TERM PAPER ARTICLE - Learning Apart, Living Apart- How the Racial and Ethnic Segregation of Schools and Colleges Perpetuates Residential Segregation

Learning Apart, Living Apart: How theRacial and Ethnic Segregation of Schoolsand Colleges Perpetuates ResidentialSegregation

PAT RUBIO GOLDSMITH

University of Wisconsin-Milwaukee

Background: Despite a powerful civil rights movement and legislation barring discrimina-tion in housing markets, residential neighborhoods remain racially segregated.Purpose: This study examines the extent to which neighborhoods’ racial composition isinherited across generations and the extent to which high schools’ and colleges’ racial com-position mediates this relationship. To understand the underlying social processes responsi-ble for racial segregation, I use the spatial assimilation model, the place stratification model,and perpetuation theory.Population: Data for this project are from the National Education Longitudinal Study(NELS), the Integrated Postsecondary Education Data System (IPEDS), and the U.S.Census.Research Design: A longitudinal design tracks the racial composition of the schools, col-leges, and neighborhoods from adolescence through age 26.Findings: Holding constant the percent white in teenagers’ neighborhoods, socioeconomicstatus, and other variables, the percent white that students experience in high school and col-lege has a lasting influence, affecting the percent white in young adult neighborhoods andexplaining 31% of intergenerational continuity of neighborhood racial composition.Conclusions: The analyses suggest that racial segregation in high schools and colleges rein-forces racial segregation in neighborhoods.

In 1976, Bowles and Gintis proposed the existence of a rigid stratificationsystem in which an individual’s position in the social class hierarchy was

Teachers College Record Volume 112, Number 6, June 2010, pp. 1602–1630Copyright © by Teachers College, Columbia University0161-4681

Learning Apart, Living Apart 1603

largely inherited from his or her parents. Upward and downward mobil-ity across generations, they argue, was relatively rare. They also argue thatschools were an integral component of this relationship. Schools did notcreate paths for upward mobility, but were institutions that reinforced theintergenerational transmission of class position.

In this article, I adopt a similar theoretical perspective, but instead ofanalyzing social class, I examine the residential racial segregation system.The work follows directly from Sharkey (2008), who argues that neigh-borhood contexts are stratified into more advantaged and less advan-taged areas, and individuals’ positions in these strata are largely inheritedfrom the residential backgrounds of their parents’ household. His empir-ical analyses demonstrated little intergenerational mobility in regard toneighborhood strata defined by income levels.

My main contribution to the study of intergenerational mobility inregard to neighborhood context, in addition to looking at race ratherthan class, is to examine the role of schools and colleges. I examinewhether segregation in schools and colleges contributes to the intergen-erational transmission of residential locations in terms of racial composi-tion. I ask: To what extent does the racial composition of teenagers’neighborhoods associate with the racial composition of their adult neigh-borhoods, and to what extent can the relationship be explained by theracial composition of students’ high schools and colleges?

Considering the role of schools and colleges in the intergenerationaltransmission of context makes sense for a number of reasons. The racialcomposition of neighborhoods is similar in youth and in adulthood(Dawkins 2005). Residential racial segregation systems can also be viewedas stratification systems because the percent white in a neighborhood isrelated to youths’ life chances. More racially segregated metropolitanareas have greater racial differences in educational achievement, highschool completion, youth employment, and single parenthood (Cardand Rothstein 2005; Cutler and Glaeser 1997). Compared with blackswho move from one inner-city neighborhood to another, blacks whomove from an inner-city neighborhood to a suburban neighborhood findmore prejudice and discrimination from peers and adults, but they go toschools with higher academic standards, receive more help in school,attain more education, and, when they are adults, have higher employ-ment rates (Kaufman and Rosenbaum 1992; Rosenbaum and Popkin1991). Predominantly black and Latino neighborhoods, in comparisonwith predominantly white ones, often have inferior institutions (includ-ing schools), less network density, and less mutual support for raisingchildren (Kozol 2005; Sampson, Morenoff, and Earls 1999).

The racial segregation of schools also can be considered a form of

1604 Teachers College Record

stratification. Although students of color often are devalued and stigma-tized in predominantly white schools (Foster 1997), attendance at theseschools privileges students in many ways. Compared with schools withfewer whites, predominantly white schools have smaller class sizes, higheraverage socioeconomic status (SES), a greater curricular focus on collegepreparation, and climates of high expectations (Goldsmith 2003; Oakesand Guiton 1995). Politicians stigmatize and neglect predominantlyminority schools (Kozol 2005), while students attending predominantlywhite schools attain more education and are less likely to go to prison(Guryan 2004; LaFree and Arum 2006; Wells and Crain 1994).

Understanding the extent to which school segregation mediates theintergenerational inheritance of neighborhood racial composition isboth theoretically and practically important. Theoretically, it will improveour understanding of the role of schools in perpetuating residential seg-regation and creating different life chances for blacks, Latinos, andwhites. Politically, it will inform debates about the benefits of schooldesegregation programs. Will school desegregation programs promoteresidential integration? A finding that levels of exposure to whites inschools and colleges explain part of the intergenerational inheritance ofneighborhood racial compositions would suggest that they can.

This article begins with a review of the literature on neighborhood andschool segregation. I use three theoretical models: the spatial assimila-tion model, the place stratification model, and perpetuation theory.After reviewing the relevant literature, I report findings from an empiri-cal study of these issues. I employed research methods widely used instudies of residential segregation (e.g., Crowder and South 2005;Crowder, South, and Chavez 2006). In these studies, researchersappended information about the racial and ethnic composition of resi-dential neighborhoods to individual-level data. By doing so, they couldidentify which people tended to leave which neighborhoods and wherethey tended to move. In my study, I appended information on percentwhite in residential areas during the high school years and at age 26 tosee how strongly racial compositions are inherited. I also appended infor-mation about the percent white in students’ high schools and colleges,making it possible to describe the percent white that students experienceas they transition from their neighborhoods of origin through highschool and college, and then into their young adult neighborhoods.

THEORIES OF RACIAL SEGREGATION

The United States is a racially hierarchical society that privileges peoplewith white identities culturally, economically, and socially (Bonilla-Silva


1997). The racial segregation of institutions monopolizes resources forwhites and isolates them from other groups. Residential segregation isthe bedrock upon which other forms of segregation are formed. AsFeagin (2006) writes, “Residential segregation reinforces, even creates,segregated schools, religious organizations, recreational facilities, andworkplaces. All such segregated organizations in turn reinforce residen-tial segregation—and thus reinforce white isolation . . . from people ofcolor” (247).

Residential segregation in terms of race in the United States developedover the 20th century as “[t]he real estate industry, banks, appraisers,and insurance agents translated private prejudice into public action ulti-mately sanctioned by the federal government in Federal HousingAdministration loan policies and the federal highway program” (Denton2001, 94). These structural and individual processes resulted in whitesbecoming increasingly concentrated in suburban and gentrified neigh-borhoods, and blacks and Latinos becoming more isolated in racially dis-tinct neighborhoods within the core cities of large metropolises.Reinforcing and adding to this process was the widespread flight of whitefamilies with school-age children from neighborhoods with predomi-nantly nonwhite schools to more-white areas (Clotfelter 2004).

