The purpose of this research is to assess data from the new Child Well-Being Topical Module of the Survey of Income and Program Participation (SIPP), collected in the fall of 1994. We test the data within established conceptual frameworks with logistic regression, and measure correlates with children's current well-being status as indicated by their current grade and age. Our findings identify the expected background correlates of child well-being, in addition to showing associations between child well-being and household stressors, family characteristics, and participation in enrichment activities. Despite the absence of a greater breadth of data on child well-being, and the inherent temporal/causal problems in this analysis we find that the SIPP can measure child well-being in the cross-section. Also we find that these panels are suited to serve as the foundation for a longitudinal analysis using the Survey of Program Dynamics (SPD), which follows these households in the future.
This paper was originally presented at the Annual Meeting of the Population Association of America (PAA) Chicago, IL, April 1998.

Disclaimer

The views expressed in this paper are solely attributable to the authors and do not necessarily reflect the position of the United States Bureau of the Census.

Introduction
Background
Data and Methods
Results
Discussion and Conclusions
References
Contacts

The purpose of this research is twofold. Primarily, this is an examination of the child well-being data from the 1992 wave nine and 1993 wave six Survey of Income and Program Participation (SIPP) collected in the fall of 1994. These data are among the first collected by the Census Bureau with the intention of measuring child well-being. We measure child well-being in this paper by the current grade for age status of school age children, from age six through 17 in the survey. Second, we expand the range of correlates tested to include more complete financial information for the families and households, and a more complete measurement of program participation which in many ways is unique to the SIPP. We also bring additional information on stress and social capital from the other modules of the SIPP into our analysis at the family, household, and neighborhood level.

Research on child well-being is certainly not a new area. However, the promotion and assessment of child well-being are topics of increasing concern to researchers, policy makers and the public as a whole. In recent research, the assessment of child well-being has been targeted primarily at two age groups of children, the young pre-adolescent children (Entwisle and Alexander, 1996; Morrison and Cherlin, 1995; Bianchi and Robinson, 1997; and Cooksey, 1997), and young adults making the transition to adulthood (Haveman and Wolfe, 1994; Amato, Loomis, and Booth, 1995; Astone and McLanahan, 1991; Teachman, Paasch, and Carver, 1997, 1996; Aquilino, 1996; Harris and Marmer, 1996; Manning and Lichter, 1996; and Weinick and Astone, 1996). The younger population has been assessed through a number of cognitive development, social environment, and time allocation measures. Young adults have been assessed in terms of their successful or "on-time" transition to adulthood (Hogan and Astone, 1986; McLanahan and Sandefur, 1994) and their general economic well-being.

This research will address questions in three specific areas with respect to differences in current child well-being as indicated by current age and grade information. This is consistent with the previous research using high school non-completion as a measure of well-being for teenagers.

Income and Poverty

Does the addition of household sources of income increase the association between income and the measurement of child well-being?

How are program receipts, home ownership, and assistance programs associated with children's well being?

Neighborhood/Community Context

Are perceived indicators of the neighborhood's level of social capital and safety correlated with children's well-being?

Family and Household Characteristics

How are characteristics such as family structure, number of adults in the household, presence of siblings, education of the parents and other adults in the household associated with children's well-being?

Are measures of household stress such as presence of a disabled parent or child, or recent unemployment correlated with child well-being?

How are different patterns of employment among adults in the household and family associated with child well-being?

Children's successful progress in the school system is one important marker for their well-being. Grade for age measurement is one way to track this progression. Child well-being encompasses other facets of child development, beyond whether a child is academically 'on-track'. However, repeating a grade, that is falling behind or being retained in grade, may be a first indication of potential risk for an off-time transition to adulthood (Hogan and Astone 1986). Falling behind while in school may also serve as a predictor of future negative academic achievement and social adjustment outcomes (Alexander, Entiwsle, and Horsey 1997; Rumberger, 1987).

In some cases, grade retention may be the wake up call which spurs parents and/or a school system to act, in manners such as increasing the supervision of the child's school work. In other cases, being left back is the first notable experience on the continuum of academic disengagement (Alexander, Entiwsle, and Horsey 1997) which can culminate in undesirable outcomes such as dropping out of school. Grade retention has a strong association with dropping out of school (Guo, Brooks-Gunn, and Harris 1996; Dawson 1991), and dropping out of high school is a strong predictor of non-marital pregnancy, long-term unemployment, receipt of welfare, and persistent poverty (Guo, Brooks-Gunn, and Harris 199; Harris 1993; and Haveman and Wolfe 1994). Although high school completion is the conventional child well-being outcome of interest, grade retention may be just as important to study because a more comprehensive understanding of the correlates of grade retention may enable parents and school systems to prevent an undesirable outcome such as dropping out of school.

Retention in elementary school is a form of stratification of students and usually sets them in a remedial track (Dauber, Alexander, and Entwisle 1996). Students in remedial courses are not usually tracked for continued education, thus the expectation placed on these students is lower. Previous research has found that family and school expectations and interactions, and other sources of social capital available to the child are important factors influencing cognitive ability, academic success, engagement in school, and high school completion (Astone and McLanahan 1991; Coleman 1988; Entwisle and Alexander 1996; Teachman, Paasch, and Carver 1997; Weinick and Astone 1996.)

