日本產險公司市場競爭度與風險關係之研究 - 政大學術集成

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(1)國立政治大學風險管理與保險學研究所碩士學位論文. 日本產險公司市場競爭度與風險關係之研究 A study of the Relation between Market Competition and Company’s Risk in Japanese Property-Liability Industry. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. 指導教授：鄭士卿博士研究生：王咨渝撰. 中華民國 104 年 7 月.

(2) 摘要本研究以日本國內產物保險公司作為研究對象，研究期間為 1986 年至 2010 年，共 25 個年度。本研究應用固定效果之 GMM（一般化動差法）模型，以 Lerner index 衡量市場獨占力（競爭度），檢驗對日本產險公司之市場獨占力（競爭度）對於公司的財務. 政治大. 穩定度以及各種風險之影響，包括總風險、核保風險以及投資風險。本研究發現日本國. 立. 內產險公司獨占力與各風險大多存在負相關：越高獨占力（越低競爭度）的公司其總風. ‧ 國. 學. 險及核保風險越低。然而，越高獨占力（越低競爭度）的公司容易面對越高的投資風險。. ‧. 本研究結果亦顯示，獨占力對於風險之影響於 1997 至 2010 之研究期間有顯著效果，惟. Nat. io. sit. y. 其於 1986 至 1996 之研究期間效果不顯著。. n. al. er. 關鍵字：日本、市場競爭度、保險、風險、勒納指數、穩定度. Ch. engchi. i n U. v.

(3) Abstract This paper chooses Japanese domestic general insurance company as the objective and the research period is from 1986 to 2010, 25 sample years in total. We apply fixed-effect in the GMM (Generalized method of moments) to examine the impact of the market power (Lerner index) specifically in insurance market on financial stability and different risks of insurance. 政治大. company, including total risk, underwriting risk and investment risk. The result suggests in. 立. general, negative relation exist between market power and risks in Japanese domestic general. ‧ 國. 學. insurance industry: We find that the higher the market power (the lower competition), the lower. ‧. the total risk and underwriting risk. On the other hand, higher marker power (lower competition). y. Nat. er. io. sit. leads to higher investment risk. Our finding also shows that market power has an impact in the period 1997-2010 but not for the period of 1986-1996, which confirms that Japanese financial. al. n. v i n C risks and financial stability. reform in 1996 might have influence on h engchi U. Keywords: Competition, insurance, Japan, Lerner index, Risk, Stability.

(4) 謝. 辭. 七年的日子終於要離開政大，最要感謝的是我的指導老師鄭士卿教授，從我還是清純小大一就一直耐心鼓勵直到我長大成人，非常感謝老師在學業以及人生的幫助讓我順利拿到碩士學歷。感謝身邊的好夥伴一路陪我走來，風管所的紀宛庭、倪世涵、郭渟渟、盧彥宥，風管系的謝丞婷、洪偉豪、傅曉婷。感謝范庭欣學妹以及蔡易達學弟在我論文草創階段的傾囊相助。感謝蕭維萱學. 治政大姊一起努力奮鬥讓彼此都不孤單。感謝系上凌玉、薏臻、欣伶以及立 ‧ 國. 學. 椀婷助教們的幫助讓我沒有後顧之憂完成口試以及學業。感謝口試委員汪琪玲教授以及邱于芬教授給予的提點以及指教。. ‧. sit. y. Nat. 感謝楊勝惟在我人生中最難捱的時刻陪我走了過來，在不見光. er. io. 明的黑暗裡點了一盞燈。最感謝的是父母一直以來的支持以及包容. n. al 無比任性的自己。最後感謝自己曾經那麼堅毅的踩過人生最艱難的 iv Ch. n U engchi. 障礙走到現在，未來人生路上還存在很多未知，你正在前進，願你勇敢。願你永遠記得最純粹的自己。. 小酌紅酒的夜裡 20150726.

(5) CONTENTS Abstract ........................................................................................................................................... 0 Chapter 1 Introduction .................................................................................................................. 1 1.1. Motivation ................................................................................................................... 1. 1.2. Thesis Organization ................................................................................................... 3. 政治大 Chapter 3 An Overview of Japanese 立 Non-life Insurance Market ............................................. 8. Chapter 2 Literature Review ........................................................................................................ 4. ‧ 國. 學. Chapter 4 Methodology ............................................................................................................... 15. ‧. 4.1 Sample Selection ............................................................................................................. 15. sit. y. Nat. 4.2 Model description ........................................................................................................... 15. n. al. er. io. Chapter 5 Empirical Results ....................................................................................................... 28. Ch. i n U. v. 5.1 Result Analysis ............................................................................................................... 28. engchi. 5.2 Robustness Checks ......................................................................................................... 40 Chapter 6 Conclusion .................................................................................................................. 45 Reference ....................................................................................................................................... 47 APPENDIX I. Japanese Consumer Price Index ....................................................................... 50 APPENDIX II. The Effect of Market Power on Risks with Time Interaction ....................... 51.

(6) List of Figures Figure 1. Number of non-life companies and growth rate of total premium from 1986 to 2012 .......................................................................................................... 13 Figure 2. Loss ratio and expense ratio from 1986 to 2012........................................ 14. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v.

(7) List of Tables Table 1. Total premium volume in millions of USD in 2013 ...................................... 8 Table 2. Insurance density: premiums per capita in USD in 2013 ............................ 9 Table 3. Insurance penetration: premiums as a % of GDP in 2013 .......................... 9 Table 4. The statistics of variables in translog cost function model ........................ 24 Table 5. The definitions and calculations of control variables................................. 27 Table 6. Estimated Coefficient of Translog Cost Function ...................................... 28. 政治大 Table 7. Descriptive Statistics ..................................................................................... 31 立. ‧ 國. 學. Table 8. Pearson correlation coefficient ..................................................................... 32 Table 9. The effect of market power on risks. ........................................................... 34. ‧. sit. y. Nat. Table 10. The effect of market power on risks for separated periods. .................... 35. n. al. er. io. Table 11. The effect of market power on financial stability..................................... 39. Ch. i n U. v. Table 12. The effect of market power on risks without using. engchi. in the model .. 41. Table 13. The effect of adding group (GRP) variable .............................................. 43 Table 14. The effect of investment (SIR) variable..................................................... 44.

(8) Chapter 1 Introduction 1.1 Motivation Operating performance and financial stability of insurance industry are very important, which is closely related with the public. Therefore, whether the insurance industry can sustainably develop and stabilize its financial performance is a highly-focused issue throughout the world. Insurance companies have to deal with every aspect of risks. For instance,. 政治大 underwriting risk may occur when Insurance underwriters incorrectly evaluate the risk and 立. ‧ 國. 學. exposures of potential clients, resulting in the failure to meet the insurance company’s expectations; another example is investment risk resulting from the inaccurate investment. ‧. sit. y. Nat. decision-making or from the suddenly negative changes in economic climate, which might make. n. al. er. io. insurance company suffer formidable financial loss.. Ch. i n U. v. Japan used to show very excellent performance on economy and financial market, and its. engchi. insurance market had been the second place throughout the world. Therefore, the perfection of its management and awareness on risk plays an important role for companies. The authority enacted regulations on the asset of insurance company in the 1940s in case the risk of the asset portfolio might be too high, too centralized or the liquidity might be too low. Nevertheless, Japanese asset price bubble burst in the 1990s, which exposed the deficiency of both the risk management of insurance industry and the supervision of the authority.. 1.

(9) There were many restrictions in Japanese insurance industry, but with the advance of economy and the trend of internationalization, it is inevitable to gradually deregulate the financial and insurance market so as to respond to the whole new economic situation. Therefore, the authority of Japan had commenced a series of financial reforms called Big Bang since 1996 including the new insurance law, the permission of cross-industry operation between life insurance company and property-liability insurance company by holding subsidiary company, and introducing insurance broker to Japan, which resulted in a big change in Japanese insurance. 治政大 competition had been triggered; industry. For the sake of the rate liberalization in 1998, the price 立 ‧ 國. 學. moreover, the authority allowed insurance industry and other financial industry to mutually operate in 2000, which gradually pushed the price competition to the peak leading to many. ‧. mergers and acquisitions appearing in Japanese insurance industry.. sit. y. Nat. io. n. al. er. Regarding the impact of competition on financial market, Keeley (1990) suggests that. i n U. v. banks with higher market-power face lower default risk. In the 1980s, the competition between. Ch. engchi. banks increases in the United States, which decreases the marker power, and thus results in ascending bankruptcies. Berger et al. (2008) suggest that less bank competition increases market power and decreases overall risk exposure, and marker power increases loan portfolio risk under the competition-fragility view. However, there are seldom researches and literatures on the correlation between market power and risks in insurance industry; they mainly focus on banking industry but insurance industry is also closely related to the interest of the public. The insurance premium volume of 2.

