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Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims

Author

Listed:
  • Daniel Aaronson
  • Scott A. Brave
  • R. Andrew Butters
  • Daniel W. Sacks
  • Boyoung Seo

Abstract

We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.

Suggested Citation

  • Daniel Aaronson & Scott A. Brave & R. Andrew Butters & Daniel W. Sacks & Boyoung Seo, 2020. "Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims," Working Paper Series WP 2020-10, Federal Reserve Bank of Chicago.
    • Handle: RePEc:fip:fedhwp:87770
    DOI: 10.21033/wp-2020-10
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    Cited by:

    1. Magdalena Kozera-Kowalska & Jarosław Uglis, 2021. "Students’ Perception of Education as a Preparation to Enter the Labour Market: A Case Study from a Polish University," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 338-349.
    2. Willem Thorbecke, 2020. "The Impact of the COVID-19 Pandemic on the U.S. Economy: Evidence from the Stock Market," JRFM, MDPI, vol. 13(10), pages 1-30, October.
    3. Christopher Foote & Tyler Hounshell & William D. Nordhaus & Douglas Rivers & Pamela Torola, 2021. "Measuring the U.S. Employment Situation Using Online Panels: The Yale Labor Survey," Cowles Foundation Discussion Papers 2282, Cowles Foundation for Research in Economics, Yale University.
    4. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
    5. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    6. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    7. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," Working Papers 2020-04, Joint Research Centre, European Commission.
    8. Asfaw, Abraham Abebe, 2021. "The effect of income support programs on job search, workplace mobility and COVID-19: International evidence," Economics & Human Biology, Elsevier, vol. 41(C).
    9. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    10. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    Covid-19; Google trends; hurricanes; unemployment; unemployment insurance;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management
    • J65 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment Insurance; Severance Pay; Plant Closings

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