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National income and its distribution

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Abstract

This paper revisits the effect of national income on distributional equality. Although the link between the two has featured prominently in the literature, a causal effect has been difficult to pin down due to the endogeneity of these variables. We use plausibly exogenous variations in the incomes of countries’ trading partners weighted by the level of trade flows, and international oil price shocks, as instruments for within-country variations in countries’ real GDP per capita. Controlling for country and time fixed effects, our instrumental variables regressions show that increases in national income have a significant moderating effect on income inequality: a 1 % increase in real GDP per capita reduces the Gini coefficient by around 0.08 percentage points on average. We document that education is one possible channel that mediates this relationship, and explore the implications of our findings for the welfare effect of national income growth.

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Notes

  1. Other models such as Banerjee and Newman (1993), and Greenwood and Jovanovic (1990), also suggest that credit market imperfections play a role in shaping income inequality.

  2. See also Brueckner and Gradstein (2013), for further analysis on the causal effect of income on education.

  3. We also explore several other possible channels such as the size of government and financial development and find that education is the most important one.

  4. Windfalls may also be detrimental for governance, and there is a substantial volume of work on this issue; see Alexeev and Conrad (2009), for a recent example, for a discussion of the literature, and for additional references.

  5. Dollar et al. (2013) update and extend this work to argue that the poorest 40 % benefit from economic growth. Pinkovskiy and Sala-i-Martin (2013), find that economic growth in Africa has reduced inequality there.

  6. This work is rooted in early writings (such as Kaldor 1955), but has been reinvigorated more recently, see Galor (2011a, b).

  7. We obtain a coefficient (standard error) on the Gini coefficient of 0.0025 (0.0018); Forbes reports in her paper as a baseline, i.e. column (1) of Table 3, a coefficient of 0.0036 (0.0015).

  8. Barro (2000), finds a coefficient (standard error) on log GDP of 0.407 (0.090) and on the square of log GDP of \(-\)0.0275 (0.0056). (We note that the Gini coefficient ranges in his study between 0 and 1; in our paper it ranges between 0 and 100, i.e. it is in percentage points). If we regress, using least squares and without controlling for country fixed effects as in Barro’s main specification, the Gini coefficient (now scaled between 0 and 1 to facilitate comparison) on log GDP and the square of log GDP we obtain a coefficient (standard error) on log GDP of 0.261 (0.065) and on the square of log GDP of \(-\)0.018 (0.004), thus similar to Barro (2000).

  9. Appendix Table 3 shows the first stage effects of OPS and TWWI on countries’ real GDP per capita. As expected from previous literature (e.g. Brueckner et al. 2012a, b; Acemoglu et al. 2008) the instruments’ first-stage fit is positive and highly significant.

  10. See, for example, Brueckner et al. (2010), who show both empirically and theoretically that greater inequality can be associated with a higher return to capital.

  11. We are grateful to an anonymous referee for pointing this out.

  12. A further potential concern with the instruments is that trade affects a country’s income distribution beyond its effect on GDP per capita (a similar concern is discussed in Acemoglu et al. 2008, where TWWI is used as an instrument for national income; Acemoglu et al.’s endeavor is to examine whether income has a causal effect on democracy). For example, exports of certain commodities may be monopolized by elites; alternatively, imports from developing countries may change the income distribution by putting domestic producers of the imported goods out of work. Both of these cases would imply, however, that trade decreases the share of income held by the poor, thus contrary to what we find in the instrumental variables estimation. We are grateful to a referee for pointing this out.

  13. The estimating equation is the same as Eq. (1), except that contemporaneous GDP in period t is replaced with an average of GDP over current and past periods. That is, in Table 6, GDP over 10 years refers to the average of GDP in periods t and t-1; GDP over 15 (20) years refers to the average of GDP in periods t, t\(-\)1, and t\(-\)2 (t, t\(-\)1, t\(-\)2, and t\(-\)3).

  14. For example, in column (1) where the dependent variable is the Gini coefficient the estimated coefficient (standard error) on log GDP per capita is \(-\)8.48 (4.09) when country and time fixed effects are controlled for but not country-specific linear time trends; when country-specific linear time trends are added the coefficient (standard error) on log GDP per capita is \(-\)11.04 (15.56).

