Financial Services Review, 32(4) 27 Racial/Ethnic Disparities in Financial Advice Seeking: A Decomposition Analysis Di Qing1 and Miranda Reiter2 Abstract Financial advice seeking is associated with many positive benefits for consumers. Yet, most U.S. households are not working with a financial planner and research has pointed to the lack of racial and ethnic diversity among those who do. This study examines racial/ethnic disparities in using a financial planner. Logistic regression analyses show that Black and White consumers are more likely than Asian and Hispanic consumers to use financial planners for saving and investment decisions. A Fairlie decomposition analysis shows racial/ethnic differences among the determinants that are associated with financial advice seeking. The differences in the determinants were large between White and Hispanic consumers and much narrower between Black and Hispanic consumers. Risk tolerance, objective financial knowledge, and income were the most important determinants to explain racial/ethnic differences in financial planner use. This study provides insight into possible barriers to working with financial planners for a diverse group of consumers. Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License Recommended Citation Qing, D., & Reiter, M. (2024). Racial/ethnic disparities in financial advice seeking: A decomposition analysis. Financial Services Review, 32(4), 27-50. Introduction There is a growing demand for financial planners (U.S. Bureau of Labor Statistics, 2023) as financial advice is becoming more important to ensure the success of long-term financial goals for consumers in the United States (Harlow et al., 2020). Financial advice is particularly important given the change in the types of employer- sponsored retirement plans offered to workers. Over the past few decades, the industry has gradually shifted from offering employees defined benefit plans to defined contribution 1 Corresponding author (dzq0007@auburn.edu). Auburn University, Auburn, USA 2 Texas Tech University, Lubbock, Texas, USA plans (Estreicher & Gold, 2007; Myers & Topoleski, 2021). In 1975, 27.2 million private sector employees reported having defined benefit plans, whereas 11.2 million reported having defined contribution plans. By 2021, less than 15 million employees reported having defined benefit plans, and over 85 million reported defined contribution plans (Myers & Topoleski, 2021). As a result, responsibility for funding employee retirement plans has shifted from the employer to the employee. One unintended consequence of this shift is lower levels of retirement preparedness. Munnell and Suden https://creativecommons.org/licenses/by-nc/4.0/ mailto:dzq0007@auburn.edu https://creativecommons.org/licenses/by-nc/4.0/ Financial Services Review, 32(4) 28 (2006) note that employees with 401(k) contribution plans will likely have a shortfall in retirement assets. The authors note that this shortfall may be possibly due to the fact that employees are now faced with multiple investment decisions, such as diversification and rebalancing (Munnell & Suden, 2006). Many of these decisions, which traditionally fall on employers, can leave employees feeling unequipped to make the right choices. Financial advice can help consumers improve savings behavior (Liu et al., 2019), stock market expectations (Reiter & Seay, 2022), investment outcomes (Gaudecker, 2015), asset allocation (Marsden et al., 2011), and the value of their assets (Goetz et al., 2020; Montmarquette & Viennot-Briot, 2015). Specifically for long-term goals, financial planners, when compared to other financial professionals, play a more important role in helping clients (Kim et al., 2018). As such, a financial planner may serve as an invaluable resource for increasing the overall financial well- being of consumers as well as preparing a diverse group of clients for long-term financial success. Non-White consumers may face unique financial challenges that make financial planning even more relevant to addressing their needs. For Black consumers, given historical events and systemic challenges, there are considerable financial barriers and constraints compared to their White peers (Craemer et al., 2020). For example, Black consumers report the lowest median household wealth when compared to other racial/ethnic groups (Aladangady et al., 2023) and have lower ownership than White consumers in high-return investments such as stocks, real estate, and business assets (Hanna et al., 2010). Black households are also more susceptible to economic shocks, such as the Great Recession than White households, which may decrease retirement preparedness (Wolff, 2023). In addition, Black consumers have historically had lower retirement plan participation than Whites (Porto, 2016). Similarly, research on Hispanic households has found that they have lower retirement preparedness than White households. Blanco et al. (2017) posit that this is partially attributed to Hispanic consumers’ plans to continue working rather than retire, which could be attributed to lower savings rates during working years or a lack of retirement savings. Moreover, Hispanic consumers may feel greater financial responsibility towards their families. For example, Hispanic individuals report a greater tendency than non-Hispanic individuals to financially support family abroad through remittances (López-Anuarbe et al., 2016). Similarly, some Asian consumers face family- oriented financial pressures, such as supporting aging parents (Merril, 2023a). While the literature is scant on the experience of Asian Americans in financial services (Hanna et al., 2015), Asian consumers generally have lower risk tolerances, higher levels of aversion towards debt, and a greater sense of self-reliance compared to consumers from other racial/ethnic backgrounds (Merril, 2023a). However, there is evidence that Asian consumers save for retirement on par with White consumers, which is higher than that of Black and Hispanic consumers (Yao, 2016). Research investigating racial/ethnic differences in financial planner use has uncovered that the majority of financial planning clients are White individuals. Despite this, there is also demand from individuals of color for financial advice. Some research has shown that Black consumers are more likely to hire a financial planner when factors such as income and education are controlled for in regression models (Elmerick et al., 2002; Hanna, 2011; Reiter & Qing, 2023; White & Heckman, 2016). Conversely, Hispanic consumers are less likely to work with financial planners (Hanna, 2011; White & Heckman, 2016). Current literature on Asian consumers shows some mixed results. While some sources show that Asian consumers are less likely to seek financial advice (Hanna et al., 2015), other sources show that Asian consumers are more likely to seek financial advice (White & Heckman, 2016). Ultimately, it may depend on the type of financial advice sought (White & Heckman, 2016). While researchers have investigated racial/ethnic differences in seeking financial advice, it is important to understand what contributes to these differences. Reiter and Qing (2023) conducted a study on both gender and Black-White racial Qing & Reiter 29 differences in seeking financial advice using 2012 data from the National Financial Capability Study (NFCS) dataset. They employed a decomposition technique from Jackson and Lindley (1989). This technique allowed them to examine the effects of independent variables, which varied by race, and to isolate the effects, specifically, of being Black or White on seeking financial advice. In other words, Reiter and Qing (2023) interacted the race variable with all other predictors and utilized the likelihood ratio test to compare the intermediate model (model without interaction) and the interacted model (model with interaction). The results showed a statistically significant difference between the two models and suggested that the interacted model was more appropriate. The results indicated that asking for financial advice is not determined by race or gender in and of itself but by other variables. However, the decomposition technique from Jackson and Lindley (1989) could