IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v222y2025ics0167715225000215.html
   My bibliography  Save this article

Unbiased estimation of the Gini coefficient

Author

Listed:
  • Baydil, Banu
  • de la Peña, Victor H.
  • Zou, Haolin
  • Yao, Heyuan

Abstract

The Gini coefficient is a fundamental statistical measure of dispersion used widely across multiple fields. The interest in the study of the properties of the Gini coefficient is highlighted by the fact that every year the World Bank ranks the level of income inequality between countries using it. In order to calculate the coefficient, it is common practice to assume a Gamma-distributed set of values when modeling the dispersion of individual incomes in a given population. The asymptotic behavior of the sample Gini coefficient for populations following a Gamma distribution has been well-documented in the literature. However, research on the finite sample behavior has been absent due to the challenge posed by the denominator. This study aims to fill this gap by demonstrating theoretically that the sample Gini coefficient is an unbiased estimator of the population Gini coefficient for a population having Gamma (α, β) distribution. Furthermore, our result provides a way to quantify the downward bias due to grouping when the population Gini coefficient is estimated using the sample Gini coefficient of equal-sized groups.

Suggested Citation

  • Baydil, Banu & de la Peña, Victor H. & Zou, Haolin & Yao, Heyuan, 2025. "Unbiased estimation of the Gini coefficient," Statistics & Probability Letters, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:stapro:v:222:y:2025:i:c:s0167715225000215
    DOI: 10.1016/j.spl.2025.110376
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715225000215
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2025.110376?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.

    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:stapro:v:222:y:2025:i:c:s0167715225000215. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.