A Little Help from My Friend's Parents: Intergenerational Closure and Educational Outcomes

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  • A Little Help from My Friend's Parents: Intergenerational Closure and Educational OutcomesAuthor(s): William J. CarbonaroSource: Sociology of Education, Vol. 71, No. 4 (Oct., 1998), pp. 295-313Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/2673172 .Accessed: 28/06/2014 07:43

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  • A Little Help from My Friend's Parents:

    Intergenerational Closure and Educational Outcomes

    William J. Carbonaro University of Wisconsin-Madison

    Coleman's theory of social capital predicts that students who have high levels of "intergenerational closure'-that is, whose parents know more of their chil- dren's friends' parents-will have better educational outcomes than will stu- dents with low levels of intergenerational closure. This study used datafrom the National Education Longitudinal Study of 1988 to test whether intergenerational closure affects children's educational outcomes. The main findings were that closure was positively associated with mathematics achievement, but not signif- icantly associated with achievement in any other subject, closure was not sig- nificantly associated with 12th-grade grade point averages, and students with more closure were less likely to drop out of high school by the 12th grade.

    M any researchers have exam- ined the different ways in which parents influence stu-

    dents' performance in school. Some prominent examples include the impact of social class (Heyns 1978; Jencks et al. 1972; Kohn 1977; Shavit and Blossfeld 1993), social psychological influences (Sewell, Haller, and Ohlendorf 1970; Sewell, Haller, and Portes 1969), parental involvement at home and school (Clark 1983; Ho and Willms 1996; Lareau 1989), and sociolinguistic practices (Bernstein 1975; Heath 1983). Some researchers have hypothesized that social capital-the quantity and quality of relationships among parents, their children, and other adults in the community-is also an important determinant of student outcomes (Coleman 1987, 1990; Coleman and Hoffer 1987; Loury 1977).

    In my study, I investigated whether a specific set of social rela- tionships discussed by Coleman

    (1990, 1991)-whether parents know the parents of their children's friends, which he labeled intergener- ational closure-influences students' outcomes. My central research ques- tion was whether higher levels of intergenerational closure among stu- dents, their friends, their parents, and their friends' parents influence educational outcomes for students. In addition, and perhaps more important, I investigated the factors that may underlie any observed rela- tionships between intergenerational closure and educational outcomes.

    IMPORTANCE OF SOCIAL CAPITAL

    Traditionally, economists have emphasized the importance of human capital for individual prosper- ity (Becker 1964; Schultz 196 1). More recently, some sociologists have argued that another important resource, social capital, can also shape a person's life chances. Loury

    SOCIOLOGY OF EDUCATION 1998, VOL. 71 (OCTOBER): 295-313 295

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  • 296 Carbonaro

    (1977) introduced the term to describe the familial and communal resources that promote children's cognitive or social development. Coleman (1990:302) defined social capital as a "social structural resource" that serves as a "capital asset for the individual" and facili- tates certain actions and outcomes for those who occupy a given social structure. Trust, obligations and expectations, norms, relations of authority, and shared information are all examples of social capital because they are resources that arise from the social relations of individu- als who share membership in a com- mon social structure.

    These resources allow actors to improve their performance in a vari- ety of activities in which they engage. For example, trust and sharing valu- able information can lower the costs of ensuring compliance with contrac- tual agreements among economic actors (Williamson 1981); high expectations for students can pro- duce behavior that is consistent with those expectations and may lead to desired educational outcomes; and mutual obligations between employ- ers and employees can mitigate adversarial attitudes in the work- place, which can reduce productivity. Although both human and social capital can enhance productivity, human capital is possessed by indi- viduals, whereas social capital exists in the relationships between people.

    Coleman (1990) emphasized that social networks that are character- ized by closure can more easily gen- erate social capital. By "closure," he meant that individuals are in contact with others, so information can be gathered, and common expectations and norms can be enforced through the use of sanctions and rewards. These connections among individuals serve to strengthen the level of social capital that exists between them. To

    use Coleman and Hoffer's (1987) example, "intergenerational closure" exists when Parent A, mother of Child a, knows Parent B, mother of child a's friend Child b. When Parent A and Parent B know each other, they can

    set norms and standards for their children, and are not vulnerable to their children's exploitation of what rules exist for other children. In addi- tion, Parent A can provide support for Child b when necessary, and can sometimes serve as a bridge if the child's communication with his or her. parent has broken down. (Coleman 199 1: 1 1)

    When social networks lack closure, parents lose an important resource for dealing with their children. According to Coleman and Hoffer (1987:226), such parents "are not in a position to discuss their children's activities, to develop common evalua- tions of these activities, and to exer- cise sanctions that guide and con- strain these activities."

    How may intergenerational clo- sure have an impact on students' educational outcomes? For instance, two students (Alan and Bob) devise a plan to skip school; Alan tells his mother that Bob's mother will drive him to school, and Bob tells his mother that Alan's mother will do the same. Both parents are deceived (thinking the other will drive Alan and Bob to school), and Alan and Bob meet at the mall and spend the day there. If Alan's and Bob's parents know each other and communicate with each other frequently, the sub- terfuge will be discovered and sanc- tions will be applied. Thus, in this example, a high degree of intergener- ational closure will allow commonly held norms to be enforced through shared information resulting from frequent social contact.

    If Alan and Bob regularly miss

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  • Intergenerational Closure and Educational Outcomes 297

    school through such schemes, their educational careers may be adversely affected. High rates of truancy and absenteeism are often associated with a higher likelihood of dropping out of school (Wehlage and Rutter 1986). Also, by skipping school fre- quently, Alan and Bob will miss out on lessons, which may adversely affect their grades and achievement levels, both of which are associated with dropping out (Rumberger 1987).

