Risk in Emerging Markets and Demand for Life Insurance: Evidence from China

Transcription

1 Risk in Emerging Markets and Demand for Life Insurance: Evidence from China By Jiang Cheng*, Lu Yu This version: *Corresponding author and presenter. Jiang Cheng is Associate Professor at the School of Finance, Shanghai University of Finance and Economics. Yu Lu is Ph.D. student at the School of Finance, Shanghai University of Finance and Economics. Authors can be contact by 1

2 Risk in Emerging Markets and Demand for Life Insurance: Evidence from China Abstract This research investigates the life insurance consumption in China and exploits the significant regional difference in socio-economic, demographic, and environmental factors in explaining consumers life insurance purchasing behavior. This paper further decomposes the life insurance consumption into three types: protection, investment, and healthcare products. We find that longevity risks associated with aging population, imbalanced dependency ratio and the one-child policy planning rate significantly influence the demand for life insurance. Environmental degrading risk affects the demand for protection- and healthcare-type life insurance. The empirical results also suggest that insurance firm characteristics have remarkable impacts on life insurance demand. 2

3 1. Introduction This paper investigates the life insurance consumption in China. We focus on this single economy for four reasons. First, China is the largest emerging economy, along with a rapid growing life insurance market for the last two decades. China has enjoyed rapid economic growth since its economic reforms in On average, the annual real GDP growth rate was around 9.8% over the past two decades according to the World Bank statistics. China also led the world in terms of insurance premium growth from 2000 to 2010 with a compound annual growth rate of close to 20%. China is now the world s fourth biggest life insurance market, accounting for 6% of the world s premium volume and more than one third of the emerging markets premiums (Swiss Re., Sigma 2014). Despite the fact that China has become one of the most important insurance markets in the world, there is very limited paper studying exclusively the life insurance market in China (Hwang and Gao, 2003; Hwang and Greenford. 2005). Second, while life insurance witnesses a significant growth, the levels of insurance market development vary significantly by region in China. For example, Jiangsu, one of the most developed coastal province, enjoys life insurance density (penetration) at RMB (1.57%), which is three times larger than Guizhou with life insurance density (penetration) at RMB (1.16%) according to the report of China Insurance Regulatory Commission (CIRC), the insurance regulatory authority in China. 1 Further, China has enough regional variability in socioeconomic, demographic, and environmental factors among provinces, allowing for meaningful cross-sectional analysis within a single economy. The sharp differences in life insurance consumption behavior among provinces provides us an unique opportunity to study what factors affect the life insurance consumption in a single economy without worrying about controlling unobservable cross-country differences in institutional factors (Millo and Carmeci, 2014). Third, China has been undertaking substantial changes in socioeconomic and demographic 1 Even within four municipalities directly under the jurisdiction of central government, life insurance density varies significantly, from RMB of Shanghai to RMB of Chongqing. 3

4 structures over a relatively short period of time, accompanying with the rapid economic growth. The most important impact on population structure changes probably comes from the one-child policy officially instituted in While the one-child policy has successfully control the growth rate of the population, the unexpected side effect is that China has entered an aging society before it is ready. The Chinese population has been increasing to 1.36 billion in In the meantime, the birth rate has decreased from in 1970 to in 2013 (National Bureau of Statistics of China). Life expectancy in China has increased from 67.9 and 70.2 in 1980 to 74 and 77 in 2013, for males and females, respectively. According to the World Bank report, the Chinese population has become older as the population ages 65 and above is 9% in 2013, compared to 5% in The significant changes in potential determinants of life insurance demand partially alleviates the econometric concerns that most socioeconomic and demographic variables change slowly over time in time-series analysis or studies using a short panel data. Fourth, two important emerging risks most relevant to China s experience, aging population and worsening environmental degradation, are expected to have a huge impact on the life insurance demand. Partially due to the one-child policy, we witness an imbalanced population structure with a high old dependency ratio reaches at 13.1% but a low young dependency ratio at 22.2% in 2013, compared to the corresponding number of 8.0% and 54.6% in The current imbalance of the high old dependency ratio and low young dependency ratio might eventually create an inverted pyramid population structure. 3 Already worsening financial burdens of the social welfare system in China thus expects further 2 Generally, the proportion more than 7% means that the population of the country or region has entered into the aging society. Please see The Aging of Populations and its Economic and Social Implications, Population Studies, No. 26 (United Nations publication, Sales No XIII.6). 3 Although China has altered the one-child policy in 2013 to address the low birth rate and deteriorating population structures, allowing some couples to have a second child if both couples are the single child of their parents. However, the response to the change of the policy is not very exciting, i.e., most eligible families do not plan to have a second child, probably due to the high cost of raising a child. Thus, the deteriorating population structure seems to be lasting if not exacerbating at least in the next few years, evidenced by the released low birth rate at in 2013, only slightly higher than the history lowest number in 2010 (National Bureau of Statistics of China). 4

5 exacerbation in the next decade if nothing else is done to change the current trend of decreasing birth rate. The government may tend to reduce the level of benefit payable for the elderly to relive the burden. This will aggravate the longevity risk of the society. The exacerbating population structure and uncertainty of whether or not the state social welfare system will be able to provide an adequate level of benefits for retirement provide potentially strong demands for life insurance. The second emerging risk faced by the society is the environmental degradation, i.e., pollution. Various forms of pollution have increased while China has industrialized, which has caused widespread environmental and health problems. Taken air pollution as an example. Many regions in China are suffering from PM2.5, which is a particulate matter with diameter of 2.5 micrometers or less. According to the U.S. Environmental Protection Agency, such fine particles induce asthma, bronchitis, and acute and chronic respiratory symptoms such as shortness of breath and painful breathing, and premature deaths. People in metropolitan cities, such as Beijing and Shanghai, have been advised to watch daily reports of the PM2.5 level to adjust their outdoor activities accordingly. According to the report by Ministry of Environmental Protection, only 6 out of 74 cities under monitoring meet the standard of air quality in 2014 measured by the PM2.5 level. The accelerating urbanization in China also raises new challenges for the pollution control endurance. Rising costs of healthcare associated with the pollution have exposed the whole society to a significant health risk, calling for solutions include healthcare life insurance. Neither imminent longevity risk nor deteriorating environmental risk is unique to China. This research thus provides a useful attempt to stress how these emerging risks affect life insurance demands. While two emerging risks are expected to impact the demand of life insurance, China s growing life insurance industry is also reshaping people s consuming behavior in various ways. There are now 71 companies conducting life insurance business, compared to 12 life insurance companies in The fierce market competition has provided all insurance firms strong incentives to innovate new products to 5

6 carter to changing consumer demands as well as strengthen their financial conditions and marketing commitments to attract business. The purpose of this research is to identify factors influencing life insurance consumption from both demand side and supply side. We first apply provincial level aggregate data, pinpointing important socioeconomic, demographic and environmental determinants of life insurance demand. This research proceeds to use data at individual insurer-province level to capture the impact of supply side factors, insurer characteristics as well as product price, on the life insurance consumption. The research also conducts analysis by decomposing life insurance products into three types: protection purpose, investment purpose and healthcare purpose. Finally, we explore the differences between individual life insurance consumptions and group life insurance consumptions which usually are purchased by employer. The sample period is from 2005 to 2013, covering the rapidest growth period of life insurance in China with the detailed information available for our research purpose. By way of preview, empirical investigation shows that the rapid change of social structure and environment risk significantly affect the life insurance demands in China. The one-child policy and urbanization process promote life insurance demands. Environmental risk proxied by air pollution increases people s purchase of life insurance. Environment protection proxied by green coverage ratio also stimulates the life insurance consumptions. Further study finds that consumers tend to purchase life insurance products from insurers with good reputations and low-pricing strategies. We find that factors affecting the consumption of three types of life insurance products are in different ways. For example, life expectancy at birth affects the consumption of all three type of life insurance products with opposite directions. The education levels positively and significantly affect the life insurance consumptions on protection- and healthcare-type products but are negatively (although not significant at the conventional level) associated with the investment-type life insurance consumptions. 6

