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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.

Suggested Citation

  • 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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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Climent Quintana-Domeque & Nicola Barban & Elisabetta De Cao & Sonia Oreffice, 2016. "Assortative Mating on Education: A Genetic Assessment," Economics Series Working Papers 791, University of Oxford, Department of Economics.
    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. 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.
    4. 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.
    5. 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.
    6. Dolton, Peter & Xiao, Mimi, 2017. "The intergenerational transmission of body mass index across countries," Economics & Human Biology, Elsevier, vol. 24(C), pages 140-152.
    7. Kooreman, Peter & Scherpenzeel, Annette, 2014. "High frequency body mass measurement, feedback, and health behaviors," Economics & Human Biology, Elsevier, vol. 14(C), pages 141-153.
    8. Nicola Barban & Elisabetta De Cao & Sonia Oreffice & Climent Quintana-Domeque, 2016. "Assortative Mating: A Genetic Assessment," Working Papers 2016-034, Human Capital and Economic Opportunity Working Group.
    9. repec:spr:eujhec:v:18:y:2017:i:6:d:10.1007_s10198-016-0833-y is not listed on IDEAS
    10. Dolton, Peter & Xiao, Mimi, 2015. "The intergenerational transmission of BMI in China," Economics & Human Biology, Elsevier, vol. 19(C), pages 90-113.

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    Keywords

    Instrumental variables; Fixed effects; ALSPAC;

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