Abstract

Gender asymmetry in mixed-race heterosexual partnerships and marriages is common. For instance, black men marry or partner with white women at a far higher rate than white men marry or partner with black women. This article asks if such gender asymmetries relate to the racial character of the neighborhoods in which households headed by mixed-race couples live. Gendered power imbalances within households generally play into decisions about where to live or where to move (i.e., men typically benefit more than women), and we find the same in mixed-race couple arrangements and residential attainment. Gender interacts with race to produce a measurable race-by-gender effect. Specifically, we report a positive relationship between the percentage white in a neighborhood and the presence of households headed by mixed-race couples with a white male partner. The opposite holds for households headed by white-blacks and white-Latinos if the female partner is white; they are drawn to predominantly nonwhite neighborhoods. The results have implications for investigations of residential location attainment, neighborhood segregation analysis, and mixed-race studies.

Introduction

Some of the most striking aspects of racial mixing in the United States are the gender asymmetries associated with heterosexual mixed-race partnerships. Asian women and white men are much more likely to marry or partner than Asian men and white women, for example. In contrast, the incidence of black men being married to or partnered with white women is far more likely than the reverse. To complicate things further, marriage and partnership between a Latina and a white1 man is roughly the same as the likelihood of a marriage or partnership between a white woman and a Latino (cf. Passel et al. 2010). These configurations originate in the complex intersections of race and gender. Interpretations of these patterns range widely across a palette of theories, ontologies, and methodologies, but no researcher, as far as we are aware, has asked whether the gender asymmetries in mixed-race partnering have spatial expressions. This study takes an interest in these geographies and to this general question: do the gendered patterns of households headed by mixed-race couples in the United States have distinctive cartographies at the neighborhood level? Specifically, is the racial composition of neighborhoods in which mixed-race couples live contingent on gender?

The fact of gender asymmetry in racially mixed couples is plain to see, yet the issue of how to translate the effect of entrenched gender relations in particular types of mixed-race partnerships to space is challenging. Such a project has to wrestle with the unresolved debate over the forces that produce such asymmetries as well as face up to the form and fluidity of U.S. racial hierarchies while weighing considerations of gendered axes of power among couples in households. It also has to fold all this into the mix of household mobility and location as well as the geographical scale of analysis. Accordingly, we confine the empirical ambitions of this study to an examination of the neighborhood residential patterns of a sample of heterosexual mixed-race couples taken from 12 large U.S. metropolitan areas. These places contain a considerable share of all mixed-race couples in the country and consequently have sufficient numbers of the most frequently observed types of such partnerships for analysis at the census tract scale. Restricted Census 2000 long-form data furnish the necessary fine-grained information needed for the investigation.

In terms of theory, scholars usually understand the racial geography of urban residential spaces by relying on theories of spatial assimilation, place stratification, or a combination of both. Most studies drawing on these approaches focus on individuals or households. When households become the object of analysis, such research time and again conceives of them as monoracial; differences within the household have not been the immediate concern of researchers trying to unpack the mechanics of residential sorting or other social processes (exceptions include Ellis et al. 2007; Holloway et al. 2005; Iceland and Nelson 2010; Smith et al. 2011; Wright and Ellis 2006; White and Sassler 2000; Wright et al. 2003). When considering the neighborhood locations of households headed by racially mixed couples, however, the issue of gender asymmetry in such units places the question of how gender interacts with race in residential processes squarely in the spotlight.

Viewing the dynamics of mixed-race household residential location through the lens of race, in fact, sharpens the focus on the effects of gender. Whites, when faced with a choice, opt for white neighborhoods over other areas that are more racially mixed (e.g., Alba and Logan 1993; Farley et al. 1997). In making the interaction of gender and race the center of attention, we instead want to answer the question, Does the gender of the (non)white person in white-nonwhite couples affect the likelihood of living in white neighborhoods? We also ask the converse: Does the gender of the (non)white person in these couples affect the probability of living in neighborhoods in which the nonwhite partner’s group predominates? Although most residential attainment studies imagine neighborhood location in terms of community types defined by the presence or share of only one race group—either whites or a specific nonwhite group—a small body of research suggests an alternative perspective in which households headed by racially mixed couples are attracted to racially mixed neighborhoods (e.g., Dalmage 2000; Ellis et al. 2012; Holloway et al. 2005; Wright et al. 2011). Consequently, we also extend this line of thought by inquiring whether such a tendency depends on the gender of the (non)white person in the relationship.

Gender asymmetry in mixed-race couples requires us to consider the ways in which both race and gender condition the residential dynamics of mixed-race couples. As most locational attainment research uses spatial assimilation and place stratification theoretical frameworks—indeed, many are posed as a test of the relative merits of the two perspectives—we try to work out the extent to which these theories allow us to (1) anticipate the presence of a gendered race effect and (2) anticipate the direction of such an effect. To develop the conceptual foundations of our study, we also take note of the trailing spouse migration literature and related research on gendered commuting to argue that the locational attainment of racially mixed couples must take into account domestic gender regimes. This article’s analytical section addresses the residential patterns of households headed by heterosexual mixed-race couples that are black-white, Asian-white, and Latino-white. It builds on some initial descriptive findings and reports on a series of residential attainment models for these households where the race of the (fe)male partner becomes the object of analysis in explaining neighborhood outcomes.

