Still the Linchpin: Segregation and Stratification in the USA

Abstract

High levels of black residential segregation emerged over the course of the twentieth century as the black population urbanized. Segregation was achieved by means of different mechanisms at different times and places, beginning with targeted violence directed at African Americans followed later by discrimination in real estate and banking using devices such as deed restrictions, restrictive covenants, and racial redlining, practices that were institutionalized in federal policies during the New Deal. By 1977, however, discrimination had been outlawed in most US markets and average black segregation began to decline. The declines, however, were inversely proportional to the size of the black community. As Latinos grew in number after the 1970s, levels of racial isolation within Hispanic neighborhoods also rose. At the same time, class segregation increased, especially among families with children, and inequalities of wealth and income grew to create a polarized urban geography. High concentrations of affluence now prevail for affluent whites and Asians living in wealthy post-industrial coastal areas, with high concentrations of poverty prevailing for poor blacks and Hispanics in older, declining industrial areas in the Midwest and the South, which also contain pockets of white and Asian poverty. The new political geography of race and class effectively denies blacks and Hispanics their access to quality education and undermines their lifetime earning prospects to create self-perpetuating system of social and economic stratification.

More than forty years ago, Pettigrew (1979) identified black residential segregation as “the structural linchpin of modern race relations,” arguing that the spatial separation of African Americans from whites was the core societal feature by which racial stratification was created and maintained in the USA. Although average levels of black–white segregation have moderated over the ensuing decades, the declines have been uneven and black segregation has by no means disappeared. Indeed, in some metropolitan areas, it remains extreme, and in other areas Hispanic segregation has risen substantially. Throughout the USA, class segregation has risen (Reardon et al. 2018) and the spatial isolation of both the poor and the affluent has increased (Massey and Rugh 2020). Segregation thus remains an important nexus in America’s system of socioeconomic stratification.

Stratification is a process that produces inequalities in access to important societal resources and it is achieved through two basic social mechanisms: opportunity hoarding and exploitation (Tilley 1998). The former is enacted through social structures and attendant processes that limit the access of outgroup members to resources controlled by the ingroup. The latter is achieved by means of institutional arrangements and practices that compel outgroup members to work for less than the value added of their labor, thereby creating a surplus that can be commandeered by the ingroup. These structural arrangements may be formal or informal and historically they have been deployed across human societies to stratify people along the lines such as race, class, ethnicity, gender, sexuality, and religion.

Whenever ingroup and outgroup members in a stratified society are spatially segregated from one another, processes of exclusion and discrimination are greatly facilitated because the targeted individuals are conveniently concentrated in space, thereby rendering opportunity hoarding and exploitation easy and efficient (Massey and Denton 1993). Residential segregation acts as a kind of spatial glue that holds a stratification system together and intensifies its effects (Massey 2016; Sewell 2016). Segregated societies tend to generate sharp differentials in status and well-being between spatially separated social groups; however, they are defined.

Creating the Black Ghetto

Although the social mechanisms to achieve segregation may be applied to any socially defined group, no community in the USA has ever experienced the degree of residential segregation visited upon African Americans during the twentieth Century. In 1900 the vast majority of African Americans still lived in the rural south where they were subject to the strictures of de jure social segregation though not necessarily separated spatially from their white oppressors (Grigoryeva and Ruef 2015; Logan 2017). In the ensuing decades, African Americans in the rural south migrated in droves to industrial cities in the Northeast, Midwest, and West (Wilkerson 2010). In response, city councils throughout the country began to pass laws to set aside separate neighborhoods for black and white residents, in effect creating a formal apartheid system. In 1917, however, the legal foundations of legally mandated residential segregation were struck down by the US Supreme Court, which held that municipal-level apartheid laws were unconstitutional (Rice 1968).

In the wake of this decision, the entry of black migrants into white urban spaces was met by a rising tide of violence, culminating in the Great Chicago Race Riot of 1919 (Hartfield 2018). Shocked by the wanton destruction of lives and property, the real estate industry stepped in to institutionalize private discrimination in housing markets and assert control over processes of neighborhood racial change (Massey and Denton 1993). In 1924, the National Association of Real Estate Brokers adopted a code of ethics stating that “a Realtor should never be instrumental in introducing into a neighborhood… members of any race or nationality… whose presence will clearly be detrimental to property values in that neighborhood” (Helper 1969, p. 201).

In 1927, the Chicago Real Estate Board went further and devised a racial covenant model that could be copied for use in other metropolitan areas. A covenant is a private contract in which property owners within a defined geographic area collectively agree not to rent or sell homes to African Americans (Philpott 1978). Once approved by a majority of whites in a covered area, the contract becomes enforceable in civil court. As a result of covenants and other practices devised by the real estate industry, racial segregation steadily increased over the twentieth century as the residential color line hardened throughout urban America (Lieberson 1980).

With the onset of the Great Depression in 1929 and US entry into the Second World War in 1941, the federal government became centrally involved in the US political economy, and thus in the perpetuation of black segregation (Massey and Denton 1993). The loan programs sponsored by the Federal Housing Administration and the Veterans Administration required mortgaged properties to be covered by racially restrictive covenants, and the agencies recommended the use of Residential Security Maps to assess the credit worthiness of neighborhoods (Katznelson 2005). These maps color coded as red all areas in or near any black neighborhood, thus rendering them ineligible for FHA or VA loans. In this way, the federal government supported the institutionalization of the practice known as “redlining.” The FHA’s Underwriting Manual justified its actions by claiming that “if a neighborhood is to retain stability, it is necessary that properties shall continue to be occupied by the same social and racial classes” (Jackson 1985, p. 208).

Black neighborhoods in central cities expanded geographically during the postwar era as white urban dwellers migrated en masse to the burgeoning suburbs, and in response white political and business elites turned to the government for help in shoring up the walls of segregation. Using federal urban renewal and public housing programs, white leaders strategically razed and redeveloped neighborhoods to block ghetto expansion into threatened white districts (Hirsch 1983; Bauman 1987). With federal support, public and private discrimination against African Americans in real estate and banking kept blacks highly segregated from whites well into the civil rights era. Racial discrimination in the rental or sale of housing was not outlawed until 1968; mortgage discrimination against black borrowers was not prohibited until 1974; and discrimination against black neighborhoods in lending (redlining) was not barred until 1977 (Metcalf 1988).

