This lifetime, the level of interest rates, the

This chapter presents the theoretical framework and various techniques which
are applied for the quantitative analysis of data to get the objectives and to
test the hypotheses of the study. It also outlines the scope of study,
identifies the variables, highlights methods of data collection and
explains the model of the study.


The scope of the study is based on
complete observation of Macroeconomic conditions of Pakistan, mainly focusing
on the education level, gross savings, crude death rate and price level of life
insurance, which influence life insurance demand and its business in Pakistan. This
research is taken to analyze the determinants of life insurance demand in
Pakistan during 1987-2016 which is a period of 29 years.


insurance demand.

Theoretical Underpinnings

theoretical framework which explains the life insurance demand was developed
for first time by Yaari (1965) and Hakansson (1969). According to this
framework, the demand for life insurance depends on person’s decision to leave
funds for dependents and save income after retirement. The consumer will
maximize utility of his life by focusing on a interest rates and a prices
including insurance premium rates.  According to the framework the demand for life
insurance depends on wealth, expected income over an individual’s lifetime, the
level of interest rates, the cost of life insurance policies (administrative
costs), and the assumed subjective discount rate for current over future

and Life Cycle Income Hypothesis was propounded by Milton Friedman. It defines
that the spending conduct of people relies on upon the present level of wealth
(pay) and the long-run expectations of wage (Friedman, 1957). This theory keeps
up that people arrange utilization (consumption) and savings utilizing
capability of future pay streams. In 1963, Ando and Modigliani hypothesized the
life cycle theory. Like the permanent income suggestion, this hypothesis
expects that saving and consumption (utilization) choices made by people are
elements of present and potential salary.

Fortune (1973) examined the empirical
inference of expected utility theory of choice under insecurity for demand for
life insurance and came with conclusion that demand depends on income,
non-human wealth and the rate of discount.

The expected utility theory (EUT)
which was developed by John von Neumann and Oskar Morgenstern (Varian, 1993).
According to that an individual chooses the plans set which guarantee high
level of satisfaction. However, satisfaction is an element of the individual’s
choices. Customers fluctuates their options of consumption with the measure of

(1989) extended this framework by focusing on the preferences of the dependents
and beneficiaries into the model. Precisely, he develops the demand for life
insurance taking a problem of maximization of the benefits receivers, the wife
and the children of the life insurance consumers. He Derives the utility
maximization by taking both spouse and offspring and assumed that there is no
inheritance by the policyholder, Lewis illustrates that total life insurance
demand as belows:

)F = max{ 1-lp/ l(1-p) 1/ ? TC – W,0……………………………………………………………..(1)

l shows the policy loading factor – the ratio of the costs of the insurance to
its actuarial value -, p the probability of the primary wage earner’s death, F
the face value of all life insurance written on the primary wage earner’s life,
? a measure of the beneficiaries’ relative risk aversion, TC the present value
of consumption of each children until he/she leaves the household and of the
wife over his/her forecasted life and W shows net wealth of the household.

to him, demand for Life insurance increases with the probability of death of
the income earner, his consumption and degree of risk aversion. Demand for Life
insurance reduces with the loading factor and the household’s wealth. According
to him consumer demand is not only driver of Life insurance consumption but
supply- side factors can also affect price of life insurance. Both the human
and information resources are the required for the determination of the pricing
and sufficient opportunities in financial markets and are the investment
function for the insurance companies. An effective enforcement of contracts
also derives the investment of life insurance companies. Thus, the costs of
life insurance products depend on these supply-side factors.

According to Lewis model, these supply-side
factors would be indicated by the policyholding factor. While attempts have
been made to model the relationship of both the supply and demand of life
insurance separately, but the hurdle of data limitations has failed the
empirical testing of these hypotheses. To observe the total money spent on life
insurance policies, by using premium data, distinguish between supply and
demand could be difficult. Thus, as premium data is joint measure of price and
level of life insurance, therefore premium cannot be taken to measure the
amount of life insurance. As price is an essential determinant in deriving the
demand of life insurance, and by omitting price from model may result to
omitted variable bias. This problem can be solved in two ways. Firstly, by
taking price as a function of many supply-side factors that affects the
companies in marketing and distributing the policies cost-effectively. we
assume that the price is a function of several supply-side factors that are
likely to affect the ability of insurers to market and distribute policies
cost-effectively. Different levels of urbanization, monetary stability,
bureaucratic quality, rule of law, corruption, and banking sector development all
affect the companies to deliver cost-effective insurance. By taking these
supply-side factors in model thus the biasness produced by the missing price
variable become low. Second, by using use panel estimation techniques biasness
produced due to omitted variables, such as the price variable in our model, can
be emitted.

