Contextual Explanations of School Choice

Douglas Lee LauenUniversity of North Carolina at Chapel Hill

Participation in school-choice programs has been increasing across the country since the early

1990s. While some have examined the role that families play in the school-choice process,

research has largely ignored the role of social contexts in determining where a student attends

school. This article improves on previous research by modeling the contextual effects of ele-

mentary schools and neighborhoods on high school enrollment outcomes using population-

level geocoded administrative data on an entire cohort of eighth graders from one of the

largest urban school districts in the United States. The results of hierarchical multinomial logis-

tic models suggest that the contextual effects of percentage black, poverty, and neighborhood

concentrated disadvantage reduce the likelihood of students attending private or elite public

high schools. Students in schools with high average achievement are less likely to attend selec-

tive-enrollment magnet schools, perhaps because of a "frog pond" effect. Finally, the study

found evidence of peer effects on attending non-neighborhood schools. Together, these find-

ings suggest a new way of conceptualizing the causes of school choice at a time when such

programs are becoming more prevalent.

Sociology of Education 2007, Vol. 80 (July): 179–209 179

Throughout most of the 20th century inthe United States, home address essen-tially determined where a child attend-

ed school. Beginning with school desegrega-tion efforts in the 1960s, mandatory and vol-untary school reassignment policies (e.g.,busing and magnet schools) weakened theconnection between residence and school-ing. Today, increasingly large fractions of stu-dents are opting out of neighborhoodschools for alternatives such as charterschools, distance learning, home schooling,and private schools funded by public vouch-ers, (Bielick & Chapman 2003).

The erosion of support for the neighbor-hood school model of student enrollment is aresult of policies to promote racial integrationand to encourage innovation in an education-al system that has been subject to constantcriticism and demands for reform (Berliner &Biddle 1997). In turn, the deregulation ofschooling assignment policies means less pre-

dictability and more mobility for both familiesand organizations within a school system. Themobility of individuals can have consequencesfor organizations and relationships (Coleman1988), schools (Hirschman 1970), and publicfinance (Peterson 1981; Tiebout 1956).Finally, it is also perhaps an indication thatschools, like neighborhoods, are becomingcommunities of “limited liability” (Janowitz1967) in which families engage with institu-tions and form relationships but are quick toexit if their needs are not met. The decline ofthe neighborhood school model suggests anew role for students in a school system—onein which students are treated less as subjectsin a one-size-fits-all system and more as clientswhose individual needs must be addressed bya wide variety of schooling options that maxi-mize freedom of choice and multiple path-ways to social mobility.

The increasing deregulation of the educa-tional sector raises the question of how to

180 Lauen

explain the causes of school selections. Theanswer to this question is critical in under-standing the different ways in which school-choice policy may affect students in particularcommunities and the implications forbetween-school segregation and achieve-ment variation. One approach to the school-selection question is to focus on the prefer-ences and actions of families. It could be, forexample, that in a struggle for status, parentsand students compete for advantageousplacements in an educational marketplace, agame in which those with higher status, high-er income, better education, and more infor-mation would likely win.

Another approach to explaining schoolselections is to analyze how students’ propen-sities to exit neighborhood schools vary in dif-ferent contexts. It could be that students inparticular types of neighborhoods or schoolsmay be more likely to attend alternatives totheir assigned neighborhood schools. It hasbeen argued, for example, that students indisadvantaged neighborhoods would be like-ly to benefit from the expansion of schoolchoice because such students would be giventhe opportunity to escape substandardschools (Friedman 1955).

The family plays an important role in pro-viding resources to children within the house-hold (Conley 2004) and managing children’seducational careers (Furstenberg et al.1999), but as children reach adolescence,schools, neighborhoods, and peers becomeincreasingly salient. Schools, through varioussocialization and sorting mechanisms, shapethe educational careers of students, providingthem with course-taking and extracurricularopportunities, which, in turn, affect the stu-dents’ ability to access high-status destina-tions (e.g., elite high schools, four-year col-leges, and high-wage jobs). Neighborhoodsalso shape the educational careers of studentsthrough access to criminal subcultures,extrafamilial mentors and youth advocates,peer groups that have been educationally andoccupationally successful, and geographiccentrality that may permit feasible commutesto a wide range of schools.

Although there have been numerous stud-ies of school and neighborhood effects onstudents’ achievement and attainment

(Ainsworth, 2002; Bryk, Lee, and Holland1993; Harding, 2003; Lee and Burkam 2003),little is known about how schools and neigh-borhoods shape the school-selection process.This article presents a study whose goal wasto test the ability of contextual explanationsto account for schooling enrollment out-comes. In other words, the study askedwhether the same child from the same familywould attain a different high school destina-tion if her or his elementary school or neigh-borhood of residence were changed.Specifically, controlling for student and fami-ly covariates, it tested the adequacy of bothelementary school and neighborhood con-textual factors in explaining variation in thehigh school destinations of an entire cohort ofeighth graders. The data for this analysiscame from the 2000 census and population-level geocoded administrative data on school-children from Chicago, a large city with arapidly expanding menu of educationaloptions, high residential segregation, and apredominately disadvantaged student popu-lation. The study improved on previousschool-choice research by using population-level data in a multinomial multilevel modelto increase the efficiency of variance esti-mates.

This article begins with a summary of pre-vious research on school choice. It then turnsto a discussion of the features of the socialcontext that could be theoretically relevant tothe study of school choice.

SCHOOL CHOICE ANDCONTEXTUAL EFFECTS

Evaluations of school-choice experimentshave been numerous and hotly contested(Green, Peterson, and Du 1997; Howell andPeterson 2002; Krueger and Zhu 2004; Rouse1998; Witte 2000). Studies on Chicagoschools that have attempted to estimate theeffect of attending a school of choice net ofselection effects have presented mixedresults. A study of three charter schools foundsome positive results in the early grades butnot in the later grades (Hoxby 2005).Students who were lotteried into a Chicago

Contextual Explanations of School Choice 181

magnet high school or program were nomore likely to graduate, have higher gains intest scores, or have higher credit-accumula-tion rates, but were less likely to be arrestedand less likely to report disciplinary incidences(Cullen, Jacob, and Levitt 2003). With theexception of career academy students, thosewho participated in the city’s extensive openenrollment program were no more likely tograduate from high school net of selectioneffects (see Cullen et al. 2005). Althoughthese studies suggested that there may besome benefit from attending a school ofchoice in Chicago, research has suggestedthat there are racial, ethnic, and class dispari-ties in the propensity to choose schools. Aconsistent finding from the choice-disparityliterature is that the disadvantaged are lesslikely to exercise school choice. For example,one study found that low-income and minor-ity parents are less aware of magnet programs(Henig 1995). Another found that amonglow-income parents, those with higher edu-cational levels are more likely to exerciseschool choice for their children (Lee,Croninger, and Smith 1996). Still anotherstudy found that parents with higher socioe-conomic status and white parents are morelikely to exercise school choice (Teske andSchneider 2001).

