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Modeling Household Poverty Status Using Repeated Cross-sectional Surveys

In: Research on Economic Inequality: Poverty, Inequality and Shocks

Author

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  • Maria Grazia Pittau
  • Roberto Zelli
  • Saida Ismailakhunova

Abstract

The authors propose a framework to estimate the probability of being poor in a dynamic setting based on a large information set that includes individual characteristics and macro-economic variables. The joint inclusion of personal characteristics along with contextual factors allows separation of idiosyncratic shocks from aggregate shocks affecting poverty. The authors combine data from different cross-sectional surveys and fit a dynamic logistic hierarchical model within a Bayesian framework using standard Markov chain Monte Carlo techniques. The authors’ approach is exemplified by estimating household poverty status in Kyrgyz Republic as a function of time, regions, country, regional level variables and household level socio-demographic characteristics.

Suggested Citation

  • Maria Grazia Pittau & Roberto Zelli & Saida Ismailakhunova, 2021. "Modeling Household Poverty Status Using Repeated Cross-sectional Surveys," Research on Economic Inequality, in: Research on Economic Inequality: Poverty, Inequality and Shocks, volume 29, pages 57-76, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:reinzz:s1049-258520210000029004
    DOI: 10.1108/S1049-258520210000029004
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    More about this item

    Keywords

    Poverty dynamics; repeated cross-sectional surveys; hierarchical models; Bayesian framework; C33; I32;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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