Wealth: Factors That Effect African American Wealth

Abstract

As accumulating wealth is a vital component of financial well-being, this paper aims to gain a better understanding of why African Americans do not accumulate wealth at the same rate as other racial groups. Researchers utilize data from the 2019 Survey of Consumer Finances (SCF) to estimate the parameters of logistic regression specifications to examine the factors that determine wealth accumulation for African Americans. Researchers found that African Americans were less likely to have low wealth if they owned their own homes and more likely to have low wealth if they did not save. Saving and homeownership had more of an impact on wealth accumulation for African Americans than for White Americans. Furthermore, African Americans who did not invest in the stock market were more likely to have low wealth, as investing also had more of an effect on wealth accumulation for White Americans than for African Americans.

Keywords

African American wealth homeownership financial literacy

Introduction

Wealth can be defined as an abundance of valuable material possessions or recourses (Hacker, 2011). It is essential for families, because it provides financial well-being, financial security, and opportunities (Wolfe, 1998). Specifically, wealth provides housing security, liquidity for immediate consumption, a safety net against financial stresses; in addition, wealth represents power and democracy (Wolfe, 1998). It has been well-documented that wealth accumulation among African Americans lags behind that of the general population, and much attention has been given to the Black-White wealth gap (Long & Van Dam, 2020; McIntosh et al., 2020). The latter has widened over the past 30 years, because African Americans’ wealth has remained stagnant while White Americans’ wealth has grown (Long & Van Dam, 2020; McIntosh et al., 2020). For example, in 1968, African American middle-class wealth was worth $6,674, while White American middle-class wealth was worth $70,786, which is 10 times that of African Americans’ (Long & Van Dam, 2020). In 2016, African American middle-class wealth was worth $13,024, while White Americans’ middle-class wealth was worth $149,703, which is 11.5 times that of African Americans’ (Long & Van Dam, 2020). Why aren't African Americans generating as much wealth as other segments of the population. In other words, what factors are limiting African Americans from generating wealth at least at the same rate as others?

A legacy of racial discrimination and segregation at the national, state, and local levels have had a devastating effect on African American wealth (The; ; McIntosh et al., 2020). Mainly, discrimination in the workplace and the housing market has denied African Americans access to professional and managerial positions, higher or adequate wages, education, and homeownership (McIntosh et al., 2020). Homeownership is a vital way for African Americans to accumulate wealth (Shapiro, 2006). However, housing discrimination and redlining have denied African Americans access to affordable housing (Shapiro, 2006).

Many researchers have found that deep-seated structures that perpetuate racism are likely the cause of this wealth gap, suggesting that finding a solution to this widening gap may be outside the control of African Americans (Hamilton & Darity, 2017; McIntosh et al., 2020). In comparison, other researchers feel that financial literacy or financial education is the key to narrowing this black-white wealth gap. This narrowing might suggest that African Americans alone should take accountability and educate themselves in financial matters, and then the wealth gap can be narrowed (Al-Bahrani et al., 2019; White et al., 2021). The researchers of this study feel that there is no single answer to this problem. Both discrimination and financial literacy contribute to this widening wealth gap, along with many other factors. These researchers would also argue that discrimination would prevent African Americans’ from increasing their financial literacy, specifically on mortgages and investing, which would impact them owning homes or investing in the stock market.

This study explores the effects of confidence, financial knowledge, saving, homeownership, and investing in the stock market on wealth. If these factors are found to positively affect wealth accumulation, African Americans could adopt these financial behaviors and attitudes to accumulate more wealth. This study is vital, because wealth could provide African American families with financial security and well-being (Wolfe, 1998). Financially secure families can lead to the improvement of entire communities and cities (Wolla & Sullivan, 2017). This study is unique and adds to the body of knowledge because it focuses on middle-income African Americans, which is the segment of the African American community that has been growing over the past several years and narrowing the Black-White wealth gap (Henderson, 2020). Understanding how they’re accumulating wealth would be vital to the overall African American community.

Literature Review

Confidence and Financial Knowledge

Often, confidence in one's financial ability impacts financial well-being, financial literacy, and behaviors (Asad 2015). Confidence influences financial behaviors such as investing (Asaad, 2015; Bannier & Schwarz, 2018 Fereidouni & Tajaddini, 2017; Mossakowski, 2012). The terms “financial knowledge” and “financial literacy” are often used interchangeably, but there is a difference. Financial knowledge is the awareness and understanding of personal finance topics (Huston, 2010). By contrast, financial literacy is the process of using financial education and information to increase one's financial knowledge; then, one applies that financial knowledge through skills and tools to make sound financial decisions (Huston, 2010). Research has shown that people with greater financial literacy are more likely to have a higher net worth than people who do not (Hastings & Mitchell, 2018).

