Summary:
Insurers and insureds having different information advantages, the insurance market is not only an essential source of adverse selection theory, but also an important place to verify the theory. Adverse selection in the market affects the profit of the life insurance company as well as the existence of the market, it is, therefore, of great practical significance to judge whether there is such selection in the life insurance market, which directly determines the behavior of the regulator, the insurer and the insured. If consumers are free to choose the security, the higher the security, the higher the price of insurance products, then the risk level of consumers is positively related to the security they choose. This is the positive correlation theory to verify the existence of adverse selection. Robust as it is, it does not rely on the setting of functional forms, nor does it require special assumptions about preferences, technology, and equilibrium. In this paper, CHARLS data and positive correlation theory are used to test the adverse selection in China's term and whole life insurance market. The risk of death is measured in accordance with the respondents' probability of living to a certain age and their self-rated health, then whether there is a correlation between the risk of death and the security is obtained. The combination of long-term and short-term indicators overcomes the above-mentioned shortcomings of using only mortality indicators. Of course, this correlation may also be caused by the omission of some unobservable variables. To eliminate the impact of missing variables, this paper, in reference to the existing literature, controls the consumer's personal and income characteristics, altruistic motivation, risk attitude and family financial indicators. Not only does these data test the correlation between risk and security, but the robustness of this correlation. The final empirical results indicate there is no adverse selection in China's life insurance market due to altruistic motives. This article for the first time uses CHARLS data to study the problem of adverse selection in China's life insurance market. There are three innovations in testing methods and research ideas: First, unlike the existing literature that uses only mortality indicators, we use both the long and the short-term indicators to measure the risk of death. Therefore, the conclusions obtained are more robust. Second, having controlled altruistic motivations, risk attitudes, and household financial indicators, we analyze the adverse selection of the life insurance market from the perspective of generalized and intensive margins, divide the sample into term life insurance and whole life insurance for regression, making the model conclusion more credible. Besides, though the existing literature believes that altruistic motivation and risk attitude are the two reasons leading to the absence of adverse selection in the life insurance market, empirical evidence cannot be given yet. This paper, with the bivariate Probit model, finds out that for consumers of China's term life insurance and whole life insurance altruistic motivation affects the correlation between death risk and security. The core conclusion of this paper is of great significance to the healthy development of China's life insurance industry. In the first place, product innovation is one of the important ways to ensure no adverse selection in China's life insurance market. Moreover, Chinese consumers purchase term life insurance or whole life insurance products out of altruistic motives. In order to attract these people, insurers should appropriately reduce the price of life insurance products. In this article, the adverse selection of the life insurance market is tested based on CHARLS data and the conclusion that there is no adverse selection in the life insurance market is obtained. Yet, there are still many follow-up issues to be further explored. For example, this paper has listed four evidences to prove no adverse selection in life insurance markets, but due to data limitations, only two empirical evidences are given. Therefore, using macro and micro data of China to provide empirical evidence for other reasons is our next research direction.
范庆祝, 孙祁祥. 中国寿险市场存在逆向选择吗?——来自CHARLS数据的经验证据[J]. 金融研究, 2020, 482(8): 112-129.
FAN Qingzhu, SUN Qixiang. Is There Adverse Selection in China's Life Insurance Market? Empirical Evidence from CHARLS Data. Journal of Financial Research, 2020, 482(8): 112-129.
