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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-7514977 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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