论文标题
使用面板数据和作业位移的应用程序数据界定分布处理效果参数
Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement
论文作者
论文摘要
本文开发了依赖于潜在结果的联合分布的边界分布处理效应参数的新技术 - 该对象未通过标准识别假设（例如选择可观察到的选择，甚至在随机分配治疗时）所识别的对象。我表明，随着时间的推移，与现有的界限相比，面板数据和对处理组的未处理潜在结果之间的依赖性（i）比现有界限提供了更多的识别能力，并且（ii）与获得点识别的现有方法相比，提供了更合理的条件。我将这些界限应用于大衰退期间工作流离失所的影响。使用标准技术，我发现在大衰退期间流离失所的工人平均失去了其收入的34％相对于其反事实收入，如果他们没有流离失所。使用当前论文中开发的方法，我还表明，平均效应掩盖了工人之间的实质异质性。
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.
