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

TL;DR

This study investigates the causal impact of COVID-19 lockdowns on public sentiment and uncertainty on Twitter, revealing nuanced effects on health and political sentiments but limited impact on economic uncertainty.

Contribution

It employs a quasi-experimental design with deep learning analysis to uncover heterogeneous effects of lockdowns on various sentiment categories during COVID-19.

Findings

Lockdowns increased health and political uncertainty.

Lockdowns led to more negative political sentiments.

No significant impact on economic uncertainty.

Abstract

We study the causal effects of lockdown measures on uncertainty and sentiment on Twitter. To this end, we exploit the quasi-experimental framework created by the first COVID-19 lockdown in a high-income economy--the unexpected Italian lockdown in February 2020. We measure changes in public sentiment using deep learning and dictionary-based methods on the text of daily tweets geolocated within and near the locked-down areas, before and after the treatment. We classify tweets into four categories--economics, health, politics, and lockdown policy--to examine how the policy affected emotions heterogeneously. Using a staggered difference-in-differences approach, we show that the lockdown did not have a significantly robust impact on economic uncertainty and sentiment. However, the policy came at the price of higher uncertainty on health and politics and more negative political sentiments. These results, which are robust to a battery of robustness tests, show that lockdowns have relevant non-health related implications.