The background characteristics of those who experience grade retention are consistent across studies. Retention rates seem to be higher among males, poor and minority children, children in urban schools, those who live in the South, and children with behavioral problems (Alexander, Entwisle, and Dauber 1993; Bianchi 1994; Cadigan, Entwisle, Alexander, and Pallas 1988; Dawson 1991; Guo, Brooks-Gunn, and Harris 1996; Madigan 1998; Rumberger 1987; Zill and Nord 1994.) Children from single parent families, and those with many of the correlated risk factors associated with single parenthood such as lower socioeconomic status, and lower parental educational achievement tend to repeat grades, and/or drop-out (McLanahan and Astone 1991; McLanahan and Sandefur 1994; Haveman and Wolfe 1994). In addition, children who change schools are also more likely to drop out of school (Teachman, Paasch, and Carver 1996).

Haveman and Wolfe (1994) provide a framework that focuses on the interrelationships of government, family, and individual investments in children. We focus on this framework and its elements throughout this analysis. Haveman and Wolfe found relationships among the elements of their framework and educational achievement, teen child bearing, welfare recipiency, and economic inactivity. The government certainly plays a role by setting levels of aid for low income families, providing assistance for the disabled, setting and monitoring school policies, and making investments in communities. And while not difficult to understand, government's role in the investment in children's well-being is difficult to measure.

Family investment is a more quantifiable measure. Families decide to have children, to marry, where to live, whether to work or buy a house, what rules to have in the household, how much education to get, in addition to other aspects of daily life that directly impact children's well-being. A governing assumption that is implicit in this framework is that a family's decisions are rational or purposive, and that they are made with respect to the well-being of the children. Following this assumption, Haveman and Wolfe point out that their framework is deterministic, that is, the well-being of the child can be predicted by the investment/decision behavior of the parent/family, government, and child.

Some of the characteristics of the household environment are outside of the parents' control. Families weather many potential stressors, some are avoided, but many are inevitable. Given enough economic and/or social resources, a family may be able to mediate the stress involved with caring for a disabled adult or child by bringing external assistance in to aid with care, for example. Families may also try to avoid potential stresses. An example of this would be not getting a divorce "for the children." Yet this may only subject the children to increased tensions at home and possibly negate the benefit gained by avoiding the disruption (Morrison and Cherlin 1995).

According to Coleman (1988), financial capital is measured by a family's income and human capital is measured by the parent's education level. Social capital, he explains, is measured by the density of interaction among parents, children and schools, and it is through the positive parent-child interactions that the financial and human capital is transferred. Thus, social capital can exist in the relationships among individuals within communities and neighborhoods, as well as within the family.

Neighborhood, communities, and schools often serve as institutions that promote transfers of social capital, as they constitute social structures which tie people together. Having strong neighborhood or community connections can provide an environment that reinforces school commitment and helps children remain engaged in school. Exchanges and support between parents, schools, and children are particular areas that can provide increased resources necessary for improving children's well being. Teachman, et. al. studied students who drop out of school prior to the 10th grade. Their research shows that children who move or change schools, and suffer losses in the density of ties available for providing support, are more likely to drop out of school (Teachman, Paasch, and Carver 1996).

Previous research has established a link between family structure and various measures of child development and well-being. Children in two-parent families fare better than single-parent children, with children of divorce having the most problems (McLanahan and Sandefur 1994). Research has also shown that children living with two biological parents are less likely to have problems than children living with one biological parent and one step parent (Astone and McLanahan 1991). Indicators of child well-being associated with divorce or single parent status include low measures of academic achievement (repeated grades, low marks, low class standing), increased likelihood of dropping out of high school or early childbearing, and increased levels of depression, stress, anxiety, and aggression (Amato, Loomis, and Booth 1995; Astone and McLanahan 1991; Dawson 1991; McLanahan and Sandefur 1994; Teachman, Paasch, and Carver 1996; Wojtkiewicz 1993; Wu and Martinson 1993). Social capital upon which a child can draw diminishes in the absence of one parent because one-parent families have less time and energy available to invest in parent-child interactions.

Family structure may be a proxy for other problematic issues within the family. Familial stress associated with marital conflict and divorce can negatively impact a child's development process (Amato, Loomis, and Booth 1995; Cherlin 1992; Morrison and Cherlin 1995). Diminished contact with the non-custodial parent can result in a loss of emotional support and supervision received from adults (Astone and McLanahan 1991). Children of single parents may experience inconsistent parenting styles, decreased time spent with a parent, and less social control than children of two parent families. In addition, children of single parents (from divorce or a never married parent) generally have a lower standard of living, are more likely to be in poverty and more frequently participate in government assistance programs than do children from two parent families (Cherlin 1992; McLanahan and Sandefur 1994).

Entwisle and Alexander (1996) find that mothers in two-parent families have higher expectations in terms of school achievement for their children than mothers in one parent-families. And their expectations seem to be borne out since children in one-parent families tend to receive lower marks than children of two-parent families. In a study of first graders in Baltimore, MD, Entwisle and Alexander (1995) show that the differences achievement scores from the beginning of the summer to the end of the summer is associated with family resources rather than family type; whereas, over the winter months neither the families' economic status nor the number of parents in the home affect achievement in standardized tests. They demonstrate that schools can neutralize the negative influence of poverty on children of single parents in terms of educational achievement.

