Recent Competition in Japanese Life
Insurance Industry
Toshiyuki Souma
Yoshiro Tsutsui
Discussion Paper 00-19

December 2000
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

Recent Competition in Japanese Life Insurance Industry∗

Toshiyuki Souma(Osaka University)
Yoshiro Tsutsui(Osaka University and University of California, San Diego)

November 2000

Correspondence:
Yoshiro Tsutsui
Graduate School of International Relations and Pacific Studies,
University of California, San Diego
9500 Gilman Drive, La Jolla
CA 92093-0519 U.S.A.
Phone: +1-858-822-4314
Fax: +1-858-534-3939
e-mail: ytsutsui@ ucsd.edu∗

An earlier version of this paper was presented at MEW (Monetary Economics Workshop) and the 2000 Spring
Meeting of Japan Economic Society. The authors are grateful to Yasuhiko Tanigawa, Shinsuke Ikeda, Hirofumi
Uchida, Takashi Kaneko, Takao Ohkawa, Tomio Iguchi, and Andrew Coors for their comments.

Abstract
This paper examines transition in the level of competition in the Japanese life insurance
industry over the last ten years. We estimate the first order condition for profit maximizing
insurance oligopolies to obtain the degree of non-competition and collusion. Estimation results
suggest: 1) mutual companies, like stock companies, seek to maximize their own profits rather
than pay out dividends to policyholders, 2) the degree of non-competition has fallen since 1995,
and 3)

the degree of non-competition and collusion among incumbent firms are higher

compared with that of the whole industry.

JEL Classification Number: G22, L13, L21
Keywords: Life insurance, Degree of competition, Collusion, Japan1. Introduction
The purpose of this paper is to examine whether the Japanese life insurance industry has become
more competitive in the last ten years. The financial liberalization, initiated in the 1970s, has not
resulted in increased competition in traditional banking, securities, and insurance industries (seeIkeo, 1995 and Horiuchi, 1999). In particular, liberalization in insurance industry is behind that of
the other financial industries. Thus the level of competition and economic efficiency of the life
insurance industry has been considered low. 1

In 1996, a New Insurance Industry Law was

enforced; it resulted in the creation of eleven subsidiaries of non-life insurance companies, whichbegan doing business in the life insurance industry-- the number of life insurance companies
jumped to 41 instantaneously. In November of the same year, the Prime Minister Hashimotodeclared the commencement of Financial Big Bang, and by June of 1997, the insurance council
submitted a report that outlined the anticipated schedule of the liberalization for the following four
years. Although it is controversial whether the tempo of the scheduled liberalization is quick
enough, such a movement towards liberalization unambiguously suggests improvement of thecompetition in the life insurance industry. This paper tries to confirm this suggestion.
Tsutsui (1990) examined a transition of the competition in the life insurance industry fromthe end of the Second World War to 1986, using the industrial organization concepts of ‘market
structure’ and ‘market performances’. Tsutsui (1990) concluded that transition of market structure
and performances since 1980 suggested increase of competition. Considering that he found signsof liberalization in the data until 1986, we may find more vivid changes in the level of competition
in a more recent sample.

1

Chuma et al. (1993) estimate the technical efficiency of Japanese life insurance companies.

1

The original Insurance Industry Law was enacted in 1939 and remained intact throughout the
postwar period.

It was the final step of the transition to a system in which premium rates,

dividend rates, and solicitations were regulated. New entry has been strictly regulated since theSecond World War, leading to the maintenance of the so-called “20 firms system”. Indeed, noentry had been allowed until December 1975, when Seibu-All State obtained a business license(see Iguchi, 1996).

Revision of the Law in 1996 aimed to keep up with a possible transition fromthe regulated system to a liberalized one.
The new Law permits entry by establishing subsidiaries of non-life insurance companies.
Due to the Law, eleven life insurance subsidiaries of non-life insurance companies were
established in 1996. In 1997, the number of new entry firms since 1975 reached 21. The logicalquestion is the following: Does this expanded number of firms imply a considerable increase incompetition in the life insurance industry?
Although the number of the new entries is quite large, the share of the new entry firms istrivial. Transition of the share of the assets of the new entry firms to the total assets is shown in
Figure 1.

