regional determinants of life insurance consumption: evidence from selected asian economies

Regional determinants of life insuranceconsumption: evidence from selected

Asian economies

Subir Sen and S Madheswaran*

The life insurance industry in developing Asian economies is under-developed compared with global standards. The low market penetrationis attributed to full or partial government ownership and entry restrictionson foreign insurers. Regulatory changes and adoption of liberal policieshave aided the growth of the life insurance industry in the past decade. Atthe same time, economic and social factors were expected to promoteinsurance awareness and consumption. In this context, the paper analysesthe factors explaining life insurance demand in 12 Asian economies,including economies from the South Asian Association for RegionalCooperation and Association of Southeast Asian Nations regions, as wellas China. The results suggest that income, financial depth, inflation, thereal interest rate, and the youth dependency ratio are significant determi-nants of life insurance consumption. Foreign ownership and improvedregulations may foster growth. But urbanisation and the literacy rate areamong the few determinants found not to have the impact observed inprevious studies. The research highlights the limitations of studies usingmacrodata and contributes to understanding of the growing insuranceindustry in the region.

Introduction

In the 1990s, the financial sector of many Asianeconomies went through a phase of institu-tional restructuring. Economies in the regionemphasised regulatory reforms to improve theperformance and strength of financial institu-tions, especially of banking and insurance1

to meet the growing demand for financial

services. Traditional measures of the growthof the insurance sector, insurance densityand penetration, for these economies haveimproved in the past decade. However, closeexamination of the data reveals that the perfor-mance of the insurance industry in the regionis short of global standards.

The identification of the determinants of thedemand and supply of financial services isan ongoing subject of research. Studies by

* Subir Sen, Assistant Professor, Department of Business Sustainability, TERI University, New Delhi, India, Email:[email protected]; and S Madheswaran, Professor, Centre for Economic Studies and Policy, Institute for Social andEconomic Change, Bangalore, India. An earlier version of this paper was presented at the 11th Annual Asia Pacific Riskand Insurance Association Meeting, 22–25 July 2007 with support from Max New York Life Insurance Co. Ltd., India.Helpful comments from Jean Kwon and the anonymous reviewers are gratefully acknowledged. The usual disclaimerapplies.

1 Lee and Park (2009) present a lengthy discussion on reform measures in Asia to strengthen financial institutions and alignthem to the region’s needs, especially after the Asian financial crisis of 1997 and the global crisis thereafter.

doi: 10.1111/apel.12024

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© 2013 Crawford School of Public Policy,The Australian National University and Wiley Publishing Asia Pty Ltd.

mailto:[email protected]

Hammond et al. (1967), Beenstock et al. (1986),Truett and Truett (1990), Browne and Kim(1993), Outreville (1996), Beck and Webb(2003), and Li et al. (2007), among others, haveidentified variables explaining life insuranceconsumption. A recent study by Outreville(2011) reports 80 studies identifying variouseconomic, demographic, socio-cultural, andstructural factors fostering insurance con-sumption. The important predictors arenational income, savings, the price of insur-ance, the interest rate, expected inflation, socialsecurity, bequest motives (family size, depend-ency ratio, etc.), life expectancy at birth, educa-tion, the legal system, regulations, and religion.In general, the results suggest that economicgrowth need not necessarily contribute to theexpansion of the insurance industry. Govern-ment controls, lack of competition, strict entrynorms, and limited foreign participation mayhinder the growth of the industry and therebylimit consumption. As a result, the industrymay fail to exploit the scope and scale of econo-mies in enhancing the reach of insurance.Against this backdrop, this study examines thedeterminants of demand for life insurance in12 Asian economies between 1994 and 2008.

The economies were selected on the basis oftheir life insurance penetration level for thereference year 1994 and the intensity of reformmeasures at the industry level. They comprisefour South Asian Association for RegionalCooperation (SAARC) economies, two econo-mies from the greater China region, and sixAssociation of Southeast Asian Nations(ASEAN) economies. A limited number ofstudies have been conducted on these econo-mies. The results show per capita income,savings, financial depth, inflation, and the realinterest rate as the main determinants of insur-ance consumption. Foreign ownership andadequate regulatory and supervisory measuresmay foster growth, but because of cultural andpolitical differences across selected economiesin the Asian region, experiences may widelyvary. Although some of our observations are inline with previous studies, we also come acrosscontradictory results that raise questions.

The paper is organised as follows. The nextsection presents a thematic review of studies

on the demand for insurance in general andlife insurance in particular. The review gener-ates the broad researchable issues and scope ofthe study, which are discussed in detail in thethird section. The fourth section describes theselection of the sample, variable selection, datasources, and the econometric framework. Theempirical results are discussed in the fifthsection. The final section summarises the obser-vations from the study.

Review of literature

Theoretical studies of life insurance consump-tion date back to Huebner (1916) who postu-lated that human life has certain qualitativeaspects that give rise to its economicvalue. Contributions from von Neumann andMorgenstern (1947), Arrow (1953), and Debreu(1953), and theoretical studies by Yaari (1965),Mossin (1968), Hakansson (1969), Fisher (1973),Borch (1977), Pissarides (1980), Campbell(1980), Karni and Zilcha (1985, 1986), Lewis(1989), and Bernheim (1991), among others,assimilated the developments in the field of theeconomics of risk and uncertainty. Over time,the economics of insurance demand becamemore focused on evaluating the amount of riskto be shared or distributed between the insuredand the insurer.

The theory of demand for life insur-ance developed further through the lifecycle hypothesis of consumption proposedby Modigliani and Brumberg (1954), andFriedman (1957). In the macroeconomic litera-ture, the Keynesian consumption hypothesisand permanent income hypothesis, togetherwith the life cycle models, explain individualconsumption patterns in relation to income,price, and interest rate. It is assumed that indi-viduals wish to maximise consumption butfail to do so because of constraints such asdebt, retirement, and premature death. Conse-quently, failure to make a bequest severelyaffects a person’s savings. Taking into accountthe uncertainties associated with the time ofdeath, individuals can improve lifetime utilityvia the purchase of a life insurance policy (pureendowment insurance) and can leave a bequest

SEN AND MADHESWARAN — REGIONAL DETERMINANTS OF LIFE INSURANCE CONSUMPTION

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as a portion of their income for dependents(Yaari 1965). Pissarides (1980) extended Yaari’swork to show that life insurance was capable ofabsorbing the impact of fluctuations in lifetimeincome. Therefore, insurance plays a role inconsumption smoothing, bequest or repay-ment of debt once an insured is no longerearning an income.

