New Paradigm for International Insurance Comparison: With an Application to Comparison of Seven Insurance Markets. Wei Zheng

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1 New Paradigm for International Insurance Comparison: With an Application to Comparison of Seven Insurance Markets Wei Zheng Associate Professor and Vice Chair Department of Risk Management and Insurance, School of Economics Secretary General China Center for Insurance and Social Security Research (CCISSR) Peking University, Beijing 0087, China Yongdong Liu China Academy of Sciences, Beijing, China Yiting Deng China Center for Economic Research (CCER) Peking University, China We are very grateful to Prof. Tong Yu and Prof. Hong Zou for their helpful comments and suggestions. Of course, the usual disclaimer applies.

2 New Paradigm for International Insurance Comparison: With an Application to Comparison of Seven Insurance Markets Abstract Considering the limitations of the traditional methods, we propose a new paradigm for the international insurance comparison in this paper. We do both the aggregate analysis of the insurance growth level and the structural analysis of the insurance growth structure, and also investigate the respective impacts of economic factors and institutional factors on the insurance growth under various scenarios. The main findings are as follows. First, we should have a new recognition for the insurance growth level of each country, and the judgment for the growth potential of both emerging and developed countries should be adjusted accordingly. Second, 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. Third, 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. In accordance with this judgment, it is extremely important for the insurance industry in the emerging countries to upgrade its growth strategy to attain a sustainable development. Key Words:international insurance; comparison; insurance growth. Introduction International insurance comparison is an issue of great importance, as it not only gives us insight into the growth potential of the insurance industry in a certain country, All the country in this paper refers to country (region). 2 but also helps us in the policy-making process for the insurance industry. In the existing literatures on the international insurance comparison, commonly used methods are premium income method, insurance density method and insurance penetration method. The premium income method measures the overall scale of

3 insurance market in each country. However, it fails to take into consideration the population factor, and thus cannot well represent the true level of insurance growth. The insurance density method (premium/population) measures the per capita premium by taking population into consideration, and it will better reflect the true level of insurance growth of each country compared with the premium income method. However, it only considers the development of the insurance industry, and neglects the economic development, thus fails to take into consideration the relationship between the insurance industry and the economy. The insurance penetration method (premium/gdp, or insurance density/gdp per capita) could be considered to an adjustment with economic factors added to the insurance density method. But it fails to reflect the reality that different stages of economic development are accompanied by different insurance penetrations. To make a better international comparison for insurance, we not only need to look at the overall scale of insurance market of each country, but also need to take into account its population, economy and the relationship between insurance penetration and economic development stage. Thus, the central idea here is to measure the benchmark-adjusted insurance growth level. Unfortunately, the existing methods can not provide such information. In this paper, we propose a new method (Benchmark Ratio of Insurance Penetration, BRIP) for comparing insurance growth across different countries. Restricted by the length of the paper, we will not compare the insurance growth of all the world countries, instead, we focus on 7 countries: the U.S., Japan, the U.K., Brazil, Russia, India and China. The U.S., Japan and the U.K. are those countries whose premiums have been the top 3 in the world for many years, and are typical representatives of the highly developed insurance markets. Brazil, Russia, India and China, the BRIC, are the largest and most populous countries in Asia, Latin America and Europe and well represent the emerging insurance markets. It is also worth mentioning that the two estimations needed in this paper are both based on a pooled (cross-country, time-series) dataset consisting of 95 countries and regions over It could be observed from the following data and analysis that as a general rule, the insurance penetration will be higher when the GDP per capita is higher. 3

