insurance, which was because of that same investment

but inspire them to reduce their consumption which was because of that same
investment have higher expected benefits. They recommended that these findings
of the study may be helpful for life insurance companies in various parts of
their corporate strategy—for example, the seasonal issues. They suggested
Further research to simplify the results of the comparison of forecasting
models to other categories of financial intermediaries.

Lin and Grace (2006) did research on household life
cycle protection: life insurance holdings, financial vulnerability and
portfolio implications in Georgia State during 2005 and he analyzed demand of
life insurance by a decomposing the whole demand into demand for term life and
whole life insurance, with additional of index of financial vulnerability etc.
they Used the Survey of Consumer Finances (SCF) in their study to examined
demand of life cycle for various types of life insurance. They first developed
a financial vulnerability index to controlled for the risk to a household then
examined the determinants of life insurance. His result showed that there is
relationship between demand for life insurance and financial vulnerability.
They concluded that life insurance demand was less by older household than by
younger. It demands to manage with the certain level of financial vulnerability.
They gave recommendations of how individual households can bring variation in
their portfolio over the life cycle and how can they relate that to the demand
for life insurance.

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Li et al. (2007) did a
cross-sectional analysis in 30 OECD economies using OLS and GMM methods over a period
of 1993-2000 and examined that income, dependency ratio, education, social
security, life expectancy, inflation and real interest rate with the topic of the demand for Life insurance in OECD
Countries. They found that dependency ratio and education are positively
associated with demand for life insurance while social security, life
expectancy, inflation and real interest rate are indirectly related with life
insurance demand. They suggested that life insurance
demand can be better explain when the product market and socioeconomic factors
will consider together.

Sen and Madheswaran (2007)
studied the impact of economic and political variables on the demand for life
insurance in six Asian and two economies in the China sub region from time-period
of 1994 to 2004. They took insurance penetration and density as the dependent
variables. The findings showed that incomes, savings and inflation were the
main factors which help in determination of life insurance demand. The study
carried time series analysis of demand for life insurance in India in period of
1965-2004. The outcomes showed that income (GDP per capita), financial
development, and price of insurance per policy and the real rates of interest
were essential factors of life insurance demand. They suggested that other
researcher should examined factors of life insurance demand to rise the life
insurance market.

Nesterova (2008) investigated the
variations in life insurance demand in fourteen countries of former Soviet
Union and Central and Eastern Europe including Ukraine based on panel data
analysis over time-period of 1996-2006. He found that countries with higher
education level, income and life expectancy at birth had greater demand for
life insurance. While there is negative relationship between development of the
financial sector, inflation and real interest rate and the life insurance
demand. His results which he found help the policymaker in making the policies
for the progress of their life insurance business and may can help to diminish
the variance in life and non-life insurance markets of both developed and
developing countries. Therefore, he suggested to examine the determinants of
life insurance in Pakistan

Celik and Kayali (2009) conducted rese arch
on the determinants of demand for life insurance in 31 European countries. The
results showed that income is the main variable which has influence on
consumption of life insurance. They also found that there is positive
relationship between population and income and demand for life insurance, and
education level and inflation were negatively related with consumption of life

Ibiwoye (2010) examined the factors
of life insurance consumption in Nigeria by applying Co integration and Error
Correction Model over period of 1970-2005. This was achieved by considering the
order of integration of each series using the Augmented Dickey – Fuller (ADF)
class of unit root tests. According to Co-integration results, there were
direct influence of real gross domestic product on consumption of Life
Insurance and inverse relationship between interest rate and life insurance demand.
While, inflation was considering as insignificant determinant of life insurance
demand. they also found that return on investment (RW), inflation rate (INFL),
openness of the economy (OPEN) and political instability were insignificant
determinants of Life Insurance Consumption (LIC) in Nigeria. It was suggested that
for the improvement of Life Insurance consumption(LIC) in Nigeria, that policies
should be focused which increases real gross domestic product (RGDP) and
economic liberalization should be pursued while that policies should be removed
which emphasized indigenization. furtherer, government should make efforts to
reduce domestic interest rate (RD) which will increase Life Insurance Consumption
(LIC) in the economy.

Wang (2010) examined factors influencing
consumers’ life insurance purchasing decisions in china during a time-period of
2000-2009. He obtained the data for the analysis from the survey of the 258
respondents. he employed factor analysis to find factors which were essential
for Chinese consumers regarding life insurance. The results of Factor analysis
showed that four factors were importance of product attributes, consumers’
financial strength, consumers’ attitude and trust toward the life insurance
industry, and consumer attributes. he used Cluster analysis to examined
multivariate data to obtained the relationships between different clusters in
selected variables and to identified the characteristics and information about
customers of life insurance. The companies are recommended to emphasize more on
these characteristics to obtain target the “right” potential consumers in

Kjosevski (2012) examined the determinants
of Life insurance demand in 14 Central and Southeastern Europe during the time-period
1998-2010 in his study. He used panel model for this period 1998 – 2010 to find
the relationship between independent and dependent variables. He used life
insurance penetration and life insurance density as a proxy of demand of life
insurance.  He used GDP per capita,
inflation, health expenditure, level of education and rule of law as
independent variables in model. the estimations showed that higher, GDP per
capita, inflation, health expenditure, level of education and rule of law and
demand of life insurance had a high associations and Real interest rates, ratio
of quasi-money, young dependency ratio, old dependency ratio control of
corruption and government effectiveness had less relationship with life
insurance demand. It was recommended for future researcher to find the effect
of insurance development on economic development and researchers are suggested
that they should take more time-period to get more knowledge about it and to
understand the determinants of life insurance demand more easily.

& Goh (2012) conducted their research study Purchase Decision of Life
Insurance Policies among Malaysians to examined demand for life insurance in
Malaysia. They used A hurdle count-data model to find the individual decisions
on the life insurance demand which was studied in two divisions: one whether to
buy a life insurance policy and how many policies are going to buy. They found
the factors which determined the decision to buy life insurance policy and the
factors which determined the that how many to buy were different little. He
found that socio-economic factors which influence the demand for life insurance
are age, income, education, occupation, marital status and risk aversion.
However, they found that gender and number of dependents have no