Obtaining consistent estimates of spillovers in an educational context is ham-
pered by at least two issues: selection into peer groups and peer effects emanat-
ing from unobservable characteristics. We develop an algorithm for estimating
spillovers using panel data that addresses both of these problems. The key inno-
vation is to allow the spillover to operate through the fixed effects of a student’s
peers. The only data requirements are multiple outcomes per student and hetero-
geneity in the peer group over time. We first show that the nonlinear least squares
estimate of the spillover parameter is consistent and asymptotically normal for a
fixed T . We then provide an iterative estimation algorithm that is easy to imple-
ment and converges to the nonlinear least squares solution. Using University of
Maryland transcript data, we find statistically significant peer effects on course
grades, particularly in courses of a collaborative nature. We compare our method
with traditional approaches to the estimation of peer effects, and quantify sepa-
rately the biases associated with selection and spillovers through peer unobserv-
ables.
Keywords. Panel data, fixed effects, peer effects, education.
JEL classification. C18, C23, I21.