(10) Japanese insurance market ranks second in the world and thus the stability of Japanese insurance industry is worthy of our attention. Besides, there were many mergers and acquisitions after the financial reform in 1996, this may change the market power of firms and further have impact on their risks as well as financial stability. In view of the above, this research examines the impact of the market power specifically in insurance market on different risks of insurance company and aims to provide a valuable reference for insurance industry as a precaution for its risks and financial stability.. 政治大. 1.2 Thesis Organization 立. ‧ 國. 學. Our research is arranged as follows. We first review the literature about the effect of market. ‧. power on financial stability in chapter 2 and overview the Japanese insurance market in chapter 3.. y. Nat. er. io. sit. In chapter 4, we next introduce our sample selection and our model description. The empirical results are discussed and analyzed as well as robustness check in chapter 5. Lastly, we make a. n. al. Ch. conclusion of our research in chapter 6.. engchi. 3. i n U. v.

(11) Chapter 2 Literature Review There hasn’t been general agreement on whether the authority should increase or decrease the level of competition to reduce risk to achieve more efficient management and better performance. Researches examining the impact of market competition on risks focus more on banking industry than on insurance industry and therefore our research try to apply the conclusions of former literatures to insurance industry.. 政治大. Keeley (1990) took American banks as research objective and introduced Tobin’s Q to. 立. evaluate the market power of banks. He found that banks with stronger market power have larger. ‧ 國. 學. capital and face lower default risk. For instance, the level of competition in banking industry in. ‧. the United States ascended in the 1980s, leading to the reduction of market power and the. y. Nat. al. er. io. sit. franchise value and the increasing of bankruptcies. Salas and Saurian (2003) followed Keeley. v. n. (1990) and applied the method to Spanish banking industry, and they further considered the. Ch. engchi. i n U. regulations of government and the risk-taking on asset aspect. The result suggests that standard measures of market concentration do not affect bank risk-taking, but negative relation is found between market power measured by Lerner index based on bank-specific interest rates and bank risk. Chang et al. (2008) tested the relation between non-performing loans (NPL) of the Brazilian banking system and macroeconomic factors, systemic risk, and banking concentration. They applied a panel data approach with fixed effects. The results find a significant impact of banking 4.

(12) concentration on NPL, which indicates that financial stability may be improved by more concentrated banking system. Turk-Ariss et al. (2010) studies how the impact of the degree of market power on efficiency and financial stability in the banking industry for a group from emerging economies. They apply three different specifications of the Lerner index of competition and use a Z-index to proxy for financial stability. The results indicate that increased market power brings about greater bank stability. Boyd and De Nicolò (2005) present contrasting models that relate banking competition to. 治政大when the banking market is more stability and test the relation empirically. They suggest that 立 ‧ 國. 學. centralized, banks with higher market power would raise loaning rate, the borrower would take the investment with higher risk because of the increasing of capitol cost and thus banks have to. ‧. take higher default risk. De Nicolò and Loukoianova (2007) also find that the positive and. sit. y. Nat. io. n. al. er. significant relationship between bank concentration and bank risk of failure is stronger when. i n U. v. bank ownership is taken into account, and it is strongest when state-owned banks have sizeable. Ch. engchi. market shares. André Uhde and Ulrich Heimeshoff (2009) collected European banks’ data from 1997 to 2005 to test the relation between Consolidation in banking and financial stability. They suggest that national banking market concentration has a negative impact on European banks’ financial soundness. Ren (2006) examined the relation of competitive on risk specifically in the insurance industry. She simultaneously considered the impact of franchise value and the level of competition on risks taken by insurance industry and further applied the effect of underwriting 5.

(13) cycle. The result shows that when competition becomes intense, firms with high franchise value appear to take more risk in order to maintain their existing market position for stock insurers. For mutual insurers, the risk-constraining effect of franchise value tends to be strengthened when the competition increases. Besides, for homeowner insurance and general liability insurance, when the market is in a slump, the risk-constraining effect of franchise value and risk-increasing effect of competition appear to be stronger. However, the effect of underwriting cycle is relatively not significant for auto personal liability insurance and commercial multiple perils insurance.. 治政大is a U-shaped relation between the Martínez Miera and Repullo (2010) also prove that there 立 ‧ 國. 學. competition and bankruptcy risk in banking industry. The result shows that the entry of new banks would decrease the bankruptcy risk in a more centralized market, and in a more. ‧. competitive market, the entry of new banks would increase the bankruptcy risk. Therefore, the. sit. y. Nat. io. n. al. er. relation between the competition and bankruptcy risk is U-shaped. Liu et al. (2013) apply the. i n U. v. Lerner index and Z-index as bank competition and bank stability respectively to examine the. Ch. engchi. competition-stability relationship in 11 European banking industries for the period 2000-2008. The results suggest that a U-shaped relationship between competition and stability existing in European banking. Jiménez, Lopez and Saurina (2013) use data from Spanish banking system to test relationship between bank competition and risk-taking in the loan market. The result supports this nonlinear relationship by using standard measures of market concentration in both the loan and deposit markets. In addition, when using a direct indicator as a proxy for market power, such as 6.

(14) Lerner indices, the results appear to be stronger to support the original franchise value hypothesis, but only in the loan market. Concerning the research about Asia, Liu, Molyneux, and Nguyen (2012) examined the effects of competition on bank risk-taking behavior in four South East Asian countries (Indonesia, Malaysia, Philippines and Vietnam). They find that competition would not increase bank risk-taking behavior in different model specifications, estimation approaches and variable construction. Even so, the result suggests that there is an inverse relation between concentration. 治政大 risk varies across bank types in and bank risk. However, Liu and Wilson (2013) suggest that 立 ‧ 國. 學. Japanese banking industry. In addition, the relationship between competition and risk also varies across bank types based on different initial levels of risk. Increasing competition appears to. ‧. reduce the risk of (City) banks with higher initial levels of risk, but increase the risk of their. sit. y. Nat. io. n. al. er. (Regional, Tier 2 Regional, Shinkin and Credit Cooperative) counterparts with lower initial levels of risk.. Ch. engchi. i n U. v. Regarding the research in Taiwan, Liu (2009) took the Taiwanese banking industry during 1996-2005 as research objective and used Lerner index to evaluate the market competition. The finding shows that higher market concentration and lower marker competition leads to lower credit risk and earnings variability risk. However, the competition in banking industry doesn’t conduce to the negative relation between marker concentration and credit risk and earnings variability risk.. 7.

(15) Chapter 3 An Overview of Japanese Non-life Insurance Market Japanese Insurance market has ranked second only to the United States for the past decades all over the world. Japanese insurance industry can be classified into life-insurance and non-life insurance. The total insurance premium written in 2013 was 531,506 million USD, accounting for 11.45% of the share of world market. As shown in table 1, the premium volumes of life insurance and non-life insurance were 422,733 and 108,773 separately in millions of USD next only to the. 政治大. United States.. 立. Table 1. Total premium volume in millions of USD in 2013. South Korea. Canada Netherlands. 422,733. 108,773. 531,506. 11.45. 222,896 152,121 160,156 114,349 117,978 91,204 52,334 26,006. 106,750 125,844 94,598 132,813 50,576 54,223 73,010 75,136. 329,643 277,965 254,754 247,162 168,554 145,427 125,344 101,140. al. Ch. engchi. y. U.K. RP China France Germany Italy. 27.13. sit. 3 4 5 6 7 8 9 10. 1,259,255. er. Japan. Share of World market (%). 726,397. n. 2. Total premium volume. 532,868. io. U.S.. Nat. 1. Non-life Insurance premium. ‧. ‧ 國. Life Insurance premium. 學. Ranking Country. i n U. v. 7.10 5.99 5.49 5.33 3.63 3.13 2.70 2.18. Resource: Swiss Re, Sigma, No. 3, 2014 Besides, we can also learn more about the importance of Japanese insurance market from other indicators that are insurance density and insurance penetration. As shown in table 2, the insurance density in Japan was 4,207 in USD and ranked second in Asia in 2012, which means that every resident in Japan averagely paid $4,207 USD as their insurance premium. 8.

