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

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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
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    References listed on IDEAS

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    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.
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    Keywords

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    JEL classification:

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

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