2016-2
Factors Affecting Inefficiency Level:Stochastic Frontier
Analysis of Public Utility Firms in Japan
Fumitoshi Mizutani
Eri Nakamura

Factors Affecting Inefficiency Level:
Stochastic Frontier Analysis of Public Utility Firms in Japan

By

Fumitoshi Mizutani
Correspondent Author
Kobe University
Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501 Japantoshi@kobe-u.ac.jp

andEri Nakamura
Kobe University
Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501 Japanenakamura@person.kobe-u.ac.jp

January 8th, 2016

1

Factors Affecting Inefficiency Level:
Stochastic Frontier Analysis of Public Utility Firms in Japan[Abstract]: The main purpose of this study is to investigate what types of internal and external
factors affect technical inefficiency in public utility firms. We consider governance structure andbusiness diversification strategy as internal factors and governmental intervention and competition
as external factors.

By using 1106 observations comprised of Japanese public utility firms from1989 to 2002, we estimate the stochastic frontier production function. The main findings are as
follows: (i) The governance factor has an important effect on a firm’s inefficiency.

As ownershipby foreign shareholders and investment funds increase, the technical inefficiency of a public utility
firm decreases.

(ii) A business diversification strategy increases inefficiency, though the magnitudeof increase is relatively small.
inefficiency.

(iii) Governmental intervention does not have a clear effect on(iv) The monopoly level shows a quite clear effect on a firm’s inefficiency. Overall,our empirical results suggest that internal factors such as governance structure and ownership byforeign shareholders and investment funds, and external factors such as monopoly level are factors
especially important to the reduction of technical inefficiency in public utilities.

[Key Words]: Stochastic Frontier Analysis, Governance, Diversification Strategy, Regulation,
Competition, Public Utility Firms[JEL Classification Number]: M11, M21, L14, L51

1. Introduction
In studies of inefficiency in public utility firms, external factors such as governmental
intervention, including regulations and competition, are typically examined as important
determinants (e.g. Berg and Jeong (1991), Antel et al. (1995), Pantalone and Platt (1997), Schneider(2003), Fenn et al. (2008), Ter-Martirosyan and Kwoka (2010)). However, recent developments
among private firms indicate that internal corporate management could induce effective management.
For example, corporate governance and business diversification strategy have been shown to beimportant factors in firms’ cost efficiency (e.g. Berger and Hannan (1998), Fries and Taci (2005),
Jeng and Lai (2005), Zelenyuk and Zheka (2006) and Berger et al. (2009)).

Thus, not only external

but also internal factors can play an important role in improving efficiency. Nevertheless, theseinternal factors are rarely investigated in research on the efficiency of public utility firms.
By using a stochastic frontier production model, this study aims to identify what types ofinternal and external factors affect public utility firms’ technical inefficiency.

We consider

governance structure and business diversification strategy as internal factors, and governmental2intervention in the form of regulation and industry structuring through competition as external
factors.
This study makes several contributions to the literature. First, this is the only study so far
to consider together the four important factors mentioned above (i.e. governance structure, businessdiversification strategy, governmental intervention, and industry structure) and to investigate which
are effective in reducing the technical inefficiency of public utility firms.

Recently public utility

firms are under great pressure from governance forces such as foreign shareholders.

Byconsidering both internal and external factors in the analysis, we can identify which internal orexternal factors are important to the reduction of inefficiency among public utility firms.
Second, we apply to our technical inefficiency analysis of public utility firms the factors
considered important in private firms’ performance analysis. Since the reduction of inefficiency is
one process for improving a firm’s performance, we can distinguish the effects of these internalmanagement factors on inefficiency from their effects on performance.

For example, while studies

on corporate governance in private firms examine performance by looking at profitability andcorporate value as factors impacted by governance structure, we investigate to what degree thegovernance structure improves production efficiency, by looking closely at previous research (e.g.
Berger and Hannan (1998), Fries and Taci (2005), Jeng and Lai (2005), Zelenyuk and Zheka (2006)
and Berger et al. (2009)).

Similarly, we include a variable for the strategic behavior of a firm.

Many public utility firms operate in more than two industries.

In this study, the effect of adiversified strategy (i.e. a multi-segment strategy) is investigated.
Third, this study uses the quantity rather than the quality variable of governmental
intervention.

In previous studies (e.g. Berg and Jeong (1991), Antel et al. (1995), Ai and

Sappington (2002), Schneider (2003), Fabrizio et al. (2007), Ter-Martirosyan and Kwoka (2010)),
regulation, one form of governmental intervention, is commonly represented as a dummy variable,
whether regulation is applied or not, or whether it has been enacted or not. On the other hand, in
this study, we use “the degree of regulation” as a quantity variable. Similarly, we try to obtain
more general results for the effect of governmental intervention. Heretofore, analysis has focused
either only on specific types of regulation: environmental regulation (e.g. Nowell and Shogren(1994)), incentive regulation (e.g. Berg and Jeong (1991), Vogelsang (2002), Mizutani et al. (2009),
Ter-Martirosyan and Kwoka (2010)), and price regulation (e.g. Cabral and Riordan (1989), Bös andPeters (1995), Vogelsang (2002)); or on specific industries: the energy industry (e.g. Nelson andWohar (1983) and Majumdar and Marcus (2001)), rail (e.g. Mizutani et al. (2009)), and postal
service (e.g. Mizutani and Uranishi (2003)).

It cannot be assumed that, when the focus shifts togeneral regulation or to other industries, the results will be the same as in the specific cases listed
here.
By stochastic frontier production analysis using 1106 observations comprised of Japanese3

public utility firms from 1989 to 2002, we conclude that ownership by foreign shareholders, top
management, and investment funds, and the monopoly level of an industry, are the important factors
in reducing a firm’s technical inefficiency.
individual

shareholders,andbusiness

In contrast, ownership by the government anddiversification,

increasetechnical

inefficiency.