Although the share has been growing since 1986, it was still less than 1.5% in 1997.

If the new entry firms are remained as only fringe firms, their effect on competition may be limited.
According to ‘market structure-performance hypothesis ’, if the market concentration
decreases as the results of the new entry, the degree of competition should increase. To investigate
this point, it is possible to examine the market concentration. The Herfindahl index does this bytaking the total assets as a proxy for firm size. Its results are shown in Figure 2. The Herfindahl
index decreases from 1986 until 1991, but only slightly. In 1950, the Herfindahl index was at 0.1,
well below the recent level. What is unexpected is that the Herfindahl index increases in 1996 and2

1997, when extensive new entry occurred.

Thus, there is no evidence that the market

concentration decreased substantially in this period.
While the fact that many firms made entries in 1996 suggests improvement of competition,
the Herfindahl index suggests that on the contrary the degree of competition is unchanged.

Wewill examine which is really the case by conducting an econometric analysis. This paper takes
more theoretical approach than Tsutsui (1990) and directly estimates the degree of competition.
Specifically, we assume that the insurance companies conduct oligopolistic quantity competition.
Estimating the regression equations with panel data from 1986 to 1997, we make clear a transitionof the degree of competition in the period.
The rest of the paper is organized as follows. In the next section, we derive regression
equations to elucidate the behavior of mutual and stock companies and to estimate the degree ofcompetition. Section 3 is devoted to a presentation of estimation results. Section 4 summarizes
our conclusions.

2.

Model

2.1 Objective of mutual insurance companies
In this section, we derive a model for an estimation of the degree of competition, assuming that N
firms in the life insurance industry conduct quantity competition. 2

First, let us introduce thevariables used in this paper.
q i, t ：policies in force of firm i at period t

2

As for a survey of empirical studies on the degree of competition, see Martin (1993) and Bresnahan(1989).

3

N

Qt ≡ ∑ qi, t
j =1

I i,t ：premium income of firm i at period t

Zi ,t ：claims paid by firm i at period t
Di ,t ：dividends paid by firm i at period t
Ai,t ：outstanding assets of firm i at period t

ri ,t ：yields of assets of firm i at period t
Ci, t ：operating costs of firm i at period t

Then, profits π i, t of firm i at period t is:(1)

π i ,t = I i,t − Z i ,t − Di,t − Ci,t ( qi, t ) + ri ,t Ai, t≡ Pt (Qt ) q i,t − C i,t ( qi, t ) + ri ,t Ai, t ,where Pt (Q t ) ≡I i ,t − D i,t − Z i ,tqi ,tis the inverse demand function for life insurance, and Ci, t ( qi ,t ) is

the cost function of firm i . Here, we assume that dividends and the mean of claims to be paid are
known to policyholders, so that they regard net premium as the price of a policy. 3 Subtracting
claims paid at the definition of the price of an insurance policy implies that the policyholders buy
reduction of the risk of future income variation due to their death with this price.
The stock insurance company i chooses q i, t to maximize the profits, given Ai,t and ri ,t .4
The first order condition of the profit maximization is3

In reality, dividends and claims will be paid in the future periods. In our one period analysis, this aspect isdisregarded.
4
We assume that profits gained at period t are added into assets and are invested at period t+1.

4

(2)

∂π i ,t∂q i , twhere MC i,t ≡= Pt +dPtµ i, t qi ,t − MC i ,t = 0 ,dQt∂C i , t∂Qtand µ i,t ≡. Now, assuming that µ i,t is common for all the firms and∂qi,t∂qi, trewriting it µ t , equation (2) becomes(3)

Ri,t = MC i,t qi ,t +where ηt ≡ −Pt dQtQt dPtµt
R MS i,t ,ηt i , tis the price elasticity, MS i, t ≡

qi, t
Qt

is the market share of firm i , andRi,t ≡ Pt qi, t = I i ,t − Z i, t − Di, t .

