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. 2021 Sep;125(9):1200-1207.
doi: 10.1016/j.healthpol.2021.06.010. Epub 2021 Jul 2.

School openings and the COVID-19 outbreak in Italy. A provincial-level analysis using the synthetic control method

Vincenzo Alfano  1 , Salvatore Ercolano  2 , Lorenzo Cicatiello  3
Affiliations

School openings and the COVID-19 outbreak in Italy. A provincial-level analysis using the synthetic control method

Vincenzo Alfano et al. Health Policy. 2021 Sep.

Abstract

Schools have been central in the debate about COVID-19. On the one hand, many have argued that they should be kept open, given their importance to youngsters and the future of the country, and the effort many countries have made in establishing protocols to keep them safe. On the other hand, it has been argued that open schools further the spread of the virus, given that these are places with large-scale interaction between teenagers and adults accompanying their children, as well as a major source of congestion on public transportation. We aim to identify the effect of school openings on the spread of COVID-19 contagion. Italy offers an interesting quasi-experimental setting in this regard due to the scattered openings that schools have experienced. By means of a quantitative analysis, employing a synthetic control method approach, we find that Bolzano, the first province in Italy to open schools after the summer break, had far more cases than its synthetic counterfactual, built from a donor pool formed from the other Italian provinces. Results confirm the hypothesis that despite the precautions, opening schools causes an increase in the infection rate, and this must be taken into account by policymakers.

Keywords: COVID-19; Coronavirus; Italy; School openings; Synthetic control method.

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Figures

Fig. 1
Fig. 1
Total COVID-19 cases in Italy, from the beginning of the pandemic (22 February) and from 1 August (second wave). Vertical line represents 1 August.
Fig. 2
Fig. 2
SCM with synthetic Bolzano built from a donor pool composed of all Italian provinces.
Fig. 3
Fig. 3
Effects estimated for all the provinces in the donor pool for the same treatment period.
Fig. 4
Fig. 4
Non-parametric SCM with synthetic Bolzano built from a donor pool composed of all Italian provinces. Kernel: Tricube, Bandwidth 0.1.
Fig. 5
Fig. 5
SCM with synthetic Bolzano built from a donor pool composed of all Italian provinces.
Fig. 6
Fig. 6
Effects estimated for all the provinces in the donor pool for the same treatment period.
Fig. 7
Fig. 7
Non-parametric SCM with synthetic Bolzano built from a donor pool composed of all Italian provinces. Kernel: Tricube, Bandwidth 1.4.
See this image and copyright information in PMC

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