Breaking the Stigma of Health Insurance

Abstract

The research has attempted to assess the effects of income tax benefits on adoptions of health insurance and on tax compliances. Following thematic literature review, four hypotheses have been set and following the cross-sectional research design through it have gathered data from 120 respondents chosen through stratified random sampling technique oscillated in four stratums. The significant inferential statistical results have indicated to nullify all the hypotheses and it has been documented that selective demographics and enhanced tax deductions are likely to motivate health insurance enrolments; and such tax deductions would increase tax compliances and reduce tax evasions.

Keywords

Health insurance tax deduction tax evasions survey inferential statistics

Introduction

Health insurance (hereafter HI) has been referred to as any stipulated premium-based financial mechanism, which is operated by the insured for minimizing the expenditure on healthcare whenever any health emergencies arise (Bodenheimer & Grumbach, 1994; Gupta & Alam, 2000). The healthcare budget of India has reached a trivial amount of 1.5 per cent of its gross domestic product (GDP) from 0.9 per cent in 2006 (Baru, Acharya, & Acharya, 2010). Interestingly, many countries have been spending significantly in double digits, for example, the USA (17.1%), Tuvalu (16.5%), Sweden (11.9%), Switzerland (11.7%), Austria (11.2%), Cuba (11.1%) and New Zealand (11%) (WHO, 2014). Such meagre spending has catalyzed for launching of multiple HI schemes, for example, social health insurance (SHI), community-based health insurance (CBHI), micro health insurance scheme and private health insurance (PHI) (Chakrabarti & Shankar, 2015; Duggal, 2007b; Forbes, 2012). Literature has validated notwithstanding, the number of schemes that have been introduced by the central and state governments; the quality of Indian healthcare system has not improved as per expectations (Ahlin, Nichter, & Pillai, 2016). Inasmuch as the poor healthcare system has been identified as the major impediment for healthy India, private healthcare system has been booming and consequently, the HI schemes have flourished (Forgia & Nagpal, 2012; Reddy, 2012; Sharma, 2015). Research has validated around 10 per cent Indian population were within the coverage of the CBHI in 2012 (Selvaraj & Karan, 2012). The poor performance of government-sponsored health programmes compelled the Indians to spend heavily on private treatments like expenditures on out of pockets (OOPs) and on medicine (Dror, van Putten-Rademaker, & Koren, 2008; Ghosh, 2011; Mohanty, Chauhan, Mazumdar, & Srivastava, 2014; Porter & Grills, 2015). Taking these into cognizance, the Government of India has adopted the National Health Policy (NHP) with the coexistence of public and private HI providers (Reddy et al., 2011). It has attempted to promote the HI by enhancing the tax benefits on the payment of premiums for senior citizens up to the tune of 0.05 million per annum (under Sec. 80D of the Income Tax Act, 1961) (Sharma, 2015). Literature is divided into two streams of opinions about improving the Indian healthcare system. The first school of thought has advocated HI coverage to cope with catastrophic medical events (Dao & Nichter, 2016; Sodhi & Rabbani, 2014), while the other has emphasized on wide coverage of HI for reducing the substantial expenditure on healthcare (Patel et al., 2011; Wagstaff & Bergkvist, 2011; Yellaiah, 2013). Post liberalizing the HI sector and streamlining the entry norms by Insurance Regulatory and Development Authority of India (IRDA) in 1999, around 40 private HI companies have been operating in India but have just covered merely 2 per cent of the population in the first part of the current decade (Sen, Pickett, & Burns, 2014). Multiple impediments in the success of the sector have been pointed out, viz. poor infrastructure and lower levels of awareness (Vellakkal & Ebrahim, 2013) notwithstanding, that HI schemes have good prospects for expansions (Burns, Srinivasan, & Vaidya, 2014).

Literature has concurred government-subsidized healthcare schemes practising globally, for example, in China (Pan & Liu, 2012; Yip, Hsiao, & Chen, 2012), in Canada (Thompson, 1991) and those schemes assessed by the scholars (Qin, Pan & Liu, 2014). The tax benefits on the payment of HI premiums were reported in many countries such as in Canada (Stabile, 2001), the USA (Falkenberg, 2011; Tanner, 2013), Chile (Frenz, Delgado, Kaufman, & Harper, 2014), Ghana (Mills et al., 2012), China (Chen, 2013), Indonesia (Sparrow, VandePoel, & Hadiwidjaja, 2014) and in Tanzania (Levin, 2005). Research has pointed out the impediments on wider HI coverage such as language barriers, especially the English HI literature in Mexico (Schur & Albers, 1996) and even the HI-related jargons (Kim, Braun, & Williams, 2013; Paez et al., 2014).

In delve studies so far have been attempted addressing the multiple facets of the Indian HI saga such as the determinants (Chakrabarti & Shankar, 2015), the ability of the poor to participate in the schemes (Ahuja & Narang, 2005), skewed socio-economic determinants (Bhat & Jain, 2006), acceptability of HI (Jain, Selva, Zeena, & Raghavan, 2014) and reduction in health expenditures in the community health insurance (CHI) schemes in rural India (Aggarwal, 2010; Devadasan et al., 2010; Rajendran & Raya, 2010). Inadequate government support for participations (Devadasan, Ranson, Van Damme, & Criel, 2004), poor quality of healthcare facilities (Drechsler & Jütting, 2005), inaccessibility to information (Bhat & Jain, 2006) and low premium collections in CHI schemes (Criel & Waelkens, 2003) along with lack of government’s requisite and timely emphasis on CHI schemes (Michielsen et al., 2011) have also been reported. Attracting multinational insurance companies with tax incentives (Duggal, Sunil, & Vadir, 1995; Purohit, 2001) all-round innovative measures (Shaw, 2017) and long-term sustainability of healthcare-providing NGOs (Baru et al., 2010; Duggal, 2007a) have been emphasized. The substandard quality of services by the Health Department staffs has been identified as a major impediment in accessing quality healthcare (Berman, Ahuja, & Bhandari, 2010). Further, mechanisms to improve the quality of healthcare under the CHI schemes (Devadasan et al., 2010) with consumer-centric empowerment approaches (Bhatia, Yesudian, & Gorter, 2006) and subsidized rural HI schemes (Forbes, 2012) have been reported. The gravity of multiple diseases in Northeast India (NEI) such as malaria, Japanese encephalitis, lymphatic filariasis and dengue/chikungunya as well as the determinants of healthcare utilization in the rural NEI have been reported (Barman, Bhattacharya, Lyngdoh, & Jamil, 2015; Dev, Sharma, & Barman, 2015; Lyngdoh, 2016). The regional disparities in healthcare system (Lyngdoh, 2015; Neogi, 2010; Sheet & Roy, 2013) and the poorest conditions of healthcare infrastructure of NEI (Saikia & Das, 2012; Sengupta, 2009) have also been highlighted. The status of healthcare facility, challenges, remedial measures and scope for further improvements of healthcare facilities in the entire NEI have been documented (Hazarika, 2014; Sinha, 2014). Further, the significant improvement in healthcare infrastructure has been reported after the implementation of National Rural Health Mission (NHRM) in the NEI, nevertheless in a skewed manner (Baru, Acharya, & Acharya, 2010; Hazarika, 2013; Kumar, 2013). A careful review of literature has produced a trend of studies so far been attempted, encompassing divergent issues across the states but no single study has been traced to measure the relationships between individual subscriber’s tax benefits and their tendency for HI adoption not even in the NEI. The study has spotted the shortfall in the literature and has attempted to close the gap to the extent possible.

