national culture and life insurance consumption

National culture and life insurance

consumption

Andy CW Chui1 andChuck CY Kwok2

1School of Accounting and Finance, Hong KongPolytechnic University; 2University of South

Carolina, Columbia, USA

Correspondence:Chuck CY Kwok, Moore School of Business,University of South Carolina, 1705 CollegeStreet, Columbia, SC 29208, USA.Tel: þ1 803 777 3606;Fax: þ1 803 777 3609;E-mail: [email protected]

Received: 31 May 2005Revised: 29 December 2006Accepted: 24 June 2007Online publication date: 13 September 2007

AbstractThis cross-disciplinary study examines the way national culture affectsconsumption 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 insuranceproduct, 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. Thefindings 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.Journal of International Business Studies (2008) 39, 88–101.

doi:10.1057/palgrave.jibs.8400316

Keywords: national culture; insurance; insurance consumption; Hofstede

INTRODUCTIONThis cross-disciplinary study examines the way differences incultures across countries affect national consumption patterns oflife insurance. It is a topic related to cross-cultural consumerbehavior. The study also fills an important gap in the insuranceliterature, in which there is at present little discussion on the effectof national culture on insurance consumption.

Hofstede and Bond (1988) define culture as ‘‘the collectiveprogramming of the mind that distinguishes the members of onecategory of people from those of another. Culture is composed ofcertain values, which shape behavior as well as one’s perception ofthe world.’’ Studies have been conducted to investigate the effectsof culture on various business practices. These include the study ofmanagerial attitudes (Kelley, Whatley, & Worthy, 1987), businessperformance (Newman & Nollen, 1996), cross-border acquisitionperformance (Morosini, Shane, & Singh, 1998), investor stocktrading decisions (Grinblatt & Keloharju, 2001), and corporatecapital structures (Chui, Lloyd, & Kwok, 2002).

In the marketing area, extant research in consumer behaviorsuggests that culture influences the cognitive processes underlyingconsumer judgments and decisions (e.g., see Briley, Morris, &Simonson, 2000; Johar, Maheswaran, & Peracchio, 2006; Mandel,2003; Steenkamp, ter Hofstede, & Wedel, 1999). Indeed, afterreviewing the consumer research literature from the past 15 years,

Journal of International Business Studies (2008) 39, 88–101& 2008 Academy of International Business All rights reserved 0047-2506 $30.00

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Johar et al. (2006) conclude that it is time forresearchers to take a broader look at consumers ofdifferent cultural backgrounds in order to under-stand how differences in cultural beliefs (such asindividualism) may affect consumption. Asreviewed in Hofstede (2001), De Mooij (1998a, b,2001) correlates Hofstede’s cultural dimensionswith data from consumer surveys across Europeancountries by market research agencies, and gener-ates some interesting preliminary findings. Forinstance, De Mooij finds that, relative to people incollectivistic countries, people in individualisticcountries are more likely to engage in do-it-yourselfactivities: painting walls and woodwork, homecarpentry, and plumbing. They tend to live indetached houses instead of apartments or flats, andare more likely to have a private garden. Thesefindings suggest a lifestyle in which the person triesto be self-sufficient. In the consumption of bev-erages and food, De Mooj finds that people fromsocieties with high uncertainty avoidance index(UAI) tend to use more mineral water, buy morefresh fruit, and consume less ice cream or frozenfood. These findings indicate a search for simplicityand purity in food consumption.

National cultures not only affect the consump-tion of physical goods; they also affect purchase inthe service industries. A higher percentage ofpeople from masculine societies agree with thestatement that ‘‘I often enjoy advertising on TV.’’De Mooij’s (1998b) interpretation is that theskepticism of feminine cultures toward advertisingis due to the perception that their markets havebeen swamped by advertising reflecting Americanmasculine values. In the area of investment andfinancial services, De Mooij finds that people fromhigh-UAI countries tend to invest less in stocks andmore in precious metals and gems. Consistent withDe Mooij’s results, Kwok and Tadesse (2006) findthat high-UAI societies such as Japan and Germanytend to adopt a bank-based financial system, whereaslow-UAI societies such as the US and UK tend toadopt a market-based financial system. Bank depositsprovide high-UAI investors with stable returnsbecause the payoff is contractually fixed, and usuallyguaranteed by deposit insurance. By contrast, low-UAI investors prefer to invest in stock markets, whichyield higher but more volatile returns.

Closely related to the finance and investmentindustry is the insurance industry. While peopleinvest to reap returns from their investment, manyof them also purchase life insurance to safeguardtheir family members’ welfare in the case of

accidental death. We believe that national cultureshould play a significant role in explaining thedifferences of life insurance consumption patternsacross countries. The reason for this stems fromsome recent findings in the consumer behaviorliterature, which indicate that cultural effects onthe consumer decision process depend on theparticular product. For instance, Briley et al.(2000) propose that culture is influential whenthe decision task requires the consumer to providereasons for their consumption. This is because,when people search for reasons, they will accessdecision rules (existing norms) that, to a largeextent, differ cross-culturally. Leung, Bhagat,Buchan, Erez, and Gibson (2005), in their reviewof studies in culture and international business,highlight a stream of research that indicates thatculture tends to have a strong impact on individualbehavior under conditions of uncertainty andambiguity. The above findings suggest that cultureis likely to have a significant impact on decision-making when consumers need to provide reasonsfor their consumption, and this cultural impacton behavior is amplified by uncertainty andambiguity.

