Discussion Paper Details

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Title: Exclusion bias and the estimation of peer effects

Author(s): Bet Caeyers and Marcel Fafchamps

Publication Date: February 2020

Keyword(s): Autoregressive Models, Exclusion bias, Linear-in-means, peer effects, Random peer assignment, Reflection bias and Social interactions

Programme Area(s): Development Economics and Labour Economics

Abstract: We examine a largely unexplored source of downward bias in peer effect estimation, namely, exclusion bias. We derive formulas for the magnitude of the bias in tests of random peer assignment, and for the combined reflection and exclusion bias in peer effect estimation. We show how to consistently test random peer assignment and how to estimate and conduct consistent inference on peer effects without instruments. The method corrects for the presence of reflection and exclusion bias but imposes restrictions on correlated effects. It allows the joint estimation of endogenous and exogenous peer effects in situations where instruments are not available and cannot be constructed from the network matrix. We estimate endogenous and exogenous peer effects in two datasets where instrumental approaches fail because peer assignment is to mutually exclusive groups of identical size. We find significant evidence of positive peer effects in one, negative peer effects in the other. In both cases, ignoring exclusion bias would have led to incorrect inference. We also demonstrate how the same approach applies to autoregressive models.

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Bibliographic Reference

Caeyers, B and Fafchamps, M. 2020. 'Exclusion bias and the estimation of peer effects'. London, Centre for Economic Policy Research.