Abstract
The study has motivated to assay the comparatively better saving tool—small saving schemes or mutual funds—based on empirical evidence. The related literature has been extensively reviewed to frame a conceptual model and has adopted survey strategy with stratified random sampling technique for gathering data from 150 respondents. Inferential statistics have supported to reject the null hypotheses and has concluded that selective demographics, risk, returns, tax benefits, inflation beating capability, and liquidity significantly influence in savings. The national saving certificate and fixed deposits have been identified as most preferred saving instruments while mutual funds have also been gaining popularity. Policy implications have been derived from the study.
Introduction
Literature has validated households’ substantial demands for saving 1 and even for sustainable development. 2 The influence of varying degrees of financial literacy (hereafter, FL) on financial decision making processes have been validated, for example, borrowings 3 and portfolio com-position. 4 In contrast, few scholars have not traced any improvements in financial decisions due to FL. 5 The current study has carefully reviewed the literature on FL—its pros and cons, the debate about its meanings along with lack of consensus about its uniform definition 6 and accordingly it has treated FL as a combination of financial knowledge and financial skills, where the former has been identified as the level of confidence that a saver is likely to possess 7 and the latter has been indicated as the art of managing one’s personal finance efficiently.8,9 Literature has concluded with mix results about the impacts of taxation on retirement saving decisions, for example, positive influence 10 while others could not have found any such evidence. 11 Only few studies have been undertaken to assay the invest-ment pattern in mutual funds (MFs) by the insurance policyholders, 12 the divergent factors of investors’ attitudes such as risk attitudes 13 and saving purposes. 14 The saving problems have also been shortlisted in countries like China, for example, the population structure problem, 15 precautionary saving, 16 skewed gender figure, 17 and income fluctuations. 18 Behavioural finance that has propounded some sort of irrational investment behaviour is likely to have remained present in every human being 19 and such irrationality has been yielding mixed results from investment decisions. 20 The investment decisions have also been motivated by a number of factors such as sentiment, emotion, and mood 21 beside nature of occupations, for instance; family businesses have strong motivation for higher investments. 22
Scholarship has concurred inasmuch the small retail savers generally have preferred saving avenues that generate returns on or above the market benchmarks, the professional fund managers have been choosing the MFs which likely to be the outperformers.23–25 Multiple deter-minants on the relationships of MFs returns and flow have also been studied in delve, for example, the earnings news associations with MFs flow, 26 the investment decisions, 27 and the impacts of advertisements. 28 Few recent studies have highlighted the importance of con-sumption smooth-ing and provision for social security provisions for rural people, for example, in China.29,30 Furthermore, scholarship has also indicated gold as a saving instrument. 31
Literature in Indian context has addressed divergent aspects like preference for savings in Small Saving Schemes (SSSs) and MFs. The multiple aspects of MFs such as its preference,32,33 aware-ness and investors’ attitudes towards MFs,34,35 influencing factors on the MFs’ performance,36,37 brokers’ attitudes towards MFs 38 women savers’ preference for MFs, 39 and perceptions analysis about MFs40,41 have been documented. The influence of demographics on saving behaviours have also been studied in delve (e.g., family size,42,43 age, 44 education levels, 45 income levels, 46 and occupations 47 ). The motivators for SSSs have also been assessed by scholars, for example, post office schemes (such as recurring deposits, 48 Kisan Vikas Patra, 49 tax deductable products such as national saving certificates [NSCs], 50 Sukanya Samriddhi Scheme 51 ), banking products (e.g., Pradhan Mantri Jan Dhan Yojana 52 ), life insurance products,53,54 retirement solutions (such as provident funds, 55 social security schemes for unorganized sector, 56 com-parative analysis of national pension system [NPS], and employees’ provident funds [EPF] 57 ). The trend of studies carried out in India has validated multi-dimensional aspects of personal finance in general and savings behaviour in particular but literature probably has been in deficit to address any comprehensive study pinpointing the savings decisions about SSSs and MFs. The current study has motivated to close the identified gap in the body of knowledge based on empirical evidence.
