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 to
attract the savings of private households. The 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. 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. We also discuss
various methodological approaches under-lying the aforesaid empirical analyses. 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. We highlight the need for further research in this
field.
JEL-Classification: C20, C51, D91, E21
Key words: Life insurance demand, consumption theories, regression analysis, tax
incentives, profitability
Dr. Carsten-Henning Schlag
Swiss Re, Economic Research & Consulting, Mythenquai 50/60, CH-8022 Zurich
Tel. +41 43 285 2428, Fax +41 42 282 2428, Email: [email protected]
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 underlying 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
2
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 macroeconometric 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 particularly 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
3
(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 consumption 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.
4
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.
5
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)
LD = F ( p, TC, δ, L, W )
( + ) ( + ) ( + ) ( − ) ( −)
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
6
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 variables", "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 insurance 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.
7
Table 1
Determinants of life insurance demand in empirical studies
(1)
Categories
(2)
Variables
(4)
Expected signa
(5)
Statistical
resultsb
+
+
+
+
***
***
#
**
Beenstock/Dickinson/Khajuria
(1996), Browne/Kim (1993),
Beck/Webb (2002),
Truett/Truett (1986)
Diacon (1980)
+
+
***
**
Beck/Webb (2002), Truett/
Truett (1986), Browne/Kim
(1993)
-
**
+
#
Browne/Kim (1993),
Beck/Webb (2002)
Beck/Webb (2002)
+
***
• Economic growth
• Ratio of young dependents to working
population
• Ratio of old dependents to working
population
• Number of marriages
• Birth rate
• Average years of school attendance
• Proportion of persons with (tertiary)
education in the population between 20 and
24 (UNESCO)
• Dummy variables for predominantly Islamic,
Protestant and Catholic countries
• Ratio of urban population to overall
population
• Life expectancy at birth (UN statistics)
• Life expectancy of persons aged over 40 in
1975
• Growth of (per capita) GDP
+
**
• Income
• Disposable income of private households
+
***
• Prices
• Aggregate price level
+
**
• Monetary stability
• Inflation rate
• Expected inflation rate (average of past years,
average of current and next year inflation rate)
• Long-term interest rate (real or nominal
terms)
?
**
(-), **
?
#
• Population
Demographic
variables
• Education
• Religion
• Urbanity
• Life expectancy
Macroeconomic
variables
(6)
Source
(3)
Operationalisation
• Interest rate
Beck/Webb (2002), Beenstock/
Dickinson/Khajuria (1996),
Outreville (1996),
Schwebler (1994), Beck/Webb
(2002), Zhuo (1998)
Schwebler (1984),Lewis (1989),
Beenstock/Dickinson/Khajuria
(1986), Truett/Truett (1990),
Browne/Kim (1993), Outreville
(1996), DePamphilis (1977)
Beenstock/Dickinson/Khajuria
(1986)
Schwebler (1994), Beck/Webb
(2002), Browne/Kim (1993),
Diacon (1980)
Beenstock/Dickinson/Khajuria
(1986), Outreville (1996)
8
• Development of the
financial market and
banking sector
• Ratio of quasi-money (M2-M1) to the broad
definition of money (M2)
• Total claims of deposit money banks on
domestic non-financial sectors as a share of
GDP
• Unemployment rate
• Government social security expenditure in
relation to GDP
+
***
Outreville (1996)
+
***
Beck/Webb (2002)
-
**
**
•
•
•
•
•
+
?
?
?
**
***
**
(-), ***
?
#
• Saving
Average income tax rate
Ratio of net to gross income
Gini coefficient
Theil's inequality coefficient
Dummy variable indicating whether the
market is a monopolistic one
• Dummy variable indicating whether foreign
companies are writing business in the market
• Private saving rate
Schwebler (1984)
Browne/Kim (1993), Beck/
Webb (2002), Beenstock/
Dickinson/Khajuria (1986),
DePamphilis (1977),
Diacon (1980)
Müller (1998)
Beck/Webb (2002), Beenstock/
Dickinson/Khajuria (1986)
Outreville (1996)
+
**
Socio-psychological factors
• Moods (present) and
anticipations for the
future
• Allensbach indicator
• Consumer climate index
• EU consumer confidence
+
+
+
***
**
**
Institutional
determinants
• Rule of law
• Well-defined property
rights
• Regulatory quality
• Control of corruption
Average of six indicators
+
**
Macroeconomic
variables
(continued)
• Labour market
• Social security
• Tax system
• Income distribution
• Competitive situation
Schwebler (1984),
Beck/Webb (2002)
Schwebler (1984), GDV (1983)
Müller (1998)
Beck/Webb (2002)
Not operationalised in the empirical studies
• Pricing
• Product design
• Advertising
• Sales channels
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.
