A New Logit-Based Gini Coefficient (2019.05)

• 저자 : Hang K. Ryu, Daniel J. Slottje  and Hyeok Y. Kwon
• 학술지명 : Entropy
• 발행처 : MDPI
• 권호 : 21(5)
• 게재년월 : 2019년 5월
• DOI : https://doi.org/10.3390/e21050488
• 초록 : 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.