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The Association of Opening K-12 Schools and Colleges with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis
COVID-19 Research Area(s): Economics & Business, Epidemiology & Public Health
The paper examines whether the opening of K-12 schools and colleges/universities may lead to the spread of COVID-19. Analyzing how an increase of COVID-19 cases is related to the timing of opening K-12 schools across different counties in the United States, we find that counties that opened K-12 schools with in-person learning modes experienced an increase in the growth rate of cases by 5 percentage points on average. This association of K-12 school visits with case growth is stronger when mask-wearing is not mandated for staff at school. This finding has significant implications for the need for enforcing precautionary actions at school and for giving vaccine priority to education workers.
This paper empirically examines how the opening of K-12 schools and colleges is associated with the spread of COVID-19 using county-level panel data in the United States. Using data on foot traffic and K-12 school opening plans, we analyze how an increase in visits to schools and opening schools with different teaching methods (in-person, hybrid, and remote) is related to the 2-weeks forward growth rate of confirmed COVID-19 cases. Our debiased panel data regression analysis with a set of county dummies, interactions of state and week dummies, and other controls shows that an increase in visits to both K-12 schools and colleges is associated with a subsequent increase in case growth rates. The estimates indicate that fully opening K-12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the positive association of K-12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These results have a causal interpretation in a structural model with unobserved county and time confounders. Sensitivity analysis shows that the baseline results are robust to timing assumptions and alternative specifications.