In Search of a Job: Forecasting Employment Growth Using Google Trends

Daniel Borup and Erik Christian Schütte

Journal of Business & Economic Statistics, 2022, vol. 40, issue 1, 186-200

Abstract: We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the United States over the period 2004–2019 at both short and long horizons. Starting from an initial search term “jobs,” we construct a large panel of 172 variables using Google’s own algorithms to find semantically related search queries. The best Google Trends model achieves an out-of-sample R2 between 29% and 62% at horizons spanning from one month to one year ahead, strongly outperforming benchmarks based on a single search query or a large set of macroeconomic, financial, and sentiment predictors. This strong predictability is due to heterogeneity in search terms and extends to industry-level and state-level employment growth using state-level specific search activity. Encompassing tests indicate that when the Google Trends panel is exploited using a nonlinear model, it fully encompasses the macroeconomic forecasts and provides significant information in excess of those.

Date: 2022
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Citations: View citations in EconPapers (14)

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Working Paper: In search of a job: Forecasting employment growth using Google Trends (2019)
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DOI: 10.1080/07350015.2020.1791133

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