Community black business ownership prevalence and self-reported stroke risk factors among black residents

Abstract

Background

Non-Hispanic Black Americans have a greater risk of stroke than other racial and ethnic groups in the United States. However, race is primarily a sociopolitical construct associated with differential access to power related to inequities in social, economic, and political resources. Racial differences in stroke incidence may be explained by this power differential. We sought to examine the relationship between Black business ownership, viewed as an economic resource that facilitates racial power, and stroke risk factors.

Methods

This was a cross-sectional analysis using data from the pooled 2017 and 2019 Selected Metropolitan/Micropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System; 2017 Annual Business Survey; 2015–2019 American Community Survey; and 2019 Small Business Policy Index. Black business ownership was estimated at the micropolitan or metropolitan statistical area (MMSA) level as a continuous measure defined as: (1) (Black-owned firms/ non-Black-owned firms)/ (Black population/ non-Black population), BBO1; and (2) (Black-owned firms/ White-owned firms)/ (Black population/ White population), BBO2. MMSAs with scores greater than 1 represented places where Black residents were overrepresented as business owners relative to their share of the local population. We examined the relationship between the Black business ownership ratios (BBO1 and BBO2) and self-reported stroke risk factors (hypertension, diabetes, smoking, cholesterol) among non-Hispanic Black residents (N = 31,149). Multivariate logistic regression models controlled for participant age, gender, income, education, region, and state-level business friendliness.

Results

In multivariate logistic regressions, a one-point increase in BBO1 was associated with lower odds (95% CIs) of self-reported hypertension (0.48 [0.29–0.81]), diabetes (0.51 [0.35–0.75]), and hypercholesterolemia (0.70 [0.51–0.96]), but not associated with smoking (1.14 [0.60, 2.19]). A one-point increase in BBO2 was associated with greater odds of smoking (1.90 [1.26–2.87]), but was not associated with hypertension, diabetes, and hypercholesterolemia.

Conclusions

Among Black individuals, living in a community with a higher prevalence of Black business ownership was linked to lower levels of stroke biological risk factors. However, it may also be associated with higher levels of stress-related behaviors, such as smoking. Policies that create an environment conducive to Black businesses may also create environments that reduce racial disparities in vascular risk factors and consequent disease burden.

Background

Black adults are 50% more likely to experience a stroke than their White counterparts [1]. This higher risk is in part attributable to the greater prevalence of hypertension (the single greatest risk factor for stroke) [2] seen among Black Americans. Phelan and Link’s fundamental cause theory posits that health inequities between social groups result from differential access to flexible resources—such as income, knowledge, and social connections—that can be mobilized to avoid disease or minimize its consequences across a range of changing risk conditions [3, 4]. Political economy of health theories assert that powerful groups are able to shape policies affecting resource allocation and, therefore, health [5]. As such, racial inequities in power may facilitate policies and practices that create racial inequities in health promoting factors and, thus, health. Racial differences in stroke incidence may be explained by power differentials between racial groups. This position is supported by research demonstrating direct associations between area-level structural racism—frequently defined as the totality of historical and present policies, practices, and laws that create racial inequity in all aspects of society [6] and poor cardiovascular health in Black people [7–10].

Power is commonly defined as the ability to influence the actions of others to facilitate desired outcomes [11, 12]. In political sociology, Power Resource Theory has been used to explain the relationship between economic class, resources, and policies regarding resource distribution [13]. This theory holds that groups leverage their resources to influence policies in ways that favor their interests. Economic resources such as business ownership are not themselves power but serve as vehicles through which communities accumulate and exercise political and social influence. More broadly, the economic resources of a community can enhance its power by collectively shaping societal practices and policies.