One reason why disadvantaged familiesare less likely to exercise school choice is thatminority and low-income parents may havelimited access to useful information aboutschools through their social networks (Teskeand Schneider 2001). More advantaged par-ents, on the other hand, generally have bet-ter knowledge that is relevant to choosingschools (Archbald 2000) and about objectivemeasures of school quality (Schneider et al.1998). Another reason why the disadvan-taged may be less likely to attend non-neigh-borhood schools may be that middle-classwhite parents, by virtue of their cultural capi-tal, face an advantage when negotiating edu-cational bureaucracies and in relating toteachers (Lareau 1989).

This research suggests that the family playsa central role in negotiating the school-choiceprocess. Because of the decentralized natureof the U.S. educational system, parents arefree to shop for school districts through resi-

dential mobility and to advocate on behalf oftheir children for favorable course and schoolplacements. Transition points, such as thosebetween primary and secondary school, arecritical junctures when choices and familyinterventions become particularly important(Baker and Stevenson 1986). As a result, theinfluence of particular family managementstrategies (Furstenberg et al. 1999) on theschool-choice process is likely to be particu-larly salient during the transition from ele-mentary to secondary school. Research hasfound, for example, that parents who exert ahigh degree of control over the managementof their children’s educational careers aremore likely to be successful in placing theirchildren in non-neighborhood schools and inkeeping them in these schools once they areenrolled (Wells and Crain 1997).

While past research addressed the familyas an important context from which to ana-lyze school-choosing behavior, the role thatneighborhood and school contexts play inshaping schooling enrollment outcomes haslargely been unaddressed. This omissionassumes that the school-choice process ispurely a family affair—that factors, such asschool staff, geographic location, school andneighborhood poverty and segregation, andtransportation constraints, are inconsequen-tial.1 By controlling for the wide variance inthe availability of public non-neighborhoodchoice options across rural, suburban, andurban school districts in a nationwide sample,Schneider, Schiller, and Coleman (1996)found that black and Hispanic students andthose whose parents have lower educationallevels have a higher propensity to attend pub-lic nonassigned schools. This finding suggeststhat taking into account the geographic vari-ance in the opportunity to exercise schoolchoice may overturn some widely held beliefsabout the role that race, ethnicity, andparental education plays in the school-choiceprocess.

Allocation theory provides an importantlens through which to view the influence ofschools and neighborhoods on the selectionof high schools (Kerckhoff 1976). At the indi-vidual level, guidance counselors and teach-ers place students in course-taking levels onthe basis of test scores, behavior, and grades.

182 Lauen

Parental social status (Lareau 1989; Oakes1985; Rosenbaum 1976) and older peers caninfluence the aspirations of adolescents (Shawand McKay 1942). At the institutional level,scholars have analyzed institutional effectsonce the effects of individual characteristicshave been taken into account (Dreeben1994; Kerckhoff 1976). Here, it is importantto distinguish between compositional andcontextual effects. Compositional effects aredifferences between subgroups that arebased on the characteristics of the individuals.Contextual effects are differences betweensubgroups that are attributable to emergentgroup-level properties (such as social interac-tion that reinforces norms of behavior), hold-ing compositional differences between sub-groups constant.

Schools and neighborhoods are two con-texts that are especially relevant to the edu-cational development of adolescents. Whileidentifying school and neighborhood effectson outcomes for adolescents is methodologi-cally challenging and a matter of extensivescholarly debate, because these contexts pro-vide various levels of access to influentialadults, peers, and structures of opportunity,one may expect that schools and neighbor-hoods should influence students’ enrollmentoutcomes. The following section presents atheoretical account of how particular featuresof school and neighborhood contexts affectschool choice.

RELEVANT FEATURES OF THESOCIAL CONTEXT

Previous research on school, peer, and neigh-borhood effects on student outcomes, suchas academic achievement, teenage pregnan-cy, and academic attainment (Browning,Leventhal, and Brooks-Gunn 2005; Bryk andThum 1989; Harding 2003; Lee and Bryk1988), has suggested that social contextsmatter for adolescents’ development. Here, Ihighlight three features of the social contextthat are relevant to the study of schoolchoice: social and economic disadvantage,academic press, and peer effects.

Social and Economic Disadvantage

Decades of sociological research on social andeconomic isolation have shown that segrega-tion is associated with higher rates of crime,unemployment, and teenage childbearingand lower academic attainment (Bursik 1988;Elliott et al. 1996; Harding 2003; Shaw andMcKay 1942). The associations between con-centrated disadvantage and such social prob-lems have been attributed to social disorgani-zation (Bursik 1988; Shaw and McKay 1942),industrial restructuring (Wilson 1987), andthe lack of interracial contact (Massey andDenton 1993). In Wilson’s (1987) account,concentrated disadvantage creates the condi-tions in which inner-city residents becomedisconnected from access to useful informa-tion about jobs, hiring requirements, and therelevance of education to chances for mobili-ty. Moreover, schools, as social institutionsthat are sustained by middle-class culture,deteriorate as the class structure of the neigh-borhood changes.

By this account, I can posit that concen-trated disadvantage would be negativelyassociated with the propensity to exerciseschool choice: In socially isolated neighbor-hoods, students would lack the socialresources to seek, apply for, and travel toschools of choice owing to the tenuous con-nection between schooling and social mobili-ty in such neighborhoods. Because schools indisadvantaged neighborhoods are likely to beperceived as being of low quality, however,students in disadvantaged neighborhoodswould perhaps have a greater incentive toexercise choice than would students in moreaffluent neighborhoods. In fact, a central the-sis of the school-choice literature is that stu-dents in disadvantaged communities mayaccess educational opportunity by choosingto attend schools in more affluent communi-ties (Friedman 1955). This thesis suggeststhat concentrated disadvantage would bepositively associated with the propensity toexercise school choice.

Academic Press

Educational research has stressed the associa-tion between “academic press” and student

Contextual Explanations of School Choice 183

outcomes, such as the achievement of testscores and graduation from high school.Research on Catholic schools, for example,has called attention to how a constrained aca-demic curriculum and the normative dimen-sions of schooling affect students’ motiva-tions to learn (Bryk et al. 1993; Lee and Bryk1988; Lee et al. 1998). In schools with astrong academic press, teachers and adminis-trators set high expectations, eliminate low-level courses, and restrict students’ curricularchoice. Students in schools with greateraccess to more rigorous curricula are likely tobe better prepared for more selective highschool placements. Moreover, students fromcompetitive schools could translate into notonly higher achievement for a particular stu-dent, but a higher likelihood of exercisingschool choice. For example, high-achievingpeers could push students to take algebra inthe eighth grade or to apply to “reach” highschools.

Increasing academic press could, however,have the unintended consequence of reduc-ing some students’ chances of attending highschools of their choice. It is possible that thescramble for advantageous high school place-ments could be subject to the same “frogpond” effects (Davis 1966) that are present inthe graduate school and college admissionsprocess. Research has found that students inelite public high schools are at a disadvantagebecause of the emphasis placed on class rankin the admissions process (Attewell 2001). Inelementary schools with a surplus of qualifiedcandidates for elite high schools, therefore,teachers and guidance counselors mayneglect students who would be academicstars in other schools. Moreover, students inelite elementary schools may suffer blows totheir self-esteem that they would not experi-ence in less elite settings. In sum, researchsuggests that not only a student’s academicpotential, but her or his position in a peckingorder, is predictive of academic success.

