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Poverty Orderings

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  • Buhong Zheng

Abstract

This paper reviews the literature of partial poverty orderings. Partial poverty orderings require unanimous poverty rankings for a class of poverty measures or a set of poverty lines. The need to consider multiple poverty measures and multiple poverty lines arises inevitably from the arbitrariness inherent in poverty comparisons. In the paper, we first survey the ordering conditions of various individual poverty measures for a range of poverty lines; for some measures necessary and sufficient conditions are identified while for others only some easily verifiable sufficient conditions are established. These ordering conditions are shown to have a close link with the stochastic dominance relations which are based on the comparisons of cumulative distribution functions. We then survey the ordering conditions for various classes of poverty measures with a single or a set of poverty lines; in all cases necessary and sufficient conditions are established. These conditions again rely on the stochastic dominance relations or their transformations. We also extend the relationship between poverty orderings and stochastic dominance to higher orders and explore the possibility and the conditions of increasing the power of poverty orderings beyond the second degree dominance condition.

Suggested Citation

  • Buhong Zheng, 2000. "Poverty Orderings," Journal of Economic Surveys, Wiley Blackwell, vol. 14(4), pages 427-466, September.
    • Handle: RePEc:bla:jecsur:v:14:y:2000:i:4:p:427-466
    DOI: 10.1111/1467-6419.00117
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    Cited by:

    1. Udo Ebert, 2010. "Equity‐regarding poverty measures: differences in needs and the role of equivalence scales," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 43(1), pages 301-322, February.
    2. Xiuqing Wang & Shujie Yao & Juan Liu & Xian Xin & Xiumei Liu & Wenjuan Ren, 2007. "Measuring Rural Poverty in China: a Case Study Approach," Working Papers PMMA 2007-27, PEP-PMMA.
    3. B. Essama-Nssah & Peter J. Lambert, 2013. "Counterfactual decomposition of pro-poorness using influence functions," Working Papers 309, ECINEQ, Society for the Study of Economic Inequality.
    4. B. Essama-Nssah & Saumik Paul & Léandre Bassolé, 2013. "Accounting for Heterogeneity in Growth Incidence in Cameroon Using Recentered Influence Function Regression," Journal of African Economies, Centre for the Study of African Economies, vol. 22(5), pages 757-795, November.
    5. Tabri, Rami V., 2015. "Empirical Likelihood for Robust Poverty Comparisons," Working Papers 2015-02, University of Sydney, School of Economics, revised May 2015.

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