The Politics of Financialization and Income InequalityBowei Hu

Department of Sociology, University of California, Los Angelesboweihu@ucla.edu

Thung-hong LinInstitute of Sociology, Academia Sinica

Recent cross-national studies of income inequality have debated whether financialization exacerbates or ameliorates the income inequality. This article proposes a theory of the politics of financialization indicating that the two seemingly contradictory effects of financialization are in fact two sides of the same coin. We argue that income inequality is exacerbated not only by increased financialization in the private sector but also by escalated financialization in the public sector, where state elites mobilize financial credit to obtain enormous revenues through the financialization of state. However, income inequality is ameliorated by the financialization in democracies, where credit is more accessible to non-elites and diverse groups than in autocracies. We pooled various cross-national datasets including 103 countries between 1981 and 2012 and analyzed with fixed-effects models. Our results show that while financialization increases income inequality within countries, the financialization in democracies reduces income inequality. Furthermore, the negative impact of financialization is larger than the equalizing effect, so financialization leads to a relatively modest income gap in democracies than in autocracies. These results not only substantiate that the theory of the politics of financialization is more general in explaining income inequality across different political regimes but also imply that the policy of democratizing credit access might reduce income inequality.

Keywords: Financialization, Income Inequality, Democracy, Credit Access

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The expansion of financial capital and profit is among the most crucial causes of increased income inequality in the era of globalization (Piketty 2014). Empirical evidence from affluent countries shows that the relationship between financialization and income inequality is still disputed among social scientists. These studies point out two contradictory findings about the relationship between financialization and inequality. The inequality-widening hypothesis suggests that financialization increases income inequality (Epstein and Jayadev 2005; Flaherty 2015; Fligstein and Goldstein 2015; Godechot 2016; Kus 2012; Lin and Tomaskovic-Devey 2013; Nau 2013; Roberts and Kwon 2017; Tomaskovic-Devey and Lin 2011). Inequality-narrowing hypothesis suggests that financialization exacerbates income inequality. (Beck et al. 2007; Clarke, Xu, and Zou 2006; Delis, Hasan, and Kazakis 2014). In this article we try to initiate a new discussion, rather than end this debate, by proposing a theory of the politics of financialization to enrich a theoretical understanding about why financialization generates income inequality within and between countries.

The underdevelopment of a theory of the politics of financialization might be responsible for the unceasing debate of the relationship between financialization and income inequality among social scientists. First, previous studies often overlooked the public sector and ruled out the financialization of state, which is especially more salient in autocracies in recent years (Wang 2015; Shih 2008). To capture state’s influence on income inequality through financialization, a theory of the politics of financialization should take the financial activities in the public sector including governments and the state-owned enterprises (SOEs), namely the financialization of state, into account. Second, many cross-national studies about the relationship between financialization and income inequality focusing on affluent and mostly democratic countries unconsciously ignore the variance of political regimes especially in developing and autocratic countries. Financial institutions under different political regimes are distinct (Acemoglu and Robinson 2012; Bhattacharyya 2013; Calomiris and Haber 2014; Haber, North, and Weingast 2008). Hence, the politics of financialization should also include the financialization tailored by different political regimes.

Reframing the two features of financialization, we propose the politics of financialization—the expansion and access of financial credit are ruled by states and political regimes (Calomiris and Haber 2014; Krippner 2012)—to explain the increase of income inequality. We argue (1) credit expansion in the economy (the private sector) and in the state itself (the public sector) yields considerable financial resources that could be exploited by economic and political elites to generate enormous revenues; (2) credit expansion is more accessible to diverse groups of people in democracies than in autocracies, which ameliorates the reproduction of income inequality by decentralizing financial resources. Foregrounding the roles of states and political regimes in expanding financial credit, the theory of the politics of financialization offers two mechanisms—the financialization of state and the financialization across political regimes— to enrich the understanding if the link between financialization and income inequality within and between countries.

We pooled panel data including 103 countries between 1981 and 2012 from various cross-national datasets and examined the association between financialization and income inequality through fixed-effects models (FEMs). Our statistical results indicate a significantly positive association between financialization and income inequality within countries. The financialization in the public sector still holds the same positive association with income inequality even though we have taken the financialization in the private sector into account. This result is consistent with our first mechanism that the financialization of state leads to the increase of income inequality

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within countries as the financialization of the private sector does. Furthermore, the interaction term between democracy and financialization displays negative coefficients. It shows that in contrast to autocratic countries, the financialization in democratic countries leads to lower income inequality. Taking the main effect and the interaction effect together into consideration, the financialization in democracies generates less income inequality than it does in autocracies, which supports our second mechanism that in democracies the financialization with more diverse credit access results in less income inequality. Overall, these results indicate that although the financialization in both private and public sectors rises income inequality within countries, this effect is ameliorated in democratic countries.

If we adopt the theory of the politics of financialization in explaining income inequality, the seemingly conflicting findings are not because of different data coverage or the period of time but just two sides of the same coin. We believe that the theoretical insights of the politics of financialization shed a new light on the debate about the relationship between financialization and inequality. Our findings imply the role of the financialization of state is also an origin of income inequality, which was overlooked by previous research. They intensively focus on the shift of power resources from labor to economic elites within organizations to explain the increase of income inequality (Dünhaupt 2017; Lin and Tomaskovic-Devey 2013; Nau 2013; Volscho and Kelly 2012). Nonetheless, the financialization of state suggests an inconspicuous pathway that political elites in public sector also harness valuable resources from state’s financial channel to generate considerable revenues. More importantly, our findings further suggest that democracy moderates the impact of financialization on income inequality. It brings the perspective of comparative political regimes (Acemoglu and Robinson 2012; Bueno de Mesquita and Smith 2012; Calomiris and Haber 2014) into existing knowledge, injecting a fresh impetus to uncover an equalizing channel between financialization and income inequality. One caveat is that our finding should not be interpreted as financialization would narrow income gap in democracies. In fact, the real degree of financialization in democracies is usually higher than in autocracies. What our argument suggests is a decentralized distribution of credit access in democracies, a moderation of the impact of financialization that does make an odd relationship (relatively high financialization accompanies relatively low income inequality in democracies) penetrable. It also lays down a policy foundation for the claim of the democratization of finance (Block 2014; Shiller 2012).

THE FINANCIALIZATION AND INEQUALITY DEBATE

The definition of financialization has varied across studies. Epstein broadly defined financialization as “the increasing role of financial motives, financial markets, financial actors, and financial institutions in the operation of the domestic and international economies” (2005: 3). Based on the definition, in this article financialization is referred to credit expansion in the private sector including the financial and non-financial sector and households, and to credit expansion in the public sector including government and SOEs to sustain the production of wealth (Foster 2007; Magdoff and Sweezy 1987) Considering the influence of politics of financialization, we estimate two similar indicators to explore the credit expansion in the private and public sectors. The indicator of private sector financialization is the private sector credit (% of gross domestic product (GDP)) (Beck et al. 2007; Cihák et al. 2013; Calomiris and Haber 2014; Flaherty 2015), whereas the financialization of state has been directly measured by public sector credit (% of GDP). We obtained data on these indicators from the Global Financial

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Development Database (Čihák et al. 2012), which allowed us to examine the different degrees of financialization across countries.

Arguments about the relationship between financialization and income inequality are mainly demarcated by the boundaries of academic disciplines: some economists of finance and development argue that financialization decreases income inequality (Banjeree and Newman 1993; Galor and Zeira 1993), but sociologists and some economists counter that it increases income inequality (Alvaredo, Chancel, Piketty, Saez, and Zucman 2018). Before defining these competing hypotheses and our alternative hypotheses, we briefly review their empirical and theoretical background.

Developmental and financial economists have advocated the importance of access to finance, arguing that limited access to credit contributes to income inequality (Beck, Demirgüç-kunt, and Honohan 2008). Clarke et al. (2006) examined the data on private sector credit (% of GDP) and Gini coefficients among 91 countries in 1960–1995 to suggest that financial development decreases income inequality. Some economists have argued that increasing financial intermediaries could reduce income inequality by raising incomes among the impoverished (Beck et al. 2007). Similarly, numerous studies have claimed that financial contracts, markets, and intermediaries could be improved to reduce income inequality (Demirgüç-Kunt and Levine 2009). A recent study (Delis et al. 2014) observed a heterogeneous effect, namely that eliminating bank regulations (i.e., credit and interest rate controls) reduces income inequality, but not all liberalization policies have similar effects across countries. In underdeveloped countries, removing barriers to entry and enhancing privatization have not resulted in decreased income inequality (Delis et al. 2014). Although in some countries financial liberalization has not had similar results, most relevant economic literature substantiates the argument that financialization decreases income inequality by enhancing economic opportunities for innovative firms or disadvantaged groups.

