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Segregation is the differential distribution of social groups. Most commonly, the use of the term refers to residential segregation, the differential distribution of social groups across geography. Residential segregation has served as a key indicator of social differentiation for decades. Segregation is usually measured by one or more summary indices, designed to reﬂect the differential distribution of persons or other social units across neighborhoods, occupations, or other categories (White 1986). Segregation is often seen as a multidimensional phenomenon tapped by a variety of indices (Massey et al. 1996). While the bulk of work has centered on residential segregation among ethnic groups, virtually any trait could be considered, and research has examined segregation by income, education, occupation, nativity, age, and other demographic traits. Racial segregation across schools and occupations remains an important indicator of social distance, and residential segregation can feed school segregation and limit access to jobs, city services, and other public goods (Farley 1984, 1996).
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Beyond the measurement of segregation itself, there has always been interest in the consequences of residential clustering. Theory can be invoked to suggest positive as well as negative consequences (Cutler and Glaeser 1997). This ranges from the ways in which residential concentration might generate employment networks or social support institutions, to the consequences of residential isolation for educational attainment or health outcomes. From the point of view of policy, the determinants and consequences of segregation are of particular interest. There has been heightened attention to the aftermath of living in, or being raised in, a segregated or dis- advantaged neighborhood. More recent discussions of social capital are pertinent, as the concept of social capital is extended to include the inﬂuence of neighbors and nearby peers and role models on such outcomes.
Empirical work at the beginning of the twenty-ﬁrst century has begun to evaluate the effects of residential segregation. The underlying concept is that residential clustering carries behavioral consequences beyond those of ethnicity, socioeconomic status, or lifestyle. Such notions have been present in demography for some time, but data and multilevel statistical techniques are now available to directly evaluate hypotheses. Social behaviors clearly vary across urban space, or equivalently other categorizations. Yet it is important to distinguish the inﬂuence of composition from that of causation. For instance the receipt of public assistance varies appreciably across urban neighborhoods. The degree to which this behavior can be linked causally to the concentration of social groups in neighborhoods (and the sociological pat- terns that arise through clustering) needs to be weighed against an explanation based just on composition. The key question turns on whether geographic clustering itself inﬂuences human behavior, beyond that attributable to other background traits, such as age and education.
Many demographic traits are segregated in urban neighborhoods, and there is a consistency to the ranking. Although empirical results on these matters derive preponderantly from North America, similar results have been obtained for other high-income societies. Race and ethnic groups remain very highly segregated in most urban areas. Several decades of study, from straightforward segregation measures through factorial ecology and the like, have veriﬁed the sorting of urban space along the lines of socio- economic status. Life cycle stage is also differentiated. Segregation indices for sex and age are modest, although segregation for household size and type is more discernable. Of course, certain special concentrations of population, such as group quarters for military populations, students, and those under correctional supervision, are highly segregated and are often excluded from consideration by analysts.
While one can identify and rank the levels of segregation illustrated by various traits, the consequences of this segregation are more difficult to discern. A common causal model advanced in these cases was that persons, especially children, adapted to their disadvantaged environment, learning criminal behavior, reducing achievement motivation for school, and developing dependence on the dole. Observers cited the higher incidence of single parenthood, labor force participation, high school dropout, and the like in such disadvantaged, inner-city neighborhoods.
A demonstration of the presence or absence of the links remained for subsequent waves of investigations. Statistical estimates of neighborhood effects rely on hierarchical and multilevel models. While strategies differ, the general approach is to develop a multivariate model, in which the behavioral and demographic outcome (employment, childbearing) is taken to be ﬁrst a function of unchanging (ascriptive) personal traits and a set of family and household background factors. Neighborhood and community characteristics are introduced to the model simultaneously or hierarchically. Thus, neighborhood effects are generally measured net of other characteristics. With the availability of multilevel statistical techniques and associated software, it is possible to derive more sophisticated (and econometrically appropriate) estimates of these neighborhood or community effects. Some of the same conceptual and methodological apparatus can be applied to patterns or demographic unevenness found in other social institutions, such as the workplace or schools.
