Policy Evaluation with Nonlinear Trended Outcomes: Covid‐19 Vaccination Rates in the United States

B-Tier
Journal: Journal of Applied Econometrics
Year: 2025
Volume: 40
Issue: 6
Pages: 697-714

Score contribution per author:

0.670 = (α=2.01 / 3 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:wly:japmet:v:40:y:2025:i:6:p:697-714
Journal Field
Econometrics
Author Count
3
Added to Database
2026-01-29