Duncan Raudenbush Context & Youth

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Assesing the effects of context in studies of child and youthdevelopmentGreg J. Duncan & Stephen W. RaudenbushPublished online: 08 Jun 2010.

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EDUCATIONAL PSYCHOLOGIST, 34(1), 29-41 Copyright O 1999, Lawrence Erlbaum Associates, Inc.

Assessing the Effects of Context in Studies of Child and Youth Development

Greg J. Duncan Northwestern University

Stephen W. Raudenbush University of Michigan

Children develop in a multitude of social environments (Bronfenbrenner, 1979). Indeed, human development is un- thinkable without social settings created to protect, feed, and nurture children and to teach them to speak and interact with others; or without inputs from a broader community of family friends, relatives, and institutions (Haveman & Wolfe, 1994).

Because social settings such as the family and larger community-including neighborhoods, schools, and peers-are essential to making the child fully human, it may seem odd that social science research is far from definitive about whether "context matters." This is due in large part to the varying senses in which context might be said to matter within the logic of social science. For many social scientists, including sociologists who study status attainment and psy- chologists who focus on individual differences, context can be said to matter if differences among social contexts are found to be important in explaining individual differences in achieving ends most of us value-mental health, literacy, intellectual growth, educational attainment, occupational status, and the like. Program evaluators wony primarily about effect sizes, rather than explained variance, and economists extend that wony to include benefits associated with effect sizes relative to cost.

In considering the methodological issues facing the design- ers of studies of contextual effects, we generally refer to such effects in the narrow sense used by social scientists who study individual differences. Thus, we consider past research on the ability of variations in contexts to account for variation in child outcomes and to identify how designs of future studies might use such variation to identify important contextual influences.

This research is relevant to social policy aimed at improv- ing settings such as neighborhoods and schools; for if certain

Requests for reprints should be sent to Greg J. Duncan, Institute for Policy Research, Northwestern University, 2040 Sheridan Road, Evanston, IL

settings are found to be especially helpful in promoting de- sired child and youth outcomes, policy might aim to recreate those settings on a broader scale. And this research is also relevant for the design of new studies of child development that would rely on "naturally occurring" variation in social settings to gauge contextual effects. However, it is important to realize that effects may turn out to be small because the degree of natural variation is small, rather than because the setting is irrelevant. Correlational research based on naturally occurring variation can identify plausible consequences only if that variation currently exists.

The purpose of this article is to formulate research designs for studies of child and youth development that would "do context right." We choose neighborhood contexts to illustrate our points, but much of what we say also carries over to contexts such as schools. We begin with theoretical sto- ries about neighborhood effects to indicate the kinds of theories that one might want to collect data to test. We then present some evidence regarding where the contextual "action" might be.

We next present an overview of practical design and statistical issues for modeling contextual effects, emphasizing that although there is a need for theoretically appropriate contextual information from record-based sources, it is generally unavailable. For example, with respect to the neighborhood literature, we may believe that the degree of informal social control is a key theoretical construct (Sampson & Groves, 1989), but we may be constrained to neighborhood measures that appear on the decennial census form. Special data collection efforts can be undertaken to measure constructs more precisely, but they are often very expensive and become even more so in longitudinal studies, because geographic mobility increases the number of neighborhoods or schools exponen- tially as a sample is followed year by year.

In addition to measurement problems, a host of statistical difficulties is caused by the nonrandom selection of parents

60208. E-mail: greg-duncan@ nwu.edu and children into neighborhood, school, and other contexts.

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30 DUNCAN AND RAUDENBUSH

Most important, apparent effects of neighborhood characteristics may merely reflect unobserved parental characteristics such as concern for their children's development, parental mental health, or permanent family income. Residential mobility caused by natural or randomized experiments is rare. Researchers relying on nonexperimental data must find model-based solutions to these problems.

We conclude with a discussion of implications for study designs. One possible approach to the measurement problem is to geographically cluster the initial sample selection so that measurements taken from individual respondents can be aggregated across all respondents in a given geographic cluster to provide the contextual measures. As appealing as this strategy may seem, we argue that it is rarely satisfactory. For example, if measures of neighborhood quality are obtained by aggregating reports of survey participants and used as predictors of the mental health outcomes of those same participants, a same-source bias is likely. On the other hand, aggregating from independent samples of informants to predict outcomes of others can be a very attractive, if expensive, method for obtaining contextual information. Less expensive and also attractive is intensive interviewer-based observation of the environments. Except in cases of true or quasiexperimental designs, definitive solutions to the problem of possible biases caused by nonrandom parental selection of context are more elusive.

THEORIES AND EVIDENCE ABOUT NEIGHBORHOOD EFFECTS

Why might extrafamilial contexts such as a neighborhood affect a child's development? The literature is filled with proposed answers to this question, some, but not all, of which argue that environments of higher socioeconomic status (SES) are better for children. We provide in this section a brief and selective review of theories and evidence of neighborhood effects.

Why Neighborhood Conditions Might Matter

Neighborhoods have been conceptualized in various ways, encompassing geographic areas that range from a few blocks to entire community areas and defined by both objective and subjective means (Gephart, 1997). Jencks and Mayer (1990) developed a taxonomy of theoretical ways in which neighborhoods may affect child development. They distinguished (a) "epidemic" theories, based primarily on the power of peer influences to spread problem behavior; (b) theories of "collective socialization," in which neighborhood role models and monitoring are important ingredients in a child's socialization; (c) "institutional" models, in which the neighborhood's institutions (e.g., schools, police protection) rather than neighbors per se make the difference; (d) "competition" models, in which neighbors (including classmates) compete for

scarce neighborhood resources; and (e) models of "relative deprivation," in which individuals evaluate their situation or relative standing vis-h-vis their neighbors (or classmates).

