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Latent class Markov models for addressing measurement problems in poverty dynamics

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  • Giovanni Marano

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  • Gianni Betti

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  • Francesca Gagliardi

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    Abstract

    The traditional approach to poverty measurement utilises only monetary variables as indicators of individuals’ intensity of the state of deprivation, causing measurement errors of the phenomenon under investigation. Moreover, when adopted in a longitudinal context, this approach tends to overestimate transition poverty. Since poverty is not directly observable, a latent definition can be adopted: in such a conception is possible to use Markov chain models in their latent acceptation. This paper proposes to use Latent class Markov models which allow taking into account more observed (manifest) variables. We define those variables via monetary and non-monetary fuzzy indicators.

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    Bibliographic Info

    Paper provided by Department of Economics, University of Siena in its series Department of Economics University of Siena with number 695.

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    Date of creation: Apr 2014
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    Handle: RePEc:usi:wpaper:695

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    Keywords: Poverty dynamics; Measurement errors; LCMM;

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    1. Alkire, Sabina & Foster, James, 2011. "Counting and multidimensional poverty measurement," Journal of Public Economics, Elsevier, vol. 95(7), pages 476-487.
    2. Sabina Alkire & Maria Emma Santos, 2010. "Acute Multidimensional Poverty: A New Index for Developing Countries," Human Development Research Papers (2009 to present) HDRP-2010-11, Human Development Report Office (HDRO), United Nations Development Programme (UNDP).
    3. Cesar Calvo & Stefan Dercon, 2007. "Chronic Poverty and All That: The Measurement of Poverty over Time," Economics Series Working Papers WPS/2007-04, University of Oxford, Department of Economics.
    4. Carlos Gradín & Coral del Río & Olga Cantó, 2010. "Measuring poverty accounting for time," Working Papers 169, ECINEQ, Society for the Study of Economic Inequality.
    5. Bossert, Walter & Chakravarty, Satya R. & d’Ambrosio, Conchita, 2010. "Poverty and Time," Working Paper Series wp2010-74, World Institute for Development Economic Research (UNU-WIDER).
      • BOSSERT, Walter & CHAKRAVARTY, Satya R. & D’AMBROSIO, Conchita, 2008. "Poverty and Time," Cahiers de recherche 05-2008, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
      • Walter Bossert & Satya R. Chakravarty & Conchita D’Ambrosio, 2008. "Poverty and Time," Working Papers 87, ECINEQ, Society for the Study of Economic Inequality.
      • BOSSERT, Walter & CHAKRAVARTY, Satya R. & D’AMBROSIO, Conchita, 2008. "Poverty and Time," Cahiers de recherche 2008-05, Universite de Montreal, Departement de sciences economiques.
    6. Daria Mendola & Annalisa Busetta, 2012. "The Importance Of Consecutive Spells Of Poverty: A Path-Dependent Index Of Longitudinal Poverty," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 58(2), pages 355-374, 06.
    7. Antonello Maruotti, 2011. "Mixed Hidden Markov Models for Longitudinal Data: An Overview," International Statistical Review, International Statistical Institute, vol. 79(3), pages 427-454, December.
    8. Jean-Yves Duclos & Abdelkrim Araaryand & John Giles, 2006. "Chronic and Transient Poverty: Measurement and Estimation, with Evidence from China," Working Papers 35, ECINEQ, Society for the Study of Economic Inequality.
    9. François Bourguignon & Satya Chakravarty, 2003. "The Measurement of Multidimensional Poverty," Journal of Economic Inequality, Springer, vol. 1(1), pages 25-49, April.
    10. A. Atkinson, 2003. "Multidimensional Deprivation: Contrasting Social Welfare and Counting Approaches," Journal of Economic Inequality, Springer, vol. 1(1), pages 51-65, April.
    11. Hoy, Michael & Zheng, Buhong, 2011. "Measuring lifetime poverty," Journal of Economic Theory, Elsevier, vol. 146(6), pages 2544-2562.
    12. Hulme, David & Shepherd, Andrew, 2003. "Conceptualizing Chronic Poverty," World Development, Elsevier, vol. 31(3), pages 403-423, March.
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