The contribution of this study towards literature has been categorized as first, it has reported the effects of income tax benefits on HI enrolments resulting in claiming of tax deductions, that is, tax compliances, likely be the first on in NEI. Second, it has conceded that demographics such as education levels, age, marital status and castes have significantly impacted HI enrolments (Lin, Liu, & Chen, 2009) but, has contradicted with few studies (Wagstaff, 2007). Moreover, influence of family income has been validated, aligned with Indian and overseas studies (Chakrabarti & Shankar, 2015), but has contested with few Indian studies (Bhat & Jain, 2006). As far as impacts of gender are concerned, it has been in corollary with few studies about significant positive impacts (Bucher-Koenen, Lusardi, Alessie, & VanRooij, 2014), but has differed from few authors who have conceded with no such precedence (Bartholomae, Russell, Bonnie, & Teresa, 2016). Third, it has validated the positive impacts of tax deductions on higher rate of HI enrolments, in tune with prior studies (Saravanan & MuthuLakshmi, 2017). Finally, following the Indian and global tax literature, it has reported the significant influence of tax compliance, resulting in reductions in tax evasions (Deb & Chakraborty, 2017; Zaklan, Westerhoff, & Stauffer, 2009).

The study has attempted to assess the effects of income tax benefits on adoptions of health insurance and on tax compliances.

The study has been designed in the remaining eight sections. A strand of prior studies have been thematically reviewed, which has been reported with the research hypotheses in the second section, the adopted research methodology has been elaborated in the third section, findings and discussion have been presented in eth fourth and fifth sections, respectively, the conclusion has been drawn in the sixth section. Finally, the managerial implications and future research roadmap have been presented in the seventh and eighth sections, respectively.

Related Literature and Hypotheses

The study has reviewed the prior literature to set the research hypotheses.

Demographics and Health Insurance

Gender

Literature has shown women have low rate participation in HI plans, for example, in the USA (Bucher-Koenen et al., 2014; Peerson & Saunders, 2011; Vart, 2010), while few studies have concluded no such findings (Barber & Odean, 2001; Bartholomae et al., 2016).

Age

Scholars have assayed the impacts of age on HI, for example, on child healthcare, in particular in Vietnam (Nguyen & Wang, 2013) and in general in Chile (Frenz et al., 2014; Sojo, 2011). The young population is mostly uninsured in the USA (Tanner, 2013).

Income and Education Levels

Income and education levels of insured people have been identified as significant motivators for taking the HI (Chakrabarti & Shankar, 2015; Logan et al., 2015; Lusardi & Mitchell, 2011; Mee-Udon, 2014).

Caste

Literature has validated marginalized population, that is, the scheduled caste (SC), scheduled tribe (ST) and other backward communities (OBC) people, have been lagging behind in HI coverage (Chanana, 1993; Filmer & Pritchett, 2001), while in the Indian scenario SC and OBC population have high inclinations towards enrolment in CGHS (Chakrabarti & Shankar, 2015).

Marital Status

The marital status and decision for enrolments in the HI coverage have been extensively studied (Lin et al., 2009; Monheit & Harvey, 1993; Wang, Zhang, & Yip, 2006). Based on these, the study has framed the research hypothesis as:

H1: Demographics significantly influence health insurance enrolments.

Tax Deductions and Health Insurance

Scholars have validated the complexities of the Indian tax system and ways for its rationalization (Jha, 2013; Kumar, 2014; Rao, 2005), highlighting the Chapter VI (Tax deduction sections) of the Income Tax Act, 1961. Tax benefits US$80 have been highlighted in the literature (Ghuge & Katdare, 2015; Saravanan & MuthuLakshmi, 2017), which has risen cumulatively with the assessee and her senior citizen parents to ₹0.1 million for the assessment year 2019–2020 (The Business Line, 2018). Globally, tax deductions in the form HI premiums have been reported (Goldhill, 2013; Richmond & Fein, 2005). Based on this, the relevant research hypothesis has been framed as follows:

H2: Enhanced tax deductions significantly influence health insurance enrolments.

Tax Deductions and Tax Compliances

Literature has divided tax compliance costs into different categories such as computational costs, planning costs and advisory costs (Chan, Cheung, Ariff, & Loh, 1999) and into commencement costs, temporary costs and regular costs (Evans & Walpole, 1997). Studies have indicated ill-effects of tax complexities like risk-taking attitudes (Ackermann, Fochmann & Mihm, 2013), enhanced tendency for taking tax advices (Eichfelder & Schorn, 2012) and even less tax compliances (Hodge & Guyton, 2014). Further, reduced tax burden has enhanced tax compliances (Arsić, Arandarenko, Radulovic, Randelovic, & Jankovic, 2015). Taking into cognizance the commencement costs which are likely to arise when government introduces major changes in tax rates and/or tax system, the current study has hypothesized that such cost would decrease as tax deductions ceiling US$80 have been raised significantly in the Union Budget of 2018, which would motivate assessees for more tax compliances. Accordingly, the research hypothesis has been set as:

H3: Enhanced tax deductions significantly enhance tax compliances.

Tax Compliances and Tax Evasions

High rate of tax compliances has inverse relations with tax evasions and such compliances have been influenced by multiple factors (Alm, McClelland, & Schulze, 1992) such as economic freedom, effective competition laws, societal perceptions and legitimacy (Gilligan & Richardson, 2005). Morality has a significant influence on compliance since whenever tax evasion has been assumed as unethical, tax compliance is likely to increase (Morris & McDonald, 1995). Moreover, tax payers have responded to the tax authorities in a systematic manner while complying with their tax liabilities (Feld & Frey, 2002; Frey & Feld, 2002). Literature has concurred inasmuch as tax compliances and tax evasions have been by and large inversely related, taxation is considered as a social act and compliance decisions are influenced by the perceptions of honesty and social norms (Frey & Torgler, 2007), trust in government and pride in citizenship (Torgler, 2003). A strand of global studies have concurred multiple mechanisms such as shaming tax payers (Ricardo & Troiano, 2015), higher rates of penalty and prosecution (Park & Hyun, 2003), conducting audit (Slemrod, Blumenthal, & Christian, 2001) and reduction in tax rates (Takáts & Papp, 2008) have significantly improved tax compliances. In the Indian context, post demonetization, higher degree of tax compliances resulting substantial increase in income tax collections have been reported (Nataraj, 2017; Singh, 2018; Singh & Bagchi, 2018). Furthermore, key policy changes such as changes in tax rates, quantum of penalty and prosecution rates and the probability of audit have also significantly influenced the tax compliances and have minimized the tax evasion tendencies (Tandon & Rao, 2017). Hence, the study has framed the hypothesis as follows:

H4: Tax compliances have significant impact on tax evasion decisions.

A conceptual model has been designed as exhibited in Figure 1 for carrying out the study. In the model, the respondents’ demographics (predictor) likely to influence the HI enrolment decision (outcome), which also is impacted by tax deductions (predictor). Moreover, tax deductions, being a predictor, are likely to have influence on tax compliances (outcome) and in turn, the latter act as a predictor probably to reduce tax evasions (outcome).

Figure 1.

Source: The authors.

Methodology

Research Design

For picturing the holistic aspects of the research problem, it has adopted cross-sectional study design and for gathering primary data, the survey has been conducted.

Method

Designing the Interview Schedule

The preference for in-person interviews in social science studies for accessing the multiple benefits like flexibilities has been indicated in literature (Mack & Ryan, 2006). The study has set the interview schedule in four steps. First, applying few key words and accessing the digital library around 178 research papers with full text have been downloaded. Second, the revision of the papers has helped in framing the hypotheses and to build 25-item inventories (5-point in Likert scale) has been developed. In the third stage, a pre-test with 30 randomly chosen respondents has been conducted to judge the reliability of the items (Zikmund & Babin, 2012) and banking upon Cronbach’s alpha threshold scores exceeding 0.5 (Nunnally, 1978), 2 items are dropped. Finally, during March–April, 2018, the survey was completed.

Sampling Technique

The HI policy holders who have claimed tax benefits US$80 during the assessment year 2017–2018, especially the salaried class and individuals involved in trades and businesses of Agartala city have been assumed as the study population. For lack of contact details of the study population, the sampling frame could not be set. Stratified random sampling technique has been applied where the study population has been divided into few stratums containing homogeneous characteristics. Sampling units have been divided into strata containing four different stratums: central government employees, state government employees, traders and businessmen. The sample size (n) of the study has been fixed at 120, following the advices of scholars, which is adequate for conducting social science research (Isreal, 2013; Roscoe, 1975). Adopting disproportionate stratified sampling technique, eventually 30 sample respondents from each of the stratums have been chosen randomly.

