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How do governance factors affect inefficiency? Stochastic frontier analysis of public utility firms in Japan

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Abstract

The main purpose of this study is to investigate how governance factors affect technical inefficiency in public utility firms. Inefficiency analysis in public utilities so far has focused mainly on industry-level treatments such as competition policy and regulations. However, since these industry-level treatments incur significant government costs and are difficult to monitor closely, management-level corporate governance, which has been widely discussed in private firms, has been attracting increased attention in recent years. In our analysis, we examine the effects of various shareholders as governance actors on technical inefficiency in public utilities. By using a panel data of 369 Japanese firms in seven public utilities from 1989 to 2015, we estimate the stochastic frontier production function. The main findings are as follows: (1) some governance factors clearly reduce inefficiency; as ownership by domestic companies and individual shareholders increases, the technical inefficiency of a public utility firm decreases. (2) However, we cannot determine the effects of foreign shareholders and financial institutions. These actors can have different effects depending on the situation. (3) Industry-level factors such as regulation and competition are shown to increase inefficiency. This indicates that conventional industry-level involvement is no longer effective in public utilities and that in the efficiency analysis more attention must be paid to the managerial improvement of public utilities.

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Notes

  1. Because our data for the cost function did not have sufficient credibility when estimated, we specify the production function here rather than the cost function. For example, some of the estimated coefficients were not reasonable in light of economic theory, and the magnitudes of the key variables were not stable. This may have occurred because, as our sample includes multiple public utility industries for multiple years, we estimated for the key variables certain data that were unavailable directly from published information.

  2. The alternative specification can be the conditional mean model proposed by Kumbhakar et al. (1991). However, since our model here uses the scale function (exponential function) for inefficiency and thus has a scaling property, as Wang and Schmidt (2002) and Alvarez et al. (2006) state, we decided to use the model of Eqs. (3) and (4) rather than conditional mean model.

  3. We tested whether there is heteroskedasticity in our data by Log-Likelihood Ratio test referring to Wiggins and Poi (2001). The LR value is 226.73 and P value is 0.00, and thus we decided to use this heteroskedastic model.

  4. We also estimated the production function assuming a case with three inputs (capital, labor, and materials), but this case lacked sufficient credibility with regard to the stability and the reasonability of the results due to the lack of data availability.

  5. In order to control for differences among industries, industry dummy variables have frequently been used in previous studies. However, we did not include industry dummies for two reasons. First, since our sample consists of many public utility industries such as electricity supply, gas supply, transportation (i.e. air, railway, bus, and truck), and telecommunications, the model has too many industry dummies if included, which preclude the convergence of ML estimation. Second, we do not include industry dummies because we believe that industry differences can be controlled by other variables such as ICMP HHI and IND PRF , which influence a substantial part of the industry’s cost structure. Moreover, since these variables are adjusted by divisional sales ratio by firm, the effects on a firm by each industry are more properly reflected than with simple industry dummies. For example, to control the industry effect for a firm belonging to both the railway and bus industries, the variables reflecting the level of business activity of the firm in each industry would be more suitable than a simple industry dummy.

  6. The estimation results are shown in “Appendix”. The estimation result shows that the effects of variables can be different depending on the industry. In road transportation, the effects of individual investors and large shareholders are significant among governance factors. These two reduce inefficiency, while the others are not significant. This might be because, as the ownership structure is not largely different within the same industry, we do not have sufficient variation to capture the effect of governance factors. In contrast, regulation is shown to increase inefficiency, while competition policy is not effective in this industry. As the coefficient of ICMP HHI is negative, a more monopolistic industry is more efficient. This might be because there was deregulation in the Japanese local bus sector in 2000, and the severe competition arising from it damaged firms’ structure. Thus, analysis focusing on a specific industry can be influenced by the situation surrounding the industry. In order to obtain the variations of governance variables and to avoid the influence of a specific situation in each industry, our estimation in Table 2 includes several public utility industries.

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Authors and Affiliations

  1. Graduate School of Business Administration, Kobe University, 2-1 Rokkodai, Nada-ku, Kobe, 657-8501, Japan

    Fumitoshi Mizutani & Eri Nakamura

  2. Vienna University of Economics and Business, Welthandelsplatz 1, Building D5, 1020, Vienna, Austria

    Eri Nakamura

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  1. Fumitoshi Mizutani
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Correspondence to Fumitoshi Mizutani.

Appendix

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Table 6 Estimation results in road transport industry
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Mizutani, F., Nakamura, E. How do governance factors affect inefficiency? Stochastic frontier analysis of public utility firms in Japan. Econ Polit Ind 44, 267–289 (2017). https://doi.org/10.1007/s40812-016-0066-1

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