Life insurance companies sell various kinds of policies, so that even if the policy in force is
identical for two companies, the price, and therefore Ri ,t , differs depending on the composite ofpolicies. In order to eliminate the effect of this composition of policies, we add the ratio of groupinsurance Gi ,t and the ratio of saving insurance Li,t to explanatory variables (see Tsutsui et al.,1992). Thus, the regression equation becomes(3’)

Ri,t = MC i,t qi ,t +µt
R MS i ,t + a1Gi, t + a 2 Li ,t .
η t i,tThe signs of a1 and a 2 are not known a priori. 5 We estimate (3’) together with the cost function
because marginal cost, MC, is not observable. We assume a translog cost function:
(4)

(

) (

)

2

ln Ci ,t = (b0,i + b0,t ) + b1,t ln qi, t − ln qt + b2 ln qi ,t − ln qt + a3Gi,t + a4Li,t ,5

An increase in saving life insurance results in an increase in premium income. It also leads to an increasein the amount of policy paid, however, so that the sign of a 2 is not determined.

5where ln qt ≡1 N
∑ln qi,t . Input prices are suppressed because of the data availability. We allow
N i=1

for time-variant intercepts bo,t , firm-specific intercepts bo,i , and firm-specific slopes b1,i . Timevariant slopes are tried, but they are not included in the final estimation because they are notsignificant. Expected sings of a3 and a 4 are negative and positive, respectively (see Tsutsui et al.,1992). Marginal cost is(5)

MC i ,t =C i ,t ∂ ln C i ,tq i, t ∂ ln q i ,t=C i, t[b + 2b (ln q − ln q )]1, t

q i ,t2i ,ttTherefore, equation (3’) now becomes

(3”)

(

)

Ri ,t = b1t Ci,t + 2b2 ln qi,t − ln qt Ci,t +µt
MSi ,t Ri,t + a1Gi ,t + a2 Li,t .
ηt

We estimate equations (3”) and (4) simultaneously, putting the restriction on the parameters overthe equations.
Most of the incumbent life insurance companies are mutual companies.

Because legal

owners of mutual companies are policyholders, dividends are not costs, but what the companies
should pursue. Thus, mutual insurance companies may maximize the surplus defined in equation(1) plus dividends. 6 In this case, assuming that Di,t is proportional to q i, t , the first order condition
becomes:(6)µ~
Ri,t = MC i,t qi ,t + t Ri, t MS i,t ,ηt~where Ri,t ≡ Pt qi, t + Di,t = I i, t − Z i, t . Following the same procedure as the above, we estimate
6

Note that the surplus is attributed to policyholders of mutual companies. Here, we disregard the fact that

6

(6’)

(

)

µ
~
Ri , t = b1t C i ,t + 2 b2 ln q i , t − ln q t C i , t + t MSi , t Ri , t + a1 Gi , t + a 2 L i ,tηttogether with equation (4).
Many people question that the mutual insurance companies are really regulated bypolicyholders, and argue that the mutual companies do not act differently from stock companies(see Komiya, 1994). On the other hand of the argument is that mutual companies are less efficientbecause the supervision by policyholders is weaker than by stockholders. 7 Therefore, it remains
controversial whether mutual life insurance companies operate for the advantage of policyholders,or they seek only surplus. 8

We will investigate which supposition is closer to the reality bycomparing equations (3”) and (6’).

2.2 Degree of non-competition
In what follows, we develop a model assuming that mutual companies maximize their profits.
Then we relax the assumption, and obtain results by substituting Ri,t in the left-hand side of the~obtained equations with Ri,t .
Let us defineµtmultiplied with market share by the variable

λi ,t .

