PolicyEngine / PolicyEngine/macro
Integrate OG-USA through the common OLG adapter contract
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Description
Objective
Add OG-USA as a separate US overlapping-generations member, following the integration pattern already established for OG-UK while preserving the differences in policy vocabulary, calibration data, units, runtime, and validation.
This is not a fork or reimplementation. The model stays upstream at PSLmodels/OG-USA; PolicyEngine Macro supplies a thin, tested adapter, common result metadata, CLI/MCP discovery, and documentation.
Upstream feasibility baseline
- Package:
ogusa0.4.0, Python 3.12–3.13. - Source: PSLmodels/OG-USA, CC0-1.0.
- Core solver: PSLmodels/OG-Core.
- Public packaged calibration assets include default parameter JSON, CPS, SCF and PSID files.
- Optional/restricted profiles include PUF/TMD inputs; some calibration paths require a FRED API key.
- Upstream states that a full baseline/reform transition-path run can exceed two hours. Hosted synchronous execution must not be promised before measurement.
- Documentation: https://pslmodels.github.io/OG-USA/content/intro/intro.html
Integration design
1. Keep the UK and US models distinct
- Add canonical registry ID
og-us; retainog-uk. - Let
score_reform(country="us", model="og")route explicitly to OG-USA andcountry="uk"to OG-UK, or expose unambiguousog-us/og-ukselectors. Test whichever public contract is chosen. - Never label OG-UK as UK + US or treat the two calibrations as interchangeable.
- Record
ogusa,ogcore, adapter, tax-calculator and calibration-data versions separately in provenance.
2. Start with a public-data profile
- Establish a clean installation using
pip install ogusa==0.4.0(or a reviewed commit pin). - Run the baseline from packaged/public CPS calibration without PUF, TMD or private credentials.
- State exactly which CPS, SCF, PSID, demographic, FRED and Tax-Calculator inputs are read, including dates and hashes where feasible.
- Keep PUF/TMD profiles optional and local; tests requiring restricted data must be separated from the public acceptance gate.
- Fail with actionable errors when an optional API key or restricted dataset is requested.
3. Build the adapter beside the OG-UK adapter
Use the existing OG-UK section in integration/src/policyengine_macro/core.py and integration/tests/test_og.py as the structural reference, not as code to generalize prematurely.
- Add lazy
_import_ogusa()loading with an exact installation error. - Add a cached US baseline keyed by every result-changing input: start year, dataset profile, model dimensions, solver configuration, policy baseline and source revision.
- Implement an initial steady-state-only adapter; expose transition paths only after runtime, storage and schema review.
- Return baseline and reform solver residuals/convergence status. A non-converged solve must fail, not serialize a plausible-looking result.
- Map OG-USA/OG-Core outputs into the common
ScoreResultwithout dropping native output. - Declare USD units, price basis, annual/steady-state time basis, geography, baseline definition, uncertainty and comparability on every quantity.
- Preserve GDP, consumption, investment, wages, interest rates, government debt and tax-revenue outputs where supported; do not invent a quantity merely to match OG-UK.
4. Use a reviewed reform bridge
OG-USA currently calibrates federal taxes through Tax-Calculator, while PolicyEngine Macro accepts PolicyEngine reform objects. Similar names do not establish equivalent definitions.
- Create a small, versioned catalogue of reviewed PolicyEngine-US → OG-USA/Tax-Calculator mappings.
- For each mapping, declare source path, destination parameter, units, effective date, federal/state scope, transformation and tests.
- Initially support only reforms whose semantics can be shown equivalent. Reject every unsupported path with an explanation and list of supported examples.
- Do not use an LLM or fuzzy name matching to invent mappings or unit conversions.
- Provide a separate expert escape hatch for direct OG-Core specification overrides; label these structural scenarios rather than PolicyEngine reform scores.
- Do not pass state or local tax reforms into a federal OG-USA calibration unless an explicit model channel exists.
5. Access and runtime policy
- Benchmark cold import, calibration, baseline steady state, reform steady state and full TPI separately on representative hardware.
- Launch as local/experimental unless a steady-state pair fits hosted limits with safe memory, timeout and cost headroom.
- If custom hosted runs exceed synchronous limits, use asynchronous jobs or precomputed reviewed scenarios; do not raise Modal timeout indefinitely.
- Cache only immutable, provenance-keyed baselines. Never share a baseline across vintages or calibration profiles.
Verification and acceptance gates
Fast contract tests
- Mocked baseline/reform calls verify routing, caching, schema, units and provenance.
- Tests reject unsupported reforms, wrong geography, missing data, failed convergence and mismatched units.
- Capability registry, CLI, MCP and website claims are generated from or tested against the same metadata.
-
og-ukbehavior remains unchanged.
Real public-data benchmark
- In a clean Python 3.12 environment, install the documented dependencies and run one CPS-profile baseline plus one small reviewed reform.
- Reproduce a frozen upstream OG-USA example or published fixture at declared tolerances.
- Store immutable expected outputs, upstream commit/package versions, data manifests, runtime and solver residuals.
- Run this benchmark on a scheduled/manual workflow where failure fails rather than skips.
Economic validation
- Publish achieved-versus-target calibration moments.
- Label matched targets as calibration by construction, not outcome validation.
- Add at least one independent published US reform benchmark before promoting beyond experimental.
- Report sensitivity to closure rule, Frisch elasticity, discount factors, tax-function profile and foreign-capital assumptions.
- Do not claim forecasting validity: OG-USA is a structural fiscal counterfactual model.
Product and documentation
- Add OG-USA to the capability registry with US geography, supported questions, unsupported uses, access, runtime, data vintage and validation status.
- Present OG-UK and OG-USA as two country calibrations in the OLG family, not one cross-country model.
- Add CLI examples for the public baseline, one reviewed PolicyEngine-US reform and one direct structural scenario.
- Document private-data boundaries and reproducibility differences between public CPS and restricted PUF/TMD profiles.
- Update model, comparison, validation and paper surfaces only after the corresponding executable capability exists.
Definition of done
- A clean public-data installation executes an OG-USA baseline and one reviewed reform.
- The output satisfies the common result/provenance schema with explicit USD units and steady-state horizon.
- Unsupported PolicyEngine-US reforms are refused rather than guessed.
- Solver convergence, calibration targets, source versions and runtime are visible.
- A frozen upstream replication and an independent US benchmark are published.
- OG-UK remains intact and country routing is unambiguous.
- Access is labelled local/experimental unless hosted feasibility is demonstrated.
Related: #69, #71. This implements the OG-USA direction originally suggested during the PolicyEngine Macro architecture review.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Read the existing OG-UK section in integration/src/policyengine_macro/core.py and the structural tests in integration/tests/test_og.py first. Verify the documented ogusa installation and public CPS baseline before designing the adapter. Done requires explicit routing, provenance, convergence and unit metadata, reviewed reform handling, public-data tests, and unchanged OG-UK behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- api, backend, documentation, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100