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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.

Suggested Citation

  • 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. 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.
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    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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