Estimation of Income Inequality from Grouped Data

Vanesa Jorda (), José María Sarabia () and Markus Jäntti ()

No 804, LIS Working papers from LIS Cross-National Data Center in Luxembourg

Abstract: Grouped data in the form of income shares have conventionally been used to estimate income inequality due to the lack of individual records. We provide guidance on the choice between parametric and nonparametric methods and its estimation, for which we develop the GB2group R package. We present a systematic evaluation of the performance of parametric distributions to estimate economic inequality. The accuracy of these estimates is compared with those obtained by nonparametric techniques in more than 5000 datasets. Our results indicate that even the simplest parametric models provide reliable estimates of inequality measures. The nonparametric approach, however, fails to represent income distributions accurately.

JEL-codes: C13 C18 D31 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2020-12
New Economics Papers: this item is included in nep-cwa, nep-ecm and nep-isf
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Journal of Royal Statistical Society Series A. (2021), https://doi.org/10.1111/rssa.12702

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Persistent link: https://EconPapers.repec.org/RePEc:lis:liswps:804

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