Schools or Neighborhoods or Both? Race and Ethnic ...

Schools or Neighborhoods or Both? Race and Ethnic Segregation and Educational Attainment Pat Rubio Goldsmith, University of Wisconsin-Milwaukee Whites, blacks and Latinos in the United States tend to live in different neighborhoods and attend different schools. Does this segregation influence youth in the long run? This study used longitudinal data from the NELS to see whether neighborhoods' or schools' proportion black and/or Latino during the high school years influences educational attainment through age 26. The analyses indicate that concentrations of blacks and Latinos in schools, but not zip code areas, associates with lower attainment in the long run. Students in predominantly black and Latino schools are less likely to earn a high school diploma or equivalent and to earn a bachelor's degree or more than similar students in predominantly white schools. The latter half of the 20'^ century featured a powerful civil rights movement, dozens of successful legal challenges to segregation and the enactment of numerous laws to reduce segregation, but whites, blacks and Latinos still tend to live in different neighborhoods and attend different schools. School segregation levels are high and continue to rise. Recent data show that 72 percent of blacks, 76 percent of Latinos, but only 11 percent of whites, attend schools where half or more of the students are not white (Frankenberg, Lee and Orfield 2003). Residential segregation also remains high. Data from the 2000 U.S. Census indicate that 62 percent of blacks and 48 percent of Latinos would need to move to eradicate neighborhood segregation in metropolitan areas (Charles 2003). Policies and practices that promote integration are controversial. Recent court decisions have weakened school desegregation requirements (Orfield 1996) but endorsed the principle of diversity in university admissions (Grutterv. Boliinger 2003). Some research has suggested that the general public values integration, but in-depth interviews with whites and studies of white flight have indicated that most whites are unlikely to freely integrate with blacks and Latinos (Bonilla-Silva 2001; Massey and Dentón 1993; Orfield 1996; Renzulli and Evans 2005). This research was supported by a grantfrom the American Educational Research Association, which receives funds for its AERA Grants Program from the U.S. Department of Education's National Center for Education Statistics of the Institute of Education Sciences, and the National Science Foundation under NSE Grant #RED-0310268. Direct correspondence to Pat Rubio Goldsmith, University of Wisconsin-Milwaukee, Department of Sociology, Bolton 778, P.O. Box 413, Milwaukee, WI 53201. E-mail: [email protected]. © The University of North Carolina Press

Social Forces 87(41:1913-42. June 2009

1914 . Socia/Forces 87(4)

Progressives also disagree on the viability of integration as a strategy to end racial inequality. Kozol (2005), for example, described schpol segregation as theShame of the Nation, and he asked how quality schooling for blacks and Latinos could occur without integrating schools. Likewise, the pro-integration Harvard Civil Rights Project studies school segregation to raise awareness and foster change. In contrast. Bell (2004) questiorled how much impact desegregation can have and whether alternative polic|ies could do more to improve blacks' and Latinos' education. I Social scientists can contribute to these debates by carefully assessing the rôle of segregation in the perpetuation of racial inequality. The soéial science literature examining the consequences of school and residential segregation is vast. In the research on racial segregation and educational attainment, the focus of this study, evidence has indicated that tjhe percent black or Latino in a neighborhood negatively affects individuals' educational attainment (Brooks-Gunn et al. 1993; Cutler and Glaeser 1997; Duncan 1994; Rosenbaum et al. 1993; South, Baumer and Lutz 2003). Studies also have argued that schools' proportion black redueles educational attainment (Dawkins and Braddock 1994; Guryan2004; Wells and Crain 1994). However, others have concluded that neighborhoiDd or school effects are weak and that such methodological problems 'as selection bias mar studies in this area (Evans, Oates and Schwab 1992; Hanushek 1994; Jencks and Mayer 1990; Plotnick and Hoffman 1999).! Studying the minority-concentration and educational-attainmeW relationship is important because racial segregation results in blacks and Latinos attending schools and living in neighborhoods with highjer proportions of minorities than whites. If schools or neighborhoods with proportionatdy more blacks and/or Latinos reduce educational attainment, then it will widen the racial and ethnic gaps in this area. I contribute to tine literature by estimating the association between minority concentrations during the high school years and educational attainment at age 26, holding constant the differences among students during 8'^ grade. This researcii is needed for four reasons. i First, consider the position of Wells and Crain (1994:531 ) who wrote, "For the last 30 years, the bulk of research on school desegregation has focused on the short-term effects of this policy on the achievement, self-esteern, and intergroup relations of students in racially mixed versus segregated schools." Many years later, there is still little research on the long-term effects of segregation. This is unfortunate. Wells and Crain explained, because long-term outcomes provide the best test of the policy. They argued that the goal of desegregation is to break long-term cycles of disadvantage, and that studies of long-term outcomes are needed to address this. i Wells and Crain suggested that the long-term outcomes of desegregation can be understood with perpetuation theory. This theory maintains that

