Discussion Paper No. 637

RECENT COMPETITION IN
THE JAPANESE LIFE INSURANCE INDUSTRY

Toshiyuki Souma
andYoshiro Tsutsui

June 2005

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Recent Competition in the Japanese Life Insurance Industry∗
Toshiyuki Souma(Kyoto Gakuen University)
Yoshiro Tsutsui†(Osaka University)
suggested running head: Recent Competition in the Japanese Life Insurance Industry

Abstract
This paper examines a change in the level of competition in the Japanese life insurance
industry over the last 17 years. We estimate the first order condition for profit-maximizing
insurance oligopolies to obtain the degree of non-competition and collusion.

Estimation

results suggest that: 1) not only stock companies, but also mutual companies maximize theirown profits rather than pay out dividends to policyholders; 2) competition has become stronger
since 1995; 3) revision of Insurance Industry Law and failures of insurance companies
promoted the competition; and 4) the competition in the recent years is still more lax than the
pre-war period.

JEL Classification Number: G22, L13, L21
Keywords: Life insurance, Degree of competition, Collusion, Japan∗An earlier version of this paper was presented at the Monetary Economics Workshop (MEW) and theAnnual Meeting of the Japan Economic Society. The authors are grateful to Charles Yuji Horioka,
Yasuhiko Tanigawa, Shinsuke Ikeda, Hirofumi Uchida, Takashi Kaneko, Takao Ohkawa, Tomio Iguchi,
and Andrew Coors for their comments.
†
Corresponding author: Yoshiro Tsutsui, Institute of Social and Economic Research, Osaka University,
6-1, Mihogaoka, Ibaraki, 567-0047 Japan, Phone: +81-6-6879-8560, Fax: +81-6-6878-2766, e-mail:tsutsui@econ.osaka-u.ac.jp

1. Introduction
The purpose of this paper is to examine whether or not the Japanese life insurance industry has
become more competitive in the last 17 years. The financial liberalization introduced in the
1970s has not resulted in increased competition in the traditional banking, securities, andinsurance industries (see Ikeo, 1995; Horiuchi, 1999). Liberalization in the insurance industry
in particular is lagging behind that of the other financial industries. The level of competition
and the economic efficiency of the life insurance industry have thus been considered low.1
In 1996, a new Insurance Industry Law was enacted. This resulted in the formation of 11
non-life insurance companies’ subsidiaries, which then began business in the life insurance
industry. The number of life insurance companies immediately jumped to 41. In November ofthe same year, Prime Minister Hashimoto declared the commencement of the Financial Big
Bang, and in June 1997 the Insurance Council submitted a report that outlined the anticipatedschedule of liberalization for the following four years. Although there is debate as to whether
the tempo of the scheduled liberalization was quick enough, such a movement toward
liberalization unambiguously suggests that competition in the life insurance industry is
improving. This paper attempts to confirm this suggestion.
A huge literature considers the efficiency, productivity, and the economies of scale and of
scope in the life insurance industry.2 These studies, however, mainly focus on US firms. Few
analyses are known regarding Japanese life insurance firms. Tsutsui et al. (1992) and Kitasaka(1996) analyzed scale and scope economies in the Japanese life insurance industry for 20
incumbent firms. Fukuyama (1997) considered the efficiency and productivity growth in theJapanese life insurance industry during the period 1988–1993. McKenzie (2002) estimated the1

Chuma et al. (1993) examined the technical efficiency of Japanese life insurance companies. They
reported that efficiency differs substantially between insurance companies and that it does not dependon the form of the company, i.e., whether it is a mutual or stock company.
2
Cummins and Weiss (2000) surveyed papers on these topics focusing on the studies using modern
frontier efficiency methodologies in both the life and property-liability insurance industries.

1

cost function, taking into account ownership types.
With regard to the competitiveness of the Japanese life insurance industry, Tsutsui (1990)
examined the change in competition in the industry from the end of the Second World War
until 1986, using the industrial organization concepts of ‘market structure’ and ‘market
performance’. He concluded that the change in market structure and performance since 1980
suggested an increase in competition. When considering that he found signs of liberalization in
the data available up until 1986, we may discover that there are more pronounced changes in
the level of competition to be revealed in a more recent sample.
This paper takes a more theoretical approach than that of Tsutsui (1990) and directly
estimates the degree of competition. Utilizing the regression equations with panel data from1986 to 2002, we clearly establish that there has been a change in the degree of competition
during that period. One merit of the estimation method used for this paper is that by using
panel data, the estimates of the degree of competition are given for each year.3 This enables us
to investigate the short-term changes in the degree of competition.4 The method proposed by
Bresnahan (1982) and Lau (1982) only reveals the average degree of competition for a long
period because it uses aggregated time series data.
The rest of the paper is organized as follows. In the next section, the recent status of theJapanese life insurance industry is summarized. In section 3, we derive regression equations toclarify the behavior of mutual and stock companies, and to estimate the degree of competition.
Section 4 is devoted to a presentation of the estimation results. Firstly, we show theconclusions derived for the purpose of insurance companies. Secondly, we present theindicated levels of non-competition and collusion. Thirdly, the causes for change in these

Angelini and Cetorelli (1999) analyzed 1983–97 panel data for Italian banks to estimate the Lernerindex.
4
The method of Panzar and Rosse (1987) enables us to estimate the degree of competition for each year.
However, it requires data on input prices for each insurance firm, which are not available to us.
32

competitive indexes are examined. Fourthly, the recent degree of competition is compared withthat which existed in the pre-war period. Section 5 summarizes our conclusions.