Children also make investments in their own well-being through their actions (Haveman and Wolfe 1994). There is limited research on the behavior, expectations and desires of children in relation to child-well being outcomes. Bianchi (1997) has studied the daily activities of children aged 3 to 11 in California in terms of four activities: time spent reading, watching tv, studying and doing housework. She found that children of parents with higher educational attainment read and studied more frequently than children of parents with lower education levels, but that family structure did not seem to influence the activities of the children. This study, however, did not look at the resulting educational achievement of the children by their activity level.

Participation in extracurricular activities are among the more voluntary types of behaviors of children that have been examined. McNeal (1995), using longitudinal data from High School and Beyond finds that participation in extracurricular activities such as athletics and fine arts significantly reduce a student's likelihood of dropping out, whereas participation in academic or vocational clubs have no effect. McNeal also emphasizes the role that increased social control vis-a-vis participation in these groups may have in mediating the tendency for deviant acts. This can also be viewed in terms of involvement in groups which lead to greater access to resources and support via increased social capital. However, these types of activities may represent "rewards" to children with good academic records and may be the outcome rather than the forerunner of academic success when considered in a cross-sectional context such as the one we are using in this analysis.

Conceptual Framework:

The conceptual foundation of this research draws heavily on the "investment-in-children" theoretical framework described by Haveman and Wolfe (1994.) The effect of government policies on children have never been more relevant than in this period of transition from pre to post welfare reform era. Parental choices have always been of utmost importance to children's well-being. In this framework, parents' decisions that shape the child's environment include the number and timing of the children they decide to have, whether and how much to work, where to live, where to send their children to school, stability of their union, and others.

Children have a direct impact on their own well-being as well. Among many other decisions, they may choose to put more effort into school or not, or participate in extracurricular activities. This framework allows the effect of poverty and program participation to be incorporated at both the societal and familial level. To this framework, we also add an input for household stressors which are included in Haveman and Wolfe's framework as family circumstances. The primary indicators of household stress that we include in this category are presence of disabled adults in the household, absence of employment, and disabilities of the child relating to school. The latter also falls into each of the other categories for investment as well. While Haveman and Wolfe use longitudinal data from the PSID to examine the determinants of childhood success, we are bound by the constraints imposed by a cross-sectional analysis, and as such are examining correlations with child success/well-being.

The process of investing in children and promoting their successful development is intertwined with the concepts of social, financial, and human capital. Coleman (1988) describes the concept of social capital as existing in the relationships people have within families and communities. This is a resource on which members of the family and community, especially children, can draw. It is a resource that is embedded in these relationships, and is believed to be important in children's successful development (Coleman, 1988). Financial capital is generally defined as income and/or wealth, and human capital is usually defined as education and skills. These are central resources from the family that are believed necessary for a child's successful development, and resources which will be valuable for the child's success in the future.

For this analysis we attempt to operationalize as many aspects of capital as possible for the child and family within the context of Haveman and Wolfe's framework. Limitations within the data prevent us from including the depth and breadth of covariates that are indicated by previous research. Further, confinement to a cross-sectional perspective limits the analysis to a measure of associations, rather than using the data longitudinally to conduct a causal analysis.

Data:

The data for this analysis come from a combined sample from the 1992 and 1993 panels of the Survey of Income and Program Participation. These two panels were designed to overlap and provide a more substantial sample for estimation of cross-sectional statistics. The panels were designed to produce nationally representative samples of households. The 1992 panel consisted of an initial 19,600 households, and the 1993 sample was started with 19,900 households. The 9th wave of the 1992 panel and the 6th wave of the 1993 panel use the same instrument and were conducted between October 1994 and January 1995. In addition to the income, assets, program, and basic demographic data contained in the core instrument, topical modules are included on a variety of specialized areas through the life of the panel. The waves used in this study include topical modules on work history, adult and child disability, adult and child health care utilization, child care, child support, and child well-being.

Combining wave 9 of the 1992 panel with wave 6 of the 1993 panel yield 40,141 unweighted households including households created during the life of the panel and 100,939 unweighted persons. For this analysis we examine the children from the child well-being topical module attached to the core instrument. Respondents identified as designated parents, usually the mother, responded to a variety of items for each of their children under 18 years old. There were 24,994 children from age 0 through age 17 for whom the child well-being topical module was administered.

The subsample selected for this analysis consists of children who were between the ages of 6 and 17, not missing information on their reported grade or enrollment, not systematically missing for a number of covariates, and whose designated parents are age 18 or over. The weighted and unweighted sample sizes are presented in table 1. Frequency distributions for the population excluded as a result of missing grade distributions, interview fallout, and young designated parents are included in appendix table 1. Although exclusions based on missing information are large and significant, the remaining sample reflects the grade for age distribution based on the 1994 October CPS estimate almost exactly (Bruno and Curry 1996).