Business ownership is an economic resource that can provide the owner with wealth and prestige that enhances the owner’s ability to influence others and exercise political power. Some researchers have posited that increasing entrepreneurship among Black Americans would increase Black wealth, reducing the racial wealth gap between the White and Black community. A study by William Bradford [14] found that self-employed Black entrepreneurs had similar upward and downward wealth mobility as self-employed White entrepreneurs, and that both groups had greater upward mobility than Black and White employees, respectively. He also found that Black employees had lower upward mobility than White employees. A subsequent analysis by Kroeger and White [15] confirmed Bradford’s findings, but discovered that Black entrepreneurs were more likely to fail—and experience downward mobility—emphasizing the importance of successful entrepreneurship. Both studies suggest that an increased prevalence of Black-owned businesses—reflecting both greater business formation and maintenance (i.e. success)—should reduce the racial wealth gap. However, Black businesses are greatly underrepresented in the US. While, 13.7% of the population identifies as Black [16], a 2020 Brookings Institute analysis of the Census Bureau’s Annual Business Survey (ABS) found that Black-owned businesses represent only 2.4% of all non-farm businesses with employees [17].

Consistent with both Fundamental Cause Theory and Power Resource Theory, we hypothesize that higher prevalence of Black-owned businesses reflects greater collective access to resources that buffer against structural disadvantage and improve health. In areas where Black people own businesses, the economic opportunities for other Black people may expand. Businesses operate as a network, connecting manufacturers, distributors and retailers. Not only can Black businesses improve the employment opportunities for Black people [18–20], but they may also increase the financial wellbeing of other Black businesses if Black businesses preferentially use each other’s services. This collective increase in economic resources resulting from business ownership could theoretically improve the social and political influence of Black people in the area, resulting in more favorable treatment of and policies towards Black residents, and, possibly, a favorable health impact. For example, a Black person who lives in an area where there is greater ownership, and, thus, economic parity, between Black people and others may be less likely to be stereotyped (by Black and non-Black people alike) as illiterate, followed in stores, or encounter negative, stress-inducing behaviors that may impact their health. In places where Black wealth facilitates Black political representation, there may be greater success in implementing antipoverty, health sustaining policies that have been shown to have higher support among Black people [21, 22]. The financial importance of successful Black businesses has been highlighted by policy makers [23, 24], but the health implications of Black business ownership has yet to be explored.

We sought to explore how racial differences in economic resources, measured by Black business ownership, related to health outcomes among Black individuals. We hypothesized that Black people living in areas with greater Black business ownership would report fewer stroke risk factors (i.e., hypertension, hyperlipidemia, smoking, and diabetes).

Methods

Data sources

We tested our hypotheses by linking area-level and individual-level data from multiple national data sets. Specifically, we used individual-level data from the Selected Metropolitan/Micropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System (SMART BRFSS) [25] and area-level data from the Annual Business Survey (ABS) [26] and the American Community Survey (ACS) [27]. We also included a state-level Small Business Policy Index (SBPI), a measure of business friendliness, developed by the Small Business and Entrepreneurship Council [28].

SMART BRFSS provides individual-level data about covariates and health outcomes in our analysis. These data are a subset of the broader BRFSS survey, administered in partnership between the Centers for Disease Control and Prevention and state health departments. BRFSS conducts health interviews with approximately 400,000 respondents annually in all 50 states, the District of Columbia, and several US territories. The BRFSS data are designed to generate statewide statistics about health behaviors and outcomes. The SMART BRFSS restricts the BRFSS data to respondents living in metropolitan or micropolitan statistical areas (MMSAs) with at least 500 respondents in the sample. These data are then reweighted to represent the MMSA in which respondents live. For our analysis, we pooled the 2017 and 2019 SMART BRFSS data sets.

The ABS provides MMSA-level data about business ownership among employer firms (businesses that employ other people). These data are collected by the US Census Bureau and the National Center for Science and Engineering Statistics within the National Science Foundation. All nonfarm businesses filing 941, 944, or 1120 tax forms are included in the ABS sampling frame. Approximately 300,000 businesses are sampled in noneconomic census years and 800,0000 businesses sampled in economic census years (years ending in 2 and 7). Crucially, this survey contains demographic data about the business owner, defined as the person who has more than 50% of the stock or equity in the business. We used the 2017 ABS data to estimate the number of Black-owned and White-owned businesses in each MMSA.

The ACS is an annual survey of American households conducted by the US Census Bureau to provide population estimates between decennial censuses. Approximately 3.5 million households are recruited for the survey each year, and participants can respond via the internet, mail, telephone, or in-person. The Census Bureau publishes pooled 5-year ACS data sets to generate more reliable estimates for small areas. We used the combined 2015–2019 ACS data to estimate the number of Black and White residents in each MMSA.

