Bayesian Impact Evaluation With Informative Priors: An Application to a Colombian Management and Export Improvement Program
中文摘要
政策制定者常常在相对较小的样本上测试成本高昂的新项目。将信息性贝叶斯先验正式纳入影响评估，有望从这些实验中获取更多信息。我们评估了一项面向200家企业、旨在提高出口的哥伦比亚项目。先验是从学者、政策制定者和企业中引出的。与这些先验相反，频率学派估计在2019年无法拒绝零效应，并在2020年发现了一些负面影响。对于企业是否出口这类二元结果，频率学派估计相对精确，贝叶斯后验区间更新后与标准置信区间几乎完全重叠。对于增加出口种类这类先验与数据相符的结果，这些先验的价值体现在后验区间明显窄于置信区间。最后，对于出口额这类噪声较大的结果，后验区间几乎没有相对于先验发生更新，这凸显出数据对此类结果的信息量是多么低。未来的政策实验可以将这些后验用作贝叶斯或经验贝叶斯分析中的先验。
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.