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Financial liberalization, political institutions, and income inequality

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Abstract

Rising income inequality coinciding with financial liberalization has stimulated extensive studies on the possible links between income inequality and different forms of financial liberalization, both inputs and outputs, since the 1990s. Nonetheless, empirical investigations remain inconclusive. To provide new and robust evidence, this study investigates the distributional repercussions of financial liberalization and the role played by democratization in this process. Focusing on the outcome measures of financial liberalization, we find, in a panel of developing and developed countries for 1989–2011, that (1) financial openness alleviates income inequality, particularly for less democratic countries; (2) stock market development mitigates income inequality, whereas its volatility exacerbates it, with both effects decreasing with democratization; (3) banking development strengthens income inequality, whereas its volatility alleviates it, with both effects again moderating with democratization; and (4) these effects are mediated by human capital accumulation and entrepreneurship development. The data thus suggest that financial reforms toward capital account openness and more liquid, stable stock markets are beneficial to income distribution, as such reforms allow more previously excluded households and firms to access financial funds and services, thereby increasing human capital and entrepreneurship, especially in less democratic countries.

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Notes

  1. For instance, Das and Mohapatra (2003), Beck et al. (2010), Agnello et al. (2012), Delis et al. (2014), Bumann and Lensink (2016), Christopoulos and McAdam (2017), Haan and Sturm (2017), Furceri and Loungani (2018), and Manish and O’Reilly (2018) consider the input/policy measure of financial liberalization such as bank regulation and capital market liberalization policy. Others including Jalilian and Kirkpatrick (2005), Clarke et al. (2006), Beck et al. (2007), Kim and Lin (2011), Law et al. (2014), Jauch and Watzka (2016), D’Onofrio et al. (2017), Haan and Sturm (2017), and Blau (2018) focus on outcome measures such as capital (e.g., foreign direct investment, FDI) flows and the size and stability of the financial sector.

  2. For instance, Law et al. (2014), Cepparulo et al. (2017), and De Hann and Strum (2017) also explore the role of political institutions. Cepparulo et al. (2017), in a sample of developing countries from 1984 to 2012, find that the pro-poor impact of banking development decreases as the quality of institutions rises. Law et al. (2014), using a sample of 81 countries averaged over 1985–2010, show that banking development tends to reduce income inequality only after a certain threshold level of institutional quality has been achieved. Using a panel fixed effects model for a sample of 121 countries covering 1975–2005, De Hann and Strum (2017) demonstrate that banking development raises income inequality irrespective of the quality of political institutions. We differ from these studies by considering not only banking development but also stock market development. We also consider actual financial openness and financial volatility as well as the mechanisms of how these financial variables affect income inequality.

  3. Indeed, Boix (2003) and Acemoglu and Robinson (2006) show that income inequality affects democratization. The causality running from inequality to finance is also recognized by Claessens and Perotti (2007).

  4. We also check whether the diminishing effects of financial liberalization on inequality with increasing democratization are simply because of too much finance, as suggested by Arcand et al. (2015). Including the squares of the financial liberalization indicators, we continue to find that the financial variables and their interaction with democratization retain their signs and significance. This means that democratization captures more than the financial curse of Arcand et al. (2015). Further, we replace GDP growth with log real GDP per capita and its quadratic term; the financial variables and their interaction with democratization retain their signs and significance. These results are not reported to save space but are available on request.

  5. The only exception is for the Column (3) regression perhaps because of using too many endogenous variables. We find a similar case for the following two tables.

  6. Liu et al. (2017) also find that a rise in turnover in the stock market augments income inequality in China.

  7. This might also explain the larger number of instruments than countries.

  8. The quantile regression approach, introduced by Koenker and Bassett (1978), has gained increasing popularity in applied economics and has recently been extended to panel data with additive fixed effects and combined instrumental variable techniques in a quantile setting (e.g., Koenker 2004; Galvao 2011; Harding and Lamarche 2014). However, all these estimators are consistent in large-T samples. More recently, Powell (2016) and Powell and Wagner (2014) exploit the ideas of Chernozhukov and Hansen (2008) and propose a different estimator, based on instrumental variables. The estimator is consistent for small T. This is the most suitable method for our study given the small T and endogeneity of the regressors. Please see Powell (2016) for more details.

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Acknowledgements

We are grateful to the anonymous referee for valuable suggestions and comments that improve the paper.

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Authors and Affiliations

  1. Department of Food and Resource Economics, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Korea

    Dong-Hyeon Kim

  2. Department of International Business, Tamkang University, 151 Yingzhuan Rd., Danshui Dis., New Taipei City, 25137, Taiwan

    Joyce Hsieh

  3. Department of Economics, SungKyunKwan University, 25-2 SungKyunKwan-ro, Jongno-gu, Seoul, 03063, Korea

    Shu-Chin Lin

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Appendices

Appendix 1: measuring volatility

1.1 Measuring volatility

We follow Byrne and Davis (2005a, b) by proxying for financial volatility using conditional standard deviations obtained from the GARCH (1, 1) model. A standard GARCH model consists of mean and variance equations. For each country \( i \), we fit an ARMA (\( p, q \)) (autoregressive moving average) model to the mean equation of financial development \( \left( {finvol_{t} } \right): \)

$$ finvol_{t} = b_{0} + \mathop \sum \limits_{j = 1}^{p} b_{j} finvol_{t - j} + \mathop \sum \limits_{j}^{q} c_{j} \varepsilon_{t - j} $$
(4)

where \( \varepsilon_{t} \) is the white noise at time \( t \). We choose a model for \( p \) and \( q \) using the Schwarz Bayesian criterion. For conditional heteroskedasticity, we assume that.

$$ \varepsilon_{t} |\varOmega_{t - 1} = h_{t}^{1/2} \eta_{t} $$

where \( \varOmega_{t - 1} \) denotes the information set to time \( t - 1 \), \( h_{t} \) represents conditional variance at time \( t \), and \( \eta_{t} \sim NID\left( {0,1} \right). \) We then use the GARCH(1,1) specification proposed by Bollerslev (1986) as follows:

$$ h_{t} = \alpha_{1} + \alpha_{2} \varepsilon_{t - 1}^{2} + \beta h_{t - 1} $$
(5)

Appendix 2: descriptive statistics and list of Countries

See Tables 8 and 9.

Table 8 Descriptive statistics
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Table 9 List of sample countries
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Kim, DH., Hsieh, J. & Lin, SC. Financial liberalization, political institutions, and income inequality. Empir Econ 60, 1245–1281 (2021). https://doi.org/10.1007/s00181-019-01808-z

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