Awareness and willingness to pay for health insurance and it's financial benefits

1
A PROJECT REPORT
On
Awareness and Willingness to Pay for Health Insurance and its Financial
Benefits: An Empirical Study with Reference to Durgapur, West Bengal
By: Rajesh Kumar (12/MBA/23) Under the guidance of: Dr. CA Anupam De
Submitted to:
“NATIONAL INSTITUTE OF TECHNOLOGY”
“In partial fulfilment of the requirements for the award of the degree
of Master of Business Administration (MBA)”
Through
Department of Management Studies
National Institute of Technology, Durgapur
Mahatma Gandhi Avenue, Durgapur – 713209
West Bengal, India

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Contents
S.No. Topic Page No.
1. Abstract 04
2. Introduction 05
3. Literature Review 06
4. Objective of the study 07
5. Hypothesis 08
6. Database and Research Methodology
- Chi-square 09
- Factor Analysis 10
- Analysis and Interpretation 11
- Awareness and Knowledge of Health Insurance 12
- Barriers in the Subscription of Health Insurance 13
- Factor Extraction 16
- Regression 17
- Willingness to join and pay health insurance by non-
health insurance policy holder. 24
7. Hypothesis Testing through Chi-square 25
8. Financial Benefits of Health Insurance
- Section 80 D 30
- Six Financial Benefits of Health Insurance 31
9. Conclusion 34
10. Annexure
- Questionnaire 35
- References 38

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Declaration
I hereby declare that the project report titled “Awareness and Willingness to Pay for Health
Insurance and its Financial Benefits: An Empirical Study with Reference to Durgapur, West
Bengal” is my original work and has not been published or submitted for any degree, diploma
or other similar titles elsewhere. This has been undertaken for the purpose of partial fulfilment of
Master of Business Administration (MBA) at National Institute of Technology, Durgapur (West
Bengal).
Date: 16/ 04/2014 RAJESH KUMAR
Place: Durgapur, West Bengal Roll No.: 12/MBA/23

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Abstract
The present study is an effort in the area of health insurance and the peculiar feature of it lies
in multi-dimensions. As firstly, it examines the respondents who are aware or not aware
about health insurance as well as various sources of awareness; secondly, those who are
aware have subscribed it or not; thirdly, those who have not subscribed what are the reasons
behind the same; and last but not least are they willing to join and pay for it? The study was
conducted in Durgapur, West Bengal and 200 questionnaires were got filled from randomly
selected general public, out of which 114 found to be suitable for analysis. The results shown
low level of awareness and willingness to join and six key factors are barrier in subscription
of health insurance. Moreover significant association exist between the gender; age; marital
status; education; occupation; income of respondents with their willingness to pay for health
insurance.
The present paper concludes that the determinants of awareness of health insurance were
religion, type of the family, education, occupation, annual income, when considered except
type of the family, the other determinants had a statistically significant. The higher education
and higher income had positive relation to the awareness of health insurance.

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Introduction
Socio-Economic development and health of community are related with each other in such a
way that it is impossible to achieve one without other i.e. one cannot be achieved in isolation.
No doubt, the economic development in India is gaining momentum over the last few
decades because of the government initiatives in public health care facilities, yet its health
system is at crossroad today. As these initiatives’ outcome are only moderate by international
standards, because India is ranked 118 among 191 WHO members countries on the basis of
overall health performance. To a large extent the health indices of a country is determined
with reference to the ways with which its health care gets financed. Although, in India the
total health care expenditure is increasing steadily, but the mix of public and private spending
is a major area of concern (Bhat and Jain, 2006). As the various studies reveal that in India
more than 80 percent of health care’s expenditure is borne by individuals i.e. health care
financing is mainly in the form of out-of-pocket which gradually pushing them in to a vicious
circle of poverty. In such a situation health insurance is a widely recognized and preferable
mechanism to finance the health care expenditure of the individuals. The credit for the
origination of concept of health insurance goes to Hugh the Elder Chamberlen from the Peter
Chamberlen family, who proposed it for the first time in the year 1694.
In the late 19th
century “accidental insurance” began which operated much like modern
“disability insurance”.
It was firstly offered by Franklin Health Assurance Co of US, which was founded in 1850. It
provides coverage for the accident arising from rail, road and steamboat accident. This
payment model continued until the start of 20th
century in some jurisdictions (like California),
where all laws regulating health insurance actually referred to disability insurance (Source:
http://en.wikipedia.org/wiki/Health_insurance). As far as the stage of development of health
insurance in India is concerned, it is in the embryonic stage. As the people of India are not
much aware about it and very few part of the population is taking the advantages of it.
Moreover those who are aware about it are not actively participating for one reason or
another and thereby making it difficult to bring it to the stage of expansion. Beside this, very
few insurers are actively venturing in it and thereby making it difficult to construct inroads
for health insurance in India. But there is terrible need of health insurance in India as the
World Bank Report reveals that 85% of the working populations in India do not have Rs.
5,00,000 as instant cash; 14% have Rs. 5,00,000 instantly but will subsequently will face a
financial crunch; Only 1% can afford to spend Rs. 5,00,000 instantly and easily; and 99% of
Indians will face financial crunch in case of any critical illness.
Hence the need for health insurance in India cannot be overlooked (
www.healthinsuranceindia.org).

