diff --git a/.gitattributes b/.gitattributes index c05cd55d..ed51b616 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,5 +33,10 @@ data/external/psid_modern_job_context_raw_extraction_specs_v1.json text eol=lf data/external/snapshots/** binary data/external/snapshots/covered_earnings_methodology_capture1/** binary +# Baseline input captures: SHA-256-pinned source bytes, never normalized +data/external/tr2026/sources/** binary +data/external/cbo2026/sources/** binary +data/external/*.source.html.gz binary + # The depletion-cut sources are byte-pinned to their fetch records (SHA-256). docs/analysis/pe_us_depletion_cut_20261001/sources/** binary diff --git a/data/external/cbo2026/cbo2026_demographics.json b/data/external/cbo2026/cbo2026_demographics.json new file mode 100644 index 00000000..c6d82908 --- /dev/null +++ b/data/external/cbo2026/cbo2026_demographics.json @@ -0,0 +1,483 @@ +{ + "asfr": { + "2021": {"all": [0.0, 2.1, 4.88, 9.79, 19.24, 33.9, 45.46, 53.6, 62.55, 69.6, 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100700.0, "total_workers_thousands": 189400.0}, + "2082": {"covered_earnings_trillions": 99.264, "female_workers_thousands": 88400.0, "male_workers_thousands": 100600.0, "total_workers_thousands": 188900.0}, + "2083": {"covered_earnings_trillions": 102.76, "female_workers_thousands": 88100.0, "male_workers_thousands": 100400.0, "total_workers_thousands": 188500.0}, + "2084": {"covered_earnings_trillions": 106.379, "female_workers_thousands": 87800.0, "male_workers_thousands": 100100.0, "total_workers_thousands": 187900.0}, + "2085": {"covered_earnings_trillions": 110.126, "female_workers_thousands": 87600.0, "male_workers_thousands": 99900.0, "total_workers_thousands": 187500.0}, + "2086": {"covered_earnings_trillions": 114.004, "female_workers_thousands": 87300.0, "male_workers_thousands": 99700.0, "total_workers_thousands": 187000.0}, + "2087": {"covered_earnings_trillions": 118.02, "female_workers_thousands": 87000.0, "male_workers_thousands": 99500.0, "total_workers_thousands": 186500.0}, + "2088": {"covered_earnings_trillions": 122.176, "female_workers_thousands": 86800.0, "male_workers_thousands": 99300.0, "total_workers_thousands": 186000.0}, + "2089": {"covered_earnings_trillions": 126.479, "female_workers_thousands": 86600.0, "male_workers_thousands": 99100.0, "total_workers_thousands": 185700.0}, + "2090": {"covered_earnings_trillions": 130.934, "female_workers_thousands": 86300.0, "male_workers_thousands": 98900.0, "total_workers_thousands": 185200.0}, + "2091": {"covered_earnings_trillions": 135.545, "female_workers_thousands": 86100.0, "male_workers_thousands": 98700.0, "total_workers_thousands": 184800.0}, + "2092": {"covered_earnings_trillions": 140.319, "female_workers_thousands": 85900.0, "male_workers_thousands": 98500.0, "total_workers_thousands": 184400.0}, + "2093": {"covered_earnings_trillions": 145.261, "female_workers_thousands": 85600.0, "male_workers_thousands": 98400.0, "total_workers_thousands": 184000.0}, + "2094": {"covered_earnings_trillions": 150.378, "female_workers_thousands": 85400.0, "male_workers_thousands": 98200.0, "total_workers_thousands": 183600.0}, + "2095": {"covered_earnings_trillions": 155.674, "female_workers_thousands": 85100.0, "male_workers_thousands": 97900.0, "total_workers_thousands": 183100.0}, + "2096": {"covered_earnings_trillions": 161.157, "female_workers_thousands": 84900.0, "male_workers_thousands": 97700.0, "total_workers_thousands": 182600.0}, + "2097": {"covered_earnings_trillions": 166.833, "female_workers_thousands": 84700.0, "male_workers_thousands": 97600.0, "total_workers_thousands": 182200.0}, + "2098": {"covered_earnings_trillions": 172.709, "female_workers_thousands": 84400.0, "male_workers_thousands": 97400.0, "total_workers_thousands": 181800.0}, + "2099": {"covered_earnings_trillions": 178.791, "female_workers_thousands": 84200.0, "male_workers_thousands": 97100.0, "total_workers_thousands": 181300.0}, + "2100": {"covered_earnings_trillions": 185.089, "female_workers_thousands": 83900.0, "male_workers_thousands": 97000.0, "total_workers_thousands": 180900.0} + }, + "long_term": { + "1996": {"cpiu_growth_percent": 2.936, "cpiu_index_1982_1984_100": 156.8, "real_earnings_growth_percent": 1.885}, + "1997": {"cpiu_growth_percent": 2.337, "cpiu_index_1982_1984_100": 160.5, "real_earnings_growth_percent": 2.608}, + "1998": {"cpiu_growth_percent": 1.547, "cpiu_index_1982_1984_100": 163.0, "real_earnings_growth_percent": 4.536}, + "1999": {"cpiu_growth_percent": 2.193, "cpiu_index_1982_1984_100": 166.5, "real_earnings_growth_percent": 2.782}, + "2000": {"cpiu_growth_percent": 3.366, "cpiu_index_1982_1984_100": 172.10000000000002, "real_earnings_growth_percent": 2.91}, + "2001": {"cpiu_growth_percent": 2.816, "cpiu_index_1982_1984_100": 177.0, "real_earnings_growth_percent": 1.19}, + "2002": {"cpiu_growth_percent": 1.595, "cpiu_index_1982_1984_100": 179.8, "real_earnings_growth_percent": 1.097}, + "2003": {"cpiu_growth_percent": 2.297, "cpiu_index_1982_1984_100": 184.0, "real_earnings_growth_percent": 0.59}, + "2004": {"cpiu_growth_percent": 2.667, "cpiu_index_1982_1984_100": 188.9, "real_earnings_growth_percent": 1.977}, + "2005": {"cpiu_growth_percent": 3.365, "cpiu_index_1982_1984_100": 195.2, "real_earnings_growth_percent": -0.614}, + "2006": {"cpiu_growth_percent": 3.222, "cpiu_index_1982_1984_100": 201.5, "real_earnings_growth_percent": 1.599}, + "2007": {"cpiu_growth_percent": 2.87, "cpiu_index_1982_1984_100": 207.29999999999998, "real_earnings_growth_percent": 0.201}, + "2008": {"cpiu_growth_percent": 3.814, "cpiu_index_1982_1984_100": 215.20000000000002, "real_earnings_growth_percent": -1.937}, + "2009": {"cpiu_growth_percent": -0.32, "cpiu_index_1982_1984_100": 214.5, "real_earnings_growth_percent": 0.744}, + "2010": {"cpiu_growth_percent": 1.636, "cpiu_index_1982_1984_100": 218.00000000000003, "real_earnings_growth_percent": 2.854}, + "2011": {"cpiu_growth_percent": 3.139, "cpiu_index_1982_1984_100": 224.9, "real_earnings_growth_percent": 0.371}, + "2012": {"cpiu_growth_percent": 2.073, "cpiu_index_1982_1984_100": 229.5, "real_earnings_growth_percent": 1.076}, + "2013": {"cpiu_growth_percent": 1.465, "cpiu_index_1982_1984_100": 232.9, "real_earnings_growth_percent": 0.225}, + "2014": {"cpiu_growth_percent": 1.615, "cpiu_index_1982_1984_100": 236.7, "real_earnings_growth_percent": 1.165}, + "2015": {"cpiu_growth_percent": 0.121, "cpiu_index_1982_1984_100": 237.0, "real_earnings_growth_percent": 2.414}, + "2016": {"cpiu_growth_percent": 1.267, "cpiu_index_1982_1984_100": 240.0, "real_earnings_growth_percent": -0.098}, + "2017": {"cpiu_growth_percent": 2.131, "cpiu_index_1982_1984_100": 245.1, "real_earnings_growth_percent": 1.329}, + "2018": {"cpiu_growth_percent": 2.438, "cpiu_index_1982_1984_100": 250.99999999999997, "real_earnings_growth_percent": 0.694}, + "2019": {"cpiu_growth_percent": 1.813, "cpiu_index_1982_1984_100": 255.6, "real_earnings_growth_percent": 1.679}, + "2020": {"cpiu_growth_percent": 1.252, "cpiu_index_1982_1984_100": 258.8, "real_earnings_growth_percent": 6.721}, + "2021": {"cpiu_growth_percent": 4.679, "cpiu_index_1982_1984_100": 270.90000000000003, "real_earnings_growth_percent": 0.907}, + "2022": {"cpiu_growth_percent": 7.992, "cpiu_index_1982_1984_100": 292.6, "real_earnings_growth_percent": -4.974}, + "2023": {"cpiu_growth_percent": 4.127, "cpiu_index_1982_1984_100": 304.7, "real_earnings_growth_percent": -0.52}, + "2024": {"cpiu_growth_percent": 2.951, "cpiu_index_1982_1984_100": 313.6, "real_earnings_growth_percent": 1.126}, + "2025": {"cpiu_growth_percent": 2.767, "cpiu_index_1982_1984_100": 322.3, "real_earnings_growth_percent": 1.494}, + "2026": {"cpiu_growth_percent": 2.924, "cpiu_index_1982_1984_100": 331.8, "real_earnings_growth_percent": 1.127}, + "2027": {"cpiu_growth_percent": 2.547, "cpiu_index_1982_1984_100": 340.2, "real_earnings_growth_percent": 1.514}, + "2028": {"cpiu_growth_percent": 2.361, "cpiu_index_1982_1984_100": 348.20000000000005, "real_earnings_growth_percent": 1.516}, + "2029": {"cpiu_growth_percent": 2.288, "cpiu_index_1982_1984_100": 356.2, "real_earnings_growth_percent": 1.251}, + "2030": {"cpiu_growth_percent": 2.263, "cpiu_index_1982_1984_100": 364.29999999999995, "real_earnings_growth_percent": 1.104}, + "2031": {"cpiu_growth_percent": 2.258, "cpiu_index_1982_1984_100": 372.5, "real_earnings_growth_percent": 1.11}, + "2032": {"cpiu_growth_percent": 2.257, "cpiu_index_1982_1984_100": 380.90000000000003, "real_earnings_growth_percent": 1.07}, + "2033": {"cpiu_growth_percent": 2.256, "cpiu_index_1982_1984_100": 389.5, "real_earnings_growth_percent": 1.054}, + "2034": {"cpiu_growth_percent": 2.256, "cpiu_index_1982_1984_100": 398.3, "real_earnings_growth_percent": 1.063}, + "2035": {"cpiu_growth_percent": 2.257, "cpiu_index_1982_1984_100": 407.30000000000007, "real_earnings_growth_percent": 1.073}, + "2036": {"cpiu_growth_percent": 2.257, "cpiu_index_1982_1984_100": 416.5, "real_earnings_growth_percent": 1.089}, + "2037": {"cpiu_growth_percent": 2.258, "cpiu_index_1982_1984_100": 425.90000000000003, "real_earnings_growth_percent": 1.1}, + "2038": {"cpiu_growth_percent": 2.258, "cpiu_index_1982_1984_100": 435.50000000000006, "real_earnings_growth_percent": 1.104}, + "2039": {"cpiu_growth_percent": 2.259, "cpiu_index_1982_1984_100": 445.4, "real_earnings_growth_percent": 1.1}, + "2040": {"cpiu_growth_percent": 2.26, "cpiu_index_1982_1984_100": 455.40000000000003, "real_earnings_growth_percent": 1.141}, + "2041": {"cpiu_growth_percent": 2.26, "cpiu_index_1982_1984_100": 465.7, "real_earnings_growth_percent": 1.137}, + "2042": {"cpiu_growth_percent": 2.261, "cpiu_index_1982_1984_100": 476.3, "real_earnings_growth_percent": 1.115}, + "2043": {"cpiu_growth_percent": 2.262, "cpiu_index_1982_1984_100": 487.0, "real_earnings_growth_percent": 1.114}, + "2044": {"cpiu_growth_percent": 2.263, "cpiu_index_1982_1984_100": 498.09999999999997, "real_earnings_growth_percent": 1.116}, + "2045": {"cpiu_growth_percent": 2.264, "cpiu_index_1982_1984_100": 509.3, "real_earnings_growth_percent": 1.089}, + "2046": {"cpiu_growth_percent": 2.265, "cpiu_index_1982_1984_100": 520.9, "real_earnings_growth_percent": 1.067}, + "2047": {"cpiu_growth_percent": 2.265, "cpiu_index_1982_1984_100": 532.7, "real_earnings_growth_percent": 1.06}, + "2048": {"cpiu_growth_percent": 2.266, "cpiu_index_1982_1984_100": 544.8000000000001, "real_earnings_growth_percent": 1.054}, + "2049": {"cpiu_growth_percent": 2.267, "cpiu_index_1982_1984_100": 557.1, "real_earnings_growth_percent": 1.055}, + "2050": {"cpiu_growth_percent": 2.267, "cpiu_index_1982_1984_100": 569.7, "real_earnings_growth_percent": 1.052}, + "2051": {"cpiu_growth_percent": 2.268, "cpiu_index_1982_1984_100": 582.7, "real_earnings_growth_percent": 1.053}, + "2052": {"cpiu_growth_percent": 2.269, "cpiu_index_1982_1984_100": 595.9, "real_earnings_growth_percent": 1.055}, + "2053": {"cpiu_growth_percent": 2.269, "cpiu_index_1982_1984_100": 609.4, "real_earnings_growth_percent": 1.056}, + "2054": {"cpiu_growth_percent": 2.27, "cpiu_index_1982_1984_100": 623.3, "real_earnings_growth_percent": 1.058}, + "2055": {"cpiu_growth_percent": 2.27, "cpiu_index_1982_1984_100": 637.4, "real_earnings_growth_percent": 1.065}, + "2056": {"cpiu_growth_percent": 2.271, "cpiu_index_1982_1984_100": 651.9, "real_earnings_growth_percent": 1.071} + }, + "schema_version": "cbo2026_economics.v1", + "source_keys": ["long_term_economics", "ten_year_economics", "covered_earnings", "actual_awi_anchor"], + "ten_year_cpiu_index_1982_1984_100": {"2023": 304.704, "2024": 313.697, "2025": 322.378, "2026": 331.807, "2027": 340.26, "2028": 348.295, "2029": 356.264, "2030": 364.328, "2031": 372.557, "2032": 380.967, "2033": 389.564, "2034": 398.355, "2035": 407.346, "2036": 416.542} +} diff --git a/data/external/cbo2026/provenance.md b/data/external/cbo2026/provenance.md new file mode 100644 index 00000000..17014394 --- /dev/null +++ b/data/external/cbo2026/provenance.md @@ -0,0 +1,146 @@ +# CBO 2026 baseline input provenance + +These are baseline demographic and economic inputs. No Social Security +cost, income, balance or policy-option table was parsed. Source captures +were provided read-only under `followup/inputs/baseline-inputs/`, captured +2026-10-01. This builder fetched nothing from the web. + +## Captures and identities + +`sources.json` records original URLs, captured relative paths, locators, +byte counts, full SHA-256, computed SHA-1/base32, archive attribution and +retained compressed-source hashes. Every CBO file came from Internet +Archive raw `id_` captures after direct requests served a DataDome CAPTCHA. +The captured research lane recorded the CDX digests; this builder computed +and matched those digests from the provided bytes. It did not re-query +CDX or assume that the live files still match the captured vintages. + +| File | Wayback capture ID | Captured CDX SHA-1/base32 | +| --- | --- | --- | +| `57059-2026-01-Demographic-Projections.zip` | `20260211074115` | `AUU73LM3P3IH4ITJSNMHQM7T7UEHJCDD` | +| `57054-2026-02-LTBO-econ.xlsx` | `20260726122828` | `OWABBCNJ5O2UJGAZDVBAK2SLWSCHOFLK` | +| `51135-2026-02-Economic-Projections.xlsx` | `20260802034021` | `SLIHKFJTIHK62CO3YN5CYBRU4PIQCB4D` | +| `62556-2026-Additional-Info.xlsx` | `20260919143154` | `DTGXZD5RVUA4T4SH3HTVHBHFOFS3DR3I` | + +The two demographic CSVs and `61879-Demographic-Projections.xlsx` were +checked byte-for-byte against their members of the verified archive. +Only these input rows are transcribed: + +- `fertilityRates_byYearAgePlace.csv`: single ages 14-49, 2021-2099, all, + native-born and foreign-born women; births per 1,000 females. JSON keeps + printed units; the accessor divides by 1,000 and returns births per + woman. It does not convert rates to Bernoulli probabilities. +- `mortalityRates_byYearAgeSex.csv`: ages 0-119, both sexes, 2021-2099; + deaths per 1,000 people. The accessor divides by 1,000 to obtain q(x), + following the empirical life-expectancy check below. +- `61879-Demographic-Projections.xlsx`, `1. Population and growth`: row 8 + years, row 39 TFR, row 41 period life expectancy at birth, row 42 at 65; + 2026-2099. This workbook remains external with a pinned hash. +- `57054-2026-02-LTBO-econ.xlsx`, `1. Econ Vars_Annual Rates`: row 8 + years, row 31 growth of real earnings per worker, row 40 CPI-U growth; + `3. Econ Vars_Annual Levels`: row 7 years, row 13 CPI-U (1982-84=1). + JSON converts the latter to 1982-84=100. Coverage is 1996-2056. +- `51135-2026-02-Economic-Projections.xlsx`, `2. Calendar Year`: row 7 + years, row 53 CPI-U levels (1982-84=100); 2023-2036. +- `62556-2026-Additional-Info.xlsx`, `1. Covered Workers`: numeric year + rows, male/female/total workers in thousands; `2. Covered and Taxable + Earnings`: numeric year rows, covered earnings column B in trillions + of dollars; 2026-2100. Beneficiary, interest-rate and taxable-payroll + columns are not transcribed; workbook footnotes are not displayed. + The workbook's sheets are `Contents`, `1. Covered Workers`, + `2. Covered and Taxable Earnings`, `3. OASDI Beneficiaries` and + `4. Average Interest Rates`; only sheets 1 and 2 are read. It holds + no outlay, revenue, cost-rate or balance table (CBO's main + 62556 projections workbook, which does, was not captured). +- The AWI anchor is CY2024 historical AWI, captured TR2026 single-year + VI.G1 (`tr2026/lr6g1.html`), SHA-256 + `2e0214278e0b2271616093af02280277f6e363d112c47a90b49fe7168e795e75`. + Nonempty cell order is year, adjusted CPI, AWI. An AWI header check and + a differential test against the separate TR2026 accessor guard the + column selection. The SSA capture lane used User-Agent `Wget/1.21.4` + after a browser User-Agent returned 403. HTML has per-request Akamai + markup; exact captured bytes and parsed values are pinned, without + assuming future fetches will hash identically. + +Stable raw CSV/XLSX/PDF captures are retained under `sources/` only when +the payload is below 2 MB and deterministic gzip is below 500 KB, the +repository's stricter precommit limit. Compression uses +`GzipFile(filename='', mtime=0)`; hashes of payload and compressed bytes +are recorded. Larger inputs and HTML carry URL, captured path and hash +only. Rebuilding external inputs requires the captures via `--inputs`. + +## Mortality interpretation + +CBO labels mortality "deaths per 1,000 people" without an explicit q/m +definition. Both interpretations were checked against every published +2026-2099 period life expectancy at birth and 65: + +1. q(x) = printed rate / 1,000. +2. m(x) = printed rate / 1,000, converted using q = m/(1 + m/2). + +For each sex, a period life table uses uniform deaths within each year, +including infancy (`f0=0.5`). Arithmetic mean male/female life expectancy +reproduces the combined-sex summary. This averaging convention and q +interpretation are empirical findings, not quoted CBO definitions. Every +source row has q(119)=1 in the q interpretation, with terminal +person-years l(119)/2. + +`transcription_check.json` records all published/recomputed values and +both interpretations' errors. Maximum q error is 0.000703364 year; +minimum m error is 0.241771105 year. q passes every summary within +0.0011 year; every m interpretation fails. The tolerance allows rounded +CSV rates and three-decimal published life expectancy. Tests compare +forward life-table columns with independent backward recursion as well. + +## Fertility check + +Summed all-women ASFR over ages 14-49, divided by 1,000, matches each +published TFR within 0.0023 births per woman. The observed maximum error +is 0.0005. The conservative rounding bound is 36 age rates at half of +0.1 birth per 1,000 plus half of 0.001 in the summary TFR. Native-born +and foreign-born schedules remain published schedules; no unweighted +averaging creates the all-women schedule. Before 2026, TFR is explicitly +derived from the all-women ASFR sum. + +## Builder defaults awaiting ratification + +The module docstring and per-value metadata name these model-builder +defaults, each awaiting ratification: + +- `ten_year_cpiu_then_long_term_growth`: ten-year CPI-U levels through + 2036, growth from adjacent levels; 2023 uses the 2022 LT denominator. + After 2036 compound published LT growth on the final ten-year level. +- `hold_2056_economic_growth`: final LT CPI-U and real earnings growth + held through 2100, with CPI levels compounded continuously. No + demographic extension after 2099 is supplied. +- `cpiw_equals_cpiu_growth` and `cola_equals_annual_cpiu_growth`: annual + CPI-U growth proxies for series CBO does not publish. This is not a + statutory Q3-to-Q3 COLA computation. Derived COLA covers 2024-2100. +- `awi_bridge_2025_2026_real_earnings_times_cpiu`: compound the actual + 2024 AWI with `(1 + real_growth/100) * (1 + cpiu_growth/100)` in 2025 + and 2026. Both bridge years are needed because covered inputs begin + in 2026 and have no 2025 denominator. +- `awi_growth_of_covered_earnings_per_worker`: from 2027 apply growth in + covered earnings divided by covered workers to the 2026 bridge. + 1e12 dollars/1e3 workers gives nominal dollars per worker; the unit + factor cancels in growth ratios. Covered inputs continue to 2100; + AWI needs no post-2056 economic extrapolation. Every analog value is + tagged derived; only the 2024 anchor is actual. +- `tfr_sum_asfr_before_2026`: use all-women ASFR sums in 2021-2025, + when no summary TFR is published; published TFR thereafter. + +## Deterministic reproduction + +From the repository root: + +```sh +PYTHONPATH=src /Users/maxghenis/PolicyEngine/microcosm-dynamics/.venv/bin/python scripts/extract_cbo2026_parameters.py --inputs /path/to/baseline-inputs --check +``` + +The builder re-parses every pinned source, verifies stated CDX digests +and demographic archive membership, and recomputes mortality/fertility +checks. `--check` compares every JSON byte and retained compressed hash, +and writes nothing. Without `--check`, it rebuilds JSON and eligible raw +captures. The accessor separately verifies `FILE_SHA256` for every +consumed JSON on first load. A changed source requires an intentional +rebuild and repin; stale or outside-coverage data are refused. diff --git a/data/external/cbo2026/sources.json b/data/external/cbo2026/sources.json new file mode 100644 index 00000000..90fcd078 --- /dev/null +++ b/data/external/cbo2026/sources.json @@ -0,0 +1,16 @@ +{ + "capture_method": "CBO raw Internet Archive id_ captures; original cbo.gov served DataDome CAPTCHA; no live download attempted by this builder. Source identities from captured research summary; computed SHA-1/base32 matches every stated CDX digest.", + "compression": "gzip mtime=0; hashes refer to uncompressed bytes", + "retention": "Raw sources retained only if payload <2MB and deterministic gzip <500KB (repository precommit limit); otherwise URL/hash only", + "schema_version": "cbo2026_sources.v1", + "sources": { + "actual_awi_anchor": {"archive_source_key": null, "bytes": 226764, "capture_date": "2026-10-01", "captured_relative_path": "tr2026/lr6g1.html", "cdx_digest_matches": null, "cdx_sha1_b32": null, "committed_file": null, "compressed_sha256": null, "computed_sha1_b32": "JOIWG64Q3FURI4SHN2BQB36EPOCMLJSQ", "locator": "TR2026 Table VI.G1, Historical data, CY2024 AWI column", "original_url": "https://www.ssa.gov/oact/TR/2026/lr6g1.html", "sha256": "2e0214278e0b2271616093af02280277f6e363d112c47a90b49fe7168e795e75", "wayback_timestamp": null}, + "covered_earnings": {"archive_source_key": null, "bytes": 36896, "capture_date": "2026-10-01", "captured_relative_path": "cbo/62556-2026-Additional-Info.xlsx", "cdx_digest_matches": true, "cdx_sha1_b32": "DTGXZD5RVUA4T4SH3HTVHBHFOFS3DR3I", "committed_file": "sources/62556-2026-Additional-Info.xlsx.gz", "compressed_sha256": "27d44ad6297248558c2034754f3b08e1a9752a7cb22e2255f8617fa7454740fc", "computed_sha1_b32": "DTGXZD5RVUA4T4SH3HTVHBHFOFS3DR3I", "locator": "'1. Covered Workers': numeric year rows,male/female/total (thousands); '2. Covered and Taxable Earnings': numeric year rows,covered earnings column B (trillions of dollars)", "original_url": "https://www.cbo.gov/system/files/2026-09/62556-2026-Additional-Info.xlsx", "sha256": "7a7247651f3ae6820c9263a16f11b9cc9cf24cd7dcb9e18bb76e44711924d73b", "wayback_timestamp": "20260919143154"}, + "demographic_archive": {"archive_source_key": null, "bytes": 4904312, "capture_date": "2026-10-01", "captured_relative_path": "cbo/57059-2026-01-Demographic-Projections.zip", "cdx_digest_matches": true, "cdx_sha1_b32": "AUU73LM3P3IH4ITJSNMHQM7T7UEHJCDD", "committed_file": null, "compressed_sha256": null, "computed_sha1_b32": "AUU73LM3P3IH4ITJSNMHQM7T7UEHJCDD", "locator": "Archive containing the demographic CSV files and workbook", "original_url": "https://www.cbo.gov/system/files/2026-01/57059-2026-01-Demographic-Projections.zip", "sha256": "083ea91701c1af901cd378211a34c8c8a941c8833c2507564af89551d63392bf", "wayback_timestamp": "20260211074115"}, + "demographic_workbook": {"archive_source_key": "demographic_archive", "bytes": 2237636, "capture_date": "2026-10-01", "captured_relative_path": "cbo/extracted/57059-2026-01-Demographic-Projections/61879-Demographic-Projections.xlsx", "cdx_digest_matches": null, "cdx_sha1_b32": null, "committed_file": null, "compressed_sha256": null, "computed_sha1_b32": "ZNHSZNBNLIBOBZECU27AQRN4P53LOU5O", "locator": "61879-Demographic-Projections.xlsx: '1. Population and growth'; row 8 years; r39 TFR; r41 life expectancy at birth; r42 at 65", "original_url": "https://www.cbo.gov/system/files/2026-01/57059-2026-01-Demographic-Projections.zip", "sha256": "aca3322e0a74fee39e669d953201075887da13e5e3792e08b8b6f7d1f86d1006", "wayback_timestamp": null}, + "fertility": {"archive_source_key": "demographic_archive", "bytes": 206838, "capture_date": "2026-10-01", "captured_relative_path": "cbo/extracted/57059-2026-01-Demographic-Projections/CSV Files/fertilityRates_byYearAgePlace.csv", "cdx_digest_matches": null, "cdx_sha1_b32": null, "committed_file": "sources/fertilityRates_byYearAgePlace.csv.gz", "compressed_sha256": "903fff07de1aecea2dee059f8246d8230f0a81dbba9f95a05aff473bd08406f7", "computed_sha1_b32": "MGZI7VZYHYFWFEXSXCJESRUFGWKSZDEW", "locator": "CSV Files/fertilityRates_byYearAgePlace.csv: year,age,place_of_birth,births_per_1000_females", "original_url": "https://www.cbo.gov/system/files/2026-01/57059-2026-01-Demographic-Projections.zip", "sha256": "8ed8615ed7561d6c08c3005dc516b8dd78c16072020e8c106a5738a5563b0f1c", "wayback_timestamp": null}, + "long_term_economics": {"archive_source_key": null, "bytes": 572138, "capture_date": "2026-10-01", "captured_relative_path": "cbo/57054-2026-02-LTBO-econ.xlsx", "cdx_digest_matches": true, "cdx_sha1_b32": "OWABBCNJ5O2UJGAZDVBAK2SLWSCHOFLK", "committed_file": "sources/57054-2026-02-LTBO-econ.xlsx.gz", "compressed_sha256": "13caae990b76e28a36bf5c6fde34ad6e9b014ee8752ff013ae38b283c0391021", "computed_sha1_b32": "OWABBCNJ5O2UJGAZDVBAK2SLWSCHOFLK", "locator": "'1. Econ Vars_Annual Rates': r8 years,r31 real earnings growth,r40 CPI-U growth; '3. Econ Vars_Annual Levels': r7 years,r13 CPI-U", "original_url": "https://www.cbo.gov/system/files/2026-02/57054-2026-02-LTBO-econ.xlsx", "sha256": "6f6e66d7e2aaf187e0d13aa90767a39e682a6a5ab2be87b0cc2221b7370e6929", "wayback_timestamp": "20260726122828"}, + "mortality": {"archive_source_key": "demographic_archive", "bytes": 410627, "capture_date": "2026-10-01", "captured_relative_path": "cbo/extracted/57059-2026-01-Demographic-Projections/CSV Files/mortalityRates_byYearAgeSex.csv", "cdx_digest_matches": null, "cdx_sha1_b32": null, "committed_file": "sources/mortalityRates_byYearAgeSex.csv.gz", "compressed_sha256": "58d5114d019ff91fa0ecf4938a523af4023f8ed2131feb3e0726d4c9fbeab61f", "computed_sha1_b32": "R3756O6XYAYQGFEUGRPJOWZLIQJ3LBTM", "locator": "CSV Files/mortalityRates_byYearAgeSex.csv: year,age,sex,deaths_per_1000_people", "original_url": "https://www.cbo.gov/system/files/2026-01/57059-2026-01-Demographic-Projections.zip", "sha256": "378defc1d6eee7b6870cc7ddaf534dd782d51bf3f686a39a8fd7cd7cba2beb63", "wayback_timestamp": null}, + "ten_year_economics": {"archive_source_key": null, "bytes": 1503579, "capture_date": "2026-10-01", "captured_relative_path": "cbo/51135-2026-02-Economic-Projections.xlsx", "cdx_digest_matches": true, "cdx_sha1_b32": "SLIHKFJTIHK62CO3YN5CYBRU4PIQCB4D", "committed_file": null, "compressed_sha256": null, "computed_sha1_b32": "SLIHKFJTIHK62CO3YN5CYBRU4PIQCB4D", "locator": "'2. Calendar Year': r7 years; r53 CPI-U (1982-84=100)", "original_url": "https://www.cbo.gov/system/files/2026-02/51135-2026-02-Economic-Projections.xlsx", "sha256": "ae8f4920702fabf8fb3136bc94a42c53466cff5e890dec22396d8dc49dc2f776", "wayback_timestamp": "20260802034021"} + } +} diff --git a/data/external/cbo2026/sources/57054-2026-02-LTBO-econ.xlsx.gz b/data/external/cbo2026/sources/57054-2026-02-LTBO-econ.xlsx.gz new file mode 100644 index 00000000..827c4711 Binary files /dev/null and b/data/external/cbo2026/sources/57054-2026-02-LTBO-econ.xlsx.gz differ diff --git a/data/external/cbo2026/sources/62556-2026-Additional-Info.xlsx.gz b/data/external/cbo2026/sources/62556-2026-Additional-Info.xlsx.gz new file mode 100644 index 00000000..cb6c9356 Binary files /dev/null and b/data/external/cbo2026/sources/62556-2026-Additional-Info.xlsx.gz differ diff --git a/data/external/cbo2026/sources/fertilityRates_byYearAgePlace.csv.gz b/data/external/cbo2026/sources/fertilityRates_byYearAgePlace.csv.gz new file mode 100644 index 00000000..1ee79bd9 Binary files /dev/null and b/data/external/cbo2026/sources/fertilityRates_byYearAgePlace.csv.gz differ diff --git a/data/external/cbo2026/sources/mortalityRates_byYearAgeSex.csv.gz b/data/external/cbo2026/sources/mortalityRates_byYearAgeSex.csv.gz new file mode 100644 index 00000000..0be812ac Binary files /dev/null and b/data/external/cbo2026/sources/mortalityRates_byYearAgeSex.csv.gz differ diff --git a/data/external/cbo2026/transcription_check.json b/data/external/cbo2026/transcription_check.json new file mode 100644 index 00000000..b504756d --- /dev/null +++ b/data/external/cbo2026/transcription_check.json @@ -0,0 +1,91 @@ +{ + "all_passed": true, + "checks": [{"id": "q_range", "passed": true}, {"id": "q119_closed", "passed": true}, {"id": "q_reproduces_published_le", "passed": true}, {"id": "m_interpretation_rejected", "passed": true}, {"id": "asfr_sums_to_published_tfr", "max_error": 0.0004999999999999449, "passed": true, "tolerance": 0.0023}, {"id": "covered_inputs_positive", "passed": true}], + "mortality_interpretation": { + "inference": "Empirical interpretation, not an explicit CBO units definition. q reproduces every summary; m does not.", + "m_max_absolute_error_years": 0.2730957516606054, + "m_min_absolute_error_years": 0.24177110533041457, + "method": "Rates/1000 as q versus central m converted with q=m/(1+m/2); uniform deaths including age 0 (f0=.5); published total LE reproduced by arithmetic mean of male/female period LE at birth and 65, every 2026-2099 year", + "q_max_absolute_error_years": 0.0007033631681423458, + "rows": [ + {"interpretations": {"m": {"0": {"error": 0.24254024717347988, "published": 78.975, "recomputed": 79.21754024717347}, "65": {"error": 0.2724917702935379, "published": 19.788, "recomputed": 20.060491770293538}}, "q": {"0": {"error": 0.000454590074269845, "published": 78.975, "recomputed": 78.97545459007426}, "65": {"error": -0.00044264109557801135, "published": 19.788, "recomputed": 19.787557358904422}}}, "year": 2026}, + {"interpretations": {"m": {"0": {"error": 0.2420677993720517, "published": 78.996, "recomputed": 79.23806779937205}, "65": {"error": 0.2730957516606054, "published": 19.844, "recomputed": 20.117095751660607}}, "q": {"0": {"error": 0.000233804427395512, "published": 78.996, "recomputed": 78.99623380442739}, "65": {"error": 0.0003749975301623465, "published": 19.844, "recomputed": 19.844374997530164}}}, "year": 2027}, + {"interpretations": {"m": {"0": {"error": 0.24177110533041457, "published": 79.12, "recomputed": 79.36177110533042}, "65": {"error": 0.27258652439527253, "published": 19.917, "recomputed": 20.189586524395274}}, "q": {"0": {"error": -0.00011010654515075657, "published": 79.12, "recomputed": 79.11988989345485}, "65": {"error": 3.627488971602588e-05, "published": 19.917, "recomputed": 19.917036274889718}}}, "year": 2028}, + {"interpretations": {"m": {"0": {"error": 0.24194950413587435, "published": 79.243, "recomputed": 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entitlement, 1998–2025", + "supplement_year": 2026, + "provenance": { + "source": "SSA Annual Statistical Supplement 2026, Table 6.B5.1", + "source_url": "https://www.ssa.gov/policy/docs/statcomps/supplement/2026/6b.html", + "source_sha256": "1f9eceb2cfa45e181aa38ca7ce33b5babc77e8cb6d77fd1343650224a0a7d06a", + "committed_source": "data/external/ssa_supplement_2026_6b.source.html.gz", + "retrieved_utc": "2026-10-02T12:08:05Z", + "locator": "div#table6.b5.1; thead/tbody/tfoot", + "bytes": 177407, + "compression": "gzip mtime=0; SHA-256 is of decompressed captured bytes", + "fetch_method": "Supplied local captures; no network fetch by this builder. SSA baseline captures used User-Agent Wget/1.21.4; the supplement capture client is not recorded in the supplied retrieval marker.", + "captures": { + "2025": { + "source_url": "https://www.ssa.gov/policy/docs/statcomps/supplement/2025/6b.html", + "source_sha256": "1ba2e4f895da924286401189ab1144406640b58e56ce1b2e74f00f9caba405a2", + "committed_source": "data/external/ssa_supplement_2025_6b.source.html.gz", + "retrieved_utc": "2026-10-02T12:08:05Z", + "locator": "div#table6.b5.1; thead/tbody/tfoot", + "bytes": 192026, + "compression": "gzip mtime=0; SHA-256 is of decompressed captured bytes" + }, + "2026": { + "source_url": "https://www.ssa.gov/policy/docs/statcomps/supplement/2026/6b.html", + "source_sha256": "1f9eceb2cfa45e181aa38ca7ce33b5babc77e8cb6d77fd1343650224a0a7d06a", + "committed_source": "data/external/ssa_supplement_2026_6b.source.html.gz", + "retrieved_utc": "2026-10-02T12:08:05Z", + "locator": "div#table6.b5.1; thead/tbody/tfoot", + "bytes": 177407, + "compression": "gzip mtime=0; SHA-256 is of decompressed captured bytes" + } + }, + "selection": "2026 edition selected: twelve-column layout matches the 2023 schema; rows through 2025. 2025 edition also re-parsed and its hash recorded.", + "data_basis": "Social Security Administration, Master Beneficiary Record, 100 percent data.", + "table_notes": "NOTES: The statistics in this table differ from those in Table 6.B5 because awards are summarized here by the year in which entitlement began rather than by the year the award action was posted on the Master Beneficiary Record. The year of entitlement often precedes the year of action because of retroactive entitlements, delayed award actions, and other reasons. Because entitlements can be awarded retroactively, statistics for current and prior years are subject to revision with each annual update of this table. Because of differences in data sources and calculation methods, statistics reported in this table may differ from those reported by Actuarial Services. Totals do not necessarily equal the sum of rounded components. FRA = full retirement age; . . . = not applicable.", + "footnote_a_fra_schedule": "FRA is 65 for workers attaining age 65 before 2003. It increases in 2-month increments for workers attaining age 65 in each of the years 2003 through 2008. It is 66 for workers attaining age 65 in the years 2008 through 2019. It again increases in 2-month increments for workers attaining age 65 in the years 2020 through 2025. It is 67 for workers attaining age 65 in 2025 or later.", + "footnote_b_disability_conversion": "Disabled-worker benefit automatically converts to a retired-worker benefit in the month the worker attains FRA.", + "not_applicable_marker": ". . .", + "contact": "statistics@ssa.gov", + "consumer_note": "claiming.load_claim_age_reference(path) loads this schema and reference.row accesses all years. Legacy claiming.claim_age_distribution and claim_age_pmf retain their global 2022 cap; a new baseline must resolve against reference.years rather than those legacy convenience functions." + }, + "column_schema": { + "raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age65_at_fra", + "age65_after_fra", + "age66_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "collapsed_categories": [ + "age62", + "age63", + "age64", + "age65", + "age66", + "disability_conversion", + "age67_69", + "age70plus" + ], + "fra_at_note": "Exactly one at-FRA column per row; a subset of age65/age66, never an additional partition member.", + "disability_conversion_note": "Automatic conversion at FRA, not a voluntary claiming choice.", + "era_map": [ + { + "entitlement_years": "1998-2002", + "fra_regime": "FRA = 65 (attain-65 years <= 2002)", + "applicable_65": [ + "age65_at_fra", + "age65_after_fra" + ], + "applicable_66": [ + "age66_after_fra" + ], + "at_fra_column": "age65_at_fra", + "note": "FRA is exactly 65y0m; a 65-year-old is at FRA (spike) or just past it. No 65-before-FRA (nobody's FRA exceeds 65) and no 66-at/before-FRA (FRA has not reached 66)." + }, + { + "entitlement_years": "2003-2008", + "fra_regime": "FRA = 65y2m..66y0m (attain-65 years 2003-2008)", + "applicable_65": [ + "age65_before_fra", + "age65_at_fra", + "age65_after_fra" + ], + "applicable_66": [ + "age66_after_fra" + ], + "at_fra_column": "age65_at_fra", + "note": "FRA crosses 65-and-months, so age 65 splits three ways. 66 is still beyond FRA for every cohort (66-after-FRA only)." + }, + { + "entitlement_years": "2009-2020", + "fra_regime": "FRA = 66y0m (attain-65 years 2008-2019)", + "applicable_65": [ + "age65_before_fra" + ], + "applicable_66": [ + "age66_at_fra", + "age66_after_fra" + ], + "at_fra_column": "age66_at_fra", + "note": "FRA is 66y0m: age 65 is entirely before FRA; age 66 is at FRA (spike) or just past. No 65-at/after-FRA and no 66-before-FRA." + }, + { + "entitlement_years": "2021-2025", + "fra_regime": "FRA = 66y2m.. (attain-65 years 2020+)", + "applicable_65": [ + "age65_before_fra" + ], + "applicable_66": [ + "age66_before_fra", + "age66_at_fra", + "age66_after_fra" + ], + "at_fra_column": "age66_at_fra", + "note": "FRA crosses 66-and-months, so age 66 splits three ways; age 65 remains entirely before FRA." + } + ] + }, + "fra_schedule": { + "unit": "months", + "keyed_by": "year worker attains age 65", + "birth_year_equivalent": "attain_65_year - 65", + "schedule": [ + { + "attain_65_year_from": 1900, + "fra_months": 780 + }, + { + "attain_65_year_from": 2003, + "fra_months": 782 + }, + { + "attain_65_year_from": 2004, + "fra_months": 784 + }, + { + "attain_65_year_from": 2005, + "fra_months": 786 + }, + { + "attain_65_year_from": 2006, + "fra_months": 788 + }, + { + "attain_65_year_from": 2007, + "fra_months": 790 + }, + { + "attain_65_year_from": 2008, + "fra_months": 792 + }, + { + "attain_65_year_from": 2020, + "fra_months": 794 + }, + { + "attain_65_year_from": 2021, + "fra_months": 796 + }, 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"disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 99.9, + "residual": -0.1 + }, + "2016": { + "number_thousands": 1393, + "average_age": 64.5, + "published_total": 100.0, + "raw": { + "age62": 35.6, + "age63": 6.5, + "age64": 6.8, + "age65_before_fra": 9.9, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": null, + "age66_at_fra": 12.8, + "age66_after_fra": 2.9, + "disability_conversion": 15.8, + "age67_69": 4.5, + "age70plus": 5.2 + }, + "categories": { + "age62": 35.6, + "age63": 6.5, + "age64": 6.8, + "age65": 9.9, + "age66": 15.7, + "disability_conversion": 15.8, + "age67_69": 4.5, + "age70plus": 5.2 + }, + "fra_at": { + "share": 12.8, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 100.0, + "residual": 0.0 + }, + "2017": { + "number_thousands": 1449, + "average_age": 64.6, + "published_total": 100.0, + "raw": { + "age62": 32.7, + "age63": 6.3, + "age64": 6.8, + "age65_before_fra": 10.2, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": null, + "age66_at_fra": 13.7, + "age66_after_fra": 3.1, + "disability_conversion": 16.1, + "age67_69": 4.8, + "age70plus": 6.2 + }, + "categories": { + "age62": 32.7, + "age63": 6.3, + "age64": 6.8, + "age65": 10.2, + "age66": 16.8, + "disability_conversion": 16.1, + "age67_69": 4.8, + "age70plus": 6.2 + }, + "fra_at": { + "share": 13.7, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 99.9, + "residual": -0.1 + }, + "2018": { + "number_thousands": 1519, + "average_age": 64.7, + "published_total": 100.0, + "raw": { + "age62": 30.7, + "age63": 6.3, + "age64": 6.9, + "age65_before_fra": 10.2, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": null, + "age66_at_fra": 14.4, + "age66_after_fra": 3.4, + "disability_conversion": 16.1, + "age67_69": 5.3, + "age70plus": 6.6 + }, + "categories": { + "age62": 30.7, + "age63": 6.3, + "age64": 6.9, + "age65": 10.2, + "age66": 17.8, + "disability_conversion": 16.1, + "age67_69": 5.3, + "age70plus": 6.6 + }, + "fra_at": { + "share": 14.4, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 99.9, + "residual": -0.1 + }, + "2019": { + "number_thousands": 1563, + "average_age": 64.8, + "published_total": 100.0, + "raw": { + "age62": 28.8, + "age63": 6.2, + "age64": 7.1, + "age65_before_fra": 10.6, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": null, + "age66_at_fra": 14.6, + "age66_after_fra": 3.5, + "disability_conversion": 16.2, + "age67_69": 5.6, + "age70plus": 7.4 + }, + "categories": { + "age62": 28.8, + "age63": 6.2, + "age64": 7.1, + "age65": 10.6, + "age66": 18.1, + "disability_conversion": 16.2, + "age67_69": 5.6, + "age70plus": 7.4 + }, + "fra_at": { + "share": 14.6, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 100.0, + "residual": 0.0 + }, + "2020": { + "number_thousands": 1661, + "average_age": 65.0, + "published_total": 100.0, + "raw": { + "age62": 25.7, + "age63": 6.0, + "age64": 6.9, + "age65_before_fra": 10.8, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": null, + "age66_at_fra": 17.2, + "age66_after_fra": 4.1, + "disability_conversion": 15.9, + "age67_69": 5.8, + "age70plus": 7.6 + }, + "categories": { + "age62": 25.7, + "age63": 6.0, + "age64": 6.9, + "age65": 10.8, + "age66": 21.3, + "disability_conversion": 15.9, + "age67_69": 5.8, + "age70plus": 7.6 + }, + "fra_at": { + "share": 17.2, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 100.0, + "residual": 0.0 + }, + "2021": { + "number_thousands": 1604, + "average_age": 65.0, + "published_total": 100.0, + "raw": { + "age62": 25.9, + "age63": 6.4, + "age64": 7.2, + "age65_before_fra": 11.0, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": 1.0, + "age66_at_fra": 14.1, + "age66_after_fra": 4.9, + "disability_conversion": 13.8, + "age67_69": 6.8, + "age70plus": 9.0 + }, + "categories": { + "age62": 25.9, + "age63": 6.4, + "age64": 7.2, + "age65": 11.0, + "age66": 20.0, + "disability_conversion": 13.8, + "age67_69": 6.8, + "age70plus": 9.0 + }, + "fra_at": { + "share": 14.1, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 100.1, + "residual": 0.1 + }, + "2022": { + "number_thousands": 1706, + "average_age": 65.1, + "published_total": 100.0, + "raw": { + "age62": 24.3, + "age63": 6.6, + "age64": 7.3, + "age65_before_fra": 11.5, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": 1.9, + "age66_at_fra": 13.4, + "age66_after_fra": 4.8, + "disability_conversion": 13.0, + "age67_69": 7.6, + "age70plus": 9.7 + }, + "categories": { + "age62": 24.3, + "age63": 6.6, + "age64": 7.3, + "age65": 11.5, + "age66": 20.1, + "disability_conversion": 13.0, + "age67_69": 7.6, + "age70plus": 9.7 + }, + "fra_at": { + "share": 13.4, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 100.1, + "residual": 0.1 + }, + "2023": { + "number_thousands": 1788, + "average_age": 65.2, + "published_total": 100.0, + "raw": { + "age62": 24.0, + "age63": 6.6, + "age64": 7.2, + "age65_before_fra": 11.6, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": 2.8, + "age66_at_fra": 13.1, + "age66_after_fra": 4.1, + "disability_conversion": 12.5, + "age67_69": 8.1, + "age70plus": 9.9 + }, + "categories": { + "age62": 24.0, + "age63": 6.6, + "age64": 7.2, + "age65": 11.6, + "age66": 20.0, + "disability_conversion": 12.5, + "age67_69": 8.1, + "age70plus": 9.9 + }, + "fra_at": { + "share": 13.1, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 99.9, + "residual": -0.1 + }, + "2024": { + "number_thousands": 1847, + "average_age": 65.3, + "published_total": 100.0, + "raw": { + "age62": 23.1, + "age63": 6.4, + "age64": 6.9, + "age65_before_fra": 11.6, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": 3.7, + "age66_at_fra": 12.8, + "age66_after_fra": 3.7, + "disability_conversion": 12.2, + "age67_69": 9.7, + "age70plus": 10.0 + }, + "categories": { + "age62": 23.1, + "age63": 6.4, + "age64": 6.9, + "age65": 11.6, + "age66": 20.2, + "disability_conversion": 12.2, + "age67_69": 9.7, + "age70plus": 10.0 + }, + "fra_at": { + "share": 12.8, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 100.1, + "residual": 0.1 + }, + "2025": { + "number_thousands": 1797, + "average_age": 65.0, + "published_total": 100.0, + "raw": { + "age62": 23.8, + "age63": 6.9, + "age64": 7.3, + "age65_before_fra": 13.1, + "age65_at_fra": null, + "age65_after_fra": null, + "age66_before_fra": 4.9, + "age66_at_fra": 12.1, + "age66_after_fra": 2.7, + "disability_conversion": 12.4, + "age67_69": 9.7, + "age70plus": 7.0 + }, + "categories": { + "age62": 23.8, + "age63": 6.9, + "age64": 7.3, + "age65": 13.1, + "age66": 19.7, + "disability_conversion": 12.4, + "age67_69": 9.7, + "age70plus": 7.0 + }, + "fra_at": { + "share": 12.1, + "at_age": 66 + }, + "applicable_raw_columns": [ + "age62", + "age63", + "age64", + "age65_before_fra", + "age66_before_fra", + "age66_at_fra", + "age66_after_fra", + "disability_conversion", + "age67_69", + "age70plus" + ], + "component_sum": 99.9, + "residual": -0.1 + } + } + } +} diff --git a/data/external/ssa_supplement_2025_6b.source.html.gz b/data/external/ssa_supplement_2025_6b.source.html.gz new file mode 100644 index 00000000..edec9123 Binary files /dev/null and b/data/external/ssa_supplement_2025_6b.source.html.gz differ diff --git a/data/external/ssa_supplement_2026_6b.source.html.gz b/data/external/ssa_supplement_2026_6b.source.html.gz new file mode 100644 index 00000000..a580210f Binary files /dev/null and b/data/external/ssa_supplement_2026_6b.source.html.gz differ diff --git a/data/external/ssa_supplement_2026_6b.source.provenance.json b/data/external/ssa_supplement_2026_6b.source.provenance.json new file mode 100644 index 00000000..e1bf366d --- /dev/null +++ b/data/external/ssa_supplement_2026_6b.source.provenance.json @@ -0,0 +1,39 @@ +{ + "source": "SSA Annual Statistical Supplement 2026, Table 6.B5.1", + "source_url": "https://www.ssa.gov/policy/docs/statcomps/supplement/2026/6b.html", + "source_sha256": "1f9eceb2cfa45e181aa38ca7ce33b5babc77e8cb6d77fd1343650224a0a7d06a", + "committed_source": "data/external/ssa_supplement_2026_6b.source.html.gz", + "retrieved_utc": "2026-10-02T12:08:05Z", + "locator": "div#table6.b5.1; thead/tbody/tfoot", + "bytes": 177407, + "compression": "gzip mtime=0; SHA-256 is of decompressed captured bytes", + "fetch_method": "Supplied local captures; no network fetch by this builder. SSA baseline captures used User-Agent Wget/1.21.4; the supplement capture client is not recorded in the supplied retrieval marker.", + "captures": { + "2025": { + "source_url": "https://www.ssa.gov/policy/docs/statcomps/supplement/2025/6b.html", + "source_sha256": "1ba2e4f895da924286401189ab1144406640b58e56ce1b2e74f00f9caba405a2", + "committed_source": "data/external/ssa_supplement_2025_6b.source.html.gz", + "retrieved_utc": "2026-10-02T12:08:05Z", + "locator": "div#table6.b5.1; thead/tbody/tfoot", + "bytes": 192026, + "compression": "gzip mtime=0; SHA-256 is of decompressed captured bytes" + }, + "2026": { + "source_url": "https://www.ssa.gov/policy/docs/statcomps/supplement/2026/6b.html", + "source_sha256": "1f9eceb2cfa45e181aa38ca7ce33b5babc77e8cb6d77fd1343650224a0a7d06a", + "committed_source": "data/external/ssa_supplement_2026_6b.source.html.gz", + "retrieved_utc": "2026-10-02T12:08:05Z", + "locator": "div#table6.b5.1; thead/tbody/tfoot", + "bytes": 177407, + "compression": "gzip mtime=0; SHA-256 is of decompressed captured bytes" + } + }, + "selection": "2026 edition selected: twelve-column layout matches the 2023 schema; rows through 2025. 2025 edition also re-parsed and its hash recorded.", + "data_basis": "Social Security Administration, Master Beneficiary Record, 100 percent data.", + "table_notes": "NOTES: The statistics in this table differ from those in Table 6.B5 because awards are summarized here by the year in which entitlement began rather than by the year the award action was posted on the Master Beneficiary Record. The year of entitlement often precedes the year of action because of retroactive entitlements, delayed award actions, and other reasons. Because entitlements can be awarded retroactively, statistics for current and prior years are subject to revision with each annual update of this table. Because of differences in data sources and calculation methods, statistics reported in this table may differ from those reported by Actuarial Services. Totals do not necessarily equal the sum of rounded components. FRA = full retirement age; . . . = not applicable.", + "footnote_a_fra_schedule": "FRA is 65 for workers attaining age 65 before 2003. It increases in 2-month increments for workers attaining age 65 in each of the years 2003 through 2008. It is 66 for workers attaining age 65 in the years 2008 through 2019. It again increases in 2-month increments for workers attaining age 65 in the years 2020 through 2025. It is 67 for workers attaining age 65 in 2025 or later.", + "footnote_b_disability_conversion": "Disabled-worker benefit automatically converts to a retired-worker benefit in the month the worker attains FRA.", + "not_applicable_marker": ". . .", + "contact": "statistics@ssa.gov", + "consumer_note": "claiming.load_claim_age_reference(path) loads this schema and reference.row accesses all years. Legacy claiming.claim_age_distribution and claim_age_pmf retain their global 2022 cap; a new baseline must resolve against reference.years rather than those legacy convenience functions." +} diff --git a/data/external/tr2026/provenance.md b/data/external/tr2026/provenance.md new file mode 100644 index 00000000..6b7efc41 --- /dev/null +++ b/data/external/tr2026/provenance.md @@ -0,0 +1,122 @@ +# TR2026 intermediate baseline inputs + +The 2026 OASDI Trustees Report was released June 9, 2026. The captures +used here were retrieved October 1, 2026 into the authorized NASI follow-up +`inputs/baseline-inputs/` directory. This builder fetched nothing from the +network and transcribed no cost, income, cost-rate, income-rate or balance +cells. Files are rebuilt by `scripts/extract_tr2026_parameters.py`. + +## Capture and identity + +The capture lane used curl with `User-Agent: Wget/1.21.4`: SSA served these +files, while a browser user agent received Akamai HTTP 403. CSV and PDF +payload hashes were stable at capture (a repeated projected male q download +matched). HTML hashes are unstable across live fetches because Akamai adds +per-request markup. `sources.json` pins the captured byte SHA-256; the typed +accessor separately pins each compact parsed JSON file by SHA-256. No new +live download is needed to reproduce these artifacts. + +The OACT current-year Downloadables URLs use `/CY/`. A durable annual +locator is expected to be +`https://www.ssa.gov/OACT/Downloadables/2026/TR2026.html` after TR2027 is +released; this expectation is recorded separately from the exact captured +URL. A changing live URL does not replace the captured hash. + +Only raw CSV/PDF payloads under 2,000,000 bytes are retained. Period-life +CSVs above 500,000 bytes are stored with deterministic gzip `mtime=0`; +source digests identify the decompressed bytes. HTML is recorded by URL and +SHA-256 rather than committed. V.C1's chapter capture exceeds 2 MB. The +extractor selects only Table V.C1 (anchor #1047210) from it. Other raw +captures can be supplied with `--input-dir`; the builder's default is the +authorized staged follow-up input directory. + +## Values, units and coverage + +`tr2026_parameters.json` contains compact arrays with explicit columns: +V.C1 COLA percent (determination year 1975-2035) and overlapping AWI; +V.B1 annual CPI-W growth percent (1960-2100); VI.G1 AWI dollars and adjusted +CPI (1970-2100, CPI indexed to 2026=100); V.A1 TFR births per woman and +ASADR deaths per 100,000 (1940-2100, standardized to the April 1, 2010 +population); V.A4 period life expectancy at birth and 65 by sex +(1940-2100). Historical and Intermediate sections are preserved. V.C1's +2025 COLA is actual (footnote g); VI.G1's 2025 AWI is estimated; V.B1's +2025 row is estimated. V.A1 2023 total/65+ ASADR is preliminary (footnote d); its under-65 rate is final. The 2024 ASADRs are preliminary (e), while the unmarked TFR is historical. The 2025 TFR/ASADRs are partial-year provisional (f). Per-cell footnotes are retained and source classes distinguish these standings. + +`tr2026_death_probabilities.json` preserves all DeathProbsE q values for +exact ages 0-119 and both sexes: historical 1900-2023, Alternative II +2024-2100. The terminal probability is kept as published, even when below +one. Check-only f0 and published e0/e65 values are also retained, with +`f0=(l0-L0)/(l0*q0)` inferred from the printed life-table integers. This is +a derived check input, not a published mortality probability or an +accessor default. + +TR2026 has no published single-age fertility schedule in these sources. +No ASFR schedule was invented. Optional SS-area population was omitted. + +## Builder defaults awaiting ratification + +`post_2035_cola_annual_cpiw`: V.C1 ends in 2035; use V.B1 annual-average +CPI-W growth as COLA for determination years 2036-2100. This approximates +the Q3-to-Q3 statutory adjustment; entries are tagged +`derived_annual_cpiw_cola`. No extrapolation beyond 2100 is allowed. + +`constant_terminal_q` in the shared life-table helper: hold the age-119 q +at later ages solely to close the life-expectancy computation. The actual +q vector is never changed. The equations, limitation and builder-default +standing are documented in `src/populace_dynamics/data/life_table.py`. + +## Invariants and transcription checks + +The extractor verifies every captured payload hash, reparses every numeric +input source, and checks unique consecutive year/age coverage. Every q is +finite and in [0,1]. All q columns equal the corresponding period-life +CSV q cells exactly. Overlapping V.C1/VI.G1 AWI cells agree exactly. +The life-table checks use the f0 inferred above and the named terminal +rule: e0/e65 agree with OACT's two-decimal period-life values within .006 +years, and with V.A4's one-decimal values within .051 years. Ages 0-109 +with positive printed survivors agree with period-life e within .006; +OACT prints zero for extinct cohorts and uses different conventions at +some ages 110-119. Neither convention is silently substituted for q. +The observed maxima and coverage are recorded below and in the pinned +`transcription_check.json`. + +Property tests cover path partitioning, positive AWI and singleton +consistency, bounded q and the historical/Alt2 source boundary. Example +and differential tests reparse all committed q sources, verify exact +terminal q preservation, recompute e0/e65, verify captured and JSON hashes, +refuse invalid years/sex/group selectors, verify immutable arrays/records, +and check continuity across the COLA extension with rounded CPI indices. + +Run the deterministic transcription check (writes nothing): + +```sh +PYTHONPATH=src /Users/maxghenis/PolicyEngine/microcosm-dynamics/.venv/bin/python scripts/extract_tr2026_parameters.py --check +``` + +| Check | Maximum error (years) | Tolerance (years) | +|---|---:|---:| +| male_Hist_life_table_e0_to_e109 | 0.005137236 | 0.006 | +| male_Alt2_life_table_e0_to_e109 | 0.005176972 | 0.006 | +| female_Hist_life_table_e0_to_e109 | 0.005122680 | 0.006 | +| female_Alt2_life_table_e0_to_e109 | 0.005164487 | 0.006 | +| male_q_reproduces_V_A4 | 0.049965548 | 0.051 | +| male_q_reproduces_PerLifeTables_e0_e65 | 0.005074466 | 0.006 | +| female_q_reproduces_V_A4 | 0.049510023 | 0.051 | +| female_q_reproduces_PerLifeTables_e0_e65 | 0.005050713 | 0.006 | + +## Captured source ledger + +- **DeathProbsE_F_Alt2_TR2026.csv**: [q](https://www.ssa.gov/OACT/Downloadables/CY/DeathProbsE_F_Alt2_TR2026.csv); Header Year,0..119; one row per year. SHA-256 `e3178487512f23666ca05754246798557275bc0728f3f937bbca97358f32532b`; 84,092 bytes. Committed `sources/DeathProbsE_F_Alt2_TR2026.csv`. +- **DeathProbsE_F_Hist_TR2026.csv**: [q](https://www.ssa.gov/OACT/Downloadables/CY/DeathProbsE_F_Hist_TR2026.csv); Header Year,0..119; one row per year. SHA-256 `1e2ecd7d24233310108da582ae922c53205d1d239ce3a484ecc5fd6cdb88d0c8`; 135,134 bytes. Committed `sources/DeathProbsE_F_Hist_TR2026.csv`. +- **DeathProbsE_M_Alt2_TR2026.csv**: [q](https://www.ssa.gov/OACT/Downloadables/CY/DeathProbsE_M_Alt2_TR2026.csv); Header Year,0..119; one row per year. SHA-256 `4e2a632a964fadb4cc3fe6127a97ef656eafd43348bf8c7408b018b83840db27`; 84,090 bytes. Committed `sources/DeathProbsE_M_Alt2_TR2026.csv`. +- **DeathProbsE_M_Hist_TR2026.csv**: [q](https://www.ssa.gov/OACT/Downloadables/CY/DeathProbsE_M_Hist_TR2026.csv); Header Year,0..119; one row per year. SHA-256 `ee423873ef2b532fc53c97312a9d6162b876f56e50bd227d45f6c9d9a1cefc8f`; 135,132 bytes. Committed `sources/DeathProbsE_M_Hist_TR2026.csv`. +- **LifeTableDefinitions.pdf**: [life_table_definitions](https://www.ssa.gov/OACT/Downloadables/LifeTableDefinitions.pdf); Life-table function definitions and separation factor at age zero. SHA-256 `94c8fd5806cdb5be3762d1284155e4827a2971d3845088943e3f96a14f26e889`; 130,536 bytes. Committed `sources/LifeTableDefinitions.pdf`. +- **PerLifeTables_F_Alt2_TR2026.csv**: [life_table_check](https://www.ssa.gov/OACT/Downloadables/CY/PerLifeTables_F_Alt2_TR2026.csv); Header line 5; Year,x,q(x),l(x),d(x),L(x),T(x),e(x),...; ages 0..119. SHA-256 `80f612185a269069d097fc32b52ba8f65cb24e5605c2201068d4ca414728bef8`; 777,447 bytes. Committed `sources/PerLifeTables_F_Alt2_TR2026.csv.gz`. +- **PerLifeTables_F_Hist_TR2026.csv**: [life_table_check](https://www.ssa.gov/OACT/Downloadables/CY/PerLifeTables_F_Hist_TR2026.csv); Header line 5; Year,x,q(x),l(x),d(x),L(x),T(x),e(x),...; ages 0..119. SHA-256 `a5e71b5a5011468e87b6c0407d60eef956039eb09524c94a3cc5bf6cf165ab97`; 1,225,687 bytes. Committed `sources/PerLifeTables_F_Hist_TR2026.csv.gz`. +- **PerLifeTables_M_Alt2_TR2026.csv**: [life_table_check](https://www.ssa.gov/OACT/Downloadables/CY/PerLifeTables_M_Alt2_TR2026.csv); Header line 5; Year,x,q(x),l(x),d(x),L(x),T(x),e(x),...; ages 0..119. SHA-256 `0d3f38d38d7428e286bb00ae5a318df4eb100e2c801e1487a0410c2f917bc938`; 773,818 bytes. Committed `sources/PerLifeTables_M_Alt2_TR2026.csv.gz`. +- **PerLifeTables_M_Hist_TR2026.csv**: [life_table_check](https://www.ssa.gov/OACT/Downloadables/CY/PerLifeTables_M_Hist_TR2026.csv); Header line 5; Year,x,q(x),l(x),d(x),L(x),T(x),e(x),...; ages 0..119. SHA-256 `bcf123b13a107e3a5d55a3e47e5129b470624e047346171cf1ee3a0a1542d1a5`; 1,216,677 bytes. Committed `sources/PerLifeTables_M_Hist_TR2026.csv.gz`. +- **V_C_prog.html**: [V.C1](https://www.ssa.gov/oact/TR/2026/V_C_prog.html#1047210); Table V.C1, anchor #1047210; historical and Intermediate rows. SHA-256 `b6af98b6e8f131ef17b3208ef0fbfb8f9fbc494bc6d33dbee5c1bcbbfc99d93a`; 2,748,991 bytes. Captured HTML is recorded only. +- **lr5a1.html**: [V.A1](https://www.ssa.gov/oact/TR/2026/lr5a1.html); Single-year V.A1; historical and Intermediate; TFR and ASADR. SHA-256 `7476bb5e9e99a04bd1b7e77afd6f367c4f94d6eb9aff74e33724b5db9d1dc357`; 180,149 bytes. Captured HTML is recorded only. +- **lr5a4.html**: [V.A4](https://www.ssa.gov/oact/TR/2026/lr5a4.html); Single-year V.A4; historical table and first alternative in projected table. SHA-256 `ab45c5e84253cea122ba0f8a284335d03a5819051914422f5980412822a2b919`; 171,472 bytes. Captured HTML is recorded only. +- **lr5b1.html**: [V.B1](https://www.ssa.gov/oact/TR/2026/lr5b1.html); Single-year V.B1; historical and Intermediate; final CPI column. SHA-256 `f1e1347ac2e9013d53a517e0acf05e692660113acd6cbe7554f0efd7c1aaa3a4`; 258,383 bytes. Captured HTML is recorded only. +- **lr6g1.html**: [VI.G1](https://www.ssa.gov/oact/TR/2026/lr6g1.html); Single-year VI.G1; historical and Intermediate; AWI and adjusted CPI. SHA-256 `2e0214278e0b2271616093af02280277f6e363d112c47a90b49fe7168e795e75`; 226,764 bytes. Captured HTML is recorded only. diff --git a/data/external/tr2026/sources.json b/data/external/tr2026/sources.json new file mode 100644 index 00000000..25512f0c --- /dev/null +++ b/data/external/tr2026/sources.json @@ -0,0 +1 @@ +{"fetch_method":"Captured with curl User-Agent Wget/1.21.4; browser User-Agent received Akamai 403. 