Policy Evaluation during a Pandemic
疫情期间的政策评估AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接比较疫情政策评估中的无混杂和双重差分识别策略。
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疫情期间的政策评估
- 研究问题
- 利用不同地区政策实施时间的差异评估疫情政策时，常见识别策略是否兼容疫情传播模型，疫情非线性又如何影响经济结果评估？
- 方法与思路
- 作者将常见识别策略与流行病模型比较，讨论控制政策前疫情状态的无混杂方法及双重差分的局限，并提出替代评估办法。
- 材料陈述的贡献
- 摘要认为，无混杂类方法在此背景下可能更有用；双重差分的问题还可传导到经济结果，作者据此研究早期州级居家令。
- 阅读关注点
- 应核对疫情状态如何定义和测量、替代方法需要何种识别条件，以及疫情模型与双重差分不兼容的具体推导和应用设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明替代方法的完整假设或居家令效果，不能据此普遍否定双重差分。
原文摘要
National and local governments have implemented a large number of policies in response to the Covid-19 pandemic. Evaluating the effects of these policies, both on the number of Covid-19 cases as well as on other economic outcomes is a key ingredient for policymakers to be able to determine which policies are most effective as well as the relative costs and benefits of particular policies. In this paper, we consider the relative merits of common identification strategies that exploit variation in the timing of policies across different locations by checking whether the identification strategies are compatible with leading epidemic models in the epidemiology literature. We argue that unconfoundedness type approaches, that condition on the pre-treatment "state" of the pandemic, are likely to be more useful for evaluating policies than difference-in-differences type approaches due to the highly nonlinear spread of cases during a pandemic. For difference-in-differences, we further show that a version of this problem continues to exist even when one is interested in understanding the effect of a policy on other economic outcomes when those outcomes also depend on the number of Covid-19 cases. We propose alternative approaches that are able to circumvent these issues. We apply our proposed approach to study the effect of state level shelter-in-place orders early in the pandemic.