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 open-ended question to specify the maximum amount they are willing to pay for the proposed scheme. Thus, in the open-ended 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 socio-economic 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 socio-economic 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 open-ended 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 open-ended question

Amount in Rupees

Percentage of Respondents WTP

1-250

28

251-500

25.8

501-1,000

23.8

1,001-2,000

16.2

2,000-5,000

6.2

Total

100.0

Source: Primary survey

In Figure 6.3, rhe X-axis represents the percentage of respondents and the Y-axis 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 open-ended question is a downward sloping curve with a steep slope. The demand curve is relatively elastic, which shows that respondents’ demand for green insurance is very sensitive to the change in the amount to be paid.

Demand curve for amount respondents are willing to pay for green insurance

Figure 6.3 Demand curve for amount respondents are willing to pay for green insurance

Source: Primary survey

 
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