Follow us
Publications Commentary Research People Events News Resources and Videos About IFS
Home Publications Exclusion bias in the estimation of peer effects

Exclusion bias in the estimation of peer effects

Bet Caeyers and Marcel Fafchamps
External publication

We formalize a noted [Guryan et al., 2009] but unexplored source of bias in peer effect estimation, arising because people cannot be their own peer. We derive, for linear-in-means models with non-overlapping peer groups, an exact formula of the bias in a test of random peer assignment. We demonstrate that, when estimating endogenous peer effects, the negative exclusion bias dominates the positive reflection bias when the true peer effect is small. We discuss conditions under which exclusion bias is aggravated by adding cluster fixed effects. By imposing restrictions on the error term, we show how to consistently estimate, without the need for instruments, all the structural parameters of an endogenous peer effect model with an arbitrary peer-group or network structure. We show that, under certain conditions, 2SLS do not suffer from exclusion bias. This may explain the counter-intuitive observation that OLS estimates of peer effects are often larger than their 2SLS counterpart.

Find out more

External publication
This working paper was published in February 2014 by the Centre for the Study of African Economies at the University of Oxford. In the paper, Manski's (1993) standard linear-in-means model is used to estimate endogenous peer effects on the awareness of vulnerable groups on Tanzania Social Action ...
IFS Working Paper W15/31
The objective of this paper is to understand and test empirically the relationship between group size and informal risk sharing. Models of informal risk sharing with limited commitment and grim-trigger punishments upon deviation imply that larger groups provide better informal insurance.