Determinants of demand for life insurance products –

Theoretical concepts and empirical evidence

Carsten-Henning Schlag

Swiss Re

Economic Research & Consulting

July 2003

Abstract

The "life insurance" product is in constant competition with alternative forms of investment toattract the savings of private households. The study gives an overview of the theoreticalconcepts that have been elaborated to explain the demand for life insurance and to provideempirical evidence of various factors that determine life insurance demand. Themacroeconometric studies available show that a combination of demographic,macroeconomic and socio-psychological factors is capable of significantly explaining lifeinsurance demand both in an international cross-section and also over time. We also discussvarious methodological approaches under-lying the aforesaid empirical analyses. It should benoted that the econometric methods used in the literature are not always state-of-the-art interms of modern research methodology. We highlight the need for further research in thisfield.

JEL-Classification: C20, C51, D91, E21

Key words: Life insurance demand, consumption theories, regression analysis, taxincentives, profitability

Dr. Carsten-Henning SchlagSwiss Re, Economic Research & Consulting, Mythenquai 50/60, CH-8022 ZurichTel. +41 43 285 2428, Fax +41 42 282 2428, Email: Carsten_Schlag@swissre.com

2

Determinants of demand for life insurance products –

theoretical concepts and empirical evidence

1. Introduction

The "life insurance" product is in constant competition with alternative forms of investment to

attract the savings of private households. Especially in times when the growth of the economy

as a whole is only sluggish, the question as to which criteria private households apply to their

investment portfolio decisions is particularly relevant. If the only modest macroeconomic

development in the OECD area were not already enough, the turbulences that shook the

financial markets in 2002 had a further adverse impact on the economic stability of the life

insurance industry. Against this background, the suppliers of life insurance products found it

more and more difficult to achieve adequate returns. How will the sources of demand for the

various life insurance products respond? What are the prevailing factors influencing demand

for the various life insurance products, and how do they take effect?

The present study gives an overview of the theoretical concepts that have been elaborated to

explain the demand for life insurance and to provide empirical evidence of various factors that

determine life insurance demand. It also discusses various methodological approaches under-

lying the aforesaid empirical analyses and highlights the need for further research in this field.

The study is structured as follows: Section 2 contains fundamental reflections on how the

demand for life insurance products can be suitably quantified. Section 3 discusses various

determinants of life insurance demand. Some of these are derived from macroeconomic

consumption theories. This section also describes some influencing factors that are taken into

account in the available empirical studies. In addition to income parameters and prices, such

3

factors include, for instance, uncertainty about lifetimes, personal levels of risk aversion, and

bequest motives. Section 4 is dedicated in particular to discussing and evaluating the macro-

econometric studies to be found in the literature. Empirical evidence of the effects of tax

incentives on the profitability of life insurance as a form of saving and thus on life insurance

demand is given in Section 5. Of the numerous determinants discussed in Section 3, this

section focuses on the "tax system" factor. Section 6 contains some concluding remarks.

2. Demand for life insurance products

2.1 Product diversity and specific characteristics

The gamut of life insurance products is relatively diverse, the range of different products

varying widely from country to country. In the light of this heterogeneity, it is difficult to

speak of "the" demand for "the" life insurance "product". Life insurance products differ par-

ticularly in terms of their insurance and savings elements, with insurers offering not only pure

forms but also a wide variety of hybrids. Life insurance differs from other insurance products

especially by virtue of the long-term nature of the contracts, the relatively high premiums, the

direct association of the conclusion of the policy with the negatively rated events of death or

disability, and the high complexity of the products, resulting eg from the preferential tax

treatment for which many life insurance products qualify (Müller, 1998, pp. 17 et seq.).

2.2 Concepts for indicators of demand

Any empirical study of life insurance demand first needs suitable indicators with which to

operationalise the demand. The simplest approach is to use the number of insurance policies

4

(quantitative indicator) or the premium volume or the sums insured (value indicators)1. A

peculiarity by comparison with measurement of demand in the context of conventional

commodities is that in life insurance it is necessary to distinguish between "new business" and

"business in force". The aforesaid valuation standards "number", "sums insured" and

"premium volume" can in principle be applied to both baselines, yielding a total of six

primary indicators for measuring life insurance demand. The demand indicators are often also

related to macroeconomic or socio-economic variables. Examples of this are insurance

expenditure per capita (insurance density) and insurance expenditure divided by gross

domestic product (insurance penetration), which are useful particularly for international

comparisons of the importance of life insurance in different countries.

3. Determinants of life insurance demand

The starting point for any theoretical approach to explaining demand for life insurance is con-

sumption theory in its various forms. Other factors are also of importance, but these concern

more the supply-side influences on the activities on the markets for life insurance products.

Supply-side factors affect the cost of life insurance products and thus their prices. These

factors include various attributes of the economic, political and social environments in which

the companies of the insurance industry operate.

3.1 Determinants deriving from consumption theory

The macroeconomic literature distinguishes various consumption theories which focus on

incomes, price and interest levels, and the population structure as important determinants for

1 The number of insurance policies and the sums insured are stock variables, the premium volume a flow

variable.

5

consumption. These include in particular the Keynesian consumption hypotheses (absolute

and relative income hypotheses), from which the permanent income hypothesis (Friedman,

1957) and the life-cycle hypothesis, which can be traced back to Modigliani (1963), must be

distinguished. The traditional approaches of consumption theory are extended by theoretical

approaches to explaining life insurance demand, which take into account not only income

parameters and prices but also factors such as uncertainty about lifetimes, bequest motives,

and personal levels of risk aversion. Concluding a life insurance contract, with the associated

obligation to make regular premium payments, reduces the individual's or a family's ongoing

ability to consume during the earning phase. Voluntarily foregoing current consumption is

here aimed at achieving a regular or a higher income in the retirement phase or of

safeguarding the beneficiaries' (eg surviving spouse or children's) future ability to consume.

Initial theoretical models to explain demand for life insurance policies were advanced by

Yaari (1965) and Hakansson (1969). In his life-cycle approach, Yaari (1965) explicitly

considers the uncertainty about lifetimes. He shows that, given the uncertainty about the

time of the individual's death and the desire to leave an adequate income for dependants

(spouse or children), buying a life insurance policy enhances the lifetime utility. Works by

Hakansson (1969) and Fischer (1973) corroborate this cardinal theoretical finding. The

importance of the bequest motive is discussed in the literature particularly in connection with

the quantitative significance of intergenerational transfers. In addition to the bequest motive,

further transfer motives are cited, including especially involuntary devolution due to accident,

or just plain altruism. Bernheim (1991) studies the strength of the bequest motive in a

theoretical setting. He is able to demonstrate empirically that a significant part of overall

savings is motivated by the desire to provide security for surviving dependants.

6

Pissarides (1980) extends upon Yaari's analysis (1965) by making allowance for a motive of

saving for the retirement phase in addition to the bequest motive. In his model approach, he

shows that life insurance is theoretically able to absorb all fluctuations in lifetime incomes, so

that consumption and bequest become independent of when the income is generated. In effect,

if a "perfect" life insurance strategy is applied, the same consumption path is obtained as

would be the case if the time of death were known with certainty.

Lewis (1989) makes explicit allowance in his model for the preferences of dependants and

beneficiaries, thereby extending Yaari's (1965) approach, whose utility considerations took

into account only the preferences of the head of the household or the principal earner. This

model approach will be examined in more detail here, as it forms the theoretical starting point

for various empirical works and will be referenced at several points in the later discussion.