Mossakowski (2012) used the National Longitudinal Survey of Youth to examine the relationship between self-concept, locus of control, self-esteem, and wealth across four different assets among the overall population as well as middle-class African Americans. The researchers found that having a stronger sense of positive personal control over one's life and higher self-esteem significantly increased the likelihood of achieving positive net worth and homeownership in the general population. It was also found that self-esteem on positive net worth did not significantly vary between races (Mossakowski, 2012).

Bannier and Schwarz (2018) examined the relationship between actual accessed financial knowledge and confidence, measured as perceived financial knowledge, versus household wealth. Using data from the German SAVE initiative, the researchers found that gender and education were moderators for wealth (Bannier & Schwarz, 2018). In other words, individuals with greater financial literacy tended to have greater financial wealth; while higher education had a stronger positive effect on wealth among female participants, this was not necessarily the case for male participants (Bannier & Schwarz, 2018). Conversely, confidence had a significant positive effect on men's wealth but no significant effect on women's wealth (Bannier & Schwarz, 2018). Likewise, Fereidouni and Tajaddini (2017) examined consumer confidence in the relationship between housing, financial wealth, and four categories of consumption expenditures. They found that consumer confidence had a significant positive relationship with both housing wealth and consumer expenditures but a negative relationship with financial wealth (Fereidouni & Tajaddini, 2017).

Hastings and Mitchell (2018) examined the link between financial literacy, impatience, and wealth through retirement savings and investment behavior. The researchers used data from the 2009 Encuesta de Proteccion Social (EPS) survey, a nationally representative microeconomic panel of 14,000 Chilean participants who were surveyed over a period of 10 years. They found that impatience was a strong predictor of retirement savings and investment behavior; moreover, financial literacy was correlated with retirement savings but at a weaker intensity (Hastings & Mitchell, 2018). Similarly, Behrman et al. (2010) also examined the link between financial literacy, educational attainment, and household wealth accumulation. The researchers found that financial literacy and educational attainment were both strongly positively associated with household wealth. More importantly, investments in financial literacy could have large positive effects on household wealth accumulation (Behrman et al., 2010).

Rooj et al. (2012) investigated the relationship between financial literacy and household wealth using data from the Dutch Central Bank Household Survey. The researchers found a strong association between financial literacy and net worth. Moreover, there were two channels through which financial literacy could facilitate wealth (Rooj et al., 2012). First, financial knowledge increased the likelihood of investing in the stock market, allowing individuals to benefit from their equity premium. Second, financial literacy related to retirement planning and the development of a savings plan also boosted wealth. Overall, financial literacy directly and indirectly impacted household wealth (Rooj et al., 2012)

Savings, Homeownership, and Investing in the Stock Market

Saving and investing in the stock market are all positive financial behaviors that are necessary for wealth creation (Belke et al., 2012; Thompson & Suazas, 2012). Homeownership is also essential for accumulating wealth, especially for African Americans (Shapiro, 2006). Typically, a rigorous savings plan is needed to generate sufficient funds to invest in the stock market, and planning is typically required to achieve one's goals (Belke et al., 2012; Thompson & Suazas, 2012). All of these behaviors are typically associated with high wealth (Belke et al., 2012; Thompson & Suazas, 2012). Although investing in the stock market is undoubtedly one way to accumulate wealth, African Americans have not invested in the stock market at the same rate as the general population (Hudson et al., 2018; Richard, 2014; Zan et al., 2017).

Thompson and Suazas (2012) explored the relationship between investing activity and wealth by measuring the number of different asset holdings. The researchers examined 25 years of data (1989–2013) from the SCF and found that wealth differences between African Americans and White Americans were attributable to different asset holdings (Thompson & Suazas, 2012). Similarly, Herring and Henderson (2016) examined cultural differences between African Americans and White Americans to determine the causes of the Black-White wealth gap. By performing OLS and quantile regression analysis on data from the 2013 SCF, the researchers found that income, stock ownership, and business ownership accounted for much of the disparity (Herring & Henderson, 2016).