Data Collection Procedure

Primary data: A self-administered interview schedule comprising of four sections has been used. In the schedule, Section I has 13 questions primarily focussed on respondents’ basic information. Section II has 23 items addressing the research problem; Section III has incorporated six questions designed in nominal scale for assessing the relations between demographics and research aspects and eventually in Section IV three items incorporated structured in 5-point Likert scale for assaying the overall perception for describing the research aspect. The Likert scale has been used extensively in business and social science studies since long past for its inherent advantages such as easy coding and tabulations and the current study has framed its questions in 5-point Likert scale. A cover letter comprising users’ instructions for filling the items has been included at the top, in tune with scholarship (Dillman, 1978). Moreover, following the advices of the scholars, for minimizing the threat of non-comprehensions, the enumerator whenever requested has translated the items into local language (Peytchev, Conrad, Couper, & Tourangeau, 2010) and assured the respondents about strict adherence with the data collection ethics (Oberhofer & Dieplinger, 2014).

Secondary data: The Primary Sources of the secondary data are the original research papers accessed from e-journals. The Indian literature has been retrieved mostly from J-Gate and UGC Shodhsindhu. The secondary sources comprise selected review articles, expert opinions from newspapers and few pertinent websites, which have been reviewed. Finally, multiple tertiary sources for example, Research Gate, Google Scholars, Indian Citation Index (ICI), J-Gate and Social Science Research Networks (SSRN) have also been explored.

Data Analysis Strategy

The statistical software has been used for data processing and analysis, and accordingly, it has accessed the 22nd version of the IBM’s Statistical Package for Social Science (SPSS).

Study Variables

From Table 1, the parameters of the study have been segregated into three types: the predictors, outcomes and extraneous. It has been assumed there likely that an association exists between the respondents’ demographics and their decisions for subscribing HI. Further, such decisions and the available income tax deductions on premium payments have been hypothesized. It has been expected that to avail the tax deductions on premium payments US$80, the enrolments are likely to be influenced. The tax deductions likely to establish an association with tax compliances and in turn, such compliance would reduce the tax evasions. To arrest the peer-group influences, it has conducted the interviews separately, as pointed out in the literature (O’Shaughnessy, 2014).

Table 1.

Variables

Predictors	Outcomes	Extraneous
Demographics	Health insurance enrolment	Peer-group influences
Tax deductions	Health insurance enrolment
	Tax compliances
Tax compliances	Tax evasions

Source: The authors.

Significance Level

The significance level (α) for conducting statistical tests has been fixed at 5 per cent, which has implied the confidence level was at 95 per cent.

Statistical Tests

The study has summarized the rationale for applying the inferential statistics for testing the null hypotheses as cited in Tables 2 and 3, respectively.

Table 2.

Choice of Statistical Tests

Tests	Variables						Objectives	Null Hypotheses
	Predictors			Outcomes
	Name	Measurement	No.	Name	Measurement	No.
Cross tabulations	Demographics	Nominal (2 or more types of categories)	6	Health insurance	Interval (continuous)	1	To determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups.	H01
Pearson’s correlation	Tax deductions	Interval (continuous)	1	Health insurance, tax compliances	Interval (continuous)	1,1	To measure the strength and direction of association that exists between two variables measured on at least an interval scale.	H02, H03
Simple regression	Tax compliances	Interval (continuous)	1	Tax evasions	Interval (continuous)	1	To predict the value of a variable based on the value of another variable.	H04

Source: The authors.

Table 3.

Assumptions Hold for Selected Statistical Tests

Tests	Type	Rationale
Cross tabulations	Parametric	The outcome has been computed on continuous level (interval data); six independent variables are considered, each consisting of more than two categorically independent groups; no significant outliers, independent groups linearly related, homogeneity of variances, that is the equality of variances of the differences between measurements hold, the Sampling distribution is normally distributed and sample size (n) >30.
Pearson’s correlation	Parametric	The assumptions required to hold have included interval data, has been linearly related, the sample size (n) has exceed 30, the nature of the sampling distribution has been bivariate and has been normally distributed.
Simple regression	Parametric	The assumptions have included that the data are interval, linearly related sample size has exceeded 30, and the sampling distribution is multivariate and has been normally distributed.

Source: The authors.

Research Validities

Research validity has been referred to as a right measure, that is, the truthfulness of the findings (Altheide & Johnson, 1994) about the extent of the capability of a research instrument, which has intended to measure the research questions (Robson, 2011). It has achieved different research validities such as construct (schedule items), content (boundary of the schedule), instrument (use of uniform items in pre-test and in final survey), internal (the research findings have been confined based on statistical results), concurrent (the findings have been mostly correlated with literature) and conclusions (the significant statistical results have generalized to the studied population). Further, to arrest the internal validity threats, the stratums have been chosen judiciously and interviews have been carried out separately.

Limitations

In tune with the research objective, it has framed only four hypotheses, used selective variables and the related literature in English has only been considered. Due to parsimony and time constraint, selective respondents covering confined geographical areas have been considered. The data collection instrument has been self-designed in preference to any established questionnaire. Moreover, for data analysis convenience, it has used pre-coded close-ended schedule and no open-ended questions have been used. The adoption of 5-point Likert scale with an option ‘neutral’ instead a 4-point scale could have been applied, in line with scholarship (Losby & Wetmore, 2012). Factor names unlikely have accurately reflected the variables within the factor. Different inferential statistical tools applied have their specific limitations, which could have at least partial impacts on the findings (Bethlehem, 2008). Finally, the probability of social–desirability bias (Crowne & Marlow, 1960)—a tendency of the participants to put their responses as expected by the enumerator unlikely is completely ruled out.

Results

Descriptive Statistics

The sample statistics of the study have been presented by mode (for nominal data), mean and standard deviation (SD) (for interval data). It has been reported that 55.83 per cent participants are men, 40.83 per cent of them have been in the age group of 45–54 years, 81.67 per cent of the participants are married, 28.33 per cent are graduates, 40 per cent belong to general caste, 41.67 per cent have monthly income of ₹0.025–0.05 million, 94.17 per cent of them have enrolled in health insurance scheme, 60.83 per cent have included their dependents under the insurance coverage, 40 per cent of them have sum assured up to ₹0.1 million, 45.83 per cent have been paying annual premiums in the tune of ₹0.01–0.02 million, 90 per cent of them have accessed the knowledge about tax benefits US$80 for payment of premiums but only 65 per cent of the respondents have claimed the same.

Principal Component Analysis

The study has run the Principal Component Analysis (PCA) on the presumption that the total variance of the variables would be accounted for by means of its components (or factors), and hence that there would be no error variance (Field, 2000).

Reliability has been referred as the instrument likely to produce stable and consistent results, that is, in other words, a good measure. It has run Kaiser–Meyer–Olkin (KMO) test for assessing the sample adequacy and Bartlett’s test of sphericity to examine whether the matrix is an identity matrix or not. The significant KMO test results have supported the sample adequacy and the significant Chi-square results (p < 0.05) have affirmed that matrix is not an identity matrix (as presented in the Tables 4 and 5 respectively).

Table 4.

Reliability Statistics

Cronbach’s Alpha	Cronbach’s Alpha Based on Standardized Items	No. of Items
0.665	0.669	23

Source: Primary data.

Table 5.

Sample Adequacy Statistics

Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy		0.703
Bartlett’s test of sphericity	Approx. Chi-square	533.440
	df	253
	Sig.	0.000

Source: Primary data.

The communality—proportion of common variance present in a variable has been extracted by applying the PCA as presented in Table 6.

Table 6.

