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STATE OF THE ART
SEGREGATION IN POST-CIVIL
Stalled Integration or End of the Segregated Century?
Jacob S. Rugh
Department of Sociology, Brigham Young University
Douglas S. Massey
Office of Population Research, Princeton University
In this paper we adjudicate between competing claims of persisting segregation and rapid integration by analyzing trends in residential dissimilarity and spatial isolation for African Americans, Hispanics, and Asians living in 287 consistently defined metropolitan areas from 1970 to 2010. On average, Black segregation and isolation have fallen steadily but still remain very high in many areas, particularly those areas historically characterized by hyper-segregation. In contrast, Hispanic segregation has increased slightly but Hispanic isolation has risen substantially owing to rapid population growth. Asian segregation has changed little and remains moderate, and although Asian isolation has increased it remains at low levels compared with other groups. Whites remain quite isolated from all three minority groups in metropolitan America, despite rising diversity and some shifts toward integration from the minority viewpoint.
Multivariate analyses reveal that minority segregation and spatial isolation are actively produced in some areas by restrictive density zoning regimes, large and/or rising minority percentages, lagging minority socioeconomic status, and active expressions of anti-Black and anti-Latino sentiment, especially in large metropolitan areas. Areas displaying these characteristics are either integrating very slowly (in the case of Blacks) or becoming more segregated (in the case of Hispanics), whereas those lacking these attributes are clearly moving toward integration, often quite rapidly.
Keywords: Segregation, African Americans, Latinos, Discrimination, Land Use Zoning
Analyses of racial and ethnic segregation in the United States indicated three basic trends at the end of the twentieth century: (1) slow but steady declines in the degree of Black-White segregation (measured by the index of dissimilarity) with parallel
Du Bois Review, 10:2 (2013) 1-28.
© 2013 W. E. B. Du Bois Institute for African and African American Research 1742-058X/13 $15.00 doi:10.1017/S1742058X13000180
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declines in Black spatial isolation (measured by the P* index); (2) the continued residential segregation and spatial isolation of Asians at low to moderate levels with no significant trend upward or downward; and (3) steady Hispanic segregation at moderate to high levels combined with rising levels of Hispanic spatial isolation (Iceland 2009; Logan et al., 2004; Massey et al., 2009). Preliminary work based on the 2010 census has yielded widely discrepant reports on America's progress toward integration. Whereas Logan and Stults (2011) see the persistence of segregation and argue that "the pace of integration has slowed to a standstill," Glaeser and Vigdor (2011) proclaim "the end of the segregated century."
The past forty years have witnessed a plethora of powerful demographic, economic, and social shifts that have transformed race relations in the United States to produce a more complicated residential configuration in American cities. Demographically, the nation has been reshaped by mass immigration from Asia and Latin America, changing the paradigmatic urban structure from the "chocolate city and vanilla suburbs" of the 1960s (Farley et al., 1978) to the "prismatic metropolis" of the new millennium (Zubrinsky and Bobo, 1996). In economic terms, inequalities of income and wealth have risen to record levels (Keister 2000; Piketty and Saez, 2007; Wolff 2010), class segregation has increased (Massey and Fischer, 2003; Reardon and Bischoff, 2011), and the socioeconomic gap between Whites and minorities has widened, even as many minority members have moved into the middle class (Massey 2007).
In the social realm, attitudes towards African Americans have shifted so that Whites no longer support segregation and discrimination as matters of principle, though many continue to harbor negative racial stereotypes, display limited tolerance of racial mixing, and offer little support for any form of civil rights enforcement (Bobo 2004; Bobo and Charles, 2009; Massey 2011; Schumanet al., 1998). Latinos, meanwhile, have increasingly been demonized as a threat to American society and depicted in harsh; racially coded terms (Chavez 2001, 2008; Massey 2009; Massey and Pren, 2012a, b; Massey and Sanchez, 2010; Santa Ana 2002). 'With respect to both groups, unconscious racism and prejudice also appear to be prevalent American social cognition (Banaji 2001; Fiske et al., 2009; Lee and Fiske, 2006; Quillian 2006) and play at least some role in shaping behavior (Bargh 2004; Ziegert and Hanges, 2005).
