Name Your Friends, but Only Five? The Importance of Censoring in Peer Effects Estimates Using Social Network Data
研究发现同伴效应研究中常用的删失同伴数据会导致估计低估真实影响，并提出修正和边界方法，对使用社交网络数据的经济学者有重要参考价值。
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.