Abstract
The paper evaluates the dynamic impact of various policies adopted by US states on the growth rates of confirmed Covid-19 cases and deaths as well as social distancing behavior measured by Google Mobility Reports, where we take into consideration people’s voluntarily behavioral response to new information of transmission risks in a causal structural model framework. Our analysis finds that both policies and information on transmission risks are important determinants of Covid-19 cases and deaths and shows that a change in policies explains a large fraction of observed changes in social distancing behavior. Our main counterfactual experiments suggest that nationally mandating face masks for employees early in the pandemic could have reduced the weekly growth rate of cases and deaths by more than 10 percentage points in late April and could have led to as much as 19 to 47 percent less deaths nationally by the end of May, which roughly translates into 19 to 47 thousand saved lives. We also find that, without stay-at-home orders, cases would have been larger by 6 to 63 percent and without business closures, cases would have been larger by 17 to 78 percent. We find considerable uncertainty over the effects of school closures due to lack of cross-sectional variation; we could not robustly rule out either large or small effects. Overall, substantial declines in growth rates are attributable to private behavioral response, but policies played an important role as well. We also carry out sensitivity analyses to find neighborhoods of the models under which the results hold robustly: the results on mask policies appear to be much more robust than the results on business closures and stay-at-home orders. Finally, we stress that our study is observational and therefore should be interpreted with great caution. From a completely agnostic point of view, our findings uncover predictive effects (association) of observed policies and behavioral changes on future health outcomes, controlling for informational and other confounding variables.
Highlights
Accumulating evidence suggests that various policies in the US have reduced social interactions and slowed down the growth of Covid-19 infections.1 An important outstanding issue, is how much of the observed slow down in the spread is attributable to the effect of policies as opposed to a voluntarily change in people’s behavior out of fear of being infected
Cases masks for employees closed K-12 schools stay at home business closure policies
Deaths masks for employees closed K-12 schools stay at home business closure policies
Summary
Accumulating evidence suggests that various policies in the US have reduced social interactions and slowed down the growth of Covid-19 infections. An important outstanding issue, is how much of the observed slow down in the spread is attributable to the effect of policies as opposed to a voluntarily change in people’s behavior out of fear of being infected. We evaluate the dynamic impact of the following three counterfactual policies on Covid-19 cases and deaths: (1) mandating face masks, (2) allowing all businesses to open, and (3) not implementing a stay-at-home order. Percentage points 16–20 days after implementation, out of which 5.9 percentage points are attributable to shelter in place orders Both Hsiang et al (2020) and Courtemanche et al (2020) adopt a reduced-form approach to estimate the total policy effect on case growth without using any social distancing behavior measures.. To the best of our knowledge, our paper is the first empirical study that shows the effectiveness of mask mandates on reducing the spread of Covid-19 by analyzing the US state-level data This finding corroborates and is complementary to the medical observational evidence in Howard et al (2020). The causal model for the effect of policies, behavior, and information on growth of infection
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