not capture specific differences in predictors when comparing racial groups; specifically, the results could not show which predictor contributes the most difference in seeking financial advice between the two groups. As such, Fairlie's (2005) decomposition technique is required. The Fairlie (2005) decomposition method can estimate the significance of various observed characteristics that account for differences in financial planner use between groups. This study aims to investigate racial/ethnic differences in using financial planners to make investment and saving decisions. We use the Fairlie (2005) decomposition technique to examine if racial/ethnic differences are attributed to various consumer and household characteristics or unobserved variables. Specifically, we examine which determinants help explain racial/ethnic differences in financial planner use and which determinants contribute to the gap in financial planner use between racial/ethnic groups. In this study, six pairwise comparisons were conducted: (1) Blacks versus Whites, (3) Blacks versus Hispanics, (3) Blacks versus Asians/others, (4) Whites versus Hispanics, (5) Whites versus Asians/others, and (6) Asians/others versus Hispanics. We calculate the part of the observed differences in the utilization of financial planners attributable to variations in household and economic characteristics. This paper contributes to the literature in a few important ways. First, we investigate the racial/ethnic differences in seeking financial advice (i.e., using a financial planner) for saving and investment decisions using the more recent 2016 and 2019 waves of the Survey of Consumer Finances (SCF). The data from the SCF allow us to include net worth as a variable, unlike the work from Reiter and Qing (2023), which does not include this important variable, due to a limitation with the NFCS dataset. Also, the financial advice seeking question from the NFCS asks about financial planner use within the past five years, whereas the SCF uses a broader question. Second, we employ the Fairlie decomposition method to understand the significance of the racial/ethnic differences in financial advice seeking characteristics for six pairwise groups, which, to the best of our knowledge, has not been examined before. As such, this paper provides additional insights beyond the findings of Reiter and Qing (2023), who examined disparities in financial planner use between Black and White consumers. The paper will be organized as follows. First, we introduce theoretical considerations, and then, we discuss our methodology, including the dataset, sample, dependent variable, independent variables, and the empirical model specification. Next, we provide an analysis of the results, which include descriptive statistics, logistic regression analyses, and decomposition analyses. Finally, we discuss the results with implications, suggestions for future research, and limitations. Theoretical Considerations According to the life-cycle hypothesis by Ando and Modigliani (1963), seeking help from financial professionals assists individuals with making financial decisions that maximize utility over the life cycle. The pattern of wealth accumulation follows a “hump shape”; in other words, individuals accumulate wealth when they are young and distribute wealth when they are old. To allocate resources optimally, asking for help from financial professionals helps smooth utility during a lifetime. According to economic theory, there should be no racial/ethnic Financial Services Review, 32(4) 30 differences when seeking help from financial professionals. However, economic theories may not explain why those who belong to non-White groups behave differently from Whites, even if they have the same characteristics, such as income, net worth, or financial knowledge (Shin & Hanna, 2015). Grable and Joo (1999) introduced the financial help-seeking framework as an expansion of Suchman’s (1966) help-seeking framework. The framework posits that seeking financial advice happens in five steps: exhibiting financial behaviors, analyzing one's financial behaviors, identifying the causes of the behaviors, deciding to seek help, and finally choosing among help options. While the framework does not explain racial/ethnic differences in seeking financial help, it establishes the process that individuals take to arrive at the action of seeking advice, and it highlights some of the characteristics attributed to advice seekers. Contrary to some other studies, Grable and Joo (1999) found that those most likely to seek a third party for financial assistance had undesirable financial behaviors and financial stressors. Grable and Joo (1999) explained that seeking financial help could be a coping response. Using the 2012 National Financial Capability Study, Fan (2021) used stress-coping theories to understand more about individuals’ propensity to engage in professional financial advice. Like Grable and Joo (1999), Fan found that having a recent experience with financial stress is positively associated with seeking assistance from a financial advisor. This association could explain the greater propensity for Black consumers to seek financial advice, as they have been found to experience greater financial challenges when compared to others in general (Lim et al., 2014). However, it does not explain the lack of financial professional use among Hispanic consumers, as they also experience financial issues similar to those of Black consumers (Martin & Dwyer, 2021). It is imperative to understand more about these disparities in seeking financial advice. Based on prior literature and theoretical considerations, we propose the following hypotheses: H1: White consumers will be less likely to seek financial advice for savings and investing decisions when compared to Black consumers. H2a: Hispanic consumers will be less likely to seek financial advice for savings and investing decisions when compared to Black consumers. H2b: Hispanic consumers will be less likely to seek financial advice for savings and investing decisions when compared to White consumers. H3a: Asian consumers will be less likely to seek financial advice for savings and investing decisions when compared to Black consumers. H3b: Asian consumers will be less likely to seek financial advice for savings and investing decisions when compared to White consumers. H4: The factors associated with seeking financial advice for savings and investing decisions will differ across racial/ethnic groups. H5: The determinants that explain the racial/ethnic disparities in seeking financial advice for savings and investing decisions will differ between racial/ethnic groups. Methods Dataset and Sample This study utilizes the 2016 and 2019 waves of the Survey of Consumer Finances (SCF), a nationally representative triennial cross-sectional survey of families in the United States. The SCF, sponsored by the Federal Reserve Board, collects consumer data on various topics. For the 2016 wave of the survey, 6,248 households were interviewed, and 5,777 households were interviewed for the 2019 survey. Multiple imputation is used within the SCF to provide respondent privacy and avoid missing data. Multiple imputation produces five sets of data, called implicates, for each respondent, representing a range of likely responses (Lindamood et al., 2007). Previous literature (Lindamood et al., 2007; Rubin, 1987) recommends using all five implicates via repeated imputation inference (RII). As such, the current study uses RII to apply all five implicates (Montalto & Sung, 1996). According to Pence (2015), the “scfcombo” command in Stata Qing & Reiter 31 software combines imputation uncertainty and bootstrapped standard errors; in other words, this command applies RII to improve the accuracy of the estimation. Previous studies using the Survey of Consumer Finances (Chang, 2005; Lei & Kordes, 2020; White & Heckman, 2016) applied weights for descriptive statistics. However, applying weights to logistic regression models has yielded more conservative results (Lindamood et al., 2007). Therefore, we applied weights to our descriptive analyses but not to the regression models, consistent with previous research (e.g., Shin & Hanna (2015)). Dependent Variable There were two questions asked in the 2016 and 2019 waves of the SCF related to financial advice seeking: (1) “What sources of information do you (and your husband/wife/partner) use to make decisions about borrowing or credit?” and (2) What sources of information do you (and your husband/wife/partner) use to make decisions about saving and investments?”