By contrast, there is another stream of research considering financialization to be an important source of increased income inequality, especially from the 1980s. The Kuznets curve (inverted U-shape) indicates that inequality first increases and then declines with economic development (Kuznets 1955). Industrial dualism argues that growth of the rural youth population, which provides large numbers of unskilled workers to the urban labor market, expands income inequality between the agricultural and industrial sectors (Robinson 1976). By contrast, some scholars have described a negative association between economic growth and income inequality, departing from the inverted U-curve (Bandelj and Mahutga 2010; Fei, Ranis, and Kuo 1979; Huber et al. 2006). Nevertheless, advanced countries have experienced a “great U-turn,” suggesting reverse correlation in which inequality first decreases and then increases with economic growth (Alderson and Nielsen 2002). Following the logic of industrial dualism implied in Kuznets’ curve, some studies emphasize on the increasing average salary of financial industry in contrast to the stagnated average salary of the non-financial industries to explain the great U-turn (Galbraith and Berner 2001). The empirical evidence shows that the expansion of financial sector in revenue and employment amplified household income inequality due to the new industrial dualism between financial sector and the other industries in the United States in the last half century (Galbraith and Hale 2014).

Second, some studies focus on financialization of non-financial firms and its influence on the organized labor. According to power resource theory, left-wing political parties and labor unions can address the imbalance in the bargaining power of labor and management, who have conflicts regarding income distribution (Bradley et al. 2003; Flaherty 2015; Kelly 2005; Korpi 1978;

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Volscho and Kelly 2012). The expansion of financial capital has been paralleled by the weakening of labor rights and elevation of management compensation, resulting in greater income inequality (Dünhaupt 2017; Nau 2013; Volscho and Kelly 2012). Financialization has undermined the bargaining power of organized labor when management reallocates corporate resources from production operations to financial units. This reallocation strengthens management’s position by increasing the independence of overall corporate revenues from the production process, whereas it erodes labor’s ability to bargain for better payment (Lin and Tomaskovic-Devey 2013). Financialization also empowered the financial market shareholders and thus guided managers to focus on shareholders’ interests (Davis 2005; Fligstein and Shin 2007). This triggers an increase in the capital incomes of shareholders and managers (Alvaredo et al. 2018). Previous studies have observed that the soaring compensation of chief executive officers after the 1990s was typically tied to corporations’ market performance (Davis and Kim 2015). Executive compensation that corresponds to stock price encourages managers to increase stock prices to maximize their own incomes (Dünhaupt 2014). Income inequality has been exacerbated by increased interest and other investment incomes that largely benefit shareholders and managers, and by increased financial profits earned by both banks and non-financial firms; the financial profits of non-financial firms are often greater than the profits of their core production activities (Davis and Kim 2015; Nau 2013; Roberts and Kwon 2017). Hence, by reducing labor’s share of corporate revenue, increasing profits of shareholders, and soaring compensation for top managers, financialization undermined organized labor and promotes income inequality (Darcillon 2015).

Third, financialization shaped income inequality through the disparity of household finance. The gap of income and wealth between rich and poor households have dramatically increased since the 1980s (Epstein and Jayadev 2005; Palley 2013). Although financial capital has increased among all households, the effects of social stratification have shaped the uses of financial resources (Fligstein and Goldstein 2015;). Rich households typically utilize credit to invest in real estates and financial assets, thus increasing their capital incomes (Fligstein and Goldstein 2015; Foster and Holleman 2010; Godechot 2016; Stockhammer 2012). The increase in investor gains in 1992–2010 led to income concentration and increased overall income inequality (Nau 2013). On the contrary, poor households typically use credit for living, medical, and educational expenses which became their liabilities in the future. The households who applying consumer loan or sub-mortgage usually bankrupted and evicted by financial institutions in the recession (Desmond 2016). Accordingly, financialization of households amplified the income gap between rich and poor households.

In contrast to the inequality-narrowing hypothesis, we proposed the inequality-widening hypothesis of financialization, namely private sector credit expansion increases income inequality through mechanisms of industrial change and household financialization. Additionally, we used the weakening of labor rights and protections, which signal decreases in the power of organized labor under financialization, as a control variable in our models to help clarify the direct effects of financialization on income inequality as measured by Gini coefficients.

Hypothesis 1: An increase in a country’s private sector credit exacerbates its income inequality.

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THE POLITICS OF FINANCIALIZATION

The previous financialization-inequality debate underestimated the role of states and regime types that certainly shape financial institutions and financial resource distribution. Financialization could not have developed without the state, and state-building accompanied the construction and maintenance of financial institutions (Carruthers 1996; Calomiris and Haber 2014). Recent studies of financialization have observed that states expand financial markets and increase the provision of credit to palliate fiscal pressures and to substitute social spending in some capitalist countries (Krippner 2012; Prasad 2012; Quinn 2017). Some argue that the interactions of politics, institutions, and bankers shall be responsible for the Great Recession (Bell and Hindmoor 2015). In contrast to the “unintended consequences” of financialization in the capitalist countires (Krippner 2012), the post-communist countries such as Russia and China are taken as the example of intended large-scale privatization of public property which shall be responsible for the increasing income inequality in the process of market transition (Novokmet, Piketty, Yang, and Zucman, 2018; Walder, Isaacson and Lu 2015). The cycle of financialization in some post-communist countries such as China might drive by factional politics inside the party-states (Shih 2008). However, these studies have not been able to clarify the intersection of the state and financialization and its association with income inequality.

A close focus on financialized segments of states is necessary to discern the mechanism between the financialization of states and income inequality. Although privatization of public property has been criticized as a major origin amplifying income inequality when departed from a state command economy (Novokmet et al. 2018), states, including the survived public sector in the post-communist countries, might not be more effective and more decentralized in allocating credit and other financial resources than a market economy. For example, central and local government governments in China both expect to benefit from financial policy intervention, which leads to inefficient capital allocation of the state-owned banking system (Tsai 2002; Shih 2008). When financialization of state was found to be considerably concentrated on a few SOEs (Wang 2015), financial revenues enlarge the income gap between SOE managers and workers, but not necessarily related to the income difference between SOEs’ employees and private sector employees (Wu and Xie 2003; Walder and Nguyen 2008). Instead, some studies of post-communist transitions have found that financial capital revenues of SOEs are channeled to a few communist power elites and therefore increased income inequality (Huang 2008; Bian and Logan 1996; Szelenyi 1978). In other words, financialization of state also leads to income inequality because the revenues from increasing financial capital to the public sector are concentrated on or corrupted by small groups of politically and managerially powerful people.

Hypothesis 2: An increase in a country’s public sector credit exacerbates its income inequality.

Advancing on the financialization-inequality debate, we argue that the contradictory results between financialization and income inequality from previous cross-national studies (for negative effects, see Beck et al. 2007; Delis, Hasan, and Kazakis 2014; and for positive effects, see Epstein and Jayadev 2005; Flaherty 2015; Hyde, Vachon, and Wallace 2018; Kus 2012) may be partially due to their ignorance on the real differences of political regimes. Although previous studies have focused on the origins of financialization (Davis 2009; Fligstein 2001; Haber et al. 2008; Krippner 2012; La Porta et al. 1998), comparisons of political regimes in

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studies of financialization have been inadequate (Calomiris and Haber 2014; Haber et al. 2008). Because distinct political regimes tend to form different types of financial institutions (Acemoglu and Robinson 2012; Bhattacharyya 2013; Calomiris and Haber 2014; Davis 2012; Haber et al. 2008), exploring how political regimes shape financialization may help disentangle the conflicting results of the debate.

We implemented a cross-national comparison of 103 countries (see Appendix A) to explore how three regime types, autocracy, anocracy and democracy, shape financialization of private and public sectors during 1981–2012. First of all, we observed that financialization in democratic countries resulted in considerable credit increases in the private sector but moderate credit decreases in the public sector. By contrast, financialization in autocratic countries was a hybrid process that credit expands mainly in the private sector and subordinately in the public sector.

It might be helpful to have a glance of the global aggregative trends of financialization in democracies and autocracies before the analysis. As Figure 1 illustrates, private sector financialization conspicuously increased in both democracies and autocracies in 1981–2012. The private sector received considerably greater amounts of credit than the public sector in this period, implying that financial capital’s increased economic role mainly manifested in the private sector. The relatively small amount of credit allocated to the public sector is exhibited in their flatter slope in Figure 1. Moreover, the total quantity of credit in autocratic countries was deficient (Calomiris and Haber 2014), but credit to the public sector was more prominent in autocratic countries than in democratic countries. In spite of the smaller proportion, credit to the public sector in autocracies was critical in the financialization process (Wang 2015). This illustrates that the processes of financialization in democratic and autocratic countries are distinct. The expansion of financial capital in democratic countries is mainly dependent on the private sector, whereas the growth of financial capital in autocratic countries is a hybrid process that takes place in both the private and the public sectors. Additionally, financialization has been relatively ambiguous in anocratic countries that operate so-called competitive and electoral authoritarianism because of their complex political contexts and high regime instability (Bernhard and Leblang 2006; Hadenius and Teorell 2007; Levitsky and Way 2002; Linz and Stepan 1996). Thus, this paper limits its discussion to comparisons between democratic and autocratic countries, although we nevertheless included the sample data from the anocratic countries.

