Discussion Papers In Economics
And Business

Degree of Competition in the Japanese Securities Industry

Yoshiro Tsutsui
Akiko Kamesaka
Discussion Paper 02-10-Rev.

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDegree of Competition in the Japanese Securities Industry

Yoshiro Tsutsui
Akiko Kamesaka
Discussion Paper 02-10-Rev.

Jul 2004

この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDegree of Competition in the Japanese Securities Industry∗
Yoshiro Tsutsui (Osaka University)
Akiko Kamesaka (Ryukoku University)
Running title: Competition in Japanese Securities Industry
Abstract
This paper investigates the competitive condition of the Japanese securities industry overthe period 1983 to 2002 using the H-statistic proposed by Panzar and Rosse. Our results
indicate that the Japanese securities industry was not in monopoly equilibrium in period 1(1983–88) and period 3 (1997–2002), while this possibility cannot be rejected in period 2(1991–96). Thus, competition deteriorated in period 2, and then it recovered and became
more competitive recently, even compared with period 1. The industry has not faced
perfect competition, notwithstanding its undergoing financial reform.
JEL Classification Numbers: G24, L13
Keywords: Panzar-Rosse, H-statistic, degree of competition, securities industry, Japan
Yoshiro Tsutsui(corresponding author)
Graduate School of Economics,
Osaka University,
1-7 Machikaneyama,
Toyonaka,
560-0043 Japan
Phone: +81-6-6850-5223
Fax: +81-6-6850-5274
e-mail: tsutsui@econ.osaka-u.ac.jp

Akiko Kamesaka
Faculty of Economics,
Ryukoku University,
67 Tsukamoto-cho,Fukakusa, Fushimi-ku, Kyoto,
612-8577 Japan
Phone: +81-75-645-8431
Fax: +81-75-645-6444
e-mail: murase@econ.ryukoku.ac.jp

An earlier version of this paper was presented at µ (Monetary Economics Workshop), Spring
Meeting of the Japan Association of Monetary Economics, and at the Annual Meeting of the
Nippon Finance Association. We thank Yukitami Tsuji, Toshihiro Matsumura, and seminar
participants for their comments. Needless to say, sole responsibility for any errors remaining is
ours.

∗

1.

Introduction

The purpose of this paper is to elucidate how the competition of Japanese securities
companies changed over the period 1983 to 2002. We apply the method proposed by
Panzar and Rosse (1987) that measures the competitive condition of an industry byexamining elasticity of revenue with respect to input price.
After the unprecedented stock price bubbles in the late 1980s and the stock market
crash in the 1990s, the Japanese government introduced several measures to promotecompetition in the securities industry. The industry has been gradually deregulated since
1993, including a liberalization of the brokerage fee. Until 1998, a license was required
to start up a securities company in Japan. Registration is now the only requirement.
Consequently, the securities industry might have become more competitive over theperiod. However, degree of competition may be influenced by business conditions. The
bubble of the 1980s and the stagnation of the 1990s, called the Heisei Depression, might
have impacted on the competitiveness of securities firms.
There is a considerable literature on the application of the Panzar-Rosse method tothe banking industry (Nathan and Neave 1989, Shaffer and DiSalvo 1994, Molyneux et al.
1994, Hondroyiannis et al. 1999, Coccorese 1998, Bikker and Groeneveld 2000).
However, there are only a few studies of the competitive condition of the financial sectors

1

in Japan.
Molyneux et al. (1996) estimated the H-statistic for Japanese banks in 1986 and1988, and they could not reject the hypothesis that bank revenues behave as if earned
under monopoly in 1986, but as if under monopolistic competition in 1988. Alley (1993)
estimated the degree of collusion in the Japanese banking sector and found a high degreeof collusion in 1986 and 1987. Uchida and Tsutsui (2004) proposed a new method andestimated the degree of competition in the Japanese banking sector from 1974 to 2000.
They found that the market had become more competitive in the 1970s, and judged thatJapanese city banks faced perfect competition in the middle of 1990s. Souma and Tsutsui(2000) applied a similar method to the Japanese life insurance industry for the period
1986 to 1997. They found that the industry was not very competitive, but had become
more competitive since 1995, when the New Insurance Industry Law was promulgated.
Fukuyama and Weber (1999) estimated the efficiency of production for theJapanese securities industry.
The rest of the paper is organized as follows. In section 2, we give an overview ofthe history of liberalization of the Japanese securities industry. In section 3, we explain
the model used in this paper. Results are presented in section 4. In section 5, we provide
a check on the robustness of our results. In section 6, we draw a conclusion.

2

2.

Liberalization of the Japanese Securities Industry

Liberalization of Japanese financial industries has been ongoing since 1974 (Cargill andRoyama 1988). The liberalization of foreign exchange and deposit interest rates was animportant area of deregulation in the 1980s. Private banks and the Ministry of Posts andTelecommunications, which operates postal savings, were allowed to sell government
bonds in 1983 and 1988, respectively. Consequently, pressure may have been applied on
the securities industry, affecting its competitiveness.
In 1993, regulation governing segregation of business areas in the financial
industries was relaxed. Banks and securities companies were allowed to enter eachother’s industry by establishing subsidiaries. Based on this deregulation, many securities
companies were established as subsidiaries of banks, but no new banks, other than trustbanks, were established as subsidiaries of securities companies. This is probably because
banks had more competitive power than securities companies in Japan. Because thesubsidiaries of banks were not allowed to conduct brokerage business, they mainly
entered underwriting and distributing businesses.

Their share of the market forunderwriting and distribution of corporate bonds increased substantially in subsequent
years.
In January 1996, regulation of the issuing of corporate bonds was relaxed.

3

Consequently, the amount of corporate bonds issued increased significantly. In Figure 1
we show the amount of corporate bonds issued from 1983 to 2002. The corporate bondhere is defined as excluding those issued by nine electric power companies, Nippon
Telegraph and Telephone Corporation and Japan Railway Companies.1 The amount wasalmost zero until 1990, then rapidly increased until 1998, and became stagnant thereafter.
In November 1996, the then Prime Minister Hashimoto commenced ‘the Financial
Big Bang’. In the securities industry, the most important reforms were liberalization of
brokerage commissions and conversion from a licensing system to a registration system.
Liberalization of brokerage commissions in 1999 and 2000 induced a significant decline
in the level of commission. Until 1999, the commission fee was fixed at 1.15% for all
trades equal to or less than one million yen. However, after the liberalization, this fee has
decreased dramatically, and each securities firm applied its highest discount rate ontransactions through internet accounts. One of the famous internet trading securities
companies in Japan, Matsui Securities, ceased to charge a trading commission fee forinternet trades from April 2004, as long as their customer’s trades for one day do not
exceed ten thousand yen. When their customer’s trades exceed ten thousand yen, they1Electric power companies and Nippon Telegraph and Telephone Corporation were a few
exceptions that issued bonds earlier than our period. This is why we exclude their data. However,if their data are added, the essentials do not change except that issues before 1990 were not close
to zero.

