Social Networks with Mismeasured Links

We consider estimation of peer effects in social network models where some network links are incorrectly measured. We show that if the number of mismeasured links does not grow too quickly with the sample size, then standard instrumental variables estimators that ignore the measurement error remain consistent, and standard asymptotic inference methods remain valid. These results hold even when measurement errors in the links are correlated with regressors, or with the model errors. Monte Carlo simulations and real data experiments confirm our results in finite samples. These findings imply that researchers can ignore small amounts of measurement errors in networks.