It is well known that life insurance is a servicethat is very abstract, complex, and focused onunsure future benefits; it is a service difficult forconsumers to evaluate even after purchase (Crosby& Stephens, 1987). In other words, inherentuncertainty and ambiguity are involved in theconsumption of life insurance. Furthermore, tofind a life insurance policy that can best suit theneeds of the beneficiaries, consumers will usuallyconsider the proposals, the agent, and the image ofthe company. They will search for reasons that areprobably based on culturally conferred decisionrules to justify their choices of insurance proposals.Indeed, Crosby and Stephens (1987), in their studyof consumer behavior in the life insurance industry,conclude that it is naive to assume that people‘‘buy’’ the agent and the company without tryingto verify their performance in delivering theservice. Because of the unique nature of lifeinsurance, culture is expected to be a criticaldeterminant underlying the systematic differencesin consumption of life insurance across countries.Previous studies in life insurance consumption,however, have not yet investigated the relationshipbetween national culture and life insurance (e.g.,Beck & Webb, 2003; Browne & Kim, 1993; Out-reville, 1996). The current research aims to fill thisgap. In addition to examining whether national

National culture and life insurance consumption Andy CW Chui and Chuck CY Kwok

89

Journal of International Business Studies

culture affects life insurance consumption, it is alsointeresting to explore whether the same set ofnational-culture dimensions that affect the invest-ment industry also affect the insurance industry.Furthermore, are the effects of national culturesufficiently significant, economically speaking, towarrant the attention of practitioners in the lifeinsurance industry?

The remainder of this paper is organized asfollows. In the second section, we hypothesizethe relationships between national culture andlife insurance consumption. In the third section, wedescribe the data and methodology. The results arereported in the fourth section. The fifth sectiondiscusses the theoretical and practical implicationsof the findings. The paper ends with a summaryand conclusions section.

NATIONAL CULTURE AND LIFE INSURANCECONSUMPTION

The Effect of IndividualismBased on the literature of cross-cultural psychology,the primary focus of this study is on how indivi-dualism, a common dimension of national culture,may affect national consumption patterns of lifeinsurance. Markus and Kitayama (1991) provide anintegrative model based on the concept of self tosummarize much of the cross-cultural research ofpsychology. The model has gained widespreadsupport among social psychologists. Markus andKitayama contend that the various cultures of theworld place differing emphasis on two aspects oftasks relevant to everyday life: independence (i.e.,tasks related to agency and autonomy) and inter-dependence (i.e., tasks related to communion andaffiliation). Cultures in which the first process isprimary are said to foster an independent construalof self, whereas cultures in which the secondprocess is dominant are said to foster an inter-dependent construal of self. The independentconstrual of self is more often seen in NorthAmerican and Western European cultures, whereasthe interdependent construal of self is often foundin Asian cultures (Heine & Lehman, 1995). In thisstudy, we relate these self concepts to life insuranceconsumption, contending that people with anindependent construal of self tend to rely moreon market life insurance and less on social networksecurity (financial help offered by fellow kinsmento one’s dependants upon one’s death) than peoplewith an interdependent construal of self. We shallpresent our arguments under two aspects: the

availability and the desirability of social networksecurity.

People with an interdependent construal of selfare constantly aware of others, and focusing ontheir needs, desires and goals. The assumption isthat, while promoting the goals of other people,one’s own goals will be attended to by the personwith whom one is interdependent. Obviously,interdependent selves cannot and do not attendto the needs and goals of all others. Their attentionis usually confined to members of a social groupwith whom they share a common fate (Markus &Kitayama, 1991). This group is likely to extendbeyond one’s immediate family to close friends andrelatives. Should one member die early, others areexpected to and will probably provide financialhelp to that member’s dependants. With socialnetwork security in place, one has less need ofmarket life insurance. In fact, insurance purchasemay be considered redundant, because it incursunnecessary expenditure. By contrast, people withan independent construal of self strive to asserttheir individuality and stress their separatenessfrom the social world (Heine & Lehman, 1995).They do not focus on others’ needs, desires andgoals. Their social group of relevant others isusually small, often confined to members of thenuclear family. Should one die early, one cannotcount on fellow kinsmen to provide financialsupport for one’s dependants. In fact, it is unrea-sonable to count on relatives’ help, as it is quitelikely that one did not extend such help to otherrelatives either. As social network security isunavailable, one needs to resort to market-basedlife insurance to safeguard the welfare of one’sdependants.

Putting the availability issue aside, an indepen-dent self may also consider the social networksecurity less desirable than market life insurance.Empirical studies have repeatedly shown thatpeople with an independent construal of self tendto exhibit self-enhancement biases, whereas thosewith an interdependent construal of self do nothave such a tendency (Gelfand et al., 2002; Heine &Lehman, 1995). By engaging in self-enhancementbiases, they view themselves in unrealisticallypositive terms. They tend to draw satisfaction fromthe belief that they can exercise inner attributes toovercome the environment. Reliance on others’help may be viewed as a sign of weakness.Consequently, to an independent self, a socialnetwork security system is less desirable than amarket-based insurance system. By contrast, people


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with an interdependent construal of self do notexhibit self-enhancement biases. To them, themarket insurance system is not necessarily moredesirable than social network security.

Since we cannot readily measure the degree ofindependent/interdependent construal of selfacross cultures, we need an empirical proxy forthese self concepts. In the present study, we use theindividualism index developed by Hofstede (1983,2001) as our proxy.1 We use Hofstede’s culturaldimensions because they are the most widely usedcultural indices in the international business litera-ture and contain a relatively large number ofcountry observations.2 In explaining his conceptof individualism, Hofstede (2001: 210) notes that‘‘the central element in our mental programminginvolved in this case is the self-concept.’’ Hence,according to Hofstede (2001), individualism per-tains to the degree to which people in a countrytend to have an independent rather than aninterdependent self-construal; the reverse is thecase for collectivism. Following this line of reason-ing, we put forward the following hypothesis:

Hypothesis 1: The life insurance consumption ofa country is positively related to its level ofindividualism.

