Increased testing for Covid-19 is seen as one of the most important steps to re-open the economy. The current paper considers Tennessee's "open-testing" policy where the state substantially increased testing while removing symptom requirements for individuals to be tested. To understand the effect of the policy, we combine standard identifying assumptions with additional weak assumptions to deal with non-random testing that lead to bounds on policy effects of interest. Our results suggest that Tennessee's open-testing policy has reduced total cases (which are not fully observed), confirmed cases, and trips to work among counties with a fast-growing number of confirmed cases .
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 .
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Increased testing for
During the early stages of the
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is seen as one of the most important steps to re-open the economy. The current paper considers Tennessee's "open-testing" policy where the state substantially increased testing while removing symptom requirements for individuals to be tested. To understand the effect of the policy, we combine standard identifying assumptions with additional weak assumptions to deal with non-random testing that lead to bounds on policy effects of interest. Our results suggest that
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
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has reduced total cases (which are not fully observed), confirmed cases, and trips to work among counties with a fast-growing number of confirmed cases
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