An Exploration of the Role of Risk Tolerance in the Financial Empowerment of Black Women

Data for this study were obtained from the National Longitudinal Survey of Youth 1979 (NLSY79) dataset. The NLSY79, which is sponsored by the Bureau of Labor Statistics (BLS) of the United States Department of Labor, includes a set of surveys designed to gather information, at multiple points in time, on the labour-market experiences and other significant life events of a panel of men and women who were young adults in 1979 (i.e., survey respondents were ages 14 to 22 when first interviewed in 1979). The NLSY79 is a representative sample of 12,686 young men and women born from 1957 through 1964 and living in the United States when the survey began. Data were, and continue to be, gathered via interviews. At the project’s outset, data were collected annually from 1979 to 1994. Since 1994, data have been collected biennially.

Data for this study were downloaded from the NLSY79 investigator website in February 2021 and were recoded to include only women, with male respondents removed from the dataset.

While we acknowledge that using older data may be problematic in drawing contemporary conclusions, we believe the strengths offered by the NLSY79 dataset outweigh the potential limitations. Specifically, the NLSY79 includes validated measures of financial risk tolerance, but these were administered only in select waves (1993, 2002, 2004, 2006, 2008, and 2010). These data have not been replicated in newer surveys or subsequent waves of this cohort. These measures offer rare and valuable insights into individual-level behavioural and psychological traits associated with wealth accumulation, particularly when viewed through an intersectional lens. Furthermore, we believe that because risk tolerance is widely regarded in behavioural finance and psychology as a relatively stable, trait-like characteristic, especially in adulthood (e.g., Grable, 2016; Finke & Guillemette, 2016), risk attitudes do not exhibit the same volatility over time as situational factors like as market returns. Thus, relying on data from 2010 does not significantly impair our ability to draw valid inferences about long-term patterns in wealth formation or financial behaviour.

Moreover, incorporating a historical perspective is critical when attempting to understand persistent wealth disparities. The wealth gap between Black women and other demographic groups is not a recent phenomenon; it is a result of decades of cumulative disadvantage. A contemporary snapshot alone cannot fully account for the generational influences and policy contexts that shape economic outcomes over time. Using data from 2010 allows us to explore how financial attitudes and structural constraints evolve in tandem. For example, analysing risk tolerance patterns before the COVID-19 pandemic provides a rich narrative about why Black women continue to face compounded barriers to wealth accumulation, despite targeted interventions aimed at reducing the wealth gap. To provide confidence in the findings, robustness checks were undertaken for each model using the 2010 weighting variable in the NLSY79. The core findings reported in this study were observed with and without the weighting variable.

In accordance with Irwin and Millstein’s (1986) and Irwin’s (1993) biopsychosocial model of risk-taking behaviours, this study evaluated six variables: gender, race, risk tolerance, wealth, education, and gender-role attitude. Risk tolerance was used as the outcome variable in the first model, whereas wealth was used as the outcome variable in the second model. The operationalization of each variable is described in detail below.

Several analytic techniques were used to evaluate the research hypotheses. Descriptive statistics — in the form of means, standard deviations, and correlation coefficients — were calculated to provide insights about the sample and bivariate relationships between and among the variables of interest in this study. This was followed by a series of multivariate tests.

The following ordinary least squares (OLS) regression model was used to estimate the relationship between the Black female variable and risk tolerance, controlling for a respondent’s educational level, gender role attitude, and wealth situation: Eq 1. RTi=β0+β1E1+β2GR2+β3BF3+β4W4+ε1 R{T_i} = {\beta _0} + {\beta _1}{E_1} + {\beta _2}G{R_2} + {\beta _3}B{F_3} + {\beta _4}{W_4} + {\varepsilon _1} where RT_i is a respondent’s risk tolerance, E₁ is a respondent’s education in years in 2008, GR₂ is gender role attitude, BF₃ is Black female, and W_i is household wealth.

