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The effect of fat mass on educational attainment: Examining the sensitivity to different identification strategies

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  • von Hinke Kessler Scholder, Stephanie
  • Davey Smith, George
  • Lawlor, Debbie A.
  • Propper, Carol
  • Windmeijer, Frank

Abstract

The literature that examines the relationship between child or adolescent Body Mass Index (BMI) and academic attainment generally finds mixed results. This may be due to the use of different data sets, conditioning variables, or methodologies: studies either use an individual fixed effects (FE) approach and/or an instrumental variable (IV) specification. Using one common dataset, the Avon Longitudinal Study of Parents and Children, and a common set of controls, this paper compares the different approaches (including using different types of IV's), discusses their appropriateness, and contrasts their findings. We show that, although the results differ depending on the approach, most estimates cannot be statistically distinguished from OLS, nor from each other. Examining the potential violations of key assumptions of the different approaches and comparing their point estimates, we conclude that fat mass is unlikely to be causally related to academic achievement in adolescence.

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Bibliographic Info

Article provided by Elsevier in its journal Economics & Human Biology.

Volume (Year): 10 (2012)
Issue (Month): 4 ()
Pages: 405-418

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Handle: RePEc:eee:ehbiol:v:10:y:2012:i:4:p:405-418

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Web page: http://www.elsevier.com/locate/inca/622964

Related research

Keywords: Instrumental variables; Fixed effects; ALSPAC;

References

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  1. Anne Case & Christina Paxson, 2008. "Stature and Status: Height, Ability, and Labor Market Outcomes," Journal of Political Economy, University of Chicago Press, vol. 116(3), pages 499-532, 06.
  2. Hamermesh, Daniel S & Biddle, Jeff E, 1994. "Beauty and the Labor Market," American Economic Review, American Economic Association, vol. 84(5), pages 1174-94, December.
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  4. John Cawley & Chad Meyerhoefer, 2010. "The Medical Care Costs of Obesity: An Instrumental Variables Approach," NBER Working Papers 16467, National Bureau of Economic Research, Inc.
  5. Susan Averett & David Stifel, 2010. "Race and gender differences in the cognitive effects of childhood overweight," Applied Economics Letters, Taylor & Francis Journals, vol. 17(17), pages 1673-1679.
  6. Edward C. Norton & Euna Han, 2008. "Genetic information, obesity, and labor market outcomes," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1089-1104.
  7. Kaestner, Robert & Grossman, Michael, 2009. "Effects of weight on children's educational achievement," Economics of Education Review, Elsevier, vol. 28(6), pages 651-661, December.
  8. John Cawley & Euna Han & Edward C. Norton, 2011. "The validity of genes related to neurotransmitters as instrumental variables," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 884-888, 08.
  9. Joseph J. Sabia, 2007. "The Effect of Body Weight on Adolescent Academic Performance," Southern Economic Journal, Southern Economic Association, vol. 73(4), pages 871–900, April.
  10. Weili Ding & Steven Lehrer & J. Niles Rosenquist & Janet Audrain-McGovern, 2006. "The Impact of Poor Health on Education: New Evidence Using Genetic Markers," Working Papers 1045, Queen's University, Department of Economics.
  11. Burkhauser, Richard V. & Cawley, John, 2008. "Beyond BMI: The value of more accurate measures of fatness and obesity in social science research," Journal of Health Economics, Elsevier, vol. 27(2), pages 519-529, March.
  12. Burkhauser, Richard V. & Cawley, John & Schmeiser, Maximilian D., 2009. "The timing of the rise in U.S. obesity varies with measure of fatness," Economics & Human Biology, Elsevier, vol. 7(3), pages 307-318, December.
  13. Angrist, Joshua D & Graddy, Kathryn & Imbens, Guido W, 2000. "The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish," Review of Economic Studies, Wiley Blackwell, vol. 67(3), pages 499-527, July.
  14. John Cawley, 2004. "The Impact of Obesity on Wages," Journal of Human Resources, University of Wisconsin Press, vol. 39(2).
  15. Jason M. Fletcher, 2011. "The promise and pitfalls of combining genetic and economic research," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 889-892, 08.
  16. Stephanie von Hinke Kessler Scholder & George Davey Smith & Debbie A. Lawlor & Carol Propper & Frank Windmeijer, 2011. "Mendelian randomization: the use of genes in instrumental variable analyses," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 893-896, 08.
  17. Jason M. Fletcher & Steven F. Lehrer, 2009. "Using Genetic Lotteries within Families to Examine the Causal Impact of Poor Health on Academic Achievement," NBER Working Papers 15148, National Bureau of Economic Research, Inc.
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Cited by:
  1. Fang, Muriel Zheng, 2014. "Violating the Monotonicity condition for instrumental variable—Dimorphic patterns of gene–behavior association," Economics Letters, Elsevier, vol. 122(1), pages 59-63.
  2. Donal O’Neill & Olive Sweetman, 2013. "The consequences of measurement error when estimating the impact of obesity on income," IZA Journal of Labor Economics, Springer, vol. 2(1), pages 1-20, December.

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