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Literature Review
Various studies related directly or indirectly with the objectives of the present study were
reviewed. Purohit and Siddiqui (1994) examined the utilization of health services in India
by making the comparison of Indian states in terms of low, medium and high household
expenditure on health care and concluded that there is no serious government initiative to
encourage utilization of health services by means of devising health insurance. Sanyal (1996)
examined that the burden of health care expenditure in rural areas was twice in 1986-87 as
compared to 1963-64 and also provided that household is the main contributor to the
financing of health care in India, so the health planners would have to pay more consideration
regarding this. Gumber and kulkarni (2000) undertaken a case study in Gujarat and
provided that SEWA a type of health insurance scheme is strongly preferred by those who
can’t afford and also not access the services of various other schemes. Asgary, Willis,
Taghvari and Refeian (2004) estimated the demand and willingness to pay for health
insurance by rural households in Iran and concluded that a significant percentage of
population (more than 38%) live in rural areas, but the health care insurance currently
operating in urban areas.
In order to provide rural areas with same level of protection as urban areas, the difference
would have to be subsidized. Ahuja and De (2004) confirmed that the demand for health
insurance is limited where supplies of health services is weak and explained interstate
variation in demand for health insurance by poor in relation to variation in healthcare
infrastructure. Beside this the study also provided that healthcare infrastructure is positively
related to demand for health insurance by poor, whereas the proportion of Below Poverty
Line (BPL) population is negatively related. In order to build demand for health insurance, it
is necessary to address the demand side and at the same time design the insurance schemes by
taking into consideration the paying capacity of the poor. Ahuja and Narang (2005)
provided an overview of existing forms and emerging trends in health insurance for low
income segment in India and concluded that health insurance schemes have considerable
scope of improvement for a country like India by providing appropriate incentives and
bringing these under the regulatory ambit. The study suggested that in order to develop health
insurance for poor in a big way, health care provisions need to be strengthened and
streamlined as well as coordination among multiple agencies is needed.
Dror (2006) laid seven myths regarding health insurance and examined the realities behind
these myths. The evidence shown that most people are willing to pay 1.35% of income or
more for health insurance and the solvent market for health insurance business exist in India;
however tapping of it is contingent upon understanding the customer’s needs and wants.
Dror (2007) examined why the “one-size-fits-all” health insurance products are not suitable
to low income people in India and provided that there is presence of considerable variability
to pay for health insurance which is because of multiple reasons like variability in income,
frequency of illness among households, quality and proximity of providers (private, public) in
different locations.

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Joglekar (2008) examined the impact of health insurance on catastrophic out-of-pocket
(OOP) health expenditure in India and taken zero percent as threshold level to define and
examine such impact. It showed that in India, OOP health expenditure by households account
for around 70% of total expenditure on health and thereby pushes households in to poverty.
Garg and Karan (2009) assessed the differential impact of out-of-pocket (OOP) expenditure
and its components between developed and less developed regions in India. The results
showed that OOP expenditure is about 5% of total households’ expenditure (ranging from
about 2% in Assam to 7% in Kerala) with higher proportion in rural areas. Further in order to
reduce OOP expenditure targeted policies are needed which in turn could help to prevent
almost 60% of poverty.
Objective of the study
The present study is an effort in the area of health insurance to assess the individuals’
awareness level and willingness to join and pay for it. With a view to develop a sound
theoretical framework for investigation, a review of literature regarding health insurance in
India and abroad has been made. Although the present study is an effort in the same direction
yet it differs in terms of its peculiar features of multi-dimensions. As firstly, it examines the
respondents who are aware or not aware about health insurance as well as various sources of
awareness; secondly, those who are aware have subscribed it or not; thirdly, those who have
not subscribed what are the reasons behind the same; and last but not least are they willing to
join and pay for it? So the study has been conducted basically with the following objectives
in mind:
• To assess the awareness echelon regarding health insurance as well as various sources of
awareness for it.
• To examine and explore the various factors which act as barriers and ultimately obstruct
the subscription of health insurance.
• To determine the willingness to join and pay for health insurance by non health insurance
holders.

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Hypothesis
Besides this, in the present study following hypothesis has been formulated and tested:
Case 1: H0: There is no significant difference between the gender of respondents and their
willingness to pay for health insurance.
Case 2: H01: There is no significant difference between the age of respondents and their
Case 3: H02: There is no significant difference between the marital status of respondents
and their willingness to pay for health insurance.
Case 4: H03: There is no significant difference between the education level of
respondents and their willingness to pay for health insurance.
Case 5: H04: There is no significant difference between the occupation of respondents
Case 6: H05: There is no significant difference between the income of respondents and
their willingness to pay for health insurance.

9
Database and Research Methodology
For the purpose of present study specified area selected on the assumption that specific area
based studies expected to give more meaningful and significant information. Accordingly the
present study was done in Durgapur, West Bengal. Thereafter selection of sample of
respondents was made by following random sampling and on the whole a sample size of 200
respondents was planned from the general public. In the view of fact that in the present study
general public has been considered as unit of investigation, a sample framework consisting of
equal number of respondents has been taken. In other words the questionnaire were got filled
from 200 respondents, out of which 114 was found to be suitable for the purpose of analysis.
The data has been collected from the general public by administering the self-structured
questionnaire from them. The preliminary draft of the questionnaire was pretested on 60
respondents. This helped in improving the questionnaire and also gave an indication as to
kind of responses that would be forthcoming with few addition and deletion; the final
questionnaire was developed and used for collection of information from respondents. The
analysis of data collected has been carried out by using simple frequencies, multiple
frequencies and percentages for multiple responses as well as rank has been calculated.
Beside this the use of factor analysis, linear regression and chi-square has been made to draw
the meaningful inference from the study. All this was done with the help of SPSS software
package.
Chi-square
The Chi-square statistics is used to test the statistical significance of the observed association
in a cross-tabulation. It assists us in determining whether a systematic association exists
between two variables. The null hypothesis is that there is no association between the
variables. The test is conducted by computing the cell frequencies. These expected cell
frequencies, denoted, are then compared to the actual observed frequencies fo, found in the
cross-tabulation to calculate the chi-square statistics. The greater the discrepancies between
the expected and actual frequencies, the larger the value of the statistic. Assume the cross-
tabulation has r rows and c columns and a random sample of n observation. Then the
expected frequency for each cell can be calculated by using a simple formula shown below in
equation (1):
݂݁=݊‫݊/ܿ݊ݎ‬
Where = total number in the row, = total number in the column, n = total
sample size Then the value of chi-square is calculated by using the formula
shown in equation (2):
ߣ2 = Σ(݂0−݂݁)/݂

