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.

We use a field experiment to show that referral-based hiring has the potential to disadvantage qualified women, highlighting another potential channel behind gender disparities in the labor market. Through a recruitment drive for a firm in Malawi, we look at men’s and women’s referral choices under different incentives and constraints. We find that men systematically refer few women, despite being able to refer qualified women when explicitly asked for female candidates. Performance pay also did not alter men’s tendencies to refer men. In addition, women did not refer enough high-quality women to offset men’s behavior.

Factor market failures can limit adoption of profitable technologies. We leverage a plot-level spatial regression discontinuity design in the context of irrigation use by farmers provided free access to water. Using irrigation boosts profits by 43–62 percent. Yet, farmers only irrigate 30 percent of plots because of labor costs. We demonstrate inefficient irrigation use, by showing farmers irrigating one plot reduce their irrigation use on other plots. This inefficiency is largest for smaller households and wealthier households, suggesting labor market frictions constrain use of irrigation.

Can targeting information to network-central farmers induce more adoption of a new agricultural technology? By combining social network data and a field experiment in 200 villages in Malawi, we find that targeting central farmers is important to spur the diffusion process. We also provide evidence of one explanation for why centrality matters: a diffusion process governed by complex contagion. Our results are consistent with a model in which many farmers need to learn from multiple people before they adopt themselves. This means that without proper targeting of information, the diffusion process can stall and technology adoption remains perpetually low.