The third paper in CLASP’s Building Skills, Remodeling the HEA series, Better Data, Better College Workforce Programs, takes a look at how data practices can be improved to promote success in workforce training initiatives. Informed by our discussions with community colleges, evaluators, and federal officials, we identified two potential areas of inquiry: efforts to build employer and institutional connections to reach low-skilled individuals; and program innovations institutions have undertaken with data that is available to them, along with suggestions for reform to meet institutional data needs that go beyond the limits of what currently accessible data can do.
The federal government’s ultimate goals for investing in large-scale job training initiatives are to help educate more students and support them in attaining successful outcomes, such as finding a job that pays a family-supporting wage. These programs should be accountable for achieving their desired completion and employment outcomes, particularly for non-traditional students and those living in poverty. For low-income individuals, the best protection against falling back, or further, into poverty is gaining skills that allow them to get a job in demand in their local labor market.
Too often, however, these programs are measured based on such inputs as the number of students enrolled, rather than on outcomes. And the input data is not always of great quality; many training programs are unable to demonstrate how often or how well students with non-traditional characteristics are participating in training programs, and how training providers might mitigate the additional barriers to completion faced by many of these students.
To promote success, future investments in such programs should include provisions enabling the reporting of more rigorous data on outcomes. We recommend the following policy solutions to the problem:
Institutions must be actively engaged with their local workforce development board and connected with employers. This facilitates the development of relevant training programs, job placements for students, and monitoring of former student success—all activities that support future program improvements.
Training programs must incentivize career pathway students’ efforts to upskill while consciously including opportunities for low-skilled individuals, rather than churning students through the program as quickly as possible. Moreover, federal funding for these initiatives should require that high school-level credentials earned as part of the training program’s career pathway count toward institutional outcomes.
Allow a student-level data collection. This would provide, for instance, a foundation for a more robust federal education and workforce data construct that allows federal investments across programs and agencies to be evaluated and given better consideration for potential future investments.
Training programs should be able to determine student employment and earnings outcomes by being allowed to compare their student data with wage record data from the state’s Unemployment Insurance system, with appropriate privacy safeguards.
These solutions are not just abstract ideas; they would directly address issues that institutions are struggling with as they implement federal workforce training programs. Current attempts by workforce programs to innovate around the limitations created by the federal Higher Education Act have caused an undue burden on these programs’ ability to effectively operate and evaluate success.