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On Extending Stochastic Dominance Comparisons to Ordinal Variables and Generalising Hammond Dominance

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  • Gordon John Anderson
  • Teng Wah Leo

Abstract

Following the increasing use of discrete ordinal data for well-being analysis, this note builds on Hammond (H-) dominance concepts developed in Gravel et al. (2020) for discrete ordinal variables by observing and exploiting the fact that the coefficients associated with successive sums of cumulative distribution functions are Binomial coefficient functions of the order of dominance under consideration. Drawing first on notions of stochastic dominance relations for continuous variables to develop analogous concepts for discrete ordinal variables, it highlights the important limitation that increasing orders of dominance lead to loss of degrees of freedom which can be significant when the number of categories is low, as is common among ordered categorical variables, effectively bounding the maximum order of dominance. However, expanding on H- dominance by utilising the Binomial coefficients facilitates sequential consideration of higher orders of H- dominance without this loss, thereby surmounting the limitation.

Suggested Citation

  • Gordon John Anderson & Teng Wah Leo, 2021. "On Extending Stochastic Dominance Comparisons to Ordinal Variables and Generalising Hammond Dominance," Working Papers tecipa-705, University of Toronto, Department of Economics.
  • Handle: RePEc:tor:tecipa:tecipa-705
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    References listed on IDEAS

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    1. Anderson, Gordon & Leo, Teng Wah, 2021. "Sufficient conditions for jth order stochastic dominance for discrete cardinal variables, and their formulae," Economics Letters, Elsevier, vol. 209(C).
    2. Schröder, Carsten & Yitzhaki, Shlomo, 2017. "Revisiting the evidence for cardinal treatment of ordinal variables," European Economic Review, Elsevier, vol. 92(C), pages 337-358.
    3. Ekern, Steinar, 1980. "Increasing Nth degree risk," Economics Letters, Elsevier, vol. 6(4), pages 329-333.
    4. Nicolas Gravel & Brice Magdalou & Patrick Moyes, 2021. "Ranking distributions of an ordinal variable," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 71(1), pages 33-80, February.
    5. Russell Davidson & Jean-Yves Duclos, 2000. "Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality," Econometrica, Econometric Society, vol. 68(6), pages 1435-1464, November.
    6. Grogan, Colleen M., 1995. "Urban economic reform and access to health care coverage in the People's Republic of China," Social Science & Medicine, Elsevier, vol. 41(8), pages 1073-1084, October.
    7. Timothy N. Bond & Kevin Lang, 2019. "The Sad Truth about Happiness Scales," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1629-1640.
    8. Daniel Kahneman & Alan B. Krueger, 2006. "Developments in the Measurement of Subjective Well-Being," Journal of Economic Perspectives, American Economic Association, vol. 20(1), pages 3-24, Winter.
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    Cited by:

    1. Anderson, Gordon & Fu, Rui & Leo, Teng Wah, 2022. "Health, loneliness and the ageing process in the absence of cardinal measure: Rendering intangibles tangible," The Journal of the Economics of Ageing, Elsevier, vol. 22(C).

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    More about this item

    Keywords

    Stochastic Dominance; Discrete Variables; Ordinal Variables; Hammond Transfers;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty

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