Prerpints.org logo
Preprint
Article

Factors Affecting Financial Risk Tolerance of Rural Households in Ludhiana District of Punjab

Altmetrics

Downloads

38

Views

19

Comments

0

This version is not peer-reviewed

Submitted:

16 September 2024

Posted:

17 September 2024

You are already at the latest version

Alerts
Abstract
The present study aims to find out the financial risk tolerance scores of the rural residents of Ludhiana district. Also, how drivers such as socio-demographic factors, namely, gender, age, education, annual income, existence of loan, occupation, employment status and psychological factors, namely, deliberative thinking, optimism and personality type A/ B impact financial risk tolerance of rural residents. The study is important for financial policy makers as they will become aware about scenario of rural residents and frame policies accordingly. Stepwise regression analysis was carried on. The result was found out that optimism positively and deliberative thinking negatively impacted financial risk tolerance, but personality type A/B did not have a significant impact on financial risk tolerance. Socio-demographic factors such as existence of loan, full-time salaried individuals, marital status significantly impacted financial risk tolerance. Other factors failed to have impact on financial risk tolerance of rural residents in the current study.
Keywords: 
Subject: Business, Economics and Management  -   Business and Management

1. Introduction

Rising life expectancy and falling birth rates are putting pressure on resources of every economy. Today, economies of this world are facing extremities in form of COVID-19 pandemic, food shortage, trade deficit, inflation, recession and so on. As a unit of this world human beings need to slog day and night to be self-sufficient. In this light, right investment is significantly important to increase savings which will further help humans to have a better standard of living, be ready for emergencies and to attain peace of mind (Bayar et al 2020). The circumstances keep the human on his/her toes to make both ends meet. It is obvious for people to use experience and acquired skills to deal with risky situations as mentioned by Grable and Rabbani (2014). Attitude towards money is another important factor that shapes life. Tang (1995) opined that people who value money as achievement may become slaves rather than masters and lead a stressful life. Money should be used as a medium to enhance their skill set. Kim (2003) states that saving moves individual towards safety. Market is unpredictable and volatile; therefore, risk is an inherent part of an investment (Bayar et al 2020). Roszkowski and Davey (2010), Prasanna C (2001) defined risk as when probability of a negative outcome of a decision is known, on the other hand, the uncertainty refers to a situation where possible outcomes are not known. It is difficult to identify odds of every desired outcome as there are factors outside individual’s control. An effective financial management requires a person to identify goals, time horizon, financial stability and financial risk tolerance and accordingly prepare for it (Grable and Lytton 1999). One who understands these inputs makes better use of his/her money and is called financially literate (Lusardi and Mitchell 2011). A person’s level of willingness to take the risk when outcome could be negative is known as financial risk tolerance (Grable 2000, 2008; International Organization for Standardization 2006, Nobre and Grable 2015). Campbell (2006) explained risk tolerance as choosing how much debt a person can bear, finding financing option best suitable, allocating income to fixed and variable expenditures, savings. Financial risk tolerance is reverse of risk aversion (Roszkowski and Davey 2010, Ryack and Sheikh 2016). In simple language, Roszkowski and Grable (2010) mention risk tolerance as a desire to take a chance. Individuals with low level of risk tolerance do not accept loss and uncertainties (MacCrimmon and Wehrung 1990). Literature cites risk tolerance as a multi-dimensional trait (Weber et al 2002, Soane and Chmiel 2005). Investment risk, risk comfort and experience, speculative risk are various dimensions of financial risk tolerance as mentioned by Grable and Lytton (1999). Financial risk tolerance is a key ingredient of effective financial management and is an important study with huge scope.
Understanding financial risk tolerance within the context of rural and urban areas is vital for policy making. Financial markets and financial products need to be carefully developed (Moreschi, 2004). According to Reserve Bank of India, rural area still accounts for 70% of India’s population. Few research has focused on rural investors, a factor that motivates this study. The study basically intends to plug this gap by focusing on rural residents in terms of their financial risk tolerance and its drivers. This study is an attempt to throw light on rural residents of Ludhiana region in Punjab. Ludhiana is the pioneer smart city of Punjab (Anonymous 2024a). It has a wide array of flourishing small-scale industries ranging from hosiery, garment, apparels to industrial goods, machine parts, tractor parts, household appliances. It is the prime center of bicycle manufacturing and textile industry in India.
The study aims to determine whether socio-demographic variables and psychological factors (Grable 2016) could be used to differentiate among levels of risk tolerance and classify individuals into risk tolerance categories (Grable 1997, Hanna et al 2008, Owusu et al 2023). Grable and Joo (2004) classified factors influencing financial risk tolerance into two broad categories- biopsychosocial and environmental factors. Biopsychosocial factors are immutable personal characteristics that is, those characteristics that cannot change such as age, gender, ethical background, birth order (Payne et al 2019). These features are deeply rooted to personality. Talking about birth order, an interesting notion mentioned by Foster (2024) is that parents become very protective about their first-born child. They take better care of needs of their first-born child than second one. Protective environment makes first born child dependable and fearful to take risk. Environmental factors are namely, income, education, Marital status. Psychological factors also called as behavioral factors (Grable 2016) are personality traits, life satisfaction level, anxiousness, extraversion, emotions, pride, regret, loss aversion bias, deliberative thinking etc. People who were cautious and risk avoiders had less percentage of their income allocated to equities. Fernandes (2013) stated a risk averse person is uncomfortable in making unpredictable financial decision. They fear to lose their money (Booij et al 2010). Researchers also mentioned that for human being pain of losing money is greater than earning money (Rabin and Thaler 2001). This trait is known as sensitivity towards loss. The degree of loss sensitivity will affect degree of taking risk (Dimmock and Kouwenberg 2010). Loss sensitive people were seen to have low income. Morin and Suarez (1983), Markowitz (1991) also added that risk averse person will maintain a diversified portfolio. Grable et al (2009), Duasa and Yusof (2013) further pointed that risk averse people are more interested in investing in safe assets such as saving account, cash and government bond. People who were willing to take risk or are gamblers in nature are more ready to buy equities, futures and options.
The current study aims to put light on how socio-demographic factors such as gender, employment status, occupation, existence of loan, marital status, education, age and psychological factors such as personality type A/B, optimism, deliberative thinking impact financial risk tolerance among rural residents. A few research studies talk about financial risk tolerance when it comes to rural residents. Most of research has focused urban population such as Nobre and Grable (2015) and Heo et al (2021). The same holds true for the impact of socio-demographic and psychological influences on financial risk tolerance and financial behavior of individuals (for example, Sulaiman 2012 and Riaz and Hunjra 2015).
The paper has five sections. Second section provides literature background of the study, while section three presents methodology, reliability and sample characteristics. Section four covers results and discussions of the study, conclusion drawn, limitation of the study and scope of future research forms part of section five.

2. Literature Review

2.1. Income

Income and financial risk tolerance has a positive connection as mentioned in studies such as Grable and Roszkowski (2008), Ardehali et al (2005), Grable and Joo (2004) and Sung and Hanna (1996). As income increases financial risk tolerance level also increase (MacCrimmon and Wehrung 1990, Duasa and Yusof 2013). The money makes individual powerful. Access to resources becomes fast and easy. In other words, financial risk tolerance is negatively associated with income. Household income act as a cushion to save from financial loss.