Hakansson (1969) examined the bequestmotive in detail using a discrete-time model ofdemand for financial assets in general and lifeinsurance purchase in particular. Borch (1990)showed that saving through life insurancetakes place at a higher rate of interest than con-ventional saving, and therefore, an inverserelationship exists between the interest rateand demand for life insurance. Simple demandmodels were proposed by Pratt (1964), Mossin(1968), and Smith (1968). They considered arisk-averse decision-maker endowed withinitial wealth and concluded that demand forlife insurance varies inversely with the amountof wealth an individual possesses. Karni andZilcha (1985) developed a methodology tomeasure risk faced by individuals and how riskperceptions influence demand for insurance.

Mossin (1968) identified insurance con-sumption to be an inferior good, as opposed toit being traditionally seen as a luxury goodwith high-income elasticity of demand. Hoyand Robson (1981), and Briys et al. (1989) heldinsurance to be a ‘Giffen’ good. Lewis (1989)concluded that the number of dependents alsoinfluences the demand for life insurance.

The ‘prospect theory’ propounded byKahneman and Tversky (1979) argued that anindividual takes a decision with respect to areference point. Gains through purchase ofinsurance receive little consideration againstlosses with respect to the reference point.During the early 1990s, studies augmented theeffects of ‘framing’ on decision-making todescribe individual behaviour (Machina 1982,1987). Different frames or reference pointsmay represent heterogeneous, region-specificcharacteristics, and therefore, the traditionaleconomic variables explaining insurance con-sumption often may not be sufficient.

In sum, from the theoretical viewpoint,income, wealth, rate of interest, current con-

sumption, anticipated future consumption, anddebt are a few of the economic variables affect-ing insurance consumption among risk-averseindividuals. The demographic and social vari-ables having a potential impact on life insuranceconsumption are probability of death, risk aver-sion, age at death, and number of dependents,among others. Most of these variables may notbe appropriate for a study focused at a macrolevel. But, variables representing individualbehaviour are often difficult to obtain. Thisleads us to review the large volume of empiricalexercises and identification of variables easilyavailable and open to interpretation for thecurrent study. In the next few paragraphs, wediscuss selected empirical studies.

As already mentioned a large volume ofempirical studies exists on analysis of insur-ance demand and supply side factors. Mantisand Farmer (1968) studied a multi-variabledemand forecast for a cross-section data.Fortune (1973) studied the empirical implica-tions of the expected utility hypothesis ofchoice under uncertainty for the demand forlife insurance and concluded that demanddepends on income, non-human wealth, andthe rate of discount. Headen and Lee (1974)considered three sets of variables that stimulatedemand, viz. variables representing insurer’sefforts, those influencing household savingdecisions and the ability to pay, and marketsize. They concluded that life insurancedemand is inelastic and is positively affectedby changes in consumer sentiments and inter-est rates. In a separate empirical exercise usingdata for the US life insurance industry,Hammond et al. (1967), identified factors asso-ciated with household life insurance premiumexpenditures and estimated the income elas-ticity of life insurance. On the contrary, Babbel(1985) obtained a strong negative price elas-ticity of demand using US data. Cargill andTroxel (1979) highlighted the absence of aunified theoretical explanation of savings in lifeinsurance and empirically analysed the rela-tionship between life insurance demand, andprice changes or anticipated inflation. The find-ings suggest that such a relationship is heavilydependent on the period under review and onwhether or not there were structural changes.

ASIAN-PACIFIC ECONOMIC LITERATURE

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Beenstock et al. (1986) analysed panel dataof ten industrialised economies over 12 yearsand found that life assurance premiums variedwith selected demographic variables andincome, but an inverse relation was notedwith regard to social security expenditure.Beenstock et al. (1988) investigated the rela-tionship between property liability insurancepremiums and income for 12 economies overa period of 12 years and found that the mar-ginal propensity to insure differed across theeconomies and the premium varied directlywith real rates of interest. Truett and Truett(1990) discussed the growth pattern of lifeinsurance consumption in Mexico and in theUSA in a comparative setting between 1964and 1984. Their results also showed the exist-ence of a higher income inelasticity of demandfor life insurance in Mexico at low incomelevels. Age, level of education, and incomewere significant factors affecting the demandfor life insurance in both economies. Browneand Kim (1993) analysed 45 economies overtwo separate time periods (1980 and 1987) andconcluded that income and social securityexpenditures are significant determinants ofinsurance demand, while inflation was foundto have an inverse relationship with demand.The incorporation of religion,2 as a dummyvariable, revealed that Islamic nations havea significant negative affinity towards lifeinsurance.

A cross-sectional analysis of 45 developingeconomies conducted by Outreville (1996) for1986 considered variables such as agriculturalstatus of the economy and the health status ofthe economy in terms of access to amenities,such as percentage of population with access tosafe drinking water, percentage of the labourforce with higher education, and level offinancial development, among many others.Dummy variables reflected competition in thedomestic market and foreign insurer participa-tion. The results showed that personal dispos-able income and level of financial developmentsignificantly relate to insurance development.

Because institutions vary across economies, thesignificance of the market structure dummyappeared to be of importance.

