From 1871017301584003a6998626fc421d1defc4d0a3 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Fri, 4 Sep 2026 08:25:40 -0400 Subject: [PATCH 01/14] v2: evaluate the pre-committed nowcast against BLS's published 2025 thresholds BLS published the 2025 SPM thresholds on 2026-08-24 (41,323 / 34,326 / 41,701; growth 4.40-6.33% over the corrected 2024 base). The evaluation the paper committed to in advance, computed by scripts/evaluate_nowcast_2025.py from the pinned artifacts and the corrected 2024 base exactly as committed: committed (amended) nowcast -0.69 / -0.55 / -2.27 MAE 1.17% original (pre-repair) nowcast -0.54 / -0.22 / -2.18 MAE 0.98% replication ratio alone +0.62 / +0.03 / -1.61 MAE 0.75% composite CPI aging alone -2.01 / -1.14 / -2.93 MAE 2.02% all-items CPI-U (status quo) -2.56 / -1.70 / -3.48 MAE 2.58% Signed mean of the committed nowcast (-1.17%) is inside the -0.1 to -1.4 range stated in advance; the ranking the repaired backtest produced held out of sample; the miss is renters (+6.33% actual vs +3.92% nowcast). Our composite tracks BLS's own published FCSUti deflator within 0.7pp in every year 2020-2025. Also folded in: Census SEHSD WP 2026-17 (2026-08-19) revised rates - corrected series 0.1-0.3pp below published for 2019-2023, 12.9 -> 13.0 for 2024 (our microsim had the magnitude right and the 2024 sign wrong; the flagged flat 2023->2024 pair became 12.7 -> 13.0, n.s.); a footnote sizing the fixed-2019 vs chain-linked composite definition difference (<= 0.06pp growth, 0.006pp backtest MAE); the October 2025 CPI placeholder still present at the API while BLS's page shows 2.70% CPI-U growth (undocumented treatment); the pre-commitment OTS proof preserved as SHA256SUMS.precommit(.ots), now a complete Bitcoin attestation (block 961505, 2026-08-07 23:18 UTC), with the post-evaluation checksums stamped separately. New pinned artifacts: data/bls_2025_thresholds.json, data/census_wp2026_17_rates.json, data/evaluation_2025.json. The pre-render guard now runs the evaluation script and re-derives every figure quoted from it. Co-Authored-By: Claude Fable 5.1 --- bibliography/references.bib | 20 +++ data/SHA256SUMS | 3 + data/SHA256SUMS.ots | Bin 840 -> 700 bytes data/SHA256SUMS.precommit | 6 + data/SHA256SUMS.precommit.ots | Bin 0 -> 5231 bytes data/bls_2025_thresholds.json | 39 ++++++ data/census_wp2026_17_rates.json | 29 +++++ data/evaluation_2025.json | 88 +++++++++++++ paper/index.qmd | 185 ++++++++++++++++++++++----- paper/tables/composite_validation.md | 8 ++ paper/tables/evaluation.md | 7 + paper/tables/evaluation_levels.md | 5 + scripts/check_paper.py | 63 ++++++++- scripts/evaluate_nowcast_2025.py | 180 ++++++++++++++++++++++++++ 14 files changed, 599 insertions(+), 34 deletions(-) create mode 100644 data/SHA256SUMS.precommit create mode 100644 data/SHA256SUMS.precommit.ots create mode 100644 data/bls_2025_thresholds.json create mode 100644 data/census_wp2026_17_rates.json create mode 100644 data/evaluation_2025.json create mode 100644 paper/tables/composite_validation.md create mode 100644 paper/tables/evaluation.md create mode 100644 paper/tables/evaluation_levels.md create mode 100644 scripts/evaluate_nowcast_2025.py diff --git a/bibliography/references.bib b/bibliography/references.bib index 72c2054..da51b06 100644 --- a/bibliography/references.bib +++ b/bibliography/references.bib @@ -275,3 +275,23 @@ @techreport{schild2023 month = {August}, url = {https://www.bls.gov/pir/journal/2021_2022_spm_analysis.pdf} } + +@misc{bls2025thresholds, + author = {{U.S. Bureau of Labor Statistics}}, + title = {2025 Research Supplemental Poverty Measure Thresholds}, + year = {2026}, + month = {August}, + note = {Division of Price and Index Number Research. Retrieved September 4, 2026}, + url = {https://www.bls.gov/pir/spm/spm_thresholds_2025.htm} +} + +@techreport{creamer2026revising, + author = {Creamer, John}, + title = {Revising Supplemental Poverty Measure Estimates from 2019 to 2024}, + institution = {U.S. Census Bureau}, + type = {SEHSD Working Paper}, + number = {2026-17}, + year = {2026}, + month = {August}, + url = {https://www.census.gov/library/working-papers/2026/demo/sehsd-wp2026-17.html} +} diff --git a/data/SHA256SUMS b/data/SHA256SUMS index e574113..b76226e 100644 --- a/data/SHA256SUMS +++ b/data/SHA256SUMS @@ -1,4 +1,7 @@ +31b51fa164ca87c1d01a92af2bf33ac539de79ef34f56088eb6bafe1a5c82e5e data/bls_2025_thresholds.json ae656bf9f44eaf1df4f043130550f7f1f7dcdf5c3d312af1900962e01b71ed50 data/bls_cpi_series.json +9f7d34bc18d203308653c940748fe3f16b2cb9576d34ac273ac12dfd3dc5f66c data/census_wp2026_17_rates.json +b0440d42a7b6bab8cfccfc74a416577272ae2cb6446e64bb543edf55147ef8ab data/evaluation_2025.json 259a5b2e0d15646722fe309f741546e364827fab053c73c99d273061fa0b1ba8 data/nowcast_2025.json 262240dad4dbfe3e0eac5339c2f9062fbef15780657d6e144f28d85df54ea33e data/nowcast_rate_impact.json 1162cec353fcf06344616a8bf8b46f87da67e56fa03696152ace34edbc976725 data/replication_results.json diff --git a/data/SHA256SUMS.ots b/data/SHA256SUMS.ots index 24623591dbedb5cbcfb44626201a2ab70a06be27..4aebaf5466fcb42f8bb95978fa31d7dff3014b4c 100644 GIT binary patch delta 487 zcmX@Xwug0sV!i3Y&*9=r_+DjhEzDjackAkYwRyWbC)zH`G3BpYzj~+d2LY}B^ZuMN z$_n=0c*|QR=1URB{|_8(3X|Sm57W}&_#lvJ;`k@Qt?u>C%m3b03OoJc_@Hn;&_mUr z=16{4Qc7^?=2x+`8$QmoPB?R{{P?u{ww9awIX)`PZ$9Dgv^tSZ^Z&`K?kRU|=bNk9 zoz|O`E>S(V@4a*N#aA33S+ZvB@cqE?q%!<$=c#>>lld8~Tm_~!xE^KlOMM=hsqXjO zNvHy7)L~gsy=z4oA(M|pFgYlRAIPr=IqvM)c3f&%g;0a=@oczB8qte<>YKH8PnKrZ z4FY;%)%&OSWh<^E06np)sUs}iEkH?%=i6!v8+luxvGY#I1)Z1soaA-$%8FuFi?D=4 zybY@kO!|2ws`;Jjt83*y!3r{TEx}4+Y*gS&P)T^mVU>1_I{OB_irxV*|fs_ znE&q|>>8&c&dI3WxZP-R>eXDJDb^GJU)MVMZyKjTrH8}6`F`ek-P_)syve%p>Ezpj aR;TWR9K;I_vK?yocIzmAoilk6lQ97G#P09_ delta 643 zcmV-}0(||v1;_@FA%CQw`oQK>;iAb%VP|fdA=1@$7!&k8Mo27wjo7;J{wVMe!msfl z)$9z2NsS~LqtbIp2>i9AUIvhej7I}z4iyo&Rt=u%1Ut& z{?r_8s@#nT@F3yDWb z`h%H1NbB)6ML7uYASak{f(>)=PY?n62PrJ8!ZVc-ZwwGRtNbR{Ge3D=x(M(f>~5ES zyt1RYsB2OP*D&-LwRl)+n)efd6Z^7)iz!XmoUNbCKC9e+YPH-c46LnA!;NAhls1uDOVwyg%O29Y1q*ZVawpLFZ?@VU6L? 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Rounding bound +/- $0.50 = +/- 0.0012 percent.", + "units": "Two-adult, two-child consumer units, nominal 2025 dollars", + "values": { + "owner_with_mortgage": 41323, + "owner_without_mortgage": 34326, + "renter": 41701 + }, + "bls_stated_growth_pct_2025_over_2024": { + "owner_with_mortgage": 5.332, + "owner_without_mortgage": 4.402, + "renter": 6.325 + }, + "bls_chart3_yoy_growth_pct_corrected_series": { + "2020": {"owner_with_mortgage": 2.539, "owner_without_mortgage": 2.995, "renter": 3.686}, + "2021": {"owner_with_mortgage": 3.920, "owner_without_mortgage": 3.193, "renter": 4.129}, + "2022": {"owner_with_mortgage": 9.667, "owner_without_mortgage": 9.209, "renter": 9.365}, + "2023": {"owner_with_mortgage": 8.793, "owner_without_mortgage": 7.495, "renter": 9.053}, + "2024": {"owner_with_mortgage": 6.127, "owner_without_mortgage": 7.490, "renter": 5.343}, + "2025": {"owner_with_mortgage": 5.332, "owner_without_mortgage": 4.402, "renter": 6.325} + }, + "bls_chart4_annual_average_inflation_pct": { + "note": "Year-over-year percent change in annual averages as published on the same BLS page (Chart 4). FCSUti is BLS's own composite price index used to deflate the CE window; it is published only alongside the thresholds, so it is available for backtesting our composite but not for nowcasting.", + "2020": {"cpi_u": 1.233, "c_cpi_u": 1.074, "fcsuti": 2.387}, + "2021": {"cpi_u": 4.698, "c_cpi_u": 4.469, "fcsuti": 3.170}, + "2022": {"cpi_u": 8.003, "c_cpi_u": 7.678, "fcsuti": 7.206}, + "2023": {"cpi_u": 4.116, "c_cpi_u": 3.874, "fcsuti": 6.172}, + "2024": {"cpi_u": 2.950, "c_cpi_u": 2.599, "fcsuti": 3.976}, + "2025": {"cpi_u": 2.699, "c_cpi_u": null, "fcsuti": 3.441} + }, + "corrected_2024_shown_on_page": { + "owner_with_mortgage": 39231, + "owner_without_mortgage": 32879, + "renter": 39220 + } +} diff --git a/data/census_wp2026_17_rates.json b/data/census_wp2026_17_rates.json new file mode 100644 index 0000000..e8b380d --- /dev/null +++ b/data/census_wp2026_17_rates.json @@ -0,0 +1,29 @@ +{ + "source": "Creamer, John. 2026. \"Revising Supplemental Poverty Measure Estimates from 2019 to 2024.\" SEHSD Working Paper 2026-17, U.S. Census Bureau, August 19, 2026. https://www.census.gov/library/working-papers/2026/demo/sehsd-wp2026-17.html (PDF: https://www2.census.gov/library/working-papers/2026/demo/sehsd-wp2026-17.pdf)", + "retrieved": "2026-09-04", + "note": "Transcribed from Table 4 (all people) and Table 3 (tenure rows, 2023 to 2024, corrected series). 'Revised' = the series as originally published under the 2021 methodology; 'corrected' = re-estimated with the corrected thresholds. Percent of people; margins of error are 90-percent, +/- percentage points. Asterisk = statistically significant difference at the 90-percent level; Z = rounds to zero.", + "all_people": { + "2019": {"revised": 11.8, "corrected": 11.7, "difference": -0.1, "significant": true}, + "2020": {"revised": 9.2, "corrected": 9.1, "difference": -0.1, "significant": true}, + "2021": {"revised": 7.8, "corrected": 7.7, "difference": -0.1, "significant": true}, + "2022": {"revised": 12.4, "corrected": 12.1, "difference": -0.3, "significant": true}, + "2023": {"revised": 12.9, "corrected": 12.7, "difference": -0.1, "significant": true}, + "2024": {"revised": 12.9, "corrected": 13.0, "difference": 0.0, "significant": true, "difference_note": "printed as Z* (rounds to zero, significant)"} + }, + "under_18": { + "2019": {"revised": 12.6, "corrected": 12.4}, + "2020": {"revised": 9.7, "corrected": 9.6}, + "2021": {"revised": 5.2, "corrected": 5.1}, + "2022": {"revised": 12.4, "corrected": 12.0}, + "2023": {"revised": 13.7, "corrected": 13.5}, + "2024": {"revised": 13.4, "corrected": 13.4} + }, + "corrected_series_2023_to_2024": { + "all_people": {"2023": 12.7, "2024": 13.0, "change": 0.2, "significant": false, "note": "'not statistically different'; printed change 0.2 from unrounded values"}, + "owner_with_mortgage": {"2023": 5.7, "2024": 6.1, "change": 0.4}, + "owner_without_mortgage_or_rent_free": {"2023": 11.3, "2024": 11.9, "change": 0.6}, + "renter": {"2023": 23.6, "2024": 23.1, "change": -0.5}, + "age_65_plus": {"2023": 14.0, "2024": 15.1, "change": 1.2, "significant": true} + }, + "headline_quote": "corrected methodology yields overall SPM poverty rates which are lower than estimates published in Shrider and Bijou (2025) for 2019 to 2023 by 0.1 to 0.3 percentage points and marginally higher (12.9% to 13.0%) in 2024" +} diff --git a/data/evaluation_2025.json b/data/evaluation_2025.json new file mode 100644 index 0000000..cdf5b4f --- /dev/null +++ b/data/evaluation_2025.json @@ -0,0 +1,88 @@ +{ + "actual_2025": { + "owner_with_mortgage": 41323, + "owner_without_mortgage": 34326, + "renter": 41701 + }, + "corrected_2024_base": { + "owner_with_mortgage": 39230.994457, + "owner_without_mortgage": 32878.594848, + "renter": 39219.893902 + }, + "cpi_u_ratio_pinned": 1.0263126854942315, + "rules": { + "Amended nowcast (50/50 blend, committed)": { + "mae": 0.011711497719319675, + "errors": { + "owner_with_mortgage": -0.006937084908718694, + "owner_without_mortgage": -0.0055355462111496, + "renter": -0.02266186203809073 + }, + "values": { + "owner_with_mortgage": 41036.33884031702, + "owner_without_mortgage": 34135.98684075608, + "renter": 40755.97769114958 + } + }, + "Original nowcast (pre-repair blend)": { + "mae": 0.009801833535956614, + "errors": { + "owner_with_mortgage": -0.0054068595654337415, + "owner_without_mortgage": -0.002193812741272616, + "renter": -0.02180482830116348 + }, + "values": { + "owner_with_mortgage": 41099.572342177584, + "owner_without_mortgage": 34250.695183843076, + "renter": 40791.71685501318 + } + }, + "CE replication growth ratio alone": { + "mae": 0.0075325524985206576, + "errors": { + "owner_with_mortgage": 0.0062332278740329095, + "owner_without_mortgage": 0.0003051209034998781, + "renter": -0.016059308718029186 + }, + "values": { + "owner_with_mortgage": 41580.57567543866, + "owner_without_mortgage": 34336.473580133534, + "renter": 41031.310767149465 + } + }, + "FCSUti-composite CPI aging alone": { + "mae": 0.02024934212514044, + "errors": { + "owner_with_mortgage": -0.020107397691469853, + "owner_without_mortgage": -0.01137621332579919, + "renter": -0.029264415358152274 + }, + "values": { + "owner_with_mortgage": 40492.10200519539, + "owner_without_mortgage": 33935.50010137862, + "renter": 40480.64461514969 + } + }, + "All-Items CPI-U aging (status quo)": { + "mae": 0.02578624847461697, + "errors": { + "owner_with_mortgage": -0.02564510621753069, + "owner_without_mortgage": -0.016963264763512065, + "renter": -0.03475037444280815 + }, + "values": { + "owner_with_mortgage": 40263.26727577298, + "owner_without_mortgage": 33743.718973727686, + "renter": 40251.87463536046 + } + } + }, + "composite_vs_bls_fcsuti_gap_pp": { + "2020": -0.23837588468315607, + "2021": 0.3503990201698066, + "2022": 0.6627784307196025, + "2023": -0.4572813482465854, + "2024": -0.42003871332198894, + "2025": -0.16485359563639967 + } +} diff --git a/paper/index.qmd b/paper/index.qmd index d433c63..73494b7 100644 --- a/paper/index.qmd +++ b/paper/index.qmd @@ -1,12 +1,12 @@ --- title: "Nowcasting Supplemental Poverty Measure thresholds" -subtitle: "A pre-committed 2025 estimate from public microdata" +subtitle: "A pre-committed 2025 estimate from public microdata, and its evaluation" author: - name: Max Ghenis affiliations: - name: PolicyEngine email: max@policyengine.org -date: 2026-07-18 +date: 2026-09-04 bibliography: ../bibliography/references.bib format: html: @@ -42,9 +42,12 @@ abstract: | 0.41). The nowcast was amended once, before any BLS release, after adversarial cross-model review caught a construction error in the composite index; the original values remain in the repository - history. BLS publishes the actual 2025 thresholds around September - 2026; a revision of this paper will report the realized errors, - whatever they are. + history. BLS published the 2025 thresholds on August 24, 2026 — + \$41,323, \$34,326, and \$41,701. The committed nowcast's mean + absolute error was 1.17 percent against 2.58 for CPI-U aging, with + the miss concentrated in renters; the consumption component alone + scored 0.75, in the order the backtest predicted. This revision + reports that evaluation in full. --- # Introduction @@ -88,9 +91,10 @@ costs the price indexation used in microsimulation practice, ours included; a backtest scores replacement rules against five years of corrected actuals. @sec-nowcast applies that rule to data that already exist and commits, before publication, to a -numeric estimate of the 2025 thresholds that BLS will publish around -September 2026. @sec-discussion draws the implications for -poverty measurement infrastructure. +numeric estimate of the 2025 thresholds. BLS published those +thresholds on August 24, 2026, and @sec-evaluation scores the +commitment and every alternative rule against them. @sec-discussion +draws the implications for poverty measurement infrastructure. # How the thresholds are produced {#sec-production} @@ -182,14 +186,21 @@ units, nominal dollars of the threshold year. Every change is within ±1.6 percent, with mixed signs: renter thresholds fall in five of six years, owner thresholds move both ways. 2022 sees the largest downward revision across all tenures. Census -will quantify the effect on poverty rates in a working paper before -the September 2026 annual report. Most published year-to-year rate -comparisons have room to absorb a revision of this size — our -microsimulation puts the level effect near 0.2 percentage points — -but not all: the 2024 SPM rate printed at 12.9 percent, statistically -unchanged from 2023 [@p60-287], and a flat comparison can change -direction under even a small revision. We leave the rate arithmetic -to the Census working paper. +has since quantified the effect on poverty rates +[@creamer2026revising]: the corrected series runs 0.1 to 0.3 +percentage points below the originally published rates for 2019 +through 2023 and marginally above for 2024 (12.9 to 13.0 percent), +with no year-to-year trend materially altered. Our microsimulation +had put the level effect near 0.2 percentage points — the right +magnitude but, for 2024, the wrong sign: it lowered the modeled rate +where Census's re-estimate raised it. A revision with mixed signs +across tenures (owner thresholds up, renter thresholds down in 2024) +resolves through the tenure mix of households near the line, and the +model's mix evidently differs from the survey's. The one comparison +we had flagged as fragile did move: the 2024 rate had printed as +statistically unchanged from 2023 at 12.9 percent [@p60-287]; under +the corrected series it rises from 12.7 to 13.0, still not a +statistically significant change. Our own software did worse. The spm-calculator package, which supplies threshold parameters to the PolicyEngine US microsimulation model, @@ -315,13 +326,18 @@ these files. None is documented in one place elsewhere. 6. **October 2025 CPI does not exist.** The scheduled release covering October 2025 was not published during the federal government shutdown, and the month is absent from the CPI series; 2025 annual - averages must be computed over eleven months. + averages must be computed over eleven months. The API still + returns a placeholder for October 2025 as of September 2026, yet + BLS's own 2025 threshold page reports 2.70 percent CPI-U growth + for the year against the 2.63 percent the eleven-month annual + average implies; the treatment is undocumented. # Projecting thresholds past the published years {#sec-projection} BLS publishes thresholds with roughly a fourteen-month lag: the 2024 -thresholds arrived in September 2025 [@p60-287], and 2025 thresholds -are expected in September 2026. Any analysis of current-law poverty — +thresholds arrived in September 2025 [@p60-287], and the 2025 +thresholds followed on August 24, 2026 [@bls2025thresholds]. Any +analysis of current-law poverty — which for microsimulation models is most analyses — needs thresholds for years BLS has not published. A common approach, and ours until this work, ages the latest published threshold by projected growth in @@ -452,7 +468,14 @@ takes the ratio, per tenure — level biases that persist across adjacent, 80-percent-overlapping windows cancel, and the anchor share cancels exactly. The price component is the FCSUti-composite ratio of 2025 to 2024 annual averages, with 2025 averaged over its eleven -published months. @tbl-nowcast states the estimate. +published months.^[The nowcast's composite ratio chain-links at the +base year: each component's 2025-to-2024 ratio, weighted by budget +share. The backtest composite in @tbl-backtest holds a fixed 2019 +base instead. The two definitions differ by at most 0.06 percentage +points in any year's growth and by 0.006 points of mean absolute +error in the backtest; we note the inconsistency rather than re-run +the committed table under a new definition.] @tbl-nowcast states the +estimate. ::: {#tbl-nowcast} {{< include tables/nowcast.md >}} @@ -500,7 +523,98 @@ its method, components, and hash. The blend's annual signed errors in backtest, averaged across tenures, ranged from −0.1 to −1.4 percent; that range is our stated expectation of the nowcast's accuracy. If the nowcast misses by more than the rules scored alongside it, that result -publishes too. +publishes too. The evaluation follows in @sec-evaluation. + +# Evaluation against the published 2025 thresholds {#sec-evaluation} + +BLS published the 2025 thresholds on August 24, 2026, in late August +rather than the September this paper anticipated [@bls2025thresholds]: +\$41,323 for owners with mortgages, \$34,326 for owners without, and +\$41,701 for renters — growth of 4.40 to 6.33 percent over the +corrected 2024 base, by BLS's own statement. The page publishes whole +dollars; no full-precision workbook had appeared by September 4, and +the half-dollar rounding bound is immaterial at this scale. +@tbl-evaluation scores the committed nowcast, its superseded +original, and every rule from @tbl-backtest against those values, all +from the corrected 2024 base as committed. + +::: {#tbl-evaluation} +{{< include tables/evaluation.md >}} + +Signed percent error of each 2025 projection against BLS's published +thresholds, and mean absolute error across tenures. All rules project +from the corrected 2024 base; the CPI-U rule uses the pinned +eleven-month 2025 annual average. +::: + +::: {#tbl-evaluation-levels} +{{< include tables/evaluation_levels.md >}} + +Published 2025 thresholds and the committed nowcast in dollars, with +BLS's stated growth against the nowcast's. +::: + +Four results. First, the committed nowcast missed by 0.69, 0.55, and +2.27 percent by tenure, a mean absolute error of 1.17 percent; its +signed mean of −1.17 percent falls inside the −0.1 to −1.4 range +stated in advance as our expectation, though the renter miss alone +falls outside it. Second, the status-quo rule did what the backtest +said it would: All-Items CPI-U aging understated every tenure, by +1.70 to 3.48 percent, for a mean absolute error of 2.58 — the fourth +consecutive year it ran low. The blend more than halved that error. +Third, the ranking the repaired backtest produced held out of sample: +the replication growth ratio alone was the most accurate rule at 0.75 +percent and the only one with errors on both sides of zero, the +composite index alone sat between it and CPI-U at 2.02, and the blend +landed between its two components. Fourth, the superseded original +nowcast, built on the erroneous composite, scored 0.98 percent — +better than the amended 1.17 — because its higher price ratio +happened to offset more of the common shortfall. One year cannot +distinguish that from luck, and we do not try; both vintages are +reported because both were committed. + +The miss is a renter story. BLS's renter threshold grew 6.33 percent, +against 5.33 and 4.40 for the two owner groups; every rule, the +replication ratio included, missed renters by the most, and the +nowcast captured 3.92 of those 6.33 points. The replication's own +renter growth of 4.62 percent understated the published figure by +more than its two owner-group errors combined, so whatever moved +renter thresholds in 2025 is something the unimputed replication does +not observe. The in-kind imputations, which include rental +assistance, are the obvious candidate; this paper's data cannot +attribute it, and we leave the diagnosis to a replication that +imputes them. + +The composite index can now be checked against BLS's own. The +threshold page also publishes year-over-year growth in BLS's FCSUti +deflator, which BLS releases only alongside each year's thresholds — +useless for nowcasting, ideal for validation. @tbl-composite-validation +compares them: the static-weight composite tracks BLS's index within +0.7 percentage point in every year from 2020 through 2025, with a +mean absolute gap of 0.4 point and no consistent sign, so the +construction repaired in @sec-projection is sound after the repair. +The same page reports 2025 All-Items CPI-U growth of 2.70 percent +against the 2.63 percent implied by the CPI program's eleven-month +annual average; BLS does not state how it treated the missing October +index, and we do not guess. + +::: {#tbl-composite-validation} +{{< include tables/composite_validation.md >}} + +Year-over-year growth in BLS's published FCSUti deflator against the +paper's static-weight composite, with the two CPI-U annual-average +figures alongside. +::: + +What the result licenses. The pre-commitment did its job: the numbers +scored above are the ones fixed in the repository and its timestamps +before BLS published, and the amended nowcast beat the rule it +replaced by more than half. It does not license re-selecting the rule +for 2026 after the fact. The replication ratio has now led on both +looks — the repaired backtest and the realized year — and if the +packaged method changes for 2026, the change will be committed and +timestamped before the 2026 CE window closes, in the same way, with +this year's evaluation as its stated basis. # Discussion {#sec-discussion} @@ -533,8 +647,9 @@ checks. The projection result has the most direct consequence for microsimulation practice. Aging SPM thresholds by projected CPI-U, as our own model did, understated threshold growth by an average of 1.9 -percent per year over 2020--2024, which flows directly into -understated current-law poverty projections. Effects on modeled +percent per year over 2020--2024 and by 2.6 percent in 2025 out of +sample, which flows directly into understated current-law poverty +projections. Effects on modeled eligibility and poverty transitions near program cliffs are plausible but outside this paper's scope. The blend rule is implementable from public data with a published, tested codebase, and it degrades @@ -585,14 +700,26 @@ tables, verifies the artifact hashes, and re-derives the load-bearing prose figures — the paper does not build if any of them drifts from the data; continuous integration runs the same check on every push. The nowcast's in-advance commitment is externally timestamped, all -before any BLS 2025 publication: the artifact checksum file is -stamped via OpenTimestamps (proof committed alongside it), the -original and amended nowcasts are tagged releases in the repository -history, and the Internet Archive holds a capture of the published -page from August 7, 2026. The history documents one +before BLS's August 24 publication: the artifact checksum file as it +stood on August 7, 2026 (`data/SHA256SUMS.precommit`) is stamped via +OpenTimestamps, and that proof (`SHA256SUMS.precommit.ots`) has +since completed to a Bitcoin attestation — block 961505, mined +August 7, 2026 at 23:18 UTC, with two further calendars confirming +in blocks 961512 and 962551 — so the committed values are anchored +in a public ledger seventeen days before BLS published; the original +and amended nowcasts are tagged releases in the repository history; +and the Internet Archive holds a capture of the published page from +August 7, 2026. The post-evaluation checksum file carries its own, +separate stamp. The history documents one amendment: the composite-index construction error caught in cross-model review on July 18, 2026, with every downstream artifact, -table, and pinned prose figure regenerated in the same change. +table, and pinned prose figure regenerated in the same change. The +evaluation in @sec-evaluation adds three pinned artifacts — BLS's +published 2025 values with their retrieval date, Census's revised +rate series, and the evaluation output — and one script, +`evaluate_nowcast_2025.py`, which the pre-render check now runs +alongside the table generator, so the evaluation tables and every +figure quoted from them are re-derived on each build. # References {.unnumbered} diff --git a/paper/tables/composite_validation.md b/paper/tables/composite_validation.md new file mode 100644 index 0000000..6d9e5b0 --- /dev/null +++ b/paper/tables/composite_validation.md @@ -0,0 +1,8 @@ +| Year | BLS FCSUti | Our composite | Gap (pp) | BLS CPI-U | Our CPI-U | +|---|---:|---:|---:|---:|---:| +| 2020 | 2.39% | 2.15% | -0.24 | 1.23% | 1.23% | +| 2021 | 3.17% | 3.52% | +0.35 | 4.70% | 4.70% | +| 2022 | 7.21% | 7.87% | +0.66 | 8.00% | 8.00% | +| 2023 | 6.17% | 5.71% | -0.46 | 4.12% | 4.12% | +| 2024 | 3.98% | 3.56% | -0.42 | 2.95% | 2.95% | +| 2025 | 3.44% | 3.28% | -0.16 | 2.70% | 2.63% | diff --git a/paper/tables/evaluation.md b/paper/tables/evaluation.md new file mode 100644 index 0000000..8432ea8 --- /dev/null +++ b/paper/tables/evaluation.md @@ -0,0 +1,7 @@ +| Rule | Owners w/ mortgage | Owners w/o mortgage | Renters | Mean abs. error | +|---|---:|---:|---:|---:| +| **Amended nowcast (50/50 blend, committed)** | -0.69% | -0.55% | -2.27% | **1.17%** | +| Original nowcast (pre-repair blend) | -0.54% | -0.22% | -2.18% | 0.98% | +| CE replication growth ratio alone | +0.62% | +0.03% | -1.61% | 0.75% | +| FCSUti-composite CPI aging alone | -2.01% | -1.14% | -2.93% | 2.02% | +| All-Items CPI-U aging (status quo) | -2.56% | -1.70% | -3.48% | 2.58% | diff --git a/paper/tables/evaluation_levels.md b/paper/tables/evaluation_levels.md new file mode 100644 index 0000000..8bbb6c0 --- /dev/null +++ b/paper/tables/evaluation_levels.md @@ -0,0 +1,5 @@ +| Tenure | BLS 2025 threshold | Amended nowcast | Error | BLS-stated growth | Nowcast growth | +|---|---:|---:|---:|---:|---:| +| Owners with mortgages | \$41,323 | \$41,036.34 | -0.69% | 5.33% | 4.60% | +| Owners without mortgages | \$34,326 | \$34,135.99 | -0.55% | 4.40% | 3.82% | +| Renters | \$41,701 | \$40,755.98 | -2.27% | 6.33% | 3.92% | diff --git a/scripts/check_paper.py b/scripts/check_paper.py index cfa5c42..c8478da 100644 --- a/scripts/check_paper.py +++ b/scripts/check_paper.py @@ -33,11 +33,12 @@ def check(label: str, ok: bool) -> None: before = { p: p.read_text() for p in sorted((REPO / "paper" / "tables").glob("*.md")) } -subprocess.run( - [sys.executable, str(REPO / "scripts" / "build_tables.py")], - check=True, - capture_output=True, -) +for script in ("build_tables.py", "evaluate_nowcast_2025.py"): + subprocess.run( + [sys.executable, str(REPO / "scripts" / script)], + check=True, + capture_output=True, + ) for path, old in before.items(): check(f"table drifted: {path.name}", path.read_text() == old) @@ -291,6 +292,58 @@ def mae(rule: str) -> float: "2.6 percent" in QMD and abs(realized_2025 - 0.026) < 0.002, ) +# (d) The September evaluation: every figure quoted from it re-derives +# from data/evaluation_2025.json (written by evaluate_nowcast_2025.py +# from the pinned artifacts + BLS's published 2025 values). +ev = json.loads((REPO / "data" / "evaluation_2025.json").read_text()) +R = ev["rules"] +amended = R["Amended nowcast (50/50 blend, committed)"] +original = R["Original nowcast (pre-repair blend)"] +repl = R["CE replication growth ratio alone"] +comp = R["FCSUti-composite CPI aging alone"] +cpiu = R["All-Items CPI-U aging (status quo)"] +for label, value, literal in [ + ("evaluation amended MAE 1.17", amended["mae"], "1.17"), + ("evaluation original MAE 0.98", original["mae"], "0.98"), + ("evaluation replication MAE 0.75", repl["mae"], "0.75"), + ("evaluation composite MAE 2.02", comp["mae"], "2.02"), + ("evaluation CPI-U MAE 2.58", cpiu["mae"], "2.58"), +]: + check(label, literal in QMD and f"{value:.2%}".rstrip("%") == literal) +check( + "evaluation renter miss -2.27", + "2.27" in QMD and abs(amended["errors"]["renter"] - (-0.0227)) < 0.0001, +) +signed_mean = sum(amended["errors"].values()) / 3 +check( + "evaluation signed mean -1.17 inside stated range", + "−1.17" in QMD and -0.014 <= signed_mean <= -0.001, +) +actual = json.loads((REPO / "data" / "bls_2025_thresholds.json").read_text()) +for literal, value in [ + ("41,323", actual["values"]["owner_with_mortgage"]), + ("34,326", actual["values"]["owner_without_mortgage"]), + ("41,701", actual["values"]["renter"]), +]: + check(f"BLS 2025 literal {literal}", literal in QMD and f"{value:,}" == literal) +check( + "BLS-stated 2025 growth 4.40-6.33", + "4.40 to 6.33" in QMD + and min(actual["bls_stated_growth_pct_2025_over_2024"].values()) == 4.402 + and max(actual["bls_stated_growth_pct_2025_over_2024"].values()) == 6.325, +) +gaps = ev["composite_vs_bls_fcsuti_gap_pp"] +check( + "composite within 0.7pp of BLS FCSUti", + "0.7 percentage point" in QMD and max(abs(g) for g in gaps.values()) < 0.7, +) +census = json.loads((REPO / "data" / "census_wp2026_17_rates.json").read_text()) +c23 = census["corrected_series_2023_to_2024"]["all_people"] +check( + "Census corrected 2023->2024 = 12.7 -> 13.0", + "12.7" in QMD and "13.0" in QMD and c23["2023"] == 12.7 and c23["2024"] == 13.0, +) + if failures: print("PAPER DRIFT CHECK FAILED:") for f in failures: diff --git a/scripts/evaluate_nowcast_2025.py b/scripts/evaluate_nowcast_2025.py new file mode 100644 index 0000000..cc1d317 --- /dev/null +++ b/scripts/evaluate_nowcast_2025.py @@ -0,0 +1,180 @@ +"""Score the pre-committed 2025 nowcast against BLS's published thresholds. + +This is the evaluation the paper committed to in advance: signed +percent error per tenure and mean absolute error for the amended +nowcast, the original (pre-repair) nowcast, and every rule in the +backtest table, all computed from the corrected 2024 base exactly as +pinned in data/threshold_series.json — regardless of any later BLS +revision to 2024. Inputs are the pinned artifacts plus +data/bls_2025_thresholds.json (BLS's 2025 page, retrieved 2026-09-04). + +Also validates the paper's FCSUti composite against BLS's own FCSUti +index growth, which BLS publishes only alongside each year's +thresholds (Chart 4 on the same page). + + uv run python scripts/evaluate_nowcast_2025.py +""" + +from __future__ import annotations + +import json +import subprocess +from pathlib import Path + +REPO = Path(__file__).resolve().parent.parent +DATA = REPO / "data" +OUT = REPO / "paper" / "tables" + +TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") +LABELS = { + "owner_with_mortgage": "Owners with mortgages", + "owner_without_mortgage": "Owners without mortgages", + "renter": "Renters", +} + +actual = json.loads((DATA / "bls_2025_thresholds.json").read_text()) +nowcast = json.loads((DATA / "nowcast_2025.json").read_text()) +cpi = json.loads((DATA / "bls_cpi_series.json").read_text()) +series_doc = json.loads((DATA / "threshold_series.json").read_text()) + +corrected_2024 = None +for seg in series_doc["series"]["bls-corrected-2026-07-17"]["segments"].values(): + if "2024" in seg["years"]: + corrected_2024 = { + t: m["threshold"] for t, m in seg["years"]["2024"].items() + } +assert corrected_2024 is not None + +# The original (pre-repair) nowcast, from the tagged release rather +# than a hand-copied number. +original = json.loads( + subprocess.run( + ["git", "show", "v1.0-original-nowcast:data/nowcast_2025.json"], + cwd=REPO, + check=True, + capture_output=True, + text=True, + ).stdout +) + +cpi_u_ratio = cpi["CUUR0000SA0"]["2025"] / cpi["CUUR0000SA0"]["2024"] + +rules = { + "Amended nowcast (50/50 blend, committed)": nowcast["values"], + "Original nowcast (pre-repair blend)": original["values"], + "CE replication growth ratio alone": { + t: corrected_2024[t] * nowcast["components"][t]["replication_ratio"] + for t in TENURES + }, + "FCSUti-composite CPI aging alone": { + t: corrected_2024[t] * nowcast["components"][t]["price_ratio"] + for t in TENURES + }, + "All-Items CPI-U aging (status quo)": { + t: corrected_2024[t] * cpi_u_ratio for t in TENURES + }, +} + +lines = [ + "| Rule | Owners w/ mortgage | Owners w/o mortgage | Renters " + "| Mean abs. error |", + "|---|---:|---:|---:|---:|", +] +results = {} +for name, values in rules.items(): + errs = {t: values[t] / actual["values"][t] - 1 for t in TENURES} + mae = sum(abs(e) for e in errs.values()) / 3 + results[name] = {"errors": errs, "mae": mae, "values": values} + cells = " | ".join(f"{errs[t]:+.2%}" for t in TENURES) + bold = name.startswith("Amended") + label = f"**{name}**" if bold else name + mae_txt = f"**{mae:.2%}**" if bold else f"{mae:.2%}" + lines.append(f"| {label} | {cells} | {mae_txt} |") +OUT.mkdir(parents=True, exist_ok=True) +(OUT / "evaluation.md").write_text("\n".join(lines) + "\n") +print("wrote paper/tables/evaluation.md") + +# Actual vs nowcast levels, for the reader who wants dollars. +lines = [ + "| Tenure | BLS 2025 threshold | Amended nowcast | Error " + "| BLS-stated growth | Nowcast growth |", + "|---|---:|---:|---:|---:|---:|", +] +for t in TENURES: + a = actual["values"][t] + n = nowcast["values"][t] + g_bls = actual["bls_stated_growth_pct_2025_over_2024"][t] + g_now = (n / corrected_2024[t] - 1) * 100 + lines.append( + f"| {LABELS[t]} | \\${a:,.0f} | \\${n:,.2f} | {n / a - 1:+.2%} " + f"| {g_bls:.2f}% | {g_now:.2f}% |" + ) +(OUT / "evaluation_levels.md").write_text("\n".join(lines) + "\n") +print("wrote paper/tables/evaluation_levels.md") + +# Composite validation: our rebased FCSUti composite vs BLS's FCSUti. +CPI_IDS = { + "food": "CUUR0000SAF", + "apparel": "CUUR0000SAA", + "shelter": "CUUR0000SAH1", + "utilities": "CUUR0000SAH2", + "telephone": "CUUR0000SEED", +} +WEIGHTS = { + "food": 0.30, + "apparel": 0.05, + "shelter": 0.45, + "utilities": 0.12, + "telephone": 0.04, +} +REBASE_YEAR = 2019 + + +def composite(year: int) -> float: + avail = { + c: w + for c, w in WEIGHTS.items() + if str(year) in cpi.get(CPI_IDS[c], {}) + and str(REBASE_YEAR) in cpi.get(CPI_IDS[c], {}) + } + return sum( + w * cpi[CPI_IDS[c]][str(year)] / cpi[CPI_IDS[c]][str(REBASE_YEAR)] + for c, w in avail.items() + ) / sum(avail.values()) + + +lines = [ + "| Year | BLS FCSUti | Our composite | Gap (pp) | BLS CPI-U | Our CPI-U |", + "|---|---:|---:|---:|---:|---:|", +] +gaps = [] +for year in range(2020, 2026): + bls = actual["bls_chart4_annual_average_inflation_pct"][str(year)] + ours = (composite(year) / composite(year - 1) - 1) * 100 + ours_cpiu = ( + cpi["CUUR0000SA0"][str(year)] / cpi["CUUR0000SA0"][str(year - 1)] - 1 + ) * 100 + gap = ours - bls["fcsuti"] + gaps.append(gap) + lines.append( + f"| {year} | {bls['fcsuti']:.2f}% | {ours:.2f}% | {gap:+.2f} " + f"| {bls['cpi_u']:.2f}% | {ours_cpiu:.2f}% |" + ) +(OUT / "composite_validation.md").write_text("\n".join(lines) + "\n") +print("wrote paper/tables/composite_validation.md") + +summary = { + "actual_2025": actual["values"], + "corrected_2024_base": corrected_2024, + "cpi_u_ratio_pinned": cpi_u_ratio, + "rules": { + k: {"mae": v["mae"], "errors": v["errors"], "values": v["values"]} + for k, v in results.items() + }, + "composite_vs_bls_fcsuti_gap_pp": dict(zip(range(2020, 2026), gaps)), +} +(DATA / "evaluation_2025.json").write_text(json.dumps(summary, indent=1) + "\n") +print("wrote data/evaluation_2025.json") +for k, v in results.items(): + print(f"{k:45s} MAE {v['mae']:.2%} " + " ".join(f"{v['errors'][t]:+.2%}" for t in TENURES)) +print("composite gap vs BLS FCSUti (pp):", [f"{g:+.2f}" for g in gaps]) From 6a5f1b661ed0d5dd5b440c6d013b2542c8df9120 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Fri, 4 Sep 2026 08:41:14 -0400 Subject: [PATCH 02/14] Evaluate at full precision from BLS's current workbook; disclose code-review findings BLS's current thresholds workbook (spm_thresholds.xlsx, linked from the SPM home page; sheet still titled 2005-2024) carries 2025 at full precision with standard errors: 41,322.707 / 34,325.998 / 41,700.556 (SE 327 / 560 / 393). Bundled with hash pinned; evaluation re-run - MAEs unchanged at two decimals. Misses in SE units: owners -0.87 and -0.34, renters -2.4. From the 2026-09-04 cross-model code review (verified): footnote on three remaining replication approximations (annual deflation of the terminal Q1, tenure-code folding, GROCER allocation scope), and a reproducibility disclosure that the implementation's default CPI store carried a mis-mapped internet series (SEEE) and so returned a different 2025 price ratio (1.0297) from the committed six-series composite (1.0322); reconciliation is on the PR #32 branch and the 2026 series choice is spm-calculator issue #35. Co-Authored-By: Claude Fable 5.1 --- data/SHA256SUMS | 5 +- data/SHA256SUMS.ots | Bin 700 -> 630 bytes data/bls_2025_thresholds.json | 103 ++++++++++++++++---- data/evaluation_2025.json | 46 ++++----- data/spm_thresholds_current_2026-09-04.xlsx | Bin 0 -> 19696 bytes paper/index.qmd | 44 +++++++-- paper/tables/evaluation.md | 4 +- scripts/check_paper.py | 30 +++++- 8 files changed, 179 insertions(+), 53 deletions(-) create mode 100644 data/spm_thresholds_current_2026-09-04.xlsx diff --git a/data/SHA256SUMS b/data/SHA256SUMS index b76226e..4a5bd1c 100644 --- a/data/SHA256SUMS +++ b/data/SHA256SUMS @@ -1,9 +1,10 @@ -31b51fa164ca87c1d01a92af2bf33ac539de79ef34f56088eb6bafe1a5c82e5e data/bls_2025_thresholds.json +38c359e458061c31e5e8aa22d80c54368f52f644fb49be9ca3291e9e53614b2f data/bls_2025_thresholds.json ae656bf9f44eaf1df4f043130550f7f1f7dcdf5c3d312af1900962e01b71ed50 data/bls_cpi_series.json 9f7d34bc18d203308653c940748fe3f16b2cb9576d34ac273ac12dfd3dc5f66c data/census_wp2026_17_rates.json -b0440d42a7b6bab8cfccfc74a416577272ae2cb6446e64bb543edf55147ef8ab data/evaluation_2025.json +0c86b0c207e85d05228f0c19778d98c394ca125f7e6d8c3091b73b86be15d0f0 data/evaluation_2025.json 259a5b2e0d15646722fe309f741546e364827fab053c73c99d273061fa0b1ba8 data/nowcast_2025.json 262240dad4dbfe3e0eac5339c2f9062fbef15780657d6e144f28d85df54ea33e data/nowcast_rate_impact.json 1162cec353fcf06344616a8bf8b46f87da67e56fa03696152ace34edbc976725 data/replication_results.json 3915ec86251a6b244962b7f72f12875b2536703380bdb5ca4c511c0ff1fefd7d data/threshold_series.json e7931a1f2540d52877d6fd14a8ed1e421e977a85c952ae1b6f690a04d904f2cb data/spm_threshold_200524_corrected.xlsx +95f39fb5479ee5c196de627e06e10efd59af5148cf1c8466924e3fb2e5e2bd3b data/spm_thresholds_current_2026-09-04.xlsx diff --git a/data/SHA256SUMS.ots b/data/SHA256SUMS.ots index 4aebaf5466fcb42f8bb95978fa31d7dff3014b4c..ea338b201c43169ae1a27a91cba3a4b86c448e64 100644 GIT binary patch delta 414 zcmdnP`i*6RV*LW+XJsEB?qm49j$P<-?QQe3MKiB$m6`l2MX@2zE^Si52Z0x1`*TxH ze#y#PWtD!yRDU_g{|^G$Gc+eYT>M49xVJ>+-jfx(I6f%c4!EGPvpf6$U5PJ;T^&qr zsg$iz;LHCix@1P+`~7@9TYyTUpWEuUy!HF|D;W(8*lMROzvbdb_d$m-o@(U|5eXtr@2*H_Z1m4j*kik`bDR2 zMyfv$Il=lkvSjV`W8c_16@&Z2g({uu{y$q&1Tib|1ILUbci22H2QQiI!D!&WNNsz2 z`{6lTuO&X6c+w*91II?!(-j3i>a9RSjX#$u6>Jar_U`zjjC$SZ zMxdegvnzZa9(MM*-omi`>}8#l_U&e=JC%m3b03OoJc_@Hn;&_mUr z=16{4Qc7^?=2x+`8$QmoPB?R{{P?u{ww9awIX)`PZ$9Dgv^tSZ^Z&`K?kRU|=bNk9 zoz|O`E>S(V@4a*N#aA33S+ZvB@cqE?q%!<$=c#>>leaTkxc~nkFtx$;D3f36^T3*y!3r{TEx}4+Y*gS&P)T^mVU>1_I{OB_irxV*|fs_ znE&q|>>8&c&dI3WxZP-R>eXDJDb^GJU)MVMZyKjTrH8}6`F`ek-P_)syve%p>Ezpj ZR;TWR9K;I_u^npncIzmAoilNtF#xu8?@<5% diff --git a/data/bls_2025_thresholds.json b/data/bls_2025_thresholds.json index 6f2f7fd..3d0cf5d 100644 --- a/data/bls_2025_thresholds.json +++ b/data/bls_2025_thresholds.json @@ -1,13 +1,13 @@ { - "source": "U.S. Bureau of Labor Statistics, Division of Price and Index Number Research, \"2025 Research Supplemental Poverty Measure Thresholds\", https://www.bls.gov/pir/spm/spm_thresholds_2025.htm", + "source": "U.S. Bureau of Labor Statistics, Division of Price and Index Number Research, current SPM thresholds workbook spm_thresholds.xlsx (linked from https://www.bls.gov/pir/spmhome.htm; sheet still titled '2005-2024' but carrying 2025), cross-checked against the 2025 threshold page https://www.bls.gov/pir/spm/spm_thresholds_2025.htm", "retrieved": "2026-09-04", "page_last_modified": "August 24, 2026", - "precision": "Page publishes whole dollars; no full-precision workbook for 2025 located as of retrieval. Rounding bound +/- $0.50 = +/- 0.0012 percent.", + "precision": "Full precision from the current workbook; the threshold page publishes the same values rounded to whole dollars (41,323 / 34,326 / 41,701).", "units": "Two-adult, two-child consumer units, nominal 2025 dollars", "values": { - "owner_with_mortgage": 41323, - "owner_without_mortgage": 34326, - "renter": 41701 + "owner_with_mortgage": 41322.707394, + "owner_without_mortgage": 34325.99772, + "renter": 41700.555713 }, "bls_stated_growth_pct_2025_over_2024": { "owner_with_mortgage": 5.332, @@ -15,25 +15,94 @@ "renter": 6.325 }, "bls_chart3_yoy_growth_pct_corrected_series": { - "2020": {"owner_with_mortgage": 2.539, "owner_without_mortgage": 2.995, "renter": 3.686}, - "2021": {"owner_with_mortgage": 3.920, "owner_without_mortgage": 3.193, "renter": 4.129}, - "2022": {"owner_with_mortgage": 9.667, "owner_without_mortgage": 9.209, "renter": 9.365}, - "2023": {"owner_with_mortgage": 8.793, "owner_without_mortgage": 7.495, "renter": 9.053}, - "2024": {"owner_with_mortgage": 6.127, "owner_without_mortgage": 7.490, "renter": 5.343}, - "2025": {"owner_with_mortgage": 5.332, "owner_without_mortgage": 4.402, "renter": 6.325} + "2020": { + "owner_with_mortgage": 2.539, + "owner_without_mortgage": 2.995, + "renter": 3.686 + }, + "2021": { + "owner_with_mortgage": 3.92, + "owner_without_mortgage": 3.193, + "renter": 4.129 + }, + "2022": { + "owner_with_mortgage": 9.667, + "owner_without_mortgage": 9.209, + "renter": 9.365 + }, + "2023": { + "owner_with_mortgage": 8.793, + "owner_without_mortgage": 7.495, + "renter": 9.053 + }, + "2024": { + "owner_with_mortgage": 6.127, + "owner_without_mortgage": 7.49, + "renter": 5.343 + }, + "2025": { + "owner_with_mortgage": 5.332, + "owner_without_mortgage": 4.402, + "renter": 6.325 + } }, "bls_chart4_annual_average_inflation_pct": { "note": "Year-over-year percent change in annual averages as published on the same BLS page (Chart 4). FCSUti is BLS's own composite price index used to deflate the CE window; it is published only alongside the thresholds, so it is available for backtesting our composite but not for nowcasting.", - "2020": {"cpi_u": 1.233, "c_cpi_u": 1.074, "fcsuti": 2.387}, - "2021": {"cpi_u": 4.698, "c_cpi_u": 4.469, "fcsuti": 3.170}, - "2022": {"cpi_u": 8.003, "c_cpi_u": 7.678, "fcsuti": 7.206}, - "2023": {"cpi_u": 4.116, "c_cpi_u": 3.874, "fcsuti": 6.172}, - "2024": {"cpi_u": 2.950, "c_cpi_u": 2.599, "fcsuti": 3.976}, - "2025": {"cpi_u": 2.699, "c_cpi_u": null, "fcsuti": 3.441} + "2020": { + "cpi_u": 1.233, + "c_cpi_u": 1.074, + "fcsuti": 2.387 + }, + "2021": { + "cpi_u": 4.698, + "c_cpi_u": 4.469, + "fcsuti": 3.17 + }, + "2022": { + "cpi_u": 8.003, + "c_cpi_u": 7.678, + "fcsuti": 7.206 + }, + "2023": { + "cpi_u": 4.116, + "c_cpi_u": 3.874, + "fcsuti": 6.172 + }, + "2024": { + "cpi_u": 2.95, + "c_cpi_u": 2.599, + "fcsuti": 3.976 + }, + "2025": { + "cpi_u": 2.699, + "c_cpi_u": null, + "fcsuti": 3.441 + } }, "corrected_2024_shown_on_page": { "owner_with_mortgage": 39231, "owner_without_mortgage": 32879, "renter": 39220 + }, + "workbook_file": "data/spm_thresholds_current_2026-09-04.xlsx", + "page_values_whole_dollars": { + "owner_with_mortgage": 41323, + "owner_without_mortgage": 34326, + "renter": 41701 + }, + "standard_errors": { + "owner_with_mortgage": 327.35414802, + "owner_without_mortgage": 560.28664871, + "renter": 393.03526083 + }, + "tenure_shares": { + "owner_with_mortgage": 0.51035789503, + "owner_without_mortgage": 0.10217639667, + "renter": 0.38746570830000004 + }, + "corrected_2024_in_current_workbook": { + "owner_with_mortgage": 39230.994457, + "owner_without_mortgage": 32878.594848, + "renter": 39219.893902 } } diff --git a/data/evaluation_2025.json b/data/evaluation_2025.json index cdf5b4f..f179ed1 100644 --- a/data/evaluation_2025.json +++ b/data/evaluation_2025.json @@ -1,8 +1,8 @@ { "actual_2025": { - "owner_with_mortgage": 41323, - "owner_without_mortgage": 34326, - "renter": 41701 + "owner_with_mortgage": 41322.707394, + "owner_without_mortgage": 34325.99772, + "renter": 41700.555713 }, "corrected_2024_base": { "owner_with_mortgage": 39230.994457, @@ -12,11 +12,11 @@ "cpi_u_ratio_pinned": 1.0263126854942315, "rules": { "Amended nowcast (50/50 blend, committed)": { - "mae": 0.011711497719319675, + "mae": 0.011705660816454744, "errors": { - "owner_with_mortgage": -0.006937084908718694, - "owner_without_mortgage": -0.0055355462111496, - "renter": -0.02266186203809073 + "owner_with_mortgage": -0.006930053032405126, + "owner_without_mortgage": -0.005535480156872774, + "renter": -0.02265144926008633 }, "values": { "owner_with_mortgage": 41036.33884031702, @@ -25,11 +25,11 @@ } }, "Original nowcast (pre-repair blend)": { - "mae": 0.009801833535956614, + "mae": 0.009795989903586691, "errors": { - "owner_with_mortgage": -0.0054068595654337415, - "owner_without_mortgage": -0.002193812741272616, - "renter": -0.02180482830116348 + "owner_with_mortgage": -0.005399816853597805, + "owner_without_mortgage": -0.002193746465031343, + "renter": -0.021794406392130927 }, "values": { "owner_with_mortgage": 41099.572342177584, @@ -38,11 +38,11 @@ } }, "CE replication growth ratio alone": { - "mae": 0.0075325524985206576, + "mae": 0.007531455316659959, "errors": { - "owner_with_mortgage": 0.0062332278740329095, - "owner_without_mortgage": 0.0003051209034998781, - "renter": -0.016059308718029186 + "owner_with_mortgage": 0.0062403530093022574, + "owner_without_mortgage": 0.00030518734572515527, + "renter": -0.016048825594952465 }, "values": { "owner_with_mortgage": 41580.57567543866, @@ -51,11 +51,11 @@ } }, "FCSUti-composite CPI aging alone": { - "mae": 0.02024934212514044, + "mae": 0.020243559886267765, "errors": { - "owner_with_mortgage": -0.020107397691469853, - "owner_without_mortgage": -0.01137621332579919, - "renter": -0.029264415358152274 + "owner_with_mortgage": -0.020100459074112176, + "owner_without_mortgage": -0.011376147659470925, + "renter": -0.029254072925220198 }, "values": { "owner_with_mortgage": 40492.10200519539, @@ -64,11 +64,11 @@ } }, "All-Items CPI-U aging (status quo)": { - "mae": 0.02578624847461697, + "mae": 0.025780498913155443, "errors": { - "owner_with_mortgage": -0.02564510621753069, - "owner_without_mortgage": -0.016963264763512065, - "renter": -0.03475037444280815 + "owner_with_mortgage": -0.025638206812674702, + "owner_without_mortgage": -0.016963199468286727, + "renter": -0.0347400904585049 }, "values": { "owner_with_mortgage": 40263.26727577298, diff --git a/data/spm_thresholds_current_2026-09-04.xlsx b/data/spm_thresholds_current_2026-09-04.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..98673826f2a39e32f0bcdadc42d2a8ea9dc3b7f2 GIT binary patch literal 19696 zcmeIab97|g);Aj4w$rg~+vwQpm=!x6+qP}ncG4Z&wmY4hgZDn?^f})c_x}C8yGE^D zyY`;HF>2MEYtA*LC<6+H1_S{F1q1{{3Sgu2Xvuv3D1t!EBkArL( 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YctsiTPcZ-jLil`@eM%CO)ZecDA2A None: ).hexdigest() == BLS_WORKBOOK_SHA256, ) +# The current-thresholds workbook as BLS served it on 2026-09-04, which +# carries 2025 at full precision (its sheet is still titled 2005-2024). +BLS_CURRENT_WORKBOOK_SHA256 = ( + "95f39fb5479ee5c196de627e06e10efd59af5148cf1c8466924e3fb2e5e2bd3b" +) +check( + "bundled current BLS workbook hash", + hashlib.sha256( + (REPO / "data" / "spm_thresholds_current_2026-09-04.xlsx").read_bytes() + ).hexdigest() + == BLS_CURRENT_WORKBOOK_SHA256, +) # (c) Load-bearing prose numbers match artifacts. TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") @@ -325,7 +337,23 @@ def mae(rule: str) -> float: ("34,326", actual["values"]["owner_without_mortgage"]), ("41,701", actual["values"]["renter"]), ]: - check(f"BLS 2025 literal {literal}", literal in QMD and f"{value:,}" == literal) + check( + f"BLS 2025 literal {literal}", + literal in QMD and f"{round(value):,}" == literal, + ) +# Misses in units of BLS's published standard errors. +se = actual["standard_errors"] +miss_se = { + t: (amended["values"][t] - actual["values"][t]) / se[t] + for t in TENURES +} +check( + "owner misses within one SE; renter about 2.4 SE", + "2.4 standard errors" in QMD + and abs(miss_se["owner_with_mortgage"]) < 1 + and abs(miss_se["owner_without_mortgage"]) < 1 + and abs(abs(miss_se["renter"]) - 2.4) < 0.05, +) check( "BLS-stated 2025 growth 4.40-6.33", "4.40 to 6.33" in QMD From 8d8a8b4960eb22e207e5e4e735277754ddf12cad Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Fri, 4 Sep 2026 08:50:34 -0400 Subject: [PATCH 03/14] Harden the pre-render guard; disclose its former scope; fix two miscounts it found A 2026-09-04 reproducibility audit showed the substring-only guard passing under edits it should reject: 41,036.34 -> 141,036.34, tenure amounts swapped, a table include redirected, a generated table missing, and an emptied SHA256SUMS (zero entries, zero failures). The guard now: enforces allowlists of generated tables and QMD includes; requires SHA256SUMS to cover exactly the artifacts present; matches registered figures at numeric-token boundaries and the tenure amounts as ordered triples in the sentences that state them; registers counts and ranges derived from the artifacts (correction <=1.6%, package errors 7.4% in N of six years, renter falls in N years, SE range, effective shelter weight 55 vs 47, evaluation per-tenure and range figures, BLS 2025 SEs, Census 2023->2024); restores the tables on failure instead of leaving the checkout mutated; and reports scope honestly (external citation figures and dates are not registered). On its first run it found two miscounts that survived every prior review: the package was wrong in five of six years (2021's owner values were ~1% off), not four; renter thresholds fell in all six years, not five. Both fixed and the catch is noted in the paper. Also from the audit: data/PROVENANCE.md (generator, inputs, source of every artifact); the sensitivity script now takes --output and stamps its own provenance; data/COMMITMENT-MANIFEST.txt lists both nowcast vintages (tag objects, commits, artifact hashes) and is stamped; README gains a clean-room recipe with toolchain versions and the pre-commitment evidence; the paper pins the full implementation SHA, says "every push to master and every pull request", links the exact Wayback capture, states how the original values are anchored (capture + release timestamps; the Bitcoin proof covers the amended manifest), and lists replication_results.json among the artifacts untracked in the implementation at the pinned commit. Co-Authored-By: Claude Fable 5.1 --- README.md | 48 ++- data/COMMITMENT-MANIFEST.txt | 19 + data/COMMITMENT-MANIFEST.txt.ots | Bin 0 -> 1015 bytes data/PROVENANCE.md | 30 ++ data/SHA256SUMS | 2 +- data/SHA256SUMS.ots | Bin 630 -> 1015 bytes data/nowcast_rate_impact.json | 4 +- paper/index.qmd | 81 ++-- scripts/check_paper.py | 476 +++++++++++++++--------- scripts/simulate_nowcast_rate_impact.py | 43 ++- 10 files changed, 492 insertions(+), 211 deletions(-) create mode 100644 data/COMMITMENT-MANIFEST.txt create mode 100644 data/COMMITMENT-MANIFEST.txt.ots create mode 100644 data/PROVENANCE.md diff --git a/README.md b/README.md index 1b5c68f..4e825cc 100644 --- a/README.md +++ b/README.md @@ -13,20 +13,48 @@ revision. quarto render # runs scripts/check_paper.py pre-render, then HTML + PDF to _output/ ``` -The pre-render check regenerates every table from `data/` via -`scripts/build_tables.py`, verifies `data/SHA256SUMS`, and re-derives -the load-bearing prose figures; the render fails if any of them -drifts from the artifacts. +The pre-render check (`scripts/check_paper.py`) regenerates every +table from `data/`, enforces allowlists of generated tables, QMD +includes, and checksummed artifacts, verifies `data/SHA256SUMS`, and +re-derives the registered prose figures at numeric-token boundaries; +the render fails if any registered figure, table, or artifact drifts. +Figures quoted from external publications are bound to citations, +not artifacts. `data/PROVENANCE.md` names the generator, inputs, and +source of every artifact. -Every numeric table is generated from the artifacts in `data/` -(SHA-256 sums in `data/SHA256SUMS`), which are produced by the scripts -in [PolicyEngine/spm-calculator](https://github.com/PolicyEngine/spm-calculator) -(v0.4.0, PR #32). The prose cannot drift from the data without the -build failing. +## Clean-room build + +Tested on 2026-09-04 with Python 3.14.6, Quarto 1.9.36, and TeX Live +2026 (LuaHBTeX). The check and the table generators use only the +Python standard library and need a writable checkout. + +```bash +git clone https://github.com/PolicyEngine/spm-threshold-paper.git +cd spm-threshold-paper +git checkout +python3 scripts/check_paper.py # guard only; no Quarto needed +quarto render --to html # HTML; add --to pdf for the PDF (needs TeX) +``` + +Rendered output lands in `_output/paper/`. The published site is a +static deploy of that directory. + +## Pre-commitment evidence + +- `data/SHA256SUMS.precommit` — the artifact manifest as of 2026-08-07, + before BLS published the 2025 thresholds; `SHA256SUMS.precommit.ots` + is its completed OpenTimestamps proof (Bitcoin block 961505, mined + 2026-08-07 23:18 UTC). Verify with + `ots verify -f data/SHA256SUMS.precommit data/SHA256SUMS.precommit.ots`. +- `data/COMMITMENT-MANIFEST.txt` — both nowcast vintages (tags + `v1.0-original-nowcast`, `v1.1-amended-nowcast`), their commits and + artifact hashes; stamped as `COMMITMENT-MANIFEST.txt.ots`. +- Internet Archive capture of the page, 2026-08-07: + ## Companion code -- spm-calculator 0.4.0: corrected/published/legacy threshold series +- spm-calculator PR #32 (0.4.0 on merge): corrected/published/legacy threshold series with provenance, CE replication, benchmark, backtest, nowcast, and the weekly BLS drift-watch workflow. - policyengine-us #9081: adopts the corrected series in the US diff --git a/data/COMMITMENT-MANIFEST.txt b/data/COMMITMENT-MANIFEST.txt new file mode 100644 index 0000000..f9aaa39 --- /dev/null +++ b/data/COMMITMENT-MANIFEST.txt @@ -0,0 +1,19 @@ +# Commitment manifest — 2025 SPM threshold nowcast, both vintages +# Written 2026-09-04 after BLS's 2025 publication; stamped separately from the pre-publication proof. +# Pre-publication anchors: SHA256SUMS.precommit.ots (Bitcoin block 961505, 2026-08-07 23:18 UTC) covers the amended manifest; +# the Internet Archive capture https://web.archive.org/web/20260807211521/https://spm-threshold-paper.vercel.app/ shows both sets; +# GitHub release timestamps for both tags date 2026-08-07. + +tag v1.0-original-nowcast + tag_object 12b7042a960412477af53c62ebd961ded1a2625e + commit a8b7c51869e64e36517d4eca8d5dbd6e2e0cde5b + nowcast_2025.json sha256 85049a018bbf319d900320fceed6b740f83ab52efbf11a795250066f69c8bfd8 + values 41099.57 / 34250.70 / 40791.72 + +tag v1.1-amended-nowcast + tag_object 3759670e314b762fa24348a3c9a16526cdc74b47 + commit d72e1dc083ad1abc95b1c88fd1b782944454074b + nowcast_2025.json sha256 ec0cd0f8b629fe79d9db24e0f8fcd0bac4f172817f940376d8f1c80fbb823028 + values 41036.34 / 34135.99 / 40755.98 + +evaluation: PR #1 (branch v2-evaluation) of PolicyEngine/spm-threshold-paper; this manifest is stamped as COMMITMENT-MANIFEST.txt.ots diff --git a/data/COMMITMENT-MANIFEST.txt.ots b/data/COMMITMENT-MANIFEST.txt.ots new file mode 100644 index 0000000000000000000000000000000000000000..eb4ce42413ffde1c99f8237071af8c3f4de1df84 GIT binary patch literal 1015 zcmZSZFG$S`$;?eHE=kNSC}v;?D9X=IW7yyM=tawmNmCd(ByMP3kzX6OMfcBB^B}u` ztvhCh?GTea#+PuDzdd;KZ1WES?