The current study has contributed in literature in many ways. At first, it has produced a ready reference about Indian savers’ preferred saving instruments rather focusing on any particular saving instrument. Such comprehensive evidence in Indian socio-economic context is likely to contribute in literature as most of the similar type of global studies has been conducted in completely different socio-economic conditions. Second, amongst the demographics, income level has been identified as the most influential factor especially for saving in diversified saving instru-ments, in tune with literature, 58 but has differed with few studies where gender has been identified as the most influencing factor. 59 Third, it has validated a new vista of study for saving in MFs schemes especially by young savers whereas middle-aged savers have preferred FDs and the NSC in spite of their relatively lower returns in compared to MFs, in contrast to global trend. 24 Fourth, it has documented that risk averse attitudes and tax-saving tendency have been prioritized by sample savers like earlier Indian studies in compared to high returns51,50; in contrast with global studies. 60 It has revealed an interesting fact that most of the respondents have claimed themselves as financial literate and have been reluctant for taking professional advices but likely have not acted as expected, for example, have been treating insurance products as saving tool and showing risk averse attitudes while saving in MFs, in contrast with literature where such professional advices have been indicated as prominent for savers.61,62 Further, the seriousness of lack of such advices has been identified in the current study, in conformity with literature. 63 Literature has been equipped with the findings of the current study that have highlighted the relevance of FL in Indian household saving decisions. Finally, tax benefits, inflation beating capabilities, and liquidity feature of the saving instruments have also been considered by Indian savers while designing their saving portfolios in conformity with literature. 64 Moreover, it has indicated that the NSC and bank FD have been preferred by Indian savers likely due to the tax benefits of the former and premature liquidity facility of both of the saving tools hence richer the literature.
The study has motivated to answer the research question about the relatively best savings avenues—small saving schemes or MFs based on empirical evidence.
The rest of the article has been designed as follows. In the second section, hypotheses have been framed based on review of related literature. Research methodology adopted for the study has been explained in the third section. Findings and discussion of those findings have been enumerated in the fourth and fifth sections, respectively. Finally, in Section 6, it has reached in its conclusion.
Related Literature and Hypotheses
Based on review of prior literature, it has set the following research hypotheses for conducting the study.
Demographics and Savings
Impacts of selective demographics on savings behaviours have been studied in delve and the results have been summarized in setting the hypotheses.
Gender
Literature has validated multiple objectives for saving motives by men and women, for example, for retirement, growth, and for child by men. 65 The higher average longevity of women likely to have motivated for retire-ment savings59,10 in risk-averse 66 lower volatile 67 saving instruments. In contrast, in three Indian matrilineal dominated states Meghalaya, Mizoram, and Nagaland, no gender differences have been reported as far as saving behaviours have been concerned 68 that largely have contradicted other Indian states. Moreover, few studies have concluded with opposite outcome, that is, women have found less interested than men as savers. 59
Age
Literature has reported the influence of age on saving decisions. It has validated the aggressive saving attitudes by young people up to the age of 40 years in India 69 while the older population has preferred to save in safer instruments. 63
Marital Status
Research has validated that marital status significantly influences in saving decisions like having bank accounts 70 and retirement decisions, even in combination with gender rather than only gender.71,72 The timings of spouse’s retirement have catalyzed in chalking out saving plans. 73 Moreover, for savings couples, joint decisions instead of individual spouse’s decisions have also been extensively studied. 19
Education Levels
Positive role of education in saving decisions has also been acknowledged in literature.74–76
Income Levels
Literature has concluded with interesting evidence where rich people likely to have lesser tendencies for saving but higher inclination for diversifying saving instruments.77,58 Instability in income levels has made savings difficult.78,79
Financial Literacy
Research has documented the need of FL for dealing with complex financial products
62
and lack of it may cause to serious problems.80,63 Studies have indicated that FL has positive influence on saving behaviors.
61
Based on these, it has been hypothesized:
Returns and Savings
Literature has validated that fair understandings of different facets of returns such as interest compounding,68,4 time value of money,
80
numeracy,
80
inflation impacts,
4
and equity exposures
81
influence the saving decisions. So it has been hypothesized:
Risk Literacy and Savings
Risk has been identified an important variable for choosing a saving instrument. Risk aspects such as risk diversification
80
and risk tolerance
82
significantly influence saving decisions even though many households have not been taken appropriate care in terms of financial wellbeing.