Peer Effects

Economic theory would predict that familiesmake schooling decisions for their children onthe basis of a rational assessment of the costsand benefits of the various options that are

open to them. A shortcoming of the standardeconomic approach to decision making isthat it ignores the endogeneity of prefer-ences—that students’ preferences are sociallyconstructed through interaction with peersand other significant persons. A key finding ofthe Coleman report (Coleman et al. 1966)was that students’ achievement is stronglyrelated to the educational backgrounds andaspirations of other students. Status attain-ment research has demonstrated the impor-tant role that peers’ aspirations play in shap-ing students’ aspirations (Hout and Morgan1975; Sewell, Haller, and Portes 1969). Anemerging field of economic theory addresseshow social influence shapes decision making(Brock and Durlauf 2001; Manski 1993b). Afinding from this field suggests that adoles-cent econometricians make schooling deci-sions on the basis of the experience of theirpredecessors (Manski 1993a). Therefore, onemay view the school-choice aspirations andmobility histories of students as a form ofsocial capital (Coleman 1988) that studentsmay use when they make their own school-enrollment decisions.

In summary, research has shown that cer-tain features of the social context are relevantto educational aspirations and outcomes.Social and economic disadvantage, academicpress, and peer effects are all hypothesized toaffect students’ school-choice enrollment out-comes. Therefore, the analysis presented hereestimates the size and direction of these con-textual effects net of student-level covariates.

DATA AND METHODS

Data for this study came from geocoded pop-ulation-level confidential administrative dataon children in the Chicago Public Schools.This article focuses on a cohort of studentswho were eighth graders in the spring of2000 and who, according to administrativerecords, were enrolled in a Chicago public orprivate2 high school in spring 2001. Virtuallyall students in the eighth grade in 2000 wereenrolled in an elementary school (elementaryeducation in Chicago is predominately fromkindergarten to Grade 8; there were only 15middle schools at the time of the study).

184 Lauen

Because the purpose is to predict high schoolchoices in Chicago, the study omitted stu-dents whose choice of high school was con-strained or predetermined (e.g., studentswho were retained in grade for low academicperformance). Student-level data about thiscohort were matched through administrativerecords with the characteristics of each stu-dent’s elementary school, neighborhood highschool, and the high school that the studentactually attended. In addition, students’records were linked to 2000 census datathrough the census tract in which the stu-dents resided. After sample-selection ruleswere applied, the data that were available foranalysis represented 60 percent of the eighthgraders in the Chicago Public Schools, livingin 84 percent of the census tracts, and attend-ing 91 percent of the public elementaryschools in Chicago during spring 2000.3

Dependent Variable

To test whether contextual factors predictenrollment in different types of destinations, Idivided high school choices into four mutual-ly exclusive categories: private school,4 publicselective-enrollment high school, non-neigh-borhood high school,5 and neighborhoodhigh school.6 The descriptive results indicatethat 6 percent of this cohort attended privateChicago high schools, 10 percent attendedpublic selective high schools, 43 percentattended non-neighborhood schools, and 41percent attended their assigned neighbor-hood high schools. Because students aresometimes chosen by high schools on thebasis of individual characteristics (e.g., testscores and race-ethnicity) and their families’willingness and ability to pay, I included theseindividual-level controls to avoid biasing thecontextual effects. I discuss the rationale forincluding these controls and describe theschool and neighborhood contextual effectsin the next section. Descriptive statistics for allthe variables are presented in Table 1.

Independent Variables

Student and Family CharacteristicsBecause previous research (Reay and Ball1997; Saporito and Lareau 1999) indicated

that participation in school choice varies byrace, ethnicity, and class, I included a vector ofindicators for whether a student was white,black, Latino, or Asian and whether the stu-dent’s family income was low enough to qual-ify him or her for a free or reduced- pricelunch. These characteristics can play severaldifferent roles in the choice process. Someschools and programs market themselves toparents of “at-risk” youths (black or Latinoyouths, for example), and magnet schoolsmust achieve court-ordered racial balancingimperatives.7 Students who receive a free orreduced-price lunch may have a more difficulttime accessing private schools, for example,because of the lack of parental resources. Inaddition to these factors, research has shownthat regardless of social background, parentalmotivation is particularly salient in predictingparticipation in school choice (Wells and Crain1997). Therefore, to proxy for prior parentalmotivation to manage the child’s educationalcareer, I controlled for whether a studentopted into his or her elementary school.Finally, I controlled for the number of schoolsa student had attended in the previous fiveyears to capture the extent of social and eco-nomic disruption in a student’s family.

Since students with particular learning oremotional disabilities are less likely to partici-pate in school-choice programs or may bemore likely to choose one school over another,depending on the types of services that are pro-vided, I controlled for whether a studentreceived special educational services. I also con-trolled for students’ academic confidence andachievement through two measures: (1)whether the student was aged 14 or older inthe eighth grade (a measure of whether thestudent had been held back or started schoollate) and (2) the student’s score on the eighth-grade mathematics test.8 Being old for one’sgrade is likely to inhibit social adjustment andself-confidence, which may reduce the chancesof participating in school choice. High testscores, on the other hand, can be viewed as anindication of academic motivation, which mayenhance students’ and parents’ willingness totake academic and social risks. In addition, testscores are highly predictive of attending selec-tive-enrollment schools because such schoolschoose students largely on the basis of such

Contextual Explanations of School Choice 185Ta

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Contextual Explanations of School Choice 187

scores. Finally, I controlled for gender becausestudies have shown that adolescent boys, par-ticularly those from minority groups, lagbehind adolescent girls on some measures ofacademic achievement and attainment(Leventhal and Brooks-Gunn 2004).

Quality of the Neighborhood High SchoolBecause the quality of the assigned neighbor-hood school (the “default” choice) is likely tobe related to a student’s propensity to attend anon-neighborhood school, I controlled for thepercentage of students at national norms, thepercentage of students who received free orreduced-price lunches, and the size of the stu-dent’s assigned neighborhood high school (thenumber of students enrolled in Grades 9–12).9

Elementary School Characteristics Tomeasure the contextual effects of elementaryschool on the probability of attending a non-neighborhood high school, I entered a vectorof elementary school characteristics as predic-tors to capture salient features of a student’s“sending school” experience. The percentageof students receiving free or reduced-pricelunches was used as a predictor of the socioe-conomic context. Also included was a mea-sure of the mobility of the school’s studentpopulation (the number of enrollments inand transfers out of a school after October 1,2000, divided by membership on October 1,2000). The average mathematics test scorewas used as a proxy for school academicpress. The analyses included an indicator vari-able for whether the student attended a mag-net school in the eighth grade, which may beconsidered another measure of elementaryschool academic press because these types ofschools are academically selective. Finally,two variables were introduced to capture theextent to which attendance at a non-neigh-borhood school is or is not a normative activ-ity: (1) a measure of high school choice in the“sending” elementary school among previouscohorts of graduates (the average percentageof students who attended a nonassignedpublic high school from the previous threecohorts (1997–98, 1998–99, and 1999-2000)and (2) the percentage of students whoopted into the elementary school as elemen-tary school choosers in the first place.

