A babel of web-searches: Googling unemployment during the pandemic
提出一种利用机器学习从网络搜索中识别与失业相关查询词的方法，构建搜索失业指数，并用双重差分法发现封锁导致该指数显著持续上升，而原始搜索数据无法反映这一动态。
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.