Abstract
This paper examines the impact of students' network size, distance, prestige and connections to influential individuals on academic performance. Larger and closer networks facilitate information exchange, but may also increase distractions that decrease productivity. To resolve this ambiguity, we use administrative data from a business school setting that features both randomly assignment of students to multiple overlapping sets of peers, allowing us to calculate degree, closeness, eigenvector and Katz-Bonacich centrality for each node, as well as a cleanly defined measure of academic achievement. We find that increasing eigenvector centrality within the network has a negative effect on student performance as measured by grade point average, suggesting that synergy reduction and information processing costs outweigh benefits from greater information access.
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