The Impact of Shutdown Policies on Unemployment During a Pandemic

50 Pages Posted: 21 Apr 2020 Last revised: 5 May 2020

See all articles by Edward Kong

Edward Kong

Harvard University, Department of Economics; Harvard Medical School

Daniel Prinz

Harvard University

Date Written: April 20, 2020


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 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.

Keywords: COVID-19, coronavirus, Non-pharmaceutical Interventions, NPIs, Unemployment, Google Trends, Economics, Health Policy

JEL Classification: C81, E24, I18, I38, J21, J23

Suggested Citation

Kong, Edward and Prinz, Daniel, The Impact of Shutdown Policies on Unemployment During a Pandemic (April 20, 2020). Available at SSRN: or

Edward Kong (Contact Author)

Harvard University, Department of Economics ( email )

Cambridge, MA 02138

Harvard Medical School ( email )

25 Shattuck St
Boston, MA 02115
United States

Daniel Prinz

Harvard University ( email )

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