4

still charge only 3150 yen for all transactions executed on the same day, as long as thetotal trades for the day do not exceed three million yen, no matter how many times thecustomer trades. As shown in this example, some securities companies seem to haveengaged in price competition in the brokerage business since 1999.
In Figure 2 we show stock transactions listed in the first section of the Tokyo Stock
Exchange. The Figure clearly shows the vivid increase of the bubble period of the 1980s,
and remarkable decline and consecutive long stagnation in the 1990s. Transactions
recovered substantially in 1999, although the Japanese economy did not fully recover.
How did securities companies respond to these changes in business conditions? In Figure
3 we show the number of securities firms, their branches and employees. The number of
employees and branches increased until 1990 and 1991, respectively, and then decreased
until 1999. After 1999, they have not decreased monotonically. Comparing Figure 3 with
Figure 2, we see that the number of branches and employees synchronized with thebusiness condition represented by the volume of stock transactions. It is reasonable forfirms to expand when revenues increase and to shrink with adversity. The number offirms in Figure 3, however, changed quite differently from the number of branches andemployees: it increased steadily except for the first and the last two years and 1990–93.
Considering that the first two years were not in the bubble period and that the bubble burst5

in 1990–92, the change might be interpreted as reflecting the change in businessconditions. It is not easy to interpret the rise in the number of firms in 1994–97 and thedecline after 2000 in terms of the degree of competition in the industry.

3.

Model

We employ the method proposed by Panzar and Rosse (1987) to estimate the degree ofcompetition. Panzar and Rosse focused on the sum of input elasticity of revenue,
K

pk ∂R

k =1

k

∑ R ∂p , which is called the H-statistic, where R is the revenue, p , the k-th inputkprice, and K is the number of inputs. They demonstrated that H ≤ 0 in case of amonopoly, H ≤ 1 in case of monopolistic competition (Chamberlinian equilibrium), in
which marginal revenue equals marginal cost and profit is zero, and H = 1 in case of
perfect competition in the long run.
Under a monopoly, an increase in input prices will increase marginal costs, so thatit decreases output and revenues. Under perfect competition in the long run, an increase
in input prices increases both marginal and average costs, so as to equalize an increase inrevenue to the increase in costs. Although different interpretations of the H-statistic havebeen offered (see Hondroyiannis et al. 1999; p.382), many scholars believe that a positive
H rejects monopoly equilibrium (Shaffer and DiSalvo 1994; p.1075). We should be6

aware that Panzar and Rosse (1987) only show a necessary condition for monopoly,monopolistic competition, and perfect competition. Thus, we should be careful ininterpreting our results.
However, Shaffer (1983) showed that under some conditions, the H-statistic is
inversely related to Bresnahan’s (1982) index of monopoly power, which is equal toq ∂Qunder quantity competition, where q is a firm’s output and Q is total market output.
Q ∂q
According to this relation, we interpret the H-statistic value as the degree of competition.
Panzar and Rosse denoted a revenue function as

R = R( y, z ) ,(1)

where y is output, and z stands for exogenous variables that affect revenue.
The cost function is assumed to be
C = C ( y , p, t ) ,(2)

where p represents input prices, and t stands for other exogenous variables. Since profits
are defined as π ≡ R( y, z ) − C ( y, p, t ) , profit-maximizing y is written as y * = y ( z , p, t ) .
Substituting this into (1), the reduced form of the revenue function can be written as~
R * = R( y ( z, p, t ), z ) ≡ R ( z , p, t ) .

(3)Thus, Panzar-Rosse’s H-statistic is calculated using estimates from equation (3).
Securities companies often operate four types of businesses: brokerage,

7

underwriting and distributing, dealing, and margin transaction businesses.2 Thus, it isdifficult to specify what is the relevant measure of outputs of securities firms. To estimate
the H-statistic, however, we need not specify the output level. It is necessary only to
specify the securities firm’s revenue R, and z, p, and t. This is a great advantage of the
H-statistic as a measure of the degree of competition in the securities industry.
Now, let us specify the variables in eq. (3). We use operating revenue for R. For
input prices, p, we use the wage rate, w, financial cost, r, and capital equipment cost, k.
For wage rate and financial cost, we definew≡

personnel expenses,
number of employeesandr ≡ (total financial cost) / (short-term loans + borrowings from securities financecompany + borrowings for margin transaction + fixed debt).
Capital equipment cost is difficult to define. We use two definitions, k1 and k2, for thisvariable. Specifically, we define
k1 ≡depreciation expenses + equipment expenses + expenses related to real estate
fixed asset

k2 ≡real estate expense.
total area of allbranches

and

2 Securities companies in Japan do not correspond to any financial institutions in the U.S.

do investment banking and also brokerage business.

8

They

In addition, we estimate the equations excluding the capital equipment cost variable tocheck the robustness of the results.
For the exogenous variables in the revenue function, z, and cost function, t, we
define variables that represent the composition of the four businesses, because we believethat business composition generally affects revenues as well as costs. Suppose there are
two firms, A and B, whose total assets are the same. Firm A conducts only brokeragebusiness, while firm B conducts only underwriting business. Then revenues of thesefirms are generally different. If the revenue of firm A is larger than that of firm B,
brokerage business tends to earn more revenue than underwriting given total assets of thefirms.
We define three business compositions, BROKER/ASSET, DISTR/ASSET, and
MARGIN/ASSET, where BROKER stands for brokerage fee, DISTR is underwriting anddistributing fee, MARGIN is income from margin transaction, and ASSET stands for total
assets. We do not specify the fourth ratio, revenue from dealing business to total asset, toavoid co-linearity, since the operating revenue sums the four business fees.
residually determined by the three ratios above.

It is

Other specifications of businesscomposition are possible and will be examined in section 5.2. In addition, we use thelogarithm of total assets, LASSET, as a size variable of the securities companies.

9We consider other firm-specific variables—age of firms, concentration ofshareholders, and location of head office—and conduct additional analyses in section 5.4.
Descriptive statistics of the variables are given in Table 1. Revenue is about 43000
million yen on average for the whole period. The revenue of the first period is the largest,reflecting the stock market boom in the 1980s. The revenue in the third period decreased
to half that of the first period. Financial cost is about 5.8% for the whole period. It
decreased from 6.7% in the first period to 3.4% in the third period, reflecting the declineof the market interest rate. Wage rate is about 8.6 million yen for the whole period. It
slightly increased from period 1 to period 3. Capital equipment cost, k1, is about 24%,and decreased from period 1 to period 2. Capital equipment cost, k2, is about 0.2 million
yen per 1000m2, which is too small to be the rental of branches. This might be due to thefact that the data for total area of branches includes not only rented branches but also
owned branches, while the real estate expense does not include that of owned branches.
Brokerage fees decreased throughout the period. Underwriting and distributing fees
decreased from period 1 to period 2, but increased again in period 3. Income from margintransactions decreased dramatically in period 3.