Even though the Fair Housing Act of 1968 made many of the practicesinvolved in the creation of residential segregation illegal, residential seg-regation has proved to be highly persistent. Since 1970, blacks’ segrega-tion from whites has changed little, and Latinos’ has actually increased(Charles 2003). Average percent white in the neighborhoods of the 331U.S. metropolitan areas of blacks and Latinos is only 37% and 51%,respectively (Charles 2003).

Recent research on residential segregation has been guided by the spa-tial assimilation model and the place stratification model. I use these twomodels, as well as perpetuation theory, to understand the connectionsbetween neighborhood and school segregation over time.

The spatial assimilation model (Alba and Logan 1993; South andCrowder 1998; South, Crowder, and Chavez 2005) contends that neigh-borhoods and schools become more desirable as they become relativelymore white. People try to exchange their capital endowments for resi-dence in whiter neighborhoods and attendance (for their children) atwhiter schools, and therefore, people with more capital and their chil-dren are more likely to be in predominantly white contexts. Capital isconceived of broadly in this model to include economic, human, and cul-tural components. Economic capital includes income and wealth; humancapital is considered to be useful skills and is often proxied with indica-tors about educational achievement or attainment. Cultural capital does


not refer to elite culture (e.g., attending the opera) but to immigrants’levels of cultural assimilation, and it is indicated by such things as Englishfluency and generation. Suburban residence is an additional steppingstone into predominantly white contexts (Alba and Logan 1993).According to the model, whites are in schools and neighborhoods withrelatively more whites because they have relatively more capital thanblacks and Latinos.

The place stratification model views racial segregation as the outcome ofwhites’ attempts to maintain spatial distance from subordinated groups(South and Crowder 1998). Massey and Denton (1993) applied thisframework in their history of the structural causes of segregation. Thetheory contends that two dominant processes create or reproduce resi-dential segregation. The first is racial differences in preferences. In gen-eral, whites have less tolerance than blacks and Latinos for living inpredominantly nonwhite contexts and even integrated contexts (Charles2003). Blacks’ and Latinos’ attitudes show more tolerance for nonwhitecontexts or integrated contexts (Charles 2003). Racial differences in pref-erences are thought to be responsible for white flight from predominantlynonwhite neighborhoods. The other process focuses on the multipleforms of discrimination that blacks and Latinos experience when attempt-ing to enter predominantly white contexts (Charles 2003). This discrimi-nation includes unequal treatment from real estate agents, lenders, cityplanners, homeowners, renters, and neighbors (Charles 2003).

In addition to these two models, research on individual moves hasuncovered an additional pattern responsible for residential segregationnot anticipated by either model: the tendency for people to move toneighborhoods with a similar racial composition to the neighborhoodthey left. Crowder, South, and Chavez (2006) claimed that this relation is“by far the most important [empirical] explanation” for “black-white gapsin exposure to whites in residential neighborhoods” (86). In other words,the main reason that whites move to neighborhoods with relatively morewhites than blacks do is that whites move from neighborhoods with rela-tively more whites. If whites and blacks moved from neighborhoods withsimilar percentages of whites, they would move into neighborhoods withmuch more similar percentages of whites. Because the spatial assimila-tion and place stratification models do not consider the racial composi-tion of origin neighborhoods as a determinant of neighborhooddestinations, neither model can anticipate this outcome.

The similarity in the racial compositions of origin and destinationneighborhoods is one instance of a pattern noted by those who devel-oped perpetuation theory. Perpetuation theory (Braddock 1980; Braddockand McPartland 1989; Wells and Crain 1994) holds that individuals


perpetually experience the same racial compositions in their neighbor-hoods, schools, and other institutions over time. This theoretical campemerged from research on school desegregation. Researchers from thiscamp saw school desegregation as a program that took blacks from segre-gated environments and put them into integrated ones, and they wantedto know whether these experiences led blacks toward more integratedlives after they left school.

The theory acknowledges that whites actively construct exclusionarybarriers limiting black and Latino access to white contexts but argues thatblacks and Latinos who have been in integrated institutions are betterable than their counterparts from segregated institutions to navigatethese barriers. Researchers argue that blacks and Latinos who attendschools with whites become more comfortable interacting with whitesand being in white contexts and that they become more skilled at inter-acting with whites (Braddock 1980; Braddock and McPartland 1989;Eaton 2001). In addition, blacks and Latinos in places with relativelymore whites develop more social ties with whites. Even if these ties arenot close, they provide blacks and Latinos with access to information inwhite networks about opportunities or processes for entering other pre-dominantly white institutions (Dawkins and Braddock 1994; Eaton 2001;Wells and Crain 1994). This could include information about applying topredominantly white colleges or housing opportunities in predominantlywhite neighborhoods.

Recent research on whiteness is helpful for understanding the effectsof segregation on whites. Eduardo Bonilla-Silva and David Embrick(Bonilla-Silva and Embrick 2007; Bonilla-Silva, Goar, and Embrick 2006)argue that whites who grow up in all-white or nearly all-white settingsdevelop a white habitus. A white habitus is a mental lens that leads whitesto avoid and exclude blacks from their social networks and institutions.Whites who grow up in isolation from people of color, moreover, come tosee their isolation and exclusionary practices as natural and henceunproblematic, rather than as a highly discriminatory process that theyactively perform. The obfuscation of their racial motives enables them tocontinue maintaining their isolation after they become adults.

According to perpetuation theory, experiences in relatively less whitecontexts will ease whites’ resistance to integrated environments. In par-ticular, theorists working in this tradition argue that whites from desegre-gated schools become more comfortable in integrated settings thanwhites from segregated-white schools. I consider this a weakening of resis-tance to desegregation because white flight from integrated contexts hasbeen one of the primary ways that whites have resisted integration (Wellset al. 2004).


FROM NEIGHBORHOOD TO HIGH SCHOOL

The series of school desegregation programs resulting from the SupremeCourt’s decision in Brown v. Board of Education (1954) led to a dramaticincrease in interracial contact in schools, especially in the South, whichwent from the most to the least segregated region in the United Statesbetween 1968 and 1972 (Clotfelter 2004). However, schools remainedhighly segregated, even relative to neighborhoods. Most of the reasonswhy schools are so segregated are consistent with the place stratificationmodel. In residential areas where whites lived near people of color,whites persuaded local government agents, especially school officials, togerrymander school attendance zones so they corresponded to raciallydefined neighborhood boundaries (Clotfelter 2004). Whites also tendedto send their children to private schools and other schools of choice inlarger numbers than did nonwhite families, especially as the percent ofnonwhites in their school zone, neighborhood school, or neighborhoodrose (Bankston and Caldas 2002; Clotfelter 2001; Ledwith and Clark2007; Renzulli and Evans 2005; Saporito 2003).

Saporito and Lareau (1999) argue that when white families considerschooling options, they begin by eliminating all predominantly minorityschools and then choose among the remaining ones. They also arguethat nonwhites put less weight on the racial composition of schools whenmaking choices. To determine whether whites’ greater inclinations to optout of neighborhood schools resulted in greater school segregation,Saporito and Sohoni (2006) compare the percent white in school atten-dance zones with that in neighborhood schools. They found that percentwhite was, on average, greater in attendance zones than in neighborhoodschools, suggesting that whites were using school choice options to isolatetheir children from nonwhite students. Adding further support to theirargument, they found that the gap between the percent white in atten-dance zones and schools was largest in areas that had more school choiceoptions. The gap was smaller in places with school desegregation pro-grams.

