Analyzing segregation in mature and developing suburbs in the United States

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1 Journal of Urban Affairs ISSN: (Print) (Online) Journal homepage: Analyzing segregation in mature and developing suburbs in the United States Katrin Anacker, Christopher Niedt & Chang Kwon To cite this article: Katrin Anacker, Christopher Niedt & Chang Kwon (2017) Analyzing segregation in mature and developing suburbs in the United States, Journal of Urban Affairs, 39:6, , DOI: / To link to this article: Published online: 08 May Submit your article to this journal Article views: 44 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at Download by: [Mr Robert Mark Silverman] Date: 28 September 2017, At: 19:01

2 JOURNAL OF URBAN AFFAIRS 2017, VOL. 39, NO. 6, Analyzing segregation in mature and developing suburbs in the United States Katrin Anacker a, Christopher Niedt b, and Chang Kwon a a George Mason University; b Hofstra University ABSTRACT Over the past several decades, researchers have investigated segregation, differentiating between central cities and suburbs. However, suburbs have become more differentiated. Using Census 2000, Census 2010, and American Community Survey (ACS) data for , this article analyzes segregation in the 100 most populous metropolitan statistical areas in the United States, differentiating between central cities, mature suburbs, and developing suburbs. For each subgeography, we consider the racial and ethnic proportions, the dissimilarity index for each combination of racial and ethnic groups, and the isolation index for each racial and ethnic group. We find that Black White and Latino White dissimilarity levels in mature suburbs are closer to the corresponding levels in central cities. From 2000 to 2010, Black White segregation indices decreased for all subgeographies in all regions and Latino White segregation indices increased for all subgeographies in Census Region South. The findings for the dissimilarity indices suggest that finer-grained analyses of segregation could yield insights on local-level processes that may influence segregation in the 3 types of places and could suggest policy interventions to address segregation s persistence. In the decades since the civil rights movement, racial and ethnic residential segregation has been slow to change. Racial and ethnic segregation may lead to unequal access to high-quality schools, health care facilities, job opportunities, community-responsive policing, and retail establishments, including access to fresh and nutritious food. The resulting poor educational and health outcomes together with high rates of unemployment and underemployment, low-wage employment, crime, and incarceration diminish life chances and limit social mobility for those who live in segregated neighborhoods (powell, 2012). The adverse effects of segregation and of discrimination and racism more generally have inspired generations of activists, working at various scales, to open up non-hispanic White suburbs since at least World War II. Not in My Backyard (NIMBY) groups have often resisted these efforts (e.g., Bonastia, 2006; Danielson, 1976; Galster, Tatian, Santiago, Petit, & Smith, 2003; Kushner, 2009), even as some communities have pursued integration deliberately and successfully (Rusk, 1999; Saltman, 1991). In addition to community resistance, racial and ethnic segregation have persisted due to many factors, including socioeconomic inequality (Massey & Eggers, 1993; Massey, Fischer, Dickens, & Levy, 2003), residential preferences (Farley, Schuman, Bianchi, Colasantro, & Hatchett, 1978; Krysan, Farley, Couper, &Forman,2007), overt and subtle discrimination (Turner et al., 2013), and weak government enforcement (Massey, 2013). Though the U.S. Supreme Court affirmed disparate-impact claims under the Fair Housing Act in June 2015, it remains to be seen whether this ruling will strengthen fair housing advocacy and enforcement (SCOTUSBLOG.com, 2015). Past studies have analyzed segregation in select metropolitan areas (Logan & Schneider, 1984; Rugh& Massey, 2010), but few have differentiated between central cities and suburbs (Fischer, 2008; Massey& Denton, 1993; powell & Reece, 2013), and fewer still have measured differences in segregation among CONTACT Katrin Anacker kanacker@gmu.edu Schar School of Policy and Government, George Mason University, 3351 Fairfax Drive, MSN 3B1, Suite 673, Arlington, VA Urban Affairs Association