[Figure 1]

Scholars have frequently argued that political regimes shape the domestic credit allocation and expansion ( Acemoglu and Robinson 2012; Bueno de Mesquita et al. 2003; Calomiris and Haber 2014), but the associations with income inequality is not clarified yet. There is a discrepancy in credit expansion exists between autocratic countries and democratic countries. Credit expansion is more concentrated in autocracies than in democracies because political leaders in democratic countries have to diversify credit expansion whereas those of autocratic countries attempt to confine it. We argue that this discrepancy of credit expansion between political regimes led to the distinct effects of financialization on income inequality.

Leaders in different political regimes apply distinct strategies, including financial policies, to survive in office. In autocracies, political leaders must maintain coalitions consisting of small groups of supporters (Bueno de Mesquita et al. 2003). Thus, a dictator typically allocates economic resources to a few political groups (i.e., cronies) to consolidate their loyalty, and these

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coalitions also collude with the dictator to maintain their prestige and profits. As landlords, economic elites usually support the dictatorship to prevent from democratization or revolution, which might damage their own prestige, property and rents (Acemoglu, Johnson, and Robinson 2005; Moore 1966; Ziblatt 2008). As a result, allocating credit under financialization in autocracies is typically confined to a small group of political supporters.

For instance, in an analysis of the political origins of bank crises and credit institutions in five countries, Calomiris and Haber (2014) indicated that autocracies (e.g., Mexico or Brazil before democratization) created a fragile and opaque bank credit market that did not aim at allocative efficiency but rather at the institutionalized sharing of rents with political coalitions and cronies. In such autocratic regimes, political leaders provide credit to their supporters in the private and public sector as a tool of consolidating followers rather than of enhancing economic efficiency or providing incentives to the wider public (Acemoglu and Robinson 2012). Unlike those dictators, democratic leaders need to address the economic concerns of larger number of potential supporters (Bueno de Mesquita and Smith 2012). Thus, political leaders in democracies adopt different institutional arrangements to allocate credit.

[Table 1]

We selected some relatively complete international financial indicators of autocracies, anocracies and democracies, especially taking China and the United States as examples to show their differences in financialization and financial institutions in 2012, the last year in our dataset. As Table 1 shows, in contrast to the amount of the private credit in anocracies (28.4% of GDP), it is significantly higher in autocracies (46.3% of GDP). The highest private credit has been found in democracies (73.5% of GDP). Some studies suggested that democracies sometimes use financialization of private credit as a substitution of social spending under the electoral competitions and lobbies of interest groups (Calomiris and Haber 2014), but the role of the public credit expansion in autocracies is neglected. In contrast to the decline of public credit divided by private credit in democracies (A/B, 18.7%), anocracies and autocracies kept a greater scale of public credit, 35.8% and 27.8% of their private credit in 2012.

In contrast to democracies, the private credit released by the autocracies was concentrated on deposit money banks. For example, 94% of private credit in China came from banks, while only 27.5% of private credit by deposit money banks in the United States in 2012. In addition, banks are highly concentrated in China, where the state-owned big-5 controlled 59.5% of assets in the banking system; on the contrary, the mostly private-owned big-5 in the United States only controlled 46.8% of assets. Besides, people can much easily access to bank branches in democracies, where there are 24 branches per 100,000 adults, than in autocracies, where there are only less than 9 per 100,000 adults. Loans from the foreign banks, an index of financial openness, were 30.4% in democracies while only 8.2% in autocracies. All above indicators show that the banking system in autocracies is more concentrated, less accessible and less competitive, which might be associated with its higher return (2.3%) than in democracies (1.6%). Although democracies expanded private credit more than the autocracies, they performed much better in some indicators, such as higher central bank assets and a lower ratio of bank credit to deposits, related to the stability of banking system (Calomiris and Haber 2014).

Autocracies such as China did not only concentrate and expand credit by their banking system mostly controlled by the state, but also suppressed the development of non-banking financial institutions. Either life insurance or non-life insurance institutions contain much less scale of

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premium volume in autocracies and anocracies compared to democracies. Nevertheless, the scale of stock market value in democracies on average is 29% of GDP, but the scales in autocracies and anocracies are 21% and 3.6%. It might be not surprising that the stocks of listed companies are much more accessible for people in democracies than in autocracies and anocracies. Although the stock market return in anocracies is extremely high due to its lowest accessibility, the return of democracies is still better than it of autocracies. In 2012, the average stock market return is 8.8% in the United States, but -16.8% in China.

As previous studies found, financial institutions in democracies are relatively accessible, decentralized, internationalized and diversified. Financial system stability in democracies is based on the protection of private property rights, which fosters the growth of private credit and its accessibility (Calomiris and Haber 2014; Keefer 2008). Democratic leaders adopt decentralized policies to allocate credit to a relatively wide range of people because of their need to maintain voter support. In contrast to the monopolistic financial systems of autocratic countries, financial systems in democracies typically have higher degrees of competitiveness. Political leaders in anocracies and autocracies restrict the entry of new financial institutions because leaders must retain control of rents to share with their cronies and bank insiders, whereas democracies typically have more open financial systems that both expand credit and decrease operating costs, thus both enhancing financial system stability (Calomiris and Haber 2014) and attracting voter support. Moreover, many financial intermediaries in democracies are set up to fulfill the credit needs of diverse interests. For example, democratic governments establish lending institutions for specific sectors such as housing or agriculture, or for specific beneficiaries such as small firms or middle class households (Calomiris and Haber 2014). The development and accessibility of non-banking financial institutions, such as insurance system, bond and stock markets, also reflect the diversity and decentralization of financial system in democracies. As the financial sector develops widely, people gradually access to credit that moderates income inequality (Greenwood and Jovanovic 1990).

However, there are counterforces increasing income inequality under the competitive financial system in democratic countries. For example, household financialization due to the deregulation of mutual funds and securitization fosters income inequality in OECD countries during the last two decades (Godechot 2016). The capital income from the stock market and the other financial assets are responsible for the increasing percentage of the top income families’ return in the national income (Piketty 2014). In some countries, this counterforce is weakened by the sustaining welfare states or circumspection in credit provision. In well-developed welfare states with high social spending, bank deregulation exhibits no significant relationship with the expansion of credit (Prasad 2012), and restrictive regulation on consumer debt in France emphasizes the financial risks, which may cause workers to reduce their consumption (Trumbull 2014). The inequality-reducing properties of decentralized credit in most democracies and the inequality-increasing properties of household credit and indebtedness in some democracies might neutralize the effects of financialization, and result in relatively lower income inequality than in autocracies.

Credit expansion is more decentralized and more equally available in democratic countries, the leaders of which must satisfy the demands of broader population. By contrast, it is more centralized and confined in autocracies, where political leaders must share rents with smaller coalitions and cronies. We hypothesized that the interaction of credit to the private sector and democracies would reduce income inequality (Hypothesis 3). Moreover, it could be reasonable to expect that the effects of private sector credit are the same as the effects of credit to the public

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sector, which is easily controlled by the leaders through the state ownership. For example, China’s central bank provides credit to SOEs not only because allocating financial resources to political coalitions strengthens the central bank’s relationship with the communist regime, but also because it keeps jobs of SOE employees who are viewed as the supporters of the regime (Calomiris and Haber 2014). After the Great Recession, the expansion of money supply and credits, either from the state-owned banks or the shadow banking, went to local governments by mortgage of public land, mostly expropriated from poor peasants to initiate urban development projects (Wallace 2014; Shen 2016). The financialization of state might also amplify income inequality in China. Therefore, we argue that the public sector in autocracies usually receive impartial financial support circumscribed by the need for the rulers to maintain supportive coalitions, which results in increased corruption and income inequality (Lee 2005). By contrast, the regulations of the public sectors in democracies are usually more transparent and less corrupted than in autocracies (Kotera, Okada, and Samreth 2012; Sung 2004). Similar to the recent findings of the association between the greater size of public sector and lower income inequality especially in the European democratic countries (Alvaredo, et al. 2018), we hypothesized that the interaction of public sector credit and democracies reduces income inequality but the interaction of public credit and autocracies increases it (Hypothesis 4).

Hypothesis 3: The positive effect of private sector credit on income inequality is exacerbated by autocracies and ameliorated by democracies.

Hypothesis 4: The positive effect of public sector credit on income inequality is exacerbated by autocracies and ameliorated by democracies.

