Super Efficiency Method Research Articles

Efficiency Evaluation of International Airports in Iran using Data Envelopment Analysis

This paper follows two main purposes. These two purposes include the analysis of the efficiency and ranking 7 domestic airports in Iran that have international airlines during 2010 - 2013. However, the indicated airports are under the ownership of the main company of the airports in the country and do not include all Iranian airports. On the other hand the Data Envelopment Analysis (DEA) is used as a non-parametric mathematical programming technique to evaluate the performance of international airports. Basically, regarding the first purpose an overall assessment of the efficiency of the airports is followed and the amount of the airport efficiency is measured and compared based on Russell’s model in DEA. Then in line with the second purpose the efficient airports are ranked based on the super-efficiency method. The structure of international airports as decision making units in DEA has been obtained by four inputs and three outputs. The results indicated that the Mehrabad, Mashhad and Bandarabbas airports had the full efficiency during all periods and these units have the first, second and third ranks, respectively. The Malmquist index of airports in these four periods can be obtained in order to better understanding of the productivity and performance of international airports.

Application of SEDEA to evaluation of degree of harmony between water resources and economic development

This paper introduces the method of data envelopment analysis (DEA) for evaluation of the degree of harmony between water resources and economic development of the water conservancy area of China's South-to-North Water Diversion Project (SNWDP). For this evaluation, a super-efficiency DEA (SEDEA) model was developed based on the super-efficiency method. To verify the applicability of the SEDEA model, both the SEDEA model and a normal-efficiency DEA (NEDEA) model were used to evaluate the degree of harmony between water resources and economic development of typical cities in the SNWDP water conservancy area. The results show that the SEDEA model ranks the degree of harmony of typical cities more efficiently than the NEDEA model, and thus can better evaluate the degree of harmony between water resources and economic development of different cities than the NEDEA model. Furthermore, the SEDEA model can be applied as an operational research tool in regional water resources management.

Can Ownership Diversification Enhance Banks’ Efficiency? An Analysis Based on Super Efficiency and Tobit Regression on the Chinese Listed Commercial Banks

Ownership diversification can improve the monitoring level in commercial banks and hence increase the efficiency. A good case is the Chinese commercial banks which have achieved outstanding performance in the recent years. In the past decade, many Chinese banks have been listed. Going listed has provided opportunities for the banks’ ownership to be diversified. Since those banks were controlled more or less by the state before going listing, it is meaningful to study the impacts of ownership diversification on the Chinese listed commercial banks’ efficiency. Our sample in this paper includes sixteen Chinese listed commercial banks with data from 2007 to 2011. Herfindahl index is the most widely used proxy to measure the degree of concentration. Hence it can reflect the degree of diversification too. In this paper, we have used Herfindhal index of the largest, the largest three, the largest five and the largest ten shareholders as the proxies for ownership structure. We include these four proxies because in the Chinese listed banks, some are owned by several largest shareholders, while others have much fewer large shareholders. There are several methods to measure the banks’ efficiency, to get more precise measurement, we adopt the super efficiency method. Super efficiency has the advantage of higher comparability for the sampled banks. on the other hand, we have used Tobit regression model rather than the ordinary least square (OLS) method because the dependent variable (the super efficiency) is positive. In this paper, we have measured the super efficiency with Data Enveloping Analysis (DEA) and then studied the impacts of the ownership diversification on the efficiency with Tobit regression model. The Chinese listed banks can be categorized into two groups. One is the four formerly state-owned largest commercial banks (hereinafter “Big Four”), the other is the group of smaller banks with less control from the government (hereafter non-Big Four). Because these two groups have different ownership structure, the ownership diversification can influence their efficiency differently. To discover the differences of the impacts from the ownership diversification, we try to compare the impacts of ownership diversification on the efficiency of the formerly state-owned larger banks (Big Four) and smaller banks (Non-Big Four). To overcome the problem of inadequate observations in Big Four sample, we first run Tobit regression with the full sample, and then run Tobit regression with non-Big Four sample. The difference in the coefficients can reveal the different impacts of ownership diversification on the efficiency. Our results indicate that ownership diversification can enhance the listed commercial banks’ efficiency. But the coefficients in the non-Big Four sample is smaller than those in the full sample models, which indicates ownership diversification has greater positive impacts on the efficiency of the formerly state-owned larger banks than that of the smaller banks. Our contribution is that our paper is the first one to study the impacts of the ownership diversification on the listed commercial banks which were previously held by the state. DOI: http://dx.doi.org/10.5755/j01.ee.24.1.2471

Ranking non-extreme efficient units based on super efficiency method in the presence of undesirable outputs: a DEA approach

Data envelopment analysis (DEA) is a non-parametric method for analysing the performance of a set of decision-making units (DMUs). Computing the efficiency scores and ranking each organisation are two important parts of analysing performance. A large number of DEA models have been proposed for ranking DMUs, but most of them are unable to rank non-extreme efficient units. In this paper, a method is proposed based on super efficiency approach for ranking all DMUs, including extreme efficient, non-extreme efficient and inefficient units. Then, the suggested method is applied for ranking DMUs in which undesirable outputs are also produced in the production process. Finally, numerical examples are provided to illustrate the proposed approach.

