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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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  • von Hinke Kessler Scholder, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2012. "The effect of fat mass on educational attainment: Examining the sensitivity to different identification strategies," Economics & Human Biology, Elsevier, vol. 10(4), pages 405-418.
  • Handle: RePEc:eee:ehbiol:v:10:y:2012:i:4:p:405-418
    DOI: 10.1016/j.ehb.2012.04.015
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    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;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 2(1), pages 1-20, December.
    3. Dolton, Peter & Xiao, Mimi, 2017. "The intergenerational transmission of body mass index across countries," Economics & Human Biology, Elsevier, vol. 24(C), pages 140-152.
    4. Jonas Minet Kinge, 2017. "Waist circumference, body mass index, and employment outcomes," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(6), pages 787-799, July.
    5. Nicole Black & David W. Johnston & Anna Peeters, 2015. "Childhood Obesity and Cognitive Achievement," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1082-1100, September.
    6. Petri Böckerman & John Cawley & Jutta Viinikainen & Terho Lehtimäki & Suvi Rovio & Ilkka Seppälä & Jaakko Pehkonen & Olli Raitakari, 2019. "The effect of weight on labor market outcomes: An application of genetic instrumental variables," Health Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 65-77, January.
    7. 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.
    8. von Hinke, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2016. "Genetic markers as instrumental variables," Journal of Health Economics, Elsevier, vol. 45(C), pages 131-148.
    9. Cawley, John, 2015. "An economy of scales: A selective review of obesity's economic causes, consequences, and solutions," Journal of Health Economics, Elsevier, vol. 43(C), pages 244-268.
    10. Kooreman, Peter & Scherpenzeel, Annette, 2014. "High frequency body mass measurement, feedback, and health behaviors," Economics & Human Biology, Elsevier, vol. 14(C), pages 141-153.
    11. Dolton, Peter & Xiao, Mimi, 2015. "The intergenerational transmission of BMI in China," Economics & Human Biology, Elsevier, vol. 19(C), pages 90-113.
    12. Olivier Bargain & Jinan Zeidan, 2019. "Heterogeneous effects of obesity on mental health: Evidence from Mexico," Health Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 447-460, April.

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