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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\u201019 vaccination policy data reveals that federal level vaccine mandates produced a merger of state vaccination rates into a single convergence cluster by mid\u2010September 2021.<\/jats:p>","DOI":"10.1002\/jae.3137","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T02:01:39Z","timestamp":1750039299000},"page":"697-714","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Policy Evaluation with Nonlinear Trended Outcomes: Covid\u201019 Vaccination Rates in the United States"],"prefix":"10.1002","volume":"40","author":[{"given":"Lynn Bergeland","family":"Morgan","sequence":"first","affiliation":[{"name":"University of Texas at Dallas  Richardson Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter C. 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