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An important characteristic of the chi-square statistics is the number of degrees of freedom
(df) associated with it. In general, the number of degree of freedom is equal to the number of
observations less than number of constraints needed to calculate a statistical term. In the case
of chi-square statistic associated with a cross-tabulation, the number of degree of freedom is
equal to the product of number of rows (r) less one and the number of columns (c) less one
i.e. df = (r - 1) x (c - 1). The null hypothesis of number of association between the two
variables will be rejected only when the calculated value of the test statistics is greater than
the critical value of chi-square distribution with the appropriate degree of freedom (Source:
Malhotra, 2007).
Factor Analysis
It is a general name denoting a class of procedures primarily used for data reduction and
summarization. Relationship among set of many interrelated variables are examined and
represented with the help of factor analysis. The approach used in the factor analysis is
“Principle Component Analysis”. In this component analysis, the total variance in the data is
considered. The diagonal of the correlation matrix consists of unities and full variance is
bought in to factor matrix. It determines the minimum number of factors that will account for
maximum variance in the data for use in subsequent multivariate analysis. The factors are
also called principal components. Although the initial or unrotated factor matrix indicates the
relationship between the factors and individual variables, it seldom results in factors that can
be interpreted, because the factors are correlated with many variables. Hence the variance
explained by each factor is redistributed by rotation. The method used for rotation in this
study is “Varimax”. It is a method of factor rotation that minimizes the numbers of variables
with high loading on a factor, thereby enhancing the interpretability of the factors (Source:
Malhotra, 2007).

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Empirical Results its Analysis and Interpretation
Table 1 shows the personal profile of the respondents
Gender Frequency Percentage
Male 122 61.0
Female 78 39.0
Total 200 100
Age Frequency
Less than 30 47 23.5
30-40 91 45.5
40-50 37 18.5
Above 50 25 12.5
Total 200 100
Marital Status Frequency
Single 87 43.5
Married 113 56.5
Total 200 100
Type of Family Frequency
Joint 127 63.5
Nuclear 73 36.5
Total 200 100
Education Frequency
Illiterate 4 2.0
Primary 7 3.5
Middle 11 5.5
Matriculation 15 7.5
Higher Education 24 12.0
Graduation 78 39.0
Post Graduation 47 23.5
Vocational 9 4.5
Other 5 2.5
Total 200 100
Occupation Frequency
Employed 58 29.0
Self employed 27 13.5
Student 60 30.0
Housewife 12 6.0
Unemployed 07 3.5
Professional 17 8.5
Family owned business 15 7.5
Retired 4 2.0
Total 200 100
Income per annum Frequency
Less than Rs 1,00,000 52 26.0
Rs 1,00,000-2,00,000 36 18.0
Rs 2,00,000-3,00,000 61 30.5
Rs 3,00,000-4,00,000 27 13.5
Rs 4,00,000-5,00,000 & Above 24 12.0
Total 200 100
A significant proportion of the sample was male members. Majority of the respondents
belonged to the age groups of 30-40 years and were married and living in joint families.
Maximum respondents were graduate followed by post graduation and higher secondary and
were employed.
As far as level of income is concerned a major percentage of the respondents were having
annual income of less than Rs. 2, 00,000-3, 00,000/-.

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Awareness, exposure and knowledge of respondents for health insurance
Although health insurance is not a new concept and people are also getting familiar with it,
yet this awareness has not reached to the level of subscription of health insurance products.
Table 2 shows the awareness level and sources of awareness for health insurance
Particulars Frequency Percentage
Awareness about Not Aware/ not exposed 04 3.50
health insurance Aware/exposed and subscribed 110 96.49
Total 114 100
Particulars Responses % of Responses
TV 22 11.0
Sources of Newspaper 14 7.0
Awareness Agents 21 10.5
Family 13 6.5
Friends 11 5.5
Movies 13 6.5
Employee of insurance company 07 3.5
Tax consultants & Doctors 09 4.5
Any other 04 2.0
Total 114 100
It is clear from the table 2 that people had already heard about health insurance yet a
significant proportion of the respondents i.e. 96.49 % are still without any form of health
insurance and presently only 3.50 %. Moreover there are number of sources creating
awareness regarding health insurance. Mainly the source of awareness is TV followed by
agents, family, friends, employees of insurance companies etc.

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Barriers in the subscription of health insurance:
Unlike reasons for having health insurance, there are numerous reasons for not having health
insurance i.e. there are number of factors which act as barriers in the subscription of health
insurance. All these reasons/barriers were taken in the form of variables and respondents who
are without health insurance were asked to give their response on five point likert scale
ranging from strongly agree to strongly disagree. Where 5 signifies strongly agree, 4 signifies
agree, 3 signifies neither agree nor disagree, 2 signifies disagree and 1 signifies strongly
disagree. Thereafter factor analysis was run in order to condense these variables. All these
variables along with their description are shown in the table 3.
Table 3 shows the list of variables along with their description
Variable Description
V1 Low salary/non availability of funds
V2 Don’t like to buy
V3 Don’t feel the need for it
V4 Prefer to invest money in some other areas
V5 Unaware about it
V6 No one suggested about it
V7 Not taken by friends, relatives etc
V8 Saving in some other areas to meet health care needs
V9 Lack of comprehensive coverage
V10 Lack of reliability and flexibility
V11 Difficulty to approach insurance agents
V12 Inadequacy of knowledge on the part of insurance agents
V13 Behaviour of insurance agents was not satisfactory
V14 Linked hospitals are not easily accessible
V15 Difficulty in availing services in hospitals
V16 Narrow policy options
V17 More copayment involved
V18 More deductible applicable
V19 More hidden cost involved
Before the application of factor analysis the reliability of scale items were tested by applying
cronbach’s alpha which is 0.81. Further to test the sampling, Kaiser-Meyer-Olin measure of
sampling adequacy is computed which is found to be 0.688. It indicates that sample is good
enough for sampling. Moreover the overall significance of correlation matrices has been
tested with Bartlett Test at 171 degree of freedom which provided as well as support for the
validity of data for factor analysis.
All this provided that we can proceed with factor analysis and the result of factor
analysis over 19 factors shown that there are 6 key factors, which was determined by
clubbing the similar variables and ignoring the rest, which majorly consider being most
affecting barriers in the subscription of health insurance. The table 4 shows the
respective percentage of variance of all these factors derived from factor analysis.

