OBR policy effects → DB ingest: 266 claims, four macro-effect metrics

[vahid-ahmadi]

Stacked on #75 (base uk/obr-policy-effects-harvest); retargets to main when #75 merges. Both branches carry a merge of current main so the post-#74 build machinery is present — the diff here is this lane's nine files only.

#75 landed 266 rows in data/externals/obr-policy-effects.json that nothing consumed: no build_db step read the file, so the claims never reached the DB or the app. This wires them in.

What's here

• scorecard_db/ingest_obr_policy_effects.py — new source obr_policy_effects, registered in build_db's chain after uk_deductions (external claims before the campaign attaches). Same contract as ingest_uk_externals: require_fields on every row, closed identity registries, values passed through verbatim, calibration_relationship from relationships.uk_relationship and never inline. One transaction replaces the source wholesale with the baseline-registration gate inside it.
• Four new Metric members. gdp_level_effect (a package's effect on the real-GDP path) and supply_side_impact (one measure's effect on potential output) are deliberately distinct rather than one "GDP effect" metric — unifying them would merge a demand-inclusive path with a supply-side scoring. decisions_effect_on_borrowing (PSNB) is likewise kept apart from revenue_change and cash_requirement_change (PSNCR), the same unconfusability rule UC deductions: FRR family re-harvested from primary sources + DB ingest #52 applied.
• Reform worlds: each row carries a policy_ref reform naming what was scored — obr_<event>_package for the chart families, obr_<event>_<measure> for the supply-side family, so the same measure re-scored at a later event is a different world. Baseline stays the null current_law: OBR scores an announcement against the law in force at its own scoring date, which is exactly the convention baselines.py documents (announcement vintage is a condition, not a baseline world). No new baseline registered.
• scorecard_db/uk_aliases.py — the source's own closed vocabulary (29 programs, 26 subgroups, geography, units). Registered as its own namespace with nothing aliased into obr's welfare programs: this source's employer_nics is a measure whose supply-side effect is scored, not a spending line.
• tests/test_obr_policy_effects_ingest.py — 24 tests; suite 277 passed / 2 skipped.

Two identity decisions the data forced

1. conditions["decomposition"] — caught by finish(), not by inspection. The October 2024 workbook prints the AB2024 package twice: chart 2.A by expenditure component, 2.B by measure/channel. Both publish a total and a demand_multipliers, so the two totals collided on one claim_id (they agree to ~1e-14, being the same series rendered twice). The decomposition is therefore identity-bearing, not provenance. It keys off (fiscal_event, sheet) because the sheet id alone is not the identity — C2.A is by-channel in the Nov 2023 and Mar 2024 workbooks and by-expenditure-component in Oct 2024. Unregistered pairs raise.

2. The supply-side horizon — the one interpretive call in this PR. Briefing paper No.10's Table 2.1 states its year in words, never as a digit: "Supply-side impact is the impact on potential output in the fifth year of our forecast." period is an int, so something had to be chosen. The note rides verbatim in conditions["horizon_note"], conditions["horizon"] names it symbolically, and the year maps in exactly one place — _BP10_HORIZON_FY = "2030-31" (the fifth year of the November 2025 forecast the paper accompanies). Flagging it explicitly: if you read the horizon differently, it is a one-constant change, and the test pins the mapping rather than the reasoning.

Verification

• uk_policy_effects step: {"claims": 266, "by_metric": {"gdp_level_effect": 151, "cpi_inflation_effect": 36, "supply_side_impact": 19, "decisions_effect_on_borrowing": 60}} — exact accounting, so a drifted harvest regeneration fails here rather than silently resizing the catalog.
• Build deterministic across two from-scratch runs (8fb2adf0…), and both no-drift gates clean — replicated exactly as ci.yml runs them (build ×2 → git diff --exit-code → pytest → git diff --exit-code).
• Table B.1's aggregate_level/parent guard survives into conditions and is asserted at DB level (6 total / 24 subtotal / 30 component, zero orphaned non-total rows), so summing borrowing effects by FY cannot double-count.
• All 266 are held_out: no pe-uk-data target or policyengine-uk parameter is fitted to a macro-effect path — they are what the Macro members get scored against.

Derived-file changes are the build's own output, committed as built: data/lanes.json gains the lane entry, mirrored to the app copy. The feed's top-level stamp stays on the UK family's constant (FEED_UPDATED) because the build ends on this step while the suite ends on whichever UK ingest test runs last — all three writers have to agree or the no-drift gate fails; the comment says so.

PE counterparts are step 3 of #55 and are not computed here.

Builds #55 (step 2: DB ingest).

🤖 Generated with Claude Code

[MaxGhenis]

Gate round 1 (reviewed as a pair with #75) — the transaction shape is right; five findings, four blocking

Clean and verified: adapter-to-DB accounting is exact (266 rows, 266 unique ids, 151/36/19/60, no ingest drops), the transaction correctly does delete + insert + register_baselines_txn + lane atomically with the feed sync post-commit, builder registration is present in build_db.py (the load-bearing post-#74 integration — results survive CI rebuilds), lane mirrors match, and no wins language anywhere.

