基于超效率DEA的城市效率演变特征

以我国15个副省级城市为研究样本,首先构建了基于效率的副省级城市的投入产出评价指标体系,采用超效率的数据包络分析模型,克服了一般文献中由于通常使用数据包络分析的DI,BC2和DEC2R模型,而陷入对有效的决策单元无法做进一步分析评价的困惑,比较客观地评价了1995-2005我国15个副省级城市效率的演变特征:其一,超效率DEA值变化呈倒U字型.且超效率值有趋同性的态势;其二,超效率DEA值大于1的副省级城市数量逐年增多;其三,超效率DEA值整体变化比较稳定,但少数某些城市的超效率值变化剧烈.     

Developing foreign bank efficiency models for DEA grounded in finance theory

We develop foreign bank technical, cost and profit efficiency models for particular application with data envelopment analysis (DEA). Key motivations for the paper are (a) the often-observed practice of choosing inputs and outputs where the selection process is poorly explained and linkages to theory are unclear, and (b) foreign bank productivity analysis, which has been neglected in DEA banking literature. The main aim is to demonstrate a process grounded in finance and banking theories for developing bank efficiency models, which can bring comparability and direction to empirical productivity studies. We expect this paper to foster empirical bank productivity studies.     

银行效率DEA分析的可信度检验

近年来涌现出大量基于DEA方法的银行效率这一热点问题的研究成果,但是由于DEA方法自身的局限性,诸如对效率值的估计偏低且离散程度较大,以及不能方便地检验结果的显著性等等问题,使其研究的结果受到影响;而Bootstrap技术是基于对经验数据及其相关估计的重复抽样来提高估计置信区间和临界值精度的统计技术,可有效地克服DEA方法结果可信度的这种内在依赖性。本文给出了基于Boot-strap技术的DEA方法来计量银行效率的手段,提高了效率分析结果的可信度,并对我国四大国有商业银行的效率进行了对比性实证分析。     

Reverse efficiency measures for environmental assessment in data envelopment analysis

Environmental issues are becoming more and more important in our everyday life. Data Envelopment Analysis (DEA) is a tool developed for measuring relative operational efficiency. DEA can also be employed to estimate environmental efficiency where undesirable outputs like greenhouse gases exist. The classical DEA method identifies best practices among a given empirical data set. In many situations, however, it is advantageous to determine the worst practices and perform efficiency evaluation by comparing DMUs with the full-inefficient frontier. This strategy requires that the conventional production possibility set is defined from a reverse perspective. In this paper, presence of both desirable and undesirable outputs is assumed and a methodological framework for performing an unbiased efficiency analysis is proposed. The reverse production possibility set is defined and new models are presented regarding the full-inefficient frontier. The operational, environmental and overall reverse efficiencies are studied. The important notion of weak disposability is discussed and the effects of this assumption on the proposed models are investigated. The capability of the proposed method is examined using data from a real-world application about paper production.     

客户信用管理理论在天然气营销中的应用

探讨了客户信用评价及管理的相关方法和理论，结合国内天然气市场的现状，提出了一种新的客户信用评价和管理的模式，并在实践中付诸应用，取得了良好的效果。客户信用管理体系的建立和应用，可为燃气市场的投资者、参与者、监管者提供一个有效的风险规避工具。     

关于DEA有效性理论的一点探索

给出经典的基于输入和输出的DEA模型,把基于DEA模型相结合的相对效率模型引入评价证券相对有效性,以风险作为输入,期望收益率作为输出,便可以用DEA模型评价每只证券相对有效性。     

Using DEA to evaluate the state of society as measured by multiple social indicators

This paper presents an approach for discussing the state of society, which is measured by multiple social indicators, using data envelopment analysis (DEA). Replacing inputs and outputs in DEA with negative and positive social indicators respectively, we analyze the desirability of living in the 47 prefectures of Japan. This is also a proposal for the potential use of DEA in multi-dimensional evaluation analysis other than the standard DEA efficiency analysis. The results using eight social indicators identify 26 DEA desirable prefectures out of the 47 and present other useful knowledge and information. It is concluded that DEA, which can avoid uniform evaluation by an a priori weighting system, provides availability as a comprehensive evaluation tool different from traditional ones.     

Assessment and budget allocation of Iranian natural gas distribution company- A CSW DEA based model

Regarding the importance of budgeting in organizations, this research proposes an empirical approach to budget allocation problems. The methodological instrument utilized is data envelopment analysis (DEA) which is a nonparametric mathematical programming technique. In the DEA methodology a standard DEA model should be independently solved to evaluate each decision making unit (DMU). Consequently, it is hard to find the magnitude of budget for each DMU by applying a budget allocation model based on standard DEA models because identifying the DMU under evaluation is problematic. Also, to overcome problems of evaluation using standard DEA models, common set of weights (CSW) DEA models were suggested. These models can be developed for use in budget allocation DEA models that lead to finding a single magnitude of budget for each DMU. Moreover, the opinion of the decision maker can be incorporated into the model using budgetary constraints. As a result, a restricted linear budget allocation CSW DEA model is proposed in which the central authority would like to plan for improving the total efficiency scores of all DMUs. In essence, the proposed model is used to reallocate the available budget and, thus, the results obtained will be a suggestion for budget allocation in subsequent periods. Finally, the proposed model is applied to budget allocation in the Iranian gas industry in which the available budget is reallocated to increase the total efficiency scores of Iranian gas distribution branches.     

