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Child Development in the Context of Family and Community Resources . An Agenda for National Data Collections Jeanne Brooks-Gunn, Brett Brown, Greg J. Duncan, and Kristin Anderson Moore INTRODUCTION The last decade has witnessed a remarkable transformation in social science data and research on child and adolescent development. Coming from quite different starting points, child development researchers, sociolo- gists, and economists have converged in their needs for rich, multilevel data based on large samples. Beginning from an interest in socioeconomic at- tainment, sociologists and economists have produced a burgeoning litera- ture on the factors that foster and undermine attainment; they now find themselves needing to delve deeper into the processes labeled socioeco- nomic status to understand how individual characteristics and family pro- cesses interact with community influences to produce socioeconomic attain- ment. Developmentalists have a tradition of conducting rich and detailed studies using small samples to examine in depth the processes by which children's characteristics interact with parental socialization practices dur- ing childhood. This approach has produced a voluminous literature that now seeks to test its theories and findings with data based on larger, more representative samples. This intersection of interests from the fields of Jeanne Brooks-Gunn is at the Center for the Study of Children and Families, Teachers College, Columbia University. Brett Brown and Kristin Anderson Moore are at Child Trends, Inc., Washington, D.C. Greg J. Duncan is at the Center for Urban Affairs and Policy Re- search, Northwestern University. 27

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28 INTEGRATING FEDERAL STATISTICS ON CHILDREN child development, sociology, and economics places great demands on ex- isting data systems (Brooks-Gunn et al., 1991; Duncan, 1991; Cherlin, 1991). Despite an almost exclusive concentration on problem behaviors and a paucity of theoretical models that evaluate the full set of factors that influ- ence children's development (Bronfenbrenner, 1979), the evolving litera- ture is demonstrating that individual, family, neighborhood, and school variables all contribute to children's development (Brooks-Gunn et al., 1993a; Rosenbaum and Popkin, 1991; Alexander et al., 1993; Furstinberg et al., 1987; Rutter, 1985; Maccoby and Martin, 1983; Eccles, 1983; Sigel, 1985; Werner and Smith, 1982; Moore et al., 1994; Duncan et al., 1994a). However, findings on the relative importance of these domains in determining varied outcomes await both better data and further research. Common to most of this research is the predominant use of large na- tional datasets compiled by federal agencies or by survey organizations funded by federal agencies. Some of these datasets, such as High School and Beyond and the National Educational Longitudinal Surveys, were de- signed explicitly by the National Center for Educational Statistics (NCES) for analyses of adolescent outcomes and transitions to adulthood. Others, such as the Child Supplements to the National Longitudinal Surveys of Youth, are based on question modules added to datasets conceived prima- rily for other (in this case, labor market) purposes. Researchers working with still other datasets, such as decennial census microdata files with matched family- and neighborhood-level data, have been able to conduct valuable research on adolescent behavior by exploiting existing information that has been organized into a more useful form. Stimulated by the increasingly widespread and complex nature of social problems involving children and adolescents (National Commission on Children, 1991; Hernandez, 1993), federal efforts to initiate or supplement data col- lection activities appear to be increasing. This is reflected in plans in the Survey of Income and Program Participation for a new supplemental mod- ule on family processes and developmental outcomes, NCES's initiation of a large cohort study of 5-year-olds, and consideration by the National Cen- ter for Health Statistics of a 1996 Child and Family Health Survey as part of the National Health Interview Survey. In this paper we suggest specific national data collection projects that could improve research on child and adolescent development.) Our explicit aim is to encourage continued expansion of both the outcome domains cov- ered and the explanatory variables measured, to enhance the richness and quality of the data obtained, and to improve the representativeness of the samples that are drawn. These improvements would serve both the policy and academic research communities in their efforts to specify and estimate causal models of child, adolescent, and young adult behavior. To this end, we begin with developmental theory and summarize key