Public policies enacted during the Civil Rights era appear largely to have ended overt racial discrimination in real estate and lending markets. Discrimination in housing was prohibited by the 1968 Fair Housing Act and discrimination in mortgage lending was banned by the 1974 Equal Credit Opportunity Act and the 1977 Community Reinvestment Act. As a result, minorities are no longer openly denied access to homes and credit, though audit studies reveal that traditional discriminatory practices continue surreptitiously (Charles 2003; Ross and Turner, 2004; Squires 1994; Turner et al., 2002). In addition, new and more subtle forms of discrimination have been invented (Massey 2005), including linguistic profiling (Fischer and Massey, 2004; Massey and Lundy, 2001; Purnell et al., 1999; Squires and Chadwick, 2006), predatory lending (Lord 2004; Renuart 2004; Squires 2004), and reverse redlining (Brescia 2009; Friedman and Squires, 2005; Rugh and Massey, 2010; Smith and DeLair, 1999; Turner et al., 2002; Williams et al., 2005).
In recent decades, density zoning has emerged as a powerful force promoting racial segregation. Limits on the density of residential construction in predominantly White communities drive up the cost of housing to make it unaffordable to low income, minority households (Glaeser et al., 2005; Pendall 2000). As result, the more restrictive the density zoning regime (the stricter the limits on residential density),
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the higher the level of racial segregation and the less the shift toward integration over time (Rothwell 2011; Rothwell and Massey, 2009). Unsurprisingly, restrictive density zoning has also been linked to higher levels of income segregation (Rothwell and Massey, 2010), and instrumental variable regressions suggest both relationships are not only strong, but causal.
In sum, whereas certain causes of segregation may have faded, new ones have appeared and the effects on levels and trends in residential segregation in the United States today are unclear. In this paper we seek to shed light on the true nature of the current situation by undertaking a systematic analysis of trends in the residential segregation and spatial isolation for Blacks, Whites, Hispanics, and Asians using a balanced panel of 287 metropolitan areas with consistently defined metropolitan boundaries from 1970 through 2010. After considering trends in segregation and spatial isolation, we specify and estimate a comprehensive explanatory model to reveal the underlying causes of residential segregation for Blacks and Hispanics in many quarters of the United States. In doing so we seek to identify the metropolitan circumstances in which segregation continues to be actively produced, and those in which shifts toward desegregation are facilitated.
DATA AND METHODS
Our principal data source is the Decennial Census of Housing and Population for 1970, 1980, 1990, 2000, and 2010 and the 2008-2010 American Community Survey. We extracted data on measures of residential segregation and spatial isolation for 1980-2010 from Logan and Stults (2011) for all metropolitan areas and divisions (hereafter MSAs) in the United States as defined in 2009. For 1970 we used data from the professional version of Social Explorer' at the census tract level to compute segregation and isolation indices for MSAs as defined in 2009. Our dataset consists of a balanced panel of 287 consistently defined MSAs for which we were able to compute segregation indices for 1970-2010. The MSAs included in our analysis are listed in Appendix A.
Here we focus on two of segregation's five constituent dimensions: unevenness and isolation (Massey and Denton, 1988a). We measure unevenness using the well-known index of dissimilarity, which captures the degree to which the residential distribution of any two groups departs from the ideal of evenness. In an even distribution, each neighborhood has the same proportion of minority and majority members as the metropolitan area as a whole. Our indicator of neighborhood is the census tract and we consider three minority groups—non-Hispanic Blacks, non-Hispanic Asians, and Hispanics and compare their residential distribution to that of non-Hispanic Whites. Under these circumstances, the index of dissimilarity states the relative percentage of minority group members and non-Hispanic Whites who would have to exchange tracts to achieve an even residential distribution.
