neighborhoods and schools as competing and reinforcing contexts for educational attainment

Neighborhoods and Schools as Competing and Reinforcing Contexts for EducationalAttainmentAuthor(s): Ann OwensSource: Sociology of Education, Vol. 83, No. 4 (OCTOBER 2010), pp. 287-311Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/25746205 .

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AMERICAN SOCIOLOGICAL ASSOCIATION

Neighborhoods and Schools as Competing and Reinforcing Contexts for Educational Attainment

Sociology of Education

83(4) 287-311 ? American Sociological Association 2010

DOI: 10.1177/0038040710383519

http://soe.sagepub.com

?SAGE

Ann Owens1

Abstract

Scholars hypothesize that both neighborhood and school contexts influence educational attainment, but few

have considered both contexts simultaneously. Using data from the National Longitudinal Study of Adolescent

Health, the author analyzes how school and neighborhood contexts are joindy related to high school and

college graduation. She finds that the absolute level of neighborhood resources positively predicts earning a bachelor's degree, while relative neighborhood socioeconomic status (SES) compared to school peers'

neighborhood SES predicts high school graduation. Interactions between school and neighborhood character

istics reveal that low odds of educational attainment among students from lower-SES neighborhoods are

reduced even more when a student attends school with more white and high-SES peers. Conversely, the

high odds of educational attainment among students from higher-SES neighborhoods are further enhanced

by attending school with more white and high-SES peers. Findings suggest that neighborhood SES may be a basis for relative deprivation within schools. Policy makers need to determine how students from different

neighborhoods are integrated into a school's structure and culture in order for policies that mix students

from different neighborhood backgrounds to succeed. Attending a high-SES, largely white school does not

eliminate (and may even exacerbate) the disadvantages of coming from a low-SES neighborhood.

Keywords

neighborhood effects, school effects, educational attainment, multiple contexts, multilevel models

Past research on how social contexts influence

educational outcomes suggests two competing

hypotheses. On the one hand, findings from the

Equality of Educational Opportunity Report (Coleman et al. 1966) suggest that, net of stu dents' own family backgrounds, attending school with more advantaged classmates positively pre dicts one's test scores. On the other hand, Davis

(1966) found that for a student of a given ability, his or her academic achievement and the prestige of the career the student chooses will be lower if he or she attends college with more advantaged peers; that is, schools can serve as "frog ponds," where being a big frog in a small pond is better than being a small frog in a big pond. While

seemingly contradictory, the results of these stud

ies suggest that the effect of attending school with

advantaged peers may vary depending on the out

come studied, the age of the student, and how the

"advantages" of peers are measured.

More broadly, these studies emphasize the

importance of social contexts for educational

'Harvard University, Cambridge, MA, USA

Corresponding Author:

Ann Owens, Harvard University, Department of

Sociology, 33 Kirkland Street, William James Hall 460,

Cambridge, MA 02138 USA

Email: [email protected]



288 Sociology of Education 83(4)

achievement. The peer effects, school composi

tion, and desegregation literatures suggest that

school peers may influence one's success because

school composition is a context for "motivation,

aspiration, and direct interactions in learning"

(Hanushek et al. 2003:529). In addition to the school, another important social context for edu

cational achievement is the student's neighbor hood. In general, past research shows that

students who live in more advantaged neighbor hoods (measured in myriad ways) have higher educational outcomes. However, neighborhoods can also serve as a frog pond in that disadvantages can accrue from living among advantaged neigh

bors, due to negative competition for scarce re

sources (Jencks and Mayer 1990). While considerable research has considered

schools and neighborhoods separately as a social

context for educational achievement, little

research has considered how they jointly influence students' outcomes. Separating out the influences

of schools and neighborhoods is complicated by the fact that school composition often mirrors

neighborhood composition. However, understand

ing how these two contexts separately and jointly affect educational outcomes at various points in

a student's career has important theoretical and

policy implications. Therefore, in this article, I use data from the National Longitudinal Study of Adolescent Health (Add Health) to look at the influence of both schools and neighborhoods on

high school and college graduation. I first identify the association between

neighborhood socioeconomic status (SES) and

educational attainment. Second, I explore whether

attending school with students from higher-SES neighborhoods puts students from lower

SES neighborhoods at a further disadvantage. Relative deprivation theory suggests that the rela tive position of one's neighborhood compared to one's classmates' neighborhoods may lead stu

dents from lower-SES neighborhoods to compare themselves unfavorably to peers from higher SES neighborhoods, which could negatively affect achievement. Third, I examine how attending a school with a particular student body composi tion interacts with the association between neigh borhood SES and educational outcomes. Cook

(2003) offers a useful framework for how multiple social contexts jointly influence outcomes: addi tive effects (both contexts are significantly associ

ated with educational attainment), substitutable

effects (neighborhood characteristics fail to be

significant once school characteristics are

included in the model), or multiplicative effects

(school characteristics interact with and alter

neighborhood influence). My analyses use this framework to identify how neighborhood and school characteristics are jointly associated with educational attainment.

These analyses have implications for policies that privilege changing the schools students attend over changing students' neighborhoods. First,

understanding whether the relative position of one's neighborhood within a school predicts educational attainment bears on policies that mix

students from various neighborhoods. Finding a negative association between relative depriva tion and educational attainment would suggest that students from lower-SES neighborhoods will fare worse among classmates from more advan

taged neighborhoods. Second, assessing how

school composition overcomes or interacts with

the association between neighborhood contexts

and educational success has implications for

school choice policies. If neighborhoods still have a significant association with educational

attainment, regardless of school composition, the

efficacy of changing the school a student attends without changing his or her neighborhood charac teristics is questionable.

NEIGHBORHOOD AND SCHOOL INFLUENCES ON EDUCATIONAL SUCCESS Research on neighborhood and school composi tion suggests that each context influences individ

uals' educational success, but very little research

examines both school and neighborhood charac

teristics simultaneously. Jencks and Mayer (1990) identify a general

framework for understanding how neighbors affect individual outcomes?the advantages of

having "advantaged" neighbors, the disadvan

tages of having "advantaged" neighbors, the irrel

evance of "advantaged" neighbors, and the

irrelevance of all neighbors?in that neighbor hood institutions and resources (schools, police, etc.) matter but neighbors do not. With respect to educational attainment, most evidence suggests the "advantages of having advantaged neighbors."

Many studies show that living in advantaged neighborhoods increases the odds of educational

success, even when individuals' own family



Owens 289

characteristics are controlled. Potential mecha

nisms underlying these findings are that advan

taged neighbors provide social networks or ties that facilitate educational attainment and/or that

advantaged neighbors enforce norms, serve as

role models, and collectively socialize youths into attitudes that lead to educational attainment

(Wilson 1987; Mayer and Jencks 1989; Sampson, Morenoff, and Gannon-Rowley 2002).

Living in a neighborhood with more affluent families, a higher proportion of managerial or pro fessional workers, a lower high school dropout

rate, a lower unemployment rate, and more ethnic

diversity increases the chances of an adolescent

completing high school and positively affects overall educational attainment (Crane 1991; Brooks-Gunn et al. 1993; Duncan 1994; Ensminger, Lamkin, and Jacobson 1996; Foster

and McLanahan 1996; Connell and Halpern Felsher 1997; Halpern-Felsher et al. 1997; Aaronson 1998). While some of these studies test interactions between individual and neighbor hood characteristics, none consider the character

istics of the school that the student attends or the potential interactions between school and

neighborhood characteristics.

Past studies have identified an association

between neighborhood characteristics and one's

educational success. However, there are some

methodological issues that make identifying exoge nous "neighborhood effects" difficult. First, neigh borhood characteristics are also a proxy for

unmeasured individual characteristics. Unobserved

characteristics or those measured with considerable

error are to some extent proxied by neighborhood level means, and so associations between neighbor hood characteristics and outcomes may really be

attributable to individual-level characteristics

(Mayer and Jencks 1989). Second, individuals select into neighborhoods. It may be the case that

neighborhood effects are really the effects of parent characteristics such as motivation or expectations that influence both where parents live and how their children perform in school. Social science data are

seldom experimental or even longitudinal, so causal

interpretations must be made with caution.

Studies focusing on school composition, rather

than neighborhood traits, also find an association with an individual's educational attainment. I focus

on school composition, or the peers that students go to school with, rather than on other measures of

school context such as school funding or teacher

quality. While school composition may reflect

neighborhood characteristics when children attend

neighborhood schools, schools are a unique context

that compels students to interact with each other

through direct competition and enforced contact

in a way that differs from the neighborhood. Research on the importance of school compo

sition became of particularly keen interest follow

ing the Equality of Educational Opportunity Report in 1966, which found that classmates'

socioeconomic backgrounds were a more substan

tial predictor of an individual's success than

school resources were (Coleman et al. 1966). Since then, research has focused on how school

composition is associated with student achieve

ment. Some evidence suggests that attending school with more black students contributes to the black?white test score gap, with black students

more adversely affected than whites (Orfield and Eaton 1997; Hanushek, Kain, and Rivkin 2002). Students who attend predominantly black or

Hispanic schools have also been shown to be

more likely to drop out, controlling for their own backgrounds (Mayer 1991). In addition to racial/ethnic composition, the class backgrounds, academic achievement, and aspirations of school

mates may affect individual outcomes. Mean SES

and mean student achievement have been shown

to positively affect high school graduation rates

(Bryk and Driscoll 1988; Mayer 1991). While attending school with higher-SES peers

may imbue benefits to students, attending school

with higher-ability peers may depress educational outcomes. Alexander and Eckland (1975) found

that although attending school with higher-SES peers elevated absolute achievement and goals,

attending school with higher-ability peers depressed students' goal setting and academic per formance. Alwin and Otto (1977) found similar results: The average ability of peers is negatively associated with the odds of being in a college preparatory curriculum, while the average SES

of peers increases one's odds of being in a

college-preparatory curriculum.

