Law, Politics and Life Insurance Consumption in Asia

The Geneva Papers on Risk and Insurance Vol. 27 No. 3 (July 2002) 395 412 Law, Politics and Life Insurance Consumption in Asia by Damian Ward and Ralf Zurbruegg This paper investigates the determinants of consumption for one of the fastest growing financial products in Asia. We find evidence that increased provision of civil rights and political stability leads to an increase in life insurance provision. However, by utilizing various estimation procedures, a number of differences between the more developed insurance markets and those in Asia are illustrated. In particular, the estimated income effect is found to be far higher in Asia than in other countries. However, the size of this difference is reduced once political and legal factors are controlled for, suggesting that future insurance market growth in Asia may not exceed that in the rest of the world. 1. Introduction Asia is fast becoming the world s leading insurance sector. According to Sigma (1999), the consumption of life insurance in Asia, on a per capita basis, is higher than the global average, with Asians spending three times as much on life insurance as on non-life insurance. As a consequence, during the last decade the consumption of life insurance within Asia has grown at more than 10 per cent per annum and currently accounts for 40.1 per cent of total global life insurance premiums. However, despite this growth in the Asian insurance market, there has been little substantial research into the sources behind the purchase of life insurance and, importantly, whether the motives for purchasing insurance differ from that of more developed life insurance markets. As a starting point, Sigma (1999) and Enz (2000) point to the S curve relationship between economic development and insurance market development. Specifically, consumption of life insurance is expected to accelerate as a developing economy grows, but then slows as economic development becomes comparable to that in the developed world. As a consequence, the income elasticity of demand for life insurance should be greater in the emerging economies of Asia than in the economies of the developed world. This argument is supported by Truett and Truett (1990), but only for the specific comparison between the U.S. and Mexico. However, it is not only the income effect that is likely to differ. Asian growth, according to Sigma (1999, 2000), stems from a set of regional specific characteristics. Savings rates in Asia are higher than in the developed world. However, capital markets which provide alternative savings vehicles, are less developed. In addition, the Asian population is extremely large, but the provision of pensions and other social welfare provisions by the state are relatively low. Taken together, all of these factors are likely to promote the consumption of life insurance in Asia. Unfortunately, existing research on aggregate level growth in the consumption of life Damian Ward, Lecturer in Economics, Bradford University School of Management, Emm Lane, Bradford, West Yorkshire, United Kingdom BD9 4JL. Ralf Zurbruegg, Associate Professor of Finance, School of Commerce, University of Adelaide. The authors wish to thank Anatoly Kiriviesky and the anonymous referees for their helpful comments and advice. Published by Blackwell Publishers, 108 Cowley Road, Oxford OX4 1JF, UK.

396 WARD AND ZURBRUEGG insurance has primarily focused upon the economic and social factors driving global insurance purchases, with little focus on Asia. Principal work includes that of Browne and Kim (1993) and Outreville (1996), whose findings indicate that the level of life insurance consumption within an economy can be explained by income levels, the price of insurance, inflation, the dependency ratio, and financial development. Also, a significant omission from previous studies and debates is the impact of legal and political factors, particularly in the less developed markets, upon the consumption of life insurance. This contrasts with growing evidence supporting the importance of legal and political factors in economic and financial growth, where work by Laporta, Lopez-de-Silanes, Shleifer and Vishny (1997, 1998, 2000) has highlighted the positive role national legal origin can have on creditor and shareholder rights, and the provision of external finance. Moreover, their research has been extended to show how a clearly defined and enforced legal system aids economic growth by facilitating better financial intermediation (see Levine, 1998, 1999; Levine, Loayza and Beck, 2000). Unfortunately, the applicability of these arguments to the insurance sector, is as yet, not well understood. However, Cummins and Danzon (1997) have shown that since insurance companies can default on claims payable, then the offering of insurance is analogous to issuing risky corporate debt. Therefore, given that insurance is conceptually a credit-based product, there is ample reason to suspect that the legal environment will impact upon the growth of the industry. However, it should also be acknowledged that the legal structure is also entwined with the political environment. Knack and Keefer(2000) argue that economic polarization within an economy leads to a greater probability of political intervention and redistribution of property rights within an economy. Hence, the government s willingness and ability to alter the legal basis over time conditions the maintenance and quality of laws and contractual obligations. This leads to the economic and legal stability required for the development of financial intermediation. To address the above issues, this paper analyses annual life insurance premium consumption in a total of 37 countries for the period between 1987 to 1998. The full set of countries are listed in the appendix. Premium data was kindly provided by Swiss Re. This panel dataset will be the largest that has been currently examined. In addition to following the previous methodologies, this paper also utilizes the General Method of Moments (GMM) dynamic system estimator developed by Arellano and Bond (1991). The estimator is considered preferable when persistence exists between sequential observations and when the independent variables are potentially endogenous. In summary, this paper as an aid to policymakers and the rapidly expanding international insurance companies that are beginning to enter the region, provides an evaluation and comparison of the determinants of life insurance consumption in Asia and the developed world. In addition to the contemporary need for such a study, this paper will also incorporate the now widely accepted role that legal and political variables have upon the life insurance sector. To accomplish this, the next section provides a description of the determinants for life insurance consumption alongside the conditioning set and additional legal and political variables used for this study. Section 3 discusses the methodology used to obtain the results tabulated in section 4. A conclusion is then presented in the final section. 2. The determinants of life insurance consumption A large amount of modern theoretical work on the demand for life insurance can be traced back to Yaari (1965) who introduced the concept of uncertainty of life into a framework