(16) Table 2. Insurance density: premiums per capita in USD in 2013 Ranking Country. Total business. Life business. Non-life business. 1 2 3 4 5 6 7 8 9. Switzerland Netherlands Denmark Finland Luxembourg Hong Kong U.K. Norway Sweden. 7,701 6,012 5,780 5,073 5,003 5,002 4,561 4,452 4,320. 4,211 1,546 4,093 4,109 2,749 4,445 3,474 2,766 3,215. 3,490 4,466 1,687 963 2,254 557 1,087 1,696 1,105. 10. Japan. 4,207. 3,346. 861. 政治大. Resource: Swiss Re, Sigma, No. 3, 2014. 立. ‧ 國. 學. Table 3. Insurance penetration: premiums as a % of GDP in 2013. n. 7 Japan 8 Finland 9 Denmark 10 Switzerland. C h11.1 engchi 10.8 9.8 9.6. y. 14.5 12.7 11.7 3.2 7.5 8.8. sit. io. al. 17.6 15.4 13.2 12.6 11.9 11.5. Life business Non-life business. ‧. Taiwan South Africa Hong Kong Netherlands South Korea U.K.. Nat. 1 2 3 4 5 6. Total business. er. Ranking Country. i n U 8.8 8.7 6.9 5.3. v. 3.1 2.7 1.5 9.4 4.4 2.8 2.3 2.0 2.9 4.4. Resource: Swiss Re, Sigma, No. 3, 2014 As shown in table 3, the insurance penetration in Japan was 11.1%, meaning that the total insurance premium accounts for 11.1% of gross domestic product in 2013. Both of the indicators reflect that Japanese insurance industry has already been highly-developed and the residents have strong sense of the importance of insurance. Hence, insurance takes a significant role in Japanese 9.

(17) economy. From the two indicators, we learn that Japanese insurance industry has already been highly-developed and the residents have strong sense of the importance of insurance in their daily life, which reflects that insurance takes a significant role in Japanese economy. Japanese authority used to implement protective policies and enacted many restrictions on financial institutions, leading to a closed market. However, Japanese authority had to make some changes in financial market so as to respond to the trend of economic globalization. In 1996, Prime Minister Hashimoto instructed the ministries and governmental agencies concerned to. 治政大Japanese financial market into an tackle the issues on financial system reform to transform the 立 ‧ 國. 學. international financial market. This is the so-called Japanese Big Bang, which aims at three main principles: “Free”, “Fair” and “Global”. "Free" means creating a free market where market. ‧. principles prevail through liberalization of such areas as entering into business, financial product. sit. y. Nat. io. n. al. er. planning, and pricing. "Fair" means transforming the financial market into a fair and reliable. i n U. v. market through clearly defined rules with increased transparency. "Global" is targeted toward. Ch. engchi. reforming the market to fit the global era through upgrading the systems of law, accounting and supervision system in compliance with global standards. In April 1996, Japanese Insurance Business Law had been amended. The regulations on the calculation of rate in non-life insurance industry had been reformulated and the cross-operation between life insurance company and property-liability insurance company had been permitted. The authority also introduced the insurance broker system, enhanced the flexibility of rate and the disclosure of finance. The procedures mentioned above facilitated the positive competition and 10.

(18) the development of insurance business, which had a significant impact on Japanese insurance industry. Nonetheless, the policy of rate liberalization in 1998 triggered vicious price competition in insurance market, resulting in some companies bankrupting and some mergers and acquisitions after then. As shown in figure 1, the growth rate of premium slumped, keeping negative for several years because of the price cutting competition. Moreover, in order to earn more market share, insurers tended to write policies applied by those with higher risk, resulting in the ascent of. 治政 loss ratio since 1998 (see figure 2). Consequently, we can 大 also observe that two years later the 立 ‧. ‧ 國. liberalization.. 學. number of non-life insurance company started declining in figure 1 due to the impact of rate. In 2000, the cross-operation between insurance industry and other financial industry had. sit. y. Nat. io. n. al. er. been permitted, and banks were allowed to sale insurance product in 2001. This created a new. i n U. v. marketing channel for insurance industry, causing the growth rate of premium soared rapidly to. Ch. engchi. 7.2% (see figure 1) and the reduction of underwriting expense, therefore we can observe the expense ratio started reducing from 2002 in figure 2. However, owing to the financial tsunami in 2008, the growth rate of premium slumped to -4.28% and kept negative for several years(see figure 1). Not until 2011 did the growth rate of net premium turn out to be positive in figure 1 due to the occurrence of Tohoku earthquake and tsunami in Japan, which spurs the need of insurance and yet causes the loss ratio hovering rapidly around 80% because of the huge loss (see figure 2). 11.

(19) As for mergers and acquisitions, Mitsui M&F and Sumitomo M&F were merged into Mitsui Sumitomo Insurance in October 2001. In April 2002, Tokio F&M, ranking first in Japanese non-life insurance industry, merged a relatively small company Nichido F&M into Tokio Marine Nichido and stayed the most profitable and highest market share in Japanese non-life insurance market. In July 2002, Yasuda F&M, Nissan F&M and Taisei F&M ,all belonging to Mizuho Financial Group, were merged into Sompo Japan and ranked second only to Tokio Marine Nichido.. 治政大and Nissay Dowa into MS&AD, In April 2010, Mitsui Sumitomo Insurance merged Aioi 立 ‧ 國. 學. becoming the largest non-life insurance group in Japan and ranking seventh all over the world. At the same year, Sompo Japan and Nippon Koa were also merged into NKSJ. Since then, the three. ‧. largest non-life insurance groups started to take shape, and they are Tokio Marine Nichido,. sit. y. Nat. io. n. al. er. MS&AD and Sompo Japan.. i n U. v. As of 2013, there are totally 52 general insurance companies operating in Japan containing. Ch. engchi. 30 domestic general insurance companies (2 exclusively operating reinsurance business insurers) and 22 foreign non-life insurance (4 operating Protection and indemnity business insurers and 5 operating reinsurance business insurers included).. 12.

(20) Figure 1. Number of non-life companies and growth rate of total premium from 1986 to 2012 35. 12.00% 33 31. 31. 31. 33 31 10.00%. 30 28 27 24. 25. 24. 23 21. 21. 21. 21. 21. 21. 立. 21. 27. 政治大. 26. 27 26. 26. 24 23. 23. 20. ‧ 國. 學. 0.00%. y. sit. n. al. -2.00%. er. io. 5. 4.00%. 2.00%. Nat. 10. 6.00%. ‧. 15. 8.00%. 25. Ch. engchi U. v i n. 0. -4.00%. -6.00% 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 # of Company. Growth Rate of Premium. Note: operating reinsurance business excluded Resource: Statistic Japan 13.

(21) Figure 2. Loss ratio and expense ratio from 1986 to 2012 120%. 110%. 100%. 90%. 立. 70%. 60%. ‧. ‧ 國. 學. 80%. 政治大. n. al. er. io. sit. y. Nat. 50%. 40%. 30%. Ch. engchi U. v i n. 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Loss ratio. Expense ratio. Resource: Statistic Japan 14. Combined ratio.

(22) Chapter 4 Methodology 4.1 Sample Selection This paper chooses Japanese domestic general insurance company as the objective in the research, and data is collected from The Statistics of Japanese Non-life Insurance Business and annual financial statements of non-life insurance companies from 1986, 25 sample years in total. Owing to cases of mergers and acquisitions in Japanese insurance industry, the number of firms. 政治大 in each year is inconsistent, which creates an unbalanced panel data for us. After deleting missing 立. ‧ 國. 學. data and abnormal samples, there are totally 535 sample numbers adopted in this research1. The sample companies in every year are listing in the appendix.. ‧. sit. y. Nat. Since the sample period includes 25 years, nominal variables are divided by consumer price. n. al. er. io. index in every year and thus we adopt real variables in the research to avoid the impact of price. Ch. i n U. v. inflation. Annual CPI are collected from the website of Statistics Japan.. 4.2 Model description. engchi. We apply fixed- effect GMM (Generalized method of moments) model developed by Lars Peter Hansen in 1982 to examine the relation between market power and risks in insurance. 1. Our initial sample size is 551. After deleting ten units of abnormal and missing observations, we have 535 samples using in the model. Abnormal and missing data are listed as follows: YKFG lacks net claim paid in 2000 and 2001. Taisei lacks data in 2001. SJFG lacks net claim paid in 2002 and data for diversification in 2003 and 2004. Anicom lacks data for diversification in 2008, 2009 and 2010. Taisho lacks data for reinsurance ratio in 1989, 1990 and 1991. Allianz has negative net premium income in 2010. Unum has negative Lerner index in 1997 and 1998. Hitachi C has abnormally large reinsurance ratio in 2005. 15.