Lewis (1989) defines life insurance demand

(1))()()()()()W,L,,TC,p(FLD

−−+++δ=

as a function of several explanatory variables. The demand for life insurance policies LD rises

with the probability of primary income-earner's death p, the present value of consumption by

the beneficiaries TC, and the relative risk aversion of the beneficiaries δ; it declines with the

household's net wealth W and the "policy loading factor" L, which describes the price of

insurance as the ratio between the cost and the actuarial value of the insurance. In the context

of empirical works, the aforesaid determinants of the model are operationalised by means of

proxy variables (Browne/Kim, 1993, Beck/Webb, 2002).

Besides the bequest motive, personal risk aversion is a further important factor determining

the individual and aggregate consumption and savings behaviour of the economic units. In

7

their work, Karni/Zilcha (1985, 1986) develop yardsticks for measuring personal risk aversion

and investigate its implications for life insurance. They show analytically that an individual

with a higher risk aversion generates more demand for life insurance than an individual with a

lower risk aversion.

3.2 Empirical determinants

Table 1 lists the determinants of life insurance demand used in the empirical studies.

Generally, the influencing factors can be assigned to five main categories: "demographic vari-

ables", "macroeconomic variables", "socio-psychological factors", "institutional determinants

and "(life) insurer action parameters". The variables listed in column 2 are operationalised in

the empirical studies by the indicators in column 3 (Table (1)). Columns 4 and 5 show the

theoretically expected signs (positive or negative) of the variables and the statistical results as

actually identified in the empirical studies.

For example the demographic variable "population" – operationalised by the population

count, number of households, number of marriages, birth rate and the proportion of young

people in the working population – is regarded as a long-term determinant for the life insur-

ance industry. A lower ratio of young people to the working population tends to curb demand

for pure life insurance policies via a reduction in the present value of consumption by

beneficiaries (variable TC in Equation (1)). The expected sign of the determinant in the

empirical study is thus positive (column 4 in Table 1). A detailed discussion of the hypotheses

associated with the various influencing factors is given in Table 6 in the Appendix.

8

Table 1

Determinants of life insurance demand in empirical studies

(1)Categories

(2) Variables

(3)Operationalisation

(4)Expected signa

(5)Statistical

resultsb

(6)Source

• Population • Ratio of young dependents to workingpopulation

• Ratio of old dependents to workingpopulation

• Number of marriages• Birth rate

++++

******

#**

Beenstock/Dickinson/Khajuria(1996), Browne/Kim (1993),Beck/Webb (2002),Truett/Truett (1986)Diacon (1980)

• Education • Average years of school attendance• Proportion of persons with (tertiary)

education in the population between 20 and24 (UNESCO)

++

*****

Beck/Webb (2002), Truett/Truett (1986), Browne/Kim(1993)

• Religion • Dummy variables for predominantly Islamic,Protestant and Catholic countries

- ** Browne/Kim (1993),Beck/Webb (2002)

• Urbanity • Ratio of urban population to overallpopulation

+ # Beck/Webb (2002)

Demographicvariables

• Life expectancy • Life expectancy at birth (UN statistics)• Life expectancy of persons aged over 40 in

1975

+ *** Beck/Webb (2002), Beenstock/Dickinson/Khajuria (1996),Outreville (1996),

• Economic growth • Growth of (per capita) GDP + ** Schwebler (1994), Beck/Webb(2002), Zhuo (1998)

• Income • Disposable income of private households + *** Schwebler (1984),Lewis (1989),Beenstock/Dickinson/Khajuria(1986), Truett/Truett (1990),Browne/Kim (1993), Outreville(1996), DePamphilis (1977)

• Prices • Aggregate price level + ** Beenstock/Dickinson/Khajuria(1986)

• Monetary stability • Inflation rate• Expected inflation rate (average of past years,

average of current and next year inflation rate)

-?

**(-), **

Schwebler (1994), Beck/Webb(2002), Browne/Kim (1993),Diacon (1980)

Macro-economicvariables

• Interest rate • Long-term interest rate (real or nominalterms)

? # Beenstock/Dickinson/Khajuria(1986), Outreville (1996)

9

• Development of thefinancial market andbanking sector

• Ratio of quasi-money (M2-M1) to the broaddefinition of money (M2)

• Total claims of deposit money banks ondomestic non-financial sectors as a share ofGDP

+

+

***

***

Outreville (1996)

Beck/Webb (2002)

• Labour market • Unemployment rate - ** Schwebler (1984)• Social security • Government social security expenditure in

relation to GDP- ** Browne/Kim (1993), Beck/

Webb (2002), Beenstock/Dickinson/Khajuria (1986),DePamphilis (1977),

• Tax system • Average income tax rate• Ratio of net to gross income

-+

*****

Diacon (1980)Müller (1998)

• Income distribution • Gini coefficient• Theil's inequality coefficient

?? **

Beck/Webb (2002), Beenstock/Dickinson/Khajuria (1986)

• Competitive situation • Dummy variable indicating whether themarket is a monopolistic one

• Dummy variable indicating whether foreigncompanies are writing business in the market

?

?

(-), ***

#

Outreville (1996)

Macro-economicvariables(continued)

• Saving • Private saving rate + ** Schwebler (1984),Beck/Webb (2002)

Socio-psycho-logical factors

• Moods (present) andanticipations for thefuture

• Allensbach indicator• Consumer climate index • EU consumer confidence

+++

*******

Schwebler (1984), GDV (1983)Müller (1998)

Institutionaldeterminants

• Rule of law• Well-defined property

rights• Regulatory quality• Control of corruption

Average of six indicators + ** Beck/Webb (2002)

Insurer actionparameters

• Pricing • Product design• Advertising• Sales channels

Not operationalised in the empirical studies

a + positive correlation, - negative correlation, ? direction of correlation not clear from theory.b *** statistically significant and coincides with the expected sign, robust result in conjunction with further explanatory factors,

** statistically significant and coincides with the expected sign, but not very robust result in conjunction with further explanatory factors,# not statistically significant. The result is given in parentheses if the expected sign cannot be unambiguously determined.

10

4. Empirical results of macroeconometric studies

The literature contains various empirical studies on the determinants of life insurance

demand. In this context, two fundamental approaches can be distinguished: macro- and micro-

econometric studies. The macroeconometric studies mainly use aggregated macroeconomic

time series, microeconometric studies aim to explain individual decision-making patterns of

the private households. These will be discussed in greater detail in Section 5.

Table 2 lists the 13 macroeconometric studies analysed in the present paper. They include not

only studies on specific countries but also cross-sectional and panel data studies covering an

extensive sample of countries.

Table 2

Macroeconometric studies

Author(s) Countries / regions Year / period Method / data Used

Babbel (1985) US 1953-1979 Time-series study, GLS estimateBeck/Webb (2002) 68 countries 1980-2000

1961-2000Cross-sectional study, LSestimate; panel study, estimationof fixed-effects and random-effects models

Beenstock/Dickinson/Khajuria (1986)

10 OECD countries 1970-1981 Panel study (time-series andcross-section data)

Browne/Kim (1993) 45 countries 1980, 1987 Cross-sectional study Cargil/Troxel (1979) US 1954-1974 Time-series studyDePamphiles (1977) US 1952-1974 Time-series studyDiacon (1980) UK 1946-1968 Time-series studyGDV (1983) Germany 1965-1980 Correlation analyses, factor

analysis, multiple regressionanalyses

Müller (1998) Germany 1985-1996 Time-series study (quarterly andannual data)

Outreville (1996) 48 developed countries 1986 Cross-sectional studySchwebler (1984) Germany 1965-1980 Time-series studyTruett/Truett (1990) US, Mexico 1960-1982 Time-series studyZhuo (1998) China: 29 regions and

14 large cities19951986-1995

Cross-sectional study,time-series study

11

4.1 Salient results

All in all, the many findings of the highly diverse studies can be summed up as follows: the

variation in life insurance demand both in an international cross-section and also over time

can be significantly explained by a combination of demographic, macroeconomic and socio-

psychological factors. Robust explanatory variables that have proved reliable in various

studies are disposable income, the inflation rate, and the variables proxying for population,

education level, life expectancy, banking sector development, and social security. The import-

ance of the various determinants is illustrated by the latest study by Beck/Webb (2002, Table

3).