Variables:

Limitations in this measure of child well-being are inherent in the measure itself. The retention indicator employed (modal age for grade statistics, responses to survey questions, and institutional records) yields differing rates. For example, modal age for grade is often used as a proxy for retention by computing a comparison of the modal grade with the child's age. However, this rate may be inflated due to the method of calculation used since students who delay entry into kindergarten are erroneously tabulated as being held back in school. In comparison, other studies ask students and parents if they (or their child) have repeated a grade. Yet, the negative association tied to being held back may deter respondents from honestly responding to sensitive questions, such as grade retention. Research using institutional records is less common due to the confidentiality of school records. An additional limitation is that studies tend to use an ever-retained measure because the timing of the retention (i.e. the age that the child repeated the grade or was held back) is not known.

Income and Poverty: The economic situation of the family is measured in this analysis in a number of ways. First, income is averaged over the four-month reference period for these waves of the survey, and divided by the four-month average poverty level assigned to each family based on their individual composition (number of persons in the family.)¹ This generated a relative income measure for both the family and the household. We have included these measures in the model in two different configurations. The first compares those families or households receiving less than 100% of poverty or more than 300% of poverty with those families or households receiving between 100% and 300% of poverty. Through preliminary analysis we found that family and household income measures did not differ with respect to being ontrack academically. As a result of this, only family income is included in this analysis. This is not completely unexpected, as it is not clear that a child in a household with extra-familial income would have access to that income, or its associated resources. Ownership of the house or apartment by the householder is included as a dummy variable coded one if the family is renting or resident without making a cash payment. The employment status of the designated parent is also included as a measure of income and poverty, and is coded one if the designated parent is employed full or part time. Variables measuring the number of working adults in the household and marital status replace an independent measure of spouse working, since this would more appropriate when all marital statuses are included in the model. That is, a variable for whether the designated parent works is inherently related to the marital status of that parent, and unemployment/non-participation in the labor force would have very different meanings by marital status.

Program participation was measured with two variables. First, public welfare recipiency was included as a dummy variable coded as '1' if the family received AFDC, WIC, Food Stamps, or General Assistance aid. Second, receipt of Supplemental Security Income (SSI) was also included as a separate variable. This item was later dropped due to colinearity with measures of disability among adults and children in the household. Participation in government assistance programs is also measured by a variable included that is marked '1' if the family is in public housing or rent subsidized housing, and '0' otherwise. Although they are not indicators of government transfer programs, receipt of alimony or child-support, and residence in a household that was not owned by the family were also included as dummy variables in this section.

Neighborhood: Measures of the designated parents perceived level of neighborhood social capital related to their children were included in the models as indices generated through factor analyses of the questions in table 2. These questions were asked on a "how much" scale with scores that range from zero through ten. Zero means "not at all" and ten means "the most". As shown in table 2, these items were broken into two sets based on the results of the factor analysis. The first set is an indication of the positive aspects of social capital relating to children, and the second set represents the negative aspects within the neighborhood or community. An index missing one or more elements was coded as missing for the index and excluded from the analysis. After removing the cases with missing data, the elements are then averaged to create the index. These indices were then divided into high, medium, and low according to their distributions. These indices were included in the analysis in a number of different arrangements. We included high trust and low danger, using medium and low trust, and medium and high danger as the reference groups. In subsequent models the indices were combined into a neighborhood assessment measure and included as a dummy variable coded one if the neighborhood assessment index had a value between zero and six, indicating a negative assessment compared to an intermediate or high neighborhood assessment.

^a The scales for these questions were reversed to make them consistent with the other items.

An item included as a neighborhood measure was the neighborhood poverty rate. This is based on information taken from the 1980 Census and updated to the date the panels were started. Large numbers of new housing (permit areas) areas were created between the 1980 Census and the sample selection for these SIPP panels. These households did not receive an estimate of their neighborhood poverty rate. After comparison with income and education, it was clear that the majority of these households were very similar to households with less than a 20% poverty rate, and were included with these for the analysis. We also include measures of urbanicity and population density. Urbanicity is included with two dummy variables identifying respondents in central city areas of metropolitan sampling areas (MSAs) and respondents in the balance of the MSA, and compares them with residents not living in an MSA. Population was divided into four categories. Residents in areas that have less than 1 million population or were not in an MSA are compared to residents in MSAs with 1 to 5 million residents, as were residents living in areas with 5 to 10 million, and areas with 10 or more million persons.

Family and household: We consider the child's family situation in the analysis in several ways. A variable is included to distinguish the previously married and never married from the currently married designated parents. Although this is a non-optimal way to consider marital status and family structure, it does represent a significant distinction in the resources available to the children, in terms of social, human, and financial capital, as well as the occurrence of marital instability. We are not, however, able to clarify the timing of the disruption, with respect to the children in the household, or their educational derailment.

There are some shortcomings that limit completeness of the measurement of the children's family situation. First, only one parent, usually the mother, is identified for the child. The relationship between the spouse of the designated parent and the child cannot be fully determined, i.e. there is no way to discriminate between biologic and step children. Second, the marital history, fertility history, and household relationship matrix for the adults in the household, which could illuminate the family situation, is only available in the second wave of each panel. Linking the files creates additional problems, not the least of which is that the two panels differ with regard to the length of time between wave two and the waves containing the child well-being topical modules. The 1992 panel has an additional year of time between the two waves.