The Mixed-Race Partnership in Residential Space

Households headed by mixed-race couples tend to reside in racially diverse neighborhoods. Ethnographies of households headed by black and white partners attest that the attraction of such places is strong because many such households feel less comfortable in predominantly white neighborhoods as well as predominantly black communities (Dalmage 2000). Census-based scholarship confirms these findings. Holloway et al. (2005) showed that U.S. mixed-race households tend to reside in less-segregated spaces than single-race households and that black-white households, in particular, live in neighborhoods characterized by their racial diversity, which also occurs in the United Kingdom (Smith et al. 2011). Wright et al. (2011) demonstrated that black-white–headed households are most often found in neighborhoods where whites constitute the majority group. Adding controls for socioeconomic status (SES) and neighborhood racial structure reveals that black-white couples are drawn to diversity no matter which racialized group forms the majority in the neighborhood. This result contrasts with the patterns that they reported for households headed by black couples (diversity acts as a draw only when they enter spaces comprising many whites or Asians) and white couples (neighborhood diversity is important when they reside in neighborhoods with many blacks or Latinos).

White and Sassler’s (2000) neighborhood attainment analysis of mixed couples also comes close to some of the questions that interest us. Marriage to white spouses affected neighborhood location for some Latino and black native-born and immigrant groups. With various controls in place, nonwhite householders partnered with whites were more likely to reside in higher-status neighborhoods than those partnered within group. In contrast, marriage to someone not white led to residence in lower-status neighborhoods. They commented that this “influence of intermarriage on neighborhood status outcomes is quite provocative. It could reflect a manifestation of interracial marriage status exchange theory, or it might indicate circumvention of discrimination where the housing search is conducted by the white spouse” (White and Sassler 2000:1007). Their suggestion that status-caste exchange might be part of the answer points to a more general consideration of gender asymmetries and mixed household neighborhood locations. The reference to discrimination in housing searches also signals their suspicion that race plays a role in racially mixed household residential attainment. The next section considers the causes of gender asymmetries in mixed-race partnering. We then reconcile that discussion with theories of residential attainment to frame our analysis.

Gender Asymmetries and Mixed-Race Partnering

Status-caste exchange theory forms part of the debate surrounding the asymmetrical gender patterns of mixed-race partnerships in the United States (e.g., Gullickson and Fu 2010; Kalmijn 2010; Rosenfeld 2005, 2010). This theory advances that minorities trade off socioeconomic resources against the social disadvantages of their racialization (Jacobs and Labov 2002). African Americans can offset their subordinate position on a racial hierarchy by using their status resources to enhance their chances of “marrying up”: that is, marrying someone in a higher position on the racial hierarchy, conventionally a white person with lower SES (Merton 1941). In mid-twentieth century U.S. society, this option was extremely limited for any black person, but particularly so for women. Viewing marriage as a market, “marrying up” was more feasible for black men than black women, which, according to this theory, produced the asymmetries of 70 years ago that persist to this day.

Critics of this approach observe that (1) black women are more educated than black men, yet it is black men who marry out at a far higher rate than black women (Belot and Fidrmuc 2010; Moran 2001:103), and (2) few differences appear in the educational attainment of black men who partner with nonblack women (Qian and Lichter 2007; Rosenfeld 2005). In economics, related Becker-type marriage-market theories (Becker 1973) also do not withstand close scrutiny (Fryer 2007). More generally, all work that relies on “marriage markets” has them operating as if a cool calculus produces sexual partnerships. Love, attraction, solidarities, and personal choice find little place in these approaches, yet these are the very forces that scholars working ethnographically find compelling (Root 2001; Spickard 1989).

From another perspective, related research highlights the prevalence of sexualized images that portray, for instance, black and Asian men and women very differently. These cultural productions and associated societal norms generate the asymmetries that we witness in mixed-race partnering (Moran 2001; Nagel 2003). Socioeconomic success for black men “lightens” and “masculinizes” them. Black women find themselves “in a double bind: they must be as least as submissive and dependent as a traditional white women to be attractive, yet they must be self sufficient to survive in the black community” (Moran 2001:105). Asian American–white gender asymmetries also grow from cultural roots. Asian American women are depicted as “hyper-feminine,” producing an assumption that they will be submissive and pliant partners; Asian American men are characterized as effeminate. Taken together, these racialized sexualities shape Asian-white heterosexual partner asymmetry (Moran 2001:107).

New research in behavioral economics also attends to physicality but in ways that can be tested via a formal hypothesis. Belot and Fidrmuc (2010) showed (again) that SES variables poorly predict gender asymmetries but that other data—specifically, height distributions—provide far more powerful predictors. The simple but widespread preference found in studies of dating—that males should be taller than their female partners—interacts with race (blacks being taller, on average, than Asians) to explain differential partnership rates with whites by gender. Relative partner height has nothing immediately to do with neighborhood location, but this finding is important. Changing demographics (via immigration and differential fertility) along with changing social norms about racial mixing may enhance (e.g., black men, Asian women) or weaken (e.g., Asian men, black women) individuals’ relative “bargaining power.” Belot and Fidrmuc concluded their essay in this way: “It would be worthwhile to investigate what are the implications in terms of household behavior and distribution of resources within the household” (Belot and Fidrmuc 2010:371). Indeed. And what of these other arguments about racialization or assimilation? How might they speak to gendered race effects within mixed partnerships and fold into residential attainment theory?

Gendering the Residential Location of Mixed-Race Couples

Insight on the processes that produce segregated (and diverse) residential spaces usually pivots on spatial assimilation (SA) and place stratification (PS) (for a thorough review, see Charles 2003; see also Alba et al. 2000; Logan et al. 1996; Iceland and Wilkes 2006; Iceland and Nelson 2010). SA holds that increases in income, occupational status, and English-language ability over time and across generations produces a spatial diffusion of immigrants from neighborhoods of initial settlement into areas that were previously the exclusive domain of the native born. Shifted from immigrant worlds into the context of ethnic and racial minority populations, it hitches individual social mobility to spatial mobility, linking them to ecological outcomes, often specified as contact with whites or Anglos (Gross and Massey 1991).