Table 3

Determinants of insurance penetrationbaseline regression (log-linear), panel of 68 countries, 1961-2000

Econometric model Parameter(t-statistics)

Constant -7.07 (1.9)GDP per capita 0.57 (2.9)Young dependency -0.36 (1.1)Old dependency 1.19 (3.9)Life expectancy -0.17 (0.2)Schooling -0.05 (0.2)Inflation -1.03 (5.2)Banking sector development 0.35 (4.6)Adjusted R2 0.67

In their study, Beck/Webb (2002) investigate the determinants of life insurance demand in a

panel of 68 countries over a period from 1961 to 2000. For their econometric estimates they

use eg insurance penetration as a yardstick for life insurance demand. The baseline regression

(fixed-effect model) reproduced in Table 3 uses a combination of demographic and macro-

economic explanatory variables. These include the per-capita income level, inflation, the ratio

of elderly and young people to the working population, life expectancy, an indicator for

12

human capital, and an indicator for the level of development of the banking system. The

seven determinants listed in Table 3 explain 67% of the international and intertemporal

variation in insurance penetration.

Table 4

Regression coefficient of the income variableand income elasticities of life insurance premiums

Author(s) Countries / regions Year / period Regressioncoefficient

Elasticity

Babbel (1985) US 1953 – 1979 0.004 – 0.008 0.62 – 1.10Beck/Webb (2002) 68 countries 1980 – 2000

1961 – 20000.34 – 0.660.18 – 0.79

1.34 – 1.66 a

1.18 – 1.79 a

Beenstock/Dickinson/Khajuria (1986)

10 OECD countries 1970 – 1981 0.57 – 2.2 0.57 – 2.2

Browne/Kim (1993) 45 countries 1980, 1987 0.32 – 0.88 0.32 – 0.88DePamphilis (1977) US 1952 – 1974 0.012 –Diacon (1980) UK 1946 – 1968 0.13 – 8.09 2.5 – 3.5GDV (1983) Germany 1965 – 1980 0.18 – 0.40 –Hammond/Houston/Melander (1967)

US 1953, 1962 0.01 – 0.04 0.3 – 1.5

Outreville (1996) 48 developed countries 1986 0.0002 0.52 – 0.62Truett/Truett (1990) US

Mexico1960 – 19821964 – 1979

0.77 – 1.103.04 – 3.87

0.77 – 1.103.04 – 3.87

a Beck/Webb (2002) use the insurance penetration (premium volume/GDP) as a dependent variable. The re-gression equation is expressed as a log-linear function, the elasticity is obtained as the regression coefficientplus one.

In nearly all the 13 studies analysed, the income variable (frequently operationalised by the

gross domestic product) is a cardinal explanatory factor. It is seen as a proxy for several

factors explaining life insurance demand. Table 4 gives an overview of the regression

coefficients identified in the literature for the respective income variable and of the directly

estimated income elasticities of life insurance premiums2. The income elasticity results lie

approximately within a range from 0.5 to 3, ie a 1% change in the income variable is

2 The regression coefficients of the respective income variable can be interpreted as elasticity if the equations

used for the estimates are expressed in log-linear terms. However, this is not the case in all studies; in some,the regression equations are estimated on a non-logarithmic basis, with the result that the regression coeffi-

13

associated with a change of 0.5% to 3% in the premium volume. The variation of the results is

due to the different country samples, periods covered and study methods used.

4.2 Selected studies in detail

We will now look at selected studies in more detail. In terms of methodological approach,

these can be divided into three categories: cross-sectional studies, time-series studies and

panel analyses.

4.2.1 Cross-sectional studies

Browne/Kim (1993) investigate the determinants of life insurance demand that explain the

variation within a sample of 45 countries. The theoretical start point for the empirical analysis

is again Lewis's model (1989). Both the premium volume and also the insurance density are

used as indicators of demand. A log-linear model is estimated for the two time-points 1980

and 1987 with the aid of the least squares (LS) method. Explanatory variables in the

regression equation are the proportion of younger people in the overall population, Islam as

professed religion, incomes, social security, the anticipated inflation rate, education levels, the

average life expectancy, and the price of insurance. The regression analysis significantly

verifies most of the signs of the variables as theoretically expected and documented in Table

1, only the proxy variable for life expectancy yields no significant contribution to explaining

the demand for life insurance.

Outreville (1996) studies the correlation between premium income on the one hand and the

level of development of the financial market and the market structure within which the insur

-------------------cients define the amount of the absolute change. Calculation of the percentage change, ie the income

14

ance companies operate on the other. The analysis is based on a sample of 48 developed

countries for 1986. Premium income is analysed as a function of the influencing factors GDP,

real interest rate, anticipated inflation, life expectancy at the time of birth, development level

of the financial market, a dummy variable for a monopolistic market structure, and a dummy

variable for activities by foreign companies in the home market. The premium income and

GDP are used in per-capita terms, the regression equations are estimated both in linear and

also in log-linear form with the aid of the LS method. The results show that the development

of life insurance premiums is significantly positively dependent on incomes and on the

development level of the local financial market3. The estimates for income elasticity lie at

around 0.5 and are thus in a similar order of magnitude as identified in the studies by Been-

stock/Dickinson/Khajuria (1986) and Browne/Kim (1993). Monopolistic market structures

have a significant negative influence on international premium development. The correlations

for anticipated inflation and life expectancy are in line with theoretical considerations. There

is no evidence of a significant influence of real interest rates or the dummy variable for

activities by foreign companies in the home market. Attempts to extend the study by

including further country-specific socio-economic variables such as the proportion of the rural

population in the overall population, human capital, religionus inclination, the proportion of

younger people in the overall population, and social transfer payments within the scope of the

regression analysis proved difficult on account of multicolinearity problems, but bivariate

correlation analyses with premium volume indicate that correlations may well exist.

The study by Beck/Webb (2002) investigates the determinants of life insurance demand in a

data set from a total of 68 countries. The study uses insurance penetration as the indicator for

demand. The regression equation is expressed as a log-linear function, the database is an

-------------------elasticity, is possible only if the ratio of incomes to premium volume is known.

15

average over the period 1980-2000, so that there is one observation for each country. The

international variation of insurance penetration can be statistically significantly explained at

70% in a baseline regression by the variation of the GDP per capita, the human capital, the

life expectancy, the inflation rate and the indicator for the development of the banking system.

If further potentially influencing factors (religious inclination, private saving rate, institutional

changes etc) are sequentially included in the regression analysis, the results for the indicators

for human capital, inflation and the indicator for the development of the banking system are

found to be extremely robust, the coefficients for incomes and for life expectancy, by

contrast, lose their statistical significance in various configurations.

Zhuo (1998) studies the influencing factors for 29 different regions and 14 large cities in

China for 1995. The model approach for the regions uses the aggregate life insurance

premiums (sum of premiums from endowment and annuity insurance) per capita and the

separate premium payments as dependent variables. The gross domestic product per capita is

positively correlated both with the aggregate life insurance premiums and also with the two

types of insurance observed. The dependency ratio of the children likewise has a significant

influence on demand for life insurance policies, and particularly endowment policies. Both

the dependency ratio of the elderly and the size of social insurance, and also the indicator for

human capital, yield no significant contribution to explaining the regional variation in insur-

ance premiums. For the 14 large cities, the aggregate life insurance premiums per capita and

the endowment premium payments per capita are used as dependent variables. The only

significant explanatory variable is the income per capita.

