2013-25
Regulation, Competition, Diversification, Governance and Costs:
An Empirical Analysis of Public Utility and Manufacturing Firms in Japan
Fumitoshi Mizutani Eri Nakamura

Regulation, Competition, Diversification, Governance and Costs:
An Empirical Analysis of Public Utility and Manufacturing Firms in Japan

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

andEri Nakamura
Shinshu University
Faculty of Economics
3-1-1 Asahi, Matsumoto, Nagano 390-8621 Japanenakamura@shinshu-u.ac.jp
Correspondent Author: Fumitoshi Mizutani
toshi@kobe-u.ac.jp1Regulation, Competition, Diversification, Governance and Costs:
An Empirical Analysis of Public Utility and Manufacturing Firms in Japan[Abstract]: The main purpose of this study is to investigate how regulation, competition,
governance structure, and business diversification strategy affect the cost structure of firms.

Byusing 358 observations comprised of public utility firms and manufacturing firms from 1989 to 2002,
we estimate the translog cost function.
obtained:

From our empirical analysis, the following results are(i) The regulation factor does not affect the cost structure. (ii) Compared with theregulation factor, the competition factor shows a quite clear effect on a firm’s cost reduction.

(iii)

As a company diversifies further from its core industry into other industries, all of the firm’s
business costs increase, indicating an apparent lack of economies of scope.
factor has an important effect on a firm’s cost structure.

(iv) The governanceAs the ratio of foreign shareholders

increases and there is more dependence on one main bank, the costs of a firm decrease.

[Key Words]: Regulation, Competition, Governance, Diversification Strategy, Japanese Firms[JEL Classification]: L5, L6, L9, M2

1. Introduction
Publicutilityindustries

such

as

electricity,gas,telecommunications and so on have traditionally been heavily regulated.

transportation,

trucking,

The advent of extensive

deregulation in the late 1990s introduced competition and changed industries across the board, notonly in public utilities but in other areas as well, such as manufacturing.

In addition to the external

forces influencing firms’ behavior, such as regulation and competition, internal factors likegovernance structure and business diversification strategy also play an important role.

Most public

utility firms are privately owned, with a myriad of management options available to each.
the correct choices has become more difficult and important than ever.

MakingRecent developments incorporate governance studies indicate that effective governance could induce efficient managementof firms, with business diversification strategy another important factor necessary for better
management.

Thus, regulation and competition as outside factors and governance structure andbusiness diversification strategy as inside factors are all important to consider when determining
how managerial efficiency can be attained.
The main purpose of this study is to investigate how four factors—regulation, competition,
governance structure, and business diversification strategy—affect the cost structure of firms.
Firms taken in this study are public utility firms and manufacturing firms.
This study makes several main contributions to the literature.

2

First, this is the only study

so far to consider together the four important factors mentioned above (i.e. regulation by government,competition among firms, governance structure, and business diversification strategy) and toinvestigate which are most effective in reducing the costs of a firm.
Second, this study uses the quantity rather than the quality variable of regulation.

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 iscommonly 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 aquantity variable. The measure of the degree of regulation refers to how many laws and regulations
are applied to each specified industry.
then the measure becomes 1.0.

If a firm in a certain industry is almost completely regulated,

But for an industry with no regulation, then the measure is 0.0.

Third, we introduce the factor of governance structure in the cost function. Most studies
on governance are comparative analyses between governance and a firm’s performance.

However,in this study, 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)), we investigate towhat degree the governance structure improves costs.
Fourth, we include a variable for the strategic behavior of a firm.

Many firms operate inmore than two industries. In this study, the effect of a diversified strategy (i.e. a multi-segment
strategy) is investigated. We define the number of segments, that is, the number of industries, inwhich each individual firm is involved.

If a firm diversifies into several industries, the cost

structure of the firm is different from that of the non-diversified firm.
include this variable.

Therefore, it is necessary to

If there exist scope economies as Ottoz and Di Giacomo (2012) indicate, thisfactor has negative effects on costs.
Last, as for regulation issues, unlike in previous studies, we try to obtain more general
results in both public utility and manufacturing industries.

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.
This paper consists of five parts after the introduction.

In the second section, wesummarize previous studies, focusing especially on the relationship between costs and the four
factors (i.e. regulation, competition, governance structure, and business diversification strategy).