Belke et al. (2012) investigated the relationship between wealth, aging, and saving behavior. The researchers used pooled cross-section data from the German Consumption Survey and found that saving behavior had the greatest effect on real estate wealth. Moreover, older households tended to increase savings in the second half of their retirement period (Belke et al., 2012). Killawald and Bryan. (2016) examined the relationship between homeownership and wealth for both African Americans and Hispanics. Homeownership is typically the largest component of wealth holdings for African Americans (Shapiro, 2006). Using data from the National Longitudinal Survey NLSY 79 and marginal structural models, Killawald and Bryan. (2016) found that each additional year of homeownership increased midlife wealth in 2008 by approximately $6,800. In addition, the benefit of homeownership for African Americans was over 48% greater than for White Americans (Killawald & Bryan. 2016).

Herbert et al. (2013) also examined the relationship between homeownership and wealth by using data from the SCF and the Panel Study of Income Dynamics (PSID). The researchers concluded that homeownership represented an opportunity for families to accumulate wealth, generated wealth through the appreciation of home prices, and forced households to save; therefore, a large increase in savings typically occurred when families owned homes (Herbert et al., 2013).

The life cycle hypothesis (LCH) is an appropriate theory for this study, because it asserts that individuals earn income, save, and accumulate enough wealth to sustain themselves throughout their lives (Modigliani, 2005). Developed by Modigliani and Brumberg, the LCH is an economic theory that describes individuals’ spending and savings habits throughout their lives (Modigliani, 2005). As individuals earn income, they save and accumulate wealth to be potentially used in retirement. At retirement, individuals dissave or consume their savings to fund the rest of their lives (Modigliani, 2005).

The LCH assumes that individuals plan their spending over their lifetime and that wealth is typically low when individuals are younger and high when they are older. As individuals earn income, they borrow when income is low and save and accumulate wealth when income is high. All of these actions are taken to smooth consumption over an individual's lifetime (Modigliani, 2005). Individuals also take on debt when they are young, with the assumption that they will pay it off in the future (Modigliani, 2005). Thus, the hypotheses below were formulated for this study.

H1: African Americans who were more financially knowledgeable were more likely to have high net worth than African Americans who were less financially knowledgeable.

H2: African Americans who invest in the stock market are more likely to have high net worth than African Americans who do not invest in the stock market.

H3: African Americans who save were more likely to have high net worth than African Americans who do not save.

H4: African Americans who are homeowners are more likely to have high net worth than African Americans who are not homeowners.

H5: African Americans who were more confident were more likely to have high net worth than African Americans who were less confident.

Methodology

Data and Sample Selection

The current study uses data from the 2019 SCF to explore differences in net worth across demographic groups, with an emphasis on African Americans. Conducted every three years by the National Opinion Research Center at the University of Chicago, the SCF is a cross-sectional survey of American families. Financial information, such as income, assets, liabilities, and investments, and socio-demographic information is surveyed. In 2019, the total sample size was 5,777. Hanna et al. (2018) and Lindamood et al. (2007) have discussed many methodological issues related to the use of SCF datasets, and the researchers in this study followed most of their recommendations. To protect respondents’ privacy, the Federal Reserve excluded some data from the publicly released files. The Federal Reserve imputes replacement values for missing data, which results in the five implicates. Researchers must consolidate the latter to account for the uncertainty caused by the omissions. This issue was addressed using a weighted sample bootstrapping technique outlined by Hanna et al. (2018). Given our research focus on African American families’ net worth, we analyzed one additional subsample consisting of 904 households.

Measurement of Variables

Dependent Variables

Net worth is defined as the difference between assets and liabilities in the household balance sheet. Assets consist of financial assets—which include all types of transaction accounts (liquid assets), certificates of deposits, directly held pooled investments funds (excluding money market funds), savings bonds, directly held stocks, directly held bonds (excluding bond funds or saving bonds), cash value of whole life insurance, other managed assets, quasi-liquid retirement accounts, and miscellaneous financial assets—and nonfinancial assets, which include vehicles, primary residence, residential property (excluding primary resident), businesses, and miscellaneous nonfinancial assets (SCF Staff, 2019). Although the SCF measure of wealth is relatively comprehensive, some assets that may be widely held, such as defined-benefit pensions and Social Security wealth, are not included in the definition of net worth. Debt includes debt secured by a primary resident (mortgages and home equity loans), debt secured by other residential property, other lines of credit, credit card balances, installment loans, and other debt.

In this study, wealth was the dependent variable. It was converted into an ordinal qualitative variable using visual binning technique in order to identify appropriate cut-off points for dividing the variable into three approximately equal groups. Equal percentiles were used based on scanned cases (equal intervals with two cut-off points). Thus, this study considered wealth as a multinomial ordinal-dependent variable (1 = low, 2 = middle, and 3 = high; Lobos et al., 2016; Nielsen, 2015).

