Chance-constrained data envelopment analysis

Data envelopment analysis (DEA) is extended to the case of stochastic inputs and outputs through the use of chance-constrained programming. The chance-constrained frontier envelops a given set of observations ‘most of the time’. As an empirical illustration, we re-examine the pioneering 1981 study of Program Follow Through by Charnes, Cooper and Rhodes.     

Performance measurement and best-practice benchmarking of mutual funds: combining stochastic dominance criteria with data envelopment analysis

We propose a method for mutual fund performance measurement and best-practice benchmarking, which endogenously identifies a dominating benchmark portfolio for each evaluated mutual fund. Dominating benchmarks provide information about efficiency improvement potential as well as portfolio strategies for achieving them. Portfolio diversification possibilities are accounts for by using Data Envelopment Analysis (DEA). Portfolio risk is accounted for in terms of the full return distribution by utilizing Stochastic Dominance (SD) criteria. The approach is illustrated by an application to US based environmentally responsible mutual funds.     

Detecting influential observations in data envelopment analysis

This paper provides diagnostic tools for examining the role of influential observations in Data Envelopment Analysis (DEA) applications. Observations may be prioritized for further scrutiny to see if they are contaminated by data errors; this prioritization is important in situations where data-checking is costly and resources are limited. Several empirical examples are provided using data from previously published studies.This research was performed while under contract with the Management Science Group, U.S. Department of Veterans Affairs, Bedford, MA 01730. Shawna Grosskopf and Richard Grabowski graciously provided data used in two of the empirical examples.     

Efficiency aggregation with enhanced Russell measures in data envelopment analysis

In aggregation for data envelopment analysis (DEA), a jointly determined aggregate measure of output and input efficiency is desired that is consistent with the individual decision making unit measures. An impasse has been reached in the current state of the literature, however, where only separate measures of input and output efficiency have resulted from attempts to aggregate technical efficiency with the radial measure models commonly employed in DEA. The latter measures are “incomplete” in that they omit the non-zero input and output slacks, and thus fail to account for all inefficiencies that the model can identify. The Russell measure eliminates the latter deficiency but is difficult to solve in standard formulations. A new approach has become available, however, which utilizes a ratio measure in place of the standard formulations. Referred to as an enhanced Russell graph measure (ERM), the resulting model is in the form of a fractional program. Hence, it can be transformed into an ordinary linear programming structure that can generate an optimal solution for the corresponding ERM model. As shown in this paper, an aggregate ERM can then be formed with all the properties considered to be desirable in an aggregate measure—including jointly determined input and output efficiency measures that represent separate estimates of input and output efficiency. Much of this paper is concerned with technical efficiency in both individual and system-wide efficiency measures. Weighting systems are introduced that extend to efficiency-based measures of cost, revenue, and profit, as well as derivatives such as rates of return over cost. The penultimate section shows how the solution to one model also generates optimal solutions to models with other objectives that include rates of return over cost and total profit. This is accomplished in the form of efficiency-adjusted versions of these commonly used measures of performance.     

Stability and integrity tests in data envelopment analysis

Studies that apply data envelopment analysis often neglect testing the stability of the efficient frontier to data perturbations, and, to a lesser extent, the ability of efficiency scores to correctly discriminate between units on performance (integrity). Our primary motivation is to demonstrate methods that can help reduce the number of managerial decisions based on results that may be unreliable. To this end, we illustrate multiple tests of stability and integrity in an environment of fully units-invariant efficiency measurement. This application of tests of stability and integrity using a slacks-based measure of efficiency is the first in a peer-reviewed journal.     

Weak congestion in output additive data envelopment analysis

Our earlier work [Wei QL, Yan H. Congestion and returns to scale in data envelopment analysis. European Journal of Operational Research 2004;153:641–60] discussed necessary and sufficient conditions for the existence of congestion together with aspects of returns to scale under an output-oriented DEA framework. In line with this work, the current paper investigates the issue of “weak congestion”, wherein congestion occurs when the reduction of selected inputs causes some, rather than all, outputs to increase, without a worsening of others. We define output efficiency for decision making units under a series of typical DEA output additive models. Based on this definition, we offer necessary and sufficient conditions for the existence of weak congestion. Numerical examples are provided for purposes of illustration.     

Extending the zero-sum gains data envelopment analysis model

Journal of Productivity Analysis - In this paper, we adapt the ZSG-DEA model to the case of a reverse output, whose larger (smaller) values reflect lower (higher) achievements. Then, we introduce...     

