IDEAS home Printed from https://ideas.repec.org/h/spr/esichp/978-0-387-72796-7_4.html
   My bibliography  Save this book chapter

Efficient Estimation of the Lorenz Curve and Associated Inequality Measures from Grouped Observations

In: Modeling Income Distributions and Lorenz Curves

Author

Listed:
  • N. C. Kakwani
  • N. Podder

Abstract

This paper introduces a new coordinate system for the Lorenz curve. Particular attention is paid to a special case of wide empirical validity. Four alternative methods have been used to estimate the proposed Lorenz curve from the grouped data. The well known inequality measures are obtained as the function of the estimated parameters of the Lorenz curve. In addition the frequency distribution is derived from the equation of the Lorenz curve. An empirical illustration is presented using the data from the Australian Survey of Consumer Expenditure and Finances 1967–68.

Suggested Citation

  • N. C. Kakwani & N. Podder, 2008. "Efficient Estimation of the Lorenz Curve and Associated Inequality Measures from Grouped Observations," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 4, pages 57-70, Springer.
    • Handle: RePEc:spr:esichp:978-0-387-72796-7_4
    DOI: 10.1007/978-0-387-72796-7_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Paul Walter & Marcus Groß & Timo Schmid & Nikos Tzavidis, 2021. "Domain prediction with grouped income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1501-1523, October.
    2. Walter, Paul & Weimer, Katja, 2018. "Estimating poverty and inequality indicators using interval censored income data from the German microcensus," Discussion Papers 2018/10, Free University Berlin, School of Business & Economics.
    3. Zoran Grubišić & Sandra Kamenković & Tijana Kaličanin, 2021. "Comparative Analysis of the Banking Sector Competitiveness in Serbia and Montenegro," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(1), pages 75-91.
    4. Dilanka S. Dedduwakumara & Luke A. Prendergast & Robert G. Staudte, 2021. "Some confidence intervals and insights for the proportion below the relative poverty line," SN Business & Economics, Springer, vol. 1(10), pages 1-22, October.

    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:spr:esichp:978-0-387-72796-7_4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.