2.2. Age

Yao et al (2004) and Yao et al (2005), Deaves et al (2007) pointed out that age and financial risk tolerance are negatively related to each other. Milligan (2004) mentioned that age and risk tolerance follow hump shaped pattern. Nairn (2005) characterized older individuals to be risk averse. When an individual grows young his/her risk tolerance increases and when an individual grows old risk tolerance decrease (Duasa and Yusof 2013, Hallahan et al 2004, Halek and Eisenhauer 2001, Nguyen et al 2021, Zhao and Zhang 2020). The reason cited behind this is that older the individuals have less time to recover from financial losses. Guillemette et al (2012) mentioned that due to decrease in the cognitive ability risk tolerance decreases after the age of 60. As age increases cognitive ability decreases and so does financial risk tolerance (Dohmen et al 2010).

2.3. Gender

In accordance with Hoe et al (2020), Hallahan et al (2004) a greater number of women are risk averse due to less financial knowledge whereas men have more financial knowledge and are keen to take more risk. Some researchers such as Miller and Stark (2002), Sapienza et al (2009), Ardehali et al (2005) and Grable and Roszkowski (2007) pointed that men and women are different psychologically due to biological difference. Other notion is that due to less socialization level women prefer safer options (Olsen and Cox, 2001; Yao and Hanna 2005; Collet and Lizardo, 2009; Bajtelsmit and Bernasek, 1996). Men have more equity investments than women have. Also, men get better opportunity to earn (Karakowsky and Elangovan 2001, Eckel and Grossman 2002, Roszkowski and Grable 2005). Yusof (2015) pointed out that only those women who have higher income are inclined to have more risk tolerance.

2.4. Education

Through exploratory study Cupples et al (2013) put to light that education can decrease the role of gender variability on financial risk tolerance. Chang and Chiremba (2004); Grable and Joo (2004); Yao and Hanna (2005) found out that education is positively associated with financial risk tolerance. Through formal education people learn cost and benefits of risky decisions. Kingston et al (2003), Bernheim et al (2001), Duasa and Yusof (2013) put to light importance of education. Higher education makes people more active, wealthier and opt for riskier financial choices. Education act as a medium of socialization. Economically, Grable (2008), Halek and Eisenhauer (2001), education increases human capital and hence, person’s future prospect of earning and thus, increase financial risk tolerance. Yusof (2015) suggested that education should be tailor made according to the needs of gender. As environment and behavior for men and women today is not same.

2.5. Marital Status

Individuals are postponing marriage to later age (Lundberg and Pollak 2013). The median age of marriage for men has shifted to 32 years and for women, the marriage age is as long as 30 years (Lee 2023). It may be perceived that single individuals have more risk tolerance than married once (Grable 2008, Yao and Hanna 2005). The notion supporting this statement is that the single individuals have less to lose in case of unfavorable risky decisions. moreover, they focus on personalized goals. Married individuals have responsibilities to support dependents such as children, parents (Yao and Hanna 2005) and losses can shake financial condition of a family (Ardehali et al 2005). Also, Siek et al (2007) mentioned right estimation of financial risk tolerance is important for married individuals as it will lead to better financial environment for the family.

2.6. Occupation

Occupation can be used as a classifying factor according to their risk tolerance (Sultana and Pardhasardhi 2011). Meyer and Reniers (2016) conducted an experiment and found that entrepreneurs are more risk takers than non-entrepreneurs. Duasa and Yusof (2013) highlighted the people working in private sector such as business, finance, hospitality, trade are more risk tolerant than other occupations. Barnewall (1988) and Masters (1989) have found out that non-professionals who have less economic risk such as clerical workers, skilled and unskilled laborers were more conservative than those who were educated professionals such as doctors, lawyers, educators, managers, owners and retired persons. Farmers engaged in agricultural activities are less willing to take risk as mentioned by Duarte et al 2023.

2.7. Employment Status

MacCrimmon and Wehrung (1990), Thanki and Baser (2021), Ansari and Phatak (2016) pointed out that self-employed individuals are more risk tolerant than salaried individuals. Because self-employed individuals have in-built characteristic of taking more risk. The current study focuses on investigating if self-employed and retired individuals had higher risk tolerance than those full-time salaried. Salaried individuals get fixed monthly salary. They avoid taking loans and get comfortable in what they have (Dohmen and Falk 2006). Retired individuals have lived their life. They are almost living the last innings of their life (Arano et al 2010). At this stage they want to use their savings which they have made throughout their life as mostly, children are independent and earn good. The relationship between retirement status and financial risk tolerance is quite complex (Kasten et al 2011, Harahap et al 2022). It depends upon multiple factors such as income stability, psychological factors, literacy (Lusardi and Mitchell 2007) and health status.

2.8. Existence of Loan

There is a common opinion that high-cost borrowers have high risk tolerance as they are able to pay high EMIs on time and maintain good credit score (Dew and Xiao 2011). Maintaining good credit score is important to get loan in future. Fernatt et al (2012) through their research found that it is wrong notion. When individuals face a financial emergency, they tend to borrow first rather than liquidating their assets. Because of the perception that in future they will not be able to buy the asset. In this situation they tend to find a source which is convenient and speedy to get cash.

2.9. Personality Type A/B

Carducci and Wong (1998) find a correlation between Type A personality and the willingness to take financial risks in everyday financial situations. Kannadhasan et al (2016), Thanki and Baser (2019) and Thanki et al (2020) in their studies found out that people with type A personality is hard driving, competitive, aggressive and time savers. These individuals had significant impact on financial risk tolerance. Type B personality individuals who are often characterized as easy going and relaxed have less risk tolerance. The current study aims to find out impact of personality type A or B on financial risk tolerance.

2.10. Optimism

Optimism is a hope that everything happens for good. Optimism leads to positivity in mind. Optimism is key to good financial behavior (Strömbäck et al 2017, Owusu et al 2023). Prospect theory opines that optimism influences financial risk tolerance. Optimism attracts hard work, savings. It’s an important psychological factor. The current study also focuses on psychological factors which is more or less ignored in the previous studies but forms part of behavioral finance. Optimistic and overconfident people are more risk tolerant (Nosic and Weber 2010). Men tend to be very optimistic and hence overconfident about their financial risk tolerance (Camerar and Lovallo 1999, Grable and Rozkowski 2007).

2.11. Deliberative Thinking

Financial decision making depends upon intuition and on deliberative thinking (Strömbäck et al 2017, Owusu et al 2023). While intuition is fast forward and based on observation and past experiences, deliberative thinking is more based on learning and calculations. Studies have revealed that expertise is learnt out of deliberative or repeated actions. Better financial choices can be made through deliberative thinking and not through intuition.

3. Methodology

In the study quantitative approach was employed, specifically, the survey method with cross-sectional data was used. Stratified random sampling was used. The data was collected from the household’s ‘breadwinner’. Breadwinner is defined as a male, or if no male was present, then the spouse, partner, or female head of the household. Only one person per family was asked to respond.
For rural residents, 5 blocks were randomly selected out of 13 blocks falling in Ludhiana district. The Block Development Officers of the selected blocks were then contacted for the villages falling under the selected blocks. Out of the villages falling under the 5 selected blocks, 2 villages from each block were chosen randomly. The Sarpanch of the selected villages were then contacted for the list of the residents. 20 residents were then selected randomly form each of 10 villages, leading to a sample of 200 rural residents. The following Table 1 shows list of selected rural blocks and villages.

