Googling unemployment during the pandemic: Inference and nowcast using search data - Giulio Caperna; Marco Colagrossi; Andrea Geraci; Gianluca Mazzarella (Publications Office of the European Union, 2020)
The economic crisis caused by the covid-19 pandemic is unprecedented in recent history. We contribute to a growing literature investigating the economic consequences of covid-19 by showing how unemployment-related online searches across the EU27 reacted to the introduction of lock-downs. We exploit Google Trends topics to retrieve over two thousand search queries related to unemployment in 27 countries. We nowcast the monthly unemployment rate in the EU Member States to assess the relationship between search data and the underlying phenomenon as well as to identify the keywords that improve predictive accuracy. Drawing from this finding, we use the set of best predictors in a Difference-in-Differences framework to document a surge of unemploymentrelated searches in the wake of lock-downs of about 30%. This effect persists for more than five weeks. We suggest that the effect is most likely due to an increase in unemployment expectations.
Keywords: E24; C21; C53; 330; Unemployment; nowcast; random forest; covid-19; Google Trends; Difference-in-Differences