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Table 4 shows the total variance explained by various factors
Reliability Statistics
Cronbach's
Alpha
Cronbach's
Alpha Based on
Standardized
Items
N of Items
.811 .815 19
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .688
Bartlett's Test of Sphericity
Approx. Chi-Square 645.144
df 171
Sig. .000
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
1 4.613 24.279 24.279 4.613 24.279 24.279 3.137 16.512 16.512
2 2.115 11.130 35.408 2.115 11.130 35.408 2.471 13.004 29.517
3 1.616 8.507 43.915 1.616 8.507 43.915 2.068 10.882 40.398
4 1.507 7.931 51.845 1.507 7.931 51.845 1.530 8.055 48.453
5 1.187 6.249 58.094 1.187 6.249 58.094 1.507 7.933 56.386
6 1.029 5.418 63.512 1.029 5.418 63.512 1.354 7.127 63.512
7 .932 4.904 68.417
8 .824 4.339 72.756
9 .735 3.871 76.627
10 .714 3.760 80.387
11 .626 3.296 83.683
12 .576 3.032 86.715
13 .536 2.823 89.538
14 .444 2.337 91.875
15 .425 2.237 94.112
16 .374 1.971 96.083
17 .321 1.688 97.771
18 .244 1.284 99.055
19 .180 .945 100.000
Extraction Method: Principal Component Analysis.

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Rotated Component Matrix
a
Component
1 2 3 4 5 6
V1 .629
V2 .645
V3 .616
V4 .830
V5 .718
V6 .811
V7 .656
V8 .675
V9 .740
V10
V11
V12 .897
V13 -.678
V14 .636
V15 .609 .536
V16 .696
V17 .501
V18 .603
V19 .674
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 26 iterations.
It is observed from table 4 that only 6 factors has Eigen value more than one, so
accordingly we preceded with these factors. The total variance explained by factor
1, 2, 3, 4, 5, and 6 is 16.512, 13.004, 10.938, 10.882, 8.055, 7.933 and 7.127
percent of variance, whereas the cumulative variance explained by all these factors
is 63.512 percent and rest of the variance is due to the factors which are beyond the
scope of the study.
The table 5 shows that each statement corresponding to the highlighted factor
loading is correlated with the factor corresponding to that factor loading. Higher
the factor loading, stronger is the correlation between the factors and statement. On
the basis of rotated component matrix the factor extraction table has been prepared
which is as:

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Table 5 Is Factor Extraction Table which is shows the variables in each factor with
corresponding loading and percentage of variance:
Factors
% of
Variance Factor Interpretation Variables Included in the factor Loading
F1 16.512
Lack of awareness and willingness
to pay Don’t like to buy(V2) .645
Don’t feel the need for it (V3) .616
Unaware about it (V5) .718
No one suggested about it (V6)
Not taken by friends, relatives etc.(V7) .656
F2 13.004 Lack of reliability, accessibility Lack of comprehensive coverage(V9) .740
& services
Linked hospitals are not accessible
(V14) .636
Difficulty services in hospitals(V15) .609
More deductible applicable(V18) .603
F3 10.882 Lack of funds to meet costly Low salary (V1) .629
affairs Narrow policy option(V16) .696
More copayments involved(V17)
More hidden cost involved(V19) .674
F4 8.055 Prefer other mode to invest Invest money in some other areas(V4) .830
F5 7.933 Lack of Intermediaries outreach Inadequacy of knowledge (V12) .897
F6 7.127 Narrow Policy Options
Saving in some other areas (V8)
Behaviour of insurance agents(V13) .531
The above stated factors are in the order of degree of importance i.e. factor 1 is more
important than factor 2; factor 2 is more important than factor 3 and so on. The factor 1
and 2 has 16.512%, and 13.004% of variance which is the highest variance as compared
with factor 3, 4, 5, and 6 where % of variance is 10.882, 8.055, 7.933 and 7.127.
Hence it is found that Lack of awareness and willingness to pay; Lack of reliability
accessibility and services of Linked Hospital; Lack of funds to meet costly affairs;
Prefer other mode to invest; Lack of intermediaries outreach and Narrow Policy Options
are acting as main barriers in the subscription of health insurance.

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Regression
Linear Regression: It is used when we want to predict the value of a variable based
on the value of another variable. The variable we want to predict is called the
dependent variable (or sometimes, the outcome variable). The variable we are using
to predict the other variable's value is called the independent variable (or sometimes,
the predictor variable). For example, you could use linear regression to understand
whether exam performance can be predicted based on revision time; whether
cigarette consumptions can be predicted based on smoking duration; and so forth.
Here, we use Linear Regression for validation of Factor Analysis (Score) Data (Calculated) or for
Cross Validation.
Table: 6
a) Variables Entered/Removed
a
Model Variables
Entered
Variables
Removed
Method
1
V7, V2, V5, V3,
V6
b
. Enter
a. Dependent Variable: REGR factor score 1 for
analysis 1
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .951
a
.904 .900 .31671495
a. Predictors: (Constant), V7, V2, V5, V3, V6
ANOVA
a
Model Sum of Squares df Mean Square F Sig.
1
Regression 102.167 5 20.433 203.705 .000
b
Residual 10.833 108 .100
Total 113.000 113
a. Dependent Variable: REGR factor score 1 for analysis 1
b. Predictors: (Constant), V7, V2, V5, V3, V6

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Coefficients
a
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -3.957 .142 -27.840 .000
V2 .185 .043 .162 4.303 .000
V3 .196 .040 .176 4.875 .000
V5 .273 .032 .310 8.599 .000
V6 .339 .038 .359 8.858 .000
V7 .268 .034 .271 7.781 .000
b) Variables Entered/Removed
a
Model Variables
Entered
Variables
Removed
Method
1
V18, V15, V9,
V14
b
. Enter
analysis 1
Model Summary
Square
Std. Error of the
Estimate
1 .906
a
.821 .814 .43133364
a. Predictors: (Constant), V18, V15, V9, V14
ANOVA
a
1
Regression 92.721 4 23.180 124.592 .000
b
Residual 20.279 109 .186
Total 113.000 113
b. Predictors: (Constant), V18, V15, V9, V14

