Bayesian Impact Evaluation With Informative Priors: An Application to a Colombian Management and Export Improvement Program
成果类型：
Article
署名作者：
Iacovone, Leonardo; McKenzie, David; Meager, Rachael
署名单位：
The World Bank; The World Bank; University of New South Wales Sydney
刊物名称：
ECONOMETRICA
ISSN/ISSBN：
0012-9682
DOI：
10.3982/ECTA21567
发表日期：
2025
页码：
1915-1935
关键词：
摘要：
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.
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