In alignment with Perry and Morris (2005), Sobel tests (Baron & Kenny, 1986) — as described by Perry and Morris (2005) — were then used to estimate the degree to which education, gender-role attitude, and wealth mediate the relationship between the variables Black female and risk tolerance. The following set of equations were used to test these mediation effects: Eq 2. Y=i1+cX Y = {i_1} + cX Eq 3. M=i2+aX M = {i_2} + aX Eq 4. Y=i3+cX+bM Y = {i_3} + cX + bM where i is the intercept coefficient, Y is the outcome variable (i.e., risk tolerance), X is the independent variable based on the mediation model being estimated, and M is the mediating variable. In the context of this study, mediation was considered present when the coefficient for X in Equation 4 was lower than the X coefficient in Equation 2 (see Hayes, 2012; Hayes & Preacher, 2010; Preacher & Hayes, 2004). The effect is referred to as inconsistent mediation in cases where the coefficient for X in Equation 4 exceeds the coefficient for X in Equation 2. Full mediation occurs when the coefficient for X in Equation 4 is zero (Baron & Kenny, 1986; Sobel, 1986).

In the context of this study, the following features were necessary to determine a mediation effect. First, the variable Black female needed to be significantly associated with the dependent variable. Second, the variable Black female needed to be significantly related to risk tolerance. Third, the effect of education, wealth, and gender-role attitude, when risk tolerance was included as a mediator, needed to be significantly associated with the dependent variable. Fourth, the effect of Black female, in the mediated model, needed to also be statistically significant. A robustness check was made using a path model. Direct, indirect, and total effects, as well as fit index scores, were used to determine the mediating roles of wealth, education, and gender-role attitude.

The following OLS regression was estimated to determine, in an exploratory manner, the degree to which wealth for Black women is moderated by risk tolerance: Eq 5. Wi=β0+β1E1+β2GR2+β3BF3+β4RT4+β5BFRT5+ε1 {W_i} = {\beta _0} + {\beta _1}{E_1} + {\beta _2}G{R_2} + {\beta _3}B{F_3} + {\beta _4}R{T_4} + {\beta _5}BFR{T_5} + {\varepsilon _1} where W_i is household wealth, E₁ is a respondent’s education in years in 2008, GR₂ is gender role attitude, BF₃ is Black female, RT₄ is risk tolerance, which was hypothesized to be an endogenous variable, and BFRT₅ is an interaction term designed to estimate the degree to which risk tolerance moderates the relationship between Black female and wealth.

Given the possibility that an endogeneity effect may have been present in the models (as well as in general and within the dataset) between risk tolerance and wealth, a two-stage least squares (2SLS) regression was estimated. The 2SLS regression was estimated based on Equation 5: Eq 6. RT^=γ0+γ1E1+γ2GR2+γ3BF3+γ4DRT4+γ5BFRT5+v1 \widehat {RT} = {\gamma _0} + {\gamma _1}{E_1} + {\gamma _2}G{R_2} + {\gamma _3}B{F_3} + {\gamma _4}DR{T_4} + {\gamma _5}BFR{T_5} + {v_1} where driving risk tolerance (DRT₄) is the instrumental variable.

Driving risk tolerance was chosen as the instrumental variable in the regression because the question meets the following requirements for such a variable (see Norusis, 2007): the variable is (a) free of causal influence from any of the variables in the equation; (b) correlated with the endogenous variables; and (c) not highly correlated with the outcome variable. Driving risk tolerance was assessed in the NLSY79 with the following question: “People can behave differently in different situations. How would you rate your willingness to take risks while driving?” Each respondent’s willingness to take risks was measured on a scale of 0 to 10, where zero indicated “unwilling to take any risks” and 10 indicated “fully prepared to take risks.” The variable was statistically associated with the risk tolerance variable (r = .405) but not statistically associated with wealth (r = −.002). The purpose of the regression shown in Equation 6 was to obtain an estimate of RT^ \widehat {RT} . The final 2SLS regression, using fitted values of RT^ \widehat {RT} derived from Equation 6, was as follows: Eq 7. Wi=β0+β1E1+β2GR2+β3BF3+β4RT^4+β5BFRT5+ε1 {W_i} = {\beta _0} + {\beta _1}{E_1} + {\beta _2}G{R_2} + {\beta _3}B{F_3} + {\beta _4}{\widehat {RT}_4} + {\beta _5}BFR{T_5} + {\varepsilon _1} where ɛ₁ is a composite error term that is not correlated with E₁,GR₂,BF₃, and RT^4 {\widehat {RT}_4} .

