Determinants of the Development of Insurance in China under the Globalization

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1 Determinants of the Development of Insurance in China under the Globalization Cuizhen Zhang (China Foreign Affairs University, China) * Nong Zhu (INRS-UCS, University of Quebec, Canada) ** Abstract: China s insurance industry has been undergoing extremely fast growth since the enforcement of open-up policy. This article, using data for 225 cities, examines the determinants of China s insurance development, measured by premium volume, insurance density, and insurance penetration. Our econometric results reveal that foreign direct investment is more significant for property than for life insurance. Per capita GDP is the only variable significant for all measures of life consumption, while the total population, savings deposit, education attainment, telephone ownership per capita, social welfare expenditure, and young dependency are significant for life premiums. Variables such as wage level, savings deposit, and investment in fixed assets, report their significant effect on the demand for property insurance. In addition, the article finds that there exists obvious regional difference in the development of China s insurance sector, and the governing factors vary from region to region. JEL Classification: C21; D91; G22; O18 Key words: Life insurance; Property-liability insurance, China 1 INTRODUCTION The insurance industry is, with no doubts, an increasingly important sector in China for recent years. According to the statistics of Swiss Reinsurance Company (2005), China s premium income mounted to USD billion, equaling to 3.26% of the gross domestic products (GDP) in 2004, compared to that of USD 1.37 billion, 0.7% of GDP, in 1986 (Swiss Reinsurance Company, 2005). Life insurance premiums, in particular, increased to USD billion, contribution to GDP being 2.21% in the year of 2004, from premiums of USD 278 million, being 0.13% of GDP in Meanwhile, China s insurance industry has been more and more significant in the world market. In terms of total written premiums, the rank of China on the world list done by Swiss Reinsurance Company rose from 29 th in 1986 to 11 th in the year of 2004, companied by the share of the world market raising from 0.16 to 1.61% for the same period. 1.2% of non-life premiums are contributed to China in 2004, while just 0.24% in For the line of life, 1.92% of written premiums in the whole world is owed to China, whereas only 0.07% in 1986 (Swiss Reinsurance Company, 2005). The insurance industry of China by 2008 is to reach a value of USD billion in gross premium income, equating to a compound annual growth rate (CAGR) of 20.4% in the period (Datamonitor, 2004). The already achieved considerable development of China s insurance industry and the forecasted great potential in the coming years have started to capture the interests of the academic staff. What factors behind are supporting the substantial growth? Whether the governing factors proved significant in the other countries also have effect in case of China? Compared with the relatively extensive empirical literatures on other countries, industrialized nations in particular, the literature on China is scarce. Zhuo (1999), * cuizhen_zhang@yahoo.co.uk ** nong_zhu@ucs.inrs.ca 1

2 Hwang and Gao (2003), Hwang and Greenford (2005) examined the determinants of China s life insurance consumption. In contrast, to our knowledge, there is no literature on the determinants of China s aggregate property-liability insurance consumption, while Zou (2003) and Zou et al. (2003) focus their interests on the determinants for corporate demand for property insurance in China. They are definitely the advancers in the kind of research on China and consequently make contributions to the literature. Nevertheless, we have to point out the shortage of the aforesaid studies on China s life consumption. Firstly, their results are not very reliable due to their limited observations. Some studies build their econometric analysis either on time-series data over the period about ten years, or on cross-sectional data collected for about twenty. As a common sense, the so short time series are not enough for econometric investigation, their results being unreliable as a consequence. Secondly, the observed premiums in reality a joint result from both supply (the insurer s side) and demand (the consumer s side), it is thereby essential that the regression model have to reflect both of these considerations (Beenstock et al., 1988). However, none of the variables reflecting the supply side is included in the published studies on China s life insurance consumption. Furthermore, any ignorance of the background such as the enforcement of open-up policy, liberalization, and gradual participation in globalization, will necessarily lead to the failure in telling the truth of China s insurance development. One of the evidence is the increasing openness degree, which is often measured by foreign direct investment (FDI). China, since 1995, has been the largest recipient of FDI in the developing countries. More than USD billion amount of FDI, in total, flowed into China over the period between 1979 and 2003 (Department of Trade and External Economic Relations Statistics, National Bureau of Statistics of China, 2005). Thomas (2002: 415) states that: As foreign investment flowed into China, the demand for insurance protection grew. The economic growth spurred by foreign investment generated additional demand for insurance in the domestic economy. As the development of China s insurance industry is motivated by China s opening-up process (Wu and Strange, 2000), openness degree is reasonably expected to have some kind of relationship with the insurance consumption. However, any variables related to open economy are excluded in the previous analysis on China. China, being a country with large dimension, differs in the level of economic development from region to region, frequently measured by the division of the eastern, the central, and the western area. As a service sector, regional insurance development shall mirror, to some degree, the overall economic disparity. Regional difference along with its reasons concerning insurance consumption within a nation has never been investigated, to the authors knowledge. This article, depending on the data collected for 225 cities, aims to analyze the prevailing factors in the development of China s insurance industry, including life and property-liability line, under the macro-economic background, and to investigate the regional difference, too. This article contributes literature in the main aspects as follows. First of all, some variables, such as the openness degree and sales condition represented by telephone ownership, are for the first time to be introduced in the econometric analysis on insurance consumption. Also, market structure measured by Herfindahl index, life expectancy, private savings deposit, and population count are firstly used to explain the development of China s life insurance. Additionally, the investment in fixed assets is never incorporated as an independent variable in the previous research on property insurance expenditure. The second contribution of this article goes to the regional difference in insurance consumption, and its causes. Sub-samples 2