Social disorganization theory suggests other neighborhood factors likely to influence child and adolescent development. For instance, following Shaw and McKay (1942), Sampson argued that a high degree of ethnic heterogeneity and residential instability leads to an erosion of adult friend- ship networks and of a values consensus in the neighborhood (Sampson & Lauritsen 1994). Wilson's (1987) explanation of inner-city poverty in Chicago relied on a more complicated model in which massive changes in the economic structure, when combined with residential mobility among more advan- taged Blacks, leave behind homogeneously impoverished neighborhoods that provide neither resources nor positive role models for children and adolescents growing up in them.

Furstenberg (1993) argued for the importance of understanding the role of family process in assessing neighborhood effects. Basing his work on ethnographic studies, he pointed out that families formulate different strategies for raising children in high-risk neighborhoods, ranging from extreme protection and insulation to an active role in developing community-based "social capital" networks that can help children at key points in their academic or labor-market careers.

Because adolescents typically spend a good deal of time away from their homes, explanations of neighborhood influences based on peers, role models, schools, and other neighborhood-based resources would appear to be more relevant for them than for younger children. However, it is possible that neighborhood influences begin long before adolescence. A substantial minority of 3- and Cyear-olds are enrolled in center-based day care or preschool (Hofferth & Chaplin, 1994). Physically dangerous neighborhoods may force mothers to be isolated in their homes and thus restrict opportunities for their children's interactions with peers and adults. Parks, libraries, and children's programs provide more enriching opportunities in relatively affluent neighborhoods than are available in resource-poor neighborhoods. Parents of high SES may be observed to resort less frequently to corporal punishment and to engage more frequently in learning-related play. Thus, there are many ways in which neighborhood conditions might affect both children and adolescents (Chase-Lansdale, Gordon, Brooks-Gunn, & Klebanov, 1997).

Distinguishing empirically among these competing theories is not an easy task. As Jencks and Mayer (1990) pointed out, relative deprivation and competition models of context generally predict negative effects of higher-SES neighbors on youth achievement and behavior, whereas all other models predict the opposite. Epidemic models focus on the presence of "problematic" peers and have often been implemented with measures of neighborhood poverty or adult unemployment.

In contrast, social control and institutional models focus more on the presence of higher SES neighbors than the pres-

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EFFECTS OF CONTEXT 31

ence or absence of low-SES neighbors. This distinction is subtle, but easily conceived if SES is thought to have at least three strata-say, low, medium, and high level of SES. The diversity of U.S. neighborhoods produces different combina- tions of these three strata, which enables researchers to distinguish empirically among their effects on developmental outcomes (Brooks-Gunn, Duncan, Klebanov, & Sealand, 1993).

Social disorganization and family process models are the hardest to implement empirically, because the required measures are not readily available from sources such as the decennial census. Thus, the empirical literature displays an uneasy tension between more representative studies using readily available but theoretically flawed neighborhood measures and smaller, more specialized studies with better context measures.

Empirical Studies of Neighborhood Effects

Every 10 years, the U.S. Census Bureau provides information that can be used to construct neighborhood-based measures, such as the fraction of individuals who are poor, the fraction of adults with a college degree, and the fraction of adult men without jobs. Such data are available for U.S. Census tracts (geographic areas encompassing 4,000 to 6,000 individuals with boundaries drawn to approximate neighborhood areas), zip codes, cities, counties, metropolitan areas, and other useful geographically defined areas.

As an example of a study of neighborhood effects using cross-sectional census data, Crane (1991) used data from a special linked family-tract file from the 1970 U.S. Cen- sus-based Public Use Microdata Sample (PUMS) file. He used these data to relate tract conditions to out-of-wedlock birth rates and high school dropout rates of adolescents in the tracts. He found highly nonlinear effects of neighborhood quality on adolescent outcomes, effects that are consistent with the so-called epidemic models of adolescent behavior. Dropping out of high school was very likely to occur among individuals, both Black and White, living in neighborhoods where fewer than 5% of workers in the neighborhood held professional or managerial jobs. Apart from neighborhoods in this extreme category, however, there was little evidence that neighborhood characteristics mattered.

The power of the epidemic model to describe patterns of neighborhood effects is called into question by Clark's (1992) failure to replicate Crane's results using similar data from the 1980 census. Although Clark found that several measures of neighborhood resources predict the high school dropout status of male adolescents, she failed to find substantial evidence of nonlinear effects such as those represented by "tipping points" beyond which neighborhood effects become visible.

As pointed out by Manski (1993), analyses such as these that are based on cross-sectional data may suffer from the "re-

flection problem." This occurs when the association between neighborhood- and family-level characteristics at the time the census is taken reflects the fact that neighborhood-level characteristics are nothing more than the aggregation of family- and individual-level characteristics. The neighborhood or peer-group crime rate may indeed correlate with the chance of observing criminal activity on the part of an adolescent living in that neighborhood or having those peers, but to what extent is this association truly causal?

Although certainly no panacea, longitudinal data provide some statistical leverage to help solve this problem, because the measurement of neighborhood characteristics can pre- cede the outcome variables of interest. Brooks-Gunn et al. (1993) used longitudinal data from the Infant Health and De- velopment Program (IHDP) and Panel Study of Income Dy- namics (PSID) to examine the impact of census-based neighborhood data-singly and in concert with family-level variables--on early-childhood IQ and behavior problems (in the IHDP) and adolescent school-leaving and out-of-wedlock childbearing (in the PSID). They found that the absence of affluent neighbors is much more important than the presence of low-income neighbors-findings that support models of beneficial institutions and collective socialization.