Racialization features more prominently in stratification models, the bedrock of which reposes on the assessment of the degree to which racialized individuals or households become sorted by neighborhood, taking into account their skills and education. It reveals the limits some people face in converting their socioeconomic standing into similar neighborhood locations compared with others who are not subject to the same racial gaze. “Whites use segregation to maintain social distance, and therefore, present-day residential segregation—particularly blacks’ segregation from whites—is best understood as emanating from structural forces tied to racial prejudice and discrimination that preserve the relative status advantages of whites” (Charles 2003:182). Charles concluded that a SA framework performs better at describing the residential mobility of white Latinos and Asians; the PS schema best captures the neighborhood dynamics of blacks and black Latinos (2003).

Almost all residential-attainment modeling studies adopt the perspective of the unitary household—single-race individuals and households, or households undifferentiated by the gender of the racialized partners (Agarwal 1997). So how do these theories apply when a minority is partnered to a white person? Does it matter whether the white person in that mixed-race relationship is a woman or a man? When the household head is assumed to represent all household members’ interests and preferences, power relations within households are imagined as equal, and gender and racial structures are ignored. In the SA model, improvements to SES accompany acculturation and map geographically via an objective process that links improvements in social status “with a marked upgrading in housing conditions and neighborhood amenities and with residence in predominantly white areas” (Alba et al. 2000:606).

When couples are classed by racial configuration, SA predicts—and the empirical results generally confirm—an intermediate residential pattern that reflects their status “in-between” single-race groups. To illustrate, households headed by black-white couples tend to locate in relatively racially diverse neighborhoods, more white than single-race black households but not as white as single-race white households (Holloway et al. 2005; Wright et al. 2011). Changing perspective from group outcomes to household-level outcomes, SA would forecast that, say, a black-white mixed-race household should equally be able to convert SES resources into improved residential circumstances regardless of whether the white partner is male or female.

We can leverage the studies of migration decision-making and axes of power in the household, however, to extract a perspective on gender from assimilation theory. The “tied migrant” literature consistently shows the employment penalties that women receive when households move (Boyle et al. 2001; Cooke and Speirs 2005; Cooke et al. 2009). Gender “is generally linked to the perceived differences between women and men and to the unequal power relations based in those perceived differences” (Hanson 2010:8). Similarly, the research on household location, work, and commuting often asks questions, directly or indirectly, about household gender regimes (Hanson and Pratt 1995; Rapino and Cooke 2011; Timmermans et al. 1992). Many such studies document the subordination of women, and these findings overlap with processes of intra-urban mobility, residential location, and, by extension, neighborhood residential segregation. We therefore seek to link gender asymmetries in heterosexual mixed-race partnerships and neighborhood location to the recurrent theme in the scholarship on family dynamics associated with the power asymmetries that favor husbands over wives in decision making (Zipp et al. 2004).

Gordon (1964) viewed mixed-race marriage and partnership as an indicator of race relations; many interpret his work to say that marital assimilation is the final “stage” in assimilation to American life (Burton et al. 2010; Kalmijn and van Tubergen 2010). Taking this as point of departure for intra- and interhousehold racial dynamics, we can posit some expectations for a gender/race effect if within each category of mixed-race household, the two gender configurations either (1) view themselves as more or less assimilated (i.e., like the dominant group) or (2) are viewed by the dominant group as more or less assimilated. Thus, white male/black female couples may be seen (or even see themselves) as “more white” than black male/white female couples. Here is how gendered power asymmetries play out within an assimilation-type framework. If the male is dominant within the relationship, then white male/black female arrangements (and other mixed-race household groupings by extension) may indeed receive a different social reception than partnerships with the opposite gender/race configuration. In terms of black-white mixed unions specifically, there may also be a backlash against black male/white female households because of the stereotypes and sexual taboos associated with this particular pairing (Moran 2001).

We ask if there is a measurable difference in terms of neighborhood location between the two types of mixed-race partnerships because of inter- and intrahousehold gender/race effects. We can anticipate two results: (1) households headed by white-nonwhite couples are more likely to gravitate to white neighborhoods when the male partner is white/the female partner is nonwhite, and (2) households headed by white-nonwhite couples are inclined to reside in nonwhite neighborhoods when the male partner is nonwhite and the female partner is white.

This discussion of acculturation and assimilation drifts into the realm of PS, which Charles (2003) suggested is more appropriate for people who are phenotypically most obviously not white. Racial stratification offers two alternative perspectives on the residential location of households headed by mixed-race couples. Mixed-race couples, as it were, “become nonwhite” (cf. Bonilla-Silva 2004). The premise here is that the presence of a racial “other” in the household, “others” all members. The literature offers evidence of such racialization by association. For example, Haslanger, who is white, has a son who is much darker. Recounting the moment in her residential neighborhood when another boy approached her and wanted to know whether the child with her was her own: he asked, “You mixed?” (Haslanger 2005). In a similar vein, Houston (2008:11) reported an interview wherein a “white” woman recounted the birth of her “black” son as not only the moment when she became a mother but also the instant when she ceased to be “Caucasian.” In terms of housing markets, Houston and Wright (2008) showed how different mixed-race couples continually and simultaneously feel both in and out of place in Seattle. Similarly, Dalmage (2000) reported on the “borderism” many black-white families face in both black and white neighborhoods—such households are seen as neither white nor black, but something else. Thus, racially mixed families may seek neighborhoods where neither group is dominant and locate in racially diverse neighborhoods irrespective of the gender of the nonwhite partner. They might also be more likely to be found in nonwhite neighborhoods or places where the minority partner’s race group predominates, no matter the race of (fe)male partner.

