From b94310b5739d1693d7135ba5f558fbbf807aaec3 Mon Sep 17 00:00:00 2001 From: vahid-ahmadi Date: Fri, 14 Aug 2026 12:58:59 +0100 Subject: [PATCH 1/3] UKMOD statistics adapter (mode 1): Country Report validation tables MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fifth UK mode-1 adapter (#38, parent #3) — the peer-model lane. Parses the UKMOD Country Report 2023-2030 (CeMPA WP 8/26, version B2026.01) validation tables into 1,131 tidy rows at data/externals/ukmod-stats.json: - Tables 4.5/4.6: simulated caseloads (thousands, unit per row: families/(c)hildren/(h)ouseholds/(i)ndividuals) and expenditure (GBPm) for 30 instruments x policy years 2023-2030, including the devolved income tax band breakdowns (Scottish advanced rate appears 2026), alongside the official estimates the report cites - Tables 4.7/4.8: income distribution (Gini, mean/median, quintile medians/shares) and BHC poverty rates below 50/60/70% of median by age group and gender Claim provenance is the variant: 'ukmod' (simulated), 'official_as_cited' (the report's transcription of DWP/HMRC/OBR/SFC estimates - annotated to resolve to primary sources, several of which are now sibling lanes), 'input_data', 'hbai'. Missing cells are absent claims, skipped rather than suppressed. Parsing is pypdf text extraction (declared dependency; the one UK adapter that needs more than stdlib because the source is a PDF) with an ordered row registry anchored on UKMOD variable names; anchor misses or token shortfalls fail the run. 12 report-pinned checks pass. Annotations record the model-vs-model decomposition contract (shared FRS base, different weights/uprating, take-up frozen at 2021 outside Scotland/pension-age per the report's own health warning), the BHC/modified-OECD triangle with the hbai-poverty lane, policy-year vs financial-year timing, and the open-source upstream path (unique among UK lanes). Lane ukmod-stats: registered -> ingested. Closes #38 Co-Authored-By: Claude Fable 5 --- app/public/data/lanes.json | 8 +- data/externals/ukmod-stats.json | 1 + data/lanes.json | 8 +- sources/ukmod-stats/adapter.py | 427 ++++++++++++++++++ sources/ukmod-stats/annotations.json | 58 +++ ...mpa8-26_ukmod_country_report_2023-2030.pdf | Bin 0 -> 2026848 bytes sources/ukmod-stats/source.json | 36 ++ 7 files changed, 530 insertions(+), 8 deletions(-) create mode 100644 data/externals/ukmod-stats.json create mode 100644 sources/ukmod-stats/adapter.py create mode 100644 sources/ukmod-stats/annotations.json create mode 100644 sources/ukmod-stats/raw/cempa8-26_ukmod_country_report_2023-2030.pdf create mode 100644 sources/ukmod-stats/source.json diff --git a/app/public/data/lanes.json b/app/public/data/lanes.json index 51fdf89..b2bffd7 100644 --- a/app/public/data/lanes.json +++ b/app/public/data/lanes.json @@ -180,10 +180,10 @@ "source": "UKMOD", "area": "published statistics", "mode": 1, - "stage": "registered", + "stage": "ingested", "running": false, - "updated": "2026-08-01", - "note": "#3" + "updated": "2026-08-14", + "note": "External side built (#38): Country Report 2023-2030 (B2026.01) validation tables 4.5-4.8 \u2014 1,131 rows, caseloads+expenditure for 30 instruments x 2023-2030 with official-as-cited columns, income distribution + poverty triangle (ukmod/input_data/hbai variants); 12 report-pinned checks pass" }, { "id": "jct-reform-scores", @@ -306,4 +306,4 @@ "note": "205 claims, 675 per-release results (5 releases, 180 attached to harvest claims); 11 unscoreable rows skipped" } ] -} \ No newline at end of file +} diff --git a/data/externals/ukmod-stats.json b/data/externals/ukmod-stats.json new file mode 100644 index 0000000..c2ddd37 --- /dev/null +++ b/data/externals/ukmod-stats.json @@ -0,0 +1 @@ +[{"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2023", "value": 75000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2024", "value": 78000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2025", "value": 73000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2026", "value": 73000.0, "status": "ok", "source_column": 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"ukmod", "geography": "UK", "unit_concept": "fraction", "period": "2028", "value": 0.15, "status": "ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}, {"source": "ukmod-stats", "country": "UK", "program": "poverty", "metric": "poverty_rate_below_60pct_median", "subgroup": "women", "variant": "ukmod", "geography": "UK", "unit_concept": "fraction", "period": "2029", "value": 0.15, "status": "ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}, {"source": "ukmod-stats", "country": "UK", "program": "poverty", "metric": "poverty_rate_below_60pct_median", "subgroup": "women", "variant": "ukmod", "geography": "UK", "unit_concept": "fraction", "period": "2030", "value": 0.15, "status": "ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}, {"source": "ukmod-stats", "country": "UK", "program": "poverty", "metric": "poverty_rate_below_60pct_median", "subgroup": "women", "variant": "input_data", "geography": "UK", "unit_concept": "fraction", "period": "2023", "value": 0.18, "status": "ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}, {"source": "ukmod-stats", "country": "UK", "program": "poverty", "metric": "poverty_rate_below_60pct_median", "subgroup": "women", "variant": "hbai", "geography": "UK", "unit_concept": "fraction", "period": "2023", "value": 