Segregation After the Civil Rights Era

More than legislation changed during the civil rights era, however. White racial attitudes also shifted significantly. Whereas upwards of 60% of white respondents to nationally representative surveys in the early 1960s insisted they had the right to keep blacks out of their neighborhoods, by the late 1980s, the share had dropped to 13% (Schuman et al. 1997). Although white support for segregation in principle may have waned, however, white tolerance for interracial contact in practice remained quite limited. The willingness of whites to enter or remain in a neighborhood declines sharply as the percentage of black neighbors rises (Charles 2003; Emerson et al. 2001). Despite declining support for segregation in principle, negative stereotyping of African Americans persisted, driving white aversion to black neighbors and opposition to race conscious civil rights policies (Bobo et al. 2012).

This new configuration of white attitudes—rejecting segregation in principle but remaining averse to the presence of blacks in practice—led to a characteristic pattern of change in racial segregation after the civil rights era. Specifically, in metropolitan areas where blacks were few in number, desegregation occurred since even with the equal distribution of blacks across neighborhoods whites would encounter few black neighbors. In areas with large black communities, however, residential integration would necessarily bring about exposure to sizeable numbers of black neighbors, exceeding the tolerance limits of most whites and thus retarding movement toward integration.

The new geographic configuration of racial segregation is demonstrated in Fig. 1, which draws on black–white dissimilarity indices computed by Rugh and Massey (2014) for 287 metropolitan areas from 1970 to 2010. The dissimilarity index varies from 0 to 100 and represents the relative share of black and white residents who would have to exchange neighborhoods (census tracts) to achieve an even residential distribution in which every neighborhood replicates the percentage of blacks in the metropolitan area as a whole.

Fig. 1
figure1

Range of black segregation trends for 287 MSAs by black population size and black percentage in 2010

By convention, index values of 60 or greater are considered “high,” while those below 30 are deemed “low” and values in-between are defined as “moderate.” In the figure, these two thresholds are indicated by dashed lines. The range of trends in black–white segregation is indicated by the bold lines at the top and bottom of the figure, which depict trends for the ten most- and ten least segregated areas in 2010. Between these two extremes, trends in segregation are presented separately by the size of the black population, dividing metropolitan areas into four ranked categories: those with black populations greater than 500,000 (with an average black percentage of 21.4%), those with black populations of 100,000 to 500,000 (average black percentage 20.0%), those with black populations of 10,000 to 100,000 (average black percentage 13.3%), and those with small black populations under 10,000 (average black percentage 2.9%).

As can be seen, in 1970, African Americans were highly segregated across all US metropolitan areas, regardless of black population size. Dissimilarity index values ranged from 64.4 among the least segregated areas to 84.3 among the most segregated areas. Thereafter segregation levels increasingly diverge until by 2010 the least segregated areas display an average dissimilarity index of just 21.1, well into the low range, whereas in the most segregated areas the index value remains extreme at 76.0. In metropolitan areas with black communities of 500,000 or more, segregation levels declined somewhat, but still remain well within the high range, beginning at 81.8 in 1970 and ending at 65.8 in 2010. For areas with black communities of 100,000 to 500,000 persons, the decline was greater, going from 73.8 to 55.5. For black communities of 10,000 to 100,000 persons, the decline was more substantial, with dissimilarity from 68.0 to 49.5, and for those under 10,000 persons, the decline was greatest, dropping from 65.8 to 37.2.

In other words, consistent with the shift of white racial attitudes toward aversive racism rather than principled racism (Dovidio and Gaertner 1986), the potential for desegregation in the post-civil rights era has depended on the size of the black community. Desegregation occurs when the number of blacks is such that integration entails little actual contact with black neighbors, and the more potential black neighbors in a metropolitan area the less the observed integration. Of course, residential segregation entails more than just unevenness in the distribution of blacks and whites across neighborhoods.

In addition to the aforementioned dimension of unevenness (measured by the dissimilarity index D), Massey and Denton (1988) identified four additional geographic dimensions: isolation (measured by the P* isolation index—see Lieberson 1981), clustering (measured by the spatial proximity index SP—see White 1986), centralization (measured by the CE index—see Duncan 1957), and concentration (measured using an index developed by the authors). In plain English, isolation is the degree to which blacks live in neighborhoods surrounded by other black residents. Clustering is the degree to which black neighborhoods adhere together in space rather than being scattered around as on a checkerboard. Centralization is the degree to which black neighborhoods are located close to the center of an urbanized area and concentration is the relative amount of physical space blacks occupy in the metropolitan landscape.

Having defined these dimensions, Massey and Denton (1989) discovered that in a subset of metropolitan areas in 1980, African Americans experienced a particularly intense form of segregation characterized by high scores (60 or greater) across multiple indices simultaneously. They coined the term “hypersegregation” to describe metropolitan areas in which blacks were highly segregated on at least four of the five geographic dimensions (Massey and Denton 1989). According to this criterion, as of 2010, a third of all black metropolitan residents continued to live under conditions of hypersegregation (Massey and Tannen 2015). An additional 21% lived under conditions of merely high segregation (D > 60), while 46% experienced moderate segregation.

The persistence of black segregation in part reflects their low degree of suburbanization relative to whites. In 2010, only 40% of metropolitan black residents lived in suburbs compared with 63% of whites. Moreover, although black suburbanites experienced lower segregation levels than city dwellers, they were still far more segregated than Hispanics or Asians in suburbs, and in central cities, black residents continued to experience high average levels of segregation (Massey and Tannen 2017). Whether in cities or suburbs, rising socioeconomic status does not bring blacks the same level of residential integration as it does for other minority groups. Even though black residential segregation by 1990 had come to decline with rising income, as of 2010, the most affluent African Americans were still more segregated from whites than the poorest Hispanics or Asians (Intrator et al. 2016).

The Continuing Causes of Segregation

Segregation has traditionally been seen as arising from a residential search process in which rational, well-informed individuals act on their preferences to search for and select a home in a neighborhood that best suits their needs and desires, subject to the constraints of income of course, but for African Americans decisions are also constrained by racial discrimination as well as the prejudicial actions of other market participants. Krysan and Crowder (2017) criticize this conceptualization and question the relevance of rationality, information, income, preferences, and discrimination in shaping locational decisions in the real world. Instead, they posit a “social structural sorting perspective” that views residential searches as embedded within racialized social, economic, cognitive, and spatial structures that serve to reproduce existing levels and patterns of segregation.

