Using LMI to help disadvantaged students

Ben Magill, associate vice chancellor of economic opportunity at Dallas College, at a press conference last fall to announce an $8.8 million federal grant to support the workforce development efforts of a local consortium that comprises other higher education institutions — including community colleges — and healthcare organizations. (Photo: Dallas College)

The odds were long, but in August 2022 a consortium led by Dallas College emerged as one of only 32 worker-centered, industry-led workforce training partnerships in the nation to receive a multimillion federal grant to help socioeconomically disadvantaged employees retrain for new jobs after Covid shut down their original low-pay, low-security retail and similar jobs.

The consortium — which also includes Tarrant County College, Collin College and the University of Texas, Arlington, BioNTX and DFW Hospital Council — received $8.8 million via the U.S. Commerce Department’s Good Jobs Challenge to build entry-level, non-degree biotech workforce training with nearby biomedical institutions. The BioWorks for North Texas Program, which after a preparation year launches this month, seeks to train 800 employees for entry-level biotech jobs, such as biomedical equipment technicians or lab technicians, and will build a career pathway model in biotechnology across all education levels to provide future career growth opportunities, says Benjamin Magill, associate vice chancellor of economic opportunity at Dallas College.

Editor’s note: This is the second story in a three-part series examining the use of labor market information (LMI) to promote community college agility. Read the first article.

“For the population we are working with, we need to get them as much help as soon as possible and get them into a job,” Magill says. “And so the idea is not that they will take our six- or eight-week boot camp and that will solve all their problems; rather, it’s more to get their foot in the door with the boot camp, and then an industry-recognized credential or certification exam, and then a job. And then we’ll take it from there and provide some additional training and show them the other pathways for training beyond that.”

Data analytics was important to building out the program, Magill says.

“Our use of data gave us a great advantage by allowing us to know which industries and what specific skills and jobs to focus on,” Magill says. “In our grant application, we used LMI (labor market information) to show the number of job openings available and the largest employers that were trying to hire for those jobs, including how many people are employed in the target industries and how many are underemployed in service sector jobs. Putting that together allowed us to show what the opportunity is to move many people to better jobs.”

Having robust data analytics is also a competitive edge that allows Dallas College to prepare more and better quantitatively supported grant applications more quickly more generally, Magill notes.

“It just makes it a lot faster for us to be able to put together well-supported, data-driven applications in a short amount of time,” he says. “It also allows us to figure out which industry partners and which of our academic subject matter experts we need to engage with, because we can look at the skills involved.”

Data’s myriad uses

Dallas College is the largest community college in Texas, with seven campuses serving more than 69,000 students. It benefits from an unusually robust LMI data center with eight employees that was established in 2015 and whose capabilities were highlighted in its Good Jobs Challenge grant application.

“Over the past two years, Dallas College has significantly invested in new research capabilities to study the barriers that impact our students. The data available includes longitudinal student data, labor market data from BLS (Bureau of Labor Statistics) and private providers, Census data, property appraisal data, and more. Analytical tools already in use include SPSS, R, Python, PowerBI, and GIS (Geographic Information Systems) from ESRI. Dallas College will continue to deploy its powerful data capabilities to ensure the project is serving high-poverty, low-resourced zip codes and share findings with partner institutions.”

As the Good Jobs Program aims to serve disadvantaged people of color, Dallas College used LMI to demonstrate that socioeconomically disadvantaged individuals in the Dallas region would benefit from the grant.

“We used LMI data to show people of color were overwhelmingly employed in those low-wage, low-security service sector jobs and that specifically women of color, were hardest hit in terms of job loss,” Magill says.

Dallas College also used LMI to develop and support reasonable targets (see below) for disadvantaged population participation in the program.

In addition, LMI helped the grantees design the BioWorks for North Texas Program training elements, Magill says.

“There’s was a lot of labor market data at use to show the gaps in that talent pipeline, and then going into, ‘Okay, well, if we’re going to build a training program, you know, what are those skills that are needed, where it is needed, and which neighborhoods should we target for potential students,’” he says. “That allows us to then convene with employers make sure that we have the right information before we build that training program.”

Like many grants, the Good Jobs Program grant comes with numerous recordkeeping burdens attached.  A robust LMI program will assist in compiling data for those reports, too, Magill notes.

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A third and final story in this series will examine how the Alabama Community College System has used LMI to design and administer a statewide employment retraining program and to more generally help its member community colleges improve educational alignment with jobs.

About the Author

David Tobenkin
David Tobenkin is a freelance journalist in the greater Washington, D.C. area.
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