Schools or Neighborhoods or Both? . 1915

many blacks and Latinos are segregated across institutions and over the life course, and as a result, do not develop networks with whites or skills for developing such networks. These networks are important because they carry high-status knowledge, for example, about college admission procedures. An inability to form social ties with whites and to access information in white networks reduces the life chances of blacks and Latinos well after adolescence. Wells and Crain, along with others (Arum and Beattie 1999; Braddock 1980; Dawkins and Braddock 1994; LaFree and Arum 2006), have found that segregated black schools negatively affect adolescents in the long run. Though understudied, the long-term effects of segregation may be considerable. Second, this study used data from a more recent cohort (i.e., 8"^ graders in 1988) than other studies of long-term effects. Analyses of more recent cohorts may be more convincing to policymakers and the courts. Levels of segregation either remain high or are rising. The residential segregation of blacks from whites has not changed in recent decades (Charles 2003), but the residential segregation of Latinos and the school segregation of Latinos and blacks are increasing (Charles 2003; Clotfelter 2004). Increases in segregation raise the proportion of blacks and Latinos that are in and potentially affected by predominantly minority schools and neighborhoods. In addition, blacks and Latinos may benefit more from integrated settings today than in the past, but researchers disagree on this point. Studies of schools changing from white, segregated schools to integrated schools have reported that teachers and staff received little preparation or training for educating in integrated settings (Schofield 1991 ). This lack of preparation reduced the achievement of students attending integrated schools early on (Grant 1997). Now that schools have had more time to prepare for integration, integrated schools may be performing better. However, other research raises doubts about improvements in integrated schools. School integration, for example, may create more interracial conflict (Goldsmith 2004). Lewis (2005) argued that race impacts educational outcomes in all schools, not just segregated ones. Still others have maintained that whites' beliefs in recent decades have not become less racist; they have changed to perpetuate new forms of racial inequality and discrimination (Bonilla-Silva 2001). Third, the analyses in this study examined the long-term outcomes for Latinos and their schools and neighborhoods, as well as those of blacks. To my knowledge, no study on the long-term effects of segregation has examined the impact of predominantly Latino neighborhoods or schools. Latinos are now the largest minority group. Predominantly Latino environments may affect adolescents differently than predominantly black ones (Goldsmith 2003; Hampton, Ekboir and Rochin 1995; Moore and Pinderhughes 1993). Although both groups are stigmatized and

1916 . Sociai forces 87(4)