(23) industry. The model is set as follows:. =. +. +. +. +. ∀ i, t (1). From the literature review, we get the conclusion that the degree of competition has a certain relation with risks. Boyd and De Nicole (2005) suggest that lower competition would bring about higher risk in banking industry. On the contrary, Keeley (1990), Salas and Saurina (2003) and Liu (2009) suggest that lower competition would bring about lower risk in banking industry. Our research focuses on Japan and Liu (2009) focuses on Taiwan, both belonging to Asia and. 治政大industrial structure, we therefore experiencing similar economic environment as well as similar 立 ‧ 國. 學. expect lower competition would bring about lower total risk and underwriting risk according to Liu (2009). We also examine the impact of competition on investment risk due to the fact that it’s. ‧. one of the main risks insurers face, yet it might not be significant because investment is not. sit. y. Nat. io. n. al. er. directly relevant to competition in general. We choose Lerner index as our measure to evaluate. i n U. v. market competition for Japanese non-life insurance companies, and how it is calculated will be defined as below.. Ch. engchi. However, Martínez Miera and Repullo (2010) prove that there is a U-shaped relation between the degree of competition and bankruptcy risk in banking industry. In view of the above, we also add the square of. as another variable in this model to test whether U-shaped. relation exists, and this model reflects the correlation between competition and risks in insurance industry.. represents the risk variable of the i. risk, underwriting risk and investment risk.. company in t year and risks includes total. represents a set of control variables and will be 16.

(24) discussed in detail later. In the analysis of our results, we will discuss where the inflection point for the distribution of Lerner index locates to investigate if the relation between competition and risks is U-shaped or linear. If Lerner index and its quadratic term are both significant, indicating the existence of U shape, we would see our observations mainly locate below or above inflection point to infer it is a positive or negative relation. On the other hand, if the inflection point locates close to half of the distribution of Lerner index, we would say it is an upwardly or downwardly U-shaped relation.. 治政 Moreover, many regulations had been amended and mergers大 and acquisitions had appeared since 立 ‧ 國. 學. Japanese Big Bang in 1996, we further separate the whole sample period (1986-2010) into two periods (1986-1996 and 1997-2010). Chang (2008) studies if the operating efficiency improves. ‧. after merger and acquisition. The result shows that both profitability and business performance. sit. y. Nat. io. n. al. er. are better before merger and acquisition than after. Merger and acquisition usually brings about. i n U. v. higher market power and we expect firms with higher market power (lower competition) might. Ch. face higher risks during 1997-2010.. engchi. The next step is to test the competition-financial stability relationship for Japanese property-liability insurance industry. The model is set as:. =. +. In formula (2),. +. +. +. represents the financial stability for the. ∀ i, t (2) firm in year t. The Z-index. is an inverse measure of firm’s overall risks, combining profitability, leverage, and return volatility in a single ratio, which will be defined as below. We specifically follow Fernández and 17.

(25) Garza-Garcíab (2015) to use the natural logarithm of the Z-index (. ) as the financial stability. variable. To address the likely endogeneity of measures of market power, we employ an instrumental variable technique with a Generalized Method of Moments (GMM) estimator. A common problem in using empirical data is heteroskedasticity, and we test for its presence by white test. Although the instrumental variables coefficient estimates remain consistent in the presence of heteroskedasticity, the estimates of their standard errors are inconsistent, preventing valid. 治政 inference and rendering the estimator inefficient. The usual大 diagnostic tests for endogeneity and 立 ‧ 國. 學. overidentifying restrictions will also be invalid if heteroskedasticity is present. Such estimation issues can partially be addressed by using heteroskedasticity consistent or robust standard errors,. ‧. but the usual approach when facing heteroskedasticity of an unknown form is to use the GMM. sit. y. Nat. io. n. al. er. estimator. We apply the Hansen J statistics to test the overidentifying restrictions. If the J statistic. i n U. v. is too small to reject the null hypothesis that J =0, the overidentification restrictions are valid,. Ch. engchi. giving us the confidence that our instrument set is appropriate. Dependent variable Risk is the most concern to all insurers. Due to the professional knowledge and technique, insurance companies afford risks the public transfer by selling insurance policies and earn profit only when they deal with different types of risks well. In this model, the i. represents risk of. company in year t. Risk variables we use here include total risk, underwriting risk,. investment risk in this research. 18.

(26) We evaluate total risk by the standard deviation of ROA (return on asset) in previous three sample years2. ROA is the ratio of net profit to asset representing how efficient the insurance companies make use of their assets. If the ROA ratio fluctuates too much, it means the company would face higher total risk and instable operation3. Firms with high market power are usually stable and experienced on their operation to minimize the possibility of loss. We expect the relation between market power and total risk is negative. Underwriting risk is the risk of unanticipated loss generated from business solicitation,. 治政大by the standard deviation of loss writing policy or relevant cost. Underwriting risk is evaluated 立 ‧ 國. 學. ratio in previous three sample years and loss ratio is calculated as losses incurred divided by premiums income. Firms with low market power may be less careful with underwriting policy in. ‧. order to earn business so we expect the effect of market power upon underwriting risk is. n. al. er. io. sit. y. Nat. negative.. i n U. v. After gathering premiums from the public, insurance companies have to make profitable. Ch. engchi. investment to ensure that their fund can afford claims when incurring losses and therefore investment risk is important for insurance companies. We evaluate investment risk by the standard deviation of ROI ratio (return on investment) in previous three sample years and ROI ratio is calculated as investment income divided by total investment assets, which represents We use year %& , year % and year % to calculate the standard deviation of ROA in year . Because many mergers and acquisitions happened in Japanese insurance industry in our sample period, the total risk of firms going through a merger is calculated by the weighted average of standard deviation of its previous entities. For example, if firm A and frim B merge with each other and become frim C in 2001, in order to get firm C’s total risk, we calculate firm A and firm B’s standard deviation of ROA in 1998-2000 respectively and calculate the weighted average of firm A and B’s total risk with the weight as firm’s total assets. The underwriting risk and investment risks are also calculated in this way. 19. 2. 3.

(27) whether insurance company’s investment is in stable status or not. Because insurers with higher market power gather greater premium volume, they may make investment decision more carefully to reduce risk. We expect a negative relation between market power and investment risk. In addition, we also introduce Z-index as a proxy for financial stability. It is estimated as:. Z = Where. 56. ()*+, -./0*+, 234 1+,. (3). is the return on assets for each firm in year t, 7/86 represents the ratio of. 治政大 deviation for return on assets equity to total assets for each firm in year t. 9 is the standard 立 ()*. ‧ 國. 學. for a period of every three years for each firm and this is the total risk we calculate for insurance companies as mentioned previously. As observed in formula (3), the Z-index increases when the. ‧. level of return on assets and the degree of capitalization increase. However, it is reduced when. sit. y. Nat. io. n. al. er. the return volatility widens. We use Z-index to examine the relation between the market competition and financial stability. Independent variable. Ch. engchi. i n U. v. Lerner suggests a method to quantify the level of market competition in 1934, which is called Lerner index. We calculate the degree of the price deviating from marginal cost to evaluate the strength of the market power, and the definition of Lerner index is as follows:. = where =. :+, %;<+, :+,. represents the unit price of output of the i. (4) insurance company in t year and is. evaluated by total income total asset, and total income includes net premium income and 20.

(28) investment income. >?. is the marginal cost of the i. insurance company in t year, meaning. that the cost occurs when additional unit output is produced. From the formula (3), we know the Lerner index is between 0 and 1, and the Lerner index has a negative relation with market competition. When Lerner index is smaller, the gap between the market price and the cost is smaller, which means the firms have smaller power to determine market price and thus have less market power, and vice versa. If the market is in perfect competition, the output price is equivalent to the marginal cost and therefore the Lerner index is zero.. 政治大. 立. ‧ 國. 學. Fernandez de Guevara and Maudos (2007) adopt Lerner index as a proxy for the degree of competition in European banking industry. With regards to the calculation, they choose the ratio. ‧. of the total income divided by the total assets as the output price and the total income includes. sit. y. Nat. io. n. al. er. interest and non-interest income. Therefore, we here use the total assets to evaluate the output of. i n U. v. insurance industry, and the output price is calculated as the total income divided by the total asset.. Ch. engchi. The total income includes the net premium income and investment income that are two major revenue sources for insurance industry. We adopt the translogarithmic cost function approach used by Fernandez de Guevara and Maudos (2007) to estimate the marginal cost of insurance company. Assuming that total cost of insurance company is a function comprised of total outputs, labor unit price, capital unit price and funds, the model is set as follows:. 21.