A data envelopment analysis of gas utilities' efficiency

This study examines the Natural Gas Policy Act of 1978 and Federal Energy Regulatory Commission (FERC) policies that culminated in Order 636 in 1992. The regulatory environment in which natural gas distribution utilities operate was altered. FERC policies forced local gas distribution utilities into an increasingly competitive environment. Restructuring of the industry may affect economic efficiency. Data Envelopment Analysis is used to examine the economic efficiency of gas distributors during 1975–94. Federal policy appears to lead to a reduction in scale due to restructuring and more competition. Reduced scale economies have not altered the economic efficiency of the utilities.     

A new method for measuring congestion in data envelopment analysis

In data envelopment analysis (DEA), there are two principal methods for identifying and measuring congestion: Those of Färe et al. [Färe R, Grosskopf S. When can slacks be used to identify congestion. An answer to W. W. Cooper, L. Seiford, J. Zhu. Socio-Economic Planning Sciences 2001;35:1–10] and Cooper et al. [Cooper WW, Deng H, Huang ZM, Li SX. A one-model approach to congestion in data envelopment analysis. Socio-Economic Planning Sciences 2002;36:231–8]. In the present paper, we focus on the latter work in proposing a new method that requires considerably less computation. Then, by proving a selected theorem, we show that our proposed methodology is indeed equivalent to that of Cooper et al.     

Data envelopment analysis and Malmquist indices for measuring group performance

This paper develops measures, based on the Malmquist index, that enable the decision making units’ internal inefficiencies to be distinguished from those associated with their group (or program) characteristics. The applicability of these measures is illustrated with the assessment of bank branches’ performance. The analysis involves the construction of an index reflecting the relative performance of branches in four different regions, which can be decomposed into an index for the comparison of within-group efficiency spread, evaluating internal managerial efficiencies, and an index for the comparison of frontier productivity, reflecting the impact of environmental factors and regional managerial policies on branches’ productivity.     

Classification transitions under expansion of inputs and outputs in data envelopment analysis

An exchange between Thomas R. Nunamaker and T. Ahn, A. Charnes and W. W. Cooper which revolves around possible behavior of the DEA efficiency measure. In particular, Ahn, Charnes and Cooper provide a counterexample to Nunamaker's assertion that adding inputs to a DMU (Decision Making Unit) which had previously attained an efficiency of 0* = 1 with fewer inputs would never alter this value, no matter how inefficient the use of the additional inputs. Here we want to add additional insight by showing the condition under which Nunamaker's proposition is true. Using the notation and concepts from Charnes, Cooper and Thrall, we shall see that this proposition is true if and only if the previously attained 0* = 1 is uniquely optimal.     

Some extensions of techniques to handle non-discretionary factors in data envelopment analysis

Data Envelopment Analysis (DEA) assumes, in most cases, that all inputs and outputs are controlled by the Decision Making Unit (DMU). Inputs and/or outputs that do not conform to this assumption are denoted in DEA asnon-discretionary (ND) factors. Banker and Morey [1986] formulated several variants of DEA models which incorporated ND with ordinary factors. This article extends the Banker and Morey approach for treating nondiscretionary factors in two ways. First, the model is extended to allow for thesimultaneous presence of ND factors in both the input and the output sets. Second, a generalization is offered which, for the first time, enables a quantitative evaluation ofpartially controlled factors. A numerical example is given to illustrate the different models.The editor for this paper was Wade D. Cook.     

Performance evaluation using data envelopment analysis in the presence of time lags

Data Envelopment Analysis (DEA) is a methodology that computes efficiency values for decision making units (DMU) in a given period by comparing the outputs with the inputs. In many applications, inputs and outputs of DMUs are monitored over time. There might be a time lag between the consumption of inputs and the production of outputs. We develop an approach that aims to capture the time lag between the outputs and the inputs in assigning the efficiency values to DMUs. We propose using weight restrictions in conjunction with the model. Our computational results on randomly generated problems demonstrate that the developed approach works well under a large variety of experimental conditions. We also apply our approach on a real data set to evaluate research institutions.     

Using data envelopment analysis to evaluate efficiency in the economic performance of Chinese cities

This paper studies the use of DEA (data envelopment analysis) as a tool for possible use in evaluating and planning the economic performance of China's cities (28 in all) which play a critical role in the government's program of economic development. DEA promises advantages which include the absence of any need for the assignment of weights on an a priori basis (to reflect the supposed relative importance of various outputs or inputs) when evaluating technical efficiency. It is also unnecessary to explicitly specify underlying functions that are intended to prescribe the analytical form of the relations between inputs and outputs. Finally, as is illustrated in the paper, DEA can be used to identify sources, and estimate amounts of inefficiencies in each city's performance as well as to identify returns-to-scale possibilities in ways that seem well-suited to the mixture of centralized and decentralized planning and performance that China is currently trying to use.     