7 Our research thus partially reconciles the conflicting findings in the previous life insurance demand research (Truett and Truett, 1990; Browne and Kim, 1993; Hwang and Gao, 2003; Li et al., 2007; Millo and Carmeci, 2014). Furthermore, individual purchasing life insurance shows a different behavior with group purchased insurance which is usually employer sponsored in China. Education level has a negative effect on individual purchased investment-type life insurance but a positive influence on group purchased investment-type life insurance. Overall, the results support the rational purchase of life insurance. Socialeconomic, demographic, environmental, psychographic, and product market factors affect consumers purchasing decisions. This study contributes to the literature in the following important ways. First, this research investigates the aforementioned two important emerging factors determining life insurance consumption in a single market using provincial data in China. As the focus of this study is only on a single country, it avoids mixing different country characteristics and heterogeneous consumer demand (Millo and Carmeci, 2014). Second, this paper contributes to the literature by analyzing not only the demand side, but also the supply side of the life insurance, i.e., insurers characteristics that affect life insurance consumptions. Third, we break total life insurance consumptions into three dimensions, life insurance consumptions for protection, investment, and healthcare purpose, which is the first attempt using aggregate market development data in literature. Differences between various types of insurance are expected to affect consumers insurance purchasing decisions but are overlooked in most life insurance demand literature (Bernheim et al., 2003). Fourth, individual purchased life insurance is studied as well as group consumption of life insurance, which has not appeared in the literature, to the best of our knowledge. Our paper is mostly like Millo and Carmeci (2014) in the spirit of studying life insurance in a single country, although we focus on the largest emerging market, China and decompose life insurance into detail types of products. Two papers have studied the life insurance market in China. Hwang and 7

8 Gao (2003) investigate the life insurance demand in China in 1990s, applying a time series study using 10 years data from 1986 to Hwang and Greenford (2005) compare the life insurance demand among mainland China, Taiwan and Hongkong from 1986 to However, none has ever explored the difference of the life insurance consumption among provinces despite that fact that the development of the life insurance market varies greatly across these geographic regions in China. Further, data of both papers is out of dated as the latest year covered in their studies are During their sample period, group purchased life insurance (normally paid by employers) generally dominates the market. Up until 1998, group purchased life insurance still enjoys about 50% of the life insurance market based on the data released by the CIRC. However, since 1999, individual life insurance has grown significantly and dominate the life insurance market. In 2005 the first year of our sample period, individual life insurance market represents 70% of total life insurance market and this number further increases to 87% in 2013, the last year of our sample period. We define this research to mainland China to avoid potential bias from institutional, regulatory, and social differences between mainland China and other regions. The remainder of the paper is organized as follows: In section 2, we review the literature on life insurance consumption. Section 3 discusses changes in China s social-economic, demographic and environmental factors that have an impact on the demand for life insurance as well as insurer characteristics that shape the supply side of market. We develop the hypotheses accordingly. Section 4 describes the dataset and methodology. Section 5 provides the results and discussion, and section 6 concludes. 2. Literature Review on Life Insurance Demand Since the seminal work by Yaari (1965), life insurance demand has been modeled to maximize a person s utility to provide income protection for retirement. Lewis (1989) extend the life insurance demand to maximize the utility of the whole family household. Bequest is expected to play an important 8

9 role in determining life insurance consumptions (Bernheim, 1991). Theoretical models also suggest that risk aversion and product designs including deductible level and loading affect life insurance demand (Campbell, 1980; Doherty, 1984; Schlesinger, 1997). Over time, more life insurance products have investment characteristics to meet the demand of consumers. Consumers eventually consider life insurance as an important financial asset of their portfolios. Richard (1975) study the relationship between consumption, portfolio and life insurance for individuals in a continuous time model. Zhu (2007) develop a theoretical model studying individuals optimal decisions on consumption, life insurance, and stock purchases in a one-period framework. They find life insurance and stock purchases are positively related with each other and are affected by all the factors, assuming power utility functions. Pliska and Ye (2007) extend the model of Richard (1975) by allowing the wage earner s lifetime exceed the planning horizon. Empirical research on the determinants of life insurance demands generally can be classified into two main categories. A few papers apply micro survey data on households or individuals to study determinants of life insurance demand, testing theories including the life-cycle hypothesis and risk aversion hypothesis (e.g., Bernheim, 1991; Lin and Grace, 2007; Liebenberg, Carson, and Hoyt, 2010). 4 The second group of empirical studies mostly focused on cross-country comparisons using aggregate market development data. Beenstock, Dickinson, and Khajuria (1986) examine life insurance market of 10 developed countries from 1970 to They find that life insurance demand is positively related to the dependency ratio, life expectancy, interest rate, and disposable income and negatively correlated to social security expenses and inflation. Browne and Kim (1993) survey a number of both developed and developing countries in 1980 and Their cross-section regressions show that life insurance demand is positively related to national income and wealth and negatively correlated with inflation. Using a set of 48 developing countries cross-section data in 1986, Outreville (1996) shows that 4 Liebenberg, Carson, and Dumm (2012) provide an excellent review of literature using household and consumer survey data. 9

10 inflation and monopolistic market structure have a negative impact on the country s life insurance development. Beck and Webb (2003) confirm that insurance demand increases with income and banking sector development, and decreases with inflation rate, using a panel data for 68 economies. Li et al. (2007) indicate that the number of dependents and level of education have a positive impact while life expectancy and social security expenditure has an opposite effect on life insurance demand in OECD countries. Millo and Carmeci (2014) comment on cross-country studies that some systematic factors shaping the demand for life insurance are complex and varied from one country to another. They instead study the life insurance market in a single country, Italy. They find a positive influence on life insurance demand from young dependency ratio and income. They suggest a negative relationship between life insurance demand and education, arguing that better education reduce consumers risk aversion. Life insurance products are broadly classified into two types in literature, protection purpose and investment purpose. For protection purpose, life insurance is considered an instrument to mitigate the risk of premature death of wage earners or lower cash flows after retirement (Campbell, 1980; Beenstock, Dickinson, and Khajuria, 1986). Two typical protection purpose products are term life insurance hedging premature death risk and annuity hedging longevity risk. Instead, some life insurance products have more investment characteristics acting as an alternative financial assets, e.g., unit-linked or universal life insurance. Some life insurance products combine the characteristics of both protection and investment purpose but mostly the weights are inclined toward investment purpose, e.g., whole life insurance which can be considered as a quasi-forced savings plan (Black and Skipper, 2000). We are able to find three papers empirically studying differences in life insurance demand for term life and whole life insurance. Using household survey data from the Survey of Consumer Finances, Lin and Grace (2007) examine the life cycle demand hypothesis, breaking the demand for life insurance into the demand for term life and whole life insurance. They find a strong relationship between financial 10

11 vulnerability and the amount of term life or total life insurance purchased, but no consistent relationship with whole life insurance. Frees and Sun (2010) use the same data source and find a negative relationship for a household s decision to own both whole and term life insurance and a positive relationship for the amount of insurance purchased. Lee, Kwon, and Chung (2010) apply 2005 consumer survey data in Korea to explore the different effect of household characteristics on demand for protection-type and savings-type life insurance. To our knowledge, there is no study explicitly investigate the demand of life insurance by decomposing into protection, investment, and healthcare purpose and make formal comparisons using aggregate market development data. With regard to the dependent variable, literature generally use premiums as the proxy for life insurance demand and estimate a reduced-form equation, due to the impossibility of observing price and quantity separately in available insurance data sets. However, the underlying assumption that insurance supply is infinitely elastic at a given price is too strong and unrealistic. We find only three papers explicitly developing a supply equation, along with the demand equation, to consider the equilibrium solutions. 5 Beenstock, Dickinson, and Khajuria (1986) model a supply of life insurance as a function of price of substitute products, life expectancy, insured s age as a proxy for mortality cost and real interest rates. Outreville (1996) formalizes the supply equation by further including the level of financial development and competitive structure of the market. Millo and Carmeci (2014) extend the model of cross-country comparisons to sub-regional analysis within a single country. They add two supply side variables: the densities of the two main distribution channels through banks and insurance agencies over population, assuming that skillful salespersons can sale the policies to potential consumers. Their results suggest that life insurance consumption is positively influenced by the density of insurance agencies but not bank 5 Most papers only implicitly include one or two variables related to supply side or do not control the supply factor at all. Beck and Webb (2003) control the supply factors by including variables related to insurance product marketing efficiencies, e.g., urbanization and banking sector development. Li et al. (2007) consider the supply factor by using actual foreign market shares as the proxy. 11

12 counters. 3. Hypotheses On the basis of existing theoretical and empirical literature, we enumerate the following set of variables to capture the determinants of life insurance consumption, from both demand and supply side. 6 We postulate that the two specific emerging risks most relevant to China experience significantly influence the demand for the life insurance market, while we also try to include social-economic and demographic variables that occur in most of the literature. More specifically, we posit that longevity risk due to deteriorating population structure and environmental risk due to worsening pollution problem are key determinants of life insurance demand in current China. We also identify important supply side factors, insurance company characteristics which are typically omitted in literature. 3.1 Longevity risk While China has experienced rapid economic growth, it is accompanied with significant changes in social and demographic structures, most strikingly, the rapidly increasing aging population. The first emerging risk faced by Chinese households is the longevity risk, i.e., how to enjoy a decent standard of living after retirement. We conjecture that the transition in this population structure has a significant impact on people s demand for life insurance. This leads to our first hypothesis: Hypothesis 1: Longevity risk positively affects life insurance demand. More specifically, we argue that three factors associated with the population structure are mostly relevant to the longevity risk in China. The first factor is the one-child policy instituted in 1980 which rapidly accelerate the aging process in China. The traditional Chinese Confucianism culture generally has 6 We distinguish consumption with demand in the article because premiums written are simultaneously determined by demand and supply of life insurance. Analysis neglecting supply factors might lead to biased conclusion on life insurance consumption. Consumers might have demand for a certain life insurance product but characteristics of supply side, life insurance companies, certainly will affect consumers purchase decisions. In the extreme case, no insurers might be willing to supply the specific product demanded by the consumers, partly due to the factors out of insurers control including the interest risk, investment risk and regulatory concerns. 12

