Beyond Rankings: Bayesian stochastic frontier analysis of the determinants of university efficiency

Zaira García-Toledo, David Conesa, Joan Crespo and Emili Tortosa-Ausina
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Zaira García-Toledo: Universitat Jaume I
David Conesa: Universitat de València
Joan Crespo: Universitat de València

No 2517, Working Papers from Department of Applied Economics II, Universidad de Valencia

Abstract: University rankings predominantly focus on outputs while neglecting the efficiency with which institutions convert resources into outcomes. We contribute to addressing this limitation by analyzing the determinants of university efficiency using a Bayesian stochastic ray frontier model applied to 47 Spanish public universities over the 2016–2021 period. Unlike traditional approaches, our methodology jointly estimates efficiency and its determinants in a single stage. We adopt a multi-output framework encompassing the three university missions: teaching, research, and knowledge transfer. Using backward stepwise selection with the deviance infor- mation criterion, we identify key efficiency determinants including the average department size, number of campuses, academic staff characteristics, and multi-province location. Results reveal substantial efficiency variations across universities, with approximately half showing positive efficiency changes over the period. The Bayesian approach provides full efficiency distributions rather than point estimates, enabling robust statistical comparisons. Our findings offer valuable insights for university managers and policymakers seeking to enhance institutional performance beyond traditional output-based rankings.

Keywords: determinants; efficiency; Bayesian; education; stochastic frontier; universities (search for similar items in EconPapers)
JEL-codes: C61 J24 R11 (search for similar items in EconPapers)
Date: 2025-12
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