As social capital adheres in the relationships between people and human capital in the characteristics of those people, identifying the number and qualities of the adults in the household should assist in capturing the social, human, and financial capital that may be available to the children. Characteristics of the household's composition included are; the number of adults (persons > 17 years old) in the household, the number of adults having attended college, and the number of adults employed. The number of adults in the household was included in a single dummy variable, coded one if there were three or more adults in the household. The reference group is households with one or two adults. Similarly the number of adults in the household who were working full or part time is included as a dummy variable comparing households with two or more working adults with households having zero or one working adults. Education of the adults in the household is also a dummy variable, and is coded one if there are two or more adult in the household with at least some college. This is compared to households with zero or one adult with college experience.

Enrichment and Rules: Social interaction and childhood enrichment of the child is measured with three items identifying the children's participation in extracurricular activities. The first is a dummy variable coded one if the child plays sports either in or out of school, and zero otherwise. A second variable is created to represent participation in clubs or organizations after school or on weekends. Scouts, religious groups, and boys and girls clubs are the examples given for the types of clubs referenced by the questionnaire item. Finally, a dummy variable is coded one if the child participates in lessons after school or on weekends in subjects like music, dance, language, or computers, and zero otherwise. Parental rules are included in reference to rules for television watching. We create a variable coded one if the parent has rules about either the number of hours, time of day, or programs that this child may watch on television.

Stressors: Household and child level stress is an important component within the conceptual framework. Presence of stressors may affect parents' ability to invest time and resources in their children, and may also affect children's ability or motivation to invest in themselves. We include three measures of household and child related stress. The first of these is a dummy variable indicating whether or not the child has changed schools since the first grade other than the transitions from elementary to junior high school or middle school, or into high school. This variable is coded one if a change occurred and zero otherwise.

The next two of these indicators of household and childhood stress are related to disability. If there is a disabled adult in the household a dummy variable is coded one. This information is from self-reported status of the adults in the household. An adult was identified as disabled if disabled is marked on the interview control card for that person. Childhood disability is also included, which indicates the presence of a current disability for the child. Stress would certainly be introduced if a sibling were disabled, however, not all of the respondents have siblings in the household. The disability being reported is specifically related to school activities, though not specifically identified. The questionnaire item asks, "Because of a physical, learning, or mental condition, does (child) have any limitations in his/her ability to do regular school work?" If the designated parent says yes to this, a dummy variable for educational limitations is coded one.

Background Socio-demographics: A number of other variables were included in the analysis to control for differences in background. For the designated parent, we include age, race/ethnicity, education, marital status, and region of residence. Age is broken into three groups, those less than 35 years old, parents 35 to 40, and parents 40 years old and over. Thirty-five to forty year old parents are the reference group. Background characteristics of the child are also included. These are the child's gender, age in single years from six through seventeen, and the number of siblings under age 18 in the household.

Methods:

We use logistic regression to measure the correlates of child well-being as measured by the children's academic progress. Three models are presented. The first model, the base relationships, presents the results of regressions for each dummy or categorical variable entered separately. This shows relationships that would be observed in a typical tabular analysis. It is followed by model 2 which includes all of the covariates in the multivariate regression. Finally, a more parsimonious model is presented in model 3. It includes only those variables related to the outcome, with the exception of employment status of the designated parent. The later variable is retained, although it fails to show a significant relationship to educational progression, because it serves as both a control variable and an income and poverty indicator. Table 3 presents percent distributions of the principal study variables for the sample analyzed. The distributions of these variables for the population age 6 through 17 that was excluded from the analysis is presented in appendix table 1.

We present the proportion of children who are academically on-track for each of the principal study variables in table 4. On average, about 76% of all children between age six and seventeen are on track academically. Just over half, 54.1%, of children with disabilities related to schooling are on track. Only 66% of children whose mothers have less than a high school degree are on track, but 80% of the children of college educated mothers are on track. Family income appears to be associated with children who are on track as well, 67% of children in families with income that is below 100% of poverty are on track compared to 81% of children whose families have incomes that are 300% of poverty or above. Approximately 80% of the children of the West and Northeast are on track while about 72% of children living in other areas are on track.

Bivariate Results:

The principal exponentiated logistic regression coefficients (odds ratios) and standard errors for models 1 through 3 are shown in table 5. The results from the bivariate regression analyses presented in model 1 show the expected results given previous research. Independently, measures of human, social, financial capital, and household stress show consistent and significant relationships with children's current status academically. The relationships from model 1 are also consistent with the nature of findings expected from more basic tabular analyses.

Parental and household measures of human capital show associations with children who are currently on-track. Parents with some college and households having two or more adults with college education are more likely to have children who are currently on-track academically. Alone, all of the measures in the household and individual stress sections show significant relationships with children who are on-track. Children living in households with stressors present are less likely to be on-track academically. In model 1, indicators in the poverty and income sections are also related to children's academic progress. Measures indicating reduced family or household financial capital are associated with children being less likely to be on-track. Employment of the designated parents, increased numbers of working adults in the household, and higher income are related to children who are also more likely to be on-track.