We measure a group's spatial isolation using the P* index, which gives the minority percentage within the neighborhood of the average minority member. The Black isolation index, for example, gives the percentage Black in the neighborhood of the average African American residing in a particular metropolitan area. Whereas the dissimilarity index is invariant with respect to the minority-majority composition of a metropolitan area, the isolation index directly depends on the relative number of minority versus majority group members.
In order to consider the determinants of residential segregation and spatial isolation we assembled pan-el data on a variety of variables that prior studies have
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shown to be relevant in shaping residential outcomes in U.S. metropolitan areas, which are listed in Table 1. Until now investigators have been unable to measure variation in the extent of racial-ethnic prejudice across metropolitan areas, owing mainly to the cost of developing reliable survey estimates from probability samples of hundreds of different areas but also to the reluctance of respondents to admit to
Table 1. Independent Variables Used to Predict Segregation Outcomes for Blacks, Hispanics, and Asians
Racial Prejudice Anti-Black Index
Percent Black Percent Hispanic Percent Asian
Ratio Minority/White HH Income
Ratio Minority/White College
Log MSA Population
Percent Foreign Born
Percent Female Headed
Percent Aged 65+
Industrial Organization Percent Manufacturing Percent FIRE
Log Military Population
Percent Unionized Patents per Capita
Violent Crime Rate Median Year Housing
Relative Frequency of Google Searches for word "Nigger"
Relative Frequency of Google Searches for "Illegal Alien"
Instrumental Variable Derived from Rothwell and Massey (2009)
Percentage Black in MSA Percentage Hispanic in MSA Percentage Asian in MSA
Ratio of Minority-to White Household Income Ratio of Minority-to White Percentage College Graduate
Percentage of Homeowners in MSA
Log of Total MSA Population
Percentage Foreign Born in MSA
Percentage of Female Headed Families in MSA Percentage of MSA Population Aged 65 or Greater
Percentage of Workers in Manufacturing
Percentage of -Workers in Finance, Insurance, & Real Estate
Percentage of 'Workers in Education
Log of Persons Housed in Military Quarters per 100,000 in MSA
Percentage of Workers in Union (for State in 1980; 2010 for MSA)
Patents per 100,000 Persons (for State in 1980; for MSA in 2010)
Percent Urban in MSA
Violent Crimes per 1,000 Persons Median Year MSA Housing was Built
Northeastern Census Region
Southern Census Region
Western Census Region
MSA Borders Atlantic, Pacific, or Gulf of Mexico
Located in State Bordering Mexico
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having prejudicial sentiments. Google Trends, however, offers new opportunities to assess topics that were previously difficult for survey researchers to tackle (Stephens-Davidowitz 2013). For example, the most extreme expression of anti-Black sentiment and the harshest epithet one can apply to an African American is the word "nigger" and when we entered variations on this term into Google Trends we found it to be the subject of a large volume of internet searches that yielded a robust and quite variable distribution of frequencies across metropolitan areas since 2004.
Stephens-Davidowitz (2013) performed a similar operation using Google Trends and found that the resulting index correlated strongly with other known measures of racial prejudice at the aggregate level; the index strongly predicted voter turnout for Obama across market areas in the 2008 presidential election. Whereas he used market areas, we employed metropolitan areas, which are smaller, and found that in some the volume of searches was too small to support a reliable index, and in these cases we substituted the state-level search frequency. On this index, the five most racist metropolitan areas were Flint, MI, Altoona, PA, Charleston, WV, Scranton, PA, and Wheeling, WV The five least racist were Salt Lake City, UT, Ogden, UT, Provo-Orem, UT, Honolulu, HI, and Napa, CA.