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CHILD DEVELOPMENT AND FAMILY AND COMMUNITY RESOURCES 29 elements of an emerging "resource" framework, which we believe provides an integrative framework for understanding how child and adolescent devel- opment is affected by the time, money, and emotional resources of parents; by the institutions and "social capital" present in communities and neigh- borhoods; and by government policies that shape the context within which parental choices are made. Next we explain and provide empirical examples of key elements of datasets that have proved especially useful in testing and drawing policy conclusions from the theoretical framework we advocate. Some of the elements we list consist of the outcomes, resources, and family processes identified by theory as important. Others are important methods for imple- menting and estimating child development models. Many of the illustra- tions are based on results from smaller-scale studies; all have implications for the data collection improvements we outline in this paper. As we detail in the sections that follow, when assessing outcomes, a number of features are critical: High-quality, longitudinal assessments of child outcomes, obtained from the child as well as the parent and, when necessary, by trained profes- sionals who test the child directly, and assessing how the factors that affect children influence development over periods of a decade or more; Measurement of age-appropriate outcomes and transitions; and Outcomes measured across multiple domains of functioning. Also, as we discuss, measurement of resources needs to attend to the fol- lowing: High-quality, longitudinal measurement of family resources, that is obtaining measures of a broad range of economic and social resources peri- odically over the years when a child or adolescent is growing up; Measures of time "inputs," including the amount of time, the activi- ties engaged in, and the persons present and interacting with a child; Measurement of family-process mediators, such as communication patterns, disciplinary style, and teaching style; Multiple levels of measurement, including the child, the family, the school, the community, the neighborhood, and the state; Measurement of school conditions, such as school organization and the socioeconomic composition of the school; Exact measurement of intrafamily relationships; and Measurement of extended-family relationships, including relationships with grandparents, aunts, and uncles. Methodological and sampling considerations are also important, including:

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30 INTEGRATING FEDERAL STATISTICS ON CHILDREN "Natural experiment" methods of model testing; Leverage for policy analyses provided by state-to-state variation in program benefits and structure; Oversamples of minority groups; Multiple informants; and Procedures to minimize and adjust for attrition in longitudinal sur veys. We next review the content of 12 existing national data collections in light of our list of desirable design features of developmental modeling, including: ( 1 ) Consumer Expenditure Surveys; (2) Decennial census; (3) High School and Beyond; (4) National Crime Victimization Survey; (5) National Educational Longitudinal Survey of 1988; (6) National Health Interview Survey (NHIS) - Child Health Supplement 1988; (7) National Longitudinal Survey of Youth (1979 Cohort); (8) NLSY Child-Mother Data; (9) National Survey of Families and Households; (10) Panel Study of Income Dynamics; ( 1 1 ) National Survey of Children; and (12) Survey of Income and Program Participation. Table 1 lists salient characteristics of each survey, including measures of family resources and processes, measures of the extrafamilial context (e.g., neighborhood, school, peer group, county, and state), special advan- tages and disadvantages, sample size, and periodicity. Table 2 summarizes available child outcome measures for each survey by age group. Our theoretical discussion and empirical illustrations lead us directly to a set of suggested improvements, involving both incremental and more sub- stantial investments in federal datasets that would enhance their value for research on child and adolescent development. In some cases, the sugges- tions involve minor and quite inexpensive changes that would produce large analytic benefits. In others, more expensive changes could open up invalu- able analytic opportunities. We conclude with ideas for an even more expensive undertaking, a new longitudinal survey of children, outlining key design elements of such a survey.

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CHILD DEVELOPMENT AND FAMILY AND COMMUNITY RESOURCES FAMILY AND COMMUNITY RESOURCES AND ClIILDREN'S DEVELOPMENT OVER TIME 31 Many different frameworks have been used to study how children de- velop and the factors that influence development during childhood, adoles- cence, and the early stages of adult life. Such frameworks include: family systems approaches, risk and resilience, family and extra-family ecology, the life course, and economic decision making. Almost all of them con- sider, at least in passing, the ecology in which development occurs (con- text), as well as the stage or phase of life in which an individual is placed (time). However, these frameworks differ markedly in their relative empha- ses on time and context. Moreover, they also differ in how they examine the individual moving through time and context. Increasing interest in interdisciplinary research has focused attention on the value of different frameworks as well as the importance of looking at multiple mechanisms underlying development in any one study (Brooks-Gunn, in press; Brooks- Gunn et al., 1991; Duncan, 1991; Cherlin, 1991~. A number of investigative teams now combine scholars of macro issues (economists, sociologists, and demographers) and scholars concerned with more micro-oriented issues (developmental and clinical psychologists, pe- diatricians). Examples of such endeavors include Cherlin et al. (1991), Duncan et al. (1994a), Baydar and Brooks-Gunn (1991), Baydar et al. (1993), and Desai et al. (1989~. The National Institute of Child Health and Human Development (NICHDJ has recently initiated a Family and Child Well- Being Research Network, comprised of seven researchers and their col- leagues; the seven teams in the NICHD network represent all six disciplines mentioned above. Discipline-focused perspectives can be integrated into a framework based on familial and extrafamilial resources. The model we employ borrows heavily from the work of Coleman on social capital theory (1988) as well as the recent work by Haveman and Wolfe on choice-investment theory (1994~. However, it departs from these two efforts in making more explicit the links with disciplines that focus on familial and extrafamilial processes, e.g., systems theory, ecological theory, and psychological-resource or social-support theory. Resources Like Haveman and Wolfe (1994), we view "resources" very broadly, defining them as consisting of the money, time, interpersonal connections, and institutions that parents and communities may use to promote the devel- opment of children. Resources actually spent on promoting child and ado- lescent development are considered "investments" since, independent of

