Estimation of social-influence-dependent peer pressure in a large network game

Research on peer effects in sociology has long been focused on social interactions and the associated social influence process. In this paper, we extend a large-network-based game model to a model that allows for the dependence of social interactions on social-influence status. In particular, we use the Katz–Bonacich centrality to measure individuals' social influences, which are obtained directly from the observation of a social network. To solve the computational burden when the data come from the equilibrium of a large network, we extend a nested pseudo-likelihood estimation approach to our large-network-based game model. Using the National Longitudinal Study of Adolescent Health (Add Health) dataset, we investigate the peer effects of dangerous behaviour among high-school students. Our results show that the peer effects are statistically significant and positive. Moreover, students benefit more (statistically significant at the 5% level) from conformity or, equivalently, pay more for disobedience, in terms of peer pressure, if their friends have a higher status of social influence.