2025 SPM poverty with thresholds anchored to 2024, from the 2026 ASEC file

analysis/asec-2026-anchored-thresholds/README.md

@@ -0,0 +1,36 @@
+# 2025 SPM poverty with thresholds anchored to 2024
+
+Computed September 15, 2026, the day Census released *Poverty in the United States: 2025* (P60-290), from the 2026 CPS ASEC public-use person file (`asecpub26csv.zip`, `pppub26.csv`, posted at www2.census.gov/programs-surveys/cps/datasets/2026/march/).
+
+## Replication
+
+Person weight `MARSUPWT`. SPM poverty (`SPM_POOR`): 13.11 percent of all people, 13.39 percent of children, 12.28 percent of people 18 to 64, 15.38 percent of people 65 and over; 44,390 thousand people and 9,653 thousand children below the threshold. Published (Table 8): 13.1 / 13.4 / 12.3 / 15.4; 44,390 and 9,657 thousand. Official poverty (`PERLIS` = 1 within `POV_UNIV` = 1): 10.20 / 13.41 / 9.20 / 9.77 against 10.2 / 13.4 / 9.2 / 9.8.
+
+## Anchoring
+
+Each unit's `SPM_POVTHRESHOLD` is scaled by tenure so that the national two-adult, two-child base equals its 2024 value (BLS corrected series: owners with a mortgage 39,231; owners without 32,879; renters 39,220) times the CPI-U ratio Census used for the 2025 official thresholds (32,649 / 31,812 = 1.02631). Tenure codes were verified from the implied 2025 bases (`SPM_POVTHRESHOLD / (SPM_EQUIVSCALE × SPM_GEOADJ)`): 1 = 41,323, 2 = 34,326, 3 = 41,701. The equivalence scale and geographic adjustment stay at their published 2025 values. A person is poor when `SPM_RESOURCES` is below the anchored threshold.
+
+| SPM poverty rate, percent | 2024 (re-based, P60-290) | 2025 published | 2025 at 2024 thresholds × CPI-U | 2025 at 2024 thresholds × C-CPI-U |
+| --- | ---: | ---: | ---: | ---: |
+| All people | 13.0 | 13.11 | 12.31 | 12.29 |
+| Under 18 | 13.5 | 13.39 | 12.31 | 12.30 |
+| 18 to 64 | 12.2 | 12.28 | 11.55 | 11.53 |
+| 65 and over | 15.1 | 15.38 | 14.66 | 14.62 |
+
+Threshold growth beyond CPI-U adds 0.80 points for all people, 1.08 for children, 0.73 for people 18 to 64 and 0.72 for people 65 and over. By tenure: renters 23.96 published against 22.13 anchored, owners without a mortgage 11.89 against 11.49, owners with a mortgage 6.00 against 5.67.
+
+The chained variant uses C-CPI-U (`SUUR0000SA0`) annual means, 174.372 for 2024 and 178.697 for 2025 (eleven months; October 2025 was not published), a ratio of 1.02480; recent C-CPI-U values are interim.
+
+## Files
+
+- `replicate_and_anchor.py`: run in a directory containing `pppub26.csv`; optional argument is the price factor (default 32649/31812).
+- `anchored_results.json`, `anchored_results_chained.json`: outputs.
+- `bls_ccpi.json`: BLS API response for `SUUR0000SA0` and `CUUR0000SA0`, 2024 to 2025.
+
+## By state and by detailed age
+
+`state_age_anchored.py` extends the recomputation to the 51 states (from `hhpub26.csv` `GESTFIPS`, joined to the person file on `H_SEQ`) and to detailed age groups, with standard errors from the 160 ASEC replicate weights (`asec_csv_repwgt_2026.csv`; variance = 4/160 times the sum of squared replicate deviations; `PWWGT0` equals `MARSUPWT`/100). Outputs: `state_age_results.json`, `by_state.csv`. Run it with the scratch directory holding the three CSV files as the first argument and the output directory as the second.
+
+## State tile maps
+
+`state_tile_map.py` renders a state tile map (pass `--logo` with the PolicyEngine teal wordmark PNG from policyengine-app-v2, `app/public/assets/logos/policyengine/teal.png`, to watermark it) (11 by 8 grid, teal shading, the layout of the PolicyEngine slides' state map) from any CSV with a `state` column. The post uses it twice: `by_state.csv` for the 2025 threshold effect (default arguments) and `census_table17_state_3yr.csv` (P60-290 Table 17, SPM percent and margin of error, three-year average 2023 to 2025) with `--value spm_pct --se spm_moe` and the 9/12/15/18 percent bins. It needs Playwright with its Chromium (`uv run --with playwright --with pandas python state_tile_map.py ...` after `playwright install chromium`).

