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

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-sampleR(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 nonlinear model, it fully encompasses the macroeconomic forecasts and provides significant information in excess of those.

Automated summary

This study demonstrates a strong correlation between Google search activity and employment growth in the United States, showing that Google search results are an effective predictor for future employment growth. By constructing a large panel of 172 variables and utilizing Google's own algorithms to find semantically related search queries, the Google Trends model outperforms other predictors in its ability to forecast employment growth.