3 for the eastern, the central, and the western cities in China make it possible to investigate the governing determinants in different regions in case of China. Thirdly, three dependent variables including total premiums, insurance expenditure per capita, and insurance penetration are adopted in our analysis, of which the ratio of premiums to GDP is not used by Hwang and Gao (2003), Hwang and Greenford (2005), and Zhuo (1999). Finally, the ever largest and latest database, composed of 225 observations by city for the year of 2003, guarantee the results more representative and reliable. In the following section, we outline the most essential characteristics of the development of insurance sector in China. Section 3 provides the related literature, on both the theoretical and the empirical perspectives, more interests will be paid to the prior studies concerning China. Section 4 explains the data and the specification of our empirical model. Results are presented in section 5. The conclusion reports in the last section. 2 DEVELOPMENT OF INSURANCE INDUSTRY IN CHINA The establishment of the People s Insurance Company of China (PICC) in October 1949 marks the very beginning of insurance industry in the People s Republic of China. However, the domestic business of PICC, the single insurance provider at that time, was suspended due to the restrictions on private ownership of property and comprehensive social entitlement programs for the period (Lee, 2002; Thomas, 2002). It is the reform and open-up policy initiated in late 1970s that resuscitated the insurance sector in China, with property business being resumed in 1980 and life insurance in The monopoly of PICC remained till the foundation of the second insurer, Xinjiang Corps Insurance Company, in 1986, 1 followed by Ping An Insurance Company of China in 1988 and China Pacific Insurance Company in In 1992, the American International Group (AIG) was granted the license to write policy in Shanghai, signaling China insurance industry door opening to the international insurers. Many more providers including local, wholly foreignowned, and joint ventures insurance companies have started operations in China, especially since China s accession to WTO in late In total, there are 61 insurance companies providing loss protection for consumers in China by the end of Motivated by the noticeable development of insurance sector, China Insurance Regulatory Commission (CIRC), an exclusive watchdog, was then set up in November We now summarize the development of China s insurance sector from the following perspectives. Figures 1 and 2 tell the stunning development of the insurance sector in China over the period of , in terms of total premiums, insurance density, and insurance penetration. The per capita expenditure in property-liability and life insurance increased from USD 1.3 and USD 0.3 in 1986, to USD 12.9 and USD 27.3 in 2004, respectively. Property-liability and life insurance s contribution to the gross domestic products grew to 1.05% and 2.21% in 2004, compared with that of 0.52% and 0.13% in Figure 1 - Premium income growth 1 Xinjiang Corps Insurance Company is in fact the second established insurer in China. But it is seldom regarded as the mark for the end of the monopoly of PICC because of its business being limited in corps insurance in Xinjiang Construction Force at the time of its establishment. Later its business region and products gradually expand, and now it has already grown to be a national insurer named as China United Property Insurance Company, covering all kinds of non-life risks across the country. 2 The central bank, i.e., the People s Bank of China, is the previous regulation body for insurance industry in China. 3

4 Million US dollars Year Non-life business Life business Source: Swiss Reinsurance Company, Sigma. Figure 2 - Evolution of insurance density and penetration Us dollars % Year 0.0 Insurance density Insurance penetration Source: Swiss Reinsurance Company, Sigma. Whereas, in 2004, on the world list released by Swiss Reinsurance Company (2005), depending on the per capita expenditure in insurance, China just holds the 72 th position. The big gap in insurance density and penetration whether compared to the world average or industrialized countries, even the emerging markets, as Table 1 shows, on the other hand, indicates the promising increase of China s insurance industry. Premiums collected are expected to exceed $100 billion in 2008, surpassing France and Germany (Binder et al., 4

5 2004). Table 1- World and China Insurance in 2004 Total Business Life Business Property-liability Business Penetration Density (USD) Penetration Density (USD) Penetration Density (USD) World Industrialized countries Emerging markets China Source: Swiss Reinsurance Company, Life insurance has been playing the leading role in the insurance sector since the year of 1997, when the life premiums for the first time surpassed property-liability. For the year of 2004, life insurance companies collected 77.59% of the total premiums in China s market. 3 Figure 3 describes the development of urban premium distribution between 1998 and The peak value of the overall premium income is on the move towards the right side, suggesting that its extremely fast growth for the studied period. Figure 3 also tells that the increase in overall premium income is significantly owed to life business because there obviously shows a rough superposition between the curve for life and that for the overall premiums. Compared to that in 1998, the curve for property in 2003 slightly moves to the right, expressing that the property premiums grew at a much lower speed than life insurance. Figure 3 Kernel density of premium income Kernel density Premium income Total business in 1998 Non-life income in 1998 Life business in 1998 Total business in 2003 Non-life income in 2003 Life business in 2003 Source: China Insurance Regulatory Commission, Yearbook of China s Insurance (various issues). 3 The percentage is calculated according to the data released by CIRC, 5

6 From the viewpoint of market structure, China s insurance market still belongs to be oligopolistic (Leung and Young, 2002) although the number of insurers, foreign ones in particular, has been increasing ever since. In contrast to only one provider in 1980, 32 life and 24 non-life insurance companies write policy in China in The market concentration still remains very high although has been gradually reducing (Leverty et al., 2004). In the year of 2004, the big-4 life and property-liability insurers collect nearly 90% and 85% of the gross premiums in their individual line (see Table 2). What needs to notice is that the China Life Insurance Company and the PICC still dominate more than half of the market, if measurd by the premiums collected. Table 2 - Market Concentration in China Insurance Market 4 Life insurance Property-liability insurance Number of firms Share of premiums written Number of firms Share of premiums written 1-Firm 2-Firm 3-Firm 4-Firm 1-Firm 2-Firm 3-Firm 4-Firm a Note a: The data concerning life insurance is for the year of Source: The share of life for the year of 1995 see CIRC and Samsung Life (2003: 53); the data concerning property insurance for the year of 1996 see Wu (2004: 105); calculation for life insurance ( ) depends on Chen (2004: 59); the others are computed on the data in Yearbook of China s Insurance (various issue). The involvement of foreign insurers is one of the evidence that China s market is in the midst of internalization. The international companies (37) including foreign-wholly owned and foreign joint ventures outnumbered the domestic ones (24) in 2003, according to the statistics from CIRC. However, foreign providers capture only a small part of the gross premiums (Leung and Young, 2002; Leverty et al., 2004; Shen, 2000). For instance, in 2004, just 2.27% of the total premiums are contributed to foreign insurance providers, 5 in large part due to the restrictions on them. 6 But when we turn eyes to the earliest opened cities such as Shanghai and Guangzhou, the case is greatly different. In Shanghai, the first opened city and the financial center in China, the market share of foreign life and property insurers respectively reached 8.87% and 14.08% in 2003, much higher than the national average. Also in 2003, foreign life insurers in Guangzhou, the second opened city and also a foreignoutnumbered city, captured 16.70% of the total life premiums (see Wu, 2004: 45). Another characteristic of China s insurance market is related to the product composition. Motor vehicle and third-party liability insurance has been the most important source for property premium income for the latest years, as Table 3 states. In earlier period, the business property and automobile insurance played nearly equal role in the premium income, whose premium share was 42% and 48% in year Motor vehicle insurance replaced the business 4 AIA Dongguan, Jiangmen, and Guangzhou Branch are counted as one company. 5 The calculation is based on the data released by CIRC, 6 According to China s regulation, foreign insurers are only permitted to write policy in their company-based geographic area. Non-life international insurers only can underwrite the risks of foreign-invested enterprises, while they are banned from writing the compulsory insurance such as third-party liability for motor vehicle. Foreign life providers are limited to sell individual life policies and prohibited from writing group life products or any type of health, pension, and annuities products. Most of the restrictions are in the middle of being removed owing to the fulfillment of the commitment for the accession to WTO. 6

