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Fair division of income distribution, development and growth:evidence from a panel of countries

Listed author(s):
  • Shao, Mingliang Frank

I use an unbalanced panel data to explore the correlation between aggregate income per capita and income inequality. A lot of studies document controversial results using the Gini index or other summary measurements of income inequality. I measure income inequality by the two dimensions of a point on the Lorenz Curve, where the Lorenz curve has unit slope. It is called the fair division point, which involves the fair population share and the fair income share. The difference between the fair population share and the fair income share approximates the Gini index of an income distribution. My analysis shows that a country’s low income population relatively decreases (the fair population share drops slightly) as the economy grows; and at the same time, those low income households are relatively worse off (the fair income share falls even though the GDP per capita increases). Inversely, as an economy becomes rich, there are more low income households (the fair population share increases), but those low income households are better off (the fair income share goes up and GDP per capita increases as well). Overall, both the Gini index and the difference between the fair population share and the fair income share have been increasing during the last half century in the panel of countries. Therefore, income inequality increases as an economy is getting richer. The analysis presents significant evidence for optimum income inequality regarding both the aggregate productivity and the growth rate of GDP, where income inequality is measured by either the Gini index or the fair division shares. But no evidence has been found for the Kuznets’ hypothesis. Both high and low inequality of income distribution could harm an economy as we compare with its potential optimum inequality. Also developed economies show different optimum inequality from that in developing economies, and there is the growth-worst fair population share that results in the lowest growth in developed economies. Measurement of income inequality matters on its economic effects for the subsamples of the panel data.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 31844.

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Date of creation: 07 May 2011
Handle: RePEc:pra:mprapa:31844
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  1. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
  2. Cecilia Garcia-Penalosa & Eve Caroli & Philippe Aghion, 1999. "Inequality and Economic Growth: The Perspective of the New Growth Theories," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1615-1660, December.
  3. Philippe Aghion, 2002. "Schumpeterian Growth Theory and the Dynamics of Income Inequality," Econometrica, Econometric Society, vol. 70(3), pages 855-882, May.
  4. repec:dau:papers:123456789/10091 is not listed on IDEAS
  5. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
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