1. Blocking — all 266 claims get the wrong baseline. The ingest defaults every claim to registered current_law (_reform() supplies no baseline; the test requires baseline is None) — contradicting OBR published economic effects of policy: AS2023–AB2025 packages, supply-side scorings, March 2026 decisions (external side) #75's own declaration that rows score against each EFO's pre-measures forecast. The registration gate passes only because it registers the wrong world. Each fiscal event needs its announcement-baseline world registered with provenance and carried on the claims (mirrored in baseline_policy), or a PE result computed against current law will silently read as comparable.

2. Blocking — all 19 BP10 claims are miskeyed to FY2030-31. BP10 reviews scores from five earlier EFOs; each value belongs to its scoring-event horizon (AS2023 NICs → 2028-29, AB2024 public investment → 2029-30, …). Period is claim identity, so all 19 need per-event periods.

3. Blocking — no deliberate unit concepts. models.py adds metrics only; the mapping collapses GDP-level deviations, CPI percentage points, and potential-output percent-of-GDP into PERCENT, and annual borrowing into bare GBP — and the staged unit is canon-checked then discarded, so a GDP row mislabeled gbp_nominal stages as percent instead of raising. The staged label must bind to the expected concept per metric (the UC deductions: FRR family re-harvested from primary sources + DB ingest #52 pattern: validate-then-map, raise on drift).

4. Blocking — 176/266 publication dates are wrong. All 187 chart claims share a generic URL + 2025-11-26, misdating the 116 AS2023/SB2024/AB2024 claims; the 60 March-2026 claims carry the Wayback capture date (2026-03-16) instead of the publication date (2026-03-03). Publication provenance is per-event, and archive-capture dates are retrieval metadata, not publication dates.

5. Closed-condition and staging gates incomplete. Seven emitted keys are absent from STANDARD_CONDITIONS (decomposition, fiscal_event, horizon, horizon_note, measure_type, scope, sign_convention); rows missing borrowing sign conventions or subtotal parents stage instead of raising; and the held-out evidence should name pe-uk-data@dd68c73 explicitly like the neighboring registry entries.

The skeleton is genuinely good — the fixes are concentrated in claim identity (baseline, period, units, dates), which is exactly where they're cheapest to fix before anything attaches.

🤖 Generated with Claude Code

[vahid-ahmadi]

All five addressed in 50f1dea (this branch now carries the repaired #75).

1. Baselines. No claim defaults to current_law any more. Each carries {policy: obr_pre_measures_<round>, counterfactual: <kind>} mirrored into conditions["baseline_policy"], with the counterfactual kind split as BP10 ch.2 splits it. March 2026 keys the November 2025 Budget forecast. All eleven (round, counterfactual) worlds are registered in baselines.py; a row whose baseline disagrees with its own round raises, and a baseline without a locator raises.

2. BP10 periods. Agreed and fixed — the paper re-states scorings from five earlier events, so "the fifth year of our forecast" is each measure's own round: 2027-28 (Mar 2023) through 2029-30 (Mar 2025). _BP10_HORIZON resolves per event and an unregistered round raises rather than borrowing another's year. A test asserts 2031 appears nowhere.

3. Units. Three new UnitConcept members replace the collapse into PERCENT, and the staged label is now compared against the metric's expected unit and raises on drift instead of being canon-checked then discarded. Bare "percent" is no longer a registered label for this source, so the three cannot collapse back.

4. Publication dates. Per artifact now: 2023-11-22 / 2024-03-06 / 2024-10-30 / 2025-11-26, and the 60 March-2026 claims carry 2026-03-03 rather than the Wayback capture. An unregistered artifact raises.

5. basis. Now forecast on every row; the scoring method moved to conditions["scoring_method"], which also keys the relationship lookup.

266 claims and the exact accounting unchanged; two builds agree on content_hash; suite 299 passed, ruff format clean.

[DTrim99]

Reviewed on tip 50f1dea (includes #75) — pytest tests/test_obr_policy_effects_ingest.py = 38 passed. Clean ingest, same discipline as the rest of the lane.

Verified:

• Accounting — no drops here (every staged row ingests), so it degenerates to staged=ingested=266, asserted via per-metric _EXPECTED (raises on drift) + the finish collision gate; 266 reconciles as 151+36+19+60.
• The four macro metrics (gdp_level_effect, cpi_inflation_effect, supply_side_impact, decisions_effect_on_borrowing) are genuine and correctly kept unconfusable — real-GDP path vs potential-output supply-side scoring, and PSNB vs revenue_change/PSNCR. CPI carried as percentage_points (a rate effect, not a level) is the right call. None should have been a drop.
• Fail-loud on unknown field / every closed axis / unit-label drift / unregistered artifact / (event,sheet) / baseline — all tested.
• held_out — all 266 → HELD_OUT with a "scored, never consumed" basis (macro paths are what Macro members are scored against, not calibration targets); the decomposition DISTINCT edge keyed on (event,sheet) correctly resolves the twice-printed AB2024 collision.
• Values pass through verbatim (no re-derivation/re-signing), baselines.py registers the 11 (round, counterfactual) pre-measures worlds + the Mar-2026 post-measures world inside the ingest txn (asserted complete, no current_law default), and the lanes.json mirror is semantically identical.

Two tiny notes: the PR body still describes an outdated single-constant _BP10_HORIZON_FY, but the code correctly uses the per-event _BP10_HORIZON (body stale, code right); and the only lanes.json byte-diff is an unrelated em-dash escaping in a _schema note. Approving — merge after #75.