The Effect of Other National Cultural DimensionsOf the four dimensions suggested by Hofstede, theindividualism/collectivism dimension is considered‘‘the most significant cultural difference amongcultures’’ (Triandis, 2001: 907). However, Kirkman,Lowe, and Gibson (2006) recommend thatresearchers should not consider only one dimen-sion in their study. They review 64 studies ofculture using Hofstede’s dimensions at the indivi-dual level of analysis, and find that 54 of thesestudies examine only the individualism/collecti-vism dimension. The remaining 12 studies, whichinclude cultural dimensions in addition to indivi-dualism/collectivism, all find significant effects ofthese other dimensions. Kirkman et al. (2006)contend that the inclusion of cultural values otherthan individualism/collectivism in the other 52studies would have led to important insights.Following their recommendation, we furtherexplore the effects of the other cultural dimensionson national patterns of life insurance consumption.

People differ in their physical and intellectualcapacities. The power distance index (PDI) reflectsthe way in which society deals with such differ-ences. Some societies let these differences grow over

time into inequalities in power and wealth, whereasother societies try to play down such inequalities asmuch as possible. Hofstede (1983) reports thatthere is a global relationship between powerdistance and collectivism: collectivist countriesalways show large power distances, but individual-ist countries do not always show small powerdistances. The Latin European countries – France,Belgium, Italy and Spain, plus (marginally) SouthAfrica – show a combination of high power distanceand individualism. In this study, we hypothesizethat countries with high power distance are asso-ciated with lower life insurance consumption.When subordinates surrender more authority totheir superiors, they expect the superiors to lookout for their welfare and provide more protection.If they die early, their family members may also betaken care of, to a certain extent. Consequently,there is less need of market insurance. Thisargument is somewhat different from our earlierargument of individualism–collectivism. In the caseof collectivism, the dependence is on the groupmembers; in the case of power distance, thedependence is on the leader, who may or may notbe a kinsman. Our second research hypothesis is asfollows:

Hypothesis 2: The life insurance consumption ofa country is negatively related to its level of powerdistance.

The uncertainty avoidance index (UAI) assessesthe extent to which people feel threatened byuncertainty and ambiguity, and try to avoid thesesituations. Low uncertainty-avoidance societiessocialize their people into accepting or toleratinguncertainty. They will take risks more readily. Bycontrast, people living in high uncertainty-avoid-ance societies tend to have a higher level of anxiety,which may manifest itself in greater nervousness,emotionality, and aggressiveness. As a copingmechanism against uncertainty, these peoplewould prefer a more predictable environment.Instead of relying on fellow kinsmen to providefinancial help for one’s dependants upon one’sdeath (which may or may not come forth), lifeinsurance purchase is more dependable, since it is afinancial contract. We therefore put forward ourthird hypothesis below:

Hypothesis 3: The life insurance consumption ofa country is positively related to its level ofuncertainty avoidance.


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The fourth cultural dimension is masculinity–femininity (MAI).3 Masculine societies emphasizethe traditional masculine values, such as theimportance of showing off, of achieving somethingvisible, and of making money. Feminine societiestend to put relationships with people beforemoney, caring for the preservation of the environ-ment, and extending help to the weak. The effect ofMAI on life insurance consumption is ambiguous.One may argue that people living in high mascu-linity societies may buy more insurance in order totake charge of their own destiny and have betterplanning. Nevertheless, people in high femininitysocieties may also use more market life insurancesince they are emotionally more sensitive to theneeds of their dependants. Because of such oppos-ing effects, instead of putting forward a formalhypothesis, we shall let the empirical findings showus the relationship between MAI and life insuranceconsumption.

DATA AND METHODOLOGYThe key dependent variable of the present study islife insurance consumption across countries. As inprevious research, such as Beck and Webb (2003),Browne and Kim (1993) and Outreville (1996), weuse life insurance density as our proxy. This ismeasured as life insurance premium per capita inconstant US dollars, and can be interpreted as theaverage amount a typical person spends on lifeinsurance in a country. However, such an indicatorhas its limitations. First, it aggregates the demandand supply factors of life insurance, and we cannotdistinguish the demand factors from the supplyfactors (Beck & Webb, 2003). Nevertheless, thisaggregation problem will bias toward finding insig-nificant relationships (Beck & Webb, 2003; Browne& Kim, 1993). Second, it is affected by the price oflife insurance across countries, and these prices areaffected by the market structure and the govern-ment policies in each country. To remedy thislimitation, we follow the method used in Beck andWebb (2003). They suggest that, since price is afunction of supply-side factors that are likely toaffect how efficiently insurance companies canoperate, the inclusion of these supply-side factorsin the regression model can control for the priceeffect. For instance, underdevelopment of thefinancial market, lack of property protection andcontract enforcement can impede life insurancecompanies from investing efficiently, and henceaffect the price of their insurance products. There-fore Beck and Webb (2003) suggest using measures

of the development of financial market andinstitutional variables as control variables to reducebias caused by the missing price variable.

Regarding the sample design, Sivakumar andNakata (2001) describe a common methodologicalproblem in cross-cultural studies. Two countries areselected as representing different levels of one ormore cultural factors. Persons and firms are sur-veyed in those countries, and differences inresponses are attributed to distinct cultural values.In fact, the differences may be due to countrydifferences other than the cultural differences.Kirkman et al. (2006) also observe that, rather thantesting for the mediating effects of cultural values,after assessing country differences on culturalvalues, some studies use a country dummy variable,rather than cultural values, as a predictor variable.Without mediation tests, researchers cannot attri-bute country differences to culture. To address thisissue, one solution is to use a very large number ofcultures, which would allow for randomization ofthe variance on non-matched cultural and spuriousrelationships and the elimination of rival hypoth-eses.4 Relative to other cross-cultural studies, ourstudy has a fairly large sample of 41 countries, withthe sample period ranging from 1976 to 2001.Furthermore, instead of using country dummyvariables, we use the cultural values as the inde-pendent variables. To control for other countryeffects, we also include a list of economic, institu-tional and demographic determinants as controlvariables. The definitions and data sources of all ourvariables are detailed in Table 1.