Independent Variables

Socioeconomic Factors. Age group was converted into an ordinal qualitative variable using visual binning technique in order to identify appropriate cut-off points for dividing the variable into three approximately equal groups. This study considered age group as a multinomial ordinal-independent variable (1 = ages 46 and younger, 2 = ages 47–62, and 3 = ages 63 and older). Marital status was determined by whether the individual was married or lived with a partner (yes) or not (no). Education level was categorized into three groups: some college or less, bachelor's degree, and postgraduate or professional degree. Employment level was categorized using several occupation classifications (1 = managerial and professional; 2 = technical, sales, and services; 3 = other; and 4 = not working). Homeownership was defined by whether or not the respondent owned a principal residence (yes or no).

Financial Factors. Confidence level was measured on a ten-point Likert scale according to the respondent's self-reported knowledge of personal finance (where 1 = not all knowledgeable and 10 = very knowledgeable). Financial knowledge was measured according to respondents’ responses to three questions about the following finance topics: stock risk, interest rates, and inflation values (where 1 indicated a correct answer and 0 indicated incorrect responses). The saving variable was measured by the respondent's response to the question “How much is in all your remaining saving accounts?” If the balance was greater than 0, then the variable was coded 1 for “yes”; otherwise, it was coded 0 for “no.” The stock variable was measured by the respondent's response to the question “What is the total value of this stock?” If the balance was greater than 0, then the variable was coded 1 for “yes”; otherwise, it was coded 0 for “no.” The Federal Reserve Board redefine the measurement of income as follows:

Income is defined as the total of SCF are wages, self-employment and business income, taxable and tax-exempt interest, dividends, realized capital gains, food stamps and other related support programs provided by the government, pensions, and withdrawals from retirement accounts, Social Security, alimony and other support payments, and miscellaneous sources of income for all members of the primary economic unit in the household (Bricker et al., 2017).

Model Specifications

A multinominal logistic regression model was employed to study the relationship of ethnicity to net worth levels. Multinomial logistic regression is similar to a series of binary logistic regressions comparing each level of the dependent variable to a chosen reference group (as measured by middle-net worth as the reference group. For example, compare the low- to middle-net-worth and high- to middle-net-worth). Multinomial logistic regression was used to explain a multi-categorical dependent variable of net worth levels. (Fukui et al., 2014).

The reason for a multinominal logistic regression is that the dependent variable is of type Interval. Interval data is a type of data that is measured along a scale, in which each point is placed at an identical distance (interval) from one another (Formplus, 2019). Unlike ordinal data, interval data always take numerical values where the distance between two points on the scale is standardized and equal. Additionally, mathematics operations can be performed on interval data (Formplus, 2019).

Net worth was assumed to be a function of financial factors, such as confidence, financial knowledge, homeownership, savings, stock ownership, and income level, and socioeconomic factors, such as age group, education level, employment status, and marital status. The model was expressed as

N e t W o r t h L e v e l s = f (f i n a n c i a l f a c t o r s a n d s o c i o e c o n o m i c f a c t o r s)

(1)

L o g i t (ρ) = \log (\frac{ρ (H i g h N e t W o r t h)}{ρ (M i d d l e N e t W o r t h)}) = β_{0} + β_{1} χ_{1} + β_{2} χ_{2} + \dots + β_{κ} χ_{κ}

(2)

L o g i t (ρ) = \log (\frac{ρ (L o w N e t W o r t h)}{ρ (M i d d l e N e t W o r t h)}) = β_{0} + β_{1} χ_{1} + β_{2} χ_{2} + \dots + β_{κ} χ_{κ}

(3)

where β is a vector of coefficients to be determined and χ is a vector of African Americans’ financial factors and socioeconomic characteristics.

Results

Descriptive Statistics

Table 1 reports descriptive statistics for the SCF 2019 dataset. For this study, net worth and age group were divided into three value groups, and income was divided into two value groups using the visual binning technique in SPSS, which enables the creation of a limited number of distinct categories from contiguous values. Net worth was divided into low, middle, and high across ethnic groups, while income was divided into low and high across ethnic groups. For example, the middle net worth category represented 48.6% of White Americans, 31.7% of African Americans, and 37.6% of all other Americans. With regard to income groupings, African Americans disproportionately fell into the low-income range at 50%, while White Americans and other Americans comprised 28.7% and 38.5%, respectively. Lastly, age group consisted of three groups: respondents aged 46 and under, 47–62, and 63 and older. The middle age group (47–62) was relatively evenly distributed: 28.5% White American, 30.3% African American, and 27.2% all other Americans. The dataset indicated a relatively low number of unmarried African Americans (33.7%) compared to White Americans (59.6%) and other Americans (62.8%). Education level was determined based on self-reported data; respondents were grouped according to whether they had some college experience or less, a college degree, or a postgraduate or professional degree. Within each ethnic group for the “some college or less” category, 59.7% were White respondents, 74.9% were African American respondents, and 69.4% in the other American group. Finally, employment type was categorized into four groups: managerial/professional, tech/sales/services, other (production/labor), and not employed. Racial groups were relatively evenly distributed across each category—except for managerial/professional, where African Americans were the least represented at 23.3%.

