  Policy decisions often depend on evidence generated elsewhere. We take a
Bayesian decision-theoretic approach to choosing where to experiment to
optimize external validity. We frame external validity through a policy lens,
developing a prior specification for the joint distribution of site-level
treatment effects using a microeconometric structural model and allowing for
other sources of heterogeneity. With data from South Asia, we show that,
relative to basing policies on experiments in optimal sites, large efficiency
losses result from instead using evidence from randomly-selected sites or,
conversely, from sites with the largest expected treatment effects.