The SBPI is a composite state-level measure developed from 62 variables that assess the economic burden placed on small businesses. Most components (n = 53) of the measure are rankings of state tax and regulatory policies, such as personal and corporate income tax rates. Individual scores are summed up to create a composite measure. The lower the score, the more business-friendly the state.

There were 32,646 non-Hispanic Black respondents living in 50 states plus the District of Columbia with non-missing BRFSS data. We excluded respondents living in MMSAs missing ACS (n = 349) and ABS (n = 1497) data necessary to determine the Black business ownership rate. Our final sample was 31,149 non-Hispanic Black respondents living in 123 unique MMSAs.

Black business ownership

Recent research on structural racism has increasingly adopted ratio-based measures to quantify racialized access to societal resources. These indicators typically compare the proportion of a racial group’s access to a resource (e.g., employment, education, income, incarceration, homeownership, or political representation) to that group’s share of the population, generating a standardized measure of racialized inequity. State-level studies have used ratio-based indicators of structural racism (e.g., Black-to-White disparities in education, employment, incarceration, income, and political representation) to show associations with Black infant mortality and self-rated health [29, 30], racial disparities in fatal police shootings [31], and chronic disease mortality [32]. These approaches build on classic measures of racial segregation as a structural condition, first formalized by Massey and Denton [33], who developed ratio-based indices to quantify the degree of spatial separation between racial groups. While these studies validate ratio-based indicators as effective proxies for structural racism, few have applied this framework to economic power via business ownership. As such, there remains a critical gap in measures that capture race-specific underrepresentation in business ownership as a form of community-level structural exclusion.

Black business ownership was estimated at the MMSA as a continuous measure defined as: (1) (Black-owned firms/non-Black-owned firms)/(Black population/non-Black population), BBO1; and (2) (Black-owned firms/White-owned firms)/(Black population/White population), BBO2. A higher score on either measure represented areas where there was greater equity between the proportion of Black people in the area and the portion of businesses owned by Black people. MMSAs with scores greater than 1 represented places where Black residents were overrepresented as business owners relative to their share of the local population.

Stroke risk factors

Prevalence of individual-level health stroke risk factors were assessed in the SMART BRFSS. The modifiable risk factors used in the Pooled Cohort Equations (PCE) [34] to calculate the risk of stroke or heart attack were examined. All variables were self-reported. Respondents who reported being informed by a health professional of having hypertension (yes/no), high cholesterol (yes/no), and diabetes, except when diagnosed only during pregnancy (yes/no) were categorized as such. Current smoking was defined as yes (smokes every day or some days) or no (former smoker, never smoked). Individuals who were unaware of a diagnosis or did not respond to the question were categorized as missing. Physical activity is not in the PCE but is an important risk factor for stroke. However, it is often poorly reported in self-assessments [35] and was therefore not included in this analysis.

Covariates

Individual-level covariates came from the SMART BRFSS. Respondents reported their ethnicity (Hispanic or non-Hispanic) and racial identification, including multiracial. Only participants identifying as non-Hispanic Black alone were retained in the dataset. Participants reported their sex as male or female. Age was reported in single years, though BRFSS data codes all respondents aged 80 years and older as “80.” We used the imputed age variable included in the BRFSS datasets that imputes age for respondents who did not report it. Household income level was categorized as less than 10,000; 10,000 to less than 15,000; 15,000 to less than 20,000; 20,000 to less than 25,000; 25,000 to less than 35,000; 35,000 to less than 50,000; 50,000 to less than 75,000; and 75,000 or greater. Level of education was categorized as less than high school, high school, some college or technical school, completed college or technical school. The region was determined based on the location of the MMSA. Regions were Northeast (ME, VT, NH, MA, RI, CT, NJ, NY, PA, MD, DE), Midwest (ND, SD, NE, KS, MN, IA, MO, WI, IL, MI, IN, OH), South (WV, DC, VA, KY, NC, TN, SC, GA, AL, MS, AR, LA, FL, OK, TX) and West (WA, OR, MT, ID, WY, CA, NV, UT, CO, NM, AZ, HI, AK). MMSAs located in more than one state were reassigned to the state holding the greatest number of people from that MSSA. Business climate was operationalized using the 2019 state-level SBPI score. For metropolitan areas representing more than one state, the score reflected the average across the represented states. Lower scores represented states that were more friendly to entrepreneurs.