Insurer action
parameters
9
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 microeconometric 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
Babbel (1985)
Beck/Webb (2002)
US
68 countries
1953-1979
1980-2000
1961-2000
Beenstock/Dickinson/
Khajuria (1986)
Browne/Kim (1993)
Cargil/Troxel (1979)
DePamphiles (1977)
Diacon (1980)
GDV (1983)
10 OECD countries
1970-1981
45 countries
US
US
UK
Germany
1980, 1987
1954-1974
1952-1974
1946-1968
1965-1980
Müller (1998)
Germany
1985-1996
Outreville (1996)
Schwebler (1984)
Truett/Truett (1990)
Zhuo (1998)
48 developed countries
Germany
US, Mexico
China: 29 regions and
14 large cities
1986
1965-1980
1960-1982
1995
1986-1995
Method / data Used
Time-series study, GLS estimate
Cross-sectional study, LS
estimate; panel study, estimation
of fixed-effects and randomeffects models
Panel study (time-series and
cross-section data)
Cross-sectional study
Time-series study
Time-series study
Time-series study
Correlation analyses, factor
analysis, multiple regression
analyses
Time-series study (quarterly and
annual data)
Cross-sectional study
Time-series study
Time-series study
Cross-sectional study,
time-series study
10
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 sociopsychological 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 importance of the various determinants is illustrated by the latest study by Beck/Webb (2002, Table
3).
Table 3
Determinants of insurance penetration
baseline regression (log-linear), panel of 68 countries, 1961-2000
Econometric model
Constant
GDP per capita
Young dependency
Old dependency
Life expectancy
Schooling
Inflation
Banking sector development
Adjusted R2
Parameter
(t-statistics)
-7.07 (1.9)
0.57 (2.9)
-0.36 (1.1)
1.19 (3.9)
-0.17 (0.2)
-0.05 (0.2)
-1.03 (5.2)
0.35 (4.6)
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 macroeconomic 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
11
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 variable
and income elasticities of life insurance premiums
Author(s)
a
Countries / regions
Year / period
Regression
coefficient
Elasticity
0.004 – 0.008
0.34 – 0.66
0.18 – 0.79
0.57 – 2.2
0.62 – 1.10
1.34 – 1.66 a
1.18 – 1.79 a
0.57 – 2.2
Babbel (1985)
Beck/Webb (2002)
US
68 countries
Beenstock/Dickinson/
Khajuria (1986)
Browne/Kim (1993)
DePamphilis (1977)
Diacon (1980)
GDV (1983)
Hammond/Houston/
Melander (1967)
Outreville (1996)
Truett/Truett (1990)
10 OECD countries
1953 – 1979
1980 – 2000
1961 – 2000
1970 – 1981
45 countries
US
UK
Germany
US
1980, 1987
1952 – 1974
1946 – 1968
1965 – 1980
1953, 1962
0.32 – 0.88
0.012
0.13 – 8.09
0.18 – 0.40
0.01 – 0.04
0.32 – 0.88
–
2.5 – 3.5
–
0.3 – 1.5
48 developed countries
US
Mexico
1986
1960 – 1982
1964 – 1979
0.0002
0.77 – 1.10
3.04 – 3.87
0.52 – 0.62
0.77 – 1.10
3.04 – 3.87
Beck/Webb (2002) use the insurance penetration (premium volume/GDP) as a dependent variable. The regression equation is expressed as a log-linear function, the elasticity is obtained as the regression coefficient
plus 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-
12
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
13
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 Beenstock/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.
14
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 insurance 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 the
financial market structure.
15
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-byyear data. The equation is estimated in the first differences of the levels and has a high explanation 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 of
insurance.
16
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
17
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 consumption) and socio-psychological indicators (Allensbach indicator). On the basis of linear multiple 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
18
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" (psychological) 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.
19
4.2.3 Panel studies
The analysis by Beenstock/Dickinson/Khajuria (1986) studies the factors governing insurance premium income in 10 OECD countries in the period from 1970 to 19815. The theoretical 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
20
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
6
The country sample comprises the US, Germany, France, Japan, the United Kingdom, Canada, Italy,
Australia, the Netherlands and Sweden.
The second part of the analysis based on year-to-year observations and the previously discussed crosssectional study serve to validate the sensitivity of the results of the panel study based on five-year averages
(Beck/Webb, 2002, p. 25).
21
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
Brunsbach/Lang (1998)
Farny (1983)
Germany
Germany
1988
1958-1983
Japelli/Pistaferri (2002)
Lang (1995)
Lewis (1989)
Walliser/Winter (1999)
Italy
Germany
US
Germany
1989-1989
1988
1976
1993
Method / data used
Household data, EVS 1988
Comparison of returns from
alternative forms of saving
Household data, SHIW
Household data, EVS 1988
Microdata, 150 US households
Household data, EVS 1993, logit
and tobit regressions
22
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. Measurement 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 allowance 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 highincome 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 saving 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
8
Data from about 20,000 households are used in the empirical analysis.