  15. We use the 1990 threshold because this provides a roughly equal number of observations (178 for the post-1990 period and 143 for the pre-1990 period).

  16. In column (1) of Table 9 the coefficient of 0.02 on the interaction between log GDP per capita and countries’ average GDP per capita (in thousands USD) suggests that taking a country from the 25th percentile of GDP per capita to the 75th percentile changes the marginal effect of income on the Gini coefficient (which ranges between 0 and 100) from \(-\)8.51 (calculated as \(-8.57+0.02*2.769\)) to \(-8.36\) (calculated as \(-8.57+0.02*10.171\)). In column (2) the coefficient of 1.03 on the interaction between log GDP per capita and the indicator variable that is unity for Low Income Countries suggests that in these countries the marginal effect of a change in log GDP per capita on the Gini coefficient is \(-7.15\) (calculated as \(-8.15+1.03\)). In column (3) the coefficient of \(-1.12\) on the interaction between log GDP per capita and the indicator variable that is unity for high income countries suggests that in these countries the marginal effect of a change in log GDP per capita on the Gini coefficient is \(-9.98\) (calculated as \(-8.77-1.12\)). In column (4) the coefficient of 5.62 on the interaction between log GDP per capita and countries’ average share of the population that lives in rural areas suggests that taking a country from the 25th percentile of the share of the population that lives in rural areas to the 75th percentile changes the marginal effect of income on the Gini coefficient from \(-10.19\) (calculated as \(-12.03+5.62*0.328\)) to \(-8.61\) (calculated as \(-12.03+5.62*0.607\)). In column (5) the coefficient of 4.31 on the interaction between log GDP per capita and countries’ average share of workers that are employed in the agricultural sector suggests that taking a country from the 25th percentile of the share of workers that are employed in the agricultural sector to the 75th percentile changes the marginal effect of income on the Gini coefficient from \(-\)9.46 (calculated as \(-9.76+4.31*0.068\)) to \(-8.17\) (calculated as \(-9.76+4.31*0.367\)).

  17. This dataset, also based on the WIID, purports to provide better comparability across countries; it does not, however, include breakdowns by income quintiles.

  18. Note that data on poverty are much sparser than income inequality data (the number of observations for the former is about one-third of the latter).

  19. GDP over 10 years refers to the average of GDP in periods t and t\(-\)1; GDP over 15 (20) years refers to the average of GDP in periods t, t\(-\)1, and t\(-\)2 (t, t\(-\)1, t\(-\)2, and t\(-\)3). The estimating equation is thus the same as Eq. (1), except that contemporaneous GDP is replaced with an average of contemporaneous and past values.

  20. Ten-year lag average years of schooling among the population of 40 years and over is a significant predictor for average years of schooling among the population of 15 years and over: in the first-stage equation for average years of schooling among the population of 15 years and over the coefficient (standard error) on 10-year lag average years of schooling among the population of 40 years and over is 0.77 (0.07). Trade-weighted world income and oil price shocks have a positive effect on log GDP per capita in this two-stage least squares estimation: in the first-stage equation for log GDP per capita the coefficient (standard error) on trade-weighted world income is 0.52 (0.09); for the oil price shock it is 2.67 (1.14).

  21. Ten-year lag share with secondary education among the population of 40 years and over is a significant predictor for share of the population with secondary education aged 15 years and over: in the first-stage equation for share of the population with secondary education aged 15 years and over the coefficient (standard error) on 10-year lag share with secondary education among the population of 40 years and over is 0.84 (0.09). Trade-weighted world income and oil price shocks have a positive effect on log GDP per capita in this two-stage least squares estimation: in the first-stage equation for log GDP per capita the coefficient (standard error) on trade-weighted world income is 0.47 (0.09); for the oil price shock it is 2.59 (1.11).

  22. In making these assumptions we follow Cordoba and Verdier (2008), and Jones and Klenow (2010).

  23. See Aitchison and Brown (1957).

References

  • Acemoglu, D., Johnson, S., Robinson, J. A., & Yared, P. (2008). Income and democracy. American Economic Review, 98, 808–842.

