Unobserved Heterogeneous Effects in the Cost Efficiency Analysis of Electricity Distribution Systems

The purpose of this study is to analyze the cost efficiency of electricity distribution systems in order to enable regulatory authorities to establish price- or revenue cap regulation regimes. The increasing use of efficiency analysis in the last decades has raised serious concerns among regulators and companies regarding the reliability of efficiency estimates. One important dimension affecting the reliability is the presence of unobserved factors. Since these factors are treated differently in various models, the resulting estimates can vary across methods. Therefore, we decompose the benchmarking process into two steps. In the first step, we identify classes of similar companies with comparable network and structural characteristics using a latent class cost model. We obtain cost best practice within each class in the second step, based on deterministic and stochastic cost frontier models. The results of this analysis show that the decomposition of the benchmarking process into two steps has reduced unobserved heterogeneity within classes and, hence, reduced the unexplained variance previously claimed as inefficiency.

[1]  Michel Mouchart,et al.  Functions and Mechanisms in Structural-Modelling Explanations , 2014 .

[2]  Andreas Behr,et al.  Quantile regression for robust bank efficiency score estimation , 2010, Eur. J. Oper. Res..

[3]  D. Aigner,et al.  P. Schmidt, 1977,?Formulation and estimation of stochastic frontier production function models,? , 1977 .

[4]  Luis R. Murillo-Zamorano.  Economic Efficiency and Frontier Techniques , 2004 .

[5]  Thierry Bréchet,et al.  Climate Policies: A Burden, or a Gain? , 2013, SSRN Electronic Journal.

[6]  R. Koenker,et al.  Robust Tests for Heteroscedasticity Based on Regression Quantiles , 1982 .

[7]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[8]  K. Kalirajan,et al.  FRONTIER PRODUCTION FUNCTION: THE STOCHASTIC COEFFICIENTS APPROACH , 2009 .

[9]  Per Joakim Agrell,et al.  Efficiency evaluation with convex pairs , 2005 .

[10]  Florentine Schwark.  Economics of Endogenous Technical Change in CGE Models - The Role of Gains from Specialization , 2010 .

[11]  E. Schokkaert,et al.  Empirical Social Choice , 2011 .

[12]  Vladimir M. Veliov,et al.  Adaptive Model-Predictive Climate Policies in a Multi-Country Setting , 2015 .

[13]  Per J. Agrell,et al.  DEA and Dynamic Yardstick Competition in , 2005 .

[14]  Per Joakim Agrell,et al.  Structural and behavioral robustness in applied best-practice regulation , 2014 .

[15]  S. Kumbhakar.  Production frontiers, panel data, and time-varying technical inefficiency , 1990 .

[16]  G. Shuttleworth,et al.  Benchmarking of electricity networks: Practical problems with its use for regulation , 2005 .

[17]  Solomon W. Polachek,et al.  Panel estimates of a two‐tiered earnings frontier , 1996 .

[18]  R. Koenker,et al.  Regression Quantiles , 2007 .

[19]  P. Schmidt,et al.  Production frontiers with cross-sectional and time-series variation in efficiency levels , 1990 .

[20]  W. Greene,et al.  Application of Panel Data Models in Benchmarking Analysis of the Electricity Distribution Sector , 2006 .

[21]  François Maniquet,et al.  Equality Of Opportunity: The Economics Of Responsibility , 2012 .

[22]  M. Steel,et al.  Stochastic frontier models: a bayesian perspective , 1994 .

[23]  S. Kumbhakar.  Estimation of technical inefficiency in panel data models with firm- and time-specific effects , 1991 .

[24]  Tooraj Jamasb,et al.  International benchmarking and regulation: an application to European electricity distribution utilities , 2003 .

[25]  R. Kitamura,et al.  Stochastic Frontier Models of Prism Vertices , 2000 .

[26]  A. Bommier.  Life-Cycle Preferences Revisited , 2011 .

[27]  Tanguy Isaac,et al.  Equity and efficiency in an overlapping generation model , 2015, Soc. Choice Welf..

[28]  Lung-fei Lee,et al.  The measurement and sources of technical inefficiency in the Indonesian weaving industry , 1981 .

[29]  Antoine Bommier,et al.  Risk Aversion and the Value of Risk to Life , 2004, SSRN Electronic Journal.

[30]  L. Bretschger,et al.  Use Less, Pay More: Can Climate Policy Address the Unfortunate Event for Being Poor? , 2012 .

[31]  U. Jensen.  Is it efficient to analyse efficiency rankings? , 2000 .

[32]  L. Bretschger,et al.  Market Concentration and the Likelihood of Financial Crises , 2010 .

[33]  J. Becker.  On the number of α-pivotal players , 2011 .

[34]  Andrew Street,et al.  How much confidence should we place in efficiency estimates? , 2003, Health economics.

[35]  E. Blankmeyer,et al.  Technical efficiency in texas nursing facilities: A stochastic production frontier approach , 2007 .

[36]  Timothy Coelli,et al.  An Introduction to Efficiency and Productivity Analysis , 1997 .