Table 1.

Descriptive Statistics for the Weighted Sample.

Overall sample (N = 5,777)
Variables	White Americans (n = 3,928)		African Americans (n = 904)		Other Americans (n = 945)
Variables	Observations	%	Observations	%	Observations	%
Net worth
Low	1,239	31.6	587	65.0	491	52.0
Middle	1,910	48.6	287	31.7	356	37.6
High	779	19.8	30	3.3	98	10.4
Income range
Low	1,128	28.7	452	50.0	364	38.5
Middle	1,298	33.0	309	34.2	307	32.5
High	1,502	38.3	143	15.8	274	29.0
Age group
46 and under	1,420	36.1	400	44.3	534	56.5
47 to 62	1,118	28.5	274	30.3	257	27.2
63 and over	1,390	35.4	230	25.4	154	16.3
Marital status
Unmarried	2,340	59.6	305	33.7	593	62.8
Married	1,588	40.4	599	66.3	352	37.2
Education level
Some college or less	2,344	59.7	677	74.9	656	69.4
College degree	924	23.5	136	15.0	160	16.9
Postgraduate degree	660	16.8	91	10.1	129	13.7
Employment type
Managerial/professional	1,220	31.1	211	23.3	275	29.1
Tech/sales/services	728	18.5	218	24.1	240	25.4
Other (production/labor/etc.)	679	17.3	179	19.8	221	23.4
Not employed	1,301	33.1	296	32.8	209	22.0

Summary statistics for the respondents’ financial characteristics are presented in Table 2. Within each ethnic group, less than half (44%) of African Americans in the sample were homeowners, compared to nearly three quarters (72.8%) of White Americans and a little over half (52.2%) of other Americans. The proportion of self-reported financial knowledge (i.e., confidence) in the survey was relatively evenly distributed across all Americans. At the lower end of the range, other Americans held the edge in the low range (44.2%), African Americans (39.5%) ranked second, and White Americans (31.4%) had the least knowledge. At the highest level of financial knowledge, White Americans demonstrated the most knowledge (49.6%), followed by other Americans (32.9%) and African Americans (26.4%).

Table 2.

Descriptive Statistics for Financial Factors.

Overall sample (n = 5,777)
Variables	White Americans (n = 3,928)		African Americans (n = 904)		Other Americans (n = 945)
Variables	Observations	%	Observations	%	Observations	%
Homeownership status
No	1,070	27.2	506	56.0	452	47.8
Yes	2,858	72.8	398	44.0	493	52.2
Confidence
Low	1,234	31.4	357	39.5	418	44.2
Moderate	1,567	39.9	291	32.2	312	33.0
High	1,127	28.7	256	28.3	215	22.8
Financial knowledge
0	110	2.8	64	7.1	54	5.7
1	576	14.7	280	31.0	218	23.1
2	1,292	32.9	321	35.5	362	38.3
3	1,950	49.6	239	26.4	311	32.9
Savings account
Yes	2,218	56.5	359	39.7	431	45.7
No	1,710	43.5	546	60.3	513	54.3
Stock ownership
Yes	711	18.1	61	6.7	106	11.2
No	3,218	81.9	844	93.3	839	88.8

The analysis found that approximately 60% of White Americans and 50% of other Americans had savings accounts. However, only about 40% of African Americans had saving accounts. White Americans’ stock ownership was nearly twice (18.1%) that of the other two groups; 11.2% of other Americans and 6.7% of African Americans owned stocks.

The dataset shown in Table 3 provided access to net worth and income data for different groups of Americans and a subset of African Americans. The interquartile range, a standard measure of dispersion, is the difference between the third quartile and the first quartile. Using the interquartile ranges to analyze the middle 50%, the results indicated when comparing African American to White Americans income ranges about 2:1, while net worth represents a 5:1 margin. In making the same comparison for other Americans, the income ranges were about 1.5:1, while net worth represents a close 2:1 margin. Moreover, we compared different income subdivisions among African Americans in order to examine variations in net worth and income within this subset. The results for low-income and middle-income African Americans followed the trend in the overall sample set (net worth differentiation ranged about 12:1 and 4:1, and income ranged about 5:1 and 4:1, respectively). Meanwhile, high-income African Americans were the only group that could compare to White Americans in the overall group on a 1:1 basis. The sample set's overall message is that, when accounting for outliers, income ranges and net worth ranges are closely related to net worth growth.