0.14, "status": "ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}] \ No newline at end of file diff --git a/data/lanes.json b/data/lanes.json index 51fdf89..b2bffd7 100644 --- a/data/lanes.json +++ b/data/lanes.json @@ -180,10 +180,10 @@ "source": "UKMOD", "area": "published statistics", "mode": 1, - "stage": "registered", + "stage": "ingested", "running": false, - "updated": "2026-08-01", - "note": "#3" + "updated": "2026-08-14", + "note": "External side built (#38): Country Report 2023-2030 (B2026.01) validation tables 4.5-4.8 \u2014 1,131 rows, caseloads+expenditure for 30 instruments x 2023-2030 with official-as-cited columns, income distribution + poverty triangle (ukmod/input_data/hbai variants); 12 report-pinned checks pass" }, { "id": "jct-reform-scores", @@ -306,4 +306,4 @@ "note": "205 claims, 675 per-release results (5 releases, 180 attached to harvest claims); 11 unscoreable rows skipped" } ] -} \ No newline at end of file +} diff --git a/sources/ukmod-stats/adapter.py b/sources/ukmod-stats/adapter.py new file mode 100644 index 0000000..a7446e3 --- /dev/null +++ b/sources/ukmod-stats/adapter.py @@ -0,0 +1,427 @@ +"""Adapter: UKMOD Country Report published statistics -> tidy rows. + +Input: raw/cempa8-26_ukmod_country_report_2023-2030.pdf — the UKMOD + Country Report 2023-2030 (CeMPA WP 8/26, March 2026, UKMOD + version B2026.01), as fetched from iser.essex.ac.uk. +Output: data/externals/ukmod-stats.json — tidy rows in the scorecard + schema. + +UKMOD is the peer FRS-based UK microsimulation model (EUROMOD platform), +so these are the highest-signal model-vs-model claims the UK side has. +This build emits the report's validation-statistics tables: + + - Table 4.5: simulated caseloads by instrument (thousands), policy + years 2023-2030, alongside the official estimates the report cites + - Table 4.6: simulated expenditure by instrument (£ millions), same + layout + - Table 4.7: household equivalised disposable income distribution + (Gini, mean/median, quintile medians and shares), UKMOD years plus + the un-simulated input data and HBAI reference columns for 2023 + - Table 4.8: poverty rates below 50/60/70% of median (BHC) by age + group and gender, same three sources + +The source variant is carried per row: 'ukmod' (simulated), +'official_as_cited' (the report's own transcription of DWP/HMRC/OBR/SFC +official estimates — provenance is this report, not the primary source), +'input_data' (statistics computed on the raw input data), and 'hbai' +(DWP HBAI as cited). Missing cells ('-', 'na') are absent claims and are +skipped, not suppressed. + +Parsing: pypdf text extraction with an ordered row registry anchored on +UKMOD variable names (bsauc_s, tin00_s, ...) and band labels; each row +consumes exactly its expected number of numeric/missing tokens, and any +anchor miss or token shortfall fails the run. Requires pypdf +(pip install pypdf); everything else is stdlib. + +Value semantics: + - caseloads are THOUSANDS -> raw units (families unless noted: + (c) children, (h) households, (i) individuals — carried as + unit_concept per row) + - expenditure is £ MILLIONS -> raw GBP + - Gini, quintile shares and poverty rates are percents -> fractions + - mean/median/quintile incomes are £ per month, as printed +""" + +import json +import re +from pathlib import Path + +try: + from pypdf import PdfReader +except ImportError as e: + raise SystemExit("this adapter needs pypdf: pip install pypdf") from e + +HERE = Path(__file__).resolve().parent +OUT_DIR = HERE.parent.parent / "data" / "externals" +OUT_DIR.mkdir(parents=True, exist_ok=True) + +SOURCE_ID = "ukmod-stats" +RAW_FILE = "cempa8-26_ukmod_country_report_2023-2030.pdf" +YEARS = [str(y) for y in range(2023, 2031)] + +# (anchor, program, subgroup, unit_concept, band_row) +# anchor is the UKMOD variable-name string printed in the table row; band +# rows (marginal-rate breakdowns) are anchored on their label and carry a +# leading '-' variable placeholder before the value tokens. +INSTRUMENT_ROWS = [ + ( + "bmascmt01_s, \nbmascmt_s", + "best_start_grant_and_foods", + "total", + "families", + False, + ), + ("bchkid_s", "child_benefit", "children_covered", "children", False), + ("bch_s", "child_benefit", "total", "families", False), + ("bfamt_s, \nbwkmt_s", "child_and_working_tax_credits", "total", "families", False), + ("bmu_s", "council_tax_reduction", "total", "households", False), + ("bsadi_s", "esa_income_based", "total", "families", False), + ("bched01_s", "free_school_meals", "total", "families", False), + ("bmamt01_s", "healthy_start_food", "total", "families", False), + ("bho_s", "housing_benefit", "total", "households", False), + ("bsa_s", "income_support", "total", "families", False), + ("bunct_s", "jsa_contribution_based", "total", "families", False), + ("boamt_s", "pension_credit", "total", "families", False), + ("bched02_s", "school_clothing_grant", "total", "families", False), + ( + "bcrdicm_s", + "scottish_carers_allowance_supplement", + "total", + "individuals", + False, + ), + ("bchmt_s", "scottish_child_payment", "total", "children", False), + ("bchht_s", "scottish_child_winter_heating", "total", "children", False), + ("bmamt_s", "sure_start_maternity_grant", "total", "families", False), + ("bsauc_s", "universal_credit", "total", "families", False), + ("boaht_s", "winter_fuel_allowance", "total", "households", False), + ("brd_s", "benefit_cap_housing_benefit", "total", "households", False), + ("brduc_s", "benefit_cap_universal_credit", "total", "households", False), + ("tscee_s", "nic_employees", "total", "individuals", False), + ("tscse_s", "nic_self_employed", "total", "individuals", False), + ("tscer_s", "nic_employers", "total", "individuals", False), + ("tin_s", "income_tax", "total", "individuals", False), + ("tin00_s", "income_tax_non_devolved", "total", "individuals", False), + ("Savers