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Ranking Inequality: Applications of Multivariate Subset Selection

William Horrace (), Joseph Marchand () and Timothy M. Smeeding
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Timothy M. Smeeding: Center for Policy Research, Maxwell School of Citizenship and Public Affairs, Syracuse University

No 21, Working Papers from ECINEQ, Society for the Study of Economic Inequality

Abstract: Inequality measures are often presented in the form of a rank ordering to highlight their relative magnitudes. However, a rank ordering may produce misleading inference, because the inequality measures themselves are statistical estimators with different standard errors, and because a rank ordering necessarily implies multiple comparisons across all measures. Within this setting, if differences between several inequality measures are simultaneously and statistically insignificant, the interpretation of the ranking is changed. This study uses a multivariate subset selection procedure to make simultaneous distinctions across inequality measures at a pre-specified confidence level. Three applications of this procedure are explored using country-level data from the Luxembourg Income Study. The findings show that simultaneous precision plays an important role in relative inequality comparisons and should not be ignored.

Keywords: Income distribution; Inference; Poverty; Subset Selection (search for similar items in EconPapers)
JEL-codes: C12 C15 D31 D63 I32 (search for similar items in EconPapers)
Date: 2006
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Journal Article: Ranking inequality: Applications of multivariate subset selection (2008) Downloads
Working Paper: Ranking Inequality: Applications of Multivariate Subset Selection (2005)
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