We use high-frequency Google search data, combined with data on the announcement dates of nonpharmaceutical interventions (NPIs) during the Covid-19 pandemic in U.S. states, to isolate the impact of NPIs on unemployment in an event-study framework. Exploiting the differential timing of the introduction of restaurant and bar limitations, non-essential business closures, stay-at-home orders, large-gatherings bans, school closures, and emergency declarations, we analyze how Google searches for claiming unemployment insurance (UI) varied from day to day and across states. We describe a set of assumptions under which proxy outcomes (e.g., Google searches) can be used to estimate the causal parameter of interest (e.g., share of UI claims caused by NPIs) when data on the outcome of interest (e.g., daily UI claims) 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 NPIs. We find that between March 14 and 28, restaurant and bar limitations and non-essential business closures could explain 4.4% and 8.5% of UI claims respectively, while the other NPIs did not increase UI claims.
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Edward Kong |
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Harvard University |
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