Economic DeterminantsSince consumption usually increases with income,we expect life insurance consumption to alsoincrease with income. However, life insuranceprovides benefits over the long term, and theselong-term benefits will be reduced by inflation.Hence we expect inflation to be negatively relatedto life insurance consumption. Previous studieshave shown that, although life insurance consump-tion is positively related to income, it is negativelyrelated to expected inflation (Beck & Webb, 2003;Browne & Kim, 1993; Outreville, 1996).5 Just as inthe banking business, moral hazard and adverseselection are the two most important factorsaffecting the profitability of the insurance business.Any financial innovations, such as effective infor-mation processing, that can help banks reducemoral hazard and adverse selection in their lendingbusiness will also benefit the insurance industry.


92


Hence it is expected that a strong bank sector canboost the development of the insurance market.Indeed, it is quite common in several countries toobserve that banks are directly involved in the lifeinsurance business. In a recent study, Beck and Webb(2003) suggest that bank sector development is oneof the most robust determinants for life insuranceconsumption across countries and over time.

Life insurance companies, like banks, also play animportant role in channeling funds from lenders toborrowers. Life insurance companies use the pre-miums received on their policies to invest in assetssuch as stocks, and use the earnings to pay outclaims on their policies. A better-developed stockmarket may therefore enhance the growth of thelife insurance market. To our knowledge, therelationship between life insurance consumption

and stock market development has not been testedso far.

Eight countries in our sample are (or previouslywere) socialist countries, and their life insurancemarkets have been determined by their govern-ments for some years.6 Without the appropriateworking incentive, state-owned life insurance com-panies are inefficient, and this usually results inunderproduction. We expect that the life insurancemarkets in these socialist countries will be lessdeveloped than in the rest of the countries in oursample. We use a dummy variable, State, to capturethis effect.

Institutional DeterminantsLevine (1999) records that financial intermediariesare better developed in countries with legal and

Table 1 Definitions and sources of variables

Density The per capita life insurance premiums in constant 1995 US dollars.

Source: Beck, Demirguc-Kunt and Levine (2003).

Cultural indexes The individualism index (IDV), power distance index (PDI), uncertainty avoidance index (UAI), and

masculinity/femininity index (MAI)

Source: Hofstede (2001).

GDP per capita GDP per capita in constant 1995 US dollars.

Source: World Development Indicators, World Bank.

Expected inflation

rate (%)

The average inflation rate in the prior five years. Inflation rate is calculated as the natural log difference of

the consumer price index.

Source: International Financial Statistics, IMF.

Bank sector

development (%)

The ratio of the total deposit money bank assets and gross domestic product.

Source: Beck, Demirguc-Kunt, and Levine (2003).

Stock market

development (%)

The ratio of the stock market capitalization to gross domestic product.

Source: Beck, Demirguc-Kunt, and Levine (2003).

State State takes the value 1 if the country is (or previously was) a socialist country, and 0 otherwise.

Creditor right Credit¼SECURED1+D1+D2, where D1 takes the value 1 if AUTOSTAY¼0, and 0 otherwise; and D2 takes the

value 1 if MANAGES¼0, and 0 otherwise. AUTOSTAY¼1 if there is restriction in the legal code that prevents

secured creditors from gaining possession of collateral or liquidating a firm to meet obligations, and 0

otherwise. MANAGES¼1 if the firm continues to manage its property pending the resolution of the

reorganization process, and 0 otherwise. SECURED1¼1 if secured creditors are ranked first in the

distribution of the proceeds that results from the liquidation of a firm, and 0 otherwise. By construction,

Credit is increasing with creditor right, and is ranges from 0 to 3.

Source: Levine (1999).

Contract enforcement ConRisk reflects the risk that a government will modify a contract after it has been signed; it ranges from 1

(low contract enforcement) to 10 (high contract enforcement).

Source: Levine (1999).

Dependency ratio (%) The dependency ratio is the sum of the ratio of the population under age 15 to the population ages 15–65

and the ratio of the population over age 65 to the population ages 15–65.

Sources: World Development Indicators, World Bank.

Religion (%) The percentage of the population with Protestant, Catholic or Muslim beliefs.

Source: La Porta, Lopez-De-Silanes, Shleifer, and Vishny (1999).


93


regulatory systems that give a high priority to theprotection of creditors and to the effective enforce-ment of contracts. Beck and Webb (2003) also arguethat the protection of private property rights andcontract enforcement are important for the growthof the life insurance market. Using the rule of lawobtained from the Political Risk Services as ameasure of the degree to which people in a countrytrust the legal system to settle disputes andenforce contracts, Beck and Webb (2003), however,find that the rule of law cannot explain thevariation in life insurance consumption acrosscountries. In the current study, creditor right(Credit) is measured as the sum of the AUTOSTAY,MANAGES, and SECURED1 scores obtained fromLevine (1999). We also use Levine’s contract riskindex (ConRisk). It is expected that the lifeinsurance market will be better developed in acountry that has high scores on creditor rights andcontract enforcement.

