Abstract

Researchers are increasingly exploiting web-searches to study phenomena for which timely and high-frequency data are not readily available. We propose a data-driven procedure which, exploiting machine learning techniques, solves the issue of identifying the list of queries linked to the phenomenon of interest, even in a cross-country setting. Queries are then aggregated in an indicator which can be used for causal inference. We apply this procedure to construct a search-based unemployment index and study the effect of lock-downs during the covid-19 pandemic. In a Difference-in-Differences analysis, we show that the indicator rose significantly and persistently in the aftermath of lock-downs.

Highlights

  • 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 focus on unemployment and investigate the response to lock-down measures enacted by Governments to ght the spread of the SARS-CoV-2 virus

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Summary

Introduction

The economic crisis caused by the covid-19 pandemic is unprecedented in recent history. The literature on the labour market impacts of the pandemic and subsequent containment measures has focused on single countries (e.g., Aaronson et al, 2020; Amburgey et al, 2020; Baert et al, 2020; GoldsmithPinkham and Sojourner, 2020; Kahn et al, 2020; “ahin et al, 2020) or few selected countries (Adams-Prassl et al, 2020). Once we add all the keywords linked to the topic unemployment and perform variable selection using random forest-based methods, the predictive accuracy increases signi cantly in almost all countries Drawing from this nding, we select the queries that best predict the unemployment rate, separately for each country, and aggregate them to create a daily indicator of unemployment-related searches.

Google searches
Google searches and unemployment rate in the EU
Measuring the effect of lock-down measures on online search activities
Conclusion
Regression Trees and Random Forest
Variable importance and selection
Full Text
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