Poverty reduction is the main goal of global development policy today. A com-prehensive framework to evaluate the effectiveness of single policy measures and of policy packages for poverty reduction, growth and pro-poor growth is lacking, though.Bayesian Model Averaging is very valuable in this context as it addresses the pa-rameter and model uncertainty inherent in development policies by not choosing asingle model but averaging over all possible ones. Using data for the 61 Vietnamese provinces we are able to ascertain the most important determinants of poverty, growth and pro-poor growth out of a large number of potential explanatory variables