}S}!UKB5SxFTnctBzmxXO52w&+BhSztjHj`tV7? z+Ir)XgUerDjH&nQWw=?x`(l&bg0CF^KXAyT*j#nLe(eKLxs^E2(cPR*f8Vm2oZ0!V zHYD29_u%4$GeN7a#jQzSZ>P=iL4ozBQ)_JBv+Yx-WjC$s;n>TwQsim>r>T#Hs!yCy z-1+e{#|ME4P9iVA7}g|7|9W-r*1H|=fd)VM$t^IgSS(d#$thXAAE{+3e6GDZw89FqJCJ-4hgro5hq|%^`;JPOglwNB`|rqsr|X|yGdgy3LFn9zK)WNCC`j*% zm)WfKRZAi6_{*q;qUDp{pS)2SV-xV#z^bv1<0DJftbJ}DI9yp{pa1T^DZ$Wu|1q!L zPo6#2Y^S@uPTXI2&(iH=Z)gKsu zUO(w|l^^)^* zKL}h{``PZ6Nw%=L<3&!n2MjzwS9Ll@c5ZMx?GYxqshg#+Q@plFp4DgbaqjtFw@x{K zdPW;iN#!eltw;NPEZP|tlo)%@WM*91u|6w({bI#!0r{~X9p?a*_)7G(_!K#RWiP#b z*|Bwt@;~>>TxQRfYd2oved+1Rp%3)i4b`g7HOtgKuAN=gSrd@q8*pnAZ#4JI{J6J= ytsk1KIrc1osD!A%D27ft^sCy2g_;!Eb0oD0|L_PL zWIWYJ*Vga|@gO=E48^+%LjCOpG|ai~eNUVp4-uRbdX@E`^GLWNz9 z=C_rWYl5zf2)zuc6y=ZdmE#h7$jBkN@$(4q5RgI?>GUytVjKGE-rDZC?+EcAG$=G>ZJW%~GI)Q!&@dRp` zzDDo}MFm~w`;XZelRyGCfB*0ZL&AhqfmS|Q2=O48TK(o)*U9PVj1uw%L$AjAK!p(L zeS@L?;DI&GX1K2i@DT1{m@_t%SHfED*Rf8<=$i=eAk}DC6d@m7E@WBcK0eWVkWfQb zR0Dpj^&NH<+c81DQV8%Maf5d2CV$D)z|(64xo*sY>k)R=d^f428Bh_m7HVB0j0o`r zYMQ=B@CbkjPEj~ggg=wF0XTpE@DNS}ILzO4s}lXqza(^XydwzkAoBvK3Yu?-A+!9y zb7}0<9`$xl-*CGbpj0B%Fet0=00{9Q$x7J5@e=|pUJS)yG^>iUi4YM#gR1Vgy`gaX zuckZJ2=E}R*X&7GxBkD55?i0xIr<$as#Wa5ne#zUc3N9VbZ5i}@dOTPn!ZNx2&NJ- zu7C`(f0H-?Er0M3(5>@6+B9nuH$l+}9^e2B2=O3^L0O5gM$1T68M2E6afugwavudu zv&Rdc^|h4G%b11;@gRBXPb%WSOgM)DpmZ}!nF9f-h_7mAucINhP;XuFL7WKjAWayK zgiLZn^#^s^(?Nx_BmPIz3O44aD}mAt=}AclF9`4;*f1q}iL9n3@vWPBiF{CKO;Fmh k4OHq)$ delta 418 zcmey){*7gVV*LW+XJsEB?qm49j$P<-?QQe3MKiB$m6`l2MX@2zE^Si52Z0x1`*TxH ze#y#PWtD!yRDU_g{|^G$Gc+eYT>M49xVJ>+-jfx(I6f%c4!EGPvpf6$U5PJ;T^&qr zsg$iz;LHCix@1P+`~7@9TYyTUpWEuUy!HF|D;W(8*lMROg_SF><+ZAy^Gby|Er$QPIIfY?kh5893K@7^ovg4 zj8uOha)R}7WXam=$G)+3DhBt33spMR{eQNm2x3;^2aXv>?yz}Y4qh_(1(R;j{|_9C z)V8;`AD(lVF;o~|hHQEvsBYW%rOsbG7^w|B=M zWz_3NHv&zypIzbe@UXMb^%jQhXD{odv~M>{?Qt&ZPe1s$dz0$P<8a4@WE5{cGvPt& I}}`` names a table outside that + allowlist; +(c) ``data/SHA256SUMS`` does not list exactly the artifacts present in + ``data/`` or any listed hash mismatches (an emptied manifest fails); +(d) a figure quoted in the prose no longer matches the artifact it + derives from. Literals are matched at numeric-token boundaries + (so ``41,036.34`` does not match ``141,036.34``), and the three + tenure amounts are matched as ordered triples in the sentences + that state them, so a swap between tenures fails. + +Scope, stated honestly: the guard covers artifact hashes, generated +tables, and the figures registered below. Numbers quoted from +external publications (P60 reports, BLS pages, Census working papers) +are bound to their citations, not to artifacts, and are the citation +referee's job; dates and ordinals are not registered. A 2026-09-04 +reproducibility audit demonstrated the earlier substring-only version +passing under several of these attacks. """ from __future__ import annotations import hashlib import json +import re +import shutil import subprocess import sys +import tempfile from pathlib import Path REPO = Path(__file__).resolve().parent.parent +DATA = REPO / "data" +TABLES = REPO / "paper" / "tables" QMD = (REPO / "paper" / "index.qmd").read_text() +TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") + +EXPECTED_TABLES = { + "correction.md", + "package_errors.md", + "replication.md", + "backtest.md", + "nowcast.md", + "evaluation.md", + "evaluation_levels.md", + "composite_validation.md", +} +GENERATORS = ("build_tables.py", "evaluate_nowcast_2025.py") + failures: list[str] = [] @@ -29,123 +63,182 @@ def check(label: str, ok: bool) -> None: failures.append(label) -# (a) Tables regenerate identically. -before = { - p: p.read_text() for p in sorted((REPO / "paper" / "tables").glob("*.md")) -} -for script in ("build_tables.py", "evaluate_nowcast_2025.py"): - subprocess.run( - [sys.executable, str(REPO / "scripts" / script)], - check=True, - capture_output=True, - ) -for path, old in before.items(): - check(f"table drifted: {path.name}", path.read_text() == old) +def has_number(literal: str, text: str = QMD, min_count: int = 1) -> bool: + """Literal present at numeric-token boundaries: no digit, comma-digit, + or dot-digit continuation on either side.""" + pattern = r"(?= min_count -# (b) Artifact hashes match SHA256SUMS. -for line in (REPO / "data" / "SHA256SUMS").read_text().splitlines(): - digest, name = line.split() - actual = hashlib.sha256((REPO / name).read_bytes()).hexdigest() - check(f"artifact hash mismatch: {name}", actual == digest) -# The bundled BLS workbook is the exact file BLS published 2026-07-17 -# (verified against the live site 2026-08-06). -BLS_WORKBOOK_SHA256 = ( - "e7931a1f2540d52877d6fd14a8ed1e421e977a85c952ae1b6f690a04d904f2cb" -) +def has_ordered(literals: list[str], window: int = 400) -> bool: + """The literals appear in this order within one window of text.""" + pattern = r"[\s\S]{0,%d}" % window + body = pattern.join(re.escape(x) for x in literals) + return re.search(body, QMD) is not None + + +# (a) Tables regenerate identically, and the set is exactly as expected. +present_before = {p.name for p in TABLES.glob("*.md")} check( - "bundled BLS workbook hash", - hashlib.sha256( - (REPO / "data" / "spm_threshold_200524_corrected.xlsx").read_bytes() - ).hexdigest() - == BLS_WORKBOOK_SHA256, + f"generated tables present == expected (missing {EXPECTED_TABLES - present_before}, extra {present_before - EXPECTED_TABLES})", + present_before == EXPECTED_TABLES, ) -# The current-thresholds workbook as BLS served it on 2026-09-04, which -# carries 2025 at full precision (its sheet is still titled 2005-2024). -BLS_CURRENT_WORKBOOK_SHA256 = ( - "95f39fb5479ee5c196de627e06e10efd59af5148cf1c8466924e3fb2e5e2bd3b" +before = {p.name: p.read_text() for p in TABLES.glob("*.md")} +with tempfile.TemporaryDirectory() as tmp: + backup = Path(tmp) / "tables" + shutil.copytree(TABLES, backup) + for script in GENERATORS: + proc = subprocess.run( + [sys.executable, str(REPO / "scripts" / script)], + capture_output=True, + text=True, + ) + if proc.returncode != 0: + failures.append(f"{script} failed:\n{proc.stderr[-2000:]}") + after = {p.name: p.read_text() for p in TABLES.glob("*.md")} + for name in EXPECTED_TABLES: + check(f"table drifted: {name}", before.get(name) == after.get(name)) + if any(before.get(n) != after.get(n) for n in EXPECTED_TABLES): + # Report without leaving the checkout mutated. + shutil.rmtree(TABLES) + shutil.copytree(backup, TABLES) + +# (b) Every QMD include names an expected table. +includes = set(re.findall(r"\{\{<\s*include\s+tables/([^\s>]+)\s*>\}\}", QMD)) +check( + f"qmd includes outside allowlist: {includes - EXPECTED_TABLES}", + includes <= EXPECTED_TABLES, ) check( - "bundled current BLS workbook hash", - hashlib.sha256( - (REPO / "data" / "spm_thresholds_current_2026-09-04.xlsx").read_bytes() - ).hexdigest() - == BLS_CURRENT_WORKBOOK_SHA256, + f"expected tables never included: {EXPECTED_TABLES - includes}", + EXPECTED_TABLES <= includes, ) -# (c) Load-bearing prose numbers match artifacts. -TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") +# (c) SHA256SUMS lists exactly the artifacts present, and every hash holds. +listed: dict[str, str] = {} +for line in (DATA / "SHA256SUMS").read_text().splitlines(): + digest, name = line.split() + listed[name] = digest +present_artifacts = { + f"data/{p.name}" for p in DATA.iterdir() if p.suffix in (".json", ".xlsx") +} +check( + f"SHA256SUMS coverage (unlisted {present_artifacts - set(listed)}, stale {set(listed) - present_artifacts})", + set(listed) == present_artifacts and len(listed) > 0, +) +for name, digest in listed.items(): + path = REPO / name + check( + f"artifact hash mismatch: {name}", + path.exists() and hashlib.sha256(path.read_bytes()).hexdigest() == digest, + ) -nowcast = json.loads((REPO / "data" / "nowcast_2025.json").read_text()) -for literal, value in [ - ("41,036.34", nowcast["values"]["owner_with_mortgage"]), - ("34,135.99", nowcast["values"]["owner_without_mortgage"]), - ("40,755.98", nowcast["values"]["renter"]), -]: +# Bundled BLS workbooks are the exact files BLS served on the stated dates. +BLS_WORKBOOK_SHA256 = ( + "e7931a1f2540d52877d6fd14a8ed1e421e977a85c952ae1b6f690a04d904f2cb" +) +BLS_CURRENT_WORKBOOK_SHA256 = ( + "95f39fb5479ee5c196de627e06e10efd59af5148cf1c8466924e3fb2e5e2bd3b" +) +for fname, expected in ( + ("spm_threshold_200524_corrected.xlsx", BLS_WORKBOOK_SHA256), + ("spm_thresholds_current_2026-09-04.xlsx", BLS_CURRENT_WORKBOOK_SHA256), +): check( - f"nowcast literal {literal}", - literal in QMD and f"{value:,.2f}" == literal, + f"bundled BLS workbook hash: {fname}", + hashlib.sha256((DATA / fname).read_bytes()).hexdigest() == expected, ) -# The superseded pre-amendment values quoted in the amendment note, -# and the stated 0.1-to-0.3-percent size of the amendment. +# (d) Load-bearing prose figures re-derive from the artifacts. +nowcast = json.loads((DATA / "nowcast_2025.json").read_text()) +AMENDED = {t: f"{nowcast['values'][t]:,.2f}" for t in TENURES} +for t, literal in AMENDED.items(): + check(f"nowcast literal {literal}", has_number(literal, min_count=2)) +# Ordered tenure triples: abstract, amendment sentence, table caption order. +check( + "abstract states the amended triple in tenure order", + has_ordered( + [AMENDED["owner_with_mortgage"], "owners with", AMENDED["owner_without_mortgage"], "owners without", AMENDED["renter"], "renters"], + window=120, + ), +) ORIGINAL = { - "owner_with_mortgage": ("41,099.57", 41099.57), - "owner_without_mortgage": ("34,250.70", 34250.70), - "renter": ("40,791.72", 40791.72), + "owner_with_mortgage": 41099.57, + "owner_without_mortgage": 34250.70, + "renter": 40791.72, } -shifts = [] -for t, (literal, value) in ORIGINAL.items(): - check(f"pre-amendment literal {literal}", literal in QMD) - shifts.append(abs(nowcast["values"][t] / value - 1)) +ORIG_LIT = {t: f"{v:,.2f}" for t, v in ORIGINAL.items()} +for t in TENURES: + check(f"pre-amendment literal {ORIG_LIT[t]}", has_number(ORIG_LIT[t])) +check( + "amendment sentence pairs original -> amended per tenure, in order", + has_ordered( + [ORIG_LIT["owner_with_mortgage"], AMENDED["owner_with_mortgage"], + ORIG_LIT["owner_without_mortgage"], AMENDED["owner_without_mortgage"], + ORIG_LIT["renter"], AMENDED["renter"]], + window=60, + ), +) +shifts = [abs(nowcast["values"][t] / ORIGINAL[t] - 1) for t in TENURES] check( "amendment size 0.1 to 0.3 percent", - "0.1 to 0.3 percent" in QMD - and 0.0005 <= min(shifts) - and max(shifts) < 0.0035, + has_number("0.1 to 0.3") and 0.0005 <= min(shifts) and max(shifts) < 0.0035, ) -# Provenance strings inside the nowcast artifact carry the repaired -# backtest numbers, not the pre-repair ones (a stale 1.35% survived -# there once — caught in colleague review, 2026-08-07). -_nowcast_meta = json.loads( - (REPO / "data" / "nowcast_2025.json").read_text() -) -_prov = _nowcast_meta["method"] + " ".join(_nowcast_meta["caveats"]) +_prov = nowcast["method"] + " ".join(nowcast["caveats"]) check( "nowcast provenance strings repaired", - "0.76" in _nowcast_meta["method"] - and "0.41" in _nowcast_meta["method"] + "0.76" in nowcast["method"] + and "0.41" in nowcast["method"] and "1.35" not in _prov and "biased low in 2022" not in _prov, ) -rate = json.loads((REPO / "data" / "nowcast_rate_impact.json").read_text()) +rate = json.loads((DATA / "nowcast_rate_impact.json").read_text()) delta_2025 = rate["2025"]["all"]["delta_pp"] check( "poverty sensitivity 0.4pp", - "0.4 percentage points" in QMD and abs(delta_2025 - 0.4) < 0.05, + has_number("0.4 percentage points", min_count=1) and abs(delta_2025 - 0.4) < 0.05, ) -backtest = (REPO / "paper" / "tables" / "backtest.md").read_text() +backtest = (TABLES / "backtest.md").read_text() for literal in ("2.23%", "1.57%", "0.41%", "**0.76%**"): check(f"backtest table literal {literal}", literal in backtest) for literal in ("2.23", "1.57", "0.41", "0.76"): - check(f"backtest prose literal {literal}", literal in QMD) - -# Recompute all four backtest rules from the artifacts (mirrors -# build_tables.py, including the rebased composite). -cpi = json.loads((REPO / "data" / "bls_cpi_series.json").read_text()) -replication = json.loads( - (REPO / "data" / "replication_results.json").read_text() -) -series_doc = json.loads((REPO / "data" / "threshold_series.json").read_text()) -corrected = {} -for seg in series_doc["series"]["bls-corrected-2026-07-17"][ - "segments" -].values(): - for y, tenures in seg["years"].items(): - corrected[int(y)] = {t: m["threshold"] for t, m in tenures.items()} + check(f"backtest prose literal {literal}", has_number(literal)) + +# Recompute all four backtest rules (mirrors build_tables.py). +cpi = json.loads((DATA / "bls_cpi_series.json").read_text()) +replication = json.loads((DATA / "replication_results.json").read_text()) +series_doc = json.loads((DATA / "threshold_series.json").read_text()) + + +def flat(series_name: str) -> dict[int, dict[str, float]]: + entry = series_doc["series"][series_name] + out: dict[int, dict[str, float]] = {} + segments = ( + entry["segments"].values() + if "segments" in entry + else [{"years": entry["years"]}] + ) + for seg in segments: + for y, tenures in seg["years"].items(): + out[int(y)] = {t: m["threshold"] for t, m in tenures.items()} + return out + + +def flat_measure(series_name: str, measure: str) -> dict[int, dict[str, float]]: + entry = series_doc["series"][series_name] + out: dict[int, dict[str, float]] = {} + for seg in entry["segments"].values(): + for y, tenures in seg["years"].items(): + out[int(y)] = {t: m.get(measure) for t, m in tenures.items()} + return out + + +corrected = flat("bls-corrected-2026-07-17") +published = flat("census-published-pre-correction") +legacy = flat("package-legacy-0.3") CPI_IDS = { "food": "CUUR0000SAF", @@ -200,14 +293,10 @@ def composite(year: int) -> float: "replication_ratio": r_rep, "blend": (f_fcs + r_rep) / 2, }[rule] - errs.append( - corrected[year - 1][t] * factor / corrected[year][t] - 1 - ) + errs.append(corrected[year - 1][t] * factor / corrected[year][t] - 1) signed_by_rule.setdefault(rule, []).extend(errs) annual_signed.setdefault(rule, []).append(sum(errs) / 3) - abs_by_rule.setdefault(rule, []).append( - sum(abs(e) for e in errs) / 3 - ) + abs_by_rule.setdefault(rule, []).append(sum(abs(e) for e in errs) / 3) def mae(rule: str) -> float: @@ -217,20 +306,16 @@ def mae(rule: str) -> float: signed_mean = sum(signed_by_rule["cpi_u"]) / len(signed_by_rule["cpi_u"]) check( "signed CPI-U bias -1.9", - "1.9 percent" in QMD and abs(signed_mean - (-0.019)) < 0.002, + has_number("1.9 percent") and abs(signed_mean - (-0.019)) < 0.002, ) +cpiu_low_years = sum(1 for m in annual_signed["cpi_u"] if m < 0) +check("CPI-U understated four of five years", "four of five" in QMD and cpiu_low_years == 4) check( "composite removes ~thirty percent of CPI-U error", - "thirty percent" in QMD - and abs((1 - mae("fcsuti_cpi") / mae("cpi_u")) - 0.30) < 0.02, -) -rep_signed = sum(signed_by_rule["replication_ratio"]) / len( - signed_by_rule["replication_ratio"] -) -check( - "replication signed mean +0.1", - "+0.1" in QMD and abs(rep_signed - 0.001) < 0.001, + "thirty percent" in QMD and abs((1 - mae("fcsuti_cpi") / mae("cpi_u")) - 0.30) < 0.02, ) +rep_signed = sum(signed_by_rule["replication_ratio"]) / len(signed_by_rule["replication_ratio"]) +check("replication signed mean +0.1", has_number("+0.1") and abs(rep_signed - 0.001) < 0.001) check( "replication annual signed range +0.5 to −0.3", "+0.5 to −0.3" in QMD @@ -244,8 +329,53 @@ def mae(rule: str) -> float: and abs(min(annual_signed["blend"]) - (-0.014)) < 0.001, ) -# Replication level fidelity ranges quoted in prose (per-year mean -# absolute deviation across tenures, matched anchors). +# Effective shelter weight under the pre-repair raw-level construction. +raw_2024 = {c: cpi[CPI_IDS[c]]["2024"] for c in WEIGHTS} +eff_shelter = WEIGHTS["shelter"] * raw_2024["shelter"] / sum( + WEIGHTS[c] * raw_2024[c] for c in WEIGHTS +) +stated_shelter = WEIGHTS["shelter"] / sum(WEIGHTS.values()) +check( + "effective shelter weight ~55 vs stated 47", + "near 55 percent" in QMD + and "stated 47" in QMD + and abs(eff_shelter - 0.55) < 0.02 + and abs(stated_shelter - 0.47) < 0.005, +) + +# Correction and package-error magnitudes. +corr_changes = [ + corrected[y][t] / published[y][t] - 1 for y in range(2019, 2025) for t in TENURES +] +check("correction within ±1.6 percent", has_number("1.6 percent") and max(abs(c) for c in corr_changes) < 0.016) +renter_falls = sum(1 for y in range(2019, 2025) if corrected[y]["renter"] < published[y]["renter"]) +FALL_WORDS = {4: "fall in four of six years", 5: "fall in five of six years", 6: "fall in all six years"} +check( + f"renter thresholds {FALL_WORDS.get(renter_falls)} (computed {renter_falls})", + FALL_WORDS.get(renter_falls, "") in QMD, +) +check( + "reproducibility note cites the two miscounts the check found", + "five of six\nyears, not four" in QMD or "five of six years, not four" in QMD, +) +pkg_err = { + y: max(abs(legacy[y][t] / published[y][t] - 1) for t in TENURES) for y in range(2019, 2025) +} +check("package errors reach 7.4 percent", has_number("7.4 percent") and abs(max(pkg_err.values()) - 0.074) < 0.001) +wrong_years = sum(1 for y, e in pkg_err.items() if e > 0.001) +WORDS = {3: "three of the six", 4: "four of the six", 5: "five of the six", 6: "all six"} +check( + f"package wrong in {WORDS.get(wrong_years)} years (computed {wrong_years}; per-year max errors {dict((y, round(e, 4)) for y, e in pkg_err.items())})", + WORDS.get(wrong_years, "") in QMD, +) +se = flat_measure("bls-corrected-2026-07-17", "standard_error") +se_ratio = [se[y][t] / corrected[y][t] for y in range(2019, 2025) for t in TENURES if se[y][t]] +check( + "SE range 0.7 to 2.1 percent", + has_number("0.7") and has_number("2.1") and abs(min(se_ratio) - 0.007) < 0.001 and abs(max(se_ratio) - 0.021) < 0.001, +) + +# Replication level fidelity ranges. mad_corrected, mad_published = [], [] for year in range(2019, 2025): for anchor, key, out in ( @@ -263,51 +393,32 @@ def mae(rule: str) -> float: out.append(sum(abs(v) for v in row[key].values()) / 3) check( "replication fidelity 1.5 to 2.1 vs corrected", - "1.5 to 2.1" in QMD - and abs(min(mad_corrected) - 0.015) < 0.001 - and abs(max(mad_corrected) - 0.021) < 0.001, + has_number("1.5 to 2.1") and abs(min(mad_corrected) - 0.015) < 0.001 and abs(max(mad_corrected) - 0.021) < 0.001, ) check( "replication fidelity 1.0 to 1.6 vs published", - "1.0 to 1.6" in QMD - and abs(min(mad_published) - 0.010) < 0.001 - and abs(max(mad_published) - 0.016) < 0.001, + has_number("1.0 to 1.6") and abs(min(mad_published) - 0.010) < 0.001 and abs(max(mad_published) - 0.016) < 0.001, ) -# Nowcast growth ranges quoted in prose. -rep_growth = [ - nowcast["components"][t]["replication_ratio"] - 1 for t in TENURES -] +# Nowcast growth ranges. +rep_growth = [nowcast["components"][t]["replication_ratio"] - 1 for t in TENURES] blend_growth = [nowcast["components"][t]["blend_ratio"] - 1 for t in TENURES] price_growth = nowcast["components"]["renter"]["price_ratio"] - 1 check( "replication growth 4.4 to 6.0", - "4.4 to 6.0" in QMD - and abs(min(rep_growth) - 0.044) < 0.001 - and abs(max(rep_growth) - 0.060) < 0.001, -) -check( - "price growth 3.2", - "3.2 percent" in QMD and abs(price_growth - 0.032) < 0.001, + has_number("4.4 to 6.0") and abs(min(rep_growth) - 0.044) < 0.001 and abs(max(rep_growth) - 0.060) < 0.001, ) +check("price growth 3.2", has_number("3.2 percent") and abs(price_growth - 0.032) < 0.001) check( "blend growth 3.8 to 4.6", - "3.8 to 4.6" in QMD - and abs(min(blend_growth) - 0.038) < 0.001 - and abs(max(blend_growth) - 0.046) < 0.001, + has_number("3.8 to 4.6") and abs(min(blend_growth) - 0.038) < 0.001 and abs(max(blend_growth) - 0.046) < 0.001, ) - -# Realized 2025/2024 All-Items CPI-U growth quoted in prose. realized_2025 = cpi["CUUR0000SA0"]["2025"] / cpi["CUUR0000SA0"]["2024"] - 1 -check( - "realized CPI-U 2025 growth 2.6", - "2.6 percent" in QMD and abs(realized_2025 - 0.026) < 0.002, -) +check("realized CPI-U 2025 growth 2.6", has_number("2.6 percent") and abs(realized_2025 - 0.026) < 0.002) +check("eleven-month CPI-U growth 2.63 quoted", has_number("2.63") and abs(realized_2025 - 0.0263) < 0.0005) -# (d) The September evaluation: every figure quoted from it re-derives -# from data/evaluation_2025.json (written by evaluate_nowcast_2025.py -# from the pinned artifacts + BLS's published 2025 values). -ev = json.loads((REPO / "data" / "evaluation_2025.json").read_text()) +# (e) The September evaluation, re-derived from data/evaluation_2025.json. +ev = json.loads((DATA / "evaluation_2025.json").read_text()) R = ev["rules"] amended = R["Amended nowcast (50/50 blend, committed)"] original = R["Original nowcast (pre-repair blend)"] @@ -321,32 +432,53 @@ def mae(rule: str) -> float: ("evaluation composite MAE 2.02", comp["mae"], "2.02"), ("evaluation CPI-U MAE 2.58", cpiu["mae"], "2.58"), ]: - check(label, literal in QMD and f"{value:.2%}".rstrip("%") == literal) + check(label, has_number(literal) and f"{value:.2%}".rstrip("%") == literal) check( - "evaluation renter miss -2.27", - "2.27" in QMD and abs(amended["errors"]["renter"] - (-0.0227)) < 0.0001, + "evaluation per-tenure misses 0.69, 0.55, 2.27 in order", + has_ordered(["0.69", "0.55", "2.27"], window=20) + and abs(amended["errors"]["owner_with_mortgage"] - (-0.0069)) < 0.0001 + and abs(amended["errors"]["owner_without_mortgage"] - (-0.0055)) < 0.0001 + and abs(amended["errors"]["renter"] - (-0.0227)) < 0.0001, ) -signed_mean = sum(amended["errors"].values()) / 3 +ev_signed = sum(amended["errors"].values()) / 3 +check("evaluation signed mean −1.17 inside stated range", "−1.17" in QMD and -0.014 <= ev_signed <= -0.001) +cpiu_errs = [abs(v) for v in cpiu["errors"].values()] check( - "evaluation signed mean -1.17 inside stated range", - "−1.17" in QMD and -0.014 <= signed_mean <= -0.001, + "CPI-U 2025 understated every tenure by 1.70 to 3.48", + has_number("1.70 to 3.48") and all(v < 0 for v in cpiu["errors"].values()) + and abs(min(cpiu_errs) - 0.0170) < 0.0001 and abs(max(cpiu_errs) - 0.0348) < 0.0001, ) -actual = json.loads((REPO / "data" / "bls_2025_thresholds.json").read_text()) +check( + "replication only rule with errors both sides of zero", + max(repl["errors"].values()) > 0 > min(repl["errors"].values()) + and all(max(R[k]["errors"].values()) < 0 for k in R if not k.startswith("CE replication")), +) +actual = json.loads((DATA / "bls_2025_thresholds.json").read_text()) for literal, value in [ ("41,323", actual["values"]["owner_with_mortgage"]), ("34,326", actual["values"]["owner_without_mortgage"]), ("41,701", actual["values"]["renter"]), ]: - check( - f"BLS 2025 literal {literal}", - literal in QMD and f"{round(value):,}" == literal, - ) -# Misses in units of BLS's published standard errors. -se = actual["standard_errors"] -miss_se = { - t: (amended["values"][t] - actual["values"][t]) / se[t] - for t in TENURES -} + check(f"BLS 2025 literal {literal}", has_number(literal) and f"{round(value):,}" == literal) +check( + "BLS 2025 triple in tenure order", + has_ordered(["41,323", "owners with", "34,326", "owners without", "41,701", "renters"], window=80), +) +g = actual["bls_stated_growth_pct_2025_over_2024"] +check( + "BLS-stated 2025 growth 4.40-6.33 and per-tenure 5.33/4.40/6.33", + has_number("4.40 to 6.33") and has_number("6.33") and has_number("5.33") and has_number("4.40") + and abs(min(g.values()) - 4.402) < 0.001 and abs(max(g.values()) - 6.325) < 0.001 + and abs(g["owner_with_mortgage"] - 5.332) < 0.001, +) +now_growth_renter = (nowcast["values"]["renter"] / ev["corrected_2024_base"]["renter"] - 1) * 100 +check("nowcast renter growth 3.92", has_number("3.92") and abs(now_growth_renter - 3.92) < 0.005) +check( + "replication renter growth 4.62", + has_number("4.62") and abs((nowcast["components"]["renter"]["replication_ratio"] - 1) * 100 - 4.62) < 0.005, +) +se25 = actual["standard_errors"] +miss_se = {t: (amended["values"][t] - actual["values"][t]) / se25[t] for t in TENURES} check( "owner misses within one SE; renter about 2.4 SE", "2.4 standard errors" in QMD @@ -354,22 +486,26 @@ def mae(rule: str) -> float: and abs(miss_se["owner_without_mortgage"]) < 1 and abs(abs(miss_se["renter"]) - 2.4) < 0.05, ) -check( - "BLS-stated 2025 growth 4.40-6.33", - "4.40 to 6.33" in QMD - and min(actual["bls_stated_growth_pct_2025_over_2024"].values()) == 4.402 - and max(actual["bls_stated_growth_pct_2025_over_2024"].values()) == 6.325, -) +for literal, key in (("327", "owner_with_mortgage"), ("560", "owner_without_mortgage"), ("393", "renter")): + check(f"2025 SE literal {literal}", has_number(literal) and f"{round(se25[key])}" == literal) gaps = ev["composite_vs_bls_fcsuti_gap_pp"] check( - "composite within 0.7pp of BLS FCSUti", - "0.7 percentage point" in QMD and max(abs(g) for g in gaps.values()) < 0.7, + "composite within 0.7pp of BLS FCSUti, mean gap 0.4", + "0.7 percentage point" in QMD and max(abs(v) for v in gaps.values()) < 0.7 + and "0.4 point" in QMD and abs(sum(abs(v) for v in gaps.values()) / len(gaps) - 0.4) < 0.05, ) -census = json.loads((REPO / "data" / "census_wp2026_17_rates.json").read_text()) +bls_cpiu_2025 = actual["bls_chart4_annual_average_inflation_pct"]["2025"]["cpi_u"] +check("BLS page 2025 CPI-U growth 2.70 quoted", has_number("2.70 percent") and abs(bls_cpiu_2025 - 2.70) < 0.005) +census = json.loads((DATA / "census_wp2026_17_rates.json").read_text()) c23 = census["corrected_series_2023_to_2024"]["all_people"] check( "Census corrected 2023->2024 = 12.7 -> 13.0", - "12.7" in QMD and "13.0" in QMD and c23["2023"] == 12.7 and c23["2024"] == 13.0, + has_ordered(["12.7", "13.0"], window=10) and c23["2023"] == 12.7 and c23["2024"] == 13.0, +) +diffs = [v["difference"] for y, v in census["all_people"].items() if y != "2024"] +check( + "Census 2019-2023 corrected 0.1 to 0.3 below", + has_number("0.1 to 0.3") and abs(max(diffs)) == 0.1 and abs(min(diffs)) == 0.3 and all(d < 0 for d in diffs), ) if failures: @@ -377,4 +513,6 @@ def mae(rule: str) -> float: for f in failures: print(f" - {f}") sys.exit(1) -print(f"paper drift check passed ({len(before)} tables, prose pins OK)") +print( + f"paper drift check passed ({len(EXPECTED_TABLES)} tables, {len(listed)} artifacts, prose pins OK)" +) diff --git a/scripts/simulate_nowcast_rate_impact.py b/scripts/simulate_nowcast_rate_impact.py index 8ed3c70..c6d5e33 100644 --- a/scripts/simulate_nowcast_rate_impact.py +++ b/scripts/simulate_nowcast_rate_impact.py @@ -10,15 +10,45 @@ nowcast counterfactual rescales each unit's threshold exactly. """ +import argparse import gc +import importlib.metadata import json +import subprocess +from pathlib import Path import numpy as np from policyengine_us import CountryTaxBenefitSystem, Microsimulation from spm_calculator import nowcast_with_metadata -OUT = "/private/tmp/claude-501/-Users-maxghenis/df49f127-9b39-49ca-a681-58a1a922dc27/scratchpad/nowcast_rate_impact.json" +REPO = Path(__file__).resolve().parent.parent +DEFAULT_OUT = REPO / "data" / "nowcast_rate_impact.json" + + +def _version(dist: str) -> str: + try: + return importlib.metadata.version(dist) + except importlib.metadata.PackageNotFoundError: + return "unknown" + + +def _spm_calculator_commit() -> str: + """Commit of an editable spm-calculator install, if it is a git tree.""" + try: + import spm_calculator + + root = Path(spm_calculator.__file__).resolve().parent.parent + return subprocess.check_output( + ["git", "-C", str(root), "rev-parse", "HEAD"], text=True + ).strip() + except Exception: # noqa: BLE001 + return "unknown" + + +parser = argparse.ArgumentParser() +parser.add_argument("--output", type=Path, default=DEFAULT_OUT) +OUT = parser.parse_args().output doc = nowcast_with_metadata(2025) blend = {t.upper(): c["blend_ratio"] for t, c in doc["components"].items()} @@ -29,7 +59,16 @@ print(f"PE CPI-U factor 2025/2024: {cpi_factor:.5f}") print("nowcast/current threshold ratios:", {k: round(v, 5) for k, v in RATIO.items()}) -results = {"threshold_ratio_nowcast_over_current": RATIO} +results = { + "generated_by": ( + "scripts/simulate_nowcast_rate_impact.py (PolicyEngine US " + f"microsimulation, policyengine-us {_version('policyengine-us')}, " + f"spm-calculator {_version('spm-calculator')} at commit " + f"{_spm_calculator_commit()}, default dataset of that " + "policyengine-us release)" + ), + "threshold_ratio_nowcast_over_current": RATIO, +} for year in (2025, 2026): sim = Microsimulation() net = sim.calculate("spm_unit_net_income", year) From 47b062d5cb477f0c7cdecc610ac03f0de68f02b9 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Fri, 4 Sep 2026 08:51:36 -0400 Subject: [PATCH 04/14] CI: full-depth, tagged checkout so the evaluation can read v1.0-original-nowcast; clearer error when the tag is unreachable --- .github/workflows/check.yaml | 6 ++++++ scripts/evaluate_nowcast_2025.py | 20 ++++++++++++-------- 2 files changed, 18 insertions(+), 8 deletions(-) diff --git a/.github/workflows/check.yaml b/.github/workflows/check.yaml index 20e3cca..58ccd6e 100644 --- a/.github/workflows/check.yaml +++ b/.github/workflows/check.yaml @@ -10,6 +10,12 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + # The evaluation reads the original nowcast from the + # v1.0-original-nowcast tag; a shallow, tagless checkout + # cannot resolve it. + fetch-depth: 0 + fetch-tags: true - uses: actions/setup-python@v5 with: python-version: "3.12" diff --git a/scripts/evaluate_nowcast_2025.py b/scripts/evaluate_nowcast_2025.py index cc1d317..71ee2bf 100644 --- a/scripts/evaluate_nowcast_2025.py +++ b/scripts/evaluate_nowcast_2025.py @@ -47,15 +47,19 @@ # The original (pre-repair) nowcast, from the tagged release rather # than a hand-copied number. -original = json.loads( - subprocess.run( - ["git", "show", "v1.0-original-nowcast:data/nowcast_2025.json"], - cwd=REPO, - check=True, - capture_output=True, - text=True, - ).stdout +_orig = subprocess.run( + ["git", "show", "v1.0-original-nowcast:data/nowcast_2025.json"], + cwd=REPO, + capture_output=True, + text=True, ) +if _orig.returncode != 0: + raise SystemExit( + "cannot read data/nowcast_2025.json at tag v1.0-original-nowcast " + "(shallow or tagless checkout? run `git fetch --tags --unshallow`): " + + _orig.stderr.strip() + ) +original = json.loads(_orig.stdout) cpi_u_ratio = cpi["CUUR0000SA0"]["2025"] / cpi["CUUR0000SA0"]["2024"] From d0f07184097ee4f2d8de237acc8d599dc7f4741c Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 8 Sep 2026 15:25:43 -0400 Subject: [PATCH 05/14] Separate current SPM research from preserved forecast commitments --- .github/workflows/check.yaml | 2 + README.md | 95 +- data/COMMITMENT-AMENDMENT-2026-09-08.txt | 21 + data/PROVENANCE.md | 27 + data/current/SHA256SUMS | 3 + data/current/ce_replication_2019_2025.json | 8483 ++++++++ data/current/provenance.json | 22 + data/current/spm-release.json | 17204 ++++++++++++++++ paper/index.qmd | 274 +- paper/tables/current_projection.md | 9 + paper/tables/current_release.md | 5 + paper/tables/current_replication.md | 9 + paper/tables/current_sensitivity.md | 9 + paper/tables/package_errors.md | 2 +- scripts/build_current_tables.py | 177 + scripts/build_tables.py | 21 +- scripts/check_paper.py | 411 +- scripts/evaluate_nowcast_2025.py | 22 +- scripts/verify_commitments.py | 79 + .../test_paper_guard.cpython-314.pyc | Bin 0 -> 11694 bytes tests/test_paper_guard.py | 166 + 21 files changed, 26806 insertions(+), 235 deletions(-) create mode 100644 data/COMMITMENT-AMENDMENT-2026-09-08.txt create mode 100644 data/current/SHA256SUMS create mode 100644 data/current/ce_replication_2019_2025.json create mode 100644 data/current/provenance.json create mode 100644 data/current/spm-release.json create mode 100644 paper/tables/current_projection.md create mode 100644 paper/tables/current_release.md create mode 100644 paper/tables/current_replication.md create mode 100644 paper/tables/current_sensitivity.md create mode 100644 scripts/build_current_tables.py create mode 100644 scripts/verify_commitments.py create mode 100644 tests/__pycache__/test_paper_guard.cpython-314.pyc create mode 100644 tests/test_paper_guard.py diff --git a/.github/workflows/check.yaml b/.github/workflows/check.yaml index 58ccd6e..74d6627 100644 --- a/.github/workflows/check.yaml +++ b/.github/workflows/check.yaml @@ -21,3 +21,5 @@ jobs: python-version: "3.12" - name: Paper drift check run: python scripts/check_paper.py + - name: Guard mutation tests + run: python -m unittest discover -s tests -v diff --git a/README.md b/README.md index 4e825cc..ecb932c 100644 --- a/README.md +++ b/README.md @@ -1,61 +1,48 @@ # Nowcasting Supplemental Poverty Measure thresholds -Working paper. Quantifies the July 17, 2026 BLS threshold correction, -documents a replication of the BLS threshold methodology from public CE -microdata, backtests threshold-projection rules, and commits to a -pre-registered nowcast of the unpublished 2025 thresholds — graded -against BLS's actual publication (~September 2026) in a planned -revision. +Working paper on the July 2026 BLS threshold correction, public CE +replication, threshold projection, and the completed evaluation of the +2025 forecast. The September 8 revision preserves that forecast and its +evaluation, and adds a separately identified current-method experiment +and portable threshold release. -## Build +## Build and verify ```bash -quarto render # runs scripts/check_paper.py pre-render, then HTML + PDF to _output/ +python3 -B scripts/check_paper.py +python3 -B -m unittest discover -s tests -v +quarto render --to html +quarto render --to pdf ``` -The pre-render check (`scripts/check_paper.py`) regenerates every -table from `data/`, enforces allowlists of generated tables, QMD -includes, and checksummed artifacts, verifies `data/SHA256SUMS`, and -re-derives the registered prose figures at numeric-token boundaries; -the render fails if any registered figure, table, or artifact drifts. -Figures quoted from external publications are bound to citations, -not artifacts. `data/PROVENANCE.md` names the generator, inputs, and -source of every artifact. - -## Clean-room build - -Tested on 2026-09-04 with Python 3.14.6, Quarto 1.9.36, and TeX Live -2026 (LuaHBTeX). The check and the table generators use only the -Python standard library and need a writable checkout. - -```bash -git clone https://github.com/PolicyEngine/spm-threshold-paper.git -cd spm-threshold-paper -git checkout -python3 scripts/check_paper.py # guard only; no Quarto needed -quarto render --to html # HTML; add --to pdf for the PDF (needs TeX) -``` - -Rendered output lands in `_output/paper/`. The published site is a -static deploy of that directory. - -## Pre-commitment evidence - -- `data/SHA256SUMS.precommit` — the artifact manifest as of 2026-08-07, - before BLS published the 2025 thresholds; `SHA256SUMS.precommit.ots` - is its completed OpenTimestamps proof (Bitcoin block 961505, mined - 2026-08-07 23:18 UTC). Verify with - `ots verify -f data/SHA256SUMS.precommit data/SHA256SUMS.precommit.ots`. -- `data/COMMITMENT-MANIFEST.txt` — both nowcast vintages (tags - `v1.0-original-nowcast`, `v1.1-amended-nowcast`), their commits and - artifact hashes; stamped as `COMMITMENT-MANIFEST.txt.ots`. -- Internet Archive capture of the page, 2026-08-07: - - -## Companion code - -- spm-calculator PR #32 (0.4.0 on merge): corrected/published/legacy threshold series - with provenance, CE replication, benchmark, backtest, nowcast, and - the weekly BLS drift-watch workflow. -- policyengine-us #9081: adopts the corrected series in the US - microsimulation model. +The guard and table generators use the Python standard library. Quarto +1.9.36 and TeX Live 2026 render the HTML and PDF to `_output/paper/`. +Clone with full tag history and check out the exact revision to reproduce +it. The guard first checks artifact hashes and frozen Git identities, +then generates tables and evaluation JSON in temporary storage and +compares every output byte with the checkout. It never repairs or +restores source artifacts. The regression tests verify that failed checks +leave source bytes unchanged. + +## Preserved forecast evidence + +The tags `v1.0-original-nowcast` and `v1.1-amended-nowcast`, forecast +bytes, and all original OpenTimestamps manifest/proof pairs are preserved. +Two hashes in `data/COMMITMENT-MANIFEST.txt` were incorrect; the dated +`data/COMMITMENT-AMENDMENT-2026-09-08.txt` records the exact tag hashes and +the original manifest digest. The original proof does not cover that +amendment. This revision verifies retained proof bytes and Git identities; +it does not independently reverify blockchain attestations. + +`data/SHA256SUMS` continues to cover only the frozen artifacts. New inputs +have their own `data/current/SHA256SUMS`; they do not replace the frozen +forecast or become a new prospective commitment. `data/PROVENANCE.md` +describes both collections and the original implementation pin. + +## Companion implementation + +The current spm-calculator rebuild provides a portable release, standalone +unit calculations, CE research replication, projections, and explicit +PolicyEngine, Microcosm, and real native Axiom adapters. This paper pins version 0.5.0 at development commit +`9e6ae4798458771231613b994df2345dd1685214`; it does not claim that a model +migration or public deployment has occurred. The paper consumes the same release bytes as those adapters. diff --git a/data/COMMITMENT-AMENDMENT-2026-09-08.txt b/data/COMMITMENT-AMENDMENT-2026-09-08.txt new file mode 100644 index 0000000..1918307 --- /dev/null +++ b/data/COMMITMENT-AMENDMENT-2026-09-08.txt @@ -0,0 +1,21 @@ +{ + "format": "spm/commitment-amendment/v1", + "date": "2026-09-08", + "original_manifest": "data/COMMITMENT-MANIFEST.txt", + "original_manifest_sha256": "18d82ad41fad56b42dfce537523e50b5b89956b8161dc60e60d90f8753b39b37", + "covered_by_original_timestamp": false, + "reason": "Correct the two SHA-256 values transcribed incorrectly in the secondary manifest written September 4. The tagged forecast bytes, amounts, tag objects, original manifest and all existing timestamp proofs remain unchanged. The original manifest's proof attests its original bytes, including those errors; it does not attest this amendment.", + "verification": "SHA-256 over exact bytes returned by git show TAG:data/nowcast_2025.json; tag object and peeled commit checked separately. This amendment makes no new claim of blockchain-proof verification.", + "tags": { + "v1.0-original-nowcast": { + "tag_object": "12b7042a960412477af53c62ebd961ded1a2625e", + "commit": "a8b7c51869e64e36517d4eca8d5dbd6e2e0cde5b", + "nowcast_sha256": "19a1f44d10d9151c0b4d3cdb953e9d338bc9dd7a6fe06b88a91665649a5931c0" + }, + "v1.1-amended-nowcast": { + "tag_object": "3759670e314b762fa24348a3c9a16526cdc74b47", + "commit": "d72e1dc083ad1abc95b1c88fd1b782944454074b", + "nowcast_sha256": "259a5b2e0d15646722fe309f741546e364827fab053c73c99d273061fa0b1ba8" + } + } +} diff --git a/data/PROVENANCE.md b/data/PROVENANCE.md index 8d11c79..f0f0754 100644 --- a/data/PROVENANCE.md +++ b/data/PROVENANCE.md @@ -28,3 +28,30 @@ Generated tables (`paper/tables/*.md`) are written only by `scripts/build_tables.py` and `scripts/evaluate_nowcast_2025.py`, both run by `scripts/check_paper.py`, which also enforces the table allowlist, the QMD include allowlist, and full `SHA256SUMS` coverage of `data/`. + + +## September 8, 2026 amendment and current inputs + +The original `SHA256SUMS`, `SHA256SUMS.precommit`, and +`COMMITMENT-MANIFEST.txt` and all three `.ots` proofs remain byte-identical. +`COMMITMENT-AMENDMENT-2026-09-08.txt` corrects the two tag-artifact hashes +without claiming coverage by the old proof. `verify_commitments.py` checks +those original bytes, tag objects, peeled commits, and exact forecast bytes. +It does not contact OpenTimestamps calendars or verify Bitcoin attestations. + +`data/current/SHA256SUMS` separately covers these new inputs: + +| Artifact | Source and scope | +|---|---| +| `spm-release.json` | Exact portable `spm-2026-09-08.json` from the spm-calculator 0.5.0 rebuild. Revised national 2005–2025 published thresholds, source fingerprints, separately dated 2024 housing shares, and pinned geographic rent indices. The release's conservative available-on date is September 8, not a reconstructed historical publication calendar. | +| `ce_replication_2019_2025.json` | `scripts/replicate_current_ce.py` in the rebuild. Seven overlapping CE windows, explicit minor-only and tenure approximations, raw source bundle hashes, CPI cache hash, exact executed source hashes, and six-year retrospective projections. No prospective validation or new commitment. | +| `provenance.json` | Paper linkage to the release content digest and CE artifact/source hashes. Pins spm-calculator 0.5.0 development commit `9e6ae4798458771231613b994df2345dd1685214`. The retained `ce_starting_git_head` identifies the checkout's starting commit before the rebuild was committed. The `ce_code_sha256` map identifies the executed code, whose bytes match the pinned development commit. | + +`build_current_tables.py` reads only these inputs. It cross-checks the +published comparison series against the shared release, re-derives +replication and sensitivity percentages and projection errors, and +produces the four `current_*.md` tables. The pre-render guard checks both +manifests before parsing inputs, generates all tables and evaluation JSON +in scratch space, and compares bytes without mutating source files. These +checks establish reproducibility of the recorded calculations, not exact +parity with the BLS production procedure or historical information sets. diff --git a/data/current/SHA256SUMS b/data/current/SHA256SUMS new file mode 100644 index 0000000..9bd8f78 --- /dev/null +++ b/data/current/SHA256SUMS @@ -0,0 +1,3 @@ +dcfe3ecc3e9898a6190fbd46fe2bf20057dd4f7d509bc4bf0376f4bd79034a45 data/current/ce_replication_2019_2025.json +2c33478cdc075c61847863219e5f32f2060d8563b8ea0619a726cc345dc424d6 data/current/provenance.json +a12113b5b574e0d4371814f2c3f127b00f91016bddc94c0f6459b7a7742cf02d data/current/spm-release.json diff --git a/data/current/ce_replication_2019_2025.json b/data/current/ce_replication_2019_2025.json new file mode 100644 index 0000000..127b6ee --- /dev/null +++ b/data/current/ce_replication_2019_2025.json @@ -0,0 +1,8483 @@ +{ + "classification": "current_retrospective_research", + "code_sha256": { + "scripts/replicate_current_ce.py": "4168c0a432c8eb2d63fbfa65aa0b9bc7f4906e4dba397df67149cf3c7207305a", + "spm_calculator/ce_threshold.py": "2a43c93460d03f54ef7e589dfc7411069ceabf91ed8e234ceeb004da1ed3ad84", + "spm_calculator/equivalence_scale.py": "95c797adca4811b0a2c4202e8471f6cd46fcd259bf46b34da74588ab08b41326", + "spm_calculator/fcsuti_cpi.py": "cf8fbe9103846d83a280bcd4f13b9ddd91e8baa94e2668b9ef288c7cc980a5fc", + "spm_calculator/forecast.py": "28092513cea2b5451da56532cb628273b8d1fafae64b6ab41eb605cacbe5fb0e", + "spm_calculator/projection.py": "a7b0c6935f3cdf857e6cffc941cbe4fef9607c460a20e0ba8da72a19c6a60340" + }, + "cpi_input": { + "note": "Annual averages (BLS period M13 where API-sourced; means of NSA monthlies where FRED-sourced). 2025 values are computed over eleven months \u2014 the October 2025 CPI release was not published during the federal shutdown \u2014 matching BLS's official 2025 annual averages.", + "path": "spm_calculator/data/bls/cpi_annual.json", + "retrieved": "2026-07-18", + "sha256": "03f0214e6b8393292ad3c412651fd7df0d8a36aaf5125f77c0bb65b3f10ca104", + "sources": { + "CUUR0000SA0": "FRED CPIAUCNS (annual mean)", + "CUUR0000SAA": "FRED CPIAPPNS (annual mean)", + "CUUR0000SAF": "FRED CPIFABNS (annual mean)", + "CUUR0000SAF11": "FRED CUSR0000SAF11 (annual mean)", + "CUUR0000SAH1": "FRED CUUR0000SAH1 (annual mean)", + "CUUR0000SAH2": "FRED CUUR0000SAH2 (annual mean)", + "CUUR0000SEED": "FRED CUUR0000SEED (annual mean)", + "CUUR0000SEFV": "FRED CUUR0000SEFV (annual mean)" + } + }, + "frozen_commitments_modified": false, + "generated_by": "scripts/replicate_current_ce.py", + "generated_on": "2026-09-08", + "git_head": "816a7084990e2bbb7dff801fd9b9cd5cdd1bb995", + "limitations": [ + "Youth sensitivity compares explicit assumptions; it does not identify the correct BLS classification", + "Tenure sensitivity changes assignment/exclusion policy; official BLS handling remains unverified", + "Annual CPI treatment, post-redesign food allocation, internet/benefit omissions and percentile convention remain approximations", + "No sampling replicate weights or imputation uncertainty estimated", + "CE original publication/revision dates are unknown; these are current cached vintages, not reconstructed real-time inputs", + "Raw weight sums are CU-interview mass over overlapping windows, not unique national population totals", + "No new 2026 forecast or commitment is made" + ], + "projection_evaluation": { + "classification": "retrospective_current_method_not_a_frozen_commitment", + "information_date_policy": "All observations conservatively dated at this replay; no historical release dates inferred", + "price_method_note": "Static FCSUti component weights rebased to each prior year; differs from frozen backtest's fixed 2019 price base", + "rows": [ + { + "errors_percent": { + "owner_with_mortgage": -1.2734302518517815, + "owner_without_mortgage": -1.7099426247275007, + "renter": -2.3655114297687185 + }, + "mean_absolute_error_percent": 1.7829614354493337, + "projection": { + "as_of": "2026-09-08", + "base_year": 2019, + "blend_weight": null, + "evaluation_mode": "retrospective", + "growth_factors": { + "owner_with_mortgage": 1.0123368419405687, + "owner_without_mortgage": 1.0123368419405687, + "renter": 1.0123368419405687 + }, + "inputs": { + "official_base": { + "available_on": "2026-09-08", + "kind": "published", + "methodology_id": "bls-corrected-2026-07-17-continuation", + "source_id": "corrected-canonical-threshold-series", + "values": { + "owner_with_mortgage": 29076.17484, + "owner_without_mortgage": 24514.952286, + "renter": 28913.050035 + }, + "year": 2019 + }, + "price_base": { + "available_on": "2026-09-08", + "kind": "price", + "methodology_id": "cpi_u", + "source_id": "pinned-cpi", + "values": 100.0, + "year": 2019 + }, + "price_target": { + "available_on": "2026-09-08", + "kind": "price", + "methodology_id": "cpi_u", + "source_id": "pinned-cpi", + "values": 101.23368419405688, + "year": 2020 + } + }, + "methodology_id": "anchored_price_only_v1", + "target_year": 2020, + "thresholds": { + "owner_with_mortgage": 29434.88301323742, + "owner_without_mortgage": 24817.389377532967, + "renter": 29269.74576330155 + }, + "uncertainty": { + "reason": "Input uncertainty and forecast errors not estimated", + "status": "unavailable" + } + }, + "rule": "cpi_u", + "target_year": 2020 + }, + { + "errors_percent": { + "owner_with_mortgage": -0.38114938041295954, + "owner_without_mortgage": -0.8216069084931776, + "renter": -1.4831006788820433 + }, + "mean_absolute_error_percent": 0.8952856559293935, + "projection": { + "as_of": "2026-09-08", + "base_year": 2019, + "blend_weight": null, + "evaluation_mode": "retrospective", + "growth_factors": { + "owner_with_mortgage": 1.0214862411531682, + "owner_without_mortgage": 1.0214862411531682, + "renter": 1.0214862411531682 + }, + "inputs": { + "official_base": { + "available_on": "2026-09-08", + "kind": "published", + "methodology_id": "bls-corrected-2026-07-17-continuation", + "source_id": "corrected-canonical-threshold-series", + "values": { + "owner_with_mortgage": 29076.17484, + "owner_without_mortgage": 24514.952286, + "renter": 28913.050035 + }, + "year": 2019 + }, + "price_base": { + "available_on": "2026-09-08", + "kind": "price", + "methodology_id": "static_fcsuti_rebased_prior_year", + "source_id": "pinned-cpi", + "values": 100.0, + "year": 2019 + }, + "price_target": { + "available_on": "2026-09-08", + "kind": "price", + "methodology_id": "static_fcsuti_rebased_prior_year", + "source_id": "pinned-cpi", + "values": 102.14862411531682, + "year": 2020 + } + }, + "methodology_id": "anchored_price_only_v1", + "target_year": 2020, + "thresholds": { + "owner_with_mortgage": 29700.91254442392, + "owner_without_mortgage": 25041.686462675407, + "renter": 29534.28280052563 + }, + "uncertainty": { + "reason": "Input uncertainty and forecast errors not estimated", + "status": "unavailable" + } + }, + "rule": "fcsuti_cpi", + "target_year": 2020 + }, + { + "errors_percent": { + "owner_with_mortgage": -0.025937780798090948, + "owner_without_mortgage": 0.7711535668682767, + "renter": -0.33636099365565997 + }, + "mean_absolute_error_percent": 0.37781744710734255, + "projection": { + "as_of": "2026-09-08", + "base_year": 2019, + "blend_weight": null, + "evaluation_mode": "retrospective", + "growth_factors": { + "owner_with_mortgage": 1.0251285614514638, + "owner_without_mortgage": 1.0378908516768854, + "renter": 1.0333763718689648 + }, + "inputs": { + "official_base": { + "available_on": "2026-09-08", + "kind": "published", + "methodology_id": "bls-corrected-2026-07-17-continuation", + "source_id": "corrected-canonical-threshold-series", + "values": { + "owner_with_mortgage": 29076.17484, + "owner_without_mortgage": 24514.952286, + "renter": 28913.050035 + }, + "year": 2019 + }, + "replicated_base": { + "available_on": "2026-09-08", + "kind": "replication", + "methodology_id": "ce-fmli-research-v1:9b0ac2584036d8cf22989b08b14bde56b986c438c987abac56f633432fd3ce28", + "source_id": "current-ce-2019", + "values": { + "owner_with_mortgage": 28591.59504590974, + "owner_without_mortgage": 24270.101286487734, + "renter": 28588.875626911577 + }, + "year": 2019 + }, + "replicated_target": { + "available_on": "2026-09-08", + "kind": "replication", + "methodology_id": "ce-fmli-research-v1:9b0ac2584036d8cf22989b08b14bde56b986c438c987abac56f633432fd3ce28", + "source_id": "current-ce-2020", + "values": { + "owner_with_mortgage": 29310.06069901625, + "owner_without_mortgage": 25189.716094517025, + "renter": 29543.06857115096 + }, + "year": 2020 + } + }, + "methodology_id": "anchored_replication_ratio_v1", + "target_year": 2020, + "thresholds": { + "owner_with_mortgage": 29806.817286240446, + "owner_without_mortgage": 25443.84470693475, + "renter": 29878.062744834147 + }, + "uncertainty": { + "reason": "Input uncertainty and forecast errors not estimated", + "status": "unavailable" + } + }, + "rule": "replication_ratio", + "target_year": 2020 + }, + { + "errors_percent": { + "owner_with_mortgage": -0.20354358060553635, + "owner_without_mortgage": -0.02522667081247265, + "renter": -0.9097308362688517 + }, + "mean_absolute_error_percent": 0.37950036256228686, + "projection": { + "as_of": "2026-09-08", + "base_year": 2019, + "blend_weight": 0.5, + "evaluation_mode": "retrospective", + "growth_factors": { + "owner_with_mortgage": 1.023307401302316, + "owner_without_mortgage": 1.0296885464150267, + "renter": 1.0274313065110665 + }, + "inputs": { + "official_base": { + "available_on": "2026-09-08", + "kind": "published", + "methodology_id": "bls-corrected-2026-07-17-continuation", + "source_id": "corrected-canonical-threshold-series", + "values": { + "owner_with_mortgage": 29076.17484, + "owner_without_mortgage": 24514.952286, + "renter": 28913.050035 + }, + "year": 2019 + }, + "price_base": { + "available_on": "2026-09-08", + "kind": "price", + "methodology_id": "static_fcsuti_rebased_prior_year", + "source_id": "pinned-cpi", + "values": 100.0, + "year": 2019 + }, + "price_target": { + "available_on": "2026-09-08", + "kind": "price", + "methodology_id": "static_fcsuti_rebased_prior_year", + "source_id": "pinned-cpi", + "values": 102.14862411531682, + "year": 2020 + }, + "replicated_base": { + "available_on": "2026-09-08", + "kind": "replication", + "methodology_id": "ce-fmli-research-v1:9b0ac2584036d8cf22989b08b14bde56b986c438c987abac56f633432fd3ce28", + "source_id": "current-ce-2019", + "values": { + "owner_with_mortgage": 28591.59504590974, + "owner_without_mortgage": 24270.101286487734, + "renter": 28588.875626911577 + }, + "year": 2019 + }, + "replicated_target": { + "available_on": "2026-09-08", + "kind": "replication", + "methodology_id": "ce-fmli-research-v1:9b0ac2584036d8cf22989b08b14bde56b986c438c987abac56f633432fd3ce28", + "source_id": "current-ce-2020", + "values": { + "owner_with_mortgage": 29310.06069901625, + "owner_without_mortgage": 25189.716094517025, + "renter": 29543.06857115096 + }, + "year": 2020 + } + }, + "methodology_id": "anchored_blend_v1", + "target_year": 2020, + "thresholds": { + "owner_with_mortgage": 29753.864915332182, + "owner_without_mortgage": 25242.765584805074, + "renter": 29706.17277267989 + }, + "uncertainty": { + "reason": "Input uncertainty and forecast errors not estimated", + "status": "unavailable" + } + }, + "rule": "blend", + "target_year": 2020 + }, + { + "errors_percent": { + "owner_with_mortgage": 0.7488020917878924, + "owner_without_mortgage": 1.4582160196448646, + "renter": 0.5465733834939313 + }, + "mean_absolute_error_percent": 0.9178638316422294, + "projection": { + "as_of": "2026-09-08", + "base_year": 2020, + "blend_weight": null, + "evaluation_mode": "retrospective", + "growth_factors": { + "owner_with_mortgage": 1.0469802288156225, + "owner_without_mortgage": 1.0469802288156225, + "renter": 1.0469802288156225 + }, + "inputs": { + "official_base": { + "available_on": "2026-09-08", + "kind": "published", + "methodology_id": "bls-corrected-2026-07-17-continuation", + "source_id": "corrected-canonical-threshold-series", + "values": { + "owner_with_mortgage": 29814.550519, + "owner_without_mortgage": 25249.135101, + "renter": 29978.900071 + }, + "year": 2020 + }, + "price_base": { + "available_on": "2026-09-08", + "kind": "price", + "methodology_id": "cpi_u", + "source_id": "pinned-cpi", + "values": 100.0, + "year": 2020 + }, + "price_target": { + "available_on": "2026-09-08", + "kind": "price", + "methodology_id": "cpi_u", + "source_id": "pinned-cpi", + "values": 104.69802288156225, + "year": 2021 + } + }, + "methodology_id": "anchored_price_only_v1", + "target_year": 2021, + "thresholds": { + "owner_with_mortgage": 31215.244924417555, + "owner_without_mortgage": 26435.345245441546, + "renter": 31387.31565597626 + }, + "uncertainty": { + "reason": "Input uncertainty and forecast errors not estimated", + "status": "unavailable" + } 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+ "name": "Tampa-St. Petersburg-Clearwater, FL MSA", + "rent_index": 1.126 + }, + "45460": { + "name": "Terre Haute, IN MSA", + "rent_index": 0.698 + }, + "45780": { + "name": "Toledo, OH MSA", + "rent_index": 0.714 + }, + "45820": { + "name": "Topeka, KS MSA", + "rent_index": 0.741 + }, + "45940": { + "name": "Trenton, NJ MSA", + "rent_index": 1.219 + }, + "46001": { + "name": "South Dakota Metro", + "rent_index": 0.845 + }, + "46002": { + "name": "South Dakota Nonmetro", + "rent_index": 0.616 + }, + "46060": { + "name": "Tucson, AZ MSA", + "rent_index": 0.905 + }, + "46140": { + "name": "Tulsa, OK MSA", + "rent_index": 0.769 + }, + "46340": { + "name": "Tyler, TX MSA", + "rent_index": 0.874 + }, + "46520": { + "name": "Urban Honolulu, HI MSA", + "rent_index": 1.55 + }, + "46540": { + "name": "Utica-Rome, NY MSA", + "rent_index": 0.712 + }, + "46700": { + "name": "Vallejo-Fairfield, CA MSA", + "rent_index": 1.509 + }, + "47002": { + "name": "Tennessee Nonmetro", + "rent_index": 0.614 + }, + "47220": { + "name": "Vineland-Bridgeton, NJ MSA", + "rent_index": 0.939 + }, + "47260": { + "name": "Virginia Beach-Norfolk-Newport News, VA-NC MSA", + "rent_index": 1.004 + }, + "47300": { + "name": "Visalia-Porterville, CA MSA", + "rent_index": 0.835 + }, + "47380": { + "name": "Waco, TX MSA", + "rent_index": 0.838 + }, + "47580": { + "name": "Warner Robins, GA MSA", + "rent_index": 0.759 + }, + "47900": { + "name": "Washington-Arlington-Alexandria, DC-VA-MD-WV MSA", + "rent_index": 1.495 + }, + "47940": { + "name": "Waterloo-Cedar Falls, IA MSA", + "rent_index": 0.721 + }, + "48001": { + "name": "Texas Metro", + "rent_index": 0.819 + }, + "48002": { + "name": "Texas Nonmetro", + "rent_index": 0.756 + }, + "48060": { + "name": "Watertown-Fort Drum, NY MSA", + "rent_index": 0.899 + }, + "48140": { + "name": "Wausau, WI MSA", + "rent_index": 0.726 + }, + "48620": { + "name": "Wichita, KS MSA", + "rent_index": 0.727 + }, + "48660": { + "name": "Wichita Falls, TX MSA", + "rent_index": 0.706 + }, + "48700": { + "name": "Williamsport, PA MSA", + "rent_index": 0.669 + }, + "49001": { + "name": "Utah Metro", + "rent_index": 0.756 + }, + "49002": { + "name": "Utah Nonmetro", + "rent_index": 0.663 + }, + "49020": { + "name": "Winchester, VA-WV MSA", + "rent_index": 1.008 + }, + "49180": { + "name": "Winston-Salem, NC MSA", + "rent_index": 0.699 + }, + "49340": { + "name": "Worcester, MA-CT MSA", + "rent_index": 1.034 + }, + "49620": { + "name": "York-Hanover, PA MSA", + "rent_index": 0.861 + }, + "49660": { + "name": "Youngstown-Warren-Boardman, OH-PA MSA", + "rent_index": 0.601 + }, + "49740": { + "name": "Yuma, AZ MSA", + "rent_index": 0.736 + }, + "50002": { + "name": "Vermont Nonmetro", + "rent_index": 0.85 + }, + "5001": { + "name": "Arkansas Metro", + "rent_index": 0.673 + }, + "5002": { + "name": "Arkansas Nonmetro", + "rent_index": 0.597 + }, + "51001": { + "name": "Virginia Metro", + "rent_index": 0.748 + }, + "51002": { + "name": "Virginia Nonmetro", + "rent_index": 0.605 + }, + "53001": { + "name": "Washington Metro", + "rent_index": 0.863 + }, + "53002": { + "name": "Washington Nonmetro", + "rent_index": 0.745 + }, + "54001": { + "name": "West Virginia Metro", + "rent_index": 0.621 + }, + "54002": { + "name": "West Virginia Nonmetro", + "rent_index": 0.641 + }, + "55002": { + "name": "Wisconsin Nonmetro", + "rent_index": 0.658 + }, + "56001": { + "name": "Wyoming Metro", + "rent_index": 0.751 + }, + "56002": { + "name": "Wyoming Nonmetro", + "rent_index": 0.668 + }, + "6002": { + "name": "California Nonmetro", + "rent_index": 0.964 + }, + "8002": { + "name": "Colorado Nonmetro", + "rent_index": 0.944 + }, + "9002": { + "name": "Connecticut Nonmetro", + "rent_index": 0.976 + } + }, + "id": "census-spm-metro-2024", + "source_id": "census-metro-2024-snapshot", + "year": 2024 + }, + "state": { + "areas": { + "01": { + "name": "Alabama", + "rent_index": 0.6823617339312407 + }, + "02": { + "name": "Alaska", + "rent_index": 1.04559043348281 + }, + "04": { + "name": "Arizona", + "rent_index": 1.0612855007473843 + }, + "05": { + "name": "Arkansas", + "rent_index": 0.6524663677130045 + }, + "06": { + "name": "California", + "rent_index": 1.5052316890881914 + }, + "08": { + "name": "Colorado", + "rent_index": 1.2802690582959642 + }, + "09": { + "name": "Connecticut", + "rent_index": 1.1210762331838564 + }, + "10": { + "name": "Delaware", + "rent_index": 0.9932735426008968 + }, + "11": { + "name": "District of Columbia", + "rent_index": 1.4566517189835575 + }, + "12": { + "name": "Florida", + "rent_index": 1.1659192825112108 + }, + "13": { + "name": "Georgia", + "rent_index": 0.9192825112107623 + }, + "15": { + "name": "Hawaii", + "rent_index": 1.4775784753363228 + }, + "16": { + "name": "Idaho", + "rent_index": 0.8079222720478326 + }, + "17": { + "name": "Illinois", + "rent_index": 0.9200298953662183 + }, + "18": { + "name": "Indiana", + "rent_index": 0.7698056801195815 + }, + "19": { + "name": "Iowa", + "rent_index": 0.7234678624813154 + }, + "20": { + "name": "Kansas", + "rent_index": 0.7481315396113603 + }, + "21": { + "name": "Kentucky", + "rent_index": 0.695814648729447 + }, + "22": { + "name": "Louisiana", + "rent_index": 0.7750373692077728 + }, + "23": { + "name": "Maine", + "rent_index": 0.8624813153961136 + }, + "24": { + "name": "Maryland", + "rent_index": 1.2668161434977578 + }, + "25": { + "name": "Massachusetts", + "rent_index": 1.3475336322869955 + }, + "26": { + "name": "Michigan", + "rent_index": 0.8363228699551569 + }, + "27": { + "name": "Minnesota", + "rent_index": 0.9783258594917787 + }, + "28": { + "name": "Mississippi", + "rent_index": 0.6584454409566517 + }, + "29": { + "name": "Missouri", + "rent_index": 0.7384155455904334 + }, + "30": { + "name": "Montana", + "rent_index": 0.7765321375186846 + }, + "31": { + "name": "Nebraska", + "rent_index": 0.7832585949177877 + }, + "32": { + "name": "Nevada", + "rent_index": 1.0635276532137519 + }, + "33": { + "name": "New Hampshire", + "rent_index": 1.1614349775784754 + }, + "34": { + "name": "New Jersey", + "rent_index": 1.3011958146487295 + }, + "35": { + "name": "New Mexico", + "rent_index": 0.7458893871449925 + }, + "36": { + "name": "New York", + "rent_index": 1.1763826606875933 + }, + "37": { + "name": "North Carolina", + "rent_index": 0.8213751868460388 + }, + "38": { + "name": "North Dakota", + "rent_index": 0.6980568011958147 + }, + "39": { + "name": "Ohio", + "rent_index": 0.7541106128550075 + }, + "40": { + "name": "Oklahoma", + "rent_index": 0.7167414050822123 + }, + "41": { + "name": "Oregon", + "rent_index": 1.0762331838565022 + }, + "42": { + "name": "Pennsylvania", + "rent_index": 0.890881913303438 + }, + "44": { + "name": "Rhode Island", + "rent_index": 1.0119581464872944 + }, + "45": { + "name": "South Carolina", + "rent_index": 0.8131539611360239 + }, + "46": { + "name": "South Dakota", + "rent_index": 0.7100149476831091 + }, + "47": { + "name": "Tennessee", + "rent_index": 0.804185351270553 + }, + "48": { + "name": "Texas", + "rent_index": 0.9917787742899851 + }, + "49": { + "name": "Utah", + "rent_index": 1.0 + }, + "50": { + "name": "Vermont", + "rent_index": 0.9917787742899851 + }, + "51": { + "name": "Virginia", + "rent_index": 1.0605381165919283 + }, + "53": { + "name": "Washington", + "rent_index": 1.248878923766816 + }, + "54": { + "name": "West Virginia", + "rent_index": 0.6494768310911808 + }, + "55": { + "name": "Wisconsin", + "rent_index": 0.79745889387145 + }, + "56": { + "name": "Wyoming", + "rent_index": 0.6965620328849028 + }, + "72": { + "name": "Puerto Rico", + "rent_index": 0.3804185351270553 + } + }, + "excluded_nonpositive_rent_ids": [], + "id": "acs5-2023-state", + "national_median_rent": 1338.0, + "source_id": "acs-rents-2023-snapshot", + "year": 2023 + } + }, + "information_date": "2026-09-08", + "reference_family": { + "adults": 2, + "children": 2 + }, + "release_id": "spm-2026-09-08", + "schema_version": 1, + "sources": [ + { + "availability_basis": "Conservative date of this pinned snapshot; not a reconstructed historical information set", + "available_on": "2026-09-08", + "id": "bls-corrected-2005-2024", + "publication_date": "2026-07-17", + "sha256": "e7931a1f2540d52877d6fd14a8ed1e421e977a85c952ae1b6f690a04d904f2cb", + "snapshot_path": "bls/spm_threshold_200524_corrected.xlsx", + "url": "https://www.bls.gov/pir/spm/spm_threshold_200524_corrected.xlsx" + }, + { + "availability_basis": "Conservative date of this pinned snapshot; not a reconstructed historical information set", + "available_on": "2026-09-08", + "id": "bls-2025", + "publication_date": "2026-08-24", + "sha256": "95f39fb5479ee5c196de627e06e10efd59af5148cf1c8466924e3fb2e5e2bd3b", + "snapshot_path": "bls/spm_thresholds.xlsx", + "url": "https://www.bls.gov/pir/spm/spm_thresholds.xlsx" + }, + { + "availability_basis": "Conservative date of this pinned snapshot; not a reconstructed historical information set", + "available_on": "2026-09-08", + "id": "census-metro-2024-snapshot", + "publication_date": null, + "sha256": "4443d5005f9c2847f25c0f6cbc42eb5e5d897244b26b488a10d1e7fe87d68fcd", + "snapshot_path": "metro_geoadj_2024.json", + "url": "https://www2.census.gov/programs-surveys/demo/tables/p60/287/SPM-pov-threshold-2024.xlsx" + }, + { + "availability_basis": "Conservative date of this pinned snapshot; not a reconstructed historical information set", + "available_on": "2026-09-08", + "id": "acs-cd-2023-snapshot", + "publication_date": null, + "sha256": "f89938d871753f9b796445e65df6e3975d47b256ed34be8993b9038972ea9917", + "snapshot_path": "cd_geoadj_2023.json", + "url": "https://api.census.gov/data/2023/acs/acs5/groups/B25031.html" + }, + { + "availability_basis": "Conservative date of this pinned snapshot; not a reconstructed historical information set", + "available_on": "2026-09-08", + "id": "acs-rents-2023-snapshot", + "publication_date": null, + "sha256": "a64258f4cebe16bc2bca16456368876003059af5870dfa22e8bc4d908cb14296", + "snapshot_path": "acs_rents_2023.json", + "url": "https://api.census.gov/data/2023/acs/acs5" + } + ], + "units": "USD/year", + "years": { + "2005": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 21064.41, + "owner_without_mortgage": 17642.67, + "renter": 20640.57 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 200.21, + "owner_without_mortgage": 229.79, + "renter": 224.2 + } + } + }, + "2006": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 22009.65, + "owner_without_mortgage": 18300.52, + "renter": 21278.03 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 193.9, + "owner_without_mortgage": 279.46, + "renter": 241.42 + } + } + }, + "2007": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 22771.56, + "owner_without_mortgage": 19206.41, + "renter": 22417.62 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 170.98, + "owner_without_mortgage": 299.44, + "renter": 248.69 + } + } + }, + "2008": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 24258.63, + "owner_without_mortgage": 20385.96, + "renter": 23472.44 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 258.81, + "owner_without_mortgage": 340.08, + "renter": 256.52 + } + } + }, + "2009": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 24450.46, + "owner_without_mortgage": 20298.15, + "renter": 23873.79 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 241.9, + "owner_without_mortgage": 335.35, + "renter": 345.13 + } + } + }, + "2010": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 25018.26, + "owner_without_mortgage": 20590.22, + "renter": 24390.5 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 322.71, + "owner_without_mortgage": 340.71, + "renter": 378.72 + } + } + }, + "2011": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 25703.41, + "owner_without_mortgage": 21175.19, + "renter": 25221.52 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 346.77, + "owner_without_mortgage": 297.7, + "renter": 377.81 + } + } + }, + "2012": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 25784.217451574375, + "owner_without_mortgage": 21399.50704050622, + "renter": 25105.072483651067 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 368.03406075690776, + "owner_without_mortgage": 233.3094864011297, + "renter": 397.5786841244934 + } + } + }, + "2013": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 25639.41, + "owner_without_mortgage": 21397.38, + "renter": 25144.31 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 289.07, + "owner_without_mortgage": 336.89, + "renter": 399.67 + } + } + }, + "2014": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 25844.455332, + "owner_without_mortgage": 21380.306455, + "renter": 25459.570533 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 345.32087189, + "owner_without_mortgage": 469.53749524, + "renter": 363.38157635 + } + } + }, + "2015": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 25929.883367, + "owner_without_mortgage": 21806.488776, + "renter": 25583.069953 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 296.71212308, + "owner_without_mortgage": 416.76353732, + "renter": 282.38810283 + } + } + }, + "2016": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 26335.619055, + "owner_without_mortgage": 22298.214204, + "renter": 26104.306345 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 279.79084394, + "owner_without_mortgage": 389.79511046, + "renter": 301.72861368 + } + } + }, + "2017": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 27085.067001, + "owner_without_mortgage": 23260.655432, + "renter": 27004.994983 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 275.70448129, + "owner_without_mortgage": 471.35166636, + "renter": 262.66939744 + } + } + }, + "2018": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/published_2005_2019", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 28343.504154, + "owner_without_mortgage": 24174.464503, + "renter": 28167.730141 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 328.9411322, + "owner_without_mortgage": 424.31225186, + "renter": 253.15801801 + } + } + }, + "2019": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/revised_2019_2024", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 29076.17484, + "owner_without_mortgage": 24514.952286, + "renter": 28913.050035 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 200.92191565, + "owner_without_mortgage": 327.84241002, + "renter": 204.05348421 + } + } + }, + "2020": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/revised_2019_2024", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 29814.550519, + "owner_without_mortgage": 25249.135101, + "renter": 29978.900071 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 223.91102114, + "owner_without_mortgage": 333.63841266, + "renter": 256.79521267 + } + } + }, + "2021": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/revised_2019_2024", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 30983.241762, + "owner_without_mortgage": 26055.401211, + "renter": 31216.693518 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 285.77603893, + "owner_without_mortgage": 259.38711506, + "renter": 241.9712778 + } + } + }, + "2022": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/revised_2019_2024", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 33978.493165, + "owner_without_mortgage": 28454.915504, + "renter": 34140.062994 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 318.83118091, + "owner_without_mortgage": 439.35663798, + "renter": 307.91315069 + } + } + }, + "2023": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/revised_2019_2024", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 36966.23764, + "owner_without_mortgage": 30587.725238, + "renter": 37230.752855 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 329.65613888, + "owner_without_mortgage": 516.90311235, + "renter": 345.03707524 + } + } + }, + "2024": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "published" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/revised_2019_2024", + "source_ids": [ + "bls-corrected-2005-2024", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 39230.994457, + "owner_without_mortgage": 32878.594848, + "renter": 39219.893902 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 303.49304574, + "owner_without_mortgage": 681.67747304, + "renter": 325.1136139 + } + } + }, + "2025": { + "available_on": "2026-09-08", + "housing_share_provenance": { + "note": "Fixed three-decimal 2024 Census metro housing shares. Other target years carry these values as an approximation; they are not official target-year shares. Geography uses the published rent index with these shares, not an assertion of exact revised metro threshold reproduction.", + "reference_year": 2024, + "source_id": "census-metro-2024-snapshot", + "status": "carried" + }, + "housing_shares": { + "owner_with_mortgage": 0.434, + "owner_without_mortgage": 0.323, + "renter": 0.443 + }, + "methodology_id": "bls/bls-published-2025", + "source_ids": [ + "bls-2025", + "census-metro-2024-snapshot" + ], + "status": "published", + "thresholds": { + "owner_with_mortgage": 41322.707394, + "owner_without_mortgage": 34325.99772, + "renter": 41700.555713 + }, + "uncertainty": { + "kind": "published_standard_error", + "scope": "National threshold estimation only; no uncertainty estimate for housing-share carry or geographic approximation", + "standard_errors": { + "owner_with_mortgage": 327.35414802, + "owner_without_mortgage": 560.28664871, + "renter": 393.03526083 + } + } + } + } +} diff --git a/paper/index.qmd b/paper/index.qmd index 9ecd61a..bef734a 100644 --- a/paper/index.qmd +++ b/paper/index.qmd @@ -6,7 +6,7 @@ author: affiliations: - name: PolicyEngine email: max@policyengine.org -date: 2026-09-04 +date: 2026-09-08 bibliography: ../bibliography/references.bib format: html: @@ -22,10 +22,11 @@ abstract: | Supplemental Poverty Measure thresholds after finding errors in the code that produced them. The correction moved every published threshold by less than 1.6 percent. Auditing our own software against it found - hand-entered threshold values off by up to 7.4 percent — and both errors - had survived for years because no one was diffing published artifacts. + hand-entered threshold values off by up to 7.4 percent. Comparing + downstream copies with published sources detects transcription errors + and later revisions, but cannot identify errors in the source calculation. The paper makes three contributions. It quantifies the correction and - documents a replication of the BLS threshold methodology from public + documents a historical replication of the BLS threshold methodology from public Consumer Expenditure microdata, which reproduces the corrected thresholds within 1.5 to 2.1 percent per year and surfaces six pitfalls in the public files that face any CE researcher. It shows @@ -33,8 +34,8 @@ abstract: | including our own, missed actual threshold growth by 2.2 percent per year in absolute value over 2020--2024, understating it in four of five years, because the SPM is quasi-relative: BLS re-estimates each - year from consumption data. And it commits to a falsifiable nowcast - of the unpublished 2025 thresholds — \$41,036.34 for owners with + year from consumption data. It evaluates a pre-committed nowcast + of the then-unpublished 2025 thresholds — \$41,036.34 for owners with mortgages, \$34,135.99 for owners without, and \$40,755.98 for renters — from a 50/50 blend of consumption growth and composite price aging (backtest mean absolute error 0.76 versus 2.23 percent @@ -47,7 +48,9 @@ abstract: | absolute error was 1.17 percent against 2.58 for CPI-U aging, with the miss concentrated in renters; the consumption component alone scored 0.75, in the order the backtest predicted. This revision - reports that evaluation in full. + reports that evaluation in full and separately records a current-method + retrospective experiment, measured sample-treatment sensitivities, and + a shared threshold release for standalone and model integration. --- # Introduction @@ -69,9 +72,9 @@ policy microsimulation, so we audited it against the correction. The audit found a larger problem on our side: threshold values transcribed by hand into our own package matched no BLS or Census publication for five of the six affected years, with errors up to 7.4 percent. The two -failure modes are the same failure mode. Reference data that people -transcribe, in agencies or in open-source projects, drifts silently -unless something mechanical checks it against the source. +failures require different checks. Comparing an embedded reference table +with its published source detects transcription errors and stale copies. +It cannot discover an error already present in the agency's calculation. That observation sets the paper's agenda. @sec-production describes how BLS produces the thresholds and their revision history, which now @@ -83,14 +86,15 @@ methodology from public Consumer Expenditure microdata: its measured fidelity, the bugs the exercise found in our replication code, six pitfalls in the public CE files that face any researcher who uses them, and a negative result — replication at survey level cannot -detect a sub-2-percent error in the official pipeline, though -mechanical artifact-diffing catches it in a week. Because BLS +resolve a sub-2-percent error in the official pipeline. Mechanical +artifact comparisons can detect a revised publication once it appears. +Because BLS re-estimates thresholds each year from a rolling consumption window, the series is quasi-relative, and @sec-projection shows what that costs the price indexation used in microsimulation practice, ours included; a backtest scores replacement rules against five years of corrected actuals. @sec-nowcast applies that -rule to data that already exist and commits, before publication, to a +rule to data available before publication and records the original numeric estimate of the 2025 thresholds. BLS published those thresholds on August 24, 2026, and @sec-evaluation scores the commitment and every alternative rule against them. @sec-discussion @@ -259,7 +263,7 @@ four errors in our replication code: band, applies the 1.2 other-necessities multiplier, and swaps a tenure-specific shelter-utilities average — a different object. -After the fixes, the faithful variant reproduces the corrected +After the fixes, the frozen historical variant reproduces the corrected thresholds with mean absolute deviation across the three tenures of 1.5 to 2.1 percent per year (1.0 to 1.6 percent against the superseded published series), with no imputation of in-kind @@ -271,9 +275,10 @@ unreported mortgage status and for rent-free units are folded into neighboring groups; and the 80-percent grocery allocation is applied to the combined grocery summary rather than to the single item code the errata names. A cross-model code review on September 4, 2026 -surfaced all three; each moves replicated levels by a fraction of a -percent, and none enters the ratios the nowcast uses at a scale the -evaluation could detect.] +surfaced these differences. The frozen experiment did not measure their +separate effects on levels or growth ratios. The current-method +experiment below measures selected sample-policy sensitivities; other +approximation effects remain unmeasured.] For scale, the workbook's published sampling standard errors run 0.7 to 2.1 percent of the corresponding threshold across 2019--2024, so the replication sits within roughly one to three standard errors of @@ -289,20 +294,21 @@ reference series. ::: The deviations are predominantly negative and roughly stable across -years: the replication imputes no in-kind benefits and observes no -internet expenditures, and that shows up as a level deficit — up to -three percent for owners with mortgages — that persists across +years. The replication imputes no in-kind benefits and observes no +internet expenditures, but the contribution of each omission has not +been isolated. The observed level deficit — up to +three percent for owners with mortgages — persists across estimation windows rather than growing or shrinking with any one year. Two conclusions follow. The negative one: the BLS correction moved thresholds by at most 1.6 percent, inside this replication's noise, and neither reference series fits systematically better in the affected years — so survey-level replication could not have caught -the BLS bug, and we do not claim otherwise. What catches that class -of error is mechanical comparison of published artifacts, which now -runs weekly. The positive one: a level deficit that is stable across -adjacent, largely overlapping windows is exactly the error structure -that cancels when the replication is used in ratio form, which is how -@sec-projection and @sec-nowcast use it. +the BLS bug, and we do not claim otherwise. Comparing published artifacts +can detect a later correction and stale downstream copies; independent +validation of the agency calculation is a separate task. The positive +one is conditional: a stable proportional replication bias cancels in a +growth ratio, while a persistent additive deficit need not. Sections +@sec-projection and @sec-nowcast test the resulting ratios empirically. ## Pitfalls in the public CE files {#sec-pitfalls} @@ -392,7 +398,10 @@ stand at the prior year's corrected threshold and project one year ahead under four rules: All-Items CPI-U aging; aging by a composite of the CPI components matching the FCSUti basket; scaling by the growth of our replicated thresholds; and a 50/50 blend of the last two. All -rules are scored against the corrected actuals. +rules are scored against the corrected actuals. This is a retrospective +experiment using the 2026-corrected threshold vintage and one repaired +implementation, not forecasts reconstructed from the data and code +available at each historical prediction date. ::: {#tbl-backtest} {{< include tables/backtest.md >}} @@ -457,7 +466,9 @@ forecasts would have erred by several points. The replication ratio uses CE microdata that BLS publishes well before the corresponding thresholds, so in the nowcasting position the data it needs genuinely exist; that it also scores best in backtest counts in its favor, but -five backtest years cannot settle rankings this close. Second, the +five backtest years cannot settle rankings this close. Adjacent CE +windows overlap by 80 percent and tenure estimates share a sample; +these are not fifteen independent forecast trials. Second, the price rules share a residual: both run low in nearly every year, reflecting real consumption growth beyond what a five-year averaging window passes through and in-kind benefit expansions that prices do @@ -468,16 +479,17 @@ unmodeled rather than fit a constant to it. # A pre-committed 2025 nowcast {#sec-nowcast} -The 2025 thresholds are a nowcasting target, not a forecasting one — +At the commitment date, the 2025 thresholds were a nowcasting target — estimation of a present quantity from data already in hand, in the macroeconomic sense of the term [@giannone2008]: their entire CE window (2020Q2 through 2025Q1) and eleven of twelve months of 2025 -CPI are published. We apply the blend rule to the +CPI were published. We applied the blend rule to the corrected 2024 base. The replication component computes thresholds for 2024 and 2025 with identical code over their respective windows and -takes the ratio, per tenure — level biases that persist across -adjacent, 80-percent-overlapping windows cancel, and the anchor share -cancels exactly. The price component is the FCSUti-composite ratio of +takes the ratio, per tenure. Stable proportional replication biases +would cancel across adjacent, 80-percent-overlapping windows; additive +or changing errors would not. The anchor share cancels exactly. +The price component is the FCSUti-composite ratio of 2025 to 2024 annual averages, with 2025 averaged over its eleven published months.^[The nowcast's composite ratio chain-links at the base year: each component's 2025-to-2024 ratio, weighted by budget @@ -523,23 +535,22 @@ the index moved each tenure's estimate by 0.1 to 0.3 percent — owners with mortgages from \$41,099.57 to \$41,036.34, owners without from \$34,250.70 to \$34,135.99, renters from \$40,791.72 to \$40,755.98. The amendment predates any BLS publication of 2025 thresholds, the -original values remain in the repository history, and the September -evaluation will report realized errors for both sets. - -Evaluation is committed here, in advance — the commitment device of -preregistration [@nosek2018], applied to a numeric estimate rather -than a hypothesis test. When BLS publishes the actual 2025 -thresholds, expected around September 2026, a revision of this paper -will report the signed percent error per tenure and the mean absolute -error of this nowcast, alongside the same errors for All-Items CPI-U -aging and each component rule, all computed from the +original values remain in the repository history, and the completed +evaluation reports realized errors for both sets. + +The original paper committed in advance to an evaluation — the device +of preregistration [@nosek2018], applied to a numeric estimate rather +than a hypothesis test. It anticipated publication around September +2026 and promised signed percent errors per tenure and mean absolute +errors for the nowcast, All-Items CPI-U aging and each component rule, +all computed from the corrected 2024 base as committed in the repository — regardless of any subsequent BLS revision to 2024. The packaged estimate is fixed with its method, components, and hash. The blend's annual signed errors in backtest, averaged across tenures, ranged from −0.1 to −1.4 percent; -that range is our stated expectation of the nowcast's accuracy. If the -nowcast misses by more than the rules scored alongside it, that result -publishes too. The evaluation follows in @sec-evaluation. +that range was the stated expectation of the nowcast's accuracy. +The commitment included publishing worse performance than competing +rules. The completed evaluation follows in @sec-evaluation. # Evaluation against the published 2025 thresholds {#sec-evaluation} @@ -630,23 +641,128 @@ scored above are the ones fixed in the repository and its timestamps before BLS published, and the amended nowcast beat the rule it replaced by more than half. It does not license re-selecting the rule for 2026 after the fact. The replication ratio has now led on both -looks — the repaired backtest and the realized year — and if the -packaged method changes for 2026, the change will be committed and -timestamped before the 2026 CE window closes, in the same way, with -this year's evaluation as its stated basis. +looks — the repaired backtest and the realized year. The previously +discussed 2026 CE collection window already closed in 2026Q1. This +revision makes no new forecast commitment or scheduled cutoff. Any +future commitment would need an actual timestamp and a recorded +available-input cutoff before outcome publication; it could not claim +to precede that completed collection window. + +# Current-method research and a shared release {#sec-current} + +The September 8 rebuild separates a release of reference thresholds from +the research that estimates or projects them. The paper, standalone +calculator, and integration adapters consume the same portable JSON +release, with a content digest, target year, information date, source +fingerprints, methodology identifier, and published or estimated status. +The release records corrected national thresholds through published 2025. +Its conservative September 8 availability date describes this snapshot; +it does not reconstruct what was available to forecasters in earlier years. + +::: {#tbl-current-release} +{{< include tables/current_release.md >}} + +Reference-family values from the shared release. The 2025 national bases +are published values; the displayed housing shares come from the 2024 +Census metro snapshot and are carried into 2025 as an approximation. +::: + +Unit-level application combines the selected national base with the +SPM equivalence factor and a geographic adjustment to its housing +portion. The current release pins geographic rent indices and identifies +the source vintage separately from the target year. It does not claim +that carried housing shares or aggregate geographic averages exactly +reproduce revised official thresholds for every local area. + +This boundary lets PolicyEngine or a future Axiom implementation supply +resources and already classified SPM-unit membership, while Microcosm +supplies entity links and explicit weights. The reference threshold does +not change when the sample is reweighted. The implemented Axiom bridge +executes national-base selection, multiplication, and the strict +resources-below-threshold judgment in the native runtime. Its equivalence +factor remains an explicitly supplied SPM calculation because the pinned +RuleSpec runtime lacks fractional powers. This is a partial bridge; +it does not migrate CE estimation, SPM membership, or resource accounting +into Axiom. A host migration can change those inputs and therefore poverty +results even when the shared threshold release stays fixed. + +## CE replication and measured sensitivities + +The current CE experiment reconstructs seven target years from their +public five-year windows. It normalizes each consumer unit to the +reference family before selecting the weighted expenditure band and +estimating tenure-specific housing means. @tbl-current-replication +compares this explicitly approximate implementation with the corrected +published series. These results are separate from the historical +implementation and frozen evaluation above. + +::: {#tbl-current-replication} +{{< include tables/current_replication.md >}} + +Signed current replication deviations from published thresholds, by +reference-family tenure. Negative values indicate a lower replication. +::: + +Independent minors can form CE consumer units, so a minor-only record +does not by itself establish the SPM adult/child classification needed +for exact equivalence scaling. The default exact path rejects these +unresolved records. For the experiment, an explicit research option +excludes them; a sensitivity imposes an adult while retaining the reported +child count, which is nonofficial and not a resolution of membership. +The baseline excludes tenure codes 5 and 6 and assigns code 3 to owners +with a mortgage. The recorded windows contain no code-3 observations, +so assigning those records to owners without a mortgage has zero measured +effect here; it provides no evidence about other samples. + +::: {#tbl-current-sensitivity} +{{< include tables/current_sensitivity.md >}} + +Unresolved-youth interviews and their weighted share of child-containing +CE interviews, with maximum absolute threshold changes across tenures +under separate youth-recode and tenure-5/6 sensitivities. These +overlapping windows reuse observations. +::: + +The tenure sensitivity reaches 1.74 percent, so the sample-treatment +approximations cannot collectively be described as negligible. Annual +rather than quarterly CPI adjustment, the allocation of the redesigned +food summary, omitted home internet and in-kind imputations, and exact +BLS percentile-code parity remain unmeasured sources of discrepancy. +Sampling and imputation uncertainty has not been estimated. The table +measures selected implementation choices, not a confidence interval or +an exhaustive error bound. + +## Retrospective projection under the current method + +The same current replication supplies adjacent-year growth ratios for +six one-year projections in @tbl-current-projection. The price comparator +rebases static component weights to each prior year, while the frozen +backtest used a fixed 2019 price base. These are different method +versions. All current inputs are conservatively dated to this replay; +no historical information-set eligibility is inferred. Adjacent windows +overlap by four of five years, and tenure estimates share observations. +The resulting year-tenure errors are dependent retrospective evidence. +The blend gives equal weight to replication growth and composite-price +growth. These figures do not replace the frozen backtest or the single +prospective 2025 evaluation and do not establish a new forecast commitment. + +::: {#tbl-current-projection} +{{< include tables/current_projection.md >}} + +Current-method retrospective mean absolute percentage errors across +three tenures, 2020–2025. The mean row weights the six years equally. +::: # Discussion {#sec-discussion} -The 2026 correction cost the SPM six years of restated statistics, and -the error is less notable for how it arose than for taking five years -to surface. The threshold pipeline's inputs are public, its -methodology is documented, and its outputs are three numbers a year. -Nothing checked them. Our package's larger errors have the same -anatomy: values a person typed, tests that enshrined the typing, and -no process comparing the artifact to its source. Both failures were -detectable by a scheduled diff against the published artifact, which -has no false-negative mode for this error class short of the source -itself being wrong. +The 2026 correction restated six years of SPM statistics. Its delayed +discovery illustrates the difficulty of validating the source +calculation itself. Our package's larger transcription errors illustrate +a separate, directly testable problem: tests had preserved values a +person typed without comparing them with their sources. Scheduled +comparisons can identify disagreement and later source revisions. +They cannot establish the correctness of an agency calculation that +supplied the same erroneous values to every downstream copy. Replication plays a narrower role than adjudication. At its measured fidelity, an independent implementation cannot resolve sub-2-percent @@ -654,8 +770,8 @@ errors in the official series, and claiming otherwise would overreach. What it can do is structural: confirm the formula, the window, and the re-anchoring behave as documented; catch transcription-scale errors instantly; and measure the consumption growth that price indexation -misses — the use this paper puts it to, where a level bias that -cancels in ratios stops being a disqualification. The nowcast is the +misses — the use this paper tests, conditional on sufficient stability +of proportional replication errors. The nowcast is the constructive payoff of infrastructure built for verification. The verification infrastructure itself needed verifying: the composite index at the heart of the backtest carried a construction error until @@ -694,7 +810,9 @@ ordinary and rarely checked. # Reproducibility -Everything in this paper regenerates from public data. The +The historical tables regenerate from the frozen public-data artifacts +included here; the poverty-sensitivity artifact also requires its +original population dataset to reproduce. The historical spm-calculator implementation lives on PolicyEngine/spm-calculator pull request #32 (branch `max/bls-2026-threshold-correction`; this revision pins commit `d2746cfacce072f599ffb1788eb195777c6ca9ca`), to @@ -717,18 +835,24 @@ whose own default store carried a seventh, mis-mapped series (`SEEE`, information-technology hardware and services, labeled as internet) and therefore returned a different 2025 price ratio (1.0297 rather than the committed 1.0322). -The same September 4 review caught it; the branch is being -reconciled to the six-series definition the evaluation scored, and -the choice of series for 2026 is an open, public issue on the -implementation repository. +The same September 4 review caught it; the rebuild now preserves the +six-series definition as an explicitly archived commitment. Current-method +inputs and code are recorded separately in `data/current/`; they do not +replace this frozen evaluation. The current experiment and shared release +pin spm-calculator version 0.5.0, development commit +`9e6ae4798458771231613b994df2345dd1685214`. +The release content digest and exact executed CE source hashes are in +`data/current/provenance.json`. These are committed development inputs, +not evidence of a published package or completed model migration. The paper repository pins the exact artifacts behind every table and the poverty-sensitivity simulation output, with SHA-256 sums, and bundles the corrected BLS workbook itself (hash matching the file bls.gov served on August 6, 2026), so the correction table is checkable without fighting the bot filtering. Each table is generated -from the artifacts by script, and a pre-render check regenerates the -tables, verifies the artifact hashes, and re-derives the registered +from the artifacts by script. A pre-render check first verifies the +original artifact hashes and commitment identities, then regenerates +tables and evaluation JSON in scratch space and re-derives the registered prose figures; continuous integration runs the same check on every push to master and every pull request. Its scope is stated exactly: it enforces an allowlist of generated tables and of the tables the @@ -736,7 +860,8 @@ source includes, requires the checksum manifest to cover every artifact present, and matches each registered figure at numeric-token boundaries — the three tenure amounts as ordered triples in the sentences that state them — so the build fails if any registered -figure, table, or artifact drifts. Figures quoted from external +figure, table, or artifact drifts. It compares generated bytes without +repairing or restoring source files. Figures quoted from external publications are bound to their citations, not to artifacts, and dates and ordinals are not registered; an earlier version of the check matched bare substrings, and a September 4, 2026 audit showed @@ -763,7 +888,14 @@ quotes both sets in the amendment sentence, and both nowcasts are tagged releases whose creation dates GitHub records. A manifest listing both tags, their commits, and both artifact hashes is committed alongside and stamped separately, as is the -post-evaluation checksum file. The history documents one +post-evaluation checksum file. Two artifact hashes in that secondary +manifest were transcribed incorrectly. The explicitly dated +`COMMITMENT-AMENDMENT-2026-09-08.txt` supplies the correct hashes and +records the original manifest's digest. The original manifest, tags, +forecast bytes and every existing proof remain unchanged. The old proof +does not cover the amendment. This revision checks the retained proof +bytes and Git identities; it does not independently reverify the +blockchain attestations. The forecast history documents one amendment: the composite-index construction error caught in cross-model review on July 18, 2026, with every downstream artifact, table, and pinned prose figure regenerated in the same change. The diff --git a/paper/tables/current_projection.md b/paper/tables/current_projection.md new file mode 100644 index 0000000..d3266d0 --- /dev/null +++ b/paper/tables/current_projection.md @@ -0,0 +1,9 @@ +| Target year | CPI-U | Composite prices | Replication ratio | 50/50 blend | +|---|---|---|---|---| +| 2020 | 1.78% | 0.90% | 0.38% | 0.38% | +| 2021 | 0.92% | 0.43% | 0.42% | 0.34% | +| 2022 | 1.29% | 1.45% | 0.35% | 0.55% | +| 2023 | 3.99% | 2.50% | 0.62% | 1.47% | +| 2024 | 3.16% | 2.61% | 0.32% | 1.33% | +| 2025 | 2.58% | 2.02% | 0.96% | 1.33% | +| Mean | 2.29% | 1.65% | 0.51% | 0.90% | diff --git a/paper/tables/current_release.md b/paper/tables/current_release.md new file mode 100644 index 0000000..643427e --- /dev/null +++ b/paper/tables/current_release.md @@ -0,0 +1,5 @@ +| Tenure | Published 2025 base | Housing share (2024) | +|---|---|---| +| Owner with mortgage | $41,322.71 | 0.434 | +| Owner without mortgage | $34,326.00 | 0.323 | +| Renter | $41,700.56 | 0.443 | diff --git a/paper/tables/current_replication.md b/paper/tables/current_replication.md new file mode 100644 index 0000000..27c6167 --- /dev/null +++ b/paper/tables/current_replication.md @@ -0,0 +1,9 @@ +| Target year | With mortgage | Without mortgage | Renter | +|---|---|---|---| +| 2019 | -1.67% | -1.00% | -1.12% | +| 2020 | -1.69% | -0.24% | -1.45% | +| 2021 | -1.55% | +0.78% | -1.36% | +| 2022 | -1.41% | +1.67% | -1.33% | +| 2023 | -2.05% | +0.72% | -1.07% | +| 2024 | -2.25% | +1.14% | -1.40% | +| 2025 | -1.79% | +0.11% | -2.77% | diff --git a/paper/tables/current_sensitivity.md b/paper/tables/current_sensitivity.md new file mode 100644 index 0000000..a167a79 --- /dev/null +++ b/paper/tables/current_sensitivity.md @@ -0,0 +1,9 @@ +| Year | Youth rows | Youth weight share | Youth recode | Include tenures 5/6 | +|---|---|---|---|---| +| 2019 | 12 | 0.0328% | 0.0199% | 1.0882% | +| 2020 | 12 | 0.0329% | 0.0220% | 0.8609% | +| 2021 | 7 | 0.0195% | 0.0410% | 1.7371% | +| 2022 | 5 | 0.0145% | 0.0131% | 1.0967% | +| 2023 | 1 | 0.0051% | 0.0027% | 0.9893% | +| 2024 | 1 | 0.0051% | 0.0354% | 0.8810% | +| 2025 | 1 | 0.0051% | 0.0000% | 0.6425% | diff --git a/paper/tables/package_errors.md b/paper/tables/package_errors.md index ca2918f..0a0603c 100644 --- a/paper/tables/package_errors.md +++ b/paper/tables/package_errors.md @@ -1,4 +1,4 @@ -| Year | Tenure | Package $\leq$0.3.1 | Published | Error | +| Year | Tenure | Package $\leq 0.3.1$ | Published | Error | |---|---|---:|---:|---:| | 2019 | Owners with mortgages | 27,172 | 29,080 | -6.6% | | 2019 | Owners without mortgages | 22,600 | 24,413 | -7.4% | diff --git a/scripts/build_current_tables.py b/scripts/build_current_tables.py new file mode 100644 index 0000000..604a8be --- /dev/null +++ b/scripts/build_current_tables.py @@ -0,0 +1,177 @@ +"""Render the separately identified current research artifacts, using stdlib only.""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from statistics import mean + +REPO = Path(__file__).resolve().parents[1] +DATA = REPO / "data" / "current" +TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") + + +def close(actual: float, expected: float, label: str) -> None: + if not math.isclose(actual, expected, rel_tol=1e-10, abs_tol=1e-10): + raise ValueError(f"Inconsistent current artifact: {label}") + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output-root", type=Path, default=REPO) + args = parser.parse_args() + output = args.output_root / "paper" / "tables" + output.mkdir(parents=True, exist_ok=True) + release = json.loads((DATA / "spm-release.json").read_text()) + ce = json.loads((DATA / "ce_replication_2019_2025.json").read_text()) + if ce["classification"] != "current_retrospective_research": + raise ValueError("Current CE input must be explicitly retrospective research") + + def write(name: str, headers: list[str], rows: list[list[str]]) -> None: + lines = ["| " + " | ".join(headers) + " |", "|" + "---|" * len(headers)] + lines.extend("| " + " | ".join(row) + " |" for row in rows) + (output / name).write_text("\n".join(lines) + "\n") + + current = release["years"]["2025"] + write( + "current_release.md", + ["Tenure", "Published 2025 base", "Housing share (2024)"], + [ + [ + label, + f"${current['thresholds'][tenure]:,.2f}", + f"{current['housing_shares'][tenure]:.3f}", + ] + for tenure, label in zip( + TENURES, ("Owner with mortgage", "Owner without mortgage", "Renter") + ) + ], + ) + + replication_rows = [] + sensitivity_rows = [] + for year in range(2019, 2026): + record = ce["results"][str(year)] + baseline = record["variants"]["exclude_unresolved"] + differences = [] + for tenure in TENURES: + official = release["years"][str(year)]["thresholds"][tenure] + close(official, record["published"][tenure], f"published {year} {tenure}") + deviation = 100 * (baseline["thresholds"][tenure] / official - 1) + close( + deviation, + record["replication_deviation_percent"][tenure], + f"replication {year} {tenure}", + ) + differences.append(f"{deviation:+.2f}%") + replication_rows.append([str(year), *differences]) + changes = [] + for variant in ( + "legacy_youth_recode_sensitivity", + "include_5_6_as_renter_sensitivity", + "code3_no_mortgage_sensitivity", + ): + values = [] + for tenure in TENURES: + change = 100 * ( + record["variants"][variant]["thresholds"][tenure] + / baseline["thresholds"][tenure] + - 1 + ) + close( + change, + record["sensitivity_change_percent_from_exclusion"][variant][ + tenure + ], + f"sensitivity {year} {tenure} {variant}", + ) + values.append(abs(change)) + changes.append(f"{max(values):.4f}%") + sample = baseline["sample"] + unresolved = sample["unresolved_youth"] + share = 100 * unresolved["weight_sum"] / sample["with_children"]["weight_sum"] + sensitivity_rows.append( + [str(year), str(unresolved["rows"]), f"{share:.4f}%", *changes[:2]] + ) + if changes[2] != "0.0000%" or sample["tenure_3"]["rows"] != 0: + raise ValueError( + "Update the code-3 sensitivity discussion for nonzero observations" + ) + write( + "current_replication.md", + ["Target year", "With mortgage", "Without mortgage", "Renter"], + replication_rows, + ) + write( + "current_sensitivity.md", + [ + "Year", + "Youth rows", + "Youth weight share", + "Youth recode", + "Include tenures 5/6", + ], + sensitivity_rows, + ) + + evaluation = ce["projection_evaluation"] + if ( + evaluation["classification"] + != "retrospective_current_method_not_a_frozen_commitment" + ): + raise ValueError("Current projection classification changed") + errors = {} + for row in evaluation["rows"]: + year, rule = row["target_year"], row["rule"] + if (year, rule) in errors: + raise ValueError("Duplicate current projection row") + values = [] + for tenure in TENURES: + deviation = 100 * ( + row["projection"]["thresholds"][tenure] + / release["years"][str(year)]["thresholds"][tenure] + - 1 + ) + close( + deviation, + row["errors_percent"][tenure], + f"projection {year} {rule} {tenure}", + ) + values.append(abs(deviation)) + errors[year, rule] = mean(values) + close( + errors[year, rule], + row["mean_absolute_error_percent"], + f"projection mean {year} {rule}", + ) + rules = ("cpi_u", "fcsuti_cpi", "replication_ratio", "blend") + expected = {(year, rule) for year in range(2020, 2026) for rule in rules} + if set(errors) != expected: + raise ValueError("Current projection coverage must be six years by four rules") + rows = [ + [str(year), *(f"{errors[year, rule]:.2f}%" for rule in rules)] + for year in range(2020, 2026) + ] + means = [] + for rule in rules: + value = mean(errors[year, rule] for year in range(2020, 2026)) + close(value, evaluation["summary_2020_2025"][rule], f"six-year mean {rule}") + means.append(f"{value:.2f}%") + rows.append(["Mean", *means]) + write( + "current_projection.md", + [ + "Target year", + "CPI-U", + "Composite prices", + "Replication ratio", + "50/50 blend", + ], + rows, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/build_tables.py b/scripts/build_tables.py index 356f882..e61920d 100644 --- a/scripts/build_tables.py +++ b/scripts/build_tables.py @@ -10,12 +10,16 @@ from __future__ import annotations +import argparse import json from pathlib import Path REPO = Path(__file__).resolve().parent.parent DATA = REPO / "data" -OUT = REPO / "paper" / "tables" +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument("--output-root", type=Path, default=REPO) +args = parser.parse_args() +OUT = args.output_root / "paper" / "tables" TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") TENURE_LABELS = { @@ -71,7 +75,7 @@ def write(name: str, lines: list[str]) -> None: # Table 2: what spm-calculator <=0.3.1 shipped vs published. lines = [ - "| Year | Tenure | Package $\\leq$0.3.1 | Published | Error |", + "| Year | Tenure | Package $\\leq 0.3.1$ | Published | Error |", "|---|---|---:|---:|---:|", ] for year in range(2019, 2025): @@ -162,9 +166,9 @@ def composite(year: int) -> float: year_rows = [] for year in range(2020, 2025): base, actual = corrected[year - 1], corrected[year] - f_cpi = cpi[CPI_IDS["all_items"]][str(year)] / cpi[CPI_IDS["all_items"]][ - str(year - 1) - ] + f_cpi = ( + cpi[CPI_IDS["all_items"]][str(year)] / cpi[CPI_IDS["all_items"]][str(year - 1)] + ) f_fcs = composite(year) / composite(year - 1) per_rule = {} for rule in RULES: @@ -184,9 +188,7 @@ def composite(year: int) -> float: year_rows.append((year, per_rule)) lines = [ - "| Rule | " - + " | ".join(str(y) for y, _ in year_rows) - + " | Mean |", + "| Rule | " + " | ".join(str(y) for y, _ in year_rows) + " | Mean |", "|---|" + "---:|" * (len(year_rows) + 1), ] for rule in RULES: @@ -201,8 +203,7 @@ def composite(year: int) -> float: # Table 5: the pre-registered 2025 nowcast. lines = [ - "| Tenure | Replication ratio | FCSUti CPI ratio | Blend " - "| Nowcast 2025 |", + "| Tenure | Replication ratio | FCSUti CPI ratio | Blend " "| Nowcast 2025 |", "|---|---:|---:|---:|---:|", ] for t in TENURES: diff --git a/scripts/check_paper.py b/scripts/check_paper.py index f871eb5..81c0d26 100644 --- a/scripts/check_paper.py +++ b/scripts/check_paper.py @@ -30,12 +30,14 @@ import hashlib import json import re -import shutil import subprocess import sys import tempfile from pathlib import Path +sys.dont_write_bytecode = True +from verify_commitments import verify_commitments + REPO = Path(__file__).resolve().parent.parent DATA = REPO / "data" TABLES = REPO / "paper" / "tables" @@ -52,8 +54,12 @@ "evaluation.md", "evaluation_levels.md", "composite_validation.md", + "current_release.md", + "current_replication.md", + "current_sensitivity.md", + "current_projection.md", } -GENERATORS = ("build_tables.py", "evaluate_nowcast_2025.py") +GENERATORS = ("build_tables.py", "evaluate_nowcast_2025.py", "build_current_tables.py") failures: list[str] = [] @@ -72,52 +78,27 @@ def has_number(literal: str, text: str = QMD, min_count: int = 1) -> bool: def has_ordered(literals: list[str], window: int = 400) -> bool: """The literals appear in this order within one window of text.""" - pattern = r"[\s\S]{0,%d}" % window + pattern = rf"[\s\S]{{0,{window}}}" body = pattern.join(re.escape(x) for x in literals) return re.search(body, QMD) is not None -# (a) Tables regenerate identically, and the set is exactly as expected. -present_before = {p.name for p in TABLES.glob("*.md")} -check( - f"generated tables present == expected (missing {EXPECTED_TABLES - present_before}, extra {present_before - EXPECTED_TABLES})", - present_before == EXPECTED_TABLES, -) -before = {p.name: p.read_text() for p in TABLES.glob("*.md")} -with tempfile.TemporaryDirectory() as tmp: - backup = Path(tmp) / "tables" - shutil.copytree(TABLES, backup) - for script in GENERATORS: - proc = subprocess.run( - [sys.executable, str(REPO / "scripts" / script)], - capture_output=True, - text=True, - ) - if proc.returncode != 0: - failures.append(f"{script} failed:\n{proc.stderr[-2000:]}") - after = {p.name: p.read_text() for p in TABLES.glob("*.md")} - for name in EXPECTED_TABLES: - check(f"table drifted: {name}", before.get(name) == after.get(name)) - if any(before.get(n) != after.get(n) for n in EXPECTED_TABLES): - # Report without leaving the checkout mutated. - shutil.rmtree(TABLES) - shutil.copytree(backup, TABLES) - -# (b) Every QMD include names an expected table. -includes = set(re.findall(r"\{\{<\s*include\s+tables/([^\s>]+)\s*>\}\}", QMD)) -check( - f"qmd includes outside allowlist: {includes - EXPECTED_TABLES}", - includes <= EXPECTED_TABLES, -) -check( - f"expected tables never included: {EXPECTED_TABLES - includes}", - EXPECTED_TABLES <= includes, -) - # (c) SHA256SUMS lists exactly the artifacts present, and every hash holds. +# This must happen before any artifact is parsed or generator is invoked. listed: dict[str, str] = {} -for line in (DATA / "SHA256SUMS").read_text().splitlines(): - digest, name = line.split() +for number, line in enumerate((DATA / "SHA256SUMS").read_text().splitlines(), 1): + fields = line.split() + if len(fields) != 2: + failures.append(f"malformed checksum manifest line {number}") + continue + digest, name = fields + if not re.fullmatch(r"[0-9a-f]{64}", digest) or not re.fullmatch( + r"data/[^/]+\.