83
Risk tolerance has been defined as the most uncertainty a household would face while taking a saving decision while the avoidance of risk has been coined as risk aversion.
84
Research has proven during financial turmoil households’ risk attitudes largely been differed from that of during normal periods.
85
Further, risk adverse savers have been saving smaller quantum in risky instruments
86
but such relative risk aversions have decreased when household wealth have been increased.
87
Accordingly, it has been hypothesized:
Personal Income Taxes and Savings
Favourable impacts of taxation on interest incomes have been reported in delve
88
while tax rate uncertainty,
60
taxation policy change
89
likely to lower savings in risky instruments as well as have been causing delay in the saving decisions.
90
Scholars have concluded that tax is an important parameter for saving decisions
91
especially stock market savings have largely been affected by changes in capital gain taxes.
92
So it has been hypothesized:
Inflation and Savings
Research has pointed out that household estimation about inflation has been relatively largely biased and unsound than professionals’ predictions because of over depen-dence on mass media93,64 resulting poor portfolio choices. Macro-economic variables
94
and market sentiments
95
have also been identified as inflation predictors for household saving decisions. Hence, it has been hypothesized:
Liquidity and Savings
Households’ liquidity need has been studied by scholars.96,97 Literature has validated the liquidity factors like transaction costs in selecting different saving instru-ments
98
; rich people have been jointly holding debt and liquid savings while poor people have preferred liquid savings and borrow from informal sources.
20
So it has been hypothesized:
The study has framed a conceptual model as depicted in Figure 1 where six predictors are likely to have impacts on the outcome. It has assumed a research paradigm having an ontology (the existence of reality), has based on an epistemology (for assaying the research problem it has gathered primary data), is following the axiology (purpose of the study), adopted a methodology (an overall approach for conducting the study), and eventually applied a method (appropriate technique for data collection and analysis) for conducting the study.
Research Methodology
Research methodology has been identified as an overall approach encompassing theoretical underpinning to reach-ing conclusions 99 and has been included therein the under-stated sub-sections.
Study Design
For assessing the savers’ perceptions, it has adopted a cross-sectional study design with survey strategy for accessing the latter’s inherent benefits such as easy coding and quantifications. 100
Method
Data collection and analysing technique, specific to any particular research problem, has been termed as method. 101
Structuring Interview Schedule
Multiple advantages of in-person interviews for personal finance studies have been recommended by scholars. 102 The items of the schedule have been developed in the following manner. First, applying selective key words around 269 relevant academic papers have been down-loaded by accessing a university e-library. Second, by reviewing those papers, hypotheses, and 40-item interview schedule has been prepared. Third, following the scholars’ advices, 103 a pre-test with 30 randomly chosen respondents has been carried out to assess the order and wording of the items. The reliability (good measure) of the responses has been tested using Cronbach alpha and five items scored below. A total of five have been dropped, in line with scholars’ advices. 104 Finally, during March–April 2018, the final survey has been carried out.
Sampling Technique
The savers in small saving schemes and MFs of Mohanpur and Northern Agartala have been assumed as study population and since contact details of all of them were inaccessible, it could not set the sampling frame. The studied population has been divided into three stratums and by adopting disproportionate stratified random sampling technique, it has gathered data from 150 respondents divided equally into three stratums—service holders, businessmen, and self-employed. The sample size has been lying within the stipulated range between 30 and 500 as per the guidelines suggested by social scientists. 105
Data Collection Design
Primary Data
The interview schedule has been designed in four sections. Section I has incorporated 10 questions that primarily have intended to gather basic information of the participants. Section II has six items set in nominal scale to assay how the selective demographics likely to influence saving decisions. A total of 17 items have been kept in Section-III in five-point Likert scale for addressing the respondents’ perceptions about MFs. Eventually in Section-IV, 18 items have been incorporated addressing SSSs aspects framed in five-point Likert scale. The choice of the Likert scale has been supported in literature, for example, extensive application in business research 106 and for accessing the inherent advantages like ease in coding. 107
Data Analysis Strategy
Statistical Package for Social Science (SPSS) of IBM (version 22) has been applied for data analysis.
