Modeling Household Poverty Status Using Repeated Cross-sectional Surveys

Maria Grazia Pittau, Roberto Zelli and Saida Ismailakhunova

A chapter in Research on Economic Inequality: Poverty, Inequality and Shocks, 2021, vol. 29, pp 57-76 from Emerald Group Publishing Limited

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.

Keywords: Poverty dynamics; repeated cross-sectional surveys; hierarchical models; Bayesian framework; C33; I32 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eme:reinzz:s1049-258520210000029004

DOI: 10.1108/S1049-258520210000029004

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