Family socioeconomic status (SES) and child health are so strongly related that scholars have speculated child health to be an important pathway through which a cycle of poverty is reproduced across generations. Despite increasing recognition that SES and health work reciprocally and dynamically over the life course to produce inequality, research has yet to address how these two pathways simultaneously shape children’s development. Using longitudinal data from the Fragile Families and Child Wellbeing Study and marginal structural models, we ask three questions: (1) how does the reciprocal relationship between socioeconomic disadvantage and child health affect estimates of each circumstance on children’s cognitive development?; (2) how do their respective effects vary with age?; and (3) do family SES and child health have differential effects on cognitive development across population subgroups? The results show that the negative effects of socioeconomic disadvantage and poor health are insensitive to their reciprocal relationships over time. We find divergent effects of socioeconomic disadvantage and poor health on children’s cognitive trajectories, with a widening pattern for family SES effects and a leveling-off pattern for child health effects. Finally, the effects of socioeconomic disadvantage are similar across all racial/ethnic groups, while the effects of child health are largely driven by white children. We discuss theoretical and policy implications of these findings for future research.
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Researchers have also identified collider stratification bias as potentially arising with this approach (Morgan and Winship 2007; Pearl 2009). Suppose that unobserved factors influence time-varying child health and the outcome but not the treatment (i.e., time-varying exposure to socioeconomic disadvantage). Conditioning on child health generates an unnecessary correlation between its common causes—socioeconomic disadvantage and unobserved factors—even under the assumption of no unobserved heterogeneity of the treatment effect. Because unobserved factors also affect the outcome, conditioning on child health makes it impossible to distinguish the effects of socioeconomic disadvantage from those of unobserved factors.
For example, the Early Childhood Longitudinal Study-Birth Cohort tracks children from birth only through kindergarten entry. The National Longitudinal Survey of Youth 1979 (NLSY79) Children and Young Adults collects information on children born only to NLSY79 mothers.
Given our analytic strategy described later, we exclude mother-child pairs lost to follow-up from the analysis. They consist of those who permanently dropped out of the survey and those who left the survey but rejoined later. To address this issue of whole-wave missingness, we incorporate censoring weights in our marginal structural models.
The analysis examines only those children whose cognitive achievement test is conducted in English because of its incompatibility with that in Spanish (Dunn and Dunn 1997). For this reason, the respondents contribute an average of 2.3 person-years to our panel data.
For the Year 1 measure of child mental health, we use the Emotionality, Activity, and Sociability Temperament Survey for children as the CBCL scales are available from Year 3 on (Mathieson and Tambs 1999).
We specify our models such that the quartile measures of socioeconomic disadvantage and child health are linearly linked to children’s cognitive development. In a supplementary analysis (results available upon request), we test whether the associations should be specified to be nonlinear by introducing quadratic terms in the models, and reject the nonlinear specification.
Given the research design of the FFCWS, family structure at baseline refers only to a mother’s relationship with her child’s biological father.
Weights are truncated at the first and ninety-ninth percentiles to avert disproportionate influence from outlying observations (Cole and Hernán 2008).
If our MSMs are correctly specified, in expectation, the distributions of stabilized IPT, censoring, and final weights should be centered on values close to 1, have small variance, and be symmetric (Hernán et al. 2002). As shown in Table S3 (Online Resource 1), all three weights meet these conditions. They have a mean close to 1, are highly clustered around the mean, and are only slightly skewed to the right. Furthermore, Tables S4 and S5 (also in Online Resource 1) document that the IPT weighting achieves covariate balance, suggesting that socioeconomic disadvantage and poor health are largely independent of observed time-constant and time-varying covariates in the IPT weighted data.
For the purpose of consistency, the analysis incorporates censoring weights into all models.
Supplemental analysis (not shown) indicates that the effect of poor health at age 5 is mostly driven by children who transition to formal schooling.
Descriptive evidence also supports divergent age-specific patterns of socioeconomic disadvantage and poor health. Nearly one-fourth of children (24 %) experience long-term exposure to the highest level of socioeconomic disadvantage (being on the fourth quartile at least two of three time points), whereas only one-eighth (13 %) do so with respect to poor health.
Although the main goal of this study is to examine how the reciprocity between social causation and health selection processes affects children’s cognitive trajectory, unobserved confounding remains a concern. To address this issue, we estimate child fixed-effects models. By using within-child variation in exposures to socioeconomic and health disadvantages and cognitive achievement, these models account for selection bias due to unobserved time-constant characteristics. The results (see Table S7 in Online Resource 1) suggest that the age-specific patterns of socioeconomic disadvantage and child health effects are substantively similar to those reported here.
We exclude other racial/ethnic groups because of their small sample sizes.
Although we use IPT and censoring weights in the analysis, an additional examination that incorporates survey weights produces similar results (not shown). Our findings are therefore relevant to children growing up in large urban cities.
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Both authors contributed equally to the article. An earlier version of this article was presented at the 2015 meeting of the RC28 of ISA in Philadelphia. We gratefully acknowledge the Demography Editors and reviewers, Larry Wu, Florencia Torche, Mike Hout, and Paula England for their valuable feedback on earlier drafts of this article. This research was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2014S1A3A2044496).
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Lee, D., Jackson, M. The Simultaneous Effects of Socioeconomic Disadvantage and Child Health on Children’s Cognitive Development. Demography 54, 1845–1871 (2017). https://doi.org/10.1007/s13524-017-0605-z
- Socioeconomic disadvantage
- Child health
- Cognitive development