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column","original_url":"https://www.ssa.gov/oact/TR/2026/lr5b1.html","retrieved":"2026-10-01","role":"V.B1","sha256":"f1e1347ac2e9013d53a517e0acf05e692660113acd6cbe7554f0efd7c1aaa3a4"},"lr6g1.html":{"bytes":226764,"capture_relative_path":"tr2026/lr6g1.html","committed_file":null,"compression":null,"durable_downloadables_locator":null,"locator":"Single-year VI.G1; historical and Intermediate; AWI and adjusted CPI","original_url":"https://www.ssa.gov/oact/TR/2026/lr6g1.html","retrieved":"2026-10-01","role":"VI.G1","sha256":"2e0214278e0b2271616093af02280277f6e363d112c47a90b49fe7168e795e75"}}} diff --git a/data/external/tr2026/sources/DeathProbsE_F_Alt2_TR2026.csv b/data/external/tr2026/sources/DeathProbsE_F_Alt2_TR2026.csv new file mode 100644 index 00000000..4d1d184c --- /dev/null +++ b/data/external/tr2026/sources/DeathProbsE_F_Alt2_TR2026.csv @@ -0,0 +1,79 @@ +Probability of death for females by calendar year and by single year of age (ages 0 to 119). +Year,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119 +2024,0.005242,0.000375,0.000218,0.000184,0.000159,0.000140,0.000125,0.000110,0.000102,0.000099,0.000103,0.000116,0.000134,0.000154,0.000178,0.000208,0.000245,0.000287,0.000334,0.000371,0.000399,0.000423,0.000448,0.000468,0.000489,0.000516,0.000542,0.000573,0.000623,0.000688,0.000757,0.000828,0.000890,0.000958,0.001035,0.001105,0.001174,0.001242,0.001313,0.001399,0.001491,0.001581,0.001703,0.001837,0.001971,0.002106,0.002232,0.002349,0.002483,0.002656,0.002893,0.003142,0.003376,0.003625,0.003918,0.004277,0.004690,0.005126,0.005604,0.006114,0.006673,0.007274,0.007910,0.008601,0.009332,0.010051,0.010766,0.011531,0.012379,0.013358,0.014509,0.015797,0.017260,0.018915,0.020918,0.023230,0.025847,0.028770,0.032437,0.036225,0.040488,0.045208,0.050495,0.056332,0.062920,0.070506,0.079138,0.089311,0.100808,0.113398,0.127470,0.142360,0.158429,0.175964,0.195000,0.215788,0.237063,0.258222,0.280132,0.301479,0.323476,0.346030,0.369035,0.392373,0.415915,0.440870,0.467322,0.495361,0.525083,0.556588,0.589983,0.625382,0.662905,0.702680,0.744840,0.789531,0.835315,0.877081,0.920935,0.966982 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diff --git a/data/external/tr2026/sources/DeathProbsE_F_Hist_TR2026.csv b/data/external/tr2026/sources/DeathProbsE_F_Hist_TR2026.csv new file mode 100644 index 00000000..c4144fa3 --- /dev/null +++ b/data/external/tr2026/sources/DeathProbsE_F_Hist_TR2026.csv @@ -0,0 +1,126 @@ +Probability of death for females by calendar year and by single year of age (ages 0 to 119). +Year,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119 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diff --git a/data/external/tr2026/sources/DeathProbsE_M_Alt2_TR2026.csv b/data/external/tr2026/sources/DeathProbsE_M_Alt2_TR2026.csv new file mode 100644 index 00000000..4a18b869 --- /dev/null +++ b/data/external/tr2026/sources/DeathProbsE_M_Alt2_TR2026.csv @@ -0,0 +1,79 @@ +Probability of death for males by calendar year and by single year of age (ages 0 to 119). +Year,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119 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diff --git a/data/external/tr2026/sources/DeathProbsE_M_Hist_TR2026.csv b/data/external/tr2026/sources/DeathProbsE_M_Hist_TR2026.csv new file mode 100644 index 00000000..82f6a49d --- /dev/null +++ b/data/external/tr2026/sources/DeathProbsE_M_Hist_TR2026.csv @@ -0,0 +1,126 @@ +Probability of death for males by calendar year and by single year of age (ages 0 to 119). +Year,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119 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index 00000000..f34ff740 Binary files /dev/null and b/data/external/tr2026/sources/PerLifeTables_M_Alt2_TR2026.csv.gz differ diff --git a/data/external/tr2026/sources/PerLifeTables_M_Hist_TR2026.csv.gz b/data/external/tr2026/sources/PerLifeTables_M_Hist_TR2026.csv.gz new file mode 100644 index 00000000..fe25857d Binary files /dev/null and b/data/external/tr2026/sources/PerLifeTables_M_Hist_TR2026.csv.gz differ diff --git a/data/external/tr2026/tr2026_death_probabilities.json b/data/external/tr2026/tr2026_death_probabilities.json new file mode 100644 index 00000000..b4580d15 --- /dev/null +++ b/data/external/tr2026/tr2026_death_probabilities.json @@ -0,0 +1 @@ +{"age_first":0,"age_last":119,"f0_rule":"(l0-L0)/(l0*q0) from OACT integer person-years; 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4177,0.778228,0.824921,0.874417,0.926882,0.982495],"2014":[0.005313,0.000346,0.000209,0.000168,0.000136,0.000115,0.000101,9.2e-05,8.8e-05,8.6e-05,8.7e-05,9.4e-05,0.000107,0.000123,0.000146,0.000179,0.00022,0.000266,0.000314,0.000359,0.000398,0.000433,0.000452,0.000462,0.000482,0.000515,0.00055,0.000577,0.000606,0.000635,0.000663,0.000706,0.00076,0.00082,0.000881,0.00095,0.001009,0.001077,0.001146,0.001222,0.001326,0.001453,0.001576,0.001696,0.001839,0.002,0.002187,0.002402,0.00265,0.002913,0.003196,0.003505,0.003805,0.004117,0.004448,0.004815,0.005173,0.005525,0.005882,0.006273,0.00673,0.007235,0.007776,0.008355,0.009026,0.009845,0.01079,0.011835,0.013091,0.014367,0.015748,0.017257,0.018978,0.020854,0.022954,0.02532,0.027969,0.031078,0.034685,0.038763,0.04332,0.048206,0.053548,0.059555,0.066505,0.07415,0.082739,0.092436,0.103273,0.115673,0.129908,0.145242,0.161449,0.178193,0.196197,0.215627,0.2363,0.257604,0.278366,0.29785,0.318103,0.339097,0.360799,0.383168,0.406159,0.430528,0.45636,0.483741,0.512766,0.543532,0.576144,0.610712,0.6448,0.67704,0.710892,0.746437,0.783759,0.822947,0.864094,0.907299],"2015":[0.005374,0.000353,0.000216,0.00017,0.000135,0.000116,0.000107,9.8e-05,9e-05,8.4e-05,8.5e-05,9.6e-05,0.000113,0.000137,0.000163,0.000193,0.000237,0.00029,0.000348,0.000393,0.000424,0.000451,0.000471,0.000492,0.000517,0.000546,0.000573,0.000605,0.000641,0.000682,0.000729,0.00078,0.000834,0.000882,0.000928,0.000981,0.001046,0.001102,0.001161,0.001226,0.001322,0.001446,0.001577,0.001705,0.001853,0.002016,0.002203,0.002409,0.002647,0.0029,0.003174,0.003471,0.003792,0.004114,0.004442,0.004798,0.005195,0.005586,0.005969,0.00635,0.006759,0.007257,0.007824,0.008428,0.009093,0.009865,0.010736,0.011728,0.012868,0.014314,0.015775,0.01735,0.019062,0.02097,0.023095,0.025492,0.028155,0.031238,0.034801,0.038867,0.043408,0.048541,0.054314,0.060609,0.067614,0.075533,0.084503,0.094651,0.106055,0.118996,0.133348,0.148611,0.165073,0.182874,0.201658,0.222278,0.243824,0.265535,0.28779,0.307971,0.328944,0.350679,0.37314,0.396284,0.420062,0.445265,0.471981,0.5003,0.530318,0.562137,0.595865,0.631617,0.669514,0.709685,0.752266,0.797402,0.845246,0.895961,0.949719,1.0],"2016":[0.005331,0.000359,0.000229,0.000183,0.00015,0.000127,0.000112,0.000105,9.9e-05,9.7e-05,9.7e-05,0.000103,0.000112,0.000132,0.000164,0.0002,0.000248,0.000303,0.000357,0.0004,0.000441,0.00048,0.000511,0.000537,0.000561,0.000596,0.000635,0.000673,0.000707,0.000745,0.000796,0.000853,0.000903,0.000946,0.001002,0.001064,0.001127,0.00119,0.001256,0.001318,0.001389,0.001476,0.0016,0.001741,0.001887,0.002047,0.002211,0.002397,0.002633,0.002905,0.00318,0.003468,0.003781,0.004124,0.004475,0.004842,0.005217,0.005607,0.006009,0.00644,0.006889,0.007352,0.007898,0.008481,0.009128,0.009851,0.010672,0.011619,0.012672,0.013871,0.015374,0.016979,0.018741,0.020667,0.022851,0.025214,0.02776,0.03073,0.034133,0.038117,0.042658,0.047678,0.053278,0.059284,0.066136,0.073966,0.082583,0.092375,0.10346,0.115788,0.129677,0.144697,0.16052,0.177627,0.196284,0.216127,0.237141,0.258801,0.280163,0.301896,0.324255,0.347129,0.370398,0.393923,0.417559,0.442612,0.469169,0.497319,0.527158,0.558788,0.592315,0.627854,0.665525,0.705457,0.747784,0.792651,0.84021,0.890623,0.94406,0.995424],"2017":[0.005229,0.000342,0.000199,0.000164,0.000137,0.000116,0.000107,0.000103,0.0001,9.7e-05,9.6e-05,0.000101,0.000112,0.000133,0.000164,0.000201,0.000241,0.000286,0.000342,0.000393,0.00044,0.000483,0.000517,0.000548,0.000576,0.000609,0.000644,0.000697,0.000742,0.000773,0.000817,0.000869,0.000926,0.000986,0.001037,0.001086,0.001137,0.001194,0.001253,0.001337,0.001428,0.001525,0.001632,0.001742,0.00186,0.002011,0.002196,0.002394,0.002605,0.002843,0.003116,0.0034,0.003694,0.004016,0.004382,0.004779,0.005194,0.005628,0.006052,0.006487,0.006952,0.007432,0.007934,0.008515,0.009161,0.009873,0.010649,0.011582,0.01267,0.013859,0.015186,0.016903,0.018766,0.020697,0.022767,0.025146,0.027769,0.030752,0.034185,0.038058,0.042549,0.047632,0.053442,0.059991,0.067006,0.074668,0.08345,0.093387,0.104463,0.116926,0.13118,0.146922,0.163568,0.181096,0.200531,0.221574,0.24307,0.265454,0.28757,0.307162,0.32759,0.348844,0.370909,0.393767,0.417393,0.442436,0.468983,0.497122,0.526949,0.558566,0.59208,0.627605,0.665261,0.705176,0.747487,0.792336,0.839876,0.890269,0.943685,0.999329],"2018":[0.005058,0.000326,0.000207,0.000162,0.000131,0.000113,0.000103,9.8e-05,9.9e-05,0.000101,0.000105,0.000112,0.000121,0.000136,0.000158,0.000192,0.000234,0.000285,0.000339,0.000386,0.000424,0.000456,0.000485,0.000516,0.000549,0.000588,0.000633,0.000681,0.000732,0.000787,0.000835,0.000874,0.000921,0.000966,0.001015,0.001074,0.001132,0.001193,0.001264,0.001343,0.00141,0.001485,0.001584,0.001706,0.001847,0.002014,0.00218,0.002337,0.002521,0.002744,0.003008,0.003304,0.003601,0.003921,0.004287,0.004682,0.005102,0.005525,0.005957,0.006406,0.006876,0.007384,0.007917,0.008475,0.009079,0.009781,0.010551,0.011404,0.012378,0.013499,0.014823,0.016378,0.018334,0.020359,0.022549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probability of death at exact age"} diff --git a/data/external/tr2026/tr2026_parameters.json b/data/external/tr2026/tr2026_parameters.json new file mode 100644 index 00000000..726a91d6 --- /dev/null +++ b/data/external/tr2026/tr2026_parameters.json @@ -0,0 +1 @@ +{"alternative":"intermediate","release_date":"2026-06-09","schema_version":"tr2026_parameters.v1","tables":{"V.A1":{"asadr_unit":"deaths per 100000; April 1 2010 standard 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That world +includes how fast prices and wages grow, how long people live, when they +claim benefits, who becomes disabled, how many children are born and who +immigrates. Taken together, these assumptions make up the *baseline*. +This page lists the baseline behind each result Microcosm Dynamics has +published and shows where the code sets each assumption. It also +describes the selectable baselines now being added, and records known +discrepancies and corrections. + +Code paths are relative to `src/populace_dynamics/` unless they start +with `scripts/`, `data/`, `docs/` or `paper/`, or name `gates.yaml`; +those are relative to the repository root. Line numbers refer to commit +`75cd3524` on `master` (1 October 2026). + +## Terms used on this page + +- **Gate.** A pass/fail test registered before its scored run. Before + that run, `gates.yaml` fixes which statistics the test scores and the + tolerance on each one. The model passes only if every statistic stays + within its tolerance. This page uses the word for nothing else. +- **Benchmark.** A comparison between the model's results and another + model's published results, here DYNASIM3's. A benchmark has no pass + mark: it reports the gaps and does not grade them. The four blind + tests against DYNASIM3 are benchmarks. +- **Registration.** The plan for a run, posted as a comment on issue + [#42](https://github.com/PolicyEngine/microcosm-dynamics/issues/42) + before the run starts. +- **Trustees Report.** The annual report of Social Security's trustees. + Its *intermediate* assumptions are the trustees' central path. On this + page TR2008 means the 2008 Report and TR2026 the 2026 Report. OACT is + SSA's Office of the Chief Actuary, which prepares the Report's + projections. +- **COLA.** The cost-of-living adjustment: the yearly percentage + increase in benefits already being paid. SSA sets it from the consumer + price index for urban wage earners and clerical workers (CPI-W). +- **AWI.** The national average wage index. SSA uses it to index each + worker's past earnings and to set the dollar thresholds, called bend + points, of the benefit formula. +- **Published or derived.** A *published* value is printed in its + source. A *derived* value is one we compute from published values by a + rule we state. A derived value is our number, not the source's. + +## The baseline behind each published result + +Exercises 1 and 3 and Registration 18 share every baseline input, so +they share one column. + +| | Exercises 1 and 3, Registration 18 | Exercise 2 | Exercise 4 | Gates m6, 2, 2b, 2c and w1 | +|---|---|---|---|---| +| **What it is** | The projection tests. A closed PSID cohort from the 2011 wave is aged to 2030 in 20 random draws. Exercise 1's alternative R6 uses the 2009 wave instead. Exercise 1 cuts each COLA by one percentage point, and exercise 3 raises the full retirement age to 68. Registration 18 (A-COLA) applies a one-point cut from December 2027. It is registered but had not run by 1 October 2026. | A static calculation on realized PSID incomes at age 67. It covers people born in the odd years 1937 to 1945, so the income years run from 2004 to 2012. | A static calculation on the PSID's 2023 wave: 2022 incomes and benefits, plus earnings histories for 1968–2022. The reform's policy dates move back to 2004. | Fitted components scored three ways: against held-out PSID records (2, 2b, 2c); against the PSID's 2015–2019 records after a fit on data through 2014 (m6); or against the CPS frame (w1). | +| **Assumption vintage** | TR2008, intermediate (`cola_track_a/config.py:264`; `fra68_track/config.py:628`). Registration 18 keeps TR2008 and names the gap from OACT's 2026 Report basis as a delta. | No projection: realized 2004–2012. Named delta: "realized COLAs versus 2002 Trustees assumptions" (`uniform_cut_track_u/runner.py:102`). | No projection: realized 2022. Named deltas: "realized 2022 against DYNASIM's projected 2025" and "realized AWI and CPI against the 2005 Trustees paths" (`min_benefit_track_m/pipeline.py:112`, `:115`). | No Trustees vintage. m6 admits only data dated 2014 or earlier (`engine/refit.py:70`). It binds policyengine-us 1.752.2 parameters, the 2014 Statistical Supplement and the NCHS 2010 life tables (`scripts/registered_m6_inputs.py:14-44`). w1's frame has a 2024 reference period. | +| **COLA path** | SSA's realized COLAs for determination years 1979–2007 (`data/external/ssa_cola_history.json`). TR2008 Table V.C1 (intermediate) for 2008–2017. From 2018, the intermediate ultimate CPI growth of 2.8 percent, a derived value. The path stops at 2030 (`cola_track_a/runner.py:231-258`; `data/tr2008.py:707-735`; `cola_track_a/config.py:258`, `:268`). | None applied. Realized COLAs are already inside the benefits people reported. | SSA's realized COLA history through the COLA determined in 2021. Only alternative MS5 uses it (`min_benefit_track_m/rules.py:133-138`; `scripts/run_track_m_registered.py:352`). | None. No gated cell scores a benefit amount. | +| **AWI** | TR2008 from 1975: Table V.C1 (historical rows 1975–2006, intermediate estimates 2007–2017), then SSA's single-year Table VI.F6 for 2018–2085. Before 1975, SSA's realized series as policyengine-us records it (`cola_track_a/runner.py:193-228`; `data/tr2008.py:750-767`). The registered-run check covers 1975–2030 (`cola_track_a/runner.py:342-344`). | Not used. | SSA's realized series, read from policyengine-us's `nawi.yaml` (pinned at `estimates/parameters.py:56`; loaded at `scripts/run_track_m_registered.py:294`). | m6 only: realized values through 2014, then a log-linear trend fitted to 2005–2014 (`scripts/registered_m6_inputs.py:26-31`; `engine/forward_earnings.py:435-460`). | +| **Mortality** | SSA's 2004 period life table by single year of age and sex. Each year it is multiplied by the ratio of TR2008's intermediate age-sex-adjusted death rate for that year to the 2004 rate. There is one ratio for ages under 65 and one for 65 and older, and each is the same for men and women (`cola_track_a/mortality.py:105-118`, `:126-181`; `data/tr2008.py:949-980`). | Used only to price the wealth annuity. The primary uses NCHS 2000 U.S. life tables by sex, and alternative U9 uses SSA's 2004 period table. Both use a 3 percent real interest rate (`estimates/adjusted_poverty.py:404-415`, `:763-819`). | None. Survival is observed. | m6: PSID death rates by age band and sex, set against NCHS 2010 rates and held constant over time (`engine/refit.py:1059-1119`). The other gates project no mortality. No gate certifies how mortality changes over time. | +| **Claiming** | SSA's distribution of retired-worker awards by age (2014 Statistical Supplement, Table 6.B5.1), with rows through 2008. Every later year reuses the 2008 row, and no claim responds to a reform (`cohorts/psid2010.py:161-166`; `cola_track_a/adapters.py:161-175`; `engine/steps.py:367-376`). | None. Receipt is observed. | None. Entitlement years are read from observed receipt. | m6: the same 2014 Supplement table, rows through 2014 (`engine/refit.py:991-1014`). | +| **Disability insurance** | Award, recovery and death rates fit on 2008 data and held constant in every year. Disabled workers' death rates move with population mortality through a multiplier (`engine/di_entitlement_rates.py:1-56`, `:163-213`). | None. | None projected. Records of benefits that began as disability benefits are observed. | No SSDI entitlement. m6 runs the work-limitation disability module that gate m4 scores. | +| **Fertility** | None. The cohort is closed and no births are drawn (`cola_track_a/adapters.py:19-22`). | None. | None. | Births come from a PSID-fit table by the mother's single year of age (15–49), her number of earlier births (capped at 3) and her birth decade (candidate 16). Gate 2 scores births, and m6 projections draw them (`models/family_transitions/components/fertility.py:26-31`; `engine/marital.py:338-401`). | +| **Earnings** | None after 2010. The benefit formula counts every later year as zero (`cola_track_a/adapters.py:25-27`). | Realized, as reported. | Realized, as reported, 1968–2022. | m6 uses the forward rank-chain law fitted on PSID earnings through 2014 at ages 25–64. | +| **Immigration** | None. No entrants are scheduled (`cola_track_a/runner.py:11-16`). | None. Named delta: immigrant under-coverage (`uniform_cut_track_u/runner.py:103-105`). | None. | None. The entrant schedule is report-only (`engine/entrant_schedule.py:1-6`). | +| **Other inputs** | Statutory parameters from policyengine-us, captured in `data/external/track_a_statutory_parameters.json`. Bend points come from the TR2008 AWI (`cola_track_a/statutory.py:13-25`). | Census weighted-average poverty thresholds for 2004–2012, and SSI parameters captured from policyengine-us (`estimates/adjusted_poverty.py:953-975`, `:1085-1097`). | The Census weighted-average poverty threshold for one person aged 65 and over, captured for 1982, 1986, 1988–1992 and 1994–2022 (`min_benefit_track_m/rules.py:47-53`). | — | + +: The baseline behind each published result. Exercise 1 is the one-point COLA cut, exercise 3 the full retirement age of 68, exercise 2 the uniform 13 percent cut and exercise 4 the minimum benefit. {#tbl-baselines} + +## How the projection tests set each assumption + +Exercise 3's configuration copies exercise 1's baseline settings +(`fra68_track/config.py:627-632`) and passes them to the Track A +configuration (`fra68_track/config.py:707-711`). The two registered +runners build the same inputs +(`scripts/run_track_a_registered.py:302-340`; +`scripts/run_fra68_registered.py:222-259`). The Track A v2 rerun code, +which has not yet run on real data, calls the same constructors +(`track_a_v2/protocol.py:318-385`). + +### Assumption vintage + +DYNASIM3's run for *Details Matter* used the 2008 Trustees assumptions, +so the projection tests use the same vintage: TR2008's intermediate +alternative (`cola_track_a/config.py:264`). The specification that fixed +this choice is A1 §4 (`docs/design/urban2010_cola_comparison.md:160-167`). +Where TR2008 publishes no series a test needs, the code names a +substitute and records it as a gap (`data/tr2008.py:285-411`). + +### Cost-of-living adjustments + +The projection tests need a COLA for every year from each benefit's +start to 2030. + +- **Before 2008.** SSA's realized COLAs, from + `data/external/ssa_cola_history.json` (OACT's COLA series page). The + runtime series covers determination years 1979–2022 + (`estimates/parameters.py:42`, `:406-505`), and the TR2008 path + replaces every year from 2008 on (`scenario_benefits.py:311-330`). So + the realized values the tests read run from 1979 to 2007. +- **2008 to 2017.** TR2008 Table V.C1 prints one intermediate COLA per + year: 2.7 percent for 2008, 2.5 for 2009 and 2.8 for each year + 2010–2017 (`docs/design/urban2010_cola_comparison.md:177-181`; + `data/tr2008.py:639-654`). +- **2018 onward.** TR2008 prints no COLA after 2017, so the code uses + the intermediate ultimate CPI growth rate from Table II.C1, 2.8 + percent (`data/tr2008.py:33-41`, `:707`, `:730-735`). This value is + derived. TR2008's CPI rates are annual averages, while the statute + measures the COLA from one third quarter to the next + (`data/external/tr2008/provenance.md`, caveat on V.B1). +- **The path stops at 2030.** The accessor could supply COLAs through + 2085, the last year TR2008 projects (`data/tr2008.py:155`, + `:725-729`). The tests request the path only through their reference + year, 2030 (`cola_track_a/config.py:258`; + `scripts/run_track_a_registered.py:324-329`). Any test that needs a + COLA after 2030 needs a new path. + +TR2008's rates for 2008, 2009 and 2010 differ from the COLAs SSA +actually paid: 5.8, 0 and 0 percent. The A1 specification bars realized +values from any year that enters a reform ratio +(`docs/design/urban2010_cola_comparison.md:247-259`). People already +receiving benefits when the projection opens keep their observed 2010 +amounts, which include the realized increases of December 2008 and +December 2009. The specification lists that as a named delta +(`docs/design/urban2010_cola_comparison.md:260-271`). + +### Average wage index + +From 1975, the AWI is TR2008's (`cola_track_a/runner.py:193-228`): + +- **1975–2006:** the values TR2008 Table V.C1 prints as historical + data. +- **2007–2017:** TR2008's intermediate estimates in V.C1. +- **2018–2085:** the single-year Table VI.F6 that SSA published with + the Report (`data/tr2008.py:29-32`, `:750-767`). Where both exist, + its values equal the printed VI.F6 values for 2020, 2025 and 2030 + (`data/external/tr2008/provenance.md`, §2). + +The constructor loads the series through 2085 +(`cola_track_a/runner.py:197`). A registered run compares it with the +committed TR2008 capture for 1975–2030 (`cola_track_a/runner.py:342-344`). +Before 1975, TR2008 prints no AWI, so those years keep SSA's realized +series as policyengine-us records it (`cola_track_a/runner.py:215-220`). +The bend points follow from the TR2008 AWI +(`cola_track_a/statutory.py:23-25`). The contribution and benefit base +keeps its realized values, because no earnings after 2010 enter +(`cola_track_a/runner.py:205-206`). + +### Mortality + +TR2008 prints no projected death probabilities by single year of age and +sex (`data/tr2008.py:285-306`). The code therefore builds a substitute +(`cola_track_a/mortality.py:1-33`). For projection year $t$, single age +$x$ and sex $s$: + +$$ +q_t(x, s) = \min\left(1,\; q_{2004}(x, s) \times +\frac{A_t(g)}{A_{2004}(g)}\right) +$$ + +- $q_{2004}$ is SSA's period life table for 2004, ages 0–119, posted in + 2008 (`data/tr2008.py:1020-1038`). Ages above 119 use the age-119 row + (`cola_track_a/mortality.py:141-143`). +- $A_t(g)$ is TR2008's intermediate age-sex-adjusted death rate in + single-year Table V.A1 for group $g$: under 65, or 65 and over + (`data/tr2008.py:928-980`). +- One ratio applies to every age in the group and to men and women + alike (`cola_track_a/mortality.py:117-118`). +- The ratios are loaded for 2009–2030 + (`scripts/run_track_a_registered.py:335-340`). The 2011-wave cohort + uses them from 2011, and only exercise 1's alternative R6, which opens + in 2008, uses 2009 and 2010. + +Because the base year is 2004, the 65-and-over ratio exceeds 1 from 2005 +through 2012 (1.0127 in 2010). V.A1's 2004 rate at 65 and over is below +its estimates for 2005–2007, and this is recorded where the base year is +chosen (`data/tr2008.py:255-270`). So in 2011 and 2012, and in 2009 and +2010 for R6, death probabilities at 65 and over sit slightly above the +2004 table's. +The substitute is a builder default, fixed by the A1 ratification and +the registration, not a ruling (`data/tr2008.py:174-184`). + +Disabled workers die at rates from SSA's Actuarial Study 118, multiplied +by the ratio of this year's population probability to the NCHS 2000 +probability, so they inherit the TR2008 improvement +(`engine/di_entitlement_rates.py:29-43`). Everyone else's probabilities +are scaled so that each cell's expected deaths stay equal to the +population table's (`engine/di_entitlement_rates.py:44-52`). + +### Claiming + +The claim-age table is the 2014 Statistical Supplement's Table 6.B5.1, +pinned by SHA-256 (`cohorts/psid2010.py:161-166`; +`cola_track_a/runner.py:167-190`). That table is SSA's distribution of +retired-worker awards by age at entitlement, by sex and entitlement +year, 1998–2013. + +- Conversions from disability benefits at full retirement age are + dropped, because they are not claims. The published 67–69 band is + split evenly across ages 67, 68 and 69 (`claiming.py:90-99`, + `:262-303`). +- The projection tests keep the rows through 2008 + (`cola_track_a/config.py:274`; `cola_track_a/adapters.py:161-175`). + The schedule takes the nearest available year + (`engine/steps.py:367-376`), so every projection year uses the 2008 + row. +- Disabled workers who convert at full retirement age count as + claimants without a draw (`cola_track_a/adapters.py:28-34`). +- No claim responds to a reform. Exercise 3 holds each person's + projected claim age fixed when the full retirement age rises. + +### Disability insurance + +The disability insurance rates have fit year 2008, which uses 2008 data +published in July 2009 (`engine/di_entitlement_rates.py:172-176`): + +- **Awards.** Awards by age at entitlement (DI Annual Statistical + Report 2008, Table 36), divided by the July 1 resident population + minus the December stock of disabled workers (Table 20). The + denominator is the whole non-entitled population, so the model never + encodes insured status (`engine/di_entitlement_rates.py:8-17`). +- **Recovery.** Actuarial Study 118's age and sex profile (1996–2000 + experience), rescaled to the 2008 recoveries in the Annual + Statistical Report's Table 50 (`engine/di_entitlement_rates.py:18-28`). +- **Death.** Actuarial Study 118's profile, multiplied by the + population mortality ratio described above + (`engine/di_entitlement_rates.py:29-43`). + +The fitted arrays are indexed by sex and age only, with no year +(`engine/di_entitlement_rates.py:421-450`). The 2008 award and recovery +rates therefore apply unchanged in every year from 2009 to 2030, and +only death rates move, through the mortality multiplier. TR2008 +publishes no disability rates by age, which is why the 2008 fit stands +in (`data/tr2008.py:11-16`, `:319-366`). + +### Fertility, earnings and immigration + +The projection tests draw no births, no earnings after 2010 and no +immigrants. Everyone aged 50 or older in 2030 was alive in 2010, so the +closed cohort needs no births (`cola_track_a/adapters.py:19-28`). No +entrants are scheduled (`cola_track_a/runner.py:11-16`). TR2008 does +publish a total fertility rate, captured in +`data/external/tr2008/tr2008_single_year.json`, but no accessor reads it +(`data/tr2008.py:940-945`). The only field read from TR2008's ultimate +assumptions is the CPI (`data/tr2008.py:445-447`, `:707`). + +### Registration 18 + +Registration 18 (A-COLA) compares a one-point COLA cut from December +2027 with the 2030 cost effects that OACT and the Penn Wharton Budget +Model publish for that provision. Its "Baseline COLA path" line states +that "the realized series runs through 2007", that TR2008 intermediate +rates apply from 2008, and that the path stops at 2030, "so 2030 is the +only year this test can read". Its population is exercise 1's R0. Its +"Baseline vintage" line names TR2008, set against OACT's 2026 Report +basis, as a delta +([issue #42, comment 5919983573](https://github.com/PolicyEngine/microcosm-dynamics/issues/42#issuecomment-5919983573)). +When it runs, it reads the same baseline inputs as exercise 1. + +## Exercises 2 and 4: realized incomes + +Exercises 2 and 4 project nothing. They apply a reform to incomes the +PSID already recorded. DYNASIM3's figures for the same reforms came from +projections on older Trustees assumptions, so part of any gap can come +from the vintage of the population and the economy, not from the rules. +Each exercise names that as a delta. + +### Exercise 2: the 13 percent cut + +- **Incomes.** The sample member's family income in the year they + turned 67, income years 2004–2012. Reported Social Security is cut by + 13 percent from 2004 (`estimates/adjusted_poverty.py:404-415`). + Realized COLAs are inside those reported benefits. The named delta + says DYNASIM3 projected them from the 2002 Trustees assumptions + (`uniform_cut_track_u/runner.py:98-102`). +- **Poverty line.** The Census weighted-average threshold of the income + year, using the 65-and-over column for one- and two-person units + (`estimates/adjusted_poverty.py:71-75`, `:953-975`). +- **SSI.** A static Supplemental Security Income offset, using SSI + parameters captured from policyengine-us + (`estimates/adjusted_poverty.py:1085-1097`). +- **Wealth annuity.** Eighty percent of the family's wealth excluding + home equity, divided by the price of a level real annuity. The + annuity pays at the end of each year the annuitant survives, at a 3 + percent real interest rate with no load. A head with a co-resident + spouse gets a joint annuity that pays half to the survivor; anyone + else gets a single-life annuity + (`estimates/adjusted_poverty.py:49-70`, `:404-415`). + - The primary prices the annuity on the NCHS 2000 U.S. life tables by + sex. Alternative U9 uses SSA's 2004 period table + (`estimates/adjusted_poverty.py:295-301`, `:763-819`; + `uniform_cut_track_u/runner.py:199-213`). + - Both are period tables. A named delta notes that DYNASIM3's + mortality follows the 2002 Trustees projections, and that period + tables overstate the annuity when mortality is falling + (`uniform_cut_track_u/runner.py:144-148`). + +### Exercise 4: the minimum benefit + +- **Incomes and careers.** 2022 incomes and benefits from the PSID's + 2023 wave, with earnings histories for 1968–2022. The named deltas + set "realized 2022 against DYNASIM's projected 2025" and "realized + AWI and CPI against the 2005 Trustees paths" + (`min_benefit_track_m/pipeline.py:110-115`). +- **AWI.** SSA's realized series, read from policyengine-us's + `nawi.yaml` and pinned by SHA-256 (`estimates/parameters.py:56`; + `scripts/run_track_m_registered.py:294`). The AWI enters the PIA and + the wage-indexed options (`min_benefit_track_m/rules.py:12-14`). It + also scales the quarters of coverage before 1978 + (`min_benefit_track_m/coverage.py:229-254`). + - In the pinned file, the values through 2024 are SSA's published + series. The later entries are projections, which the file derives + from CBO's projected taxable maximum. + - Exercise 4's records reach entitlement year 2022, whose indexing + year is 2020 under 42 U.S.C. 415(b)(3), so they read only + published values. +- **COLA.** SSA's realized COLA history through the COLA determined in + 2021, used only by alternative MS5. That alternative takes the PIA + from the benefit the person receives + (`min_benefit_track_m/rules.py:133-138`; + `min_benefit_track_m/careers.py:27-38`). +- **Threshold.** The Census weighted-average threshold for one person + aged 65 and over, in the worker's threshold year + (`min_benefit_track_m/rules.py:47-53`; + `min_benefit_track_m/thresholds.py:71-72`). +- **Mortality.** Exercise 4 uses no life table. + +## The gated components + +The gates score fitted components. None of them scores a benefit amount, +uses a COLA path or has a Trustees vintage, and none certifies how +mortality changes over time. + +- **m6** (`gates.yaml:5324`). The temporal holdout fits the whole engine + on data dated 2014 or earlier and projects 2015–2019 against the + PSID's records. Its inputs are fixed to sources dated 2014 or earlier + (`scripts/registered_m6_inputs.py:14-44`): + - policyengine-us 1.752.2, with the AWI realized through 2014 and + then a log-linear trend fitted to 2005–2014; + - the 2014 Supplement claim table, rows through 2014; + - PSID death rates by age band and sex, set against the NCHS 2010 + life table, with no change over time (`engine/refit.py:1059-1119`). + + Births come from the candidate-16 table, and earnings from the + forward rank-chain law. +- **2, 2b and 2c** (`gates.yaml:634`, `:1325`, `:2022`). Family + transitions, household composition and the joint of marital status + and earnings, each scored on PSID persons held out of the fit. + - Gate 2 scores birth rates by five-year maternal age band and + completed fertility (`gates.yaml:1242-1254`). + - NCHS birth rates serve only as an anchor for the noise floor + (`scripts/build_gate2_floors.py:684-712`). +- **w1** (`gates.yaml:3701`). Transport of the PSID-estimated processes + onto the CPS frame, scored on cross-sectional joint distributions. + The frame's reference period is 2024. + +Gates 1 and m4, which the table does not list, also score fitted +components against PSID records. Gate m4 is also scored against the age +shape of SSA's 2023 disability statistics. A gate's result is its +committed file under `runs/`, so choosing a baseline changes no gate +result. + +## Choosing a baseline + +The baselines package (`src/populace_dynamics/baselines/`) lets a +projection name the baseline it runs on. `get_baseline(name)` returns +one of three: + +- **`tr2008_intermediate`** reproduces today's inputs exactly. It is + the default. +- **`tr2026_intermediate`** follows the 2026 Trustees Report's + intermediate assumptions. +- **`cbo2026_long_term`** follows CBO's 2026 long-term projections. + +**Every registered result keeps `tr2008_intermediate`, and no +published number changes.** A registered run is a one-shot run whose +inputs the registration fixes in advance. A rerun on another baseline +would be a new run and would need its own registration on issue #42. +The code enforces this: `build_track_a_inputs` refuses a real-data run +under any baseline other than `tr2008_intermediate` +(`baselines/track_a.py`), and `project_track_a` runs the new baselines +only on invented cohorts: it refuses any cohort that is not labelled +invented (`baselines/track_a.py`, `_check_invented`). Each baseline's own +provenance records, not this page, are the authority for every +derivation rule. `scripts/describe_baseline.py --baseline +--years 2026-2035` prints a baseline's input paths, which are inputs +only and no model output. + +Both 2026 baselines splice realized values before any projected or +derived one: COLAs through determination year 2025 and the AWI through +2024. + +| Assumption | `tr2008_intermediate` (default) | `tr2026_intermediate` | `cbo2026_long_term` | +|---|---|---|---| +| COLA | As in @tbl-baselines. Published through 2017, derived after. | Published: Table V.C1 intermediate, 2025–2035; the 2025 value is an actual amount. Derived: from 2036, the annual change in the CPI-W from single-year Table V.B1. | Realized COLAs through 2025, as under `tr2026_intermediate`. Derived from 2026: CBO publishes no COLA and no CPI-W, so by builder default the COLA and the CPI-W grow at CBO's annual CPI-U growth (ten-year levels through 2036, long-term growth after, held at its 2056 rate to 2100). That is an annual average, not the statutory third-quarter calculation. Through 2025 the CPI-W itself comes from the 2026 Report's Table V.B1. | +| AWI | As in @tbl-baselines. Published (V.C1 and single-year VI.F6). | Published: single-year Table VI.G1, historical 1970–2024 and intermediate 2025–2100. | Derived: from the actual 2024 AWI, 2025–2026 grow at CBO's real earnings per worker growth times CPI-U growth; from 2027, at the growth of covered earnings per covered worker from CBO's September 2026 Social Security inputs (to 2100). CBO publishes no AWI. | +| Mortality | The 2004 table scaled by two TR2008 ratios. Derived. | Published: OACT's projected probabilities of death by single year of age (0–119) and sex, historical through 2023 and intermediate for 2024–2100. | Published for 2021–2099: CBO's mortality rates by single year of age (0–119) and sex. Before 2021, where CBO publishes nothing, the baseline splices OACT's historical probabilities from the 2026 Report (a builder default); a projection that opens in 2010 first draws deaths in 2011, so it reads OACT's values for 2011–2020. After 2099 the baseline supplies nothing and refuses. CBO labels them deaths per 1,000 people without saying which kind. Read as probabilities of death, they reproduce every life expectancy CBO publishes for 2026–2099 within 0.0011 years; read as central death rates, they do not. The baseline therefore treats them as probabilities. | +| Fertility | None. TR2008 publishes a total rate only, and no accessor reads it. | Published: the total fertility rate in single-year Table V.A1 (historical 1940–2024, a partial-year provisional 2025 value, projected 2026–2100). Derived: rates by single year of age, scaling the NCHS 2024 age shape to each year's total (see the next section). | Published: rates by single year of age, 14–49, for 2021–2099. CBO prints its total fertility rate from 2026; for 2021–2025 the baseline's total is the sum of the published rates by age. | +| Other economic series | None beyond the COLA path. | Single-year Table V.B1 (historical 1960–2024, an estimated 2025 value, projected 2026–2100) supplies the CPI-W change after 2035. | CBO's February 2026 long-term economics: CPI-U growth and growth of real earnings per worker, 1996–2056. After 2056 the baseline holds each final growth rate. | +| Claiming | The 2014 Supplement, rows through 2008. | The 2026 Statistical Supplement's Table 6.B5.1 (entitlement years through 2025), with its last row reused for later years. | Same as `tr2026_intermediate`. | +| Disability insurance | 2008 fit, held constant. | 2008 fit, held constant: a named gap. | 2008 fit, held constant: a named gap. | +| Immigration | None (closed cohort). | None in a closed cohort. Single-year Table V.A2 publishes inflows, outflows and net change, for lawful permanent residents and for temporary or unlawfully present immigrants. | None in a closed cohort. CBO publishes gross migration by age, sex, status and flow. | + +: What each selectable baseline draws from its sources. "Published" marks a series printed in the source; "derived" marks one we compute from published values. {#tbl-selectable} + +### Notes on each assumption + +**COLA after 2035 under the 2026 Report.** V.C1 prints COLAs only +through 2035. Later years take the annual change in the CPI-W from +single-year Table V.B1. That change is an annual average, so it only +approximates the third-quarter-to-third-quarter change the statute +uses. TR2008's derived ultimate rate carries the same caveat. + +**CBO's prices and wages.** CBO publishes neither the CPI-W nor the +AWI. Its long-term economic projections stop in 2056, while its Social +Security inputs run to 2100 and its demographic files cover 2021–2099. +The `cbo2026_long_term` baseline therefore applies these rules, each a +builder default awaiting ratification and each named in the code +(`data/cbo2026.py`, `baselines/cbo2026_long_term.py`): + +- **CPI-U splice.** Ten-year CPI-U levels for 2023–2036, then the + long-term workbook's annual growth compounded on the last ten-year + level, so the two workbooks join without a jump. +- **CPI-W and COLA from 2026.** Both grow at the spliced CPI-U growth. + Realized COLAs run through 2025, and the CPI-W through 2025 comes + from the 2026 Trustees Report. +- **AWI bridge, 2025–2026.** From the actual 2024 AWI, the two years + grow at real earnings per worker growth times CPI-U growth, because + CBO's covered earnings and workers start in 2026. +- **AWI from 2027.** Growth of covered earnings per covered worker. + That series is a different one from the AWI, not a version of it. +- **After 2056.** Each final long-term economic growth rate is held to + 2100. +- **Total fertility rate before 2026.** The sum of CBO's rates by age. +- **Mortality before 2021.** OACT's historical probabilities from the + 2026 Report. + +The baseline supplies no demographic value after 2099 and refuses those +years. + +**Mortality.** Both new baselines replace TR2008's substitute, a 2004 +table scaled by two broad ratios, with probabilities that vary by single +year of age, sex and year. Disabled workers' death rates are a multiple +of the population probability, so they move with whichever mortality +the baseline supplies (`engine/di_entitlement_rates.py:29-43`). + +**Claiming.** Under the new baselines, claiming uses Table 6.B5.1 of +SSA's 2026 *Annual Statistical Supplement*, which covers entitlement +years through 2025 (`data/external/ssa_claim_ages_2026supplement.json`). +Nearest-year selection reuses its last row for later years +(`engine/steps.py:367-376`). Claiming still does not respond to a +reform. + +**Disability rates stay 2008-fit under every baseline.** Neither source +publishes disability incidence or termination rates by age. The 2026 +Trustees materials give age-sex-adjusted summary measures, plus charts +by age group, in the disability assumptions memorandum. None of the CBO +files listed below contains disability rates by age. The model's award, +recovery and death profiles therefore keep the 2008 fit described +above. Only disabled workers' death rates change, through the mortality +multiplier. This is a named gap. + +### Sources + +The 2026 Trustees Report pages, as of 1 October 2026: + +- Table V.C1: +- Single-year Tables V.A1, V.B1 and VI.G1: + , + and + +- OACT's projected probabilities of death: + + and its `_F_` counterpart for women, with `_Hist_` files for history +- *2026 Long-Range OASDI Projection Model Documentation*: + + (SHA-256 `e3a73c80db785bf38aa334ac7315cc7372906e5dbae627ed2bb7d473cb8073f2`) +- *The Long-Range Disability Assumptions for the 2026 Trustees Report*: + + +OACT's `CY` folder always holds the current year's files. Earlier +Reports' files sit under year folders, such as +, and the 2026 +files should move to the same pattern once the 2027 Report appears. + +CBO's files. cbo.gov blocks automated downloads, so the copies we +examined came from Internet Archive captures: + +| File | SHA-256 | +|---|---| +| Demographic projections, January 2026, supplementing *The Demographic Outlook: 2026 to 2056*: | `083ea91701c1af901cd378211a34c8c8a941c8833c2507564af89551d63392bf` | +| Long-term economic projections, February 2026: | `6f6e66d7e2aaf187e0d13aa90767a39e682a6a5ab2be87b0cc2221b7370e6929` | +| Projections underlying Social Security estimates, September 2026: | `7a7247651f3ae6820c9263a16f11b9cc9cf24cd7dcb9e18bb76e44711924d73b` | + +## Fertility by age + +No registered result draws a birth, so nothing in this section affects +a published number. Fertility matters for the open-population +projections that the new baselines make possible. The package's opt-in +birth step (`baselines/fertility.py`) draws births to women aged 14–49 +at their single-age rates. It is not gated, it is report-only, and it +does not touch the gated fertility law that gate m6's projections use. + +### Why rates by age and not one total rate + +The number of births in a year equals the sum, over each age, of the +number of women at that age times the birth rate at that age. The +total fertility rate (TFR) adds up one year's birth rates across ages. +It reads as the number of children a woman would have if she lived +through her childbearing years at that year's rates. It does not say at +which ages those births happen. + +That matters in two ways: + +- **Age structure.** Two populations with the same TFR produce + different numbers of births in a year if their women are spread + differently across ages. A large cohort of women in their late + twenties produces more births than the same number in their early + forties. +- **Timing.** A shift toward later childbearing changes births year by + year even while the TFR holds steady. + +A projection that knows only the TFR still needs an age pattern to +place births. The model's own birth step already works by the mother's +single year of age (`models/family_transitions/components/fertility.py:26-31`), +so any outside baseline has to supply rates by single year of age to +plug into the same structure. + +### What each source publishes + +- **CBO** publishes rates by single year of age, 14 to 49, for every + year 2021–2099. They are in births per 1,000 women, for all women and + separately for native-born and foreign-born women (file + `CSV Files/fertilityRates_byYearAgePlace.csv` in the January 2026 + demographic projections). These are published rates and need no + derivation. +- **The 2026 Trustees Report** publishes only the total fertility rate, + in single-year Table V.A1. OACT's downloadable files for the 2026 + Report hold life tables, death probabilities and population, but no + fertility file. + - OACT does project birth rates by single year of age 14 to 49 + internally. Its 2026 model documentation (§1.1 "Fertility", + Demography pages 2–4, PDF pages 10–12) describes the method. OACT projects each age's rate as a ratio + to the age-30 rate, carrying forward the average year-to-year + change in those ratios, and moves each age to its ultimate rate on + its own schedule (age 30 reaches its ultimate in 2037). The age-30 + rate is chosen to hit the ultimate cohort TFR, which the 2026 + Report sets at 1.75 from the cohort born in 2012. + - Those single-year rates are not published. +- **TR2008** likewise publishes a total rate only + (`data/tr2008.py:445-447`, `:940-945`). + +### A Trustees-consistent age schedule + +The `tr2026_intermediate` baseline derives rates by age. It scales a +documented age shape to the V.A1 total fertility rate each year. The +default shape is NCHS's final 2024 rates in five-year bands +(`data/external/nchs_asfr_2024.json`), spread to single ages by a +mean-preserving interpolation that keeps each band's total +(`baselines/interpolation.py`, `baselines/asfr.py`). CBO's 2026 +single-age pattern is the registered alternative shape. With $s(a)$ the +shape's share at single age $a$: + +$$ +f_t(a) = \mathrm{TFR}_t \times s(a), \qquad \sum_{a=14}^{49} s(a) = 1, +$$ + +so that $\sum_a f_t(a) = \mathrm{TFR}_t$ in every year: the schedule +reproduces the published total exactly. Rates here are births per +woman. CBO's rates, in births per 1,000 women, are divided by 1,000 to +compare. + +The schedule is derived, not published, in two respects: + +- **The shape is an assumption.