Thus, we know that businesscomposition changed considerably throughout the period.
De Bandt and Davis (2000) specify the revenue equation for banks as

10

J

K

N

j =1

k =1

n =1

ln Ri ,t = ∑ α j ln p j i ,t + ∑ β k ln S k i ,t + ∑ γ n X in,t + ε i ,t , where p is a three-dimensional

vector of factor prices (wages, interest rate on liabilities, and other cost), S is a vector ofscale variables, and X is a vector of exogenous and bank-specific variables that may shiftbusiness mix. It is remarkable that our variable selection conforms closely to their
specification. We employ three prices, log of scale variable, and business mix variables,as they do. We also adopt exogenous firm-specific variables, such as firm age and firm
location in section 5.4.
For our basic regression equation, we examine the following Cobb-Douglas andtranslog functions. We call the following ‘basic equations’.
Cobb-Douglas function:ln Ri ,t = a0 + a1 ln ri ,t + a 2 ln wi ,t + a3 ln k i ,t + a 4+ a6

MARGIN i ,t
ASSETi ,t

BROKERi ,tASSETi ,t+ a5

DISTRi ,t
ASSETi ,t(4)+ a7 ln ASSETi ,t + u i ,tFull translog function:ln Ri ,t = a 0 + a1 ln ri ,t + a 2 ln wi ,t + a3 ln k i ,t + a 4+ a6

MARGIN i ,t
ASSETi ,t

BROKERi ,tASSETi ,t+ a5

DISTRi ,t
ASSETi ,t+ a 7 ln ASSETi ,t + b1 (ln ri ,t ) 2 + b2 (ln wi ,t ) 2 + b3 (ln k i ,t ) 2+ b4 (ln ri ,t )(ln wi ,t ) + b5 (ln ri ,t )(ln k i ,t ) + b6 (ln wi ,t )(ln k i ,t ) + u i ,tPartial translog function:

11

(5)

ln Ri ,t = a0 + a1 ln ri ,t + a2 ln wi ,t + a3 ln ki ,t + a4

BROKERi ,t
DISTRi ,t+ a5
ASSETi ,t
ASSETi ,t

MARGIN i ,t
+ a6
+ a7 ln ASSETi ,t + b1 (ln ri ,t ) 2 + b2 (ln wi ,t ) 2 + b3 (ln ki ,t ) 2 + ui ,tASSETi ,t(6)

where variables with upper bars are deviations from their means.3
H-statistic is defined as
H ≡ a1 + a2 + a3

(7)

where it is evaluated at the mean of price variables for translog cases.4

4.

Basic Results

Our sample firms are 54 to 62 securities firms depending on the year. There are a total of
247 to 297 securities firms in our observation period. The number of firms in our sample
is only about 1/5 of this total. However, since our sample of companies includes all thoselisted on the stock exchange or that publish financial reports, we have relatively large
samples. For example, if we look at the number of employees, our sample firms employ
42% of the total for all firms in 1999 and 57% in 1983, implying that our sample covers
the principal part of the Japanese securities industry.
As De Bandt and Davis (2000) mentioned, the estimates of year-by-yearcross-section analysis are usually unstable and this is really the case in our study;
3

Using deviations from the means in the translog function is a convention to avoid possible
multi-collinearity.

12

therefore, we pool our data for several years to guarantee stable estimates. Based on thecompetitive background and stock market conditions in Japan, we pool our data andestimate the model for three periods between 1983 and 2002. Period 1 is from 1983 to
1988, when Japan went through the unprecedented stock price bubble; period 2 is from1991 to 1996, when the market faced the large stock price crash; and period 3, from 1997
to 2002, is when the government implemented several regulatory changes to promotecompetition in the securities industry. Nonetheless, Japanese financial industries fell into
crisis during period 3.
We excluded 1989 as the accounting period changed in that year. Before 1989, theperiod was the twelve-months ending September 30, and after 1989, it ended March 31.
Because the dataset for 1989 accounts for only six months, October 1988 to March 1989,
we deleted that year.5
We also excluded 1990, since our financial data in that year reflect not only 1989
but also 1990. While the stock price index, Nikkei average, grew from 9893 yen in 1983,
the first year of our dataset of period 1, to 38915 yen in 1989, the index started to fall
steeply from the beginning of 1990. Thus, by deleting 1990, period 1 is purely a period of

4

At the mean of price variables, variables with upper bars are zero.
In this paper, we designated the year by the end of the accounting year. Before 1989, using 1984
as an example, the accounting year was from October 1, 1983 to September 30, 1984. After 1990,
using 1995 as an example, the accounting year was from April 1, 1994 to March 31, 1995.

5

13

the Japanese stock market boom or bubble.
All the data were extracted from the Nikkei NEEDS Corporate Financial Data File
(Securities Companies), unless otherwise mentioned.
We estimated equations (4)–(6) with plain OLS, fixed effect model, and random
effect model. As plain OLS was always rejected against the fixed effect model by theF-test for intercepts, and the random effect model was rejected against the fixed effectmodel for most cases by the Hausman test, we will present the results of the fixed effectmodel. We will present the results using k1 for capital equipment cost, k.
To discover the appropriate functional form, a conventional F-test was applied tothe three equations and the results are presented in Table 2. Table 2 makes it clear that the
Cobb-Douglas function is not rejected against the partial translog function in any period,
but it is rejected against the full translog function in periods 2 and 3. Given these results,
we will report the results of the Cobb-Douglas and full translog functions in this section.
The results of partial translog function are not remarkably different.
In Table 3, we present the results of eq. (4). The fit was good for all periods. The
coefficient of LASSET ( ln ASSET ) is highly significant and takes on the value of 0.9
throughout the period. The coefficient of BROKER/ASSET was positive and also highly
significant throughout the period, implying that the brokerage business earned relatively

14more revenue. The coefficient of DISTR /ASSET was significantly positive in periods 1
and 3, but was negative in period 2, suggesting that profitability of the distributing
business in period 2 was poor. The coefficient of MARGIN/ASSET was insignificant inperiods 1 and 3, but significantly positive in period 2.
The H-statistic is shown in Figure 4 with a 95% confidence interval. The H-statistic
took on a value of 0.216 in period 1, and was significant. In period 2, the H-statistic
decreased to 0.085, and standard error increased resulting in a wider confidence interval,so that the H-statistic was statistically indifferent from zero. In period 3, the H-statistic
increased to 0.338 and was significant at a 1% level. The H-statistic was significantly
different from unity for all periods, implying that the industry was not in perfectcompetition.
In Table 4, we present the results of eq. (5). The signs of the coefficients of
BROKER/ASSET, DISTR/ASSET, and LASSET we found to be almost the same as in
Table 3. However, MRAGIN/ASSET became significantly positive in period 2. The
coefficients of DLR ( ln r ) , DLW ( ln w) , and DLK (ln k ) are also similar to those in Table
3. The second order terms of DLW and DLK are only significant in period 3 and in period
2, respectively. The cross terms were not significant except for DLW*DLK in period 3.
The H-statistic is almost the same as in Figure 4.

