شیوه های فرهنگی و مصرف بیمه عمر: تجزیه و تحلیل بین المللی با استفاده از نمرات GLOBE

This cross-disciplinary study examines how national culture practice affects cross-country variations in life insurance consumption. To proxy for national culture dimensions, we use the refined measure of the GLOBE project which includes several additional cultural dimensions not included in Hofstede's analysis. Using 1966–2004 data across thirty-eight countries, our analysis reveals a strong relationship between life insurance consumption and the practice scores of in-group collectivism as well as power distance. These relationships continue to hold, even after controlling for other country-level variables such as national income, expected inflation rate, banking sector development, investor protection index, dependency ratio, life expectancy, and religion.

Despite the fact that the life insurance market has become an increasingly important financial sector in the world economy, there is a large variation in life insurance consumption across countries. We select 32 countries out of our sample of 38 countries and classify these countries, from low (bottom 30%) to high (top 30%), into three groups based on their average Gross Domestic Product (GDP) per capita in 2000 constant US dollar calculated over the period from 1985 to 2004.2Fig. 1 indicates that although life insurance density increases from low income group to high income group, there is a substantial variation in life insurance density across-country in each income group. Our diagrams suggest that national income is not the only determinant for cross-country variation in life insurance consumption.Previous studies have identified a set of economic, demographic, and institutional factors to explain the variations in life insurance consumption measured as life insurance density across countries. Using data from 45 countries in years 1980 and 1987, Browne and Kim (1993) suggest that life insurance consumption across countries is positively related to national income, dependency ratio, and government spending on social security, while it is negatively related to the religion (Muslim), inflation, and the policy loading charge. Using data from 48 developing countries in 1986, Francois (1996) also finds that life insurance consumption across countries is positively related to national income and negatively related to inflation rate. In addition, Francois indicates that while life insurance consumption in a country is positively related to financial market development in that country, it is negatively related to the monopolistic market structure in some of the countries in his sample. Even though the above two studies cover a wide range of countries, their data are collected from one or two particular years. Therefore, the results of these two studies are subject to selection year bias. To alleviate this selection year bias, Beck and Webb (2003) use a panel data for 68 countries over the period from 1961 to 2000 to investigate the determinants of cross-country variation in life insurance consumption. Beck and Webb (2003) document that, out of the eight economic variables, the six demographic variables and the three institutional variables included in their study, national income, inflation, and banking sector development are the most robust determinants of life insurance consumption across countries and over time. In particular, while life insurance consumption is positively related to national income and banking sector development, it is negatively related to inflation. The aforementioned studies are built on the same theoretical ground which assumes that the decision maker is a rational economic being, aiming to maximize the benefits of his/her dependents after his/her death. In these studies, decision makers are assumed to have the same cognitive machinery for making economic decisions across countries. Therefore, people in different countries may have about the same life insurance consumption when they are faced with similar economic circumstances. These economic circumstances are the decision maker's demographic characteristics, his/her economic situations as well as the institutional environment that he/she is living in. In other words, cross-country variation in life insurance consumption is solely owing to the differences in these economic circumstances across countries. Recently, Henrich (2000) finds experimental evidence indicating that economic decisions are heavily influenced by cultural differences and concludes that “the assumption that humans share the same economic decision-making processes must be reconsidered” (p. 973). The basic need underlying the purchase of life insurance is to take care of one's dependents after one's death and this basic need is likely influenced by national culture. In an exploratory study using survey data collected from Winnipeg of Canada and Grand Forks of the United States, Wharton and Harmatz (1989) find that the two city groups have different life insurance consumption merely because they have different cultural attitudes toward life insurance. This finding suggests that people in different cultures may have different life insurance consumption even if they have similar constraints. In a recent study using Hofstede's cultural dimensions and data from 1976–2001 across 41 countries, Chui and Kwok (2008) documents that individualism has a significant, positive effect on life insurance consumption while power distance and masculinity/femininity have significant, negative effects.3 There is a caveat, however, in the use of Hofstede's cultural dimensions to explain the cross-country variation of life insurance