The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy

TL;DR

This study evaluates the impact of Italy's COVID-19 lockdown on mortality, demonstrating that the intervention significantly saved lives, with the new ASCM estimator providing more reliable results than traditional methods.

Contribution

The paper introduces the Augmented Synthetic Control Method (ASCM), improving causal inference in policy impact studies, and applies it to assess Italy's lockdown effectiveness during COVID-19.

Findings

Lockdown prevented approximately 20,400 deaths in Italy.

ASCM outperforms SCM by selecting a more appropriate donor set.

Non-pharmaceutical interventions significantly reduced COVID-19 mortality.

Abstract

This paper estimates the effects of non-pharmaceutical interventions - mainly, the lockdown - on the COVID-19 mortality rate for the case of Italy, the first Western country to impose a national shelter-in-place order. We use a new estimator, the Augmented Synthetic Control Method (ASCM), that overcomes some limits of the standard Synthetic Control Method (SCM). The results are twofold. From a methodological point of view, the ASCM outperforms the SCM in that the latter cannot select a valid donor set, assigning all the weights to only one country (Spain) while placing zero weights to all the remaining. From an empirical point of view, we find strong evidence of the effectiveness of non-pharmaceutical interventions in avoiding losses of human lives in Italy: conservative estimates indicate that for each human life actually lost, in the absence of lockdown there would have been on average other 1.15, the policy saved in total 20,400 human lives.