Davis (1966) explained the negative impact of

peers' ability levels by applying relative depriva tion theory to demonstrate how schools can serve

as frog ponds for students, concluding that it is better to be a big frog in a small pond than a small

frog in a big pond. Davis found that for a student of a given ability, the odds of selecting a high prestige career were lower for those who attended




school with higher-ability peers than those who at

tended school with lower-ability peers. Students tend to compare themselves to those in their local

(school) environment rather than across schools, so those attending more competitive schools

develop a lower academic self-concept and are

less likely to strive for high-performance careers

than peers of equal ability who attend less

competitive schools, that is, those who are big frogs in small ponds. More recently, Espenshade,

Hale, and Chung (2005) found further evidence for frog pond effects: Attending high school with

higher-ability peers decreases one's odds of admis

sion to a highly selective college, holding individ ual academic performance constant.

School composition acts through two channels:

by influencing the school's formal structure (i.e., teacher quality, curriculum, and guidance counse

lors) and by generating a climate or culture of achievement in schools (Meyer 1970). With re

gard to structure, schools that serve majority low-income populations and majority minority populations tend to have fewer instructional re

sources, less competitive curriculums, and lower

quality teachers (Orfield and Eaton 1997; Phillips and Chin 2004). With regard to school culture, attending school with students from

higher-SES backgrounds may expose less-advan

taged students to norms about achievement or

educational attainment. In turn, this culture of suc

cess influences the expectations of teachers and

the organizational features of the school (Jencks and Mayer 1990; Rumberger and Palardy 2005). Of course, it is possible that norms about educa

tional attainment may result in lowered academic

aspirations for students who perceive themselves

to be small frogs in big ponds. Past research has shown some frog pond effects for peers' individ

ual background characteristics, but it is less clear

how other dimensions of school composition, such as neighborhood origin, influence educa tional outcomes.

While neighborhood and school studies each

identify compositional features of these contexts associated with educational success, few studies

consider how both neighborhoods and schools influence educational outcomes. Examining

multiple social contexts is important because in

dividuals experience social contexts of varying quality (Cook et al. 2002). While higher-SES schools are more likely to be located in higher SES neighborhoods, correlations at the

individual level suggest that students from disad

vantaged neighborhoods often attend schools that are much more advantaged than their neighbor

hoods, or vice versa (Cook et al. 2002). Considering just one context without accounting for the characteristics of other social contexts

biases the estimated effect of the first context, as the effects of social contexts can overlap or

interact with one another.

Cook (2003) suggests that social contexts

jointly affect outcomes in three ways: effects can be additive, multiplicative, or substitutable.

Several researchers have found additive or substi

tutable effects of school and neighborhood con texts. Catsambis and Beveridge (2001) find that

being from a more disadvantaged neighborhood and attending a school with a higher proportion of students on free or reduced-price lunch are

both negatively associated with math achieve

ment, so the additive effect for students who expe rience both of these contexts is quite large and

negative. Ainsworth (2002) finds that the additive

joint effect on math and reading achievement for

students who come from high-SES neighborhoods but attend lower-quality schools is positive, as the

positive neighborhood coefficient is about five times larger than the negative school coefficient.

Card and Rothstein (2006) compare the effects of neighborhood segregation and school segrega tion on the black-white test score gap. They find that, when considered separately, segregation in

both contexts negatively affects the black?white

gap in SAT scores and SAT participation.

However, when the segregation measures are con

sidered jointly, substitutive effects emerge: Neighborhood segregation still matters for the black?white test-score gap, but school segregation does not.

While some past research considers both

school and neighborhood characteristics, only one study (Cook et al. 2002) includes interaction effects between neighborhood and school con texts. The authors find that the social contexts of the peer group, the family, the neighborhood, and the school all jointly affect students' grades, attendance, and school activities from seventh to

eighth grade. However, no interactions between

contexts were significant, suggesting that the joint effects were additive rather than multiplicative.

In this article, I test how high school gradua tion and college graduation vary according to both school and neighborhood contexts. Based



Owens 291

on the research summarized above, I hypothesize the following:

1. Neighborhood SES will be positively associated with educational attainment.

2. Attending school with peers from

higher-SES neighborhoods than one's own will be positively associated with educational attainment. Past research

generally suggests that attending school with higher-ability peers might create

negative competition but that attending school with higher-SES peers positively influences one's academic achievement.

I hypothesize that peers' neighborhood SES backgrounds will also positively influence attainment.

3. Both school and neighborhood contexts

will be associated with educational attainment (additive effects). I also

expect there to be interactions between

neighborhood and school contexts (multi plicative effects): On the one hand, inter actions between school and neighborhood SES could reveal that attending school with high-SES peers is most beneficial for students from higher-SES neighbor hoods. On the other hand, interactions

between school and neighborhood SES could show that attending school with high-SES school peers is most beneficial for students from lower

SES neighborhoods.

DATA Add Health provides extensive information about

adolescents' family, school, and neighborhood characteristics. Add Health collects longitudinal data from a sample of students who were in 7th to 12th grade in 1994-1995. From all high schools in the United States, Add Health selected a random, stratified sample of 80 high schools and 52 "feeder" middle schools, resulting in a nationally representative sample of high schools. A subsample of students chosen with unequal probability of selection (consisting of a core sample plus over

samples of minority racial/ethnic and disability groups, 21,000) participated in longitudinal data collection, which included an in-home ques

tionnaire, a parent survey, and contextual infor

mation obtained by linking geocoded student

addresses to the 1990 census, the School District Dataset, and other national dataseis.

(For a more complete description of the Add Health study, see http://www.cpc.unc.edu/ad

dhealth.) The Add Health data set is one of the few social science data sets that provides infor

mation about multiple social contexts, allowing for examination of family background, school, and neighborhood influences.

This article analyzes family background, school, and neighborhood data from Wave I (1994-1995) and educational outcome variables from Wave

(collected in 2000-2001 when respondents were

ages 18 to 26).1 Educational outcome data exist

for 15,168 students at Wave III, but I include only those students who attended one of the sampled high schools (not a feeder school) at Wave I.2

Excluding students who attended feeder schools

(n ? 3,200) reduces the sample size to approxi mately 12,900.1 fiirther limit the sample to students for whom geocoded neighborhood context data are

available and those who did not attend one of the

high schools where only twins were sampled at Wave I. My final sample consists of 11,097 students from 77 high schools and 1,709 census tracts.

Outcome Variables The Wave III survey measures educational attain

ment in several ways. I use responses to the ques tion "Have you received a high school diploma?" to measure high school graduation.3 Students are

also asked, "What degrees or diplomas have you received?" and I use the response "Bachelor's

degree?a BA, AB, or BS" to identify students

who have received a college degree (my use of A refers to any bachelor's degree). Students who

have not received a BA and have not yet turned

22, a typical minimum age for BA receipt, are

excluded from analyses predicting college gradua tion (n

= 2,560). The distribution of these two out

come variables is presented in Table 1. Table 1

presents both unweighted and weighted descriptive statistics, using the stratified weighting scheme identified by Add Health. About 83 percent of the students graduated from high school, and about 18 percent of students earned a A by Wave III.

Control Variables At the individual level, I control for gender, race/

ethnicity, the family's public assistance status (as



NO

Table I. Descriptive Statistics for Dependent and Independent Variables

Unweighted (%)

Unweighted (%)

Yes

Weighted %

No

Weighted

%

Dependent variables

Graduated from high school

Earned a BA

Independent

variables

Controls ( = I 1,097)

Male

Non-Hispanic white Non-Hispanic black

Hispanic

Non-Hispanic Asian Other/mixed race

Family

on public aid

Age Grade

PPVT score Parents' education

Family income

(in thousands)

Parents' expectations Own expectations

Time at residence (years)

Census tract characteristics ( = 1,709) Index I: Concentrated Disadvantage

Proportion of families headed by a single female Proportion of children under 18 living in poverty Proportion of all residents living in poverty

Unemployment rate

9,167 (82.6) 1,570 (18.4)

Unweighted mean (SD)

80.8 18.1

Weighted mean

1,930 (17.4) 6,967 (81.6)

Minimum

5,332 5,683 2,069 1,899

883 563

1,082

Unweighted 16.204 10.238 100.774 2.825 46.141

4.777 4.121 7.558 0.000 0.125 0.222 0.168 0.084

(48.0)

(51.2)

(18.6) (17.1) (8.0) (5.1)

(9.8)

mean (SD) (1.458) (1.336) (14.634)

(1.066) (53.067)

(0.779) (1.180)

(5.862) (1.000)

(0.102) (0.183) (0.136) (0.059)

52.0 63.3 16.1 12.4 3.9 4.2 9.1

Weighted mean 16.271

10.270

101.551

2.787

46.024 4.773

4.067 7.599 -0.116 0.119 0.216 0.163 0.079

5,765

5,414 9,028

9,198 10,214

10,534 10,015

(52.0)

(48.8)

(81.4) (82.9)

(92.0)

(94.9) (90.2)

Minimum II 7 13

I 0 I I 0

-1.199 0.000 0.000 0.000 0.000

18.76 81.90 Maximum 49.0 34.9 85.1 88.3 96.1 95.6

90.8

Maximum 21 12 146

4 999

5 5 21

4.954

0.712 1.000 0.864 0.658

(continued)



Table I. (continued)

Unweighted (%)

Unweighted (%)

Yes

Weighted %

No

Weighted '

Proportion of

white

residents 0.664 (0.332) Proportion of

black

residents 0.236 (0.329)

Index 2: Educational and Occupational Attainment 0.000 (1.000)

Proportion of residents 25 and

older without

a high school degree 0.295 (0.159)