rate", "income_tax_non_devolved", "savers_rate", "individuals", True), + ("Basic rate", "income_tax_non_devolved", "basic_rate", "individuals", True), + ("Higher rate", "income_tax_non_devolved", "higher_rate", "individuals", True), + ( + "Additional rate", + "income_tax_non_devolved", + "additional_rate", + "individuals", + True, + ), + ("tin11_s", "income_tax_welsh", "total", "individuals", False), + ("tin12_s", "income_tax_scottish", "total", "individuals", False), + ("Starter rate", "income_tax_scottish", "starter_rate", "individuals", True), + ("Basic rate", "income_tax_scottish", "basic_rate", "individuals", True), + ( + "Intermediate rate", + "income_tax_scottish", + "intermediate_rate", + "individuals", + True, + ), + ("Higher rate", "income_tax_scottish", "higher_rate", "individuals", True), + ("Advanced rate", "income_tax_scottish", "advanced_rate", "individuals", True), + ("Top rate", "income_tax_scottish", "top_rate", "individuals", True), +] + +# Table 4.6 adds the NIC total row (between brduc_s and tscee_s) and has a +# single Child Benefit row (no children-covered caseload analogue). +EXPENDITURE_ROWS = ( + [r for r in INSTRUMENT_ROWS[:21] if r[0] != "bchkid_s"] + + [("NIC all", "nic_all", "total", "individuals", True)] + + INSTRUMENT_ROWS[21:] +) + +TOKEN = re.compile(r"^(-|na|-?\d+(?:\.\d+)?)$") + + +def page_text(reader, indices): + return "\n".join(reader.pages[i].extract_text() or "" for i in indices) + + +def parse_rows(text, registry, n_values): + """Walk the registry in order, reading n_values tokens after each anchor.""" + pos = 0 + out = [] + for anchor, program, subgroup, unit_concept, band in registry: + anchor_flat = anchor.replace("\n", "") + # search on a whitespace-normalized shadow so wrapped anchors match + flat = re.sub(r"\s+", " ", text) + m = re.search(re.escape(re.sub(r"\s+", " ", anchor_flat)), flat[pos:]) + if not m: + raise ValueError(f"anchor not found: {anchor!r}") + start = pos + m.end() + tokens = [] + for tok in flat[start:].split(): + if TOKEN.match(tok): + tokens.append(tok) + elif tokens: + break + elif band and tok == "-": + continue + else: + break + if band and tokens and len(tokens) == n_values + 1: + tokens = tokens[1:] # leading variable-column placeholder + if len(tokens) < n_values: + raise ValueError( + f"{anchor!r}: expected {n_values} tokens, got {len(tokens)}: {tokens}" + ) + out.append((program, subgroup, unit_concept, tokens[:n_values])) + pos = start + return out + + +def emit( + rows, program, metric, subgroup, variant, unit_concept, period, value, source_column +): + rows.append( + { + "source": SOURCE_ID, + "country": "UK", + "program": program, + "metric": metric, + "subgroup": subgroup, + "variant": variant, + "geography": "UK", + "unit_concept": unit_concept, + "period": period, + "value": value, + "status": "ok", + "source_column": source_column, + } + ) + + +def emit_instrument_table(text, registry, metric, scale, rows, table_id): + for program, subgroup, unit_concept, tokens in parse_rows(text, registry, 16): + for k, tok in enumerate(tokens): + if tok in ("-", "na"): + continue # absent claim, not suppression + variant = "ukmod" if k < 8 else "official_as_cited" + period = YEARS[k % 8] + emit( + rows, + program, + metric, + subgroup, + variant, + unit_concept, + period, + float(tok) * scale, + f"{table_id}:{program}/{subgroup}", + ) + + +DISTRIBUTION_ROWS = [ + ("Gini", "gini", "total", 0.01, "fraction"), + ("Mean (£ per month)", "mean_income", "total", 1, "gbp_per_month_equivalised"), + ("Median (£ per month)", "median_income", "total", 1, "gbp_per_month_equivalised"), + ("lowest", "quintile_median_income", "q1", 1, "gbp_per_month_equivalised"), + ("2", "quintile_median_income", "q2", 1, "gbp_per_month_equivalised"), + ("3", "quintile_median_income", "q3", 1, "gbp_per_month_equivalised"), + ("4", "quintile_median_income", "q4", 1, "gbp_per_month_equivalised"), + ("highest", "quintile_median_income", "q5", 1, "gbp_per_month_equivalised"), + ("lowest", "quintile_income_share", "q1", 0.01, "fraction"), + ("2", "quintile_income_share", "q2", 0.01, "fraction"), + ("3", "quintile_income_share", "q3", 0.01, "fraction"), + ("4", "quintile_income_share", "q4", 0.01, "fraction"), + ("highest", "quintile_income_share", "q5", 0.01, "fraction"), +] + +POVERTY_ROWS = [ + (anchor, metric, subgroup, 0.01, "fraction") + for anchor, metric, subgroup in [ + ("below 50% of median", "poverty_rate_below_50pct_median", "total"), + ("below 60% of median", "poverty_rate_below_60pct_median", "total"), + ("below 70% of median", "poverty_rate_below_70pct_median", "total"), + ("below 50% of median", "poverty_rate_below_50pct_median", "children"), + ("below 