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.     

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.     

A decomposition analysis of managerial efficiency for the insurance companies: A data envelopment analysis approach

The insurance industry worldwide has become less fragmented through liberalization reforms. Yet, following the transformational changes, there is little empirical evidence on identifying the determinants of managerial efficiency. This paper employs a truncated regression analysis to determine the factors driving managerial efficiency of Malaysian insurers. Overall and divisional efficiencies are derived using data envelopment analysis. The decomposition analysis reveals that the observed inefficiency is mainly caused by investment capability division. The regression findings reveal that determinants of efficiency have dissimilar impacts on overall and divisional efficiencies. This study broadly supports financial liberalization as a means of promoting managerial efficiency.     

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.     

Distance optimization approach to ratio-form efficiency measures in data envelopment analysis

This paper examines the development pattern of the DEA (Data Envelopment Analysis) research field using Social Network Analysis. Nine stylized facts are verified: it is shown that the distribution of research performance is highly skewed, satisfying a power law, and that collaborative research activity is concentrated achieving higher performance. Moreover, economics and OR (operations research)/MS (management science) groups developed without collaboration until the 1980s; however, the merger of the two groups began in 1994 with the collaboration of frontier researchers. Now the two groups have merged significantly, but they are still identifiable. Finally, research hubs and the emergence of new groups are examined. Nine stylized facts show that DEA has developed with unique attributes. Although it shares common characteristics with other academic fields, including a highly skewed distribution of publications and expansion of the network over time, the DEA field has had a pattern of development that is different from those of other academic fields. Independent development and interrelated evolution between economics and OR/MS contributed to the exchange of knowledge of these two separate fields.     

Using data envelopment analysis to measure the efficiency of non-profit organizations: A critical evaluation

This study examines the potential effects of variable set expansion and data variations upon the efficiency scores generated using the Data Envelopment Analysis (DEA) model. It was found that variable set expansion (either through disaggregation of existing variables or addition of new factors) should produce an upward trend in efficiency scores. In addition, ample opportunity exists for ‘decision-making units’ to increase their efficiency scores through manipulation of reported data. In real-world applications of DEA, these problems must be resolved as much as possible (e.g. increased audit of data) in order to improve DEA's practical usefulness and reliability.     

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.     

Production frontier estimation to measure efficiency: A critical evaluation in light of data envelopment analysis

The ranking and measurement of efficiency of decision-making units by two methods—data envelopment analysis and frontier production function—may not always lead to identical results. In this framework we attempt here a critical evaluation of the frontier production function theory in terms of theoretical and empirical implications. It is shown that under certain conditions the two approaches to effciency measurement may lead to identical results.     

Productive efficiency of English football teams—a data envelopment analysis approach

This paper investigates how close to their potential English Premier League Clubs play. Using a deterministic Data Envelopment Analysis Approach, the productive efficiency of 20 teams in the 2000/2001 season is measured and weaknesses of individual teams are disclosed. The sensitivity of results is analyzed with regard to different model specifications and variable combinations. Copyright © 2003 John Wiley & Sons, Ltd.     

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.     

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。     

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.

     

Hypothesis tests using data envelopment analysis

A substantial body of recent work has opened the way to exploring the statistical properties of DEA estimators of production frontiers and related efficiency measures. The purpose of this paper is to survey several possibilities that have been pursued, and to present them in a unified framework. These include the development of statistics to test hypotheses about the characteristics of the production frontier, such as returns to scale, input substitutability, and model specification, and also about variation in efficiencies relative to the production frontier.     

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.     

A quantitative study of financing efficiency of low‐carbon companies: A three‐stage data envelopment analysis

This study set out to evaluate the financing efficiency of low‐carbon companies. Applying a three‐stage data envelopment analysis with the data from 85 listed companies in China's low‐carbon industries over the period 2011 to 2017, this study has found that the overall financing efficiency of low‐carbon companies was relatively high, and the pure technical efficiency was quite steady over the period. The overall financing efficiency of these low‐carbon companies on average tended to change with the scale efficiency. This study has also shown that the scale efficiency was the main constraint influencing the financing efficiency of low‐carbon companies in China over the period. Our results are robust and have significant implications for policy makers and corporate managers.     

Regional technical efficiency of Chinese Iron and steel industry based on bootstrap network data envelopment analysis

This paper analyses the regional technical efficiency of Chinese iron and steel industry from 1996 to 2010 by a network DEA procedure, and provides the smoothed bootstrap network DEA strategy for the sensitivity analysis of the efficiency measure to sampling variation of the estimated frontier. Furthermore, the evolution and convergence characteristics of regional technical efficiency are examined by a dynamic regression model based on different regional divisions of China. The empirical results show that there exist significant geographical differences in the technical efficiency of Chinese iron and steel industry. On the one hand, the technical efficiency of the eastern area, the central area and the western area is unbalanced, with a lower efficiency in the west and a higher one in the east. On the other hand, technical efficiency of Central Bohai, Yangtze River Delta and Pearl River Delta economic zones is higher than that of the other economic zones. In addition, the technical efficiency has a significant improvement during the period of the Eleventh Five-Year Plan. Following the convergence notation in economic growth theory, this paper also gives some convergence evidence of the technical efficiency towards the efficient frontier due to the catching-up effect. Finally, this paper explores the determinants of the technical efficiency, and discusses policy implications for Chinese iron and steel industry.     

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.