Parameters
The study variables as summarized in Table 1 have assumed that six predictors likely to have significant impacts on the outcome, that is, the saving decisions. The extraneous variables, that is, presence of girl child, quantum of family debts (e.g., home, education, auto, and personal loans) have been controlled. Furthermore, for curbing the influ-ence of referral group members, personal interviews have been conducted separately (i.e., to control the internal validity threats).
Study Variables
Choice of Statistical Tests
Assumptions Hold for Selected Statistical Tests
Significance Level
For carrying out inferential statistical tests the significance level (α) has been assumed as 5 per cent, that is, the confidence level has fixed 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.
Arguments for Statistical Techniques
Factor Analysis
Factor analysis has been used for easy understandability of data relationships and patterns. 108 The present study has applied the technique for regrouping the survey items into confined set of clusters based on their shared variance that has supported to isolate constructs and concepts—in other words, for decreasing data dimensionality. 109 Applying math-ematical procedures, it has unearthed the interrelated measures within the variables, that is, the items of the interview schedule, 110 inasmuch parsimony—the simplest procedure to interpret the surveyed data has remained the purpose of the factor analysis.111,112 Furthermore, the ab-sence of univariate and multivariate outliers in the dataset has justified the selection of the factor analysis, as literature has shown. 113
Cross-tabulations
For analysing nominal (categorical) data, cross-tabulation (also known as contingency table analysis) has been widely used in social science studies. 114 A cross-tabulation having a two- (or more) dimensional table has been used for recording the responses (frequency) characterized speci-fically in the cells of the table, 115 for example, gender of the respondents has been categorically spilt into men and women. Such tabulation has exhibited the frequency of co-occurrence of the mutually exclusive characteristics of each variable (i.e., high, medium, and low) that have been labelled by the rows, columns, and other layers of the cross-tabulation. In a 2×2 contingency table, the row total has corresponded with the responses (frequencies) of the categories of the row variable. Further, the probabilities of all the occurrences in these cells (say, e.g., gender) have been in agreement with the sum total with the table total. The chi-square statistic has used to assess whether the two variables (demographics and savings) have been in-de-pendent or not. The objective of this statistic has been focused on the results that would either show no relationship (non-significant) or relationship (significant) amongst the variables. For the occurrence of the former, the study would accept the H01 and in case of the latter it would reject the H01. 116
Pearson’s Correlation
Correlation analysis is a widely used statistical tool in social science research 117 for determining the relationships or associations between the study variables118,119 that have been categorized in different ways like simple, partial, and multivariate. 120 The objective of applying this tool in the present study has been categorical, that is, to assess whether the risks and returns have been related with the saving decisions positively or negatively or even not at all related (for testing H02 and H03, respectively). The study has taken precautionary measures about the fallacy that correlation has been treated as causality inasmuch the data would not indicate any cause-and-effect relationships (i.e., higher risk and returns would not necessarily result with more savings and vice versa). 121
Multiple Regressions
Multiple regressions has been defined as a multivariate technique applied for determining the correlation between an outcome and two or more predictors122,123 has been widely used in social science studies. 124 The current study has applied the tool for assaying the impacts of three predictors, namely, income tax, inflation, and liquidity on the outcome saving decisions (H04, H05, and H06, respectively). The ANOVA results have indicated the overall effect of the predictors on the outcome; on the other hand, regression models have reported the how the models (Model 1 and Model 2) have been able to predict the outcome. 125
Findings
Descriptive Statistics
The sample statistics have been presented using mode (for nominal data), mean, and SD (for interval data). It has reported that 74 per cent of the respondents are men, 32 per cent of them have been aged 45–54 years, 65.34 per cent of them are married, 32.67 per cent are postgraduate, 36.67 per cent belonging from scheduled caste, 92 per cent have reported they are finically literate, 36 per cent have reported having monthly income ₹0.025–0.050 million, monthly saving has been ranging between ₹0.01–0.02 million and 29.33 per cent respondents have chosen the NSC as a saving instrument.
