AER: Insights · 2021 · Daron Acemoğlu、Victor Chernozhukov、Iván Werning、Michael D. Whinston
中文摘要
我们研究多群体SIR模型中的定向封锁,其中感染率、住院率和致死率因群体而异——尤其是"年轻人"、"中年人"与"老年人"之间。我们的模型使得对最优政策进行可处理的定量分析成为可能。将针对COVID-19疫情的基准参数值应用于美国,我们发现,对风险/年龄群体进行差异化定向的最优政策显著优于最优的统一政策,且大部分收益可以通过对更脆弱的老年群体实施更严格的保护措施(如封锁)来实现。直观而言,对老年人实施严格且长期的封锁既能减少感染,又能使较低风险群体得以采取较宽松的封锁措施。(JEL H51, I12, I18, J13, J14)
Abstract
We study targeted lockdowns in a multigroup SIR model where infection, hospitalization, and fatality rates vary between groups—in particular between the “young,” the “middle-aged,” and the “old.” Our model enables a tractable quantitative analysis of optimal policy. For baseline parameter values for the COVID-19 pandemic applied to the US, we find that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter protective measures such as lockdowns on the more vulnerable, old group. Intuitively, a strict and long lockdown for the old both reduces infections and enables less strict lockdowns for the lower-risk groups. (JEL H51, I12, I18, J13, J14)