Bayesian Impact Evaluation with Informative Priors: An Application to a Colombian Management and Export Improvement Program
Leonardo Iacovone, David McKenzie and Rachael Meager
Abstract
Policymakers often test expensive new programs on relatively small samples. Formally incorporating informative Bayesian priors into impact evaluation offers the promise to learn more from these experiments. We evaluate a Colombian program for 200 firms which aimed to increase exporting. Priors were elicited from academics, policymakers, and firms. Contrary to these priors, frequentist estimation can not reject null effects in 2019, and finds some negative impacts in 2020. For binary outcomes like whether firms export, frequentist estimates are relatively precise, and Bayesian credible posterior intervals update to overlap almost completely with standard confidence intervals. For outcomes like increasing export variety, where the priors align with the data, the value of these priors is seen in posterior intervals that are considerably narrower than frequentist confidence intervals. Finally, for noisy outcomes like export value, posterior intervals show almost no updating from the priors, highlighting how uninformative the data are about such outcomes.