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This paper investigates one of the potential costs of rising segregation in American cities by evaluating empirically the extent at which ethnic-based segregation contributes to the onset and the speed of propagation of the COVID-19 pandemic.

Regression analysis based on matched data on early incidence of COVID-19 cases, segregation and covariates. Identification resorts on variations in segregation across MSAs and heterogeneity in the geography and timing of stay-at-home orders.

One cross-MSA standard deviation increase in segregation leads to a significant and robust rise of COVID-19 cases of 8.7 per 100,000 residents across urban counties.

Combines spatial data on COVID-19 cases and segregation; use of a new segregation measure; focus on early incidence of the pandemic and its drivers.

The paper aims is to see why social conflict caused by class segregation did not occur in Gading Serpong? What factors prevent conflict from occurring? This research seeks to find the causes of the nonoccurrence of social conflict due to class segregation in the Gading Serpong cluster area and explore the factors that restrain conflict there.

This research is qualitative research with data collection techniques through in-depth interviews with several parties identified as brokerages in the research object area. In this context, one of the media and analytical tools is to recognize agents or brokers who connect two groups of people. Brokerage occurs in sectors, patterns or forms of informal, personal relationships; to understand it, one must pay close attention to micro-level relationships and social psychological processes. However, brokerage can have a significant impact on macro-level social relations, as it is generally associated with social integration processes.

The lack of involvement of developers in overcoming social conflicts that occur between Gading Serpong natives and migrants in Gading Serpong housing has given rise to new actors. These new actors are what we can call brokers, where they have a role as brokers who are able to connect between migrants and natives in the Gading Serpong area. The broker phenomenon is actually familiar in academia, where in practice the broker acts as someone who is able to find solutions to problems. The broker is the reason even social segregation is created between migrant citizens and native citizens in Gading Serpong but never becomes a conflict between them.

Even if the brokerage phenomenon is the reason why there is no conflict over social segregation brokerage is not the only factor in this nonconflict segregation. Therefore, to cover the larger area of these suburban segregation problems, there must be further research on this topic.

The practical implication of this research is to encourage the housing developers that create urban housing, such as clusters or other gated communities, to evaluate the social factors, such as potential segregation and conflict management. Also to encourage the developers to get involved and create some social engineering systems, like brokerage, market and other social agents, to create some nonconflict segregation or even more inclusive communities.

This research is uncovering the main reason why social segregation between migrant and native people in Gading Serpong, which could potentially lead to conflict, is never a conflict. The main reason is social actors like brokerage.

This paper defines local segregation measures that are sensitive to status differences among organizational units. So far as we know, this is the first time that status-sensitive segregation measures have been offered in a multigroup context with a cardinal measure of status. These measures allow researchers to aggregate employment gaps of a target group by penalizing its concentration in low-status occupations. They are intended to complement rather than substitute for previous local segregation measures. The usefulness of these tools is illustrated in the case of occupational segregation by race and ethnicity in the United States.

Purpose – We propose the Information Theory of Segregation, which holds that all measures of segregation and of inequality are united within a single conceptual framework. Accepting this framework implies that all measures of inequality can also be used to measure segregation and that all measures of segregation are fundamentally based on measures of inequality.

Methodology – We state several propositions that follow from the information theory perspective, and show mathematically that many common measures of inequality and segregation satisfy the propositions.

Findings – We show that all common measures of inequality can be used to form measures of segregation and that the resulting measures can be applied to binary, polytomous, and continuous variables. Further, we develop several new measures, including a Gini Segregation Index (GS) for continuous variables and Income Dissimilarity Index (ID), a version of the Index of Dissimilarity suitable for measuring economic segregation. We show that segregation measures can easily be adapted to handle persons of mixed race, and describe the Non-Exclusive Index of Dissimilarity (NED) and the Non-Exclusive Entropy Index of Segregation (NEH). We also develop a correction for structural constraints on the value of segregation measures, comparable to capacity constraints in a communications channel, which prevent them from reaching their theoretical maximum or minimum value.