RESEARCH DESIGN

Previous studies have limited their data samples to OECD countries (Epstein and Jayadev 2005; Flaherty 2015; Kus 2012). This study examined a sample of 103 countries, comprising both advanced and developing countries, as a more representative data set than those of previous studies. We used unbalanced panel data for the 103 countries in 1981–2012. After excluding missing values, the sample included 1,659 observations. The sample period was limited to 1981–2012 because data on some variables was not recorded or was poorly maintained before 1981, and because the data set was updated only to 2012. However, this range should suffice to capture the recent prominent trend of financialization across countries. Table 2 lists the data resources and a statistical summary.1

[Table 2]

Data

Dependent variable: Gini coefficients

I examined net Gini coefficients as a proxy for income inequality. A large Gini coefficient indicates a large income gap within a country. The most appropriate source of income inequality data is the Standardized World Income Inequality Database (SWIID) (Solt 2016), which has been widely used in recent inequality studies for cross-national comparisons (Jauch and Watzka

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2016; Kerrissey 2015; Kus 2012). Compared with other data sets (e.g., the World Income Inequality Dataset or the Luxembourg Income Study), the strength of the SWIID is that it comprises the broadest sample of countries and years, thus providing enhanced cross-national comparability for studying income inequality (Solt 2016). The SWIID comprises average estimations of 100 imputations of the Gini coefficient using Monte Carlo simulation to huddle missing data. We applied the net Gini coefficient in equalized household disposable incomes as the dependent variable for our models.

Independent variables: Private sector credit, public sector credit and political regimes

Using the Global Financial Development Database (GFDD) from the World Development Indicators (WDI) and the World Bank (Čihák et al. 2012), we selected domestic private sector credit (% of GDP) to approximate financialization of private sector in each country (Beck et al. 2007; Čihák et al. 2013; Clomiris and Harber 2014; Flaherty 2015). Domestic private sector credit indicates the financial resources (such as loans, nonequity securities, and trade credit) that are provided to private sector entities. It excludes credit to government agencies and public enterprises, as well as credit issued by central banks and development banks (Čihák et al. 2013).2

By contrast, our study of financialization in autocratic countries, encompassed both the private and the public sectors because of states expanded role as both regulators and shareholders (Wang 2015). Hence, we examined credit to the public sector including government and SOEs (% of GDP), which we also obtained from the GFDD, to measure the financialization of states.

This paper follows the classification of political regimes implemented by Polity IV (Marshall, Gurr and Jaggers 2016), which uses polity scores to define coherent democracies (score ≥ 6) and coherent autocracies (score ≤ −6), with all remaining states (score between −5 and +5) defined as anocracies (Jaggers and Gurr 1995). As illustrated in Figure 1, financial development under the three regimes indicates that anocracies have a relatively smooth trend because their complex political contexts and high regime instability might limit their financialization. Thus, we used a trichotomy rather than of a dichotomous or continuous variable, to identify distinct political regimes. The effect of democracy on income inequality identified in previous studies has been inconsistent, including negative associations, nonlinear relationships, and no effect (Kerrissey 2015). Relying on more extensive cross-country databases on income inequality as estimated by Gini coefficients (such as SWIID) has had similar empirical results, namely, that democracy does not associate significantly with Gini coefficients (Acemoglu et al. 2015; Kerrissey 2015). Although there might be no direct effects on inequality, the interaction variable with democracy was identified, revealing the heterogeneous effect of democracy. For example, Lee (2005) argued that because nondemocratic countries prefer to aid certain industries through tax reduction or transfer systems, public sector expansion in such countries increases income inequality. The influence of public sector expansion on income inequality switches from increasing to decreasing only in institutionalized democracies. Therefore, we examined interaction variables between credit to the private and the public sectors, and political regimes (the reference group is autocracy) to test Hypotheses 3 and 4.

Control variables

Demographic and economic variables that are baseline factors of income inequality include GDP per capita (GDPPC), population growth, agricultural employment, secondary school enrollment,

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and foreign direct investment (FDI) (Alderson and Nielsen 2002; Kerrissey 2015; Lee 2005). These variables are collected by the WDI. GDPPC growth typically has two relationships with Gini coefficients: the Kuznets curve (the inverted U-shape) and the great U-turn hypothesis (Alderson and Nielsen 2002). Traditional explanations for income inequality have been economic growth and industrial dualism; however, the findings of numerous studies have remained inconsistent. Following the logic of dualism in education, a few studies have supported the hypothesis that a higher secondary enrollment ratio decreases income inequality in developing countries (Alderson and Nielsen 1999; Lee 2005).

Population growth exhibits a negative association with Gini coefficients (Bandelj and Mahutga 2010; Huber et al. 2006) but either a positive effect (Lee et al. 2007) or no effect (Kerrissey 2015) with Gini coefficients. Agricultural employment and secondary school enrollment also have exhibited mixed results (Alderson and Nielsen 1999; Kerrissey 2015; Lee 2005). Finally, FDI exacerbates income inequality (Alderson and Nielsen 1999), but with the expansion of the public sector, this effect can be positive at first but then attenuated or even negative (Lee et al. 2007). These basic variables exhibit mixed results because relevant studies have typically covered distinct data samples such as different periods or limited parts of the world. Most of them omitted the key variables, regime type, private credit and public credit, in this study. Thus, we required a more comprehensive cross-national database to correct these findings.

Labor protection. Effective labor rights require workers to have bargaining power to argue effectively for higher wages against corporate management. For example, Kerrissey (2015) observed that strong labor rights are significantly associated with lower income inequality in 100 developed and less-developed countries. However, obtaining an ideal measurement of labor rights in countries beyond the OECD members is difficult. The Varieties of Democracy (V-Dem) project (Coppedge, Gerring, Lindberg, Skaaning, Teorell, Altman, Bernhard, Fish, Glynn, Hicken, Knutsen, Krusell, Lührmann, Marquardt, McMann, Mechkova, Olin, Paxton, Pemstein, Pernes, Petrarca, Römer, Saxer, Seim, Sigman, Staton, Stepanova, and Wilson 2017) may have provided a suitable indicator. Initiated by scholars from the University of Gothenburg in Sweden, V-Dem included data from 206 political jurisdictions during 1900–2012. V-Dem asked, “Are adult citizens free from servitude and other kinds of forced labor?” The response scale ranges from 0 (widely existent) to 4 (nonexistent), suggesting that a higher score represents strong labor protection, which might allow workers to consolidate their power through collective action.

Government Expenditure (% of GDP). Although tax revenue (% of GDP) is commonly adopted by literature, government expenditure is a better measure that captures comprehensive social policies on distribution (Lee, Nielsen, and Alderson 2007). State policies are a pivotal role to improve income distribution (Brady, Blome, and Kleider 2016). Government spending including social security, welfare programs, health care and education enhances opportunities for the poor and decreases inequality through redistribution policies (Morgan and Kelly 2013). We may expect higher government expenditure (% of GDP) reduce overall income inequality across countries. This hypothesis has been supported firmly not only in rich democracies, the redistributive state retrenchment has also an impact on income inequality in Central and Eastern Europe (Bandelj and Mahutga 2010). But the effect of government expenditures invert in autocratic countries, where state elites tend to appropriate government resources for their own interests (Lee 2005). Therefore, the higher government expenditure in autocracies leads to severe income inequality. In following models, there are no interaction items between the government

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expenditure and the political regime. We thus expect that the association is not significant because of the mixed effect.

Methods

The unbalanced data from 103 countries in 1981–2012 included 1,659 country-year observations. Constructing the models by the traditional ordinary least squares (OLS) method would have been inappropriate because of the assumption of uncorrelated errors does not hold when applied to the panel data. We primarily used an fixed-effet model (FEM) for the following reasons. First, our FEM examines information within countries and discards information between countries. This method has the advantage of excluding the effects of unobserved variables (Allison 2009). In addition, the FEM is appropriate for analyzing data without time-invariant variables (e.g., geographic regions) because they are eliminated in the model. However, FEMs occasionally produce large standard errors and incorrect estimations when some dependent variables’ variations are similar within individuals (Allison 2009). In an Random-effect model (REM), unobservations are uncorrelated with observations, and a part of the error term involves country-specific components rather than a fixed country-specific intercept (Alderson and Nielsen 2002). Second, from a statistical perspective, the Hausman test (1978) examines the coefficients between an FEM and an REM. The test results are significant (p < 0.05), suggesting that using the FEM is appropriate.

It shall be noticed that no consensus of the model selection between FEM and REM has been reached among researchers. Some scholars have adopted REMs (e.g., Bandelj and Mahutga 2010; Kerrissey 2015; Lee 2005; Lee et al. 2007) and a few studies have used FEMs to address the same topic (e.g., Adams and Klobodu 2016; Jauch and Watzka 2015). However, to make the relationship between financialization and income inequality clear, FEMs are adopted to rule out confounding factors (Acemoglu et al. 2015). Therefore, this paper’s regression table provides the statistical results of the FEM primarily. The results of the REM are provided for reference. Our research indicated only a minimal difference among control variables; and most of the results were consistent across the model specifications.

