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疫情早期的政策评估:在数据非随机缺失条件下界定政策效应

Evaluating Policies Early in a Pandemic: Bounding Policy Effects with Nonrandomly Missing Data
Review of Economics and Statistics · 2023 · Brantly Callaway、Tong Li

中文摘要

在新冠疫情初期,各国及地方政府出台了多项政策以遏制新冠病毒的传播。本文提出一种新方法,用于界定此类疫情早期政策对新冠病例数及其他结果的影响,同时处理以下因素引发的复杂问题:(i)新冠病毒检测的可获得性有限;(ii)不同地区新冠病毒检测的可获得性存在差异;以及(iii)个人接受检测须满足资格要求。我们运用该方法研究田纳西州在疫情早期扩大新冠病毒检测的效果,发现该政策减少了新冠病例数。

Abstract

Abstract During the early part of the COVID-19 pandemic, national and local governments introduced a number of policies to combat the spread of COVID-19. In this paper, we propose a new approach to bound the effects of such early-pandemic policies on COVID-19 cases and other outcomes while dealing with complications arising from (i) limited availability of COVID-19 tests, (ii) differential availability of COVID-19 tests across locations, and (iii) eligibility requirements for individuals to be tested. We use our approach study the effects of Tennessee’s expansion of COVID-19 testing early in the pandemic and find that the policy decreased COVID-19 cases.
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