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

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  • Lynn Bergeland Morgan
  • Peter C. B. Phillips
  • Donggyu Sul

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.

Suggested Citation

  • Lynn Bergeland Morgan & Peter C. B. Phillips & Donggyu Sul, 2025. "Policy Evaluation with Nonlinear Trended Outcomes: Covid‐19 Vaccination Rates in the United States," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(6), pages 697-714, September.
  • Handle: RePEc:wly:japmet:v:40:y:2025:i:6:p:697-714
    DOI: 10.1002/jae.3137
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