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The Gray Zone

Authors: Federico Crudu; Roberta Di Stefano; Giovanni Mellace; Silvia Tiezzi;
Abstract

The Gray Zone ∗ Federico Crudu† University of Siena and CRENoS Roberta Di Stefano ‡ Sapienza University of Rome Giovanni Mellace§ University of Southern Denmark Silvia Tiezzi¶ University of Siena March 2022 Abstract On March 23, 2020, in response to the COVID-19 pandemic, Italy declared a nation- wide lockdown. A month earlier, on February 23, the Italian government ordered its military police to seal the borders and declared a Red Zone around 10 municipalities of the province of Lodi and in Vo’ Euganeo, a small town in Padua province. On the same day, Confindustria Bergamo, the province’s industrial association, posted a video on social media against having a lockdown in the area of Bergamo and was supported by key business leaders and local administrators. Despite having a similar infection rate to the Red Zone municipalities, the government decided not to extend the Red Zone to the municipalities of Bergamo province with high infection rates. Bergamo later became one of the deadliest outbreaks of the first wave of the virus in the Western world. What would have happened had the Red Zone been extended to that area? We use the Synthetic Control Method to estimate the causal effect of (not) declaring a Red Zone in the Bergamo area on daily excess mortality. We find that about two-thirds of the reported deaths could have been avoided had the Italian government declared the area a Red Zone.

Countries
Denmark, Italy
Keywords

synthetic control method, causal impact, COVID-19, COVID-19, causal impact, synthetic control method, Red Zone, Bergamo, non- pharmaceutical interventions., non- pharmaceutical interventions., Red Zone, Bergamo

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
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