Start Date
11-12-2016 12:00 AM
Description
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 for 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 a variety of regression models. We find this type of analysis may be useful for ruling out endogenous peer effects as a driver of behavior. Omitted variables and violation of exogeneity assumptions can bias regression coefficients toward finding statistically significant peer effects.
Recommended Citation
Rock, Daniel; Aral, Sinan; and Taylor, Sean J., "Identification of Peer Effects in Networked Panel Data" (2016). ICIS 2016 Proceedings. 7.
https://aisel.aisnet.org/icis2016/Economics/Presentations/7
Identification of Peer Effects in Networked Panel Data
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 for 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 a variety of regression models. We find this type of analysis may be useful for ruling out endogenous peer effects as a driver of behavior. Omitted variables and violation of exogeneity assumptions can bias regression coefficients toward finding statistically significant peer effects.