Analysis

Descriptive statistics were used to describe the study sample. Bivariate logistic regressions were used to assess the relationship between each measure of Black business ownership (BBO1 and BBO2) and self-reported hypertension, diabetes, high cholesterol, and smoking status. Multivariate logistic regression models (controlled for age, gender, income, education, region, and state-level business friendliness) examined the same relationships. All variables were determined a priori. BRFSS survey weights were used to account for the complex survey design and standard errors were clustered at the MMSA level. Marginal effects, the change in the predicted probability of the outcome measures with one point change in the exposure (BBO1 and BBO2), were reported. As self-reported health data can be less accurate among those not receiving care, we conducted a sensitivity analysis where we restricted the analysis to those who reported receiving an annual physical. Analyses were conducted in Stata 18 (College Station, TX).

Results

Participants had a median age of 55, were majority female (62%), attended some college or technical school or more (64%), and lived in the South (51.4%) (Table 1). Among participants, 51% reported hypertension, 20% diabetes, 16% smoking, and 35% high cholesterol. The BBO1 scores ranged from 0.05 to 1.34 (median: 0.14, IQR: 0.12–0.22). El Paso Texas was the only MMSA in which Black business ownership was overrepresented relative to the Black population (BB01 score = 1.34). The BBO2 scores ranged from 0.05 to 0.58 (median: 0.12, IQR: 0.09–0.16). Histograms of BBO1 and BBO2 scores in the 123 unique MMSA’s are provided in Fig. 1.

Table 1.

Demographic characteristics of Black participants from pooled 2017 and 2019 SMART BRFSS^a stratified by median BBO1^b

	Total	BBO1 > 0.14	BBO1 < = 0.14
Characteristic (n, %)	N = 31,149	N = 17,473	N = 13,676
Age, median (IQR)	55.00 (40.00, 66.00)	54.00 (40.00, 65.00)	55.00 (41.00, 66.00)
Male	11,859 (38.1%)	6900 (39.5%)	4959 (36.3%)
Income
< $10,000	2627 (8.4%)	1344 (7.7%)	1283 (9.4%)
$10,000 to < $15,000	2084 (6.7%)	981 (5.6%)	1103 (8.1%)
$15,000 to < $20,000	3181 (10.2%)	1621 (9.3%)	1560 (11.4%)
$20,000 to < $25,000	3407 (10.9%)	1783 (10.2%)	1624 (11.9%)
$25,000 to < $35,000	3635 (11.7%)	1967 (11.3%)	1668 (12.2%)
$35,000 to < $50,000	4337 (13.9%)	2416 (13.8%)	1921 (14.0%)
$50,000 to < $75,000	4299 (13.8%)	2505 (14.3%)	1794 (13.1%)
> = $75,000	7579 (24.3%)	4856 (27.8%)	2723 (19.9%)
Education level
< High school	2328 (7.5%)	1150 (6.6%)	1178 (8.6%)
High school	8771 (28.2%)	4685 (26.8%)	4086 (29.9%)
Some college/technical school	9262 (29.7%)	5222 (29.9%)	4040 (29.5%)
College or technical school	10,788 (34.6%)	6416 (36.7%)	4372 (32.0%)
Region
Northeast	6876 (22.1%)	3661 (21.0%)	3215 (23.5%)
Midwest	6719 (21.6%)	3150 (18.0%)	3569 (26.1%)
South	16,019 (51.4%)	9176 (52.5%)	6843 (50.0%)
West	1535 (4.9%)	1486 (8.5%)	49 (0.4%)
BBO1, median (IQR)b	0.14 (0.12, 0.22)
BBO2, median (IQR)b	0.12 (0.09, 0.16)
Stroke risk factors
Hypertension	15,971 (51.3%)	8616 (49.3%)	7355 (53.8%)
Diabetes	6074 (19.5%)	3220 (18.4%)	2854 (20.9%)
High Cholesterol	10,891 (35.0%)	5903 (33.8%)	4988 (36.5%)
Smoker	4977 (16.0%)	2723 (15.6%)	2254 (16.5%)

^aSMART BRFSS: Selected Metropolitan/Micropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System

^bBBO1 (ratio of Black to non-Black business ownership) and BBO2 (ratio of Black to White business ownership) calculated using the 2017 Annual Business Survey and 2015–2019 American Community Survey

Fig. 1.