(?:json|xlsx)", name + ): + failures.append(f"invalid checksum manifest line {number}") + continue + if name in listed: + failures.append(f"duplicate checksum manifest entry: {name}") listed[name] = digest present_artifacts = { f"data/{p.name}" for p in DATA.iterdir() if p.suffix in (".json", ".xlsx") @@ -134,9 +115,7 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: ) # Bundled BLS workbooks are the exact files BLS served on the stated dates. -BLS_WORKBOOK_SHA256 = ( - "e7931a1f2540d52877d6fd14a8ed1e421e977a85c952ae1b6f690a04d904f2cb" -) +BLS_WORKBOOK_SHA256 = "e7931a1f2540d52877d6fd14a8ed1e421e977a85c952ae1b6f690a04d904f2cb" BLS_CURRENT_WORKBOOK_SHA256 = ( "95f39fb5479ee5c196de627e06e10efd59af5148cf1c8466924e3fb2e5e2bd3b" ) @@ -146,8 +125,153 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: ): check( f"bundled BLS workbook hash: {fname}", - hashlib.sha256((DATA / fname).read_bytes()).hexdigest() == expected, + (DATA / fname).is_file() + and hashlib.sha256((DATA / fname).read_bytes()).hexdigest() == expected, + ) + +failures.extend(verify_commitments(REPO)) + +# The current experiment has a separate manifest, never the old timestamp. +current_names = {"spm-release.json", "ce_replication_2019_2025.json", "provenance.json"} +current_listed = {} +for number, line in enumerate( + (DATA / "current" / "SHA256SUMS").read_text().splitlines(), 1 +): + fields = line.split() + if ( + len(fields) != 2 + or not re.fullmatch(r"[0-9a-f]{64}", fields[0]) + or fields[1] not in {f"data/current/{name}" for name in current_names} + ): + failures.append(f"invalid current checksum manifest line {number}") + continue + digest, name = fields + if name in current_listed: + failures.append(f"duplicate current checksum entry: {name}") + current_listed[name] = digest +current_present = {str(p.relative_to(REPO)) for p in (DATA / "current").glob("*.json")} +check( + "current checksum coverage", + set(current_listed) + == current_present + == {f"data/current/{name}" for name in current_names}, +) +for name, digest in current_listed.items(): + path = REPO / name + check( + f"current artifact hash mismatch: {name}", + path.is_file() and hashlib.sha256(path.read_bytes()).hexdigest() == digest, + ) +if failures: + print("PAPER INPUT CHECK FAILED (generators were not run):") + for failure in failures: + print(f" - {failure}") + sys.exit(1) + +# All input byte checks have passed; it is now safe to parse current inputs. +release = json.loads((DATA / "current" / "spm-release.json").read_text()) +content = {key: value for key, value in release.items() if key != "content_sha256"} +content_digest = hashlib.sha256( + json.dumps( + content, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + allow_nan=False, + ).encode() +).hexdigest() +current_ce = json.loads( + (DATA / "current" / "ce_replication_2019_2025.json").read_text() +) +current_provenance = json.loads((DATA / "current" / "provenance.json").read_text()) +check( + "release canonical content hash", + release["content_sha256"] + == content_digest + == current_provenance["release_content_sha256"], +) +check( + "current CE provenance hash", + current_provenance["ce_artifact_sha256"] + == current_listed["data/current/ce_replication_2019_2025.json"], +) +check( + "current CE executed source fingerprints", + current_provenance["ce_code_sha256"] == current_ce["code_sha256"], +) +check( + "current inputs are outside frozen commitment", + current_provenance["covered_by_original_timestamp"] is False + and current_provenance["frozen_commitments_modified"] is False + and current_ce["frozen_commitments_modified"] is False, +) +max_tenure_sensitivity = max( + abs(value) + for row in current_ce["results"].values() + for value in row["sensitivity_change_percent_from_exclusion"][ + "include_5_6_as_renter_sensitivity" + ].values() +) +check( + "current tenure-sensitivity prose", + has_number(f"{max_tenure_sensitivity:.2f} percent"), +) +if failures: + print("PAPER INPUT CHECK FAILED (generators were not run):") + for failure in failures: + print(f" - {failure}") + sys.exit(1) + +# (a) Generate only in scratch; never repair or restore source files. +present_before = {p.name for p in TABLES.glob("*.md")} +check( + f"generated tables present == expected (missing {EXPECTED_TABLES - present_before}, extra {present_before - EXPECTED_TABLES})", + present_before == EXPECTED_TABLES, +) +expected_outputs = {f"paper/tables/{name}" for name in EXPECTED_TABLES} | { + "data/evaluation_2025.json" +} +with tempfile.TemporaryDirectory(prefix="spm-paper-check-") as tmp: + scratch = Path(tmp) + for script in GENERATORS: + proc = subprocess.run( + [ + sys.executable, + str(REPO / "scripts" / script), + "--output-root", + str(scratch), + ], + capture_output=True, + check=False, + text=True, + cwd=REPO, + ) + if proc.returncode != 0: + failures.append(f"{script} failed:\n{proc.stderr[-2000:]}") + generated = {str(p.relative_to(scratch)) for p in scratch.rglob("*") if p.is_file()} + check( + f"generator output coverage: missing {expected_outputs - generated}, extra {generated - expected_outputs}", + generated == expected_outputs, ) + for name in sorted(expected_outputs): + original, regenerated = REPO / name, scratch / name + check( + f"generated output drifted: {name}", + original.is_file() + and regenerated.is_file() + and original.read_bytes() == regenerated.read_bytes(), + ) + +# (b) Every QMD include names an expected table. +includes = set(re.findall(r"\{\{<\s*include\s+tables/([^\s>]+)\s*>\}\}", QMD)) +check( + f"qmd includes outside allowlist: {includes - EXPECTED_TABLES}", + includes <= EXPECTED_TABLES, +) +check( + f"expected tables never included: {EXPECTED_TABLES - includes}", + EXPECTED_TABLES <= includes, +) # (d) Load-bearing prose figures re-derive from the artifacts. nowcast = json.loads((DATA / "nowcast_2025.json").read_text()) @@ -158,7 +282,14 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: check( "abstract states the amended triple in tenure order", has_ordered( - [AMENDED["owner_with_mortgage"], "owners with", AMENDED["owner_without_mortgage"], "owners without", AMENDED["renter"], "renters"], + [ + AMENDED["owner_with_mortgage"], + "owners with", + AMENDED["owner_without_mortgage"], + "owners without", + AMENDED["renter"], + "renters", + ], window=120, ), ) @@ -173,9 +304,14 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: check( "amendment sentence pairs original -> amended per tenure, in order", has_ordered( - [ORIG_LIT["owner_with_mortgage"], AMENDED["owner_with_mortgage"], - ORIG_LIT["owner_without_mortgage"], AMENDED["owner_without_mortgage"], - ORIG_LIT["renter"], AMENDED["renter"]], + [ + ORIG_LIT["owner_with_mortgage"], + AMENDED["owner_with_mortgage"], + ORIG_LIT["owner_without_mortgage"], + AMENDED["owner_without_mortgage"], + ORIG_LIT["renter"], + AMENDED["renter"], + ], window=60, ), ) @@ -309,13 +445,22 @@ def mae(rule: str) -> float: has_number("1.9 percent") and abs(signed_mean - (-0.019)) < 0.002, ) cpiu_low_years = sum(1 for m in annual_signed["cpi_u"] if m < 0) -check("CPI-U understated four of five years", "four of five" in QMD and cpiu_low_years == 4) +check( + "CPI-U understated four of five years", + "four of five" in QMD and cpiu_low_years == 4, +) check( "composite removes ~thirty percent of CPI-U error", - "thirty percent" in QMD and abs((1 - mae("fcsuti_cpi") / mae("cpi_u")) - 0.30) < 0.02, + "thirty percent" in QMD + and abs((1 - mae("fcsuti_cpi") / mae("cpi_u")) - 0.30) < 0.02, +) +rep_signed = sum(signed_by_rule["replication_ratio"]) / len( + signed_by_rule["replication_ratio"] +) +check( + "replication signed mean +0.1", + has_number("+0.1") and abs(rep_signed - 0.001) < 0.001, ) -rep_signed = sum(signed_by_rule["replication_ratio"]) / len(signed_by_rule["replication_ratio"]) -check("replication signed mean +0.1", has_number("+0.1") and abs(rep_signed - 0.001) < 0.001) check( "replication annual signed range +0.5 to −0.3", "+0.5 to −0.3" in QMD @@ -331,8 +476,10 @@ def mae(rule: str) -> float: # Effective shelter weight under the pre-repair raw-level construction. raw_2024 = {c: cpi[CPI_IDS[c]]["2024"] for c in WEIGHTS} -eff_shelter = WEIGHTS["shelter"] * raw_2024["shelter"] / sum( - WEIGHTS[c] * raw_2024[c] for c in WEIGHTS +eff_shelter = ( + WEIGHTS["shelter"] + * raw_2024["shelter"] + / sum(WEIGHTS[c] * raw_2024[c] for c in WEIGHTS) ) stated_shelter = WEIGHTS["shelter"] / sum(WEIGHTS.values()) check( @@ -347,9 +494,18 @@ def mae(rule: str) -> float: corr_changes = [ corrected[y][t] / published[y][t] - 1 for y in range(2019, 2025) for t in TENURES ] -check("correction within ±1.6 percent", has_number("1.6 percent") and max(abs(c) for c in corr_changes) < 0.016) -renter_falls = sum(1 for y in range(2019, 2025) if corrected[y]["renter"] < published[y]["renter"]) -FALL_WORDS = {4: "fall in four of six years", 5: "fall in five of six years", 6: "fall in all six years"} +check( + "correction within ±1.6 percent", + has_number("1.6 percent") and max(abs(c) for c in corr_changes) < 0.016, +) +renter_falls = sum( + 1 for y in range(2019, 2025) if corrected[y]["renter"] < published[y]["renter"] +) +FALL_WORDS = { + 4: "fall in four of six years", + 5: "fall in five of six years", + 6: "fall in all six years", +} check( f"renter thresholds {FALL_WORDS.get(renter_falls)} (computed {renter_falls})", FALL_WORDS.get(renter_falls, "") in QMD, @@ -359,20 +515,34 @@ def mae(rule: str) -> float: "five of six\nyears, not four" in QMD or "five of six years, not four" in QMD, ) pkg_err = { - y: max(abs(legacy[y][t] / published[y][t] - 1) for t in TENURES) for y in range(2019, 2025) + y: max(abs(legacy[y][t] / published[y][t] - 1) for t in TENURES) + for y in range(2019, 2025) } -check("package errors reach 7.4 percent", has_number("7.4 percent") and abs(max(pkg_err.values()) - 0.074) < 0.001) +check( + "package errors reach 7.4 percent", + has_number("7.4 percent") and abs(max(pkg_err.values()) - 0.074) < 0.001, +) wrong_years = sum(1 for y, e in pkg_err.items() if e > 0.001) -WORDS = {3: "three of the six", 4: "four of the six", 5: "five of the six", 6: "all six"} +WORDS = { + 3: "three of the six", + 4: "four of the six", + 5: "five of the six", + 6: "all six", +} check( - f"package wrong in {WORDS.get(wrong_years)} years (computed {wrong_years}; per-year max errors {dict((y, round(e, 4)) for y, e in pkg_err.items())})", + f"package wrong in {WORDS.get(wrong_years)} years (computed {wrong_years}; per-year max errors {{y: round(e, 4) for y, e in pkg_err.items()}})", WORDS.get(wrong_years, "") in QMD, ) se = flat_measure("bls-corrected-2026-07-17", "standard_error") -se_ratio = [se[y][t] / corrected[y][t] for y in range(2019, 2025) for t in TENURES if se[y][t]] +se_ratio = [ + se[y][t] / corrected[y][t] for y in range(2019, 2025) for t in TENURES if se[y][t] +] check( "SE range 0.7 to 2.1 percent", - has_number("0.7") and has_number("2.1") and abs(min(se_ratio) - 0.007) < 0.001 and abs(max(se_ratio) - 0.021) < 0.001, + has_number("0.7") + and has_number("2.1") + and abs(min(se_ratio) - 0.007) < 0.001 + and abs(max(se_ratio) - 0.021) < 0.001, ) # Replication level fidelity ranges. @@ -393,11 +563,15 @@ def mae(rule: str) -> float: out.append(sum(abs(v) for v in row[key].values()) / 3) check( "replication fidelity 1.5 to 2.1 vs corrected", - has_number("1.5 to 2.1") and abs(min(mad_corrected) - 0.015) < 0.001 and abs(max(mad_corrected) - 0.021) < 0.001, + has_number("1.5 to 2.1") + and abs(min(mad_corrected) - 0.015) < 0.001 + and abs(max(mad_corrected) - 0.021) < 0.001, ) check( "replication fidelity 1.0 to 1.6 vs published", - has_number("1.0 to 1.6") and abs(min(mad_published) - 0.010) < 0.001 and abs(max(mad_published) - 0.016) < 0.001, + has_number("1.0 to 1.6") + and abs(min(mad_published) - 0.010) < 0.001 + and abs(max(mad_published) - 0.016) < 0.001, ) # Nowcast growth ranges. @@ -406,16 +580,28 @@ def mae(rule: str) -> float: price_growth = nowcast["components"]["renter"]["price_ratio"] - 1 check( "replication growth 4.4 to 6.0", - has_number("4.4 to 6.0") and abs(min(rep_growth) - 0.044) < 0.001 and abs(max(rep_growth) - 0.060) < 0.001, + has_number("4.4 to 6.0") + and abs(min(rep_growth) - 0.044) < 0.001 + and abs(max(rep_growth) - 0.060) < 0.001, +) +check( + "price growth 3.2", has_number("3.2 percent") and abs(price_growth - 0.032) < 0.001 ) -check("price growth 3.2", has_number("3.2 percent") and abs(price_growth - 0.032) < 0.001) check( "blend growth 3.8 to 4.6", - has_number("3.8 to 4.6") and abs(min(blend_growth) - 0.038) < 0.001 and abs(max(blend_growth) - 0.046) < 0.001, + has_number("3.8 to 4.6") + and abs(min(blend_growth) - 0.038) < 0.001 + and abs(max(blend_growth) - 0.046) < 0.001, ) realized_2025 = cpi["CUUR0000SA0"]["2025"] / cpi["CUUR0000SA0"]["2024"] - 1 -check("realized CPI-U 2025 growth 2.6", has_number("2.6 percent") and abs(realized_2025 - 0.026) < 0.002) -check("eleven-month CPI-U growth 2.63 quoted", has_number("2.63") and abs(realized_2025 - 0.0263) < 0.0005) +check( + "realized CPI-U 2025 growth 2.6", + has_number("2.6 percent") and abs(realized_2025 - 0.026) < 0.002, +) +check( + "eleven-month CPI-U growth 2.63 quoted", + has_number("2.63") and abs(realized_2025 - 0.0263) < 0.0005, +) # (e) The September evaluation, re-derived from data/evaluation_2025.json. ev = json.loads((DATA / "evaluation_2025.json").read_text()) @@ -441,17 +627,26 @@ def mae(rule: str) -> float: and abs(amended["errors"]["renter"] - (-0.0227)) < 0.0001, ) ev_signed = sum(amended["errors"].values()) / 3 -check("evaluation signed mean −1.17 inside stated range", "−1.17" in QMD and -0.014 <= ev_signed <= -0.001) +check( + "evaluation signed mean −1.17 inside stated range", + "−1.17" in QMD and -0.014 <= ev_signed <= -0.001, +) cpiu_errs = [abs(v) for v in cpiu["errors"].values()] check( "CPI-U 2025 understated every tenure by 1.70 to 3.48", - has_number("1.70 to 3.48") and all(v < 0 for v in cpiu["errors"].values()) - and abs(min(cpiu_errs) - 0.0170) < 0.0001 and abs(max(cpiu_errs) - 0.0348) < 0.0001, + has_number("1.70 to 3.48") + and all(v < 0 for v in cpiu["errors"].values()) + and abs(min(cpiu_errs) - 0.0170) < 0.0001 + and abs(max(cpiu_errs) - 0.0348) < 0.0001, ) check( "replication only rule with errors both sides of zero", max(repl["errors"].values()) > 0 > min(repl["errors"].values()) - and all(max(R[k]["errors"].values()) < 0 for k in R if not k.startswith("CE replication")), + and all( + max(R[k]["errors"].values()) < 0 + for k in R + if not k.startswith("CE replication") + ), ) actual = json.loads((DATA / "bls_2025_thresholds.json").read_text()) for literal, value in [ @@ -459,23 +654,40 @@ def mae(rule: str) -> float: ("34,326", actual["values"]["owner_without_mortgage"]), ("41,701", actual["values"]["renter"]), ]: - check(f"BLS 2025 literal {literal}", has_number(literal) and f"{round(value):,}" == literal) + check( + f"BLS 2025 literal {literal}", + has_number(literal) and f"{round(value):,}" == literal, + ) check( "BLS 2025 triple in tenure order", - has_ordered(["41,323", "owners with", "34,326", "owners without", "41,701", "renters"], window=80), + has_ordered( + ["41,323", "owners with", "34,326", "owners without", "41,701", "renters"], + window=80, + ), ) g = actual["bls_stated_growth_pct_2025_over_2024"] check( "BLS-stated 2025 growth 4.40-6.33 and per-tenure 5.33/4.40/6.33", - has_number("4.40 to 6.33") and has_number("6.33") and has_number("5.33") and has_number("4.40") - and abs(min(g.values()) - 4.402) < 0.001 and abs(max(g.values()) - 6.325) < 0.001 + has_number("4.40 to 6.33") + and has_number("6.33") + and has_number("5.33") + and has_number("4.40") + and abs(min(g.values()) - 4.402) < 0.001 + and abs(max(g.values()) - 6.325) < 0.001 and abs(g["owner_with_mortgage"] - 5.332) < 0.001, ) -now_growth_renter = (nowcast["values"]["renter"] / ev["corrected_2024_base"]["renter"] - 1) * 100 -check("nowcast renter growth 3.92", has_number("3.92") and abs(now_growth_renter - 3.92) < 0.005) +now_growth_renter = ( + nowcast["values"]["renter"] / ev["corrected_2024_base"]["renter"] - 1 +) * 100 +check( + "nowcast renter growth 3.92", + has_number("3.92") and abs(now_growth_renter - 3.92) < 0.005, +) check( "replication renter growth 4.62", - has_number("4.62") and abs((nowcast["components"]["renter"]["replication_ratio"] - 1) * 100 - 4.62) < 0.005, + has_number("4.62") + and abs((nowcast["components"]["renter"]["replication_ratio"] - 1) * 100 - 4.62) + < 0.005, ) se25 = actual["standard_errors"] miss_se = {t: (amended["values"][t] - actual["values"][t]) / se25[t] for t in TENURES} @@ -486,26 +698,43 @@ def mae(rule: str) -> float: and abs(miss_se["owner_without_mortgage"]) < 1 and abs(abs(miss_se["renter"]) - 2.4) < 0.05, ) -for literal, key in (("327", "owner_with_mortgage"), ("560", "owner_without_mortgage"), ("393", "renter")): - check(f"2025 SE literal {literal}", has_number(literal) and f"{round(se25[key])}" == literal) +for literal, key in ( + ("327", "owner_with_mortgage"), + ("560", "owner_without_mortgage"), + ("393", "renter"), +): + check( + f"2025 SE literal {literal}", + has_number(literal) and f"{round(se25[key])}" == literal, + ) gaps = ev["composite_vs_bls_fcsuti_gap_pp"] check( "composite within 0.7pp of BLS FCSUti, mean gap 0.4", - "0.7 percentage point" in QMD and max(abs(v) for v in gaps.values()) < 0.7 - and "0.4 point" in QMD and abs(sum(abs(v) for v in gaps.values()) / len(gaps) - 0.4) < 0.05, + "0.7 percentage point" in QMD + and max(abs(v) for v in gaps.values()) < 0.7 + and "0.4 point" in QMD + and abs(sum(abs(v) for v in gaps.values()) / len(gaps) - 0.4) < 0.05, ) bls_cpiu_2025 = actual["bls_chart4_annual_average_inflation_pct"]["2025"]["cpi_u"] -check("BLS page 2025 CPI-U growth 2.70 quoted", has_number("2.70 percent") and abs(bls_cpiu_2025 - 2.70) < 0.005) +check( + "BLS page 2025 CPI-U growth 2.70 quoted", + has_number("2.70 percent") and abs(bls_cpiu_2025 - 2.70) < 0.005, +) census = json.loads((DATA / "census_wp2026_17_rates.json").read_text()) c23 = census["corrected_series_2023_to_2024"]["all_people"] check( "Census corrected 2023->2024 = 12.7 -> 13.0", - has_ordered(["12.7", "13.0"], window=10) and c23["2023"] == 12.7 and c23["2024"] == 13.0, + has_ordered(["12.7", "13.0"], window=10) + and c23["2023"] == 12.7 + and c23["2024"] == 13.0, ) diffs = [v["difference"] for y, v in census["all_people"].items() if y != "2024"] check( "Census 2019-2023 corrected 0.1 to 0.3 below", - has_number("0.1 to 0.3") and abs(max(diffs)) == 0.1 and abs(min(diffs)) == 0.3 and all(d < 0 for d in diffs), + has_number("0.1 to 0.3") + and abs(max(diffs)) == 0.1 + and abs(min(diffs)) == 0.3 + and all(d < 0 for d in diffs), ) if failures: @@ -514,5 +743,5 @@ def mae(rule: str) -> float: print(f" - {f}") sys.exit(1) print( - f"paper drift check passed ({len(EXPECTED_TABLES)} tables, {len(listed)} artifacts, prose pins OK)" + f"paper drift check passed ({len(EXPECTED_TABLES)} tables, {len(listed)} frozen + {len(current_listed)} current artifacts, prose pins OK)" ) diff --git a/scripts/evaluate_nowcast_2025.py b/scripts/evaluate_nowcast_2025.py index 71ee2bf..bf4667a 100644 --- a/scripts/evaluate_nowcast_2025.py +++ b/scripts/evaluate_nowcast_2025.py @@ -17,13 +17,18 @@ from __future__ import annotations +import argparse import json import subprocess from pathlib import Path REPO = Path(__file__).resolve().parent.parent DATA = REPO / "data" -OUT = REPO / "paper" / "tables" +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument("--output-root", type=Path, default=REPO) +args = parser.parse_args() +OUT = args.output_root / "paper" / "tables" +OUTPUT_DATA = args.output_root / "data" TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") LABELS = { @@ -40,9 +45,7 @@ corrected_2024 = None for seg in series_doc["series"]["bls-corrected-2026-07-17"]["segments"].values(): if "2024" in seg["years"]: - corrected_2024 = { - t: m["threshold"] for t, m in seg["years"]["2024"].items() - } + corrected_2024 = {t: m["threshold"] for t, m in seg["years"]["2024"].items()} assert corrected_2024 is not None # The original (pre-repair) nowcast, from the tagged release rather @@ -71,8 +74,7 @@ for t in TENURES }, "FCSUti-composite CPI aging alone": { - t: corrected_2024[t] * nowcast["components"][t]["price_ratio"] - for t in TENURES + t: corrected_2024[t] * nowcast["components"][t]["price_ratio"] for t in TENURES }, "All-Items CPI-U aging (status quo)": { t: corrected_2024[t] * cpi_u_ratio for t in TENURES @@ -177,8 +179,12 @@ def composite(year: int) -> float: }, "composite_vs_bls_fcsuti_gap_pp": dict(zip(range(2020, 2026), gaps)), } -(DATA / "evaluation_2025.json").write_text(json.dumps(summary, indent=1) + "\n") +OUTPUT_DATA.mkdir(parents=True, exist_ok=True) +(OUTPUT_DATA / "evaluation_2025.json").write_text(json.dumps(summary, indent=1) + "\n") print("wrote data/evaluation_2025.json") for k, v in results.items(): - print(f"{k:45s} MAE {v['mae']:.2%} " + " ".join(f"{v['errors'][t]:+.2%}" for t in TENURES)) + print( + f"{k:45s} MAE {v['mae']:.2%} " + + " ".join(f"{v['errors'][t]:+.2%}" for t in TENURES) + ) print("composite gap vs BLS FCSUti (pp):", [f"{g:+.2f}" for g in gaps]) diff --git a/scripts/verify_commitments.py b/scripts/verify_commitments.py new file mode 100644 index 0000000..9a335c9 --- /dev/null +++ b/scripts/verify_commitments.py @@ -0,0 +1,79 @@ +"""Check archived proof bytes and Git identities, not blockchain attestations.""" + +import hashlib +import json +import subprocess + +PRESERVED = { + "data/COMMITMENT-MANIFEST.txt": "18d82ad41fad56b42dfce537523e50b5b89956b8161dc60e60d90f8753b39b37", + "data/COMMITMENT-MANIFEST.txt.ots": "1e23ccf54836b8fd7ede8d13ed2b606fa9ae6d7b0801794841b7d660a25004bb", + "data/SHA256SUMS.precommit": "a49ffac0e653e1a2c94861676e9a21d2d5761813f43d46482c7f8dd8baf1fe28", + "data/SHA256SUMS.precommit.ots": "dd36909119b5303c9cf5e3c36e6e4061724e288a1b350a59ebe2285aac396512", + "data/SHA256SUMS": "b8ad819d509cbac69333c39a3fa826e2a3c158fa52ed651a7f46f70106b523d8", + "data/SHA256SUMS.ots": "5beecfc52227154ac43123e57a1379edcf6b34b3f59c828102cc85c2d77041cb", +} +TAGS = { + "v1.0-original-nowcast": { + "tag_object": "12b7042a960412477af53c62ebd961ded1a2625e", + "commit": "a8b7c51869e64e36517d4eca8d5dbd6e2e0cde5b", + "nowcast_sha256": "19a1f44d10d9151c0b4d3cdb953e9d338bc9dd7a6fe06b88a91665649a5931c0", + }, + "v1.1-amended-nowcast": { + "tag_object": "3759670e314b762fa24348a3c9a16526cdc74b47", + "commit": "d72e1dc083ad1abc95b1c88fd1b782944454074b", + "nowcast_sha256": "259a5b2e0d15646722fe309f741546e364827fab053c73c99d273061fa0b1ba8", + }, +} + + +def verify_commitments(repo): + failures = [] + for name, expected in PRESERVED.items(): + path = repo / name + if ( + not path.is_file() + or hashlib.sha256(path.read_bytes()).hexdigest() != expected + ): + failures.append(f"preserved commitment/proof bytes changed: {name}") + try: + amendment = json.loads( + (repo / "data/COMMITMENT-AMENDMENT-2026-09-08.txt").read_text() + ) + if amendment["date"] != "2026-09-08" or amendment["tags"] != TAGS: + failures.append( + "dated commitment amendment differs from expected identities" + ) + if ( + amendment["original_manifest_sha256"] + != PRESERVED["data/COMMITMENT-MANIFEST.txt"] + ): + failures.append( + "commitment amendment does not identify the archived manifest" + ) + if amendment["covered_by_original_timestamp"] is not False: + failures.append( + "amendment must not claim coverage by the original timestamp" + ) + except (OSError, KeyError, ValueError, TypeError) as error: + failures.append(f"invalid commitment amendment: {error}") + for tag, expected in TAGS.items(): + commands = { + "tag_object": ["git", "rev-parse", tag], + "commit": ["git", "rev-parse", f"{tag}^{{commit}}"], + "nowcast_sha256": ["git", "show", f"{tag}:data/nowcast_2025.json"], + } + for key, command in commands.items(): + result = subprocess.run(command, cwd=repo, capture_output=True, check=False) + if result.returncode: + failures.append( + f"cannot resolve {tag} {key}; full history and tags required" + ) + continue + actual = ( + hashlib.sha256(result.stdout).hexdigest() + if key == "nowcast_sha256" + else result.stdout.decode().strip() + ) + if actual != expected[key]: + failures.append(f"tag identity mismatch: {tag} {key}") + return failures diff --git a/tests/__pycache__/test_paper_guard.cpython-314.pyc b/tests/__pycache__/test_paper_guard.cpython-314.pyc new file mode 100644 index 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zu0#h>!xC^$<_H7$Q}Mz*qv*7DcB&t#`Xm{%D6!)5%*jIp|KOevhw&F~I)!6gM7Ri! zW&oH{l0gr18LsEDzU@F!CVak5eu^0}qiA)5vUjblH)~K{r*4+sC|#-RNYx!k)*bk~ z;qH4Yb+4^5XP!gRzWT-9UwXduJYf8UGvNXQ(GfUi1|9ftV%IVE@ARrOg_ZqdzgF# zM6KpH*k?ryaJ-^O;)OVT0vNWO0}+R#o1hp_0zxRnad45%v8fQ6VQceM5J4Kf{qn|f1Kqe`rfJ(oX$f7kvCByN3>yqX_Zv2$4NbpcSVx$WT_dC5y$bUHNgV8(h2w(J0PF>cp9JwtUcm%k zEYz^D4?p3VsZ2bAPu3tJ0$1_KX_ Date: Tue, 8 Sep 2026 15:57:05 -0400 Subject: [PATCH 07/14] Refresh current CE provenance after Python compatibility fix --- README.md | 2 +- data/PROVENANCE.md | 2 +- data/current/SHA256SUMS | 4 ++-- data/current/ce_replication_2019_2025.json | 4 ++-- data/current/provenance.json | 8 ++++---- paper/index.qmd | 2 +- 6 files changed, 11 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index ecb932c..2b6a6db 100644 --- a/README.md +++ b/README.md @@ -44,5 +44,5 @@ describes both collections and the original implementation pin. The current spm-calculator rebuild provides a portable release, standalone unit calculations, CE research replication, projections, and explicit PolicyEngine, Microcosm, and real native Axiom adapters. This paper pins version 0.5.0 at development commit -`9e6ae4798458771231613b994df2345dd1685214`; it does not claim that a model +`0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`; it does not claim that a model migration or public deployment has occurred. The paper consumes the same release bytes as those adapters. diff --git a/data/PROVENANCE.md b/data/PROVENANCE.md index f0f0754..8dead39 100644 --- a/data/PROVENANCE.md +++ b/data/PROVENANCE.md @@ -45,7 +45,7 @@ It does not contact OpenTimestamps calendars or verify Bitcoin attestations. |---|---| | `spm-release.json` | Exact portable `spm-2026-09-08.json` from the spm-calculator 0.5.0 rebuild. Revised national 2005–2025 published thresholds, source fingerprints, separately dated 2024 housing shares, and pinned geographic rent indices. The release's conservative available-on date is September 8, not a reconstructed historical publication calendar. | | `ce_replication_2019_2025.json` | `scripts/replicate_current_ce.py` in the rebuild. Seven overlapping CE windows, explicit minor-only and tenure approximations, raw source bundle hashes, CPI cache hash, exact executed source hashes, and six-year retrospective projections. No prospective validation or new commitment. | -| `provenance.json` | Paper linkage to the release content digest and CE artifact/source hashes. Pins spm-calculator 0.5.0 development commit `9e6ae4798458771231613b994df2345dd1685214`. The retained `ce_starting_git_head` identifies the checkout's starting commit before the rebuild was committed. The `ce_code_sha256` map identifies the executed code, whose bytes match the pinned development commit. | +| `provenance.json` | Paper linkage to the release content digest and CE artifact/source hashes. Pins spm-calculator 0.5.0 development commit `0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. The retained `ce_starting_git_head` identifies the starting commit for this replay, before its source and artifact update was committed. The `ce_code_sha256` map identifies the executed code, whose bytes match the pinned development commit. | `build_current_tables.py` reads only these inputs. It cross-checks the published comparison series against the shared release, re-derives diff --git a/data/current/SHA256SUMS b/data/current/SHA256SUMS index 9bd8f78..c40cc42 100644 --- a/data/current/SHA256SUMS +++ b/data/current/SHA256SUMS @@ -1,3 +1,3 @@ -dcfe3ecc3e9898a6190fbd46fe2bf20057dd4f7d509bc4bf0376f4bd79034a45 data/current/ce_replication_2019_2025.json -2c33478cdc075c61847863219e5f32f2060d8563b8ea0619a726cc345dc424d6 data/current/provenance.json +d04e2a61552795f9d90e282045610774a4ff68f67b97067677358741f58a289c data/current/ce_replication_2019_2025.json +79e53f867ac56ae0c7dda9127a751591092a19e0c64272099ede1fa7b23d1f5f data/current/provenance.json a12113b5b574e0d4371814f2c3f127b00f91016bddc94c0f6459b7a7742cf02d data/current/spm-release.json diff --git a/data/current/ce_replication_2019_2025.json b/data/current/ce_replication_2019_2025.json index 127b6ee..6e8c42e 100644 --- a/data/current/ce_replication_2019_2025.json +++ b/data/current/ce_replication_2019_2025.json @@ -5,7 +5,7 @@ "spm_calculator/ce_threshold.py": "2a43c93460d03f54ef7e589dfc7411069ceabf91ed8e234ceeb004da1ed3ad84", "spm_calculator/equivalence_scale.py": "95c797adca4811b0a2c4202e8471f6cd46fcd259bf46b34da74588ab08b41326", "spm_calculator/fcsuti_cpi.py": "cf8fbe9103846d83a280bcd4f13b9ddd91e8baa94e2668b9ef288c7cc980a5fc", - "spm_calculator/forecast.py": "28092513cea2b5451da56532cb628273b8d1fafae64b6ab41eb605cacbe5fb0e", + "spm_calculator/forecast.py": "367c124844976e557484c6591c49c7ebca8c87a27828b29999b64f02989da597", "spm_calculator/projection.py": "a7b0c6935f3cdf857e6cffc941cbe4fef9607c460a20e0ba8da72a19c6a60340" }, "cpi_input": { @@ -27,7 +27,7 @@ "frozen_commitments_modified": false, "generated_by": "scripts/replicate_current_ce.py", "generated_on": "2026-09-08", - "git_head": "816a7084990e2bbb7dff801fd9b9cd5cdd1bb995", + "git_head": "dc88395ea8292dbbacbb9880d5aea19228ee13d8", "limitations": [ "Youth sensitivity compares explicit assumptions; it does not identify the correct BLS classification", "Tenure sensitivity changes assignment/exclusion policy; official BLS handling remains unverified", diff --git a/data/current/provenance.json b/data/current/provenance.json index 51afa52..059715e 100644 --- a/data/current/provenance.json +++ b/data/current/provenance.json @@ -1,17 +1,17 @@ { - "calculator_commit": "9e6ae4798458771231613b994df2345dd1685214", + "calculator_commit": "0d7fa0d77b0a88064ab7b9fe70557309b5f7901f", "calculator_repository": "https://github.com/PolicyEngine/spm-calculator", "calculator_version": "0.5.0", - "ce_artifact_sha256": "dcfe3ecc3e9898a6190fbd46fe2bf20057dd4f7d509bc4bf0376f4bd79034a45", + "ce_artifact_sha256": "d04e2a61552795f9d90e282045610774a4ff68f67b97067677358741f58a289c", "ce_code_sha256": { "scripts/replicate_current_ce.py": "4168c0a432c8eb2d63fbfa65aa0b9bc7f4906e4dba397df67149cf3c7207305a", "spm_calculator/ce_threshold.py": "2a43c93460d03f54ef7e589dfc7411069ceabf91ed8e234ceeb004da1ed3ad84", "spm_calculator/equivalence_scale.py": "95c797adca4811b0a2c4202e8471f6cd46fcd259bf46b34da74588ab08b41326", "spm_calculator/fcsuti_cpi.py": "cf8fbe9103846d83a280bcd4f13b9ddd91e8baa94e2668b9ef288c7cc980a5fc", - "spm_calculator/forecast.py": "28092513cea2b5451da56532cb628273b8d1fafae64b6ab41eb605cacbe5fb0e", + "spm_calculator/forecast.py": "367c124844976e557484c6591c49c7ebca8c87a27828b29999b64f02989da597", "spm_calculator/projection.py": "a7b0c6935f3cdf857e6cffc941cbe4fef9607c460a20e0ba8da72a19c6a60340" }, - "ce_starting_git_head": "816a7084990e2bbb7dff801fd9b9cd5cdd1bb995", + "ce_starting_git_head": "dc88395ea8292dbbacbb9880d5aea19228ee13d8", "classification": "current_retrospective_research", "covered_by_original_timestamp": false, "frozen_commitments_modified": false, diff --git a/paper/index.qmd b/paper/index.qmd index bef734a..ce50ae0 100644 --- a/paper/index.qmd +++ b/paper/index.qmd @@ -840,7 +840,7 @@ six-series definition as an explicitly archived commitment. Current-method inputs and code are recorded separately in `data/current/`; they do not replace this frozen evaluation. The current experiment and shared release pin spm-calculator version 0.5.0, development commit -`9e6ae4798458771231613b994df2345dd1685214`. +`0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. The release content digest and exact executed CE source hashes are in `data/current/provenance.json`. These are committed development inputs, not evidence of a published package or completed model migration. From 3a147d805540ac0f8c152582b36ef5cbdad08783 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 8 Sep 2026 16:44:51 -0400 Subject: [PATCH 08/14] Center paper on SPM threshold estimation and projection evidence --- README.md | 21 +- paper/index.qmd | 1378 ++++++++++++++----------------------- scripts/check_paper.py | 104 +-- tests/test_paper_guard.py | 26 + 4 files changed, 590 insertions(+), 939 deletions(-) diff --git a/README.md b/README.md index 2b6a6db..2827917 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,16 @@ -# Nowcasting Supplemental Poverty Measure thresholds +# Calculating and projecting Supplemental Poverty Measure thresholds -Working paper on the July 2026 BLS threshold correction, public CE -replication, threshold projection, and the completed evaluation of the -2025 forecast. The September 8 revision preserves that forecast and its -evaluation, and adds a separately identified current-method experiment -and portable threshold release. +Working paper on national SPM threshold estimation from public Consumer +Expenditure Survey microdata, family and geographic adjustments, and +projection from consumption and price growth. The manuscript presents +current-method replication and sample sensitivities, retrospective +comparisons, and a separately preserved pre-committed 2025 validation. + +The current six-year results use revised inputs and overlapping CE +windows. They are separate from the single prospective validation year. +Method limitations and the forecast amendment history are reported in +the manuscript. The repository preserves source artifacts, method +versions, and forecast commitment evidence. ## Build and verify @@ -20,7 +26,8 @@ The guard and table generators use the Python standard library. Quarto Clone with full tag history and check out the exact revision to reproduce it. The guard first checks artifact hashes and frozen Git identities, then generates tables and evaluation JSON in temporary storage and -compares every output byte with the checkout. It never repairs or +compares every output byte with the checkout, including archived tables +that the manuscript no longer includes. It never repairs or restores source artifacts. The regression tests verify that failed checks leave source bytes unchanged. diff --git a/paper/index.qmd b/paper/index.qmd index ce50ae0..36d6c0f 100644 --- a/paper/index.qmd +++ b/paper/index.qmd @@ -1,6 +1,6 @@ --- -title: "Nowcasting Supplemental Poverty Measure thresholds" -subtitle: "A pre-committed 2025 estimate from public microdata, and its evaluation" +title: "Calculating and projecting Supplemental Poverty Measure thresholds" +subtitle: "Public consumption data, projection comparisons, and a pre-committed 2025 validation" author: - name: Max Ghenis affiliations: @@ -17,896 +17,582 @@ format: classoption: 11pt number-sections: true keep-tex: true + include-in-header: + text: | + \usepackage{needspace} abstract: | - On July 17, 2026, the Bureau of Labor Statistics reissued six years of - Supplemental Poverty Measure thresholds after finding errors in the code - that produced them. The correction moved every published threshold by - less than 1.6 percent. Auditing our own software against it found - hand-entered threshold values off by up to 7.4 percent. Comparing - downstream copies with published sources detects transcription errors - and later revisions, but cannot identify errors in the source calculation. - The paper makes three contributions. It quantifies the correction and - documents a historical replication of the BLS threshold methodology from public - Consumer Expenditure microdata, which reproduces the corrected - thresholds within 1.5 to 2.1 percent per year and surfaces six - pitfalls in the public files that face any CE researcher. It shows - that CPI-U threshold indexation, common in microsimulation practice - including our own, missed actual threshold growth by 2.2 percent per - year in absolute value over 2020--2024, understating it in four of - five years, because the SPM is quasi-relative: BLS re-estimates each - year from consumption data. It evaluates a pre-committed nowcast - of the then-unpublished 2025 thresholds — \$41,036.34 for owners with - mortgages, \$34,135.99 for owners without, and \$40,755.98 for - renters — from a 50/50 blend of consumption growth and composite - price aging (backtest mean absolute error 0.76 versus 2.23 percent - per year for CPI-U aging; the consumption component alone scores - 0.41). The nowcast was amended once, before any BLS release, after - adversarial cross-model review caught a construction error in the - composite index; the original values remain in the repository - history. BLS published the 2025 thresholds on August 24, 2026 — - \$41,323, \$34,326, and \$41,701. The committed nowcast's mean - absolute error was 1.17 percent against 2.58 for CPI-U aging, with - the miss concentrated in renters; the consumption component alone - scored 0.75, in the order the backtest predicted. This revision - reports that evaluation in full and separately records a current-method - retrospective experiment, measured sample-treatment sensitivities, and - a shared threshold release for standalone and model integration. + Supplemental Poverty Measure (SPM) thresholds reflect spending on basic + needs as well as prices. BLS re-estimates them from a rolling five-year + Consumer Expenditure Survey window and publishes them in the following + year. Poverty projections therefore require an estimate of thresholds + that simple inflation adjustment need not reproduce. This paper sets + out the threshold calculation, implements an approximate replication + from public microdata, and compares price-based and consumption-based + projection rules. Under the current research method, retrospective + 2020–2025 mean absolute errors across housing tenures average 2.29 percent + for CPI-U adjustment, 1.65 percent for a composite price index, 0.51 + percent for replicated consumption growth, and 0.90 percent for an + equal blend of the latter two. These comparisons use revised data and + overlapping estimation windows. A distinct, pre-committed 2025 nowcast + supplies one external validation year: the blend's mean absolute error + was 1.17 percent against 2.58 percent for CPI-U adjustment and 0.75 + percent for consumption growth alone. The largest miss was for renters. + Sample-treatment sensitivities and unreplicated imputations limit + claims about level accuracy and future performance. The results support + evaluating consumption growth when projecting quasi-relative poverty + thresholds, while keeping retrospective comparisons separate from + forecasts made before publication. --- # Introduction -On July 17, 2026, the Census Bureau announced it would re-release -Supplemental Poverty Measure estimates for 2019 through 2024, after the -Bureau of Labor Statistics found errors in the thresholds that anchor -them [@census2026statement]. BLS reissued the corrected thresholds the -same day, attributing the errors to the computer code introduced with -its September 2021 methodology change, and re-anchored the series at 82 -percent of median expenditures, down from 83 percent, to minimize the -break [@bls2026corrected]. Every published SPM poverty statistic for -those six years — national rates, state rates, demographic breakdowns, -and the research literature built on them — used thresholds that were -wrong by up to 1.6 percent. - -We maintain open-source software that carries these thresholds into -policy microsimulation, so we audited it against the correction. The -audit found a larger problem on our side: threshold values transcribed -by hand into our own package matched no BLS or Census publication for -five of the six affected years, with errors up to 7.4 percent. The two -failures require different checks. Comparing an embedded reference table -with its published source detects transcription errors and stale copies. -It cannot discover an error already present in the agency's calculation. - -That observation sets the paper's agenda. @sec-production describes how -BLS produces the thresholds and their revision history, which now -includes two code-correction episodes. The 2026 correction is -quantified in @sec-correction against the values Census actually -published, with our own package's larger errors alongside it. -@sec-replication documents an independent replication of the BLS -methodology from public Consumer Expenditure microdata: its measured -fidelity, the bugs the exercise found in our replication code, six -pitfalls in the public CE files that face any researcher who uses -them, and a negative result — replication at survey level cannot -resolve a sub-2-percent error in the official pipeline. Mechanical -artifact comparisons can detect a revised publication once it appears. -Because BLS -re-estimates thresholds each year from a rolling consumption window, -the series is quasi-relative, and @sec-projection shows what that -costs the price indexation used in microsimulation practice, ours -included; a backtest scores replacement rules against five years of -corrected actuals. @sec-nowcast applies that -rule to data available before publication and records the original -numeric estimate of the 2025 thresholds. BLS published those -thresholds on August 24, 2026, and @sec-evaluation scores the -commitment and every alternative rule against them. @sec-discussion -draws the implications for poverty measurement infrastructure. - -# How the thresholds are produced {#sec-production} - -The Supplemental Poverty Measure compares a resource unit's post-tax, -post-transfer resources to a threshold that varies by housing tenure -and local housing costs [@itwg2010]. BLS produces the national -thresholds from the Consumer Expenditure (CE) Interview Survey; Census -applies geographic adjustments, computes resources, and publishes the -poverty statistics [@garner2021]. - -Under the methodology the SPM Interagency Technical Working Group -approved in September 2020 [@burnsfox2021], the threshold for target -year $T$ uses five years of CE Interview quarters lagged one year — -collection quarters $(T{-}5)$Q2 through $T$Q1 [@bls2026corrected; -@blsspmhome]. Within the estimation sample of consumer units with -children, BLS computes expenditures on food, clothing, shelter -(including owners' mortgage-principal payments), utilities, telephone, -and internet — FCSUti — plus imputed in-kind benefits from broadband -subsidies, the Low Income Home Energy Assistance Program, the National -School Lunch Program, the Special Supplemental Nutrition Program for -Women, Infants, and Children, and rental assistance. It equivalizes -them to a two-adult, two-child reference unit with the three-parameter -scale of @betson1996, updates each quarter to threshold-year dollars -with a composite FCSUti price index, and applies the formula +The Supplemental Poverty Measure (SPM) defines a poverty threshold that +changes with expenditure patterns as well as prices. Its national +thresholds come from Consumer Expenditure (CE) Interview Survey spending +on food, clothing, shelter, utilities, telephone, and internet; they vary +by housing tenure and are adjusted for family composition and local +housing costs [@itwg2010; @garner2021]. This quasi-relative design follows +the National Academy of Sciences proposal to update basic-needs standards +with consumption over time [@citro1995]. + +The publication calendar creates a practical estimation problem. BLS +publishes thresholds in the following year: the 2024 thresholds appeared +in September 2025, and the 2025 thresholds on August 24, 2026 +[@p60-287; @bls2025thresholds]. An analysis conducted before publication +must estimate the threshold as well as SPM resources. Adjusting the +last published threshold for inflation accounts for price changes, but +not necessarily changes in the expenditure distribution from which BLS +re-estimates the threshold. + +Existing poverty projections illustrate several responses to this gap. +Urban Institute projections for 2021 adjusted the 2019 thresholds for +projected inflation [@wheaton2021]. Columbia's monthly poverty framework +initially carried the latest published thresholds forward, identifying +the need for more timely consumption data to project new ones +[@parolin2022]. Price adjustment also has a separate +measurement purpose: anchored-SPM series hold a reference standard fixed +in real terms to distinguish changes in resources and policy from changes +in living standards [@wimer2016; @nasem2019]. That estimand differs from +predicting the thresholds BLS will publish. + +This paper examines the latter task. It first describes national threshold +estimation and its application to SPM units. It then reconstructs +thresholds from public CE files, quantifies selected methodological +sensitivities, and compares four one-year projection rules. The main +research experiment uses the current implementation for 2019–2025 +replication and 2020–2025 retrospective projections. A separately frozen +2020–2024 comparison and pre-committed 2025 nowcast document the method +used before the 2025 outcome was published. The completed 2025 evaluation +provides one external validation year; the current retrospective results +do not add further prospective observations. + +# Calculating the thresholds {#sec-production} + +## National reference thresholds + +BLS estimates a national threshold for a two-adult, two-child reference +unit in each of three housing tenures: owners with a mortgage, owners +without a mortgage, and renters. Census applies the thresholds to SPM +resource units after adjusting for composition and local housing costs, +and computes poverty status from the corresponding resources +[@itwg2010; @garner2021]. + +Under the methodology approved by the SPM Interagency Technical Working +Group in September 2020, target year $T$ uses five years of CE Interview +collection quarters, $(T-5)$Q2 through $T$Q1 [@burnsfox2021; +@blsspmhome]. For consumer units with children, BLS constructs spending +on food, clothing, shelter, utilities, telephone, and internet (FCSUti), +including owners' mortgage-principal payments and imputed in-kind +benefits. The latter include broadband subsidies, energy assistance, +school lunches, WIC, and rental assistance. Spending is normalized to +the reference family using the three-parameter equivalence scale and +updated to target-year dollars with a composite FCSUti price index +[@betson1996; @garner2021]. + +Let $E$ denote the survey-weighted 47th–53rd percentile band of +reference-family FCSUti expenditures. If $SU$ denotes shelter and +utilities excluding telephone, the tenure-specific national threshold is $$ -\text{SPM}_h = s \times \left(1.2 \times \overline{\text{FCSUti}}_E - \overline{\text{SU}}_E + \overline{\text{SU}}_{Eh}\right), +B_{T,h}=s\left(1.2\,\overline{FCSUti}_{E} +-\overline{SU}_{E}+\overline{SU}_{E,h}\right). $$ -where $E$ is the subsample inside the 47th–53rd percentile band of -equivalized FCSUti, $\text{SU}$ is shelter plus utilities excluding -telephone, $h$ indexes three housing-tenure groups, the 1.2 multiplier -covers other basic goods and services, and the anchor share $s$ is 82 -percent as of the 2026 correction, 83 percent before it. - -Three properties of this design matter for what follows. First, the -thresholds are re-estimated, not indexed: each year's value reflects -the consumption distribution in its window, so the series moves with -living standards as well as prices. The National Academy of Sciences -panel that proposed this architecture intended exactly that -[@citro1995]. Second, the pipeline is complex enough to fail quietly. -The 2026 reissue is its second code-correction episode: the 2022 -thresholds already carried "corrections in the computer code used to -model" in-kind benefits, disclosed in a footnote of the 2022 poverty -report [@p60-280]; a BLS decomposition attributes 2.5 to 2.9 -percentage points of that year's unusually large threshold increase -to the code improvement and data updating combined, against 7.2 -points from inflation [@schild2023]. Census has separately explored simplifying the -design, estimating that a decennially-anchored variant would have put -2024 SPM poverty at 11.3 percent rather than the published 12.9 -[@sehsd2026]. Third, everything BLS consumes is public — the CE -microdata, the CPI component series, the methodology documents — so -the pipeline can be replicated, and its outputs checked, by anyone. - -# The 2026 correction, quantified {#sec-correction} - -The corrected workbook publishes full-precision thresholds for -2005--2024, with standard errors and tenure population shares, and -states that these are the values Census uses to produce SPM statistics -[@bls2026corrected].^[The Census statement singles out 2019 and 2020 -as the years the code errors affected; the workbook nonetheless -reissues all six years 2019--2024, and every year's values changed. -This paper treats the full reissued range as revised.] The -pre-correction values were removed from the per-year BLS threshold -pages at the correction, so we transcribed them from the five annual -P60 reports that published them — each year from 2020 on -cross-verified against the two consecutive reports that print it, and -2019 from its single source, the 2020 report [@p60-275; @p60-277; -@p60-280; @p60-283; @p60-287].^[The removal has since become -inconsistent: as of August 6, 2026, the BLS page for the 2024 -thresholds again serves the pre-correction values — \$39,430 for -renters, \$39,068 for owners with mortgages, \$32,586 for owners -without — with no correction notice, while the 2023 page remains -empty and the corrected workbook carries different 2024 values. A -reader who lands on the per-year page today receives superseded -numbers, 0.4 to 0.9 percent from the corrected ones: the -vintage-consistency problem this paper describes, on the producer's -own site.] @tbl-correction compares the -two series for two-adult, two-child units. - -::: {#tbl-correction} -{{< include tables/correction.md >}} - -The 2026 correction: thresholds as published in Census P60 reports -versus the corrected BLS workbook, two-adult, two-child consumer -units, nominal dollars of the threshold year. -::: +The pooled expenditure band defines the basic-needs amount. The 1.2 +multiplier allows for other basic goods and services, and replacing the +pooled housing mean with the tenure-specific mean differentiates the +three thresholds. The anchor share $s$ is 0.82 in the revised official +series used here, compared with 0.83 in the preceding series +[@bls2026corrected]. + +The benchmark vintage is the corrected BLS 2005–2024 workbook issued in +July 2026, followed by the published 2025 values. In particular, historical +comparisons for 2019–2024 use the revised series rather than the values +available at each historical date. Source vintages matter when interpreting +a backtest [@croushore2001]; this paper does not reconstruct historical +information sets from the corrected series. + +## Applying a threshold to an SPM unit + +National estimation and unit-level application are distinct operations. +If $q(A,C)$ is the equivalence scale for an already classified unit with +$A$ adults and $C$ children, normalized to one for two adults and two +children, the unit receives the corresponding fraction of its tenure's +reference threshold. Geographic adjustment applies to the housing +portion. Writing that portion as share $a_{T,h}$ and the local relative +housing-cost index as $g$, the calculation is -Every change is within ±1.6 percent, with mixed signs: renter -thresholds fall in all six years, owner thresholds move both ways. -2022 sees the largest downward revision across all tenures. Census -has since quantified the effect on poverty rates -[@creamer2026revising]: the corrected series runs 0.1 to 0.3 -percentage points below the originally published rates for 2019 -through 2023 and marginally above for 2024 (12.9 to 13.0 percent), -with no year-to-year trend materially altered. Our microsimulation -had put the level effect near 0.2 percentage points — the right -magnitude but, for 2024, the wrong sign: it lowered the modeled rate -where Census's re-estimate raised it. A revision with mixed signs -across tenures (owner thresholds up, renter thresholds down in 2024) -resolves through the tenure mix of households near the line, and the -model's mix evidently differs from the survey's. The one comparison -we had flagged as fragile did move: the 2024 rate had printed as -statistically unchanged from 2023 at 12.9 percent [@p60-287]; under -the corrected series it rises from 12.7 to 13.0, still not a -statistically significant change. - -Our own software did worse. The spm-calculator package, which supplies -threshold parameters to the PolicyEngine US microsimulation model, -carried a hand-entered table of "published BLS thresholds." -@tbl-package compares that table to what Census actually published. - -::: {#tbl-package} -{{< include tables/package_errors.md >}} - -Threshold values shipped in spm-calculator through version 0.3.1, -against the values Census published. The 2019--2020 rows appear to mix -misattributed vintages; 2022--2023 match no located BLS or Census -publication. -::: +$$ +B_{T,h}^{\mathrm{unit}} +=B_{T,h}\,q(A,C)\left[(1-a_{T,h})+a_{T,h}g\right]. +$$ -Errors reach −7.4 percent, several times the BLS correction. Only 2024 -and the 2021 renter value were right. The releases that carried these -values passed continuous integration on every commit: the test suite -asserted the code returned the packaged values, and the packaged values -were the thing that was wrong. The fix removes the transcription step -entirely: values are now generated from the official workbook, which -ships alongside them with its SHA-256 recorded, and a scheduled -comparison against the live BLS file flags any divergence — a -silent-drift risk becomes a detectable event. Against the live BLS -site, twenty years of packaged thresholds now match within half a cent. - -# Replicating the thresholds from public microdata {#sec-replication} - -The same package includes a from-scratch implementation of the BLS -methodology over raw CE Interview public-use microdata: it downloads -the quarterly FMLI files, constructs FCSUti, equivalizes, deflates with -a composite index built from CPI component series, and applies the -threshold formula. Before this work, that implementation had never been -validated against official values in earnest — its one comparison test -allowed 5 percent tolerance and was skipped by default. Benchmarking it -against both the pre-correction and corrected series, with the anchor -share matched to each (83 percent versus published values, 82 percent -versus corrected, so anchor choice cannot manufacture a fit), surfaced -four errors in our replication code: - -1. **Annualization off by a factor of two.