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

36 Pages Posted: 22 Jul 2019 Last revised: 1 Jul 2020

See all articles by Daniel Borup

Daniel Borup

Aarhus University, CREATES, DFI

Erik Christian Montes Schütte

Aarhus University - CREATES; DFI

Date Written: July 19, 2019


We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the US 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 R^2 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 non-linear model, it fully encompasses the macroeconomic forecasts and provides significant information in excess of those.

Keywords: Google Trends, forecast comparison, US employment growth, targeting predictors, random forests, keyword search

JEL Classification: C22, C53, E24

Suggested Citation

Borup, Daniel and Schütte, Erik Christian Montes, In Search of a Job: Forecasting Employment Growth Using Google Trends (July 19, 2019). Journal of Business and Economic Statistics, forthcoming, Available at SSRN: or

Daniel Borup (Contact Author)

Aarhus University, CREATES, DFI ( email )

School of Business and Social Sciences
Fuglesangs Alle 4
Aarhus V, 8210

Erik Christian Montes Schütte

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C

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