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Network Search: Climbing the Job Ladder Faster

We introduce an irregular network structure into a model of frictional, on-the-job search in which workers find jobs through their network connections or directly from firms. We show that jobs found through network search have wages that stochastically dominate those found through direct contact. Because we consider irregular networks, heterogeneity in the worker's position within the network leads to heterogeneity in wage and employment dynamics: better connected workers climb the job ladder faster and do not fall off it as far. These workers also pass along higher quality referrals, which benefits their connections. Despite this rich heterogeneity from the network structure, the mean-field approach allows the problem of our workers to be formulated tractably and recursively. We then calibrate and study the wage and employment dynamics coming from our job ladder with network heterogeneity. This quantitative version of our mechanism is consistent with several features of empirical studies on networks and labor markets: jobs found through networks have higher wages and last longer.