3.1. Variables and Measures

Primary data was collected using a questionnaire. Keeping in mind the target population the questionnaire was converted into regional Punjabi language. The questionnaire was divided into three parts. Part 1 had questions related to socio-demographic variables such as gender, marital status, age, education level, existence of loan, employment status, occupation, annual income. These were independent variables in the study. Part two included question related to measuring financial risk tolerance-13-item scale developed by Grable and Lytton 1999. The construct is dependent variable. According to Grable and Lytton (1999, 2003), Kuzniak et al (2015) scores of the scale can range from 13 (low risk tolerance) to 47 (high risk tolerance). In the study scores of the financial risk tolerance ranged from 13 to 39. Part three covered constructs related to psychological influences which were considered as independent variables. Deliberative thinking was adapted from Pachur and Spaar (2015). Optimism scale designed by Scheier et al (1994). Eaker and Castelli (1988) personality type A/B scale was used.
In the study, IBM SPSS (Statistical Package for Social Sciences) version 26 was used. Socio-demographic variables such as annual income, age, occupation, employment was dummy coded by converting each category into binary variable 0 and 1. Single = 1, married = 0; female = 0, male = 1; loan takers = 1, loan non-takers = 0. Stepwise multiple regression analysis was carried to examine the relationship between dependent variable, namely, financial risk tolerance and independent variables such as socio-demographic variables and psychological factors (e.g., personality type, optimism and deliberative thinking). Reference categories were age: 18-25, annual income:- less than ₹5,00,000, occupation:- government job, employment status:- retired individuals.

3.2. Reliability

Reliability is the measure of internal consistency of the constructs in the study. Construct reliability was assessed using Cronbach’s Alpha. The reliability results are summarized in the following table.
Table 2. Cronbach’s alpha results of different constructs used in the study.
Table 2. Cronbach’s alpha results of different constructs used in the study.
Constructs No. of items Alpha (α)
Financial risk tolerance scale 13 0.738
Personality scale 6 0.71
Optimism scale 5 0.692
Deliberative thinking scale 2 0.628
The index was found to be reliable for all constructs. The results revealed that the financial risk tolerance scale with the thirteen items had α = 0.738. Personality scale had α = 0.71, deliberative thinking had α = 0.628.
From Table 3, it has been inferred that males dominate the sample. As 85% were males and female consisted of 15%. Respondents falling in the age group of 18-25 years is 11.5%. Population falling in the working age group of 26-55 years is 81%. Age group of 56-65 years consist of 5% of population and only 2.5% lie in upper age of 66 years and above. Matriculate residents are 13.5% and maximum residents (37%) have completed post-graduation. 35% of population have done graduation and 14.5% is 12th pass. 50% of residents earned annual income less than 7.5 lakh and 38.55 of residents earned annual income of 7.5 lakh to 12.5 lakh. Nearly 11.5% of residents earned above 12.5 lakh. 63% of residents were married and 37% were single. Only 3.5% of residents were retired, 55.5% were full-time salaried and 41% were self-employed. 20.5% of residents were doing government job where as 26% of residents had private job, 11.5% had self-owned business, 38% of residents are engaged in agriculture. Maximum number of residents (61%) of residents have availed bank loan where as 39% have not taken.

4. Results

5. Discussion

The mean financial risk tolerance score of the rural residents is 27.3, 41.5% of residents are conservative whereas 58.5% of residents are aggressive. The dependent variable (financial risk tolerance) was stepwise regressed on predicting variables, namely, deliberative thinking, optimism, personality and socio-demographic variables. In rural area the independent variable significantly predicted financial risk tolerance. Table 4 and Table 5 shows that F (7,192) = 11.52, p < 0.05, which indicates that the factors under the study have a significant impact on financial risk tolerance. Moreover, the adjusted R2 = 0.27 depicts that the variables explain 27% of the variance in financial risk tolerance due to independent variable. From Table 6 and Table 7 the fitted equation developed for rural area is as follows:
Financial risk tolerance = 23.08 + 0.43 (optimism) + 3.64 (Loan existence) – 1.61 (Full-time salaried) -3.63 (ANNUAL_INC=Rs 7,50,000 to Rs 10,00,000) + 1.93 (age group = 46-55 years) + 3.213 (Marital status status) – 0.43 (Deliberative Thinking).
There is positive relationship between presence of loan and financial risk tolerance. Loan takers had more financial risk tolerance than those who do not have loan on themselves. The result is in line with Wang (2023) but contrary to Fernatt et al (2012). If an individual deliberately manages his/her debt creditworthiness and investment opportunities increase and hence, financial risk tolerance also increase manifold. Carvalho et al (2019) mentions to be careful when borrowing. Cost of borrowing must be kept in mind while availing loan. Borrowing at high cost can prove to be fatal.
Negative relationship was found between full-time salaried individuals and financial risk tolerance. This implies the reference category: retired individuals had more financial risk tolerance than full-time salaried residents. According to Arano et al 2010, retired individuals want to use their savings which they have made throughout their life as mostly, children of retired individuals are independent and start earning for themselves. The results are opposite to Kasten et al 2011, Harahap et al 2022. According to them retired individuals may or may not have good financial risk tolerance.
Occupation did not significantly impact financial risk tolerance. The result was in contrast with Sultana and Pardhasardhi (2011) but in line with Duasa and Yusof (2013) Individuals running their own business had more financial risk tolerance than government job holders. Individuals having agriculture as their occupation defer from taking bonds or stocks. They believe in buying agricultural land to be best investment and have low risk tolerance.
Education did not significantly impact financial risk tolerance of rural residents. The results were in contrast to Cupples et al (2013), Chang and Chiremba (2004), Grable and Joo (2004), Yao and Hanna (2005), Kingston et al (2003), Bannier and Schwarz (2017) and Heo et al (2021).
Relationship between income and financial risk tolerance scores was found not to be significant. The results were in contrary to Grable et al (2008), Ardehali et al (2005), Grable and Joo (2004); Huston et al (1997) and Sung and Hanna (1996). Only one group of individuals earning Rs 7,50,000 to Rs 10,00,000 had significant but negative financial risk tolerance. This indicates lower income earners (Less than ₹5,00,000) had better risk tolerance. The reason may be that lower income groups want to park their funds in better avenues and earn returns as more risk means more returns (Ricciardi V 2008). Higher income earners may not want to park their hard-earned money in risky assets and live in comfort zone (Bernstein 1996, Thanki and Baser 2021). During the survey it was observed that low age group residents who earned a meagre income wanted to opt for shortcuts and earn easy money through bitcoins or futures and options, of which they had little knowledge. They want to opt for shortcuts which may not benefit them for long term.
Age was also found not to be significantly associated with financial risk tolerance. Only the age group 46 to 55 years was found to have significant impact. At this age individual have experience and knowledge about market and children are mostly, of marriageable age. Taking financial risk can better off the financial conditions. The results were inconclusive and in line with Yang (2004), Chaulk et al (2003) and Ardehali et al (2005) but in contrast to Forenseca et al (2012), Yusof (2015) who found a positive association between age and financial risk tolerance.
Marital status had positive relationship with financial risk tolerance. Single individuals had higher financial risk tolerance than married individuals. Single individuals have less to lose in case of unfavorable risky decisions. The result is in line with Nosita et al (2020), Grable (2008), Yao and Hanna (2005). Family structure plays an important role in deciding financial risk tolerance according to Chaulk et al (2003) Moreover, single individuals focus on personalized goals. Married individuals have responsibilities. Financial expenditures are more for married ones compared to single ones.
Talking about psychological factors, in the present study, personality did not have significant impact on financial risk tolerance of rural residents. The results were in contrast to Kannadhasan et al (2016), Thanki and Baser (2019) and Thanki et al (2020).
Deliberative thinking had significant but negative impact on financial risk tolerance. This implies that as deliberative thinking improves financial risk tolerance decreases. The results show that when people are more calculative and planned, financial risk tolerance decreases.
Optimism had positive relation with risk tolerance. The results were consistent with Nosic and Weber (2010), Camerar and Lovallo (1999), Grable and Rozkowski (2007). Owusu et al (2023) did not find a significant relationship of optimism with financial risk tolerance whereas prospect theory opines that optimism influences financial risk tolerance. Optimism attracts hard work, savings and thus, increases risk tolerance.