Proportion of residents

25

and older with a BA 0.234 (0.146) Median household income (in thousands) 29.463 (13.282)

Proportion of residents with a

managerial/professional

job 0.234 (0.118)

Neighborhood relative deprivation (N = 1,709)

Based on

concentrated disadvantage

0.636 0.955

Based on educational and occupational attainment 0.624 0.693

School characteristics

(N = 77)

School Index I : Socioeconomic

Status

and Expectations 0.000 ( 1.000)

Average college

expectations

4.843 (0.873)

Average expectations for middle-class

income 4.407 (0.761 )

Average parents' education 2.298 (0.569)

Proportion of students living with two parents 0.653 (0.131)

School Index 2: Racial Composition 0.000 (1.000) Proportion of

white

students 0.602 (0.273) Proportion of

black

students 0.158 (0.212)

0.719 0.193

0.002 0.285

0.232 29.444

0.235

0.519 0.574 -0.113 4.689 4.317

2.239

0.651 0.106

0.643

0.147

0.000 0.000 -2.994

0.000 0.000

-12.168

0.000 0.000 0.000 -3.838

1.333 1.494 0.469 0.210 -3.106

0.008 0.000

1.000 1.000 4.084 0.787 0.825 125.053

0.704 6.929 4.687 1.962 6.621 5.540 3.732 0.872 1.100 0.925 0.871

Values were imputed for the following individual-level variables: Peabody Picture Vocabulary Test (PPVT) score (n = 517), parents' educational attainment (n = 382), family income

(n = 2,842), parents' educational expectations (n

= 294), and students' educational expectations (n = 73).




reported by the student), age, grade, score on an

abridged version of the Peabody Picture

Vocabulary Test (PPVT; a commonly used indica tor of cognitive skill), parents' educational attain

ment, family income, parents' educational

expectations, student's educational expectations, and number of years the student has lived at his or her current residence, all measured at Wave I. I

measure parents' education based on students' re

sponses about their parents' education: 1 = less

than high school; 2 = high school graduate; 3 =

some college; 4 = BA or higher. Family income is

measured by the parent's responses to the question "About how much total income, before taxes, did

your family receive in 1994?" For the parents' ex

pectations measure, students are asked, "On a scale

of 1 to 5, where 1 is low and 5 is high, how disap pointed would [your parent] be if you did not grad uate from college?" about both their mothers and fathers. For both parents' education and parents' ex

pectations, I use the higher of the mother's or fa

ther's response. I measure students' educational

expectations as their response to "On a scale of 1

to 5, where 1 is low and 5 is high, how likely do

you think it is that you will attend college?" As seen in Table 1, the sample includes

slightly more girls than boys. Because there is a purposive oversample of minority students, the unweighted data show that the sample is about 50% white and 50% minority students. The weighted means more closely resemble the

national student population: 63% white, 16% African American, 12% Hispanic, and 4% each Asian American and those of mixed or other

races. About 10 percent of students in the sample are from families receiving public assistance at

Wave I, the average student at Wave I was about

16 years old and in the 10th grade, the cognitive test score has a mean of around 101, the average

parent has some college education, the average

family income is around $46,000, parents and students hold high expectations for the student

graduating from college, and the average student has lived at his or her current residence for 8

years. I used multiple imputation to estimate

missing values for PPVT score, parents' educa

tional attainment, family income, parents' ex

pectations, and students' own expectations from

parents' and students' observed characteristics.4

The number of imputed values is noted below Table 1. In addition, 286 students did not have

grade data, and their grade levels were calculated based on their age, with 12-year-olds assigned to

7th grade, 13- and 14-year-olds to 8th grade, 15

year-olds to 9th grade, 16-year-olds to 10th

grade, 17-year-olds to 11th grade, and 18-year olds and older to 12th grade.

Neighborhood Characteristics I define students' neighborhoods as the 1990 cen sus tract to which their residential address was

geocoded at Wave I. The sample includes students

from 1,709 census tracts, with a mean of 6.5 stu

dents in each tract (SD = 15.4). Add Health pro

vides myriad information about the students'

census tracts. Drawing on variables used in past

research, I selected 10 neighborhood characteris

tics for analyses: proportion of residents 25 years and older without a high school degree, proportion of residents 25 years and older with a BA, propor tion of families headed by a single female, propor tion of children living in poverty, proportion of all residents living in poverty, median household income, proportion of residents holding a profes sional or managerial job, unemployment rate, pro

portion of residents who are white, and proportion of residents who are black.

Because of the high correlations among these

variables, I used principal components analysis to create indices of neighborhood SES. The varia

bles loaded onto two factors, rotated using promax

rotation, which allows for correlation between the

two indices (R =

?.519,/? < .001; for factor load

ings, see Appendix Table 1; all appendices are available at http://soe.sagepub.com/supplemen

tal). The neighborhood indices are a linear combi

nation of the standardized neighborhood variables,

weighted by the factor loading. The first neighbor hood index measures concentrated disadvantage: this index is a linear combination of the proportion of families headed by a single female, proportion of children living in poverty, proportion of residents

living in poverty, unemployment rate, proportion white (negatively loaded), and proportion black. The race variables have the highest loadings on this factor, but because the proportion of families headed by a single female also loads highly and because the race factors do not load on a separate

index, I label this index concentrated disadvantage rather than race. Concentrated disadvantage refers

to the ecological concentration of factors that can

create "structural social disorganization'' and "cul

tural social isolation," which can lead to negative outcomes for residents (Sampson and Wilson,

1995:44).



Owens 295

The second neighborhood index measures the

educational and occupational attainment of resi

dents, and this index is a linear combination of the proportion of residents 25 and older without a high school degree (negatively loaded), propor tion of residents 25 and older with a BA, median household income, and proportion of residents

holding a professional or managerial job. Proportion of residents with a BA and proportion with a professional or managerial job have the

highest loadings on this factor. I present descriptive statistics for the two indices as well as each tract characteristic in Table 1. Descriptive statistics for all neighborhood characteristics are calculated at the neighborhood level (i.e., each tract's value on

the variable is counted once, regardless of the num

ber of residents). The unweighted means for the two indices are 0 and standard deviations are 1

because they are standardized variables.

School Characterises I include 77 schools in my analyses. Of these, 72 are

public, 2 are Catholic, and 3 are private. I include all three types of schools because a public school indi cator variable did not significantly predict the out comes, nor did running the models with only public schools change results. I also cannot identify schools' attendance algorithms (i.e., geographically based vs. choice schools). School administrators' re

sponses to survey items about their school types and their attendance algorithms are contradictory?for

example, an administrator who responded that the

school was a "choice" school also responded that

all students in the area attended the school.

Because I do not have definitive information about

what sort of attendance algorithm a school uses, I

include all schools. On average, there are 144.1 stu

dents in each school (SD = 161.9).

Every student present in each sampled high school during Wave I data collection completed an in-school survey that collected information on

demographic characteristics, family relationships, school activities, and attitudes, resulting in a cen

sus of the student body (n ? 70,000 high school

students). Using this data, I am able to calculate school-level averages, excluding the respondent's own characteristics so that the measures capture the traits of the respondent's peers. Based on

past research showing the importance of racial/ ethnic and socioeconomic composition, as well

as mean aspirations in the school, I include six

measures of student body composition: proportion

of the student body that is white; proportion that is black; average college expectations, scored on

a scale of 1 to 8, with 8 = it will happen; average expectations for earning a middle-class family income by age 30, scored on a scale of 1 to 8,

with 8 = it will happen; average parent education

(on the same scale of 1 to 4 as the individual-level control variable); and proportion of students living with two parents (biological, step-, foster, or adop tive parents). I do not have information about the

academic ability of students in the in-school sam

ple. As is the case for neighborhood characteristics, these variables are highly correlated, so I con

structed two indices of school composition, using principal components analysis (correlation between the two indices is .398, < .001; see Appendix Table 1 for factor loadings). School Index 1 meas ures students' SES and expectations?a linear com

bination of students' educational and income

expectations, their parents' educational attainment, and whether they live with both parents. School Index 2 measures school racial composition?the

proportion black and the proportion white (propor tion black is loaded negatively so that a higher score on the school race variable corresponds to

a school with a higher proportion of white stu

dents). Table 1 presents the average values of the

characteristics of the 77 schools. The unweighted means and standard deviations are 0 and 1.

Analyses I use multilevel logit models to predict the likeli hood of graduating from high school and the like lihood of earning a A.5 Because of the large number of models tested, all analyses include

Bonferroni adjustments to reduce the odds of re

jecting the null hypothesis when it is actually true (Type I error). I do not use weights, for the sake of efficiency. Appendix Table 2 presents es

timates of how much variance the school and

neighborhood explain.6 The adjusted R2 estimates are from logit models predicting high school grad uation and earning a BA with no other predictors except for school and neighborhood indicator

(dummy) variables. The adjusted R2 estimates indicate that schools explain about 3.5 percent of the variance in high school graduation and neigh borhoods explain an additional 2.5 percent. For

earning a A, schools account for 12 percent of the variance, while neighborhood differences

within schools account for an additional 4 percent. It is surprising that high schools explain more




variance in college graduation than in high school

graduation. One explanation is the sampling frame

of Add Health: Only students who have stayed in

high school are sampled, so the students sampled may be those who have found a way to succeed in

their school, regardless of its characteristics.

In the first set of models, I estimate the likeli hood of graduating from high school and of earn

ing a A based on the student's individual and

neighborhood characteristics, including a random

school intercept. The likelihood of each outcome is estimated using Equation 1 (notation is modeled after that used in Gelman and Hill 2007).