** The default is the NCHS 2024 shape; + the choice is a builder default awaiting ratification, and every + derived rate carries its shape's tag. +- **The shape does not change over time.** OACT's internal method moves + each age toward its own ultimate rate on its own schedule. The derived + schedule therefore matches the Trustees' totals but not their timing + of births across ages. + +CBO's published schedule needs neither step. + +## Known discrepancies + +1. **The A1 specification's AWI wording.** A1 §15 says the AWI for + 2018–2029 is a "geometric interpolation between published points" + (`docs/design/urban2010_cola_comparison.md:638`). The code instead + reads the single-year Table VI.F6 that SSA published with TR2008 + (`data/tr2008.py:29-32`, `:761-767`). Where both exist, that table's + values equal the printed VI.F6 points for 2020, 2025 and 2030 + (`data/external/tr2008/provenance.md`, §2). Between those points the + two methods can differ. The A1 specification is frozen, so this page + records the mismatch and the specification stays as ratified. +2. **Which edition of the claiming table.** The paper and A1 §15 point + to `claiming.py`, whose default table is the 2023 Supplement + (`claiming.py:81-83`). The projection tests pass the 2014 Supplement + instead (`cohorts/psid2010.py:161-166`; + `cola_track_a/runner.py:167-190`). The two editions' 2008 rows agree + in every cell but one: men claiming at 67–69 are 1.3 percent of + awards in the 2014 edition and 1.4 percent in the 2023 edition + (`data/external/ssa_claim_ages_2014supplement.json`; + `data/external/ssa_claim_ages_2023supplement.json`). +3. **"2008" disability rates.** The paper calls the disability rates + "2008-vintage" and says they were "observed through 2008" + (`paper/paper.qmd`, the "Disability, mortality and claiming" paragraph and the first paragraph of "The projections"). The fit uses 2008 data, published in + July 2009 (`engine/di_entitlement_rates.py:172-176`). Both phrases + describe the data year correctly. This page states the publication + date so that no one reads "2008-vintage" as "published by 2008". +4. **The AWI's span.** The code loads TR2008's AWI through 2085 + (`cola_track_a/runner.py:197`), while the paper says the tests take + it "for 1975–2030" (`paper/paper.qmd`, first paragraph of "The projections"). The registered-run check + covers exactly 1975–2030 (`cola_track_a/runner.py:342-344`), and the + tests compute nothing after 2030. The paper's statement is right and + stays as written. +5. **Planning documents.** Two planning documents describe mechanisms + no registered result uses: + - `docs/methodology.md` describes fertility predictors aligned to + national vital statistics (lines 392–396), differential mortality + by earnings and education (lines 430–434), alignment to Trustees + demographic rates (line 480) and new cohorts entering from the CPS + at age 18 (lines 488–497); + - `docs/operationalizing-mortality-and-projection-drift.md` says the + first phase should not reduce mortality to age and sex alone (lines + 212–222), which is exactly what the projection tests do. + + Both describe plans, not the code. + +## Corrections made with this page + +- **Projected death probabilities.** The paper said that "The Trustees + Report publishes no projected death probabilities by single year of + age and sex" (`paper/paper.qmd`, the "Disability, mortality and claiming" paragraph). That holds for the 2008 Report + as printed, but not in general. OACT publishes projected death + probabilities by single year of age and sex in downloadable files + alongside its Reports. We found those files for the 2014 Report + onward: on 3 October 2026 the 2014, 2015 and 2026 files answered at + `https://www.ssa.gov/OACT/Downloadables//DeathProbsE_M_Alt2_TR.csv` + (`CY` in place of the year for 2026), and the same address returned + "not found" for 2010–2013. We did not search for earlier files under + other addresses. For 2026 + the men's intermediate file is + . + The sentence now limits the claim to the 2008 Report and says where + the later files are. No number in the paper changed. + +These are corrections to prose, not to any committed artifact, so +nothing is filed under `docs/errata/`. diff --git a/paper/paper.qmd b/paper/paper.qmd index 36d433e6..8034f330 100644 --- a/paper/paper.qmd +++ b/paper/paper.qmd @@ -175,7 +175,7 @@ No blind test yet draws forward earnings, and the projection tests draw no earni **Disability, mortality and claiming.** The disability module passes gate m4 in 5 of 5 seeds (`runs/gate_m4_hazard_v1.json`). It models self-reported work limitation: incidence, recovery, occupancy and exits near full retirement age. Gate m4 scores it against held-out PSID employment-status records and against the age shape and exit composition of SSA's 2023 disability-insurance statistics. It never scores SSA's levels. Work limitation is a different concept from a Social Security Disability Insurance (SSDI) award. The projection tests use a separate SSDI entitlement component, which draws awards, recoveries and conversion at full retirement age at rates observed through 2008 (`engine/di_entitlement.py`). No gate scores it. -The engine's mortality step uses a table of death probabilities by age band and sex, and gate m6 certifies nothing about how mortality drifts over time. The projection tests use SSA's 2004 period life table. Each year we multiply its death probabilities by the ratio of that year's age-sex-adjusted death rate to the 2004 rate, both from the 2008 Trustees Report's intermediate assumptions. We use one ratio for ages under 65 and another for 65 and over, and each applies to men and women alike. The Trustees Report publishes no projected death probabilities by single year of age and sex. Claiming runs with no gate. The claiming step draws a claim age from SSA's published distribution of retired-worker awards by sex and entitlement year (`claiming.py`). For entitlement years after 2008, the projection tests reuse the 2008 distribution. No claim responds to a reform. Exercise 3, which raises the full retirement age to 68, therefore holds each person's projected claim age fixed under the reform. Its registration, the plan we post before the run, lists claiming responses only as alternatives that we report beside the main result (@sec-blind). +The engine's mortality step uses a table of death probabilities by age band and sex, and gate m6 certifies nothing about how mortality drifts over time. The projection tests use SSA's 2004 period life table. Each year we multiply its death probabilities by the ratio of that year's age-sex-adjusted death rate to the 2004 rate, both from the 2008 Trustees Report's intermediate assumptions. We use one ratio for ages under 65 and another for 65 and over, and each applies to men and women alike. The 2008 Trustees Report prints no projected death probabilities by single year of age and sex. SSA's Office of the Chief Actuary publishes such probabilities in downloadable files alongside its Reports, which we found for the 2014 Report onward; for the 2026 Report, the men's intermediate file is . Claiming runs with no gate. The claiming step draws a claim age from SSA's published distribution of retired-worker awards by sex and entitlement year (`claiming.py`). For entitlement years after 2008, the projection tests reuse the 2008 distribution. No claim responds to a reform. Exercise 3, which raises the full retirement age to 68, therefore holds each person's projected claim age fixed under the reform. Its registration, the plan we post before the run, lists claiming responses only as alternatives that we report beside the main result (@sec-blind). **Population.** Gate w1 passes (`runs/gate_w1_candidate4_v1.json`). When we carry the transitions we estimate on the PSID onto the CPS frame (@sec-model), they match the frame on all 44 gated cells: 43 cross-sectional joint distributions and one ordering check. The entrant schedule lets new people enter a projection. We report its output, and no gate scores it. No fitted law takes that output as an input. diff --git a/scripts/describe_baseline.py b/scripts/describe_baseline.py new file mode 100644 index 00000000..d2afd3c4 --- /dev/null +++ b/scripts/describe_baseline.py @@ -0,0 +1,158 @@ +"""Print a baseline's input paths for documentation (input values only). + +Usage:: + + python scripts/describe_baseline.py --baseline tr2026_intermediate \ + --years 2026-2035 [--sources] + +Prints, for every year in the range, the baseline's COLA (determination +year, percent), annual CPI-W growth (percent), AWI growth over the prior +year (percent), the death probability at 65 by sex, the TFR and the ASFR +at ages 20, 30 and 40 (births per 1,000 women). ``--sources`` adds each +value's source tag (derived values are tagged as derived). These are +the baseline's inputs; nothing is projected and no outcome is computed. +A value the baseline does not supply prints as ``gap``. +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT / "src") not in sys.path: + sys.path.insert(0, str(ROOT / "src")) + +from populace_dynamics.baselines import ( # noqa: E402 + BASELINE_NAMES, + DEFAULT_BASELINE, + BaselineGapError, + get_baseline, +) + +COLUMNS = ( + ("year", 4), + ("COLA%", 6), + ("CPI-W%", 6), + ("AWIg%", 6), + ("q65 M", 8), + ("q65 F", 8), + ("TFR", 6), + ("f20", 6), + ("f30", 6), + ("f40", 6), +) +_GAP = "gap" +#: The first determination year taken from the baseline's own tables, as +#: in the projections (``TrackAConfig.tr2008_first_rate_year``). +FIRST_RATE_YEAR = 2008 + + +def _years(text: str) -> range: + first, _, last = text.partition("-") + try: + start, stop = int(first), int(last or first) + except ValueError as error: + raise argparse.ArgumentTypeError( + f"--years must look like 2026-2035, got {text!r}" + ) from error + if start > stop: + raise argparse.ArgumentTypeError("--years must be increasing") + return range(start, stop + 1) + + +def _value(function, fmt: str) -> str: + try: + return format(float(function()), fmt) + except (BaselineGapError, KeyError, ValueError): + return _GAP + + +def _series(function) -> dict: + """A year-keyed path, or an empty one if the range is not covered.""" + try: + path = function() + except (BaselineGapError, KeyError, ValueError): + return {} + if hasattr(path, "by_determination_year"): + return dict(path.by_determination_year) + return dict(path) + + +def describe(name: str, years: range, *, sources: bool = False) -> str: + """The text the CLI prints for ``name`` over ``years``.""" + baseline = get_baseline(name) + first = min(FIRST_RATE_YEAR, years[-1]) + cola = _series(lambda: baseline.cola_rates(first, years[-1])) + awi = _series(lambda: baseline.awi(years[0] - 1, years[-1])) + lines = [ + f"Baseline: {baseline.name}", + f"Vintage: {baseline.vintage}", + "Input values only (no projection, no outcome).", + "COLA% is the determination-year increase; AWIg% the AWI growth " + "over the prior year; f20/f30/f40 are births per 1,000 women.", + "", + " ".join(label.rjust(width) for label, width in COLUMNS), + ] + for year in years: + cells = ( + str(year), + _value(lambda year=year: 100.0 * cola[year], ".2f"), + _value(lambda year=year: baseline.cpiw_growth(year), ".2f"), + _value( + lambda year=year: 100.0 * (awi[year] / awi[year - 1] - 1.0), + ".2f", + ), + *( + _value( + lambda year=year, sex=sex: baseline.qx(year)[sex][65], + ".6f", + ) + for sex in ("male", "female") + ), + _value(lambda year=year: baseline.tfr(year), ".3f"), + *( + _value( + lambda year=year, age=age: 1000.0 + * baseline.asfr(year)[age], + ".2f", + ) + for age in (20, 30, 40) + ), + ) + lines.append( + " ".join( + cell.rjust(width) + for cell, (_, width) in zip(cells, COLUMNS, strict=True) + ) + ) + if sources: + lines += ["", "Sources by year:"] + for year in years: + try: + tags = baseline.value_sources(year) + except (BaselineGapError, KeyError, ValueError): + lines.append(f" {year}: {_GAP}") + continue + lines.append( + f" {year}: " + + "; ".join(f"{key}={value}" for key, value in tags.items()) + ) + return "\n".join(lines) + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument( + "--baseline", choices=BASELINE_NAMES, default=DEFAULT_BASELINE + ) + parser.add_argument("--years", type=_years, default=_years("2026-2035")) + parser.add_argument("--sources", action="store_true") + args = parser.parse_args(argv) + print(describe(args.baseline, args.years, sources=args.sources)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/extract_cbo2026_parameters.py b/scripts/extract_cbo2026_parameters.py new file mode 100644 index 00000000..b740d775 --- /dev/null +++ b/scripts/extract_cbo2026_parameters.py @@ -0,0 +1,722 @@ +"""Rebuild CBO2026 baseline inputs from pinned, captured source bytes. + +Only demographic inputs, economic inputs and the covered-worker/earnings +rows of Additional-Info sheets 1-2 are parsed. No fiscal outcome tables or +Additional-Info footnotes are emitted. CBO files were recovered from raw +Wayback captures; SHA-1/base32 digests are checked against captured CDX +records as well as SHA-256. See ``data/external/cbo2026/provenance.md``. + +``--inputs`` names the captured ``baseline-inputs`` directory. Small raw +sources may be bundled; larger sources remain external, by URL and hash. +``--check`` rebuilds in memory and compares every output byte, writing +nothing. Inputs are never downloaded by this script. +""" + +from __future__ import annotations + +import argparse +import base64 +import csv +import gzip +import hashlib +import io +import json +import math +import re +import sys +import zipfile +from collections.abc import Mapping, Sequence +from dataclasses import dataclass +from html.parser import HTMLParser +from pathlib import Path +from typing import Any +from xml.etree import ElementTree as ET + +from populace_dynamics.data.life_table import life_expectancy + +ROOT = Path(__file__).resolve().parents[1] +OUT_DIR = ROOT / "data" / "external" / "cbo2026" +DEFAULT_INPUTS = Path( + "/Users/maxghenis/microcosm-launch-evidence/nasi-meeting-20261001/" + "followup/inputs/baseline-inputs" +) +DEMOGRAPHIC_PREFIX = "cbo/extracted/57059-2026-01-Demographic-Projections/" +PLACES = ("all", "native-born", "foreign-born") +SEXES = ("male", "female") +LE_TOLERANCE = 0.0011 +TFR_TOLERANCE = 0.0023 +NS = {"s": "http://schemas.openxmlformats.org/spreadsheetml/2006/main"} + + +@dataclass(frozen=True) +class Source: + """An immutable captured-source identity, never a live URL request.""" + + relative_path: str + sha256: str + url: str + locator: str + timestamp: str | None = None + cdx_sha1_b32: str | None = None + + +ZIP_URL = ( + "https://www.cbo.gov/system/files/2026-01/" + "57059-2026-01-Demographic-Projections.zip" +) +SOURCES: Mapping[str, Source] = { + "demographic_archive": Source( + "cbo/57059-2026-01-Demographic-Projections.zip", + "083ea91701c1af901cd378211a34c8c8a941c8833c2507564af89551d63392bf", + ZIP_URL, + "Archive containing the demographic CSV files and workbook", + "20260211074115", + "AUU73LM3P3IH4ITJSNMHQM7T7UEHJCDD", + ), + "fertility": Source( + DEMOGRAPHIC_PREFIX + "CSV Files/fertilityRates_byYearAgePlace.csv", + "8ed8615ed7561d6c08c3005dc516b8dd78c16072020e8c106a5738a5563b0f1c", + ZIP_URL, + "CSV Files/fertilityRates_byYearAgePlace.csv: year,age," + "place_of_birth,births_per_1000_females", + ), + "mortality": Source( + DEMOGRAPHIC_PREFIX + "CSV Files/mortalityRates_byYearAgeSex.csv", + "378defc1d6eee7b6870cc7ddaf534dd782d51bf3f686a39a8fd7cd7cba2beb63", + ZIP_URL, + "CSV Files/mortalityRates_byYearAgeSex.csv: year,age,sex," + "deaths_per_1000_people", + ), + "demographic_workbook": Source( + DEMOGRAPHIC_PREFIX + "61879-Demographic-Projections.xlsx", + "aca3322e0a74fee39e669d953201075887da13e5e3792e08b8b6f7d1f86d1006", + ZIP_URL, + "61879-Demographic-Projections.xlsx: '1. Population and growth'; " + "row 8 years; r39 TFR; r41 life expectancy at birth; r42 at 65", + ), + "long_term_economics": Source( + "cbo/57054-2026-02-LTBO-econ.xlsx", + "6f6e66d7e2aaf187e0d13aa90767a39e682a6a5ab2be87b0cc2221b7370e6929", + "https://www.cbo.gov/system/files/2026-02/" + "57054-2026-02-LTBO-econ.xlsx", + "'1. Econ Vars_Annual Rates': r8 years,r31 real earnings growth," + "r40 CPI-U growth; '3. Econ Vars_Annual Levels': r7 years,r13 CPI-U", + "20260726122828", + "OWABBCNJ5O2UJGAZDVBAK2SLWSCHOFLK", + ), + "ten_year_economics": Source( + "cbo/51135-2026-02-Economic-Projections.xlsx", + "ae8f4920702fabf8fb3136bc94a42c53466cff5e890dec22396d8dc49dc2f776", + "https://www.cbo.gov/system/files/2026-02/" + "51135-2026-02-Economic-Projections.xlsx", + "'2. Calendar Year': r7 years; r53 CPI-U (1982-84=100)", + "20260802034021", + "SLIHKFJTIHK62CO3YN5CYBRU4PIQCB4D", + ), + "covered_earnings": Source( + "cbo/62556-2026-Additional-Info.xlsx", + "7a7247651f3ae6820c9263a16f11b9cc9cf24cd7dcb9e18bb76e44711924d73b", + "https://www.cbo.gov/system/files/2026-09/" + "62556-2026-Additional-Info.xlsx", + "'1. Covered Workers': numeric year rows,male/female/total " + "(thousands); '2. Covered and Taxable Earnings': numeric year " + "rows,covered earnings column B (trillions of dollars)", + "20260919143154", + "DTGXZD5RVUA4T4SH3HTVHBHFOFS3DR3I", + ), + "actual_awi_anchor": Source( + "tr2026/lr6g1.html", + "2e0214278e0b2271616093af02280277f6e363d112c47a90b49fe7168e795e75", + "https://www.ssa.gov/oact/TR/2026/lr6g1.html", + "TR2026 Table VI.G1, Historical data, CY2024 AWI column", + ), +} + + +def sha256(raw: bytes) -> str: + return hashlib.sha256(raw).hexdigest() + + +def sha1_b32(raw: bytes) -> str: + return base64.b32encode(hashlib.sha1(raw).digest()).decode("ascii") + + +def deterministic_gzip(raw: bytes) -> bytes: + """Use a portable gzip header across supported Python versions.""" + buffer = io.BytesIO() + with gzip.GzipFile(filename="", mode="wb", fileobj=buffer, mtime=0) as f: + f.write(raw) + return buffer.getvalue() + + +def read_source(name: str, inputs: Path) -> bytes: + """Verify captured or bundled payload before parsing any data value.""" + spec = SOURCES[name] + captured = inputs / spec.relative_path + bundled = OUT_DIR / "sources" / (Path(spec.relative_path).name + ".gz") + if captured.is_file(): + raw = captured.read_bytes() + elif bundled.is_file(): + raw = gzip.decompress(bundled.read_bytes()) + else: + raise FileNotFoundError( + f"source {name} is external: provide --inputs containing " + f"{spec.relative_path}; expected sha256 {spec.sha256}" + ) + if sha256(raw) != spec.sha256: + raise ValueError(f"{name} captured-source SHA-256 mismatch") + if spec.cdx_sha1_b32 and sha1_b32(raw) != spec.cdx_sha1_b32: + raise ValueError(f"{name} Wayback CDX digest mismatch") + return raw + + +def workbook_rows( + raw: bytes, sheet: str, *, selected: set[int] | None = None +) -> dict[int, dict[str, str]]: + """Read cached XLSX values from one named input sheet. + + XML row filtering avoids traversing fiscal sheets or printing workbook + strings. ``selected`` is used for summaries; callers reading numeric + year tables consume only rows whose column A is a year. + """ + with zipfile.ZipFile(io.BytesIO(raw)) as archive: + strings = [ + "".join(node.itertext()) + for node in ET.fromstring( + archive.read("xl/sharedStrings.xml") + ).findall("s:si", NS) + ] + relationships = { + node.attrib["Id"]: node.attrib["Target"] + for node in ET.fromstring( + archive.read("xl/_rels/workbook.xml.rels") + ) + } + sheets = ET.fromstring(archive.read("xl/workbook.xml")).findall( + "s:sheets/s:sheet", NS + ) + matching = [node for node in sheets if node.attrib["name"] == sheet] + if len(matching) != 1: + raise ValueError(f"expected one input sheet {sheet!r}") + relationship = matching[0].attrib[ + "{http://schemas.openxmlformats.org/officeDocument/" + "2006/relationships}id" + ] + target = relationships[relationship] + path = target.lstrip("/") if target.startswith("/") else "xl/" + target + rows = {} + for node in ET.fromstring(archive.read(path)).findall( + "s:sheetData/s:row", NS + ): + number = int(node.attrib["r"]) + if selected is not None and number not in selected: + continue + cells = {} + for cell in node: + value = cell.find("s:v", NS) + if value is None or value.text is None: + continue + text = value.text + if cell.attrib.get("t") == "s": + text = strings[int(text)] + cells[re.sub(r"[0-9]+$", "", cell.attrib["r"])] = text + rows[number] = cells + return rows + + +def year_columns(row: Mapping[str, str], first: int, last: int) -> dict: + out = { + int(value): column + for column, value in row.items() + if value.isdigit() and first <= int(value) <= last + } + selected_count = sum( + value.isdigit() and first <= int(value) <= last + for value in row.values() + ) + if len(out) != selected_count: + raise ValueError("duplicate year columns") + if set(out) != set(range(first, last + 1)): + raise ValueError(f"year columns do not cover {first}..{last}") + return out + + +def parse_age_csv( + raw: bytes, + group_column: str, + value_column: str, + groups: Sequence[str], + first_age: int, + last_age: int, +) -> dict[str, dict[str, list[float]]]: + """Refuse duplicate or missing age cells and preserve printed units.""" + cells: dict[tuple[int, str], dict[int, float]] = {} + reader = csv.DictReader(io.StringIO(raw.decode("utf-8-sig"))) + expected = {"year", "age", group_column, value_column} + if ( + len(reader.fieldnames or ()) != len(expected) + or set(reader.fieldnames or ()) != expected + ): + raise ValueError("unexpected demographic CSV columns") + for row in reader: + year, age = int(row["year"]), int(row["age"]) + group = row[group_column] + if not 2021 <= year <= 2099 or group not in groups: + raise ValueError("demographic CSV has unexpected year/group") + if not first_age <= age <= last_age: + raise ValueError("demographic CSV has unexpected age") + key = year, group + ages = cells.setdefault(key, {}) + if age in ages: + raise ValueError(f"duplicate demographic cell {key}, age {age}") + value = float(row[value_column]) + if not math.isfinite(value) or value < 0: + raise ValueError("demographic CSV has invalid rate") + ages[age] = value + out = {} + for year in range(2021, 2100): + out[str(year)] = {} + for group in groups: + ages = cells.get((year, group), {}) + if set(ages) != set(range(first_age, last_age + 1)): + raise ValueError( + f"missing demographic ages for {year},{group}" + ) + out[str(year)][group] = [ + ages[age] for age in range(first_age, last_age + 1) + ] + return out + + +class _Year2024(HTMLParser): + """Select the AWI anchor row without extracting unrelated columns.""" + + def __init__(self) -> None: + super().__init__() + self.in_cell = False + self.row: list[str] = [] + self.text: list[str] = [] + self.anchors: list[float] = [] + self.has_awi_header = False + + def handle_starttag(self, tag, attrs): + if tag == "tr": + self.row = [] + elif tag in ("td", "th"): + self.in_cell = True + self.text = [] + + def handle_data(self, data): + if self.in_cell: + self.text.append(data) + + def handle_endtag(self, tag): + if tag in ("td", "th"): + text = "".join(self.text).strip() + if text: + self.row.append(text) + self.in_cell = False + elif tag == "tr": + label = re.sub(r"\s+", " ", " ".join(self.row)).casefold() + if "wage index" in label: + self.has_awi_header = True + if len(self.row) >= 3 and re.fullmatch( + r"2024\s*[a-z]?", self.row[0] + ): + # VI.G1 nonempty cells: year,adjusted CPI,AWI,... + self.anchors.append(float(self.row[2].replace(",", ""))) + + +def parse_awi_anchor(raw: bytes) -> float: + parser = _Year2024() + parser.feed(raw.decode("utf-8")) + if ( + not parser.has_awi_header + or len(parser.anchors) != 1 + or parser.anchors[0] <= 0 + ): + raise ValueError("expected one positive actual 2024 AWI anchor") + return parser.anchors[0] + + +def build_demographics(raw: Mapping[str, bytes]) -> dict[str, Any]: + asfr = parse_age_csv( + raw["fertility"], + "place_of_birth", + "births_per_1000_females", + PLACES, + 14, + 49, + ) + mortality = parse_age_csv( + raw["mortality"], + "sex", + "deaths_per_1000_people", + SEXES, + 0, + 119, + ) + rows = workbook_rows( + raw["demographic_workbook"], + "1. Population and growth", + selected={8, 39, 41, 42}, + ) + columns = year_columns(rows[8], 2026, 2099) + summary = { + str(year): { + "tfr": float(rows[39][column]), + "life_expectancy_birth": float(rows[41][column]), + "life_expectancy_65": float(rows[42][column]), + } + for year, column in columns.items() + } + return { + "schema_version": "cbo2026_demographics.v1", + "asfr_ages": [14, 49], + "asfr_units": "births_per_1000_females", + "asfr": asfr, + "mortality_ages": [0, 119], + "mortality_units": "deaths_per_1000_people (empirically q*1000)", + "mortality": mortality, + "published_summary": summary, + "source_keys": ["fertility", "mortality", "demographic_workbook"], + } + + +def build_economics(raw: Mapping[str, bytes]) -> dict[str, Any]: + rates = workbook_rows( + raw["long_term_economics"], + "1. Econ Vars_Annual Rates", + selected={8, 31, 40}, + ) + levels = workbook_rows( + raw["long_term_economics"], + "3. Econ Vars_Annual Levels", + selected={7, 13}, + ) + columns = year_columns(rates[8], 1996, 2056) + level_columns = year_columns(levels[7], 1996, 2056) + long_term = { + str(year): { + "cpiu_growth_percent": float(rates[40][column]), + "real_earnings_growth_percent": float(rates[31][column]), + "cpiu_index_1982_1984_100": ( + 100.0 * float(levels[13][level_columns[year]]) + ), + } + for year, column in columns.items() + } + ten = workbook_rows( + raw["ten_year_economics"], "2. Calendar Year", selected={7, 53} + ) + ten_columns = year_columns(ten[7], 2023, 2036) + ten_year = { + str(year): float(ten[53][column]) + for year, column in ten_columns.items() + } + workers = workbook_rows( + raw["covered_earnings"], + "1. Covered Workers", + selected=set(range(9, 90)), + ) + earnings = workbook_rows( + raw["covered_earnings"], + "2. Covered and Taxable Earnings", + selected=set(range(9, 90)), + ) + covered = {} + for row in workers.values(): + year = row.get("A", "") + if year.isdigit() and 2026 <= int(year) <= 2100: + if year in covered: + raise ValueError(f"duplicate covered-worker year {year}") + covered[year] = { + "male_workers_thousands": float(row["B"]), + "female_workers_thousands": float(row["C"]), + "total_workers_thousands": float(row["D"]), + } + for row in earnings.values(): + year = row.get("A", "") + if year.isdigit() and year in covered: + if "covered_earnings_trillions" in covered[year]: + raise ValueError(f"duplicate covered-earnings year {year}") + covered[year]["covered_earnings_trillions"] = float(row["B"]) + if set(covered) != {str(y) for y in range(2026, 2101)}: + raise ValueError("covered earnings/workers must cover 2026..2100") + if any(len(row) != 4 for row in covered.values()): + raise ValueError("covered earnings column missing") + return { + "schema_version": "cbo2026_economics.v1", + "long_term": long_term, + "ten_year_cpiu_index_1982_1984_100": ten_year, + "covered": covered, + "awi_anchor": { + "year": 2024, + "amount": parse_awi_anchor(raw["actual_awi_anchor"]), + "source": "tr2026_vi_g1_actual", + }, + "source_keys": [ + "long_term_economics", + "ten_year_economics", + "covered_earnings", + "actual_awi_anchor", + ], + } + + +def build_checks(demographics: Mapping, economics: Mapping) -> dict: + """Compare q and m interpretations to every published LE summary.""" + rows = [] + errors: dict[str, list[float]] = {"q": [], "m": []} + for year, summary in demographics["published_summary"].items(): + interpretations = {} + for kind in ("q", "m"): + by_sex = {} + for sex in SEXES: + rate = [ + x / 1000.0 for x in demographics["mortality"][year][sex] + ] + q = ( + rate + if kind == "q" + else [x / (1.0 + x / 2.0) for x in rate] + ) + by_sex[sex] = { + str(age): life_expectancy(q, age, f0=0.5) + for age in (0, 65) + } + interpretations[kind] = {} + for age, field in ( + (0, "life_expectancy_birth"), + (65, "life_expectancy_65"), + ): + estimate = sum(by_sex[s][str(age)] for s in SEXES) / 2.0 + error = estimate - summary[field] + errors[kind].append(abs(error)) + interpretations[kind][str(age)] = { + "recomputed": estimate, + "published": summary[field], + "error": error, + } + rows.append({"year": int(year), "interpretations": interpretations}) + q_max = max(errors["q"]) + m_min = min(errors["m"]) + tfr_errors = [ + abs(sum(demographics["asfr"][y]["all"]) / 1000.0 - row["tfr"]) + for y, row in demographics["published_summary"].items() + ] + all_q = [ + value / 1000.0 + for sexes in demographics["mortality"].values() + for rates in sexes.values() + for value in rates + ] + checks = [ + {"id": "q_range", "passed": all(0 <= x <= 1 for x in all_q)}, + { + "id": "q119_closed", + "passed": all( + rates[-1] == 1000.0 + for sexes in demographics["mortality"].values() + for rates in sexes.values() + ), + }, + {"id": "q_reproduces_published_le", "passed": q_max <= LE_TOLERANCE}, + {"id": "m_interpretation_rejected", "passed": m_min > LE_TOLERANCE}, + { + "id": "asfr_sums_to_published_tfr", + "passed": max(tfr_errors) <= TFR_TOLERANCE, + "max_error": max(tfr_errors), + "tolerance": TFR_TOLERANCE, + }, + { + "id": "covered_inputs_positive", + "passed": all( + value > 0 + for row in economics["covered"].values() + for value in row.values() + ), + }, + ] + return { + "schema_version": "cbo2026_transcription_check.v1", + "all_passed": all(check["passed"] for check in checks), + "checks": checks, + "mortality_interpretation": { + "selected": "q", + "tolerance_years": LE_TOLERANCE, + "q_max_absolute_error_years": q_max, + "m_min_absolute_error_years": m_min, + "m_max_absolute_error_years": max(errors["m"]), + "method": ( + "Rates/1000 as q versus central m converted with " + "q=m/(1+m/2); uniform deaths including age 0 (f0=.5); " + "published total LE reproduced by arithmetic mean of " + "male/female period LE at birth and 65, every 2026-2099 year" + ), + "inference": ( + "Empirical interpretation, not an explicit CBO units " + "definition. q reproduces every summary; m does not." + ), + "rows": rows, + }, + "reparse": ( + "--check re-parses all pinned CSV/XLSX/HTML inputs and compares " + "every JSON byte; source SHA-256 and available CDX digests verified" + ), + } + + +def compact_json(payload: Any) -> str: + """Keep scalar arrays on one line, following the TR2008 data pattern.""" + + def encode(value: Any, level: int) -> str: + direct = json.dumps(value, sort_keys=True, ensure_ascii=False) + if not isinstance(value, (dict, list)) or len(direct) <= 1400: + return direct + pad = " " * (level + 1) + if isinstance(value, dict): + entries = [ + f"{pad}{json.dumps(key)}: {encode(value[key], level + 1)}" + for key in sorted(value) + ] + opening, closing = "{", "}" + else: + entries = [pad + encode(item, level + 1) for item in value] + opening, closing = "[", "]" + return ( + opening + + "\n" + + ",\n".join(entries) + + "\n" + + " " * level + + closing + ) + + text = encode(payload, 0) + "\n" + if json.loads(text) != payload: + raise AssertionError("JSON encoder changed values") + return text + + +def build_all(inputs: Path = DEFAULT_INPUTS) -> dict[str, str]: + raw = {name: read_source(name, inputs) for name in SOURCES} + archive = zipfile.ZipFile(io.BytesIO(raw["demographic_archive"])) + for name in ("fertility", "mortality", "demographic_workbook"): + member = SOURCES[name].relative_path.removeprefix("cbo/extracted/") + if archive.read(member) != raw[name]: + raise ValueError( + f"{name} differs from verified demographic archive" + ) + archive.close() + demographics = build_demographics(raw) + economics = build_economics(raw) + checks = build_checks(demographics, economics) + if not checks["all_passed"]: + failures = [c["id"] for c in checks["checks"] if not c["passed"]] + raise ValueError(f"CBO2026 checks failed: {failures}") + sources = {} + for name, spec in SOURCES.items(): + bundled_name = Path(spec.relative_path).name + ".gz" + compressed = ( + deterministic_gzip(raw[name]) + if len(raw[name]) < 2_000_000 + else b"" + ) + # User requests <~2MB payloads; the repository hook is stricter. + bundled = ( + Path(spec.relative_path).suffix in (".csv", ".xlsx", ".pdf") + and len(raw[name]) < 2_000_000 + and len(compressed) < 500_000 + ) + sources[name] = { + "captured_relative_path": spec.relative_path, + "sha256": spec.sha256, + "bytes": len(raw[name]), + "original_url": spec.url, + "locator": spec.locator, + "wayback_timestamp": spec.timestamp, + "cdx_sha1_b32": spec.cdx_sha1_b32, + "computed_sha1_b32": sha1_b32(raw[name]), + "cdx_digest_matches": ( + sha1_b32(raw[name]) == spec.cdx_sha1_b32 + if spec.cdx_sha1_b32 + else None + ), + "committed_file": "sources/" + bundled_name if bundled else None, + "compressed_sha256": sha256(compressed) if bundled else None, + "archive_source_key": ( + "demographic_archive" + if name in ("fertility", "mortality", "demographic_workbook") + else None + ), + "capture_date": "2026-10-01", + } + return { + "cbo2026_demographics.json": compact_json(demographics), + "cbo2026_economics.json": compact_json(economics), + "transcription_check.json": compact_json(checks), + "sources.json": compact_json( + { + "schema_version": "cbo2026_sources.v1", + "sources": sources, + "capture_method": ( + "CBO raw Internet Archive id_ captures; original cbo.gov " + "served DataDome CAPTCHA; no live download attempted by " + "this builder. Source identities from captured research " + "summary; computed SHA-1/base32 matches every stated CDX digest." + ), + "compression": "gzip mtime=0; hashes refer to uncompressed bytes", + "retention": ( + "Raw sources retained only if payload <2MB and deterministic " + "gzip <500KB (repository precommit limit); otherwise URL/hash only" + ), + } + ), + } + + +def main(argv: Sequence[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--inputs", type=Path, default=DEFAULT_INPUTS) + parser.add_argument("--check", action="store_true") + args = parser.parse_args(argv) + outputs = build_all(args.inputs) + if args.check: + stale = [ + name + for name, text in outputs.items() + if not (OUT_DIR / name).is_file() + or (OUT_DIR / name).read_text(encoding="utf-8") != text + ] + sources = json.loads(outputs["sources.json"])["sources"] + for source in sources.values(): + if source["committed_file"]: + path = OUT_DIR / source["committed_file"] + if ( + not path.is_file() + or sha256(path.read_bytes()) != source["compressed_sha256"] + ): + stale.append(source["committed_file"]) + if stale: + print(f"stale CBO2026 outputs: {stale}", file=sys.stderr) + return 1 + print("CBO2026 outputs re-parsed and current; all input checks passed") + return 0 + OUT_DIR.mkdir(parents=True, exist_ok=True) + sources_dir = OUT_DIR / "sources" + sources_dir.mkdir(exist_ok=True) + source_metadata = json.loads(outputs["sources.json"])["sources"] + for name, spec in source_metadata.items(): + if spec["committed_file"]: + (OUT_DIR / spec["committed_file"]).write_bytes( + deterministic_gzip(read_source(name, args.inputs)) + ) + for name, text in outputs.items(): + (OUT_DIR / name).write_text(text, encoding="utf-8") + print(f"wrote {name}, SHA-256={sha256(text.encode())}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/extract_ssa_claim_ages_2026.py b/scripts/extract_ssa_claim_ages_2026.py new file mode 100644 index 00000000..ba845764 --- /dev/null +++ b/scripts/extract_ssa_claim_ages_2026.py @@ -0,0 +1,396 @@ +"""Rebuild Supplement 2026 Table 6.B5.1 from the supplied SSA captures. + +The captured 2026 edition has the same twelve age columns as the 2023 +reference, with entitlement years 1998-2025. Every cell is parsed using +its HTML ``headers`` attribute; unexpected headers, missing cells, +duplicate years, and incomplete coverage are refused. The published +rounding residuals are retained, never normalized in the artifact. + +No network is used. Both the 2025 and 2026 captures are SHA-256 verified. +The 2026 edition is selected because its layout matches the existing +``ssa_claim_ages.v1`` schema. ``--check`` re-parses both captured tables +and compares the deterministic outputs without writing. Small, exact +captures are committed as deterministic gzip streams for offline replay. + +Source: SSA Annual Statistical Supplement 2026, section 6.B, +https://www.ssa.gov/policy/docs/statcomps/supplement/2026/6b.html, +div ``table6.b5.1``, thead/tbody/tfoot. Captured 2026-10-02T12:08:05Z. +""" + +from __future__ import annotations + +import argparse +import gzip +import hashlib +import io +import json +import math +import re +from html.parser import HTMLParser +from pathlib import Path +from typing import Any + +from build_ssa_claim_ages import ( + COLLAPSED_CATEGORIES, + FRA_MONTHS_BY_ATTAIN_65_YEAR, + RAW_COLUMNS, + SUM_TOLERANCE, + _collapse, + _era_mapping, + _fra_at, +) + +ROOT = Path(__file__).resolve().parents[1] +EXTERNAL = ROOT / "data" / "external" +OUT_PATH = EXTERNAL / "ssa_claim_ages_2026supplement.json" +PROVENANCE_PATH = EXTERNAL / "ssa_supplement_2026_6b.source.provenance.json" +CAPTURED_UTC = "2026-10-02T12:08:05Z" +SOURCE_SHA256 = { + 2025: "1ba2e4f895da924286401189ab1144406640b58e56ce1b2e74f00f9caba405a2", + 2026: "1f9eceb2cfa45e181aa38ca7ce33b5babc77e8cb6d77fd1343650224a0a7d06a", +} +LEAF_FIELDS = dict( + zip( + ( + "c2", + "c3", + "c5", + "c6", + "c7", + "c8", + "c14", + "c15", + "c16", + "c17", + "c18", + "c19", + "c11", + "c12", + "c13", + ), + ("number_thousands", "average_age", "published_total", *RAW_COLUMNS), + strict=True, + ) +) +EXPECTED_HEADERS = { + "c1": "Year of entitlement", + "c2": "Number (thousands)", + "c3": "Average age", + "c4": "Percentage distribution by age at month of entitlement", + "c5": "Total, all ages", + "c6": "62", + "c7": "63", + "c8": "64", + "c9": "65 a", + "c10": "66 a", + "c11": "Disability conversions b", + "c12": "67–69", + "c13": "70 or older", + "c14": "Before FRA", + "c15": "At FRA", + "c16": "After FRA", + "c17": "Before FRA", + "c18": "At FRA", + "c19": "After FRA", +} + + +def source_name(edition: int) -> str: + return f"ssa_supplement_{edition}_6b.source.html.gz" + + +def gzip_bytes(raw: bytes) -> bytes: + """Stable gzip metadata across supported Python versions.""" + output = io.BytesIO() + with gzip.GzipFile( + filename="", mode="wb", fileobj=output, mtime=0 + ) as stream: + stream.write(raw) + return output.getvalue() + + +def read_source(edition: int, inputs: Path | None = None) -> bytes: + """Verify exact supplied bytes, using committed gzip if no input root.""" + if inputs is None: + raw = gzip.decompress((EXTERNAL / source_name(edition)).read_bytes()) + else: + raw = (inputs / f"supplement_{edition}_6b.html").read_bytes() + if hashlib.sha256(raw).hexdigest() != SOURCE_SHA256[edition]: + raise ValueError(f"Supplement {edition} source SHA-256 mismatch") + return raw + + +class TableParser(HTMLParser): + """Collect rows/cells and text from an isolated table, preserving labels.""" + + def __init__(self) -> None: + super().__init__(convert_charrefs=True) + self.rows: list[list[tuple[str, dict[str, str], str]]] = [] + self.caption = "" + self._row: list[tuple[str, dict[str, str], str]] = [] + self._cell: tuple[str, dict[str, str]] | None = None + self._text: list[str] = [] + self._in_caption = False + + def handle_starttag(self, tag: str, attrs: list) -> None: + if tag == "tr": + self._row = [] + elif tag in ("th", "td"): + self._cell = (tag, dict(attrs)) + self._text = [] + elif tag == "caption": + self._in_caption = True + + def handle_data(self, data: str) -> None: + if self._cell is not None: + self._text.append(data) + if self._in_caption: + self.caption += data + + def handle_endtag(self, tag: str) -> None: + if tag in ("th", "td") and self._cell is not None: + kind, attrs = self._cell + value = " ".join("".join(self._text).split()) + self._row.append((kind, attrs, value)) + self._cell = None + elif tag == "tr": + self.rows.append(self._row) + elif tag == "caption": + self._in_caption = False + + +def parse_table(raw: bytes, edition: int) -> dict[str, Any]: + """Extract exact cells and notes; refuse schema and coverage changes.""" + text = raw.decode("utf-8") + anchor = text.find('id="table6.b5.1"') + start = text.find("", anchor) + end = text.find("
", start) + if anchor < 0 or start < 0 or end < 0: + raise ValueError("Table 6.B5.1 boundaries missing") + parser = TableParser() + parser.feed(text[start : end + len("")]) + header_cells = [ + (attrs["id"], value) + for row in parser.rows + for kind, attrs, value in row + if kind == "th" and attrs.get("id", "").startswith("c") + ] + headers = dict(header_cells) + if len(headers) != len(header_cells) or headers != EXPECTED_HEADERS: + raise ValueError("Table 6.B5.1 column layout changed") + data: dict[str, dict[str, Any]] = {"male": {}, "female": {}} + notes: list[str] = [] + sex = None + for row in parser.rows: + panels = [v for _, a, v in row if a.get("class") == "panel"] + if panels: + if panels not in (["Men"], ["Women"]): + raise ValueError(f"Unknown sex panel: {panels}") + sex = "male" if panels == ["Men"] else "female" + continue + years = [v for _, a, v in row if a.get("class") == "stub0"] + if not years: + notes.extend( + v + for _, a, v in row + if "Note" in a.get("class", "") or a.get("class") == "note" + ) + continue + if ( + sex is None + or len(years) != 1 + or not re.fullmatch(r"\d{4}", years[0]) + ): + raise ValueError("Unexpected year row or missing sex panel") + year = years[0] + if year in data[sex]: + raise ValueError(f"Duplicate {sex} year {year}") + fields = {} + for kind, attrs, value in row: + if kind != "td": + continue + leaf = attrs.get("headers", "").split()[-1:] + if not leaf or leaf[0] not in LEAF_FIELDS: + raise ValueError("Missing or unknown cell header") + field = LEAF_FIELDS[leaf[0]] + if field in fields: + raise ValueError("Duplicate cell header") + if value == ". . .": + fields[field] = None + else: + try: + parsed = float(value.replace(",", "")) + if not math.isfinite(parsed): + raise ValueError("nonfinite numeric cell") + fields[field] = parsed + except ValueError as exc: + raise ValueError(f"Unrecognized cell {value!r}") from exc + if set(fields) != set(LEAF_FIELDS.values()): + raise ValueError("Missing published cells") + raw_values = {k: fields[k] for k in RAW_COLUMNS} + if any( + v is not None and not 0 <= v <= 100 for v in raw_values.values() + ): + raise ValueError("Claiming share outside [0,100]") + at_columns = [raw_values[k] for k in ("age65_at_fra", "age66_at_fra")] + if sum(v is not None for v in at_columns) != 1: + raise ValueError("Expected one at-FRA column") + number = fields["number_thousands"] + if number is None or number != int(number) or number <= 0: + raise ValueError("Invalid number of awardees") + if fields["average_age"] is None: + raise ValueError("Missing average age") + if fields["published_total"] != 100.0: + raise ValueError("Expected published total 100") + categories = _collapse(raw_values) + component_sum = round(sum(categories.values()), 1) + residual = round(component_sum - 100.0, 1) + if abs(residual) > SUM_TOLERANCE: + raise ValueError("Shares exceed published rounding tolerance") + data[sex][year] = { + "number_thousands": int(number), + "average_age": fields["average_age"], + "published_total": fields["published_total"], + "raw": raw_values, + "categories": categories, + "fra_at": _fra_at(raw_values), + "applicable_raw_columns": [ + k for k in RAW_COLUMNS if raw_values[k] is not None + ], + "component_sum": component_sum, + "residual": residual, + } + expected_years = [str(y) for y in range(1998, edition)] + if any(list(rows) != expected_years for rows in data.values()): + raise ValueError(f"Expected complete 1998-{edition - 1} coverage") + if len(notes) != 8: + raise ValueError("Unexpected table footnotes") + return { + "title": " ".join(parser.caption.split()), + "data": data, + "notes": notes, + } + + +def build(inputs: Path | None = None) -> dict[Path, bytes]: + """Reparse both supplied editions and build deterministic output bytes.""" + captures = {y: read_source(y, inputs) for y in SOURCE_SHA256} + tables = {y: parse_table(raw, y) for y, raw in captures.items()} + chosen = tables[2026] + notes = chosen["notes"] + sources = { + str(y): { + "source_url": f"https://www.ssa.gov/policy/docs/statcomps/supplement/{y}/6b.html", + "source_sha256": SOURCE_SHA256[y], + "committed_source": f"data/external/{source_name(y)}", + "retrieved_utc": CAPTURED_UTC, + "locator": "div#table6.b5.1; thead/tbody/tfoot", + "bytes": len(captures[y]), + "compression": "gzip mtime=0; SHA-256 is of decompressed captured bytes", + } + for y in captures + } + provenance = { + "source": "SSA Annual Statistical Supplement 2026, Table 6.B5.1", + **sources["2026"], + "fetch_method": "Supplied local captures; no network fetch by this builder. SSA baseline captures used User-Agent Wget/1.21.4; the supplement capture client is not recorded in the supplied retrieval marker.", + "captures": sources, + "selection": "2026 edition selected: twelve-column layout matches the 2023 schema; rows through 2025. 2025 edition also re-parsed and its hash recorded.", + "data_basis": notes[0].removeprefix("SOURCE: "), + "table_notes": " ".join(notes[1:5]), + "footnote_a_fra_schedule": notes[5].removeprefix("a. "), + "footnote_b_disability_conversion": notes[6].removeprefix("b. "), + "not_applicable_marker": ". . .", + "contact": "statistics@ssa.gov", + "consumer_note": "claiming.load_claim_age_reference(path) loads this schema and reference.row accesses all years. Legacy claiming.claim_age_distribution and claim_age_pmf retain their global 2022 cap; a new baseline must resolve against reference.years rather than those legacy convenience functions.", + } + eras = _era_mapping() + eras[-1] = {**eras[-1], "entitlement_years": "2021-2025"} + residuals = [ + r["residual"] + for rows in chosen["data"].values() + for r in rows.values() + ] + document = { + "schema_version": "ssa_claim_ages.v1", + "table": "6.B5.1", + "title": chosen["title"].removeprefix("Table 6.B5.1 "), + "supplement_year": 2026, + "provenance": provenance, + "column_schema": { + "raw_columns": list(RAW_COLUMNS), + "collapsed_categories": list(COLLAPSED_CATEGORIES), + "fra_at_note": "Exactly one at-FRA column per row; a subset of age65/age66, never an additional partition member.", + "disability_conversion_note": "Automatic conversion at FRA, not a voluntary claiming choice.", + "era_map": eras, + }, + "fra_schedule": { + "unit": "months", + "keyed_by": "year worker attains age 65", + "birth_year_equivalent": "attain_65_year - 65", + "schedule": [ + {"attain_65_year_from": y, "fra_months": m} + for y, m in FRA_MONTHS_BY_ATTAIN_65_YEAR + ], + "source": "Table 6.B5.1 footnote a; initial year 1900 is the existing schema's lower-bound sentinel, not a source datum.", + }, + "validation": { + "sum_tolerance": SUM_TOLERANCE, + "max_abs_residual": max(map(abs, residuals)), + "n_rows": len(residuals), + "years": "1998-2025", + "sexes": ["male", "female"], + "reparsed_editions": [2025, 2026], + }, + "build": { + "built_by": "scripts/extract_ssa_claim_ages_2026.py", + "reproducible": "Exact supplied HTML bytes, no clock or network dependency", + }, + "data": chosen["data"], + } + outputs = {OUT_PATH: document, PROVENANCE_PATH: provenance} + result = { + p: (json.dumps(d, indent=2, ensure_ascii=False) + "\n").encode() + for p, d in outputs.items() + } + result.update( + { + EXTERNAL / source_name(y): gzip_bytes(raw) + for y, raw in captures.items() + } + ) + return result + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument( + "--inputs", + type=Path, + help="Supplied ssa-supplement directory; default committed captures", + ) + parser.add_argument("--check", action="store_true") + args = parser.parse_args() + outputs = build(args.inputs) + if args.check: + stale = [ + str(p.relative_to(ROOT)) + for p, raw in outputs.items() + if not p.exists() or p.read_bytes() != raw + ] + if stale: + raise SystemExit(f"Stale supplement outputs: {stale}") + print( + "Supplement 2026 outputs match re-parsed sources (both editions)" + ) + return 0 + for path, raw in outputs.items(): + path.write_bytes(raw) + print( + f"Wrote Supplement 2026 reference: {hashlib.sha256(outputs[OUT_PATH]).hexdigest()}" + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/extract_tr2026_parameters.py b/scripts/extract_tr2026_parameters.py new file mode 100644 index 00000000..3105e274 --- /dev/null +++ b/scripts/extract_tr2026_parameters.py @@ -0,0 +1,578 @@ +"""Rebuild intermediate TR2026 baseline inputs from pinned captured sources. + +Only the specific input tables V.C1, V.B1, VI.G1, V.A1 and V.A4 are +parsed; no cost, income or balance table is transcribed. Captures were made +2026-10-01 with the Wget/1.21.4 user agent. HTML digests identify captured +bytes, not stable live pages. ``--check`` reparses every source, verifies +its capture digest and compares every generated artifact without writing. +The raw CSV/PDF sources under 2 MB are retained (gzip mtime=0 for CSVs +above 500 KB); HTML is identified by URL and capture SHA-256 only. + +Usage: PYTHONPATH=src python scripts/extract_tr2026_parameters.py + --input-dir /path/to/captured/baseline-inputs [--check] +""" + +from __future__ import annotations + +import argparse +import csv +import gzip +import hashlib +import io +import json +import re +import sys +from collections.abc import Sequence +from html.parser import HTMLParser +from pathlib import Path +from typing import Any + +from populace_dynamics.data.life_table import life_expectancy_profile + +ROOT = Path(__file__).resolve().parents[1] +OUT_DIR = ROOT / "data" / "external" / "tr2026" +DEFAULT_INPUT_DIR = ( + Path.home() + / "microcosm-launch-evidence" + / "nasi-meeting-20261001" + / "followup" + / "inputs" + / "baseline-inputs" +) +RETRIEVED = "2026-10-01" +RELEASED = "2026-06-09" + +# Digests from the capture SHA256SUMS.txt; keys are capture-relative paths. +SOURCE_SHA256: dict[str, str] = { + "tr2026/V_C_prog.html": "b6af98b6e8f131ef17b3208ef0fbfb8f9fbc494bc6d33dbee5c1bcbbfc99d93a", + "tr2026/lr5b1.html": "f1e1347ac2e9013d53a517e0acf05e692660113acd6cbe7554f0efd7c1aaa3a4", + "tr2026/lr6g1.html": "2e0214278e0b2271616093af02280277f6e363d112c47a90b49fe7168e795e75", + "tr2026/lr5a1.html": "7476bb5e9e99a04bd1b7e77afd6f367c4f94d6eb9aff74e33724b5db9d1dc357", + "tr2026/lr5a4.html": "ab45c5e84253cea122ba0f8a284335d03a5819051914422f5980412822a2b919", + "oact_downloadables_tr2026/DeathProbsE_M_Hist_TR2026.csv": "ee423873ef2b532fc53c97312a9d6162b876f56e50bd227d45f6c9d9a1cefc8f", + "oact_downloadables_tr2026/DeathProbsE_M_Alt2_TR2026.csv": "4e2a632a964fadb4cc3fe6127a97ef656eafd43348bf8c7408b018b83840db27", + "oact_downloadables_tr2026/DeathProbsE_F_Hist_TR2026.csv": "1e2ecd7d24233310108da582ae922c53205d1d239ce3a484ecc5fd6cdb88d0c8", + "oact_downloadables_tr2026/DeathProbsE_F_Alt2_TR2026.csv": "e3178487512f23666ca05754246798557275bc0728f3f937bbca97358f32532b", + "oact_downloadables_tr2026/PerLifeTables_M_Hist_TR2026.csv": "bcf123b13a107e3a5d55a3e47e5129b470624e047346171cf1ee3a0a1542d1a5", + "oact_downloadables_tr2026/PerLifeTables_M_Alt2_TR2026.csv": "0d3f38d38d7428e286bb00ae5a318df4eb100e2c801e1487a0410c2f917bc938", + "oact_downloadables_tr2026/PerLifeTables_F_Hist_TR2026.csv": "a5e71b5a5011468e87b6c0407d60eef956039eb09524c94a3cc5bf6cf165ab97", + "oact_downloadables_tr2026/PerLifeTables_F_Alt2_TR2026.csv": "80f612185a269069d097fc32b52ba8f65cb24e5605c2201068d4ca414728bef8", + "oact_downloadables_tr2026/LifeTableDefinitions.pdf": "94c8fd5806cdb5be3762d1284155e4827a2971d3845088943e3f96a14f26e889", +} + +TABLE_LOCATORS = { + "V_C_prog.html": ( + "V.C1", + "Table V.C1, anchor #1047210; historical and Intermediate rows", + ), + "lr5b1.html": ( + "V.B1", + "Single-year V.B1; historical and Intermediate; final CPI column", + ), + "lr6g1.html": ( + "VI.G1", + "Single-year VI.G1; historical and Intermediate; AWI and adjusted CPI", + ), + "lr5a1.html": ( + "V.A1", + "Single-year V.A1; historical and Intermediate; TFR and ASADR", + ), + "lr5a4.html": ( + "V.A4", + "Single-year V.A4; historical table and first alternative in projected table", + ), +} + + +def _sha256(raw: bytes) -> str: + return hashlib.sha256(raw).hexdigest() + + +def _json_bytes(value: Any) -> bytes: + return ( + json.dumps(value, sort_keys=True, separators=(",", ":")) + "\n" + ).encode("utf-8") + + +def _gzip(raw: bytes) -> bytes: + """Deterministic across Python versions: fixed timestamp, no filename.""" + buffer = io.BytesIO() + with gzip.GzipFile( + filename="", mode="wb", fileobj=buffer, mtime=0 + ) as stream: + stream.write(raw) + return buffer.getvalue() + + +def _read(input_dir: Path, name: str) -> bytes: + raw = (input_dir / name).read_bytes() + observed = _sha256(raw) + if observed != SOURCE_SHA256[name]: + raise ValueError(f"capture sha256 mismatch for {name}: {observed}") + return raw + + +class TableRows(HTMLParser): + """Collect cells from an already selected input table.""" + + def __init__(self) -> None: + super().