60% of median", "poverty_rate_below_60pct_median", "children"), + ("below 70% of median", "poverty_rate_below_70pct_median", "children"), + ("below 50% of median", "poverty_rate_below_50pct_median", "over_spa"), + ("below 60% of median", "poverty_rate_below_60pct_median", "over_spa"), + ("below 70% of median", "poverty_rate_below_70pct_median", "over_spa"), + ("men", "poverty_rate_below_60pct_median", "men"), + ("women", "poverty_rate_below_60pct_median", "women"), + ] +] + + +def emit_reference_table(text, registry, program, rows, table_id): + """Tables 4.7/4.8: 8 UKMOD years + input-data 2023 + HBAI 2023.""" + reg = [ + (anchor, metric, subgroup, scale, unit) + for anchor, metric, subgroup, scale, unit in registry + ] + parsed = parse_rows( + text, + [(a, m, s, u, False) for a, m, s, _, u in reg], + 10, + ) + for (anchor, metric, subgroup, scale, unit), (_, _, _, tokens) in zip(reg, parsed): + for k, tok in enumerate(tokens): + if tok in ("-", "na"): + continue + if k < 8: + variant, period = "ukmod", YEARS[k] + elif k == 8: + variant, period = "input_data", "2023" + else: + variant, period = "hbai", "2023" + emit( + rows, + program, + metric, + subgroup, + variant, + unit, + period, + float(tok) * scale, + f"{table_id}:{metric}/{subgroup}", + ) + + +def run(): + reader = PdfReader(HERE / "raw" / RAW_FILE) + rows = [] + + text = page_text(reader, range(89, 92)) + text = text[text.find("Table 4.5") :] + emit_instrument_table(text, INSTRUMENT_ROWS, "caseload", 1000, rows, "4.5") + + text = page_text(reader, range(92, 95)) + text = text[text.find("Table 4.6") :] + emit_instrument_table(text, EXPENDITURE_ROWS, "expenditure", 1e6, rows, "4.6") + + text = page_text(reader, [98]) + text = text[text.find("Table 4.7") :] + emit_reference_table(text, DISTRIBUTION_ROWS, "income_distribution", rows, "4.7") + + text = page_text(reader, [100]) + text = text[text.find("Full population") :] + emit_reference_table(text, POVERTY_ROWS, "poverty", rows, "4.8") + + out = OUT_DIR / f"{SOURCE_ID}.json" + out.write_text(json.dumps(rows)) + print(f"{len(rows)} rows -> {out}") + + # --- validation against the report's printed values --- + def get(program, metric, period, variant, subgroup="total"): + for r in rows: + if ( + r["program"] == program + and r["metric"] == metric + and r["subgroup"] == subgroup + and r["variant"] == variant + and r["period"] == period + ): + return r["value"] + raise KeyError((program, metric, subgroup, variant, period)) + + checks = [ + ( + "UC caseload 2025 (UKMOD) 6.941M", + get("universal_credit", "caseload", "2025", "ukmod"), + 6_941_000, + ), + ( + "UC caseload 2025 (official) 6.493M", + get("universal_credit", "caseload", "2025", "official_as_cited"), + 6_493_000, + ), + ( + "UC expenditure 2025 (official) £79,525m", + get("universal_credit", "expenditure", "2025", "official_as_cited"), + 79_525_000_000, + ), + ( + "child benefit children 2024 (UKMOD) 12.863M", + get( + "child_benefit", + "caseload", + "2024", + "ukmod", + subgroup="children_covered", + ), + 12_863_000, + ), + ( + "Scottish advanced-rate payers 2026 (UKMOD) 144k", + get( + "income_tax_scottish", + "caseload", + "2026", + "ukmod", + subgroup="advanced_rate", + ), + 144_000, + ), + ( + "income tax expenditure 2023 (official) £297,480m", + get("income_tax", "expenditure", "2023", "official_as_cited"), + 297_480_000_000, + ), + ( + "Gini 2023 (UKMOD) 0.32", + get("income_distribution", "gini", "2023", "ukmod"), + 0.32, + ), + ( + "Gini 2023 (HBAI) 0.33", + get("income_distribution", "gini", "2023", "hbai"), + 0.33, + ), + ( + "median income 2023 (input data) £2,818/mo", + get("income_distribution", "median_income", "2023", "input_data"), + 2818, + ), + ( + "child poverty <60% 2025 (UKMOD) 19%", + get( + "poverty", + "poverty_rate_below_60pct_median", + "2025", + "ukmod", + subgroup="children", + ), + 0.19, + ), + ( + "child poverty <60% 2023 (HBAI) 20%", + get( + "poverty", + "poverty_rate_below_60pct_median", + "2023", + "hbai", + subgroup="children", + ), + 0.20, + ), + ( + "women poverty <60% 2023 (UKMOD) 15%", + get( + "poverty", + "poverty_rate_below_60pct_median", + "2023", + "ukmod", + subgroup="women", + ), + 0.15, + ), + ] + failed = False + for label, got, want in checks: + ok = got is not None and abs(got - want) < 1e-9 + print(f" {'OK ' if ok else 'FAIL'} {label}: {got}") + failed = failed or not ok + if failed: + raise SystemExit(1) + + +if __name__ == "__main__": + run() diff --git a/sources/ukmod-stats/annotations.json b/sources/ukmod-stats/annotations.json new file mode 100644 index 0000000..7357a80 --- /dev/null +++ b/sources/ukmod-stats/annotations.json @@ -0,0 +1,58 @@ +{ + "_schema": { + "applies_to": "matcher on {program, metrics, subgroups}; null = any", + "severity": "period | definitional | construction | caution | gap", + "basis": "traceability: the country report's own notes and health warnings, a GitHub issue, or a measured diagnostic in this repo's data" + }, + "annotations": [ + { + "id": "model-vs-model", + "applies_to": {"program": null, "metrics": null}, + "severity": "construction", + "text": "UKMOD 'ukmod'-variant rows are simulated statistics from a peer FRS-based microsimulation, not measurements: same survey base as PE UK but different weighting (HBAI-adjusted FRS 2023/24), different