Non-degree credentials (NDCs)—badges, certificates, certifications, licenses, and microcredentials—have proliferated as purported alternatives to traditional education, yet evidence on their labor market value remains limited.
Using 37.7 million U.S. worker resumes, we employ machine learning to identify genuine NDCs and map them to a standardized taxonomy, enabling the first large-scale analysis of NDC wage returns.
We find returns to NDC possession depend critically on job relevance: Workers’ first job-relevant NDC yields a 3.8% wage premium, more than double the 1.8% premium for their first job-irrelevant NDC. And returns to accumulation depend entirely on relevance: Each additional job-relevant NDC increases wages by 1.0%, while irrelevant accumulation shows either no gains or a wage penalty.
Returns vary substantially across worker characteristics: Non-college workers realize premiums 1.5 to 2 times larger than college graduates, and early-career workers show similarly elevated returns.
Disaggregating by NDC type reveals distinct mechanisms: Certifications exhibit returns to accumulation only when job-relevant, patterns consistent with human capital acquisition, while badges and certificates deliver one-off premiums independent of relevance, consistent with signaling.
Our findings highlight both the promise and risks of NDC proliferation: Rigorous, job-relevant NDCs can narrow earnings gaps for non-college and early-career workers, but absent quality assurance and transparent information, NDC market expansion risks exposing workers to low-value investments.
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