Wei Zheng, Peking University Yongdong Liu, China Academy of Sciences

1. 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

4. 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.

7. 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.

8. Estimates of “Ordinary Growth Model” The Robust t-statistics is in parentheses. The term of “***” means the level of significance is 1%.

9. Regression Curves of “Ordinary Growth Model”

10. 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. What’s the economic implications of BRIP ? • 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.

12. 2.3 Comparison of Ranking Results (2006)

13. 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

14. 3. Comparison of Insurance Growth Structure 3.1 Introduction to “Trichotomy” 3.2 Adjusted model of insurance growth 3.3 Comparison of Growth Structure

15. 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

16. Trichotomy of Insurance Growth Structure Adjusted Growth Curve of World Insurance Penetration D C B A GDP per Capita

17. Trichotomy of Insurance Growth Structure Adjusted Growth Curve of World Insurance Penetration D C B A GDP per Capita

18. Trichotomy of Insurance Growth Structure Adjusted Growth Curve of World Insurance Penetration D C B A GDP per Capita

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

20. Estimates of “Adjusted Growth Model” The Robust t-statistics is in parentheses. The term of “***” means the level of significance is 1%.

21. Regression Curves of “Adjusted Growth Model”

22. 3.3 Comparison of Growth Structure

23. 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

24. 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

25. 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

27. Comparison of Two Models for Non-Life Insurance

28. Comparison of Two Models for Insurance Industry

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.

30. 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)

31. 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.

32. 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.

33. 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

34. 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.

35. 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.

36. 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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