Distribution of BBO1^a and BBO2^a scores among Black participants from pooled 2017 and 2019 SMART BRFSS^b. a BBO1(ratio of Black to non-Black businesses ownership) and BBO2(ratio of Black to White businesses ownership) calculated using the 2017 Annual Business Survey and 2015–2019 American Community Survey. b SMART BRFSS: Selected Metropolitan/Micropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System

In bivariate logistic regressions, BBO1 was inversely associated with hypertension (OR: 0.55; 95% CI: 0.35,0.87; marginal effect [dy/dx]: −0.15), diabetes (OR: 0.51; 95% CI: 0.33,0.78; dy/dx: −0.09), cholesterol (OR: 0.51; 95% CI: 0.31,0.86; dy/dx: −0.14), and not associated with smoking (OR: 1.26; 95% CI: 0.27,5.76; dy/dx 0.03). The marginal effect score indicated that a one-unit higher BBO1 score was associated with a 15% lower predicted hypertension prevalence. BBO2 was inversely associated with hypertension (OR: 0.66; 95% CI: 0.44,0.98; dy/dx: −0.10), diabetes (OR: 0.64; 95% CI:0.45,0.91; dy/dx: −0.06), cholesterol (OR: 0.66; 95% CI: 0.46,0.96; dy/dx: −0.08), but directly associated with smoking (OR: 3.29; 95% CI:1.50,7.20; dy/dx: 0.16).

In multivariate logistic regressions (Table 2), BBO1 was inversely associated with hypertension (OR: 0.48; 95% CI: 0.29,0.81; dy/dx: −0.14), diabetes (OR: 0.51; 95% CI: 0.35,0.75; dy/dx: −0.07), and cholesterol (OR: 0.70; 95% CI 0.51,0.96; dy/dx: −0.06) and not associated with smoking (OR 1.14; 95% CI: 0.60,2.19; dy/dx: 0.02). In multivariate logistic regressions, BBO2 (Table 2) was directly associated with smoking (OR: 1.90, 95% CI:1.26,2.87; dy/dy: 0.08) but not associated with hypertension (OR: 0.75; 95% CI: 0.44,1.26; dy/dx: −0.05), diabetes (OR: 0.76; 95% CI: 0.41,1.39; dy/dx: −0.03), and cholesterol (OR: 0.91; 95% CI: 0.59,1.41; dy/dx: −0.02). After limiting the sample size to those who reported an annual physical in the prior year, all associations except the one between cholesterol and BBO1, were unchanged (Table 3).

Table 2.

Relationship (odds ratio [OR]^a and margins [dy/dx]) between BBO1^b and BBO2^b and stroke risk factors^c among Black survey participants from pooled 2017 and 2019 SMART BRFSS.^d (N = 31,149)

Stroke risk factor	BBO1 OR (95% CI)	BBO1 dy/dx (95% CI)	BBO2 OR (95% CI)	BBO2 dy/dx (95% CI)
Hypertension	0.48 (0.29, 0.81)*	−0.14 (−0.23, −0.04)*	0.75 (0.44, 1.26)	−0.05 (−0.15, 0.04)
Diabetes	0.51 (0.35, 0.75)*	−0.07 (−0.12, −0.03)*	0.76 (0.41, 1.39)	−0.03 (−0.10, 0.04)
Cholesterol	0.70 (0.51, 0.96)*	−0.06 (−0.12, −0.01)*	0.91 (0.59, 1.41)	−0.02 (−0.09, 0.06)
Smoking	1.14 (0.60, 2.19)	0.02 (−0.07, 0.10)	1.90 (1.26, 2.87)*	0.08 (0.03, 0.13)*

^aChange in OR for a 1-unit increase in BBO1 or BBO2

^bBBO1 (ratio of Black to non-Black business ownership) and BBO2 (ratio of Black to White business ownership) calculated using the 2017 Annual Business Survey and 2015–2019 American Community Survey

^cControlling for age, gender, income, education, region, and state-level business friendliness

^dSMART BRFSS: Selected Metropolitan/Micropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System

^*p value < 0.05

Table 3.

