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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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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    3. Tatyana Deryugina & Laura Kawano & Steven Levitt, 2018. "The Economic Impact of Hurricane Katrina on Its Victims: Evidence from Individual Tax Returns," American Economic Journal: Applied Economics, American Economic Association, vol. 10(2), pages 202-233, April.
    4. Tanya Suhoy, 2009. "Query Indices and a 2008 Downturn: Israeli Data," Bank of Israel Working Papers 2009.06, Bank of Israel.
    5. Tatyana Deryugina, 2017. "The Fiscal Cost of Hurricanes: Disaster Aid versus Social Insurance," American Economic Journal: Economic Policy, American Economic Association, vol. 9(3), pages 168-198, August.
    6. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    7. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2019. "Tracking the Labor Market with "Big Data"," FEDS Notes 2019-09-20-1, Board of Governors of the Federal Reserve System (U.S.).
    8. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    9. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    10. Ortega, Francesc & Taspinar, Süleyman, 2016. "Rising Sea Levels and Sinking Property Values: The Effects of Hurricane Sandy on New York's Housing Market," IZA Discussion Papers 10374, Institute of Labor Economics (IZA).
    11. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    12. Justin Gallagher & Daniel Hartley, 2017. "Household Finance after a Natural Disaster: The Case of Hurricane Katrina," American Economic Journal: Economic Policy, American Economic Association, vol. 9(3), pages 199-228, August.
    13. Daniel Aaronson & Scott Brave, 2016. "Using Private Sector “Big Data” as an Economic Indicator: The Case of Construction Spending," Chicago Fed Letter, Federal Reserve Bank of Chicago.
    14. Ortega, Francesc & Taṣpınar, Süleyman, 2018. "Rising sea levels and sinking property values: Hurricane Sandy and New York’s housing market," Journal of Urban Economics, Elsevier, vol. 106(C), pages 81-100.
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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. O'Donnell, Niall & Shannon, Darren & Sheehan, Barry, 2023. "A vaccine for volatility? An empirical analysis of global stock markets and the impact of the COVID-19 vaccine," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    10. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    11. 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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