economically disadvantaged, they differ considerably in English language ability, family structure and immigrant status (Goldsmith 2003). Studying how Latinos' segregation affects long-term outcomes is important for Latinos as well as for all adolescents, because the desegregation of this large minority group would affect the racial and ethnic composition! of many schools and neighborhoods. ; Fourth, the analyses examined the effects ofsoiiooi and neigtiboriiood segregation. The racial segregation of neighborhoods is principally responsible for the racial segregation of schools (Clotfelter 2004). Since t|he Supreme Court decided that desegregation mandates apply within distrióts but not between them (Milliken v. Bradley 1974), the racial composition' of district areas has limited the amount of desegregation possible. Cities such as Chicago, Detroit and Milwaukee, for example, have many predominantly black and Latino schools because their districts do not encompass t|he suburban ring where many whites reside. Consequently, adolescetjits have similar proportions of blacks and Latinos in their neighborhoods and schools (Braddock 1980). Because these proportions are so similar, it| is easy to mistake the effects of one institution for the effects of the other. They fieed to be studied together. '• Neighborhood characteristics also can shape the quality of the neighborhood school. Wilson's (1987) analyses pointed to a reduced tax base jn inner cities created by the flight of middle-class whites and blac;ks, and the exodus of businesses to other places, as a cause of lower school quality in inner-city neighborhoods. Because many districts rely heavily J3n local taxes for funding, schools in inner-city districts often have less fundipgj | and higher costs per student than schools in suburban districts (Boozer and Rouse 2001; Connell and Halpem-Felsher 1997). In addition, Kozol (2005) argued that politicians stigmatize schools in predominantly black and Latino neighborhoods, further reducing public support for improving these schools. Schools in predominantly minority neighborhoods also may have greater difficulty attracting and keeping high-quality teachers and staff, even net of their pay, because of the community's physical and demographic characteristics (Connell and Halpern-Felsher 1997). : While researchers theorize the connection between neighborhoods and schools, the empirical research on contextual effects and educatiotjial outcomes has examined schools or neighborhoods, but not both. Arum (2000;:401), in a review of research about neighborhood effects and educational outcomes, stated: "[T]hese studies usually model neighborhood effects on individual educational outcomes without incorporating consideration of variation in the structure [that is, quality] of schooling across neighborhoods: i.e., ignoring

Schools or Neighborhoods or Both? »1917

the most important probable source of institutional variation affecting educational achievement within neighborhoods." Arum's remarks are truer of research in the past than today, as researchers are beginning to consider school effects and neighborhood effects at the same time (e.g., Ainsworth 2002; Pong and Hao 2007; Teitler and Weiss 2000; Wilson 2001). Ainsworth (2002) and Wilson (2001) have shown that net of school effects, neighborhoods with relatively more highstatus residents (in terms of income, education and occupational prestige) associate with improved educational outcomes. Neighborhood effects studies that did not examine school effects have found similar results regarding the relative presence of high-status adults in neighborhoods (Brooks-Gunn et al. 1997; Duncan 1994; South, Baumer and Lutz 2003). Teitler and Weiss (2000) found that their outcome, first sexual intercourse, varies more by school than by neighborhood, and that not examining school effects alongside neighborhood effects can lead to an overestimation of neighborhood effects. Pong and Hao (2007) and Wilson (2001) also have reported that neighborhood and school characteristics influence educational outcomes. The effects of school and neighborhood racial composition also can be studied together. Not all students have the same proportions of blacks and Latinos in these two institutions. Communities with many private, charter and magnet schools tend to have relatively more school segregation than neighborhood segregation because white families utilize these educational options more than blacks and Latinos (Clotfelter 2004; Saporito and Sohoni 2006). Also, some desegregation programs effectively reduce racial differences in schools' proportion minority (Clotfelter 2004; Saporito and Sohoni 2006). Possibly as a result of these options and policies, as many as half of all students in urban areas do not attend the high school associated with their neighborhood (Lauen 2007). Furthermore, the proportion minority in schools is usually higher than in neighborhoods for all groups because of the greater diversity within the school-age population than in the total population. One quantitative study of educational outcomes compared the effects of proportion black and Latino in these two institutions. In this study. Card and Rothstein (2005) found that, holding other things constant, metropolitan areas with larger racial differences in proportion black and Latino in schools have larger racial differences in SAT scores. The result regarding racial differences in proportion black and Latino in neighborhoods was the same. However, when examined together, only the neighborhood effects were significant. These authors concluded that racial differences in proportion black and Latino in neighborhoods, but not schools, create racial

