In search of a job: Forecasting employment growth using Google Trends

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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 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 non-linear model, it fully encompasses the macroeconomic forecasts and provides significant information in excess of those.
Original languageEnglish
JournalJournal of Business and Economic Statistics
Pages (from-to)186-200
Number of pages15
Publication statusPublished - 2022

    Research areas

  • Elastic Net, Google Trends, Machine learning, Random forests, Targeting predictors, U.S. employment growth, ECONOMIC TIME-SERIES, TESTS, PERFORMANCE, BUSINESS, S, U, PREDICTORS, SELECTION, employment growth

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