13 a strong obligation of providing mutual support towards the members of an extended family (Kotlikoff and Summers, 1981). The extended family relationship can be considered as an effective instrument to provide economic security and is a substitute of commercial insurance in managing longevity and healthcare risk. The members of the extend family organize as an informal insurance pool which all members share risks with. However, the one-child policy has effectively reduce the number of children in a single family which makes the informal insurance pool among extended family members unsustainable. With only one child, parents fell less economically secure and has to resort to alternative resources of income to warrant their personal long-term financial security. Furthermore, the one-child policy also increases parents concerns for their only child s economic security as their child can no longer expect mutual support from brothers or sisters in emergency. Consumers thus shift their expectation of retirement and healthcare financial support from family to life insurance companies. Hwang and Greenford (2005) compare life insurance consumptions in mainland China, Hong Kong, and Taiwan and find that the one-child policy in mainland China reduces the life insurance consumption, arguing decline in the number of dependents reduces the demand for life insurance. However, their results might be driven by unobservable characteristics among regions. They do not distinguish the impact of the one-child policy on different types of life insurance either. Due to the regional policy and varying social-economic and race-religion factors, the one-child family planning rate varies significantly by regions and by time in China. 7 We postulate that the success of the execution of the one-child policy positively affects life insurance consumption. Particular, we expect that the one-child policy planning rate should increase the demand for the protection and healthcare-type life insurance products due to the dominant impact of peoples concerns on their future uncertainty. The impact of the one-child policy planning rate on investment-type life insurance is ambiguous. 7 One child policy was modified in 2013, allowing spouses who both of them are the single child of their family, to have a second child. As 2013 is the first year of the change on the one-child policy, we do not expect our results will be affected. 13

14 On one hand, reducing dependents reduce the demand for purchasing investment-type life insurance for dependent s education purpose. On the other hand, financial resources available for household investment increase with the reducing cost of raising fewer children. Thus, we offer no prediction, concerning the influence of the one-child policy planning rate on the investment-type life insurance, but await empirical adjudication. The second factor associated with the population structure affecting the longevity risk is the young and old age dependency ratio. The young dependency ratio is expected to increase insurance consumptions hedging mortality risk because one of the main purposes of life insurance is to provide dependents financial security in the case of the wage earner's premature death (Beenstock, Dickinson, and Khajuria, 1986; Browne and Kim, 1993). In contrast, expected effect of young dependency ratio on annuity component is negative because the need of annuity to hedging longevity risk is not immediate. Giving our data of protection-type insurance consists of both mortality and longevity risk components, contradictory effects of the young dependency ratio on two components of protection-type insurance make its sign an empirical issue. The young dependency ratio should reduce the investment-type life insurance consumption because a larger share of population is too young to consider saving for retirement (Beck and Webb, 2003). Thus, we predict a negative relationship between the young dependency ratio and the investment-type life insurance consumption. The relationship between healthcare-type life insurance consumption and the young dependency ratio is ambiguous. With regard to the old dependency ratio, high proportion of old dependents should increase the demand for the healthcare-type life insurance products to hedge the health risk. The effects of the old dependency ratio on the protection-type life insurance demand is again unclear because it is expected to increase the demand for annuity components but decrease the demand for life insurance to hedge mortality 14

15 risk. We do not have a priori on the impact of the old dependency ratio on the investment-type life insurance either. The third factor impairing the informal insurance mechanism within extended family members is the urbanization process in China. The rapid growth in the urban population over the recent decades also has a significant impact on demographic structure in China. Between 1990 and 2013, the urban residents rose from percent to percent (National Bureau of Statistics of China). Both inter and intra province labor migration, mostly move from rural villages to urban cities, widen the interpersonal relationship and reduce the traditional mutual dependency of family members (Zhang and Song, 2003). The growth of an urban population has coincided with the decline of family size and lead to a more financially independent social structure. This is also accompanied with delayed marriage and reduced birth rate, partly due to the soaring cost of raising a child and housing price in cities. Further, urbanization rate is expected to positively influence the insurers facility to distribute products at a low cost due to concentration of consumers in space. Outreville (2015) suggests that urbanization rate of the population is positively and significantly related to relative risk aversion and thus might affect life insurance purchase. Therefore, we expect a positive impact of urbanization on (all three types of) life insurance consumptions Environmental risk It s now a tremendous area of concern to the health impacts of environmental degradation in China. 9 Over the past few years, people living in China have been bombarded almost daily by media reports about the health impacts of environmental pollution, including air, soil and water pollution. With the economic growth and increasing disposable income per capita, the focus of health risk has been shifting 8 Existing literature does not empirically confirm the relationship between urbanization and life insurance (Outreville, 1996; Beck and Webb, 2003). Millo and Carmeci (2014) apply population density as an alternative proxy for urbanization and also fail to find a significant relationship between population density and life insurance. 9 One-fifth of China s land is polluted, according to a recent report from the Ministry of Environmental Protection. Nearly 60% of the country s groundwater is of poor or extremely poor quality, according to the Ministry of Land and Resources. 15

16 from problems associated with poverty towards diseases associated with growing pollution problem. The price of environmental degradation and pollution is expressed in human suffering and increasing rates of diseases related to pollution. 10 Poel, O Donnell and Doorslaer (2012) suggest that rapid urbanization processes raises the probability of reporting of poor health in China. China s size, uneven economic development and regional diversity make pollution problem vary significantly across provinces. In 2013, a report published in the U.S. journal Proceedings of the National Academy of Sciences, suggests that residents of south China could expect to live five years longer than their 500 million neighbors in the northern China. The reason was due to the predominance of coal fired heating systems helping people through the harsh winters in the northern China creates a level of air pollution produced by burning coal, measured by particulate matter, that is about 55% greater than in south China. We postulate that the prevalence of environmental health risks has a significant impact on the demand for life insurance, in particular for the healthcare-type life insurance. Hypothesis 2: Environmental risk is positively associated with life insurance demand. To capture the effect of the environmental risk, we adopt the sulfur dioxide emissions per capita, a popular measure for air pollution, and hypothesize a positive relationship with the protection- and healthcare-type life insurance. We also include the green coverage ratio to capture the potential effect of environmental improvement/protection on life insurance consumptions. To the extent that people anticipate a long life-expectancy associated with environment improvement, the demand of annuity, a type of protection-type life insurance should increase. People should also purchase more investment- and healthcare-type life insurance to hedge longevity risk. Thus, we expect a positive relationship between the green coverage ratio and all three types of life insurance. 10 China s health statistics reveal that the country ranks second in the world for lung disease and 12 th for lung cancer. Chinese people also suffer from the world s third highest rates of stomach cancer and the third highest rates of liver cancer, ill health that is highly likely to have environmental causes. 16

17 3.3 Life Insurance Supply Side Factors: Insurer Reputation Life insurance policies generally compensate or pay a designated beneficiary a sum of money or cash flows upon the occurrence of a certain event or at a certain point in the future time. Consumers always pay the premium ahead and have to rely on the insurers to keep their promises of paying in the long term. Insurers reputations play a critical role in convincing potential consumers to purchase their policies. We expect that consumers consider insurers reputation in making their choice of suppliers. Hence Hypothesis 3 states, Hypothesis 3: Insurer reputation is associated with life insurance consumption. We apply several characteristics of insurance companies to proxy for firm reputation. The first variable is firm size measured by total assets. Large firms are expected to have more financial resources to honor their promises and thus firm size should have a positive impact on life insurance demand. The second variable to gauge the effect of insurer reputation is years of business operation. The longer the insurance company operates, the higher the reputation accumulates. We postulate that the business age variable is positively related to life insurance consumption. We next include the rate of return on investment to capture insurers ability to use their investment funds effectively. This variable is expected to be particular relevant to investment-type life insurance because consumers are more likely to purchase products from insurers with better investment expertise. We include firm ownership structure as our fourth proxy to capture insurer reputation. We categorize insurance companies operating in China into two broad types based on their ownership structure: insurers with and without certain degrees of foreign shares, following the categorization of the CIRC. Among 62 companies with data available in our sample, 37 are domestic companies without foreign ownership, and 25 firms having varying degrees of foreign insurers ownership. 11 Insurers with foreign 11 AIA is the only firm with 100 percent owned by a foreign company. Other insurers are jointly owned by domestic enterprises and foreign insurers. 17