Factor Analysis
The study has run factor analysis to club the factors into suitable factors. The reliability of the items have been tested by running Cronbach alpha score (0.713) and sample adequacy by Kaiser–Mayer–Olkin (KMO) scored as 0.688; both the values have comfortably exceeded the threshold limit as scholars advocated.107,126
It has applied principal component analysis technique for clubbing the items into five factors that have been assigned appropriate titles. Factor-1 has named as “mutual fund uniqueness” incorporating nine items with average means 4.15 and average SD 0.812. Factor-2 has been titled as “small saving schemes’ uniqueness” with nine items with average means 4.17 and average SD 0.840. The third factor has labelled with “risks & returns” having six items, average means 4.09 and average SD 0.889. Factor-4 has named as “tax & liquidity” incorporating six items, average means 4.13 and average SD 0.827. Factor-5 has been titled as “inflation & instrument features” having five items with average means 4.06 and average SD 0.895.
Cross-tabulations
The influence of demographics on saving decisions (H01) has been assessed through running cross-tabulations and the results have been summarized in Table 4.
Summary Results of Cross-tabulations
From Table 4, in result details the first column has indicated summarized Pearson chi-square scores that has assessed the null hypothesis that row and column variables have likely been independent, which has been supported by the corresponding lower significant values as reported in the final column, and based on these, it has probably to reject the null hypothesis of “no relationship.” In the second column, the likelihood ratio has been interpreted similarly like the Pearson chi-square. Moreover, the ratio has supported the findings as smaller sample size has minimal effect on overall results, as literature has repor-ted. 127 The third column has exhibited the results of linear-by-linear Association test used for ordinal type data that has assumed equal and ordered intervals and relying on the Pearson correlation coefficient and has an app-roximately chi-squared distribution on 1 df, all the values have found significant (p < 0.05). Banking on all the significant results, it has got sufficient evidence to likely reject H01 and accordingly the corresponding research hypothesis has probably to accept. Further, it has validated that men have more saving tendencies than women, in line with most of the Indian states. 59 It has reported middle-aged people have been saving in more having higher incomes and additional responsibilities for their members of families, supporting prior studies. 128 The married people have likely been saving more than single and widows, probably due to their responsibilities towards for members of their families, and their spouses’ suggestions likely have catalyzed for saving, in tune with literature. 129 It has indicated that highly educated people likely have more tendencies in savings in a planned manner, correlated with literature. 75 Again, respondents in the middle-income groups likely to save more as significant result has pointed out. Finally, most of the respondents have claimed that their prior financial knowledge has assisted them in saving decisions particularly in designing their portfolios. The association between FL and savings decisions has produced significant results 0.000 (p < 0.05), in tune with literature. 61
Pearson’s Correlation Analysis
To assess the influences of returns and risks on saving decisions (H02 and H03), it has run Pearson’s correlation analysis. The significant results as reported in Table 5 have supported to reject both of the null hypotheses. There have strong positive and significant associations between saving decisions with having both the returns prospects (r = 0.782, n = 150, p = 0.004) and risks (r = 0.709, n = 150, p = 0.037) been established. Based on these, H02 and H03 have been rejected and have provided evidence likely to accept the study’s research hypotheses, that is, both returns and risks have significant impacts in saving decisions, in corollary with literature.80,4
Correlations Between Returns, Risk, and Saving Decision
Multiple Regressions
Literature has validated that savers usually have been designing their portfolios keeping in mind the tax-saving instruments, prospective returns to beat the heat of the inflations and the associated liquidities. To assess the impacts of these predictors on saving decisions, it has applied multiple regressions and the results have been summarized in Tables 6 and 7, respectively.
Model Summary
Table 6 has reported that in Model 1, it has used tax benefits as predictor while in Model 2, inflations and liquidity have been applied to assay their combined effects on saving decisions. Amongst the different methods of measuring goodness of fit of the multiple regressions model, the square of the multiple correlation coefficient R2 (i.e., the coefficient of multiple determination) and adjusted R2 have been chosen.130,131 The first column (R) has representing association, that is, simple correlation between the first predictor and the outcome calculated as 0.416. The second column (R2) has been valued as 0.390, that is, 39 per cent of the outcome has been represented by the income tax benefits. In Model 2, the R2 value has raised to 0.846 that has indicated that the addition of the remaining two predictors (inflation and liquidity) have contributed 48.1 per cent (0.871–0.390) of the outcome. In both of the models, the third columns (adjusted R2) have produced values that are close to the values of R2, indicating that the models have been derived from study population. In change statistics details, R2 has been changed from 0 to 0.593, and that of in Model 2 to 0.304 with significant F-ratios (p < 0.05). Eventually, the Durbin–Watson test has computed with 1.97, that is, close to 2 that has validated the assumption of independent error.
