Estimating peer effects in networked panel data

[摘要] After product adoption, consumers make decisions about continued use. These choices can be influenced by peer decisions in networks, but identifying causal peer influence effects is challenging. Correlations in peer behavior may be driven by correlated effects, exogenous consumer and peer characteristics, or endogenous peer effects of behavior (Manski 1993). Extending the work of Bramoullé et al. (2009), we apply proofs of peer effect identification in networks under a set of exogeneity assumptions to the panel data case. With engagement data for Yahoo Go, a mobile application, we use the network topology of application users in an instrumental variables setup to estimate usage peer effects, comparing the performance of a variety of regression models. We find analyses of this type may be especially useful for ruling out endogenous peer effects as a driver of behavior. Omitted variables (especially ones related to network homophily) and violation of the exogeneity assumptions can bias regression coefficients toward finding statistically significant peer effects.

[发布机构] Massachusetts Institute of Technology