IDEAS home Printed from https://ideas.repec.org/a/eme/jespps/jes-11-2017-0346.html
   My bibliography  Save this article

Workopolis or The Pirate Bay: what does Google Trends say about the unemployment rate?

Author

Listed:
  • Maryam Dilmaghani

Abstract

Purpose - The purpose of this paper is to use data mined from Google Trends, in order to predict the unemployment rate prevailing among Canadians between 25 and 44 years of age. Design/methodology/approach - Based on a theoretical framework, this study argues that the intensity of online leisure activities is likely to improve the predictive power of unemployment forecasting models. Findings - Mining the corresponding data from Google Trends, the analysis indicates that prediction models including variables which reflect online leisure activities outperform those solely based on the intensity of online job search. The paper also outlines the most propitious ways of mining data from Google Trends. The implications for research and policy are discussed. Originality/value - This paper, for the first time, augments the forecasting models with data on the intensity of online leisure activities, in order to predict the Canadian unemployment rate.

Suggested Citation

  • Maryam Dilmaghani, 2019. "Workopolis or The Pirate Bay: what does Google Trends say about the unemployment rate?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(2), pages 422-445, March.
    • Handle: RePEc:eme:jespps:jes-11-2017-0346
    DOI: 10.1108/JES-11-2017-0346
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/JES-11-2017-0346/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/JES-11-2017-0346/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/JES-11-2017-0346?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    2. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving : Evidence from SMS Donations," The Warwick Economics Research Paper Series (TWERPS) 1325, University of Warwick, Department of Economics.
    3. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2022. "Does online salience predict charitable giving? Evidence from SMS text donations," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 134-149.
    4. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
    5. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    6. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.
    7. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    8. Lolić, Ivana & Matošec, Marina & Sorić, Petar, 2024. "DIY google trends indicators in social sciences: A methodological note," Technology in Society, Elsevier, vol. 77(C).

    More about this item

    Keywords

    Canada; Unemployment; Leisure; Time-use; Google Trends data; J20; J21; J22;
    All these keywords.

    JEL classification:

    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

    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:eme:jespps:jes-11-2017-0346. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Emerald Support (email available below). General contact details of provider: .

    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.