(29) 8? =. +. 86 + &. 1 2. + D EF GF, FI &. + D JF 86 FI &. + D MF 8. &. &. 1 + D D EFH GF, 2. GH,. FI HI. GF, + K 8. L+K. 1 8 2. L + K& 8. represents the total cost of the. insurance company in year t, RSPTSP. 治政 insurance company in year 大 t, G G G 立. represents the output of the. L 86. L GF, + N + O ∀ , P (5). FI. Where 8?. ( 86 ). ‧ 國. indicates the unit input. L is the time variable and it equals 1 to 5 for. 學. price of labor, capital, and fund separately. 8. &. five different periods categorized by Japanese economic situation4.. ‧. sit. y. Nat. Total cost is the sum of personnel expense, commission and general operating expense. G. n. al. er. io. is calculated as personnel expenses divided by number of the staffs. G is the ratio of. i n U. v. commission plus general operating expense to number of policies. We evaluate G& by ROE. Ch. engchi. (return on equity), and ROE is estimated by the ratio of net profit for the year to total net worth. Notice that we have imposed the following restrictions in the translog cost function in order to obtain a valid cost function. Homogeneous of degree one should be satisfied in factor prices, that is ∑F λF = 1,. 4. ∑F λFH = 0 ∀ , ∑F JF = 0 and ∑F ∅F = 0, and symmetry restriction should be. Japan was in a good ecomonic staus during 1986 to 1990 and thus we set the trend as 1. Owing to the burst of economic bubble during 1990 to 1996, risks may become higher and thus we set the trend as 2. Risks may grow during 1996 to 2000 because of Big Bang and the liberalization of rate, and thus we set the trend as 3. The peak of mergers and acquisitions occurred during 2000 to 2005 and thus we set the trend as 4.and we set the rest of the time period equals 5. 22.

(30) satisfied, that is EFH = EHF , in formula (5). Table 4 shows the statistics of variables in translog cost function model. The mean of total asset is 11,384,581 thousand yen and the distribution is left-skewed, indicating that the majority of the non-life insurance companies are large-scale. After obtaining the estimators, we are able to further estimate the marginal cost by regression equation as follows: [0<+,. [0*+,. =. 0<+,. 0*+,. (. 立. +. 86 + ∑&FI JF GF, + K& 8. 政治大. 學 ‧. ‧ 國 io. sit. y. Nat. n. al. er. >? = . Ch. engchi. 23. i n U. v. L). (6).

(31) Table 4. The statistics of variables in translog cost function model Variable. definition. Calculation. TC. Total cost. personnel expense + commission + general operating expense. TA. Total asset. Mean. 政治大11,384,581. 立. \. Unit input price of labor. personnel expense number of staffs. f. Unit input price of capital. commission + general operating expense number of policies. Unit input price of fund. ROE. 0.124. 8,529. 7,260,194. 1,595,211. 6,005,292. 20,314. 98,121,787. 16,121,433. 100.600. 1.550. 189.620. 21.147. 0.137. 0.005. 0.694. 0.073. 0.038. -2.120. 0.587. 0.238. sit. y. -0.018. n. Ch. engchi U. v i n. 2. Nominal sample data are divided by annual consumer price index and thus transformed into real variables. 3. The sample number is 535 in total. 4. We increase all the ROE for 2.12 to have the minimum ROE equal zero to deal with the problem that ROE are negative.. 24. Standard Deviation. 790,972. er. io. al. Maximum. ‧. ‧ 國. 101.286. Minimum. 學. Nat. Note: 1. TC, TA, W1 and W2 are in thousand Japanese yen.. 1,330,823. Medium.

(32) Control variable As mentioned before, we introduce a set of control variables (K j ) in model (1). Firm size (FS), percentage of long-tailed business (PLT), reinsurance ratio (RR), the degree of business diversification (DVF), Herfindahl-Hirschmani index (HHI) and the organization form of insurance company (OF) are selected as the control variables in the model. Firm size reflects how much business volume an insurance company is able to accept. The larger the company is, the more business volume the company can take. If an insurance company. 治政大absorbing risk in comparison with owns larger asset, when severe event occurs, it would afford 立 ‧ 國. 學. small companies. Fernandez de Guevara and Maudos (2011) show a negative relation between bank size and financial stability, but beyond a threshold, greater bank size decreases the possibility of. ‧. bankruptcy. Craig and Dinger (2013) find humped shape of the relation between bank size and. sit. y. Nat. io. n. al. er. bank risk, we thus also add the square of total asset as a control variable to test if the U-shaped. i n U. v. relation exists between firm size and risks in insurance industry. We apply the natural logarithm. Ch. engchi. of total assets as the firm size in our model.. According to Lee and Urrutia (1996), the proportion of premiums written in long-tailed lines has statistically significant impacts on the probability of insolvency of a property-liability insurer in their logit model. If the percentage of long-tailed business of a general insurance company is high, it has to take the longer responsibility, which represents the longer duration that insurer has to face uncertain risk than insurers with low percentage of long-tailed business. The proportion of. 25.

(33) premiums written in long-tailed lines is measured by the ratio of long-tailed lines5 net premiums written to total net premiums written. Berger, Cummins, and Tennyson (1992) suggest that profitability is improved by the ceding of reinsurance, consistent with a view of reinsurance as an alternative to other risk diversification devices. Shiu (2011) and Aunon-Nerin and Ehling (2007) show that increasing use of reinsurance leads to higher risk-taking. Reinsurance ratio of a company reflects the degree of risk transferred to other insurer or reinsurer. When loss occurs, companies can be compensated by reinsurance to. 治政大arrangements incline to face lower stabilize their operation, and thus insurers doing reinsurance 立 ‧ 國. 學. risk. The reinsurance ratio is calculated as reinsurance premium expense to net premiums income. Odesanmi S. and Wolfe S. (2007) find that diversification across and within both interest. ‧. and non-interest income generating activities decreases insolvency risk. When a huge loss occurs. sit. y. Nat. io. n. al. er. for a certain kind of insurance policy, insurers with high degree of revenue diversification can be. i n U. v. compensated from other kinds of insurance business, and therefore their risk is relatively smaller.. Ch. engchi. We sum the square premium of every line first6, dividing it by the square of total premium and then minus the value by 1 to obtain the diversification for every firm in every year. Fernandez de Guevara and Maudos (2009) use HHI as a proxy for market concentration and they find that increases in concentration lead to a lowering of financial stability. HHI indicates the 5. Long-tailed lines include Compulsory Automobile Liability Insurance and General Liability Insurance. Short-tailed lines include Fire Insurance, Savings-type Fire Insurance, Hull Insurance, Cargo Insurance, Transit Insurance, Automobile Insurance, Personal Accident Insurance and Savings-type Personal Accident Insurance. 6 According to The Statistics of Japanese Non-life Insurance Business, we select Compulsory Automobile Liability Insurance and General Liability Insurance, Fire Insurance, Savings-type Fire Insurance, Hull Insurance, Cargo Insurance, Transit Insurance, Automobile Insurance, Personal Accident Insurance and Savings-type Personal Accident Insurance to calculate diversification as our variable. 26.

(34) level of concentration, evaluating whether the market share is highly-concentrated on some specific companies, and risk may vary across different degree of market concentration. The HHI index is calculated by squaring the market share of each firm competing in a market, and then summing the resulting numbers. Organization form of insurance company represents whether insurance company is a stock or mutual company. Investment decision and operation can be affected to some extent according to the way insurance company being hold and thus insurance companies face different degree of. 治政 risk. Lamm-Tennant and Starks (1993) find that stock firms大 face higher total risk (measured by 立 ‧ 國. 學. the variance of loss ratio) than mutual firms in the U.S. property-liability insurance industry. In our model, if the insurance company is held as a mutual firm, the dummy variable is set as 0,. ‧. otherwise it is set as 1.. er. io. sit. y. Nat. al. n. Table 5. The definitions and calculations of control variables Variables Definition FS FSS PLT RR DVF HHI OF. Ch. Firm Size Square of FS percentage of long-tailed business reinsurance ratio degree of business diversification Herfindahl-Hirschmani index Organization form of company. Calculation. i n U. v. e natural h i of total assets n g clogarithm square of natural logarithm of total assets Premiums of long-tailed lines / Total Net Premiums Reinsurance Premium Expense / Net Premiums Income 1-( ∑((Premiums of lines)^2) / (∑Premiums of lines)^2 ) ∑(Market Share)^2 0:Mutual 1:Stock. 27.