RESULTS

The models encompassed 1,659 observations among 103 countries during 1981–2012. Models 1–5 in Tables 2–3 adopted a FEM, whereas Model 6 used a REM for reference. The methods yielded similar results. We ruled out dynamic effects of income inequality by controlling for year dummies in all models.

Model 1 documents the effects of the control variables on income inequality. GDPPC is positive, suggesting that countries with higher economic growth exhibit higher inequality. This result is consistent with the great U-turn hypothesis (Alderson and Nielsen 2002) but the squared term of GDPPC is also positive, indicating the impact on income inequality as GDPPC increases has inclined in recent years. However, the significance of GDPPC disappears in following models after the variables of financialization (private sector credit and public sector credit) are included. Economic growth thus should not be understood as an origin of income inequality but should be considered as a trigger of financialization, which indirectly exacerbates income inequality.

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Besides, population growth, agricultural employment, and secondary school enrollment have exhibited mixed results in previous studies (Kerrissey 2015). In our models population growth and agricultural employment have consistently exhibited a negative effect on income inequality and secondary school enrollment has no significance across models. Although some studies have pointed out that population growth has a positive effect on income inequality, our results shows a negative effect on the inequality, which still yields a similar finding as Bandelj and Mahutga (2010) and Huber et al. (2006). In our models a larger portion of agricultural employment is associated with smaller income inequality. A higher agricultural employment in traditional society leads to a more equal income distribution because compared to an industrial society, the occupational structure in a traditional society results in a more equal share of income (Lee, Kim, and Shim 2011).

Although a few scholars have reported a positive association of FDI with Gini coefficients (Alderson and Nielsen 1999; Kerrissey 2015), Model 1 revealed that FDI is not significant, and this outcome is consistent across all models. The correlation of FDI and Gini coefficient (see Appendix B) exhibited only a very weak and negative coefficient (−0.0580). Labor protection exhibited a significantly negative association (p < 0.05) with income inequality in all models. Higher labor protection implies stronger labor rights, and the negative association suggests that labor protection decreases income inequality (Kerrissey 2015). The final control variable, government expenditure, as expected by the prediction that the association is not significant between government expenditure and Gini coefficients. Although recent research (Kollmeyer 2015) substantiated that a larger public sector moderates income inequality, the effect of government expenditure is not universal. This effect inverts in autocratic countries (Lee 2005). Hence, this control variable without interaction items of political regimes resulting in no significant effect on income inequality is consistent with Lee’s finding.

[Tables 3]

In Model 2 private sector credit and public sector credit are included to examine the key relationship between financialization and Gini coefficients (Hypotheses 1 and 2). The results revealed a significantly (p < 0.05) positive association between both private and public sector credit and Gini coefficients, indicating that an increase in credit in the private and public economy leads to a larger income gap in society. These results are similar to those of previous studies on the United States and other OECD countries (Flaherty 2015; Jauch and Watzka 2016; Kus 2012). These results support Hypothesis 1 and Hypothesis 2 that financialization in the private and public sector exacerbates income inequality.

To test Hypothesis 3, Model 3 added interaction items: private sector credit with political regimes. The main effect of private sector credit remains positive, but the interaction with democracy exhibits a significant (p < 0.001) negative effect on Gini coefficients. This result support Hypothesis 3 that democracy moderates the positive effect of private sector credit on the Gini coefficient. Additionally, although financial market instability created by highly uncertain political events and information in anocracies (Bernhard and Leblang 2006), the anocracy does also exhibit a similar but smaller effect. Before testing Hypothesis 4, the changing results of political regimes across models should not be ignorant. Democracy yields no significance in Model 1 but it becomes significant and has a positive effect in following models. According to a recent thorough investigation (Acemoglu et al. 2015), democracy does enhance tax revenues but does not directly affect income inequality. The result in Model 1 also concurs with their finding.

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Even though the positive effect of democracy in Model 2-6 is statistically significant, it may result from the influence of financialization’s main effect and interaction items.

To test Hypothesis 4, Model 4 in Table 3 added another interaction variable: public sector credit with political regimes. The full model is presented as Model 5. The results in Model 4 and 5 support Hypothesis 4 that increased credit to the public sector moderates income inequality in democracies. Public sector credit also has a similar but smaller effect in anocracies. The combined coefficient (-0.045) from the main effect of public sector credit (2.037 in Model 5) and democracy’s interaction item (-2.082 in Model 5) indicates that public sector credit in democracies reduces income inequality but its effect is slight. The final results are also confirmed by Model 6 with random-effects. FEM often has a large standard error (Allison 2009) when it contains numerous interaction variables. To avoid a risky conclusion, Model 6 adopted an REM to serve as a reference for evaluating the results. Private and public sector credit in democracies remained significant (p < 0.001) in Model 6, bolstering our confidence in the argument that although financialization exacerbates income inequality, its influence is moderated in democratic political regimes.

[Tables 4]

CONCLUSIONS AND DISCUSSION

This study reexamined the influence of financialization on income inequality through the political perspective focusing on the vital role of political regimes and states. This analysis suggests that financial credit expansion, which allows economic and political elites to mobilize credit to earn vast profits, results in increased income inequality. Furthermore, we argue that financialization shaped by political regimes lead to distinct aftermaths. Credit expansion is more decentralized and equal in democracies than in autocratic states, which centralize credit expansion to small groups of political coalition members. Therefore, by allocating decentralized credit more broadly, democratic political institutions moderate the positive association between financialization and income inequality. Moreover, the financialization of state through the public sector credit does not necessarily reduce income inequality; instead, it usually increases income inequality especially in autocracies. The statistical results substantiate all hypotheses: the expansion of private and public sector credit increases income inequality but the association is moderated in democracies.

There are some limitations mostly due to the restrictions of data in this study. Although we chose the most popular applied measurements of financialization, they might not better than some alternatives (Abiad, Detragiache, and Tressel 2010). The application of Gini coefficients has the same problem in measuring income inequality (Houle 2009). Moreover, we adopt Polity IV to categorize regime types, which is still a contentious issue for political scientists (e.g., Cheibub, Gandhi and Vreeland 2010; Geddes, Wright and Frantz 2013; Linz 2000; Munck and Verkuilen 2002; Svolik 2012). Indeed, some studies focusing on democracy and inequality continuously using Polity IV (e.g., Ansell, and Samuels 2010) before the consensus of political scientists on regime types (Lueders and Lust 2018). On the other hand, we acknowledge the arbitrary distinction and mobility of some cases between the edges of democracy and anocracy, and of anocracy and autocracy, and follow the majority of studies investigating the association between democracy and income inequality.

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Although emphasizing on the politics of financialization has been advocated in previous research (Bell and Hindmoor 2015; Davis 2012; Krippner 2012), examining the mechanisms that states and political regimes and their interaction shaping financialization might solve the financialization-inequality debate and provide a novel understanding of financialization and a timely explanation to the severe income inequality in the past three decades. Our results provide the study of financialization and income inequality with theoretical and policy implications. First, we linked the financialization studies of sociologists and economists, who have focused on inequality (Flaherty 2015; Fligstein and Goldstein 2015; Lin and Tomaskovic-Devey 2013; Nau 2013; Tomaskovic-Devey and Lin 2011), to those of political scientists, who have emphasized the power of political regimes (Calomiris and Haber 2014; Haber et al. 2008), to enrich our understanding that the effects of financialization differ according to political regimes because of their distinct credit expansion and distribution mechanisms. By observing that centralized financialization further aggravates income inequality, this paper confirms that financial markets are not independent from politics (Carruthers and Kim 2011) and also illustrates how the varieties of capitalism display distinct trajectories regarding financialization (Davis and Kim 2015).

Second, we examined not only the expansion of private sector credit but also financialization of states, that is, the expansion of public sector credit, especially in autocratic countries, linking financialization and income inequality. To sustain political survival, autocracies tend to distribute the financial resources, sometimes through the corrupted public sector, to a relatively small group of followers. By contrast, the expansion of credit, either to the private or to the public sectors, are relatively decentralized and transparent under the democratic countries’ regulations. In addition, high tax ratio and government expenditure divided by GDP usually reduced income inequality especially in democracies as earlier studies expected.

Democracy alleviated the undesirable influence of financialization on income inequality in the past three decades, implies that decentralized and transparent financial development reduces income inequality. Decentralized finance enhances the ability of innovative firms or disadvantaged groups to access credit. Shiller (2012: 326-340) stated that policies aimed at increasing the benefits of financial development should emphasize decentralized capital both to inhibit ownership centralization and to target the most innovative small firms. Block (2014) also argued for the democratization of finance, suggesting that credit should be established on a decentralized basis such that “financial intermediaries can reorganize the positive synergies that come from multiple investments in the same locality.” Therefore, decentralized credit moderates the influence of financialization on income inequality and also bolsters the access of innovative entrepreneurs to sufficient credit.