Communalities

Items	Initial	Extraction
Health costs are soaring	1.000	0.651
Low public spending	1.000	0.670
Low penetration of health insurance	1.000	0.679
Reduction of out-of-pocket spending	1.000	0.506
Covers pre-and post-hospitalization expenses	1.000	0.524
The demand for health insurance	1.000	0.603
Willingness to pay higher premiums	1.000	0.592
More generous health insurance coverage	1.000	0.666
Government is increasing the tax deduction	1.000	0.698
Health insurance lessens the financial impact	1.000	0.678
Insured have higher rates of accessing hospitalizations	1.000	0.581
People with diabetes and hypertension	1.000	0.513
Lack of a comprehensive benefits package	1.000	0.608
Enhanced tax deductions	1.000	0.610
A lower after-tax price	1.000	0.583
Higher tax compliances	1.000	0.624
Increased tax anomalies	1.000	0.589
Tax perception influences tax evasion	1.000	0.631
Higher the tax rates, the greater will be the tax evasion	1.000	0.615
Tax evasion is related to tax structures	1.000	0.537
Tax evasion deprives governments	1.000	0.624
Tax morale decreases	1.000	0.632
Tax compliance will lower the tax evasions	1.000	0.681

Source: Primary data, extraction method: principal component analysis.

Table 7.

Total Variance Explained

Components	Initial Eigen values			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
Components	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	7.214	35.52	35.52	7.214	35.52	35.52	5.648	26.37	26.37
2	5.885	27.61	63.13	5.885	27.61	63.13	4.135	17.28	43.65
3	4.221	14.33	77.46	4.221	14.33	77.46	3.004	8.31	51.96
4	1.979	7.59	85.05	1.979	7.59	85.05	1.237	3.72	55.68

Source: Primary data, extraction method: principal component analysis.

Note: Components: health insurance uniqueness, health insurance perspectives, tax compliance perspectives, and tax evasion perspectives.

PCA technique has deduced four components, which have been labelled with appropriate titles as presented in Table 7 along with corresponding Eigen values—the total of squared values of factor loadings of a factor. Inasmuch as Eigen values have represented the amount of variation explained by a component and such Eigen value of 1 has represented a significant amount of variation (Kaiser, 1960). The deduced components have reorientation of around 81.65 per cent of the variables, a significant figure for inferring the conclusion (Pett, Lackey, & Sullivan, 2003). Component 1 has been assigned the title Health Insurance Uniqueness consisting of 7 items with the average mean = 3.78, SD = 1.070, where the mean scores have been oscillating between 3.67 and 3.92. Cronbach’s alpha scores of the components have been computed as 0.678, 0.702, 0.598, 0.515, 0.555, 0.571 and 0.580, respectively. Component 2 has been captioned as Health Insurance Perspectives incorporating 6 items with average mean = 3.79, S.D. = 1.115, mean scores have been oscillating between 3.76 and 3.81. Cronbach’s alpha scores of the components have been computed as 0.539, 0.561, 0.622, 0.673, 0.595 and 0.613, respectively. Component 3 has been named as Tax Compliance Perspectives, covering 4 items with average mean value = 3.63, SD = 1.211 and the mean scores have been distributed between 3.58 and 3.73. Cronbach’s alpha scores have been calculated as 0.581, 0.619, 0.601 and 0.609, respectively. Component 4 has assigned the name of Tax Evasion Perspectives, containing 6 items with average mean value = 3.75, SD = 1.187 and the mean score have been ranging between 3.57 and 3.96. Cronbach’s alpha scores have been computed as 0.633, 0.607, 0.584, 0.569, 0.597 and 0.605, respectively.

Inferential Statistics

Cross Tabulations

To assess the influence of selective demographics (predictors) on HI enrolment decision (outcome), that is, to test H₀₁, cross tabulations have been applied; the significant outcomes are presented in Table 8.

In the results section of Table 8, the first column shows the Pearson’s Chi-square values, which are used for testing whether row and column variables are independent or not. The lower significance values present in the fourth column have evidence to nullify H₀₁. The second column summarizes the corresponding Likelihood Ratios, which have also been explained in corollary with Pearson’s Chi-square values. Since the current study has been carried out with relatively lower sample size of 120, the Likelihood ratios would validate the findings as the findings likely been less affected by smaller samples, as literature indicated (Hays, 1963). The Linear-by-Linear Association test has been conducted for ordinal type data on the assumptions of equal and ordered intervals and the results have been summarized in the third column. The significant results of the Pearson’s correlation coefficient with an approximately chi-squared distribution on 1 df, ( p < 0.05) have supported to reject the H₀₁.

Table 8.

Summary Results of Cross Tabulations

Variables		Results
Demographics (predictors)	Health Insurance Enrolment (outcome)	Pearson’s Chi-square Value	Likelihood Ratio	Liner-by-Liner Association	Significance Value*
Gender	Health insurance enrolment	24.478	40.123	5.81	0.000
Age	Health insurance enrolment	33.361	42.145	5.80	0.002
Marital status	Health insurance enrolment	24.391	33.105	5.96	0.003
Education level	Health insurance enrolment	30.741	40.111	5.77	0.008
Caste	Health insurance enrolment	33.210	45.028	5.33	0.006
Income level	Health insurance enrolment	33.210	45.028	5.17	0.006

Source: Primary data.

Note: *p < 0.05.

Pearson’s Correlation

Pearson’s correlation has been applied for assessing the associations between tax deductions and HI enrolment as well as the former’s associations with the tax compliances. The significant and positive associations between tax deductions with HI enrolments (r = 0.751, n = 120, p = 0.001) and with tax compliances (r = 0.770, n = 120, p = 0.003) have supported to nullify both H₀₂ and H₀₃. The findings as reported in Table 9 have supported the research hypotheses that due to enhanced tax deductions on payment of HI premium, the uninsured and under-insured are likely to enrol in different plans for accessing the tax benefits US$80. Such claiming of tax deductions would be tantamount to enhanced tax compliances. To sum up, it has drawn the conclusions that tax deduction has positive significant association with increased number of HI enrolments and increased tax compliances.

Table 9.

Correlations between Tax Deduction and Health Insurance Enrolment

Predictor	Outcome	Correlation Value (r)	Significance Value (p)
Tax deduction	Health insurance enrolment	0.751	0.001*
Tax deduction	Tax compliances	0.770	0.003*

Source: Primary data.

Note: * p < 0.05.

Simple Regression

To investigate the effect of tax compliances in evading tax evasions (H₀₄), the simple regression analysis has been applied.

The influence of tax compliances on tax evasions has been tested using simple regression, which has deduced significant result as evident in Table 10. The first column R exhibits the simple correlation between the variables scored 0.513, moderate relations between themselves. However, the direction of this relationship has not been reflected. The second column R² shows a score of 0.482, which indicates 48.2 per cent variations of the tax evasions likely as explained by tax compliances, a moderate figure even though the higher the R², the better the model fits the data (Frost, 2013). Moreover, it has been indicated that it is likely to have other variables which have influences on tax evasions. The next column, adjusted R², is an adjustment of the R², which has penalized the addition of extraneous predictors in the model (i.e., Model 1). To avoid a bias towards complex models, the adjusted R² has been used. The model is unlikely to interpret the adjusted R² as the percentage of explained variance in the sample used for regression analysis. The adjusted R² has only been used as a measure of how much the model has been able to explain while controlling for model complexity. The fifth column presents the S.D. of the error term and the square root of the Mean Square (MS) for the residuals.

The result of Analysis of Variance (ANOVA), indicating whether the applied model has been significantly better in comparison to the mean value for predicting the outcome, is shown in Table 11. In the fourth column, the MS has been derived dividing the SS (col. 2) by the df (col. 3), and in the fifth column the associated F ratio has reported significant scores (p < 0.05) [F (1, 118) = 6904.73, p = 0.001]. The significant result has conceded that there has been less than 0.001 per cent chance that an F-ratio this large would happen if the H₀₄ were be accepted. It is likely to conclude that the regression model findings are significantly better in predicting the tax evasions than if the mean value of tax compliance could have been used. Finally, the significant result has emphatically pointed to nullify the H₀₄, hence the corresponding research hypothesis is likely be accepted.

Table 10.

Model Summary Results^a

Model	R	R²	Adjusted R²	Standard Error of Estimate
1	0.513^a	0.482	0.439	57.22

Source: Primary data.

