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. Author manuscript; available in PMC: 2015 Jan 2.
Published in final edited form as: City Community. 2010 Jul 23;9(3):299–319. doi: 10.1111/j.1540-6040.2010.01338.x

The Racial Residential Segregation of Black Single Adults Living Alone

Kris Marsh 1, John Iceland 2
PMCID: PMC4281966  NIHMSID: NIHMS639346  PMID: 25558183

Abstract

While many studies have examined the intersection of race and class with residential segregation and residential preferences, very little is known about the role played by household composition in shaping residential patterns. This paper focuses on the residential patterns of a particular kind of household: those consisting of persons single and living alone (SALA). We compare the residential segregation of black SALA households—an important subset of non-family households and a rapidly growing segment of the population—from white SALA households and both white and black married-couple households. We examine how group and metropolitan characteristics influence segregation levels for these household types. Using data from the 2000 census, we find that black SALA households are less segregated from white SALA households than from white married-couple households. Multivariate analyses show that smaller income differences across SALA households account for these segregation patterns, indicating the importance of economic resources in influencing residential patterns. Nevertheless, race continues to play an important role, as black SALA household segregation from both kinds of white households is high in absolute terms and relative to their segregation from black married-couple households.

Scholars have examined the intersection of race and class with residential segregation (Adelman, 2004, 2005; Iceland and Wilkes, 2006; Montgomery, 2006; Pattillo-McCoy, 1999; Wilson, 1978, 1987), residential preferences (Adelman, 2005), and identity and space (Lacy, 2004). Much of the emphasis in these studies has been on housing for ethnic groups taken as a whole, but very little is known about the residential patterns of a particular kind of household: persons single and living alone (SALA). To better understand how racial residential segregation corresponds to changing household composition in the U.S., this paper moves beyond the existing literature and focuses on the spatial segregation patterns of SALA households as a group. We know there are racial differences in rates of SALA households by race. In 2000, a full quarter of black and white households were in the SALA category, compared to only 17 percent of all Latino households and 20 percent of all Asian households.

Residential segregation studies are typically organized around black/white, poor/non-poor, or some times married couple/single-parent dichotomies or an amalgam of these binaries. Take, for example, William Julius Wilson’s (1978, 1987) work on the social and spatial divide between poor and middle-class urban blacks. Based primarily on a study of middle-class married couples with children, Wilson claimed these divisions emerged mainly as a result of the financial ability of middle-class blacks to move away from lower-income neighborhoods.

Massey and Denton (1993) challenged Wilson’s claim, arguing that racial segregation between whites and blacks (as opposed to class segregation among blacks) is omnipresent and continues to produce economic disadvantages for poor and non-poor blacks alike. In keeping with Massey and Denton and as another example of reliance on binaries, Pattillo-McCoy (1999) suggested that affluent and middle-class blacks literally occupy a buffer zone between white middle-class married-couple households in suburbia and poor black single-parent households in inner cities. We maintain that these dichotomous models of family types should be expanded to include the growing number of SALA households.

Urban sociologists should be concerned with how household composition (and SALA households in particular) affects racial residential segregation patterns for two important reasons. First, we know that people are not marrying and having children at the same rate as in previous years. People are staying single for longer periods of time, and currently a higher proportion of them end up never marrying than was the case a few decades ago. This phenomenon is more pronounced among some racial and ethnic groups, such as blacks (Frey and Berube, 2002). Second, previous studies have suggested that household composition—and life cycle factors more generally—affect mobility patterns and where people choose to reside (Guest, 1972; Howell and Frese, 1983; Johnson and Roseman, 1990; McAuley and Nutty, 1983; Rossi, 1955; Speare, Goldstein, and Frey, 1975). These factors in turn may affect levels of racial segregation through their interaction with patterns of discrimination, residential preferences that vary by household composition, and the economic constraints faced by different household types (we elaborate on these factors below). In short, while there are reasons to believe that household structure influences patterns of racial and ethnic residential segregation, there are few studies that directly focus on this issue. The goal of our study is to therefore provide a detailed analysis of how racial and ethnic residential segregation varies by household structure in U.S. metropolitan areas, focusing on black single adults living alone (SALA) households.

To this end, this study utilizes 2000 census data to: (1) compare the degree of residential segregation of black SALA households from white SALA households and from both white and black married-couple households1 and (2) explore the extent to which economic characteristics of households in metropolitan areas and other metropolitan area characteristics influence segregation patterns. This study contributes to the larger stratification literature by examining how a broader range of household types, especially SALA households, fits into ongoing discussions of life chances and racial residential segregation. This study also contributes to the urban sociology literature because SALA households are particularly prominent in urban areas, and increasingly so in the past few decades. SALA households are more likely to be renters than owners. SALA households are one-person households that potentially allow them to have more discretionary income for local consumption than suburban married-couple households with children. Finally, SALA households may have the proclivity to live in more racially diverse cities than other types of households, and our study examines the implications of these patterns for neighborhood-level segregation.

Theoretical Framework

A number of individual and structural factors affect patterns of racial residential segregation. One factor that has received relatively little attention is household type. This section discusses how household structure itself and closely related life course factors potentially affect levels and trends in racial residential segregation. Among household types, this paper pays particular attention to the residential patterns of SALA individuals. The discussion of residential patterns by household type is followed by a very brief review of common perspectives offered to explain racial residential patterns.

The Rise of SALA Households

SALA households comprised well over a quarter (27.8 percent) of all households in 2008 (U.S. Census Bureau 2008), up from 18.5 percent in 1973 and a mere 9.3 percent in 1950 (Kobrin, 1976). In the larger US context, half of all households are married-couple families (with or without children), close to one in six households (17%) are other-family types (with or without children), and approximately one-third (34%) are non-family types (U.S. Census Bureau, 2008). Members of a non-family household, as the U.S. Census Bureau defines it, are not related through blood or marriage. The category of non-family household can be divided into two subcategories: a one-person household (SALA), and two or more unrelated or unmarried persons sharing a living unit. The non-family household category may include unrelated adults who do not have a sexual relationship (housemates) as well as an unmarried heterosexual or homosexual partnership without children. Among non-family households, a majority (83%) live alone (U.S. Census Bureau, 2008). The latter households are the focus of our study.

There are a several earlier and pivotal studies that examine the rise of single-person households in the United States – what we are now coining SALA households (Guest, 1972; Kobrin, 1976; & Ruggles 1988). Kobrin (1976) and Ruggles (1988) noted the steady increase in the number of people living alone over the course of the 20th century, especially among young adults and the elderly. Today, a little over a third of SALA households are over the age of 65 (Frey and Berube, 2002).

