  The evaluation of a multifaceted program against extreme poverty in different
developing countries gave encouraging results, but with important heterogeneity
between countries. This master thesis proposes to study this heterogeneity with
a Bayesian hierarchical analysis. The analysis we carry out with two different
hierarchical models leads to a very low amount of pooling of information
between countries, indicating that this observed heterogeneity should be
interpreted mostly as true heterogeneity, and not as sampling error. We analyze
the first order behavior of our hierarchical models, in order to understand
what leads to this very low amount of pooling. We try to give to this work a
didactic approach, with an introduction of Bayesian analysis and an explanation
of the different modeling and computational choices of our analysis.