2. The Japanese Life Insurance Industry from 1986 to 2002
2.1 The life insurance business under the Bubble Economy and the Heisei Depression
The period from 1986 to 2002, which we analyze in this paper, consists of the bubble period
and the long stagnation. The purpose of the analysis is to evaluate to what extent liberalization
proceeded in these 17 years and how the non-competitive situation, which was dominant
throughout the post-war period, has changed. We need to be careful that the dramatic
movements in Japanese business conditions over these 17 years do not in fact conceal along-term structural change, if such exists.
The Japanese economy enjoyed a boom, the so-called ‘Bubble Economy’, in the late 1980s.
However, after the stock price bubble burst in 1990 and the land price bubble did likewise in
1991, the Japanese economy fell into a long period of stagnation called ‘the Heisei Depression’.
In Figure 1, we show the GDP growth rate and the growth rate of the value of policies in force
over the whole life insurance industry. The GDP growth rate has been close to zero since 1992.
While it rose temporarily in 1995 and 1996, it fell to a negative figure in 1998, and except for2000 remained negative. Figure 1 shows that the contrast between the boom in the late 1980s
and the depression in the 1990s is even more pronounced for the life insurance industry.
Specifically, whilst the annual growth rate of the value of policies in force was over 10% in thelate 1980s, it declined and reached –10% in 1997 and was negative in consecutive years
thereafter except for 2001.
Figure 2 shows the change in the current profit rate as the current profit/asset ratio of thecorporate sector for all industries and of the life insurance industry for the same period.
Whereas the profit rate of the whole industrial sector declined from the 1980s to the 1990s, the3decline of that rate within the life insurance industry was much larger, as that sector
deteriorated more than the average for the whole of the sector. The profit rate of the life
insurance business was less than 1% after 1993, whereas that for the whole of industry was
greater than 2%.
It is not easy to discover the reasons for this deterioration in the life insurance sector
during the 1990s. There is no doubt, however, that the long-term contracts of companies were
one cause. Life insurance contracts imply the collection of a premium and payments of claims
over a couple of decades, however revenues were not necessarily invested in equivalent
long-term assets. Insurance companies thus assumed interest rate variation risks. Such risks
were realized in the depression following an unanticipated fall in interest rates when the
average for a loan fell from 7.6% in 1991 to 1.9% in 2001.
In Figure 3, the change in yield of assets and the assumed interest rate are shown. The
assumed interest rate is the expected return on the assets of the company considered in setting
the insurance premium rate. To maintain solvency, the assumed interest is set at a lower level
than the premium rate, and the ex-post gain is distributed to policyholders as dividends. In
Figure 3, data on the yield of assets are taken from Insurance: issue of life insurance statistics,
and data on assumed interest rates are taken from Toyo Keizai Weekly: special issue of life
insurance. The assumed interest rate is not a new one, but the average such rate for outstanding
contracts that have been available since 1992. Yields of assets have been declining since 1987,
falling remarkably after the burst of the bubble. Although the assumed interest rate has also
been lowered, the back spread ran to 1.57% in 1993 and was not dissolved in 2002, because the
new assumed interest rate in the bubble period was over 6%.
Reflecting these severe business conditions, several insurance companies, including
medium-sized ones such as Nissan, Toho, Daihyaku, Taisho, Chiyoda, Kyoei, and Tokyo Life,
went bankrupt in the 1997–2000 fiscal years.

42.2 New entry and exit
The original Insurance Industry Law was established in 1939 and remained intact throughout
the post-war period. This enactment was the final step in the transition to a system in which
premium rates, dividend rates, and solicitations were regulated. The entry of new firms wasstrictly regulated following the Second World War, leading to the maintenance of the so-called‘20 firms system’. Indeed, no new entry was allowed until December 1975, when Seibu-All
State obtained a business license (see Iguchi, 1996). The revision of the Law in 1996 wasaimed at keeping pace with an expected transition from the regulated system to a liberalizedone.
In the post-war period, financial institutions, banking, securities, and insurance companies,
have been segregated from each other. Although in 1993 banks and securities companies wereallowed to enter each other’s domain by creating subsidiaries, the insurance sector has been
kept isolated from the other industries. Deposit interest rates were thoroughly deregulated in
1993 and 1994, and the trade commissions for stocks trading were deregulated in 1998 and1999. However, the rates of non-life insurances were not deregulated until 2001, and thepremium and dividend rates of the life insurance industry still seem to be under the control of
the authorities. Essentially, the insurance industry has been left behind by the financial
liberalization of the 1980s and 1990s, so that it has remained uncompetitive.
The most remarkable change in the life insurance industry was brought about by therevision of the Insurance Industry Law. The new law permitted mutual entry between life andnon-life companies through the establishment of subsidiaries.5 Thereafter, a series of failures
and mergers of small and medium-sized firms occurred.
The failure of Nissan Life Insurance in April 1997 collapsed the myth that life insurance
5

Life and non-life insurance companies were permitted to enter the third sector of insurance, which isrepresented by medical and personal accident insurance, from July 2001.