Table 3 shows the first multivariate test designed to determine the relationship between being a Black woman and risk tolerance. It was determined that the Black female variable was positively associated with risk tolerance. The positive coefficient suggests that, on average, Black women exhibit higher levels of financial risk tolerance compared to other women. The model was statistically significant — F_{(4, 1857)} = 12.749, p < .01 — with approximately 3% of risk-tolerance scores explained by the model (R² = .027).

Other significant relationships were observed. Wealth and risk tolerance were positively associated, as was the relationship with education. The association between gender-role attitude and risk tolerance was negative, indicating that those who held an affinity for traditional gender roles exhibited lower levels of risk tolerance.

Sobel mediation tests were used to further examine the relationship between being a Black female and risk tolerance. The mediating roles of wealth, education, and gender role attitude were examined. The analysis revealed that being a Black female is positively associated with risk tolerance. However, this effect was only partially mediated by wealth. The negative coefficient for “Black Female → Wealth” suggests that Black women tend to hold less wealth. The positive coefficient for “Wealth → Risk Tolerance” indicates that higher wealth is positively associated with risk tolerance. The mediated effect of being a Black female through wealth was statistically significant, suggesting that wealth was positively associated with the degree of risk tolerance exhibited by Black women. Risk tolerance was the lowest for those holding the lowest levels of wealth.

Similarly, while the results showed a positive association between being a Black female and risk tolerance, the relationship was partially mediated by education. The negative coefficient for “Black Female → Education” indicates that Black women tend to have lower educational attainment levels. Higher educational attainment was observed to be positively associated with risk tolerance. The mediated effect of being a Black woman and having more education was thus a higher level of risk tolerance. Conversely, risk tolerance was lowest for those with less formal attained education.

The last Sobel test was not significant. This finding suggests that gender-role attitude, while important in describing risk tolerance, does not mediate a Black woman’s risk attitude.

While Sobel tests indicate potential mediation across variables, the approach fails to account for the simultaneous effects of variables on an outcome. For this, reason the associations among the variables from Table 4 were retested with a path model. Results from the path model provided support for the Sobel tests, although the model did not fit the data particularly well. The chi-square statistic was significant (X² = 502.695, df = 3, p < .001). The RMSEA, NFI, and CFI coefficients for the model were .163, .469, and .464, respectively. The key variable of non-significance was gender-role attitude; this variable was not directly associated with risk tolerance, and as such, it provided no mediation in the model. Table 5 shows the standardized coefficients from the test.

Table 6 shows the total effects of each variable in the path model based on standardized direct and indirect effects. Being a Black woman was only marginally positively associated with holding a traditional gender-role attitude. However, in support of previous findings, wealth and education were negatively associated with being a Black woman. Black women, compared to other women, exhibited a higher level of risk tolerance.

Table 7 shows the direct effects from the test. Being a Black woman was directly and positively associated with risk tolerance. This means that, in a bivariate sense, the willingness to take risks among Black women was higher than that of non-Black women. This finding confirms previous reports.

Table 8 shows the indirect effects of each variable on risk tolerance. These indirect effects consider the pathways between variables and risk tolerance. As shown in Table 8, the indirect effect of being a Black woman on risk tolerance was negative. This means that when the indirect pathways to risk tolerance were estimated jointly, Black women exhibited lower risk tolerance than other women. The indirect effect was produced by the general tendency of Black women to report lower education and wealth levels, both of which were found to be positively associated with risk tolerance and to mediate the relationship between being a Black woman and risk tolerance.