7 property to be the dominating product for the first time in 1988, and its share increased to the summit in 1997 (Wu, 2004: ) and remains at 60% or so ever since then. The product composition proves what Lee (2002:17) says: To date, personal property and casualty insurance - including individual automobile, dwelling, and third-party liability policies - remains significantly underdeveloped. For personal insurance, in 2003, life insurance, pension policy, and healthcare insurance, separately accounts for 74.28%, 14.34% and 8.06% of the total life premiums. And the policy covering medical expense has increased at a stead pace for recent years. Lately, participating and unit-linked policy grab quite a large number of Chinese consumers, which jointly contributes over 60% to the total premiums in 2003(see Wu, 2004: ). In reality, foreign providers failure in realizing that many Chinese consumers interest in insurance or financial products is closely linked to invested and financial management, partly lead to their low market share (see Ji and Thomas, 2001). Table 3 - Product Composition of Property insurance in China Automobile and third-party Business property Cargo and transport Private property General Liability Others Total Source: The composition for the year of 1985 and 2001 see Wu (2004: 108); the rest is calculated from the data in Yearbook of China s Insurance (various issues). Regional disparity in the insurance consumption obviously appears in China, with the coastal area being the leader, followed by the central and the western region. Table 4 summaries the premium regional distribution in China. In 2003, more than 2/3 of gross premiums were collected from the coastal region, and the central and western region just 1/3. The similar situation shows even when we separate the overall business into life and propertyliability line. If analyzed the regional distribution from the perspective of annual growth rate, the coastal region is also the leading one, especially in life business. It is obvious that the coastal area was and will be the most important contributor for the development of insurance industry in China. Total business Table 4 - Premium income distribution in 2003 Premium income (million yuan) Property business Life business Total business Percentage Property business Life business annual growth rate Total business Property business Life business China Coastal region Central region Western region Source: China Insurance Regulatory Commission, Yearbook of China s Insurance (various issues). 7

8 We also can investigate the regional distribution, using provincial data. In 2003, overall premiums collected in Jiangsu and Guangdong, located along the coastal line, accounted for more than 9.5% of national premiums, respectively. Meanwhile, the market share of Tibet, Qinghai, and Ningxia, belonging to the western region, is individually less than 0.5% for the same year. Regarding life insurance, more than 10% of written premiums are contributed to Jiangsu Province, whereas the joint share of the western region just 13.1%. For property business, the market share of Guangdong Province reached 13.8% of the national premium income. Figure 4 presents the evolution and the decomposition between interior and coastal areas of the Theil index, computed on the basis of the share of a given province s premium income and population, over period. We can observe that, compared with the population s regional distribution, inter-provincial differences in terms of premium income decreases to the year of 2000 and then goes up. The decomposition of the Theil index clearly indicates that the decline in disparities that occurred over the period resulted essentially from the reduction of the gap within the region concerned. On the contrary, the surge in total disparity in 2000 is rooted in the widening disparity between coastal and interior. In other words, premium income levels have diverged between coastal and interior of China, while premium income s homogeneity has improved within each region. 0.5 Figure 4 - Evolution of the Theil Index 0.4 Theil index Year Inter-regional Theil index Intra-regional Theil index 3 DETERMINANTS OF THE DEMAND FOR INSURANCE We firstly shed light on the determinants of life insurance demand. Nearly all the theories on the demand for life insurance purchase have been developing on the basis of Yaari (1965). Bequest motives and risk aversion are analyzed as the main factors to motivate individuals to buy life insurance in the early theoretical literature. 7 Among of them, Lewis (1989), from the 7 See for instance Bernheim (1991), Campbell (1980), Fisher (1973), Fitzgerald (1987), Hakansson (1969), Karni 8

9 perspective of beneficiaries, presents a function of life insurance demand, which in reality builds the theoretical starting point for many empirical works. Mantis and Farmer (1968) examine the determinants of America s life insurance demand, measured by total sales per year over the period, which may be one of the earliest empirical studies in this field. According to Zietz (2003), twenty-six academic empirical studies examining the determining factors associated with a consumer s life insurance demand had been published. Five categories of parameters including demographic, macroeconomic, socio-psychological, institutional, and insurer-side variables are investigated to be the governing factors for the life insurance coverage. The literature specifies that disposable income, the inflation rate, financial development, social security, and some population variables such as young dependency, aged dependency, birth rate, educational level, life expectancy, are verified as the robust explanatory variables for the life insurance consumption. 8 However, the determinants for life insurance demand vary from country to country. In case of China, the determining factors are not conclusive in the published studies. Zhuo (1999) adopts three models to test the determinants of China s life insurance consumption. Using cross-sectional data for all (29 observations) except Tibet for the year of 1995, his study finds that per capita GDP, young dependency rate are the significant determinants. He then employs the national time-series data for the period of (10 observations) and just per capita GDP proves significant, which also the only significant determinants in his third model based on the cross-sectional data for 14 large cities in China. Hwang and Gao (2003) introduce four variables of income, inflation, urbanization and education to examine their influence on the life insurance consumption over the period of GDP per capita, urbanization, and educational level are found significant and positive, while the influence of inflation shows not significant. Lately in 2005, Hwang and Greenford (2005) use variables including income, education, social security, social structure, one-child policy, price of insurance, economic development to try to explain the development of life insurance from the perspective of comparison among China mainland, Hong Kong and Taiwan. Their study expresses that real GDP per capita, education level, social structure measured by agricultural population ratio of total employed affect the insurance consumption in a significant and positive way, whereas one-child policy is significantly negative. We now turn to the determinants for the property insurance consumption. Based on the expected utility paradigm, the insurance demand theory suggest that such factors as individual s income and wealth, the price of insurance, the individual s degree of risk aversion, and the probability of loss decide the individual s insurance purchase. 9 Many theories suggest that market imperfections motivate corporate to take out policy for loss protection (e.g. see Smith, 1986; Grace and Rebello, 1993; MacMinn, 1987; Skogh, 1989). In contrast to life insurance, the empirical studies on property-liability insurance consumption is less extensive (see Beenstock et al., 1988; Zietz, 2003). Many prior researches focus their interests on the corporate demand for insurance (Core, 1997; Davidson III et al., 1992; Mayers and Smith, 1990; Yamori, 1999; Zou, 2003; Zou et al., 2003). Beenstock et al. (1988) proves the influence of income on the property-liability expenditure. Browne et al. (2000), relying on the data for OECD countries, verifies that income, wealth, the percent of a country s insurance market controlled by foreign firms, and the form of legal system in the country all have and Zilcha (1985; 1986), Lewis (1989), Moffet (1979), Pissariades (1980), Yaari (1965). 8 See for instance, Beck and Webb (2003), Beenstock et al. (1986), Browne and Kim (1993), DePamphillis (1977), Diacon (1980), Hwang and Gao (2003), Hwang and Greenford (2005), Lewis (1989), Lim and Haberman (2004), Outreville (1996), Schwebler (1984), Truett and Truett (1990), Zhuo (1999). 9 See for instance, Cleenton and Zellner (1993), Mossin (1968), Schlesinger and Graf (1987), Smith and Buser (1987). 9

10 governing effect on the property-liability insurance consumption. As our interest lies in the determinants for China s non-life insurance consumption as a whole, we follow Browne et al. (2000) that the governing factors for corporate demand for insurance are the same as the individual. 4 DATA AND VARIABLES In this study, we use the Yearbook of China s Insurance (various issues), China Urban Statistical Yearbook (various issues), China Labor and Social Security Yearbook (various issues) and China Statistical Yearbook for Regional Economy (various issues) as the sources of our data. The data cover 225 cities in total, either at the provincial level or at the prefecture level. Our objective is to study the determinants of the development of insurance industry in China, as well as their regional difference. The choice of the dependant variable requires some indicators to measure the development of insurance industry. Although the number of insurance policies, or the premium volume, or the sums insured is the simplest approach to measure insurance demand, in the literature, three widely adopted indicators related to macroeconomic or socio-economic variables are (i) premium volume, (ii) insurance expenditure per capita (insurance density) and (iii) insurance penetration (insurance premiums divided by gross domestic product). Premium volume represents total insurance premiums written in a given region/country and is a major indicator of the importance of the insurance industry in the economy of that region/country, which is popularly adopted as the dependent variable in prior empirical studies. 10 Insurance density is calculated by dividing direct gross premiums by the population and represents average insurance spending per capita in a given region/country, which is often used as the dependent variable in the previous studies. 11 Insurance penetration is the ratio of direct gross premiums to GDP, indicating the relative importance of the insurance business in the given economy, which is less frequently used in the prior research. 12 This article at the same time employs the penetration together with aggregate premiums and per capita expenditure in insurance as the dependent variable. For the independent variables, we introduce the following indicators, which are supposed to determine the development of insurance industry. The independent variables common for both life and property are composed of total population, per capita GDP, wage level, private savings deposit, FDI, education attainment, telephone ownership, Herfindahl Index. In addition, such variables as investment in fixed assets and dwelling space per capita are special for non-life insurance, while social security expenditure, life expectancy, young and old dependency ratio special for life line. (i) Total population. Mantis and Farmer (1968) use population as an independent variable to explain the life insurance demand in U.S for the period of Schlag (2003) also tells that the size of population determines the operating background, that is to say, the size of market, for the life insurance industry in the long term. Besides, size of population is often an element for the potential of China s insurance market, life insurance in particular (e.g., see Lai, 2002; D arcy and Xia, 2003). We, therefore, include the total population for each city into 10 For instance, Babbel (1985), Beenstock et al. (1986), Browne and Kim (1993), DePamphillis (1977), Diacon (1980), Lim and Haberman (2004), Mantis and Farmer (1968), Schwebler (1984), Ward and Zurbruegg (2002), Zhuo (1999). 11 For example, Beck and Webb (2003), Browne and Kim (1993), Hwang and Gao (2003), Hwang and Greenford (2005), Outreville (1996), Truett and Truett (1990), Zhuo (1999). 12 See for instance Beck and Webb (2003). 10

11 our regressions and assume that its effect on the life and property insurance demand is positive. (ii) Per capita GDP. We adopt the per capita GDP to represent the level of regional economic development and expect it has positive effect on life insurance consumption, as quite a number of researches proved. From the perspective of property insurance, previous studies consistently verify GDP among the most important determining factors for the insurance consumption (Beenstock et al., 1988; Browne et al., 2000; Outreville, 1990a; 1990b). This article also hypothesizes it positive to China s property insurance consumption. (iii) Average wage of staff and workers. The income variable is previously represented by various proxy such as normalized disposable personal income - disposable personal income divided by total household net worth (Cargil and Troxel, 1979), the real disposable income (Babbel, 1985), national income - GNP minus depreciation and indirect business tax (Browne and Kim, 1993), GDP (Outveville, 1996), per capita GDP (Hwang and Gao, 2003; Lim and Haberman, 2004; Hwang and Greenford, 2005). As for property-liability insurance consumption, Beenstock et al. (1988) and Browne et al. (2000) proves the significant and positive effect of income, while the latter of which takes GNP per capita as the proxy for income. In our study, the wage level is used to measure the level of income, and per capita GDP work as an indicator for macroeconomic development for a given city. And we suppose that the wage level is positively related to both the life and the property insurance demand. (iv) Savings. Researchers have no common opinion about the relationship between savings and demand for life insurance. Rose and Mehr (1980) believe that savings will reduce the life insurance coverage because of it being a protection deductible for surviving dependents. However, Headen and Finley (1974) argues that, on the perspective of increasing household assets resulting from savings, savings may play a pushing role in life insurance demand. Some other scholars also suggest that the level of savings tells the private household s propensity to save, and therefore also the background condition for the life insurance industry (Schlag, 2003). Schwebler (1984) and Beck and Webb (2003) prove the significant and positive influence of savings on life insurance demand. We introduce both total savings deposit and per capita savings deposit, as measures of wealth, into our analysis. Considering China being a nation with high savings propensity, the positive effect on insurance consumption is therefore expected. (v) Foreign direct investment (FDI). A number of studies have investigated issues related to the determinants of FDI in various industries, but only a few have included insurance-specific content. Ma and Pope (2003) use per capita FDI to examine the premium written per capita by international insurer in host markets by supposing that the inflow of FDI favors the level of international insurer participation in a given host market, depending on the client-following suggestions by Price Waterhouse Coopers (1991) and Skipper (1998). FDI can also be used to measure the openness degree of a city. The open-door policy and the export-oriented sectors greatly contribute to the modernization and marketization of local economy, and hence would have effect on the demand for insurance, as stated in Section 1. In particular, D Arcy and Xia (2003) partly contribute the continuous growth of property-liability insurance to more foreign investment in the eastern coastal area. We, in our regressions, introduce respectively FDI amount and ratio of FDI to GDP to examine whether the foreign investment has significant influence on the demand for insurance. Taking the unequal regional distribution of FDI in China into consideration, FDI is hypothesized positive to both non-life and life consumption, especially in the coastal region. (vi) Fixed assets investment to GDP ratio. The variable has a direct impact on production 11

12 power, urban infrastructure and, in particular, it has an impact on property. We introduce this variable to measure the potential of the local economy and examine whether it influences the demand for non-life insurance. Positive result is expected in this article. (vii) Education. The individual s high education attainment determines his or her level of risk aversion (Schlag, 2003), leading to higher probability to buy life insurance as Karni and Zilcha (1985; 1986) suggests. Pratt (1964), Arrow (1971), and Szpiro (1985) show that in theory the more risk averse an individual is, the higher the amount insured. Some studies also find the positive effect of education on China s life insurance consumption in China (Hwang and Gao, 2003; Hwang and Greenford, 2005). We adopt the variable, measured by university enrollment per million population, to investigate its influence in our sample. The positive result is assumed. (viii) Per capita number of telephones. This variable can serve as a proxy of the living standard for a given region; it can also reveals the conditions of the local communication channels as well as the power of information transmission in the city in question. Communication convenience has so far never been included in the analysis of the demand for insurance. We, here, incorporate it into our regressions to represent the sales condition in the current China. The saying that insurance is sold not to buy is very popular in insurance industry. It is the very case in China because of the low awareness of insurance even among well-educated population. To contact the potential clients easily with low cost is crucial for salespersons to sell policy. Number of telephones per capita therefore can indicate the level of sales condition, to a great extent. The positive effect is therefore expected. (ix) Per capita dwelling space. Arrow (1965) suggests that the insurance demand increases with wealth when individuals are characterized by increasing relative risk aversion. In contrast, Mossin (1968) postulates conditions under which the optimal level of insurance coverage decreases with increases in wealth. The conflicting theoretical suggestions of Arrow (1971) and Mossin (1968) on the influence of wealth on insurance demand make it difficult to judge the variable s empirical results. This study employ per capita dwelling space as a measure of private wealth and suppose its effect is positive, although Browne et al. (2000) finds the negative influence of wealth on the consumption of property-liability insurance. (x) Herfindahl Index (HI). 13 The relationship between competitive situation and insurance ownership is inconclusive and has not been widely proved in empirical research. Following the National Association of Insurance Commissioners (NAIC) criteria for defining a competitive marketplace, Ma and Pope (2003) consider countries with HI measurements greater than to be noncompetitive and assigned a value of one for the dummy variable. They conclude that comparably competitive markets are more likely to include the participation of international insurers. Outreville (1996) takes a dummy variable indicating either whether the market is a monopolistic one or whether foreign companies are writing business in the market into his regressions, with data collected from 48 developing countries, concluding that monopolistic markets are significantly less developed than competitive markets. Simultaneously, he suggests that the appropriate variable to test the influence of foreign insurers involvement would be a concentration ratio instead of the dummy variable. Kwon (2002) use Herfindal Index as one of the means to measure market structure. In case of China, measured in written premiums share, the insurance market is typical oligopoly in most cities, along with the very low foreign share. The dummy variable is thereby of no use in 13 Summing the squared market shares of each insurer in the market and multiplying the result by determine this index. In a monopolistic market, the HI would be , and the index would become smaller as the market becomes more competitive - suggesting either more companies competing in the market and/or market share becoming more evenly distributed among the competitors (Ma and Pope, 2003). 12

13 analyzing the effect of market structure within China. For these reasons, following Bajtelsmit and Bouzoutia (1998) and Kwon (2002), we calculate for each city the HI of non-life insurance and that of life insurance, and, use directly the value of HI to measure the degree of market concentration, expecting a negative result. Moreover, as the observation in our study is city but not country, the value of HI is much higher than other studies because of lower number of insurers in most of cities in China.. (xi) Social security expenditure. Life insurance and government-provided social security systems interact each other. Some early theoretical research suggests that the increase of social security will result in the decrease of private life insurance (see Yaari, 1965; Lewis, 1989; Berheim, 1991). However, Fitzgerald (1987) says that social security has little net effect on life insurance demand empirically. 14 The contrasting suggestions encourage us to incorporate the variable, indicated by social security expenditure ratio to GDP, into our regressions to examine its effect in the case of China. We still expect it is negatively associated with private life insurance ownership, even though the previous study on China does not prove it significant (Zhuo, 1999; Hwang and Greenford, 2005). (xii) Life expectancy. Societies with high expectancy tend to have lower demand for pure life insurance policies on the one hand; on the other hand, life expectancy may have positive relationship with products bearing high savings element. Beenstock et al. (1986) and Outreville (1996) in their empirical analysis corroborate the positive influence of life expectancy. However, this variable is never examined in case of China. We expect the positive effect also exists in our regressions. (xiii) Dependency ratio of children. The influence of young dependency on life insurance consumption is inconclusive. Some research Schlag (2003) states that lower proportion of young people in the working population tends to curtail demand for pure life insurance policies via a reduction in the present value of consumption by the beneficiaries. On the other hand, the high ratio of the young to the working population makes it reasonable a negative effect on the demand for life policy with high savings element (see for instance, Beck and Webb, 2003; Schlag, 2003). We, following Zhuo (1999), Beenstock et al. (1986) and Browne and Kim (1993), expect the variable positive in our model. (xiv) Dependency ratio of aged. The higher ratio of aged to working population can increase the demand for insurance with savings components and annuities, while can decrease the demand for mortality risk coverage. Beck and Webb (2003) proves this variable significantly related to the life insurance demand. Taking the reduced pension benefits from the Social Security into consideration, we still expect the aged dependency ratio has positive influence on China s life insurance consumption. It s obvious that the effect of feedback exists between the growth of premium income and its explanatory variables. For example, the increase of income level would lead to the rise of insurance consumption; but on the other hand, the development of insurance industry may exert effect on the development of other sectors. It s difficult to identify this causality in the regressions. In order to avoid the problem of endogenity, we follow the method of lagged variables. This method supposes that premium income responds to regional development with a time difference. In other words, any improvement in socio-economic conditions at the present time will encourage the growth of the premium income in the future. For the above 14 Fitzgerald (1987) states that social security survivor benefits are found to decrease the demand for life insurance on an earner, while social security benefits conditional on the earner s survival increase the demand. And empirically the two effects largely offset one another and so that social security has little net effect on life insurance demand. 13

14 reasons, in our estimation, the dependant variables take their values at the year of 2003, and all the explanatory variables take their values at More precisely, we assume that, during a period of five years, the growth of premium income is determined by certain socioeconomic characteristics at the beginning of that period. All premium data including total written premiums, per capita life insurance expenditure, life premium ratio to GDP, are drawn from Yearbook of China s Insurance 2004, compiled by CIRC. The independent variables such as population, GDP, wage, savings, FDI, fixed assets investment, university student enrollment, number of telephones, per capita dwelling space, come from the China Urban Statistical Yearbook. The above variables are based on city data. The four other variables are based on provincial data, including social insurance expenditure from China Labor and Social Security Yearbook, and life expectancy, dependency ratio of children and dependency ratio of aged from China Statistical Yearbook for Regional Economy. Table 5 reports the summary statistics for the main variables concerned. On average, Chinese consumers expend significantly more money in life than property insurance. The life sector, bearing more providers, is comparatively more competitive, although the market as a whole belongs to oligopoly with Herfindahl Index for both lines being more than All the cities Table 5 - Descriptive statistics Coastal Central Western province 2.0 millions or above millions City size millions Below 0.5 millions Premium income (million yuan) Total , Property insurance Life insurance , Insurance density (yuan) Total Property insurance Life insurance Insurance penetration Total Property insurance Life insurance Herfindahl index Property insurance ,708 7,991 8,553 Life insurance ,115 7,662 8,565 Number of insurance companies Property insurance Life insurance Number of cities Note: The data for premium income, insurance density, insurance penetration, and the number of insurers, are for the year of The Herfindahl index is for the year of The regional difference also describes in Table 5. The eastern coastal cities are absolutely the leader in the China s insurance development whether measured by premium income, per 15 Due to the data limitation, the HI is calculated from the data of Yearbook of China s Insurance

15 capita expenditure, or insurance penetration. The western and the central cities rank the second in property and life insurance respectively. In addition, there are much more providers writing policies in the coastal line than the rest of the country, indicating that competition is more severe in coastal areas than in inland areas (see Sun, 2003). If we divide the cities according to the size of population, we will find that the insurance consumption gets the highest in cities where there is population of 2.0 million or above, and then decreases gradually with the reducing size of city. For instance, citizens living in cites of more than 2 million population spend 593 yuans in life insurance policy, over 2 times of that for cities with population of million. Meanwhile, insurance companies prefer larger cities to sell policy. For example, there are, on average, more than six insurers writing non-life policy in the largest cities, compared with less than three providers in the smallest cities, as Table 5 shows. 5 RESULTS AND DISCUSSIONS Insurance is, both in theory and in practice, generally classified into life and propertyliability. The estimations are correspondingly run for life and for property respectively. Table 6 and Table 7 present the results of the regressions. The dependant variables are premium income, insurance density and insurance penetration. For each measure, the regressions are run separately for the whole sample and for the three sub-samples of the coastal, the central and the western, which help us examine the effect of the explanatory variables on the insurance industry in different areas. For life insurance, we find that per capita GDP is the only variable which has significant influence on all three dependent variables. Meanwhile, the governing factors for different region also prove different. The results are discussed in details hereinafter. As reported in Table 6, the effect of population size on written premiums is positive and statistically significant, in particular for central and western. This result consists with the expectations that the population count determines the operating background for the life insurance industry in the long term (Schlag, 2003) and the empirical results by Mantis and Farmer (1968). That indicates, with the expanding size of population, the capacity of life insurance market in China is likely to increase in the future. The effect of total population on per capita life insurance expenditure proves positive and significant for the whole sample and the sub-sample for coastal cites. This finding supports insurer s preference for populous metropolitan centers with established service networks (see Wu and Strange, 2000). Actually large cities in China have more insurers than small ones. However, the population size doesn t exert significant influence on insurance penetration. The coefficient of per capita GDP is by and large positive and significant in the regressions with written premium and insurance density as dependant variable. This is also in harmony with the results from previous studies on life insurance consumption in China (Hwang and Gao, 2003; Hwang and Greenford, 2005; Zhuo, 1999). It proves the conclusion that life insurance sector will significantly benefit from the dramatic increase of the national economy. In particular, the effect of per capita GDP is significant for western province, suggesting that the rise of per capita GDP is actually a fundamental factor for the development of life insurance in less developed regions. In contrast, the effect of per capita GDP is negative and statistically significant for insurance penetration in the whole sample and the sub-sample for central. A possible reason may lie in that insurance penetration is an indicator influenced by both the insurance sector and the gross domestic products for a given region. 15

16 Table 6 Determinants of the development of life insurance Dependant variable Logarithm of total premium income Logarithm of insurance density Insurance penetration All the cities Coastal Central Western province All the cities Coastal Central Western province All the cities Coastal Central Western province Logarithm of total population 0.342*** *** 0.566*** 0.160*** 0.182** (3.53) (-0.03) (2.63) (2.83) (3.16) (2.54) (0.45) (-1.21) (0.62) (-0.18) (-1.32) (-1.18) Logarithm of per capita GDP 0.257** * 0.301*** * ** *** (2.46) (0.46) (0.78) (1.90) (3.05) (1.03) (1.46) (1.76) (-2.42) (-1.45) (-3.94) (1.49) Logarithm of average wage *** 1.184*** * (1.13) (0.90) (0.71) (-0.11) (3.61) (3.78) (0.82) (1.53) (0.58) (1.90) (-0.02) (-1.08) Logarithm of total savings deposit 0.301*** 0.604** 0.217** (3.55) (2.62) (2.18) (0.88) Logarithm of per capita savings deposit 0.234** 0.425*** * (2.47) (2.64) (-0.15) (-1.44) (1.44) (0.87) (0.55) (-1.70) Logarithm of FDI (-0.36) (0.53) (1.41) (-0.72) FDI to GDP ratio ** *** (-0.32) (2.17) (-0.61) (-0.39) (-2.72) (-0.52) (-1.46) (-1.03) University student enrollment per million population 0.188*** 0.224*** ** 0.234*** (4.58) (3.45) (1.63) (1.66) (0.03) (0.53) (1.13) (0.40) (2.28) (3.30) (1.55) (0.69) Per capita number of telephones 0.910*** ** *** * 1.948** 4.817*** (2.71) (0.34) (2.10) (0.27) (2.86) (-1.69) (2.56) (3.84) (1.03) (-1.31) (0.73) (1.62) Herfindahl index of life insurance (/10000) * *** ** ** * *** ** (-0.42) (0.75) (-0.23) (-1.05) (-1.53) (-1.85) (-3.33) (-2.17) (-2.49) (-1.76) (-4.00) (-2.43) Expenditure for social insurance and welfare to GDP ratio ** * ** ** *** 0.157*** 0.200** * 0.342* (-2.39) (-1.86) (-2.04) (0.92) (0.95) (-2.19) (-0.76) (3.22) (3.29) (2.40) (-1.89) (2.00) Life expectancy *** * 0.100** 0.077* *** 0.146** (-0.02) (-0.82) (3.04) (0.03) (1.05) (-0.26) (1.87) (2.12) (1.94) (-0.16) (4.19) (2.04) Dependency ratio of children; *** *** ** *** 0.055*** * 0.111*** (-3.40) (-3.45) (0.96) (-0.77) (-2.09) (-4.55) (4.31) (-1.16) (-0.16) (-1.91) (5.15) (-0.73) Dependency ratio of aged ** *** *** (1.34) (1.44) (-0.71) (0.92) (-2.46) (0.80) (-5.93) (0.91) (-1.29) (1.08) (-5.14) (0.08) Constant * *** ** (0.83) (0.76) (-1.83) (0.21) (-2.72) (-0.90) (-0.61) (-1.50) (-0.87) (-0.20) (-2.05) (0.14) 2 R Number of observations The t-students are in brackets. *** significance at 1%; ** significance at 5%; * significance at 10%. 16

17 Table 7 Determinants of the development of property-liability insurance Dependant variable Logarithm of total premium income Logarithm of insurance density Insurance penetration All the cities Coastal Central Western province All the cities Coastal Central Western province All the cities Coastal Central Western province Logarithm of total population 0.564*** 0.566*** 0.315*** 0.574*** 0.231*** 0.323*** (7.43) (4.24) (2.69) (3.35) (4.40) (4.49) (0.95) (-0.16) (0.73) (1.35) (-0.84) (0.02) Logarithm of per capita GDP 0.400*** 0.416** 0.287** 0.430** 0.392*** 0.429** 0.454*** *** (4.71) (2.58) (2.61) (2.09) (3.69) (2.31) (2.93) (1.68) (-1.45) (-2.71) (-0.29) (0.68) Logarithm of per capita wage 0.499*** 0.890*** *** 1.082*** ** 0.277*** 0.512*** (3.49) (2.84) (0.87) (1.19) (4.33) (3.24) (0.48) (2.36) (3.61) (3.67) (0.48) (0.26) Logarithm of total savings deposit 0.147** ** (2.20) (1.51) (2.31) (1.66) Logarithm of per capita savings deposit 0.267*** ** 0.176*** (2.86) (1.58) (0.64) (-0.40) (2.06) (2.88) (1.00) (-0.27) Logarithm of FDI 0.037** * (2.06) (-0.36) (1.86) (0.03) FDI to GDP ratio (0.09) (1.09) (0.54) (-0.16) (-1.23) (-0.07) (1.42) (-0.88) Fixed investment to GDP ratio 0.478*** 0.638** 0.469** ** *** 0.174* * (2.82) (2.30) (2.19) (0.11) (2.08) (0.16) (0.49) (4.07) (1.89) (0.48) (-0.05) (1.92) University student enrollment per million population 0.084** 0.102** 0.149*** (2.45) (2.05) (2.67) (0.96) (-1.57) (-0.87) (0.43) (-0.08) (-0.55) (-0.61) (0.61) (-0.32) Per capita number of telephones 1.142*** 1.211*** * ** (4.00) (3.37) (0.77) (0.00) (1.69) (0.43) (1.25) (2.29) (-0.25) (0.16) (-0.26) (1.39) Per capita dwelling space ** ** (1.27) (1.19) (0.60) (-1.27) (1.21) (2.09) (-2.19) (0.19) (-0.23) (0.55) (-1.63) (-0.16) Herfindahl index of non-life insurance (/10000) * ** * (-0.47) (1.70) (-0.81) (-0.88) (-0.91) (0.09) (-0.94) (-1.17) (-2.53) (-0.47) (-1.89) (-1.22) Constant *** *** *** *** ** *** *** (-3.04) (-3.22) (0.46) (-1.45) (-6.96) (-5.13) (-0.86) (-2.37) (-2.98) (-3.47) (0.33) (-0.13) 2 R Number of observations The t-students are in brackets. *** significance at 1%; ** significance at5%; * significance at10%. 17

18 As our results show, the relationship between insurance premium and income level, measured by average wage per employee, is very interesting. The coefficient is significantly positive in the regression in which the insurance density acts as the dependent variable, while not significant for total premiums. In addition, the independent variable shows its significant and positive effect on the insurance density and penetration in the sub-sample for coastal cities. This result proves the matter of fact that the increase of income will not serve as a pushing factor for insurance purchase unless the income reaches a certain level, which is also verified by Beenstock et al. (1988) in property-liability. That is to say, the income is not high enough in the central and western cities that it cannot encourage consumers to buy life coverage. On the other hand, the disposable income instead of wage level shall be a better indicator to measure income. Our results show that total savings deposit and per capita savings are significantly and positively associated with total premium and per capita consumption in life insurance, respectively. Its effect is particularly significant for the eastern coastal region that achieves the highest level of savings rate. 16 This gives support to Headen and Finley (1974) and is also consistent with the conclusions from prior studies (Beck and Webb, 2003; Eisenharuer and Halek, 1999; Schwebler, 1984). Compared to other countries, the savings rate in China is extremely high. By the end of 2004, the total savings account amounted to billon yuan, equaling to 88.9% of GDP (National Bureau of Statistics of China, 2005). We therefore can forecast that the big volume of savings will provide great potential for the growth of life insurance. We use two indicators to represent the level of foreign direct investment in our regressions, one is logarithm of FDI, and the other is FDI to GDP ratio. FDI has, in statistics, not significant influence on life insurance development, which is in contrast with our expectation. A possible explanation is that the sector distribution of FDI in China. As statistics reports, most of FDI has been invested in manufacturing industries in China. In 2003, for instance, 69% of FDI, USD million, flowed into the manufacturing industry (Department of Trade and External Economic Relations Statistics National Bureau of Statistics, 2005), whose major risk lies in property and liability but not life. Also in the year of 2003, just 0.43% of FDI went to finance and insurance, compared to 22.6% for the Association of South-East Asian Nations (ASEAN) in 2001 (ASEAN, 2002). We can come to a conclusion that, on the supply side, FDI has no direct effect on the development of life insurance. However, we have to notice that FDI ratio to GDP shows positive and significant effect on per capita life insurance consumption in the east of China. This can be explained by FDI s regional distribution in China. According to the figure released by China National Statistics Bureau, in 2003, the eastern regions along the coastal line in China captured the FDI USD million, equaling more than 85.7% of the gross amount (National Bureau of Statistics of China, 2005). Ten per cent of China s labor force are employed by foreign-invested enterprises (see Ji and Thomas, 2001), and their wage is usually much higher than their domestic counterparts. On the other hand, the replacement ratio of their pension benefits from the social insurance system remains the lowest among enterprises, being 55.2% in 2003 (National Bureau of Statistics of China, 2003). The contrast between far higher income and the lower social security benefit encourage them to buy more life insurance to increase their expected utility after retirement. University student enrollment per million population expresses to be positive and significant in the two first regressions (the whole sample and the sub-samples of the coastal 16 In 1998, per capita savings deposit reached yuans for the coastal, compared to 8007 yuans for the central and 7095 yuans for the western. 18

