Forecasting unemployment with Google Trends: age, gender and digital divide

Rodrigo Mulero () and Alfredo Garcia-Hiernaux
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Rodrigo Mulero: Universidad Complutense de Madrid

Empirical Economics, 2023, vol. 65, issue 2, No 2, 587-605

Abstract: Abstract This paper uses time series of job search queries from Google Trends to predict the unemployment in Spain. Within this framework, we study the effect of the so-called digital divide, by age and gender, from the predictions obtained with the Google Trends tool. Regarding males, our results evidence a digital divide effect in favor of the youngest unemployed. Conversely, the forecasts obtained for female and total unemployment clearly reject such effect. More interestingly, Google Trends queries turn out to be much better predictors for female than male unemployment, being this result robust to age groups. Additionally, the number of good predictors identified from the job search queries is also higher for women, suggesting that they are more likely to expand their job search through different queries.

Keywords: Digital divide; Forecasting; Gender; Google Trends; Unemployment (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00181-022-02347-w

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