Hyper Segregation

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Hyper segregation occurs when a race/ethnic group is highly segregated in multiple ways, no matter how segregation is conceptualized or measured. It is an explicit recognition of the fact that segregation by race is a complex phenomenon that is multidimensional in nature. First used in 1989 in an article by Massey and Denton about patterns of black-white segregation in large US metropolitan areas in 1980, the term now occurs in both the academic and popular literature to describe the extremely high segregation experienced by African Americans, Hispanic Non-White, Native Americans, and Asian Americans in the US. Conceptually, hyper segregation occurs when a group has high segregation scores on four or five different dimensions of segregation. The first dimension is evenness: the extent to which all the neighborhoods in a metropolitan area show the same distribution of groups as the total area. Thus, if an area is 20 percent black and 80 percent white, there would be no segregation if each neighborhood had that racial distribution as well.

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Evenness is measured by the Index of Dissimilarity (D), the most commonly used measure of segregation. The next dimension is isolation: the extent to which a group shares its neighborhoods with only members of its own group. While evenness looks at distributions across all neighborhoods in a city or metropolitan area, isolation provides the view from within neighborhoods. A group may live in only a subset of the neighborhoods in a city, but if those neighborhoods are relatively integrated the group has contacts outside their group and their segregation is not as severe as when their neighborhoods are occupied only by their own group. The third dimension, concentration, refers to the relative proportion of the total land area a group occupies, relative to the group’s size. This dimension addresses the issues of crowding, population density, and the advantages associated with housing on spacious suburban lots. Centralization, the fourth dimension, measures how close to the central business district a group resides. In the past, the central business district was not a desirable place to live because of the presence of factories, and in more recent years it reflects the disadvantage associated with not living in the suburbs, where many jobs are now located. The last dimension of segregation, clustering, looks at whether the neighborhoods where a group lives are themselves clustered into one large area or are scattered throughout the metropolitan area. It addresses the aspect of whether a group member, regardless of the composition of their neighborhood of residence, interacts with non-group members if they leave their neighborhood.

In hyper segregated metropolitan areas, black neighborhoods tend to form large contiguous ghettos. In summary, to define hypersegregation requires three decisions: first, which index will be used to measure each of the five dimensions; second, what value of each index will be considered high; and third, on how many of the five dimensions must a group be highly segregated to be called hypersegregated. Choices on each of these are made based on both the extant literature and the judgment of the researchers: in short, there is no correct choice, and changes will yield different lists of hyper segregated places and groups. While this may at first seem to imply that hypersegregation is an arbitrary idea, what it really reflects is that segregation is a continuous variable. Furthermore, as will be seen when specific hypersegregated places are discussed below, varying these choices does not dramatically change the overall pattern of results. Economics plays a huge role in the hypersegregation of the Education system. Segregation by income very often moves in tandem with segregation by race. In addition to attending racially segregated schools, black and Latino students are significantly more likely to attend high-poverty schools.

The Civil Rights Project at the University of California, Los Angeles, calls this phenomenon double segregation. Educational disparities between lower- and higher-income students have noticeably widened in recent years. In fact, income-based disparities among students are now larger than racial disparities, and low-income children are 15 percent less likely to graduate from high school than their high-income peers. The causes of this gap are many and well-documented. Many low-income students encounter a host of disadvantages outside of school that are likely to affect their educational achievement. For instance, low-income students are less likely to benefit from parents with postsecondary degrees. Studies have shown that the mother’s education level strongly predicts the achievement of the child, and among low-income families, the mother’s education level usually does not exceed a high school diploma. Children living in low-income neighborhoods also have increased exposure to hardship in their communities. These communities tend to lack access to meaningful job opportunities and face chronic unemployment.

As a consequence, members are more likely to be distressed by mental health challenges, substance abuse, crime, and high levels of incarceration. Furthermore, residents of these communities are also excessively exposed to pollutants and environmental hazards. The trauma associated with all of these conditions poses serious negative consequences for a child’s well-being and brain development. But while family and community factors are strong predictors of student achievement, school-level factors matter as well. In fact, in 1966, James Coleman, an American sociologist and researcher, released a report that studied more than 650,000 students nationwide and found that the level of student poverty in a school is the single most determinative school-level factor in a student’s academic achievement. Since the Coleman report, study after study has shown that low-income children who attend high-poverty schools fare worse than low-income children who attend low-poverty schools.

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Hyper Segregation. (2019, Nov 12). Retrieved February 3, 2023 , from

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