Research has also shown, consistent with the spatial assimilation model,that immigrants are less likely than natives to send their children toschools of choice, although results vary by ethnic group (Betts and Fairlie2001). Independent of their neighborhood context, children from high-SES backgrounds are also more likely to attend schools of choice (Lauen2007).

The outlined considerations about neighborhoods and high schoolshelp explain the kinds of racial compositions that teenagers have in thesetwo institutions. The prevalence of neighborhood schools results in many


teenagers experiencing similar racial compositions in their high schoolsand neighborhoods. Net of the percentage white in neighborhoods,whites are likely to have greater percentages of whites in their schoolsthan blacks and Latinos, who, along with immigrants and those from low-SES backgrounds, experience downward mobility into schools with rela-tively fewer whites in the transition from neighborhood to school (Bettsand Fairlie 2001; Clotfelter 2004; Lauen 2007; Saporito and Sohoni2006).

FROM NEIGHBORHOOD AND HIGH SCHOOL TO COLLEGE

Colleges and universities within the United States constitute a highlystratified system of schools that range from public two-year colleges thathave largely open admissions that generally serve local constituents, tohighly selective private institutions that recruit nationally. As in K–12 edu-cation, interracial contact in higher education increased during the 20thcentury. As late as 1954, 83% of black collegians were in HistoricallyBlack Colleges and Universities (HBCUs). By 1998, this number droppedto 20%, and large numbers of blacks were attending historically white col-leges (Clotfelter 2004).

Like K–12 education, colleges and universities are also racially segre-gated. Blacks and Latinos tend to be concentrated in two-year collegesand nonselective four-year colleges, and whites are concentrated in selec-tive four-year colleges (Deil-Amen and Lopez Turley 2007). However,researchers have not examined how the racial compositions of a student’sneighborhood and high school relate to the racial composition of col-leges and universities to the same extent that they have examined otherforms of segregation.

Most researchers have seen colleges and universities as heavily stratifiedalong the dimensions of SES and standardized test scores (Davies andGuppy 1997; Karen 2002). As per the spatial assimilation model, theracial segregation of colleges is partially attributable to differencesamong whites, blacks, and Latinos in SES and standardized test scores(Alon and Tienda 2007). In fact, blacks are more likely than whites, andLatinos are equally likely as whites, to attend selective colleges and uni-versities net of differences in SES and test scores (Alon and Tienda 2007).Karen (2002) attributes blacks’ and Latinos’ high rates of attendance atthese institutions to social pressure from these groups. In addition,researchers working from the perspective of perpetuation theory haveshown that in comparison with blacks from predominantly black schools,those from integrated schools attended colleges and universities with rel-atively more whites (Braddock 1980; Braddock and McPartland 1982;


Crain and Weisman 1972 [as cited in Dawkins and Braddock 1994]).In addition, the prevalence of high-SES students with high standard-

ized test scores in predominantly white neighborhoods and high schools(Goldsmith 2003) may contribute to segregation in college, as these stu-dents often attend more selective colleges and universities than theircounterparts from predominantly nonwhite contexts. Nevertheless, thetransition from neighborhoods and high schools to college should allowfor much more mobility than the transition from neighborhoods to highschools because colleges enroll students from geographic areas muchlarger than neighborhoods.

FROM NEIGHBORHOOD, HIGH SCHOOL, AND COLLEGETO NEIGHBORHOOD

The spatial assimilation model, the place stratification model, and per-petuation theory are all important for studying residential segregation.Empirical research on the spatial assimilation model shows that racial dif-ferences in destination neighborhoods between whites and blacks, and toa lesser extent between whites and Latinos, is weakly related to differ-ences in economic and human capital. People with more education,income, and wealth tend to live in whiter neighborhoods, though theassociations are weak. Many educated middle-class whites live in raciallymixed neighborhoods through processes such as gentrification, andmany middle-class blacks and Latinos live in predominantly nonwhiteneighborhoods (Crowder and South 2005; Crowder, South, and Chavez2006).

The spatial assimilation model proved more useful for explaining themovement patterns of immigrants (Charles 2003; South, Crowder, andChavez 2005). Non-English speakers and first-generation immigrants areless integrated into white contexts than English speakers and second- andlater-generation immigrants.

Consistent with the place stratification model, empirical research hasshown that whites move into neighborhoods with relatively more whitesthan the neighborhoods that blacks and Latinos move into (Crowder,South, and Chavez 2006; South, Crowder, and Chavez 2005). In addition,whites’ movement patterns have shown a strong disinclination toward liv-ing with nonwhites. As the percent white in the neighborhood of origindeclines, whites become much more likely than blacks and Latinos toleave the neighborhood—a process usually referred to as “white flight”(Crowder 2000; Quillian 2001). Because these differences betweenwhites, blacks, and Latinos are observed even with controls for dif-ferences in human, economic, and cultural capital, researchers have


interpreted their effects as evidence of some combination of racial differ-ences in preferences or in discriminatory or exclusionary barriers thatlimit nonwhites’ entrance into white contexts. However, the researchtracking which individuals leave or enter which neighborhoods has notincluded measures of these underlying mechanisms (Sharkey 2008).

Perpetuation theorists have shown desegregated schooling to fostergreater integration among blacks. In comparison with blacks from black-segregated schools, those from integrated schools, colleges, and universi-ties move to neighborhoods with relatively more whites (Crain et al. 1992;Crain and Weisman 1972 [as cited in Dawkins and Braddock 1994]). Inother words, black adults tend to live in neighborhoods with a racial con-text that mirrors that of the schools they attended. For this reason, Iexpect to observe an association between the racial compositions ofschools and adult neighborhoods for all groups. Despite these impressivefindings, researchers working in this theoretical tradition have not usu-ally studied how or why these outcomes occur (Wells et al. 2004).

The only research on a large quantitative data set that has spoken tomechanisms suggested by this theory is Dawkins (2005). Dawkins foundthat for blacks, having a household head with more integrated friend-ships is associated with living in a more integrated neighborhood as anadult. Research using in-depth interviews has shown evidence for the the-ory’s mechanisms. Wells and others (2005) have shown that adults whoattended integrated schools describe themselves as being more comfort-able in integrated settings than other people they know. Eaton (2001)also found through interviews that blacks from integrated schools tappedinto white networks and gained information about applying to predomi-nantly white colleges and finding job opportunities.

In addition, research linking the racial composition of schools to theracial composition of neighborhoods has not controlled for the racialcomposition of the neighborhoods in which students were raised.Without controlling for the racial composition of neighborhoods, it isnot possible to determine if school racial compositions have an indepen-dent affect on later neighborhood compositions. This follows becauseneighborhood and school racial compositions are very similar for youngpeople, and it is easy to mistake the effect of one for the other unless theyare examined together. In addition, people often develop very strong psy-chological ties to places and people, and these ties may root individualswithin geographic areas (Altman and Low 1992). Thus, many youngadults may establish independent residences in the same neighborhoodswhere they lived as teenagers. A finding that high schools’ and colleges’racial composition influences where young adults live, controlling for theracial composition of prior neighborhoods, will provide strong evidence


that the racial segregation of schools and colleges reinforces neighbor-hood segregation.