Originality – Placing inequality and segregation measures in a common framework is useful for several reasons. It highlights a common mathematical structure shared by many different segregation measures, and it suggests certain useful variants of these measures that have not been recognized previously.

Models of race-based segregation establish that individual characteristics or housing market attributes are complementary causes of the observed level of races’ concentration inside an urban space. The goal of this work is to establish which variables, and in which order of magnitude, among individual characteristics, housing features, and local amenities correlate with immigrants’ segregation, in the case of consistent within-city immigrants’ mobility. We capture the degree of segregation for different immigration groups by a local concentration statistics that is directly obtained from segregation curves, and we use data on the Verona Municipality as a case study. We find strong evidence in favor of the role of the housing market and housing ownership distribution across city areas.

Purpose – This chapter presents information about the residential patterns and reported segregation or discrimination of Latinos in the greater Washington, DC, metropolitan region. The author provides definitions, associated concepts, causes and consequences, selected data findings, and a historical and demographic overview of the Latino population in the region.

Methodology/approach – A literature review of scholarly articles from the social sciences, policy reports, census data, and other public use data, and other publications.

Findings – Data from the Harvard University DiversityData Project (2012) reveals evidence of Hispanic residential segregation throughout the Washington, DC, metropolitan region. In addition, Hispanic children are more racially isolated, have less exposure to Whites, and are more densely populated and residentially clustered in the region.

Research limitations/implications (if applicable) – This chapter does not present new research or original evidence about residential patterns, residential segregation, or housing discrimination among Latinos in the greater Washington, DC, metropolitan region.

Practical/social implications – The prevalence of residential discrimination, segregation and its impact on the restricted residential patterns, social mobility, and isolation of Latinos is a regional and national social problem. The greater Washington, DC, region will continue to receive Latino newcomers who will disperse into areas where they have not resided before. The ways in which they and their families are received and treated by their neighbors can provide context into race relations in a so-called post-racial America.

Originality/value of chapter – The residential patterns of Latinos in the greater Washington, DC, metropolitan region and evidence of the segregation and discrimination they have encountered caution us to examine how segregation perpetuates disadvantage, inequality, racialization, social distance, and other kinds of discrimination. Whether residential segregation is voluntary or involuntary, its remnants are a visceral force that cannot be ignored.

Purpose – The Gini coefficient is a widely used measure of income inequality. It has also been used as a segregation measure, but only in the case of binary variables, for example race or gender. We develop a general version of the Gini Segregation Index (Gs) that can accommodate either continuous or binary variables, and discuss its relationship to existing measures.

Methodology – The Gini Index of Segregation is developed graphically and derived mathematically, illustrating the relationship between Gini's use in segregation and inequality applications.

Findings – Using the Public Use Microdata Sample for 25 U.S. metropolitan areas from the 2000 Census, we illustrate the calculation of the index and show that it is highly correlated with an existing measure of economic segregation.

Originality – This paper develops and illustrates a measure of segregation for continuous variables, a task for which there are few alternative measures.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

Scholarship has demonstrated important consequences of segregation on health and health care outcomes, yet the mechanisms behind this association remain poorly understood. Several recent studies have shown inequities in the distribution of a wide variety of health-related organizations across urban neighborhoods, which may account for some portion of this negative health association. Though, within this literature, relatively little attention has been given to the distribution of health care facilities in particular.

Here, I consider how segregation is related to the distribution of several auxiliary health care practitioners in a series of spatial regression models of zip codes across the United States using data from the 2010 US Census and County Business Patterns (CBP).

I find that both Black and Latino segregation is negatively related to the density of a number of auxiliary health care practitioners, including mental health providers, dentists, physical/occupational/speech therapists, chiropractors, optometrists, podiatrists, and miscellaneous health care practitioners. However, this association is reduced (in certain instances to non-significance) with the inclusion of socioeconomic indicators, chiefly the percent of college educated individuals and the unemployment rate of the zip code. This is association is reduced for both Black and Latino segregation, with a larger reduction in the size of the effects for Latino segregation.

This research suggests that segregation plays an important role in the distribution of health care facilities, but that policy and public health interventions should focus on the intersection between racial residential segregation and socioeconomic considerations.