Permutation Tests for Comparing Inequality Measures

Jean-Marie Dufour, Emmanuel Flachaire and Lynda Khalaf
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Jean-Marie Dufour: McGill University = Université McGill [Montréal, Canada]

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Abstract: Asymptotic and bootstrap tests for inequality measures are known to perform poorly in finite samples when the underlying distribution is heavy-tailed. We propose Monte Carlo permutation and bootstrap methods for the problem of testing the equality of inequality measures between two samples. Results cover the Generalized Entropy class, which includes Theil's index, the Atkinson class of indices, and the Gini index. We analyze finite-sample and asymptotic conditions for the validity of the proposed methods, and we introduce a convenient rescaling to improve finite-sample performance. Simulation results show that size correct inference can be obtained with our proposed methods despite heavy tails if the underlying distributions are sufficiently close in the upper tails. Substantial reduction in size distortion is achieved more generally. Studentized rescaled Monte Carlo permutation tests outperform the competing methods we consider in terms of power.

Keywords: Bootstrap; Income distribution; Inequality measures; Permutation test (search for similar items in EconPapers)
Date: 2019-07
New Economics Papers: this item is included in nep-cmp, nep-gen and nep-ore
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-02172793v1
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Citations: View citations in EconPapers (7)

Published in Journal of Business and Economic Statistics, 2019, 37 (3), pp.457-470. ⟨10.1080/07350015.2017.1371027⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02172793

DOI: 10.1080/07350015.2017.1371027

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