During the coronavirus disease 2019 (COVID-19) pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant activity data, we find that the implementation of stay-at-home orders initially had a limited impact, but that activity rose quickly after states' reopenings. The results suggest that consumers inferred from reopening that it was safer to eat out. The rational, but mistaken inference that occurs in our model may explain why a sharp rise of COVID-19 cases followed reopening in some states.

中文翻译：

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从放松管制中学习：封锁和重新开放对 COVID-19 期间危险行为的不对称影响

在 2019 年冠状病毒病 (COVID-19) 大流行期间，各州发布并随后撤销了限制流动性的居家令。我们开发了一种通过放松管制来学习的模型，该模型预测解除居家令可以表明外出变得更加安全。使用餐厅活动数据，我们发现实施居家订单最初产生的影响有限，但在各州重新开放后，该活动迅速上升。结果表明，消费者从重新开业推断出外出就餐更安全。我们的模型中出现的理性但错误的推论可以解释为什么在某些州重新开放之后，COVID-19 病例急剧增加。