Publication Type

Journal Article

Version

submittedVersion

Publication Date

9-2025

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.

Keywords

Two-way Fixed Effects Regression, Robust Clustering Algorithm, Relative Convergence, Automatic Clustering Mechanism, Panel logit Regression

Discipline

Econometrics | Public Affairs, Public Policy and Public Administration | Public Health

Research Areas

Econometrics

Publication

Journal of Applied Econometrics

Volume

40

Issue

6

First Page

697

Last Page

714

ISSN

0883-7252

Identifier

10.1002/jae.3137

Publisher

Wiley

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1002/jae.3137

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