6. Conclusions

In conclusion, financial risk tolerance in rural areas is influenced by a variety of factors, including socioeconomic status, access to financial education, and cultural attitudes toward risk and debt. Rural residents often face unique challenges such as limited access to financial services and markets, which can impact their risk tolerance. However, studies have found out that those with higher risk tolerance in rural areas are more likely to engage in entrepreneurial activities and take loans to invest in agriculture or small business ventures. According to the present study, loan takers had higher risk tolerance scores than non-loan takers. Rural residents use mobile phones and internet to not to be left behind and stay updated. Income and education impacts living style. In the present study annual income, age, education and occupation did not significantly impact risk tolerance scores. But according to literature these factors impact financial risk tolerance. Retired individuals have tendency to spend more on themselves and save for children as well. They had higher risk tolerance scores than full-time salaried and self-employed residents. Psychological factor such as personality did not impact financial risk tolerance. Deliberative thinking negatively impacted risk tolerance scores which means planning hard can decrease risk tolerance scores but being optimistic can certainly improve the scores.

6.1. Limitations

The data collected may be subject to biasness due to respondents self-reported data. The answers provided may be more socially acceptable rather than representing respondent’s true feelings. Cultural differences may limit the scope of the study. The questions used to measure variables may be differently interpreted by residents. Actual response may not be captured due to this limitation. This study suffers from all the limitations associated with studies based on survey data.

6.2. Future Research

Future research could benefit from longitudinal data to examine changes in risk tolerance over time. Impact of economic cycles, technological advancements such as mobile banking, digital wallets etc. can be further studied. Additionally, exploring other psychological variables such as risk perception, trust, loss sensitivity, mental health etc. in more depth could provide a more comprehensive understanding. Urban and rural residents financial risk tolerance can be studied further according to different parameters.

Conflicts of interest

The authors declare no conflict of interest.

