Evaluating Policies Early in a Pandemic: Bounding Policy Effects with Nonrandomly Missing Data.

During the early stages of the Covid-19 pandemic, national and local governments introduced a large number of policies, particularly non-pharmaceutical interventions, to combat the spread of Covid-19. Understanding the effects that these policies had (both on Covid-19 cases and on other outcomes) is particularly challenging though because (i) Covid-19 testing was not widely available, (ii) the availability of tests varied across locations, and (iii) the tests that were available were generally targeted towards individuals meeting certain eligibility criteria. In this paper, we propose a new approach to evaluate the effect of policies early in the pandemic that accommodates limited and nonrandom testing. Our approach results in (generally informative) bounds on the effect of the policy on actual cases and in point identification of the effect of the policy on other outcomes. We apply our approach to study the effect of Tennessee's open-testing policy during the early stage of the pandemic. For this policy, we find suggestive evidence that the policy decreased the number of Covid-19 cases in the state relative to what they would have been if the policy had not been implemented.