Some variables which affect sales of insurance companies – after 1998 in Japan Hideto Azegami (Kyotogakuen University)
1. Introduction
The Japanese life insurance market experienced considerable change as a result of mergers,
acquisitions, and reorganizations including demutualization after 1990s. At the same time,
the financial authorities conducted liberalization and deregulation. Mahlberg and Url (2003)
measured the effects of liberalization on technical efficiency and the productivity developmentof the Austrian insurance industry. They predicted that bancassurance could improve
productivity of insurance companies. Bancassurance is defined as selling insurance through abank's established distribution channels. Teunissen (2008) introduces the four typicalbancassurance models; the pure distributor model, the strategic alliance model, the joint
venture model, and the financial holding company model. Although the common styles ofbancassurance in Japan belong to the pure distribution model, other models of bancassurance
may prevail in the future. Consequently, liberalization, deregulation, and competition
diversified the distribution channels of insurance products.
Obviously, prevalence of the internet influences the insurance markets in the world. Brown
and Goolsbee (2002) investigated the market for term life insurance in the United States from1992 to 1997. They already found that expanding the share of people in a group that used theinternet to research insurance on-line lowered their quality-adjusted prices.
Brockett et al. (2005) presented the financial intermediary approach with a new DEA
model to examine the efficiency of the marketing distribution channel and organizational
structure for insurance companies. Trigo-Gamarra (2008) measured the service quality
provided by insurance intermediaries in Germany. She compared it between independent andexclusive agencies and showed that the former had higher levels of service quality.
Although customers have been able to select the most suitable one among the multiple
distribution channels, a new problem has been pointed out. Cooper and Nakabayashi (2010)
compared the views of groups of leading U.S. and Japanese life insurance sales professionals
on the extent to which certain ethical issues were perceived as having presented problems tothose working in their respective industries during the selected periods of unethical behavior(1990-2006). Chen and Mau (2009) analyzed the relationship between ethical sales behavior
and customer loyalty in the life insurance industry in Taiwan. They found that thesalesperson’s ethical sales behavior was important for the life insurance company’scompetitive advantage as manifested by customer loyalty through the mediation of customer
trust.
2. Sales of insurance products
Figure 1 displays the transition of individual life insurance from fiscal 1998 to 2009. Although
the number of policies for individual life insurance in force turned to grow after 2008, theamount of policies in force continues to fall during a period of time.
Figure 1 (omitted)
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Figure 2 displays the transition of individual annuities during the same period. Both thenumber and the amount of contracts of new business for individual annuity were rapidly
growing from fiscal 2002 to 2006. Moreover, the efficiency defined as the number or theamount of contracts per branch office was improved after 2002. In table1, although thenumber of contracts of new business for individual annuity per branch office was 32.7 in fiscal
2001, it rose up to 56.5 in fiscal 2002 and 88.6 in fiscal 2003. Table1 also shows that theefficiency became different among prefectures because the coefficient of variation for thenumber of contracts per branch office was relatively high from fiscal 2002 to 2006. Forexample, the number of contracts per branch office was 45.6 and 100.6 in fiscal 2001 and 2002
in Nara prefecture. In Okinawa prefecture, on the other hand, it was 12.3 and 14.7 in fiscal
2001 and 2002.
Figure 2 (omitted)
Table 1

FY
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

New Business for Individual Annuity
Number of Contracts
Ammount of Contracts
mean (No.)
coefficient of variation
mean (mil. Yen)
coefficient of variation
70.9
0.2034344.7
0.219345.0
0.2249195.90.238258.1
0.2072246.9
0.2346
32.7
0.2167137.8
0.266856.5
0.4425261.4
0.423888.6
0.3466411.5
0.3822104.7
0.3312542.6
0.4015118.6
0.3196625.1
0.3836125.1
0.3041668.0
0.3639123.6
0.2983636.7
0.3639135.2
0.2398644.7
0.2876141.1
0.2149673.7
0.2776

