Band 11, Heft 3 - “Socioeconomic Status and Health Across Generations and Over the Life Course” edited by Thomas C Buchmueller

Selective attrition potentially biases estimation of intergenerational links in health and economic status. This paper documents attrition in the PSID through 2007 for a cohort of children, and investigates attrition bias in intergenerational models predicting adult health, education and earnings, including models based on sibling differences. Although attrition affects unconditional means, the weighted PSID generally maintains its representativeness along key dimensions in comparison to the National Health Interview Survey. Using PSID, sibling correlations in outcomes and father-son correlations in earnings are not significantly affected by attrition. Models of intergenerational links with covariates yield more mixed results with females showing few robust impacts of attrition and males showing potential attrition bias for education and earnings outcomes. For adult health outcomes conditional on child background, neither gender shows significant impacts of attrition for the age ranges and models considered here. Sibling models do not produce robustly higher attrition impacts than individual models.

Using national data from the U.S., we find that poor health at birth and limited parental resources (including low income, lack of health insurance, and unwanted pregnancy) interfere with cognitive development and health capital in childhood, reduce educational attainment, and lead to worse labor market and health outcomes in adulthood. These effects are substantial and robust to the inclusion of sibling fixed effects and an extensive set of controls. The results reveal that low birth weight ages people in their 30s and 40s by 12 years, increases the probability of dropping out of high school by one-third, lowers labor force participation by 5 percentage points, and reduces labor market earnings by roughly 15 percent. While poor birth outcomes reduce human capital accumulation, they explain only 10 percent of the total effect of low birth weight on labor market earnings. Taken together, the evidence is consistent with a negative reinforcing intergenerational transmission of disadvantage within the family; parental economic status influences birth outcomes, birth outcomes have long reaching effects on health and economic status in adulthood, which in turn leads to poor birth outcomes for one’s own children.

This paper investigates the extent and ways in which childhood family and neighborhood quality influence later-life health outcomes. The study analyzes the health trajectories of children born between 1950 and 1970 followed through 2005. Data from the Panel Study of Income Dynamics (PSID) spanning four decades are linked with information on neighborhood attributes and school quality resources that prevailed at the time these children were growing up.There are several key findings. First, estimates of sibling and child neighbor correlations in health are used to bound the proportion of inequality in health status in childhood through mid-life that are attributable to childhood family and neighborhood quality. Estimates based on four-level hierarchical random effects models (neighborhoods, families, individuals, over time) consistently show a significant scope for both childhood family and neighborhood background (including school quality). The results imply substantial persistence in health status across generations that are linked in part to low intergenerational economic mobility. Sibling correlations are large throughout at least the first 50 years of life: roughly three-fifths of adult health disparities may be attributable to family and neighborhood background. Childhood neighbor correlations in adult health are also substantial (net of the similarity arising from similar family characteristics), suggesting that disparities in neighborhood background account for more than one-third of the variation in health status in mid life.Second, exposure to concentrated neighborhood poverty during childhood has significant deleterious impacts on adult health. The results reveal that even a large amount of selection on unobservable factors does not eliminate the significant effect of child neighborhood poverty on health status later in life. Thus, racial differences in adult health can be accounted for by childhood family, neighborhood, and school quality factors, while contemporaneous economic factors account for relatively little of this gap.

This paper presents new estimates of sibling correlations in health and socioeconomic outcomes over the life course in the U.S. Sibling correlations provide an omnibus measure of the importance of all family and community influences. I find that sibling correlations in a range of health and socioeconomic outcomes start quite high at birth and remain high over the life course. The sibling correlation in birth weight is estimated to be 0.5. Sibling correlations in test scores during childhood are as high as 0.6. Sibling correlations in adult men’s wages are also around 0.5. Decompositions provide suggestive evidence on which pathways may account for the gradients in health and SES by family background. For example, sibling correlations in cognitive skills and non-cognitive skills during childhood are lower controlling for family income. Similarly, parent education levels can account for a sizable portion of the correlation in adult health status among brothers.

Intergenerational disparity in income and health violates the norm of equal opportunity and deserves the attention of researchers and policy makers. To understand changes in intergenerational disparity, we created the intergenerational mobility index (IMI), which can simultaneously measure changes in income rankings and in health outcomes across two generations. We selected obesity as one health outcome to illustrate the application of IMI due to its severe health and financial consequences for society and the significant changes in the distribution of obesity across income groups. Although obesity has increased in all income groups in the last four decades, higher income groups have tended to have a faster increase in obesity, which has reduced the disparity in obesity across income groups. The strength of our intergenerational approach within families is to control the genetic influence, which is one of the strongest determinants of obesity. The decomposition of the IMI illustrates that it captures changes in obesity distribution (holding constant income rankings between generations) and changes in income rankings (holding constant the obesity distribution across generations), simultaneously. We used the data of the Panel Study of Income Dynamics (PSID), which have been collected since 1967, is the longest longitudinal survey in the U.S. The PSID surveyed respondents’ height and weight were recorded in 1986 and from 1999 to 2007. We selected respondents from 1986 as the parental generation and respondents from 2007 as the adult children’s generation. To make the adult children’s body weight status and income comparable to their parents’, we stratified the analysis by gender. For the pairs of fathers and adult sons, we found the intergenerational disparity in overweight, a less severe indicator of excessive fatness, across income was decreasing. This was partially due to the up-swing in the adult children’s income status. For the pairs of mothers and adult daughters, we found a similar decrease in socioeconomic disparity in obesity. However, decomposition of the IMI indicated that changes in income distributions between mothers and adult daughters contributed smaller effects than that between fathers and adult sons. Our study has demonstrated that the IMI and its decomposition are useful tools for analyzing intergenerational disparity in income and health.

We estimate a dynamic model of health that is rooted in "stress models" from Epidemiology. Health is determined by time-invariant endowments, permanent shocks, and transitory shocks. We estimate that the variance in health at age 60 ranges between 2.5 and five times its variance at age 25 depending on which demographic group we consider. We show that the stress model performs better than a simple alternative, the random effects Probit, particularly for less educated people.

While considerable research studies the association between education and health, no prior research estimates the magnitude of health improvements associated with more schooling, accounting for both quantity and quality of life. We use longitudinal PSID data that combines repeated survey measures of health status with mortality data to measure the number of Quality-Adjusted Life Years accruing over a 23-year period. To reduce endogeneity concerns we utilize detailed survey information on family background, cognitive, and non-cognitive skills; and we estimate sibling fixed effects models. An additional year of schooling is associated with 0.20-0.38 additional QALYs over the 23-year study period. These estimates are considerably larger than looking at quality-unadjusted life years. We estimate that an additional year of schooling is associated with an incremental discounted lifetime value of $26,000-$49,000 of health. To the extent that our results approximate causal estimates, increasing education may be a cost-effective way to improve population health.

We use longitudinal data from the 1984-2007 waves of the Panel Study of Income Dynamics to examine how occupational status is related to the health transitions of 30-59 year-old U.S. males. A recent history of blue-collar employment predicts a substantial increase in the probability of transitioning from very good into bad self-assessed health, relative to white-collar employment, but with no evidence of a difference in movements from bad to very good health. Service work is also associated with a higher probability of transitioning into bad health and possibly with a lower probability of recovery. These findings suggest that blue-collar and service workers “wear out” faster with age because they are more likely than their white-collar counterparts to experience negative health shocks. There is also evidence that this partly reflects differences in the physical demands of jobs.