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 crosscountry 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.

A babel of web-searches: Googling unemployment during the pandemic / G. Caperna, M. Colagrossi, A. Geraci, G. Mazzarella. - In: LABOUR ECONOMICS. - ISSN 0927-5371. - 74:(2022 Jan), pp. 102097.1-102097.12. [10.1016/j.labeco.2021.102097]

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

G. Mazzarella
Ultimo
2022-01

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 crosscountry 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.
Covid-19; Difference-in-Differences; Google trends; Nowcast; Random forest; Unemployment;
Settore SECS-P/01 - Economia Politica
Settore SECS-P/02 - Politica Economica
Settore SECS-P/05 - Econometria
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/937649
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