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

Author

Listed:
  • Lynn Bergeland Morgan

    (University of Texas at Dallas)

  • Peter C. B. Phillips

    (Yale University)

  • Donggyu Sul

    (University of Texas at Dallas)

Abstract

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 ÔlogtÕ 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.

Suggested Citation

  • Lynn Bergeland Morgan & Peter C. B. Phillips & Donggyu Sul, 2023. "Policy Evaluation with Nonlinear Trended Outcomes: COVID-19 Vaccination Rates in the US," Cowles Foundation Discussion Papers 2380, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:2380
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    File URL: https://cowles.yale.edu/sites/default/files/2024-02/d2380.pdf
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    References listed on IDEAS

    as
    1. Peter C. B. Phillips & Donggyu Sul, 2007. "Transition Modeling and Econometric Convergence Tests," Econometrica, Econometric Society, vol. 75(6), pages 1771-1855, November.
    2. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    3. Phillips, Peter C.B. & Sul, Donggyu, 2007. "Some empirics on economic growth under heterogeneous technology," Journal of Macroeconomics, Elsevier, vol. 29(3), pages 455-469, September.
    4. Clément de Chaisemartin & Xavier D'Haultfœuille, 2020. "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects," American Economic Review, American Economic Association, vol. 110(9), pages 2964-2996, September.
    5. Greenaway-McGrevy, Ryan & Han, Chirok & Sul, Donggyu, 2012. "Asymptotic distribution of factor augmented estimators for panel regression," Journal of Econometrics, Elsevier, vol. 169(1), pages 48-53.
    6. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    7. Peter C. B. Phillips & Donggyu Sul, 2009. "Economic transition and growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1153-1185.
    8. Margaret E. Brehm & Paul A. Brehm & Martin Saavedra, 2022. "The Ohio Vaccine Lottery and Starting Vaccination Rates," American Journal of Health Economics, University of Chicago Press, vol. 8(3), pages 387-411.
    9. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    10. Harsha Thirumurthy & Katherine L Milkman & Kevin G Volpp & Alison M Buttenheim & Devin G Pope, 2022. "Association between statewide financial incentive programs and COVID-19 vaccination rates," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-7, March.
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