4 the past 27 years ( ). This rest of the paper is organized as follows:section 2 compares the insurance growth level of the countries under consideration based on the new BRIP method. Section 3 compares the insurance growth structure of those countries based on the new trichotomy method. Section 4 analyzes the respective impacts of economic factors and institutional factors on the insurance growth. Section 5 draws conclusions. 2. Comparison of Insurance Growth Level In this section, we will first introduce a new method BRIP. Then for each country, we will compare its new ranking of insurance growth level with that under the traditional methods. Finally we will further analyze the economic implications of the differences between the rankings. 2. New Method: BRIP In view of the shortcomings of the existing methods ( premium income method, insurance density method and insurance penetration method ), we develop a new method the method of Benchmark Ratio of Insurance Penetration. The core indicator of this method is the Benchmark Ratio of Insurance Penetration or BRIP, which is a measure of the benchmark-adjusted insurance growth level. The BRIP evaluates the relative relationship between a country s insurance penetration and the world s average penetration at an economic level equal to the country s GDP per capita. If we define the world average insurance penetration at the same economic level as benchmark penetration, a country s BRIP can be calculated as follows: actual penetration BRIP = 00% () benchmark penetration The denominator, benchmark penetration, refers to the world average insurance penetration at a country s economic level, and the numerator, actual penetration, refers to a country s actual penetration. 4

5 To get the measure of the world average penetration at a certain economic level, we have to establish an insurance growth model to depict the relationship between the economic growth and the insurance growth. There are currently three major types of insurance growth models available: the first type is the simple linear model; the second is the logarithmic linear model and the third one is the logistic model. Each type has some advantages as well as limitations, but comparatively, the logistic model is superior to the first two models for this research. 2.2 Ordinary model of insurance growth Carter & Dickinson (992) and Enz (2000) developed a logistic model to depict the relationship between insurance penetration and GDP per capita. We label it as the ordinary growth model. Based on this model, we will do the estimation on the ordinary growth model 2 with a large amount of historical data. The expression of the ordinary growth model is the following: = Y + ε C + C C X 2 3 Where Y is the insurance penetration, X is the GDP per capita, C, C2 andc 3 are three parameters3,ε is the residual.4 This paper uses the data of 95 countries and regions over the past 27 years ( ) as the sample. There are 2,052 observations in the life insurance sample, 2,07 observations in the non-life sample, and 2,0 observations in the insurance sample.5 (2) Refer to Zheng et al. (2007) for the detailed discussion on the model setting. We name this model as ordinary growth model because we will make adjustment to it in the following section, and this term could help to distinguish between the two models.. Refer to Zheng et al. (2008) for the detailed discussion on the detailed description of the three parameters. Some researches believe that insurance has some impacts on economic growth on the same period (e.g. 5 Outreville 990; Soo, 996; Webb et al., 2002; Arena, 2006), while some others indicate that such an endogenous relationship does not really exit (e.g. Ward and Zurbruegg, 2000; Kugler and Ofoghi, 2005; Adams et al., 2005),. Apparently, there is no consensus on the impact of insurance on economic growth on a short term. In order to deal with the potential endogeneity problem, we have tried to use one-period lagged GDP per capita as the regressor instead, and the results are very close to our existing estimates, and this implies that endogeneity is probably not a serious concern for our model. So that we accept the estimates obtained from the current model. The GDP, population and GDP per capita data of every country and region come from the National Accounts 5

6 Table shows the estimates of the ordinary growth model for world life insurance, non-life insurance and the insurance industry, and Figure depicts the regression curves of life insurance, non-life insurance and the insurance industry accordingly, which represent the term of in Equation (2). X C + C2 C (6.59) 3 Table Estimates of Ordinary Growth Model of World Insurance (2.83) (47.53) (33.35) Life Non-Life Insurance Insurance (68.4) (9.93) Insurance (5.46) (7.32) (8.33) Industry 24.37*** 35.45*** 4.47*** C.03*** 62.72*** 42.07*** C *** *** *** C 3 R Adjusted-R Number of Observations 2,052 2,07 2,0 Note:The Robust t-statistics is in parentheses. The term of *** means the level of significance is %. Main Aggregates Database which is provided by the United Nations. The data on total premium, life premium, non-life premium, total insurance penetration, life penetration and non-life penetration are taken from the Sigma database provided by Swiss Re. GDP per capita figures are at constant 990 prices in US Dollars. The penetration figures are the relative ratio (Premium/GDP) and are not affected by the prices. Specifically, in the data used in this paper, the classification of life insurance and non-life insurance follows the tradition of the EU and OECD, in which health insurance and accident insurance are all in the catalog of non-life insurance. 6