18 ownership structure have gained significant market share over time. The market share measured by premiums written in 2013 is 43.6% and 56.4%, for insurers with and without certain degrees of foreign shares, respectively. Domestic firms without foreign ownership have a long history of operating in local markets and the largest ownerships are usually controlled by the government. They generally react slowly to the market change than insurers with foreign ownership structure. Insurers with foreign ownership structure are recognized as having better reputation by consumers in China, probably due to their extended globally operation experience, high technical expertise in risk management and strong financial conditions due to their explicit financial ties with a global financial conglomeration, at least in the opinion of wealthy individuals who is the main customers for individually purchased insurance. Overall, we expect that insurers with foreign ownership structure have an advantage in selling life insurance products. We control whether the insurer is publicly listed at the Chinese stock market as the fifty proxy for insurer reputation. Publicly traded insurers are under strict scrutinize by the capital market and followed by financial analyst. Publicly traded stocks are generally well known to the consumers and have better reputation than private stocks. Thus, we expect that the status of publicly trading on the stock market has a positive impact on all types of life insurance products. As a last proxy for insurer reputation, we also control insurance salespersons ability by including the percentage of employees with insurance intermediate or advanced professional titles. The title can be earned by passing insurance professional exams sponsored by the Insurance Association of China authorized by the CIRC and the Ministry of Human Resources. This variable potentially captures the professional knowledge of insurers salesperson. Acting as personal financial consultants, it is the responsibility of the salesperson to clarify the jargon of life insurance products and justify the appropriateness of certain products to meet consumers specific financial needs. Further, Chinese culture considers purchasing protection- or healthcare-type life insurance is unlucky because it forces people to 18

19 face the possibility of premature death or illness. Firms with higher percentage of employees having intermediate or advanced professional titles are expected to have more capable salespersons with superior financial knowledge and thus are expected to have competitive advantages in the life insurance market, especially prompting attitudinal change towards the protection- and healthcare-type life insurance. 3.4 Price Our last formal hypothesis is on the life insurance price which is jointly determined by demand and supply. Price is expected to have a negative influence on the insurance demand (Babbel and Stacking, 1983; Babbel, 1985). Hypothesis 4 states, Hypothesis 4: High policy price is associated with low life insurance demand. Unfortunately, the price level of life insurance products is unavailable. We consider two alternative variables to proxy for life insurance price in China. First, we postulate the policy loading charge as a potential proxy for the price of life insurance products. The policy loading can be interpreted as the cost per dollar of life insurance coverage. Because we are studying life insurance in a single country, the mortality table should be identical. While other factors (expectation on interest rate, inflation rate, etc.) might be different in calculating actuarially fair premiums among insurers, we do not expect systematic differences in pricing life insurance products except for the loading factor. Therefore, it is plausible that the policy loading is a good proxy for price. Following Browne and Kim (1993), we define the policy loading as the expense ratio, i.e., the ratio of total expenditures on life insurance premiums to the premiums written. 12 The expense ratio is hypothesized to be negatively related to insurance consumption. Second, several papers use life expectancy at birth to proxy for the actuarially fair price of life 12 We extract firm expenditures from the income statement page of annual statement submitted by all insurer to the regulator. Expenditures include net commissions and expense allowances on reinsurance, commissions and brokerage fees on direct business, and other general insurance expenses. 19

20 insurance in cross-country studies (Beenstock, Dickson, and Khajuria, 1986; Outreville, 1996; Ward and Zurbruegg, 2002; Hwang and Greenford, 2005). The rationale is that the price of life insurance products is generally calculated with an assumed mortality rate, interest rate, and expense rate. A high life expectancy leads to the low pricing of insurance products and thus increases the demand of life insurance (Outreville, 1996). We have somewhat different arguments because we are looking at the demand of life insurance in a single country instead of conducting cross-country comparisons. Arguably, the life insurance product is pricing according the mortality rate of total population in China. The same product of a specific insurer is sold at the same price across the whole country. Consumers in provinces with a low life expectancy purchasing protection-type life insurance hedging mortality risks are actually subsidized by their peers in regions with a high life expectancy. In other words, consumers in regions with a low life expectancy should consider a specific life insurance policy hedging mortality risks as actuarially priced at a low level. The case for annuity hedging longevity risk is just opposite. 13 Because our data does not allows us to further break protection-type life insurance into products hedging mortality or longevity risks, the sign of life expectancy rate remains an empirical question. As literature demonstrates gender differences in risk aversion and attitudes in financial decision-making, we include average life expectancy for males and females separately, defined as the number of years the average individual males or females in a province is expected to live (Schubert, et al., 1999; Borghans, et al., 2009). 3.5 Other Control Variables In addition to aforementioned factors related to our main hypotheses, we identify a set of control variables to give a clearer picture of the life insurance consumption in China. These variables include social-economic, demographic, and market factors, selected on the basis of the review of the literature. Disposable income 13 We acknowledge that the increase in life expectancy itself provides incentives for consumers to purchase annuities for their retirement. 20

21 The relation between the life insurance consumption and income is unclear because of the impact of consumers risk tolerance. Increasing in income suggests increasing in human capital needed to be protected and thus a positive impact on life insurance consumption is expected. Alternatively, if the consumer has decreasing absolute risk aversion, she will purchase less life insurance at higher levels of income because of decreasing marginal utility of income. Disposable income is found to have a positive influence on insurance consumptions in most literature although no paper distinguishes detail types of life insurance products in this matter (Burnett and Palmer, 1984; Beenstock, Dickinson, and Khajuria, 1986; Outreville, 1996; Beck and Webb, 2003; Li et al., 2007). We thus expect a positive sign of the coefficient of disposable income for all types of life insurance. Social security expenditures Two competing hypotheses exist as to how social security expenditures affect life insurance consumption. On one hand, if the social security benefits effectively alleviate consumers uncertainty about the future, the incentives of purchasing life insurance, especially for protection- and healthcare-type insurance, are significantly reduced. Social security system might be considered as a substitute for private life insurance (Lewis, 1989). Further, the social security benefits come from taxes, which represents a significant chunk of wage earners income in China. According to national statistics, various types of social security taxes constitutes about average 40 to 45 percent of wages in China. The social security tax is thus expected to crow-out the life insurance consumption. This leads to a negative relationship between social security benefits and life insurance consumptions (Beenstock, Dickson, and Khajuria, 1986). On the other hand, to the extent that the social security expenditures can be considered as a source of household asset and disposable income, social security benefits might be positively correlated with life insurance consumptions (Bernheim, 1991; Browne and Kim; 1993). Li et al. (2007) find mixed results with regard to social security expenditures on life insurance when using alternative models. Our data 21

22 allows us to separately test the effect of social security on three types of life insurance. We measure the social security expenditures as social security spending per capita. We also include a variable to capture similar but another type of important social security benefits: government spending on healthcare per capita. This variable has not been tested in any literature and allows us to investigate its direct impact on the healthcare-type insurance as well as other types of life insurance. Health status Viscusi and Evans (1990) suggest that health status could alter the structure of the utility functions and thus the risk aversion of consumers. A better health status should reduce the life insurance consumption on mortality protection and healthcare-type life insurance. Health status variable also captures the adverse selection of consumers to this extent. To control the impact of health status on life insurance demand, we use the diagnosis treat number per capita as our proxy. As higher diagnosis treat number per capita indicates a relatively worse health status, we postulate that this health status proxy positively influence healthcare- and protection-type insurance. We do not have a prior on the impact of health status on investment-type insurance. Education Education level is correlated to human capital investment and earning ability over the long term. It is also associated with wealth, financial vulnerability, and risk attitude. Thus, education should have an impact on life insurance consumption. However, literature is mixed on the results. While some studies find education is positively related to life insurance consumption (Burnett and Palmer, 1984; Lin and Grace, 2007), others find a negative relationship (Goldsmith, 1983). We contend that the conflicting results might stem from combining protecting- and investment-type insurance in previous demand studies. Literature on insurance demand commonly argues that a higher level of education leads to a greater risk aversion and greater awareness of the necessity of insurance (Truett and Truett, 1990; Browne and 22

