The Effect of Stay-at-Home Orders on COVID-19 Infections in the United States

James H. Fowler; Seth J. Hill; Nick Obradovich; Remy Levin

Our independent variable, stay-at-home order status, measures a policy intervention that was often implemented simultaneously or within days of several other local interventions, such as bans on mass gatherings and closures of schools, non-essential businesses, and/or public areas. Given the uncertainty about how many days infected individuals are contagious both before and after the onset of symptoms, efforts to generate a sharp estimate of the effects of policies that were implemented within days of each other are difficult. Moreover, our analysis suggests these other local interventions might also have an effect on infection growth. On average, the peak of infections happens three days prior to the stay-at-home order. In addition, we see significant reductions in the growth rate of infections at just two days after the order. This is in spite of the fact that case identification during the early part of our observations was based on tests that often took a week to be resolved. 2 , 18 With our current empirical approach we cannot perfectly separate the effects of these other local interventions from that of stay-at-home orders. This means that our estimates should properly be interpreted as the effect of stay-at-home orders bundled with the effects of these other local interventions. As such, our model compares a "do everything" approach to a counterfactual mix of "do something" and "do nothing" approaches at the local level, which is the status quo that prevailed in the United States until mid-March. An interesting question which we leave for future work is which local interventions in the policy mix helped the most.