Governmental intervention does not have a significant effect on inefficiency.
This paper consists of five parts after the introduction.

In section 2, we summarize

previous studies, focusing especially on the relationship between efficiency and the four factors
mentioned above.

In section 3, the empirical model is specified.

Section 4 presents an

explanation of the data and the definitions of variables used in this study.

In section 5, theempirical results are summarized. Section 6 contains concluding remarks.

2. Previous Studies
In this section, we will summarize previous studies concerning the relationship between
the four important factors (i.e. governance structure, business diversification, governmental
intervention, and industry structure) and firms’ inefficiency.

2.1 Governance Structure
Previous studies in the area of private firms show that governance structure affects a firm’s
inefficiency significantly through the discipline of corporate management. For example, Berger
and Hannan (1998), Fries and Taci (2005), Jeng and Lai (2005), Zelenyuk and Zheka (2006) andBerger et al. (2009) evaluate to what degree governance structure improves cost efficiency.
As for the measures of governance structure, there are (i) insider ownership (e.g. Berger
and Hannan (1998)), (ii) foreign ownership (e.g. Fries and Taci (2005), Zelenyuk and Zheka (2006),
Berger et al. (2009)), (iii) large shareholders (e.g. Berger and Hannan (1998) and Berger et al.
(2009)) and (iv) governmental or public ownership (e.g. Berger et al. (2009)).
These measures of governance structure are commonly defined as either the ratio or
dummy variable.

For example, some studies (e.g. Berger and Hannan (1998), Fries and Taci (2005),

Zelenyuk and Zheka (2006)) take the ratio of these governance measures to total shares or total
assets.

On the other hand, some studies (e.g. Berger and Hannan (1998) and Weistain and Yafeh(1998)) treat the governance measure as a dummy variable.

Furthermore, in addition to thesemethods, some studies analyze the effect of the governance structure by comparing results obtained
from different observations according to type of governance structure (e.g. Berger et al. (2009) and
Jeng and Lai (2005)).
Finally, we will summarize the empirical results of our study of governance structure’s
effect on efficiency as follows.

First, foreign ownership improves efficiency, as Zelenyuk andZheka (2006) and Berger et al. (2009) show.

Second, the existence of large shareholders has

4differing results.

Berger and Hannan (1998) obtain the result that large shareholders decrease

efficiency. On the other hand, a more recent study by Berger et al. (2009) produced the opposite
result that large shareholders increase cost efficiency.

Furthermore, Berger et al. (2009) also obtaina result contradictory to what is commonly perceived as the effect of governmental ownership.
According to their results, governmental ownership increases efficiency.

As for insider ownership,

an increase in the manager’s ownership tends to decrease efficiency but the effect is not statisticallysignificant.

2.2 Diversification Strategy
There are few previous studies analyzing to what extent a business diversification strategy
affects the inefficiency of public utility firms. Business diversification could propel the costs of a
firm in two different directions.

If there exist economies of scope among diversified businesses,

the more diversified firms have smaller costs than otherwise. On the other hand, more diversified
firms might have bigger costs if they are promoting excess investment and cross-subsidies among
diversified divisions.
Some previous empirical studies investigate the relationship between businessdiversification and the cost efficiency of a firm (e.g. Ferrier et al. (1993), Rajan et al. (2000), Jengand Lai (2005)). These studies focus on industries such as banking (e.g. Ferrier et al. (1993), Rajan
et al. (2000)) and insurance (e.g. Jeng and Lai (2005)) and conclude that diversification causes costinefficiency through the promotion of excess investing and cross-subsidizing. On the other hand,in the Italian bus industry, there exist economies of scope (e.g. Ottoz and Di Giacomo (2012)).
Although his study is a productivity analysis of Italian manufacturing firms, Vannoni (2000)
concludes that the degree of diversification is not significantly related to productivity.

2.3 Governmental Intervention
Studies have been done to determine the degree to which governmental intervention
affects a firm’s inefficiency.

The most common approach is to use a cost of production function

such as the translog cost function (e.g. Berg and Jeong (1991), Antel et al. (1995), Pantalone andPlatt (1997), Schneider (2003), Ter-Martirosyan and Kwoka (2010)). Some studies analyze the cost
efficiency change due to regulatory reform, one of the forms of governmental intervention, by using
the stochastic cost frontier function (e.g. Kleit and Tecrell (2001) Mizutani et al. (2009)). Also,
theoretical studies have been done which construct the relationship between governmental
intervention and the costs of a firm (e.g. Cabral and Riordan (1989), Bös and Peters (1995),
Vogelsang (2002)).
Second, in most previous studies, the measure of governmental intervention is used as adummy variable, whether or not there is regulation (e.g. Berg and Jeong (1991), Antel et al. (1995),

5

Ai and Sappington (2002), Schneider (2003), Fabrizio et al. (2007), Ter-Martirosyan and Kwoka(2010)).

Other measures of governmental intervention vary by individual study, with, for example,

the revenue ratio of a hospital under regulation defined as a proxy variable for regulation in Antel etal. (1995).

There are almost no studies in which the degree of governmental intervention ismeasured as a directly obtained continuous variable.
Third, most previous studies focus on some specific governmental interventions:environmental regulation (e.g. Nowell and Shogren (1994)), incentive regulation (e.g. Berg andJeong (1991), Vogelsang (2002), Mizutani et al. (2009), Ter-Martirosyan and Kwoka (2010)), andprice regulation (e.g. Cabral and Riordan (1989), Bös and Peters (1995), Vogelsang (2002)). There
are few studies on how governmental intervention itself in general affects the efficiency of
individual firms.
Fourth, previous studies have produced conflicting results, with many studies supporting
the idea that governmental intervention reduces inefficiency (e.g. Cabral and Riordan (1989), Kleit
and Tecrell (2001), Ai and Sappington (2002), Ter-Martirosyan and Kwoka (2010), Nakamura(2010) and Buranabunyut and Peoples (2012), and other studies showing that governmental
intervention increases productivity (e.g. Dufour et al. (1998), Berman and Bui (2001), Majumdar
and Marcus (2001), Alpay et al. (2002) and Knittel (2002)). On the other hand, some studies showthat governmental interventions increase the costs of firms (e.g. Gollop and Roberts (1983), Berg etal. (2005) and Fabrizio et al. (2007)), while others suggest that governmental intervention decreases
firms’ productivity (e.g. Christainsen and Haveman (1981), Gollop and Roberts (1983), Gray (1987),
Majumdar and Marcus (2001) and Nicoletti et al. (2003)).
In addition to these results, some studies show that governmental intervention does notaffect inefficiency.