Measuring the performance of Internet companies using a two-stage data envelopment analysis model

In exploring the business operation of Internet companies, few researchers have used data envelopment analysis (DEA) to evaluate their performance. Since the Internet companies have a two-stage production process: marketability and profitability, this study employs a relational two-stage DEA model to assess the efficiency of the 40 dot com firms. The results show that our model performs better in measuring efficiency, and is able to discriminate the causes of inefficiency, thus helping business management to be more effective through providing more guidance to business performance improvement.     

Performance improvement decision aid systems (PIDAS) in retailing organizations using data envelopment analysis

This paper investigates the usefulness of Data Envelopment Analysis (DEA) models to aid decision making in multi-level retail organizations. It is argued that market efficiency is a key performance measurement dimension in retail organizations. The paper proposes three variants of market efficency that correspond to different tiers of management in a multi-level setting. The disentanglement of market efficiency will lead to the development of the Performance Improvement Decision Aid System (PIDAS) which seeks to classify units on clusters of different performance profile. The method is illustrated using data from a restaurant chain in the UK.     

The efficient use of enterprise information for strategic advantage: A data envelopment analysis

A majority of manufacturers make use of some form of enterprise systems (ES), yet on average, the financial impact of ES adoption is essentially neutral. We propose that in an ES environment of easy information access, competitive success depends, in part, on the policies regulating enterprise information use. To explore this proposition, we examine the efficient use of different types of enterprise information in the realization of strategic performance. Efficient firms will devote fewer resources to information use to achieve the same strategic performance as less efficient firms.     

基于数据包络分析法的涉农金融机构经营效率分析

文章以2012年246家涉农金融机构的4项投入因素(员工人数、总利息支出、非利息费用与净值)与3项产出因素(贷款收入、非贷款收入与逾期贷款比率)为研究对象,利用非预期因素数据包络分析法,分析涉农金融机构经营效率的评估,并提出达到相对有效率应改善的方向。研究发现:舍弃逾期贷款比率与不舍弃逾期贷款比率的产出导向BCC模型间,占总体样本的8.54%,显现两种方法有其差异性。这是因为涉农金融逾期贷款比率愈高,需要付出更多的人力去催讨与处理抵押品的损失,皆会影响涉农金融机构经营效率。此外,投入或产出项需要调整的涉农金融机构占80.89%。涉农金融机构经营效率低下的主要原因是资源分配不当,如何降低经营成本与开拓业务实属当务之急。     

A two-stage double-bootstrap data envelopment analysis of efficiency differences of New Zealand secondary schools

We conduct a two-stage (DEA and regression) analysis of the efficiency of New Zealand secondary schools. Unlike previous applications of two-stage semi-parametric modelling of the school “production process”, we use Simar and Wilson’s double bootstrap procedure, which permits valid inference in the presence of unknown serial correlation in the efficiency scores. We are therefore able to draw robust conclusions about a system that has undergone extensive reforms with respect to ideas high on the educational agenda such as decentralised school management and parental choice. Most importantly, we find that school type affects school efficiency and so too does teacher quality.     

Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models

Non-discretionary or environmental variables are regarded as important in the evaluation of efficiency in Data Envelopment Analysis (DEA), but there is no consensus on the correct treatment of these variables. This paper compares the performance of the standard BCC model as a base case with two single-stage models: the Banker and Morey (1986a) model, which incorporates continuous environmental variables and the Banker and Morey (1986b) model, which incorporates categorical environmental variables. Simulation analyses are conducted using a shifted Cobb-Douglas function, with one output, one non-discretionary input, and two discretionary inputs. The production function is constructed to separate environmental impact from managerial inefficiency, while providing measures of both for comparative purposes. Tests are performed to evaluate the accuracy of each model. The distribution of the inputs, the sample size and the number of categories for the categorical model are varied in the simulations to determine their impact on the performance of each model. The results show that the Banker and Morey models should be used in preference to the standard BCC model when the environmental impact is moderate to high. Both the continuous and categorical models perform equally well but the latter may be better suited to some applications with larger sample sizes. Even when the environmental impact is slight, the use of a simple two-way split of the sample data can produce significantly better results under the Categorical model in comparison to the BCC model.