études et Dossiers - Amazon S3...The Geneva Association_____Etudes et Dossiers no. 369 Document free to download - 1 - I. Introduction The Korean non-life insurance industry has witnessed

International Association for the Study of Insurance Economics études et Dossiers

études et Dossiers No. 369 World Risk and Insurance Economics Congress

25-29 July 2010

Singapore

Working Paper Series ofThe Geneva Association

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International Association for the Study of Insurance Economics Études et Dossiers

Études et Dossiers No. 369

World Risk and Insurance Economics Congress

25-29 July 2010

Singapore

February 2011

Working Paper Series of The Geneva Association

© Association Internationale pour l'Etude de l'Economie de l'Assurance

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A Study on the Measurement of the Degree of Competition in the Korean Non-life Insurance Industry

NAM, Sangwook*1)/ KIM, Jean Yong*

<Abstract>

This study empirically estimates the degree of competition in the Korean non-life insurance industry which has witnessed a remarkable growth due to the rapid economic development of the country, its government's policies to foster the industry and insurance firms' endeavors to expand the market. Although the general consensus is that the competition in the industry has been intensified over the years, the extent of it and its directions are yet to be empirically examined. In particular, even the effects of major structural breaks such as introduction of bancassurance and price liberalization, the Asian currency crisis in the late 1990s, and the global financial crisis in 2008 on the level of competition are yet to be verified.

Accordingly, this study aims to empirically determine the degree of competition in the Korean non-life insurance sector. The parameter for the degree of competition from FY1996 to FY2008 was estimated using FIML(full information maximum likelihood) under the assumption of profit maximization and marginal cost function derived from translog cost function. The result indicates that the level of competition in the Korean non-life insurance industry has been consistently rising to the point near perfect competition. Moreover, the rise in the level of competition after introduction of price liberalization and bancassurance was statistically significant, implying that such regulatory measures altering the structure of the industry affected the degree of competition.

Key Words : Korean non-life insurance, degree of competition, perfect competition, bancassurance, price liberalization

* Senior Research Fellow, Samsung Research Institute of Finance. The study's contents are solely the works and opinions of the authors and do not necessarily represent those of the authors' affiliation.

The Geneva Association________________________Etudes et Dossiers no. 369

Document free to download www.genevaassociation.org

- 1 -

I. Introduction

The Korean non-life insurance industry has witnessed a remarkable growth due

to the rapid economic development of the country, its government’s policies to

foster the advancement of the insurance industry and the industry’s efforts to

expand the market; and as of the end of 2008, the industry ranks the twelfth

largest in the world with 30.6 billion dollars in non-life premium.

Through out the industry's development, non-life insurance companies have gone

through major internal, as well as external, environmental changes. In particular,

when Korea received the emergency funds from IMF due to the currency crisis

of the region in 1997, some insurance companies were forced to exit the market

and even the surviving ones suffered extensive policy cancellation. Moreover,

the Korean insurance regulatory body raised the solvency margin in 1998, further

limiting usage of capital and exacerbating difficulties.

In August of 2001, price liberalization was introduced in the insurance industry,

and the price of auto-insurance which was highly regulated until then became

fully liberalized. In the same year, the insurance law was revised, enabling the

establishment of unit insurance; and following the enactment, online auto

insurance companies were established. The competition in the industry in

intensified even more; and the advent of online auto insurance firms has changed

the competitive landscape of the non-life sector, as online firms currently

comprise nearly twenty percent of the total auto-insurance market.

Moreover, the information on agent fees of non-life products were made public,

enabling easier comparison among non-life firms; and the competition in the

market further intensified as the distribution channel widened to include banks



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with the introduction of bancassurance from September of 2003. More recently,

the Korean non-life sector has been adversely affected by the global economic

crisis, ignited by the fall of Lehman Brothers in 2008.

