IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2112.15291.html
   My bibliography  Save this paper

A simple method for estimating the Lorenz curve

Author

Listed:
  • Thitithep Sitthiyot
  • Kanyarat Holasut

Abstract

Given many popular functional forms for the Lorenz curve do not have a closed-form expression for the Gini index and no study has utilized the observed Gini index to estimate parameter(s) associated with the corresponding parametric functional form, a simple method for estimating the Lorenz curve is introduced. It utilizes 3 indicators, namely, the Gini index and the income shares of the bottom and the top in order to calculate the values of parameters associated with the specified functional form which has a closed-form expression for the Gini index. No error minimization technique is required in order to estimate the Lorenz curve. The data on the Gini index and the income shares of 4 countries that have different level of income inequality, economic, sociological, and regional backgrounds from the United Nations University-World Income Inequality Database are used to illustrate how the simple method works. The overall results indicate that the estimated Lorenz curves fit the actual observations practically well. This simple method could be useful in the situation where the availability of data on income distribution is low. However, if more data on income distribution are available, this study shows that the specified functional form could be used to directly estimate the Lorenz curve. Moreover, the estimated values of the Gini index calculated based on the specified functional form are virtually identical to their actual observations.

Suggested Citation

  • Thitithep Sitthiyot & Kanyarat Holasut, 2021. "A simple method for estimating the Lorenz curve," Papers 2112.15291, arXiv.org.
    • Handle: RePEc:arx:papers:2112.15291
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2112.15291
    File Function: Latest version
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Konstantinos N. Zafeiris, 2023. "Greece since the 1960s: the mortality transition revisited: a joinpoint regression analysis," Journal of Population Research, Springer, vol. 40(1), pages 1-31, March.
    2. Pauliuk, Stefan, 2024. "Decent living standards, prosperity, and excessive consumption in the Lorenz curve," Ecological Economics, Elsevier, vol. 220(C).
    3. Subhadip Sarkar, 2023. "ABC classification using extended R-model, SVM and Lorenz curve," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1433-1455, September.

    More about this item

    Statistics

    Access and download statistics

    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:arx:papers:2112.15291. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.