5

companies never go bankrupt. This failure may also have indicated the end of the so-called‘convoy administration system’ and may have disciplined life insurance firms. On the other
hand, this failure triggered the passage of many bills treating insolvency cases in suchcompanies. For example, in order to protect the policyholders of failed companies, the Life
Insurance Policyholders Protection Corporation was formed in December 1998. Thereafter, abill for a prompt corrective action was promulgated in December 1998 and enforced in April
1999. The establishment of this act caused each of the life insurance companies on the verge ofinsolvency to form a capital alliance with, and become a subsidiary of, a foreign-based
company.6
In spite of these endeavors, the five small and medium-sized life insurance companies,
Daihyaku, Taisyo, Chiyoda, Kyoei, and Tokyo, collapsed in the 2000 fiscal year. The mergerof parent non-life insurance companies led to the merger of their life subsidiaries.7
In summary, between 1975 and 2002, more than 20 new firms entered the market. For thesame period, seven firms exited. These changes resulted in an increase in the number of life
insurance companies from 1975 to 1999, whereas the number decreased from 1999 to 2002(see Figure 4). In view of these new entrants, a logical question is: Does this change in thenumber of firms reflect a change in competition in the life insurance industry?
According to the ‘market structure–performance hypothesis’, if the market concentration
decreases as the result of a new entry, the degree of competition should increase. Therefore, let
us investigate how the market concentration has changed in the 17-year period. We use theHerfindahl index, taking the total assets as a proxy for firm size. The results are shown in
6

For example, Toho Mutual Life and Daihyaku Mutual Life formed a capital alliance respectively with
GE Capital and Manulife Financial of Canada to establish their joint venture. Both companies, however,failed after about a year. Heiwa Life and Nippon Dantai Life become a subsidiary of Aetna and Axa
groups in March 2000, respectively.
7
For example, in April 2001, Nippon Fire Partner Life and Koa Life merged into NIPPONKOA Life,
and Chiyodakasai EBISU Life and Dai-Tokyo Happy Life merged into Aioi Life. Mitsui Mirai Life andSumitomo Yu-Yu Life merged into Mitsui Sumitomo Kaijo Life in October 2001.

6

Figure 5. The index decreases from 1986 until 1991, but only slightly. In 1950, the index was
0.1, well below the recent level. Unexpectedly, the Herfindahl index increases for the years
after 1996, although extensive new entry occurred at that time. There is thus no evidence thatthe market concentration decreased substantially in this period.
Whilst the large number of new entrants in 1996 suggests an improvement in competition,
the increase in the Herfindahl index after 1996 suggests that, if we rely upon the standard‘market structure–performance hypothesis’, the degree of competition decreased. The‘efficiency structure hypothesis’ proposed by Demsetz (1973), however, contrarily predicts thatthe increase in the Herfindahl index is caused by the enhanced competition. We will examine
which scenario is really the case by conducting a regression analysis in section 4.3.

3. The Model
3.1 The basic model
In this section, we derive a model to estimate the degree of competition.8 First, let us introduceN

the variables used in this paper. q i ,t is the value of the policies in force, Qt ≡ ∑ qi ,t , I i ,t isi =1

premium income, Zi ,t represents claims paid, Di ,t represent dividends paid, Ai ,t isoutstanding assets, ri ,t represents yields of assets, and C i ,t is operating costs. Subscripts iand t represent firm i and period t . Then, the profits π i,t of firm i at period t are:

π i ,t = Pt (Qt )qi ,t − C i ,t (qi ,t ) + ri ,t Ai ,t − ∆RSVi ,t(1)where Pt (Qt ) is the inverse demand function for life insurance, C i ,t (q i ,t ) is the cost functionof firm i , and ∆RSVi ,t stands for the change in reserve of firm i . Here we assume that8

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

7

dividends and the mean of claims to be paid are known to policyholders, so that they regard thenet premium as the price of a policy.9 Subtracting claims paid at the definition of the price ofan insurance policy implies that, with this price, the policyholders buy a reduction of the risk of
future income variation by their death.
The stock insurance company i chooses q i ,t to maximize the profits, given Ai ,t , ri ,t ,and ∆RSVi ,t .10 From the first order condition of the profit maximization, we obtain:
Ri , t = MCi , t qi , t +where MCi ,t ≡∂Ci ,t∂qi ,tµt
R MS ,
ηt i , t i , t(2)

is the marginal cost, η t ≡ −Pt dQtQt dPtis the price elasticity, MS i ,t ≡is the market share of firm and Ri ,t ≡ Pt q i ,t = I i ,t − Z i ,t − Di ,t . µ i ,t ≡q i ,tQt∂Qt
is assumed to be∂qi ,tcommon for all the firms and is denoted as µ t in order to capture the industry average degreeof competition.11
We estimate (2) together with the cost function because marginal cost, MC, is notobservable. We assume a translog cost function:(

ln Ci,t = b0,t + b0,i + b1,i ln qi,t + b2 ln qi,t

) + b ln w + b ln p + b (ln w ) + b (ln p )
2

2

3

t

+ a3Gi,t + a4 Li,t ,

4

t

5

t

2

6

t

(3)

where w is the wage rate of the finance and insurance industries and p is the deflator of fixed
capital formation. These data are not available for each firm, so we use the data which vary
only over time. ln q, ln w, and ln p are deviations from their means. 12 We allow for9

In reality, dividends and claims will be paid in future periods. In our one-period analysis, this aspect isdisregarded.
10
We assume that profits gained at period t are added into assets and are invested at period t+1.
11
See Bresnahan (1989) for this point.
12
Using deviations from the means in the translog function is a convention to avoid possible
multi-colinearity.