When we entered various pejorative terms for Asians (chink, gook, etc.) into Google Trends, we did not find a sufficient volume of searches to provide a reliable index of Anti-Asian bias across metropolitan areas, suggesting a much lower level of hostility against this group. Likewise, when we entered various pejorative terms for Latinos into the system (spic, beaner, etc.) we also came up empty. However, recent decades have seen the rise of a Latino threat narrative in the media and public discourse tied to the framing of Latino immigrants as "illegal" (owing to undocumented migration) and therefore by definition "criminals" and "lawbreakers" (Chavez 2001, 2008). When we entered variations on the term "illegal immigrant" into Google Trends we again encountered a rather large volume of searches that yielded a robust and variable distribution of frequencies across metropolitan areas. As before, we substituted the state-level frequency whenever the volume was too low to sustain measurement within a particular metropolitan area. According to this index the lowest levels of anti-Latino sentiment were observed in Honolulu, HI, Bangor, ME, Cleveland, OH, Detroit, MI, and Lewiston, ME, whereas the highest levels occurred in Santa Barbara, CA, El Paso, TX, Brownsville, TX, Phoenix, AZ, and Tucson, AZ.
In addition to White animus toward certain minority groups, as noted above researchers have also demonstrated a strong causal connection between restrictive density zoning and both class and racial segregation (Rothwell and Massey, 2009, 2010). In their survey of local land regulations prevailing in forty-nine metropolitan areas, Pendall et al. (2006) asked 1,677 governmental units to report the maximum allowable density permitted in the jurisdictions they controlled. Localities that allowed less than four units per acre were coded 1; those that allowed four to seven units were coded 2; those permitting eight to fifteen units were coded 3; those allowing sixteen to thirty were coded 4; and those permitting thirty or more units per acre were coded 5. Rothwell and Massey (2009) computed the average density score across jurisdictions for each metropolitan area and found that it strongly predicted segregation, even after the application of rigorous controls.
In order to establish the causal effect of density zoning on segregation, they derived a prediction equation to estimate the density score as an instrumental variable, using year of statehood as the principal exogenous identifier. We borrowed their prediction equation and used it here to estimate density instruments for all metropolitan areas in our data set according to the following formula (1). Following Rothwell and Massey (2009), all predictors are defined as of 1980 except for the
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intergovernmental transfer variable, which was defined as of 1967 according to their formula.
Density Instrument = —48.394 + Year of Statehood * 0.007 + Percent Black * —0.006 + Percent Hispanic + 0.022 + Log of Population * —0.080 + Unionization Rate * —0.015 + Intergovernmental Transfers as a Share of Local Revenues * 0.005 + Coastal Location * 0.297 + Percent Urban * 0.004 + Share in Manufacturing * 0.021 + Median Age of Housing * 0.021 + Percent Homes Owner Occupied * —0.010 + Violent Crime Rate * —0.0002 + Ratio of Minority to White Median Family Income * 0.114.
The relative size of minority groups has long been recognized as a key determinant of segregation and discrimination. Sociologists have long argued that larger minority groups pose a greater threat to majority interests than smaller ones, in both symbolic and practical terms (Blalock 1967; Blau 1977; Blauner 1972; Lieberson 1985). Symbolically, a larger minority population increases the visibility and salience of group members in public. Practically, more minority group members intensify competition for scarce public and private resources and can be expected to yield higher levels of residential dissimilarity.
In addition, the minority percentage has a direct mathematical relationship with the P* isolation index, essentially setting its lower bound. In a metropolitan area that is 20% Black and 80% White, for example, the minimum possible isolation index for African Americans is twenty, which would occur when the two groups are evenly distributed across neighborhoods (yielding a dissimilarity index of zero), thereby producing a distribution where every neighborhood is 20% Black. The upper bound, of course, would be 100, which would occur when all Blacks lived in neighborhoods that were 100% Black and all Whites lived in neighborhoods that were 100% White.