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 for
private health, accident, and (personal and motor) liability insurance policies (Lang, 1995).
23
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, sociodemographic 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 endowment 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
10
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 government
social insurance system than among the reference group of persons in dependent employment.
This finding supports the assumption that life insurance policies are frequently concluded in order to qualify
for low-interest mortgage loans from insurance companies (Lang, 1995).
24
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 influences 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 corroborated; 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
25
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 households by a change in the tax framework for life insurance policies. The tax reform of 19921994 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.
26
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 calculation 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.
27
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 observed 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
28
formation is not significantly higher among the household groups targeted by the tax legislation 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 possible to recognise how the authors arrive at the processes they use for evaluating the econometric 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.
29
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.
30
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 Lebensversicherungssparens, 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.
31
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 Versicherungswirtschaft 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.
32
–, 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, ZEWDiscussion 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, 451457.
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.
33
Zhuo, Z., 1998, Die Nachfrage nach Lebensversicherungenn: Eine empirische Analyse für
China, Manheimer Manuskripte zu Risikotheorie, Portfolio Management und
Versicherungswirtschaft 112, Mannheim.
34
Appendix
Table 6
Empirical determinants of life insurance demand
Categories
Demographic
variables
Variables
• Population
• Education
• Religion
• Urbanity
• Life expectancy
Macroeconomic
variables
• Economic growth
• Disposable income
Hypotheses
Demographic trends (eg population count, number of households, number of marriages, birth rate, ratio of young people
to the working population etc) determine the operating background for the life insurance industry in the long term. A
lower proportion of young people in the working population tends to curtail demand for pure life insurance policies via a
reduction in the present value of consumption by the beneficiaries (variable TC in Equation (1)). On the other hand a
high proportion of young people in the population makes it reasonable to expect a negative effect on the demand for
insurance policies with a high savings element.
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-term
savings. A higher education level can also be expected to be reflected in a higher level of personal risk aversion (variable
δ in Equation (1)).
The religion of a population can influence the risk aversion and the fundamental institutional configuration of the insurance industry. This influence can be considerable in international comparisons of life insurance demand. A low life
insurance demand is to be expected in predominantly Islamic countries.
It is to be expected that regions with a large proportion of city dwellers in relation to the overall population will exhibit
a high demand for life insurance products. While in rural regions close family ties make for an intact solidarity community, city life tends to be associated with anonymity and low neighbourhood solidarity. Furthermore, the concentration of
consumers within a compact geographical area simplifies the distribution and marketing of life insurance products; this
should on the whole tend to reduce costs. Cost and thus price considerations can have a positive influence on the supply
of life insurance products (effect on the "policy-loading factor" L in Equation (1)).
Societies with a high life expectancy tend to exhibit lower demand for pure life insurance policies; however, demand for
life 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).
The growth of the domestic product can be seen as a "proxy" for a whole package of other influencing factors and is thus
often used in empirical studies. On the whole it can be assumed that an acceleration (slowdown) of macroeconomic
growth can be expected to have positive (negative) effects particularly on new business.
The change in the disposable income of the private households (eg through changes in taxation) influences their ability
to set money aside for the future and thus affects the margin for growth in life insurance demand. If disposable incomes
rise, this can result in a higher demand for life insurance to safeguard the income potential of the dependants (this affects
the variable TC in Equation (1)).
35
Macroeconomic
variables
(continued)
• Monetary stability
• Interest rate
• Development of the
financial market and
banking sector
• Demand for credit
• Labour market
• Social security
In this context, the scale of the increase in price levels or, more accurately, anticipated inflation is of interest. As long as
the inflation rate is felt to be tolerable, it is reflected in nominal growth in premiums and sums insured. Furthermore, if
monetary devaluation is moderate, life insurance can be seen as a relatively safe form of investment, as ongoing
premium 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. A
high inflation rate can also trigger structural effects, with demand for life insurance products with a high savings element
being shifted towards pure term life insurance policies, if interest rates are negative in real terms.
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 be
expected 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 investment
that are decisive. The direction in which changes in interest rates and returns will drive life insurance demand is hard to
say a priori. Not only different motives for buying life insurance products but also their dependence on pre-tax and aftertax returns and the associated need to distinguish between real and nominal scenarios make it difficult to predict a clear
trend.
On the one hand, new business in life insurance is in competition with alternative forms of investment for the savings of
the private households. Changes in the preferences of the private households influence the ratio of life insurance to total
savings. On the other hand, it is reasonable to surmise that an efficiently functioning banking system will enhance the
private households' confidence in other financial and insurance institutions. Progress made towards a more efficient
financial market system makes it easier for the insurer to invest more efficiently and thus to offer a more competitive
price for life insurance policies.