Note: ^aPredictor: (constant), tax compliance.

Table 11.

ANOVA^a Results

Model	Sum of Squares (SS)	df	Mean Square (MS)	F	Sig.
Col. 1	Col. 2	Col. 3	Col. 4 [Col. 2/Col. 3]	Col. 5	Col. 6
Regression Residual Total	220145.10 814758.74 1034903.84	1 118 119	220145.10 6904.73	82.39	0.001

Source: Primary data.

Note: ^aOutcome variable: tax evasions.

Discussion

The study has run PCA, a set of techniques which has economically clubbed the underlying variables into fewer components. Furthermore, it has established the underlying dimensions between measured components and latent constructs (i.e., things that cannot directly be measured), thereby allowing the formation and refinement of theory. Moreover, the respondents have been chosen randomly and the variables measured have constituted the study population of variables, the core of the study. Notwithstanding, techniques are likely be developed to generalize the findings to the wider study population, but the constraint is that the results would hold true only for the set of variables measured. In Table 12, the extracted components with the number of items and their corresponding statistical values have been summarized.

The outcomes of cross tabulations have indicated a significant result and accordingly H₀₁ could probably be rejected and the corresponding research is likely to be accepted. The relationship between tax deductions and HI enrolment decision has been tested by applying Pearson’s Correlation coefficient technique, and based on significant result H₀₂ has likely been rejected. Further, the Pearson’s Correlation coefficient technique has been adopted to assay the relations between tax deductions and tax compliances and a significant result has also supported to reject H_03. Banking upon those significant results, it has to be concluded that HI enrolment decision probably is significantly influenced by tax deduction quantum and such enhanced tax deduction, in turn, is likely to motivate increased tax compliances. Eventually, H₀₄ has assessed whether enhanced tax compliance has reduced the instances of tax evasions, and to test the same simple regression analysis has been conducted. The significant finding has indicated to reject H₀₄, that is, likely to conclude that tax evasions probably be reduced due to increased tax compliances.

The success of HI has largely depended on the individuals’ behavioural change. The Union Budget 2018 has replaced the erstwhile Rashtriya Swasthya Bima Yojana (RSBY) by National Health Protection Scheme (NHPS) which has a coverage of ₹0.5 million per family without any subscription fee. The scheme notwithstanding has been operating on the principle of ‘risk pooling’, but it, unlikely, has resembled a classical HI scheme where the individual subscribers or employers would pay for protections (Reddy, 2018). Further, HI schemes have different plans with varying premiums depending on the sum assured, which has indicated the NHPS as a government healthcare scheme rather purely a HI. The present study at this juncture has its unique relevance as poor people have preferred OPD and primary healthcare services instead of hospitalization for the fear of losing wages. The NHPS is likely to give indirect boost for the healthcare sector, for example, for service providers, medical and surgical instrument producers and HI companies along with other allied healthcare services. Accordingly, effective participation along with public policy promoting awareness about HI plans is the need of the hour. For accessing HI, aggressive awareness programmes along with drives for HI literacy campaigns need to be organized at a large scale. Media, especially social media, is likely to play a significant role in closing the gap. The HI coverage has the potential to significantly reduce the out-of-pocket expenditure, which currently constitutes 62 per cent of the total health expenditure for Indians (Kapoor, 2018). Adoption of HI probably extends twin benefits not only for subscribers but also for government since the individuals enter within the formal HI ambit and would claim tax deductions for the payment of premiums. Such voluntary enrolment would likely enhance the number of subscribers and claim of tax deductions US$80 would boost the revenue collections and tax compliances which, in turn, are likely to reduce the tax evasions.

Table 12.

Summary Results of PCA and Descriptive Statistics

Factor No.	Name of the Components	No. of items	Cronbach’s Alpha Values	Composite Mean	SD
1	Health insurance uniqueness	7	0.630	3.78	1.070
2	Health insurance perspectives	6	0.680	3.79	1.115
3	Tax compliance perspectives	4	0.810	3.63	1.211
4	Tax evasion perspectives	6	0.710	3.75	1.187

Source: Primary data.

Conclusion

With the objective of assessing the effects of income tax benefits on adoptions of HI and on tax compliances, the current research has been conducted. By adopting the cross-sectional study design and applying stratified random sampling technique, data have been gathered from four different stratums: central government employees, state government employees, traders and businessmen. A total of 30 respondents have been chosen from each of the four stratums using a self-designed interview schedule. The schedule has been divided into four sections to collect respondents’ basic information; a section designed in nominal scale has been applied to assay the relations between demographics and HI perception, and two sections have been incorporated in 5-point Likert scale for assessing HI aspects in particular as well as holistically. The null hypotheses have been tested and significant statistical results have documented to nullify all these and accordingly the corresponding research hypotheses are likely to be accepted.

Managerial Implications

The study has indicated how demographic variables impacted HI enrolments, which may be used by the potential customers while taking their HI enrolment decisions. The significant positive influence of increased deductions on HI enrolments is likely to motivate the insured to include their parents in the ambit of HI coverage for accessing the full benefits of the Section 80D. It is well settled that India’s tax challenges unlikely for exorbitant tax rates per se, rather due to the associated complex compliance. The Economic Survey 2018–2019 has shown the tax-to-GDP ratio has slipped to 10.9 per cent and the government has been under constrained fiscal space hence unlikely to cut the tax rates (Kale, 2019). Taking this into cognizance, the tax authority may access the report for chalking out strategies to increase the direct tax revenues by enhanced tax compliance. Furthermore, such compliance would likely reduce the tax evasions substantially inasmuch as the assessees would claim deductions US$80 while filing their returns. A welfare state government needed revenues for achieving the growth and development objectives, which has created choices between tax rate cuts and widening the tax base. Tax literature has shown high tax rates have adversely affected investments, hence policymakers may use the report for enhancing the narrow tax base by attracting the potential tax payers under the ambit of tax through extending tax deductions for payments of HI premiums. Finally, the HI providers may design their advertising strategies by focusing on the tax benefits which, in turn, would not only attract new customers but also retain the existing customers even with enhanced sum assured.

Future Research Roadmap

The role of media in understanding complexities of HI literature while taking HI coverage (Chakrabarti & Shankar, 2015) may be studied in NEI where multiple languages and dialects have been using by inhabitants. Future research may be directed to assess the potential insured’s level of health literacy in a comparative manner district-wise or inter-state as literature indicated (Kim et al., 2013) for chalking out policies for HI awareness. The HI companies’ performances in the light of the enhanced tax deductions US$80 may be studied through intra-firm and inter-firm comparisons. Future research agenda may be channelized to assess the impacts of excluded variables such as social, cultural and psychographic demographics on tax evasions (Bobek, Hageman, & Kelliher, 2013). Finally, the role of Third Party Administrators (TPAs) for accelerating the HI growth may be studied in an era of enhanced tax deductions US$80.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

References

Ackermann

, Fochmann

, & Mihm

(2013). Biased effects of taxes and subsidies on portfolio choices. Economics Letters, 120, 23–26.

Aggarwal

(2010). Impact evaluation of India’s Yeshasvini community-based health insurance programme. Health Economics, 19(S1), 5–35.

Ahlin

, Nichter

, & Pillai

(2016). Health insurance in India: What do we know and why is ethnographic research needed. Anthropology & Medicine, 23(1), 102–124.

Ahuja

, & Narang

(2005). Emerging trends in health insurance for low-income groups. Economic and Political Weekly, 40, 4151–4157.

Alm

, McClelland

G. H.

, & Schulze

W. D.

(1992). Why do people pay taxes? Journal of Public Economics, 48, 21–38.

Altheide

D. L.

, & Johnson

J. M.

(1994). Criteria for assessing interpretive validity in qualitative research. In Denzin

N. K.

& Lincoln

Y. S.

(Eds.), Handbook of qualitative research (pp. 485–499). Thousand Oaks, CA: SAGE Publications.