1918 . Social Forces 87(4)

differences in SAT scores. However, these authors found no evidence that racial' differences in either institution affected racial differences in high school completion.^ ; Further research is needed. Card and Rothstein's study did not explore whether students in predominantly black and Latino neighborhoods, or schools have lower educational outcomes than students in predominaritly white contexts, a finding that may influence policymakers and courts.^ In addition, it is not completely surprising that Card and Rothstein did not find effects of school segregation on test scores or high school completion. Reviews of research on the effect of schools' racial composition on shprtterm educational outcomes are inconclusive (Crain and Mahard 1983; Jencks and Mayer 1990). However, many studies have found that schools' racial composition has long-term consequences for students (see revievvs by Dawkins and Braddock 1994; Wells and Crain 1994). Thus, schools' proportion minority is likely to affect educational attainment in the long run. Methods Data for this project are from the restricted version of the National EÎducation Longitudinal Study 1988-2000 and Summary File 3B of the 1990 Census of the population. The NELS provides multistage, longitudinal data begirining in 1988 that are stratified and clustered. The strata ensure participation of schools from different sectors, regions and levels of minonty representation. Within strata are randomly selected schools with an ¡8'^ gradé - the primary sampling unit. Within schools are random samples of 8"" grade students. Asians and Hispanics are over-sampled. Follow-ups ¡occurred in 1990,1992,1994 and 2000. All waves contain data from studeipts. Data from parents are in the 1988 and 1992 waves, and data from teachers and school principals are in the first three waves. I used the sample^ of 10,827 students who participated in all waves. It includes 7,632 whites, 975 blacks and 1,362 Latinos. Most students are 26 years old in the 2000 waye. Data from the 1990 U.S. Census about students' residential zip cojde areas in 1990 and 1992 are merged with the NELS data. ZCAs are seco^nd to tracts as the most often-used ecological unit to study neighborhood characteristics (e.g., Ainsworth 2002; South and Baumer 2000; South et al.^2003). ZCAs are designed to facilitate mail delivery, so residents within the same ZCA are connected by streets. People on the same street almost always are in the same ZCA because ZCA boundaries follow suchithings as backyards, alleys, rivers and so on. I used them because they are the smallest ecological unit describing residential locations; of NELS respondents. Census tracts are smaller than ZCAs. In the 19^90 U.S. Census, there are 29,470 ZCAs and 50,690 tracts. The larger size' of ZCAs may introduce more error in the measurement of "neighborhood"

Schools or Neighborhoods or Both? • 1919

characteristics, but Sampson, Morenoff and Gannon-Rowley (2002), in their recent review of research on neighborhood effects, argued that findings have not varied by the ecological units used. For example, BrooksGunn and colleagues (1993) reported similar results using ZCAs and tracts. Logic of Analyses The analyses are designed to estimate, net of individual-level control variables, the total association between schools' and neighborhoods' proportion black and Latino during the high-school years and educational attainment at age 26. To explain the analyses, I use "context" and "contextual" to refer to proportion black and Latino in schools and neighborhoods. "Control variables" refer to a set of factors that influence educational attainment and in this study include family-level characteristics: socio-economic status, family size, family structure, parents' educational expectations for their child and parents' immigrant status. They also include student characteristics: two measures of ability (test scores and grade-point average), multiple attitudinal questions, race-ethnicity, gender and language background. They also include school sector and region. The analyses exploit the longitudinal nature of the data by measuring the contextual variables during the high-school years (1990 and 1992), the control variables during 8'^ grade (1988), and the outcome variable at age 26 (2000). Control variables are used to hold constant the confounding influences of variables related to the contexts and to reduce omitted-variable bias and selection bias. These two forms of bias are the same thing, and both can plague studies of contextual effects. The concern is that unmeasured variables are related to the sorting of families and students into different contexts and to educational attainment. For example, high-SES families may self-select into predominantly white neighborhoods or send their children to predominantly white schools, and students from these families attain relatively more education. The SES differences across contexts can make it appear as if contexts affect attainment even if they do not. Controlling for family SES eliminates the omitted-variable bias that results from SES differences. If all variables related to the context and the outcome are controlled, then contextual effects can be estimated without omitted variable bias. While controlling for all variables is not possible, many studies have suggested that the commonly used set of control variables is capable of eliminating omitted-variable bias in the estimation of contextual effects. These studies produced similar results in (1. analyses that used the common control variables and (2. analyses that used more sophisticated methodological corrections for omitted-variable bias, such as fixed-effects models, instrumental variables, counterfactual models and an extremely extensive set of control variables (Card and