Demand for mortgages tends to be complementary to demand for life insurance policies. It can be assumed that life
insurance policies are frequently concluded in order to qualify for low-interest mortgage loans from insurance
companies.
Rising employment and the associated job certainty create favourable conditions for life insurance business. Declining
employment can be expected to have negative effects not only on new business, but also on the business in force, ie by
triggering cancellations.
Life insurance and state-run social security systems interact with each other. The scale of state welfare and the associated
legislation establish the backdrop for the conclusion of life insurance policies. It is to be expected that the size and
efficiency of a country's social security system will show a negative correlation with the demand for life insurance
products. Such a substitutive relationship between the private and the public sector makes itself felt in the net wealth of
the households via income transfers (this affects variable W in Equation (1)).
36
Macroeconomic
variables
(continued)
• Tax system
• Income distribution
• Saving
Sociopsychological
factors
• Present moods and
anticipations of the
future
Institutional
determinants
• Rule of law
• Well defined property
rights
• Regulatory quality
• Control of corruption
The features of a country's tax system can influence demand for life insurance products. One of the major purposes of
life insurance – to make provision for the future – can be further promoted by opportunities to take advantage of tax
benefits. For example, the German income tax system encourages saving in the form of endowment insurance in two
ways: firstly, the interest earned on the savings accumulated during the term of the policy is exempt from tax at the time
of pay-out (by contrast, interest earned from other forms of investment is counted as taxable income). Secondly, life
insurance premiums, unlike other forms of monetary wealth formation (with the exception of long-term building society
savings accounts), can be deducted from the individual's taxable income as part of a personal tax-free allowance. In
Lewis's theoretical model (1989), the "tax system" determinant is represented by the "policy-loading factor" L. Apart
from this supply-side effect, the particularities of the tax system can directly affect the disposable incomes of the private
households on the demand side.
The effect of income distribution on the demand for life insurance products is not clear a priori. Beenstock/Dickinson/Khajuria (1986) argue that wealthy sections of the population do not need insurance, while poorer sections of the
population generate only limited demand, because they operate under tight budget constraints. They conclude that a
more equal distribution of incomes would lead to a broader middle class and would thus create stronger demand for life
insurance. In their study they are able to empirically confirm their hypothesis. This contrasts with the findings of
Beck/Webb (2002), which identify either a negative or no significant influence. They argue that rich population groups
need life insurance products in order to transfer their affluence to their descendants. A more equal distribution of
incomes in the population would expand the middle class, but since a certain minimum level of income is necessary in
order to be able to afford life insurance products at all, the authors assume that changes in demand for life insurance are
more likely to exhibit a negative correlation with a more uniform income distribution.
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 be
expected that a high propensity to save has a favourable effect on life insurance demand.
Psychological factors such as, for example, moods and anticipations of the future play an important part in decisionmaking by individuals. It is to be assumed that they exert a not inconsiderable influence on the propensity to take out a
life insurance policy. Various mood indicators eg the "Allensbach" indicator in Germany, the "consumer confidence
index " in Switzerland or a so-called "business climate index" (likewise for Germany) attempt to quantify the
expectations and moods of the households and enterprises. Müller (1998, pp 244 et seq.) points out that quantitative
results on the expectations and attitudes to topic areas such as "past and future development of the general economic
situation", "past and future development of price levels", "expected future unemployment", "past and future development
of the individual's financial situation", "assessment of the expedience of present savings and opportunities for future
savings" can be helpful for drawing conclusions as to people's future ability and willingness to conclude life insurance
policies. Müller (1998) shows that a synthesis of "hard" economic and "soft" psychological factors influencing demand
for individual life insurance products exhibits a significantly higher explanation value than isolated observation of only
one of these groups of factors.
Besides the determinants of life insurance demand already discussed, the institutional features of a country are, of
course, also important. Particularly in less-developed industrial nations, factors such as the rule of law, well-defined
property rights, a high-quality regulatory regime and control of corruption are important to ensuring that the insurance
industry can operate in a stable and secure environment in which potential insurance purchasers have confidence. Lack
of public confidence in the institutional environment curbs demand for life insurance products.
37
Insurer action
parameters
•
•
•
•
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 their
own, 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 demand
responds to price changes. The "policy loading factor" L in Lewis's (1989) model, reflecting the price of insurance, as
expected shows a negative correlation with life insurance demand. The higher the market price of life insurance
products, the lower, ceteris paribus, should be the demand for such products. However, the effective price, ie the price
after 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 in
force, are able to demonstrate a significant negative price elasticity of life insurance demand. Babbel (1985) also
identifies a significant negative price elasticity for new business.
38