Disentangling Policy Effects Using Proxy Data: Which Shutdown Policies Affected Unemployment During the COVID-19 Pandemic?

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We use high-frequency Google search data, combined with data on the announcement dates of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic in U.S. states, to disentangle the short-run direct impacts of multiple different state-level NPIs in an event study framework. Exploiting differential timing in the announcements of restaurant and bar limitations, non-essential business closures, stay-at-home orders, large-gatherings bans, school closures, and emergency declarations, we leverage the high-frequency search data to separately identify the effects of multiple NPIs that were introduced around the same time. We then describe a set of assumptions under which proxy outcomes can be used to estimate a causal parameter of interest when data on the outcome of interest are limited. Using this method, we quantify the share of overall growth in unemployment during the COVID-19 pandemic that was directly due to each of these state-level NPIs. We find that between March 14 and 28, restaurant and bar limitations and non-essential business closures can explain 6.0% and 6.4% of UI claims respectively, while the other NPIs did not directly increase own-state UI claims. This suggests that most of the short-run increase in UI claims during the pandemic was likely due to other factors, including declines in consumer demand, local policies, and policies implemented by private firms and institutions.


SSRN 3581254 
Covid Economics 17: 24-72

Publisher's Version

Last updated on 08/23/2020