These examples suggest that race trumps gender. Both partners in a mixed-race relationship encounter racist ideologies about socially appropriate relationships. Commonly held social proscriptions about appropriate romantic partners still inhibit marriage or household formations that cross racial lines (e.g., Romano 2001). Diverse neighborhoods may offer the best—that is, the most socially comfortable—places to enact such complex racial identities, especially when raising mixed-race children. Such broad accounts, however, ignore the specific asymmetries that motivate this article. If gender did not matter in mixed-race partnering, then any variation in gender asymmetry in such pairings would be simply random. That is not the case. Gender roles in relationships are weighted unequally, with women in general continuing to be marginalized by a dominant male culture. If these norms also play out among mixed-race couples, it follows that neighborhood outcomes should favor the male partner. So even in relationships that many find racially transgressive and progressive, the irony is that gender practices in such partnerships may still follow conventional norms. Thus, if a racially mixed couple’s male partner is white, we predict an elevated probability that the couple resides in a white neighborhood. If the man is, say, Latino, then we would expect a positive relationship between residential location and neighborhood percentage Latino. We can also anticipate an inverse relationship between the female partner being white and neighborhood percentage white. We can also speculate that a mixed-race couple with a white male partner will have a reduced likelihood of making their home in a diverse locale.

Data and Analysis

We use 2000 U.S. census confidential data to perform our analyses. Although publicly released data offer information about the location of mixed-race couples down to the scale of the PUMA (Public Use Microdata Sample Area, an area of about 100,000 people), confidential census data provide information about the location of such couples by census tract. This level of geographic detail requires that research be carried out in secure facilities, and our results were screened by Census Bureau employees to maintain confidentiality. We examine residential patterns of our sampled couples in 12 large metropolitan areas: Atlanta, Chicago, Dallas, Detroit, Houston, Los Angeles, Miami, New York, Philadelphia, San Diego, San Francisco, and Washington, DC.2 This group comprises 11 of the 12 most populous metropolitan areas in the United States.3 These places range in racial composition and rates of mixing, capturing variations in racial population diversity in different parts of the country. The concentrations of mixed-race couples in these locations, combined with their large populations, provide samples big enough to sustain the analysis. Table 1 shows that these metropolitan areas were home to more than one-third of the nation’s mixed-race partnered couples in 2000 and that, on average, 8.8 % of couples were mixed, compared with the national average of almost 7 %. The averages mask variations within the sample, with the three West Coast metropolitan areas having double the share of mixed-race than same-race couples. In contrast, Atlanta, Detroit, and Philadelphia have fewer racially mixed-race than same-race couples.

Our investigation features the three most common heterosexual mixed-race household types: namely, those headed by black-white couples, Asian-white couples, and Latino/Latina–white couples (Passel et al. 2010). Overall, heterosexual couples head more than two-thirds of all mixed-race households. The remaining one-third of mixed-race households include same-sex couples, unrelated housemates, and households where children, many adopted, are reported as having a race different from their parent(s). We predict that the nonheterosexual share of mixed-race households will increase over time and warrant targeted and extensive analysis. Still, nonheterosexual mixed-race households are a broadly heterogeneous group whose locational decisions reflect a wide array of processes. Moreover, our theoretical focus on the possibility of a gender-by-race interaction effect precludes the inclusion of mixed-race households that do not have a male–female couple.

Table 2 shows the degree of variation in the gender configuration of the three types of mixed-race couples studied. The patterns exhibit no clear geography at this scale. For example, among black-white couples, Los Angeles comes closest to gender parity; among black-white couples in San Francisco, however, almost 79 % involve a black man partnered with a white female. Asian-white partnerships exhibit the common pattern of being dominated by a white male/Asian female configuration. Latino-white couples, by metropolitan area, cluster closely to the mean of 46 % white female/Latino male.

We next explore the typical neighborhoods of the three classes of mixed-race couples, contingent on the gender of the white person in the pairing. We do this by first comparing the typical neighborhoods of mixed couples with those of single-race black, Asian, and Latino couples.4 We then compare the racial diversity of the typical neighborhood of households headed by mixed-race couples with those headed by single-race pairs. This analysis relies on two variants of the exposure index. Conventionally, P* represents neighborhood exposure:
formula
(1)
where j indexes census tracts, w and x index racial groups, and t is the total population of all racial groups. W is the total population of group w across all tracts; and wj, xj, and tj are tract counts of the respective groups. characterizes group x’s population share in group w’s typical tract: that is, the residential exposure of group w to group x. As we aim to assess the exposure of certain mixed-race households to whites (and, depending on the mix in the household, to blacks, Asians, and Latinos), we modify such that w represents counts of households, and x represents individuals (cf. Holloway et al. 2005). We can further modify by specifying the race of the (fe)male partner and thus describe the exposure, say, of households headed by a black man and a white woman to blacks (or whites): that is, the average tract percentage black (white) of the typical household headed by a black man and white woman.5

Figure 1 illustrates the patterns of exposure of the three different classes of couples to (1) whites and (2) the minority population associated with the nonwhite partner, summarized for all 12 metropolitan areas. Ignoring the race of the (fe)male partner for a moment, the values in Fig. 1 depicting exposure to white neighbors are considerably greater than those associated with nonwhite neighbors. Mixed-race households with one white partner are far more likely to encounter whites in their neighborhoods of residence than individuals who are the race of the nonwhite partner. Figure 1 also shows that an increased neighborhood exposure to whites occurs when the male in the partnership is white, regardless of the race of the female partner. The differences are small relative to the differences revealed between exposure to whites versus nonwhites, but are nevertheless consistent across the groups. The raw data in Fig. 1 support the ideas that households headed by mixed-race couples tend to reside in white neighborhoods, and there appears to be a small gender effect.

