Abstract:

In collecting data on network connections, a common practice is to prompt respondents to name up to a certain number of network links, potentially leading to censoring. This censored data is then used to estimate parameters of peer effects models in a wide variety of economic applications. In this paper, I first provide an analytic form of the bias induced by this practice, showing that this bias decreases as the number of observed links increases. I then conduct a series of Monte Carlo experiments to demonstrate the magnitude of the bias, providing suggestive evidence that the it may be substantively meaningful. Using network data from Add Health, I show that different censoring rules induce substantially different estimates of peer-effects parameters. After documenting the possible bias, I propose a number of strategies for researchers working with censored network data. These findings and proposed solutions have potentially wide-ranging applications to research on peer effects through networks as well as the practice of collecting network data.