Research Seminar

Title: ”The Earth is Not Flat: A New World of High-Dimensional Peer Effects”, Aurélien Sallin and Simone Balestra

Abstract: We use a novel machine learning empirical framework to better understand peer effects in the classroom. This approach accounts for systematic interactions between peer types and nonlinearities of peer effects. We use machine-learning methods to (i) understand which dimensions of peer characteristics are the most predictive of academic performance, (ii) estimate high-dimensional peer effects functions, and (iii) investigate performance-improving classroom allocation through policy-relevant simulations. First, we find that students’ own characteristics are the most predictive of own academic performance, and that the strongest peer effects are generated by students with special needs, low-achieving students, and male students. Second, we show that classroom peer effects reported by the literature likely miss important nonlinearities in the distribution of peer proportions. Third, we determine that classroom compositions that are the most balanced in students’ characteristics are the ones that reach maximal aggregated school performance.