Specifically, they posit a two-stage model in which people first select a small subset of neighborhoods to focus their search, and then move on to consider homes within this limited choice set. The first stage is critical because actors generally lack full information about the range of neighborhoods available to them and therefore tend to rely on heuristics rather than reason in choosing where to look. A heuristic is a cognitive shortcut that individuals deploy to simplify decision-making. In residential searches, especially, people tend to fall back on the “correlated characteristics heuristic” in which a single salient neighborhood trait—in this case racial composition—is used to represent an area’s acceptability as a potential destination.

In the minds of whites, the mere presence of blacks denotes lower property values, higher crime rates, and struggling schools, irrespective of what the objective neighborhood conditions actually are (Quillian 2014). Although whites say they would welcome the presence of black neighbors, in practice they avoid neighborhoods containing more than a few blacks and confine their search to overwhelmingly white residential areas exhibiting white percentages well above those they offer in describing their “ideal” neighborhood to survey researchers (Krysan and Crowder 2017).

In sum, although white racial prejudice may have moderated, it certainly has not disappeared. For example, Rugh and Massey (2014) developed an index of anti-black sentiment by tabulating Google search frequencies on the epithet “nigger” across metropolitan areas and found that it strongly predicted an area’s level of black segregation and spatial isolation. In later work, they found that the anti-black prejudice index also predicted the degree to which a city’s suburbs were covered by restrictive density zoning regimes (Massey and Rugh 2018a), which Rothwell and Massey (2009) had earlier identified as an emergent and increasingly powerful cause of racial segregation in the post-civil rights era.

Nonetheless, in keeping with the moderation of white racial attitudes, direct and open discrimination against blacks in housing and lending markets appears to have declined over time (see Turner et al 2013; Delis and Papadopoulos 2019). However, subtler forms of discrimination persist and new modes of exclusion appear to have emerged. Rental and sales agents today are less likely to respond to emails from people with stereotypically black names (Carpusor and Loges 2006; Hanson and Hawley 2011) or to reply to phone messages left by speakers who “sound black” (Massey and Lundy 2001; Massey and Fischer 2004). A recent audit study conducted in Suburban Long Island by the newspaper Newsday (Choi et al. 2019) documented discrimination in 49% of real estate transactions involving African Americans and 39% of those involving Hispanics, compared to a figure of only 19% for Asians.

The continued relevance of discrimination in the production of racial segregation is underscored by the meta-analysis that Lincoln Quillian undertook for this volume. His paper considers results from 16 field experiments in housing discrimination and 19 observational studies of mortgage discrimination conducted since 1970. Although his analysis confirmed that the absolute denial of housing to black and Hispanic clients has indeed declined to relatively low levels, discrimination in the number of units they were recommended and invited to inspect remained significant. He also found that racial differentials in mortgage denial rates and lending costs have barely changed over the past four decades. Thus, despite Krysan and Crowder’s (2017) demonstration that segregation can be generated in the absence of discrimination, Quillian’s analysis clearly shows that discrimination remains a potent force in the US housing and lending markets and continues to contribute to the residential segregation of African Americans in metropolitan America.

The Continuing Consequences of Segregation

Opportunities and resources are always distributed unevenly in space. Some locations have better access to jobs, transportation, services, and safety than others, and therefore, to advance and prosper in a competitive market society people need freedom of mobility. Especially in post-industrial societies where knowledge and information are the principal sources of economic growth and material well-being, investments in human capital are critical. Unfortunately, in the USA, the quality of schools and the education they provide varies sharply from neighborhood to neighborhood and district to district. Given this reality, the restrictions on residential mobility implied by racial segregation necessarily constitute barriers to social mobility.

Parents clearly take school quality into account when making residential decisions. Families with children are more segregated by race and income than others (Owens 2016, 2017) and in recent decades income segregation has increased most sharply among families with children (Reardon et al. 2018). The vast majority of American children continue to attend local public schools that are populated from the surrounding neighborhood, and residential segregation therefore necessarily leads automatically to school segregation. Across US states, black–white segregation by neighborhood explains 61% of the variation in black–white segregation by school district (Massey and Tannen 2016).

In her contribution to this volume, Ann Owens updates the “savage inequalities” in American public education caused by the inherent link between residential and school segregation, long ago documented by Kozol (1992). Whereas white children attend schools that are 74% white or Asian and only a third of the children qualify for free or reduced price lunches, black children attend schools that are 67% black or Hispanic where 63% qualify for subsidized lunches. These differences parallel neighborhood-level data, which show that white children occupy census tracts that are 81% white or Asian and just 11% poor, whereas black children inhabit tracts that are 59% black or Hispanic and 23% poor.

A key consequence of segregation by race and income in the USA is the spatial concentration of poverty and its sequelae in black and Hispanic neighborhoods and the spatial concentration of affluence and its correlates in white and Asian neighborhoods (Massey and Rugh 2018a, 2020). Across all groups—whites, Asians, blacks, and Hispanics—the spatial concentration poverty in 2010 was strongly predicted by the pairing of a high rate of poverty with a high degree of poor-affluent segregation. Among African Americans, however, the concentration of poverty was additionally increased by high levels of black–white segregation paired with a large black population (Massey and Rugh 2020).

At the other end of the socioeconomic spectrum, the spatial concentration of affluence for all groups was predicted by the pairing of a high rate of affluence with a high level of affluent-poor segregation, but among African Americans a high degree of black–white segregation significantly reduced the concentration of black affluence, essentially forcing well-off blacks to share residential space with their less advantaged fellows. As a result, across US metropolitan areas in 2010 the average affluent white resident lived in a census tract that was 45% affluent compared to a figure of only 35% for affluent black residents (Massey and Rugh 2020). At the same time, the average poor black resident lived in a census tract that was 33% poor, while the average poor white resident lived in one that was only 21% poor.

The differential concentration of affluence and poverty across neighborhoods contributes directly to the stagnation of social mobility and rising income inequality in the USA (Chetty 2018a, b). Rothwell and Massey (2015) found that in predicting adult income, the effect of the neighborhood income experienced by individuals in childhood was fully half that of the parental income in childhood. Moreover, when adjustments for regional purchasing power were introduced, the effect of neighborhood income grew to two-thirds of parental income. Rothwell and Massey estimate that lifetime income would have been $635,000 greater if people born into a bottom quartile neighborhood had instead been born into a top quartile neighborhood, figure that rose to $910,000 after adjusting for regional purchasing power.