METHODS

Data for this project are from the restricted version of the NationalEducation Longitudinal Study 1988-2000 (NELS), the IntegratedPostsecondary Education Data System (IPEDS), and Summary File 3B ofthe 1990 and the 2000 Censuses of Population and Housing. The NELSprovides longitudinal data that follow a base-year sample of respondentswho were eighth graders in the United States in 1988. Follow-upsoccurred in 1990, 1992, 1994, and 2000. The respondents were about 26years of age in the final wave. I used the sample of 10,827 respondentswho participated in all panels from 1988 to 2000. Information from theNELS about respondents’ families (from parents or the respondent) andschools (from principals) is attached to respondents’ records.

Information about NELS respondents’ postsecondary institutions isincluded by appending IPEDS data on the first postsecondary institutionattended (hereafter referred to as colleges). I averaged the IPEDS dataover the years 1992–96 because most students began college during thatperiod. Information about NELS respondents’ residential neighbor-hoods was created by appending census data to individual cases via theirfive-digit residential zip code numbers, which were recorded separately in1990, 1992, and 2000. I attached 1990 census data to the 1990 and 1992waves and 2000 census data to the 2000 wave.

Zip code areas (ZCAs), which are called zip code tabulation areas inthe 2000 Census, are second to census tracts as the most-often-used levelof aggregation to study “neighborhood” effects (e.g., Ainsworth 2002;South, Baumer, and Lutz 2003). I used ZCAs rather than tracts becauseNELS data cannot be linked to tracts. The results of using one level ofaggregation rather than other are unlikely to vary. In their recent reviewof a large body of research on neighborhood effects, Sampson, Morenoff,and Gannon-Rowley (2002) argue that places are stratified by race andclass at many different levels and that “empirical results have not variedmuch with the operational unit of analyses” (446). Regardless of whethertracts or zip codes are used, the type of error introduced is likely to besimilar when measuring exposure to whites. If there is segregation withinZCAs, then ZCAs’ percent white will, on average, underestimate the per-cent white that whites directly encounter and overestimate the percentwhite that blacks and Latinos directly encounter.

In all, these data contain information on individuals’ exposure towhites in their teenage ZCAs, high schools, colleges, and young adult


ZCAs. The age of these individuals is apt for studying the causes of resi-dential segregation because their mobility rates are very high. During thisperiod, many individuals leave home as they take on adult roles (Long1988).

LOGIC OF ANALYSES

The analyses are designed to estimate the extent to which ZCAs’ percentwhite is similar in youth and adulthood and how much of this relation-ship is mediated by percent white in schools and colleges. The analysesused data measured at four time points. At Time 1, which is 1988, oreighth grade, I measured all control variables and race/ethnicity. Thecontrol variables include the various forms of capital and regions. Thesevariables are described in more detail next. At Time 2, the teenage years(1990–92), I measured high schools’ and ZCAs’ percent white. Colleges’percent white is measured at Time 3, which for most students occursbetween 1992 and 1996. The final outcome, ZCAs’ percent white inyoung adulthood, was measured at Time 4, which is age 26, or year 2000.

The control variables were measured during eighth grade to reduceovercontrol bias (Sampson, Morenoff, and Gannon-Rowley 2002).Overcontrol bias occurs when one or more of the control variables areendogenous to the contextual effects. The inclusion of such variablesdownwardly biases the estimates of neighborhood effects and schooleffects. Because the control variables were measured during eighthgrade—before the contextual effects—they could not be endogenous tothem.

To account for problems from missing data, I followed the advice ofAllison (2002) and used SAS’s PROC MI to calculate multiple imputa-tions. This method imputes missing values from an algorithm that pre-dicts these values as a linear combination of other variables in theanalyses. A random variance component was added to the imputed val-ues so that their variances equaled the variance of the real scores. Irecoded imputed values to be within the range of the nonimputed dataand so that discrete variables would have discrete imputed scores. Thisprocess was followed five times, creating five different data sets (andhence, multiple imputations). In multiple imputations, the different datasets are used to mitigate the chances that the results obtained from theanalyses are influenced by the imputed scores rather than the real ones.This is done by using the results from the five data sets to calculate (inSAS’s PROC MIANALYZE) the mean coefficients and standard errorsthat account for the variation of the coefficients across data sets.

Models that used colleges’ percent white as a dependent variable


omitted students who did not attend college by assigning their weight avalue of 0 (Lee and Forthofer 2006). When colleges’ percent white wasused as an independent variable, missing values were assigned the mean.A dummy variable is used to flag where this occurred. Readers alsoshould keep in mind that the samples of Asians and American Indianswere small, and therefore, any inferences regarding these populationsare speculative.

Estimates and standard errors also were adjusted for the survey design.The sample of respondents participating in all waves of the NELS wasconstructed by the National Center for Education Statistics (NCES) fromthe sampling frames of earlier waves and by oversampling individualswho were unlikely to respond or who were difficult to locate. Because ofthe unequal probabilities of selection, I used the weight (F4PNLWT) con-structed by the NCES to make the sample representative of the eighth-grade class of 1988 (Curtin et al. 2002). Using the weight reduces the biasthat might result from calculating estimates from a nonrepresentativesample, and it protects estimates from model misspecification (Lee andForthofer 2006).

Survey design was accounted for because NELS data are stratified, andmultiple students were selected within base-year schools, which are theprimary sampling unit (PSU). Both sampling properties can bias stan-dard errors, but the latter is far more important because the homogene-ity of students within PSUs will downwardly bias standard errors andmake hypothesis tests too liberal. I used the NCES-created variablesSTRATA and PSU to adjust standard errors using the Taylor Seriesmethod (Lee and Forthofer 2006), which increases standard errors by aseparate amount for each variable based on the size of the intraclass cor-relation coefficient for the variable within PSUs. Linear regression mod-els adjusting for survey design were estimated using the SAS procedureSURVEYREG.

MEASUREMENT

Table 1 reports the weighted means for the entire sample for all variables.Exposure to whites in ZCAs during the teenage years is the average per-cent non-Hispanic white in respondents’ 1990 and 1992 ZCAs. Exposureto whites in schools during the teen years is the average percent non-Hispanic white in students’ 1990 and 1992 schools, as reported by princi-pals in the NELS data. I averaged the 1990 and 1992 reports to reducemeasurement error from point-in-time estimates (Solon 1992).

Race and ethnicity were measured with dummy variables for Latinos(of any race), Asians, African Americans, and American Indians. Non-


Hispanic whites are the reference category. Effects of race and ethnicity,which exist net of all other factors, are typically interpreted in theresearch on residential segregation as evidence of social processes pro-posed by place stratification models, such as differences in preferences ordiscrimination (Charles 2003; Sharkey 2008). Unfortunately, the data Ipossess, like other data sets that examine the movement of individualsinto and out of different contexts, do not contain measures of the under-lying mechanisms (Sharkey 2008).