Echoing the asymmetry of the exposure index itself, we next ask whether households headed by mixed-race couples live in neighborhoods associated with the nonwhite partner. Figure 1 shows that gender has a relatively small effect for black-white and Latino-white couples. If the male partner is black or Latino, the chances of being exposed to black and Latino neighbors is slightly higher than if the female partner is black or Latina. There is no gender difference in the neighborhood exposure to Asians for household headed by Asian-white partners.

Figure 2 provides additional perspective, describing the likelihood of exposure to neighborhood racial compositions by households headed by mixed-race couples compared with their relevant single-race household referent groups. (As before, these data are averages of the 12 metropolitan area values.) Accordingly, Fig. 2, panel A, shows not only the exposure of mixed-race households headed by black-white couples with whites, blacks, Asians, and Latinos, but also the neighborhood exposure of (1) black and (2) white single-race households to those same groups.6

Figure 2, panel A, reveals that blacks (in this case, black couples) are far less likely to have white neighbors than are either Latinos or Asians. White couples are also far more likely to have white neighbors than any other racial group. Households headed by racially mixed couples occupy a median position, if you will, in their exposure to these four racial groups when compared with their same-race referents. Furthermore, Fig. 2 expands on the subtle gender differences depicted in Fig. 1. For example, black male/white female household types are not only more exposed to black neighbors than are black female/white male couples, but they are also comparatively more exposed to Latino neighbors. White-Latino couples replicate a similar pattern regarding exposure to blacks (panel C). Both Figs. 1 and 2 suggest that at least in the case of white-black and white-Latino household heads, a minority male partner increases the likelihood of having both black and Latino neighbors.

To begin to examine the question of whether gender asymmetries of couples heading mixed-race households is related to neighborhood racial diversity, we deploy a second variant of neighborhood exposure: the Neighborhood Diversity Exposure index (NDE) (Holloway et al. 2005). The NDE indexes the amount of racial diversity (measured using scaled entropy) in the typical neighborhood of a particular group. It captures a group’s exposure to racial diversity in their typical residential neighborhood. The standardized entropy diversity measure for each tract is
formula
(2)
where k indexes the racial groups. The maximum value of Ej is obtained when tract j’s population is evenly divided between the k racial groups; the constant s (1 / ln(k)) ensures that Ej ranges between 0 and 1. The NDE captures the racial diversity for group w’s typical tract in the following way:
formula
(3)

If group w—say, a household headed by an Asian woman and a white man—disproportionately concentrates in neighborhoods with considerable racial diversity, the NDE takes on a relatively large positive index value. Conversely, if such a household disproportionately concentrates in tracts with little racial diversity, the NDE takes on a relatively small positive value.

Figure 3 portrays the results of this analysis. The three sets of pairs record roughly similar exposures to neighborhood racial diversity; the scores range from a low of .44 (white male/Latina couples) to a high of .51 (black male/white female couples). Figure 3 reveals that mixed-raced couples with a nonwhite male partner encounter elevated levels of neighborhood racial diversity in their place of residence relative to those encountered by mixed-race couples with a white male partner. This is consistent across the three sets of pairs of partners under investigation and also adds a gender dimension to the conjecture that white-nonwhite couples gravitate to racially diverse neighborhoods. This elevated likelihood of exposure to neighborhood diversity is attenuated if the male partner is white.

Figure 3 also illustrates that white-Asian and white-Latino couples (but not white-black couples) encounter higher levels of neighborhood racial diversity than their white same-race counterparts but lower levels than their nonwhite same-race reference groups. White-black couples, however, encounter more neighborhood racial diversity than either white or black same-race partners.7 Again, in registering the small gender effect, Fig. 3 reveals that mixed-race households headed by a white male partner trend consistently toward the patterns of single-race white households.

A set of models assesses whether the differences detected in the descriptive phase of the research are statistically significant, taking account of an extensive set of control variables. With tracts serving as proxies for neighborhoods, we estimate three sets of logistic regression models, one set for each mixed-race couple classification, with the following form:
formula
(4)
where γij is a community-level measure for tract j and thus is assumed to be constant for all household types i in the same tract j. We estimate this model using three measures of community racial composition as dependent variables: tract racial diversity, measured by scaled entropy, which ranges between 0 and 1; the proportion of whites among tract residents; and, depending on the mixed-race couple being analyzed, the proportion of blacks or Asians or Latinos in tract j. The specifications of the dependent variables match with the descriptive analysis that focused on P* and neighborhood racial diversity. P* measures the neighborhood proportion white (or black, Asian, and Latino) in which the average white or minority person (in our case, mixed-race couple) lives. Scaled entropy measures racial diversity in a census tract.

Given that our dependent variables have ranges restricted to fall between 0 and 1, all models are specified as generalized linear models with a binomial variance function and a logit link function. Parameters appear in log odds form and are estimated using maximum likelihood; robust standard errors account for the clustering of observations within tracts. The estimation and significance of the variables of principal interest are very stable throughout this process.