Demographic DeterminantsPrevious studies have proposed many demographicvariables to explain life insurance consumptionacross countries. Among them, dependency ratioand religion are the most consistent and robustdeterminants of cross-country life insurance con-sumption. The positive relationship betweendependency ratio and life insurance consumptionis intuitive. When the number of dependants of aperson has increased, this person needs to buy morelife insurance. Whereas Browne and Kim (1993) andBeck and Webb (2003) find a significant, positive

relationship between dependency ratio and lifeinsurance consumption across countries, Outreville(1996) finds the relationship to be insignificant.

Browne and Kim (1993) suggest that religiouspeople tend to purchase less life insurance, becausethey perceive that buying life insurance shows adistrust of God’s protection. Browne and Kim(1993) and Beck and Webb (2003) find that lifeinsurance consumption is significantly lower inIslamic countries than in other countries. However,Outreville (1996) find that the relationshipbetween Muslim belief and life insurance countriesin developing countries is weak. Our religionvariable (Religion) shows the share of the totalpopulation (in percentage) with Protestant, Catholic,or Muslim beliefs.7

Regression ModelTables 2 and 3 provide the descriptive statistics andthe correlation matrix of our explanatory vari-ables.8 Since some of the explanatory variables arehighly correlated, putting these variables in thesame regression model leads to a severe multi-collinearity problem. To remedy this problem ingeneral, we use the principal component method tocondense all the economic variables, institutionalvariables and demographic variables into theeconomic component (PrinEcon), the institutionalcomponent (PrinInst) and the demographic compo-nent (PrinDemo) respectively. These three compo-nents are selected because they are the only oneswith eigenvalues greater than 1 in the principal

Table 2 Descriptive statistics

Variable Mean Median Standard deviation Minimum Maximum Observations No. of countries

Life insurance density 341.79 103.56 763.87 0.012 11201.08 1477 58

Life insurance penetration (%) 1.94 1.15 2.56 0.004 28.29 1477 58

Individualism 51.80 54.00 24.32 6.00 91.00 1477 58

Power distance 53.20 55.00 22.22 11.00 104.00 1477 58

Uncertainty avoidance 65.47 68.00 22.55 8.00 112.00 1477 58

Masculinity/femininity 51.52 53.00 20.29 5.00 110.00 1477 58

Real GDP per capita 13240.9 11248.02 11327.84 218.889 58915.33 1474 58

Expected inflation (%) 15.66 7.25 29.29 �0.103 270.66 1296 58

Bank sector development (%) 57.27 48.43 36.19 0.047 189.74 1417 58

Stock market development (%) 41.55 22.38 54.44 0.040 510.50 1047 56

Social security to GDP (%) 14.29 13.15 9.24 0.000 42.70 1120 54

Creditor right 1.51 1.00 0.99 0.000 3.00 1227 41

Contract enforcement right 8.21 8.96 1.53 4.680 9.98 1250 42

Accounting standard 63.75 64.00 11.24 31.000 83.00 1174 38

Dependency ratio (%) 59.17 55.59 13.18 37.067 107.21 1477 58

Religion (%) 70.69 83.85 32.51 1.500 99.60 1474 57


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component analysis. Our basic empirical model isdescribed by the following equation:9

Insit ¼ aþ bXi;Cul þ Z1PrinEcon;it þ Z2PrinInst;i

þ Z3PrinDemo;it þ lDYear þ eit ð1Þ

where Insit is the life insurance consumptionmeasured in the natural logarithm of density(Ln Den) for country i in year t. Xi,Cul is an arrayof cultural variables that includes the indexes onindividualism (IDV), power distance (PDI), uncer-tainty avoidance (UAI) and masculinity/femininity(MAI) for country i. These cultural variables varyacross countries, but they are invariant across timefor a given country. b is a vector of coefficientscorresponding to these cultural variables. Ourhypotheses predict that whereas the coefficientson IDV and UAI are positive, the coefficient on PDIis negative. The coefficient on MAI should bedetermined empirically. PrinEcon,it and PrinDemo,it

are, respectively, the scores on the economiccomponent and the demographic component ofcountry i in year t. PrinInst,i is the score on theinstitutional component of country i.10 DYear is anarray of year dummy variable that is used to capturethe time effect on life insurance consumptionacross countries. Finally, eit is the error term forcountry i in year t. Since we have a pool of timeseries and cross-sectional data, we use the pooledGLS technique to estimate Equation (1).11,12

EMPIRICAL FINDINGSWe use an unbalanced panel data of 41 countriesover the years from 1976 to 2001 to estimateEquation (1), and Table 4 reports the results. Whenlife insurance density is regressed on the threeprincipal components, the estimated coefficientson the economic and institutional components aresignificantly positive, and the estimated coefficienton the demographic component is negative butinsignificant. These findings agree with our expec-tation. When individualism (IDV) is added into themodel, we find that the estimated coefficient onIDV is significantly positive, and the estimatedcoefficient on PrinDemo becomes significantly nega-tive. The adjusted R2 increased from 0.70 to 0.83,and the increase in R2 is also highly significant. Thisfinding suggests that, after taking into account theeconomic, institutional and demographic factors,the difference in individualism can explain anadditional 13% of the variation in life insurancedensity across countries. This finding supportsour Hypothesis 1, that the consumption of lifeT

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95


insurance is higher in individualistic countries thanin collectivistic countries.

When life insurance density is regressed on powerdistance (PDI) and the three principal components,we find that PDI has a strong negative relationshipwith life insurance density. The coefficient of�0.032 is statistically significant at the 0.01 level.This finding is consistent with our Hypothesis 2.Alternatively, when life insurance density isregressed on uncertainty avoidance (UAI) and thethree principal components, we find that therelationship between life insurance consumptionand UAI is insignificant. This finding does notsupport our Hypothesis 3. There is a possible reason

for this. Hofstede (2001: 148) warns that ‘‘uncer-tainty avoidance does not equal risk avoidance.’’Risk is often expressed as a percentage of prob-ability that a particular event may happen. Anxietyand uncertainty are both diffuse feelings, with nospecific object. Rather than lead toward an escapefrom risk, uncertainty avoidance leads to an escapefrom ambiguity. When UAI is replaced by mascu-linity/femininity (MAI) in the model, we find thatthe estimated coefficient on MAI is negative(�0.008) and statistically significant at the 0.01level. The significant, negative effect of MAI on lifeinsurance consumption seems to indicate that thefemininity effect dominates the masculinity effect.