analysis/asec-2026-anchored-thresholds/anchored_results.json

@@ -0,0 +1,46 @@
+{
+ "cpi_factor": 1.02631,
+ "anchor_factors": {
+  "1": 0.9744,
+  "2": 0.983,
+  "3": 0.9653
+ },
+ "spm_published_replication": {
+  "all": 13.11,
+  "children": 13.39,
+  "18-64": 12.28,
+  "65+": 15.38
+ },
+ "official_replication": {
+  "all": 10.2,
+  "children": 13.41,
+  "18-64": 9.2,
+  "65+": 9.77
+ },
+ "spm_anchored_2024_thresholds": {
+  "all": 12.31,
+  "children": 12.31,
+  "18-64": 11.55,
+  "65+": 14.66
+ },
+ "by_tenure": {
+  "owner_with_mortgage": {
+   "published": 6.0,
+   "anchored": 5.67
+  },
+  "owner_without_mortgage": {
+   "published": 11.89,
+   "anchored": 11.49
+  },
+  "renter": {
+   "published": 23.96,
+   "anchored": 22.13
+  }
+ },
+ "threshold_effect_pp": {
+  "all": 0.8,
+  "children": 1.08,
+  "18-64": 0.73,
+  "65+": 0.72
+ }
+}

analysis/asec-2026-anchored-thresholds/anchored_results_chained.json

@@ -0,0 +1,46 @@
+{
+ "cpi_factor": 1.0248,
+ "anchor_factors": {
+  "1": 0.9729,
+  "2": 0.9816,
+  "3": 0.9638
+ },
+ "spm_published_replication": {
+  "all": 13.11,
+  "children": 13.39,
+  "18-64": 12.28,
+  "65+": 15.38
+ },
+ "official_replication": {
+  "all": 10.2,
+  "children": 13.41,
+  "18-64": 9.2,
+  "65+": 9.77
+ },
+ "spm_anchored_2024_thresholds": {
+  "all": 12.29,
+  "children": 12.3,
+  "18-64": 11.53,
+  "65+": 14.62
+ },
+ "by_tenure": {
+  "owner_with_mortgage": {
+   "published": 6.0,
+   "anchored": 5.66
+  },
+  "owner_without_mortgage": {
+   "published": 11.89,
+   "anchored": 11.48
+  },
+  "renter": {
+   "published": 23.96,
+   "anchored": 22.09
+  }
+ },
+ "threshold_effect_pp": {
+  "all": 0.82,
+  "children": 1.09,
+  "18-64": 0.75,
+  "65+": 0.76
+ }
+}

