A Bayesian Measure of Poverty in the Developing World

30 Pages Posted: 24 Sep 2018

See all articles by Michel Lubrano

Michel Lubrano

Ecole des Hautes Etudes en Sciences Sociales (EHESS)

Date Written: September 2018

Abstract

We propose a new methodology to revise the international poverty line (IPL) after Ravallion et al. (2009) using the same database, but augmented with new variables to take into account social inclusion in the definition of poverty along the lines of Atkinson and Bourguignon (2001). We provide an estimation of the world income distribution and of the corresponding number of poor people in the developing world. Our revised IPL is based on an augmented two‐regime model estimated using a Bayesian approach, which allows us to take into account uncertainty when defining the reference group of countries where the IPL applies. The influence of weighting by population is discussed, as well as the IPL revision proposed in Deaton (2010). We also discuss the impact of using the new 2011 PPP and the recent IPL revision made by the World Bank.

Keywords: Bayesian inference, poverty line, social inclusion, WDI

JEL Classification: C11, C21, I32

Suggested Citation

Lubrano, Michel, A Bayesian Measure of Poverty in the Developing World (September 2018). Review of Income and Wealth, Vol. 64, Issue 3, pp. 649-678, 2018. Available at SSRN: https://ssrn.com/abstract=3254459 or http://dx.doi.org/10.1111/roiw.12295

Michel Lubrano

Ecole des Hautes Etudes en Sciences Sociales (EHESS) ( email )

Greqam, Vieille Charité
2 rue de la Charité
13002 Marseille
France

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