significant of the research should be taken into

significant effect on the demand for life insurance. Furthermore, he found that higher education hasno such effect on demand for life insurance. They suggested future researchersto find other factors for the progress of life insurance business andgovernment should increase the real income to enhance the life insurance demandin Malaysia. (2013) conducted research on social and demographic determinants of lifeinsurance demand in Croatia. They collected the data from observations of 95 respondents.He examined age, employment, education, factors-gender, marital status andnumber of family members. He observed that age, education and employment arethe main factors that had an impact on life insurance demand of household inCroatia while gender, marital status and number of family members have no statisticallysignificant influence on demand for life insurance. It was suggested that lifeinsurance companies should introduce more banc assurance in distribution oftheir products for the progress of the life insurance companies and toencourage life insurance demand macroeconomic decision makers should providepolicies that ensure employment and encourage education. This is especiallyimportant in situation of lowering pensions and other social welfareprovisions.

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The findings of the research should be taken into consideration bylife insurance companies especially in planning their distribution channels andbanks have these information on their customers, life insurance companiesshould use banc assurance more in distributing their products. For the futureresearcher should wide for social and demographic variables for expectedlifetime, urbanization, and social welfare system.Aderaw (2013) in his research study ondeterminants of life insurance conducted in Ethiopia observed that the determinantsof 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 rateand inflation were the main determinants of life insurance demand. He suggestedthat in Ethiopia these variables should be considered for growth of lifeinsurance business. It was recommended the government should emphases toincrease the real income of income which will rise the life expectancy ratio. Sliwinski et al.

(2013) examined the determination oflife insurance demand in Poland by comprising the ten peak rising markets andother CEE countries such as Hungary and the Czech Republic. Consequently, heapplied factor analysis to differentiate independent factors which were GDP, percent rate, inflation, financial development, men andwomen’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. Heemployed a linear regression model to discover both the factors fordetermination of life insurance in Poland. He found but were not agreed withthe factors such as education level and social benefits that were found byprevious 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 researchershould compare life insurance demand determinants in Poland with the otherconditions of CEE countries.Sherif & Shaairi (2013) et al. examined differenteconomics and socio-demographic factors that affect the takaful demand byidentifying the driving forces that influence family Takaful demand inMalaysia.

They used least square (OLS) and generalized method of moments (GMM)techniques on data to analyzed the significance of many economic andsocio-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 offamily Takaful. They found that income,Islamic banking development, education, dependency ratio and Muslim populationfactors were having positive relationship with Takaful demand. He also foundthat inflation, real interest rate, financial development and life expectancyhad adverse significant relationship with total family Takaful demand. Theyrecommended that future researcher should also analyze the impact of possible influentialfactors which may be government social security expenditures, price of takafuland level of competition on the takaful and insurance was alsorecommended that more researches should be conducted to check the influence oflegal system and government policies on the family takaful consumption.

Furthermore, their study also focused on the demand side of family takafultherefore it was suggested by them to analysis on the supply-side of familytakaful system should also be taken in focus.Mahdzan & Victorian (2013) did on research on TheDeterminants of Life Insurance Demand: A Focus on Saving Motives and FinancialLiteracy to explore the determinants of life insurance demand between lifeinsurance policyholders of five big life insurance companies in Malaysia. Heused a non-probability sampling method on data collected from a sample of 259life insurance consumers from five main life insurance companies in KualaLumpur Malaysia. He employed purposeful sampling method and used one-way ANOVAtests to test the hypothesis.

According to their findings demographic variablesand saving motives are positively related with life insurance demand. While, theyfound that the variable which insignificantly determines life insurance demandwas financial literacy. They showed that education level and life insurancedemand were significantly related, showed that people having high educationlevel demands more life insurance. He recommended that Other areas of lifeinsurance demand must also be examined, like, on other behavioral aspects offinancial decision-making, such as heuristics and risk aversionBryan at el. (2015) to examined the effect of thegross national income per capita on the premiums per capita of life insurancein the Organization for Economic Co­operation and Development (OECD) countriesfor three years, from 2010 to 2012 by using data from 22 of the 46 OECDcountries.

They developed a restricted model of six variables: gross nationalincome per capita, life expectancy, youth dependency population (0­17), longterm interest rates, life insurance as share of the entire insurance market,and fertility rate and applied a simple and multiple regression models. Theycame with results and concluded that there is the positive correlation betweengross national income per capita and premiums per capita of life insurance inOECD countries.Redzuan (2014)examined analysis of the demand for life insurance and familytakaful in Malaysia during time-period of 1970-2008 and investigated thelong-run and short-run relationship of different factors with life insuranceand family takaful. He employed autoregressive distributed lag (ARDL) boundstesting to test the Significance of the number of dependents, level of education, savings in theEmployees’ Provident Fund (EPF), life expectancy and price of insurance.He identified that income is the key determinantin the consumption of life insurance both in the long- and short-run. He foundthat income had more effect on family tactful consumption in the long-run, butits effect than in short-run.

He concluded from his estimations that the numberof dependents, level of education, savings in the Employees’ Provident Fund(EPF), life expectancy and price of insurance are different variables whichdetermines the demand for life insurance and family takaful. hesuggested that level of income and level of education should be take intoconsideration by the government for the progress of life insurance consumption.Sarkodie & Yusif (2015) et al. examined thedeterminants of life insurance demand in the Ayeduase-Kumasi community from theperspective of consumers in Ghana in 2004.

They used Logistic regressionmodeling technique to analyze 256 cross section data. They examined Income,higher education, number of dependents, employment in their study. They foundthat Age however, had negative association with life insurance demand at 5%significance and Number of dependents was statistically significant at 1%. Typeof employment was significant at 5% while’s income had positive relationshipwith odds of life insurance at significance of 10%. the results of Their studyand the results of Celik and Kayali were same and found that income positivelyaffect the odds of taking insurance except Celik and Kayali found that apositive relationship higher education and odds of taking life insurance.

Tomake the policies more advance for consumers of life insurance they dividedcustomers into various groups on the type of employment and identified thebehavior of consumers of life insurance policies they should take intoconsideration the various variables which strongly influence the life insurancedemand is recommended that life insurance companies should not increasethe premium to attract more customers. Furthermore, the companies should beactive in dealing contracts with their consumers till they are not satisfied bythem.


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