Abstract
Conventional data envelopment analysis (DEA) models assume that all decision-making units (DMUs) are homogenous. While higher education institutions (HEIs) of very different sizes challenge the homogeneity of DMUs, DEA studies have paid relatively little attention to university size when assessing the performance of HEIs. This article proposes novel, effective methods for evaluating university performance and identifying useful benchmarks for improving the operations of inefficient performers. Specifically, DEA and cluster analysis (CA) are applied for the evaluation of the performance of traditional Spanish public universities. DEA is utilized to examine the relative performance of these universities in terms of undergraduate teaching output. CA is applied to find similar-in-scale universities prior to the DEA to facilitate peer-groupings. The advantage of this method is that when DMUs are clustered based on their size, one can obtain homogenous groups of units with comparable operating environments. Furthermore, using the meta-frontier framework, this research finds significant evidence that there is an efficiency advantage for medium- and large-sized universities over small ones in providing undergraduate teaching. A bootstrapped, non-parametric meta-frontier approach also verifies this latter result. Some of the factors that contribute to the differences in the relative efficiencies are identified as well.
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