3Inthe third section, the empirical model is specified.
analysis.

We employ the translog cost function for the

The fourth section presents an explanation of the data used in this study.

There are atotal of 358 observations here, from the years between 1989 and 2002, obtained from public utility
and manufacturing firms.

The definitions of variables are also given in this section.

section, the empirical results are summarized.

In the fifth

An explanation of the regression results is followedby an evaluation of the effects of the four factors on cost.

The last section contains concluding

remarks.

2. Previous Studies
In this section, we will summarize previous studies concerning the relationship between
the four important factors (i.e. competition, regulation, governance structure and businessdiversification) and firms’ costs.

2.1 Regulation
Studies have been done to determine the degree to which regulation affects a firm’s costs.
The most common approach is to use a cost function such as the translog cost function (e.g. Bergand Jeong (1991), Antel et al. (1995), Pantalone and Platt (1997), Schneider (2003), Ter-Martirosyan
and Kwoka (2010)).

Some studies analyze the cost efficiency change due to regulatory reform byusing 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 regulation and thecosts of a firm (e.g. Cabral and Riordan (1989), Bös and Peters (1995), Vogelsang (2002)).
Second, in most previous studies, the measure of regulation is used as a dummy variable,
whether or not there is regulation (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)). Most suchstudies are limited to public utility industries such as electric power, gas, and transport industries.
Other measures on regulation vary by individual study, with, for example, the revenue ratio of a
hospital under regulation defined as a proxy variable for regulation in Antel et al. (1995). There arealmost no studies in which the degree of regulation is measured as a directly obtained continuous
variable.
Third, most previous studies focus on some specific 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. Cabraland Riordan (1989), Bös and Peters (1995), Vogelsang (2002)).

There are few studies on how

regulation itself in general affects the efficiency of individual firms.
Fourth, previous studies have produced conflicting results, with many studies supporting4

the idea that regulation reduces costs (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 regulation increases productivity (e.g. Dufour etal. (1998), Berman and Bui (2001), Majumdar and Marcus (2001), Alpay et al. (2002) and Knittel(2002)).

On the other hand, some studies show that regulations increase the costs of firms (e.g.

Gollop and Roberts (1983), Berg et al. (2005) and Fabrizio et al. (2007)), while others suggest that
regulation 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 regulation does not affect cost structure.
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.

Pantaloneand Platt (1997) conclude that effect on costs by regulation varies according to the difference inability to respond to environmental change. Although Gutierrez’ study (2003) is not a cost but a
productivity study, Gutierrez concludes that regulatory governance has a positive effect on sector
performance and efficiency.
Thus, empirical results have not produced a consistent conclusion.

In general,

regulations create extra constraints for each individual firm and could therefore increase a firm’s
costs.

However, under the private interest theory (capture hypothesis), it is also possible that

regulations reduce costs.

If regulations are created under the capture hypothesis, regulatory

programs could be advantageous for the regulated firms, resulting in lower costs than for
unregulated firms.

In fact, some studies support the private interest theory from a theoretical andempirical point of view: for example, Stigler (1971), Peltzman (1976), Primeaux Jr. et al. (1984),
Nowell and Shogren (1994), Antel et al. (1995), Dnes et al. (1998), Dnes and Seaton (1999),
Kroszner and Strahan (1999), Nakamura (2010) and Benmelech and Moskowitz (2010).

On theother hand, Smyth and Soderberg (2010) support the public interest theory.

2.2 Competition
First, competition among firms certainly affects firms’ costs.

Most previous studies takecost efficiency as dependent variables: for example, Berger and Hannan (1998), Sari (2003) andFenn 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.

According toFenn et al. (2008), competitive pressures impose the threat of bankruptcy on firm managers and thus
work as an incentive to cut inefficiency. Moreover, firm owners can judge the performance of theircompany by comparing it to rival firms when the industry is competitive, which results in5

appropriate pressure on the firm.

On the other hand, Nakamura (2010) shows that competition

sometimes worsens internal efficiency, since competitive pressures can drive firm managers to
reduce necessary investment and costs. Sari (2003) integrates these conflicting results by pointing
out that the relationship between cost inefficiency and competition is U-shaped, indicating that whilea certain degree of competition improves cost efficiency, too much competition creates the opposite
effect.