Analysts contributing chapters to Brooks-Gunn, Duncan, and Aber (1997) matched a number of developmental data sets to census-based neighborhood data and subjected them to parallel analyses. They found that (a) although there is some evidence of neighborhood effects in the preschool years, the most consistent evidence shows up among school-age children; (b) cognitive and achievement measures appear somewhat more sensitive to neighborhood influences than do behavioral and mental-health measures; (c) among the five neighborhood factors used (low SES, high SES, ethnic diversity, male joblessness, and the concentration of families in the neighborhood), the high-SES factor had the most consistently powerful effects; (d) Blacks were somewhat less affected by the neighborhood measures than Whites; and (e) important from a methodological point of view, multicolinearity problems arose in attempts to estimate separate effects of the five neighborhood factors using data from single cities or from fairly homogeneous neighborhoods across a small number of cities, but not in using data from national (PSID, National Longitudinal Survey of Youth) or heterogeneous, multisite data (IHDP).

Garner and Raudenbush (199 1) focused on neighborhood social deprivation as a predictor of overall educational attainment in Scotland. Key to their work was that any neighborhood effect prior to secondary schooling was effectively controlled by including measures of Primary 7 achievement (verbal IQ and reading proficiency at Primary Grade 7). Thus, the test of the neighborhood effect was a stringent one. Addi- tional control variables included SES (parental education and occupation, unemployment, family size) and school attended. The model accounted for essentially all of the variation between neighborhoods (enumeration districts, which are simi-

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lar to U.S. Census tracts) and between schools, as well as for over half of the variation within schools. Neighborhood social deprivation (a composite index from the British census) was strongly negatively related to overall attainment, after controlling for the above factors.

The work of Sampson, Raudenbush, and Earls (1997) is a noteworthy exception to the rule that studies of extrafamilial context are hampered by measures of context that fail to cor- respond closely to theoretical constructs. In a study of the delinquent behavior of youth in a sample of Chicago neighborhoods, they measured the "collective efficacy" of neighborhoods by conducting a survey of adult residents in sampled neighborhoods rather than by relying exclusively on some collection of decennnial census measures. For reasons detailed later, it is important to note that their neighborhood measures were derived from an independent survey of neighborhood residents and not an aggregation of the characteristics of the youth whose possible delinquent behavior was being studied.

Collective efficacy combines social cohesion (the extent to which neighbors trust each other and share common values) with informal social control (the extent to whlch neighbors can count on each other to monitor and supervise youth and protect public order). It is thus a capacity for collective action shared by neighbors. Sampson et al. (1997) found that collective efficacy so defined relates strongly to neighborhood levels of violence, personal victimization, and homicide in Chicago, after controlling for social composition (as indicated by census variables) and for prior crime.

Sampson et al. (1997) also found that collective efficacy substantially mediates associations of concentrated disad- vantage, residential instability, and immigrant concentration with violence and crime. Key, then, is not so much the criminogenic character of neighborhoods but rather the capacity of adults to informally regulate social behavior, particularly that of young people. Thus, collective efficacy exists relative to a particular task (in this case, protecting public order), and its consequences ought to be specific to the outcome of interest (curbing antisocial behavior, especially of young people).

All of the previously cited studies relied on nonexperimental data, and none fully accounted for the possible biases caused by the unmeasured characteristics of parents that lead them to choose to live in one neighborhood over another (Duncan, Connell, & Klebanov, 1997). A more complete discussion of nonexperimental approaches to the bias problem is provided later. Here we note that Rosenbaum (1991) was able to circumvent these problems by using data from an un- usual quasiexperiment involving low-income black families from public-housing projects in Chicago. As part of the Gautreaux court case, nearly 4,000 families volunteered to par- ticipate in a subsidized program that arranged for private housing, much of it in predominantly White Chicago suburbs, but some of it in predominantly White sections of the city of Chi- cago itself. Because participants were assigned to the first

available housing and were not allowed to choose between city and suburban locations, their assignment to locations ap- proached the experimental ideal of randomized assignment.

Rosenbaum (1991) reported an impressive series of positive differences, both in the employment outcomes for adults and in developmental outcomes for their children, for the families assigned to the suburban as opposed to the city locations. A crucial question for reconciling the large effects found by Rosenbaum with the more modest ones found in the nonexperimental literature is whether these effects were produced because of the quasiexperimental nature of his data, because large neighborhood effects exist for underclass Blacks but not for other population groups, or because the volunteer nature of hls sample produced larger effects than would be the case for a more general sample of low-income, inner-city Blacks.

An alternative approach to assessing the strength of contextual effects relies on correlations between children who are neighbors or classmates or on the explained variance of neighborhoods, schools, or classmates to provide an upper bound on the possible effect of these contexts. Many studies have used sibling correlations to estimate the importance of shared family and other environmental experiences. For example, sibling correlations for years of completed schooling are quite high-around .%-indicating that there are important elements of the families (including genetic influences), neighborhoods, schools, and other aspects of the shared environments of siblings that make the siblings much more alike in terms of completed schooling than two individuals drawn at random from the population.

An analogous correlation for children growing up in the same neighborhood but not in the same family indicates how much of what is important in the shared environments of siblings lies outside the immediate family. A high completed-schooling correlation for unrelated neighbor children, for example, is consistent with a strong neighborhood effect and would imply that shared neighborhood conditions are an important component of the sibling correlations. (An alternative interpretation is that the extrafamilial correlations are driven by the often-similar family backgrounds of children in neighboring families.) Neighbor correlations close to zero suggest that family effects are driving the sibling correlations and that the scope for pure (i.e., extrafamilial) neighborhood effects is quite small.