3 820 K. ANACKER ET AL. groups of suburbs (Pfeiffer, 2016). In this article we argue that the dynamics of segregation in U.S. suburbs are better understood at finer levels of resolution. Thus, as a first step, we differentiate suburbs by the age of their housing stock and find that they fall into two different groups: first, mature suburbs where diversity and segregation are high; second, developing suburbs where diversity and segregation are low. In this context, we ask the following research questions: How do indices of racial and ethnic segregation in mature suburbs compare with those of central cities and developing suburbs? How do these indices vary by census region, racial and ethnic pair, and racial and ethnic group? How have these indices changed from 2000 to 2010, and where did segregation increase or decrease? In this article, we compare and contrast segregation within the central cities, mature suburbs, and developing suburbs of the most populous 100 metropolitan statistical areas (MSAs). We use the indices of dissimilarity and isolation to operationalize segregation (Population Studies Center, n.d.a, n.d.b). Our analysis is preliminary but lays the groundwork for future research on segregation between and within suburbs. We begin by providing a brief overview of residential segregation in the suburban context. We then describe our data and methods and answer the research questions posed above. We find that mature suburbs are as segregated as central cities, as measured by their dissimilarity indices, which suggests that measuring segregation at the suburban level in large metropolitan areas may obscure intrasuburban variations. We also find that Black White segregation has slightly declined from 2000 to 2010, but the patterns of change are more ambiguous for other racial/ethnic pairs and vary between regions and subgeographies as well. We analyze segregation trends within metropolitan statistical areas from 2000 to 2010, grouping MSAs according to whether the Asian/White, Black/White, and Latino/White dissimilarity indices declined least (or increased most) in a given MSA s cities, mature suburbs, or developing suburbs. We find that for MSAs with dissimilarity indices that declined least in mature suburbs, proportions of a non-white racial/ethnic group grew more than did the proportions in that MSA s cities or developing suburbs; likewise, in MSAs with dissimilarity indices that declined least in developing suburbs, proportions of a those groups grew more than they did in that MSA scitiesormaturesuburbs. We conclude by discussing the implications of these findings for our understanding of mature and developing suburbs as potential sites for integration, and for public policy more generally. Literature review One of the most noteworthy attempts to examine suburban racial segregation was Massey and Denton s (1987, 1988a, 1988b, 1989) research during the late 1980s, which culminated in a series of landmark articles and the book American Apartheid (Massey & Denton, 1993). This work synthesized prior methodological innovations in the measurement of segregation, suggested new ways of distinguishing hypersegregated areas, and underlined the persistence and relevance of segregation to contemporaneous debates in urban sociology. Massey and Denton s discussion of the causes of segregation focused heavily on structural racism as the major barrier to integration of Blacks/African Americans and non-hispanic Whites. Subsequent research has refined theories of stratification and assimilation, coalescing in spatial assimilation theory (Alba, Logan, & Stults, 2000; Alba& Nee, 2003; Portes, 1997; Rumbaut, 1997); place stratification theory (Friedman, Singer, Price, & Cheung, 2005; Iceland & Scopilliti, 2008; Logan, 1978; Logan & Schneider, 1981, 1984; Stahura, 1987); and segmented assimilation theory (Portes & Zhou, 1993;Zhou,1999). Suburban stratification occurs when certain racial and ethnic groups are excluded in developing suburbs, even as they become more integrated in mature suburbs (Stahura, 1987). This argument has been made in concert with a growing focus on Hispanic/Latino (and White Hispanic/Hispanic/Latino) segregation patterns (Iceland, Goyette, Nelson, & Chan, 2010; Iceland, Mateos, & Sharp, 2011; Iceland & Nelson, 2008; Iceland & Scopilliti, 2008; Park & Iceland, 2011; Pfeiffer, 2016). Recent work on segregation has considered the intersection of race and socioeconomic status (Sharp & Iceland, 2013), segregation among specific Hispanic and Asian groups (Iceland, Weinberg, & Hughes, 2014), and variation across regions (Iceland, Sharp, & Timberlake, 2013; Pfeiffer, 2016). Though many of these studies have focused on metropolitan areas, relatively few have differentiated between central cities and suburbs (aside from Massey & Denton, 1993; see Logan, Alba, & Leung, 1996;

4 JOURNAL OF URBAN AFFAIRS 821 Logan & Schneider, 1984; Pfeiffer, 2016). Those who have studied suburban segregation have found that suburban dissimilarity indices are generally lower than the corresponding urban figures. Is there a contradiction, then, between low indices and the well-documented histories of suburban exclusion? Twenty years ago, one might have argued that the low suburban segregation indices reflected successful exclusion of people of color by non-hispanic Whites (i.e., that low levels of diversity, along with token integration, produced suburbs with low numbers of people of color as well as low segregation levels). Yet as more people of color have moved to the suburbs, this explanation has become less convincing. By 2010, Hispanics/Latinos, African Americans, and Asians constituted 17, 10, and 6% of the total suburban, respectively (Frey, 2011). These proportions vary considerably by region and metropolitan area, as we discuss below. Furthermore, few scholars of segregation have differentiated among different types of suburbs. This is surprising given