The Effects of School Closures on COVID-19: A Cross-Country Panel Analysis

Abstract

Background: There has been much debate about the effects and importance of closing, keeping closed, or not opening schools in order to prevent COVID-19 contagion. This policy has been questioned regarding both its efficacy and the social cost it entails, including the possible asymmetric impact it has on genders in many societies due to traditional childcare roles. To the best of our knowledge no existing contribution has attempted to gauge the effectiveness of such a policy over time, in a longitudinal cross-country perspective.

Objectives: This paper aimed to fill the gap in the literature by assessing, at a European level, the effect of school closures (or the lack of such measures) on the numbers of new COVID-19 infections, in the absence of vaccines. Given this policy's expected change in effectiveness over time, we also measured the effectiveness of having schools closed after a given number of days (from 7 to 100).

Methods: We pursued our objectives by means of a quantitative panel analysis, building a longitudinal dataset with observations from countries in Europe, from 1 January to 30 September, and estimating the impact of school closure via feasible-generalised least-squares fixed effect and random effect estimators, and analysis of variance (ANOVA) mixed models.

Results: Our results show that having schools closed is effective in reducing the number of new cases. Countries that implement closure have fewer new COVID-19 cases than those that do not. This becomes a reality around 20 days after the implementation of the policy. Its efficacy continues to be detectable up to 100 days after implementation. The result is robust to controls for other forms of social distancing.

Conclusion: Results suggest that school closure is effective in reducing the number of people who are infected with COVID-19. Unlike what has been suggested in previous analyses or with regard to other diseases, its efficacy continues to be detectable up to 100 days after the introduction of the policy.

Fig. 1

The horizontal axis measures the value for the different betas of several school closure dummies, computed at the date of implementation of the policy, 7 days after, 8 days after, and so on, up to 100 days after (measured on the vertical axis). Lines and lighter colours represent 95% and 90% confidence intervals. Betas are estimated through the feasible-generalised least-squares fixed-effect (F-GLS-FE) model, not including the variable stringency in the regression

Fig. 2

The horizontal axis measures the value for the different betas of several school closure dummies, computed at the date of implementation of the policy, 7 days after, 8 days after, and so on, up to 100 days after (measured on the vertical axis). Lines and lighter colours represent 95% and 90% confidence intervals. Betas are estimated through the feasible-generalised least-squares fixed-effect (F-GLS-FE) model, including the variable stringency in the regression