uprating, and take-up corrections frozen at 2021 outside Scotland/pension-age (the report's own health warning). PE-vs-UKMOD divergence decomposes into data-vintage, uprating, take-up and policy-scope channels before it says anything about either engine.", + "basis": "Country report sections 3.2.3 and 4.3" + }, + { + "id": "official-as-cited", + "applies_to": {"program": null, "metrics": ["caseload", "expenditure"]}, + "severity": "caution", + "text": "'official_as_cited' rows are the report's transcription of DWP/HMRC/OBR/SFC estimates, kept here to reproduce UKMOD's own validation frame. Before scoring PE against those values, resolve them to the primary publication (several are already ingested lanes: HMRC liabilities, OBR EFO March 2026) — cite the primary, and treat any transcription mismatch as an external-issue seed against this report.", + "basis": "Table 4.5/4.6 source notes" + }, + { + "id": "caseload-unit-mix", + "applies_to": {"program": null, "metrics": ["caseload"]}, + "severity": "definitional", + "text": "Caseloads count families except where the report marks (c) children, (h) households, (i) individuals — carried per row as unit_concept. Child Benefit appears twice (families and children covered); Income Support includes income-based JSA. Match the unit before comparing.", + "basis": "Table 4.5 notes" + }, + { + "id": "policy-year-not-fy", + "applies_to": {"program": null, "metrics": null}, + "severity": "period", + "text": "Periods are UKMOD policy years (the system as at a reference date in the named calendar year), not DWP/HMRC financial years; forecasts start in 2025. A PE counterpart should simulate the matching policy year, and comparisons to FY-denominated official statistics inherit a timing wedge.", + "basis": "Country report section 3.5" + }, + { + "id": "bhc-modified-oecd", + "applies_to": {"program": ["income_distribution", "poverty"], "metrics": null}, + "severity": "definitional", + "text": "Distribution and poverty statistics are before-housing-costs household equivalised disposable income, modified OECD scale, individual unit of analysis — the same construction as the hbai-poverty lane's BHC rows, so the three-way PE/UKMOD/HBAI triangle is well-posed on this definition. The report itself flags UKMOD child poverty understating HBAI, attributing it to child-benefit take-up assumptions.", + "basis": "Table 4.7/4.8 notes; section 4.2.2" + }, + { + "id": "rounding", + "applies_to": {"program": ["income_distribution", "poverty"], "metrics": null}, + "severity": "caution", + "text": "Gini, quintile shares and poverty rates are printed to whole percents (fractions carry ±0.005); caseloads to thousands; expenditure to £ millions. Score within rounding tolerance.", + "basis": "printed table precision" + }, + { + "id": "open-source-upstream", + "applies_to": {"program": null, "metrics": null}, + "severity": "gap", + "text": "UKMOD is open source, so confirmed external-model issues here have an upstream path (unique among UK lanes). Case-level adjudication belongs to the mode-3 ukmod-cases lane via the JRC connector (#41); this lane stays at published-aggregate altitude.", + "basis": "scorecard issues #38 and #41" + } + ] +} diff --git a/sources/ukmod-stats/raw/cempa8-26_ukmod_country_report_2023-2030.pdf b/sources/ukmod-stats/raw/cempa8-26_ukmod_country_report_2023-2030.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0b01c01a368ced085d2342f8934581f3df032cda GIT binary patch literal 2026848 zcmb@sb!?rn(=HfjlO}1Hnc;+)nW3Q*W`>5DISn&2bHmKc%uFXIj16;>+uz;&_I~@v z>T0zsjVyZ}k7r)L)?;gIN+oeg1{OvxcuMg8>>@l52{Vbku@yW&KRlD&6fTvHD0t20#XzevWUj;i+AnG?>6I% z?=+D39hxga-BmeyuCADqS7)@}+Gk&Ocu>}sKfAr3YF6L!xIB+MXS^re&)#z0rTv&^ zaGXJ6qf}`z^f|e^n||2)J-*R5)RU62Ozk!GTO)19Xd}rzNu`HRy*Kq6Zwalq&A+X& zm)Q*-hhNxmtTU(5J4)QlqTChlvY>H0)kf1l3PO)wqE>p#kj!ny`J&gM?y_cVJgRLh zAWb&sd;SiNyk_zargQiUd#g8bm7^kU3Y><;GfMC_Ix0_|?U}@cC4Bs<4~dbNv$D4H zJv_3DzU+yq?}v-BQs+8mn0+O`#aG_xEbl2f>TqtYHr--+b>9JJN<5S#Xfd%pO2C@< 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zra?$PHF*8b^-w)uL$2Ax=JrD7+VbK`P>i`DLB}y)+_ZI5n^&*JQ~36zZ!%C$t}_TD z-JJZAWh@`9$gtz=lVi-HTVp?&cK=*6ZtI)eUGa+0`&}x1o3^KUCMZ9li18vUwa{G> zl9o)j#bV25?RU34GH1wPPg5q9HlB5B(P3;)2icaWUV1Gym~(`Cu%t(ZIZsyVi&S@Q z63f))`FtqZKQn2+ezU6%tH%+mNFyeP9%fM$x38&}<+P&)sS4OVHLq4Yg|>Ybuo*DgxS zg=t~8h@f7nb`CrQ>FTiC>HtfLv-;KCg>EUX|)F2#A%m+Y46b#rzj zf27^*vHrB#F#Ci>OJWnqkceHjJz9*tEHV$2|p`b3hZhzOyj1RP@+ zAqOs40shFw3mw6jP>zXZcQ956ION5w(xvmk!NA?$lSLRli2dNczM$cc-${$-)sb@UF@!G#hwb&-Q3w zAY+m(U>80AzdRK|W#jo?*A0lnUY!aUl`#pg?$Zbn%P!PAIj;_re1&bSm^?W0aI7!& z0&r0TO#xgf?;#YIVrqs1x^Iw0G#@oEqR2p1u*iEgk;(|@*YUa6zgFAqj<(eWpZQTPyVYy%GMLhm?aotmv5>5kSnTw4MqqaYRhP)WIy=C{Z-~&ZiMHK_EUyV3T~d{+VdZ7jXZst$8W%6f9<6gT zNbjZ0eF`TeN%{5|vT6@>dRaE&sEI*V2-bwGstNg`#d6qkai(SCoWrO5eviw{Um1CL(MjC2FsJoojK7 z{=OROnn(TWATOvparvg78)PVhl^sA!aN1{f-H_BqmLZ=g;AqGDZbs&b= zU{zHg`QM#Qss;GZsF!25`9)8*U7b`-{Q5LE2s#7B+oq516J+*#`gq{{{XAVC@ Date: Fri, 14 Aug 2026 13:50:38 +0100 Subject: [PATCH 2/3] Fix Table 4.7 quintile rows: token-anchored parsing + structural invariants MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A code review caught a real data-corruption defect in this adapter. The parser matched row anchors as SUBSTRINGS of whitespace-flattened text, so the bare digit anchors of Table 4.7's quintile rows ('2', '3', '4') matched digits INSIDE preceding numbers — the '2' anchor hit the trailing 2 of '1352', shifting five rows onto their neighbours' values. 