the voluminous literature on place stratification theory (Friedman et al., 2005; Iceland & Scopilliti, 2008; Logan, 1978), including works on White flight and residential preferences (Adelman, 2005; Crowder, 2000; Crowder & South, 2008; Crowder, South, & Chavez, 2006; Farley, Fielding, & Krysan, 1997; Kruse, 2005; Krysan, 2002; Krysan & Farley, 2002), the diversity in mature and developing suburbs (Anacker, 2015b; Frey, 2015; Hanlon, 2010; Hanlon, Short, & Vicino, 2010; Hudnut, 2003; Lucy & Phillips, 2000, 2006; Vicino, 2008), and the growth of suburban poverty, particularly in older, postwar suburbs (Allard, 2009; Anacker, 2015b; Kneebone & Berube, 2013; Orfield, 2002; Pfeiffer, 2016; Vicino, 2013). This literature suggests that the explanation for lower levels of suburban segregation overall may be differences among groups of suburbs. In this study, we follow much of the recent segregation literature, which has placed renewed emphasis on scale (Allen & Turner, 2012; Östh, Clark, & Malmberg, 2014). Fischer s (2008) recent useful contribution, for example, decomposes the entropy index to show that residential sorting within the suburbs accounts for a growing proportion of Black White segregation generally. Her findings, along with those of Parisi, Lichter, and Taquiano (2015), lead us to take a closer look at differences in discrimination indices among groups of suburbs. Most recently, Pfeiffer (2016) has examined racial and ethnic equity outcomes in post civil rights suburbs developing suburbs, built largely after the wave of Black activism in the 1950s and 1960s that produced landmark legislation such as the Civil Rights Act of 1968 (which banned housing discrimination in the private market). After finding that people of color experienced more racially and ethnically equitable neighborhood conditions in post civil rights suburbs than in the city or mature suburbs, Pfeiffer (2016) analyzes how levels of racial/ethnic segregation, housing age, and income parity (between subgeographies of central city, mature suburbs, and newer suburbs) drive intermetropolitan variations in racial and ethnic equity. She concluded that these newer, developing suburbs offer opportunities to improve racial and ethnic equity for families of color, largely because patterns of segregation are less entrenched. Pfeiffer (2016) challenges place stratification theory, which anticipates that continuing discrimination (and structural racism more generally) will stymie the progress toward integration. Instead, her findings lend support to the housing availability model, which suggests that the reduced incidence of discrimination generally and new construction in the developing suburbs in particular allow people of color to attain high-quality housing and achieve integration and equity. Though our analysis is limited, our consideration of dissimilarity levels in mature and developing suburbs does provide one perspective on suburban progress toward integration (see Denton, 2010, on the measurement of segregation versus integration). Data and methods Data Our study measures segregation among census tracts, our unit of observation, within the 100 most populous MSAs, our geography. We differentiate among central cities, suburbs, mature suburbs, and developing suburbs, utilizing the indices of dissimilarity and isolation (Population Studies Center, n.d.a, n.d.b).

5 822 K. ANACKER ET AL. TheOfficeofManagementandBudget(OMB;2013) establishes and maintains the delineations of MSAs and designates central cities. However, the OMB does not define suburbs. We follow the common practice of treating areas within MSAs but outside central cities as suburban (Anacker, 2010, 2012, 2015a; Anacker& Morrow-Jones, 2008, among others). Using this definition of suburbs provides the advantage of comparability with other studies of suburbanization. Because MSAs are delineated at the county level, they often include areas on the suburban rural fringe and rural s. We utilized the GeoLytics Neighborhood Change Database, which provides 2000 and 2010 decennial census data, normalized to 2010 tract boundaries. We supplemented this data set with 5-year American Community Survey (ACS) data for journey-to-work variables to identify the census tracts (with any workers) as urban (Living in a Principal City: B ) or suburban (Living Outside Any Principal City: B ). To differentiate between mature and developing suburbs we use the Median Year Housing Unit Built variable, defining mature suburbs as those suburbs where the majority housing was built in 1969 or earlier and developing suburbs as those built in 1970 or later, consistent with the literature (Hanlon, 2009; Lucy&Phillips,2000, 2006). We excluded tracts with a zero and those where more than 50% of the tract s was housed in group quarters, because institutional s are often by their nature segregated from the tracts and communities that contain them. We also excluded the few remaining tracts that had a non-zero and no workers. 