A funding and program design model in which public or private funds are disbursed to apprenticeship sponsors (e.g., employers, training providers, intermediaries) based on the achievement of pre-defined outcomes or performance milestones instead of traditional upfront or cost-reimbursement funding. These milestones can include metrics such as apprentice enrollment, retention in employment during the apprenticeship, successful completion of apprenticeship requirements, and wage progression upon completion. This strategy moves practitioners away from input-based funding toward outcomes that demonstrate quality and impact for apprentices and employers.

In workforce development, pay-for-performance strategies are gaining traction as policymakers, funders, and workforce system leaders seek greater accountability and results from apprenticeship investments. Rather than funding programs based on projected activities, this approach ties dollars to measurable deliverables, incentivizing high-quality design, strong employer partnership, and successful apprentice outcomes. It can help ensure that public funds support programs that demonstrably close skills gaps, improve workforce readiness, and align with labor market needs.

Examples

• In the United States, the U.S. Department of Labor announced a large pay-for-performance incentive payments program to expand registered apprenticeships. The funding is structured so that payments are tied to apprenticeship milestones, signaling a shift from traditional grant models toward performance-based outcomes funding.
• Some U.S. states, like California and Maryland, have piloted outcomes-linked apprenticeship funding that pays sponsors based on successful apprentice milestones, including program completion and retention.
• In several workforce systems internationally, pay-for-performance or pay-for-success (social impact bond) models are being explored where private investors fund apprenticeship up-front costs and receive returns when programs meet agreed outcomes, though these models require robust measurement and data systems.