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New Paradigm for International Insurance Comparison:
With an Application to Comparison of Seven Insurance Markets
New Paradigm for International Insurance Comparison:
With an Application to Comparison of Seven Insurance Markets
Wei Zheng, Peking University
Yongdong Liu, China Academy of Sciences
Yiting Deng, Peking University
2
Outline
1. Introduction
2. Comparison of Insurance Growth Level
3. Comparison of Insurance Growth Structure
4. Economic and Institutional Factors in Insurance Growth
5. Conclusion
3
1. Introduction
Commonly used methods for international insurance comparison premium income method insurance density method insurance penetration method
Limitations of the above methods They fail to take into consideration the relationship between insurance
penetration and economic development stage
A new paradigm is proposed BRIP: comparison of Insurance Growth Level Trichotomy: comparison of Insurance Growth Structure
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1. Introduction
Application to seven markets
U.S. Japan U.K.
Brazil Russia India China
Sometimes we also refer to data of OECD average, BRIC average and world average.
5
2. Comparison of Insurance Growth Level
2.1 New Method: BRIP
2.2 Ordinary model of insurance growth
2.3 Comparison of Ranking Results under the New Method and the Traditional Methods
6
2.1 New Method: BRIP
Benchmark Ratio of Insurance Penetration
“benchmark penetration” refers to “the world average insurance penetration at a country’s economic level”
“actual penetration” refers to a country’s actual penetration
The central idea here is to measure the “benchmark-adjusted insurance growth level” instead of a traditional one.
The difficulty is how to get the benchmark penetration.
100%
actual penetrationBRIP
benchmark penetration
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2.2 Ordinary model of insurance growth
Carter & Dickinson (1992) and Enz (2000) developed a logistic model to depict the relationship between insurance penetration and GDP per capita.
Y: insurance penetration X: GDP per capita C1, C2, C3: three parameters ε: residual
This paper uses the data of 95 countries (regions) over the past 27 years (1980-2006) as the sample.
XCCC
Y321
1
8
Estimates of “Ordinary Growth Model”
Life Insurance
Non-Life Insurance
Insurance Industry
C124.37***(16.59)
35.45***(47.53)
14.47***(33.35)
C2111.03***
(12.83)62.72***(19.93)
42.07***(17.32)
C30.8671***
(68.14)0.8276***
(51.46)0. 8592***
(81.33)
R2 0.5362 0.8115 0.7393
Adjusted-R2 0.5356 0.8112 0.7389
Number of Observations 2,052 2,071 2,011
The Robust t-statistics is in parentheses. The term of “***” means the level of significance is 1%.
9
Regression Curves of “Ordinary Growth Model”
0%
2%
4%
6%
8%
10%
12%
14%
10 100 1000 10000 100000
GDP per Capita (US Dollars)
Pene
trat
ion
Real Insurance Penetration Life Insurance Growth Curve Non-Life Insurance Growth Curve Insurance Growth Curve
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Why use BRIP ?
The international insurance comparison will make more sense only when it is based on the comparable “benchmark-adjusted insurance growth level”.
BRIP is such a “benchmark” adjustment to insurance penetration.
So, BRIP represents a more reasonable indicator for the international insurance comparison.
11
BRIP = 1: the country’s actual penetration is equal to the world average penetration at that country’s economic development stage
BRIP < 1: the actual penetration is less than the average
BRIP > 1: the actual penetration is greater than the average
There is a positive correlation between the BRIP and the relative insurance growth level of that country.
What’s the economic implications of BRIP ?