For example, studies such as Antel et al. (1995), Berg and Jeong (1991) and

Bös and Peters (1995) conclude that the cost effect of regulation is not significant. Furthermore,
there are two studies, Pantalone and Platt (1997) and Meyer and Leland (1980), which have different
results.

Pantalone and Platt (1997) conclude that effect on costs by regulation varies according to

the difference in ability to respond to environmental change. Although Gutierrez’ study (2003) is nota cost but a productivity study, Gutierrez concludes that regulatory governance has a positive effecton sector performance and efficiency.

2.4 Competition
First, competition among firms certainly affects firms’ inefficiency.

Most previous

studies take cost efficiency as dependent variables: for example, Berger and Hannan (1998), Sari
(2003) and Fenn et al. (2008).

As a definition of competition, the Herfindahl-Hirschman index (e.g.

Berger and Hannan (1998), Sari (2003)) and a concentration ratio of the top 5 firms (e.g. Fenn et al.
(2008)) are often used.

Most studies conclude that competition can improve cost efficiency.

6

According to Fenn et al. (2008), competitive pressures impose the threat of bankruptcy on firmmanagers and thus work as an incentive to cut inefficiency.

Moreover, firm owners can judge theperformance of their company by comparing it to rival firms when the industry is competitive, which
results in appropriate pressure on the firm.

On the other hand, Nakamura (2010) shows thatcompetition sometimes worsens internal efficiency, since competitive pressures can drive firmmanagers to reduce necessary investment and costs.

Sari (2003) integrates these conflicting resultsby pointing out that the relationship between cost inefficiency and competition is U-shaped,
indicating that while a certain degree of competition improves cost efficiency, too much competition
creates the opposite effect.

3. Empirical Model
As we mentioned before, the main purpose of this study is to investigate what types offactors most affect a firm’s technical inefficiency.

For the empirical analysis, we use the stochastic

frontier production model

1, which is frequently used because it can identify the level of inefficiencyof each individual firm.

In order to examine the effects of various factors on inefficiency, we

consider four types of factors: governance structure, diversification structure, government
intervention, and competition, the former two types being internal factors and the latter two external
factors relative to a firm. Next, we will explain the stochastic frontier production model and thendiscuss the determinants of inefficiency of public utility firms to construct the model.
Figure 1 shows the stochastic frontier production model and technical inefficiency.

Inthis figure, technical inefficiency is shown as υ . The set of the most efficient levels that firms canachieve is shown as the production frontier. When a firm has no inefficiency, the firm’s observed
output level is located on the production frontier line.

In this figure, firm A is considered theefficient firm. However, as firm B is located below the production frontier, the firm is considered
as inefficient, as situation A shows.

************
Figure 1************In this study, we assume a two-input production function, with Q as observed productionlevel, K as capital and L as labor of a firm. The public utility firm’s production function is shown
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 coefficientswere 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 publishedinformation.

7

as equation (1). The function f(K, L) is the production frontier, and thus equation (1) shows that theobserved level of production is equal to the production frontier.

Q=f(K, L)(1)

On the other hand, when a firm has inefficiency (υ), the observed level of output is lessthan the level of the production frontier by υ. Therefore, the firm’s production function is shown asequation (2).
Q=f(K, L) - υ(2)

The inefficiency level, υ, is affected by four factors mentioned earlier.

The inefficiency

is assumed as Equation (3). Equation (3) shows that inefficiency υ follows the truncated normal
distribution Νt and has the structure of A’X as the mean and σ2 as the variance.

X is the vector offactors influencing inefficiency and A is the parameter vector of X. These factors, X, are four.

Inthis study, equations (2) and (3) are estimated simultaneously with the maximum likelihood method.

υ∼Νt(A’X,σ2)(3)

Next, we explain the factors influencing the technical inefficiency hereafter to specify X.
As many public utility firms are privately owned, governance by significant actors is important.
Proper governance structure, including private ownership and monitoring, might reduce theinefficiency of a firm.

Furthermore, many firms have diversified their business.

If there exist

economies in business diversification, a more diversified firm can attain a higher efficiency level
than others.

On the other hand, a diversification strategy might generate extra costs throughunnecessary investment and cross-subsidies.

As public utility firms are in general regulated andlimited in terms of competition, too much intervention by the government in the form of regulation
might lead to the over-costing of a firm. Thus, we included these four important factors (i.e.
governance structure, diversification strategy, governmental intervention, and competition) as thedeterminants of production inefficiency.
It is worth noting that governance structure and diversification strategy are factors overwhich a firm can exercise a degree of control.
environment.

Each individual firm can change its internal

If a firm can gain cost advantages from business diversification, then the firm has

incentive to diversify.

At the same time, it can be easily seen that governance structure affects afirm’s cost structure, in that a firm with strict management discipline adopting proper governancestructure would generate less cost than a firm with loose management. On the other hand, variables

8such as governmental intervention and competition reflect the situation of the external environment
in which each firm is involved.

These are factors the firm cannot change, as set by the governmentor as the industry was created by the market.
In addition to these four factors, we included various factors as control variables.

For

macro-level differences among industries, an industry’s characteristics variables are included ascontrol variables (IND).