1 Methods We measured segregation within suburban subgeographies with the index of dissimilarity and the index of isolation, both of which have been widely used in the literature (e.g., Farley, 2011; Frey, 2015; Iceland et al., 2010, 2011, 2013, 2014; Iceland & Nelson, 2008; Iceland & Scopilliti, 2008; Logan,2004; Logan& Stults, 2011; Logan, Stults, & Farley, 2004; Massey & Denton, 1987, 1993; Park& Iceland,2011; Sharp & Iceland, 2013). For each subgeography (city, mature suburbs, developing suburbs), we calculated the racial and ethnic composition and isolation indices for non-hispanic Whites, non-hispanic Blacks/ African Americans, non-hispanic Asians, and Hispanics/Latinos. We measured the dissimilarity indices for each pair of these racial/ethnic groups, although in this article we focus upon the non-hispanic White/non-Hispanic Black and non-hispanic White/Hispanic indices. Dissimilarity indices indicate unevenness in the distribution of racial and ethnic groups within component geographies across a larger area in our case, tracts within the geographies of MSAs and the subgeographies of central cities, mature suburbs, and developing suburbs. We calculated the dissimilarity and isolation indices for 2000 and 2010, based on data provided by the U.S. Bureau of the Census (GeoLytics, n.d.). The index of dissimilarity can be calculated in many ways. In this article, we use the formula 1 X n a t b t 2 t¼1 A B ; where a t and b t are the of racial or ethnic groups a and b in tract t, A and B are the total of the metropolitan area, and n is the number of census tracts within a given metropolitan area. The index of dissimilarity produces values ranging from 0 to 1, representing uniformly even and extremely uneven distributions, respectively. The values are commonly multiplied by 100, such that the index values are equivalent to the percentage of people who need to move from one tract to another to achieve an even distribution across all tracts. Massey and Denton (1993) established an index value of 60 as a threshold of high segregation, a value of 30 as a threshold of moderate segregation, and a gain or drop of 10 points as a significant change (Logan & Stults, 2011). The meaningfulness of these thresholds can vary considerably across metros and across time (Denton, 2010).

6 JOURNAL OF URBAN AFFAIRS 823 We also measured the isolation index (i.e., the degree to which residents of particular races and ethnicities are likely to live in communities with a high percentage of residents of the same racial and ethnic background). For this article, we calculate the isolation index as follows: X n t¼1 a t A where p t indicates total for the tract. These resulting indices are most meaningful when presented side by side. The basic proportion does not shed much light on the distribution of racial and ethnic s, though the dissimilarity index does not take proportion into consideration; the isolation index reflects both proportion and distribution, albeit imperfectly, as the two terms of the equation reflect distribution and proportion. We therefore provide all three calculations below, along with an analysis of how they are related to one another in each type of suburb. a t p t ; Results As stated above, we ask the following research questions: how do indices of racial and ethnic segregation in mature suburbs compare to those of central cities and developing suburbs? How do these indices vary by census region, racial/ethnic pair, and racial and ethnic group? How have these indices changed from 2000 to 2010, and where did segregation increase or decrease? First, we analyzed the racial and ethnic composition for all central cities, mature suburbs, and developing suburbs in 2000 and 2010 in the 100 most populous MSAs (see Table 1). Whites were the largest racial and ethnic group as a percentage of the total in the 100 most populous MSAs, followed by Hispanics/Latinos, non-hispanic Blacks/African Americans, and non-hispanic Asians. At the national level, the proportions of Hispanic/Latinos and non-hispanic Black/African Americans have remained larger in central cities and smaller in the Table 1. Racial and ethnic composition for 100 most populous MSAs. Proportion of non-hispanic White (%) Proportion of non- Hispanic Black/African American (%) Proportion of non-hispanic Asian (%) Proportion of Hispanic/Latino (%) Proportion of non-hispanic other (%) All CMSAs All census regions Census region Northeast Census region Midwest Census region South Census region West All central cities All census regions Census region Northeast Census region Midwest Census region South Census region West All mature suburbs All census regions Census region Northeast Census region Midwest Census region South Census region West All developing suburbs All census regions Census region Northeast Census region Midwest Census region South Census region West