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Data envelopment analysis (DEA) is in fact more than just being an instrument for measuring the relative efficiencies of a group of decision making units (DMU). DEA models are also means of expressing appreciative democratic voices of DMUs. This paper proposes a methodology for allocating premium points to a group of professors using three models sequentially: (1) a DEA model for appreciative academic self-evaluation, (2) a DEA model for appreciative academic cross-evaluation, and (3) a Non-DEA model for academic rating of professors for the purpose of premium allocations. The premium results, called DEA results, are then compared with the premium points “nurtured” by the Dean, called N bonus points. After comparing DEA results and N bonus points, the Dean reassessed his initial bonus points and provided new ones – called DEA-N decisions. The experience indicates that judgmental decisions (Dean's evaluations) can be enhanced by making use of formal models (DEA and Non-DEA models). Moreover, the appreciative and democratic voices of professors are virtually embedded in the DEA models.     

Stochastic leader–follower 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 leader–follower 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 leader–follower 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 leader–follower DEA models are also discussed. An example of the commercial banks in China is considered using the proposed models under different risk levels.     

评价与优选线路设计方案的DEA方法

根据数据包络分析方法的特点,提出了运用DEA模型对线路设计方案进行评价与优选,阐述了应用的步骤。通过实例验证了方法的可行性与有效性,并可得出非有效方案的缺陷所在,提出改进措施,为决策者提供更多管理信息。     

DEA分析方法研究综述

介绍了数据包络分析(DEA)的产生和基本思想,然后对DEA分析的经济意义进行了详细介绍,突出综述了DEA分析的发展和应用,最后指出了应用中应该注意的问题。     

DEA, DFA and SFA: A comparison

The nonparametric data envelopment analysis (DEA) model has become increasingly popular in the analysis of productive efficiency, and the number of empirical applications is now very large. Recent theoretical and mathematical research has also contributed to a deeper understanding of the seemingly simple but inherently complex DEA model. Less effort has, however, been directed toward comparisons between DEA and other competing efficiency analysis models. This paper undertakes a comparison of the DEA, the deterministic parametric (DFA), and the stochastic frontier (SFA) models. Efficiency comparisons across models in the above categories are done based on 15 Colombian cement plants observed during 1968–1988.     

Hierarchies and Groups in DEA

Conventional applications of data envelopment analysis (DEA) presume the existence of a set of similar decision making units, wherein each unit is evaluated relative to other members of the set. Often, however, the DMUs fall naturally into groupings, giving rise first to the problem of how to view the groups themselves as DMUs, and second to the issue of how to deal with several different ratings for any given DMU when groupings can be formed in different ways. In the present paper we introduce the concept of hierarchical DEA, where efficiency can be viewed at various levels. We provide a means for adjusting the ratings of DMUs at one level to account for the ratings received by the groups (into which these DMUs fall) at a higher level. We also develop models for aggregating different ratings for a DMU arising from different possible groupings. An application of these models to a set of power plants is given.     

基于L-R型模糊DEA的模型改进

本文首先介绍了前人建立的模糊DEA模型,考虑这些模型存在人为提高效率值、未充分利用模糊信息、计算量过大的问题,建立了一个新的L-R DEA模型,该模型基于α-截集的模糊数变换,将指标集分解为多个指标子集,分别对每个子集进行效率计算,最后应用熵值法确定决策单元(DMU)的最终效率值。该方法可以对DMU进行充分排序,扩大了DEA的应用领域。文末通过一算例来说明新模型的有效性。     

基于DEA的油料供应标准效果评价

首先,介绍了DEA评价模型的基本特点和评价步骤,建立了油料供应标准效果评价指标体系,而后利用DEA法对油料供应标准效果进行评价．并分析了DEA评价法的优缺点。     

DEA and Activity Planning under Asymmetric Information

The productivity analysis literature has traditionally focused on the evaluation of past performances. In this paper, we consider the post productivity analysis problem of deciding which production plans to choose in the future given information from a productivity analysis. In particular, we demonstrate that Data Envelopment Analysis (DEA) has an important role to play in the reallocation game following a normal productivity analysis. DEA estimates reduce the information rents an agent can extract by claiming high costs for the least reduced or most expanded activity. We also examine how to optimally combine DEA estimates with other information in a planning context, including preference information, ex-ante cost reports and ex-post cost data.     

Research performance evaluation of Chinese university: A non-homogeneous network DEA approach

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments’ performance of a university, and especially, none of them consider the non-homogeneity among the universities’ departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.     

Incorporating quality into the measurement of hospital efficiency: a double DEA approach

This paper uses “double DEA” to assess how accounting for quality influences DEA technical efficiency scores of a sample of 1,074 US hospitals. In the first use of DEA, quality indices are estimated using a variety of process and outcome measures of quality. In the second use of DEA, technical efficiency is assessed while controlling for quality. A variety of DEA quality indices and a DEA variety of efficiency models are compared to determine how the treatment of quality influences findings regarding technical efficiency. Controlling for efficiency does matter, with outcome measures having an apparently greater impact than process measures. Given the call for improved quality and better cost containment, controlling for quality is an important contribution to efficiency analysis.