15

In sum, estimation results of Cobb-Douglas and translog revenue function suggest
the following:1) That the industry was in monopoly equilibrium is rejected in periods 1 and 3, but thepossibility that the industry was in monopoly equilibrium in period 2 cannot berejected.
2) Firms with higher brokerage business ratio earned more revenue than others in all
sub-periods.
3) Period 2 was difficult for the underwriting and distributing business.
These conclusions are robust to the Cobb-Douglas and translog functional forms, and tothe variable used to measure capital equipment cost.
We interpret the first conclusion as follows. In period 1, 1983–88, which ischaracterized by a big bubble, the Japanese stock market was excited, with a sharp rise ofstock prices along with a sharp increase in trading volume (see Figure 2). In thisatmosphere, securities companies expanded their businesses enthusiastically. In period 2,
1991–96, facing dramatic decline of transaction demand, securities companies contracted
their businesses and their firm size, losing their eagerness to expand (see Figure 3).
Although liberalization, such as relaxation of the segregation of business areas in thefinancial industries, was implemented in this period, its impact on competition was

16

limited. Our result, a high H-statistic in period 1 and a low one in period 2, reflects
business conditions for securities companies and their attitude toward expansion of theirbusinesses. However, after the failures of large banks and securities companies in 1997
and 1998, brokerage commissions were liberalized, which was a remarkable achievement,
which clearly caused a decrease of commission fees. Our finding that the H-statistic
recovered in period 3 reflects the effect of liberalization.

5.

Checking Robustness of the Results

In this section, we provide a check on the robustness of the results in the previous section.

5.1 Simple specification
First, we check whether the conclusion based on the basic estimation presented in section
4 remains valid, when we delete the variables representing the business composition ofeach firm. The regression equation is now
ln Ri ,t = a0 + a1 ln ri ,t + a 2 ln wi ,t + a3 ln ki ,t + a7 ln ASSETi ,t + ui ,t(8)

The estimates of eq. (8) with within-estimation are shown in Table 5.

Thecoefficients of LASSET and all the coefficients of the input prices, except for thecoefficient of LR for period 2, are significant in this Table. The estimates of the17

H-statistic become substantially larger when we exclude the variables of businesscomposition for the periods 1, 2, and 3, respectively. However, the results are consistent
with Table 3 in that the H-statistic is significantly different from zero in periods 1 and 3,
while it is not in period 2 at a 5% significant level. In addition, the H-statistic in period 3
is slightly larger than that in period 1.
Excluding the business composition variables, similar results are obtained when we
estimate translog function, and when k2 is used as the measure of k (results are not shown).
Therefore, our conclusion based on our basic model in the previous section is robust
when we exclude the variables of business composition.

5.2 Other specifications of ‘business composition’ variables
In section 4, we used the revenue of each business divided by total asset,
BROKER/ASSET, DISTR/ASSET, and MARGIN/ASSET for the data of businesscomposition variables. In this section, we check whether the conclusions in the previous
section are robust, when we adopt other specifications for these variables.
We examine the case in which the revenues of each business, i.e. brokerage fee,
underwriting and distributing fee, and income from margin transactions, are adopted as
the business composition variables. Thus, the regression equation becomes18

ln Ri ,t = a0 + a1 ln ri ,t + a2 ln wi ,t + a3 ln k i ,t + a 4 BROKERi ,t + a5 DISTRi ,t + a6 MARGINi ,t+ a7 ln ASSETi ,t + ui ,t(9)

Since BROKER, DISTR, and MARGIN may be endogenous variables, we estimate
eq. (9) using the instrumental variables method. Specifically, we used rank variables ofthese variables as the instrumental variables for the estimation.6
The estimation results using within-estimation are shown in Table 6.

Thecoefficients of these business composition variables become smaller in magnitude thanthose in Table 3, but the signs are unchanged except that the coefficient of MARGIN
becomes negative and insignificant in the second and third periods and significantly
positive in period 1.
We show the H-statistic in Figure 5. The H-statistic in the first period becomesinsignificant when we use revenues of each business as the business condition variables.
Since the H-statistic is also insignificant in the second period, the results do not reject the
proposition that the security industry was in monopoly equilibrium in both the first andthe second periods. Looking at the point estimates of the H-statistic, the results indicatethat competition grew stronger over the period.
Next, we use the business composition variables divided by total revenue. Thus, theregression equation is6

Rank variable of Xi is the variable whose value is the order of Xi ranked in ascending order. See
Maddala (1977; pp.297–298).

19

ln Ri ,t = a0 + a1 ln ri ,t + a 2 ln wi ,t + a3 ln k i ,t + a 4+ a6

MARGIN i ,tREVi ,t

BROKERi ,tREVi ,t+ a5

DISTRi ,tREVi ,t(10)+ a7 ln ASSETi ,t + ui ,tThe estimates are shown in Table 7. They are quite different from previous
estimations. First, the H-statistic takes on the larger values 0.279, 0.600, and 0.763 inperiods 1, 2, and 3, respectively. These are all significantly different from zero, implying
a rejection of monopoly equilibrium in all periods (see Figure 6). They are relatively
larger than those in previous results, implying that the industry was more competitive
than the other estimations suggest. Second, the signs of the coefficients of businesscomposition variables are all negative, implying that income from dealing, which is
excluded to avoid co-linearity, has a strong positive effect on revenue throughout theperiod. This result is quite different from results in both Tables 3 and 5, leading to doubt
about the results in Table 7.
In sum, these two investigations suggest that the selection of the businesscomposition variables critically influences the estimated value of the H-statistic.
Although reaching a decisive conclusion is a task for the future, we consider that ourconclusion based on the basic model in section 4 is more reliable because the simple
models in section 5.1, which do not use business composition variables, produce results
consistent with those of the basic models.
20

5.3 Endogeneity of the wage rate
Some may argue that the wage rate is not exogenous, but endogenously determined,reflecting the performances of securities companies. Considering such a characteristic of
the wage rate, we estimate equation (4) treating wi,t as an endogenous variable.
Specifically, we use rank variables of LW as the instrumental variable for the estimation.
The within-estimation results for the Cobb-Douglas function are shown in Table 8.
The coefficient of LW does not change dramatically, although that for period 3 became
smaller. The coefficients of BROKER/ASSET and DISTR/ASSET are significant and are
all positive except for the coefficient of DISTR/ASSET for period 2.
The results confirm the previous results in Table 3, in that the H-statistic issignificantly different from zero in periods 1 and 3, but not in period 2. The estimates ofthe H-statistic in period 3 are slightly larger than for period 1.
Similar results are obtained for the translog function. Therefore, we conclude that
our results are robust when we consider the wage rate to be endogenous.

