This chapter presents the theoretical framework and various techniques whichare applied for the quantitative analysis of data to get the objectives and totest the hypotheses of the study. It also outlines the scope of study,identifies the variables, highlights methods of data collection andexplains the model of the study.4.1.SCOPE OF THE STUDY:The scope of the study is based oncomplete observation of Macroeconomic conditions of Pakistan, mainly focusingon the education level, gross savings, crude death rate and price level of lifeinsurance, which influence life insurance demand and its business in Pakistan.
Thisresearch is taken to analyze the determinants of life insurance demand inPakistan during 1987-2016 which is a period of 29 years.4.3. THEORITICALFRAMEWORKinsurance demand.3.1.
Theoretical Underpinnings atheoretical framework which explains the life insurance demand was developedfor first time by Yaari (1965) and Hakansson (1969). According to thisframework, the demand for life insurance depends on person’s decision to leavefunds for dependents and save income after retirement. The consumer willmaximize utility of his life by focusing on a interest rates and a pricesincluding insurance premium rates. According to the framework the demand for lifeinsurance depends on wealth, expected income over an individual’s lifetime, thelevel of interest rates, the cost of life insurance policies (administrativecosts), and the assumed subjective discount rate for current over futureconsumption.Permanentand Life Cycle Income Hypothesis was propounded by Milton Friedman. It definesthat 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 keepsup that people arrange utilization (consumption) and savings utilizingcapability of future pay streams. In 1963, Ando and Modigliani hypothesized thelife cycle theory.
Like the permanent income suggestion, this hypothesisexpects that saving and consumption (utilization) choices made by people areelements of present and potential salary. Fortune (1973) examined the empiricalinference of expected utility theory of choice under insecurity for demand forlife 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 highlevel of satisfaction. However, satisfaction is an element of the individual’schoices. Customers fluctuates their options of consumption with the measure ofuncertainty.Lewis(1989) extended this framework by focusing on the preferences of the dependentsand beneficiaries into the model.
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Precisely, he develops the demand for lifeinsurance taking a problem of maximization of the benefits receivers, the wifeand the children of the life insurance consumers. He Derives the utilitymaximization by taking both spouse and offspring and assumed that there is noinheritance by the policyholder, Lewis illustrates that total life insurancedemand as belows:(1-lp)F = max{ 1-lp/ l(1-p) 1/ ? TC – W,0……………………………………………………………..(1)wherel shows the policy loading factor – the ratio of the costs of the insurance toits actuarial value -, p the probability of the primary wage earner’s death, Fthe 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 valueof consumption of each children until he/she leaves the household and of thewife over his/her forecasted life and W shows net wealth of the household.Accordingto him, demand for Life insurance increases with the probability of death ofthe income earner, his consumption and degree of risk aversion. Demand for Lifeinsurance reduces with the loading factor and the household’s wealth.
Accordingto him consumer demand is not only driver of Life insurance consumption butsupply- side factors can also affect price of life insurance. Both the humanand information resources are the required for the determination of the pricingand sufficient opportunities in financial markets and are the investmentfunction for the insurance companies. An effective enforcement of contractsalso derives the investment of life insurance companies. Thus, the costs oflife insurance products depend on these supply-side factors.According to Lewis model, these supply-sidefactors would be indicated by the policyholding factor. While attempts havebeen made to model the relationship of both the supply and demand of lifeinsurance separately, but the hurdle of data limitations has failed theempirical testing of these hypotheses. To observe the total money spent on lifeinsurance policies, by using premium data, distinguish between supply anddemand could be difficult.
Thus, as premium data is joint measure of price andlevel of life insurance, therefore premium cannot be taken to measure theamount of life insurance. As price is an essential determinant in deriving thedemand of life insurance, and by omitting price from model may result toomitted variable bias. This problem can be solved in two ways. Firstly, bytaking price as a function of many supply-side factors that affects thecompanies in marketing and distributing the policies cost-effectively.