Source: Insurance Research Institute “Statistics of Life Insurance Business in Japan”
Table 2 shows the distribution of population, establishments (all industries and insuranceinstitutions), post offices, and offices of financial institutions in 1795 regions. 1795 regions
consist of municipalities at 2009 but we use the data at 2006 because of availability. Although
the number of municipalities decreased by local government amalgamation policy in 2000s,
we can find that a lot of small villages and towns still exist in 2009. For instance, 286 regions
have no establishments of insurance institutions, so that the “0-ratio” is 0.159.
Table 2officepopulation**establishments (all industries)
establishments (insurance institutions)***post officecity banktrust bankregional bankregional bank II
shinkin bankcredit union
labor bankNo.
127,809,599
5,910,305
41,05624,060
1,925
255
7,399
3,255
7,737
1,886
685

mean median
71,203 25,190
3,293
1,191
22.87
5
13.40
7
1.07
0
0.14
0
4.12
1
1.81
1
4.31
1
1.05
0
0.38
0

s.d.
0-ratio*
179,390 8,777
0.000
61.42
0.159
23.65
0.000
5.96
0.812
0.82
0.942
10.32
0.249
6.30
0.484
10.41
0.262
3.09
0.655
0.78
0.704

Source: The Statistics Bureau and the Director-General for Policy Planning of Japan“Establishment and Enterprise Census 2006”
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The Japan Financial News Co., Ltd. “Nihon Kin’yu Meikan 2007”
Note: * “0-ratio” stands for the ratio of the number of municipalities which have noestablishments or branch offices of a bank per the number of all municipalities.
** Miyake village (Tokyo) is excluded.
*** Insurance institutions include non-life insurance institutions.
3. Empirical studies
3.1. Cross-sectional study
The issues on deregulation of financial systems have been argued for a long time. Evanoff(1988) tested whether branch banking affected service accessibility in the United States. He
measured accessibility as the number of banking facilities per square mile and regressed it on
independent variables depicting population, the distribution of the population, per capita
income, the degree of state regulatory stringency, and branching restrictions. Avery et al.
(1999) examined the association between consolidation and changes in levels of banking
branching as measured by changes in the number of bank branches per capita. Azegami(2010) also regressed the number of branches or offices of banks on independent variables
depicting population, the number of establishments in a business area, and the size of anarea.
In this study, we select the number of establishments of insurance institutions, post offices,and financial institutions as explained variables and regress them on several explanatory
variables, such as the size of an inhabitable area (km2), the number of establishments of allindustries, the share of elderly (65+) in the population, the total fertility rate, and the sum ofthe taxable income.
[Notation]
NI: the number of establishments of insurance institutions
NF: the number of branches or offices of financial institutions
NP: the number of post offices
INH: the size of an inhabitable area (km2)
OFC: the number of establishments of all industries
ER: the share of elderly (65+) in the population
FR: the total fertility rate
TX: the sum of the taxable income
We use the Box-Cox transformation as follows: For variable X,(1) X[λ]＝(X^λ－1)/λ, 0<λ<1.
Thus, our linear regression equation is written as (2).
(2) X[λ]＝β0＋β1*INH[λ]＋β2*OFC[λ]＋β3*ER＋β4*FR＋β5*TX[λ],where X[λ]＝NI[λ], NF[λ], NP[λ].
Table 3 offers three regressions for each dependent variable. It is not surprising that moreoffices are in a large municipality, so that the sign of the coefficient of the size of an
inhabitable area is positive. The older the population, the fewer the establishments ofinsurance institutions. On the other hand, the influence on the number of establishment ofpost offices is significantly positive. The sum of the taxable income gives an opposite influence
3on these two variables.
Table3
Insurance institutions

Financial institutions

NINF

Coef.
Constant (β0)
INH
OFCERFRTX
R2

-3.54
0.11
0.25
-5.52
0.14
-0.001
0.89

S. E.
0.38
0.01
0.01
0.67
0.19
0.00

Coef.
***
***
***
***
**

NP

S. E.

-3.44
0.07
0.12
-0.11
0.28
0.00
0.92

Post Offices
Coef.

0.27
0.01
0.01
0.48
0.14
0.00

**
***
***
*

S. E.

-6.76
0.22
0.59
5.00
0.07
0.14
0.87

0.63
0.01
0.04
0.31
0.07
0.05

***
***
***
******

Note: Standard errors are heteroscedastic-consistent. Parameterλ is selected such that
NI[0.33], NF[0.38],and NP[0.000001].

