Relationship between Income and Risk taking

Background

            Different research studies have documented a relationship between risk taking behavior and personal level of income. Accordingly, risk taking behaviors encompass venturing into decisions of chance where the outcome can be costly or beneficial. On the other hand, income level includes the total money that individuals receive on regular patterns from their investments or work done – indicating that it is a compensation for time, skills and monetary assets invested. A study by Kanbur (2009) examined the relationship between these variables with a claim that income inequality was a driving force for risk taking behaviors in a society. Conversely, patterns of risk taking in professional choice can be traced back to Adam Smith’s research on societal inequalities arising from types of unemployment – which emphasized that compensation in the labor market differ depending on the probability of success. Therefore, such probability explains the development of chance and choice, and the distribution of personal income. Ideally, individuals are faced with a trade-off between making choice among many alternatives which involve risk, a key aspect which Kanbur (2009) claims to be driven by the level of income.

            Another study by MacDonald, Piquero and Valois (2006) assessed the relationship between risk taking behaviors, youth violence and life satisfaction. The level of life satisfaction was measured through income level of the participants. The study found that high levels of income level (which is associated with high life satisfaction) were associated with reduced chances of being involved in youth violence and other risk-taking behaviors such as unprotected sex, alcoholism and drugs. The study was later advanced by Abber et al. (2016) in evaluating conflict resolution programs which concluded that to reduce risk behaviors within a society increasing income opportunities for the youth to raise their income would be effective in reducing such behaviors. Therefore, the two studies depict that income inequality in a society is a potential factor which triggers increased risky behaviors. Another study by West and Worthington (2014) assessed how personal attributes was associated with financial risk taking, taking a case study of Australian population.

Ideally, personal attributes were measured using the level of household income and labor dynamics. West and Worthington (2014) claimed that personal preferences for taking financial risky decisions have high influence on their financial decision-making process. The study added that risk-averse investors are attracted by safe investments which are associated with low yields but less risk. One differentiating feature between high chances of risk-taking decisions and low risky decisions is the level of income and expected returns. The study noted that individuals, who were associated with high income level, had a high chance of taking risky-behaviors (West and Worthington, 2014). Conversely, high income individuals were seen to be aggressive in looking to creating more wealth and had a high chance of taking risky financial decisions expecting high returns. Also, the study found that risky behavior decisions increased with age until the age mark of 65 years, and reduced significantly thereafter. Further, West and Worthington (2014) assessed if there was a significance difference between risky behaviors between males and females – with men having a high chance of taking risky behavior holding income constant. 

Perotti (2003) investigated the relationship between risk taking and income inequality and found that inequalities among the high income class individuals promote the growth of risky ventures among the rich people. The research used a behavioral study approach to assess how these variables behaved. Accordingly, based on the traditional decision-making theory, risky behaviors are determined by the expected pay-off associated with a given venture. Therefore, individuals with high income always associated risky ventures with high returns and will prefer such risky activities to generate more income. However, a counterargument study by Hill and Buss (2010) claimed that the low income individuals may adopt the social comparison phenomenon – which makes them compare their social status with the rich people and to close this gap, the low income individuals may be driven to making risky behaviors to close the income gap. Therefore, such an aspect points that, based on the social comparison approach, low income individuals would take risky ventures particularly in generation of wealth to close the high-low income gap.

 Fried et al. (2014) added that the social comparison approach, income was positively correlated with chances of taking risky behaviors by the low income group with a motive of gaining more monetary wealth to break-even with the high income individuals. Conversely, the rich individuals may avoid risky behavior with a motive of avoiding losing their wealth to fall behind the poor individuals. In so doing, the social comparison approach considers a negative correlation between high income (wealth) and taking risky behavior among the wealthy groups. However, Rhode and Rhode (2011) dismissed the claim by Hill and Buss (2010) and Fried et al. (2014) by claiming that, the fact that risky ventures may demand high levels of capital which these low income people may be lacking – hence risky behaviors by this group may be motivated by other social consideration. However, these studies are more oriented to financial decisions than social and behavioral decisions and the social comparison model may not exactly depict how income may affect behavioral and social behaviors.

