Breaking Down the Lockdown: The Causal Effect of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic

We study the causal effects of targeted lockdown measures on uncertainty and sentiments on Twitter. By exploiting the quasi-experimental setting induced by the first Western COVID-19 lockdown -- the unexpected lockdown implemented in Northern Italy in February 2020 -- we measure changes in public uncertainty and sentiment expressed on daily pre and post-lockdown tweets geolocalized inside and in the proximity of the lockdown areas. Using natural language processing, including dictionary-based methods and deep learning models, we classify each tweet across four categories -- economics, health, politics, and lockdown policy -- to identify in which areas uncertainty and sentiments concentrate. Using a Difference-in-Differences analysis, we show that areas under lockdown depict lower uncertainty around the economy and the stay-at-home mandate itself. This result likely stems from an informational asymmetry channel, for which treated individuals (those in the lockdown areas) adjust their expectations once the policy is in place, while uncertainty persists around the untreated (those in the proximity of the lockdown areas). On the other hand, we find that the lockdown comes at a cost as sentiments around health issues worsen.