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Gini estimation under infinite variance

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  • Fontanari, Andrea
  • Taleb, Nassim Nicholas
  • Cirillo, Pasquale

Abstract

We study the problems related to the estimation of the Gini index in presence of a fat-tailed data generating process, i.e. one in the stable distribution class with finite mean but infinite variance (i.e. with tail index α∈(1,2)). We show that, in such a case, the Gini coefficient cannot be reliably estimated using conventional nonparametric methods, because of a downward bias that emerges under fat tails. This has important implications for the ongoing discussion about economic inequality.

Suggested Citation

  • Fontanari, Andrea & Taleb, Nassim Nicholas & Cirillo, Pasquale, 2018. "Gini estimation under infinite variance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 256-269.
    • Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:256-269
    DOI: 10.1016/j.physa.2018.02.102
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    References listed on IDEAS

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    Cited by:

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    3. John C. Stevenson, 2021. "Population and Inequality Dynamics in Simple Economies," Papers 2101.09817, arXiv.org, revised Aug 2021.
    4. Chan, Terence, 2022. "On a new class of continuous indices of inequality," Mathematical Social Sciences, Elsevier, vol. 120(C), pages 8-23.
    5. Maia, Adriano & Matsushita, Raul & Da Silva, Sergio, 2020. "Earnings distributions of scalable vs. non-scalable occupations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    6. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2021. "Measuring income inequality: A robust semi-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    7. John C. Stevenson, 2021. "Dynamics of Wealth Inequality in Simple Artificial Societies," Papers 2108.11892, arXiv.org, revised Oct 2021.
    8. Sabiou Inoua, 2021. "Beware the Gini Index! A New Inequality Measure," Papers 2110.01741, arXiv.org.

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