Vasu Chaudhary

Social and economic networks are often multiplexed, meaning that people are connected by different types of relationships—such as borrowing goods and giving advice. We make two contributions to the study of multiplexing and the understanding of simple versus complex contagion. On the theoretical side, we introduce a model and theoretical results about diffusion in multiplex networks. We show that multiplexing impedes the spread of simple contagions, such as diseases or basic information that only require one interaction to transmit an infection. We show, however that multiplexing enhances the spread of a complex contagion when infection rates are low, but then impedes complex contagion if infection rates become high. On the empirical side, we document empirical multiplexing patterns in Indian village data. We show that relationships such as socializing, advising, helping, and lending are correlated but distinct, while commonly used proxies for networks based on ethnicity and geography are nearly uncorrelated with actual relationships. We also show that these layers and their overlap affect information diffusion in a field experiment. The advice network is the best predictor of diffusion, but combining layers improves predictions further. Villages with greater overlap between layers—more multiplexing—experience less overall diffusion. Finally, we identify differences in multiplexing by gender and connectedness. These have implications for inequality in diffusion-mediated outcomes such as access to information and adherence to norms.

Draft coming soon.

Social networks help workers find jobs, but fear of being blamed when a shared job turns out badly may discourage information sharing. We study whether these relational consequences discourage job-information sharing in informal labor markets in India. In a field experiment with 620 daily-wage workers at 32 labor stands in Delhi, we offer workers a one-day paid job organized by our research team and ask them to nominate another worker to hear about the same opportunity, randomizing whether the nominated worker will be told who provided his name. Anonymity increases the probability of nominating another worker by 8.0 percentage points (11%). The effect is larger (14.6 percentage points) among workers who perceive a higher likelihood of problems with the job. Anonymity also increases an index of nominations of workers perceived as able and reliable by 0.215 s.d., without a statistically detectable increase in nominations of close contacts. Descriptive and experimental evidence further supports anticipated relational costs as an explanation. Workers report conflict, demands for compensation, and reduced social and financial exchange following jobs they shared that turned out badly. In a partially incentivized vignette experiment, employer underpayment increases the share of workers willing to give up money to penalize the information source from 11% to 45%, even though the source had no control over payment. These findings show how anticipated relational costs can limit the flow of job information through social networks, potentially reducing their ability to lower search costs for workers and firms.