23 Kim, 1993; Hwang and Gao, 2003; Li et al., 2007). Education level varies significantly among regions in China. While education lengthens the period of dependency, there should be a greater demand for life insurance in regions where individuals are educated over a longer period. Furthermore, a more educated consumer has a greater likelihood of understanding the need for insurance to protect their human capital and hedging the risk of soaring healthcare costs. We thus posit that education is positively related to consumptions for protection- and healthcare-type life insurance. Alternatively, well-educated people have generally better access to financial knowledge and more likely to diversify their asset portfolios. Millo and Carmeci (2014) argue that education increase the ability of consumers to manage their portfolios risk and thus reduce the life insurance consumption which is usually considered as safe assets. Many investment-type insurance products are analogous to deposits, being much less risky and having lower returns than direct investments in stock markets. To the extent that well-educated people usually have higher income and better investment knowledge, education level should be inversely related to investment-type life insurance. Competing financial assets: stocks and housing Households hold various asset items in their portfolios and life insurance can be a substitute for financial assets such as equities and other lower risk assets (Fortune, 1973). Many life insurance products in China are typically packaged as investment products with life insurance benefits. Housing is also considered as an important investment instrument to secure the real purchasing power of wealth. 14 We thus include the change in annual stock market price index and housing price index in our analysis. To the extent that these two variables capture the substitute effects of these two important assets on life 14 Headen and Lee (1974) propose a four-component interrelated household asset model including primary securities, money, time deposits, and life insurance sales. Real estates is a primarily asset of households in China. Bernheim et al. (2001) state that housing may also affect life insurance needs. We consider housing as a part of household assets based on China s circumstances. It is not unusual that households purchase term life insurance to ensure that mortgages are paid on the debtor/insured s death (Lin and Grace, 2007). 23

24 insurance demand, a negative relationship is expected. However, increase in the stock market index and housing consumption might suggest the increase of household wealth and stimulate consumers purchasing of life insurance products. Thus, we do not have a prior on the signs of annual stock market index growth and housing price index. Demand for insurance is also negatively influenced by inflation as the deleterious effects of inflation on the value of life insurance (Fortune, 1973; Browne and Kim, 1993; Outreville, 1996). Literature also controls interest rates, arguing that the cost of insurance is affected by the interest rate. Interest rate also influences the expected return of life insurance products (Outreville, 1996; Beck and Webb, 2003; Li et al., 2007). Thus, we also include inflation rate proxied by CPI and interest rate for one year bank deposits as additional control variables. Finally, following the spirit of Outrevill (1996), we calculate the Herfindahl Hirschman Index (HHI) of the premiums written for each province each year to capture the regional market competition structure. 15 Table 1 summarizes our hypotheses and expectation of the impact of control variables on three types of life insurance consumptions. 4. Data and Methodology We augment the model of Beenstock, Dickinson, and Khajuria (1986) and Millo and Carmeci (2014) by decomposing total life insurance premium (V) into three categories protection (V1), investment (V2), and healthcare (V3), based on our data availability. 16 A premium data further broken down into detail types of insurance product provides insight into the purpose for which life insurance is purchased. Factors influencing three types of insurance product might be different and the same factor theoretically can have contradictory influences on different insurance products demand as argued in the 15 Outreville (1996) proxy market structure by introducing two dummy variables indicating whether the market is a monopolistic one and whether there is a foreign insurer writing business. 16 Beenstock, Dickinson, and Khajuria (1986) classify life insurance into three types life protection, pension plans, and saving elements. However, both papers estimate one model for V as a whole due to data availability. Our data does not allow us to separate the components of life protection hedging mortality risk and pension plans hedging longevity risk either. 24

25 hypothesis section. Because we cannot observe directly the price of insurance policies, we follow the equilibrium solutions suggested by aforementioned two papers and estimate the models as following: V1 = F1(longevity risk proxies, environmental risk proxies, control variables) V2 = F2(longevity risk proxies, environmental risk proxies, control variables) V3 = F3(longevity risk proxies, environmental risk proxies, control variables) We first conduct the sub-regional analysis to test Hypotheses 1 and 2, using aggregate annual data at the province level for two reasons. 17 Regional socio-economic and political condition are relative homogenous at the province level in China. Further, the CIRC sets 31 local offices in every province to regulate the insurance products and services market and maintain legal and stable operations of the insurance industry. We next apply insurer firm-province level data to test our Hypotheses 3 and 4. While all three types of life insurance can be considered as sort of financial investments, consumers decisions on purchase one or a combination of insurance products should be correlated with their total budgets constraint. In other words, the error terms of three equations are unlikely to be independent. Thus, we adopt seemingly unrelated regression to estimate the three equations simultaneously. This method also allows us to test the difference of the impact of same variable on the demand of three types of insurance. Since most literature explores the relationship based on the total insurance premiums, we also study this relationship by using the sum of all types of life insurance as the dependent variable and applying GMM estimation models, following Li, et.al (2007). We manually collect insurance premiums of life insurance industry as well as individual insurers operation data from the CIRC annual reports (Yearbook of China Insurance) for the period Data for demographic variables, social security, economic development, education, life expectancy, and health status are obtained from several sources. The one-child policy planning rate, young and old 17 We include the four municipalities directly under the jurisdiction of central government in results reported. Results dropping observations belong to the four municipalities remain robust. 25

26 dependence ratio, urbanization ratio and life expectancy at birth are obtained from the China Statistical Yearbook. Air pollution measured by sulfur dioxide emissions per capita, green coverage ratio, disposable income, and health status measured by diagnosis treat number per capita are obtained from the CEInet Statistics Database. Social security expenditure and government expenditure on healthcare are obtained from National Bureau of Statistics of China. Finally, data for the education, housing price index, stock index growth, inflation rate and interest rate were obtained from the CSMAR (China Stock Market & Accounting Research) and the Wind databases, various years. In the final sample at the individual firmprovince level, we have 62 firms and 2,047 observations, after eliminating observations with missing information requested in the regression analysis. The sample firms represent 85% of total life insurance industry premiums written in 2010 and comparable percentages for other years. Because of the skewness of the life insurance premiums and some control variables (e.g., social security expenditures), we use a logarithmic transformation, following Beenstock, Dickinson, and Khajuria (1986) and Li et al. (2007). 18 Variables that are not expressed as logarithms are already expressed in percentage terms or dummy variables, e.g. interest rate, education level and the publicly traded dummy. 5. Results 5.1 Descriptive Statistics Descriptive statistics are contained in Table 2. Panel A shows the mean, median and related statistics for the aggregate data at the province level. The table indicates that the premiums written per capita vary significantly among provinces with mean and median at 563RMB and 455RMB per capita (value before taking logarithm). The lowest premiums written per capita is 112RMB while the highest number reaches 2179RMB, suggesting the validity of the logarithmic transformation due to data skewness. The sharp difference in life insurance development applies to all three types of products. The most popular 18 The estimated coefficients of log-linear specification can be interpreted as elasticity and common in demand functions specified on macroeconomic variables, which tend to display exponential growth (Outreville, 1996; Browne and Kim, 1993). 26

27 life insurance is investment-type product, followed by protection-type product. Untabulated results show that all three types of life insurance have grown significantly during our sample period from 2005 to The annual compound growth rates are 4.08%, 18.1% and 11.42%, for protection-, investment-, and healthcare-type life insurance. Double-digit increases in premiums indicate that the society has increasingly rely on commercial insurance to hedge healthcare risk even though the government expenditures on social welfare have increased over time. The mean and median social security expenditures are 712RMB and 3158RMB (value before taking logarithm), respectively. Similarly, the mean and median government expenditures on healthcare are 371RMB and 1343RMB, respectively. There also exists significant variance in demographic, socio-economic and environmental risk variables. The highest urbanization rate reaches 85%, while the lowest is only 30%. The proportion of population with college degree or above ranges from the highest at 30% and the lowest only 3%. The green coverage ratio varies from 43% to 27%. The results in Table 2, Panel B contain the means for the samples at individual insurer-province level. Approximately 16 percent of the sample consists of insurers with foreign-ownership structure. Roughly 16 percent observations are publicly-traded insurers. Expense ratios vary significantly from 584% to 10% among insurer-state-year observations. It is not surprising as many new insurers expend their business and commonly spend a significant amount of expenditures on marketing endeavor, striving to attract customers from existing insurers during our sample period. 5.2 Regression Results Regression results for the provincial level data are presented in Table 3. Column 1 reports results of using total premiums written as the dependent variable, applying the GMM estimation method, to facilitate comparisons with previous literature (e.g., Li et al., 2007). The models using premiums written of protection, investment and healthcare type as the dependent variable are jointly estimated using the 27

