IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2001.03401.html
   My bibliography  Save this paper

Text as Data: Real-time Measurement of Economic Welfare

Author

Listed:
  • Rickard Nyman
  • Paul Ormerod

Abstract

Economists are showing increasing interest in the use of text as an input to economic research. Here, we analyse online text to construct a real time metric of welfare. For purposes of description, we call it the Feel Good Factor (FGF). The particular example used to illustrate the concept is confined to data from the London area, but the methodology is readily generalisable to other geographical areas. The FGF illustrates the use of online data to create a measure of welfare which is not based, as GDP is, on value added in a market-oriented economy. There is already a large literature which measures wellbeing/happiness. But this relies on conventional survey approaches, and hence on the stated preferences of respondents. In unstructured online media text, users reveal their emotions in ways analogous to the principle of revealed preference in consumer demand theory. The analysis of online media offers further advantages over conventional survey-based measures of sentiment or well-being. It can be carried out in real time rather than with the lags which are involved in survey approaches. In addition, it is very much cheaper.

Suggested Citation

  • Rickard Nyman & Paul Ormerod, 2020. "Text as Data: Real-time Measurement of Economic Welfare," Papers 2001.03401, arXiv.org.
    • Handle: RePEc:arx:papers:2001.03401
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2001.03401
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthew Gentzkow & Bryan Kelly & Matt Taddy, 2019. "Text as Data," Journal of Economic Literature, American Economic Association, vol. 57(3), pages 535-574, September.
    2. Ron S. Jarmin, 2019. "Evolving Measurement for an Evolving Economy: Thoughts on 21st Century US Economic Statistics," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 165-184, Winter.
    3. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    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. Chris Florackis & Christodoulos Louca & Roni Michaely & Michael Weber & Itay Goldstein, 2023. "Cybersecurity Risk," The Review of Financial Studies, Society for Financial Studies, vol. 36(1), pages 351-407.
    2. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    3. Michael Ryan, 2020. "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics 20/10, University of Waikato.
    4. Baranowski, Paweł & Doryń, Wirginia & Łyziak, Tomasz & Stanisławska, Ewa, 2021. "Words and deeds in managing expectations: Empirical evidence from an inflation targeting economy," Economic Modelling, Elsevier, vol. 95(C), pages 49-67.
    5. Massimo Ferrari Minesso & Laura Lebastard & Helena Mezo, 2023. "Text-Based Recession Probabilities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 415-438, June.
    6. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.
    7. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    8. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
    9. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    10. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    11. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
    12. Consoli, Sergio & Pezzoli, Luca Tiozzo & Tosetti, Elisa, 2021. "Emotions in macroeconomic news and their impact on the European bond market," Journal of International Money and Finance, Elsevier, vol. 118(C).
    13. Nicolò Fraccaroli & Alessandro Giovannini & Jean-François Jamet & Eric Persson, 2023. "Central Banks in Parliaments: A Text Analysis of the Parliamentary Hearings of the Bank of England, the European Central Bank, and the Federal Reserve," International Journal of Central Banking, International Journal of Central Banking, vol. 19(2), pages 543-600, June.
    14. Hansen, Stephen & Davis, Steven & Seminario-Amez, Cristhian, 2020. "Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19," CEPR Discussion Papers 15314, C.E.P.R. Discussion Papers.
    15. Heng Chen & Li Han, 2022. "Do the Media Bow to Foreign Economic Powers? Evidence from a News Website Crackdown," HKUST CEP Working Papers Series 202201, HKUST Center for Economic Policy.
    16. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    17. Aromi, J. Daniel & Clements, Adam, 2021. "Facial expressions and the business cycle," Economic Modelling, Elsevier, vol. 102(C).
    18. Lucas, David S., 2024. "The effect of regime change on entrepreneurship: A real options approach with evidence from US gubernatorial elections," Journal of Business Venturing, Elsevier, vol. 39(4).
    19. Amarasinghe, Ashani, 2022. "Diverting domestic turmoil," Journal of Public Economics, Elsevier, vol. 208(C).
    20. Tarek A. Hassan & Stephan Hollander & Laurence Van Lent & Ahmed Tahoun, 2024. "The Global Impact of Brexit Uncertainty," Journal of Finance, American Finance Association, vol. 79(1), pages 413-458, February.

    More about this item

    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:arx:papers:2001.03401. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.