Structural Barriers to Small Business Success

Entrepreneurship is often thought of as an arena of equal opportunity: it is thought that through time, hard work, and patience, business owners can transform their lives and make a better future for their children. However, the reality is more complicated.
Structural racism describes the way that systems in society advantage people from certain racial or ethnic backgrounds and disadvantage people from other backgrounds.
Disadvantages like discrimination in commercial lending, the racial wealth gap, and the historical legacy of redlining affect business owners’ odds of successfully starting and growing their companies.
This tool enables you to visualize the effects of structural racism on businesses as they relate both to the race of the business’s owner and to the social and economic characteristics of the neighborhood in which they are located.
For more information about the tool and data visit About and Methodology (note: some data is suppressed to protect business owners' privacy). For tips on using the tool, see our brief Tutorial.

About

Structural racism consists of a set of interconnected business- and neighborhood-level factors that differentially affect small business owners’ odds of successfully starting, sustaining, and growing their businesses.

Already disadvantaged by the racial wealth gap, business owners of color face systemic barriers in capital access, such as racial disparities in rates of loan approval, higher credit and debt burden, and poorer access to quality, non-predatory financial institutions. People of color are under-represented not only among business owners but also within the finance industry. White-owned and publicly held businesses disproportionately award and receive favorable loans, private equity, angel investment, and other desirable forms of business capital.

Taxi driving under elevated tracks

Conditions of place also reinforce historical patterns of disadvantage. Redlining and other forms of discrimination in housing created many of the racially segregated neighborhoods and areas of high poverty that exist today.

Residents of under-resourced communities— predominantly people of color and disproportionately Black and Latine— experience higher rates of poverty and unemployment, have a harder time accessing quality jobs, have fewer opportunities for advancement through education, and often contend with a dangerous, inhospitable built environment in the places they live. Businesses in black neighborhoods have slower growth rates and are smaller, even among businesses with high consumer ratings. This reality draws into question the common assumption that entrepreneurship is a clear path toward inclusive prosperity.

While all these factors stem from structural racism, the relationships between place-based disadvantage, discriminatory business practices, and underrepresentation are complex, and can be difficult to describe.

But what if there were a way to visualize these effects directly? Our tool offers users the ability to view the effects of structural racism on businesses as they relate both to the race of the business’s owner, and the social and economic characteristics of the neighborhood in which they are located. Looking at the 100 largest metro areas in the United States, we combine several data sources to describe how revenue and business presence vary by the racial and economic characteristics of where a business is located, the business’s industry, and who owns the business.

We invite you to explore our interactive tool! For more information, please contact [email protected].

Support for this tool was provided by the Robert Wood Johnson Foundation (RWJF). The views expressed here do not necessarily reflect the views of RWJF.

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Tutorial

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Partners

Small Business Majority’s mission is to empower America’s diverse entrepreneurs to build a thriving and equitable economy. Throughout its work, Small Business Majority prioritizes support for entrepreneurs who have been historically blocked from accessing business capital and assistance, including women, people of color, immigrants and those operating in low- to moderate-income communities.

ICIC drives inclusive prosperity in under-resourced communities through innovative research and programs designed to create jobs, income, and wealth for local residents. Our business technical assistance programs work to address the effects of inequitable access to capital and business education.

One Visual Mind brings excellence in design solutions, providing functional, well-designed products for electronic and print media. One Visual Mind specializes in working with educational, governmental, and non-profit clients. They help research organizations illuminate their work and make their findings accessible to a broad audience.

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Methodology

Overview

This tool combines data from several sources to offer users unique access to information about variation in the revenue and employment of businesses in the nation’s top 100 metro areas and the wages that they pay. We demonstrate how these characteristics are related to the race of business owners, their industry, and the social and economic characteristics of the neighborhood in which they operate.

Corner convenience store in a city

Census Tract Data

To show the race of owner(s), median revenue, industry, and employment size of businesses, we purchased business records for the year 2023 from Data Axle, a data provider that maintains an extensive private dataset of company-level information. We used 2-digit North American Industry Classification System (NAICS) codes, which are the most general standardized categories available. While Data Axle reports industry information at the 6-digit (most specific) level, concerns over maintaining data confidentiality and ensuring the best functionality of our tool led us to report higher-level 2-digit industry information. To further protect the confidentiality of individual businesses, we do not report data on employment size, revenue, or industry if a tract has three or fewer businesses in any employment size class, revenue range, or industry group. For this reason, the unfiltered tract totals will not always align with the sum of businesses created by selecting all categories in any one of the business characteristics filters.