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Rothstein 2005; Cutler and Glaeser 1997; Duncan et al. 1997; Harcling 2003; Guryan 2004; but see Evans, Oates and Schwab 1992 and Plotiiiick and Hoffman 1999 for contrasting views). The set of control variables I gse is at least as extensive as those used in the other studies. Some researchers have emphasized using many control variables because of the threat of omitted-variable bias. However, doing so can result in a different kind of bias - over-control bias (Sampson et al. 20|02). This ;form of bias occurs when one or more of the control variables iare

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To remedy problems associated with missing data, I followed the procedures outlined by Allison (2002) and used SAS's PROC Ml to calculate multiple imputations. This method imputes missing values from an algorithm that predicts their values as a linear combination of all other variables in the analyses. This process is followed six times, creating six different data sets (and hence, muitipie imputations). Each data set was analyzed separately and then the results were combined in SAS's PROC MIANALYZE, which uses the variation in results across the multiple analyses to correct standard errors for the potential effects of imputed values. Estimates and standard errors also were adjusted for the sample and the survey design. The NCES constructed the 1988-2000 sample by using the third follow-up sample frame and by over-sampling individuals from earlier waves who were unlikely to respond or who were difficult to locate (Curtin et al. 2002). Because of the unequal probabilities of selection, I used

1922 . Soc/íj/Forces 87(4)

the weight (F4PNLWT) constructed by the NCES to make the sample representative of the eighth-grade class of 1988. The analyses also correct for two sampling properties of NELS data mentioned earlier, strata and PSUs. Both sampling properties can bias stancjard errors, but the latter is more important because the homogeneity of students within PSUs will downwardly bias standard errors and make hypothesis tests too liberal. I used the NCES-created variables STRATA and PSU to adjust standard errors using the Taylor Series method (iiee an'd Porthofer 2006). This method increases standard errors by a separate amount for each variable, based on the size of the intra-class correlation coefficient for the variable within PSUs. i The models were calculated using SAS's PROC SURVEYLOGISTIC, which computed multinomial logit models and ordered logit models (Liao 1994) predicting educational attainment. These models are appropriate because educational attainment is measured as both a nominal and ¡an ordinal variable. I also compared results produced in (1. models adjusting for survey design and (2. cross-classified multilevel models (Goldstein 1994). The latter models can account for the clustering of students in schools and neighborhoods by treating these institutions as random effects. This comparison required me to use only that part of the sample that had at least two students in each ZCA and first follow-up school (the two random effects),! as indicated by Teitler and Weiss (2000). These comparisons also assunned a continuous dependent variable. The two models produced nearly identical coefficients. However, the standard errors were consistently 1.3 times larger in the models adjusting for the survey design than in the CCM models, indicating that the former are more conservative (results available upon request). For this reason, I report analyses of the entire sample using the multihomial and ordered logit models that adjust for survey design. | Multicollinearity

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Students tend to have similar proportions of blacks and of Latinos in their neighborhood and school. Just how similar these proportions are in the data needs to be examined closely. Some sample members need to have different proportions in these two institutions for the independent effect of each to be estimated. As their correlations rise, so might multicollinearity, which is a lack of information about a variable's independent impact on the outcome (Hanushek and Jackson 1977). Multicollinearity does not bias the estiniates of coefficients (Hanushek and Jackson 1977); so the estimates of coefficients should not be too positive or too negative. Multicollinearity makes estimates of collinear terms imprecise by inflating their standard errors. This, in turn, makes their hypotheses tests too conservative. '.