In short, accordingly with the
hypothetical survey, many variables such as income, rate of interest, current
consumption and accumulated savings in wealth affect life insurance demand.
Socials factors were also focused in theoretical models and investigated that
they can also effect on person’s life insurance demand decision. Price of life
insurance has been referred to as a main driver of demand of life insurance. To
comprehensive them up, hypothetical examination demonstrates the main
components, for example, income, price of life insurance, interest rate, recent
consumption and buildup saving are the most important determinants of life


this study, time series annual secondary data of all variables has been
collected for the empirical analysis of determinants of life insurance demand
in Pakistan in the time period of 1985-2016. The required data are collected from different sources. Data on gross saving, consumer
price index, level of education and crude death rate are obtained from World
Development Indicator (WDI) and data on price of life insurance and sum insured
(used as a proxy of demand for life insurance) are collected were annual
reports of state life corporation of Pakistan.

data collection, for data processing and its analyzing Eviews-9 is used as
statistical package for multiple regression analysis.:

software has been used.
All the data were entered in Eviews-9 software and ARDL technique has been
applied for statistical analysis of data.


demand for life insurance is taken as dependent variable and the macroeconomic
factors are taken as explanatory variables in model of this study. The
macroeconomic variables which are included in model of this study are gross
saving, crude death rate, inflation level, education level and price of life


insured (DEMAND)

insured is taken as the dependent variable. It refers to the percentage
calculated as the ratio of the new sums insured in a year to the total sums
insured in force in the preceding year of ordinary life business (comprising
policies such as whole life, endowment, temporary, and others). In this study
sums insured is denoted by “lid”.


is rise in the general prices of goods and services in a country. The inflation
rate rose to 4.57% in December 2017. Previous researches have analyzed that
there is a significant negative relation between the inflation and demand for
life insurance consumption (see Fortune,1973; Babbel, 1981; Browne & Kim,
1993; Beck & Webb, 2003; Hwang & Greenford, 2005). During economic
instability, the demands for life insurance usually declined (Black &
Skipper, 2000). The rate of inflation affects the life demand in negative way
(Beck and Webb(2003), Li (2007), Nesterova(2008), Çelik and Kayali(2009),
Ibiwoye In this study, consumer price index (CPI) taken as a
proxy of inflation level. Inflation is denoted by “INF” in model of this study.


Saving is that part of income which is not spent which
means that when income rises the saving will increase. The gross saving (as %
of GDP) is 23.29% during December 2017 in Pakistan. In this study, gross saving
is taken as independent variable to find the effect of saving and income on
demand for life insurance. Saving is closely related to investment. Therefore,
the income left after consumption of goods and services is invest in life
insurance. Savings have therefore a positive effect on demand for life
insurance and contributes to economic growth. Gross saving in this study is
represented by “GS”.



According to previous studies, the level of education
has significant and positive effect on demand for life insurance (Truett and
Truett(1990) and Browne and Kim(1993), Li, Kakar and Shukla(2010),
Mahdzan & Victorian(2013) found that when education level is higher, people
are more aware of types of life insurance and they attempt to secure themselves
and dependent relative by consuming it. It is denoted by “ED” in this study.


The crude death rate stands for the average annual
number of deaths during a year per 1,000 persons in the population at mid-year,
which is also referred to as crude death rate. The death rate is 7.5 deaths per
100 general crude death rate has positive relation with demand for
life insurance. In this study, crude death rate is denoted by “CDR”.


It is one of important determinants of life insurance
demand. It is the premium rate of life insurance which is charge annually,
quarterly or monthly. More formally, the price is the cost per 1,000 of
ordinary life insurance coverage defined as the ratio of the total annual
premium in force to the total sums insured in force in a year.

The price of insurance has significant and inverse
relationship with the demand for life insurance because high life insurance
cost tends the price of life insurance which is taken as measure to determine
demand in this study is based on the model used by Browne and Kim (1993). It is
represented by “PLI” in this study.