Browne et al. (2000) explained the differ-ences in property liability insurance consump-tion across economies and analysed the OECDeconomies to conclude that in general, insur-ance purchase is influenced by various eco-nomic and demographic conditions. A separatestudy with nine OECD economies by Wardand Zurbruegg (2000) concluded that country-specific factors3 influence the causal relation-ship between economic growth and insurancemarket development. Enz (2000) proposed anS-curve relationship between per capitaincome and insurance penetration. Using thisone-factor model, a long-run forecast of lifeinsurance consumption was generated. Byobserving the outlier economies, it was pos-sible to identify structural factors such as insur-ance environment and taxation regimeresulting in such deviations. Park et al. (2002)studied the impact of socio-cultural variableson the degree of insurance pervasiveness andusing a representative sample of 37 economiesthey found out that certain socio-political vari-ables can significantly influence the level ofinsurance pervasiveness but not the nationalculture. Two exhaustive studies, one by Wardand Zurbruegg (2002), and the other by Beckand Webb (2003) looked for the causes behindthe variations in life insurance consumptionacross economies. The studies highlighted thedifficulties in explaining the low per capitaconsumption of insurance in Asian economieswith higher savings rates, a sound capitalmarket in some of them, and large andgrowing populations with low provision forpensions or other social security. Except forJapan, Asian economies have low insurancedensity and penetration.

The study by Ward and Zurbruegg(2002) indicated that improved civil rightsand political stability lead to an increase in theconsumption of life insurance, both in theAsian and OECD (Organisation for Economic

2 ‘Religion can provide weights into individuals . . . and life insurance consumption is less in predominantly Islamiccountries’ Browne and Kim (1993), p 621.

3 Country-specific factors referred to by Ward and Zurbruegg (2000) include attitudes towards risk and risk management,regulatory factors, the legal environment, and availability of financial intermediation.


89


Co-operation and Development) regions. Fol-lowing Laporta et al. (1997, 1998, 2000), worksrelating to the supportive aspects of the legalenvironment for finance considered the samelegal variables in determining demand forinsurance. Lenten and Rulli (2006) exploredthe time series properties of the demand for lifeinsurance in Australia using a novel statisticalprocedure that allows unobservable compo-nents to be extracted. Li et al. (2007) examinedthe determinants of life insurance consump-tion in OECD economies and concluded that asignificant positive income elasticity ofdemand for life insurance exists and demand isdependent on the number of dependents andlevel of education but falls with life expectancyand social security expenditure. They usedGMM (Generalised Method of Moments) andthat improved the robustness of results com-pared to ordinary least square (OLS) estimates.

A few studies have focused on single econo-mies, such as Zhu (2002), and Hwang andGao (2003), which focused on the individualdemand for life insurance in the Chineseeconomy. Lim and Haberman (2004) consid-ered Malaysia, while Hwang and Greenford(2005) concentrated on Mainland China, HongKong, and Taiwan. A study by Hatekar andSingh (2004) explored the relationship of 14different life insurance determinants with fourdifferent measures and demand for life insur-ance in 18 Indian states between 1981 and 2000.However, the paper failed to explain thechanges that emerged following liberalisationof the insurance sector in 1999–2000.

Zietz (2003) and Hussels et al. (2005) docu-mented most of the studies that haveattempted to explain consumer behaviour con-cerning the purchase of life insurance over thepast 50 years. Both reviews suggest that themajority of empirical exercises concur that anincrease in savings and growth of the financialservices industry increases demand for lifeinsurance.

This review of empirical studies drawsattention to variables not considered in theo-retical models of the demand for insurance. Wehave presented the review chronologically tohighlight the emergence of new insurancedeterminants and the application of advancedeconometric techniques to handle the complex-ities of the data. Note that the majority ofempirical studies used panel data, but fewchecked for the robustness of the results.

Methodology

The methodology adopted in this study isbased on the objective of testing the signifi-cance of traditional determinants of thedemand for life insurance. This section isdivided into four subsections describing thesample, the variables, the data sources, and theanalytical framework.

Sample selection

The sample comprises four SAARC economies(India, Bangladesh, Pakistan, and Sri Lanka4),China and Hong Kong from the Greater ChinaRegion, and six ASEAN states (Indonesia,Malaysia, Philippines, Singapore, Thailand,and Vietnam5). Except for Hong Kong andSingapore, these economies had per capitaGDP less than US$10,000 in 20086. The selectedeconomies are participants of the ASEANRegional Forum, which is a formal, multilateraldialogue in the Asia-Pacific region. We have notconsidered two ASEAN Plus-Three economies,namely Japan and South Korea, and Taiwan,although one may argue that they are similar tothe 12 selected economies. However, it wasnoted that they had a stronger insurancemarket in terms of life insurance premium con-tributions to GDP as compared with theirAsian neighbours (see Table 1).

4 SAARC; the other three countries are Maldives, Nepal, and Bhutan.5 The ASEAN and the other members are Brunei Darussalam, Cambodia, Lao, and Myanmar.6 The sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of

output, divided by mid-year population. Gross national income per capita 2008, PPP (purchasing power parity) adjusted(in international dollars) for selected economies are as follows: Bangladesh, US$1,450; India, US$2,930; Pakistan,US$2,590; Sri Lanka, US$4,460; Indonesia, US$3,600; Malaysia, US$13,740; Philippines, US$3,900; Singapore, US$47,970;Thailand, US$7,770; Vietnam, US$2,700; Hong Kong, US$44,000; and China, US$6,010.


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Insurance business statistics are presentedin Table 1. During the period 1994–2008, theshare of life insurance in total insurance busi-ness increased, and there is a similar trend inlife insurance penetration in the 12 economies.In these economies, the insurance market con-tributed less to GDP. For example, insurancedensity of only three economies was close to 5percent in 2008.

The selected economies are heterogeneousnot only in terms of economic and social char-acteristics but also with respect to institutionalfeatures. All are members of the World TradeOrganisation and, except for Bangladesh andIndonesia, are members of the InternationalAssociation of Insurance Supervisors. Thedegree of openness, however, is not uniform.

Regulatory reforms took place at differentpoints in time. China and India liberalised theinsurance market in 1992 and 1999, respec-tively. The number of life insurers in theseeconomies varies, with China having themaximum number of 61 life insurers comparedwith Pakistan and Vietnam with only nine andten, respectively. Insurance statistics are verypoor in this region, which forced us to leaveout many small economies. Appendix 1describes the development of the life insuranceindustry in the 12 economies.

