Direct and Indirect Treatment Effects With Time‐Varying Covariates
提出一种在时间期数少且平行趋势因交互固定效应不成立时，仍能有效估计面板数据处理效应的方法，可分离协变量带来的间接效应，并以中国加入WTO对加价离散度的影响为例说明。
ABSTRACT We propose a simple approach to treatment effect estimation in panel data that is valid when the number of time periods is small and the parallel trends condition is violated due to the presence of interactive fixed effects. The procedure allows the covariates to be affected by treatment and enables separation of the part of the estimated treatment effect that is due to the covariates from the part that is not. The asymptotic properties of the new approach are established, and their accuracy in small samples is investigated using Monte Carlo simulations. The procedure is illustrated using as an example the effect of increased trade competition on firm markups in China. We estimate that about half of the impact of China's entrance into the WTO on markup dispersion came from the changes in industry‐level productivity.