Table 4 National culture and life insurance density

Model 1 2 3 4 5 6

PrinEcon 1.743*** 1.238*** 1.526*** 1.737*** 1.783*** 1.285***

(29.61) (24.99) (29.50) (29.31) (30.48) (26.93)

[1.93] [2.33] [2.04] [1.96] [1.96] [2.39]

PrinInst 0.200*** 0.128*** �0.018 0.185*** 0.148** 0.064

(3.34) (2.78) (�0.34) (2.95) (2.46) (1.33)

[2.24] [2.25] [2.37] [2.46] [2.31] [2.67]

PrinDemo �0.078 �0.243*** �0.206*** �0.089 �0.115* �0.288***

(�1.31) (�5.27) (�4.01) (�1.46) (�1.95) (�6.47)

[1.86] [1.91] [1.90] [1.95] [1.89] [1.94]

IDV 0.035*** 0.030***

(24.53) (17.42)

[1.39] [2.19]

PDI �0.032*** �0.011***

(�17.77) (�5.99)

[1.28] [2.02]

UAI �0.001 0.004***

(�0.83) (3.11)

[1.20] [1.32]

MAI �0.011*** �0.008***

(�5.15) (�5.21)

[1.05] [1.12]

F-stat 73.19 140.38 107.23 70.66 73.65 142.61

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Wald 1953.50 3933.10 2993.00 1955.80 2040.70 4431.20

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Condition 10.17 11.82 12.09 12.03 11.91 21.98

Adjusted R2 0.70 0.83 0.78 0.70 0.71 0.84

R2 0.71 0.83 0.79 0.71 0.72 0.85

F(DR2) 580.31*** 304.74*** 0.57 25.75*** 724.16***

Notes: This table reports the results of the pooled GLS regressions of the natural logarithm life insurance density on individualism index (IDV),uncertainty avoidance index (UAI), power distance (PDI), masculinity/femininity index (MAI), and the three principal components over the years from1976 to 2001. The F-statistic (F-stat) is used to test the null hypothesis that all the estimated coefficients on the independent variables including the timedummy variables are equal to zero. The Wald statistic (Wald) is used to test the null hypothesis that all the reported estimated coefficients are equal tozero. The p-values of F-stat and Wald are in parentheses. The row ‘Condition’ reports the condition number. F(DR2) is the F-statistic for the change in R2.The changes in R2 in models 2–6 are in comparison with the R2 in model 1; the t-statistics are in parentheses and the variance inflation factors are insquare brackets.***Significance at 1% level; **significance at 5% level; *significance at 10% level.


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In the full model, which includes all the culturalvariables and the economic, institutional anddemographic principal components, we find thatthe estimated coefficients on IDV and UAI aresignificantly positive and the estimated coefficientson PDI and MAI are significantly negative. Thesefindings support our three hypotheses.

Criticism may be raised regarding the use ofHofstede’s cultural dimensions, since Hofstede’scultural scores are based on his empirical work in1967–1973, which may be outdated (Shenkar,2001). To investigate whether our results areaffected by the possible changes in these scoresover time, we collected updated scores on indivi-dualism, power distance, and masculinity/femi-ninity from the GLOBE project conducted byHouse, Hanges, Javidan, Dorfman, and Gupta(2004). We obtained similar findings.13

DISCUSSIONAfter controlling for the economic, institutionaland demographic principal components, Hofstede’scultural dimensions explain an additional 14% ofthe variation of life insurance density acrosscountries (the adjusted R2 increases from 0.70 to0.84). Such a finding confirms that national cultureplays a significant role in explaining the nationaldifferences in life insurance consumption. Of thefour cultural dimensions, individualism shows thehighest level of significance, followed by powerdistance and masculinity–femininity. In an indivi-dualistic society, the social group of relevant othersis usually small – often confined to members of thenuclear family. Should one die early, one cannotcount on fellow kinsmen to provide financialsupport for one’s dependants. Furthermore, peopletend to draw satisfaction from the belief that theycan exercise their inner attributes to overcome theenvironment. Reliance on others’ help may beviewed as a sign of weakness. Instead of relyingon social network security, which is less desirable oravailable, they resort to market-based life insuranceto safeguard the welfare of their dependants.Alternatively, power distance also shows a strongnegative relationship with life insurance consump-tion. In a country with high power distance,subordinates surrender more authority to theirsuperiors. In return, they expect their family needsto be better taken care of by their superiors in caseof emergency. There is less need for marketinsurance. Similarly, masculinity is also inverselyrelated to life insurance density. While peopleliving in high masculinity societies may buy more

insurance in order to take charge of their owndestiny and have better planning, people in highfemininity societies may use more market lifeinsurance since they are emotionally more sensitiveto the needs of their dependants. The negative,significant coefficient of MAS indicates that thefemininity effect dominates the masculinity effect.