2.3 Governance Structure
Although governance structure affects a firm’s costs through the discipline of corporate
management, few studies have investigated the relationship between governance structure and thecosts of a firm.

There are, however, a few studies, such as Berger and Hannan (1998), Fries andTaci (2005), Jeng and Lai (2005), Zelenyuk and Zheka (2006) and Berger et al. (2009), which
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)).

In addition to thesemeasures, as characteristics of Japanese governance structure, (v) ‘keiretsu’ (e.g. Jeng and Lai(2005)) and (vi) main bank (e.g. Weinstain and Yafeh (1998)) are often used. ‘Keiretsu’ is anindustrial group of firms affiliated with each other for mutual help and monitoring throughshareholding, an arrangement typical of Japanese manufacturing and financial industries. Mainbank is a bank that has established a close relationship with firms by providing extensive financing
and consulting services through a system unique to Japanese governance.
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 cost efficiency as follows.

First, foreign ownership improves cost efficiency, as Zelenyuk

and Zheka (2006) and Berger et al. (2009) show. Second, the existence of large shareholders has
differing results.

Berger and Hannan (1998) analyze the banking industry and they obtain the resultthat large shareholders decrease cost efficiency. On the other hand, a more recent study by Berger6et al. (2009), an analysis of the banking industry in China, produced the opposite result that large
shareholders increase cost efficiency.

Furthermore, Berger et al. (2009) also obtain a result

contradictory to what is commonly perceived as the effect of governmental ownership.

Accordingto their results, governmental ownership increases cost efficiency. As for insider ownership, anincrease in the manager’s ownership tends to decrease cost efficiency but the effect is notstatistically significant.

As for the characteristics of Japanese governance structure, ‘keiretsu’

increases cost efficiency, according to Jeng and Lai (2005), who argue that ‘keiretsu’ fosters strong
relationships among firms inside the ‘keiretsu,’ and main bank. As a result, cost efficiency is
increased by the fact that firms inside the ‘keiretsu’ monitor each other, the search costs for
information are reduced, and firms can attain cheaper capital costs.

It is worth noting that one

result has shown that the main bank system has merit only for the main bank and that the main banksystem causes firms to increase capital costs.

2.4 Diversification Strategy
There are few previous studies analyzing to what extent a business diversification strategy
affects the costs of firms.
directions.

Business diversification could propel the costs of a firm in two different

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 havebigger 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.

3. Empirical Model
The main purpose of this study is to determine what factors affect public utility firms’ cost
structure, with a special focus on the following: regulations, governance structure, diversification
strategy, and competition. As public utility firms are in general regulated and limited in terms ofcompetition, too much regulation might lead to the over-costing of a firm.

Manufacturing firms arealso partly regulated but the market of the industry itself is characterized by competition among7

firms.

At the same time, as many public utility firms and manufacturing firms are privately owned,

proper governance structure might reduce the costs of a firm.

Furthermore, many firms havediversified their business. If there exist economies of scope in business diversification, a morediversified firm can attain lower costs than others.

On the other hand, a diversification strategy

might generate extra costs through unnecessary investment and cross-subsidies. We will analyze
how these four important factors (i.e. regulations, competition, governance structure anddiversification strategy) affect firms’ costs.
To investigate this theme, we estimate the cost function because it can reveal the current
situation relatively accurately.

With regard to the cost function, the following points are important.

As stated in the purpose of our analysis above, in addition to output (Q) and input factor prices (w),
we include the four key variables of regulation (RG), competition (CMP), governance structure (GS)and diversification strategy (STR).

First, variables such as regulation and competition reflect thesituation of industries in which each firm is involved. These are factors the firm cannot change, as
set by the government or as the industry was created by the market.

On the other hand, governancestructure and diversification strategy are factors over which a firm can exercise a degree of control.
Each individual firm can change its environment.

If a firm can gain cost advantages from businessdiversification, then the firm has incentive to diversify. At the same time, it can be easily seen thatgovernance structure affects a firm’s cost structure, in that a firm with strict management discipline
adopting proper governance structure would generate less cost than a firm with loose management.
In addition to these four factors, it is also necessary to control for differences among industries.
Some industries might be declining while others are growing.
characteristics variables as control variables (EX).