References

  1. Anonymous (2024a) About Ludhiana district. Retrieved on 1st June, 2024 from https://ludhiana.nic.in/about-district.
  2. Arano, K., Parker, C., & Terry, R. (2010). Gender-based risk aversion and retirement asset allocation. Economic inquiry, 48(1), 147-155. [CrossRef]
  3. Ardehali, P. H., Paradi, J. C., & Asmild, M. (2005). Assessing financial risk tolerance of portfolio investors using data envelopment analysis. International Journal of Information Technology & Decision Making, 4(03), 491-519. [CrossRef]
  4. Ansari, S., & Phatak, Y. (2016). A study of assessment of financial risk tolerance and preferred investment avenues of investors. Prestige International Journal of Management and Research, 9(2/1), 1-7.
  5. Bajtelsmit, V. L., & Bernasek, A. (1996). Why do women invest differently than men?. Financial counseling and planning, 7.
  6. Bayar, Y., Sezgin, H. F., Öztürk, Ö. F., & Şaşmaz, M. Ü. (2020). Financial literacy and financial risk tolerance of individual investors: Multinomial logistic regression approach. Sage Open, 10(3). [CrossRef]
  7. Bernheim, B. D., Garrett, D. M., & Maki, D. M. (2001). Education and saving:: The long-term effects of high school financial curriculum mandates. Journal of public Economics, 80(3), 435-465.
  8. Booij, A. S., Van Praag, B. M., & Van De Kuilen, G. (2010). A parametric analysis of prospect theory’s functionals for the general population. Theory and Decision, 68, 115-148.
  9. Camerer, C., & Lovallo, D. (1999). Overconfidence and excess entry: An experimental approach. American economic review, 89(1), 306-318. [CrossRef]
  10. Campbell, J. Y. (2006). Household finance. The journal of finance, 61(4), 1553-1604.
  11. Carvalho, L., Olafsson, A., & Silverman, D. (2019). Misfortune and mistake: The financial conditions and decision-making ability of high-cost loan borrowers. [CrossRef]
  12. Carducci, B. J., & Wong, A. S. (1998). Type A and risk taking in everyday money matters. Journal of business and psychology, 12, 355-359. [CrossRef]
  13. Chaulk, B., Johnson, P. J., & Bulcroft, R. (2003). Effects of marriage and children on financial risk tolerance: A synthesis of family development and prospect theory. Journal of Family and Economic Issues, 24, 257-279. [CrossRef]
  14. Chang, C. C., DeVaney, S. A., & Chiremba, S. T. (2004). Determinants of subjective and objective risk tolerance. Journal of Personal finance, 3(3), 53-67.
  15. Collett, J. L., & Lizardo, O. (2009). A power-control theory of gender and religiosity. Journal for the Scientific Study of Religion, 48(2), 213-231. [CrossRef]
  16. Cupples, S., Rasure, E., & Grable, J. (2013). Educational achievement as a mediator between gender and financial risk tolerance: An exploratory study. Ewha Journal of Social Sciences, 29(1). [CrossRef]
  17. Davey G A (2006) Measuring risk tolerance. Retrieved on December 12, 2023 from https://www.moneymanagement.com.au/features/editorial/measuring-client-risk- tolerance.
  18. Dew, J., & Xiao, J. J. (2011). The financial management behavior scale: Development and validation. Journal of Financial Counseling and Planning, 22(1), 43.
  19. Dimmock, S. G., & Kouwenberg, R. (2010). Loss-aversion and household portfolio choice. Journal of Empirical Finance, 17(3), 441-459. [CrossRef]
  20. Dohmen, T., Falk, A., Huffman, D., & Sunde, U. (2010). Are risk aversion and impatience related to cognitive ability? American Economic Review, 100(3), 1238-1260.
  21. Duarte, D. H. J. D., de Souza Morais, E., Siqueira, E. S., Nobre, F. C., & Nobre, L. H. N. (2023). Perception and tolerance to the risk of family agriculture managers. Revista de Gestão Social e Ambiental, 17(2), 1-19.
  22. Duasa, J., & Yusof, S. A. (2013). Determinants of risk tolerance on financial asset ownership: A case of Malaysia. International Journal of Business and Society, 14(1), 1-16.
  23. Eaker, E. D., & Castelli, W. P. (1988). Type A behavior and coronary heart disease in women: Fourteen-year incidence from the Framingham Study.
  24. Eckel, C. C., & Grossman, P. J. (2002). Sex differences and statistical stereotyping in attitudes toward financial risk. Evolution and human behavior, 23(4), 281-295. [CrossRef]
  25. Fernatt, F., Stolberg, S., Blue, J., & Grable, J. (2012). A Risk-Tolerance Paradox: Are Payday and Car Title Loan Customers Really More Risk Tolerant than Others?. International Review of Social Sciences and Humanities, 3(2), 214-229.
  26. Fernandes S (2013) How risk tolerant or risk averse are you? Retrieved on December 20, 2020 from https://www.business-standard.com/article/pf/how-risk-tolerant-or-risk-averse-are- you-113020901086_1.html.
  27. Foster K (2024). Birth Order Theory: Insights Into Your Personality. Retrieved on 1st July 2024 from https://www.betterhelp.com/advice/family/birth-order-theory-insights-into-your-personality/.
  28. Grable, J. E. (1997). Investor risk tolerance: Testing the efficacy of demographics as differentiating and classifying factors.
  29. Grable, J. E. (2000). Financial risk tolerance and additional factors that affect risk taking in everyday money matters. Journal of business and psychology, 14, 625-630. [CrossRef]
  30. Grable, J. E. (2008). Risk tolerance. In Handbook of consumer finance research (pp. 3-19). New York, NY: Springer New York.
  31. Grable J E (2016) Financial risk tolerance. Pp 19-31. Handbook of consumer finance research.
  32. Grable, J. E., & Joo, S. H. (2004). Environmental and biophysical factors associated with financial risk tolerance. Journal of Financial Counseling and Planning, 15(1).
  33. Grable, J., & Lytton, R. H. (1999). Financial risk tolerance revisited: the development of a risk assessment instrument☆. Financial services review, 8(3), 163-181.
  34. Grable, J. E., & Rabbani, A. (2014). Risk tolerance across life domains: Evidence from a sample of older adults. Journal of Financial Counseling and Planning, 25(2), 174-183.
  35. Grable, J. E., & Roszkowski, M. J. (2007). Self-assessments of risk tolerance by women and men. Psychological Reports, 100(3), 795-802. [CrossRef]
  36. Grable, J. E., & Roszkowski, M. J. (2008). The influence of mood on the willingness to take financial risks. Journal of Risk Research, 11(7), 905-923. [CrossRef]
  37. Grable, J., Roszkowski, M., Joo, S. H., O'Neill, B., & Lytton, R. H. (2009). A test of the relationship between self-classified financial risk-tolerance and investment risk-taking behaviour. International Journal of Risk Assessment and Management, 12(2-4), 396-419. [CrossRef]
  38. Guillemette, M., Finke, M. S., & Gilliam, J. (2012). Risk tolerance questions to best determine client portfolio allocation preferences. Journal of Financial Planning, 25(5), 36-44.
  39. Hallahan, T. A., Faff, R. W., & McKenzie, M. D. (2004). An empirical investigation of personal financial risk tolerance. Financial Services Review-greenwich-, 13(1), 57-78.