28 seemingly unrelated regression method. 19 Results of three types of life insurance are presented in column 2 to 4, respectively. We focus primarily on the results of three types of life insurance in columns 2-4 and indicate only the noteworthy result of aggregate premiums in column 1. Hypothesis 1 is generally borne out in the results in Table 3, indicating that longevity risk factors significantly affect life insurance consumptions. First, the coefficients for the one-child policy planning rate are positive and significant at the 1 percent level in columns 2 to 4, consistent with Hypothesis 1. Provinces with a high one-child policy planning rate tend to have larger life insurance consumption on all three types of products, suggesting that households having less children are more likely to rely on life insurance to manage the economic uncertainty. Second, the coefficients of the young dependency ratio are significantly negative in columns 2 to 4, suggesting a higher young dependency ratio reduces the consumers incentive to purchase all types of life insurance. A large proportion of population is too young to consider saving for investment or longevity concerns (Beck and Webb, 2003). This result is in contrast to Millo and Carmeci (2014), indicating that there might be institutional difference between China and Italy, a developing country and a developed country. At the same time, the coefficient is positive and significant at the 1 percent level for the old dependency ratio in column 3, suggesting that increasing proportion of old age population provides additional incentives for consumers to purchase investment insurance products. These results are consistent with Hypothesis 1 which states that changes in population structure in China relative to longevity risk have a direct effect on the life insurance demand. Third, the coefficients are positive and significant for the urbanization rate for all three types of life insurance in columns 2 to 4. These results again provide strong support to Hypothesis 1. Accelerating 19 Correlation matrix of residuals of three-types of life insurance show that correlations between protection and investment, protection and healthcare, investment and healthcare type life insurance are , , and , respectively. Breusch- Pagan test of independence is highly significant, suggesting the validity of using the seemingly unrelated regression model. Similar test results apply for estimations in Table 4. 28

29 urbanization process has significant impacts on the population structure and social connections in China. Urbanization might also enable insurers to distribute products more efficiently (Outreville, 1996; Beck and Webb, 2003). Hypothesis 2 considers the impact of environmental risk on life insurance demand. The results in columns 2 and 4 suggest that worse air pollution condition is positively associated with life insurance demand for protection- and healthcare-type products, consistent with Hypothesis 2. The significant coefficients for the green coverage ratio in columns 3 and 4 provide additional support to Hypothesis 2, suggesting that improved environmental provides people incentives to purchase supplementary commercial life insurance hedging potential longevity risk. Therefore, the results are consistent with Hypothesis 2. The coefficient for the life expectancy for males is positive and significant in column 3, while it is not significant in other columns. Alternatively, the coefficient for the life expectancy for females is negative and significant in column 3. Although the life expectancy does not seem to have impact on overall life insurance demand, a high life expectancy for males (females) increases (decreases) demand for investment-type products. This is consistent with the argument that male and female have different risk attitudes when making financial decisions. The results also underscore the importance of differentiate life insurance demands into detail products due to varying consumers economic incentives to purchase life insurance. Overall, we find evidence supporting Hypothesis 4 with respect to the impact of life expectancy on life insurance demand. Turning to our additional explanatory variables, disposable income is positively related to protection- and healthcare-type life insurance, consistent with most literature (Beck and Webb, 2003; Lin and Grace, 2007). The social security expenditure is negatively related to the protection- and healthcaretype life insurance consumption, suggesting the crowd-out effect of social security expenditures on 29

30 commercial life insurance. Social security benefits may diminish the demand for life insurance to deal with the longevity risk. The government expenditure on healthcare variable also has a negative and significant sign in column 2. However, it carries a positive and significant sign in column 3, suggesting higher government expenditures on healthcare increase investment-type life insurance consumptions. Perhaps this result is due to the fact that higher government expenditures on healthcare alleviate people s concerns on economic security due to rising healthcare costs. That is, if the main concern on healthcare cost has been mitigated, consumers may be able to be spend more on investment-type life insurance. The positive relationship between health status and investment-type life insurance in column 3 is probably due to the bequest motive (Inkmann and Michaelides, 2012). Recall that higher diagnosis treat number per capita indicates worse health status. Parents with an inferior health condition might want to provide economic safety for their children through life insurance, an important type of safety investment assets as well as an estate-tax-free financial instrument. The results for the education level variable suggests that the impact of education on varying types of life insurance is different. The results in columns 2 and 4 suggest that well-educated people tend to purchase more protection- and healthcare-type life insurance, consistent with the argument that education increase people s risk aversion and awareness of the necessity of insurance to hedge life uncertainties (Truett and Truett, 1990; Brown and Kim, 1993). Highly educated people should have a better understanding of life insurance in offering economic security. In contrast, the negative (albeit not significant) coefficient in column 3 suggests that better educated people are inclined to reduce their investments in safe assets such as life insurance, confirming the conjecture of Millo and Carmeci (2014). Taken as a whole, these results reconcile the conflicting findings with regard to the impact of education on life insurance consumption and underscore the necessity of decomposing total life insurance consumption into products designed for different purposes and target consumers. 30

31 The variables proxy for competing financial assets, the growth in stock market price index and the ratio of housing consumption to total disposable income, are significant and negative in column 3. This provides evidence that investment-type life insurance is considered as substitutes of other financial assets. The coefficient of the ratio of housing consumption is also negative and significant in column 2, suggesting rising housing consumptions in current China further squeezing out normal consumptions on protectiontype life insurance. The coefficient of inflation is negative and significant in column 4, indicating high inflation rates suppress the life insurance demand for healthcare-type products. However, we find a positive relationship between inflation rate and protection-type life insurance demand in column 2, which is unexpected. Interest rate enters positively in column 4, suggesting that high interest rate probably increases insures investment returns and thus potential ability to offer competitive healthcare-type products at a lower price (Beck and Webb, 2003). In contrast, we find a negative relationship between interest rate and investmenttype products in column 3. High interest rate might indicate a significant opportunity cost for consumers purchasing investment-type products. Therefore, consumers tend to purchase investment-type life insurance when interest rate is low if life insurance products do not immediately alter price according to the change in interest rate. Finally, the coefficient of the HHI is significant and negative in column 4, suggesting that a higher degree of competition (associated with a low HHI) might provide insurers incentive to innovate in healthcare-type products and thus increase the consumption. Overall, we find results supporting our Hypotheses 1 and 2 in Table 3. Empirical results also support Hypothesis 4 regarding to the price effect on life insurance consumptions using the life expectancy as the proxy. Now we turn to Table 4 containing results using individual insurer firm-province level data with firm characteristics variables added and re-estimating the model of Table 3, mainly designed to test Hypothesis 3 and Hypothesis 4. 31

32 Hypothesis 3 indicates that the firm reputation is associated with life insurance demand. The coefficients for the firm size are positive and significant in all of the equations in Table 4, supporting Hypothesis 3. Interestingly, coefficients for the firm business age variable are negative in columns 1 to 3, contrary to our expectation. A plausible explanation is that new opening firms have more inputs in the marketing or are willing to reduce the price level to attract more cash inflow in a short-term and take advantage of the favorable investment opportunities in current China. Old companies instead usually have more stable marketing strategy and business structure and are less likely to trade huge business expenditures for a short-term unusual increase in business. However, demand for healthcare-type insurance is positively related to the business age in column 4, consistent with our hypothesis. Insurers with longer operation history generally have better connections with local healthcare providers and enjoy competitive advantage in health-care life insurance products, ceteris paribus. The coefficient of insurers rate of return on investment is positive and significant in column 4, indicating insurers superior investment ability positively affects the demand for healthcare-type life insurance. It might be due to the reason that insurers with better investment returns can lowers the cost of their healthcare-type life insurance products. However, this variable is marginally negatively related to investment-type life insurance in column 3, in contrary to our hypothesis. A potential explanation is that insurers in China generally do not redistribute or only redistribute a small fraction of its profits to consumers purchasing investment-type insurance. Insurers make higher investment return when the overall macro-investment opportunities are ample. This favorable macro-economic environment induces consumers to other investment products with higher profits. The coefficient of foreign ownership structure indictor variable is positive and significant at the 1% level in columns 2 to 4 in Table 4. This implies a significantly positive relationship between foreign ownership structure and the life insurance consumption, consistent with the argument that people tend to 32

