Multidimensional diffusion processes in dynamic online networks.

We develop a dynamic matched sample estimation algorithm to distinguish peer influence and homophily effects on item adoption decisions in dynamic networks, with numerous items diffusing simultaneously. We infer preferences using a machine learning algorithm applied to previous adoption decisions, and we match agents using those inferred preferences. We show that ignoring previous adoption decisions leads to significantly overestimating the role of peer influence in the diffusion of information, mistakenly confounding influence-based contagion with diffusion driven by common preferences. Our matching-on-preferences algorithm with machine learning reduces the relative effect of peer influence on item adoption decisions in this network significantly more than matching on earlier adoption decisions, as well other observable characteristics. We also show significant and intuitive heterogeneity in the relative effect of peer influence.

Weight, reference points, and the onset of eating disorders.

We investigate whether the development of eating disorders, in the form of purging, is influenced by peers' body size through interpersonal comparisons. Using detailed information on recent cohorts of U.S. teenagers, we document a sizeable and significant negative effect of high school peers' body mass index (BMI) on purging behavior during the adolescence for females, but not for males. Interpersonal comparisons operate through the formation of a distorted self-perception: teenage girls with relatively thin female peers perceive themselves as heavier than they actually are. The girls who are more susceptible to peer influences are those having peers who are thinner, more popular, more (verbally) able, and with more educated parents.

Peer effects in bedtime decisions among adolescents: a social network model with sampled data.

Using unique information on a representative sample of US teenagers, we investigate peer effects in adolescent bedtime decisions. We extend the nonlinear least-squares estimator for spatial autoregressive models to estimate network models with network fixed effects and sampled observations on the dependent variable. We show the extent to which neglecting the sampling issue yields misleading inferential results. When accounting for sampling, we find that, besides the individual, family and peer characteristics, the bedtime decisions of peers help to shape one's own bedtime decision.