  40. Halek, M., & Eisenhauer, J. G. (2001). Demography of risk aversion. Journal of Risk and Insurance, 1-24.
  41. Hanna, S. D., Wang, C., & Lindamood, S. (2008). Household ownership of stocks, business assets, and investment real estate: An endogenous probit model. Proceedings of the Academy of Financial Services.
  42. Harahap, S., Thoyib, A., Sumiati, S., & Djazuli, A. (2022). The impact of financial literacy on retirement planning with serial mediation of financial risk tolerance and saving behavior: Evidence of medium entrepreneurs in Indonesia. International Journal of Financial Studies, 10(3), 66. [CrossRef]
  43. Heo, W., Rabbani, A., & Grable, J. E. (2021). An evaluation of the effect of the COVID-19 pandemic on the risk tolerance of financial decision makers. Finance Research Letters, 41, 101842. [CrossRef]
  44. International Organization for Standardization (2006) Personal Financial Planning 22222:2005. Geneva: ISO.
  45. Kannadhasan M, Aramvalarthan S, Mitra S K and Goyal V (2016) Relationship between biopsychosocial factors and financial risk tolerance: An empirical study. Vikalpa 41: 117- 131.
  46. Karakowsky, L., & Elangovan, A. R. (2001). Risky decision making in mixed-gender teams: Whose risk tolerance matters?. Small Group Research, 32(1), 94-111.
  47. Kasten, G. W., & Kasten, M. W. (2011). The impact of aging on retirement income decision making. Journal of Financial Planning, 24(6), 60-69.
  48. Kingston P W, Hubbard R, Lapp B, Schroeder P and Wilson J (2003) Why education matters. Sociol Educ 76:53-70.
  49. Kim, K. (2003). Money attitudes and personal finance of college students. Journal of Consumption Culture, 6(3), 33-50.
  50. Lee E (2023) The average age of marriage 2022. Retrieved on September 25, 2023 from https://www.theknot.com/content/average-age-of-marriage.
  51. Lusardi, A., & Mitchell, O. S. (2007). Financial literacy and retirement planning: New evidence from the Rand American Life Panel. Michigan Retirement Research Center Research Paper No. WP, 157.
  52. Lusardi, A., & Mitchell, O. S. (2011). Financial literacy around the world: an overview. Journal of pension economics & finance, 10(4), 497-508. [CrossRef]
  53. Lundberg S and Pollak R A (2013) Cohabitation and the uneven retreat from marriage in the U.S., 1950–2010. Retrieved on 8th September, 2022 from http://www.nber.org/papers/w19413.
  54. MacCrimmon, K. R., & Wehrung, D. A. (1990). Characteristics of risk taking executives. Management science, 36(4), 422-435. [CrossRef]
  55. Markowitz, H. M. (1991). Foundations of portfolio theory. The journal of finance, 46(2), 469-477.
  56. Miller, A. S., & Stark, R. (2002). Gender and religiousness: Can socialization explanations be saved?. American journal of sociology, 107(6), 1399-1423. [CrossRef]
  57. Milligan, K. (2005). Life-cycle asset accumulation and allocation in Canada. Canadian Journal of Economics, 38(3), 1057-1106. [CrossRef]
  58. Moreschi, R. W. (2005). An analysis of the ability of individuals to predict their own risk tolerance. Journal of Business & Economics Research (JBER), 3(2). [CrossRef]
  59. Morin, R. A., & Suarez, A. F. (1983). Risk aversion revisited. The journal of finance, 38(4), 1201-1216.
  60. Nguyen, T. A. N. (2022). Does financial knowledge matter in using fintech services? Evidence from an emerging economy. Sustainability, 14(9), 5083. [CrossRef]
  61. Nosita, F., Pirzada, K., Lestari, T., & Cahyono, R. (2020). IMPACT OF DEMOGRAPHIC FACTORS ON RISK TOLERANCE. Journal of Security & Sustainability Issues, 9(4). [CrossRef]
  62. Nobre, L. H. N., & Grable, J. E. (2015). The role of risk profiles and risk tolerance in shaping client investment decisions. Journal of Financial Service Professionals, 69(3).
  63. Olsen, R. A., & Cox, C. M. (2001). The influence of gender on the perception and response to investment risk: The case of professional investors. The journal of psychology and financial markets, 2(1), 29-36. [CrossRef]
  64. Owusu, G. M. Y., Korankye, G., Yankah, N. Y. M., & Donkor, J. B. A. (2023). Financial risk tolerance and its determinants: The perspective of personnel from security services in Ghana. Borsa Istanbul Review, 23(4), 852-864. [CrossRef]
  65. Pachur, T., & Spaar, M. (2015). Domain-specific preferences for intuition and deliberation in decision making. Journal of Applied Research in Memory and Cognition, 4(3), 303-311. [CrossRef]
  66. Payne, P., Kalenkoski, C. M., & Browning, C. (2019). Risk Tolerance and the Financial Satisfaction of Credit Card Users. Journal of Financial Counseling and Planning, 30(1), 110-120. [CrossRef]
  67. Prasanna C (2001) Financial management theory and practice. Tata McGraw.
  68. Rabin, M., & Thaler, R. H. (2001). Anomalies: risk aversion. Journal of Economic perspectives, 15(1), 219-232.
  69. Riaz, L., & Hunjra, A. I. (2015). Relationship between psychological factors and investment decision making: The mediating role of risk perception. Pakistan Journal of Commerce and Social Sciences, 9(3), 968-981.
  70. Ryack, K., & Sheikh, A. (2016). The relationship between time perspective and financial risk tolerance in young adults. Financial Services Review: The Journal of Individual Financial Management, 25(2), 157-180. [CrossRef]
  71. Roszkowski, M. J., & Grable, J. (2005). Gender stereotypes in advisors' clinical judgments of financial risk tolerance: Objects in the mirror are closer than they appear. The Journal of Behavioral Finance, 6(4), 181-191. [CrossRef]
  72. Roszkowski, M. J., & Davey, G. (2010). Risk perception and risk tolerance changes attributable to the 2008 economic crisis: A subtle but critical difference. Journal of financial service professionals, 64(4), 42-53.
  73. Sapienza, P., Zingales, L., & Maestripieri, D. (2009). Gender differences in financial risk aversion and career choices are affected by testosterone. Proceedings of the National Academy of Sciences, 106(36), 15268-15273. [CrossRef]
  74. Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test. Journal of personality and social psychology, 67(6), 1063.
  75. Sieck, W. R., Merkle, E. C., & Van Zandt, T. (2007). Option fixation: A cognitive contributor to overconfidence. Organizational Behavior and Human Decision Processes, 103(1), 68-83. [CrossRef]
  76. Soane, E., & Chmiel, N. (2005). Are risk preferences consistent?: The influence of decision domain and personality. Personality and Individual Differences, 38(8), 1781-1791. [CrossRef]
  77. Strömbäck, C., Lind, T., Skagerlund, K., Västfjäll, D., & Tinghög, G. (2017). Does self-control predict financial behavior and financial well-being?. Journal of behavioral and experimental finance, 14, 30-38. [CrossRef]
  78. Sulaiman, E. K. (2012). An empirical analysis of financial risk tolerance and demographic features of individual investors. Procedia Economics and Finance, 2, 109-115. [CrossRef]
