Name Your Friends, but Only Five? The Importance of Censoring in Peer Effects Estimates Using Social Network Data

Alan Griffith

Journal of Labor Economics, 2022, vol. 40, issue 4, 779 - 805

Abstract: Empirical peer effects research often employs censored peer data. Individuals may list only a fixed number of links, implying mismeasured peer variables. I first document that censoring is widespread in network data. I then introduce an estimator and characterize its inconsistency analytically; an assumption on the ordering of peers implies that censoring causes attenuated peer effects estimates. Next, I demonstrate the effect of censoring in two data sets, showing that estimates with censored data underestimate peer influence. I discuss interpretation of estimates, propose methods for correction and bounding, and give implications for the design of network surveys.

Date: 2022
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