Life course variables such as the presence of children and marital status help shape both the tastes for different kinds of dwellings and neighborhoods and the likelihood of acting on those tastes (Landale and Guest, 1985; Lee, Oropesa, and Kanan, 1994; McHugh, Gober, and Reid, 1990; South and Deane 1993). Guest (1972) found that larger households seek larger homes in low density suburban areas while SALA households seek smaller homes in higher density areas, i.e., central cities. Thus, SALA households self-segregate based on their stage in the life cycle, as they require less space than a household with a spouse and children (South and Deane 1993). Even married-couple households without children have residential patterns that more closely resemble those of families with children than nonfamily households, suggesting that the former households perhaps either anticipate having children soon or recently had children leave the home (Iceland et al., 2010; Frey and Berube, 2002).

More recent work by Frey and Berube (2002) describes how the proportion of nonfamily households (a vast majority which are SALA households, as reported above) has continued to grow, even in suburbs, though SALA households remain concentrated in central cities. Non-family households comprise 40 percent of central city households and 29 percent of suburban households, while corresponding figures for married-couple families are 39 percent and 56 percent, respectively. Their report and other work has indicated that suburbs have become more diverse along several dimensions (household composition, racial/ethnic diversity, economic diversity) in recent years, but this does not necessarily translate into declining racial or economic segregation at the neighborhood level in suburban counties (Farrell, 2008; Jargowsky, 2003).

Racial Residential Segregation

The three leading explanations for racial residential segregation are: (1) discrimination, (2) preferences, and (3) economics (Charles, 2003; Cutler, Glaeser, and Vigdor, 1999; Massey and Denton, 1993; Squires, Friedman, and Saidat, 2002). Previous research has indicated that segregation by race is higher than segregation by other kinds of demographic characteristics (White, 1987). Our focus here is to examine how the consideration of SALA households could enrich these three theoretical perspectives and deepen our understanding of black-white residential segregation patterns.

SALA Households and Residential Discrimination

Household type could be a conceivable basis for systematic discrimination in the housing market alongside race, class, and gender. Discrimination against blacks and other minority groups in the housing market has been widely documented (Ross and Turner, 2005; Turner and Ross, 2003; Turner et al., 2002). Over the years, these discriminatory practices have included real estate agents steering racial groups to certain neighborhoods and providing less information and assistance to minority home seekers, lenders’ unequal provision of access to mortgage credit or credit at high interest rates (e.g., subprime mortgages), and neighbors’ hostility (Goering and Wienk, 1996; Squires and Kubrin, 2006; Yinger, 1995).

Real estate agents may view SALAs, unlike married families, as more likely to be irresponsible, overly social, perpetual renters, or unstable. For these reasons, housing representatives may not show married couples real estate properties in areas dominated by SALA households and vice versa. This may result in more segregation between black SALA households and white households, and white married-couple households in particular, although perhaps relatively lower levels of segregation between white and black SALA households.

SALA Households and Residential Preferences

Residential preferences could interact with household structure to affect racial residential segregation patterns. Racial residential preferences have been documented in a number of studies (Bobo and Zubrinksky, 1996; Charles 2006; Farley et al., 1994; Krysan and Bader, 2007). Respondents of all races tend to express a desire to live in neighborhoods where people of the same ethnicity are present. Early ecological studies stemming from the Chicago School and popularized by Park, Burgess, and McKenzie (1925) suggest that people who are similar and at the same stage in their life cycle often sort themselves into relatively similar and homogenous areas.

Residential mobility is often a response to changing housing needs that are often a function of changing household composition (Rossi, 1955; Speare, Goldstein, and Frey, 1975). For example, married couples may desire more space than single individuals. In addition to space, families with children are also likely to be concerned about particular kinds of neighborhood amenities such as good schools, parks, and safe spaces (Rosenbaum and Friedman, 2001). Moving beyond the impact that preferences may have on the involuntary or voluntary sorting of household types, we must not overlook the idea of segregation among housing types. For instance, SALA households are more likely than married-couple households to occupy apartments, condominiums, and other residences in multi-unit dwellings. Furthermore, zoning regulations spatially isolate these dwellings from detached single-family units, suggesting that the geographical location of SALA households could be subject to powerful structural constrains (Guest 1972).

Conversely, SALA households, unlike married-couple households with children, may be in pursuit of a single social life and therefore favor areas where it is easier to meet other singles; such places may be relatively well-integrated racially or at least with respect to black and white SALA households. As mentioned above, SALA households are indeed overrepresented in central cities (Frey and Berube, 2002). Just one example of a racially diverse urban area is the Adams Morgan neighborhood in Washington, DC. The zip code in which it is located is 42 percent non-Hispanic white, 30 percent non-Hispanic black, and 20 percent Hispanic. At the same time, 56 percent of households in that zip code are SALA households—a significantly higher percent than in D.C. as a whole (44 percent) or the nation (26 percent) (U.S. Census Bureau, 2000). This at least anecdotally suggests that black and white SALA households in urban areas may be less segregated than are black SALA-white married-couple households.

Adams Morgan and similarly diverse communities in other cities are of significant interest to urban sociologists. Adams Morgan and the adjoining residential community, Mount Pleasant, while far from racially or socioeconomically homogeneous, are also to some extent gentrified (Modan 2007). Adams Morgan is a site of many restaurants and bars where singles congregate. It is the quintessential location for young people to meet and socialize, and at the same time parts of it have attracted immigrant settlement. There are several international restaurants, bars, coffee shops, and specialty stores flanked by a metro system, as well as row houses and apartments. It is a walking community, and day and evening events and attractions are easily accessible without a car. Adams Morgan has a very cosmopolitan feel. There is a sense that racial, social, cultural, and sexual orientation diversity is accepted, embraced, and welcomed in this urban space.

SALA Households and Economic Constraints

Household structure also may interact with economic factors to influence patterns of racial residential segregation. The economic perspective of racial segregation suggests that the spatial concentration of racial and ethnic groups reflects their relative financial status (Clark, 1989). As early as the 1920s, Robert Park and others argued that the spatial distance between blacks and whites was rooted at least in part in socioeconomic inequality (Park, Burgess, and McKenzie, 1925). For instance, married couples, as well as cohabiting couples and same-sex partners with children, are more likely to be dual-earner households, while single-person and single-parent households are more likely to be single-earner households. Unlike single-parent families, however, SALA households are less likely to have the burden of supporting dependent children. As a result, statistics generally show that singles are more likely than married-couple households to be poor, but less likely than single-parent households with children to be poor (Iceland, 2006). Additionally, when per-person income is calculated for middle class households, a similar pattern persists (Marsh et al., 2007). Thus, households vary by type in their access to economic resources that would let them afford to live in higher socioeconomic status areas—those often containing a higher proportion of whites.