In sum, the Korean non-life insurance sector has been exposed to seemingly an

endless array of regulatory and environmental changes. Surprisingly, however,

empirical studies on the effects of such changes on the competitive landscape of

the industry are rare. In particular, even the effects of ground breaking

regulations, such as bancassurance, price liberalization, and higher capital

adequacy, whose consequences were rather controversial, are yet to be

empirically examined. Therefore, this study aims to empirically assess the

degree of competition in the Korean non-life insurance industry.

In general, the degree of competitiveness characterizes the nature of players in a

given industry to advance their positions within the industry. Accordingly, the

level of competition and changing direction of it are imperative in determining

the efficiency of a given market; and the changing nature of competition also

renders significance for regulatory perspectives, as effectiveness of regulation

introduced to foster competition can be measured by the actual changes in the

level of competition before and after the introduction of it.

As the effects of structural break points such as introduction of bancassurance,

price liberalization and online auto insurance are to be examined in this study;

the results will provide a yet another set of ground on which to assess the

effectiveness of the past regulatory changes and their effects on the efficiency of

the Korean non-life insurance industry.

The study is organized as follows. Following the introduction in the first



- 3 -

chapter, theoretical background and related researches are reviewed in the second

chapter. The model used in this study and its results are reported in the third

chapter, and the last chapter summarizes the study.

II. Theoretical Background

1. The Lerner Index and conjectural variation

To estimate competitiveness, the Lerner Index which measures competitiveness

from difference between price and marginal cost is first considered.

Under perfect competition any single firm(i) may not increase or decrease price

level; and price equals the firm i's marginal cost(); but under imperfect

competition, price may exceed marginal cost. Accordingly, the Lerner Index

defines competitiveness by the magnitude of difference between price and

marginal cost( ).

Further, the Lerner Index may be extended into conjectural variation which

measures market competitiveness by increase or decrease in total output of

industry, when a single firm within the industry changes its output level

momentarily. Conjectural variation assumes that each of firms(I) within an

industry pursues profit maximization; and price(P) is defined from an inverse

demand function( )of total output(≡

), where stands for firm

i’s profit, revenue, cost and output. For revenue, , is defined as

, the first order condition for profit maximization is as follows.



- 4 -

As is

≠

, and ≠

is defined as, , the above first

order condition may be defined as follows.

Also, as is an estimation of other firms’ changes in outputs, it is an

assessment by firm i’s of competitors' increase in output when firm i changes its

output by . Further, may be defined in terms of price elasticity of

demand≡ and the firm i's market share ≡

as follows.

As is the product of the Lerner Index and (price elasticity of demand /

market share -1), perfect competition is achieved when is -1, Cournot

competition when , and perfect collusion when . Iwata(1974)’s

empirical work on the estimation of and its changes through out time in the

flat glass industry in Japan is an application of the above conjectural variation.

2. Bresnahan-Lau’s Value

Bresnahan(1982) and Lau(1982) proposed a means to estimate market power in

oligopoly as follows. First, as profit is the difference between revenue

and cost, profit maximization requires marginal revenue to equal

marginal cost. Under perfect competition, as revenue is a product of

price and output, ≡, and as marginal revenue() equals price( ),

the profit maximization condition yields . On the other hand, as a



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firm’s output equals total output() under monopoly, becomes an inverse

dement function( ) of , and the first order condition for profit

maximization is as follows.

Under monopoly, firm i's profit maximization condition requires

when

, or

.

By defining as

, the relationship between marginal revenue and

marginal cost is defined as follows.

By substituting for , the relationship between and is defined as

, where is a constant, for it is the same for every firm; and

under perfect competition, , under monopoly, and equals under

symmetric Cournot oligopoly with n firms.

Accordingly, the level of competitiveness can be determined by the value of ,

which is determined by the following demand and marginal cost functions.

where, and are exogenous variables



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As =

, marginal cost becomes

,

and price becomes . By re-arranging the above terms, the

following equation is derived by which the value of may be estimated.

Shaffer(1989) applied Bresnahan(1982) and Lau(1982)’s methodology in

empirically evaluating the competitiveness of the U.S. banking industry from

1941 to 1983. Shaeffer estimated the competitiveness, , with loan size, and

lending rate and corporate bond rate as external variables. The result indicated

that value of for the U.S. banking industry was not statistically different from

0, and that profitability improved as the industry became more competitive.