Table 3.

Summary Statistics for Sample Set.

Overall Sample (N = 5,777)
Variables	Net worth						Income
Variables	n	M	SD	Mdn	Q1	Q3	IQR	M	SD	Mdn	Q1	Q3	IQR
Overall groups
White Americans	3,928	951,348	105,084	181,516	34,090	554,859	520,769	120,630	8,657	68,213	34,616	122,174	87,558
African Americans	904	139,797	29,238	20,781	388	113,983	113,595	58,406	4,100	38,929	21,558	68,213	46,655
Other Americans	945	477,395	107,878	59,441	6,146	265,674	259,528	92,255	7,020	49,887	28,507	96,721	68,213
African American groups
Low income	452	32,181	3,353	4,744	20	43,988	43,968	22,031	472	21,837	13,235	30,543	17,308
Middle income	309	107,642	11,555	40,353	793	143,452	142,658	59,269	780	57,014	47,851	69,232	21,380
High income	143	548,258	178,895	215,985	69,354	540,891	471,537	171,221	23,417	125,221	100,793	174,273	73,479

N ote. M = mean; SD = standard deviation; Mdn = median; Q1 = first quartile; Q3 = third quartile; IQR = interquartile range.

Low Net Worth versus Middle Net Worth

Table 4 offers several insights into the characteristics of Americans’ net worth levels. The younger age group (46 and under) was significant across all groups, indicating that age is a barrier for low-net worth Americans to move into the middle net worth group. The odds that White and African Americans to be classified at the low net worth were about 5:1 (p <.001) compared to 3:1 (p < 0.01) for other Americans. The middle age group (ages 47–62) was significant (p < 0.001) for White Americans (odds ratio 1.5:1) and African Americans (odds ratio 2:1) but not for other Americans. Marital status remained consistent throughout the study; unmarried low-income respondents were at a disadvantage with regard to moving into the middle net worth group. More specifically, White Americans (36.3%, p < 0.001) and African Americans (48.7%, p < 0.01) were significantly less likely to be classified in the middle wealth group; this finding did not hold for other Americans. The association between low education level (“some college or less”) and low net worth was significant across all groups of Americans. Low-income Other Americans were 152.8% (p < 0.05) more likely to be included in this category, followed by African Americans at 103.6% (p < 0.05) and White Americans 60.2% (p < 0.01) in being classified at the low net worth level. However, it is worth noting that when compared to the reference group no significant relationship was found for African Americans and other Americans, while White Americans (52%, p < 0.001) employed in managerial and professional occupations were less likely to be classified in the low net worth group.

Table 4.

Multinomial Logistical Analysis of the Likelihood of Net Worth Levels.

Net Worth Levels (N = 5,777)
Parameter estimates	White Americans (n = 3,928)				African Americans (n = 904)				Other Americans (n = 945)
	Low		High		Low		High		Low		High
	β	Exp(β)	β	Exp(β)	β	Exp(β)	β	Exp(β)	β	Exp(β)	β	Exp(β)
Intercept	1.021	−2.870	.177	−3.689	.749	−3.301
Age group (reference category = 63 and over)
46 and under	1.532***	4.626	−1.762***	.172	1.496***	4.464	−1.433	.239	1.095**	2.991	−2.182***	.113
47–62	.404**	1.498	−0.287*	.750	.699**	2.012	.135	1.145	.119	1.127	−1.084*	.338
Marital status (reference category = married)
Unmarried	−0.450***	.637	.732***	2.079	−0.667**	.513	.884*	2.419	.099	1.105	1.088**	2.968
Education level (reference category = postgraduate degree)
Some college or less	.471**	1.602	−1.314***	.269	.711*	2.036	−0.233	.792	.927*	2.528	−1.724***	.178
College degree	.005	1.005	−0.419**	.658	.078	1.081	.316	1.371	.237	1.268	−0.464	.629
Employment type (reference category = not employed)
Managerial/professional	−0.734***	.480	.482**	1.619	−0.223	.800	.263	1.301	−0.647	.524	1.466**	4.334
Tech/sales/services	−0.253	.777	−0.288	.750	−0.260	.771	−0.632	.532	−0.014	.987	1.102*	3.011
Other (prod/labor/etc.)	−0.323*	.724	−0.200	.819	−0.057	.944	−0.026	.975	−0.620	.538	.721	2.057
Homeownership status (reference category = no)
Yes	−2.657***	.070	.646**	1.909	−2.960***	.052	.475	1.608	−3.399***	.033	1.284*	3.611
Confidence (scale from 1 to 10)
Knowledge scale	−0.102***	.903	.140***	1.151	−0.034	.966	.112	1.119	−0.076	.927	.096	1.101
Financial knowledge (reference category = scale from 0 to 3)
Literacy scale	−0.342***	.710	.621***	1.860	−0.034	1.034	.461	1.585	−0.390**	.677	.508*	1.661
Savings account (reference category = yes)
No	.548***	1.730	−0.029	.971	.790***	2.203	−0.287	.751	.312	1.366	−0.519	.595
Stock ownership (reference category = yes)
No	1.021***	2.776	−1.040***	.354	.965*	2.624	−1.123*	.325	1.462***	4.313	−0.885**	.413

