  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.