7 824 K. ANACKER ET AL. developing suburbs and the proportion of non-hispanic Asians has remained larger in central cities and smaller in mature suburbs, consistent with the literature (Anacker, 2010, 2012, 2015a, 2015b). The proportions of non-hispanic Black/African Americans and Hispanics/Latinos were higher in mature suburbs than in developing suburbs, illustrating the widely noted diversity of mature suburbs (Anacker, 2010, 2012, 2015a, 2015b; Kneebone & Berube, 2013). The proportion of non-hispanic Asians, on the other hand, was higher in the developing suburbs than in mature ones. This may reflect somewhat different mobility patterns among racial/ethnic subgroups with high immigration rates (Anacker, 2013; Clark, 2003; Jones, 2008; Li & Skop, 2007; Massey & Denton, 1988a, 1988b, Zhou & Gatewood, 2007). These proportions mask considerable variability among census regions and at the regional and metropolitan levels, and there are many exceptions to the general pattern of mature suburbs falling between central cities and developing suburbs. The proportion of Hispanics/Latinos in the mature suburbs of the West, for example, is higher (37.9%) than that of both central cities (29.8%) and developing suburbs (25.8%) in that census region. At the metropolitan level, a higher proportion of non-hispanic African Americans lived in the mature suburbs than in the developing suburbs of 75 out of the 100 most populous MSAs in Most of the remaining 25 MSAs were located in western and southwestern regions and had small mature suburban rings (due to annexation or recent growth; e.g., Las Vegas), small overall Black s, or both. Hispanics/Latinos were present in higher proportions in the mature suburbs in 82 MSAs, and more than half of the remainder were located in southern MSAs. Whites and Asians were present in greater proportions in the developing suburbs in 86 and 90 MSAs, respectively. We have similar findings in regards to segregation. The dissimilarity index is a measure of evenness, indicating the percentage of people who need to move from one tract to another within the Consolidated Metropolitan Statistical Area (CMSA) to achieve an even distribution. As pointed out above, Massey and Denton (1993) established an index value of 60 as a threshold of high segregation. Among the pairs of racial ethnic groups (Hispanic/Latinos, non-hispanic Whites, non- Hispanic Blacks/African Americans, and non-hispanic Asians), we find that dissimilarity indices in mature suburbs generally exceed dissimilarity indices for developing suburbs (see Table 2). Moreover, Black/White and Hispanic/Latino/non-Hispanic White dissimilarity levels in mature suburbs are closer to segregation levels in central cities. Whereas in the Northeast, the Black/White dissimilarity index for mature suburbs for 2000 and 2010 was at the midpoint between high urban and much lower developing suburban levels, the Black/White dissimilarity index for mature suburbs was much closer to central cities in the South, West, and Midwest. The same held true for White/Latino dissimilarity indices in every region except for the South, where the dissimilarity index in the developing suburbs was notably higher than in other regions. In the West, the Latino/White dissimilarity index for mature suburbs exceeded the figure for central cities. Similarly, Black/Asian and Black/Latino dissimilarity indices in the mature suburbs exceeded the index in central cities and developing suburbs in 2000, with the Black/Latino dissimilarity index falling to a level only slightly below that of central cities in The White Asian dissimilarity index, on the other hand, was highest in the developing suburbs. Table 2 also indicates changes in ethnic and racial segregation by region and subgeography from 2000 to Several broad trends are apparent: Black/White segregation indices decreased for all regions and subgeographies, with the sole exception of northeastern central cities. Latino/White segregation indices generally decreased to a smaller degree but increased across the South and in mature suburbs of the West. The Asian/White dissimilarity index also generally declined but increased in the mature suburbs of the West and the developing suburbs of the Northeast and South. Although there was wide variation in the size and direction of change across racial/ethnic pairs, there were a few common trends among the 100 MSAs we studied. We measured the shift in segregation for each metro subgeography, comparing it to the shift in other subgeographies within the same metro, utilizing Black/White, Latino/White, and Asian/White dissimilarity indices. In MSAs with Asian/White, Black/White, or Latino/White dissimilarity indices that declined least in mature suburbs, proportions of the corresponding racial/ethnic group grew more than did the proportions in that MSA s cities or developing suburbs; likewise, in MSAs with dissimilarity indices that declined least in developing

8 JOURNAL OF URBAN AFFAIRS 825 Table 2. Dissimilarity indexes for 100 most populous MSAs. White vs. non- Hispanic Black/ African American White vs. non- Hispanic Asian White vs. Hispanic/ Latino Black vs. non- Hispanic Asian Black vs. Hispanic/ Latino Non- Hispanic Asian vs. Hispanic/ Latino All CMSAs All census regions Census region Northeast Census region Midwest Census region South Census region West All central cities All census regions Census region Northeast Census region Midwest Census region South Census region West All mature suburbs All census regions Census region Northeast Census region Midwest Census region South Census region West All developing suburbs All census regions Census region Northeast Census region Midwest Census region South Census region West suburbs, proportions of the corresponding groups grew more than they did in that MSA s cities or mature suburbs. The Black/White dissimilarity index experienced the smallest decline in the developing suburbs of 41 MSAs, where the Black developing suburban as a proportion of the total rose from 9.8 to 12.2%, as the mature suburban proportion increased from 10.2 to 10.7%. In these areas, mature suburban Black/White dissimilarity fell from 60.0 to 57.0, whereas the developing suburbs fell only slightly from 49.5 to The metros where the urban change was worst (32 metros) or the mature suburban change was worst (27 metros) had smaller Black developing suburban s in 2000 (7.1 and 7.3%) and