Very clearly, then, people coming of age in a white neighborhood of concentrated affluence experience very different social worlds and life chances than those growing up in black neighborhoods of concentrated poverty (see Massey and Tannen 2016). Indeed, Sharkey (2013) argues that neighborhood poverty is the principal structural feature of US society most responsible for the stagnation of black socioeconomic status and social mobility in contemporary society. Among African Americans born after the civil rights era, two-thirds grew up in neighborhoods that were more than 20% poor, compared to just five percent of whites born at the same time. In addition, half of blacks born after the civil rights era had lived in the poorest quarter of urban neighborhoods for at least two generations, compared with just seven percent of comparable whites. In Sharkey’s estimation, “the reason children end up in neighborhood environments similar to those of their parents is not that their parents have passed on a set of skills, resources, or abilities to their children.... Instead, parents pass on the place itself to their children’’ (Sharkey 2013, p. 21).

As Fahle et al. demonstrate their paper in this volume, the extremes of racialized neighborhood poverty inevitably extend into America’s public schools. Although exposure to school poverty has steadily increased for whites as well as for black and Hispanic students, marked racial gaps in the degree of exposure to school poverty have persisted, whether measured at the district, metropolitan, state, or national level. Racial segregation in schools is strongly correlated with racial achievement gaps, but this association is mediated entirely by racial differences in exposure to school poverty (Reardon 2016; Reardon, Kalogrides, and Shores 2019a, b. Exposure to school poverty also predicts the rate at which the racial achievement gap grows over years of schooling (Reardon et al. 2019a, b). The central role of racial segregation in stratification is underscored by the fact that racial differences in exposure to school poverty are greatest in declining districts with large minority enrollments.

As mentioned at the outset of this article, segregation renders the stratifying processes of exclusion and exploitation both easy and efficient. This fact is nowhere clearer than in the home mortgage industry. African Americans historically were excluded from access to capital and credit by institutionalized redlining, which over the years produced low rates of home ownership and limited accumulations of black housing wealth (Baradaran 2017). This situation changed markedly during the 1980s and 1990s, however, as black individuals and neighborhoods were increasingly targeted for predatory lending using exploitative high-cost, high-risk financial instruments (Massey et al. 2016; Sewell 2016).

The shift from exclusion to exploitation was triggered by the invention of mortgage-backed securities, in which mortgages were bundled together to back bonds that were then sold to investors, with interest payments covered from the monthly installments sent in by mortgage holders (Steil et al. 2018). In this new system, mortgages were originated not by banks but by independent brokers who sold the loans to a financial firm that bundled the loans to back securities, which were duly assigned a low risk score by bond rating agencies before finally being sold to investors. Everyone along this chain of transactions made money but assumed no risk in the event of mortgage default, a risk that was instead offloaded onto investors who ultimately purchased the bonds.

With the advent of mortgage-backed securities, the number of mortgages that could be issued was no longer limited by the funds a bank had on hand to lend, only by what the market for derivative securities would bear, thereby creating a huge demand for mortgage originations (Massey and Rugh 2018b). Even high-risk mortgages were in demand because financial specialists could combine them with less risky mortgages to create bonds with different levels of risk and rates of return. In this context, black borrowers in black neighborhoods became attractive prospects for high-cost, high-risk loans, which were attractive to lenders because they generated higher fees. Mortgage brokers saw black borrowers as financially unsophisticated, though they often owning homes that could be targeted for predatory lending, yielding a new process of “reverse redlining” (Squires 2005).

The end result was the systematic channeling of blacks, and later Hispanics, into very risky and costly loan products that put their home equity at risk. In their detailed analysis of mortgage discrimination in Baltimore, Rugh, Albright, and Massey (2015) found that during the housing boom of 2000–2008, the monthly payments of black borrowers in black neighborhoods were 6.4% greater than those of comparable white borrowers in white neighborhoods, and that these excess payments accumulated to $1900 from the point of loan origination. With respect to risk, black borrowers in black neighborhoods were 48% more likely than white borrowers in white neighborhoods to be channeled into high-risk loans.

As a result of their exposure to excess costs and higher risks, black borrowers in black neighborhoods were 57% more likely to enter foreclosure and 76% more likely to have their home repossessed than white borrowers in white neighborhoods, placing a total of $3.5 million in black home equity at risk and $2.0 million already lost to forfeiture. Higher class status offered no protection to African Americans, since more affluent black borrowers received even more costly and higher-risk loan packages than the average black borrower. Predation was especially severe for borrowers whose mortgages were securitized privately rather than publicly (by a federal agency such as Fannie Mae or Freddie Mac). For black borrowers in black neighborhoods receiving privately securitization loans, monthly payments were 11.3% greater than comparable white borrowers in white neighborhoods, and the excess payments totaled more than $3300 from the point of loan origination.

A content analysis of testimony provided by persons deposed in federal court cases alleging lending discrimination in mortgage lending revealed evidence of structural discrimination in 76% of declarations sampled (with an inter-rater coding reliability of 86%). Structural discrimination was deemed to exist whenever standard institutional procedures and practices guaranteed that minority borrowers would receive disadvantageous lending products (Massey et al. 2016). In the wake of the mortgage bust, Rugh and Massey (2010) found that black residential segregation was the strongest single predictor of the number and rate of foreclosures across US metropolitan areas.

In the later stages of housing boom, Hispanics were increasingly targeted for predation as well, especially in the booming “sand states” of Arizona, California, Florida, and Nevada (Rugh 2015). Unlike blacks, however, they were vulnerable to foreclosure not only because of the higher costs and greater risks of the loans they received, but also because the massive increase in deportations during the Bush and Obama administrations systematically removed key wage earners from Hispanic households, causing them to miss mortgage payment, enter foreclosure proceedings, and experience repossession (Rugh and Hall 2016).

Between 2004 and 2013, 3.3 million Latinos were deported from the USA and over this period 23% of all Latino homeowners forfeited their homes through foreclosure, compared with 19% of blacks, 11% of Asians, and 9% of non-Hispanic whites (Rugh and Hall 2016). Census data indicate that black home ownership peaked at 49.7% in 2006 for Hispanics and at 49.1% in 2004 for African Americans, but by 2019 the respective figures had fallen to 47.3% and 41.5%, with the latter being the lowest level recorded since the passage of the 1968 Fair Housing Act.

