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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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    2. Gordon Anderson, Alessio Farcomeni, Maria Grazia Pittau and Roberto Zelli, 2019. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development Since 1990," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(1), pages 1-22, March.
    3. Achille Lemmi & Donatella Grassi & Alessandra Masi & Nicoletta Pannuzi & Andrea Regoli, 2019. "Methodological Choices and Data Quality Issues for Official Poverty Measures: Evidences from Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 299-330, January.
    4. Edwin Fourrier-Nicolai & Michel Lubrano, 2019. "The Effect of Aspirations on Inequality: Evidence from the German Reunification using Bayesian Growth Incidence Curves," AMSE Working Papers 1914, Aix-Marseille School of Economics, France.
    5. Edwin Fourrier-Nicolaï & Michel Lubrano, 2021. "Bayesian Inference for Parametric Growth Incidence Curves," Research on Economic Inequality, in: Research on Economic Inequality: Poverty, Inequality and Shocks, volume 29, pages 31-55, Emerald Group Publishing Limited.
    6. Francesca Mariani & Gloria Polinesi & Maria Cristina Recchioni, 2022. "A tail-revisited Markowitz mean-variance approach and a portfolio network centrality," Computational Management Science, Springer, vol. 19(3), pages 425-455, July.
    7. Edwin Fourrier-Nicolaï & Michel Lubrano, 2020. "Bayesian inference for TIP curves: an application to child poverty in Germany," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(1), pages 91-111, March.
    8. Francesca Mariani & Mariateresa Ciommi & Francesco M. Chelli & Maria Cristina Recchioni, 2022. "An Iterative Approach to Stratification: Poverty at Regional Level in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 873-903, June.
    9. Gianni Betti & Lucia Mangiavacchi & Luca Piccoli, 2020. "Women and poverty: insights from individual consumption in Albania," Review of Economics of the Household, Springer, vol. 18(1), pages 69-91, March.
    10. Matheus Pereira Libório & Petr Yakovlevitch Ekel & Oseias da Silva Martinuci & Letícia Ribeiro Figueiredo & Renato Moreira Hadad & Renata de Mello Lyrio & Patrícia Bernardes, 2022. "Fuzzy set based intra-urban inequality indicator," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(2), pages 667-687, April.
    11. Moatsos, Michail, 2020. "The devil in the details: The core disadvantage of the International Poverty Line," EconStor Preprints 218971, ZBW - Leibniz Information Centre for Economics.

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    More about this item

    Keywords

    Poverty frontiers; Mixture models; Gibrat’s law; C14; I32; O1;
    All these keywords.

    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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