    One could easily think of other examples of behaviors that are detri- mental to students' success in school that might be affected by closure. For example, students who engage in deviant behavior in school (like cut- ting classes and breaking school rules) may be able to keep their par- ents from finding out about it if the parents do not have frequent contact with their children's friends' parents. Furthermore, parents can use their children's friends' parents as an important resource in assessing whether their children's peers have goals and aspirations that are con- sistent with the ones they hold for their children. Thus, parents can more easily determine whether their children are hanging out with the wrong crowd and intervene (if neces- sary). These examples illustrate that intergenerational closure may cru- cially affect many important factors that ultimately influence numerous educational outcomes for students.

    EVIDENCE OF COLEMAN'S CLAIMS

    What evidence is there to support Coleman's claims about the link among social capital, intergenera- tional closure, and educational out- comes? There is certainly a good deal of evidence to support the claim that strong connections between parents and students enhance students' edu- cational outcomes. Ethnographers,

    such as Clark (1983), have found that poor children whose parents were more involved in their children's schoolwork and emphasized good study habits were more successful in school than their counterparts whose parents were less concerned with their children's schooling (see also Comer 1988; Williams and Komblum 1985).

    Lareau (1989) stated that first- grade students of middle-class pro- fessional parents benefited from a high degree of parental involvement both at home and in school. By tai- loring educational programs to their children's needs, offering extensive help for their children at home, and taking an active interest in their chil- dren's early careers, these parents gave their children a decided advan- tage in school over the children of working-class parents. Ho and Willms (1996) found that more home discussion of school-related matters between parents and their children had a positive effect on eighth-grade reading and mathematics achieve- ment.

    Researchers have also found that increased levels of "parental support" (such as the presence of study aids at home; differences in parenting styles; and parents' monitoring of their children, reactions to grades, and involvement in academic mat- ters) encourage students to finish high school (Ekstrom Goertz, Pollack, and Rock 1986; Rumberger, Ghatak, Poulos, Ritter, and Dornbusch 1990).

    The Wisconsin model of status attainment (Sewell et al. 1969) emphasized the importance of "sig- nificant others" (defined as parents, teachers, and close friends) in engen- dering expectations that shape the educational and occupational attain- ment of young people. This body of research found that students who have significant others with high aspirations serve as models for other

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  • 298 Carbonaro

    students who increase their own educational aspirations. High levels of aspirations positively affect educa- tional performance and ultimately enhance an individual's level of edu- cational and occupational attainment (Sewell et al. 1970; see Alexander, Eckland, and Griffin 1975, and Jencks, Crouse, and Mueser 1983 for replications that support the find- ings of the Wisconsin model). Thus, the Wisconsin model suggested that parents, teachers, and peers play a crucial role in shaping educational outcomes for students.

    Clearly, the findings of a sub- stantial amount of research support Coleman's claims about the impor- tance of social capital for students. However, one may question whether Coleman discovered something new or merely "put old wine in new bot- tles," as one commentator charged (Brown 1991:566). Much of Coleman's discussion of social capi- tal among parents, students, and other members of the community did not differ substantially from the con- nections earlier researchers had made. Indeed, Coleman's measures of social capital were similar to those used by previous researchers, including parental involvement with the school, parental expectations, and the degree of communication between parents and their children (see Coleman and Hoffer 1987). Thus, Coleman may have popular- ized a unique way of thinking about these behaviors (labeled social capi- tal in contrast to human capital), but it is not clear that he gained new insights on these behaviors.

    However, I believe that Coleman's notion of intergenerational closure is an original conceptual contribution. Previous research did not focus on the extent to which relations among parents, children, c'hildren's friends, and friends' parents constitute net- works that gather information, form

    norms and expectations, and enforce standards of behavior. Unfortunate- ly, Coleman and Hoffer's (1987) study provided no measure of inter- generational closure, and these authors' statements on this aspect of social capital are hypotheses based on theory, rather than empirically grounded assertions. In their study of Catholic schools, Bryk, Lee, and Holland (1993) reported that their field observations of Catholic schools did not support Coleman and Hoffer's hypothesis, but they did not provide any quantitative evidence to refute Colman and Hoffer's claims.

    In short, the aspect of Coleman's theory of social capital that is the least original has been relatively well researched, but the most novel aspect of his theory still awaits empirical testing. My goal was to take a first step in testing Coleman's theory of intergenerational closure. Data from the National Education Longitudinal Study (NELS) of 1988 allowed me to do so because it included information on whether parents knew their children's friends' parents. By using this information, I assessed whether closure has any impact on students' educational out- comes, and if so, how closure oper- ates in influencing those outcomes.

    METHODS

    Data Set

    The data set used in NELS is a two-stage stratified cluster sample. In 1988, 24,599 8th-grade students were surveyed to generate a national- ly representative sample of 8th graders. The students were resur- veyed in 1990 and 1992 (the 10th and 12th grades) to make longitudi- nal analyses possible. Dropouts were included in both follow-ups. Parents were surveyed in the base year and the second follow-up, but not in the

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  • Intergenerational Closure and Educational Outcomes 299

    first follow-up. In my study, I ana- lyzed the responses of students and parents from the 8th- to the 12th- grade longitudinal cohort, with a total sample size of 16,489. Table 1 pre- sents the means and standard devia- tions or frequency distributions for the variables used in the analyses.

    To provide a sufficient number of

    respondents for analyses of small subpopulations of students (such as private school students, Asians, and Latinos), NELS oversampled certain subgroups. As a result, weights must be used to calculate proper estimates of population parameters. In my analyses, I used weights throughout to estimate all population parameters.