Participation in enrichment activities and having rules about television watching are independently associated with children's academic progress. These enrichment activities could represent increased peer level social capital for the child. They could also be simultaneously determined, that is, children who are doing well are more likely to be able/allowed to also participate in these enrichment activities. Nonetheless, participation in enrichment activities is also considered an indicator of child well-being, and as such, is appropriately correlated with children who are doing well academically. The characteristics of the child's neighborhood show interesting bivariate associations with academic progress. Children whose parents have a poor impression of the neighborhood are less likely to also be on-track. This indication of neighborhood social capital is the combination of the elements measuring the designated parents' impressions relating to trust and danger in the neighborhood. Impressions of low trust in others to care for the child and high levels of danger for the child are significantly related to children who are less likely to be on-track.

Like the other conceptual groups, characteristics of the neighborhood, or area of residence, also show independent associations. Central city residence and residence in a non-metropolitan area is associated with a decreased likelihood of the child being on-track academically compared to children residing in the suburbs. Lower poverty rates and higher population MSAs are also independently associated with children who are more likely to also be on-track.

Multivariate Results:

Model 2 presents regression results from a multivariate analysis of all of the variables included in a single model. The one exception to this is the set of variables for designated parents' perceived trust and danger in the community. These are removed from the analysis and the single measure of negative neighborhood impression is used. Not surprisingly, many of the relationships that appear in the bivariate model disappear when the effects of the other variables are being controlled.

All of the significant household characteristics presented in model 1 fail to attain significance in model 2. With the exception of family income ratio and housing ownership, all of the family/designated parent level poverty indicators lose significance. Low income ratio and high income ratio are associated with marginally significant decreases and increases, respectively, in the probability that the child is also on-track. Children who are living in housing that is not owned by their family are significantly less likely to also be on-track.

The characteristics of the neighborhood follow a similar path as the characteristics of the household. The designated parents' impressions of child related social capital and safety are not significantly related with their child's current educational progress. Neither the local poverty rate nor the family's residential proximity to a central city are significantly related to whether the child is currently on-track or not. Residence in a highly populated MSA continues to be significantly related to being currently on-track. Among the enrichment activities, children who play sports or participate in clubs are significantly more likely to be children who are also progressing appropriately academically. Participation in lessons does not show significant relationships with current academic progress in a multivariate context. Children's educational status remains significantly related to whether children have rules about watching television, however the direction changes dramatically. In model 1 children with rules about television are 20% more likely to also be children who are on-track. In the multivariate model, where parents' education level, poverty status, and age of the child are controlled for, children with rules are about 17% less likely to also be on-track. This may be a result of television rules being used by parents as an intervening act when children are doing poorly.

Household and child level stressors continue to be related significantly with children who are less likely to be currently on-track. Children living in households with at least 1 disabled adult are about 14% less likely to also be on-track, and children who have disabilities or limitations related to schooling are about 54% less likely to also be on-track. Among the background variables for the child and their parent, four notable shifts in significance occur. In model 1, the age of the designated parent showed no relationship to the current on-track status of their child, but in model 2 we find that older designated parents are marginally more likely to have children who are currently on-track. Previously, black designated parents were significantly less likely to have children who were currently on-track academically. This relationship disappears in a multivariate context. However, children whose designated parent is not white, black, or Hispanic are more likely than whites to also be on-track academically.

The designated parents marital status also changes with respect to its relationship to the child's current academic status. In model 1, designated parents who were never married and those who were previously married were less likely to have children who were also on-track academically. This finding was consistent with the results expected from decreased social, human, and financial capital, and instability in family structure. When the other aspects of the household are controlled (economic situation, availability of adults, age, education, etc.) designated parents who were never married are not significantly different from those who are married with respect to the current academic status of their children. Designated parents who were previously married are still less likely to have a child who is also on-track, though to a lesser degree than in model 1. This may indicate a residual association of divorce or household instability, not controlled for by the other covariates.

Finally, the number of siblings in the household presents a different association from model 1. In model 1, having four or more siblings was the only significant association, and it reduced the likelihood that the child was also currently on-track. In model 2 this is no longer significant, however having only one child is associated with a 20% greater likelihood that the child is also currently on-track.

The final model, model 3, presents results for the more parsimonious model based on the results of model 2. In models not shown we tested each of the variables excluded one at a time with the variables remaining in model 3. None of them were significantly related to the child's academic status. We also tested interaction terms for race and education of the designated parents, marital status and education, marital status and poverty, and marital status and age, none of which were significantly related to the child's current academic status. The associations identified for the retained covariates remained as reported in model 2, with three exceptions. The negative association for blacks that lost significance in model 2, regains marginal significance in model 3 indicating that black designated parents are less likely to have a child who is currently on-track academically, than are white designated parents. Residence in the Northeast shows a marginally significant association with children who are more likely to be on-track. However, children living in the West are still about 50% more likely to be on track than are children living in the South. Finally, having a family income ratio less than 100% of poverty increases in significance from model 2 to model 3, but still indicates that families with income less than 100% of poverty are about 15% less likely to have children who also are currently on-track compared to families with income levels between 100% and 300% of poverty.

The purpose of this research was to address two objectives, to examine the child well-being data from the 1992 and 1993 panels of the SIPP, and, second, to identify correlates of child well-being. With respect to new correlates of child well-being, we include measures of human, social, and financial capital as well as measures of household and individual stressors. Of the covariates included, measures of household and individual stress proved to have the most persistent relationship with current child well-being as measured by the child's current grade and age. Young women, only children, residents of the West, and participants in sports and clubs are more likely to be on track. Also, children from families with higher income and from larger population centers are more likely to be on track academically. More detailed measures of the economic status and program receipt in the family and household failed to add significantly to the models. Additional information about the characteristics of the household in terms of the adults and their characteristics also did not add to model.