5.4 Characteristics of firms
In section 4, we reported that the plain OLS estimation was rejected in favor of the fixed
effect model (within-estimation). This implies that the characteristics of securities firms

21are important to explain their revenues.

Thus, it is interesting to see how thecharacteristics of the firms affect the degree of competition. In this section, we use
information on the age of the firm, AGE, its location, TOKYO, and the holding ratio of thelargest shareholder, SHARE, and analyze their impact on competitiveness.

The

regression equation is now:ln Ri ,t = a0 + a1 ln ri ,t + a2 ln wi ,t + a3 ln k i ,t + a4
+ a6

MARGIN i ,t
ASSETi ,t

BROKERi ,tASSETi ,t+ a5

DISTRi ,t
ASSETi ,t+ a7 ln ASSETi ,t + a8 AGEi ,t + a9 SHAREi + a10TOKYOi+ c1 AGEi ,t ln ri ,t + c2 AGEi ,t ln wi ,t + c3 AGEi ,t ln k i ,t(11)

+ c4 SHAREi ln ri ,t + c5 SHAREi ln wi ,t + c6 SHAREi ln k i ,t
+ c7TOKYOi ln ri ,t + c8TOKYOi ln wi ,t + c9TOKYOi ln k i ,t + ui ,tThe H-statistic is defined as
H ≡ a1 + a2 + a3 + (c1 + c2 + c3 ) AGE + (c4 + c5 + c6 )SHARE+ (c7 + c8 + c9 )TOKYO(12)

where underlined variables stand for their means.
The precise definition of the firm-specific variables is as follows.
AGE: age of company since its establishment (years);
SHARE: the holding ratio of the top shareholder in March, 2000 (%);7TOKYO: a dummy variable which is unity if the head office is located in Tokyo, and zerootherwise.

7

For some firms, this information was not available for the year 2000. Thus, we use the data for22

Data on these variables are taken from Nikkei Kaisha Joho (Nikkei Information on
Companies), Annual Corporation Reports, and several other sources including webpagesof the companies. Their descriptive statistics are shown in the bottom rows of Table 1.
Average AGE of these firms is about 52 years. Average SHARE is about 19%. Average
TOKYO is 0.745, implying 2/3 of the securities firms of our sample have their head
offices in Tokyo.8
We present the results of OLS estimation in Table 9, since SHARE and TOKYO are
only firm-specific, and do not vary over time, so that within-estimation is impossible inthis case.
The estimates are somewhat different from those of Table 3. For example, thecoefficient of LW in the third period was 0.352 in Table 3, and 0.788 in Table 9. However,the signs of the coefficients of the price variables are not dramatically different from
Table 3. The signs of BROKER/ASSET and DISTR/ASSET of Table 3 and Table 9 aresimilar except for DISTR/ASSET in period 2, which becomes insignificant, and thecoefficients of MARGIN /ASSET, which are insignificant throughout.

1995, 1999, and 2002 for 13, one, and 10 firms, respectively.
8

Although SHARE and TOKYO show different figures for each period in Table 1, this is because

the observations are slightly different between periods.

23

The level of the H-statistic becomes slightly lower compared with the otherestimations without firm-specific variables. However, the result that the H-statistic issignificantly different from zero in the first and the third periods, while it is insignificant
in the second period, is unchanged. Thus, the basic conclusion is confirmed.
As shown in equation (12), the H-statistic consists of four parts:
a1 + a 2 + a3 , which we name PH (pure H); (c1 + c 2 + c3 ) AGE , which represents theeffect of AGE, which we name EFA; (c 4 + c5 + c6 ) SHARE , which represents the effectof SHARE, which we name EFS; and (c7 + c8 + c9 )TOKYO , which represents the effectof TOKYO, which we name EFT. If we consider that young firms are innovative andeager to compete, we expect EFA to be negative. Similarly, if we consider that managersof the firms with a higher holding owned by the largest shareholder will be moredisciplined and tend to be more eager to seek profits, EFS is positive. We can also expect
competition in Tokyo to be more severe, since there are many securities firms. If this is
the case, we expect a positive value for EFT.
The estimates of EFA, EFS, and EFT are shown in the bottom rows of Table 9. PH
shows the same tendency as the H-statistic though its magnitude is larger: it is significant
in periods 1 and 3 and insignificant in period 2. Unfortunately, EFA, EFS, and EFT are
insignificant in most cases. Interestingly, however, EFS is significantly positive at a 1%24

level in the third period, implying that firms with a higher holding owned by the largest
shareholder are more competitive. In contrast, EFT in the third period is unexpectedly
negative at a 1% significance level, implying that firms in Tokyo are less competitive.

6.

Conclusions

This paper investigates changes in the competitive condition of the Japanese securities
industry from 1983 to 2002. The H-statistic is estimated for three periods reflecting
differences in the competitive environment. Our results indicate that the Japanese
securities industry was not in monopoly equilibrium in period 1 (1983–88) or period 3(1997–2002), while that possibility cannot be denied in period 2 (1991–96). Looking at
the point estimates of the H-statistic, competition deteriorated in period 2, and thenrecovered and became more competitive recently even compared with period 1. The
industry has not faced perfect competition, notwithstanding the financial reforms
implemented during this time. This conclusion is robust to both the Cobb-Douglas andtranslog revenue functions.
We interpret this result as the outcome of business conditions cum development offinancial liberalization. Specifically, we argue that a bubble in the 1980s accelerated
competition, while stagnation in the 1990s depressed competition. While relaxation of25

segregation of business lines in 1993 did not promote competition, deregulation of
brokerage fees in the late 1990s did have a strong impact on competition. This suggeststhat a policy measure does not have a constant influence on competition. Relaxation ofsegregation of business lines did not have prompt strong impact because it was done at atime of very severe business conditions for securities companies.

