Abstract
In a sample of advanced and developing countries observed over the 1980–2007 period, this paper documents that the ability to use financial instruments and deal with financial market complexity that indicators of economic literacy proxy for is significantly and robustly associated to a lower variation in income inequality. The direct association between financial development and inequality usually referred to as the “finance-inequality nexus”, instead, is not significant in long-run regressions that control for the level of economic literacy nor in panel regressions.
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Notes
The study by Beck et al. (2007) considers both the 1960–2005 period and the shorter 1980–2005 period.
The unit of analysis indicates if income inequality data are based on actual observation of individual units, drawn from household surveys (“household”), or on national statistics (“person”). The choice of the “household” statistical unit of analysis is preferred in international comparisons because estimates drawn from national statistics rely on strong assumption regarding patterns of inequality across countries or over time that cannot be tested if such information is included in the data set, and that are normally used only when household surveys are not available (for a detailed discussion, see e.g. Deiniger and Squire 1996).
The indicator of economic literacy among the population was compiled for the first time in 1995 for 45 countries. Afterwards, the number of countries included in the survey increased up to 55 in 2008. The choice of using the country-level 1995–2007 average (as in Jappelli 2010) allows to use the maximum number of observations available for the cross-sectional analysis. The results presented in Sects. 4 and 5 are robust to measuring economic literacy as the value in the last year of the sample (i.e. 2007).
Economic literacy is a significant determinant of income inequality in regressions that considers the role of potential outliers in Fig. 1, by dropping Romania or by introducing a dummy variable for transitions economies.
To remove the bias related to cross-country unobservable characteristics, it would be necessary to find source of cross-country variation that can serve as instruments, or to use fixed-effect or first-difference specifications that cannot estimate the coefficient of time-invariant literacy.
The strength of investor protection might have a direct impact on the dynamics of the income distribution if protection existed only for small groups of well-connected people (see Pagano and Volpin 2005, and related literature). Results from IV regressions where the set of instruments includes legal origin dummies only confirm the findings on the relations of interest, as historical differences in legal systems may arguably capture well cross-country differences in legal protection (La Porta et al. 1997).
Results are robust to alternative ways of measuring time-varying economic-specific competences, e.g. as the last value of economic literacy in each sub-period, that would allow to run regressions on four sub-periods.
References
Banerjee, A., & Newman, A. (1993). Occupational choice and the process of development. Journal of Political Economy, 101(2), 274–298.
Barro, R., & Lee, J. (2013). A new data set of educational attainment in the world, 1950–2010. Journal of Development Economics, 104, 184–198.
Baum, C. F., Schaffer, M. E., & Stillman, S. (2007). Enhanced routines for instrumental variables/GMM estimation and testing. Boston College Economics Working Paper 667.
Beck, T., & Demirgüç-Kunt, A. (2009). financial institutions and markets across countries and over time: Data and analysis. World Bank Policy Research Working Paper 4943.
Beck, T., Demirgüç-Kunt, A., & Levine, R. (2007). Finance, inequality and the poor. Journal of Economic Growth, 12, 27–49.
Bertola, G., & Lo Prete, A. (2009). Openness, financial markets, and policies: Cross-country and dynamic patterns. Annals of Economics and Statistics, 95(96), 167–182.
Caliendo, F., & Findley, T. (2013). Time inconsistency and retirement planning. Economics Letters, 121(1), 30–34.
Claessens, S., & Perotti, E. (2007). Finance and inequality: Channels and evidence. Journal of Comparative Economics, 35, 748–773.
Clarke, G., Xu, L., & Zou, H. (2006). Finance and income inequality: What do the data tell us? Southern Economic Journal, 72(3), 578–596.
Deiniger, K., & Squire, L. (1996). A new data set measuring income inequality. The World Bank Economic Review, 10(3), 565–591.
Deiniger, K., & Squire, L. (1998). New ways of looking at old issues: Inequality and growth. Journal of Development Economics, 57(2), 259–287.
Demirgüç-Kunt, A., & Levine, R. (2009). Finance and inequality: Theory and evidence. Annual Review of Financial Economics, 1, 287–318.
Dollar, D., & Kraay, A. (2002). Growth is good for the poor. Journal of Economic Growth, 7, 195–225.
Eichengreen, B. (2015). The next financial crisis. Economia Politica, 32(1), 53–66.
Fornero, E., & Lo Prete, A. (2017). Voting in the aftermath of a pension reform: The role of financial literacy. CeRP Working Paper 171/17.
Galor, O., & Moav, O. (2004). From physical to human capital accumulation: inequality and the process of development. Review of Economic Studies, 71(4), 1001–1026
Galor, O., & Zeira, J. (1993). Income distribution and macroeconomics. Review of Economic Studies, 60, 35–52.
Greenwood, J., & Jovanovic, B. (1990). Financial development, growth, and the distribution of income. Journal of Political Economy, 98, 1076–1107.
