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Conditions for the most robust multidimensional poverty comparisons using counting measures and ordinal variables

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  • Gaston Yalonetzky

    () (University of Leeds & OPHI)

Abstract

A natural concern with multivariate poverty measures, as well as with other composite indices, is the robustness of their ordinal comparisons to changes in the indices’ parameter values. Applying multivariate stochastic dominance techniques, this paper derives the distributional conditions under which a multidimensional poverty comparison based on the popular counting measures, and ordinal variables, is fully robust to any values of the indices.parameters. As the paper shows, the conditions are relevant to most of the multidimensional poverty indices in the literature, including the Alkire-Foster family, upon which the UNDP.s "Multidimensional Poverty Index" (MPI) is based. The conditions are illustrated with an example from the EU-SILC dataset.

Suggested Citation

  • Gaston Yalonetzky, 2012. "Conditions for the most robust multidimensional poverty comparisons using counting measures and ordinal variables," Working Papers 257, ECINEQ, Society for the Study of Economic Inequality.
  • Handle: RePEc:inq:inqwps:ecineq2012-257
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    References listed on IDEAS

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    1. Joachim Merz & Tim Rathjen, 2011. "Intensity of Time and Income Interdependent Multidimensional Poverty: Well-Being and Minimum 2DGAP ; German Evidence," SOEPpapers on Multidisciplinary Panel Data Research 411, DIW Berlin, The German Socio-Economic Panel (SOEP).
    2. Sabina Alkire and Maria Emma Santos, "undated". "Acute Multidimensional Poverty: A New Index for Developing Countries," OPHI Working Papers ophiwp038, Queen Elizabeth House, University of Oxford.
    3. BOSSERT, Walter & CHAKRAVARTY, Satya R. & D’AMBROSIO, Conchita, 2009. "Multidimensional Poverty and Material Deprivation," Cahiers de recherche 2009-11, Universite de Montreal, Departement de sciences economiques.
    4. Sen, Amartya, 2001. "Development as Freedom," OUP Catalogue, Oxford University Press, number 9780192893307.
    5. Gaston Yalonetzky, 2013. "Stochastic Dominance with Ordinal Variables: Conditions and a Test," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 126-163, January.
    6. Jean-Yves Duclos & David E. Sahn & Stephen D. Younger, 2006. "Robust Multidimensional Poverty Comparisons," Economic Journal, Royal Economic Society, vol. 116(514), pages 943-968, October.
    7. Gordon Anderson, 2008. "The empirical assessment of multidimensional welfare, inequality and poverty: Sample weighted multivariate generalizations of the Kolmogorov–Smirnov two sample tests for stochastic dominance," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(1), pages 73-87, March.
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    Citations

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    Cited by:

    1. José V. Gallegos & Gastón Yalonetzky & Francisco Azpitarte, 2015. "Robust Pro-Poorest Poverty Reduction with Counting Measures: The Anonymous Case," Melbourne Institute Working Paper Series wp2015n22, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    2. Alkire, Sabina & Santos, Maria Emma, 2014. "Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index," World Development, Elsevier, vol. 59(C), pages 251-274.
    3. Espinoza-Delgado, Jose & Klasen, Stephan, 2017. "Gender and Multidimensional Poverty in Nicaragua, An Individual-based Approach," MPRA Paper 81907, University Library of Munich, Germany.
    4. repec:kap:jecinq:v:15:y:2017:i:2:d:10.1007_s10888-017-9349-7 is not listed on IDEAS
    5. Rolf Aaberge & Andrea Brandolini, 2014. "Social evaluation of deprivation count distributions," Working Papers 342, ECINEQ, Society for the Study of Economic Inequality.

    More about this item

    Keywords

    Multidimensional poverty; stochastic dominance;

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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