33 consider insurers with certain foreign ownership having a better reputation and risk management techniques in all types of life insurance products. The publicly traded status indicator variable is positive and statistically significant in columns 2 to 4, consistent with our prediction that publicly-traded insurers have better reputations and can attract more business. The proxy for insurance salespersons ability is also positive and significant in column 2 and 4, suggesting capable employees can explain the necessity of protection- and healthcare-type life insurance better to customers. Surprisingly, this variable is negatively related to investment-type life insurance consumptions. Overall, our empirical results support Hypothesis 3. The last main hypothesis concerns the effect of price on life insurance consumption. The coefficient for the expense ratio isnegative and significant in column 3 supporting Hypothesis 4. This suggests that insurers with high expense ratios are associated with low life insurance demand, especially for investment-type products, ceteris paribus. However, we find a positive relationship between the expense ratio and both protection- and healthcare-type life insurance consumptions. One potential explanation is that healthcare life insurance is a sort of relatively new life insurance products in China. Insurers which spend more in marketing newly innovated healthcare-type life insurance products can potentially attract more business. Protection-type insurance products also requires high marketing expenses to push consumers to purchase due to the culture reason in China (Hwang and Greenford, 2005). It is noteworthy that the alternative proxies for price, life expectancy variables, remain significant in columns 2 and 4 in Table 4, complementing the results of Table 3. The coefficients for the life expectancy for females also carry exactly opposite signs with those of males in columns 2 and 4, consistent with the argument that that male and female have different risk attitudes when making financial decisions. Males seem to be more aggressive in risk taking than females. It is also noteworthy that the significant coefficient for the green coverage ratio in column 2 provides additional support to Hypothesis 2, suggesting that 33

34 improved environmental might evoke consumers to purchase supplementary commercial annuity products. Results with respect to the control variables in Table 4 are generally consistent with Table 3 with a few exceptions. Disposable income now is significantly and negatively correlated with life insurance consumption. The result is not very surprising if consumers have decreasing marginal utility of income and thus decreasing absolute risk aversion. We also find a positive relationship between our health status variable in columns 2 and 4, suggesting people with a worse health condition increase life insurance consumptions in protection- and healthcare-type products, as expected. This is consistent with the prediction of adverse selection theory. Surprisingly, the coefficient of health status proxy is negatively related to the investment-type life insurance in column 3 now. One potential explanation here is the consumers face budget constraints and allocate expenses on investment-type products away towards protection- and healthcare-type products. We further estimate similar models using individual purchased and group purchased life insurance data. The dependent variable in these regressions is the life insurance consumption purchased by individuals or by groups mostly paid by employers in China. Regression results delineated between individual purchased and group purchased life insurance are not reported for space concern and available upon request from the authors. We mention a few noteworthy findings. As individual purchased life insurance comprises a majority of total life insurance consumption in China, it is not surprising that the results using individual purchased life insurance data are almost duplicates of those using the aggregate life insurance consumption data. The results using group life insurance data are also mostly consistent with the total consumption data except for a few exceptions. The first notable result is that the one-child policy planning rate is negatively and significantly correlated with the protection- and healthcare-type life insurance. This is not surprising as employers have less need to purchase protection- and healthcare-type insurance for children when the number of young dependent is 34

35 relatively low. 20 The second difference lies in the positive impact of education on group purchased investment-type life insurance. We believe that it is also due to the reality in China. While well-educated people tend to diversify their asset allocation away from low risk investment-type life insurance, investment-type group life insurance is preferred as the cost comes from the employer and is considered as a bonus for employees. Third, we find a positive and significant relationship between the old dependency ratio and group purchased healthcare life insurance, indicating employers tend to purchase group life insurance to manage the risk of soaring healthcare cost for the aged. Finally, we conduct a few robustness tests, including winsorizing all continuous variables at the 1 and 99 percent level or removing these outliers, replacing the air pollution proxy with nitric oxide emissions per capita, applying OLS model and random effect model for panel data instead of seemingly unrelated model and GMM model. All our main results remain robust. Because headquarters of most insurance companies located in Beijing or Shanghai, we re-estimate all models dropping observations of these two cities. We also conduct robustness tests by further dropping observations of other two municipalities (Tianjin and Chongqin) directly under the jurisdiction of central government. The case of these municipalities might distort the results because they have different characteristics or regulatory attentions compared with provinces, e.g., urbanization rates and social security expenditures. Again, our main results remain unchanged. 6. Conclusion This research investigates the life insurance market in China. We incorporate not only aggregate provincial panel data but also individual insurance company data. We examine the determinants of life insurance consumption using aggregate premiums as well as three types of life insurance: protection-type, investment-type, and healthcare-type products. 20 Note that group purchased life insurance usually covers employees children but not the spouse in China. 35

36 The results indicate that longevity risks represented particular by population structure changes significantly affect the life insurance demand. As birth rates have reduced, falling below the replacement rate due to the one-child policy and populations have enjoyed the benefits of improved medical care, the population in China over the age of 65 has increased both absolutely and proportionally. This significantly increases the demand for commercial life insurance to hedge the longevity risk. Accelerating urbanization process also has a positive impact of the life insurance demand. Environmental risk is found to have remarkable influences on the life insurance demand, especially the healthcare-type products. Further, the results suggest that insurers have competitive advantage in at least some types of life insurance if they have a larger size, associated with a foreign insurer or are publicly traded, and have more capable employees. We also find some evidences that price is negatively related to life insurance consumption. Overall, the results indicate that the firm s reputation and price are additional important determinants of life insurance demand, as well as demographic and socio-economic factors illustrated in previous literature. Our results of the impact of education and life expectancy at birth on life insurance also challenge the literature using total life insurance consumption instead of decomposing them into detail types of products. As different type of life insurance products are designed to hedge different risks, treating all life insurance products as the same blurs the economic incentives of demand of varying consumers, potentially leading to biased results. These results are important because economic security for the elderly has become a social and political risk in China. As extended families and other traditional methods of supporting the elderly are weakening, China faces challenges in the wake of this demographic evolution which are underpinned by the longevity risk. There is uncertainty over future life expectancy, around the costs to cater to the needs of increasingly elderly populations, about whether economy will keep growing providing support for the 36

37 social security system, and, perhaps to some extents, about how birth rate reacts to the relaxed one-child policy in Households come to realize that public social security system can only cover basic and a small part of their future financial need after retirement or unexpected shock like premature death. High enrolment in tertiary education over the last decades increases the human capital needed to be protected and the awareness of risk management as well. Overall, the aging of Chinese population and worsening pollution problem provide challenge to the society but great opportunities for the life insurance industry. The current trend of deregulation of investment and product innovation in China increases the overall revenues of life insurance companies, as well as the supply of capital, and therefore the ability and incentives of the insurance companies to answer to the potential demand. The research results suggest that while life insurance companies should improve their company reputation and financial strength, they should also enhance their agents ability. As consumers become more educated and sophisticated in financial knowledge, insurance companies should innovate more suitable products to targeting customers and improving their marketing strategy such as online sales of insurance products. 37

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40 Table 1. Summary of Variables and the Predicted Signs This table reports our expectation of the relationship between dependent variables and life insurance consumptions on three types of products. The one-child policy planning rate is the percentage of children born conform to the planned onechild policy birth to the total number of birth cohorts in the same year. Young dependency ratio is the ratio of the total number of children under 15 to the total number of persons between 15 and 64. Old dependency ratio is the ratio of the total number of adults aged 65 and over to the total number of persons between 15 and 64. Urbanization ratio is the share of the urban population in the total population. Air pollution is the sulfur dioxide emissions (in ton) per capita. Green coverage ratio is the urban built-up area green coverage rate. Disposable income is the logarithm of disposable income (in RMB) per capita. Social security expenditure is the logarithm of the amount of expenditure for social security and employment (in RMB) per capita. Government expenditure on healthcare is the logarithm of the amount of expenditure for medical healthcare (in RMB) per capita. Health status is the number of diagnosis and treatment by medical institution per capita. Education is the proportion of population with college degree or above. Stock index growth is the annual growth rate of the Shanghai stock exchange composite index measured in closing price. Housing price index is ratio of commercial housing sales to disposable income. Inflation rate is the annual consumer price index. Interest rate is the oneyear term bank deposit interest rate. Life expectancies at birth male and female are the logarithm of the number of years the average individual males or females is expected to live. Herfindahl-Hirschman Index is Herfindahl indexes of premiums written by province. Insurer size is the logarithm of total assets. Insurer business age is the logarithm of total years in business. Insurer investment return is the rate of return on investment. Insurer with foreign-ownership dummy is a dummy variable equal to one if the firm has a certain shares owned by foreign insurers and zero otherwise. Insurer with publicly traded status dummy is an indicator variable equal to one for insurers are publicly listed at the stock market, and zero otherwise. Insurer employee ability is the percentage of employees with insurance intermediate or advanced professional titles. Insurer expense ratio is the ratio of total expenditures on life insurance premiums to the premiums written. Variable Expected Sign on Life Insurance Consumption Protection-type Investment-type Healthcare-type Longevity Risk Proxy The one-child policy planning rate Positive Uncertain Positive Young dependency ratio Uncertain Negative Uncertain Old dependency ratio Uncertain Positive Positive Urbanization ratio Positive Positive Positive Environmental Risk Proxy Air pollution Positive Uncertain Positive Green coverage ratio Positive Positive Positive Supply Side Proxy Insurer size Positive Positive Positive Insurer business age Positive Positive Positive Insurer investment return Positive Positive Positive Insurer with foreign-ownership dummy Positive Positive Positive Insurer with publicly traded status dummy Positive Positive Positive Insurer employee ability Positive Positive Positive Herfindahl-Hirschman Index Uncertain Uncertain Uncertain Price Proxy Insurer expense ratio Negative Negative Negative Life expectancy at birth male Uncertain Uncertain Positive Life expectancy at birth female Uncertain Uncertain Positive Other Control Variables Disposable income Positive Positive Positive Social security expenditure Uncertain Uncertain Uncertain Government expenditure on healthcare Uncertain Uncertain Uncertain Health status Positive Uncertain Positive Education Positive Negative Positive Stock index growth Uncertain Uncertain Uncertain Housing price index Uncertain Uncertain Uncertain Inflation rate Uncertain Uncertain Uncertain Interest rate Uncertain Uncertain Uncertain 40

