Despite 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, we present a novel method for linking non-completers with completers according to their program of study. We use this method to calculate the labor market returns to programs of study, accounting for those who obtain an award and those who do not. We use a large dataset of community college transcripts matched with earnings data. We find that different classification systems – by algorithm, intent or goal – yield very different enrollment patterns across programs. Importantly, these classifications make a substantial difference to earnings patterns. Returns vary by program completion and by program non-completion. Consequently, combining completers and non-completers yields a new pattern of returns. We find that the variance in returns by subject of study is reduced when we combine data on completers and non-completers. In particular, the large returns to nursing awards are substantially lower when we account for the probability of completing a nursing program and the returns to not completing a nursing program. In addition, progression per se does not lead to higher earnings for non-completers: progressing further in a nursing program is no different from accumulating general college credits. If validated, these findings have significant implications for policies on program choice and on student retention policies.