Nevertheless, we also note that Marsh et al. (2007) found that black SALA households, a growing proportion of all black households, increasingly consist of college-educated homeowners. Based on 2000 data, these SALAs were the household type with the second largest share of black middle class households after married-couple households with children (Marsh et al. 2007). The perceived socioeconomic opportunities related to a college degree and the economic freedom of being childless may allow many SALA households, especially blacks SALA households, to integrate residentially more than previously believed.

We find the residential segregation literature, specifically the models and theories used to explain the persistence of segregation, to be a useful foundation for our research. The aim of our study is to build on the segregation literature by expanding the range of household types under consideration. Because household composition is known to have economic consequences, we do not know yet whether SALA households tend to congregate in certain areas or whether they are the most racially integrated of all households types. For this reason, we explore the residential segregation of SALA households. In addition, we examine how group and metropolitan characteristics influence these patterns. It has already been established that race and class are two possible factors that predict residential segregation. This study contributes new findings by exploring whether household type should be an additional factor for social scientists to examine in residential segregation models.

Hypotheses

Our data are not detailed enough to distinguish between the relative importance played by residential preferences, economics, and discrimination in shaping the residential patterns of SALA households. However, these perspectives do offer some general predictions that we examine in our analysis. First, drawing from the preferences approach, we offer the hypothesis that black and white SALA household segregation will be lower than black SALA and white married-couple household segregation, net of other factors. Second, based on economic factors, we further propose that the closer the ratio of income, homeownership, and poverty between black SALA households and white households (SALAs, and married couples), the lower the degree of segregation between these three groups. Third, from the discrimination framework, we theorize that racial residential segregation between blacks and whites (and more specifically black SALA households from white SALA households and white married-couple households) is high, regardless of household type and economic factors, although we again note that we cannot directly test the role of discrimination with our data.

Data and Methods

We present segregation estimates averaged for metropolitan statistical areas (MSAs), primary metropolitan statistical areas (PMSAs), and for the New England states, New England county metropolitan areas (NECMAs) together, hereafter referred to as metropolitan areas (MAs), as defined by the Office of Management and Budget (OMB) on June 30, 1999. Using this definition, there are 318 MAs in the U.S. We use census tracts to approximate neighborhoods. Census tracts typically have between 2,500 and 8,000 people, are defined with local input, are intended to represent neighborhoods, and typically do not change much from census to census, except to subdivide. In addition, census tracts are by far the unit most often selected by other researchers.

To examine the residential patterns of households by race and composition across neighborhoods in U.S. metropolitan areas, we use data from the 2000 Decennial Census Summary File 3 (SF3). We focus on metropolitan areas with at least 1,000 households each in the black SALA, black married-couple, white SALA, and white married-couple categories. Our analysis includes only counts of households and thus excludes all people in group quarters (such as prisons, dorms, or other kinds of group houses).

The first part of our analysis consists of a series of cross-tabulations showing the association between race, household type, and patterns of residential segregation. In particular, we focus on a few key comparisons. First, we calculate segregation indices for all households by race. Households are classified by the race of the householder, and the analysis focuses on those who were either non-Hispanic white or black. Then we calculate indices for three different categories: all households; single, living alone households (SALA); and married-couple households.

We measure segregation using the dissimilarity index (Dxy), a measure of evenness (Massey and Denton, 1988). Dissimilarity is the most widely used measure of residential segregation. We also conducted the analysis with the information theory index (also sometimes known as Theil’s H), but we only show results with Dxy because the conclusions do not change when using the information theory index.

The dissimilarity index is formally computed as:

Dxy=0.5∑∣(xi/X)-(yi/Y)∣

where X and Y represent the total number of households of a particular type in the metropolitan area as a whole, and xi and yi are the number of these household types within census tract i. When the index is converted to a percentage, it varies from zero (no separation or complete integration) to 100 (complete separation). Dxy can be understood as the percentage of a given group that would have to move to another census tract to make their distribution proportionally equal (Jakubs, 1977, 1979, 1981; Sims, 1999). Generally, scores below 30 are considered low, scores between 30 and 60 are moderate, and those above 60 are high (Iceland, 2009; Massey and Denton, 1993; Sims, 1999).

A descriptive figure based on race and household type with segregation scores will be followed by a multivariate analysis focusing on these same factors. We use several economic-based independent variables while controlling for various metropolitan area characteristics and for region. These analyses will shed light on the extent to which the variability in segregation by race and household type can be explained by socioeconomic differences across the groups.

Our dependent variables are the levels of segregation of black SALA households from white SALA households, white married-couple households, and black married-couple households. Given our interest in black SALA households and based on descriptive results, we run two sets of models. The first set of models addresses the segregation of black SALA households from white SALA households and white married-couple households. The second set examines black SALA household segregation, white SALA household segregation, and black married-couple household segregation in order to shed light on the role of race versus family structure in shaping levels of segregation.

Again, the goal here is to see whether differences in the segregation of black SALA households from other households are driven by socioeconomic differences across groups, or whether significant differences by household type remain. It is important to note that if significant differences by household type persist, we cannot definitively conclude whether these are being driven by the residential preferences of different kinds of households, by differential discrimination by household type, or by other factors.

We estimate the following general model:

Yji=B0+B1Xji+B2Zj+eji. (1)

where Yji is the dissimilarity score for metropolitan area j and household of interest i for each metropolitan area where at least 1,000 group i members are present, Xji is a vector of group i characteristics in metropolitan area j, and Zj is a vector of metropolitan characteristics for metropolitan area j. The unit of analysis is the metropolitan area, though models include multiple observations per metropolitan area that contain information on the different households of interest, depending on the model.

This approach essentially follows Massey and Denton’s (1989) and Iceland and Scopilliti’s (2008) strategy of pooling group metropolitan dissimilarity scores together and including dummy variables for each group comparison. For example, in the models, each metropolitan area contributes up to two observations: one indicates the dissimilarity index for black SALA-white SALA households and the other the dissimilarity index for black SALA-white married-couple households. A dummy variable for household type will indicate whether dissimilarity scores are higher for the black SALA-white SALA or black SALA-white married-couple households. Because the same metropolitan areas are included more than once in all of the models, we produce corrected standard errors by using Generalized Linear Regression models that account for the correlated error structure among the independent variables (i.e., because we are using repeated, clustered observations).

The X-vector variables in the regression models represent group i characteristics in metropolitan area j. They include median household income relative to the reference group, housing tenure (percentage owning homes) relative to the reference group, and poverty rate relative to the reference group. The expectation is that the closer groups are to parity, the lower the levels of segregation between them.

Z is a vector of metropolitan area characteristics that has been shown to be associated with segregation (Frey and Farley, 1996; Logan et al., 2004; Wilkes and Iceland, 2004). This includes percentage of the population that is black, proportion of the population 18 or older that is enrolled in college, percentage of the civilian labor force in manufacturing and government, percentage of the labor force in the military, percentage of the population over 65 years old, metropolitan area size, percentage of housing units built in the last 10 years, percentage of the metropolitan area population in the suburbs, and region. All of the regression models are unweighted. Our models do, however, include controls for the size of the group in question (an Xji variable).

