Income Dynamics and Behavioral Trajectories across Childhood and Adolescence: Implications for Functioning into Adulthood

University Of Pittsburgh At Pittsburgh, Pittsburgh PA

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Abstract

Project Summary Over 20% of kindergarteners meet the criteria for internalizing and/or externalizing disorders (Carter et al., 2010). While the problem behaviors of most of these children subside over time, roughly 10% will maintain elevated problem behavior through adolescence (Costello et al., 2003; Shaffer et al., 1996), placing them at greater risk for worse psychological, social, and economic outcomes in adulthood (e.g. Bongers et al., 2004; Campbell et al., 2010; Shaw & Shelleby, 2014). Children from low-income homes are more likely to exhibit internalizing and externalizing symptoms (Carter et al., 2010; Votruba-Drzal, 2006). Yet we have a narrow understanding of the role family income plays in shaping behavioral development due to several limitations in the literature. This two-year project uses nationally representative, longitudinal data on more than 10,000 children matched to their mothers from the National Longitudinal Survey of Youth and its Child and Young Adult Supplements to address extant weaknesses in the literature examining income?s effects on behavioral development. First, it examines the links between income dynamics (i.e. cumulative family income, income volatility) and internalizing and externalizing problems during early childhood, middle childhood, and adolescence. Second, it looks at how income dynamics predict children?s trajectories of internalizing and externalizing from childhood through adolescence. Lastly, it examines whether behavioral trajectories are pathways through which youth income dynamics relate to wellbeing in early adulthood (age 24-28). Internalizing and externalizing will be drawn from the parent-reports on the Behavior Problems Index (BPI), and young adult wellbeing will be measured by self-reports of educational attainment, childbearing, labor market experiences, mental health, relationships, attitudes, and risky behaviors. Study aims will be addressed using several techniques that exploit the strengths of the data, including General Additive Modeling to examine nonlinearities in links between income dynamics and behavior, regression-based strategies to address timing-specific income effects, and Growth Mixture Modeling to answer questions regarding behavioral trajectories. Moreover, we will use quasi-experimental methods, including instrumental variable analyses, to strengthen our ability to draw causal inferences from results. Results will guide prevention and intervention efforts aimed at narrowing income gaps in behavioral functioning and stemming the intergenerational transmission of economic disadvantage.