Some industries might be declining while others are growing.

For theprogress in technology that can affect the production frontier, technology trend (T) is included inaddition to two input factors, capital (K) and labor (L). Thus, considering the above discussion, we
specified our basic model as the following system of equations.
Q=f(K, L, T)- υυ∼Νt(A’X,σ2)υ =g(GS, STR, GOV, ICMP, IND)
where

(4)

Q: observed level of output,
K: capital input,
L: labor input
T: technology,

υ: technical inefficiency,
GS: governance structure,
STR: diversification strategy,
GOV: governmental intervention,
ICMP: competition,
IND: characteristics of industry as a control variable.

Based on the above basic model, we specify the variables included in four factors. As forthe governance structure, we consider the following variables: stock ratio held by the top 10
shareholders (GSTOP), stock ratio held by foreign shareholders (GSFRN), stock ratio held by top
management (GSMANAG), stock ratio held by government and public organizations (GSPUB), stock
ratio held by investment funds (GSFUND), and stock ratio held by individuals (GSINDIV).

As for the

strategy variable, we define the number of industries in which each firm is involved (STRDIV).

As

for governmental intervention, we take the degree of regulation (GOVREG). This variable is aquantity variable showing the magnitude of regulation.

As for the competition factor, we consider

the Herfindahl-Hirschman index (ICMPHHI). This is the inverse variable of competition, since thisindex shows the extent of monopoly in an industry.

Thus, when ICMPHHI is higher, this means thatthe industry is highly monopolistic. Finally, as for control variables for industries in which eachfirm is involved, we consider the industry’s profitability (INDPRF). The empirical function in this9

study is the Cobb-Douglas production function assuming two inputs and one output2.
As a result, the empirical model to be estimated is expressed as follows.
lnQ = α0 + αK lnK + αL lnL + τT T - υ + ε(5)

υ = β0 +ΣmγGSm GSm + γDIV STRDIV + γREG GOVREG +γHHI ICMPHHI + γPRF INDPRF

(6)

where

Q: revenues,
K: capital,
L: labor,
T: time trend,

υ: inefficiency term,ε: ,GSm: m = TOP (stock ratio held by top 10 shareholders), FRN (stock ratio held
by foreign shareholders), MANAG (stock ratio held by top management),
PUB (stock ratio held by government and public organizations), FUND
(stock ratio held by investment funds), INDIV (stock ratio held byindividuals),
STRDIV: number of segments,
GOVREG: degree of regulation,
ICMPHHI : Herfindahl-Hirschman index,
INDPRF: industry’s profitability.
The estimation method is the maximum likelihood (ML) method for the system of
equations (5) simultaneously. We also impose restrictions in equation (5) as αK +αL =1.
4. Data
4.1 Sample
As mentioned earlier, we collected observations from public utility industries in Japan.
At first, we selected a total of 150 public utility firms in Japan for the 14 years from 1989 to 2002.
The public utility industries in this study are electricity supply, gas supply, transportation (i.e. air,2

In an analysis of a stochastic frontier production model, the Cobb-Douglas function is frequently used
for feasible estimation instead of such complicated functions as the translog function (e.g. Nakamura
2010). This is because a function with many variables tends to over-absorb the inefficiency term anderror term, precluding convergence by the maximum likelihood method. We also estimated the
production function assuming a case with three inputs (capital, labor, and materials), but this case also
lacked sufficient credibility with regard to the stability and the reasonability of the results due to the lackof data availability. Thus, we decided to use the case of the Cobb-Douglas production function,
assuming two inputs.

10railway, bus, and truck), telecommunications, and broadcasting. Since our data is not a balanced
panel data, the total sample size is reduced to 1106 as pooling data. This reduction occurs becausesome variables such as diversification strategy are not available for all observations in the years from1989 to 2002.

4.2 Definition of Variables
Table 1 shows the definition of all variables used for the estimation in this study.

First,

we define output (Q) as total sales because there are many variations if we select physical output
measures. Therefore, we choose output measure as monetary values.

We use two kinds of input

factors: capital (K) defined as property, plant, and equipment, and labor (L) defined as number of
employees.

Time trend (T) is a proxy variable for technology progress.

In this study, 1989 isdefined as the starting year.

************
Table 1************

As for the governance structure, we consider six kinds of measures: the stock ratio held by
the top 10 shareholders (GSTOP), the stock ratio held by foreign shareholders (GSFRN), the stock ratio
held by top management (GSMANAG), the stock ratio held by government and public organizations(GSPUB), the stock ratio held by investment funds (GSFUND), and the stock ratio held by individuals(GSINDIV).
As for the diversification strategy variable (STRDIV), the number of segments is defined as
the number of industries in which each firm is involved. This variable expresses the extent ofdiversification of the firm.
As for governmental intervention (GOVREG), we choose “degree of regulation.” Thedegree of regulation shows to what degree each firm is subjected to regulation, or its “regulation
weight,” as originally defined by the Management and Coordination Agency (Somucho).

The

original data source for regulation weight is the JIP database for 2006, issued by the ResearchInstitute of Economy, Trade and Industry (RIETI). This measure is obtained by counting the numberof existing laws and regulations in each industry, by which process we can determine quantitatively
the degree of industries’ regulation.

However, this measure does not include information regarding

types of regulations (i.e. environment, safety, and price regulation). Finally, it is worth noting thatwhile this measure is based on industry summaries, many firms provide various services indiversified industries. The degree of regulation referred to in this study is calculated by weighted
revenues of the industries in which each firm is involved.

11

As for competition factors, we define ICMPHHI as the Herfindahl-Hirschman index. Thismeasure is obtained on a revenues basis and again is obtained by using weights of each firm’s
individual industry’s revenues.
As for characteristics of industry as a control variable 3 , we define the industry’s
profitability (INDPRF) as the weighted average profitability of the industries to which each firm
belongs.