Shaeffer claimed that the U.S. banking industry was approaching perfect

competition, and that the rise in profitability resulted from improved efficiency

rather than market concentration.

Shaeffer(1993) then evaluated the competitiveness of the Canadian banking

industry from 1965 to 1989. The result indicated that the value of for the

Canadian banking industry was 0, as was the case in the U.S., implying perfect

competition.

Moreover, Zardkoohi and Fraser(1998) applied Shaeffer’s methodology to evaluate

market competitiveness of the banking industry by each State in the U.S. from

1964 to 1993, and the estimated values were not statistically different from 0,

unable to refute the absence of perfect competition.



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3. Panzar-Rosse's H-Value

As the sum of elasticity in production cost cannot be positive (+) in income

function under monopoly setting when profit maximization is assumed, Panzar

and Rosse(1987) have measured the degree of competitiveness from changes in

monopoly's income as cost increases by 1%. The H-Value is obtained from

summing input price elasticity of revenue,

, where is the

price of th input and K the number of inputs. Under perfect competition,

, and ≤ under monopoly. Also, when profit becomes zero as marginal

cost equals marginal revenue under oligopoly, ≺ ≺ .

The methodology has been employed in Nathan and Neave(1989); Shaffer and

DiSalvo(1994); Molyneux et al.(1996); Coccorese(1998); Hondroyiannis et

al.(1999); De Bandt and Davis(2000); and Bikker and Haaf(2002). Of the above

studies, Nathan and Neave(1989) estimated the H-value for the Canadian banking

industry with the panel data consisted of revenues, capital, and labor costs of

banks, trust and mortgage companies from 1982 to 1984. The result indicated

that the Canadian banking industry was near perfect competition, and the

H-value for banks and trusts were larger than that of mortgage companies,

implying greater competition.

Also, Shaffer and DiSalvo(1994) estimated the H-value, along with conjectural

variation, of banks in Pennsylvania from 1970 to 1986. The conjectural

variation estimates were between -1 (i.e. perfect competition) and 0 (i.e. Cournot

competition), while the H-value was 1.19 implying the competitive level near

perfect competition.



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III. The degree of competition in the Korean non-life insurance industry

1. Data and methodology

Although Bresnahan(1982) and Lau(1982)’s measure allows estimation of average

competitiveness from time series data within an observation period when supply

and demand functions are given, variable choices for given functions, particularly

price, are restricted; and assessing changes over time is limited.

On the other hand, Panzar and Rosse(1987)’s H-value estimation allows

assessment of changing competitiveness throughout observation period and is

often applied in estimating changing competitiveness of financial industries.

However, the estimation of H-value presupposes one price of output per one

input; and as Panzar and Rosse(1987) assumes production of one good, the

methodology has innate problems to be applied in this study, as a majority of

non-life insurance companies offer many different products.

Accordingly, this study assumes that non-life insurance companies pursues profit

maximization and estimated the competitiveness of the Korean non-life insurance

industry by fiscal year with a variation of Uchida and Tsutsui(2005)’s first order

profit maximization condition and cost function.

The definition of the variables used in this study is as follow:

: output of non-life company i during a period, t

: sum of all the non-life companies' outputs during a period, t ≡

: revenue of non-life company i during a period, t



- 9 -

: cost of non-life company i during a period, t

: proportion of fire insurance of non-life company i during a period, t

: proportion of marine insurance of non-life company i during a period, t

: proportion of automobile insurance of non-life company i during a period, t

: proportion of casualty insurance of non-life company i during a period, t

: proportion of long-term insurance & private annuity of non-life company i during a period, t

: market share of non-life company i during a period, t

: price elasticity of insurance demand during a period t

: unit business cost of non-life company i during a period, t

A proxy for non-life company's output is to be determined first. Although the

definition of bank and other financial institutions's output has been discussed is

many studies, such as Berger et al(1987), a general consensus is yet to be

reached; and output of non-life insurance is no exception. Nevertheless, Garner

and Grace(1993) and, Donni and Fecher(1997) have applied the methodology in

Berger et al.(1987) and have used premium as a proxy of insurance company's

output.

