Abstract
Obesity among U.S. adolescents ages 12–19 rose from 4.6% in 1963–1965 to 17.4% in 2003–2004. This paper contributes to the literature on the impact of unhealthy body mass index (BMI) on health (e.g., obesity) and human capital (e.g., schooling) investments of adolescents. We use the propensity score method to study 8,388 individuals who responded to survey Waves I through III of the National Longitudinal Study of Adolescent Health (Add Health), for students in grades 7–12. We estimate an economic model that captures longer-term effects of BMI categories (obesity and overweight separately) on on-time (dichotomous) high school graduation. We control for characteristics at the individual, household, and community levels. Baseline probit regression estimates were improved upon by using matching estimators (propensity scores yield consistent estimate of the average treatment on the treated) based on the nearest neighbor and the more robust kernel density weighting schemes. Results from both full and reduced models suggest no adverse impact of overweight or obesity on timely high school completion for males, but a significant average negative effect on females. Investigating disparities in effects across both gender and race, we isolate the adverse effects primarily to white and Asian females. No significant effects were found for African-Americans. One of the novel contributions of our research is that the significant effects of gender- and race- specific adolescent obesity and overweight conditions reach beyond high school GPA standing to also impact on-time high school graduation status.
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Notes
Obesity may affect earnings through a number of channels. It may cause decreased productivity on the job in a direct (physical) sense, or productivity may suffer indirectly through psychological effects. It may limit the ability of individuals to take full advantage of labor market opportunities by increasing the fixed cost of movement between jobs. Finally, employer discrimination may lead to decreased earnings or employment offers for the pathological overweight. Therefore, to the degree that obesity is a chronic condition, lifestyle habits at an early age may affect one’s body weight for years to come.
Actual gender and age-specific cutoffs are given in Carriere (2003).
In addition to this full specification containing all of the variables in Table 1, it should be noted that results are robust to the inclusion of an even larger set of covariates. We attempted inclusion of additional behavioral variables and household characteristics, but removed them due to lack of statistical significance, collinearity with the included variables, or lack of effect on the estimated coefficients of interest.
The pseudo-R 2 increased only slightly (to 0.25 or 0.26) with the inclusion of the additional variables in the full specification.
The c-statistic is a measure of the predictive power of the first-stage. Generally, a higher c-statistic is viewed favorably, though there is some debate on this issue (Fu and Li 2008).
The standardized bias is the difference of the sample proportions in the treated and non-treated groups as a percentage of the square root of the mean of their sample variances (Rosenbaum and Rubin 1985).
Despite the potential improvement of propensity score methods relative to ordinary regression, it should be emphasized that potential endogeneity problems remain. That is, the independence assumption may be violated\—those individuals chosen as controls because of their similarity to the treatment group may still differ in unobserved ways. The standard approach to dealing with endogeneity is to use instrumental variables. However, identifying an instrument with sufficient association with obesity, but that is also plausibly otherwise unrelated to academic outcomes, is a difficult feat. Nonetheless, we attempted a two-stage least squares (2SLS) regression estimation using as instruments both the percentage of obese individuals in the school and parents’ obesity status. While the resulting estimates were generally consistent in sign as the results presented here, the estimates in general were much larger in magnitude and less precisely estimated (suggesting that the instruments may not fully satisfy the exogeneity condition).
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This study was partly funded through the University of Memphis (Fogelman College of Business & Economics) faculty Summer Research Grant Awarded to Okunade and Hussey. Additional funding for Okunade came from his First Tennessee Professorship fund at the University of Memphis. This work uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, The University of North Carolina at Chapel Hill, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc.edu). No direct support was received from grant P01-HD31921 for this analysis.
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Okunade, A.A., Hussey, A.J. & Karakus, M.C. Overweight Adolescents and On-time High School Graduation: Racial and Gender Disparities. Atl Econ J 37, 225–242 (2009). https://doi.org/10.1007/s11293-009-9181-y
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DOI: https://doi.org/10.1007/s11293-009-9181-y