Life Insurance Demand

GROSS SAVING RATE: 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”.

LEVEL OF EDUCATION: 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 et.al(2007), 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.

CRUDE DEATH RATE: 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 people.in general crude death rate has positive relation with demand for life insurance. In this study, crude death rate is denoted by “CDR”.

PRICE OF INSURANCE. 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.

Econometric Modelling for life insurance demand:

Keeping in view the above arguments the multiple econometric model is predicted to find the determinants of life insurance demand in Pakistan. Babbel (1981), Truett and Truett(1990) and Browne and Kim(1993), Hwang & Greenford (2005), Li et.al(2007), Nesterova(2008), Çelik and Kayali(2009), Ibiwoye et.al(2010)). Kakar and Shukla(2010), Mahdzan & Victorian(2013) are the studies on the basis of which the econometric model is designed. They designed life insurance demand as a function of explanatory variables.

LIDD  =   f  ( ß0 + ß1 GSt  +  ß2 INFt +  ß3 PLIt + ß4 EDt + ß5 CDRt )

LIDD  =   ß0 +  ß1 GSt  +  ß2 INFt  +  ß3 PLIt  +  ß4 EDt  +  ß5 CDRt  +  et Where ß1 >0, ß20, ß5>0,

Here in this study, LIDD= Life Insurance Demand. Sums insured (total business in force of life insurance) is used to measure life insurance demand.

GS= Gross saving,

INF=Inflation, i.e., consumer price index

PLI=Price of life insurance i.e. annual gross premium of life insurance

ED=education, i.e., Government education spending as a percent of GDP

CDR= Crude death rate, And e is the error term of model, ß0 is the constant value of the regression surface. ß1, ß2, ß3, ß4, ß5 are parameters to be estimated and t = time-period.

The transformation of some variables is carried out in empirical analysis of data collected (Koop, 2000; Gujarati, 2003) for obtaining appropriate model in this study. The sum insured (demand for life insurance) and education level are subject to transformation by taking the natural logarithm of their level values. The transformed variables are named as LNLIDD and LNEDT. Hence, these transformed variables are used in model of analysis.  
LNLIDD  =   ß0 +  ß1 GSt  +  ß2 INFt  +  ß3 PLIt  +  ß4LNEDt  +  ß5 CDRt  +  et

ANALYSIS TECHNIQUE: Before analyzing the time-series data collected through different sources for estimation of Time- series Model in Statistical Package EVIEWS 9. The stationarity of data is checked by using ADF  test or unit root test .

UNIT-ROOT TEST: It is one of the assumptions of the standard regression analysis that all the variables being tested should be stationary at level or at first difference. In statistics, a unit root test is a test to check stationarity of time series variables for using an autoregressive model because this problem is very common in time series data. A well-known test that is valid in large samples is the augmented Dickey–Fuller test. These tests use the existence of a unit root as the null hypothesis. That is, the series with unit root present in it, is said to be non-stationary and a series when no unit root is present is said to be a stationary series. There are many methods to test unit roots:

• Dickey Fuller
• Augmented Dickey Fuller
• Philips-Perron (PP)

Test In this study ADF is used as, this is the most commonly used unit root test by econometricians.

CO-INTEGRATION Test: The two stage approaches are used to test the Co-integration of the variables and confirm whether there exists a long-term balanced association or not (Engle and Granger, 1987). The concept of co-integration implies that even if many economic variables are non-stationary, their linear combination may be stationary through time (Greene, 2006). Spurious results will be obtained in case of having no stationary variable and having no co integration between the variables (Chan and Lee, 1997). For checking the co-integration this study used ARDL technique because some variables are stationary at level and some at first difference.

THE ERROR CORRECTION MODEL (ECM) After Finding that variables have the long-run co-integration, the short-term relationship among variables are find by applying the ECM. This approach is useful in finding both short-term and long-term response of time-series on other time-series.it finds the speed with which the data of dependent variable restore to equilibrium by change in other time series. The ECM is made by combining the error term with the first difference of the variables (short-run indicators). This shows that the variables have long run relationships.

COEFFICIENT OF MULTIPLE DETERMINATION (R2): The R squared (R2) statistic show that how much the model is good fitted and confirms the strength of the joint explanatory variables in forecasting the values of the dependent parameter. It also displays how much the regression line is fit to the sample of data in the model. The definition of R-squared is the percentage of the response variable variation that is explained by a linear model. In general, the higher the R-squared, the better the model fits your data. R-squared is always between 0 and 100%: 0% indicates that the model explains none of the variability of the response data around its mean. 100% indicates that the model explains all the variability of the response data around its mean.

F-STATISTIC AND PROBABILITY VALUE: The F statistic test shows statistically significance of overall model while the probability value (p-value) indicates whether the parameters are the statistical significant. The thumb rule for statistical significance for a parameter at 5% or 10% is that its p-value must be less than 0.05 or 0.1 respectively.