  79. Sultana, D. T., & Pardhasardhi, S. (2011). An empirical investigation of the relation between risk tolerance and socioeconomic characteristics of individual investors. QUEST-Journal of Management and Research, 2(1), 57-66.
  80. Sung, J., & Hanna, S. D. (1996). Factors related to risk tolerance. Financial counseling and planning, 7.
  81. Tang, T. L. P., & Gilbert, P. R. (1995). Attitudes toward money as related to intrinsic and extrinsic job satisfaction, stress and work-related attitudes. Personality and Individual Differences, 19(3), 327-332. [CrossRef]
  82. Thanki, H., & Baser, N. (2019). Interactive impact of demographic variables and personality type on risk tolerance. Emerging Economy Studies, 5(1), 42-54. [CrossRef]
  83. Thanki H and Baser N (2021) Determinants of financial risk tolerance (FRT): An empirical investigation. J Wealth Manag.
  84. Thanki, H., Karani, A., & Goyal, A. K. (2020). Psychological antecedents of financial risk tolerance. The Journal of Wealth Management, 23(2), 36-51. [CrossRef]
  85. Wang, S. (2023). Risk preference, payday loans and other alternative financial services. Review of Behavioral Finance. [CrossRef]
  86. Weber, E. U., Blais, A. R., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. Journal of behavioral decision making, 15(4), 263-290. [CrossRef]
  87. Yao, R., Hanna, S. D. & Lindamood, S. (2004). Changes in financial risk tolerance, 1983-2001. Financial Services Review, 13(4), 249-266.
  88. Yao, R., & Hanna, S. D. (2005). The effect of gender and marital status on financial risk tolerance.
  89. Yusof, S. A. (2015). Household bargaining, financial decision-making and risk tolerance. International journal of business and society, 16(2).
  90. Zhao, H., & Zhang, L. (2020). Talking money at home: the value of family financial socialization. International Journal of Bank Marketing, 38(7), 1617-1634. [CrossRef]
Table 1. list of selected Rural blocks and villages.
Table 1. list of selected Rural blocks and villages.
Sr. No. Block Villages
1. Jagraon Manuke, Hathur
2. Doraha Jandali, Dhamot
3. Khanna Kalal Majra, Isru
4. Ludhiana West Ayali Kalan, Birmi
5. Ludhiana East Bhamian Kalan, Dehlon
Table 3. Demographic characteristics of the respondents.
Table 3. Demographic characteristics of the respondents.
Area → Rural (N=200)
Frequency (%)
Gender Female 30 15
Male 170 85
Age 18-25 23 11.5
26-35 59 29.5
36-45 44 22
46-55 59 29.5
56-65 10 5
66 and above 5 2.5
Education Upto matriculation 27 13.5
Up to 12th 29 14.5
Graduation 70 35
Post-Graduation 74 37
Annual Income Less than ₹5,00,000 42 21
₹5,00,000 to ₹7,50,000 58 29
₹7,50,000 to ₹10,00,000 37 18.5
₹10,00,000 to ₹12,50,000 40 20
₹12,50,000 to ₹15,00,000 10 5
Above ₹15,00,000 13 6.5
Marital status Single 74 37
Married 126 63
Employment Status Retired 7 3.5
Full time Salaried 111 55.5
Self Employed 82 41
Occupation Government Job 41 20.5
Private Job 52 26
Business 23 11.5
Professional 8 4
Agricultural 76 38
Bank loan availed No 79 39.5
Yes 121 60.5
Table 4. financial risk tolerance score.
Table 4. financial risk tolerance score.
Rural Area Conservative (less than average risk tolerance score) Aggressive (greater than average risk tolerance score)
(Mean = 27.27) 83(41.5%) 117 (58.5%)
Table 5. Model Summary.
Table 5. Model Summary.
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .283a 0.08 0.075 5.65571
2 .368b 0.135 0.126 5.49716
3 .447c 0.2 0.188 5.29993
4 .496d 0.246 0.231 5.1574
5 .516e 0.266 0.247 5.10227
6 .530f 0.281 0.259 5.06437
7 .544g 0.296 0.27 5.02446
Notes: a. Predictors: (Constant), optimism; b. Predictors: (Constant), optimism, loan; c. predictors: (constant), optimism, loan, marital status; d. predictors: (constant), optimism, loan, marital status, ₹7,50,000 to ₹10,00,000; e. predictors: (constant), optimism, loan, marital status, ₹7,50,000 to ₹10,00,000, deliberative thinking; f. predictors: (constant), optimism, loan, marital status, ₹7,50,000 to ₹10,00,000, deliberative thinking, 36-45; g. predictors: (constant), optimism, loan, marital status, ₹7,50,000 TO ₹10,00,000, Deliberative Thinking, 36-45, Full time salaried.
Table 6. Analysis of Variance Results (ANOVA)a.
Table 6. Analysis of Variance Results (ANOVA)a.
Model Sum of Squares df Mean Square F Sig.
1 Regression 549.972 1 549.972 17.194 .000b
Residual 6333.448 198 31.987
Total 6883.42 199
2 Regression 930.329 2 465.165 15.393 .000c
Residual 5953.091 197 30.219
Total 6883.42 199
3 Regression 1377.916 3 459.305 16.352 .000d
Residual 5505.504 196 28.089
Total 6883.42 199
4 Regression 1696.651 4 424.163 15.947 .000e
Residual 5186.769 195 26.599
Total 6883.42 199
5 Regression 1832.993 5 366.599 14.082 .000f
Residual 5050.427 194 26.033
Total 6883.42 199
6 Regression 1933.388 6 322.231 12.564 .000g
Residual 4950.032 193 25.648
Total 6883.42 199
7 Regression 2036.344 7 290.906 11.523 .000h
Residual 4847.076 192 25.245
Total 6883.42 199
Notes: a Dependent Variable: SUM_FRT; b Predictors: (Constant), optimism; c Predictors: (Constant), optimism, loan; D predictors: (constant), optimism, loan, marital status; E predictors: (constant), optimism, loan, marital status, annual income: ₹7,50,000 to ₹10,00,000; F predictors: (constant), optimism, loan, marital status, annual income: ₹7,50,000 to ₹10,00,000, deliberative thinking; G predictors: (constant), optimism, loan, marital status, annual income: ₹7,50,000 to ₹10,00,000, deliberative thinking, age: 36-45; H predictors: (constant), optimism, loan, marital status, annual income: ₹7,50,000 to ₹10,00,000, deliberative thinking, age: 36-45, Full time salaried.
Table 7. Coefficients Table.
Table 7. Coefficients Table.
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 21.173 1.524 13.896 0
optimism 0.434 0.105 0.283 4.147 0
2 (Constant) 19.867 1.526 13.018 0
optimism 0.405 0.102 0.264 3.97 0
LOAN 2.83 0.798 0.236 3.548 0
3 (Constant) 18.334 1.521 12.057 0
optimism 0.384 0.099 0.25 3.899 0
LOAN 3.853 0.811 0.321 4.753 0
MARITAL STATUS 3.267 0.818 0.269 3.992 0
4 (Constant) 18.407 1.48 12.438 0
optimism 0.424 0.097 0.276 4.391 0
LOAN 4.039 0.791 0.337 5.109 0
MARITAL STATUS 2.912 0.803 0.24 3.627 0
₹7,50,000 TO ₹10,00,000 -3.323 0.96 -0.22 -3.462 0.001
5 (Constant) 21.615 2.027 10.663 0
optimism 0.441 0.096 0.287 4.6 0
LOAN 3.718 0.795 0.31 4.678 0
MARITAL STATUS 3.021 0.796 0.249 3.796 0
₹7,50,000 TO ₹10,00,000 -3.352 0.95 -0.222 -3.529 0.001
DELIBERATIVE THINKING -0.424 0.185 -0.144 -2.289 0.023
6 (Constant) 21.494 2.013 10.678 0
optimism 0.448 0.095 0.291 4.699 0
LOAN 4.062 0.808 0.338 5.028 0
MARITAL STATUS 2.788 0.799 0.229 3.491 0.001
₹7,50,000 TO ₹10,00,000 -3.635 0.954 -0.241 -3.812 0
DELIBERATIVE THINKING -0.378 0.185 -0.129 -2.036 0.043
36-45 -1.806 0.913 -0.128 -1.978 0.049
7 (Constant) 23.076 2.145 10.756 0
optimism 0.433 0.095 0.282 4.571 0