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Coefficients
a
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -4.859 .230 -21.132 .000
V9 .515 .053 .458 9.778 .000
V14 .277 .054 .248 5.136 .000
V15 .307 .048 .297 6.430 .000
V18 .324 .063 .238 5.117 .000
c) Variables Entered/Removed
a
Model Variables
Entered
Variables
Removed
Method
1
V19, V17, V1,
V16
b
. Enter
analysis 1
Model Summary
Square
Std. Error of the
Estimate
1 .947
a
.896 .893 .32785766
a. Predictors: (Constant), V19, V17, V1, V16
ANOVA
a
1
Regression 101.284 4 25.321 235.564 .000
b
Residual 11.716 109 .107
Total 113.000 113
b. Predictors: (Constant), V19, V17, V1, V16

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Coefficients
a
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -5.162 .177 -29.217 .000
V1 .380 .034 .370 11.143 .000
V16 .498 .038 .441 13.252 .000
V17 .297 .039 .248 7.652 .000
V19 .353 .035 .345 9.994 .000
d) Variables Entered/Removed
a
Model Variables
Entered
Variables
Removed
Method
1 V4
b
. Enter
analysis 1
Model Summary
Square
Std. Error of the
Estimate
1 .830
a
.689 .686 .55995354
a. Predictors: (Constant), V4
ANOVA
a
1
Regression 77.883 1 77.883 248.391 .000
b
Residual 35.117 112 .314
Total 113.000 113
b. Predictors: (Constant), V4
Coefficients
a
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -2.934 .193 -15.170 .000
V4 .826 .052 .830 15.760 .000

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e) Variables Entered/Removed
a
Model Variables
Entered
Variables
Removed
Method
1 V12
b
. Enter
analysis 1
Model Summary
Square
Std. Error of the
Estimate
1 .897
a
.805 .803 .44410349
a. Predictors: (Constant), V12
ANOVA
a
1
Regression 90.910 1 90.910 460.941 .000
b
Residual 22.090 112 .197
Total 113.000 113
b. Predictors: (Constant), V12
Coefficients
a
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -3.399 .164 -20.765 .000
V12 .996 .046 .897 21.470 .000
f) Variables Entered/Removed
a
Model Variables
Entered
Variables
Removed
Method
1 V8, V13
b
. Enter
analysis 1

22
Model Summary
Square
Std. Error of the
Estimate
1 .886
a
.785 .781 .46779880
a. Predictors: (Constant), V8, V13
ANOVA
a
1
Regression 88.709 2 44.355 202.685 .000
b
Residual 24.291 111 .219
Total 113.000 113
b. Predictors: (Constant), V8, V13
Coefficients
a
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -.050 .253 -.196 .845
V13 -.590 .045 -.582 -13.049 .000
V8 .613 .047 .578 12.949 .000
Interpretation of Linear Regression:
Variables Entered/Removed:
It is a part of the output simply states which independent variables is part of the equation and
what the dependent variable is.
Model Summary:
The Model Summary part of the output is most useful when we are performing multiple
regressions (which we are NOT doing.) Capital R is the multiple correlation coefficients that
tell us how strongly the multiple independent variables are related to the dependent variable.
In the simple bivariate case (what we are doing) R = | r | (multiple correlation equals the
absolute value of the bivariate correlation.) R square is useful as it gives us the coefficient of
determination.
Hence, this section shows you the correlation between the two variables (R).

23
ANOVA:
The ANOVA part of the output is not very useful for our purposes. It basically tells us
whether the regression equation is explaining a statistically significant portion of the
variability in the dependent variable from variability in the independent variables. This
section shows the p-value (“sig” for “significance”) of the predictor’s effect on the criterion
variable. P-values less than .05 are generally considered “statistically significant.”
Coefficients:
The Coefficients part of the output gives us the values that we need in order to write the
regression equation. The regression equation will take the form:
Predicted variable (dependent variable) = slope * independent variable + intercept
The slope is how steep the line regression line is. A slope of 0 is a horizontal line, a slope of 1
is a diagonal line from the lower left to the upper right, and a vertical line has an infinite
slope. The intercept is where the regression line strikes the Y axis when the independent
variable has a value of 0.
This section shows the beta coefficients for the actual regression equation. Usually, we want
the “un-standardized coefficients,” because this section includes a y-intercept term (beta zero)
as well as a slope term (beta one). The “standardized coefficients” are based on a re-scaling
of the variables so that the y-intercept is equal to zero.
Hence, from the above output result (From Table 6: a, b, c, d, e and f) we can say that our
data’s are good and very much sufficient to explain each and every factors (Score).

24
Willingness to join and pay health insurance by non health insurance policy
holders: Further the analysis of non health insurance policy holders has been made in
order to know about their willingness to join and pay for health insurance. For this
respondents were asked to give answer for followings: ready to buy; still need some
time; not ready to buy; no response or buy if certain conditions fulfilled. As far as
conditional buying of health insurance is concerned, the respondents were asked to rank
the conditions in the order of priority by assigning 5 to most prefer and 1 to least prefer
condition for the buying of health insurance policy.
Table 7 shows the willingness level of non health insurance policy holders and scores of
various conditions
Particulars Frequency Percentage
Ready to buy 95 83.33
Not ready to buy 03 2.631
No response 02 1.75
Still need some time 04 3.5
Buy if conditions fulfilled 10 8.77
Total 114 100
Amount willing to pay for Health Insurance WAS Rank
Up to Rs 5,000/- 13 3
Rs 5,000- 10,000/- 24 2
Rs 10,000- 15,000/- 61 1
Rs 15,000- 20,000/- 09 4
Rs 20,000- 25,000/- & Above 07 5
It is clear from the Table 7 that 2.631%, 1.75% and 3.5% of non health insurance
policy holders are not ready to buy, not provided any response and still need
sometime. Whereas a very good percentage i.e. 83.33% are ready to buy health
insurance without any conditions and remaining are willing to buy only if certain
conditions will fulfil. As far as ranking of different amount conditions where
respondents are willing to pay health insurance are: Rank 1 and the amount range is
Rs 10,000- 15,000.

