This paper reports the results of a Bayesian analysis on large-scale empirical data to assess the effectiveness of eleven types of COVID-control policies that have been implemented at various levels of intensity in 40 countries and U.S. states since the onset of the pandemic. The analysis estimates the marginal impact of each type and level of policy as implemented in concert with other policies. The purpose is to provide policymakers and the general public with an estimate of the relative effectiveness of various COVID-control strategies. We find that a set of widely implemented core policies reduces the spread of virus but not by enough to contain the pandemic except in a few highly compliant jurisdictions. The core policies include the cancellation of public events, restriction of gatherings to fewer than 100 people, recommendation to stay at home, recommended restrictions on internal movement, implementation of a partial international travel ban, and coordination of information campaigns. For the median jurisdiction, these policies reduce growth rate in new infections from an estimated 270% per week to approximately 49% per week, but this impact is insufficient to prevent eventual transmission throughout the population because containment occurs only when a jurisdiction reduces growth in COVID infection to below zero. Most jurisdictions must also implement additional policies, each of which has the potential to reduce weekly COVID growth rate by 10 percentage points or more. The slate of these additional high-impact policies includes targeted or full workplace closings for all but essential workers, stay-at-home requirements, and targeted school closures.

This paper reports the results of a Bayesian analysis on large-scale empirical data to assess the effectiveness of eleven types of COVID-control policies that have been implemented at various levels of intensity in 40 countries and U.S. states since the onset of the pandemic. The analysis estimates the marginal impact of each type and level of policy as implemented in concert with other policies. The purpose is to provide policymakers and the general public with an estimate of the relative effectiveness of various COVID-control strategies. We find that a set of widely implemented core policies reduces the spread of virus but not by enough to contain the pandemic except in a few highly compliant jurisdictions. The core policies include the cancellation of public events, restriction of gatherings to fewer than 100 people, recommendation to stay at home, recommended restrictions on internal movement, implementation of a partial international travel ban, and coordination of information campaigns. For the median jurisdiction, these policies reduce growth rate in new infections from an estimated 270% per week to approximately 49% per week, but this impact is insufficient to prevent eventual transmission throughout the population because containment occurs only when a jurisdiction reduces growth in COVID infection to below zero. Most jurisdictions must also implement additional policies, each of which has the potential to reduce weekly COVID growth rate by 10 percentage points or more. The slate of these additional high-impact policies includes targeted or full workplace closings for all but essential workers, stay-at-home requirements, and targeted school closures.

Research Summary Platform companies use design changes to govern their participants. The success of a design change depends on participants' responses, which are influenced by their local environments. Our study focuses on an important aspect of the local environment—rural versus urban. Using data from a leading e‐commerce platform, we find that relative to urban sellers, rural sellers were particularly poor at adjusting to a major design change, resulting in a persistent performance gap. We attribute these misaligned responses to rural sellers' lack of local access to rich information. This study shows that sellers' local heterogeneity generates equivocal responses and carries unintended consequences for platform governance. It also enriches our understanding of digital inequality and algorithmic design by highlighting the importance of the “offline interface.” Managerial Summary Digital platforms frequently change their design rules (e.g., ranking algorithms) to guide the behavior of participants. However, participants are inherently heterogeneous, and their abilities to understand and follow a design change also vary across populations. This study examines a major design change on a leading e‐commerce platform. We find that, compared to urban sellers, rural sellers developed responses that detracted from the platform's design goals and resulted in lower sales. This study highlights the need for digital platforms to understand how participants' offline environments affect their online behavior. This study also shapes the conversation on digital inequality: despite being connected online, entrepreneurs in traditionally disadvantaged regions may still suffer from a lack of accessible local information channels.

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