    Table 1. Means, Standard Deviations (SDs), and Frequency Distributions for Variables Used in the Analysis: 12th-Grade Longitudinal Cohort

    Variable Mean SD Percentage

    Closure 2.87 1.56 Base-year SES -.11 .77 Base-year reading 26.53 8.60 Base-year mathematics 35.28 11.78 Base-year history 29.30 4.59 Base-year science 18.49 4.84 Senior-year reading 32.75 10.15 Senior-year mathematics 47.59 14.43 Senior-year history 34.58 5.41 Senior-year science 23.20 6.22 12th-grade GPA 13.84 22.22 Parent-school communication 6.87 1.32 Parents' participation in school 19.97 4.98 12th-grade suspensions .32 1.54 12th-grade absenteeism 5.49 4.68 12th-grade class skipping 2.89 4.50 Gender

    Male 50.0 Female 50.0

    Race White, Non-Hispanic 71.7 Asian-Pacific Islanders 3.5 Black, Non-Hispanic 13.2 Hispanic 10.4 Native American 1.2

    Family Composition Intact 64.8 Single-parent family 17.5 Stepfamily 13.9 Nonparent family 3.3

    Region Northeast 19.1 Midwest 25.8 South 35.6 West 19.5

    Urban Location Urban 28.0 Suburban 40.4 Rural 31.5

    (Continued)

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  • 300 Carbonaro

    Table 1 (Continued)

    Variable Mean SD Percentage

    Parents' Aspirations Less than high school 0.5 GED 0.2 High school graduation 12.8 Vocational, trade, less than 1 year 1.1 Vocational, trade, 1-2 years 3.9 Vocational, Trade, 2 or more years 3.0 College: fewer than 2 years 5.3 College: 2 or more years 10.0 College: finish a 2-year program 4.8 College: finish a 4-5-year program 35.8 Master's degree 10.8 Professional degree (Ph.D., MD) 9.5

    When using NELS, it is necessary to account for the effect of the sam- pling design on the estimation of the standard errors. Since NELS is a multistage cluster sample, estimates of the standard errors that assume simple random sampling are smaller than they should be. To deal with this problem, I used the Stata soft- ware package, which uses informa- tion on the sampling strata and pri- mary sampling unit to generate weighted point estimates that are then used in a first-order matrix Taylor series expansion to generate the proper standard errors.

    Dependent Variables

    I chose three different educational outcomes for students as the depen- dent variables: 12th-grade achieve- ment, 12th-grade grade point average (GPA), and 12th-grade dropout status. NELS used four academic achieve- ment tests (reading, mathematics, sci- ence, and history-citizenship-geogra- phy) to measure students' achieve- ment (see National Center for Education Statistics, NCES, 1994). The students took the four tests in 1988 when they were 8th graders and then took them again in the follow-ups in 1990 and 1992.1 I used the 12th-

    grade "IRT-estimated right"2 scores for the four subjects as my measure of academic achievement.

    The second dependent variable, 12th-grade cumulative GPA, mea- sures a student's GPA across all sub- jects. I used this measure in addition to 12th-grade achievement because grades may be more sensitive to behavior in the classroom than are test scores. However, there is a major drawback in using grades as a dependent variable: Grades are determined differently from school to school and even within schools; in other words, an A in one school may not be equivalent to an A in another school. Also, many schools weight grades by track, so a C in the high track is equivalent to an A in the low track. In short, GPA is likely to be an unreliable indicator of students' per- formance. It is important to keep this caveat in mind when interpreting the results.

    The third dependent variable, 12th-grade dropout status, has sever- al response categories. I recoded it as a dichotomous variable. That is stu- dents were classified either as 'in school" or 'out of school" during their senior year, regardless of whether they had dropped out and returned to school or not; thus, 'did not drop

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  • Intergenerational Closure and Educational Outcomes 301

    out" and "dropped out, but returned" were coded 0, and "school reported dropout" and "dropped out" were coded 1. Although some students who are not in school during their senior year may return later and graduate or obtain a general equiva- lency diploma (GED), many will not. Thus, this measure is a relatively good predictor of which students will not graduate from high school.

    Independent Variables

    The most important independent variable in my study was the mea- sure of intergenerational closure. NELS surveyed parents about whether they knew the parents of their children's friends. The parents were asked variants of the question, "Do you know the parents of your child's first friend?" five times, with "first friend" replaced by "second friend," "third friend," and so on to "fifth friend." By establishing whether parents know their children's friends' parents, researchers can discern the degree of intergenerational closure that exists: The more parents who are known, the more closure exists. To measure the degree of closure, I totaled the responses to the five questions and created a new vari- able, closure.3

    These five questions were asked in the base year and the second fol- low-up. There is a potential problem in using the 8th-grade responses in a longitudinal study because when students change schools between the 8th and 12th grade, their friends may change as well. Thus, the 8th- grade items may not accurately reflect the amount of closure stu- dents have in high school. Since I was interested in student outcomes from high school, I preferred a mea- sure of closure from when the stu- dents were in high school. Accordingly, I used the responses

    from the second follow-up in these analyses. However, in using the 12th-grade responses in an analysis of 12th-grade dropout status, any observed association between the two variables may be the result of a cont- amination effect (the dependent vari- able may be responsible for changes in the independent variable). This is an important concern that I address in the Results section.