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32 INTEGRATING FEDERAL STATISTICS ON CHILDREN TABLE 1 Review of Federal Survey Contents and Characteristics Family Material Family Contextual Survey Resources Process Data Panel Study of income spend neighed Income Dynamics source marhist zip (PSID) welfare biopar county hlthins state assets move tenure month year multi National income time school Longitudinal source biopar county Survey of Youth welfare state (NLSY) hlthins peer assets move tenure month year National income ppcnflct county Longitudinal source commun state Survey of Youth welfare marhist move Child-Mother hlthins biopar Data (NLSY-CM) assets tenure month year National income time neighed Educational assets activty school Longitudinal multi rules Survey of 1988 common (NELS88) spend National Survey income time neighed of Children (NSC) source activty school welfare ppcnflct zip tenure pccnflct state multi soccap peers rules move commun violnce marhist deprsn biopar

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CHILD DEVELOPMENT AND FAMILYAND COMMUNITY RESOURCES 33 Survey Characteristics Special Special Sample Advantages Problems Size Periodicity sibs foster 4,800 Annual, since black institut households 1968 Latino in 1968; 7,900 absparent households in exact 1993 tract sibs nopar 12,686 in 1979 Annual, since black 1979 Latino child exact cheval sibs follow 6,503 children 1986, 88, 90, black foster in 1992 92, 94, Latino institut (biennial) exact cheval unrep Latino foster 24,599 in 1988; 1988, 90, 92, Asian 21,188 in 1992 94, 98 child teach exact sibs institut 2,301 in 1976; 1976, 1981, black 1,147 in 1987 1987 child abspar teach exact cheval

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34 TABLE 1 Continued INTEGRATING FEDERAL STATISTICS ON CHILDREN Survey Family Material Resources Family Process Contextual Data Survey of Income and Program Participation (SIPP) National Survey of Families and Households (NSFH) High School and Beyond (HS&B) Consumer Expenditure Surveys (CEX) National Crime Victimization Survey (NCVS) Decennial Census, Public Use Micro- Sample (5%) Income source welfare hlthins assets Income source welfare assets tenure multi marhist biopar time ppcnflct pccnflct soccap rules commun violnce marhist deprsn biopar income commun source spend welfare assets tenure multi income source welfare hlthins assets tenure Income hlthins tenure year Income source welfare tenure state move county move school peer spend violnce move county state move

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CHILD DEVELOPMENT AND FAMILY AND COMMUNITY RESOURCES 35 Survey Characteristics Special Special Sample Advantages Problems Size Periodicity bothpar follow 20,000 households Every 4 absparent institut in 1993 months for exact 30 months sibs sib institut 13,017 households 1987-1988, black in 1987-1988; 1992-1993 Latino 7,926 children child bothpar absparent exact sib foster 58,270 in 1980; 1980, 82, 84 black nopar 24,354 in 1986 86; 1992 Latino (small sub teach sample) cheval foster 5,000 households Every quarter institut for 5 quarters sib follow 47,600 households Every 6 child foster in 1990; 9,400 months for bothpar institut children (age 12+) 36 months immig in 1990 sib 15 million in 1990 Decennial (cross-sectional)