Selection of variables

The dependent and independent variables areshown in Table 2. We consider the following

Table 1Life insurance premiums, share of life premium in total insurance premium, life density, and

penetration figures for the 12 Asian economies

Year 1994 2008

EconomyTotalprea

Lifeprea

Lifeshare

Lifepenc

Lifedend

Totalprea

Lifeprea

Lifeshare

Lifepenc

Lifedend

Emerging insurance marketsBangladesh 112 42 37.96 0.13 0.3 751 568 75.63 0.7 3.9China 5747 1880 32.71 0.34 1.6 140,814 95,828 68.05 2.2 71.7Hong Kong 4439 2520 56.78 1.86 417.6 24,060 21,324 88.63 9.9 2929.6India 5332 3653 68.51 1.13 3.9 55,527 48,229 86.86 4.0 41.2Indonesia 1988 733 36.88 0.37 3.8 6904 4705 68.15 0.9 20.1Malaysia 3115 1538 49.38 1.95 76.6 9044 5869 64.9 2.8 225.9Pakistan 425 184 43.23 0.36 1.5 1088 495 45.5 0.3 2.8Philippines 941 384 40.86 0.61 5.7 2346 1489 63.47 0.9 16.2Singapore 3666 1957 53.39 2.77 580.1 14,948 10,121 67.7 6.3 2549Sri Lanka — 0.01b — 0.0006b — 590 236 40.0 0.6 12.8Thailand 3391 1347 39.73 0.93 23.7 9997 5676 56.78 1.8 77.2Vietnam — 0.01b — 0.0002b — 1304 632 48.47 0.7 7.5Developed insurance marketsJapan 588,680 469,705 79.79 9.53 3756.2 483,083 380,060 78.67 7.7 2985.7South Korea 45,638 35,222 77.18 7.74 789.0 90,623 60,577 66.85 7.2 1247.6Taiwan 12,922 8790 68.02 3.48 416.4 64,265 52,748 82.08 13.1 2288.1

a Dollars in Millions of US$ at constant 2000 prices.b Figures are extrapolated.c Life penetration: life insurance premium as percentage of GDP.d Life density: life insurance premium per capita (in US$).Source: Sigma (various issues), Swiss Re.


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Table 2Description of life insurance determinants

Variables Variable name Description

Economic GDP It is the sum of value added by all resident producersplus any product taxes (less subsidies) not included inthe valuation of output

GDP per capita GDP divided by mid-year populationGDSPC or GDS

per-capitaThe gross domestic savings divided by mid-year

populationConsumer price index

(CPI)Reflects changes in the cost to the average consumer of

acquiring a basket of goods and services that may befixed or may change at specified intervals, such asyearly

Liquid liabilities as a %of GDP (for financialdepth) (FIND)

They include bank deposits of generally less than oneyear, plus currency. It is the sum of currency anddeposits in the central bank, plus transferabledeposits, electronic currency, plus savings in time andsavings deposits, foreign currency transferabledeposits, certificate of deposit, and securitiesrepurchase agreements

Inflation The inverse of CPIReal interest rate (RIR) Deposit interest rate minus the inflation rate

Demographic Population Total mid-year populationTotal dependency ratio

(TDR)The ratio of dependent youth population and dependent

aged population to the working age population (thoseaged 15–64)

Youth dependency ratio(YDR)

The ratio of dependents (people younger than 15 yearsof age) to the working age population (those aged15–64)

Age dependency ratio(ODR)

The ratio of dependents (people older than 64 years ofage) to the working age population (those aged 15–64)

Adult literacy rate (ADL) The percentage of people aged 15 and older who can,with understanding, read and write a short, simplestatement about their everyday life

Life expectancy at birth(LEXB)

The number of years a new-born infant would live ifprevailing patterns of mortality at the time of its birthwere to stay the same throughout its life

Urbanisation (URB) The population of the urban agglomeration, acontiguous inhabited territory without regard toadministrative boundaries

Insurance business Premiums Total premiums generated (net)Insurance density (DEN) Total premiums per capitaInsurance penetration

(PEN)Total premiums as a percentage of GDP

Dummy variables Religion (REL) 0 for Islamic country, 1 otherwiseRegulatory changes

(REG)Years post-regulatory change (1 if yes, 0 otherwise)

Foreign ownership(FOR)

Time since foreign participation was allowed (1 if yes,0 otherwise)

Source: Swiss Re (various issues) Sigma; IMF (various issues) International Financial Statistics; World Bank (various issues)World Development Indicators.


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economic variables: GDP, per capita GDP, grossdomestic savings (GDS), GDS per capita, finan-cial depth, price indices, the inflation rate, andthe real interest rate. GDP and per capita GDPwere found to be highly correlated with insur-ance density and penetration. Therefore, GDSper capita is used based on an assumption thatas consumers’ incomes grow, insurancedemand will rise through the rise in savings.The log difference of consumer price index rep-resents inflation, and the real interest rate isdefined as the deposit interest rate minus infla-tion. All economic variables are expressed inmonetary values (US dollars), considered atconstant prices in the year 2000.

The demographic variables considered aretotal population, dependency ratios, life expec-tancy, adult literacy rate, and rate of urbanisa-tion. The gross enrolment ratio was used inprevious studies to show the impact of educa-tion on insurance consumption, but this vari-able is unavailable for most of the economies.Therefore, the adult literacy rate is used toascertain the impact of education on the con-sumption of insurance.

The selected economies display immensediversity in terms of ethnicity, culture, and reli-gion. For example, a major segment of thepopulation is Buddhist in Singapore, Sri Lanka,Thailand, and Vietnam; Bangladesh, Indonesia,Malaysia, and Pakistan are Islamic nations;China and Hong Kong are Confucian; themajority of the population of India is Hindu;and the Philippines is mainly Roman Catholic.Population growth rates also vary across these12 economies with China, Thailand, and SriLanka now growing at less than 1 per cent,Bangladesh, Malaysia, and Philippines at closeto 2 per cent, and Pakistan at well above 2 percent. The legal systems also vary but mostlyfollow the European legal system because of theimprint of colonialism. Regulatory changeshave mostly taken place after 1990, and exceptin the case of Bangladesh, foreign ownership iseither fully or partially allowed.