Somewhat surprising is the relatively weak sig-nificance of the uncertainty-avoidance dimension.The estimated coefficient on UAI is significantlypositive only in the full regression model. Whenlife insurance density is regressed on uncertaintyavoidance (UAI) and the three principal compo-nents, we find that the coefficient of UAI isinsignificant. We expect that people in high-UAIcountries will be more likely to buy life insurance,as insurance is a financial contract that is morereliable than financial help from fellow kinsmen.One possible explanation for the weak significance,as warned by Hofstede (2001: 148), is that uncer-tainty avoidance is not necessarily the same as riskavoidance. This result is different from the findingin the finance and investment area. Kwok andTadesse (2006) find that high-UAI societies tend toadopt a bank-based financial system, whereas low-UAI societies tend to adopt a market-based finan-cial system. Bank deposits provide high-UAI inves-tors with stable returns because the payoff iscontractually fixed and usually guaranteed bydeposit insurance. Alternatively, low-UAI investorswould prefer to invest in stock markets, which yieldhigher but more volatile returns. Kwok and Tadesse(2006) find that the coefficient of UAI is consis-tently negative and significant, despite addingvarious control variables.

Leung et al. (2005) provide a state-of-the-artreview of studies in culture and internationalbusiness. They observe that, although in manystudies the cultural variable is statistically signifi-cant, the strength of the relationship (i.e., the sizeof the coefficient) is relatively weak in practicalterms, reflecting the fact that culture does notexplain a large amount of variance. However, in ourstudy of cross-country life insurance consumption,the cultural variables are both statistically andeconomically significant. The cultural dimensionstogether explain an additional 14% of the variationof life insurance density. The estimated coefficienton IDV of 0.03 indicates that the per capita lifeinsurance consumption in 1995 constant US dollarsper year (Ln Den) will increase by 30% for every10-point increase in Hofstede’s individualism index(the index ranges from 6 to 91). Similarly, the


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estimated coefficient on PDI of �0.011 indicatesthat Ln Den will decrease by 11% for every 10-pointincrease in Hofstede’s power distance index (theindex ranges from 11 to 104). The effects ofnational culture are economically significant, andwarrant the attention of practitioners who work inthe insurance industry.

We can think of several implications for multi-national insurance companies that sell life insur-ance polices around the world. The first implicationrefers to the choice of market location. Whenmultinational insurance companies survey possiblelocations for foreign market entry, in addition toconsidering economic, demographic and institu-tional factors, they should also consider nationalculture. Given limited financial and humanresources, Ceteris paribus, the firms should firstapproach countries with higher scores of individu-alism (IDV) and lower scores of power distance(PDI) and masculinity/femininity (MAI).

The second implication refers to the marketsegmentation within a host country. As explainedin Hofstede (1983), differences in national culturesare statistical in nature. Characterizing a nationalculture does not mean that every individual withinthat culture is mentally programmed in the sameway. The national culture reflects the averagepattern of beliefs and values, around which indivi-duals in the country differ. Researchers have shownthat there is indeed plenty of within-countryvariation on cultural values (e.g., Au, 1999). Forinstance, on the average, Chinese are more likely tohave an interdependent self-construal than Amer-icans. However, an entrepreneur living in Shanghai(the commercial center of China) may have ahigher independent self-construal than a small-business owner living in a rural town in the US.When a multinational insurance company entersChina, it may target population segments that areless influenced by the traditional culture. In itsmarketing efforts, the firm may focus on youngexecutives in big cities. These people study Westerntextbooks, and are exposed to Western massmedia. Their relocation into big cities because ofemployment opportunities separates them fromtheir kinsmen in the rural areas. With the suppor-tive social security network far away, they aremore likely to resort to market life insurance forthe protection of their dependants.

The third business implication refers to thecontents of advertising. In promoting life insuranceto this group of potential customers, the insu-rance company may highlight the fact that the

traditional dependence on fellow kinsmen orleaders in supporting one’s dependants may nolonger function well under the changing environ-ments. With rising income, one can afford thepurchase of market life insurance. It is better to getthings well planned and put them under one’scontrol in order to ensure that their dependants’needs will be taken care of.

SUMMARY AND CONCLUSIONSLinking the two streams of literature in culture andinsurance, this cross-disciplinary study examineshow national culture affects the different consump-tion patterns of life insurance across countries. Lifeinsurance is a service that is abstract, complex, andfocused on unsure future benefits. Because of theuncertainty and ambiguity of the life insuranceproduct, consumers are more likely to respondaccording to their cultural prescriptions. Ourempirical findings show that individualism indeedhas a significant, positive effect on life insuranceconsumption, whereas power distance and mascu-linity/femininity have significant, negative effects.The results are robust, even after controlling foreconomic, institutional and demographic deter-minants. However, the relationship betweenuncertainty avoidance and life insurance consump-tion is weak.

As in other studies, the present study has itslimitations. First, because of data availabilityproblems, we do not include any pricing variablesin our analysis, and we can estimate only a reduced-form model. A higher price of life insurance policywill definitely reduce the demand for life insurance.Following the suggestion of Beck and Webb (2003),we include financial market development andinstitutional variables in the regression model toreduce the bias caused by the missing price variable.Second, current research in cognitive psychologyshows that the human mind is fluid and adaptive.This conception of the human mind gives rise to adynamic view of culture, which contrasts sharplywith traditional views that regard culture as moreor less stable and static. This dynamic view ofculture argues that it is represented by cognitivestructures and processes that are sensitive toenvironmental influences. Our study follows thetraditional, static approach; it does not examinesuch cognitive processes. As recommended byLeung et al. (2005), such a cognitive, dynamicprocess is better studied using the experimentalapproach. Our study confirms the presence of acultural effect at the country level. It lays the


98


groundwork for more complex experiments to testhow differences in the levels of a cultural dimen-sion influence behaviors and perceptions.

ACKNOWLEDGEMENTSWe thank our colleagues Helen Doerpinghaus and XuHuang for providing helpful comments. Andy Chuiacknowledges the financial support from the HongKong Polytechnic University (A-PG29), and ChuckKwok gratefully acknowledges the support of theCenter for International Business Education andResearch (CIBER) at the University of South Carolinafor this research project.

