论文标题
分解锁定：住院任务对COVID-19大流行期间的不确定性和情感的因果影响
Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic
论文作者
论文摘要
我们研究了锁定措施对Twitter上不确定性和情感的因果影响。为此，我们利用了第一个Covid-19在高收入经济中创建的准实验框架，这是2020年2月在高收入经济中的意外意大利锁定。我们使用深度学习和基于词典的方法对公共情感的变化进行了对日常推文的基于深度学习和基于词典的方法的变化。我们将推文分为四类 - 经济学，健康，政治和锁定政策 - 研究政策如何异构影响情绪。使用交错差异的方法，我们表明锁定对经济不确定性和情感没有显着强大的影响。但是，该政策是以更高的健康和政治不确定性以及更多负面政治情感的代价。这些结果对一系列鲁棒性测试是可靠的，表明锁定具有相关的非健康含义。
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.