Note. *p < 0.05, **p < 0.01, ***p < 0.001. The reference category for the model is middle level net worth.

The five financial variables listed in Table 4 were studied to gain insight on net worth levels. For all respondents, homeownership was significantly associated (p < 0.001) with a lower likelihood of having a low net worth: White Americans’ homeownership rate at 93%, African Americans’ homeownership at 94.8%, and other Americans’ homeownership at 96.7%. Self-reported financial knowledge or confidence (subjective measure) was not found to be significantly associated with a low net worth for African Americans and other Americans, but it was highly significant for White Americans (9.7%, p < 0.001). Financial knowledge level (objective measure) where African Americans were not significant, while White Americans (29%, p < 0.001) and other Americans (32.3%, p < 0.01) were less likely to be classified at the middle level. Not having a savings account was significant. Financial knowledge level (objective measure) where African Americans were not significant, while White Americans (29%, p < 0.001) and other Americans (32.3%, p < 0.01) were less likely to be classified at the middle level. Not having a savings account was significant for African Americans (120.3%, p < 0.001), more likely identified at the low net worth level, and White Americans (73%, p < 0.001), while other Americans were not significant. Compared to middle net worth respondents, not owning stocks was least significant for African Americans, indicating that 1 out of 3 respondents were low net worth respondents (p < 0.05). White Americans who did not own stocks were (1 out 3 respondents, p < 0.001) more likely to be grouped at the low net worth compared to other Americans who were (1 out of 4 respondents, p < 0.001) more likely to be grouped here. Not having a saving account was significant for African Americans (120.3%, p < 0.001) most likely identified at the low net worth level and White Americans (73%, p < 0.001), while other Americans were not significant. Not owning stocks was least significant for African Americans 2.6:1 (p < 0.05). White Americans who did not own stocks were 2.8:1 (p < 0.001) more likely to be grouped at the low net worth compared to other Americans at 4.3:1 (p < 0.001) more likely to be grouped here.

High Net Worth versus Middle Net Worth

When reviewing high net worth respondents relative to middle net worth respondents, being in the younger age group (46 and under) was found to be significant for White Americans and other Americans but not African Americans. Younger White American respondents and other Americans were 82.8% (p < 0.001) and 88.7% (p < 0.001) less likely to be classified in the high-net-worth group, respectively, when compared to their reference group. Being unmarried was significant for all groups of respondents; in other words, unmarried people were more likely to be classified in the high net worth group than married respondents. Furthermore, when compared to the unmarried reference group, White Americans at 107.9% (p < 0.001), African Americans at 141.9% (p < 0.05), and other Americans at 196.8% (p < 0.01), an indication of the importance of being married in building wealth and avoiding the low wealth classification. Education level was not significant for African Americans at the high wealth level. However, having some college or less was negatively associated with high net worth; White American respondents at 73.1% (p < 0.001) and other American respondents at 82.2% (p < 0.001) represents the less likelihood of being at the high-net-worth level. However, African American respondents’ employment status was not significant across all employment classifications. By contrast, White respondents and other Americans employed in managerial or professional occupations were 61.9% (p < 0.01) and 333.4% (p < 0.01) more likely to have a high net worth, respectively. In addition, other American respondents were 201.1% (p < 0.05) more likely to have a high net worth when employed in the technical, sales, and services category.

