On the regulatory choice of returns to scale in DEA MODELS: An Application to the argentine electricity distribution sector (original) (raw)
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Central European Journal of Operations Research, 2013
In this paper, we use simulations to investigate the relationship between data envelopment analysis (DEA) efficiency and major production functions: Cobb-Douglas, the constant elasticity of substitution, and the transcendental logarithmic. Two DEA models were used: a constant return to scale (CCR model), and a variable return to scale (BCC model). Each of the models was investigated in two versions: with bounded and unbounded weights. Two cases were simulated: with and without errors in the production functions estimation. Various degrees of homogeneity (of the production function) were tested, reflecting a constant increasing and decreasing return to scale. With respect to the case with errors, three distribution functions were utilized: uniform, normal, and double exponential. For each distribution, 16 levels of the coefficient of variance (CV) were used. In all the tested cases, two measures were analysed: the percentage of efficient units (from the total number of units), and the average efficiency score. We applied a regression analysis to test the relationship between these two efficiency measures and the above parameters. Overall, we found that the degree of homogeneity has the largest effect on efficiency. Efficiency declines as the errors grow (as reflected by larger CV and of the expansion of the probability distribution function away from the centre). The bounds on the weights tend to Y. Hadad (B) ·
Productivity Analysis and Variable Returns of Scale: DEA Efficiency Frontier Interpretation
Procedia Computer Science, 2015
The main objective of this paper is to analyze DMUs efficiency from the perspective of variable returns to scale. Thus, a case study is proposed, where the efficiencies of DMUs suffer variation according to the methods used in the analysis. The classic models of DEA, CCR and BCC, and a new model proposed by the authors, will have their results compared to classical foundations of the economy. The case study will examine the efficiency of administrative units selected of Undergraduate Higher Education.
Productivity Analysis and Variable Returns to Scale: DEA Efficiency Frontier Interpretation
The main objective of this paper is to analyze DMUs efficiency from the perspective of variable returns to scale. Thus, a case study is proposed, where the efficiencies of DMUs suffer variation according to the methods used in the analysis. The classic models of DEA, CCR and BCC, and a new model proposed by the authors, will have their results compared to classical foundations of the economy. The case study will examine the efficiency of administrative units selected of Undergraduate Higher Education.
DEA efficiency analysis: Efficient and anti-efficient frontier
Applied Mathematics and Computation, 2007
Data envelopment analysis (DEA) is a methodology for identifying the efficient frontier of production possibility set. Using this efficient frontier, an efficiency score is derived to each decision making units. This study, proposes an alternative efficiency measure by using efficient and anti-efficient frontiers. Numerical experiments show the validity of the proposed efficiency measure and its compatibility with other measures of efficiency. The paper addresses the super-efficiency issue by using this measure.
Frontier improvement in the DEA models
arXiv: Optimization and Control, 2018
Applications of data envelopment analysis (DEA) show that many inefficient units are projected onto the weakly efficient parts of the frontier when efficiency scores are computed. However this fact disagrees with the main concept of the DEA approach, because the efficiency score of an inefficient unit has to be measured relative to an efficient unit. As a consequence inaccurate efficiency scores may be obtained. This happens because a non-countable (continuous) production possibility set is determined on a basis of a finite number of production units. Some authors proposed to use artificial production units in the primal space of inputs and outputs as a starting point in order to improve the frontier of the DEA models. Farrell was the first who introduced artificial units in the primal space of inputs and outputs in order to secure convex isoquants. In previous papers we introduced the notion of terminal units. It was also proved that only terminal units form necessary and sufficien...
DEA efficiency analysis with identifying efficient and full-inefficient frontier
International Mathematical Forum, 2007
Data envelopment analysis (DEA) is a mathematical programming technique for identifying relative efficiency scores of decision making units (DMUs). Recently, Amirteimoori (2007) Introduced an alternative efficiency measure based on efficient and anti-efficient frontiers. In this paper we introduce a new computational framework for identifying full-efficient and inefficient frontier of production possibility set (PPS) in DEA models with variable return to scale. This facets apply in finding full-efficient and inefficient DMUs, sensitivity and stability analysis, ranking, and etc.
A Note on the Convergence of Nonparametric Dea Estimators for Production Efficiency Scores
Econometric Theory, 1998
Efficiency scores of production units are measured by their distance to an estimated production frontier+ Nonparametric data envelopment analysis estimators are based on a finite sample of observed production units, and radial distances are considered+ We investigate the consistency and the speed of convergence of these estimated efficiency scores~or of the radial distances! in the very general setup of a multioutput and multi-input case+ It is shown that the speed of convergence relies on the smoothness of the unknown frontier and on the number of inputs and outputs+ Furthermore, one has to distinguish between the output-and the input-oriented cases+
A Note on the Convergence of Nonparametric DEA Efficiency Measures
RePEc: Research Papers in Economics, 1996
Efficiency scores of production units are measured by their distance to an estimated production frontier+ Nonparametric data envelopment analysis estimators are based on a finite sample of observed production units, and radial distances are considered+ We investigate the consistency and the speed of convergence of these estimated efficiency scores~or of the radial distances! in the very general setup of a multioutput and multi-input case+ It is shown that the speed of convergence relies on the smoothness of the unknown frontier and on the number of inputs and outputs+ Furthermore, one has to distinguish between the output-and the input-oriented cases+
2003
Among the duties of the regulatory agency of the electric power supply sector in Brazil there is the periodical revision of energy prices. Such revisions involve estimating the X Factor applied to update prices so that gains in productivity are shared with consumers. To estimate the X Factor it is necessary to measure efficiency and, for this, two issues are important: the choices of benchmarks and of techniques for productivity measurement. This paper proposes an approach to define frontier efficiency of electric power distribution utilities based on clustering homogeneous utilities using neural networks and estimating the frontiers through econometric techniques.
ICORD-VI - International Conference on Operational Research for Development , 2007
This paper presents the main results of an empirical study which Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) are used to evaluate the cost efficiency of the Brazilian electricity distribution utilities. These techniques can reduce the information asymmetry and improve the regulator’s skill to compare the performance of the utilities, a fundamental aspect in incentive regulation frameworks. Based on yardstick competition scheme, a Self-Organising Map is applied to identify groups with similar utilities. In each cluster the cost efficiency scores of its electricity distribution utilities are evaluated by different specifications of the SFA and DEA models. In both models the only input variable is operational cost (OPEX) and so, the efficiency measure reflect the operational costs reduction potential of each utility. The outputs are the cost-drivers of the OPEX: the number of customers, the total electric power supplied, the distribution network size.