The correlation between schools' proportion black and neighborhoods' proportion black is high, at .82. The same correlation for proportion Latino is even higher, at .92. These correlations show that students experience similar proportions in these two institutions, and they are high enough to inflate standard errors. However, variance inflation factors suggest that the consequences of multicollinearity in these analyses are moderate. VIFs are diagnostic statistics estimating how much standard errors are inflated by multicollinearity. According to Neter, Wasserman and Kutner (1985), a VIF over 10 indicates serious multicollinearity. In OLS models containing all the variables, the VIFs for the group proportion variables are only between 3 and 5. A closer look at the data suggests why the standard errors may not be too inflated. It is common for students in the same ZCA to go to different high schools and vice versa. Among ZCAs with at least two students, 30.4 percent of the ZCAs have students that attend different schools. Among schools with more than one sampled student, 77.4 percent of the schools have students from different ZCAs. It is not unusual in the data for students in the same school to come from five to eight different ZCAs. The lack of a one-to-one correspondence between schools and ZCAs creates independent variation in the measures of group proportions. Even if the amount of independent variation is small, it can be exploited because the sample is large (nearly 11,000 students). As Hanushek and Jackson (1977) described, a large sample size can ease the problems created by multicollinearity. Measurement Educational Attainment I used the 2000 panel of data to measure educational attainment with five categories; (1. lacks a high-school diploma or equivalent, (2. has high school diploma or equivalent, but no post-secondary enrollment (3. has PSE but not a post-secondary degree or license, (4. earned a license or an associate's degree, but no higher credential, (5. earned a four-year degree or more. I treated this measure as a nominal variable and as an ordinal variable. It can be considered a nominal variable because the pathways to the different degrees, especially those for two- and four-year degrees, can be thought of as unordered alternatives. Ordering the outcomes also introduces, measurement error because some people with PSE attain more years of education than those earning a two-year degree. Unfortunately, the NELS 1988-2000 data cannot identify these people. Nevertheless, the categories also can be considered ordered because each successive level, on average, represents higher levels of prestige and value in labor markets. Thus, there are merits in both approaches.


Group Proportions The measures of racial and ethnic composition for schools and ZCAs are averaged over the two panels covering the high-school years (1990 and 1992). For schools, I used the proportion of schools' students who are non-Hispanic black and Hispanic, as reported by a school administrator. For neighborhoods, I calculated the proportion of non-Hispanic black individuals and the proportion of Hispanic individuals in the adolescents' residential ZCA. Looking at the means of the group proportion variables by race provicjes clues about the extensiveness of racial segregation. The data show whites' ZCAs and schools to contain relatively few blacks and Latinos. Their ZCAs and schools average 8 and 6 percent black and 5 and 4 percent Latino, respectively. Blacks are in places with many more blacks, averaging S3 and 48 percent black in schools and ZCAs, respectively. Similarly, Latinps' average percent Latino in schools and ZCAs is 47 and 40 percent. Blacks and Latinos are also largely separated from each other. Blacks' average percent Latino in schools and ZCAs is 8 and 8 percent. Latinos' average percent black is 12 and 9 percent, respectively. Control Variables The control variables are measured from the 1988 wave of the NELS. Jo capture the influence of socio-economic status, I used the NCES-created variable SES. SES is an index used in studies with the NELS because it ranks students along the interrelated dimensions of economic resources, parental skills and parental status. It is constructed from four underlying indicators: household income, parental education, parental occupational status and household possessions. The variable is based on parents' reports when they are available (because they are more accurate) and frc|m students' reports when they are not. Household possessions include such items as computers, dictionaries, at least 50 books, microwave ovens, e'tc. While SES captures variation in family resources, it does not account for how many people share these resources. For this reason, the analyses control for the number of people in the family. Two dummy variables control for family structure. The reference category is families with both biological p^arents, and the two dummy variables flag single-parent families and families where at least one parent is not the biological parent. Parents' educatiorial expectations were measured with a dummy variable indicating parents who expected their child to earn a bachelor's degree or more. Also included is a dummy variable to flag adolescents who have an immigrant parent. Such parents can improve educational outcomes (Kao and Tienda 1995). Two variables capture students' educational achievement. The first is adolescents' composite reading and math test scores in 8'^ grade, and the second is their self-reported GPA over the middle-school years. Because


