标题： 在移动者设计中估计处理效应 显示英文标题

标题： Estimating Treatment Effects in Mover Designs

摘要： 研究人员越来越多地利用多种处理之间的移动来估计因果效应。 虽然这些“移动回归”通常是由线性常数效应模型所驱动的，但在较弱的准实验假设下它们所捕捉的内容并不明确。 我表明，二元处理移动回归恢复了四个差分-差分比较的凸平均值，并因此在标准的平行趋势假设下具有因果解释。 然而，来自多处理模型的估计在没有对处理效应异质性和时间变化冲击的更强限制的情况下，不一定是因果性的。 我提出了一类两步估计量，以隔离并结合由移动设计产生的大量差分-差分准实验，识别在协变量条件下的平行趋势和效应同质性限制下的移动者平均处理效应。 我表征了该类中的有效估计量，并基于模型的过度识别限制推导了规范检验。 未来的稿件将把这一理论应用于Finkelstein等人（2016）的移动者设计，分析地理因素对医疗保健使用的影响的因果效应。 显示英文摘要

摘要： Researchers increasingly leverage movement across multiple treatments to estimate causal effects. While these "mover regressions" are often motivated by a linear constant-effects model, it is not clear what they capture under weaker quasi-experimental assumptions. I show that binary treatment mover regressions recover a convex average of four difference-in-difference comparisons and are thus causally interpretable under a standard parallel trends assumption. Estimates from multiple-treatment models, however, need not be causal without stronger restrictions on the heterogeneity of treatment effects and time-varying shocks. I propose a class of two-step estimators to isolate and combine the large set of difference-in-difference quasi-experiments generated by a mover design, identifying mover average treatment effects under conditional-on-covariate parallel trends and effect homogeneity restrictions. I characterize the efficient estimators in this class and derive specification tests based on the model's overidentifying restrictions. Future drafts will apply the theory to the Finkelstein et al. (2016) movers design, analyzing the causal effects of geography on healthcare utilization.