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OCR for page 27
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
OCR for page 28
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
OCR for page 32
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:
OCR for page 37
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
OCR for page 87
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
OCR for page 88
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)
OCR for page 89
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.
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Representative terms from entire chapter:
integrating federal