25
Hypothesis Testing
Thereafter hypothesis were tested with the help of chi-square: The use of chi-square was
made in order to find out the association between the variables associated with the individual
having impact on their ability and willingness to pay for health insurance.
Table 8 Show the results of Chi-Square
a) Gender * Willingness Crosstabulation
Count
Willingness Total
1 2 3 4 5
Gender
1 9 19 16 25 9 78
2 1 3 12 17 3 36
Total 10 22 28 42 12 114
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 8.861
a
4 .065
Likelihood Ratio 9.736 4 .045
Linear-by-Linear
Association
3.390 1 .066
N of Valid Cases 114
a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is 3.16.
b) Age * Willingness Crosstabulation
Count
Willingness Total
1 2 3 4 5
Age
1 7 13 14 23 6 63
2 1 7 5 10 3 26
3 2 1 4 5 2 14
4 0 1 5 4 1 11
Total 10 22 28 42 12 114

26
Chi-Square Tests
a
12 .795
Linear-by-Linear
Association
.916 1 .339
a. 11 cells (55.0%) have expected count less than 5. The minimum expected count is .96.
c) Marital * Willingness Crosstabulation
Count
Willingness Total
1 2 3 4 5
Marital
1 7 14 16 26 6 69
2 3 8 12 16 5 44
3 0 0 0 0 1 1
Total 10 22 28 42 12 114
Chi-Square Tests
a
8 .313
Linear-by-Linear
Association
1.042 1 .307

27
d) Education * Willingness Crosstabulation
Count
Willingness Total
1 2 3 4 5
Education
1 0 4 6 3 2 15
2 5 5 2 6 4 22
3 3 9 9 14 2 37
4 2 3 8 12 3 28
5 0 1 3 7 1 12
Total 10 22 28 42 12 114
Chi-Square Tests
a
16 .229
Linear-by-Linear
Association
2.356 1 .125
e) Occupation * Willingness Crosstabulation
Count
Willingness Total
1 2 3 4 5
Occupation
1 2 1 2 2 2 9
2 4 6 7 11 4 32
3 2 3 5 2 1 13
4 2 7 11 22 4 46
5 0 5 3 5 1 14
Total 10 22 28 42 12 114
Chi-Square Tests
a
16 .552
Linear-by-Linear
Association
.559 1 .455

28
f) Income * Willingness Crosstabulation
Count
Willingness Total
1 2 3 4 5
Income
1 5 4 8 10 3 30
2 4 10 4 13 3 34
3 1 4 4 4 2 15
4 0 3 6 10 2 21
5 0 1 6 5 2 14
Total 10 22 28 42 12 114
Chi-Square Tests
a
16 .469
Linear-by-Linear
Association
3.572 1 .059

29
Interpretation:
Case 1: H0: There is no significant difference between the gender of respondents and their
From Table 8: a) We can say that there is a significant difference between the gender (i.e.
Male and Female) and their willingness to pay for health insurance. So, H0 is rejected (Sig.
Value is >0.05) and we accept the alternate hypothesis.
Case 2: H01: There is no significant difference between the age of respondents and their
From Table 8: b) We can say that there is a significant difference between the age of
respondents and their willingness to pay for health insurance. So, H01 is rejected (Sig. Value
is >0.05) and we accept the alternate hypothesis.
Case 3: H02: There is no significant difference between the marital status of respondents
From Table 8: c) We can say that there is a significant difference between the marital
status of respondents and their willingness to pay for health insurance. So, H02 is rejected
(Sig. Value is >0.05) and we accept the alternate hypothesis.
Case 4: H03: There is no significant difference between the education level of
respondents and their willingness to pay for health insurance.
From Table 8: d) We can say that there is a significant difference between the education
level of respondents and their willingness to pay for health insurance. So, H03 is rejected (Sig.
Value is >0.05) and we accept the alternate hypothesis.
Case 5: H04: There is no significant difference between the occupation of respondents
From Table 8: e) We can say that there is a difference between the occupation of
is >0.05) and accept the alternate hypothesis.
Case 6: H05: There is no significant difference between the income of respondents and
their willingness to pay for health insurance.
From Table 8: f) We can say that there is a significant difference between the income of
is >0.05) and we accept the alternate hypothesis.

30
Financial Benefits of Health Insurance
Section80Dprovidestaxbenefitsformediclaimpremium:
The most commonly used deduction is section 80C of the Income-tax Act which allows you
to deduct up to Rs.1 lakh for your gross total income to arrive at the net income. This
deduction is available for investments made in specified qualifying investments—PPF, life
insurance premiums and so on. However, you have already exhausted this deduction. Another
deduction available under section 80D provides benefit in respect of payment made for
medical insurance premium subject to a ceiling ofRs.15,000 and also includes payment on
account of preventive health check-up subject to a cap of Rs.5,000 but within the overall
ceiling mentioned above. In case a policy is taken on the life of senior citizen (which as per
the Income-tax is now revised to 60 years and hence you are eligible) the additional
deduction of Rs.5,000 is available.
Other than these deductions, you are eligible to claim certain allowances within your salary
which your employer needs to factor in. For example, in case you are staying on rent, house
rent allowance under section 10(13A) can be claimed which will help in increasing your take-
home salary. And in case of any housing loan taken on self-occupied property, you will be
eligible to claim the interest on borrowed capital. The maximum ceiling of claiming interest
on borrowed capital is Rs.1.5 lakh.
Overall the deductions available are limited and you cannot increase your take-home beyond
a limit. As the options are limited, you need to check how your investments can become more
effective.
At present our savings in RD is not the best option. The gross return is good but considering
your marginal rate of tax, the net return in hand is 6.8% which then does not look that
effective.
As we are prima facie risk averse, consider debt mutual funds and here short- to medium-
term funds as well as funds which are dynamically managed are good options.