. For the current study, the authors are mostly concerned with consumers’ decisions related to using a financial planner in a more traditional sense, which would include seeking help for investments and savings rather than debt or credit. The SCF provides many answer choices for information sources, including service professionals such as lawyers, accountants, bankers, brokers, and financial planners. However, the “financial planner” answer choice was the only one used for the current study. The responses were coded as 1 if respondents chose “financial planner” and 0 if they did not. If respondents chose “other” or “inappropriate,” the responses were coded as missing values. Independent Variables The factors associated with financial planner use have been well-investigated. Independent variables included race/ethnicity, gender, age, marital status, income, net worth, risk tolerance, investment horizon, subjective knowledge, objective knowledge, household size, educational attainment, homeownership, employment status, and emergency account access. Race/ethnicity, as categorized in the Survey of Consumer Finances, includes Black/African American, Hispanic/Latino, Asian, American Indian/Alaska Native, Native Hawaiian/Pacific Islander, and White. For the current study, race/ethnicity was grouped into four categories: White, Black, Hispanic, and Asian/other. The Asian/other category is made up of mostly Asian respondents (Hanna & Lindamood, 2015) but also includes American Indian/Alaska Native, Native Hawaiian/Pacific Islander, and anyone who chose “another race.” Combining these groups into one was necessary due to the low sample sizes of each group alone. Gender has been identified as a predictor for financial advice. For example, women are more likely to pay for financial advice compared to men and are more likely to seek advice for retirement planning (Finke et al., 2011; Joo & Grable, 2001). If respondents were women (or female, as described in the survey), the value was coded as 1, and 0 if respondents identified as men (male). Age is associated with financial advice seeking (Robb et al., 2012). There is evidence that older consumers are more likely to seek specific types of financial advice, such as investment and savings advice (Hackethal et al., 2012; Lachance & Tang, 2012). For the current study, age was treated as a continuous variable. In addition, some previous research has indicated that the relationship between age and financial planner use may be nonlinear (White & Heckman, 2016), and therefore, age-squared was also included as an independent variable. Marriage has been long understood as a financially-advantaged status compared to being single. As such, married individuals tend to do better financially than those who are not married and are more likely to work with financial planners than those who are single (Lachance & Tang, 2012; Robb et al., 2012). Marital status was used as a dummy variable; those who were married were coded as 1, and all others were coded as 0. Other household characteristics, such as household size (Elmerick et al., 2002), educational attainment (Chatterjee & Zahirovic- Herbert, 2010), and employment status (Elmerick et al., 2002), have been associated with using a Financial Services Review, 32(4) 32 financial planner. Household size was used as a continuous variable that indicated the number of people within the same household. Educational attainment was organized into the following categories: (a) lower than high school, (b) a high school diploma, (c) some college, (d) a bachelor’s degree, and (d) a graduate degree. Employment status was categorized into four groups for the current study: (a) unemployed, (b) employed (e.g., working for someone else), (c) self- employed, and (d) retired. Financial variables such as wealth (Harlow et al., 2022) and income (Cummings & James, 2014; Finke et al., 2011; Joo & Grable, 2001) are positively associated with seeking financial advice. Income was made into a categorical variable and comprised three groups: (1) less than $50,000; (2) between $50,000 and $99,999; and (3) $100,000 or higher. Net worth was used as a continuous variable. If net worth was greater than 0, the log-value was used. If net worth was smaller than 0, the log (0.01) was utilized. Homeownership (Hanna, 2011) and having an emergency fund (White & Heckman, 2016) are predictors associated with using a financial planner (Hanna, 2011). Homeownership and emergency access were used as binary dummy variables. Emergency access is determined by answering the following question: “In an emergency could you (or your husband/wife/partner) get financial assistance of $3,000 or more from any friends or relatives who do not live with you?” If the respondent answered “yes,” the value was coded as 1, and 0 if the respondent answered “no.” Risk tolerance has been correlated with a higher likelihood of seeking financial advice in numerous studies (Chang, 2005; Joo & Grable, 2001; Moreland, 2018; White & Heckman, 2016). Risk tolerance was categorized into four groups: (1) not willing to take any financial risks; (2) willing to take average financial risks expecting to earn average returns; (3) willing to take above average financial risks expecting to earn above average returns; and (4) willing take substantial financial risks expecting to earn substantial returns. Investment time horizons may also impact financial help-seeking behavior, as households with long-term investment horizons are likelier to work with a financial planner than households with intermediate- or short-term investment horizons (White & Heckman, 2016). Investment time horizon was used as a categorical variable and included the following choices: (a) next few months; (b) next year; (c) next few years; (d) next 5-10 years; and (e) longer than 10 years. Objective and subjective financial knowledge predict the likelihood of working with a financial planner, although results are mixed depending on whether the association is positive or negative. There tends to be more support in the literature for a positive association between objective financial knowledge and financial advice seeking (Alyousif & Kalenkoski, 2017; Calcagno & Monticone, 2015; Seay et al., 2016), but some research has found the opposite (Hsu, 2022; Sommer & Lim, 2022). Kramer (2016) found that subjective financial knowledge is associated with a lower likelihood of seeking financial advice, but others report a positive association (Fan, 2021). The current study measured objective financial knowledge using the Big Three scale (Lusardi & Mitchell, 2011), which evaluates respondents’ knowledge of stocks, interest rates, and inflation. If respondents answered a question correctly, one point was allotted; therefore, a summation scale ranged from 0 to 3, where 0 indicated that respondents had low objective financial knowledge and 3 indicated that respondents had high objective financial knowledge. Subjective financial knowledge was measured by the question: “On a scale from 0 to 10, where zero is not at all knowledgeable about personal finance and ten is very knowledgeable about personal finance, what number would you be on the scale?” Subjective financial knowledge was treated as a continuous variable. Empirical Model Specification A binomial logistic regression model was used among a pooled sample to investigate the determinants of financial advice seeking when making decisions about saving and investments. Four additional logistic regression models were used to examine racial/ethnic groups separately. The main assumption of the regression or logit model indicates that all racial/ethnic groups have the same slope and intercept because it is an identical independent variable. However, the Qing & Reiter 33 slope and intercept can change depending on the specific group due to different