Racial and Ethnic Diversity—Why Not Measure It? Diversity in Large Urban Areas in the U.S., 1980-2010

Background While postwar suburban migration established suburbs as relatively affluent, homogeneous white enclaves distinct from the urban core, recent waves of suburbanization and exurbanization have been spurred largely by rapid growth in the nonwhite population. While these increases in suburban racial/ethnic diversity represent a significant evolution of the traditional “chocolate city, vanilla suburbs” dichotomy, scholars have expressed concern that they are worsening racial/ethnic segregation among suburban public school students. Objective In this study, we document shifts in the racial imbalance of suburban schools in terms of several racial/ethnic and geographic dimensions (i.e., multiracial, black–white; between and within suburban districts, among localities). In addition, we extend the urban/suburban dichotomy to provide initial evidence on changes in racial balance in metropolitan exurbs. Finally, we use inferential models to directly examine the impact of changes in racial/ethnic diversity on shifts in racial imbalance. Research Design Using demographic data from the National Center of Education Statistics Common Core of Data on 209 U.S. metropolitan areas, we provide a descriptive analysis of changes in segregation within and between urban, suburban, and exurban localities from 2002 to 2012. We measure segregation using Theil's entropy index, which quantifies racial balance across geographic units. We assess the relationship between demographic change and change in segregation via a series of longitudinal fixed-effects models. Results Longitudinal analyses indicate that increases in racial/ethnic diversity are positively related to change in racial imbalance. However, observed increases in diversity were generally insufficient to produce meaningful increases in segregation. As a result, suburbs and exurbs, like urban areas, experienced little change in segregation, although trends were generally in a negative direction and more localities experienced meaningful declines in segregation than meaningful increases. Findings are less encouraging for suburbs and exurbs than for urban areas and underscore the intractability of black-white racial imbalance and the emerging spatial imbalance of Asians and whites. We also document an important shift in the geographic distribution of segregation, with suburbs now accounting for a plurality of metropolitan segregation. Conclusions Contrary to previous researchers, we do not find evidence that suburban and exurban schools are resegregating, although we fail to document meaningful progress towards racial equity. Moreover, while suburbs are not necessarily resegregating, we find that segregation is suburbanizing, and now accounts for the largest share of segregation of any locality. We conclude with a discussion of recommendations for policy and research.

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Pandemic Injustice: Spatial and Social Distributions of COVID-19 in the US Epicenter

Journal of Extreme Events ◽

10.1142/s234573762150007x ◽

2021 ◽

pp. 2150007

Author(s):

Timon McPhearson ◽

Zbigniew Grabowski ◽

Pablo Herreros-Cantis ◽

Ahmed Mustafa ◽

Luis Ortiz ◽

...

Keyword(s):

New York ◽

Low Income ◽

Social Vulnerability ◽

Urban Areas ◽

Social Dynamics ◽

Negative Relationship ◽

Policy Debate ◽

The Social ◽

Vulnerability Indicators ◽

The U.S

We examine the uneven social and spatial distributions of COVID-19 and their relationships with indicators of social vulnerability in the U.S. epicenter, New York City (NYC). As of July 17th, 2020, NYC, despite having only 2.5% of the U.S. population, has [Formula: see text]6% of all confirmed cases, and [Formula: see text]16% of all deaths, making it a key learning ground for the social dynamics of the disease. Our analysis focuses on the multiple potential social, economic, and demographic drivers of disproportionate impacts in COVID-19 cases and deaths, as well as population rates of testing. Findings show that immediate impacts of COVID-19 largely fall along lines of race and class. Indicators of poverty, race, disability, language isolation, rent burden, unemployment, lack of health insurance, and housing crowding all significantly drive spatial patterns in prevalence of COVID-19 testing, confirmed cases, death rates, and severity. Income in particular has a consistent negative relationship with rates of death and disease severity. The largest differences in social vulnerability indicators are also driven by populations of people of color, poverty, housing crowding, and rates of disability. Results highlight the need for targeted responses to address injustice of COVID-19 cases and deaths, importance of recovery strategies that account for differential vulnerability, and provide an analytical approach for advancing research to examine potential similar injustice of COVID-19 in other U.S. cities. Significance Statement Communities around the world have variable success in mitigating the social impacts of COVID-19, with many urban areas being hit particularly hard. Analysis of social vulnerability to COVID-19 in the NYC, the U.S. national epicenter, shows strongly disproportionate impacts of the pandemic on low income populations and communities of color. Results highlight the class and racial inequities of the coronavirus pandemic in NYC, and the need to unpack the drivers of social vulnerability. To that aim, we provide a replicable framework for examining patterns of uneven social vulnerability to COVID-19- using publicly available data which can be readily applied in other study regions, especially within the U.S.A. This study is important to inform public and policy debate over strategies for short- and long-term responses that address the injustice of disproportionate impacts of COVID-19. Although similar studies examining social vulnerability and equity dimensions of the COVID-19 outbreak in cities across the U.S. have been conducted (Cordes and Castro 2020, Kim and Bostwick 2002, Gaynor and Wilson 2020; Wang et al. 2020; Choi and Unwin 2020), this study provides a more comprehensive analysis in NYC that extends previous contributions to use the highest resolution spatial units for data aggregation (ZCTAs). We also include mortality and severity rates as key indicators and provide a replicable framework that draws from the Centers for Disease Control and Prevention’s Social Vulnerability indicators for communities in NYC.

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