Charting How Wealth Shapes Educational Pathways from Childhood to Early Adulthood: A Developmental Process Model
Wealth plays a pervasive role in sustaining inequality and is more inequitably distributed than household income. Research has identified that wealth contributes to children’s educational outcomes. However, the specific mechanisms accounting for these outcomes are unknown. Using the Panel Study of Income Dynamics (PSID) and its supplements, SEM was used to test a hypothesized longitudinal chain of mediating processes. Framed by the parent investment model, this study tracks children and their parents over twenty-seven years, from pre-birth to early adulthood. The analytic sample was comprised of 1247 young people who were between 6–12 years of age (M= 5.66, SD= 2.12) in 1997, the first wave of the PSID’s Child Development Supplement. This analytic sample was roughly equivalent by gender (N= 774; 53% identified as female and N= 693; 47% identified as male). The racial/ethnic background of participants was nearly an equal split between individuals who identified as White (N= 666; 45%) or Black (N= 634; 43%), with an additional 7% (N= 97) who identified as “Hispanic,” 2% (N= 40) as “Other,” 1% (N= 20) as Asian or Pacific Islander, and less than 1% (N= 6) who identified as American Indian or Alaskan Native. The results indicated that wealth (a) engenders parental and child processes—primarily expectations and achievement—that promote educational success, (b) plays a different role across the life course, and (c) that pre-birth wealth has a significant mediated relationship to educational attainment seventeen years later. These findings advance understanding of specific mediating mechanisms by which wealth may foster children’s educational success across the life course, as well as how wealth may differentially shape educational outcomes in childhood, adolescence, and young adulthood.
KeywordsWealth Economic resources Inequality Social class
The first author was supported by a grant from Poverty Solutions, at the University of Michigan. Thank you to Fabian Pfeffer for his insights about wealth, stratification, and the PSID and thank you to Meichu Chen for her assistance wrangling the PSID dataset for this research.
MD conceived of the study, coordinated and conducted data analyses, and coordinated writing; AM conducted data analyses, contributed to the conceptual framework and to writing; RM contributed to the conceptual framework, interpretation of analyses and to writing. All authors contributed to the writing of, read, and approved of the final manuscript.
The first author was supported by a grant from Poverty Solutions, at the University of Michigan.
Data Sharing and Declaration
These publicly available data are freely available online, via the PSID Data Center.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
This research was conducted in accordance with American Psychological Association standards for the conduct of research. This research was deemed not human subjects research at the University of Michigan, because it analyzes publicly available data that contains no identifiers (i.e., the Panel Survey of Income Dynamics, or PSID).
Informed consent is not applicable to this secondary analysis of publicly available data, the Panel Survey of Income Dynamics (PSID).
- Bourdieu, P., & Richardson, J. G. (1986). The forms of capital. In Handbook of theory and research for the sociology of education (pp. 241–58). New York, NY: Greenwood Press.Google Scholar
- Conley, D. (1999). Being Black, living in the red: race, wealth, and social policy in America. Berkeley, CA: University of California Press.Google Scholar
- Enders, C. K. (2010). Applied missing data analysis. New York, NY: The Guilford Press.Google Scholar
- Furstenberg, F. F., Cook, T. D., Eccles, J., Elder, G. H., & Sameroff, A. (1999). Managing to make it: urban families and adolescent success. Chicago, IL: University of Chicago.Google Scholar
- Kline, R. B. (2015). Principles and practice of structural equation modeling. New York, NY: Guilford Publications.Google Scholar
- Lareau, A. (2003). Unequal childhoods: class, race, and family life. Berkeley, CA: University of California Press.Google Scholar
- MacKinnon, D. P. (2012). Introduction to statistical mediation analysis. New York, NY: Routledge.Google Scholar
- Mainieri, T. (2010). The panel study of income dynamics child development supplement: user guide for CDS-II. Ann Arbor, MI: Institute for Social Research, University of Michigan.Google Scholar
- Muthén, L. K., & Muthén, B. O. (2016). Mplus user’s guide. Los Angeles, CA: Muthén & Muthén.Google Scholar
- Reardon, S. F., Duncan, G. J., & Murnane, R. J. (2011). The widening academic achievement gap between the rich and the poor: new evidence and possible explanations. In Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 91–116). New York, NY: Russell Sage Foundation.Google Scholar
- Schneider, B. L., & Stevenson, D. (1999). The ambitious generation: America’s teenagers, motivated but directionless. New Haven, CT: Yale University Press.Google Scholar
- Sewell, W. H., & Hauser, R. M. (1975). Education, occupation, and earnings: achievement in the early career. New York, NY: Academic Press.Google Scholar
- Woodcock, R.W., & Mather, N. (1989). Woodcock-Johnson Psycho-Educational Battery–Revised. Chicago, IL: Riverside.Google Scholar