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A New Logit-Based Gini Coefficient

1
Department of Economics, Chung Ang University, Seoul 156-756, Korea
2
Department of Economics, Southern Methodist University, Dallas, TX 75275, USA
3
Department of Political Science, Korea University, Seoul 136-701, Korea
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(5), 488; https://doi.org/10.3390/e21050488
Received: 21 March 2019 / Revised: 29 April 2019 / Accepted: 9 May 2019 / Published: 13 May 2019
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Abstract

The Gini coefficient is generally used to measure and summarize inequality over the entire income distribution function (IDF). Unfortunately, it is widely held that the Gini does not detect changes in the tails of the IDF particularly well. This paper introduces a new inequality measure that summarizes inequality well over the middle of the IDF and the tails simultaneously. We adopt an unconventional approach to measure inequality, as will be explained below, that better captures the level of inequality across the entire empirical distribution function, including in the extreme values at the tails. View Full-Text
Keywords: projection of share function; logit function; maximum entropy method; inequality measure projection of share function; logit function; maximum entropy method; inequality measure
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Ryu, H.K.; Slottje, D.J.; Kwon, H.Y. A New Logit-Based Gini Coefficient. Entropy 2019, 21, 488.

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