Therefore, we include an industry’s

As a result, with these variables, the costfunction (C) is expressed as output (Q), input factor price (w), technology (T) as follows.
∑

≡
,

;
,

where

,

,

,

,

,

,

,

,

,

,

(1)
(2)

C: cost,
Q: output,
wi: input factor price of input-i,
xi: input-i,
T: technology,
RG: regulation,
CMP: competition,
GS: governance structure,
STR: diversification strategy,
EX: characteristics of industry as a control variable.

8The empirical cost model in this study is the translog cost function, which has been widelyused in previous cost studies.

Costs for the estimation are total costs, and the cost function is

specified as three-input-factor-price model of labor (wL), material and service (wM) and capital (wK).
Because we use panel data, we include time trend (T) to control for technology progress.
As for regulation, we take the degree of regulation (RGREG).
variable showing the magnitude of regulation.

This variable is a quantity

As for the competition factor, we consider two

possible variables: the Herfindahl-Hirschman index (CMPHHI) and the concentration ratio of 4 firms(CMPCR4). As for the strategy variable, we define the number of industries in which each firm is
involved (STRDIV).

As for the governance structure1, we consider the following variables: the ratioof external auditors (GSXAUD), stock ratio held by foreign shareholders (GSFRN), stock ratio held by
the top 10 shareholders (GSTOP), and main bank variable (GSBANK).

Finally, as for control variables

for industries in which each firm is involved, we consider the industry’s bankruptcy ratio (EXBKT),
and the industry’s profitability (EXPRF).
As a result, the cost function is expressed as follows.
∑

∑ ∑
∑

∑

∑
∑
(3)

where

C: total costs,
Q: revenues,
wi, wj: i, j= L (labor price), M (material and service price), K (capital price),
T: time trend,
RGREG: degree of regulation,
CMPl: l = HHI (Herfindahl-Hirschman index), CR4 (concentration ratio of 4
firms),
GSm: m = XAUD (the ratio of external auditors), FRN (stock ratio held byforeign shareholders), TOP (stock ratio held by top 10 shareholders),
BANK (main bank index),
STRDIV: number of segments,
EXn: n = BKT (industry’s bankruptcy ratio), PRF (industry’s profitability),

1

There are two reasons why our model does not include a variable for ‘keiretsu.’ First, our sample
is comprised of large firms, which are mostly parent companies influencing the management ofmember firms. This means that the effect of keiretsu on our sample firms is minor. Second, mainbank variable can control the effect of keiretsu in a group simultaneously because the power ofkeiretsu is determined by financing from the main bank (Nakamura (2010)).
9DNPU: non-public utility dummy (i.e. non-public utility =1, otherwise =0).
We apply Shepard’s lemma for the cost function.

As a result, the share equations are

obtained as follows.
∑(4)

where Si : share of input i (i, j = K, L, M).
The estimation method is the Seemingly Unrelated Regression (SUR) method forequations (3) and (4).
∑

4

0∑

We also impose restrictions in equation (3) as follows: ∑0, ∑0, QK + QL + QM = 0,

0, ∑

1,
.

Data

4.1 Sample
As the main purpose of this study is to evaluate the effects of regulation, competition,
governance structure and diversification strategy, we collected observations from both public utility
industries and manufacturing industries. As other industries such as service industries (e.g. retail,finance, agriculture, and mining) are quite different in cost structure, we limited our sample tomanufacturing industries only.

At first, we selected a total of 300 firms, consisting of 150 firms in

the public utility industry and 150 from the manufacturing industry, in Japan for the 14 years from1989 to 2002.

The public utility industries in this study are electricity supply, gas supply, watersupply, transportation (i.e. air, railway, bus, and truck), telecommunications, broadcasting, and postal
services.

In order to avoid estimation bias by size difference, in the sample selection of

manufacturing industries, we selected firms similar in size to public utility industries.

Finally, thetotal sample size is reduced to 358, since some variables such as diversification strategy are notavailable for all observations.

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

First, total costs (C) in this study are defined as the sum of labor, material (including

energy) and capital costs. Capital costs include depreciation and interest payments.
As for output (Q), we define total sales as total output because there are many variations if
we select physical output measures. Therefore, we choose output measure as monetary values.
We use three kinds of input factor prices: (i) labor, (ii) material and service, and (iii)
capital.