In contrast,

deregulation of brokerage fees was very timely, as internet trading became popular fromthat time. In addition, the failure of the Yamaichi Securities Company in 1997 made all
the securities firms feel anxious for their safety and compete with each other to attract
new customers. Policy measures should take into account business conditions for thefirms being regulated.
We provided a further check on the robustness of these conclusions. First, weestimated a simple specification excluding the variables representing firms’ businesscomposition. Our results support the conclusions of the former sections. Second, we
estimated the equations using different specifications for the variables representing
business composition. Specifically, we used the revenues from each business, as well as
those divided by total revenues. The conclusions critically depend on the specification ofbusiness composition variables. However, we consider that the results in section 4 are
more reliable. Third, we checked the possibility of the endogeneity of the wage rate. The

26

estimation with instrumental variables of wage rates supports the conclusion based on thebasic estimation. Fourth, we considered additional firm-specific variables, age of thefirms, AGE, shareholding of the largest shareholder, SHARE, and location of the firm,
TOKYO, and re-estimated the equations adding these variables. The conclusions basedon the basic equations are confirmed.

However, the effect of these variables oncompetitiveness (H-statistic) is ambiguous except the result that firms with a higher
holding of the largest shareholder are more competitive in the third period.
Finally, we compare our results with those of the other financial industries in Japan.
Souma and Tsutsui (2000) concluded that the degree of competition of the Japanese life
insurance industry was low, although it has increased since 1995. On the other hand,
Uchida and Tsutsui (2004) concluded that the Japanese city banks were subject to perfectcompetition in the middle of the 1990s. In view of these studies, competition in the
securities industry stands in the middle of the banking and life insurance industries inJapan.

27

References
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10: 87–92.
Cargill, T. F., and Royama, S. (1988.) The Transition of Finance in Japan and the UnitedStates. Stanford: Hoover Institution Press.
Coccorese, P. (Jun. 1998.) Assessing the competitive conditions in the Italian banking
system: Some empirical evidence. BNL Quarterly Review 205: 171–191.
De Bandt, O., and Davis, E. P. (2000.) Competition, contestability and market structure inEuropean banking sectors on the eve of EMU. Journal of Banking and Finance 24:1045–1066.
Fukuyama, H., and Weber, W. L. (1999.) The efficiency and productivity of Japanese
securities firms, 1988-93. Japan and the World Economy 11: 115–133.
Hondroyiannis, G., Lolos S., and Papapetrou, E. (1999.) Assessing competitive
conditions in the Greek banking system. Journal of International Financial Markets,
Institutions and Money 9: 377–391.
Maddala, G. S. (1977.) Econometrics, Tokyo: McGraw-Hill Kogakusha Ltd.
Molyneux, P., Lloyd-Williams, D. M., and Thornton, J. (1994.) Competitive conditions in
European banking. Journal of Banking and Finance 18: 445–459.
Molyneux, P., Thornton, J., and Lloyd-Williams, D. M. (1996.) Competition and market
contestability in Japanese commercial banking. Journal of Economics and Business48: 33–45.

28

Nathan, A., and Neave, E. H. (1989.) Competition and contestability in Canada’s
financial system: Empirical results. Canadian Journal of Economics 22 (3): 576–594.
Panzar, J. C., and Rosse, J. N. (1987.) Testing for ‘monopoly’ equilibrium. The Journal ofIndustrial Economics 35 (4): 443–456.
Shaffer, S. (1983.) Non-structural measures of competition: Toward a synthesis ofalternatives. Economics Letters 12: 349–353.
Shaffer, S., and DiSalvo, J. (1994.) Conduct in a banking duopoly. Journal of Banking
and Finance 18: 1063–1082.
Souma, T., and Tsutsui, Y. (2000.) Recent competition in Japanese life insurance industry.
Discussion paper 00–19 (Osaka University).
Uchida, H., and Tsutsui, Y. (2004.) Has competition in the Japanese banking sector
improved? forthcoming in Journal of Banking and Finance.

29

Table 1 Descriptive statistics of variables

operating revenue, R
financial cost, r
wage rate, w
capital equipment cost, k1
capital equipment cost, k2
brokerage fee
underwriting and distributing fee
income from margin transaction

AGE
SHARE
TOKYO
number of observations
number of observations for k2

whole period
Mean
Std Dev
42916
99220
5.798
3.627
8.642
1.766
24.162
11.999
0.201
0.546
20510
46029
3770
11050
3027
6065
52.38813.23119.03515.558
0.745
948
868

period 1
Mean
60884
6.696
7.873
27.585
0.138
33937
3764
4683
44.79519.039
0.757
300
267

Std Dev
1300031.941
1.515
10.844
0.326
69806
10853
7304
11.83315.880period 2
Mean
38910
6.835
8.335
22.981
0.223
16828
2520
3615
52.74518.612
0.747
344
338

Std Dev
84581
4.183
1.048
10.955
0.558
30223
7438
6857
11.77815.269period 3
Mean
29719
3.740
9.750
22.122
0.236
11427
5190
729
59.47719.509
0.730
304
263

Std Dev
74027
3.365
2.078
13.443
0.687
22414
14105
826
12.011
15.599Notes: Operating revenue, brokerage fee, underwriting and distributing fee, and income from margin transaction are in million yen. Financial cost,capital equipment cost, k1, and SHARE are in percentage. Wage rate is in million yen/person. Capital equipment cost, k2, is in million yen/1000m2.
AGE is in years. TOKYO is a dummy variable that takes on unity if head office is in Tokyo. Therefore, mean of TOKYO means the ratio of the numberof firms whose head office is in Tokyo to the total number of firms.
Std Dev stands for standard deviation.
Period 1 is 1983–88, period 2 is 1991–96, and period 3 is 1997–2002.

30

Table 2 F-test of the Cobb-Douglas and translog functions

H0: Cobb-Douglas vs. partial translog
H0: Cobb-Douglas vs. full translog
H0: partial translog vs. full translog

period 1
period 2
period 3
F-value P-value F-value P-value F-value P-value
1.099
0.350
1.409
0.240
1.908
0.128
7.581
0.000
1.378
0.223
2.556
0.020
13.764
0.000
1.319
0.268
3.112
0.027

Notes: Conventional F-test is applied to Cobb-Douglas, eq.(4), Full translog, eq. (5), and partial translog, eq. (6),to find out which is the appropriate functional form. Period 1 is 1983–88, period 2 is 1991–96, and period 3 is 1997–2002.

