Ranking Inequality: Applications of Multivariate Subset Selection

Author(s)/Creator(s)

William C. Horrace, Syracuse UniversityFollow
Joseph T. Marchand, University of Alberta
Timothy M. Smeeding

Description/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.

Document Type

Working Paper

Date

2005

Keywords

income distribution, inference, poverty, subset selection

Language

English

Series

Working Papers Series

Disciplines

Economics

Additional Information

Harvest from RePEc at http://repec.org

Recommended Citation

Horrace, William C.; Marchand, Joseph T.; and Smeeding, Timothy M., "Ranking Inequality: Applications of Multivariate Subset Selection" (2005). Center for Policy Research. 93.
https://surface.syr.edu/cpr/93

Source

Metadata from RePEc

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.