First, labor price (wL) is obtained by dividing labor costs by the total number of employees.
10Second, material and service price (wM) is defined as domestic Corporate Goods Price Index (CGPI)
weighted by divisional sales ratio.

Last, capital price (wK) is defined as the sum of depreciation rate

(depreciation per fixed asset) and interest rate (interest payment per debt).
Time trend (T) is a proxy variable for technology progress.

In this study, 1989 is definedas the starting year.
As for regulation, we choose “degree of regulation.” The degree of regulation (RGREG)
shows to what degree each firm is subjected to regulation, or its “regulation weight,” as originallydefined by the Management and Coordination Agency (Somucho).

The original data source forregulation weight is the JIP database for 2006, issued by the Research Institute of Economy, Tradeand Industry (RIETI). This measure is obtained by counting the number of existing laws andregulations in each industry, by which process we can determine quantitatively the degree ofindustries’ regulation.

However, this measure does not include information regarding types ofregulations (i.e. environment, safety, and price regulation).

Finally, it is worth noting that while

this measure is based on industry summaries, many firms provide various services in diversified
industries.

The degree of regulation referred to in this study is calculated by weighted revenues of

the industries in which each firm is involved.
As for competition factors, we choose two kinds of measures, the Herfindahl-Hirschmanindex (CMPHHI) and the concentration ratio of 4 firms (CMPCR4).

These measures are obtained ona revenues basis and again are obtained by using weights of each firm’s individual industry’s
revenues.
As for the governance structure, we consider four kinds of measures: the stock ratio held
by foreign shareholders (GSFRN), the stock ratio held by the top 10 shareholders (GSTOP), and themain bank (GSBANK). First, the stock ratio held by foreign shareholders (GSFRN) is defined as thenumber of shares held by non-Japanese relative to the total number of shares. Second, the stock
ratio held by the top 10 shareholders (GSTOP) is defined as the number of shares held by the largest
ten shareholders to the total number of shares.

Third, as for the characteristics of Japanese

corporate governance structure, main bank index (GSBANK) is chosen.

Main bank index is thestandard deviation of the ratios of debt loan by each financial institution.

This variable expresses

the pressure on a firm’s management by a main bank. The main bank index is large, which meansthat a firm obtains its loans mainly through a certain single bank.
becomes larger, the effect of a main bank becomes larger.

Therefore, as this variable

On the other hand, the fact that the mainbank index is small means that a firm obtains a loan by diversifying financial sources among various
financial institutions.
As for the diversification strategy variable (STRDIV), the number of segments is defined as
the number of industries in which each firm is involved.
business, the regulation effect on costs will be different.

11

If a firm diversifies into a non-regulated

As for characteristics of industry as a control variable, there are two variables: industry’s
bankruptcy ratio (EXBKT) and industry’s profitability (EXPRF).

First, industry’s bankruptcy ratio(EXBKT) is obtained by dividing the number of bankrupt firms by existing firms.

Last, industry’s

profitability (EXPRF) is defined as the weighted average profitability of the industries to which eachfirm 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 used by the firm’s revenues
from each industry.

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

5. Empirical Results
We estimate the total cost function shown in equation-(3) with equation-(4). For ourestimation, we use the seemingly unrelated regression (SUR) method according to the total costfunction and input share equations.

The estimation results of the total cost function aresummarized in Table 2. We show four cases in Table 2: (i) Case 1, regulation only; (ii) Case 2 andCase 3, with regulation and competition; (iii) Case 4 and Case 5, with regulation, competition,
governance and diversification strategy; (iv) Case 6, Case 7 and Case 8, with regulation, competition,
governance, diversification strategy and external environments.
The goodness-of-fit in the regressions is acceptably high for these cases because pseudo R2 isvery high at over 0.96. As for the required properties in the cost function, first, symmetry andhomogeneity conditions in input factor prices are satisfied, because we imposed restrictions on thecost model. Second, as for monotonicity conditions, it is necessary that the cost function be a
non-monotone decreasing function in both output and input factor prices.