31

Table 3 Estimates of Cobb-Douglas function: eq. (4)VariableLR
LW
LK
BROKER/ASSET
DISTR/ASSETMARGIN/ASSETLASSETR

period 1
Coefficient
0.032
0.142
0.041
7.325
10.6030.078
0.872

period 2
period 3
P-value Coefficient P-value Coefficient[.157]
0.002[.933]
0.016[.003]
0.151[.359]
0.352[.052]-0.068[.199]-0.029[.000]
12.950[.000]
9.120[.000]-24.562[.015]
5.838[.965]
7.507[.004]
5.866[.000]
0.894[.000]
0.863

P-value[.445][.000][.449][.000][.000][.286][.000]

2

H-statistic
number of observations
number of individual firms

0.998
0.216
300
55

[.000]

0.987
0.085
344
58

[.651]

0.988
0.338
307
62

[.000]

Notes: Equation (4)ln Ri ,t = a 0 + a1 ln ri ,t + a 2 ln wi ,t + a 3 ln k i ,t + a 4

MARGIN i ,t
BROKER i ,t
DISTR i ,t
+ a5
+ a6
+ a 7 ln ASSET i ,t + ui ,tASSET i ,t
ASSET i ,t
ASSET i ,t

is estimated by within-estimation. k1 is used for k. LR stands for ln r , LW for ln w , and LK for ln k .
Precise definitions of the variables are given in the text.

32

Table 4 Estimates of full translog function: eq. (5)Variable

DLR
DLW
DLK
BROKER/ASSET
DISTR/ASSETMARGIN/ASSETLASSETDLR*DLRDLW*DLWDLK*DLKDLR*DLWDLR*DLKDLW*DLK
2
R
H-statistic
number of observations
number of individual firms

period 1
Coefficient
0.060
0.136
0.026
7.634
12.397-1.2290.866-0.053-0.0440.056-0.073-0.001-0.124
0.998
0.222
267
54

P-value[.092][.058][.413][.000][.000][.555][.000][.244][.749][.145][.488][.988][.225][.014]

period 2
Coefficient-0.0140.165-0.05612.472-24.3418.313
0.890-0.016-0.241-0.1260.216
0.022-0.335
0.987
0.094
344
58

P-value[.652][.333][.307][.000][.017][.003][.000][.519][.708][.042][.111][.523][.247][.627]

period 3
Coefficient
0.019
0.302
-0.029
9.457
5.843
8.108
0.886
-0.011
0.339-0.0130.066-0.046-0.292
0.989
0.292
307
62

P-value[.372][.000][.463][.000][.000][.162][.000][.451][.019][.734][.368][.116][.011][.002]

Notes: Equation (5)ln Ri , t = a 0 + a1 ln ri , t + a 2 ln wi , t + a 3 ln k i , t + a 4

BROKER i , t
DISTR i , t
MARGIN i , t
+ a5
+ a6
+ a 7 ln ASSET i , t
ASSET i , t
ASSET i , t
ASSET i , t+ b1 ( ln ri , t ) 2 + b2 ( ln wi , t ) 2 + b3 ( ln k i , t ) 2 + b4 ( ln ri , t )( ln wi , t ) + b5 ( ln ri , t )( ln k i , t ) + b6 ( ln wi , t )( ln k i , t ) + ui , t

is estimated by within-estimation. See notes to Table 3. DLR stands for ln r , DLW for ln w , DLK for ln k .

33

Table 5 Estimates of simple specification: Cobb-Douglas case, eq. (8)VariableLR
LW
LK
LASSET
2
R
H-statistic
number of observations
number of individual firms

period 1
Coefficient
0.171
0.459
0.119
0.915
0.992
0.749
300
55

P-value[.000][.000][.002][.000]

period 2
period 3
Coefficient P-value Coefficient
0.000[.998]-0.0600.574[.004]
0.708-0.149[.025]
0.147
1.122[.000]
0.578[.000]

0.978
0.426
344
58

[.061]

Notes: Equation (8)ln Ri ,t = a 0 + a1 ln ri ,t + a 2 ln wi ,t + a 3 ln k i ,t + a 7 ln ASSETi ,t + ui ,tis estimated by within-estimation. See notes to Table 3.

34

0.977
0.795
307
62

P-value[.006][.000][.004][.000][.000]

Table 6 Estimates of eq. (9): revenues for each variable for business composition variables

VariableLR
LW
LK
BROKER
DISTR
MARGINLASSET
2
R
H-statistic
number of observations
number of individual firms

period 1
Coefficient-0.019
0.037
0.018
0.923
0.052
0.122
-0.180
0.998
0.036
300
55

period 2
P-value Coefficient[.506]
0.066[.407]
0.026[.371]-0.027[.000]
0.812[.000]-0.038[.029]-0.011[.074]
0.272
0.987[.580]

period 3
P-value Coefficient[.026]
0.030[.859]
0.222[.567]
0.025[.000]
0.675[.077]
0.042[.759]-0.026[.005]
0.176
0.991[.690]

0.064
344
58

0.277
307
62

Notes: Equation (9)ln Ri ,t = a0 + a1 ln ri ,t + a 2 ln wi ,t + a3 ln ki ,t + a4 BROKERi ,t + a5 DISTRi ,t + a6 MARGIN i ,t + a7 ln ASSETi ,t + ui ,tis estimated by within-estimation. See notes to Table 3.

35

P-value[.116][.000][.401][.000][.000][.550][.000][.000]

Table 7 Estimates of eq. (10): business composition variables divided by total revenues

VariableLR

LW
LK
BROKER/REVDISTR/REVMARGIN/REVLASSET
2
R
H-statistic
number of observations
number of individual firms

period 1
Coefficient
0.178
0.079
0.021
-0.821-2.289-3.238
0.880
0.996
0.279
267
54

period 2
P-value Coefficient[.000]
0.013[.251]
0.688[.486]-0.101[.000]-0.032[.000]-2.974[.000]-0.331[.000]
1.129
0.986[.000]

0.600
344
58

period 3
P-value Coefficient[.665]-0.015[.000]
0.601[.061]
0.178[.760]-0.177[.000]-0.294[.126]-2.295[.000]
0.613
0.978[.002]

P-value[.571][.000][.000][.319][.182][.000][.000]

0.763
307
62

Notes: Equation (10)ln Ri , t = a0 + a1 ln ri , t + a 2 ln wi , t + a3 ln ki , t + a 4

BROKER i , t
DISTR i , t
MARGIN i , t
+ a5
+ a6
+ a7 ln ASSETi , t + ui , t
REVi , t
REVi , t
REVi , tis estimated by within-estimation. See notes to Table 3.

36[.000]

Table 8 Estimation results with instrumental variable of wage rate: Cobb-Douglas function

VariableLR
LW
LK
BROKER/ASSET
DISTR/ASSETMARGIN/ASSETLASSET
2
R
H-statistic
number of observations
number of individual firms

period 1
Coefficient
0.032
0.128
0.041
7.347
10.5600.047
0.875

period 2
period 3
P-value Coefficient P-value Coefficient P-value[.117]
0.002 [.932]
0.012 [.504][.014]
0.146 [.372]
0.230 [.004][.030]-0.069 [.157]-0.028 [.420][.000]
12.953 [.000]
9.611 [.000][.000]-24.511 [.008]
5.825 [.000][.977]
7.516 [.001]
3.937 [.428][.000]
0.894 [.000]
0.873 [.000]

0.998
0.201 [.001]
300
55

0.987
0.080 [.668]
344
58

0.988
0.214 [.017]
307
62

Notes: Equation (4)ln Ri ,t = a 0 + a1 ln ri ,t + a 2 ln wi ,t + a 3 ln k i ,t + a 4

MARGIN i ,t
BROKER i ,t
DISTR i ,t
+ a5
+ a6
+ a 7 ln ASSET i ,t + ui ,tASSET i ,t
ASSET i ,t
ASSET i ,t

is estimated, treating wi,t as an endogenous variable. Rank variables of LW are used as an instrumental variable.
Results by within-estimation are shown.