8

time-variant intercepts bo ,t , firm-specific intercepts bo ,i and firm-specific slopes b1,i . The
ratio of group insurance Gi ,t and the ratio of saving insurance Li ,t are added to the costfunction to eliminate the effect of the composition of various kinds of policies. The expected
signs of a3 and a4 are negative and positive, respectively (see Tsutsui et al., 1992).
Substituting the definition of marginal cost, equation (2) now becomes:
Ri ,t = b1,iCi ,t + 2b2 ln qi ,t Ci ,t +µtMSi ,t Ri ,t + a1Gi ,t + a2 Li ,t .
ηt(4)Here, Gi ,t and Li ,t are added to the first order condition to eliminate the effect of thecomposition of various kinds of policies. The signs of a1 and a2 are not known a priori.13
We estimate equations (3) and (4) simultaneously, putting the restriction on the parameters
over the equations.
We obtain the estimate of µ t η t , but µ t cannot be identified. Thus, we evaluate thepossible change in µ t , assuming that ηt is constant over the estimation period. Multiplyingµ t with market share, we get the degree of non-competition λt (Bresnahan, 1982). λt = 0
corresponds to perfect competition and λt = 1 to a monopoly. In Cournot competition, when
the number of firms is n , λt = 1 n .
3.2 The degree of collusion
In order to distinguish µ t from ηt , we conduct another analysis, putting a restriction on the
conjectural variations (Clarke and Davies, 1982; Alley, 1993). Specifically, we assume thatwhen firm i increases its production by a certain rate, the other firms j ≠ i increase α-times( 0 < α t < 1 ) of that rate. Thus, for all i and for all j ≠ i :13

An increase in savings life insurance results in an increase in premium income. However, it also leads
to an increase in the amount of the policy paid, so that the sign of a2 is not determined.

9∂q j ,t∂q i ,t= αtq j ,tq i ,t.

(5)

If α t equals unity, (5) means that firm i predicts that other firms will respond to anincrease of firm i ’s production so as to keep the share of every firm unchanged. Alternatively,
if α t equals zero, it means that firm i predicts that other firms will not respond at all to its
increase in production. This model corresponds to a cooperative game, in which α t represents
the degree of collusion. The former case is interpreted as perfect collusion and the lattercorresponds to non-cooperative Cournot competition.
The concept of conjectural variation is popular in both applied theoretic and empiricalindustrial organization. Theorists of industrial organization, however, take a dim view of its ad
hoc assumptions about the conduct of firms, its lack of a game-theoretical foundation, and theforcing of dynamics into an essentially static model in which the strategy space and the time
horizon of the underlying game are only loosely defined (Fellner, 1949; Friedman, 1983,
p.110; Daughety, 1985; Makowski, 1987; and Tirole, 1989, pp. 244–245). These shortcomings
are often perceived as the cost that the modeler must pay for realism without compromising
simplicity and tractability. However, it is fortunate that Dockner (1992), Cabral (1995), andPfaffermayr (1999) showed that the concept of conjectural variation can be supported by aconsistent theoretical foundation if it is considered a reduced form of a dynamic game.14 Theirfindings can be used to justify a static conjectural variations analysis for both modelingdynamic interactions and estimating the degree of oligopoly power. From the same viewpoint,

14

Using an infinite horizon adjustment cost model, Dockner (1992) demonstrated that any steady stateclosed-loop (subgame-perfect) equilibrium coincides with a static conjectural variation equilibrium with
nonzero conjectures. Cabral (1995) proved that in linear oligopolies, and for an open set of values of the
discount factor, there exists an exact correspondence between the conjectural variation solution and thesolution of a quantity-setting repeated game with minimax punishments during T periods. Pfaffermayr(1999) followed an idea put forward by Cabral (1995) and demonstrated that the conjectural variation model
can be interpreted as the joint-profit-maximizing steady-state reduced form of a price-setting supergame in adifferentiated product market under optimal punishment strategies.

10

we believe that the use of the static model is rationalized by considering it as a reduced form ofan (unmodeled) dynamic game.
Summing up (5) over all j ≠ i , we obtain:µ i ,t MS i ,t = α t + (1 − α t ) MS i ,t .

(6)

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 (6) into (4), we obtain, instead of (4):Ri ,t = b1,i Ci ,t + 2b2 ln qi ,t Ci ,t +αt1−αtRi ,t +MS i ,t Ri ,t + a5 Gi ,t + a6 Li ,t
ηt
ηt

(7)

3.3 The objective of mutual insurance companies
Two ownership structures, mutual and stock, coexist in the life insurance industry, since life
insurance was started as mutual aid. Thus, the question: ‘Which is the better organizational
form, a stock or a mutual company?’ has been the concern of much research.15 For example,
Mayers and Smith (1986) examined the relative efficiency of a stock versus amutual-ownership structure from the perspective of the agency cost approach. Using the data of
30 companies changing from a stock to a mutual structure, they concluded that this change ison average efficiency-enhancing, whereas Spiller (1972) concluded that the mutual company isless efficient.
Mayers et al. (1997) examined the relationship between board structure and organizational
forms and found that mutual companies employ a significantly larger fraction of outside
directors than do stock companies. These studies, however, mainly focus on US firms.
Nevertheless, quite a few consider the Japanese life insurance industry. Notable are Fukuyama(1997) and McKenzie (2002), which compare the efficiency of Japanese firms of both
15

Mayers and Smith (2000) produced a detailed review of the literature relevant to this topic in theinsurance industry.

11

organizational forms. Fukuyama (1997) found that mutuals and stocks had an identicaltechnology and that the major sources of overall technical inefficiency were pure technicalinefficiency in the case of mutuals and scale inefficiency in the case of stocks. He concluded
that it is not clear which organizational form, mutual or stock, is preferable in the Japanese life
insurance industry. McKenzie (2002) reported that the rate of return equation does not differ
between mutual and stock companies, whereas the cost function does.
Most of the incumbent life insurance companies in Japan are mutual companies.16 Because
the legal owners of mutual companies are policyholders, dividends are not costs, but represent
the main objective that the companies should pursue. Mutual insurance companies may thus
maximize the surplus defined in equation (1) plus dividends.17 In this case, assuming that Di ,tis proportional to q i ,t , we obtain:µ~Ri ,t = b1,i C i ,i + 2b2 ln q t C i ,t + t MS i ,t Ri ,t + a1Gi ,t + a 2 Li ,t
ηt

(8)

~
where Ri ,t ≡ Pt qi ,t + Di ,t = I i ,t − Z i ,t .
Many people question if the mutual insurance companies are really regulated by thepolicyholders, arguing that the mutual companies act no differently from stock companies (seeKomiya, 1994). On the other hand, some insist that mutual companies are less efficient because
the supervision by policyholders is weaker than that by stockholders. It therefore remains
controversial whether or not mutual life insurance companies operate for the advantage ofpolicyholders, or if they only seek profits. We will investigate which situation is the closer toreality by comparing equations (4) and (8).
16

Out of the 20 firms existing in 1997, 16 were mutual companies. In recent years, however, it has beenargued that the stock company is a more flexible style of insurance company, and consequently, themutual companies are examining conversion to stock companies. Daido Life converted to a stockcompany on April 1, 2002.
17
Note that the surplus is attributed to policyholders of mutual companies. Here we disregard the factthat policyholders change over time, so that the problem of the transfer of the surplus between
policyholders emerges.

124. The Estimation Results
Our estimation period is from 1986 to 2002, and the samples are restricted to ‘domestic
corporations’ as defined by the Insurance Industry Law. Data used for the estimation are I i ,t ,Z i ,t , Di,t, q i ,t , C i ,t , Gi ,t , and Li ,t , which are taken from Statistics of Life Insurance Businessin Japan, edited and published by the Insurance Research Institute, together with wt and pt ,which are taken from the NIKKEI NEEDS Macro data file.
4.1 Do mutual companies maximize profits or dividends?
Firstly, let us examine which equation, (4) or (8), better describes the behavior of mutual
insurance companies. Constructing:µ~
Ri ,t = b1,i C i ,t + 2b2 ln q t C i ,t + t MS i ,t Ri ,t + a1Gi ,t + a 2 Li ,t + βDi ,t ,ηt(9)(4) is derived when β = 1 , and (8) is derived when β = 0 . Therefore, equations (4) and (8)constitute a non-nested hypothesis.
We apply the double log likelihood ratio test, in which we construct a general specification,i.e., (9), which includes the two equations as nested hypotheses. Then, we conduct two
likelihood ratio tests, (4) against (9), and (8) against (9), and compare the results. The
estimation method used is three-stage least squares.18 The test results are presented in Table 1.
When mutual companies are taken as samples, the specification that they maximize dividends
plus profits is rejected at the 1% significance level, whilst the hypothesis that they maximize
profits is not rejected. The same results are obtained when the stock companies are taken as
18

The instrumental variables are Gi,t , Li ,t , (ln q t −1 )2 , d i ln q t −1 , d t −1 MS i ,t −1 Ri ,t −1 , ln p t −1 , ln wt −1 , (ln pt−1 )2 ,(ln w ) , R
2t −1

i , t −1

, MSi ,t −1 , d t , di , Di ,t −1 and constants. The variables d t and di are the time and firm

dummies, respectively.

13

samples, as we expected. Thus, we conclude that both mutual and stock companies seek only
profits, rather than dividends plus profits. The behavior of these two types of company does notdiffer, at least with respect to their objectives.
4.2 The results of the basic analysis
Given the results of the former subsection, we conduct the following analyses assuming thatboth mutual and the stock companies maximize their profits. We define the two models that wewish to estimate. The first model, described by equations (3) and (4), shall henceforth be called
the estimation of the degree of non-competition. The second model, given by equations (3) and(7), will be called the estimation of the degree of collusion.
The result of the three-stage least squares estimation of the degree of non-competition isshown in Table 2.19 The model fits well since the determination coefficient of equation (4) isover 0.99 and that of the translog cost function (3) is 0.90.
In the estimation of first order condition (4), the coefficients of group insurance and saving
insurance a1 and a2 are significantly positive, implying that companies with more of theseinsurances tend to earn more revenues. On the other hand, a3 and a4 are not significant in thecost function, implying that these variables do not affect costs. b2 , which appears in both (3)and (4), is positive and highly significant and the b1,i are all positive; 28 of the 52 b1,i are
significant at the 5% level. However, the coefficients of ln wt and ln pt and their squared
terms are all insignificant. This is probably because yearly data only are available for thesevariables. µt /ηt are all highly significant, implying that the market has not been perfectlycompetitive.

In the estimation, we use Gi,t , Li ,t , (ln q t −1 )2 , d i ln q t −1 , d t −1 MS i ,t −1 Ri ,t −1 , ln p t −1 , ln wt −1 , (ln pt−1 )2 ,(ln wt −1 )2 , R i ,t −1 , MSi,t −1 , d t , di , and constants as instrumental variables.