National surveys such as the PSID and the National Longi- tudinal Survey of Youth draw their samples from a set of tightly clustered neighborhood areas. Thus, these clusters approximate neighborhood areas and, using an anonymous cluster identification, it is possible to calculate both sibling and neighbor correlations for various outcomes of interest. Solon, Page, and Duncan (1997) calculated such sibling and neighbor correlations with a representative PSID sample con- sisting of individuals between the ages of 8 and 16 years in 1968. Neighborhoods were defined by either sampling clusters (if available) or census tracts. For their outcome measure---years of completed schooling-the sibling correlation

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must be taken to account for the clustered nature of the data; statistical methods for doing so are now widely available.3

The second type of analysis uses repeated measurements of the same outcome variable to estimate trajectories of individual growth or change. For example, Huttenlocher, Haight, Bryk, and Seltzer (1991) asked: How does maternal speech affect vocabulary growth during the 2nd year of achild's life? To answer this question, vocabulary was assessed monthly and maternal speech was hypothesized to predict acceleration in vocabulary. We might similarly be interested in the chang- ing propensity to commit crimes during early, middle, and late adolescence, or rates of change in externalizing behaviors during the transition to middle school. Because they must in- corporate the within-subject serial correlation in the errors, analysis methods for such data differ quite dramatically from those used for cross-sectional models.

Modeling growth versus status. Although most studies of neighborhood and school effects are static in the sense that they relate the individual's status at a particular point to his or her environment, there is growing evidence that a static focus is misguided. The status of a chlld or adolescent at a given time reflects the cumulative effects of all past contexts-including home learning and learning and other experiences occurring in past neighborhoods and schools-and may reflect only slightly the contribution of the current neighborhood or school. This concern is amplified by evidence of substantial mobility across neighborhood and school contexts.

In contrast, a neighborhood or school can more legiti- mately be held accountable for a change in a child's achievement or behavior during the time that child lives in that neighborhood or attends that school. Bryk and Raudenbush (1988) and Bryk, Raudenbush, and Congdon (1996) found in two data sets that more than 50% of the variation in rates of math learning can be attributed to differences between elementary schools. In the same data sets, less than 20% of the variation in status can be attributed to differences between schools. The implications are that a longitudinal design is required to understand the nature of school effects and that

here are two widely used approaches for ensuring that statistical inferences appropriately reflect the clustered character of the data. First, one may explicitly model the variability at each level in a hierarchical model (cf., Bryk & Raudenbush. 1992), also known as a multilevel model (Goldstein, 1995) or a random coefficients model (Longford, 1993). This approach enables study of variation and covariation at each level and ensures that standard errors reflect this variation and covariation. The approach can be imple mented by a variety of statistical packages, including HLM, MLN, Mixor, and SAS Proc Mixed. The second approach uses standard least squares estimation of regression coefficients, but computes "robust" standarderrors (cf., Liang & Zeger, 1986). This approach is useful when the regression coefficients are of sole interest, for it gives no information on the variability at each level. However, a benefit of the approach is that inferences do not depend on distributional assumptions. The approach can be implemented in several software packages, including STATA. Cheong, Raudenbush, and Fotiu (1998) compare the approaches and discuss how they can be used together.

school effects are best conceptualized as effects on growth rates rather than on status.

Similar results may hold for neighborhood effects. Be- cause people move across social settings throughout life, their status on an outcome at any time represents the cumulative effect of all past settings. However, their rate of change while in a setting is more directly influenced by that setting. Moreover, repeated measures data, when analyzed effi- ciently, provide dense information about development-more than can be provided by a snapshot at one time-with likely increases in statistical precision.

Social settings and mobility. If participants stayed within a single school or neighborhood during the course of a study, the study of contextual effects on development would be simpler than it is. One could compare contexts by comparing the rates of growth of participants in those contexts. How- ever, mobility is exceedingly common during childhood-five in six children move at least once by age 1 5 . ~ Roughly one third of all children move more than three times, and one sixth move more than five times. Geographic mobility is especially common in early childhood, with more than half of all children moving at least once between birth and age 3, nearly half moving between age 4 and 6, and between 25% and 41% moving at least once in the 3-year periods in middle childhood and early adolescence.

Thus, in contrast to participants in a cross-sectional analysis, people in longitudinal studies will generally not be purely nested within contexts. Rather, the data will have a cross-classified structure, with time series data cross-classified by individuals and settings. Raudenbush (1993) proposed and estimated a cross-classified random-effects model for studying the growth of children as they move across social contexts.

Sample Sizes and Resource Allocation

Number of time points per person. The number of time points per person is determined by the frequency of observation and the length of the study. More time points are needed when trajectories are more complex. For example, vocabulary over the life course likely follows an exponential growth and decay function, or "S curve." Multiple assessments are needed to fit such a curve. However, one sees only a piece of that curve during a small range of ages. Thus, since we see upward acceleration during the 2nd year of life, we need a quadratic function to represent growth; growth may look nearly linear for several years thereafter. Thus, more fre-

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4 ~ h e s e mobility calculations are based on unpublished data from the nationally representative PSID sample of children.

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quent observations are required to study vocabulary growth around age 2 than around age 10.

The association between age and antisocial behavior looks like a bell-shaped curve between ages 1 1 and 21 (an accelerat- ing propensity during early adolescence reaching a peak at around age 17 and then declining during early adulthood). A nonlinear function with at least three parameters is required to fit this curve. Apart from the dramatically different learning rates during the summer and the academic year, growth in reading and math look nearly linear during the early school years.

The total number of participants. Of course, adding time points costs money and thus reduces the overall sample size, given fixed resources for the study. The trade-offs need not be one for one, however. Duncan, Juster, and Morgan (1984) estimated that interviewer costs associated with con- tact and persuasion make a second observation on the same family cost only about two thirds as much as the first observation on that or another family. Overall, the sample size must be very large if it is believed essential to describe mean trajectories accurately for many subgroups defined, for example, by gender, ethnicity, and social status. The precision of estimation of the coefficients associated with time-invariant covariates will depend largely on the total number of participants.

