A babel of web-searches: Googling unemployment during the pandemic
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.
CAPERNA Giulio;
COLAGROSSI Marco;
GERACI Andrea;
MAZZARELLA Gianluca;
2021-12-15
ELSEVIER
JRC125630
0927-5371 (online),
https://www.sciencedirect.com/science/article/pii/S0927537121001329?via%3Dihub,
https://publications.jrc.ec.europa.eu/repository/handle/JRC125630,
10.1016/j.labeco.2021.102097 (online),
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