For example, if a firm is involved in four industries, this variable is obtained by theweighted average profitability of the four industries.

The weight is derived from the firm’s

revenues from each industry.

5. Empirical Results
The estimation results of the system of equations shown as (5) are summarized in Table 2.
We show six cases in Table 2: Case 1 as full variables included; Case 2 as competition variable
excluded; Case 3 as governmental intervention excluded; Case 4 as diversification strategy andgovernmental intervention excluded, Case 5 as governmental intervention and industry’s profitability
excluded, and Case 6 as competition and industry’s profitability excluded.
The results satisfy all of the required properties in the production function.

First, thecondition that the output is zero when all the inputs are zero, that is, f(0, 0) = 0, is satisfied since theconstant α0 is not significant in all cases.

Second, homogeneity condition in input factors is

satisfied, because we imposed restrictions on the model. Third, as for monotonicity conditions, it isnecessary that the production function be a non-monotone decreasing function in input factors.
Whether or not the monotonicity conditions are satisfied was evaluated by checking that the partial
derivative of the production function with respect to input factors is not negative (i.e. ∂lnQ/∂lnK > 0,
∂lnQ/∂lnL > 0). The estimated coefficients of K and L are all positive with statistical significance,
which means that monotonicity conditions are satisfied. Fourth, the concavity condition is alsosatisfied since the Cobb-Douglas function by nature incorporates concavity.
The results also have sufficient robustness, since key variables such as competition factor
3

In order to control the difference among industries, industry dummy variables have frequently beenused 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), telecommunications, and broadcasting, the model has too many industry
dummies if included, which preclude the convergence of ML estimation. Similarly, if we estimate themodel by industry instead of estimating with a whole sample including industry dummies, other trouble
arises because ML estimation requires a large sample for convergence. Since our sample consists of1106 firms in total, estimation by industry cannot be feasible in some industries due to small sample size.
Second, we do not include industry dummies because we believe that industry differences can becontrolled by other variables such as ICMPHHI and INDPRF, which influence a substantial part of theindustry’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 railway and bus industries, thevariables reflecting the level of business activity of the firm in each industry would be more suitable thana simple industry dummy.

12

show the correct sign, and the magnitudes of these variables are stable among cases. Moreover, weestimated cases with different combinations of explanatory variables in addition to those listed inTable 2.

For example, we excluded the variable of corporate governance one by one for each case,then two variables, three variables, and so on. We found that the sign and the magnitude in theseresults were also stable.

Thus, the results shown in Table 2 are robust and reasonable enough toserve as the basis for our discussion.

************
Table 2************First, governance factors are clearly important in their impact on technical inefficiency.
Among six governance variables, five variables are statistically significant in all cases and theirsigns and magnitudes are stable.

As the ratio of foreign shareholders becomes larger, the technicalinefficiency of a public utility firm becomes smaller.
(2006) and Berger et al. (2009).

This is consistent with Zelenyuk and Zheka

Foreign shareholders tend to monitor a firm’s management morestrictly than domestic shareholders. Similarly, as ownership by top management becomes larger,
production inefficiency becomes smaller, which means that holding shares of the firms works as anincentive for top management to improve efficiency.

This is consistent with previous studies

arguing that stock options can be effective as an incentive for managers (e.g. Nakamura 2010).
However, ownership by government and public organizations increases production inefficiency.
According to Berger et al. (2005), since the goals of the government and the firm are different,
agency problems occur and thus inefficiency increases. Ownership by investment funds increases
production efficiency very strongly, which is also consistent with our expectation that funds havemore incentives to monitor firms since they are subject to pressures to maximize the investment
return for their customers.

In contrast, individual shareholders tend to increase inefficiency,

probably because their information on firms’ management might be limited compared withorganizational shareholders.

The extent of the concentration of ownership, expressed as GSTOP,

tends to improve production inefficiency, but the coefficients in Cases 2, 5, and 6 are not significant.
Thus, the effect is minor compared with the other governance variables.
Second, the fact that all empirical diversification strategy (STRDIV) shows the positive signwith a statistical significance of 1% means that as a company diversifies more from its core industry
to other industries, the technical inefficiency of all the firm’s business increases.

This result is

consistent with previous studies, which were analyses of the financial industry, such as Ferrier et al.
(1993), Rajan et al. (2000) and Jeng and Lai (2005). However, the magnitude of increase intechnical inefficiency for diversification is relatively small.

13

Third, governmental intervention (GOVREG) does not affect technical inefficiency.
Although it shows the positive sign statistically in Case 1, which means that governmental
intervention does increase technical inefficiency, the coefficient in the other cases is not statisticallysignificant.

These results suggest that the effect of governmental intervention is not stable butchangeable according to the situation of the other factors. Thus, previous studies such as Antel et al.
(1995), Berg and Jeong (1991) and Bös and Peters (1995) might reflect only one aspect ofgovernmental intervention in a specific situation.
Fourth, compared with governmental intervention, the competition factor is quite clear.
As the empirical results show, the monopoly level (ICMPHHI) has the effect of reducing theinefficiency of a firm. The market becomes more competitive, which means that as ICMPHHI
decreases, inefficiency increases.

This is probably because public utilities are network industries

with scale economy, and thus large and monopolistic firms tend to be efficient.
Last, as for characteristics of industry as a control variable, as industry’s profitability(INDPRF) increases, the inefficiency of a firm decreases.
In summary, our empirical results suggest that internal as well as external factors areimportant for cost reduction or efficiency. Both governance factors and the diversification factor
are statistically significant. On the other hand, as governmental intervention does not have a clear
effect, among external factors, traditionally considered the main determinants of a firm’s efficiency,
there are some factors whose effects on a firm’s production structure are minor.

6. Conclusion
The main purpose of this study is to investigate how four factors—governance structure,business diversification strategy, governmental intervention, and competition—affect the technicalinefficiency of public utility firms.