            Another counterargument study by Agnew (2001) investigated how income disparities explained taking chancy behaviors such as delinquency and crime. The study adopted the General Strain Theory to assess the relationship between taking chancy behaviors (crime and delinquency) and income level. Ideally, the model postulates that stressors in human life have a high chance of inducing a negative emotion and frustrations in life. Therefore, such emotions exert pressure to the individual for adopting a corrective action where crime is one of these actions. Other stressors such as poverty (low income) have a high chance of stealing or taking other risky chances to deliver oneself from such a straining situation. Therefore, Agnew (2001) a negative correlation relationship between income and taking risky decisions such as stealing (behavioral) and drug trafficking to make more money.

Hypothesis statement

Given the above accounts on the relationship between income and risky decision making, there is no consensus between the ideal relationships between these two variables because there are different approaches exploring the same using different models. For example, from a behavioral approach low income is seen as a stressor which exerts strain on the individual to execute a risky decision such as stealing, gambling and other decisions which would increase individual’s wealth position. Contrary, from a financial perspective low income is associated with low risky behaviors while individuals with high income have a chance of taking risky decisions or investments. This study uses the financial approach to examine how these two variables are related. Stated as the study hypotheses are:

1. There is a positive correlation between income and risk-taking decisions
2. Women have a lower chance of taking high risky decisions – Risk-taking behavior is lower in females than males.
3. Risk-taking is higher in single individuals than married individuals

Method

            The study adopted a random sampling approach to recruit 400 participants from an undisclosed organization. The participants were given the informed consent for signing which explained the purpose, benefits, objectives and potential impacts of the study. Thereafter, the participants were given a study questionnaire which focused on assessing their social, demographic, and perceptions regarding their financial positions such as financial comfortability, risk-taking behaviors, lifestyle, and (satisfaction) happiness among other aspects. The data collected was recorded in a SPSS spreadsheet for analysis. The researcher was interested in assessing the correlation between the levels of income and risk-taking behavior and if risk-taking varied with gender.

Findings

Descriptive statistics

Table 1: Descriptive statistics

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
Income	400	$0	$285,000	$47,112.92	$40,477.089
Age	400	21	63	33.12	9.453
Risk-Taking	400	15	58	36.51	8.343
Valid N (listwise)	400

            Based on the above descriptive analysis, it shows that age of the participants ranged between 21 and 63 years (M = 33.12, SD = 9.453). Income ranged between $0 and $285,000 (M = $47,112.92, SD = $40,477.089) while the Mean risk-taking score ranged between 15 and 58 (M = 36.51, SD = 8.343).

Gender

Table 2: Gender frequency

Gender
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	200	50.0	50.0	50.0
Female	200	50.0	50.0	100.0
Total	400	100.0	100.0

            The sample had 50% males and 50% females indicating that the sample representation was fair.

Marital status

Table 3: Marital status frequency

Marital Status
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Single	163	40.8	40.8	40.8
Married	237	59.3	59.3	100.0
Total	400	100.0	100.0

            40.8% of the study participants were single while 59.2% of them were married as seen in the table above.

Inferential statistics

Correlational analysis

Table 4: Correlation between Income and Risk-taking

Correlations
	Income	Risk-Taking
Income	Pearson Correlation	1	.228**
Sig. (2-tailed)		.000
N	400	400
Risk-Taking	Pearson Correlation	.228**	1
Sig. (2-tailed)	.000
N	400	400
**. Correlation is significant at the 0.01 level (2-tailed).

