Webinars
Virtual Workshop Schedule 2026
Join our virtual webinar series for periodic methodological webinars on field experiments! Stay updated by registering with the community.
Advantages and Limitations of Small-Sample Evidence in Developing Economies
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 cannot 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 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 the confidence intervals. Finally, for noisy outcomes like export value, posterior intervals show almost no updating from priors, highlighting how uninformative the data are about such outcomes. Future policy experiments could use these posteriors as priors in a Bayesian or empirical Bayesian analysis.
Methodological Webinars & Design Discussions
Share your early work on experimental design with the AFFE community. We organize webinars to discuss experimental designs together before running experiments, helping you refine your approach and get valuable feedback from peers.
Share Your Experimental Design
Are you working on an experimental design and would like to discuss it with the AFFE community before running your experiments? Submit your early work through the form below, and we'll organize a webinar to discuss your experimental design together. This is a great opportunity to refine your approach and get valuable feedback from peers.