Among the four factors, governance structure and businessdiversification strategy are considered internal factors amenable to change at the will of a firm
seeking to achieve managerial efficiency.

On the other hand, governmental intervention andcompetition are considered external factors that are beyond a firm’s control.

A firm must accept

these factors, while striving to attain efficiency under the given conditions. Our main researchquestion is this: among these four factors, which is the most influential?
From the empirical analysis, we found the following results.
(i) The governance factor has an important impact on firms’ technical inefficiency. Empiricalresults show that as ownership by foreign shareholders, top management, and investment funds
becomes larger, a firm’s technical inefficiency decreases. The effect of investment funds islargest, followed by that of foreign shareholders.

In contrast, ownership by the government

and public organizations and individuals increases inefficiency significantly.

The

concentration rate of shareholding structure tends to decrease inefficiency, though the effect is

14

relatively minor.
(ii) As a company diversifies more from its core industry into other industries, the inefficiency of alla firm’s business increases.

Our results are consistent with previous studies such as Ferrier etal. (1993), Rajan et al. (2000) and Jeng and Lai (2005).

However, the magnitude of increasein efficiency for diversification is relatively small.
(iii) Governmental intervention does not have a clear effect on inefficiency.

The effect depends onother variables included in the model, which suggests that the effects of governmental
intervention suggested in previous studies such as Antel et al. (1995), Berg and Jeong (1991)
and Bös and Peters (1995) might reflect one aspect of the effects in a specific situation.
(iv) Compared with governmental intervention, the competition factor shows a quite clear effect intechnical inefficiency.

As the industry tends to be monopolistic, the production efficiency

increases, probably because of the scale economy of public utility industries.

This result is

consistent with theory and supports previous literature.
(v) Another important finding is that as an industry’s profitability increases, the inefficiency of a firm
decreases.
Overall, we can conclude that certain governance factors, that is, ownership by foreign
shareholders and investment funds, and the monopoly level of an industry are the important factors
in reducing a firm’s technical inefficiency, and that, contrary to traditional expectations,
governmental intervention as an external factor does not have a significant effect on inefficiency.

References
Ai, C. and D.E.M. Sappington (2002) “The Impact of State Incentive Regulation on the U.S.
Telecommunications Industry,” Journal of Regulatory Economics, Vol.22, No.2, pp.133-160.
Alpay, E., S. Buccola, J. Kerkvliet (2002) “Productivity Growth and Environmental Regulation in
Mexican and U.S. Food Manufacturing,” American Journal of Agricultural Economics, Vol.84,
No.4, pp.887-901.
Antel, J.J., R.L. Ohsfeldt and E.R. Becker (1995) “State Regulation and Hospital Costs,” Review of
Economics and Statistics, Vol.77, No.3, pp.416-422.
Benmelech, E. and T.J. Moskowitz (2010) “The Political Economy of Financial Regulation:
Evidence from U.S. State Usury Laws in the 19th Century,” Journal of Finance, Vol.65, No.3,
pp.1029-1073.
Berg, S.V. and J. Jeong (1991) “An Evaluation of Incentive Regulation for Electric Utilities,”
Journal of Regulatory Economics, Vol.3, No.1, pp.45-55.
Berg, S., C. Lin and V. Tsaplin (2005) “Regulation of State-Owned and Privatized Utilities: Ukraine
Electricity Distribution Company Performance,” Journal of Regulatory Economics, Vol.28, No.3,
pp.259-287.

15

Berger, A.N. and T.H. Hannan (1998) “The Efficiency Cost of Market Power in the Banking
Industry: A Test of the ‘Quiet Life’ and Related Hypotheses,” Review of Economics and Statistics,
Vol.80, No.3, pp.454-465.
Berger, A.N., G.R.G. Clarke, R. Cull, L. Klapper, and G.F. Udell (2005) “Corporate Governance andBank Performance: A Joint Analysis of the Static, Selection, and Dynamic Effects of Domestic,
Foreign, and State Ownership,” Journal of Banking and Finance, Vol.29, Issues.8-9,
pp.2179-2221.
Berger, A.N., I Hasan and M. Zhou (2009) “Bank Ownership and Efficiency in China: What Will
Happen in the World’s Largest Nation?,” Journal of Banking and Finance, Vol.33, No.1,
pp.113-130.
Berman, E. and L.T.M. Bui (2001) “Environmental Regulation and Productivity: Evidence from Oil
Refineries,” Review of Economics and Statistics, Vol.83, No.3, pp.498-510.
Bös, D. and W. Peters (1995) “Double Inefficiency in Optimally Organized Firms,” Journal of
Public Economics, Vol.56, No.3, pp.355-375.
Buranabunyut, N. and J. Peoples (2012) “An Empirical Analysis of Incentive Regulation and the
Allocation of Inputs in the US Telecommunications Industry,” Journal of Regulatory Economics,
Vol.41, No.2, pp.181-200.
Cabral, L.M.B. and M.H. Riordan (1989) “Incentives for Cost Reduction Under Price Cap
Regulation,” Journal of Regulatory Economics, Vol.1, No.2, pp.93-102.
Christainsen, G.B. and R.H. Haveman (1981) “Public Regulations and the Slowdown in Productivity
Growth,” American Economic Review, Vol.71, No.2, pp.320-325.
Dnes, A.W., D.G. Kodwani, J.S. Seaton, and D. Wood (1998) “The Regulation of the United
Kingdom Electricity Industry: An Event Study of Price-Capping Measures,” Journal ofRegulatory Economics, Vol.13, No.3, pp.207-225.
Dnes, A. W., and J. S. Seaton (1999) “The Regulation of Electricity: Results from an Event Study,”
Applied Economics, Vol.31, No.5, pp.609-618.
Dufour, C., P. Lanoie, and M. Patry (1998) “Regulation and Productivity,” Journal of Productivity
Analysis, Vol.9, No.3, pp.233-247.
Fabrizio, K.R., N.L. Rose, and C.D. Wolfram (2007) “Do Markets Reduce Costs? Assessing the
Impact of Regulatory Restructuring on US Electric Generation Efficiency,” American Economic
Review, Vol.97, No.4, pp.1250-1277.
Fenn, P., D. Vencappa, S. Diacon, P. Klumpes and C. O’Brien (2008) “Market Structure and the
Efficiency of European Insurance Companies: A Stochastic Frontier Analysis,” Journal of
Banking and Finance, Vol.32, No.1, pp.86-100.
Ferrier, G. D., S. Grosskopf, K. J. Hayes and S. Yaisawarng (1993) “Economies of Diversification in
the Banking Industry,” Journal of Monetary Economics, Vol.31, No.2, pp.229-249.

