Ranking Inequality: Applications of Multivariate Subset Selection
William Horrace (),
Joseph Marchand () and
Timothy M. Smeeding
No 70, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University
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. Wityhin 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: D31 C12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm
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Journal Article: Ranking inequality: Applications of multivariate subset selection (2008)
Working Paper: Ranking Inequality: Applications of Multivariate Subset Selection (2006)
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Persistent link: http://EconPapers.repec.org/RePEc:max:cprwps:70
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