Policy Evaluation with Nonlinear Trended Outcomes: Covid‐19 Vaccination Rates in the United States
指出当结果变量包含非线性趋势时，双向固定效应回归会扭曲政策效果估计，并提出基于相对收敛检验的动态俱乐部成员方法，应用于美国新冠疫苗接种政策评估，发现联邦疫苗强制令使各州接种率在2021年9月中旬合并为一个收敛集群。
ABSTRACT This paper discusses pitfalls in two way fixed effects (TWFE) regressions when the outcome variables contain nonlinear and possibly stochastic trend components. If a policy change shifts trend paths of outcome variables, TWFE estimation can distort results and invalidate inference, especially in a context of evolving policy decisions. A robust solution is proposed by allowing for dynamic club membership empirically using a relative convergence test procedure. The determinants of respective club memberships are assessed by panel ordered logit regressions. The approach allows for policy evolution and shifts in outcomes according to a convergence cluster framework with transitions over time and the possibility of eventual convergence to a single cluster as policy impacts mature. The long run impact of a policy can thus be examined via its impact on convergence club membership. An application to new weekly US Covid‐19 vaccination policy data reveals that federal level vaccine mandates produced a merger of state vaccination rates into a single convergence cluster by mid‐September 2021.