Direct certification has been described by policymakers and academics as a tool which may replace National School Lunch Program (NSLP) eligibility data (Douglas Geverdt, National Center for Education Statistics, personal communication, August 28, 2023). It suggests a policy future in which we change the metric of how we identify disadvantage. On the state level, this impacts allocations, program evaluations, and sponsored research by research institutions. Historically, NSLP eligibility data has been an effective predictor of student outcomes such as achievement and attendance. Little is currently known if direct certification will continue the policy legacy of NSLP eligibility data by accounting for differences in student level outcomes in the same way. Using student level data, this study finds that direct certification meets or exceeds the predictive validity of Montana eligibility data. This means that how direct certification explains student outcomes is like how NSLP eligibility data does, most notably in areas of student achievement. Direct certification does carry its own limitations, for example, comparability between states and systems. Measures for SNAP, TANF, and student statuses that are categorically eligible also differ in the predictive validity of each measure. Due to the differences between these component measures, it may be said that the overall construct is "accidental." Between states, there is no set formula for the percentage of students that come from SNAP, TANF, Medicaid, or categorically eligible groups. This study establishes that there are differences between these groups in the degree to which the poverty measure predicts variation in a student outcome.