    I also used a series of back- ground variables as controls for stu- dents' characteristics that may be associated with closure. These vari- ables were the NELS composite for the base-year socioeconomic status (SES) of the family, which consists of each parent's education and occupa- tion, and family income; the stu- dent's self-reported race, coded as three dummy variables (Asians, blacks, and Hispanics, with whites and Native Americans as the omitted category); the student's gender (with males as the omitted category); the base-year family composition, coded as a series of dummy variables (intact families, in which both natur- al parents were present; single-par- ent families, in which only the moth- er or father was present; stepfami- lies, in which one natural parent and a guardian were present; and non- parent families, in which neither nat- ural parent was present and the stu- dent lived with another relative or nonrelative); region of the country, coded as a series of dummy variables (South, Midwest, and West, with Northeast as the omitted category); and dummy variables to indicate whether the students lived in urban, suburban, or rural areas (urban was the omitted category).

    It is possible that any association between closure and educational out- comes may simply reflect differences in expectations among parents; that is, parents with high expectations for their children may consider it impor-

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  • 302 Carbonaro

    tant to know their children's friends' parents, and this relationship between expectations and closure may make any observed association between closure and educational outcomes spurious. I controlled for educational aspirations by using the 8th-grade reports by the parent who responded to the survey of how far she or he hoped his or her child would go in school.

    If one knew the level of aspira- tions in the network of a child's friends and their parents, one could test for an interaction between clo- sure and the aspirations in the net- work. Such an interaction would allow one to test Portes and Sensenbrenner's (1993) contention that social capital can have either negative or positive outcomes, depending on the values and/or norms held by the members of a net- work. In this case, closure could have positive effects for students in a closed network with high expecta- tions and negative effects for stu- dents in a closed network with low expectations. Unfortunately, NELS did not collect information on the aspirations of the network. Therefore, I used parents' aspirations when the students were in the 8th grade in its place as a proxy for the aspirations of a given network and tested for an interaction between closure and aspirations.

    Another possible source of spuri- ousness is parental involvement. Parents who make the effort to famil- iarize themselves with their chil- dren's friends' parents may be gener- ally more concerned with their chil- dren's academic careers and there- fore may have more contact and involvement with their children's schools. This higher level of parental involvement with the students' schools may be responsible for any observed association between closure and students' outcomes.

    To control for this possibility, I included two measures of parental involvement, borrowed from Ho and Willms (1996). The first is parent- school communication, which used items from the parent's second fol- low-up survey to measure the degree to which parents used the school as a resource to gather information about their children's school perfor- mance. The items used were how many times since school opened last fall (or during the last year the child attended school) did the school con- tact the parents regarding the child's (1) academic performance, (2) acade- mic program, (3) plans after high school, (4) selection of college preparatory courses, (5) attendance, (6) behavior, and (7) information to help the child with homework and school skills, and how many times since school opened last fall (or dur- ing the last year the child attended school) did the respondent contact the school regarding items 1-6? I summed these items to form a scale with an alpha of .82 1.

    The second scale captured the degree of parents' participation in school. It included the following four items from the base-year parent's survey: whether the respondent (or spouse or partner) had (1) volun- teered at the school (2) belonged to the parent-teacher organization (PTO), (3) attended PTO meetings, (4) and took part in PTO activities. Although I would have preferred to have the parents' responses to these items from the second follow-up to give me a measure of parental involvement concurrent with my clo- sure measure, these questions were asked only in the base year and thus were the only available information on parents' participation in school. I added these items together in a scale with an alpha of .735.

    Finally, I included controls for four behaviors that may be related to

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  • Intergenerational Closure and Educational Outcomes 303

    achievement and dropout status as independent variables: 12th-grade reports by students of how often they skipped class, were absent from school, and were suspended from school4 and whether they had close friends who were dropouts.5 Ekstrom et al. (1986) found that the impact of a student's background characteris- tics on dropping out was largely mediated by school-related factors, such as grades and the frequency of class cutting, talking back, and sus- pensions. Rumberger (1987) noted that several researchers found that dropouts were more likely than non- dropouts to have friends who were dropouts. It is possible that students with low levels of closure may be more likely to engage in these vari- ous behaviors that directly affect stu- dent outcomes. For example, high levels of closure may enable parents to keep their children from hanging out with the wrong crowd and there- by protect them from any negative effect this behavior might have on their children's school careers. Entering these variables in the regressions allowed me to examine whether these behaviors might be responsible for any observed associa- tion between closure and student outcomes.

    Missing Data

    In preliminary analyses, I encountered problems with missing data in the most elaborate ordinary least-squares and logistic regression models when using listwise deletion. In these models, from two-fifths to one half the cases were lost because of missing data. To ensure that changes in the coefficients from model to model were due to the addi- tion of new variables and not the attrition of cases owing to missing data, I substituted the variable-spe- cific mean for cases with missing

    data.6 I also created a dummy vari- able for each of these variables that identified the cases with a mean sub- stitution. By entering these dummy variables in my regression equations, I could control for any effect the mean substitution might have on my results. In my regression analyses, all the new variables entered in Models 3 and 4 were transformed in this manner.

    RESULTS

    Academic Achievement and Closure

    The first set of analyses focuses on 12th-grade achievement. I ran four sets of regressions, each with the same set of independent vari- ables but with a different test subject as the dependent variable. Model 1, a baseline model, included only the student background variables as independent variables. In Model 2, the closure variable was added to the list of independent variables in Model 1. In reading, history, and science, the coefficients for closure were all positive, but the p values were all .130 or greater. However, closure has a positive, statistically significant association with math achievement. Table 2 shows the results for the regressions using the mathematics test as the dependent variable.

    Controlling for closure did not substantially reduce the coefficients of any of the social background vari- ables from Model 1. Apparently clo- sure operates independently of stu- dents' background characteristics.

