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Does education improve health more for one sex than the other? We develop a theory of resource substitution which implies that education improves health more for women than men. Data from a 1995 survey of U.S. adults with follow-ups in 1998 and 2001 support the hypothesis. Physical impairment decreases more for women than for men as the level of education increases. The gender gap in impairment essentially disappears among people with a college degree. Latent growth SEM vectors also show that among the college educated, men's and women's life course patterns of physical impairment do not differ significantly.

ACKNOWLEDGMENTS

This research was funded by grants from the National Institute on Aging: “Aging, Status, and the Sense of Control” (RO1-AG12393) (Mirowsky, p.i.), and “Education, Resource Substitution, and Health” (RO1-AG023380) (Ross, p.i.), and from the National Institute of Child Health and Human Development: “Educational Differences in U.S. Adult Mortality” (RO1-HD053696) (Robert Hummer, p.i.).

Notes

1 We define a resource as something that helps one achieve goals, and a disadvantaged status as one with fewer resources overall. A disadvantaged group has less power, that is less of an ability to achieve goals. Although women may have more of some kinds of resources, like social support, overall, women have fewer socioeconomic resources than men.

2 Models are estimated using partitioned full information maximum likelihood (FIML) estimation that maximizes the casewise likelihood (Wothke 2000 Wothke, W. 2000. “Longitudinal and Multigroup Modeling with Missing Data.” Pp. 21940 in Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches and Specific Examples, edited by T. D. Little, K. U. Schnabel, and J. Baumert. Mahwah, NJ: Lawrence Erlbaum Associates. [Google Scholar]), as implemented in the AMOS structural equation modeling program. FIML procedures use the full sample, partitioned into subpopulations defined by patterns of missing data. The method corrects for data “Missing at Random” or MAR, assuming multivariate normality. It assumes that the absence of values depends on a combination of random chance and tendencies predictable from the observed values. Dropouts are missing information on physical impairment at time 2 or 3. The FIML estimates are robust to the extent that the observed baseline values predict either the follow-up outcome or the tendency to drop out. The model includes baseline measures of education, income, gender, and age, known to influence attrition (Mirowsky and Reynolds 2000 Mirowsky, John and John R. Reynolds. 2000. “Age, Depression and Attrition in the National Survey of Families and Households.” Sociological Methods and Research 28:476504.[Crossref], [Web of Science ®] [Google Scholar]), strengthening the robustness.

3 An index that combines physical functioning and self-reported health shows the same substantive results, as does an index of musculoskeletal impairment which removes vision and hearing from the physical impairment index.

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