40 of Table 4.7's 130 values were wrong, including quintile 2's 2024 median stored as GBP2/month (the row label itself) instead of GBP2,333, and quintile 3's 2023 median as 8.0 instead of 2,869. All 12 spot-checks printed OK throughout, because none of them touched those rows. Fixes: - parse_rows now matches WHOLE TOKENS, never substrings, and requires a numeric anchor to sit at the start of its line (a bare '2' mid-line is a data value, not a row label). Values are read from the anchor's own line and continue onto following lines only while those lines hold nothing but values — which keeps Table 4.6's wrapped benefit-cap rows working without letting a row run on into the next labelled row. - Tables 4.5/4.6/4.8 are byte-identical after the rewrite (independently machine-diffed against the PDF at 0 mismatches before the fix); only the 40 corrupt Table 4.7 cells changed, and each now matches the report exactly. - Expenditure rows carried the caseload registry's counting unit ('families' on a GBP value); money rows now carry gbp_nominal. - The 325 absent (unpublished '-'/'na') cells are now tallied and reported instead of silently skipped. New defences, because spot-checks structurally cannot see a shifted row: - structural_invariants(): per variant and year, quintile shares sum to 1, quintile medians increase, the middle quintile's median equals the population median, and median <= mean. 40 invariants, all pass; they fire 27 times on the pre-fix data. - tests/test_ukmod_adapter.py puts those identities plus hand-read PDF ground truth in CI's path (CI runs pytest, never sources/). 5 of the 7 new tests fail on the pre-fix output. - pypdf is now imported lazily so the invariants are importable, and testable in CI, without it. Co-Authored-By: Claude Fable 5 --- app/public/data/lanes.json | 2 +- data/externals/ukmod-stats.json | 2 +- data/lanes.json | 2 +- sources/ukmod-stats/adapter.py | 181 +++++++++++++++++++++++++++----- tests/test_ukmod_adapter.py | 106 +++++++++++++++++++ 5 files changed, 264 insertions(+), 29 deletions(-) create mode 100644 tests/test_ukmod_adapter.py diff --git a/app/public/data/lanes.json b/app/public/data/lanes.json index b2bffd7..6806e7f 100644 --- a/app/public/data/lanes.json +++ b/app/public/data/lanes.json @@ -183,7 +183,7 @@ "stage": "ingested", "running": false, "updated": "2026-08-14", - "note": "External side built (#38): Country Report 2023-2030 (B2026.01) validation tables 4.5-4.8 \u2014 1,131 rows, caseloads+expenditure for 30 instruments x 2023-2030 with official-as-cited columns, income distribution + poverty triangle (ukmod/input_data/hbai variants); 12 report-pinned checks pass" + "note": "External side built (#38): Country Report 2023-2030 (B2026.01) validation tables 4.5-4.8 \u2014 1,131 rows, caseloads+expenditure for 30 instruments x 2023-2030 with official-as-cited columns, income distribution + poverty triangle (ukmod/input_data/hbai variants); 12 report-pinned checks + 40 structural invariants pass (quintile-row parse defect fixed)" }, { "id": "jct-reform-scores", diff --git a/data/externals/ukmod-stats.json b/data/externals/ukmod-stats.json index c2ddd37..3f552d9 100644 --- a/data/externals/ukmod-stats.json +++ b/data/externals/ukmod-stats.json @@ -1 +1 @@ -[{"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2023", "value": 75000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2024", "value": 78000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2025", "value": 73000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2026", "value": 73000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2027", "value": 76000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2028", "value": 76000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2029", "value": 76000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "ukmod", "geography": "UK", "unit_concept": "families", "period": "2030", "value": 76000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "best_start_grant_and_foods", "metric": "caseload", "subgroup": "total", "variant": "official_as_cited", "geography": "UK", "unit_concept": "families", "period": "2023", "value": 21000.0, "status": "ok", "source_column": "4.5:best_start_grant_and_foods/total"}, {"source": "ukmod-stats", "country": "UK", "program": "child_benefit", "metric": "caseload", "subgroup": "children_covered", "variant": "ukmod", "geography": "UK", "unit_concept": "children", "period": "2023", "value": 11909000.0, "status": "ok", "source_column": "4.5:child_benefit/children_covered"}, {"source": "ukmod-stats", "country": "UK", "program": "child_benefit", "metric": "caseload", "subgroup": "children_covered", "variant": "ukmod", "geography": "UK", "unit_concept": "children", "period": "2024", "value": 12863000.0, "status": "ok", "source_column": "4.5:child_benefit/children_covered"}, {"source": "ukmod-stats", "country": "UK", "program": "child_benefit", "metric": "caseload", "subgroup": "children_covered", "variant": "ukmod", "geography": "UK", "unit_concept": "children", "period": "2025", "value": 