[1]段白鸽、王永钦和夏梦嘉,2019,《金融创新如何缓解信任品市场失灵?——中国食品安全责任强制保险的自然实验》,《金融研究》第9期,第75~93页。 [2]樊纲治和王宏扬,2015,《家庭人口结构与家庭商业人身保险需求——基于中国家庭金融调查(CHFS)数据的实证研究》,《金融研究》第7期,第174~193页。 [3]范庆祝、贾若和孙祁祥,2017,《寿险供给侧指标对寿险消费的影响——基于寿险供给质量、动能和效率的视角》,《金融研究》第9期,第119~133页。 [4]何文和申曙光,2020,《灵活就业人员医疗保险参与及受益归属——基于逆向选择和正向分配效应的双重检验》,《财贸经济》第3期,第36~48页。 [5]孙祁祥和王向楠,2013,《家庭财务脆弱性、资产组合与人寿保险需求:指标改进和两回归分析》,《保险研究》第6期,第23~34页。 [6]王珺和高峰,2007,《我国汽车保险市场逆向选择实证研究》,《金融研究》第12期,第223~230页。 [7]吴卫星、邵旭方和陶利斌,2016,《家庭财富不平等会自我放大吗?——基于家庭财务杠杆的分析》,《管理世界》第9期,第44~54页。 [8]臧文斌、赵绍阳和刘国恩,2012,《城镇基本医疗保险中逆向选择的检验》,《经济学(季刊)》第1期,第47~70页。 [9]钟晓敏、杨六妹和鲁建坤,2018,《城乡居民医疗保险中逆向选择效应的检验》,《财贸经济》第10期,第118~130页。 [10]Beck, T., and I. Webb. 2003. “Economic, Demographic, and Institutional Determinants of Life Insurance Consumption Across Countries”, World Bank Economic Review, 17(1): 51~88. [11]Boyer, M., and G. Dionne. 1989. “An Empirical Analysis of Moral Hazard and Experience Rating”, Review of Economics and Statistics, 71: 128~134. [12]Cawley, J., and T. Philipson. 1999. “An Empirical Examination of Information Barriers to Trade in Insurance”, American Economic Review, 89: 827~846. [13]Chiappori, P. A., and B. Salanié. 2000. “Testing for Asymmetric Information in Insurance Markets”, The Journal of Political Economy, 108(1): 56~78. [14]Cohen, A., and P. Siegelman. 2010. “Testing for Adverse Selection in Insurance Markets”, Journal of Risk and Insurance, 77(1): 39~84. [15]Dardanoni, V., A. Forcina, and P. L. Donni. 2016. “Testing for Asymmetric Information in Insurance Markets: a Multivariate Ordered Regression Approach”, Journal of Risk and Insurance, 85(1): 107~125. [16]Finkelstein, A., and K. McGarry. 2006. “Multiple Dimensions of Private Information: Evidence from the Long-Term Care Insurance Market”. American Economic Review, 96(4): 938~958. [17]Fang, H., M. P. Keane, and D. Silverman. 2008. “Sources of Advantageous Selection: Evidence from the Medigap Insurance Market”, The Journal of Political Economy, 116(2): 303~350. [18]Harris, T., and A. Yelowitz. 2014. “Is There Adverse Selection in the Life Insurance Market? Evidence from a representative sample of purchasers”, Economics Letters, 124(3): 520~522. [19]Han, L., D. Li, F. Moshirian, and Y. Tian. 2010. “Insurance Development and Economic Growth”, Geneva Papers on Risk and Insurance, 35(1): 183~199. [20]Idler, E. L., and S. V. Kasl. 1991. “Health Perceptions and Survival: Do Global Evaluations of Health Status Really Predict Mortality”, Journal of Gerontology: Social Sciences, 46(2): S55~65. [21]Mahdavi, G. 2005. “Advantageous Selection Versus Adverse Selection in Life Insurance Market”, Japanese Society for the Promotion of Science, Working Paper. [22]Meza, D. D., and D. C. Webb. 2000. “Advantageous Selection in Insurance Market”, The RAND Journal of Economics, 32(2): 249~262. [23]Mossey, J. M., and E. Shapiro. 1982. “Self-rated Health: A Predictor of Mortality Among the Elderly”, American Journal of Public Health, 72(8): 800~808. [24]McCarthy, D., and O.S. Mitchell. 2003. “International Adverse Selection in Life Insurance and annuities”, Ssrn Electronic Journal, 8: 119~135. [25]Mor, V., J. Murphy, S. M. Allen, C. Willey, A. Razmpour, M. E. Jackson, D. Greer, and S. Katz. 1989. “Risk of Functional Decline Among Well Elders”, Journal of Clinical Epidemiology, 42(9): 895~904. [26]Puelz, R., and A. Snow. 1994. “Evidence on Adverse Selection: Equilibrium Signaling and Cross-Subsidization in the Insurance Market”, Journal of Political Economy, 102(2): 236~257. [27]Rothschild, M., and J. E. Stiglitz. 1976. “Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information”, Quarterly Journal of Economics, 90(4): 629~649. [28]Rakowski, W., and V. Mor. 1992. “The Association of Physical Activity with Mortality Among Older Adults in the Longitudinal Study of Aging (1984-1988)”, Journal of Gerontology: Medical Sciences, 47(4): M122-29. [29]Robinson, P. A., F. A. Sloan, and L. M. Eldred. 2018. “Advantageous Selection, Moral Hazard, and Insurer Sorting on Risk in the U.S. Automobile Insurance Market”, Journal of Risk and Insurance, 85(2): 545~575. [30]Smith, V. K., D. H. Taylor, and F. A. Sloan. 2001. “Longevity Expectations and Death: Can People Predict Their Own Demise”, American Economic Review, 91(4): 1126~1134. [31]Schlesinger, H. 1981. “The Optimal Level of Deductibility in Insurance Contracts”, Journal of Risk and Insurance, 48(2): 465~481. [32]Siegelman, P. 2004. “Adverse Selection in Insurance Markets: An Exaggerated Threat”, Yale Law Journal, 113(6): 1223~1281.