Arsić

, Arandarenko

, Radulovic

, Ranđelovic

S. A.

, & Jankovic

(2015). Causes of the shadow economy. In Krstic´

& Schneider

(Eds.), Formalizing the shadow economy in Serbia (pp. 21–46). Dordrecht, The Netherlands: Springer.

Barber

B. M.

, & Odean

(2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261–292.

Barman

, Bhattacharya

P. K.

, Lyngdoh

, & Jamil

(2015). Alcoholism—a health burden: A study from north eastern India. Indian Journal of Medical Specialties, 6, 55–58.

10.

Bartholomae

, Russell

M. B.

, Bonnie

, & Teresa

(2016). Building health insurance literacy: Evidence from the smart choice health insurance program. Journal of Family Economic Issues, 37, 140–155.

11.

Baru

, Acharya

, & Acharya

(2010). Inequities in access to health services in India: Caste, class and region. Economic & Political Weekly, 45, 49–58.

12.

Berman

, Ahuja

, & Bhandari

(2010). The impoverishing effect of healthcare payments in India: New methodology and findings. Economic & Political Weekly, 45, 65–71.

13.

Bethlehem

J. G.

(2008). Representatives of web surveys—an illusion? In Stoop

& Wittenberg

(Eds.), Access panels and online research, panacea or pitfall? Proceedings of the DANS symposium (pp. 19–44). Amsterdam, The Netherlands: Aksant Academic Publishers.

14.

Bhat

, & Jain

(2006). Factors affecting the demand for health insurance in a micro insurance scheme. Working Paper No. 2006-07-02. Ahmadabad, India: Indian Institute of Management.

15.

Bhatia

M. R.

, Yesudian

C. A. K.

, & Gorter

(2006). Demand side financing for reproductive and child health services in India. Economic and Political Weekly, 41, 279–284.

16.

Bobek

D. D.

, Hageman

A. M.

, & Kelliher

C. F.

(2013). Analyzing the role of social norms in tax compliance behavior. Journal of Business Ethics, 115(3), 451–468.

17.

Bodenheimer

, & Grumbach

(1994). Paying for health care. Journal of American Medical Association, 272(8), 634–639.

18.

Bucher-Koenen

, Lusardi

, Alessie

, & VanRooij

(2014). How financially literate are women? An over view and new insights. Working Paper No. w20793, National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w20793.pdf

19.

Burns

L. R.

, Srinivasan

, & Vaidya

(2014). India’s hospital sector: The journey from public to private healthcare delivery. In Burns

L. R.

(Ed.), India’s healthcare industry: Innovation in delivery, financing, and manufacturing (pp. 169–218). New York, NY: Cambridge University Press.

20.

Chakrabarti

, & Shankar

(2015). Determinants of health insurance penetration in India: An empirical analysis. Oxford Development Studies, 43(3), 379–401.

21.

Chan

S. Y. S.

, Cheung

D. K. C.

, Ariff

, & Loh

A. L. C.

(1999). Compliance costs of corporate taxation in Hong Kong. The International Tax Journal, 24(4), 42–68.

22.

Chanana

(1993). Accessing higher education: The dilemma of schooling women, minorities, scheduled castes and scheduled tribes in contemporary India. Higher Education, 26, 69–92.

23.

Chen

(2013). The issues of long-term care budget and fairness in financing in China (in Chinese). Journal of Finance and Economics, 39, 106–118.

24.

Criel

, & Waelkens

M. P.

(2003). Declining subscriptions to the Maliando mutual health organisation in Guinea-Conakry (West Africa): What is going wrong? Social Science Medicine, 57(7),1205–1219.

25.

Crowne

D. P.

, & Marlowe

(1960). A new scale of social desirability independent of psychopathology. Journal of Consulting Psychology, 24, 349–354.

26.

Dao

, & Nichter

(2016). The social life of health insurance in low-to middle-income countries: An anthropological research agenda. Medical Anthropology Quarterly, 30(1), 122–143.

27.

Deb

, & Chakraborty

(2017). Tax perception and tax evasion. IIM Kozhikode Society & Management Review, 6(2), 174–185.

28.

Dev

, Sharma

V. P.

, & Barman

(2015). Mosquito-borne diseases in Assam, north-east India: Current status and key challenges. WHO South-East Asia Journal of Public Health, 4, 20–29.

29.

Devadasan

, Ranson

, Van Damme

, & Criel

(2004). Community health insurance in India: An overview. Economic and Political Weekly, 39, 3179–3183.

30.

Devadasan

, Criel

, VanDamme

, Manoharan

, Sarma

P.S.

, & Van der Stuyft

(2010). Community health insurance in Gudalur, India, increases access to hospital care. Health Policy Plan, 25(2), 145–154.

31.

Dillman

D. A.

(1978). Mail and telephone surveys: The total design method. New York, NY: John Wiley.

32.

Drechsler

, & Jütting

J. P.

(2005). Private health insurance in low and middle-income countries. Issy-les-Moulineaux: OECD Development Centre.

33.

Dror

D. M.

, van Putten-Rademaker

, & Koren

(2008). Cost of illness: Evidence from a study in five resource-poor locations in India. Indian Journal of Medical Research, 127(4), 347–361.

34.

Duggal

(2007a). Health care in India: Changing the financing strategy. Social Policy & Administration, 41, 386–394.

35.

Duggal

(2007b). Poverty & health: Criticality of public financing. Indian Journal of Medical Research, 126(4), 309–317.

36.

Duggal

, Sunil

, & Vadair

(1995). Health expenditure across states—Part I. Economic and Political Weekly, XXX(15), 834–844.

37.

Eichfelder

, & Schorn

(2012). Tax compliance costs: A business administration perspective. FinanzArchiv/Public Finance Analysis, 68, 191–230.

38.

Evans

, & Walpole

(1997). Compliance costs and taxation impact statements. Australian Tax Forum, 13, 227–274.

39.

Falkenberg

(2011). First responder. Forbes, December 5, 96–104. Retrieved from www.forbes.com/forbes/2011/1205/feature-passions-health-care-reform-jim-yong-kimfalkenberg.html

40.

Feld

L. P.

, & Frey

(2002, January). Trust breads trust: how taxpayers are treated. Working Paper No. 98. Zurich, Switzerland: University of Zurich.

41.

Field

(2000). Discovering statistics using SPSS for windows. London, UK; Thousand Oaks, CA; New Delhi, India: SAGE Publications.

42.

Filmer

, & Pritchett

(2001). Estimating wealth effects without expenditure data or tears: An application to educational enrollments in states of India. Demography, 38, 115–132.

43.

Forbes

D. J.

(2012). Healthcare provision in rural India: Does micro health insurance help or hinder? International Journal of Health Care Quality Assurance, 25(3), 216–225.

44.

Forgia

G. L.

, & Nagpal

(2012). Government-sponsored health insurance in India: Are you covered? Directions in development: Human development. Washington, DC: The World Bank.

45.

Frenz

, Delgado

, Kaufman

J. S.

, & Harper

(2014). Achieving effective universal health coverage with equity: Evidence from Chile. Health Policy and Planning, 29, 717–731.

46.

Frey

, & Feld

L. P.

(2002, August). Deterrence and morale in taxation: An empirical analysis. Working Paper No. 760. Munich, Germany: CESifo.

47.

Frey

, & Torgler

(2007). Tax morale and conditional cooperation. Journal of Comparative Economics, 35, 136–159.

48.

Frost

(2013, May 30). Regression analysis: How do i interpret R-squared and assess the goodness-of-fit? Retrieved from http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-doi-interpret-r-squared-and-assess-the-goodness-of-fit

49.

Ghosh

(2011). Catastrophic payments and impoverishment due to out-of-pocket health spending. Economic & Political Weekly, 46, 63–70.

50.

Ghuge

N. R.

, & Katdare

V. K.

(2015). Indian tax structure—An analytical perspective. International Journal in Management and Social Science, 3(9), 242–252.

51.

Gilligan

, & Richardson

(2005). Perceptions of fairness and tax compliance in Australia and Hong Kong— A preliminary study. Journal of Financial Crime, 12(4), 331–343.

52.

