In this paper we analyze a unique micro-level panel data set encompassing all publicschool students in grades 3-10 in the state of Florida for each of the years 1999/2000-2003/2004.We are able to directly link each student and teacher to a specific classroom and can thus identifyeach member of a student’s classroom peer group. The ability to track individual studentsthrough multiple classrooms over time and multiple classes for each teacher enables us to controlfor many sources of spurious peer effects such as fixed individual student characteristics andfixed teacher inputs, as well as to compare the strength of peer effects across different groupingsof peers, across grade levels, and to compare the effects of fixed versus time-varying peercharacteristics. We find mixed results on the importance of peers in the linear-in-means model,and resolve some of these apparent conflicts by considering non-linear specifications of peereffects. The results suggest that some grouping by ability may create Pareto improvements overuniformly mixed classrooms. In general we find that contemporaneous behavior wields strongerinfluence than peers’ fixed characteristics.
Peer Effects, Student Achievement
In this paper we analyze the impact of classroom peers on individual student performance with a unique longitudinal data set covering all Florida public school students in grades 3-10 over a five-year period. Unlike many previous data sets used to study peer effects in education, our data set allow us to identify each member of a given student’s classroom peer group in elementary, middle, and high school as well as the classroom teacher responsible for instruction. As a result, we can control for individual student fixed effects simultaneously with individual teacher fixed effects, thereby alleviating biases due to endogenous assignment of both peers and teachers, including some dynamic aspects of such assignments. Our estimation strategy, which focuses on the influence of peers' fixed characteristics—both observed and unobserved—on individual test score gains, also alleviates potential biases due to error in measuring peer quality, simultaneity of peer outcomes, and mean reversion. Under linear-in-means specifications, estimated peer effects are small to non-existent, but we find some sizable and significant peer effects within non-linear models. For example, we find that peer effects depend on an individual student’s own ability and on the ability level of the peers under consideration, results that suggest Pareto-improving redistributions of students across classrooms and/or schools. Estimated peer effects tend to be smaller when teacher fixed effects are included than when they are omitted, a result that suggests co-movement of peer and teacher quality effects within a student over time. We also find that peer effects tend to be stronger at the classroom level than at the grade level.
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