Policy Evaluation with Nonlinear Trended Outcomes: COVID-19 Vaccination Rates in the US

This paper points out some pitfalls in the use of two-way fixed effects (TWFE) regressions when outcome variables contain nonlinear or stochastic trend components.  When a policy change shifts trend paths of outcome variables conventional TWFE estimation can distort results and invalidate inference. A robust solution is proposed by identifying determinants of dynamic club membership based on the idea of relative convergence, which can be assessed empirically by the so-called ‘logt’ test (Phillips & Sul, 2007a). Club membership in each time period is estimated by recursive regression, transforming outcome variables to statistically stable, stationary status. Time varying club membership can then be used to identify the determinants of club memberships by running a panel logit or ordered logit regression. This approach is applied to study COVID-19 vaccination data across 50 states and the District of Columbia (DC). A new weekly database is created to track individual state and DC vaccination policies and mandates over the period from March 2021 to February 2022. Initially two convergent clubs are identified. Later evidence of the vaccination rates across states reveals a single convergent club. The primary determinant of this merger of sub-clubs is found to be federal-level vaccine mandates.