7 Figure Regression Curves of Ordinary Growth Model of World Insurance As is illustrated in Figure, under the ordinary growth model supported by the statistical tests, the regression curves of life insurance, non-life insurance and the insurance industry all resemble the shape of the letter S, and this is the reason why the model is called ordinary S-curve. As could be seen from the figure, the insurance penetration (the ratio of the premium to the GDP) rises with the GDP per capita, but different levels of GDP per capita are accompanied by different growth rates of insurance penetration. When the level of the GDP per capita is low, the growth rate of insurance penetration is relatively slow. As the GDP per capita increases, the growth rate of insurance penetration increases as well. However, after the GDP per capita reaches a higher level, the insurance penetration tends to plateau. This means, with the increase of the GDP per capita, the growth rate of premium will exceed that of the GDP per capita. The gap of the two growth rates is smaller when the GDP per capita is low, and expands with the growth of GDP per capita, but will eventually narrow after a certain stage. Based on the ordinary growth model, we can calculate the BRIP of a country as follows: first, calculate the benchmark insurance penetration for the country, which is the world s average penetration at a country s economic level ; second, calculate this country s actual insurance penetration; third, divide the actual insurance penetration by the benchmark insurance penetration and obtain the value of BRIP. The calculation of the benchmark insurance penetration is an essential step in this process, and it could be obtained from the ordinary growth model. Actually, the benchmark insurance penetration is represented by C + C C X 2 3 in Equation (2). As far as we are concerned, the insurance industry is one of the economic segments, and its growth is to some degree endogenous to the development of the economy, and cannot have an unlimited growth surpassing the economic development. This is to say, when discussing the growth level of a country s insurance industry, the economic 7

8 development stage cannot be neglected. The international insurance comparison, likewise, will make more sense only when it is based on the comparable economic-adjusted insurance growth level. As has been mentioned above, insurance density is an adjustment to premium income by adding the population factor, insurance penetration is an adjustment to insurance density by taking into account the economic factor, and the BRIP is a benchmark adjustment to insurance penetration, with the key being considering the important rule that different stages of economic development are accompanied by different insurance penetrations. So to speak, the BRIP represents the comparable economic-adjusted insurance growth level, and is a more reasonable indicator for the international insurance comparison than premium, insurance density and insurance penetration. If BRIP equals to, then the country s actual penetration is equal to the world average penetration at that country s economic development stage. If BRIP is less than, then the actual penetration is less than the world average level. If the BRIP is greater than, then the actual penetration is greater than the world average level. The higher the BRIP is, the higher the economic-adjusted insurance growth level will be. In other words, with a comprehensive consideration of the premium, population, economy, as well as the relationship between the insurance penetration and the economy, there is a positive correlation between the BRIP and the relative insurance growth level of that country. The opposite also holds. 2.3 Comparison of Ranking Results under the New Method and the Traditional Methods From what has been discussed above, we can see that the BRIP method is different from traditional methods of premium income, insurance density and insurance penetration. Such a difference makes the ranking results based on these various methods different. Table 2 shows the results of the BRIP, the traditional indicators, the GDP per capita of the 7 countries, and it also shows those results of the OECD average, BRIC average and the world average. Table 3 presents the rankings of these indicators. 8

9 Table 2 General Situations of Insurance Industry of Seven Countries in 2006 Traditional indicators GDP per Premium capita Market BRIP Income US$) (%) Insurance Insurance (US$) density penetration (million (US$) U.S..3,70,0 3, ,632 Japan ,26 3, ,48 U.K ,366 6, ,29 Brazil.2 30, ,47 Russia , ,843 India , China.3 70, ,466 OECD Average.22 3,338,60 2, ,280 BRIC Average.45 65, ,424 World Average.2 3,723, ,263 Note: The US$ is the constant 990 price. Data source: National Accounts Main Aggregates Database by United Nations, Sigma database by Swiss Re, etc; author s calculation. Table 3 Rankings of Insurance Industry of Seven Countries in 2006 Traditional methods GDP per Market BRIP Insurance Insurance premium capita density penetration U.S Japan U.K Brazil Russia India China Note: The numbers in the table refer to the ranking order. There are 88 countries or regions being ranked for insurance comparison and 20 for GDP per capita. Data source: National Accounts Main Aggregates Database by United Nations, Sigma database by Swiss Re, etc; author s calculation. This includes all of 30 OECD countries:australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Luxemburg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Republic of Korea, Slovakia, Spain, Sweden, Switzerland, Turkey, United Kingdom and United States. 9