analysis/asec-2026-anchored-thresholds/bls_ccpi.json

@@ -0,0 +1,5 @@
+{"status":"REQUEST_SUCCEEDED","responseTime":94,"message":[],"Results":{
+"series":
+[{"seriesID":"SUUR0000SA0","data":[{"year":"2025","period":"M12","periodName":"December","value":"179.775","footnotes":[{"code":"U","text":"Interim"}]},{"year":"2025","period":"M11","periodName":"November","value":"179.889","footnotes":[{"code":"U","text":"Interim"}]},{"year":"2025","period":"M09","periodName":"September","value":"180.373","footnotes":[{}]},{"year":"2025","period":"M08","periodName":"August","value":"179.839","footnotes":[{}]},{"year":"2025","period":"M07","periodName":"July","value":"179.272","footnotes":[{}]},{"year":"2025","period":"M06","periodName":"June","value":"178.991","footnotes":[{}]},{"year":"2025","period":"M05","periodName":"May","value":"178.427","footnotes":[{}]},{"year":"2025","period":"M04","periodName":"April","value":"178.081","footnotes":[{}]},{"year":"2025","period":"M03","periodName":"March","value":"177.520","footnotes":[{}]},{"year":"2025","period":"M02","periodName":"February","value":"177.134","footnotes":[{}]},{"year":"2025","period":"M01","periodName":"January","value":"176.362","footnotes":[{}]},{"year":"2024","period":"M12","periodName":"December","value":"175.219","footnotes":[{}]},{"year":"2024","period":"M11","periodName":"November","value":"175.180","footnotes":[{}]},{"year":"2024","period":"M10","periodName":"October","value":"175.346","footnotes":[{}]},{"year":"2024","period":"M09","periodName":"September","value":"175.099","footnotes":[{}]},{"year":"2024","period":"M08","periodName":"August","value":"174.848","footnotes":[{}]},{"year":"2024","period":"M07","periodName":"July","value":"174.792","footnotes":[{}]},{"year":"2024","period":"M06","periodName":"June","value":"174.721","footnotes":[{}]},{"year":"2024","period":"M05","periodName":"May","value":"174.685","footnotes":[{}]},{"year":"2024","period":"M04","periodName":"April","value":"174.424","footnotes":[{}]},{"year":"2024","period":"M03","periodName":"March","value":"173.796","footnotes":[{}]},{"year":"2024","period":"M02","periodName":"February","value":"172.700","footnotes":[{}]},{"year":"2024","period":"M01","periodName":"January","value":"171.649","footnotes":[{}]}]},
+{"seriesID":"CUUR0000SA0","data":[{"year":"2025","period":"M13","periodName":"Annual","value":"321.943","footnotes":[{}]},{"year":"2025","period":"M12","periodName":"December","value":"324.054","footnotes":[{}]},{"year":"2025","period":"M11","periodName":"November","value":"324.122","footnotes":[{}]},{"year":"2025","period":"M10","periodName":"October","value":"-","footnotes":[{"code":"X","text":"Data unavailable due to the 2025 lapse in appropriations"}]},{"year":"2025","period":"M09","periodName":"September","value":"324.800","footnotes":[{}]},{"year":"2025","period":"M08","periodName":"August","value":"323.976","footnotes":[{}]},{"year":"2025","period":"M07","periodName":"July","value":"323.048","footnotes":[{}]},{"year":"2025","period":"M06","periodName":"June","value":"322.561","footnotes":[{}]},{"year":"2025","period":"M05","periodName":"May","value":"321.465","footnotes":[{}]},{"year":"2025","period":"M04","periodName":"April","value":"320.795","footnotes":[{}]},{"year":"2025","period":"M03","periodName":"March","value":"319.799","footnotes":[{}]},{"year":"2025","period":"M02","periodName":"February","value":"319.082","footnotes":[{}]},{"year":"2025","period":"M01","periodName":"January","value":"317.671","footnotes":[{}]},{"year":"2024","period":"M13","periodName":"Annual","value":"313.689","footnotes":[{}]},{"year":"2024","period":"M12","periodName":"December","value":"315.605","footnotes":[{}]},{"year":"2024","period":"M11","periodName":"November","value":"315.493","footnotes":[{}]},{"year":"2024","period":"M10","periodName":"October","value":"315.664","footnotes":[{}]},{"year":"2024","period":"M09","periodName":"September","value":"315.301","footnotes":[{}]},{"year":"2024","period":"M08","periodName":"August","value":"314.796","footnotes":[{}]},{"year":"2024","period":"M07","periodName":"July","value":"314.540","footnotes":[{}]},{"year":"2024","period":"M06","periodName":"June","value":"314.175","footnotes":[{}]},{"year":"2024","period":"M05","periodName":"May","value":"314.069","footnotes":[{}]},{"year":"2024","period":"M04","periodName":"April","value":"313.548","footnotes":[{}]},{"year":"2024","period":"M03","periodName":"March","value":"312.332","footnotes":[{}]},{"year":"2024","period":"M02","periodName":"February","value":"310.326","footnotes":[{}]},{"year":"2024","period":"M01","periodName":"January","value":"308.417","footnotes":[{}]}]}]
+}}