Our hypotheses on the association of these Z vector variables with segregation and are consistent with those in Lee et al. (2008). We hypothesize that segregation will be greater in metropolitan areas with a larger percentage of blacks (isolated black “ghettos” are more common in such places). Segregation should be lower in metropolitan areas with institutions that are often thought to promote racial equality—MAs with a higher proportion of residents in college, government, or the military. Segregation will be greater in areas with a higher percentage of adults over 65 years old because older whites are thought to have a greater preference for racial homogeneity. We hypothesize that segregation is higher in metropolitan areas with a larger population size and therefore take the logarithm of total population size. Segregation is expected to be lower in metropolitan areas with more new housing, as new housing construction may circumvent the racialized reputation of older neighborhoods. Finally, we use region characteristics and follow previous researcher by using the West as the reference category. The West generally has lower levels of black-white segregation and does not have the long history of segregation and discrimination relative to the Northeast, Midwest, and South.

Limitations

Two important limitations of our analyses need to be noted. First, the cross-sectional nature of the data imposes limitations on claims of causality. Segregation can affect groups’ levels of income, homeownership, and poverty and vice versa. Rather, our goal is to examine the relationship between segregation and these group characteristics, as well as how these characteristics might help explain the broader association between household type and segregation. Second, ideally we would like to have direct measures of residential preferences in our models, but these are not available by group (and not at the metropolitan level). Likewise, our analyses lack measures of discrimination. Without these, we can at best make indirect inferences about the effect of these factors on observed residential patterns.

Results

Before reporting our results of residential segregation by race and household type, we note that the overall SALA-married couple dissimilarity index (when both blacks and whites are combined) in 2000 is only 0.252. This score shows that household type is a weaker basis for segregation than race, consistent with previous research by White (1987).

Figure 1 shows levels of segregation by race and household type in 2000 using the dissimilarity index. The segregation estimates are weighted by the population size of group of interest (all black households and black SALA households for the two set of bars in the figure, respectively). This weighting gives relatively little weight to metropolitan areas with small populations of these groups. The first set of bars shows the segregation of all black households from various groups. The next set of bars focuses on the segregation of black SALA households from different groups.

Figure 1.

Figure 1

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Segregation of Black Households by Household Type and Reference Group: Dissimilarity, 2000

Source: Census 2000 Summary File 3.

Notes: Means are weighted. Metropolitan areas with at least 1,000 members in the reference group, and group of interest are included in the calculations.

All black households are more segregated (0.689) from white married-couple households than from other kinds of white households and from white SALA households in particular (0.624). Segregation of all black households from whites is nevertheless high, regardless of the type of white household (as mentioned above, the rule of thumb is that D scores above 0.60 are high in absolute terms).

Now we turn our attention to the segregation patterns of black SALA households. Similar to all black households in relation to various kinds of white households, black SALA households are more segregated from white married-couple households (0.725) and least segregated from white SALA households (0.633). When we turn our attention to both sets of bars, we find two interesting patterns. First, consistent across the two sets of bars is that black households are more segregated from white married-couple households than from white households in general. It is clear, however, that all black households are less segregated from white married-couple households than are black SALA households. This could suggest that marriage (or non SALA status) may buy blacks into more integrated neighborhoods to a greater extent than other types of black households.

The other interesting finding is the asymmetry in the patterns of segregation of white SALA and black SALA households. White SALA households are similarly segregated from black SALA households (0.633) as from all black households (0.624). In contrast, black SALA households are less segregated from white SALA households (0.633) than from all white households (0.675). This could, in part, reflect socioeconomic differences across various groups. In particular, it could be that black SALA households are less segregated from white SALA households than from white married-couple households because of smaller income differences among the former two groups, as white married-couple households are generally the most affluent of all the household types considered here. In contrast, white SALA households, which tend to have higher incomes, may be more similar to other kinds of black households with multiple earners than to black SALA households.

Focusing just on the black segregation patterns, as expected, we see that black SALA households are less segregated from black married-couple households (0.328) than from other kinds of white households, suggesting that race plays a key role in the residential patterns of blacks, and that race is indeed more important than household structure in shaping residential patterns in U.S. metropolitan areas.

Multivariate Analysis

Our multivariate analysis examines in more detail the role that socioeconomic factors—as a consequence of household type—play in producing the patterns seen in Figure 1. More specifically, we investigate the extent to which factors such as income, homeownership, and poverty are associated with levels of segregation among household types. We hypothesize that in metropolitan areas where socioeconomic differences between black SALA households and white households are smaller segregation will be lower. We also test whether differences in the segregation of black SALA households from white SALA households and from both white and black married-couple households is being driven by socioeconomic factors, or differences remain even when we control for these and other factors.

Table 1 shows descriptive statistics for the variables in our analyses. The segregation estimates are unweighted, which gives relatively equal weight to metropolitan areas with small populations. We run the regressions using unweighted data (in contrast to the numbers in descriptive Figure 1) because here we are interested in examining which factors explain the variation in segregation patterns across metropolitan areas.2 Table 1 also includes socioeconomic type ratios for SALA and married households by race and descriptive statistics for the control variables and region.

Table 1.

Descriptive Statistics

Variable Mean SD
Segregation Indices
 Black SALA/White SALA .556 .107
 Black SALA/White Married .658 .107
 Black SALA/Black Married .330 .083
Household Median Income Ratios
 Black SALA/White SALA .670 .107
 Black SALA/White Married .273 .050
 Black SALA/Black Married .336 .054
Homeownership Ratios
 Black SALA/White SALA .605 .121
 Black SALA/White Married .410 .098
 Black SALA/Black Married .524 .097
Poverty Rate Ratios
 Black SALA/White SALA 2.062 .456
 Black SALA/White Married 11.188 3.641
 Black SALA/Black Married 3.909 1.020
MSA Characteristics
 % black .152 .108
 % of people enrolled in college .085 .044
 % in manufacturing .141 .063
 % over 65 years old .124 .032
 % in government .157 .050
 % in military .011 .031
 Population size 13.20 1.058
 % new housing .181 .070
 % not in central city .620 .193
Region
 West .120 .326
 North East .154 .361
 Midwest .211 .409
 South .514 .501
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Source: Census 2000 Summary File 3.

Notes: Includes metropolitan areas with at least 1000 black SALA households in 2000.

Means are unweighted by the size of the group.

