[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2023-35.html
   My bibliography  Save this paper

Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data

Author

Listed:
Abstract
We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like unemployment insurance claims provided an imperfect signal of job loss. Additionally, we find that our Twitter job loss indicator provides incremental information in predicting official unemployment flows in a given month beyond what weekly unemployment insurance claims offer.

Suggested Citation

  • Anbar Aizenman & Connor M. Brennan & Tomaz Cajner & Cynthia L. Doniger & Jacob Williams, 2023. "Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data," Finance and Economics Discussion Series 2023-035, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2023-35
    DOI: 10.17016/FEDS.2023.035
    as

    Download full text from publisher

    File URL: https://www.federalreserve.gov/econres/feds/files/2023035pap.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.17016/FEDS.2023.035?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    2. John H. Boyd & Jian Hu & Ravi Jagannathan, 2005. "The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks," Journal of Finance, American Finance Association, vol. 60(2), pages 649-672, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elenev, Vadim & Law, Tzuo-Hann & Song, Dongho & Yaron, Amir, 2024. "Fearing the Fed: How wall street reads main street," Journal of Financial Economics, Elsevier, vol. 153(C).
    2. Marc Poitras, 2004. "The Impact of Macroeconomic Announcements on Stock Prices: In Search of State Dependence," Southern Economic Journal, John Wiley & Sons, vol. 70(3), pages 549-565, January.
    3. Norbert Funke & Akimi Matsuda, 2006. "Macroeconomic News and Stock Returns in the United States and Germany," German Economic Review, Verein für Socialpolitik, vol. 7(2), pages 189-210, May.
    4. Marfatia, Hardik A., 2015. "Monetary policy's time-varying impact on the US bond markets: Role of financial stress and risks," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 103-123.
    5. Zhao, Yuanying & Pawlak, Jacek & Sivakumar, Aruna, 2022. "Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 101-134.
    6. Christiansen, Charlotte & Ranaldo, Angelo, 2009. "Extreme coexceedances in new EU member states' stock markets," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1048-1057, June.
    7. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
    8. Ernst Konrad, 2009. "The impact of monetary policy surprises on asset return volatility: the case of Germany," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(2), pages 111-135, June.
    9. Samara Ahmed & Adil E. Rajput & Akila Sarirete & Asma Aljaberi & Ohoud Alghanem & Abrar Alsheraigi, 2020. "Studying Unemployment Effects on Mental Health: Social Media versus the Traditional Approach," Sustainability, MDPI, vol. 12(19), pages 1-14, October.
    10. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    11. James Ming Chen, 2017. "Systematic Risk in the Macrocosm," Quantitative Perspectives on Behavioral Economics and Finance, in: Econophysics and Capital Asset Pricing, chapter 0, pages 239-274, Palgrave Macmillan.
    12. Caporale, Guglielmo Maria & Spagnolo, Fabio & Spagnolo, Nicola, 2016. "Macro news and stock returns in the Euro area: A VAR-GARCH-in-mean analysis," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 180-188.
    13. Bailliu, Jeannine & Han, Xinfen & Kruger, Mark & Liu, Yu-Hsien & Thanabalasingam, Sri, 2019. "Can media and text analytics provide insights into labour market conditions in China?," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1118-1130.
    14. Javier Morales & V'ictor Tercero & Fernando Camacho & Eduardo Cordero & Luis L'opez & F-Javier Almaguer, 2014. "Trend and Fractality Assessment of Mexico's Stock Exchange," Papers 1411.3399, arXiv.org.
    15. Giot, Pierre & Petitjean, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," International Journal of Forecasting, Elsevier, vol. 23(2), pages 289-305.
    16. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.
    17. Farka, Mira, 2009. "The effect of monetary policy shocks on stock prices accounting for endogeneity and omitted variable biases," Review of Financial Economics, Elsevier, vol. 18(1), pages 47-55, January.
    18. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
    19. Albuquerque, Rui & Vega, Clara, 2006. "Asymmetric Information in the Stock Market: Economic News and Co-movement," CEPR Discussion Papers 5598, C.E.P.R. Discussion Papers.
    20. Madhuri Malhotra & M. Thenmozhi & Arun Kumar Gopalaswamy, 2011. "Evidence on Changes in Time Varying Volatility around Bonus and Rights Issue Announcements," Working Papers 2011-061, Madras School of Economics,Chennai,India.

    More about this item

    Keywords

    Job Loss; Natural Language Processing; Neural Networks;
    All these keywords.

    JEL classification:

    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedgfe:2023-35. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ryan Wolfslayer ; Keisha Fournillier (email available below). General contact details of provider: https://edirc.repec.org/data/frbgvus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.