Defining premium as output is rather controversial; however, as premium can

also be considered as price. In particular, as premium of life insurance

companies and that of long-term insurance sold by non-life insurance companies

is level premium from averaging premium over entire terms of policy, it does

not account for the full amount of risks covered by insurance companies in

earlier periods in insurance contracts.



- 10 -

However, as premium is an estimated value created by acquiring risks by

insurance companies, previous studies have concluded that premium is not an

inappropriate measure of insurance company's output; and this study defines

earned premium during a fiscal year as output.

Also, costs for human resources and other materials used to produce and

maintain earned premium need to be defined; and this study defined net

operation expenses comprised of personnel expenses, including benefits and

severance pay, general expenses, agent fees and other costs during a fiscal year

as costs.

Revenue of non-life insurance company is defined by subtracting net claims paid

and refund of long term insurance and dividend from earned premium.

As profit of non-life insurance company, i, is derived from subtracting a

cost function from an inverse-demand function, , the first order condition

for profit maximization is as follows.

[Eq. 1]

where, ≡ , ≡

Defining price elasticity of demand during a period t as ≡

, and

firm i's market share as ≡ , [Eq. 1] can be re-written in terms of price

elasticity of demand and market share as . As is

common for every firm, [Eq. 2] is derived by substituting for and



- 11 -

multiplying on both sides.

[Eq. 2]

However, as non-life companies often sell many different products, the effect of

product portfolio on price level needs to be controlled. Although, assessing

price by each product may be more desirable, this study categorizes non-life

products into five groups, fire insurance, marine insurance, automobile insurance,

casualty insurance, long-term insurance & private annuity, and use their

proportions as explanatory variables.

The variation in weight among product groups is quite large, as general

non-life insurance policies such as fire and marine constitutes a very small

portion from one to three percent while long-term insurance policies, similar to

life-insurance policies, and private annuities comprise sixty to seventy percent of

operation. Accordingly, the following revenue function [Eq. 3] is derived,

accounting for differences in proportion among product groups.

[Eq. 3]

A translog function is assumed for the cost function(Shaffer 1993, Uchida and

Tsutsui 2005), and price for inputs is determined as unit operation cost

from considering the number of employees, solicitors and agents (i.e. expenses

and benefits including severance pay per employee, initial expenses and premium

collection expenses per solicitor; commission per agency, and average general

expenses). The cost function is as follows.



- 12 -

ln ln ln ln ln ln ln ln ln

ln lnln ln

[Eq. 4]

where ln≡

ln , ln≡

ln

Accordingly, marginal cost may be derived from the cost function ([Eq. 4]) as

ln ln

ln ln ln ln, and applying

the term in [Eq. 3] yields the following.

ln ln ln ln

[Eq. 5]

Although the value of can be estimated from [Eq. 4] and [Eq. 5], which

is required for estimating ≡ ≡

, is still not identified.

Therefore, to estimate and , when a non-life insurance company i increases

its output , competitors is assumed to increase their output in proportion to

, which is defined as × (Clarke and Davies 1982, Alley 1993, Uchida

and Tsutsui 2005).

Then as

, it is re-arranged as

; and for

equals , becomes , and [Eq. 5] is re-written

as follows.



- 13 -

ln ln ln ln

[Eq. 6]

From [Eq. 6] and are estimated and is identified, yielding .

Interpretation of which is a proxy for market competitiveness follows that of

Bresnahan(1982): perfect competition when , monopoly when , and

Cournot competition when ( where is the number of firms).

As for , when , it implies that non-life insurance firm i had anticipated

competitors' responses to maintain their market share when it was to increase its

total amount of insurance in force; and implies that non-life insurance

firm i had anticipated no reaction from its competitors. Accordingly, Clarke and

Davies(1982), Alley(1993), and Uchida and Tsutsui(2005) have defined as the

degree of collusion.