characteristics based on the group. This study utilizes Fairlie decomposition techniques (2005), developed by Blinder-Oaxaca (1994), which capture intercept and slope differences in racial/ethnic groups. Fairlie’s decomposition technique is an ideal research method to identify inter-group differences. It allows us to address the explained and unexplained segments and quantify the significance levels of different variables. The explained segments indicate how well the observed variables explain financial advice seeking when making decisions about savings and investments. In contrast, the unexplained segments demonstrate how the unobserved variables, which are not included in this model, explain financial advice seeking. This research method has been previously used to explore racial/ethnic differences in financial behaviors. For example, Shin and Hanna (2015) utilized a decomposition analysis to examine the racial/ethnic differences in high-return investment ownership after the Great Recession. Lee and Kim (2022) investigated racial/ethnic differences in financial knowledge using decomposition techniques. However, to the best of our knowledge, this technique has not yet been applied to racial/ethnic differences in seeking financial advice. For the current study, the decomposition of the racial/ethnic groups was estimated as follows: 𝐻 = 𝐹(𝑋�̂�) (1) �̄�1 − �̄�2 = [ 1 𝑁1 ∑ 𝐹(𝑋𝑖 1�̂�1) −𝑁1 𝑖=1 1 𝑁2 ∑ 𝐹(𝑋𝑖 2�̂�1)𝑁2 𝑖=1 ] + [ 1 𝑁2 ∑ 𝐹(𝑋𝑖 2�̂�1) −𝑁2 𝑖=1 1 𝑁2 ∑ 𝐹(𝑋𝑖 2�̂�2)𝑁2 𝑖=1 ] (2) The first equation indicates general logistic regression. The second equation addresses the differences between two groups. More specifically, this paper examined differences in financial planner use between Blacks and Whites, Hispanics and Whites, Asians/others and Whites, Blacks and Hispanics, Blacks and Asians/others, and Hispanics and Asians/others. The second equation �̄�𝑗(�̄�1, �̄�2) indicates the average probability of using a financial planner for different racial/ethnic groups (j); 𝑁𝑗(𝑁1, 𝑁2) represents the sample size for group 1 and group 2; �̂�𝑗(�̂�1, �̂�2) shows the vector of coefficient estimates for group 1 and group 2; 𝑋𝑖 𝑗 (𝑋𝑖 1, 𝑋𝑖 2) is a row vector of average values of the independent variables. [ 1 𝑁1 ∑ 𝐹(𝑋𝑖 1�̂�1) − 1 𝑁2 ∑ 𝐹(𝑋𝑖 2�̂�1)𝑁2 𝑖=1 𝑁1 𝑖=1 ] (3) [ 1 𝑁2 ∑ 𝐹(𝑋𝑖 2�̂�1) − 1 𝑁2 ∑ 𝐹(𝑋𝑖 2�̂�2)𝑁2 𝑖=1 𝑁2 𝑖=1 ] (4) Following Fairlie’s techniques (2005), equation (3) is the first part of equation (2) and measures the racial/ethnic gap due to the different distribution of groups 𝑋𝑖 𝑗 (𝑋𝑖 1, 𝑋𝑖 2). Equation (4) is the second part of equation (2) and measures the “unexplained” portion of the racial/ethnic gap due to unobservable or unmeasurable endowments, such as data limitations. This study focuses on explaining the “explained” part from the first part of equation (2). Since most of the respondents in the SCF are White, Fairlie’s measurement provides for an adjustment in sample selection bias due to an uneven sample size. In this study, we specified 100 decomposition replications and utilized the mean of 100 estimations to conduct the differences across racial/ethnic groups. The degree to which a variable contributes to the racial/ethnic gap in financial planner use could vary slightly based on the order in which an independent variable is placed in the model (Fairlie, 2005). For the decomposition analyses in this paper, the independent variables will be ordered as follows: gender, age, marital status, income, net worth, risk tolerance, investment horizon, subjective financial knowledge, objective financial knowledge, household size, educational attainment, homeownership, employment status, and emergency account access. Results Descriptive Statistics Table 1 shows the percentage of populations using a financial planner for saving and investment decisions by race and ethnicity. For the 2016 wave of the SCF, about 24% of Black, 17% of Hispanic, 30% of Asian/other, and 38% of White households used financial planners for Financial Services Review, 32(4) 34 saving and investing decisions. Usage was similar for the 2019 wave at 23%, 21%, 32% and 37%, respectively. When combining the 2016 and 2019 waves, about 24% of Black, 19% of Hispanic, 31% of Asian/other, and 38% of White consumers used financial planners for saving and investing decisions. Table 1. Mean Levels of Financial Planner Use for Savings and Investments Decisions by Race/Ethnicity Racial/Ethnic Category Black Hispanic Asian/other White Total Survey year 2016 Percent of group using 24% 17% 30% 38% 34% N 204 106 97 1,723 2,130 Survey year 2019 Percent of group using 23% 21% 32% 37% 33% N 174 115 103 1,537 1,929 Pooled years (2016 and 2019) Percent of group using 24% 19% 31% 38% 34% N 378 221 200 3,260 4,059 Note: Weighted proportion. Table 2 shows the characteristics of respondents who used financial planners for savings and investment decisions in the combined 2016 and 2019 waves of the SCF. About 80% of the respondents were White, while the rest were 9% Black, 5% Hispanic and 5% Asian/other. About 58% of Black respondents were women, compared to 39% White, 49% Hispanic, and 35% Asian/other respondents. White respondents had the oldest mean age of 56, while Hispanic respondents were the youngest, with a mean age of 45. White, Hispanic, and Asian/other respondents were more likely to be married than Black respondents. White and Asian/other respondents tended to have higher levels of income. As for risk tolerance, Black and Hispanic respondents had higher proportions who reported taking no risk or taking substantial risk compared to White and Asian/other respondents. In terms of investment horizon, a higher percentage of White (32%), Hispanic (27%), and Asian/other (30%) fell into the “5-10 years investment horizon" category. In comparison, more Black respondents (28%) chose “next few years” as their investment time horizon. In addition, White and Asian/other respondents had higher subjective and objective financial knowledge scores than Black and Hispanic respondents. White and Asian/other respondents had similar educational attainment rates at the bachelor’s degree or higher level, with over 70% of their samples represented in this category. Black and Hispanic respondents were also similar to one another in that 44% and 47% of these groups had attained a bachelor’s degree or higher. About 80% of the sample owned homes, and about 75% were employed. About 78% had access to emergency funds, with Black and Hispanic respondents having the least access compared to the other groups. Qing & Reiter 35 Table 2. Descriptive Statistics of Respondents Using Financial Planners by Race/Ethnicity Pooled Sample N=4,059 Black N= 378 Hispanic N=221 Asian/other N=200 White N= 3,260 Variables Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D. Race/Ethnicity White 0.8031 0.3977 - - - - - - - - Black 0.0931 0.2906 - - - - - - - - Hispanic 0.0544 0.2269 - - - - - - - - Asian/other 0.0494 0.2167 - - - - - - - - Female 0.4102 0.4919 0.5788 0.4944 0.4932 0.5011 0.3533 0.4792 0.3885 0.4875 Age 54.9076 14.7980 49.6646 14.1296 45.3765 12.9053 49.9232 14.2646 56.4682 14.5806 Married 0.6738 0.4689 0.4132 0.4931 0.5656 0.4968 0.6836 0.4662 0.7108 0.4535 Income Less than $50K 0.1954 0.3966 0.4434 0.4974 0.3339 0.4727 0.1916 0.3946 0.1575 0.3643 $50K-$99,999 0.2069 0.4051 0.2741 0.4466 0.3050 0.4614 0.1786 0.3840 0.1942 0.3956 $ 100K above 0.5977 0.4904 0.2825 0.4508 0.3611 0.4814 0.6297 0.4841 0.6483 0.4776 Net worth (Log value) 12.8774 4.9852 8.5356 6.6113 10.0247 5.6837 12.6897 5.2382 13.5858 4.3346 Risk tolerance No risk 0.1620 0.3685 0.2942 0.4563 0.2941 0.4567 0.1507 0.3587 0.1384 0.3454 Average risk 0.4948 0.5000 0.4307 0.4958 0.3937 0.4897 0.4860 0.5011 0.5096 0.5000 Above average risk 0.2898 0.4537 0.1931 0.3953 0.2262 0.4193 0.3134 0.4650 0.3038 0.4600 Substantial risk 0.0535 0.2250 0.0820 0.2747 0.0860 0.2810 0.0499 0.2183 0.0482 0.2141 Investment horizon Next few months 0.1101 0.3130 0.2196 0.4145 0.2380 0.4268 0.1277 0.3346 0.0876 0.2828 Next year 0.0929 0.2903 0.1492 0.3568 0.1457 0.3536 0.0998 0.3005 0.0823 0.2749 Next few years 0.2513 0.4338 0.2783 0.4488 0.2344 0.4246 0.2605 0.4400 0.2488 0.4324 Next 5-10 years 0.3093 0.4623 0.2317 0.4225 0.2688 0.4443 0.2954 0.4574 0.3219 0.4673 