The effect of friends on adolescent body weight

Renna, Francesco; Grafova, Irina B.; Thakur, Nidhi

doi:10.1016/j.ehb.2008.06.005

Cited by 105 publications

(129 citation statements)

References 27 publications

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“…They find that an one point increase in peers' average BMI increases own BMI by 0.52 point. Using a similar approach and based on Add Health dataset, Renna et al (2008) also find positive peer effects. These effects are significant for females only (= 0.25 point).…”

Section: Previous Literaturementioning

confidence: 90%

“…Rather, they are considered to have social interactions within a school network. This allows for the construction of a social interaction matrix that reflects how social interaction between adolescents in schools occurs in a more realistic setting (as in Trogdon et al, 2008;Renna et al, 2008). An additional originality of our paper lies in the fact that it relies upon a structural (linear-in-means) approach when relating an adolescent's behaviour to that of his peers.…”

Section: Previous Literaturementioning

confidence: 99%

“…Also, Renna et al (2008) reports endogenous effects of 0.16 for OLS estimates. This makes our OLS estimates comparable to those obtained in the literature on obesity.…”

Section: Baseline: Ols Peer Effects Estimatesmentioning

confidence: 99%

“…The evidence suggests the presence of strong peer effects in weight gain. Christakis and Fowler (2007), Trogdon et al (2008) and Renna et al (2008) seem to be unanimously pointing to the social multiplier as an important element in the obesity epidemics. 1 A social multiplier may amplify, at the aggregate level, the impact of any shock that affects obesity at the individual level.…”

Section: Introductionmentioning

confidence: 99%

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Peer effects, fast food consumption and adolescent weight gain

Fortin

Yazbeck

2015

Journal of Health Economics

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This paper aims at opening the black box of peer effects in adolescent weight gain. Using Add Health data on secondary schools in the U.S., we investigate whether these effects partly flow through the eating habits channel. Adolescents are assumed to interact through a friendship social network. We first propose a social interaction model of fast food consumption. Our approach allows to control for correlated effects at the network level and to solve the simultaneity (reflection) problem. We exploit results by Bramoullé, Djebbari and Fortin (2009) which show that if there are two agents who are separated by a link of distance 3 within a network (i.e., two adolescents who are not friends but are linked by two friends), peer effects are identified. The model is estimated using maximum likelihood and generalized 2SLS strategies. We also estimate a panel dynamic weight gain production function relating an adolescent's Body Mass Index (BMI) to his current fast food consumption and his lagged BMI level. Results show that there are positive significant peer effects in fast food consumption among adolescents belonging to a same friendship school network. The estimated social multiplier is 1.59. Our results also suggest that, at the network level, an extra day of weekly fast food restaurant visits increases BMI by 2.4%, when peer effects are taken into account.

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Section: Previous Literaturementioning

confidence: 90%

Section: Previous Literaturementioning

confidence: 99%

“…Also, Renna et al (2008) reports endogenous effects of 0.16 for OLS estimates. This makes our OLS estimates comparable to those obtained in the literature on obesity.…”

Section: Baseline: Ols Peer Effects Estimatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Peer effects, fast food consumption and adolescent weight gain

Fortin

Yazbeck

2015

Journal of Health Economics

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“…Research continually uncovers adult-to-adult [8], adult-to-child [10][11][12][13][14], and child-to-child [14][15][16][17][18] associations and influence in terms of obesity and obesityrelated attitudes, norms, and behaviors (i.e., nutrition and physical activity). Furthermore, a few recent obesity interventions found that targeting parents only may have a significant residual impact on children in regards to behavior and weight change [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Modeling Social Transmission Dynamics of Unhealthy Behaviors for Evaluating Prevention and Treatment Interventions on Childhood Obesity

Frerichs¹,

Araz²,

Huang³

2015

The Childhood Obesity Epidemic

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Research evidence indicates that obesity has spread through social networks, but lever points for interventions based on overlapping networks are not well studied. The objective of our research was to construct and parameterize a system dynamics model of the social transmission of behaviors through adult and youth influence in order to explore hypotheses and identify plausible lever points for future childhood obesity intervention research. Our objectives were: (1) to assess the sensitivity of childhood overweight and obesity prevalence to peer and adult social transmission rates, and (2) to test the effect of combinations of prevention and treatment interventions on the prevalence of childhood overweight and obesity. To address the first objective, we conducted two-way sensitivity analyses of adult-to-child and child-to-child social transmission in relation to childhood overweight and obesity prevalence. For the second objective, alternative combinations of prevention and treatment interventions were tested by varying model parameters of social transmission and weight loss behavior rates. Our results indicated child overweight and obesity prevalence might be slightly more sensitive to the same relative change in the adult-to-child compared to the child-to-child social transmission rate. In our simulations, alternatives with treatment alone, compared to prevention alone, reduced the prevalence of childhood overweight and obesity more after 10 years (1.2-1.8% and 0.2-1.0% greater reduction when targeted at children and adults respectively). Also, as the impact of adult interventions on children was increased, the rank of six alternatives that included adults became better (i.e., resulting in lower 10 year childhood overweight and obesity prevalence) than alternatives that only involved children. The findings imply that social transmission dynamics should be considered when designing both prevention and treatment intervention approaches. Finally, targeting adults may be more efficient, and research should strengthen and expand adult-focused interventions that have a high residual impact on children.

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