    How large is the association between closure and mathematics achievement? From the 8th to the 12th grade, the average increase in achievement among students was 3.08 more questions answered cor- rectly per year.7 For each friend's parent who was known, the students'

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  • 304 Carbonaro

    Table 2. Unstandardized Regression Coefficients of Mathematics Achievementa

    Variable Model 1 Model 2 Model 3 Model 4

    Single-parent family - 1.225 -.845 - 1.122 -.825 Stepfamily -2.456 -2.645 - 1.767 - 1.212 Nonparent family -3.942 -2.548 -2.197 -1.443 SES 7.599 7.627 5.436 4.773 Asian 3.262 3.153 1.585 1.172 Hispanic -2.952 -2.507 -3.313 -3.036 Black -6.156 -6.240 -6.976 -7.075 Female -1.029 -.838 - 1.442 -1.581 West -2.237** -1.955** -1.709* -.943 Midwest -1.408 - 1.357 - 1.306 - 1.286 South -3.094 -3.050 -3.505 -3.230 Suburban -.568 -.586 -.474 -.707 Rural -.180 -.381 -.123 -.544 Closure - .490 .308 .138 Parents' expectations - - 1.560** 1.459** Communication with the

    schoolb - - -.296 .214 Participation in

    the school - - -.259 -.110 Class skippinga - - - -.202 Absenteeism - - - -.239 Suspensions - - - -.694 Friends as dropouts?b - - - -3.318 Intercept 52.642 51.541 46.450 48.574 R2 .255 .259 .338 .376 p value of F-test .000 .000 .000 .000 Number of cases 12,306 10,323 10,323 10,323

    Note: All regressions were weighted by the appropriate weight. The weight was transformed by dividing the original weight by its own average for the valid cases in the equation.

    a Student background characteristics, closure, parental expectations and involve- ment, and at-risk behaviors were controlled for.

    b The "missing values" flag for this variable had a p value less than .05 in Models 3 and/or 4.

    *p< .05, **p

  • Intergenerational Closure and Educational Outcomes 305

    achievement and closure. Thus, a good portion of the association between mathematics achievement and closure reflects prior differences in parental expectations among stu- dents with different levels of closure.

    In Model 4, four behaviors that may be related to closure and school performance were added: how often the students skipped class, were sus- pended, and were absent and whether the students had friends who were dropouts. The analysis revealed that all four variables are negatively associated with achieve- ment, that the association between closure and 12th-grade achievement is reduced by 55 percent, and that the coefficient is no longer statistical- ly significant. The coefficients for clo- sure in the analyses using the tests of the three other subjects as depen- dent variables also dropped substan- tially when these variables were added to the model. Thus, Model 4 suggests that the positive association between closure and achievement may be due to the fact that students with low levels of closure are more likely to engage in these deviant behaviors that have a negative impact on achievement.

    As was suggested earlier, having a high degree of closure may not be sufficient to enhance achievement. It is likely that a high degree of closure increases achievement only in combi- nation with norms and expectations of the network that support high lev- els of achievement. Since data on the network's level of aspirations was not available, I substituted parents' expectations and added an interac- tion term between parents' aspira- tions and closure to Model 4 to test this possibility. The interaction term was small and statistically nonsignif- icant, indicating that closure has the same impact for students, regardless of their parents' expectations for them.

    Finally, in Model 5 (results not shown), I added the students' scores on the four 8th-grade achievement tests as controls for prior achieve- ment. This addition enabled me to establish whether closure was associ- ated primarily with students' learning in high school or operated through higher achievement before the stu- dents entered high school. After prior achievement was controlled for, the coefficient of the closure variable was reduced by roughly 75 percent (.066) and was no longer statistically signifi- cant (p = .347). Apparently, closure operates primarily through students' prior achievement.

    One puzzling aspect of this analy- sis requires attention: Why is closure positively associated with achieve- ment in mathematics but not in any other subject? Although one should always be careful when offering ret- rospective explanations for findings, I believe that Bryk et al.'s (1993) argu- ment may clarify this issue. These authors contended that achievement in mathematics is more dependent on the impact of schooling than is achievement in other subjects. This explanation is consistent with the finding that a substantial portion of the association of closure with achievement is explained by vari- ables that measure how much time students spend in the classroom.

    GPA and Closure

    In the next set of analyses, I changed the dependent variable to 12th-grade cumulative GPA and used the same models used in the analysis of 12th-grade achievement. Model 1 includes students' background char- acteristics and Model 2 adds closure to the regression equation. As Table 3 indicates, closure is positively related to the 12th-grade cumulative GPA, but the association is not sta- tistically significant (p = .335). Since

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  • 306 Carbonaro

    Table 3: Unstandardized Regression Coefficients of Cumulative 12th-Grade GPAa

    Variable Model 1 Model 2 Model 3 Model 4

    Single-parent family -1.265 -.897 -.957 -.858 Stepfamily -1.556 - 1.763 - 1.695 - 1.616 Nonparent family -2.706 -2.775 -2.813 -2.715 SES .398 .519 .268 .150 Asian -.006 -.349 -.422 -.486 Hispanic 4.678 4.974 4.781 4.802 Black -2.332 -2.640 -2.892 -3.001 Female -.536 -.670 .669 .715 West -18.948 - 19.305 - 19.333 - 19.225 Midwest -15.540 - 16.189 - 16.073 - 16.123 South -6.699 -6.830 -6.775 -6.756 Suburban 1.522 1.275 1.366 1.284 Rural 1.453 1.072 1.231 1.137 Closure .176 .152 .124 Parents' expectations .129 .104 Communication with

    the school .018 .035 Participation in

    school -.203 -.156 Skipping classesb - -.031 Absenteeismb - -.103 Suspensions .078 Friends as dropouts? -.500 Intercept 22.972 23.013 22.701 23.178 R2 .093 .095 .096 .097 p value of F-test .000 .000 .000 .000 Number of cases 15,669 12,860 12,860 12,860

    Note: All regressions were weighted by the appropriate weight, which was trans- formed by dividing its own average for the valid cases in the equation.

    a Student background characteristics, closure, parental expectations and involve- ment, and at-risk behaviors were controlled.

    b The "missing values" flag for this variable had a p value less than .05 in Models 3 and/or 4.

