significant Croatia while gender, marital status and number of

significant effect on the demand for life insurance. Furthermore, he found that higher education has
no such effect on demand for life insurance. They suggested future researchers
to find other factors for the progress of life insurance business and
government should increase the real income to enhance the life insurance demand
in Malaysia.

Curak
et.al (2013) conducted research on social and demographic determinants of life
insurance demand in Croatia. They collected the data from observations of 95 respondents.
He examined age, employment, education, factors-gender, marital status and
number of family members. He observed that age, education and employment are
the main factors that had an impact on life insurance demand of household in
Croatia while gender, marital status and number of family members have no statistically
significant influence on demand for life insurance. It was suggested that life
insurance companies should introduce more banc assurance in distribution of
their products for the progress of the life insurance companies and to
encourage life insurance demand macroeconomic decision makers should provide
policies that ensure employment and encourage education. This is especially
important in situation of lowering pensions and other social welfare
provisions. The findings of the research should be taken into consideration by
life insurance companies especially in planning their distribution channels and
banks have these information on their customers, life insurance companies
should use banc assurance more in distributing their products. For the future
researcher should wide for social and demographic variables for expected
lifetime, urbanization, and social welfare system.

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Aderaw (2013) in his research study on
determinants of life insurance conducted in Ethiopia observed that the determinants
of life insurance by time-series data over 1991-2010. He applied multiple linear regression on data to analyzed the data.
He made it cleared that per capita income, life expectancy, real interest rate
and inflation were the main determinants of life insurance demand. He suggested
that in Ethiopia these variables should be considered for growth of life
insurance business. It was recommended the government should emphases to
increase the real income of income which will rise the life expectancy ratio.

Sliwinski et al. (2013) examined the determination of
life insurance demand in Poland by comprising the ten peak rising markets and
other CEE countries such as Hungary and the Czech Republic. Consequently, he
applied factor analysis to differentiate independent factors which were GDP, percent rate, inflation, financial development, men and
women’s life expectancy, market monopolization, share of foreign capital,
population, level of education, expenditures on health and social care,
dependency ratio to find out demand for life insurance. He
employed a linear regression model to discover both the factors for
determination of life insurance in Poland. He found but were not agreed with
the factors such as education level and social benefits that were found by
previous studies. Though,
the transition period should change to alter the attitude of Polish customers,
which will be obtain by employing lags to this study. The future researcher
should compare life insurance demand determinants in Poland with the other
conditions of CEE countries.

Sherif & Shaairi (2013) et al. examined different
economics and socio-demographic factors that affect the takaful demand by
identifying the driving forces that influence family Takaful demand in
Malaysia. They used least square (OLS) and generalized method of moments (GMM)
techniques on data to analyzed the significance of many economic and
socio-demographic variables such as income, Islamic banking development,
education, dependency ratio, Muslim population, inflation, real interest rate,
financial development and life expectancy factors that determines the demand of
family Takaful. They found that income,
Islamic banking development, education, dependency ratio and Muslim population
factors were having positive relationship with Takaful demand. He also found
that inflation, real interest rate, financial development and life expectancy
had adverse significant relationship with total family Takaful demand. They
recommended that future researcher should also analyze the impact of possible influential
factors which may be government social security expenditures, price of takaful
and level of competition on the takaful and insurance demand.it was also
recommended that more researches should be conducted to check the influence of
legal system and government policies on the family takaful consumption.
Furthermore, their study also focused on the demand side of family takaful
therefore it was suggested by them to analysis on the supply-side of family
takaful system should also be taken in focus.

Mahdzan & Victorian (2013) did on research on The
Determinants of Life Insurance Demand: A Focus on Saving Motives and Financial
Literacy to explore the determinants of life insurance demand between life
insurance policyholders of five big life insurance companies in Malaysia. He
used a non-probability sampling method on data collected from a sample of 259
life insurance consumers from five main life insurance companies in Kuala
Lumpur Malaysia. He employed purposeful sampling method and used one-way ANOVA
tests to test the hypothesis. According to their findings demographic variables
and saving motives are positively related with life insurance demand. While, they
found that the variable which insignificantly determines life insurance demand
was financial literacy. They showed that education level and life insurance
demand were significantly related, showed that people having high education
level demands more life insurance. He recommended that Other areas of life
insurance demand must also be examined, like, on other behavioral aspects of
financial decision-making, such as heuristics and risk aversion

Bryan at el. (2015) to examined the effect of the
gross national income per capita on the premiums per capita of life insurance
in the Organization for Economic Co­operation and Development (OECD) countries
for three years, from 2010 to 2012 by using data from 22 of the 46 OECD
countries. They developed a restricted model of six variables: gross national
income per capita, life expectancy, youth dependency population (0­17), long
term interest rates, life insurance as share of the entire insurance market,
and fertility rate and applied a simple and multiple regression models. They
came with results and concluded that there is the positive correlation between
gross national income per capita and premiums per capita of life insurance in
OECD countries.

Redzuan (2014)
examined analysis of the demand for life insurance and family
takaful in Malaysia during time-period of 1970-2008 and investigated the
long-run and short-run relationship of different factors with life insurance
and family takaful. He employed autoregressive distributed lag (ARDL) bounds
testing to test the Significance of the number of dependents, level of education, savings in the
Employees’ Provident Fund (EPF), life expectancy and price of insurance.
He identified that income is the key determinant
in the consumption of life insurance both in the long- and short-run. He found
that income had more effect on family tactful consumption in the long-run, but
its effect than in short-run. He concluded from his estimations that the number
of dependents, level of education, savings in the Employees’ Provident Fund
(EPF), life expectancy and price of insurance are different variables which
determines the demand for life insurance and family takaful. he
suggested that level of income and level of education should be take into
consideration by the government for the progress of life insurance consumption.

Sarkodie & Yusif (2015) et al. examined the
determinants of life insurance demand in the Ayeduase-Kumasi community from the
perspective of consumers in Ghana in 2004. They used Logistic regression
modeling technique to analyze 256 cross section data. They examined Income,
higher education, number of dependents, employment in their study. They found
that Age however, had negative association with life insurance demand at 5%
significance and Number of dependents was statistically significant at 1%. Type
of employment was significant at 5% while’s income had positive relationship
with odds of life insurance at significance of 10%. the results of Their study
and the results of Celik and Kayali were same and found that income positively
affect the odds of taking insurance except Celik and Kayali found that a
positive relationship higher education and odds of taking life insurance. To
make the policies more advance for consumers of life insurance they divided
customers into various groups on the type of employment and identified the
behavior of consumers of life insurance policies they should take into
consideration the various variables which strongly influence the life insurance
demand and.it is recommended that life insurance companies should not increase
the premium to attract more customers. Furthermore, the companies should be
active in dealing contracts with their consumers till they are not satisfied by
them.

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