Extending Productivity Research Frontiers: DEA Resource of Datasets and Errata

Website databases provide the opportunity to fundamentally change the way research and knowledge are advanced and disseminated. This paper describes a website, http://www.etm.pdx.edu/dea/dataset/, focused on Data Envelopment Analysis and DEA researchers.     

Measuring productivity of research in economics: A cross-country study using DEA

We measure productivity in leading edge economic research by using data envelopment analysis (DEA) for a sample of 21 countries belonging to the Organization for Economic Cooperation and Development (OECD). Publications in ten top journals of economics from 1980 to 1998 are taken as the research output. Inputs are measured by R&D expenditure, the number of universities with economics departments and (as an uncontrollable variable) population. Under constant returns-to-scale, the US emerges as the only efficient country. Under variable returns-to-scale, the efficiency frontier is defined by the US, Ireland and New Zealand. With the exception of the US, all countries in our sample display increasing returns-to-scale, and thus have the potential to raise their efficiency by scaling up their research activities.     

Target and technical efficiency in DEA: controlling for environmental characteristics

In this paper we propose a target efficiency DEA model that allows for the inclusion of environmental variables in a one stage model while maintaining a high degree of discrimination power. The model estimates the impact of managerial and environmental factors on efficiency simultaneously. A decomposition of the overall technical efficiency into two components, target efficiency and environmental efficiency, is derived. Estimation of target efficiency scores requires the solution of a single large non-linear optimization problem and provides both a joint estimation of target efficiency scores from all DMUs and an estimation of a common scalar expressing the environmental impact on efficiency for each environmental factor. We argue that if the indices on environmental conditions are constructed as the percentage of output with certain attributes present, then it is reasonable to let all reference DMUs characterized by a composed fraction lower than the fraction of output possessing the attribute of the evaluated DMU enter as potential dominators. It is shown that this requirement transforms the cone-ratio constraints on intensity variables in the BM-model (Banker and Morey 1986) into endogenous handicap functions on outputs. Furthermore, a priori information or general agreements on allowable handicap values can be incorporated into the model along the same lines as specifications of assurance regions in standard DEA.

Economic interpretations of DEA

Research on productive efficiency at the firm level has developed as an important and active strand of research the last decades, both within operations research, management science and economics. Two apparently different definitions of efficiency are examined, but it is shown that when both estimation methods are based on solving linear programming problems the definitions of efficiency are identical. The purpose of the paper is to give the basic ideas of efficiency analyses using DEA as a tool for researchers not so familiar with efficiency analysis and DEA. The concept of shadow prices is given special attention.     

Classification in automobile insurance using a DEA and discriminant analysis hybrid

In this article, we adopt an efficiency approach to the two-group linear programming method of discriminant analysis (DA), using principles taken from data envelopment analysis (DEA), to predict group membership in an insurance underwriting scheme. Using an empirical insurance data base we illustrate the effectiveness of our model as a decision-making tool to distinguish among automobile insurance applicants by contrasting our hybrid model with both statistical and LP methods of discriminant analysis. We find for this insurance application that our hybrid model significantly outperforms the more traditional methods in separation and misclassification outcomes.     

The benefits of non-radial vs. radial super-efficiency DEA: an application to burden-sharing amongst NATO member nations

Using radial super-efficiency data envelopment analysis (DEA) has improved the discriminating performance across efficient decision-making units (DMUs). This paper extends the super-efficiency approach to a non-radial super-efficiency DEA (NRSE-DEA) index. NRSE-DEA is shown to be invariant to units of input (output) measurement. NRSE-DEA is illustrated here via an application to NATO burden-sharing assessment in which the DMUs are the member nations of NATO. The NRSE-DEA provides additional insights into the ranking of efficient countries, suggesting which are absorbing a particularly large share of NATO responsibilities. The NRSE-DEA generates a smaller set of efficient DMUs. This, in turn, provides more discriminatory power, a more accurate measure of super-efficiency, a more meaningful ranking of the efficient burden sharing countries, and a more reliable assessment of contributions by NATO members, amongst other policy issues.     

A Computational Framework for Accelerating DEA

We introducea new computational framework for DEA that reduces computationtimes and increases flexibility in applications over multiplemodels and orientations.The process is based on the identificationof frames--minimal subsets of the data needed to describethe models in the problems--for each of the four standardproduction possibility sets. It exploits the fact that the framesof the models are closely interrelated. Access to a frame ofa production possibility set permits a complete analysis in asecond phase for the corresponding model either oriented or orientation-free.This second phase proceeds quickly especially if the frame isa small subset of the data points. Besides accelerating computations,the new framework imparts greater flexibility to the analysisby not committing the analyst to a model or orientation whenperforming the bulk of the calculations. Computational testingvalidates the results and reveals that, with a minimum additionaltime over what is required for a full DEA study for a given modeland specified orientation, one can obtain the analysis for thefour models and all orientations.     

