Stay-at-home orders and the willingness to stay home during the COVID-19 pandemic: A stated-preference discrete choice experiment

Abstract

The spread of COVID-19 in the Spring of 2020 prompted state and local governments to implement a variety of policies, including stay-at-home (SAH) orders and mandatory mask requirements, aimed at reducing the infection rate and the severity of the pandemic’s impact. We implement a discrete choice experiment survey in three major U.S. States—California, Georgia, and Illinois—to empirically quantify individuals’ willingness to stay (WTS) home, measured as the number of weeks of a potential new SAH order, to prevent the spread of the COVID-19 disease and explore factors leading to their heterogeneous WTS. Our results demonstrate broad support for statewide mask mandates. In addition, the estimate of WTS to lower new positive cases is quite large, approximately five and half weeks, even though staying home lowers utility. We also find that individuals recognize the trade-offs between case reduction and economic slowdown stemming from SAH orders when they decide to stay home or not. Finally, pandemic related factors such as age, ability to work from home, and unemployment status are the main drivers of the heterogeneity in individuals’ WTS.

Trade-offs during the COVID-19 pandemic: A discrete choice experiment about policy preferences in Portugal

“…Our results are broadly in line with other DCE studies focusing on a similar question in the Netherlands [8], United States of America [12], Australia [10] and Germany [11], even though these studies collected responses at an earlier phase of the pandemic: Chorus (2020) [8] in April 2020, Reed (2020) [12] in May 2020, Manipis (2020) [10] between July and August 2020, Li (2021) [9] end of August 2020 and Mu ¨hlbacher (2022) [11] from October to November 2020). Although there are some differences between the domains studied, attributes related to COVID-19 excess mortality were consistently those with a larger effect on individual's preferences.…”

“…It is particularly relevant in the context of a crisis, when unprecedented measures must be adopted in short time frames [6,7]. DCE studies have been used in the context of COVID-19, not only to measure how the citizens value the costs and benefits of lockdown policies [8][9][10][11][12] but also to understand potential barriers and preferences towards the uptake of COVID-19 measures, such as testing, contact-tracing and vaccination [13][14][15][16].…”

Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics

“…No mask requirement was clearly rejected. Other preference studies show similar results [ 4 , 42 ]. Compared with the number of preference studies on vaccine acceptability, there appear to be fewer studies examining and evaluating the acceptability of non-pharmaceutical interventions in the general population [ 2 , 21 , 42 , 46 ].…”

“…This is evident in the different setups of preference studies on corona pandemic interventions and its potential effects on the economy and society. Decision context in preference studies ranges from mainly measure-related attributes and levels such as “stay-at-home orders” [ 42 ] to attributes and levels that represent exclusively pandemic indicators such as “number of new cases” [ 20 ]. Although some of the attributes and levels are similar to those used in other studies, the present study attempted to create a more holistic approach that considers interventions and effects of interventions equally.…”

Service-level barriers to and facilitators of access to services for the treatment of alcohol use disorder and problematic alcohol use: protocol for a scoping review

“…14 27 However, little is known about how the COVID-19 pandemic may affect preferences for the utilisation of SUHSs for the treatment of AUD or PAU. The available literature on individual preferences in the context of the pandemic has focused almost entirely on vaccinations, [28][29][30][31] public health measures [32][33][34] and resource allocation. 35 We propose to conduct a scoping review to address the gap related to individual preferences for SUHSs for the treatment of AUD/PAU by identifying characteristics of the services that affect access to them, based on the perspectives of individuals with current or past AUD or PAU and those who deliver treatment services (eg, clinicians, other care providers and administrators).…”