Table 9 presents the results from the regression model used to estimate the association between being a Black woman and wealth status, as moderated by risk tolerance. The model was statistically significant, F_{(5, 1856)} = 138.725, p < .001, with approximately 27% of the wealth estimates explained by the model (R² = .272). The relationship between wealth and being a Black woman was moderated by risk tolerance. Gender role attitude was not significant in the model when the interaction term was included. This suggests that while gender role attitude appears to be a significant variable when viewed in a bivariate sense, its importance is diminished when the interaction between being a Black woman and risk tolerance is included in the model.

One obvious constraint associated with the estimates presented in Table 9 is the potential endogeneity related to the relationship between wealth status and risk tolerance. While the model was premised on the assumption that risk tolerance is a predisposing factor associated with wealth accumulation, it is also possible that greater wealth levels may explain risk tolerance. If this is true, then the moderation effect observed in Table 9 may be spurious. A two-stage least squares regression was estimated to account for potential endogeneity in the model.

Driving risk tolerance was selected as the instrumental variable. It was thought that being willing to take risks whilst driving should be associated with the risk-tolerance measure used in this study, but not significantly associated with wealth (i.e., someone’s wealth status should not cause someone to take more or fewer risks when operating a motor vehicle). The variable’s mean, median, standard deviation, minimum score, and maximum score were 3.220, 2.00, 3.320, .00, and 10.00, respectively. Table 10 shows the correlations between driving risk tolerance and the other variables in the model.

The two-stage regression model was statistically significant, F_{(5, 1846)} = 72.264, p < 0.001, with 27% of the wealth estimates explained by the model (R² = .270). The test results shown in Table 11 mirrored those of the hypothesized model, suggesting that the path from the Black woman variable to wealth status is moderated by risk tolerance.

Figures 4 through 7 illustrate the general moderation effect of risk tolerance on being a Black woman and wealth status. Figure 4 shows the partial regression plot of wealth status by the Black woman variable. The fitted regression line shows the slope of the effect from non-Black women (i.e., the left side of the figure) to Black women (i.e., the right side of the figure). Black women were, on average, less likely to report high levels of wealth. Figure 5 shows the partial regression plot of the interaction term. The moderating effect of risk tolerance is evident in the reduced slope of the regression line compared to the line in Figure 4.

Figure 4.

Partial Regression Plot Showing the Wealth Status of Black Women and non-Black Women.

Figure 5.

Partial Regression Plot Showing the Interaction Effect of Risk Tolerance and Black Female on Wealth Status.

Figure 6.

Interaction Effect of Risk Tolerance and Black Female on Wealth Status.

Figure 7.

Illustration of Wealth Gap Closing as Risk Tolerance Increases.

Figure 6 illustrates the overall interaction effect of risk tolerance and being a Black woman on wealth status. As risk tolerance increases, reported wealth is shown to decrease for Black women when holding the other factors at the sample mean or median level. A separate model was estimated (not shown) that included marital status (1 = single, 0 = otherwise). The model was statistically significant, with single individuals reporting lower levels of wealth. An interaction term was created to determine whether marital status moderated the association between the Black woman variable and wealth. When this variable was included in the model, no interaction was noted. This suggests that in addition to being a Black woman, those who are single are less likely to accumulate wealth, but that the association between Black woman and marital status is not interactive.

The model was estimated again without an interaction effect. The model was statistically significant, F_{(4, 1857)} = 90.695, p < .001, with approximately 27% of the wealth estimate explained by the model (R² = .269). In each of the models, Black women were observed to exhibit a lower wealth status compared to non-Black women. However, the slope of the decline, as noted above, was reduced for Black women with a higher level of risk tolerance. Although risk tolerance did not reverse the wealth gap between Black women and others, it did moderate, to some extent, the degree to which the gap could be observed.