Six Financial Benefits of Health Insurance
1.Putting more money in families’ pockets, boosting demand, and bringing down
unemployment today: As of Janu
health insurance marketplace, and nearly 80 percent of those people will
– benefit from tax credits to help pay their premiums. All told, the Congressional Budget
Office estimates that over the entirety of 2014,
tax credits and help with cost
people are estimated to benefit, rising to 19 million in 2016. Many millions more will gain
affordable health insurance cov
2. Helping slow the growth of health care costs, boosting hiring in the near term, and
bolstering workers’ paychecks.
slowdown in the growth of health care costs. From 2010
spending grew at an average annual rate of just 1.1 percent, and
data and projections imply that this slow growth continued in 2013.
31
Six Financial Benefits of Health Insurance
As of January 1, more than 2 million people had selected a plan in the
health insurance marketplace, and nearly 80 percent of those people will –
benefit from tax credits to help pay their premiums. All told, the Congressional Budget
that over the entirety of 2014, 5 million people will benefit from premium
tax credits and help with cost-sharing averaging $4,700 per person. In 2015, 11 million
affordable health insurance coverage through Medicaid.
Helping slow the growth of health care costs, boosting hiring in the near term, and
bolstering workers’ paychecks. The United States is currently experiencing a historic
slowdown in the growth of health care costs. From 2010 to 2012 real per
spending grew at an average annual rate of just 1.1 percent, and
imply that this slow growth continued in 2013.
had selected a plan in the
thanks to the ACA
benefit from tax credits to help pay their premiums. All told, the Congressional Budget
5 million people will benefit from premium
In 2015, 11 million
Helping slow the growth of health care costs, boosting hiring in the near term, and
The United States is currently experiencing a historic
to 2012 real per-capita health
spending grew at an average annual rate of just 1.1 percent, and preliminary

3. Reducing our long-term deficit and laying the foundation for future growth.
Congressional Budget Office (CBO) has
the ACA will reduce the deficit by $109 billion.
4. Improving health and making workers more productive.
both for people who would otherwise not have had hea
already insured.
5. Reducing “job lock” and encouraging job mobility and entrepreneurship.
ACA, many Americans’ only source of secure health insurance coverage was through their
jobs. For people with pre-existing medical conditions, purchasing coverage on their own was
often unaffordable or even impossible since insurance companies could simply refuse to
provide coverage.
32
term deficit and laying the foundation for future growth.
Congressional Budget Office (CBO) has estimated that over fiscal years 2013 through 2022,
the ACA will reduce the deficit by $109 billion.
4. Improving health and making workers more productive. The ACA is improving health
both for people who would otherwise not have had health insurance and for people who are
Reducing “job lock” and encouraging job mobility and entrepreneurship.
xisting medical conditions, purchasing coverage on their own was
term deficit and laying the foundation for future growth. The
that over fiscal years 2013 through 2022,
The ACA is improving health
lth insurance and for people who are
Reducing “job lock” and encouraging job mobility and entrepreneurship. Before the
xisting medical conditions, purchasing coverage on their own was

6. Improving financial security in the face of illness.
health insurance coverage, the ACA is helping to ensure that getting sick no longer means
financial ruin. Recent research
Oregon confirmed the important role that having insurance plays in ensuring financial
security.
33
Improving financial security in the face of illness. By expanding access to aff
Recent research examining an expansion of Medicaid coverage in the State of
regon confirmed the important role that having insurance plays in ensuring financial
By expanding access to affordable
examining an expansion of Medicaid coverage in the State of
regon confirmed the important role that having insurance plays in ensuring financial

34
Conclusion
Although the health insurance is not a new concept and the people are also getting aware
about it, which mainly comes from TV followed by newspaper, agents, friends etc, but this
awareness has not yet reached the level of subscription. Moreover it was observed that there
are 6 key factors by clubbing the related variables under it which are acting as barrier in the
subscription of health insurance. Lack of awareness and willingness to pay; Lack of
reliability accessibility and services of Linked Hospital; Lack of funds to meet costly affairs;
Prefer other mode to invest; Lack of intermediaries outreach and Narrow Policy Options are
acting as main barriers in the subscription of health insurance.
Alternatively, 2.631%, 1.75% and 3.5% of non health insurance policy holders are not ready
to buy not provided any response and still need sometime. Whereas a very good percentage
i.e. 83.33% are ready to buy health insurance without any conditions and remaining are
willing to buy only if certain conditions will fulfil. As far as ranking of different amount
conditions where respondents are willing to pay health insurance are: Rank 1 and the amount
range are Rs 10,000- 15,000.
Besides this the difference between the various variables linked with the respondents has
been determined with their willingness to pay for health insurance and the results provided
that on the one hand significant difference exist between the gender; age; marital status;
education; occupation; income of respondents with their willingness to pay for health
insurance.

35
Annexure
Questionnaire
Dear Respondent,
This survey is being conducted to know your awareness and willingness to pay for health insurance. The
whole questionnaire is divided into three parts: Part-A for General Information, Part-B for Health
Insurance Policy Holders and Part-C for Non- Health Insurance Policy Holders. The information provided
by you will be kept confidential and will be used for academic purpose only.
Part- A: General Information; Name: __________________________________________________
(Mark in your appropriate option)
1. Gender: a) Male b) Female
2. Age:
a) Less than 30 years c) 40-50 years
b) 30-40 years d) Above 50 years
3. Marital status:
a) Single b) Married
4. Type of family:
a) Joint b) Nuclear
5. Education:
a) Illiterate d) Matriculation g) Post Graduation
b) Primary e) Higher Secondary h) Vocational
c) Middle f) Graduation i) Other (Specify)_______
6. Occupation:
a) Employed d) Housewife g) Family owned business
b) Self-employed e) Unemployed h) Retired
c) Student f) Professional
7. Income per annum (in Rs.):
a) Less than 1,00,000 d) 3,00,000- 4,00,000
b) 1,00,000- 2,00,000 e) 4,00,000 – 5,00,000
c) 2,00,000- 3,00,000 f) 5,00,000 & Above
8. Do you have health insurance policy?
a) Yes b) No
If yes, then provide the answer for Part-B and if No, then provide the answer for Part-C.
Part-B: For Health Insurance Policy Holder
9. Who is your insurer?
a) Public Company c) Any other (Specify)____________________________
b) Private company