consumption. Hofstede's survey relies on a value-based framework for measuring cultures and this framework implicitly assumes that knowing values in a culture tells us about what actually happens in that culture (Javidan et al., 2006). In contrast to Hofstede's survey, GLOBE uses a set of values and practices to measure national cultures. In the early 1990s, the GLOBE (Global Leadership and Organizational Behavior Effectiveness) project was started by a group of scholars with a focus on culture and leadership in 61 countries, who surveyed thousands of middle managers in various organizations in three industries: food processing, telecommunications services and financial services (House et al., 2002). In this project, national culture is defined as a set of shared values and beliefs. While values are people's aspirations about the way things should be done, beliefs are people's perceptions of how things are done (Javidan and House, 2001). In other words, values reflect people's desired practices and beliefs reflect people's actual practices. In the survey, GLOBE used two sets of questions to assess values and beliefs separately. From the findings, GLOBE classifies national culture into nine dimensions: assertiveness, institutional collectivism, in-group collectivism, future orientation, genders egalitarianism, humane orientation, performance orientation, power distance, and uncertainty avoidance. Each of the dimensions has a score on value (i.e. desired practice) as well as a score on belief (i.e. actual practice). In the current study, we shall focus on beliefs in each country rather than values. In other words, we shall concentrate on the relationship between cultural practices and life insurance consumption across countries. In this regard, this study is complementary to Chui and Kwok (2008). In addition, there are two more advantages in using the GLOBE cultural dimensions. First, while Hofstede's survey was conducted in early 1980s, GLOBE conducts its survey in early 1990s. Since cultural values might change overtime, GLOBE scores provide us with updated cultural values. Second, GLOBE includes several cultural dimensions that are not included in Hofstede's analysis. These additional cultural dimensions, however, might have significant effect on life insurance consumption across countries. Our analysis of the life insurance market in thirty-eight countries reveals a strong relationship between life insurance consumption and the indices on in-group collectivism as well as power distance. These relationships continue to hold, even after controlling for other country-level variables that are known to be the determinants of cross-country variation in life insurance consumption. These variables include national income, expected inflation rate, banking sector development, investor protection index, dependency ratio, life expectancy, and religion. Furthermore, we also notice that the relationship between life insurance consumption and in-group collectivism/power distance is robust to estimation methods. The remainder of this paper is organized as follows. In the second section, we explore the relationship between GLOBE's cultural values and life insurance consumption. In the third section, we describe the data and methodology used in the paper. In the fourth section, we report the results and the fifth section concludes the paper.

The GLOBE project classifies national culture into nine dimensions. Each of the dimensions has a score on value (i.e. desired practice) as well as a score on belief (i.e. actual practice). In the current study, we focus on beliefs in each country rather than values and concentrate on the relationship between cultural practices and life insurance consumption across countries. Of the nine dimensions, two shows robustly significant effects on life insurance consumption: in-group collectivism (ING) and power distance (PDI). One contribution of the GLOBE project relative to Hofstede's dimensions is that it refines the measure of collectivism, differentiating collectivism into two aspects. One is the institutional collectivism dimension (INS) which reflects the degree to which societal institutions encourage collective distribution of resources and collective action. The other is the in-group collectivism which refers to the extent to which individuals of a country take pride in membership in small groups such as their family and circle close friends ( Javidan and House, 2001). Such distinction indeed makes a difference in the empirical findings. The robustly strong result is with ING and the sign is negative. Such finding is consistent with Chui and Kwok (2008) who argue that since people in collectivistic countries foster an interdependent construal of self, people tend to rely more on social network support than market life insurance. Their self-concept argument is more related to in-group collectivism. What Chui and Kwok (2008) do not show is the institutional aspect of collectivism. Indeed, we find that the coefficients of institutional collectivism carry positive signs, opposite to those of in-group collectivism. Organizations in high institutional collectivism countries tend to take responsibility for employee welfare ( Javidan and House, 2001) and provide more protection to their employees. However, instead of relying on providing financial support to the family members upon the death of their employees, our findings suggest that companies in high institutional collectivism countries tend to utilize the purchase of group life insurance as a means to providing more protection for their employees. It should be noted that the findings of ING are stronger than those