The overall dissimilarity index again suggests that black SALA households are the least segregated from black married-couple households (0.330) and the most segregated from white married-couple households (0.658). As expected, the median income is higher for white (SALA and married couples) than black households (data not shown). Black and white SALA households have the highest income ratio (0.670) while black SALA households and white married-couple households have the lowest income ratio (0.273). Specifically, black SALA households’ median income is a quarter of white married-couple households’ income, a third of black married-couple households’ income and two thirds of white SALA households’ income. It is worth noting that SALA households are more likely to be single earning households than married-couple households, and when measuring median household income, this might account for some of the disparity in income between SALA households and married-couple households. The median income for each household is as follows: black SALA ($16,842); white SALA ($24,972); black married couple ($49,565); and white married couple ($61,157). Close to 35 percent of black SALA households live in owner occupied homes compared with 57 percent for white SALA households, 66 percent of black married-couple households, and 85 percent for white married-couple households. Similar to the income results, the ratio of homeownership is highest among black and white SALA households (0.605) and lowest between black SALA households and white married-couple households (0.410).

We have to reverse our thinking when it comes to the poverty ratio. The higher the ratio, the greater the poverty disparity between the two household types under consideration. Take, for example, black SALA households and white married-couple households with a poverty ratio of 11.19, which is the highest of the household pairings. This suggests that considerably more black SALA households are impoverished than white married-couple households. Put another way, the poverty rate of black SALA households is over 11 times that of white married-couple households. The lowest poverty ratio is between black SALA and white SALA households (2.062). The proportion in poverty for each household type is as follows: black SALA (0.297); white SALA (0.151); white married couple (0.029); and black married couple (0.081).

As described above, we run two sets of models—one for the segregation of black SALA households from white SALA households and white married-couple households (Table 2) and the other for the segregation of black SALA households from white SALA households and black married-couple households (Table 3). There are six models in each table. Model 1 is the baseline model, while models 2 to 4 include the economic variables one at a time. Model 5 includes these economic factors simultaneously, and Model 6 is the full model with controls for metropolitan area characteristics and region.

Table 2.

Generalized Linear Regressions Indicating the Association Between Group and Metropolitan Characteristics With Levels of Dissimilarity of Black SALA Households and White SALA Households from Black SALA Households and White Married-Couple Households, 2000

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Intercept 0.658** 0.693** 0.766** 0.604** 0.740** 0.273**
Household Type
 White married couples (reference)
 White SALA −0.103** −0.052** −0.051** −0.059** 0.043* 0.015
Group Specific Economic
 Income Ratio −0.128** −0.158** −0.151**
 Tenure Ratio −0.262** −0.215** −0.100*
 Poverty Ratio 0.005** 0.004** 0.004**
MSA
 % black 0.368
 % of people enrolled in college −0.089
 % in manufacturing −0.196
 % in government −0.401**
 % in military −0.180
 % over 65 years old 0.438**
 Population size 0.037**
 % of new housing −0.421**
 % of population in suburbs 0.041
Region
 West (omitted) --------
 Northeast 0.019
 Midwest 0.067**
 South 0.017
N 416 416 416 416 416 416
−Log -Likelihood 1275.86 1381.11 1383.65 1756.55 1809.22 3336.34
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Notes: The unit of analysis is the segregation score of a group in a given metropolitan area (there are two observations for each MA). This includes metropolitan areas with at least 1000 Black SALA households. See the Data and Methods section for details.

*

p < .05;

**

p < .01 (two-tailed tests)

Table 3.

Generalized Linear Regressions Indicating the Association Between Group and Metropolitan Characteristics With Levels of Dissimilarity of Black SALA Households and White SALA Households from Black SALA Households and Black Married-Couple Households, 2000

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Intercept 0.330** 0.332** 0.502** 0.223** 0.412** 0.059
Household Type
 Black married couples (reference)
 White SALA 0.225** 0.227** 0.252** 0.276** 0.274** 0.283**
Group Specific Economic
 Income Ratio −0.006 0.193 −0.088
 Tenure Ratio −0.328** −0.295** −0.074
 Poverty Ratio 0.027 0.017** 0.012**
MSA
 % black −0.137**
 % of people enrolled in college 0.041
 % in manufacturing −0.163*
 % in government −0.216
 % in military −0.195
 % over 65 years old 0.255*
 Population size 0.030**
 % of new housing −0.222**
 % of population in suburbs −0.003
Region
 West (omitted) --------
 Northeast −0.024
 Midwest −0.015
 South −0.027*
N 416 416 416 416 416 416
−Log -Likelihood 662.99 698.03 642.60 737.45 753.34 1357.10
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Notes: The unit of analysis is the segregation score of a group in a given metropolitan area (there are two observations for each MA). This includes metropolitan areas with at least 1000 Black SALA households. See the Data and Methods section for details.

*

p < .05;

**

p < .01 (two-tailed tests)

Black SALA, White SALA and White Married Couples

Results from Model 1 of Table 2 indicate that, consistent with our descriptive findings, black SALA-white SALA households on average have considerably lower levels of segregation (−0.103 points) than black SALA-white married-couple households (dissimilarity scores range from 0 to 1). Moving from Model 1 to Model 2, we see that the effect of black SALA-white SALA household segregation is cut in nearly half (it is cut to −0.052) by the inclusion of the income ratio variable. As expected, this coefficient indicates that greater median income parity is associated with lower levels of segregation between black SALA households and both types of white households.

When we look at the other two economic variables, tenure and poverty, a generally similar pattern persists. Homeownership parity reduces the size of the black SALA-white SALA household coefficient from −0.103 (Model 1) to −0.051 (Model 3). The poverty ratio variable reduces the white SALA household coefficient to −0.059 (Model 4). The coefficients of the tenure and poverty ratio variables are in the expected direction: greater parity is associated with less segregation.3

When all three indicators are included in Model 5, two things happen. First, the economic factors maintain their statistical significance and direction. Second, the sign of the coefficient for the white SALA household indicator in fact changes direction, becoming positive. This shift indicates that black SALA households would in fact be more segregated from white SALA households than white married-couple households were it not for the economic similarities between the two SALA groups. However, this coefficient becomes insignificant in the final full model, which includes metropolitan area and region controls, indicating that the positive sign in Model 5 was mainly a function of other metropolitan characteristics. Income and poverty ratios remain statistically significant in the expected direction in the final model, though the coefficient for tenure becomes less significant.

Among the control variables in model 6, we see that metropolitan areas with a higher percentage of government and newer home construction have lower levels of black-white household segregation. On the other hand, larger metropolitan areas with a large retired community have higher levels of segregation. Metropolitan areas in the Midwest have higher black-white household segregation than areas in the West. Our results are generally consistent with previous studies (e.g., Logan, Stults, and Farley, 2004; Lee et al., 2008).