[37]  Dimitri Paolini,et al.  MANAGEMENT EFFICIENCY IN FOOTBALL: AN EMPIRICAL ANALYSIS OF TWO EXTREME CASES , 2015 .

[38]  A. Bommier,et al.  Comparative Risk Aversion: A Formal Approach with Applications to Saving Behaviors , 2010 .

[39]  William H. Greene,et al.  Reconsidering heterogeneity in panel data estimators of the stochastic frontier model , 2005 .

[40]  M. Farsi,et al.  Efficiency Measurement in the Electricity and Gas Distribution Sectors , 2009 .

[41]  Paul F. Lazarsfeld,et al.  Latent Structure Analysis. , 1969 .

[42]  Jacques-François Thisse,et al.  Economic Geography and the Unequal Development of Regions , 2012 .

[43]  P. Peretto,et al.  Growth on a finite planet: resources, technology and population in the long run , 2011, Journal of Economic Growth.

[44]  M. Fleurbaey,et al.  A Theory of Fairness and Social Welfare , 2011 .

[45]  A. U.S.,et al.  FORMULATION AND ESTIMATION OF STOCHASTIC FRONTIER PRODUCTION FUNCTION MODELS , 2001 .

[46]  W. Meeusen,et al.  Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error , 1977 .

[47]  C. Lovell,et al.  Stochastic Frontier Analysis: Frontmatter , 2000 .

[48]  G. Battese,et al.  Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data , 1988 .

[49]  Marc Nerlove,et al.  RETURNS TO SCALE IN ELECTRICITY SUPPLY , 1961 .

[50]  S. Afriat.  Efficiency Estimation of Production Function , 1972 .

[51]  H. Gersbach,et al.  Channeling the Final Say in Politics , 2012 .

[52]  P. Schmidt,et al.  Stochastic Frontier Analysis and Efficiency Estimation , 2008 .

[53]  Andreas Lange,et al.  Offsetting Versus Mitigation Activities to Reduce CO2 Emissions: A Theoretical and Empirical Analysis for the U.S. And Germany , 2012 .

[54]  A. Cullmann.  Benchmarking and firm heterogeneity: a latent class analysis for German electricity distribution companies , 2012 .

[55]  Michael G. Pollitt,et al.  Efficiency analysis of energy networks: An international survey of regulators , 2009 .

[56]  L. Bretschger,et al.  Long-Run Effects of Post-Kyoto Policies: Applying a Fully Dynamic CGE Model with Heterogeneous Capital , 2010 .

[57]  C. Lovell,et al.  On the estimation of technical inefficiency in the stochastic frontier production function model , 1982 .

[58]  W. Greene.  The Econometric Approach to Efficiency Analysis , 2008 .

[59]  Neil Henry.  Latent structure analysis , 1969 .

[60]  A. Shleifer,et al.  A Theory of Yardstick Competition , 1985 .

[61]  Luc BAUWENS,et al.  Handbook of Volatility Models and Their Applications , 2012 .

[62]  Per J. Agrell,et al.  DEA and Dynamic Yardstick Competition in Scandinavian Electricity Distribution , 2005 .

[63]  H. Gersbach,et al.  Unraveling short- and farsightedness in politics , 2012, Public Choice.

[64]  M. Farsi,et al.  Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities , 2004 .

[65]  G. Chamberlain.  Multivariate regression models for panel data , 1982 .

[66]  E. Bjørndal.  Energy, natural resources and environmental economics , 2010 .

[67]  A.A.R. Madhavi,et al.  Efficiency Estimation of Production Functions , 2013 .

[68]  C. Brunnschweiler,et al.  International Partnerships, Foreign Control and Income Levels: Theory and Evidence , 2011 .

[69]  M. Ohndorf,et al.  Optimal monitoring of credit-based emissions trading under asymmetric information , 2011, Journal of Regulatory Economics.

[70]  C. N. Brunnschweiler,et al.  Rebellion Against Reason? A Study of Expressive Choice and Strikes , 2012 .

[71]  F. Krauss.  Latent Structure Analysis , 1980 .

[72]  Harmonizing the Nordic Regulation of Electricity Distribution , 2010 .

[73]  Christian A. Ruzzier,et al.  The Case for International Coordination of Electricity Regulation: Evidence from the Measurement of Efficiency in South America , 2002 .

[74]  G. Battese,et al.  Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India , 1992 .

[75]  Maik T. Schneider,et al.  ENDOGENOUS ENFORCEMENT OF INTELLECTUAL PROPERTY, NORTH–SOUTH TRADE, AND GROWTH , 2013, Macroeconomic Dynamics.

[76]  P. Picard,et al.  State Owned Firms: Private Debt, Cost Revelation and Welfare , 2012 .

[77]  Lester C. Hunt,et al.  International Handbook on the Economics of Energy , 2011 .

[78]  R. Shephard.  Cost and production functions , 1953 .

[79]  Yair Tauman,et al.  Cooperation in R&D: patenting, licensing and contracting , 2014 .

[80]  P. Schmidt,et al.  Production Frontiers and Panel Data , 1984 .

[81]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

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

[83]  S. Kumbhakar,et al.  Efficiency measurement using a latent class stochastic frontier model , 2004 .