O4O Insights
This Is a Pilot Site
This is an advanced prototype of data products combined
with data from public sources. Stories may not be fully
contextualized and the data fully validated.

Exploring WIOA Participants with Barriers to Employment

When examining WIOA and ETPL data provided by our O4O pilot partners, the data validates several long-standing assumptions held by stakeholders across the workforce development ecosystem. The O4O team’s insights are available in the ending commentary below. We encourage you to interact with the data visuals to see if you identify similar or new insights.

Exploring Barriers by Program Participation

Most job seekers entering the workforce system are currently experiencing at least one of the challenges identified in the dashboard to the right. A large majority are experiencing more than one of these challenges.

As you explore the visuals, we encourage you to pay special attention to how this data changes from 2019 to 2020 as the pandemic began to significantly impact the labor market. Similarly, examining how the barriers impact particular populations can lend insight into how these challenges are being approached on a programmatic basis within your local area. For example, when looking at the data unfiltered, 75 percent of WIOA participants are experiencing two or more barriers to employment. Thirty-eight percent of people are facing three or more barriers.

Exploring Job Placement by Barriers and Race/Ethnicity

The O4O pilot has enabled us to gather and analyze demographic data as it relates to the life situations that impact a person’s ability to be successful in their job search. Data that explores the job placement rates for specific races and ethnicities as well as how these populations are impacted by barriers to employment can help reveal systemic inequities and support data-driven decision making that helps prioritize funding and strategic investments.

Using the filters provided, explore how job placement rates are impacted by common barriers and whether specific races are experiencing placement at higher or lower rates than others. What affirms some of the anecdotal assumptions you’ve heard from your colleagues and workforce partners? What additional questions does this data raise for you? How might you be able to use this data to drive decision making in the future as more data becomes available?

Exploring the Disproportionality of Program Participation to Barriers

In general, the rate at which specific races are impacted by common barriers to employment misaligns to the rate at which these populations are enrolled in WIOA. The graphics to the right allow you to explore the rate at which specific challenges are impacting the various races and ethnicities that participate in programs.

The radar chart explores the proportion of individuals within each race/ethnicity that are experiencing each of the identified challenges. Inversely, the Sanke chart provides a snapshot of how the total population experiencing each barrier breaks down across the various races/ethnicities.

Use the gender and race/ethnicity filters provided to explore the rates at which various populations are experiencing the different barriers to employment. These data demonstrate that while specific populations represent a certain proportion of all participants, they are experiencing specific barriers at much higher or lower rates than others.

Our Insights

In exploring the population of participants that are experiencing at least one barrier to employment, basic skills development and low-income background are the two challenges being identified most frequently. Of the total population, 26 percent require basic skills development (which includes English language learners) and 84 percent come from low-income backgrounds. When further exploring the breakdown of barriers by WIOA program or race, these two barriers continue to surface as the most frequently identified.

For individuals experiencing two or more barriers to training completion or employment, Black (non-Hispanic) people represent the largest population by a significant margin in each category while Hispanic people represent the second largest population for each category.

Overall, with a job placement rate of 90 percent, Hispanic participants are placed into jobs more frequently than their White or Black (non-Hispanic) counterparts. Black (non-Hispanic) WIOA participants have a higher job placement rate than White people. The placement rate for Black (non-Hispanic) participants is 87 percent overall, with the placement rate for White people resting at 83 percent (three percentage points lower than the overall average job placement rate for all participants). The placement rate for American Indian or Alaska Native is highest, overall, at 94 percent; however, their participation in this sample is relatively low at just 33 participants.


Black, female participants are disproportionately impacted by being a single parent with dependents. While just 38 percent of the total population with barriers to employment identify as Black females, the overwhelming majority (57 percent) that selected “single parent with dependents” as a barrier identify as Black (non-Hispanic) females.

Black males disproportionately identify as people with records. Overall, Black males represent just 34 percent of the total WIOA population with barriers to employment; however, 56 percent of participants that have selected this as a barrier identify as Black (non-Hispanic) males.

As we think about how to best address the diverse and complex needs of those entering the workforce system, it’s important to consider how the presence of these life situations impact an individual’s ability to focus on and prioritize skill development, training completion, and job search activities. It’s also imperative that workforce boards acknowledge the racial disparities that exist when working with their community partners to design programs, policies, and processes that seek to address inequities and alleviate challenges associated with various barriers.

For information about data sources, refer to Data Resources. Terminology provides descriptions for industry and common terms.

Have a comment for our project team? See an issue you want to report? Contact us at dataservices@jff.org