Neighborhood Characteristics To testwhether the neighborhood context predictsthe probability of attending a non-neighbor-hood school, I introduced tract-level predic-tors to measure the level of (dis)advantage ofa neighborhood; residential mobility; andwhether attending non-neighborhoodschools, both public and private, is sociallynormative. Specifically, the analyses includedmeasures of concentrated disadvantage(Wilson 1987) and concentrated affluence(Brooks-Gunn et al. 1993) to capture thesocial and economic environment of neigh-borhoods. Both measures were constructedfrom data from the 2000 census. Concentrat-ed disadvantage is the average of the z-scoresof the percentage below poverty, percentagereceiving public assistance, percentageunemployed, percentage of female-headedhouseholds with children, and percentageblack. Concentrated affluence is the averageof the z-scores of the percentage of familieswith incomes higher than $75,000, the per-centage of adults with a four-year collegedegree, and the percentage of the civilianlabor force who were employed in profes-sional or managerial occupations. Also on thebasis of census data, the analysis included ameasure of residential mobility and housingtenure. This measure is the average of the z-scores of the percentage of residents aged 5or older who resided in the same house in1995 and the percentage of owner-occupiedhomes.10

The analysis used two measures to indicatewhether attendance at non-neighborhoodschools is socially normative. The first mea-sure, from the 2000 census, is the percentageof elementary- and high school-aged childrenwho attended a private school. The secondmeasure, from the Chicago Public Schoolsadministrative files, is the average percentageof students who resided in the census tractfrom the previous three cohorts who attend-ed nonassigned public high schools.

Analytical Strategy

The study used a multilevel modelingapproach to estimate contextual effects(Goldstein 2003; Raudenbush and Bryk2002). Although it is possible to estimate

188 Lauen

unbiased and efficient estimates of coeffi-cients using ordinary least-squares (OLS)regression, standard errors of estimates aretypically too conservative because the depen-dence of grouped observations is ignored.Better estimates of standard errors are pro-duced by an OLS between-groups regressionanalysis, but since the designs depart from aperfect balance, estimates of fixed effectsbecome less and less efficient relative to thosein multilevel models. In addition, multilevelmethods provide superior estimates of vari-ance relative to OLS regression (Raudenbushand Bryk 2002).

To estimate the relationship between con-textual factors and schooling-enrollmentdecisions, I controlled for student-level fac-tors, such as test-score achievement, parentalmotivation, and income, to reduce the bias inorganizational-level effects that would occurif such factors were uncontrolled. Randomintercept models were estimated to examinethe reduction in between-context (i.e., schoolor neighborhood) variance, τ00, that is attrib-utable to compositional and contextual fac-tors. Because of the complexity of this analy-sis and computational limitations, all slopeeffects are fixed (i.e., they are not allowed tovary).

To facilitate comparison and statistical test-ing of the likelihoods of attending one type ofschool over another, I used multinomialregression analysis with a logit link.11 For eachalternative category m = 1, 2, 3, of the depen-dent variable, I model

ηmij = log

(ϕmijϕmij

)= log

(Prob(Rij = m)Prob(Rij = 4)

)

or the log-odds of falling into one of m cate-gories relative to the base category M. Theanalysis modeled the log-odds of attending oneof three alternatives—(1) a private high school,(2) a selective-enrollment public high school,and (3) a non-neighborhood public highschool—to the assigned neighborhood school,which is coded 4, or the base category:

Prob(Rij = 1) = ϕ1ij = private high schoolProb(Rij = 2) = ϕ2ij = selective enrollment

public high school

Prob(Rij = 3) = ϕ3ij = non-neighborhood public high school

Prob(Rij = 4) = ϕ4ij = assigned public neighborood high school

The analysis estimated two models. First,there is a model of students nested within ele-mentary schools:

ηmij = βoj(m) + ΣAm

a=0 βaj(m)(Xaij – X

–..)

+ ΣBm

b=0 αbj(m)(Xbij – X

–..)

+ ΣCm

c=0 γcj(m)(Wcij – W

–c.)

+ uoj(m)

where βaj(m) are the coefficients of students’characteristics; αbj(m) are the coefficients ofneighborhood high school characteristics;12

γcj(m) are the coefficients of elementary schoolcharacteristics; u0j(m) are school-specific ran-dom effects; and m indexes categories of thedependent variable, i indexes students, j index-es schools, and individual and school-level vari-ables are centered on their grand means.

Second, there is a model of students nest-ed within neighborhoods:

ηmik = βok(m) + ΣAm

a=0 βak(m)(Xaik – X

–..)

+ ΣBm

b=0 αbk(m)(Xbik – X

–..)

+ ΣCm

c=0 γck(m)(Wcik – W

–c.)

+ uok(m)

where βak(m) are the coefficients of students’characteristics; αbk(m) are the coefficients ofneighborhood high school characteristics;δdk(m) are the coefficients of neighborhoodcharacteristics; u0k(m) are neighborhood-specific random effects; and m indexes cate-gories of the dependent variable, i indexesstudents, k indexes neighborhood, and indi-vidual and neighborhood-level variables arecentered on their grand means.

Contextual Explanations of School Choice 189

In both the school and neighborhoodmodels, random effects are assumed to benormally distributed with means of zero andthe following variance/covariance structure:(

uoj(1)uoj(2)uoj(3)

)~N

[(000

),

(τ00(1)00(1) τ00(1)00(2) τ00(1)00(3)τ00(2)00(1) τ00(2)00(2) τ00(2)00(3)τ00(3)00(1) τ00(3)00(2) τ00(3)00(3)

)

Centering variables on their grand meansresults in an average effect across contextsthat adjusts for compositional differencesbetween schools and neighborhoods. Grand-mean centering also allows one to comparethe within-group student effect βwithin withthe group-level contextual effect βcontext(Raudenbush and Bryk 2002).

RESULTS

By grand-mean centering both the student-and contextual-level predictors, I estimate theadjusted log-odds of attending one of thethree destinations relative to a neighborhoodschool for the average student in the averageschool. Table 1, which presents descriptivestatistics of the variables in the analysis, showsthat 18,477 students nested within 399schools and 697 census tracts are available forthis analysis.

Modeling Variance in High SchoolDestination

A fully unconditional model with no predic-tors indicates that there is a great deal ofbetween-school and between-neighborhoodvariance in high school destinations toexplain. All the variance components (τ00)are close to or above 1, indicating that aboutfour logits separate 95 percent of schools orneighborhoods. The variance components inthe elementary model for private, selective-enrollment, and non-neighborhood schools,respectively, are 1.74, 1.78, and 1.05. Thesame components from the neighborhoodmodel are, respectively, 1.11, .92, and 1.13.

Although these variance componentsappear to be large, it is useful to decomposetotal variance into its between and withinportions. The fact that high school choice is a

categorical dependent variable, however,complicates the interpretation. I cannotdecompose the within and between varianceof a categorical variable, but I can insteadestimate an uncertainty index H = Σ

ipi log 1—

pi

(Shannon 1948) and decompose this mea-sure to a between and within component(Teachman 1980).

Assuming that population events can begrouped on two dimensions, X with I cate-gories and Y with J categories, and probabili-ties pij, i = 1 . . . , I and j = 1 . . . J, subject tothe constraint that Σ

iΣ

jpij = 1, I can define

the within and between sums of squares:

Hwss = Σi

Σj Pijlog

(p. j—pij

)

HBSS = HTSS – Hwss = Σi

Σj Pijlog

(pij

—pi.

