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Bounds of the Gini Index Using Sparse Information on Mean Incomes

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  • Tomson Ogwang

Abstract

In this paper, bounds of the Gini index, based on grouped data, are proposed assuming sparse information on mean incomes in the sense that data on either the overall mean income or some of the group mean incomes are not reported. It turns out that the proposed bounds are identical to those proposed by other authors that have more stringent information requirements with respect to mean incomes.

Suggested Citation

  • Tomson Ogwang, 2003. "Bounds of the Gini Index Using Sparse Information on Mean Incomes," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 49(3), pages 415-423, September.
    • Handle: RePEc:bla:revinw:v:49:y:2003:i:3:p:415-423
    DOI: 10.1111/1475-4991.00096
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    Cited by:

    1. Yves Tillé, 2016. "The legacy of Corrado Gini in survey sampling and inequality theory," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 167-176, August.
    2. Philip Clarke & Tom Van Ourti, 2009. "Correcting the Bias in the Concentration Index when Income is Grouped," CEPR Discussion Papers 599, Centre for Economic Policy Research, Research School of Economics, Australian National University.
    3. Tom Van Ourti & Philip Clarke, 2008. "The Bias of the Gini Coefficient due to Grouping," Tinbergen Institute Discussion Papers 08-095/3, Tinbergen Institute.
    4. Andreas Kuhn, 2020. "The individual (mis-)perception of wage inequality: measurement, correlates and implications," Empirical Economics, Springer, vol. 59(5), pages 2039-2069, November.

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