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Testing for Peer Effects Using Genetic Data

Author

Listed:
  • John Cawley
  • Euna Han
  • Jiyoon (June) Kim
  • Edward C. Norton

Abstract

Estimating peer effects is notoriously difficult because of the reflection problem and the endogeneity of peer group formation. This paper tests for peer effects in obesity in a novel way that addresses these challenges. It addresses the reflection problem by using the alter’s genetic risk score for obesity, which is a significant predictor of obesity, is determined prior to birth, and cannot be affected by the behavior of others. It addresses the endogeneity of peer group formation by examining peers who are not self-selected: full siblings. Using data from the National Longitudinal Survey of Adolescent Health, we find evidence of positive peer effects in weight and obesity; having a sibling with a high genetic predisposition raises one’s risk of obesity, even controlling for one’s own genetic predisposition to obesity. Implications of the findings include that peer effects may be an explanation for continued worldwide increases in weight, and that, because of social multipliers, the cost-effectiveness of obesity treatment and prevention programs may have been underestimated.

Suggested Citation

  • John Cawley & Euna Han & Jiyoon (June) Kim & Edward C. Norton, 2017. "Testing for Peer Effects Using Genetic Data," NBER Working Papers 23719, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23719
    Note: AG CH EH
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    Cited by:

    1. Han Yu & Naci Mocan, 2018. "The Impact of High School Curriculum on Confidence, Academic Success, and Mental and Physical Well-Being of University Students," NBER Working Papers 24573, National Bureau of Economic Research, Inc.
    2. Ashani Amarasinghe & Roland Hodler & Paul A. Raschky & Yves Zenou, 2018. "Spatial Diffusion of Economic Shocks in Networks," CESifo Working Paper Series 7001, CESifo.
    3. Brunello, Giorgio & Sanz-de-Galdeano, Anna & Terskaya, Anastasia, 2020. "Not only in my genes: The effects of peers’ genotype on obesity," Journal of Health Economics, Elsevier, vol. 72(C).
    4. Ushchev, Philip & Zenou, Yves, 2020. "Social norms in networks," Journal of Economic Theory, Elsevier, vol. 185(C).
    5. Lim, Jaegeum & Meer, Jonathan, 2018. "How do peers influence BMI? Evidence from randomly assigned classrooms in South Korea," Social Science & Medicine, Elsevier, vol. 197(C), pages 17-23.
    6. Hodor, Michal, 2021. "Family health spillovers: evidence from the RAND health insurance experiment," Journal of Health Economics, Elsevier, vol. 79(C).
    7. Ana Balsa & Carlos Díaz, 2018. "Social interactions in health behaviors and conditions," Documentos de Trabajo/Working Papers 1802, Facultad de Ciencias Empresariales y Economia. Universidad de Montevideo..
    8. Nuñez, Roy, 2020. "Obesity and labor market in Peru," MPRA Paper 105621, University Library of Munich, Germany.
    9. Mecheva, Margarita de Vries & Rieger, Matthias & Sparrow, Robert & Prafiantini, Erfi & Agustina, Rina, 2021. "Snacks, nudges and asymmetric peer influence: Evidence from food choice experiments with children in Indonesia," Journal of Health Economics, Elsevier, vol. 79(C).

    More about this item

    JEL classification:

    • D1 - Microeconomics - - Household Behavior
    • I1 - Health, Education, and Welfare - - Health
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z18 - Other Special Topics - - Cultural Economics - - - Public Policy

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