—p.j

)

And the proportion of uncertainty that liesbetween groups:

PRUx.y = HBSS——HTSS

Using these formulas, I estimate that 20percent of the uncertainty in high schoolenrollment outcomes lies between elemen-tary schools. The proportion of uncertainty(PRUx.y) that lies between neighborhoods isalso 20 percent. Because a significant propor-tion of uncertainty in enrollment outcomeslies between school and neighborhood con-texts and there is a wide range of outcomesacross schools and neighborhoods, it appearsthat modeling context effects will be fruitful.

If elementary and neighborhood contextare unrelated to high school destinations, theinclusion of contextual-level factors would failto reduce between-school and between-neighborhood variance over and above thataccounted for by student-level predictors.Controlling for student-level characteristics,such as race/ethnicity, gender, and test-scoreachievement, accounts for between one-tenth and one-half the between-school andbetween-neighborhood variance in highschool destinations (see Tables 2 and 3).Including predictors of elementary schooland neighborhood context further reduces

190 Lauen

between-context variance by an additional17 to 38 percentage points, however, evi-dence that schooling decisions are driven bymore than just student-level factors. Giventhat school and neighborhood contextsreduce variance in high school destinations,these contexts matter. I now turn to whichfeatures of schools and neighborhoods arethe most relevant in explaining where a stu-dent attends high school.

Disadvantaged Contexts Depressthe Propensity for Choice

Net of compositional factors, the contextualeffects of percentage black, poverty, andaffluence are associated with at least oneenrollment outcome.13 For example, schoolswith higher percentages of blacks are less like-ly to send students to selective-enrollmenthigh schools, net of school composition andstudent-level covariates (see Table 2). A 10-percentage-point increase in the percentageblack in an elementary school reduces theodds of attending a selective-enrollmentschool by 11 percent (see Table 4, column 3).The corresponding figure in the neighbor-hood selective equation is 8 percent (seeTable 4, column 4).

Like the effect of percentage black, theeffects of poverty and mobility both inhibitthe likelihood that a student will attend a pri-vate or selective-enrollment high school. A10-percentage-point increase in the povertylevel of an elementary school reduces theodds of attending either a private or a selec-tive-enrollment school by about 10 percent(see Table 4, columns 1 and 3). A one-unitincrease in concentrated disadvantage (whichis a little less than 1 standard deviation)reduces the likelihood of attending either aprivate or a selective-enrollment high schoolby about one-quarter and one-third, respec-tively (see Table 4, columns 2 and 4).

Relative to students of other racial ethnicbackgrounds, white students are the mostlikely to attend private schools. Hispanics andAsians have roughly equal chances of attend-ing private schools, whereas blacks have thelowest predicted probabilities relative to theother racial/ethnic groups. The predictedprobabilities of attending a private school in a

more advantaged neighborhood (-1 standarddeviation in concentrated disadvantage) forwhite, Hispanic, Asian, and black students,respectively, are 28 percent, 15 percent, 14percent, and 9.5 percent. In less advantagedneighborhoods (+1 standard deviation inconcentrated disadvantage), the predictedprobabilities of attending a private school forwhite, Hispanic, Asian, and black students,respectively, are 18 percent, 9 percent, 9 per-cent, and 6 percent.

High-Achieving Schools andNeighborhoods Depress Choice

Whereas students’ math achievement exerts apositive effect on the propensity for choice,average elementary school and average neigh-borhood math achievement are negativelyassociated with it. Specifically, after students’background characteristics and the quality ofthe assigned neighborhood high school arecontrolled, the average student in a schoolwith a one-unit higher average math achieve-ment (a one grade-equivalent increase), faces36 percent lower odds of attending a selectivehigh school. The average student in a neigh-borhood with one-unit higher average mathscores has 28 percent lower odds of attendinga selective high school.

One explanation for the negative contex-tual effect of achievement on transition prob-abilities is the frog-pond effect (Davis 1966),in which it is better to be a big frog in a smallpond than a small frog in a big pond. Forexample, a student whose math score is atthe ninth-grade level who attends an elemen-tary school in which the average math scoreis at the seventh-grade level (i.e., a big fish ina small pond) has about a 25-percent chanceof attending a selective-enrollment school. Astudent whose math score is at the seventh-grade level and who attends an elementaryschool in which the average math score is atthe ninth-grade level (i.e., a small fish in a bigpond) has only a 1-percent chance of attend-ing a selective-enrollment school.

Despite this paradoxical achievementeffect, students in elementary magnet schoolsenjoy a selective school advantage net ofschool and student achievement. That stu-dents in elementary magnet schools have 79

Contextual Explanations of School Choice 191Ta

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192 LauenTa

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196 Lauen

percent higher odds of attending a selective-enrollment school suggests that features ofschool organization besides academic press,such as feeder-pattern relationships, may beat work.

Choice Flows Tend to Persist OverTime

The history of public school choice outflows inan elementary school or neighborhood exerts aresidual effect when compositional effects andthe contextual effects of percentage black,poverty, mobility, academics, and quality of theneighborhood high school are controlled. Itested the effects of three types of previousenrollment flows, percentage of public highschool choice from previous cohorts, percent-age of elementary choosers, and percentage ofthe census tract that attends a private elemen-tary or secondary school. The enrollment expe-rience of previous cohorts is among thestrongest and most consistent predictors of2001 enrollment patterns. A 10-percentage-point increase in the percentage who chose apublic high school of choice increased the oddsthat a member of the 2000–01 cohort wouldattend a public or private school choice bybetween one-quarter and one-half.

Two other enrollment-flow measures, thepercentage of students who opt into their ele-mentary school and the percentage whochoose a private school, are negatively relat-ed to attending selective and non-neighbor-hood schools. A 10-percentage-point increasein elementary choosers decreased the odds ofattending a non-neighborhood high schoolby 5 percent. (These analyses do not estimatea similar effect for private and selective highschools.) A 10-percentage-point increase inthe percentage who choose a private schoolin the census tract reduces the likelihood ofattending a selective or non-neighborhoodhigh school by about 15 percent.

High-Quality Assigned SchoolsDepress Choice

The quality of students’ assigned school affectthe propensity for choice (see Table 4).Students who are assigned to high schoolswith high levels of achievement are less likely

to exercise choice (odds of choice fallbetween one-fifth and one-third).14 Theeffects of the poverty of the neighborhoodhigh school are less consistent. Higher levelsof poverty of a neighborhood high schoolreduce the odds of attending a private highschool, but the results from the other equa-tions are not statistically significant and are, insome cases, in the opposite direction as thisresult.

DISCUSSION

In the study, I investigated whether elemen-tary school and neighborhood context influ-ence students’ propensities to exercise highschool choice. The high school destinations ofan entire eighth-grade cohort of ChicagoPublic School children were divided into fourcategories: assigned public, nonassignedpublic, public selective, and private. By esti-mating contextual effects within a multilevelmodeling framework, I tested hypothesesthat move beyond sociological theories thatfocus on students and families to focus on therole that schools and neighborhood play inthe choice process.