Table 7 has reported the analysis of variance (ANOVA) results that have pointed out the improvement in model fitness through F-ratio. The ratio has increased from 90.57 to 100.91 significant at p < 0.05; have supported likely to reject H04, H05, and H06, that is, tax benefits, inflation beating capabilities, and liquidity feature of the saving instruments are likely to have a significant influence in saving decisions. The findings have correlated with lite-rature as far as the influence of these predictors on the outcome has been concerned.96,94
ANOVA Results
Discussion
Factor analysis has extracted five factors that have been captioned appropriately and the summarized findings with Cronbach Alpha scores, average means, and average SD have been presented in Table 8.
Summary Results of Factor Analysis and Descriptive Statistics
The study has run factor analysis for data reduction dimensionality that has produced five factors amongst which the items have been distributed. The items have further been reported along with their means, standard deviations, and factor loading in descending order. A careful scrutiny of the assigned items in each of the factors have motivated the study for labelling appropriate titles for the five factors and, accordingly, five titles have been given. Factor-1 has named as “mutual funds uniqueness” having nine items addressing different uniqueness of the MFs which the prospective savers likely to give due diligence. Further, inasmuch the current research has motivated to address the savers’ puzzle for choosing between the SSSs and MFs for parking their surplus funds, the given title for Factor-1 has been justified. Similarly, Factor-2 has been assigned the title “small saving schemes’ uniqueness” with nine more items primarily incorporated important features of the SSS schemes. The items like the former one have been reported with their means, standard deviations, and factor loading in descending order. Following the same procedures, Factor-3 has named as “risks & returns” incorporating six items about saving risks and returns issues. Factor-4 has been labelled with “tax & liquidity” addressing tax implications/deductions during deposits, accumulations as well as on maturities. Even-tually, Factor-5 has been captioned as “inflation & instrument features” having five items focused on inflation impacts and instrument-specific hidden aspects such as expenses ratios and instrument comparative items. A total of five items with their means, standard deviations, and factor loading in descending order has been exhibited.
It has reported few interesting facts about saving attitudes of the sample respondents as shared in course of personal interviews. As far as saving avenues have concerned, their responses were skewed even though most of them have preferred the NSC, probably due to the twin benefits-assured return and tax benefits. The savings in MFs have also been gaining popularity as indicated from results. Interestingly, FDs and public provident fund (PPF) have also equally been preferred. Data has indicated that in spite of attracting returns by MFs, savers likely to perk their hard core funds in secured instruments like the NSC and FDs as those having the features of pre-matured surrender facility for supporting the precautionary needs. The PPF and life insurance products have also been preferred as saving tools. The choice of PPF has been justified by the respondents having its unique EEE tax benefits, that is, during deposits, the accumulations, and the maturity amount. The preference of life insurance plans (other than term plans) has also been reported a saving avenue for a good number of savers even though the returns likely been lower than FD/PPF/NSC/MFs. Unfortunately, most of the respondents have been assuming life insurance plans as a saving tool rather than a protection tool and such fallacy has been continued since long past. They have confessed that they had been misstated by the insurance agents and they had presumed life insurance plans as a perfect saving tool rather as a protection fund. The participants have unequivocally shared their concerns for security of the deposited funds than returns and have been saving in FD/PPF/NSC. Post office monthly income scheme (PO MIS) has been preferred by few respondents as minimum lump sum money required to be deposited to get monthly returns and such returns are taxable as well. Moreover, in recent past, the returns have substantially been cut by government hence the savers have preferred alternative avenues. A few respondent parents of less than 10 years old girl child have been saving in Samajik Suraksha Yojana (SSY) with the twin objectives—higher education and marriage of their girl children. They have shared the higher rate of retunes with EEE flavour that have attracted them in the scheme. Further, they have opined there should be changes in the terms and conditions of the scheme and the tenure of savings need to be extended up to 25 years or the girl child get married, whichever is earlier. They have also suggested in coming year, the annual ceiling of saving should be increased to at least to ₹2 million. As far as savings in MFs has concerned, the perceptions have likely been skewed. A section of the respondents have reported their aggressive attitudes and preference for superior returns and volatility in scheme sweeping practices without focusing on any tax benefits u/s 80C of the Income Tax Act. On the other hand, rest of the savers have considered the tax benefits along with better results hence deposited in ELSS. The attraction towards ELSS has also been justified based on the shortest mandatory lock-in period of three years, amongst the available array of saving instruments of Section 80C. Further, they have shared their experiences with ELSS and have concluded double-digit yields likely to derive when one remain invested beyond the compulsory lock-in period. The longer time horizon has been identified as a shock absorber for subsuming the market volatility for yielding superior returns.