(35) Chapter 5 Empirical Results 5.1 Result Analysis We use the translogarithmic cost function discussed as above to estimate the coefficient, and the results are shown in table 6. The adjusted R square is 0.966, indicating that this model can be highly-explained by the variables. The coefficients of β 、β 、J 、J J& and θ& are -0.025, 0.073, 0.043, 0.024, -0.067 and -0029 respectively, and nearly all of these coefficients are. 政治大. significant in the model. The next step is to apply these values into formula (6) to obtain the. 立. marginal cost, and then we further calculate the Lerner index by formula (4).. (mnop)f. -1.43. lnTATrend. 8.1. (mn \)f. io. 0.073*** -1.579**. lnW2. 1.101**. lnW3. 1.479***. Trend. -0.029*** 0.002. a l -2.47 (mn f)f v -0.201*** i n C h (mn )f U 2.48 0.087*** engchi. n. lnW1. Estimate. y. -0.253. Variable. sit. lnTA. T value. er. Estimate. Nat. Variable. ‧. ‧ 國. 學. Table 6. Estimated Coefficient of Translog Cost Function. -3.72 0.13 -6.01 4.29. 3.3. lnW1lnW2. 0.697***. 2.96. lnW1lnW3. 0.204. 1.56. 0.021. 1.22. lnW2lnW3. 0.958***. 8.46. lnTAlnW1. 0.043**. 2.01. lnW1Trend. lnTAlnW2. 0.024*. 1.93. lnW2Trend. -0.067**. -2.51. lnW3Trend. oqrnsf. lnTAlnW3 f. Adjusted t. 0.966. N. 535. Note: * represents significance level of 10% ** represents significance level of 5% *** represents significance level of 1%. 28. -1.050***. T value. -0.054 0.055*** -0.001. -4.78. -1.05 3.33 -0.02.

(36) Table 7 presents the descriptive statistics of all dependent and independent variables. The mean of Lerner index (LI) is 0.569, implying that on average, the majority of the insurers have a certain level of market power. The insurer with highest market power is MS&AD insurance group after merger in 2010 as mentioned above and insurer with lowest market power is Sony Assurance Company in 2002. The firm with the highest output price (P) and marginal cost (MC) is H.S. in 20107. The data shows that firm size (FS) is a left-skewed distribution and the mean is 15.028,. 治政大 with largest firm size is TMN indicating that large insurers are more than small ones. Insurer 立 ‧ 國. 學. insurance group and the smallest firm size is H.S. insurance in 2010. For the proportion of long-tail business (PLT), the mean is 0.129 and the maximum is only 0.248, from which we. ‧. observe that Japanese nonlife insurers prefer taking short-tail business more than long-tail. sit. y. Nat. io. n. al. er. business. The mean of business diversification (DVF) is 0.619, indicating that most firms are. i n U. v. moderately diversified with their business and nearly 85% of the samples are higher than 0.5.. Ch. engchi. The mean of reinsurance rate (RR) is 18.8% in Japanese non-life insurance market and this indicates that most insurers choose to retain risk instead of transferring risk to other firms8. In Japanese insurance industry, the market concentration (HHI) grows from 864.15 to 1693.19 year by year from 1986 to 2010. As for organization form (OF), the only two insurers held as mutual. 7. H.S. has the lowest total assets in 2010, leading to the highest output price, and in the same year H.S. has the highest ratio of total cost to total asset, causing its highest marginal cost. 8 Unum has reinsurance ratio of 60% in1999 and this is the only observation higher than 0.5. 29.

(37) company are Kyoei mutual fire & marine insurance company and Dai-Ichi life insurance company. The descriptive statistics of dependent variables are discussed as below. The mean of total risk (SROA) is 0.011, the firm with the highest total risk is Sony Assurance in 2005 and the firm with the lowest total risk (SROA) is Taiyo in 1997. The mean of underwriting risk (SLR) is 0.035, Meiji Yasuda General Insurance faces the highest underwriting risk in 2007 and JI Accident & Fire Insurance faces the lowest underwriting risk in 2007. The mean of investment risk (SROI) is. 治政 0.041, the highest investment risk occurs to Fuji Fire and大 Marine Insurance in 1998 and the 立 ‧ 國. 學. lowest investment risk occurs to Sonpo 24 Insurance Company Limited in 2004. On average, underwriting risk is higher than investment risk in our data. The range is wide for total risk and. ‧. especially for underwriting risk, indicating that the loss ratio varies widely with different insurers,. sit. y. Nat. io. n. al. er. and this can be attributed to their ways of operation.. i n U. v. As for financial solvency, we use the natural logarithm of the Z-index as the financial. Ch. engchi. solvency variable in our model. The mean of the natural logarithm of the Z-index (lnZ) is 4.368. Taiyo Life Insurance Company has the highest solvency in 1997 due to its very low standard deviation of return on asset (0.00002). Besides, owing to its negative return on asset (-0.0471) and relatively low ratio of equity to total asset (0.05031), ACE Insurance Japan has the lowest financial stability in 2002.. 30.

(38) Table 7. Descriptive Statistics Variable. N. Mean. Std.. P. 535. 0.320. 0.143. 0.072. 1.032. MC. 535. 0.122. 0.051. 0.027. 0.376. LI. 535. 0.569. 0.076. 0.136. 0.814. FS. 535. 15.028. 1.897. 9.919. 18.402. PLT. 535. 0.129. 0.056. 0.000. 0.248. RR. 535. 0.188. 0.085. 0.001. 0.600. DVF. 535. 0.619. 0.175. 0.000. 0.832. HHI. 535. 864.150. 1693.190. OF. 535. 0.955. 0.207. 0.000. 1.000. SROA. 535. 0.011. 0.025. 0.000. 0.232. SLR. 535. 0.035. 0.510. SROI. 535. 0.053 治 0.000 政 0.041 0.030 0.000 大. lnZ. 535. 1.863. 8.560. 1161.740 317.871. 立 4.368. Minimum Maximum. 0.147. -1.963. ‧ 國. 學. Table 8 presents the Pearson correlation coefficient between the independent variables. It. ‧. shows none of the correlation coefficient bigger than 0.7 or smaller than -0.7, meaning that no. sit. y. Nat. strong relation exists, and therefore implies that no multi-collinearity problem exists. We observe. er. io. that firm size has a moderate positive relation with percentage of long-tailed business and. al. n. v i n diversification and the correlation coefficient of long-tailed business and C h between percentage engchi U diversification is 0.633, indicating that firms with larger size may be willing to take on more long-tailed business and therefore they would manage to diversify their revenue sources. HHI also has a moderate negative relation with diversification, and the possible reason is that the increases in market concentration insurers may reduce the motivation of firms with high market share to diversify their premium sources and choose to consolidate their original business revenue. 31.

(39) instead. With regards to Lerner index, it doesn’t show any strong relation with other control variables. Table 8. Pearson correlation coefficient. LI FS. LI. FS. PLT. 1. 0.049. -0.021. 1. RR -0.283***. 0.539*** 1. PLT. DVF -0.103**. 0.071. -0.217***. -0.089**. 0.105**. 0.633***. -0.040. -0.040. 1. 0.222***. 0.047. 0.053. 1. 立. 0.226***. 0.459***. DVF. OF. OF. -0.268***. RR. HHI. HHI. -0.417***. 政治大. 1. -0.160*** -0.006 1. Note: * Statistical significance at 10 % level. ‧ 國. 學. ** Statistical significance at 5% level *** Statistical significance at 1% level. ‧. Table 9 shows the results of the relation between risks and the degree of competition in. y. Nat. er. io. sit. Japanese property-liability insurance industry. We first show the results of using total risks as our dependent variable in the first column. In the total risk model, the coefficient of Lerner index is. al. n. v i n C h term is positiveU(1.113) and both are significant at 5 negative (-1.277), the coefficient of quadratic engchi. percent level. The inflection point for Lerner index in the total risk model is 0.574 and it is approximately 62th percentile of the Lerner index distribution. This implies that nearly 62% of the sample presents higher market power (lower competition) decreasing total risk and indicates that our main data supports the inverse relation between market power and total risk.. We find a similar result for underwriting risk. In the second column, the coefficient of Lerner index is negative (-1.880), the coefficient of quadratic term is positive (1.655) and both 32.

