arXiv 26 Dec 2025 · Econometrics
arXiv:2512.21862 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes asymptotically distribution-free inference methods for comparing a broad range of welfare indices across dependent samples, including those employed in inequality, poverty, and risk analysis. Two distinct situations are considered. First, we propose asymptotic and bootstrap intersection methods which are completely robust to arbitrary dependence between two samples. Second, we focus on the common case of overlapping samples -- a special form of dependent samples where sample dependence arises solely from matched pairs -- and provide asymptotic and bootstrap methods for comparing indices. We derive consistent estimates for asymptotic variances using the influence function approach. The performance of the proposed methods is studied in a simulation experiment: we find that confidence intervals with overlapping samples exhibit satisfactory coverage rates with reasonable precision, whereas conventional methods based on an assumption of independent samples have an inferior performance in terms of coverage rates and interval widths. Asymptotic inference can be less reliable when dealing with heavy-tailed distributions, while the bootstrap method provides a viable remedy, unless the variance is substantial or nonexistent. The intersection method yields reliable results with arbitrary dependent samples, including instances where overlapping samples are not feasible. We demonstrate the practical applicability of our proposed methods in analyzing dynamic changes in household financial inequality in Italy over time.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dufour, Jean-Marie and He, Tianyu (2024) Measuring inequality for winners and losers: extended Lorenz curves and Gini indices for possibly negative variables self | 0.928 | 5 | 3 | 80% |
| 2 | Frank A. Cowell and Emmanuel Flachaire (2015) Chapter 6 - Statistical Methods for Distributional Analysis | 0.874 | 5 | 2 | 100% |
| 3 | Martin Biewen (2002) Bootstrap inference for inequality, mobility and poverty measurement | 0.843 | 3 | 3 | 100% |
| 4 | Zheng, Buhong and J. Cushing, Brian (2001) Statistical inference for testing inequality indices with dependent samples | 0.843 | 3 | 3 | 100% |
| 5 | Zheng, Buhong (2002) Testing Lorenz Curves with Non-Simple Random Samples | 0.843 | 3 | 3 | 100% |
| 6 | Zheng, Buhong (2004) Poverty comparisons with dependent samples | 0.843 | 3 | 3 | 100% |
| 7 | Davidson, Russell (2009) Reliable inference for the Gini index | 0.811 | 4 | 2 | 100% |
| 8 | Garry F. Barrett and Stephen G. Donald (2009) Statistical inference with generalized Gini indices of inequality, poverty, and welfare | 0.747 | 8 | 2 | 62% |
| 9 | Jean-Marie Dufour and Emmanuel Flachaire and Lynda Khalaf (2019) Permutation Tests for Comparing Inequality Measures self | 0.737 | 3 | 2 | 100% |
| 10 | Ibragimov, Rustam and Kattuman, Paul and Skrobotov, Anton (2025) Robust inference on income inequality: t- statistic based approach | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 55 scored citations.