J*r(Ed Outcomeihs = 1) ?

logit-1(a? + as + ?^ontrolsi),

aA~N(?2Neighh,a2),for h = 1,..., (1)

1,709 (1,474 for earning a BA)

a5~ ( a, ^), ) 5= 1,...,77,

where Ed Outcome is either high school graduation or earning a BA for individual i in neighborhood h and school s, a represents random intercepts for the

neighborhood (h) and school (s), and Controls is the vector of control variables for individual i. The second equation shows that estimates for the

random neighborhood intercept (et/,) are normally distributed (N) with variance \. The intercept is calculated from the neighborhood's deviation

from the mean educational outcome within each

school. This deviation is predicted by Neighh, the

neighborhood's score on each neighborhood index.

The random intercept for schools (as) is also nor

mally distributed with variance ^, and it is the deviation of each school from the grand mean of

each educational outcome ( a).

Next, I test the influence of the relative position of each student's neighborhood within his or her school on educational attainment. The relative dep rivation measure takes into account the context of

school peers' neighborhoods. That is, this measure

captures what the individual's neighborhood is like in relation to what school peers' neighborhoods are like, and it accounts for both how many people are from better-off neighborhoods than an individ ual and how much better off the neighborhoods are. This measure helps identify how students will fare in a school where more students are from more

advantaged neighborhoods. I use the formula suggested by Stark and Taylor

(1989) to calculate relative deprivation indices

based on each neighborhood SES index: RD? =

AD(YijP(Yi), where relative deprivation (RD) equals the product of the individual's absolute dep rivation (AD) within a school, or the average differ ence between one's neighborhood index score (1?) and the neighborhood index score of those higher than the individual, and the proportion (P) of those within the school who have a higher neighborhood index score (Yj) than the individual. It should be noted that because the measure emphasizes relative

position compared to others, it does not differenti ate between a student who is the most advantaged in his or her school and a student attending a homo

geneous school, since in both scenarios the student

would have a score on the relative deprivation index of 0. Equation 2 represents the next models,

which include individual controls and the relative

deprivation measures.

?r(Ed Outcomeihs = 1) = logit-1 (ah + as + ? ! Control^ ),

aA~N(?2Neighh + ?2RDh, 2,), for h = 1,...,

1,709 (1,474 for earning a BA)

a5~ ( a, 2), 5= 1, ...,77.

(2)

The neighborhood intercept ah is now esti mated by the relative deprivation score based on each index RDh within one's school, controlling for the neighborhood indices Neighh. Here, rela

tive rather than absolute resources are tested.

Finally, I simultaneously examine the contexts

of schools and neighborhoods by including specific school and neighborhood characteristics. I estimate

the likelihood of each outcome variable from the individual-level control variables, the neighbor hood indices, and the school indices. Then, I add interaction terms between each neighborhood and

school index. Equation 3 displays this model.

?r(Ed Outcomeihs ? 1) = logit-1 (ah + as + ?jControlSi)

ah ~N(?2Neighh + ?3(NeighhXSchools), 2), for h = 1,1,709 (1,474 for earning a BA)

a5~ ( a + ?4Schools, 2), for s =

1,..., 77.

(3)

Schools, each school index variable, predicts the school intercept as. Neighh X Schools is the



Owens_297

interaction term between each neighborhood index and each school index. The coefficients ?2 and ?4 represent additive or substitutable effects of school and neighborhood characteristics, while

?3 represents multiplicative effects between the two contexts.

RESULTS

Predicting Educational Attainment with Neighborhood Characteristics

First, I examined how neighborhood characteris

tics relate to educational attainment. Table 2

presents logistic regression coefficients predicting graduating from high school, and Table 3 presents logistic regression coefficients predicting earning aBA.

I include individual-level control variables in all analyses, and coefficients for the control vari

ables are displayed in Appendix Table 3 for both

high school graduation and earning a BA. For

high school graduation and earning a BA, students in higher grades and those who are younger within those grades (i.e., those who have not been held

back) are more likely to graduate, as are female

students. Students whose parents have higher edu

cational attainment, who are not on public assis

tance, who have higher educational expectations, who have higher cognitive ability, and who have lived longer at their current residences are more

likely to graduate from both high school and col

lege. Students who perceive their parents to have

higher expectations for their future are more likely to graduate from high school, but parental expect ations do not significantly predict earning a BA.

Family income positively and significantly pre dicts earning a BA but does not predict graduating from high school, controlling for other family background characteristics. Asian American stu

dents are more likely than whites to graduate from college, but otherwise a student's race is

not a statistically significant predictor of educa tional attainment, holding other characteristics constant.

Next, I enter the neighborhood SES indices, as in Equation 1. Concentrated disadvantage in

cludes the proportion of families headed by a sin

gle female, proportion of children living in

poverty, proportion of all residents living in pov erty, unemployment rate, proportion white (nega

tively loaded), and proportion black. Educational

and occupational attainment includes the propor tion of residents 25 and older without a high school degree (negatively loaded), proportion of residents 25 and older with a BA, median house

hold income, and proportion of residents holding a professional or managerial job. Overall, results

follow Hypothesis 1: Neighborhood SES is posi tively associated with educational attainment.

For high school graduation, models including individual-level control variables and the neigh borhood SES indices show that neighborhood con centrated disadvantage is a significant and

negative predictor, while neighborhood educa

tional and occupational attainment does not pre dict high school graduation (Table 2, Models 1

3). However, including an interaction between

neighborhood SES index and student's grade re

veals that neighborhood educational and occupa tional attainment may matter for students in the

lowest grades. Because adolescents are only sam

pled in Wave I if they are still in school, students enrolled in higher grades are more likely to grad uate than those in lower grades: The graduation rate for those initially in 7th grade is 78%, 8th

grade is 79%, 9th grade is 78%, 10th grade is

81%, 11th grade is 84%, and 12th grade is 89%

(the differences among 7th-10th graders are not

significant; all other differences are significant). These differences suggest that coefficients for

neighborhood SES derived exclusively from sam

ples of older students tend to be biased downward, since their association with high school attrition is underestimated, and that grade bias terms should

be included. For high school graduation, the

neighborhood educational and occupational attainment index becomes a significant and posi tive predictor when a grade bias term is included, but only for 7th and 8th graders (Table 2, Model

5). See the technical appendix for a full discussion of the grade bias terms and results.

For college graduation, only the index measur

ing neighborhood educational and occupational attainment is a significant and positive predictor when individual-level characteristics are con

trolled (Table 3, Models 1-3).7 Students who come from neighborhoods with higher average educational attainment, higher median household

income, and more neighbors with managerial or

professional jobs are more likely to earn a A. An increase of 1 standard deviation on the neigh borhood educational and occupational attainment

index corresponds to a 34 percent increase in the

odds of earning a BA, exp(0.292) = 1.339.



sO 00

Table 2. Neighborhood Socioeconomic Status (SES) and Relative Deprivation Measures Predicting High School Graduation

Neighborhood SES

Model I

Model 2

Model 3

Grade bias terms

Model 4

Model 5

Neighborhood SES + relative deprivation

Model 6

Model 7

Neighborhood indices

Concentrated disadvantage

Educational and occupational attainment

Concentrated disadvantage X Grade

Attainment X Grade Relative deprivation

Based on concentrated disadvantage

Based on educational and occupational attainment

Intercept

Log likelihood

-.113* (.055) 2.811 -186,466

.025 (.047)

2.868 -186,466

-.124*

(.063) .019 (.053) 2.797 -186,466

-.211* (.100) .032 (.027)

2.755

-186,466

.372*< (.095)

-.112*' (.026) 2.900

-186,465

.016 (.097) -.I86t (.100) 2.886 -186,467

.321** (.099)

-.117*** (.026)

-.276** (.083) 2.940

-186,467

= 11,097 students, 1,709 census tracts, and 77 schools. Logistic regression coefficients are displayed with standard errors in parentheses beneath. All models include individual level controls for Wave I at age, grade, gender, race, parents' education, family income, public assistance, parents' expectations, student's own expectations, Peabody Picture

Vocabulary Test score, and number of years at current residence, tp < .10. *p < .05. **p < .01. ***p < .001 (two-tailed tests).



io NO NO

Table 3. Neighborhood Socioeconomic Status (SES) and Relative Deprivation Measures Predicting Earning a Bachelor's Degree (BA)

Neighborhood

SES +

Neighborhood SES Grade bias terms relative deprivation

Model I Model 2 Model 3 Model 4 Model 5 Model 6 Model 7


Concentrated disadvantage -.088 .082 .156 .019

(.071)

(.079)

(.214) (.126)

Educational and occupational attainment .292*** .311*** .237 .316***

(.053) (.058) (.176) (.060)

Concentrated disadvantage X Grade -.061

(.052)

Attainment

X Grade .014

(.043)

Relative deprivation

Based on concentrated disadvantage -.163

(.139)

Based on educational and occupational attainment .119

(.103)

Intercept -9.214 -9.007 ^8.970 -9.111 -8.867 -9.214 -8.981 Log likelihood -17,101 -17,974 -18,033 -17,166 -17,975 -16,812 -18,040

= 8,537 students, 1,474 census tracts, and 77 schools. Logistic regression coefficients are displayed with standard errors in parentheses beneath. All models include individual-level controls at Wave I for age, grade, gender, race, parents' education, family income, public assistance, parents' expectations, student's own expectations, Peabody Picture Vocabulary

Test score, and number of years at current residence.