36
10. What type of health insurance policy you have?
a) Individual Health Insurance c) Family Floater Health Insurance
b) Group Health Insurance d) Other (Specify)_______________________________
11. Who persuaded you to purchase the policy?
a) Insurance officials e) Income tax advocate
b) Relatives f) Colleagues
c) Friends g) Yourself
d) Advertisement h) Other (Specify)______________________________
12. Which of the following factors did you consider being the most important while choosing your
insurance policy? Please give your response for the following statements on Five Point Likert’s Scale
ranging from Strongly Agree to Strongly Disagree.
Note: Strongly Agree=5; Agree=4; Neither Agree Nor Disagree=3; Disagree=2; Strongly
Disagree=1.
S.No. Statement Scale
a) Name and reputation of the insurance company.
b) Use of modern technology by insurance company.
c) Courteousness of employees, brokers and corporate agents.
d) Capability and knowledge of employees, brokers and corporate agents.
e) Services provided by the employees, brokers and corporate agents.
f) Use of extensive promotional activities.
g) Maximum customer’s satisfaction.
h) Prompt claim processing with least of formalities.
i) Availability of loan facility to meet all associated cost of health insurance.
j) Minimum co-payment involved.
k) Minimum deductible applicable.
l) Nominal premium charged.
m) Reliability of services offered.
n) Comprehensive coverage.
o) Cash less facility.
p) Easy accessibility of linked hospitals.
q) Easy availability of services in hospitals.
r) Flexibility of policy offered.
s) Availability of tax benefits.
t) Goodwill and linkage of company with Third Party Administrators (TPA’s).
Part-C: For Non-Health Insurance Policy Holders
13. Do you know about Health Insurance?
a) Yes b) No
14. If yes, what are the sources of information?
a) TV d) Friends g) Employees of Insurance Companies
b) Family e) Agents h) Tax consultants and Doctors
c) Newspaper f) Movies i) Other (Specify)________________________

37
15. Why you have not taken Health Insurance Policy? Please give your response for the following
statements on Five Point Likert’s Scale ranging from Strongly Agree to Strongly Disagree.
Note: Strongly Agree=5; Agree=4; Neither Agree Nor Disagree=3; Disagree=2; Strongly
Disagree=1.
S.No. Statement Scale
a) Low salary or non- availability of funds.
b) Don’t like to buy.
c) Don’t feel the need for it.
d) Prefer to invest money in some other areas.
e) Unaware about it.
f) No one suggested about it.
g) Not taken by friends, relatives etc.
h) Saving in some other areas to meet health care needs.
i) Lack of comprehensive coverage.
j) Lack of reliability and flexibility.
k) Difficulty to approach insurance agents.
l) Inadequacy of knowledge on the part of insurance agents.
m) Behaviour of insurance agents was not satisfactory.
n) Linked hospitals are not easily accessible.
o) Difficulty in availing services in hospitals.
p) Narrow policy options.
q) More co-payment involved.
r) More deductible applicable.
s) More hidden cost involved.
16. Are you willing to purchase Health Insurance Policy?
a) Ready to buy d) Not ready to buy
b) Still need sometime e) No response
c) Buy only if certain conditions will fulfil
17. Which insurance company you will prefer?
a) Public Company b) Private Company c) Any other (Specify)__________
18. Which type of health insurance policy do you prefer more?
a) Individual Health Insurance c) Family Health Insurance
b) Family Floater Health Insurance d) Other (Specify)_________________________
19. What amount are you willing to pay for health insurance per family member per annum?
a) Up to Rs 5,000 d) Rs 15,001-20,000
b) Rs 5,001-10,000 e) Rs 20,001-25,000
c) Rs 10,001-15,000 f) Rs 25,000 & Above
20. How you would like to pay health insurance premium amount?
a) Monthly c) Half-yearly
b) Quarterly d) Yearly
Thanks for your cooperation

38
References
Linear Regression: https://statistics.laerd.com/spss-tutorials/linear-regression-using-spss-
statistics.php
Council of Economic Advisor: http://www.whitehouse.gov/healthreform
Asgary, A., Willis, K., Taghvaei, A.A. and Rafeian, M. (2004) “Estimating Rural Households
Willingness to Pay for
Health Insurance” The European Journal of Health Economics, Vol. 5, No. 3, pp. 209-215.
Ahuja, R. and De, I. (2004) “Health Insurance for the Poor Need to Strengthen Healthcare
Provision” Economic and
Political Weekly, Vol. 39, No. 41, pp. 4491-4493.
Ahuja, R. and Narang, A. (2005) “Emerging Trends in Health Insurance for Low-Income
Groups” Economic and
Political Weekly, Vol. 40, No. 38, pp. 4151- 4157.
Bhat, R. and Jain, N. (2006) “Factors Affecting the Demand for Health Insurance in a Micro
Insurance Scheme” Indian
Institute of Management Ahmadabad, working paper no. 02.
Dror, M.D. (2006) “Health Insurance for the Poor: Myths and Realities” Economic and
Political Weekly, Vol. 41, No. 43
& 44, pp. 4541-4544.
Dror, M.D. (2007) Why “one-size-fits-all” health insurance products are unsuitable for low-
income persons in the informal economy in India, Asian Economic Review, Vol. 49, No. 4.
Gumber, A. and Kulkarani, V. (2000) “Health Insurance for Informal Sector Case Study of
Gujarat” Economic and Political Weekly, Vol. 35, No. 40, pp. 3607-3613.
Garg, C.C. and Karan, K.A. (2009) “Reducing out-of-pocket expenditure to reduce poverty: a
disaggregated analysis at rural-urban and state level in India” Health Policy and Planning, pp.
116-128.
Joglekar, R. (2008) “Can Insurance Reduce Catastrophic Out-of-Pocket Health
Expenditure?” Indira Gandhi Institute of
Development Research (IGIDR), paper series-July 28.
Malhotra .N.K. (2007) “Marketing Research An Applied Orientation,” 5th
Edition, Pearson
Publication, New Delhi.
Purohit, C.B. and Siddiqui, A.T. (1994) “Utilization of Health Services in India” Economic
and Political Weekly, Vol. 29, No.18, pp 1071-1080.
www.wikipedia.com
Sanyal, K.S. (1996) “Household Financing of Health Care” Economic and Political
Weekly, Vol. 31, No. 20, pp. 1216-1222. www.healthinsuranceindia.org
http://en.wikipedia.org/wiki/Health_insurance

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