LOAN 3.644 0.828 0.304 4.402 0
MARITAL STATUS 3.213 0.82 0.264 3.92 0
₹7,50,000 TO ₹10,00,000 -3.631 0.946 -0.24 -3.837 0
DELIBERATIVE THINKING -0.425 0.185 -0.145 -2.291 0.023
36-45 -1.929 0.908 -0.136 -2.124 0.035
FULL TIME SALARIED -1.607 0.796 -0.136 -2.019 0.045
Table 8. Excluded Variables.
Table 8. Excluded Variables.
Model Beta In t Sig. Partial Correlation Collinearity Statistics
Tolerance
1 LOAN .236 3.548 0.00 0.245 0.994
FULL TIME SALARIED -.103 -1.516 0.131 -0.107 0.992
SELF EMPLOYED .055 0.803 0.423 0.057 0.989
PRIVATE JOB .020 0.287 0.775 0.02 0.983
BUSINESS -.001 -0.009 0.993 -0.001 0.974
PROFESSIONAL .112 1.655 0.099 0.117 1
AGRICULTURAL .051 0.746 0.456 0.053 0.999
26-35 .048 0.695 0.488 0.049 0.997
36-45 -.076 -1.114 0.267 -0.079 0.999
46-55 -.007 -0.1 0.921 -0.007 0.994
56-65 -.026 -0.375 0.708 -0.027 1
66 AND ABOVE .006 0.088 0.93 0.006 0.923
₹5,00,000 TO ₹7,50,000 -.037 -0.535 0.593 -0.038 0.999
₹7,50,000 TO ₹10,00,000 -.219 -3.272 0.001 -0.227 0.985
₹10,00,000 TO ₹12,50,000 .197 2.942 0.004 0.205 0.996
₹12,50,000 TO ₹15,00,000 .155 2.295 0.023 0.161 1
ABOVE ₹15,00,000 .114 1.666 0.097 0.118 0.982
PERSONALITY -.080 -1.151 0.251 -0.082 0.971
DELIBERATIVE THINKING -.168 -2.494 0.013 -0.175 0.996
MARITAL STATUS .168 2.491 0.014 0.175 0.999
UP TO 12TH .057 0.833 0.406 0.059 0.995
GRADUATION .057 0.837 0.404 0.06 0.993
POST-GRADUATION .187 2.785 0.006 0.195 0.993
GENDER .032 0.464 0.643 0.033 0.979
2 FULL TIME SALARIED -.029 -0.415 0.679 -0.03 0.885
SELF EMPLOYED -.010 -0.145 0.885 -0.01 0.915
PRIVATE JOB .074 1.075 0.284 0.077 0.938
BUSINESS -.022 -0.321 0.749 -0.023 0.967
PROFESSIONAL .123 1.861 0.064 0.132 0.998
AGRICULTURAL -.014 -0.208 0.836 -0.015 0.925
26-35 .084 1.251 0.212 0.089 0.976
36-45 -.137 -2.029 0.044 -0.143 0.946
46-55 -.045 -0.673 0.502 -0.048 0.969
56-65 -.048 -0.715 0.476 -0.051 0.992
66 AND ABOVE .001 0.015 0.988 0.001 0.922
₹5,00,000 TO ₹7,50,000 -.005 -0.073 0.942 -0.005 0.981
₹7,50,000 TO ₹10,00,000 -.249 -3.839 0.00 -0.264 0.973
₹10,00,000 TO ₹12,50,000 .146 2.15 0.033 0.152 0.931
₹12,50,000 TO ₹15,00,000 .135 2.038 0.043 0.144 0.992
ABOVE ₹15,00,000 .106 1.583 0.115 0.112 0.98
PERSONALITY -.043 -0.636 0.526 -0.045 0.947
DELIBERATIVE THINKING -.125 -1.851 0.066 -0.131 0.954
MARITAL STATUS .269 3.992 0.00 0.274 0.9
UP TO 12TH .039 0.584 0.56 0.042 0.989
GRADUATION .025 0.379 0.705 0.027 0.974
POST-GRADUATION .204 3.126 0.002 0.218 0.988
GENDER -.015 -0.221 0.825 -0.016 0.942
3 FULL TIME SALARIED -.107 -1.522 0.13 -0.108 0.826
SELF EMPLOYED .051 0.738 0.461 0.053 0.872
PRIVATE JOB .020 0.291 0.771 0.021 0.898
BUSINESS -.014 -0.222 0.824 -0.016 0.966
PROFESSIONAL .085 1.315 0.19 0.094 0.974
AGRICULTURAL .051 0.749 0.455 0.054 0.873
26-35 -.021 -0.295 0.769 -0.021 0.825
36-45 -.107 -1.629 0.105 -0.116 0.932
46-55 .063 0.894 0.372 0.064 0.827
56-65 -.008 -0.124 0.902 -0.009 0.968
66 AND ABOVE .033 0.496 0.62 0.036 0.909
₹5,00,000 TO ₹7,50,000 -.022 -0.339 0.735 -0.024 0.976
₹7,50,000 TO ₹10,00,000 -.220 -3.462 0.001 -0.241 0.957
₹10,00,000 TO ₹12,50,000 .143 2.188 0.03 0.155 0.931
₹12,50,000 TO ₹15,00,000 .099 1.536 0.126 0.109 0.971
ABOVE ₹15,00,000 .101 1.574 0.117 0.112 0.98
PERSONALITY -.048 -0.732 0.465 -0.052 0.947
DELIBERATIVE THINKING -.141 -2.179 0.031 -0.154 0.95
UP TO 12TH .054 0.837 0.404 0.06 0.986
GRADUATION .076 1.157 0.249 0.083 0.941
POST-GRADUATION .134 1.976 0.05 0.14 0.88
GENDER .054 0.79 0.431 0.056 0.883
4 FULL TIME SALARIED -.103 -1.515 0.131 -0.108 0.826
SELF EMPLOYED .045 0.676 0.5 0.048 0.871
PRIVATE JOB -.005 -0.074 0.941 -0.005 0.887
BUSINESS .013 0.199 0.842 0.014 0.951
PROFESSIONAL .099 1.574 0.117 0.112 0.97
AGRICULTURAL .042 0.627 0.532 0.045 0.871
26-35 -.009 -0.124 0.901 -0.009 0.823
36-45 -.144 -2.237 0.026 -0.159 0.911
46-55 .098 1.426 0.155 0.102 0.811
56-65 .014 0.224 0.823 0.016 0.958
66 AND ABOVE .019 0.29 0.772 0.021 0.905
₹5,00,000 TO ₹7,50,000 -.092 -1.403 0.162 -0.1 0.896
₹10,00,000 TO ₹12,50,000 .089 1.332 0.184 0.095 0.861
₹12,50,000 TO ₹15,00,000 .077 1.214 0.226 0.087 0.96
ABOVE ₹15,00,000 .070 1.109 0.269 0.079 0.958
PERSONALITY -.095 -1.469 0.143 -0.105 0.91
DELIBERATIVE THINKING -.144 -2.289 0.023 -0.162 0.95
UP TO 12TH .025 0.399 0.691 0.029 0.968
GRADUATION .119 1.844 0.067 0.131 0.911
POST-GRADUATION .107 1.607 0.11 0.115 0.867
GENDER .069 1.035 0.302 0.074 0.88
5 FULL TIME SALARIED -.127 -1.865 0.064 -0.133 0.81
SELF EMPLOYED .084 1.244 0.215 0.089 0.825
PRIVATE JOB -.019 -0.294 0.769 -0.021 0.879
BUSINESS -.028 -0.431 0.667 -0.031 0.882
PROFESSIONAL .100 1.615 0.108 0.115 0.97
AGRICULTURAL .083 1.224 0.223 0.088 0.821
26-35 -.055 -0.779 0.437 -0.056 0.762
36-45 -.128 -1.978 0.049 -0.141 0.897
46-55 .120 1.756 0.081 0.125 0.798
56-65 .030 0.481 0.631 0.035 0.946
66 AND ABOVE -.010 -0.145 0.885 -0.01 0.872
₹5,00,000 TO ₹7,50,000 -.079 -1.219 0.225 -0.087 0.889
₹10,00,000 TO ₹12,50,000 .107 1.606 0.11 0.115 0.851
₹12,50,000 TO ₹15,00,000 .063 0.99 0.323 0.071 0.949
ABOVE ₹15,00,000 .081 1.296 0.197 0.093 0.953
PERSONALITY -.083 -1.284 0.201 -0.092 0.903
UP TO 12TH .045 0.705 0.481 0.051 0.952
GRADUATION .111 1.732 0.085 0.124 0.908
POST-GRADUATION .096 1.445 0.15 0.103 0.861
GENDER .057 0.872 0.384 0.063 0.875
6 FULL TIME SALARIED -.136 -2.019 0.045 -0.144 0.807
SELF EMPLOYED .101 1.498 0.136 0.107 0.814
PRIVATE JOB -.011 -0.162 0.871 -0.012 0.875
BUSINESS -.026 -0.393 0.695 -0.028 0.881
PROFESSIONAL .091 1.476 0.142 0.106 0.964
AGRICULTURAL .101 1.496 0.136 0.107 0.808
26-35 -.100 -1.383 0.168 -0.099 0.705
46-55 .070 0.891 0.374 0.064 0.599
56-65 .009 0.145 0.885 0.01 0.918
66 AND ABOVE -.021 -0.326 0.745 -0.024 0.865
₹5,00,000 TO ₹7,50,000 -.097 -1.49 0.138 -0.107 0.875
₹10,00,000 TO ₹12,50,000 .120 1.814 0.071 0.13 0.843
₹12,50,000 TO ₹15,00,000 .060 0.958 0.339 0.069 0.949
ABOVE ₹15,00,000 .090 1.446 0.15 0.104 0.949
PERSONALITY -.085 -1.321 0.188 -0.095 0.902
UP TO 12TH .044 0.708 0.48 0.051 0.952
GRADUATION .126 1.964 0.051 0.14 0.898
POST-GRADUATION .093 1.423 0.156 0.102 0.861
GENDER .047g 0.716 0.475 0.052 0.868
7 SELF EMPLOYED -.166 -0.945 0.346 -0.068 0.119
PRIVATE JOB .042 0.605 0.546 0.044 0.761
BUSINESS -.059 -0.892 0.374 -0.064 0.834
PROFESSIONAL .111 1.799 0.074 0.129 0.945
AGRICULTURAL .048 0.623 0.534 0.045 0.628
26-35 -.094 -1.305 0.194 -0.094 0.703
46-55 .067 0.862 0.39 0.062 0.599
56-65 -.005 -0.076 0.939 -0.006 0.907
66 AND ABOVE -.055 -0.826 0.41 -0.06 0.817
₹5,00,000 TO ₹7,50,000 -.072 -1.082 0.28 -0.078 0.834
₹10,00,000 TO ₹12,50,000 .111 1.684 0.094 0.121 0.839
₹12,50,000 TO ₹15,00,000 .048 0.764 0.446 0.055 0.939
ABOVE ₹15,00,000 .058 0.895 0.372 0.065 0.865
PERSONALITY -.099 -1.557 0.121 -0.112 0.892
UP TO 12TH .075 1.182 0.239 0.085 0.907
GRADUATION .115 1.812 0.072 0.13 0.892
POST-GRADUATION .093 1.436 0.153 0.103 0.861
GENDER .038 0.583 0.56 0.042 0.864
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated