The Bayesian approach to poverty measurement

Michel Lubrano and Zhou Xun ()
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Zhou Xun: NUFE - Nanjing University of Finance and Economics

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Abstract: This chapter reviews the recent Bayesian literature on poverty measurement together with some new results. Using Bayesian model criticism, we revise the international poverty line. Using mixtures of lognormals to model income, we derive the posterior distribution for the FGT, Watts and Sen poverty indices, for TIP curves (with an illustration on child poverty in Germany) and for Growth Incidence Curves. The relation of restricted stochastic dominance with TIP and GIC dominance is detailed with an example based on UK data. Using panel data, we decompose poverty into total, chronic and transient poverty, comparing child and adult poverty in East Germany when redistribution is introduced. When panel data are not available, a Gibbs sampler can be used to build a pseudo panel. We illustrate poverty dynamics by examining the consequences of the Wall on poverty entry and poverty persistence in occupied West Bank.

Keywords: Bayesian inference; mixture model; poverty indices; stochastic dominance; poverty dynamics (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-eur and nep-ltv
Note: View the original document on HAL open archive server: https://cnrs.hal.science/hal-04347292
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Published in Jacques Silber. Research Handbook on Measuring Poverty and Deprivation, Edward Elgar, pp.475-487, 2023, Economics 2023, 9781800883444. ⟨10.4337/9781800883451.00059⟩

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Related works:
Chapter: The Bayesian approach to poverty measurement (2023)
Working Paper: The Bayesian approach to poverty measurement (2023)
Working Paper: The Bayesian approach to poverty measurement (2021)
Working Paper: The Bayesian approach to poverty measurement (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04347292

DOI: 10.4337/9781800883451.00059

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