Exclusion bias in empirical social interaction models: causes, consequences and solutions

Bet Caeyers

No 2014-05, CSAE Working Paper Series from Centre for the Study of African Economies, University of Oxford

Abstract: This paper formalises an unproven source of ordinary least squares estimation bias in standard linear-in-means peer effects models. I derive a formula for the magnitude of the bias and discuss its underlying parameters. I show the conditions under which the bias is aggravated in models adding cluster fixed effects and demonstrate how it affects inference and interpretation of estimation results. Further, I reveal that two-stage least squares (2SLS) estimation strategies eliminate the bias and provide illustrative simulations. The results may explain some counter-intuitive findings in the social interaction literature, such as the observation of OLS estimates of endogenous peer effects that are larger than their 2SLS counterparts.

Date: 2014
New Economics Papers: this item is included in nep-ecm
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