In Search of a Job: Forecasting Employment Growth Using Google Trends
36 Pages Posted: 22 Jul 2019 Last revised: 1 Jul 2020
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
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