Abstract
I examine inefficiencies in Japanese water utility companies. Efficiency in this context is defined as a firm’s capacity to maximize output given a fixed level of inputs. The findings suggest that the average operation rate, customer density and size variables are associated with lower levels of inefficiency (or higher levels of efficiency), while water purification (a conditioning variable capturing low initial water quality), subsidies and outsourcing are associated with higher levels of inefficiency. Since inefficiency exists, there is an opportunity to improve Japanese water utilities by working on emulating “best practice” firms whenever possible and by providing a regulatory framework that can set appropriate incentive schemes to do so.
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Notes
The focus of the Yane and Berg (forthcoming 2013) paper is on the robustness of efficiency score rankings across different specifications for SFA models. The present paper focuses on policy implications of inefficiency effects. The Yane and Berg paper does not explore specific policy implications for the Japanese water utility sector because it focuses on the methods used and robustness when different distributional assumptions are utilized in correcting for heteroscedasticity. Yane and Berg narrowly focus on error terms associated with doubly heteroscedastic SFA.
If all firms were able to operate at the frontier (i.e., be fully efficient), SFA would not be required.
These models are estimated using the maximum likelihood method. The “maximum likelihood estimate of an unknown parameter is defined to be the value of the parameter that maximizes the probability (or likelihood) of randomly drawing a particular sample of observations” (Coelli et al. 2005). For a well-behaved linear function, maximization usually involves taking first derivatives with respect to unknown parameters and setting them equal to zero. However, because these specific problems cannot be solved analytically (given the existing non-linearities and the large number of parameters) iterative optimization procedures are used. Essentially, a computer program selects starting values for these unknown parameters and updates them until the values that maximize the log likelihood function are obtained Coelli et al. (2005). Unfortunately, sometimes iterative optimization procedures do not converge to a maximum at all. In other words, the computer program is unable to find the values that maximize the log likelihood function, which is what happened with the translog specification for this paper using the selected distributional assumptions.
The \(\mathrm{{z}}_\mathrm{it}\)’s are referred to as the “inefficiency effects model variables” throughout this paper.
This paper uses the same output and inputs as Yane and Berg (forthcoming, 2013). For input observations that have original values of zero, I adopt the standard practice of calculating log values by adding one to these original values.
Estimated staff from outsourcing is calculated following Yane and Berg (forthcoming, 2013). Virtual staff is calculated by dividing outsourcing expenditures by payment per employee in each prefecture.
Total intake water capacity \(=\) self-produced capacity \(+\) purchased water capacity.
Where gamma is defined as \(\gamma ={\sigma _{u}^{2}}/{\sigma ^{2}}\) and varies from 0 to 1. A gamma value of 0 would indicate that OLS provides consistent estimates.
Perard also discusses why introducing competition for the market doesn’t solve this problem. Readers are referred to Perard (2009) for more information on this topic and for a complete summary of the results of privatization in different countries.
Even though anecdotal evidence suggests that the outsourcing decision is due to an aging workforce there is a possibility that inefficient firms may be precisely the ones turning to outsourcing, so causation is reversed. Therefore, it is conceivable that this result could stem from endogeneity, where firms that experience relatively high costs turn to outsourcing. More research is needed to resolve the issue of causation.
A listing of the regions is available upon request.
Certain characteristics influencing efficiency are clearly outside of managerial control. Customer density, for instance, depends on where customers decide to live. It would be illogical to suggest improvements in efficiency based on increases in customer density to a manager, particularly in rural towns facing emigration towards the big cities.
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Acknowledgments
This research was conducted while I was a Research Associate at the Public Utility Research Center (PURC) of the University of Florida. I would like to thank Shinji Yane for assistance in interpreting the data and for providing information on the Japanese water systems. I am very thankful to Sanford Berg for his advice and suggestions. I also received valuable comments from David Denslow, Lawrence Kenny, Chunrong Ai, Katsufumi Fukuda, and two anonymous referees. All remaining errors are my own.
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Phillips, M.A. Inefficiency in Japanese water utility firms: a stochastic frontier approach. J Regul Econ 44, 197–214 (2013). https://doi.org/10.1007/s11149-013-9225-8
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DOI: https://doi.org/10.1007/s11149-013-9225-8