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2.3 Comparison of Ranking Results (2006)
Market BRIP
Traditional methodsGDP
per capitapremium
Insurancedensity
Insurancepenetration
U.S. 26 1 6 14 15
Japan 14 2 9 7 16
U.K. 4 3 1 1 26
Brazil 36 19 49 44 86
Russia 52 22 52 56 80
India 5 15 76 31 157
China 27 9 70 47 122
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To sum it up,
We should have a new recognition for the insurance growth level of each country: the benchmark-adjusted insurance growth level of the
emerging countries is not as low as what traditional methods indicate
the benchmark-adjusted insurance growth level of the developed countries is not as high as what traditional methods imply
Put it in another way, for the year 2006, the ranking of the growth potential of the seven countries would be like this (from large to small): Russia, Brazil, China, US, Japan, India and UK
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3. Comparison of Insurance Growth Structure
3.1 Introduction to “Trichotomy”
3.2 Adjusted model of insurance growth
3.3 Comparison of Growth Structure
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3.1 Introduction to “Trichotomy”
Insurance growth can be decomposed into three parts
Regular growth• Insurance growth accompanying the economic growth
assuming the insurance penetration is unchanged Deepening growth
• Insurance growth brought about by the increase of insurance penetration induced by economic growth
Institutional growth• The remaining part of the growth, which is brought about by
the institutional factors after the economic factors have been deducted
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Penetration
B
D
C
A
GDP per Capita
Adjusted Growth Curve of World Insurance
Trichotomy of Insurance Growth Structure
17
Penetration
B
D
C
A
GDP per Capita
Adjusted Growth Curve of World Insurance
Trichotomy of Insurance Growth Structure
18
Penetration
B
D
C
A
GDP per Capita
Adjusted Growth Curve of World Insurance
Trichotomy of Insurance Growth Structure
19
3.2 Adjusted model of insurance growth
Y : insurance penetration X : GDP per capita C’1, C’2, and C’3 : three parameters Di （ i=1,…94 ） : country dummy with respect to
country i λi （ i=1,…94 ） : coefficient for Di
ε: residual
94
' ' '11 2 3
1i iX
i
Y DC C C
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Estimates of “Adjusted Growth Model”
Life Insurance
Non-Life Insurance
Insurance Industry
C110.76***(24.22)
40.09***(14.07)
8.49***(26.23)
C2154.27***
(5.24) 155.35***
(5.31)76.65***(6.93)
C30.8408***(110.54)
0.7367***(28.82)
0. 8505***(126.74)
R2 0.9079 0.9508 0.8771
Adjusted-R2 0.9033 0.8112 0.7389
Number of Observations 2,052 2,071 2,011
The Robust t-statistics is in parentheses. The term of “***” means the level of significance is 1%.
21
Regression Curves of “Adjusted Growth Model”
0%
2%
4%
6%
8%
10%
12%
14%
10 100 1000 10000 100000
GDP per Capita (US Dollars)
Pene
trat
ion
Real Insurance Penetration Life Insurance Growth Curve Non-Life Insurance Growth Curve Insurance Growth Curve
22
3.3 Comparison of Growth Structure
Economic Factors (%)Institutional Factor(%)Regular growth Deepening growth
U.S. 78 37 -15
Japan 69 27 4
U.K. 34 24 41
Brazil 24 6 71
Russia 25 8 67
India 22 2 76
China 5 9 86
OECD Average 63 34 3
BRIC Average 15 20 65
World average 63 6 31
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To sum it up,
In developed countries, the insurance growth is mainly driven by the economic factors (including regular and deepening factors)
In emerging countries, the insurance growth is largely driven by the institutional factors
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4. Economic and Institutional Factors in Insurance Growth
4.1 Comparison of Two Growth Models
4.2 Discussion on “Institutional Factors”
4.3 Discussion on Developed and Emerging Countries
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4.1 Comparison of Two Growth Models
Ordinary growth model combines both the economic factors and institutional
factors that influence the insurance growth
Adjusted growth model separates the country-specific institutional influences and
the common economic influences
26
Comparison of Two Models for Life Insurance
0%
2%
4%
6%
8%
10%
12%
14%
10 100 1000 10000 100000
GDP per Capita (US Dollars)
Pene
trat
ion
Real Life Penetration Adjusted Growth Curve Ordinary Growth Curve
27
Comparison of Two Models for Non-Life Insurance
0%
2%
4%
6%
8%
10%
12%
14%
10 100 1000 10000 100000
GDP per Capita (US Dollars)
Pene
trat
ion
Real Non-Life Penetration Adjusted Growth Curve Ordinary Growth Curve
28
Comparison of Two Models for Insurance Industry
0%
2%
4%
6%
8%
10%
12%
14%
10 100 1000 10000 100000
GDP per Capita (US Dollars)
Pene
trat
ion
Real Insurance Penetration Adjusted Growth Curve Ordinary Growth Curve
29
Comparison of Two Models for Insurance Industry
In the figure Ordinary growth curve: combines both economic and institutional
factors Adjusted growth curve: reflects only pure economic factors
When GDP per capita is low, the ordinary curve is higher than the adjusted curve, which indicates that institutional factors facilitate the growth of the insurance industry to some degree.