Goldhill

(2013). Catastrophic care. New York, NY: Alfred A. Knopf.

53.

Gupta

, & Alam

(2000). Health insurance in India: Prognosis and prospectus. Economic and Political Weekly, 35, 207–217.

54.

Hays

W. L.

(1963). Statistics. New York, NY: Holt, Reinhart & Winston.

55.

Hazarika

(2013). Health workforce in India: Assessment of availability, production and distribution. South-East Asia Journal of Public Health, 2(2), 106–112.

56.

Hazarika

(2014). Barriers to health care access for rural women: A case study of selected villages of Majuli, Assam. Health and Development in North-East India, 29–38. New Delhi, India: Mittal Publications.

57.

Hodge

, & Guyton

(2014). The compliance costs of IRS post-filing processes. IRS Research Bulletin: Proceedings of the IRS Research Conference 2014, pp. 3956–3957, Washington, DC.

58.

Isreal

G. D.

(2013). Determining sample size. PEOD6, one of a series of the Agricultural Education and Communication Department, UF/IFAS Extension. Retrieved from http://edis.ifas.ufl.edu

59.

Jain

, Selva

, Zeena

, & Raghavan

(2014). Acceptability of, and willingness to pay for, community health insurance in rural India. Journal of Epidemiology and Global Health, 4, 159–167.

60.

Jha

(2013). Tax structure in India and effect on corporates. International Journal of Management and Social Sciences research (IJMSSR), 2(10), 80–82.

61.

Kaiser

H. F.

(1960). The application of electronic computers to factor analysis. Educational and Psychological Measurements, 20, 141–151.

62.

Kale

(2019, August 22). Tax compliance complexity is the challenge. The Financial Express, p. 8. Retrieved from https://www.financialexpress.com/opinion/explained-here-is-how-tax-compliance-complexity-is-the-challenge/1682478/

63.

Kapoor

(2018, March 1). A booster shot. The Business Line, p. 9. Retrieved from https://www.thehindubusinessline.com/opinion/health-insurance-for-poor-will-bolster-economy/article22881400.ece.

64.

Kim

, Braun

, & Williams

A. D.

(2013). Understanding health insurance literacy: A literature review. Family and Consumer Sciences Research Journal, 42(1), 3–13.

65.

Kumar

J. R.

(2013). Role of public health systems in the present health scenario: Key challenges. Indian Journal of Public Health, 57(3), 133–137.

66.

Kumar

(2014). Taxation laws of India: An overview and fiscal analysis 2013–14. Indian Journal of Applied Research, 4(9), 82–84.

67.

Lin

, Liu

G. G.

, & Chen

(2009). The urban resident basic medical insurance: A landmark reform towards universal coverage in China. Health Economics, 18, S83–S96.

68.

Logan

R. A.

, Wong

W. F.

, Villaire

, Daus

, Parnell

T. A.

, Willis

, & Paasche-Orlow

M. K.

(2015). Health literacy: A necessary element for achieving health equity. Retrieved from http://nam.edu/wp-content/uploads/2015/07/NecessaryElement.pdf

69.

Losby

, & Wetmore

(2012). CDC coffee break: Using Likert scales in evaluation survey work. Atlanta, GA: National Centre for Chronic Disease Prevention and Health Promotion.

70.

Lusardi

, & Mitchell

O. S.

(2011). Financial literacy and planning: Implications for retirement well-being. In Lusardi

& Mitchell

O. S.

(Eds). Financial literacy: Implications for retirement security and the financial marketplace (pp. 17–39). Oxford, UK: Oxford University Press.

71.

Lyngdoh

L. M.

(2015). Inter-state variations in rural healthcare infrastructure in North-East India. The NEHU Journal, XIII(2), 31–48.

72.

Lyngdoh

L. M.

(2016). Determinants of healthcare utilization in rural North East India. EPRA International Journal of Economic Business Review, 4(3), 156–162.

73.

Mack

, & Ryan

(2006). Reflections on the theoretical underpinnings of the general-purpose financial reports of Australian government departments. Accounting, Auditing and Accountability Journal, 19(4), 592–612.

74.

Mee-Udon

(2014). Universal health coverage scheme impact on well-being in rural Thailand. International Journal of Health Care Quality Assurance, 27(6), 456–467.

75.

Michielsen

, Criel

, Devadasan

, Soors

, Wouters

, & Meulemans

(2011). Can health insurance improve access to quality care for the Indian poor? International Journal for Quality in Health Care, 23(11), 4471–4486.

76.

Mills

, Ataguba

J. E.

, Akazili

, Borghi

, Garshong

, Makawia

& McIntyre

(2012). Equity in financing and use of health care in Ghana, South Africa and Tanzania: Implications for paths to universal coverage. The Lancet, 380, 126–133.

77.

Mohanty

S. K.

, Chauhan

R. K.

, Mazumdar

, & Srivastava

(2014). Out-of-pocket expenditure on healthcare among elderly and non-elderly households in India. Social Indicators Research, 115(3), 1137–1157.

78.

Monheit

A. C.

, & Harvey

P. H.

(1993). Sources of health insurance for the self-employed: Does differential taxation make a difference? Inquiry, 30, 293–305.

79.

Morris

S. A.

, & McDonald

R. A.

(1995). The role of moral intensity in moral judgments: An empirical investigation. Journal of Business Ethics, 14(9), 715–726.

80.

Nataraj

(2017). Demonetisation and its impact. Theme Paper. Indian Institute of Public Administration.

81.

Neogi

(2010). Disparity in socio-economic development and its implications on communal conflicts: A study on India’s north eastern region. International Journal of Human and Social Sciences, 4(3), 259–266.

82.

Nguyen

, & Wang

(2013). The effects of free government health insurance among small children—Evidence from the free care for children under six policies in Vietnam. International Journal of Health Planning and Management, 28, 3–15.

83.

Nunnally

J. C.

(1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hill.

84.

Oberhofer

, & Dieplinger

(2014). Sustainability in the transport and logistics sector: Lacking environmental measures. Business Strategy and the Environment, 23(4), 236–253.

85.

O’Shaughnessy

D. B.

(2014). Tax compliance determinants: A proposed model for cross-country analysis. El Paso, TX: ETD Collection for University of Texas.

86.

Paez

K. A.

, Mallery

C. J.

, Noel

, Pugliese

, McSorley

V. E.

, & Lucado

J. L.

(2014). Development of the Health Insurance Literacy Measure (HILM): Conceptualizing and measuring consumer ability to choose and use private health insurance. Journal of Health Communication, 19(sup 2), 225–239.

87.

Pan

, & Liu

G.G.

(2012). The determinants of Chinese provincial government health expenditures: Evidence from 2002–2006 data. Health Economics, 21, 757–777.

88.

Park

C. G.

, & Hyun

J. K.

(2003). Examining the determinants of tax compliance by experimental data: A case of Korea. Journal of Policy Modeling, 25(8), 673–684.

89.

Patel

, Chatterji

, Chisholm

, Ebrahim

, Gopalakrishna

, Mathers

… Reddy

K. S.

(2011). Chronic diseases and injuries in India. The Lancet, 377(9763), 413–428.

90.

Peerson

, & Saunders

(2011). Men’s health literacy in Australia: In search of a gender lens. International Journal of Men’s Health, 10(2), 111–135.

91.

Pett

, Lackey

, & Sullivan

(2003). Making sense of factor analysis: A practical guide to understanding factor analysis for instrument development in health care research. Thousand Oaks, CA: SAGE Publications.

92.

Peytchev

, Conrad

F. G.

, Couper

M. P.

, & Tourangeau

(2010). Increasing respondents’ use of definitions in web surveys. Journal of Official Statistics, 26(4), 633–650.

93.

Porter

, & Grills

(2015). Medication misuse in India: A major public health issue in India. Journal of Public Health, 38(2), e150–7.

94.

Purohit

B. C.

(2001). Private initiatives and policy options: Recent health system experience in India. Health Policy and Planning, 16(1), 87–97.

95.