    *p< .05, **p< .01.

    closure was not significantly associ- ated with the cumulative GPA, I will not discuss the results of Models 3 and 4 (see Table 3 for the results).

    Dropping Out and Closure

    In the final set of analyses, I changed the dependent variable to 12th-grade dropout status and ran Models 1-4. As Table 4 shows, Model

    2 reveals that closure is positively associated with staying in school after students' background characteristics are controlled for. For each additional friend's parent who was known, a stu- dent's odds of staying in school increased by a factor of 1.4. For the students at the extremes of the clo- sure distribution, the likelihood of dropping out differed dramatically: The odds of dropping out for students

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  • Intergenerational Closure and Educational Outcomes 307

    Table 4. Odds Ratios of 12th-Grade Dropout Status

    Variable Model 1 Model 2 Model 3 Model 4

    Single-parent family 1.530 1.424 1.501 1.308 Stepfamily 2.241 2.673 2.379 2.295* Nonparent family 3.327 3.655 3.994 3.827 SES .299 .356 .458 .534 Asian .532 .402 .555 .605 Hispanic 1.028 1.118 1.300 1.382 Black .526 .445 .497 .600 Female 1.043 1.074 1.269 1.282 West 1.276 1.493 1.518 1.460 Midwest 1.308 1.457 1.452 1.604 South 1.409 1.597 1.897 2.304 Suburban .929 .991 .922 1.034 Rural .814 .898 .873 .932 Closure .708** .724 .735** Parents' expectations - .843 .855 Communication with

    the school 1.079 1.056 Participation in

    school 1.270 1.196 Skipping classes 1.038 Absenteeism - 1.116 Suspensionsb - 1.034 Friends as dropouts?b 3.604 p value of F-test .000 .000 .000 .000 Number of cases 15,669 12,860 12,860 12,860

    Note: All regressions were weighted by the appropriate weight. The weight was transformed by dividing the original weight by its own average for the valid cases in the equation.

    aStudent background characteristics, closure, parental expectations and involve- ment, and at-risk behaviors were controlled for.

    b The "missing values" flag for this variable had a p value less than .05 in Models 3 and/or 4.

    *p< .05, **p< ;O1.

    whose parents knew none of their friends' parents were seven times greater than the odds of their counter- parts whose parents knew all five of their children's friends' parents.

    When parental expectations and involvement were added as controls in Model 3, the odds ratio for closure increased only slightly. This finding casts doubt on the idea that closure's positive association with staying in school simply reflects higher expecta- tions and more involvement with school by parents with more closure.

    In Model 4, after skipping class-

    es, suspensions, absenteeism, and whether the students had friends who were dropouts were controlled for, the odds ratio for closure increased by only 1.5 percent. Thus, unlike the analysis of students' achievement, these variables accounted for only a small proportion of the association between dropping out and closure.

    Next, controls for academic per- formance were added to Model 4. Earlier research noted that poor aca- demic performance is often associat- ed with dropping out (Ekstrom et al.

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  • 308 Carbonaro

    1986; Rumberger 1987). A student's GPA is typically a good predictor of dropping out. However, when I used the cumulative GPA as a control in the model, there was too much miss- ing data among the dropouts, so I substituted the four 8th-grade achievement tests to control for aca- demic performance. Adding these variables to Model 4 had little effect on the results displayed in Table 4. Most important, the coefficient for closure increased by only .002 and remained statistically significant (p = .000). Therefore, the association between closure and dropout status is not merely a reflection of students with more closure being more capa- ble students.

    Furthermore, having a high degree of closure may not be a suffi- cient condition for reducing one's odds of dropping out. It is possible that a high degree of closure only lowers a student's odds of dropping out when it exists in combination with norms and behaviors that are conducive to staying in school. To investigate this possibility, I entered some interaction terms into Model 4. First, I added an interaction term between parents' aspirations and clo- sure. A high degree of closure may be effective only when the norms and expectations of the network support staying in school. As with the analy- ses of mathematics achievement, I used parents' expectations as a proxy for the network's expectations. Since the interaction term was small and statistically nonsignificant, closure has the same impact for students regardless of their parents' expecta- tions for them.

    Second, I added an interaction term between whether the student had friends who were dropouts and clo- sure. It is possible that students who have friends who dropped out of school receive an extra benefit from a high degree of closure: Their parents

    use their contacts with their children's friends' parents to ascertain whether their children are hanging out with the wrong crowd. The interaction term was statistically insignificant, indicat- ing that closure has the same impact regardless of a student's peer network.

    An Endogeneity Problem?

    One potential objection to the finding of a strong association between 12th-grade closure and 12th-grade dropout status is that the observed relationship may be the result of a contamination effect (when a dependent variable causes change in an independent variable). In other words, it may be that drop- ping out of school causes a student to associate with friends outside the network to which his or her parents are linked, thereby reducing the stu- dent's degree of closure. If this was the case, one would find no differ- ence in the levels of closure between dropouts and nondropouts before students dropped out and expect dif- ferences in levels of closure to emerge only after the dropouts left school.