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36 TABLE 1 Continued INTEGRATING FEDERAL STATISTICS ON CHILDREN Survey Family Material Family Resources Process Contextual Data National Health Interview Survey Child Health Supplement Income welfare hlthins marhist move Family Material Resources income: summary measures of family income source: sources of family income are identified welfare: welfare receipt hlthins: health insurance coverage assets: family assets tenure: whether rent or own home month: year: multi: income data reported on a monthly basis income data reported on a yearly basis income data reported every few years Family Process time: amount of time spent by parent(s) with child activty: activities between parent and child ppcnflct: conflict between parents pccnflct: conflict between parent(s) and child soccap: social capital measures (e.g., extended kin and community contact(s) rules: house rules for child regarding homework, television watching, bed time, dating, etc. frequency, styles and/or content of communication between parent and child reports of physical violence within the family family spending patterns parental marital histories parental depression measures all biological parents of child within household are identifiable commun: violnce: spend: marhist: deprsn: biopar:

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CHILD DEVELOPMENT AND FAMILY AND COMMUNITY RESO URCES 37 Survey Characteristics Special Special Sample Advantages Problems Size Periodicity black institut 17,110 children 1981, 1988 absparent immig in 1988 (cross-sectional) exact Contextual Data neighhd: measures of neighborhood characteristics school: measures of school and/or classroom characteristics (e.g., curriculum, student body demographics) zip: zip code level data available, or zip code identified county: county-level characteristics available, or county identified state: state-level data available, or state identified information on peers of child (e.g., characteristics, attitudes) residential mobility history Special Advantages sibs: siblings are included in the samp Black: Black oversample Latino: Latino oversample Asian: Asian oversample child: child is surveyed bothpar: both parents are surveyed, if in same household exact relationship of child to all household members is determined tract-level data has been appended to the survey absparent: data on the absent (noncustodial) parent is gathered cheval: child evaluations are performed in person or through standardized tests peer: move: exact: tract: le and identified Special Problems follow: foster: institut: unrep: child is not followed if child moves to a new household foster children not included is sample frame, or not separately identified institutionalized children not included in sample frame sample is not nationally representative nopar: parents are not surveyed immi.: cannot identify children of immigrants

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CHILD DEVELOPMENT AND FAMILY AND COMMUNITY RESOURCES ACKNOWLEDGMENTS 87 The authors shared equally in the preparation of this paper and are listed in alphabetical order. Work on the paper was supported by the workshop's sponsors and by the National Institute of Child Health and Human Development's Family and Child Well-Being Research Network, of which Brooks-Gunn, Duncan, and Moore are members. Two of the authors are affiliated with datasets reviewed in this paper: Moore with the National Survey of Chil- dren and Duncan with the Panel Study of Income Dynamics. In addition, Child Trends, Inc., and Brooks-Gunn are part of a consortium of organiza- tions that have bid to design the Department of Education's Early Child- hood Longitudinal Survey. In writing this paper we have drawn freely upon a number of earlier efforts: Moore et al. (1994J, Haveman and Wolfe (1994), Moore (1993), Watts and Hernandez ~ 1982), Zill ~ 1989), and Zill et al. ~ 19841. We are indebted to Deborah Phillips for helping us conceptualize our task and to helpful comments from Don Hernandez, Gary Sandefur, Terry Adams, Dor- othy Duncan, Valerie Lee, Susan Mayer, Robert Moffitt, Bruce Taylor, Diane Hansen, Dennis Carol, Susan Mayer, Jennifer Maddens, Michael Pergamit, Felicia LeClere, and the NICHD Research Network on Family and Child Well-Being. NOTES 1. There are several exciting new federal data collection efforts, currently in the planning stages, that are not reviewed in this paper. The Department of Educa- tion intends to fund an Early Childhood Longitudinal Educational Study. This is to be a large, school-based, nationally representative, longitudinal survey of kindergar- ten children. A second effort, recently funded by the National Institute of Child Health and Human Development, is a National Longitudinal Study of Adolescent Health. This survey has as its goal to provide a better understanding of the complex forces that promote good health and those that increase risk among the nation's adolescents. 2. Randomized trials allow for an estimation of the effects of a particular treatment, in this case a family or community resource. However, in many cases involving federal or state programs, it is impossible to conduct randomized trials (see, for example, the paucity of such research in the literature on Head Start and the Supplemental Food Program for Women, Infants, and Children; McKey, 1985; Zigler and Muenchow, 1992; Lee et al., 1990; Rush et al., 19801. In addition, when community-level resources are the target of intervention, randomized trials are often not appropriate (i.e., randomizing communities is difficult, since the sample size is based on communities, not individuals, and communities are usually not comparable on all the possible dimensions of interest). 3. In an analysis of the determinants of cognitive test scores of 3-7-year-olds, Moore and Snyder found that mother's education and poverty status were not sig

