Operating efficiency in Chinese universities:An extended two-stage network DEA approach

Operating efficiency of universities is widely concerned by the education community.As a non-parametric method for efficiently handling multiple inputs and outputs,data envelopment analysis(DEA)is often used for measuring the operating efficiency.However,shared input resources are often ignored in the existing DEA studies.In order to remedy the shortcoming with a focus on teaching and research processes of universities,this paper adopts an extended two-stage network DEA approach to measure the operating efficiency of 52 universities in China using a data set in 2014.The main findings show that:(1)Among the operating efficiency of 52 universities,about one third and two thirds of universities are efficient and inefficient,respectively.It may reflect some problems such as inefficient use of resources or unsatisfactory outcomes for these inefficient universities.By giving first priority to universities’teaching or research process,we provide alternative ways for teaching-oriented or research-oriented universities to benchmark and improve their performance.(2)For the heterogeneity efficiency analysis of different universities,the operating efficiency of“non-985”universities are significantly higher than that of“985”universities,while there is only a small difference on the operating efficiency between comprehensive universities and science&engineering universities.Although the efficiency of the central and western universities is slightly better than that of the eastern universities in terms of the average efficiency,there is no significant efficiency difference among the eastern,central,and western regions statistically.Hence,to improve the operating efficiency of Chinese universities,the Chinese government should improve the financial allocation mechanism and introduce successful budget performance management.For the Chinese universities,they should formulate teaching and scientific research plans according to their own research needs and development goals.

Overall Efficiency and its Decomposition in a Two-Stage Network DEA Model

This paper aims to propose a new approach to decompose an overall data envelopment analysis model into equivalent two-stage models.In this approach,we use a minimax reference point method to set the weights and reliabilities of the two stage models so that the combined efficiency of the two stages is equal to the overall efficiency.The equivalent multi-stage models are useful to support planning for performance improvement.An illustrative example is first explored to compare the results from the new approach with those of four other existing approaches.The main finding from the comparisons is that the new decomposition approach of this paper satisfies the proposed assumptions.A case study is then conducted on a two-stage process of steel manufacturing to illustrate the validity and applicability of the proposed approach.

Stochastic leaderefollower DEA models for two-stage systems

Data envelopment analysis(DEA)is a non-parametric approach for measuring the relative efficiencies of peer decision making units(DMUs).In recent years,it has been widely used to evaluate two-stage systems under different organization mechanisms.This study modifies the conventional leaderefollower DEA models for two-stage systems by considering the uncertainty of data.The dual deterministic linear models are first constructed from the stochastic CCR models under the assumption that all components of inputs,outputs,and intermediate products are related only with some basic stochastic factors,which follow continuous and symmetric distributions with nonnegative compact supports.The stochastic leaderefollower DEA models are then developed for measuring the efficiencies of the two stages.The stochastic efficiency of the whole system can be uniquely decomposed into the product of the efficiencies of the two stages.Relationships between stochastic efficiencies from stochastic CCR and stochastic leaderefollower DEA models are also discussed.An example of the commercial banks in China is considered using the proposed models under different risk levels.

Evaluating Efficiency of Decision Making Units with Network Structures

This paper considers the problem of evaluating efficiency of Decision Making Units （DMUs） with network structures of divisions by the Data Envelopment Analysis （DEA） model. All divisions in the network are under a decentralized authority organiza- tion. That is, each division in a decision making unit has its own authority to adjust its input and output. By incorporating the division operations in the DEA model, we discuss the sufficient and necessary conditions for a DMU to be network efficient in series structure and general structure respectively.