Ohio data provides early insights into the value of short-term credentials – and the need for stronger oversight and data systems as Workforce Pell rolls out.

Recently, workforce advocates in Ohio used a public records request to obtain data on the state’s TechCred program. TechCred reimburses employers up to $2,000 for each short-term technology credential their workers earn.

Rebecca Kusner of the Ohio Workforce Coalition (NSC’s SkillSPAN lead for Ohio) sat down with Amanda Bergson-Shilcock to talk about what she and her colleagues have learned from the data, what’s working well, and what policymakers need to do to strengthen the program and others like it.

Amanda: Can you give us a quick summary of what you found?

Rebecca: Absolutely. Let’s start strong: It’s popular with employers, has strong support from the state administration, and is easy to access. These are all important considerations!

Overall, I’d group our findings into three main categories:

• Awareness: TechCred is reaching a lot of employers and intermediaries, with a wide range of training. However, there seems to be uneven awareness about the program, and difficulty reaching different industries and types of workers.
• Operations: Tech Cred is set up to be easy and flexible. But it lacks guidelines about quality of training or provider and provides insufficient data on outcomes.
• Impact: Because of gaps in the data, loose reporting requirements, and lag times, it’s impossible to determine impact. For example, we don’t know whether TechCred is helping workers actually improve their career trajectories and earn wage increases or retain positions they might otherwise have lost – or if it is simply helping to augment the skills of people who are already in stable, living-wage jobs.

Note: Now that Ohio Workforce Coalition has analyzed the data, they have specific recommendations for policymakers, which are outline at the end of this interview.

Amanda: Big picture, what do your findings mean for other states?

Rebecca: I appreciate this question! As Workforce Pell Implementation moves forward, states and the federal government are trying to set guardrails for what constitutes a “good” short-term credential, how to best monitor and support training providers, and what kind of data they need to collect to measure the return on investment (ROI).

TechCred started doing some of this a while ago, so our analysis gives us early insight into what works and what needs to change. Our experience also shows the importance of enacting policies that have strong data plans right from the beginning – because if you don’t collect it, you can’t analyze it.

This data isn’t just relevant for Workforce Pell conversations. It’s also helpful for any state that is trying to improve an incumbent-worker training program, or to understand what kinds of digital skills are in demand from local businesses.

Amanda: Stepping back, can you tell us a little about how TechCred works?

Rebecca: TechCred was launched in 2019, and there are six rounds of funding per year. Employers can apply to be reimbursed for up to $2,000 per credential for sending existing employees through a short-term technology skills training program. People can earn more than one credential. The program is also open to new jobseekers – if they pitch TechCred to a potential employer, and they are ultimately hired, they can be eligible for funding (assuming that the credential is approved).

To be approved for TechCred, credentials must be: 1) industry-recognized; 2) technology-focused; and 3) short-term certificates or certifications that can be completed in less than 12 months or fewer than 900 clock hours or thirty credit hours. Technology-focused credentials include those related to software development or utilization, advanced manufacturing and construction technology, data analytics, cybersecurity, broadband and 5G technology, and other emerging fields.

Credentials on the approved list have been recommended by the Technology Review of Educational Credentials panel of experts and approved by the Chancellor of the Ohio Department of Higher Education. However, there is no publicly available list of who sits on this panel and how these decisions are made. This is unusual, given that TechCred is funded with public dollars.

(In contrast, states’ process for defining High Priority Occupations under the federal Workforce Innovation and Opportunity Act is generally spelled out in more detail.)

Amanda: Why did you seek out this program data?

Rebecca: We were curious to see how TechCred was doing in meeting its goals—and actually, to better understand what the goals were. The program is regularly referenced and promoted, but there hasn’t been good public information sharing about outcomes. The basic data on the program website just says how many employers have been approved, which credentials they were approved for, and how much money was allocated.

We thought that if we could get more detailed data, we could understand which employers were applying for funds, for what type of training, and whether workers were successful in obtaining credentials.

These questions became more urgent as instances of fraud in TechCred emerged, including a high-profile case in which an employer and a training provider were alleged to have enrolled fake participants. If policymakers are going to add safeguards to the program, we want to give them informed recommendations about other ways to strengthen it.

Amanda: What did you learn?

Rebecca: The first thing we learned is that the data is really incomplete! Part of that is just that there is lag time between when employers apply for the funding and when people complete training, and then more lag time before employers report on outcomes. In addition, some data is missing, or messy (because of spelling errors), or hard to compare across funding rounds.