Homeownership for African American respondents was not significant. By contrast, for White American respondents (90.9%, p < 0.01) and other American respondents (261.1%, p < 0.05), homeownership increased the likelihood of having a high net worth. The relationship between confidence in financial knowledge and net worth was only found to be significant for White American respondents (15.1%, p < 0.001). At 86% (p < 0.001), White American respondents were more likely to be at the high-net-worth level from a financial literacy perspective. In contrast, other Americans, 66.1% (p < 0.05), were more likely to be classified at a high-net-worth level, as well. Financial knowledge was not significant for African Americans. Not having a savings account was not significant across all groups of respondents with a high net worth. However, stock ownership was significant for the high-net-worth group, while homeownership was significant across all net worth groups. Interestingly, lack of homeownership indicates that White Americans (64.6%, p < 0.001), African Americans (67.5%, p < 0.05), and other Americans (58.7%, p < 0.01) were all less likely to be included in the high-net-worth group.

Based on the analysis of results from Table 4, Hypothesis H₁ is rejected. Among African Americans, the financial knowledge level was not significant compared with a low net worth or high net worth respondents than the reference group of middle net worth respondents. Based on the results, Hypothesis H₂ cannot be rejected. African Americans who invested in stocks were 67.5% more likely than middle net worth African Americans to be classified as having high net worth; this relationship was significant (p < 0.05). Moreover, low net worth respondents were 162.4% more likely not to be classified in the middle wealth level. Based on the results, Hypothesis H₃ cannot be rejected. African Americans who saved were 2.2 times more likely to be classified at the middle net worth than low net worth respondents who do not save; this relationship was significant (p < 0.001). Hypothesis H₄ also cannot be rejected. Among African American respondents with a low net worth, lack of homeownership was significantly (94.8%, p < 001) associated with accumulating net worth compared to the reference group of middle net worth families. Lastly, H₅ is rejected, as confidence did not have a significant effect on African Americans’ net worth.

Discussion

This study found a significant positive relationship between having a savings account and wealth and homeownership and wealth. The results are consistent with previous studies that explored the same relationships (Belke et al., 2012; Thompson & Suazas, 2012). The current study segmented the dependent variable of wealth into three distinct categories: low, middle, and high wealth. By contrast, previous studies often measured wealth as a continuous variable. This difference in the way that wealth was measured did not seem to impact the results. Specifically, the current study found that African Americans who owned homes were less likely to have low wealth and African Americans who did not save were more likely to have low wealth; this contradicts previous findings that they were more likely to have high wealth. This may be due to the relatively low number of African Americans with high wealth.

Previous studies that examined the relationship between financial knowledge and wealth typically found a positive and significant association (Bannier & Schwarz, 2018; Hastings & Mitchell, 2018; Rooj et al., 2012.). Conversely, this study did not find a significant positive relationship between financial knowledge and wealth among African Americans. The current study measured financial knowledge in a similar way as previous studies, so this probably did not account for the difference in results. However, the way in which this study measured wealth may account for the difference in results related to financial knowledge.

Moreover, previous studies that examined the relationship between confidence and wealth found a positive and significant association (Mossakowski, 2012). However, this study did not find a significant positive relationship between confidence and wealth among African Americans. In the current study, confidence was a self-reported variable, which differs from how previous studies measured confidence (Asaad, 2015; Mossakowski, 2012; Bannier & Schwarz, 2018; Fereidouni & Tajaddini, 2017). This difference in measurement may account for the difference in results.

The researchers also found no differences in wealth accumulation between African Americans with a bachelor's degree and those with a postgraduate degree. This is inconsistent with previous studies that showed that a higher education level was associated with a higher likelihood that education level would have a significant effect on wealth (Wolla & Sullivan). In the current study, White Americans with a postgraduate degree were more likely to have higher wealth than those with a bachelor's degree.

Conclusion and Implications

We find that African Americans were less likely to have low wealth if they owned homes and more likely to have low wealth if they did not save. Moreover, it was found that African Americans who did not invest in the stock market were less likely to have high wealth. Many studies have found that homeownership is a great way to generate wealth, especially for African Americans (Herbert et al., 2013; Killewald & Bryan, 2016). In fact, homes comprise the largest portion of many African Americans’ net worth (Herbert et al., 2013; Killewald & Bryan, 2016).

Previous studies have found that African Americans are less likely to invest in the stock market (Hudson et al., 2018). The financial services industry would likewise be interested in the results of this study. Since a significant and positive relationship was found between African Americans’ investment in the stock market and wealth, more attention should be given to teaching African Americans about investing and the stock market. If African Americans understood vital elements of the stock market, such as risk/return and asset allocation, they may be more confident investing in it. In addition, the financial services industry should provide more homeownership and mortgage education to the African American community, which would be key to helping African Americans purchasing their homes.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Crystal R. Hudson

John Young

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