I also included a dummy variable indicating which students lived withtheir parents at age 26. I did this because the percent white in these stu-dents’ teenage and adult neighborhoods are highly related because manyof them have (presumably) not moved.

Table 1. Descriptions of Variables, Weighted Means, and Standard Errors Adjusted for Survey Design.

Variable Description M SE

Percent white inHigh school (1990–92) 70.65 0.01College (1992–96) 71.19 0.01ZCA (1990–92) 76.02 0.01ZCA (2000) 69.24 0.01

Control variablesNon-Hispanic Black (1 = yes) Reference: non-Hispanic white 0.12 0.01Latina/o (1 = yes) Reference: non- Hispanic white 0.11 0.01Non-Hispanic Asian (1 = yes) Reference: non- Hispanic white 0.03 0.00Non-Hispanic Amer. Indian (1 = yes) Reference: non- Hispanic white 0.01 0.01SESa Index of family income, education, occupational

prestige, and possessions -0.09 0.02Family sizea Number of people in the family 4.62 0.02One-parent family (1 = yes) Reference: both biological parents 0.17 0.01Other nontraditional family (1 = yes) Reference: both biological parents 0.16 0.01Parent expects BA (1 = yes; 0 = no) 0.60 0.01Has an immigrant parent (1 = one or more; 0 = none) 0.13 0.01Language minoritya (1 = yes; 0 = no) 0.12 0.01Test score compositea Summary of math and reading test scores 50.98 0.20Grade point averagea Self-report, covers middle school years 2.91 0.02Expects a BA or more (1 = yes; 0 = no) 0.66 0.01Female (1 = yes; 0 = no) 0.50 0.01Inner-city residence (1 = yes) Central city of metropolitan area; reference:

suburban area 0.25 0.01Rural residence (1 = yes) Outside of metropolitan area; reference:

suburban area 0.32 0.01North Central (1 = yes) Reference category: South 0.26 0.01Northeast (1 = yes) Reference category: South 0.19 0.01West (1 = yes) Reference category: South 0.20 0.01a Indicates a variable created by the NCES that is available in the NELS.


The remaining variables were measured to correspond to 1988, thebase year of the NELS. As per the spatial assimilation model, I controlledfor variables related to family background, individual achievement, andacculturation. I used the NCES-created variable SES to control for varia-tion in economic and human capital among students’ families. SES is avariable created by the NCES using information on family income, par-ents’ occupations and education, and household possessions. I alsoincluded an indicator of family size to adjust the SES measure for thenumber of people in the family. I used two dummy variables to controlfor variation in family structure. The reference category was families withboth biological parents present, and the two dummy variables flag stu-dents in families with one parent and with some other family structure.The models also include a dummy variable that indicates parents whoexpect their child to earn at least a bachelor’s degree.

I controlled for the students’ human capital with self-reported highschool grade point averages (GPAs) and standardized test scores. Sinceattitudes also influence educational and occupational mobility later inlife, I controlled for educational expectations with a dummy variableindicating those who expected to earn at least a bachelor’s degree. Tocapture variation in acculturation, I used dummy variables to identify stu-dents who had (a) a minority language background and (b) at least oneimmigrant parent. I included dummy variables for residency in non-metropolitan areas and inner-city areas, using suburban residency as thereference category. Finally, I controlled for regions, as other researchershave found them to be related to the extent of school segregation andthe racial mix of destination neighborhoods (Clotfelter 2004; South andCrowder 1998). I measured regions with dummy variables for the West,North Central, and Northeast. The South is the reference category. NextI examine the linear regression models.

RESULTS

NEIGHBORHOOD TO HIGH SCHOOL

Table 2 shows models predicting high schools’ percent white. Model 1includes the effects of ZCAs’ percent white. Because many studentsattend neighborhood schools, this relationship should be very strong.However, there should be some mobility in the transition from ZCAs tohigh schools because some students will attend schools of choice or be indesegregation programs. Mobility rates can be measured with an elastic-ity coefficient, which equals the square root of the coefficient of determi-nation (Solon 1992). An elasticity coefficient of one (1.0) signifies no


mobility. Model 1, which estimates an elasticity coefficient of [square root(0.74) =] 0.86, shows very little mobility in the transition from ZCAs tohigh schools. As a point of reference, elasticity coefficients for the inter-generational inheritance of income are believed to be about 0.6 (Solon1992); for the intergenerational inheritance of neighborhood economiccontext, it is 0.64 (Sharkey 2008).

Model 1 also shows that as percent white in ZCAs increases, so does per-cent white in schools, although with a slight curve. This is seen in themodel by the positive and significant linear and squared term for ZCAs’percent white. Model 2 removes the ZCA effects and adds the racedummy variables. This estimates the aggregate amount of segregation inhigh schools because the coefficients show that on average, blacks’ andLatinos’ high schools have 48.2 and 48.5 percentage points fewer white,respectively, than the high schools of whites, which are, on average, 83.3percent white (as indicated by the intercept). Adding the ZCA effects(Model 3) shows that the primary reason that schools are so segregated

Table 2. Regression of Selected Variables onto High Schools’ Percent White.

Variable Model 1 Model 2 Model 3 Model 4

Intercept 5.32 ** 83.32 *** 16.28 *** 19.78 ***ZCA % white (1990–92) 0.54 *** 0.45 *** 0.42 ***ZCA % white squared (1990–92) 0.004 *** 0.004 *** 0.003 ***Black -48.20 *** -9.80 *** -9.31 ***Latina/o -48.52 *** -10.97 *** -7.09 ***Asian -26.14 *** -7.53 *** -4.71 **American Indian -48.83 *** -15.39 *** -13.01 **Family SES 0.83Family size 0.13One-parent family -1.31Other nontraditional family 0.08Parent expects college 0.75Immigrant parent 0.62Grade point average -0.57Standardized test score 0.07Language minority -5.84 ***Expects BA or more 1.16Female 0.09Core city residence -5.13 ***Rural residence 1.34Northeast 1.19West -1.41North Central 1.38Adjusted R2 0.74 0.44 0.75 0.76

*p < .05. ** p < .01. ***p < .001.


is due to differences in the racial composition of residential neighbor-hoods (that is, because of neighborhood segregation). Holding ZCAs’percent white constant reduces the black-white and Latino-white gaps tojust 9.8 and 11.0 percentage points, respectively, suggesting that ZCAs’percent white explains [(48.2 – 9.8) / 48.2 * 100 =] 80 and 77% of theschool segregation of these two groups, respectively.

Figure 1 graphs the estimated effect of ZCAs’ percent white fromModel 3 on high schools’ percent white for whites, blacks, and Latinos.1

The figure helps us see who is upwardly and downwardly mobile in thetransition from neighborhoods to high schools. Upward mobility occursfor individuals above the identity line; downward mobility occurs for indi-viduals below the identity line. As the graph shows, the only teenagerswho, on average, experience upward mobility are whites from ZCAs withvery few whites. This finding is likely to result from white flight: whiteteenagers in very nonwhite ZCAs find their way into schools with rela-tively more whites than in their neighborhoods. Other whites, in contrast,have nearly identical percentages of whites in their ZCAs and highschools.

