Income vs Poverty Reassessment Note Generated: 2026-02-20 Question Does the new ODE "Students Experiencing Poverty" field require reassessment of prior assumptions about income and its interactions with attendance/adult education? Short answer Yes, a partial reassessment is warranted. - Income remains useful, but the new poverty field adds substantial independent signal. - The prior role of income should be interpreted more as a broad community proxy, not the primary hardship measure. - Income interaction analyses should be retained, but run alongside poverty-aware specifications. What we tested - School-level models (non-charter, non-virtual), weighted by participants. - Outcome: Percent Proficient. - Core predictors: BA+ rate, attendance, median household income. - New predictor: Students Experiencing Poverty. - Compared in-sample R2 and 5-fold cross-validated R2. Key empirical findings 1) Poverty aligns strongly (but not perfectly) with income context - Students Experiencing Poverty vs ACS Median HH Income: weighted r about -0.59 to -0.62. - vs ACS Per Capita Income: weighted r about -0.65 to -0.66. - Interpretation: strong overlap, but not redundancy. 2) Adding poverty materially improves predictive fit beyond income Cross-validated R2 (non-charter/non-virtual): - ELA: - BA+ + Attendance + Income: 0.5190 - BA+ + Attendance + Poverty: 0.6408 - BA+ + Attendance + Income + Poverty: 0.6508 - Math: - BA+ + Attendance + Income: 0.6429 - BA+ + Attendance + Poverty: 0.6705 - BA+ + Attendance + Income + Poverty: 0.6726 - Science: - BA+ + Attendance + Income: 0.3966 - BA+ + Attendance + Poverty: 0.5077 - BA+ + Attendance + Income + Poverty: 0.5195 Interpretation: - Poverty contributes large incremental signal in ELA and Science, moderate in Math. - Income still adds some incremental value when poverty is present, but much less than before. 3) Income interactions remain relevant, but poverty-aware interaction models are often stronger Cross-validated R2 gains from interaction terms: - Income interactions (income*BA, income*attendance) improve fit in all subjects. - Poverty interactions (poverty*BA, poverty*attendance) are competitive and in Math outperform income interactions. - Best fit typically uses both interaction families or whichever is stronger by subject. Practical implications for our project 1) Do not drop income entirely. - Keep it as community-resource context. 2) Promote Students Experiencing Poverty to a core factor. - Treat it as a direct hardship/composition indicator, distinct from tract income. 3) Update interpretation language. - Prior language implying income as the main socioeconomic channel should be softened. - Better framing: "Income and poverty both matter; poverty appears to carry additional school-level signal beyond tract income." 4) For interaction analyses and highlights - Run dual specs: - Income-centric interactions (for continuity with prior work), and - Poverty-aware interactions (for updated robustness). - If choosing one headline interaction lens by subject: - Math: poverty interactions look especially strong. - ELA/Science: both income and poverty interaction sets are useful. Bottom line The new field does not invalidate earlier income findings, but it does change emphasis: poverty should now be treated as a first-tier explanatory variable, with income interpreted as a related but broader community proxy.