Evaluating Policies Early in a Pandemic: Bounding Policy Effects with Nonrandomly Missing Data

TL;DR

This paper introduces a new method to estimate the effects of early-pandemic policies on Covid-19 outcomes, accounting for missing and uneven testing data, and applies it to Tennessee's testing expansion showing it reduced cases.

Contribution

A novel bounding approach for policy effects during pandemics that handles nonrandom missing data and testing disparities, demonstrated on Tennessee's Covid-19 testing policy.

Findings

Tennessee's testing expansion decreased Covid-19 cases.

The method effectively accounts for testing data limitations.

Policy effects can be bounded despite data gaps.

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.