Relationship (odds ratio [OR]^a and margins [dy/dx]) between BBO1^b and BBO2^b and stroke risk factors^c among Black survey participants who had an annual physical in the prior year from pooled 2017 and 2019 SMART BRFSS.^d (N = 26,728)

Stroke risk factor	BBO1 OR (95% CI)	BBO1 dy/dx (95% CI)	BBO2 OR (95% CI)	BBO2 dy/dx (95% CI)
Hypertension	0.47 (0.30, 0.75)*	−0.14 (−0.23, −0.06)*	0.70 (0.38, 1.30)	−0.07 (−0.18, 0.05)
Diabetes	0.60 (0.41, 0.88)*	−0.06 (−0.11, −0.02)*	0.84 (0.43, 1.65)	−0.02 (−0.11, 0.06)
Cholesterol	0.74 (0.52, 1.06)	−0.06 (−0.12, 0.01)	0.89 (0.49, 1.63)	−0.02 (−0.14, 0.09)
Smoking	1.12 (0.64, 1.96)	0.01 (−0.05, 0.08)	1.80 (1.11, 2.93)*	0.07 (0.01, 0.13)*

^aChange in OR for a 1-unit increase in BBO1 or BBO2

^bBBO1 (ratio of Black to non-Black business ownership) and BBO2 (ratio of Black to White business ownership) calculated using the 2017 Annual Business Survey and 2015–2019 American Community Survey

^cControlling for age, gender, income, education, region, and state-level business friendliness

^dSMART BRFSS: Selected Metropolitan/Micropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System

^*p value < 0.05

Discussion

Our study investigates the relationship between Black business ownership and stroke risk factors in Black communities with the goal of understanding how community-level economic power influences health outcomes. Existing research predominantly focuses on individual socioeconomic factors, such as income and education, leaving a gap in understanding the broader structural and community-level economic determinants of health. We conceptualize Black business ownership as a novel indicator of economic power at the community level and examine its association with key health risks, including hypertension, diabetes, high cholesterol and smoking. In doing so, we respond to recent calls for more explicit investigations into the role of structural racism in shaping health outcomes [36], and our results highlight complexities in the relationship between economic empowerment at the community level and ongoing health disparities among marginalized communities.

Our findings reveal complex associations between Black business ownership and stroke risk factors. Greater Black business ownership relative to the Black population (BBO1) was associated with lower prevalences of biological stroke risk factors (i.e., hypertension, diabetes, hypercholesterolemia), suggesting that increased economic power within Black communities may help mitigate chronic disease risks. In contrast, greater Black business ownership relative to the Black/White population (BBO2) was associated with higher smoking prevalence, indicating that economic empowerment in a White-dominated business environment may not uniformly promote protective health behaviors. It is also possible that when considering racial power hierarchies, comparisons may be most appropriate between non-Hispanic Black people and remaining racial groups instead of just between non-Hispanic Black and White peoples. This could be of particular relevance in Southern and Southwestern regions, such as Southern Florida and New Mexico [37], where Hispanic/Latino/Latine people hold considerable political power. For example, Hispanic/Latino/Latine people are 20% of the US population [38], but comprise only 11% of the senators and representatives in Congress [39]. In Florida, Hispanic/Latino/Latine people are both 27% of the population [38] and 27% of US senators and representatives [40].

Our findings conceptually align with existing research on socioeconomic determinants of health and structural racism, which typically show an inverse relationship between economic resources and chronic disease risk [41]. For example, Daughtery and colleagues found that county-level White/Black socioeconomic inequities were associated with higher BMI among Black people [7]. Our finding that group level hierarchies impact health even after accounting for individual-level socioeconomic status is likely explained by the fact that minority-owned businesses, having also been shown to foster social cohesion, provide culturally relevant services, and build social and economic capital to support overall well-being [42].

