IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/20010.html
   My bibliography  Save this paper

Using Social Media to Measure Labor Market Flows

Author

Listed:
  • Dolan Antenucci
  • Michael Cafarella
  • Margaret Levenstein
  • Christopher Ré
  • Matthew D. Shapiro

Abstract

Social media enable promising new approaches to measuring economic activity and analyzing economic behavior at high frequency and in real time using information independent from standard survey and administrative sources. This paper uses data from Twitter to create indexes of job loss, job search, and job posting. Signals are derived by counting job-related phrases in Tweets such as "lost my job." The social media indexes are constructed from the principal components of these signals. The University of Michigan Social Media Job Loss Index tracks initial claims for unemployment insurance at medium and high frequencies and predicts 15 to 20 percent of the variance of the prediction error of the consensus forecast for initial claims. The social media indexes provide real-time indicators of events such as Hurricane Sandy and the 2013 government shutdown. Comparing the job loss index with the search and posting indexes indicates that the Beveridge Curve has been shifting inward since 2011. The University of Michigan Social Media Job Loss index is update weekly and is available at http://econprediction.eecs.umich.edu/.

Suggested Citation

  • 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.
  • Handle: RePEc:nbr:nberwo:20010
    Note: EFG LS ME PR
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w20010.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Régis Barnichon & Bart Hobijn & Ayşegül Şahin, 2010. "Which industries are shifting the Beveridge curve?," Working Paper Series 2010-32, Federal Reserve Bank of San Francisco.
    2. Miles Kimball & Helen Levy & Fumio Ohtake & Yoshiro Tsutsui, 2006. "Unhappiness after Hurricane Katrina," NBER Working Papers 12062, National Bureau of Economic Research, Inc.
    3. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
    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. Regis Barnichon & Andrew Figura, 2015. "Labor Market Heterogeneity and the Aggregate Matching Function," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(4), pages 222-249, October.
    2. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    3. Peter Diamond, 2011. "Unemployment, Vacancies, Wages," American Economic Review, American Economic Association, vol. 101(4), pages 1045-1072, June.
    4. Xiao Zhou & Rui Zhen & Xinchun Wu, 2019. "Understanding the Relation between Gratitude and Life Satisfaction among Adolescents in a Post-Disaster Context: Mediating Roles of Social Support, Self-Esteem, and Hope," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 12(5), pages 1781-1795, October.
    5. Frijters, Paul & Johnston, David W. & Knott, Rachel & Torgler, Benno, 2021. "Resilience to Disaster: Evidence from Daily Wellbeing Data," IZA Discussion Papers 14220, Institute of Labor Economics (IZA).
    6. Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.
    7. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    8. Takuya Ishino & Akiko Kamesaka & Toshiya Murai & Masao Ogaki, 2014. "Effects of the Great East Japan Earthquake on Subjective Well-Being," Keio-IES Discussion Paper Series 2014-010, Institute for Economics Studies, Keio University.
    9. Yumin Shu & Zhongying Qi, 2020. "The Effect of Market-Oriented Government Fiscal Expenditure on the Evolution of Industrial Structure: Evidence from Shenzhen, China," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    10. Keita, Sekou & Schewe, Paul, 2021. "Out of sight, out of mind? Terror in the home country, family reunification options, and the well-being of refugees," World Development, Elsevier, vol. 146(C).
    11. Christopher L. Foote & Richard W. Ryan, 2015. "Labor-Market Polarization over the Business Cycle," NBER Macroeconomics Annual, University of Chicago Press, vol. 29(1), pages 371-413.
    12. Berlemann, Michael, 2016. "Does hurricane risk affect individual well-being? Empirical evidence on the indirect effects of natural disasters," Ecological Economics, Elsevier, vol. 124(C), pages 99-113.
    13. Plamen Nenov, 2013. "Regional Mismatch and Labor Reallocation in an Equilibrium Model of Migration," 2013 Meeting Papers 565, Society for Economic Dynamics.
    14. Jinzhu Chen & Prakash Kannan & Prakash Loungani & Bharat Trehan, 2012. "New evidence on cyclical and structural sources of unemployment," Proceedings, Federal Reserve Bank of San Francisco, issue March, pages 1-23.
    15. Bricker, Jesse & Bucks, Brian, 2016. "Negative home equity, economic insecurity, and household mobility over the Great Recession," Journal of Urban Economics, Elsevier, vol. 91(C), pages 1-12.
    16. Akay, Alpaslan & Bargain, Olivier & Elsayed, Ahmed, 2020. "Global terror, well-being and political attitudes," European Economic Review, Elsevier, vol. 123(C).
    17. Fumio Ohtake & Katsunori Yamada, 2013. "Appraising the Unhappiness due to the Great East Japan Earthquake: Evidence from Weekly Panel Data on Subjective Well-being," ISER Discussion Paper 0876, Institute of Social and Economic Research, Osaka University.
    18. Berlemann, Michael, 2015. "Hurricane Risk, Happiness and Life Satisfaction. Some Empirical Evidence on the Indirect Effects of Natural Disasters," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113073, Verein für Socialpolitik / German Economic Association.
    19. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    20. Rocío Calvo & Mariana Arcaya & Christopher Baum & Sarah Lowe & Mary Waters, 2015. "Happily Ever After? Pre-and-Post Disaster Determinants of Happiness Among Survivors of Hurricane Katrina," Journal of Happiness Studies, Springer, vol. 16(2), pages 427-442, April.

    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General

    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:nbr:nberwo:20010. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.