9 826 K. ANACKER ET AL. smaller increases by 2010 (rising to only 8.6 and 8.8%); Black/White developing suburb dissimilarity indices fell from 48.5 to 45.0 in the mature suburban group and 49.5 to 45.1 in the city group. Because we use the size and direction of the change to define the groups, this is not surprising; what is noteworthy is that the smaller decreases in Black White segregation in the developing suburbs appear to be associated with larger increases in Black developing suburban. Along the same lines, in most metros (53), the Asian/White index declined least or grew most in the developing suburbs. In this group of metros, the developing suburban non-hispanic Asian grew at a faster rate (gaining 2.2 percentage points), compared to metros where mature suburbs or cities had recorded the smallest declines in Asian/White segregation (1.8 and 1.7 points). The overall pattern is somewhat different for the Latino/White dissimilarity index. Here, cities experienced the smallest declines in Latino/White dissimilarity overall (49 metros), followed by mature suburbs (35 metros). In the latter group, the proportion of Latinos in the mature suburbs began at a higher level (26.1%) and rose to a higher level (31.0%) in 2010 than the mature suburban Latino proportions in the urban (8.2 to 12.7%) or developing suburb group (9.6 to 14.0%). Latino/White dissimilarity in the mature suburb group rose from 47.7 to We further compared segregation in central cities, mature suburbs, and developing suburbs by examining the isolation index for Hispanic/Latino and Black/African American residents. The isolation index measures the degree to which residents of particular races and ethnicities are likely to live in communities with a high percentage of residents of the same racial and ethnic background. Our results show great variation among central cities, mature suburbs, and developing suburbs in 2000 and 2010 (see Table 3). Because the isolation index reflects the presence and the spatial distribution of racial and ethnic groups, the indices in 2000 and 2010 in Table 3 reflect the shifts illustrated in Tables 1 and 2. For non- Hispanic African Americans, proportions in cities, mature suburbs, and developing suburbs have increased only slightly, whereas dissimilarity indices have decreased across U.S. regions and suburban subgeographies; isolation has generally decreased. The proportion of non-hispanic Asians, on the other hand, has grown across regions and geographies, even as the direction of the change in dissimilarity has varied across subgeographies and regions, and isolation has generally risen. The proportion of Latino/as grew as well but by a greater degree (i.e., more percentage points) than non-hispanic Asians. Latino/a isolation accordingly increased across all regions and subgeographies; in the mature suburbs of the West, which includes notable groups of Latino-majority and -plurality suburbs outside of Los Angeles, the Latino/a isolation index rose sharply from 51.9 to 56.4, approaching the threshold of 60.0 that many demographers believe indicates a high level of segregation. Discussion and future research Overall, we find significant differences in the dissimilarity indices of mature and developing suburbs across regions and race/ethnic pairs. Black/White and Latino/White dissimilarity indices for mature suburbs tend to be closer to the figures for central cities, and in some cases they exceed them. By contrast, the Asian/White index for developing suburbs now exceeds mature suburbs and central cities in most regions. Our analysis shows that Black/non-Hispanic White and Asian/non-Hispanic White dissimilarity shows the smallest decline (or greatest increase) in the developing suburbs for a plurality of MSAs; central cities, followed by mature suburbs, showed the smallest decline in the Latino/White dissimilarity index in a plurality of MSAs. For each racial and ethnic group the subgeographies that showed the smallest declines in the dissimilarity index had proportions of the corresponding s of color that exceeded the percentage point change in the other subgeographies. One can view these results optimistically or pessimistically. In the case of Blacks/African Americans, for example, the group of MSAs where dissimilarity indices decreased slowest in the developing suburbs still saw the dissimilarity index fall (49.5 to 49.3). In these 41 MSAs, the index in developing suburbs remained lower than the index in mature suburbs (57.0), even as the proportion of Blacks/African Americans in the developing suburbs exceeded the proportion in mature suburbs. This might provide

