Data structure.

<p>Data structure.</p

Peer effects on adolescent smoking: Are popular teens more influential?

<div><p>Previous research on adolescent cigarette adoption has focused on peer influence and the perceived status gain from smoking but has ignored the status effects on peer influence. We analyze adolescent peer effects on cigarette consumption while considering the popularity of peers. The analysis is based on a four wave panel survey representative of American high school students. We measure peers’ popularity by their eigenvector centrality in high school social networks. Using lagged peers’ behavior, school fixed effects, and instrumental variables to control for homophily and contextual confounds, we find that the probability of smoking the following year increases with the mean popularity of smokers, while the popularity of non-smokers has the opposite effect. These effects persist seven and fourteen years later (wave 3 and 4 of the data). In addition, the probability of smoking increases with the smoking propensity of the 20% most popular teens and decreases with the smoking propensity of the bottom 80%. The results indicate the importance of knowing not only the smoking propensity within a school but also the location of smokers within the social hierarchy.</p></div

Correlates of popularity.

<p>Weighted standardized eigenvector centrality.</p

Summary statistics.

<p>Summary statistics.</p

Probability of smoking and popularity of smokers/non-smokers—Probit average marginal effects.

<p>Probability of smoking and popularity of smokers/non-smokers—Probit average marginal effects.</p

Trends over time along four dimensions of specialization.

The distributions of (a) focus, (b) rarity, (c) novelty, and (d) technical exclusivity over time suggest that each measure captures a different dimension of specialization. Meetup groups trend towards associating with rarer topics, and the introduction of new topics declines over time.</p

Meetup groups with the most and least common topics across the years they were active.

Meetup groups with the most and least common topics across the years they were active.</p

Technical appendix that discusses alternative specifications of focus and robustness check of the Cox proportional hazards assumption for survival models.

Technical appendix that discusses alternative specifications of focus and robustness check of the Cox proportional hazards assumption for survival models.</p

Linear model of group performance predicted by topic focus.

Coefficient estimates of cross-sectional linear regression models for predicting a group’s size, growth, audience retention, and survival, using a group’s topic focus score as the lone predictor.</p

Cross-sectional linear regression model of group performance predicted by multiple dimensions of specialization.

The table reports coefficient estimates for predicting a group’s size, growth retention, and survival using a group’s topic focus, rarity, novelty and technical exclusivity scores.</p