The number of contexts. Sampling many contexts is generally expensive because travel costs for interviewing grow with the geographic dispersion of the participants. In the case of schools, there is also often a large cost of obtaining entry to that context. On the other hand, the precision of estimation of contextual effects (coefficients associated with contexts) depends strongly on sampling a large number of contexts.

Variabiiity in contextual characteristics. The relatively limited variability in neighborhood conditions found in sections of cities or even in entire geographic areas of cities poses difficult trade-offs for study design. To illustrate the scope of the problem, we drew tract-based data from the 1980 decennial census. We formed subsets of tracts to approximate typical study designs: (a) all tracts in the United States (to approximate national samples); (b) all tracts in the city of Chi- cago (to approximate a large study in a single but diverse city); (c) all Chicago tracts with a 30% or greater poverty rate (to approximate an "underclass" study in a large city); (d) all tracts in the city of Atlanta, GA (to approximate a large study in a less diverse city); (e) all Atlanta tracts with a 30% or greater rate (to approximate an "underclass" study in a less diverse large city); and (f) all tracts in the city of Rochester, NY (to approximate a study in a medium-sized city).

We drew from the census files seven tract-level demographic measures often used in neighborhood-based re-

search: race-the percentage of individuals in the tract who are Black; female headship-the percentage of households headed by women; welfare-the percentage of households receiving public assistance; poverty-the percentage of nonelderly individuals with below-poverty household incomes; high education-the percentage of adults with college degrees; neighborhood stability-the percentage of households who lived in the same dwelling 5 years before; and joblessness-the percentage of adult men who worked less than 26 weeks in 1979.

Descriptive statistics for these measures differed dramatically across the subsets. Although the standard deviations of the seven measures were as great in Chicago, Atlanta, and Rochester as in the entire set of U.S. Census tracts, limiting tracts to areas of cities with a relatively high poverty rate re- duced the standard deviations substantially, especially for the schooling and residential stability measures.

An important analytic concern is the extent to which sampled neighborhoods enable analysts to estimate the distinct effects of neighborhood characteristics. For example, there are theoretical reasons to suspect that concentrations of low- and high-SES neighbors have distinct effects on child outcomes. But if measures of low and high SES are too closely correlated in the chosen sample of tracts, then it will be im- possible to distinguish their separate effects.

To assess potential multicolinearity problems, we took our collection of tracts and regressed each of the neighborhood measures on the remaining six neighborhood measures.5 A high R-squared indicates a great deal of multicolinearity; modest R-squared suggest the potential for estimating distinct effects. Table 2 presents the R-squared from the 42 (7 measures by 6 geographic areas) regressions.

In almost all cases, multicolinearity is considerably higher in the city-specific samples than for the national set of tracts. This was particularly hue for the high-SES, stability, and joblessness indicators. For example, only 29% of the variation in the fraction of college-graduate adults could be accounted for by the other six measures in the national sets of tracts. In the city-specific samples, the squared multiple correlations ranged from .31 to -75. Overall, the average extent to which the city-specific squared correlations exceeded those for all U.S. tracts ranged from .07 to .25.

Endogeneity of Contextual Effects

The contexts in which children develop are not allocated by a random process. This is most clearly seen in the case of selection of preschool child-care arrangements, in which the deci- sion is almost always made by the parent and is affected by parental preferences, financial constraints, and local supply

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'For example, the .53 entry in the first row and column of Table 2 comes from a regression, using all U.S. Census tracts, of race on the six other tract-based measures of SES. The R-squared from that regression was .53.

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TABLE 2 Fraction of Variance in a Given Census-Based Neighborhood Measure Explained by Six Other Census-Based Measures,

by Geographic Area

U.S." chicagob Chicago 30+%' ~ t l a n t a ~ Atlanta 30+%' ~ochesref

Race Black

Female headship Households headed by women

Welfare Households receiving public assistance

Poverty Nonelderly individuals with below-poverty

household incomes High education

Adults with college degrees Neighborhood stability

Households living in the same dwelling 5 years before

Joblessness Adult men working less than 26 weeks in

1979 Average difference from all U.S. tracts

Note. All data are in fractions. "All U.S. tracts. b ~ l l Chicago tracts. 'All Chicago tracts with 30+% poverty rates

Rochester. NY, tracts.

conditions. A child's immediate neighborhood and, to a somewhat smaller extent, schools also have an element of parental choice. The propensity of individuals to choose higher or lower quality child care or to move to better or worse neighborhoods or schools depends on background characteristics and current circumstances. Apart from the rare case of pseudoexperiments such as Gautreaux or genuine experiments such as the one in Tennessee with class size (Mosteller, 1995), substantial effort is required to model these propensi- ties as a precondition to drawing conclusions regarding the causal nature of context influences.

The possibility of bias in estimates from nonexperimental data arising from nonrandom parental selection of context is more certain than its likely direction. Suppose parents choose between (a) holding two jobs and using the extra income to buy a better neighborhood and (b) having a single earner and living in a poorer neighborhood. Suppose further that those who live in poorer neighborhoods or send their children to worse schools, or both, make up for the deficiencies of the neighborhood or school through the additional time that mothers spend with their children. Neighborhood or school conditions matter in this scenario, but an empirical analysis will show this to be the case only if it adjusts for differences in parental time use. Failure to adjust for parental employment will cause conventional regression-based approaches to un- derstate neighborhood or school effects.