(40) are significant at 10 percent level. The inflection point for Lerner index here is 0.568, which lies approximately the 60th percentile of the sample. Our main sample shows that market power is negatively associated to underwriting risk, implying higher market power would improve underwriting risk. An opposite situation happens in investment risk model. In the last column, the coefficient of Lerner index is significantly positive (1.778) at 5 percent level and the quadratic term is significantly negative (-1.536) at 1 percent level. With 63% of our data lying below the inflection. 治政 point (0.579) of Lerner index, the result presents a positive大 relation established between market 立 ‧ 國. 學. power and investment risk. The higher market power, the higher investment risk might be. As for other control variables, the percentage of long-tail business (PLT) is negatively. ‧. related to all of the three kinds of risks, indicating that firms with higher ratio of long-tail. sit. y. Nat. io. n. al. er. business would face lower risks in Japanese general insurance industry. Diversification variable. i n U. v. (DVF) is negatively significant to underwriting and investment risks, meaning that higher degree. Ch. engchi. of diversification would decrease underwriting and investment risk. The reinsurance ratio (RR) has a positive relation with total risk and underwriting risk. We reckon that insurers may write policies without carefulness on account of reinsurance arrangement, contributing the positive relation between underwriting risk and reinsurance ratio. In this result, firm size (FS) is only negatively significant related to investment risk and its squared term is positively significant. With 90% of the data lying above the inflection point (12.5), an inverse relationship is found between frim size and investment risk. The greater size of 33.

(41) a firm may improve its investment risk. Table 9. The effect of market power on risks. Total Risk Variable Intercept. Underwriting Risk. Estimate. T value. 0.000. -0.18. Estimate. Investment Risk. T value. Estimate. T value. 0.000. 0.15. 0.002. 0.46. Lerner Index. -1.277**. -2.29. -1.880*. 1.63. 1.778**. 2.45. urqnrq vnsrw f. 1.113**. 2.39. 1.655*. 1.80. -1.536***. -2.59. FS. -0.012. -1.41. 0.002. 0.09. -0.025**. -2.34. FSS. 0.000. 1.27. 0.000. 0.12. 0.001**. 2.38. -1.73. -0.097**. -2.40. -2.53. 0.000. 0.31. 0.000. -0.11. -0.004. -0.63. <.0001 0.525. al. 80.48*** 1.67 0.004 535. Ch. engchi. sit. 246*** 1.29 0.36 535. y. 3.88. n. Adjusted tf N. -0.088**. 0.000***. io. White Test Hansen’s J. -1.38. Nat. OF. -0.023. 3.01. 0.032***. ‧. HHI. 1.94. 學. DVF. 0.009*. 0.007 0.434. er. RR. ‧ 國. PLT. 治政 -0.074*** -3.27 -0.098* 大立. i n U. v. Note: The rejection of White test implies the inexistence of heteroscedasticity . The rejection of Hansen’s J test implies the invalidity of instrument variables used. * Statistical significance at 10 % level ** Statistical significance at 5% level *** Statistical significance at 1% level. 34. 0.006. 1.39. -0.025*. -1.80. 0.000***. -0.002 328.9*** 4.66 0.06 535. -8.95 -0.49 <.0001 0.198.

(42) Table 10. The effect of market power on risks for separated periods. 1986 - 1996. Estimate. T value. Estimate. Intercept. -0.014***. -3.37. 0.038. 1.37. Lerner Index. -1.980**. -2.28. 7.040. 1.06. -17.715. urqnrq vnsrw f. 1.621**. 2.27. -5.665. -1.05. FS. -0.019***. -3.41. -0.069. -1.61. FSS. 0.001***. 3.37. 0.002. PLT. -0.069***. -2.94. -0.093. 1.79. -0.006 -0.035. -3.18. HHI. 0.000***. -2.85. OF. -1.01. Hansen’s J. 0.31. Adjusted tf. 0.282. N. 213. 0.857. 0.537. 0.16. 0.022. 1.27. -0.002. -0.44. 0.001 -1.229**. Estimate. T value. 1.72. 14.267. 2.19. 6.899**. 2.08. -0.733*. -1.84. 立 0.000 1.51. -1.33. 0.184***. 2.21. -0.021**. -2.14. 1.18. -0.006***. -2.24. 0.001**. 2.17. -0.114***. -2.89. 治 1.080** 政 1.53 大 -0.010 -0.24 -0.012 0.28. 0.000. -0.25. -0.055. -0.28. -0.081**. -2.29. -0.594**. -2.03. -0.12. 0.071. 0.78. 0.010*. 1.88. 0.076**. 2.14. 0.004. 0.9. -0.56. -0.059. -1.43. 0.101. 0.27. 0.005. 0.44. 0.000***. 4.33. 0.000***. -4.67. -0.002. -0.33. 1.70. -2.08. 95.83*** <.0001 7.64*. T value. 0.830*. al. n. 163.6*** <.0001. -0.150***. Estimate. -2.05. io. White Test. -0.001. 0.000*. T value. 0.054. 0.001***. 6.22. 0.023. 1.41. 79.54*** 0.005. -0.023. ‧. -0.018***. -1.56. Investment Risk. -8.244**. Nat. DVF. 6.07. Underwriting Risk. -2.10. ‧ 國. 0.015*. 0.285***. Estimate. 學. RR. T value. Total Risk. -1.14. y. Estimate T value. Investment Risk. 3.07. sit. Variable. Underwriting Risk. 0.000***. -0.27. -0.001. er. Total Risk. 1997 - 2010. 142.5*** <.0001. iv. n1.41 C3.97 U h e n g0.265 i ch 0.180. 0.289. 0.495. -0.022. -1.45. 103.9*** <.0001 0.16. 0.983. 0.081. 322. Note: * Statistical significance at 10 % level ** Statistical significance at 5% level *** Statistical significance at 1% level The rejection of White test implies the inexistence of heteroscedasticity . The rejection of Hansen’s J test implies the invalidity of instrument variables used.. 35. 125.4*** <.0001 7.44* 0.102. 0.059.

(43) Table 10 presents the results of market power on risks in the two separated periods9. For 1986-1996, market power only has impact on total risk. The coefficient of Lerner index is negatively significant (-1.980) at 10% level and the quadratic term is positively significant (1.621) at 10% level. With 93% of the data lying below the inflection point (0.611) of the accumulated Lerner distribution, this presents an inverse relation between market power and total risk. For 1986-1996, firms with higher market power tend to face lower total risk. As of the effect in the period of 1997-2010, for total risk, the Lerner index is negatively. 治政大 significant at 5% level. The significant (-1.229) at 5% level and its square term is positively 立 ‧ 國. 學. inflection point is 0.569 and it represents approximately 45th percentile of the distribution, implying the relation found between market power and total risk is nearly U-shaped. The firm. ‧. with higher market power would decrease total risk, but when threshold obtained, higher market. sit. y. Nat. io. n. al. er. power might increase total risk. But for underwriting risk, our main sample suggests a negative. i n U. v. effect of market power upon underwriting risk10 during 1997-2010. For investment risk, because. Ch. engchi. this model failed to pass the Hansen’s J test, the result doesn’t show support of the relation between market power and investment risk if the period is separated. As for other control variables, practically all the variables except for organization form (OF) have impact on total risk in the period from 1986-1996 but firm size (FS), diversification (DVF) and are not significant in the next period. In the period of 1997-2010, most of the variables have 9. We also try to introduce time variable and its interaction term with other variables to test the relation. The result is showed in appendix II. 10 The coefficient of Lerner index is negatively significant (-8.244) at 5 percent level and its quadratic term is positively significant (6.899) at 5 percent level. Its inflection point (0.597) lies the 59th percentile of the distribution. 36.