LAW, POLITICS AND LIFE INSURANCE CONSUMPTION IN ASIA 397 explaining an individual s lifetime consumption allocation process. Other work has extended Yarri s theory by incorporating additional determinants. This includes Hakansson (1969) who discussed the impact wealth, an income stream, interest rates and the price of insurance has on the demand function. Karni and Zilcha (1986) also incorporated a measure of risk aversion into the model. Alternative frameworks also exist such as Lewis (1989) model, which treated the demand function slightly differently by defining the goal of a policyholder to maximize the utility of any beneficiaries, rather than maximizing personal consumption utility. From the above theoretical research, empirical tests for international life insurance demand have attempted to measure the significance of these factors. Moreover, many have also tried to account for cross-national differences in risk perception and consumer utility maximization. Examples would include Ram and Schultz (1979), Szpiro and Outreville (1988), Viscusi and Evans (1990), Browne and Kim (1993) and Outreville (1996) who have examined how consumer utility functions can be dependent upon life expectancy, human capital endowment, health, religion and differing lifecycle stages for cross-country populations. As it is the intention of this paper to examine the impact legal and political factors can have on life insurance purchases, the basic set of economic and social variables used in these previous studies are also incorporated into the possible explanatory factors that affect life insurance density, LIFEDEN, 1 in countries. Following Yuengert (1993), it is acknowledged that total premiums are not a pure measure of output as they represent total sales which is equal to price times output. Cummins, Tennyson and Weiss (1999) in a cost study preferred to measure output by the value adding activities of risk transfer and financial intermediation. Such measures would be useful here but are unfortunately unavailable in macro-level datasets. However, the value added approach does not exclude the use of total premiums as the amounts paid by policyholders should correlate with the value added by the insurance company. For expository purposes, each of the factors contained within the final estimation model are explained in depth below. 2.1 Legal and political factors Recent literature by Laporta et al. (1997, 1998, 2000) has highlighted the supportive aspects of the legal environment for finance. A central recognition is that investors are at risk of opportunistic behaviour unless the legal system offers protection. Not surprisingly, recent empirical evidence (see Levine, 1998, 1999) shows that where legal environments provide good investor protection then economic growth and financial intermediation will tend to be higher. Moreover, it can be further argued that there are even more reasons to believe the legal environment may be more important for the life insurance market where a client s financial relationship with life companies tends to be long term. With life policies offering investment opportunities, the variation in investment returns over prolonged time periods leads to an element of risk being associated with the policies final value. Furthermore, when this risk is coupled with the complexity of life insurance products, policyholders can suffer from informational asymmetry. This combination of problems for policyholders can provide managers with the ability to pursue opportunistic behaviour. This can invariably realize itself 1 Specifically, our dependent variable is the logarithm of the real value of life insurance premiums written in any given year divided by the total population.

398 WARD AND ZURBRUEGG via increased expense loadings, reduced annuity rates or even higher dividend payments to shareholders. All of which result in lower financial returns to policyholders. Traditionally, the mutual mode of corporate governance, originally discussed by Mayers and Smith (1981), has been seen as a way of controlling managerial opportunism and the expropriation of policyholders wealth. However, more recently Baker and Thompson (2000) have noted that the governance of managerial opportunism in life insurance companies extends beyond modes of corporate ownership and includes the legal and regulatory environment of the insurance industry. 2 Specifically, life insurance is often subject to investor protection regulation, where governments provide industry specific laws that seek to protect investors from opportunistic behaviour by insurance companies. This provision of protection should smooth the progress of efficient long-term contracting in the life insurance industry and accordingly aid the development of the industry while encouraging the purchase of life products. But importantly, given the existence of industry specific regulation, it is almost selfevident that the legal environment as it relates to the consumption of life insurance is tremendously important to policyholders. Therefore, in order to examine the legal environment we use two variables to capture a host of legal factors that may affect a consumer s choice to purchase life insurance products. The primary tool is CIVRIGHTS, with an alternative variable called ROL. CIVRIGHTS is an index formed by the company Freedom House and incorporates human rights, the rule of law, personal autonomy and economic rights. As a broad measure, it seeks to capture the functioning of the legal system, the enforcement of property rights and the ability to contract for commercial services, as they relate to individuals. The lower the score, the better the provision of civil rights within the economy. We therefore expect that CIVRIGHTS will be negatively related to the consumption of life insurance. As it is arguable that the results obtained are a consequence of the legal variable chosen, a second variable, ROL is also analysed. It is derived from the International Country Risk Guide Dataset (ICRG) utilized by La Porta et al. (1998), among others, as a basic measure of the law and order tradition (rule of law) within a country. As it is more specific, we use ROL as a benchmark for the preferred measure of CIVRIGHTS which covers a broader range of civil liberties. With improvements to the rule of law in a given country we would expect the protection and enforcement of property rights to facilitate the transaction of insurance. Therefore, it is hypothesized that there is a positive relationship between the consumption of life insurance and ROL. Unfortunately, even though the written law may be supportive of the insurance market, the protective benefit of the law for policyholders needs to be recognized within the constraints of the institutions and the political system that provides and enforces such rules. For example, Pistor, Raiser and Gelfer (2000) show that in transition economies it is the effectiveness of legal systems in enforcing the law, rather than the actual law on the books, 2 Indeed, as an example, two key features of the current U.K. life insurance market have been the use of the courts in settling disputes between managers and policyholders at the Equitable and the AXA insurance companies. In the high inflation, high interest rate environment of the 1970s, Equitable offered guaranteed annuity rates which it never envisaged having to pay. In the recent low inflation, low interest environment, the company faced crippling liabilities but was forced by the courts to honour its contracts. In the case of AXA, following unprecedented investment returns in the 1950s and 1960s its life fund contained unallocated reserves, or so called orphan assets. Again, the courts were used to allocate these between policyholders and shareholders. Such examples show clearly how uncertainty in investment returns over long time periods can generate scope for opportunism and a reliance on the courts is inevitable in many cases.

