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Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data

Giulio Capema (), Marco Colagrossi (), Andrea Geraci () and Gianluca Mazzarella ()
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Giulio Capema: European Commission,
Marco Colagrossi: European Commission,
Andrea Geraci: European Commission,
Gianluca Mazzarella: European Commission,

No 2020-04, Working Papers from Joint Research Centre, European Commission (Ispra site)

Abstract: 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: Unemployment; nowcast; random forest; covid-19; Google Trends; Difference-in-Differences (search for similar items in EconPapers)
JEL-codes: E24 C21 C53 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2020-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mac
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Published by Publications Office of the European Union, 2020

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