When GDP per capita is high, the ordinary curve is obviously lower than the adjusted curve, which indicates that institutional factors markedly restrain the growth of the insurance industry.
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4.2 Discussion on “Institutional Factors”
Major institutions social security system (systematic institution)
• dominantly affects the life insurance
legal system (systematic institution)• dominantly affects the non-life insurance, with its most
typical components being the compulsory insurance and liability insurance
culture (non-systematic institution)
religion (non-systematic institution)
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Effects of institutional factors on life insurance
Relationship between life insurance and social security usually substitutable the better developed the social security system is, the more the life
insurance growth is restricted
Relationship between social security and GDP per capita usually positive correlation low GDP per capita countries: social security system is usually under-
developed high GDP per capita countries: social security system is usually well-
developed
Thus, as the GDP per capita increases (with the improvement of social security system), the negative effects of institutional factors on life insurance would gradually increase.
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Effects of institutional factors on non-life insurance
Relationship between non-life insurance and certain legal policies usually complementary the more compulsory insurance and liability insurance are implemented,
the more growth opportunities will be created for the non-life insurance
Relationship between certain legal policies and GDP per capita usually no direct relation the government’s decision of whether to adopt those legal policies (the
compulsory insurance and liability insurance) is mainly based on the consideration of social policy (such as equity and justice), and generally is not related to GDP per capita
Thus, no matter how large GDP per capita is, institutional factors will always bring positive effects to the growth of non-life insurance.
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Effects of institutional factors on insurance industry
When GDP per capita is low institutions have some positive effects on both the life
insurance and the non-life insurance with its net effects on the insurance industry being positive
When GDP per capita is high institutions have remarkably negative effects on the life
insurance and some positive effects on the non-life insurance
with its net effects on the insurance industry being negative, and the negative effects are notable
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4.3 Discussion on Developed and Emerging Countries
For the emerging countries institutional factors facilitate the growth of the insurance industry to
some degree
For the developed countries institutional factors notably restrain the growth of the insurance
industry
It could also imply that as the economy develops, the contribution of the institutional factors to the insurance growth would gradually decrease, and the economic factors would play a more active role in driving the insurance growth.
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4.3 Discussion on Developed and Emerging Countries
This implication suggests that, for those emerging countries, after the insurance industry having experienced a period of “taking-off”, its growth will gradually change from being “driven by both economic and institutional factors” to being “driven mainly by economic factors”.
Following this judgment, it is extremely important for the insurance industry in the emerging countries to upgrade its growth strategy from the extensive developing pattern to a refined and sustainable developing pattern, for the former one will lose its foundation for surviving.
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5. Conclusion
1. We should have a new recognition for the insurance growth level of each country. BRIP gives a different and probably more reasonable answer
2. The insurance growth in developed countries is mainly driven by the economic factors, while that in emerging countries is largely driven by the institutional factors.
3. As the economy develops, the contribution of the institutional factors to the insurance growth would gradually decrease, and the economic factors would play a more active role in driving the insurance growth.
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Thank you for your kind attention ！
Comments are welcome ！
Thank you for your kind attention ！
Comments are welcome ！