***p < .001 (two-tailed tests).




Comparing students from the most versus the least

advantaged neighborhoods, the odds of graduating from college more than triple for students from the

neighborhoods with the highest mean educational and occupational attainment versus students from

neighborhoods with the lowest mean educational and occupational attainment (0.292 X 4 standard

deviation change, which compares those at the

97.5th percentile with those at the ? 2.5th per centile on the neighborhood index, is 1.168; exp( 1.168)

= 3.216). College graduation rates do not significantly vary by grade level at Wave I

(perhaps because few of the 7th-9th graders are 22 or older at Wave 3), and no interaction terms

between grade and neighborhood indices were sig nificant (Table 3, Models 4 and 5).8

All attempts at estimating causality require as

sumptions, even with experimental data. In this

article, to interpret the relationship between neigh borhood SES and educational outcomes as causal, one must assume that neighborhood SES is exog enous to the relationship between the students'

background characteristics and their educational

outcomes. With survey data, this assumption

may be problematic. First, several control varia

bles may be endogenous to the relationship between neighborhood characteristics and educa

tional outcomes. For example, parents' educa

tional attainment increases neighborhood-level educational attainment. There is also a selection

issue; higher-SES parents or those with higher ex

pectations may choose their neighborhood because of its schools or the attainment of its res

idents. While I cannot control for all observable

and unobservable background characteristics, I

consider alternative explanations and include

a comprehensive set of controls, including some

factors that might be endogenous to neighborhood context. While results may be biased, they are consistent with an interpretation of how neighbor hood context affects educational outcomes above

and beyond background characteristics.

Overall, results show that different dimensions of neighborhood SES matter for high school grad uation versus college graduation and that the mag nitude of the association between neighborhood SES and earning a BA is more than double that for graduating from high school. Both these re

sults suggest that students draw on different skills, resources, and reference groups to complete high school versus college. For students already enrolled in high school, the odds of graduation may depend mainly on staying out of trouble

and completing school requirements. This may be more difficult in neighborhoods of concen trated disadvantage: A high proportion of female-headed households and a high poverty rate often signal that a neighborhood has lower levels of trust and social cohesion (Wilson 1987; Sampson and Wilson 1995). Parents may not

have the desire or social resources to watch out

for other children in the neighborhood. However, factors such as school attendance and completion of work may depend more on individual charac

teristics like motivation, peer influence, and

parental monitoring than on neighborhood charac

teristics, and so the magnitude of the association

between neighborhood SES and high school grad uation is smaller compared to that for college

graduation. In contrast, earning a BA depends on skills and

resources drawn from a wider reference group than one's individual or family traits. High-status

neighbors, measured as those with professional or

managerial jobs or bachelor's degrees, can pro vide information about particular colleges, pro

grams, and financial aid; can create norms in

one's neighborhood with respect to what success

ful adults have done; and can pass these resources

on to their children, with whom neighborhood children interact. Students from low-SES families

or whose own parents did not go to college may be less familiar with the complicated application and financial aid process for college, and having

neighbors who provide information about college or the cultural tools to succeed in the college classroom may aid them in setting higher goals and navigating the college experience. Students

who observe more highly educated neighbors with higher-status jobs may see them as positive role models or as an example of what someone

from his or her neighborhood should achieve. That neighborhood SES seems to matter more for earning a BA than for high school graduation emphasizes the need to develop a cohesive theory of how neighborhoods matter for educational attainment.

Relative Deprivation of One's

Neighborhood within One's School Models 6 and 7 of Tables 2 and 3 display results for relative deprivation measures: how much

more advantaged school peers' neighborhoods are and the proportion of school peers who come



Owens 301

from better-off neighborhoods than one's own.

This measure captures the types of neighborhoods represented in the school and thus the context within which learning and competition play out. The results suggest that relative neighborhood

position matters for high school graduation but not for earning a BA.

Being more relatively deprived in one's school in terms of neighborhood educational and occupa tional attainment negatively predicts high school

graduation (Table 2, Model 7). A 1-point increase on the relative deprivation scale based on neighbor hood educational and occupational attainment re

sults in a 24% reduction in the odds of graduating from college, exp(-0.276)

= 0.759. This finding sug gests that school composition in terms of neighbor hood background produces frog pond effects: Being among students from more advantaged neighbor hoods than your own, or being a small frog in a big pond, is detrimental. My results suggest that if two students from the same neighborhood go to different high schools, the student who goes to school with peers from lower-SES neighborhoods is more likely to complete high school. I cannot test this proposition directly with these data, since Add Health samples only one high school per city; I do not know if a student who is a big frog in a small

pond (who attends high school with peers from lower-SES neighborhoods) would outperform a neighbor who attends school with students from

more advantaged neighborhoods. However, the

findings imply that the reduced odds of high school

graduation associated with being from a lower-SES

neighborhood are even lower among more peers from higher-SES neighborhoods.

As I state in Hypothesis 2, one would expect that attending school with students from higher SES neighborhoods would have the same positive effect as found in most research on peers' individ

ual SES, but this does not seem to be the case.

My findings are consistent with those of Crosnoe

(2009), who found that low-income students attend

ing schools with higher-SES peers perform worse than low-income students attending lower-SES

schools. My results and Crosnoe's suggest that

other individual, school, and neighborhood traits

may serve as bases for negative competition than

suggested by past research on frog pond effects.

Why does attending school with peers from

higher-SES neighborhoods disadvantage those from lower-SES neighborhoods? First, it may be the case that neighborhood SES predicts curriculum track. Many elementary and middle schools serve

neighborhood students, and it is possible that schools serving lower-SES neighborhoods have

less-rigorous curricula than those serving higher SES neighborhoods. Therefore, when students

from diverse neighborhoods come together in high

school, students from more disadvantaged neigh borhoods might be placed in a lower academic track if their feeder schools have inadequately prepared them. This placement could in turn shape their aca

demic self-concept, interactions with teachers and

guidance counselors, and academic achievement.

Second, attending school where one perceives the

majority of peers to come from a more advantaged

neighborhood may result in a climate of negative competition; that is, students may feel that they can not compete against those with more resources than

them and so adjust their goals and achievement

accordingly. Neighborhood context may shape stu

dents' ideas about their potential or ability, what

goals are appropriate, or how to present themselves

or relate to others. Students may know what people from their neighborhood are "supposed" to do and

compare themselves to students from other neigh

borhoods, leading those from lower-SES neighbor hoods to lose motivation in the face of what seems like insurmountable disadvantages. The mecha

nisms behind why neighborhood status produces negative competition need further investigation.

Neither relative deprivation index significantly predicts college graduation (Table 3, Models 6 and 7). These results suggest that the relative posi tion of one's neighborhood influences high school

graduation, while the absolute level of neighbor hood resources is associated with college gradua tion. For high school graduation, students'

reference group for how they assess their own aca

demic abilities or goal setting is primarily their classmates, the "local" context (Meyer 1970). In

contrast, one's reference group for earning a A

is much broader, as students look to adults who

have achieved this goal, which may include adults in their neighborhood. Therefore, it is not surpris

ing that the absolute status of one's neighbors, rather than the position of one's neighborhood compared to school peers' neighborhoods, influ

ences college graduation.

Neighborhood and School Characteristics

While the previous set of analyses explored how

neighborhood characteristics play out within the




school as a context for comparison, the next set of

analyses examines how neighborhood and school

characteristics are jointly associated with educa

tional outcomes. Because of the school-based

sampling, all high school students in the sample who live in the same census tract attend the

same school. I cannot make comparisons between

students who live in the same neighborhood and attend different schools, only comparisons between students who go to the same school but live in different neighborhoods. As Cook (2003) suggests, the joint results might be additive

(school and neighborhood characteristics are both significant predictors), substitutable (when school characteristics are entered into the model,

neighborhood characteristics are no longer signif icant), or multiplicative (school context may dif

ferentially influence students who come from

different neighborhoods). First, I predicted high school graduation and

earning a BA using individual-level controls and the school composition indices to see if educa

tional attainment varies by school peers' charac

teristics. The first two columns of Table 4 (high school graduation) and Table 5 (earning a BA) present these results. School-level SES and ex

pectations includes students' educational and

income expectations, their parents' educational

attainment, and whether they live with both pa rents. A higher score on racial composition signi fies more white students and fewer black students

in the school. Individual-level control variables

are included in all models (coefficients are similar to those presented in Appendix Table 3).

As the first two columns of Table 4 show, nei

ther classmates' SES and expectations nor school

racial composition significantly predicts one's

likelihood of graduating from high school. However, for earning a A, school peers' SES

and expectations is a significant and positive pre dictor (Table 5, Model 1). An increase of 1 stan dard deviation on the index measuring SES and

expectations increases the odds of earning a BA

by 51 percent, exp(0.410) = 1.507. Consistent

with past literature, attending school with peers with higher expectations and higher-SES back

grounds increases one's educational attainment,

holding individual traits constant. School compo sition reflects both school structure (if the major ity of students plan to go to college, the school is

likely facilitating that in some way, e.g., by pro viding information on application and financial aid processes or inviting college admissions

representatives to the school) and school climate

(having schoolmates with higher aspirations and more highly educated parents might influence the norms about college held in the school).

The next set of models (Models 3-6 of Tables 4 and 5) includes both neighborhood and school indices in predicting educational attainment. For

high school graduation, only neighborhood educa tional and occupational attainment is a significant

predictor (again, decreasing in magnitude by grade, as described in the technical appendix), and school composition is not. The odds of high school graduation vary by neighborhood charac

teristics but do not vary by the characteristics of schoolmates, suggesting that regardless of

who students attend school with, their achieve

ment is still associated with the traits of their

neighborhoods (although it should be noted that I measure different characteristics of schools

and neighborhoods). School SES and expectations positively predict

earning a A, controlling for neighborhood SES. When neighborhood educational and occupational attainment is controlled for, there is a positive additive effect of the two contexts: Both neighbor hood educational and occupational attainment and

schoolmates' SES and expectations significantly predict earning a BA, controlling for individual characteristics (Table 5, Model 5). Somewhat sur

prising results occur with regard to school racial

composition. Once either dimension of neighbor hood SES is controlled, the coefficient for school race is significant (or borderline significant) and

negative, suggesting that attending high school with more black students (a lower score on the

race index) is positively associated with earning a A (Table 5, Models 4 and 6). This contrasts with past research that shows that attending school

with more white peers is beneficial for high school

graduation (Mayer 1991). What might explain these findings? First, it

should be noted that of the 77 schools, only 6 are composed of more than 50 percent black stu dents. Therefore, most students attend school

with mostly nonblack peers, so this coefficient in dicates that attending school with fewer white stu dents is beneficial. Second, as the literature on

frog pond effects showed, some dimensions of school composition can have negative effects on

students' outcomes. Perhaps school racial compo sition is a proxy for school structure or climate

characteristics that operate in this same way.

