Efficiency analysis of Chinese universities with shared inputs: An aggregated two-stage network DEA approach

Abstract

Evaluating the resource utilization efficiency of a country's universities is a prerequisite for implementing a balanced development strategy. Universities often share some resources such as the teachers and fixed assets used for teaching and research. Data envelopment analysis (DEA), as a non-parametric method for performance evaluation of decision-making units (DMUs), has been used for measuring the operating efficiency of universities with shared inputs based on radial measures under multiplier DEA models. However, few existing studies consider non-radial measures and different perspectives of external and internal evaluations. From both external and internal perspectives, we propose aggregated two-stage DEA models to measure the efficiency scores of 52 Chinese universities in 2014. The main results indicate that: (1) both internal and external evaluations yield relatively high average operating efficiency, with around 53 % of universities being efficient. The allocation ratios of shared inputs for the same university differ according to the internal and external evaluations. (2) Heterogeneity analysis reveals that 985 universities are less efficient than non-985 universities, and comprehensive universities operate more efficiently than science and engineering universities. (3) There are efficiency differences between 985 and non-985 universities in the teaching stage. Science and engineering as well as comprehensive universities also differ in the teaching efficiency.