37

Table 9 Estimates of Equation (11): firm-specific variables

Variable
CONSTANTLR
LW
LK
BROKER/ASSET
DISTR/ASSETMARGIN/ASSETLASSET
AGE
SHARE
TOKYO
AGE*LR
AGE*LW
AGE*LKSHARE*LRSHARE*LWSHARE*LKTOKYO*LRTOKYO*LWTOKYO*LKR

2

H-statistic

PH
EFA
EFS
EFT
number of observations

period 1
Estimate-3.540
0.274
0.120
0.073
8.545
15.990
3.275
0.974
0.012
0.010
-0.070-0.0030.000-0.002-0.001-0.0070.002

period 2
P-value Estimate[.000]-3.791[.027]-0.020[.540]-0.003[.420]
0.122[.000]
15.476[.000]
8.325[.119]-0.370[.000]
1.030[.343]
0.009[.254]
0.000[.778]-0.340[.275]
0.001[.953]-0.001[.255]-0.004[.714]
0.002[.021]
0.001[.256]-0.002

period 3
P-value Estimate[.000]-4.227[.805]
0.110[.993]
0.788[.283]-0.079[.000]
11.184[.275]
9.796[.848]-3.906[.000]
0.958[.578]
0.022[.996]-0.032[.469]
1.078[.333]-0.001[.893]-0.011[.059]
0.001[.116]
0.001[.930]
0.015[.178]-0.001

P-value[.000][.242][.025][.455][.000][.000][.443][.000][.198][.003][.003][.627][.104][.551][.290][.000][.325]-0.084
0.057
0.054
0.992

[.161][.491][.230]-0.002
0.126
0.038
0.981

[.967][.550][.458]-0.032-0.415-0.0360.980[.334][.002][.453]

0.143
0.467-0.222-0.1220.020
300[.031][.096][.409][.131][.815]

0.040
0.098-0.182
0.003
0.122
344

[.733][.816][.665][.984][.461]

0.137
0.820-0.6200.290-0.353304[.088][.060][.185][.002][.003]

Notes: Equation (5)ln Ri ,t = a 0 + a1 ln ri ,t + a 2 ln wi ,t + a 3 ln k i ,t + a 4

BROKER i ,t
ASSET i ,t

+ a5

DISTR i ,t
ASSET i ,t

+ a6

MARGIN i ,t
ASSET i ,t

a 7 ln ASSET i ,t

+ a 8 AGE i ,t + a 9 SHARE i + a10 TOKYO i + c1 AGE i ,t ln ri ,t + c 2 AGE i ,t ln wi ,t + c 3 AGE i ,t ln k i ,t + c 4 SHARE i ln ri ,t+ c5 SHARE i ln wi ,t + c 6 SHARE i ln k i ,t + c 7 TOKYO i ln ri ,t + c8 TOKYO ln wi ,t i + c 9 TOKYO i ln k i ,t + u i ,t

is estimated by OLS estimation. See notes to Table 3.

38

Ｆｉｇｕｒｅ １ Issues of corporate bonds

hundred million yen
90000

80000

70000

60000

50000

40000

30000

20000

10000

0
1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

Note: Issues of private corporate bonds are shown from fiscal 1983 to 2002. They are measured in hundred million yen. The corporate bond is definedas the amount excluding those issued by nine electric power companies, Nippon Telegraph and Telephone Corporation and Japan Railway Companies.
Data source is Nikkei NEEDS Macro Data File.

39

Figure 2 Transaction of listed stocks

million yen
1400000

1200000

1000000

800000600000400000200000

0
1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

Note: Amount of stock transactions of stocks listed in the first section of Tokyo Stock Exchange is shown from 1983 to 2002. They are measured inmillion yen. Data source is Nikkei NEEDS Macro Data File.

40

Figure 3 Number of firms, branches and employees
350

170000330

160000310

150000290

140000270130000250120000230110000210100000

190

90000

170
150

80000
1983

1984

1985

1986

1987

1988

1989

number of companies (left scale)

1990

1991

1992

1993

1994

1995

number of branches (left scale x 10)

1996

1997

1998

1999

2000

2001

2002

number of employees (right scale)Note: The number of securities firms (left scale), number of their branches (left scale x 10), and number of their employees (right scale) are shown.
Data source is Nikkei NEEDS Macro Data File.

41

Figure 4 H-statistic based on eq. (4)
H
0.6
0.5
0.4
0.3
0.2
95%upper95%lower
mean

0.1
0
-0.1
-0.2
-0.3
-0.4
period 1

period 2

period 3

Notes: H-statistic, which is based on the estimation of eq. (4) ln R i ,t = a 0 + a 1 ln ri ,t + a 2 ln w i ,t + a 3 ln k i ,t + a 4 BROKER i ,t + a 5 DISTR i ,t
ASSET i , t+ a6

MARGINASSETi ,t+ a 7 ln ASSETi ,t+ u i , t is shown with its 95% confidence interval.

i ,t

42

ASSET i ,tFigure 5 H-statistic based on eq. (9)

H
0.5

0.4

0.3

0.2
95%upper95%lower
mean

0.1

0

-0.1

-0.2

-0.3
period 1

period 2

period 3

Notes: H-statistic. which is based on the estimation of eq. (9) ln Ri ,t = a 0 + a1 ln ri ,t + a 2 ln wi ,t + a3 ln k i ,t + a 4 BROKERi ,t + a5 DISTRi ,t+ a6 MARGINi ,t + a7 ln ASSETi ,t + ui ,t is shown with its 95% confidence interval.

43

Figure 6 H-statistic based on eq. (10)

H
1.2

1

0.8

95%upper95%lower
mean

0.6

0.4

0.2

0
period 1

period 2

period 3

Notes: H-statistic, which is based on the estimation of eq. (10) ln Ri ,t = a0 + a1 ln ri ,t + a2 ln wi ,t + a3 ln ki ,t + a4

+ a6

MARGIN i ,tREVi ,t+ a7 ln ASSETi ,t + ui ,t is shown with its 95% confidence interval.

44

BROKERi ,tREVi ,t+ a5

DISTRi ,tREVi ,t