Control of corruption also corresponds with these statements regarding democratized finance. Corruption is more serious in autocracies than democracies (Song 2004), and it increases when bureaucrats have enormous rents to exploit (Ades and DiTella 1999). A study documenting 21 sub-Saharan African countries during 1980–2011 indicated that although financial development exacerbates income inequality, greater control of corruption and transparency ameliorates the income inequality arising from financialization (Adams and Klobodu 2016). Hence, amid the increasing expansion of finance, effective and transparent governance is required both to ameliorate its effects on income inequality and to boost innovation.

Indeed, this is not to say that democratic regimes always prevent the monopolization or corruption of financial systems. For example, the big five Canadian banks once controlled the country’s entire financial system, which resulted in a significant decline in the number of bank

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branches in 1990–2003; still, the banking systems in Brazil and Mexico are plagued by spread corruptions after democratization (Calomiris and Haber 2014). As we examined in the empirical study, nevertheless, democratic regulations moderating income inequality might be damaged under the expansion of private credit. However, financialization of states happens and increases income inequality especially in autocracies. In most aspects redistributing financial resources to the poor, dictatorship is the worst. This conclusion may enhance confidence in democracy in a permissive era under the shadow of Great Recession and the ongoing challenge of populism and authoritarianism (Bermeo 2016).

REFERENCE

Abiad, Abdul, Enrica Detragiache, and Thierry Tressel. 2010. “A New Database of Financial Reforms.” IMF Staff Papers (57):281–302.

Acemoglu, Daron, Simon Johnson, and James Robinson. 2005. “The Rise of Europe: Atlantic Trade, Institutional Change, and Economic Growth.” American Economic Review 95(3):546–79.

Acemoglu, Daron, Suresh Naidu, Pascual Restrepo, and James A. Robinson. 2015. “Democracy, Redistribution, and Inequality.” Pp. 1885–1966 in Handbook of Income Distribution. Volume 2B, edited by A. B. Atkinson and F. Bourguignon. Amsterdam: North Holland.

Acemoglu, Daron and James Robinson. 2012. Why Nations Fail: The Origins of Power, Prosperity, and Poverty. New York: Crown.

Adams, Samuel and Edem Kwame Mensah Klobodu. 2016. “Financial Development, Control of Corruption and Income Inequality.” International Review of Applied Economics 30(6):790–808.

Ades, Alberto and Rafael DiTella. 1999. “Rents, Competition, and Corruption.” American Economic Review 89(4):982–93.

Alderson, Arthur S. and François Nielsen. 1999. “Income Inequality, Development, and Dependence: A Reconsideration.” American Sociological Review 64(4):606.

Alderson, Arthur S. and François Nielsen. 2002. “Globalization and the Great U-Turn: Income Inequality Trends in 16 OECD Countries.” American Journal of Sociology 107(5):1244–99.

Allison, Paul David. 2009. Fixed Effects Regression Models. London: Sage Publications.Ansell, Ben and David Samuels. 2010. “Inequality and Democratization: A Contractarian

Approach.” Comparative Political Studies 43(12): 1543-1574.Bandelj, N. and M. C. Mahutga. 2010. “How Socio-Economic Change Shapes Income Inequality

in Post-Socialist Europe.” Social Forces 88(5):2133–61.Banjeree, Abhijit V. and Andrew F. Newman. 1993. “Occupational Choice and the Process of

Development.” Journal of Political Economy 101(2):274–98.Beck, Thorsten, Asli Demirgüç-kunt, and Patrick Honohan. 2008. “Access to Financial

Services.” The World Bank Research Observer 24:70p.Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine. 2007. “Finance, Inequality and the

Poor.” Journal of Economic Growth 12(1):27–49.Bermeo, Nancy. 2016. “On Democratic Backsliding.” Journal of Democracy 27(1): 5-19.Bernhard, William and David Leblang. 2006. Democratic Processes and Financial Markets:

Pricing Politics. New York: Cambridge University Press.Bhattacharyya, Sambit. 2013. “Political Origins of Financial Structure.” Journal of Comparative

Economics 41(4):979–94.

17

Bian, Yanjie and John R. Logan. 1996. “Market Transition and the Persistence of Power: The Changing Stratification System in Urban China.” American Sociological Review 61(5):739.

Block, Fred. 2014. “Democratizing Finance.” Politics and Society 42(1):3–28.Bradley, David, Evelyne Huber, Stephanie Moller, François Nielsen, and John D.Stephens. 2003.

“Distribution and Redistribution in Postindustrial Democracies.” World Politics 55(2):193–228.

Brady, David, Agnes Blome, and Hanna Kleider. 2016. “How Politics and Institutions Shape Poverty and Inequality.” Pp. 167–140 in The Oxford Handbook of the Social Science of Poverty, edited by D. Brady and B. Linda M. New York: Oxford University Press.

Bueno de Mesquita, Bruce and Alastair Smith. 2012. The Dictator’s Handbook: Why Bad Behavior Is Almost Always Good Politics. New York: PublicAffairs.

Bueno de Mesquita, Bruce, Alastair Smith, Randolph M. Siverson, and James D. Morrow. 2003. The Logic of Political Survival. Cambridge: MIT Press.

Calomiris, Charles W. and Stephen H. Haber. 2014. Fragile by Design : The Political Origins of Banking Crises and Scarce Credit. Princeton, NJ: Princeton University Press.

Carruthers, Bruce G. 1996. City of Capital : Politics and Markets in the English Financial Revolution. Princeton, NJ: Princeton University Press.

Carruthers, Bruce G. and Jeong-Chul Kim. 2011. “The Sociology of Finance.” Annual Review of Sociology 37(1):239–59.

Cheibub, José A., Jennifer Gandhi and James R. Vreeland. 2010. “Democracy and Dictatorship Revisited.” Public Choice 143(1-2): 67-101.

Čihák, Martin, Asli Demirgüč-Kunt, Erik Feyen, and Ross Levine. 2012. “Benchmarking Financial Systems around the World.” World Bank Policy Research Working Papers WPS6175 (6175):1–58.

Čihák, Martin, Asli Demirgüč-Kunt, Erik Feyen, and Ross Levine. 2013. “Financial Development in 205 Economies, 1960 to 2010.” Journal of Financial Perspectives, EY Global FS Institute, 1(2):17–36.

Clarke, George R. G., Lixin Colin Xu, and Heng-fu Zou. 2006. “Finance and Income Inequality : What Do the Data Tell Us ?” Southern Economic Journal 72(3):578–96.

Coppedge, Michael, John Gerring, Staffan I. Lindberg, Svend-Erik Skaaning, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Carl Henrik Knutsen, Joshua Krusell, Anna Lührmann, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Moa Olin, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Constanza Sanhueza Petrarca, Johannes von Römer, Laura Saxer, Brigitte Seim, Rachel Sigman, Jeffrey Staton, Natalia Stepanova, and Steven Wilson. 2017. “V-Dem [Country-Year/Country-Date] Dataset v7.1” Varieties of Democracy (V-Dem) Project.

Darcillon, Thibault. 2015. “How Does Finance Affect Labor Market Institutions? An Empirical Analysis in 16 OECD Countries.” Socio-Economic Review 13(3):477–504.

Davis, Gerald F. 2005. “New Directions in Corporate Governance.” Annual Review of Sociology 31(1):143–62.

Davis, Gerald F. 2009. Managed by the Markets : How Finance Reshaped America. New York: Oxford University Press.

Davis, Gerald F. 2012. “Politics and Financial Markets.” Pp. 33–51 in The Oxford Handbook of the Sociology of Finance, edited by K. K. Cetina and A. Preda. New York: Oxford University Press.

18

Davis, Gerald F. and Suntae Kim. 2015. “Financialization of the Economy.” Annual Review of Sociology 41(1):203–21.

Delis, Manthos D., Iftekhar Hasan, and Pantelis Kazakis. 2014. “Bank Regulations and Income Inequality: Empirical Evidence.” Review of Finance 18(5):1811–46.

Demirgüç-Kunt, Asli and Ross Levine. 2009. “Finance and Inequality: Theory and Evidence.” Annual Review of Financial Economics 1(1):287–318.

Dünhaupt, Petra. 2014. An Empirical Assessment of the Contribution of Financialization on Corporate Governance to the Rise in Income Inequaltiy.

Dünhaupt, Petra. 2017. “Determinants of Labour’s Income Share in the Era of Financialisation.” Cambridge Journal of Economics 41(1):283–306.

Epstein, Gerald. 2005. “Introduction: Financialization of the World Economy.” Pp. 3–16 in Financialization and the World Economy, edited by G.Epstein. Cheltenham, UK: Edward Elgar Publishing.