Thus,

1
1
∂ Qt q i ,t
. Defining MS t ≡MS i ,t =and λt ≡ µ t MS t , λt is determined
N j=1
N∂ q i , t QtN

λ i ,t ≡ µ t MS i ,t ≡∑with the number of firms and conjectural variations, which represents the degree of noncompetition (Bresnahan, 1982). For example, λt = 0 corresponds to the perfect competition, andpolicyholders change over time, so that transfer problem of the surplus between policyholders emerges.
7
McKenzie (2000) reports that rate of return equation does not differ between mutual and stock companies,
while cost function does.

7λt = 1 , to a monopoly. In Cournot competition, when the number of firms is n , λt = 1 n . In thesimultaneous estimation of (3”) and (4), we obtain the estimate of µ t η t , but µ t cannot be
identified. Thus, we evaluate the possible transition of µ t , assuming that η t is constant over theestimation period.

2.3 Degree of collusion
In order to identify µ t , we conduct another analysis, putting a restriction on the conjecturalvariations (Clarke and Davies, 1982, and Alley, 1993). Specifically, we assume that when firm iincreases its production by a certain rate, the other firms j ≠ i increase α -times （ 0 < α t < 1 ）ofthat rate. Thus, for all i and for all j ≠ i ,(7)∆ q j ,t=αtq j, t∆q i, tqi , t.

If α t equals to unity, (7) means that firm i predicts that other firms respond to an increase of
firm i so as to keep the share of every firm unchanged. On the contrary, if α t equals to zero, itmeans that firm i predicts that other firms do not respond at all to its increase. This model
corresponds to a cooperative game, in which α t represents the degree of collusion. The formercase is interpreted as the perfect collusion, and the latter corresponds to non-cooperative Cournot
competition.
Assuming that the changes are infinitesimal, equation (7) can be rewritten as(8)8∂q j ,t∂q i,t= αtq j ,tq i,t.

Surplus is accumulated inside the company and can be spent in various ways by managers.

8Summing up this over all j ≠ i , equation (8) becomes
(9)

∂Q t
Q
− 1 = α t ( t − 1) , so that we obtain∂qi ,tqi, tµ i, t MS i,t = α t + (1 − α t ) MS i, t .

When α t = 0 , µ i, t = 1 , corresponding to the case of Cournot competition.
Assuming that µ i, t is constant over i , and substituting (9) into (3”), we obtain(10)

Ri,t − MC i,t qi ,t =αt1 −α tRi , t +MS i, t Ri ,t + a5 Gi ,t + a6 Li,t .
ηtηt

Therefore, first we estimate equation (4) to calculate the marginal cost, MC. Then, using thisestimate, we regress equation (10) to estimate α t and η t .

3. Estimation Results
Estimation period is from 1986 to 1997, and the samples are restricted to ‘domestic corporations ’defined by Insurance Industry Law. Data used for the estimation are I i,t , Z i,t , Di,t , q i, t , Ci, t , Gi ,t ,and Li,t , which are taken from Statistics of Life Insurance Business in Japan edited by Insurance
Research Institute.

3.1 Do mutual companies maximize profits or dividends?
First, let us examine which equation, (3”) or (6’) better describe the behavior of mutual insurancecompanies. Constructing
(11)

(

)

µ
~
Ri , t = b1t Ci ,t + 2 b2 ln q i , t − ln q t C i , t + t MSi , t Ri ,t + a1 Gi ,t + a 2 L i ,t + βD ,ηt9(3”) is derived when β = 1 , and (6’) is derived when β = 0 . So, equations (3”) and (6’) constitutea non-nested hypothesis.
We apply the double log likelihood ratio test (DLLR), in which we construct a general
specification, i.e. (11), which includes the two equations as nested hypotheses. Then we conduct
two likelihood ratio tests, (3”) against (11) and (6’) against (11), and compare the results.

