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Demographic and Socioeconomic Determinants of Financial Risk Aversion

Paper Type: Free Essay Subject: Finance
Wordcount: 2578 words Published: 8th Feb 2020

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I. Project Outline


 An individual’s level of risk aversion has long been considered to be a critical factor in financial decision making. Riskier assets are associated with higher rates of return since investors who take higher risks need to be compensated for them and so they earn more than those who prefer safer assets (concept known as the risk premium). Since the more risk averse an investor is, the less their expected returns from their investments are. And so, it is widely assumed that lower risk aversion is associated with higher levels of wealth accumulation (Shaw,1996). But financial risk aversion has implications in other areas, as well. Financial advisers and retirement policy makers are concerned about how families allocate their savings for retirement and whether they are exposed to financial distress in the event of an economic shock. At a corporate level, the more risk tolerant a manager is financially, the more likely they are able to implement risky decisions which can generate a boost in profits for the company (Johnson and Powel, 1994). The level of risk aversion is, therefore, an important factor in determining promotion in the workplace. It is important to establish which characteristics, whether innate or developed throughout life, are determinant for an individual’s degree of risk aversion in order to discover whether there are any improvements that can be done to alleviate it, potentially leading to better financial decisions being employed.


 Either at an individual or at a collective level (i.e. household), financial risk aversion reaches into almost every aspect of social and economic life (Grable, 2000). But perhaps it does now even more, after the Financial Crisis of 2008, than before, since riskless alternatives of investments have reached insignificantly low, or even negative rates of return, which incentivised investors to opt for riskier investment opportunities. It would be interesting to compare findings from data from post-financial crisis to the previous ones and to examine how strong the determinants of risk aversion were then compared to now. Additionally, the research on this topic is also important in determining whether there are any racial and gender differences that could undermine performance in positions of high financial risk, such as asset or investment management. It would also help in providing an explanation for payment gaps in the workplace, as women are believed to negotiate less their wages compared to their male counterparts (Chauvin and Ash, 1994).


 The objective of this paper is to examine whether the determinants of risk aversion that are innate, fixed, unchangeable or “biopsychological” (Irwin, 1993), such as gender, race or age are stronger than those which are nurtured, developed through life, or “environmental” (Irwin, 1993), such as income, net worth, education or marital status.

 The nature of the variables mentioned above, and the previous literature are best suitable for an objective measurement of risk aversion, such as the proportion of risky assets held in the overall savings and investments portfolio of an individual or household. Additionally, this research is not limited to using only objective means of measurement, but also to explore a subjective measurement, as well, in the form of a self-assessment or situational questions.


 Scholars have tried to identify factors which affect an individual’s risk behaviour in order to explain why some households invest more in risky assets than others. Since stocks have the highest rate of return in the long term, it is only rational for them to hold stocks in order to maximise lifetime returns (Siegel, 1994). Also, studying the changes in risk aversion through different periods of the financial cycle would enable policy makers and financial advisers to make more accurate decisions meant to influence someone’s willingness to invest or consume (Chang and Xiao, 2017). This was an important factor to consider on the eve of the financial crisis. In practicality, understanding the determinants of risk aversion should help asset managers and pension funds to better tailor their products in accordance to the requirements of their customers. My contribution to the literature will be exploring comparatively to what extent natural factors affect financial risk aversion relative to developed ones and determining whether or not current data results are consistent with the ones conducted in the period preceding the financial crisis.


 In my analysis, similar to previous literature (Yao et al., 2005) I will use the Pratt-Arrow definition of risk aversion as objective measurement, which says that for rational decision makers, the proportion of wealth invested in risky assets is lower for individuals with a higher risk aversion (Pratt, 1964). Therefore, the variable of interest should be a ratio that reflects how much of someone’s wealth is held in risky assets, such as common stock. Since the purpose of this research is to determine the effects of demographical, socioeconomical and psychological factors on financial risk behaviour, similarly to Grable and Joo’s paper (2004), these characteristics shall be the main explanatory variables, alongside those related to environmental factors, which measure individual and family financial attributes (wealth, income, home ownership etc.).

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 This paper will use either the 2016 Survey of Consumer Finances (abbreviated: SCF), the most recent version released by the Federal Reserve, for individuals and households in the US, or the 2018 Wealth and Assets Survey, collected by the Office of National Statistics for UK respondents. The first one is cross-sectional, whereas the second is longitudinal. Both data sets contain information on the explanatory variables aforementioned, although there are limitations to each of them. Still, it is important that they address the key dependent variable of this research, which is the proportion of wealth held in risky assets, similarly to the paper by Jianakoplos & Bernasek (1998). In terms of subjective measurement, the SCF offers a good option under the form of the question: “Which of the statements on this page comes closest to the amount of financial risk that you and your (spouse/partner) are willing to take when you save or make investments?” (Yao et al., 2005). The equation of the multivariate regression(s), therefore, can take the following form:

 Financial risk aversion= fn. (biopsychological factors, environmental factors).

 In case my dependent variable is the ratio mentioned earlier, it will take values between 0 and 1, in which case a Logit or Tobit model would be most appropriate, to allow for both an upper and lower limit on this variable (Jianakopolos et al., 1998). In case the variable will be the subjective one, it is likely to be categorical, therefore the form of the regression will be an OLS multivariate regression.

II. Literature Review

 The study of risk tolerance and aversion within financial planning has focused extensively on identifying demographic and socioeconomic factors that could explain differences between individuals or between households. Findings in this field can provide a valuable insight into the attitudes and behaviours towards potential investment opportunities which could be of great benefit to financial advisers, and pension policy regulators who are interested in promoting more suitable choices for investing to achieve a financial goal.