We used data on the residential characteristics of neighborhoods, including median household income, percentage of residents from different racial and ethnic groups, poverty rate, and employment rate, from the American Community Survey (ACS) 5-year estimates for 2018-2022. We also used ACS 5-year estimates from 2018-2022 to define groups of contiguous tracts with high non-student poverty and low median income as under-resourced communities. For more information on this definition, please refer to ICIC’s report The New Face of Under Resourced Communities. We used ACS 1-year population estimates for 2022 to restrict our tool to the nation’s 100 most populous metropolitan areas, and 2022 Office of Management and Budget metropolitan area boundaries to define our metro areas.

We used 2010 Decennial census tracts to represent the geographies of neighborhoods and combined the Data Axle business records and the ACS data into these tracts. This enabled us to link the information on neighborhood residential characteristics from the ACS and business data from the Data Axle dataset while also preserving the confidentiality of individual business records. Because the boundaries of Census geographies are changed between data years, we used land area to weight and standardize the ACS and decennial census data across the multiple years we used.

Man and woman meeting in an open office area

Racial and Ethnic Definitions

Data Axle classifies the race and ethnicity of business owners using a custom coding scheme, which we recoded into categories that more closely resemble the standardized categories used by the U.S. Census Bureau. These categories are: Black, Latine, Asian, White, Other, and Unknown. While these designations are more analogous to Census categories than the original labels provided by Data Axle, there are several important distinctions. First, while Census has historically differentiated race (Black, Asian, white, etc.) from ethnicity (Hispanic, non-Hispanic), Data Axle treats racial and ethnic categories as mutually exclusive. We included multiracial people in the “Other” category, along with Indigenous people. We initially planned on reporting both on majority-Indigenous tracts and on businesses owned by Indigenous people. However, because there are very few majority-Indigenous tracts in the 100 largest metro areas, we classified majority-Indigenous tracts as having “No Racial Majority.” For a similar reason, we classified Indigenous businesses owners as business owners of “Other” race or ethnicity. Data Axle’s racial identification methodology relies on a language learning model to code first and last names of business owners into racial or nationality groups and verifies the racial identities of samples of business owners using surveys.

Metro and State Data

To develop insights at the metro level, we grouped business revenue data by the racial categories of business owners, the racial majority category of the census tract where their businesses are located, and whether the census tract where a business is part of an under-resourced community.

To find the percent of businesses in each revenue category by race/ethnicity of business owner, we found the total number of businesses in the metro in each of our owner racial categories (all Black-owned businesses, all Latine-owned businesses, all Asian-owned businesses, all white-owned businesses, all businesses owned by a person from another racial group—Middle Eastern/North African, Indigenous, other, and all businesses where the race of the owner was unknown). We then calculated the percentage of each of those groups that fell in each revenue range.

To find the percent of census tracts in each business median revenue category by neighborhood majority racial group, we calculated the median business revenue of all tracts in the metro, then found the percentage of Black-majority, Latine-majority, Asian-majority, White-majority, and No Majority tracts with median revenue in each of our revenue ranges. Similarly, to show the percent of census tracts in each business median revenue category by under-resourced status, we found the median revenue of businesses in each tract in a metro and calculated the percentage of under-resourced and non-under-resourced tracts with median revenue in each revenue range.

To show the average wages paid by businesses of different sizes of businesses, we used data from the Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW). These data provide state-wide estimates of average weekly wages paid by businesses in each of the 2-digit NAICS industries by the size of the businesses’ employment. We report these data for the state in which each metro area is located. In instances where a metro area crosses state lines, we report the data for each state that intersects the metro.

Majority Race/Ethnicity
Median Household Income
Poverty Rate
Under-resourced

Median Revenue
# of Businesses by Size
All employees
1-9 employees
10-19 employees
20-49 employees
50-99 employees
100+ employees
Unknown employees

100 Largest Metro Areas in the U.S.

Residential Data Residential data is sourced from 2018-2022 American Community Survey five-year estimates. Under-resourced communities are areas of low income and high poverty, as defined by ICIC. See ICIC report The New Face of Under-Resourced Communities to learn more.

Business Data Business data is sourced from 2023 Data Axle data. Entries with three or fewer businesses are suppressed to protect business owner privacy.

Under-resourced communities Under-resourced communities in metro areas are groups of contiguous census tracts with high non-student poverty rates and median household incomes below the national median. For more information, consult the ICIC report The New Face of Under-Resourced Communities.

Business characteristics Revenue shows ranges in sales volume in dollars. Business Size describes the range of the number of employees working at a business. Industry categories are derived from industry sectors as defined by the North American Industry Classification System (NAICS) Codes.

Metro and State Data

Metro

Metro

Metro

State

About

Tutorial

Partners

Methodology

Overview

Census Tract Data

Racial and Ethnic Definitions

Metro and State Data

Business characteristics Revenue shows ranges in sales volume in dollars.

Business Size describes the range of the number of employees working at a business.

Industry categories are derived from industry sectors as defined by the North American Industry Classification System (NAICS) Codes.