1. Mechanisms Through Which Segregation May Inﬂuence Demographic Outcomes
Beyond the simple mechanism of demographic composition itself, segregation, and differential concentration might inﬂuence outcomes in several ways. These include (a) contagion effects; (b) resource allocation; and (c) institutional structures. Contagion models, also closely linked to epidemic effects and diffusion models, have a long history in social science thinking. Simply stated, individuals alter their behavior on the basis of their interpretation of those around them (Jencks and Mayer 1990, Crane 1991). The inﬂuence of peers on child behavior is one such mechanism. Such processes have been particularly prominent in thinking about school context (Alwin 1977). Role models provide a related mechanism. Much of the recent discussion of adverse inner city outcomes draws on this line of thinking. The general reasoning (see, e.g., Wilson 1987) is that low income or disadvantaged neighborhoods offer fewer mainstream role models (dual parent families, stable adult workers, school completion), and so those growing up in such areas do not acquire mainstream norms. Massey and Denton argue that the intersection of poverty and segregation creates an environment where such conditions abound (Massey and Denton 1993). Social capital is a contemporary phrase that taps some of the same mechanisms. Resources available to the individual beyond the family or household may inﬂuence a wide range of behavioral outcomes. Certainly this is of interest in schooling and neighborhoods, and recently it has become of increasing concern in the ﬁeld of health.
A second line of thinking, far less readily articulated, involves resource allocation. Residents of affluent neighborhoods (or workers in advantaged workplaces) can provide superior public goods, either through direct community provision or through the political process. More than that, these public goods are likely to confer advantages that go beyond the immediate advantages of household and family. Thus, superior schools produce a beneﬁt for all residents in the district (neighborhood) over and above the advantage of family background. Other externalities of clustering include relative advantage or deprivation in local sanitation (exposure to pathogens and toxins), public safety, and the like. Housing experiments, such as those relocating low-income individuals to middle-income communities, are premised on such mechanisms.
The third line of thinking involves the growth and maintenance of local institutions, whether formal or informal (Duncan 1994). The literature identiﬁes both positive and negative consequences of segregation. In the realm of economic outcomes, the notion of the ethnic enclave suggests that concentration, building on ethnic networks, may provide beneﬁts. Occupational and residential segregation with co-ethnics may generate employment opportunities unavailable in a more integrated economy. A long-standing argument is that racial residential segregation enables integration by social class. In an urban neighborhood, local institutions, such as a church or neighborhood association, might foster social ties across class boundaries, although even historical accounts of ghetto life do not always portray a comfortable coexistence of disparate groups. What is relevant in the present circumstance is that these evolving structures can serve as social actors and then inﬂuence behavioral outcomes for other members of the community. Empirical social science evidence on these points is scant, especially evidence that can tease out the inﬂuences of institutions, net of individual, family, and other community traits.
2. Methodological Issues
Concerns about the effects of segregation raise several methodological issues. A fundamental issue is one of deﬁnition. A ‘neighborhood’ might have several competing geographic boundaries. Tracts, wards, block groups, and other urban statistical subdivisions become de facto neighborhoods (White 1987). This has two consequences. First, such units serve as the basic geography for calculations of segregation measures. Second, these small statistical areas are the units that serve as the proxy for social conditions in multilevel models. Another methodological evolution is the shift from ecological inference to multilevel modeling. For decades of social science research, analysts were constrained to a choice of individual level analysis or ‘ecological’ models, which relate aggregate values of demographic characteristics to one another. While ecological analysis was the only route available for certain questions, and while some might argue for the focus on the neighborhood only, multilevel analysis has grown over the last several years as the favored approach (Diez-Roux et al. 1997, Garner and Raudenbush 1991). In multilevel models, individual behaviors are taken to be the result of both individual characteristics and traits of an aggregate. The level of segregation in the city, or the demographic composition of the neighborhood, could be one of these aggregates.
It remains a challenge to properly specify models, so that individual and contextual effects are fully distinguished statistically. The very process of residential assimilation—the achievement of neighborhood status through residential mobility—also generates correlations between community and individual. Thus, separating endogenous and exogenous elements of a process is no easy task. Even then, one must still be concerned that events are properly allocated to local administrative units.