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 Empirical and theoretical frameworks in literature (Yao et al., 2005; Jianakoplos and Bernasek, 1998; Lai and Xiao, 2010; Grable and Joo, 2004 etc.) identified the following characteristics as being highly correlated with the degree of financial risk aversion of an individual or household: gender, race, education, age, marital status, number of children, income, net worth. Focuses on gender and race are important in the pursuit of equality and diversity in positions of financial risk.

Focus on gender

 Using an objective measurement of risk tolerance, as defined by the ratio of risky assets to total wealth, Jianakoplos & Bernasek (1998), using the Survey of Consumer Finances (SCF) from 1989, reported that single women exhibit higher relative levels of risk aversion than single men when facing financial decisions. They also notice that gender differences are influenced by age, race and number of children. The findings are important as they make the connection between greater risk aversion, asset-allocation practices that result in relatively lower levels of wealth and the reason why women have lower earnings relative to men. A shortcoming of using the SCF, however, is that it covers households, not individuals, and in the case of married couples, it is difficult to determine whether the decision to invest is done by men or women.

 The use of experimental analysis has also been a popular alternative to the objective measurement. Papers such as Powel & Ansic (1997) and Eckel & Grossman (2002) examine how students decide when faced with gambles or investment choices with different levels of risk and they also measure how fast they respond to these questions. The advantage of experimental measurements is that they offer insight on some behavioural perspectives of financial decision making, such as overconfidence, loss aversion or framing effects, which are believed to play a major role in the level of risk tolerance (De Bondt & Thaler, 1995). However, experimental studies usually lack the statistical power and number of variables as external determinants that the SCF provides, which is why this research paper aims to use an extensive data set.

Focus on race/ethnicity

 Papers such as Gutter et al. (1999) and Yao et al. (2005) focus on racial and ethnic differences in risk tolerance by using the data from the Survey of Consumer Finances in multiple years. The differences between the two papers is that the first uses a binary choice model by focusing on the probability that a household holds stocks in their asset portfolio, whereas the second one is looking at the willingness to take financial risk rather than portfolio allocation. The argument for the first is that the research is not focusing on the amount invested in risky assets, but on the choice to have done so. The second paper argues that asset allocation is not a good measurement since financial tolerance should also reflect future investment behaviour, not just the present, especially if there are differences in initial means of investment between households. Therefore, they use a hypothetical question on the amount of financial risk one is willing to take. For the purpose of this research, however, I am looking for a quantitative measurement of risk aversion such as risky asset ownership, since this could be a direct determinant of the higher level of wealth (Shaw, 1996).

 Regardless of the dependent variable, both studies found that Blacks and Hispanics are less likely to hold risky assets or to take some financial risk. An interesting aspect determined by the first paper is that racial differences in risk aversion are explained by racial differences in the individual determinants of risk aversion, rather than by races themselves. This conclusion serves as my motivation to do a comparative analysis on the contribution of environmental and natural factors in determining financial risk aversion.

Focus on other factors

 Xiao et al. (2001), also using the SCF, reported that households whose head has a higher education are more likely to have a higher degree of financial risk tolerance. Black & Devereux (2018) used wealth data from Swedish population to estimate the effect of education on stock market investments and concluded that men increase weight of stocks in portfolios by 10% for an extra year of education. The education effect was inconclusive for women.

 Love (2010) studied marital status and its impact on savings and portfolio decisions and predicted that married households take lower risks. This paper is important due to the introduction of panel data, as it shows time differences in financial tolerance with respect to transitions in marital status.

 Chiang & Xiao’s (2017) paper employs panel data even more extensively, as they examine how the Financial Crisis of 2008 affected household risk tolerance and how several determinants, such as race and education, influence differently the increase in risk aversion.

 But perhaps the most important paper for this research is Grable & Joo (2004), which examined the effects of “biopsychological” and “environmental” (Irwin, 1993) factors on financial risk tolerance. Their aim was to determine which set of factors is the most important. Using an OLS regression on data collected from faculty and staff of two major US universities, with the dependent variable, financial risk tolerance, being measured based on a set of answers to five questions of risk assessment proposed by MacCrimmon and Wehrung (1986), they reported that education, net worth, household income, financial knowledge and marital status (all environmental factors) were significant, whereas only one biopsychological factor (self-esteem) was significantly related to financial risk tolerance. These findings suggest that, overall, environmental factors have more influence than biopsychological ones.

 The aim of my paper is to exceed these results to a larger data set (possibly the SCF) and to find out if after the financial crisis the reported effects are different. For the examination of time changes in risk aversion, the use of panel data analysis would be the most suitable, similarly to Chiang & Xiao’s paper.


  1. Shaw, 1996
  2. Johnson and Powell, 1994
  3. Grable, 2000
  4. Chauvin and Ash, 2004
  5. Irwin, 1993
  6. Siegel, 1994
  7. Pratt, 1964
  8. Chiang and Xiao, 2017
  9. Yao et al., 2005
  10. Grable and Joo, 2004
  11. Jianakoplos and Bernasek, 1998
  12. Lai and Xiao, 2010
  13. Powel and Ansic, 1997
  14. Eckel and Grossman, 2002
  15. De Bondt and Thaler, 1995
  16. Gutter et al., 1999
  17. Xiao et al., 2001
  18. Black and Devereux, 2018
  19. Love, 2010
  20. MacCrimmon and Wehrung, 1986


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