10 According to Table 2, for insurance penetration, the OECD average (8.8%) is higher than the BRIC average (4.2%), but for the BRIP, the OECD average (.22) is lower than the BRIC average (.45). This indicates that on the average level, the ranking of the BRIP for the emerging countries would be higher than that for insurance penetration. The insurance density shares the similar characteristics with insurance penetration. Specifically, it can be seen from Table 3 that the ranking order of a certain country could vary largely by using different indicators. Taking the U.S. for instance, in 2006, its BRIP, premium income,insurance density, and insurance penetration are respectively ranked the 26 th, the st, the 6 th, and the 4 th in the world. As is revealed from these numbers, although the premium of the U.S. is the largest in the world (the st ), the insurance density (the premium per capita) is ranked lower (the 6 th ) due to the relatively large U.S. population. At the same time, the ranking of the insurance penetration (premium/gdp) is even lower (the 4 th ) due to the high economic development level. Furthermore, if taking into consideration the rule that the benchmark insurance penetration will be higher when the GDP per capita is higher, the ranking of the BRIP descends further (the 26 th ). This is to say, for the U.S., the ranking of the BRIP declines compared with the rankings of those traditional indicators like premium income, insurance density, as well as insurance penetration. Remarkably, Japan and the U.K. share similar characteristics. Take China for another example. In 2006, its BRIP, premium income, insurance density, and insurance penetration are respectively ranked the 27 th, the 9 th, the 70 th, and the 47 th in the world. As is revealed from these numbers, although the premium of China is among the largest in the world (the 9 th ), the ranking of China s insurance density (the premium per capita) is significantly lower (the 70 th ) due to the very large population. The insurance penetration is ranked higher than the insurance density (the 47 th ) due to the relatively low economic development level. Taking into consideration the rule of the benchmark insurance penetration will be lower when the GDP per capita is lower, the ranking of the BRIP rises further (the 27 th ). This is to say, for China, the ranking of the BRIP rises compared with the rankings of those traditional 0

11 indicators like insurance density and insurance penetration. Remarkably, Brazil, Russia and India share similar characteristics. We can see from the above analysis that according to the new indicator BRIP, the rankings of the insurance industries of developed countries descend relative to those under traditional indicators, and on the contrary, the rankings of emerging countries rise. Table 4 shows the ranking of the BRIP of the 7 countries during the time period We can see from the table that, generally speaking, the BRIP of 2006 is not unusual, but reflects the natural trend of the BRIP development during the period Table 4 Ranking of BRIP of Seven Countries ( ) U.S. 9(7%) 8(5%) 5(9%) 20(22%) 9(20%) 26(30%) Japan 8(5%) 5(9%) 7(9%) 8(9%) 2(3%) 4(6%) U.K. (2%) 7(3%) 6(8%) 7(8%) 7(7%) 4(5%) Brazil 48(9%) 50(9%) 63(82%) 47(5%) 54(57%) 36(4%) Russia - - 7(92%) 60(65%) 35(37%) 52(59%) India 36(68%) 33(60%) 30(39%) 39(42%) 5(6%) 5(6%) China 53(00%) 49(89%) 50(65%) 54(58%) 26(28%) 27(3%) Number of Countries Note: The numbers in the table refer to the ranking positions. The numbers in the parenthesis are the percentages of the rankings. Data source: National Accounts Main Aggregates Database by United Nations, Sigma database by Swiss Re, etc; author s calculation. In sum, as is revealed by the analysis above, we should have a new recognition for the insurance growth level of each country: as the new indicator BRIP implies, relative to their own stage of economic development, the benchmark-adjusted insurance growth level of the emerging countries is not as low as what traditional methods indicate, and the benchmark-adjusted insurance growth level of the developed countries is not as high as what traditional methods imply. Put it in another way, statically speaking for