LAW, POLITICS AND LIFE INSURANCE CONSUMPTION IN ASIA 399 which has a greater impact on the level of financial intermediation. Furthermore, it is important to recognize the dynamic variability that can exist in the enforcement and provision of legal rights. Knack and Keefer (2000) argue that increasing polarization or inequality in a society leads to a higher probability that the government will act to redistribute wealth. In doing so they may increase taxation, withdraw property rights, or pursue changes to the regulatory environment for business. In all cases such policies have the ability to reduce asset values. Moreover, in the specific case of the East Asian financial crisis, Bongini, Claessens and Ferri (2000) argue that in providing support to distressed companies on political rather than economic grounds, governments extended the resolution process and hence the financial crisis. However, even on a general basis, when faced with the potential for legal changes, both firms and individuals will be less willing to develop business relationships. This will be especially true for long-term relationships. Therefore, in order to understand the role of the legal system in promoting life insurance consumption, it is important to recognize the stability of such systems over time. In the developed world where the degree of polarization is less and the political environment is more stable, we can also expect the legal system to be stable. In contrast, within the emerging economies of Asia it is more likely that polarization is greater, political stability is reduced and changes in the legal environment more likely. In order to capture the political environment and potential for legal instability we consider the measure CHECKS, and an alternative CO, both of which are described in Beck, Clarke, Groff, Keefer and Walsh (2000). CHECKS 3 is a measure of checks and balances within a country s political system that broadly measures the number of individuals with a veto within the political apparatus of a country. The CHECKS index increases with the number of checks in the political system. Such checks should promote the effective running of the political system and lead to improvements in political actions and enforcement. For this reason we also expect CHECKS to be positively related to the consumption of life insurance. As an alternative index we also examine CO, a measure of political cohesion originally devised by Roubini and Sachs (1989). This variable captures the extent to which government control is vested in one or more political parties. Control vested in one party is associated with greater political cohesion and is expected to be positively related to greater stability and enforcement across political and legal institutions. As such, CO is expected to be positively related to the consumption of life insurance. Finally, as an alternative to utilizing political checks and balances a religious dummy, RELIGION, is included as an indicator of whether the population is predominately Muslim. In some Muslim-dominated societies there are possible legal or political conditions that may inhibit growth of the insurance sector. In fact, some Islamic laws do not encourage certain forms of insurance, which facilitate speculation of future events. Takaful, mutual insurance, is an exception but not widely common. 2.2 Economic factors Probably the most influential determinant for purchasing life insurance is income. Work dating back to Fortune (1973) has shown a direct positive link between income levels and the purchase of life insurance. In line with previous studies, income is measured as real GDP per 3 The measure applied in this paper also takes into account the policy orientation of the parties.

400 WARD AND ZURBRUEGG capita, GDPCAP. However, the significant issue for this study is the extent to which the income effect is homogeneous across the emerging and developed economies, particularly those in Asia. The S curve relationship highlighted by Sigma (1999) and Enz (2000) illustrates how the consumption of life insurance rapidly accelerates as an economy begins to develop. Their evidence seems to suggest this is particularly true for populations that have a GDP per capita of between U.S.$1,000 and U.S.$10,000. After this point the insurance industry undergoes diminishing returns to economic growth. This is not surprising, for as income levels grow individuals will begin to save. Truett and Truett (1990) provide support for these arguments by showing that the income elasticity for insurance in Mexico is around three times higher than in the U.S. A further explanation can be found in the bequeath motive for life insurance. Hau (2000), working only with U.S. data, shows that when an individual s holdings of net liquid assets are low and government-backed annuity wealth is high, there is little scope to bequeath financial assets to dependents. Hence, consumption of life insurance increases. Therefore, when focusing on emerging economies, individuals will ordinarily have accumulated few assets and will be drawn to life insurance for its bequest capabilities. Accordingly, when we take all these ideas together we expect income elasticity to be higher in the rapidly growing economies of Asia than in the developed economies of the world. Second, the real inflation rate, INFLATION, is also included as there is substantial evidence to suggest a very strong, negative relationship exists. As inflation erodes the cash value of any sums received in the future, the benefits from purchasing life insurance diminish. Further, higher levels of inflation are associated with macro-economic uncertainty and as a result the discounted value of financial assets, including life insurance, will be less. Indeed the impact of inflation is so strong that a study by Babbel (1981) noted that demand was less in periods of high inflation for indexed cash value life insurance products. Finally, Outreville (1996) has shown that financial development, DEPTH, as an aid to financial intermediation can be an important source of growth in the insurance industry. A common measure of financial development is the ratio M2/GDP. However, the more specific measure of Private Credit from Banks and other Financial Institutions over GDP, as deployed by Levine et al. (2000), is used in this study as it focuses on financial institutions involved in financial development. 2.3 Social factors As with the other empirical studies, life expectancy, LIFE, is included. It is expected to hold a positive relationship with life insurance consumption. Outreville (1996) argues that it reflects the actuarially fair price of life insurance, as the longer one is expected to live, the more premium payments will be made. However, as a proxy for the price of insurance life expectancy has a number of problems associated with it. First, it refers to the general population, not the pool of risks insured by the average life insurance company. Second, life expectancy is notoriously inaccurate (see McDonald, 1996; Blake and Burrows, 2001), with a need to price into life insurance the additional risk of life expectancy error. Third, longrunning insurance will have been underwritten and priced with very different assumptions of mortality risk from those currently used. Fourth, life expectancy does not account for the ability to discount life insurance liabilities by investment returns. Therefore, an improved proxy for price would take account of the statistical distribution associated with mortality risk over the population and the expected distribution of investment returns. However, such

