A babel of web-searches: Googling unemployment during the pandemic

Giulio Caperna, Marco Colagrossi, Andrea Geraci and Gianluca Mazzarella

Labour Economics, 2022, vol. 74, issue C

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 first wave of 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. This is not the case when using unprocessed (raw) web search data, which might return a partial figure of the labour market dynamics following lock-downs.

Keywords: Unemployment; Nowcast; Random forest; Covid-19; Google trends; Difference-in-Differences (search for similar items in EconPapers)
JEL-codes: C53 C82 E24 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:74:y:2022:i:c:s0927537121001329

DOI: 10.1016/j.labeco.2021.102097

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