PROJECTExploring Spatial Gaps in Access to Low-Wage Jobs by Race and Ethnicity

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(Marco Bottigelli/Getty Images)

Last updated January 16, 2024

In many cities across the US, access to employment opportunities is not evenly distributed. A history of racist housing policies at the local, state, and federal levels has led to segregated neighborhoods and an unequal transportation landscape that keep many people of color far from opportunity. This spatial mismatch between where jobs are located and where job seekers live can cause high unemployment rates and lead to longer spells of joblessness.

For this project, we examine how access to job opportunities for low-wage workers varies based on their race/ethnicity and where they live. For select metropolitan regions, we compare gaps in access to low-wage jobs between workers who are white and workers who are people of color. To do so, we create a measure that shows how many low-wage jobs are available to low-wage workers and people who are unemployed within a 30-minute commute via public transit. The measure is competition adjusted to account for the number of other people who can also access and potentially compete for each job in the same timeframe. Low-wage jobs are defined as those with monthly earnings of $3,333 or less.

We aggregate this access measure to the city and metro level for each race and ethnicity group using a weighted average by race/ethnicity at the census tract level. Overall, this measure reflects public transit connectivity, housing availability, and the proximity of job opportunities for communities. We display this measure as the gap in access, which is the percentage difference in access to jobs between people of color and people who are non-Hispanic white.


Calculating Gaps in Spatial Access to Jobs

Four steps in creating the access measure

Calculate transit commute time between all census tracts within each metropolitan area

Calculate competition-adjusted access to jobs via transit for each census tract

Calculate each locality and metropolitan area's average access to jobs by race/ethnicity each year from 2007 to 2019

Calculate the percentage difference in access to jobs between people of color and people who are white

Source: Framework developed by the authors. Icons by Noun Project.


This measure has several limitations. First, it only reflects spatial access to jobs—other barriers may exist that make jobs inaccessible to people of color, such as skills mismatch and racism in hiring. Second, the measure only reflects access to jobs at rush hour, when public transit is most available; people who commute during off-peak hours may have more limited job access. Third, the transit options used in the measure may not be accessible for people with disabilities. 

It is also important to note that in some metropolitan regions, non-Hispanic white residents live farther from jobs because of racist policymaking and planning that allowed them to purchase homes in suburbs with exclusionary single-family zoning and high-quality public services. Non-Hispanic white people also have higher rates of car ownership, which means their choice of homes is not limited by transit availability. This may be why in some places non-Hispanic white residents have lower rates of access to job opportunities via public transit than residents who are people of color.

This project is meant to help community members and local leaders see where their locality ranks within their metropolitan region in terms of racial gaps in access to jobs. The data can help regional planners target investments in transportation, community development, and workforce training to benefit people of color in the areas that have the largest gaps.

Choose a metropolitan region from the drop-down menu below to explore how public transit connectivity and the geography of job opportunities affect residents differently by race and ethnicity. 

To view the full dataset, visit Urban's data catalog.



For these fact sheets, we calculated public transit travel times using the r5r R package, transit agency data from the Transitland open data platform, and road data from OpenStreetMap. We sourced data on jobs and low-income workers at the census tract level from the US Census Bureau Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics. For data on the racial and ethnic composition of block groups, we drew on the US Census Bureau American Community Survey 2017–2021 five-year estimates.

We originally created these data for a forthcoming study funded by the Robert Wood Johnson Foundation that examined the effect of rent control and inclusionary zoning policies on housing production and access to jobs. We selected the 25 metropolitan regions included in this project based on their relevance to that study.

Project Credits

This project was funded by the Urban Institute’s Fleishman Innovation Fund. We are grateful to all our funders, who make it possible for Urban to advance its mission.

The views expressed are those of the author/authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts. Further information on the Urban Institute’s funding principles is available at

RESEARCH Amy Rogin, Christina Stacy, and Alena Stern

DESIGN Amy Rogin, Aleszu Bajak, and John Wehmann 

DEVELOPMENT Aleszu Bajak, Amy Rogin

EDITING Lauren Lastowka

WRITING Dana Ferrante

Research Areas Economic mobility and inequality Neighborhoods, cities, and metros
Cities Atlanta-Sandy Springs-Alpharetta, GA Baltimore-Columbia-Towson, MD Birmingham-Hoover, AL Chicago-Naperville-Elgin, IL-IN-WI Denver-Aurora-Lakewood, CO Durham-Chapel Hill, NC Los Angeles-Long Beach-Anaheim, CA Miami-Fort Lauderdale-Pompano Beach, FL Minneapolis-St. Paul-Bloomington, MN-WI New Orleans-Metairie, LA New York-Newark-Jersey City, NY-NJ-PA Ocean City, NJ Oxnard-Thousand Oaks-Ventura, CA Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Pittsburgh, PA Portland-Vancouver-Hillsboro, OR-WA Richmond, VA Riverside-San Bernardino-Ontario, CA Sacramento-Roseville-Folsom, CA San Diego-Chula Vista-Carlsbad, CA San Francisco-Oakland-Berkeley, CA San Jose-Sunnyvale-Santa Clara, CA Seattle-Tacoma-Bellevue, WA Tallahassee, FL Trenton-Princeton, NJ
Tags Transportation Job opportunities Workers in low-wage jobs
Policy Centers Metropolitan Housing and Communities Policy Center
Research Methods Quantitative data analysis
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