Longer than 10 years 0.2364 0.4249 0.1212 0.3268 0.1131 0.3175 0.2166 0.4129 0.2593 0.4383 Subjective knowledge 7.8257 1.7832 7.4884 1.9814 7.3955 2.0136 7.5798 1.9066 7.9091 1.7243 Objective knowledge 2.5424 0.7204 2.0466 0.8980 2.1520 0.8814 2.5788 0.6600 2.6241 0.6544 Household size 2.5599 1.3205 2.3799 1.3330 3.1548 1.5813 2.9371 1.5826 2.5172 1.2670 Educational attainment Less than high school 0.0184 0.1345 0.0667 0.2498 0.0814 0.2741 0.0120 0.1091 0.0090 0.0942 High school 0.1021 0.3029 0.1497 0.3573 0.1864 0.3903 0.0539 0.2264 0.0939 0.2917 Financial Services Review, 32(4) 36 Some college 0.1970 0.3978 0.3127 0.4642 0.2896 0.4546 0.1667 0.3736 0.1792 0.3835 Bachelor 0.2996 0.4581 0.2429 0.4294 0.2489 0.4333 0.2345 0.4248 0.3136 0.4640 Graduate 0.3828 0.4861 0.2280 0.4201 0.1937 0.3961 0.5329 0.5002 0.4044 0.4908 Homeownership 0.8087 0.3934 0.5397 0.4991 0.6054 0.4899 0.7595 0.4285 0.8567 0.3505 Employment Unemployed 0.0251 0.1565 0.0556 0.2294 0.0181 0.1336 0.0549 0.2283 0.0202 0.1409 Employee 0.4716 0.4993 0.5799 0.4942 0.6561 0.4761 0.5160 0.5010 0.4438 0.4969 Self-employed 0.2768 0.4474 0.1413 0.3488 0.1810 0.3859 0.2854 0.4527 0.2984 0.4576 Retired 0.2265 0.4186 0.2233 0.4170 0.1448 0.3527 0.1437 0.3517 0.2376 0.4257 Access to emergency funds 0.7775 0.4160 0.5624 0.4967 0.6389 0.4814 0.7754 0.4183 0.8120 0.3908 Note: Weighted; 2016 and 2019 Survey of Consumer Finances Logistic Regression Analyses Table 3 shows the results of five logistic regression models. The first model represents a binomial regression for the pooled sample, including all racial/ethnic groups, with the Black respondent category as the reference group. We also ran a binomial regression for the pooled sample with White as the reference group (see Appendix). The other four regression models represent the findings related to the factors significant for financial planner use when making decisions about saving and investments within the Black, Hispanic, Asian/other, and White groups. In the pooled sample, Hispanic and Asian/other respondents were significantly less likely to work with a financial planner when making decisions about saving and investments than Black respondents. No significant difference was found when comparing Black consumers to White consumers. Gender, age, income, net worth, risk tolerance, investment horizon, financial knowledge, educational attainment, homeownership, and access to emergency funds were all positively associated with seeking financial advice. The authors conducted a joint hypothesis test and Likelihood Ratio Test (LRT) to test the nonlinear effect of age. The coefficient of the squared term is significant and indicates that there is evidence to suggest a nonlinear association. Also, when comparing the full model (age-squared included) and nested model (age- squared excluded), the result of the likelihood ratio test was statistically significant (p = 0.0000), indicating that age-squared is an important predictor when determining advice- seeking behavior. Qing & Reiter 37 Table 3. Binomial Logistic Regressions of Financial Planner Use Pooled Sample Black Hispanic Asian/other White Variables Coef. S.E. Coef. S.E. Coef. S.E. Coef. S.E. Coef. S.E. Race/ Ethnicity (Ref.= Black) White -0.0435 0.0588 - - - - - - - - Hispanic -0.1897** 0.0708 - - - - - - - - Asian/other -0.2794** 0.1011 - - - - - - - - Female (Ref.= Male) 0.3284*** 0.0324 0.2601** 0.0977 0.0738 0.1126 0.1776 0.1749 0.3784*** 0.0538 Age 0.0343*** 0.0072 0.0795*** 0.0188 0.0673* 0.0312 0.0308 0.0337 0.0292** 0.0099 Age squared - 0.0003*** 0.0001 - 0.0007*** 0.0001 -0.0008* 0.0003 -0.0002 0.0003 -0.0002** 0.0001 Married (Ref.= Not married) -0.0706 0.0472 0.1885 0.0976 -0.3143* 0.1520 -0.5766* 0.1891 -0.0427 0.0600 Income (Ref.= Less than $50K) $50K-$99,999 0.2189*** 0.0487 0.1173 0.1244 0.3837* 0.1831 0.1271 0.2409 0.2003** 0.0667 $100K and above 0.5294*** 0.0506 0.4951** 0.1801 0.6671** 0.2116 0.4884 0.2949 0.4893*** 0.0687 Net worth 0.0225*** 0.0052 0.0001 0.0087 0.0070 0.0164 0.0097 0.0225 0.0350*** 0.0067 Risk tolerance (Ref.=Not willing) Average risk 0.7900*** 0.0480 0.7158*** 0.1468 0.6853*** 0.1368 0.7487*** 0.2118 0.7993*** 0.0553 Above average risk 0.7823*** 0.0518 0.6966*** 0.1469 0.7672*** 0.1741 0.6821** 0.2420 0.7932*** 0.0618 Substantial risk 0.5862*** 0.0919 0.8746*** 0.1896 0.8479** 0.2677 0.2795 0.4098 0.4911*** 0.1114 Invest horizon (Ref.=Next few months) Next year 0.0105 0.0591 -0.0909 0.1763 -0.2527 0.2072 -0.0690 0.3401 0.0938 0.0774 Next few years 0.1714** 0.0560 0.0397 0.1243 -0.1782 0.1894 0.0353 0.2884 0.2694*** 0.0683 Next 5-10 years 0.3205*** 0.0605 0.0219 0.1509 0.2480 0.2148 -0.0876 0.3232 0.4359*** 0.0692 Longer than 10 years 0.3778*** 0.0618 0.3793* 0.1673 0.1078 0.2381 -0.0147 0.3919 0.4751*** 0.0686 Subjective knowledge 0.0172* 0.0080 0.0608** 0.0212 0.0690** 0.0246 0.0560 0.0468 -0.0041 0.0103 Objective knowledge 0.1770*** 0.0254 0.0142 0.0577 0.0741 0.0752 0.3352** 0.1131 0.2147*** 0.0315 Household size -0.0353* 0.0145 -0.0921* 0.0403 0.0134 0.0573 0.0270 0.0644 -0.0331 0.0186 Educational attainment (Ref. = Less than high school) High school 0.5133*** 0.1091 -0.1838 0.2087 0.6932* 0.3344 0.7375 3.9594 0.6930*** 0.1500 Some college 0.6238*** 0.1011 0.0575 0.1873 0.7778*** 0.2385 1.1054 3.9382 0.7575*** 0.1489 Bachelor 0.8328*** 0.1031 0.4592* 0.1954 1.1322*** 0.2261 0.7006 3.9447 0.9628*** 0.1606 Graduate 0.9187*** 0.1025 0.4118 0.2159 1.1356*** 0.3134 0.8684 3.9347 1.0648*** 0.1579 Homeownership (Ref.=No) 0.1552*** 0.0486 0.1994 0.1201 0.2511 0.1397 0.3785 0.2478 0.1252* 0.0623 Employed (Ref.=Unemployed) Employee 0.0093 0.0990 -0.2892 0.2378 0.8489 1.2875 -0.0384 0.2724 0.0139 0.1186 Self-employed 0.0397 0.1013 0.1145 0.2411 0.9846 1.2813 0.2671 0.2994 -0.0236 0.1155 Retired 0.1318 0.1046 -0.2158 0.2830 1.4831 1.2767 0.2627 0.3974 0.0895 0.1191 Financial Services Review, 32(4) 38 Have emergency funds (Ref.= No) 0.1014* 0.0433 0.3116** 0.0986 -0.0153 0.1414 0.0617 0.1769 0.0725 0.0530 Intercept - 4.4140*** 0.2608 - 4.3920*** 0.6276 - 5.7947*** 1.5528 -4.7735 4.0514 - 4.6304*** 0.2751 Sample size 4,059 378 221 200 3,260 R-squared 0.1172 0.0966 0.1298 0.0889 0.1085 Note: Unweighted analysis, 2016 & 2019 SCF. *p<0.05; **p<0.01; ***p<0.001 Black Respondents. Among Black respondents who use a financial planner, being a woman, age, being married, having an income that was $100,000 or more, being willing to take at least some risk, having a time horizon greater than 10 years, subjective financial knowledge, holding at least a bachelor’s degree, and having access to emergency funds were all positively associated with using a financial planner when making decisions about saving and investing. Household size and age squared were negatively associated with using a financial planner for saving and investment decisions. Hispanic Respondents. For Hispanic respondents, age, having an income of $50,000 or more, having at least some risk tolerance, subjective financial knowledge, and having at least a high school diploma were associated positively with financial planner use. Age squared and marriage were negatively associated with using a financial planner. Asian/Other Respondents. For the Asian/other group, having average or above average risk compared to no risk was associated with seeking advice. In addition, objective financial knowledge was positively associated, while marriage was negatively associated. White Respondents. Among White respondents, being a woman, age, having income greater than $50,000, having at least some risk tolerance, having an investment time horizon of the next few years or more, and objective financial knowledge were associated positively with financial advice seeking. Net worth was also positively associated with using a financial planner. As the findings show, there were differences in the characteristics of seeking financial advice among racial/ethnic groups. As such, decomposition estimation was used to investigate further and measure the intergroup differences among these variables. Decomposition Analyses According to Fairlie (2005), when conducting decomposition analyses, the reference group should typically be the group with the lower rate of financial planner use at the descriptive level. For