Relative Efficiency Assessments Using Data Envelopment Analysis: An Application to Data on Rates Departments

This paper examines the nature of information obtained from data envelopment analysis (DEA) in comparative studies of the efficiency of decision-making units, and it discusses the interpretation and practical usefulness of such information. The themes developed in the paper are illustrated by an application of DEA to data on the rate-collection function of London Boroughs and Metropolitan District Councils. The paper begins with an overview of DEA, followed by a discussion of some of the practical considerations arising in the application of DEA. It then describes the structuring of the rate-collection function for assessment by DEA, and explores the extent to which units can be classified as relatively efficient or inefficient. In respect of relatively inefficient units, it illustrates the construction of target inputs and outputs so that their relative efficiency may improve. In respect of relatively efficient units, it is argued that their identification is weak in the sense that for some of them their apparent efficiency may be simply a reflection of an uncommon input-output profile. It is shown, nevertheless, that information about relatively efficient units can be used to identify those of them which may prove examples of good operating practice in given aspects of their function. (Readers not familiar with British taxes may wish to note that rates are a tax on property, levied by local authorities.)     

The Efficiency Distribution Approach in Data Envelopment Analysis: An Application

A method is developed here for characterizing the empirical distribution of the efficient units in data envelopment analysis. Two empirical applications illustrate the various uses of the distribution approach. One involves the cost frontier which exhibits increasing returns to scale and the other involves a dynamic production frontier, where technological change causes a shift of the production frontier over time.     

基于DEA的出版社相对有效性评价

利用数据包络分析方法(简称DEA方法)对几个教育类出版社的职工人数结构与定价总额之间关系进行了分析,发现有些出版社是DEA有效的,而有些则不是.对非DEA有效的出版社来说,可以通过调整投入以获得同样产出,从而提高经济效益.     

Efficiency bounds in Data Envelopment Analysis

This paper considers a proposal, by Chang and Guh, that the non-archimedean infinitesimal in the CCR data envelopment analysis model be replaced with a data-dependent finite magnitude. Whilst the intention of the proposal is clear: to reduce the CCR efficiency rating of certain problematic DMUs, it is found not to work in practice. An alternative implementation, which puts the CCR model into a mixed-binary linear programming framework, is developed. Chang and Guh's proposal is also related to the earlier modification to the CCR model, Constrained Facet Analysis. Both are seen as providing bounds on the relative efficiency of DMUs which are not properly enveloped in the CCR model.     

模糊数据包络分析方法有效性分析

传统数据包络分析(DEA)模型只能用来评价具有精确投入和产出数据的决策单元.然而在实践中决策单元的投入产出数据可能存在一定模糊性.为了评价具有模糊投入产出数据的决策问题,研究工作者提出了模糊数据包络分析模型,并给出了相应的有效性定义.对于不同研究者提出的有效性定义方式有众多地方需要改进.通过这些改进提出了相关模型及新的有效性定义方式,并给出了相关实例.     

数据包络分析中规模效率指数方法的一点注记

原有的规模效率指数方法是基于投入导向的模型进行分析的,有悖于规模收益的定义,应该基于产出导向的模型.为此,把基于投入导向的方法转化为基于产出导向的方法.首先,通过理论证明原方法所提出的判定原则也适用于基于产出导向的方法,但由于使用的模型不同,两种方法存在本质的区别.第二,证明了投入导向和产出导向的BCC模型是不等价的,这直接说明两个方法是不等价的.第三,通过实例表明两种方法将产生不同的计算结果,并通过理论分析了其根本原因.上述结论表明,两种方法在投影方式和计算结果等两方面存在差别.因而,开展规模收益分析应该采用基于产出导向的方法.     

A Complete Efficiency Ranking of Decision Making Units in Data Envelopment Analysis

The efficiency measures provided by DEA can be used for ranking Decision Making Units (DMUs), however, this ranking procedure does not yield relative rankings for those units with 100% efficiency. Andersen and Petersen have proposed a modified efficiency measure for efficient units which can be used for ranking, but this ranking breaks down in some cases, and can be unstable when one of the DMUs has a relatively small value for some of its inputs. This paper proposes an alternative efficiency measure, based on a different optimization problem that removes the difficulties.     

The Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation

While financial ratios are currently the method most often used to evaluate a bank's performance, there is no clear-cut rationale which would allow one to acquire a composite score on the overall financial soundness of a bank. This paper demonstrates the application of DEA (Data Envelopment Analysis) in conjunction with financial ratios to help bank regulators in Taiwan not only to distinguish the efficient banks from the inefficient ones but also to gain insight into various financial dimensions that somehow link to the bank's financial operational decisions.     