Overall, the results in Table 2 provide support for the economic perspective: black SALA households are more segregated from white married-couple households than from white SALA households (Model 1), but this appears to be primarily attributable to socioeconomic differences across these groups (Model 5). In short, black SALA households are more segregated from white married-couple households than from white SALA households mainly because black and white SALA households are more similar in their socioeconomic characteristics (income and poverty, and to a lesser extent tenure).

Black SALA, White SALA and Black Married Couples

Results from Table 3 are slightly different than the results presented in Table 2. From Table 3 we find that black SALA-white SALA households on average have significantly higher levels of segregation (0.225) than black SALA-black married-couple households, as expected. Unlike the inter-racial household comparisons results showing when income is added to the model black SALA-white SALA household segregation decreases, when income is added in Table 3 Model 2, the coefficient for the segregation among black SALA-white SALA households (0.227) remains the same as Model 1.

Greater tenure parity decreases the segregation between black SALA households and the other household types (-0.328). Unlike the previous findings for black SALA households-white households, it is clear from Models 2 to 4 that the effect of homeownership is stronger and more robust than the other economic indicators. In the Table 2 comparisons we found that income considerably reduces segregation between black SALA households and white households. When all three economic indicators are included in Model 5 of Table 3, however, the coefficient for household type (0.274) changes relatively little from the baseline model (0.225), suggesting that group economic characteristics, while at times significantly related to segregation, do not explain why black SALA households are more segregated from white SALA households than from black married-couple households. Further, the fact that the Model 5 white SALA household coefficient is higher than in Model 1 suggests that if black and white SALA households were not more similar in their economic characteristics, segregation (relative to the segregation of black SALA households from black married-couple households) would be even higher. In the full model (Model 6) the household type coefficient remains significant and at about the same magnitude (0.283), indicating that the group and metropolitan characteristics do not explain the relatively higher levels of black SALA household segregation from white SALA households.

Among the control variables in Model 6, we see that the percentage of blacks in a metropolitan area, employment in manufacturing jobs, and newer housing are all negatively associated with black SALA household segregation from the other groups in the table (white SALA households and black married-couple households). On the other hand, larger metropolitan areas with a large retired community are positively associated with black SALA household segregation. Metropolitan areas in the South have lower black SALA household segregation than those in the West.

Conclusion

The goal of this study was to examine the racial residential segregation patterns of a particular—and growing—household type: those single and living alone (SALA). We currently know little about this group. With this analysis, we also more broadly hoped to draw attention to the role—albeit a modest, subtle one—that household composition plays in shaping racial and ethnic residential patterns. We used data from the 2000 decennial censuses to calculate dissimilarity indices for black and white households in U.S. metropolitan areas, focusing on the segregation of black SALA households from white SALA households and from both white and black married-couple households. We then conducted multivariate analyses to determine the extent to which differences in residential segregation can be explained by average group specific characteristics—such as income, homeownership and poverty—controlling for metropolitan characteristics. Our findings provide broad support for the economic perspective. Specifically, black SALA households are less segregated from white SALA households than from white married-couple households, but this difference is mainly attributable to the economic similarities between the SALA groups.

The economic perspective suggests that segregation among groups reflects their relative financial status (Clark, 1989). The average socioeconomic characteristics of SALA households are generally associated with higher levels of segregation, because SALA households generally have higher poverty rates, lower incomes, and lower homeownership rates. However, black SALA households have smaller income, homeownership, and poverty differences from white SALA households (and black married-couple households) than white married-couple households. Thus, these characteristics explain all of their higher levels of segregation from white married-couple households than from white SALA households.

The fact that economic factors explain differences in segregation by household type does not necessarily mean that household type is in itself unimportant. After all, household type and the economic characteristics we examined are not independent of each other. Married-couple households, by having at least two adults present, generally have greater resources at their disposal. Likewise, SALA households by definition have only one potential earner. SALA households themselves are often either young, and thus early in their careers, or old and with limited earnings capacity. These factors suggests that the process of changing households types (increases in black SALA households) could serve to put upward pressure on segregation (holding other factors constant) in the coming years. However, Marsh et al. (2007) point out that black middle class SALAs are a growing demographic among black households, and the growth of this group could serve downward pressure on segregation over time.

We nonetheless emphasize caution in focusing too much on economic factors and household composition in shaping black-white residential patterns. First, blacks continue to be considerably segregated from whites. In absolute terms, levels of segregation are very high between black SALA households and white married-couple households, merely high between black SALA households and white SALA households, and low to moderate between black SALA households and black married-couple households. Black-white racial polarization and the continued discrimination against blacks in the housing market still likely play important roles in shaping residential patterns (Ross and Turner, 2005). Thus, it could be argued that high overall levels of segregation between black and white households provide support for the discrimination and preferences perspectives. These high levels of racial segregation suggest that black households live in very different neighborhoods than white households regardless of their socioeconomic characteristics. However, economic factors could play some role, because we see some differences among blacks and whites by household type, and some effect of socioeconomic characteristics—mainly income, homeownership and poverty.

Finally, although the purpose of this analysis was to shed light on the extent to which variability in segregation by race and household type can be explained by socioeconomic differences across the groups, some questions remain unanswered. For one, it would be useful to look at a more detailed race, class and household type analysis. As mentioned above, middle class SALA households may represent a different kind of social mobility than the more frequently discussed middle-class married-couple households. Wilson (1978) argues that racial barriers are less likely to constrain blacks’ life chances than social class. In essence, given appropriate social and economic capital, Wilson suggests that blacks can freely move into affluent suburbs and not suffer constraints based on their race. As noted earlier, Massey and Denton have disputed this claim. One avenue of future research would be to examine the residential patterns of black middle-class SALA households in particular. Unfortunately, the lack of public-use SF3 data in the 2000 data on this particular household type limits this kind of analysis. Obtaining data from other sources on these households would thus be useful for showing if middle class SALA households live in different conditions or circumstances than other middle class households.

Another avenue for future research would be to further examine the notion put forth by Iceland, Sharpe and Steinmetz (2005) that higher socioeconomic status blacks are moderately less segregated than lower socioeconomic status blacks with respect to white neighbors, but higher socioeconomic status blacks tend to have higher incomes and more education than their white neighbors. By conducting a more detailed analysis of the segregation patterns of middle class SALA households, as well as their individual characteristics and those of their neighbors, we can speculate that besides education and income, these SALA households may have lower wealth or net worth–which may offset their advantage in socioeconomic status and have some bearing on their segregation patterns.

The last avenue to consider addressing is based on the notion by Charles (2003) that it is important to look at residential patterns for multiple ethnic groups. It would be interesting to investigate if SALA households are more likely to live residentially integrated with SALA households from other racial and ethnic groups. This would include a cross-comparison of all combinations of household types in all racial and ethnic categories. SALA households, their growing proportion of all households, their increasing presence in the middle class, and their perceived desire for large urban areas to meet other singles, may have the economic freedom and the household structure (childlessness) necessary to much more likely integrate residentially than other households types.

