Wages and Employment in a Random Social Network with Arbitrary Degree Distribution

Empirical studies of labor markets show that social contacts are an important source of job-related information [Ioannides and Loury (2004)]. At the same time, wage differences among workers may be explained only in part by differences in individual background characteristics. Such findings motivate our model in which differences in "social connectedness" among otherwise identical workers result in wage inequality and differences in unemployment rates. The paper is related to theoretical contributions by Calvo- Armengol and Jackson (2004) and Calvo-Armengol and Zenou (2005) and builds on the Pissarides (2000) model. Workers may hear about job openings directly from employers or through their social contacts. We go further by introducing heterogeneity in the number of contacts each worker has with others, i.e. in the workers' degree. We utilize results from the technical literature on random graphs with arbitrary degree distributions [Newman, (2003a)] to account for a consequence of workers' receiving information about job openings from their social contacts: they compete with their social contacts' other contacts. For social networks with arbitrary degree distributions we show that people who are better connected receive a higher wage on average and face a lower unemployment rate. Numerical computations for the specific case in which connections follow a Poisson distribution show that variability in connections can result in substantial variation in the above labor market outcomes.