While Power Resource Theory and Fundamental Cause Theory suggest that access to economic resources strengthens community power and improves health, our results indicate that economic power may not consistently reduce all health risks, such as smoking.

It is possible that economic empowerment in the context of systemic racial inequities and pressures to compete in predominantly White economic systems—such as limited access to capital, heightened scrutiny, and discrimination [43]—may contribute to harmful behaviors like smoking. Conservation of Resources Theory [44] further explains this by emphasizing that the stress caused by the need to safeguard resources in environments marked by scarcity and discrimination weaken the protective effects of economic gains [45]. Thus, increases in Black business ownership, while linked to reduced chronic disease risks, may also be associated with higher levels of stress-related behaviors, such as smoking, due to persistent social stressors. Nevertheless, Power Resource Theory and Fundamental Cause Theory highlight the protective effects of economic resources on health, whereas Conservation of Resources Theory underscores how chronic stress and resource depletion can offset these benefits. Our findings suggest that economic empowerment alone may not be sufficient to fully address health disparities, as social and racial stressors continue to influence health behaviors such as smoking.

Implications

These findings suggest that policy interventions that increase Black business ownership and economic empowerment may also have benefits for health. Policies that improve access to capital for Black entrepreneurs, alongside community-based health initiatives aimed at reducing stress, could help mitigate both chronic disease risks and harmful behaviors like smoking. Specific interventions might include equitable lending practices, mentorship programs for Black business owners, and partnerships between economic development programs and public health initiatives. Such policies would foster sustainable Black-owned businesses and contribute to improved health outcomes in marginalized communities. Additionally, integrated health and economic policies could ensure that economic empowerment initiatives also address the broader social determinants of health.

Limitations and future research

There were a few limitations to this study. First, the BRFSS collects self-reported data therefore the analysis is limited by participants awareness of being diagnosed with hypertension, diabetes, and high cholesterol. However, in a sensitivity analysis where the sample was limited to those who had received preventive care in the last year, the significance of the associations did not change except for between cholesterol and BBO1. Notably, the direction of the association remained unchanged. Second, it is possible that there are missing confounders, the inclusion of which may have modified the relationship between our exposure and outcome variables. Finally, we conducted cross-sectional analysis therefore causal associations cannot be determined. Future research should explore the mechanisms by which economic empowerment interacts with social stressors in marginalized communities and how interventions can address both protective and harmful health outcomes.

Conclusion

Black Americans were less likely to report a diagnosis of hypertension and diabetes in areas with a more equitable representation of Black-owned businesses relative to the proportion of Black people living in the region. However, in regions with greater equity between the number of White and Black owned businesses, Black residents were more likely to report smoking, a finding that warrants further investigation. Notably racial difference in diabetes and hypertension are much more pronounced for Black Americans than smoking, for which Black Americans share a similar prevalence as White Americans. Fostering Black business ownership and greater economic parity between racial groups may have health benefits resulting not from individual-levels impact on wealth, but by fostering general wellbeing in the population. Therefore, the policies and practices that create an environment conducive to Black business ownership may also create environments that reduce racial disparities in vascular risk factors and consequent disease burden.

Abbreviations

ABS

Annual Business Survey

ACS

American Community Survey

BBO1

Black business ownership 1

BBO2

Black business ownership 2

MMSA

Micropolitan or Metropolitan Statistical Area

SBPI

Small Business Policy Index

SMART BRFSS

Selected Metropolitan/Micropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System

Authors’ contributions

All authors were involved in study conceptualization. EO conducted the analysis with support from ML. EO, IW, and ML wrote the first draft of the manuscript. The manuscript was critically reviewed and edited by all authors.

Funding

EDO was supported by NIH NCATS UL1TR002494. IHCW was supported by NIH NIMHD R00MD015296. PLL was supported by NIH NHLBI K24. HL159246. KL was supported by NIH NIA K24 AG078506. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Data availability

All data used in this study is publicly available and sources are cited within the manuscript.

Declarations

Ethics approval and consent to participate

This study used de-identified, publicly available data and did not involve interactions with human participants. It was reviewed by the University of Minnesota’s Institutional Review Board and was determined to be not human research.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.