12 the year 2006, the ranking of the growth potential of seven countries would be like this (from large to small): Russia, Brazil, China, US, Japan, India and UK. Of course, when speaking of a dynamic growth potential for coming future, it needs much more information about the countries, such as the mid-term and long-term projections for their GDP growth and BRIP as well as some others. 3. Comparison of Insurance Growth Structure The overall analysis of a country s insurance growth includes not only the aggregate analysis of the insurance growth level, but also the structural analysis of the insurance growth structure. This section will discuss the growth structure of each country s insurance industry by decomposing the aggregate growth into three parts. 3. Introduction to Trichotomy By analyzing the factors that drive the insurance growth, we can decompose any country s insurance growth into three parts regular growth, deepening growth and institutional growth which will be explained in detail later. This method is named as trichotomy in this paper. Figure 2 illustrates the trichotomy of the insurance growth structure. If the solid S curve in the figure represents the growth curve of the world industry, the dashed S curve passes point A and parallels the solid S curve, and point A and D show a country s GDP per capita and insurance penetration in 982 and 2006 respectively,, then the insurance growth of that country during the time period could be decomposed into three parts: Line AB, Line BC and Line CD. As for the mid-term and long-term growth potential of China s insurance industry (for the period of ), please refer to Zheng, Liu and Dickinson (2008). The same methodology can be applied to other countries. 2

13 Penetration Adjusted Growth Curve of World Insurance D C A B GDP per Capita Figure 2 Trichotomy of Insurance Growth Structure Line AB measures the insurance growth accompanying the economic growth assuming the insurance penetration is unchanged, and is named as regular growth in this paper. Line BC measures the growth brought about by the increase of insurance penetration induced by economic growth as the insurance penetration will increase when GDP per capita increases, and is named as deepening growth in this paper. Regular growth and deepening growth are both driven by economic factors. Line CD measures the remaining part of the growth, which is brought about by the institutional factors after the economic factors have been deducted, and is named as institutional growth in this paper. However, the ordinary growth model estimated in the previous section could not serve as a basis for the trichotomy, as it combines all the information about the economic and institutional factors influencing the insurance growth, and it is possible to make a decomposition of the insurance growth structure only when the economic and institutional factors are separated. Therefore, in order to separate the country-specific institutional factors and the common economic factors, we need to adjust the ordinary growth model by introducing country dummies into the model, and the obtained model is named as the adjusted growth model. The equation of the adjusted growth model is as follows: 3

14 Y 94 = + λidi + ε C + C C ' ' ' X 2 3 i= (3) ' ' Where Y is the insurance penetration, X is the GDP per capita, C, C2 and C ' 3 are three parameters, D(i=, 94)is i the country dummy with respect to country i, (i=, 94)is coefficient for D(i=, 94), i ε is the residual. The reason for adding the country dummies is to control the impacts of the country-specific factors (e.g. legal system, culture, religion, social security) on the insurance growth. Similar to the estimation of the ordinary growth model, data of 95 countries and regions over the past 27 years ( ) are used to estimate this model. Table 5 shows the estimates of the adjusted growth model for world life insurance, non-life insurance and the insurance industry, and Figure 3 depicts the regression curves of life insurance, non-life insurance and the insurance industry accordingly, which are pure economic regression curves with country specific institutional factors extracted and λi represent the term of ' ' ' C + C C X 2 3 in Equation (3). Table 5 Estimates of Adjusted Growth Model of World Insurance Life Insurance Non-Life Insurance Insurance Industry ' 0.76*** 40.09*** 8.49*** C (24.22) (4.07) (26.23) ' 54.27*** 55.35*** 76.65*** C 2 (5.24) (5.3) (6.93) ' *** *** *** C 3 (0.54) (28.82) (26.74) R Adjusted-R Number of Observations 2,052 2,07 2,0 significance is %. For a detailed discussion on the impact of institutional factors, please refer to section Note:The Robust t-statistics is in parentheses. The term of *** means the level of