    Article  Google Scholar 

  • Ahluwalia, M. (1976). Income distribution and development: Some stylized facts. American Economic Review, 1976, 128–135.

    Google Scholar 

  • Ahluwalia, M. (1976b). Inequality, poverty and development. Journal of Development Economics, 3, 307–342.

    Article  Google Scholar 

  • Aitchison, J., & Brown, J. A. C. (1957). The lognormal distribution with special reference to its uses in economics. Cambridge: Cambridge University Press.

    Google Scholar 

  • Alexeev, M., & Conrad, R. (2009). The elusive curse of oil. Review of Economics and Statistics, 91, 586–598.

    Article  Google Scholar 

  • Anand, S., & Kanbur, S. M. R. (1993). The Kuznets process and the inequality-development relationship. Journal of Development Economics, 40, 25–52.

    Article  Google Scholar 

  • Atkinson, A., Piketty, T., & Saez, E. (2011). Top incomes in the long run of history. Journal of Economic Literature, 49, 3–71.

    Article  Google Scholar 

  • Banerjee, A. V., & Newman, A. F. (1993). Occupational choice and the process of development. Journal of Political Economy, 101(2), 274–298.

    Article  Google Scholar 

  • Barro, R. J. (2000). Inequality and growth in a panel of countries. Journal of Economic Growth, 5, 5–32.

    Article  Google Scholar 

  • Bazzi, S., & Clemens, M. (2013). Blunt instruments: Avoiding common pitfalls in identifying the causes of economic growth. American Economic Journal, 2, 152–186.

    Google Scholar 

  • Barro, R., & Lee, J.W. (2010). A New Data Set of Educational Attainment in the World, 1950–2010. NBER Working Paper No. 15902.

  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143.

    Article  Google Scholar 

  • Brueckner, M., Chong, A., & Gradstein, M. (2012a). Estimating permanent income elasticity of government expenditures: Evidence on Wagner’s Law based on oil price shocks. Journal of Public Economics, 96, 1025–1034.

    Article  Google Scholar 

  • Brueckner, M., Ciccone, A., & Tesei, A. (2012b). Oil price shocks, income, and democracy. Review of Economics and Statistics, 94, 389–399.

    Article  Google Scholar 

  • Brueckner, M., Gerling, K., & Gruener, H. (2010). Wealth inequality and credit markets: Empirical evidence from three industrialized countries. Journal of Economic Growth, 15, 155–176.

    Article  Google Scholar 

  • Brueckner, M., & Gradstein, M. (2013). Income and schooling. CEPR DP 9365.

  • Cordoba, J., & Verdier, G. (2008). Inequality and growth: Some welfare calculations. Journal of Economic Dynamics and Control, 32, 1812–1829.

    Article  Google Scholar 

  • Deininger, K., & Squire, L. (1998). New ways of looking at old issues: Inequality and growth. Journal of Development Economics, 57, 259–287.

    Article  Google Scholar 

  • Dollar, D., & Kraay, A. (2002). Growth is good for the poor. Journal of Economic Growth, 7, 195–225.

    Article  Google Scholar 

  • Dollar, D., Kleineberg, T., Kraay, A. (2013). Growth is still good for the poor, mimeo. Policy Research Working Paper 6768. Washington, DC: World Bank.

  • Easterly, W. (1999). Life during growth. Journal of Economic Growth, 4, 239–276.

    Article  Google Scholar 

  • Easterly, W. (2007). Inequality does cause underdevelopment. Journal of Development Economics, 84, 755–776.

    Article  Google Scholar 

  • Fields, G. S. (2001). Distribution and development: A new look at the developing world. New York: Russell Sage Foundation.

    Google Scholar 

  • Fleurbaey, M. (2009). Beyond GDP: The quest for a measure of social welfare. Journal of Economic Literature, 47, 1029–1075.

    Article  Google Scholar 

  • Forbes, K. J. (2000). A reassessment of the relationship between inequality and growth. American Economic Review, 90, 869–887.