The lines for blacks and Latinos show that almost regardless of the per-cent white in their ZCA, they experience downward mobility. The onlyblacks and Latinos who do not are those who live in ZCAs with very fewwhites. In essence, blacks and Latinos from predominantly minority

0

20

40

60

80

100

0 20 40 60 80 100

ZCA %white

Hig

hsc

hool

%w

hit

Figure 1. Effects of ZCAs’ Percent White (1990–92) on High Schools’ Percent White, by Race

Note. The figure was created from Model 3 in Table 3.

Hig

hsc

hool

%w

hite


neighborhoods are spared downward mobility in the transition to highschool because they have no place lower to fall.

Model 4 adds the covariates (which are grand-mean centered in allmodels to make the intercept easier to interpret). These variablesimprove the adjusted coefficient of determination only slightly (to 0.76).However, the covariates explain part of the ZCA effects. The linear termand quadratic term are reduced in magnitude by [(0.54 – 0.42) / 0.54 =]22% and 12%, respectively. Presumably, the covariates explain part of theZCA effects because characteristics of parents and their children arerelated to movement patterns into neighborhoods and schools.Nevertheless, about 80% of the effect of ZCAs’ percent white is indepen-dent of the covariates.

The model shows that even net of any differences in capital, blacks andLatinos experience downward mobility relative to whites. While thecauses of the gaps cannot be further ascertained, racial and ethnic gapsthat are estimated net of capital are frequently interpreted as evidencethat is consistent with place stratification models. In this case, racial dif-ferences in mobility may result from whites’ tendency to avoid blacks andLatinos by enrolling in schools of choice (Clotfelter 2004; Saporito andSohoni 2006).

Two other groups experience downward mobility. Schools’ percentagewhite is 5.8 and 5.1 points lower for students with a language minoritybackground and for students in inner cities (rather than suburbs),respectively. Both of these gaps are consistent with the spatial assimilationmodel, but most of the effects predicted by this model, including thoseabout SES and achievement, do not reach statistical significance.

FROM NEIGHBORHOODS AND HIGH SCHOOLS TO COLLEGE

Table 3 uses colleges’ percent white as the dependent variable. Onlyrespondents with postsecondary enrollment are included. Model 1 ofTable 3 examines the amount of mobility between ZCAs and colleges byregressing ZCAs’ percent white onto colleges’ percent white. The elastic-ity coefficient [square root (0.43) =] 0.66 indicates more mobility in thistransition than in the one from ZCAs to high schools, but the amount ofmobility is still small. The coefficient for ZCAs’ percent white indicatesthat a 10-percentage-point increase in percent white in the ZCA associ-ates with a 5.5-percentage-point increase in percent white in the college.

Model 2 adds the effect of high schools’ percent white to see whetherthis variable acts as a mediator. Adding it only slightly improves the modelfit, as seen by the slightly higher adjusted r2 (0.45). However, it explains[(.55 – 0.33) / 0.55 * 100 =] 40% of the ZCA effect, indicating an


important mediating effect. The coefficient for high schools’ percentwhite indicates that controlling for ZCAs’ percent white, a 10-percentage-point increase in high schools’ percent white translates into a gain of 2.3percentage points in college.

Model 3 estimates the aggregate amount of college segregation byincluding the race dummy variables, but not the effects of percent whitein prior contexts. As seen by the intercept, whites’ colleges are, on aver-age, 79.1% white, and the net black-white and Latino-white gaps are 28.2and 31.2 percentage points, respectively. Model 4, which includes thecontextual variables and the race variables, shows that net of differencesin ZCAs’ and high schools’ percent white, the black-white and Latino-white gaps are 7.3 and 10.8 percentage points, respectively. The datatherefore suggest that the racial segregation of neighborhoods and highschools is one of the main reasons that there is racial segregation incolleges.

Model 5 adds the covariates. Once again, adding these variables only

Table 3. Regression of Selected Variables onto Colleges’ Percent White.

Variable Model 1 Model 2 Model 3 Model 4 Model 5

Intercept 29.43 *** 29.75 *** 79.07 *** 39.59 *** 42.43 ***ZCA % white (1990–92) 0.55 *** 0.33 *** 0.26 *** 0.24 ***High school % white 0.23 *** 0.20 *** 0.17 ***Black -28.22 *** -7.23 *** -9.29 ***Latina/o -31.20 *** -10.79 *** -7.50 ***Asian -21.15 *** -10.66 *** -4.71 **American Indian -25.22 *** -2.94 -3.67Family SES -1.51 *Family size 0.01One-parent family -1.07Other nontraditional family -1.21Parent expects college 0.20Immigrant parent -3.96 *Grade point average 0.41Standardized test score 0.07Language minority -2.55Expects BA or more 1.95Female 0.10Core city residence 2.91 **Rural residence 5.23 ***Northeast 1.53West -4.55 ***North Central 3.03 **Adjusted R2 0.43 0.45 0.29 0.45 0.50

*p < .05. **p < .01. ***p < .001


improves the model fit slightly, as seen by the adjusted coefficient ofdetermination, which rises to 0.50. The covariates reduce the ZCA coef-ficient by 27% and the high school coefficient by 26%. Thus, ZCA andhigh school context are less predictive of college context when other vari-ables are held constant, but about three-fourths of the effects of priorcontexts are independent of these covariates. Increases of 10 percentagepoints in ZCAs’ and high schools’ percent white are associated withincreases of 2.4 and 1.7 percentage points in colleges, respectively.

Once again, the coefficients for blacks and Latinos are significant.Consistent with place stratification models, blacks and Latinos experi-ence downward mobility relative to whites as they transition into collegenet of differences in capital. The only covariate consistent with the spa-tial assimilation model is the one for having an immigrant parent.Students with an immigrant parent attend colleges with 4 percentagepoints fewer whites than students without an immigrant parent. Theeffects of the other significant covariates are somewhat surprising.Students with high-SES backgrounds experience slight downward mobil-ity. A one-standard-deviation increase in SES results in 1.5 percentagepoints fewer whites in college. In addition, students from inner cities andfrom rural areas have 2.9 and 5.2 percentage points more whites, respec-tively, in their colleges than students from suburban backgrounds.

Determining why these counterintuitive findings occur is not withinthe bounds of this analysis, but one possibility may be that high-SES stu-dents from suburban backgrounds tend to enroll in very selective institu-tions that use affirmative action policies (Alon and Tienda 2007); thesepolicies may help provide more contact with nonwhites than would beexpected based on the percent white in their neighborhoods and highschools. Regardless, the main point is that percent white in ZCAs andhigh schools is strongly determinative of percent white in college andthat, on average, students of color experience downward mobility moreoften than white students in the transition to college.