Epstein, Gerald and Arjun Jayadev. 2005. “The Rise of Rentier Incomes in OECD Countries: Financialization, Central Bank Policy and Labor Solidarity.” Pp. 46–74 in Financialization and the World Economy, edited by G. Epstein. Cheltenham, UK: Edward Elgar Publishing.

Fei, John C. H., Gustav Ranis, and Shirley W. Y. Kuo. 1979. Growth with Equity : The Taiwan Case. Oxford: Oxford University Press.

Flaherty, Eoin. 2015. “Top Incomes under Finance-Driven Capitalism, 1990–2010: Power Resources and Regulatory Orders.” Socio-Economic Review 13(3):417–47.

Fligstein, Neil. 2001. The Architecture of Markets : An Economic Sociology of Twenty-First-Century Capitalist Societies. Princeton, NJ: Princeton University Press.

Fligstein, Neil and Adam Goldstein. 2015. “The Emergence of a Finance Culture in American Households, 1989–2007.” Socio-Economic Review 13(3):575–601.

Fligstein, Neil and Taekjin Shin. 2007. “Shareholder Value and the Transformation of the U.S. Economy, 1984-2000.” Sociological Forum 22(4):399–424.

Foster, John Bellamy. 2007. “The Financialization of Capitalism.” Monthly Review 58(11):1–12.Foster, John Bellamy and Hannah Holleman. 2010. “The Financial Power Elite.” Monthly

Review 62(1):1–19.Galor, Oded and Joseph Zeira. 1993. “Income Distribution and Macroeconomics.” The Review

of Economic Studies 60(1):35.Geddes, Barbara, Joseph Wright and Erica Frantz. 2014. “Autocratic Breakdown and Regime

Transitions: A New Data Set.” Perspectives on Politics 12(2):313-331.Godechot, Olivier. 2016. “Financialization Is Marketization! A Study of the Respective Impacts

of Various Dimensions of Financialization on the Increase in Global Inequality.” Sociological Science 3:495–519.

Greenwood, Jeremy and Boyan Jovanovic. 1990. “Financial Development, Growth, and the Distribution of Income.” The Journal of Political Economy 98(5):1076–1107.

Haber, Stephen H., Douglass C. North, and Barry R. Weingast. 2008. Political Institutions and Financial Development. Stanford, CA: Stanford University Press.

Hadenius, Axel and Jan. Teorell. 2007. “Pathways from Authoritarianism.” Journal of Democracy 18(1):143–57.

Hausman, J. A. 1978. “Specification Tests in Econometrics.” Econometrica 46(6):1251.Houle, Christian. 2009. “Inequality and Democracy: Why Inequality Harms Consolidation but

Does Not Affect Democratization. ” World Politics 61(4):589-622.

19

Huber, E., F.Nielsen, J. Pribble, and J. D. Stephens. 2006. “Politics and Inequality in Latin America and the Caribbean.” American Sociological Review 71(6):943–63.

Jaggers, K. and T. R. Gurr. 1995. “Tracking Democracy’s Third Wave with the Polity III Data.” Journal of Peace Research 32(4):469–82.

Jauch, Sebastian and Sebastian Watzka. 2016. “Financial Development and Income Inequality: A Panel Data Approach.” Empirical Economics 51(1):291–314.

Keefer, Philip. 2008. “Beyond Legal Origin and Checks and Balances: Political Credibility, Citizen Information, and Financial Sector Development.” Pp. 125–55 in Political Institutions and Financial Development, edited by S. H. Haber, Douglass C. North, and B. R. Weingast. Stanford, CA: Stanford University Press.

Kelly, Nathan J. 2005. “Political Choice, Public Policy, and Distributional Outcomes.” American Journal of Political Science 49(4):865–80.

Kerrissey, J. 2015. “Collective Labor Rights and Income Inequality.” American Sociological Review 80(3):626–53.

Kollmeyer, C. 2015. “Globalization and Income Inequality: How Public Sector Spending Moderates This Relationship in Affluent Countries.” International Journal of Comparative Sociology 56(1):3–28.

Korpi, Water. 1978. The Working Class and Welfare Capitalism: Work, Unions and Politics in Sweden. London: Routledge and Kegan Paul.

Kotera, Go, Keisuke Okada, and Sovannroeun Samreth. 2012. “Government Size, Democracy, and Corruption: An Empirical Investigation.” Economic Modelling 29(6):2340–48.

Krippner, Greta R. 2012. Capitalizing on Crisis : The Political Origins of the Rise of Finance. Cambridge, Mass: Harvard University Press.

Kus, Basak. 2012. “Financialisation and Income Inequality in OECD Nations: 1995-2007.” The Economic and Social Review 43(4):477–95.

Kuznets, Simon. 1955. “Economic Growth and Income Inequality.” American Economic Review 45(1):1–28.

Lee, Cheol-Sung. 2005. “Income Inequality, Democracy, and Public Sector Size.” American Sociological Review 70(1):158–81.

Lee, Cheol-Sung, Young-Bum Kim, and Jae-Mahn Shim. 2011. “The Limit of Equality Projects.” American Sociological Review 76(1):100–124.

Lee, Cheol-Sung, F. Nielsen, and A. S. Alderson. 2007. “Income Inequality, Global Economy and the State.” Social Forces 86(1):77–111.

Levitsky, Steven and Lucan Way. 2002. “The Rise of Competitive Authoritarianism.” Journal of Democracy 13(2):51–66.

Lin, Ken-Hou and Donald Tomaskovic-Devey. 2013. “Financialization and U.S. Income Inequality, 1970–2008.” American Journal of Sociology 118(5):1284–1329.

Linz, Juan J. 2000. Totalitarian and Authoritarian Regimes. Boulder, CO: Lynne Rienner Press.Linz, Juan J. and Alfred Stepan. 1996. Problems of Democratic Transition and Consolidation:

Southern Europe, South America, and Post-Communist Europe. Baltimore: Johns Hopkins University Press.

Lueders, Hans, and Ellen Lust. 2018. “Multiple Measurements, Elusive Agreement, and Unstable Outcomes in the Study of Regime Change.” The Journal of Politics (Forthcoming).

Magdoff, Harry. and Paul M. Sweezy. 1987. Stagnation and the Financial Explosion. New york: Monthly Review Press.

20

Marshall, Monty G., Ted R. Gurr and Keith Jaggers. 2016. “Polity IV Project, Political Regime Characteristics and Transitions, 1800-2015.” Vienna, VA: Center for Systemic Peace. Retrieved December 15, 2016 (http://www.systemicpeace.org/inscr/p4v2015.xls)

Moore, Barrington. 1966. Social Origins of Dictatorship and Democracy: Lord and Peasant in the Making of the Modern World. Boston: Beacon Press.

Morgan, Jana and Nathan J. Kelly. 2013. “Market Inequality and Redistribution in Latin America and the Caribbean.” The Journal of Politics 75(3):672–85.

Munck, Gerardo L. and Jay Verkuilen. 2002. “Conceptualizing and Measuring Democracy:Evaluating Alternative Indices.” Comparative Political Studies 35(1):5-34.

Nau, Michael. 2013. “Economic Elites and Income Inequality: Economic Elites, Investments, and Income Inequality.” Social Forces 92(2):437–61.

Palley, Thomas I. 2013. Financialization: The Economics of Finance Capital Domination. Basingstoke: Palgrave Macmillan UK.

Piketty, Thomas. 2014. Capital in the Twenty First Century. Cambridge: Belknap Press.La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny. 1998.

“Law and Finance.” The Journal of Political Economy 106(6):1113–55.Prasad, Monica. 2012. The Land of Too Much : American Abundance and the Paradox of

Poverty. Cambridge, Mass: Harvard University Press.Quinn, Sarah. 2017. “‘The Miracles of Bookkeeping’: How Budget Politics Link Fiscal Policies

and Financial Markets.” American Journal of Sociology 123(1):48–85.Roberts, Anthony and Roy Kwon. 2017. “Finance, Inequality and the Varieties of Capitalism in

Post-Industrial Democracies.” Socio-Economic Review 15(3):511–38.Robinson, Sherman. 1976. “A Note on the U Hypothesis Relating Income Inequality and

Economic Development.” The American Economic Revie 66(3):437–40.Shih, Victor C. 2008. Factions and Finance in China : Elite Conflict and Inflation. New York:

Cambridge University Press.Shiller, Robert J. 2012. Finance and the Good Society. Princeton, NJ: Princeton University

Press.Solt, Frederick. 2016. “The Standardized World Income Inequality Database.” Social Science

Quarterly.Stockhammer, Engelbert. 2012. “Financialization, Income Distribution and the Crisis.”

Investigacion Economica 71(279):39–70.Svolik, Milan. 2012. The Politics of Authoritarian Rule. New York: Cambridge University Press.Szelenyi, Ivan. 1978. “Social Inequalities in State Socialist Redistributive Economies.”