adolescents' expectations and aspirations also may influence their attainment, the analyses incorporates dummy variables flagging students who expected to earn at least a bachelor's degree, have a professional or a managerial occupation by age 30, and enter a college preparation curriculum track. The models also controls for students' race, ethnicity, gender and region. I included dummy variables for blacks. Latino/as, Asians and American Indians. Whites are the reference category. To measure students' language heritage, a dummy variable is used to designate students that have a language minority background, as defined in the NELS by the NCES. Results Effects of Schools' and ZCAs' Proportion Black and Latino The estimated coefficients for group proportions in schools and ZCAs when entered alone are shown in tables 2 and 3, respectively, and in Table 4 when entered together. All tables show coefficients from multinomial and ordered logit models. Coefficients and standard errors are adjusted for weights, survey design and multiple imputations. The multinomial models show four sets of coefficients. They are the marginal influence of each variable on the log odds of reaching the level of education heading the column versus not earning a high-school diploma or its equivalent, which is the comparison category. As seen in Table 2 (as well as tables 3 and 4), many of the control variables have robust effects in the expected direction. For example, students from high-SES families, from families with both biological parents, who expect to earn a bachelor's degree or more education, and who have strong academic abilities attain more education in the long run. The coefficient for "black" is also positive, which is consistent with other literature showing that blacks are expected to attain more education than whites, other conditions equal (e.g., Bennett and Xie 2003). The models also show that all else equal. Latinos do not attain less education than whites.^ The main interest in Table 2 is the coefficients for schools' proportion black and schools' proportion Latino. As seen in the table, all of these coefficients in the multinomial model are negative and significant except for the one for proportion Latino related to associate's degree, which is not significant. In contrast, the coefficients for these variables are not significant in the ordered logit model. As will be demonstrated later, the different results from the two models disappear when the models are better specified with the inclusion of the ZCA variables. Table 3 reports the estimated effects of ZCAs' proportion black and Latino when they are entered in models without the school effects included. As seen in this table, only one coefficient for ZCAs' proportion

1926 . Socio/forces 87(4)

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black and proportion Latino is significant. Thus, even when the school effects are not included, there is little evidence that proportion black or Latino in ZCAs reduces educational attainment. What happens when the effects from schools and ZCAs are enteredi together? These results are shown in Table 4. As discussed earlier, the multicollinearity arising from the high correlations among these variables could make the hypotheses tests of these variables too conservative, but the findings show that the school effects are significant anyway. All of the coefficients

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do not have as much power as the models using everyone because they have smaller sample sizes (especially in the models for blacks and Latinos), but they can suggest similarities and .| differences across groups. ^ In the race-specific multinomial models, all '^ 24 coefficients for school proportions have a ^ negative sign, with 9 of them reaching significance "^ (5 for whites, 2 for blacks - both in relation to ^ proportion black, and 2 for Latinos - both in '^. relation to proportion Latino). In the ordered logit o models, 5 of the 6 coefficients about schools are c negative, 2 significantly so (proportion Latino for whites and Latinos). The coefficient for schools' proportion Latino has a positive sign for blacks, 3 but its standard error is very large. In contrast, 21 9o (8 for whites, 6 for blacks and 7 for Latinos) of o the ZCA coefficients in the multinomial models 2 have a positive sign, two being significant (both g for whites). In the ordered logit models, 5 of the "S 6 ZCA coefficients are positive, none significant. .g These results are not definitive, but they suggest, ^ along with the interactions described above, that S the pattern of results exposed in Table 4 applies -è similarly to whites, blacks and Latinos. Other

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