To a large extent, therefore, residential isolation is produced in urban areas by the confluence of a high level of dissimilarity with a large percentage of minority group members. Apart from these structural influences, the degree of segregation and isolation has been found to vary according to the socioeconomic status of the group in question (Denton and Massey, 1988; Iceland 2007; Iceland et al., 2005). Given that U.S. housing markets are segmented by price, and that wealth and income continue to vary sharply by race and ethnicity, intergroup differences in socioeconomic status translate into differences in residential status, leading to segregation (Massey and Denton, 1985, 1993). In general, the greater the gap in socioeconomic status between Whites and minorities, the greater the level of minority segregation and spatial isolation (Alba and Logan, 1991, 1993; Massey and Denton, 1987). Hence our data set includes two indicators of relative status: the ratio of minority to White household income and the ratio of minority to White college graduates. In each case, we used census data to compare minority group members (Blacks, Hispanics, or Asians) to Whites living in the same metropolitan area. In addition, since homeowners have a greater stake in the status of a neighborhood than do renters and thus tend to behave more conservatively in response to perceived threats (Hirsch 1983; Sugrue 1996), we also control for the relative number of homeowners in each metropolitan area.
Research has generally shown that minority segregation and isolation vary systematically by metropolitan demographic circumstances, being greater in metropolitan areas that have more inhabitants (Massey and Denton, 1987, 1988b), more foreign born (Denton and Massey, 1988; Iceland and Scopilliti, 2008), more female-headed households (Massey et al., 1994), and more elderly (Farley and Frey, 1994;
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Massey 2006). Older Whites tend to be more prejudiced than younger persons (Maykovich 1975; Schuman et al., 1998; St. John 1996) and so are more resistant to residential integration.
Segregation has also been hypothesized to vary according to patterns of industrial organization, with large manufacturing sectors and high rates of unionization pushing segregation levels upward (Lieberson 1980; Massey and Denton, 1993). In contrast, metropolitan areas dominated by "creative class" service industries, such as finance, insurance, and real estate, tend to be more diverse (Florida 2002). Another indicator of a creative class economy is the rate of patent production, which we also include in the model. Sectoral employment data was drawn from the census whereas the unionization data came from Hirsch and McPherson (2012). Patent production was computed as the number of patents per 100,000 persons in the MSA using data on utility patents from the U.S. Patent and Trademark Office.
Researchers have also shown that metropolitan areas dominated by colleges and universities and large military populations are more integrated than others (Farley and Frey, 1994). The military is the most integrated institution in American society and its influence apparently shapes race relations and housing patterns in metropolitan areas that contain them (Moskos and Butler, 1996). Using census data we computed the number of persons living in military quarters per 100,000 persons. Expressions of prejudice decline sharply with education, and in order to assess the dominance of the educational sector in each metropolitan area we computed the proportion of workers employed in education.
We also consider the influence of several facets of urbanism. Since metropolitan areas are constructed from multiple counties, many of which contain significant non-urban populations, we measure the percentage of metropolitan inhabitants who are actually urban residents (living in census tracts with greater than 1,000 persons per square mile). Moreover, given that Whites, on average, continue to associate African Americans with higher rates of crime (Quillian and Pager, 2001, 2010) and resist integration based on this perception (Emerson et al., 2001) we also constructed violent crime rates for MSA's using count data from the U.S. Federal Bureau of Investigation (2012). Metropolitan areas with newer housing stocks built after the civil rights era tend to be less segregated than those areas built up in earlier periods (Farley and Frey, 1994), we drew upon census data to compute the median year in which an MSA's housing was built.
Finally, we control for geographic location by including dummy variables for region (with the Midwest serving as the reference category), a dummy variable for whether the MSA is located on a coast (given that coastal areas tend to have more restrictive building codes and higher housing costs (Glaeser and Gyourko, 2008)), and a final dummy for location in a border state, where levels of Hispanic segregation have historically been higher (Grebler et al., 1970; Massey 1979).