Stepwise DEA; Choosing Variables for Measuring Technical Efficiency in Norwegian Electricity Distribution

A production technology specification in electricity distribution leads to many product aspects that together with detailed input data gives a high dimensionality. Non-parametric methods may give meaningful results when parametric methods lack degrees of freedom, but have problems with collinear or irrelevant variables. Aggregate efficiency estimates will be little affected, but rates of transformation will be corrupted and extreme observations will be measured as efficient by default. This paper uses tests suggested in the literature to measure the significance of the change in results when disaggregating or introducing an extra variable, and lets the data aid in deciding the specification. JEL Classification: D24, L94, C14

[1]  Abraham Charnes,et al.  Preface to topics in data envelopment analysis , 1984, Ann. Oper. Res..

[2]  Finn R. Førsund,et al.  Efficiency analysis of Norwegian district courts , 1992 .

[3]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .

[4]  R. Färe,et al.  The measurement of efficiency of production , 1985 .

[5]  G. K. Bhattacharyya,et al.  Statistical Concepts And Methods , 1978 .

[6]  K. Salvanes,et al.  Productivity differences in multiple output industries: An empirical application to electricity distribution , 1994 .

[7]  Edward E. Leamer,et al.  Model choice and specification analysis , 1983 .

[8]  Léopold Simar,et al.  On estimation of monotone and convex boundaries , 1995 .

[9]  A. Charnes,et al.  A structure for classifying and characterizing efficiency and inefficiency in Data Envelopment Analysis , 1991 .

[10]  Michael Norman,et al.  Data Envelopment Analysis: The Assessment of Performance , 1991 .

[11]  Rolf Färe,et al.  Efficiency Measures for Multiplant Firms with Limited Data , 1988 .

[12]  R. Banker.  Maximum likelihood, consistency and data envelopment analysis: a statistical foundation , 1993 .

[13]  P. Bauer.  Recent developments in the econometric estimation of frontiers , 1990 .

[14]  E. Lehmann,et al.  Nonparametrics: Statistical Methods Based on Ranks , 1976 .

[15]  Wassily Leontief,et al.  The Theory of Econometrics. , 1942 .

[16]  A. Tsybakov,et al.  Efficient Estimation of Monotone Boundaries , 1995 .

[17]  W. R. Buckland,et al.  Distributions in Statistics: Continuous Multivariate Distributions , 1974 .

[18]  Lennart Hjalmarsson,et al.  Analyses of industrial structure : a putty-clay approach , 1987 .

[19]  T. J. Cook,et al.  Evaluating the administrative efficiency of courts , 1982 .

[20]  Russell G. Thompson,et al.  Sensitivity Analysis of Efficiency Measures with Applications to Kansas Farming and Illinois Coal Mining , 1994 .

[21]  Boaz Golany,et al.  Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions , 1985 .

[22]  Jesús T. Pastor,et al.  Linear programming approaches to the measurement and analysis of productive efficiency , 1994 .

[23]  Rolf Färe,et al.  Measuring the technical efficiency of production , 1978 .

[24]  David Kendrick,et al.  GAMS, a user's guide , 1988, SGNM.

[25]  Abraham Charnes,et al.  Sensitivity and stability analysis in dea , 1984, Ann. Oper. Res..

[26]  Vivian Valdmanis,et al.  Sensitivity analysis for DEA models: An empirical example using public vs. NFP hospitals , 1992 .

[27]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[28]  R. Shepherd.  Theory of cost and production functions , 1970 .

[29]  Rolf Färe,et al.  Aggregation and Efficiency , 1988 .

[30]  N. Petersen,et al.  Chance constrained efficiency evaluation , 1995 .

[31]  Lennart Hjalmarsson,et al.  Efficiency and ownership in Swedish electricity retail distribution , 1992 .

[32]  Robert M. Thrall,et al.  Classification transitions under expansion of inputs and outputs in data envelopment analysis , 1989 .