Policy Evaluation during a Pandemic
Brantly Callaway, Tong Li

TL;DR
This paper evaluates the effectiveness of Covid-19 policies using epidemiologically consistent identification strategies, highlighting the limitations of traditional methods and proposing alternatives for policy impact assessment.
Contribution
It compares common identification strategies against epidemic models and introduces alternative methods better suited for nonlinear pandemic data.
Findings
Unconfoundedness approaches are more suitable than difference-in-differences during a pandemic.
Traditional difference-in-differences may be biased due to nonlinear case spread.
Proposed methods effectively evaluate policy impacts on Covid-19 and economic outcomes.
Abstract
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…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Advanced Causal Inference Techniques