Figure 7 illustrates the moderation effect from the perspective of comparing Black women to non-Black women in terms of risk tolerance. The dark line indicates that the wealth status of Black women is lower than that of non-Black women. However, an interaction was observed when risk tolerance was taken into account (i.e., the relationship between risk tolerance and wealth is positive). This means that although Black women hold less wealth than similarly situated non-Black women, the gap between the two falls as risk tolerance increases.

It was determined that Black women, compared to non-Black women, exhibit a greater willingness to take financial risks. This finding hints at what in this study is termed the “wealth/risk tolerance paradox.” It has been extensively documented in the literature, including in this study, that risk tolerance and wealth are positively associated. There is likely an endogeneity factor at play in the relationship. Nonetheless, those who exhibit a greater willingness to take financial risks generally report holding more wealth. This relationship, however, does not hold true for Black women when compared to non-Black women. This is a paradox that can be addressed by providing answers to the other research questions posed in this study.

Wealth was observed to have an inconsistent mediating effect between being a Black woman and risk tolerance. Those with more net wealth reported higher levels of risk tolerance. However, the opposite was also true. As wealth decreased, so did risk tolerance. Because Black women tend to hold less wealth than non-Black women, it is not surprising that wealth was observed to have an inconsistent effect in this study.

Similarly, in the mediated models examined in this study, it was determined that education had an inconsistent mediation effect on the relationship between being a Black woman and risk tolerance. The attained educational level of Black women tends to be lower than that of other women. In confirmation with the extant literature, this was observed in this study. Those with higher levels of attained education reported more risk tolerance, and individuals with lower levels of educational attainment reported a lower willingness to take financial risks. This helps explain the wealth/risk tolerance paradox. It is also worth noting that gende-role attitude was not a significant mediator in the models examined in this study. While Black women were more likely to hold traditional gender-role attitudes, this had no effect on describing higher or lower levels of risk tolerance.

It was determined that risk tolerance does moderate the association between being a Black woman and wealth. Even though Black women hold less wealth than similar non-Black women, the gap between these two groups falls as risk tolerance increases. In this respect, risk tolerance is a key factor in describing the wealth/risk tolerance paradox noted in this study. More specifically, Black women’s wealth accumulation status appears to be negatively affected by their generally lower level of education; this can be partially offset, however, by their greater willingness to engage in financial behaviours in which the outcome is both uncertain and potentially negative.

Financial planners, financial counsellors, and financial educators who work with Black woman clients can play a pivotal role in empowering them by focusing on education and risk literacy. Recognizing the historical disparities and systemic challenges that Black women face, they can tailor their approaches to address these unique circumstances. Education becomes a key tool in this process, with financial-services professionals needing to take the time to thoroughly explain financial concepts, investment strategies, and long-term planning options. By fostering a learning environment, financial-service professionals and financial educators can empower Black woman clients to make informed decisions, demystifying the complexities of the financial landscape.

Moreover, a nuanced emphasis on risk literacy is crucial. Black women, like any other demographic group, have a diverse range of risk tolerance levels and financial goals. Financial service professionals and financial educators should be willing to engage in open and honest conversations about the way risk, both real and imaginary, is perceived, tailoring their advice to align with the specific needs and aspirations of their Black woman clients. This approach goes beyond conventional financial planning, counselling, and education by recognizing the importance of addressing unique life experiences and potential obstacles. Through education and a focus on risk literacy, financial service professionals and financial educators can significantly contribute to the financial well-being and empowerment of Black woman clients, enabling them to navigate the complexities of the financial world with confidence and resilience.

