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Poverty status probability: a new approach to measuring poverty and the progress of the poor

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  • Gordon Anderson

    ()

  • Maria Pittau

    ()

  • Roberto Zelli

    ()

Abstract

Poverty measurement and the analysis of the progress (or otherwise) of the poor, whether it is societies, families or individuals, is beset with difficulties and controversies surrounding the definition of a poverty line or frontier. Here, borrowing ideas from the mixture model literature, a new approach to assigning poverty-non poverty status is proposed which avoids specifying a frontier, the price is that an agent’s poverty status is only determined to the extent of its chance of being poor. Invoking variants of Gibrat’s law to give structure to the distribution of outcomes for homogeneous subgroups of a population within the context of a finite mixture model of societal outcomes facilitates calculation of an agent’s poverty status probability. From this it is straightforward to calculate all the usual poverty measures as well as other characteristics of the poor and non poor subgroups in a society. These ideas are exemplified in a study of 47 countries in Africa over the recent quarter century which reveals among other things a growing poverty rate and a growing disparity between poor and non poor groups not identified by conventional methods. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Gordon Anderson & Maria Pittau & Roberto Zelli, 2014. "Poverty status probability: a new approach to measuring poverty and the progress of the poor," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(4), pages 469-488, December.
  • Handle: RePEc:kap:jecinq:v:12:y:2014:i:4:p:469-488
    DOI: 10.1007/s10888-013-9264-5
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    References listed on IDEAS

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    1. Atkinson, A B, 1987. "On the Measurement of Poverty," Econometrica, Econometric Society, vol. 55(4), pages 749-764, July.
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    5. Joan R. Rodgers & John L. Rodgers, 1993. "Chronic Poverty in the United States," Journal of Human Resources, University of Wisconsin Press, vol. 28(1), pages 25-54.
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    10. Xavier Sala-i-Martin, 2006. "The World Distribution of Income: Falling Poverty and … Convergence, Period," The Quarterly Journal of Economics, Oxford University Press, vol. 121(2), pages 351-397.
    11. Sen, Amartya, 1983. "Poor, Relatively Speaking," Oxford Economic Papers, Oxford University Press, vol. 35(2), pages 153-169, July.
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    Citations

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    Cited by:

    1. Edwin Fourrier-Nicolai & Michel Lubrano, 2017. "Bayesian Inference for TIP curves: An Application to Child Poverty in Germany," AMSE Working Papers 1710, Aix-Marseille School of Economics, Marseille, France.
    2. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2018. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development since 1990," Working Papers tecipa-602, University of Toronto, Department of Economics.

    More about this item

    Keywords

    Poverty frontiers; Mixture models; Gibrat’s law; C14; I32; O1;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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