For example, in rounds 1-3, the TechCred application asked employers to identify specific individuals for training (rather than allowing them to be identified later) and collected data on their current hourly wage and expected hourly wage after credential. Unfortunately, rounds 4-17 no longer asked employers to identify specific individuals at the time of submission, and switched to collecting wage averages.

We expected that the data would be incomplete. What we didn’t expect was how much of the data was considered optional, and how little standardization there seemed to be. For example, demographic information on gender and race was optional. Wage information was self-reported and was provided in varying formats that made it harder to compare across workers (e.g., hourly wage vs. annual salary).

Amanda: What kinds of digital skills credentials are people earning?

Rebecca: The data lists 2,600 training providers delivering a total of 38,167 credentials. Because of gaps in the data, we can’t tell how many workers that represents. Also, we can see some obvious duplication in provider and credential count, which we’re still working on de-duplicating.

It’s worth noting that TechCred data is a moving target; since we received this information there have been more changes to the program and additional people have completed their training.

Of all of the credentials people have earned through TechCred, the three most common are related to Excel (see table). Together, these made up about 9% of all credentials earned.

10 Most Common Credentials Earned by TechCred Participants

And overall, Microsoft Office-related training accounted for 26% of all credentials.

Excel comes up frequently in job postings, so I’m not surprised that workers want to learn it and employers want to train on it. But I am surprised we’d use TechCred funding to do so when Microsoft Office training is widely available for free or at a very low cost.

Amanda: What did you learn about the industries that are being served by TechCred?

Rebecca: TechCred works well in helping workers earn manufacturing-related tech credentials (28.5% of credentials earned) but it’s working less well for healthcare credentials (just 0.8% of credentials earned). We know from NSC’s earlier research that 95% of jobs in the health sector likely require digital skills. We also know from talking to Ohio employers that healthcare employers have applied for TechCred – they’re just not getting approved at the same rate.

Amanda: Here’s the big question: What do you know about the workers? Do wages go up?

Rebecca: You want to see that if people gain new skills and credentials, they get paid more. You also want to see that different types of workers have similar access to the program.

Unfortunately, only about half of the records include demographic data. Within those records, we found disparities. Specifically, while men comprise 52% of Ohio’s total workforce, men make up 75% of the TechCred recipients for whom we have data. Race also shows disparities: White workers compose about 77% of Ohio’s workforce but make up 87% of TechCred recipients.

This might be attributable to gender dynamics in key sectors — manufacturing being overrepresented and healthcare underrepresented in TechCred — or geographic location of recipients or any number of other factors.

Now, onto wages. Again, there is a lot of missing data, but we analyzed what we did have. The average hourly wage before training was $30.74, and the average hourly wage after training was $32.18. That’s a 4.7% increase, or about $2,880 a year. But we don’t know whether workers’ wages went up because of TechCred, or because of other factors like cost-of-living raises, promotions, or job changes.

What we can see is that TechCred is going to workers who are already earning nearly double the median hourly wages of high school graduates across Ohio ($16.87).

Amanda: What do you recommend policymakers do to improve programs like TechCred?

Rebecca: If I had to boil it down to a few recommendations, they would be:

1. Determine and clearly communicate what the program is trying to accomplish. Establish clear parameters for which types of employers, jobs, and workers should benefit from these limited dollars. Set a wage ceiling to avoid using public funds to pay for training that can be paid for by workers or employers, and identify a targeted wage increase goal.
2. Carefully identify what data is needed to show whether the program is achieving its goals and make the investments necessary to get complete data. This may include requiring employers to provide more robust reporting, adequately resourcing state agencies so they can analyze relevant administrative data, or other approaches. Because programs like TechCred are paid for with public dollars, we must be able to see where funds are going, who and what they are paying for, and whether they are having the impact intended.
3. Ensure accountability by enforcing rigorous standards and evaluating the program’s ability to accomplish its goals. In addition to the data gathering mentioned above, it’s vital to ensure that there are clear, transparent parameters in place to identify and vet training providers. Similarly, programs need to have quality control standards and processes in place to determine which credentials are eligible to be earned with public funds. Finally, once we have gathered data on program outcomes, it’s vital that we close the feedback loop and use that data to improve programs and policies.

Amanda: Anything else you’d like to add?

Rebecca: Just that we are continuing our analysis of TechCred data and working to get more information that will provide a fuller picture. We’re looking forward to sharing our findings with Ohio policymakers—especially in this important election year!