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Methods: Longitudinal data on 1036 children from 991 households, drawn from the National Longitudinal Survey of Youths 1979 Mother and Child datasets, was used in this study. The children were born in either 1986 or 1987, and were followed from around the time of their birth to the year 2006, when they were ages 19 to 20. To adjust for non-independence of observations for children belonging to the same household, the children were clustered by their mothers. This paper uses structural equation modeling techniques, including growth mixture models and path models, to explore the asset accumulation patterns of families with young children, and to examine the educational effects of the different asset accumulation trajectories.

Results and Implications: Using growth mixture modeling, four asset accumulation trajectory classes are identified, controlling for household income and other socio-demographic characteristics. The results indicate that around 78% of households belong to the Low Stable trajectory class that started with low initial net worth, and which experienced non-significant growth in assets over time, while only 4% of households with lower initial wealth levels experienced significant asset increases over the same period (Low Accumulator class). The reminding households belonged to trajectories where initial net-worth was significantly higher than 0, and where subsequent asset accumulation patterns were stable (12%) or increasing (6%) over time. Path model analyses further indicate that children from the Low Stable class have significantly poorer educational outcomes compared to children from the other asset trajectories. In addition, children from the Low Accumulator class have similar outcomes compared to children from households with higher initial net worth, regardless of subsequent growth trajectories. The effects of assets on children's educational outcomes appear to be fully mediated by the quality of home cognitive stimulation and the level of mothers' educational expectations for their children.

Methods: Data on 1342 children ages 7 to 14 in the year 2000 and on their mothers drawn from the NLSY79 and the NLSY79 Children and Young Adults datasets are used for this study. The outcome measures are children's PIAT standardized scores in math and reading in 2000. The process of asset accumulation is measured by whether there is an increase in net-worth, net-worth less home equity, and in liquid assets, from 1996 to 2000. Income is measured be averaging the total net family income over 1996 to 2000. Other variables include various mothers' socio-demographic data. A series of OLS regressions by the different income quartiles were conducted to examine the relationship between changes in assets and children's educational outcomes.

Results: The findings support our hypothesis that the different asset measures have differential effects for the various income groups. Increases in net worth significantly predict better math scores for children from the 2nd (b=4.04, t=2.19, p=0.03, N=173) and 4th income quartiles (b=4.70, t=2.08, p<0.04, N=171). Increases in net-worth less home equity is also significantly associated with better math outcomes for the 2nd income quartile (b=4.70, t=2.65, p=0.009, N=180) but lower math scores for children from the 3rd income quartile (b=-4.78, t=-2.21, p=0.03, N=165). Similarly, increases in liquid assets significantly predicts better math outcomes for children from the 2nd (b=3.63, t=2.19, p=0.03, N=201) and 4th (b=4.75, t=2.22, p=0.03, N=193) income quartiles, but lower math scores for the 3rd (b=-4.31, t=-2.03, p=0.04, N=181) income quartile. No significant associations are found for reading scores across the different asset measures. Race and mother's education are also found to significant predict children's math scores in the models reported above.

Implications: Our study calls for additional longitudinal research to further explicate the dynamic relationship between the process of asset accumulation across the different asset measures and children's outcomes for the different income groups. Diverse policy approaches would also need to be introduced to maximize the effects of parental assets on children's outcomes across income status.