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.
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.
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.
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.
The author of this article has not made their research data set openly available. Any enquiries regarding the data set can be directed to the corresponding author.
Dilmaghani, M. (2019), "Workopolis or The Pirate Bay: what does Google Trends say about the unemployment rate?", Journal of Economic Studies, Vol. 46 No. 2, pp. 422-445. https://doi.org/10.1108/JES-11-2017-0346
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