Another scenario, also leading to an understatement of neighborhood or school effects, is one in which parents well-equipped to resist the effects of bad neighborhoods

. d ~ l l Atlanta, GA, tracts."All Atlanta, GA, tracts with 30+% poverty rates. 'AH

choose to live in them to take advantage of cheaper housing or perhaps shorter commuting times. Unless measures of parental competence are included in the model, the estimated effects of bad neighborhoods or schools on child development will be smaller than if parents were randomly allocated across neighborhoods.

It is perhaps more likely that parents especially ill-equipped to handle bad neighborhoods or schools are most likely to live in them, because these parents lack the (partly unmeasured) wherewithal to move to better neighborhoods. In this case, the coincidence of a poor neighborhood or school and the poor developmental outcomes of their children results from their inability to avoid either, thus leading to an overestimation of the effects of current neighborhood conditions. Conversely, parents who are effective in promoting the developmental success of their children may find their neighborhood choices dominated by considerations of developmental consequences. If this capacity is not captured in measured parental characteristics, then the coincidence of positive developmental outcomes for their children and living in a better neighborhood would be misattributed to current neighborhood conditions and also lead to an overestimation of neighborhood effects. In terms of a regression model relating some child outcome to family and neighborhood characteristics, the omitted factors amount to unobserved characteristics of the parents (e.g., concern for their children's development) that affect developmental outcomes. A key problem with most existing studies is that they estimate regressions without controlling for all relevant

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parenting variables, thus biasing estimates of contextual effects.

There are three approaches for addressing this problem. The best situation would be one in which families are randomly assigned to child care settings, neighborhoods, or schools. The Department of Housing and Urban Develop- ment's Moving to Opportunity experiment contains such experimental data on neighborhood context. With funding for 10 years, Moving to Opprotunity is randomly assigning housing-project residents in five of the nation's largest cities to (a) a group that is moved to a low-poverty area; (b) a control group receiving conventional Section 8 housing assistance; (c) a second control group receiving no special assistance. Second-best solutions to the nonrandom neighborhood selection problem are quasiexperimental data such as Gautreaux.

A nonexperimental approach to the selection bias problem is to locate data that measure the crucial omitted variables. Some child-development data sets contain fairly sophisti- cated measures of parenting characteristics, including, for example, an assessment of the home learning environment provided by parents. Controls for such measures in regression-based analyses can help reduce the omitted-variable bias.

Another nonexperimental approach to the bias problem is to replace the contextual measures in the regression analysis of interest with an instrumental variable for those variables. This instrumental variable is purged of the measures' spuri- ous correlation with unobserved parenting measures. Instru- mental variables estimation is a two-step procedure. The first consists of predicting the contextual variables themselves, ideally using at least some independent variables that do not belong in the child outcome equation. In the second, the child outcome equation is estimated using the predicted value of context obtained in the first stage.

A study by Evans, Oates, and Schwab (1992) adopted this strategy to investigate selection bias in school-based effects, although it relied on dubious instrumental variables. Their dependent variables of interest are high school completion and out-of-wedlock teen childbearing. Their contextual variable was the SES of the student body. When they ignored selection issues and regressed their outcomes on student-body SES and family-level controls, they found highly significant, beneficial effects of high student-body SES. However, when they estimated a two-equation model, with the first equation regressing student-body SES on characteristics of the metropolitan area in which the student resided and the second regressing the developmental outcomes on predicted student-body SES and family-level controls, the effects of student-body SES disappeared.

Yet another approach to the bias problem is to eliminate the biasing influence of omitted persistent, unmeasured parental characteristics by differencing them out using sibling-based fixed-effects models. In fixed-effects models, each sibling's scores on the dependent and independent variables are subtracted from the average values of all siblings in

the family. The influence of persistent family characteristics that affect residential choices, whether measurable or not, are differenced out of the model. However, as Griliches (1979) pointed out, differencing between siblings reduces but does not eliminate endogenous variation in neighborhood regressors and, at the same time, filters out much of the exog- enous variation as well.

Aaronson (in press) demonstrated the feasibility of this approach on PSID adolescents. He used family residential changes as a source of neighborhood background variation within families to estimate sibling-based neighborhood effects that are substantially free of family-specific heterogeneity biases associated with neighborhood selection. Using a sample of multiple-child PSID families in which the adolescent siblings are separated in age by at least 3 years, he estimated family fixed-effect equations of children's educational outcomes and found that the impact of neighborhoods exists even when family-specific unobservables are controlled for. In fact, family fixed-effect regressions that use the neighborhood poverty rate as the measure of neighborhood conditions show even larger neighborhood effects on high-school gradu- ation and grades completed than the models without fixed effects.

Measuring Contextual Characteristics

Alternative approaches to assessment of contextual characteristics have strong implications for design. First, neighborhood data from the decennial census offer a number of measures of the economic and demographic composition of individuals and families in the census tracts in which sample children live. The data provided by the decennial census about these neighborhood areas come from the census forms the population is asked to fill out on April 1 of the 1st year of every decade. Abundant information about the economic and demographic characteristics of the population is provided by the completed census forms. As illustrated by the data presented in Table 2, this enables one to characterize neighborhoods according to a number of key dimensions, such as the extent of neighborhood poverty, female headship, public assistance receipt, and male joblessness.

Regrettably absent from the census forms are measures of crime, drugs, gang activity, neighborhood collective efficacy, churches, community centers, and school quality. Other national databases on neighborhood conditions provide such data, but on either county or zip code areas. These are large geographic areas that contain substantial internal variation in neighborhood conditions. Other administrative databases can be used for measuring certain physical characteristics of neighborhoods and schools as well as certain ecological risk factors (such as crime and infant mortality rates of neighborhoods). However, none of these data sources are appropriate for assessing the social organizational dynamics that have more proximal theoretical linkages to outcomes.