Whether or not themonotonicity conditions are satisfied was evaluated by checking that the partial derivative of thecost function with respect to output and input factor prices is not negative (i.e. lnC/lnQ > 0,
lnC/lnwj > 0).

Around the sample mean, these conditions are satisfied.

Determining whether or

not the Hessian matrix holds negative semi-definite can test for the concavity condition in input
factor prices.

In our test results, 12.6% to 20.7% of observations satisfy the concavity condition.

Among these cases, Case 4 is the best because the log of likelihood statistics and R2 are large,
the concavity condition is relatively high, and key variables such as competition factor show thecorrect sign.

If we consider the external environment, Case 6 seems to be second.

discuss our findings based mainly on these results.

12

Therefore, we**********
Table 2**********First, the regulation factor (RGREG) does not affect the cost structure.

Although it shows

the negative sign, which means that regulation does reduce the cost, the regulation factor is notstatistically significant.

These results are consistent with previous studies such as Antel et al.

(1995), Berg and Jeong (1991) and Bös and Peters (1995).
either the public or private interest theory.

Our empirical results do not support

We think that regulation itself does not affect costs.

Compared with the regulation factor, the competition factor is quite clear and consistent
with previous results.

As the empirical results show, competition (CMPHHI, CMPCR4) has the effectof reducing the costs of a firm.

The market becomes more competitive, which means that as

CMPHHI or CMPCR4 decreases, the cost becomes smaller.
Third, governance factors are also important in the cost structure.

Among three

governance variables, both the stock ratio held by foreign shareholders (GSFRN) and main bank
(GSBANK) are statistically significant.

As the foreign shareholders’ ratio becomes larger and moreconcentrated in a single main bank, the costs of a firm become smaller.

As for foreign shareholders’effect, our result is consistent with Zelenyuk and Zheka (2006) and Berger et al. (2009).

The resultthat firms under main bank influence have smaller costs needs to be discussed, since main bank has
both positive and negative effects on firm management. While Weinstain and Yafeh (1998) state thatthe main bank system increases the cost of capital, Jeng and Lai (2005) argue that the close
relationship with main bank results in better monitoring and reduced information costs. Our result is
consistent with Jeng and Lai (2005).
Fourth, 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 costs of all the firm’s business increase.

Our results are consistent withprevious studies, which are analyses of the financial industry, such as Ferrier et al. (1993), Rajan etal. (2000) and Jeng and Lai (2005).

This result shows that there are no economies of scope.

Fifth, the non-public utility dummy (DNPU) shows the correct sign (i.e. the negative sign)in case 4 but with a statistical significance of at most 10%.
statistically significant.

However, results in other cases are not

Presumably, as we include many factors in order to explain the costdifference, the non-public utility dummy does not affect the costs.
Last, as for characteristics of industry as a control variable, as industry’s bankruptcy ratio

13

(EXBKT) increases and industry’s profitability (EXPRF) decrease, the cost of a firm becomes large.

6. Conclusion
The main purpose of this study is to investigate how the four factors of regulation,competition, governance structure, and business diversification strategy affect the cost structure offirms.

Among the four factors, regulation and competition are considered as outside factors that are

beyond a firm’s control, factors it must accept while striving to attain efficiency under the given
conditions.

On the other hand, governance structure and business diversification strategy are

considered as internal factors amenable to change at the will of a firm seeking to achieve managerial
efficiency.

Our main research question is this: which, among these four factors, is the mostinfluential?

Furthermore, although the regulation factor in most previous studies is treated as adummy variable, the regulation factor in this study is treated as a continuous variable representing
the degree of regulation affecting a firm’s cost structure.

To analyze these questions, we estimatetranslog cost functions by using 358 observations obtained from public utility firms andmanufacturing firms from 1989 to 2001. In order to avoid estimation bias by size difference, in thesample selection of manufacturing industries, we selected firms similar in size to public utility
industries.
From this empirical analysis, we found the following results.
(i) The regulation factor does not affect the cost structure.

Our results are consistent with previous

studies such as Antel et al. (1995), Berg and Jeong (1991) and Bös and Peters (1995).
(ii) Compared with the regulation factor, the competition factor shows a quite clear effect inreducing a firm’s costs.

This result is consistent with theory and supports previous literature.

(iii) The governance factor has an important impact on firms’ cost structure.

