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Childhood overweight in the United States: A quantile regression approach

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  • Stifel, David C.
  • Averett, Susan L.

Abstract

The prevalence of overweight children in the United States has increased dramatically over the past two decades, and is creating well-known public health problems. Moreover, there is also evidence that children who are not overweight are becoming heavier. We use quantile regression models along with standard ordinary least squares (OLS) models to explore the correlates of childhood weight status and overweight as measured by the Body Mass Index (BMI). This approach allows the effects of covariates to vary depending on where in the BMI distribution a child is located. Our results indicate that OLS masks some of the important correlates of child BMI at the upper and lower tails of the weight distribution. For example, mother's education has no effect on black children, but is associated with improvements in BMI for overweight white boys and underweight white girls. Conversely, mother's cognitive aptitude has no effect on white boys, but is associated with BMI improvements for underweight black children and overweight white girls. Further, we find that underweight white children and black girls experience similar improvements in BMI as they get older, but that for black boys there is little if any association between age and BMI anywhere in the BMI distribution.

Suggested Citation

  • Stifel, David C. & Averett, Susan L., 2009. "Childhood overweight in the United States: A quantile regression approach," Economics & Human Biology, Elsevier, vol. 7(3), pages 387-397, December.
  • Handle: RePEc:eee:ehbiol:v:7:y:2009:i:3:p:387-397
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Millimet, Daniel L. & Tchernis, Rusty, 2015. "Persistence in body mass index in a recent cohort of US children," Economics & Human Biology, Elsevier, vol. 17(C), pages 157-176.
    2. Classen, Timothy J., 2010. "Measures of the intergenerational transmission of body mass index between mothers and their children in the United States, 1981-2004," Economics & Human Biology, Elsevier, vol. 8(1), pages 30-43, March.
    3. Dodd, Mark C., 2014. "Intertemporal discounting as a risk factor for high BMI: Evidence from Australia, 2008," Economics & Human Biology, Elsevier, vol. 12(C), pages 83-97.
    4. Jolliffe, Dean, 2011. "Overweight and poor? On the relationship between income and the body mass index," Economics & Human Biology, Elsevier, vol. 9(4), pages 342-355.
    5. Fiese, Barbara H. & Hammons, Amber & Grigsby-Toussaint, Diana, 2012. "Family mealtimes: A contextual approach to understanding childhood obesity," Economics & Human Biology, Elsevier, vol. 10(4), pages 365-374.
    6. Butzlaf, Iris & Minos, Dimitrios, 2016. "Understanding the Drivers of Overweight and Obesity in Developing Countries: The Case of South Africa," Discussion Papers 232025, Georg-August-Universitaet Goettingen, GlobalFood, Department of Agricultural Economics and Rural Development.
    7. Chiang-Ming Chen & Chen-Kang Chang & Chia-Yu Yeh, 2012. "A quantile regression approach to re-investigate the relationship between sleep duration and body mass index in Taiwan," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(3), pages 485-493, June.
    8. Nakamura, R., 2012. "Intergenerational effect of schooling and childhood overweight," Health, Econometrics and Data Group (HEDG) Working Papers 12/02, HEDG, c/o Department of Economics, University of York.
    9. Forste, Renata & Moore, Erin, 2012. "Adolescent obesity and life satisfaction: Perceptions of self, peers, family, and school," Economics & Human Biology, Elsevier, vol. 10(4), pages 385-394.

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