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A New Logit-Based Gini Coefficient

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 summari...

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Detalles Bibliográficos
Autores principales: Ryu, Hang K., Slottje, Daniel J., Kwon, Hyeok Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514977/
https://www.ncbi.nlm.nih.gov/pubmed/33267202
http://dx.doi.org/10.3390/e21050488
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author Ryu, Hang K.
Slottje, Daniel J.
Kwon, Hyeok Y.
author_facet Ryu, Hang K.
Slottje, Daniel J.
Kwon, Hyeok Y.
author_sort Ryu, Hang K.
collection PubMed
description 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.
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spelling pubmed-75149772020-11-09 A New Logit-Based Gini Coefficient Ryu, Hang K. Slottje, Daniel J. Kwon, Hyeok Y. Entropy (Basel) Article 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. MDPI 2019-05-13 /pmc/articles/PMC7514977/ /pubmed/33267202 http://dx.doi.org/10.3390/e21050488 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ryu, Hang K.
Slottje, Daniel J.
Kwon, Hyeok Y.
A New Logit-Based Gini Coefficient
title A New Logit-Based Gini Coefficient
title_full A New Logit-Based Gini Coefficient
title_fullStr A New Logit-Based Gini Coefficient
title_full_unstemmed A New Logit-Based Gini Coefficient
title_short A New Logit-Based Gini Coefficient
title_sort new logit-based gini coefficient
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514977/
https://www.ncbi.nlm.nih.gov/pubmed/33267202
http://dx.doi.org/10.3390/e21050488
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