10 JOURNAL OF URBAN AFFAIRS 827 some evidence that developing suburbs may be providing opportunities for Blacks/African Americans to live in more integrated communities (see Pfeiffer, 2016). Yet in these 41 MSAs, the dissimilarity index did remain stable, rather than declining. Similarly, in the MSAs where the greatest increase in the Latino/ White dissimilarity index was in the MSA s mature suburbs, the mature suburban dissimilarity index increased. This raises the question of whether increasing suburban diversity, whether in the mature or developing suburbs, is increasing segregation or at least halting trends toward integration. This finding could suggest that the growing tolerance for diversity and the opportunities presented by new housing developments on the periphery may occur amidst ongoing discrimination and structural racism. If this is the case, then it remains an open question whether the low segregation levels, which Pfeiffer (2016) argues are essential to the beneficial racial and ethnic equity outcomes of the developing suburbs, will withstand growing diversity. Could lower segregation levels in many developing suburbs represent a bare tokenism that is unthreatening to the local racial order but upended once certain demographic thresholds or tipping points are crossed (just as they are elsewhere)? If so, then the clean racial slate presented by housing availability in the developing suburbs rests in tension with more familiar theories of suburban stratification (Logan & Schneider, 1981, 1984; Stahura, 1987). Our argument is consistent with the recent literature on the suburbanization of poverty (Anacker, 2015b; Covington, 2015; Kneebone & Berube, 2013; Lassiter, 2006; Mikelbank, 2004; Nicolaides, 2002; Pfeiffer, 2016; Singer, Hardwick, & Brettell, 2008; Wiese 2004) and on the scanty nonprofit safety net in mature and especially developing suburbs, which is insufficient for addressing the outcomes of racial and ethnic segregation (Kneebone & Berube, 2013; Roth & Allard, 2015). Ultimately, we believe that these questions cannot be answered at the national scale but at the level of the region, metropolitan area, municipality, and neighborhood. This would include recognizing the regional patterning of White privilege (Nagel, 2013) as well as the particularity of local racial formations (Cheng, 2013) that cannot be readily operationalized quantitatively and yet must provide a context for our interpretation of measures of segregation (Denton, 2010). We recommend moving away from analyses that implicitly assume that all suburbs are the same and that the political and economic forces driving segregation within them are the same as well, especially given that most academic research and even the popular press has repeatedly underscored the great and growing diversity of the suburbs. We see mature and developing suburbs as distinct but grouped entities that have commonalities that result in similar levels of segregation, yet these levels also reflect region- and MSA-specific processes that may or may not be quantified easily. Decisions about the proper scale for the analysis of segregation have direct bearing upon policy. Massey and Denton s(1993) work moved segregation to the forefront of the debate in urban studies, at a moment when the political and social divide between cities and suburbs was arguably at its starkest (see, e.g., Gainsborough, 2001). Debates raged about the existence of a specifically Black urban underclass, whose hypersegregation cut off households from suburban educational and economic opportunities, underutilized opportunities, and most controversially perpetuated cultures and values inimical to social and economic mobility. These debates supported the development of policies oriented toward deconcentration that sought to decrease racial and ethnic segregation by moving urban residents toward suburban opportunities (Massey, Albright, Casciano, Derickson, & Kinsey, 2013; for critiques, see Chapple & Goetz, 2010; Imbroscio, 2008). Since the early 1990s, there has been a growing recognition that suburban opportunities vary across space (Orfield, 2002; powell & Reece, 2013). The renewed attention to suburban poverty and the challenges that mature suburbs face reflect a growing concern that disadvantage may be reconcentrated in suburban locations (Kneebone & Berube, 2013). This is particularly the case in metropolitan areas where urban gentrification has proceeded rapidly in the 21st century, raising the prospects that poorer residents, who are disproportionately residents of color, will be displaced to the mature suburbs (Ehrenhalt, 2012). Our future research will thus concentrate on two major avenues of inquiry. First, we will broaden our evaluation of segregation patterns in the suburbs by measuring the prevalence of segregation using other indices such as Thiel s H, which allows for the decomposition of metropolitan