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Second, the use of participants or parents of participants as informants about the qualities of their neighborhoods or schools is generally not to be recommended. One reason for this is that measurement errors in these assessments are likely to be correlated with the measurement errors of other predictors and many outcomes. For example, parents' mental health may lead their reports of neighborhood context to be spuriously correlated with their reports of their children's social behavior. And participant reports of peer attitudes will be spuriously correlated with participant self-reports of attitudes and behavior. We do not dispute the utility of aggregating demographic characteristics such as ethnicity, sex, or social class to construct segregation indexes or other measures of social composition. For example, Lee and Bryk (1989) con- structed measures of the social and ethnic composition of U.S. high schools from student survey data and used those measures to predict the same students' academic achievement. There is a small risk that a student's report of demographic background is influenced by his or her achievement. In contrast, an aggregated measure of perceived instructional quality would quite plausibly reflect the achievement of the reporters, and it would therefore be inadvisable to use such a measure of instructional quality as a predictor of their achievement.

A much more satisfying, if expensive, strategy is to obtain an independent sample of capable informants about a context and to pool their reports to create context-level measures. Ttus approach has been successfully used in national data on school climate (cf., Raudenbush, Rowan, & Kang, 1991), with multiple teachers surveyed about their degree of control, collaboration, and supportive administrative leadership; and in data assessing the social cohesion, informal social control, and collective efficacy of neighbors in Chicago (Sampson et al., 1997). In both cases, 15 to 30 informants per context were required to obtain reliable contextual-level measures. Clearly, the expense of this measurement strategy grows rap- idly with the number of contexts sampled, and it will increase during the course of a longitudinal study as mobility creates greater dispersion of participants across contexts and, hence, produces more contexts to be assessed.

Systematic social observation (SSO; Reiss, 1971) provides an alternative source of contextual information independent of the sample of participants. Using this approach, trained observers can fairly quickly assess aspects of a neighborhood, such as its degree of social and physical disorder. In- terviewers dispatched to conduct interviews can also be used to conduct such observations at an expense that is far less than that of conducting an independent survey of residents.

The Project on Human Development in Chicago Neigh- borhoods implemented SSO by having a van drive 5 miles an hour down every street within 80 target neighborhood clusters. Videotape recorders on both sides of the van captured physical characteristics of the streets and buildings on each side of the street as well as visible aspects of social interac- tion. Trained observers then coded the videotapes, noting the

status of buildings (residential versus commercial, detached homes or apartments, whether vacant or burned out, their general condition, presence of security precautions such as bars or grates, etc.), presence of garbage, litter, graffiti, drug paraphernalia, broken bottles, abandoned cars, and other aspects of the physical environment.

The driver and a second rider in the van, trained to observe social interactions, also recorded their observations via audiotape. Social interactions included, for example, adults drinking in public, drug sales, children playing in the street, and apparent gang activity. Scales tapping social and physical disorder, housing conditions, and other aspects of the neighborhood environment showed high internal consistency across face blocks within neighborhood clusters and reasonably high construct validity as indicated by correlations with theoretically linked constructs measured by an independent community survey, by the census, and by official crime data. Analyses now underway are estimating the value added by the videotapes, above the information gleaned from the au- diotapes. Generally, the videotaped data are far more expensive than the audiotaped data. It is feasible to use the audiotape strategy even when samples are not highly clustered because data collection per block face is comparatively cheap.

SSO has substantial promise for efficient collection of data on the social organization of neighborhoods-data not available from administrative records. However, some of the constructs that can be captured through interviews, such as "collective efficacy" in Sampson et al. (1997), are not acces- sible via observational methods. Given the expense of interviewing residents in unclustered samples, researchers interested in neighborhood effects face difficult trade-offs, which are discussed later. Similar trade-offs face school researchers, who might opt for observational measures (cf., Mortimore, Sammons, Stoll, Lewis, & Ecob, 1988) as an alternative to survey methods designed to capture school organization and climate.

BOTTOM-LINE RECOMMENDATIONS

The multitude of possible designs for developmental studies makes it difficult to present a succinct set of recommendations. We organize our summary discussion with recommendations relevant for any developmental study, followed by recommendations for studies that focus on specific periods of childhood.

Universal Recommendations

1. The diversity of individual developmental trajectories argues for a longitudinal design in which outcomes and ex- planatory factors of interest are measured on at least several occasions. Capturing the dynamics of achievement or behav-

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ior surrounding transitions (e.g., from elementary to middle school) or conditions (e.g., summer vs. school months) of interest requires outcome measurement before and after the transitions or conditions. Measurement at equal intervals across the study period is less important than measurement surrounding key transitions and conditions of interest.

2. All else equal, nonexperimental studies of contextual effects are best conducted using data collected from participants living in very diverse contexts. In the case of neighborhood variation, this argues for representative samples drawn from many diverse neighborhoods. In the case of schools, this argues for samples drawn from many schools in varied settings.

3. If the only goal of a study is to estimate effect sizes via regression coefficients, and if the cost of data collection were irrelevant, clustering samples by context (e.g., sampling multiple families within neighborhoods or multiple students within classrooms or schools) is undesirable. Such clustering creates a (statistically) inefficient dependence across observations. In the absence of cost savings in collecting interview or contextual information, the optimal design would sample one participant per context. Although such a design would be optimal for estimating regression coefficients, it would provide no information about variation within and between contexts. To the extent it is important to gauge the magnitude of unmeasured sources of variation within and between contexts, the unclustered sample design is problematic, even when costs are ignored.

4. In most instances, interviewing costs associated with additional participants per context are substantially lower than costs associated with participants drawn from different contexts. The trade-off between interviewing costs and statistical efficiency has long been a concern of sampling statisti- cians and typically leads to designs with relatively modest cluster sizes.

