Beyond homophilic dyadic interactions: the impact of network formation on individual outcomes

Models of social interaction have been mostly focusing on the dyad, the smallest possible social structure, as a unit of network analysis. In the context of friendship networks, we argue that the triad could also be seen as a building block to ensure cohesion and stability of larger group structures. By explicitly modeling the mechanism behind network formation, individual attributes (such as gender and ethnicity) are often dissociated from purely structural network effects (such as popularity) acknowledging the presence of more complex configurations. Allowing structural configurations to emerge when nodes share similar attribute values, real-world networks are more adequately described. We present a comprehensive set of network statistics that allow for continuous attributes to be accounted for. We also draw on the important literature on endogenous social effects to further explore the role of network structures on individual outcomes. A series of Monte Carlo experiments and an empirical example analyzing students’ friendship networks illustrate the importance of properly modeling attribute-based structural effects. In addition, we model unobserved nodal heterogeneity in the network formation process to control for possible friendship selection bias on educational outcomes. A critical issue discussed is whether friendships are related to homogeneity across several attributes or by a balance between homophily on some, such as gender and race, but heterophily on others, such as socio-economic factors.