To test the effect of gender on the tract location of households headed by mixed-race couples, we create a simple dummy variable for the male partner being white. (We can equivalently call this dummy variable “female partner not white.”) The controls are other individual- and household-level variables that predict residence in a community with a given level of the trait measured by the dependent variables. These controls are of the following general types: (1) a pair of dummy variables that account for the racial ancestry of the partners in the household; (2) for Asian and Latino/Latina partners, a set of national-origin ancestry dummy variables; (3) a set of controls that account for mobility, migration, and immigration history, such as location of previous residence and places of birth; (4) standard socioeconomic variables, many of which are used in both SA- and PS-type models, such as household income, education (the education variables are specified as a polychotomous suite of dummy variables that reflect both overall educational attainment and the homogamy of attainment between the partners), and age; (5) two variables that account for military service; and (6) a set of metropolitan area fixed-effect dummy variables.

The decision to aggregate the 12 metropolitan area samples into a single pool represents a trade-off between examining three outcomes for a set of metropolitan areas and examining one outcome for each metropolitan area separately. Our core research hypotheses require that we examine tract percentage white, percentage nonwhite, and racial diversity. Attempting this analysis separately for a dozen metropolitan areas would have produced an unwieldy amount of output. Accordingly, we maintain our attention on these three variables of interest and, following convention, use metropolitan fixed-effect dummy variables to control for unobserved locational heterogeneity across the sample. Aggregating the 12 metropolitan areas and leveraging the 1-in-6 sample, these data produce large samples of each household type. Our models are based on samples of 15,700 households headed by black-white married and partnered couples, 32,338 Asian-white households, and 92,644 white-Latino households. The analysis thus boils down to an analysis of the residential geography of households headed by mixed-race couples composed of a white male partner or a nonwhite male partner from a sample pooled from 12 large U.S. metropolitan areas.

We begin with the models in which neighborhood percentage white serves as the dependent variable (see Table 3). To reduce clutter we exclude the estimations, where applicable, of dummy fixed-effects controls for ancestry.

An examination of the coefficients along with robust standard errors (where bold numbers reflect statistical significance at p < .05) reveals that these three classes of households headed by mixed-race couples generally follow conventional renditions of spatial assimilation models wherein neighborhood proximity to whites is associated with elevated SES. More specifically, if these racially mixed households have higher incomes, own homes, and possess advanced educational qualifications, they are likely to gravitate to white neighborhoods. This result is new but not surprising. Most previous tests of spatial assimilation theory examine the economic status of monoracial households, finding that higher SES among nonwhite households is associated with proximity to whites. We find that households headed by mixed-race couples behave in largely the same way.

SES, of course, only frames our interest in spatial assimilation theory in the context of this study of gender/race effects on household geographies. The models show a consistent and statistically significant positive relationship between the male partner’s race being white and the percentage white in the neighborhood: that is, a gender bias in the residential location of these three kinds of racially mixed couples. If we exponentiate the estimate, say, for white male in the model of both black-white and Asian-white couples (1.05), then we can state that households headed by black-white couples with a white male partner are about 5 % more likely to live in a white neighborhood than a black (Asian)-white couple that includes a white woman household head, all else being equal. That effect is also statistically significant for Latino-white couples, but the effect is smaller: such couples with a white male partner are about 2.4 % more likely to live in a white neighborhood than a Latino-white couple that includes a white woman household head.

The regression estimates for the partners’ ancestries are also interesting. The results show that if the white partner has a mixed ancestry,8 they are less likely to live in a white neighborhood. Conversely, if the nonwhite partner is mixed, the likelihood of residence in a white neighborhood increases. All this, of course, lends support for the proposition that white-nonwhite–headed households are more likely to be found in white neighborhoods if the male partner is white.

We now turn to the models that examine the effect of the neighborhood structure measured as percentage nonwhite (Table 4). This works as follows. In the model of Asian-white couples, for example, the neighborhood percentage Asian serves as the dependent variable. For the Latino-white model, the neighborhood percentage Latino serves as the dependent variable. Examining the results by zeroing in on the variable of primary interest—for households headed by black-white couples—there is a significant negative relation between the presence of a white male partner in the relationship and neighborhood percentage black. Exponentiating the estimate for white male in the model of black-white couples, we can state that households headed by black-white couples with a white male partner are about 4 % less likely to live in a black neighborhood than a black-white couple that includes a white woman household head, all else being equal. The same effect is significant but about 2 % less for Latino-white couples. We find no relationship between race of the male partner in a household headed by an Asian-white couple and the proportions of Asians in the neighborhood of residence.

Given the findings of the first suite of regression estimates, these three models help confirm the marginal (i.e., net of other factors in the model) effect of gender on mixed-race residential location in another way. By discovering an effect in two of the models, this analysis provides support for the idea that white-nonwhite couples gravitate to neighborhoods where the minority partner’s race is a larger fraction of the population, especially if the household head is a black male or Latino.

Last, we examine the results of the logistic regression analysis in which we assess the relationship between neighborhood racial diversity and the race of the (fe)male partner in the three types of racially mixed households (Table 5).