15 Figure 3 Regression Curves of Adjusted Growth Model of World Insurance As is illustrated in Figure 3, following the adjusted growth model supported by the statistical tests, the regression curves of life insurance, non-life insurance and the insurance industry all resemble the shape of S, and this is the reason why the model gets the name of adjusted S-curve. 3.2 Comparison of Growth Structure According to the trichotomy that decomposes insurance growth into regular growth, deepening growth and institutional growth based on the adjusted growth model, we list the insurance growth structure of the seven typical countries during the time period of in Table 6. Table 6 Insurance Growth Structure of Seven Countries ( ) (%) Economic Factors Institutional Factor Regular growth Deepening growth Institutional growth U.S Japan U.K Brazil Russia India China

16 OECD Average BRIC Average World average Note: Due to the data availability, the interval of Russian data is Because the data of Czech Republic, Hungary, Poland and Slovakia in 982 are unavailable, these counties are not included in the calculation of OECD Average. For the same reason, Russia is not included in the calculation of BRIC Average. World Average only covers 53 countries or regions, whose data in 982 are available. Data source: National Accounts Main Aggregates Database by United Nations, Sigma database by Swiss Re, etc; author s calculation. As is shown by Table 6, during the period of , the insurance growth of the U.S. and Japan is mainly constituted by regular growth (78% and 69% respectively) and institutional growth is quite limited (even negative). On the contrary, the insurance growth of Brazil, Russia, India and China is mainly formed by institutional growth (7%, 67%, 76% and 86% respectively) and deepening and institutional growth is relatively limited. Taking a look at the average value, we can find that on the OECD average level, regular growth, deepening growth and institutional growth respectively account for 63%, 34% and 3% of the insurance growth during the period of In contrast, the three ratios for the BRIC average are 5%, 20% and 65% respectively. As mentioned above, regular growth and deepening growth are both driven by economic factors, so that the sum of them could be entitled economic growth, as is opposed to institutional growth. So as to say, on the average, the economic factor and institutional factor account for 97% and 3% for the OECD, 35% and 65% for the BRIC, and 69% and 3% for the world average. The UK is a little bit different. The three types of insurance growth in UK are relatively balanced. 6 From what has been discussed above, we can see that in general, the insurance growth in developed countries is mainly driven by economic factors (including regular and

17 deepening factors), while institutional factors act as the major driving power for the insurance growth in emerging countries. As a matter of fact, during the process of economic transition, most emerging countries have undertaken dramatic change to their social institutions, which has created great opportunities for the insurance industry. For instance, before the economic reform in China, the Chinese government was responsible for providing old-age pension, low-price health care service and housing service to all the urban employees, so that there was no enough demand for the private insurance. During the transition, however, the government has been gradually decreasing its provision of these services, and the market (including the insurance market) is playing a more and more active role. So as to say, the economic reforms of the emerging countries have created huge institutional opportunities for the growth of the insurance industry. 4. Economic and Institutional Factors in Insurance Growth As is revealed by the last section, economic factors and institutional factors play different roles in the insurance growth in developed countries and emerging countries. However, the above result is drawn from a local analysis based on the data of the seven typical countries during a specific time period. In this section, in order to analyze the impacts of economic factors and institutional factors on the insurance growth in different stages of economic development, we will make a generalization to the above result through the comparison of the ordinary growth model and the adjusted growth model established respectively in the second section and the third section. 4. Comparison of the Ordinary and the Adjusted Growth Models We can see from the previous analysis that both the ordinary growth model and the adjusted growth model have their own properties and applicability. While the ordinary growth model takes into account both the economic factors (including regular and deepening factors) and institutional factors that influence the insurance growth, the 7

18 adjusted growth model separates the country-specific institutional influences and the common economic influences. In order to make a more explicit contrast of these two models and show their differences, we compare these two models for life insurance, non-life insurance and the insurance industry respectively in Figure 4, Figure 5 and Figure 6. Figure 4 Comparison of Two Models for Life Insurance Figure 5 Comparison of Two Models for Non-Life Insurance The reason why we contrast the two models for life insurance and non-life insurance separately lies in that the institutional factors play different roles in the growth of life insurance and non-life insurance. 8