11 828 K. ANACKER ET AL. Table 3. Isolation indexes for 100 most populous MSAs. White Black/ African American Asian Hispanic/Latino other All CMSAs All census regions Census region Northeast Census region Midwest Census region South Census region West All central cities All census regions Census region Northeast Census region Midwest Census region South Census region West All mature suburbs All census regions Census region Northeast Census region Midwest Census region South Census region West All developing suburbs All census regions Census region Northeast Census region Midwest Census region South Census region West segregation indices into their component parts (Lichter, Parisi, & Taquino, 2015), or information theory index H, a census tract free approach that focuses on local environments of varying size (Lee et al., 2008). Second, we will examine how suburban racial and ethnic segregation intersects with patterns of class segregation. It is our hope that future finer-grained analyses of segregation, placed in regional and metropolitan contexts as described above, will enable activists and policymakers to craft better responses to the stubborn problem of suburban segregation. Note 1. This group of tracts had 292 residents total nationwide, a tiny proportion of all residents in our select metropolitan areas. Less than 1% of the analyzed census tracts were split by the municipal boundaries of a

12 JOURNAL OF URBAN AFFAIRS 829 central city. In this case, a census tract was classified as belonging to the central city when more than 50% of workers resided in the city part of the census tract that fell in the central city, whereas the remaining tracts were classified as suburban. Acknowledgments We thank the attendees at the Association for Public Policy Analysis and Management (APPAM) annual fall research conference in Washington, DC, in November 2013 and at the American Sociological Association (ASA) annual meeting in Chicago, Illinois, in August 2015 for invaluable comments. Matt Ogborn provided superb assistance with copyediting. About the authors Katrin Anacker is Associate Professor at the Schar School of Public Policy and Government at George Mason University in Arlington, Virginia. Her work has been published in the Journal of Urban Affairs, Housing Policy Debate, Housing Studies, the International Journal of Urban and Regional Research, and Urban Geography, among others. She is the co-editor of Suburbia in the 21st Century: From Dreamscape to Nightmare? (Routledge, in progress) and Introduction to Housing (University of Georgia Press, 2017) and the editor of The New American Suburbs: Poverty, Race, and the Economic Crisis (Ashgate, 2015). Christopher Niedt is Academic Director of the National Center for Suburban Studies and Associate Professor in the Department of Sociology at Hofstra University. He studies the effects of metropolitan growth and decline on racial/ ethnic and class inequality, particularly as these relate to the history, politics, and redevelopment of inner-ring suburbs. His most recent research projects focus on suburban racial/ethnic diversity, suburban social movements, the foreclosure crisis, and the use of eminent domain. He is co-author of Foreclosed America (with Isaac Martin; Stanford University Press, 2015) and An Uneven Road to Recovery: Place, Race, and Mortgage Lending on Long Island (with Marc Silver; National Center for Suburban Studies, 2014) and editor of Social Justice in Diverse Suburbs (Temple University Press, 2013). Chang Kwon is currently a PhD candidate in public policy at George Mason University s Schar School of Policy and Government and an external consultant at the Inter-American Development Bank s Institutional Capacity and Finance Sector. He was the research fellow for the Tuscany s Quality of Life project at the Milan Expo He also served as a public policy research assistant at George Mason University from 2011 to His main research areas are public finance and social policy. ORCID Katrin Anacker References Adelman, R. M. (2005). The roles of race, class, and residential preferences in the neighborhood racial composition of middle-class Blacks and Whites. Social Science Quarterly, 86, Alba, R. D., Logan, J. R., & Stults, B. J. (2000). The changing neighborhood contexts of the immigrant metropolis. Social Forces, 79, Alba, R. D., & Nee, V. (2003). Remaking the American mainstream: Assimilation and contemporary immigration. Cambridge, MA: Harvard University Press. Allard, S. W. (2009). Out of reach: Place, poverty, and the new American welfare state. New Haven, CT: Yale University Press. Allen, J., & Turner, E. (2012). Black-White and Hispanic-White segregation in U.S. counties. The Professional Geographer, 64, Anacker, K. B. (2010). Still paying the race tax? Analyzing property values in homogenous and mixed-race suburbs. Journal of Urban Affairs, 32, Anacker, K. B. (2012). Shaky palaces? Analyzing property values and their appreciation rates in minority first suburbs. International Journal of Urban and Regional Research, 36, Anacker, K. B. (2013). Immigrating, assimilating, cashing in? Analyzing property values in suburbs of immigrant gateways. Housing Studies, 28, Anacker, K. B. (2015a). Analyzing Census tract foreclosure risk rates in mature and developing suburbs in the United States. Urban Geography, 36,

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