Modeling Household Poverty Status Using Repeated Cross-sectional Surveys
Research on Economic Inequality: Poverty, Inequality and Shocks
ISBN: 978-1-80071-558-5, eISBN: 978-1-80071-557-8
Publication date: 2 December 2021
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
Acknowledgements
Acknowledgments
This work was partially supported by the 2017 Sapienza Research Grant 000317. We would like to thank participants at the IPPA Public Lecture, University of Central Asia, Bishkek, and at the IARIW-World Bank Conference on “New approaches to defining and measuring poverty in a growing world,” Washington DC. A special thanks to the Kyrgyz National Statistical Committee and the Macroeconomic Policy Department of the Ministry of Economy of the Kyrgyz Republic.
Citation
Pittau, M.G., Zelli, R. and Ismailakhunova, S. (2021), "Modeling Household Poverty Status Using Repeated Cross-sectional Surveys", Bandyopadhyay, S. (Ed.) Research on Economic Inequality: Poverty, Inequality and Shocks (Research on Economic Inequality, Vol. 29), Emerald Publishing Limited, Leeds, pp. 57-76. https://doi.org/10.1108/S1049-258520210000029004
Publisher
:Emerald Publishing Limited
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