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88 INTEGRATING FEDERAL STATISTICS ON CHILDREN nificant predictors once controls for mother's score on the Armed Forces Qualifica- tion Test and measures of the home environment (as measured by the HOME scale), which were highly significant, were included. 4. Both Brooks-Gunn et al. (1993a) and Clark (1993) find that measures of affluent neighbors are more important than measures of low-income neighbors. Crane (1991) interprets his neighborhood measure (the percentage of workers in profes- sional or managerial occupations) in terms of epidemic models, but it clearly mea- sures the presence or absence of affluent neighbors. In one exception, Brown (1990), using the 1970 U.S. Census Neighborhood PUMS file, found evidence consistent with an "underclass neighborhood" or epidemic hypothesis for both white and black females when looking at teen nonmarital births and for white females when looking at high school completion. Evidence for white and black males regarding high school completion and idleness was not consistent with such hypotheses, however. 5. It is considerably more expensive to geocode addresses beyond wave 1 in longitudinal studies, since address matching would be involved and it is impossible to match addresses to census geocodes without at least some tedious map work. There would be substantial value in this additional geocoding. But the greatest value, especially given its low cost, would be in geocoding the wave 1 addresses and matching STF3 census data to these addresses. 6. One valuable piece of neighborhood information that could be distributed more widely is a scrambled version of the tract/BNA identifier. Clustered samples typically select several families per block (or adjacent blocks). It is analytically very useful to be able to sort children into groups (1) same family (i.e., siblings), same neighborhood; (2) different family, same neighborhood; and (3) different family, different neighborhood even if the actual characteristics of the neighborhoods are not known. These groups form the basis of an analysis-of-variance type of account- ing of family and neighborhood effects. To perform this kind of analysis, one need not know any of the actual characteristics of the neighborhoods, but only whether survey families share the same neighborhood. Although this analysis-of-variance accounting capability would be quite useful analytically, it is less valuable than and no substitute for the actual decennial census measures matched to the family- and individual-level survey data. 7. An example of the importance of studying different family structures as well as the processes within families is the well-documented fact that the movement from a traditional family structure to a single-parent structure (divorce) results in less optimal functioning across domains. However, the effect is not just due to a single-parent household, in that remarriage, and the entrance of a stepparent, do not alter substantially the well-being of children (Hetherington, 1993; McLanahan et al., 1991; Garfinkel and McLanahan, 1986; Kiernan, 1992). 8. For more detailed descriptions of each survey, consult Child Trends (1993). 9. Possible national sampling frames include census-based dwellings and list- based schools. The former provides national samples of noninstitutionalized fami- lies and children; the latter national samples of school-age children. The former clusters children within neighborhoods and families; the latter within schools. As already noted, clustering provides analytic advantages in the form of sibling, neigh- bor, and classmate comparisons. Choice of sampling frame depends on the value of covering preschool children (which is part of dwelling but not school-based frames)

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CHILD DEVELOPMENT AND FAMILY AND COMMUNITY RESO URCES 89 as well as the comparative advantages of clustering by siblings and neighbors versus classmates. Although we see some arguments for school-based clustering, there is greater analytic value in clustering by family and neighbor and in coverage of preschool children. 10. A new cohort of the NLSY will be fielded in spring 1996. All adolescents ages 12-17 in the household will be interviewed and followed over time; however, there are no plans to interview children age 11 or younger. REFERENCES Alexander, K.L., D.R. Entwisle, and S.L. Dauber First-grade classroom behavior: Its short- and long-term consequences for school performance. Child Development 64(3):801-814. Baydar, N. 1988 Effects of parental separation and re-entry into unicorn on the emotional well-being of children. Journal of Marriage and the Family 50:967-981. Baydar, N., and J. Brooks-Gunn 1991 Effects of maternal employment and child-care arrangements in infancy on preschoolers' cognitive and behavioral outcomes: evidence from the children of the NLSY. Developmental Psychology 27(6):932-945. Baydar, N., J. Brooks-Gunn, and F.F. Furstenberg, Jr. 1993 Early warning signs of functional illiteracy: predictors in childhood and adoles- cence. Child Development 64(3):815, 829. Belsky, J. 1984 The determinants of parenting: a process model. Child Development 55(1): 83-96. Berendes, H., S. Kessell, and S. Yaffe, eds. 1991 Advances in Low Birthweight: An Interactional Symposium. National Center for Education in Maternal and Child Health. Bornstein, M., ed. 1995 Handbook of Parenting. Hillsdale, N.J.: Erlbaum. Brim, O.G., and J. Kagan 1980 Constancy and Change in Human Development. University Press. Bronars, S., and J. Grogger 1992 Bronfenbrenner, U. Washington, D.C.: Cambridge, Mass.: Harvard The Economic Consequences of Teenage Childbearing: Results from a Natural Experiment. Paper presented at the National Institute for Child Health and Devel- opment Conference on outcomes of early childbearing, Bethesda, Md., May 1992. 1979 Contexts of child rearing: problems and prospects. American Psychologist 34:844 850. Brooks-Gunn, J. 1 990 in press Research on step-parenting families: integrating discipline approaches and in- forming policy. In A. Booth and J. Dunn, eds., Step-Parent Families with Chil- dren: Who Benefits and Who Does Not? Hillsdale, NJ: Erlbaum. Identifying the vulnerable young child. Pp. 104-124 in D.E. Rogers and E. Ginzberg, eds., Improving the Life Chances of Children at Risk. Boulder, Colo.: Westview Press. Brooks-Gunn, J., and P.L. Chase-Lansdale 1995 Adolescent parenthood. Pg. 10 in M. Bornstein, ea., Handbook of Parenting. Hillsdale, N.J.: Erlbaum.

