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Dynamic Measurement of Poverty: Modeling and Estimation

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  • Guglielmo D’Amico

    (Universitá “G. D’Annunzio”)

  • Philippe Regnault

    (Université de Reims Champagne-Ardenne)

Abstract

This study presents a model of income evolution from which dynamic versions of commonly used static poverty measures are derived. The dynamic indexes are calculated both for finite- and infinite-size economic systems. Probabilistic convergence results prove that the infinite-size system can be conveniently used to approximate the finite-size system in an effective way. Secondly, poverty indexes estimation based on micro-data are discussed under different sampling schemes and it is proved that they are strongly consistent. A hypothetical example is used to show the dynamic evolution of the poverty and the estimation methodologies.

Suggested Citation

  • Guglielmo D’Amico & Philippe Regnault, 2018. "Dynamic Measurement of Poverty: Modeling and Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 305-340, November.
    • Handle: RePEc:spr:sankhb:v:80:y:2018:i:2:d:10.1007_s13571-018-0153-6
    DOI: 10.1007/s13571-018-0153-6
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    Cited by:

    1. Guglielmo D'Amico & Filippo Petroni & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2019. "A copula based Markov Reward approach to the credit spread in European Union," Papers 1902.00691, arXiv.org.
    2. Guglielmo D'Amico & Riccardo De Blasis & Philippe Regnault, 2020. "Confidence sets for dynamic poverty indexes," Papers 2006.06595, arXiv.org.
    3. Guglielmo D’Amico & Shakti Singh & Dharmaraja Selvamuthu, 2023. "Analysis of fair fee in guaranteed lifelong withdrawal and Markovian health benefits," Annals of Finance, Springer, vol. 19(3), pages 383-400, September.

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