    To investigate whether a contami- nation effect was present in the results, I divided the sample into stu- dents who dropped out by the 12th grade and those who did not. I then compared the means for closure for both groups in the 8th grade (when none of the students had yet dropped out) and the 12th grade. The com- parisons revealed that in both the 8th and 12th grades, the 12th-grade dropouts had lower levels of closure than did the nondropouts and that the difference in means between the two groups was constant over time. This finding suggests that a contami- nation effect is not present in the results and that the act of dropping out did not influence the level of clo- sure among the dropouts.

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  • Intergenerational Closure and Educational Outcomes 309

    I also ran Models 2-4 (Tables 2-4), replacing the 12th-grade reports of closure with the 8th-grade reports. Although the 8th-grade reports are not ideal for reasons previously stat- ed, these items have the advantage of having been measured before the out- come variables and thus cannot be effects of these outcomes. Table 5 shows the comparisons between the two sets of analyses.

    When 12th-grade closure was replaced by 8th-grade closure in Models 2-4 with the achievement tests as the dependent variables, there were only minor changes in the results. There was no statistically significant association between 8th- grade closure and achievement in either history or science, but unlike 12th-grade closure, 8th-grade clo- sure had a positive, statistically sig- nificant association with reading achievement (b = .2 14, p = .012). However, in Models 3 and 4, this association became small and statis- tically nonsignificant. In mathemat- ics, as with 12th-grade closure, there was a positive association between 8th-grade closure and achievement. The association was slightly smaller for 8th-grade than for 12th-grade closure and became nonsignificant in Models 3 and 4.

    As with 12th-grade closure, 8th- grade closure did not have a signifi- cant association with the cumulative GPA in any model. Finally, 8th-grade closure was positively associated with the likelihood of staying in school, but the size of the coefficient was about half that of 12th-grade closure in Model 2. In Models 3 and 4, the size of the association between 8th-grade closure and staying in school decreased more dramatically than that of the association between 12th-grade closure and staying in school. Overall, the similar results produced by these additional analy- ses provide added confidence that there was no serious endogeneity problem in the analyses.

    DISCUSSION

    According to the findings, higher degrees of intergenerational closure have mixed effects on educational outcomes. For academic achieve- ment, they have a significantly posi- tive association with mathematics achievement but not with reading, history, or science achievement. A substantial portion of this associa- tion is a reflection of differences in parents' expectations. When the amount of skipping classes, absen-

    Table 5. Comparison of Analyses Using 8th- and 12th-Grade Closure

    Dependent Variable Model 2 Model 3 Model 4

    12th-Grade Mathematics Achievement

    8th-grade closure .406** .065 -.050 12th-grade closure .490 .308 .138

    12th-Grade Dropout Status 8th-grade closure .854 .902 .949

    12-grade closure .708 .724 .735

    Note: The coefficients for the mathematics achievement analyses are unstandard- ized regression coefficients, and the coefficients for the dropout analyses are odds ratios (or, explB]).

    *p< .05, **p< .01.

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  • 310 Carbonaro

    teeism, and suspensions and whether students had friends who were dropouts were controlled for, the association between closure and mathematics achievement decreased substantially and became statistical- ly insignificant. In addition, closure was not significantly associated with the cumulative GPA. Finally, increased levels of closure substan- tially decreased the odds that a stu- dent would drop out; for each friend's parent who was known, a student's odds of dropping out decreased by almost a third. When the amount of class skipping, absen- teeism, and suspensions and whether students had friends who were dropouts were controlled for, the association between closure and 12th-grade dropout status remained roughly the same.

    These findings raise important new questions: What is it about social networks with more closure that improves certain educational outcomes? Is it the greater and/or more reliable information about stu- dents' behavior that parents receive? Is it the stronger and more consis- tently well-defined norms or the greater social pressure to induce conformity?

    Unfortunately, these questions cannot by answered using the NELS data because information on these aspects of social capital (such as norms, information flows, expecta- tions) was not collected. Therefore, a major limitation of my closure mea- sure is that although it measures the size of a social network, it does not evaluate whether any of the behav- iors that Coleman and Hoffer (1987) claimed are present in closed net- works are actually occurring. Just because a parent knows all his or her child's friend's parents does not mean that all the parents have com- mon values and norms about educa- tional outcomes. Furthermore, these

    parents may not choose to use their connections with each other to enforce any common values and norms that they may have. An ideal measure of closure would examine the quality of the interactions among persons in a network, rather than simply the size of the network as my measure of closure does.

    Thus, it is possible that I may have underestimated the impact of closure on educational outcomes in this study because I did not distin- guish between families who knew all five of their children's friends' par- ents and engaged in the behaviors that Coleman described and those who knew all five friends' parents but did not engage in such behaviors. In other words, it is the interaction between the size of the network and the behaviors that the members of the network engage in that is essen- tial to Coleman's theory. In short, a more rigorous test of Coleman's theo- ry of intergenerational closure would involve the use of data on the quality, as well as the quantity, of the social networks in which children are situ- ated.8

    Although it is possible to gather this type of information from sur- veys, ethnographic research in this area would be especially helpful. It might provide some finer details about closed networks that might better guide those who are interested in collecting more data and perform- ing more sophisticated analyses on this issue than the one performed here. Ethnographic research on kin networks provided much valuable information that could not be dis- cerned from quantitative analyses (see Stack 1974). Perhaps ethno- graphic studies on closed networks and educational outcomes might pro- duce important revelations as well.

