IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v502y2018icp256-269.html
   My bibliography  Save this article

Gini estimation under infinite variance

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118301894
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.02.102?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cirillo, Pasquale, 2013. "Are your data really Pareto distributed?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5947-5962.
    2. Eliazar, Iddo & Sokolov, Igor M., 2010. "Maximization of statistical heterogeneity: From Shannon’s entropy to Gini’s index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3023-3038.
    3. Taleb, Nassim Nicholas & Douady, Raphael, 2015. "On the super-additivity and estimation biases of quantile contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 252-260.
    4. Fontanari, Andrea & Cirillo, Pasquale & Oosterlee, Cornelis W., 2018. "From Concentration Profiles to Concentration Maps. New tools for the study of loss distributions," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 13-29.
    5. Yang, Yingying & Hu, Shuhe & Wu, Tao, 2011. "The tail probability of the product of dependent random variables from max-domains of attraction," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1876-1882.
    6. Nolan, John P., 1998. "Parameterizations and modes of stable distributions," Statistics & Probability Letters, Elsevier, vol. 38(2), pages 187-195, June.
    7. Eliazar, Iddo I. & Sokolov, Igor M., 2010. "Gini characterization of extreme-value statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4462-4472.
    8. Li, Deli & Bhaskara Rao, M. & Tomkins, R. J., 2001. "The Law of the Iterated Logarithm and Central Limit Theorem for L-Statistics," Journal of Multivariate Analysis, Elsevier, vol. 78(2), pages 191-217, August.
    9. Eliazar, Iddo I. & Sokolov, Igor M., 2012. "Measuring statistical evenness: A panoramic overview," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1323-1353.
    10. Nassim Nicholas Taleb & Raphaël Douady, 2015. "On the Super-Additivity and Estimation Biases of Quantile Contributions," Post-Print hal-01477963, HAL.
    11. repec:cup:cbooks:9781107013445 is not listed on IDEAS
    12. Taleb, Nassim Nicholas & Douady, Raphael, 2015. "On the super-additivity and estimation biases of quantile contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 252-260.
    13. Eliazar, Iddo & Cohen, Morrel H., 2014. "On social inequality: Analyzing the rich–poor disparity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 148-158.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arthur Charpentier & Emmanuel Flachaire, 2022. "Pareto models for top incomes and wealth," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 1-25, March.
    2. Wang, Zheng-Xin & Jv, Yue-Qi, 2023. "Revisiting income inequality among households: New evidence from the Chinese Household Income Project," China Economic Review, Elsevier, vol. 81(C).
    3. 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).
    4. John C. Stevenson, 2021. "Population and Inequality Dynamics in Simple Economies," Papers 2101.09817, arXiv.org, revised Aug 2021.
    5. 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).
    6. John C. Stevenson, 2021. "Dynamics of Wealth Inequality in Simple Artificial Societies," Papers 2108.11892, arXiv.org, revised Oct 2021.
    7. Sabiou Inoua, 2021. "Beware the Gini Index! A New Inequality Measure," Papers 2110.01741, arXiv.org.
    8. Chan, Terence, 2022. "On a new class of continuous indices of inequality," Mathematical Social Sciences, Elsevier, vol. 120(C), pages 8-23.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Fontanari & Nassim Nicholas Taleb & Pasquale Cirillo, 2017. "Gini estimation under infinite variance," Papers 1707.01370, arXiv.org, revised Dec 2017.
    2. Thomas Blanchet & Ignacio Flores & Marc Morgan, 2022. "The weight of the rich: improving surveys using tax data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 119-150, March.
    3. Nassim Nicholas Taleb, 2015. "How to (Not) Estimate Gini Coefficients for Fat Tailed Variables," Papers 1510.04841, arXiv.org.
    4. Demetrio Guzzardi & Elisa Palagi & Andrea Roventini & Alessandro Santoro, 2022. "Reconstructing Income Inequality in Italy: New Evidence and Tax Policy Implications from Distributional National Accounts," SciencePo Working papers Main halshs-03693201, HAL.
    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. Ignacio Flores, 2021. "The capital share and income inequality: Increasing gaps between micro and macro-data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 685-706, December.
    7. Pablo Gutiérrez Cubillos, 2022. "Gini and undercoverage at the upper tail: a simple approximation," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 29(2), pages 443-471, April.
    8. Carranza, Rafael & De Rosa, Mauricio & Flores, Ignacio, 2023. "Wealth Inequality in Latin America," IDB Publications (Working Papers) 12906, Inter-American Development Bank.
    9. Fontanari, Andrea & Cirillo, Pasquale & Oosterlee, Cornelis W., 2018. "From Concentration Profiles to Concentration Maps. New tools for the study of loss distributions," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 13-29.
    10. Amparo Ba'illo & Javier C'arcamo & Carlos Mora-Corral, 2021. "Extremal points of Lorenz curves and applications to inequality analysis," Papers 2103.03286, arXiv.org.
    11. Viktor Stojkoski & Petar Jolakoski & Arnab Pal & Trifce Sandev & Ljupco Kocarev & Ralf Metzler, 2021. "Income inequality and mobility in geometric Brownian motion with stochastic resetting: theoretical results and empirical evidence of non-ergodicity," Papers 2109.01822, arXiv.org.
    12. José Manuel Gavilan-Ruiz & África Ruiz-Gándara & Francisco Javier Ortega-Irizo & Luis Gonzalez-Abril, 2024. "Some Notes on the Gini Index and New Inequality Measures: The nth Gini Index," Stats, MDPI, vol. 7(4), pages 1-12, November.
    13. Thomas Blanchet & Lucas Chancel & Amory Gethin, 2019. "How Unequal is Europe? Evidence from Distributional National Accounts, 1980-2017," World Inequality Lab Working Papers hal-02877000, HAL.
    14. Thomas Blanchet & Juliette Fournier & Thomas Piketty, 2022. "Generalized Pareto Curves: Theory and Applications," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(1), pages 263-288, March.
    15. Heinrich, Torsten & Yang, Jangho & Dai, Shuanping, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," MPRA Paper 105011, University Library of Munich, Germany.
    16. A. B. Atkinson, 2017. "Pareto and the Upper Tail of the Income Distribution in the UK: 1799 to the Present," Economica, London School of Economics and Political Science, vol. 84(334), pages 129-156, April.
    17. Graves, Timothy & Franzke, Christian L.E. & Watkins, Nicholas W. & Gramacy, Robert B. & Tindale, Elizabeth, 2017. "Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 60-71.
    18. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    19. Menn, Christian & Rachev, Svetlozar T., 2006. "Calibrated FFT-based density approximations for [alpha]-stable distributions," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 1891-1904, April.
    20. Abraham Nunes & Thomas Trappenberg & Martin Alda, 2020. "Measuring heterogeneity in normative models as the effective number of deviation patterns," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-16, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:256-269. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.