The Bayesian approach to poverty measurement

Abstract

This survey paper reviews the recent Bayesian literature on poverty measurement. After introducing Bayesian statistics, we show how Bayesian model criticism could help to 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, then for TIP curves (with an illustration on child poverty in Germany) and finally for Growth Incidence Curves. The relation of restricted stochastic dominance with TIP and GIC dominance is detailed with an example on UK data. Using panel data, we show how to decompose poverty into total, chronic and transient poverty, comparing child and adult poverty in East Germany when redistribution is introduced. When a panel is not available, a Gibbs sampler is 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.