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Inequality measurement with grouped data: Parametric and non-parametric methods
Stockholm University, Faculty of Social Sciences, The Swedish Institute for Social Research (SOFI).
Number of Authors: 32021 (English)In: Journal of the Royal Statistical Society: Series A (Statistics in Society), ISSN 0964-1998, E-ISSN 1467-985X, Vol. 184, no 3, p. 964-984Article in journal (Refereed) Published
Abstract [en]

Grouped data in the form of income shares have conventionally been used to estimate income inequality due to the lack of individual records. We present a systematic evaluation of the performance of parametric distributions and non-parametric techniques to estimate economic inequality using more than 3300 data sets. We also provide guidance on the choice between these two approaches and their estimation, for which we develop the GB2group R package. Our results indicate that even the simplest parametric models provide reliable estimates of inequality measures. The non-parametric approach, however, fails to represent income distributions accurately.

Place, publisher, year, edition, pages
2021. Vol. 184, no 3, p. 964-984
Keywords [en]
generalised beta distribution of the second kind, kernel density estimator, Lorenz curve, minimum distance estimators
National Category
Sociology Mathematics
Identifiers
URN: urn:nbn:se:su:diva-196124DOI: 10.1111/rssa.12702ISI: 000659040000001OAI: oai:DiVA.org:su-196124DiVA, id: diva2:1590628
Available from: 2021-09-03 Created: 2021-09-03 Last updated: 2022-02-25Bibliographically approved

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Jäntti, Markus

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