Workforce Pell Is a State Data Policy

When Congress created Workforce Pell, it didn’t just expand financial aid to short-term programs; it created an entirely new oversight and approval regime. This new regime puts states front and center of Workforce Pell implementation. That’s especially true when it comes to one important piece of this implementation puzzle: data.

If states want to do this right and reap the program’s potential benefits, they need to be actively thinking about their data systems. If states wait to do so, they will already be behind.

One of the most consequential pieces of the Workforce Pell policy is how clearly it shifts program accountability to states. Under the law and proposed regulations, states are no longer just intermediaries between institutions and the federal government; they are central actors in determining which programs qualify, remain eligible, and ultimately succeed. Governors must certify programs for initial and continued approval, determine whether programs align with high-skill, high-wage, or in-demand occupations, and attest—using administrative data—that programs meet required employment outcomes. At a basic level, states need to be able to answer the following questions reliably:

1. Who enrolled in each Workforce Pell–eligible program?
   
   
2. Did they complete and earn the credential?
   
   
3. Were they employed after completion, and in what occupation?
   
   
4. Do the program’s costs make sense relative to earnings?
   
   
5. Did they continue their education in a for-credit program related to the occupation or industry for which they were trained, and did they receive academic credit for their Workforce Pell program?

Answering those questions requires integration across systems that, in many states, still don’t talk to each other: postsecondary unit records, noncredit data collections, unemployment insurance wage records, workforce systems, and sometimes licensure data. In many states, answering them will also require adding additional data collection in wage records and noncredit offerings. Workforce Pell is not just a higher education policy. It’s a state data infrastructure policy.

One challenge is that states are not starting from the same place when it comes to data capacity. State longitudinal data systems vary widely in scope, maturity, and governance. Some states have well-established P–20W systems that already link K–12, postsecondary, and workforce data at the individual level, with clear authority for cross-agency data sharing and routine use for policy and accountability. Others have more fragmented systems—strong in one sector, such as K–12 or workforce development, but weak or underdeveloped in noncredit postsecondary education, short-term credentials, or adult learners. In many states, noncredit workforce programs still sit outside the longitudinal system entirely, making it difficult to connect Workforce Pell participants to enrollment, completion, and subsequent education data in a reliable way.

Workforce Pell assumes a level of integration and coordination that many states have not historically needed for higher education oversight. Measuring labor market demand, job placement by occupation, and participation in noncredit programs will not be simple. Building a state data system capable of adequately overseeing Workforce Pell programs requires consistent identifiers, aligned program taxonomies, and agreements that allow data to flow between higher education agencies, workforce agencies, and institutional systems.

States with robust longitudinal infrastructure can build on what they already have to create an effective Workforce Pell accountability system. States without it will need to make choices—about governance, data standards, and investment—if they want to avoid Workforce Pell becoming an exercise in ad hoc reporting rather than a durable accountability system.

Employment outcomes sit at the core of Workforce Pell accountability. The law requires that Workforce Pell programs have a job placement rate of 70 percent, and unemployment insurance wage records are the primary data source for measuring that. However, beyond states’ general data capacity, there are two challenges for many states in calculating this rate.

The first data challenge that states will likely face in calculating the job placement rate is that students who complete their program and move out of state may not be included in the state’s data. Some states have entered into data-sharing agreements with their neighboring states, but not all have. Granted, it is possible that many students will remain in the state, given what we know about the kinds of programs these are likely to be, and because we know most low-income students attend college and work very close to home. However, one more aspect of this could be challenging: online programs that enroll students from many states.

The second data challenge is meeting the full federal requirements for the job placement rate, which will not take effect until a few years after the program begins. Initially, states must calculate whether participants are employed in the second quarter after program exit. Over time, the requirements become more demanding, asking whether participants are employed in the occupation for which the program prepares them or in a comparable, in-demand occupation.

Most UI wage records today are designed for tax and benefit purposes, not education accountability. They reliably capture earnings and employer identifiers, but often lack occupational information, hours worked, or precise work location. Adding occupational enhancements to wage records is possible, but it is neither quick nor simple. In some states, statutory authority is required. In others, employer reporting systems must be modified and phased in. Even once implemented, new data elements must be tested and validated before they can be used for high-stakes determinations. States that delay this work risk finding themselves unable to meet Workforce Pell requirements when occupationally aligned placement becomes mandatory.

Taken together, these challenges make clear that calculating and enforcing the job placement rate will be one of the most technically demanding aspects of Workforce Pell implementation for states, particularly over the long term.

Workforce Pell also emphasizes short-term workforce programs functioning as on-ramps to further education rather than dead ends—and this is an area where states may be better positioned to act. The law requires that Workforce Pell–eligible programs prepare students for continued postsecondary education and that states, through the Governor’s approval process, determine whether programs support credential stackability. Under the proposed regulations, states must have a policy describing how academic credit is awarded for Workforce Pell programs and must certify that each approved program prepares students for enrollment in one or more eligible for-credit certificate or degree programs.

At a minimum, states must ensure that programs seeking Workforce Pell approval can demonstrate how credit is awarded, whether through articulation agreements, transfer-of-credit policies, credit for prior learning, or other written agreements. In practice, this means states need clear documentation at the point of program approval. But documentation alone is not sufficient to ensure that Workforce Pell's policy intent is met.

States can—and should—use their longitudinal data systems to examine whether Workforce Pell completers actually continue their education in for-credit programs and whether those programs are meaningfully related to the occupation or industry for which the student was trained. Tracking subsequent enrollment allows states to assess whether credit articulation is functioning in practice. Just as importantly, states can observe whether students receive academic credit for their Workforce Pell program once they enroll, providing a concrete measure of whether articulation agreements are working as intended.

States can also use these data proactively during program approval and reapproval processes. For programs seeking initial approval, states can examine historical data from similar programs offered by the institution or provider to see how students have progressed in the past. For programs seeking continued approval, states can look at actual post-completion outcomes—whether students re-enrolled, what programs they entered, and whether those programs aligned with the original field of training. Over time, this allows states to not just certify that pathways exist, but also validate that they are being used and lead to something meaningful.

Workforce Pell will test state data systems in ways few higher education policies ever have. Some of those tests—particularly around job placement rates and occupational alignment—will be technically complex, resource-intensive, and slow to fully resolve. States will need time, coordination, and sustained investment to meet those requirements in a durable way.

In areas like credit articulation and postsecondary progression, states have a real opportunity to use data they already collect—or could collect with modest changes—to strengthen oversight and improve outcomes. By tracking whether Workforce Pell completers continue into for-credit programs, whether those programs align with the original field of training, and whether students actually receive academic credit, states can move beyond paper pathways and begin validating whether stackability is working in practice across their higher education systems.

The difference between states that struggle and states that succeed under Workforce Pell will not simply be whether they comply with federal requirements. It will be whether they treat data as an after-the-fact reporting obligation or as a core policy tool. States that invest in longitudinal data systems, align identifiers and program taxonomies, and use outcomes data to inform approval decisions will be positioned to make Workforce Pell a lever for quality, mobility, and long-term value for students and workers alike.