Of the copious research on the labor market returns to college, very little has adequately modeled the pathways of non-completers or compared their outcomes with those of award holders. In this paper, the authors present a novel method for linking non-completers with completers according to their program of study. This method allows us to calculate the labor market returns to programs of study, accounting both for those who obtain an award and those who do not. The authors use a large dataset of community college transcripts matched with earnings data and find that different classification systems—by algorithm, intent, or goal—yield very different enrollment patterns across programs. These classifications make a substantial difference to earnings patterns. The results show that returns vary not only by program completion, but they also vary by program non-completion. Consequently, combining completers and non-completers yields a new pattern of returns. For some awards, this leads to wider earnings differentials. However, the variance in returns by subject of study is reduced when data on completers and non-completers is combined. Finally, progression in a program per se does not lead to higher earnings for students who do not complete (even as it demonstrably does for students who complete their program). If validated, these findings have significant implications for policies on program choice and on student retention policies.

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