-------------------3 A suitable variable is found to be the ratio (M2-M1)/M2, which describes the ratio of quasi-money (M2-M1)

to the broad definition of money (M2) and is intended as a proxy variable representing the complexity of thefinancial market structure.

16

4.2.2 Time-series studies

DePamphilis (1977) formulates a model framework which assumes that the development of

the premium income is positively dependent on incomes and negatively dependent on the

insurance business in force. The basic model is supplemented by price, interest-rate and social

transfer variables to make allowance for the cyclic effects that become apparent in time-series

studies. The study covers the period from 1952 to 1974 for the US on the basis of year-by-

year data. The equation is estimated in the first differences of the levels and has a high ex-

planation value – measured by the R-squared statistic –, the variables are all statistically

significant and the correlations match those theoretically expected. The stability of the

parameters and the forecast accuracy are checked with the Chow test and the mean squared

errors (MSE). The principal findings can be summed up as follows: premium incomes are

positively (negatively) correlated with disposable income (consumer price index); social

transfer payments are identified as substitutes for private life insurance; higher interests rates

reduce growth in premium income with a time lag of one year; and an increase in the business

in force in the previous period reduces premium income in the current period.

The analysis by Diacon (1980) studies the factors determining the aggregate premium volume

from new business in the United Kingdom for the period from 1946 to 1968. As regards the

dependent variable, a distinction is made between demand and supply, while the premiums

are broken down according to the two motives "protection" and "saving". In addition to a

constant and the prices for the two kinds of insurance, a set of exogenous demand and supply

variables4 is used in the model equations, of which there are four in all. All four regression

equations are estimated simultaneously with the aid of the two-stage LS method. In some

4 These include the inflation rate, income, the number of births and marriages, the average income tax, the

unemployment rate, the sums insured under the business in force, various returns indicators and the cost ofinsurance.

17

versions, an adaptive expectation model is integrated into the model to make allowance for

anticipated inflation as an explanatory variable. The principal findings in respect of the

demand equations can be summed up as follows: in none of the demand equations can a

significant influence of prices be demonstrated; income elasticity is higher in the case of

premium income due to the "saving" motive than in the case of that due to the "protection"

motive; the average income tax is a significant explanatory variable (positive influence on

premium income due to the "saving" motive and negative influence on the premium income

due to the "protection" motive); and the unemployment rate has a positive and significant

influence on the premium income due to the "protection" motive. The principal findings in

respect of the supply equations can be summed up as follows: the supply of premium income

due to the "saving" motive is positively dependent on the price; other important explanatory

factors are the administration cost of insurance and the yield from the investment fund. The

supply of premium income due to the "protection" motive is explained primarily by the price

and the yield of the investment fund. If an adaptive expectation model is introduced to make

allowance for anticipated inflation as an explanatory variable, the inflation variable yields

significantly negative explanation contributions for both demand equations. All in all, it is

true to say that the empirical analysis is hampered by the problems of "multicolinearity" and

"auto-correlation". Prices have no significant influence on demand, the income variable being

by far the most important explanatory factor.

In their study, Truett/Truett (1990) analyse the determinants of life insurance demand on the

basis of aggregate time series for Mexico and the US in the period 1960 to 1982 (1964 to

1979 for Mexico). As the indicator for life insurance demand in the US, they use the average

spending on life insurance products per family, in Mexico the life insurance in force in

relation to the working population. The demand equations for the US and for Mexico are each

dependent on an indicator for age (average age of the population, proportion of earners in the

18

overall population), an indicator for the education level of the population (average years of

school attendance) and the real per-capita income. In the case of the income variable, two

variants are distinguished, on the one hand the current income at time t and on the other the

future income at time t+3, in order to model the household's hypothetical decision on the basis

of its anticipated future income. The demand equations for the two countries are separately

estimated in log-linear form. All three explanatory variables show the expected positive

correlation in the regression analysis and are statistically significant. The estimates of the

demand equations based on future income are found to be statistically superior to the variant

based on current income, as they exhibit no auto-correlation.

Schwebler (1984) and the GDV (1983) analyse the factors influencing life insurance demand

in the period 1965 to 1980 for Germany. As their indicators for demand they use the growth

rates of the premium volume, the sums insured under the business in force and the sums

insured under new business. The departure point for the multiple regression analysis is a

factor analysis that serves to identify groups of variables and to reduce the high number of

separate influencing factors to the essential minimum. The findings identify three groups of

influencing factors: the macroeconomic situation (economic growth, unemployment rate and

price inflation), income trends (disposable income, private saving rate and private consump-

tion) and socio-psychological indicators (Allensbach indicator). On the basis of linear multi-

ple regression equations, the determinants used are capable of explaining almost 90% of the

variation in the respective demand indicators.

In his study, Müller (1998) explains life insurance demand in Germany on the basis of

economic and psychological factors. He studies the determinants of aggregate life insurance

demand on the one hand and those of the specific life insurance products "capital-forming life

insurance", "endowment insurance", "term life insurance" and "annuity insurance" on the

19

other. As indicators for the respective demand, the multiple regression approaches use the

sums insured under new business and the number of new policies. The empirical analysis

covers the period from 1975 to 1996 and uses both quarterly and annual data.

The main finding of the study is that combined use of "hard" (economic) and "soft" (psycho-

logical) determinants for various life insurance products and for the aggregate life insurance

demand yields the best-quality explanations. Some exemplary results are: 88% of the demand

for capital-forming life insurance policies (indicator: sums insured) can be explained by

means of the ratio of net to gross income, the number of unemployed, anticipated

opportunities for future saving, private sight deposits and a dummy variable for the 1st quarter

of 1995 (introduction of compulsory long-term care insurance in Germany). 83% of

endowment insurance demand (indicator: sums insured) can be explained on the basis of the

ratio of net to gross income, the individual's assessment of his/her personal financial situation

in the past, the sight deposits of private individuals, expectations of future price trends, and

the dummy variable for the 1st quarter of 1995.

Zhuo (1998) studies the factors determining aggregate life insurance premiums per capita and

endowment insurance premiums per capita in China for the period from 1986 to 1995. As

explanatory variables he uses per capita GDP, the consumer price index, an interest variable,

and social security spending; the regression equation is estimated in log-linear form with the

aid of the LS method. The regression results exhibit significant results only for income; the

income elasticity is considerably higher than in Zhoe's (1998) cross-sectional study.

20

4.2.3 Panel studies

The analysis by Beenstock/Dickinson/Khajuria (1986) studies the factors governing insur-

ance premium income in 10 OECD countries in the period from 1970 to 19815. The theoreti-

cal model explains the demand for and the supply of life insurance on the basis of economic

and demographic variables. It distinguishes expenditure on pure risk coverage, later annuity

payments and pure savings. It determines the factors governing demand and supply for all

three products, as a result of the assumption of perfect market competition, supply and

demand are identical. Addition of the total expenditures on the three types of product gives

the aggregate premium revenue in dependence on income, life expectancy, the aggregate price

level, the proportion of the resident population with dependants, the average age of this

proportion of the resident population, the real interest rate, aggregate savings, the level of the

government-organised social transfer payments, and the implicit tax savings. The function is

specified in log-linear form and is estimated with a country-specific dummy variable as a

constant and with a country-specific dummy variable in combination with income (in order to

model country-specific rises in income). The principal findings of the study can be summed

up as follows: the income elasticity of life insurance demand is positive and varies widely

from one country to another; life expectancy and the demographic variables used significantly

explain the variation in insurance demand; government social transfer payments tend to

displace private insurance demand.

