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

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  • Alan Griffith

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.

Suggested Citation

  • Alan Griffith, 2022. "Name Your Friends, but Only Five? The Importance of Censoring in Peer Effects Estimates Using Social Network Data," Journal of Labor Economics, University of Chicago Press, vol. 40(4), pages 779-805.
    • Handle: RePEc:ucp:jlabec:doi:10.1086/717935
    DOI: 10.1086/717935
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    Cited by:

    1. Yong Cai, 2022. "Linear Regression with Centrality Measures," Papers 2210.10024, arXiv.org.

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