__init__() + self.rows: list[list[str]] = [] + self.row: list[str] | None = None + self.cell: list[str] | None = None + + def handle_starttag(self, tag: str, attrs: Any) -> None: + if tag == "tr": + self.row = [] + elif tag in ("td", "th"): + self.cell = [] + + def handle_data(self, data: str) -> None: + if self.cell is not None: + self.cell.append(data) + + def handle_endtag(self, tag: str) -> None: + if tag in ("td", "th") and self.cell is not None: + if self.row is None: + raise ValueError("table cell outside a row") + value = " ".join("".join(self.cell).split()) + if value: + self.row.append(value) + self.cell = None + elif tag == "tr" and self.row is not None: + self.rows.append(self.row) + self.row = None + + +def _table_rows(table: str) -> list[list[str]]: + parser = TableRows() + parser.feed(table) + return parser.rows + + +def _tables(raw: bytes) -> list[str]: + return re.findall( + r"]*>.*?", raw.decode("utf-8"), re.I | re.S + ) + + +def _number(text: str) -> float: + matches = re.findall(r"[-+]?(?:\d[\d,]*\.?\d*|\.\d+)", text) + if len(matches) != 1: + raise ValueError(f"expected one numeric table cell, got {text!r}") + return float(matches[0].replace(",", "")) + + +def _year(text: str) -> int | None: + match = re.fullmatch(r"(\d{4})(?:\s+[a-z])?", text) + return int(match.group(1)) if match else None + + +def _section_rows(raw: bytes, *, vc1: bool = False) -> list[list[Any]]: + tables = _tables(raw) + if vc1: + selected = [table for table in tables if "Table V.C1" in table] + if len(selected) != 1: + raise ValueError("expected exactly one V.C1 input table") + table = selected[0] + else: + table = max(tables, key=len) + section = None + out = [] + for cells in _table_rows(table): + if not cells: + continue + if cells[0] == "Historical data:": + section = "historical" + continue + if cells[0] == "Intermediate:": + section = "intermediate" + continue + if cells[0] in ("Low-cost:", "High-cost:"): + break + year = _year(cells[0]) + if year is not None and section in ("historical", "intermediate"): + out.append([year, section, *cells[1:]]) + return out + + +def _coverage(rows: list[list[Any]], first: int, last: int) -> None: + years = [row[0] for row in rows] + if years != list(range(first, last + 1)): + raise ValueError( + f"table lacks unique consecutive {first}..{last} rows" + ) + + +def parse_parameters(raws: dict[str, bytes]) -> dict[str, Any]: + """Transcribe only the published input columns, preserving units.""" + vc1 = _section_rows(raws["tr2026/V_C_prog.html"], vc1=True) + vb1 = _section_rows(raws["tr2026/lr5b1.html"]) + g1 = _section_rows(raws["tr2026/lr6g1.html"]) + a1 = _section_rows(raws["tr2026/lr5a1.html"]) + for rows, first, last in ( + (vc1, 1975, 2035), + (vb1, 1960, 2100), + (g1, 1970, 2100), + (a1, 1940, 2100), + ): + _coverage(rows, first, last) + expectancy = [] + for table in _tables(raws["tr2026/lr5a4.html"]): + for cells in _table_rows(table): + if cells and (year := _year(cells[0])) is not None: + if len(cells) not in (5, 13): + raise ValueError("V.A4 expectancy layout changed") + expectancy.append([year, *map(_number, cells[1:5])]) + _coverage(expectancy, 1940, 2100) + return { + "schema_version": "tr2026_parameters.v1", + "release_date": RELEASED, + "alternative": "intermediate", + "tables": { + "V.C1": { + "source_file": "V_C_prog.html", + "columns": ["year", "section", "cola_percent", "awi"], + "rows": [ + [y, s, _number(c), _number(w)] for y, s, c, w, *_ in vc1 + ], + }, + "V.B1": { + "source_file": "lr5b1.html", + "columns": ["year", "section", "cpiw_growth_percent"], + "rows": [[r[0], r[1], _number(r[-1])] for r in vb1], + }, + "VI.G1": { + "source_file": "lr6g1.html", + "columns": ["year", "section", "awi", "adjusted_cpi_2026_100"], + "rows": [ + [y, s, _number(w), _number(c)] for y, s, c, w, *_ in g1 + ], + }, + "V.A1": { + "source_file": "lr5a1.html", + "columns": [ + "year", + "section", + "tfr", + "total", + "under_65", + "65_and_over", + ], + "cell_footnotes": { + str(row[0]): { + column: cell.split()[0] + for column, cell in zip( + ("tfr", "total", "under_65", "65_and_over"), + row[2:], + strict=True, + ) + if re.match(r"^[def]\s", cell) + } + for row in a1 + if any(re.match(r"^[def]\s", cell) for cell in row[2:]) + }, + "asadr_unit": "deaths per 100000; April 1 2010 standard population", + "rows": [[r[0], r[1], *map(_number, r[2:])] for r in a1], + }, + "V.A4": { + "source_file": "lr5a4.html", + "columns": [ + "year", + "male_birth", + "female_birth", + "male_65", + "female_65", + ], + "rows": expectancy, + }, + }, + } + + +def _death_rows(raw: bytes, first: int, last: int) -> dict[str, list[float]]: + rows = list(csv.reader(io.StringIO(raw.decode("utf-8-sig")))) + header = rows[1] + if header != ["Year", *map(str, range(120))]: + raise ValueError("DeathProbsE age header changed") + out = {} + for row in rows[2:]: + if not row: + continue + year = int(row[0]) + q = list(map(float, row[1:])) + if len(q) != 120 or any(not 0 <= value <= 1 for value in q): + raise ValueError(f"invalid q vector for {year}") + if str(year) in out: + raise ValueError(f"duplicate mortality year {year}") + out[str(year)] = q + if list(map(int, out)) != list(range(first, last + 1)): + raise ValueError("mortality year coverage changed") + return out + + +def _life_rows(raw: bytes) -> dict[str, list[dict[str, str]]]: + lines = raw.decode("utf-8-sig").splitlines() + header_index = next( + i for i, line in enumerate(lines) if line.startswith("Year,x,q(x),") + ) + rows = csv.DictReader(lines[header_index:]) + out: dict[str, list[dict[str, str]]] = {} + for row in rows: + out.setdefault(row["Year"], []).append(row) + for year, ages in out.items(): + if [int(r["x"]) for r in ages] != list(range(120)): + raise ValueError(f"life table age coverage changed for {year}") + return out + + +def parse_mortality( + raws: dict[str, bytes], +) -> tuple[dict[str, Any], list[dict[str, Any]]]: + """Preserve published q and derive f0 solely for expectancy checks.""" + result: dict[str, Any] = { + "schema_version": "tr2026_death_probabilities.v1", + "age_first": 0, + "age_last": 119, + "unit": "one-year probability of death at exact age", + "f0_rule": "(l0-L0)/(l0*q0) from OACT integer person-years; check-only", + "sexes": {}, + } + checks = [] + for sex, code in (("male", "M"), ("female", "F")): + q_by_year: dict[str, list[float]] = {} + check_inputs = {} + for variant, first, last in ( + ("Hist", 1900, 2023), + ("Alt2", 2024, 2100), + ): + q_name = f"oact_downloadables_tr2026/DeathProbsE_{code}_{variant}_TR2026.csv" + life_name = f"oact_downloadables_tr2026/PerLifeTables_{code}_{variant}_TR2026.csv" + q_rows = _death_rows(raws[q_name], first, last) + life_rows = _life_rows(raws[life_name]) + if q_rows.keys() != life_rows.keys(): + raise ValueError("DeathProbsE and PerLifeTables years differ") + q_by_year.update(q_rows) + max_error = 0.0 + for year, q in q_rows.items(): + ages = life_rows[year] + if q != [float(row["q(x)"]) for row in ages]: + raise ValueError(f"q columns disagree at {sex} {year}") + birth = ages[0] + f0 = (float(birth["l(x)"]) - float(birth["L(x)"])) / ( + float(birth["l(x)"]) * q[0] + ) + ex = life_expectancy_profile(q, f0=f0) + check_inputs[year] = [ + f0, + float(ages[0]["e(x)"]), + float(ages[65]["e(x)"]), + ] + # OACT's e for extinct lx=0 cohorts is printed as zero. + errors = [ + abs(ex[age] - float(ages[age]["e(x)"])) + for age in range(110) + if float(ages[age]["l(x)"]) > 0 + ] + max_error = float(max(max_error, max(errors))) + checks.append( + { + "id": f"{sex}_{variant}_life_table_e0_to_e109", + "source": life_name, + "years": [first, last], + "compared_q_cells": len(q_rows) * 120, + "max_absolute_expectancy_error_years": max_error, + "tolerance_years": 0.006, + "passed": max_error <= 0.006, + } + ) + result["sexes"][sex] = { + "q": q_by_year, + "check_inputs_f0_e0_e65": check_inputs, + } + return result, checks + + +def _source_records(raws: dict[str, bytes]) -> dict[str, Any]: + records = {} + for relative, raw in raws.items(): + name = Path(relative).name + stored = None + if name.endswith((".csv", ".pdf", ".xlsx")) and len(raw) < 2_000_000: + stored = "sources/" + name + (".gz" if len(raw) > 500_000 else "") + if name in TABLE_LOCATORS: + table, locator = TABLE_LOCATORS[name] + url = "https://www.ssa.gov/oact/TR/2026/" + name + if table == "V.C1": + url += "#1047210" + role = table + else: + url = "https://www.ssa.gov/OACT/Downloadables/" + ( + name if name == "LifeTableDefinitions.pdf" else "CY/" + name + ) + locator = ( + "Life-table function definitions and separation factor at age zero" + if name.endswith(".pdf") + else ( + "Header Year,0..119; one row per year" + if name.startswith("DeathProbs") + else "Header line 5; Year,x,q(x),l(x),d(x),L(x),T(x),e(x),...; ages 0..119" + ) + ) + role = ( + "q" + if name.startswith("DeathProbs") + else ( + "life_table_definitions" + if name.endswith(".pdf") + else "life_table_check" + ) + ) + records[name] = { + "capture_relative_path": relative, + "original_url": url, + "sha256": SOURCE_SHA256[relative], + "bytes": len(raw), + "retrieved": RETRIEVED, + "locator": locator, + "role": role, + "committed_file": stored, + "compression": ( + "gzip mtime=0; hash identifies decompressed bytes" + if stored and stored.endswith(".gz") + else None + ), + "durable_downloadables_locator": ( + "https://www.ssa.gov/OACT/Downloadables/2026/TR2026.html" + if relative.startswith("oact_") + else None + ), + } + return { + "schema_version": "tr2026_sources.v1", + "fetch_method": "Captured with curl User-Agent Wget/1.21.4; browser User-Agent received Akamai 403. No fetch performed by this builder.", + "html_hash_policy": "Live HTML contains per-request Akamai markup; pin captured bytes and parsed JSON values, not future live HTML.", + "sources": records, + } + + +def build_all(input_dir: Path) -> dict[str, bytes]: + """Reparse every pinned capture and return deterministic artifacts.""" + raws = {name: _read(input_dir, name) for name in SOURCE_SHA256} + parameters = parse_parameters(raws) + mortality, checks = parse_mortality(raws) + expectancy = { + str(row[0]): row[1:] for row in parameters["tables"]["V.A4"]["rows"] + } + for sex, birth_index, age65_index in (("male", 0, 2), ("female", 1, 3)): + data = mortality["sexes"][sex] + errors = [] + life_errors = [] + for year, inputs in data["check_inputs_f0_e0_e65"].items(): + f0, e0, e65 = inputs + ex = life_expectancy_profile(data["q"][year], f0=f0) + life_errors.extend( + (float(abs(ex[0] - e0)), float(abs(ex[65] - e65))) + ) + if year in expectancy: + published = expectancy[year] + errors.extend( + ( + float(abs(ex[0] - published[birth_index])), + float(abs(ex[65] - published[age65_index])), + ) + ) + checks.extend( + [ + { + "id": f"{sex}_q_reproduces_V_A4", + "years": [1940, 2100], + "ages": [0, 65], + "max_absolute_error_years": max(errors), + "tolerance_years": 0.051, + "passed": max(errors) <= 0.051, + }, + { + "id": f"{sex}_q_reproduces_PerLifeTables_e0_e65", + "years": [1900, 2100], + "ages": [0, 65], + "max_absolute_error_years": max(life_errors), + "tolerance_years": 0.006, + "passed": max(life_errors) <= 0.006, + }, + ] + ) + vc1 = {row[0]: row[3] for row in parameters["tables"]["V.C1"]["rows"]} + g1 = {row[0]: row[2] for row in parameters["tables"]["VI.G1"]["rows"]} + checks.append( + { + "id": "V_C1_AWI_equals_VI_G1", + "years": [1975, 2035], + "compared_cells": len(vc1), + "passed": all(g1[y] == amount for y, amount in vc1.items()), + } + ) + sources = _source_records(raws) + transcription = { + "schema_version": "tr2026_transcription_check.v1", + "all_passed": all(c["passed"] for c in checks), + "checks": checks, + "source_capture_sha256": { + Path(n).name: h for n, h in SOURCE_SHA256.items() + }, + "reparse_check": "--check verifies capture hashes, rebuilds every JSON and compares exact bytes; no network required", + "rounding": "V.A4 printed to 0.1 years; PerLifeTables e to 0.01; f0 inferred from integer L0. Tail rule is documented in data.life_table.", + "q119": "Preserved exactly; OACT does not force q119 to 1. No terminal-age replacement or clipping.", + } + if not transcription["all_passed"]: + raise ValueError( + f"transcription checks failed: {[c for c in checks if not c['passed']]}" + ) + outputs = { + "tr2026_parameters.json": _json_bytes(parameters), + "tr2026_death_probabilities.json": _json_bytes(mortality), + "transcription_check.json": _json_bytes(transcription), + "sources.json": _json_bytes(sources), + } + for record in sources["sources"].values(): + name = record["committed_file"] + if name: + raw = raws[record["capture_relative_path"]] + outputs[name] = _gzip(raw) if name.endswith(".gz") else raw + return outputs + + +def main(argv: Sequence[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--input-dir", type=Path, default=DEFAULT_INPUT_DIR) + parser.add_argument( + "--check", + action="store_true", + help="Reparse and compare; write nothing", + ) + args = parser.parse_args(argv) + outputs = build_all(args.input_dir) + if args.check: + stale = [ + name + for name, raw in outputs.items() + if not (OUT_DIR / name).exists() + or (OUT_DIR / name).read_bytes() != raw + ] + if stale: + print(f"stale TR2026 outputs: {stale}", file=sys.stderr) + return 1 + print( + f"TR2026: {len(outputs)} artifacts reproduced from {len(SOURCE_SHA256)} pinned captures" + ) + return 0 + for name, raw in outputs.items(): + path = OUT_DIR / name + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(raw) + print(f"TR2026: wrote {len(outputs)} deterministic artifacts") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/first_estimates_birth_evidence.py b/scripts/first_estimates_birth_evidence.py index 6344f8d0..dc7e3d75 100644 --- a/scripts/first_estimates_birth_evidence.py +++ b/scripts/first_estimates_birth_evidence.py @@ -351,6 +351,24 @@ Path("src/populace_dynamics/harness/epuf_cells.py"), Path("src/populace_dynamics/harness/epuf_gate.py"), Path("src/populace_dynamics/harness/epuf_run.py"), + # The selectable projection baselines (NASI follow-up, 2026-10-01) are + # opt-in inputs for unregistered projections; every registered result + # keeps the TR2008 path, and nothing historical imports them. + Path("src/populace_dynamics/data/tr2026.py"), + Path("src/populace_dynamics/data/cbo2026.py"), + Path("src/populace_dynamics/data/life_table.py"), + Path("src/populace_dynamics/baselines/__init__.py"), + Path("src/populace_dynamics/baselines/asfr.py"), + Path("src/populace_dynamics/baselines/base.py"), + Path("src/populace_dynamics/baselines/cbo2026_long_term.py"), + Path("src/populace_dynamics/baselines/claim_tables.py"), + Path("src/populace_dynamics/baselines/common2026.py"), + Path("src/populace_dynamics/baselines/fertility.py"), + Path("src/populace_dynamics/baselines/interpolation.py"), + Path("src/populace_dynamics/baselines/legacy.py"), + Path("src/populace_dynamics/baselines/realized.py"), + Path("src/populace_dynamics/baselines/tr2026_intermediate.py"), + Path("src/populace_dynamics/baselines/track_a.py"), ) POST_REVIEW_SHARED_SOURCE_BLOBS = { Path( diff --git a/src/populace_dynamics/baselines/__init__.py b/src/populace_dynamics/baselines/__init__.py new file mode 100644 index 00000000..56a417a8 --- /dev/null +++ b/src/populace_dynamics/baselines/__init__.py @@ -0,0 +1,92 @@ +"""Selectable projection baselines (NASI follow-up package B2). + +A baseline bundles the paths a projection reads besides its cohort: +COLA, CPI-W, AWI, population mortality by single age, sex and year, +fertility, the claim-age table and DI rates +(:class:`~populace_dynamics.baselines.base.Baseline`). Three are +registered: + +``tr2008_intermediate`` (default) + :class:`~populace_dynamics.baselines.legacy.TR2008Legacy`, the 2008 + Trustees baseline the registered projection tests (exercises 1 and + 3) use, delegating to their existing constructors unchanged. +``tr2026_intermediate`` + :class:`~populace_dynamics.baselines.tr2026_intermediate. + TR2026Intermediate`, the 2026 Trustees intermediate assumptions. +``cbo2026_long_term`` + :class:`~populace_dynamics.baselines.cbo2026_long_term. + CBO2026LongTerm`, CBO's 2026 long-term projections. + +The 2026 baselines splice realized COLAs through determination year 2025 +and realized AWI through 2024 before any projected or derived value, and +tag every derived value (:meth:`Baseline.value_sources`). They are for +invented-data projections only: +:func:`~populace_dynamics.baselines.track_a.build_track_a_inputs` refuses +a real-data run under them (see :mod:`~populace_dynamics.baselines. +track_a` for why), and :func:`~populace_dynamics.baselines.track_a. +project_track_a` projects invented cohorts under any baseline without +the TR2008-bound checks of ``run_track_a``. +:mod:`~populace_dynamics.baselines.fertility` adds an opt-in, not gated, +report-only ASFR birth step for open populations. +""" + +from __future__ import annotations + +from collections.abc import Callable + +from populace_dynamics.baselines.base import ( + Baseline, + BaselineGapError, + BaselineYearAwareMortality, +) +from populace_dynamics.baselines.cbo2026_long_term import CBO2026LongTerm +from populace_dynamics.baselines.legacy import TR2008Legacy +from populace_dynamics.baselines.tr2026_intermediate import ( + TR2026Intermediate, +) +from populace_dynamics.baselines.track_a import ( + INVENTED_SEED, + build_track_a_inputs, + invented_track_a_cohorts, + project_track_a, + track_a_config_for, +) + +__all__ = [ + "BASELINE_NAMES", + "DEFAULT_BASELINE", + "INVENTED_SEED", + "Baseline", + "BaselineGapError", + "BaselineYearAwareMortality", + "CBO2026LongTerm", + "TR2008Legacy", + "TR2026Intermediate", + "build_track_a_inputs", + "get_baseline", + "invented_track_a_cohorts", + "project_track_a", + "track_a_config_for", +] + +DEFAULT_BASELINE = "tr2008_intermediate" +_REGISTRY: dict[str, Callable[[], Baseline]] = { + "tr2008_intermediate": TR2008Legacy, + "tr2026_intermediate": TR2026Intermediate, + "cbo2026_long_term": CBO2026LongTerm, +} +BASELINE_NAMES: tuple[str, ...] = tuple(_REGISTRY) + + +def get_baseline(name: str = DEFAULT_BASELINE) -> Baseline: + """The registered baseline called ``name`` (default: the legacy one).""" + try: + factory = _REGISTRY[name] + except KeyError: + raise KeyError( + f"unknown baseline {name!r}; registered: {list(BASELINE_NAMES)}" + ) from None + baseline = factory() + if baseline.name != name: + raise RuntimeError(f"registry entry {name!r} built {baseline.name!r}") + return baseline diff --git a/src/populace_dynamics/baselines/asfr.py b/src/populace_dynamics/baselines/asfr.py new file mode 100644 index 00000000..2623a1ac --- /dev/null +++ b/src/populace_dynamics/baselines/asfr.py @@ -0,0 +1,152 @@ +"""Single-age fertility shapes for baselines that publish only a TFR. + +The 2026 Trustees Report publishes the period total fertility rate +(Table V.A1) but no age-specific fertility schedule in the captured +sources (``data/external/tr2026/provenance.md``: "No ASFR schedule was +invented"). A TR2026 baseline therefore derives single-age rates as a +fixed age *shape* scaled each year to the published TFR. Two shapes +are supplied; the choice is a builder default awaiting ratification and +every derived value is tagged with it: + +``nchs2024`` (default) + NCHS final 2024 age-specific fertility rates in eight five-year + bands, 10-14 to 45-49 (``data/external/nchs_asfr_2024.json``, from + *Births: Final Data for 2024*, NVSR 75(2); the file records the + Socrata query, its response SHA-256 and the NCHS TFR check). Each + band total (five times the rate per 1,000, divided by 1,000) is + spread over its single ages by + :func:`~populace_dynamics.baselines.interpolation.ungroup_band_totals` + (mean-preserving, non-negative). The fertility step exposes ages + 14-49, so the mass the interpolation places at ages 10-13 is added + to age 14 (``fold_under_14_into_14``): the shape keeps every birth + and its sum equals the NCHS TFR, 1.5995. NCHS's 45-49 rate counts + births to women 45 and over (the file's ``band_note``), and stays in + ages 45-49. +``cbo2026`` + CBO's January 2026 single-age (14-49) all-women schedule for the + same year, as published (:func:`populace_dynamics.data.cbo2026.asfr`), + used only for its age pattern; covered 2021-2099. + +``asfr(year) = shape(age) * TFR(year) / sum(shape)``, so the derived +schedule sums to the baseline's TFR to floating-point rounding. +""" + +from __future__ import annotations + +import hashlib +import json +from functools import cache +from pathlib import Path +from types import MappingProxyType +from typing import Literal + +import numpy as np + +from populace_dynamics.baselines.interpolation import ungroup_band_totals +from populace_dynamics.data import cbo2026 + +__all__ = [ + "AsfrShape", + "FERTILE_AGES", + "NCHS_ASFR_PATH", + "NCHS_ASFR_SHA256", + "NCHS_BANDS", + "NCHS_VINTAGE", + "SHAPES", + "age_shape", + "nchs_published_tfr", + "nchs_single_age_shape", + "scale_shape_to_tfr", +] + +AsfrShape = Literal["nchs2024", "cbo2026"] +SHAPES: tuple[AsfrShape, ...] = ("nchs2024", "cbo2026") +#: Single ages at which the fertility step exposes women (CBO's ages). +FERTILE_AGES: tuple[int, ...] = tuple(range(14, 50)) +_ROOT = Path(__file__).resolve().parents[3] +NCHS_ASFR_PATH = _ROOT / "data" / "external" / "nchs_asfr_2024.json" +NCHS_ASFR_SHA256 = ( + "578742b151d5656231527175e5e4c29c0554ade8bb56807235d6a75890594f60" +) +NCHS_VINTAGE = "2024" +#: NCHS's eight published bands as single-year edges [lower, upper). +NCHS_BANDS: tuple[tuple[str, int, int], ...] = ( + ("10-14", 10, 15), + ("15-19", 15, 20), + ("20-24", 20, 25), + ("25-29", 25, 30), + ("30-34", 30, 35), + ("35-39", 35, 40), + ("40-44", 40, 45), + ("45-49", 45, 50), +) + + +@cache +def _nchs_document() -> dict: + raw = NCHS_ASFR_PATH.read_bytes() + digest = hashlib.sha256(raw).hexdigest() + if digest != NCHS_ASFR_SHA256: + raise ValueError( + f"{NCHS_ASFR_PATH.name} sha256 {digest} != pinned " + f"{NCHS_ASFR_SHA256}; re-pin deliberately" + ) + document = json.loads(raw) + if document.get("measure") != "age_specific_fertility_rate_per_1000_women": + raise ValueError("NCHS ASFR file measure is not per 1,000 women") + return document + + +@cache +def nchs_single_age_shape() -> MappingProxyType: + """NCHS 2024 rates spread to single ages 14-49 (births per woman). + + Read-only mapping; its values sum to the NCHS 2024 TFR (per woman). + """ + table = _nchs_document()["tables"][NCHS_VINTAGE] + if set(table) != {label for label, _, _ in NCHS_BANDS}: + raise ValueError("NCHS ASFR table bands differ from NCHS_BANDS") + edges = [NCHS_BANDS[0][1], *(upper for _, _, upper in NCHS_BANDS)] + totals = [ + (upper - lower) * float(table[label]) / 1000.0 + for label, lower, upper in NCHS_BANDS + ] + values = ungroup_band_totals(edges, totals) + ages = range(edges[0], edges[-1]) + by_age = dict(zip(ages, values.tolist(), strict=True)) + first = FERTILE_AGES[0] + shape = {age: by_age[age] for age in FERTILE_AGES} + shape[first] = sum(value for age, value in by_age.items() if age <= first) + return MappingProxyType(shape) + + +def nchs_published_tfr() -> float: + """The NCHS 2024 TFR the file records, per woman (1599.5 / 1000).""" + return float(_nchs_document()["total_fertility_rate"][NCHS_VINTAGE]) / ( + 1000.0 + ) + + +def age_shape(shape: AsfrShape, year: int) -> dict[int, float]: + """The unscaled single-age shape ``shape`` supplies for ``year``.""" + if shape == "nchs2024": + return dict(nchs_single_age_shape()) + if shape == "cbo2026": + return cbo2026.asfr(year, "all") + raise ValueError(f"unknown ASFR shape {shape!r}; expected {SHAPES}") + + +def scale_shape_to_tfr( + shape: dict[int, float], tfr: float +) -> dict[int, float]: + """``shape`` rescaled so that its single-age values sum to ``tfr``.""" + values = np.asarray(list(shape.values()), dtype=np.float64) + if not np.isfinite(tfr) or tfr < 0.0: + raise ValueError(f"TFR must be finite and non-negative, got {tfr!r}") + if not np.all(np.isfinite(values)) or np.any(values < 0.0): + raise ValueError("shape values must be finite and non-negative") + total = float(values.sum()) + if total <= 0.0: + raise ValueError("an all-zero shape cannot be scaled to a TFR") + factor = float(tfr) / total + return {int(age): float(value) * factor for age, value in shape.items()} diff --git a/src/populace_dynamics/baselines/base.py b/src/populace_dynamics/baselines/base.py new file mode 100644 index 00000000..2037a139 --- /dev/null +++ b/src/populace_dynamics/baselines/base.py @@ -0,0 +1,315 @@ +"""The ``Baseline`` interface and the pieces every baseline shares. + +A baseline is the set of economic and demographic paths a projection +reads besides its cohort: the COLA path, the AWI, population mortality +by single age, sex and year, fertility by single age, the claim-age +table and the DI rates. :class:`Baseline` is the protocol the three +registered baselines implement (:mod:`~populace_dynamics.baselines` +registry); each method's docstring here is the contract. + +Units follow the projection code: COLA rates are fractions keyed by +determination year (``0.028`` is 2.8 percent), as a +:class:`~populace_dynamics.estimates.parameters.COLASeries` that is also +a :class:`~populace_dynamics.scenario_benefits.COLARateSource`; CPI-W +growth is a percentage; AWI is dollars; ``q`` is a one-year death +probability at exact age (ages 0-120, :data:`MAX_AGE`); ASFR is births +per woman at single ages 14-49 (not a probability); TFR is births per +woman. + +:class:`BaselineYearAwareMortality` is the generic year-aware population +mortality model: the same interface as +:class:`~populace_dynamics.cola_track_a.mortality.Tr2008YearAwareMortality` +(``bands`` of single ages, ``__call__(frame, context)``, no +``probabilities`` attribute, so the A4 DI-aware adapter passes the period +context), with ``q`` stored by year and sex instead of a 2004 table times +a ratio. Convention (as A5's): the probability applied in projection +year ``t``, deaths between the ``t - 1`` and ``t`` states, is the year- +``t`` period ``q`` at the frame's start-of-year age ``t - 1 - +birth_year``; ages above 120 use age 120. +""" + +from __future__ import annotations + +import dataclasses +import hashlib +from collections.abc import Mapping +from dataclasses import dataclass, field +from pathlib import Path +from types import MappingProxyType +from typing import Any, Protocol, runtime_checkable + +import numpy as np +import pandas as pd + +from populace_dynamics import scenario_benefits as sb +from populace_dynamics.engine.di_entitlement_rates import ( + MAX_AGE, + SINGLE_YEAR_AGE_BANDS, + DIEntitlementRates, + DIEntitlementSpec, +) +from populace_dynamics.engine.loop import PeriodContext +from populace_dynamics.estimates.parameters import COLASeries +from populace_dynamics.ss.params import SSAParameters + +__all__ = [ + "AWI_FIRST_REPLACED_YEAR", + "SEXES", + "Baseline", + "BaselineGapError", + "BaselineYearAwareMortality", + "file_sha256", + "pad_to_max_age", + "repo_relative", + "replace_awi", + "splice_cola_percent", +] + +SEXES: tuple[str, ...] = ("female", "male") +#: Every baseline replaces the oracle's NAWI from this year on and keeps +#: the oracle's (policyengine-us) realized series before it. It is the +#: legacy baseline's year (TR2008 V.C1 starts its AWI rows in 1975, +#: ``cola_track_a.runner``), kept for the 2026 baselines so that every +#: baseline shares the pre-1975 series and differs only by vintage. +AWI_FIRST_REPLACED_YEAR = 1975 + + +class BaselineGapError(LookupError): + """An input this baseline's sources do not supply (a named gap).""" + + +_ROOT = Path(__file__).resolve().parents[3] + + +def file_sha256(path: Path) -> str: + """SHA-256 of a file's bytes.""" + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def repo_relative(path: Path) -> str: + """``path`` relative to the repository root (for provenance keys).""" + return str(Path(path).resolve().relative_to(_ROOT)) + + +def pad_to_max_age(q: np.ndarray) -> np.ndarray: + """``q`` for ages 0-119 extended to 0-120 by repeating age 119. + + Both 2026 sources stop at age 119 (A5's 2004 table too); the engine's + DI rates and mortality cells run to :data:`MAX_AGE` = 120. The + legacy model pads the same way (``cola_track_a.mortality``). + """ + values = np.asarray(q, dtype=np.float64) + if values.ndim != 1 or values.size < 1 or values.size > MAX_AGE + 1: + raise ValueError(f"q must be a vector of at most {MAX_AGE + 1} ages") + padded = np.full(MAX_AGE + 1, values[-1]) + padded[: values.size] = values + return padded + + +def splice_cola_percent( + realized: COLASeries, + projected_percent: Mapping[int, float], + *, + label: str, +) -> COLASeries: + """Realized history with ``projected_percent`` from its first year on. + + Percentages become fractions rounded to ten decimals, as the legacy + constructor (``cola_track_a.runner.tr2008_baseline_cola``) does, and + :func:`~populace_dynamics.scenario_benefits.extend_cola_series` + splices them (consecutive years, no gap, fractions in [0, 1)). + """ + projected = { + int(year): round(float(percent) / 100.0, 10) + for year, percent in projected_percent.items() + } + return sb.extend_cola_series(realized, projected, projection_label=label) + + +def replace_awi( + params: SSAParameters, + awi: Mapping[int, float], + *, + revision_suffix: str, +) -> SSAParameters: + """``params`` with NAWI replaced by ``awi`` for every year it covers. + + Years before the first year of ``awi`` keep ``params``'s series (the + legacy ``tr2008_ssa_parameters`` rule); ``awi`` must cover + consecutive years with positive finite amounts. + """ + years = sorted(int(year) for year in awi) + if not years or years != list(range(years[0], years[-1] + 1)): + raise ValueError("awi must cover consecutive years") + for year in years: + amount = float(awi[year]) + if not np.isfinite(amount) or amount <= 0.0: + raise ValueError(f"AWI for {year} must be positive and finite") + nawi = { + year: value for year, value in params.nawi.items() if year < years[0] + } + nawi.update({year: float(awi[year]) for year in years}) + return dataclasses.replace( + params, + nawi=dict(sorted(nawi.items())), + pe_us_revision=f"{params.pe_us_revision}+{revision_suffix}", + ) + + +@dataclass(frozen=True) +class BaselineYearAwareMortality: + """Population death probabilities by single age, sex and year. + + ``qx_by_year[year][sex]`` is a read-only vector for ages 0-120. See + the module docstring for the age and year convention. + """ + + baseline: str + qx_by_year: Mapping[int, Mapping[str, np.ndarray]] + bands: tuple[tuple[int, int], ...] = SINGLE_YEAR_AGE_BANDS + provenance: Mapping[str, Any] = field(default_factory=dict) + + def __post_init__(self) -> None: + if tuple(self.bands) != SINGLE_YEAR_AGE_BANDS: + raise ValueError("bands must be the single-year age bands") + frozen: dict[int, Mapping[str, np.ndarray]] = {} + for year, by_sex in self.qx_by_year.items(): + if set(by_sex) != set(SEXES): + raise ValueError(f"q for {year} must cover {SEXES}") + arrays = {} + for sex in SEXES: + array = np.array(by_sex[sex], dtype=np.float64) + if array.shape != (MAX_AGE + 1,): + raise ValueError( + f"{year} {sex} q must cover ages 0-{MAX_AGE}" + ) + if ( + not np.isfinite(array).all() + or ((array < 0.0) | (array > 1.0)).any() + ): + raise ValueError(f"{year} {sex} q must lie in [0, 1]") + array.setflags(write=False) + arrays[sex] = array + frozen[int(year)] = MappingProxyType(arrays) + object.__setattr__(self, "qx_by_year", MappingProxyType(frozen)) + object.__setattr__( + self, "provenance", MappingProxyType(dict(self.provenance)) + ) + + @property + def years(self) -> tuple[int, ...]: + return tuple(sorted(self.qx_by_year)) + + def probabilities_for_year( + self, frame: pd.DataFrame, year: int + ) -> np.ndarray: + """Death probabilities in ``year`` for the frame's rows.""" + missing = {"age", "sex"} - set(frame.columns) + if missing: + raise ValueError( + f"mortality frame is missing columns {sorted(missing)}" + ) + try: + by_sex = self.qx_by_year[int(year)] + except KeyError as error: + raise KeyError( + f"no {self.baseline} mortality for {year}; load the model " + "for every projection year" + ) from error + age = frame["age"].to_numpy(dtype=np.int64) + if (age < 0).any(): + raise ValueError("mortality ages must be non-negative") + index = np.minimum(age, MAX_AGE) + sex = frame["sex"].astype(str).to_numpy() + out = np.full(len(frame), np.nan) + for label in SEXES: + rows = sex == label + out[rows] = by_sex[label][index[rows]] + if np.isnan(out).any(): + unknown = sorted(set(sex[np.isnan(out)])) + raise ValueError(f"unknown mortality sex labels {unknown}") + return out + + def __call__( + self, frame: pd.DataFrame, context: PeriodContext + ) -> np.ndarray: + return self.probabilities_for_year(frame, context.year) + + +@runtime_checkable +class Baseline(Protocol): + """What a selectable projection baseline supplies. + + ``name`` is the registry key, ``vintage`` a human-readable label of + the sources' vintage. ``claim_table_max_year`` is the last claim-age + table row the projection may read (every later year snaps to it). + """ + + name: str + vintage: str + claim_table_max_year: int + + def cola_rates(self, first: int, last: int) -> COLASeries: + """Determination-year COLA fractions, realized history to ``last``. + + The series starts at the realized history's first year (1979) and + is consecutive through ``last``. ``first`` is the caller's first + rate year (the A1 section 4 splice year, ``TrackAConfig. + tr2008_first_rate_year``): the legacy baseline takes TR2008's + rates from it on. Every 2026 baseline instead keeps the + committed history through 2022 and the realized V.C1 rows through + the last actual determination year (2025) whatever ``first`` is, + so its path does not depend on ``first``. + """ + ... + + def cpiw_growth(self, year: int) -> float: + """Annual-average CPI-W growth in ``year``, percent.""" + ... + + def awi(self, first: int, last: int) -> dict[int, float]: + """AWI in dollars for every year ``first..last``.""" + ... + + def ssa_parameters(self, params: SSAParameters) -> SSAParameters: + """``params`` with NAWI replaced by the baseline's AWI. + + Years from :data:`AWI_FIRST_REPLACED_YEAR` take the baseline's + AWI (``dataclasses.replace(params, nawi=...)``); earlier years + keep ``params``'s series. + """ + ... + + def qx(self, year: int) -> dict[str, np.ndarray]: + """Death probabilities by sex for ages 0-120 in ``year``.""" + ... + + def population_mortality(self, years: range) -> Any: + """A year-aware single-age model covering every year in ``years``.""" + ... + + def tfr(self, year: int) -> float: + """Period total fertility rate in ``year``, births per woman.""" + ... + + def asfr(self, year: int) -> dict[int, float]: + """Births per woman at single ages 14-49 in ``year``.""" + ... + + def claim_pmf(self) -> dict[tuple[str, int], dict[int, float]]: + """Claim-age PMFs by (sex, entitlement year), every table row.""" + ... + + def di_rates( + self, spec: DIEntitlementSpec | None = None + ) -> DIEntitlementRates: + """DI incidence, recovery and death rates for ``spec``.""" + ... + + def value_sources(self, year: int) -> dict[str, str]: + """Source tag of every series' value in ``year`` (derived tagged).""" + ... + + def provenance(self) -> dict[str, Any]: + """Sources, SHA-256 of every file read, splices and gaps.""" + ... diff --git a/src/populace_dynamics/baselines/cbo2026_long_term.py b/src/populace_dynamics/baselines/cbo2026_long_term.py new file mode 100644 index 00000000..8cf83ca3 --- /dev/null +++ b/src/populace_dynamics/baselines/cbo2026_long_term.py @@ -0,0 +1,336 @@ +"""``cbo2026_long_term``: CBO's 2026 long-term demographic and economic paths. + +Every CBO value comes from B1's pinned accessor +(:mod:`populace_dynamics.data.cbo2026`; January 2026 demographic +projections, February 2026 ten-year and long-term economics, September +2026 covered workers and earnings) and keeps its tags and builder +defaults there. CBO publishes no COLA, CPI-W or AWI, and its +demographic files start in 2021, so this baseline splices realized +values first (:mod:`~populace_dynamics.baselines.realized`): + +* **COLA.** The committed realized history through 2022, the TR2026 + V.C1 historical rows for 2023 and 2024 and the 2025 actual + (2.8 percent), then from 2026 annual CPI-U growth as the COLA proxy + (cbo2026 builder defaults ``cpiw_equals_cpiu_growth``, + ``cola_equals_annual_cpiu_growth``, ``ten_year_cpiu_then_long_term_ + growth`` and, after 2056, ``hold_2056_economic_growth``). The proxy is + an annual-average growth, not the statutory third-quarter comparison. +* **CPI-W.** TR2026 V.B1 through 2025 (historical through 2024; the + 2025 value is TR2026's estimate, tagged so), CPI-U growth from 2026. +* **AWI.** TR2026 VI.G1 realized values from 1975 through 2024, then + the CBO analog (cbo2026 builder defaults ``awi_bridge_2025_2026_real_ + earnings_times_cpiu`` and ``awi_growth_of_covered_earnings_per_ + worker``), which starts from the same 2024 actual + (:func:`~populace_dynamics.baselines.realized.check_awi_anchor`). +* **Mortality.** CBO's printed rate per 1,000 divided by 1,000 is + ``q`` (B1 verified the q reading against every published 2026-2099 + life expectancy; the m reading fails), ages 0-119 with ``q(119) = 1``, + 2021-2099; age 120 repeats age 119. Before 2021, where CBO publishes + nothing, the OACT TR2026 historical period ``q`` is spliced (builder + default ``cbo_mortality_before_2021_oact_historical``, awaiting + ratification). After 2099 nothing is supplied. +* **Fertility.** CBO's all-women single-age (14-49) ASFR as published, + 2021-2099, and CBO's TFR (published from 2026; the ASFR sum before). +* **Claiming and DI.** :mod:`~populace_dynamics.baselines.common2026`. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +import numpy as np + +from populace_dynamics.baselines.base import ( + AWI_FIRST_REPLACED_YEAR, + SEXES, + BaselineYearAwareMortality, + pad_to_max_age, + replace_awi, + splice_cola_percent, +) +from populace_dynamics.baselines.common2026 import ( + DI_GAP, + Vintage2026Inputs, +) +from populace_dynamics.baselines.realized import ( + FIRST_SPLICED_COLA_YEAR, + LAST_ACTUAL_AWI_YEAR, + LAST_ACTUAL_COLA_YEAR, + REALIZED_HISTORY_LAST_YEAR, + check_awi_anchor, + check_cola_request, + check_realized_cola_agreement, + realized_awi, + realized_cola_percent, +) +from populace_dynamics.data import cbo2026, tr2026 +from populace_dynamics.estimates.parameters import ( + COLASeries, + load_cola_history, +) +from populace_dynamics.ss.params import SSAParameters + +__all__ = ["CBO2026LongTerm"] + +#: CBO's economic paths (as extended by B1) end in this year. +LAST_ECONOMIC_YEAR = 2100 +#: CBO's demographic projections cover 2021-2099. +FIRST_DEMOGRAPHIC_YEAR = 2021 +LAST_DEMOGRAPHIC_YEAR = 2099 +_MORTALITY_SPLICE_DEFAULT = "cbo_mortality_before_2021_oact_historical" + + +@dataclass(frozen=True) +class CBO2026LongTerm(Vintage2026Inputs): + """CBO's 2026 long-term baseline (see module docstring).""" + + name: str = "cbo2026_long_term" + vintage: str = ( + "CBO 2026 long-term projections (demographics January 2026, " + "economics February 2026, covered earnings September 2026)" + ) + + # -- COLA, CPI and AWI -------------------------------------------------- + def cola_rates(self, first: int, last: int) -> COLASeries: + """History through 2022, V.C1 2023-2025, CBO's CPI-U proxy after. + + ``first`` is validated but does not move the splice (realized + increases are kept through 2025; :func:`~populace_dynamics. + baselines.realized.check_cola_request`). + """ + first, last = check_cola_request(first, last) + if last > LAST_ECONOMIC_YEAR: + raise KeyError( + f"CBO2026 projects no COLA after {LAST_ECONOMIC_YEAR}" + ) + check_realized_cola_agreement() + projected = { + year: realized_cola_percent(year)[0] + for year in range( + FIRST_SPLICED_COLA_YEAR, min(last, LAST_ACTUAL_COLA_YEAR) + 1 + ) + } + if last > LAST_ACTUAL_COLA_YEAR: + projected.update( + { + entry.determination_year: entry.percent + for entry in cbo2026.cola_path( + LAST_ACTUAL_COLA_YEAR + 1, last + ) + } + ) + return splice_cola_percent( + load_cola_history(), + projected, + label=( + "CBO 2026 long-term: realized increases through " + f"{LAST_ACTUAL_COLA_YEAR} (TR2026 V.C1 historical rows and " + "the 2025 actual), then annual CPI-U growth as the COLA " + "proxy (cbo2026 builder defaults), via " + "populace_dynamics.baselines.cbo2026_long_term" + ), + ) + + def cpiw_growth(self, year: int) -> float: + year = int(year) + if year <= LAST_ACTUAL_COLA_YEAR: + return tr2026.cpiw_growth(year) + return cbo2026.cpiw_growth(year) + + def awi(self, first: int, last: int) -> dict[int, float]: + first, last = int(first), int(last) + if first > last: + raise ValueError("first must not exceed last") + check_awi_anchor() + out = { + year: realized_awi(year)[0] + for year in range(first, min(last, LAST_ACTUAL_AWI_YEAR) + 1) + } + if last > LAST_ACTUAL_AWI_YEAR: + out.update( + { + entry.year: entry.amount + for entry in cbo2026.awi( + max(first, LAST_ACTUAL_AWI_YEAR + 1), last + ) + } + ) + return out + + def ssa_parameters(self, params: SSAParameters) -> SSAParameters: + return replace_awi( + params, + self.awi(AWI_FIRST_REPLACED_YEAR, LAST_ECONOMIC_YEAR), + revision_suffix=( + f"cbo2026_awi_analog_{AWI_FIRST_REPLACED_YEAR}_" + f"{LAST_ECONOMIC_YEAR}" + ), + ) + + # -- Mortality ---------------------------------------------------------- + def qx(self, year: int) -> dict[str, np.ndarray]: + year = int(year) + if year > LAST_DEMOGRAPHIC_YEAR: + raise KeyError( + f"CBO2026 projects no mortality after {LAST_DEMOGRAPHIC_YEAR}" + ) + out = {} + for sex in SEXES: + source = ( + cbo2026.mortality(year, sex) + if year >= FIRST_DEMOGRAPHIC_YEAR + else tr2026.death_probability(year, sex) + ) + values = pad_to_max_age(source) + values.setflags(write=False) + out[sex] = values + return out + + def population_mortality(self, years: range) -> BaselineYearAwareMortality: + return BaselineYearAwareMortality( + baseline=self.name, + qx_by_year={int(year): self.qx(year) for year in years}, + provenance={ + "basis": ( + "CBO 2026 deaths per 1,000 / 1,000 read as q (B1 " + f"check), {FIRST_DEMOGRAPHIC_YEAR}-" + f"{LAST_DEMOGRAPHIC_YEAR}; OACT TR2026 historical q " + f"before {FIRST_DEMOGRAPHIC_YEAR} " + f"({_MORTALITY_SPLICE_DEFAULT})" + ), + "accessor": "populace_dynamics.data.cbo2026.mortality", + "ages_above_table": "age 120 repeats age 119 (q = 1)", + "age_convention": "start-of-year age (year - 1 - birth_year)", + "years": [int(years.start), int(years.stop) - 1], + "files_verified_by": ( + "data.cbo2026.FILE_SHA256 and data.tr2026.FILE_SHA256" + ), + }, + ) + + # -- Fertility ---------------------------------------------------------- + def tfr(self, year: int) -> float: + return cbo2026.tfr(year) + + def asfr(self, year: int) -> dict[int, float]: + return cbo2026.asfr(year, "all") + + # -- Provenance --------------------------------------------------------- + def value_sources(self, year: int) -> dict[str, str]: + year = int(year) + if year <= LAST_ACTUAL_COLA_YEAR: + cola = realized_cola_percent(year)[1] + cpiw = tr2026.value_provenance("cpiw_growth", year).source + else: + cola = cbo2026.value_provenance("cola", year).source + cpiw = cbo2026.value_provenance("cpiw_growth", year).source + awi = ( + realized_awi(year)[1] + if year <= LAST_ACTUAL_AWI_YEAR + else cbo2026.value_provenance("awi", year).source + ) + demographic = FIRST_DEMOGRAPHIC_YEAR <= year <= LAST_DEMOGRAPHIC_YEAR + return { + "cola": cola, + "cpiw_growth": cpiw, + "awi": awi, + "mortality": ( + cbo2026.value_provenance("mortality", year, sex="male").source + if demographic + else ( + f"{_MORTALITY_SPLICE_DEFAULT}:" + + tr2026.value_provenance( + "death_probability", year, sex="male" + ).source + if year < FIRST_DEMOGRAPHIC_YEAR + else "not_supplied" + ) + ), + "tfr": ( + cbo2026.value_provenance("tfr", year).source + if demographic + else "not_supplied" + ), + "asfr": ( + cbo2026.value_provenance("asfr", year).source + if demographic + else "not_supplied" + ), + "claiming": ( + "ssa_supplement_2026_6b5_1_row_" + f"{min(max(year, 1998), self.claim_table_max_year)}" + ), + "di_rates": "di_entitlement_fit_2008", + } + + def provenance(self) -> dict[str, Any]: + return { + "name": self.name, + "vintage": self.vintage, + "splices": { + "cola": ( + "committed realized history through " + f"{REALIZED_HISTORY_LAST_YEAR}; realized TR2026 V.C1 " + f"rows {FIRST_SPLICED_COLA_YEAR}-2024 and the " + f"{LAST_ACTUAL_COLA_YEAR} actual; CBO annual CPI-U growth " + f"{LAST_ACTUAL_COLA_YEAR + 1}-{LAST_ECONOMIC_YEAR}. " + "Realized rates run through the last actual " + "determination year, whatever the caller's first rate " + "year" + ), + "cpiw_growth": ( + "TR2026 V.B1 through 2025 (2025 estimated); CBO CPI-U " + "growth after" + ), + "awi": ( + f"oracle NAWI before {AWI_FIRST_REPLACED_YEAR}; TR2026 " + f"VI.G1 realized {AWI_FIRST_REPLACED_YEAR}-" + f"{LAST_ACTUAL_AWI_YEAR} (the last actual year); CBO " + f"analog {LAST_ACTUAL_AWI_YEAR + 1}-{LAST_ECONOMIC_YEAR} " + f"from the same {LAST_ACTUAL_AWI_YEAR} actual" + ), + "mortality": ( + "OACT TR2026 historical q before " + f"{FIRST_DEMOGRAPHIC_YEAR}; CBO q " + f"{FIRST_DEMOGRAPHIC_YEAR}-{LAST_DEMOGRAPHIC_YEAR}" + ), + "claiming": ( + "Supplement 2026 Table 6.B5.1, rows 1998-" + f"{self.claim_table_max_year}; later years snap to " + f"{self.claim_table_max_year}" + ), + }, + "builder_defaults_awaiting_ratification": { + **{ + name: "data.cbo2026 builder default" + for name in cbo2026.PENDING_RATIFICATION + }, + _MORTALITY_SPLICE_DEFAULT: ( + "OACT TR2026 historical period q (SSA area) for years " + "before CBO's first demographic year, 2021" + ), + "claim_table_last_row": ( + f"the 2026 Supplement's {self.claim_table_max_year} row " + "(SSA: current-year rows are revised each update) is " + "used for every later year" + ), + "awi_first_replaced_year": ( + f"{AWI_FIRST_REPLACED_YEAR}, the legacy baseline's year" + ), + }, + "gaps": [ + DI_GAP, + "CBO publishes no COLA, CPI-W or AWI: proxies are tagged " + "derived (cbo2026 builder defaults)", + f"no demographic inputs after {LAST_DEMOGRAPHIC_YEAR} and " + f"no CBO fertility before {FIRST_DEMOGRAPHIC_YEAR}", + ], + "files_sha256": { + **self.shared_files_sha256(), + **self.cbo_files_sha256(), + }, + "captured_sources_sha256": self.captured_sources_sha256( + include_cbo=True + ), + } diff --git a/src/populace_dynamics/baselines/claim_tables.py b/src/populace_dynamics/baselines/claim_tables.py new file mode 100644 index 00000000..4255cf5d --- /dev/null +++ b/src/populace_dynamics/baselines/claim_tables.py @@ -0,0 +1,104 @@ +"""Claim-age PMFs from the latest committed SSA awards distribution. + +The 2026 baselines claim from SSA *Annual Statistical Supplement, 2026*, +Table 6.B5.1 (retired-worker awardees by age at entitlement, by sex and +entitlement year 1998-2025), committed as +``data/external/ssa_claim_ages_2026supplement.json`` by +``scripts/extract_ssa_claim_ages_2026.py`` and pinned here by SHA-256. +The table's notes warn that "statistics for current and prior years are +subject to revision with each annual update", so the 2025 row, the last +and the row every later projection year snaps to, is the least settled. + +:func:`populace_dynamics.claiming.claim_age_pmf` cannot read rows after +2022: it snaps every request to its module-level 1998-2022 span before +reading the reference. :func:`claim_pmf_from_reference` therefore +applies the same category-to-age rule directly to each row: ages 62-66 +and 70+ from their categories, the 67-69 aggregate split uniformly over +67, 68 and 69, disability conversions excluded and the rest renormalized +to one. For every row in 1998-2022 the two constructions must agree +exactly (differential test in +``tests/baselines/test_asfr_and_claim_tables.py``). +""" + +from __future__ import annotations + +import hashlib +from functools import cache +from pathlib import Path + +from populace_dynamics import claiming + +__all__ = [ + "CLAIM_AGES_2026_PATH", + "CLAIM_AGES_2026_SHA256", + "claim_pmf_2026_supplement", + "claim_pmf_from_reference", + "claim_row_pmf", + "last_year_2026_supplement", +] + +_ROOT = Path(__file__).resolve().parents[3] +CLAIM_AGES_2026_PATH = ( + _ROOT / "data" / "external" / "ssa_claim_ages_2026supplement.json" +) +CLAIM_AGES_2026_SHA256 = ( + "1721f82ae6bc865a1e19f22a9578963032e7b30da9ba3f6f53448ab49fd17210" +) +_BAND_67_69 = (67, 68, 69) + + +def claim_row_pmf(categories: dict[str, float]) -> dict[int, float]: + """Claim-age PMF of one row's collapsed categories, no conversions.""" + band = float(categories["age67_69"]) / len(_BAND_67_69) + mass = { + 62: float(categories["age62"]), + 63: float(categories["age63"]), + 64: float(categories["age64"]), + 65: float(categories["age65"]), + 66: float(categories["age66"]), + 67: band, + 68: band, + 69: band, + 70: float(categories["age70plus"]), + } + if any(value < 0.0 for value in mass.values()): + raise ValueError("claim-age category shares must be non-negative") + total = sum(mass.values()) + if total <= 0.0: + raise ValueError("degenerate claim-age row") + return {age: value / total for age, value in mass.items()} + + +def claim_pmf_from_reference( + reference: claiming.ClaimAgeReference, +) -> dict[tuple[str, int], dict[int, float]]: + """Every (sex, entitlement year) PMF of a Table 6.B5.1 reference.""" + return { + (sex, year): claim_row_pmf(reference.row(sex, year)["categories"]) + for sex in ("female", "male") + for year in reference.years() + } + + +@cache +def _reference_2026() -> claiming.ClaimAgeReference: + digest = hashlib.sha256(CLAIM_AGES_2026_PATH.read_bytes()).hexdigest() + if digest != CLAIM_AGES_2026_SHA256: + raise ValueError( + f"{CLAIM_AGES_2026_PATH.name} sha256 {digest} != pinned " + f"{CLAIM_AGES_2026_SHA256}; re-pin deliberately" + ) + reference = claiming.load_claim_age_reference(CLAIM_AGES_2026_PATH) + if reference.supplement_year != 2026 or reference.table != "6.B5.1": + raise ValueError("the 2026 claim-age file is not Supplement 6.B5.1") + return reference + + +def claim_pmf_2026_supplement() -> dict[tuple[str, int], dict[int, float]]: + """Every row of the 2026 Supplement table (1998-2025), as PMFs.""" + return claim_pmf_from_reference(_reference_2026()) + + +def last_year_2026_supplement() -> int: + """The last entitlement year the 2026 Supplement table prints.""" + return max(_reference_2026().years()) diff --git a/src/populace_dynamics/baselines/common2026.py b/src/populace_dynamics/baselines/common2026.py new file mode 100644 index 00000000..8c1e9eea --- /dev/null +++ b/src/populace_dynamics/baselines/common2026.py @@ -0,0 +1,136 @@ +"""Inputs the two 2026 baselines share: claiming, DI and file identity. + +* **Claiming.** The latest SSA awards distribution committed in + ``data/external`` is Supplement 2026 Table 6.B5.1 (entitlement years + 1998-2025, :mod:`~populace_dynamics.baselines.claim_tables`). Every + row is supplied, and the projection reads rows at or before its last + row, 2025 (``claim_table_max_year``), so every projection year after + 2025 snaps to the 2025 row. The registered tests' table is the 2014 + Supplement capped at 2008 (``baselines.legacy``). +* **DI.** The DI incidence, recovery and death rates are the 2008 fit + (``load_di_entitlement_rates(DIEntitlementSpec())``) under every + baseline, a named gap: no projected DI incidence or termination rates + by age were found for TR2008 or any later vintage, and the fitted + arrays have no year axis. In the default multiplier death mode DI + death is the AS118 rate times the baseline's population ``q`` over the + NCHS 2000 ``q``, so it follows the baseline's mortality. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +from populace_dynamics.baselines.asfr import NCHS_ASFR_PATH, NCHS_ASFR_SHA256 +from populace_dynamics.baselines.base import file_sha256, repo_relative +from populace_dynamics.baselines.claim_tables import ( + CLAIM_AGES_2026_PATH, + CLAIM_AGES_2026_SHA256, + claim_pmf_2026_supplement, + last_year_2026_supplement, +) +from populace_dynamics.data import cbo2026, tr2026 +from populace_dynamics.engine.di_entitlement_rates import ( + DEFAULT_INPUTS_PATH, + NCHS_2000_PATH, + DIEntitlementRates, + DIEntitlementSpec, + load_di_entitlement_rates, +) +from populace_dynamics.estimates.parameters import ( + COLA_FILE_SHA256, + COLA_HISTORY_PATH, +) + +__all__ = [ + "CLAIM_TABLE_MAX_YEAR_2026", + "DI_GAP", + "Vintage2026Inputs", +] + +#: The last row of the 2026 Supplement claim-age table. +CLAIM_TABLE_MAX_YEAR_2026 = 2025 +DI_GAP = ( + "DI incidence, recovery and death are the 2008 fit (A4, " + "load_di_entitlement_rates(DIEntitlementSpec())) under every baseline; " + "no projected DI rates by age are captured for any vintage. DI death " + "follows the baseline's population mortality in multiplier mode" +) + + +def _captured_sources(data_dir: Path) -> dict[str, str]: + """Captured upstream source SHA-256s recorded in ``sources.json``.""" + document = json.loads((data_dir / "sources.json").read_bytes()) + return { + name: record["sha256"] + for name, record in sorted(document["sources"].items()) + } + + +class Vintage2026Inputs: + """Claiming, DI and file-identity methods of the 2026 baselines.""" + + claim_table_max_year: int = CLAIM_TABLE_MAX_YEAR_2026 + + def claim_pmf(self) -> dict[tuple[str, int], dict[int, float]]: + """Every 2026 Supplement row (1998-2025) as a claim-age PMF.""" + if last_year_2026_supplement() != self.claim_table_max_year: + raise ValueError( + "the 2026 Supplement table's last row is not " + f"{self.claim_table_max_year}" + ) + return claim_pmf_2026_supplement() + + def di_rates( + self, spec: DIEntitlementSpec | None = None + ) -> DIEntitlementRates: + """The 2008 DI fit (a named gap; see the module docstring).""" + return load_di_entitlement_rates(spec or DIEntitlementSpec()) + + @staticmethod + def shared_files_sha256() -> dict[str, str]: + """Pinned or hashed identity of every shared input file read.""" + tr2026.verify_files() + cbo2026.verify_files() + for path, pinned in ( + (NCHS_ASFR_PATH, NCHS_ASFR_SHA256), + (CLAIM_AGES_2026_PATH, CLAIM_AGES_2026_SHA256), + (COLA_HISTORY_PATH, COLA_FILE_SHA256), + ): + observed = file_sha256(path) + if observed != pinned: + raise ValueError( + f"{path.name} sha256 {observed} != pinned {pinned}" + ) + return { + **{ + f"data/external/tr2026/{name}": digest + for name, digest in tr2026.FILE_SHA256.items() + }, + repo_relative(COLA_HISTORY_PATH): COLA_FILE_SHA256, + repo_relative(CLAIM_AGES_2026_PATH): CLAIM_AGES_2026_SHA256, + repo_relative(NCHS_ASFR_PATH): NCHS_ASFR_SHA256, + repo_relative(DEFAULT_INPUTS_PATH): file_sha256( + DEFAULT_INPUTS_PATH + ), + repo_relative(NCHS_2000_PATH): file_sha256(NCHS_2000_PATH), + } + + @staticmethod + def cbo_files_sha256() -> dict[str, str]: + cbo2026.verify_files() + return { + f"data/external/cbo2026/{name}": digest + for name, digest in cbo2026.FILE_SHA256.items() + } + + @staticmethod + def captured_sources_sha256(*, include_cbo: bool) -> dict[str, Any]: + """Upstream capture hashes the extractors recorded (pinned JSON).""" + out: dict[str, Any] = { + "tr2026": _captured_sources(tr2026.DATA_DIR), + } + if include_cbo: + out["cbo2026"] = _captured_sources(cbo2026.DATA_DIR) + return out diff --git a/src/populace_dynamics/baselines/fertility.py b/src/populace_dynamics/baselines/fertility.py new file mode 100644 index 00000000..e2678600 --- /dev/null +++ b/src/populace_dynamics/baselines/fertility.py @@ -0,0 +1,181 @@ +"""Opt-in ASFR fertility for open-population projections (report-only). + +NOT GATED, REPORT-ONLY. This step draws births from a baseline's +single-age fertility schedule (:meth:`Baseline.asfr`). It is a separate +path from the gated candidate-16 fertility law (``models/family_ +transitions/components/fertility.py``, drawn by ``engine.marital`` and +materialized by ``engine.steps.apply_fertility``; gate 2 locks its +``asfr.*`` cells), which it neither imports nor changes. Nothing +constructs it by default: the registered Track A and FRA-68 projections +are closed cohorts whose fertility slot draws no births +(``cola_track_a.adapters.adopt_marital_state``), and no registered test +uses this module. + +The draw. In projection year ``t`` every woman (``sex == "female"``) +whose ``age`` column, after step 2 (aging, which sets it to the age +attained in ``t``), is a key of ``asfr(t)`` gives birth with probability +``asfr(t)[age]``: one Bernoulli draw per woman-year. ASFR is births per +woman, so using it as a probability needs every rate in [0, 1] (refused +otherwise); at most one birth per woman-year is drawn, so multiple +births in a year are not represented. The expected number of births is +the sum of the rates over the exposed women. Using the attained age for +the age at birth is a builder convention (a half-year average offset). + +Randomness is the engine's: each woman's uniform comes from the RNG +registry's tagged child stream ``(period, FERTILITY, ASFR_STREAM_TAG, +person ordinal)``, so a woman's draw does not depend on who else is in +the frame, and the tag keeps it apart from the module stream and from +person streams. Births become child rows through +:func:`populace_dynamics.engine.steps.materialize_maternal_births` +(imported read-only): new identifiers from the projection's allocator, +age 0, the mother's weight and household, and a 50/50 sex draw from the +module stream the loop passes. +""" + +from __future__ import annotations + +from collections.abc import Callable, Mapping, MutableMapping +from dataclasses import dataclass + +import numpy as np +import pandas as pd + +from populace_dynamics.engine.loop import ( + MaritalReaderStep, + MaritalStepResult, + PeriodContext, +) +from populace_dynamics.engine.rng import ProjectionModule +from populace_dynamics.engine.steps import materialize_maternal_births + +__all__ = [ + "ASFR_FERTILITY_LABEL", + "ASFR_STREAM_TAG", + "AsfrFertilityStep", + "draw_asfr_births", + "expected_births", +] + +ASFR_FERTILITY_LABEL = "not gated, report-only" +#: Component tag of the per-woman fertility streams ("AF"). +ASFR_STREAM_TAG = 0x4146 + + +def _validated_rates(rates: Mapping[int, float]) -> dict[int, float]: + out = {} + for age, rate in rates.items(): + value = float(rate) + if not np.isfinite(value) or not 0.0 <= value <= 1.0: + raise ValueError( + f"ASFR at age {age} is {value!r}; the Bernoulli draw needs " + "a rate in [0, 1]" + ) + out[int(age)] = value + return out + + +def _exposed( + frame: pd.DataFrame, rates: Mapping[int, float] +) -> tuple[np.ndarray, np.ndarray]: + """Row positions of exposed women and their rates.""" + missing = {"person_id", "age", "sex"} - set(frame.columns) + if missing: + raise ValueError( + f"fertility frame is missing columns {sorted(missing)}" + ) + age = frame["age"].to_numpy(dtype=np.int64) + female = frame["sex"].astype(str).to_numpy() == "female" + rate = np.array([rates.get(int(value), 0.0) for value in age]) + exposed = np.flatnonzero(female & np.isin(age, list(rates))) + return exposed, rate[exposed] + + +def expected_births(frame: pd.DataFrame, rates: Mapping[int, float]) -> float: + """Sum of the rates over the women the step exposes.""" + _, rate = _exposed(frame, _validated_rates(rates)) + return float(rate.sum()) + + +def draw_asfr_births( + frame: pd.DataFrame, + context: PeriodContext, + rates: Mapping[int, float], +) -> pd.DataFrame: + """One Bernoulli birth draw per exposed woman; returns birth records. + + Columns ``parent_person_id`` and ``birth_year`` (the step-3 birth + record shape :func:`materialize_maternal_births` reads). + """ + if context.rng_registry is None: + raise RuntimeError( + "ASFR fertility needs the loop's RNG registry (person-keyed " + "streams)" + ) + exposed, rate = _exposed(frame, _validated_rates(rates)) + person_ids = frame["person_id"].to_numpy()[exposed] + uniforms = np.array( + [ + context.rng_registry.tagged_child_generator( + context.period_index, + ProjectionModule.FERTILITY, + ASFR_STREAM_TAG, + context.person_ordinals[person_id], + ).random() + for person_id in person_ids + ], + dtype=np.float64, + ) + mothers = person_ids[uniforms < rate] + return pd.DataFrame( + { + "parent_person_id": pd.Series(mothers, dtype="int64"), + "birth_year": pd.Series( + np.full(len(mothers), int(context.year)), dtype="int64" + ), + } + ) + + +@dataclass(frozen=True) +class AsfrFertilityStep: + """A ``PeriodModules.fertility`` adapter drawing births from ASFR. + + ``asfr`` maps a year to ``{age: births per woman}`` (for example a + baseline's :meth:`asfr`). ``marital_reader``, when given, runs first + on the frame (for example Track A's ``adopt_marital_state``, the + merge point the fertility slot otherwise fills). ``birth_log[year]`` + records the year's birth records, and ``expected_log[year]`` the + expected number of births, when given. + """ + + asfr: Callable[[int], Mapping[int, float]] + marital_reader: MaritalReaderStep | None = None + birth_log: MutableMapping[int, pd.DataFrame] | None = None + expected_log: MutableMapping[int, float] | None = None + label: str = ASFR_FERTILITY_LABEL + + def __post_init__(self) -> None: + if self.label != ASFR_FERTILITY_LABEL: + raise ValueError( + f"the ASFR fertility step is labelled {ASFR_FERTILITY_LABEL!r}" + ) + + def __call__( + self, + frame: pd.DataFrame, + context: PeriodContext, + marital: MaritalStepResult, + rng: np.random.Generator, + ) -> pd.DataFrame: + current = frame + if self.marital_reader is not None: + current = self.marital_reader(frame, context, marital, rng) + rates = self.asfr(int(context.year)) + births = draw_asfr_births(current, context, rates) + if self.birth_log is not None: + self.birth_log[int(context.year)] = births + if self.expected_log is not None: + self.expected_log[int(context.year)] = expected_births( + current, rates + ) + return materialize_maternal_births(current, births, context, rng) diff --git a/src/populace_dynamics/baselines/interpolation.py b/src/populace_dynamics/baselines/interpolation.py new file mode 100644 index 00000000..b5d4fe05 --- /dev/null +++ b/src/populace_dynamics/baselines/interpolation.py @@ -0,0 +1,131 @@ +"""Mean-preserving ungrouping of banded schedules into single years. + +A five-year age-specific fertility rate is the mean of its five single +ages' rates in a population with equal exposure at every age, so the +band's *total* (width times rate) is the sum of the single-age rates the +band contains. :func:`ungroup_band_totals` spreads band totals over the +single years of each band so that + +1. every band's single-year values sum exactly to the band total + (mean preservation), and +2. every single-year value is non-negative, + +using a monotone piecewise-cubic interpolation of the cumulative schedule +(Fritsch and Carlson, 1980, "Monotone piecewise cubic interpolation", +*SIAM Journal on Numerical Analysis* 17(2):238-246; SciPy's +``PchipInterpolator``, whose derivative estimates follow Fritsch and +Butland, 1984). The cumulative ``C(x)``, the sum of every band total +below edge ``x``, is known exactly at the band edges and is +non-decreasing because no total is negative. A monotone interpolant of +those points never decreases, so its first differences over single +years, the single-year values, are never negative; and because it passes +through ``C`` at every edge, each band's differences telescope to the +band total. The implied single-year density (the interpolant's +derivative) is piecewise quadratic and continuous. This is the +cumulative monotone-spline graduation that demographers use to split +grouped counts (for example DemoTools' ``graduate_mono``); it is chosen +over Beers or Sprague multipliers, which preserve band sums but can +return negative values at the edges of a schedule. + +Floating-point differences can leave values of order ``1e-16`` below +zero or a band's sum off by a few ulps. Values below ``-1e-12`` times +the largest band total are refused as a defect; smaller ones are set to +zero, and each band with a positive total is then rescaled so its values +sum to the total (to rounding). A band whose total is zero returns +zeros. A positive total below the cumulative's floating-point +resolution (about ``1e-16`` of the running sum, for example ``1e-208`` +after a band of ``1.0``) is absorbed when the cumulative is formed, so +its interpolated values are all zero; such a band takes its total +spread uniformly over its years, which keeps both invariants. No +fertility schedule comes near this case (Hypothesis found it). + +Invariants (property-tested in ``tests/baselines/test_interpolation.py``): +band sums preserved, every value non-negative, homogeneity of degree one +(scaling every total by ``c >= 0`` scales every value by ``c``), and a +schedule whose bands have equal per-year means is returned flat. +""" + +from __future__ import annotations + +from collections.abc import Sequence + +import numpy as np +from scipy.interpolate import PchipInterpolator + +__all__ = [ + "NEGATIVE_TOLERANCE", + "ungroup_band_totals", +] + +#: Relative size of a negative single-year value that is treated as +#: floating-point noise rather than a defect. +NEGATIVE_TOLERANCE = 1e-12 + + +def _validated( + edges: Sequence[int], totals: Sequence[float] +) -> tuple[np.ndarray, np.ndarray]: + edge_array = np.asarray(edges) + if edge_array.ndim != 1 or edge_array.size < 2: + raise ValueError("edges must list at least two band edges") + if not np.issubdtype(edge_array.dtype, np.integer): + raise ValueError("edges must be integers (single-year boundaries)") + edge_array = edge_array.astype(np.int64) + if np.any(np.diff(edge_array) <= 0): + raise ValueError("edges must be strictly increasing") + total_array = np.asarray(totals, dtype=np.float64) + if total_array.shape != (edge_array.size - 1,): + raise ValueError("totals must have one value per band") + if not np.all(np.isfinite(total_array)): + raise ValueError("band totals must be finite") + if np.any(total_array < 0.0): + raise ValueError("band totals must be non-negative") + return edge_array, total_array + + +def ungroup_band_totals( + edges: Sequence[int], totals: Sequence[float] +) -> np.ndarray: + """Single-year values for bands ``[edges[k], edges[k+1])``. + + ``totals[k]`` is the sum of the single-year values of band ``k`` + (for a rate schedule, the band width times the band rate). Returns + one value per single year from ``edges[0]`` to ``edges[-1] - 1``. + See the module docstring for the method and its invariants. + """ + edge_array, total_array = _validated(edges, totals) + cumulative = np.concatenate(([0.0], np.cumsum(total_array))) + single_edges = np.arange(edge_array[0], edge_array[-1] + 1) + if total_array.max(initial=0.0) == 0.0: + return np.zeros(single_edges.size - 1) + # Subnormal slopes overflow SciPy's harmonic-mean derivative to an + # infinite reciprocal, i.e. a zero derivative, which is the intended + # flat limit; the band fallback below handles the absorbed totals. + with np.errstate(over="ignore", divide="ignore"): + interpolant = PchipInterpolator( + edge_array.astype(np.float64), cumulative, extrapolate=False + ) + values = np.diff(interpolant(single_edges.astype(np.float64))) + scale = float(total_array.max()) + if not np.all(np.isfinite(values)): + raise ValueError("ungrouping produced a non-finite value") + if values.min() < -NEGATIVE_TOLERANCE * scale: + raise ValueError( + "ungrouping produced a negative single-year value " + f"({values.min()!r}); the monotone interpolant should not" + ) + values = np.maximum(values, 0.0) + for lower, upper, total in zip( + edge_array[:-1], edge_array[1:], total_array, strict=True + ): + start, stop = lower - edge_array[0], upper - edge_array[0] + band = values[start:stop] + band_sum = float(band.sum()) + if total == 0.0: + values[start:stop] = 0.0 + elif band_sum > 0.0: + values[start:stop] = band * (total / band_sum) + else: + # Absorbed by the cumulative's rounding (module docstring). + values[start:stop] = total / (upper - lower) + return values diff --git a/src/populace_dynamics/baselines/legacy.py b/src/populace_dynamics/baselines/legacy.py new file mode 100644 index 00000000..591222e3 --- /dev/null +++ b/src/populace_dynamics/baselines/legacy.py @@ -0,0 +1,252 @@ +"""``tr2008_intermediate``: the registered tests' baseline, unchanged. + +:class:`TR2008Legacy` adds no rate of its own. Every input delegates to +the constructor the registered Track A and FRA-68 entry points already +call (``scripts/run_track_a_registered.py``, ``run_fra68_registered.py`` +and the dry runs), so its outputs are those constructors' outputs, value +for value (differential tests in +``tests/baselines/test_legacy_differential.py``): + +* COLA: :func:`~populace_dynamics.cola_track_a.runner.tr2008_baseline_cola` + on the realized history + (:func:`~populace_dynamics.estimates.parameters.load_cola_history`): + realized rates before ``first``, TR2008 V.C1 through 2017 and the + derived ultimate CPI (2.8) after (A1 section 4). The 2008-vintage + splice is the registered convention: TR2008 rates replace realized + ones from 2008, so this baseline does not splice realized COLAs through + the last actual year, unlike the 2026 baselines. +* AWI: :func:`~populace_dynamics.cola_track_a.runner. + tr2008_ssa_parameters` (TR2008 from 1975 through 2085). +* Mortality: :func:`~populace_dynamics.cola_track_a.mortality. + load_tr2008_mortality` (the A2 substitute, 2004 period table times the + V.A1 ASADR ratio; base year 2004). +* Claiming: :func:`~populace_dynamics.cola_track_a.runner. + load_claiming_pmf` (Supplement 2014 Table 6.B5.1, pinned by A3), read + through rows at or before 2008 (``claim_table_max_year``). +* DI: :func:`~populace_dynamics.engine.di_entitlement_rates. + load_di_entitlement_rates` (the 2008 fit). +* Fertility: none. TR2008 publishes no age-specific fertility, and the + registered projection tests draw no births, so :meth:`TR2008Legacy.asfr` + and :meth:`TR2008Legacy.tfr` refuse with :class:`BaselineGapError`. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +import numpy as np +import pandas as pd + +from populace_dynamics.baselines.base import ( + AWI_FIRST_REPLACED_YEAR, + SEXES, + BaselineGapError, + file_sha256, + repo_relative, +) +from populace_dynamics.cohorts import psid2010 +from populace_dynamics.cola_track_a.mortality import ( + Tr2008YearAwareMortality, + load_tr2008_mortality, +) +from populace_dynamics.cola_track_a.runner import ( + load_claiming_pmf, + tr2008_baseline_cola, + tr2008_ssa_parameters, +) +from populace_dynamics.data import tr2008 +from populace_dynamics.engine.di_entitlement_rates import ( + DEFAULT_INPUTS_PATH, + MAX_AGE, + NCHS_2000_PATH, + DIEntitlementRates, + DIEntitlementSpec, + load_di_entitlement_rates, +) +from populace_dynamics.estimates.parameters import ( + COLA_FILE_SHA256, + COLA_HISTORY_PATH, + COLASeries, + load_cola_history, +) +from populace_dynamics.ss.params import SSAParameters + +__all__ = ["TR2008Legacy"] + +#: ``tr2008_ssa_parameters``'s default last AWI year. +_LEGACY_AWI_LAST_YEAR = 2085 + + +@dataclass(frozen=True) +class TR2008Legacy: + """The 2008 Trustees intermediate baseline the registered tests use.""" + + alternative: str = "intermediate" + mortality_base_year: int = 2004 + claim_table_max_year: int = 2008 + name: str = "tr2008_intermediate" + vintage: str = ( + "2008 Trustees Report, intermediate (registered projection tests)" + ) + + def __post_init__(self) -> None: + if self.claim_table_max_year != 2008: + raise ValueError( + "the legacy baseline's claim-table cap is the registered " + "2008 (TrackAConfig.claim_table_max_year)" + ) + + # -- COLA, CPI and AWI -------------------------------------------------- + def cola_rates(self, first: int, last: int) -> COLASeries: + return tr2008_baseline_cola( + load_cola_history(), + first_year=first, + last_year=last, + alternative=self.alternative, + ) + + def cpiw_growth(self, year: int) -> float: + """TR2008 single-year V.B1 CPI growth (CPI-W), 1960-2082.""" + return tr2008.economic_assumptions( + year, alternative=self.alternative + ).cpi + + def awi(self, first: int, last: int) -> dict[int, float]: + return { + entry.year: entry.amount + for entry in tr2008.awi_path( + first, last, alternative=self.alternative + ) + } + + def ssa_parameters(self, params: SSAParameters) -> SSAParameters: + return tr2008_ssa_parameters(params, alternative=self.alternative) + + # -- Mortality ---------------------------------------------------------- + def population_mortality(self, years: range) -> Tr2008YearAwareMortality: + return load_tr2008_mortality( + years, + alternative=self.alternative, + base_year=self.mortality_base_year, + ) + + def qx(self, year: int) -> dict[str, np.ndarray]: + """The A2 substitute's probabilities by age, as the model applies. + + Evaluated through the model itself + (:meth:`Tr2008YearAwareMortality.probabilities_for_year`), so it is + the clipped product ``qx_2004 x ratio(year, group)``. + """ + model = self.population_mortality(range(int(year), int(year) + 1)) + ages = np.arange(MAX_AGE + 1) + out = {} + for sex in SEXES: + frame = pd.DataFrame({"age": ages, "sex": sex}) + values = model.probabilities_for_year(frame, int(year)) + values.setflags(write=False) + out[sex] = values + return out + + # -- Fertility ---------------------------------------------------------- + def tfr(self, year: int) -> float: + raise BaselineGapError( + "the tr2008_intermediate baseline supplies no fertility: the " + "registered projection tests draw no births and no TR2008 " + "accessor reads its V.A1 TFR column" + ) + + def asfr(self, year: int) -> dict[int, float]: + raise BaselineGapError( + "the tr2008_intermediate baseline supplies no age-specific " + "fertility: TR2008 publishes none and the registered " + "projection tests draw no births" + ) + + # -- Claiming and DI ---------------------------------------------------- + def claim_pmf(self) -> dict[tuple[str, int], dict[int, float]]: + return load_claiming_pmf() + + def di_rates( + self, spec: DIEntitlementSpec | None = None + ) -> DIEntitlementRates: + return load_di_entitlement_rates(spec or DIEntitlementSpec()) + + # -- Provenance --------------------------------------------------------- + def value_sources(self, year: int) -> dict[str, str]: + year = int(year) + cola = tr2008.cola_path(year, year, alternative=self.alternative)[0] + awi = tr2008.awi_path(year, year, alternative=self.alternative)[0] + return { + "cola": ( + f"{cola.source} (realized history is used for determination " + "years before the caller's first rate year)" + ), + "cpiw_growth": tr2008.economic_assumptions( + year, alternative=self.alternative + ).source, + "awi": awi.source, + "mortality": ( + "a2_substitute_qx2004_times_tr2008_v_a1_asadr_ratio_base_" + f"{self.mortality_base_year}" + ), + "tfr": "gap_not_supplied", + "asfr": "gap_not_supplied", + "claiming": ( + "ssa_supplement_2014_6b5_1_row_" + f"{min(year, self.claim_table_max_year)}" + ), + "di_rates": "di_entitlement_fit_2008", + } + + def provenance(self) -> dict[str, Any]: + tr2008.verify_files() + return { + "name": self.name, + "vintage": self.vintage, + "alternative": self.alternative, + "constructors": { + "cola": "cola_track_a.runner.tr2008_baseline_cola", + "awi": "cola_track_a.runner.tr2008_ssa_parameters", + "mortality": "cola_track_a.mortality.load_tr2008_mortality", + "claiming": "cola_track_a.runner.load_claiming_pmf", + "di": "engine.di_entitlement_rates.load_di_entitlement_rates", + }, + "splices": { + "cola": ( + "realized history before the caller's first rate year " + "(A1 section 4: 2008), TR2008 V.C1 to 2017, derived " + "ultimate CPI after 2017" + ), + "awi": ( + f"oracle NAWI before {AWI_FIRST_REPLACED_YEAR}; TR2008 " + f"V.C1/VI.F6 {AWI_FIRST_REPLACED_YEAR}-" + f"{_LEGACY_AWI_LAST_YEAR}" + ), + "claiming": f"rows at or before {self.claim_table_max_year}", + }, + "mortality_base_year": self.mortality_base_year, + "claim_table_max_year": self.claim_table_max_year, + "gaps": [ + "no fertility (TR2008 publishes no ASFR; the registered " + "projection tests draw no births)", + "DI rates are the 2008 fit (A4); DI death follows the " + "population mortality in multiplier mode", + tr2008.MORTALITY_SUBSTITUTE_STANDING, + ], + "files_sha256": { + **{ + f"data/external/tr2008/{name}": digest + for name, digest in tr2008.FILE_SHA256.items() + }, + "data/external/ssa_cola_history.json": COLA_FILE_SHA256, + "data/external/ssa_claim_ages_2014supplement.json": ( + psid2010.CLAIMING_REFERENCE_SHA256 + ), + repo_relative(DEFAULT_INPUTS_PATH): file_sha256( + DEFAULT_INPUTS_PATH + ), + repo_relative(NCHS_2000_PATH): file_sha256(NCHS_2000_PATH), + }, + "cola_history_path": repo_relative(COLA_HISTORY_PATH), + } diff --git a/src/populace_dynamics/baselines/realized.py b/src/populace_dynamics/baselines/realized.py new file mode 100644 index 00000000..9b1d7170 --- /dev/null +++ b/src/populace_dynamics/baselines/realized.py @@ -0,0 +1,142 @@ +"""Realized values the 2026 baselines splice before any projected value. + +Every 2026 baseline keeps realized values through the last actual year +before it uses a projected or derived one: + +* **COLA**: realized through determination year 2025. The committed + realized history (``data/external/ssa_cola_history.json``, loaded by + :func:`~populace_dynamics.estimates.parameters.load_cola_history`) + ends in 2022 and supplies every year through 2022 unchanged (the same + floats the legacy baseline's realized years carry); TR2026 Table V.C1 + supplies the historical increases for 2023 and 2024 and the 2025 + increase as an actual (V.C1 footnote g, 2.8 percent, first paid + January 2026), so every 2026 baseline's projected or derived rates + start in :data:`FIRST_SPLICED_COLA_YEAR` (2023) with the realized + rows. V.C1's historical rows equal the committed history in every + year both cover (1979-2022), and :func:`check_realized_cola_agreement` + refuses any difference before a splice is built. +* **AWI**: realized through 2024, TR2026 Table VI.G1's last historical + row; VI.G1's 2025 value is an estimate and later values projections. + CBO publishes no AWI; its analog starts from the same 2024 actual + (``data.cbo2026.awi``), and :func:`check_awi_anchor` refuses any + difference between the two 2024 amounts. +""" + +from __future__ import annotations + +from functools import cache +from numbers import Integral + +from populace_dynamics.data import cbo2026, tr2026 +from populace_dynamics.estimates.parameters import ( + REQUIRED_COLA_DETERMINATION_YEARS, + load_cola_history, +) + +__all__ = [ + "FIRST_SPLICED_COLA_YEAR", + "LAST_ACTUAL_AWI_YEAR", + "LAST_ACTUAL_COLA_YEAR", + "REALIZED_HISTORY_LAST_YEAR", + "check_awi_anchor", + "check_cola_request", + "check_realized_cola_agreement", + "realized_awi", + "realized_cola_percent", +] + +#: TR2026 V.C1: 2025 is the last actual automatic increase (footnote g). +LAST_ACTUAL_COLA_YEAR = 2025 +#: TR2026 VI.G1: 2024 is the last historical AWI (2025 is estimated). +LAST_ACTUAL_AWI_YEAR = 2024 +#: The committed realized COLA history ends with this determination year. +REALIZED_HISTORY_LAST_YEAR = REQUIRED_COLA_DETERMINATION_YEARS[-1] +#: The first determination year a 2026 baseline splices onto the history. +FIRST_SPLICED_COLA_YEAR = REALIZED_HISTORY_LAST_YEAR + 1 + +_REALIZED_COLA_SOURCES = ("tr2026_v_c1_historical", "tr2026_v_c1_actual") + + +def check_cola_request(first: int, last: int) -> tuple[int, int]: + """Validate a 2026 baseline's ``cola_rates(first, last)`` request. + + ``first`` (the caller's first rate year, ``TrackAConfig. + tr2008_first_rate_year``) must be an integer year no later than + ``last``; it does not move the splice, because a 2026 baseline keeps + every realized increase through :data:`LAST_ACTUAL_COLA_YEAR`. + ``last`` must follow the committed history, which already covers + every earlier year (use ``load_cola_history()`` for those). + """ + for label, value in (("first", first), ("last", last)): + if isinstance(value, bool) or not isinstance(value, Integral): + raise TypeError(f"{label} must be an integer year") + first, last = int(first), int(last) + if first > last: + raise ValueError(f"first ({first}) must not follow last ({last})") + if last < FIRST_SPLICED_COLA_YEAR: + raise ValueError( + f"last ({last}) must be at least {FIRST_SPLICED_COLA_YEAR}: the " + "committed realized history covers every earlier year" + ) + return first, last + + +def realized_cola_percent(year: int) -> tuple[float, str]: + """The realized increase for ``year`` (percent) and its source tag.""" + year = int(year) + if year > LAST_ACTUAL_COLA_YEAR: + raise KeyError( + f"no realized COLA for determination year {year}; the last " + f"actual increase is {LAST_ACTUAL_COLA_YEAR}" + ) + (entry,) = tr2026.cola_path(year, year) + if entry.source not in _REALIZED_COLA_SOURCES: + raise ValueError( + f"TR2026 V.C1 {year} is {entry.source!r}, not a realized row" + ) + return entry.percent, entry.source + + +def realized_awi(year: int) -> tuple[float, str]: + """The realized AWI for ``year`` (dollars) and its source tag.""" + year = int(year) + if year > LAST_ACTUAL_AWI_YEAR: + raise KeyError( + f"no realized AWI for {year}; the last historical AWI is " + f"{LAST_ACTUAL_AWI_YEAR}" + ) + (entry,) = tr2026.awi(year, year) + if entry.source != "tr2026_vi_g1_historical": + raise ValueError(f"TR2026 VI.G1 {year} is {entry.source!r}") + return entry.amount, entry.source + + +@cache +def check_realized_cola_agreement() -> None: + """Refuse a splice if V.C1's history differs from the committed one.""" + realized = load_cola_history() + mismatched = {} + for entry in tr2026.cola_path( + REQUIRED_COLA_DETERMINATION_YEARS[0], REALIZED_HISTORY_LAST_YEAR + ): + year = entry.determination_year + committed = 100.0 * realized.rate_for_determination_year(year) + if round(committed, 10) != round(entry.percent, 10): + mismatched[year] = (committed, entry.percent) + if mismatched: + raise ValueError( + "TR2026 V.C1 historical increases differ from the committed " + f"realized history: {mismatched}" + ) + + +@cache +def check_awi_anchor() -> None: + """Refuse if CBO's 2024 AWI anchor is not TR2026's 2024 actual.""" + (anchor,) = cbo2026.awi(LAST_ACTUAL_AWI_YEAR, LAST_ACTUAL_AWI_YEAR) + amount, _ = realized_awi(LAST_ACTUAL_AWI_YEAR) + if anchor.amount != amount: + raise ValueError( + f"CBO's {LAST_ACTUAL_AWI_YEAR} AWI anchor {anchor.amount!r} is " + f"not TR2026 VI.G1's {amount!r}" + ) diff --git a/src/populace_dynamics/baselines/tr2026_intermediate.py b/src/populace_dynamics/baselines/tr2026_intermediate.py new file mode 100644 index 00000000..1cb0c02e --- /dev/null +++ b/src/populace_dynamics/baselines/tr2026_intermediate.py @@ -0,0 +1,254 @@ +"""``tr2026_intermediate``: the 2026 Trustees Report intermediate paths. + +Every value comes from B1's pinned accessors +(:mod:`populace_dynamics.data.tr2026`, Tables V.C1, V.B1, VI.G1 and V.A1 +and OACT's DeathProbsE files, captured 2026-10-01) or from the files +named below; nothing is typed in. + +* **COLA.** The committed realized history (``ssa_cola_history.json``) + through 2022, then V.C1: historical rows for 2023 and 2024, the 2025 + actual (2.8 percent, footnote g) and the intermediate projection + 2026-2035, then annual-average CPI-W growth from V.B1 for 2036-2100 + (B1's builder default ``post_2035_cola_annual_cpiw``, awaiting + ratification, tagged ``derived_annual_cpiw_cola``). Realized rates + therefore run through 2025 whatever the caller's first rate year is + (:mod:`~populace_dynamics.baselines.realized`). +* **AWI.** VI.G1 from 1975 (historical through 2024, the last actual; + 2025 estimated; projected 2026-2100); the oracle's NAWI before 1975. +* **Mortality.** OACT's period ``q`` by single age (0-119) and sex: + historical tables through 2023, Alternative II from 2024 to 2100; + age 120 repeats age 119, whose published ``q`` (below one) is kept. +* **Fertility.** V.A1 TFR (1940-2100). TR2026 publishes no age + schedule, so ASFR is derived (tag ``derived_shape_scaled_to_v_a1_ + tfr``): a fixed single-age shape scaled each year to the TFR + (:mod:`~populace_dynamics.baselines.asfr`). The default shape is + NCHS 2024 spread to single ages by the mean-preserving monotone + interpolation; CBO's single-year shape is the registered alternative + (``asfr_shape="cbo2026"``, 2021-2099 only). Both are builder defaults + awaiting ratification. +* **Claiming and DI.** :mod:`~populace_dynamics.baselines.common2026`. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +import numpy as np + +from populace_dynamics.baselines.asfr import ( + SHAPES, + AsfrShape, + age_shape, + scale_shape_to_tfr, +) +from populace_dynamics.baselines.base import ( + AWI_FIRST_REPLACED_YEAR, + SEXES, + BaselineYearAwareMortality, + pad_to_max_age, + replace_awi, + splice_cola_percent, +) +from populace_dynamics.baselines.common2026 import ( + DI_GAP, + Vintage2026Inputs, +) +from populace_dynamics.baselines.realized import ( + FIRST_SPLICED_COLA_YEAR, + LAST_ACTUAL_AWI_YEAR, + LAST_ACTUAL_COLA_YEAR, + REALIZED_HISTORY_LAST_YEAR, + check_cola_request, + check_realized_cola_agreement, +) +from populace_dynamics.data import tr2026 +from populace_dynamics.estimates.parameters import ( + COLASeries, + load_cola_history, +) +from populace_dynamics.ss.params import SSAParameters + +__all__ = ["TR2026Intermediate"] + +#: The last year TR2026's single-year tables and q files project. +LAST_YEAR = 2100 +_LAST_HISTORICAL_Q_YEAR = 2023 +_ASFR_TAG = "derived_shape_scaled_to_v_a1_tfr" + + +@dataclass(frozen=True) +class TR2026Intermediate(Vintage2026Inputs): + """The 2026 Trustees intermediate baseline (see module docstring).""" + + asfr_shape: AsfrShape = "nchs2024" + name: str = "tr2026_intermediate" + vintage: str = "2026 Trustees Report, intermediate (released 2026-06-09)" + + def __post_init__(self) -> None: + if self.asfr_shape not in SHAPES: + raise ValueError(f"asfr_shape must be one of {SHAPES}") + + # -- COLA, CPI and AWI -------------------------------------------------- + def cola_rates(self, first: int, last: int) -> COLASeries: + """History through 2022, V.C1 2023-2035, V.B1 CPI-W after. + + ``first`` is validated but does not move the splice (realized + increases are kept through 2025; :func:`~populace_dynamics. + baselines.realized.check_cola_request`). + """ + first, last = check_cola_request(first, last) + check_realized_cola_agreement() + projected = { + entry.determination_year: entry.percent + for entry in tr2026.cola_path(FIRST_SPLICED_COLA_YEAR, last) + } + return splice_cola_percent( + load_cola_history(), + projected, + label=( + "TR2026 intermediate: Table V.C1 (historical through 2024, " + f"actual {LAST_ACTUAL_COLA_YEAR}, projected 2026-2035), " + "then V.B1 annual-average CPI-W growth from 2036 (builder " + "default post_2035_cola_annual_cpiw), via " + "populace_dynamics.baselines.tr2026_intermediate" + ), + ) + + def cpiw_growth(self, year: int) -> float: + return tr2026.cpiw_growth(year) + + def awi(self, first: int, last: int) -> dict[int, float]: + return {entry.year: entry.amount for entry in tr2026.awi(first, last)} + + def ssa_parameters(self, params: SSAParameters) -> SSAParameters: + return replace_awi( + params, + self.awi(AWI_FIRST_REPLACED_YEAR, LAST_YEAR), + revision_suffix=( + f"tr2026_awi_intermediate_{AWI_FIRST_REPLACED_YEAR}_" + f"{LAST_YEAR}" + ), + ) + + # -- Mortality ---------------------------------------------------------- + def qx(self, year: int) -> dict[str, np.ndarray]: + out = {} + for sex in SEXES: + values = pad_to_max_age(tr2026.death_probability(year, sex)) + values.setflags(write=False) + out[sex] = values + return out + + def population_mortality(self, years: range) -> BaselineYearAwareMortality: + return BaselineYearAwareMortality( + baseline=self.name, + qx_by_year={int(year): self.qx(year) for year in years}, + provenance={ + "basis": ( + "OACT TR2026 DeathProbsE q by single age and sex: " + f"historical through {_LAST_HISTORICAL_Q_YEAR}, " + "Alternative II after" + ), + "accessor": "populace_dynamics.data.tr2026.death_probability", + "ages_above_table": "age 120 repeats age 119", + "age_convention": "start-of-year age (year - 1 - birth_year)", + "years": [int(years.start), int(years.stop) - 1], + "files_verified_by": "data.tr2026.FILE_SHA256", + }, + ) + + # -- Fertility ---------------------------------------------------------- + def tfr(self, year: int) -> float: + return tr2026.tfr(year) + + def asfr(self, year: int) -> dict[int, float]: + return scale_shape_to_tfr( + age_shape(self.asfr_shape, int(year)), self.tfr(year) + ) + + # -- Provenance --------------------------------------------------------- + def value_sources(self, year: int) -> dict[str, str]: + year = int(year) + (cola,) = tr2026.cola_path(year, year) + (awi,) = tr2026.awi(year, year) + return { + "cola": cola.source, + "cpiw_growth": tr2026.value_provenance("cpiw_growth", year).source, + "awi": awi.source, + "mortality": tr2026.value_provenance( + "death_probability", year, sex="male" + ).source, + "tfr": tr2026.value_provenance("tfr", year).source, + "asfr": f"{_ASFR_TAG}:{self.asfr_shape}", + "claiming": ( + "ssa_supplement_2026_6b5_1_row_" + f"{min(max(year, 1998), self.claim_table_max_year)}" + ), + "di_rates": "di_entitlement_fit_2008", + } + + def provenance(self) -> dict[str, Any]: + return { + "name": self.name, + "vintage": self.vintage, + "asfr_shape": self.asfr_shape, + "splices": { + "cola": ( + "committed realized history through " + f"{REALIZED_HISTORY_LAST_YEAR}; TR2026 V.C1 from " + f"{FIRST_SPLICED_COLA_YEAR} (historical through 2024, " + f"actual {LAST_ACTUAL_COLA_YEAR}, projected 2026-2035); " + "V.B1 CPI-W growth 2036-2100. Realized rates run through " + f"{LAST_ACTUAL_COLA_YEAR}, the last actual determination " + "year, whatever the caller's first rate year" + ), + "awi": ( + f"oracle NAWI before {AWI_FIRST_REPLACED_YEAR}; TR2026 " + f"VI.G1 {AWI_FIRST_REPLACED_YEAR}-{LAST_YEAR}: realized " + f"through {LAST_ACTUAL_AWI_YEAR}, the last actual year, " + f"estimated {LAST_ACTUAL_AWI_YEAR + 1}, projected after" + ), + "mortality": ( + f"OACT historical q through {_LAST_HISTORICAL_Q_YEAR}, " + f"Alternative II {_LAST_HISTORICAL_Q_YEAR + 1}-{LAST_YEAR}" + ), + "claiming": ( + "Supplement 2026 Table 6.B5.1, rows 1998-" + f"{self.claim_table_max_year}; later years snap to " + f"{self.claim_table_max_year}" + ), + }, + "builder_defaults_awaiting_ratification": { + **dict(tr2026.PENDING_RULINGS), + "asfr_shape": ( + f"{self.asfr_shape}: a fixed single-age shape scaled each " + "year to the V.A1 TFR (TR2026 publishes no ASFR); the " + "default is NCHS 2024 ungrouped by the mean-preserving " + "monotone interpolation, the alternative CBO's 2026 " + "single-year shape" + ), + "fold_under_14_into_14": ( + "NCHS 10-14 band mass interpolated below age 14 is added " + "to age 14, the youngest exposed age" + ), + "claim_table_last_row": ( + f"the 2026 Supplement's {self.claim_table_max_year} row " + "(SSA: current-year rows are revised each update) is " + "used for every later year" + ), + "awi_first_replaced_year": ( + f"{AWI_FIRST_REPLACED_YEAR}, the legacy baseline's year" + ), + }, + "gaps": [DI_GAP], + "files_sha256": self.shared_files_sha256(), + "captured_sources_sha256": self.captured_sources_sha256( + include_cbo=self.asfr_shape == "cbo2026" + ), + **( + {"cbo_files_sha256": self.cbo_files_sha256()} + if self.asfr_shape == "cbo2026" + else {} + ), + } diff --git a/src/populace_dynamics/baselines/track_a.py b/src/populace_dynamics/baselines/track_a.py new file mode 100644 index 00000000..936112c2 --- /dev/null +++ b/src/populace_dynamics/baselines/track_a.py @@ -0,0 +1,424 @@ +"""One factory for Track A inputs under a selectable baseline. + +Five places build :class:`~populace_dynamics.cola_track_a.runner. +TrackAInputs` by hand from the same TR2008 constructors +(``scripts/run_track_a_registered.py``, ``scripts/run_fra68_registered. +py``, ``scripts/track_a_dry_run.py``, ``scripts/fra68_dry_run.py`` and +``track_a_v2.protocol.load_registered_inputs``). Those files are +registered entry points and are not edited; :func:`build_track_a_inputs` +composes the same inputs from a :class:`~populace_dynamics.baselines. +base.Baseline` instead. Under the default ``tr2008_intermediate`` +baseline every input it returns equals the hand-built one value for +value (``tests/baselines/test_track_a_inputs.py``: the Track A and FRA-68 +dry runs' inputs), and the configuration is not touched, so +``TrackAConfig.as_dict()`` and every artifact field it feeds are +unchanged. The baseline's name, vintage and file hashes are recorded +under ``provenance["baseline"]``. + +Scope of the 2026 baselines. ``run_track_a`` (``cola_track_a/runner. +py``, not edited) is hard-wired to TR2008 in three ways, so the 2026 +baselines are exposed for invented-data projections only: + +1. A ``registered_real`` run compares the COLA path, AWI, mortality, DI + rates and claim table with the TR2008 captures and refuses any + mismatch. :func:`build_track_a_inputs` refuses ``registered_real`` + under any other baseline first: a real-data run under a non-legacy + baseline needs its own registered entry point and an issue #42 + registration (future work). +2. The run artifact labels the rate path ``"TR2008 "`` + (A7 upstream conventions) and records a population mortality that is + not the A2 class as having "no year axis". Under a 2026 baseline both + labels are wrong; ``inputs_provenance["baseline"]`` (this module's + record, which the runner copies into the result) is authoritative and + says so in ``runner_label_caveat``. +3. A1's floor assertion (``runner._check_floor``) requires every reduced + COLA from the first reduced year (2009 or 2010) to be positive. The + 2026 baselines keep realized increases, which were zero for + determination years 2009, 2010 and 2015, so ``run_track_a`` refuses a + one-point cut under them. Running exercise 1 under a 2026 baseline + needs a floor ruling (``NegativeRatePolicy.FLOOR_AT_ZERO`` or later + first years). :func:`project_track_a` runs the projection alone + (mortality, DI, claiming) on invented cohorts under any baseline, the + path the invented-data smoke tests use. + +The claim table: ``run_track_a`` reads claim rows at or before +``config.claim_table_max_year``. :func:`track_a_config_for` returns the +configuration a baseline needs (the legacy one unchanged; a 2026 one +with the cap at its last row, 2025), and :func:`build_track_a_inputs` +refuses a configuration whose cap is not the baseline's, which would +otherwise silently read an old row of the new table. Pass the same +configuration to ``run_track_a`` or :func:`project_track_a`. + +Invented cohorts: when no cohorts are passed, an ``invented`` call +builds them as the dry runs do (``scripts/track_a_dry_run.py`` +``_prepared_cohort``): the INVENTED generator +(:func:`~populace_dynamics.cola_track_a.invented. +invented_psid2010_inputs`) with seed :data:`INVENTED_SEED` and the +baseline's claim-age table, the A3 builder and +:func:`~populace_dynamics.cola_track_a.opening.prepare_track_a_cohort`. +A ``registered_real`` cohort is never built here: the registered entry +point's cohort code prepares it and passes it in, so this module never +reads PSID files. +""" + +from __future__ import annotations + +import dataclasses +import warnings +from collections.abc import Mapping, MutableMapping +from typing import Any + +import pandas as pd + +from populace_dynamics.baselines.base import Baseline +from populace_dynamics.baselines.legacy import TR2008Legacy +from populace_dynamics.cohorts import psid2010 +from populace_dynamics.cola_track_a import invented +from populace_dynamics.cola_track_a.adapters import ( + build_period_modules, + claiming_schedule, +) +from populace_dynamics.cola_track_a.config import TrackAConfig +from populace_dynamics.cola_track_a.opening import ( + TrackACohort, + prepare_track_a_cohort, + track_a_cohort_sha256, +) +from populace_dynamics.cola_track_a.runner import TrackAInputs +from populace_dynamics.engine.di_entitlement import ( + fra_schedule_from_parameters, +) +from populace_dynamics.engine.loop import ProjectionEngine, ProjectionResult +from populace_dynamics.estimates.cola_age_profile import ( + INVENTED, + REGISTERED_REAL, +) +from populace_dynamics.ss.params import SSAParameters, load_ssa_parameters + +__all__ = [ + "INVENTED_SEED", + "RUNNER_LABEL_CAVEAT", + "build_track_a_inputs", + "invented_track_a_cohorts", + "project_track_a", + "track_a_config_for", +] + +#: The invented generator's seed in both dry runs (``--seed`` default). +INVENTED_SEED = 20260922 +RUNNER_LABEL_CAVEAT = ( + "cola_track_a.runner labels the rate path 'TR2008 ' and " + "records a population mortality that is not the A2 class as having no " + "year axis; under a non-legacy baseline both labels are wrong and this " + "baseline record is authoritative" +) +#: The warning the DI-aware mortality adapter emits for an infeasible +#: cell; ``run_track_a`` counts it rather than failing, and so does +#: :func:`project_track_a`. +_INFEASIBLE_CELL_WARNING = "DI-origin expected deaths exceed" + + +def track_a_config_for( + config: TrackAConfig, baseline: Baseline +) -> TrackAConfig: + """``config`` with the claim-table cap ``baseline`` reads. + + The legacy baseline returns ``config`` itself (the registered cap, + 2008), so a default configuration's ``as_dict()`` is unchanged. + """ + if config.claim_table_max_year == baseline.claim_table_max_year: + return config + return dataclasses.replace( + config, claim_table_max_year=baseline.claim_table_max_year + ) + + +def invented_track_a_cohorts( + config: TrackAConfig, + baseline: Baseline, + *, + seed: int = INVENTED_SEED, +) -> dict[int, TrackACohort]: + """INVENTED cohorts for every anchor wave ``config`` projects. + + The dry runs' construction (module docstring), with ``baseline``'s + claim-age table as the generator's claiming parameter. Under the + legacy baseline that table is ``load_claiming_pmf()``, the one the + dry runs pass, so the cohorts are the dry runs' cohorts. + """ + claiming_pmf = baseline.claim_pmf() + cohorts = {} + for wave in config.anchor_waves: + raw = invented.invented_psid2010_inputs( + seed=seed, claiming_pmf=claiming_pmf, anchor_wave=wave + ) + a3 = psid2010.build_psid2010_cohort( + raw, psid2010.Psid2010CohortSpec(anchor_wave=wave) + ) + cohorts[wave] = prepare_track_a_cohort( + a3, data_provenance=INVENTED, config=config + ) + return cohorts + + +def _check_legacy_conventions( + config: TrackAConfig, baseline: TR2008Legacy +) -> None: + for field, expected in ( + ("tr2008_alternative", baseline.alternative), + ("mortality_base_year", baseline.mortality_base_year), + ): + if getattr(config, field) != expected: + raise ValueError( + f"config.