41 Table 2. Descriptive Statistics This table reports summary statistics for the years 2005 to Panel A reports summary statistics for province level data and has 176 observations Panel B shows summary statistics for firm characteristics using the firm-province level data and has 2,047 observations. Premiums written per capita are the logarithm of total premiums written per capita. Premiums written per capita-protection, investment, and healthcare are the logarithm of premiums written for three types of life insurance products per capita, respectively. The one-child policy planning rate is the percentage of children born conform to the planned one-child policy birth to the total number of birth cohorts in the same year. Young dependency ratio is the ratio of the total number of children under 15 to the total number of persons between 15 and 64. Old dependency ratio is the ratio of the total number of adults aged 65 and over to the total number of persons between 15 and 64. Urbanization ratio is the share of the urban population in the total population. Air pollution is the sulfur dioxide emissions (in ton) per capita. Forest coverage ratio is the urban built-up area green coverage rate. Disposable income is the logarithm of disposable income (in RMB) per capita. Social security expenditure is the logarithm of the amount of expenditure for social security and employment (in RMB) per capita. Government expenditure on healthcare is the logarithm of the amount of expenditure for medical healthcare (in RMB) per capita. Health status is the number of diagnosis and treatment by medical institution per capita. Education is the proportion of population with college degree or above. Stock index growth is the annual growth rate of the Shanghai stock exchange composite index measured in closing price. Housing price index is ratio of commercial housing sales to disposable income. Inflation rate is the annual consumer price index. Interest rate is the one-year term bank deposit interest rate. Life expectancies at birth male and female are the logarithm of the number of years the average individual males or females is expected to live. Herfindahl- Hirschman Index is Herfindahl indexes of premiums written by province. Insurer size is the logarithm of total assets. Insurer business age is the logarithm of total years in business. Insurer investment return is the rate of return on investment. Insurer with foreign-ownership dummy is a dummy variable equal to one if the firm has a certain shares owned by foreign insurers and zero otherwise. Insurer with publicly traded status dummy is an indicator variable equal to one for insurers are publicly listed at the stock market, and zero otherwise. Insurer employee ability is the percentage of employees with insurance intermediate or advanced professional titles. Insurer expense ratio is the ratio of total expenditures on life insurance premiums to the premiums written. All continuous variables are winsorized at the 5 and 95 percentile to remove the excess effects of outliers. Panel A. Summary statistics for the province level data Variable Mean Median Std. Min Max Premiums written per capita Premiums written per capita-protection Premiums written per capita-investment Premiums written per capita-healthcare The one-child policy planning rate Young dependency ratio Old dependency ratio Urbanization ratio Air pollution Green coverage ratio Disposable income Social security expenditure Government expenditure on healthcare Health status Education Stock index growth Housing price index Inflation rate Interest rate Life expectancy at birth male Life expectancy at birth female Herfindahl-Hirschman Index Panel B. Summary statistics for firm characteristics using the firm-province level data Insurer size Insurer business age Insurer investment return Insurer with foreign-ownership dummy Insurer with publicly traded status dummy Insurer employee ability Insurer expense ratio

42 Table 3. Determinants of Life Insurance Consumption: Provincial Level This table reports results testing the determinants of life insurance consumption in China using aggregate provincial level data for the sample period Column 1 (2-4) report results using the total life insurance premiums (three types of life insurance) as the dependent variable. We use GMM method in column 1 and seemingly unrelated regression panel model in column 2-4. All other variables are defined in Table 2. Robust standard errors are reported in parentheses below each coefficient. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively. Independent Variables (1) Total Premiums (2)Protectiontype insurance (3) Investmenttype insurance (4)Healthcaretype insurance The one-child policy planning rate *** 0.021*** 0.017*** (0.008) (0.006) (0.007) (0.006) Young dependency ratio *** *** ** (0.012) (0.004) (0.005) (0.004) Old dependency ratio *** (0.016) (0.007) (0.009) (0.007) Urbanization ratio * 0.012*** 0.007*** (0.015) (0.003) (0.003) (0.002) Air pollution ** *** (12.905) (1.397) (2.043) (1.338) Green coverage ratio *** 0.007* (0.008) (0.004) (0.005) (0.004) Disposable income 0.871** 0.576*** *** (0.404) (0.145) (0.185) (0.138) Social security expenditure *** *** (0.053) (0.039) (0.042) (0.037) Government expenditure on healthcare ** *** 0.117* (0.110) (0.069) (0.070) (0.065) Health status *** (0.037) (0.018) (0.020) (0.017) Education *** *** (0.017) (0.006) (0.007) (0.006) Stock index growth *** *** * (0.000) (0.000) (0.000) (0.000) Housing price index *** *** (0.308) (0.210) (0.225) (0.198) Inflation rate * *** (0.009) (0.009) (0.011) (0.009) Interest rate *** 0.126** (0.085) (0.062) (0.070) (0.059) Life expectancy at birth male *** (15.451) (1.194) (1.384) (1.125) Life expectancy at birth female *** (10.442) (1.108) (1.245) (1.043) Herfindahl-Hirschman Index * (0.325) (0.194) (0.224) *** (0.183) Number of Observations

43 Table 4. Determinants of Life Insurance Consumption: Insurance Firm-Province Level This table reports results testing the determinants of life insurance consumption in China using individual insurance firmprovince level data for the sample period Column 1 (2-4) report results using the total life insurance premiums (three types of life insurance) as the dependent variable. We use GMM method in column 1 and seemingly unrelated regression panel model in column 2-4. All other variables are defined in Table 2. Robust standard errors are reported in parentheses below each coefficient. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively. Independent Variables (1) Total Premiums (2)Protectiontype insurance (3) Investmenttype insurance (4)Healthcaretype insurance The one-child policy planning rate (0.013) (0.010) (0.012) (0.012) Young dependency ratio ** *** (0.020) (0.006) (0.007) (0.007) Old dependency ratio ** (0.031) (0.011) (0.013) (0.013) Urbanization ratio *** 0.035*** 0.017*** (0.032) (0.005) (0.005) (0.005) Air pollution *** (32.813) (2.476) (2.863) (2.614) Green coverage ratio ** 0.013* 0.015* (0.011) (0.007) (0.008) (0.008) Disposable income *** *** ** (0.485) (0.250) (0.290) (0.271) Social security expenditure 0.221** *** *** *** (0.089) (0.063) (0.076) (0.072) Government expenditure on healthcare (0.165) (0.108) (0.123) (0.118) Health status ** 0.090*** *** 0.061* (0.049) (0.032) (0.037) (0.036) Education 0.040** *** (0.018) (0.010) (0.012) (0.011) Stock index growth ** *** (0.001) (0.001) (0.001) (0.001) Housing price index * *** (0.468) (0.371) (0.427) (0.418) Inflation rate *** *** *** (0.016) (0.019) (0.021) (0.020) Interest rate *** (0.119) (0.122) (0.139) (0.133) Life expectancy at birth male *** ** (49.477) (2.078) (2.409) (2.271) Life expectancy at birth female *** *** (39.848) (1.936) (2.281) (2.136) Herfindahl-Hirschman Index *** *** *** (0.500) (0.329) (0.392) (0.376) Insurer size 0.294*** 1.370*** 1.161*** 0.812*** (0.101) (0.036) (0.045) (0.041) Insurer business age ** *** *** 0.291*** (0.253) (0.080) (0.100) (0.087) Insurer investment return * 0.099*** (0.030) (0.025) (0.031) (0.025) Insurer with foreign-ownership dummy *** 0.491*** 0.691*** (0.320) (0.155) (0.189) (0.162) Insurer with publicly traded status dummy *** 0.621*** 0.303*** (0.084) (0.065) (0.075) (0.072) Insurer employee ability *** *** 1.901*** (0.483) (0.445) (0.533) (0.521) Insurer expense ratio *** * 0.354*** (0.097) (0.074) (0.090) (0.083) Number of Observations