The correlations observed with participation in enrichment activities reinforce these concepts as relevant co-indicators of child well-being. The pattern observed with parental rules about television viewing may be explained by the temporal problems with this cross-sectional analysis. It is likely that the children who have rules about their television viewing behavior, are likely to have fallen off-track in the past and presently have parental rules to attempt to correct poor study habits, or as another form of discipline/structure. We are not surprised that the metropolitan area residence and the neighborhood poverty estimates fail to be significantly related to children's current educational status. One limitation of these measures is that they are updated estimates based on the results of the 1980 decennial Census. Ideally, we would like to improve neighborhood context measurement in the SIPP by incorporating 1990 Census data for future analyses.

The population of the metropolitan area is highly related to children's current academic status. This is likely representing several important characteristics. First, the very large area variable is a proxy for the New York and Los Angeles metropolitan areas. Second, the effects observed for the children living in areas with five or more million people, higher odds for being currently on track, are likely biased. The first source of bias is the potential that urban school systems engage in "Social Promotion", that is, for financial and other reasons these school systems are keeping children on track when their achievement indicates that they should be held back. Another source of bias is that, the wealthier populations in these MSAs are likely to have better schools and/or have the means to make sure that their children learn what is necessary to stay successful in school. An additional possibility is that these are areas that have a more developed school system and are doing a better job educating their children.

We had held out a great deal of hope that the questions that the designated parent answered regarding his/her impression of the safety of the neighborhood and the availability of trusted adults would be a very good indicator of the levels of social capital in the neighborhood, specifically related to children. Our results, however, somewhat dashed this hope. While the responses to these items may indeed represent the designated parents' perceived levels of social capital, they do not show any association with the current academic status of the children. However, a more salient measure might have been neighborhood measures from the child's point of view.

This analysis had a few fairly significant faults. The first and by far the most significant is the causal problem inherent in our dependent outcome. The child's current educational status is a product of his/her academic and life experiences to this point. Measuring the correlates of his/her current status provides a nice descriptive picture of the children who are currently on or off-track. However this precludes any ability to determine predictors for becoming off-track or remaining on-track. We are also unable to determine the simultaneity of the correlates we are observing. The previous example regarding rules about watching television illustrates this point. It is also highly likely that in a prospective analysis some of the covariates that we dropped from our final model would be significantly related to staying on-track versus beginning the process of academic disengagement.

Family structure is also not adequately measured in these waves of the 1992 and 1993 SIPP panel. We would have preferred to have been able to identify the relationships between the child and all of the other persons in the household. However, this is only possible when using the household matrix collected in wave 2 of the SIPP. Linking the cross-sectional data between waves presents significant logistical difficulties which were beyond the time available for this project. The SIPP waves currently in the field as part of the 1996 panel will correct at least part of this by collecting the person number and relationship for both mothers and fathers present in the household for each child. This will enable construction of more complete family structure measures.

We would also have preferred to have had additional measures of child well-being. As mentioned earlier, previous research considered a variety of early childhood measures of child well-being that would certainly add to this analysis. Since the data in the child well-being topical module is limited to children under 18, transitions to adulthood are difficult to measure. Beyond the age limitation, there are systematic problems with studying the transition to adulthood in the SIPP. The first of these is that if a child in this sample had a child, he/she would have become a related subfamily, and the original child would disappear from the sample as a child and would reappear as a designated parent. Second, a similar fate befalls those children who marry early. They would leave the sample and become a related husband/wife subfamily if they stayed in the household, or they would become a new household.

In spite of these limitations we are able to make some conclusions regarding the child well-being data. The data from the SIPP child well-being module confirm the basic relationships that were hypothesized based on previous research, and can produce a cross-sectional picture of child well-being as measured by the child's educational status. Also, other measures of child well-being such as participation in sports and clubs show the expected correlations with educational status as co-indicators of child well-being. These findings also suggest that including measurement of household stressors such as the presence of a disabled adult, childhood disabilities, or the occurrence of marital disruption is important. This would certainly be useful in a longitudinal analysis of determinants of child well-being, but also may have utility as indicators of the current environment and well-being of children.

While our primary conclusion is that the data, as they are, can measure children's current status, we feel that these data can be developed to have additional utility. We are excited by the prospect of using these data as the foundation for a longitudinal analysis of child well-being and the transition to adulthood using data from the Survey of Program Dynamics which follow a portion of the sample from the 1992 and 1993 SIPP panels beyond the year 2000.

¹  Annual or 12 month income data is not available in the waves of SIPP as only the income for each of the 4 months prior to the interview was collect

Alexander, Karl, Doris Entwisle, and Susan Dauber. 1993. "First-Grade Classroom Behavior: Its Short and Long-Term Consequences for School Performance." Child Development 64:801-814.

Alexander, Karl, Doris Entwisle, and Carrie Horsey. 1997. "From First Grade Forward: Early Foundations of High School Dropout." Sociology of Education 70(April):87-107.