Identification and Use of Efficient Faces and Facets in DEA

This paper provides an outline of possible uses of complete information on the facial structure of a polyhedral empirical production possibility set obtained by DEA. It is argued that an identification of all facets can be used for a characterization of basic properties of the empirical production frontier. Focus is on the use of this type of information for (i) the specification of constraints on the virtual multipliers in a cone-ratio model, (ii) a characterization of the data generation process for the underlying observed data set, and (iii) the estimation of isoquants and relevant elasticities of substitution reflecting the curvature of the frontier. The relationship between the so-called FDEF approach and the cone-ratio model is explored in some detail. It is demonstrated that a decomposition of the facet generation process followed by the use of one of the available (exponential) convex hull algorithms allows for an explicit identification of the facial structure of the possibility set in fairly large DEA data sets. It is a main point to be made that the difficulties encountered for an identification of all facets in a DEA-possibility set can be circumvented in a number of empirical data sets and that this type of information can be used for a characterization of the structural properties of the frontier.     

Causes of inefficiency in Japanese railways: Application of DEA for managers and policymakers

Although railway services have been suffering financially due to modal shifts and aging populations, they have been, and will continue to be, an essential component of nations' basic social infrastructures. Since railway firms generate positive externalities, and are required to operate in pre-determined licensed areas, governmental intervention/support may, in some cases, be justified. Indeed, many types of subsidies are created and offered for railway operations in Japan; while some are meant to cover large investments, others are used as compensation for regional disparities. However, thus far, no attempt has been made to analyze the reasons for the underperformance of Japanese railway services. In other words, it is unclear whether this underperformance can be attributed to exogenous and uncontrollable causes, or endogenous phenomena and, hence, capable of being handled by managers. The optimal degree of intervention is thus not sufficiently known. In the current paper, we propose a method based on data envelopment analysis (DEA) to analyze the causes of inefficiency in Japanese railway operations, and, further, to calculate optimal subsidy levels. The latter are designed to compensate for railways' lack of complete discretion in changing location of their operations and/or increasing/decreasing these operations since they are a regulated service. Our proposed method was applied to 53 Japanese railway operators. In so doing, we identified several key characteristics related to their inefficiencies, and developed optimal subsidies designed to improve performance.     

Investment and dynamic DEA

A dynamic version of data envelopment analysis (DEA) is developed in the present paper. Our model introduces investment in traditional DEA and imposes intertemporal cost minimization. Adding an intertemporal adjustment constraint into the cost minimization problem, we derive the relation between the DEA variables of the variable cost function and those of the primary production frontiers’ coefficients. The augmented DEA model can be solved using standard linear programming. This dynamic framework enables computing the production frontiers, measuring the productive efficiencies and evaluating the potential economies all in the presence of adjustment costs.

Using DEA to improve the management of congestion in Chinese industries (1981–1997)

Congestion is said to be present when increases in inputs result in output reductions. An “iron rice bowl” policy instituted in China shortly after the revolution led by Mao Tze Tung resulted in congestion that ultimately led to bankruptcy in the textile industry, and near bankruptcy in other industries. A major policy shift away from the “iron rice bowl policy” in 1990 led to massive layoffs and increasing social tensions. Were these massive layoffs necessary? Extensions of data envelopment analysis models effected in the present paper identified inefficiencies in the management of congestion. Using textiles and automobiles for illustration, it is shown how elimination of such managerial inefficiencies could have led to output augmentation without reducing employment. Thus, even in the presence of congestion, it proved to be possible to identify additional (managerial) inefficiencies that provided opportunities for improvement. In the heavily congested textile industry, these output augmentations could have been accompanied by reductions in the amounts of capital used (as an added bonus). In any case, we show how to identify and evaluate new types of efficiency—viz., the efficiency with which needed (or desired) inefficiencies are managed.     

Measuring residential energy efficiency improvements with DEA

This paper measures energy efficiency improvements of US single-family homes between 1997 and 2001 using a two-stage procedure. In the first stage, an indicator of energy efficiency is derived by means of Data Envelopment Analysis (DEA), and the analogy between the DEA estimator and traditional measures of energy efficiency is demonstrated. The second stage employs a bootstrapped truncated regression technique to decompose the variation in the obtained efficiency estimates into a climatic component and factors attributed to efficiency improvements. Results indicate a small but significant improvement of energy efficiency over the studied time interval, mainly accounted for by fuel oil and natural gas users.     

