This paper examines the dynamic implications of social networks for the labour market outcomes of refugees resettled in the U.S. A theoretical model of job information transmission shows that the relationship between social network size and labour market outcomes is heterogeneous and depends on the vintage of network members: an increase in network size can negatively impact some cohorts in a network while benefiting others. To test this prediction, I use new data on political refugees resettled in the U.S. and exploit the fact that these refugees are distributed across cities by a resettlement agency, precluding individuals from sorting. The results indicate that an increase in the number of social network members resettled in the same year or one year prior to a new arrival leads to a deterioration of outcomes, while a greater number of tenured network members improves the probability of employment and raises the hourly wage.

We use recruitment into a laboratory experiment in Kolkata, India to analyze how social networks select individuals for jobs. The experiment allows subjects to refer actual network members for casual jobs as experimental subjects under exogenously varied incentive contracts. We provide evidence that some workers, those who are high ability, have useful information about the abilities of members of their social network. However, the experiment also shows that social networks provide incentives to refer less qualified workers, and firms must counterbalance these incentives in order to effectively use existing employees to help overcome their screening problem.