What Drives the Effectiveness of Social Distancing in Combating COVID-19 across U.S. States?

We combine structural estimation with ideas from Machine Learning to estimate a model with information-based voluntary social distancing and state lockdowns to analyze the factors driving the effect of social distancing in mitigating COVID-19. The model allows us to estimate how contagious social interactions are by state and enables us to control for several unobservable, time-varying confounders such as asymptomatic transmission, sample selection in testing and quarantining, and time-varying fatality rates. We find that information-based voluntary social distancing has saved three times as many lives as lockdowns. Second, information policy effects are asymmetric: `least informed' responses would have implied 240,000 more fatalities by June 2020 while `most informed' responses would have saved 25,000 more lives. Third, our estimates suggest that contagion externalities from social interactions are large enough that a lockdown response could have been 25\% less costly for the median state and still saved an equivalent number of lives.