  We provide new identification results for panel data models with peer effects
operating through unobserved individual heterogeneity. The results apply for
general network structures governing peer interactions and allow for correlated
effects. Identification hinges on a conditional mean restriction requiring
exogenous mobility of individuals between groups over time. We apply our method
to surgeon-hospital-year data to study take-up of keyhole surgery for cancer,
finding a positive effect of the average individual heterogeneity of other
surgeons practicing in the same hospital
