Elsevier

Addictive Behaviors

Volume 35, Issue 4, April 2010, Pages 337-342
Addictive Behaviors

Social network effects in alcohol consumption among adolescents

https://doi.org/10.1016/j.addbeh.2009.12.002Get rights and content

Abstract

In this paper we seek to empirically quantify the role of peer social networks in explaining drinking behavior among adolescents. Using data from a nationally representative sample of adolescents we utilize a multivariate structural model with school-level fixed effects to account for the problems of contextual effects, correlated effects and peer selection to purge the potential biases from the estimates of peer influence. Our peer group measures are drawn not only from the nomination of close friends, but also from classmates. Drinking behavior among the peer groups was constructed using the peers' own report of their alcohol consumption. Controlling for parent level characteristics, and other demographic parameters, we find that a 10% increase in the proportion of classmates who drink will increase the likelihood of drinking participation and frequency by approximately four percentage points. We also find evidence to show that the influence of close friends, while still significant, diminishes in magnitude after accounting for unobserved environmental confounders. Our findings support the literature that peer effects are important determinants of drinking behavior even after controlling for potential biases. Effective policy aimed at reducing alcohol consumption among adolescents would consider these significant peer effects.

Introduction

Alcohol consumption among adolescents is a major public health concern in the United States (Kosterman et al., 2000, Johnston et al., 2003). A 2000 Center for Disease Control and Prevention (CDC) study revealed that a third of all youths surveyed report beginning to drink before the age of 13. In addition, a high prevalence of alcohol use and a trend toward earlier onset have been observed among middle and high school students (Guo, Elder, Cai, & Hamilton, 2009). Besides being associated with poor outcomes such as type II diabetes, coronary artery disease, cardiac arrhythmias and stroke (Puddey, Rakic, Dimmitt, & Beilin, 1999), adolescent drinking is also correlated with risk behaviors, such as poor school performance, violence, delinquency and suicide (Windle, 2003, Moore et al., 2005).

Research on adolescent substance use has consistently identified a strong relationship between adolescent behavior and the behavior of their peers (Clark & Loheac, 2007, Evans et al., 1995, Lundborg, 2006, Norton et al., 1998). From a policy perspective, the potential existence and the magnitude of the social network effects are of interest since “peer effects may serve to amplify the effects of interventions” (Lundborg, 2006). However, peer effects are difficult to estimate and causal interpretations must be undertaken with caution since individuals in most cases choose with whom to associate (Bullers et al., 2001, Kremer & Levy, 2008). In other words, estimates without accounting for peer selection are unable to identify accurately whether an individual's behavioral choices in some way vary with behavior of the reference group (Manski, 1993). Peer selection implies that the correlation in behavior could be attributed to the similarity among individuals, whereas, peer influence implies that the correlation is due to the peer behavior. Disentangling the peer influence from spurious unobserved factors associated with peer selection (Alexander et al., 2001, Bullers et al., 2001) is important if we are to accurately predict the success of policies aimed at reducing alcohol consumption among adolescents. Thus, if there are common underlying attributes of individuals within a peer group that drive behavior more than peer influence, policies aimed at taking advantage of peer influence may not realize the desired effects (Ali & Dwyer, 2009).

Building on the existing literature on peer effects we extend our analysis by empirically quantifying the role of the peer social network to explain alcohol consumption behavior among adolescents. Our peer measures are drawn not only from the nomination of close friends, but also from classmates within a grade. This allows us to identify the differences in effects that could be exerted by different compositions of reference groups. It is also important to note that our second reference group is not driven by selective peer sorting (Clark & Loheac, 2007, Fletcher, in press) and might be more relevant for policy purposes, since most interventions (the DARE program for example) aimed towards reducing adolescent risky behaviors are implemented at the school level. Further we implement two stage least-squares modeling approaches with school-level fixed effects to purge potential biases from the peer estimates in order to give it a causal interpretation.

Section snippets

Estimating social networks

A standard linear regression using an average contemporaneous measure by a reference group (for example, by the school level, by workplace or by closest friends identified by the individuals) as a proxy for social interactions is easy to estimate. However, such measures of peer networks, or social interactions, have quite a few problems of interpretation (Manski, 1993). A significant effect of a peer indicator could be the consequence of three different interpretations according to Manski (1993)

Data

We utilize data from the National Longitudinal Study of Adolescent Health (Add Health). Add Health consists of data on adolescents in 132 schools nationwide between grades 7 and 12. The in-school portion of the first wave of the survey (1994) contains a cross-section of data on about 90,000 adolescents. A subset of the initial sample (20,745 respondents) was also interviewed in their homes with follow-up surveys in 1996 and in 2002, when most respondents had made a transition to adulthood. The

Empirical model

We estimate a model of peer effects where drinking behavior by adolescent i at school s during time t, Yist (a participation indicator or drinking frequency) is given byYist=β0+β1Fist+β2Xist+β3Pist+β4Sist+εistwhere Fist refers to our peer drinking measures, pertaining either to the adolescent's nomination of close friends or their classmates. Xist is a vector of personal or demographic characteristics and Pist is a vector of parent and family characteristics. Sist is a vector of school dummy

Results

We begin by presenting OLS results for the effects of peer drinking on individual drinking behavior. Least-square estimates of coefficients in linear probability models are consistent estimates if standard errors are adjusted for the presence of heteroskedasticity (Angirst & Kruger, 1999). We report standard error estimates that are robust to any form of heteroskedasticity. Linear probability also converges to normal when samples are large (Mittelhammer, Judge, Miller, 2000). Table 2 presents

Discussion

In this paper, we estimated models of adolescent drinking behavior to identify the role of social networks or peer groups on drinking propensities and frequencies. In particular, we used a two stage least squares with school-level fixed effects methodology to purge potential biases from the estimates of peer effects. Our estimation strategy allowed us to account for the contextual effects, correlated effects and the reflection problem, which are present in empirically measuring social influence.

Role of Funding Sources

This research was not funded by any external or internal source.

Contributors

Both authors contributed equally in writing the manuscript and interpreting the results. M.M. Ali analyzed the data.

Conflict of Interest

We have no conflict of interest.

Acknowledgements

We would like to thank Elizabeth A. Vanner at the Department of Health, Technology and Management, Stony Brook University for her helpful comments. This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R. Rindfuss and Barbara

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