Theestimation method used is three-stage least squares, and the instrumental variables are Gi ,t , Li,t ,

(ln q

)

2

i ,t −1()− ln qt −1 , d i ln q i,t −1 − ln q t−1 , d t MS i ,t −1 Ri, t−1 , Ri,t −1 , MS i,t −1 , D i,t −1 , and constants. The

variables d t and d i are respectively the time and firm dummies.
The test results are in Table 1. When mutual companies are taken as samples, thespecification that they maximize dividends plus profits is rejected at a 5% significance level, while
the hypothesis that they maximize profits is not rejected. The tests with the stock companies bringabout similar results. Thus, we conclude that both mutual and stock companies seek for only
profits, rather than dividends plus profits. The behavior of these two types of companies does notdiffer, at least, with respect of their objectives.

3.2 Results of the basic analysis
Given the results of the former subsection, we conduct the following analyses assuming that theboth mutual and stock companies maximize their profits. We define the two models, which wewish to estimate. The first one, described by equations (3”) and (4), shall henceforth be called theestimation of non-competition degree. The second, given by equations (10) and (4) will be called
the estimation of collusion degree.

10

Results of three-stage least squares estimation of non-competition degree are in Table 2. In
the estimation, we use

Gi ,t ,()

2

Li,t , ln q i,t −1 − ln qt −1 ,ln q i,t −1 − ln q t−1 ,()

d i ln qi, t −1 − ln q t−1 ,d t MS i,t −1 Ri ,t −1 , Ri,t −1 , d t , and constants as instrumental variables.

The model fits well: the determination coefficient of (3”) is over 0.99 and that of the translog
cost function is over 0.95. The coefficient representing the degree of competition, λ t ηt , takes on
the value of 0.25-0.29 from 1986 to 1994, and thereafter decreases remarkably to 0.11 in 1997.
Thus, we conclude that life insurance industry has become more competitive since 1995.
Table 3 presents the results of estimation of collusion degree. In this estimation, different
from Table 2, we first estimate equation (4) alone and construct the estimates of the marginal costs.
Then, (10) is estimated with instrumental variables method.

The index of collusion, α t , issignificantly positive for all years, rejecting the hypothesis of no collusion (i.e. Cournot
competition). While the value of α t does not reject the hypothesis of ‘perfect collusion (α t =1)’
until 1996, it decreases since 1992, and rejects the hypothesis in 1997. It seems that the decline
has been accelerated since 1995. The demand elasticity, η t , is significantly positive and takes on
the value between 0.88 and 1.18.
Both results of Tables 2 and 3 reveal that the life insurance industry have become morecompetitive since 1995, implying that this conclusion is robust for these two methods. The New
Insurance Industry Law was passed in the Diet and promulgated in 1995.

The insurancecompanies probably started various reformulations, including the preparation of the establishmentof their subsidiaries in 1995, to get ready for the enforcement of the Law in the next year. The
results reasonably reflect this fact.

11

3.3 Competition among 20 incumbent firms
In the previous subsection, we conduct the analysis using all firms in the industry as samples;
however, Tsutsui (1990) reports that the transition to the liberalization becomes ambiguous when
the sample is restricted to the incumbent 20 companies. This is plausible because incumbent firms
are probably not quick enough in responding to a new competitive environment brought about by a
new entry.

Thus, it is interesting to investigate whether the degree of non-competition andcollusion of 20 incumbent firms are higher than the whole samples.
The results of estimation of degree of non-competition with the data of 20 incumbents are
essentially similar to those of Table 2. In Figure 3, we plot λ t ηt for the cases of all firms and 20
incumbents. Looking at the Figure, we find that while the pattern of the transition is similar eachother, they are different with respect to the following two points:
1) The value of λ t ηt is higher for the case of 20 incumbent firms throughout the period except
1986, and
2) While λ t ηt began to fall since 1995 for the case of all firms, that for 20 incumbent firms
began in 1996 and was not as dramatic as the case of all firms.
These results suggest that the competition among 20 incumbent firms has become stronger since
1996, but not in comparison to competition among new entry firms.
In Figure 4, we plot α t for the cases of all firms and 20 incumbents. The degree of collusion
sharply rises in the late 1980s and then declines consecutively since 1989. Although this spike of
α t in the bubble period may reflect some change in the behavior of 20 incumbent firms, the fact

12

that α t exceeds unity is difficult to interpret from viewpoint of the degree of collusion. 9 Since
1993, α t takes the value less than unity and consistently declines, rejecting α t = 1 in 1997. Thisimplies that competition among 20 incumbents is getting stronger, at least, since 1993. Moreover,the fact that α t of 20 incumbent firms is larger than that of all firms implies that competition
among incumbent firms is weaker than among all firms.