Amato, Paul, Laura Spencer Loomis, and Alan Booth. 1995. "Parental Divorce, Marital Conflict, and Offspring Well-being during Early Adulthood." Social Forces 73:895-915.

Aquilino, William. 1996. "The Life Course of Children Born to Unmarried Mothers: Childhood Living Arrangements and Young Adult Outcomes." Journal of Marriage and the Family 58:293-310.

Astone, Nan Marie and Sara McLanahan. 1991. "Family Structure, Parental Practices and High School Completion." American Sociological Review 56:309-320.

Bane, Mary Jo and David Ellwood. 1994. Welfare Realities: From Rhetoric to Reform. Cambridge, MA.: Harvard University Press.

Bianchi, Suzanne. 1984. "Children's Progress Through School: A Research Note." Sociology of Education 57:184-92.

Bianchi, Suzanne and John Robinson. 1997. "What Did You Do Today? Children's Use of Time, Family Composition, and the Acquisition of Social Capital." Journal of Marriage and the Family 59:332-344.

Bruno, Rosalind and Andrea Curry. 1996. "School Enrollment-Social and Economic Characteristics of Students: October 1994." U.S. Bureau of the Census. P20-487.

Cadigan, Doris, Doris Entwisle, Karl Alexander, and Aaron Pallas. 1988. "First-Grade Retention Among Low-Achieving Students: A Search for Significant Predictors." Merrill-Palmer Quarterly 34:71-88.

Cherlin, Andrew. 1992. Marriage, Divorce, and Remarriage. Cambridge, Mass.: Harvard University Press.

Coleman, James S. 1988. "Social Capital in the Creation of Human Capital." American Journal of Sociology 94(Supplement):S95-S120.

Cooksey, Elizabeth. 1997. "Consequences of Young Mother's Marital Histories for Children's Cognitive Development." Journal of Marriage and the Family 59:245-261

Dauber, Susan, Karl Alexander, and Doris Entwisle. 1996. "Tracking and Transitions through the Middle Grades: Channeling Educational Trajectories." Sociology of Education 69(October):290-307.

Dawson, Deborah. 1991. "Family Structure and Children's Health and Well-Being: Data from the 1988 National Health Interview Survey on Child Health." Journal of Marriage and the Family 53(August):573-84.

Entwisle, Doris and Karl Alexander. 1996. "Family Type and Children's Growth in Reading and Math Over the Primary Grades." Journal of Marriage and the Family 58:341-355.

Guo, Guang, Jeanne Brooks-Gunn, and Kathleen Mullan Harris. 1996. "Parents' Labor Force Attachment and Grade Retention among Urban Black Children." Sociology of Education  69(July):217-236.

Harris, Kathleen and Jeremy Marmer. 1996. "Poverty, Paternal Involvement, and Adolescent Well-Being." Journal of Family Issues 17:614-640.

Haveman, Robert and Barbara Wolfe. 1994. Succeeding Generations: On the Effects of investments in Children. New York, NY.: Russell Sage Foundation.

Hogan, Dennis and Nan Astone. 1986. "The Transition to Adulthood." Annual Review of Sociology 12:109-130.

Madigan, Timothy. 1998. "Changes During The 1980's In The Rates Of And Types Of Students Affected By Grade Retention." Unpublished Paper. U.S. Bureau of the Census.

Manning, Wendy and Daniel Lichter. 1996. "Parental Cohabitation and Children's Economic Well-Being." Journal of Marriage and the Family 58:998-1010.

McLanahan, Sara and Gary Sandefur. 1994. Growing Up with a Single Parent: What Hurts, What Helps. Cambridge, MA.: Harvard University Press.

McNeal, Ralph. 1995. "Extracurricular Activities and High School Dropouts." Sociology of Education 68:62-80.

Morrison, Donna and Andrew Cherlin. 1995. "The Divorce Process and Young Children's Well-Being: A Prospective Analysis." Journal of Marriage and the Family 57:800-812

Rumberger, R. W. 1987. "High school dropouts: A review of issues and evidence." Review of Educational Research 57:101-21.

Teachman, Jay, Kathleen Paasch, and Karen Carver. 1997. "Social Capital and the Generation of Human Capital." Social Forces 75:1343-59.

Teachman, Jay, Kathleen Paasch, and Karen Carver. 1996. "Social Capital and Dropping Out of School Early." Journal of Marriage and the Family 58:773-83.

Weinick, Robin and Nan Astone. 1996. "Family Structure, Social Capital and Children's Well-Being." Unpublished paper presented at the 1996 PAA meeting in New Orleans, LA.

Wojtkiewicz, Roger. 1993. "Simplicity and Complexity in the Effects of Family Structure on High School Graduation." Demography 30:701-18.

Wu, Lawrence and Brian Martinson. 1993. "Family Structure and the Risk of a Premarital Birth." American Sociological Review 58, 2(April):210-232.

Zill, Nicholas and Christine Nord. 1994. Running in Place. Washington, D.C.: Child Trends.

For further information contact:
Fertility and Family Statistics Branch
Population Division
FB #3, Room 2353
Washington, DC 20233
301-457-2465

Jason M. Fields: jfields@census.gov
Kristin E. Smith: ksmith@census.gov