A Review of Ranking Models in Data Envelopment Analysis

In the course of improving various abilities of data envelopment analysis (DEA) models, many investigations have been carried out for ranking decision-making units (DMUs). This is an important issue both in theory and practice. There exist a variety of papers which apply different ranking methods to a real data set. Here the ranking methods are divided into seven groups. As each of the existing methods can be viewed from different aspects, it is possible that somewhat these groups have an overlapping with the others. The first group conducts the evaluation by a cross-efficiency matrix where the units are self- and peer-evaluated. In the second one, the ranking units are based on the optimal weights obtained from multiplier model of DEA technique. In the third group, super-efficiency methods are dealt with which are based on the idea of excluding the unit under evaluation and analyzing the changes of frontier. The fourth group involves methods based on benchmarking, which adopts the idea of being a useful target for the inefficient units. The fourth group uses the multivariate statistical techniques, usually applied after conducting the DEA classification. The fifth research area ranks inefficient units through proportional measures of inefficiency. The sixth approach involves multiple-criteria decision methodologies with the DEA technique. In the last group, some different methods of ranking units are mentioned.

Efficient and Super-Efficient Use of Broadband by U.S. States

In an earlier study, Parajuli and Haynes (2012) used multiple-input/multiple-output Data Envelopment Analysis (DEA) models. They found that a number of states in the U.S. assumed an efficiency score of one for broadband adoption and use. While this is not unusual, the commonly used DEA methods — the Charnes, Coopers, and Rhodes model and the Banker, Charnes, and Cooper extension — do not rank relative efficient decision making units (DMUs) across the efficient frontier set. The super-efficiency estimation overcomes this inherent limitation of the common DEA methods and allows for ranking the efficient DMUs. This paper uses super-efficiency methods to rank states that were efficient in broadband adoption and use in the U.S. from 2005 through 2007.

Super-efficiency in DEA by effectiveness of each unit in society

One of the most important topics in management science is determining the efficiency of Decision Making Units (DMUs). The Data Envelopment Analysis (DEA) technique is employed for this purpose. In many DEA models, the best performance of a DMU is indicated by an efficiency score of one. There is often more than one DMU with this efficiency score. To rank and compare efficient units, many methods have been introduced under the name of super-efficiency methods. Among these methods, one can mention Andersen and Petersen’s (1993) [1] super-efficiency model, and the slack-based measure introduced by Tone (2002) [4]. Each of the methods proposed for ranking efficient DMUs has its own advantages and shortcomings. In this paper, we present a super-efficiency method by which units that are more effective and useful in society have better ranks. In fact, in order to determine super-efficiency by this method, the effectiveness of each unit in society is considered rather than the cross-comparison of the units. To do so, we divide the inputs and outputs into two groups, desirable and undesirable, at the discretion of the manager, and assign weights to each input and output. Then we determine the rank of each DMU according to the weights and the desirability of inputs and outputs.

Intellectual property rights and information technology industry development

We adopt two DEA methods, including the BCC and SBM Super-Efficiency methods, to investigate whether a patent is an important factor in the development of the information technology (IT) industry of Taiwan. The data consists of 24 Taiwanese IT sub-industries covering the period 2002 to 2003. Our empirical results show that (1) A patent is an influential factor in evaluating the efficiency of the IT industry. (2) The average efficiency scores of the IT sub-industries with high royalty fees are significantly higher than those with lower royalty fees. (3) Based on the non-radial and slacks-based Malmquist TFP index, we find that the productivity of Taiwan’s IT industry has increased and that technical change has improved during the data period.

Review of ranking methods in the data envelopment analysis context

Within data envelopment analysis (DEA) is a sub-group of papers in which many researchers have sought to improve the differential capabilities of DEA and to fully rank both efficient, as well as inefficient, decision-making units. The ranking methods have been divided in this paper into six, somewhat overlapping, areas. The first area involves the evaluation of a cross-efficiency matrix, in which the units are self and peer evaluated. The second idea, generally known as the super-efficiency method, ranks through the exclusion of the unit being scored from the dual linear program and an analysis of the change in the Pareto Frontier. The third grouping is based on benchmarking, in which a unit is highly ranked if it is chosen as a useful target for many other units. The fourth group utilizes multivariate statistical techniques, which are generally applied after the DEA dichotomic classification. The fifth research area ranks inefficient units through proportional measures of inefficiency. The last approach requires the collection of additional, preferential information from relevant decision-makers and combines multiple-criteria decision methodologies with the DEA approach. However, whilst each technique is useful in a specialist area, no one methodology can be prescribed here as the complete solution to the question of ranking.