19

14

The estimates of the degree of non-competition λt ηt are depicted in Figure 6, together
with their 95% confidence interval. This takes on a value between 0.25 and 0.31 during theperiod from 1986 to 1994, and then decreases sharply, reaching 0.17 in 1996. Thereafter, itremains almost constant. We thus conclude that the life insurance industry has become morecompetitive since 1995. The new Insurance Industry Law was passed in the Diet andpromulgated in 1995. The insurance companies probably started various reforms, including the
preparation of the establishment of their subsidiaries in 1995, to prepare for the enforcement of
the law in the next year. The results reasonably reflect this fact.
Table 3 presents the estimation results of the degree of collusion. The coefficient of groupinsurance has a significantly negative sign in this case, whilst that of saving insurance has apositive sign in equation (7). The former is a different result from that in Table 2, but the sign
is not known a priori. Group insurance has a significant negative coefficient in the costfunction as expected, whereas that of saving insurance is insignificant. Notably b2 is positiveand highly significant. Estimates of b1,i are all positive, and 34 of the 46 b1,i are significant
at the 5% level. 20 Input prices are not significant in the cost function.
Estimates of the degree of collusion α t are close to unity and highly significant, implyingthat the market was close to perfect collusion and Cournot competition is rejected. Estimates ofdemand elasticity ηt are also close to unity and decrease over the sample period.
The estimates of the degree of collusion α t are depicted in Figure 7 together with their
95% confidence interval. α t remains almost at the same level around unity until 1991, andthen falls sharply, reaching 0.7 in 2002. The degree of collusion does not reject perfect20

In this estimation, we assume that six firms that existed for only two years of our sample period havethe same firm-specific intercepts bo,i and firm-specific slopes b1,i as each other in order to avoid the

problems of the singularities of the data and derivatives.

15

collusion until 1994, but rejects it thereafter. However, the value of α t suggests that thecompetition is still lax. Whereas α t rejects ‘perfect collusion’ at the 5% significance level
after 1995, it is far from Cournot competition.
The result for the degree of collusion is consistent with the results of the degree of
non-competition in that the insurance industry became competitive over the whole observation
period. However, the patterns are somewhat different. The degree of collusion remained
constant until 1991, and then decreased monotonically. On the other hand, the degree of
non-competition decreased moderately until 1994, and fell sharply in 1995 and 1996, tobecome stagnant thereafter.
Which pattern is more reliable? We should notice that the degree of competition assumes
constant demand elasticity η t . If this elasticity varies substantially, the estimates do not
represent competitiveness correctly. We obtained the estimates of η t from the estimation ofcollusion. So let us apply the estimates to calculate λt . The results are presented in Figure 8
and in the right-hand column of Table 2. Looking at the figure, we notice that it rejects
monopoly ( λt = 1 ) as well as Cournot oligopoly ( λt = 1 n ) throughout the period. That theCournot oligopoly is rejected reflects the same outcome as for the degree of collusion.
However, the fact that monopoly is rejected might contradict the result that perfect collusion
prevailed until 1994. The pattern of the graphs shows that the degree of non-competition wasalmost constant around 0.35 until 1991, which is consistent with the observed degree ofcollusion. However, the degree of non-competition is seen to have been stagnant after 1996.
This agrees with Figure 6 and differs from that of the degree of collusion.
We are not sure which pattern is correct a priori. However, considering that various policy
measures were taken that related to the failure of insurance companies, the results indicatingthat competition improved consecutively after 1995, the pattern of collusion, best fit our

16intuition.
4.3 Causes of the progress in competitivenessIn this subsection, let us try to find out how the degree of collusion and degree of
non-competition depend on the various elements. Here, we consider the effect of deregulation,i.e., the promulgation of the Insurance Law and the change of policy that ended the convoy
system and allowed the failure of insurance firms. In addition, we consider the variables
representing business condition (composite index) and market concentration (Herfindahl index).
The regression equation is

Yt = A0 + A1CIX t + A2 HI t + A3 DILt + A4 DFAILt + ut ,Y = α , λ / η , or λ(10)Here, CIX is the composite index, HI is the Herfindahl index, DIL is a dummy variable that
represents the revision of the Insurance Industry Law, which takes the value 1 in 1995 and1996, and 0 otherwise, and DFAIL is a dummy variable that represents the failure of insurancecompanies, which takes the value 1 in the fiscal years 1997–2000, and 0 otherwise.
If these new policies have a significant effect on competition, A3 and A4 are negative. We
cannot predict the sign of business conditions a priori. The coefficient of the Herfindahl index
will be positive if the structure–performance hypothesis applies, and negative if theefficient-structure hypothesis is the case (Demsetz, 1973, Berger, 1995).
The estimation results are presented in Table 4. In the left columns, the results for the
degree of collusion are shown. The coefficient of composite index is significantly positive,
implying that competition was more lax than when business conditions were better. The
coefficient of the Herfindahl index is significantly negative, supporting the efficient-structure
hypothesis. Herfindahl index rose after 1995 because the insurance companies became more
efficient. A3 and A4, which represent the effect of policy measures, are negative, implying that17

