PolicyEngine / PolicyEngine/microcosm

SSA SSI recipients by age band: mint as ledger facts, bind as registry count targets

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Python
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Description

Verified gap (Max, 7/21): the three SSA age-band recipient counts (under-18 / 18–64 / 65+, December 2024: 1,001,922 / 3,905,779 / 2,382,142) exist only as hardcoded constants in ssi_take_up.py (US_SSI_TAKE_UP_AGE_TARGETS) driving the assignment machinery — they were never minted as ledger facts nor bound as calibration targets. The registry's SSA SSI surface today: ssi_recipients national+state totals, ssi_total national dollars, ssi_state_payments state dollars; the supplement's aged/blind/disabled category sub-rows stay reviewed-exclusions (engine models no category split — that reasoning does NOT apply to age, which the frame carries directly).

Work: (1) ledger — mint the SSA SSI recipients-by-age table (the source the constants were copied from) as a package with oracle-verified rows; (2) populace — age-band indicator targets on the take-up flag (the reference builder needs age-bound metadata like the AGI-band slices already carry); (3) delete the constants with #469's machinery removal — the bands live in the registry like every other count, and the scorecard reports their misses. Should have been targets even under the old contraption; mandatory once take-up is plain Bernoulli (#469).

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start in ssi_take_up.py at US_SSI_TAKE_UP_AGE_TARGETS, then trace the ledger, populace, registry, and reference-builder paths involved in age-band targets. Review #469 for the machinery-removal context. Done means the source table is minted as oracle-verified ledger facts, age-band indicator targets are bound with age metadata, the constants are removed, and scorecard misses are reported.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
45/100

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