基于面板数据的分布型处理效应参数的界及其在工作置换中的应用
Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement
摘要：
本文开发了新的技术来约束分布处理效应参数，这些参数取决于潜在结果的联合分布--一个对象不被标准识别假设所识别，例如选择可观察量，甚至是随机分配治疗。我表明，面板数据和一个额外的假设之间的依赖性治疗组随着时间的推移，未经治疗的潜在结果（i）提供更多的识别能力的分布处理效应参数比现有的界限和（ii）提供一个更合理的条件比现有的方法，获得点识别。我应用这些界限来研究大衰退期间工作置换效应的异质性。使用标准技术，我发现在大衰退期间被解雇的工人平均损失了34%的收入，相对于他们没有被解雇的反事实收入。使用本文中开发的方法，我还表明，平均效应掩盖了工人之间的实质性异质性。
英文摘要：
This paper develops new techniques to bound distributional treatment effect parameters that depend on the joint distribution of potential outcomes -- an object not identified by standard identifying assumptions such as selection on observables or even when treatment is randomly assigned. I show that panel data and an additional assumption on the dependence between untreated potential outcomes for the treated group over time (i) provide more identifying power for distributional treatment effect parameters than existing bounds and (ii) provide a more plausible set of conditions than existing methods that obtain point identification. I apply these bounds to study heterogeneity in the effect of job displacement during the Great Recession. Using standard techniques, I find that workers who were displaced during the Great Recession lost on average 34\% of their earnings relative to their counterfactual earnings had they not been displaced. Using the methods developed in the current paper, I also show that the average effect masks substantial heterogeneity across workers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