The degree of competitiveness in the Korean non-life insurance industry is

estimated with the above analytical models using FIML(full information

maximum likelihood).

In general, 3SLS(three stage least squares) and FIML are used to estimate

variables in simultaneous equations. However, 3SLS which accounts for

correlations between error terms of equations as well as error terms of

explanatory variables requires instrument variables whose selection may affect the

estimation result; and according to Toolsema(2002), parameters from 3SLS are

rather sensitive to initial values used to estimate them.



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On the other hand, FIML does not require instrument variables, and each or all

of the equations may be solved simultaneously by eliminating a set of variables

according to given conditions, leading to a better estimation. Accordingly, this

study uses FIML as an estimation methodology and Marquardt algorithm over a

period from FY1996 to FY2008. All the time series data on non-life insurance

companies have been provided by the Korea Non-life Insurance Association.

2. Result

[Table 1] reports the parameters estimated from the model which defined earned

premium as non-life firm's output; and [Figure 1] reports by fiscal year.

[ Table 1 ] Estimation Result

FY Obs. 1996 14 0.0268(.000)*** 0.0019 0.0030 0.6459(.000)*** 0.6458(.000)*** 1.5481 0.9998

1997 14 0.0175(.100)* 0.0013 0.0030 0.4406(.000)*** 0.4347(.000)*** 2.2694 0.9865

1998 14 0.0176(.464) 0.0013 0.0037 0.3414(.000)*** 0.3318(.001)*** 2.9294 0.9719

1999 14 0.0074(.666) 0.0005 0.0016 0.3334(.000)*** 0.2760(.005)*** 2.9994 0.8280

2000 15 0.0164(.333) 0.0011 0.0038 0.2874(.000)*** 0.3310(.000)*** 3.4793 1.1515

2001 16 0.0186(.000)*** 0.0012 0.0021 0.5487(.000)*** 0.5039(.000)*** 1.8227 0.9184

2002 16 0.0194(.000)*** 0.0012 0.0024 0.5095(.000)*** 0.5649(.000)*** 1.9626 1.1087

2003 16 0.0194(.000)*** 0.0012 0.0021 0.5671(.000)*** 0.5461(.000)*** 1.7633 0.9630

2004 15 0.0209(.000)*** 0.0014 0.0024 0.5796(.002)*** 0.5751(.000)*** 1.7253 0.9922

2005 15 0.0209(.000)*** 0.0014 0.0024 0.5822(.001)*** 0.5773(.000)*** 1.7177 0.9916

2006 17 0.0217(.000)*** 0.0013 0.0022 0.5754(.000)*** 0.5685(.000)*** 1.7381 0.9881

2007 17 0.0223(.000)*** 0.0013 0.0022 0.5967(.000)*** 0.5914(.000)*** 1.6758 0.9910

2008 18 0.0209(.000)*** 0.0012 0.0021 0.5459(.000)*** 0.5406(.000)*** 1.8318 0.9903

Adjusted R2 0.9793(Eq. 4) / 0.9639(Eq. 5) 0.9789(Eq. 4) / 0.9977(Eq. 6)

Log Likelihood -2681.641 -2430.371

Note : ***, **, * refer to significance at p-level of 1%, 5%, 10%

During the observed period, the results indicate that the Korean non-life

insurance industry is approaching perfect competition. The -value fluctuate

from time to time, however, and the magnitude of change was large around the

Asian currency crisis in 1997. As the crisis dissolved in the 2000's, the level

of competition in the Korean non-life insurance industry has been steadily



- 15 -

approaching perfect competition without much fluctuation.

[ Figure 1 ] estimation by fiscal year

0.000

0.001

0.002

0.003

0.004

0.005

1996 1998 2000 2002 2004 2006 2008

Asia CurrencyCris is

Higher solvencymargin

Bancassurance1st step 2nd step 3rd step

The Insurance Law

Price liberalization

By fiscal year, the -value which is a proxy for competitiveness was at the

lowest level (i.e. intense competition) in 1999 during which the adverse effect of

the Asian currency crisis was the greatest. As FY1999 was characterized by

turmoil in the financial sector and exit of insolvent insurance companies from

the industry; non-life firms seem to have exerted greater efforts to boost sales to

overcome the crisis, leading to the heightened level of competition.

