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Ranking Inequality: Applications of Multivariate Subset Selection

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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. 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.

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  • William C. Horrace & Joseph T. Marchand & Timothy M. Smeeding, 2005. "Ranking Inequality: Applications of Multivariate Subset Selection," Center for Policy Research Working Papers 70, Center for Policy Research, Maxwell School, Syracuse University.
  • Handle: RePEc:max:cprwps:70
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    Cited by:

    1. Jun-ichiro Fukuchi, 2020. "A Note on Bootstrap for Gupta’s Subset Selection Procedure," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 96-114, February.
    2. Tarsitano Agostino & Lombardo Rosetta, 2013. "A Coefficient of Correlation Based on Ratios of Ranks and Anti-ranks," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(2), pages 206-224, April.
    3. Agostino Tarsitano & Rosetta Lombardo, 2011. "An Exhaustive Coefficient Of Rank Correlation," Working Papers 201111, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    4. Alfonso Flores-Lagunes & William C. Horrace & Kurt E. Schnier, 2007. "Identifying technically efficient fishing vessels: a non-empty, minimal subset approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 729-745.
    5. William C. Horrace & Christopher F. Parmeter, 2017. "Accounting for Multiplicity in Inference on Economics Journal Rankings," Southern Economic Journal, John Wiley & Sons, vol. 84(1), pages 337-347, July.
    6. Marchand, J. & Smeeding, T., 2016. "Poverty and Aging," Handbook of the Economics of Population Aging, in: Piggott, John & Woodland, Alan (ed.), Handbook of the Economics of Population Aging, edition 1, volume 1, chapter 0, pages 905-950, Elsevier.
      • Marchand, Joseph & Smeeding, Timothy, 2016. "Poverty and Aging," Working Papers 2016-11, University of Alberta, Department of Economics, revised 20 Nov 2016.
    7. Lena Lindahl, 2011. "A comparison of family and neighborhood effects on grades, test scores, educational attainment and income—evidence from Sweden," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(2), pages 207-226, June.

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    More about this item

    Keywords

    income distribution; inference; poverty; subset selection;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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