Statistical analysis part II: Multiple regression analysis
The current study reveals that about 59.11 per cent of the respondents have accepted the BID amount offered to them randomly, showing their WTP for green insurance. The remaining 40.89 per cent of the respondents decline to pay the BID value offered to them. The latter group are asked in the form of an openended question to specify the maximum amount they are willing to pay for the proposed scheme. Thus, in the openended question, the respondents are asked to state the maximum amount of money they are willing to pay to get the benefit of green insurance. That would give a continuous measure of the amount that respondents are willing to pay for green insurance and reveal how the amount of WTP varies with the specified socioeconomic and other related determinants. Hence, it would help in finding the direction and magnitude of the determinants by using multiple regression analysis. This study uses the Ordinary Least Squares regression to determine the relationship between the actual amount respondents are willing to pay for green insurance and their socioeconomic determinants.
Model specification
The empirical specification of the model is:
WTPA = Amount respondents are willing to pay
The multiple regression analysis (Table 6.11) shows that the maximum amount of money that the respondents are willing to pay is positively
Table 6.11 The amount respondents are willing to pay for green insurance, using the regression model
Model 
Unstandardised coefficients 
Standardised coefficients 
T 
% 

6 
Std. Error 
Beta 

(Constant) 
317.915 
202.322 
1.571 
.116 

Age of respondent 
59.065 
23.076 
.078 
2.560 
.011** 
Gender 
138.746 
41.135 
.081 
3.373 
.001*** 
Educational level 
152.718 
20.302 
.341 
7.522 
.000*** 
Marital status 
79.427 
45.208 
.043 
1.757 
.079* 
Family type 
18.942 
64.166 
.01 1 
.295 
.768 
Number of family members 
65.508 
32.777 
.074 
1.999 
.046** 
Own house 
47.866 
45.035 
.028 
1.063 
.288 
Family income 
51.977 
19.867 
.080 
2.616 
009*** 
Number of earning members in the family 
1 16.659 
27.529 
.105 
4.238 
.000*** 
Environment pollution in the area 
202.539 
77.783 
.123 
2.604 
009*** 
Existence of disease in the area 
1 13.912 
67.562 
.069 
1.686 
.092* 
Frequency of occurrence of disease 
35.530 
17.564 
.047 
2.023 
.043** 
Hospital type 
24.661 
42.599 
.014 
.579 
.563 
Interest in conservation of green belt 
7.356 
32.868 
.007 
.224 
.823 
Dependent variable: Amount respondents are willing to pay (in Rupees) * indicates 10% level of significance.
 ** indicates 5% level of significance,
 *** indicates 1% level of significanceSource: Primary survey
Table 6.12 Test statistics of the amount respondents are willing to pay for green insurance, using the regression model
R 
0.733 
R squared 
0.S37 
Adjusted R square 
0.529 
F 
73.254 
Significance of F 
.000 
Degree of freedom 
14 
Source: Primary Survey
related to the level of education, family income, number of earning members in the family, existence of disease in their area, frequency of occurrence of disease, number of visits to private hospitals, interest in conservation of green belt and environment pollution in their area. Male members are more WTP for green insurance than female. At the same time it is also found that married people are more WTP than unmarried. In fact, the level of education, marital status, family income, number of earning members in the family and environmental pollution in the area are seen to be positive and highly significant factors in influencing the amount of money respondents are willing to pay for green health insurance.
Table 6.12 shows the test statistics. R is the multiple correlation coefficient that tells us how strongly the multiple independent variables are related to the dependent variable. The regression coefficient R value is 0.73, which shows that there is a high positive correlation between the dependent variable and all the independent variables in the model. R square gives the coefficient of determination. It implies that (0.537), that is, 54 per cent variation in the dependent variable (amount respondents are willing to pay for green insurance) is explained by variations in the independent variables in the model. The adjusted R square value is 0.529, which gives us the idea of how well the model generalises. Ideally, we would like its value to be the same or very close to the value of R square, which is 0.537 in the model. The test statistic from the results of the multiple regression model demonstrates that the model is quite robust with F statistic value of 73.254, with over 99 per cent level of confidence.
Derivation of demand curve for the amount respondents are willing to pay for green insurance
Table 6.13 shows the amount of money respondents are willing to pay for health insurance in the openended question. The WTP for green insurance is higher at the lower premium amount of insurance than that of the higher premium amount. This shows that health insurances with lower premium are highly demanded by the respondents for health insurance.
Table 6.13 Amount respondents are willing to pay for green insurance, using the openended question
Amount in Rupees 
Percentage of Respondents WTP 
1250 
28 
251500 
25.8 
5011,000 
23.8 
1,0012,000 
16.2 
2,0005,000 
6.2 
Total 
100.0 
Source: Primary survey
In Figure 6.3, rhe Xaxis represents the percentage of respondents and the Yaxis shows the amount of money that respondents are willing to pay. The demand curve for the amount that respondents are willing to pay for green insurance in the openended question is a downward sloping curve with a steep slope. The demand curve is relatively elastic, which shows that respondentsâ€™ demand for green insurance is very sensitive to the change in the amount to be paid.
Figure 6.3 Demand curve for amount respondents are willing to pay for green insurance
Source: Primary survey