This cross-disciplinary study examines the way national culture affects consumption patterns of life insurance across countries. Life insurance is a service that is abstract, complex, and focused on unsure future benefits. Because of the uncertainty and ambiguity inherent in the life insurance product, consumers are more likely to respond according to their cultural prescriptions. Our research hypotheses are tested empirically using Hofstede's cultural dimensions, and data from 1976–2001 across 41 countries. The findings show that individualism indeed has a significant, positive effect on life insurance consumption, whereas power distance and masculinity/femininity have significant, negative effects. The results are robust, even after controlling for economic, institutional and demographic determinants.
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Several concerns have been raised regarding the Hofstede dimensions: (1) that his empirical work is based on a single, multinational corporation; (2) that the dimensions are too broad, and some other important value dimensions have not been included; and (3) that his cultural scores based on his empirical work in 1967–1973 may be dated. Despite these criticisms, Hofstede's dimensions are still the most widely used cultural indices in the international business literature.
The initial dimensions of Hofstede are derived from his empirical work that took place in 1967–73. As reviewed in Leung et al. (2005), there have been novel dimensions. For instance, Schwartz (1994) has identified seven cultural-level dimensions of values. Leung et al. (2002) create a social axiom survey based on items culled from the psychological literature as well as from qualitative research conducted in Hong Kong and Venezuela. Alternatively, House et al. (2004) conduct a GLOBE project that adopts a theory-based approach. Despite the use of different items to identify cultural dimensions, House et al.'s results are consistent with previous findings, and most of the cultural dimensions identified are related conceptually and correlated empirically with Hofstede's dimensions, suggesting that the Hofstede dimensions are quite robust (Leung et al., 2005: 366).
In the 2001 update of his book, Hofstede proposes one more cultural dimension: long-term orientation. However, the data for this dimension are less complete than those for the four original dimensions. If we include this variable in our test, our sample of countries will be reduced from 41 to 18, and the number of observations in our sample will be reduced from 853 to 377. Therefore this cultural dimension is not included in this study.
However, a study using a large sample of countries is a rarity in international business research. Typically two to four countries are compared in cross-national studies (Samiee & Jeong, 1994). To alleviate this issue, Sivakumar and Nakata (2001) suggest an algorithm in choosing country combinations that would strengthen hypothesis testing.
Social security is not included in the current study, because previous studies indicate that the relationship between life insurance consumption and social security is insignificant (Beck & Webb, 2003; Outreville, 1996). Our conclusion will not be affected even if we include social security in our analysis.
These socialist countries are Bulgaria, China, Czech Republic, Hungary, Poland, Romania, Slovak Republic, and Vietnam. Most of these socialist countries started to transform into market economies in the 1990s. However, the initial conditions of their life insurance markets are still quite different from those of the non-socialist countries. These differences in initial conditions could be an important determinant for the development of the life insurance markets across countries. The authors thank the referee for bringing up this point.
We do not separate our religion measure into Protestant, Catholic and Muslim, because we believe that a religious person will generally purchase less life insurance if he/she perceives that buying life insurance shows a distrust of God's protection. Our measure on religion may underestimate the share of religious people out of the total population, because this measure only counts those people with a Protestant, Catholic or Muslim belief. For example, since Buddhism is very popular in Asia, our religion measure will underestimate the proportion of religious people in Asian countries. Data on other religions, however, are not available to us.
We first compute the time-series means of the time-varying variables for each country over the years from 1976 to 2001 and then estimate the correlations across countries. We require each country to have at least 20 observations for this variable over the period from 1976 to 2001. Since none of the eight socialist countries has 20 observations on life insurance density, these countries are excluded from the correlation analysis.
As a robustness test, we also estimate Equation (1) using the data for the explanatory variables instead of the three principal components. We obtain similar results for the relationship between life insurance consumption and the cultural variables.
The economic component (Prin Econ ) is positively correlated to national income, and developments in the bank and stock sectors, but it is negatively correlated to expected inflation rate and State. Therefore we expect the coefficient on Prin Econ (η1) to be positive. Since the institutional component (Prin Inst ) is positively correlated to Credit and ConRisk, we expect the coefficient on Prin Inst (η2)to be positive. On the other hand, the demographic component (Prin Demo ) is positively correlated to the dependency ratio and Religion. As a result, the sign of the coefficient on Prin Demo (η3) has to be determined empirically.
To save space, we do not report the intercept estimate (α) and the estimated coefficients on the year dummy variables, because time effects are not the focus of this study.
Judge, Hill, Griffiths, Lutkepohl, and Lee (1988) suggest that a value of variance inflation factor (condition number) that is 5 (30) or more is an indication of severe multicollinearity. In our regression analysis, all the estimated coefficients have variance inflation factors much less than 5, and the condition numbers of our regression are all less than 30.
To conserve space, the additional findings using the GLOBE dimensions are not reported in this paper, but will be made available to readers upon request.
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We thank our colleagues Helen Doerpinghaus and Xu Huang for providing helpful comments. Andy Chui acknowledges the financial support from the Hong Kong Polytechnic University (A-PG29), and Chuck Kwok gratefully acknowledges the support of the Center for International Business Education and Research (CIBER) at the University of South Carolina for this research project.
Accepted by Lorraine Eden, Departmental Editor, 24 June 2007. This paper has been with the authors for two revisions.
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Chui, A., Kwok, C. National culture and life insurance consumption. J Int Bus Stud 39, 88–101 (2008). https://doi.org/10.1057/palgrave.jibs.8400316
- national culture
- insurance consumption