19 ), in which the total written premiums and insurance penetration are dependent variables respectively. This not only proves the theory (see for instance, Karni and Zilcha, 1985; 1986) and our hypothesis that education attainment is positively related to life insurance development, but also supports previous empirical findings (Beck and Webb, 2003; Browne and Kim, 1993; Hwang and Gao, 2003; Hwang and Greenford, 2005; Truett and Truett, 1990). The result indicates that, in China, individuals with higher educational attainment are more likely to buy life insurance. Number of telephones owned by per capita affects the premiums in a significant and positive way for the whole sample and the sub-sample for the central. The coefficient is positive and statistically significant for the expenditure per capita in the whole sample and all three sub-samples. The result verifies our expectation that the improvement in communication will encourage the sales of insurance policy in China. That is to say, insurance is easy to sell in the regions with more telephones the sales condition is also a robust explanatory variable for the development of insurance. The result of the effect of Herfindahl index is very interesting. HI has no significant relationship with the premiums volume, even in the coastal, where most of the insurers writing policies in China have established entities. As Section 2 states that most of premiums are concentrated on the top 3 firms, the market influence of new entrants is comparatively low, owing to their low market share. Therefore, we believe that the change in market structure resulting from the increasing number of entrants is not enough to push the growth of the total amount of written premiums significantly. Contrary to the above results, the association of HI with per capita consumption and ratio of premiums to GDP is significant and negative especially in the central and western part of China, being consistent with our hypothesis that competitive market is a pushing factor for the insurance ownership. The earlier theory suggests that the relationship between social security benefits and private insurance demand is substitutive (see Berheim, 1991; Lewis, 1989; Yaari, 1965). Our results verify that social security expenditure has significant and negative effect on private life insurance premium income. This is in harmony with the previous studies (Beck and Webb, 2003; Beenstock et al., 1986; Browne and Kim, 1993; DePamphilis, 1977). The reform of the social insurance system over recent years gives supports to our results. We here take pension or old age insurance as an example. The replacement ratio of pension benefits provided by the currently implemented system is about 60% of their working wages, 17 compared to the previous percentage of 80% or so (Leung, 2003; Li, 2000; Saunders and Shang, 2001; Song, 2001). 18 The reduced replacement ratio requires employees to seek financial support from other insurance schemes, which is actually regarded as one pillar to supplement to the social security system. The other type of social insurance system such as health care is nearly the same The old age insurance system has been implemented since the year of The new system replaced the previous pay-as-you-go system with three-pillar system. Pillar 1: a mandatory, defined-benefit social pension from social account funded by payroll tax. Pillar 2: a mandatory, defined-contribution, individual pension with individual accounts funded by contributions from employee and employer. Pillar 3: a voluntary supplementary pension (see State Council, 1997). 18 The old social insurance system of China, only covering the employees in state-owned enterprises and government employees in urban area, is a defined-benefit, pay-as-you-go type. The system provides benefits for retirement, disability, health care, unemployment, housing, and all other risks employees may face (see Leung, 2003; Li, 2000). 19 A new health social insurance system called three-tier system has been in force since 1999, which combines social pooling with individual accounts. The system covers all sectors and is pooled above the county level. Tier 19

20 Social security expenditure ratio to GDP does not have significant influence on the per capita consumption in the whole sample, consistent with the suggestion of Fitzgerald (1987). However, the independent variable is significantly and positively related to the insurance density in the west of China. The local economy in the western part of China is still dominated by state-owned enterprises, whose employees are well covered by the social insurance system. What s more, the employees in such enterprises as oil, gas, or steel companies often are paid much higher than the other sectors. That is to say, individuals with higher benefits are also ones with higher income, who are the main purchasers of insurance policy in the west of China. The influence of life expectancy appears different for different dependent variables. As Table 4 shows, life expectancy only affect significantly and positively the premium volume in the central cities in China. For cities in both the central and the western part of China, life expectancy s influence on per capita life insurance expenditure is positive and significant. In the regressions with premiums ratio to GDP as the dependent variable, life expectancy appears positive and significant in all samples. Our results are in consistent with the theoretical suggestions and the previous empirical findings (Beck and Webb, 2003; Beenstock et al., 1986; Outreville, 1996), indicating that life expectancy is actually a determining factor for life insurance demand in China. Consistent with the positive effect of ratio of young dependents in previous studies, 20 young dependency ratio is also related to life insurance expenditure per capita and premiums ratio to GDP in the central area of China, proving the bequest motives in theory. In contrast, the variable appears a significantly negative determinant to the life premium volume for the whole sample and eastern sub-sample, which supports the Schlag s expectation that a high proportion of young people possibly have a negative effect on the demand for insurance policies with a high savings element (Schlag, 2003). The results may come from the two possible reasons. First, a large number of families, especially those in the coastal area, spend a large part of household income into children s education and daily consumption, reducing the purchase power for insurance, to some degree. Second, as reported in Section 3, due to the scarcity of data by city, we use the dependency ratio by province, which may lead to some deviation. The aged dependency ratio to working population affects per capita consumption in a negative and significant way while its effect in east of China is positive but not significant. The significant and negative effect is different from the positive and significant result of Beck and Webb (2003). We owe the contradicting results to the following reasons. Normally with the increase of aged population, more will buy insurance to protect their economic requirement for retirement. However, the currently aged (who are retirees at present) often can be given relatively higher pension from the social security system, reducing the necessity to purchase private insurance. From the perspective of private insurers, the aged, the high aged in particular, generally do not meet the underwriting requirements such as age and health, and therefore cannot get coverage even if they would like to. Even so, we still believe that, with the rising portion of aged population, 21 the reducing family size, and the furthering reform of 1: individual health account funded by employee whole contribution and a small portion of employer s contribution. Tier 2: social health account funded by the rest of employer s contribution. Medical expenses up to 4 times the local average wage will be covered by the social account. Expenses above the ceiling will have to be financed by commercial insurance, which is the third tier (see State Council, 1998). 20 See Beenstock et al. (1986), Beck and Webb (2003), Browne and Kim (1993); Diacon (1980), Truett and Truett (1986), Zhuo (1999). 21 The proportion of the aged population, 65 years old or over, will reach 9% by 2010 and further to 21% by 2050 (Zhang, 2000). 20