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

Maryam Dilmaghani (Department of Economics, Saint Mary’s University, Halifax, Canada)

Journal of Economic Studies

ISSN: 0144-3585

Article publication date: 4 March 2019

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.

Keywords

Acknowledgements

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.

Citation

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

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited