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Inequality Measures as Tests of Fairness in an Economy




Standard measures of inequality have been criticized for a long time on the grounds that they are snap shot measures which do not take into account the process generating the observed distribution. Rather than focusing on outcomes, it is argued, we should be interested in whether the underlying process is "fair". Following this line of argument, this paper develops statistical tests for fairness within a well defined income distribution generating process and a well specified notion of "fairness". We find that standard test procedures, such as LR, LM and Wald, lead to test statistics which are closely related to standard measures of inequality. The answer to the "process versus outcomes" critique is thus not to stop calculating inequality measures, but to interpret their values differently - to compare them to critical values for a test of the null hypothesis of fairness.

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  • Ravi Kanbur & Stuart Sayer & Andy Snell, 2007. "Inequality Measures as Tests of Fairness in an Economy," ESE Discussion Papers 174, Edinburgh School of Economics, University of Edinburgh.
  • Handle: RePEc:edn:esedps:174

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    References listed on IDEAS

    1. Steven Haider & Gary Solon, 2006. "Life-Cycle Variation in the Association between Current and Lifetime Earnings," American Economic Review, American Economic Association, vol. 96(4), pages 1308-1320, September.
    2. Michael Baker & Gary Solon, 2003. "Earnings Dynamics and Inequality among Canadian Men, 1976-1992: Evidence from Longitudinal Income Tax Records," Journal of Labor Economics, University of Chicago Press, vol. 21(2), pages 267-288, April.
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    Cited by:

    1. Nicholas Rohde, 2016. "J-divergence measurements of economic inequality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 847-870, June.

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