Empirical results

show that as the ratio of foreign shareholders becomes larger and there is more concentration ona single bank, a firm’s costs decrease.

As for foreign shareholders’ effect, our result is

consistent with Zelenyuk and Zheka (2006) and Berger et al. (2009).
(iv) As a company diversifies more from its core industry into other industries, the costs of all afirm’s business increases. Our results are consistent with previous studies such as Ferrier et al.
(1993), Rajan et al. (2000) and Jeng and Lai (2005). There are apparently no economies ofscope.
(v) Another important finding is that as an industry’s bankruptcy ratio increases and its profitability
decreases, the costs of a firm increase.

14

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18

Table 1 Definition and Statistics of Variables

Mean

Standard
Deviation

Minimum

Maximum

408,031543,665

1,843

3,042,338

415,140

554,060

1,926

3,000,000

6,260

2,375

1,068

15,907-95.789926.4882

7.3416249.9917-

0.11310.03570.02580.2802-8.4916

3.78171.000015.0000-

0.45850.39200.00001.0000-0.46770.25050.01611.00000.71540.29360.01191.00003.43301.36982.000012.0000

0.05840.05910.00000.40240.38130.12300.08370.78760.09080.06450.01980.65660.20320.36220.00080.9828-0.87310.38840.25105.9841-0.50000.50070.00001.0000-0.16570.07630.03930.5384-0.72930.08680.41580.9067-0.10500.06620.01770.3374

Variable

Unit

TC(total cost)Q(output)wL(labor price)wM(material price)wK(capital price)T(time trend)
RGREG(degree of regulation)
CMPHHI(Herfindahl-Hirschman index)
CMPCR4(concentration ratio of 4 firms)
STRDIV (numbers of industries
which a firm involves)
GS FRN (stock ratio held
by foreign shareholders)
GS TOP (stock ratio heldby top 10 shareholders)
GS BANK(main bank dummy)
EX BKT(industry’s bankruptcy ratio)
EXPRF(industry’s profitability)
DNPU(non-public utility dummy)
SL(share of labor)
SM(share of material and service)SK(share of capital)

Millionyen
Millionyen
Thousandyen

19

Table 2 Estimation Results
Variableln Q

ln CMPHHI

Case 1
0.9670***(0.0100)
0.1974***(0.0050)
0.6386***(0.0076)
0.1640***(0.0057)-0.0038**(0.0015)0.0358***(0.0073)-0.0698***(0.0077)0.0340***(0.0068)
0.2088***(0.0119)-0.1390***(0.0092)
0.1051***(0.0108)-0.0228(0.0154)0.0162(0.0186)
0.0066(0.0164)
0.0224***(0.0029)-0.0007(0.0014)-ln CMPCR4

-

ln wL
ln wM
ln wK
(ln Q)2(ln wL)2
ln wL•ln wM
ln wL•ln wK(ln wM)2
ln wM•ln wK(ln wK)2
ln Q•ln wL
ln Q•ln wK
ln Q•ln wMT
ln RGREG

Case 20.9690***(0.0100)
0.1973***(0.0050)
0.6399***(0.0076)
0.1628***(0.0057)-0.0038**(0.0015)
0.0361***(0.0073)-0.0699***(0.0077)
0.0339***(0.0067)
0.2064***(0.0118)-0.1364***(0.0092)
0.1025***(0.0107)-0.0219(0.0153)0.0148(0.0186)
0.0071(0.0164)
0.0225***(0.0029)-0.0006(0.0014)0.0292*(0.0153)

Case 30.9653***(0.0099)
0.1975***(0.0050)
0.6390***(0.0076)
0.1635***(0.0057)-0.0038**(0.0015)
0.0363***(0.0073)-0.0703***(0.0077)0.0340***(0.0068)0.2079***(0.0118)-0.1376***(0.0092)
0.1036***(0.0108)-0.0257*(0.0153)
0.0134(0.0185)0.0123(0.0164)
0.0217***(0.0029)-0.0001(0.0014)0.0428***