Nevertheless, the estimate for 1999 was not statistically significant due to

abnormal shocks affecting the volume of new business and premium, as the

market conditions fluctuated rather dramatically.

In 2000, the -value was 0.0038, implying somewhat subdued competition. As

non-life insurance firms were forced to meet higher solvency level that year,

push for sales seem to have been restrained. In 2001, the results imply that the

competition re-intensified, as the -value decreased.

In 2001, the Korean non-life insurance industry experienced considerable



- 16 -

environmental changes. Above all, as price setting was liberalized, insurance

premiums for automobile insurance, which comprises the largest portion in the

industry was fully liberalized. Also, in 2001, the insurance law was revised,

allowing establishment of unit insurance; and although small in sizes, online

specific automobile insurance firms emerged; and as meeting higher solvency

requirement became less demanding due to overall recovery of the economy,

competition to earn greater market share intensified as well.

As the first stage of bancassurance and mandatory announcement of agent fees

were implemented in 2003, the level of competition increased even further.

Moreover, as the second stage of bancassurance was implemented in 2005, the

competition became even more intense. Although the change in the -value

around the implementation of bancassurance is not substantial, bancassurance

seems to have affected the competitive landscape.

In 2005, as the limitation on insured term of the 3rd sector insurance (for a

period greater than one year but less then fifteen years) was nullified, non-life

insurance companies eagerly expanded into the accident insurance market.

2007 was characterized by the bullish stock market, increased consumer

confidence and investment sentiment. However, as the global economic crisis

spread throughout 2008, the financial market contracted, adversely affecting the

demand for financial products. Thus, non-life insurance firms seem to have

exerted greater efforts to maintain sales level, as volume of new business

decreased dramatically, resulting in increased competition in 2008.

Price elasticity of non-life insurance demand, , and degree of collusion, ,

were also estimated in assessment of -value; and over the observed period,



- 17 -

became less elastic, as exhibited in [Figure 2].

[ Figure 2 ] Estimated by fiscal year

0.0

1.0

2.0

3.0

4.0

1996 1998 2000 2002 2004 2006 2008

Asia CurrencyCris is

Price liberalization

Over the Asian currency crisis in 1997, the price elasticity of insurance demand

increased drastically, and as policy holders' decreased income from the crisis led

to greater price elasticity, it steadily increased up to 2000. Also, as

auto-insurance premium was fully liberalized in 2001, price elasticity plunged in

2001, as non-life firms competitively decreased premium leading to lower price

elasticity. [ Figure 3 ] reports estimated which fluctuates throughout the

observed period until 2004.

[ Figure 3] Estimated by fiscal year

0.8

1

1.2

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008



- 18 -

During the Asian economic crisis, value was estimated at a level below 1, as

non-life firms were not in shape to observe and react to competitors' actions in

a dire market condition where the entire industry was threatened. As the

general economy and the insurance market environment recovered in 2000,

estimates became larger, implying that non-life insurance firm took into

consideration its competitors' reaction to maintain market control when it

increased its output to gain greater market share.

However, value fell below 1 in 2001 due to price liberalization. It

rebounded in 2002, but fell again in 2003, as bancassurance was introduced.

From 2004, value has been stabilizing around 1, again implying that

competitors' reaction to maintain market share is taken into consideration when a

firm moves to enlarge market share.

Ⅳ. Conclusion

This study empirically examined the level of competitiveness in the Korean

non-life insurance sector from the Asian currency crisis in 1997 to the global

economic crisis, ignited by the fall of Lehman Brothers in 2008. In particular,

the effects of many environmental and structural changes on the competitive

nature of the non-life industry have been observed.