Footnotes

1

This study is limited to black and white households. Although other racial and ethnic groups have a presence of SALA households, they are lower than the roughly one in four black and white households that are SALA households. The lower percentages of these other racial and ethnic groups do not suggest that they are uninteresting. However, for practical reasons, an analysis that includes multiple racial and ethnic groups by various household types is out of the scope of our present study.

2

In other words, while Figure 1 describes the extent of segregation experienced by the typical black household and typical black SALA household, the multivariate analysis focuses on what explains the variation in metropolitan-level segregation. Weighted black-white segregation scores are generally higher than unweighted ones because blacks tend to be more segregated in metropolitan areas with larger black populations (Iceland, Weinberg, and Steinmetz 2002).

3

As mentioned above, in contrast to income and tenure ratios, black SALAs have higher poverty levels than both reference groups. In this case, then, a higher ratio of black SALA poverty to reference group poverty indicates greater disparity, while higher income and tenure ratios indicate greater parity (see Table 1).

Contributor Information

Kris Marsh, Email: kmarsh1@umd.edu, Associate Professor, University of Maryland.

John Iceland, Email: jdi10@psu.edu, Professor of Sociology and Demography, Department Head, Department of Sociology and Criminology, The Pennsylvania State University.

References

  1. Adelman RM. The Roles of Race, Class and Residential Preferences in the Neighborhood Racial Composition of Middle-Class Blacks and Whites. Social Science Quarterly. 2005;85:209–228. [Google Scholar]
  2. Adelman RM. Neighborhood Opportunities, Race, and Class: The Black Middle Class and Residential Segregation. City & Community. 2004;3:43–63. [Google Scholar]
  3. Bobo L, Zubrinsky CL. Attitudes Toward Residential Integration: Perceived Status Differences, Mere In-Group Preference, or Racial Prejudice? Social Forces. 1996;74:883–909. [Google Scholar]
  4. Charles CZ. Neighborhood Racial-Composition Preferences: Evidence from a Multiethnic Metropolis. Social Problems. 2000;47:370–407. [Google Scholar]
  5. Charles CZ. Dynamics of Racial Residential Segregation. Annual Review of Sociology. 2003;29:167–207. [Google Scholar]
  6. Charles CZ. Won’t You Be My Neighbor: Race, Class, and Residence in Los Angeles. New York: Russell Sage Foundation; 2006. [Google Scholar]
  7. Clark WAV. Residential Preferences and Neighborhood Racial Segregation: A Test of the Shelling Segregation Model. Demography. 1989;28:1–19. [PubMed] [Google Scholar]
  8. Culter DM, Glaeser EL, Vigdor JL. The Rise and Decline of the American Ghetto. Journal of Political Economy. 1999;107:455–506. [Google Scholar]
  9. Darden JT. A Socioeconomic Status and Racial Residential Segregation: Blacks and Hispanics in Chicago. International Journal of Comparative Sociology. 1987;28:1–13. [Google Scholar]
  10. Erbe BM. Race and Socioeconomic Segregation. American Sociological Review. 1975;40:801–812. [Google Scholar]
  11. Farley R. Blacks, Hispanics, and White Ethnic Groups: Are Blacks Uniquely Disadvantaged? The American Economic Review. 1990;80:237–241. [Google Scholar]
  12. Farley R, Steeh C, Krysan M, Jackson T, Reeves K. Stereotypes and Segregation: Neighborhoods in the Detroit Area. American Journal of Sociology. 1994;100:750–80. [Google Scholar]
  13. Farrell CR. Bifurcation, Fragmentation, or Integration? The Racial and Geographic Structure of U.S. Metropolitan Segregation, 1990–2000. Urban Studies. 2008;45(3):467–499. [Google Scholar]
  14. Fischer MJ. The Relative Importance of Income and Race in Determining Residential Outcomes in U.S. Urban Areas, 1970–2000. Urban Affairs Review. 2003;38:669–696. [Google Scholar]
  15. Frey WH, Berube A. City Families and Suburban Singles: An Emerging Household Story from Census 2000. 2002. The Brookings Institution Census 2000 Series Report (February) [Google Scholar]
  16. Goering J, Wienk R. Mortgage Lending, Racial Discrimination, and Federal Policy. Washington, DC: Urban Institute Press; 1996. [Google Scholar]
  17. Guest AM. Patterns of Family Location. Demography. 1972;9:156–171. [PubMed] [Google Scholar]
  18. Howell FM, Wolfgang F. Size of Place, Residential Preferences and the Life Cycle: How People Come to Like Where They Live. American Sociological Review. 1983;48:569–80. [Google Scholar]
  19. Iceland J. Where We Live Now: Immigration and Race in the United States. Berkeley, CA: University of California Press; 2009. [Google Scholar]
  20. Iceland J. Poverty in America. 2. Berkeley, CA: University of California Press; 2006. [Google Scholar]
  21. Iceland J. Beyond Black and White: Residential Segregation in Multiethnic America. Social Science Research. 2004;33, 2(June):248–271. [Google Scholar]
  22. Iceland J, Goyette KA, Nelson KA, Chan C. Racial and Ethnic Residential Segregation and Household Structure: A Research Note. Social Science Research. 2010;39(1):39–47. doi: 10.1016/j.ssresearch.2009.06.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Iceland J, Weinberg DH, Steinmetz E. US Census Bureau, Census Special Report, CENSR-3. Washington, DC: U.S. Government Printing Office; 2002. Racial and Ethnic Residential Segregation in the United States: 1980–2000. [Google Scholar]
  24. Iceland J, Sharpe C, Steinmetz E. Class Differences in African American Residential Patterns in US Metropolitan Areas: 1990–2000. Social Science Research. 2005;34:252–266. [Google Scholar]
  25. Iceland J, Scopilliti M. Immigrant Residential Segregation in U.S. Metropolitan Areas, 1990–2000. Demography. 2008;45:79–94. doi: 10.1353/dem.2008.0009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Iceland J, Wilkes R. Does Socioeconomic Status Matter? Race, Class, and Residential Segregation. Social Problems. 2006;52:248–273. [Google Scholar]
  27. Jargowsky P. Stunning Progress, Hidden Problems: The Dramatic Decline of Concentrated Poverty in the 1990s. 2003. The Brookings Institution Center on Urban and Metropolitan Policy report (May) [Google Scholar]
  28. Jakubs JF. Residential Segregation: The Taueber Index Reconsidered. Journal of Regional Science. 1977;17:281–303. [Google Scholar]