Owing to high rates of foreclosure and sharp decreases in home values, black wealth fell by 35% from 2007 to 2013, going from $156,000 to $102,000, and over the same period Hispanic wealth dropped by 48%, going from $216,000 to $111,000 (Massey and Rugh 2018b). As a result, both groups fell further behind whites in terms of average wealth, with the black–white gap in mean wealth rising from $257,000 in 1983 to $780,000 in 2016 and the Hispanic-white gap grew from $261,000 to $728,000. In his contribution to this volume, Jacob Rugh uncovers other unexpected consequences of predatory lending that go well beyond reductions in minority home ownership and wealth.

Among African Americans, in particular, he showed that changes in black home ownership from 2006 to 2016 were strongly correlated with shifts in black voter turnout. Using a state-level fixed effects model he further estimated that each ten-point increase in the black home ownership rate yielded a nine-point increase in the rate of black voter turnout, meaning that a ten-point decline would likewise produce a nine-point decline in turnout. In a very real way, therefore, rising black ownership boosted black turnout to help elect Barack Obama in 2008, while falling black ownership thereafter lowered black turnout to help elect Donald Trump in 2016. A lagged specification of his statistical model for the 2016 election suggests that sizeable declines in black home ownership in critical swing states such as Michigan, Pennsylvania, and Wisconsin likely made a difference in the final outcome in the election.

Residential segregation is just the tip of a much larger iceberg of social separation between blacks and whites in American society. It only indicates the degree of racial separation in where people sleep at night, not in the social and geographic spaces they occupy during the rest of the day. Heretofore, it has not been possible to measure racial segregation across the various “mobility spaces” people occupy in the course of their daily lives, but the article in this volume by Sampson and Levy signals a major leap in analytic capabilities with respect to the study of racial segregation in the era of “big data.”

Although the connection between residential segregation and violence is well established in the research literature (see Massey and Denton 1993; Peterson and Krivo 2010; Sampson 2012), Sampson and Levy build on work by Phillips et al. (2019) that used machine learning to estimate home locations and inter-neighborhood mobility patterns for some 375,000 Twitter users over 18 months during 2013–2015. These data were used to derive two novel spatial measures. The equitable mobility index indicates the degree to which residents of particular neighborhoods travel to other neighborhoods in the city equally, and the concentrated mobility index captures the degree to which trips away from a resident’s home neighborhood are confined to a small number of receiving destinations.

Sampson and Levy combine these measures with Theil indices of multigroup segregation by race and income computed from the American Community Survey and then move on to assess their correlations with one another and with homicide rates obtained from the Bureau of Justice Statistics. Focusing on the 50 largest US cities, they uncover significant associations between racial segregation, on the one hand, and income segregation, equitable mobility, and concentrated mobility, on the other. Multivariate regression models reveal that racial segregation and the interaction between indices of equitable and concentrated mobility very strongly and significantly predict homicide rates across cities, explaining more than two-thirds of the interurban variation in lethal violence with no significant effects observed for controls introduced for the share of college-educated adults, population density, or income segregation.

In their multivariate model, the main effects of equitable mobility and concentrated mobility are negative and insignificant, whereas the interaction between them is positive and highly significant. Graphing the effects of equitable mobility on homicide rates at high and low levels of concentrated mobility reveals that under conditions of highly concentrated mobility (involving moves to a small number of neighborhoods) rising levels of equitable mobility have little effect on the homicide rate, so that cities with low-mobility equity but high levels of segregation such as Baltimore, Philadelphia, Cleveland, and Detroit tend to experience high levels of lethal violence. However, as mobility becomes less concentrated (embracing a larger number of neighborhoods), rising levels of equitable mobility increasingly act to reduce the homicide rate. As a result, cities with high levels of equitable mobility experience low homicide rates, especially when they are not segregated as in Portland, Raleigh, and Seattle.

Segregation and Inequality in the Twenty First Century

In 2010 a third of all black metropolitan residents still lived under conditions of hypersegregation, with 21% living under conditions of high segregation, 46% under conditions of moderate segregation, and less than 1% under conditions of low segregation. Among Latinos, in 2010 only 0.4% lived in a hypersegregated metropolitan area (down from 14% in 2000), but 23% still lived under conditions of high segregation, with three-quarters living under conditions of moderate segregation, and just 2% under conditions of low segregation. No Asians lived under conditions of either hyper- or high segregation. Instead, 82% experienced moderate levels of segregation and 8% experienced low levels of segregation.

Thus, we see a very clear ranking of American minority groups with respect to the residential segregation they experience in American society. Blacks remain by far the most segregated group, followed by Hispanics and then at some distance Asians. Racial segregation stems from multiple causes, but negative anti-black and anti-Latino stereotypes persist and discrimination remains a potent force in channeling African Americans and Hispanics to separate neighborhoods from whites. Consistent with their rank ordering with respect to segregation and in keeping with the results of audit studies, Asians appear to experience much less discrimination than African Americans or Hispanics.

Owing to ongoing racial segregation, rising class segregation, growing income inequality, and the resultant geographic concentration of both affluence and poverty, African Americans and Hispanics experience high levels of neighborhood poverty and low concentrations of affluence compared to whites and Asians. These racial disparities are confirmed in Tables 1 and 2, which draw on computations done by Massey and Rugh (2020). Table 1 lists the 30 metropolitan areas with the highest concentrations of affluence for whites, Asians, blacks, and Hispanics, drawing on P* isolation indices to indicate the percentage of affluent people in the census tract of the average affluent metropolitan resident. Affluence is defined to include all persons in households with incomes at least four times the poverty rate for a family of four in 2010. Remarkably, all the metropolitan areas listed for whites and Asians display isolation indices above 50, meaning that in these areas affluent whites and Asians occupy neighborhoods in which a majority of other residents are also affluent.

Table 1 US metropolitan areas with the highest concentrations of affluence in 2010
Table 2 US metropolitan areas with the highest concentrations of poverty in 2010

We observe notable clusters of concentrated affluence in and around Boston (Cambridge, Boston, Peabody, and Manchester), New York City (New York, Nassau-Suffolk, Poughkeepsie, Bridgeport, Hartford, Newark, and Edison), Philadelphia (Philadelphia, Camden, Trenton, and Wilmington), Washington (Baltimore, Bethesda, and Washington), San Francisco (San Francisco, San Jose, and Oakland), and Los Angles (Los Angeles, Santa Ana, Oxnard), as well as in Boulder, Dallas, Honolulu, Houston, and Seattle. These are all places that tend to house the nation’s economic, political, and academic elites. Given the current political polarization of the country, it is perhaps unsurprising to note that the elite are most isolated in Washington, DC, where two-thirds of all affluent whites and Asians live in neighborhoods where two-thirds of their neighbors are also affluent.