Dummy variables have been considered toaccount for these cross-sectional differences inreligion (whether or not the majority popula-tion follows Islam or not), the impact of regu-latory changes, and of foreign participation

(economies where 100 per cent participation isallowed).

Data sources

The panel data for the 12 selected Asian econo-mies for the period 1994–2008 were constructedusing annual data from the following datasources: insurance premium figures are fromSwiss Re publications, the economic and demo-graphic variables are from the IMF’s (Interna-tional Monetary Fund) International FinancialStatistics and the World Bank’s World Develop-ment Indicators. In earlier studies, data pertain-ing to socioeconomic variables were collectedfrom various sources. Data consolidation fromvaried sources ignores the inherent differencesin data because of definitional differences.Therefore, we have considered only data avail-able from World Bank and IMF databases.Therefore, the analysis is conducted using abalanced panel data set.

Analytical framework

The econometric specifications used in thestudy are presented in Table 3. These specifica-tions are grouped under two separate paneldata regression models where insurance pen-etration and insurance density are the depend-ent variables. Panel data analysis is useful incontrolling individual (cross-section) heteroge-neity and effects that are difficult to determinevia pure cross-sections or pure time series data.The specifications are as follows:

log loglog log

PEN GDSPCFIND URB YD

( ) = + ( )+ ( ) + +

it i it

it it

α ββ β β

1

2 3 4 RRODR ADLLEXB RIR

( )+ ( ) + ( )+ ( ) + +

it

it it

it it

β ββ β β

5 6

7 8 9

log loglog (11 CPI)it it+ ε

(1)

log loglog log

DEN GDSPCFIND URB YD

( ) = + ( )+ ( ) + +

it i it

it it

γ δδ δ δ

1

2 3 4 RRODR ADLLEXB RIR

( )+ ( ) + ( )+ ( ) + +

it

it it

it it

δ δδ δ δ

5 6

7 8 9

log loglog loog 1 CPI( ) +it itν

(2)

Where, PEN = insurance penetration; DEN =insurance density; GDSPC = GDS per capita;FIND = financial depth; URB = urban


93


population; YDR = youth dependency ratio;ODR = age dependency ratio; ADL = adult lit-eracy rate; LEXB = life expectancy at birth; RIR= real interest rate; and CPI = consumer priceindex.

In Equations (2) and (3), subscript i denotesthe country, and the subscript t represents time.We begin with pooled OLS estimation, whichignores the panel structure of the data anddraws conclusions regarding the poolabilityof the data. Fixed-effect model (FEM) andrandom-effect model (REM) are estimatedthereafter. Use of dummy variables only facili-tates analysis of cross-section effects. However,given the importance of these variables, wehave tested them through separate econometricspecifications as detailed under Models 9–12.Selection between FEM and REM is based onthe Hausman test.7 Tests for heteroscedasticityand serial correlation were conducted, and

robust relationships between the dependentand independent variables were obtained.

Major findings

In this section, results obtained from the 12regression models are discussed. The resultsare presented in Tables 4 and 5.

Following Baltagi (2005), we assume thatindividual heterogeneity would be controlledin a panel, but since factors such as religion andregulation play a crucial role, we tested thefeasibility of pooling these independent indi-viduals in a panel. The poolability test wasundertaken to test whether the parameters ofthe insurance determinants equation vary fromone year to the next over the 15 years of dataor whether the parameters may vary across

7 Mundlak (1978), cited in Hsiao (2003).

Table 3Model specifications

Model no. Remarks Equation estimated

1 Pooled OLS with robust standard errors log (PEN)it = ai + b1 log (GDSPC)it + b2 log (FIND)it +b3 URBit + b4 log (YDR)it + b5 log (ODR)it + b6 log(ADL)it + b7 log (LEXB)it + b8 RIRit + b9 (1/CPI)it + εit

2 Fixed-effect model3 Random-effect model4 Panel-corrected standard error model5 Pooled OLS with robust standard errors log (DEN)it = γi + δ1 log (GDSPC)it + δ2 log (FIND)it +

δ3 URBit + δ4 log (YDR)it + δ5 log (ODR)it + δ6 log(ADL)it + δ7 log (LEXB)it + δ8 RIRit + δ9 (1/CPI)it + vit

6 Fixed-effect model7 Random-effect model8 Panel-corrected standard error model9 Fixed-effect model log (PEN)it = ai + b1 log (GDSPC)it + b2 log (FIND)it +

b3 URBit + b4 log (YDR)it + b5 log (ODR)it + b6 log(ADL)it + b7 log (LEXB)it + b8 RIRit + b9 (1/CPI)it +b10 RELit + b11 REGit + b12 FORit + εit

10 Corrected fixed-effect model

11 Fixed-effect model log (DEN)it = γi + δ1 log (GDSPC)it + δ2 log (FIND)it +δ3 URBit + δ4 log (YDR)it + δ5 log (ODR)it + δ6 log(ADL)it + δ7 log (LEXB)it + δ8 RIRit + δ9 (1/CPI)it + δ8

RELit + δ8 REGit + δ8 FORit + vit

12 Corrected fixed-effect model

The binary dummy variables are REL = religion (0 for Islamic country, 1 otherwise); REG = number of years post-regulatorychange (1 if yes, 0 otherwise); and FOR = time since foreign participation was allowed (1 if yes, 0 otherwise).ADL = adult literacy rate; CPI = consumer price index; DEN = insurance density; FIND = financial depth; GDSPC = grossdomestic savings per-capita; LEXB = life expectancy at birth; ODR = age dependency ratio; PEN = insurance penetration; RIR= real interest rate; URB = urban population; YDR = youth dependency ratio. Author’s own data.


94


Tab

le4

Res

ult

sof

pan

eld

ata

esti

mat

ion

(Mod

els

1–8)

Mod

el1

Mod

el2

Mod

el3

Mod

el4

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Dep

end

ent

vari

able

s→In

sura

nce

pene

trat

ion

Insu

ranc

ed

ensi

ty

Ind

epen

den

tva

riab

les

↓E

xpec

ted

sign

Con

stan

t9.

815

(9.1

02)

−20.