In their study, Beck/Webb (2002) investigate the determinants of life insurance demand in a

panel of 68 countries over a period from 1961 to 2000. In the first stage of the study, eight

five-year averages are analysed. Random-effects and fixed-effects models are used for the

econometric estimates of the yardsticks for measurement of life insurance demand (insurance

21

penetration, insurance density and business in force in relation to GDP). The per-capita

income level, inflation, the ratio of the elderly to the working population, and the indicator for

the development of the banking system are robust explanatory factors for the international and

intertemporal variation of insurance penetration, insurance density and life insurance business

in force in relation to GDP. The other variables (life expectancy, expected inflation, human

capital, ratio of young people to the working population, real interest rates, private saving

rate, etc) sequentially introduced into the respective estimates cannot be attributed any further

major contribution to the explanation.

In a second stage, the annual observations are analysed instead of five-year averages6. The

study focuses on the insurance penetration as the demand indicator. The results differ only

slightly from those obtained from the five-year averages. Further to the per-capita income

level, the ratio of the elderly to the working population, the indicator for the development of

the banking sector, and the inflation rate, another significant explanatory factor is the ratio of

young people to the working population.

All in all, the findings of the study by Beck/Webb (2002) suggest that the per-capita income,

inflation, and the indicator for the banking sector development level are the most robust

factors for explaining the international and intertemporal variation in life insurance demand.

However, religious and institutional factors also account for some of the international

variation.

-------------------5 The country sample comprises the US, Germany, France, Japan, the United Kingdom, Canada, Italy,

Australia, the Netherlands and Sweden.6 The second part of the analysis based on year-to-year observations and the previously discussed cross-

sectional study serve to validate the sensitivity of the results of the panel study based on five-year averages(Beck/Webb, 2002, p. 25).

22

5. Analysis of tax incentives

By contrast with the macroeconometric studies in the previous section, the microeconometric

studies focus on explaining how the individual households decide how they want to spend

their income. Table 5 lists the studies analysed in the present paper. Economic literature

devotes a lot of space to explaining the savings behaviour of the economic units. The political

debate about the reliability of the pensions and social insurance systems in many countries is

closely linked to the theoretical debate about the private households' portfolio decisions about

how to use their savings. This general debate provides the background against which savings

can be used to purchase life insurance products. The portfolio decision in favour of life

insurance products depends eg on the returns offered by alternative forms of investment and

on the tax incentives available to purchasers of life insurance products. How preferential tax

treatment can influence saving by means of life insurance products has up to now been the

subject of only a few empirical works.

Table 5

Microeconometric analyses

Author(s) Countries / regions Year / period Method / data used

Brunsbach/Lang (1998) Germany 1988 Household data, EVS 1988Farny (1983) Germany 1958-1983 Comparison of returns from

alternative forms of savingJapelli/Pistaferri (2002) Italy 1989-1989 Household data, SHIWLang (1995) Germany 1988 Household data, EVS 1988Lewis (1989) US 1976 Microdata, 150 US householdsWalliser/Winter (1999) Germany 1993 Household data, EVS 1993, logit

and tobit regressions

23

5.1 Econometric studies

In his analysis for Germany, Lang (1995) uses the results of the consumer expenditure survey

(EVS) performed in 1988 by the German Federal Statistics Bureau. With the aid of an income

tax simulation, the potential and actually claimed tax benefit achievable through saving in

endowment insurance policies can be calculated for each household in the sample7. Measure-

ment of the tax subsidy distinguishes between two channels: tax exemption for interest earned

and tax deductibility of insurance premiums paid. The advantage accruing to a household

from the tax exemption for the interest earned under endowment insurance policies is all the

greater, the higher the household's marginal tax burden. The personal tax rate is thus the

yardstick for the tax incentive offered through this channel. The extent to which a household

can save tax by deducting life insurance premiums from its taxable income (as "expenses of a

providential nature") depends in particular on how much of the maximum deductible allow-

ance has already been dedicated to purposes other than the life insurance premiums. The

following conclusions can be drawn from descriptive statistics: the margin available to high-

income households for deducting insurance premiums is becoming smaller all the time, more

than two-thirds of all households deriving their income from dependent employment have no

opportunity to deduct their life insurance premiums, because their "unavoidable" expenses8

already exceed the ceiling of the deductible allowance. The scale of the tax subsidies for sav-

ing in endowment policies thus differs widely between various population strata; on average

over all households eligible to deduct life insurance premiums, only about one-third of the

deductible allowance is actually exhausted.

7 Data from about 20,000 households are used in the empirical analysis.8 The term "unavoidable" expenses is understood to mean compulsory contributions to the statutory pensions,

unemployment and health insurance schemes, voluntary contributions to these schemes, and premiums forprivate health, accident, and (personal and motor) liability insurance policies (Lang, 1995).

24

In the microeconometric analysis, the effect of the potentially achievable tax savings on

investment by private households is isolated from other influencing factors. The savings

element in endowment insurance policies is modelled as a component of a demand system

that serves to allocate the entire budget available for monetary wealth formation. The variable

to be explained in the estimating equation is thus the absolute amount allocated to saving in

endowment insurance policies in relation to the total funds dedicated to monetary wealth

formation by each household. In addition to the budget as the explanatory variable, a variation

of the estimating equation constant as a function of the size of the tax subsidy, socio-

demographic household features (family status, number of children, employment status of the

spouse, occupational category) and home ownership (as a potential substitute for endowment

insurance) is modelled. The principal findings of the regression analysis (Tobit regression)

can be summed up as follows: the budget elasticity of saving in endowment policies declines

significantly with rising budget; the occupational category to which the household belongs9

has a major influence on the amount of savings in endowment policies; the proportion of the

budget dedicated to savings in endowment policies rises, ceteris paribus, with the number of

children living in the household and is larger for married than for single persons; a household

with mortgage commitments dedicates a higher proportion of its budget to saving in endow-

ment insurance than a comparable household without debts10; real-estate ownership has a

negative effect on the proportion of the budget dedicated to saving in endowment policies; the

influence of tax incentives on the relative amount of savings in endowment insurance policies

is low. Tax savings through deduction of insurance premiums from taxable income under the

personal tax allowance make no significant contribution to explaining the proportion of the

budget dedicated to saving in endowment policies. The marginal tax rate as the measure of the

9 For example, the proportion of the budget dedicated to saving in endowment policies is, ceteris paribus,

28 percentage points higher among self-employed persons not eligible for benefits from the governmentsocial insurance system than among the reference group of persons in dependent employment.

10 This finding supports the assumption that life insurance policies are frequently concluded in order to qualifyfor low-interest mortgage loans from insurance companies (Lang, 1995).

25

extent to which the individual benefits from tax exemption of the interest earned under

endowment insurance policies has a significant, but relatively low effect.

In their study, Walliser/Winter (1998) investigate the quantitative importance of tax influ-

ences and of the bequest motive to demand for life insurance in Germany. The empirical

analysis is driven by a theoretical life-cycle model that postulates three reasons for buying a

life insurance product: to enhance bequeathable wealth; tax advantages over other forms of

saving; and the saving motive, which is modelled by allowing for lump-sum payments as well

as regular annuity payments. Like Lang (1995), the study uses the consumer expenditure

survey (EVS) performed by the German Federal Statistics Bureau as its database; in this case,

however, the data stem from the 1993 wave. In the first stage of the microeconometric

analysis, life insurance demand is estimated on the basis of a probit regression, ie the

dependent variable takes the value 1 if the household holds one or more insurance policies,

otherwise the value is zero. The explanatory variables used include age, net earned income,

various proxies for lifetime income, number of children, family status and average tax rate.