12746000.0, "status": "ok", "source_column": "4.5:child_benefit/children_covered"}, {"source": "ukmod-stats", "country": "UK", "program": "child_benefit", "metric": "caseload", "subgroup": "children_covered", "variant": "ukmod", "geography": "UK", "unit_concept": "children", "period": "2026", "value": 12681000.0, "status": "ok", "source_column": "4.5:child_benefit/children_covered"}, {"source": "ukmod-stats", "country": "UK", "program": "child_benefit", "metric": "caseload", "subgroup": "children_covered", "variant": "ukmod", "geography": "UK", "unit_concept": "children", "period": "2027", "value": 12582000.0, "status": "ok", "source_column": "4.5:child_benefit/children_covered"}, {"source": "ukmod-stats", "country": "UK", "program": "child_benefit", "metric": "caseload", "subgroup": "children_covered", "variant": "ukmod", "geography": "UK", "unit_concept": "children", 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"ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}, {"source": "ukmod-stats", "country": "UK", "program": "poverty", "metric": "poverty_rate_below_60pct_median", "subgroup": "women", "variant": "ukmod", "geography": "UK", "unit_concept": "fraction", "period": "2030", "value": 0.15, "status": "ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}, {"source": "ukmod-stats", "country": "UK", "program": "poverty", "metric": "poverty_rate_below_60pct_median", "subgroup": "women", "variant": "input_data", "geography": "UK", "unit_concept": "fraction", "period": "2023", "value": 0.18, "status": "ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}, {"source": "ukmod-stats", "country": "UK", "program": "poverty", "metric": "poverty_rate_below_60pct_median", "subgroup": "women", "variant": "hbai", "geography": "UK", "unit_concept": "fraction", "period": "2023", "value": 0.14, "status": "ok", "source_column": "4.8:poverty_rate_below_60pct_median/women"}] \ No newline at end of file diff --git a/data/lanes.json b/data/lanes.json index b2bffd7..6806e7f 100644 --- a/data/lanes.json +++ b/data/lanes.json @@ -183,7 +183,7 @@ "stage": "ingested", "running": false, "updated": "2026-08-14", - "note": "External side built (#38): Country Report 2023-2030 (B2026.01) validation tables 4.5-4.8 \u2014 1,131 rows, caseloads+expenditure for 30 instruments x 2023-2030 with official-as-cited columns, income distribution + poverty triangle (ukmod/input_data/hbai variants); 12 report-pinned checks pass" + "note": "External side built (#38): Country Report 2023-2030 (B2026.01) validation tables 4.5-4.8 \u2014 1,131 rows, caseloads+expenditure for 30 instruments x 2023-2030 with official-as-cited columns, income distribution + poverty triangle (ukmod/input_data/hbai variants); 12 report-pinned checks + 40 structural invariants pass (quintile-row parse defect fixed)" }, { "id": "jct-reform-scores", diff --git a/sources/ukmod-stats/adapter.py b/sources/ukmod-stats/adapter.py index a7446e3..44d8db6 100644 --- a/sources/ukmod-stats/adapter.py +++ b/sources/ukmod-stats/adapter.py @@ -46,10 +46,16 @@ import re from pathlib import Path -try: - from pypdf import PdfReader -except ImportError as e: - raise SystemExit("this adapter needs pypdf: pip install pypdf") from e + +def _pdf_reader(): + """Imported lazily so the parsing helpers and structural_invariants() + stay importable (and CI-testable) without pypdf installed.""" + try: + from pypdf import PdfReader + except ImportError as e: # pragma: no cover - environment-dependent + raise SystemExit("this adapter needs pypdf: pip install pypdf") from e + return PdfReader + HERE = Path(__file__).resolve().parent OUT_DIR = HERE.parent.parent / "data" / "externals" @@ -143,36 +149,81 @@ def page_text(reader, indices): return "\n".join(reader.pages[i].extract_text() or "" for i in indices) +def tokenize(text): + """[(token, line_index, is_line_start)] over the extracted page text. + + Tokens, not characters, are the matching unit. Substring matching on + flattened text is what made a bare '2' anchor match the trailing digit + of '1352' in Table 4.7, shifting five quintile rows onto their + neighbours' values; whole-token equality cannot do that. + """ + out = [] + for line_no, line in enumerate(text.split("\n")): + for i, tok in enumerate(line.split()): + out.append((tok, line_no, i == 0)) + return out + + +def _match_anchor(stream, start, anchor_tokens, numeric_anchor): + """First index >= start where anchor_tokens match consecutively.""" + n = len(anchor_tokens) + for i in range(start, len(stream) - n + 1): + if [t for t, _, _ in stream[i : i + n]] != anchor_tokens: + continue + # A numeric label ('2', '3', '4' — the quintile rows) is only a row + # label at the start of its line; anywhere else it is a data value. + if numeric_anchor and not stream[i][2]: + continue + return i + return -1 + + def parse_rows(text, registry, n_values): - """Walk the registry in order, reading n_values tokens after each anchor.""" - pos = 0 + """Walk the registry in order, reading n_values values after each anchor. + + A row's values are taken from the anchor's OWN line, and continue onto + following lines only while the anchor line has not yet supplied enough + (Table 4.6 wraps the two benefit-cap rows) and each continuation line + holds nothing but values. That bound is what stops a row from running + on into the next labelled row. + """ + lines = text.split("\n") + pure_value_line = [ + bool(line.split()) and all(TOKEN.match(t) for t in