Conclusion
The study has motivated to find out which saving tools—MFs or small saving schemes are the best for household savers based on empirical evidence. Accessing digital library and reviewing the related literature, it has framed six research hypotheses and a self-administered interview schedule for gathering data applying survey strategy. Appropriate inferential statistical tools have been used to test the null hypotheses and based on significant statistical findings, all those null hypotheses have likely been rejected. It has concluded selective demographics, risks, returns, income tax benefits; inflation-absorbing capacity and liquidity features of the saving instruments probably significantly influence households in designing their saving portfolios.
The academic audience should consider the following limitations before drawing any conclusions. First, it has reviewed only that literature published in different academic e-journals and expert opinions published in four business newspapers in English language hence literature published in other languages have not been considered. Second, due to parsimony and stipulated time line, it has set only six hypotheses and selective parameters, relatively small sample size collected from confined geographic areas for conducting the study. Third, instead of adopting and adapting any established questionnaire, it has designed a self-administered interview schedule for collecting pri-mary data. Fourth, two sections of the schedule have been framed in five-point Likert scale with the third option as “neutral” instead of a four-point scale as suggested by scholars. 132 Fifth, the respondents might not likely have behaved similarly while designing their saving portfolios, that is, the exact moment of effects (telescoping)—under or over reporting might remain present as literature has indicated. 133 Sixth, in lieu of advanced statistical software such as R, E-view, Strata and SPSS latest versions, it has applied SPSS-20. Final, the applied statistical tools have their inherent limitations that is likely to have impacts on null hypotheses test results, as scholars have indicated. 134
The practical implications of the study for multiple stakeholders have been enumerated. First, it has indicated savers design their portfolios taking into cognizance factors such as risk, prospective returns, income tax benefits, post-inflation yield, and liquidity. Moreover, income levels, marital status, age profiles, education, and FL levels have a significant influence in portfolio designs. Second, the NSC and FDs have been identified as the most preferred saving instruments due to the tax benefits of the former and pre-matured surrender facility of the latter. Third, it has pointed out savings in MFs has been gaining popularity amongst the young savers but more awareness programmes need to be arranged for its wide range popularity. The market-linked returns likely has created panic amongst the middle- and above middle-aged savers as they expect at least their principal amount should be protected and even have been satisfied with nominal returns. Fourth, the policy makers should use the report for revisiting the features of saving instruments for attracting more savers, for example, the annual ceiling of contribution of ₹15 million in Sukanya Samriddhi Yojana should be increased to at least ₹2 million, as respondents shared in course of personal interviews during the survey. Finally, the fallacy that life insurance is a saving tool instead of as a protection avenue has also been identified in the current study probably due to its attached tax benefits on premium payments and the sum assured. The banks and MFs agents should take appropriate measures to attract the savers in SSS and in MFs schemes as life insurance products, in true sense, are not saving tools.
The study has chalked out future research avenues. First, the excluded variables, for example, impacts of financial advices on saving decisions 76 and retirement motives 137 should be studied in Northeast Indian context where the rates of FL have been recorded as comparatively lower than national standard. Second, the impacts of self-efficacy—the belief of a saver that she could achieve to accumulate the target corpus 135 —may be studied in a comparative manner. Third, impacts of gender differences within households on saving decisions may be studied gathering empirical data as the current study has not addressed the issue. Fourth, the gender dimension of future retirement savings and current household expenditures as researchers have validated 136 may be studied in Northeast Indian context and even state-wise comparative manner. Finally, studies may address the impact of religious affiliation, influence of referral groups, caste traditions on savings. Moreover, perception studies may be attempted to address loopholes of MFs and SSSs with wider geographical region accessing established database with greater sample size.
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.