16

Fries, S. and A. Taci (2005) “Cost Efficiency of Banks in Transition: Evidence from 289 Banks in 15
Post-Communist Countries,” Journal of Banking and Finance, Vol.29, No.1, pp.55-81.
Gollop, F.M. and M.J. Roberts (1983) “Environmental Regulations and Productivity Growth: The
Case of Fossil-fueled Electric Power Generation,” Journal of Political Economy, Vol.91, No.4,
pp.654-674.
Gray, W.B. (1987) “The Cost of Regulation: OSHA, EPA and the Productivity Slowdown,”
American Economic Review, Vol.77, No.5, pp.998-1006.
Gutierrez, L.H. (2003) “The Effect of Endogenous Regulation on Telecommunications Expansion
and Efficiency in Latin America,” Journal of Regulatory Economics, Vol.23, No.3, pp.257-286.
Jeng, V. and G. C. Lai (2005) “Ownership Structure, Agency Costs, Specialization, and Efficiency:
Analysis of Keiretsu and Independent Insures in the Japanese Nonlife Insurance Industry,”
Journal of Rsik and Insurance, Vol.72, No.1, pp.105-158.
Kleit, A.N. and D. Tecrell (2001) “Measuring Potential Efficiency Gains from Deregulation ofElectricity Generation: A Bayesian Approach,” Review of Economics and Statistics, Vol.83, No.3,
pp.523-530.
Knittel, C.R. (2002) “Alternative Regulatory Methods and Firm Efficiency: Stochastic Frontier
Evidence from the U.S. Electricity Industry,” Review of Economics and Statistics, Vol.84, No.3,
pp.530–540.
Kroszner, R.S. and P.E. Strahan (1999) “What Drives Deregulation? Economics and Politics of theRelaxation of Bank Branching Restrictions,” Quarterly Journal of Economics, Vol.114, No.4,
pp.1437-1467.
Majumdar, S.K. and A.A. Marcus (2001) “Rules versus Discretion: The Productivity Consequencesof Flexible Regulation,” Academy of Management Journal, Vol.44, No.1, pp.170-179.
Meyer, R.A. and H.E. Leland (1980) “The Effectiveness of Price Regulation,” Review of Economics
and Statistics, Vol.62, No.4, pp.555-566.
Mizutani, F., H. Kozumi and N. Matsushima (2009) “Does Yardstick Regulation Really Work?
Empirical Evidence from Japan’s Rail Industry,” Journal of Regulatory Economics, Vol.36, No.3,
pp.308-323.
Mizutani, F. and S. Uranishi (2003) “The Post Office vs. Parcel Delivery Companies: Competition
Effects on Costs and Productivity,” Journal of Regulatory Economics, Vol.23, No.3, pp.299-319.
Nakamura, E. (2010) “The Effect of Public Involvement on Firm Inefficiency: Evidence Using
Japanese Private Firms,” Review of Managerial Science, Vol.4, No.3, pp.217-258.
Nelson, R.A. and M.E. Wohar (1983) “Regulation, Scale Economies, and Productivity in
Steam-Electric Generation,” International Economic Review, Vol.24, No.1, pp.57-79.
Nicoletti, G., S. Scarpetta, and P.R. Lane (2003) “Regulation, Productivity and Growth: OECD
Evidence,” Economic Policy, Vol.18, No.36, pp.9-72.

17

Nowell, C. and J. Shogren (1994) “Challenging the Enforcement of Environmental Regulation,”
Journal of Regulatory Economics, Vol.6, No.3, pp.265-282.
Ottoz, E. and M. Di Giacomo (2012) “Diversification Strategies and Scope Economies: Evidence
from a Sample of Italian Regional Bus Transport Providers,” Applied Economics, Vol.44,
No.22-24, pp.2867-2880.
Pantalone, C.C. and M.B. Platt (1997) “Thrift Cost Inefficiencies: Did Deregulation Help?”
Quarterly Review of Economics and Finance, Vol.37, No.1, pp.39-57.
Peltzman, S. (1976) “Toward a More General Theory of Regulation,” Journal of Law andEconomics, Vol.19, No.2, pp.211-240.
Primeaux Jr., W. J., J. E. Filer, R. S. Herren, and D. R. Hollas (1984) “Determinants of Regulatory
Policies toward Competition in the Electric Utility Industry,” Public Choice, Vol.43, No.2,
pp.173-186.
Rajan, R., H. Servaes and L. Zingales (2000) “The Cost of Diversity: The Diversification Discountand Inefficient Investment,” Journal of Finance, Vol.55, No.1, pp.35-80.
Sari, N. (2003) “Efficiency Outcomes of Market Concentration and Managed Care,” International
Journal of Industrial Organization, Vol.21, No.10, pp.1571-1589.
Schneider, J.E. (2003) “Changes in the Effects of Mandatory Rate Regulation on Growth in Hospital
Operating Costs, 1980-1996,” Review of Industrial Organization, Vol.22, No.4, pp.297-312.
Smyth, R. and M. Söderberg (2010) “Public Interest versus Regulatory Capture in the Swedish
Electricity Market,” Journal of Regulatory Economics, Vol.38, No.3, pp.292-312.
Stigler, G.J. (1971) “The Theory of Economic Regulation,” Bell Journal of Economics andManagement Science, Vol.2, No.1, pp.3-21.
Ter-Martirosyan, A. and J. Kwoka (2010) “Incentive Regulation, Service Quality, and Standards in
U.S. Electricity Distribution,” Journal of Regulatory Economics, Vol.38, No.3, pp.258-273.
Vannoni, D. (2000) “Diversification, the Resource View and Productivity: Evidence from Italian
Manufacturing Firms,” Empirica, Vol.27, No.1, pp.47-63.
Vogelsang, I. (2002) “Incentive Regulation and Competition in Public Utility Markets: A 20-Year
Perspective,” Journal of Regulatory Economics, Vol.22, No.1, pp.5-27.
Weinstain, D. E. and Y. Yafeh (1998) “On the Costs of a Bank-Centered Financial System: Evidence
from the Changing Main Bank Relations in Japan,” Journal of Finance, Vol.53, No.2,
pp.635-672.
Zelenyuk, V. and V. Zheka (2006) “Corporate Governance and Firm’s Efficiency: the Case of aTransitional Country, Ukraine,” Journal of Productivity Analysis, Vol.25, No.1-2, pp.143-157.

