Finding the single efficient unit in data envelopment analysis with flexible measures

Purpose – The last decade witnessed the reform of China's financial sector, during which Chinese commercial banks’ ownership and operation had been significantly changed in order to improve efficiency. The purpose of this paper is to investigates whether these banks have improved their productivity efficiency during their rapid expansion and growth in the post reform era from 2004 to 2011. Design/methodology/approach – Data envelopment analysis is used to investigate the production efficiency of Chinese commercial banks during 2004-2011. First, the technical efficiency (TE) score is constructed to evaluate bank productivity. The TE score is disintegrated into pure technical efficiency (PTE) and scale efficiency (SE) to examine the effects of technical factors and scale economies. Second, the Malmquist index is constructed to explore the year-by-year productivity. Lastly, regression analysis examines how bank characteristics and ownership structure affect productivity efficiency. Findings – The Big Four banks are less efficient than other commercial banks, and public banks are less efficient than private banks. The low efficiency is primarily due to scale inefficiency, rather than PTE. In addition, ownership structure impacts production efficiency. Specifically, foreign ownership is related to high efficiency while state ownership is associated with lower productivity. Research limitations/implications – There were small observations of public banks in China. Thus, a more comprehensive test is impractical to explore whether or not annual changes in ownership structure improve their production efficiency. With more date, such a test will reveal further information about the relationship between ownership and productivity. Originality/value – The authors are the first to assess the production efficiency of Chinese commercial banks after the recent financial reform during which Chinese commercial banks had undergone significant structural changes. The lower overall productivity of Big Four and public banks is a result of scale inefficiency, although these banks are better than their peers with respect to input-output transformation.

Survey on rank preservation and rank reversal in data envelopment analysis

A cross-dependence based ranking system for efficient and inefficient units in DEA

Fintech and Banking Efficiency: Evidence from Chinese Commercial Banks

Efficiency evaluation based on data envelopment analysis in the big data context

One of the major research streams in data envelopment analysis (DEA) is ranking decision-making units (DMUs). Utilizing a multicriteria decision-making technique, we develop a novel approach to fully rank all units. Motivated by the convex cone-based total order for multiple criteria alternatives proposed by Dehnokhalaji et al. (Nav Res Logist 61(2):155–163, 2014), we consider DMUs in DEA as multiple criteria alternatives and obtain their total ordering. Initially, some pairwise preference information is provided by the decision maker for units and the concepts of convex cones and polyhedral sets are defined in a DEA framework, correspondingly. We apply a modification of Dehnokhalaji et al. method to extract additional preference information for each pair of units and consequently obtain a full ranking (strict total ordering) of DMUs. The benefit of our approach to their method is that we apply non-radial models to overcome the instability drawback of radial models and their infeasibility occurring in DEA applications. The proposed approach is implemented for two numerical examples, and the accuracy of it is investigated through a computational test.

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

Data envelopment analysis (DEA) with considering the best condition for each decision making unit (DMU) assesses the relative efficiency for it and divides a homogenous group of DMUs into two categories: efficient and inefficient, but traditional DEA models can not rank efficient DMUs. Although some models were introduced for ranking efficient DMUs, Franklin Lio & Hsuan peng (2008), proposed a common weights analysis (CWA) approach for ranking them. These DMUs are ranked according to the efficiency score weighted by the common set of weights and shadow prices. This study shows there are some cases that shadow prices of efficient DMUs are equal, hence this method is not applicable for ranking them. Next, we propose a new method for ranking units with equal shadow prices.

Data envelopment analysis (DEA) identifies an empirical efficient frontier of a set of peer decision making units (DMUs) with multiple inputs and outputs. The efficient frontier is characterized by the DMUs with an unity efficiency score. The performance of inefficient DMUs is characterized with respect to the identified efficient frontier. If the performance of inefficient DMUs deteriorates or improves (up to the frontier), the efficient DMUs still have an unity efficiency score. However, the performance of DMUs may be influenced by the context — e.g. a product may appear attractive against a background of less attractive alternatives and unattractive when compared to more attractive alternatives. With an application to Tokyo public libraries, the current paper presents and demonstrates a context-dependent DEA which measures the relative attractiveness of libraries on a specific performance level against libraries exhibiting poorer performance. The set of libraries are grouped into different levels of efficient frontiers. Each efficient frontier (on a specific performance level) is then used as evaluation context for the relative attractiveness. The performance of the efficient libraries changes as the inefficient libraries change their performance. The context-dependent DEA can also be used to differentiate the performance of efficient DMUs. The context-dependent DEA provides finer DEA results with respect to the performance of all DMUs.

Compensating for non-homogeneity in decision-making units in data envelopment analysis

Efficiency and performance evaluation is an important task in governmental decision making. This paper aims in application of analytic hierarchy process (AHP) for efficiency analysis of a general set of decision making units (DMUs). Conventional tool for analysis of efficiency of DMUs is data envelopment analysis (DEA). DEA models allow splitting the set of DMUs into two subsets - efficient and inefficient. The inefficient ones can be ranked according to their efficiency scores given by the DEA model but the efficient units cannot be ranked easily as their have identical maximal efficiency score. Various models have been proposed for ranking of efficient units in DEA. The aim of the paper is to discuss the possibility of application of AHP models for ranking of efficient or entire set of DMUs. The proposed approach is illustrated on performance evaluation of 19 Czech economic faculties. The results given by the AHP ration model are compared to those given by conventional approaches.

The paper aims in application of analytic hierarchy process (AHP) for efficiency analysis of the set of decision making units (DMUs).Conventional tool for analysis of efficiency of DMUs is data envelopment analysis (DEA).DEA models allow splitting the set of DMUs into two subsets -efficient and inefficient.The inefficient ones can be ranked according to their efficiency scores given by the DEA model but the efficient units cannot be ranked easily as their have identical maximal efficiency score.Many models have been proposed for ranking of efficient units in DEA models.The aim of the paper is to discuss the possibility of application of AHP models for ranking of efficient or entire set of DMUs.Numerical experiments are realized on the set of 20 commercial banks operating on the Czech financial market.The study is based on a real data set containing financial characteristics of the banks.

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.

Data envelopment analysis (DEA) is a common non-parametric frontier analysis method. The multiplier framework of DEA allows flexibility in the selection of endogenous input and output weights of decision making units (DMUs) as to cautiously measure their efficiency. The calculation of DEA scores requires the solution of one linear program per DMU and generates an individual set of endogenous weights (multipliers) for each performance dimension. Given the large number of DMUs in real applications, the computational and conceptual complexities are considerable with weights that are potentially zero-valued or incommensurable across units. In this paper, we propose a two-phase algorithm to address these two problems. In the first step, we define an ideal DMU (IDMU) which is a hypothetical DMU consuming the least inputs to secure the most outputs. In the second step, we use the IDMU in a LP model with a small number of constraints to determine a common set of weights (CSW). In the final step of the process, we calculate the efficiency of the DMUs with the obtained CSW. The proposed model is applied to a numerical example and to a case study using panel data from 286 Danish district heating plants to illustrate the applicability of the proposed method.

Prioritization models for frontier decision making units in DEA