Advancing Workforce Equity Through Integrated Labor Market Data

Many communities aspire to create a more inclusive economy characterized by equitable economic growth for a diverse workforce. But practical plans for achieving this goal are harder to come by. 

Despite the best of intentions, data limitations often make it challenging to accurately assess, let alone improve, the diversity and equity of a regional workforce. Traditional government sources of labor market information (e.g. BLS) provide comprehensive coverage backed by robust statistical methodologies — but typically lack the precision to accurately capture real-world job titles, let alone pinpoint the skills needed to transition from one job to another. 

Fortunately, real-time data sources, like employer job postings and professionals’ online profiles, provide exactly the kind of precision those traditional sources lack — but can have comparatively uneven coverage across industries and job types.

The good news is, these complementary data sources can be integrated to amplify their strengths, overcome their limitations, and provide a best-of-both worlds data set that serves as a roadmap to greater equity and economic mobility. 

While “integrating complementary data sources” may sound a bit theoretical, the results couldn’t be more practical. In fact, the team of economists, data scientists, and researchers at Emsi Burning Glass has already used this approach to empower and guide strategies for education providers, non-profit organizations, policy makers, and other stakeholders in economic and workforce development. We’ll show how this integration is helping to unlock diversity, equity, and upward mobility in three basic steps:

• Get a more precise picture of current workforce diversity
• Identify promising career pathways based on skill similarity
• Create a better equity benchmark and deploy targeted reskilling programs

1) Get a more precise picture of current workforce diversity

As usual, an effective strategy begins with an honest, accurate assessment of current conditions. In this case, you’ll want to establish a baseline for the diversity (or lack thereof) of your region’s workforce, and how it varies across different sectors and jobs.

At a basic level, this information is relatively easy to obtain from a source like the Bureau of Labor Statistics (BLS). But there’s a catch: BLS data is classified by SOC, O*NET, and NAICS codes which provide a useful-but-simplified way of understanding the labor market. After all, in the real world, it’s very rare that an individual’s job title is identical to the SOC code classification the BLS would give them.

Professional profile data, on the other hand, is incredibly precise and lets us analyze the labor market in terms of the actual job titles individuals and businesses use. This data does have its own quirks, like its tendency to offer stronger coverage of “white collar” jobs where individuals are more likely to share job changes and skills via their online profile. But these are not the same quirks as BLS data. Which is why integrating the two sources is so effective: the official government statistics keep the real-time data anchored in reality while the real-time data enhances the granularity of the government sources.

In practical terms, integrating these two data sources means that we can go from having ethnicity and gender estimates for 400 – 500 occupations in a given metropolitan area to producing diversity figures for over 1,300 detailed occupations. These detailed occupations better reflect the actual job titles used by employers and workers, and therefore allow us to analyze the nuances between different specialties within broad career areas.

For example, whereas BLS data can tell us what percentage of Medical Records Specialists are women of color, it can’t tell us how many of those women work as medical coders vs medical record technicians vs clinical systems analysts. Detailed occupation data, enhanced by online profiles, can.

With this insight, community leaders can create, fund, and support targeted training programs laser-focused on the specific skill gaps that keep workers from making the jump to desirable destination jobs. And, combined with the detailed diversity analysis from step one, these programs can be targeted to reach underserved minority populations who are overrepresented in lower-wage jobs. Which brings us to step three.

3) Create a better equity benchmark and deploy targeted reskilling programs

Now comes the exciting part where we put it all together. From step one, we have detailed, job-level data outlining workforce diversity in our region. We know which occupations in our region are disproportionately filled by minority or disadvantaged populations. And we know which high-growth, high-wage jobs are noticeably lacking in diverse representation.

From step two, we can identify realistic career pathways based on the similarity of skills, experience, and education required for various jobs. We’ve also evaluated the desirability of those transitions based on factors like whether or not the destination job offers a higher wage and has an acceptably low automation risk.

Putting these insights together, we can create a better benchmark for workforce diversity and craft an actionable plan for beating that benchmark in the future.

Establishing the benchmark

For our benchmark, we can now construct an “Equation for Equality Index,” like the one used in the aforementioned Npower report. This involves comparing the diversity of the current workforce in a given sector of the economy (say, IT or allied health or media production, etc.) with the diversity of the broader “skills-similar workforce,” comprised of individuals who work outside the target sector but who have enough skills overlap to be considered a viable talent pool.

This index provides a meaningful benchmark that is equal parts realistic and idealistic.

• Realistic, because it is rooted in hard data about the number of underrepresented individuals working in skills-similar jobs
• Idealistic, because it shows the potential for greater diversity if those individuals can be reskilled for higher paying, in-sector jobs

Beating the benchmark

To improve the equation for equality index number, community leaders can implement programs that teach the specific bridge skills and last-mile skills needed to move individuals from their current role into a higher-growth, higher-wage job. Furthermore, these programs can be targeted towards individuals working in career areas with the highest percentage of diverse representation. In this way, communities can design programs to naturally oversample the most diverse sectors of the economy, thereby boosting equity and economic mobility at the same time.

Conclusion

There’s nothing new about communities pursuing economic growth. But the recent focus on ensuring this growth is inclusive and equitable has highlighted the need for better data — data that not only captures how things are, but illuminates the path to how they could be. 

The team at Emsi Burning Glass is committed to delivering this data through innovative, customized approaches that combine the best of traditional and real-time sources. If you’d like to explore how this data can help unlock inclusive growth in your region, please reach out. We’d love to learn more about your community and partner with you to help it thrive.

Learn more about Emsi Burning Glass’s consulting work by checking out our highlight reel of major projects from 2021.

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