While the racial disparities in participa-tion in the choice process vary somewhat bydestination, the contextual effects of per-centage black and poverty on attending pri-vate and selective public high schools aremore consistent. Attending a predominant-ly black elementary school, living in a pre-dominantly black neighborhood, or living ina neighborhood with a high degree of con-centrated disadvantage decreases thechances of attending a selective-enrollmentschool. Affluence has the opposite effect,enhancing students’ chances of attending aselective school.

Second, neighborhoods have various levelsof school quality, which partially explainswhether a student exercises school choice.Students who live in neighborhoods withhigh-quality neighborhood schools, forexample, are less likely to attend a choiceschool outside their attendance area. Thisfinding suggests that, as in research on immi-gration and residential mobility, one mustconsider not only the characteristics of the

Contextual Explanations of School Choice 197

individuals, but the “push” and “pull” factorsthat are relevant to their decision making.

Finally, the results suggest that peer andwithin-school institutional effects are newavenues for research on school choice. Boththe experience of previous cohorts withschool choice and the achievement level ofpeers are relevant. While the effects of indi-vidual math achievement are strong and con-sistently positive, attending a high-achievingelementary school can actually hurt students’chances of attending a selective or non-neighborhood public high school. There areseveral possible explanations for the seeming-ly paradoxical effect of average math achieve-ment on attending a selective-enrollmenthigh school.

First, if there were quotas on the number ofstudents who were accepted from any oneelementary school, students in high-achievingelementary schools would be at a relative dis-advantage compared with those in low-achieving schools.15 Second, students mayself-select themselves out of contention forslots in elite high schools because their statusin the academic pecking order suggests a lowprobability of success. Third, elementaryschool teachers and counselors may informal-ly select students for slots in elite high schoolson the basis of a relative ranking of students.Students who are at the top of the academicpecking order may receive extra attention andfavorable recommendation letters, for exam-ple. Alternatively, students in elite elementaryschools may face stiffer grading standards,which may hurt them in competition with ele-mentary schools with more lax grading stan-dards because seventh-grade course marks areused in the selective-enrollment admissionsprocess. These explanations are consistentwith research that has shown that students inelite public high schools are at a collegeadmissions disadvantage because of theemphasis placed on class rank in the admis-sions process (Attewell 2001). Finally, neigh-borhoods with higher achieving schools andcommunities may confer noneducational ben-efits that may inhibit the likelihood that stu-dents will leave their neighborhoods and riskweakening these other positive attachments.

Although relative ranking among a set ofcurrent peers may be a disadvantage for

some students, attending an elementaryschool in which high school choice is thenorm has a strong and positive effect on thepropensity for choice. Whether a function ofpeer effects or informal institutional relation-ships, the percentage of prior cohorts whoattended a school of choice is an importantform of social capital that increases students’likelihood of exercising school choice.

In many respects, these findings conformwith the findings of school-choice researchthat has focused on students’ and families’disparities in participation. The results pre-sented here suggest that the home environ-ments and school experiences of certain at-risk populations present substantial barriers toexercising school choice. Students with edu-cational disadvantages (being old for theirgrade level, having low test scores, or havinghigh elementary school mobility) are less like-ly to attend any choice option. Consistentwith prior research on the growing gendergap between males and females in postsec-ondary attendance (Jacob 2002), the findingrevealed that boys are less likely than are girlsto exercise public school choice. Moreover,consistent with prior research, the findingsindicated that among the student populationof Chicago, those with highly motivated par-ents are better able to succeed in the educa-tional marketplace.

Unlike previous research, which has oftenfound that black students are less likely totake advantage of choice options, however,these findings suggest that a more complexnarrative may be necessary to describe therole of race and ethnicity in schooling enroll-ment decisions. The results revealed that raceis an inconsistent predictor of high schooldestination. In other words, the effect of racedepends on the destination in question.While blacks are less likely to attend privateschools (when poverty and math achieve-ment are controlled), the role of race inattending a selective-enrollment high schoolis less clear. Furthermore, blacks are no lesslikely to attend non-neighborhood highschools than are whites and Asians. In addi-tion, Hispanic students are more likely thanare blacks to attend private schools, but areless likely than blacks to attend non-neigh-borhood public schools.

198 Lauen

This study has shown that both school andneighborhood contexts matter for highschool enrollment outcomes. This findingraises the question of whether elementaryschool contextual estimates are biased by theomission of neighborhood contextual con-trols (and vice versa). A cross-classified modelwould require a much more parsimoniousspecification of contextual effects and individ-ual controls. The virtue of the analysis pre-sented here is that it tested some importanttheoretical concepts on an entire cohort witha fairly rich set of predictors and controls.Explicitly modeling the cross-nesting of stu-dents in different contexts is a logical nextstep for this type of analysis.

Furthermore, while the models presentedhere account for the nonrandom sorting ofstudents into schools and neighborhoods, across-sectional design limits the extent towhich a study, such as this one, can makecausal inferences. For example, because ofschool and residential mobility, students havehad different levels of exposure to school andneighborhood contexts, which in this articlewas measured only in the eighth grade. Thisstudy was not designed to assess “exposureto treatment” in this sense. That said, schoolcontext among mobile students does notvary a great deal. On average, when studentsin Chicago switch elementary schools, theydo not typically go to schools that are muchdifferent from the ones they left. Among stu-dents in the study who switched schoolsbetween fall 1998 and fall 1999, the correla-tion of each school’s poverty level is .35.Among students who switched schoolsbetween spring 1998 and spring 1999, thecorrelation of each school’s test score is .45.Therefore, while this study was not designedto test the longitudinal effects of context, Iwould not expect the results to be substan-tially different from those in the presentstudy.

The strength of the administrative dataused for this study are completeness withrespect to coverage of a population. Theweakness is that important features of stu-dents’ background that are often available insurvey samples, such as self-esteem, parentaleducation, and sibship, remain uncontrolled.Ideally, this model would include better mea-

sures of family background and more detailsabout the disposition of students’ applica-tions. This was a study of realized outcomes,not of students’ or families’ ambitions. Datalimitations prevented this analysis from distin-guishing between the factors that influence astudent to apply, be accepted, and enroll in aschool of choice. Although the outcomeprobably describes an underlying ambition ofa student or parent in most cases, it is possi-ble that the realized ambition is, in fact, a sec-ond or third choice. In other words, it is pos-sible that some of the students in this studywere “failed choosers” who wanted to attenda non-neighborhood school, but were reject-ed or ended up in a private school instead ofthe selective-enrollment school they pre-ferred. Moreover, it is possible that some fam-ilies who moved out of Chicago between theeighth and ninth grades did so for their chil-dren to attend higher-quality schools thanthey thought were available to their childrenwithin the city limits. These mobile familiescould also have moved exclusively for better-quality housing, proximity to jobs, crime, or amix of such factors. To assess the effect ofschool quality on residential mobility deci-sions would require a different type of datathan were available for this study. One wouldalso like to take students’ attendance, sev-enth-grade course grades, and scores on theselective-enrollment entrance test statisticallyinto account. Unfortunately, these data andinformation about the disposition of individ-ual students’ applications were not availableto me for this study.

