一项自适应定向实地实验：约旦难民求职援助

Journal of the European Economic Association · 2023 · Stefano A. Caria、Grant Gordon、Maximilian Kasy、Simon Quinn、Soha Shami、Alexander Teytelboym

中文摘要

我们提出了一种用于实地实验的自适应定向处理分配方法。我们提出的经调节的汤普森算法（Tempered Thompson Algorithm）在最大化处理效应估计精度与最大化实验参与者福利这两个目标之间进行权衡。分层贝叶斯模型使我们能够自适应地定向分配处理。我们在约旦实施了这一方法，检验旨在帮助叙利亚难民和当地求职者找到工作的政策。小额现金补助、信息和心理支持对就业的即时影响较小，但定向分配使就业率提高了1个百分点（20%）。4个月后，现金补助对叙利亚人的就业和劳动收入产生了相当大的影响。

Abstract

Abstract We introduce an adaptive targeted treatment assignment methodology for field experiments. Our Tempered Thompson Algorithm balances the goals of maximizing the precision of treatment effect estimates and maximizing the welfare of experimental participants. A hierarchical Bayesian model allows us to adaptively target treatments. We implement our methodology in Jordan, testing policies to help Syrian refugees and local jobseekers to find work. The immediate employment impacts of a small cash grant, information and psychological support are small, but targeting raises employment by 1 percentage-point (20%). After 4 months, cash has a sizable effect on employment and earnings of Syrians.