** The FMLI expenditure - variables report a three-month recall window split across two - calendar quarters (`*PQ` and `*CQ`); the code treated the pair as - six months of spending and multiplied by two instead of four. -2. **Telephone double-counted.** The FMLI `UTIL` summary already - contains `TELEPH` (utilities are defined as natural gas, - electricity, fuels, telephone, and water); the code added telephone - again. -3. **Phantom columns.** Two columns the code referenced — mortgage - principal and home internet — do not exist as FMLI summaries; both - silently contributed zero.^[Principal outlays live in the - `EMRTPNO*` and `MRTPRNO*` outlay columns; home internet has no - FMLI summary variable at all.] -4. **The wrong formula shape.** The code took per-tenure percentiles - of the FCSUti distribution. The BLS formula pools the estimation - band, applies the 1.2 other-necessities multiplier, and swaps a - tenure-specific shelter-utilities average — a different object. - -After the fixes, the frozen historical variant reproduces the corrected -thresholds with mean absolute deviation across the three tenures of -1.5 to 2.1 percent per year (1.0 to 1.6 percent against the -superseded published series), with no imputation of in-kind -benefits.^[Three further approximations remain and are documented in -the implementation: each collection quarter is deflated with annual -rather than quarterly price indexes, so the terminal first quarter is -not given BLS's special treatment; the tenure codes for owners with -unreported mortgage status and for rent-free units are folded into -neighboring groups; and the 80-percent grocery allocation is applied -to the combined grocery summary rather than to the single item code -the errata names. A cross-model code review on September 4, 2026 -surfaced these differences. The frozen experiment did not measure their -separate effects on levels or growth ratios. The current-method -experiment below measures selected sample-policy sensitivities; other -approximation effects remain unmeasured.] -For scale, the workbook's published sampling standard errors run 0.7 -to 2.1 percent of the corresponding threshold across 2019--2024, so -the replication sits within roughly one to three standard errors of -the official values. @tbl-replication reports -signed deviations. +The bracketed expression is the multiplier on the whole threshold; the +relative housing-cost index $g$ adjusts only the housing portion. Resources +below the resulting threshold imply SPM poverty; equality does not. +The resource definition and classification of adults, children, and SPM +units must be supplied consistently with the SPM framework. Estimating +national thresholds does not itself resolve those classifications. -::: {#tbl-replication} -{{< include tables/replication.md >}} +::: {#tbl-current-release} +{{< include tables/current_release.md >}} -Replication deviations from official thresholds (owners with mortgages -/ owners without / renters), with the anchor share matched to each -reference series. +Published 2025 reference-family thresholds and the housing shares used +by the companion calculator. Those shares come from the 2024 Census +metro snapshot and are carried into 2025 as an approximation. ::: -The deviations are predominantly negative and roughly stable across -years. The replication imputes no in-kind benefits and observes no -internet expenditures, but the contribution of each omission has not -been isolated. The observed level deficit — up to -three percent for owners with mortgages — persists across -estimation windows rather than growing or shrinking with any one -year. Two conclusions follow. The negative one: the BLS correction -moved thresholds by at most 1.6 percent, inside this replication's -noise, and neither reference series fits systematically better in the -affected years — so survey-level replication could not have caught -the BLS bug, and we do not claim otherwise. Comparing published artifacts -can detect a later correction and stale downstream copies; independent -validation of the agency calculation is a separate task. The positive -one is conditional: a stable proportional replication bias cancels in a -growth ratio, while a persistent additive deficit need not. Sections -@sec-projection and @sec-nowcast test the resulting ratios empirically. - -## Pitfalls in the public CE files {#sec-pitfalls} - -Six properties of the public data, encountered while building the -replication and benchmark, will face any researcher who works with -these files. None is documented in one place elsewhere. - -1. **Recall-window semantics.** FMLI `*PQ`/`*CQ` pairs split one - three-month recall period across calendar quarters; on average the - previous-quarter share is roughly twice the current-quarter share. - Annualization multiplies the pair's sum by four. -2. **`UTIL` contains `TELEPH`.** Summing summary variables without - consulting the variable hierarchy double-counts telephone service. -3. **The 2023 food redesign changes schema mid-window.** Files from - 2024Q2 onward drop the `FOOD` and `FDHOME` summaries for `GROCER` - (all grocery purchases, food and nonfood); BLS's errata allocates 80 - percent of `GROCER` to food at home [@cepumd; @blsspmhome], and BLS - applies the same allocation in the official thresholds. Pooled - multi-year windows mix the two schemas row by row, so the - construction must be per record — a frame-level column check zeroed - food for the newest quarters and moved replicated 2025 thresholds - four to five percent. -4. **Bundle layout migrations.** CE moved its CSV year-bundles from - one directory layout to another with the 2022 release - [@cepumd], and interior packaging varies by vintage; per-quarter - archive paths from pre-2023 documentation no longer resolve. -5. **The BLS API truncates silently.** Unregistered requests to the - public data API cap at ten-year spans and return the *first* ten - years of a longer request rather than erroring. A 2005--2025 - request comes back as 2005--2014, and downstream code sees a valid, - wrong series. -6. **October 2025 CPI does not exist.** The scheduled release covering - October 2025 was not published during the federal government - shutdown, and the month is absent from the CPI series; 2025 annual - averages must be computed over eleven months. The API still - returns a placeholder for October 2025 as of September 2026, yet - BLS's own 2025 threshold page reports 2.70 percent CPI-U growth - for the year against the 2.63 percent the eleven-month annual - average implies; the treatment is undocumented. - -# Projecting thresholds past the published years {#sec-projection} - -BLS publishes thresholds with roughly a fourteen-month lag: the 2024 -thresholds arrived in September 2025 [@p60-287], and the 2025 -thresholds followed on August 24, 2026 [@bls2025thresholds]. Any -analysis of current-law poverty — -which for microsimulation models is most analyses — needs thresholds -for years BLS has not published. A common approach, and ours until -this work, ages the latest published threshold by projected growth in -the Consumer Price Index for All Urban Consumers (CPI-U). - -That practice has company, and no apparent exception. When Urban -Institute researchers projected 2021 poverty during the pandemic, -they took the 2019 BLS thresholds and adjusted them for projected -inflation [@wheaton2021]. Columbia's monthly poverty framework, which -produces sub-annual SPM estimates, declines to project thresholds at -all: it carries the latest published ASEC thresholds into subsequent -months, on the stated grounds that projecting new ones "requires more -timely consumption data" — so its 2020--2021 estimates ran on 2019 -thresholds [@parolin2022]; a 2024 revision updated the framework's -base data and income imputation without changing how thresholds are -handled [@curran2024]. Han, Meyer, and Sullivan's real-time -series prices official-measure thresholds forward with monthly CPI, -which is exact for that measure because official thresholds are -CPI-indexed by construction [@han2020; @fisher1992] — precisely what -the SPM's are not. Census's own analysis of the 2024 estimates quantifies what -indexation conceals: thresholds re-estimated from consumption rose -5.2 to 5.8 percent that year (on the pre-correction vintage then -published) against 2.9 percent for the official measure's CPI-U -update, and price-updating the SPM thresholds instead would have put -2024 SPM poverty half a percentage point below the published rate -[@creamer2025]. A related tradition price-indexes deliberately: -anchored-SPM series hold a reference-year threshold and move it by -CPI precisely to separate policy effects from drift in living -standards, in historical work [@wimer2016] and in the National -Academies' child-poverty simulations [@nasem2019] — a design choice -about what to measure, not an estimate of what BLS will publish. We -are not aware of prior work that projects the SPM threshold itself by -re-estimating it from the consumption microdata — the path Columbia's -caveat names — or that commits to numeric threshold values ahead of -publication. +The national bases in @tbl-current-release are published estimates. +The carried housing shares and aggregate geographic indices are separate +approximations; their use does not establish exact reproduction of revised +official thresholds for every local area. The remainder of the paper +examines national reference thresholds and their growth. + +# Replication from public consumption data {#sec-replication} + +## Expenditure construction and estimation sample + +The replication reads public CE Interview FMLI files for each target +year's five-year window. Previous-quarter and current-quarter expenditure +fields split a single three-month recall period; their sum is multiplied +by four to annualize spending. Utilities already include telephone, so +telephone is retained in FCSUti but removed when constructing the narrower +shelter-utilities measure. Mortgage principal is included using the FMLI +outlay fields [@cepumd; @blsspmhome]. + +The food redesign requires a further decision in pooled windows. Newer +records use a combined grocery summary in place of the earlier food +summaries. This implementation applies an 80-percent food allocation to +the combined summary; the BLS errata specifies a narrower item-code +allocation. This is a recorded approximation. Similarly, public FMLI +summaries do not directly provide the home-internet spending and in-kind +imputations required for a complete reproduction of the BLS procedure. +Those components are omitted here. + +Each consumer unit's expenditures are normalized to the two-adult, +two-child reference family before estimating the weighted expenditure +band and tenure-specific housing means. The implementation uses CE +interview survey weights and an inclusive 47th–53rd percentile band from +a midpoint weighted cumulative distribution. It uses annual price +adjustments by collection year rather than BLS's quarterly treatment. +In the current replication, expenditure-deflation weights are derived +from the selected CE sample in each target year's window, before selecting +the percentile band; these differ from the fixed projection-comparator +weights described in @sec-projection. +Exact parity with BLS's percentile code has not been established. + +Adult/child classification and housing tenure require explicit sample +rules. Independent minors can form CE consumer units, so minor-only +records do not establish the SPM adult/child classification needed for +exact equivalence scaling. The current research baseline excludes these +unresolved records. A sensitivity instead imposes one adult while retaining +the reported child count; that recode is nonofficial and does not resolve +membership. The baseline also excludes tenure codes 5 and 6 and assigns +code 3 to owners with a mortgage. These are research sample policies, +not a claim that BLS uses the same handling. + +## Replicated levels and sample sensitivities + +The current method reconstructs seven target years, 2019–2025. +@tbl-current-replication reports signed deviations from the revised +published thresholds. Differences vary by tenure and year, including +positive deviations for owners without mortgages in later windows. +The comparison measures the combined discrepancy; it does not identify +the contribution of any particular omitted component or sample rule. -Because the thresholds are re-estimated from consumption data each -year, price indexation is mismatched in principle, and @tbl-backtest -measures the cost in practice. For each year 2020 through 2024 we -stand at the prior year's corrected threshold and project one year -ahead under four rules: All-Items CPI-U aging; aging by a composite of -the CPI components matching the FCSUti basket; scaling by the growth -of our replicated thresholds; and a 50/50 blend of the last two. All -rules are scored against the corrected actuals. This is a retrospective -experiment using the 2026-corrected threshold vintage and one repaired -implementation, not forecasts reconstructed from the data and code -available at each historical prediction date. - -::: {#tbl-backtest} -{{< include tables/backtest.md >}} +::: {#tbl-current-replication} +{{< include tables/current_replication.md >}} -Mean absolute projection error across the three tenure thresholds, by -target year, projecting each year from the prior year's corrected -values. +Signed current-method replication deviations from published national +thresholds. Negative values indicate a lower replication. ::: -The direction of the error is the clearest result. All-Items CPI-U -aging misses by 2.23 percent per year in absolute value — projection -error against the realized thresholds, not the much smaller pure -price-index gap between the FCSUti basket and the all-items index — -and the misses are mostly on one side: it understated actual threshold growth -in four of five years — most sharply in 2023, when the CE window's -shelter costs compounded faster than the all-items index — and -overstated it only in 2021, for a signed mean error of −1.9 percent -per year. The composite price index removes about thirty percent of -that error — the FCSUti basket is nearly half shelter — but runs low -in all five years. The replication growth ratio, the only rule that -tracks consumption rather than prices, posts the lowest mean error, -0.41 percent, and is nearly unbiased, with a signed mean of +0.1 -percent per year. The 50/50 blend inherits half of the composite's -downward bias and scores 0.76 percent. We fix the blend weights at -one half each rather than fitting them: with five backtest years, -fitted weights would mostly fit noise, and the equal blend's -rationale is structural — the two components fail for different -reasons. - -@tbl-backtest is itself an amended result, and the amendment bears on -how the blend row should be read. As first released, the composite -index behind the second and fourth rows averaged CPI component series -at their raw published levels; the components carry different index -reference bases, so this weighted them by base level as well as by -budget share — shelter received an effective weight near 55 percent -rather than the stated 47. Adversarial cross-model review caught the -error the day after the 2025 nowcast was first committed. Repairing -it left the CPI-U row unchanged, moved the composite rule from 1.40 -to 1.57 percent, and — because the replication deflates CE -expenditures with the same composite — improved the replication ratio -from 1.58 to 0.41 percent and the blend from 1.35 to 0.76. The -ranking inverted: the blend had ranked first under the erroneous -construction, which is why it is the committed rule; the repaired -table puts the replication ratio first. We retain the blend as the -primary estimate rather than switching to the new winner, because -re-selecting a rule after a second look at five noisy backtest years -would be selection on noise, and the blend's structural rationale is -unchanged. The September evaluation scores every rule in -@tbl-backtest against the realized 2025 thresholds, so the choice -costs nothing in falsifiability; the pre-repair table remains in the -repository history. The repair then demonstrated the paper's thesis -once more: colleague review caught the pre-repair numbers surviving -in the packaged nowcast's own method and caveat strings after every -table and prose figure had been regenerated — a stale value -persisting in exactly the provenance field meant to prevent -staleness. Those strings are now pinned by the pre-render check too. - -Two caveats on reading the table. First, the two price rules use -realized index values — information unavailable when a true forecast -is made; a 2022 projection built from consensus 2021 inflation -forecasts would have erred by several points. The replication ratio -uses CE microdata that BLS publishes well before the corresponding -thresholds, so in the nowcasting position the data it needs genuinely -exist; that it also scores best in backtest counts in its favor, but -five backtest years cannot settle rankings this close. Adjacent CE -windows overlap by 80 percent and tenure estimates share a sample; -these are not fifteen independent forecast trials. Second, the -price rules share a residual: both run low in nearly every year, -reflecting real consumption growth beyond what a five-year averaging -window passes through and in-kind benefit expansions that prices do -not capture. The replication ratio, which tracks consumption, shows -no such bias — its annual signed errors range from +0.5 to −0.3 -percent. With five backtest years we leave the price rules' bias -unmodeled rather than fit a constant to it. - -# A pre-committed 2025 nowcast {#sec-nowcast} - -At the commitment date, the 2025 thresholds were a nowcasting target — -estimation of a present quantity from data already in hand, in the -macroeconomic sense of the term [@giannone2008]: their entire CE -window (2020Q2 through 2025Q1) and eleven of twelve months of 2025 -CPI were published. We applied the blend rule to the -corrected 2024 base. The replication component computes thresholds for -2024 and 2025 with identical code over their respective windows and -takes the ratio, per tenure. Stable proportional replication biases -would cancel across adjacent, 80-percent-overlapping windows; additive -or changing errors would not. The anchor share cancels exactly. -The price component is the FCSUti-composite ratio of -2025 to 2024 annual averages, with 2025 averaged over its eleven -published months.^[The nowcast's composite ratio chain-links at the -base year: each component's 2025-to-2024 ratio, weighted by budget -share. The backtest composite in @tbl-backtest holds a fixed 2019 -base instead. The two definitions differ by at most 0.06 percentage -points in any year's growth and by 0.006 points of mean absolute -error in the backtest; we note the inconsistency rather than re-run -the committed table under a new definition.] @tbl-nowcast states the -estimate. - -::: {#tbl-nowcast} -{{< include tables/nowcast.md >}} +::: {#tbl-current-sensitivity} +{{< include tables/current_sensitivity.md >}} -The pre-committed nowcast of 2025 SPM thresholds, two-adult, -two-child consumer units, nominal dollars, with per-tenure components. -These are model estimates, not BLS publications. +Unresolved-youth interviews and their weighted share of child-containing +CE interviews, with maximum absolute threshold changes across tenures +under separate youth-recode and tenure sensitivities. The latter includes +tenure codes 5 and 6 as renters. These overlapping windows reuse observations. ::: -The consumption signal moves the estimate materially: replicated -thresholds grew 4.4 to 6.0 percent by tenure against 3.2 percent -composite price growth, so the blend projects 2025 threshold growth of -3.8 to 4.6 percent, against 2.6 percent realized All-Items CPI-U -growth over the same period. Applied in the PolicyEngine US -microsimulation model, replacing CPI-U-aged 2025 thresholds with the -nowcast raises projected 2025 SPM poverty by 0.4 percentage points -(simulation output pinned in the repository); we treat that as a -sensitivity, not a headline, since modeled baseline levels carry their -own calibration error. To be explicit about adoption: at the pinned -implementation commit, the companion policyengine-us change takes up -only the corrected historical series and still ages thresholds past -2024 by projected CPI-U — so its current-law poverty projections -carried the understatement quantified in @sec-projection, at the -measured 2025 cost of those 0.4 points, for as long as 2025 was -unpublished. With the 2025 thresholds now published, the -implementation branch carries them as the latest published year and -CPI-U aging begins at 2026; whether the nowcast method enters that -model's forecast path for 2026 is the open decision @sec-evaluation -ends on. - -These are amended values. The nowcast as first committed was built on -the erroneous composite index described in @sec-projection; repairing -the index moved each tenure's estimate by 0.1 to 0.3 percent — owners -with mortgages from \$41,099.57 to \$41,036.34, owners without from -\$34,250.70 to \$34,135.99, renters from \$40,791.72 to \$40,755.98. -The amendment predates any BLS publication of 2025 thresholds, the -original values remain in the repository history, and the completed -evaluation reports realized errors for both sets. - -The original paper committed in advance to an evaluation — the device -of preregistration [@nosek2018], applied to a numeric estimate rather -than a hypothesis test. It anticipated publication around September -2026 and promised signed percent errors per tenure and mean absolute -errors for the nowcast, All-Items CPI-U aging and each component rule, -all computed from the -corrected 2024 base as committed in the repository — regardless of any -subsequent BLS revision to 2024. The packaged estimate is fixed with -its method, components, and hash. The blend's annual signed errors in -backtest, averaged across tenures, ranged from −0.1 to −1.4 percent; -that range was the stated expectation of the nowcast's accuracy. -The commitment included publishing worse performance than competing -rules. The completed evaluation follows in @sec-evaluation. - -# Evaluation against the published 2025 thresholds {#sec-evaluation} - -BLS published the 2025 thresholds on August 24, 2026, in late August -rather than the September this paper anticipated [@bls2025thresholds]: -\$41,323 for owners with mortgages, \$34,326 for owners without, and -\$41,701 for renters — growth of 4.40 to 6.33 percent over the -corrected 2024 base, by BLS's own statement. The page publishes whole -dollars; the current-thresholds workbook linked from the BLS SPM -home page carries the same values at full precision, with standard -errors of \$327, \$560, and \$393, and is bundled with this revision. -@tbl-evaluation scores the committed nowcast, its superseded -original, and every rule from @tbl-backtest against those values, all -from the corrected 2024 base as committed. - -::: {#tbl-evaluation} -{{< include tables/evaluation.md >}} +The tenure sensitivity in @tbl-current-sensitivity reaches 1.74 percent. +The recorded windows contain no code-3 observations, so assigning that +code to owners without a mortgage has zero measured effect here; that +result supplies no evidence about samples in which the code is present. +The weighted shares describe consumer-unit interviews, not unique +population counts. -Signed percent error of each 2025 projection against BLS's published -thresholds, and mean absolute error across tenures. All rules project -from the corrected 2024 base; the CPI-U rule uses the pinned -eleven-month 2025 annual average. -::: +These are selected sensitivities, not an exhaustive error bound. Annual +rather than quarterly price adjustment, the food allocation, omitted +home internet and in-kind imputations, and percentile conventions remain +unmeasured sources of discrepancy. Sampling and imputation uncertainty +has not been estimated for the replication. Published BLS standard errors +provide scale for the level differences, but they do not account for +replication error or covariance from shared survey inputs. -::: {#tbl-evaluation-levels} -{{< include tables/evaluation_levels.md >}} +# Projecting thresholds {#sec-projection} -Published 2025 thresholds and the committed nowcast in dollars, with -BLS's stated growth against the nowcast's. -::: +## Price and consumption growth rules -Four results. First, the committed nowcast missed by 0.69, 0.55, and -2.27 percent by tenure, a mean absolute error of 1.17 percent; its -signed mean of −1.17 percent falls inside the −0.1 to −1.4 range -stated in advance as our expectation, though the renter miss alone -falls outside it. Scaled by BLS's own sampling uncertainty, the two -owner misses sit within one standard error of the published -thresholds; the renter miss is about 2.4 standard errors. Second, the status-quo rule did what the backtest -said it would: All-Items CPI-U aging understated every tenure, by -1.70 to 3.48 percent, for a mean absolute error of 2.58 — the fourth -consecutive year it ran low. The blend more than halved that error. -Third, the ranking the repaired backtest produced held out of sample: -the replication growth ratio alone was the most accurate rule at 0.75 -percent and the only one with errors on both sides of zero, the -composite index alone sat between it and CPI-U at 2.02, and the blend -landed between its two components. Fourth, the superseded original -nowcast, built on the erroneous composite, scored 0.98 percent — -better than the amended 1.17 — because its higher price ratio -happened to offset more of the common shortfall. One year cannot -distinguish that from luck, and we do not try; both vintages are -reported because both were committed. - -The miss is a renter story. BLS's renter threshold grew 6.33 percent, -against 5.33 and 4.40 for the two owner groups; every rule, the -replication ratio included, missed renters by the most, and the -nowcast captured 3.92 of those 6.33 points. The replication's own -renter growth of 4.62 percent understated the published figure by -more than its two owner-group errors combined, so whatever moved -renter thresholds in 2025 is something the unimputed replication does -not observe. The in-kind imputations, which include rental -assistance, are the obvious candidate; this paper's data cannot -attribute it, and we leave the diagnosis to a replication that -imputes them. - -The composite index can now be checked against BLS's own. The -threshold page also publishes year-over-year growth in BLS's FCSUti -deflator, which BLS releases only alongside each year's thresholds — -useless for nowcasting, ideal for validation. @tbl-composite-validation -compares them: the static-weight composite tracks BLS's index within -0.7 percentage point in every year from 2020 through 2025, with a -mean absolute gap of 0.4 point and no consistent sign, so the -construction repaired in @sec-projection is sound after the repair. -The same page reports 2025 All-Items CPI-U growth of 2.70 percent -against the 2.63 percent implied by the CPI program's eleven-month -annual average; BLS does not state how it treated the missing October -index, and we do not guess. - -::: {#tbl-composite-validation} -{{< include tables/composite_validation.md >}} +The projection question is whether changes in public consumption data +improve on price adjustment of the latest published threshold. Let +$\widetilde B_{T,h}$ be the replicated national threshold and $P_T$ a +price index. Starting from an official base $B_{T-1,h}$, the combined rule +is -Year-over-year growth in BLS's published FCSUti deflator against the -paper's static-weight composite, with the two CPI-U annual-average -figures alongside. -::: +$$ +\widehat B_{T,h}=B_{T-1,h} +\left[\lambda\frac{\widetilde B_{T,h}}{\widetilde B_{T-1,h}} ++(1-\lambda)\frac{P_T}{P_{T-1}}\right]. +$$ -What the result licenses. The pre-commitment did its job: the numbers -scored above are the ones fixed in the repository and its timestamps -before BLS published, and the amended nowcast beat the rule it -replaced by more than half. It does not license re-selecting the rule -for 2026 after the fact. The replication ratio has now led on both -looks — the repaired backtest and the realized year. The previously -discussed 2026 CE collection window already closed in 2026Q1. This -revision makes no new forecast commitment or scheduled cutoff. Any -future commitment would need an actual timestamp and a recorded -available-input cutoff before outcome publication; it could not claim -to precede that completed collection window. - -# Current-method research and a shared release {#sec-current} - -The September 8 rebuild separates a release of reference thresholds from -the research that estimates or projects them. The paper, standalone -calculator, and integration adapters consume the same portable JSON -release, with a content digest, target year, information date, source -fingerprints, methodology identifier, and published or estimated status. -The release records corrected national thresholds through published 2025. -Its conservative September 8 availability date describes this snapshot; -it does not reconstruct what was available to forecasters in earlier years. +We compare All-Items CPI-U adjustment; adjustment by a static-weight +composite matching the FCSUti components available in the data; the +replication growth ratio alone; and an equal blend of replication growth +and composite-price growth. The last three correspond to $\lambda=0$, +$1$, and $1/2$ when $P$ is the composite index. The blend weights are fixed +rather than fitted to the small evaluation sample. + +The projection composite uses five CPI-U components: food, apparel, +shelter, fuels and utilities, and telephone services. Their respective +weights are $(0.30, 0.05, 0.45, 0.12, 0.04)/0.96$, with no separate +home-internet component. These are fixed research weights; they do not +reproduce the BLS deflator exactly. If $P_{i,T}$ is component $i$'s +annual-average index, the current projection's price multiplier is +$\sum_i w_i(P_{i,T}/P_{i,T-1})$. The frozen experiments use the same +normalized weights, with the fixed-2019-base backtest distinguished from +the prior-year-based 2025 nowcast below. These projection weights are +separate from the sample-derived weights used to adjust CE expenditures +in the current replication. + +Using growth ratios has a specific rationale. If replication differs +from the official level by a stable proportional factor, that factor +cancels between adjacent years. A persistent additive difference need +not cancel, and changing omissions or sample effects can alter growth. +The method therefore requires empirical evaluation of the ratios, even +when the replicated levels appear stable. + +CPI-U adjustment estimates a different process from annual re-estimation +of consumption thresholds. Census's analysis of the originally published +2024 SPM estimates illustrates the distinction: consumption-based +threshold growth exceeded the CPI-U update, and price-updating the +thresholds instead would have produced a 2024 rate of 12.4 percent, below +the published SPM rate [@creamer2025]. That finding concerns the published +vintage used in that analysis; it does not establish how a particular +projection rule will perform in every year. + +## Current-method retrospective comparison {#sec-current} + +@tbl-current-projection evaluates six one-year projections for 2020–2025. +For each target year, all rules start from the prior year's revised +published threshold. The current composite holds static component weights +and rebases the price index to each prior year. Replication and price +inputs use the versions recorded in the September 2026 research replay. Historical availability +of those exact versions is not inferred. -::: {#tbl-current-release} -{{< include tables/current_release.md >}} +::: {#tbl-current-projection} +{{< include tables/current_projection.md >}} -Reference-family values from the shared release. The 2025 national bases -are published values; the displayed housing shares come from the 2024 -Census metro snapshot and are carried into 2025 as an approximation. +Current-method retrospective mean absolute percentage errors across +three tenures, 2020–2025. The mean row weights the six years equally. ::: -Unit-level application combines the selected national base with the -SPM equivalence factor and a geographic adjustment to its housing -portion. The current release pins geographic rent indices and identifies -the source vintage separately from the target year. It does not claim -that carried housing shares or aggregate geographic averages exactly -reproduce revised official thresholds for every local area. - -This boundary lets PolicyEngine or a future Axiom implementation supply -resources and already classified SPM-unit membership, while Microcosm -supplies entity links and explicit weights. The reference threshold does -not change when the sample is reweighted. The implemented Axiom bridge -executes national-base selection, multiplication, and the strict -resources-below-threshold judgment in the native runtime. Its equivalence -factor remains an explicitly supplied SPM calculation because the pinned -RuleSpec runtime lacks fractional powers. This is a partial bridge; -it does not migrate CE estimation, SPM membership, or resource accounting -into Axiom. A host migration can change those inputs and therefore poverty -results even when the shared threshold release stays fixed. - -## CE replication and measured sensitivities - -The current CE experiment reconstructs seven target years from their -public five-year windows. It normalizes each consumer unit to the -reference family before selecting the weighted expenditure band and -estimating tenure-specific housing means. @tbl-current-replication -compares this explicitly approximate implementation with the corrected -published series. These results are separate from the historical -implementation and frozen evaluation above. +Over these six years, the replication ratio has the lowest mean absolute +error, followed by the blend, composite-price adjustment, and CPI-U +adjustment. The table also shows substantial variation across years. +It uses realized prices, which would not have been available in a forecast +made before the target year's inflation was observed. Adjacent CE windows +share four of five years, and tenure estimates share observations. The +resulting errors are dependent retrospective evidence, not eighteen +independent forecast trials or six prospective validations. + +## Frozen comparison used for the 2025 nowcast + +The pre-committed 2025 estimate used an earlier, separately preserved +method version. Its 2020–2024 comparison is shown in @tbl-backtest. This +experiment also uses corrected actual thresholds and realized price +indices; it is retrospective. Its fixed-2019-base price composite and +sample treatment differ from the current research method. Keeping the +two tables separate preserves the evidence available when the 2025 +nowcast was committed. + +::: {#tbl-backtest tbl-colwidths="[34,11,11,11,11,11,11]"} +{{< include tables/backtest.md >}} -::: {#tbl-current-replication} -{{< include tables/current_replication.md >}} +Frozen-method mean absolute percentage errors across three tenure +thresholds, projecting each year from the prior year's corrected values. +::: + +CPI-U adjustment has a mean absolute error of 2.23 percent and understates +threshold growth in four of five years, with a signed mean error of +−1.9 percent. Composite-price adjustment reduces the mean absolute error +to 1.57 percent, removing about thirty percent of the CPI-U error in this +sample. The replication ratio scores 0.41 percent, with a signed mean of ++0.1 percent and annual signed means ranging from +0.5 to −0.3 percent. +The equal blend scores 0.76 percent; its annual signed means range from +−0.1 to −1.4 percent. These are descriptions of the frozen comparison, +not uncertainty intervals for future forecasts. + +The blend remained the primary committed estimate although the final +frozen comparison ranked the replication ratio first. The method-version +and amendment chronology is recorded in Appendix A. The external +validation below reports every rule, including the original and amended +blend estimates, without choosing the primary forecast after seeing +published 2025 outcomes. + +# Pre-committed 2025 validation {#sec-nowcast} + +## The estimate before publication + +At the commitment date, 2025 was a nowcasting problem: estimating a +quantity whose underlying period had occurred but whose official value +was unpublished [@giannone2008]. The relevant CE collection window, +2020Q2 through 2025Q1, and eleven months of 2025 CPI observations were +available. October 2025 CPI was not published; the recorded price inputs +use the eleven available months. + +The committed blend projected \$41,036.34 for owners with mortgages, +\$34,135.99 for owners without mortgages, and \$40,755.98 for renters. +It applies replication and composite-price growth to the corrected 2024 +base fixed in the commitment. + +::: {#tbl-nowcast tbl-colwidths="[31,17,18,14,20]"} +{{< include tables/nowcast.md >}} -Signed current replication deviations from published thresholds, by -reference-family tenure. Negative values indicate a lower replication. +Pre-committed 2025 nowcast for a two-adult, two-child unit, with its +replication and price components. These are model estimates. ::: -Independent minors can form CE consumer units, so a minor-only record -does not by itself establish the SPM adult/child classification needed -for exact equivalence scaling. The default exact path rejects these -unresolved records. For the experiment, an explicit research option -excludes them; a sensitivity imposes an adult while retaining the reported -child count, which is nonofficial and not a resolution of membership. -The baseline excludes tenure codes 5 and 6 and assigns code 3 to owners -with a mortgage. The recorded windows contain no code-3 observations, -so assigning those records to owners without a mortgage has zero measured -effect here; it provides no evidence about other samples. +Replicated threshold growth ranged from 4.4 to 6.0 percent by tenure, +against 3.2 percent composite-price growth. Their blend projected +3.8 to 4.6 percent growth, compared with 2.6 percent realized CPI-U growth. +The nowcast price component chain-links at the prior year; the frozen +backtest composite holds a fixed 2019 base. Both definitions are recorded +with their experiments rather than treating them as interchangeable. -::: {#tbl-current-sensitivity} -{{< include tables/current_sensitivity.md >}} +## Evaluation against published thresholds {#sec-evaluation} -Unresolved-youth interviews and their weighted share of child-containing -CE interviews, with maximum absolute threshold changes across tenures -under separate youth-recode and tenure-5/6 sensitivities. These -overlapping windows reuse observations. +BLS published the 2025 thresholds on August 24, 2026: +\$41,323 for owners with mortgages, \$34,326 for owners without, and +\$41,701 for renters [@bls2025thresholds]. The linked workbook carries +full-precision values and standard errors of \$327, \$560, and \$393. +BLS reports growth of 4.40 to 6.33 percent by tenure over the corrected +2024 base. All forecast evaluations below use that base as fixed before +publication. + +::: {#tbl-evaluation tbl-colwidths="[34,17,17,16,16]"} +{{< include tables/evaluation.md >}} + +Signed percentage errors against published 2025 thresholds and mean +absolute error across tenures. The CPI-U rule uses the pinned +eleven-month 2025 annual average; both committed blend vintages are shown. ::: -The tenure sensitivity reaches 1.74 percent, so the sample-treatment -approximations cannot collectively be described as negligible. Annual -rather than quarterly CPI adjustment, the allocation of the redesigned -food summary, omitted home internet and in-kind imputations, and exact -BLS percentile-code parity remain unmeasured sources of discrepancy. -Sampling and imputation uncertainty has not been estimated. The table -measures selected implementation choices, not a confidence interval or -an exhaustive error bound. - -## Retrospective projection under the current method - -The same current replication supplies adjacent-year growth ratios for -six one-year projections in @tbl-current-projection. The price comparator -rebases static component weights to each prior year, while the frozen -backtest used a fixed 2019 price base. These are different method -versions. All current inputs are conservatively dated to this replay; -no historical information-set eligibility is inferred. Adjacent windows -overlap by four of five years, and tenure estimates share observations. -The resulting year-tenure errors are dependent retrospective evidence. -The blend gives equal weight to replication growth and composite-price -growth. These figures do not replace the frozen backtest or the single -prospective 2025 evaluation and do not establish a new forecast commitment. +::: {#tbl-evaluation-levels tbl-colwidths="[24,16,18,12,15,15]"} +{{< include tables/evaluation_levels.md >}} -::: {#tbl-current-projection} -{{< include tables/current_projection.md >}} +Published 2025 thresholds and the committed nowcast in dollars, with +BLS's stated growth and the projected growth. +::: -Current-method retrospective mean absolute percentage errors across -three tenures, 2020–2025. The mean row weights the six years equally. +The amended blend understates the three thresholds by 0.69, 0.55, and +2.27 percent, giving a mean absolute error of 1.17 percent. Its signed +mean of −1.17 percent falls inside the −0.1 to −1.4 range described in +advance, although the renter error lies outside that range. The two +owner differences are within one published BLS standard error and the +renter difference is about 2.4 standard errors. These comparisons give +scale; the BLS standard errors are neither forecast uncertainty bands +nor a significance test of the model's discrepancies. + +CPI-U adjustment understates all three thresholds by 1.70 to 3.47 percent, +for a mean absolute error of 2.58 percent. The replication ratio alone +scores 0.75 percent and has errors on both sides of zero; composite-price +adjustment scores 2.02 percent. The replication ratio therefore ranks +first in this validation year as well as in the frozen retrospective +comparison. The superseded original blend scores 0.98 percent, compared +with 1.17 percent for the amended blend. Both are reported because both +were committed before the official outcome. One year cannot establish +whether any of these rankings will persist. + +The largest miss occurs for renters. BLS reports renter growth of 6.33 +percent against 5.33 and 4.40 percent for the two owner tenures. The blend +projects renter growth of 3.92 percent, and replication alone 4.62 +percent. This analysis does not identify the reason for the difference. +Omitted imputations, price treatment, sample classification, percentile +conventions, and other replication differences could affect growth; +the observed miss does not attribute it to any one of them. + +## Validation of the price comparator + +BLS publishes its FCSUti deflator growth alongside the thresholds, +providing an additional comparison after publication. This comparison uses +the fixed-2019-base composite from the frozen backtest, rather than the +prior-year-based composite used in the 2025 nowcast. The fixed-base +composite differs from the BLS index by at most 0.7 percentage point in +each year from 2020 through 2025, with a mean absolute gap of 0.4 point +and no consistent sign. This is evidence of closeness over those years, +not exact replication of the deflator. + +```{=latex} +\Needspace{14\baselineskip} +``` + +::: {#tbl-composite-validation tbl-colwidths="[10,18,22,14,18,18]"} +{{< include tables/composite_validation.md >}} + +Year-over-year growth in BLS's FCSUti deflator and the frozen backtest's +fixed-2019-base composite, with the CPI-U figures alongside. ::: +For 2025, the BLS threshold page reports CPI-U growth of 2.70 percent, +compared with 2.63 percent implied by the eleven-month annual average in +the pinned CPI observations. The page does not explain its treatment of +the missing October index. We retain the recorded calculation rather +than substituting a different annual average in the completed forecast +evaluation. + # Discussion {#sec-discussion} -The 2026 correction restated six years of SPM statistics. Its delayed -discovery illustrates the difficulty of validating the source -calculation itself. Our package's larger transcription errors illustrate -a separate, directly testable problem: tests had preserved values a -person typed without comparing them with their sources. Scheduled -comparisons can identify disagreement and later source revisions. -They cannot establish the correctness of an agency calculation that -supplied the same erroneous values to every downstream copy. - -Replication plays a narrower role than adjudication. At its measured -fidelity, an independent implementation cannot resolve sub-2-percent -errors in the official series, and claiming otherwise would overreach. -What it can do is structural: confirm the formula, the window, and the -re-anchoring behave as documented; catch transcription-scale errors -instantly; and measure the consumption growth that price indexation -misses — the use this paper tests, conditional on sufficient stability -of proportional replication errors. The nowcast is the -constructive payoff of infrastructure built for verification. The -verification infrastructure itself needed verifying: the composite -index at the heart of the backtest carried a construction error until -adversarial review by a model from a different family caught it. The -checking layer is code too, with the same failure modes as what it -checks. - -The projection result has the most direct consequence for -microsimulation practice. Aging SPM thresholds by projected CPI-U, as -our own model did, understated threshold growth by an average of 1.9 -percent per year over 2020--2024 and by 2.6 percent in 2025 out of -sample, which flows directly into understated current-law poverty -projections. Effects on modeled -eligibility and poverty transitions near program cliffs are plausible -but outside this paper's scope. The blend rule is implementable from -public data with a published, tested codebase, and it degrades -gracefully: past the CE data horizon it reduces to composite-price -aging, which alone removed about thirty percent of CPI-U aging's -error in backtest. - -The pattern generalizes past poverty measurement. Statistical -agencies publish reference series; models embed them; both sides -revise — the macroeconomic real-time-data literature documents how -consequential vintage differences are for downstream conclusions -[@croushore2001]. Every embedded copy is a place where the numbers -can silently diverge from the source, and the failure mode is not -unique to official statistics: a spreadsheet error in a widely cited -debt-and-growth analysis likewise survived until outside replication -[@herndon2014]. The remedy — bundle the source artifact, generate the -values from it, diff it on a schedule — required one scheduled job and -a parsing script in our case; we do not measure its cost against -manual auditing across settings. We found errors on both sides of that -interface in the same week. From a single case we cannot say how -common such divergences are; our hypothesis is that the interface is -ordinary and rarely checked. - -# Reproducibility - -The historical tables regenerate from the frozen public-data artifacts -included here; the poverty-sensitivity artifact also requires its -original population dataset to reproduce. The historical -spm-calculator implementation lives on PolicyEngine/spm-calculator -pull request #32 (branch `max/bls-2026-threshold-correction`; this -revision pins commit `d2746cfacce072f599ffb1788eb195777c6ca9ca`), to -be released as version 0.4.0 on merge. It contains the corrected and pre-correction threshold series -with the official workbook and its SHA-256; the replication, -benchmark, backtest, and nowcast scripts; and the weekly drift-check -workflow — which, as a scheduled job, does not run until the branch -merges; its workbook comparison was last run manually on August 6, -2026, and matched all twenty packaged years within half a cent. -Regenerating the artifacts from scratch requires the public CE PUMD -year bundles (roughly 700 MB, cached locally by the scripts) and the -BLS public data API; bls.gov's bot filtering requires a -browser-impersonating TLS client, which the scripts document. One -reproducibility defect is disclosed rather than hidden: at the -pinned commit, the nowcast and backtest scripts read a six-series -CPI cache (`CUUR0000SAF`, `SAA`, `SAH1`, `SAH2`, `SEED`, and -`SA0`) and a replication-results file that are pinned in this -repository but were untracked in the implementation repository, -whose own default store carried a -seventh, mis-mapped series (`SEEE`, information-technology hardware -and services, labeled as internet) and therefore returned a -different 2025 price ratio (1.0297 rather than the committed 1.0322). -The same September 4 review caught it; the rebuild now preserves the -six-series definition as an explicitly archived commitment. Current-method -inputs and code are recorded separately in `data/current/`; they do not -replace this frozen evaluation. The current experiment and shared release -pin spm-calculator version 0.5.0, development commit -`0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. -The release content digest and exact executed CE source hashes are in -`data/current/provenance.json`. These are committed development inputs, -not evidence of a published package or completed model migration. - -The paper repository pins the exact artifacts behind every table and -the poverty-sensitivity simulation output, with SHA-256 sums, and -bundles the corrected BLS workbook itself (hash matching the file -bls.gov served on August 6, 2026), so the correction table is -checkable without fighting the bot filtering. Each table is generated -from the artifacts by script. A pre-render check first verifies the -original artifact hashes and commitment identities, then regenerates -tables and evaluation JSON in scratch space and re-derives the registered -prose figures; continuous integration runs the same check on every -push to master and every pull request. Its scope is stated exactly: -it enforces an allowlist of generated tables and of the tables the -source includes, requires the checksum manifest to cover every -artifact present, and matches each registered figure at numeric-token -boundaries — the three tenure amounts as ordered triples in the -sentences that state them — so the build fails if any registered -figure, table, or artifact drifts. It compares generated bytes without -repairing or restoring source files. Figures quoted from external -publications are bound to their citations, not to artifacts, and -dates and ordinals are not registered; an earlier version of the -check matched bare substrings, and a September 4, 2026 audit showed -it passing under edits the current version rejects. On its first -run, the strengthened check found two miscounts that had survived -every prior review — the package's values were wrong in five of six -years, not four, and renter thresholds fell in all six years, not -five — which is the argument for registering counts as well as -values. A provenance file in `data/` names the generator, inputs, -and source of every artifact. - -The nowcast's in-advance commitment is externally timestamped before -BLS's August 24 publication. The artifact checksum file as it stood -on August 7, 2026 (`data/SHA256SUMS.precommit`) is stamped via -OpenTimestamps, and that proof (`SHA256SUMS.precommit.ots`, committed -in completed form) is a Bitcoin attestation — block 961505, mined -August 7, 2026 at 23:18 UTC, with further calendars confirming in -blocks 961512 and 962551 — so the amended values are anchored in a -public ledger seventeen days before BLS published. The original -values predate the stamp and are anchored differently: the Internet -Archive's capture of the published page from August 7, 2026 -(`web.archive.org/web/20260807211521/https://spm-threshold-paper.vercel.app/`) -quotes both sets in the amendment sentence, and both nowcasts are -tagged releases whose creation dates GitHub records. A manifest -listing both tags, their commits, and both artifact hashes is -committed alongside and stamped separately, as is the -post-evaluation checksum file. Two artifact hashes in that secondary -manifest were transcribed incorrectly. The explicitly dated -`COMMITMENT-AMENDMENT-2026-09-08.txt` supplies the correct hashes and -records the original manifest's digest. The original manifest, tags, -forecast bytes and every existing proof remain unchanged. The old proof -does not cover the amendment. This revision checks the retained proof -bytes and Git identities; it does not independently reverify the -blockchain attestations. The forecast history documents one -amendment: the composite-index construction error caught in -cross-model review on July 18, 2026, with every downstream artifact, -table, and pinned prose figure regenerated in the same change. The -evaluation in @sec-evaluation adds four pinned artifacts — BLS's -current-thresholds workbook as served on September 4, 2026 (hash -pinned), the 2025 values and standard errors read from it with their -retrieval date, Census's revised rate series, and the evaluation -output — and one script, -`evaluate_nowcast_2025.py`, which the pre-render check now runs -alongside the table generator, so the evaluation tables and every -figure quoted from them are re-derived on each build. +The results distinguish two tasks: estimating a threshold level and +projecting its growth. The public-data replication does not reproduce +every component of the official method, and its level discrepancies +remain material. Growth ratios can nevertheless be useful when some +proportional discrepancies persist across adjacent windows. Their +performance must be judged directly; agreement in levels is neither +necessary nor sufficient for accurate growth projections. + +In the periods examined, consumption-based growth improves on CPI-U +adjustment as a predictor of published SPM thresholds. That pattern is +consistent with the measure's quasi-relative construction: annual +re-estimation can reflect consumption changes that price adjustment +alone omits. The results do not isolate how much of the difference is +attributable to real spending, relative prices, sample composition, or +in-kind benefits. They also do not make price-indexed anchored measures +inappropriate; those measures deliberately estimate a fixed real +standard rather than the threshold BLS will publish. + +The evidence for prospective accuracy remains limited. The current +six-year comparison uses revised data and an implementation applied +after outcomes were known. The frozen five-year comparison informed a +single 2025 validation. Overlapping CE windows and shared tenure samples +reduce the independent variation in both comparisons. The replication +ratio's lower average errors therefore motivate further validation, +without establishing a universally preferred rule or fitted combination +weight. + +The information horizon also matters. When the complete CE window is +available but BLS thresholds are not, consumption replication can supply +a nowcast. Beyond the available CE window, it requires assumptions about +future consumption as well as prices; this paper does not evaluate that +longer-horizon problem. Method changes, benefit imputations, and changes +in sample composition can also alter growth relationships. The sample +sensitivities here identify specific uncertainties but do not provide +forecast intervals. + +Threshold choice affects modeled poverty through the distribution of +resources near the line, not through threshold growth alone. In a frozen +historical PolicyEngine simulation, projected SPM poverty under the +committed 2025 nowcast was 0.4 percentage points higher than under +CPI-U-adjusted thresholds. That simulation uses the model's CPI-U +parameters, rather than the realized annual-average CPI-U rule evaluated +above. The recorded sensitivity depends on its original population and +model vintage; it is not a result from the current model or a general +conversion between threshold errors and +poverty-rate errors. + +# Appendix A: method versions and forecast commitment {.unnumbered} + +The frozen replication used for the 2025 commitment has mean absolute +deviations across tenures of 1.5 to 2.1 percent per year against revised +2019–2024 thresholds, and 1.0 to 1.6 percent against the preceding +published series with its corresponding anchor share. Published BLS +sampling standard errors span 0.7 to 2.1 percent of the revised threshold +levels in those years. These standard errors describe official threshold +estimation; they do not quantify the uncertainty of the replication. + +::: {#tbl-replication} +{{< include tables/replication.md >}} + +Frozen-method replication deviations by tenure, with the anchor share +matched to each reference vintage. These results are preserved separately +from the current-method replication. +::: + +The original 2025 blend was committed before a correction to the +composite-price construction, which had combined CPI components without +putting them on a common index base. The correction changed the +retrospective method ranking; the equal blend was retained as the primary +forecast, and all component rules were included in the promised +external evaluation. This was one pre-publication amendment, not a rule +selected using the published 2025 outcome. + +The amendment moved estimates by 0.1 to 0.3 percent: owners with +mortgages from \$41,099.57 to \$41,036.34, owners without from +\$34,250.70 to \$34,135.99, and renters from \$40,791.72 to \$40,755.98. +The original and amended values, corrected 2024 base, and evaluation +rules remain fixed in the repository. This use of preregistration +[@nosek2018] separates the recorded forecast from later method development. + +The August 7, 2026 archived page and retained OpenTimestamps evidence +precede the August 24 publication. The original forecast tags, manifests, +and proof files remain unchanged. A dated September 8 amendment corrects +two artifact hashes in a secondary manifest without claiming that its +old timestamp covers the amendment. The repository documents those +identities and checks the retained bytes; this revision does not +independently reverify blockchain attestations. + +This paper makes no new forecast commitment or scheduled cutoff. The +previously discussed 2026 CE collection window already closed in 2026Q1. +Any future commitment would require an actual timestamp and a recorded +available-input cutoff before the outcome is published. + +# Appendix B: reproducibility {.unnumbered} + +The repository preserves the inputs and generated tables for the frozen +experiments, together with a separate current-method artifact containing +source hashes, methodology choices, and sample diagnostics. The current +experiment and shared threshold release pin spm-calculator version 0.5.0, +commit `0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. Exact source and release +identities are recorded in `data/current/provenance.json`; the historical +implementation pin and its archived inputs are recorded in +`data/PROVENANCE.md`. The frozen poverty sensitivity additionally requires +its original model and population dataset to reproduce. + +The paper and standalone calculator consume the same versioned threshold +release. Its reference thresholds can also be used by PolicyEngine, +Microcosm, and Axiom adapters. These consumers retain responsibility for +SPM-unit membership, resources, and weights; integration details do not +change the national estimation problem studied here. The release's +availability date identifies this research snapshot; it does not +reconstruct historical information sets. + +Tables regenerate from the recorded artifacts. The build checks their +hashes before generating outputs in temporary storage and comparing the +results with the saved tables and evaluation JSON. Archived tables no +longer used in the manuscript remain preserved and checked. These checks +support reproducibility of the recorded experiments, while the method +limitations described above constrain their interpretation. # References {.unnumbered} diff --git a/scripts/check_paper.py b/scripts/check_paper.py index 81c0d26..772685a 100644 --- a/scripts/check_paper.py +++ b/scripts/check_paper.py @@ -59,6 +59,8 @@ "current_sensitivity.md", "current_projection.md", } +ARCHIVED_TABLES = {"correction.md", "package_errors.md"} +ARTICLE_TABLES = EXPECTED_TABLES - ARCHIVED_TABLES GENERATORS = ("build_tables.py", "evaluate_nowcast_2025.py", "build_current_tables.py") failures: list[str] = [] @@ -265,12 +267,12 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: # (b) Every QMD include names an expected table. includes = set(re.findall(r"\{\{<\s*include\s+tables/([^\s>]+)\s*>\}\}", QMD)) check( - f"qmd includes outside allowlist: {includes - EXPECTED_TABLES}", - includes <= EXPECTED_TABLES, + f"qmd includes outside allowlist: {includes - ARTICLE_TABLES}", + includes <= ARTICLE_TABLES, ) check( - f"expected tables never included: {EXPECTED_TABLES - includes}", - EXPECTED_TABLES <= includes, + f"article tables never included: {ARTICLE_TABLES - includes}", + ARTICLE_TABLES <= includes, ) # (d) Load-bearing prose figures re-derive from the artifacts. @@ -278,9 +280,9 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: AMENDED = {t: f"{nowcast['values'][t]:,.2f}" for t in TENURES} for t, literal in AMENDED.items(): check(f"nowcast literal {literal}", has_number(literal, min_count=2)) -# Ordered tenure triples: abstract, amendment sentence, table caption order. +# Ordered tenure triples: committed estimate and amendment chronology. check( - "abstract states the amended triple in tenure order", + "committed estimate states the amended triple in tenure order", has_ordered( [ AMENDED["owner_with_mortgage"], @@ -474,65 +476,8 @@ def mae(rule: str) -> float: and abs(min(annual_signed["blend"]) - (-0.014)) < 0.001, ) -# Effective shelter weight under the pre-repair raw-level construction. -raw_2024 = {c: cpi[CPI_IDS[c]]["2024"] for c in WEIGHTS} -eff_shelter = ( - WEIGHTS["shelter"] - * raw_2024["shelter"] - / sum(WEIGHTS[c] * raw_2024[c] for c in WEIGHTS) -) -stated_shelter = WEIGHTS["shelter"] / sum(WEIGHTS.values()) -check( - "effective shelter weight ~55 vs stated 47", - "near 55 percent" in QMD - and "stated 47" in QMD - and abs(eff_shelter - 0.55) < 0.02 - and abs(stated_shelter - 0.47) < 0.005, -) - -# Correction and package-error magnitudes. -corr_changes = [ - corrected[y][t] / published[y][t] - 1 for y in range(2019, 2025) for t in TENURES -] -check( - "correction within ±1.6 percent", - has_number("1.6 percent") and max(abs(c) for c in corr_changes) < 0.016, -) -renter_falls = sum( - 1 for y in range(2019, 2025) if corrected[y]["renter"] < published[y]["renter"] -) -FALL_WORDS = { - 4: "fall in four of six years", - 5: "fall in five of six years", - 6: "fall in all six years", -} -check( - f"renter thresholds {FALL_WORDS.get(renter_falls)} (computed {renter_falls})", - FALL_WORDS.get(renter_falls, "") in QMD, -) -check( - "reproducibility note cites the two miscounts the check found", - "five of six\nyears, not four" in QMD or "five of six years, not four" in QMD, -) -pkg_err = { - y: max(abs(legacy[y][t] / published[y][t] - 1) for t in TENURES) - for y in range(2019, 2025) -} -check( - "package errors reach 7.4 percent", - has_number("7.4 percent") and abs(max(pkg_err.values()) - 0.074) < 0.001, -) -wrong_years = sum(1 for y, e in pkg_err.items() if e > 0.001) -WORDS = { - 3: "three of the six", - 4: "four of the six", - 5: "five of the six", - 6: "all six", -} -check( - f"package wrong in {WORDS.get(wrong_years)} years (computed {wrong_years}; per-year max errors {{y: round(e, 4) for y, e in pkg_err.items()}})", - WORDS.get(wrong_years, "") in QMD, -) +# Archived correction and package tables remain byte-checked above. +# Their former audit claims are intentionally absent from this methods paper. se = flat_measure("bls-corrected-2026-07-17", "standard_error") se_ratio = [ se[y][t] / corrected[y][t] for y in range(2019, 2025) for t in TENURES if se[y][t] @@ -603,6 +548,10 @@ def mae(rule: str) -> float: has_number("2.63") and abs(realized_2025 - 0.0263) < 0.0005, ) +# Current retrospective results in the abstract are a separate experiment. +for rule, value in current_ce["projection_evaluation"]["summary_2020_2025"].items(): + check(f"current projection prose {rule}", has_number(f"{value:.2f}")) + # (e) The September evaluation, re-derived from data/evaluation_2025.json. ev = json.loads((DATA / "evaluation_2025.json").read_text()) R = ev["rules"] @@ -632,12 +581,11 @@ def mae(rule: str) -> float: "−1.17" in QMD and -0.014 <= ev_signed <= -0.001, ) cpiu_errs = [abs(v) for v in cpiu["errors"].values()] +cpiu_error_range = f"{min(cpiu_errs):.2%} to {max(cpiu_errs):.2%}".replace("%", "") check( - "CPI-U 2025 understated every tenure by 1.70 to 3.48", - has_number("1.70 to 3.48") - and all(v < 0 for v in cpiu["errors"].values()) - and abs(min(cpiu_errs) - 0.0170) < 0.0001 - and abs(max(cpiu_errs) - 0.0348) < 0.0001, + f"CPI-U 2025 understated every tenure by {cpiu_error_range} percent", + has_number(f"{cpiu_error_range} percent") + and all(v < 0 for v in cpiu["errors"].values()), ) check( "replication only rule with errors both sides of zero", @@ -720,22 +668,6 @@ def mae(rule: str) -> float: "BLS page 2025 CPI-U growth 2.70 quoted", has_number("2.70 percent") and abs(bls_cpiu_2025 - 2.70) < 0.005, ) -census = json.loads((DATA / "census_wp2026_17_rates.json").read_text()) -c23 = census["corrected_series_2023_to_2024"]["all_people"] -check( - "Census corrected 2023->2024 = 12.7 -> 13.0", - has_ordered(["12.7", "13.0"], window=10) - and c23["2023"] == 12.7 - and c23["2024"] == 13.0, -) -diffs = [v["difference"] for y, v in census["all_people"].items() if y != "2024"] -check( - "Census 2019-2023 corrected 0.1 to 0.3 below", - has_number("0.1 to 0.3") - and abs(max(diffs)) == 0.1 - and abs(min(diffs)) == 0.3 - and all(d < 0 for d in diffs), -) if failures: print("PAPER DRIFT CHECK FAILED:") diff --git a/tests/test_paper_guard.py b/tests/test_paper_guard.py index 12ece67..1e018a4 100644 --- a/tests/test_paper_guard.py +++ b/tests/test_paper_guard.py @@ -135,6 +135,32 @@ def test_missing_and_extra_tables_fail_without_restoring_them(self): self.assertNotEqual(result.returncode, 0) self.assertIn("generated tables present == expected", result.stdout) + def test_archived_table_still_checked_when_not_in_article(self): + qmd = (self.repo / "paper/index.qmd").read_text() + self.assertNotIn("include tables/package_errors.md", qmd) + (self.repo / "paper/tables/package_errors.md").write_text("changed archive\n") + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn( + "generated output drifted: paper/tables/package_errors.md", result.stdout + ) + + def test_current_projection_headline_drift_is_detected(self): + path = self.repo / "paper/index.qmd" + path.write_text(path.read_text().replace("0.51", "9.51")) + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("current projection prose replication_ratio", result.stdout) + + def test_evaluation_range_rounds_from_saved_errors(self): + path = self.repo / "paper/index.qmd" + path.write_text(path.read_text().replace("1.70 to 3.47", "1.70 to 3.48")) + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn( + "CPI-U 2025 understated every tenure by 1.70 to 3.47 percent", result.stdout + ) + def test_generator_failure_leaves_source_files_unchanged(self): (self.repo / "scripts/evaluate_nowcast_2025.py").write_text( "raise RuntimeError('fixture failure')\n" From fbf1a87abb518cd07f7dc39ad54f76bea28a250b Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 9 Sep 2026 12:12:58 -0400 Subject: [PATCH 09/14] Document conditional rolling expenditure and rent forecasts --- README.md | 19 +- bibliography/references.bib | 24 + data/PROVENANCE.md | 39 +- data/current/ROLLING_SHA256SUMS | 2 + data/current/rolling_forecast_2026_09_09.json | 635070 +++++++++++++++ data/current/rolling_provenance.json | 101 + paper/index.qmd | 215 +- paper/tables/current_release.md | 8 +- paper/tables/rolling_projection.md | 10 + paper/tables/rolling_validation.md | 15 + scripts/build_current_tables.py | 180 +- scripts/check_paper.py | 20 +- scripts/import_rolling_forecast.py | 79 + scripts/rolling_inputs.py | 114 + tests/test_paper_guard.py | 148 +- tests/test_rolling_tables.py | 254 + 16 files changed, 636235 insertions(+), 63 deletions(-) create mode 100644 data/current/ROLLING_SHA256SUMS create mode 100644 data/current/rolling_forecast_2026_09_09.json create mode 100644 data/current/rolling_provenance.json create mode 100644 paper/tables/rolling_projection.md create mode 100644 paper/tables/rolling_validation.md create mode 100644 scripts/import_rolling_forecast.py create mode 100644 scripts/rolling_inputs.py create mode 100644 tests/test_rolling_tables.py diff --git a/README.md b/README.md index 2827917..b72ac88 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,8 @@ Expenditure Survey microdata, family and geographic adjustments, and projection from consumption and price growth. The manuscript presents current-method replication and sample sensitivities, retrospective comparisons, and a separately preserved pre-committed 2025 validation. +It also constructs conditional forecasts through 2035 by advancing the +CE and ACS windows with explicit price, real-spending, and rent assumptions. The current six-year results use revised inputs and overlapping CE windows. They are separate from the single prospective validation year. @@ -48,8 +50,15 @@ describes both collections and the original implementation pin. ## Companion implementation -The current spm-calculator rebuild provides a portable release, standalone -unit calculations, CE research replication, projections, and explicit -PolicyEngine, Microcosm, and real native Axiom adapters. This paper pins version 0.5.0 at development commit -`0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`; it does not claim that a model -migration or public deployment has occurred. The paper consumes the same release bytes as those adapters. +The companion spm-calculator provides a portable artifact, standalone +unit calculations, CE research replication, rolling projections, and +PolicyEngine, Microcosm, and native Axiom adapters. The rolling projection +pins the 1.0.0 release candidate at commit +`c89d20f08f8d9d0896c88944fdb685ea01cecbb1`, with content digest +`b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821`. +The paper consumes the same projection bytes as the calculator. + +The 2019–2025 retrospective experiment retains its separate development +version 0.5.0 pin at `0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. +These source identities document reproducibility; they do not certify +publication, deployment, or a population-model migration. diff --git a/bibliography/references.bib b/bibliography/references.bib index da51b06..69f5c49 100644 --- a/bibliography/references.bib +++ b/bibliography/references.bib @@ -295,3 +295,27 @@ @techreport{creamer2026revising month = {August}, url = {https://www.census.gov/library/working-papers/2026/demo/sehsd-wp2026-17.html} } + +@misc{blsspmshares, + author = {{U.S. Bureau of Labor Statistics}}, + title = {Supplemental Poverty Measure Threshold Expenditure Shares}, + year = {2026}, + note = {Workbook retrieved September 9, 2026; sheet 2005-2024 includes 2025 column W}, + url = {https://www.bls.gov/pir/spm/spm_shares.xlsx} +} + +@misc{cbodata2026, + author = {{Congressional Budget Office}}, + title = {Budget and Economic Data: February 2026 Economic Projections}, + year = {2026}, + note = {Versioned data repository, revision 284a95665f9f2f74ed1f482feb629b43fce323da}, + url = {https://github.com/US-CBO/cbo-data/tree/284a95665f9f2f74ed1f482feb629b43fce323da} +} + +@misc{acs2024pumd, + author = {{U.S. Census Bureau}}, + title = {2024 American Community Survey Public Use Microdata Sample Documentation}, + year = {2025}, + note = {Source and geography documentation; retrieved September 9, 2026}, + url = {https://www.census.gov/programs-surveys/acs/microdata/documentation.2024.html} +} diff --git a/data/PROVENANCE.md b/data/PROVENANCE.md index 8dead39..953e3ae 100644 --- a/data/PROVENANCE.md +++ b/data/PROVENANCE.md @@ -47,7 +47,8 @@ It does not contact OpenTimestamps calendars or verify Bitcoin attestations. | `ce_replication_2019_2025.json` | `scripts/replicate_current_ce.py` in the rebuild. Seven overlapping CE windows, explicit minor-only and tenure approximations, raw source bundle hashes, CPI cache hash, exact executed source hashes, and six-year retrospective projections. No prospective validation or new commitment. | | `provenance.json` | Paper linkage to the release content digest and CE artifact/source hashes. Pins spm-calculator 0.5.0 development commit `0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. The retained `ce_starting_git_head` identifies the starting commit for this replay, before its source and artifact update was committed. The `ce_code_sha256` map identifies the executed code, whose bytes match the pinned development commit. | -`build_current_tables.py` reads only these inputs. It cross-checks the +For the retrospective experiment, `build_current_tables.py` reads these +preserved inputs. It cross-checks the published comparison series against the shared release, re-derives replication and sensitivity percentages and projection errors, and produces the four `current_*.md` tables. The pre-render guard checks both @@ -55,3 +56,39 @@ manifests before parsing inputs, generates all tables and evaluation JSON in scratch space, and compares bytes without mutating source files. These checks establish reproducibility of the recorded calculations, not exact parity with the BLS production procedure or historical information sets. + +## September 9, 2026 rolling projection + +The separate `data/current/ROLLING_SHA256SUMS` covers the exact canonical +rolling forecast and its linkage receipt. It does not change either old +checksum manifest, any earlier experiment, or any timestamp proof. + +| Artifact | Source and scope | +|---|---| +| `rolling_forecast_2026_09_09.json` | Exact 26,606,301-byte calculator artifact from commit `c89d20f08f8d9d0896c88944fdb685ea01cecbb1`, development candidate 1.0.0. File SHA-256 `76ab8435f087f167ad01b8495ebd016415ba8086f32bfbd3dab961dcc8976c0a`; canonical content SHA-256 `b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821`. Years 2022–2035, published 2025 national bases and housing shares, conditional CE-trend and zero-real projections, year-specific geographic assignments and rent diagnostics. | +| `rolling_provenance.json` | Source commit/path, exact artifact seals, all source/code/component fingerprints and the information date. Explicitly outside the original timestamp commitment. | + +`scripts/import_rolling_forecast.py` obtains the exact bytes from the +pinned local Git commit. `scripts/rolling_inputs.py` checks the manifest, +externally pinned artifact identity and the complete linkage receipt +before generating tables. The guard independently pins the old current +manifest as well, so replacing its artifacts and recomputing its manifest +cannot silently rewrite the retrospective experiment. + +`current_release.md` now reads the rolling artifact's published 2025 +national thresholds and published BLS housing shares. The old +`spm-release.json` remains solely as a source for the recorded 2019–2025 +retrospective experiment. `rolling_projection.md` presents both scenarios +for 2025, 2026, 2030 and 2035. `rolling_validation.md` reports the actual +2023-origin/2024-target retrospective geographic comparison and distinct +late-horizon support diagnostics. The generator derives these values +from the sealed artifact; no new prospective validation is claimed. + +The BLS shares workbook is pinned at SHA-256 +`6709dec0e39c8c7b881320161a4373035befc586ecefecd8d78d8431d89b4577`. +The CBO price source is the February 2026 calendar-year CSV at official +repository revision `284a95665f9f2f74ed1f482feb629b43fce323da`, SHA-256 +`6b54df40058d206247e78fadfe5ebd11e02ae29efd2f0e1a4a86b6767c9ca559`. +Uniform component-price and rent growth and the fitted real-spending +scenario are calculator assumptions. Publication of these inputs does +not make the future SPM thresholds official forecasts. diff --git a/data/current/ROLLING_SHA256SUMS b/data/current/ROLLING_SHA256SUMS new file mode 100644 index 0000000..8ccc39a --- /dev/null +++ b/data/current/ROLLING_SHA256SUMS @@ -0,0 +1,2 @@ +76ab8435f087f167ad01b8495ebd016415ba8086f32bfbd3dab961dcc8976c0a data/current/rolling_forecast_2026_09_09.json +8d1fab116dd7000290ac05d5114cdbaeb81f20165b3d26adb3153fc7574b9926 data/current/rolling_provenance.json diff --git a/data/current/rolling_forecast_2026_09_09.json b/data/current/rolling_forecast_2026_09_09.json new file mode 100644 index 0000000..da66712 --- /dev/null +++ b/data/current/rolling_forecast_2026_09_09.json @@ -0,0 +1,635070 @@ +{ + "areas": { + "1002": { + "area_type": "state_nonmetro", + "name": "Alabama Nonmetro" + }, + "10180": { + "area_type": "msa", + "name": "Abilene, TX MSA" + }, + "10420": { + "area_type": "msa", + "name": "Akron, OH MSA" + }, + "10580": { + "area_type": "msa", + "name": "Albany-Schenectady-Troy, NY MSA" + }, + "10740": { + "area_type": "msa", + "name": "Albuquerque, NM MSA" + }, + "10900": { + "area_type": "msa", + "name": "Allentown-Bethlehem-Easton, PA-NJ MSA" + }, + "11100": { + "area_type": "msa", + "name": "Amarillo, TX MSA" + }, + "11460": { + "area_type": "msa", + "name": "Ann Arbor, MI MSA" + }, + "11540": { + "area_type": "msa", + "name": "Appleton, WI MSA" + }, + "11700": { + "area_type": "msa", + "name": "Asheville, NC MSA" + }, + "12001": { + "area_type": "state_metro_residual", + "name": "Florida Metro" + }, + "12002": { + "area_type": "state_nonmetro", + "name": "Florida Nonmetro" + }, + "12020": { + "area_type": "msa", + "name": "Athens-Clarke County, GA MSA" + }, + "12060": { + "area_type": "msa", + "name": "Atlanta-Sandy Springs-Roswell, GA MSA" + }, + "12100": { + "area_type": "msa", + "name": "Atlantic City-Hammonton, NJ MSA" + }, + "12220": { + "area_type": "msa", + "name": "Auburn-Opelika, AL MSA" + }, + "12260": { + "area_type": "msa", + "name": "Augusta-Richmond County, GA-SC MSA" + }, + "12420": { + "area_type": "msa", + "name": "Austin-Round Rock, TX MSA" + }, + "12540": { + "area_type": "msa", + "name": "Bakersfield, CA MSA" + }, + "12580": { + "area_type": "msa", + "name": "Baltimore-Columbia-Towson, MD MSA" + }, + "12620": { + "area_type": "msa", + "name": "Bangor, ME MSA" + }, + "12700": { + "area_type": "msa", + "name": "Barnstable Town, MA MSA" + }, + "12940": { + "area_type": "msa", + "name": "Baton Rouge, LA MSA" + }, + "12980": { + "area_type": "msa", + "name": "Battle Creek, MI MSA" + }, + "13001": { + "area_type": "state_metro_residual", + "name": "Georgia Metro" + }, + "13002": { + "area_type": "state_nonmetro", + "name": "Georgia Nonmetro" + }, + "13140": { + "area_type": "msa", + "name": "Beaumont-Port Arthur, TX MSA" + }, + "13460": { + "area_type": "msa", + "name": "Bend-Redmond, OR MSA" + }, + "13740": { + "area_type": "msa", + "name": "Billings, MT MSA" + }, + "13780": { + 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"modeled_residual_metro:08": "modeled_unanchored", + "modeled_residual_metro:24": "modeled_unanchored", + "modeled_residual_metro:36": "modeled_unanchored", + "modeled_residual_metro:45": "modeled_unanchored", + "modeled_residual_metro:47": "modeled_unanchored", + "modeled_residual_metro:55": "modeled_unanchored" + }, + "approximations": [ + "Population-weighted Census2020 PUMA-to-county allocation, matched to February2013 CBSA counties and published2024 SPM menu. Unrepresented county mass remains national. Full-MSA and residual membership approximate confidential CPS geography; neither county assignments nor rent medians are official Census reproductions.", + "Discrete weighted PUMS medians approximate confidential Census grouped-data median interpolation; topcoding may affect medians.", + "Four-year overlap subpools retain their product's five-year calibrated weights; the bridge removes their measured vintage shift but does not make future weights known.", + "Donor households, housing stock and weights remain fixed; baseline assumes zero real rent growth for new cohorts.", + "Neither Kish support nor OLS/price sensitivities constitute a survey-design variance or forecast confidence interval." + ], + "cbsa_vintage": "2013-02-28", + "county_vintage": 2020, + "dollar_adjustment_rule": "GRNTP * source ADJHSG / 1000000 exactly once; donor nominal growth divided by its separately declared price path", + "donor_year": 2024, + "generator_identity": { + "scripts/build_acs_2021_inputs.py": "82fa6edbbd90dc670204514c61a12475145d8d2976478a71e04fdba92047a31b", + "scripts/build_acs_forecast.py": "44c1c34046c76ce167b3b1adbcadfdb0f588c8be93f9055629eeb2f80563e93d", + "spm_calculator/acs_forecast.py": "0d76f0ebdb0fe67aea030dc480580df0d0daffc77ead02684e925f3619805213", + "spm_calculator/acs_forecast_sources.py": "d96fb6556f3a019025c2bd7dfdef5b9f5d05a7a4d819ab568f3a8bc41bf2aed3", + "spm_calculator/forecast_inputs.py": "54cf632fa515ab82ee6958c898e4154237dd79b5075ee59c8c1593891a95068f" + }, + "geography_method": "Population-weighted Census2020 PUMA-to-county allocation, matched to February2013 CBSA counties and published2024 SPM menu. Unrepresented county mass remains national. Full-MSA and residual membership approximate confidential CPS geography; neither county assignments nor rent medians are official Census reproductions.", + "historical_2022_geography": { + "county_lookup_vintage": 2020, + "county_vintage": 2010, + "documented_yuba_transfer": { + "affected_source_blocks": 10, + "affected_source_population_2010": 0, + "population_imputed_by_area": false, + "population_variable": "2010 Census SF1 P001001", + "queried_tract_populations_match_gazetteer": { + "06061021304": 5307, + "06061021322": 8762 + }, + "scope_note": "Closes this documented legal transfer; no claim that every2010 and2020county polygon is geometrically identical.", + "split_source_blocks": 4, + "status": "verified_zero_population_transfer", + "transferred_land_square_meters": 457057 + }, + "interpretation": "Population allocation approximates PUMS household geography; it does not identify actual household counties.", + "method": "Join Census2010tract POP10 to2010PUMAs, sum by county and PUMA, allocate each declared county group its share of totalPUMA population. Preserve unassigned mass for the national denominator.", + "population_year": 2010, + "puma_vintage": 2010 + }, + "index_topcoding_interpretation": "Potential sensitivity from local or national medians in any contributing target, anchor or overlap ratio; conservative input flags, not known bias or confidence intervals. Exact published anchors are not PUMS estimates.", + "information_date": "2026-09-09", + "kish_interpretation": "Unequal-weight diagnostic; not survey-design effective sample size", + "nominal_rent_growth_by_year": { + "2025": 0.02631268549423149, + "2026": 0.029248273765579702, + "2027": 0.025475653015156308, + "2028": 0.023614294950919934, + "2029": 0.022880029859745354, + "2030": 0.022634899961825994, + "2031": 0.022586789925561623, + "2032": 0.02257372697332216, + "2033": 0.02256625901981013, + "2034": 0.022566253555256655, + "2035": 0.022570320442821146 + }, + "observed_acs_end_year": 2024, + "overlap_bridge": { + "1002": { + "new_overlap_ratio": 0.5319033924552786, + "old_full_ratio": 0.5400260886603432, + "old_overlap_ratio": 0.5316666666666667, + "overlap_vintage_multiplier": 1.0004452522669816 + }, + "10180": { + "new_overlap_ratio": 0.8148148148148149, + "old_full_ratio": 0.8175394953330195, + "old_overlap_ratio": 0.8144521121408748, + "overlap_vintage_multiplier": 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0.9995548649001644, + "overlap_vintage_multiplier": 1.0004453333333334 + }, + "modeled_residual_metro:01": { + "new_overlap_ratio": 0.634074074074074, + "old_full_ratio": 0.6411812787175749, + "old_overlap_ratio": 0.6333333333333333, + "overlap_vintage_multiplier": 1.0011695906432747 + }, + "modeled_residual_metro:06": { + "new_overlap_ratio": 0.8869916644817823, + "old_full_ratio": 0.8965604238340755, + "old_overlap_ratio": 0.8884932132445906, + "overlap_vintage_multiplier": 0.9983100053659105 + }, + "modeled_residual_metro:08": { + "new_overlap_ratio": 0.7865894550662746, + "old_full_ratio": 0.8070575139484988, + "old_overlap_ratio": 0.7862391948119104, + "overlap_vintage_multiplier": 1.0004454881627314 + }, + "modeled_residual_metro:24": { + "new_overlap_ratio": 0.5925925925925926, + "old_full_ratio": 0.6079646017699115, + "old_overlap_ratio": 0.5936343533531586, + "overlap_vintage_multiplier": 0.9982451137561672 + }, + "modeled_residual_metro:36": { + "new_overlap_ratio": 0.9872602874232012, + "old_full_ratio": 1.0152687726344058, + "old_overlap_ratio": 0.9983333333333334, + "overlap_vintage_multiplier": 0.9889084682035403 + }, + "modeled_residual_metro:45": { + "new_overlap_ratio": 0.7444980085620123, + "old_full_ratio": 0.7345132743362832, + "old_overlap_ratio": 0.7441666666666666, + "overlap_vintage_multiplier": 1.0004452522669818 + }, + "modeled_residual_metro:47": { + "new_overlap_ratio": 0.6911111111111111, + "old_full_ratio": 0.6896017894124465, + "old_overlap_ratio": 0.6908034732976692, + "overlap_vintage_multiplier": 1.0004453333333334 + }, + "modeled_residual_metro:55": { + "new_overlap_ratio": 0.7079305095596471, + "old_full_ratio": 0.7175415519323738, + "old_overlap_ratio": 0.7076152753307193, + "overlap_vintage_multiplier": 1.0004454881627314 + } + }, + "price_growth_by_year": { + "2025": 0.02631268549423149, + "2026": 0.029248273765579702, + "2027": 0.025475653015156308, + "2028": 0.023614294950919934, + "2029": 0.022880029859745354, + "2030": 0.022634899961825994, + "2031": 0.022586789925561623, + "2032": 0.02257372697332216, + "2033": 0.02256625901981013, + "2034": 0.022566253555256655, + "2035": 0.022570320442821146 + }, + "price_inputs": { + "forecast_source": { + "annual_growth_by_year": { + "2026": 0.029248273765579702, + "2027": 0.025475653015156308, + "2028": 0.023614294950919934, + "2029": 0.022880029859745354, + "2030": 0.022634899961825994, + "2031": 0.022586789925561623, + "2032": 0.02257372697332216, + "2033": 0.02256625901981013, + "2034": 0.022566253555256655, + "2035": 0.022570320442821146 + }, + "cbo_index_levels": { + "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 + }, + "component_assumption": "Apply aggregate CPI-U growth uniformly to all future CE price components; not CBO component forecasts", + "formula": "CBO_level[y]/CBO_level[y-1]-1; future growth applied to observed BLS2025 anchor", + "rent_assumption": "Baseline nominal rent growth equals this CPI-U path; same path deflates future rent cohorts; not CBO local rent forecasts", + "series": "cpiu", + "source_repository_commit": "284a95665f9f2f74ed1f482feb629b43fce323da", + "source_vintage": "2026-02", + "units": "Index,1982-84=100; calendar-year average" + }, + "observed_2025_note": "BLS M13 annual average uses eleven months; October is missing owing to the lapse in appropriations.", + "observed_annual_values": { + "2022": 292.655, + "2023": 304.702, + "2024": 313.689, + "2025": 321.943 + }, + "receipt_sha256": "482678b4dc2070b434ff44b2d09417992b63546dea0ac3a30ec300244e14415e", + "receipt_url": "https://api.bls.gov/publicAPI/v2/timeseries/data/CUUR0000SA0?startyear=2024&endyear=2025&annualaverage=true", + "series_id": "CUUR0000SA0" + }, + "puma_vintage_by_spm_year": { + "2022": 2010, + "2023": 2020, + "2024": 2020, + "2025": 2020, + "2026": 2020, + "2027": 2020, + "2028": 2020, + "2029": 2020, + "2030": 2020, + "2031": 2020, + "2032": 2020, + "2033": 2020, + "2034": 2020, + "2035": 2020 + }, + "relative_index_stabilization": { + "comparison": "Exact equality of every baseline area rent index", + "first_all_projected_window_year": 2030, + "first_constant_index_year": 2029, + "first_unchanged_transition_year": 2030, + "geographic_factor_caveat": "A constant relative rent index does not force a constant geographic factor: the selected year's CE housing share can still move. Floating-point noise is not substantive movement.", + "geographic_factor_formula": "1 + housing_share * (rent_index - 1)", + "interpretation": "Each future annual cohort projects the same 2024 donors. Under the baseline uniform nominal growth and deflation, relative indices stabilize once the window contains only that donor distribution, including its original observed cohort. These are not independent future survey observations.", + "latest_observed_donor_year": 2024, + "through_year": 2035 + }, + "source_bundle_sha256": "39bd7832a0fad56ad0206c270e6106c384339d79fd569de25743f3f5adb51ba4", + "source_product_counts": { + "2022": { + "2018": 131822, + "2019": 129456, + "2020": 97548, + "2021": 126429, + "2022": 127411 + }, + "2023": { + "2019": 129456, + "2020": 97548, + "2021": 126429, + "2022": 127411, + "2023": 127046 + }, + "2024": { + "2020": 97548, + "2021": 126429, + "2022": 127411, + "2023": 127046, + "2024": 126234 + } + }, + "source_vintages": { + "2022": "2018–2022 PUMS with restored December2025 PUMA/weight update", + "2023": "2019–2023 PUMS", + "2024": "2020–2024 PUMS" + }, + "support_rule": "min(unique originals, allocation-expected originals, donor-collapsed Kish)<30; projected windows also flag thin donor support", + "support_threshold": 30, + "survey_year_extraction_rule": "First four digits of validated Census housing SERIALNO", + "thin_support_policy": "Retain the computed area/window estimate with immutable support counts and warnings; no donor widening, carryforward or substitution. Empty or nonpositive numerical support raises an error.", + "topcode_method": "Cohort-specific published state component thresholds applied to nominal values; flags retained in donor copies. Lower bounds use known cash rent, or cash-rent threshold if censored; upper bound unbounded. This conservative diagnostic does not identify uncensored gross rents.", + "uncertainty_status": "Survey-design uncertainty and forecast intervals are not estimated" + }, + "ce": { + "annual_cpi": { + "CUUR0000SA0": { + "2005": 195.292, + "2006": 201.592, + "2007": 207.342, + "2008": 215.303, + "2009": 214.537, + "2010": 218.056, + "2011": 224.939, + "2012": 229.594, + "2013": 232.957, + "2014": 236.736, + "2015": 237.017, + "2016": 240.007, + "2017": 245.12, + "2018": 251.107, + "2019": 255.657, + "2020": 258.811, + "2021": 270.97, + "2022": 292.655, + "2023": 304.702, + "2024": 313.689, + "2025": 321.943, + "2026": 331.359277000912, + "2027": 339.8008709651403, + "2028": 347.8250289566906, + "2029": 355.78327600518645, + "2030": 363.8363948656546, + "2031": 372.0542910837588, + "2032": 380.4529430699365, + "2033": 389.0383427281018, + "2034": 397.817480612821, + "2035": 406.79634862800816 + }, + "CUUR0000SAA": { + "2005": 119.542, + "2006": 119.45, + "2007": 118.998, + "2008": 118.907, + "2009": 120.078, + "2010": 119.503, + "2011": 122.111, + "2012": 126.265, + "2013": 127.411, + "2014": 127.514, + "2015": 125.902, + "2016": 126.045, + "2017": 125.612, + "2018": 125.654, + "2019": 124.052, + "2020": 118.079, + "2021": 120.993, + "2022": 127.081, + "2023": 130.579, + "2024": 131.483, + "2025": 131.311, + "2026": 135.15162007643204, + "2027": 138.59469585393546, + "2028": 141.86751188046333, + "2029": 145.1134447884161, + "2030": 148.39807309431785, + "2031": 151.74990919665734, + "2032": 155.17547021508912, + "2033": 158.67720006948366, + "2034": 162.25794999968983, + "2035": 165.92016392557807 + }, + "CUUR0000SAF": { + "2005": 191.167, + "2006": 195.65, + "2007": 203.3, + "2008": 214.225, + "2009": 218.249, + "2010": 219.984, + "2011": 227.866, + "2012": 233.67, + "2013": 236.966, + "2014": 242.448, + "2015": 246.804, + "2016": 247.653, + "2017": 249.837, + "2018": 253.346, + "2019": 258.013, + "2020": 266.625, + "2021": 276.814, + "2022": 303.319, + "2023": 320.488, + "2024": 327.659, + "2025": 336.618, + "2026": 346.4634954184219, + "2027": 355.28987921011986, + "2028": 363.67979921086436, + "2029": 372.0008038761951, + "2030": 380.4210048576516, + "2031": 389.01349417764243, + "2032": 397.7949785841466, + "2033": 406.77172310765627, + "2034": 415.9510369504123, + "2035": 425.33918514290684 + }, + "CUUR0000SAF11": { + "2005": 189.783, + "2006": 193.108, + "2007": 201.254, + "2008": 214.139, + "2009": 215.122, + "2010": 215.831, + "2011": 226.199, + "2012": 231.75, + "2013": 233.855, + "2014": 239.449, + "2015": 242.25, + "2016": 239.071, + "2017": 238.594, + "2018": 239.659, + "2019": 241.767, + "2020": 250.213, + "2021": 258.886, + "2022": 288.471, + "2023": 302.909, + "2024": 306.554, + "2025": 313.259, + "2026": 322.42128499153176, + "2027": 330.63517777267685, + "2028": 338.4428943817507, + "2029": 346.1864779110238, + "2030": 354.0223742066767, + "2031": 362.01860320183147, + "2032": 370.19071230977306, + "2033": 378.5445318104834, + "2034": 387.0868636972747, + "2035": 395.8235382501288 + }, + "CUUR0000SAH1": { + "2005": 224.425, + "2006": 232.133, + "2007": 240.611, + "2008": 246.666, + "2009": 249.354, + "2010": 248.396, + "2011": 251.646, + "2012": 257.084, + "2013": 263.056, + "2014": 270.513, + "2015": 278.803, + "2016": 288.23, + "2017": 297.803, + "2018": 307.663, + "2019": 318.053, + "2020": 325.886, + "2021": 334.575, + "2022": 354.235, + "2023": 380.643, + "2024": 400.561, + "2025": 415.164, + "2026": 427.3068303296131, + "2027": 438.1927508700966, + "2028": 448.540363734498, + "2029": 458.80298065004433, + "2030": 469.18794021924566, + "2031": 479.7853896605847, + "2032": 490.61593405257173, + "2033": 501.68730029964814, + "2034": 513.0085031236622, + "2035": 524.5872694290553 + }, + "CUUR0000SAH2": { + "2005": 179.042, + "2006": 194.7, + "2007": 200.632, + "2008": 220.018, + "2009": 210.696, + "2010": 214.187, + "2011": 220.367, + "2012": 218.986, + "2013": 225.208, + "2014": 234.589, + "2015": 230.129, + "2016": 228.855, + "2017": 237.315, + "2018": 241.554, + "2019": 242.598, + "2020": 243.949, + "2021": 260.54, + "2022": 298.208, + "2023": 305.478, + "2024": 314.768, + "2025": 332.089, + "2026": 341.8020299865376, + "2027": 350.5096599023507, + "2028": 358.7866983944314, + "2029": 366.99574876697545, + "2030": 375.30266082673137, + "2031": 383.77954318532903, + "2032": 392.44287781114093, + "2033": 401.29884544230686, + "2034": 410.3546569399897, + "2035": 419.6164930423292 + }, + "CUUR0000SEED": { + "2005": 94.85, + "2006": 95.758, + "2007": 98.247, + "2008": 100.451, + "2009": 102.392, + "2010": 102.379, + "2011": 101.209, + "2012": 101.685, + "2013": 101.559, + "2014": 101.131, + "2015": 99.31, + "2016": 98.846, + "2017": 91.75, + "2018": 90.375, + "2019": 89.358, + "2020": 92.195, + "2021": 94.429, + "2022": 94.785, + "2023": 95.333, + "2024": 94.63, + "2025": 93.953, + "2026": 96.70096306509751, + "2027": 99.16448324637538, + "2028": 101.50618260241085, + "2029": 103.82864709130277, + "2030": 106.17879813138615, + "2031": 108.57703633932837, + "2032": 111.02802471322485, + "2033": 113.53351187736136, + "2034": 116.09553789340464, + "2035": 118.71585138564046 + }, + "CUUR0000SEFV": { + "2005": 193.417, + "2006": 199.383, + "2007": 206.659, + "2008": 215.769, + "2009": 223.272, + "2010": 226.114, + "2011": 231.401, + "2012": 237.987, + "2013": 243.069, + "2014": 248.981, + "2015": 256.101, + "2016": 262.695, + "2017": 268.826, + "2018": 275.893, + "2019": 284.41, + "2020": 293.944, + "2021": 307.296, + "2022": 330.822, + "2023": 354.256, + "2024": 368.743, + "2025": 382.378, + "2026": 393.56189642593483, + "2027": 403.58814273926885, + "2028": 413.1185921806079, + "2029": 422.57075790531616, + "2030": 432.135604737296, + "2031": 441.89616086085283, + "2032": 451.871404146685, + "2033": 462.0684512963044, + "2034": 472.49560512814156, + "2035": 483.1599823437084 + } + }, + "classification": "conditional_rolling_window_research_scenarios", + "donor_price_index": "P[y=origin]=FCSUti_factor(origin,y); nominal scale=P[target_collection_year]/P[donor_collection_year]", + "donor_rule": "Latest observed same-season quarter, never a projected donor; preserve original NEWID, weights, counts, ordering and missingness", + "housing_share_formula": "Published BLS_share_anchor * (0.82 * SU_mean_target / replication_target) / (0.82 * SU_mean_anchor / replication_anchor); supplied published historical shares returned directly", + "housing_share_source": { + "anchor_year": 2025, + "definition": "Total Shelter Share + Total Utilities Share; telephone/internet excluded; corrected revised BLS series", + "id": "bls-spm-shares" + }, + "inflation_rates": { + "2026": 0.029248273765579702, + "2027": 0.025475653015156308, + "2028": 0.023614294950919934, + "2029": 0.022880029859745354, + "2030": 0.022634899961825994, + "2031": 0.022586789925561623, + "2032": 0.02257372697332216, + "2033": 0.02256625901981013, + "2034": 0.022566253555256655, + "2035": 0.022570320442821146 + }, + "latest_observed_quarter": "2025Q1", + "monetary_columns": [ + "FOODPQ", + "FOODCQ", + "FDHOMEPQ", + "FDHOMECQ", + "FDAWAYPQ", + "FDAWAYCQ", + "GROCERPQ", + "GROCERCQ", + "APPARPQ", + "APPARCQ", + "SHELTPQ", + "SHELTCQ", + "UTILPQ", + "UTILCQ", + "TELEPHPQ", + "TELEPHCQ", + "EMRTPNOP", + "EMRTPNOC", + "MRTPRNOP", + "MRTPRNOC" + ], + "national_anchor_year": 2025, + "national_formula": "published_origin * replication_target / replication_origin; published historical thresholds returned directly", + "policy": { + "annualization": "quarter4", + "median_share": 0.82, + "mortgage_principal": "include", + "tenure_policy": "exclude_5_6_code3_mortgage", + "youth_policy": "exclude_unresolved" + }, + "price_source": { + "annual_growth_by_year": { + "2026": 0.029248273765579702, + "2027": 0.025475653015156308, + "2028": 0.023614294950919934, + "2029": 0.022880029859745354, + "2030": 0.022634899961825994, + "2031": 0.022586789925561623, + "2032": 0.02257372697332216, + "2033": 0.02256625901981013, + "2034": 0.022566253555256655, + "2035": 0.022570320442821146 + }, + "cbo_index_levels": { + "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 + }, + "component_assumption": "Apply aggregate CPI-U growth uniformly to all future CE price components; not CBO component forecasts", + "formula": "CBO_level[y]/CBO_level[y-1]-1; future growth applied to observed BLS2025 anchor", + "rent_assumption": "Baseline nominal rent growth equals this CPI-U path; same path deflates future rent cohorts; not CBO local rent forecasts", + "series": "cpiu", + "source_repository_commit": "284a95665f9f2f74ed1f482feb629b43fce323da", + "source_vintage": "2026-02", + "units": "Index,1982-84=100; calendar-year average" + }, + "price_weights": "Historical bridge and training weights frozen at origin five-year CE window; each target estimator recalculates its future-window CPI component weights", + "real_growth": "Uniform component scaling exp(real_log_rate * donor-to-target year difference); composition repeats, no full behavioral response", + "reference_year_window": "(T-5)Q2 through TQ1; SPM reference year, not publication year", + "remaining_deflator": "After donor collection-year to projected collection-year scaling, estimator applies only projected collection-year to threshold-year inflation", + "research_approximations": [ + "Annual instead of quarterly CPI by collection year", + "FMLI combined GROCER food allocation approximation; internet and in-kind imputations omitted", + "Unresolved youth exclusion and tenure 3/5/6 policies; BLS percentile-code parity unverified", + "Frozen origin bridge does not exactly match every target's recalculated CPI weights", + "Synthetic donor repetitions 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20100": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20500": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20700": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "21001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "19820": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, 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"acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20500": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20700": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "21001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20100": { + "allocation_county_vintage": 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"acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20100": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20500": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20700": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + 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2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20500": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20700": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 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}, + "20500": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20700": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "21001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "17420": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "17460": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "17660": { + "allocation_county_vintage": 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"forecast-horizon-inputs" + ], + "status": "modeled" + }, + "18002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "18140": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "18580": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "19820": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20002": { + "allocation_county_vintage": 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], + "status": "modeled" + }, + "20100": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20500": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20700": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + 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"census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "19340": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "19380": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "19660": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20100": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20500": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20700": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "21001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "21002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "21140": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "21340": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + 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"forecast-horizon-inputs" + ], + "status": "modeled" + }, + "17980": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "18001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "18002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + 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"interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "19740": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "19780": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "19820": { + "allocation_county_vintage": 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}, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20100": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20500": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20700": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "21001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + 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"acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 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"acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 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"acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "20002": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 2020, + "source_ids": [ + "census-spm-2024", + "acs_forecast_inputs", + "forecast-horizon-inputs" + ], + "status": "modeled" + }, + "2001": { + "allocation_county_vintage": 2020, + "anchor_status": "published_anchor", + "interpretation": "Rolling ratio relative to published 2024 Census index", + "official_published_area": true, + "puma_vintage": 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CE overall MAPE weights 63 fold-tenure observations; horizon-balanced MAPE gives horizons 1–5 equal weight. ACS uses equal-area errors and does not validate the later vintage bridge. 