DEA方法在供应链绩效评价中的应用

阐述了供应链绩效评价的特征及重要性。叙述了数据包络分析方法(DEA)方法的基本原理和模型,进而利用DEA中的C2R模型和C2GS2模型对供应链绩效进行评价分析。算例分析表明该方法可以有效地评价不同供应链体系的相对规模性和技术有效性,找出其非DEA有效的影响因素,并通过在生产前沿面上的投影分析,给出了改进措施。评价过程与结果均较客观、合理。     

基于DEA的区域高等院校协作效率评价

本文应用DEA方法从定量的角度研究通过区域内和区域之间的合作带来的益处。通过构建评价高等院校协作效率的DEA模型,对某地14所院校的区域内和区域间的高等院校协作效率进行评价。计算结果表明,通过区域内合作,每一个区域内都有部分院校的效率有较大的提高,最高的超过30％。     

Economic development and growth in Colombia: An empirical analysis with super-efficiency DEA and panel data models

In this paper, we analyse economic development and growth through traditional measures (gross domestic product and human development index) and Data Envelopment Analysis (DEA) in Colombian departments over the period 1993–2007. We use a DEA model to measure and rank economic development and growth from different approaches such as poverty, equality and security. The results show considerable variation in efficiency scores across departments. A second-stage panel data analysis with fixed effects reveals that higher levels of economic activity, quality life, employment and security are associated with a higher efficiency score based on the standards of living, poverty, equality and security. All findings of this analysis should demonstrate that economic development and growth could be achieved most effectively through a decrease in poverty, an increase in equality, a reduction in violence, and improved security. This indicates the need to generate effective policies that guarantee the achievement of these elements in the interest of all members of society.     

The Effect of Sample Size on the Mean Efficiency in DEA with an Application to Electricity Distribution in Australia,Sweden and New Zealand

This study examines the effect of sample size on the mean productive efficiency of firms when the efficiency is evaluated using the non-parametric approach of Data Envelopment Analysis. By employing Monte Carlo simulation, we show how the mean efficiency is related to the sample size. The paper discusses the implications for international comparisons. As an application, we investigate the efficiency of the electricity distribution industries in Australia, Sweden and New Zealand.     

Multiple Objective Approach as an Alternative to Radial Projection in DEA

Radial projection is a standard technique applied in data envelopment analysis (DEA) to calculate efficiency scores for input and/or output variables. In this paper, we have studied the appropriateness of radial projection for target setting. We have created a situation where the decision making units (DMUs) are free to choose their own target values on the efficient frontier and then compared the results to those of radial projection. In practice, target values are primarily used for future goal attainment; hence, not only preferences but also, and on the whole, change in time frame, affect the choice of target values. Based on that, we conducted an empirical experiment with an aim to study how the DMUs choose their most preferred target values on the efficient frontier. The subjects, who all were students of the Helsinki School of Economics, were given the freedom to explore their personalized efficient frontiers by using a multiple objective linear programming (MOLP) approach. To study various and relevant scenarios, the personalized efficient frontiers for all students were constructed in such a way that the current position of each student in relation to the frontier made him/her inefficient, efficient, or super-efficient. The results show that the use of radial projection for target setting is too restrictive.     

Comparing and Combining Two Approaches for Chance Constrained DEA

This paper presents a comparison of two different models (Land et al (1993) and Olesen and Petersen (1995)), both designed to extend DEA to the case of stochastic inputs and outputs. The two models constitute two approaches within this area, that share certain characteristics. However, the two models behave very differently, and the choice between these two models can be confusing. This paper presents a systematic attempt to point out differences as well as similarities. It is demonstrated that the two models under some assumptions do have Lagrangian duals expressed in closed form. Similarities and differences are discussed based on a comparison of these dual structures. Weaknesses of the each of the two models are discussed and a merged model that combines attractive features of each of the two models is proposed.

DEA方法在城市经营效率分析中的应用

加快城市化建设是我国21世纪发展的伟大战略决策,是促进我国经济快速、健康、持续增长的重要动力,同时也是解决我国当前经济发展中一系列深层次矛盾和问题(特别是就业问题和三农问题)的重要途径。目前全国大、中、小城市都在快速加强自身建设,城市规模扩张很快。在这种形势下,有必要寻找一种评估城市建设是否有效的方法。本文以山东省17个市为例,运用DEA方法探讨城市经营效率的评估方法。     

Using DEA and Worst Practice DEA in Credit Risk Evaluation

The purpose of this paper is to introduce the concept of worst practice DEA, which aims at identifying worst performers by placing them on the frontier. This is particularly relevant for our application to credit risk evaluation, but this also has general relevance since the worst performers are where the largest improvement potential can be found. The paper also proposes to use a layering technique instead of the traditional cut-off point approach, since this enables incorporation of risk attitudes and risk-based pricing. Finally, it is shown how the use of a combination of normal and worst practice DEA models enable detection of self-identifiers. The results of the empirical application on credit risk evaluation validate the method. The best combination of layered normal and worst practice DEA models yields an impressive 100% bankruptcy and 78% non-bankruptcy prediction accuracy in the calibration data set, and equally convincing 100% and 67% out-of-sample classification accuracies.