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Poverty Measurement with Ordinal Data

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Author Info

  • Chrysanthi Hatzimasoura

    ()
    (Department of Economics, George Washington University)

  • Christopher J. Bennett

    ()
    (Department of Economics, Vanderbilt University.)

Abstract

The Foster, Greer, Thorbecke (1984) class nests several of the most widely used mea- sures in theoretical and empirical work on economic poverty. Use of this general class of measures, however, presupposes a dimension of well-being that, like income, is cardinally measurable. Responding to recent interest in dimensions of well-being where achievements are recorded on an ordinal scale, this paper develops counterparts to the popular FGT measures that are still meaningful when applied to ordinal data. The resulting ordinal FGT measures retain the simplicity of the classical FGT measures and also many of their desirable features, including additive decomposability. This paper also develops ordinal analogues of the core axioms from the literature on economic poverty, and demonstrates that the ordinal FGT measures indeed satisfy these core axioms. Moreover, new domi- nance conditions, which allow for poverty rankings that are robust with respect to the choice of poverty line, are established. Lastly, the ordinal FGT measures are illustrated using self-reported data on health status in Canada and the United States.

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Bibliographic Info

Paper provided by The George Washington University, Institute for International Economic Policy in its series Working Papers with number 2011-14.

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Length: 26 pages
Date of creation: Jul 2011
Date of revision:
Handle: RePEc:gwi:wpaper:2011-14

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Related research

Keywords: poverty measurement; ordinal data; FGT poverty measures; social welfare; dominance conditions;

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References

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  1. Sen, Amartya K, 1976. "Poverty: An Ordinal Approach to Measurement," Econometrica, Econometric Society, vol. 44(2), pages 219-31, March.
  2. John C. Harsanyi, 1953. "Cardinal Utility in Welfare Economics and in the Theory of Risk-taking," Journal of Political Economy, University of Chicago Press, vol. 61, pages 434.
  3. Gaston Yalonetzky, 2013. "Stochastic Dominance with Ordinal Variables: Conditions and a Test," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 126-163, January.
  4. David Madden, 2008. "Ordinal and Cardinal Measures of Health Inequality - An Empirical Comparison," Working Papers 200813, School Of Economics, University College Dublin.
  5. Spector, Yishay & Leshno, Moshe & Horin, Moshe Ben, 1996. "Stochastic dominance in an ordinal world," European Journal of Operational Research, Elsevier, vol. 93(3), pages 620-627, September.
  6. Allison, R. Andrew & Foster, James E., 2004. "Measuring health inequality using qualitative data," Journal of Health Economics, Elsevier, vol. 23(3), pages 505-524, May.
  7. Ravallion, Martin, 1994. "Measuring Social Welfare with and without Poverty Lines," American Economic Review, American Economic Association, vol. 84(2), pages 359-64, May.
  8. Zheng, Buhong, 1997. " Aggregate Poverty Measures," Journal of Economic Surveys, Wiley Blackwell, vol. 11(2), pages 123-62, June.
  9. Atkinson, Anthony B., 1970. "On the measurement of inequality," Journal of Economic Theory, Elsevier, vol. 2(3), pages 244-263, September.
  10. Ramses H. Abul Naga & Tarik Yalcin, 2007. "Inequality Measurement forOrdered Response Health Data," STICERD - Distributional Analysis Research Programme Papers 92, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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Citations

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Cited by:
  1. Gaston Yalonetzky, 2012. "Poverty measurement with ordinal variables: A generalization of a recent contribution," Working Papers 246, ECINEQ, Society for the Study of Economic Inequality.
  2. Michał Brzeziński, 2013. "Accounting for trends in health poverty: A decomposition analysis for Britain, 1991-2008," Working Papers 2013-02, Faculty of Economic Sciences, University of Warsaw.

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