  29. Johnson JH, Jr, Roseman CC. Increasing Black Outmigration from Los Angeles: The Role of Household Dynamics and Kinship Systems. Annals of the Association of American Geographers. 1990;80:205–222. [Google Scholar]
  30. Kobin F. The Fall in Households Size and the Rise of the Primary Individual in the United States. Demography. 1976;13:127–138. [PubMed] [Google Scholar]
  31. Krysan M. Whites Who Say They’d Flee: Who Are They, and Why Would They Leave? Demography. 2002;39:675–696. doi: 10.1353/dem.2002.0037. [DOI] [PubMed] [Google Scholar]
  32. Krysan M, Bader M. Perceiving the Metropolis: Seeing the City Through a Prism of Race. Social Forces. 2007;86:699–633. [Google Scholar]
  33. Lacy KR. Black Spaces, Black Places: Strategic Assimilation and Identity Construction in Middle-Class Suburbia. Ethnic and Racial Studies. 2004;27:908–930. [Google Scholar]
  34. Landale NS, Guest A. Constraints, Satisfaction, and Residential Mobility: Speare’s Model Reconsidered. Demography. 1985;22:199–222. [PubMed] [Google Scholar]
  35. Lee BA, Oropesa RS, Kanan JW. Neighborhood Context and Residential Mobility. Demography. 1994;31 (2):249–270. [PubMed] [Google Scholar]
  36. Lee BA, Reardon SF, Firebaugh G, Farrell CR, Matthews SA, O’Sullivan D. Beyond the Census Tract: Patterns and Determinants of Racial Segregation at Multiple Geographic Scales. American Sociological Review. 2008;73:766–791. doi: 10.1177/000312240807300504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Logan JR, Stults BJ, Farley R. Segregation of Minorities in the Metropolis: Two Decades of Change. Demography. 2004;41:1–22. doi: 10.1353/dem.2004.0007. [DOI] [PubMed] [Google Scholar]
  38. Logan JR, Alba RD, Leung SY. Minority Access to White Suburbs: A Multiregional Comparison. Social Forces. 1996;74:851–882. [Google Scholar]
  39. Logan JR, Zhang W. Identifying Ethnic Neighborhoods with Census Data: Group Concentration and Spatial Clustering. In: Goodchild Michael, Janelle Donald., editors. Spatially Integrated Social Science. New York: Oxford University Press; 2004. pp. 113–126. [Google Scholar]
  40. Marsh K, Darity WA, Cohen PN, Casper LM, Salter D. The Emerging Black Middle Class: Single and Living Alone. Social Forces. 2007;86:735–762. [Google Scholar]
  41. Massey DS, Denton NA. American Apartheid: Segregation and the Making of the Underclass. Cambridge, MA: Harvard University Press; 1993. [Google Scholar]
  42. Massey DS, Denton NA. The Dimensions of Residential Segregation. Social Forces. 1988;67:281–315. [Google Scholar]
  43. McAuley WJ, Nutty CL. Residential Preferences and Moving Behavior: A Family Life-Cycle Analysis. Journal of Marriage and Family. 1982;82:301–09. [Google Scholar]
  44. McHugh KE, Gober P, Reid N. Determinants of Short- and Long-term Mobility Expectations for Home Owners and Renters. Demography. 1990;27:81–95. [PubMed] [Google Scholar]
  45. Modan GG. Turf Wars: Discourse, Diversity, and the Politics of Place. Malden, MA: Blackwell Publishing; 2007. [Google Scholar]
  46. Montgomery AF. Living in Each Other’s Pockets: The Navigation of Social Distances by Middle Class Families in Los Angeles. City and Community. 2006;5:425–450. [Google Scholar]
  47. Park R, Burgess EW, McKenzie RD. The City. Chicago: University of Chicago Press; 1925. [Google Scholar]
  48. Pattillo-McCoy M. Black Picket Fences: Privilege and Peril in the Black Middle Class Neighborhood. Chicago: University of Chicago Press; 1999. [Google Scholar]
  49. Quillian L. Why is Black-White Residential Segregation So Persistent? Evidence on Three Theories from Migration Data. Social Science Research. 2002;31:197–229. [Google Scholar]
  50. Roseman CC, Lee SW. Linked and Independent African American Migration from Los Angeles. Professional Geographer. 1998;50:204–214. [Google Scholar]
  51. Rosenbaum E, Friedman SR. Differences in the Locational Attainment of Immigrant and Native-Born Households with Children in New York City. Demography. 2001;38:337–348. doi: 10.1353/dem.2001.0030. [DOI] [PubMed] [Google Scholar]
  52. Ross SL, Turner MA. Housing Discrimination in Metropolitan America: Explaining Changes between 1989 and 2000. Social Problems. 2005;52:152–180. [Google Scholar]
  53. Rossi PH. Why Families Move: A Study in the Social Psychology of Urban Residential Mobility. New York: The Free Press; 1955. [Google Scholar]
  54. South SJ, Deane GD. Race and Residential Mobility: Individual Determinants and Structural Constraints. Social Forces. 1993;72(1):147–167. [Google Scholar]
  55. Speare A, Goldstein S, Frey WH. Residential Mobility, Migration, and Metropolitan Change. Cambridge, MA: Ballinger; 1975. [Google Scholar]
  56. Squires GD, Kubrin CE. Privileged Places: Race, Residence, and the Structure of Opportunity. Boulder: Lynne Rienner; 2006. [Google Scholar]
  57. Squires GD, Friedman S, Saidat CE. Experiencing Residential Segregation. Urban Affairs Review. 2002;38:155–183. [Google Scholar]
  58. Turner MA, Ross SL. Discrimination in Metropolitan Housing Markets: Phase 2—Asians and Pacific Islanders of the HDS 2000. Washington, DC: US Department of Housing and Urban Development; 2003. [Google Scholar]
  59. Turner MA, Ross SL, Galster G, Yinger J. Discrimination in Metropolitan Housing Markets: National Results from Phase 1 of the Housing Discrimination Study (HDS) Washington, DC: U.S. Department of Housing and Urban Development; 2002. [Google Scholar]
  60. U.S. Census Bureau. Households and Families. American Community Survey data, Table S1101 (1-Year Estimates) American FactFinder. 2008 http://factfinder.census.gov, Retrieved December 11, 2009.
  61. U.S. Census Bureau. Household size, household type, and presence of own children. 2000 Census SF1 data, TP18 American FactFinder. 2000 http://factfinder.census.govRetrieved May 2, 2010.
  62. White M. American Neighborhoods and Residential Differentiation. New York: Russell Sage Foundation; 1987. [Google Scholar]
  63. Wilson WJ. The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press; 1978. [Google Scholar]
  64. Wilson WJ. The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy. Chicago: University of Chicago Press; 1987. [Google Scholar]
  65. Yinger J. Closed Doors, Opportunities Lost: The Continuing Costs of Housing Discrimination. New York: Russell Sage Foundation; 1995. [Google Scholar]

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