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Disentangling policy effects using proxy data: Which shutdown policies affected unemployment during the COVID-19 pandemic?

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  • Kong, Edward
  • Prinz, Daniel

Abstract

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.

Suggested Citation

  • Kong, Edward & Prinz, Daniel, 2020. "Disentangling policy effects using proxy data: Which shutdown policies affected unemployment during the COVID-19 pandemic?," Journal of Public Economics, Elsevier, vol. 189(C).
  • Handle: RePEc:eee:pubeco:v:189:y:2020:i:c:s0047272720301213
    DOI: 10.1016/j.jpubeco.2020.104257
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    References listed on IDEAS

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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Angela Devine’s journal round-up for 28th September 2020
      by Angela Devine in The Academic Health Economists' Blog on 2020-09-28 11:00:07

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    Cited by:

    1. Leonardo Martins & Marcelo C. Medeiros, 2021. "The Impacts of Mobility on Covid-19 Dynamics: Using Soft and Hard Data," Papers 2110.00597, arXiv.org.
    2. Kyriakos C. Neanidis & Maria Paola Rana, 2021. "Crime in the Era of COVID-19: Evidence from England," Economics Discussion Paper Series 2103, Economics, The University of Manchester.
    3. Mario J. Crucini & Oscar O'Flaherty, 2020. "Stay-at-Home Orders in a Fiscal Union," NBER Working Papers 28182, National Bureau of Economic Research, Inc.
    4. Alipour, Jean-Victor & Fadinger, Harald & Schymik, Jan, 2021. "My home is my castle – The benefits of working from home during a pandemic crisis," Journal of Public Economics, Elsevier, vol. 196(C).
    5. Brodeur, Abel & Cook, Nikolai & Wright, Taylor, 2021. "On the effects of COVID-19 safer-at-home policies on social distancing, car crashes and pollution," Journal of Environmental Economics and Management, Elsevier, vol. 106(C).
    6. Gros, Daniel & Ounnas, Alexandre, 2021. "Labour market responses to the Covid-19 crisis in the United States and Europe," CEPS Papers 32985, Centre for European Policy Studies.
    7. L'aszl'o Czaller & GergH{o} T'oth & Bal'azs Lengyel, 2021. "Vaccine allocation to blue-collar workers," Papers 2104.04639, arXiv.org.
    8. Spyros Niavis & Dimitris Kallioras & George Vlontzos & Marie-Noelle Duquenne, 2021. "COVID-19 Pandemic and Lockdown Fine Optimality," Economies, MDPI, Open Access Journal, vol. 9(1), pages 1-26, March.
    9. Brantly Callaway & Tong Li, 2021. "Policy Evaluation during a Pandemic," Papers 2105.06927, arXiv.org.
    10. Akos Horvath & Benjamin S. Kay & Carlo Wix, 2021. "The COVID-19 Shock and Consumer Credit: Evidence from Credit Card Data," Finance and Economics Discussion Series 2021-008, Board of Governors of the Federal Reserve System (U.S.).

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