192

(8.0

75)

−11.

162

(7.0

89)

5.21

2(4

.370

)0.

302

(10.

326)

−19.

383*

(8.3

02)

−14.

293*

(7.5

38)

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0.96

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1.34

1***

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1.10

1***

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0.87

2***

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Fina

ncia

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1.71

5**

(0.5

89)

2.86

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(0.2

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2.85

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(0.2

48)

1.62

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(0.3

13)

1.78

3*(0

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474*

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513*

**(0

.254

)1.

524*

**(0

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)U

rban

isat

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.115

*(0

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)−0

.058

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.027

)−0

.063

*(0

.027

)−0

.033

*(0

.019

)−0

.153

(0.0

77)

−0.0

57*

(0.0

27)

−0.0

61*

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−0.0

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95


economies. The test rejects poolability acrosseconomies8 but does not reject poolabilityacross time.9 Therefore, the panel data analysisallows us to control for differences rangingfrom cultural factors to business practicesacross economies.

The robust OLS estimates (Model 1 andModel 5) show that variations in insurancepenetration are explained by GDS per capita,financial depth, urbanisation, and inflation.But, following our poolability test results, wecan assume further that individual heterogene-ity exists, and we incorporate it via the leastsquare dummy variable regression and obtainsignificant dummy variables absorbing theeffects particular to each economy. This leads

us to the estimation of FEMs (Model 2 andModel 6). From Model 2, we see that eight outof nine determinants are statistically signifi-cant, suggesting that the model is good with ahigh intra-class correlation. Similarly, forModel 6, seven out of nine determinants aresignificant, satisfying the conditions for thedata to be correctly structured as a group. Thesetwo models were further subjected to tests fortime fixed effects and cross-section-fixed (indi-vidual) effects. For Model 2, we reject the twonull hypotheses that all year coefficients arejointly equal to zero and that all individualcoefficients are jointly equal to zero. This neces-sitates inclusion of both time-specific fixedeffects as well as individual effects. However,

8 F and FPEN DEN110 60 110 6023 06 30 35, ,. .( ) ( )= = rejects poolability across economies.

9 F and FPEN DEN140 30 140 301 18 0 66, ,. .( ) ( )= = confirms poolability over time.

Table 5Panel data estimation with dummy variables (Models 9–12)

Dependent Variable →

Model 9 Model 10 Model 11 Model 12

Insurance penetration Insurance density

Independent variables ↓ ExpectedConstant Sign −19.620** (7.550) −19.620 (17.945) −17.850* (7.700) −17.848 (18.402)GDS per-capita + 0.469*** (0.167) 0.469* (0.242) 1.148*** (0.170) 1.148*** (0.277)Financial depth + 2.713*** (0.227) 2.713*** (0.474) 2.387*** (0.231) 2.387*** (0.579)Urbanisation + 0.0123 (0.009) 0.013 (0.010) 0.019* (0.009) 0.019 (0.011)Youth dependency ratio − 2.503*** (0.882) 2.503 (2.367) 1.550* (0.900) 1.550 (2.312)Total life expectancy at birth - 8.543*** (4.004) 8.543 (8.226) 7.851* (4.083) 7.851 (8.605)Adult literate population + −3.788* (0.871) −3.788** (1.155) −3.210*** (0.889) −3.210 (1.427)Inflation − −23.102* (12.312) −23.102* (9.543) −44.861*** (12.557) −44.861*** (8.604)Real interest rate − −0.019*** (0.004) −0.018* (0.011) −0.021*** (0.004) −0.021* (0.011)Foreign ownership + 0.335*** (0.084) 0.335* (0.193) 0.344*** (0.086) 0.344* (0.187)Regulatory changes + 0.111* (0.063) 0.111 (0.103) 0.104 (0.060) 0.104 (0.101)Observations 180 180 180 180F test statistics/χ2 60.55 306.08 84.26 1988.50

Within 0.793 0.793 0.842 0.842R2 Between 0.532 0.532 0.840 0.840

Overall 0.504 0.504 0.811 0.811Hausman test 385.53*** 511.13***Breusch–Pagan LM testa 25.76*** 60.62***Pesaran CSD test −2.014 −1.799Modified Wald test 2863.42*** 5697.92***Woolridge test for autocorrelation 181.995*** 71.520***

Source: Author’s calculations.Notes: *, **, and *** represent statistical significance at 0.05, 0.01, and 0.001 levels, respectively. Figures in parentheses arestandard errors (SE). For Model 10 and Model 12, SEs are autocorrelation (AR1 assumed) and heteroscedasticity correctedstandard errors.a This test is applied to the random effects model to compare with a simple pooled regression model.CSD = Cross-sectional dependence; GDS = gross domestic savings; LM = Lagrange multiplier.


96


tests on Model 6 reveal that cross-sectioneffects are required to be tested. We estimatedthe REMs to analyse further whether time-invariant variables play a role as explanatoryvariables. For example, religion plays animportant role in determining consumption offinancial products in general. This variable maybe arbitrarily correlated with another explana-tory variable, but it cannot be considered as anunobserved variable, equivalent to randomerror. Therefore, as our analysis shows, religionincorporated as a time invariant dummydescribing a fixed country attribute is not ofinterest in fixed effect specifications.

In the random effect Models 3 and 7, six andseven determinants, respectively, are statisti-cally significant. The significant F-statistics onceagain highlight the correctness of the models.To draw final inferences, a test was conducted tochoose between REMs and FEMs. Based on theHausman (1978) test comparing Model 2 andModel 3, we reject the possibility of individualeffects being uncorrelated with other regres-sors. This suggests that the FEM (Model 2) is abetter choice. Following the same procedureand comparing Model 6 and Model 7 yieldresults in favour of the FEM (Model 6). Usingthe Breusch and Pagan (1980) test for the rel-evance of random effect for Model 3 and Model7 suggests no significant differences acrossunits. A significant Breusch–Pagan Lagrangemultiplier (LM) test may force us to abandonthe assumption that the individual effects arefixed and estimable, but given the power of theHausman test over the LM test and followingdiscussion of selecting between random andfixed effects in Greene (2002), we considerthis to indicate a possible heteroscedasticityproblem.

