Individual Components of Three Inequality Measures for Analyzing Shapes of Inequality

Autor/in	Liao, Tim Futing
Titel	Individual Components of Three Inequality Measures for Analyzing Shapes of Inequality
Quelle	In: Sociological Methods & Research, 51 (2022) 3, S.1325-1356 (32 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Liao, Tim Futing)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124119875961
Schlagwörter	Sociology; Income; Social Differences; Social Science Research; National Surveys; Evaluation Methods; Individual Characteristics; Comparative Analysis; Error of Measurement; Current Population Survey + Suchen Sie Ihr Suchwort? Soziologie; Einkommen; Sozialer Unterschied; Social scientific research; Sozialwissenschaftliche Forschung; Personality characteristic; Personality traits; Persönlichkeitsmerkmal; Messfehler
Abstract	In common sociological research, income inequality is measured only at the aggregate level. The main purpose of this article is to demonstrate that there is more than meets the eye when inequality is indicated by a single measure. In this article, I introduce an alternative method that evaluates individuals' contributions to inequality as well as the between-group and within-group components of these individual contributions. I first highlight three common inequality measures, the Gini index and two generalized entropy measures--Theil's T and Theil's L indices--by presenting their individual components as a method for evaluating inequality. Five artificial data examples illustrate the use of these individual components first. An empirical analysis of the 2007 and 2017 Current Population Survey data then focuses on the differences in inequality revealed by such individual inequality components between the 2007 and 2017. The individual-level inequality measures can reveal patterns of inequality concealed by single measures at the aggregate level. In particular, the Gini individual measures differentiate cases better than the generalized entropy measures and tend to have smaller standard errors in a regression analysis. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01