Lockdown effects in US states: an artificial counterfactual approach

TL;DR

This paper uses an artificial counterfactual method to evaluate how lockdowns affected COVID-19 case and death trajectories in US states, revealing that without lockdowns, cases could have doubled in the short term.

Contribution

It introduces an artificial counterfactual approach to quantify the immediate impact of lockdown policies on COVID-19 spread in US states.

Findings

Lockdowns reduced short-term case numbers by approximately half.

Counterfactual scenarios suggest cases would be twice as high without lockdowns.

The method provides a new way to assess policy impacts during pandemics.

Abstract

We adopt an artificial counterfactual approach to assess the impact of lockdowns on the short-run evolution of the number of cases and deaths in some US states. To do so, we explore the different timing in which US states adopted lockdown policies, and divide them among treated and control groups. For each treated state, we construct an artificial counterfactual. On average, and in the very short-run, the counterfactual accumulated number of cases would be two times larger if lockdown policies were not implemented.