3. Empirical Evidence
Interest in community effects in urban settings was rekindled with the discussion and debate over the urban ‘underclass’ in the 1980s social science literature. Again much of this debate was focused on US settings, but parallel issues of urban neighborhood deterioration and behavioral consequences can be raised for other high-income nations. Despite the level of concern for the effects of residing in ghettoes and inner-city disadvantaged zones, one major contemporary review argued that national social and economic conditions more powerfully determined the incidence of and concentration of poverty (Lynn and McGeary 1990). After expressing reservations about the quality and consistency of data, a related recent review of empirical studies of the effects of neighborhood SES, racial mix, for both neighborhoods and schools, concluded that there is no general pattern of effects across a wide range of outcomes (Jencks and Mayer 1990). There is some modest evidence that growing up with affluent neighbors (for those of modest means) and in less segregated environment (for blacks) predicts higher levels of educational attainment (Jencks and Mayer 1990). One recent review of about 24 studies of neighborhood effects did tend to ﬁnd that community-level (often census tract) measures of neighborhood advantage were associated with superior individual-level behavioral outcomes in several phases of childhood (Gephardt 1997). Not surprisingly, this review was critical of the state of knowledge, for the weakness of the link to the appropriate community-level empirical measures and the absence of other characteristics (school, family traits) that would be likely to exert an inﬂuence on individual outcomes. One comprehensive compendium of neighborhood level studies on poverty, a study that coordinated data and research approach, again detected neighborhood effects in several investigations (Brooks-Gunn et al. 1997). Typically family-level measures swamped neighborhood-level measures in predicting child behavioral outcomes. Perhaps more interesting is the ﬁnding that neighborhood measures could be discerned for pre-school children, despite the conventional thinking that such youngsters’ social world was limited to the home.
Substantial effects of neighborhood factors, particularly neighborhood affluence and poverty, have been linked to later life achievement (IQ, teenage childbearing, and school-leaving) in US nationally representative longitudinal data (Brooks-Gunn et al. 1993). Longitudinal data only sometimes detect effects of neighborhood socioeconomic status and racial composition on later earnings, after controlling for selected background traits (Jencks and Mayer 1990). Conceptual models of the inﬂuence of neighborhood effects concentrate on the pathways through which these aggregate effects carry to the individual. At the same time qualitative approaches can suggest coping mechanisms for those in disadvantaged neighborhoods (Jarrett 1997). There is some empirical evidence that local organizational structure may mediate community disadvantage (Elliott et al. 1996). The effect of schools may outweigh neighborhoods on risky health behavior (Ennett et al. 1997).
One reasonably long-standing line of work looks at the inﬂuence of segregation itself, often introducing the value of a segregation statistic to a regression model of a behavioral outcome. The behavioral mechanism underlying the inclusion of such a covariant is not always clear, but higher level residential segregation may tap into underlying housing market constraints, a more hostile climate of local race relations, or differential access to public sector resources. Predictive power of such segregation indices is not always strong, but on balance they tend to show that individuals (usually minority group members) living in more segregated urban areas experience detrimental outcomes, such as higher levels of unemployment, or violence, or less mobility out of poverty (Iceland 1997, Peterson and Krivo 1993, South and Crowder 1997, Wilson et al. 1995). Even after including several individual and neighborhood controls in a multilevel model, residential segregation does appear to be associated with lower residential mobility on the part of both blacks and whites, supporting the housing market constriction idea (South and Dean 1993). Neighborhood concentration and metropolitan racial segregation, especially among African Americans does appear to be linked to adverse health outcomes for both adults and infants (Williams and Collins 1999, Hart et al. 1998, LaVeist 1989, LeClere et al. 1997, Polednak 1996). There is no consensus on the scale of neighborhood effects, and the magnitude of such effects would be expected to differ by behavioral outcome. The current state of knowledge is that neighborhood effects on demographic outcomes can be detected—especially when investigators use more robust data and more contemporary methodology—yet these neighborhood effects are modest by comparison to personal ﬁxed traits and household characteristics.
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