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PR #68 fixes several healthcare target-mapping bugs in the fiscal target registry. This issue tracks the remaining ACA data/calibration concerns that are intentionally out of scope for that PR.
What I observed
Using the published 2024 Populace dataset in a PolicyEngine-US microsimulation:
takes_up_aca_if_eligible is true for every tax unit in the released HDF5.
selected_marketplace_plan_benchmark_ratio is 1.0 for every tax unit in the released HDF5.
Because of those inputs, assigned_aca_ptc, used_aca_ptc, and raw aca_ptc are identical in the current dataset.
Total PTC is about $92.10B, and positive PTC returns are about 12.95M tax units.
Compared with the SOI TY2022 all-income PTC targets:
PTC amount target: $53.91B; current sim: $92.10B (+70.8%).
PTC returns target: 7.84M; current sim: 12.95M (+65.2%).
Average PTC per positive return is not wildly off: target is about $6.9k, current sim is about $7.1k. So the main issue appears to be too many positive PTC returns, not credits that are too large conditional on receipt.
The excess is concentrated in middle/high AGI bins:
$75k-$100k: 1.94M positive returns vs 0.47M target.
$100k-$200k: 2.16M positive returns vs 0.36M target.
$200k-$500k: 0.31M positive returns vs 0 target.
For contrast, the CMS APTC recipient benchmark looks much better after the PR #68 mapping fix: eligible people with positive assigned PTC are about 20.24M vs the CMS target of 19.74M (+2.5%), with state-level correlation around 0.996.
Marketplace enrollment is still a separate concern. The reported Marketplace coverage input is about 8.77M people vs the CMS target of 21.45M. A direct-purchase union gets closer nationally but looks worse state-by-state, so this should be handled as a data/calibration design question rather than a simple variable-map change.
PR #68 fixes cases where targets were wired to variables with the wrong semantics. The remaining ACA concern is different: the current dataset appears to have degenerate ACA take-up and selected-plan inputs. Even after mapping targets to assigned_aca_ptc, the model still overstates SOI PTC returns/spending because everyone eligible is effectively assumed to take up ACA coverage and select a benchmark-like plan.
Suggested next steps
Locate where takes_up_aca_if_eligible and selected_marketplace_plan_benchmark_ratio are generated or defaulted; this may be outside PolicyEngine/populace.
Add a dataset diagnostic or release check that flags degenerate health input columns, especially all-true take-up and all-1.0 plan ratio.
Decide what the CMS marketplace enrollment benchmark should calibrate: reported Marketplace coverage, modeled ACA take-up, direct-purchase coverage, or some combined construct.
Design an ACA take-up/plan-choice imputation or calibration strategy that jointly considers CMS enrollment, CMS APTC recipients, and SOI PTC amount/return targets.
Context
PR #68 fixes several healthcare target-mapping bugs in the fiscal target registry. This issue tracks the remaining ACA data/calibration concerns that are intentionally out of scope for that PR.
What I observed
Using the published 2024 Populace dataset in a PolicyEngine-US microsimulation:
takes_up_aca_if_eligibleis true for every tax unit in the released HDF5.selected_marketplace_plan_benchmark_ratiois1.0for every tax unit in the released HDF5.assigned_aca_ptc,used_aca_ptc, and rawaca_ptcare identical in the current dataset.$92.10B, and positive PTC returns are about12.95Mtax units.Compared with the SOI TY2022 all-income PTC targets:
$53.91B; current sim:$92.10B(+70.8%).7.84M; current sim:12.95M(+65.2%).$6.9k, current sim is about$7.1k. So the main issue appears to be too many positive PTC returns, not credits that are too large conditional on receipt.The excess is concentrated in middle/high AGI bins:
$75k-$100k:1.94Mpositive returns vs0.47Mtarget.$100k-$200k:2.16Mpositive returns vs0.36Mtarget.$200k-$500k:0.31Mpositive returns vs0target.For contrast, the CMS APTC recipient benchmark looks much better after the PR #68 mapping fix: eligible people with positive assigned PTC are about
20.24Mvs the CMS target of19.74M(+2.5%), with state-level correlation around0.996.Marketplace enrollment is still a separate concern. The reported Marketplace coverage input is about
8.77Mpeople vs the CMS target of21.45M. A direct-purchase union gets closer nationally but looks worse state-by-state, so this should be handled as a data/calibration design question rather than a simple variable-map change.Why PR #68 does not cover this
PR #68 fixes cases where targets were wired to variables with the wrong semantics. The remaining ACA concern is different: the current dataset appears to have degenerate ACA take-up and selected-plan inputs. Even after mapping targets to
assigned_aca_ptc, the model still overstates SOI PTC returns/spending because everyone eligible is effectively assumed to take up ACA coverage and select a benchmark-like plan.Suggested next steps
takes_up_aca_if_eligibleandselected_marketplace_plan_benchmark_ratioare generated or defaulted; this may be outsidePolicyEngine/populace.