Finance, income inequality and income redistribution

ABSTRACT

Using a panel fixed effects model for a large sample of countries covering 1975–2005, we test the hypothesis that income inequality caused by finance (financial development, financial liberalization and banking crises) is related to more income redistribution than inequality caused by other factors. Our results provide evidence in support of this hypothesis. We also find that the impact of inequality on redistribution is conditioned by ethno-linguistic fractionalization. Our findings are robust to the inclusion of several control variables suggested by previous studies.

KEYWORDS:

• Income inequality
• income redistribution
• financial liberalization
• financial sector size
• financial crises

JEL CLASSIFICATION:

• D31
• D63
• F02
• O11
• O15

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ We should add that notably the growth enhancing effects of capital account liberalization are hard to identify; see, for instance, Kose et al. (2009).

² For instance, Arcand et al. (2015) report that at intermediate levels of financial depth, there is a positive relationship between the size of the financial system and economic growth, but at high levels of financial depth, more finance is associated with less growth. In fact, the marginal effect of financial depth on output growth becomes negative when credit to the private sector reaches 80–100 per cent of GDP.

³ However, there are also several studies suggesting that financial development and/or financial liberalization reduce(s) income inequality. de Haan and Sturm (2017) provide an extensive discussion of the literature.

⁴ Further evidence on the importance of ethnic fractionalization is provided by Fum and Hodler (2010), who find that natural resources raise (after tax) income inequality in ethnically polarized societies, but reduce income inequality in ethnically homogenous societies.

⁵ We acknowledge that the Gini coefficient is less than perfect and that other measures, such as the share of income of the lowest quintile, may sometimes be more appropriate. Data availability, however, dictates our choice. A downside of the use of Gini coefficients is that it is rather insensitive to movements at the end of the tails, the very rich and very poor. This is partly because it is hard to get the richest and poorest household to contribute to household surveys and hence they are underrepresented. This is unfortunate, since much of the increase in inequality is expected at the highest 1 per cent of the distribution (Alvaredo, 2011).

⁶ The next paragraphs heavily draw on de Haan and Sturm (2017).

⁷ We acknowledge that the Gini coefficient is an imperfect measure of income inequality. The main problem is that several welfare state characteristics may affect incentives to supply labor and capital, thereby affecting the market Gini coefficient. Other indicators of income inequality are often not available for most countries in our sample. Furthermore, the difference between the market and net Gini coefficients is a much better proxy for income redistribution than frequently-used other measures such as government transfers and subsidies, as also taxes may have redistribution consequences (Sturm and de Haan 2015).

⁸ Data for GDP per capita and natural resource rents come from the World Bank’s World Development Indicators. The natural resource rents measure the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents taken as percentage of GDP. These rents are estimated as the difference between the price of a commodity and the average cost of extracting these resources.

⁹ We measure the quality of political institutions using the democratic accountability variable included in the ICRG database. Our indicator of the quality of economic institutions is the sum of three ICRG variables, namely bureaucratic quality, corruption and law and order (taking differences in scaling of these indicators into account).