/fsolt/swiid/swiid.html) Economic Freedom: The world economic freedom summary index as well as its five sub-indices are used as alternative indicators for economic freedom across countries. The Fraser Institute‟s economic freedom of the world summary index (EF) that ranges from 0-10 where 0 corresponds to „less
48
ACCEPTED MANUSCRIPT economic freedom‟ and 10 to „more economic freedom‟, compiled from the Fraser Institute database (see http://www.freetheworld.com/). EF has five different components, for example, (a) Government size index (GV) that ranges from 0-10 where 0 corresponds to „large general government consumption‟, „large transfer sector‟, „many government enterprises‟, and „high marginal tax rates and low income thresholds‟, and 10 to „small general government consumption‟, „small transfer sector‟,
CR IP T
„few government enterprises‟, and „low marginal tax rates and high income thresholds‟, (b) Legal structure and security of property rights index (PR) that ranges from 0-10 where 0 corresponds to „no judicial independence‟, „no trusted legal framework exists‟, „no protection of intellectual property‟, „military interference in rule of law‟, and „no integrity of the legal system‟ and 10 corresponds to „high judicial independence‟, „trusted legal framework exists‟, „protection of
AN US
intellectual property‟, „no military interference in rule of law‟, and „integrity of the legal system‟, (c) Access to sound money index (SM) that ranges from 0-10 where 0 corresponds to „high annual money growth‟, „high variation in the annual rate of inflation‟, „high inflation rate‟, and „restricted foreign currency bank accounts‟ and 10 corresponds to „low annual money growth‟, „low or no
M
variation in the annual rate of inflation‟, „low inflation rate‟, and „foreign currency bank accounts are
ED
permissible without restrictions‟, (d) Freedom to trade internationally index (FT) that ranges from 0-10 where 0 corresponds to „increasing tax rate on international trade‟, „slow import or export
PT
process‟, „small trade sectors relative to the population and geographic size‟, „exchange rate controls are present and a black-market exists‟, and „restrictions on the freedom of citizens to engage in capital
CE
market exchange with foreigners‟ and 10 corresponds to „no specific taxes on international trade‟, „swift import or export process‟, „large trade sectors relative to the population and geographic size‟,
AC
„no black-market exchange rate‟, and „no restrictions on the freedom of citizens to engage in capital market exchange with foreigners‟, and (e) Regulation of credit, labour and business index (RG) that ranges from 0-10 where 0 corresponds to „low percentage of deposits held in privately owned banks‟, „high foreign bank license denial rate‟, „private sector‟s share of credit is close to the base-yearminimum‟, „deposit and lending rates are fixed by the government and real rates are persistently negative‟, „high impact of minimum wage‟, „widespread use of price controls throughout various sectors of the economy‟, and „starting a new business is generally complicated‟ and 10 corresponds to 49
ACCEPTED MANUSCRIPT „high percentage of deposits held in privately owned banks‟, „low foreign bank license denial rate‟, „private sector‟s share of credit is close to the base-year-maximum‟, „interest rate is determined primarily by market forces and the real rate is positive‟, „low impact of minimum wage‟, „no price controls or marketing boards‟, and „starting a new business is generally easy‟. For more detailed explanations, see Gwartney et al. (2016) and the Quality of Government Online Dataset (see
CR IP T
http://www.qog.pol.gu.se/data/datadownloads/qogstandarddata/). Trade Union Density: Trade union density is used as an instrument of wealth inequality. Trade union density corresponds to the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earners; and the data are extracted from OECD.Stat database (see
AN US
https://stats.oecd.org/Index.aspx?DataSetCode=UN_DEN).
Democracy: Polity2 as a proxy for the democracy (political freedom) is measured by the revised combined polity score (Polity2) and is obtained from the Polity IV project of the University of Maryland (available at http://www.systemicpeace.org/polity/polity4.htm). The Polity score combines
M
the scores on the democracy and autocracy indices to a single regime indicator. The score captures the regime authority spectrum on a 21-point scale ranging from -10 (hereditary monarchy) to +10
ED
(consolidated democracy). Polity2 is the revised combined polity score. Control Variables: Data on real GDP per capita growth rate are collected from the Conference Board Economy
Database
(see
https://www.conference-
PT
Total
board.org/data/economydatabase/index.cfm?id=27762). Data on inflation rate (consumer price index),
CE
population size, and resource rents to GDP ratio (oil, gas and mineral rents to GDP) are compiled from the World Development Indicators online database (see http://data.worldbank.org/data-catalog/world-
AC
development-indicators). Data on school attainment (measured as the average years of schooling in the population aged 15 years and over) are collected from Barro and Lee‟s Educational Attainment online database (see http://www.barrolee.com/).
50