    Article  Google Scholar 

  • Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52, 761–766.

    Article  Google Scholar 

  • Galor, O. (2011a). Inequality, human capital formation and the process of development. Handbook of the economics of education. Amsterdam: North-Holland.

    Google Scholar 

  • Galor, O. (2011b). Unified growth theory. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Galor, O., Moav, O., & Vollrath, D. (2009). Inequality in landownership, the emergence of human-capital promoting institutions, and the great divergence. Review of Economic Studies, 76, 143–179.

    Article  Google Scholar 

  • Galor, O., & Zeira, J. (1993). Income distribution and macroeconomics. Review of Economic Studies, 60, 35–52.

    Article  Google Scholar 

  • Greenwood, J., & Jovanovic, B. (1990). Financial development, growth, and the distribution of income. Journal of Political Economy, 98, 1076–1107.

    Article  Google Scholar 

  • Halter, D., Oechslin, M., & Zweimüller, J. (2014). Inequality and growth: The neglected time dimension. Journal of Economic Growth, 19, 81–104.

    Article  Google Scholar 

  • Heston, A., Summers, R., & Aten, B. (2012). Penn World Table Version 7.1. Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.

  • Jaumotte, F., Lall, S., & Papageorgiou, C. (2013). Rising income inequality: Technology, or trade and financial globalization? IMF Economic Review, 61, 271–309.

    Article  Google Scholar 

  • Jones, C. I., & Klenow, P. I. (2010). Beyond the GDP? Welfare across countries and time, mimeo. NBER Working Paper 16352. Stanford, CA: Stanford University.

    Book  Google Scholar 

  • Kaldor, N. (1955). Alternative theories of distribution. Review of Economic Studies, 23, 83–100.

    Article  Google Scholar 

  • Kraay, A. (2006). When is growth pro-poor? Evidence from a panel of countries. Journal of Development Economics, 80, 198–227.

    Article  Google Scholar 

  • Kuznets, S. (1955). Economic growth and income inequality. American Economic Review, 45, 1–28.

    Google Scholar 

  • Lundberg, M., & Squire, L. (2003). The simultaneous evolution of growth and inequality. Economic Journal, 113, 326–344.

    Article  Google Scholar 

  • Milanovic, B. (2000). Determinants of cross-country income inequality: An augmented Kuznets’ Hypothesis. In V. Franicevic & M. Uvalic (Eds.), Equality, participation, transition. London: Macmillan Press.

    Google Scholar 

  • Paukert, F. (1973). Income distribution of different levels of development: A survey of the evidence. International Labor Review, 108, 97–125.

    Google Scholar 

  • Pinkovskiy, M., & Sala-i-Martin, X. (2013). African poverty is falling... much faster than you think!. NBER Working Paper #15775.

  • Solt, F. (2009). Standardizing the World Income Inequality Database. Social Science Quarterly, 90, 231–242.

    Article  Google Scholar 

  • Staiger, D., & Stock, J. (1997). Instrumental variables regressions with weak instruments. Econometrica, 65, 557–586.

    Article  Google Scholar 

  • WDI (2013). World Development Indicators. Available for download at http://data.worldbank.org/.

  • WIID (2008). World Income Inequality Database V2.0. Available for download at http://www.wider.unu.edu/research/Database.

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Acknowledgments

We thank three referees and the editor, as well as participants in several seminar presentations, for their very thoughtful comments and suggestions. Camelia Minoiu’s dedicated assistance in putting together the inequality dataset is gratefully acknowledged. This paper is part of a research project on macroeconomic policy in low-income countries and was partially supported by the United Kingdom’s Department for International Development (DFID).

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  1. University of Queensland, Brisbane, Australia

    Markus Brueckner

  2. IMF, Washington, USA

    Era Dabla Norris

  3. BGU, Beer Sheva, Israel

    Mark Gradstein

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  1. Markus Brueckner
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Correspondence to Mark Gradstein.

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Brueckner, M., Dabla Norris, E. & Gradstein, M. National income and its distribution. J Econ Growth 20, 149–175 (2015). https://doi.org/10.1007/s10887-015-9113-4

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