Qin

, Pan

, & Liu

G. G.

(2014). Does participating in health insurance benefit the migrant workers in China? An empirical investigation. China Economic Review, 30, 263–278.

96.

Rajendran

, & Raya

R. P.

(2010). Impact of microfinance-an empirical study on the attitude of SHG leaders in Vellore district (Tamil Nadu, India). Global Journal of Financial Management, 2(1), 59–68.

97.

Rao

G. M.

(2005). Tax system reform in India: Achievement and challenges ahead. Journal of Asian Economics, 16(6), 993–1011.

98.

Richmond

J. B.

, & Fein

(2005). The health care mess. Cambridge, MA: Harvard University Press.

99.

Reddy

K. S.

(2012). Universal health coverage in India: The time has come. National Medical Journal of India, 25(2), 65–67.

100.

Reddy

K. S.

(2018, February 9). Insurance can’t replace primary healthcare. The Financial Express, p. 8. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/22686710

101.

Reddy

K. S.

, Patel

, Jha

, Paul

V. K.

, Shiva Kumar

A. K.

, & Dandona

(2011). Towards achievement of universal healthcare in India by 2020: A call to action. The Lancet, 377(9767), 760–768.

102.

Ricardo

P. T.

, & Troiano

(2015). Shaming tax delinquents: Theory and evidence from a field experiment in the United States. Working Paper No. w21264. National Bureau of Economic Research.

103.

Robson

(2011). Real world research: A resource for users of social research methods in applied settings (2nd ed.). Sussex, UK: John Wiley and Sons Ltd.

104.

Roscoe

J. T.

(1975). Fundamentals research statistics for behavioral sciences (2nd ed., pp. 483). New York, NY: Holt, Rinehart and Winston.

105.

Saikia

, & Das

K. K.

(2012). Rural health infrastructures in the North-East. MPRA Paper No. 41859. Retrieved from https://mpra.ub.uni-muenchen.de/41859/1/MPRA_paper_41859.pdf

106.

Saravanan

, & MuthuLakshmi

(2017). Tax saving instruments of income tax in India: A study on tax Assessee in Trichy City. International Journal of Trend in Scientific Research and Development, 1(5), 1258–1266.

107.

Schur

C. L.

, & Albers

L. A.

(1996). Language, socio demographics, and health care use of Hispanic adults. Journal of Health Care Poor Underserved, 7, 140–158.

108.

Selvaraj

, & Karan

A. K.

(2012). Why publicly-financed health insurance schemes are ineffective in providing financial risk protection. Economic & Political Weekly, 47(11), 60–68.

109.

Sengupta

(2009). Genesis of human development in North East India: A diagnostic analysis. New Delhi, India: Concept Publishing Company.

110.

Sen

, Pickett

, & Burns

L. R.

(2014). The health insurance sector in India: History and opportunities. In Burns

L. R.

(Ed.), India’s healthcare industry: Innovation in delivery, financing, and manufacturing (pp. 361–399). Delhi, India: Cambridge University Press.

111.

Sharma

D. C.

(2015). Concern over private sector tilt in India’s new health policy. The Lancet, 9965(385), 317.

112.

Shaw

K. M.

(2017). Leveraging affordable innovation to tackle India’s healthcare challenge. IIMB Management Review, 30(1), 37–50.

113.

Sheet

, & Roy

T. A.

(2013). Micro level analysis of disparities in health care infrastructure in Birbhum District, West Bengal, and India. IOSR Journal of Humanities and Social Science, 7(3), 25–31.

114.

Sinha

(2014). Status of health in rural North East India: Introduction. In Sinha

(Ed.), Health and development in North-East India (pp. 1–12). New Delhi, India: Mittal Publications.

115.

Singh

(2018). India since demonetisation. IIMB Working Paper, No: 567. Bangalore, India: Indian Institute of Management.

116.

Singh

R. R.

, & Bagchi

(2018). Demonetisation: A golden opportunity for widening the taxpayer base. ICRIER Working Paper, No: 351. New Delhi, India: Indian Council for Research on International Economic Relations.

117.

Slemrod

, Blumenthal

, & Christian

(2001). Taxpayer response to an increased probability of audit: Evidence from a controlled experiment in Minnesota. Journal of Public Economics, 79(3), 455–483.

118.

Sodhi

, & Rabbani

(2014). Health service system in India: Is insurance the way forward. Economic and Political Weekly, 49(35), 26–28.

119.

Sojo

(2011). Conditions for universal access to health in Latin America: Social rights, social protection and financial and political constraints. Ciencia & Saude Coletiva (in Spanish), 16, 2673–2685.

120.

Sparrow

, VandePoel

, & Hadiwidjaja

(2014). Coping with the economic consequences of ill health in Indonesia. Health Economics, 23, 719–728.

121.

Stabile

(2001). Private insurance and public health care subsidies: Evidence from Canada? Canadian Journal of Economics, XXXIV, 921–942.

122.

Tandon

, & Rao

R. K.

(2017). Tax compliance in India: An experimental approach. NIPFP Working Paper No. 207. New Delhi, India: National Institute of Public Finance and Policy.

123.

Takáts

, & Papp

T. K.

, (2008). Tax rate cuts and tax compliance—The Laffer-Curve revisited. Washington, DC: Working Paper No. 08/7.

124.

Tanner

(2013). The patient protection and affordable care act: A dissenting opinion. Journal of Family and Economic Issues, 34(1), 3–15.

125.

The Business Line (2018, February 2). Raises limit of deduction for health insurance premium and medical expenditure to Rs 50,000 from Rs 30,000 under section 80D. The Business Line, Kolkata, p. 5. Retrieved from https://www.thehindubusinessline.com/economy/budget/budget-2018-exemption-on-interest-income-for-senior-citizens-hiked-to-rs-50000/article22622345.ece

126.

Thompson

L. H.

(1991). Canadian health insurance: Lessons for the United States. Washington, DC: General Accounting Office.

127.

Torgler

(2003). To evade taxes or not to evade: That is the question. Journal of Socio-Economics, 32, 283–302.

128.

Vart

G. F.

(2010). How men differ from women in their attitudes towards bowel cancer screening and intentions to be screened. Journal of Men’s Health, 7(3), 241–248.

129.

Vellakkal

, & Ebrahim

(2013). Publicly-financed health insurance schemes: Concerns about impact assessment. Economic & Political Weekly, 48(18), 25.

130.

Wagstaff

(2007). Health insurance for the poor: Initial impacts of Vietnam’s health care fund for the poor. Policy Research Working Paper Series, 4134, The World Bank. Retrieved from http://documents.worldbank.org/curated/en/600521468131108417/Health-insurance-for-the-poor-initial-impacts-of-Vietnams-health-care-fund-for-the-poor

131.

Wagstaff

, & Bergkvist

(2011). Health system innovation in India, Part II: Aarogyasri. Let’s talk development: A blog hosted by the World Bank’s Chief Economist, 2 August. Retrieved from https://blogs.worldbank.org/developmenttalk/health-system-innovation-in-india-part-ii-aarogyarsi

132.

Wang

, Zhang

, & Yip

(2006). Adverse selection in a voluntary rural mutual health care health insurance scheme in China. Social Science & Medicine, 63, 1236–1245.

133.

World Health Organization (WHO) (2014). Global health expenditure database. Retrieved from https://data.worldbank.org/indicator/SH.XPD.TOTL.ZS?locations=NO

134.

Yellaiah

(2013). Health insurance in India: Rajiv Aarogyasri health insurance scheme in Andhra Pradesh. Journal of Humanities and Social Science, 8(1), 714.

135.

Yip

W. C. M.

, Hsiao

W. C.

, & Chen

(2012). Early appraisal of China’s huge and complex health-care reforms. The Lancet, 379, 833–842.

136.

Zaklan

, Westerhoff

, & Stauffer

(2009). Analysing tax evasion dynamics via the Ising model. Journal of Economic Interaction Coordination, 4(1), 1–14.

137.

Zikmund

W. G.

, & Babin

B. J.

(2012). Marketing research (10th ed.). Mason, OH: South-Western/Cengage Learning.