3.2. Panel data analysis
It is possible to use data about the business results of insurance companies in the LIAJ
statistics. Panel data set for 12 years from fiscal 1998 to 2009 and 47 prefectures was created.
We examine the quantitative disparities of contracts of new business for individual annuity
per branch office among prefectures. The dependent variable is the amount of contracts,denoted by AM, and the independent variables are as follows.
[Notation]
TXTP: the taxable income per capita (previous term)
HOS: the number of households per square kilometer of inhabitable land
BBRS: the number of offices or branches of banks per square kilometer of inhabitable land
POS: the number of post offices per square kilometer of inhabitable landdBD: the changes in bank deposits (from the previous term)dPD: the changes in postal savings (from the previous term)
Since each variable is observed every year and prefecture, our data set is a balanced panel. If
one denotes time period and prefecture as t and p respectively, then variable X might bewritten such as X , t=1998,…,2009, and p=1,…,47. But we omit these indices in order forbrevity. (3) is a linear regression equation.
(3) AM＝β0＋β1*TXTP＋β2*HOS＋β3*BBRS＋β4*POS＋β5*dBD＋β6*dPD
After testing several estimations, we adopted fixed-effects model. The results of theestimations are shown in Table4.
Table4 (omitted)
The presence of autocorrelation in residuals is detected by the Durbin-Watson statistics inTable4. Therefore, we modify this model by AR1 with maximum likelihood method. Table 5
depicts the results.
Table5 (1998-2009)
Coef.
S. E.
p-values

TXTP
-0.92
0.06 ***
0.00

HOS
0.82
0.25 ***
0.00

BBRS-401.16396.050.31

Note: *** significant at 1 percent level.
4POS-863.142027.88
0.67

dBD
-0.01
0.00 ***
0.00

dPD
0.00
0.00
0.17

ρ
0.59
0.03 ***
0.00

The signs of coefficients in Table 5 are consistent with Table4 except for the number of post
offices per square kilometer. It is unexpected that the sign of the coefficient of the taxable
income per capita is significantly negative. While the share of age 60 and over in those whocontracted an individual annuity in fiscal 2002 was 23.9 per cent, it rose up to 32.8 per cent infiscal 2003 and continued to be above 30 per cent ever since. Since the elderly get lower
income than the younger, we suggest that the high share of elderly induced the negative signof this coefficient.
The number of households per square kilometer of inhabitable land is proportion topopulation density, so that insurance branches in an area with higher HOS can sale insurance
products more efficiently. Accordingly, the sign of the coefficient is significantly positive.
Table1 showed disparity among prefectures was enlarged after fiscal 2002. In order toconsider this phenomenon, we estimate equation (3) for the period from fiscal 2002 to 2009.
The results are listed in Table6. The signs of the coefficients of the taxable income per capita,
the number of households per square kilometer of inhabitable land, and the changes in bankdeposits are the same as in Table5. The coefficient of the changes in postal savings becomessignificantly negative in Table6. The outstanding balance of postal savings is decreasing since2000 and it is said that the customers use some amount of their postal savings to purchaseindividual annuity.
Table6 (2002-2009)
Coef.
S. E.
p-values

TXTP-0.8350.056 ***
0.000

HOS
1.681
0.255 ***
0.000

BBRS
-1113.040
517.009 **
0.031

POS
dBD
-191.233 -0.017
1876.960
0.002 ***
0.919
0.000

dPD
-0.0240.004 ***
0.000

ρ0.4230.057 ***
0.000

Finally, we convert the amount of contracts of new business for individual annuity perbranch office into the amount of contracts of new business for individual insurance per branch
office as the dependent variable. The independent variables are the same, so that theregression equation is still (3). The results are listed in Table7.
The sign of the coefficient of the taxable income per capita is significantly positive while itis negative in Table5 and Table6. Since the share of age 60 and over in those who contract theindividual insurance is lower than that in the case of the individual annuity, the amount ofcontracts is proportion to the sum of the taxable income. The estimated coefficient on thenumber of offices or branches of banks per square kilometer of inhabitable land is positive inthis case. Although the business results of insurance companies are strongly affected by salesof the individual annuity through banks, the present results suggest that the density of banks
is positively related to the sales of individual insurance rather than individual annuity.
Table7Coef.
S. E.
p-values

TXTP
5.507
0.280 ***0.000

HOSBBRS1.609 7199.580
1.097 1814.700 ***
0.142
0.000

POS
dBD
3597.380 -0.040
9912.1100.011 ***
0.717
0.001

dPD
-0.1150.013 ***
0.000

ρ
0.415
0.042 ***
0.000

4. Conclusions
There still exist a number of small villages and towns without establishment of financial
institutions in Japan. The accessibility of insurance services is also different among regions.
5Deregulation and competition diversified service channels in the insurance markets.
Nevertheless regional disparity of accessibility has not diminished yet.
In the aspect of insurance companies, they may make their best effort to acquire acustomer through various channels. Although bancassurance is expected to be one of themajor sales channels, the number of establishments of financial institutions is decreasing. We
presume that regional disparities will remain hereafter. Accordingly, if an insurance company
develops a delivery channel to improve accessibility, it can have a dominant position in themarket.
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