these changes in policy really promoted competition.21
In the middle and right columns, we show the results of λ / η and λ , respectively. Here,
we calculate λ using the value of η in the estimation of the degree of collusion. They
confirm the results based on α except that the business condition does not affect competitionin the case of λ and λ / η . Thus, we conclude that the recent deregulation policy, including
the revision of the insurance law and the abolition of the convoy administration, made theinsurance industry more competitive.
4.4 Comparison with the pre-war period
It is said that in the pre-war period, the competition amongst life insurance companies wasquite severe (Tsutsui et al., 2004). Premium and dividend rates were not regulated until 1937,
nor was sales staff activity (in terms of insurance solicitation) restricted until 1931. Insurancecompanies tried to mitigate competition by means of different premium-dividend schemes in
1917–1937 (Tsutsui et al., 2000).
It is interesting to investigate, therefore, whether the current degree of competition is as
great as that in the pre-war period. We estimated the degree of non-competition and degree ofcollusion using cross-section data for 1916, 1917, and 1922, when the competition was quite
fierce. It was not necessary to include wt and p t because the regression is cross-sectional,
and Gi ,t and Li ,t were omitted from our estimations because of the unavailability of data.
In Figure 7, we show the degree of collusion for those years. It is about 0.5 and lower thanfor recent years, suggesting that the current competition is still weaker than during the pre-war
period.
Revision of the Insurance Industry Law induced a remarkable increase in the number of firms, so that
we estimated eq. (10) replacing DIL with the net change in their number. The coefficient was negative,
but the p value was only 15%. We also estimated eq. (10) replacing DFAIL with the asset value of thecompanies in default. The coefficient was negative, but insignificant.
21

18

The degree of non-competition for the pre-war years is shown in Figure 6. These values
are around 0.19 and slightly higher than that for the current years. However, since the degree of
non-competition is divided by demand elasticity, and since it is not convincing to assume thiselasticity to have been the same for the pre- and post-war periods, we should not directlycompare these values. Let us therefore adopt the value of η t for the estimation of the degreeof collusion and multiply these estimates to get λt . The result is shown in Figure 8. λt in
2002 is 0.15, whilst it is 0.134 in 1916, suggesting that the current competition is almost at thelevel that was attained during the pre-war period.
Which result, Figure 7 or Figure 8, is more appropriate with regard to the level ofcompetition? The suggestion that the current competition is as severe as that which occurred in
the pre-war period when there were no particular policies that would restrict competition is notvery convincing. In this regard, the collusion result appears to be the more reliable.

5. Conclusions
In this paper, we have examined the change in the degree of competition existing in theJapanese life insurance industry for the 1986–2002 period. Firstly, we investigated whether
Japanese life insurance companies of both stock and mutual structure seek profits or dividends.
Then, estimating the first order condition of the profit maximization, together with the costfunction, we obtained estimates of the degree of non-competition and collusion. We alsosought the causes of progress in competition and compared the competitiveness with that of the
pre-war period. The conclusions we obtained from the estimation results are summarized asfollows:
1) Both stock and mutual companies seek to maximize profits rather than dividends topolicyholders.

192) The degree of non-competition fell until 1996, and has remained almost unchanged
thereafter. It fell particularly sharply in 1995 and 1996, when the new Insurance Industry Law
was promulgated and enforced.
3) The estimation of the degree of collusion reveals that competition has improved
monotonically since 1992. The degree of collusion does not reject perfect collusion until 1994,
but rejects it thereafter.
4) The degree of collusion has improved following policy changes such as the revision of the
Insurance Industry Law and the abolition of the convoy system, since which the failure of
insurance companies has been admitted. The Herfindahl index has a positive correlation withthe degree of competition, as the efficient-structure hypothesis predicts.
5) The degree of collusion in the later years is greater than that for the pre-war period,
suggesting that there remains room for more competition.
Let us compare the results of this paper with those available for the competitiveness of theother Japanese financial industries. Uchida and Tsutsui (2005), using an approach similar to theone applied in this paper, considered the Japanese banking industry and estimated the degree ofcompetition from 1974 to 2000. They found that the market had become more competitive inthe 1970s, and judged that the city banks faced perfect competition in the middle of the 1990s.
Estimating Panzar-Rosse’s H-statistic, Tsutsui and Kamesaka (2005) found that the Japanese
securities industry was in monopolistic competition equilibrium in the 1980s and late 1990s
and was in monopoly equilibrium in the early 1990s. In view of these findings, life insurance inJapan seems to be a less competitive industry than banking. Competition in banking improvedin the late 1970s. Whereas it improved in the life insurance sector during the late 1990s, it did
not reach perfect competition.

20

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23

Table 1 Results of the Double Log Likelihood Ratio Tests of the Objectives of
Mutual and Stock Life Insurance Companies(4) against (9): p-values(8) against (9): p-values

Mutual companies
0.157
0.002

Stock companies
0.760
0.001

Note: (4) and (8) represent models of the maximization of profits and the maximization ofdividends plus profits, respectively. (9) is a general specification that includes (4) and (8) as
special cases.

24

Table 2 Estimates of the Degree of Non-competition
Parameter

Estimate

P-valueλt η tλtµ1986 η1986µ1987 η1987µ1988 η1988µ1989 η1989µ1990 η1990µ1991 η1991µ1992 η1992µ1993 η1993µ1994 η1994µ1995 η1995µ1996 η1996µ1997 η1997µ1998 η1998µ1999 η1999µ 2000 η 2000
µ 2001 η 2001
µ 2002 η 2002

7.507[.000]

0.313

0.339

7.333

[.000]

0.306

0.325

7.194

[.000]

0.288

0.328

7.200

[.000]

0.288

0.338

7.361

[.000]

0.283

0.342

7.339

[.000]

0.272

0.338

6.922

[.000]

0.256

0.287

6.784

[.000]

0.251

0.269

6.869

[.000]

0.254

0.267

6.657

[.000]

0.230

0.224

6.826

[.000]

0.166

0.159

6.730

[.000]

0.168

0.157

6.846

[.000]

0.171

0.155

6.582

[.000]

0.161

0.136

6.068

[.000]

0.156

0.133

6.215

[.000]

0.164

0.133

[.000]

0.181

0.150

6.171

a1

4.569 × 10

4[.025]

a2

4.796 × 10 5[.000]

a3-0.250[.885]

a4

0.068[.973]

b2
b3

0.276

[.000]-165.856[.678]

b4
b5-48.206[.728]

798.980[.676][.709]

b6-422.421

R

2

for (3)

0.899

R

2

for (4)0.992

Note: The results of simultaneous estimation of eqs. (3) and (4) are shown. Estimates of bo ,t ,bo ,i , and b1,i are omitted to save space. Observations are for all existing life insurance firms.