(44) effect on underwriting risk except for diversification (DVF) and organization form (OF), and the same situation is also presented for investment risk but reinsurance ratio (RR) is not significant. The directions of effect in the regression are consistent with our discussion above. To sum up, firms with higher market power may decrease total risk before 1996 but afterwards the relation changes into U-shaped. For underwriting risk, an inverse relation appears in the period from 1996 to 2010 in our research. There is no evidence indicating that marker power is associated with investment risk if the period is separated. From these results, we infer. 治政大 in 1996 have transformed the that the series of changes in Japanese financial regulations starting 立 ‧ 國. 學. pattern of market power’s impact on risks, which is consistent with our initial anticipation11. Lastly, we follow Turk-Ariss et al. (2010) to adopt z-index as a proxy for financial solvency. ‧. in the GMM model to examine the relationship between competition and solvency in Japanese. sit. y. Nat. io. n. al. er. property-liability insurance industry and we also test it for whole period, 1986-1996 and 1996-2010.. Ch. engchi. i n U. v. Table 11 shows the impact of market power on financial stability. For the whole period, the coefficient of Lerner index is positively significant (77.122) at 5 percent level and its quadratic term is negatively significant (-67.572) at 5% level. The inflection point (0.571) covers 60% of the Lerner indices distribution, implying a nearly positive relation existing between market power and firms’ financial stability. 11. We also divide 1997-2010 into 1997-2004 and 2005-2010 according to the peak of mergers and acquisitions before 2005 and the financial crisis after 2005. We find that the competition has positive impact on total risk in 1997-2004. In addition, we introduce merger and acquisition variable and its interaction term with Lerner index, and the result shows merger and acquisition and the interaction term are significant. 37.

(45) For the period of 1986-1996, we don’t find evidence to prove the existence of relation. For the period of 1997-2010, the Lerner index term is positively significant (99.712) at 5 percent level and its square term is negatively significant (-85.036) at 5% level. The inflection point (0.586) lies in approximately 55th percentile of the distribution, representing a nonlinear relation existing between market power and firms’ financial stability. For other independent variables, firm size (FS) is positively related to solvency before 1996 but has no influence afterwards. In addition, the results show that if firms take on more. 治政大 and if they arrange more long-tailed business, they would have better financial stability 立 ‧ 國. 學. reinsurance, they would suffer worse financial stability. The organization form of insurance company (OF) has impact on financial stability before 1986, indicating that stock firms have. ‧. better financial stability than mutual firms.. sit. y. Nat. io. n. al. er. As observed from the result, we infer that firms with higher market power would improve. i n U. v. their financial stability at first, but after the threshold obtained, higher market power may decrease financial stability.. Ch. engchi. To investigate the robustness of the regression for z-index, we also apply three more sets of different instrument variables to test the model. Our finding of the relation between market power and financial stability is consistent to the above-discussed.. 38.

(46) Table 11. The effect of market power on financial stability. 1986-2010 Estimate T value. Variable. 1986-1996 Estimate T value. 1997-2010 Estimate T value -21.595*. Intercept. -19.826**. -2.05. -29.168. -0.78. Lerner Index. 77.122**. 2.33. -58.117. -0.70. 99.712**. 2.49. urqnrq vnsrw f. -67.572**. -2.44. 48.087. 0.67. -85.036**. -2.55. FS. 0.449. 0.82. 2.76. 0.106. 0.16. FSS. -0.008. -0.44. -0.167*. -2.54. 0.002. 0.09. 3.50. 7.356. 0.93. 7.636***. 2.77. -2.01. -0.974 ***. -3.91. -1.971. -1.63. -0.004***. -8.54. 6.471***. PLT. -0.888*** -4.60. RR. 5.413***. -3.098**. 0.78 治 -0.002*** -6.76政 0.002 大0.40 4.93 1.018* 立 1.91 4.281*** -1.913*. HHI OF Hansen’s J Adjusted t N. 0.12. 0.092. 173.8*** <.0001. 0.942. 1. 0.355. 0.487. 525. 213. 0.608. y. Nat. sit. The rejection of Hansen’s J test implies the invalidity of instrument variables used.. er. io. al. n. ** Statistical significance at 5% level. Ch. *** Statistical significance at 1% level. engchi. 39. 0.69 0.306. Note: The rejection of White test implies the inexistence of heteroscedasticity .. * Statistical significance at 10 % level. 261.2***. ‧. f. 346*** <.0001. 1.943. 學. White Test. -1.80. ‧ 國. DVF. i n U. v. 312. -1.87. 0.22 <.0001 0.710.

(47) 5.2 Robustness Checks Recalculating Lerner Index without ROE To test the robustness of the model, we try to remove the variable W& (ROE) in the translogarithmic cost function and then calculate Lerner index to run GMM model. Table 12 shows the result of the model. The coefficient of Lerner index in total risk model is negatively significant (-36.385) at 10 percent level and the squared Lerner index is positively significant (20.729) at 10 percent level. The inflection point (0.877) covers approximately 80% of. 政治大 the accumulated distribution, implying our main sample suggests firms with higher market power 立. ‧ 國. 學. improve their total risk.. In the underwriting risk model, the coefficient of Lerner index is negatively significant. ‧. sit. y. Nat. (16.755) at 5 percent level and the squared Lerner index is positively significant (9.328) at 5. n. al. er. io. percent level. The inflection point (0.898) represents approximately 85th percentile of the Lerner. Ch. i n U. v. indices distribution, supporting the existence of inverse relation between market power and. engchi. underwriting risk. However, there is no evidence supporting that market power is associated with investment risk in this test. We also try another test: replacing number of policy with net premium income for W2 and keeping W3 in cost function to calculate Lerner index and rerun the GMM model. The result of total risk and investment risk is consistent to the above-mentioned, but for underwriting risk, a reasonable adjusted R square isn’t obtained. As mentioned above, the robustness tests are. 40.

(48) supportive of our testing for the inverse relation between market power and total/underwriting risk in Japanese non-life insurance industry.. Table 12. The effect of market power on risks without using Variable. Total Risk. in the model. Underwriting Risk. Investment Risk. Estimate. T value. Estimate. T value. Estimate. T value. Intercept. 15.862. 1.88. 7.522. 2.15. 1.561. 0.56. Lerner Index. -36.385*. -1.88. -16.755**. -2.09. -3.221. -0.50. urqnrq vnsrw f. 20.729*. 1.88. 1.785. 0.49. FS. 0.014. -0.002. -0.16. FSS. -0.001. -1.12. 0.000. -0.20. 0.000. 0.16. -0.022. -0.42. -0.038. -1.10. -0.010. -0.24. 0.015. 0.96. -0.046*. -1.91. -0.036. -1.60. DVF. 0.035***. 0.011. 0.79. -0.017*. -1.71. OF. Nat. 3.28. 0.000. 0.18. 0.000. -0.82. 0.000***. -7.15. HHI. 0.000. 0.13. 0.002. 0.008**. 2.07. White Test. 376.2***. i v0.48 n U 0.003. 51***. <.0001. Hansen’s J. 6.6. 0.478. 5.29. 0.152. Adjusted tf N. 0.11 527. n. y. sit. er. ‧ 國. io. al. ‧. RR. 立. 學. PLT. 2.03 治政 9.328** 大 0.40 1.00 0.004. Ch. e n g83.19*** chi. <.0001 0.159. 3.5 0.02 527. Note: The rejection of White test implies the inexistence of heteroscedasticity . The rejection of Hansen’s J test implies the invalidity of instrument variables used. * Statistical significance at 10 % level ** Statistical significance at 5% level *** Statistical significance at 1% level. 41. 0.21 527.

(49) Introducing Group Variable During 1986 to 1996, whether a financial institute belongs to financial group is a key factor to its operation and performance in Japanese financial industry, but after 1997, many merges and acquisitions occur and this substantially reduces the impact of financial group. We thus add group (GRP) variable and its interaction term with Lerner index (GLI) and with Lerner Index (GLIS) in this model to test the relation during 1986-1996. Table 13 shows the effect of adding group (GRP) variable in the period of 1986-1996. From. 治政大 indices in the three models all the result, we find that after adding group (GRP) variable, Lerner 立 ‧ 國. 學. become significant. For total risk, Lerner index is negatively significant (-3.509) at 10% level and its squared term (2.855) is positive significant at 10% level. The inflection point (0.614) locates. ‧. approximately 94th percentile in the Lerner index distribution, indicating nearly all the. sit. y. Nat. io. n. al. er. observations imply negative relation between market power and total risk and this is consistent with our test discussed above.. Ch. engchi. i n U. v. For underwriting risk, Lerner index is negatively significant (-28.193) at 5% level and its squared term (23.149) is positively significant at 5% level. With 93% data lying below the inflection point (0.609), our main sample suggests that higher marker power might bring about lower underwriting risk. A similar situation happens in investment risk model, Lerner index is negatively significant (-14.536) at 1% level and its squared term (11.947) is positively significant at 1% level. With 93% data lying below the inflection point (0.608), our main sample suggests that higher marker power might decrease underwriting risk. 42.