A Bayesian measure of poverty in the Developing World

Prof. Michel Lubrano, Le Centre National de la Recherche Scientifique (CNRS)

In this paper, we propose a new methodology to revise the international poverty line (IPL) after Ravallion et al. (2009) using the same data base, 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.



About the speaker

Michel Lubrano is a senior researcher at le Centre National de la Recherche Scientifique (CNRS), and a member of Greqam in Marseille, France. His current research interests are Bayesian econometrics and poverty and inequality measurement.



Venue: Conference room (0.16 & 0.17)

Date: 02 February 2017

Time: 12:00 - 13:00


UNU-MERIT