{field} is {getattr(config, field)!r} but the " + f"legacy baseline uses {expected!r}" + ) + + +def _checked_cohorts( + config: TrackAConfig, + cohorts: Mapping[int, TrackACohort], + data_provenance: str, +) -> dict[int, TrackACohort]: + waves = config.anchor_waves + missing = [wave for wave in waves if wave not in cohorts] + if missing: + raise ValueError(f"no prepared cohort for anchor waves {missing}") + for wave in waves: + cohort = cohorts[wave] + if int(cohort.anchor_wave) != int(wave): + raise ValueError( + f"the cohort given for wave {wave} has anchor wave " + f"{cohort.anchor_wave}" + ) + if cohort.data_provenance != data_provenance: + raise ValueError( + f"the anchor-wave {wave} cohort is labelled " + f"{cohort.data_provenance!r}, not {data_provenance!r}" + ) + return {wave: cohorts[wave] for wave in waves} + + +def build_track_a_inputs( + config: TrackAConfig, + baseline: Baseline, + *, + data_provenance: str, + cohorts: Mapping[int, TrackACohort] | None = None, + base_params: SSAParameters | None = None, + invented_seed: int = INVENTED_SEED, + provenance: Mapping[str, Any] | None = None, +) -> TrackAInputs: + """The Track A inputs for ``config`` under ``baseline``. + + ``data_provenance`` is ``"invented"`` or ``"registered_real"`` (the + latter under the legacy baseline only). ``cohorts`` holds a + prepared cohort for every anchor wave the configured rows project; + the first wave's goes in ``cohort`` and the others in + ``additional_cohorts``, as the dry runs do. Omitted for an + ``invented`` call, the INVENTED cohorts are built + (:func:`invented_track_a_cohorts`, seed ``invented_seed``); a + ``registered_real`` call must pass the cohorts its entry point's + cohort code prepared. ``base_params`` are the oracle's statutory + parameters before the baseline replaces the AWI (default + :func:`~populace_dynamics.ss.params.load_ssa_parameters`, the + policyengine-us checkout every builder reads). ``provenance`` holds + the caller's other records (statutory capture, script name); it may + not carry a ``"baseline"`` key. + + The inputs: ``params = baseline.ssa_parameters(base_params)``; + ``baseline = baseline.cola_rates(config.tr2008_first_rate_year, + config.reference_year)``; ``di_rates = baseline.di_rates(config. + di_spec)``; population mortality for every projection year of every + population (the earliest start year plus one through the reference + year); ``claiming_pmf = baseline.claim_pmf()``. + """ + if data_provenance not in (INVENTED, REGISTERED_REAL): + raise ValueError( + f"data_provenance must be {INVENTED!r} or {REGISTERED_REAL!r}" + ) + if not isinstance(baseline, Baseline): + raise TypeError("baseline must implement the Baseline protocol") + legacy = isinstance(baseline, TR2008Legacy) + if data_provenance == REGISTERED_REAL and not legacy: + raise ValueError( + f"a registered_real run under the {baseline.name!r} baseline " + "is not supported: run_track_a checks a real run against the " + "TR2008 captures, so a real-data run under a non-legacy " + "baseline needs its own registered entry point and an issue " + "#42 registration (future work)" + ) + if legacy: + _check_legacy_conventions(config, baseline) + if config.claim_table_max_year != baseline.claim_table_max_year: + raise ValueError( + f"config.claim_table_max_year is {config.claim_table_max_year} " + f"but the {baseline.name!r} baseline reads claim rows through " + f"{baseline.claim_table_max_year}; pass " + "track_a_config_for(config, baseline)" + ) + extra = dict(provenance or {}) + if "baseline" in extra: + raise ValueError("provenance['baseline'] is written by this factory") + built_invented = cohorts is None + if cohorts is None: + if data_provenance != INVENTED: + raise ValueError( + "a registered_real call must pass the cohorts its entry " + "point's cohort code prepared; this factory builds only " + "INVENTED cohorts and never reads PSID files" + ) + cohorts = invented_track_a_cohorts( + config, baseline, seed=invented_seed + ) + cohorts = _checked_cohorts(config, cohorts, data_provenance) + if base_params is None: + base_params = load_ssa_parameters() + first_projection_year = min(config.start_years.values()) + 1 + mortality = baseline.population_mortality( + range(first_projection_year, config.reference_year + 1) + ) + di_rates = baseline.di_rates(config.di_spec) + cola = baseline.cola_rates( + config.tr2008_first_rate_year, config.reference_year + ) + params = baseline.ssa_parameters(base_params) + record = { + **baseline.provenance(), + "cola_series_provenance": dict(cola.provenance), + "mortality": dict(mortality.provenance), + "di_rates_sha256": { + key: value + for key, value in di_rates.provenance.items() + if key.endswith("sha256") + }, + "ssa_parameters": params.pe_us_revision, + "claim_table_max_year": baseline.claim_table_max_year, + "first_rate_year": config.tr2008_first_rate_year, + "projection_years": [first_projection_year, config.reference_year], + } + if built_invented: + record["invented_cohorts"] = { + "seed": int(invented_seed), + "builder": "baselines.track_a.invented_track_a_cohorts", + } + if not legacy: + record["runner_label_caveat"] = RUNNER_LABEL_CAVEAT + primary, *others = cohorts.values() + return TrackAInputs( + cohort=primary, + params=params, + baseline=cola, + di_rates=di_rates, + population_mortality=mortality, + claiming_pmf=baseline.claim_pmf(), + provenance={**extra, "baseline": record}, + additional_cohorts=tuple(others), + ) + + +def _check_invented(cohort: TrackACohort) -> None: + """``run_track_a``'s source checks for an invented cohort, repeated. + + The label, the A3 source kind, the seal and the generator's persons, + weights and family units must all say INVENTED, so no PSID cohort + is projected here however it was relabelled. + """ + recorded = dict(cohort.source_provenance) + if cohort.data_provenance != INVENTED: + raise ValueError( + f"project_track_a projects INVENTED cohorts only; the " + f"anchor-wave {cohort.anchor_wave} cohort is labelled " + f"{cohort.data_provenance!r}. A real-data projection runs " + "through a registered entry point after its issue #42 " + "registration" + ) + if recorded.get("kind") != psid2010.INVENTED: + raise ValueError( + f"the anchor-wave {cohort.anchor_wave} cohort's A3 source " + f"provenance is {recorded.get('kind')!r}, not invented" + ) + if cohort.seal is None or cohort.seal != track_a_cohort_sha256(cohort): + raise ValueError( + f"the anchor-wave {cohort.anchor_wave} cohort does not match " + "the seal prepare_track_a_cohort set" + ) + invented.check_invented_population( + persons=cohort.persons, + initial_slice=cohort.initial_slice, + career_ids=cohort.careers.keys(), + seed=recorded.get("seed"), + anchor_wave=int(cohort.anchor_wave), + ) + + +def project_track_a( + inputs: TrackAInputs, + config: TrackAConfig, + *, + draw_index: int = 0, + death_log: ( + MutableMapping[int, MutableMapping[int, pd.DataFrame]] | None + ) = None, +) -> dict[int, ProjectionResult]: + """Project every INVENTED population once through ``config``'s year. + + The projection ``run_track_a`` runs for one draw (the runner's + ``build_period_modules`` adapters: DI-aware population mortality, the + DI step, claiming from the inputs' table capped at + ``config.claim_table_max_year``, no earnings, births or entrants), + without the A1 floor assertion, the benefit computation or the A7 + tabulation, so it runs under any baseline. Invented cohorts only + (:func:`_check_invented`); results are keyed by anchor wave. + ``death_log[wave]``, when given, receives that population's death + records by year. The DI-aware mortality adapter's infeasible-cell + warnings are absorbed, as ``run_track_a`` counts them; any other + warning is an error. + """ + recorded = dict(inputs.provenance).get("baseline", {}) + cap = recorded.get("claim_table_max_year") + if cap is not None and int(cap) != config.claim_table_max_year: + raise ValueError( + f"the inputs were built for claim rows through {cap} but " + f"config.claim_table_max_year is {config.claim_table_max_year}; " + "pass the configuration the inputs were built with" + ) + cohorts = inputs.cohorts_by_wave() + missing = [wave for wave in config.anchor_waves if wave not in cohorts] + if missing: + raise ValueError(f"no cohort for anchor waves {missing}") + schedule = claiming_schedule( + inputs.claiming_pmf, max_table_year=config.claim_table_max_year + ) + fra_schedule = fra_schedule_from_parameters(inputs.params) + results = {} + for wave in config.anchor_waves: + cohort = cohorts[wave] + _check_invented(cohort) + log: MutableMapping[int, pd.DataFrame] = {} + modules = build_period_modules( + roster_ids=cohort.roster_ids, + population_model=inputs.population_mortality, + di_rates=inputs.di_rates, + fra_schedule=fra_schedule, + schedule=schedule, + death_log=log, + ) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always", RuntimeWarning) + results[wave] = ProjectionEngine(modules).project( + cohort.initial_slice, + end_year=config.reference_year, + draw_index=int(draw_index), + metadata={}, + ) + other = [ + str(item.message) + for item in caught + if _INFEASIBLE_CELL_WARNING not in str(item.message) + ] + if other: + raise RuntimeError(f"unexpected projection warnings: {other[:3]}") + if death_log is not None: + death_log[wave] = log + return results diff --git a/src/populace_dynamics/data/cbo2026.py b/src/populace_dynamics/data/cbo2026.py new file mode 100644 index 00000000..3a48efa6 --- /dev/null +++ b/src/populace_dynamics/data/cbo2026.py @@ -0,0 +1,426 @@ +"""Typed, SHA-pinned CBO2026 baseline input accessors. + +Sources: CBO January 2026 demographic projections (57059/61879), February +2026 ten-year and long-term economics (51135/57054), and September 2026 +covered workers/earnings (62556 Additional-Info sheets 1-2). The captured +bytes, Wayback IDs, CDX checks and locators are recorded in +``data/external/cbo2026/``. Rates are annual percentages unless an accessor +explicitly returns probabilities or births per woman. Demographic inputs +cover 2021-2099; there is no demographic extension after 2099. + +Mortality ``deaths_per_1000_people / 1000`` is interpreted as q(x). This +is an inference tested against every published 2026-2099 life expectancy +at birth and 65: arithmetic mean of male/female period life expectancy, +uniform deaths within each age including infancy, reproduces CBO sheet 1 +within 0.0011 year; central-m conversion does not. Age 119 is closed with +q=1 in every source row. See ``transcription_check.json`` for both checks. + +Builder defaults awaiting ratification +------------------------------------- +``ten_year_cpiu_then_long_term_growth``: use ten-year CPI-U levels in +2023-2036; growth is their year-on-year ratio (2023 uses the 2022 LT +level). After 2036, compound the LT published annual growth on the last +ten-year level, avoiding a level jump between workbook vintages. +``hold_2056_economic_growth``: after 2056 hold each final LT economic +growth rate, through 2100; no new demographic rates are invented. +``cpiw_equals_cpiu_growth`` and ``cola_equals_annual_cpiu_growth``: CBO +publishes neither CPI-W nor COLA. Their annual growth proxy uses the +spliced CPI-U growth, not a statutory Q3-to-Q3 COLA calculation. The COLA +proxy is exposed only in 2024-2100, where that proxy is nonnegative. +``awi_bridge_2025_2026_real_earnings_times_cpiu``: start from the last +actual AWI (2024, TR2026 VI.G1); use compounded real earnings per worker +growth and CPI-U growth for 2025 and 2026, because covered earnings and +workers begin in 2026 and supply no earlier growth ratio. +``awi_growth_of_covered_earnings_per_worker``: from 2027 compound growth +in covered earnings/covered workers on that 2026 bridge. This is an AWI +analog, not a CBO-published AWI. The covered-input source runs to 2100, +so AWI needs no post-2056 economic extrapolation. +``tfr_sum_asfr_before_2026``: in 2021-2025, where sheet 1 publishes no +TFR, sum the all-women ASFRs. From 2026 use the printed summary TFR; +the ASFR sum matches within rounding (0.0023 births per woman). + +Every scalar has a corresponding :func:`value_provenance` record. Path +entries also carry provenance-class tags directly. No fallback outside +these stated coverages is allowed. +""" + +from __future__ import annotations + +import hashlib +import json +from collections.abc import Mapping +from dataclasses import dataclass +from functools import lru_cache +from numbers import Integral +from pathlib import Path +from types import MappingProxyType +from typing import Any, Literal + +import numpy as np + +Sex = Literal["male", "female"] +Place = Literal["all", "native-born", "foreign-born"] + +DATA_DIR = ( + Path(__file__).resolve().parents[3] / "data" / "external" / "cbo2026" +) +FILE_SHA256: Mapping[str, str] = MappingProxyType( + { + "cbo2026_demographics.json": "2d09b326bc1ac460ac038e4da3a8a37a12929b50173fa5cd6117bd9c904173fc", + "cbo2026_economics.json": "314d5d10753ddb9c6aa124ec0ddbd3ed55ad9e5b0bd625138283dcc73927a43f", + "transcription_check.json": "324dc88e305d48987846814c1594e0dd3a01d68ecac36b83ddcd62adf943f34e", + "sources.json": "a00644af2aaa87fe4fbdd3fe72e5dd5b4ba7d0d76f6a74423094933552a6ded7", + } +) +PLACES: tuple[Place, ...] = ("all", "native-born", "foreign-born") +SEXES: tuple[Sex, ...] = ("male", "female") +PENDING_RATIFICATION: tuple[str, ...] = ( + "ten_year_cpiu_then_long_term_growth", + "hold_2056_economic_growth", + "cpiw_equals_cpiu_growth", + "cola_equals_annual_cpiu_growth", + "awi_bridge_2025_2026_real_earnings_times_cpiu", + "awi_growth_of_covered_earnings_per_worker", + "tfr_sum_asfr_before_2026", +) + + +@dataclass(frozen=True) +class ColaEntry: + """One derived CBO annual inflation proxy for a benefit increase.""" + + determination_year: int + percent: float + source: str + effective_month: Literal["December"] = "December" + + +@dataclass(frozen=True) +class AwiEntry: + """Actual 2024 AWI or one CBO-derived AWI analog amount.""" + + year: int + amount: float + source: str + + +@dataclass(frozen=True) +class ValueProvenance: + """Source identities and named derivations for one returned value.""" + + source: str + source_keys: tuple[str, ...] + locators: tuple[str, ...] + sha256: tuple[str, ...] + builder_defaults: tuple[str, ...] = () + + +__all__ = [ + "AwiEntry", + "ColaEntry", + "DATA_DIR", + "FILE_SHA256", + "PENDING_RATIFICATION", + "PLACES", + "SEXES", + "ValueProvenance", + "asfr", + "awi", + "cola_path", + "cpiu_growth", + "cpiu_index", + "cpiw_growth", + "mortality", + "real_earnings_growth", + "tfr", + "value_provenance", + "verify_files", +] + + +def verify_files(data_dir: Path = DATA_DIR) -> None: + """Refuse any changed input, check record or source identity bytes.""" + if not FILE_SHA256: + raise ValueError("CBO2026 file pins have not been populated") + for name, expected in FILE_SHA256.items(): + actual = hashlib.sha256((data_dir / name).read_bytes()).hexdigest() + if actual != expected: + raise ValueError(f"{name} SHA-256 {actual} != pinned {expected}") + + +@lru_cache(maxsize=1) +def _data() -> dict[str, Any]: + payloads = {} + for name, expected in FILE_SHA256.items(): + raw = (DATA_DIR / name).read_bytes() + actual = hashlib.sha256(raw).hexdigest() + if actual != expected: + raise ValueError(f"{name} SHA-256 {actual} != pinned {expected}") + # Parse the same bytes whose identity was checked. + payloads[name] = json.loads(raw) + return payloads + + +def _demographics() -> dict[str, Any]: + return _data()["cbo2026_demographics.json"] + + +def _economics() -> dict[str, Any]: + return _data()["cbo2026_economics.json"] + + +def _year(year: int, first: int, last: int) -> int: + if isinstance(year, bool) or not isinstance(year, Integral): + raise ValueError("year must be an integer") + result = int(year) + if not first <= result <= last: + raise ValueError(f"year {result} outside coverage {first}..{last}") + return result + + +def _interval(first: int, last: int, start: int, stop: int) -> range: + first = _year(first, start, stop) + last = _year(last, start, stop) + if first > last: + raise ValueError("first year must not exceed last year") + return range(first, last + 1) + + +def _sex(sex: str) -> None: + if sex not in SEXES: + raise ValueError(f"sex must be one of {SEXES}, got {sex!r}") + + +def _place(place: str) -> None: + if place not in PLACES: + raise ValueError(f"place must be one of {PLACES}, got {place!r}") + + +def asfr(year: int, place: Place = "all") -> dict[int, float]: + """Single-year age 14-49 births per woman, not a birth probability. + + Values may exceed one birth per woman in principle; no clipping or + conversion to a Bernoulli probability is part of this data accessor. + """ + year = _year(year, 2021, 2099) + _place(place) + rates = _demographics()["asfr"][str(year)][place] + return {age: float(value) / 1000.0 for age, value in enumerate(rates, 14)} + + +def mortality(year: int, sex: Sex) -> np.ndarray: + """One-year q(x) for ages 0..119, as a fresh read-only float array.""" + year = _year(year, 2021, 2099) + _sex(sex) + q = np.array(_demographics()["mortality"][str(year)][sex], dtype=float) + q /= 1000.0 + q.setflags(write=False) + return q + + +def tfr(year: int) -> float: + """Published 2026-2099 TFR; derived sum of ASFR for 2021-2025.""" + year = _year(year, 2021, 2099) + if year < 2026: + return sum(asfr(year).values()) + return float(_demographics()["published_summary"][str(year)]["tfr"]) + + +def cpiu_growth(year: int) -> float: + """Annual CPI-U growth in percent, with named splice/extension rules.""" + year = _year(year, 1996, 2100) + econ = _economics() + if 2023 <= year <= 2036: + current = econ["ten_year_cpiu_index_1982_1984_100"][str(year)] + previous = ( + econ["ten_year_cpiu_index_1982_1984_100"][str(year - 1)] + if year > 2023 + else econ["long_term"]["2022"]["cpiu_index_1982_1984_100"] + ) + return 100.0 * (current / previous - 1.0) + source_year = min(year, 2056) + return float(econ["long_term"][str(source_year)]["cpiu_growth_percent"]) + + +def cpiu_index(year: int) -> float: + """Spliced CPI-U level (1982-84=100), continuous at 2036 and 2056.""" + year = _year(year, 1996, 2100) + econ = _economics() + if year < 2023: + return float(econ["long_term"][str(year)]["cpiu_index_1982_1984_100"]) + if year <= 2036: + return float(econ["ten_year_cpiu_index_1982_1984_100"][str(year)]) + level = float(econ["ten_year_cpiu_index_1982_1984_100"]["2036"]) + for current in range(2037, year + 1): + level *= 1.0 + cpiu_growth(current) / 100.0 + return level + + +def cpiw_growth(year: int) -> float: + """CPI-U annual growth proxy for CPI-W; a builder default.""" + return cpiu_growth(year) + + +def real_earnings_growth(year: int) -> float: + """Published LT real earnings per worker growth; hold 2056 thereafter.""" + year = _year(year, 1996, 2100) + return float( + _economics()["long_term"][str(min(year, 2056))][ + "real_earnings_growth_percent" + ] + ) + + +def cola_path(first: int, last: int) -> tuple[ColaEntry, ...]: + """Annual CPI-U proxy for December COLA, determination years 2024-2100.""" + years = _interval(first, last, 2024, 2100) + return tuple( + ColaEntry( + determination_year=year, + percent=cpiu_growth(year), + source=value_provenance("cola", year).source, + ) + for year in years + ) + + +def _covered_average(year: int) -> float: + row = _economics()["covered"][str(year)] + return 1_000_000_000.0 * ( + row["covered_earnings_trillions"] / row["total_workers_thousands"] + ) + + +def awi(first: int, last: int) -> tuple[AwiEntry, ...]: + """2024 actual AWI and 2025-2100 CBO AWI analog, inclusive interval. + + Growth in covered earnings per worker is applied only after 2026; + the explicit 2025-2026 bridge supplies the missing growth ratios. + """ + years = _interval(first, last, 2024, 2100) + amount = float(_economics()["awi_anchor"]["amount"]) + out = [] + for year in range(2024, years.stop): + if year in (2025, 2026): + amount *= (1.0 + real_earnings_growth(year) / 100.0) * ( + 1.0 + cpiu_growth(year) / 100.0 + ) + elif year >= 2027: + amount *= _covered_average(year) / _covered_average(year - 1) + if year in years: + out.append( + AwiEntry(year, amount, value_provenance("awi", year).source) + ) + return tuple(out) + + +def value_provenance( + series: str, + year: int, + *, + sex: Sex | None = None, + place: Place = "all", +) -> ValueProvenance: + """Frozen metadata for the named public series/year and group. + + Accepted names: asfr,mortality,tfr,cpiu_growth,cpiu_index,cpiw_growth, + real_earnings_growth,cola,awi. Source tags distinguish all derivations. + The complete source identities remain available through sources.json. + """ + if sex is not None and series != "mortality": + raise ValueError("sex qualifier is supported only for mortality") + if place != "all" and series != "asfr": + raise ValueError("place qualifier is supported only for asfr") + defaults: tuple[str, ...] = () + keys: tuple[str, ...] + if series in ("asfr", "mortality", "tfr"): + year = _year(year, 2021, 2099) + if series == "asfr": + _place(place) + keys = ("fertility",) + source = f"cbo2026_asfr_{place}" + elif series == "mortality": + if sex is None: + raise ValueError("mortality provenance needs sex") + _sex(sex) + keys = ("mortality", "demographic_workbook") + source = "cbo2026_mortality_q_verified" + elif year < 2026: + keys = ("fertility",) + source = "derived_cbo2026_tfr_asfr_sum" + defaults = ("tfr_sum_asfr_before_2026",) + else: + keys = ("demographic_workbook",) + source = "cbo2026_summary_tfr" + elif series == "awi": + year = _year(year, 2024, 2100) + if year == 2024: + keys = ("actual_awi_anchor",) + source = "tr2026_vi_g1_actual" + else: + keys = ( + "actual_awi_anchor", + "long_term_economics", + "ten_year_economics", + ) + defaults = ("awi_bridge_2025_2026_real_earnings_times_cpiu",) + source = "derived_cbo2026_awi_bridge" + if year >= 2027: + keys += ("covered_earnings",) + defaults += ("awi_growth_of_covered_earnings_per_worker",) + source = "derived_cbo2026_awi_covered_earnings_per_worker" + elif series in ( + "cpiu_growth", + "cpiu_index", + "cpiw_growth", + "cola", + "real_earnings_growth", + ): + year = _year(year, 2024 if series == "cola" else 1996, 2100) + if series == "real_earnings_growth": + keys = ("long_term_economics",) + source = "cbo2026_real_earnings_growth" + elif 2023 <= year <= 2036: + keys = ("ten_year_economics",) + if year == 2023: + keys += ("long_term_economics",) + source = ( + "cbo2026_ten_year_cpiu_level" + if series == "cpiu_index" + else "derived_cbo2026_ten_year_cpiu_growth" + ) + defaults = ("ten_year_cpiu_then_long_term_growth",) + else: + keys = ("long_term_economics",) + source = "cbo2026_long_term_cpiu" + if series == "cpiu_index" and year >= 2037: + keys += ("ten_year_economics",) + defaults = ("ten_year_cpiu_then_long_term_growth",) + source = "derived_cbo2026_spliced_cpiu_level" + if year > 2056: + defaults += ("hold_2056_economic_growth",) + source = "derived_cbo2026_hold_2056_growth" + if series == "cpiw_growth": + defaults += ("cpiw_equals_cpiu_growth",) + source = "derived_cbo2026_cpiw_from_cpiu" + elif series == "cola": + defaults += ( + "cpiw_equals_cpiu_growth", + "cola_equals_annual_cpiu_growth", + ) + source = "derived_cbo2026_cola_from_cpiu" + else: + raise ValueError(f"unknown CBO2026 series {series!r}") + specs = _data()["sources.json"]["sources"] + selection = f"requested series={series},year={year}" + if series == "asfr": + selection += f",place_of_birth={place},ages=14..49" + elif series == "mortality": + selection += f",sex={sex},ages=0..119" + return ValueProvenance( + source, + keys, + tuple(specs[key]["locator"] + "; " + selection for key in keys), + tuple(specs[key]["sha256"] for key in keys), + defaults, + ) diff --git a/src/populace_dynamics/data/life_table.py b/src/populace_dynamics/data/life_table.py new file mode 100644 index 00000000..796ff040 --- /dev/null +++ b/src/populace_dynamics/data/life_table.py @@ -0,0 +1,203 @@ +"""Period life-table functions from one-year death probabilities. + +The equations are SSA Office of the Chief Actuary's *Definitions of Life +Table Functions* (``LifeTableDefinitions.pdf``, linked from the OACT +Downloadables page; captured 2026-10-01, SHA-256 +``94c8fd5806cdb5be3762d1284155e4827a2971d3845088943e3f96a14f26e889``; +https://www.ssa.gov/OACT/Downloadables/LifeTableDefinitions.pdf): + +* ``l0 = 100,000`` and ``lx = lx-1 * (1 - qx-1)`` for ``x >= 1``; +* ``dx = lx * qx``; +* ``L0 = l0 - f0 * d0``, where the separation factor ``f0`` is "the + average number of years not lived by those age 0 who die at age 0"; +* ``Lx = lx - 0.5 * dx`` for ``x >= 1`` (uniform distribution of deaths + within each year of age above 0); +* ``Tx = Lx + Lx+1 + ...`` and ``ex = Tx / lx``. + +The definitions do not say how the table ends. OACT's death-probability +files stop at age 119 with ``q119`` usually below 1 (0.949919 for both +sexes in the TR2026 intermediate 2030 row), and its period life tables +print a positive ``e119`` (0.55 there). This module closes the last +tabulated age ``w`` with the person-years lived at ``w`` and every later +age by a population whose central death rate stays at the uniform-deaths +value ``m_w = q_w / (1 - q_w / 2)``:: + + L_w+ = l_w / m_w = l_w * (1 - q_w / 2) / q_w + +which is also the sum of ``Lx`` if ``q`` were held at ``q_w`` at every age +past ``w``. It gives ``e119 = (1 - 0.949919/2)/0.949919 = 0.5527`` for +that row, and when ``q_w = 1`` it reduces to ``L_w = l_w - 0.5 * d_w``, +the closed-table value. ``constant_terminal_q`` is a builder default +awaiting ratification, rather than a published OACT equation. The TR2026 +transcription check records comparisons with OACT's printed period-life +expectancies, inferring ``f0`` from each row's printed ``l(0)``, ``L(0)`` +and ``q(0)``. See ``data/external/tr2026/transcription_check.json`` for +coverage, tolerances and observed errors. The check excludes ages 110-119 +from its all-age comparison because OACT uses different printing +conventions at those ages, including zero expectancy where ``q = 1``. + +Invariants: survivorship and remaining person-years are nonnegative and +nonincreasing with age; before the closing age, deaths equal the loss of +survivors over the next year. Changing the radix scales ``l, d, L, T`` +by the same factor and leaves conditional life expectancy unchanged. +Increasing any death probability cannot increase life expectancy at any +earlier age, with the same separation factor and closing rule. + +Two implementations are kept on purpose: :func:`period_life_table` builds +the ``l, d, L, T, e`` columns as defined above, and +:func:`life_expectancy` uses the equivalent backward recursion +``e_x = 1 - q_x / 2 + (1 - q_x) * e_{x+1}`` (``e_0 = 1 - f0 * q_0 + +(1 - q_0) * e_1``), which stays defined where ``lx`` underflows to 0. +The tests check that they agree. +""" + +from __future__ import annotations + +import math +from collections.abc import Sequence +from dataclasses import dataclass + +import numpy as np + +__all__ = [ + "PeriodLifeTable", + "RADIX", + "life_expectancy", + "life_expectancy_profile", + "period_life_table", +] + +#: OACT's radix, ``l0 = 100,000``. +RADIX = 100_000.0 + + +@dataclass(frozen=True) +class PeriodLifeTable: + """The columns of a period life table, ages ``0..w`` (read-only). + + ``Lx[w]`` and ``Tx[w]`` are the person-years lived at age ``w`` and + every later age under the closing rule in the module docstring. + """ + + qx: np.ndarray + lx: np.ndarray + dx: np.ndarray + Lx: np.ndarray + Tx: np.ndarray + ex: np.ndarray + f0: float + radix: float + + +def _validated_q(qx: Sequence[float] | np.ndarray) -> np.ndarray: + q = np.array(qx, dtype=np.float64) + if q.ndim != 1: + raise ValueError("qx must be one-dimensional (ages 0..w)") + if q.size < 2: + raise ValueError("qx needs at least ages 0 and 1") + if not np.all(np.isfinite(q)): + raise ValueError("qx must be finite") + if np.any(q < 0.0) or np.any(q > 1.0): + raise ValueError("every qx must lie in [0, 1]") + if q[-1] <= 0.0: + raise ValueError( + "the last qx must be positive: with q_w = 0 nobody dies at or " + "after the last age and Tx is unbounded" + ) + return q + + +def _validated_f0(f0: float) -> float: + value = float(f0) + if not math.isfinite(value) or not 0.0 <= value <= 1.0: + raise ValueError("f0 must lie in [0, 1]") + return value + + +def _read_only(array: np.ndarray) -> np.ndarray: + array.setflags(write=False) + return array + + +def period_life_table( + qx: Sequence[float] | np.ndarray, + *, + f0: float, + radix: float = RADIX, +) -> PeriodLifeTable: + """Build ``l, d, L, T, e`` from ``qx`` for ages ``0..w``. + + ``f0`` is required: the definitions give no default separation factor. + ``ex`` is ``NaN`` at ages where ``lx`` is exactly 0 (an earlier ``qx`` + equals 1, or numerical underflow); :func:`life_expectancy` returns the + conditional value there. + """ + q = _validated_q(qx) + f0 = _validated_f0(f0) + if not math.isfinite(radix) or radix <= 0.0: + raise ValueError("radix must be positive") + survival = np.concatenate(([1.0], np.cumprod(1.0 - q[:-1]))) + lx = radix * survival + dx = lx * q + big_l = lx - 0.5 * dx + big_l[0] = lx[0] - f0 * dx[0] + big_l[-1] = lx[-1] * (1.0 - 0.5 * q[-1]) / q[-1] + tx = np.cumsum(big_l[::-1])[::-1] + with np.errstate(invalid="ignore", divide="ignore"): + ex = np.where(lx > 0.0, tx / np.where(lx > 0.0, lx, 1.0), np.nan) + return PeriodLifeTable( + qx=_read_only(q), + lx=_read_only(lx), + dx=_read_only(dx), + Lx=_read_only(big_l), + Tx=_read_only(tx), + ex=_read_only(ex), + f0=f0, + radix=float(radix), + ) + + +def life_expectancy_profile( + qx: Sequence[float] | np.ndarray, *, f0: float | None = None +) -> np.ndarray: + """``e(x)`` at every age ``0..w`` by the backward recursion. + + With ``f0=None`` the age-0 entry is ``NaN``: it is the only age whose + value depends on the separation factor. + """ + q = _validated_q(qx) + out = np.empty_like(q) + out[-1] = (1.0 - 0.5 * q[-1]) / q[-1] + for age in range(q.size - 2, 0, -1): + out[age] = 1.0 - 0.5 * q[age] + (1.0 - q[age]) * out[age + 1] + if f0 is None: + out[0] = np.nan + else: + f0 = _validated_f0(f0) + out[0] = 1.0 - f0 * q[0] + (1.0 - q[0]) * out[1] + return _read_only(out) + + +def life_expectancy( + qx: Sequence[float] | np.ndarray, + age: int, + *, + f0: float | None = None, +) -> float: + """Period life expectancy at exact ``age`` (backward recursion). + + ``f0`` is needed only at age 0; asking for age 0 without it raises. + """ + q = _validated_q(qx) + try: + integer_age = int(age) + except (TypeError, ValueError, OverflowError) as error: + raise ValueError("age must be an integer") from error + if isinstance(age, bool) or integer_age != age: + raise ValueError("age must be an integer") + age = integer_age + if not 0 <= age < q.size: + raise ValueError(f"age must lie in 0..{q.size - 1}") + if age == 0 and f0 is None: + raise ValueError("life expectancy at birth needs the separation f0") + return float(life_expectancy_profile(q, f0=f0)[age]) diff --git a/src/populace_dynamics/data/tr2026.py b/src/populace_dynamics/data/tr2026.py new file mode 100644 index 00000000..d3558fe9 --- /dev/null +++ b/src/populace_dynamics/data/tr2026.py @@ -0,0 +1,357 @@ +"""Pinned 2026 Trustees Report intermediate baseline input accessors. + +Data are transcribed from Tables V.C1, V.B1, VI.G1, V.A1 and V.A4 and +OACT DeathProbsE CSVs captured 2026-10-01. See +``data/external/tr2026/provenance.md`` for source URLs, locators, capture +SHA-256s, exact-byte reproduction and life-expectancy checks. Captured +HTML bytes are pinned, while live Akamai HTML hashes can change between +requests. Every JSON file read here is verified against FILE_SHA256. + +Units: COLA and CPI-W growth are percentages (2.4 means 2.4%); AWI is +dollars; TFR is births per woman; ASADR is deaths per 100,000 standardized +to the April 1, 2010 population; q is a one-year probability at exact age. +Hist/Alt2 q is preserved, including q119 below one where OACT publishes it. + +Builder default awaiting ratification: ``post_2035_cola_annual_cpiw`` uses +the V.B1 annual-average CPI-W growth as the COLA for determination years +2036-2100. This approximates the statutory Q3-to-Q3 adjustment and is tagged +``derived_annual_cpiw_cola``. No extension after 2100 is provided. Tables +otherwise have no inferred values, interpolation or silent fallback. + +Value source classes distinguish historical, actual, estimated, +preliminary, provisional and projected rows. Path entries carry source classes; +:func:`value_provenance` adds the captured source hash, locator and units +for every scalar/path value and every sex-specific q vector. +""" + +from __future__ import annotations + +import hashlib +import json +from collections.abc import Mapping +from dataclasses import dataclass +from functools import cache, lru_cache +from numbers import Integral +from pathlib import Path +from types import MappingProxyType +from typing import Any, Literal + +import numpy as np + +__all__ = [ + "AwiEntry", + "ColaEntry", + "DATA_DIR", + "FILE_SHA256", + "PENDING_RULINGS", + "ValueProvenance", + "asadr", + "awi", + "cola_path", + "cpiw_growth", + "death_probability", + "tfr", + "value_provenance", + "verify_files", +] + +Sex = Literal["male", "female"] +AsadrGroup = Literal["total", "under_65", "65_and_over"] +_PROJECT_ROOT = Path(__file__).resolve().parents[3] +DATA_DIR = _PROJECT_ROOT / "data" / "external" / "tr2026" +FILE_SHA256: Mapping[str, str] = MappingProxyType( + { + "tr2026_parameters.json": "15d890db61dc8cdcb4a22cc0427e091ee062d85f7265902d74362bd51c7525d8", + "tr2026_death_probabilities.json": "f711c087bcf907a91b78b076a7fc340ffb931cc26453ace7965512152aeca424", + "transcription_check.json": "5942720fc9ac7a633680279a2a3de0a4e973f1a9e0952cac2482c485eae245bb", + "sources.json": "8f618f5deda22f65fdf0be010101af6170af86b965b6d531bb2366bf891295d3", + } +) +PENDING_RULINGS = MappingProxyType( + { + "post_2035_cola_annual_cpiw": ( + "Use V.B1 annual-average CPI-W growth for COLA in 2036-2100; " + "builder default awaiting ratification" + ), + } +) + + +@dataclass(frozen=True) +class ColaEntry: + """Automatic benefit increase in percent, by determination year.""" + + determination_year: int + percent: float + source: str + effective_month: Literal["June", "December"] + + +@dataclass(frozen=True) +class AwiEntry: + """One published national average wage index in dollars.""" + + year: int + amount: float + source: str + + +@dataclass(frozen=True) +class ValueProvenance: + """Source identity for one year's value or single-sex q vector.""" + + series: str + year: int + source: str + source_file: str + source_sha256: str + locator: str + unit: str + derived_rule: str | None = None + + +def _year(year: int) -> int: + if isinstance(year, bool) or not isinstance(year, Integral): + raise ValueError("year must be an integer (not bool or float)") + return int(year) + + +def _range(first: int, last: int) -> range: + first, last = _year(first), _year(last) + if first > last: + raise ValueError("first must not exceed last") + return range(first, last + 1) + + +def _sex(sex: str) -> Sex: + if sex not in ("male", "female"): + raise ValueError("sex must be 'male' or 'female'") + return sex + + +def verify_files(data_dir: Path = DATA_DIR) -> None: + """Verify every consumed artifact against its hard-coded SHA-256.""" + for name, expected in FILE_SHA256.items(): + observed = hashlib.sha256((data_dir / name).read_bytes()).hexdigest() + if observed != expected: + raise ValueError( + f"{name} sha256 {observed} != pinned {expected}; rebuild and re-pin" + ) + + +@lru_cache(maxsize=1) +def _data() -> dict[str, Any]: + verify_files(DATA_DIR) + return { + name: json.loads((DATA_DIR / name).read_text(encoding="utf-8")) + for name in FILE_SHA256 + } + + +@cache +def _table(table: str) -> dict[int, dict[str, Any]]: + table_data = _data()["tr2026_parameters.json"]["tables"][table] + return { + int(row[0]): dict(zip(table_data["columns"], row, strict=True)) + for row in table_data["rows"] + } + + +def _row(table: str, year: int) -> dict[str, Any]: + year = _year(year) + try: + return _table(table)[year] + except KeyError: + raise KeyError(f"TR2026 {table} has no coverage for {year}") from None + + +def _row_source(table: str, year: int, column: str | None = None) -> str: + _row(table, year) + stem = "tr2026_" + table.lower().replace(".", "_") + if table == "V.C1": + standing = ( + "historical" + if year <= 2024 + else "actual" if year == 2025 else "projected" + ) + elif table == "VI.G1": + standing = ( + "historical" + if year <= 2024 + else "estimated" if year == 2025 else "projected" + ) + elif table == "V.B1": + standing = ( + "historical" + if year <= 2024 + else "estimated" if year == 2025 else "projected" + ) + else: + table_data = _data()["tr2026_parameters.json"]["tables"][table] + footnote = table_data["cell_footnotes"].get(str(year), {}).get(column) + standing = ( + "preliminary" + if footnote in ("d", "e") + else ( + "provisional" + if footnote == "f" + else "historical" if year <= 2025 else "projected" + ) + ) + return f"{stem}_{standing}" + + +def cola_path(first: int, last: int) -> tuple[ColaEntry, ...]: + """COLA percent for determination years 1975-2100, inclusive. + + V.C1 through 2035; the named builder default uses annual-average CPI-W + growth thereafter. June is effective in 1975-82, December thereafter + (first paid January of the next year), following V.C1 footnote a. + """ + out = [] + for year in _range(first, last): + if year <= 2035: + row = _row("V.C1", year) + percent = float(row["cola_percent"]) + source = _row_source("V.C1", year) + else: + percent = cpiw_growth(year) + source = "derived_annual_cpiw_cola" + out.append( + ColaEntry( + year, percent, source, "June" if year <= 1982 else "December" + ) + ) + return tuple(out) + + +def cpiw_growth(year: int) -> float: + """Annual-average CPI-W growth in percent, 1960-2100 (V.B1).""" + return float(_row("V.B1", year)["cpiw_growth_percent"]) + + +def awi(first: int, last: int) -> tuple[AwiEntry, ...]: + """Published VI.G1 AWI in dollars for 1970-2100, inclusive.""" + return tuple( + AwiEntry( + year, float(_row("VI.G1", year)["awi"]), _row_source("VI.G1", year) + ) + for year in _range(first, last) + ) + + +def death_probability(year: int, sex: Sex) -> np.ndarray: + """Published exact-age q for ages 0-119, 1900-2100; immutable array. + + Historical rows end in 2023; Alt2 starts in 2024. Terminal q is kept + exactly as published. Sex must be the literal 'male' or 'female'. + """ + year, sex = _year(year), _sex(sex) + try: + q = _data()["tr2026_death_probabilities.json"]["sexes"][sex]["q"][ + str(year) + ] + except KeyError: + raise KeyError( + f"TR2026 death probability has no coverage for {year}" + ) from None + # Bytes backing prevents callers from re-enabling write access. + return np.frombuffer( + np.array(q, dtype=np.float64).tobytes(), dtype=np.float64 + ) + + +def tfr(year: int) -> float: + """Period total fertility rate in births per woman, 1940-2100.""" + return float(_row("V.A1", year)["tfr"]) + + +def asadr(year: int, group: AsadrGroup) -> float: + """V.A1 deaths per 100,000, April 1 2010 standardized population. + + Group is 'total', 'under_65' or '65_and_over'; coverage is 1940-2100. + """ + if group not in ("total", "under_65", "65_and_over"): + raise ValueError("group must be 'total', 'under_65' or '65_and_over'") + return float(_row("V.A1", year)[group]) + + +def value_provenance( + series: Literal[ + "cola", "cpiw_growth", "awi", "death_probability", "tfr", "asadr" + ], + year: int, + *, + sex: Sex | None = None, + group: AsadrGroup | None = None, +) -> ValueProvenance: + """Captured hash, source class, units and locator for every value. + + ``sex`` is required only for death_probability and ``group`` only for + asadr; unused qualifiers are refused to catch mistaken selections. + """ + year = _year(year) + if series != "death_probability" and sex is not None: + raise ValueError("sex applies only to death_probability provenance") + if series != "asadr" and group is not None: + raise ValueError("group applies only to asadr provenance") + derived_rule = None + if series == "death_probability": + if sex is None: + raise ValueError("death_probability provenance needs sex") + death_probability(year, sex) + code = "M" if sex == "male" else "F" + variant = "Hist" if year <= 2023 else "Alt2" + filename = f"DeathProbsE_{code}_{variant}_TR2026.csv" + source = ( + f"tr2026_death_probs_{'historical' if year <= 2023 else 'alt2'}" + ) + unit = "one-year probability at exact age" + else: + if series == "cola": + entry = cola_path(year, year)[0] + table = "V.C1" if year <= 2035 else "V.B1" + source, unit = entry.source, "percent" + if year > 2035: + derived_rule = "post_2035_cola_annual_cpiw" + elif series == "cpiw_growth": + cpiw_growth(year) + table, unit = "V.B1", "percent" + source = _row_source(table, year) + elif series == "awi": + source = awi(year, year)[0].source + table, unit = "VI.G1", "dollars" + elif series == "tfr": + tfr(year) + table, unit = "V.A1", "births per woman" + source = _row_source(table, year, "tfr") + elif series == "asadr": + if group is None: + raise ValueError("asadr provenance needs group") + asadr(year, group) + table, unit = ( + "V.A1", + "deaths per 100000; April 1 2010 standard population", + ) + source = _row_source(table, year, group) + else: + raise ValueError(f"unknown series {series!r}") + filename = _data()["tr2026_parameters.json"]["tables"][table][ + "source_file" + ] + record = _data()["sources.json"]["sources"][filename] + locator = f"{record['locator']}; year {year}" + if sex: + locator += f"; sex {sex}; ages 0-119" + if group: + locator += f"; group {group}" + return ValueProvenance( + series, + year, + source, + filename, + record["sha256"], + locator, + unit, + derived_rule, + ) diff --git a/tests/README-tiers.md b/tests/README-tiers.md index a48a1eda..41af880d 100644 --- a/tests/README-tiers.md +++ b/tests/README-tiers.md @@ -39,9 +39,9 @@ pytest --collect-only -q -m oracle_policyengine | tail -1 | Tier | Tests at HEAD | |---|---:| -| `unit` | 5,960 | -| `artifact` | 3,361 | +| `unit` | 6,029 | +| `artifact` | 3,536 | | `integration_psid` | 1,341 | | `reproduction_legacy` | 520 | | `oracle_policyengine` | 220 | -| **Total** | **11,402** | +| **Total** | **11,646** | diff --git a/tests/baselines/__init__.py b/tests/baselines/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/baselines/test_asfr_and_claim_tables.py b/tests/baselines/test_asfr_and_claim_tables.py new file mode 100644 index 00000000..1f44b4fe --- /dev/null +++ b/tests/baselines/test_asfr_and_claim_tables.py @@ -0,0 +1,154 @@ +"""The derived TR2026 fertility shape and the 2026 claim-age PMFs. + +Artifact tier: reads committed inputs under ``data/external`` only +through their pinned accessors (``nchs_asfr_2024.json``, +``ssa_claim_ages_2026supplement.json``, the TR2026 and CBO2026 JSON). +No projection runs and no outcome is computed. + +Invariants: + +* the NCHS single-age shape is non-negative, keeps every NCHS band's + total (ages 10-13 folded into 14) and sums to the NCHS TFR; +* a derived TR2026 ASFR sums to the V.A1 TFR and is proportional to the + shape in every year; +* the 2026 claim-age PMFs equal ``claiming.claim_age_pmf`` exactly in + every row both can read (1998-2022, a differential test), sum to one + and cover 1998-2025. +""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +import numpy as np +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics import claiming +from populace_dynamics.baselines import TR2026Intermediate, asfr +from populace_dynamics.baselines import claim_tables as tables +from populace_dynamics.data import cbo2026, tr2026 + +ROOT = Path(__file__).resolve().parents[2] +DATA_EXTERNAL = ROOT / "data" / "external" + + +def test_the_pinned_files_are_the_committed_ones(): + for path, pinned in ( + (asfr.NCHS_ASFR_PATH, asfr.NCHS_ASFR_SHA256), + (tables.CLAIM_AGES_2026_PATH, tables.CLAIM_AGES_2026_SHA256), + ): + assert path.parent == DATA_EXTERNAL + assert hashlib.sha256(path.read_bytes()).hexdigest() == pinned + + +def test_nchs_shape_keeps_every_band_total_and_the_tfr(): + shape = asfr.nchs_single_age_shape() + assert tuple(shape) == asfr.FERTILE_AGES + values = np.array(list(shape.values())) + assert np.all(values >= 0.0) + table = json.loads(asfr.NCHS_ASFR_PATH.read_text())["tables"]["2024"] + for label, lower, upper in asfr.NCHS_BANDS: + expected = (upper - lower) * table[label] / 1000.0 + ages = range(max(lower, 14), upper) + observed = sum(shape[age] for age in ages) + assert observed == pytest.approx(expected, rel=1e-12, abs=1e-15) + assert values.sum() == pytest.approx(asfr.nchs_published_tfr(), 1e-12) + # The NCHS 2024 schedule peaks in the 30-34 band (file validation). + assert max(shape, key=shape.get) in range(28, 35) + + +def test_nchs_shape_is_read_only(): + with pytest.raises(TypeError): + asfr.nchs_single_age_shape()[30] = 1.0 + + +@settings(max_examples=40, deadline=None) +@given(year=st.integers(1940, 2100)) +def test_tr2026_asfr_is_the_shape_scaled_to_the_v_a1_tfr(year): + rates = TR2026Intermediate().asfr(year) + shape = asfr.nchs_single_age_shape() + assert tuple(rates) == asfr.FERTILE_AGES + assert sum(rates.values()) == pytest.approx(tr2026.tfr(year), 1e-12) + ratio = {age: rates[age] / shape[age] for age in shape if shape[age]} + assert max(ratio.values()) == pytest.approx(min(ratio.values()), 1e-12) + + +@settings(max_examples=30, deadline=None) +@given(year=st.integers(2021, 2099)) +def test_cbo_shape_alternative_scales_cbo_ages_to_the_tr2026_tfr(year): + rates = TR2026Intermediate(asfr_shape="cbo2026").asfr(year) + published = cbo2026.asfr(year) + assert sum(rates.values()) == pytest.approx(tr2026.tfr(year), 1e-12) + factor = tr2026.tfr(year) / sum(published.values()) + for age, value in published.items(): + assert rates[age] == pytest.approx(value * factor, rel=1e-12) + + +def test_cbo_shape_alternative_refuses_years_cbo_does_not_cover(): + baseline = TR2026Intermediate(asfr_shape="cbo2026") + with pytest.raises(ValueError, match="outside coverage"): + baseline.asfr(2100) + with pytest.raises(ValueError, match="asfr_shape"): + TR2026Intermediate(asfr_shape="flat") + + +def test_scale_shape_refuses_degenerate_inputs(): + with pytest.raises(ValueError, match="all-zero"): + asfr.scale_shape_to_tfr({20: 0.0}, 1.5) + with pytest.raises(ValueError, match="TFR"): + asfr.scale_shape_to_tfr({20: 0.1}, float("nan")) + with pytest.raises(ValueError, match="non-negative"): + asfr.scale_shape_to_tfr({20: -0.1, 21: 0.2}, 1.5) + + +# --------------------------------------------------------------------------- +# Claim-age PMFs from the 2026 Supplement +# --------------------------------------------------------------------------- +def test_2026_claim_pmf_equals_the_claiming_module_where_both_read(): + reference = claiming.load_claim_age_reference(tables.CLAIM_AGES_2026_PATH) + ours = tables.claim_pmf_2026_supplement() + compared = 0 + for (sex, year), pmf in ours.items(): + if claiming.MIN_YEAR <= year <= claiming.MAX_YEAR: + theirs = claiming.claim_age_pmf( + sex, year, exclude_conversions=True, reference=reference + ) + assert pmf == theirs, (sex, year) + compared += 1 + assert compared == 2 * (claiming.MAX_YEAR - claiming.MIN_YEAR + 1) + + +def test_the_claiming_module_snaps_rows_after_2022_which_ours_reads(): + reference = claiming.load_claim_age_reference(tables.CLAIM_AGES_2026_PATH) + snapped = claiming.claim_age_distribution( + "male", 2025, reference=reference + ) + assert snapped.nearest_year_used and snapped.entitlement_year == 2022 + ours = tables.claim_pmf_2026_supplement()[("male", 2025)] + direct = tables.claim_row_pmf(reference.row("male", 2025)["categories"]) + assert ours == direct + + +def test_2026_claim_pmfs_cover_1998_2025_and_sum_to_one(): + pmfs = tables.claim_pmf_2026_supplement() + assert {year for _, year in pmfs} == set(range(1998, 2026)) + assert {sex for sex, _ in pmfs} == {"female", "male"} + assert tables.last_year_2026_supplement() == 2025 + for pmf in pmfs.values(): + assert set(pmf) == set(range(62, 71)) + assert all(value >= 0.0 for value in pmf.values()) + assert sum(pmf.values()) == pytest.approx(1.0, abs=1e-12) + + +def test_claim_row_pmf_refuses_degenerate_rows(): + zero = dict.fromkeys( + ("age62", "age63", "age64", "age65", "age66", "age67_69"), 0.0 + ) + with pytest.raises(ValueError, match="degenerate"): + tables.claim_row_pmf({**zero, "age70plus": 0.0}) + with pytest.raises(ValueError, match="non-negative"): + tables.claim_row_pmf({**zero, "age62": -1.0, "age70plus": 2.0}) diff --git a/tests/baselines/test_baseline_invariants.py b/tests/baselines/test_baseline_invariants.py new file mode 100644 index 00000000..663d5e78 --- /dev/null +++ b/tests/baselines/test_baseline_invariants.py @@ -0,0 +1,546 @@ +"""Invariants every registered baseline must satisfy. + +Stated invariants (each is tested below, with Hypothesis where the +domain is large): + +1. **Mortality.** ``q`` is finite and in [0, 1] for every age 0-120, + both sexes and every covered year; age 120 repeats age 119; the + year-aware model returns ``q[year][sex][min(age, 120)]`` for any + frame. +2. **Fertility.** ASFR covers single ages 14-49, is non-negative and + below one (a Bernoulli probability per woman-year), and sums to the + baseline's TFR: exactly (to 1e-12) where the TFR is derived from the + schedule or the schedule from the TFR, and within the printed + rounding where both are published (CBO 2026-2099). +3. **AWI.** Strictly positive and finite; ``ssa_parameters`` keeps the + oracle's pre-1975 series and replaces every later year it covers. +4. **COLA splices are continuous.** The path covers every + determination year from 1979 consecutively, each year from one named + source; the 2026 baselines keep realized increases through 2025 (the + committed history through 2022, then TR2026 V.C1's historical rows + and the 2.8 percent 2025 actual) and their path does not depend on + the first rate year; the committed history and V.C1 agree in every + year both cover. The CBO AWI analog starts from the realized 2024 + level. +5. **Claiming.** Every claim-age PMF is non-negative on ages 62-70 and + sums to one. +6. **DI.** Every baseline supplies the 2008 fit (a named gap). +7. **Provenance.** Every recorded file hash is the hash of that file's + bytes under ``data/external``; derived values carry derived tags. + +Artifact tier: reads the committed TR2008, TR2026, CBO 2026, NCHS, +claim-age, COLA-history and DI inputs under ``data/external`` through +their pinned accessors. No projection runs and no outcome is computed. +""" + +from __future__ import annotations + +import dataclasses +import hashlib +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics.baselines import ( + BASELINE_NAMES, + DEFAULT_BASELINE, + Baseline, + BaselineGapError, + BaselineYearAwareMortality, + CBO2026LongTerm, + TR2008Legacy, + TR2026Intermediate, + get_baseline, + realized, +) +from populace_dynamics.baselines.base import ( + SEXES, + pad_to_max_age, + replace_awi, +) +from populace_dynamics.cola_track_a.runner import _same_values +from populace_dynamics.data import cbo2026, tr2026 +from populace_dynamics.engine.di_entitlement_rates import ( + MAX_AGE, + SINGLE_YEAR_AGE_BANDS, + DIEntitlementSpec, + load_di_entitlement_rates, +) +from populace_dynamics.engine.loop import PeriodContext +from populace_dynamics.estimates.parameters import load_cola_history +from populace_dynamics.ss.params import SSAParameters + +ROOT = Path(__file__).resolve().parents[2] +DATA_EXTERNAL = ROOT / "data" / "external" +#: Years each baseline supplies mortality for (TR2008: the V.A1 ASADR +#: ratios; CBO: its 2021-2099 tables with OACT history spliced before). +MORTALITY_YEARS = { + "tr2008_intermediate": range(2005, 2086), + "tr2026_intermediate": range(1950, 2101), + "cbo2026_long_term": range(1950, 2100), +} +LAST_COLA_YEAR = { + "tr2008_intermediate": 2085, + "tr2026_intermediate": 2100, + "cbo2026_long_term": 2100, +} +#: CBO prints ASFR per 1,000 women to two decimals and the TFR to three +#: (``cbo2026_demographics.json``), so the 36 rates per woman sum to the +#: TFR within 36 half-units of 1e-5 plus half a unit of 1e-3. +CBO_PRINTED_ROUNDING = 36 * 0.5e-5 + 0.5e-3 +DI_FIELDS = ( + "incidence", + "recovery_attained", + "death_attained", + "population_reference_death", + "recovery_select", + "death_select", + "recovery_level_factor", + "death_level_factor", +) + + +def invented_base_params() -> SSAParameters: + """INVENTED oracle parameters (4 percent wage growth from 1951).""" + return SSAParameters( + nawi={ + year: 2_800.0 * 1.04 ** (year - 1951) for year in range(1951, 2061) + }, + wage_base={1937: 3_000.0}, + pia_factors=(0.9, 0.32, 0.15), + fra_months_by_birth_year=[(1900, 792)], + early_monthly_rates=(5 / 900, 5 / 1200), + early_first_bracket_months=36, + pe_us_revision="INVENTED", + ) + + +@pytest.fixture(scope="module", params=BASELINE_NAMES) +def baseline(request) -> Baseline: + return get_baseline(request.param) + + +# --------------------------------------------------------------------------- +# Registry +# --------------------------------------------------------------------------- +def test_registry_names_and_default(): + assert BASELINE_NAMES == ( + "tr2008_intermediate", + "tr2026_intermediate", + "cbo2026_long_term", + ) + assert DEFAULT_BASELINE == "tr2008_intermediate" + assert isinstance(get_baseline(), TR2008Legacy) + assert isinstance(get_baseline("tr2026_intermediate"), TR2026Intermediate) + assert isinstance(get_baseline("cbo2026_long_term"), CBO2026LongTerm) + with pytest.raises(KeyError, match="unknown baseline"): + get_baseline("tr2025_intermediate") + + +def test_every_baseline_implements_the_protocol_and_is_frozen(baseline): + assert isinstance(baseline, Baseline) + assert baseline.name in BASELINE_NAMES + assert baseline.vintage + with pytest.raises(dataclasses.FrozenInstanceError): + baseline.name = "other" + + +# --------------------------------------------------------------------------- +# 1. Mortality +# --------------------------------------------------------------------------- +def test_q_lies_in_the_unit_interval_every_year_age_and_sex(baseline): + for year in MORTALITY_YEARS[baseline.name]: + qx = baseline.qx(year) + assert set(qx) == set(SEXES) + for sex in SEXES: + values = qx[sex] + assert values.shape == (MAX_AGE + 1,) + assert np.isfinite(values).all(), (year, sex) + assert ((values >= 0.0) & (values <= 1.0)).all(), (year, sex) + assert values[MAX_AGE] == values[MAX_AGE - 1] + assert not values.flags.writeable + + +def test_q_outside_coverage_refuses(baseline): + years = MORTALITY_YEARS[baseline.name] + with pytest.raises((KeyError, ValueError)): + baseline.qx(years.stop + 1) + + +def test_published_q_are_used_unaltered(): + tr = TR2026Intermediate() + cbo = CBO2026LongTerm() + for year in (1990, 2023, 2024, 2050, 2099): + for sex in SEXES: + published = tr2026.death_probability(year, sex) + assert np.array_equal(tr.qx(year)[sex][:120], published) + if year >= 2021: + assert np.array_equal( + cbo.qx(year)[sex][:120], cbo2026.mortality(year, sex) + ) + assert cbo.qx(year)[sex][119] == 1.0 + else: + assert np.array_equal(cbo.qx(year)[sex][:120], published) + # OACT publishes q(119) below one and the TR2026 baseline keeps it. + assert tr.qx(2050)["male"][119] < 1.0 + + +@settings(max_examples=30, deadline=None) +@given( + name=st.sampled_from(BASELINE_NAMES), + year=st.integers(2009, 2085), + ages=st.lists(st.integers(0, 140), min_size=1, max_size=40), + data=st.data(), +) +def test_the_year_aware_model_returns_q_at_the_capped_age( + name, year, ages, data +): + baseline = get_baseline(name) + model = baseline.population_mortality(range(year, year + 1)) + assert tuple(model.bands) == SINGLE_YEAR_AGE_BANDS + assert not hasattr(model, "probabilities") + sexes = data.draw( + st.lists( + st.sampled_from(SEXES), min_size=len(ages), max_size=len(ages) + ) + ) + frame = pd.DataFrame({"age": ages, "sex": sexes}) + context = PeriodContext( + period_index=1, year=year, draw_index=0, metadata={} + ) + observed = model(frame, context) + qx = baseline.qx(year) + expected = [ + qx[sex][min(age, MAX_AGE)] + for age, sex in zip(ages, sexes, strict=True) + ] + assert np.array_equal(observed, np.asarray(expected)) + + +def test_the_generic_model_refuses_bad_inputs(): + good = {2030: {sex: np.full(MAX_AGE + 1, 0.01) for sex in SEXES}} + model = BaselineYearAwareMortality(baseline="INVENTED", qx_by_year=good) + frame = pd.DataFrame({"age": [30], "sex": ["female"]}) + with pytest.raises(KeyError, match="no INVENTED mortality for 2031"): + model.probabilities_for_year(frame, 2031) + with pytest.raises(ValueError, match="sex labels"): + model.probabilities_for_year( + pd.DataFrame({"age": [30], "sex": ["other"]}), 2030 + ) + with pytest.raises(ValueError, match="non-negative"): + model.probabilities_for_year( + pd.DataFrame({"age": [-1], "sex": ["male"]}), 2030 + ) + with pytest.raises(ValueError, match="missing columns"): + model.probabilities_for_year(pd.DataFrame({"age": [30]}), 2030) + bad = {2030: {"female": np.full(MAX_AGE + 1, 1.5), "male": good[2030]}} + with pytest.raises(ValueError, match=r"\[0, 1\]"): + BaselineYearAwareMortality(baseline="INVENTED", qx_by_year=bad) + short = {2030: {sex: np.full(MAX_AGE, 0.01) for sex in SEXES}} + with pytest.raises(ValueError, match="ages 0-120"): + BaselineYearAwareMortality(baseline="INVENTED", qx_by_year=short) + with pytest.raises(ValueError, match="single-year"): + BaselineYearAwareMortality( + baseline="INVENTED", qx_by_year=good, bands=((0, 65), (65, 121)) + ) + with pytest.raises(TypeError): + model.qx_by_year[2030]["male"] = np.zeros(MAX_AGE + 1) + with pytest.raises(ValueError, match="read-only"): + model.qx_by_year[2030]["male"][30] = 0.5 + assert not model.qx_by_year[2030]["male"].flags.writeable + with pytest.raises(ValueError, match="at most"): + pad_to_max_age(np.zeros(MAX_AGE + 2)) + + +# --------------------------------------------------------------------------- +# 2. Fertility +# --------------------------------------------------------------------------- +def _check_schedule(rates: dict[int, float]) -> np.ndarray: + assert tuple(rates) == tuple(range(14, 50)) + values = np.asarray(list(rates.values())) + assert np.isfinite(values).all() + assert (values >= 0.0).all() and (values < 1.0).all() + return values + + +def test_tr2026_asfr_sums_to_the_v_a1_tfr_every_year(): + baseline = TR2026Intermediate() + for year in range(1940, 2101): + values = _check_schedule(baseline.asfr(year)) + assert values.sum() == pytest.approx(baseline.tfr(year), rel=1e-12) + assert baseline.tfr(year) == tr2026.tfr(year) + + +def test_cbo_asfr_is_published_and_sums_to_the_tfr_within_rounding(): + baseline = CBO2026LongTerm() + for year in range(2021, 2100): + rates = baseline.asfr(year) + values = _check_schedule(rates) + assert rates == cbo2026.asfr(year, "all") + if year < 2026: + assert values.sum() == pytest.approx(baseline.tfr(year), 1e-12) + else: + assert abs(values.sum() - baseline.tfr(year)) <= ( + CBO_PRINTED_ROUNDING + ), year + with pytest.raises(ValueError, match="outside coverage"): + baseline.asfr(2020) + + +def test_legacy_supplies_no_fertility(): + with pytest.raises(BaselineGapError): + TR2008Legacy().asfr(2030) + + +# --------------------------------------------------------------------------- +# 3. AWI +# --------------------------------------------------------------------------- +def test_awi_is_positive_and_ssa_parameters_splice_it(baseline): + last = LAST_COLA_YEAR[baseline.name] + awi = baseline.awi(1975, last) + assert list(awi) == list(range(1975, last + 1)) + values = np.asarray(list(awi.values())) + assert np.isfinite(values).all() and (values > 0.0).all() + base = invented_base_params() + params = baseline.ssa_parameters(base) + assert all(np.isfinite(v) and v > 0.0 for v in params.nawi.values()) + for year in range(1951, 1975): + assert params.nawi[year] == base.nawi[year] + for year in range(1975, last + 1): + assert params.nawi[year] == awi[year] + assert params.pe_us_revision.startswith("INVENTED+") + assert dataclasses.replace(params, nawi=base.nawi, pe_us_revision="X") == ( + dataclasses.replace(base, pe_us_revision="X") + ) + + +def test_realized_awi_runs_through_2024_in_both_2026_baselines(): + tr, cbo = TR2026Intermediate(), CBO2026LongTerm() + tr_awi, cbo_awi = tr.awi(1975, 2100), cbo.awi(1975, 2100) + for year in range(1975, 2025): + assert tr_awi[year] == cbo_awi[year] == realized.realized_awi(year)[0] + assert tr.value_sources(year)["awi"] == "tr2026_vi_g1_historical" + assert cbo.value_sources(year)["awi"] == "tr2026_vi_g1_historical" + # The CBO analog starts from the realized 2024 level (no level jump). + (anchor,) = cbo2026.awi(2024, 2024) + assert anchor.amount == cbo_awi[2024] + for year in range(2025, 2101): + (entry,) = cbo2026.awi(year, year) + assert cbo_awi[year] == entry.amount + assert cbo.value_sources(year)["awi"].startswith("derived_cbo2026") + assert tr.value_sources(2025)["awi"] == "tr2026_vi_g1_estimated" + assert tr.value_sources(2026)["awi"] == "tr2026_vi_g1_projected" + + +def test_replace_awi_refuses_gaps_and_non_positive_amounts(): + base = invented_base_params() + with pytest.raises(ValueError, match="consecutive"): + replace_awi(base, {1975: 1.0, 1977: 1.0}, revision_suffix="x") + with pytest.raises(ValueError, match="positive"): + replace_awi(base, {1975: 0.0}, revision_suffix="x") + with pytest.raises(ValueError, match="positive"): + replace_awi(base, {1975: float("nan")}, revision_suffix="x") + + +# --------------------------------------------------------------------------- +# 4. COLA splices +# --------------------------------------------------------------------------- +def test_cola_covers_consecutive_years_from_1979(baseline): + last = LAST_COLA_YEAR[baseline.name] + series = baseline.cola_rates(2008, last) + years = sorted(series.by_determination_year) + assert years == list(range(1979, last + 1)) + values = np.asarray([series.rate_for_determination_year(y) for y in years]) + assert np.isfinite(values).all() and (values >= 0.0).all() + + +def test_the_committed_history_and_v_c1_agree_where_both_cover(): + realized.check_realized_cola_agreement() + history = load_cola_history() + for entry in tr2026.cola_path(1979, 2022): + year = entry.determination_year + committed = 100.0 * history.rate_for_determination_year(year) + assert round(committed, 10) == round(entry.percent, 10), year + + +def test_the_agreement_check_refuses_a_different_history(monkeypatch): + history = load_cola_history() + changed = dict(history.by_determination_year) + changed[2015] = 0.017 + tampered = dataclasses.replace(history, by_determination_year=changed) + monkeypatch.setattr(realized, "load_cola_history", lambda: tampered) + with pytest.raises(ValueError, match="differ"): + realized.check_realized_cola_agreement.