The results are as follow. First, the Korean non-life insurance industry is

characterized by near perfect competition. Second, despite minor fluctuations, the

competition in the sector has increasingly intensified over the observed period.

Third, regulatory changes such as price liberalization and establishment of online

auto-insurance companies resulted in statistically significant increased level of



- 19 -

competition. Fourth, price elasticity is increasingly becoming inelastic, as policy

holders are less sensitive to lower premium. The above results will prove useful

in assessing the effects of regulatory and structural changes on the

competitiveness of the Korean non-life insurance sector is to be determined.

However, this study is limited as follow. First, the analytical model used in

this study assumes that non-life firms pursue profit maximization. However,

the business model of the Korean non-life exhibits the industry's focus on size

maximization rather than profit. Size maximization behavior observed during the

industry expansion in the 1980's and 1990's before the Asia currency crisis has

largely disappeared, but irrational competitive behavior over agency, operation

expenses, and collaboration with banks as well as online shopping malls

post-bancassurance are still witnessed.

Accordingly, from the cost perspective, the assumption of profit maximization

may need to be altered, if the model is to be more representative of the

industry. Particularly, when profit maximization is not sought, management's

expense preference behaviors can alter cost structure; and in such case, decreases

in marginal cost as well as profit may ultimately lead to a condition under

perfect competition, which is common to not only non-life insurance, but also to

life and banks.

Also, this study defined earned premium as a proxy of non-life insurance firm's

output, but premium, amount of contract and loans are still used as valid proxies

for output in various studies. As all of these variables are innately price-

variables whose value fluctuates pending on the valuation point, the results from

this study based on the model with earned premium as output need to be

interpreted carefully.



- 20 -

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< Appendix >

1. Descriptive statistics

Obs Mean Std. Dev. Min Maxq 201 1148639 1572673 556 8992603

MS 201 6.467647 7.981202 0.003 32.965R 201 637544.8 946650.8 -9268 5281185C 201 286392.2 362108.3 1405 2062462E 201 1.900498 1.581155 0 11F 201 4.81592 6.371887 0 42G 201 35.30846 25.03087 0 100H 201 22.93035 30.27268 0 99I 201 35.0796 23.56234 0 78w 201 104.1493 66.76367 43 512

2. Correlation matrix　 q MS R C E F G H I w

q 1.0000 　　　　　　　　　

MS0.7236

(0.0000) 1.0000

R0.6480

(0.0000)0.8967

(0.0000) 1.0000 　　　　　　　

C0.7113

(0.0000)0.9325

(0.0000)0.9769

(0.0000) 1.0000 　　　　　　

E-0.0241(0.7341)

-0.0881(0.2134)

-0.1670(0.0178)

-0.1462(0.0383) 1.0000 　　　　　

F-0.4822(0.0000)

-0.2721(0.0001)

-0.2431(0.0005)

-0.2694(0.0001)

-0.0182(0.7976) 1.0000 　　　　

G0.3783

(0.0000)0.0558

(0.4311)0.034

(0.6316)0.0247

(0.7282)-0.1128(0.1109)

-0.5329(0.0000) 1.0000 　　　

H-0.7761(0.0000)

-0.3939(0.0000)

-0.3091(0.0000)

-0.3533(0.0000)

0.0674(0.3415)

0.5733(0.0000)

-0.7152(0.0000) 1.0000 　　

I0.7317

(0.0000)0.5299

(0.0000)0.4415

(0.0000)0.5137

(0.0000)-0.0249(0.7259)

-0.443(0.0000)

0.0107(0.8797)

-0.6891(0.0000) 1.0000 　

w -0.3132(0.0000)

-0.1310(0.0638)

-0.0086(0.9032)

-0.0404(0.5694)

-0.3077(0.0000)

0.1347(0.0566)

-0.4941(0.0000)

0.3940(0.0000)

-0.0002(0.9978) 1.0000



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études et Dossiers - Amazon S3...The Geneva Association_____Etudes et Dossiers no. 369 Document free to download - 1 - I. Introduction The Korean non-life insurance industry has witnessed