            There was a significant positive weak correlation between income level of an individual and their risk-taking behavior (R = 0.228, p = 0.000 < 0.05). Such an aspect shows that risk-taking decisions and behaviors increase with income level. Therefore, this analysis supported the hypothesis I – which suggested a positive correlation between these two variables.

Table 5: Group Mean analysis across gender  

Descriptives
Risk-Taking
	N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean	Minimum	Maximum
Lower Bound	Upper Bound
Male	200	38.80	7.765	.549	37.71	39.88	15	58
Female	200	34.23	8.294	.586	33.07	35.39	15	53
Total	400	36.51	8.343	.417	35.69	37.33	15	58

ANOVA
Risk-Taking
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	2083.923	1	2083.923	32.287	.000
Within Groups	25688.015	398	64.543
Total	27771.938	399

The second hypothesis suggested that men had a more chance of taking risky decisions than women. Therefore, this focused on assessing if the scores for risk-taking were different across males and females. Accordingly, the study there was a significant difference between the group means with male having higher risk-taking score (F _{1, 398} = 32.287, p = 0.000 < 0.05). Therefore this analysis supported the second hypothesis in this study.

Table 6: Group mean analysis across marital status

Descriptives
Risk-Taking
	N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean	Minimum	Maximum
Lower Bound	Upper Bound
Single	163	36.30	8.296	.650	35.02	37.58	15	58
Married	237	36.66	8.390	.545	35.58	37.73	15	58
Total	400	36.51	8.343	.417	35.69	37.33	15	58

ANOVA
Risk-Taking
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	12.351	1	12.351	.177	.674
Within Groups	27759.586	398	69.748
Total	27771.938	399

            The third hypothesis claimed single individuals had a high chance of making risky decisions when compared to married individuals. Therefore, the analysis focused on assessing if the group means were statistically different. However, there was no significant difference in group means (F _{1, 398} = 0.177, P = 0.674 > 0.05) indicating that the null hypothesis was rejected and this did not support the third hypothesis.

Conclusion

            Using the financial inequality approach, income is positively correlated with risk-taking decisions as postulated by Kanbur (2009), Abber et al. (2016), and Piquero and Valois (2006) in their research on this field. Therefore, high income individuals would tend to consider risky investments which are associated with high returns – hence opportunities for making more money. The study makes it clear that men tend to make more risky decisions while marital status does not affect risk-taking behavior. A future research may focus on the behavioral approach using the General Strain Theory to assess if there is any change in the study findings.

References

Aber, J. L., Brown, J. L., Chaudry, N., Jones, S. M., & Samples, F. (1996). The evaluation of the Resolving Conflict Creatively program: Anoverview. American Journal of Preventive             Medicine, 12(Suppl. 5), 82-90

Agnew, R. (November 01, 2001). Building on the Foundation of General Strain Theory:   Specifying the Types of Strain Most Likely to Lead to Crime and Delinquency. Journal    of Research in Crime and Delinquency, 38, 4.)

Fried, A., De Miranda, K. L., & Schmidt, U. (2014). “Insurance demand and social comparison:   An experimental analysis”. Journal of Risk and Uncertainty, 48(2): 97-109.

Hill, S. E. & Buss, D. M. (2010). “Risk and relative social rank: positional concerns and risky        shifts in probabilistic decision-making.” Evolution and Human Behavior 31: 219-226.

Kanbur, S. M. (August 01, 2009). Of Risk Taking and the Personal Distribution of Income.          Journal of Political Economy, 87, 4, 769-797.

MacDonald, J., Piquero, A., & Valois, R. (April 01, 2006). The relationship between life   satisfaction, risk-taking behaviors, and youth violence. Violence & Abuse Abstracts, 12,                    2.)

Rohde, I. & Rohde, K. (2011). “Risk attitudes in a social context.” Journal of Risk and     Uncertainty, 43: 205-225.

West, T., & Worthington, A. C. (January 01, 2014). Personal attributes and financial risk-taking   in Australia. Jassa, 1, 25-31.