LAW, POLITICS AND LIFE INSURANCE CONSUMPTION IN ASIA 401 measures do not currently exist. 4 Therefore, following the previous literature we will continue using life expectancy while suggesting the development of a better pricing proxy would be an excellent topic for further research. WELFARE measures average social welfare expenditure as a percentage of GDP. It is expected that as a government increases its social provision, the need for individuals to make private provision against longevity or early death, via life insurance, is reduced. The final two variables are annual education and dependency ratios for each country. The ratio, EDUCATION, measures the proportion of the population completing secondary schooling. Following Browne and Kim (1993), it serves a dual purpose of not only measuring risk aversion (on the assumption that an individual s education level is positively related to greater risk aversion) but signifies that with more people completing secondary education, the more individuals are dependent on family income earners. The dependency ratio, YOUNG, measures this last point more succinctly. The chosen measure is the number of young dependents over the total working population. As this ratio grows, the more likely it is that income-earners will purchase life insurance in the event of a premature death. 5 The full list of variables and their hypothesized signs are tabulated in Table 1. Also, with Hypotheses Table 1: Summary of Hypotheses Proxies Expected relationship with demand for life insurance Economic and social factors Real GDP per capita GDPCAP +ve Inflation rate INFLATION ve Financial development DEPTH +ve Social welfare expenditure WELFARE ve Young dependency ratio YOUNG +ve Risk aversion EDUCATION +ve Price of insurance LIFE +ve Political rights Checks and balances CHECKS +ve Political cohesion CO +ve Legal rights Civil rights CIVRIGHTS ve Rule of law ROL +ve Muslim country RELIGION ve 4 For an indication of the problems associated with discounting a stochastic variable with a stochastic discount factor see Klumpes (2001). 5 Recent empirical studies that have employed a similar dependency ratio include, among others, Truett and Truett (1990), Browne and Kim (1993) and Outreville (1996).

402 WARD AND ZURBRUEGG the exception of WELFARE and figures relating to Taiwan, all the data for the social and economic variables were collected from the World Bank. For WELFARE, some of the World Bank statistics were supplemented with welfare expenditure data obtained from national statistical reports. Taiwanese government statistical yearbooks were also used for the collection of data not available from the World Bank. 3. Descriptive statistics and methodology Table 2 provides descriptive statistics for the full unbalanced 6 panel dataset used. The choice of countries included within the sample was based on the availability of life insurance premium data for the countries between 1987 and 1998. As a comparison, the sample is also split into two: one for Asian countries and one for developed/oecd countries. There are five exceptions to this rule. Due to the highly developed nature and size of the insurance market in Japan, it is excluded from the Asian sample. Also, the samples are not mutually exclusive, with Singapore, 7 South Korea, Israel and Turkey being represented in both sub-samples, primarily due to the fact that they also have developed economies. Despite this, it is noticeable that the level of life insurance consumption is higher in the developed economies. This is not surprising given the higher level of GDP per capita and the lower rate of inflation. In addition, financial development, civil rights, political checks and balances, rule of law and political cohesion, all thought to promote insurance consumption, are more favourable in the OECD sub-sample. The table also contains the correlation scores for the entire sample. With the exception of the young dependency ratio, all the figures support our hypothesized relationships between the independent variables and life insurance density. However, it is also notable that the correlation between GDPCAP and LIFE is 0.91. This figure is not surprising when one considers that income and general living standards will inevitably lead to a longer life expectancy. Including both of these measures as independent regressors will increase the risk of generating spurious results due to the high level of multicollinearity. Although previous studies have not dealt with this potential problem, we will exclude LIFE in order to alleviate the problems of collinearity. However, we also conduct ancillary regressions that include LIFE, which show its significance as an explanatory variable. The reason for the negative relationship between YOUNG and life insurance density will be explored in the empirical section, as part of the explanation for this lies in differences in population growth between the developed and developing countries. To provide a more comprehensive examination of the relationships between these explanatory factors and life insurance density, two specific methodologies are applied. In both cases logarithmic values are taken. This creates linearity in the data and also provides for the estimation of elasticities. First, pooled cross-section OLS regressions are performed for the two primary sub-samples with only the basic economic and social set of variables included. These regressions include time-specific dummies to account for time variation, but no country specific effects in order to maximize cross-country variation. The results are presented to make a direct comparison with the existing literature on life insurance which has primarily used cross-sectional datasets. Secondly, to determine whether there are any transitory or 6 Where possible, data was interpolated to maximize the number of observations. 7 Singapore is not a member of the OECD, although for the purposes of this study we also place it in the OECD sub-sample.

Asia Table 2: Descriptive statistics LIFEDEN INFLATION GDPCAP EDUCATION DEPTH WELFARE YOUNG CIVRIGHT CHECKS ROL LIFE CO Mean 219.58 12.78 5263.29 63.79 0.67 3.02 61.87 4.11 3.65 3.64 68.78 0.94 Median 34.61 07.19 3916.50 60.00 0.67 1.46 60.32 4.00 3.00 4.00 69.02 1.00 Std. Dev. 302.19 18.36 3846.52 22.49 0.40 3.61 18.04 1.35 2.44 1.40 4.88 1.00 OECD LIFEDEN INFLATION GDPCAP EDUCATION DEPTH WELFARE YOUNG CIVRIGHT CHECKS ROL LIFE CO Mean 551.04 07.49 12060.79 99.95 0.89 11.41 51.28 1.76 4.03 5.27 75.41 1.18 Median 505.20 3.41 12969.00 100.00 0.81 11.53 49.76 1.00 3.00 6.00 76.40 1.00 Std. Dev. 382.69 14.34 3927.72 20.61 0.47 6.48 7.00 1.19 2.12 1.18 3.57 1.11 Full sample LIFEDEN INFLATION GDPCAP EDUCATION DEPTH WELFARE YOUNG CIVRIGHT CHECKS ROL RELIGION LIFE CO LIFEDEN 0.6109 0.8372 0.7271 0.7126 0.0183 0.5450 0.6850 0.1916 0.6203 0.6467 0.7236 0.1554 INFLATION 0.5427 0.4317 0.5671 0.0449 0.3941 0.4238 0.1640 0.5531 0.3984 0.5222 0.1812 GDPCAP 0.7534 0.5824 0.1652 0.6145 0.7402 0.2276 0.7888 0.4844 0.9144 0.2168 EDUCATION 0.4300 0.2397 0.5002 0.6597 0.0419 0.6434 0.5820 0.7548 0.1615 DEPTH 0.0430 0.4787 0.3147 0.0723 0.5258 0.4657 0.5507 0.0170 WELFARE 0.0112 0.0024 0.1546 0.1526 0.1175 0.2047 0.2059 YOUNG 0.4263 0.1457 0.6196 0.6686 0.6461 0.0960 CIVRIGHTS 0.2039 0.5643 0.5585 0.6905 0.2558 CHECKS 0.0907 0.0024 0.1981 0.6054 ROL 0.4330 0.7892 0.1836 RELIGION 0.5494 0.0467 LIFE 0.2053 CO LAW, POLITICS AND LIFE INSURANCE CONSUMPTION IN ASIA 403

404 WARD AND ZURBRUEGG persistence effects within the life market from previous life insurance consumption, panel regressions are performed that examine within-country differences. Specifically, rather than conduct a standard fixed effects model, a General Method of Moments (GMM) system estimation is performed that allows for the inclusion of a lagged dependent variable in order to distinguish between transitory and permanent influences upon life insurance consumption. Specifically, this model can be notionally written as: y i,t ¼ Æ y i,t 1 þ â x i,t þ ì i þ ô t þ å i,t (1) where y is the dependent variable, y i,t 1 is the lagged dependent variable, x is the set of explanatory variables, ì is a country-specific effect, ô is a time-specific effect, å is a timevarying error term, and i and t denote country and time periods, respectively. While the fixed effects model is able to account for both time and country-specific effects, estimates will be biased and inconsistent when a lagged dependent variable is included. 8 For this reason this procedure is not appropriate and the preferred estimation technique is the GMM dynamic system estimator developed by Arellano and Bond (1991) and Arellano and Bover (1995), and also employed by Levine et al. (2000), among others. The GMM estimator provides the ability to model lagged dependent variables as regressors within a dynamic framework, while assuming weak exogeneity. 9 In brief, the GMM system estimator involves the simultaneous estimation of the model in differences and levels, with lagged levels of the independent variables used as instruments in the differences equation and lagged differences used as instruments in the levels equation. Blundell and Bond (1998) provide appropriate moment conditions for the estimation of this system and go onto show the GMM estimator offers significant improvements in terms of bias and precision over non-system estimators. Importantly, the consistency of the GMM system estimator depends on a number of assumptions. Specifically, the assumption of no serial correlation in the residuals, å i,t, and on the validity of the instruments. Under the null hypothesis of no serial correlation, the test of first-order serial correlation of the differenced residuals should be significantly negative, and the second-order test should be insignificant. The Sargan test of over-identifying restrictions can also be conducted, and is based on the null hypothesis that the instruments are valid (Arellano and Bond, 1991). Failure to reject the null hypothesis supports the model specification. 10 For this reason, both the Sargan and serial correlation test statistics are presented alongside the GMM system results to test the validity of the estimation procedure. Although the purpose of including the GMM estimator results are also to partially provide a comparison with the cross-sectional regressions, slightly different datasets are used. Despite annual data being collected, many of the variables do not experience much time variation on an annual basis. This is particularly true of the legal and political factors for the more developed countries. In order to deal with this problem, one technique is to segment and average the data. In this paper non-overlapping two-year averages are calculated and used for 8 See Verbeek (2000) for details on the limitation of fixed effects models with lagged dependent explanatory variables. 9 Strict exogeneity implies that the explanatory variables are uncorrelated with current, future and past values of the error term. This is in contrast to weak exogeneity that allows for current explanatory variables to be affected by past and current values of the dependent variable, but are not affected by future changes in the dependent variable. 10 We use the Dynamic Panel Data (DPD) for Ox program written by Doornik, Arellano and Bond (1999) to implement the GMM system estimator.

LAW, POLITICS AND LIFE INSURANCE CONSUMPTION IN ASIA 405 the GMM estimation. Although this halves the number of observations, it also increases the time variation within the data. 4. Empirical results Table 3 presents the pooled cross-section OLS regression results. 11 The first set of regressions only include a subset of social and economic variables which can lead to a comparison of the results from previous research by, for example, Browne and Kim (1993) and Outreville (1996). With the exception of the young dependency ratio in the ASIA sample, the estimated co-efficients have the expected sign and are statistically significant to at least the 10 per cent level. The negative sign for the YOUNG dependency ratio may in our dataset proxy for the rise in the young adult population over the period, rather than proxy for the number of dependents. Unlike the more developed countries, population growth is still evident in most parts of Asia. Therefore, a growing young adult population, with few family commitments, is unlikely to seek life insurance. However, aside from this, these initial results are similar to previous research on life insurance markets and support the premise that our underlying data is comparable. Since all of the variables have undergone a logarithmic transformation, the estimated coefficients in Table 3 are measures of elasticity. Any value greater than 1 indicates that a change in that variable will drive an even bigger change in the consumption of life insurance. So for the first set of results the Asian sub-sample has an estimated co-efficient of 1.313 for GDPCAP, signifying that consumption is income elastic. More specifically, an increase in income of 10 per cent will increase the consumption of life insurance by 13.13 per cent. At this point it is therefore worth noting the differences in the magnitude of the estimated coefficients across the two samples. In contrast to Asia, the consumption of life insurance in the OECD sample is around three times less sensitive to changes in income with an estimated coefficient of 0.4485. This result is not surprising given the higher average income level in the OECD sample and is supportive of the S curve hypothesis where, at higher levels of income, insurance consumption becomes less sensitive to income growth. Following the above reasoning, inflation and, we would hazard, economic uncertainty, is around two and a half times more important in the ASIA region as opposed to the OECD sample. Conversely, in the developed economies a 10 per cent improvement in financial development, DEPTH, will lead to approximately a 12 per cent increase in life insurance consumption. In the ASIA sample the improvement in consumption would only be around 2 per cent. This is an interesting result as it would imply that the importance of the financial intermediary sector is to complement insurance consumption in developed economies. In the Asian economies where financial intermediation is not as broad the impact is not as great. The second group of results augments the earlier specification with legal and political variables. In the first instance we use CIVRIGHTS and CHECKS as our measures of the legal and political environment, while in the later specification we opt for our alternative measures of ROL, CO and RELIGION. These alternative regressions using differing legal and political determinants will help to provide an indication of the robustness of the results, independent of the variables used. Taking ASIA first, improvements in civil rights have a significant and positive impact on 11 All regressions are de-trended for time by the inclusion of time dummies and co-efficient tests are corrected for heteroskedasticity using White s standard errors.

Table 3: Determinants of life insurance consumption in ASIA and OECD OLS pooled cross-section estimates a Basic conditioning variables Legal and political (1) Legal and political (2) Exclusion of GDPCAP Asia sub-samples ASIA OECD ASIA OECD ASIA OECD ASIA OECD ASIA (non-tiger) Asia Tigers Variable Constant 7.7810 6.2220 0.2716 13.3770 7.8081 8.8901 17.7297 16.9031 0.7921 15.3662 (2.6242) (2.0290) (2.8888) (2.6379) (3.0640) (1.9591) (6.0718) (8.6980) (2.3522) (2.1775) INFLATION 0.6354 0.2450 0.5841 0.2113 0.6172 0.2354 0.6164 0.2642 0.8134 0.2008 (0.1302) (0.0589) (0.0961) (0.0606) (0.1062) (0.0606) (0.1033) (0.0665) (0.1542) (0.1702) DEPTH 0.1850 1.1974 0.4614 1.0677 0.2824 1.0715 0.5037 1.1676 0.3363 0.0567 (0.1119) (0.0933) (0.1464) (0.1007) (0.1674) (0.1038) (0.1897) (0.0970) (0.2074) (0.5016) EDUCATION 1.1055 1.2188 0.8609 2.0602 0.8010 1.4778 0.9931 1.9872 0.5810 0.3166 (0.2633) (0.2561) (0.2123) (0.2885) (0.1732) (0.2866) (0.2617) (0.2805) (0.1668) (0.4274) YOUNG 0.6738 1.2267 0.8720 1.2062 0.6925 0.8268 1.2226 0.3130 0.6360 1.3889 (0.3809) (0.3875) (0.4282) (0.3864) (0.5180) (0.4004) (0.3771) (0.4201) (0.4033) (0.4069) GDPCAP 1.3130 0.4485 0.8984 0.5728 1.6989 0.7098 0.7328 1.7990 (0.1575) (0.1405) (0.1364) (0.1619) (0.1235) (0.1552) (0.2088) (0.2461) LIFE 6.1675 3.1300 (1.4007) (1.9459) WELFARE 0.0024 0.0227 0.0259 0.0162 0.0826 0.0613 1.6379 0.3930 (0.0131) (0.0061) (0.0130) (0.0065) (0.0397) (0.0085) (0.3891) (0.1610) CIVRIGHTS 0.5492 0.2324 0.6210 0.0628 0.5069 0.2873 (0.0532) (0.1476) (0.0492) (0.0584) (0.0630) (0.0866) CHECKS 0.0345 0.0357 0.0610 0.0960 0.0517 0.2246 (0.0313) (0.0146) (0.0355) (0.0175) (0.0398) (0.0810) ROL 0.4958 0.2176 (0.0714) (0.0521) CO 0.2573 0.0123 (0.0667) (0.0343) RELIGION 0.7291 (0.1428) Observations 156 273 150 273 150 273 151 273 95 55 F test /Chi Square 39.2241 53.8027 70.4938 52.6007 56.2903 49.5857 57.3720 52.2066 32.7183 19.0047 Adjusted R 2 0.7872 0.7444 0.8936 0.7735 0.8758 0.7628 0.8647 0.8291 0.8516 0.8334 a, and indicate significance at the 1%, 5% and 10% levels respectively. 406 WARD AND ZURBRUEGG

LAW, POLITICS AND LIFE INSURANCE CONSUMPTION IN ASIA 407 life insurance consumption. With a 10 per cent improvement generating a 5.5 per cent increase in consumption of life insurance. However, the make-up of the political system is found to be statistically insignificant. It should also be noted that the inclusion of legal and political variables markedly reduces the income elasticity measure in ASIA, indicating that after controlling for legal and political effects income elasticity for life insurance in Asia is inelastic. Finally, for the Asia region WELFARE is insignificant which provides little support for the argument that consumption of life insurance in the Asia region is driven in part by low public sector provision of social insurance. By contrast, in the OECD sample, welfare provision has a significant and detrimental effect on the consumption of life insurance, with a 10 per cent increase in welfare provision leading to a 0.2 per cent reduction in life insurance consumption. However, for the OECD sample, civil rights are found to be statistically insignificant, perhaps reflecting that the level of civil rights do not vary across these economies very much and have reached an acceptably high standard. This is despite the fact that the political environment is found to be a positive and significant determinant of life insurance consumption, with a 10 per cent improvement in the political process generating a 0.4 per cent improvement in consumption. In order to check that these results are not spurious to our choice of variables we also present results for our alternative measures of the legal and political environment. For the rule of law (ROL), it turns out to be a significant determinant of life insurance consumption in both the OECD and ASIA samples. However, with an estimated co-efficient of 0.4958, the impact of the law is two and a half times more important in ASIA than in the OECD sample. Moreover, with higher estimated co-efficients for the ASIA sample, the impact of the political system (CO and RELIGION) and the welfare state are also of greater importance for insurance consumption in ASIA, as opposed to the OECD sample. 12 In conjunction, the results presented do sometimes contrast with each other. In particular, welfare provision seems important in one regression for ASIA, but not the other, while evidence of legal determinants, as measured by CIVRIGHTS, is weak for the developed countries. However, these results may be more reflective of the differences between what the various political and legal variables are measuring. Despite this, the results do show broad evidence to support the hypothesis that the legal and political environment are key determinants of life insurance consumption in both developed and emerging economies. This is clearly important to policymakers and new entrants to the Asian insurance markets, as it highlights the legal and political environments as key structural determinants of future expansion in these markets. Policymakers can use this knowledge to aid the development of these markets and entrants can use this knowledge to select markets that they should and should not enter. Significantly, the results so far are restricted through the omission of LIFE, which was seen in Table 2 to be highly correlated with GDPCAP. To recognize this problem the model is re-estimated with LIFE taking the place of GDPCAP. The results in Table 3 show that an improvement in LIFE expectancy is important in ASIA, but not in the OECD sample. Whether it is possible to consider LIFE as being a measure of the price of insurance is questionable, given its close relationship with income and the wealth of a country. Indeed, the results suggest that if life expectancy does increase by 10 per cent there would be a corresponding rise of over 60 per cent in life insurance consumption. In the OECD economies 12 RELIGION was excluded from the OECD sample due to the dummy variable only being equal to 1 for Turkey.