To comprehensively understand the relationship between being a Black woman and risk tolerance, researchers should delve into the intricate interplay of various socio-economic factors, including wealth, education, and gender role attitudes. Wealth plays a pivotal role in describing an individual’s risk tolerance, as financial stability often influences one’s ability to absorb potential losses. Black women, who may face systemic barriers to wealth accumulation, may exhibit unique risk tolerance patterns influenced by economic disparities and distinct perspectives on what it means to be a saver and an investor. Moreover, education serves as a crucial determinant, empowering individuals with the knowledge and skills needed to navigate financial complexities. A nuanced exploration of how educational attainment intersects with being a Black woman can unveil insights into this group’s risk-taking behaviour, shedding light on whether education mitigates or exacerbates risk aversion within this demographic. In terms of specific steps researchers can take to inform educational interventions, it will be important for future studies to incorporate mediation and moderation effects in models. It would also be helpful, as illustrated in Figure 9, to account for possible curvilinear effects between risk tolerance and wealth, as well as risk tolerance and other factors.

Gender role attitudes further complicate the relationship between being a Black woman and risk tolerance. Societal expectations and cultural norms often prescribe specific gender roles that may intersect with racial identities, influencing individuals’ perceptions of risk and financial decision-making. By scrutinizing how gender role attitudes are internalized and expressed by Black women, researchers can elucidate whether traditional gender norms amplify or mitigate risk aversion. This multifaceted analysis, which considers the mediating roles of wealth, education, and gender role attitudes, is crucial for developing targeted interventions and policies that foster financial inclusion and empowerment among Black women.

To better understand the relationship between being a Black woman and risk tolerance, policymakers and researchers must examine the complex interplay of socio-economic factors, such as wealth, education, gender role attitudes, and risk-taking attitudes. Effective policy development to expand access to investing opportunities for traditionally underrepresented groups requires a nuanced approach that moves beyond surface-level solutions. Wealth, as demonstrated in the literature, is positively associated with risk tolerance, as financial stability allows individuals to absorb potential losses. Simultaneously, risk tolerance itself can contribute to wealth accumulation, creating a feedback loop. However, systemic barriers to wealth accumulation that Black women are uniquely likely to face may limit their responsiveness to traditional incentives for savings and investments. Divergent perceptions of risk and preferences for financial products, shaped by these economic disparities, must be taken into account when designing policies aimed at fostering financial inclusivity.

Policymakers should recognize that financial knowledge alone is insufficient to resolve the wealth/risk tolerance paradox experienced by Black women. As illustrated in this study, Black women already exhibit higher risk tolerance compared to non-Black women. What is critically needed is enhanced access to education and support systems that help Black women conceptualize wealth as a tool to strengthen the financial capacity of their households. Education is a key determinant that empowers individuals with the knowledge and skills to navigate financial systems and make informed, strategic decisions. A deeper exploration of how educational attainment intersects with being a Black woman can provide valuable insights into whether education mitigates or exacerbates risk aversion.

To address the challenges faced by Black women, policymakers have an opportunity to incentivize the accumulation of human capital through targeted initiatives, such as tax credits, tax subsidies, refundable tax credits, or direct tuition support for Black women who demonstrate educational need. The long-term societal and household benefits of such policies are significant. A higher level of educational attainment enhances human capital, fostering a greater willingness to take financial risks. This willingness drives wealth accumulation, further strengthening risk capacity and accelerating wealth growth. Over time, this accumulated wealth reduces reliance on government funding for health and retirement needs, creating positive economic outcomes for households and society as a whole.

In addition, gender role attitudes must be carefully considered, as they further complicate the relationship between being a Black woman and risk tolerance. Societal and cultural norms often impose traditional gender roles that intersect with racial identity, influencing financial decision-making and perceptions of risk. Understanding how Black women internalize and express these attitudes is critical for determining whether traditional gender norms amplify or mitigate risk aversion. By accounting for the mediating roles of wealth, education, and attitudes toward gender roles, policymakers can develop targeted, evidence-based interventions that foster financial empowerment and inclusivity for Black women. This multifaceted approach is essential for dismantling systemic barriers, promoting equitable access to financial opportunities, and supporting long-term economic stability.