In contrast to whites and Asians, the spatial concentration of affluence begins at a lower level and drops off much more quickly for blacks and Hispanics. Affluent members of these groups experience concentrations of affluence that average ten points lower than those observed among their white and Asian counterparts. Moreover, instead of 30 areas with isolation indices above 50, the number is only seven for blacks and six for Hispanics. These other areas of black- and Hispanic concentrated affluence tend to be areas housing specialized institutions that attract well-educated affluent workers, such as Boulder, Cambridge, Santa Cruz, Provo, Ann Arbor, and Bloomington (all college towns), Rochester, Minnesota (home of the Mayo Clinic), and Huntsville, Alabama (home of the Marshall Space Flight Center and several colleges and universities).

Table 2 lists the 30 metropolitan areas with the highest concentrations of poverty for whites, blacks, Hispanics, and Asians, with P* isolation indices indicating the percentage of poor persons in the neighborhood of average poor person. In general, living in a neighborhood where more than 40% of residents fall below the poverty line for a family of four constitutes an “extreme” concentration of poverty, whereas neighborhoods that are 20% to 40% poor indicate a “high” concentration of poverty. By these criteria, in no metropolitan area do poor whites experience an extreme concentration of poverty, though in 14 areas they do exhibit concentrations of poverty in the top half of the high range (i.e., with poor isolation indices in the 30–40 range).

High concentrations of white poverty are notably prevalent in the southern metropolitan areas, such as College Station, Waco, Lubbock, McAllen, and Brownsville in Texas; Gainesville and Tallahassee in Florida; Tuscaloosa, Florence, and Gadsden in Alabama; Blacksburg and Danville in Virginia; Huntington,Cleveland, and Kingsport in Tennessee; and Forth Smith in Arkansas as well as metropolitan areas in Appalachia (Cumberland in Maryland and Wheeling in West Virginia). Indiana is also a standout for high concentrations of white poverty, with Bloomington, Lafayette, Muncie, and Terre Haute all appearing on the list.

Unlike whites, Asians experience extreme concentrations of poverty in 19 different metropolitan areas, including three where poor Asians inhabit neighborhoods in which the majority of residents are also poor, such as Waco and College Station in Texas (with isolation indices of 57.6 and 50.5) as well as Champaign, Illinois (52.1). However, concentrated poverty for Asians appears to be most common in older industrial centers such as Lafayette, Bloomington, Muncie in Indiana; Utica and Syracuse in New York; Kalamazoo and Lansing in Michigan; Sioux City, Ames, and Dubuque in Iowa, along with assorted areas in the Midwest (Madison, Columbia, Erie, Youngstown, Decatur) and South (Athens, Huntington, Pine Bluff, Gainesville, Florence, and Cleveland, Tennessee). Most of these areas contain few Asians, but significant numbers of blacks and Hispanics, suggesting the possibility that what we are observing is Asian small business owners inhabiting storefront residences in poor, minority neighborhoods.

All the areas on the list of areas with the highest concentrations of black poverty display isolation indices above 40, indicating the unique degree to which extreme concentrations of poverty prevail for African Americans. The highest concentration of poverty experienced by poor blacks occurs in Lewiston, Maine (with an isolation index of 50.7), reflecting that area’s large Somali refugee population. The rest of list is dominated by rust belt cities in states such as Ohio (Steubenville, Youngstown, Toledo, Cleveland, Lima), Michigan (Muskegon, Jackson, Detroit, Flint, Saginaw), Indiana (Muncie, Bloomington), Pennsylvania (Johnstown, Erie), and New York (Utica, Syracuse) along with metropolitan areas in the South (Gadsden, Jackson, Knoxville, Monroe, Huntington, Chattanooga, and Louisville).

Many of the same metropolitan areas populate the Hispanic list of areas of greatest poverty concentration. However, extreme concentrations of Hispanic poverty prevail in only 12 of the 30 metropolitan areas. As with African Americans, the concentration of poverty for Hispanics is evident in older industrial areas such as Akron, Cleveland, Steubenville, and Youngstown in Ohio; Buffalo, Elmira, Rochester, Syracuse, and Utica in New York; Philadelphia, Reading, and Erie in Pennsylvania, as well as metropolitan areas in the South (Albany GA, Athens GA, Gadsden, AL, Gainesville, FL, and Tallahassee, FL) and New England (Springfield MA, Providence RI). The Hispanic list also includes many metropolitan areas in the southwest, such as Brownsville, College Station, Laredo, and McAllen in Texas and Pueblo, Colorado.

The foregoing extremes of concentrated poverty and affluence—and the ongoing residential segregation of blacks, rising spatial isolation of Hispanics, increasing segregation by income, and growing inequalities of wealth that produced them—are the critical components of the American system of stratification. Exposure to neighborhood-based advantage and disadvantage play a huge role in structuring life chances. As we have seen from the studies presented here, segregation and the concentration of neighborhood poverty are tied to continued discrimination in the real estate and banking industries, and neighborhood segregation and poverty lead directly to racial isolation and the concentration of poverty within schools, creating stark inequalities in access to education.

Growing up in a racially isolated neighborhood of concentrated poverty also dramatically reduces lifetime earnings in adulthood and increases exposure to violence in both childhood and adulthood. Segregation does so not only directly within neighborhoods, but indirectly through its association with the spatial environments that people move through in daily life. At the other extreme, growing up in a neighborhood of concentrated affluence not only assures access to a high-quality public education and a healthier, wealthier, and safer adult life, it also isolates elite decision makers within very privileged social worlds that are far removed from conditions experienced by the poor and those in middle and working classes. Given the gaping fissures in America’s social and economic geography documented here, is it any wonder that the USA is a polarized and divided nation?

References

  1. Baradaran, M. (2017). The color of money: Black banks and the racial wealth gap. Cambridge, MA: Harvard University Press.

    Google Scholar 

  2. Bauman, J. F. (1987). Public housing, race, and renewal: Urban planning in Philadelphia, 1920–1974. Philadelphia, PA: Temple University Press.