The probability of buying a life insurance policy shows a non-linear dependence on age and

net earned income. In line with the theoretical considerations, the bequest motive is corrobor-

ated; married persons and families with children are more likely to take out insurance

policies. The assumption that tax incentives have a positive influence on demand is

confirmed. A higher average tax rate increases the probability of owning a life insurance

policy. A second stage in the analysis studies whether the right-hand variables of the probit

regression can also explain the face value of life insurance. With the aid of a tobit regression

that takes into account the fact that the variable to be explained cannot be observed in every

26

household 11, tax incentive effects and the bequest motive are shown to have a significant

influence on the face value of life insurance.

The start point of the study by Jappelli/Pistaferri (2002) is the postulate in economic theory

that portfolio decisions are dependent on the disposable income of the household and on the

remaining returns after taxes. The authors attempt to test the theory empirically by

investigating in their analysis the effects produced on the portfolio decisions of private house-

holds by a change in the tax framework for life insurance policies. The tax reform of 1992-

1994 in Italy, which forms the basis of the study, abolished tax incentives benefits for life

insurance policies bought by households with high marginal tax rates and introduced tax

advantages for life insurance policies bought by households with low marginal tax rates. As a

result, the relative profitability of life insurance policies by comparison with other financial

investments was reduced for the rich and increased for the poor, while for the middle-income

groups it remained unchanged. The study sample covers the years 1989 to 1998, allowing the

effects of the tax reform on the portfolio decisions of various household groups to be

investigated in detail. The effects of the tax reform on demand for life insurance policies can

be observed in isolation, as the tax reform changed the structure of the incentives, but without

changing the tax categories or tax rates.

The microeconometric analysis uses the household panel drawn up by the Bank of Italy as its

database; the panel comprises the results of polls of nearly 8,000 representative households

conducted for the years 1989, 1991, 1993, 1995 and 1998. In line with the strategy of the

study, the regressions are performed not only for various time periods, but also for various

household groups that differ according to whether or not they are affected by the tax reform.

Probit and tobit regressions are used to investigate on the one hand the probability of a house

11 Elderly persons and households typically have zero demand for life insurance policies.

27

hold investing in life insurance policies (probit regressions) and on the other hand the amount

invested (tobit regressions). The specifications for the different regressions comprise, among

other features, dummies for various age groups, dummies for various education levels,

indicators for gender, family status, number of children and a number of income variables.

Neither the tobit nor the probit regressions provide evidence of any significant tax effects. All

in all, the two authors find no statistically significant portfolio effects resulting from the

change in the tax incentives for life insurance policies. Both the households that had no life

insurance policies before the tax reform and those that already had one or more did not

significantly change their investment patterns after the tax reform. The authors explain their

results in particular with reference to the design of the insurance products available at the

time, which provided little incentive for private households. Such aspects included eg high

minimum payments for capital accumulation, which tend to deter households with low

incomes. The high cost of obtaining information is cited as a further explanatory factor.

5.2 Tax incentives and profitability of life insurance as a form of saving

Closely linked to the microeconometric analysis of the tax incentives for saving by means of

life insurance is the question as to the profitability of the savings component of long-term life

insurance policies. In this context, not only the absolute profitability is of interest, but also

how it relates to that of other forms of saving and capital investment by private households.

In his analysis, Farny (1983) studies the profitability of endowment insurance in Germany.

The profitability values for common types of endowment insurance, determined by calcula-

tion for the past and extrapolation for the future, are in the long term higher than the historic

inflation rates. Thus, endowment insurance in the past achieved profitability in real terms.

Part of this profitability effect is due to the preferential tax treatment of life insurance.

28

Calculation of the profitability of endowment insurance before and after tax effects shows that

the tax advantages enhance profitability, the scale of this improvement depending on the

policyholder's personal tax situation.

The work by Brunsbach/Lang (1998) studies the effects of tax benefits (incentives for saving

in endowment insurance policies under the German income tax system) on the yield of the

savings component of life insurance policies. The study uses microdata from the 1988 EVS

survey to estimate the tax-related scatter of yields within the German household population.

For this purpose, it assigns one and the same model insurance policy to each household ob-

served and calculates the returns after taxes achieved by the household taking into account its

specific tax characteristics. This household-specific return achieved in the context of the real

tax system is compared with the return the household would have obtained from the same

insurance contract in a fictitious tax system without preferential treatment for life insurance

policies. Comparison of the two levels of return reveals the tax advantage that the individual

household can achieve by saving in endowment insurance. On the basis of an exogenous

average return before taxes, the increase in the after-tax return as a result of the preferential

treatment can be calculated. The findings of the study show that the preferential tax treatment

significantly enhances the profitability of investments in endowment insurance: saving in

endowment insurance brings the average household a return of 6.67% after taxes, 54% higher

than the return from another form of monetary wealth formation that generates the same

return before tax, but does not enjoy preferential tax treatment. It also becomes clear that the

scale of the tax advantage varies widely between different sectors of the population. If the

household-specific return is compared with the actual monetary wealth formation of the

households, there is no evidence to indicate that the preferential tax treatment constitutes any

effective incentive to invest more in life insurance as a savings vehicle. On the contrary, it is

apparent that the proportion of savings in endowment policies within overall monetary wealth

29

formation is not significantly higher among the household groups targeted by the tax legis-

lation than among those households that do not benefit from the preferential tax treatment.

This outcome coincides with the conclusions reached by Lang (1995), as discussed earlier,

whose microeconometric analysis features additional controls for other possible influencing

factors.

6. Concluding remarks

The present analysis of the empirical literature on the determinants of demand for life

insurance products does not claim to be exhaustive, but nevertheless affords an initial

overview of the discussion status. The macroeconometric studies available show that a

combination of demographic, macroeconomic and socio-psychological factors is capable of

significantly explaining life insurance demand both in an international cross-section and also

over time. By way of criticism, however, it should be noted that the econometric methods

used in the literature are not always state-of-the-art in terms of modern research methodology.

Even the latest panel study by Beck/Webb (2002) is no exception to this. It is generally true of

all the studies analysed that they completely ignore or at least treat only superficially such

problem fields as "multicolinearity", "auto-correlation", "structural constancy of parameters",

"non-stationarity of macroeconomic time series", "causality", "interdependence of variables",

"model analysis on the basis of specification tests". Only in a few empirical studies is it pos-

sible to recognise how the authors arrive at the processes they use for evaluating the econo-

metric model approaches chosen. Future research works on the topic will have to address

these problem areas. In particular, the correlations discussed should be statistically validated

by means of stationarity and cointegration analyses, error correction models to distinguish

between short and long-term effects, and estimates from VAR models to verify the results

obtained from structural approaches.

30

Up to now, little attention has been given in the literature to the effects of tax incentives for

saving in life insurance products. The studies examined in the present paper show that

analysis of the tax effects is relatively complex and that it is not always possible to draw clear

conclusions. In particular, it is apparent that the importance of tax effects to life insurance

demand or the profitability of life insurance as a form of investment depends heavily on the

specific tax situation of the private households. There is broad scope for further research work

in the form of theoretical and empirical analyses of tax implications12.

12 Guiso, Haloassos and Jappelli (2002) offer a good overview of the current works in this topic area.

31

References

Anderson, D. R. and J. R. Nevin, 1975, Determinants of Young Marrieds’ Life Insurance

Purchasing Behaviour – An Empirical Investigation, The Journal of Risk and Insurance

42, 375-387.

