Testing Club Convergence in COVID-19 Vaccine Doses Administered in the United States

Kolawole Ogundari ()

MPRA Paper from University Library of Munich, Germany

Abstract: The development of vaccines is considered an important public health tool to combat the spread of the COVID-19 virus globally. And this study examines whether convergence exists in COVID-19 vaccinations across U.S. states and territories. We employed the club convergence test and data on the number of COVID-19 vaccine doses administered daily from December 13, 2020, to October 11, 2022. This yields balanced panel data covering all 50 U.S. states, the District of Columbia (D.C.), and 8 U.S. territories with 39 412 observations. The empirical result shows a lack of support for the overall convergence in the data. We employed the club clustering algorithm to check whether club convergence exists in the sample. And the results identify two initial convergence clubs and a divergence group in COVID-19 vaccinations across U.S. states and territories. Finally, we performed a club merger test because of the possible overestimation of the number of clubs. This resulted in identifying one final convergence club and a divergence group in the sample. The evidence supporting the lack of overall convergence in the data implies that the states/territories with lower COVID-19 vaccination rates are not catching up with those with higher vaccination rates over time. Also, a convergence club means that states/territories in the club with lower vaccination rates are catching up with those with higher vaccination rates over time. Therefore, increasing vaccination rates might require specific interventions targeting states and territories within the convergence club.

Keywords: COVID-19; Convergence; Divergence; Panel data; Vaccine; United States (search for similar items in EconPapers)
JEL-codes: I1 I10 I18 O1 O11 (search for similar items in EconPapers)
Date: 2023-03-08
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