    Google Scholar 

  3. Bobo, L. D., Charles, C. Z., Krysan, M., & Simmons, A. D. (2012). The real record on racial attitudes. In P. V. Marsden (Ed.), Social trends in the United States: Evidence from the General Social Survey since 1972 (pp. 38–83). Princeton, NJ: Princeton University Press.

    Google Scholar 

  4. Carpusor, A. G., & Loges, W. E. (2006). Rental discrimination and ethnicity in names. Journal of Applied Social Psychology,36(4), 934–952.

    Google Scholar 

  5. Charles, C. Z. (2003). The dynamics of racial residential segregation. Annual Review of Sociology,29, 167–207.

    Google Scholar 

  6. Chetty, R., & Hendren, N. (2018a). The impacts of neighborhoods on intergenerational mobility I: Childhood exposure effects. Quarterly Journal of Economics,133(3), 1107–1162.

    Google Scholar 

  7. Chetty, R., & Hendren, N. (2018b). The impacts of neighborhoods on intergenerational mobility II: County-Level Estimates. Quarterly Journal of Economics,133(3), 1163–1228.

    Google Scholar 

  8. Choi, A., Herbert, L., Winslow, O., Browne, A. 2019. Long Island divided. Newsday, November 17. https://projects.newsday.com/long-island/real-estate-agents-investigation/#open-paywall-message

  9. Delis, M. D., & Papadopoulos, P. (2019). Mortgage lending discrimination across the U.S.: New methodology and new evidence. Journal of Financial Services Research,56(3), 341–368.

    Google Scholar 

  10. Dovidio, J. F., & Gaertner, S. L. (1986). The aversive form of racism. In J. F. Dovidio & S. L. Gaertner (Eds.), Prejudice, discrimination and racism (pp. 61–89). New York: Academic Press.

    Google Scholar 

  11. Duncan, O. D. (1957). The measurement of population distribution. Population Studies,11(1), 27–45.

    Google Scholar 

  12. Emerson, M. O., Chai, K. J., & Yancey, G. (2001). Does race matter in residential segregation? Exploring the preferences of white Americans. American Sociological Review,66(6), 922–935.

    Google Scholar 

  13. Fischer, M. J., & Massey, D. S. (2004). The ecology of racial discrimination. City & Community,3(3), 221–243.

    Google Scholar 

  14. Grigoryeva, A., & Ruef, M. (2015). The historical demography of racial segregation. American Sociological Review,80(4), 814–842.

    Google Scholar 

  15. Hanson, A., & Hawley, Z. (2011). Do landlords discriminate in the rental housing market? Evidence from an internet field experiment in US cities. Journal of Urban Economics,70(2), 99–114.

    Google Scholar 

  16. Hartfield, C. (2018). A few red drops: The Chicago race riot of 1919. New York: Clarion Press.

    Google Scholar 

  17. Helper, R. (1969). Racial policies and practices of real estate brokers. Minneapolis: University of Minnesota Press.

    Google Scholar 

  18. Hirsch, A. R. (1983). Making the Second Ghetto: Race and Housing in Chicago, 1940–1960. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  19. Intrator, J., Tannen, J., & Massey, D. S. (2016). Segregation by race and income in the United States 1970–2010. Social Science Research,60(1), 45–60.

    Google Scholar 

  20. Jackson, K. T. (1985). Crabgrass frontier: The suburbanization of the United States. New York: Oxford University Press.

    Google Scholar 

  21. Katznelson, I. (2005). When affirmative action was white: An untold History of racial inequality in twentieth-century America. New York: W. W. Norton.

    Google Scholar 

  22. Kozol, J. (1992). Savage inequalities: Children in America's schools. New York: Harper.

    Google Scholar 

  23. Krysan, M., & Crowder, K. (2017). Cycle of segregation: Social processes and residential stratification. New York: Russell Sage Foundation.

    Google Scholar 

  24. Lieberson, S. (1980). A piece of the pie: Blacks and white immigrants since 1880. Berkeley: University of California Press.

    Google Scholar 

  25. Lieberson, S. (1981). An asymmetrical approach to segregation. In C. Peach, V. Robinson, & S. Smith (Eds.), Ethnic segregation in cities. London: Croom Helm.

    Google Scholar 

  26. Logan, J. (2017). Racial segregation in postbellum Southern cities: The case of Washington, D. C. Demographic Research,36(1), 1759–1784.

    Google Scholar 

  27. Massey, D. S. (2016). Segregation and the perpetuation of disadvantage. In L. Burton & D. Brady (Eds.), The Oxford handbook of the social science of poverty (pp. 369–393). Oxford: Oxford University Press.

    Google Scholar 

  28. Massey, D. S., & Denton, N. A. (1988). The dimensions of residential segregation. Social Forces,67, 281–315.

    Google Scholar 

  29. Massey, D. S., & Denton, N. A. (1989). Hypersegregation in U.S. metropolitan areas: Black and Hispanic segregation along five dimensions. Demography,26(3), 373–393.

    Google Scholar 

  30. Massey, D. S., & Denton, N. A. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University Press.

    Google Scholar 

  31. Massey, D. S., & Lundy, G. (2001). Use of black English and racial discrimination in urban housing markets: New methods and findings. Urban Affairs Review,36(4), 470–496.

    Google Scholar 

  32. Massey, D. S., & Rugh, J. S. (2018a). The intersection of race and class: Zoning, affordable housing, and segregation in U.S. metropolitan areas. In G. Squires (Ed.), The fight for fair housing: Causes (pp. 245–265). New York: Taylor and Francis.

    Google Scholar 

  33. Massey, D. S., & Rugh, J. S. (2018b). The great recession and the destruction of minority wealth. Current History,117(802), 298–304.

    Google Scholar 

  34. Massey, D. S., & Rugh, J. S. (2020). America's unequal metropolitan geography: Segregation and the spatial concentration of affluence and poverty. In F. Rosenbluth & M. Weir (Eds.), The new politics of insecurity. New York: Cambridge University Press.

    Google Scholar 

  35. Massey, D. S., Rugh, J. S., Steil, J. P., & Albright, L. (2016). Riding the stagecoach to Hell: A qualitative analysis of racial discrimination in mortgage lending. City & Community,15(2), 118–136.

    Google Scholar 

  36. Massey, D. S., & Tannen, J. (2015). A research note on trends in black hypersegregation. Demography, 52(3), 1025–1034.

