Abstract
This paper provides new identification results for panel data models with contextual and endogenous peer effects, respectively operating through individual heterogeneity and outcomes. The results apply for general network structures governing peer interactions, and hinge on a conditional mean restriction requiring exogenous mobility of individuals between groups over time. Some networks preclude identification, in which case we propose additional conditional variance restrictions. We apply our method to surgeon-hospital-year data to study take-up of keyhole surgery, finding a positive effect of the average individual heterogeneity of peers. This effect is equally due to endogenous and contextual effects.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