The modified Wald test is used to check forheteroscedasticity in the FEMs (Models 2 and6). The test statistics show the rejection ofhomoscedasticity (very high and significanttest statistic as shown in Table 5) and thereforesupport our earlier argument for the presenceof heteroscedasticity. Using the Pesaran (2004)tests for cross-sectional dependence in paneldata, we conclude that there is an absence of

cross-sectional dependence or no contempora-neous correlation in the models. Finally, wecheck for autocorrelation using the Wooldridge(2002) tests with a null of no first-orderautocorrelation.10 The test shows the presenceof first-order autocorrelation in both panelspecifications (Equations 2 and 3). The diagnos-tics tests confirm that even though we usefixed effect estimates, the problems of hetero-scedasticity and first-order autocorrelationwould restrict robust estimates. Therefore, weuse panel-corrected standard error (PCSE) esti-mates (Blackwell 2005) for linear panel datamodels where the parameters are estimated byPrais–Winsten regression (to control auto-correlation), and disturbances are assumed tobe only panel-level heteroscedastic (robustcoefficients are obtained) with no contempora-neous correlation across panels. The resultsof PCSE estimates with heteroscedasticity-corrected standard errors are given in Models 4and 8, respectively, in Table 4.

In Table 5, we have presented resultsobtained while testing additional variables ofinterest: foreign ownership and regulatorychanges. In Models 9–12, we had initially con-sidered three dummy variables but laterdropped religion because it is not changing forsome economies in the sample. Foreign own-ership and regulatory changes are time-varying dummy variables. The results indicatethat foreign ownership positively influencesinsurance consumption in the selected econo-mies. Model 9 indicates that regulatorychanges influence insurance consumption. Theregression results obtained from Models 4, 8,10, and 12 show that GDS per capita has apositive relationship with insurance densityand penetration. It indicates that the relation-ship between savings and insurance penetra-tion is inelastic, implying that it is a luxurygood. However, the relationship betweensavings and insurance density is elastic. Thismeans that as savings grow, it would morethan proportionately raise contributionstowards life insurance. The finding is interest-ing as the first relationship suggest that at amacro level, we may not witness significant

10 For details, refer to Drukker (2003).


97


changes in demand, but at a micro level,changes in savings promote insurance con-sumption. The significant positive relationshipbetween density and penetration with financialdepth further supports our earlier argumentsand suggests that as the financial sectorstrengthens, the insurance sector will grow.

Among the 12 selected economies, manyemphasised structural reforms in the financialand corporate sectors in addition to the imple-mentation of appropriate macroeconomic poli-cies, as a response to the Asian crisis in 1997(Lindgren et al. 1999). Unlike in the past whenthe financial sector’s supervision was based on afunctional approach, the activities of banks andother financial institutions today go beyond thetraditional banking or non-banking activities,and growth in one segment supports the other.

Urbanisation is significant in relation toinsurance penetration, but its relationship is notin line with our a priori expectation. Urbanisa-tion may not have increased insurance con-sumption in proportion to the growth of urbancentres in the selected economies. Urbanisation,especially in South-Asia and East Asia, has beenstudied in depth and scholars have termed thedevelopment as highly unsustainable where thebenefits are not shared by all inhabitants, thereare steep gradients of inequality between thehaves and have nots, and these differences maybe increasing11.

The youth dependency ratio exhibits theexpected relationship with dependent variablein Models 4 and 8. The aged dependency ratiois significant, but its relationship with thedependent variables does not corroborateearlier studies. The results suggest that a risingage dependency ratio will lead to a tapering offof demand.

To estimate the influence of education andawareness of improved health conditions oninsurance consumption, we use variables suchas adult literacy and life expectancy at birth.These variables are statistically insignificant. InFEMs (Models 2 and 6), both were significantbut yielded unexpected relationships. Thismight lead one to conclude that higher educa-tion or education above a certain minimum

fails to guarantee awareness of insurance ben-efits, and as improved living conditionsenhance life expectancy, demand for insuranceproducts would go up. As expected, inflationand the real interest rate are significantlyrelated to insurance penetration and density.Thus, the current interest rate or price situa-tion does not affect decisions on insuranceconsumption.

Moshirian (1997) noted that foreign directinvestment was increasing in the non-US insur-ance market, particularly in the Asia-Pacificregion. Studies by Ma and Pope (2008), and Yeet al. (2009) show positive effects from foreignparticipation (in the form of investments) in lifeinsurance markets. In line with these studies,Models 10 and 12 indicate a positive relation-ship between insurance consumption andforeign participation. In most of the selectedeconomies, some form of restriction applies toforeign insurers, but over time, each economyhas adopted policies facilitating entry offoreign insurers. For example, there is only oneforeign insurer in Bangladesh, but four foreigninsurers are operating in Vietnam. In India andThailand, foreign insurers can have restrictedownership in domestic insurance companies.

Conclusions

The study indicates that along with income,determinants explaining insurance consump-tion are GDS, level of development of thefinancial sector, inflation, and real interest rate.As specialised financial institutions turn intofinancial conglomerates in the selected econo-mies, one important policy implication couldbe to strengthen banking and non-bankinginstitutions to generate a positive spillovereffect on insurers. There are many economieswithin ASEAN and SAARC with virtually nomarket for insurers. As more banks enter theinsurance industry, it may boost demand forinsurance products. Theoretical studies havepointed out that insurance is not pure savingsand insurance consumption is expected tosmooth income or wealth over time. The real

11 For discussions regarding the problems related to urbanisation in Asia, refer to Ooi (2009), Kundu (2009), among others.


98


interest rate is significant in determining insur-ance consumption, suggesting that somevariant of insurance and an interest-relatedproduct (for example, an investment compo-nent) may also play an important role inmotivating individuals to choose betweeninsurance, and other investment and savinginstruments. In India, for example, the forma-tion of unit-linked investment products playedan important role in raising awareness of insur-ance products.