NOTES1Several concerns have been raised regarding the

Hofstede dimensions: (1) that his empirical work isbased on a single, multinational corporation; (2) thatthe dimensions are too broad, and some otherimportant value dimensions have not been included;and (3) that his cultural scores based on his empiricalwork in 1967–1973 may be dated. Despite thesecriticisms, Hofstede’s dimensions are still the mostwidely used cultural indices in the internationalbusiness literature.

2The initial dimensions of Hofstede are derived fromhis empirical work that took place in 1967–73. Asreviewed in Leung et al. (2005), there have been noveldimensions. For instance, Schwartz (1994) has identi-fied seven cultural-level dimensions of values. Leunget al. (2002) create a social axiom survey based onitems culled from the psychological literature as well asfrom qualitative research conducted in Hong Kongand Venezuela. Alternatively, House et al. (2004)conduct a GLOBE project that adopts a theory-basedapproach. Despite the use of different items to identifycultural dimensions, House et al.’s results are consis-tent with previous findings, and most of the culturaldimensions identified are related conceptually andcorrelated empirically with Hofstede’s dimensions,suggesting that the Hofstede dimensions are quiterobust (Leung et al., 2005: 366).

3In the 2001 update of his book, Hofstede proposesone more cultural dimension: long-term orientation.However, the data for this dimension are less completethan those for the four original dimensions. If weinclude this variable in our test, our sample ofcountries will be reduced from 41 to 18, and thenumber of observations in our sample will be reducedfrom 853 to 377. Therefore this cultural dimension isnot included in this study.

4However, a study using a large sample of countriesis a rarity in international business research. Typically

two to four countries are compared in cross-nationalstudies (Samiee & Jeong, 1994). To alleviate this issue,Sivakumar and Nakata (2001) suggest an algorithm inchoosing country combinations that would strengthenhypothesis testing.

5Social security is not included in the current study,because previous studies indicate that the relationshipbetween life insurance consumption and social secur-ity is insignificant (Beck & Webb, 2003; Outreville,1996). Our conclusion will not be affected even if weinclude social security in our analysis.

6These socialist countries are Bulgaria, China, CzechRepublic, Hungary, Poland, Romania, Slovak Republic,and Vietnam. Most of these socialist countries startedto transform into market economies in the 1990s.However, the initial conditions of their life insurancemarkets are still quite different from those of the non-socialist countries. These differences in initial condi-tions could be an important determinant for thedevelopment of the life insurance markets acrosscountries. The authors thank the referee for bringingup this point.

7We do not separate our religion measure intoProtestant, Catholic and Muslim, because we believethat a religious person will generally purchase lesslife insurance if he/she perceives that buying lifeinsurance shows a distrust of God’s protection. Ourmeasure on religion may underestimate the share ofreligious people out of the total population, becausethis measure only counts those people with aProtestant, Catholic or Muslim belief. For example,since Buddhism is very popular in Asia, our religionmeasure will underestimate the proportion of religiouspeople in Asian countries. Data on other religions,however, are not available to us.

8We first compute the time-series means of the time-varying variables for each country over the years from1976 to 2001 and then estimate the correlations acrosscountries. We require each country to have at least 20observations for this variable over the period from 1976to 2001. Since none of the eight socialist countries has20 observations on life insurance density, thesecountries are excluded from the correlation analysis.

9As a robustness test, we also estimate Equation (1)using the data for the explanatory variables instead ofthe three principal components. We obtain similarresults for the relationship between life insuranceconsumption and the cultural variables.

10The economic component (PrinEcon) is positivelycorrelated to national income, and developments inthe bank and stock sectors, but it is negativelycorrelated to expected inflation rate and State. There-fore we expect the coefficient on PrinEcon (Z1) to be


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positive. Since the institutional component (PrinInst) ispositively correlated to Credit and ConRisk, we expectthe coefficient on PrinInst (Z2)to be positive. On theother hand, the demographic component (PrinDemo) ispositively correlated to the dependency ratio andReligion. As a result, the sign of the coefficient onPrinDemo (Z3) has to be determined empirically.

11To save space, we do not report the interceptestimate (a) and the estimated coefficients on the yeardummy variables, because time effects are not thefocus of this study.

12Judge, Hill, Griffiths, Lutkepohl, and Lee (1988)suggest that a value of variance inflation factor(condition number) that is 5 (30) or more is anindication of severe multicollinearity. In our regressionanalysis, all the estimated coefficients have varianceinflation factors much less than 5, and the conditionnumbers of our regression are all less than 30.

13To conserve space, the additional findingsusing the GLOBE dimensions are not reported in thispaper, but will be made available to readers uponrequest.

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ABOUT THE AUTHORSAndy Chui is Associate Professor of the School ofAccounting and Finance at the Hong KongPolytechnic University. Born in Hong Kong, he is

a Chinese and a Hong Kong permanent resident. Hereceived his doctorate in Finance from the HongKong University of Science and Technology. Hisresearch interests are empirical asset pricing model,behavioral finance, and international finance. Hecan be reached at [email protected].

Chuck C.Y. Kwok is the Charles W. Coker Sr.Distinguished Moore Fellow and a Professor ofInternational Business at the University of SouthCarolina. He was Vice President-Administrationof the Academy of International Business in1995–1996. Born in Hong Kong, he is an Americancitizen. He received his PhD degree from theUniversity of Texas, majoring in InternationalBusiness. His research concentrates on interna-tional finance, international business education,and business in Pacific Asia. He can be reached [email protected].

Accepted by Lorraine Eden, Departmental Editor, 24 June 2007. This paper has been with the authors for two revisions.


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national culture and life insurance consumption

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