Focusing on the importance of the variable white male (nonwhite female) and neighborhood diversity as measured by scaled entropy means that the interpretation of this parameter estimate translates into the impact on neighborhood entropy of the location of households headed by mixed-race couples with a white male partner/nonwhite female partner. Thus, the negative parameter estimate for white-Asian households implies that white-Asian households with a white male live in less diverse neighborhoods, even with extensive controls, than similar households with white females. The relationship is similar for white-Latino heterosexual households: that is, exponentiating the estimate for white male in the model of Latino-white couples and controlling for other factors, we can state that households headed by black-white couples with a white male partner are 1 % to 2 % less likely to live in a diverse neighborhood than a Latino-white couple that includes a white woman household head. Surprisingly, given our descriptive analysis, we find no evidence of gender asymmetry for households headed by black-white couples after controls are included. Although the race of the male partner can significantly reduce the likelihood that the household lives in a racially diverse neighborhood for Asian-white and Latino-white couples, it has no such effect for black-white couples. In this particular instance, race effects appear to trump gender effects. Accordingly, although previous research accents the fact that race matters in particular ways for households headed by mixed-race couples, the results reported here require we modulate those findings with gender in mind.

A final word concerns estimation. With the exception of metropolitan fixed effects, which are in all the models, we estimate models in stages to assess the impact of adding controls on the parameter estimated for the race-by-gender interaction dummy variable. The first stage (M1) includes, in addition to our main variable, the dummy variables that indicate mixed ancestry for the two partners; and, for white-Asian and white-Latino pairs, the set of national-origin ancestry dummy variables (the rationale being that ancestry or mixed ancestry might be distinctive for some individuals). The second stage (M2) adds variables related to migration and immigration, which we think form a distinct cluster relating to both tied migrant theory and racialization theory. The third stage (M3) adds the remainder of the control variables. The sequential addition of new variables and clusters of variables has no effect on statistical significance, leading us to conclude that multicollinearity is not a problem with these models.

Table 6 contains goodness-of-fit measures and statistical comparisons of these models. In seven of the nine sets of models, the more complete models provide an improved fit to the data, based on lower Akaike information criterion (AIC) scores. The simplest model (M1 in Table 6) provides the lowest AIC score for white-black pairs when the dependent variable is scaled entropy. The second stage model (M2 in Table 6) provides the best AIC score for white-Asian pairs when tract percentage Asian is the dependent variable. For each of the three mixed-race pairings and for each of the three dependent variables, the last model that includes all controls (M3 in Table 6) significantly (p < .01) improves upon the initial model that controls only race, gender, and ancestry (M1 in Table 6). Moreover, in all model sets except one (white-Asian pairs with percentage Asian as the dependent variable), the most complete model is statistically superior (p < .01) to the second-stage model, which includes the migration and immigration controls (M2 in Table 6). For white-Asian pairs in the model with percentage Asian as the dependent variable, the most complete model (M3) is marginally better (p = .088) than the second-stage model (M2). Based on these results, we present the parameter estimates from the third-stage model that includes all possible controls.

Conclusions

The analysis of the neighborhood location of households headed by mixed-race couples brings the issue of gender to the surface faster than when the object of analysis is a household headed by a same-race couple. Our investigation of the gender makeup in the three most frequently occurring racially mixed partnerships and their relationship to neighborhood location found that not only does race matter, but also that gender matters. In each of these household relationships, the presence of a white male partner is associated with the percentage of the neighborhood of residence that is white. In addition, if the male partner is white, it reduces the likelihood that the households make their home in a diverse neighborhood for white-Asian and white-Latina households. We also found a statistically significant relationship between the percentage of a neighborhood that is black or Latino and the presence of households headed by heterosexual couples with, respectively, a black male or Latino partner.

These results augment those of White and Sassler (2000), who found a race/class/spouse effect in the residential attainment of mixed couples. Our research similarly detects a gender/race effect in several different models and with different racial pairings. We had access to confidential census information to conduct our investigations; nevertheless, future research, however configured, should pay more attention to intrahousehold gender regimes. Such analyses could address this issue from the “inside out” (by examining gender relations within the household and neighborhood context). The work could also be directed from the “outside in”—assessing, for instance, the differential racialization of, say, Latino-white and Latina-white couples by neighbors in preference/attitudinal surveys.

Research in related realms consistently shows that household power relations tilt in favor of male partners in heterosexual couples. What are the implications for theory? One reading of SA theory is that residential attainment is an individual or a collective process and that parsing housing power relations fits albeit awkwardly in such a rubric. SA theory posits that we should find no difference in the residential attainment of, say, white male/Latina households relative to Latino/white female households; their household-level SES characteristics generate potentially different locational outcomes. We demonstrated, both theoretically and empirically, that we can tease out gender effects from such a perspective. The charge for researchers is clear: scholars should pay more attention to gender dynamics in household neighborhood dynamics. Recent related research is trending slightly in this direction. For example, Iceland and Nelson (2010) and Ellis et al. (2006) showed that spousal characteristics usefully predict residential outcomes for immigrants to the United States, in line with spatial assimilation frameworks. Ellis et al. (2006) found suggestive gender effects for some immigrant groups, but new research has yet to build substantively on this outcome.

Place stratification theory is constructed on the idea of racial hierarchy and is associated with the inability of minorities to convert human capital into residential advantage. For households headed by racially mixed couples, the racialization of all household members as nonwhite, no matter the racial claims made by individuals in the household, creates the conditions for subordination. We maintain, and research bears out, that racially plural places provide attractive locations for mixed households (Dalmage 2000; Wright et al. 2011). Inserting gender into place stratification forces us to confront both racial and gender differences and to consider not one but two sets of social hierarchies based on these differences. Our analysis uncovers an important irony. Households headed by racially mixed heterosexual couples literally love across racial divides; gender practices in such households, in general, toe conventional lines. The race of the male partner significantly affects neighborhood location. If the male partner in a racially mixed couple is nonwhite, it raises the chances that the couple resides in a nonwhite neighborhood. Furthermore, the race of the male partner can reduce the likelihood that the household lives in a racially diverse neighborhood.