4. Conclusions
In this paper, we examine a transition of the degree of competition in the Japanese life insurance
industry for these ten years. We first investigate whether the life insurance companies in Japan
seek for profits or dividends. Then, estimating the first order condition of the profit maximization
together with the cost function, we obtain the estimates of the degree of non-competition andcollusion. Our conclusions obtained from the estimation results are summarized as follows.
1) Mutual companies, like stock companies, seek for maximizing profits rather than dividends topolicyholders,
2) The degree of non-competition for all firms has fallen since 1995, when the new Insurance
Industry Law was promulgated, and
3) The degree of non-competition and collusion among incumbent 20 firms are higher comparedwith those of the whole industry.
Needles to say, this paper suffers from various problems.

First, in estimation of non-competition degree, we cannot infer the level of the non-competition -- even if its transition is9

α t = 1 is not rejected, however, for these periods.

13inferred by the assumption of constant price elasticity. 10 Second, the estimates of cost function
may be biased because of the lack of the input price data. Third, the model we employed is staticone.

Extension to a dynamic framework is a future agenda.

Finally, we assume quantity

competition to derive equations for estimation. This assumption is restrictive, and it is desirable
to derive theoretically the degree of non-competition corresponding to various types ofcompetition and examine which type of competition best explains reality.

10

There are other methods to estimate the degree of non-competition. The method of Bresnahan (1982) and
Lau (1982) uses the time-series data, however, and does not fit to the investigation of the short-termtransition of the degree of non-competition. The method of Panzar and Rosse (1987) requires data of input
prices, which are not available to us.

14

References
Alley, W. A. (1993) “Collusion versus Efficiency in the Japanese Regional Banking Industry,”
Economic Studies Quarterly, vol. 44, pp. 206-215.
Bresnahan, T. F. (1982) “The Oligopoly Solution Concept Is Identified,” Economics Letters, vol.
10, pp. 87-92.
Bresnahan, T. F. (1989) “Empirical Studies if Industries with Market Power,” in Shmalensee, R.
and R. Willig eds. Handbook of Industrial Organization, Amsterdam: North-Holland.
Chuma, H., T. Tachibanaki, and S. Takada (1993) “Seimei Hoken Gaisha no Koritsusei no
Keisoku, ” in Chuma, H. and T. Tachibanaki eds. Seimeihoken no Keizai Bunseki (Economic
Analysis of Life Insurance), Tokyo: Nippon Hyoron Sha. (In Japanese, Estimation of the
Efficiency of Life Insurance Companies)
Clarke, R. and S. W. Davies (1982) “Market Structure and Price-Cost Margins,” Economica, vol.
49, pp. 277-287.
Horiuchi, A. (1999) Nippon Keizai to Kinyu Kiki, Tokyo: Iwanami Shoten. (In Japanese, Japanese
Economy and Financial Crisis)
Iguchi, T. (1996) Gendai Hokengyo no Sangyo Soshiki, Tokyo: NTT Publishing Ltd. (In Japanese,
Industrial Organization of the Contemporary Insurance Industry)Ikeo, K. (1995) Kinyu Sangyo eno Keikoku, Tokyo: Toyo Keizai Shinpo Sha. (In Japanese, An
Alarm to the Financial Industry)
Komiya, R. (1994) ”The Life Insurance Company as a Business Enterprise,” in Imai, K. and R.
Komiya, eds. Business Enterprise in Japan: Views of Leading Japanese Economists,
Cambridge: MIT Press.
Lau, L. J. (1982) “On Identifying the Degree of Competitiveness from Industry Price and Output
Data,” Economics Letters, vol. 10, pp. 93-99.
Martin, S. (1993) Advanced Industrial Economics, Oxford: Blackwell.
McKenzie, C. (2000) “On the Conversion of Mutual Life Insurance Companies to Stock
Companies,” mimeo..
Panzar, J. C. and J. N. Rosse (1987) “Testing for ‘Monopoly’ Equilibrium,” The Journal ofIndustrial Economics, vol. 35, pp. 443-456.