FROM NEIGHBORHOODS, HIGH SCHOOLS, AND COLLEGES TONEIGHBORHOODS

Table 4 uses adult neighborhoods’ percent white as the dependent vari-able. As seen in Model 1, ZCAs’ percent white in the teenage years has acoefficient of 0.68, and the model has an adjusted r2 of 0.56 and an elas-ticity coefficient of 0.75, suggesting a very strong intergenerational inher-itance of racial composition in neighborhoods.2

Model 2 adds the effects of high schools’ and colleges’ percent whiteto see whether they mediate the ZCA effect.3 As seen in the model, the


effects of percent white in high schools (0.13) and colleges (0.18) arevery robust. Together, they explain [(0.68 – 0.47) / 0.68 =] 31% of theeffect of ZCAs’ percent white. Thus, they are very strong mediators. Thecoefficients indicate that 10% increases in high schools’ and colleges’percent white are associated with having 1.3% and 1.8% more whites,respectively, in the adult neighborhood, holding constant percent whitein the origin neighborhood. In addition to mediating the neighborhoodeffect, they also slightly improve the model fit, as seen in the increase inthe adjusted r2 from 0.56 to 0.60.

Model 3 estimates the aggregate amount of residential segregationamong young adults by including the race variables but not the contex-tual variables. It shows that on average, whites’ neighborhoods are 79.5%white, and the black-white and Latino-white gaps are 36.9 and 37.2 per-centage points, respectively. Model 4 suggests that most of these gaps can

Table 4. Regression of Selected Variables onto ZCAs’ Percent White in Young Adulthood.

Variable Model 1 Model 2 Model 3 Model 4 Model 5

Intercept 17.96 *** 12.37 *** 79.52 *** 18.86 *** 28.18 ***ZCA % white (1990–92) 0.68 *** 0.47 *** 0.43 *** 0.42 ***High school % white 0.13 *** 0.11 *** 0.10 ***College % white 0.18 *** 0.16 *** 0.14 ***Black -36.87 *** -5.83 *** -7.37 ***Latina/o -37.20 *** -5.87 *** -4.74 ***Asian -19.40 *** -3.56 ** -0.33American Indian -30.62 *** -2.74 -3.15Family SES -0.34Family size -0.02One-parent family -2.04 *Other nontraditional family -1.34Parent expects college -2.26 ***Immigrant parent -1.79Grade point average 0.82Standardized test score -0.12 **Language minority -2.88 *Expects BA or more -0.76Female 1.01Core city residence -0.05Rural residence 1.06Northeast 1.37West -2.43 **North Central 2.28 ***Lives at home -18.31 *** -17.41 *** -4.26 *** -17.31 *** -16.96 ***Lives home * ZCA % white 0.23 *** 0.23 *** 0.23 *** 0.23 ***No college 1.99 2.09 0.18Adjusted R2 0.56 0.60 0.35 0.60 0.61

*p < .05. **p < .01. ***p < .001.


be attributed to the percent white in the residential ZCA during theteenage years, high school, and college. Holding these constant reducesthe black-white and the Latino-white gaps to just 5.8 and 5.9 percentagepoints, respectively. These are both 84% smaller, suggesting that priorexperiences in neighborhoods, high schools, and colleges are by far themost important empirical explanation for racial segregation amongyoung adults.

Adding the covariates (Model 5) improves the model fit only slightly,from 0.60 (model 4) to 0.61. The covariates explain little of the contex-tual effects. The estimated effects of the percent white of ZCAs, highschools, and colleges are just 2%, 9%, and 13% smaller, respectively,when the covariates are included. Thus, the contribution of the covari-ates to the model is far less than that of the contextual effects.

The coefficients for high schools’ and colleges’ percent white in Model5 are 0.10 and 0.14, respectively. While these coefficients are modest,their effects are important because the gaps in high schools’ and col-leges’ percent white are so large. In high schools, the black-white andLatino-white gaps are about 48 percentage points; in colleges, they are 28and 31 percentage points for blacks and Latinos, respectively. Becausethe gaps are larger in high schools, high schools’ percent white actuallyexplains more of the black-white and Latino-white gaps in ZCAs’ percentwhite in young adulthood. This can be seen by multiplying the gaps bythe coefficients. Closing blacks’ and Latinos’ gaps in high schools wouldreduce the ZCA gap by (0.1 * 48 =) 4.5 percentage points. Closing thecollege gap would reduce blacks’ and Latinos’ gaps by 3.9 and 4.3 per-centage points, respectively.

However, the effects of schools’ and colleges’ percent white are stillsmaller than teenage ZCAs’ percent white. The gap in teenage ZCAs’ per-cent white is 45 percentage points for blacks and 44 percentage points forLatinos. If the teenage ZCA gap were closed, blacks’ and Latinos’ gaps inadult ZCAs would be smaller by 18.9 and 18.5 percentage points, respec-tively.

Model 5 also shows support for the place stratification model. Otherthings being equal, the black-white gap is 7.4 percentage points, and theLatino-white gap is 4.7 percentage points. Consistent with the spatialassimilation model, language-minority and single-parent backgroundsassociate with downward mobility, but their effects are modest (2.9 and2.0 percentage points, respectively). The other two significant covariatesare surprising. Students whose parents expect them to earn at least abachelor’s degree and students with higher standardized test scores ineighth grade have relatively fewer whites in their adult neighborhoods.Interpreting these counterintuitive coefficients is complicated because


these models hold constant the percent white in their high schools andcolleges. The lack of significance for the SES coefficient is consistent withthe research showing that such factors as income, wealth, and educationplay trivial roles in explaining residential racial segregation (e.g.,Crowder, South, and Chavez 2006).

To illustrate the importance of prior context, I calculated the expectedpercent white in adult ZCAs for blacks, whites, and Latinos from teenageZCAs that are 40% and 80% white. I selected these percentages becausefew whites live in neighborhoods less than 40% white, and few blacks andLatinos live in neighborhoods that are more than 80% white. I used theslopes from Model 5 of Table 4, so these effects are net of all the covari-ates. For the levels of high schools’ and colleges’ percent white, I used thevalues calculated from Model 4 of Table 2 and Model 5 of Table 3. Thevalues for the covariates are their grand mean.

These calculations show that blacks who begin in ZCAs that are 40%white and 80% white are expected to live in ZCAs as young adults that are48% and 69% white, respectively. This is a gap of 21%. In contrast, theeffect of being black compared with white is 7.4%. A 40% difference inpercentage white in the initial ZCA is thus nearly three times as influen-tial as identifying as black rather than white. Latinos who begin in ZCAsthat are 40% white and 80% white are expected to live in ZCAs as adultsthat are 51% and 72% white, respectively; for whites, the expected ZCAsare 59% and 80% white, respectively.

DISCUSSION

This article examines how much neighborhood racial contexts are passedon intergenerationally and the extent to which the racial compositionsexperienced in schools and colleges mediate this relationship. The datashowed that when teenagers grew up and left their parents’ home, theyfrequently moved into a ZCA that had a very similar percentage of whitesas the ZCA they lived in as teenagers. In between these two time points,they also attended high schools and colleges that had similar percentagesof whites as in their teenage and young adult ZCAs. This pattern reflectswhat perpetuation theorists have long argued: that individuals tend toexperience the same racial context across institutions and over time.

However, not all individuals in these data experienced the same per-centage of whites in all these institutions. It was the small fluctuations inthese percentages—the small amounts of upward and downward mobil-ity in institutional transitions—that the analyses exploited to see the roleof schools and colleges in perpetuating neighborhood segregation acrossgenerations. Individuals who experienced upward mobility in their


transition from teenage ZCA to high school and college benefited in thelong run by having a higher percentage of whites in their young adultZCA. Similarly, individuals who experienced downward mobility in thesetransitions lost in the long run by having a lower percentage of whites intheir young adult ZCA.