The econometric results support the signifi-cance of demographic variables, namely lifeexpectancy, the youth dependency ratio, theadult literacy rate, and the rate of urbanisation,in explaining the demand for life insurance atan aggregate level. However, these variablesmay not explain insurance consumption ata micro or country level. According toRosenzweig (1988), implicit insurance pro-vided by networks of family and friends mayprovide some form of alternative arrangementto take care of life-related risks in India.12

Chan (2009) noted that there is a strong cul-tural resistance to purchase life insurance inChina.

Given the current global concerns relatingto aging, it is expected that if self-insurance isthe best alternative to pure life insurance inthe absence of or limited health, pensions, orold-age security, the demand for annuitiescould increase in developing economies(Blanchet 2007).13 This explains why agedependency ratios fail to exhibit the expectedrelationship with the demand for life insur-ance in the selected economies with poorsocial security systems. In recent years, regu-latory changes have allowed foreign insurersto either fully own or partially promote insur-ance companies in this region. Ye et al. (2009)have shown empirically that a wide range of

socioeconomic factors and market conditionsmake entry of foreign insurers favourable.Our results show that foreign ownershippositively influences demand for insurance.Skipper (1997) has put-forth several reasonswhy the entry of foreign insurers raisesdemand: enhanced customer services, tech-nological and managerial know-how, andimprovement in the quality of domestic regu-lation, to name a few.

We fail to establish empirically the influenceof religion or regulatory changes on thedemand for insurance. Nehen’s (1989) surveyshowed that in the ASEAN economies, the rateof growth of premiums was higher than that ofits GNP and argued that this showed thehuge potential for expansion of the industry.However, the adoption of policies restrictingthe number of participants did not cause stag-nation in employment generation but affectedthe development of the sector at large.

According to Liedtke (2007), insuranceshould be considered a key component of eco-nomic development and the best mechanismto take care of multidimensional risks inmodern economies. It is necessary to clear theconfusion over considering life insurance as aluxury good. Potential consumers in thisregion have low per capita incomes, but theyare slowly increasing. This may well-stimulatedemand. It is difficult to forecast demandbecause we have considered macro data. Inaddition, the State plays a crucial role inshaping the industry and the focus in thisregion has been on promotion of home growninsurers. Although many emerging economiesfailed to implement the desired strategies, theselected 12 economies may learn from theexperience of Japan, South Korea and Taiwanwhich became key agents in fostering thenational insurance industry.

12 The information was collected from the study by Townsend (1995), which dealt with some of the issues relating toevaluation of risk-bearing systems in low-income economies.

13 Kwon and Jones (2006) show that longevity risk is related to benefits obtained via consumption of life insurance.


99


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Appendix 1

Development of life insurance industry in selected 12 Asian economies

Economies Regulatory authority Pre-regulatory regime Regulatory change Foreign ownershipLife

Insurersf

Bangladesh The Department ofInsurance, headed bythe InsuranceDirectorate of theMinistry of Commerce.

After separation fromPakistan in 1972,state-owned monopolylife insurer Jiban BimaCorporation wasestablished in 1973

1984Allowed 50% private

underwriting in 1990

Allowed 19

China P.R. China InsuranceRegulatory Commission(CRIC) since 1998

PICC’sa monopoly asinsurer ended in 1985;5 new public insurerscreated; 4 regionalinsurers existed

1992; Interim ManagementRegulation for foreigninsurance institutionsallowed foreign entry

Allowed; 2002 Regulationson Admn. ofForeign-InvestedInsurance Company

61

Hong Kong Office of theCommissioner ofInsurance (OCI); 1990

Comparatively, the mostcompetitive insurancemarket with 204insurers

1997, after it becamePRC’s specialadministrative region

Allowed; 104 captiveinsurers in operationfrom 25 countries

59

India Insurance Regulatory andDevelopment Authority,IRDA,1999

The Life InsuranceCorporation Act of 1956merged 256 life insurersto form LICb

100% private participationallowed since 1999

26% equity share in localcompanies

23

Indonesia The Directorate Generalfor Financial Institutionsalong with Ministry ofFinance

– – Allowed 46

Malaysia Ministry of Finance defacto regulator withadministration by BankNegara Malaysia (BNM)

In 1984 Takaful InsuranceAct permitted insureroperations based onIslamic principles

Always open to PrivateLaw and Regulatory

changes in 1996.

Allowed 16

Pakistan Securities and ExchangeCommission of Pakistan(SECP); 1999

In 1972, 34 out of 50existing insurers weremerged to form StateLife Insurance Corp.c

1990100% private entry

Allowed since 1992

Allowed 9

Philippines The InsuranceCommission

(Komiyon ng Seguro)

Private sector was alwaysemphasised

The Republic Act No. 8179of 1996 permits 100%foreign ownership

New foreign insurer is notallowed to hold acomposite license

34

Singapore The Insurance Departmentof the MonetaryAuthority of Singapore(MAS)

Until 1960s the marketwas loosely regulated

2000The market was

liberalized to directinsurers

In 2000, 49% restriction onforeign ownership inlocal companies waslifted

17

Sri Lanka The Insurance Board of SriLanka; 2001

Insurance Corp. Act in1961 nationalised the lifeinsurance industry toform ICSd

2001, 100% privateoperation allowed since1986

Up to 90% foreigninvestment in localcompanies

13

Thailand The Department ofInsurance (of theMinistry of Commerce),independent from 2002

Closed for decades till late1990s

1992Insurance business is

relatively liberal

25% of foreign ownershipin domestic insurerse

25

Vietnam Ministry of Finance Baoviet, a state owned lifeinsurer startedoperation in 1996 butwas initially created fornon-life insurance

1999In 2000 approved and

enacted a new set oflaws

Allowed 2 foreigncompanies in 1999followed by 2 in 2000

10

a People’s Insurance Company of China.b Life Insurance Corporation of India under the control of Government of India.c The Corporation established under Article 11 of the Life Insurance (nationalisation) Order of 1972.d Insurance Corporation of Sri Lanka, renamed the Sri Lanka Insurance Corporation Ltd. in 1993.e To be lifted to 49%.f Including composite insurers.

Source: Compiled from information available in Kwon (2001, 2002a,b).


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