It is possible that the contextual effects onhigh school destinations could vary by urban-icity. The findings reported here apply only tofamilies who chose to live in the city ofChicago and are likely to be generalizable toother urban areas. It is possible that theimplicit exclusion of families who chose to livein suburban areas because of their perceptionthat these areas had higher-quality schoolscould have biased the contextual effects pre-sented here. The typical solution to this prob-lem is to use national data sets to ensure gen-eralizability. Yet, national samples are not nec-essarily appropriate for addressing importantquestions in research on school choice. Adownside to using a national data set to study

Contextual Explanations of School Choice 199

school choice is that the structure of educa-tional options varies quite a bit across schooldistricts, with some districts offering a widearray of schools that are accessible by publictransportation and others with no choices atall.

This study examined school choice in a set-ting that is rich with options for students anda public transportation system that permitsaccess to many of these options for most stu-dents, a regime shared by most of the largesturban districts in the United States. Moreover,it examined the high school choices of thepopulation that is most often targeted withschool choice policy: disadvantaged studentsin urban districts. Although the results maynot be generalizable to suburban and ruralareas, this is perhaps not a major shortcom-ing, given that the choices of students inmost rural and suburban districts are con-strained relative to those in large urban dis-tricts and school choice policy is not typicallytargeted to these populations of students.

An advantage of using geocoded popula-tion-level data from one urban school districtis robustness. One can have more confidencein the estimates of contextual effects thatwere presented in this article because of therelatively large within-school and within-neighborhood clusters of students who wereavailable for analysis and the large number ofschools and neighborhoods to compare. Thistype of analysis is often difficult to conductwith national data sets owing to small samplesizes and privacy concerns that prevent thedisclosure of geographic identifiers. In sum,studies like the one presented here sacrificegeneralizability for robustness and in-depthknowledge of the schools, neighborhoods,and admission procedures in one social sys-tem.

It should be noted that students with lowachievement are highly unlikely to gainadmittance to selective-enrollment highschools. To address this concern, I conductedseparate analyses on just the students whowere eligible to apply for admission to selec-tive-enrollment schools (i.e., those withscores on the seventh-grade math and read-ing tests that were above the 40th per-centile). In most respects, the results (avail-able from me on request) closely aligned with

the findings of the main analysis. The con-textual effects are roughly of the same orderof magnitude, and the signs of all statisticallysignificant coefficients are consistent with theanalyses on the all-students population.

While estimating students’ choice ofschools with a multinomial model is prefer-able from an efficiency standpoint, it is possi-ble that the results presented here violate theindependence of irrelevant alternativesassumption of the model. To address this pos-sibility, I also specified a congruent set ofbinary logistic models. The binary logitresults, shown in Appendix Table B1, pro-duced estimates that are almost identical tothe results produced by the multinomial spec-ification and did not alter the conclusions ofthis study in any substantial way.

In closing, this analysis speaks to the com-plexity of the newly emergent educationalmarketplace. With the rapid expansion ofcharter schools and the passage of the NoChild Left Behind Act in 2001, which man-dates that local school districts allow studentsin “failing” or “unsafe” schools to take theirper-pupil funds to attend better schools (ineffect a public school voucher), school choiceis becoming a favored policy tool for educa-tional reform. In addition, a 2002 SupremeCourt decision, Zelman vs. Simmons-Harris,permits publicly supported religious school-ing. These recent developments make under-standing how students’ social contexts influ-ence their ability to negotiate the education-al marketplace increasingly relevant.

NOTES

1. For an important exception, seeSaporito (2003).

2. Because administrative records for stu-dents who were no longer enrolled inChicago public schools include the names ofthe schools to which the school district senttranscripts, it was possible to identify whichstudents attended private high schools in theChicago area.

3. Descriptive statistics from the analyticcohort with complete data are presented inTable 1. Appendix A outlines the sample-

200 Lauen

selection rules that were used for this study. Itshows that the analytic sample is comparableto the full cohort of eighth-grade studentswho were enrolled in the Chicago PublicSchools in spring 2000.

4. Most students in this cohort whoattended a private school attended aparochial one. Among the private schoolattendees in this cohort, 82 percent attendedCatholic schools, 5 percent attended non-Catholic sectarian schools (e.g., Evangelical orJewish), 4 percent attended nonsectarianschools, and 9 percent attended schools thatare otherwise nonclassifiable. These figuresare based on my calculations using data forschool-type classifications from the 2000Private School Survey by the National Centeron Education Statistics.

5. This category includes all students whowent to charter schools or public militaryacademies; most students who attendedcareer academies, magnet schools, or magnetprograms; and all students who simply optedout of their assigned neighborhood schools.Although this is a heterogeneous grouping ofschool types, previous analyses indicated thatsimilar factors predict the probability ofattending charter schools, nonselective mag-net schools and programs, military acade-mies, and other non-neighborhood options(analyses available from me on request).

6. One may think of these labels as usefulshorthand. Defining neighborhood as a unitof analysis is notoriously difficult. For the pur-poses of this study, the “neighborhoodschool” is the one to which a student isassigned. In most cases, this school will be theone closest to a student’s residence. In somecases, however, another nonassigned schoolmay be closer. Private and selective schoolsmay also be in a student’s neighborhood, forexample, but in this analysis, they are consid-ered alternatives to the assigned publicschool. The difficulty of defining the school-neighborhood nexus stems, in part, from thefact that school boundaries do not corre-spond with neighborhood boundaries.

7. Policy-driven racial balancing impera-tives suggest that the school-choice process isa function not only of family decisions, but ofadministrative decisions in schools. Becausedata on such administrative procedures are

scarce, the selection and admission processesare not taken into account in this study. Sincestated policies and anecdotal evidence sug-gest that the criteria by which students areselected at selective-enrollment high schoolsand magnet programs within high schools(i.e., schools within schools) are representedby control variables (e.g., test scores,race/ethnicity, and gender), this should be aminor shortcoming.

8. Analyses were conducted on the stu-dents’ scores on both the reading and math-ematics tests of the Iowa Test of Basic Skills.Because math and reading scores are highlycorrelated with each other, this analysis prox-ies students’ academic proficiency with onlyone score. Using reading scores instead ofmath scores does not significantly affect theestimation of contextual effects (analysis notshown, but available from me on request).

9. It is rarely the case that a given elemen-tary school sends students to only one or twohigh schools in Chicago. There were 399 ele-mentary schools in this study and 47 neigh-borhood high schools. On average, the stu-dents of an elementary school were assignedto about 5 different neighborhood highschools, although this number varied from 1to 20 (Tukey’s hinges are 7 and 3). These sta-tistics suggest there is ample within-elemen-tary-school variability in the characteristics ofthese neighborhood high schools.

10. While using simple aggregations isstandard practice in the sociology of educa-tion literature, I chose to create measures inthe neighborhood models to reflect theoreti-cal constructs in urban sociology. This studyreplicated the definitions of concentrated dis-advantage, concentrated affluence, and tenurein Sampson, Morenoff, and Earls (1999).

11. This model has been used to analyzechoices of colleges and graduate schools(Eide, Brewer, and Ehrenberg 1998; Nguyenand Taylor 2003).

12. Because tract and high school bound-aries are noncontiguous, it is not possible toenter neighborhood high school characteris-tics as level-two effects.