5. Per-participant costs of contextual information vary widely. The costs of administrative data, such as census-tract demographic conditions, school expenditures, or labor-market employment conditions, are typically small and largely independent of the number of contexts in which participants are found. Thus, exclusive use of administrative-based contextual information provides no rationale for clustering samples within context.

6. The utility of administrative data makes it crucial to identify the location of participants on all measurement occasions. In the case of schools, this means identifying the school and school district. In the case of neighborhoods, this means the identification of census tract and zip code. Dwell- ing-based sample surveys almost always identify tract as part of the data used to draw their samples, making the identification of Wave- 1 neighborhood location exceedingly simple. Residential mobility makes it necessary to invest some resources in identification of census tract in subsequent waves. Geographic Information Systems, which facilitate geocoding of address data, pinpoint the census tract or block group in

which interviewing takes place. If addresses are tracked for purposes of respondent payment or other reasons, then commercial services are available to convert addresses into tract identifiers.

7. Contextual data drawn from nonadministrative sources-teachers, independent representative samples of individuals in the context, or time-intensive systematic observation of the context-give rise to cost functions in which study costs increase almost linearly with the number of sampled contexts. These instances may argue for more heavily clustered samples.

8. Contextual information reported by individuals themselves or obtained by aggregating information drawn from individuals who are part of the context is typically problematic. Data on ethnicity and SES may be confidently aggregated from participants to characterize those aspects of neighborhoods or schools (assuming reasonably large samples of participants per neighborhood or school). However, such data are typically available from records, and because the records are based on larger samples (or even a census), the record data will typically be more reliable. Participant reports of the organizational health or climate of neighborhoods and schools should, in general, be avoided due to reliability and bias problems.

9. Contextual information drawn from independent samples is very expensive in unclustered samples and becomes more so as mobility increases the number of contexts in which participants reside. Study objectives may require the collection of such information, in which case, sample sizes in the 15 to 25 range appear sufficient. If not, gathering such information is probably too costly to be warranted.

10. We view systematic observational methods of gathering contextual information as an underutilized but very prom- ising compromise strategy for gathering needed contextual information. Trained interviewer ratings of the learning environment of child-care settings and SSO of neighborhood or school settings are examples.

Additional Recommendations for Contextual Studies of Preschool Children

1. Child-care settings are perhaps the most important extrafamilial contexts for preschool children. Gauging the effects of child-care settings on children is exceedingly difficult given the likely biases associated with the high degree of choice in parental selection of child-care arrangements for their children. Studies focused on the effects of child-care characteristics should consider extraordinary measures (e.g., experimental or quasiexperimental design) to solve the problem of likely bias associated with nonrandom selection.

2. In the case of intensive studies of language and perhaps other aspects of the cognitive development of preschool children, the likely nonlinear trajectories argue for numerous

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measurements of outcomes over likely periods of rapid change.

Additional Recommendations for Contextual Studies of School-Age Children

1. School and neighborhood settings are the most important extrafamilial contexts for school-age children. Gauging the effects of these contexts on children is complicated by the degree of choice in parental selection of neighborhoods and schools for their children. Studies focused on the effects of neighborhoods or schools should consider carefully how their proposed designs might solve the problem of likely bias associated with nonrandom selection.

2. In the case of achievement during the school years, dif- ferential rates of learning between summer and during the school year argue for at least two measurements per year.

In summary, problems associated with obtaining reliable and valid measures of the social organization of neighborhoods or schools and unbiased estimates of the effects of these contexts on child and adolescent outcomes present the researcher with sobering design choices. If nationally representative descriptions of child development are essential, it is hard to imagine a feasible research strategy that would also provide exceptionally high-quality measures of context. For example, the decennial census provides high-quality and nationally uniform measures of neighborhood context, but only for the demographic measures sought in the census enumeration forms. The connection between these demographic measures and crucial theoretical dimensions of neighborhood conditions is often remote. Similar problems exist for school-based administrative record data and theoretically de- sirable measures of school context.

A tempting alternative for broad-based studies is to measure context using the aggregated responses to theoretically appropriate questions from clusters of neighboring children, classmates or parents. We argue against this strategy because measurement errors in these assessments are likely to be correlated with the measurement errors of other predictors and many outcomes. Moreover, individual reports of context are likely to be quite unreliable.

Smaller-scale studies are better suited for preferred measurement strategies such as SSO. The problem with such studies is that they may restrict unduly the variability of contextual conditions and not support the estimation of the rich, multidimensional contextual models implied by theory.

A final, and perhaps most difficult problem, is that of nonrandom selection of parents and children into their naturally occumng contexts. Pursuit of sturdy causal inferences in light of the high degree of self-selection in parents' choice of context pushes the designer in the direction of randomized experiments or carefully controlled quasiexperiments. Thus, it appears that there is no single study design and no single

study for best understanding how neighborhood and school contexts affect child development. Required, then, is a sensi- ble research agenda drawing evidence from nationally representative data, geographically concentrated data, experimental data, and quasiexperimental data. Durable knowledge will cumulate as the research community synthe- sizes evidence from these multiple sources.

ACKNOWLEDGMENTS

Portions of this article were presented at the conference "Re- search Ideas and Data Needs for Studying the Well-Being of Children and Families," October 2 1-23, 1997, Washington, DC. It has benefitted from discussions with Gary Solon, Jens Ludwig, and fellow members of the MacArthur Foundation Methodology Working Group-Robert Sampson, Tom Cook, Helena Kraemer, Ron Kessler, and John Nesselroade. We are grateful to the Family and Child Well-Being Research Net- work of the National Institute of Child Health and Human De- velopment for supporting this research and to Eric Petersen for research assistance.

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Duncan Raudenbush Context & Youth