example, the Hispanic group served as the reference group compared with other groups since only 19% of Hispanic respondents from the pooled sample reported using a financial planner when making decisions about saving and investments. Black respondents had lower rates (24%) than White respondents (38%), and as such, they were the reference group. Asian/other respondents had a rate of 31% and, therefore, were used as the reference group compared to White respondents. Table 4 represents the decomposition analyses utilizing Fairlie’s estimation (2005) across three pairwise comparisons between racial/ethnic groups (e.g., Black respondents versus other groups). Specification 1 shows the comparison between Black and White respondents. The total difference in using financial planners when making decisions about saving and investments between these two groups was 0.1399 based on the mean predictions of each group. The explained difference to the total difference was 110%, which indicates that the observed differences in respondents’ characteristics explained approximately 110% of the difference. If White respondents had the same characteristics as Black respondents, their probability of seeking financial advice for saving and investment decisions would be lower than that of Black respondents. Because the total difference is greater than 100%, this indicates that the Qing & Reiter 39 unexplained difference is having a negative effect on the propensity to use financial planners. Income accounted for 23.7% of the racial/ethnic gap, followed by 20.2% for net worth and 19.3% for risk tolerance. Negative percentages indicate a narrowing effect of that factor on the racial/ethnic gap in financial planner use. Gender contributed to narrowing the racial gap by 10.86% between Black and White respondents. As such, this means that gender narrows the gap in financial planner use between Black and White consumers. Table 4. Decomposition Analysis of Financial Planner Use (1) Black vs. White (2) Black vs. Hispanic (3) Black vs. Asian/other Variables Contribution to difference Percent of explained difference Contribution to difference Percent of explained difference Contribution to difference Percent of explained difference Female -0.0167*** -10.86% 0.0018*** 13.21% -0.0082 -11.46% Age 0.0077*** 5.01% -0.0016 -11.85% -0.0018 -2.50% Marital status -0.0029 -1.92% -0.0069* -51.12% -0.0441*** -61.73% Income 0.0365*** 23.71% -0.0046*** -34.09% 0.0359*** 50.29% Net worth 0.0310*** 20.15% -0.00001 -0.09% 0.0102 14.27% Risk tolerance 0.0297*** 19.29% 0.0075*** 55.49% 0.0251*** 35.13% Investment horizon 0.0152*** 9.88% 0.0006 4.79% -0.0002 -0.21% Subjective financial knowledge -0.0005 -0.35% 0.0034*** 25.60% 0.0017** 2.41% Objective financial knowledge 0.0218*** 14.17% 0.00001 0.05% 0.0290*** 40.63% Household size -0.0004*** -0.27% 0.0108*** 80.06% 0.0030 4.17% Education 0.0226*** 14.70% 0.0061** 44.93% 0.0023 3.20% Homeownership 0.0072*** 4.70% -0.0010** -7.45% 0.0173** 24.27% Employment -0.0002** -0.14% 0.0010 7.31% -0.0013 -1.76% Emergency access 0.0034** 2.23% -0.0036*** -27.06% 0.0022 3.13% Total difference 0.1399 0.0493 0.0713 Explained difference 0.1538 0.0135 0.0714 Unexplained difference -0.0138 0.0359 -0.00004 Percent of explained difference to total difference 110% 27% 100% Note: *p<0.05; **p<0.01; ***p<0.001; Decomposition analysis using 2016 & 2019 SCF. The reference group is the one that has the lower rate of financial planner usage. Financial Services Review, 32(4) 40 Specification 2 in Table 4 shows the decomposition analysis between Black and Hispanic respondents. The probability of using a financial planner between Black and Hispanic respondents was much smaller than that of the other racial/ethnic groups versus Black respondents. The total difference in the predicted mean likelihood of seeking financial advice was estimated at 0.0493. However, the characteristics of respondents accounted for 27% of the gap in the rate of using financial planners between these two groups. If Hispanic respondents had the same characteristics as Black respondents, the probability of using a financial planner when making decisions about saving and investments would be lower than that of Blacks. Household size (80.1%), risk tolerance (55.5%), and education (44.9%) were the most important factors. The largest contributors to narrowing the gap were marital status (51.1%) and income (34.1%). The results imply that, for instance, because Hispanic respondents had higher income levels than Blacks, if they had the same level of income as Black respondents, they would have a lower probability of using a financial planner. Specification 3 in Table 4 shows the contribution of each characteristic of households to the racial/ethnic difference financial advisor use between Black and Asian/other respondents; the total difference was 0.0713 based on the mean predictions of each group. The explained difference to the total difference was 100%. That is, if Black respondents had the same respondent characteristics as Asian/other respondents, the probability of using financial planners to make saving and investment decisions would be similar. The most important factor was income, which contributed to the racial/ethnic gap by 50.29%. Objective financial knowledge contributed 40.63% to the gap. In addition, marital status contributed to narrowing the financial advisor use gap by 61.73%, which indicates that if Black respondents had the same marital status as Asian/other respondents, they would have a lower probability of using a financial planner. Table 5 represents the decomposition analyses comparing three pairwise groups: (1) White- Hispanic, (2) White-Asian/other, and (3) Asian/other-Hispanic. Specification 1 of Table 5 shows the decomposition estimation between White and Hispanic respondents. The total difference in using financial planners when making decisions about saving and investments between these two groups was 0.1892 based on the mean predictions of each group. The explained difference to total difference was 92%, which indicates that respondents’ characteristics accounted for 92% of the gap in using financial advisors. If Hispanic respondents had the same characteristics as White respondents, the probability of using financial planners would be lower. In addition, the results suggest that most of the racial/ethnic gap in having financial planners when making decisions about saving and investments between White and Hispanic respondents was determined by the difference in characteristics of respondents in racial/ethnic groups, not by racial/ethnicity itself. Risk tolerance (22.30%) and educational attainment (19.20%) contributed the most among the predictors. Specification 2 of Table 5 shows the decomposition of White-Asian/other differences. However, the characteristics of respondents accounted for 32% of the gap in the rate of using financial planners between these two groups. If Asian/other respondents had the same characteristics as White respondents, the probability of using financial planners would be lower than White respondents. Age was the most important factor in explaining differences and contributed 30.73%, followed by objective financial knowledge at 20.53%. In addition, because Asian/other respondents had more education than White respondents, if they had the same level of education as White respondents, they would have a lower probability of using financial planners. When comparing Asians/others to Hispanics (Specification 3), the explained difference to total difference was 87%. The result indicates that if Hispanic respondents had the same characteristics as Asian/other respondents, the probability of using financial planners would be lower than Asian/other respondents. Risk tolerance (30.31%), income (28.68%), and objective financial knowledge Qing & Reiter 41 (25.20%) were the biggest contributors to the gap, while marital status and being female narrowed the gap. That is, if Hispanics had the same marital status as Asian/other respondents, they would have a lower probability of using financial planners. Table 5. Decomposition Analysis of Financial Planner Use (1) White vs. Hispanic (2) White vs. Asian/other (3) Asian/other vs. Hispanic Variables