Business Shutdowns and COVID-19 Mortality
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Abstract
Governments around the world have adopted unprecedented policies to deal with COVID-19. This paper zooms in on business shutdowns and investigates their effectiveness in reducing mortality. We leverage upon highly granular death registry data for over 4,000 Italian municipalities in a diff-in-diff approach that allows us to credibly mitigate endogeneity concerns. Our results, which are robust to controlling for a host of co-factors, offer strong evidence that business shutdowns are very effective in reducing mortality. We calculate that the death toll from the first wave of COVID-19 in Italy would have been twice as high in their absence. Our findings also highlight that timeliness is key – by acting one week earlier, the government could have reduced the death toll by an additional 25%. Finally, our estimates suggest that shutdowns should be targeted: closing shops, bars and restaurants saves the most lives, while shutting down manufacturing and construction activities has only mild effects.
Article activity feed
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SciScore for 10.1101/2020.10.06.20207910: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank…
SciScore for 10.1101/2020.10.06.20207910: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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