Babbel, D. F., 1985, The Price Elasticity of Demand for Whole Life Insurance, The Journal of

Finance 40, 225-239.

Beck, T. and I. Webb, 2002, Economic, Demographic, and Institutional Determinants of Life

Insurance Consumption across Countries, in: World Bank Economic Review.

Beenstock, M., Dickinson, G. and S. Khajuria, 1986, The Determination of Life Premiums:

An International Cross-Section Analysis 1970-1981, Insurance: Mathematics and

Economics 5, 261-270.

Bernheim, B. D., 1991, How strong are bequest motives? Evidence based on estimates of the

demand for life insurances and annuities, Journal of Political Economy 99, 899-927.

Browne M. J. and K. Kim, 1993, An International Analysis of Life Insurance Demand,

Journal of Risk and Insurance 60, 616-634.

Brunsbach, S. and O. Lang, 1998, Steuervorteile und die Rendite des Lebensver-

sicherungssparens, Jahrbücher für Nationalökonomie und Statistik 217, 185-213.

Campbell, R. A., 1980, The Demand of Life Insurance: An Application of the Economics of

Uncertainty, Journal of Finance 35, 1155-1172.

Cargil, T. F. and T. E. Troxel, 1979, Modeling Life Insurance Savings: Some Methodological

Issues, Journal of Risk and Insurance 46, 391-410.

Carson, J. M. and R. E. Hoyt, 1992, An Econometric Analysis of the Demand for Life

Insurance Policy Loans, The Journal of Risk and Insurance 59, 239-251.

Chin-sheng Huang, 1994, Life insurer financial distress prediction: a neural network model,

Journal of insurance regulation 13, 131-167.

32

Cummins, J. D., 1973, An Econometric Model of the Life Insurance Sector of the U.S.

Economy, Journal of Risk and Insurance 40, 533-554.

DePamphlis, D. M., 1977, Variation in individual life insurance premium revenues: An

econometric approach, Journal of Risk and Insurance 44, 67-76.

Diacon, S. R., 1980, The demand for U.K. ordinary life insurance: 1946-1968, Geneva Papers

on Risk and Insurance, June.

Farny, D., 1983, Zur Rentabilität langfristig gemischter Lebensversicherungen (Stand 1983),

Zeitschrift für die gesamte Versicherungswirtschaft, 363-380.

Fischer, S., 1973, A Life Cycle Model of Life Insurance Purchases, International Economic

Review 14, 132-152.

GDV (ed.), 1983, Gesamtwirtschaftliche Einflüsse auf die Lebensversicherung, Schriftenreihe

des Ausschusses Volkswirtschaft des Gesamtverbandes der Deutschen Versicherungs-

wirtschaft e.V. Heft 1, Karlsruhe.

Guiso, L., M. Haliassos and T. Jappelli (eds.), 2002, Household Portfolios, MIT Press,

Cambridge MA.

Hakansson, N. H., 1969, Optimal Investment and Consumption Strategies Under Risk, and

Under Uncertain Lifetime and Insurance, International Economic Review 10, 443-466.

Hammond, J. D., D. B. Houston and E. R. Melander, 1967, Determinants of Household Life

Insurance Premium Expenditures – An Empirical Investigation: The Journal of Risk and

Insurance 34, 397-408.

Jappelli, T. and L. Pistaferri, 2002, Tax incentives and the demand for life insurance:

evidence from Italy, forthcoming Journal of Public Economics.

Karni, E. and I. Zilcha, 1985, Uncertain Lifetime, Risk Aversion and Life Insurance:

Scandinavian Actuarial Journal, 109-123.

33

–, 1986, Risk Aversion in the Theory of Life Insurance: The Fisherian Model, Journal of Risk

and Insurance 53, 606-620.

Lang, O., 1995, Steuersubventionen und Savingbildung in Lebensversicherungen, ZEW-

Discussion Paper 21, Mannheim.

Lewis, F. D., 1989, Dependents and the Demand for Life Insurance, American Economic

Review 79, 452-466.

Müller, A., 1998, Erklärung der Lebensversicherungsnachfrage anhand ökonomischer und

psychologischer Einflussfaktoren: Eine quantitative Analyse des Abschlussverhaltens

bei differenzierter Betrachtung einzelner Lebensversicherungsformen, Beiträge zu

wirtschaftswissenschaftlichen Problemen der Versicherung 40, Karlsruhe.

Outreville, J. F., 1996, Life Insurance Markets in Developing Countries, Journal of Risk and

Insurance 63, 263-278.

Pissariades, C. A., 1980, The Wealth-Age Relation with Life Insurance, Economica 47, 451-

457.

Schwebler, R., 1984, Identification and quantification of the overall impact of the national

economy on life assurance in the Federal Republic of Germany, Geneva Papers on Risk

and Insurance 9, 280-298.

Truett, D. B. and L. J. Truett, 1990, The Demand for Life Insurance in Mexico and the United

States: A Comparative Study, The Journal of Risk and Insurance 57, 321-328.

Walliser, J. and J. Winter, 1998, Tax Incentives, bequest motives and the demand for life

insurance: evidence from Germany, Uni Mannheim, Sonderforschungsbereich 504:

Rationalitätskonzepte, Entscheidungsverhalten und ökonomische Modellierung, 99-28,

Mannheim.

Yaari, M. E., 1965, Uncertain Lifetime, Life Insurance, and the Theory of the Consumer,

Review of Economic Studies 32, 137-150.

34

Zhuo, Z., 1998, Die Nachfrage nach Lebensversicherungenn: Eine empirische Analyse für

China, Manheimer Manuskripte zu Risikotheorie, Portfolio Management und

Versicherungswirtschaft 112, Mannheim.

35

AppendixTable 6

Empirical determinants of life insurance demand

Categories Variables Hypotheses

• Population Demographic trends (eg population count, number of households, number of marriages, birth rate, ratio of young peopleto the working population etc) determine the operating background for the life insurance industry in the long term. Alower proportion of young people in the working population tends to curtail demand for pure life insurance policies via areduction in the present value of consumption by the beneficiaries (variable TC in Equation (1)). On the other hand ahigh proportion of young people in the population makes it reasonable to expect a negative effect on the demand forinsurance policies with a high savings element.

• Education A higher education level of the population should show a positive correlation with demand for life insurance products.The individual's education level determines his/her ability to understand the benefits of risk management and long-termsavings. A higher education level can also be expected to be reflected in a higher level of personal risk aversion (variableδ in Equation (1)).

• Religion The religion of a population can influence the risk aversion and the fundamental institutional configuration of the insur-ance industry. This influence can be considerable in international comparisons of life insurance demand. A low lifeinsurance demand is to be expected in predominantly Islamic countries.

• Urbanity It is to be expected that regions with a large proportion of city dwellers in relation to the overall population will exhibita high demand for life insurance products. While in rural regions close family ties make for an intact solidarity commun-ity, city life tends to be associated with anonymity and low neighbourhood solidarity. Furthermore, the concentration ofconsumers within a compact geographical area simplifies the distribution and marketing of life insurance products; thisshould on the whole tend to reduce costs. Cost and thus price considerations can have a positive influence on the supplyof life insurance products (effect on the "policy-loading factor" L in Equation (1)).

Demographicvariables

• Life expectancy Societies with a high life expectancy tend to exhibit lower demand for pure life insurance policies; however, demand forlife insurance products with a high savings element can be expected to show a positive correlation with life expectancy.A high life expectancy is reflected in a low p in Equation (1).