19 Figure 6 Comparison of Two Models for Insurance Industry As the adjusted S-curve reflects the impacts of pure economic factors on the insurance growth, while the ordinary S-curve reflects the combined impacts of economic and institutional factors on the insurance growth, we can see the impacts of institutional factors on the insurance growth by comparing these two S-curves,. Check the comparison for life insurance depicted in Figure 4 first. We can see from the figure that when GDP per capita is relatively low (lower than US$ 4,67), the ordinary S-curve and the adjusted S-curve are almost overlapped, with the former slightly higher than the latter. When the GDP per capita is relatively high (higher than US$ 4,67), the ordinary S-curve gradually falls below adjusted S-curve, and the gap between these two curves increases as the GDP per capita increases. When GDP per capita is higher than around US$ 35,0002, the gap gets to keep stable to some extent.. It implies that when GDP per capita is low, institutional factors slightly 2 factors notably restrain the growth of life insurance. It is the constant 990 price and same in the rest part of this section. This figure is a proximate estimation as there is no guideline to determine the stability of the gap. 9 facilitate the growth of life insurance; and when GDP per capita is high, institutional Then check the comparison for non-life insurance depicted in Figure 5. As is shown in

20 the figure, the ordinary S-curve is always slightly higher than the adjusted S-curve, though the gap between the two curves slightly varies with the GDP per capita. It implies that institutional factors facilitate the growth of non-life insurance no matter how large the GDP per capita is. Finally, check the comparison for the insurance industry depicted in Figure 6. We can see from the figure that when GDP per capita is relatively low (lower than US$ 9,975), the ordinary S-curve is slightly higher than the adjusted S-curve. When GDP per capita is relatively high (higher than US$ 9,975), the ordinary S-curve notably falls below the adjusted S-curve, and the gap between these two curves increases as the GDP per capita increases. When GDP per capita is higher than around US$ 38,000, the gap gets to keep stable to some extent. It implies that when life insurance and non-life insurance are combined together, institutional factors facilitate the growth of the insurance industry to some degree when GDP per capita is low, and it markedly restrains the growth of the insurance industry when GDP per capita is high. 4.2 Discussion on Institutional Factors Above, we have discussed the positive or negative effects of the institutional factors on the development of life insurance, non-life insurance and the insurance industry. Next, we would naturally ask what kinds of institutions have caused such effects. As far as we are concerned, although the influential institutions may take various forms, there must be certain major institutions that dominate. Institutions that have potential impacts on the insurance growth include legal system, culture, religion, social security system, etc. Institutions like culture and religion are non-systematic institutions, that is, countries at the same economic development stage may have different cultures and religions, so that the impacts of these institutions on the insurance growth could be This figure is a proximate estimation as there is no guideline to determine the stability of the gap. 20 offset on the world s average level. In contrast, institutions like legal system and social security system are systematic institutions. For instance, most countries, no matter of their economic development stage, all have certain fundamental legal systems with similar characteristics, which would have systematic impacts on the

21 insurance growth on the world s average level. Likewise, social security system, which is closely related to the economic development stage, would probably have clear positive or negative systematic impact on the insurance growth. Besides, it is worth mentioning here that instead of analyzing all the institutions that could influence the insurance growth, we will focus on the systematic institutions whose effects are dominating. As far as we are concerned, the institution that dominantly affects the life insurance is the social security system, and the institution that dominantly affects the non-life insurance is the legal system, with its most typical components being the compulsory insurance and liability insurance. Consider the social security system first. In countries with a low level of GDP per capita, the social security system is usually under-developed, while in countries with a high level of GDP per capita, the social security system is usually well-developed. The social security system and the private insurance (particularly life insurance) are usually substitutable to some extent, which means that the better developed the social security system is, the more the life insurance growth is restricted. Consequently, in countries with a high level of GDP per capita, as the social security system is relatively better developed, the institutional factors tend to restrain the growth of life insurance. In other words, as the GDP per capita increases (and the improvement of social security system accompanying), the negative effects of institutional factors on life insurance would gradually increase. Then consider the compulsory insurance and liability insurance. As we know, the government s decision of whether to carry out these policies is mainly based on the consideration of social policy (such as equity and justice), and generally is not related to GDP per capita. In addition, the compulsory insurance and liability insurance are usually provided by private insurers; which means that the more compulsory insurance and liability insurance are implemented, the more growth opportunities will be created for the non-life insurance. Thus, no matter how large GDP per capita is, institutional factors will always bring positive effects to the growth of non-life insurance. 2

