Ignoring Measurement Errors in Social Networks

Abstract

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