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.     

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.     

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.     

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。     

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.     

Decomposition of technical efficiency under fixed proportion technologies: an application of data envelopment analysis

In this paper, we extend the four-component stochastic frontier model to allow for global spatial dependence via the endogenous spatial autoregressive variable. Our proposed model is more general than the model considered by (Glass et al., 2016) in the sense that we include a random effect as well as a permanent efficiency component. With the spatial autoregressive specification, our model is able to capture the asymmetric efficiency spillovers and also decompose the persistent/transient inefficiencies into direct and indirect efficiencies. Moreover, we also investigate the marginal effects of the exogenous variables on the persistent/transient efficiency. We suggest a maximum simulated likelihood method to estimate the frontier parameters of the model, and we predict the efficiencies using the simulated estimator. Monte Carlo simulations reveal that the suggested estimator performs well in finite samples. An empirical application is considered to illustrate the usefulness of our proposed model and method.

     

Using data envelopment analysis to measure the efficiency of not-for-profit organizations: A critical evaluation—comment

This note responds to Nunamaker (1985) who supposedly deals with deficiencies in Data Envelopment Analysis (DEA) as an approach for (1) measuring efficiencies of not-for-profit entities identified as Decision Making Units (DMUs) and (2) locating sources and amounts of inefficiencies in each of the inputs used and in each of the outputs produced by each DMU. Corrections and comments are offered with references supplied for interested readers who wish to examine more detailed treatments of the topics covered.     

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.     

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.     

A cautionary note on methods of comparing programmatic efficiency between two or more groups of DMUs in data envelopment analysis

In some applications of data envelopment analysis (DEA) there may be doubt as to whether all the DMUs form a single group with a common efficiency distribution. The Mann–Whitney rank statistic has been used to evaluate if two groups of DMUs come from a common efficiency distribution under the assumption of them sharing a common frontier and to test if the two groups have a common frontier. These procedures have subsequently been extended using the Kruskal–Wallis rank statistic to consider more than two groups. This technical note identifies problems with the second of these applications of both the Mann–Whitney and Kruskal–Wallis rank statistics. It also considers possible alternative methods of testing if groups have a common frontier, and the difficulties of disaggregating managerial and programmatic efficiency within a non-parametric framework.      

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...     

Data envelopment analysis with stochastic variations in data

The mathematical programming-based technique data envelopment analysis (DEA) has often treated data as being deterministic. In response to the criticism that in most applications there is error and random noise in the data, a number of mathematically elegant solutions to incorporating stochastic variations in data have been proposed. In this paper, we propose a chance-constrained formulation of DEA that allows random variations in the data. We study properties of the ensuing efficiency measure using a small sample in which multiple inputs and a single output are correlated, and are the result of a stochastic process. We replicate the analysis using Monte Carlo simulations and conclude that using simulations provides a more flexible and computationally less cumbersome approach to studying the effects of noise in the data. We suggest that, in keeping with the tradition of DEA, the simulation approach allows users to explicitly consider different data generating processes and allows for greater flexibility in implementing DEA under stochastic variations in data.     

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.     

Measurement of technical efficiency in farrow-to-finish swine production using multi-activity network data envelopment analysis: evidence from Taiwan

This study aims to propose a dynamic multi-activity network data development analysis (DMNDEA) model to measure the technical efficiency of farrow-to-finish swine production in Taiwan. Production phases are explicitly divided into two activities; namely, the breed-to-farrow phase and the wean-to-finish phase. By using this model, the problem of shared inputs and dynamic intermediates among activities that characterize pig production are taken into account in an integrated framework, simultaneously with the consideration of non-zero slack, allowing us to examine aspects of production in a more comprehensive and factual manner. For the empirical results based on sample data from 2006 to 2007, it is shown that the overall technical inefficiencies obtained from DMNDEA are not obviously different from those obtained using a traditional one-stage model. However, the DMNDEA results explicitly show us that the sources of inefficiency for each farm are different. Furthermore, second-stage bootstrapping regression results reveal that the determinants of efficiency for each production phase are not the same, indicating the need to identify the influential factors for each production phase separately.     

Evaluating cost efficiency and returns to scale in the Life Insurance Corporation of India using data envelopment analysis

This paper applies a new variant of data envelopment analysis model to examine the performance of Life Insurance Corporation (LIC) of India. The findings show a significant heterogeneity in the cost efficiency scores over the course of 19 years. A decline in performance after 1994-1995 can be taken as evidence of increasing allocative inefficiencies arising from the huge initial fixed cost undertaken by LIC in modernizing its operations. A significant increase in cost efficiency in 2000-2001 is, however, cause for optimism that LIC may now be realizing a benefit from such modernization. This will stand them in good stead in terms of future competition. Results from a sensitivity analysis are in broad agreement with the main findings of this study.     

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.     

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.     

An application of data envelopment analysis to evaluate the efficiency level of the operational cost of Brazilian electricity distribution utilities

This paper shows efficiency indices for 60 Brazilian electricity distribution utilities. The efficiency scores are gauged by three DEA models. For both models, these quantities are evaluated under different contexts. One treats with respect to the regulator perspective. The others examine an alternative approach based on cluster analysis and restrictions on factor weights. It is worth pointing out that these developments can reduce the information asymmetry and improve the regulator's skill to compare the performance of the utilities, a fundamental in incentive regulation schemes.     

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.