Mayer (1991) found that attending school with



0

Table 4. Neighborhood and School Characteristics Predicting High School Graduation



Educational and

occupational

attainment

Attainment X Grade

School indices

Socioeconomic status (SES) and expectations

Racial composition

School X Neighborhood interactions Neighborhood Disadvantag X School SES

Neighborhood Disadvantage X School Race Neighborhood

Attainment

X School SES Neighborhood

Attainment X School Race

Intercept

Log likelihood

School composition

School

and neighborhood traits

Interactions between school and neighborhood traits

Modell Model 2 Model 3 Model 4 Model 5 Model? Model 7 Model 8 Model 9 Model 10

.059 (.050) 2.985

.056 (.050) 2.860

-186,465 -186,466

-.I03t (.057)

.045 .051

-.I03t (.062) .018

(.056)

354*** 367***

(.096)

(.095) -.110*** -.112***

(.026) (.026)

.045 (.051)

.053 (.051)

2.915 2.817 2.992 -186,466 -186,466 -186,465

2.903

-.133* (.057)

.032 (.051)

-.094* (.045)

-.180** .070 .031 .056 -.100* .045

2.869 2.768

.373***

(.098) -.108**

(.027) .038

(.051)

-186,465 -186,466 -186,466

.095* (.045) 3.027

-186,465

.367***

(.096) -.112*** (.026) .053 (.052) .000

(.051) 2.903

-186,465

= 11,097 students, 1,709 census tracts, and 77 schools. Logistic regression coefficients are displayed with standard errors in parentheses beneath. All models include individual level controls at Wave I for age, grade, gender, race, parents' education, family income, public assistance, parents' expectations, student's own expectations, Peabody Picture

Vocabulary Test score, and number of years at current residence, tp < .10. *p < .05. **p < .01. ***p < .001 (two-tailed tests).



2

Table 5. Neighborhood and School

Characteristics

Predicting Earning a Bachelor's Degree (BA)

School composition

School and neighborhood traits

Interactions between

school

and neighborhood traits

Modell Model 2 Model 3 Model 4 Model 5 Model? Model 7 Model 8 Model 9 Modello



Educational and occupational attainment

School indices

Socioeconomic

status

(SES) and expectations

410***

(.064)

Racial composition

School X

Neighborhood

interactions Neighborhood Disadvantage X School SES

Neighborhood Disadvantage X School Race Neighborhood Attainment X School SES

Neighborhood Attainment X School Race

Intercept

-8.249 Log

likelihood

-17,126

-.088 (.061)

-.014 (.072)

.409***

(.065)

-.188* (.081)

-.160* (.068)

.201***

(.056)

.350***

(.068)

.301***

(.053) -.I20f

(.061)

-.012 (.073)

.406***

(.066) -.011

(.067)

-.321** (.100)

-9.270 -8.257 -9.391 -8.257 -9.113 -8.264 -17,636 -17,128

-17,252

-18,011 -18,088 -17,119

.196***

(.057)

.340***

(.069)

.333*** (.055) -.HOt (.061)

-.175* (.069)

-.123* (.055)

.025 (.056)

.171**

(.055)

-9.500 -8.251 -9.066 -16,716

-18,019

-18,181

= 8,537 students, 1,474 census tracts, and 77 schools. Logistic regression coefficients are displayed with standard errors in parentheses beneath. All models include individual-level controls for Wave I at age, grade, gender, race, parents' education, family income, public assistance, parents' expectations, student's own expectations, Peabody Picture Vocabulary

Test score, and number of years at current residence,

tp < .10. *p < .05. **p < .01. ***p < .001 (two-tailed tests).



Owens 305

A os ?

\ 50th potile

??. (school race)

\ \ \ \

o ?.

? 90th pctile\ (school race)

-1 5

Neighborhood Concentrated Disadvantage

Figure I a. Probability of high school graduation by neighborhood concentrated disadvantage, plot ted by percentile score on the school racial com

position index; estimates from Table 4, Model 8

more white students is beneficial, but students'

neighborhood is not controlled. Without control

ling for students' neighborhood SES, perhaps the benefits accrued from norms learned in one's

neighborhood about earning a BA were being attributed to attending school with more white

peers. Controlling for neighborhood SES, perhaps attending school with more white peers is detri

mental because of competition. If white students

typically have stronger academic records or are

encouraged to attend college more than black stu

dents are, attending school with fewer white stu

dents increases the odds that one will have the academic record and resources to attend college. I do not have the data to speculate further about

this, but it appears that attending school with more white students may produce negative com

petition, much like student ability.

Finally, Models 7 through 10 of Table 4 and Table 5 present the results of interaction terms between neighborhood and school characteristics for high school graduation and receiving a BA.

Models 7 and 8 of Table 4 show that students from more disadvantaged neighborhoods have lower odds of graduating from high school and that attending school with higher-SES peers or a greater proportion of white peers reduces the odds even more (the interaction term between

neighborhood concentrated disadvantage and

each school index is negative). In addition, there

is a positive and significant interaction between

neighborhood educational and occupational attainment and school SES and expectations?

being from a high-SES neighborhood confers an even greater advantage when one attends school

with higher-SES peers (Table 4, Model 9). These interactions suggest that students from

low-SES neighborhoods do worse among more

white and higher-SES school peers than they would among fewer white and lower-SES class

mates. Figure la shows the relationship between

the probability of high school graduation and

neighborhood concentrated disadvantage for stu

dents attending schools at the 10th (fewer white

students), 50th, and 90th (more white students) percentile scores on the school race index. The

figure shows that the relationship between neigh borhood disadvantage and the probability of grad uating from high school is negative regardless of school racial composition, but students from dis

advantaged neighborhoods who go to school with more white students (the 50th and 90th per centile lines) have steeper negative slopes and lower predicted probabilities of graduation com

pared with students from disadvantaged neighbor hoods who attend school with fewer white students (the 10th percentile line). Attending school with more white students reduces the

already lower odds of high school graduation for those from low-SES neighborhoods even more.

Figure lb shows the relationship between high school graduation and neighborhood, educational

and occupational attainment for students attending schools at the 10th, 50th, and 90th percentile scores on the school SES and expectations index.

The relationship between neighborhood attain

ment and high school graduation is positive for students attending schools at the 50th and 90th

percentile of SES and expectations, and attending school with higher-SES classmates amplifies the

positive relationship between neighborhood SES and high school graduation (the 90th percentile line has a more positive slope and higher predicted values than the 50th percentile line). However, attending school with students with lower-SES

backgrounds and expectations (the 10th percentile line) actually results in a negative relationship between neighborhood attainment and high school

graduation?students from more advantaged

neighborhoods have lower odds of graduating from these schools than do students from less

advantaged neighborhoods. Conversely, students




s ? 8

Neighborhood Educ and Occ Attainment

Figure I b. Probability of high school graduation by neighborhood educational and occupational attainment, plotted by percentile score on the

school socioeconomic status (SES) and expecta tions index; estimates from Table 4, Model 9

from neighborhoods of lower educational and

occupational attainment do best in schools with

lower average SES and expectations. For college graduation, the interaction between

being from a neighborhood of concentrated disad

vantage and school racial composition is negative, as it was for predicting high school graduation (Table 5, Model 8). This finding may elucidate the finding in Models 4 and 6 that the average stu dent, controlling for individual and neighborhood characteristics, has higher odds of graduating from

college if he or she attends high school with more black peers. For those who are from disadvan

taged neighborhoods, attending school with more white peers is particularly detrimental. The inter

action term between neighborhood educational

and occupational attainment and school race is

also significant but positive, suggesting that stu dents with higher-status neighbors are more likely to earn a BA, and that if they attend schools with

more white peers, their advantage over students

from lower-SES neighborhoods is even greater

(Table 5, Model 10). Figures 2a and 2b illustrate these interactive

relationships between school and neighborhood characteristics for earning a BA. Figure 2a shows

that coming from a neighborhood of higher con centrated disadvantage lowers the odds of earning

a BA for all students, but attending school with

more white students lowers these odds even

more?the lines for schools at the 90th and 50th

percentile on the school racial composition index

(more white students) have a more negative slope and lower predicted probabilities of graduation than the line for schools at the 10th percentile (fewer white students). Figure 2b shows the posi tive relationship between neighborhood educa

tional and occupational attainment and earning a BA and also demonstrates how attending school

with more white peers (the 50th and 90th percen tile lines) increases the probability of earning a BA for students from high-SES neighborhoods even more. Conversely, the graph indicates that

students from neighborhoods of lower average attainment do best among schools with fewer

white peers (to the left of the intersection of the three lines, attending a school at the 10th percen tile for school racial composition, i.e., with fewer

white students, corresponds to the highest proba

bility of earning a BA for students scoring lower on the neighborhood SES index).

