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Increasing discriminatory power in well-being analysis using convex stochastic dominance

Author

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  • Gordon Anderson

    (University of Toronto)

  • Thierry Post

    (Nazarbayev University Graduate School of Business)

Abstract

The complementary concepts of Stochastic Dominance Optimality and Stochastic Dominance Inferiority are developed and employed to detect collections of distributions with the highest and lowest levels of shared prosperity respectively. These resulting sets can be smaller than the standard sets of maximal and minimal elements based on multiple pairwise comparisons. Linear Programming techniques for implementing the twin concepts are derived and implemented in empirical and simulated examples based on aggregated income distributions to demonstrate the potential gains in discriminatory power.

Suggested Citation

  • Gordon Anderson & Thierry Post, 2018. "Increasing discriminatory power in well-being analysis using convex stochastic dominance," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 51(3), pages 551-561, October.
    • Handle: RePEc:spr:sochwe:v:51:y:2018:i:3:d:10.1007_s00355-018-1127-3
    DOI: 10.1007/s00355-018-1127-3
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    References listed on IDEAS

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    7. Thierry Post, 2003. "Empirical Tests for Stochastic Dominance Efficiency," Journal of Finance, American Finance Association, vol. 58(5), pages 1905-1931, October.
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    Cited by:

    1. Arvanitis, Stelios & Post, Thierry & Potì, Valerio & Karabati, Selcuk, 2021. "Nonparametric tests for Optimal Predictive Ability," International Journal of Forecasting, Elsevier, vol. 37(2), pages 881-898.
    2. Mehmet Pinar & Thanasis Stengos & Nikolas Topaloglou, 2022. "Stochastic dominance spanning and augmenting the human development index with institutional quality," Annals of Operations Research, Springer, vol. 315(1), pages 341-369, August.
    3. Pinar, Mehmet & Stengos, Thanasis & Topaloglou, Nikolas, 2020. "On the construction of a feasible range of multidimensional poverty under benchmark weight uncertainty," European Journal of Operational Research, Elsevier, vol. 281(2), pages 415-427.

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