λt η t is calculated by multiplying µ t η t by the number of companies. λt is calculated bymultiplying λt η t by η t presented in Table 3.

25

Table 3 Estimates of the Degree of Collusion
Parameter

Estimate

P-valueParameter

Estimate

P-value

α 1986
α 1987
α 1988
α 1989α 1990α 1991
α 1992
α 1993α 1994
α 1995
α 1996
α 1997
α 1998
α 1999α 2000α 2001
α 2002
a3
a4
a5
a6
b2
b3
b4
b5
b6

1.022
0.998
1.082
1.110
1.121
1.144
1.032
0.982
0.956
0.880
0.844
0.830
0.787
0.737
0.752
0.703
0.708
-0.892-0.186-2.024 × 10 4
2.802 × 10 40.052
-27.056-6.649132.894-58.224
0.997
0.999

[.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.285][.000][.059][.000][.300][.461][.285][.425]

η1986

1.084
1.064
1.139
1.174
1.209
1.242
1.119
1.069
1.048
0.975
0.955
0.936
0.903
0.849
0.858
0.811
0.824

[.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000]

R 2 for (3)R 2 for (7)

η1987
η1988
η1989
η1990
η1991
η1992
η1993
η1994
η1995
η1996
η1997
η1998
η1999
η 2000η 2001
η 2002

Note: The results of simultaneous estimation of eqs. (3) and (7) are shown. Estimates of bo ,t ,bo ,i , and b1,i are omitted to save space. Observations are for all existing life insurance firms.

26

Table 4 Causes of the progress of competitionα

Dependentｔ variableλη

λVariable

Coefficient

P-value

Coefficient

P-value

Coefficient

P-valueConstant

1.599[.003]

0.539[.029]

0.625[.052]

CIX

0.006[.070]

0.000[.960]

0.002[.390]

HI-10.204[.001]-2.519[.052]-4.484[.014]

DIL-0.180[.010]-0.076[.027]-0.116[.014]

DFAIL-0.111[.042]-0.072[.014]-0.094[.015]

2

0.762

R

0.568

0.656

Note: We regress the degree of collusion and degree of non-competition over the business condition(CIX), the Herfindahl index (HI), and two year-dummy variables representing deregulations. Number ofobservations is 17.

27

Figure 1

The GDP Growth Rate and the Growth Rate of the Value of Policies in Force

%
15.0
10.0
5.0
0.0
-5.0
-10.0
-15.0

Year
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002GDP

the value of policies in force

Source: NIKKEI NEEDS Macro data file.

28

Figure 2

The Current Profit Rate

%
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

the life insurance industryYear

the corporate sector of all industry

Note: The current profit rate of the life insurance industry and that of the corporate sector of all industries are shown. Current profit rate is
the current profit/asset ratio.

29

Figure 3

The Yields of Assets and the Assumed Interest Rate

%
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

assumed interest rateYearyields of assets

Note: The assumed interest rate is the expected rate of return on the assets of the company. The assumed interest rate here is the averagefor outstanding contracts. Data on the yield of assets are taken from Insurance: issue of life insurance statistics, and data on assumed
interest rates is taken from Toyo Keizai Weekly: special issue on life insurance.

30

Figure 4

The number of Life Insurance Firms

45
40
35
30
25
20
15
10
5
0

Year
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Source: Insurance: issue of life insurance statistics.

31

Figure 5

The Herfindahl Index (HI)
HI

0.15
0.14
0.13
0.12
0.11
0.1
0.09
0.08
Year

0.07
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Note: The Herfindahl index, taking the total assets as a proxy for firm size.

32

Figure 6

Estimates of the Degree of Non-Competition ( λt η t )

0.40

0.35

0.30

0.25

0.20

0.15

0.10
1916 1917 1922 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
the lower bounds of a 95% confidence interval

the upper bounds of a 95% confidence interval

Note: Estimates are based on simultaneous estimation of eqs. (3) and (4).
33

Year

Figure 7

Estimates of the Degree of Collusion ( α t )

αt
1.4
1.2
1.0
0.8
0.6
0.4
Year

0.2
1916 1917 1922 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

the upper bounds of a 95% confidence interval

the lower bounds of a 95% confidence interval

Note: Estimates are based on simultaneous estimation of eqs. (3) and (7).

34

Figure 8

Estimates of the Degree of Non-Competition ( λt )

0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
Year

0.00
1916 1917 1922 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
the upper bounds of a 95% confidence interval

the lower bounds of a 95% confidence interval

1/nNote: λt is calculated by multiplying ηt taken from the estimation of collusion with the estimates of λt / ηt shown in
Figure 6. Line of 1/n corresponds to Cournot oligopoly.

35