weassume that the price is a function of several supply-side factors that arelikely to affect the ability of insurers to market and distribute policiescost-effectively. Different levels of urbanization, monetary stability,bureaucratic quality, rule of law, corruption, and banking sector development allaffect the companies to deliver cost-effective insurance. By taking thesesupply-side factors in model thus the biasness produced by the missing pricevariable become low. Second, by using use panel estimation techniques biasnessproduced due to omitted variables, such as the price variable in our model, canbe emitted.In short, accordingly with thehypothetical survey, many variables such as income, rate of interest, currentconsumption and accumulated savings in wealth affect life insurance demand.Socials factors were also focused in theoretical models and investigated thatthey can also effect on person’s life insurance demand decision. Price of lifeinsurance has been referred to as a main driver of demand of life insurance. Tocomprehensive them up, hypothetical examination demonstrates the maincomponents, for example, income, price of life insurance, interest rate, recentconsumption and buildup saving are the most important determinants of lifeinsurances.
4.2.METHOD OF DATA COLLECTION:Inthis study, time series annual secondary data of all variables has beencollected for the empirical analysis of determinants of life insurance demandin Pakistan in the time period of 1985-2016. The required data are collected from different sources. Data on gross saving, consumerprice index, level of education and crude death rate are obtained from WorldDevelopment Indicator (WDI) and data on price of life insurance and sum insured(used as a proxy of demand for life insurance) are collected were annualreports of state life corporation of Pakistan. Afterdata collection, for data processing and its analyzing Eviews-9 is used asstatistical package for multiple regression analysis.: statisticalsoftware has been used.All the data were entered in Eviews-9 software and ARDL technique has beenapplied for statistical analysis of data.
4.5. VARIABLES ANDMEASUREMENT OF VARIABLESThedemand for life insurance is taken as dependent variable and the macroeconomicfactors are taken as explanatory variables in model of this study. Themacroeconomic variables which are included in model of this study are grosssaving, crude death rate, inflation level, education level and price of lifeinsurance. DEPENDENT VARIABLE:· Sumsinsured (DEMAND)Sumsinsured is taken as the dependent variable. It refers to the percentagecalculated as the ratio of the new sums insured in a year to the total sumsinsured in force in the preceding year of ordinary life business (comprisingpolicies such as whole life, endowment, temporary, and others). In this studysums insured is denoted by “lid”.
INDEPENDENT VARIABLES:4.5.2.1. INFLATIONInflationis rise in the general prices of goods and services in a country. The inflationrate rose to 4.57% in December 2017.
Previous researches have analyzed thatthere is a significant negative relation between the inflation and demand forlife insurance consumption (see Fortune,1973; Babbel, 1981; Browne & Kim,1993; Beck & Webb, 2003; Hwang & Greenford, 2005). During economicinstability, 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 et.
al (2007), Nesterova(2008), Çelik and Kayali(2009),Ibiwoye et.al(2010)). In this study, consumer price index (CPI) taken as aproxy of inflation level. Inflation is denoted by “INF” in model of this study. GROSS SAVING RATE: Saving is that part of income which is not spent whichmeans 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 savingis taken as independent variable to find the effect of saving and income ondemand for life insurance.
Saving is closely related to investment. Therefore,the income left after consumption of goods and services is invest in lifeinsurance. Savings have therefore a positive effect on demand for lifeinsurance and contributes to economic growth. Gross saving in this study isrepresented by “GS”. LEVEL OF EDUCATION: According to previous studies, the level of educationhas significant and positive effect on demand for life insurance (Truett andTruett(1990) and Browne and Kim(1993), Li et.al(2007), Kakar and Shukla(2010),Mahdzan & Victorian(2013) found that when education level is higher, peopleare more aware of types of life insurance and they attempt to secure themselvesand dependent relative by consuming it. It is denoted by “ED” in this study.
CRUDE DEATH RATE: The crude death rate stands for the average annualnumber 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 per100 people.in general crude death rate has positive relation with demand forlife insurance.
In this study, crude death rate is denoted by “CDR”.PRICE OFINSURANCE. It is one of important determinants of life insurancedemand. 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 ofordinary life insurance coverage defined as the ratio of the total annualpremium in force to the total sums insured in force in a year.The price of insurance has significant and inverserelationship with the demand for life insurance because high life insurancecost tends the price of life insurance which is taken as measure to determinedemand in this study is based on the model used by Browne and Kim (1993). It isrepresented by “PLI” in this study.