The interactions between neighborhood and school SES indicate that attending school with

more white peers or those with higher SES or ex

pectations can be disadvantageous for students

from lower-SES neighborhoods. Schools serving

higher-SES student bodies may have more resour

ces and more rigorous curricula than do schools

serving lower-SES students. Students from

lower-SES neighborhoods, particularly if they at

tended a neighborhood middle school, may be less prepared to succeed in these schools com

pared with students from higher-SES neighbor hoods, depressing their academic self-concept,

college expectations, and readiness for higher edu

cation. In contrast, attending a school with higher SES peers increases the benefits of coming from

higher-SES neighborhoods. If schools serving more white peers or those with higher SES and ex

pectations have more resources, students from

neighborhoods with higher average educational and occupational attainment can capitalize on

these resources to pursue the educational plans

they learn from neighbors.9

DISCUSSION Educational attainment varies by both the neigh borhood one lives in and the school one attends.

By examining both contexts jointly, I have



Owens 307

\ .

^^**??. 50th potile

'?.. (school race) 90thpctile^ *'?... (schoolrace) "??...

Neighborhood Concentrated Disadvantage

Figure 2a. Probability of earning a bachelor's

degree (BA) by neighborhood concentrated disad

vantage, plotted by percentile score on the school

racial composition index; estimates from Table 5, Model 8

presented a picture of the complex way these two contexts affect adolescents' prospects for high school and college graduation.

First, I find that neighborhood SES is a more robust predictor of college graduation than of

high school graduation, and different dimensions of neighborhood SES matter for each outcome.

Living in high-poverty neighborhood a with more black residents, more single mothers, and

higher poverty and unemployment rates reduces

the odds of high school graduation, while living among neighbors with higher educational attain

ment, income, and job rank increases the odds of

earning a BA. That neighborhood SES seems to

matter more for college graduation may reflect

the different resources needed for earning a BA

versus graduating from high school and that stu

dents may look to neighbors for information, motivation, and as role models for earning a BA.

Since Add Health only samples students who are still in high school at Wave I, neighborhood influ ence on high school graduation may be hard to

capture if many students from low-SES neighbor hoods have already dropped out.

Second, the relative deprivation of one's

neighborhood compared to schoolmates' neigh borhoods negatively predicts high school gradua tion, while only the absolute resources of one's

90th potile (school race)

2 / .

/ / / /

/50th / petite

-3 -2 ? -1 0 1 2 3 Neighborhood Educ and Occ Attainment

Figure 2b. Probability of earning a bachelor's

degree (BA) by neighborhood educational and

occupational attainment, plotted by percentile score on the school racial composition index; es

timates from Table 5, Model 10

neighborhood matters for earning a BA. This sug

gests that neighborhood backgrounds serve as

grounds for negative competition within high schools rather than as a supportive boost as ex

pected given past research showing the benefits of high-SES peers for students' goal setting and academic achievement. Neighborhood back

ground might influence selection into academic tracks or friend groups, which may be the basis for students' (limiting) expectations for them

selves. More research is needed on how knowl

edge of one's own and of others' neighborhoods within a school produces negative competition. The fact that relative deprivation of one's neigh borhood within the high school does not influence

college graduation presumably reflects the fact that earning a BA likely occurs away from high school peers and that the influence of the back

ground of high school peers is no longer relevant.

Finally, neighborhood characteristics still pre dict educational outcomes when school character

istics are taken into account, and the school

composition measures tested here do not seem to

be able to overcome the disadvantages of coming from a low-SES neighborhood. Neither school

average SES and expectations nor school racial

composition significantly predicted high school

graduation. For earning a BA, the SES and




expectations of one's schoolmates positively pre dicted success in addition to neighborhood educa tional or occupational attainment, suggesting that

messages and norms were transmitted in both con

texts. School racial composition also predicted

earning a BA, but in an unexpected way:

Attending high school with fewer white peers was beneficial. This suggests that racial composi tion may proxy school climate or structural char

acteristics that affect educational attainment. It

could, however, also be the case that after control

ling for neighborhood SES, attending school with more white peers is itself detrimental. While this contrasts with past research, it also demonstrates

the importance of accounting for multiple con

texts: Perhaps not controlling for neighborhood SES results in the benefits of living in a high SES neighborhood being attributed to attending school with more white peers.

Interaction effects between school and neigh borhood characteristics suggest (1) that the low ered odds of educational attainment associated

with coming from a neighborhood of concentrated

disadvantage are amplified in schools with more white and higher-SES peers and, conversely, (2) that adolescents from neighborhoods with higher educational and occupational attainment are even

more likely to graduate from high school if they attend school with higher-SES peers and from col

lege if they attend school with more white peers. That is, students from low-SES neighborhoods

perform worse when mixed in schools with higher SES and more white peers, and students from

high-SES neighborhoods get a boost from being with white and high-SES peers?they would have lower educational attainment among lower

SES and minority peers. For students from low

SES neighborhoods, it is better to be among fewer white peers, those with lower expectations for the

future, and those from lower-SES homes?to be

bigger frogs in smaller ponds, in some respects. These findings may be explained by school

structure and climate, which I cannot measure

here. While the findings suggest that mixing ado lescents together from various neighborhoods puts those from lower-SES neighborhoods at a disad

vantage, this likely depends on the quality of the school they are being mixed into. In addition, the data do not reveal how the students from lower-SES neighborhoods are integrated into

more white schools or schools with students

from higher-SES families or neighborhoods. While the data measure school composition, the

degree to which students are integrated within

classes, academic tracks, and peer groups is

unknown. Perhaps if students from lower-SES

neighborhoods had access to the same resources

as students from higher-SES neighborhoods within schools, I would not find the less favorable outcomes for students from low-SES neighbor hoods of attending schools serving more white

peers and those from higher-SES families and

neighborhoods. Add Health data cannot identify the mechanisms that underlie these associations, and future research should focus on developing a theory of joint neighborhood and school effects on educational outcomes that includes mecha

nisms. Policy makers should also be attuned to

the potential negative competition occurring among students of various backgrounds and deter

mine how resources can be equitably distributed.

The Add Health data set provides more infor mation about family, neighborhood, and school

contexts for a national sample than researchers

have previously had access to, providing a com

plex picture of what predicts adolescents' out

comes. However, the data set does have several

limitations. First, the data make identifying exog enous effects of neighborhood and school charac

teristics difficult. These analyses may be subject to selection bias and measurement error at the

individual, neighborhood, and school levels.

Like many other neighborhood and school effects studies, they do not rely on experimental data. In

addition, the design of the Add Health high school

sample does not include students from the same

neighborhood who attend different schools, criti

cal for assessing policy implications. But despite its limitations, this article illuminates patterns and illustrates potential approaches in considering the joint impacts of school and neighborhood con texts that should be built upon in future research.

This article bridges the neighborhood and school effects literatures to take into account ex

periences in both contexts. From a policy perspec

tive, the results in this article suggest that

integrating students from different neighborhoods must be done with attention to how students from

disadvantaged neighborhoods are integrated into a school's structure and climate. Overall, findings

imply that changing the school students attend while ignoring the characteristics of their neigh borhood does not eliminate (and may even exacer

bate) the disadvantages of coming from a low SES neighborhood. Jointly modeling the two con texts demonstrates that both contexts matter, their



Owens 309

influences interact, and both contexts should be

considered by policy makers who hope to reduce the racial/ethnic and SES achievement gaps.

ACKNOWLEDGEMENTS I gratefully acknowledge the contributions of

Christopher Jencks, Robert Sampson, Kathryn Edin, Daniel Schr?ge, Greg Duncan, Paul Jargowsky, and

Filiz Garip. Three anonymous reviewers and the editor

provided valuable suggestions.

FUNDING This research has been supported by a graduate fellow

ship from the National Science Foundation IGERT pro

gram, Multidisciplinary Program in Inequality & Social

Policy, at Harvard University (Grant No. 0333403). This

research uses data from the National Longitudinal Study of Adolescent Health (Add Health), a program project directed by Kathleen Mullan Harris and designed by J.

Richard Udry, Peter S. Bearman, and Kathleen Mullan

Harris at the University of North Carolina at Chapel Hill and funded by grant P01-HD31921 from the

Eunice Kennedy Shriver National Institute of Child

Health and Human Development, with cooperative fund

ing from 23 other federal agencies and foundations.

Special acknowledgment is due Ronald R. Rindfuss

and Barbara Entwisle for assistance in the original

design. Information on how to obtain the Add Health

data files is available on the Add Health Web site

(http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

NOTES 1. The response rate at Wave III was between 76

percent and 79 percent for students in 7th to 11th

grade and 70% for students in 12th grade. Potential

bias due to nonresponse at Wave III is estimated

to be about 1 percent, according to National

Longitudinal Study of Adolescent Health (Add

Health) documentation.

2. In the Add Health high school sample, all students

from one census tract attend the same high school

because only one high school per city was sampled. Students from the same tract as students in the sam

ple but who attended a high school not selected by Add Health are not in the sample.

3. A separate question asks about GED or high school

equivalency diplomas, so these results refer only to

a traditional high school diploma. 4. I used Stata's ice package to conduct multiple impu

tation. I then ran analyses on five complete data sets

and used the formulas provided by King et al. (2001)

to generate estimates for coefficients and standard er

rors across the five data sets. On average, the vari ance in point estimates was very small, less than

1E-6.

5. Models were estimated in the R statistical package

using the lmer (linear mixed-effects regression)

package. 6. I cannot use this sample to estimate the proportion of

variance explained by neighborhood alone because

the sampling frame is school based. That is, the sam

ple does not include students who live in the same

neighborhoods as sampled students but who attend

different schools.

7. I do not control the parent's own occupation, as it is not asked of parents, and therefore the coefficient for

the neighborhood educational and occupational attainment index, which includes neighborhood measures of occupation type, may be upwardly biased.

8. I tested interactions between the neighborhood indi

ces and individual characteristics to see if neighbor hoods matter more for boys than for girls, for blacks

than for whites, by parent education, for those on

public assistance, or for long-time neighborhood res

idents. The only significant interaction is that, for

earning a BA, the influence of neighborhood educa

tional and occupational attainment is three times

higher for white than for black students. This sug

gests that black students, regardless of their personal or family background characteristics, may be less

able to access the benefits of living in a neighborhood of high educational or occupational attainment, per

haps because social networks within neighborhoods are racially segregated.

