“Shelter at Home, if You Can:” Community Vulnerability and Residential Sequestering During the Coronavirus Pandemic of 2020

ABSTRACT

This study examines the effects of community vulnerability on residential sequestering across counties in the United States. Powerlessness and racialized politics are two hypothesized reasons for why community vulnerability affects social distancing behavior. Powerlessness is tied to the socioeconomic disadvantages of places, which intertwines with politics and race to produce a stratified response to the pandemic. We examine these dynamics with analyses that account for the disease epidemiology and other demographic factors. Data come from multiple sources, including Google’s Mobility Reports and Cuebiq’s Mobility Insights. Growth curve analyses find that socioeconomic disadvantage, political orientation, and racial composition independently explain the rate of change in mobility and peak residential sequestering levels during the initial outbreak. These conceptually separate dimensions of community vulnerability operate in concert, rather than as substitutes or as competing explanations, to impact the behavioral response to COVID-19.

KEYWORDS:

• Disasters
• hazard risks
• health disparities
• social distancing
• social inequality

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1. Although most protests that were covered on the nightly news came after the peak in residential sequestering in the beginning of April, it is important to note that the social conditions that lead to the protest had been bubbling under the surface beginning in the late winter with the killings of Ahmaud Arbery, Daniel Prude, and Breonna Taylor. The summer protests was a manifestation of a general distrust in mainstream institutions that during the initial stages of the pandemic fostered a disregard of public health recommendations. From this perspective, the fear of authorities taking lives and livelihoods maybe greater for all types of disadvantaged communities than the fear of the disease.

2. Hawaii and Alaska are included but not shown. Puerto Rico is not included.

3. The organizations are identified in the County Business Patterns data by NAICS codes 813410, 713950, 713910, 713940, 711211, 813110, 813940, 813930, 813910 and 813920.

4. Over the course of this research we tried several approaches to operationalizing racial context, including a focus that isolated Black counties. Using percentage Black produced larger racial effects than the more inclusive measure we adopted for the final analysis. This difference does not alter the stratified order in Table 3 nor any of the substantive conclusions. The current approach provides optimal model fit via AIC criteria, and better coverage across different political contexts.

5. We use two standard deviations throughout the analysis to provide a range that captures 95% of the model implied county-level difference in effects. These reported effects should be viewed as plausible minimum or maximum values.

6. Exploratory analysis suggests a combination of time-varying effects in the Cuebiq data are responsible for this reversal, most notably the spatial lag and the weekend indicator variable, which also operates differently in the Cuebiq data than the Google data.

7. When racial composition is based off percentage of the county population that is Black, the effect for Black counties on the intercept and slope are −6.2 and −1.7, respective; the effects for predominantly white are similar. Model 3 then explained an additional 11.7% of the variation across counties in the rate of change and an additional 2.8% of variance around the level of sequestering at the peak over the variance explained in Model 2.

Additional information

Notes on contributors

Jeremy Pais

Jeremy Pais is an Associate Professor in the Department of Sociology at the University of Connecticut. A primary area of his research focuses on the social stratification dynamics that shape community vulnerability to natural disasters and environmental hazards.

Andrew Deener

Andrew Deener is a Professor of Sociology at the University of Connecticut.  His research focuses on urban inequality, organizations, infrastructure, and the environment.

Mary J. Fischer

Mary J. Fischer is an Associate Professor in the Department of Sociology at the University of Connecticut at Storrs.  Her research examines the relationship between geographic context and social inequalities, with a focus on neighborhoods and homeownership.

Zachary D. Kline

Zachary D. Kline is a graduate student at the University of Connecticut. His work models how social and community contexts constrain behavior in unequal ways. His dissertation focuses on market-based social welfare programs, with current projects examining the Affordable Care Act, retirement early withdrawal, housing vouchers, and school choice.