line.split()) + for line in lines + ] + stream = tokenize(text) + cursor = 0 out = [] for anchor, program, subgroup, unit_concept, band in registry: - anchor_flat = anchor.replace("\n", "") - # search on a whitespace-normalized shadow so wrapped anchors match - flat = re.sub(r"\s+", " ", text) - m = re.search(re.escape(re.sub(r"\s+", " ", anchor_flat)), flat[pos:]) - if not m: + anchor_tokens = anchor.split() + numeric_anchor = all(TOKEN.match(t) for t in anchor_tokens) + i = _match_anchor(stream, cursor, anchor_tokens, numeric_anchor) + if i < 0: raise ValueError(f"anchor not found: {anchor!r}") - start = pos + m.end() + start = i + len(anchor_tokens) + # values start on the LAST anchor token's line: wrapped anchors + # ("bmascmt01_s,\nbmascmt_s") put the label and the data on + # different lines + anchor_line = stream[start - 1][1] + want = n_values + 1 if band else n_values tokens = [] - for tok in flat[start:].split(): - if TOKEN.match(tok): - tokens.append(tok) - elif tokens: + for tok, line_no, _ in stream[start:]: + if len(tokens) >= want: break - elif band and tok == "-": - continue - else: + if line_no != anchor_line and not pure_value_line[line_no]: break - if band and tokens and len(tokens) == n_values + 1: + if not TOKEN.match(tok): + break + tokens.append(tok) + if band and len(tokens) == n_values + 1: tokens = tokens[1:] # leading variable-column placeholder if len(tokens) < n_values: raise ValueError( f"{anchor!r}: expected {n_values} tokens, got {len(tokens)}: {tokens}" ) out.append((program, subgroup, unit_concept, tokens[:n_values])) - pos = start + cursor = start return out @@ -198,10 +249,12 @@ def emit( def emit_instrument_table(text, registry, metric, scale, rows, table_id): + absent = 0 for program, subgroup, unit_concept, tokens in parse_rows(text, registry, 16): for k, tok in enumerate(tokens): if tok in ("-", "na"): - continue # absent claim, not suppression + absent += 1 # claim not published, not a suppressed value + continue variant = "ukmod" if k < 8 else "official_as_cited" period = YEARS[k % 8] emit( @@ -210,11 +263,14 @@ def emit_instrument_table(text, registry, metric, scale, rows, table_id): metric, subgroup, variant, - unit_concept, + # the caseload registry supplies the counting unit; money + # rows are GBP whatever their instrument counts + "gbp_nominal" if metric == "expenditure" else unit_concept, period, float(tok) * scale, f"{table_id}:{program}/{subgroup}", ) + return absent DISTRIBUTION_ROWS = [ @@ -285,17 +341,80 @@ def emit_reference_table(text, registry, program, rows, table_id): ) +QUINTILES = ("q1", "q2", "q3", "q4", "q5") + + +def structural_invariants(rows): + """[(label, ok)] — table-level identities the source guarantees. + + Shared with tests/test_ukmod_adapter.py so CI enforces them too (CI + runs pytest, never sources/). + """ + index = {} + for r in rows: + index[(r["metric"], r["subgroup"], r["variant"], r["period"])] = r["value"] + variants = ("ukmod", "input_data", "hbai") + out = [] + + for variant in variants: + periods = sorted( + { + p + for (m, _, v, p) in index + if m == "quintile_income_share" and v == variant + } + ) + for period in periods: + shares = [ + index[("quintile_income_share", q, variant, period)] for q in QUINTILES + ] + # published as whole percents; five roundings bound the error + out.append( + ( + f"{variant} {period} quintile shares sum to 1", + abs(sum(shares) - 1.0) <= 0.025, + ) + ) + medians = [ + index[("quintile_median_income", q, variant, period)] for q in QUINTILES + ] + out.append( + ( + f"{variant} {period} quintile medians increase q1<.. Date: Fri, 14 Aug 2026 13:51:13 +0100 Subject: [PATCH 3/3] Docstring: describe token-anchored parsing and the lazy pypdf import Co-Authored-By: Claude Fable 5 --- sources/ukmod-stats/adapter.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/sources/ukmod-stats/adapter.py b/sources/ukmod-stats/adapter.py index 44d8db6..70b06d0 100644 --- a/sources/ukmod-stats/adapter.py +++ b/sources/ukmod-stats/adapter.py @@ -28,10 +28,19 @@ skipped, not suppressed. Parsing: pypdf text extraction with an ordered row registry anchored on -UKMOD variable names (bsauc_s, tin00_s, ...) and band labels; each row -consumes exactly its expected number of numeric/missing tokens, and any -anchor miss or token shortfall fails the run. Requires pypdf -(pip install pypdf); everything else is stdlib. +UKMOD variable names (bsauc_s, tin00_s, ...) and band labels. Anchors +match WHOLE TOKENS, never substrings, and a numeric anchor must sit at +the start of its line — the first build matched substrings of flattened +text, so Table 4.7's bare '2' anchor matched the trailing digit of +'1352' and shifted five quintile rows onto their neighbours' values. +Values come from the anchor's own line, continuing onto later lines only +while those hold nothing but values (Table 4.6 wraps two rows). Any +anchor miss or token shortfall fails the run, and structural_invariants() +checks the table identities that spot-checks cannot see. + +pypdf is imported lazily (see _pdf_reader) so the parsing helpers and the +invariants stay importable — and CI-testable — without it; everything +else is stdlib. Value semantics: - caseloads are THOUSANDS -> raw units (families unless noted:

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