Carry educational attainment as an auxiliary variable (imputation predictor + ACS-calibratable margin)

Proposal

Carry educational attainment (ASEC A_HGA, collapsed to a small class set, e.g. the SCF's four edcl classes) on the populace-us support spine as an auxiliary variable: not a rules-engine input, but a conditioning variable for imputation and a calibratable margin.

Why

1. Imputation quality. Attainment is among the strongest available predictors of income and wealth conditionals. Standard multiple-imputation guidance is to include strong auxiliaries in the imputation model even when no downstream analysis consumes them — the joint quality of the variables we do care about depends on conditioning richness. The imputation-paper experiments (github.com/PolicyEngine/imputation-paper) had to drop education from the SCF↔CPS shared predictor set and its absence visibly weakens the conditional signal; its "populace-scale" experiment adds it back from raw ASEC and measures the effect.
2. Calibration surface. ACS publishes attainment margins by age and state (S1501), so the variable is not just a predictor: it is a target family for the local-area program, cheap to add to the Ledger lane.
3. Cost is low. A_HGA ships in every ASEC vintage the spine pools; the mapping to a 4-class attainment variable is a few lines in unit construction.

Scope note

Flag it in provenance as auxiliary (carried for conditioning/calibration, not consumed by rules), so nobody reads its presence as a tax-benefit modeling claim. Related: #253 (tuition expenses) is about a rules input and is orthogonal — this issue is about attainment as a conditioning/calibration variable.

🤖 Generated with Claude Code

Start by locating unit construction, provenance handling, and the Ledger lane for the populace-us support spine. Trace ASEC A_HGA into the proposed four-class attainment variable, then verify that it is carried for imputation and ACS calibration while marked auxiliary and excluded from rules-engine inputs.