Bayesian hierarchical analysis of a multifaceted program against extreme poverty

TL;DR

This paper uses Bayesian hierarchical models to analyze the heterogeneity in the effectiveness of a multifaceted anti-poverty program across different developing countries, revealing that most differences are genuine rather than due to sampling error.

Contribution

It introduces a Bayesian hierarchical analysis approach to quantify and interpret heterogeneity in program effects across countries, with detailed explanation of modeling and computational choices.

Findings

Low pooling indicates true heterogeneity between countries.

Bayesian models help distinguish genuine differences from sampling error.

Didactic approach enhances understanding of Bayesian analysis.

Abstract

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.