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

• 成果类型: Article; Early Access
• 署名作者: Griffith, Alan
• 署名单位: University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF LABOR ECONOMICS
• ISSN/ISSBN: 0734-306X
• DOI: 10.1086/717935
• 发表日期: 2022
• 关键词: identification; MODEL

摘要

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.