Our work reflects the growing interest in household structure and residential location—what Buzar et al. (2005:413) called the “changing social geometry of the household.” They made the case for deepening the incorporation of household demography into understanding patterns of urban structure and transformation. A focus on household racial mixing opens the literature for new questions, such as, How much of the total variation in residential segregation is explained by the racial makeup of families? Indeed, without mixed-race households, changes in neighborhood racial segregation between whites and blacks and whites and Latinos in the 1990s would be higher by nontrivial quantities (Ellis et al. 2012). Specifically, over this decade, white-black segregation without mixed-race households would be greater by 5 %, and white-Asian and white-Latino residential segregation would be 10 % greater than currently recorded.

The results reported here on specific gender effects should be seen as part of a larger project on household-level racial mixing. They suggest that scholars must attend to household power relations between male and female partners in heterosexual couples. In contrast to interregional migration, we know much less about the interaction among race, gender, and household bargaining power when it comes to neighborhood choice and urban mobility. Our research outcomes suggest future analyses might concentrate more on gender regimes and axes of power with racially mixed households. There are also lessons for work on different household arrangements, such as the place (literally) of mixed-race households headed by single parents. It is not a large conceptual leap to deploy confidential census data to examine the location of those and other types of mixed-race households.

When research on residential location and neighborhood segregation isolates the “household head” for analysis, it glosses the racial and gender variation of individual households and associated axes of power. The result is that the mixed-race household and its links to neighborhood-scale location remains understudied and undertheorized (Wright et al. 2003). Given that more than 14 % of all new marriages in 2008 cross racial or ethnic lines (Passel et al. 2010), the conceptions of households as monoracial and a place where gender is irrelevant require revision. The mixed-race household is increasingly important numerically. Moreover, because such a collective constitutes both a spatial scale at which mixed-race contact takes place and is a place for identity construction of individuals, partners, and the surrounding neighborhood, it represents a prime location from which to assess how the relationships between men and women play out in residential space.

Acknowledgments

This research was made possible by grants from the National Science Foundation and the Russell Sage Foundation. Thanks to Rebecca Acosta, Angela Andrus, and Kevin McKinney at the California Census Research Data Center for assistance with the data. Warm thanks to Margaret East, who spent far too many hours in a small, windowless room helping with the analysis, and also to Carla Castillo who provided other research support. Thanks also the referees and to audiences at Brown University and Brigham Young University who provided feedback on previous versions of this article. The results reported in this article were obtained while the authors were Census Bureau Special Sworn Status researchers. Any opinions and conclusions expressed herein are those of the author and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. Support for this research at the UCLA and Berkeley Research Data Centers from the NSF (ITR-0427889) is gratefully acknowledged.

Notes

1

Throughout the article, white refers to what the census describes as “non-Hispanic white.”

2

Specifically, we used the urbanized tracts of these metropolitan places to comply with restrictions on the nature of information that we could remove from the restricted-access research labs. Urbanized areas comprise contiguous spaces that have urbanized land use.

3

San Diego (ranked 16th overall) is the exception and was included for two reasons. The larger project comparing 1990 data with 2000, of which this article is one part, was conceived with the idea that at some point, we would be interested in looking at the effect of the military on mixed-race household formation and location. San Diego has several military installations in or near the metropolitan area. San Diego also had the related advantage of providing a fourth metropolitan area in our group (along with San Francisco, Los Angeles, and New York) with a high proportion Asian and Latino. These two factors work together to increase the number of mixed-race households in our sample relative to Boston—the excluded “top 12” metropolitan area.

4

We use racial categories consistent with those from the 1990 census requiring us to reassign some 2000 data. Some of the recategorizations are simple (e.g., merging Pacific Islanders and Asians into an aggregate Asian–Pacific Islander category). The reassignment of the 2.4 % of those who chose more than one race in 2000 used the whole-race assignment methods—Largest Group Other than White—recommended by the U.S. federal Office of Management and Budget.

5

One referee asked whether an individual could be counted in both the group of interest and the reference group that this modified P* measure. For example, does the man in a black male/white female household appear in both w (the count of mixed-race households of interest) and x (the count of blacks in a neighborhood)? The referee went on to state that if this were the case, the black male/white female index would be biased upward relative to the white male/black female household index because the former includes more counts of people exposed to themselves (because the former arrangement is more common that the latter). The right side of the P* index indeed captures the total compositional character of a tract and thus is based on the total population, not a set of remainder counts after individuals involved in mixed-race households are extracted. This, however, is not a bias, but a theoretical necessity. When the traditional P* is computed, it also includes the individual represented on the left side of the formula within the counts on the right side. Moreover, the “bias” based on the greater frequency of black male/white female households (relative to black female/white male households) is part of what we want to capture. This would be similar to our understanding of P*’s asymmetric characteristics being an asset rather than a detriment. In our case, we are asking, In what kinds of neighborhoods do these variously configured households live, with these households constituting a real and important part of the neighborhood?

6

Based on exposure indices—exposure to Indian and Other racial categories are not shown for clarity.

7

Holloway et al. (2005) also found this “in-between” pattern for white-Latinos and white-Asians of residing in less-diverse neighborhoods than their nonwhite same-race peers and in more diverse neighborhoods than white same-race couples using 1990 data.

8

For our purposes, mixed ancestry means that one or more listed ancestries are not from North America, Europe, former USSR, Australia, or New Zealand.

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