Contribution to difference Percent of explained difference Contribution to difference Percent of explained difference Contribution to difference Percent of explained difference Female -0.0112*** -6.42% 0.0020*** 9.04% -0.0058 -5.47% Age 0.0114*** 6.56% 0.0067*** 30.73% 0.0045*** 4.22% Marital status -0.0011 -0.64% 0.0003 1.20% -0.0208*** -19.70% Income 0.0312*** 17.93% -0.0003*** -1.41% 0.0303*** 28.68% Net worth 0.0224*** 12.85% 0.0029*** 13.02% 0.0067 6.31% Risk tolerance 0.0388*** 22.30% 0.0025*** 11.51% 0.0320*** 30.31% Investment horizon 0.0174*** 10.00% 0.0023*** 10.43% -0.0002 -0.21% Subjective financial knowledge -0.0007 -0.42% -0.0004 -1.77% 0.0045** 4.31% Objective financial knowledge 0.0204*** 11.71% 0.0045*** 20.53% 0.0266*** 25.20% Household size 0.0037*** 2.10% 0.0032*** 14.37% -0.0005 -0.49% Education 0.0334*** 19.20% -0.0065*** -29.77% 0.0144 13.69% Homeownership 0.0056*** 3.20% 0.0032*** 14.36% 0.0125** 11.84% Employment 0.0009** 0.50% 0.0011** 5.12% 0.0001 0.06% Emergency access 0.0021** 1.18% 0.0003* 1.42% 0.0012 1.11% Total difference 0.1892 0.0686 0.1207 Explained difference 0.1740 0.0220 0.1055 Unexplained difference 0.0153 0.0466 0.0151 Percent of explained difference to total difference 92% 32% 87% Note: Decomposition analysis using 2016 & 2016 SCF. The reference group is the group that has the lower rate of financial planner usage. *p<0.05; **p<0.01; ***p<0.001 Financial Services Review, 32(4) 42 Discussion and Implications This study examined the racial/ethnic differences in financial advice seeking for saving and investment decisions using the 2016 and 2019 waves of the Survey of Consumer Finances. Logistic regression was used to determine if there were significant racial/ethnic differences using a financial planner. The results showed that compared to Black and White respondents (see Appendix), Hispanic and Asian/other respondents were less likely to use a financial planner when making decisions about savings and investments, which supported Hypotheses 2 and 3. These findings have been corroborated in other studies (Hanna, 2011; White & Heckman, 2016) and may result from cultural differences among racial/ethnic groups. In particular, Hispanic clients may be less likely to seek financial advice if they do not intend to retire (Blanco et al., 2017). This would partially explain why nearly three-quarters of Hispanic consumers indicate they are not actively engaging in retirement planning (Hasler et al., 2023). Additionally, Asian clients may prefer to self- manage accounts instead of seeking financial advice (Merrill Lynch, 2023a). One commonality between Hispanic Americans and Asian Americans is that these groups make up a higher percentage of first-generation immigrants compared to White and Black Americans (Budiman et al., 2020; Budiman & Ruiz, 2021). It may be possible that a sense of isolation and language barriers dissuade these individuals from seeking financial advice (Pisnanont et al., 2015). One final point is that Hispanic and Asian individuals may be more likely to support aging parents and other family members (Merrill Lynch, 2023b; Merrill Lynch, 2023a). Arguably, the burden of supporting family members might warrant financial advice seeking; however, it may also reduce one's level of disposable income. With less disposable income, these consumers may feel that financial advice is too expensive, or they do not perceive themselves as having enough wealth to warrant seeking advice. The logistic regression analyses also showed that factors associated with seeking financial advice for savings and investing decisions were different across racial/ethnic groups, which supported Hypothesis 4. For example, net worth and homeownership were significant, but only among White consumers. Having an education beyond high school and age were factors in seeking advice for all consumers except for Asian/other consumers. Subjective financial knowledge was a positive and significant factor among Black and Hispanic consumers, while objective financial knowledge was a positive and significant factor among White and Asian/other consumers. Marriage was significant only for Hispanics and Asians/others. For Black consumers, emergency funds were positively associated with seeking advice, while household size was negatively associated. Differences in family dynamics, traditions, customs, and culture and their influence on financial advice seeking warrant further research. No significant difference was found between Black and White respondents seeking financial advice for saving and investment decisions, and as such, hypothesis 1 was not supported. This contradicts some previous studies that found differences in Black- White financial planner use (Chang, 2005; Elmerick et al., 2002; Hanna, 2011; Reiter & Qing, 2023). However, the discrepancy may be related to the fact that some previous studies investigated financial professional usage by including financial advice seeking behavior around debt, which this study excluded, or by defining financial planner differently than the current study. Fairlie’s decomposition estimation (2005) was employed to investigate the most important determinants that explain the racial/ethnic gaps in financial planner use. Results indicated that the determinants used to explain the racial/ethnic disparities in seeking financial advice for savings and investing decisions differed between racial/ethnic groups, supporting Hypothesis 5. The findings show that White and Hispanic households had the most significant total difference between groups (0.1892), and Black and Hispanic households had the least significant total difference between groups (0.0493). Previous research indicates that Black and Hispanic consumers track similarly in the personal finance domain (White et al., 2021). At the same time, cultural, language, and migration differences may explain why there are differences in financial planner use when these two groups Qing & Reiter 43 are compared. This difference deserves further investigation. The decomposition analyses found that risk tolerance was a key variable in explaining racial/ethnic differences in financial advice seeking. In four out of six pairwise decomposition analyses, risk tolerance was attributed to differences, and it explained most of the differences in using a financial planner between White and Hispanic households and Asian/other and Hispanic households. This finding is consistent with previous research in that risk tolerance has been positively associated with financial advice seeking (Chang, 2005; Joo & Grable, 2001; Moreland, 2018; White & Heckman, 2016). In addition, research has pointed to racial/ethnic differences in risk tolerance between Whites and Hispanics, with Hispanic consumers generally taking less risk (Fisher, 2020). Previous studies have shown that objective financial knowledge is positively related to seeking financial advice (Alyousif & Kalenkoski, 2017; Calcagno & Monticone, 2015; Seay et al., 2016). Our decomposition estimations showed that in three out of six specifications, objective financial knowledge was a key indicator for racial/ethnic differences in financial planner use. There has been an ongoing discussion on how to improve racial/ethnic differences in objective financial knowledge, and our findings serve as further evidence that this is a worthy cause. Findings indicate that Black and Hispanic consumers often have lower levels of financial literacy than White consumers (Anong, 2016; Porto, 2016) and face barriers to accessing the resources and services necessary for improving financial knowledge. While it is easy to suggest that these groups need more education, it might be more effective to consider the policy and institutional changes that can be made to improve their realities. Some may argue that individual or group characteristics are to blame for low financial literacy in Black and Hispanic groups. However, history suggests that systemic socioeconomic and political barriers, as well as discrimination, have roles to play as well (Hamilton & Darity, 2017). As such, policymakers should seek effective solutions to increase the financial literacy levels of marginalized groups. However, lifting financial literacy levels