15

Tsutsui, Y. (1990) “Seimei Hokengyo no Sijo Kozo to Seika,” Japan Financial Review, No. 12, pp.
21-40. (In Japanese, Market Structure and Performances of the Life Insurance Industry)
Tsutsui, Y., M. Sekiguchi, and T. Chano (1992) “Seimei Hokengyo no Kibo to Han’i no
Keizaisei,” Japan Financial Review, No. 15, pp. 1-15. (In Japanese, Scale and Scope
Economies of the Life Insurance Industry)16

Table 1 Results of the Double Log Likelihood Ratio Tests of the Objectives of
Mutual and Stock Life Insurance Companies(3”) against (11): p-values(6’) against (11): p-values

Mutual companies
0.796
0.045

Stock companies
0.530
0.028

Note: (3”) and (6’) represent models of the maximization of profits and maximization of dividendplus profits, respectively. (11) is a general specification that includes (3”) and (6’) as special cases.

17

Table 2 Estimates of Degree of Non-competition: All Firms
1986
μ／η
6.479
16.094t-valuenumber of firms
23
λ t / ηt
0.282
R squared of (3") 0.994

1987
6.606
17.080
24
0.275

1988
7.302
18.08825
0.292

1989 1990 1991 1992 1993 1994 1995 1996 1997
7.460 7.193 7.131 6.900 6.794 6.894 6.414 6.419 4.742
17.565 14.845 14.207 17.332 18.348 18.055 19.300 17.044 18.066
25
26
27
27
27
27
29
40
40
0.298 0.277 0.264 0.256 0.252 0.255 0.221 0.160 0.119

Note: Equations (3”) and (4) are jointly estimated by three-stage least squares.

18

Table 3 Estimates of Degree of Collusion: All Firms

at-value?
t-valuenumber of firms
R squared of (10)

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

0.961
5.375
0.948
6.567
23
0.999

0.951
6.468
0.945
7.844
24

0.962
7.473
0.961
8.827
25

0.963
6.934
0.969
8.142
25

0.981
6.172
0.994
7.279
26

0.992
5.933
1.012
7.003
27

0.972
6.444
0.996
7.661
27

0.952
6.904
0.980
8.256
27

0.924
6.528
0.959
7.839
27

0.879
7.483
0.921
9.184
29

0.841
6.878
0.894
8.575
40

0.828
7.895
0.887
10.77440

Note: Equation (10) is estimated by instrumental variables method.

19

Figure 1 Share of the Assets of New Entry Firms to the Total Assets
％
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
86

87

88

89

90

91

92

93

20

94

95

96

97 YearFigure 2 The Herfindahl Index (HI)

ＨＩ
0.15
0.14
0.13
0.12
0.11
0.1
0.09
0.08
0.07
86

87

88

89

90

91

92

93

21

94

95

96

97

Year

(λ t /ηt )Figure 3 Transition of Degree of Non-Competition (λ t / ηt )

0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
86

87

88

89

90
all firms

91

92

93
20 firms

22

94

95

96

97

Year

Figure 4 Transition of Degree of Collusion (a)

a
1.50
1.40
1.30
1.20
1.10
1.00
0.90
0.80
0.70
0.60
86

87

88

89

90
all firms

91

92

93
20 firms

23

94

95

96

97

Year