基于数据包络分析的企业资本利用有效性研究

数据包络分析(DEA)是一种评价相对有效性的线性规划方法，在多个输入、多个输出情形下的投入产出效率分析中多有应用，并可进行规模效应分析。本采用DEA方法，以沪深股市50家房地产上市公司为样本。采用其2002年度财务数据，对样本公司资本利用的有效性进行了实证分析。研究表明，我国多数房地产上市公司既存在着显的技术非有效，也存在着显的规模非有效；其中，主营业务收入5亿元以上的公司一般运行在规模效益递减区间，这意味着并不是最大的公司具有最好的资本利用有效性。     

A Semi-Infinite Programming Model In Data Envelopment Analysis

We consider a model for data envelopment analysis with infinitely many decision-making units. The determination of the relative efficiency of a given decision-making unit amounts to the solution of a semi-infinite optimization problem. We show that a decision-making unit of maximal relative efficiency exists and that it is 100% efficient. Moreover, this decision-making unit can be found by calculating a zero of the semi-infinite constraint function. For the latter task we propose a bi-level algorithm. We apply this algorithm to a problem from chemical engineering and present numerical results     

Real Estate Appraisal: A Double Perspective Data Envelopment Analysis Approach

This paper proposes a new methodology for the assessment of the value range for real estate units. The theoretical basis of the methodology is built on the Data Envelopment Analysis—DEA approach, which has its original concept adapted to the case where the units under assessment consist of transactions among sellers and buyers. The proposed approach—christened Double Perspective-Data Envelopment Analysis (DP-DEA)—is applied to a database comprising the prices and features of the units under assessment. It is shown that the DP-DEA presents some specific advantages when compared to the usual regression analysis method employed in real estate value assessment.     

Cost,Revenue and Profit Efficiency Models in Generalized Fuzzy Data Envelopment Analysis

One of the most important information given by data envelopment analysis models is the cost, revenue and profit efficiency of decision making units (DMUs). Cost efficiency is defined as the ratio of minimum costs to current costs, while revenue efficiency is defined as the ratio of maximum revenue to current revenue of the DMU. This paper presents a framework where data envelopment analysis (DEA) is used to measure cost, revenue and profit efficiency with fuzzy data. In such cases, the classical models cannot be used, because input and output data appear in the form of ranges. When the data are fuzzy, the cost, revenue and profit efficiency measures calculated from the data should be uncertain as well. Fuzzy DEA models emerge as another class of DEA models to account for imprecise inputs and outputs for DMUs. Although several approaches for solving fuzzy DEA models have been developed, numerous deficiencies including the $\alpha$-cut approaches and types of fuzzy numbers must still be improved. This scheme embraces evaluation method based on vector for proposed fuzzy model. This paper proposes generalized cost, revenue and profit efficiency models in fuzzy data envelopment analysis. The practical application of these models is illustrated by a numerical example.     

商业银行分支机构效率评价的参数与非参数方法比较研究

本文根据某商业银行反映效率的真实数据,分别采用因子分析、聚类和线性回归、DEA分析三种方法,计算了银行分支机构的效率,并对效率评价的参数与非参数方法进行了差异比较。进而,本文用历史数据对效率影响因素分析方法的有效性进行了实证检验。     

基于DEA的中国零售连锁企业经营效率比较研究

本文运用DEA的方法对中国零售连锁上市公司2005～2008年度的经营效率进行了分析。研究表明,在现有的资源和技术条件下,零售连锁企业全要素生产率变化较大,主要是得益于大幅增长的技术进步,而非技术效率的提升;不少企业经营方式仍然比较是粗放,管理上跟不上零售业扩张的步伐。要提升零售连锁企业的经营效率,提高从业人员素质,合理配置资源,提升管理水平势在必行。     

On the Use of Ordinal Data in Data Envelopment Analysis

In many problems involving efficiency analysis using DEA, certain factors may be measurable only on an ordinal scale. Specifically, it may be possible only to rank order the DMUs according to a factor, rather than being able to assign a specific numerical value of that factor to each DMU. To illustrate this, we examine a problem involving the evaluation of new technology installations. The presence of qualitative factors in such an environment motivates the need to investigate how such factors can be incorporated into existing efficiency measurement models. In particular, a procedure is presented for incorporating an ordinal factor into the DEA structure, with the resulting formulation being a particular form of cone ratio model. The model is then applied to the technology installation efficiency problem.     

DEA方法进行规模收益分析的几点注记

自从 Banker等人 ( 1 984)用 DEA方法进行规模收益分析 ,已有越来越多的学者也在进行相关的研究 .研究主要从两个方面进行 :基于 DEA输入模型的方法和基于 DEA输出模型的方法 .这两类方法所应用的模型、条件、结论都有所不同 ,但在应用中有些文章将它们混淆在一起 .本文针对应用中容易出现的错误 ,给出反例和正确的判别规模收益状况的充分必要条件 .并由规模收益的定义 ,建议使用 DEA—输出模型进行规模收益分析 .     

A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units

This paper is concerned with the comparison of two popular non-parametric methodologies—data envelopment analysis and artificial neural networks—as tools for assessing performance. Data envelopment analysis has been established since 1978 as a superior alternative to traditional parametric methodologies, such as regression analysis, for assessing performance. Neural networks have recently been proposed as a method for assessing performance. In this paper, we use a simulated production technology of two inputs and one output for testing the success of the two methods for assessing efficiency. The two methods are also compared on their practical use as performance measurement tools on a set of bank branches, having multiple input and output criteria. The results demonstrate that, despite their differences, both methods offer a useful range of information regarding the assessment of performance.