发表机构
Rochester Institute of Technology; UNC-Chapel Hill（罗切斯特理工学院; 北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对存在时变中介变量的双重差分（DiD）设计，提出可在无条件平行趋势不成立时识别平均直接效应的方法，将框架扩展至多场景，通过实证验证了其有效性。
AI 中文摘要
本文研究存在二元处理的双重差分（DiD）设计，其中该二元处理会改变内生时变（连续、离散或混合）中介变量，而该中介变量又会影响结果变量。在我们的模型假设下，我们表明常规DiD估计量会混合处理组的平均直接效应、平均间接效应和一个趋势偏误项。控制中介变量的双向固定效应（TWFE）回归无法恢复处理组的平均直接处理效应。我们表明，基于观测到的中介变量路径的DiD估计量可识别该路径下处理组单位的条件平均直接效应，且对处理组路径分布取平均可在无条件平行趋势假设不成立时仍识别平均直接效应。稳定的平均中介效应假设有助于恢复平均中介效应和间接效应。该框架可扩展至多变量中介变量、非线性DiD及多处理期场景，可使用现有双稳健估计量进行推断。通过重新考察铁路通达对农地价值的影响，该设定得到一个不受可观测市场通达中介的正向直接成分，而对应的间接成分较小且不显著；采用相同样本和基线地理协变量的TWFE基准模型得到一个较小且不显著的直接系数，而原始控制TWFE系数符号相反。
英文摘要
We study difference-in-differences (DiD) designs in which a binary treatment changes an endogenous time-varying (continuous, discrete, or mixed) mediator that in turn affects an outcome. We allow for the inclusion of lagged outcomes in the model. Under our model assumptions, we show that the usual DiD estimand mixes the average direct effect on the treated, the average indirect effect, and a trend bias term. A two-way fixed effects (TWFE) regression that controls for the mediator does not recover the average direct treatment effect on the treated. We show that a DiD estimand conditional on the observed mediator path identifies the conditional average direct effect for treated units at that path, and that averaging over the treated path distribution identifies the average direct effect even when unconditional parallel trends fails. A stable average mediator effect assumption helps recover the average mediator and indirect effects. The framework extends to multivariate mediators, nonlinear DiD, and multiple-treatment-period settings. Existing doubly robust estimators can be used to conduct inference. Revisiting the effects of railroad access on agricultural land values, the specification yields a positive direct component not mediated by measured market access, while the corresponding indirect component is small and imprecise.
Comments55 pages, 3 figures