    Finally, what are the practical implications of this study? Since the mechanism underlying the associa-

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  • Intergenerational Closure and Educational Outcomes 311

    tion between more closure and stay- ing in school remains unknown, I am reluctant to make any policy recom- mendations. However, if further research reveals that Coleman's the- ory regarding the importance of clo- sure and social capital is correct, schools may want to reassess the types of opportunities they provide for parents to interact with each other. It would not be difficult or costly to give parents more opportu- nities to become acquainted with one another to form larger, more cohesive social networks with more closure. Indeed, if social capital truly is a resource, expanding such opportuni- ties would give all parents-including the most socially isolated-access to a potentially powerful tool to enhance their children's educational outcomes.

    NOTES

    1. In the base year, all the stu- dents took the same set of tests. To avoid potentially serious ceiling and floor effects, they were given different tests that varied in difficulty in the first and second follow-ups. The stu- dents' scores on their 8th-grade reading and mathematics tests deter- mined which set of tests they would take in the follow-ups (NCES, 1994:48).

    2. Item response theory (IRT), a scaling technique, enables a researcher to estimate a student's test score even when the students have not all taken the same version of the test.

    3. I recoded these items by com- bining the legitimate skips and the no's in the same category. I then set yes equal to 1 and no equal to 0, so the range of closure would be 0-5 and a higher number would indicate more closure.

    I also experimented with another operationalization of intergenera-

    tional closure, called "closure II." I constructed this version on the assumption that a parent's acquain- tance with his or her child's closest friend's parents is more important than an acquaintance with the par- ents of the child's more distant friends. If parents rely on their chil- dren's friends' parents for valuable information about their children's behavior, it seems logical to assume that they gain more information from the parents of the friends with whom their children spend the most time. To account for this possibility, I coded closure II as follows: A parent received a score of three for each of the child's first or second friend's parents she or he knew, a score of two if he or she knew the child's third friend's parents, and a score of one for each of the child's fourth or fifth friend's parents she or he knew. By assigning different weights to dif- ferent parents, I treated parents who knew their child's closest friend's parents as having more closure than those who knew only their child's more distant friend's parents.

    To see if these two operationaliza- tions yielded different results, I ran two sets of preliminary regression equations that differed in only one respect: One used closure as an independent variable, while the other used closure II. The two sets of regressions yielded similar results. Hence, I decided to use closure because it is a simpler operational- ization that is easier to interpret.

    4. The categories for these vari- ables were recoded to the midpoint, except for the lowest category ('none'), which was coded 0, and the highest category ("15 times or more"), which was coded as one more than the number mentioned.

    5. Students who reported having no close friends who were dropouts were coded 0, and students who reported that some, most, or all their

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  • 312 Carbonaro

    close friends were dropouts were coded 1.

    6. For the variables class skip- ping, suspensions, and absenteeism, roughly 2 percent of the cases were affected by this procedure. For par- ents' expectations, the procedure affected 5 percent of the cases, and for friends as dropouts, about 8 per- cent. About 20 percent of the cases for the parents' communication scale and roughly 15 percent of the cases for the parents' participation scale were affected.

    7. I calculated this average by subtracting the average achievement level in the 8th grade from the aver- age achievement level in the 12th grade. I then divided the total by four (the number of years of high school) to get a year of growth.

    8. What criteria did the parents use to determine whether they 'knew" the parents of their children's friends? Did meeting a parent once constitute "knowing" him or her? In addition, did all the parents use the same opera- tional definition of knowing in answer- ing these survey questions? Also, it is not clear how the parents distin- guished between their child's first and fifth friend-what criteria they used in making this distinction. Did their classification concur with their chil- dren's? Unfortunately, there is no way to answer any of these questions using the NELS data. Since the NELS did not ask the students the same questions about their parents' acquaintance with their friends' par- ents, there is no way to determine how reliable my measure is.

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    William J. Carbonaro, MA, is a Ph.D. candidate, Department of Sociology, University of Wisconsin-Madison. His main fields of interest are sociology of education, social stratification, and social organization. He is currently conduct- ing a study of how cross-national differences in social-labor market institutions affect the relationships among credentials, skills, occupational attainment, and earnings.

    The author thanks Adam Gamoran for his insightful comments and Warren Hagstrom and Gary Sandefurfor their helpful suggestions on this article. The final work on this article was supported by a grant from the Office of Educational Research and Improvement to the Center on English and Learning Achievement, Wisconsin Center for Education Research, University of Wisconsin-Madison (Grant No. G-00869007-89). The findings and opinions are the author's and do not necessarily reflect the views of the supporting agen- cies. Address all correspondence to William Carbonaro, Department of Sociology, University of Wisconsin-Madison, 1180 Observatory Drive, Madison, WI 53706, or by E-mail at wjcarbon@students.wisc.edu.

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    Article Contentsp. 295p. 296p. 297p. 298p. 299p. 300p. 301p. 302p. 303p. 304p. 305p. 306p. 307p. 308p. 309p. 310p. 311p. 312p. 313

    Issue Table of ContentsSociology of Education, Vol. 71, No. 4 (Oct., 1998), pp. i-ii+269-356Volume InformationFront Matter [pp. i-ii]E Pluribus Unum: Bilingualism and Loss of Language in the Second Generation [pp. 269-294]A Little Help from My Friend's Parents: Intergenerational Closure and Educational Outcomes [pp. 295-313]Sector Differences in High School Course Taking: A Private School or Catholic School Effect? [pp. 314-335]Gender Differences in Parental Involvement and Adolescents' Mathematics Achievement [pp. 336-356]Back Matter

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