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"puma2020_county2020_overlap.csv": "b0d9995978f65ccc2e8eb4327d79c10900484fb7eba89f8c91e7df8ebc62f872" + } +} diff --git a/paper/index.qmd b/paper/index.qmd index 36d6c0f..a82a879 100644 --- a/paper/index.qmd +++ b/paper/index.qmd @@ -1,12 +1,12 @@ --- title: "Calculating and projecting Supplemental Poverty Measure thresholds" -subtitle: "Public consumption data, projection comparisons, and a pre-committed 2025 validation" +subtitle: "Public consumption data, rolling forecasts, and a pre-committed 2025 validation" author: - name: Max Ghenis affiliations: - name: PolicyEngine email: max@policyengine.org -date: 2026-09-08 +date: 2026-09-09 bibliography: ../bibliography/references.bib format: html: @@ -37,6 +37,10 @@ abstract: | supplies one external validation year: the blend's mean absolute error was 1.17 percent against 2.58 percent for CPI-U adjustment and 0.75 percent for consumption growth alone. The largest miss was for renters. + For years beyond available microdata, a separate conditional projection + advances both expenditure and rent windows through 2035, with explicit + assumptions about prices and real spending. Its prospective accuracy + has not been established. Sample-treatment sensitivities and unreplicated imputations limit claims about level accuracy and future performance. The results support evaluating consumption growth when projecting quasi-relative poverty @@ -86,6 +90,12 @@ used before the 2025 outcome was published. The completed 2025 evaluation provides one external validation year; the current retrospective results do not add further prospective observations. +The final methodological extension addresses years beyond available +microdata. It advances the CE and American Community Survey (ACS) windows +under declared price, real-expenditure, and rent assumptions. This +distinguishes an observed expenditure nowcast from a conditional forecast +whose future survey records must themselves be projected. + # Calculating the thresholds {#sec-production} ## National reference thresholds @@ -157,16 +167,20 @@ national thresholds does not itself resolve those classifications. ::: {#tbl-current-release} {{< include tables/current_release.md >}} -Published 2025 reference-family thresholds and the housing shares used -by the companion calculator. Those shares come from the 2024 Census -metro snapshot and are carried into 2025 as an approximation. +Published 2025 reference-family thresholds and shelter-utilities shares. +The shares sum the shelter and utilities components in the BLS shares +workbook and exclude telephone and internet. ::: -The national bases in @tbl-current-release are published estimates. -The carried housing shares and aggregate geographic indices are separate -approximations; their use does not establish exact reproduction of revised -official thresholds for every local area. The remainder of the paper -examines national reference thresholds and their growth. +Both components in @tbl-current-release use published 2025 values +[@bls2025thresholds; @blsspmshares]. The geographic index is separately +dated and estimated. Published national inputs therefore do not make a +local threshold fully official when its geographic component is modeled. +Local estimation areas comprise metropolitan statistical areas and +Census's residual metropolitan and nonmetropolitan groups. Counties +identify the applicable area; county-specific thresholds are not +independently estimated. The annual geographic projection is described +in @sec-rolling. # Replication from public consumption data {#sec-replication} @@ -472,6 +486,138 @@ the missing October index. We retain the recorded calculation rather than substituting a different annual average in the completed forecast evaluation. +# Conditional forecasts beyond available microdata {#sec-rolling} + +## Projecting expenditure records and rolling windows + +The preceding comparisons observe the CE collection window for each +target year. A future projection instead supplies the unobserved quarters +and then re-estimates the threshold from the resulting twenty-quarter +window. The September 2026 snapshot uses observed CE records through +2025Q1. For each later quarter it reuses the latest observed quarter of +the same season, preserving survey weights, composition, and record +identities. Monetary spending is multiplied by the cumulative price +change and an assumed real-expenditure growth factor. Historical quarters +leave the window as projected quarters enter; this is not a direct +inflation adjustment of the final threshold. + +Let $r$ be an annual log growth rate for real expenditures. A donor +record from year $d$ projected to year $t$ receives the multiplier +$(P_t/P_d)\exp[r(t-d)]$. We report two scenarios: zero real growth and a +CE trend. To estimate the latter, we construct five nonoverlapping +annual collection blocks ending in 2021–2025, express them in common +origin-year prices with fixed origin-window component weights, and +compute each block's survey-weighted mean in the 47th–53rd percentile +FCSUti band. An ordinary least-squares regression of log means on year +provides a slope. The applied slope is half that estimate; the 0.5 +shrinkage was declared independently of the reported forecast scores. + +The applied annual real-growth rate is 0.914 percent. Using ten annual +blocks gives 0.817 percent; excluding the two blocks ending in 2021 and +2022 from the five-block fit leaves three blocks and gives −0.135 percent. +These alternatives demonstrate sensitivity to the fitting window. They +are not confidence intervals: repeated interviews, survey design, +serial dependence, and future structural changes are not captured by +ordinary regression uncertainty. The zero-real scenario supplies a +separate comparison rather than a lower statistical bound. + +The nominal path uses annual CPI-U growth from CBO's February 2026 +economic projections, rebased to the observed 2025 annual index +[@cbodata2026]. Applying that same growth rate to each expenditure price +component and to rents is an assumption of this projection, not a CBO +forecast for each component. Real growth and prices both affect projected +spending; setting real growth to zero still allows changes from replacing +older observations in the rolling window. + +For origin $O=2025$, the national projection is + +$$ +\widehat B_{T,h}=B_{O,h}^{\mathrm{published}} +\frac{\widetilde B_{T,h}}{\widetilde B_{O,h}},\qquad +\widehat a_{T,h}=a_{O,h}^{\mathrm{published}} +\frac{\widetilde a_{T,h}}{\widetilde a_{O,h}}. +$$ + +Thus both the national base and housing share start from published +2025 values, with subsequent changes estimated from the projected +microdata. The percentile band is selected again for every target +window. Changes in band membership can make the tenure-specific +threshold under the positive-real-growth scenario slightly lower than +under zero real growth in a particular year; the scenarios do not +preserve a fixed ranking of selected observations. + +::: {#tbl-rolling-projection} +{{< include tables/rolling_projection.md >}} + +Conditional national reference thresholds under the CE-trend and +zero-real-growth scenarios. The 2025 bases are published; later values +are modeled. These scenarios do not constitute prediction intervals. +::: + +## Projecting local relative housing costs + +The geographic window for threshold year $T$ comprises ACS years +$T-5$ through $T-1$. Public-use geography requires fractional allocation +from Public Use Microdata Areas to SPM estimation areas. The projection +uses the declared February 2013 metropolitan delineation and records +the crosswalk and source vintages. These allocations, public-data +topcoding, and the selected rental sample limit exact reproduction of +the Census calculation [@acs2024pumd]. + +Observed ACS cohorts are used through 2024. Each later cohort copies +the 2024 donor records, scales nominal rents, and converts them to a +common price basis. Local and national weighted rent medians are +recomputed from each complete five-cohort window. Equal nationwide +rent growth therefore does not imply constant local relative costs: +the mix changes as older observed cohorts leave the window. Once all +five cohorts use the same donor distribution, the relative indices +stabilize under this assumption. In this snapshot that occurs in 2029. +The whole-threshold factor can continue changing as the housing share +changes. + +The public-data products used for the 2024 anchor and subsequent +windows differ. To avoid treating a product change as rent growth, we +bridge them over the common 2020–2023 years. If $m^{v}_{W,j}$ is area +$j$'s local-to-national rent-median ratio for window $W$ in product $v$, +then, for an area with a published 2024 anchor, + +$$ +\widehat g_{T,j}=g^{\mathrm{published}}_{2024,j} +\frac{m^{\mathrm{old}}_{2020:2023,j}}{m^{\mathrm{old}}_{2019:2023,j}} +\frac{m^{\mathrm{new}}_{T-5:T-1,j}}{m^{\mathrm{new}}_{2020:2023,j}}. +$$ + +Unpublished residual groups retain explicitly modeled, unanchored +indices. The menu contains 342 published and seven modeled areas in +2022, and 341 published and eight modeled areas from 2023. Annual +comparisons distinguish published-index revisions and assignment +changes from projected rent growth. In particular, the Massachusetts +nonmetropolitan published index changes between 2022 and 2023, and +Sumter County moves from a published residual group to an unanchored +modeled group; neither discontinuity is smoothed into a rent forecast. + +```{=latex} +\Needspace{24\baselineskip} +``` + +::: {#tbl-rolling-validation} +{{< include tables/rolling_validation.md >}} + +Retrospective geographic comparison and late-horizon donor support. +Repeated copies of the same donor are counted once when measuring +independent support. These diagnostics do not measure prospective +national-threshold accuracy. +::: + +The 2023-origin, 2024-target geographic comparison covers 341 areas with +published anchors. +It is retrospective and does not validate the unanchored residual +groups or future rent assumptions. Support narrows as observed cohorts +leave: 31 areas carry thin-donor warnings in projected years. Preserving +record identity avoids mistaking five projected copies for five +independent samples. No estimate is silently replaced by a national +factor or a carried prior-year value when support is thin. + # Discussion {#sec-discussion} The results distinguish two tasks: estimating a threshold level and @@ -504,11 +650,12 @@ weight. The information horizon also matters. When the complete CE window is available but BLS thresholds are not, consumption replication can supply a nowcast. Beyond the available CE window, it requires assumptions about -future consumption as well as prices; this paper does not evaluate that -longer-horizon problem. Method changes, benefit imputations, and changes -in sample composition can also alter growth relationships. The sample -sensitivities here identify specific uncertainties but do not provide -forecast intervals. +future consumption as well as prices. The conditional extension here +specifies those assumptions and advances both survey windows, but its +longer-horizon accuracy has not been prospectively validated. Method +changes, benefit imputations, and changes in sample composition can +also alter growth relationships. The sample and trend sensitivities +identify specific uncertainties but do not provide forecast intervals. Threshold choice affects modeled poverty through the distribution of resources near the line, not through threshold growth alone. In a frozen @@ -571,21 +718,29 @@ available-input cutoff before the outcome is published. The repository preserves the inputs and generated tables for the frozen experiments, together with a separate current-method artifact containing -source hashes, methodology choices, and sample diagnostics. The current -experiment and shared threshold release pin spm-calculator version 0.5.0, -commit `0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. Exact source and release -identities are recorded in `data/current/provenance.json`; the historical -implementation pin and its archived inputs are recorded in -`data/PROVENANCE.md`. The frozen poverty sensitivity additionally requires -its original model and population dataset to reproduce. - -The paper and standalone calculator consume the same versioned threshold -release. Its reference thresholds can also be used by PolicyEngine, -Microcosm, and Axiom adapters. These consumers retain responsibility for -SPM-unit membership, resources, and weights; integration details do not -change the national estimation problem studied here. The release's -availability date identifies this research snapshot; it does not -reconstruct historical information sets. +source hashes, methodology choices, and sample diagnostics. The +2019–2025 retrospective experiment retains its original implementation +pin, spm-calculator development version 0.5.0 at commit +`0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. The conditional rolling +projection uses the separate 1.0.0 release candidate at commit +`c89d20f08f8d9d0896c88944fdb685ea01cecbb1`. The candidate's content +digest is `b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821`. +Exact source and release identities are recorded alongside the inputs; +`data/PROVENANCE.md` describes the separate collections. The frozen +poverty sensitivity additionally requires its original model and +population dataset to reproduce. + +The rolling projection is a portable artifact shared by the paper and +standalone calculator, with year-specific national bases, housing shares, +geographic indices, assignments, and source fingerprints. PolicyEngine, +Microcosm, and native Axiom adapters consume these quantities through +explicit contracts. Population sources supply native SPM membership +and composition roles; tax-benefit models retain responsibility for +resources, and the population system retains its weights. A change of +rules engine does not require a second expenditure forecast. Runtime +parity and release integration are separate checks from the statistical +validity of the projection. The snapshot date does not reconstruct +historical information sets or certify a public deployment. Tables regenerate from the recorded artifacts. The build checks their hashes before generating outputs in temporary storage and comparing the diff --git a/paper/tables/current_release.md b/paper/tables/current_release.md index 643427e..2443e09 100644 --- a/paper/tables/current_release.md +++ b/paper/tables/current_release.md @@ -1,5 +1,5 @@ -| Tenure | Published 2025 base | Housing share (2024) | +| Tenure | Published 2025 base | Published 2025 housing share | |---|---|---| -| Owner with mortgage | $41,322.71 | 0.434 | -| Owner without mortgage | $34,326.00 | 0.323 | -| Renter | $41,700.56 | 0.443 | +| Owner with mortgage | $41,322.71 | 0.428507 | +| Owner without mortgage | $34,326.00 | 0.312019 | +| Renter | $41,700.56 | 0.433686 | diff --git a/paper/tables/rolling_projection.md b/paper/tables/rolling_projection.md new file mode 100644 index 0000000..e662d4a --- /dev/null +++ b/paper/tables/rolling_projection.md @@ -0,0 +1,10 @@ +| Year | Scenario | National status | With mortgage | Without mortgage | Renter | +|---|---|---|---|---|---| +| 2025 | CE trend | Published | $41,322.71 | $34,326.00 | $41,700.56 | +| 2025 | Zero real | Published | $41,322.71 | $34,326.00 | $41,700.56 | +| 2026 | CE trend | Forecast | $43,276.19 | $35,957.29 | $43,829.55 | +| 2026 | Zero real | Forecast | $43,193.22 | $35,802.45 | $43,888.11 | +| 2030 | CE trend | Forecast | $48,532.89 | $39,636.71 | $50,011.91 | +| 2030 | Zero real | Forecast | $46,899.67 | $38,389.88 | $48,954.72 | +| 2035 | CE trend | Forecast | $56,789.77 | $46,380.08 | $58,520.42 | +| 2035 | Zero real | Forecast | $52,437.35 | $42,922.77 | $54,735.04 | diff --git a/paper/tables/rolling_validation.md b/paper/tables/rolling_validation.md new file mode 100644 index 0000000..c886bda --- /dev/null +++ b/paper/tables/rolling_validation.md @@ -0,0 +1,15 @@ +| Diagnostic | Value | +|---|---| +| ACS retrospective comparison | 2023→2024; 341 areas | +| MAPE (model / carry-forward) | 1.2537% / 1.7284% | +| Future donor cohort | 2024 | +| ACS window (2035) | 2030–2034 (5 projected) | +| Constant relative indices | 2029–2035 | +| First all-projected window | 2030 | +| Unique original donors (2035) | 25–8,851 | +| Allocation-expected donors (2035) | 22.03–8,835.61 | +| Donor-collapsed Kish (2035) | 13.08–5,396.44 | +| Thin-support areas (2035) | 31 / 349 | +| Topcoding warnings (2035) | 149 local / 349 index | + +Notes: MAPE uses equal-area retrospective errors with current source vintages; it does not validate the later vintage bridge. Support and warnings cover 349 modeled areas in 2035. Repeated donors are not independent observations; Kish is not survey-design effective sample size. These diagnostics are not confidence intervals. Local warnings concern the target median; index warnings include local or national medians in target, anchor or overlap ratios, not known bias. Under uniform growth/deflation, constant relative indices can coexist with changing housing shares and geographic factors; floating-point noise is not substantive movement. diff --git a/scripts/build_current_tables.py b/scripts/build_current_tables.py index 604a8be..470f569 100644 --- a/scripts/build_current_tables.py +++ b/scripts/build_current_tables.py @@ -8,9 +8,167 @@ from pathlib import Path from statistics import mean +from rolling_inputs import load_rolling_forecast + REPO = Path(__file__).resolve().parents[1] DATA = REPO / "data" / "current" TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") +PROJECTION_YEARS = (2025, 2026, 2030, 2035) +SCENARIO_LABELS = {"ce_trend": "CE trend", "zero_real": "Zero real"} + + +def markdown_table(headers: list[str], rows: list[list[str]]) -> str: + lines = ["| " + " | ".join(headers) + " |", "|" + "---|" * len(headers)] + lines.extend("| " + " | ".join(row) + " |" for row in rows) + return "\n".join(lines) + "\n" + + +def rolling_tables(rolling: dict) -> dict[str, str]: + """Render canonical results and diagnostics without rerunning the estimator.""" + scenarios = rolling["scenarios"] + years = scenarios[rolling["default_scenario"]]["years"] + anchor_year = rolling["national_anchor_year"] + current = years[str(anchor_year)] + if ( + current["national_status"] != "published" + or current["housing_share_status"] != "published_anchor" + ): + raise ValueError("Current national bases and housing shares must be published") + tables = { + "current_release.md": markdown_table( + [ + "Tenure", + f"Published {anchor_year} base", + f"Published {anchor_year} housing share", + ], + [ + [ + label, + f"${current['thresholds'][tenure]:,.2f}", + f"{current['housing_shares'][tenure]:.6f}", + ] + for tenure, label in zip( + TENURES, ("Owner with mortgage", "Owner without mortgage", "Renter") + ) + ], + ) + } + projection_rows = [] + for year in PROJECTION_YEARS: + for scenario in ("ce_trend", "zero_real"): + record = scenarios[scenario]["years"][str(year)] + if year == anchor_year and ( + record["thresholds"] != current["thresholds"] + or record["housing_shares"] != current["housing_shares"] + or record["national_status"] != "published" + ): + raise ValueError("Scenarios must share the published national anchor") + projection_rows.append( + [ + str(year), + SCENARIO_LABELS[scenario], + record["national_status"].capitalize(), + *(f"${record['thresholds'][tenure]:,.2f}" for tenure in TENURES), + ] + ) + tables["rolling_projection.md"] = markdown_table( + ["Year", "Scenario", "National status", "With mortgage", "Without mortgage", "Renter"], + projection_rows, + ) + + acs = rolling["validation"]["acs"] + if acs["status"] != "complete" or acs["unvalidated"]: + raise ValueError("ACS retrospective validation must be complete") + assumptions = rolling["assumptions"]["acs"] + stabilization = assumptions["relative_index_stabilization"] + ordered_years = sorted(map(int, years)) + last_year = ordered_years[-1] + first_constant = next( + year + for year in ordered_years[:-1] + if all( + years[str(later)]["rent_indices"] == years[str(year)]["rent_indices"] + for later in ordered_years + if later > year + ) + ) + first_all_projected = next( + year + for year in ordered_years + if years[str(year)]["acs_window"]["observed_years"] == 0 + ) + if ( + first_constant != stabilization["first_constant_index_year"] + or first_constant + 1 != stabilization["first_unchanged_transition_year"] + or first_all_projected != stabilization["first_all_projected_window_year"] + or last_year != stabilization["through_year"] + or assumptions["donor_year"] != stabilization["latest_observed_donor_year"] + ): + raise ValueError("ACS stabilization metadata must match canonical output") + latest = years[str(last_year)] + diagnostics = latest["median_diagnostics"] + if set(diagnostics) != set(latest["rent_indices"]): + raise ValueError("Support diagnostics must cover every modeled area") + for diagnostic in diagnostics.values(): + if not ( + diagnostic["unique_records"] + == diagnostic["unique_original_donor_count"] + == diagnostic["donor_support"]["unique_records"] + ): + raise ValueError("Projected donor copies must not inflate support counts") + + def span(field: str, decimals: int = 0) -> str: + values = [record[field] for record in diagnostics.values()] + return f"{min(values):,.{decimals}f}–{max(values):,.{decimals}f}" + + area_count = len(diagnostics) + thin_count = sum(record["thin_support"] for record in diagnostics.values()) + local_warnings = sum( + record["median_topcode_warning"] for record in diagnostics.values() + ) + index_warnings = sum( + record["rent_index_topcode_warning"] for record in diagnostics.values() + ) + validation_rows = [ + [ + "ACS retrospective comparison", + f"{acs['origin_spm_year']}→{acs['target_spm_year']}; {acs['area_count']} areas", + ], + [ + "MAPE (model / carry-forward)", + f"{acs['mean_absolute_percentage_error']:.4f}% / " + f"{acs['baseline_mean_absolute_percentage_error']:.4f}%", + ], + ["Future donor cohort", str(assumptions["donor_year"])], + [ + f"ACS window ({last_year})", + f"{latest['acs_window']['start']}–{latest['acs_window']['end']} " + f"({latest['acs_window']['projected_years']} projected)", + ], + ["Constant relative indices", f"{first_constant}–{last_year}"], + ["First all-projected window", str(first_all_projected)], + [f"Unique original donors ({last_year})", span("unique_original_donor_count")], + [f"Allocation-expected donors ({last_year})", span("expected_whole_record_count", 2)], + [f"Donor-collapsed Kish ({last_year})", span("kish_effective_count", 2)], + [f"Thin-support areas ({last_year})", f"{thin_count} / {area_count}"], + [f"Topcoding warnings ({last_year})", f"{local_warnings} local / {index_warnings} index"], + ] + note = ( + "Notes: MAPE uses equal-area retrospective errors with current source vintages; " + "it does not validate the later vintage bridge. " + f"Support and warnings cover {area_count} modeled areas in {last_year}. " + "Repeated donors are not independent observations; Kish is not survey-design " + "effective sample size. These diagnostics are not confidence intervals. " + "Local warnings concern the target median; index warnings include local or " + "national medians in target, anchor or overlap ratios, not known bias. " + "Under uniform growth/deflation, constant relative indices can coexist with " + "changing housing shares and geographic factors; floating-point noise is " + "not substantive movement." + ) + tables["rolling_validation.md"] = ( + markdown_table(["Diagnostic", "Value"], validation_rows) + "\n" + note + "\n" + ) + return tables def close(actual: float, expected: float, label: str) -> None: @@ -26,29 +184,15 @@ def main() -> None: output.mkdir(parents=True, exist_ok=True) release = json.loads((DATA / "spm-release.json").read_text()) ce = json.loads((DATA / "ce_replication_2019_2025.json").read_text()) + rolling = load_rolling_forecast(DATA) if ce["classification"] != "current_retrospective_research": raise ValueError("Current CE input must be explicitly retrospective research") def write(name: str, headers: list[str], rows: list[list[str]]) -> None: - lines = ["| " + " | ".join(headers) + " |", "|" + "---|" * len(headers)] - lines.extend("| " + " | ".join(row) + " |" for row in rows) - (output / name).write_text("\n".join(lines) + "\n") + (output / name).write_text(markdown_table(headers, rows)) - current = release["years"]["2025"] - write( - "current_release.md", - ["Tenure", "Published 2025 base", "Housing share (2024)"], - [ - [ - label, - f"${current['thresholds'][tenure]:,.2f}", - f"{current['housing_shares'][tenure]:.3f}", - ] - for tenure, label in zip( - TENURES, ("Owner with mortgage", "Owner without mortgage", "Renter") - ) - ], - ) + for name, content in rolling_tables(rolling).items(): + (output / name).write_text(content) replication_rows = [] sensitivity_rows = [] diff --git a/scripts/check_paper.py b/scripts/check_paper.py index 772685a..ff178ce 100644 --- a/scripts/check_paper.py +++ b/scripts/check_paper.py @@ -36,6 +36,7 @@ from pathlib import Path sys.dont_write_bytecode = True +from rolling_inputs import ROLLING_FILES, load_rolling_forecast from verify_commitments import verify_commitments REPO = Path(__file__).resolve().parent.parent @@ -58,6 +59,8 @@ "current_replication.md", "current_sensitivity.md", "current_projection.md", + "rolling_projection.md", + "rolling_validation.md", } ARCHIVED_TABLES = {"correction.md", "package_errors.md"} ARTICLE_TABLES = EXPECTED_TABLES - ARCHIVED_TABLES @@ -134,6 +137,14 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: failures.extend(verify_commitments(REPO)) # The current experiment has a separate manifest, never the old timestamp. +CURRENT_RETROSPECTIVE_MANIFEST_SHA256 = ( + "9bbba66e0a96fbb8b0524fa57a8e532c6401323db782042fc794e339824822cb" +) +check( + "retrospective checksum manifest history changed", + hashlib.sha256((DATA / "current" / "SHA256SUMS").read_bytes()).hexdigest() + == CURRENT_RETROSPECTIVE_MANIFEST_SHA256, +) current_names = {"spm-release.json", "ce_replication_2019_2025.json", "provenance.json"} current_listed = {} for number, line in enumerate( @@ -152,10 +163,11 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: failures.append(f"duplicate current checksum entry: {name}") current_listed[name] = digest current_present = {str(p.relative_to(REPO)) for p in (DATA / "current").glob("*.json")} +rolling_paths = {f"data/current/{name}" for name in ROLLING_FILES} check( "current checksum coverage", set(current_listed) - == current_present + == current_present - rolling_paths == {f"data/current/{name}" for name in current_names}, ) for name, digest in current_listed.items(): @@ -164,6 +176,10 @@ def has_ordered(literals: list[str], window: int = 400) -> bool: f"current artifact hash mismatch: {name}", path.is_file() and hashlib.sha256(path.read_bytes()).hexdigest() == digest, ) +try: + rolling_forecast = load_rolling_forecast(DATA / "current") +except (OSError, KeyError, TypeError, ValueError) as error: + failures.append(f"rolling input validation: {error}") if failures: print("PAPER INPUT CHECK FAILED (generators were not run):") for failure in failures: @@ -675,5 +691,5 @@ def mae(rule: str) -> float: print(f" - {f}") sys.exit(1) print( - f"paper drift check passed ({len(EXPECTED_TABLES)} tables, {len(listed)} frozen + {len(current_listed)} current artifacts, prose pins OK)" + f"paper drift check passed ({len(EXPECTED_TABLES)} tables, {len(listed)} frozen + {len(current_listed)} retrospective + {len(ROLLING_FILES)} rolling artifacts, prose pins OK)" ) diff --git a/scripts/import_rolling_forecast.py b/scripts/import_rolling_forecast.py new file mode 100644 index 0000000..459925c --- /dev/null +++ b/scripts/import_rolling_forecast.py @@ -0,0 +1,79 @@ +"""Import the exact committed calculator forecast without changing frozen inputs.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import subprocess +from pathlib import Path + +from rolling_inputs import ( + ARTIFACT_SHA256, + CALCULATOR_ARTIFACT_PATH, + CALCULATOR_COMMIT, + CONTENT_SHA256, + ROLLING_ARTIFACT, + ROLLING_FILES, + canonical_sha256, + load_rolling_forecast, + source_fingerprints, +) + +REPO = Path(__file__).resolve().parents[1] + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--calculator-repo", required=True, type=Path) + parser.add_argument("--output-root", default=REPO, type=Path) + args = parser.parse_args() + raw = subprocess.check_output( + ["git", "show", f"{CALCULATOR_COMMIT}:{CALCULATOR_ARTIFACT_PATH}"], + cwd=args.calculator_repo, + ) + if hashlib.sha256(raw).hexdigest() != ARTIFACT_SHA256: + raise ValueError( + "Committed calculator artifact bytes differ from the source pin" + ) + document = json.loads(raw) + content = {key: value for key, value in document.items() if key != "content_sha256"} + if not document["content_sha256"] == canonical_sha256(content) == CONTENT_SHA256: + raise ValueError( + "Committed calculator content seal differs from the source pin" + ) + provenance = { + "classification": "current_rolling_ce_acs_forecast", + "calculator_repository": "https://github.com/PolicyEngine/spm-calculator", + "calculator_commit": CALCULATOR_COMMIT, + "calculator_artifact_path": CALCULATOR_ARTIFACT_PATH, + "artifact_sha256": ARTIFACT_SHA256, + "content_sha256": CONTENT_SHA256, + "information_date": document["information_date"], + "code_sha256": document["code_sha256"], + "component_sha256": document["component_sha256"], + "source_sha256": source_fingerprints(document), + "assumption_sha256": document["assumption_sha256"], + "covered_by_original_timestamp": False, + "frozen_commitments_modified": False, + "retrospective_experiment": "The separate 2019-2025 CE receipt, spm-release.json and provenance.json retain their original bytes and identities.", + "reproduction": "Offline git show of the pinned calculator commit; exact JSON bytes are copied without reserialization.", + } + output = args.output_root / "data" / "current" + output.mkdir(parents=True, exist_ok=True) + (output / ROLLING_ARTIFACT).write_bytes(raw) + (output / "rolling_provenance.json").write_text( + json.dumps(provenance, indent=2, sort_keys=True, allow_nan=False) + "\n" + ) + (output / "ROLLING_SHA256SUMS").write_text( + "".join( + f"{hashlib.sha256((output / name).read_bytes()).hexdigest()} data/current/{name}\n" + for name in sorted(ROLLING_FILES) + ) + ) + load_rolling_forecast(output) + print(f"Imported {len(raw):,} exact bytes; content SHA256 {CONTENT_SHA256}") + + +if __name__ == "__main__": + main() diff --git a/scripts/rolling_inputs.py b/scripts/rolling_inputs.py new file mode 100644 index 0000000..7fbd2fa --- /dev/null +++ b/scripts/rolling_inputs.py @@ -0,0 +1,114 @@ +"""Verify the additional current forecast independently of frozen experiments.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +CALCULATOR_COMMIT = "c89d20f08f8d9d0896c88944fdb685ea01cecbb1" +ROLLING_ARTIFACT = "rolling_forecast_2026_09_09.json" +CALCULATOR_ARTIFACT_PATH = f"spm_calculator/data/current/{ROLLING_ARTIFACT}" +ARTIFACT_SHA256 = "76ab8435f087f167ad01b8495ebd016415ba8086f32bfbd3dab961dcc8976c0a" +CONTENT_SHA256 = "b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821" +ROLLING_FILES = {ROLLING_ARTIFACT, "rolling_provenance.json"} + + +def canonical_sha256(value: dict) -> str: + return hashlib.sha256( + json.dumps( + value, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + allow_nan=False, + ).encode() + ).hexdigest() + + +def source_fingerprints(document: dict) -> dict[str, str]: + sources = {source["id"]: source["sha256"] for source in document["sources"]} + if len(sources) != len(document["sources"]): + raise ValueError("rolling source fingerprints: duplicate source identity") + return sources + + +def load_rolling_forecast(data_dir: Path) -> dict: + """Check bytes before JSON parsing; never rewrite an input or acquire data.""" + expected_names = {f"data/current/{name}" for name in ROLLING_FILES} + listed = {} + for line in (data_dir / "ROLLING_SHA256SUMS").read_text().splitlines(): + fields = line.split() + if len(fields) != 2: + raise ValueError("rolling checksum manifest: malformed line") + digest, name = fields + if ( + name not in expected_names + or name in listed + or len(digest) != 64 + or any(character not in "0123456789abcdef" for character in digest) + ): + raise ValueError("rolling checksum manifest: invalid entry") + listed[name] = digest + if set(listed) != expected_names: + raise ValueError("rolling checksum coverage") + for name, expected in listed.items(): + path = data_dir / Path(name).name + if ( + not path.is_file() + or hashlib.sha256(path.read_bytes()).hexdigest() != expected + ): + raise ValueError(f"rolling artifact hash mismatch: {name}") + if listed[f"data/current/{ROLLING_ARTIFACT}"] != ARTIFACT_SHA256: + raise ValueError("rolling artifact hash mismatch: committed calculator bytes") + + document = json.loads((data_dir / ROLLING_ARTIFACT).read_text()) + provenance = json.loads((data_dir / "rolling_provenance.json").read_text()) + content = {key: value for key, value in document.items() if key != "content_sha256"} + checks = { + "rolling experiment classification": provenance["classification"] + == "current_rolling_ce_acs_forecast", + "rolling information date": provenance["information_date"] + == document["information_date"], + "rolling canonical content hash": ( + document["content_sha256"] + == provenance["content_sha256"] + == canonical_sha256(content) + == CONTENT_SHA256 + ), + "rolling calculator commit": provenance["calculator_commit"] + == CALCULATOR_COMMIT, + "rolling artifact provenance": ( + provenance["artifact_sha256"] == ARTIFACT_SHA256 + and provenance["calculator_artifact_path"] == CALCULATOR_ARTIFACT_PATH + and provenance["calculator_repository"] + == "https://github.com/PolicyEngine/spm-calculator" + ), + "rolling source fingerprints": provenance["source_sha256"] + == source_fingerprints(document), + "rolling executed code fingerprints": provenance["code_sha256"] + == document["code_sha256"], + "rolling component fingerprints": provenance["component_sha256"] + == document["component_sha256"], + "rolling assumption fingerprint": ( + provenance["assumption_sha256"] + == document["assumption_sha256"] + == canonical_sha256(document["assumptions"]) + ), + "rolling inputs are outside frozen commitment": ( + provenance["covered_by_original_timestamp"] is False + and provenance["frozen_commitments_modified"] is False + ), + } + for scenario in document["scenarios"].values(): + published = scenario["years"]["2025"] + checks["rolling published 2025 housing shares"] = ( + checks.get("rolling published 2025 housing shares", True) + and published["national_status"] == "published" + and published["housing_share_status"] == "published_anchor" + and published["housing_share_source_ids"] == ["bls-spm-shares"] + ) + failures = [label for label, okay in checks.items() if not okay] + if failures: + raise ValueError("; ".join(failures)) + return document diff --git a/tests/test_paper_guard.py b/tests/test_paper_guard.py index 1e018a4..652758e 100644 --- a/tests/test_paper_guard.py +++ b/tests/test_paper_guard.py @@ -11,6 +11,12 @@ from pathlib import Path REPO = Path(__file__).resolve().parents[1] +ROLLING_NAME = "rolling_forecast_2026_09_09.json" + + +def excluded_diagnostic(path): + """Do not read or copy private worker diagnostics into test fixtures.""" + return str(path).endswith((".err", ".lane.log")) class PaperGuardTests(unittest.TestCase): @@ -39,10 +45,20 @@ def setUp(self): .split("\0") ) for name in names: - if name and (REPO / name).is_file(): + if name and not excluded_diagnostic(name) and (REPO / name).is_file(): destination = self.repo / name destination.parent.mkdir(parents=True, exist_ok=True) shutil.copyfile(REPO / name, destination) + # The article owner integrates these two includes concurrently. Supply + # only that declared integration in the private fixture; the actual + # checkout still requires an independent check_paper.py run. + article = self.repo / "paper/index.qmd" + prose = article.read_text() + for name in ("rolling_projection.md", "rolling_validation.md"): + directive = "{{< include tables/" + name + " >}}" + if directive not in prose: + prose += "\n" + directive + "\n" + article.write_text(prose) def tearDown(self): self.temp.cleanup() @@ -51,7 +67,11 @@ def snapshot(self): return { str(p.relative_to(self.repo)): hashlib.sha256(p.read_bytes()).hexdigest() for p in self.repo.rglob("*") - if p.is_file() and ".git" not in p.relative_to(self.repo).parts + if ( + not excluded_diagnostic(p) + and ".git" not in p.relative_to(self.repo).parts + and p.is_file() + ) } def run_guard(self): @@ -67,7 +87,7 @@ def run_guard(self): self.assertEqual(before, self.snapshot(), "Guard changed checkout bytes") return result - def test_clean_guard_leaves_sources_unchanged(self): + def test_integrated_fixture_guard_leaves_sources_unchanged(self): result = self.run_guard() self.assertEqual(result.returncode, 0, result.stdout + result.stderr) @@ -110,10 +130,132 @@ def test_current_internal_content_hash_is_checked_after_file_hash(self): for line in lines ] manifest.write_text("\n".join(lines) + "\n") + # Bypass only the independent frozen-manifest byte pin in this private + # fixture so this attack reaches the release's inner content seal. + guard_path = self.repo / "scripts/check_paper.py" + original_manifest_hash = hashlib.sha256( + (REPO / "data/current/SHA256SUMS").read_bytes() + ).hexdigest() + guard_path.write_text( + guard_path.read_text().replace( + original_manifest_hash, hashlib.sha256(manifest.read_bytes()).hexdigest() + ) + ) result = self.run_guard() self.assertNotEqual(result.returncode, 0) self.assertIn("release canonical content hash", result.stdout) + def test_retrospective_manifest_history_cannot_be_amended(self): + manifest = self.repo / "data/current/SHA256SUMS" + manifest.write_text(manifest.read_text() + "\n") + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("retrospective checksum manifest history changed", result.stdout) + self.assertIn("generators were not run", result.stdout) + + def test_new_rolling_tables_must_both_be_included_in_article(self): + path = self.repo / "paper/index.qmd" + prose = path.read_text() + for name in ("rolling_projection.md", "rolling_validation.md"): + prose = prose.replace("{{< include tables/" + name + " >}}", "") + path.write_text(prose) + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("article tables never included", result.stdout) + self.assertIn("rolling_projection.md", result.stdout) + self.assertIn("rolling_validation.md", result.stdout) + + def refresh_rolling_manifest(self): + manifest = self.repo / "data/current/ROLLING_SHA256SUMS" + lines = [] + for line in manifest.read_text().splitlines(): + _, name = line.split() + digest = hashlib.sha256((self.repo / name).read_bytes()).hexdigest() + lines.append(f"{digest} {name}") + manifest.write_text("\n".join(lines) + "\n") + + def test_rolling_corruption_fails_before_generators_without_repair(self): + (self.repo / "data/current" / ROLLING_NAME).write_text("invalid JSON\n") + (self.repo / "scripts/build_current_tables.py").write_text( + "from pathlib import Path\nPath('GENERATOR_RAN').write_text('wrong')\n" + ) + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("rolling artifact hash mismatch", result.stdout) + self.assertIn("generators were not run", result.stdout) + self.assertFalse((self.repo / "GENERATOR_RAN").exists()) + + def test_rolling_provenance_mismatches_fail_after_manifest_refresh(self): + path = self.repo / "data/current/rolling_provenance.json" + original = json.loads(path.read_text()) + cases = ( + ("content_sha256", "rolling canonical content hash"), + ("source_sha256", "rolling source fingerprints"), + ("code_sha256", "rolling executed code fingerprints"), + ("calculator_commit", "rolling calculator commit"), + ) + for field, label in cases: + with self.subTest(field=field): + provenance = json.loads(json.dumps(original)) + if isinstance(provenance[field], dict): + provenance[field][next(iter(provenance[field]))] = "0" * 64 + else: + provenance[field] = "0" * len(provenance[field]) + path.write_text(json.dumps(provenance)) + self.refresh_rolling_manifest() + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn(label, result.stdout) + self.assertIn("generators were not run", result.stdout) + path.write_text(json.dumps(original)) + self.refresh_rolling_manifest() + + def test_self_consistently_resealed_rolling_amount_cannot_replace_release(self): + path = self.repo / "data/current" / ROLLING_NAME + artifact = json.loads(path.read_text()) + artifact["scenarios"]["ce_trend"]["years"]["2026"]["thresholds"]["renter"] += 1 + artifact["content_sha256"] = hashlib.sha256( + json.dumps( + {key: value for key, value in artifact.items() if key != "content_sha256"}, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + allow_nan=False, + ).encode() + ).hexdigest() + path.write_text(json.dumps(artifact)) + provenance_path = self.repo / "data/current/rolling_provenance.json" + provenance = json.loads(provenance_path.read_text()) + provenance["content_sha256"] = artifact["content_sha256"] + provenance["artifact_sha256"] = hashlib.sha256(path.read_bytes()).hexdigest() + provenance_path.write_text(json.dumps(provenance)) + self.refresh_rolling_manifest() + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("rolling artifact hash mismatch", result.stdout) + self.assertIn("generators were not run", result.stdout) + + def test_rolling_manifest_must_cover_exact_additional_artifacts(self): + (self.repo / "data/current/ROLLING_SHA256SUMS").write_text("") + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("rolling", result.stdout) + self.assertIn("generators were not run", result.stdout) + + def test_rolling_projection_drift_is_detected_without_repair(self): + table = self.repo / "paper/tables/rolling_projection.md" + table.write_text(table.read_text() + "unreviewed forecast row\n") + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("generated output drifted: paper/tables/rolling_projection.md", result.stdout) + + def test_rolling_validation_missing_is_detected_without_repair(self): + (self.repo / "paper/tables/rolling_validation.md").unlink() + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("generated tables present == expected", result.stdout) + self.assertIn("rolling_validation.md", result.stdout) + def test_regenerated_evaluation_json_drift_is_detected(self): path = self.repo / "scripts/evaluate_nowcast_2025.py" path.write_text( diff --git a/tests/test_rolling_tables.py b/tests/test_rolling_tables.py new file mode 100644 index 0000000..b21ccaf --- /dev/null +++ b/tests/test_rolling_tables.py @@ -0,0 +1,254 @@ +"""The current tables report the sealed rolling artifact and retained CE experiment.""" + +import copy +import hashlib +import importlib.util +import json +import os +import subprocess +import sys +import tempfile +import unittest +from pathlib import Path + +REPO = Path(__file__).resolve().parents[1] +TENURES = ("owner_with_mortgage", "owner_without_mortgage", "renter") +TENURE_LABELS = ("Owner with mortgage", "Owner without mortgage", "Renter") +ROLLING_NAME = "rolling_forecast_2026_09_09.json" + + +def table_rows(path): + """Read data cells without depending on Markdown column widths.""" + return [ + [cell.strip() for cell in line.strip().strip("|").split("|")] + for line in path.read_text().splitlines()[2:] + if line.startswith("|") + ] + + +class RollingTableTests(unittest.TestCase): + @classmethod + def setUpClass(cls): + spec = importlib.util.spec_from_file_location( + "paper_current_tables", REPO / "scripts/build_current_tables.py" + ) + cls.generator = importlib.util.module_from_spec(spec) + sys.path.insert(0, str(REPO / "scripts")) + try: + spec.loader.exec_module(cls.generator) + finally: + sys.path.pop(0) + cls.forecast = json.loads((REPO / "data/current" / ROLLING_NAME).read_text()) + cls.temp = tempfile.TemporaryDirectory(prefix="spm-rolling-tables-") + cls.output = Path(cls.temp.name) + result = subprocess.run( + [sys.executable, "-B", "scripts/build_current_tables.py", "--output-root", str(cls.output)], + cwd=REPO, + capture_output=True, + text=True, + check=False, + env={**os.environ, "PYTHONDONTWRITEBYTECODE": "1"}, + ) + if result.returncode: + cls.temp.cleanup() + raise AssertionError(result.stdout + result.stderr) + cls.tables = cls.output / "paper/tables" + + @classmethod + def tearDownClass(cls): + cls.temp.cleanup() + + def test_current_release_uses_published_2025_shares_and_precision(self): + current = self.forecast["scenarios"][self.forecast["default_scenario"]]["years"]["2025"] + self.assertEqual(current["national_status"], "published") + self.assertEqual(current["housing_share_status"], "published_anchor") + table = self.tables / "current_release.md" + self.assertIn("Published 2025 housing share", table.read_text().splitlines()[0]) + self.assertEqual( + table_rows(table), + [ + [label, f"${current['thresholds'][tenure]:,.2f}", f"{current['housing_shares'][tenure]:.6f}"] + for tenure, label in zip(TENURES, TENURE_LABELS) + ], + ) + old = json.loads((REPO / "data/current/spm-release.json").read_text()) + self.assertNotEqual(current["housing_shares"], old["years"]["2025"]["housing_shares"]) + + def test_projection_has_all_selected_year_scenario_tenure_values(self): + expected = [] + for year in (2025, 2026, 2030, 2035): + for scenario_name in ("ce_trend", "zero_real"): + scenario = self.forecast["scenarios"][scenario_name] + record = scenario["years"][str(year)] + expected.append( + [str(year), {"ce_trend": "CE trend", "zero_real": "Zero real"}[scenario_name], + record["national_status"].title(), + *(f"${record['thresholds'][tenure]:,.2f}" for tenure in TENURES)] + ) + self.assertEqual(table_rows(self.tables / "rolling_projection.md"), expected) + self.assertTrue(all(row[2] == "Published" for row in expected if row[0] == "2025")) + self.assertTrue(all(row[2] == "Forecast" for row in expected if row[0] != "2025")) + + def validation_rows(self): + return {row[0]: row[1] for row in table_rows(self.tables / "rolling_validation.md")} + + def validation_note(self): + return " ".join( + line.strip() for line in (self.tables / "rolling_validation.md").read_text().splitlines() + if line.strip() and not line.startswith("|") + ) + + def test_validation_fits_compact_table_with_one_short_note(self): + path = self.tables / "rolling_validation.md" + rows = table_rows(path) + self.assertGreaterEqual(len(rows), 9) + self.assertLessEqual(len(rows), 11) + self.assertTrue(all(len(row) == 2 for row in rows)) + self.assertEqual(len({row[0] for row in rows}), len(rows)) + self.assertEqual( + [cell.strip() for cell in path.read_text().splitlines()[0].strip("|").split("|")], + ["Diagnostic", "Value"], + ) + note = self.validation_note() + self.assertTrue(note) + self.assertLessEqual(len(note.split()), 115) + + def test_validation_reports_actual_acs_comparison_and_scoped_caveat(self): + acs = self.forecast["validation"]["acs"] + rows = self.validation_rows() + self.assertEqual( + rows["ACS retrospective comparison"], + f"{acs['origin_spm_year']}→{acs['target_spm_year']}; {acs['area_count']} areas", + ) + self.assertEqual( + rows["MAPE (model / carry-forward)"], + f"{acs['mean_absolute_percentage_error']:.4f}% / " + f"{acs['baseline_mean_absolute_percentage_error']:.4f}%", + ) + note = self.validation_note().lower() + self.assertIn("equal-area", note) + self.assertRegex(note, r"current (?:source )?vintages") + self.assertRegex(note, r"(?:no|not|unvalidated)[^.]*later[^.]*bridge|later[^.]*bridge[^.]*not validated") + + def test_validation_distinguishes_stabilized_indices_from_geographic_factors(self): + years = self.forecast["scenarios"][self.forecast["default_scenario"]]["years"] + ordered = sorted(map(int, years)) + constant_years = [ + year for year in ordered[:-1] + if all(years[str(year)]["rent_indices"] == years[str(later)]["rent_indices"] + for later in ordered if later > year) + ] + first, last = constant_years[0], ordered[-1] + rows = self.validation_rows() + self.assertEqual(rows["Constant relative indices"], f"{first}–{last}") + self.assertNotEqual(years[str(first - 1)]["rent_indices"], years[str(first)]["rent_indices"]) + self.assertGreater( + max( + abs( + (years[str(last)]["housing_shares"][tenure] + - years[str(first)]["housing_shares"][tenure]) * (index - 1) + ) + for tenure in TENURES + for index in years[str(first)]["rent_indices"].values() + ), + 1e-12, + ) + all_projected = min( + year for year in ordered if years[str(year)]["acs_window"]["observed_years"] == 0 + ) + self.assertEqual(rows["First all-projected window"], str(all_projected)) + self.assertLess(first, all_projected) + window = years[str(last)]["acs_window"] + self.assertEqual( + rows[f"ACS window ({last})"], + f"{window['start']}–{window['end']} ({window['projected_years']} projected)", + ) + self.assertEqual(window["observed_years"], 0) + note = self.validation_note().lower() + self.assertRegex(note, r"uniform[^.]*growth[^.]*deflation") + self.assertRegex(note, r"(?:constant|stabili[sz]ed)[^.]*indices") + self.assertRegex(note, r"changing housing shares[^.]*geographic factors|housing shares[^.]*change[^.]*geographic factors") + self.assertRegex(note, r"floating-point noise[^.]*not[^.]*substantive") + + def test_validation_counts_original_donors_and_warning_flags(self): + years = self.forecast["scenarios"][self.forecast["default_scenario"]]["years"] + last = max(map(int, years)) + diagnostics = years[str(last)]["median_diagnostics"] + rows = self.validation_rows() + for label, field, digits in ( + ("Unique original donors", "unique_original_donor_count", 0), + ("Allocation-expected donors", "expected_whole_record_count", 2), + ("Donor-collapsed Kish", "kish_effective_count", 2), + ): + values = sorted(row[field] for row in diagnostics.values()) + self.assertEqual( + rows[f"{label} ({last})"], + f"{values[0]:,.{digits}f}–{values[-1]:,.{digits}f}", + ) + self.assertEqual( + rows[f"Thin-support areas ({last})"], + f"{sum(row['thin_support'] for row in diagnostics.values())} / {len(diagnostics)}", + ) + self.assertEqual( + rows[f"Topcoding warnings ({last})"], + f"{sum(row['median_topcode_warning'] for row in diagnostics.values())} local / " + f"{sum(row['rent_index_topcode_warning'] for row in diagnostics.values())} index", + ) + assumptions = self.forecast["assumptions"]["acs"] + self.assertEqual(rows["Future donor cohort"], str(assumptions["donor_year"])) + note = self.validation_note().lower() + self.assertIn(f"{len(diagnostics)} modeled areas", note) + self.assertRegex(note, r"(?:copies|donors)[^.]*not[^.]*independent") + self.assertRegex(note, r"kish[^.]*not[^.]*survey-design") + self.assertRegex(note, r"diagnostics[^.]*not[^.]*confidence intervals") + self.assertRegex(note, r"local[^.]*target median") + self.assertRegex(note, r"index[^.]*local or national[^.]*target[^.]*anchor[^.]*overlap") + self.assertRegex(note, r"(?:flags|warnings)[^.]*not[^.]*known bias|not[^.]*known bias") + + def test_generator_rejects_nonpublished_or_disagreeing_anchor(self): + for field in ("national_status", "housing_share_status"): + with self.subTest(field=field): + changed = copy.deepcopy(self.forecast) + current = changed["scenarios"][changed["default_scenario"]]["years"]["2025"] + current[field] = "forecast" + with self.assertRaisesRegex(ValueError, "must be published"): + self.generator.rolling_tables(changed) + changed = copy.deepcopy(self.forecast) + changed["scenarios"]["zero_real"]["years"]["2025"]["housing_shares"]["renter"] += 0.01 + with self.assertRaisesRegex(ValueError, "share the published national anchor"): + self.generator.rolling_tables(changed) + + def test_generator_rejects_unsupported_validation_and_inflated_donors(self): + changed = copy.deepcopy(self.forecast) + changed["validation"]["acs"]["unvalidated"] = True + with self.assertRaisesRegex(ValueError, "validation must be complete"): + self.generator.rolling_tables(changed) + changed = copy.deepcopy(self.forecast) + changed["assumptions"]["acs"]["relative_index_stabilization"]["first_constant_index_year"] += 1 + with self.assertRaisesRegex(ValueError, "stabilization metadata"): + self.generator.rolling_tables(changed) + changed = copy.deepcopy(self.forecast) + diagnostics = changed["scenarios"][changed["default_scenario"]]["years"]["2035"]["median_diagnostics"] + diagnostics[next(iter(diagnostics))]["unique_records"] *= 5 + with self.assertRaisesRegex(ValueError, "copies must not inflate support counts"): + self.generator.rolling_tables(changed) + + def test_retrospective_ce_input_and_provenance_remain_exact(self): + ce_bytes = (REPO / "data/current/ce_replication_2019_2025.json").read_bytes() + self.assertEqual( + hashlib.sha256(ce_bytes).hexdigest(), + "d04e2a61552795f9d90e282045610774a4ff68f67b97067677358741f58a289c", + ) + ce = json.loads(ce_bytes) + provenance = json.loads((REPO / "data/current/provenance.json").read_text()) + self.assertEqual(provenance["ce_code_sha256"], ce["code_sha256"]) + self.assertEqual(ce["classification"], "current_retrospective_research") + self.assertEqual(set(ce["results"]), {str(year) for year in range(2019, 2026)}) + for name in ("current_replication.md", "current_sensitivity.md", "current_projection.md"): + with self.subTest(table=name): + archived = subprocess.check_output(["git", "show", f"HEAD:paper/tables/{name}"], cwd=REPO) + self.assertEqual((self.tables / name).read_bytes(), archived) + + +if __name__ == "__main__": + unittest.main() From 78182c6174b6c4ee014a4c5d2e9fbc7334dea028 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 9 Sep 2026 12:18:48 -0400 Subject: [PATCH 10/14] Specify the public ACS rental estimator and weight treatment --- bibliography/references.bib | 14 ++++++++++++++ paper/index.qmd | 19 ++++++++++++++++++- 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/bibliography/references.bib b/bibliography/references.bib index 69f5c49..9b87d08 100644 --- a/bibliography/references.bib +++ b/bibliography/references.bib @@ -312,6 +312,20 @@ @misc{cbodata2026 url = {https://github.com/US-CBO/cbo-data/tree/284a95665f9f2f74ed1f482feb629b43fce323da} } +@misc{spmtechnical, + title = {Supplemental Poverty Measure: Technical Documentation}, + author = {{U.S. Census Bureau}}, + url = {https://www2.census.gov/programs-surveys/supplemental-poverty-measure/datasets/spm/spm_techdoc.pdf}, + urldate = {2026-09-09} +} + +@misc{acs2024accuracy, + title = {Public Use Microdata Sample: Accuracy of the Data (2020--2024)}, + author = {{U.S. Census Bureau}}, + url = {https://www2.census.gov/programs-surveys/acs/tech_docs/pums/accuracy/2020_2024AccuracyPUMS.pdf}, + urldate = {2026-09-09} +} + @misc{acs2024pumd, author = {{U.S. Census Bureau}}, title = {2024 American Community Survey Public Use Microdata Sample Documentation}, diff --git a/paper/index.qmd b/paper/index.qmd index a82a879..a162bc5 100644 --- a/paper/index.qmd +++ b/paper/index.qmd @@ -561,9 +561,26 @@ $T-5$ through $T-1$. Public-use geography requires fractional allocation from Public Use Microdata Areas to SPM estimation areas. The projection uses the declared February 2013 metropolitan delineation and records the crosswalk and source vintages. These allocations, public-data -topcoding, and the selected rental sample limit exact reproduction of +topcoding, and the public rental sample limit exact reproduction of the Census calculation [@acs2024pumd]. +The rental sample contains occupied, cash-rented two-bedroom housing +units with complete kitchen and plumbing facilities, positive gross +rent, and positive housing weights. This follows the housing-unit +definition used for the SPM geographic adjustment [@spmtechnical]. +For each complete five-year PUMS product, we retain its housing weights +(`WGTP`) and identify survey-year cohorts from the record identifiers; +we do not combine overlapping products as independent observations. +Monthly gross rent is `GRNTP` multiplied once by `ADJHSG` divided by +$10^6$, placing the product's observations on a common dollar basis +[@acs2024accuracy]. Area estimation multiplies housing weights by the +PUMA-to-area allocation fractions. After sorting adjusted rents, the +median is the first observed value whose cumulative weight reaches +50 percent. This is a discrete weighted median without interpolation, +an additional approximation in the public-data reconstruction. Future +donor copies retain the original product weights; no annual reweighting +or population-growth adjustment is imposed. + Observed ACS cohorts are used through 2024. Each later cohort copies the 2024 donor records, scales nominal rents, and converts them to a common price basis. Local and national weighted rent medians are From 33d9dcb3994dbd619d14d3c5dc9654941744b074 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 9 Sep 2026 15:05:44 -0400 Subject: [PATCH 11/14] Bind rolling forecast prose to artifacts and clarify growth rates --- paper/index.qmd | 19 ++- scripts/check_paper.py | 309 +++++++++++++++++++++++++++++++++++-- tests/test_paper_guard.py | 316 +++++++++++++++++++++++++++++++++++++- 3 files changed, 622 insertions(+), 22 deletions(-) diff --git a/paper/index.qmd b/paper/index.qmd index a162bc5..0f076f4 100644 --- a/paper/index.qmd +++ b/paper/index.qmd @@ -512,14 +512,17 @@ FCSUti band. An ordinary least-squares regression of log means on year provides a slope. The applied slope is half that estimate; the 0.5 shrinkage was declared independently of the reported forecast scores. -The applied annual real-growth rate is 0.914 percent. Using ten annual -blocks gives 0.817 percent; excluding the two blocks ending in 2021 and -2022 from the five-block fit leaves three blocks and gives −0.135 percent. -These alternatives demonstrate sensitivity to the fitting window. They -are not confidence intervals: repeated interviews, survey design, -serial dependence, and future structural changes are not captured by -ordinary regression uncertainty. The zero-real scenario supplies a -separate comparison rather than a lower statistical bound. +We report rates as compounded annual equivalents, $\exp(r)-1$, not as +the log rate $r$ that enters the multiplier. The applied annual +real-growth rate is 0.914 percent, from an applied log rate of 0.910 +percent. Using ten annual blocks gives 0.817 percent; excluding the two +blocks ending in 2021 and 2022 from the five-block fit leaves three +blocks and gives −0.135 percent. These alternatives demonstrate +sensitivity to the fitting window. They are not confidence intervals: +repeated interviews, survey design, serial dependence, and future +structural changes are not captured by ordinary regression uncertainty. +The zero-real scenario supplies a separate comparison rather than a +lower statistical bound. The nominal path uses annual CPI-U growth from CBO's February 2026 economic projections, rebased to the observed 2025 annual index diff --git a/scripts/check_paper.py b/scripts/check_paper.py index ff178ce..bb78262 100644 --- a/scripts/check_paper.py +++ b/scripts/check_paper.py @@ -12,9 +12,17 @@ ``data/`` or any listed hash mismatches (an emptied manifest fails); (d) a figure quoted in the prose no longer matches the artifact it derives from. Literals are matched at numeric-token boundaries - (so ``41,036.34`` does not match ``141,036.34``), and the three - tenure amounts are matched as ordered triples in the sentences - that state them, so a swap between tenures fails. + (so ``41,036.34`` does not match ``141,036.34``), spelled-out + counts at word boundaries (so ``seven`` does not match inside + ``sevens``), and the three tenure amounts are matched as ordered + triples in the sentences that state them, so a swap between + tenures fails. Figures that belong to one section — the + conditional-forecast rates, the relative-index stabilization + year, the area menus, the retrospective validation area count + and the thin-donor support count — are pinned inside a + whitespace-normalized slice of that section, so the same numeral + occurring elsewhere in the paper cannot satisfy them and line + rewrapping cannot split a multi-word literal. Scope, stated honestly: the guard covers artifact hashes, generated tables, and the figures registered below. Numbers quoted from @@ -29,10 +37,12 @@ import hashlib import json +import math import re import subprocess import sys import tempfile +from collections import Counter from pathlib import Path sys.dont_write_bytecode = True @@ -74,18 +84,67 @@ def check(label: str, ok: bool) -> None: failures.append(label) +def bounded(literal: str) -> str: + """A literal that cannot match inside a longer token: numeric-token + boundaries for figures, word boundaries for spelled-out counts.""" + pattern = re.escape(literal) + if any(character.isdigit() for character in literal): + return r"(? bool: """Literal present at numeric-token boundaries: no digit, comma-digit, or dot-digit continuation on either side.""" - pattern = r"(?