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


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Research on peer effects in sociology has long been focused on social interactions and its associated influence process. This paper extends Xu (fthc)’s large–network–based game model to a model allowing 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 Aguirregabiria and Mira (2007)’s nested pseudo likelihood estimation (NPLE) approach to our large network game model. Using the Add Health dataset, we investigate peer effects of dangerous behaviors among high school students. Our results show that the peer effects are statistically significant and positive. Moreover, a student benefits more (statistically significant at the 5% level) from her conformity, or equivalently, pays more for her disobedience, in terms of peer pressure, if her friends have higher social influence status.

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