While the findings of this study offer valuable insights, several limitations must be acknowledged to provide context and guide future research. First, this study relied on secondary data, which, while robust, has inherent constraints. Using primary data to examine the wealth-building experiences of Black women could offer a significant advantage. The collection of primary data in follow-up studies will enable the development of tailored questions that directly address the unique cultural, social, and economic factors influencing Black women’s financial trajectories — elements often underrepresented in secondary datasets like the NLSY79.

Second, the data were restricted to female respondents. While purposeful for addressing gendered wealth dynamics, this delimitation limits the generalizability of the findings to Black women. Third, the temporal scope of the financial risk tolerance measures poses an important constraint. These measures were only collected in six discrete waves between 1993 and 2010, with no subsequent updates or replications in newer waves of the NLSY79. As a result, our analysis is bound by a fixed historical period that may not fully reflect evolving attitudes shaped by recent economic or social events, such as the COVID-19 pandemic, rising student debt burdens, or post-2010 financial market shifts. While we believe that risk tolerance is a stable psychological trait in adulthood, and therefore not subject to significant temporal distortion, this assumption may not hold uniformly across all subgroups, particularly among populations exposed to chronic economic insecurity.

Additionally, reliance on retrospective data raises concerns about potential cohort effects. Respondents in the NLSY79 are part of a specific generational cohort, and their financial behaviours may be shaped by unique policy environments, labour markets, and social norms that differ markedly from those facing younger generations today. Although a historical lens is essential for interrogating the structural roots of the wealth gap experienced by Black women, especially in terms of accumulated disadvantage, caution must be exercised when extrapolating these patterns to current or future economic contexts. Another limitation deals with causality. While robustness checks using 2010 sampling weights confirm the stability of our core findings, the cross-sectional nature of the risk tolerance measures precludes definitive causal claims. Our findings should thus be interpreted as associations rather than as evidence of direct causal mechanisms.

Despite these limitations, the study provides valuable insights into the long-term relationship between psychological traits and wealth accumulation in the context of systemic inequality. The value of the NLSY79 dataset — particularly its inclusion of validated measures of financial risk tolerance — continues to provide a powerful platform for analysing enduring economic disparities and behavioural patterns. Nonetheless, future research would benefit from incorporating updated datasets or designing studies that longitudinally track changes in Black women’s financial behaviours, attitudes, and financial outcomes. Furthermore, expanding upon the biopsychosocial model of risk-taking behaviour by integrating additional endogenous, exogenous, and predisposing factors would enable a more comprehensive understanding of the variables influencing risk tolerance among Black women. This study deliberately employed a restrained model to establish foundational knowledge on this under-researched topic, but future work should expand the range of variables examined to uncover deeper insights.

Finally, while this study includes married Black women, a potential confounding effect arises from the shared nature of marital wealth. Financial outcomes within marital partnerships often reflect joint effort, making it challenging to attribute wealth accumulation solely to the individual contributions of Black women. To address this, future studies should consider focusing specifically on single Black women, as this approach would provide a more transparent lens into their economic agency and independent financial strategies. By isolating the financial decisions, investments, and entrepreneurial efforts of single Black women, researchers can better understand the challenges and opportunities they encounter in wealth-building processes. This narrower focus would mitigate the confounding influence of spousal wealth and highlight the resilience, resourcefulness, and unique financial pathways of single Black women. However, it is important to recognize that concentrating exclusively on single Black women may also introduce limitations. The Black female demographic is highly diverse, encompassing variations in educational attainment, employment opportunities, geographic location, and other socio-economic factors that impact wealth accumulation and risk tolerance. While narrowing the scope to single Black women may allow for greater specificity, doing so risks oversimplifying the broader dynamics faced by the larger population of Black women. A balanced approach — one that examines both individual financial agency and collective economic dynamics — is essential to ensure that findings represent this group’s full range of experiences. Future studies should employ data collection and methodological strategies that account for both individual and systemic factors influencing Black women’s financial outcomes. This may ultimately provide a more comprehensive and actionable understanding of wealth-building opportunities and challenges facing Black women.