Performance-Based Funding Policy to Expand Skilled Labor Supply: Evidence from Noncredit Workforce Training in Virginia

• In Event: Community Colleges in Changing Labor Markets: Enrollment Trends and Workforce Alignment

Abstract

With rapid technological change and evolving labor market demands, states are under increasing pressure to strengthen their workforce pipelines, particularly in high-demand fields. One increasingly important vehicle for meeting these needs is noncredit workforce training offered at community colleges, which provide flexible and short-term pathways into skilled occupations. Yet, despite the growing scale of noncredit programs and their policy relevance, there is limited knowledge about how public policies can shape the enrollment and outcomes of these programs. This study addresses this gap by exploring the impact of a unique statewide performance-based funding policy designed to expand the supply of skilled workers for high-demand fields through noncredit workforce training.

Specifically, Virginia launched the New Economy Workforce Credential Grant Program (WCG) in 2016 to expand short-term, noncredit workforce training programs—known as “FastForward” programs—across its 23 community colleges. The program targets fields with high labor market demand, as determined by the Virginia Board for Workforce Development, and ties funding to student success: students pay one-third of the program cost upfront. The state pays the second third upon program completion and the final third upon credential attainment (the institution is responsible for the final third if the credential is not earned). If students do not complete the program, they are responsible for the second third. As a result, this funding structure creates strong incentives for both students to enroll and complete training and for institutions to support successful outcomes.  

To estimate the policy’s effects, we employ a staggered difference-in-differences design that leverages variation in the timing of FastForward rollout across colleges and fields of study following the launch of the WCG policy. Our analysis draws on two primary datasets. First, we use administrative institution-field-year panel data from 2012 to 2021, covering over 185 noncredit programs in various high-demand occupational fields such as transportation, healthcare, construction and manufacturing. Second, to capture changes in the supply of skilled workers in targeted fields, we combine this administrative data with publicly available labor market data on employment by field and region. 

Preliminary findings indicate that the WCG led to significant increases in noncredit course offerings and student enrollments in high-demand fields, despite broader declines in community college enrollment during this period. These results suggest that the policy effectively directed noncredit training toward labor-aligned programs and boosted participation in targeted occupational areas. Following the promising evidence on academic outcomes, we are continuing to explore labor market effects to assess whether increased participation translated into increased supply of skilled workers.

Overall, this research provides new insights into the effectiveness of unique performance-based incentives in the noncredit sector and their role in meeting evolving workforce demands. By highlighting how state policy can shape the scale and focus of community college training, this study offers valuable guidance for policymakers seeking to strengthen connections between education and the labor market. More broadly, our study is among the first to provide causal evidence on the impact of performance-based funding models from a sector that has traditionally operated with limited data and understanding.

Authors

• Sabrina M. Mahendra Solanki, University of Michigan
  Non-Presenting Co-Author

• Di Xu, University of California Irvine
  Non-Presenting Co-Author

• Michael Span, University of California Irvine
  Presenting Author

• Michael Alexander Cooper, University of California Irvine
  Non-Presenting Co-Author