COVID‐19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages

Abstract

Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real‐world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real‐world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay‐at‐home percentage, miles traveled per person, trip rate, work trip rate, and non‐work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: “best performance,” “effort,” and “consistency.” We found that for all 18 indicators, high‐income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low‐income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real‐world data and offers compelling perspectives for future discussions.

Unique times, unequal mobilities: Daily mobility during the de‐escalation of the COVID‐19 pandemic

“…Yet, their mobility was mainly the result of commuting, and they were equally likely to leave their homes for care and self‐realization as the rest of the population. This detail is important, yet it is missing in studies that use aggregated mobility data or big data and do not account for different mobility types (Lee et al, 2020, 2021; Sun et al, 2022). We cannot state that people with higher SES complied with NPIs more or less than others, as we found no significant differences across SES groups with the exception of commuting, which was allowed by the law.…”

“…Scholars have reported inequalities in the ability to comply with NPIs, showing that richer states (M. Lee et al, 2020), regions (Pullano et al, 2020), health areas (Lee et al, 2021), municipalities (Dokhov & Topnikov, 2021), neighbourhoods (Checa et al, 2020) and citizens (Bracarense & Oliveira, 2021) around the world reduced their mobility levels more than their poorer counterparts. In some cases, people with higher SES also started reducing their mobility before the enactment of the NPIs (Checa et al, 2020; Lee et al, 2021; Sun et al, 2022), suggesting not only that they had access to cultural capital that allowed them to recognize the implications of the spread of the pandemic earlier than the rest (Checa et al, 2020) but also that their jobs were more easily adaptable to working from home (Angell & Potoglou, 2022).…”

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments

“…For example, Shamshiripour et al (2020a) studied the spatial-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, and characterized the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics and found that more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. Sun et al (2020) examined the difference between communities in the US responses to mobility interventions, specifically focusing on income levels, and found that the high-income individuals' social distancing behavior could be improved in various situations. Xiong et al, 2020a , Xiong et al, 2020b developed a statistical model to examine the change in mobility inflow across the nation as well as the time-varying relationship between mobility and infection.…”