408 WARD AND ZURBRUEGG this relationship is probably not displayed because of the fact that life expectancy has stabilized and thus there is little variation in the data. However, this alternative specification does also confirm the importance of legal and political factors in the ASIA and OECD samples which continue to be significant determinants of life insurance consumption, regardless of whether LIFE or GDPCAP is used. As a recognition of the fact that economic development varies across Asia and because insurance has developed most markedly within the so-called Tiger Economies of South- East Asia, we also split our Asian sample into Tiger and Non-Tiger economies. Interestingly, from the results in Table 3 it is clearly evident that even after controlling for legal and political factors, income elasticity is much higher in the Tiger countries than elsewhere. In fact, out of all our specifications the Tiger economies gain the highest and therefore most elastic measure for income. The inelasticity of the non-tiger economies is probably due to the low purchases of insurance products in these countries to start with, as life density is an average of only $45.86 per capita. Furthermore, public sector provision of welfare systems is seen to have a larger and negative impact in the Tiger economies. This possibly suggests that these governments need to recognize the negative impact that welfare provision can have on the development of life insurance, and financial intermediation more generally. This is particularly true where welfare provision is a viable alternative to private insurance. Finally, as with the previous results, even within these further sub-samples there remains strong evidence for the role of legal and political factors, measured by CIVRIGHTS and CHECKS, in the consumption of life insurance. Finally, as a means of examining the time dimension of the data and analysing any longrun persistence effects within the consumption of life insurance, Table 4 presents the GMM system estimator results for the panel data. It is important to realize that with the inclusion of country-specific effects, our political and legal measures are not in themselves proxies for other country-specific characteristics. The results show the beneficial role of the legal environment in determining life insurance density. However, no evidence is shown to support the impact of the political environment. For the elasticity of income, there is no significant difference, at the 1 per cent level, between the sub-samples. Also, not all of the remaining social and economic variables are significant, although where they are, they hold the right hypothesized sign. It is difficult to make any direct comparisons between these results and the previous cross-sectional regressions due to the inclusion of country-specific dummies in the equations and, most importantly, the dynamic specification using the lagged dependent variable. However, the results for both sub-samples do indicate that current life insurance is dependent upon approximately 40 per cent of its previous level, over a two-year period. This may be diluting the impact obtained from the other socio-economic variables within the regressions. This is particularly true of the Asian countries. 5. Conclusion Recent work by LaPorta et al. (1997, 1998, 2000) and Levine (1998, 1999) has highlighted how the financial sector is influenced by the legal system within an economy. Unfortunately, there has been little empirical attention given to such factors influencing the insurance industry. This study begins to bridge this gap in the literature while at the same time emphasizing the influence of such factors within the rapidly growing insurance markets of Asia. Using a larger dataset than has been previously examined, we find evidence that the

LAW, POLITICS AND LIFE INSURANCE CONSUMPTION IN ASIA 409 Table 4: Determinants of consumption for life insurance in ASIA and OECD Panel data results a GMM system estimator ASIA OECD Variable CONSTANT 3.0616 16.5848 (5.173) (7.1580) LIFEDEN 1 0.3924 0.3844 (0.0640) (0.0872) INFLATION 0.0201 0.0546 (0.1888) (0.1174) DEPTH 0.9635 1.0675 (0.2398) (0.1568) YOUNG 0.1671 1.8838 (0.7090) (0.9397) EDUCATION 0.5088 1.7415 (0.3313) (0.6027) GDPCAP 0.5255 0.4991 (0.2570) (0.2539) WELFARE 0.0088 0.0083 (0.0204) (0.0083) CIVRIGHTS 0.2289 0.2595 (0.0821) (0.1582) CHECKS 0.0318 0.0047 (0.0593) (0.0278) Observations 66 124 F test/chi Square Adjusted R2 Wald joint test [p value] 0.0000 0.0000 Sargan test 12.5221 13.2700 AR (1) test [N(0,1)] 1.6653 1.7361 AR (2) test [N(0,1)] 0.5492 0.7549 a, and indicate significance at the 1%, 5% and 10% levels respectively. improved provision of civil rights and political stability leads to an increase in the consumption of life insurance in both OECD countries and the ASIA region. More telling is the difference in the income elasticity between the developed economies and the emerging markets of Asia. This is consistent with Sigma s (1999) S curve for insurance growth and the findings of Truett and Truett (1990). Beyond the academic merit of this comparative study between Asian and the more developed markets of the OECD is the real significance these results have for policymakers

410 WARD AND ZURBRUEGG and foreign entrants within some of the world s most rapidly expanding insurance markets. For instance, the attraction of the Tiger economies to many insurance companies from the developed world has been the anticipation of high future growth rates linked to greater economic development. The estimated income elasticities from this study after controlling for legal, political and dynamic effects would tend to caution against such outright optimism. For while economic development may drive faster rates of insurance market development in Asia, the link may not be as strong as first thought. Indeed, economic stability conditioned on political and legal stability, as in the developed economies of the world, appears just as important for long run success. Countries included in the study are: Appendix OECD Asia Asian Tigers Australia China Hong Kong 1 Austria Hong Kong 1 Korea, South Belgium India Malaysia Canada Indonesia Singapore Denmark Iran Taiwan Finland Israel Thailand France Jordan Greece Korea, South Ireland Kuwait Israel Malaysia Italy Pakistan Japan Philippines Korea, South Singapore Netherlands Taiwan New Zealand Thailand Norway Turkey Portugal Singapore 2 South Africa Spain Sweden Switzerland Turkey United Kingdom United States 1 Some of the regressions exclude Hong Kong due to missing political and legal variables. 2 Singapore is not a member of the OECD, although for the purposes of this study it is also placed in the OECD sub-sample.

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