• Economic growth The growth of the domestic product can be seen as a "proxy" for a whole package of other influencing factors and is thusoften used in empirical studies. On the whole it can be assumed that an acceleration (slowdown) of macroeconomicgrowth can be expected to have positive (negative) effects particularly on new business.

Macro-economicvariables

• Disposable income The change in the disposable income of the private households (eg through changes in taxation) influences their abilityto set money aside for the future and thus affects the margin for growth in life insurance demand. If disposable incomesrise, this can result in a higher demand for life insurance to safeguard the income potential of the dependants (this affectsthe variable TC in Equation (1)).

36

• Monetary stability In this context, the scale of the increase in price levels or, more accurately, anticipated inflation is of interest. As long asthe inflation rate is felt to be tolerable, it is reflected in nominal growth in premiums and sums insured. Furthermore, ifmonetary devaluation is moderate, life insurance can be seen as a relatively safe form of investment, as ongoingpremium adjustments and unchanged real interest rates more or less preserve the substance of the invested funds.However, if the inflation rate exceeds a certain threshold, negative effects on life insurance demand can be expected. Ahigh inflation rate can also trigger structural effects, with demand for life insurance products with a high savings elementbeing shifted towards pure term life insurance policies, if interest rates are negative in real terms.

• Interest rate Life insurance demand is closely linked to trends on the financial markets. By analogy with its dependence on inflation,another relevant issue is the threshold as of which the interest rate triggers new behaviour patterns. Interest rates can beexpected to exert a direct influence on the wealth formation motives of "provision for old age" and "capital investment",as the achievable returns influence decisions about whether or not to conclude life insurance policies. In this context,however, it is not only the absolute interest rate, but the relative returns by comparison with other forms of investmentthat are decisive. The direction in which changes in interest rates and returns will drive life insurance demand is hard tosay a priori. Not only different motives for buying life insurance products but also their dependence on pre-tax and after-tax returns and the associated need to distinguish between real and nominal scenarios make it difficult to predict a cleartrend.

• Development of thefinancial market andbanking sector

On the one hand, new business in life insurance is in competition with alternative forms of investment for the savings ofthe private households. Changes in the preferences of the private households influence the ratio of life insurance to totalsavings. On the other hand, it is reasonable to surmise that an efficiently functioning banking system will enhance theprivate households' confidence in other financial and insurance institutions. Progress made towards a more efficientfinancial market system makes it easier for the insurer to invest more efficiently and thus to offer a more competitiveprice for life insurance policies.

• Demand for credit Demand for mortgages tends to be complementary to demand for life insurance policies. It can be assumed that lifeinsurance policies are frequently concluded in order to qualify for low-interest mortgage loans from insurancecompanies.

• Labour market Rising employment and the associated job certainty create favourable conditions for life insurance business. Decliningemployment can be expected to have negative effects not only on new business, but also on the business in force, ie bytriggering cancellations.

Macro-economicvariables(continued)

• Social security Life insurance and state-run social security systems interact with each other. The scale of state welfare and the associatedlegislation establish the backdrop for the conclusion of life insurance policies. It is to be expected that the size andefficiency of a country's social security system will show a negative correlation with the demand for life insuranceproducts. Such a substitutive relationship between the private and the public sector makes itself felt in the net wealth ofthe households via income transfers (this affects variable W in Equation (1)).

37

• Tax system The features of a country's tax system can influence demand for life insurance products. One of the major purposes oflife insurance – to make provision for the future – can be further promoted by opportunities to take advantage of taxbenefits. For example, the German income tax system encourages saving in the form of endowment insurance in twoways: firstly, the interest earned on the savings accumulated during the term of the policy is exempt from tax at the timeof pay-out (by contrast, interest earned from other forms of investment is counted as taxable income). Secondly, lifeinsurance premiums, unlike other forms of monetary wealth formation (with the exception of long-term building societysavings accounts), can be deducted from the individual's taxable income as part of a personal tax-free allowance. InLewis's theoretical model (1989), the "tax system" determinant is represented by the "policy-loading factor" L. Apartfrom this supply-side effect, the particularities of the tax system can directly affect the disposable incomes of the privatehouseholds on the demand side.

• Income distribution The effect of income distribution on the demand for life insurance products is not clear a priori. Beenstock/Dickin-son/Khajuria (1986) argue that wealthy sections of the population do not need insurance, while poorer sections of thepopulation generate only limited demand, because they operate under tight budget constraints. They conclude that amore equal distribution of incomes would lead to a broader middle class and would thus create stronger demand for lifeinsurance. In their study they are able to empirically confirm their hypothesis. This contrasts with the findings ofBeck/Webb (2002), which identify either a negative or no significant influence. They argue that rich population groupsneed life insurance products in order to transfer their affluence to their descendants. A more equal distribution ofincomes in the population would expand the middle class, but since a certain minimum level of income is necessary inorder to be able to afford life insurance products at all, the authors assume that changes in demand for life insurance aremore likely to exhibit a negative correlation with a more uniform income distribution.

Macro-economicvariables(continued)

• Saving An important economic factor influencing demand for life insurance policies is, of course, the private households'propensity to save. This is, as it were, the most important background condition for the life insurance industry. It is to beexpected that a high propensity to save has a favourable effect on life insurance demand.

Socio-psychologicalfactors

• Present moods andanticipations of thefuture

Psychological factors such as, for example, moods and anticipations of the future play an important part in decision-making by individuals. It is to be assumed that they exert a not inconsiderable influence on the propensity to take out alife insurance policy. Various mood indicators eg the "Allensbach" indicator in Germany, the "consumer confidenceindex " in Switzerland or a so-called "business climate index" (likewise for Germany) attempt to quantify theexpectations and moods of the households and enterprises. Müller (1998, pp 244 et seq.) points out that quantitativeresults on the expectations and attitudes to topic areas such as "past and future development of the general economicsituation", "past and future development of price levels", "expected future unemployment", "past and future developmentof the individual's financial situation", "assessment of the expedience of present savings and opportunities for futuresavings" can be helpful for drawing conclusions as to people's future ability and willingness to conclude life insurancepolicies. Müller (1998) shows that a synthesis of "hard" economic and "soft" psychological factors influencing demandfor individual life insurance products exhibits a significantly higher explanation value than isolated observation of onlyone of these groups of factors.

Institutionaldeterminants

• Rule of law• Well defined property

rights• Regulatory quality• Control of corruption

Besides the determinants of life insurance demand already discussed, the institutional features of a country are, ofcourse, also important. Particularly in less-developed industrial nations, factors such as the rule of law, well-definedproperty rights, a high-quality regulatory regime and control of corruption are important to ensuring that the insuranceindustry can operate in a stable and secure environment in which potential insurance purchasers have confidence. Lackof public confidence in the institutional environment curbs demand for life insurance products.

38

Insurer actionparameters

• Pricing • Product design• Advertising• Sales channels

Of course, the life insurance business cycle is subject not only to "outside" influences. Insurers have a number of theirown, ie internal, action parameters for influencing the business cycle in their own favour (pricing and product design,advertising, sales channels, commissions, etc). An interesting question in this context is how life insurance demandresponds to price changes. The "policy loading factor" L in Lewis's (1989) model, reflecting the price of insurance, asexpected shows a negative correlation with life insurance demand. The higher the market price of life insuranceproducts, the lower, ceteris paribus, should be the demand for such products. However, the effective price, ie the priceafter taxes, should be much more important than the market price for determining demand for life insurance products.Browne/Kim (1993), who approximate the "policy loading factor" as the ratio of premium volume to the life insurance inforce, are able to demonstrate a significant negative price elasticity of life insurance demand. Babbel (1985) alsoidentifies a significant negative price elasticity for new business.