Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier methods

This study estimates cost inefficiency and economies of scale of Slovenian water distribution utilities over the 1997–2003 period by employing several different stochastic frontier methods. The results indicate that significant cost inefficiencies are present in the utilities. An introduction of incentive-based price regulation scheme might help resolve this problem. However, the inefficiency scores obtained from different cost frontier models are not found to be robust. The levels of inefficiency estimates as well as the rankings depend on the econometric specification of the model. The established lack of robustness can be at least partly explained by different ability of the models to separate unobserved heterogeneity from inefficiency. Newly proposed true fixed effects model (Greene, J Econom 126:269–303, 2005; J Prod Anal 23(1):7–32, 2005) appears to perform better than the conventional panel data models with respect to distinguishing between unobserved heterogeneity and inefficiency. On the other hand, different models produce fairly robust results with respect to estimates of economies of output density, customer density and economies of scale. The optimal size of a company is found to closely corresponds to the sample median. Economies of scale are found in small-sized utilities, while large companies exhibit diseconomies of scale.

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

[2]  K. Mitchell,et al.  Economies of Scale and Scope at Large Commercial Banks: Evidence from the Fourier Flexible Functional Form , 1996 .

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

[4]  M. Farsi,et al.  Unobserved heterogeneity in stochastic cost frontier models: an application to Swiss nursing homes , 2005 .

[5]  R. Evans.  "Behavioural" Cost Functions for Hospitals , 1971 .

[6]  Douglas A. McManus,et al.  Resolving the Scale Efficiency Puzzle in Banking , 1993 .

[7]  Michael W. Tretheway,et al.  Economies of density versus economies of scale , 1984 .

[8]  M. Edward Rister,et al.  SELECTING FUNCTIONAL FORM IN PRODUCTION FUNCTION ANALYSIS , 1987 .

[9]  Rangesan Narayanan,et al.  Specification and estimation of the effect of ownership on the economic efficiency of the water utilities , 1995 .

[10]  Mark J. Roberts.  Economies of Density and Size in the Production and Delivery of Electric Power , 1986 .

[11]  Paul W. Wilson,et al.  New evidence on returns to scale and product mix among U.S. commercial banks , 2001 .

[12]  W. Greene.  Fixed and Random Effects in Stochastic Frontier Models , 2002 .

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

[14]  A. Heshmati,et al.  Farm heterogeneity and technical efficiency: Some results from Swedish dairy farms , 1994 .

[15]  A. Cameron,et al.  Microeconometrics: Methods and Applications , 2005 .

[16]  Alban Thomas,et al.  The Structure of Municipal Water Supply Costs: Application to a Panel of French Local Communities , 2001 .

[17]  Giovanni Fraquelli,et al.  Costs and Structure of Technology in the Italian Water Industry , 2000 .

[18]  M. Farsi,et al.  Cost Efficiency in Regional Bus Companies: An Application of Alternative Stochastic Frontier Models , 2006 .

[19]  F. Breyer,et al.  The specification of a hospital cost function. A comment on the recent literature. , 1987, Journal of health economics.

[20]  L. R. Christensen,et al.  Economies of Density versus Economies of Scale: Why Trunk and Local Service Airline Costs Differ , 1984 .

[21]  M. Farsi,et al.  An analysis of efficiency and productivity in Swiss hospitals , 2005 .

[22]  W. Greene,et al.  Efficiency Measurement in Network Industries: Application to the Swiss Railway Companies , 2004 .

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

[24]  M. Filippini.  Economies of scale and utilization in the Swiss electric power distribution industry , 1996 .

[25]  Subal C. Kumbhakar,et al.  Stochastic frontier analysis , 2000 .

[26]  Massimo Filippini,et al.  The use of a variable cost function in the regulation of the Italian water industry , 2001 .

[27]  Léopold Simar,et al.  Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach , 2005 .

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

[29]  Robert M. Clark,et al.  Economies of scale and scope in water supply , 1988 .

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

[31]  Thomas G. Cowing,et al.  Multiproduct Short-Run Hospital Cost Functions: Empirical Evidence and Policy Implications from Cross-Section Data , 1983 .

[32]  Gary D. Ferrier,et al.  Consistency Conditions for Regulatory Analysis of Financial Institutions : A Comparison of Frontier Efficiency Methods , 1997 .

[33]  Derivation of Long-run Factor Demands from Short-run Responses , 1989 .

[34]  Massimo Filippini,et al.  Efficiency and Regulation of the Slovenian Electricity Distribution Companies , 2004 .

[35]  Peter Schmidt,et al.  One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels , 2002 .

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

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

[38]  P. W. Wilson,et al.  Nonparametric analysis of returns to scale in the US hospital industry , 2004 .