of INS as the statistical significance of the latter varies across different estimation methods as well as different measures of life insurance consumption. Another cultural dimension which shows fairly consistent result is power distance (PDI). Chui and Kwok (2008) argue that when subordinates surrender more authority to their superiors, they expect the superiors to watch out for their welfare and provide more protection. The measure used by Chui and Kwok is Hofstede's score which combines value and practice. However, House et al. (2004) argue that value and practice may not always be consistent to each other. In countries high in power distance practice, resources are concentrated in the hands of a few people. If the superiors in those countries do not indeed provide protection to their subordinates’ family members, their subordinates have to rely more on market life insurance to safeguard the welfare of their family members. While the value score of PDI in Chui and Kwok (2008) finds significantly negative result, our practice score of PDI shows consistently positive results. It shows that value and practice are not always consistent. Indeed, the distinction made by House et al. (2004) helps refine our understanding of the effects of PDI on life insurance consumption. The effect of gender egalitarianism is not statistically significant. As discussed in the earlier hypothesis section, there may be two opposing effects of gender egalitarianism. Javidan and House (2001) indicate that countries high in gender egalitarianism tend to have a higher percentage of women participation in the labor force. Women are likely to be more risk averse than men and they tend to care more about the welfare of their children than their husbands. Since women in countries high in gender egalitarianism play a stronger role in decision-making (Javidan and House, 2001), we expect life insurance consumption will be larger in these countries. On the other hand, there is less need for life insurance when the wife works. In case the husband dies, the income of the wife can still provide for the livelihood of the children to a large extent. Because of these opposing effects, the insignificant finding of the gender egalitarianism dimension is not surprising. In the hypothesis section, we argue that assertiveness (ASI), future orientation (FOI), performance orientation (POI) should have positive effects on market life insurance consumption. When we use pooled GLS regression as the estimation method, these three cultural variables indeed show significantly positive effects. However, when we change the estimation methods, some of the coefficients become insignificant, though the signs remain the same. The results suggest that our hypothesized effects may exist but they are weak. Similarly, we hypothesized earlier that the effects of humane orientation (HOI) and uncertainty avoidance (UAI) should be negative on life insurance consumption. What we found, however, is that the effect of humane orientation is consistently insignificant. The effect of uncertainty avoidance indeed shows a negative effect as hypothesized. However the coefficients are statistically significant only in the cases of pooled GLS and Fama–MacBeth regressions. In the regressions of time-series means and random effect model, the coefficients of UAI are still negative, but not statistically significant. It shows that the effect of UAI on life insurance consumption is weak. This study contributes to the insurance literature in two main areas. Firstly, besides the general economic, institutional and demographic determinants, national culture practice indeed plays a significant role in explaining differences in life insurance consumption across countries, accounting for an additional 13% of the variation in the case of pooled GLS regressions. Together, these variables can explain most of the cross-country variation of life insurance consumption (87%); it is encouraging news to scholars who study cross-country life insurance consumption. Secondly, the refinement of the cultural dimensions (such as Hofstede's collectivism into institutional collectivism and in-group collectivism) and the distinction of the cultural dimensions into value and practice by the GLOBE project indeed leads to our deeper understanding of how national culture affects life insurance consumption. The practical implication of our findings is that when insurance companies search for foreign markets to expand their business, besides considering economic, demographic and institutional factors, they should also check the scores of national culture for these countries. Ceribus paribus, they should first enter countries with low practice scores of in-group collectivism (ING) and high practice scores of power distance (PDI) where market potentials are higher. For instance, the average PDI index for our sample countries in the low-, medium-, and high-income groups are respectively 5.32, 5.10, and 4.88. Currently, the life insurance consumption among the low-income countries is low. However, the high PDI index among these countries suggests that the life insurance markets in these countries may have good potential to grow. As in previous studies, this research project has its limitations. For example, we do not include any pricing variables in our analysis because of data availability problem. Higher price of life insurance policy will reduce demand for life insurance. To remedy this limitation, we follow the suggestion of Beck and Webb (2003) and include the financial market development and institutional variables in the regression model to reduce the bias caused by the missing price variable.