Guiso, L., & Jappelli, T. (2008). Financial literacy and portfolio diversification. CSEF Working Paper 212.
Heston, A., Summers, R., & Aten, B. (2009). Penn World Table Version 6.3. Compiled by the Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, August 2009.
Honohan, P. (2009). Household financial assets in the process of development. In J. B. Davies (Ed.), Personal wealth from a global perspective, part 3 (pp. 271–292). Oxford: Oxford University Press.
Jappelli, T. (2010). Economic literacy: An international comparison. Economic Journal, 120, F429–F451.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1997). Legal determinants of external finance. Journal of Finance, 52(3), 1131–1150.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1999). The quality of government. The Journal of Law, Economics, and Organization, 15(1), 222–279.
Li, H., Squire, L., & Zou, H. (1998). Explaining international and intertemporal variations in income inequality. Economic Journal, 108, 26–43.
Lo Prete, A. (2013). Economic literacy, inequality, and financial development. Economics Letters, 118, 74–76.
Lusardi, A., Michaud, P., & Mitchell, O. (2017). Optimal financial knowledge and wealth inequality. Journal of Political Economy, 125(2), 431–477.
Lusardi, A., & Mitchell, O. (2007). Baby boomer retirement security: The roles of planning, financial literacy, and housing wealth. Journal of Monetary Economics, 54(1), 205–224.
Lusardi, A., & Mitchell, O. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44.
Pagano, M., & Volpin, P. (2005). The political economy of corporate governance. American Economic Review, 95(4), 1005–1030.
Paukert, F. (1973). Income distribution at different levels of development: A survey of evidence. International Labor Review, 108, 97–125.
Rajan, R. G., & Zingales, L. (2003). The great reversals: The politics of financial development in the twentieth century. Journal of Financial Economics, 69, 5–50.
Summers, R., Kravis, I., & Heston, A. (1984). Changes in the world income distribution. Journal of Policy Modeling, 6, 237–269.
Van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101, 449–472.
Acknowledgements
I thank Tullio Jappelli, Maela Giofrè, Chiara Daniela Pronzato, Serena Trucchi, participants at the 7th Max Weber Fellows June Conference and at the CeRP Conference on “Financial Literacy, Saving and Retirement in Aging Society” for their helpful comments. The usual disclaimer applies.
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Data Appendix
Data Appendix
1.1 Inequality
Data on inequality are drawn from the UNU-WIDER World Income Inequality Database (version 2.0c, May 2008). This database updates the World Bank database by Deiniger and Squire (1996) and includes new estimates from the Luxembourg Income Study and from the TransMONEE.
1.2 Finance
Financial development is the “Private Credit by Deposit Money Banks and Other Financial Institutions to GDP” from the World Bank “Financial Development and Structure Database” (Beck and Demirgüç-Kunt 2009).
1.3 Competence
The World Competitiveness Yearbook compiles indexes of economic competence on the basis of interviews with senior business leaders. The “economic literacy among the population” index ranges from 0 to 10, lower values indicating that the level of competence in economics subjects is low. It is available for 55 countries over the 1995–2008 period. The “education in finance” index ranges from 0 to 10, lower values indicating that the level of competence in financial subjects does not meet the needs of the enterprises. It is available for 55 countries over the 1999–2008 period. Data on “Schooling” are drawn from the “Education Attainment for Total Population, 1950–2010” database by Barro and Lee (2013), and refer to the percentage of people with secondary school attainment over the population aged 15 years-old or later. “PISA score” is the mean value of the PISA indicator that assesses 15-year-old boys and girls’ performance in mathematics in 2006, compiled by the OECD.
1.4 Control variables
“Trade openness” is the ratio of export plus imports to GDP by the Penn World Tables (issue: June 3, 2011). “Inflation” is the annual percentage growth of the GDP deflator from the World Bank’s World Development Indicators online (issue: April 17, 2012). “GDP per capita growth” is the annual growth rate of GDP per capita from the IMF online database. “Population growth” is the annual growth of population, computed using data from the Penn World Tables, Version 6.3 (Heston et al. 2009). “Dependency ratio” measures the number of people aged below 15 and above 65 as a percentage of the total population, and is drawn from the World Bank’s World Development Indicators.
1.5 Instrumental variables
Investor protection is measured by the “strength of investor protection index” compiled by the Doing Business Project. It includes information on the extent of disclosure, the extent of director liability, and ease of shareholder suits indices, and ranges from 0 to 10, a higher value indicating stronger investor protection. Dummy variables for “legal origin” define five legal-origin groups as in La Porta et al. (1999): English Common Law; French Commercial Code; German Commercial Code; Scandinavian Commercial Code; Social/Communist Laws.
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Lo Prete, A. Inequality and the finance you know: does economic literacy matter?. Econ Polit 35, 183–205 (2018). https://doi.org/10.1007/s40888-018-0097-3
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DOI: https://doi.org/10.1007/s40888-018-0097-3