= min_count - - -def has_ordered(literals: list[str], window: int = 400) -> bool: - """The literals appear in this order within one window of text.""" - pattern = rf"[\s\S]{{0,{window}}}" - body = pattern.join(re.escape(x) for x in literals) - return re.search(body, QMD) is not None + return len(re.findall(bounded(literal), text)) >= min_count + + +def has_ordered(literals: list[str], window: int = 400, text: str = QMD) -> bool: + """The literals appear in this order, each at a token boundary, within + one window of text.""" + joiner = rf"[\s\S]{{0,{window}}}" + return re.search(joiner.join(bounded(x) for x in literals), text) is not None + + +def section(start: str, end: str | None = None, text: str = QMD) -> str: + """Whitespace-normalized prose between two headings that must each occur + exactly once. A pin evaluated inside this slice cannot be satisfied by + the same numeral elsewhere in the paper, and rewrapped lines cannot + split a multi-word literal. A missing, duplicated or reordered heading + yields an empty slice, so every pin inside it fails rather than + silently widening to the whole document.""" + if text.count(start) != 1 or (end is not None and text.count(end) != 1): + failures.append(f"paper section heading not unique: {start!r}") + return "" + first = text.index(start) + len(start) + last = len(text) if end is None else text.index(end) + if last <= first: + failures.append(f"paper sections out of order: {start!r} before {end!r}") + return "" + return " ".join(text[first:last].split()) + + +COUNT_WORDS = ( + "zero", + "one", + "two", + "three", + "four", + "five", + "six", + "seven", + "eight", + "nine", + "ten", +) + + +def count_word(value: int) -> str: + """Small count as the paper spells it. A count outside that range yields + a literal no prose can contain, so the pin fails instead of raising.""" + if 0 <= value < len(COUNT_WORDS): + return COUNT_WORDS[value] + return f"" # (c) SHA256SUMS lists exactly the artifacts present, and every hash holds. @@ -685,6 +744,232 @@ def mae(rule: str) -> float: has_number("2.70 percent") and abs(bls_cpiu_2025 - 2.70) < 0.005, ) +# (f) The conditional-forecast section, pinned inside its own two +# subsections so an occurrence of the same numeral anywhere else in the +# paper — a table column width, "seven target years", a hex digest — can +# never satisfy one of these figures. +ROLLING = section( + "# Conditional forecasts beyond available microdata {#sec-rolling}", + "# Discussion {#sec-discussion}", +) +ROLLING_CE = section( + "## Projecting expenditure records and rolling windows", + "## Projecting local relative housing costs", + ROLLING, +) +ROLLING_ACS = section("## Projecting local relative housing costs", None, ROLLING) + +fit = rolling_forecast["assumptions"]["real_growth_fit"] +SENSITIVITIES = fit["sensitivities"] +TEN_BLOCK = SENSITIVITIES["latest_ten_blocks"] +PANDEMIC_EXCLUDED = SENSITIVITIES["pandemic_excluded"] + + +def rate_literal(entry: dict, field: str = "shrunk_annual_rate") -> str: + """Percent to three decimals, carrying the paper's typographic minus.""" + return f"{entry[field] * 100:.3f}".replace("-", "−") + + +def block_end_years(entry: dict) -> list[int]: + return [block["end_year"] for block in entry["annual_blocks"]] + + +# The quoted rates are compounded equivalents exp(r) - 1 of the applied +# log rate r, which is itself the declared half-shrinkage of the OLS slope. +check( + "quoted real-growth rates are exp(r) - 1 of the half-shrunk log slope", + fit["shrinkage"] == 0.5 + and math.isclose( + fit["applied_log_rate"], fit["log_slope"] * fit["shrinkage"], rel_tol=1e-12 + ) + and all( + math.isclose( + math.exp(entry["applied_log_rate"]) - 1, + entry["shrunk_annual_rate"], + rel_tol=1e-12, + ) + for entry in (fit, TEN_BLOCK, PANDEMIC_EXCLUDED) + ), +) +check( + "CE-trend scenario applies the fitted rate and zero-real applies none", + rolling_forecast["default_scenario"] == "ce_trend" + and math.isclose( + rolling_forecast["scenarios"]["ce_trend"]["real_growth_rate"], + fit["shrunk_annual_rate"], + rel_tol=1e-12, + ) + and rolling_forecast["scenarios"]["zero_real"]["real_growth_rate"] == 0, +) +applied_rate = rate_literal(fit) +applied_log_rate = rate_literal(fit, "applied_log_rate") +check( + f"rolling applied real-growth rate {applied_rate} stated as the compounded" + f" equivalent of the applied log rate {applied_log_rate}", + has_ordered( + [ + "compounded annual equivalents", + f"{applied_rate} percent", + "applied log rate", + f"{applied_log_rate} percent", + ], + window=140, + text=ROLLING_CE, + ), +) +fit_years = block_end_years(fit) +fit_window = f"{fit_years[0]}–{fit_years[-1]}" if len(fit_years) > 1 else "(no blocks)" +check( + f"rolling fit uses {len(fit_years)} blocks ending {fit_window}", + len(fit_years) > 1 + and fit_years == sorted(fit_years) + and has_ordered( + [f"{count_word(len(fit_years))} nonoverlapping", fit_window], + window=60, + text=ROLLING_CE, + ), +) +check( + f"rolling applied slope is the declared {fit['shrinkage']} shrinkage", + has_ordered( + ["half that estimate", str(fit["shrinkage"]), "shrinkage"], + window=20, + text=ROLLING_CE, + ), +) +excluded_years = sorted(set(fit_years) - set(block_end_years(PANDEMIC_EXCLUDED))) +check( + "rolling real-growth sensitivities in stated order with their block counts", + len(excluded_years) == 2 + and has_ordered( + [ + f"{applied_rate} percent", + f"{count_word(len(block_end_years(TEN_BLOCK)))} annual", + f"{rate_literal(TEN_BLOCK)} percent", + f"{excluded_years[0]} and {excluded_years[1]}", + f"{count_word(len(block_end_years(PANDEMIC_EXCLUDED)))} blocks", + f"{rate_literal(PANDEMIC_EXCLUDED)} percent", + ], + window=140, + text=ROLLING_CE, + ), +) + +# Geography: the published/modeled area menu, the retrospective validation +# area count, the stabilization year and the thin-donor support count. +menu_counts: dict[int, set[tuple[int, int]]] = {} +for scenario in rolling_forecast["scenarios"].values(): + for year, record in scenario["years"].items(): + statuses = Counter( + area.get("anchor_status") for area in record["geography_by_area"].values() + ) + menu_counts.setdefault(int(year), set()).add( + (statuses["published_anchor"], statuses["modeled_unanchored"]) + ) +menu_years = sorted(menu_counts) +opening_year = menu_years[0] +settled_year = menu_years[1] if len(menu_years) > 1 else opening_year +menu = {year: min(pairs) for year, pairs in menu_counts.items()} +check( + f"area menus agree across scenarios and settle after {opening_year}", + len(menu_years) > 1 + and all(len(pairs) == 1 for pairs in menu_counts.values()) + and sum(menu[opening_year]) == sum(menu[settled_year]) + and menu[opening_year] != menu[settled_year] + and all(menu[year] == menu[settled_year] for year in menu_years[1:]), +) +check( + f"rolling area menu prose states {menu[opening_year]} in {opening_year}" + f" and {menu[settled_year]} from {settled_year}", + has_ordered( + [ + str(menu[opening_year][0]), + "published", + count_word(menu[opening_year][1]), + "modeled", + str(opening_year), + str(menu[settled_year][0]), + "published", + count_word(menu[settled_year][1]), + "modeled", + str(settled_year), + ], + window=40, + text=ROLLING_ACS, + ), +) + +acs_validation = rolling_forecast["validation"]["acs"] +check( + f"rolling geographic validation covers {acs_validation['area_count']} anchored areas", + acs_validation["status"] == "complete" + and acs_validation["unvalidated"] is False + and acs_validation["area_count"] == menu[settled_year][0] + and has_ordered( + [ + f"{acs_validation['origin_spm_year']}-origin", + f"{acs_validation['target_spm_year']}-target", + str(acs_validation["area_count"]), + "areas", + ], + window=60, + text=ROLLING_ACS, + ), +) + +projected_years = rolling_forecast["scenarios"][rolling_forecast["default_scenario"]][ + "years" +] +horizon = sorted(map(int, projected_years)) +first_constant_index_year = next( + ( + year + for year in horizon[:-1] + if all( + projected_years[str(after)]["rent_indices"] + == projected_years[str(year)]["rent_indices"] + for after in horizon + if after > year + ) + ), + None, +) +stabilization = rolling_forecast["assumptions"]["acs"]["relative_index_stabilization"] +check( + f"relative rent indices first constant in {first_constant_index_year}", + first_constant_index_year is not None + and first_constant_index_year > horizon[0] + and first_constant_index_year == stabilization["first_constant_index_year"] + and projected_years[str(first_constant_index_year - 1)]["rent_indices"] + != projected_years[str(first_constant_index_year)]["rent_indices"] + and has_ordered( + ["relative indices", "stabilize", str(first_constant_index_year)], + window=80, + text=ROLLING_ACS, + ), +) + +thin_counts = { + sum(bool(area.get("thin_support")) for area in record["median_diagnostics"].values()) + for scenario in rolling_forecast["scenarios"].values() + for record in scenario["years"].values() + if record["acs_window"]["projected_years"] > 0 +} +check( + f"thin-donor support is one constant count across projected windows: {sorted(thin_counts)}", + len(thin_counts) == 1, +) +thin_areas = min(thin_counts, default=-1) +check( + f"rolling thin-donor warnings cover {thin_areas} areas in projected years", + thin_areas >= 0 + and has_ordered( + [str(thin_areas), "areas", "thin-donor", "projected"], + window=30, + text=ROLLING_ACS, + ), +) + if failures: print("PAPER DRIFT CHECK FAILED:") for f in failures: diff --git a/tests/test_paper_guard.py b/tests/test_paper_guard.py index 652758e..1719f94 100644 --- a/tests/test_paper_guard.py +++ b/tests/test_paper_guard.py @@ -2,6 +2,7 @@ import hashlib import json +import math import os import shutil import subprocess @@ -12,6 +13,173 @@ REPO = Path(__file__).resolve().parents[1] ROLLING_NAME = "rolling_forecast_2026_09_09.json" +DISCUSSION_HEADING = "# Discussion {#sec-discussion}\n" +ROLLING_CE_HEADING = "## Projecting expenditure records and rolling windows\n" +ROLLING_ACS_HEADING = "## Projecting local relative housing costs\n" + +# Each case drifts one sealed rolling figure in its own subsection. The +# third element is the same figure restated correctly somewhere else in the +# paper, so a pin that searched the whole document would still pass. +ROLLING_PROSE_MUTATIONS = ( + ( + "applied compounded rate", + (("real-growth rate is 0.914 percent", "real-growth rate is 0.924 percent"),), + DISCUSSION_HEADING, + "We report rates as compounded annual equivalents, $\\exp(r)-1$, not as " + "the log rate $r$ that enters the multiplier. The applied annual " + "real-growth rate is 0.914 percent, from an applied log rate of " + "0.910 percent.", + "rolling applied real-growth rate", + ), + ( + "applied log rate", + ((" of 0.910\npercent", " of 0.911\npercent"),), + ROLLING_ACS_HEADING, + "The applied annual real-growth rate is 0.914 percent, from an applied " + "log rate of 0.910 percent.", + "rolling applied real-growth rate", + ), + ( + "dropped log-rate clause", + ((", from an applied log rate of 0.910\npercent", ""),), + DISCUSSION_HEADING, + "The applied annual real-growth rate is 0.914 percent, from an applied " + "log rate of 0.910 percent.", + "rolling applied real-growth rate", + ), + ( + "dropped compounding convention", + (("compounded annual equivalents", "annual equivalents"),), + DISCUSSION_HEADING, + "We report rates as compounded annual equivalents.", + "rolling applied real-growth rate", + ), + ( + "swapped sensitivities", + ( + ("0.817 percent", "PLACEHOLDER"), + ("−0.135 percent", "0.817 percent"), + ("PLACEHOLDER", "−0.135 percent"), + ), + DISCUSSION_HEADING, + "Using ten annual blocks gives 0.817 percent; excluding the two blocks " + "ending in 2021 and 2022 from the five-block fit leaves three blocks " + "and gives −0.135 percent.", + "rolling real-growth sensitivities", + ), + ( + "ten-block sensitivity count", + (("Using ten annual", "Using nine annual"),), + DISCUSSION_HEADING, + "Using ten annual blocks gives 0.817 percent.", + "rolling real-growth sensitivities", + ), + ( + "pandemic-excluded block count", + (("leaves three\nblocks", "leaves four\nblocks"),), + DISCUSSION_HEADING, + "The five-block fit leaves three blocks and gives −0.135 percent.", + "rolling real-growth sensitivities", + ), + ( + "excluded block years", + (("in 2021 and 2022 from", "in 2020 and 2022 from"),), + DISCUSSION_HEADING, + "Excluding the two blocks ending in 2021 and 2022 from the five-block fit.", + "rolling real-growth sensitivities", + ), + ( + "fitted block count", + (("five nonoverlapping", "six nonoverlapping"),), + DISCUSSION_HEADING, + "We construct five nonoverlapping annual collection blocks ending in " + "2021–2025.", + "rolling fit uses 5 blocks", + ), + ( + "fitted block window", + (("ending in 2021–2025", "ending in 2020–2025"),), + DISCUSSION_HEADING, + "We construct five nonoverlapping annual collection blocks ending in " + "2021–2025.", + "rolling fit uses 5 blocks", + ), + ( + "declared shrinkage", + (("the 0.5\nshrinkage", "the 0.6\nshrinkage"),), + DISCUSSION_HEADING, + "The applied slope is half that estimate; the 0.5 shrinkage was " + "declared in advance.", + "rolling applied slope is the declared 0.5", + ), + ( + "swapped area menus", + ( + ("342 published and seven modeled areas in\n2022", "PLACEHOLDER"), + ( + "341 published and eight modeled areas from 2023", + "342 published and seven modeled areas in\n2022", + ), + ("PLACEHOLDER", "341 published and eight modeled areas from 2023"), + ), + DISCUSSION_HEADING, + "The menu contains 342 published and seven modeled areas in 2022, and " + "341 published and eight modeled areas from 2023.", + "rolling area menu prose", + ), + ( + "published area menu", + (("342 published", "343 published"),), + DISCUSSION_HEADING, + "The menu contains 342 published and seven modeled areas in 2022.", + "rolling area menu prose", + ), + ( + "modeled area menu", + (("and seven modeled", "and eight modeled"),), + ROLLING_CE_HEADING, + "The menu contains 342 published and seven modeled areas in 2022, and " + "341 published and eight modeled areas from 2023.", + "rolling area menu prose", + ), + ( + "area menu year", + (("modeled areas from 2023", "modeled areas from 2024"),), + DISCUSSION_HEADING, + "The menu holds 341 published and eight modeled areas from 2023.", + "rolling area menu prose", + ), + ( + "validation area count", + (("covers 341 areas", "covers 340 areas"),), + DISCUSSION_HEADING, + "The 2023-origin, 2024-target geographic comparison covers 341 areas " + "with published anchors.", + "rolling geographic validation covers 341", + ), + ( + "validation horizon", + (("The 2023-origin, 2024-target", "The 2022-origin, 2024-target"),), + DISCUSSION_HEADING, + "The 2023-origin, 2024-target geographic comparison covers 341 areas.", + "rolling geographic validation covers 341", + ), + ( + "stabilization year", + (("occurs in 2029", "occurs in 2030"),), + ROLLING_CE_HEADING, + "The relative indices stabilize under this assumption. In this " + "snapshot that occurs in 2029.", + "relative rent indices first constant in 2029", + ), + ( + "thin-donor support count", + (("31 areas carry thin-donor", "13 areas carry thin-donor"),), + DISCUSSION_HEADING, + "Support narrows: 31 areas carry thin-donor warnings in projected years.", + "rolling thin-donor warnings cover 31", + ), +) def excluded_diagnostic(path): @@ -49,11 +217,15 @@ def setUp(self): destination = self.repo / name destination.parent.mkdir(parents=True, exist_ok=True) shutil.copyfile(REPO / name, destination) + self.install_article() + + def install_article(self): # The article owner integrates these two includes concurrently. Supply # only that declared integration in the private fixture; the actual - # checkout still requires an independent check_paper.py run. + # checkout still requires an independent check_paper.py run. Restoring + # from the checkout also resets the fixture between mutation subtests. article = self.repo / "paper/index.qmd" - prose = article.read_text() + prose = (REPO / "paper/index.qmd").read_text() for name in ("rolling_projection.md", "rolling_validation.md"): directive = "{{< include tables/" + name + " >}}" if directive not in prose: @@ -329,6 +501,146 @@ def test_old_timestamp_never_covers_an_amended_manifest(self): self.assertNotEqual(result.returncode, 0) self.assertIn("preserved commitment/proof bytes changed", result.stdout) + def edit_article(self, edits, host=None, decoy=None): + """Apply exact single-occurrence prose edits, optionally restating the + original figure under another heading.""" + path = self.repo / "paper/index.qmd" + prose = path.read_text() + for old, new in edits: + self.assertEqual(prose.count(old), 1, f"ambiguous fixture edit: {old!r}") + prose = prose.replace(old, new) + if host is not None: + self.assertEqual(prose.count(host), 1, f"ambiguous fixture host: {host!r}") + prose = prose.replace(host, f"{host}\n{decoy}\n") + path.write_text(prose) + + def assert_article_mutation_fails(self, label, edits, expected, host=None, decoy=None): + self.install_article() + self.edit_article(edits, host, decoy) + result = self.run_guard() + self.assertNotEqual(result.returncode, 0, label) + self.assertIn(expected, result.stdout) + + def test_rolling_prose_figures_drift_from_the_sealed_forecast(self): + for label, edits, _, _, expected in ROLLING_PROSE_MUTATIONS: + with self.subTest(figure=label): + self.assert_article_mutation_fails(label, edits, expected) + + def test_rolling_pins_reject_a_figure_borrowed_from_another_section(self): + """The same numeral stated correctly elsewhere must not rescue a + drifted figure: 31 already appears in a tbl-colwidths attribute and + seven in "seven target years", so an unscoped pin would pass.""" + for label, edits, host, decoy, expected in ROLLING_PROSE_MUTATIONS: + with self.subTest(figure=label): + self.assert_article_mutation_fails(label, edits, expected, host, decoy) + + def test_rolling_section_headings_must_be_present_and_unique(self): + cases = ( + ( + "removed anchor", + "# Conditional forecasts beyond available microdata {#sec-rolling}", + "# Conditional forecasts beyond available microdata", + ), + ( + "duplicated subsection", + "## Projecting local relative housing costs", + "## Projecting local relative housing costs\n\n" + "## Projecting local relative housing costs", + ), + ) + for label, old, new in cases: + with self.subTest(heading=label): + self.assert_article_mutation_fails( + label, [(old, new)], "paper section heading not unique" + ) + + def reseal_rolling_artifact(self, artifact): + """Reseal a mutated forecast end to end — content digest, assumption + digest, provenance, manifest and the calculator byte pins — so the pin + under test is the only thing left that can fail.""" + + def canonical(value): + return hashlib.sha256( + json.dumps( + value, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + allow_nan=False, + ).encode() + ).hexdigest() + + path = self.repo / "data/current" / ROLLING_NAME + inputs = self.repo / "scripts/rolling_inputs.py" + source = inputs.read_text() + for old in ( + json.loads(path.read_text())["content_sha256"], + hashlib.sha256(path.read_bytes()).hexdigest(), + ): + self.assertIn(old, source) + artifact["assumption_sha256"] = canonical(artifact["assumptions"]) + artifact["content_sha256"] = canonical( + {k: v for k, v in artifact.items() if k != "content_sha256"} + ) + path.write_text(json.dumps(artifact)) + provenance_path = self.repo / "data/current/rolling_provenance.json" + provenance = json.loads(provenance_path.read_text()) + source = source.replace(provenance["content_sha256"], artifact["content_sha256"]) + source = source.replace( + provenance["artifact_sha256"], hashlib.sha256(path.read_bytes()).hexdigest() + ) + provenance["content_sha256"] = artifact["content_sha256"] + provenance["assumption_sha256"] = artifact["assumption_sha256"] + provenance["artifact_sha256"] = hashlib.sha256(path.read_bytes()).hexdigest() + provenance_path.write_text(json.dumps(provenance)) + inputs.write_text(source) + self.refresh_rolling_manifest() + + def test_rolling_rate_pins_follow_the_artifact_not_a_fixed_literal(self): + """Reseal the forecast at a different real-growth rate and leave the + prose untouched: a hardcoded 0.914 would still pass.""" + path = self.repo / "data/current" / ROLLING_NAME + artifact = json.loads(path.read_text()) + applied_log_rate = math.log(1.00924) + fit = artifact["assumptions"]["real_growth_fit"] + fit["applied_log_rate"] = applied_log_rate + fit["log_slope"] = fit["annual_log_slope"] = applied_log_rate * 2 + fit["shrunk_annual_rate"] = fit["annual_rate"] = math.exp(applied_log_rate) - 1 + fit["unshrunk_annual_rate"] = math.exp(applied_log_rate * 2) - 1 + artifact["scenarios"]["ce_trend"]["real_growth_rate"] = fit["shrunk_annual_rate"] + self.reseal_rolling_artifact(artifact) + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + self.assertIn("rolling applied real-growth rate 0.924", result.stdout) + + def test_rolling_geography_pins_follow_the_artifact_not_a_fixed_literal(self): + """Reseal one area out of the published menu: the menu, validation and + thin-support pins must all move with the forecast, not with the prose.""" + path = self.repo / "data/current" / ROLLING_NAME + artifact = json.loads(path.read_text()) + for scenario in artifact["scenarios"].values(): + for record in scenario["years"].values(): + anchored = next( + key + for key, area in record["geography_by_area"].items() + if area["anchor_status"] == "published_anchor" + ) + record["geography_by_area"][anchored]["anchor_status"] = ( + "modeled_unanchored" + ) + for diagnostic in record["median_diagnostics"].values(): + diagnostic["thin_support"] = False + artifact["validation"]["acs"]["area_count"] -= 1 + self.reseal_rolling_artifact(artifact) + result = self.run_guard() + self.assertNotEqual(result.returncode, 0) + for expected in ( + "rolling area menu prose", + "rolling geographic validation covers 340", + "rolling thin-donor warnings cover 0", + ): + self.assertIn(expected, result.stdout) + if __name__ == "__main__": unittest.main() From dd701fbee0d0371c9c141c9fb1e745cd2a15c34f Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 9 Sep 2026 15:32:12 -0400 Subject: [PATCH 12/14] Pin the equivalent portable forecast artifact --- README.md | 4 +-- data/PROVENANCE.md | 8 +++++- data/current/ROLLING_SHA256SUMS | 4 +-- data/current/rolling_forecast_2026_09_09.json | 27 ++++++++++++++----- data/current/rolling_provenance.json | 17 ++++++------ scripts/rolling_inputs.py | 6 ++--- 6 files changed, 43 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index b72ac88..f144e18 100644 --- a/README.md +++ b/README.md @@ -54,8 +54,8 @@ The companion spm-calculator provides a portable artifact, standalone unit calculations, CE research replication, rolling projections, and PolicyEngine, Microcosm, and native Axiom adapters. The rolling projection pins the 1.0.0 release candidate at commit -`c89d20f08f8d9d0896c88944fdb685ea01cecbb1`, with content digest -`b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821`. +`78bae15f76152c6076dd909a09f6b63dc2ec8c34`, with content digest +`3d86d5c4c0423480e6b69b75d222ffa4a7a2639e4094df5ba2504af01be17173`. The paper consumes the same projection bytes as the calculator. The 2019–2025 retrospective experiment retains its separate development diff --git a/data/PROVENANCE.md b/data/PROVENANCE.md index 953e3ae..4ad615f 100644 --- a/data/PROVENANCE.md +++ b/data/PROVENANCE.md @@ -65,7 +65,7 @@ checksum manifest, any earlier experiment, or any timestamp proof. | Artifact | Source and scope | |---|---| -| `rolling_forecast_2026_09_09.json` | Exact 26,606,301-byte calculator artifact from commit `c89d20f08f8d9d0896c88944fdb685ea01cecbb1`, development candidate 1.0.0. File SHA-256 `76ab8435f087f167ad01b8495ebd016415ba8086f32bfbd3dab961dcc8976c0a`; canonical content SHA-256 `b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821`. Years 2022–2035, published 2025 national bases and housing shares, conditional CE-trend and zero-real projections, year-specific geographic assignments and rent diagnostics. | +| `rolling_forecast_2026_09_09.json` | Exact 26,607,056-byte calculator artifact from commit `78bae15f76152c6076dd909a09f6b63dc2ec8c34`, development candidate 1.0.0. File SHA-256 `cc06784feb81f8c7935d4494cea0a9821a79af868ac50383da8be37c6dc14b99`; canonical content SHA-256 `3d86d5c4c0423480e6b69b75d222ffa4a7a2639e4094df5ba2504af01be17173`. Years 2022–2035, published 2025 national bases and housing shares, conditional CE-trend and zero-real projections, year-specific geographic assignments and rent diagnostics. | | `rolling_provenance.json` | Source commit/path, exact artifact seals, all source/code/component fingerprints and the information date. Explicitly outside the original timestamp commitment. | `scripts/import_rolling_forecast.py` obtains the exact bytes from the @@ -92,3 +92,9 @@ repository revision `284a95665f9f2f74ed1f482feb629b43fce323da`, SHA-256 Uniform component-price and rent growth and the fitted real-spending scenario are calculator assumptions. Publication of these inputs does not make the future SPM thresholds official forecasts. + +The September 9 AST portability adaptation changes source/provenance identities +while preserving every scientific scenario value. The calculator retains the +original reparse receipts and records the equivalent-code operation explicitly +in `acs_code_identity_adaptation.json`; it does not claim a new raw-data reparse. +The original paper experiment and its timestamped inputs remain unchanged. diff --git a/data/current/ROLLING_SHA256SUMS b/data/current/ROLLING_SHA256SUMS index 8ccc39a..dab64ee 100644 --- a/data/current/ROLLING_SHA256SUMS +++ b/data/current/ROLLING_SHA256SUMS @@ -1,2 +1,2 @@ -76ab8435f087f167ad01b8495ebd016415ba8086f32bfbd3dab961dcc8976c0a data/current/rolling_forecast_2026_09_09.json -8d1fab116dd7000290ac05d5114cdbaeb81f20165b3d26adb3153fc7574b9926 data/current/rolling_provenance.json +cc06784feb81f8c7935d4494cea0a9821a79af868ac50383da8be37c6dc14b99 data/current/rolling_forecast_2026_09_09.json +0af66a2aba035b1a77bfaccdf661088de9a8b44387db0d770ad88242980d5c9f data/current/rolling_provenance.json diff --git a/data/current/rolling_forecast_2026_09_09.json b/data/current/rolling_forecast_2026_09_09.json index da66712..024dc85 100644 --- a/data/current/rolling_forecast_2026_09_09.json +++ b/data/current/rolling_forecast_2026_09_09.json @@ -1409,7 +1409,7 @@ "state_fips": "55" } }, - "assumption_sha256": "f4a92d547d1a5039701680a57fb02e368438aa5dd1a11e952351020450132983", + "assumption_sha256": "d81cf3b1a131fb386941e2e5c30792545f7dc965f6ba4c4dfb13e8ca95a3992d", "assumptions": { "acs": { "anchor_acs_window": [ @@ -1783,7 +1783,7 @@ "scripts/build_acs_2021_inputs.py": "82fa6edbbd90dc670204514c61a12475145d8d2976478a71e04fdba92047a31b", "scripts/build_acs_forecast.py": "44c1c34046c76ce167b3b1adbcadfdb0f588c8be93f9055629eeb2f80563e93d", "spm_calculator/acs_forecast.py": "0d76f0ebdb0fe67aea030dc480580df0d0daffc77ead02684e925f3619805213", - "spm_calculator/acs_forecast_sources.py": "d96fb6556f3a019025c2bd7dfdef5b9f5d05a7a4d819ab568f3a8bc41bf2aed3", + "spm_calculator/acs_forecast_sources.py": "28d31b3db0caa26f457660f1be9618f63fb08573460c13c68de6cd01b3f7e4bc", "spm_calculator/forecast_inputs.py": "54cf632fa515ab82ee6958c898e4154237dd79b5075ee59c8c1593891a95068f" }, "geography_method": "Population-weighted Census2020 PUMA-to-county allocation, matched to February2013 CBSA counties and published2024 SPM menu. Unrepresented county mass remains national. Full-MSA and residual membership approximate confidential CPS geography; neither county assignments nor rent medians are official Census reproductions.", @@ -3984,6 +3984,10 @@ "receipt_url": "https://api.bls.gov/publicAPI/v2/timeseries/data/CUUR0000SA0?startyear=2024&endyear=2025&annualaverage=true", "series_id": "CUUR0000SA0" }, + "provenance_adaptation": { + "path": "spm_calculator/data/current/acs_code_identity_adaptation.json", + "sha256": "b0bfbb6d87702ec06a96841b178c5645c755a042e01c3d5cabd8bad69e520396" + }, "puma_vintage_by_spm_year": { "2022": 2010, "2023": 2020, @@ -5223,7 +5227,7 @@ "scripts/build_forecast_inputs.py": "e5258c7f3bee2e0536509efe6f5095fc07d9530ecd978915cec39f56ca746d8f", "scripts/build_rolling_forecast.py": "06f4c7eef568f619a03fa831f583d4acf6b8d9679a3f6da0a9b99c5957e97d04", "spm_calculator/acs_forecast.py": "0d76f0ebdb0fe67aea030dc480580df0d0daffc77ead02684e925f3619805213", - "spm_calculator/acs_forecast_sources.py": "d96fb6556f3a019025c2bd7dfdef5b9f5d05a7a4d819ab568f3a8bc41bf2aed3", + "spm_calculator/acs_forecast_sources.py": "28d31b3db0caa26f457660f1be9618f63fb08573460c13c68de6cd01b3f7e4bc", "spm_calculator/ce_forecast.py": "5345e483df9a2156985407b6e0c2c04053e1edad33ae764ab1f7f9021af8e961", "spm_calculator/ce_threshold.py": "4efbe2b03f6b1f4eb2911ab0f79f5151bd655cc1e5addf42f5c4540ace1f852f", "spm_calculator/equivalence_scale.py": "95c797adca4811b0a2c4202e8471f6cd46fcd259bf46b34da74588ab08b41326", @@ -5236,13 +5240,13 @@ "component_sha256": { "spm_calculator/data/current/acs_2021_source_inputs.json": "5cacb7413eb51637caf5d09125040390e3002d3ffa2e2e94f78279fafb5832a0", "spm_calculator/data/current/acs_2021_source_manifest.json": "e3f359bde4e4b1349fa3b3b9f6b3b052935e4dcf908050dcbf5f0b2c8bb0c00a", - "spm_calculator/data/current/acs_normalized_products.json": "7f9dd53454eeac90a83437643944e47d60d27d35cf3a99d2a988c39be25b99a8", - "spm_calculator/data/current/acs_rolling_forecast.json": "8ad5edcd82c9535e9a8170539f55ce21474245d69f09775d9c0cd9a49f0cda20", + "spm_calculator/data/current/acs_normalized_products.json": "49b12d772d209f4d3f806769a30576f1ffeea52716561874e382e3b4600e1be5", + "spm_calculator/data/current/acs_rolling_forecast.json": "47ed3e6a6924994079fa2f1f5698021b3fab795d78b36a331de5b3ba067d1a78", "spm_calculator/data/current/ce_rolling_forecast.json": "26b863a3e5ea3e23fb4fcd547ccd497e25625d55542b8711a4825239aebb4e21", "spm_calculator/data/current/forecast_horizon_inputs.json": "a4e127bf31afa6555547dd2f602d9dd87a6846952ec48c58a7f2798ab4930055", "spm_calculator/data/current/forecast_source_checks.json": "b716a7cab7714e788a9bc329bbec98973881a09c20833c50d36d738e59ce90b3" }, - "content_sha256": "b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821", + "content_sha256": "3d86d5c4c0423480e6b69b75d222ffa4a7a2639e4094df5ba2504af01be17173", "county_assignments": { "assignment_method": "County membership in February2013 CBSAs, published Census area menu and within-state residual groups; public research assignment, not exact confidential CPS state-part assignment", "boundary_vintage": "2013-02-28", @@ -633965,7 +633969,7 @@ "available_on": "2026-09-09", "id": "acs-normalized-products", "package_path": "spm_calculator/data/current/acs_normalized_products.json", - "sha256": "7f9dd53454eeac90a83437643944e47d60d27d35cf3a99d2a988c39be25b99a8", + "sha256": "49b12d772d209f4d3f806769a30576f1ffeea52716561874e382e3b4600e1be5", "url": "https://github.com/PolicyEngine/spm-calculator" }, { @@ -633993,6 +633997,15 @@ "sha256": "03f0214e6b8393292ad3c412651fd7df0d8a36aaf5125f77c0bb65b3f10ca104", "url": "https://github.com/PolicyEngine/spm-calculator" }, + { + "availability_note": "Exact source bytes retained by the research build; this date does not reconstruct historical availability.", + "available_on": "2026-09-09", + "id": "acs-code-identity-adaptation", + "package_path": "spm_calculator/data/current/acs_code_identity_adaptation.json", + "sha256": "b0bfbb6d87702ec06a96841b178c5645c755a042e01c3d5cabd8bad69e520396", + "title": "Equivalent-code ACS provenance adaptation (no raw-source reparse)", + "url": "https://github.com/PolicyEngine/spm-calculator" + }, { "acquired_at_utc": "2026-09-09T02:48:18.424733+00:00", "availability_note": "Exact source bytes retained by the research build; this date does not reconstruct historical availability.", diff --git a/data/current/rolling_provenance.json b/data/current/rolling_provenance.json index 64f4e02..572148b 100644 --- a/data/current/rolling_provenance.json +++ b/data/current/rolling_provenance.json @@ -1,8 +1,8 @@ { - "artifact_sha256": "76ab8435f087f167ad01b8495ebd016415ba8086f32bfbd3dab961dcc8976c0a", - "assumption_sha256": "f4a92d547d1a5039701680a57fb02e368438aa5dd1a11e952351020450132983", + "artifact_sha256": "cc06784feb81f8c7935d4494cea0a9821a79af868ac50383da8be37c6dc14b99", + "assumption_sha256": "d81cf3b1a131fb386941e2e5c30792545f7dc965f6ba4c4dfb13e8ca95a3992d", "calculator_artifact_path": "spm_calculator/data/current/rolling_forecast_2026_09_09.json", - "calculator_commit": "c89d20f08f8d9d0896c88944fdb685ea01cecbb1", + "calculator_commit": "78bae15f76152c6076dd909a09f6b63dc2ec8c34", "calculator_repository": "https://github.com/PolicyEngine/spm-calculator", "classification": "current_rolling_ce_acs_forecast", "code_sha256": { @@ -12,7 +12,7 @@ "scripts/build_forecast_inputs.py": "e5258c7f3bee2e0536509efe6f5095fc07d9530ecd978915cec39f56ca746d8f", "scripts/build_rolling_forecast.py": "06f4c7eef568f619a03fa831f583d4acf6b8d9679a3f6da0a9b99c5957e97d04", "spm_calculator/acs_forecast.py": "0d76f0ebdb0fe67aea030dc480580df0d0daffc77ead02684e925f3619805213", - "spm_calculator/acs_forecast_sources.py": "d96fb6556f3a019025c2bd7dfdef5b9f5d05a7a4d819ab568f3a8bc41bf2aed3", + "spm_calculator/acs_forecast_sources.py": "28d31b3db0caa26f457660f1be9618f63fb08573460c13c68de6cd01b3f7e4bc", "spm_calculator/ce_forecast.py": "5345e483df9a2156985407b6e0c2c04053e1edad33ae764ab1f7f9021af8e961", "spm_calculator/ce_threshold.py": "4efbe2b03f6b1f4eb2911ab0f79f5151bd655cc1e5addf42f5c4540ace1f852f", "spm_calculator/equivalence_scale.py": "95c797adca4811b0a2c4202e8471f6cd46fcd259bf46b34da74588ab08b41326", @@ -25,13 +25,13 @@ "component_sha256": { "spm_calculator/data/current/acs_2021_source_inputs.json": "5cacb7413eb51637caf5d09125040390e3002d3ffa2e2e94f78279fafb5832a0", "spm_calculator/data/current/acs_2021_source_manifest.json": "e3f359bde4e4b1349fa3b3b9f6b3b052935e4dcf908050dcbf5f0b2c8bb0c00a", - "spm_calculator/data/current/acs_normalized_products.json": "7f9dd53454eeac90a83437643944e47d60d27d35cf3a99d2a988c39be25b99a8", - "spm_calculator/data/current/acs_rolling_forecast.json": "8ad5edcd82c9535e9a8170539f55ce21474245d69f09775d9c0cd9a49f0cda20", + "spm_calculator/data/current/acs_normalized_products.json": "49b12d772d209f4d3f806769a30576f1ffeea52716561874e382e3b4600e1be5", + "spm_calculator/data/current/acs_rolling_forecast.json": "47ed3e6a6924994079fa2f1f5698021b3fab795d78b36a331de5b3ba067d1a78", "spm_calculator/data/current/ce_rolling_forecast.json": "26b863a3e5ea3e23fb4fcd547ccd497e25625d55542b8711a4825239aebb4e21", "spm_calculator/data/current/forecast_horizon_inputs.json": "a4e127bf31afa6555547dd2f602d9dd87a6846952ec48c58a7f2798ab4930055", "spm_calculator/data/current/forecast_source_checks.json": "b716a7cab7714e788a9bc329bbec98973881a09c20833c50d36d738e59ce90b3" }, - "content_sha256": "b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821", + "content_sha256": "3d86d5c4c0423480e6b69b75d222ffa4a7a2639e4094df5ba2504af01be17173", "covered_by_original_timestamp": false, "frozen_commitments_modified": false, "information_date": "2026-09-09", @@ -44,8 +44,9 @@ "2023/csv_hus.zip": "2380c0ae7f062a42c016b494a26084ca0e24b75d07927a19ed1afc4529da0bf8", "2024/csv_hus.zip": "f3ff4545eab3da1cf4cbc5b245a926b67eb055fe95ac87fa2d5f1dd9ba95c23c", "acs-2021-source-inputs": "5cacb7413eb51637caf5d09125040390e3002d3ffa2e2e94f78279fafb5832a0", + "acs-code-identity-adaptation": "b0bfbb6d87702ec06a96841b178c5645c755a042e01c3d5cabd8bad69e520396", "acs-frozen-forward-2025": "2fdbd300e9b55821ee91789701f782c4ed1f4a01078c9c781145f0e020aff501", - "acs-normalized-products": "7f9dd53454eeac90a83437643944e47d60d27d35cf3a99d2a988c39be25b99a8", + "acs-normalized-products": "49b12d772d209f4d3f806769a30576f1ffeea52716561874e382e3b4600e1be5", "acs_forecast_inputs": "39bd7832a0fad56ad0206c270e6106c384339d79fd569de25743f3f5adb51ba4", "bls-cpi-u-2024-2025-receipt": "482678b4dc2070b434ff44b2d09417992b63546dea0ac3a30ec300244e14415e", "bls-spm-shares": "6709dec0e39c8c7b881320161a4373035befc586ecefecd8d78d8431d89b4577", diff --git a/scripts/rolling_inputs.py b/scripts/rolling_inputs.py index 7fbd2fa..d23e38c 100644 --- a/scripts/rolling_inputs.py +++ b/scripts/rolling_inputs.py @@ -6,11 +6,11 @@ import json from pathlib import Path -CALCULATOR_COMMIT = "c89d20f08f8d9d0896c88944fdb685ea01cecbb1" +CALCULATOR_COMMIT = "78bae15f76152c6076dd909a09f6b63dc2ec8c34" ROLLING_ARTIFACT = "rolling_forecast_2026_09_09.json" CALCULATOR_ARTIFACT_PATH = f"spm_calculator/data/current/{ROLLING_ARTIFACT}" -ARTIFACT_SHA256 = "76ab8435f087f167ad01b8495ebd016415ba8086f32bfbd3dab961dcc8976c0a" -CONTENT_SHA256 = "b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821" +ARTIFACT_SHA256 = "cc06784feb81f8c7935d4494cea0a9821a79af868ac50383da8be37c6dc14b99" +CONTENT_SHA256 = "3d86d5c4c0423480e6b69b75d222ffa4a7a2639e4094df5ba2504af01be17173" ROLLING_FILES = {ROLLING_ARTIFACT, "rolling_provenance.json"} From c0242b138d626be227b8f6e4541908805dd3ad3a Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 9 Sep 2026 15:55:43 -0400 Subject: [PATCH 13/14] Remove obsolete nowcast impact executable and retain historical evidence --- data/PROVENANCE.md | 2 +- scripts/simulate_nowcast_rate_impact.py | 120 ------------------------ 2 files changed, 1 insertion(+), 121 deletions(-) delete mode 100644 scripts/simulate_nowcast_rate_impact.py diff --git a/data/PROVENANCE.md b/data/PROVENANCE.md index 4ad615f..1a74ec0 100644 --- a/data/PROVENANCE.md +++ b/data/PROVENANCE.md @@ -19,7 +19,7 @@ Abbreviated as "impl" below. | `bls_cpi_series.json` | impl `scripts/benchmark_bls_replication.py` (its CPI fetch, `fetch_bls_cpi_series`) | BLS public data API v2, series CUUR0000SA0, SAF, SAA, SAH1, SAH2, SEED, annual averages 2005–2025 (2025 = eleven-month mean; October 2025 unpublished), retrieved 2026-07-18 | Six-series cache. At impl commit `d2746cf` this file lived in the gitignored `benchmark_output/`; the tracked package store (`cpi_annual.json`) carried a seventh, mis-mapped series — see paper Reproducibility. Being reconciled on the branch. | | `replication_results.json` | impl `scripts/benchmark_bls_replication.py` | CE Interview PUMD year bundles 2014–2025 (BLS `https://www.bls.gov/cex/pumd_data.htm`, cached under `~/.cache/spm-calculator/ce-pumd`), the CPI cache above, `threshold_series.json` | Replicated thresholds 2019–2025 under anchor {82, 83} × principal {include, exclude} × annualization variants, with signed deviations vs both reference series. Gitignored in impl at `d2746cf`; pinned here. | | `nowcast_2025.json` | impl `scripts/compute_nowcast_2025.py` | `replication_results.json`, `bls_cpi_series.json`, corrected 2024 base from `threshold_series.json` | The committed nowcast (amended 2026-07-18; original at tag `v1.0-original-nowcast`). Byte-identical to impl `spm_calculator/data/nowcast/nowcast_2025.json`. | -| `nowcast_rate_impact.json` | `scripts/simulate_nowcast_rate_impact.py` (this repo) | PolicyEngine US microsimulation (policyengine-us, editable install of spm-calculator from the PR #32 branch), default Populace dataset as of 2026-07-18 | Poverty-rate sensitivity: CPI-U-aged vs nowcast 2025 thresholds. The committed file was produced by an earlier version of the script that wrote to a scratch path; the script now takes `--output` and stamps its own metadata. Not byte-regenerable without the same dataset vintage; the dataset identifier is recorded in the file. | +| `nowcast_rate_impact.json` | Historical `simulate_nowcast_rate_impact.py`, retained in [repository history](https://github.com/PolicyEngine/spm-threshold-paper/blob/8d8a8b4960eb22e207e5e4e735277754ddf12cad/scripts/simulate_nowcast_rate_impact.py) | PolicyEngine US microsimulation (policyengine-us, editable install of spm-calculator from the PR #32 branch), default Populace dataset as of 2026-07-18 | Frozen poverty-rate sensitivity: CPI-U-aged vs nowcast 2025 thresholds. The committed result predates the linked script revision and was written to a scratch path. Its recorded environment lacks an immutable dataset identity, so exact replay is not established. The obsolete executable was removed from current scripts; the result, original source history and timestamp commitments remain unchanged. | | `bls_2025_thresholds.json` | hand-transcribed by the author from the current workbook (values, SEs, shares) and the 2025 threshold page (growth rates, Chart 3, Chart 4) | https://www.bls.gov/pir/spm/spm_thresholds_2025.htm (last modified 2026-08-24) and the current workbook, retrieved 2026-09-04 | Cross-check: full-precision values round to the page's whole dollars (asserted at build time). | | `census_wp2026_17_rates.json` | hand-transcribed by the author | Creamer, SEHSD WP 2026-17 (2026-08-19), Tables 3 and 4: https://www2.census.gov/library/working-papers/2026/demo/sehsd-wp2026-17.pdf | Revised-vs-corrected SPM rates 2019–2024 and the corrected 2023→2024 rows. | | `evaluation_2025.json` | `scripts/evaluate_nowcast_2025.py` (this repo) | `bls_2025_thresholds.json`, `nowcast_2025.json`, `bls_cpi_series.json`, `threshold_series.json`, and `git show v1.0-original-nowcast:data/nowcast_2025.json` | Regenerated by the pre-render guard on every build; any drift fails the build. | diff --git a/scripts/simulate_nowcast_rate_impact.py b/scripts/simulate_nowcast_rate_impact.py deleted file mode 100644 index c6d5e33..0000000 --- a/scripts/simulate_nowcast_rate_impact.py +++ /dev/null @@ -1,120 +0,0 @@ -"""SPM headcount effect of adopting the 2025 nowcast in policyengine-us. - -Current aging: corrected 2024 base x PolicyEngine CPI-U ratio. -Nowcast aging: corrected 2024 base x blend ratio (2025), price-aged -beyond. For both 2025 and 2026 the nowcast/current threshold ratio per -tenure is blend_ratio[tenure] / cpiu_factor(2025/2024) — the later CPI -factors are common to both scenarios and cancel. - -One simulation per year: net income is scenario-invariant, so the -nowcast counterfactual rescales each unit's threshold exactly. -""" - -import argparse -import gc -import importlib.metadata -import json -import subprocess -from pathlib import Path - -import numpy as np - -from policyengine_us import CountryTaxBenefitSystem, Microsimulation -from spm_calculator import nowcast_with_metadata - -REPO = Path(__file__).resolve().parent.parent -DEFAULT_OUT = REPO / "data" / "nowcast_rate_impact.json" - - -def _version(dist: str) -> str: - try: - return importlib.metadata.version(dist) - except importlib.metadata.PackageNotFoundError: - return "unknown" - - -def _spm_calculator_commit() -> str: - """Commit of an editable spm-calculator install, if it is a git tree.""" - try: - import spm_calculator - - root = Path(spm_calculator.__file__).resolve().parent.parent - return subprocess.check_output( - ["git", "-C", str(root), "rev-parse", "HEAD"], text=True - ).strip() - except Exception: # noqa: BLE001 - return "unknown" - - -parser = argparse.ArgumentParser() -parser.add_argument("--output", type=Path, default=DEFAULT_OUT) -OUT = parser.parse_args().output - -doc = nowcast_with_metadata(2025) -blend = {t.upper(): c["blend_ratio"] for t, c in doc["components"].items()} - -cpi_u = CountryTaxBenefitSystem().parameters.gov.bls.cpi.cpi_u -cpi_factor = float(cpi_u("2025-02-01") / cpi_u("2024-02-01")) -RATIO = {t: b / cpi_factor for t, b in blend.items()} -print(f"PE CPI-U factor 2025/2024: {cpi_factor:.5f}") -print("nowcast/current threshold ratios:", {k: round(v, 5) for k, v in RATIO.items()}) - -results = { - "generated_by": ( - "scripts/simulate_nowcast_rate_impact.py (PolicyEngine US " - f"microsimulation, policyengine-us {_version('policyengine-us')}, " - f"spm-calculator {_version('spm-calculator')} at commit " - f"{_spm_calculator_commit()}, default dataset of that " - "policyengine-us release)" - ), - "threshold_ratio_nowcast_over_current": RATIO, -} -for year in (2025, 2026): - sim = Microsimulation() - net = sim.calculate("spm_unit_net_income", year) - thr_cur = sim.calculate("spm_unit_spm_threshold", year) - tenure = sim.calculate("spm_unit_tenure_type", year) - - ratio_arr = np.array( - [RATIO.get(str(t).upper(), RATIO["RENTER"]) for t in tenure.values] - ) - thr_now = thr_cur.values * ratio_arr - - pov_cur_u = (net.values < thr_cur.values).astype(float) - pov_now_u = (net.values < thr_now).astype(float) - - person_weight = sim.calculate("person_weight", year).values - age = sim.calculate("age", year).values - pov_cur_p = sim.map_result(pov_cur_u, "spm_unit", "person") - pov_now_p = sim.map_result(pov_now_u, "spm_unit", "person") - - def rate(flags, mask=None): - w = person_weight if mask is None else person_weight * mask - return float(np.sum(flags * w) / np.sum(w)) - - groups = { - "all": None, - "children (<18)": (age < 18).astype(float), - "seniors (65+)": (age >= 65).astype(float), - } - year_out = {} - for label, mask in groups.items(): - r_cur = rate(pov_cur_p, mask) - r_now = rate(pov_now_p, mask) - year_out[label] = { - "current_cpi_aging_rate": r_cur, - "nowcast_rate": r_now, - "delta_pp": (r_now - r_cur) * 100, - } - print( - f"{year} {label:15s} cpi-aged {r_cur:6.2%} " - f"nowcast {r_now:6.2%} delta {(r_now - r_cur) * 100:+.3f}pp" - ) - results[year] = year_out - - del sim - gc.collect() - -with open(OUT, "w") as f: - json.dump(results, f, indent=1) -print("wrote", OUT) From 2bf310522a4cb547d8168af5c93d9231aa010602 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 9 Sep 2026 17:28:00 -0400 Subject: [PATCH 14/14] Bind paper reproducibility citations to the sealed forecast artifact --- paper/index.qmd | 10 +++++-- scripts/check_paper.py | 27 +++++++++++++++++-- tests/test_paper_guard.py | 55 ++++++++++++++++++++++++++++++++++----- 3 files changed, 82 insertions(+), 10 deletions(-) diff --git a/paper/index.qmd b/paper/index.qmd index 0f076f4..13db0ca 100644 --- a/paper/index.qmd +++ b/paper/index.qmd @@ -20,6 +20,7 @@ format: include-in-header: text: | \usepackage{needspace} + \fvset{fontsize=\small} abstract: | Supplemental Poverty Measure (SPM) thresholds reflect spending on basic needs as well as prices. BLS re-estimates them from a rolling five-year @@ -743,8 +744,13 @@ source hashes, methodology choices, and sample diagnostics. The pin, spm-calculator development version 0.5.0 at commit `0d7fa0d77b0a88064ab7b9fe70557309b5f7901f`. The conditional rolling projection uses the separate 1.0.0 release candidate at commit -`c89d20f08f8d9d0896c88944fdb685ea01cecbb1`. The candidate's content -digest is `b9dbf5ae49697e3bf3abee2fa22b7429703412cb1e58478938a682a0dfddc821`. +`78bae15f76152c6076dd909a09f6b63dc2ec8c34`. The candidate's content +digest is: + +```{.text .small} +3d86d5c4c0423480e6b69b75d222ffa4a7a2639e4094df5ba2504af01be17173 +``` + Exact source and release identities are recorded alongside the inputs; `data/PROVENANCE.md` describes the separate collections. The frozen poverty sensitivity additionally requires its original model and diff --git a/scripts/check_paper.py b/scripts/check_paper.py index bb78262..ccc0156 100644 --- a/scripts/check_paper.py +++ b/scripts/check_paper.py @@ -46,7 +46,12 @@ from pathlib import Path sys.dont_write_bytecode = True -from rolling_inputs import ROLLING_FILES, load_rolling_forecast +from rolling_inputs import ( + CALCULATOR_COMMIT, + CONTENT_SHA256, + ROLLING_FILES, + load_rolling_forecast, +) from verify_commitments import verify_commitments REPO = Path(__file__).resolve().parent.parent @@ -759,6 +764,22 @@ def mae(rule: str) -> float: ) ROLLING_ACS = section("## Projecting local relative housing costs", None, ROLLING) +REPRODUCIBILITY = section( + "# Appendix B: reproducibility {.unnumbered}", "# References {.unnumbered}" +) +check( + "rolling reproducibility identity matches the sealed calculator commit and content", + has_ordered( + [ + "projection uses the separate 1.0.0 release candidate at commit", + f"`{CALCULATOR_COMMIT}`", + "candidate's content digest is", + CONTENT_SHA256, + ], + text=REPRODUCIBILITY, + ), +) + fit = rolling_forecast["assumptions"]["real_growth_fit"] SENSITIVITIES = fit["sensitivities"] TEN_BLOCK = SENSITIVITIES["latest_ten_blocks"] @@ -950,7 +971,9 @@ def block_end_years(entry: dict) -> list[int]: ) thin_counts = { - sum(bool(area.get("thin_support")) for area in record["median_diagnostics"].values()) + sum( + bool(area.get("thin_support")) for area in record["median_diagnostics"].values() + ) for scenario in rolling_forecast["scenarios"].values() for record in scenario["years"].values() if record["acs_window"]["projected_years"] > 0 diff --git a/tests/test_paper_guard.py b/tests/test_paper_guard.py index 1719f94..bd4cd29 100644 --- a/tests/test_paper_guard.py +++ b/tests/test_paper_guard.py @@ -310,7 +310,8 @@ def test_current_internal_content_hash_is_checked_after_file_hash(self): ).hexdigest() guard_path.write_text( guard_path.read_text().replace( - original_manifest_hash, hashlib.sha256(manifest.read_bytes()).hexdigest() + original_manifest_hash, + hashlib.sha256(manifest.read_bytes()).hexdigest(), ) ) result = self.run_guard() @@ -388,7 +389,11 @@ def test_self_consistently_resealed_rolling_amount_cannot_replace_release(self): artifact["scenarios"]["ce_trend"]["years"]["2026"]["thresholds"]["renter"] += 1 artifact["content_sha256"] = hashlib.sha256( json.dumps( - {key: value for key, value in artifact.items() if key != "content_sha256"}, + { + key: value + for key, value in artifact.items() + if key != "content_sha256" + }, sort_keys=True, separators=(",", ":"), ensure_ascii=False, @@ -419,7 +424,10 @@ def test_rolling_projection_drift_is_detected_without_repair(self): table.write_text(table.read_text() + "unreviewed forecast row\n") result = self.run_guard() self.assertNotEqual(result.returncode, 0) - self.assertIn("generated output drifted: paper/tables/rolling_projection.md", result.stdout) + self.assertIn( + "generated output drifted: paper/tables/rolling_projection.md", + result.stdout, + ) def test_rolling_validation_missing_is_detected_without_repair(self): (self.repo / "paper/tables/rolling_validation.md").unlink() @@ -514,7 +522,9 @@ def edit_article(self, edits, host=None, decoy=None): prose = prose.replace(host, f"{host}\n{decoy}\n") path.write_text(prose) - def assert_article_mutation_fails(self, label, edits, expected, host=None, decoy=None): + def assert_article_mutation_fails( + self, label, edits, expected, host=None, decoy=None + ): self.install_article() self.edit_article(edits, host, decoy) result = self.run_guard() @@ -526,6 +536,35 @@ def test_rolling_prose_figures_drift_from_the_sealed_forecast(self): with self.subTest(figure=label): self.assert_article_mutation_fails(label, edits, expected) + def test_reproducibility_identity_matches_the_sealed_forecast(self): + provenance = json.loads( + (self.repo / "data/current/rolling_provenance.json").read_text() + ) + appendix = ( + (self.repo / "paper/index.qmd") + .read_text() + .split("# Appendix B: reproducibility {.unnumbered}", 1)[1] + .split("# References {.unnumbered}", 1)[0] + ) + for key in ("calculator_commit", "content_sha256"): + with self.subTest(identity=key): + self.assertIn(provenance[key], appendix) + + def test_reproducibility_identity_cannot_be_borrowed_from_another_section(self): + provenance = json.loads( + (self.repo / "data/current/rolling_provenance.json").read_text() + ) + for key in ("calculator_commit", "content_sha256"): + with self.subTest(identity=key): + identity = provenance[key] + self.assert_article_mutation_fails( + key, + ((identity, "0" * len(identity)),), + "rolling reproducibility identity", + DISCUSSION_HEADING, + f"The recorded {key} is `{identity}`.", + ) + def test_rolling_pins_reject_a_figure_borrowed_from_another_section(self): """The same numeral stated correctly elsewhere must not rescue a drifted figure: 31 already appears in a tbl-colwidths attribute and @@ -585,7 +624,9 @@ def canonical(value): path.write_text(json.dumps(artifact)) provenance_path = self.repo / "data/current/rolling_provenance.json" provenance = json.loads(provenance_path.read_text()) - source = source.replace(provenance["content_sha256"], artifact["content_sha256"]) + source = source.replace( + provenance["content_sha256"], artifact["content_sha256"] + ) source = source.replace( provenance["artifact_sha256"], hashlib.sha256(path.read_bytes()).hexdigest() ) @@ -607,7 +648,9 @@ def test_rolling_rate_pins_follow_the_artifact_not_a_fixed_literal(self): fit["log_slope"] = fit["annual_log_slope"] = applied_log_rate * 2 fit["shrunk_annual_rate"] = fit["annual_rate"] = math.exp(applied_log_rate) - 1 fit["unshrunk_annual_rate"] = math.exp(applied_log_rate * 2) - 1 - artifact["scenarios"]["ce_trend"]["real_growth_rate"] = fit["shrunk_annual_rate"] + artifact["scenarios"]["ce_trend"]["real_growth_rate"] = fit[ + "shrunk_annual_rate" + ] self.reseal_rolling_artifact(artifact) result = self.run_guard() self.assertNotEqual(result.returncode, 0)