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On Calculation of the Extended Gini Coefficient

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  • Chotikapanich, Duangkamon
  • Griffiths, William

Abstract

The conventional formula for estimating the extended Gini coefficient is a covariance formula provided by Lerman and Yitzhaki (1989). We suggest an alternative estimator, obtained by approximating the Lorenz curve by a series of linear segments. In a Monte Carlo experiment designed to assess the relative bias and efficiency of the two estimators, we find that, when using grouped data with 20 or fewer groups, our new estimator has less bias and lower mean squared error than the covariance estimator. When individual observations are used, or the number of groups is 30 or more, there is little or no difference in the performance of the two estimators. Copyright 2001 by The International Association for Research in Income and Wealth.

Suggested Citation

  • Chotikapanich, Duangkamon & Griffiths, William, 2001. "On Calculation of the Extended Gini Coefficient," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 47(4), pages 541-547, December.
    • Handle: RePEc:bla:revinw:v:47:y:2001:i:4:p:541-47
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    Cited by:

    1. Stephen P. Jenkins & Philippe Van Kerm, 2016. "Assessing Individual Income Growth," Economica, London School of Economics and Political Science, vol. 83(332), pages 679-703, October.
    2. Heshmati, Almas, 2004. "A Review of Decomposition of Income Inequality," IZA Discussion Papers 1221, Institute of Labor Economics (IZA).
    3. Heshmati, Almas, 2004. "Inequalities and Their Measurement," IZA Discussion Papers 1219, Institute of Labor Economics (IZA).
    4. Essama-Nssah, B., 2002. "Assessing the distributional impact of public policy," Policy Research Working Paper Series 2883, The World Bank.
    5. Diasakos, Theodoros M & Neymotin, Florence, 2013. "Coordination in Public Good Provision: How Individual Volunteering is Impacted by the Volunteering of Others," SIRE Discussion Papers 2013-119, Scottish Institute for Research in Economics (SIRE).
    6. Rolf Aaberge, 2007. "Gini’s nuclear family," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(3), pages 305-322, December.
    7. Araceli Ortega Diaz, 2004. "Assesment of the relationship between Inequality and Economic Growth: A panel Data Approach," Econometric Society 2004 Latin American Meetings 261, Econometric Society.
    8. Edna Schechtman & Ricardas Zitikis, 2006. "Gini indices as areas and covariances: what is the difference between the two representations?," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 385-397.
    9. Walter Piesch, 2005. "A look at the structure of some extended Ginis," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 263-296.
    10. Chameni Nembua, Célestin, 2008. "Measuring and explaining economic inequality: An extension of the Gini coefficient," MPRA Paper 31242, University Library of Munich, Germany, revised 2010.
    11. Xiao Ma & Feiran Wang & Jiandong Chen & Yang Zhang, 2018. "The Income Gap Between Urban and Rural Residents in China: Since 1978," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1153-1174, December.
    12. Shlomo Yitzhaki & Edna Schechtman, 2005. "The properties of the extended Gini measures of variability and inequality," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 401-433.
    13. Diasakos, Theodoros M & Neymotin, Florence, 2013. "Coordination in Public Good Provision: How Individual Volunteering is Impacted by the Volunteering of Others," SIRE Discussion Papers 2013-119, Scottish Institute for Research in Economics (SIRE).

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