__wrapped__() + + +@pytest.mark.parametrize("name", ["tr2026_intermediate", "cbo2026_long_term"]) +def test_2026_baselines_keep_realized_increases_through_2025(name): + baseline = get_baseline(name) + series = baseline.cola_rates(2008, 2100) + history = load_cola_history() + for year in range(1979, 2023): + assert series.rate_for_determination_year( + year + ) == history.rate_for_determination_year(year) + for year in range(2023, 2026): + percent, source = realized.realized_cola_percent(year) + assert series.rate_for_determination_year(year) == round( + percent / 100.0, 10 + ) + assert baseline.value_sources(year)["cola"] == source + assert series.rate_for_determination_year(2025) == 0.028 + assert baseline.value_sources(2025)["cola"] == "tr2026_v_c1_actual" + # From 2026 every rate is the projected or derived source's. + for year in range(2026, 2101): + if name == "tr2026_intermediate": + (entry,) = tr2026.cola_path(year, year) + expected = entry.percent + else: + expected = cbo2026.cpiu_growth(year) + assert series.rate_for_determination_year(year) == round( + expected / 100.0, 10 + ), year + + +@settings(max_examples=25, deadline=None) +@given( + name=st.sampled_from(["tr2026_intermediate", "cbo2026_long_term"]), + first=st.integers(1979, 2040), + last=st.integers(2026, 2100), +) +def test_2026_cola_path_does_not_depend_on_the_first_rate_year( + name, first, last +): + baseline = get_baseline(name) + first = min(first, last) + observed = baseline.cola_rates(first, last).by_determination_year + reference = baseline.cola_rates(2008, last).by_determination_year + assert observed == reference + + +def test_cola_requests_are_validated(): + for baseline in (TR2026Intermediate(), CBO2026LongTerm()): + with pytest.raises(ValueError, match="must not follow"): + baseline.cola_rates(2031, 2030) + with pytest.raises(ValueError, match="at least 2023"): + baseline.cola_rates(2008, 2022) + with pytest.raises(TypeError, match="integer"): + baseline.cola_rates(2008.0, 2030) + with pytest.raises(KeyError, match="after 2100"): + CBO2026LongTerm().cola_rates(2008, 2101) + with pytest.raises(KeyError): + TR2026Intermediate().cola_rates(2008, 2101) + + +def test_legacy_splices_tr2008_rates_from_the_first_rate_year(): + series = TR2008Legacy().cola_rates(2008, 2030) + history = load_cola_history() + for year in range(1979, 2008): + assert series.rate_for_determination_year( + year + ) == history.rate_for_determination_year(year) + + +def test_derived_values_carry_derived_tags(): + tr, cbo = TR2026Intermediate(), CBO2026LongTerm() + assert tr.value_sources(2035)["cola"] == "tr2026_v_c1_projected" + assert tr.value_sources(2036)["cola"] == "derived_annual_cpiw_cola" + assert tr.value_sources(2030)["asfr"].startswith("derived_") + assert tr.value_sources(2030)["tfr"] == "tr2026_v_a1_projected" + assert cbo.value_sources(2026)["cola"].startswith("derived_") + assert cbo.value_sources(2026)["cpiw_growth"].startswith("derived_") + assert cbo.value_sources(2025)["cpiw_growth"] == "tr2026_v_b1_estimated" + assert "oact_historical" in cbo.value_sources(2015)["mortality"] + assert cbo.value_sources(2030)["mortality"] == ( + "cbo2026_mortality_q_verified" + ) + for year in (2015, 2030, 2050): + for baseline in (tr, cbo): + assert baseline.value_sources(year)["di_rates"] == ( + "di_entitlement_fit_2008" + ) + assert tr.value_sources(2040)["claiming"].endswith("_row_2025") + assert tr.value_sources(2010)["claiming"].endswith("_row_2010") + + +def test_cpiw_growth_splices_realized_values_before_projections(): + tr, cbo = TR2026Intermediate(), CBO2026LongTerm() + for year in range(1980, 2026): + assert cbo.cpiw_growth(year) == tr.cpiw_growth(year) + assert tr.cpiw_growth(year) == tr2026.cpiw_growth(year) + for year in range(2026, 2101): + assert cbo.cpiw_growth(year) == cbo2026.cpiu_growth(year) + + +# --------------------------------------------------------------------------- +# 5. Claiming and 6. DI +# --------------------------------------------------------------------------- +def test_claim_pmfs_sum_to_one(baseline): + pmfs = baseline.claim_pmf() + years = {year for _, year in pmfs} + assert max(years) >= baseline.claim_table_max_year + assert {sex for sex, _ in pmfs} == set(SEXES) + for key, pmf in pmfs.items(): + assert set(pmf) == set(range(62, 71)), key + assert all(value >= 0.0 for value in pmf.values()), key + assert sum(pmf.values()) == pytest.approx(1.0, abs=1e-12), key + + +def test_2026_baselines_read_the_2026_supplement_through_2025(): + for baseline in (TR2026Intermediate(), CBO2026LongTerm()): + assert baseline.claim_table_max_year == 2025 + assert {year for _, year in baseline.claim_pmf()} == set( + range(1998, 2026) + ) + + +def test_every_baseline_supplies_the_2008_di_fit(baseline): + ours = baseline.di_rates() + theirs = load_di_entitlement_rates(DIEntitlementSpec()) + assert ours.spec == theirs.spec + for name in DI_FIELDS: + assert _same_values(getattr(ours, name), getattr(theirs, name)) + assert ours.provenance["information_boundary_year"] == 2008 + + +# --------------------------------------------------------------------------- +# 7. Provenance +# --------------------------------------------------------------------------- +@pytest.mark.parametrize( + "factory", + [ + TR2008Legacy, + TR2026Intermediate, + lambda: TR2026Intermediate(asfr_shape="cbo2026"), + CBO2026LongTerm, + ], +) +def test_every_recorded_file_hash_is_the_files_hash(factory): + record = factory().provenance() + files = dict(record["files_sha256"]) + files.update(record.get("cbo_files_sha256", {})) + assert files + for relative, digest in files.items(): + path = ROOT / relative + assert path.is_relative_to(DATA_EXTERNAL), relative + assert hashlib.sha256(path.read_bytes()).hexdigest() == digest, path + assert record["name"] and record["vintage"] and record["gaps"] + + +def test_2026_provenance_names_the_splices_and_pending_defaults(): + for baseline in (TR2026Intermediate(), CBO2026LongTerm()): + record = baseline.provenance() + assert "2025" in record["splices"]["cola"] + assert "2024" in record["splices"]["awi"] + assert "last actual" in record["splices"]["cola"] + assert record["builder_defaults_awaiting_ratification"] + assert any("2008 fit" in gap for gap in record["gaps"]) + assert record["captured_sources_sha256"]["tr2026"] + cbo = CBO2026LongTerm().provenance() + assert set(cbo2026.PENDING_RATIFICATION) <= set( + cbo["builder_defaults_awaiting_ratification"] + ) diff --git a/tests/baselines/test_fertility.py b/tests/baselines/test_fertility.py new file mode 100644 index 00000000..05dcd89e --- /dev/null +++ b/tests/baselines/test_fertility.py @@ -0,0 +1,386 @@ +"""The opt-in ASFR fertility step (not gated, report-only). + +Every person, age, weight and fertility rate below is INVENTED for +testing; no baseline file, PSID file or outcome is read. + +Stated invariants (Hypothesis property tests unless noted): + +1. The expected number of births is the sum of the rates over the + exposed women (``sex == "female"`` at an age the schedule keys). +2. Zero rates give zero births; unit rates give every exposed woman one + birth; men and women at unkeyed ages never give birth. +3. Over many draws the mean number of births equals the expected number + within Monte Carlo tolerance (five standard errors). +4. A woman's draw depends only on the draw, the period and her own RNG + ordinal, not on who else is in the frame; the same inputs give the + same births. +5. Births materialize through ``engine.steps.materialize_maternal_births`` + as age-0 child rows with the mother's weight and household and fresh + identifiers (example tests, including a multi-year projection). +6. The module does not import or call the gated candidate-16 fertility + law (a static check). +""" + +from __future__ import annotations + +import ast +import math +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics.baselines import fertility +from populace_dynamics.baselines.fertility import ( + ASFR_FERTILITY_LABEL, + ASFR_STREAM_TAG, + AsfrFertilityStep, + draw_asfr_births, + expected_births, +) +from populace_dynamics.engine.loop import ( + MaritalStepResult, + PeriodContext, + PeriodModules, + ProjectionEngine, + SyntheticPersonIdAllocator, +) +from populace_dynamics.engine.rng import ( + ProjectionModule, + ProjectionRNGRegistry, +) +from populace_dynamics.engine.steps import advance_age + +FERTILE = range(14, 50) + + +# -------------------------------------------------------------------------- +# INVENTED frames, rates and contexts +# -------------------------------------------------------------------------- +def invented_frame(ages, sexes, *, year=2030, first_id=1) -> pd.DataFrame: + """INVENTED persons: one household per person, weight 100 + id.""" + ids = np.arange(first_id, first_id + len(ages), dtype=np.int64) + return pd.DataFrame( + { + "person_id": ids, + "year": year, + "age": np.asarray(ages, dtype=np.int64), + "sex": list(sexes), + "weight": 100.0 + ids, + "household_id": 1_000 + ids, + } + ) + + +def invented_context(frame, *, year=2030, period=1, draw=0, ordinals=None): + """A period context with the loop's registry and roster ordinals.""" + ids = sorted(frame["person_id"].tolist()) + return PeriodContext( + period_index=period, + year=year, + draw_index=draw, + metadata={ + "synthetic_id_allocator": SyntheticPersonIdAllocator( + int(max(ids, default=0)) + 1 + ) + }, + rng_registry=ProjectionRNGRegistry(draw, max(period, 1)), + person_ordinals=( + {pid: index for index, pid in enumerate(ids)} + if ordinals is None + else ordinals + ), + ) + + +def empty_marital(frame) -> MaritalStepResult: + return MaritalStepResult( + sim_years=frame[["person_id"]].copy(), + births=pd.DataFrame(columns=["parent_person_id", "birth_year"]), + ) + + +rates_strategy = st.dictionaries( + st.sampled_from(list(FERTILE)), + st.floats(0.0, 0.6, allow_nan=False), + min_size=1, + max_size=36, +) + + +@st.composite +def frames(draw, max_size=30): + size = draw(st.integers(1, max_size)) + ages = draw(st.lists(st.integers(0, 70), min_size=size, max_size=size)) + sexes = draw( + st.lists( + st.sampled_from(["female", "male"]), min_size=size, max_size=size + ) + ) + return invented_frame(ages, sexes) + + +def exposed_rates(frame, rates): + female = frame["sex"].to_numpy() == "female" + return [ + rates[int(age)] if is_female and int(age) in rates else None + for age, is_female in zip(frame["age"], female, strict=True) + ] + + +# -------------------------------------------------------------------------- +# 1-2. Expected births, zero and unit rates +# -------------------------------------------------------------------------- +@settings(max_examples=60, deadline=None) +@given(frame=frames(), rates=rates_strategy) +def test_expected_births_is_the_sum_over_exposed_women(frame, rates): + observed = expected_births(frame, rates) + expected = sum(r for r in exposed_rates(frame, rates) if r is not None) + assert observed == pytest.approx(expected, abs=1e-12) + + +@settings(max_examples=40, deadline=None) +@given(frame=frames(), draw_index=st.integers(0, 50)) +def test_zero_rates_give_zero_births(frame, draw_index): + context = invented_context(frame, draw=draw_index) + births = draw_asfr_births(frame, context, dict.fromkeys(FERTILE, 0.0)) + assert births.empty + assert list(births.columns) == ["parent_person_id", "birth_year"] + + +@settings(max_examples=40, deadline=None) +@given(frame=frames(), draw_index=st.integers(0, 50)) +def test_unit_rates_give_every_exposed_woman_one_birth(frame, draw_index): + context = invented_context(frame, draw=draw_index) + births = draw_asfr_births(frame, context, dict.fromkeys(FERTILE, 1.0)) + female = frame["sex"] == "female" + fertile = frame["age"].between(14, 49) + mothers = set(frame.loc[female & fertile, "person_id"]) + assert set(births["parent_person_id"]) == mothers + assert len(births) == len(mothers) + assert (births["birth_year"] == context.year).all() + + +# -------------------------------------------------------------------------- +# 3. Monte Carlo: mean births equal expected births +# -------------------------------------------------------------------------- +@settings(max_examples=8, deadline=None) +@given( + rates=st.dictionaries( + st.sampled_from(list(FERTILE)), + st.floats(0.02, 0.5), + min_size=5, + max_size=36, + ), + seed=st.integers(0, 2**16), +) +def test_mean_births_equal_expected_births(rates, seed): + rng = np.random.default_rng(seed) + ages = rng.integers(10, 60, size=40) + sexes = np.where(rng.random(40) < 0.7, "female", "male") + frame = invented_frame(ages, sexes) + exposed = [r for r in exposed_rates(frame, rates) if r is not None] + expected = sum(exposed) + variance = sum(r * (1.0 - r) for r in exposed) + draws = 150 + counts = [ + len(draw_asfr_births(frame, invented_context(frame, draw=k), rates)) + for k in range(draws) + ] + tolerance = 5.0 * math.sqrt(variance / draws) + 1e-12 + assert abs(np.mean(counts) - expected) <= tolerance + + +# -------------------------------------------------------------------------- +# 4. Person-keyed, deterministic draws +# -------------------------------------------------------------------------- +@settings(max_examples=30, deadline=None) +@given( + frame=frames(), + rates=rates_strategy, + draw_index=st.integers(0, 20), + keep=st.data(), +) +def test_a_womans_draw_does_not_depend_on_the_rest_of_the_frame( + frame, rates, draw_index, keep +): + full_context = invented_context(frame, draw=draw_index) + full = set( + draw_asfr_births(frame, full_context, rates)["parent_person_id"] + ) + mask = keep.draw( + st.lists(st.booleans(), min_size=len(frame), max_size=len(frame)) + ) + subset = frame.loc[mask].reset_index(drop=True) + # The loop's ordinals are projection-wide, so the subset keeps them. + sub_context = invented_context( + subset, draw=draw_index, ordinals=full_context.person_ordinals + ) + sub = set(draw_asfr_births(subset, sub_context, rates)["parent_person_id"]) + assert sub == full & set(subset["person_id"]) + again = draw_asfr_births(frame, full_context, rates) + assert set(again["parent_person_id"]) == full + + +def test_the_stream_is_the_registry_tagged_child_of_the_fertility_module(): + frame = invented_frame([30], ["female"]) + context = invented_context(frame, draw=3) + uniform = context.rng_registry.tagged_child_generator( + 1, ProjectionModule.FERTILITY, ASFR_STREAM_TAG, 0 + ).random() + below = draw_asfr_births(frame, context, {30: min(1.0, uniform + 1e-9)}) + above = draw_asfr_births(frame, context, {30: max(0.0, uniform - 1e-9)}) + assert len(below) == 1 and above.empty + + +# -------------------------------------------------------------------------- +# 5. Materialized births +# -------------------------------------------------------------------------- +def test_the_step_materializes_age_zero_children_of_the_mothers(): + frame = invented_frame( + [25, 30, 35, 60, 30], ["female", "female", "female", "female", "male"] + ) + context = invented_context(frame) + births_log, expected_log = {}, {} + step = AsfrFertilityStep( + asfr=lambda year: dict.fromkeys(FERTILE, 1.0), + birth_log=births_log, + expected_log=expected_log, + ) + out = step(frame, context, empty_marital(frame), np.random.default_rng(0)) + children = out[out["synthetic_entry"].astype(bool)] + assert len(children) == 3 == len(births_log[2030]) + assert expected_log[2030] == 3.0 + assert set(children["parent_person_id"]) == {1, 2, 3} + assert (children["age"] == 0).all() and (children["year"] == 2030).all() + assert (children["birth_year"] == 2030).all() + assert children["person_id"].tolist() == [6, 7, 8] + mothers = frame.set_index("person_id") + for _, child in children.iterrows(): + mother = mothers.loc[child["parent_person_id"]] + assert child["weight"] == mother["weight"] + assert child["household_id"] == mother["household_id"] + assert set(children["sex"]) <= {"female", "male"} + assert len(out) == len(frame) + 3 + + +def test_a_marital_reader_runs_before_the_draw(): + frame = invented_frame([30], ["female"]) + seen = [] + + def reader(frame, context, marital, rng): + seen.append(len(frame)) + return frame.assign(marital_status="single") + + step = AsfrFertilityStep( + asfr=lambda year: {30: 0.0}, marital_reader=reader + ) + out = step( + frame, + invented_context(frame), + empty_marital(frame), + np.random.default_rng(0), + ) + assert seen == [1] and (out["marital_status"] == "single").all() + + +def test_a_multi_year_projection_grows_by_the_logged_births(): + """INVENTED open population projected 2031-2040 with the step.""" + ages = np.tile(np.arange(10, 50), 3) + sexes = np.where(np.arange(ages.size) % 2 == 0, "female", "male") + initial = invented_frame(ages, sexes, year=2030) + births_log, expected_log = {}, {} + + def keep(frame, context, rng): + return frame + + def marital(frame, context, rng): + return empty_marital(frame) + + def household(frame, context, marital, rng): + return frame + + modules = PeriodModules( + mortality=keep, + aging=advance_age, + marital_core=marital, + fertility=AsfrFertilityStep( + asfr=lambda year: dict.fromkeys(FERTILE, 0.08), + birth_log=births_log, + expected_log=expected_log, + ), + disability=keep, + earnings=keep, + claiming=keep, + household_composition=household, + ) + result = ProjectionEngine(modules).project( + initial, end_year=2040, draw_index=4 + ) + sizes = [len(frame) for frame in result.slices] + for index, year in enumerate(range(2031, 2041), start=1): + assert sizes[index] == sizes[index - 1] + len(births_log[year]) + assert expected_log[year] > 0.0 + final = result.slices[-1] + born = final[final["synthetic_entry"].astype(bool)] + assert (born["age"] == 2040 - born["birth_year"]).all() + assert born["person_id"].is_unique + assert sum(len(b) for b in births_log.values()) == len(born) > 0 + + +# -------------------------------------------------------------------------- +# Refusals +# -------------------------------------------------------------------------- +def test_rates_outside_the_unit_interval_are_refused(): + frame = invented_frame([30], ["female"]) + context = invented_context(frame) + for bad in (1.2, -0.1, float("nan")): + with pytest.raises(ValueError, match=r"\[0, 1\]"): + draw_asfr_births(frame, context, {30: bad}) + + +def test_the_step_needs_the_registry_and_its_columns(): + frame = invented_frame([30], ["female"]) + bare = PeriodContext(period_index=1, year=2030, draw_index=0, metadata={}) + with pytest.raises(RuntimeError, match="RNG registry"): + draw_asfr_births(frame, bare, {30: 0.1}) + with pytest.raises(ValueError, match="missing columns"): + expected_births(frame.drop(columns="sex"), {30: 0.1}) + with pytest.raises(ValueError, match="report-only"): + AsfrFertilityStep(asfr=lambda year: {}, label="gated") + assert AsfrFertilityStep(asfr=lambda year: {}).label == ( + ASFR_FERTILITY_LABEL + ) + assert ASFR_FERTILITY_LABEL == "not gated, report-only" + + +# -------------------------------------------------------------------------- +# 6. The gated candidate-16 law is untouched +# -------------------------------------------------------------------------- +def test_the_step_does_not_reach_the_gated_fertility_law(): + source = Path(fertility.__file__).read_text(encoding="utf-8") + tree = ast.parse(source) + imported = set() + for node in ast.walk(tree): + if isinstance(node, ast.ImportFrom): + imported.add(node.module) + imported.update(f"{node.module}.{a.name}" for a in node.names) + elif isinstance(node, ast.Import): + imported.update(alias.name for alias in node.names) + forbidden = ( + "populace_dynamics.models", + "populace_dynamics.engine.marital", + "populace_dynamics.engine.steps.apply_fertility", + ) + assert not [ + name + for name in imported + if name and any(name.startswith(bad) for bad in forbidden) + ] + assert ( + "populace_dynamics.engine.steps.materialize_maternal_births" + in imported + ) diff --git a/tests/baselines/test_interpolation.py b/tests/baselines/test_interpolation.py new file mode 100644 index 00000000..e1c34e75 --- /dev/null +++ b/tests/baselines/test_interpolation.py @@ -0,0 +1,129 @@ +"""Properties of the mean-preserving monotone ungrouping (INVENTED data). + +Every band edge and band total below is INVENTED or drawn by Hypothesis; +nothing is read from a committed file. + +Invariants (``baselines.interpolation`` module docstring): + +* band sums are preserved: the single-year values of every band add up + to its total; +* every single-year value is non-negative; +* homogeneity: scaling every total by ``c >= 0`` scales every value by + ``c``; +* bands with equal per-year means give a flat schedule. +""" + +from __future__ import annotations + +import numpy as np +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics.baselines.interpolation import ungroup_band_totals + +TOTAL = st.floats( + min_value=0.0, max_value=1e3, allow_nan=False, allow_infinity=False +) + + +@st.composite +def banded_schedules(draw): + """INVENTED bands: 1-12 widths of 1-10 years and their totals.""" + widths = draw(st.lists(st.integers(1, 10), min_size=1, max_size=12)) + start = draw(st.integers(-20, 60)) + edges = np.concatenate(([start], start + np.cumsum(widths))).tolist() + totals = draw(st.lists(TOTAL, min_size=len(widths), max_size=len(widths))) + return edges, totals + + +def _band_sums(edges, values): + return [ + float(values[lower - edges[0] : upper - edges[0]].sum()) + for lower, upper in zip(edges[:-1], edges[1:], strict=True) + ] + + +@settings(max_examples=300, deadline=None) +@given(schedule=banded_schedules()) +def test_band_sums_are_preserved_and_values_are_nonnegative(schedule): + edges, totals = schedule + values = ungroup_band_totals(edges, totals) + assert values.shape == (edges[-1] - edges[0],) + assert np.all(np.isfinite(values)) + assert np.all(values >= 0.0) + scale = max(max(totals), 1.0) + np.testing.assert_allclose( + _band_sums(edges, values), totals, rtol=1e-12, atol=1e-12 * scale + ) + for lower, upper, total in zip(edges[:-1], edges[1:], totals, strict=True): + if total == 0.0: + band = values[lower - edges[0] : upper - edges[0]] + assert np.all(band == 0.0) + + +@settings(max_examples=150, deadline=None) +@given( + schedule=banded_schedules(), + factor=st.floats(min_value=0.0, max_value=1e3, allow_nan=False), +) +def test_ungrouping_is_homogeneous_of_degree_one(schedule, factor): + edges, totals = schedule + base = ungroup_band_totals(edges, totals) + scaled = ungroup_band_totals(edges, [factor * t for t in totals]) + scale = max(max(totals), 1.0) * max(factor, 1.0) + np.testing.assert_allclose( + scaled, factor * base, rtol=1e-9, atol=1e-9 * scale + ) + + +@settings(max_examples=100, deadline=None) +@given( + widths=st.lists(st.integers(1, 10), min_size=1, max_size=10), + mean=st.floats(min_value=1e-6, max_value=1e3, allow_nan=False), +) +def test_equal_per_year_means_give_a_flat_schedule(widths, mean): + edges = np.concatenate(([0], np.cumsum(widths))).tolist() + values = ungroup_band_totals(edges, [mean * w for w in widths]) + np.testing.assert_allclose(values, mean, rtol=1e-9) + + +def test_invented_five_year_hump_is_smooth_and_band_exact(): + # INVENTED rates per woman in five-year bands 15-19 ... 40-44. + rates = [0.01, 0.06, 0.09, 0.09, 0.05, 0.01] + edges = list(range(15, 46, 5)) + totals = [5 * rate for rate in rates] + values = ungroup_band_totals(edges, totals) + np.testing.assert_allclose(_band_sums(edges, values), totals, rtol=1e-12) + # Smoother than the uniform spread: no jump between adjacent single + # years is as large as the largest jump between band means. + assert np.abs(np.diff(values)).max() < max(np.abs(np.diff(rates))) + + +@pytest.mark.parametrize( + ("edges", "totals", "match"), + [ + ([0], [], "at least two"), + ([0, 5, 5], [1.0, 1.0], "strictly increasing"), + ([0.0, 5.0], [1.0], "integers"), + ([0, 5], [1.0, 2.0], "one value per band"), + ([0, 5], [-1.0], "non-negative"), + ([0, 5], [float("nan")], "finite"), + ], +) +def test_invalid_schedules_are_refused(edges, totals, match): + with pytest.raises(ValueError, match=match): + ungroup_band_totals(edges, totals) + + +def test_all_zero_totals_return_zeros(): + assert np.all(ungroup_band_totals([0, 5, 10], [0.0, 0.0]) == 0.0) + + +def test_a_band_absorbed_by_the_cumulative_is_spread_uniformly(): + # Hypothesis counterexample: 1.0 + 4.675e-208 == 1.0 in the + # cumulative, so the tiny band's interpolated values were all zero. + tiny = 4.675152378505037e-208 + values = ungroup_band_totals([0, 1, 3], [1.0, tiny]) + assert values[0] == 1.0 + np.testing.assert_allclose(values[1:], tiny / 2, rtol=1e-12) diff --git a/tests/baselines/test_invented_smoke.py b/tests/baselines/test_invented_smoke.py new file mode 100644 index 00000000..b66f7e11 --- /dev/null +++ b/tests/baselines/test_invented_smoke.py @@ -0,0 +1,322 @@ +"""Smoke test: the invented Track A projection under every baseline. + +INVENTED DATA - NOT A COMPARISON. The cohorts are the invented +generator's people (``cola_track_a.invented``, seed 20260922, the dry +runs' default), read as the 2011 wave (row R0) and the 2009 wave (row +R6), and the oracle parameter bundle the AWI is spliced into is +INVENTED. Each baseline's committed inputs (COLA, AWI, mortality, DI, +claim-age table, read under ``data/external`` through their pinned +accessors) drive the projection to 2035. Nothing about real data is +computed or printed; the assertions are structural (completion, +finiteness, accounting), not values. + +* Under every baseline :func:`project_track_a` (the ``run_track_a`` + projection, one draw) must complete through 2035 with finite weights, + ages consistent with birth years, a closed population (alive counts + fall only by the logged deaths) and claim years no later than the + slice year. +* Under the legacy baseline the full ``run_track_a`` (projection, + benefits and A7 tabulation of rows R0 and R6) must complete with every + tabulated number finite. +* Under the 2026 baselines ``run_track_a`` refuses at the A1 floor + assertion (realized zero COLAs for 2009, 2010 and 2015 cannot take a + one-point cut), the documented scope limit of ``baselines.track_a``. +* The opt-in ASFR fertility step (not gated, report-only) projects an + INVENTED open population from 2025 to 2035 under each 2026 + baseline's ASFR and population mortality: the population changes by + exactly the logged births minus deaths, every expected-birth count is + the sum of the baseline's rates over the exposed women, every mother + is a woman aged 14-49 and total births lie within five Poisson + standard errors of the expected total. The legacy baseline, which + supplies no fertility, refuses rather than drawing zero births. +""" + +from __future__ import annotations + +import math +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from populace_dynamics.baselines import ( + BASELINE_NAMES, + BaselineGapError, + build_track_a_inputs, + get_baseline, + project_track_a, + track_a_config_for, +) +from populace_dynamics.baselines.fertility import AsfrFertilityStep +from populace_dynamics.cola_track_a import run_track_a +from populace_dynamics.cola_track_a.config import TrackAConfig +from populace_dynamics.engine.loop import ( + MaritalStepResult, + PeriodModules, + ProjectionEngine, +) +from populace_dynamics.engine.steps import advance_age +from populace_dynamics.ss.params import SSAParameters + +ROOT = Path(__file__).resolve().parents[2] +DATA_EXTERNAL = ROOT / "data" / "external" +END_YEAR = 2035 +START_YEARS = {2011: 2010, 2009: 2008} + + +def invented_base_params() -> SSAParameters: + """INVENTED oracle parameters (4 percent wage growth from 1951).""" + return SSAParameters( + nawi={ + year: 2_800.0 * 1.04 ** (year - 1951) for year in range(1951, 2061) + }, + wage_base={1937: 3_000.0, 1975: 14_100.0, 1990: 51_300.0}, + pia_factors=(0.9, 0.32, 0.15), + fra_months_by_birth_year=[(1900, 792), (1955, 804)], + early_monthly_rates=(5 / 900, 5 / 1200), + early_first_bracket_months=36, + pe_us_revision="INVENTED", + delayed_credit_by_birth_year=[(1900, 0.08)], + ) + + +@pytest.fixture(scope="module", params=BASELINE_NAMES) +def projected(request): + """One invented projection to 2035 under the named baseline.""" + baseline = get_baseline(request.param) + config = track_a_config_for( + TrackAConfig( + reference_year=END_YEAR, draw_indices=(0,), rows=("R0", "R6") + ), + baseline, + ) + inputs = build_track_a_inputs( + config, + baseline, + data_provenance="invented", + base_params=invented_base_params(), + ) + death_log = {} + results = project_track_a(inputs, config, death_log=death_log) + return baseline, config, inputs, results, death_log + + +def test_the_projection_completes_to_2035_with_finite_state(projected): + baseline, config, inputs, results, death_log = projected + assert set(results) == set(START_YEARS) + for wave, result in results.items(): + years = [int(frame["year"].iloc[0]) for frame in result.slices] + assert years == list(range(START_YEARS[wave], END_YEAR + 1)) + alive = [len(frame) for frame in result.slices] + assert alive[0] > alive[-1] > 0 + deaths = death_log[wave] + for index in range(1, len(result.slices)): + year = years[index] + previous = set(result.slices[index - 1]["person_id"]) + current = set(result.slices[index]["person_id"]) + assert current <= previous, (wave, year) + died = previous - current + logged = set(deaths.get(year, {"person_id": []})["person_id"]) + assert died == logged, (wave, year) + for frame in result.slices: + year = int(frame["year"].iloc[0]) + weight = frame["weight"].to_numpy(dtype=np.float64) + assert np.isfinite(weight).all() and (weight > 0).all() + age = frame["age"].to_numpy(dtype=np.int64) + assert (age == year - frame["birth_year"].to_numpy()).all() + claimed = frame["claim_year"].dropna().astype(int) + assert (claimed <= year).all() + for column in frame.select_dtypes("number").columns: + values = frame[column].dropna().to_numpy(dtype=np.float64) + assert np.isfinite(values).all(), (wave, year, column) + assert inputs.provenance["baseline"]["name"] == baseline.name + assert config.reference_year == END_YEAR + + +def test_the_projection_uses_the_baselines_mortality(projected): + baseline, config, inputs, results, death_log = projected + model = inputs.population_mortality + for year in range(2009, END_YEAR + 1): + frame = results[2011].slices[-1][["age", "sex"]] + expected = np.minimum(frame["age"].to_numpy(), 120) + qx = baseline.qx(year) + observed = model.probabilities_for_year(frame, year) + sexes = frame["sex"].to_numpy() + assert np.array_equal( + observed, + np.where( + sexes == "male", qx["male"][expected], qx["female"][expected] + ), + ) + + +def test_legacy_run_track_a_completes_to_2035_with_finite_tables(): + baseline = get_baseline("tr2008_intermediate") + config = TrackAConfig( + reference_year=END_YEAR, draw_indices=(0,), rows=("R0", "R6") + ) + inputs = build_track_a_inputs( + config, + baseline, + data_provenance="invented", + base_params=invented_base_params(), + ) + result = run_track_a(inputs, config=config) + assert result["data_provenance"] == "invented" + assert result["parameter_consistency"]["consistent"] + assert result["inputs_provenance"]["baseline"]["name"] == baseline.name + + def non_finite(value, path=""): + if isinstance(value, float): + return [] if math.isfinite(value) else [path] + if isinstance(value, dict): + return [ + bad + for key, item in value.items() + for bad in non_finite(item, f"{path}/{key}") + ] + if isinstance(value, list | tuple): + return [ + bad + for index, item in enumerate(value) + for bad in non_finite(item, f"{path}[{index}]") + ] + return [] + + for row in ("R0", "R6"): + record = result["rows"][row] + assert record["status"] == "tabulated", record["status"] + assert record["tabulation"] is not None + assert non_finite(record["tabulation"]) == [] + + +@pytest.mark.parametrize("name", ["tr2026_intermediate", "cbo2026_long_term"]) +def test_run_track_a_refuses_a_one_point_cut_on_realized_zero_colas(name): + baseline = get_baseline(name) + config = track_a_config_for( + TrackAConfig(reference_year=END_YEAR, draw_indices=(0,), rows=("R0",)), + baseline, + ) + inputs = build_track_a_inputs( + config, + baseline, + data_provenance="invented", + base_params=invented_base_params(), + ) + zero_years = [ + year + for year in range(2009, END_YEAR + 1) + if inputs.baseline.rate_for_determination_year(year) == 0.0 + ] + assert zero_years == [2009, 2010, 2015] + with pytest.raises(ValueError, match="A1 floor assertion"): + run_track_a(inputs, config=config) + + +# --------------------------------------------------------------------------- +# The opt-in ASFR fertility step on an INVENTED open population +# --------------------------------------------------------------------------- +OPEN_START = 2025 + + +def invented_open_population(size: int = 3_000) -> pd.DataFrame: + """INVENTED people in 2025: ages 0-89 spread evenly, sexes alternating.""" + ids = np.arange(1, size + 1, dtype=np.int64) + ages = (7 * ids) % 90 + return pd.DataFrame( + { + "person_id": ids, + "year": OPEN_START, + "age": ages, + "birth_year": OPEN_START - ages, + "sex": np.where(ids % 2 == 0, "female", "male"), + "weight": 1.0, + "household_id": ids, + } + ) + + +def open_population_modules(baseline, births, expected, deaths): + """Mortality from the baseline's model, aging and ASFR births only.""" + model = baseline.population_mortality(range(OPEN_START + 1, END_YEAR + 1)) + + def mortality(frame, context, rng): + died = rng.random(len(frame)) < model(frame, context) + deaths[context.year] = int(died.sum()) + return frame.loc[~died].reset_index(drop=True) + + def keep(frame, context, rng): + return frame + + def marital(frame, context, rng): + return MaritalStepResult( + sim_years=frame[["person_id"]].copy(), + births=pd.DataFrame(columns=["parent_person_id", "birth_year"]), + ) + + def household(frame, context, marital, rng): + return frame + + return PeriodModules( + mortality=mortality, + aging=advance_age, + marital_core=marital, + fertility=AsfrFertilityStep( + asfr=baseline.asfr, birth_log=births, expected_log=expected + ), + disability=keep, + earnings=keep, + claiming=keep, + household_composition=household, + ) + + +@pytest.mark.parametrize("name", BASELINE_NAMES) +def test_the_asfr_step_projects_an_open_population_to_2035(name): + baseline = get_baseline(name) + births, expected, deaths = {}, {}, {} + engine = ProjectionEngine( + open_population_modules(baseline, births, expected, deaths) + ) + initial = invented_open_population() + if name == "tr2008_intermediate": + with pytest.raises(BaselineGapError, match="no age-specific"): + engine.project(initial, end_year=END_YEAR, draw_index=0) + return + result = engine.project(initial, end_year=END_YEAR, draw_index=0) + years = list(range(OPEN_START + 1, END_YEAR + 1)) + assert [int(frame["year"].iloc[0]) for frame in result.slices] == [ + OPEN_START, + *years, + ] + for index, year in enumerate(years, start=1): + frame = result.slices[index] + logged = births[year] + assert len(frame) == ( + len(result.slices[index - 1]) - deaths[year] + len(logged) + ), year + assert np.isfinite(frame["weight"].to_numpy(dtype=float)).all() + assert (frame["age"] == year - frame["birth_year"]).all(), year + born = frame[frame["birth_year"] == year] + before = frame[frame["birth_year"] < year].set_index("person_id") + assert sorted(born["parent_person_id"]) == sorted( + logged["parent_person_id"] + ) + mothers = before.loc[born["parent_person_id"]] + assert (mothers["sex"] == "female").all() + assert mothers["age"].between(14, 49).all() + assert (born["age"] == 0).all() and born["person_id"].is_unique + rates = baseline.asfr(year) + exposed = before[ + (before["sex"] == "female") & before["age"].between(14, 49) + ] + assert expected[year] == pytest.approx( + sum(rates[int(age)] for age in exposed["age"]), rel=1e-12 + ) + total_expected = sum(expected.values()) + total_births = sum(len(logged) for logged in births.values()) + assert total_births > 0 + assert abs(total_births - total_expected) <= 5.0 * math.sqrt( + total_expected + ) diff --git a/tests/baselines/test_isolation.py b/tests/baselines/test_isolation.py new file mode 100644 index 00000000..2efb6782 --- /dev/null +++ b/tests/baselines/test_isolation.py @@ -0,0 +1,180 @@ +"""No registered path can reach the selectable baselines. + +``TR2008Legacy`` delegates to the constructors the registered projection +tests already call, so the differential tests in +``test_legacy_differential.py`` only show that it passes them the right +arguments. What guarantees that every registered result is unchanged +is narrower and stronger: nothing outside the ``baselines`` package +imports it, apart from the documentation CLI. This module asserts that +by scanning the source, so a later import from a registered runner, a +v1 track module or the engine fails here. +""" + +from __future__ import annotations + +import ast +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[2] +PACKAGE = "populace_dynamics.baselines" +SRC = REPO_ROOT / "src" / "populace_dynamics" +SCRIPTS = REPO_ROOT / "scripts" + +#: The only importer outside the package: it prints input paths and runs +#: no projection. +ALLOWED_IMPORTERS = frozenset({Path("scripts/describe_baseline.py")}) + +#: Entry points of the registered runs and the modules behind them. +REGISTERED_PATHS = ( + Path("scripts/run_track_a_registered.py"), + Path("scripts/run_fra68_registered.py"), + Path("scripts/run_track_u_registered.py"), + Path("scripts/run_track_m_registered.py"), + Path("src/populace_dynamics/cola_track_a"), + Path("src/populace_dynamics/fra68_track"), + Path("src/populace_dynamics/uniform_cut_track_u"), + Path("src/populace_dynamics/min_benefit_track_m"), + Path("src/populace_dynamics/track_a_v2"), + Path("src/populace_dynamics/engine"), + Path("src/populace_dynamics/ss"), + Path("src/populace_dynamics/estimates"), +) + + +def _module_package(path: Path) -> list[str]: + """The dotted package of ``path`` under ``src``, as parts. + + ``src/populace_dynamics/engine/loop.py`` gives + ``["populace_dynamics", "engine"]``; a package ``__init__.py`` gives + its own package. Files outside ``src`` (scripts, test scratch files) + have no package, so a relative import there cannot name ours. + """ + + try: + parts = list(path.resolve().relative_to(REPO_ROOT / "src").parts) + except ValueError: + return [] + return parts[:-1] + + +def _resolve_from(node: ast.ImportFrom, path: Path) -> str | None: + """The absolute module an ``ImportFrom`` names, or None if unknown.""" + + if node.level == 0: + return node.module or "" + package = _module_package(path) + if not package or node.level - 1 > len(package): + return None + base = package[: len(package) - (node.level - 1)] + return ".".join(base + ([node.module] if node.module else [])) + + +def _imports_baselines(path: Path) -> bool: + """Whether ``path`` imports the baselines package, by its syntax tree. + + Absolute and relative imports both count: a relative import is + resolved against the file's package under ``src``. + """ + + tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path)) + for node in ast.walk(tree): + if isinstance(node, ast.Import): + if any( + alias.name == PACKAGE or alias.name.startswith(PACKAGE + ".") + for alias in node.names + ): + return True + elif isinstance(node, ast.ImportFrom): + module = _resolve_from(node, path) + if module is None: + continue + if module == PACKAGE or module.startswith(PACKAGE + "."): + return True + if module == "populace_dynamics" and any( + alias.name == "baselines" for alias in node.names + ): + return True + return False + + +def _python_files(root: Path) -> list[Path]: + if root.is_file(): + return [root] + return sorted(root.rglob("*.py")) + + +def test_registered_paths_exist(): + """A renamed path must not silently drop out of the scan.""" + + missing = [ + str(path) + for path in REGISTERED_PATHS + if not (REPO_ROOT / path).exists() + ] + assert not missing + + +def test_no_registered_path_imports_the_baselines_package(): + importers = [ + str(file.relative_to(REPO_ROOT)) + for path in REGISTERED_PATHS + for file in _python_files(REPO_ROOT / path) + if _imports_baselines(file) + ] + assert not importers + + +def test_only_the_documentation_cli_imports_baselines_from_outside(): + outside = [ + file.relative_to(REPO_ROOT) + for root in (SRC, SCRIPTS) + for file in _python_files(root) + if SRC / "baselines" not in file.parents and _imports_baselines(file) + ] + assert set(outside) == ALLOWED_IMPORTERS + + +def test_the_scan_detects_each_import_form(tmp_path): + """The scanner itself is not vacuous.""" + + forms = { + "plain": f"import {PACKAGE}\n", + "submodule": f"import {PACKAGE}.track_a as t\n", + "from_package": f"from {PACKAGE} import get_baseline\n", + "from_submodule": f"from {PACKAGE}.legacy import TR2008Legacy\n", + "from_parent": "from populace_dynamics import baselines\n", + } + for name, source in forms.items(): + file = tmp_path / f"{name}.py" + file.write_text(source, encoding="utf-8") + assert _imports_baselines(file), name + clean = tmp_path / "clean.py" + clean.write_text( + "from populace_dynamics.data import tr2008\n" + "# populace_dynamics.baselines in a comment is not an import\n", + encoding="utf-8", + ) + assert not _imports_baselines(clean) + + +def test_the_scan_resolves_relative_imports(tmp_path, monkeypatch): + """Relative imports inside ``src`` are caught after resolution.""" + + fake_root = tmp_path + engine = fake_root / "src" / "populace_dynamics" / "engine" + engine.mkdir(parents=True) + top = fake_root / "src" / "populace_dynamics" + monkeypatch.setattr( + __import__(__name__, fromlist=["REPO_ROOT"]), "REPO_ROOT", fake_root + ) + cases = { + engine / "a.py": ("from ..baselines import get_baseline\n", True), + engine / "b.py": ("from ..baselines.legacy import X\n", True), + top / "c.py": ("from . import baselines\n", True), + top / "d.py": ("from .baselines import track_a\n", True), + engine / "e.py": ("from . import steps\n", False), + engine / "f.py": ("from ..data import tr2008\n", False), + } + for file, (source, expected) in cases.items(): + file.write_text(source, encoding="utf-8") + assert _imports_baselines(file) is expected, file.name diff --git a/tests/baselines/test_legacy_differential.py b/tests/baselines/test_legacy_differential.py new file mode 100644 index 00000000..63f5e612 --- /dev/null +++ b/tests/baselines/test_legacy_differential.py @@ -0,0 +1,290 @@ +"""``tr2008_intermediate`` equals the registered tests' constructors. + +Differential tests: every input :class:`TR2008Legacy` supplies is +compared, value for value (exact equality, no tolerance), with the +constructor the registered Track A and FRA-68 entry points call, over +every year, age and sex those tests read: the COLA path for +determination years 2008-2030, the AWI 1975-2030 (and the whole NAWI the +oracle receives), population mortality for projection years 2009-2030, +every claim-age PMF and every DI rate array. The registered runner's +own committed-value checks (``runner._tr2008_value_checks`` and +``runner._committed_value_checks``, which a ``registered_real`` run +enforces) must also pass on the legacy baseline's inputs. + +Artifact tier: reads the committed TR2008, COLA-history, claim-age and +DI inputs under ``data/external`` through their pinned accessors. The +SSA parameter bundle the AWI is spliced into is INVENTED (pre-1975 wage +index, wage base and formula constants); both sides receive the same +one, so the comparison does not depend on it. No projection runs. +""" + +from __future__ import annotations + +import dataclasses +import hashlib +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics.baselines import TR2008Legacy, get_baseline +from populace_dynamics.baselines.base import SEXES +from populace_dynamics.cola_track_a import runner +from populace_dynamics.cola_track_a.config import TrackAConfig +from populace_dynamics.cola_track_a.mortality import ( + Tr2008YearAwareMortality, + load_tr2008_mortality, +) +from populace_dynamics.cola_track_a.runner import ( + TrackAInputs, + load_claiming_pmf, + tr2008_baseline_cola, + tr2008_ssa_parameters, +) +from populace_dynamics.data import tr2008 +from populace_dynamics.engine.di_entitlement_rates import ( + MAX_AGE, + DIEntitlementSpec, + load_di_entitlement_rates, +) +from populace_dynamics.estimates.parameters import load_cola_history +from populace_dynamics.ss.params import SSAParameters + +ROOT = Path(__file__).resolve().parents[2] +DATA_EXTERNAL = ROOT / "data" / "external" +#: The registered tests' years (TrackAConfig defaults: first TR2008 rate +#: year 2008, reference year 2030, earliest opening year 2008). +COLA_YEARS = range(2008, 2031) +AWI_YEARS = range(1975, 2031) +MORTALITY_YEARS = range(2009, 2031) +DI_FIELDS = ( + "incidence", + "recovery_attained", + "death_attained", + "population_reference_death", + "recovery_select", + "death_select", + "recovery_level_factor", + "death_level_factor", +) + + +def invented_base_params() -> SSAParameters: + """INVENTED oracle parameters (4 percent wage growth from 1951).""" + return SSAParameters( + nawi={ + year: 2_800.0 * 1.04 ** (year - 1951) for year in range(1951, 2061) + }, + wage_base={1937: 3_000.0, 1975: 14_100.0, 1990: 51_300.0}, + pia_factors=(0.9, 0.32, 0.15), + fra_months_by_birth_year=[(1900, 792), (1955, 804)], + early_monthly_rates=(5 / 900, 5 / 1200), + early_first_bracket_months=36, + pe_us_revision="INVENTED", + delayed_credit_by_birth_year=[(1900, 0.08)], + ) + + +@pytest.fixture(scope="module") +def legacy() -> TR2008Legacy: + return TR2008Legacy() + + +def test_the_registry_default_is_the_legacy_baseline(): + baseline = get_baseline() + assert isinstance(baseline, TR2008Legacy) + assert baseline == TR2008Legacy() + assert baseline.name == "tr2008_intermediate" + assert baseline.claim_table_max_year == 2008 + with pytest.raises(ValueError, match="2008"): + TR2008Legacy(claim_table_max_year=2025) + + +# --------------------------------------------------------------------------- +# COLA +# --------------------------------------------------------------------------- +def test_cola_equals_tr2008_baseline_cola_for_the_registered_years(legacy): + ours = legacy.cola_rates(2008, 2030) + theirs = tr2008_baseline_cola( + load_cola_history(), + first_year=2008, + last_year=2030, + alternative="intermediate", + ) + assert ours == theirs + assert ours.provenance == theirs.provenance + for year in COLA_YEARS: + (entry,) = tr2008.cola_path(year, year, alternative="intermediate") + assert ours.rate_for_determination_year(year) == round( + entry.percent / 100.0, 10 + ) + + +@settings(max_examples=25, deadline=None) +@given( + first=st.integers(1983, 2010), + span=st.integers(0, 75), +) +def test_cola_equals_the_constructor_for_any_first_and_last(first, span): + last = min(first + span, 2085) + assert TR2008Legacy().cola_rates(first, last) == tr2008_baseline_cola( + load_cola_history(), first_year=first, last_year=last + ) + + +# --------------------------------------------------------------------------- +# AWI +# --------------------------------------------------------------------------- +def test_ssa_parameters_equal_tr2008_ssa_parameters(legacy): + base = invented_base_params() + ours = legacy.ssa_parameters(base) + theirs = tr2008_ssa_parameters(base, alternative="intermediate") + assert ours == theirs + assert ours.nawi == theirs.nawi + assert list(ours.nawi) == list(theirs.nawi) + assert ours.pe_us_revision == theirs.pe_us_revision + for year in AWI_YEARS: + (entry,) = tr2008.awi_path(year, year, alternative="intermediate") + assert ours.nawi[year] == entry.amount + for year in range(1951, 1975): + assert ours.nawi[year] == base.nawi[year] + + +def test_awi_equals_the_tr2008_awi_path(legacy): + path = tr2008.awi_path(1975, 2085, alternative="intermediate") + assert legacy.awi(1975, 2085) == {e.year: e.amount for e in path} + params = legacy.ssa_parameters(invented_base_params()) + assert { + year: params.nawi[year] for year in range(1975, 2086) + } == legacy.awi(1975, 2085) + + +# --------------------------------------------------------------------------- +# Mortality +# --------------------------------------------------------------------------- +def _same_mortality( + ours: Tr2008YearAwareMortality, theirs: Tr2008YearAwareMortality +) -> None: + assert type(ours) is type(theirs) is Tr2008YearAwareMortality + assert set(ours.qx_by_sex) == set(theirs.qx_by_sex) == set(SEXES) + for sex in SEXES: + assert np.array_equal(ours.qx_by_sex[sex], theirs.qx_by_sex[sex]) + assert dict(ours.ratio_by_year) == dict(theirs.ratio_by_year) + assert ours.alternative == theirs.alternative + assert ours.base_year == theirs.base_year + assert tuple(ours.bands) == tuple(theirs.bands) + assert dict(ours.provenance) == dict(theirs.provenance) + + +def test_mortality_equals_load_tr2008_mortality(legacy): + _same_mortality( + legacy.population_mortality(MORTALITY_YEARS), + load_tr2008_mortality( + MORTALITY_YEARS, alternative="intermediate", base_year=2004 + ), + ) + + +def test_qx_is_the_model_probability_at_every_age_and_year(legacy): + model = load_tr2008_mortality(MORTALITY_YEARS) + ages = np.arange(MAX_AGE + 1) + for year in MORTALITY_YEARS: + qx = legacy.qx(year) + for sex in SEXES: + frame = pd.DataFrame({"age": ages, "sex": sex}) + expected = model.probabilities_for_year(frame, year) + assert np.array_equal(qx[sex], expected) + under, over = model.ratio_by_year[year] + ratio = np.where(ages >= 65, over, under) + assert np.array_equal( + qx[sex], np.clip(model.qx_by_sex[sex] * ratio, 0.0, 1.0) + ) + + +# --------------------------------------------------------------------------- +# Claiming and DI +# --------------------------------------------------------------------------- +def test_claim_pmf_equals_load_claiming_pmf(legacy): + ours = legacy.claim_pmf() + theirs = load_claiming_pmf() + assert ours == theirs + assert {year for _, year in ours} == set(range(1998, 2014)) + + +def test_di_rates_equal_the_2008_fit_field_by_field(legacy): + ours = legacy.di_rates() + theirs = load_di_entitlement_rates(DIEntitlementSpec()) + assert ours.spec == theirs.spec == DIEntitlementSpec() + for name in DI_FIELDS: + assert runner._same_values( + getattr(ours, name), getattr(theirs, name) + ), name + assert dict(ours.provenance) == dict(theirs.provenance) + spec = dataclasses.replace(DIEntitlementSpec(), death_mode="explicit") + assert legacy.di_rates(spec).spec == spec + + +# --------------------------------------------------------------------------- +# The registered runner's own checks accept the legacy baseline's inputs +# --------------------------------------------------------------------------- +def test_the_runners_committed_value_checks_pass(legacy): + config = TrackAConfig() + inputs = TrackAInputs( + cohort=None, + params=legacy.ssa_parameters(invented_base_params()), + baseline=legacy.cola_rates( + config.tr2008_first_rate_year, config.reference_year + ), + di_rates=legacy.di_rates(config.di_spec), + population_mortality=legacy.population_mortality( + range( + min(config.start_years.values()) + 1, + config.reference_year + 1, + ) + ), + claiming_pmf=legacy.claim_pmf(), + ) + checks = { + **runner._tr2008_value_checks(inputs, config), + **runner._committed_value_checks(inputs, config), + } + assert set(checks) == { + "cola_path", + "awi", + "mortality_values", + "di_rates", + "claiming_pmf", + } + for name, check in checks.items(): + assert check["consistent"], (name, check) + + +# --------------------------------------------------------------------------- +# Provenance hashes are the files' bytes +# --------------------------------------------------------------------------- +def test_every_recorded_file_hash_is_the_files_hash(legacy): + record = legacy.provenance() + files = record["files_sha256"] + assert "data/external/ssa_cola_history.json" in files + assert "data/external/ssa_claim_ages_2014supplement.json" in files + for relative, digest in files.items(): + path = ROOT / relative + assert path.is_relative_to(DATA_EXTERNAL), relative + assert hashlib.sha256(path.read_bytes()).hexdigest() == digest + di = legacy.di_rates().provenance + assert di["inputs_sha256"] in files.values() + assert di["nchs_2000_sha256"] in files.values() + + +def test_legacy_fertility_is_a_named_gap(legacy): + from populace_dynamics.baselines import BaselineGapError + + with pytest.raises(BaselineGapError, match="no age-specific"): + legacy.asfr(2030) + with pytest.raises(BaselineGapError, match="no fertility"): + legacy.tfr(2030) + sources = legacy.value_sources(2030) + assert sources["asfr"] == sources["tfr"] == "gap_not_supplied" diff --git a/tests/baselines/test_track_a_inputs.py b/tests/baselines/test_track_a_inputs.py new file mode 100644 index 00000000..b83d0870 --- /dev/null +++ b/tests/baselines/test_track_a_inputs.py @@ -0,0 +1,446 @@ +"""``build_track_a_inputs``: the dry runs' inputs, and its refusals. + +Differential tests against the hand-built inputs of the two invented +dry runs (``scripts/track_a_dry_run.py`` and ``scripts/fra68_dry_run. +py``): each script's ``main`` is run up to the point where it hands its +``TrackAInputs`` to the runner, which is replaced by a capture, so the +comparison is with the scripts' own construction code. Under the +default ``tr2008_intermediate`` baseline every input must be equal: +the oracle parameters, the COLA path, the DI rates, the population +mortality, the claim-age table and the invented cohorts (by the seal +``prepare_track_a_cohort`` sets). ``TrackAConfig.as_dict()`` must not +change. + +INVENTED data only: the cohorts are the invented generator's people +(seed 20260922, the dry runs' default) and the oracle parameter bundle +is INVENTED (the scripts' policyengine-us loader is replaced by it, on +both sides). Artifact tier: the baselines read committed inputs under +``data/external`` through their pinned accessors. No PSID file is +opened; no outcome is computed (the runners are never called). +""" + +from __future__ import annotations + +import ast +import dataclasses +import importlib.util +from functools import cache +from pathlib import Path + +import numpy as np +import pytest + +from populace_dynamics.baselines import ( + INVENTED_SEED, + BaselineYearAwareMortality, + CBO2026LongTerm, + TR2008Legacy, + TR2026Intermediate, + build_track_a_inputs, + invented_track_a_cohorts, + project_track_a, + track_a_config_for, +) +from populace_dynamics.baselines.track_a import RUNNER_LABEL_CAVEAT +from populace_dynamics.cola_track_a.config import TrackAConfig +from populace_dynamics.cola_track_a.mortality import Tr2008YearAwareMortality +from populace_dynamics.cola_track_a.runner import _same_values +from populace_dynamics.fra68_track.config import FRA68Config +from populace_dynamics.ss.params import SSAParameters + +ROOT = Path(__file__).resolve().parents[2] +DATA_EXTERNAL = ROOT / "data" / "external" +#: The five hand-built input builders the factory replaces (not edited). +BUILDERS = ( + "scripts/run_track_a_registered.py", + "scripts/run_fra68_registered.py", + "scripts/track_a_dry_run.py", + "scripts/fra68_dry_run.py", + "src/populace_dynamics/track_a_v2/protocol.py", +) +#: The constructor each of them calls, by input. +LEGACY_CONSTRUCTORS = { + "cola": "tr2008_baseline_cola", + "awi": "tr2008_ssa_parameters", + "mortality": "load_tr2008_mortality", + "claiming": "load_claiming_pmf", + "di": "load_di_entitlement_rates", +} +DI_FIELDS = ( + "incidence", + "recovery_attained", + "death_attained", + "population_reference_death", + "recovery_select", + "death_select", + "recovery_level_factor", + "death_level_factor", +) + + +def invented_base_params() -> SSAParameters: + """INVENTED oracle parameters (4 percent wage growth from 1951).""" + return SSAParameters( + nawi={ + year: 2_800.0 * 1.04 ** (year - 1951) for year in range(1951, 2061) + }, + wage_base={1937: 3_000.0, 1975: 14_100.0, 1990: 51_300.0}, + pia_factors=(0.9, 0.32, 0.15), + fra_months_by_birth_year=[(1900, 792), (1955, 804)], + early_monthly_rates=(5 / 900, 5 / 1200), + early_first_bracket_months=36, + pe_us_revision="INVENTED", + delayed_credit_by_birth_year=[(1900, 0.08)], + ) + + +class _Captured(Exception): + """Stops a dry run at the runner call.""" + + +def _script(name: str): + path = ROOT / "scripts" / f"{name}.py" + spec = importlib.util.spec_from_file_location(f"_b2_{name}", path) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +@cache +def _dry_run_inputs(name: str, runner: str): + """The inputs a dry run hands its runner, with INVENTED oracle params.""" + module = _script(name) + captured = {} + + def capture(inputs, **kwargs): + captured["inputs"] = inputs + captured["config"] = kwargs["config"] + raise _Captured + + module.load_ssa_parameters = invented_base_params + setattr(module, runner, capture) + with pytest.raises(_Captured): + module.main(["--output-dir", str(ROOT / "unused"), "--draws", "1"]) + return captured["inputs"], captured["config"] + + +def _assert_same_inputs(ours, theirs) -> None: + assert ours.params == theirs.params + assert ours.baseline == theirs.baseline + assert ours.di_rates.spec == theirs.di_rates.spec + for name in DI_FIELDS: + assert _same_values( + getattr(ours.di_rates, name), getattr(theirs.di_rates, name) + ), name + ours_m, theirs_m = ours.population_mortality, theirs.population_mortality + assert type(ours_m) is type(theirs_m) is Tr2008YearAwareMortality + for sex in ("female", "male"): + assert np.array_equal(ours_m.qx_by_sex[sex], theirs_m.qx_by_sex[sex]) + assert dict(ours_m.ratio_by_year) == dict(theirs_m.ratio_by_year) + assert dict(ours_m.provenance) == dict(theirs_m.provenance) + assert (ours_m.alternative, ours_m.base_year) == ( + theirs_m.alternative, + theirs_m.base_year, + ) + assert ours.claiming_pmf == theirs.claiming_pmf + ours_c, theirs_c = ours.cohorts_by_wave(), theirs.cohorts_by_wave() + assert list(ours_c) == list(theirs_c) + for wave in ours_c: + assert ours_c[wave].seal is not None + assert ours_c[wave].seal == theirs_c[wave].seal, wave + assert ours_c[wave].initial_slice.equals(theirs_c[wave].initial_slice) + provenance = theirs.provenance + record = ours.provenance["baseline"] + assert record["mortality"] == provenance["mortality"] + assert record["di_rates_sha256"] == provenance["di_rates"] + assert record["ssa_parameters"] == provenance["ssa_parameters"] + files = record["files_sha256"] + for file_name, digest in provenance["tr2008_file_sha256"].items(): + assert files[f"data/external/tr2008/{file_name}"] == digest + assert provenance["cola_history_sha256"] in files.values() + assert provenance["claiming_reference_sha256"] in files.values() + + +# --------------------------------------------------------------------------- +# Differential: the legacy factory equals both dry runs' inputs +# --------------------------------------------------------------------------- +@cache +def _legacy_factory_inputs(): + """The factory's inputs for the Track A dry run's configuration.""" + return build_track_a_inputs( + TrackAConfig(draw_indices=(0,)), + TR2008Legacy(), + data_provenance="invented", + base_params=invented_base_params(), + ) + + +def test_track_a_dry_run_inputs_are_the_factorys(): + theirs, config = _dry_run_inputs("track_a_dry_run", "run_track_a") + assert config == TrackAConfig(draw_indices=(0,)) + ours = _legacy_factory_inputs() + _assert_same_inputs(ours, theirs) + assert ours.provenance["baseline"]["invented_cohorts"]["seed"] == ( + INVENTED_SEED + ) + + +def test_fra68_dry_run_inputs_are_the_factorys(): + """FRA-68 projects the same two populations (rows R0 and R6). + + The factory-built Track A cohorts are passed in (they are compared + with the FRA-68 dry run's own cohorts by seal), so the comparison + covers every input without building the invented cohorts again. + """ + theirs, config = _dry_run_inputs("fra68_dry_run", "run_fra68") + assert config == FRA68Config(draw_indices=(0,)) + ours = build_track_a_inputs( + config.track_a_config(), + TR2008Legacy(), + data_provenance="invented", + cohorts=_legacy_factory_inputs().cohorts_by_wave(), + base_params=invented_base_params(), + ) + _assert_same_inputs(ours, theirs) + + +def _called_names(relative: str) -> set[str]: + """Names of every function or method a source file calls.""" + tree = ast.parse((ROOT / relative).read_text(encoding="utf-8")) + names = set() + for node in ast.walk(tree): + if isinstance(node, ast.Call): + if isinstance(node.func, ast.Name): + names.add(node.func.id) + elif isinstance(node.func, ast.Attribute): + names.add(node.func.attr) + return names + + +def test_every_builder_calls_the_constructors_the_legacy_delegates_to(): + """A static guard that the factory still mirrors all five builders. + + The two dry runs are compared value for value above. The two + registered entry points and Track A v2 read PSID cohorts, so they + are checked statically: each builds ``TrackAInputs`` from the same + five constructors :class:`TR2008Legacy` delegates to (and records in + its provenance). A builder that changes constructor fails here, and + the legacy baseline must follow it. + """ + recorded = TR2008Legacy().provenance()["constructors"] + assert { + key: path.rsplit(".", 1)[1] for key, path in recorded.items() + } == LEGACY_CONSTRUCTORS + expected = set(LEGACY_CONSTRUCTORS.values()) + legacy = _called_names("src/populace_dynamics/baselines/legacy.py") + assert expected <= legacy + for relative in BUILDERS: + called = _called_names(relative) + assert "TrackAInputs" in called, relative + assert expected <= called, (relative, expected - called) + + +def test_the_default_configuration_is_not_touched(): + default = TrackAConfig() + before = default.as_dict() + legacy = TR2008Legacy() + assert track_a_config_for(default, legacy) is default + assert default.as_dict() == before == TrackAConfig().as_dict() + assert before["claim_table_max_year"] == 2008 + assert before["tr2008_first_rate_year"] == 2008 + assert before["reference_year"] == 2030 + + +# --------------------------------------------------------------------------- +# The 2026 baselines +# --------------------------------------------------------------------------- +@pytest.fixture(scope="module") +def inputs_2026(): + """Both 2026 baselines' inputs on one set of INVENTED cohorts. + + The two baselines share the claim-age table, so the factory would + build the same cohorts for each; they are built once (and checked + against a fresh build in ``test_shared_cohorts_are_the_factorys``). + """ + config = track_a_config_for( + TrackAConfig(draw_indices=(0,), rows=("R0", "R6")), + TR2026Intermediate(), + ) + first = build_track_a_inputs( + config, + TR2026Intermediate(), + data_provenance="invented", + base_params=invented_base_params(), + ) + out = {"tr2026_intermediate": (TR2026Intermediate(), config, first)} + baseline = CBO2026LongTerm() + assert track_a_config_for(config, baseline) == config + out["cbo2026_long_term"] = ( + baseline, + config, + build_track_a_inputs( + config, + baseline, + data_provenance="invented", + cohorts=first.cohorts_by_wave(), + base_params=invented_base_params(), + ), + ) + return out + + +def test_2026_inputs_come_from_the_baseline(inputs_2026): + for name, (baseline, config, inputs) in inputs_2026.items(): + assert config.claim_table_max_year == 2025 + assert config.as_dict() == { + **TrackAConfig(draw_indices=(0,), rows=("R0", "R6")).as_dict(), + "claim_table_max_year": 2025, + } + model = inputs.population_mortality + assert isinstance(model, BaselineYearAwareMortality) + assert model.years == tuple(range(2009, 2031)) + for year in model.years: + for sex in ("female", "male"): + assert np.array_equal( + model.qx_by_year[year][sex], baseline.qx(year)[sex] + ) + assert inputs.claiming_pmf == baseline.claim_pmf() + assert ( + inputs.baseline.by_determination_year + == baseline.cola_rates(2008, 2030).by_determination_year + ) + assert inputs.baseline.rate_for_determination_year(2025) == 0.028 + assert inputs.params == baseline.ssa_parameters(invented_base_params()) + record = inputs.provenance["baseline"] + assert record["name"] == name + assert record["runner_label_caveat"] == RUNNER_LABEL_CAVEAT + assert record["claim_table_max_year"] == 2025 + assert record["projection_years"] == [2009, 2030] + for cohort in inputs.cohorts_by_wave().values(): + assert cohort.data_provenance == "invented" + assert cohort.source_provenance["seed"] == INVENTED_SEED + + +def test_shared_cohorts_are_the_factorys(inputs_2026): + """CBO's own invented cohort is the shared one (by seal).""" + baseline = CBO2026LongTerm() + assert baseline.claim_pmf() == TR2026Intermediate().claim_pmf() + config = track_a_config_for(TrackAConfig(rows=("R0",)), baseline) + fresh = invented_track_a_cohorts(config, baseline) + assert list(fresh) == [2011] + shared = inputs_2026["cbo2026_long_term"][2].cohort + assert fresh[2011].seal is not None + assert fresh[2011].seal == shared.seal + assert fresh[2011].source_provenance["seed"] == INVENTED_SEED + + +# --------------------------------------------------------------------------- +# Refusals +# --------------------------------------------------------------------------- +def test_registered_real_is_refused_under_a_2026_baseline(): + for baseline in (TR2026Intermediate(), CBO2026LongTerm()): + config = track_a_config_for(TrackAConfig(), baseline) + with pytest.raises(ValueError, match="own registered entry point"): + build_track_a_inputs( + config, baseline, data_provenance="registered_real" + ) + + +def test_registered_real_cohorts_are_never_built_here(): + with pytest.raises(ValueError, match="must pass the cohorts"): + build_track_a_inputs( + TrackAConfig(), + TR2008Legacy(), + data_provenance="registered_real", + base_params=invented_base_params(), + ) + + +def test_inconsistent_requests_are_refused(inputs_2026): + legacy = TR2008Legacy() + base = invented_base_params() + tr2026_baseline, tr2026_config, tr2026_inputs = inputs_2026[ + "tr2026_intermediate" + ] + with pytest.raises(ValueError, match="track_a_config_for"): + build_track_a_inputs( + TrackAConfig(), + tr2026_baseline, + data_provenance="invented", + base_params=base, + ) + with pytest.raises(ValueError, match="tr2008_alternative"): + build_track_a_inputs( + TrackAConfig(tr2008_alternative="low"), + legacy, + data_provenance="invented", + base_params=base, + ) + with pytest.raises(ValueError, match="written by this factory"): + build_track_a_inputs( + tr2026_config, + tr2026_baseline, + data_provenance="invented", + cohorts=tr2026_inputs.cohorts_by_wave(), + base_params=base, + provenance={"baseline": "mine"}, + ) + with pytest.raises(ValueError, match="data_provenance"): + build_track_a_inputs( + TrackAConfig(), legacy, data_provenance="psid", base_params=base + ) + with pytest.raises(TypeError, match="Baseline protocol"): + build_track_a_inputs( + TrackAConfig(), object(), data_provenance="invented" + ) + cohorts = tr2026_inputs.cohorts_by_wave() + with pytest.raises(ValueError, match="no prepared cohort"): + build_track_a_inputs( + tr2026_config, + tr2026_baseline, + data_provenance="invented", + cohorts={2011: cohorts[2011]}, + base_params=base, + ) + with pytest.raises(ValueError, match="has anchor wave"): + build_track_a_inputs( + tr2026_config, + tr2026_baseline, + data_provenance="invented", + cohorts={2011: cohorts[2009], 2009: cohorts[2009]}, + base_params=base, + ) + with pytest.raises(ValueError, match="labelled 'invented'"): + build_track_a_inputs( + TrackAConfig(rows=("R0", "R6")), + legacy, + data_provenance="registered_real", + cohorts=cohorts, + base_params=base, + ) + + +def test_project_track_a_refuses_what_run_track_a_would(inputs_2026): + baseline, config, inputs = inputs_2026["tr2026_intermediate"] + with pytest.raises(ValueError, match="claim rows through 2025"): + project_track_a(inputs, TrackAConfig(rows=("R0", "R6"))) + # dataclasses.replace resets the seal prepare_track_a_cohort set. + unsealed = dataclasses.replace( + inputs, cohort=dataclasses.replace(inputs.cohort) + ) + assert unsealed.cohort.seal is None + with pytest.raises(ValueError, match="seal"): + project_track_a(unsealed, config) + real_label = dataclasses.replace( + inputs, + cohort=dataclasses.replace( + inputs.cohort, data_provenance="registered_real" + ), + ) + with pytest.raises(ValueError, match="INVENTED cohorts only"): + project_track_a(real_label, config) + with pytest.raises(ValueError, match="no cohort for anchor waves"): + project_track_a( + dataclasses.replace(inputs, additional_cohorts=()), config + ) diff --git a/tests/data/test_cbo2026.py b/tests/data/test_cbo2026.py new file mode 100644 index 00000000..958ac44a --- /dev/null +++ b/tests/data/test_cbo2026.py @@ -0,0 +1,305 @@ +"""Artifact invariants for the pinned public CBO2026 baseline inputs.""" + +from __future__ import annotations + +import gzip +import hashlib +import importlib.util +import json +from dataclasses import FrozenInstanceError +from pathlib import Path + +import numpy as np +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics.data import cbo2026 as cbo +from populace_dynamics.data import tr2026 as tr +from populace_dynamics.data.life_table import ( + life_expectancy, + period_life_table, +) + +ROOT = Path(__file__).resolve().parents[2] +DATA_DIR = ROOT / "data" / "external" / "cbo2026" + + +def _payload(name): + return json.loads((DATA_DIR / name).read_text(encoding="utf-8")) + + +def test_hash_pins_and_mutation_refusal(tmp_path, monkeypatch): + cbo.verify_files() + for name in cbo.FILE_SHA256: + (tmp_path / name).write_bytes((DATA_DIR / name).read_bytes()) + cbo.verify_files(tmp_path) + target = tmp_path / "cbo2026_economics.json" + target.write_bytes(target.read_bytes() + b" ") + with pytest.raises(ValueError, match="SHA-256"): + cbo.verify_files(tmp_path) + cbo._data.cache_clear() + monkeypatch.setattr(cbo, "DATA_DIR", tmp_path) + with pytest.raises(ValueError, match="SHA-256"): + cbo.cpiu_growth(2026) + cbo._data.cache_clear() + + +def test_exact_source_coverage_and_positive_inputs(): + demographic = _payload("cbo2026_demographics.json") + economics = _payload("cbo2026_economics.json") + years = {str(y) for y in range(2021, 2100)} + assert set(demographic["asfr"]) == years + assert set(demographic["mortality"]) == years + assert set(demographic["published_summary"]) == { + str(y) for y in range(2026, 2100) + } + assert set(economics["long_term"]) == {str(y) for y in range(1996, 2057)} + assert set(economics["covered"]) == {str(y) for y in range(2026, 2101)} + assert all(entry.amount > 0 for entry in cbo.awi(2024, 2100)) + for year in range(1996, 2101): + assert cbo.cpiu_index(year) > 0 + + +def test_retained_source_pins_and_size_policy(): + sources = _payload("sources.json")["sources"] + retained = [] + for source in sources.values(): + if source["committed_file"] is None: + continue + path = DATA_DIR / source["committed_file"] + retained.append(path.name) + compressed = path.read_bytes() + assert ( + hashlib.sha256(compressed).hexdigest() + == source["compressed_sha256"] + ) + raw = gzip.decompress(compressed) + assert hashlib.sha256(raw).hexdigest() == source["sha256"] + assert len(raw) == source["bytes"] + assert len(raw) < 2_000_000 + assert len(compressed) < 500_000 + assert set(retained) == { + path.name for path in (DATA_DIR / "sources").iterdir() + } + + +def test_all_year_mortality_bounds_and_source_terminal_closure(): + for year in range(2021, 2100): + for sex in cbo.SEXES: + q = cbo.mortality(year, sex) + assert q.shape == (120,) + assert np.isfinite(q).all() + assert ((q >= 0) & (q <= 1)).all() + assert q[119] == 1.0 + assert not q.flags.writeable + with pytest.raises(ValueError, match="read-only"): + cbo.mortality(2026, "male")[0] = 0.0 + + +def test_q_semantics_reproduce_every_published_life_expectancy(): + checks = _payload("transcription_check.json") + assert checks["all_passed"] + semantics = checks["mortality_interpretation"] + assert semantics["selected"] == "q" + tolerance = semantics["tolerance_years"] + assert tolerance == 0.0011 + for year, summary in _payload("cbo2026_demographics.json")[ + "published_summary" + ].items(): + for age, field in ( + (0, "life_expectancy_birth"), + (65, "life_expectancy_65"), + ): + q_le = [] + m_le = [] + for sex in cbo.SEXES: + q = cbo.mortality(int(year), sex) + table = period_life_table(q, f0=0.5) + q_le.append(table.ex[age]) + # Differential check: column construction vs recursion. + assert table.ex[age] == pytest.approx( + life_expectancy(q, age, f0=0.5), abs=1e-12 + ) + interpreted_m = q / (1.0 + q / 2.0) + m_le.append(life_expectancy(interpreted_m, age, f0=0.5)) + assert np.mean(q_le) == pytest.approx( + summary[field], abs=tolerance + ) + assert abs(np.mean(m_le) - summary[field]) > tolerance + + +def test_asfr_units_and_published_tfr_rounding(): + for year in range(2021, 2100): + for place in cbo.PLACES: + rates = cbo.asfr(year, place) + assert set(rates) == set(range(14, 50)) + assert all(0 <= value <= 1 for value in rates.values()) + assert sum(cbo.asfr(year).values()) == pytest.approx( + cbo.tfr(year), abs=0.0023 + ) + raw = _payload("cbo2026_demographics.json")["asfr"]["2026"]["all"] + assert cbo.asfr(2026)[30] == raw[30 - 14] / 1000.0 + + +def test_cpi_splices_are_continuous_and_extension_is_explicit(): + for year in (2023, 2024, 2036, 2037, 2056, 2057, 2100): + ratio = cbo.cpiu_index(year) / cbo.cpiu_index(year - 1) + assert ratio == pytest.approx(1.0 + cbo.cpiu_growth(year) / 100) + for year in range(2057, 2101): + assert cbo.cpiu_growth(year) == cbo.cpiu_growth(2056) + assert cbo.real_earnings_growth(year) == cbo.real_earnings_growth(2056) + assert ( + "hold_2056_economic_growth" + in cbo.value_provenance("cpiu_growth", 2057).builder_defaults + ) + assert cbo.cpiw_growth(2057) == cbo.cpiu_growth(2057) + assert ( + "cpiw_equals_cpiu_growth" + in cbo.value_provenance("cpiw_growth", 2057).builder_defaults + ) + + +def test_awi_anchor_bridge_and_covered_growth_identity(): + economics = _payload("cbo2026_economics.json") + entries = {entry.year: entry for entry in cbo.awi(2024, 2100)} + assert entries[2024].amount == economics["awi_anchor"]["amount"] + assert entries[2024].amount == tr.awi(2024, 2024)[0].amount + assert entries[2024].source == "tr2026_vi_g1_actual" + for year in (2025, 2026): + nominal_growth = (1 + cbo.real_earnings_growth(year) / 100) * ( + 1 + cbo.cpiu_growth(year) / 100 + ) + assert entries[year].amount == pytest.approx( + entries[year - 1].amount * nominal_growth + ) + assert entries[year].source == "derived_cbo2026_awi_bridge" + for year in range(2027, 2101): + current = economics["covered"][str(year)] + previous = economics["covered"][str(year - 1)] + ratio = ( + current["covered_earnings_trillions"] + / current["total_workers_thousands"] + ) / ( + previous["covered_earnings_trillions"] + / previous["total_workers_thousands"] + ) + assert entries[year].amount / entries[year - 1].amount == ( + pytest.approx(ratio) + ) + assert entries[year].source.startswith("derived_") + + +@pytest.mark.parametrize( + "accessor,args", + [ + (cbo.asfr, (2020,)), + (cbo.asfr, (2100,)), + (cbo.asfr, (2026, "unknown")), + (cbo.mortality, (2020, "male")), + (cbo.mortality, (2100, "male")), + (cbo.mortality, (2026, "unknown")), + (cbo.tfr, (2100,)), + (cbo.cpiu_growth, (1995,)), + (cbo.cpiu_growth, (2101,)), + (cbo.cpiu_index, (2101,)), + (cbo.cpiw_growth, (2101,)), + (cbo.real_earnings_growth, (2101,)), + (cbo.cola_path, (2023, 2030)), + (cbo.cola_path, (2030, 2029)), + (cbo.awi, (2023, 2030)), + (cbo.awi, (2030, 2029)), + (cbo.value_provenance, ("unknown", 2026)), + ], +) +def test_refusals(accessor, args): + with pytest.raises(ValueError): + accessor(*args) + + +@pytest.mark.parametrize("bad_year", [True, 2026.0, "2026", None]) +def test_year_type_is_strict(bad_year): + with pytest.raises(ValueError, match="integer"): + cbo.cpiu_growth(bad_year) + + +def test_returned_paths_and_metadata_are_immutable(): + cola = cbo.cola_path(2026, 2026)[0] + assert cola.determination_year == 2026 + assert cola.effective_month == "December" + assert cola.source.startswith("derived_") + with pytest.raises(FrozenInstanceError): + cola.percent = 0.0 + provenance = cbo.value_provenance("asfr", 2026, place="native-born") + assert provenance.sha256 + assert provenance.locators + assert "year=2026" in provenance.locators[0] + assert "place_of_birth=native-born" in provenance.locators[0] + assert provenance.source == "cbo2026_asfr_native-born" + with pytest.raises(FrozenInstanceError): + provenance.source = "changed" + rates = cbo.asfr(2026) + original = rates[30] + rates[30] = 0 + assert cbo.asfr(2026)[30] == original + + +@pytest.mark.parametrize( + "series,qualifiers", + [ + ("asfr", {"sex": "male"}), + ("mortality", {"sex": "male", "place": "foreign-born"}), + ("tfr", {"sex": "bad"}), + ("cpiu_growth", {"place": "foreign-born"}), + ], +) +def test_provenance_refuses_irrelevant_group_qualifiers(series, qualifiers): + with pytest.raises(ValueError, match="qualifier"): + cbo.value_provenance(series, 2026, **qualifiers) + + +@settings(deadline=None, max_examples=20) +@given( + first=st.integers(2024, 2099), + width=st.integers(0, 10), +) +def test_path_partition_invariant(first, width): + last = min(2100, first + width) + middle = (first + last) // 2 + for accessor in (cbo.awi, cbo.cola_path): + full = accessor(first, last) + assert len(full) == last - first + 1 + if middle < last: + assert full == accessor(first, middle) + accessor(middle + 1, last) + + +@settings(deadline=None, max_examples=20) +@given(year=st.integers(2021, 2099), place=st.sampled_from(cbo.PLACES)) +def test_asfr_conversion_invariant(year, place): + rates = cbo.asfr(year, place) + printed = _payload("cbo2026_demographics.json")["asfr"][str(year)][place] + assert [rates[age] * 1000 for age in range(14, 50)] == pytest.approx( + printed, abs=1e-12 + ) + + +def test_captured_source_reparse_and_cdx_digest_checks(): + script = ROOT / "scripts" / "extract_cbo2026_parameters.py" + spec = importlib.util.spec_from_file_location("extract_cbo2026", script) + extractor = importlib.util.module_from_spec(spec) + # The frozen Source dataclass needs its defining module registered. + import sys + + sys.modules[spec.name] = extractor + spec.loader.exec_module(extractor) + if not extractor.DEFAULT_INPUTS.is_dir(): + pytest.skip( + "captured inputs required for external-source transcription" + ) + outputs = extractor.build_all() + for name, text in outputs.items(): + assert (DATA_DIR / name).read_text(encoding="utf-8") == text + sources = json.loads(outputs["sources.json"])["sources"] + wayback = [source for source in sources.values() if source["cdx_sha1_b32"]] + assert len(wayback) == 4 + assert all(source["cdx_digest_matches"] for source in wayback) diff --git a/tests/data/test_tr2026_artifact.py b/tests/data/test_tr2026_artifact.py new file mode 100644 index 00000000..4af7ed38 --- /dev/null +++ b/tests/data/test_tr2026_artifact.py @@ -0,0 +1,273 @@ +"""TR2026 committed input invariants and differential source checks. + +Artifact tier: this module reads data/external/tr2026 inputs only. It +never reads PSID or any blind-test outcomes. +""" + +import csv +import gzip +import hashlib +import io +import json +from dataclasses import FrozenInstanceError +from pathlib import Path + +import numpy as np +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics.data import tr2026 +from populace_dynamics.data.life_table import life_expectancy_profile + +DATA_DIR = Path(__file__).resolve().parents[2] / "data" / "external" / "tr2026" + + +def _artifact(name): + return json.loads((DATA_DIR / name).read_text()) + + +def test_hash_pins_and_first_load_verification(tmp_path, monkeypatch): + tr2026.verify_files() + for name in tr2026.FILE_SHA256: + (tmp_path / name).write_bytes((DATA_DIR / name).read_bytes()) + target = tmp_path / "tr2026_parameters.json" + target.write_bytes(target.read_bytes() + b" ") + with pytest.raises(ValueError, match="sha256"): + tr2026.verify_files(tmp_path) + monkeypatch.setattr(tr2026, "DATA_DIR", tmp_path) + tr2026._data.cache_clear() + tr2026._table.cache_clear() + with pytest.raises(ValueError, match="sha256"): + tr2026.tfr(2026) + tr2026._data.cache_clear() + tr2026._table.cache_clear() + + +def test_source_payloads_are_pinned(): + for record in _artifact("sources.json")["sources"].values(): + stored = record["committed_file"] + if stored: + raw = (DATA_DIR / stored).read_bytes() + if stored.endswith(".gz"): + raw = gzip.decompress(raw) + assert len(raw) == record["bytes"] + assert hashlib.sha256(raw).hexdigest() == record["sha256"] + + +def test_all_coverage_and_published_examples(): + assert len(tr2026.cola_path(1975, 2100)) == 126 + assert len(tr2026.awi(1970, 2100)) == 131 + assert tr2026.cola_path(2025, 2026)[0].percent == 2.8 + assert tr2026.cola_path(2026, 2026)[0].percent == 2.7 + assert tr2026.cpiw_growth(2026) == 2.62 + assert tr2026.awi(2025, 2025)[0].amount == 72025.07 + assert tr2026.tfr(2026) == 1.59 + assert tr2026.tfr(2050) == 1.75 + assert tr2026.asadr(2026, "total") == 769.7 + for year in range(1960, 2101): + assert np.isfinite(tr2026.cpiw_growth(year)) + for year in range(1940, 2101): + assert tr2026.tfr(year) > 0 + for group in ("total", "under_65", "65_and_over"): + assert tr2026.asadr(year, group) > 0 + assert all(entry.amount > 0 for entry in tr2026.awi(1970, 2100)) + + +def test_q_reparse_matches_all_published_cells_and_terminal_age(): + for sex, code in (("male", "M"), ("female", "F")): + for variant in ("Hist", "Alt2"): + raw = ( + DATA_DIR + / "sources" + / f"DeathProbsE_{code}_{variant}_TR2026.csv" + ).read_text() + rows = list(csv.reader(io.StringIO(raw)))[2:] + for row in rows: + if not row: + continue + year = int(row[0]) + expected = np.array(row[1:], dtype=float) + actual = tr2026.death_probability(year, sex) + np.testing.assert_array_equal(actual, expected) + assert actual.shape == (120,) + assert np.all((actual >= 0) & (actual <= 1)) + assert actual[119] == float(row[-1]) + if expected[119] == 1: + assert actual[119] == 1 + assert tr2026.death_probability(2030, "male")[119] == 0.949919 + + +def test_q_arrays_and_path_records_are_immutable(): + q = tr2026.death_probability(2030, "female") + with pytest.raises(ValueError): + q[0] = 0 + with pytest.raises(ValueError): + q.setflags(write=True) + entry = tr2026.awi(2026, 2026)[0] + with pytest.raises(FrozenInstanceError): + entry.amount = 0 + + +def test_recomputed_expectancy_matches_published_tables(): + mortality = _artifact("tr2026_death_probabilities.json") + va4 = { + row[0]: row[1:] + for row in _artifact("tr2026_parameters.json")["tables"]["V.A4"][ + "rows" + ] + } + for sex, birth_column, age65_column in (("male", 0, 2), ("female", 1, 3)): + inputs = mortality["sexes"][sex]["check_inputs_f0_e0_e65"] + for year_text, (f0, e0, e65) in inputs.items(): + year = int(year_text) + ex = life_expectancy_profile( + tr2026.death_probability(year, sex), f0=f0 + ) + assert abs(ex[0] - e0) <= 0.006 + assert abs(ex[65] - e65) <= 0.006 + if year in va4: + assert abs(ex[0] - va4[year][birth_column]) <= 0.051 + assert abs(ex[65] - va4[year][age65_column]) <= 0.051 + checks = _artifact("transcription_check.json") + assert checks["all_passed"] + assert all(check["passed"] for check in checks["checks"]) + + +def test_cola_splice_and_provenance(): + entries = tr2026.cola_path(2035, 2036) + assert entries[0].percent == entries[1].percent == 2.4 + assert entries[0].source == "tr2026_v_c1_projected" + assert entries[1].source == "derived_annual_cpiw_cola" + metadata = tr2026.value_provenance("cola", 2036) + assert metadata.derived_rule == "post_2035_cola_annual_cpiw" + assert metadata.source_file == "lr5b1.html" + assert len(metadata.source_sha256) == 64 + assert tr2026.cola_path(1982, 1983)[0].effective_month == "June" + assert tr2026.cola_path(1982, 1983)[1].effective_month == "December" + assert tr2026.cola_path(2025, 2025)[0].source == "tr2026_v_c1_actual" + assert tr2026.value_provenance("cpiw_growth", 2025).source.endswith( + "estimated" + ) + assert tr2026.value_provenance("tfr", 2024).source.endswith("historical") + assert tr2026.value_provenance("awi", 2025).source.endswith("estimated") + assert tr2026.value_provenance( + "asadr", 2023, group="total" + ).source.endswith("preliminary") + assert tr2026.value_provenance( + "asadr", 2023, group="under_65" + ).source.endswith("historical") + assert tr2026.value_provenance( + "asadr", 2024, group="under_65" + ).source.endswith("preliminary") + assert tr2026.value_provenance("tfr", 2025).source.endswith("provisional") + for series in ("cpiw_growth", "awi", "tfr"): + assert "year 2026" in tr2026.value_provenance(series, 2026).locator + assert ( + "group total" + in tr2026.value_provenance("asadr", 2026, group="total").locator + ) + assert ( + "sex female" + in tr2026.value_provenance( + "death_probability", 2026, sex="female" + ).locator + ) + + +def test_cpi_index_continuity_around_cola_extension_splice(): + rows = _artifact("tr2026_parameters.json")["tables"]["VI.G1"]["rows"] + levels = {row[0]: row[3] for row in rows} + for year in (2035, 2036): + expected = levels[year - 1] * (1 + tr2026.cpiw_growth(year) / 100) + # Printed index levels are rounded to .01 and growth to .01 percent. + assert abs(levels[year] - expected) <= 0.02 + assert tr2026.cola_path(2036, 2036)[0].percent == tr2026.cpiw_growth(2036) + + +@pytest.mark.parametrize("first,last", [(1974, 1975), (2100, 2101)]) +def test_cola_refuses_outside_coverage(first, last): + with pytest.raises(KeyError): + tr2026.cola_path(first, last) + + +@pytest.mark.parametrize( + "call", + [ + lambda: tr2026.awi(1969, 1970), + lambda: tr2026.awi(2101, 2101), + lambda: tr2026.cpiw_growth(1959), + lambda: tr2026.cpiw_growth(2101), + lambda: tr2026.tfr(1939), + lambda: tr2026.tfr(2101), + lambda: tr2026.asadr(1939, "total"), + lambda: tr2026.asadr(2101, "total"), + lambda: tr2026.death_probability(1899, "male"), + lambda: tr2026.death_probability(2101, "female"), + ], +) +def test_refuses_missing_year(call): + with pytest.raises(KeyError): + call() + + +@pytest.mark.parametrize("year", [True, False, 2026.0, "2026", None]) +def test_refuses_noninteger_year(year): + for call in ( + lambda: tr2026.cola_path(year, year), + lambda: tr2026.awi(year, year), + lambda: tr2026.cpiw_growth(year), + lambda: tr2026.tfr(year), + lambda: tr2026.asadr(year, "total"), + lambda: tr2026.death_probability(year, "male"), + ): + with pytest.raises(ValueError, match="integer"): + call() + + +def test_refuses_invalid_selections(): + for call in ( + lambda: tr2026.cola_path(2027, 2026), + lambda: tr2026.awi(2027, 2026), + lambda: tr2026.death_probability(2026, "both"), + lambda: tr2026.asadr(2026, "65+"), + lambda: tr2026.value_provenance("asadr", 2026), + lambda: tr2026.value_provenance("death_probability", 2026), + lambda: tr2026.value_provenance("awi", 2026, sex="male"), + lambda: tr2026.value_provenance("cola", 2026, group="total"), + lambda: tr2026.value_provenance("unknown", 2026), + ): + with pytest.raises(ValueError): + call() + + +@settings(max_examples=25, deadline=None) +@given(st.integers(1975, 2099), st.integers(1, 10)) +def test_cola_path_partition_invariant(first, width): + last = min(2100, first + width) + middle = (first + last) // 2 + assert tr2026.cola_path(first, last) == tr2026.cola_path( + first, middle + ) + tr2026.cola_path(middle + 1, last) + + +@settings(max_examples=25, deadline=None) +@given(st.integers(1970, 2100)) +def test_awi_singleton_and_positive_invariant(year): + (entry,) = tr2026.awi(year, year) + assert entry.year == year + assert entry.amount > 0 + assert entry.source == tr2026.value_provenance("awi", year).source + + +@settings(max_examples=25, deadline=None) +@given(st.integers(1900, 2100), st.sampled_from(["male", "female"])) +def test_q_probability_and_source_boundary_invariant(year, sex): + q = tr2026.death_probability(year, sex) + assert len(q) == 120 + assert np.all(np.isfinite(q)) + assert np.all((q >= 0) & (q <= 1)) + expected = "historical" if year <= 2023 else "alt2" + assert tr2026.value_provenance( + "death_probability", year, sex=sex + ).source.endswith(expected) diff --git a/tests/estimates/test_birth_evidence_artifact.py b/tests/estimates/test_birth_evidence_artifact.py index 9fbe6033..5ea8e00a 100644 --- a/tests/estimates/test_birth_evidence_artifact.py +++ b/tests/estimates/test_birth_evidence_artifact.py @@ -214,6 +214,21 @@ def test_post_review_sources_are_outside_historical_reducer_identity(): Path("src/populace_dynamics/harness/epuf_cells.py"), Path("src/populace_dynamics/harness/epuf_gate.py"), Path("src/populace_dynamics/harness/epuf_run.py"), + Path("src/populace_dynamics/data/tr2026.py"), + Path("src/populace_dynamics/data/cbo2026.py"), + Path("src/populace_dynamics/data/life_table.py"), + Path("src/populace_dynamics/baselines/__init__.py"), + Path("src/populace_dynamics/baselines/asfr.py"), + Path("src/populace_dynamics/baselines/base.py"), + Path("src/populace_dynamics/baselines/cbo2026_long_term.py"), + Path("src/populace_dynamics/baselines/claim_tables.py"), + Path("src/populace_dynamics/baselines/common2026.py"), + Path("src/populace_dynamics/baselines/fertility.py"), + Path("src/populace_dynamics/baselines/interpolation.py"), + Path("src/populace_dynamics/baselines/legacy.py"), + Path("src/populace_dynamics/baselines/realized.py"), + Path("src/populace_dynamics/baselines/tr2026_intermediate.py"), + Path("src/populace_dynamics/baselines/track_a.py"), ) assert reducer.POST_REVIEW_SHARED_SOURCE_BLOBS == { Path( diff --git a/tests/test_baseline_life_table.py b/tests/test_baseline_life_table.py new file mode 100644 index 00000000..41b15cff --- /dev/null +++ b/tests/test_baseline_life_table.py @@ -0,0 +1,239 @@ +"""Unit tests with INVENTED probability schedules, never microdata. + +The independent forward-column and backward-recursion implementations +must agree wherever survivors are numerically positive. Structural +invariants apply to arbitrary schedules, rather than assuming that +conditional remaining lifetime decreases with attained age. +""" + +from __future__ import annotations + +from dataclasses import FrozenInstanceError + +import numpy as np +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics.data.life_table import ( + RADIX, + life_expectancy, + life_expectancy_profile, + period_life_table, +) + +PROBABILITY = st.floats(min_value=0.0, max_value=0.95, allow_subnormal=False) +SEPARATION = st.floats(min_value=0.0, max_value=1.0, allow_subnormal=False) + + +@st.composite +def probability_schedules(draw): + """INVENTED schedules with a positive final probability.""" + q = draw(st.lists(PROBABILITY, min_size=2, max_size=120)) + q[-1] = draw( + st.floats(min_value=0.05, max_value=1.0, allow_subnormal=False) + ) + return np.asarray(q) + + +def test_invented_closed_table_with_known_columns(): + table = period_life_table([0.1, 0.2, 1.0], f0=0.25, radix=1000) + np.testing.assert_allclose(table.lx, [1000, 900, 720]) + np.testing.assert_allclose(table.dx, [100, 180, 720]) + np.testing.assert_allclose(table.Lx, [975, 810, 360]) + np.testing.assert_allclose(table.Tx, [2145, 1170, 360]) + np.testing.assert_allclose(table.ex, [2.145, 1.3, 0.5]) + np.testing.assert_allclose( + life_expectancy_profile(table.qx, f0=0.25), table.ex + ) + + +@pytest.mark.parametrize("q", [0.1, 0.25, 0.5, 1.0]) +def test_invented_constant_probability_has_geometric_lifetime(q): + # Geometric waiting time to death, less a half-year separation. + expected = 1.0 / q - 0.5 + profile = life_expectancy_profile([q] * 120, f0=0.5) + np.testing.assert_allclose(profile, expected) + assert life_expectancy([q] * 120, 119) == pytest.approx(expected) + + +def test_invented_open_closure_matches_explicit_long_geometric_tail(): + q = np.array([0.1, 0.2, 0.25]) + table = period_life_table(q, f0=0.25, radix=1000) + survivors = table.lx[-1] + tail_person_years = [] + for _ in range(500): + tail_person_years.append(survivors * 0.875) + survivors *= 0.75 + assert table.Lx[-1] == pytest.approx(sum(tail_person_years)) + assert table.Tx[-1] == table.Lx[-1] + assert life_expectancy(q, 2) == pytest.approx(3.5) + + +def test_invented_deathless_initial_years_add_whole_years(): + assert life_expectancy([0, 0, 1], 0, f0=0.3) == 2.5 + assert life_expectancy([0, 0, 1], 1) == 1.5 + + +def test_invented_early_extinction_preserves_conditional_recursion(): + q = [1, 0.3, 0.7, 1] + table = period_life_table(q, f0=0.2) + np.testing.assert_array_equal(table.lx[1:], 0) + assert table.ex[0] == pytest.approx(0.8) + assert np.isnan(table.ex[1:]).all() + profile = life_expectancy_profile(q, f0=0.2) + assert np.isfinite(profile).all() + assert profile[0] == pytest.approx(0.8) + assert profile[-1] == 0.5 + + +def test_invented_survivorship_underflow_keeps_recursion_defined(): + q = np.full(120, 0.999) + table = period_life_table(q, f0=0.5) + assert table.lx[-1] == 0 + assert np.isnan(table.ex[-1]) + profile = life_expectancy_profile(q, f0=0.5) + assert np.isfinite(profile).all() + np.testing.assert_allclose(profile, 1 / 0.999 - 0.5) + + +def test_infant_separation_is_required_only_at_birth(): + q = [0.1, 0.2, 1] + profile = life_expectancy_profile(q) + assert np.isnan(profile[0]) + assert np.isfinite(profile[1:]).all() + assert life_expectancy(q, 1) == pytest.approx(1.3) + with pytest.raises(ValueError, match="needs the separation"): + life_expectancy(q, 0) + + +def test_columns_are_read_only_and_input_is_copied(): + q = np.array([0.1, 0.2, 1]) + table = period_life_table(q, f0=0.25) + q[0] = 0.9 + assert table.qx[0] == 0.1 + for field in ("qx", "lx", "dx", "Lx", "Tx", "ex"): + column = getattr(table, field) + assert not column.flags.writeable + with pytest.raises(ValueError, match="read-only"): + column[0] = 0 + with pytest.raises(FrozenInstanceError): + table.f0 = 0.5 + assert not life_expectancy_profile(table.qx).flags.writeable + assert table.radix == RADIX + + +@pytest.mark.parametrize( + "q", + [ + [], + [0.5], + [[0.1, 1]], + [np.nan, 1], + [np.inf, 1], + [-0.01, 1], + [1.01, 1], + [0.1, 0], + ], +) +def test_invalid_probability_schedules_are_refused(q): + with pytest.raises(ValueError): + period_life_table(q, f0=0.5) + with pytest.raises(ValueError): + life_expectancy_profile(q) + with pytest.raises(ValueError): + life_expectancy(q, 1) + + +@pytest.mark.parametrize("f0", [-0.1, 1.1, np.nan, np.inf]) +def test_invalid_infant_separation_is_refused(f0): + q = [0.1, 1] + with pytest.raises(ValueError, match="f0"): + period_life_table(q, f0=f0) + with pytest.raises(ValueError, match="f0"): + life_expectancy_profile(q, f0=f0) + with pytest.raises(ValueError, match="f0"): + life_expectancy(q, 0, f0=f0) + + +@pytest.mark.parametrize("radix", [0, -1, np.nan, np.inf]) +def test_invalid_radix_is_refused(radix): + with pytest.raises(ValueError, match="radix"): + period_life_table([0.1, 1], f0=0.5, radix=radix) + + +@pytest.mark.parametrize( + "age", [-1, 3, 0.5, True, None, np.nan, np.inf, -np.inf, "one"] +) +def test_invalid_age_is_refused(age): + with pytest.raises(ValueError, match="age"): + life_expectancy([0.1, 0.2, 1], age, f0=0.5) + + +@settings(max_examples=60, deadline=None) +@given(q=probability_schedules(), f0=SEPARATION) +def test_survivorship_person_years_and_deaths_obey_invariants(q, f0): + table = period_life_table(q, f0=f0) + assert np.all(table.lx >= 0) + assert np.all(np.diff(table.lx) <= 0) + assert np.all(table.dx >= 0) + assert np.all(table.Lx >= 0) + assert np.all(table.Tx >= 0) + assert np.all(np.diff(table.Tx) <= 0) + np.testing.assert_allclose( + table.lx[1:], table.lx[:-1] * (1 - q[:-1]), rtol=2e-13 + ) + np.testing.assert_allclose( + table.lx[:-1] - table.lx[1:], table.dx[:-1], atol=2e-11 + ) + np.testing.assert_allclose( + table.Tx[:-1] - table.Tx[1:], table.Lx[:-1], atol=2e-9 + ) + + +@settings(max_examples=60, deadline=None) +@given( + q=probability_schedules(), + f0=SEPARATION, + radix=st.floats(min_value=0.01, max_value=1e9, allow_subnormal=False), +) +def test_implementations_agree_and_expectancy_is_radix_independent( + q, f0, radix +): + standard = period_life_table(q, f0=f0) + scaled = period_life_table(q, f0=f0, radix=radix) + profile = life_expectancy_profile(q, f0=f0) + np.testing.assert_allclose(standard.ex, profile, rtol=2e-12) + np.testing.assert_allclose(scaled.ex, profile, rtol=2e-12) + for field in ("lx", "dx", "Lx", "Tx"): + np.testing.assert_allclose( + getattr(scaled, field), + getattr(standard, field) * radix / RADIX, + rtol=2e-12, + ) + for age in (0, len(q) // 2, len(q) - 1): + assert life_expectancy(q, age, f0=f0) == profile[age] + + +@settings(max_examples=60, deadline=None) +@given( + q=probability_schedules(), + f0=SEPARATION, + increase=SEPARATION, +) +def test_higher_mortality_cannot_increase_life_expectancy(q, f0, increase): + higher_q = q + (1 - q) * increase + initial = life_expectancy_profile(q, f0=f0) + higher = life_expectancy_profile(higher_q, f0=f0) + assert np.all(higher <= initial + 2e-13) + + +@settings(max_examples=40, deadline=None) +@given(q=probability_schedules(), first_f0=SEPARATION, second_f0=SEPARATION) +def test_separation_changes_only_birth_expectancy(q, first_f0, second_f0): + first = life_expectancy_profile(q, f0=first_f0) + second = life_expectancy_profile(q, f0=second_f0) + np.testing.assert_array_equal(first[1:], second[1:]) + assert second[0] - first[0] == pytest.approx( + (first_f0 - second_f0) * q[0], abs=2e-13 + ) diff --git a/tests/test_ssa_claim_ages_2026.py b/tests/test_ssa_claim_ages_2026.py new file mode 100644 index 00000000..85303a3a --- /dev/null +++ b/tests/test_ssa_claim_ages_2026.py @@ -0,0 +1,184 @@ +"""Artifact invariants for the supplied Supplement 2026 claiming reference.""" + +from __future__ import annotations + +import hashlib +import html +import importlib.util +import json +import re +import sys +from pathlib import Path + +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from populace_dynamics.claiming import load_claim_age_reference + +ROOT = Path(__file__).resolve().parents[1] +EXTERNAL = ROOT / "data" / "external" +SCRIPT_DIR = ROOT / "scripts" +sys.path.insert(0, str(SCRIPT_DIR)) +spec = importlib.util.spec_from_file_location( + "extract_ssa_claim_ages_2026", + SCRIPT_DIR / "extract_ssa_claim_ages_2026.py", +) +extract = importlib.util.module_from_spec(spec) +spec.loader.exec_module(extract) + +FILE_SHA256 = ( + "1721f82ae6bc865a1e19f22a9578963032e7b30da9ba3f6f53448ab49fd17210" +) + + +def test_pins_and_offline_reparse(): + """Exact captures and every rebuilt output match committed bytes.""" + outputs = extract.build() + for path, raw in outputs.items(): + assert path.read_bytes() == raw + assert hashlib.sha256(outputs[extract.OUT_PATH]).hexdigest() == FILE_SHA256 + for edition, digest in extract.SOURCE_SHA256.items(): + assert ( + hashlib.sha256(extract.read_source(edition)).hexdigest() == digest + ) + + +def test_schema_coverage_and_existing_loader(): + ref = load_claim_age_reference(extract.OUT_PATH) + assert ref.schema_version == "ssa_claim_ages.v1" + assert ref.supplement_year == 2026 + assert ref.years() == list(range(1998, 2026)) + assert ref.raw_columns == extract.RAW_COLUMNS + assert ref.collapsed_categories == extract.COLLAPSED_CATEGORIES + for sex in ("male", "female"): + for year in ref.years(): + row = ref.row(sex, year) + assert sum(row["categories"].values()) == pytest.approx( + row["component_sum"] + ) + assert row["residual"] == pytest.approx( + row["component_sum"] - row["published_total"] + ) + assert abs(row["residual"]) <= ref.validation["sum_tolerance"] + assert ( + row["fra_at"]["share"] + <= row["categories"][f"age{row['fra_at']['at_age']}"] + ) + assert row["applicable_raw_columns"] == [ + key for key, value in row["raw"].items() if value is not None + ] + + +def test_differential_position_parser(): + """Header-keyed parser equals independent positional source extraction.""" + raw = extract.read_source(2026) + text = raw.decode() + start = text.index('id="table6.b5.1"') + table = text[start : text.index("", start)] + source_rows = re.findall( + r']*>(\d{4})(.*?)', table, re.S + ) + document = json.loads(extract.OUT_PATH.read_text()) + assert len(source_rows) == 56 + for index, (year, body) in enumerate(source_rows): + sex = "male" if index < 28 else "female" + tokens = re.findall(r"]*>(.*?)", body, re.S) + tokens = [" ".join(html.unescape(t).split()) for t in tokens] + assert len(tokens) == 15 + row = document["data"][sex][year] + assert row["number_thousands"] == int(tokens[0].replace(",", "")) + assert row["average_age"] == float(tokens[1]) + assert row["published_total"] == float(tokens[2]) + assert list(row["raw"].values()) == [ + None if token == ". . ." else float(token) for token in tokens[3:] + ] + + +@pytest.mark.parametrize( + "old,new,error", + [ + ('id="table6.b5.1"', 'id="missing"', "boundaries"), + (">Year of entitlement<", ">Changed column<", "layout"), + ('id="c3"', 'id="c2"', "layout"), + ('id="c4"', 'id="c20"', "layout"), + ( + 'headers="r1 r2 c3">63.4<', + 'headers="r1 r2 c3">NaN<', + "Unrecognized", + ), + ( + 'headers="r1 r2 c3">63.4<', + 'headers="r1 r2 c3">Infinity<', + "Unrecognized", + ), + ( + 'headers="r1 r2 c3">63.4<', + 'headers="r1 r2 c3">. . .<', + "Missing average", + ), + ('id="r1">Men<', 'id="r1">Unknown<', "sex panel"), + ('headers="r1 r2 c2"', 'headers="r1 r2 absent"', "cell header"), + ('headers="r1 r2 c3"', 'headers="r1 r2 c2"', "Duplicate cell"), + ('headers="r1 c1">1999<', 'headers="r1 c1">1998<', "Duplicate male"), + ( + 'headers="r1 r2 c4 c6">50.8<', + 'headers="r1 r2 c4 c6">bad<', + "Unrecognized", + ), + ( + 'headers="r1 r2 c4 c6">50.8<', + 'headers="r1 r2 c4 c6">101.0<', + "outside", + ), + ], +) +def test_changed_source_layout_refused(old, new, error): + raw = extract.read_source(2026) + # Replace only inside the selected table, since section 6.B reuses ids. + start = raw.index(b'id="table6.b5.1"') + prefix, selected = raw[:start], raw[start:] + assert old.encode() in selected + changed = prefix + selected.replace(old.encode(), new.encode(), 1) + with pytest.raises(ValueError, match=error): + extract.parse_table(changed, 2026) + + +def test_source_tampering_refused(tmp_path): + (tmp_path / "supplement_2026_6b.html").write_bytes(b"INVENTED BAD CAPTURE") + with pytest.raises(ValueError, match="SHA-256 mismatch"): + extract.read_source(2026, tmp_path) + + +def test_missing_published_cell_refused(): + raw = extract.read_source(2026) + start = raw.index(b'id="table6.b5.1"') + prefix, selected = raw[:start], raw[start:] + cell = b'63.4' + assert cell in selected + changed = prefix + selected.replace(cell, b"", 1) + with pytest.raises(ValueError, match="Missing published"): + extract.parse_table(changed, 2026) + + +@settings(max_examples=60, deadline=None) +@given( + st.lists( + st.one_of(st.none(), st.integers(0, 1000)), min_size=12, max_size=12 + ) +) +def test_collapsing_preserves_partition(values): + """INVENTED tenths: collapsing age65/66 preserves all component mass.""" + raw = { + name: None if value is None else value / 10 + for name, value in zip(extract.RAW_COLUMNS, values, strict=True) + } + collapsed = extract._collapse(raw) + expected_tenths = sum(value or 0 for value in values) + assert sum(collapsed.values()) == pytest.approx(expected_tenths / 10) + assert collapsed["age65"] == pytest.approx( + sum(v or 0 for v in values[3:6]) / 10 + ) + assert collapsed["age66"] == pytest.approx( + sum(v or 0 for v in values[6:9]) / 10 + ) diff --git a/tests/tier_counts.json b/tests/tier_counts.json index 3856ea00..a5e8799b 100644 --- a/tests/tier_counts.json +++ b/tests/tier_counts.json @@ -1,8 +1,8 @@ { "schema_version": 1, "counts": { - "unit": 5960, - "artifact": 3361, + "unit": 6029, + "artifact": 3536, "integration_psid": 1341, "reproduction_legacy": 520, "oracle_policyengine": 220