18

Output
Production frontier
Inefficiency (υ )A
B

0Figure 1

Input
Stochastic Frontier Production Function and Technical Inefficiency

19

Table 1 Summary Statistics
Variable

Definition

Mean

Q

Sales (million yen)

290,830

K

Property, plant, and equipment (million yen)

LStandard

MinMax

628,6211,926

7,000,000

351,976

1,052,506

20

12,000,000

Number of employees (people)

9,511

23,90734

283,294

T

Time trend

9.653

3.810

2.000

15.000

GSTOP

Concentration ratio of 4 firms

0.427

0.159

0.000

0.928

GSFRN

Stock ratio held by foreign shareholders

0.039

0.049

0.000

0.269

GSMANAG

Stock ratio held by top management

0.026

0.055

0.000

0.418

GSPUB

Stock ratio held by government and public organizations

0.008

0.052

0.000

0.655

GSFUND

Stock ratio held by investment funds

0.008

0.011

0.000

0.100

GSINDIV

Stock ratio held by individuals

0.289

0.150

0.000

0.884

STRDIV

Number of industries in which a firm is involved

3.722

1.405

2.000

11.000

GOVREG

Regulation index

0.546

0.343

0.000

1.000

ICMPHHI Herfindahl-Hirschman index

0.388

0.250

0.004

1.000

0.880

0.382

0.244

2.536

INDPRF

Industry’s profitability(Note) The number of observations is 1106.

20

Deviation

Table 2 Regression Results: Coefficients and Standard Error
Case
Dependent variable
Capital(log(K))
Labor(log(L))Time trend(T)
Constant
Dependent variable

Case 1log(Q)
0.468***(0.015)0.532***(0.015)-0.006(0.004)2.949(21.774)

Case 2log(Q)
0.463***(0.015)0.537***(0.015)-0.005(0.004)2.918(13.094)

Case 3log(Q)
0.465***(0.015)0.535***(0.015)-0.008**(0.004)3.024(26.083)

Case 4log(Q)
0.475***(0.015)0.525***(0.015)-0.009**(0.004)3.060(31.543)

Case 5log(Q)
0.409***(0.014)
0.591***(0.014)0.006*(0.004)3.181(67.030)

Case 6log(Q)
0.408***(0.014)0.592***(0.014)0.007*(0.004)3.243(99.318)

υ

υ

υ

υ

υ

υ

Concentration ratio of 4 -0.153*-0.147-0.152*-0.159*-0.147-0.143(0.089)(0.090)(0.089)(0.090)(0.093)(0.093)
firms (GSTOP)
Stock ratio held by foreign -1.798*** -1.856*** -1.755*** -1.838*** -1.719*** -1.779***(0.285)(0.287)(0.285)(0.285)(0.295)(0.296)
shareholders (GSFRN)
Stock ratio held by top-0.867*** -0.842*** -0.893*** -0.931*** -1.418*** -1.379***(0.287)(0.290)(0.288)(0.289)(0.292)(0.293)management (GSMANAG)
Stock ratio held by
0.913*** 0.933*** 0.892*** 0.855*** 1.035*** 1.055***
government and public(0.244)(0.246)(0.245)(0.246)(0.253)(0.254)organizations (GSPUB)
Stock ratio held by-3.646*** -3.803*** -3.901*** -3.968*** -3.939*** -3.971***(1.186)(1.194)(1.184)(1.190)(1.227)(1.236)
investment funds (GSFUND)
Stock ratio held by
0.570*** 0.592*** 0.585*** 0.609*** 0.957*** 0.961***(0.109)(0.110)(0.109)(0.109)(0.105)(0.105)individuals (GSINDIV)
Number of industries in
0.032*** 0.030*** 0.031***0.038*** 0.037***
which a firm is involved(STRDIV)

Regulation index (GOVREG)
Herfindahl-Hirschmanindex (ICMPHHI)
Industry’s profitability(INDPRF)
Constant
Wald chi2
Log likelihood(0.009)(0.009)(0.009)0.115**(0.046)-0.246***(0.060)-0.308***(0.033)0.789(21.775)
1037.230-595.091

0.017(0.040)-0.170***(0.051)-0.292***(0.032)0.874(26.083)1027.790-598.181-0.293***(0.033)0.710(13.094)
1007.430-603.589(0.010)(0.010)---0.036(0.041)-0.163***(0.051)-0.302***(0.032)
1.038(31.543)
1095.930-603.637-0.162***(0.053)

-

-

-

0.906
(67.030)
887.470-637.401

0.931(99.318)875.130-641.640(Note): Numbers in parentheses are standard error.

[2016.1.8 1214]

21