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Obtaining consistent estimates of spillovers in an educational context is hampered by at least two issues: selection into peer groups and peer effects emanating from unobservable characteristics. We develop an algorithm for estimating spillovers using panel data that addresses both of these problems. The key innovation 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 heterogeneity 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 implement 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 separately the biases associated with selection and spillovers through peer unobservables.

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Workers contribute to team production through their own productivity and through their effect on the productivity of other team members. We develop and estimate a model where workers are heterogeneous both in their own productivity and in their ability to facilitate the productivity of others. We use data from professional basketball to measure the importance of peers in productivity because we have clear measures of output, and members of a worker's group change on a regular basis. Our empirical results highlight that productivity spillovers play an important role in team production, and accounting for them leads to changes in the overall assessment of a worker's contribution. We also use the parameters from our model to show that the match between workers and teams is important and quantify the gains to specific trades of workers to alternative teams. Finally, we find that worker compensation is largely determined by own productivity with little weight given to the productivity spillovers a worker creates, despite their importance to team production. The use of our empirical model in other settings could lead to improved matching between workers and teams within a firm, and compensation that is more in-line with the overall contribution that workers make to team production.