Social Network Structure, Segregation, and Equality in a Labor Market with Referral Hiring∗

We construct a model of referral hiring to examine the effects of social network structure on group level inequality. Our study departs from many studies of social networks and labor market outcomes in that we focus on groups and not on individuals. We find that more random social networks yield higher employment rates than less random social networks if the population is integrated or information flows about job vacancies are random. However if the population is highly segregated and information flows about job vacancies are nonrandom then less random social networks have higher employment rates than more random social networks. This second finding holds because non-random social networks allow a group to better contain job information inside the group when a population is segregated. We also study the robustness of this finding with respect to the size of minority and majority groups and the amount of social segregation. JEL Classification: J15, J18, J78