13. This article uses the term contextualeffects to describe an association of a featureof a neighborhood or school and high schoolenrollment outcomes, net of compositional

Contextual Explanations of School Choice 201

factors. It is important to stress that a cross-sectional design permits only the analysis ofassociations, not causal inferences in the strictsense of the phrase.

14. All the results are statistically significant

except for the private equation in the neigh-borhood model.

15. Conversations with admissions direc-tors at selective-enrollment high schools,however, indicated that no such quotas exist.

APPENDIX A

Analytic Sample-Selection Rules

The goal of the analysis was to estimate contextual effects on students’ propensities to choosehigh schools. Students who were not positioned to choose a high school during spring 2000were defined as “out of scope” for the purposes of this study. In other words, students whowere not “at risk” of exercising high school choice in Chicago were censored from the analy-sis. Table A1 identifies these students and summarizes the following sample restrictions.

1. Students who were enrolled in the Chicago Public Schools in the eighth grade, but thenmoved to an address outside the city limits were censored from the analysis because it wasimpossible to track their high school enrollment outcomes.

2. Students who were retained in the eighth grade in fall 2000 were censored from theanalysis because their choice of schools was administratively determined.

3. Students who were already in a school that included secondary grades (i.e., those greaterthan eight) were omitted from consideration because they had chosen a high school beforespring 2000.

4. Those who attended schools for students exclusively with special needs (behavioral orlearning disabilities, those who were in jail, or those who were in a school for pregnantteenagers) were censored from the analysis because students in such circumstances were gen-erally unable to choose a high school. It is important to note that this sample-definition deci-sion did not omit all students with special needs from the sample. Students with learning dis-abilities who were enrolled in regular elementary schools were included in the analysis andconstituted 17 percent of the final analytic sample.

5. Dropouts were omitted from the analysis because they did not choose a high school. 6. Those who died between spring 2000 and spring 2001 were not included in the analy-

sis. These sample decision rules resulted in an “in-scope” N of 23,535. From this population,

5,058 cases were deleted because of missing student-level data, school or tract identifiers,and/or school or neighborhood covariates, resulting in a final analytic sample of 18,477 stu-dents.

Table A1. Sample-Selection Summary

Category Number

Chicago Public Schools Eighth Graders in Spring 2000 30,624Moved out of Chicago 2,284Retained in the eighth grade 605In a school with an elementary-high school grade span 349Special population 2,199Dropout 1,638Died 14

In-Scope Population 23,535Deleted due to missing data 5,058

Final Analysis Sample 18,477

202 Lauen

Because each of these censoring decisions may be of concern, it is important to understandwhether the censoring decisions resulted in an analytic sample that is representative of theunderlying population. Table A2 shows the means of the population and analytic sample.Because the analytic sample is large, one-sample z-tests resulted in a number of statistically sig-nificant mean differences. That said, the size of these differences is relatively small. The columnlabeled “Diff (SD)” computes the ratio of the mean difference and the pooled standard devi-ation of each variable. Most of the standardized mean differences are less than one-twentiethof a standard deviation. Two differences are over a tenth of a standard deviation. The studentsin the analytic sample were less likely to have high values on previous school moves and wereless likely to be old for their grade level.

Contextual Explanations of School Choice 203Ta

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204 LauenTa

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Contextual Explanations of School Choice 205

APPENDIX B

Binary Logit Results

An assumption of the multinomial logistic model is the “independence of irrelevant alterna-tives.” It is possible that the model used in this study was not appropriate for the underlyingbehavior being modeled. If the school-choice decision process is sequential, for example, thiswould violate the assumption of the independence of irrelevant alternatives because one’spreference for one school (e.g., a private one) may depend on whether one got into anotherschool (e.g., a selective-enrollment one). While this is certainly plausible, data are not avail-able to adjudicate this kind of claim. Although multinomial models are often preferable for effi-cient estimation, it turns out that because of the sample sizes in this study, the results of thebinary logit HLM models are virtually identical to those obtained from the multinomial logitHLM models. Therefore, the decision of whether to use the multinomial model or an analo-gous series of binary logits has no effect on the statistical or substantive significance of thefindings reported.

Table B1 displays the results of the coefficients and standard errors of the primary contex-tual effects discussed in the article. All the results are in the same direction and are of approx-imately the same magnitude. No statistically significant result is rendered statistically insignif-icant by specifying the comparison as a binary logit.

206 LauenTa

ble

B1.

Com

par

ison

ofM

ulti

nom

iala

nd

Bin

ary

Log

itR

esul

ts(r

obus

tst

and

ard

erro

rsre

por

ted

bel

owth

ees

tim

ates

)

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ent

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288

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40.

456

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30.

431

0.14

20.

604*

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NSE

0.28

80.

815

0.46

20.

112

0.43

30.

139

0.59

9**

Sele

ctiv

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ta-1

.22*

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.17

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1*-0

.43*

*2.

22*

-0.4

5**

-2.9

5**

BIN

beta

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1**

-1.4

3-1

.36*

-0.3

8**

2.04

*-0

.42*

*-2

.70*

*M

NSE

0.23

20.

661

0.47

60.

102

0.44

40.

157

0.68

9BI

NSE

0.24

60.

743

0.55

10.

115

0.48

20.

141

0.81

8

Non

-Nei

ghbo

rhoo

dve

rsus

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ta-0

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-0.3

60.

42-0

.09

3.31

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.12

-3.8

6**

BIN

beta

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2-0

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03.

21**

-0.1

2-3

.85*

*M

NSE

0.16

00.

350

0.30

50.

061

0.27

90.

074

0.58

7BI

NSE

0.15

80.

351

0.29

10.

062

0.27

00.

073

0.53

0

Not

e:Th

efig

ures

labe

led

MN

are

from

the

tabl

esin

clud

edin

the

artic

le.

The

figur

esla

bele

dBI

Nar

efr

oma

supp

lem

enta

ryan

alys

isof

bina

rylo

git

HLM

s.**

p<

.01,

*p<

.05

(tw

o-ta

iled

test

s).

Contextual Explanations of School Choice 207

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Douglas Lee Lauen, Ph.D., is Assistant Professor, Department of Public Policy, University of NorthCarolina at Chapel Hill. His main fields of interest are school choice, inequality, and school effects.He is currently estimating the effects of school choice on students’ graduation rates, examining therole that teachers’ working conditions play in students’ gains in test scores, and investigating howchanges in state-level accountability policies affect teachers’ and students’ incentives to focusinstruction on particular subgroups of students.

The author thanks Andrew Abbott, Tony Bryk, Charles Bidwell, Jeffrey Henig, Susan Mayer, DanMcFarland, Brian Powell, and Kazuo Yamaguchi for their comments and advice on previous draftsof the article. A preliminary version of this article was presented at the meeting of the AmericanEducational Research Association, April 13, 2004, San Diego, CA. The author gratefully acknowl-edges support from the Alfred P. Sloan Center on Parents, Children, and Work; the Data Researchand Development Center; the Consortium on Chicago School Research; and a Spencer Foundationdissertation fellowship. The views expressed in this article are solely the responsibility of the author.Direct all correspondence to Douglas Lee Lauen, Department of Public Policy, University of NorthCarolina at Chapel Hill, Abernethy Hall, CB 3435, Chapel Hill, NC 27599; e-mail:dlauen@email.unc.edu.