    Google Scholar 

  37. Massey, D. S., & Tannen, J. (2016). Segregation, race, and the social worlds of rich and poor. In H. Braun & I. Kirsch (Eds.), The dynamics of opportunity in America: Evidence and perspectives (pp. 13–33). New York: Springer.

    Google Scholar 

  38. Massey, D. S., & Tannen, J. (2017). Suburbanization and segregation in the United States. Ethnic and Racial Studies,41(9), 1594–1611.

    Google Scholar 

  39. Metcalf, G. R. (1988). Fair housing comes of age. New York: GreenwoodPress.

    Google Scholar 

  40. Owens, A. (2016). Inequality in children’s contexts. American Sociological Review,81(3), 549–574.

    Google Scholar 

  41. Owens, A. (2017). Racial residential segregation of school-age children and adults: The role of schooling as a segregating force. The Russell Sage Foundation Journal of the Social Sciences,3(2), 63–80.

    Google Scholar 

  42. Peterson, R. D., & Krivo, L. J. (2010). Divergent social worlds: Neighborhood crime and the racial-spatial divide. New York: Russell Sage Foundation.

    Google Scholar 

  43. Phillips, N., Levy, B. L., Sampson, R. J., Small, M. L., & Wang, R. Q. (2019). The social integration of American Cities: Network neasures of connectedness based on everyday mobility across neighborhoods. Sociological Methods and Research. https://doi.org/10.1177/0049124119852386.

    Article  Google Scholar 

  44. Philpott, T. (1978). The slum and the ghetto: Neighborhood deterioration and middleclass reform, Chicago 1880–1930. New York: Oxford University Press.

    Google Scholar 

  45. Quillian, L. (2014). Social psychological processes in studies of neighborhoods and inequality. In J. McLeod, M. Schwalbe, & E. Lawler (Eds.), Handbook of the social psychology of inequality (pp. 459–484). New York: Springer.

    Google Scholar 

  46. Reardon, S. F. (2016). School segregation and racial academic achievement gaps. The Russell Sage Foundation Journal of the Social Sciences,2(5), 34–57.

    Google Scholar 

  47. Reardon, S. F., Bischoff, K., Owens, A., & Townsend, J. B. (2018). Has income segregation really increased? Bias and bias correction in sample-based segregation estimates. Demography,55(6), 2129–2160.

    Google Scholar 

  48. Reardon, S. F., Kalogrides, D., & Shores, K. (2019). The geography of racial/ethnic test score gaps. American Journal of Sociology,124(4), 1164–1221.

    Google Scholar 

  49. Reardon, S. F., Weathers, E. S., Fahle, E. M., Jang, H., & Kalogrides, D. (2019). Is separate still unequal? New evidence on school segregation and racial academic achievement gaps. CEPA Working Paper No. 19-06, Stanford, CA: Center for Education Policy Analysis. https://cepa.stanford.edu/wp19-06

  50. Rice, R. L. (1968). Residential segregation by law 1910-1917. Journal of Southern History, 64(2), 179–199.

    Google Scholar 

  51. Rothwell, J., & Massey, D. S. (2009). The effect of density zoning on racial segregation in U.S. urban areas. Urban Affairs Review,44(6), 799–806.

    Google Scholar 

  52. Rothwell, J., & Massey, D. S. (2015). Geographic effects on intergenerational income mobility. Economic Geography,91(1), 83–106.

    Google Scholar 

  53. Rugh, J. (2015). Double jeopardy: Why Latinos were hit hardest by the US foreclosure crisis. Social Forces,93(3), 1139–1184.

    Google Scholar 

  54. Rugh, J. S., & Hall, M. (2016). Deporting the American dream: Immigration enforcement and Latino foreclosures. Sociological Science,3, 1053–1076.

    Google Scholar 

  55. Rugh, J. S., & Massey, D. S. (2010). Racial segregation and the American foreclosure crisis. American Sociological Review,75(5), 629–651.

    Google Scholar 

  56. Rugh, J. S., & Massey, D. S. (2014). Segregation in the post-civil rights America: Stalled integration or end of the segregated century?”. The DuBois Review: Social Science Research on Race,11(2), 202–232.

    Google Scholar 

  57. Rugh, J. S., Albright, L., & Massey, D. S. (2015). Race, space, and cumulative disadvantage: A case study of the subprime lending collapse. Social Problems,62(2), 186–218.

    Google Scholar 

  58. Sampson, R. J. (2012). Great American city: Chicago and the enduring neighborhood effect. Chicago, IL: University of Chicago press.

    Google Scholar 

  59. Schuman, H., Steeh, S., Bobo, L., & Krysan, M. (1997). Racial attitudes in America: Trends and interpretations. Cambridge, MA: Harvard University Press.

    Google Scholar 

  60. Sewell, A. A. (2016). The racism-race reification process: A mesolevel political economic framework for understanding racial health disparities. Sociology of Race and Ethnicity,2(4), 402–432.

    Google Scholar 

  61. Sharkey, P. (2013). Stuck in place: Urban neighborhoods and the end of progress toward racial equality. Chicago, IL: University of Chicago Press.

    Google Scholar 

  62. Squires G. D. (2005). Predatory Lending: Redlining in Reverse, Shelterforce: The voice of community development, January 1, 2005. https://shelterforce.org/2005/01/01/predatory-lending-redlining-in-reverse/.

  63. Steil, J. P., Albright, L., Rugh, J. S., & Massey, D. S. (2018). The social structure of mortgage discrimination. Housing Studies,33(5), 759–776.

    Google Scholar 

  64. Tilly, C. (1998). Durable inequality. Berkeley: University of California Press.

    Google Scholar 

  65. Turner, M. A., Santos, R., Levy, D. K., Wissoker, D., Aranda, C., & Pitingolo, R. (2013). Housing Discrimination against Racial and Ethnic Minorities 2012. Washington, D.C.: Urban Institute.

    Google Scholar 

  66. White, M. J. (1986). Segregation and diversity: Measures in population distribution. Population Index, 52, 198–221.

    Google Scholar 

  67. Wilkerson, I. (2010). The warmth of other suns: The epic story of America’s great migration. New York: Random House.

    Google Scholar 

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Massey, D.S. Still the Linchpin: Segregation and Stratification in the USA. Race Soc Probl 12, 1–12 (2020). https://doi.org/10.1007/s12552-019-09280-1

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Keywords

  • Affluence
  • Poverty
  • Segregation
  • Concentration
  • Education