alone is not enough. Financial literacy is also associated with other factors, such as higher educational levels, so a holistic approach to financial well-being is necessary. Income has been positively associated with financial planner use in prior research (Elmerick et al., 2002; Hanna, 2011), and the decomposition analyses revealed that it is also a factor in racial/ethnic differences in seeking financial advice for savings and investing. It was a factor when looking at three pairwise groups: Black- White, Black-Asian/other, and Asian/other- Hispanic differences. It has long been recognized that there are persistent racial/ethnic differences in income, with White and Asian/other consumers making more, on average, than Black and Hispanic consumers (Wilson, 2020). Net worth also explained some of the racial/ethnic differences between White and Black households, White and Hispanic households, and White and Asian/other households, albeit at lower percentage rates. Net worth has often been touted as a key variable in one’s ability to engage the services of a financial planner (West, 2012). In summary, our findings show racial/ethnic gaps in financial advice seeking. Hispanic and Asian consumers were significantly less likely to seek financial advice for saving and investments than Black and White consumers. These results indicate that there is more to understand regarding attracting Hispanic and Asian/other clients to financial planning and minimizing barriers. The decomposition analyses revealed more information about the disparities in seeking advice. The findings show that income and risk tolerance explained the gaps in several of the pairwise analyses. Income was either the largest or second largest contributor to the gap in seeking financial advice among four pairwise groups: (a) Black-White, (b) Black-Asian/other, (c) White- Hispanic, and (d) Asian/other-Hispanic comparisons. Risk tolerance was the largest or second largest factor contributing to the financial advice seeking gap when comparing (a) Black- Hispanic, (b) White-Hispanic, and (c) Asian/other-Hispanic pairwise groups. These results show that if the racial/ethnic income gap and risk tolerance gaps were remedied, there would be less disparity in financial planner use. Black and Hispanic consumers lag behind White Financial Services Review, 32(4) 44 and Asian consumers in income (Greig & Eckerd, 2022). Evidence supports the idea that racial differences in income help explain the racial wealth gap (Ashman & Neumeuller, 2020). While there is no quick solution to narrowing either the racial income or wealth gaps, our findings are further evidence that these gaps have broad implications that reach beyond mere finances. This is an area in which policy can be implemented to assist consumers with gaining access to financial planning. The U.S. government has developed such solutions to assist consumers with financial issues. For example, an initiative was established in 2021 by the presidential administration to help build wealth in marginalized communities in an attempt to ameliorate the long-standing and persistent racial wealth gap (The White House, 2021). Closing the racial wealth gap is a goal that could help increase the number of marginalized consumers seeking financial advice. Similarly, policy could provide governmental financial support to minimize the gap in accessing financial planners. In the current study, the descriptive statistics show that while only 15% and 14% of Whites and Asians were willing to take no investment risk, nearly 30% of Black and Hispanics were willing to take no risk. On the other hand, nearly 8% of Black and Hispanic consumers stated they would be willing to take substantial risk, while only 4% of White and Asian/other consumers reported the same. These results align with findings from Yao et al. (2005), which found that Black and Hispanic consumers were more likely to take very high risks but were less likely to take some risks. The results seem to indicate that if Black and Hispanic consumers had more moderate risk tolerance profiles, this would help close the financial planner use gap. While risk tolerance may not be static, it may be a stretch to suggest that one’s risk tolerance should change. Many precipitating factors influence one’s risk tolerance, including experience, knowledge, skills, cognition, and financial satisfaction (Grable, 2016). As such, it is insufficient to suggest that education alone can help improve risk tolerance gaps. However, a multi-prong approach to increase awareness and financial literacy play a meaningful role. Ultimately, we recommend conducting further research on financial planner use among different racial/ethnic groups. Specifically, future research should investigate cultural, immigration, language, and other factors that may play a role in seeking financial advice. Qualitative research would be particularly useful in this process. It would allow researchers to learn more about information-seeking among diverse racial/ethnic groups in their own words. Limitations Some limitations in this study should be noted for subsequent studies. First, as stated by Fairlie (2005), unlike in the case of linear models, the matrix of independent predictors from two groups (X1 and X2) depends on the value of other variables; as a result, the order of switching the distribution may result in small differences in the outcome. Second, this study mainly examined the disparities from an economic behavior perspective; however, hiring a financial planner is a complex decision-making process. For example, future research should consider respondents’ psychological perspectives and cognitive abilities. Third, there are likely unobservable factors, such as cultural differences, at play in addition to those included in this study’s model. For example, it must be noted that certain consumers may face barriers in seeking financial advice due to the lack of access to professionals who share the same language and cultural background. Also, differences in results may be related to differences between U.S.-born respondents and respondents who migrated to the U.S. It is likely these two groups may have differing perspectives on financial advice seeking. Fourth, Asians and consumers from racial/ethnic groups with a comparatively small representation in the sample (i.e., Native Americans) were combined to make one category, given the limitations in the data. Interpretations of the results for this group should be read with an understanding that respondents with diverse racial, ethnic, and cultural identities and languages were combined. Finally, this study used cross-sectional data. It would be worthwhile to estimate differences over time utilizing panel data. Qing & Reiter 45 References Aladangady, A., Chang C. A., & Krimmel J. (2023). "Greater wealth, greater uncertainty: changes in racial inequality in the Survey of Consumer Finances," FEDS Notes. 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S.E. Race/ Ethnicity (Ref.=White) Black 0.0435 0.0588 Hispanic -0.1463** 0.0563 Asian/other -0.2359** 0.0811 Female (Ref.= Male) 0.3284*** 0.0324 Age 0.0343*** 0.0072 Age squared -0.0003*** 0.0001 Married (Ref.= Not married) -0.0706 0.0472 Income (Ref.= Less than $50K) $50K-$99,999 0.2189*** 0.0487 $ 100K above 0.5294*** 0.0506 Net worth 0.0225*** 0.0052 Risk tolerance (Ref.=Not willing) Average risk 0.7900*** 0.0480 Above average risk 0.7823*** 0.0518 Substantial risk 0.5862*** 0.0919 Invest horizon (Ref.=Next few months) Next year 0.0105 0.0591 Next few years 0.1714** 0.0560 Next 5-10 years 0.3205*** 0.0605 Longer than 10 years 0.3778*** 0.0618 Subjective knowledge 0.0172* 0.0080 Objective knowledge 0.1770*** 0.0254 Household size -0.0353* 0.0145 Educational attainment (Ref. = Less than high school) High school 0.5133*** 0.1091 Some college 0.6238*** 0.1011 Bachelor 0.8328*** 0.1031 Graduate 0.9187*** 0.1025 Homeownership (Ref.=No) 0.1552*** 0.0486 Employed (Ref.=Unemployed) Employee 0.0093 0.0990 Self-employed 0.0397 0.1013 Retired 0.1318 0.1046 Have emergency funds (Ref.= No) 0.1014* 0.0433 Intercept -4.4575*** 0.2543 Sample size 4,059 R-squared 0.1172 Note: Unweighted analysis, 2016 & 2019 SCF. *p<0.05; **p<0.01; ***p<0.001