These upwardly and downwardly mobile individuals tell us what mighthappen were there more mobility across institutions. If all students werein colleges and high schools that had the same percentage of whites (thatis, if percent white in these institutions were constant), the intergenera-tional transmission of ZCAs percent white would be 31% weaker. Thedata therefore suggest that school desegregation programs could slowlybut steadily integrate neighborhoods.

The absence of desegregation programs results in two problems. First,there is little mobility in the transitions from ZCAs to high schools and tocolleges. Second, teenage neighborhoods, high schools, and colleges arevery segregated. Because of these two underlying problems, the experi-ences that people have in school and college actually perpetuate theracial segregation of neighborhoods. To paraphrase the quote fromFeagin (2006) cited earlier, residential segregation creates segregatedschools and colleges, and segregation in these institutions reinforces res-idential segregation.

Nevertheless, why segregation is so perpetual across these institutionsis only partially clear. The data revealed that the lack of mobility in thesetransitions is largely unrelated to variation in capital. Only cultural capi-tal was consistently related to mobility. Less acculturated individualstended to experience downward mobility in each institutional transition.In the transition to college, some evidence suggested that affirmativeaction policies may be increasing interracial contact for high-achieving,affluent whites from suburban areas. Future research might explore theaffects of capital in more depth by looking for interactions by race.

In addition, blacks and Latinos consistently experience downwardmobility relative to whites. Downward mobility by blacks and Latinos inthe transition from neighborhoods to high schools is consistent withresearch showing gerrymandered school zones and whites’ inclinationsto avoid interracial contact by using school choice policies (Clotfelter2004; Saporito and Sohoni 2006). Downward mobility by blacks andLatinos in the transition to adult ZCAs is consistent with the researchshowing racial differences in preferences regarding the racial composi-tion of neighborhoods and discrimination in housing markets (Charles2003).

The racial differences in mobility are large, but they explain little ofthe contextual effects related to racial composition. They are also not as


influential as prior neighborhood context in determining the racial con-text of neighborhoods. For example, a 40-percentage-point difference inpercent white in teenagers’ ZCAs is about three times more influentialthan the effect of being black versus white, and more than four times asinfluential as being Latino versus white.

Perpetuation theory anticipates the strong connections between per-cent white in teenage ZCAs, high schools, colleges, and adult ZCAs. Itholds that people become accustomed to the racial compositions theyhave already experienced, that people develop skills for navigating theseracial compositions, that people develop social ties within their institu-tions, and that all these factors combine to orient them toward otherinstitutions with similar racial compositions (Wells and Crain 1994).Research on whites in segregated-white neighborhoods (Bonilla-Silva,Goar, and Embrick 2006) and on a more diverse sample of people whoattended integrated high schools (Wells et al. 2005) has shown with in-depth interviews the social-psychological effects of racial compositionsposited by the theory. In addition, Dawkins’s (2005) research has sug-gested that levels of interracial contact while growing up explain wholives in more or less integrated neighborhoods as an adult.

However, the data analyzed here do not contain measures of the mech-anisms posited by perpetuation theory. In the absence of these measures,the analyses are unable to rule out alternative explanations for the find-ings. In this case, at least two categories of alternative explanations maybe particularly important for future research to consider. First, individu-als may form attachments to the particular people and institutions theyinhabit, and these may be more or less independent of the racial context.In particular, individuals form strong attachments to their neighbor-hoods and the people who live there (Altman and Low 1992). Second,there may be organizational links between institutions. These links arebest exemplified by neighborhood schools. These educational institu-tions are linked concretely to neighborhoods and almost surely explainwhy there is so little mobility from neighborhoods to high school.4

The possibility that these alternative explanations—particular attach-ments and institutional links—may account for the lack of mobility acrossinstitutions means that the data must be interpreted cautiously in regardto perpetuation theory. Nevertheless, some findings lend themselves lessto alternative explanations than others. For example, the effects of highschools’ and colleges’ percent white on the percent white in adult ZCAsdo not lend themselves to other explanations so easily. The effects fromboth of these institutions exist net of the percent white in the teenageZCA, so they are unlikely to indicate particular attachments to a place. Inaddition, there is no obvious process by which high schools and colleges


link their students to adult neighborhoods. High schools and collegeshave no formal procedures guiding where students live (years) after theyleave school.

The effects of high schools’ and colleges’ percent white on percentwhite in adults’ neighborhoods imply that experiences in these educa-tional institutions affect individuals in long-lasting ways. Here, the dataindicate that blacks and Latinos who went to school or college with rela-tively more whites moved, years later, into neighborhoods with relativelymore whites. And whites who went to school or college with relativelyfewer whites moved, years later, to neighborhoods with relatively fewerwhites. These findings are consistent with the views of perpetuation the-orists, who argue that experiences in racial contexts shape young peo-ple’s skills, dispositions, and social networks in ways that direct themtoward institutions with racial contexts like those they experienced inyouth. Therefore, programs that increase interracial contact in youth,like school desegregation and affirmative action programs, may lead inthe long run to more integrated neighborhoods over time.

Acknowledgments

This research was supported by a grant from the American Educational Research Association, whichreceives funds for its AERA Grants Program from the U.S. Department of Education’s National Centerfor Education Statistics of the Institute of Education Sciences, and the National Science Foundationunder NSF Grant No. RED-0310268. Opinions reflect those of the author(s) and do not necessarilyreflect those of the granting agencies.

Notes

1. I do not use separate slopes for each racial group because interactions between theracial and ethnic dummy variables and ZCAs’ percent white are not significant.

2. All the models using ZCAs’ percent white as a dependent variable include a controlvariable for people who still live with their parents, and an interaction between this termand the effect of ZCAs’ percent white during the teenage years. Including these termsmakes the main effect of the ZCAs’ percent white its effect on those young adults who donot live with their parents. The effect of ZCAs’ percent white for those who live with theirparents is the sum of the main effect (0.68) plus the interaction (0.23), or 0.91. This effectis so strong because many of these people live in the same zip code area that they grew upin, and probably the same residence, at the two time points.

3. Students who did not attend college are assigned the mean of colleges’ percentwhite. A dummy variable flagging where this occurred is also added to the model. Thedummy variable captures the effect of colleges’ percent white for people who did not attendcollege, and the effect of colleges’ percent white captures its effect for people who didattend college. Models that exclude people who did not attend college yielded similarresults.

4. If links across institutions, like neighborhood schools, help explain why individualsperpetually experience the same racial context across institutions and over time, then


perpetuation theory should be expanded. That is, institutional links should be theoreticallyincorporated into the model.

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PAT RUBIO GOLDSMITH is associate professor in the department ofsociology at the University of Wisconsin-Milwaukee. Currently, heresearches the causes and consequences of racial segregation; race andsport; and the mistreatment of Latinos along the U.S.-Mexico border. Hehas recent publications in Social Forces, Sociology of Sport Journal, andAztlan.

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TERM PAPER ARTICLE - Learning Apart, Living Apart- How the Racial and Ethnic Segregation of Schools and Colleges Perpetuates Residential Segregation