Case 40.9661***(0.0123)0.1944***(0.0049)
0.6461***(0.0074)
0.1595***(0.0058)-0.0043**(0.0015)0.0359***(0.0073)-0.0635***(0.0076)0.0275***(0.0069)
0.1862***(0.0121)-0.1227***(0.0097)
0.0952***(0.0113)-0.0114(0.0157)-0.0012(0.0188)0.0125(0.0165)
0.0233***(0.0030)-0.0014(0.0014)0.0472***(0.0156)-

Case 50.9632***(0.0121)0.1947***(0.0049)0.6447***(0.0074)
0.1607***(0.0059)-0.0044***(0.0015)
0.0362***(0.0073)-0.0639***(0.0075)
0.0277***(0.0070)0.1886***(0.0121)-0.1248***(0.0098)
0.0971***(0.0114)-0.0155(0.0155)-0.0028(0.0187)
0.0183(0.0164)
0.0222***(0.0030)-0.0007(0.0014)0.0559***Case 6
0.9637***(0.0121)0.1930***(0.0049)0.6443***(0.0075)
0.1628***(0.0060)-0.0048***(0.0016)0.0354***(0.0073)-0.0607***(0.0075)
0.0253***(0.0071)
0.1906***(0.0121)-0.1299***(0.0099)
0.1047***(0.0116)-0.0191(0.0154)-0.0052(0.0189)0.0243(0.0167)
0.0281***(0.0034)-0.0007(0.0014)0.0426***(0.0153)-

20

Case 70.9611***(0.0120)0.1932***(0.0049)
0.6429***(0.0074)
0.1639***(0.0060)-0.0048***(0.0016)0.0356***(0.0073)-0.0611***(0.0075)
0.0255***(0.0071)
0.1929***(0.0121)-0.1319***(0.0099)
0.1064***(0.0117)-0.0226(0.0153)-0.0068(0.0188)0.0294*(0.0166)
0.0271***(0.0034)-0.0001(0.0014)0.0510***Case 80.9611***(0.0121)0.1932***(0.0049)
0.6430***(0.0075)
0.1638***(0.0060)-0.0048***(0.0016)0.0356***(0.0073)-0.0611***(0.0075)
0.0255***(0.0071)
0.1927***(0.0121)-0.1316***(0.0099)
0.1061***(0.0117)-0.0227(0.0154)-0.0068(0.0188)0.0296*(0.0168)
0.0271***(0.0034)-0.0001(0.0014)-0.0014(0.0257)
0.0521**

ln GS FRN-(0.0144)--0.0347(0.0274)
12.2080***(0.0316)902.790-0.0238***(0.0083)-0.0092(0.0356)-0.0780***(0.0229)0.1781***(0.0306)
0.0154**(0.0062)-0.1512***(0.0348)0.0255(0.0337)
12.1651***(0.0327)915.185(0.0146)-0.0234***(0.0082)
0.0022(0.0357)-0.0719***(0.0227)0.1817***(0.0304)
0.0152**(0.0061)-0.1472***(0.0346)
0.0365(0.0337)
12.1684***(0.0321)916.358(0.0247)-0.0234***(0.0082)
0.0024(0.0359)-0.0717***(0.0229)0.1817***(0.0305)
0.0152**(0.0061)-0.1473***(0.0346)
0.0366(0.0338)
12.1686***(0.0325)916.544

ln GS TOP--ln GS BANK--

ln STRDIV--

ln EX BKT-

ln EXPRF-

DNPU

Log-likelihood-0.0467*(0.0252)
12.2528***(0.0303)894.485R2

0.96760.97090.97130.97190.9722

0.9722

Concavity
12.6%
13.1%
12.8%
20.7%
Condition(Note):(1) Numbers in parentheses are standard errors.
(2) Sample size is 358.
(3) Statistically significant at 1% (***), 5% (**) and 10% (*).

19.6%

17.9%

15.4%

15.9%

Constant--0.0214***(0.0085)-0.0079(0.0359)-0.0815***(0.0232)
0.1455***(0.0302)-(0.0148)-0.0210**(0.0084)0.0047(0.0360)-0.0748***(0.0230)0.1503***(0.0300)
-

-

-

-

-0.0373(0.0256)
12.2398***(0.0310)897.344-0.0259(0.0259)
12.2359***(0.0307)900.112-0.0483*(0.0271)
12.2052***(0.0323)901.368

0.96780.9681[2013.6.13 1134]

21