International Journal of Comparative Sociology 19(1):63–87.Tomaskovic-Devey, D. and Ken-Hou Lin. 2011. “Income Dynamics, Economic Rents, and the

Financialization of the U.S. Economy.” American Sociological Review 76(4):538–59.Trumbull, Gunnar. 2014. Consumer Lending in France and America: Credit and Welfare. New

York: Cambridge University Press.Volscho, T. W. and N. J. Kelly. 2012. “The Rise of the Super-Rich: Power Resources, Taxes,

Financial Markets, and the Dynamics of the Top 1 Percent, 1949 to 2008.” American Sociological Review 77(5):679–99.

Wang, Yingyao. 2015. “The Rise of the ‘shareholding State’: Financialization of Economic Management in China.” Socio-Economic Review 13(3):603–25.

Xie, Yu. 2010. Regression Analysis. Beijing: Social Sciences Academic Press.

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Ziblatt, Daniel. 2008. “Does Landholding Inequality Block Democratization?: A Test of the ‘Bread and Democracy’ Thesis and the Case of Prussia.” World Politics 60(4):610–41.

Figure 1. Private Sector Credit (% of GDP) (triangle) and Public Sector Credit (% of GDP) (dot), 1981–2012

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Table 1. Selected Financial Indicators

Financial indicators China United States Autocracy Anocracy Democracy

Credits (Financialization in general)

A. Domestic credit to private sector (% of GDP) 128.50 178.49 46.30 28.43 73.51

B. Credit to government and state-owned enterprises (% of GDP) 10.50 9.67 12.85 10.17 13.74

Public to private credits ratio (B/A%) 8.17 5.42 27.75 35.77 18.69

Banking system

Private credit by banks/A (%) 94.34 27.50 102.98 91.38 91.33

5-bank asset concentration (%) 59.50 46.76 78.87 75.60 76.91

Bank branches per 100,000 adults 7.64 34.67 8.85 10.76 24.10

Loans from nonresident banks (% of GDP) 2.06 18.37 8.19 4.70 30.38

Bank return on assets (%, before tax) 1.71 1.40 2.26 2.13 1.60

Bank credit to bank deposits (%) 262.47 61.20 126.21 81.72 107.90

Central bank assets (% of GDP) 2.88 15.90 1.16 4.83 4.43

Insurance system

Life insurance premium volume (% of GDP) 1.67 3.67 0.43 0.30 2.10

Nonlife insurance premium volume (%of GDP) 1.00 3.12 0.88 0.76 1.46

Insurance company assets (% of GDP) 13.75 31.77 5.86 3.58 22.37

Stock market

Stock market total value traded (% of GDP) 69.55 229.36 20.91 3.58 29.06

Number of listed companies per 1,000,000 people 1.85 13.06 7.83 6.25 25.08

Stock market return (2011-2012, %) -16.79 8.81 -11.30 17.24 -5.15 Source: Global Financial Development Database (GFDD) (2016)

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Table 2. Descriptive SummaryVariables Definition Source Mean Std. Dev.

Gini CoefficientEstimate of Gini index of inequality in equalized (square root scale) household disposable (post-tax, post-transfer) income

SWIID 35.96 8.90

GDPPC a GDP per capita (constant 2010 US$) WDI 9.21 1.24

Population Growth Total population (t-1) / Total population (t) WDI 0.89 1.13

Agricultural Employment a Employment in agriculture (% of total employment)

WDI 2.38 1.05

Secondary School Enrollment

School enrollment, secondary (% gross) WDI 86.61 23.33

FDIForeign direct investment, net inflows (% of GDP)

WDI 3.45 7.13

Labor Protection Freedom from forced labor V-Dem 0.80 0.15

Government Expenditure a General government final consumption expenditure (% of GDP)

WDI 2.75 0.33

Private Sector Credit Domestic credit to private sector (% of GDP)GFDD (WDI)

3.84 0.85

Public Sector Credit Credit to government and SOEs (% of GDP)GFDD (WDI)

2.01 1.19

Note: Total observations = 1,659a Logarithms base e (natural logs)

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Table 3. Fixed-effects Models: Effects on Gini Coefficients　 Model 1 Model 2 Model 3

GDPPC a0.745* 0.535 0.519(0.335) (0.348) (0.346)

GDPPC Squared 3.80e-10** 3.97e-10** 4.57e-10**(1.39e-10) (1.40e-10) (1.41e-10)

Population Growth -0.258** -0.262** -0.259**

(0.0910) (0.0912) (0.0909)Agricultural Employment a

-0.379*** -0.389*** -0.441***(0.109) (0.109) (0.109)

Secondary School Enrollment -0.00767 -0.00913 -0.00778(0.00533) (0.00533) (0.00532)

FDI -0.00495 -0.00522 -0.00486(0.00596) (0.00594) (0.00592)

Labor Protection -4.067** -3.836** -3.086*(1.323) (1.325) (1.333)

Government Expenditure a0.142 0.0141 -0.0553

(0.324) (0.324) (0.324)Regime (Autocracy=0)

Anocracy 0.701* 0.823* 1.145***

(0.330) (0.330) (0.341)Democracy 0.600 0.669* 1.043**

(0.336) (0.335) (0.350)Private Sector Credit 0.338** 1.850***

(0.110) (0.424)Public Sector Credit 0.137* 0.124*

(0.0590) (0.0589)Regime x Private Sector Credit (Autocracy=0)

Anocracy x Private Sector Credit -1.368**(0.454)

Democracy x Private Sector Credit -1.610***(0.426)

Year Dummy

Constant 32.26*** 34.27*** 33.74***　 (3.321) (3.400) (3.389)Observations 1,659Countries 103

Note: Standard errors in parentheses; *p < .05; **p< .01; ***p< .001 (two-tailed tests)a Logarithms base e (natural logs)

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Table 4. Fixed-effects and Random-effects Models: Effects on Gini Coefficients

　 Model 4 (FE) Model 5 (FE) Model 6 (RE)

GDPPC a 0.0274 0.0196 -1.211***(0.341) (0.340) (0.286)

GDPPC Squared 2.71e-10* 3.28e-10* 8.59e-11(1.37e-10) (1.37e-10) (1.37e-10)

Population Growth -0.221* -0.218* -0.106

(0.0887) (0.0885) (0.0890)Agricultural Employment a -0.381*** -0.428*** -0.390***

(0.106) (0.106) (0.108)Secondary School Enrollment -0.0118* -0.0106* -0.0147**

(0.00518) (0.00518) (0.00526)FDI -0.00470 -0.00436 -0.00362

(0.00577) (0.00575) (0.00591)Labor Protection -6.113*** -5.421*** -7.079***

(1.329) (1.337) (1.307)Government Expenditure a 0.00154 -0.0575 -0.353

(0.317) (0.316) (0.319)Regime (Autocracy=0)

Anocracy 1.411*** 1.710*** 1.745***

(0.338) (0.347) (0.351)Democracy 1.228*** 1.574*** 1.770***

(0.339) (0.353) (0.357)Private Sector Credit 0.443*** 1.836*** 1.957***

(0.107) (0.411) (0.407)Public Sector Credit 2.064*** 2.037*** 2.241***

(0.228) (0.228) (0.230)Regime x Private Sector Credit (Autocracy=0)

Anocracy x Private Sector Credit -1.271** -1.309**(0.441) (0.438)

Democracy x Private Sector Credit -1.483*** -1.526***(0.414) (0.410)

Regime x Public Sector Credit (Autocracy=0)Anocracy x Public Sector Credit -1.421*** -1.421*** -1.643***

(0.242) (0.242) (0.245)Democracy x Public Sector Credit -2.100*** -2.082*** -2.335***

(0.234) (0.233) (0.236)Year Dummy

Constant 40.30*** 39.74*** 53.75***　 (3.357) (3.349) (2.707)Observations 1,659Countries 103

Note: Standard errors in parentheses. *p < .05; **p< .01; ***p< .001 (two-tailed tests).a Logarithms base e (natural logs)

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Note1. Continuous variables, including direct tax, private sector credit, and public sector credit, were

centered (by subtracting the mean of the observations so that the new mean equaled zero) before including them in the analysis to avoid multicollinearity when the models included interacting variables (Xie 2010).

2. Although a financial regulation data set with a few detailed indices is available (Abiad, Detragiache, and Tressel 2010), it comprises a relatively small sample (91 countries during 1973–2005). Financial development can be also measured by total banking assets to GDP, but it is limited to a smaller sample. Moreover, financial systems can be measured using other indicators, such as financial access, efficiency, and stability, but credit to GDP is more suitable for capturing the sizes of financial institutions and markets, which indicate financial depth (Čihák et al. 2013). Although these alternative measurement tools might more comprehensively measure the scope of financial systems, their use is hindered by problems of minimal data coverage. Future studies might address this limitation by comparing the various concepts of financialization.

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