9. In reality, multiple and complex interactions occur

between family, school, and neighborhood contexts.

I investigated if the interactions between neigh borhood and school characteristics actually reflected

interactions between individual and school character

istics. Overall, the interactions between neighbor hood and school characteristics hold when

including interactions between individual and school

characteristics for both high school graduation and

earning a BA, and interactions between school and

individual characteristics are not significant. A few

exceptions follow: (1) For high school graduation, the interaction term between neighborhood concen

trated disadvantage and school race is still significant and negative, and an interaction term between pa rents' expectations and school race is significant and positive, suggesting that students whose parents have high expectations do even better in more white

schools. (2) For earning a BA, an interaction term

between student's expectations and school race is

also significant and positive, suggesting that students

with high expectations perform even better in schools

with more white peers. The interaction terms between

both neighborhood concentrated disadvantage and




educational and occupational attainment and school

race are still significant. (3) For earning a A , the

interaction term between neighborhood concentrated

disadvantage and school race is still significant and

positive, and an interaction term between parents' educational attainment and school race is also signifi cant and positive, suggesting that students whose pa rents have higher educational attainment perform even better in schools with more white peers.

REFERENCES Aaronson, Daniel. 1998. "Using Sibling Data to Estimate

the Impact of Neighborhoods on Children's

Educational Outcomes." Journal of Human Resources

33(4):915-46. Ainsworth, James W. 2002. "Why Does It Take

a Village? The Mediation of Neighborhood Effects on Educational Attainment." Social Forces

81(l):117-52. Alexander, Karl and Bruce K. Eckland. 1975.

"Contextual Effects in the High School Attainment

Process." American Sociological Review 40(3):402 16.

Alwin, Duane F. and Luther B. Otto. 1977. "High School Context Effects on Aspirations." Sociology

of Education 50(4):259-73. Brooks-Gunn, Jeanne, Greg J. Duncan, Pamela Kato

Klebanov, and Naomi Sealand. 1993. "Do

Neighborhoods Influence Child and Adolescent

Development?" American Journal of Sociology

99(2):353-95. Bryk, Anthony S. and Mary Erina Driscoll. 1988. The

High School as Community: Contextual Influences, and Consequences for Students and Teachers.

Madison, WI: National Center on Effective

Secondary Schools.

Card, David and Jesse Rothstein. 2006. "Racial

Segregation and the Black-White Test Score Gap."

Working Paper No. 12078, National Bureau of

Economic Research, Cambridge, MA.

Catsambis, Sophia and Andrew A. Beveridge. 2001.

"Neighborhood and School Influences on the

Family Life and Mathematics Performance of

Eighth-Grade Students." Report No. 54. Center for

Research on the Education of Students Placed at

Risk, Baltimore, MD.

Coleman, James S., Ernest Q. Campbell, Carol J.

Hobson, James McPartland, Alexander M Mood, Frederic D. Weinfeld, and Robert L. York. 1966.

Equality of Educational Opportunity. Washington, DC: U.S. Government Printing Office.

Connell, James P. and Bonnie L. Halpern-Felsher. 1997.

"How Neighborhoods Affect Educational Outcomes

in Middle Childhood and Adolescence: Conceptual Issues and an Empirical Example." Pp. 174-99 in

Neighborhood Poverty: Context and Consequences

for Children, edited by J. Brooks-Gunn, G. J.

Duncan, and J. L. Aber. New York: Russell Sage. Cook, Thomas D. 2003. "The Case for Studying

Multiple Contexts Simultaneously." Addiction

98(Suppl. l):151-55.

Cook, Thomas D., Melissa R. Herman, Meredith

Phillips, and Richard A. Settersten, Jr. 2002.

"Some Ways in Which Neighborhoods, Nuclear

Families, Friendship Groups, and Schools Jointly Affect Changes in Early Adolescent Development." Child Development 73(4): 1283-309.

Crane, Jonathan. 1991. "The Epidemic Theory of

Ghettos and Neighborhood Effects on Dropping Out and Teenage Childbearing." American Journal

of Sociology 96(5): 1226-259.

Crosnoe, Robert. 2009. "Low-Income Students and the

Socioeconomic Composition of Public High Schools." American Sociological Review

74(5):709-30. Davis, James A. 1966. "The Campus as a Frog Pond: An

Application of the Theory of Relative Deprivation to

Career Decisions of College Men." American

Journal of Sociology 72(1):17-31. Duncan, Greg J. 1994. "Families and Neighbors as

Sources of Disadvantage in the Schooling Decisions of White and Black Adolescents."

American Journal of Education 103(1): 20-53.

Ensminger, Margaret E., Rebecca P. Lamkin, and Nora

Jacobson. 1996. "School Leaving: A Longitudinal

Perspective Including Neighborhood Effects."

Child Development 67(5):2400-416.

Espenshade, Thomas J., Lauren E. Hale, and Chang Y.

Chung. 2005. "The Frog Pond Revisited: High School Academic Context, Class Rank, and Elite

College Admission." Sociology of Education

78(4):269-93. Foster, E. Michael and Sara McLanahan. 1996. "An

Illustration of the Use of Instrumental Variables: Do Neighborhood Conditions Affect a Young Person's Change of Finishing High School?"

Psychological Methods 1(3): 249-60.

Gelman, Andrew and Jennifer Hill. 2007. Data Analysis

Using Regression and Multilevel/Hierarchical

Models. Cambridge, England: Cambridge University Press.

Halpern-Felsher, Bonnie L., James P. Connell, Margaret B. Spencer, J. Lawrence Aber, Greg J. Duncan, Elizabeth Clifford, Warren E. Crichlow, Peter A.

Usinger, and Steven P. Cole. 1997. "Neighborhood and Family Factors Predicting Educational Risk

and Attainment in African American and White

Children in Adolescents." Pp. 146-73 in

Neighborhood Poverty: Context and Consequences

for Children. Vol. 1, edited by J. Brooks-Gunn, G. J.

Duncan, and J. L. Aber. New York: Russell Sage. Hanushek, Eric A., John F. Kain, Jacob M. Markman,

and Steven G. Rivkin. 2003. "Does Peer Ability



Owens 311

Affect Student Achievement?" Journal of Applied Econometrics 18(5):527-44.

Hanushek, Eric A., John F. Kain, and Steven G. Rivkin.

2002. "New Evidence about Brown v. Board of Education: The Complex Effects of School Racial

Composition on Achievement." Working Paper No.

w8741, National Bureau of Economic Research,

Cambridge, MA.

Hoxby, Caroline. 2000. "Peer Effects in the Classroom:

Learning from Gender and Race Variation."

Working Paper No. 7867, National Bureau of

Economic Research, New York, NY.

Jencks, Christopher and Susan E. Mayer. 1990. "The

Social Consequences of Growing Up in a Poor

Neighborhood." Pp. 111-86 in Inner-City Poverty in the United States, edited by L. E. Lynn, Jr., and

M. G. H. McGeary. Washington, DC: National

Academy Press.

King, Gary, James Honaker, Anne Joseph, and Kenneth

Scheve. 2001. "Analyzing Incomplete Political

Science Data: An Alternative Algorithm for

Multiple Imputation." American Political Science

Review 95(l):49-69.

Mayer, Susan E. 1991. "How Much Does a High School's Racial and Socioeconomic Mix Affect

Graduation and Teenage Fertility Rates?" Pp. 321

41 in The Urban Underclass, edited by C. Jencks

and P. E. Peterson. Washington, DC: Brookings Institution.

Mayer, Susan E. and Christopher Jencks. 1989.

"Growing Up in Poor Neighborhoods: How Much

Does It Matter?" Science 243(4897): 1441-445.

Meyer, John W. 1970. "High School Effects on College Intentions." American Journal of Sociology 76(1): 59-70.

Orfield, Gary and Susan E. Eaton. 1997. Dismantling

Desegregation: The Quiet Reversal of Brown v.

Board of Education. New York: New Press.

Phillips, Meredith and Tiffani Chin. 2004. "School

Inequality: What Do We Know?" Pp. 467-520 in

Social Inequality, edited by K. M. Neckerman.

New York: Russell Sage. Rivkin, Steven G. 2000. "School Desegregation,

Academic Attainment, and Earnings." Journal of Human Resources 35(2):333-46.

Rumberger, Russell W. and Gregory J. Palardy. 2005.

"Does Segregation Still Matter? The Impact of

Student Composition on Academic Achievement in

High School." Teachers College Record

107(9): 1999-2045.

Sampson, Robert J., Jeffrey D. Morenoff, and Thomas

Gannon-Rowley. 2002. "Assessing 'Neighborhood Effects': Social Processes and New Directions in

Research." Annual Review of Sociology 28:443-78.

Sampson, Robert J. and William Julius Wilson. 1995.

"Toward a Theory of Race, Crime, and Urban

Inequality." Pp. 39-54 in Crime and Inequality, edi

ted by J. Hagan and R. D. Peterson. Stanford, CA:

Stanford University Press.

Stark, Oded and J. Edward Taylor. 1989. "Relative

Deprivation and International Migration."

Demography 26(1): 1-14.

Wilson, William Julius. 1987. The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy.

Chicago: University of Chicago Press.

BIO

Ann Owens is a graduate student in the Department of

Sociology and Social Policy at Harvard University. Her research interests center around inequality among

neighborhoods, educational outcomes, and families.

She is currently engaged in projects on neighborhood

mobility, public housing policy, and gentrification in the United States.



neighborhoods and schools as competing and reinforcing contexts for educational attainment

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