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9o INTEGRATING FEDERAL STATISTICS ON CHILDREN Brooks-Gunn, J., G.J. Duncan, P.K. Klebanov, and N. Sealand 1993a Do neighborhoods influence child and adolescent behavior? American Journal of Sociology 99(2):353-395. Brooks-Gunn? J., G. Guo, and F.F. Furstenberg, Jr. 1993b Who drops out of and who continues beyond high school?: a 20-year study of black youth. Journal of Research in Adolescence 3(37):271-294. Brooks-Gunn, J., P.K. Klebanov, F.R. Liaw, and D. Spiker 1993c Enhancing the development of low-birth-weight, premature infants: change in cognition and behavior over the first three years. Child Development 64(3): 736- 753. Brooks-Gunn, J., E. Phelps, and G.H. Elder 1991 Studying lives through time: secondary data analyses in developmental psychol- ogy. Developmental Psychology 27(6):899-910. Brooks-Gunn, J., and M. Weinraub 1983 Origins of infant intelligence testing. Pp. 25-66 in M. Lewis, ea., Origins of Intelligence, 2nd Edition. New York: Plenum Press. Brown, B. 1 990 The Effect of Neighborhood Characteristics on Teen Outcomes Related to Socio- economic Attainment: In Search of the Underclass Neighborhood. Dissertation. University of Wisconsin, Madison. Chase-Lansdale, P.L., J. Brooks-Gunn, and E.S. Zamsky 1994 Young African-American multigenerational families in poverty: quality of moth- ering and grandmothering. Child Development 65(2):373-393. Chase-Lansdale, P. L., F.L. Mott, J. Brooks-Gunn, and D.A. Phillips 1991 Children of the NLSY: a unique research opportunity. Developmental Psychology 27(6):918-931. Cherlin, A.J. 1991 On analyzing other people's data. Developmental Psychology 27(6):946-948. Cherlin, A.J., F.F. Furstenberg, Jr., P.L. Chase-Lansdale? K.E. Kiernan, P.K. Robins, and D.R. Morrison 1991 Longitudinal studies of effects of divorce on children in Great Britain and the United States. Science 252:1386-1389. Child Trends 1993 Researching the Family: A Guide to Survey and Statistical Data on U.S. Families. Washington, D.C.: Child Trends, Inc. Clark, R. 1993 Nei:,hbc~rhood Effects on Dropping Out of School among Teenage Boys. Mimeo. Urban Institute, Washington, D.C. Coleman, J. 1988 Social capital in the creation of human capital. American Journal of Sociology 94:95-120. Committee on Ways and Means 1993 1993 Green Book: Background Material and Data on Brogans within the Juris- diction of the Committee on Ways and Means. Washington, D.C.: U.S. Govern- ment Printing Office. Cowan, P.A., and C.P. Cowan 1990 Becoming a family: research and intervention. Pp. 1-51 in I. Sigel and A. Brody. eds., Family Research. Hillsdale, N.J.: Erlbaum. Crane, J. 1991 The epidemic theory of ghettos and neighborhood effects on dropping out and teenage childbearing. American Journal of Sociology 96(5):1126-1159.

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