22 Finally, consider the life insurance and non-life insurance as a whole. 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. 4.3 Discussion on Developed and Emerging Countries From the above discussion on GDP per capita and the effects of institutions, we can obtain some implications. 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. We 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. 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 economy and institutions to being driven mainly by economy. 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. 5. Conclusion Considering the limitations of the traditional methods, we propose a new paradigm for international insurance comparison in this paper. We do both the aggregate analysis of the insurance growth level and the structural analysis of the insurance growth structure, and also investigate the respective impacts of economic factors and institutional factors on the insurance growth under various scenarios. The main conclusions of this research are as follows: 22

23 First, we should have a new recognition for the insurance growth level of each country or region: as the new indicator BRIP implies, relative to their own stage of economic development, the benchmark-adjusted insurance growth level of the emerging countries is not as low as what traditional methods indicate, and the benchmark-adjusted insurance growth level of the developed countries is not as high as what traditional methods imply. Thus, the judgment for the static growth potential of both emerging and developed countries should be adjusted accordingly, although the estimation for the mid-term and long-term growth potential should take into account more specific information. Second, as a whole, the insurance growth in developed countries is mainly driven by the economic factors (including regular and deepening factors), while that in emerging countries is largely driven by the institutional factors. Third, 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. 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 economy and institutions to being driven mainly by economy. In accordance with this judgment, it is extremely important for the insurance industry in the emerging countries to upgrade its growth strategy to attain a sustainable development. References Adams, M., Andersson, J., and Lindmark, M., 2005, The Historical Relation between Banking, Insurance, and Economic Growth in Sweden: 830 to 998, Norwegian School of Economics Working paper. Arena, M., 2006, Does Insurance Market Activity Promote Economic Growth? A Cross-Country Study for Industrialized and Developing Countries. World Bank Policy Research Working Paper 4098, December. Carter, R. L. and Dickinson, G. M., 992, Obstacles to the Liberalization of 23

24 Trade in Insurance, London: Harvester Wheatsheaf, Enz, R., 2000, The S-Curve Relation Between Per-Capita Income and Insurance Penetration, Geneva Papers on Risk and Insurance, Vol. 25, No.3, Kugler, M., and Ofoghi, R., 2005, Does Insurance Promote Economic Growth?: Evidence from the UK, University of Southampton Working Paper. USAID 2006, Assessment of How Strengthening the Insurance Industry in Developing Countries Contributes to Economic Growth, accessed from Outreville, J. F., 990, The Economic Significance of Insurance Markets in Developing Countries. Journal of Risk and Insurance, 57(3), pp Soo, H. H., 996, Life Insurance and Economic Growth: Theoretical and Empirical Investigation, University of Nebraska Dissertatio. Sun, Q. and Zheng, W., 2006, Reflection on the Overtaking Developing Mode of China Insurance Industry and the Build-up of New Mode, Journal of Insurance Studies, No.0. Swiss Re, Sigma, related issues. Ward, D., and Zurbruegg, R., 2000, Does Insurance Promote Economic Growth? Evidence From OECD Countries. Journal of Risk and Insurance, 67(4), pp Webb, I., Grace, M. F., and Skipper, H. D., 2002, The Effect of Banking and Insurance on the Growth of Capital and Output, Center for Risk Management and Insurance Working paper 02. Zheng, W, Liu, Y, and Dickinson, G..M., 2008, The Chinese Insurance Market: Estimating its Long-Term Growth and Size, Geneva Papers on Risk and Insurance, Vol. 33, No.3, forthcoming. 24

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