analysis/asec-2026-anchored-thresholds/by_state.csv

@@ -0,0 +1,52 @@
+state,published,published_se,anchored,anchored_se,effect_pp,effect_se,n_persons,weighted_persons_thousands
+AL,15.75,1.14,14.04,1.19,1.71,0.52,2334,5079.0
+AK,10.37,1.22,10.02,1.18,0.35,0.34,1648,708.0
+AZ,14.78,1.62,13.41,1.47,1.36,0.51,2279,7666.0
+AR,12.67,1.42,11.87,1.4,0.8,0.33,2196,3073.0
+CA,17.29,0.66,16.22,0.63,1.08,0.2,13025,38913.0
+CO,11.36,2.17,10.9,2.26,0.46,0.29,1665,5950.0
+CT,10.56,1.32,9.75,1.27,0.81,0.31,1376,3651.0
+DE,7.51,1.15,6.14,0.98,1.37,0.72,1471,1066.0
+DC,16.76,1.4,15.65,1.43,1.12,0.46,1919,691.0
+FL,17.56,0.91,16.55,0.89,1.01,0.17,6342,23261.0
+GA,13.85,1.11,13.11,1.04,0.74,0.27,2881,11164.0
+HI,12.5,1.29,11.36,1.2,1.14,0.43,2427,1388.0
+ID,9.47,0.9,8.89,0.85,0.58,0.3,2366,1995.0
+IL,11.38,1.03,10.78,1.01,0.61,0.25,3297,12607.0
+IN,11.78,1.29,10.4,1.28,1.38,0.67,2024,6893.0
+IA,9.55,1.11,8.48,1.09,1.07,0.47,1634,3240.0
+KS,7.53,1.12,7.45,1.11,0.08,0.08,1678,2909.0
+KY,10.63,1.29,10.0,1.26,0.63,0.32,1461,4550.0
+LA,17.85,1.01,16.72,0.92,1.13,0.37,2869,4545.0
+ME,5.76,1.12,5.69,1.13,0.07,0.07,1061,1408.0
+MD,10.51,1.46,9.96,1.38,0.55,0.32,1724,6188.0
+MA,13.26,1.08,12.43,1.04,0.83,0.27,2732,7032.0
+MI,10.86,1.04,10.42,1.03,0.44,0.22,2517,10006.0
+MN,7.78,1.4,7.63,1.42,0.15,0.1,1694,5772.0
+MS,18.26,1.77,17.56,1.73,0.7,0.27,2446,2914.0
+MO,9.08,1.12,8.11,1.08,0.97,0.3,2010,6308.0
+MT,9.68,1.69,8.21,1.3,1.47,0.59,2006,1123.0
+NE,5.47,0.96,5.47,0.96,0.0,0.0,1885,2011.0
+NV,15.7,1.52,14.49,1.51,1.21,0.43,2116,3335.0
+NH,7.09,1.17,6.09,0.99,1.0,0.53,1608,1407.0
+NJ,11.53,1.07,10.95,1.03,0.58,0.23,2868,9488.0
+NM,14.95,1.36,14.25,1.22,0.7,0.46,3251,2103.0
+NY,16.42,1.06,14.92,1.03,1.5,0.37,5036,19683.0
+NC,11.69,1.12,11.23,1.05,0.46,0.22,3035,11050.0
+ND,8.86,1.12,8.57,1.17,0.29,0.17,1980,780.0
+OH,10.05,1.24,9.56,1.24,0.48,0.33,2952,11974.0
+OK,11.96,1.35,11.36,1.32,0.6,0.33,2090,4070.0
+OR,9.93,1.02,9.52,0.98,0.41,0.19,2241,4234.0
+PA,9.83,0.9,9.53,0.89,0.31,0.15,3367,12893.0
+RI,11.34,1.45,10.47,1.38,0.87,0.47,1196,1110.0
+SC,11.07,1.29,10.27,1.22,0.8,0.34,2302,5592.0
+SD,8.78,1.25,8.39,1.15,0.39,0.33,1455,913.0
+TN,12.41,1.13,11.66,1.09,0.75,0.27,2368,7196.0
+TX,15.27,0.74,14.25,0.71,1.02,0.22,8575,31542.0
+UT,10.35,1.04,10.03,1.04,0.31,0.13,2469,3510.0
+VT,9.08,1.14,8.76,1.11,0.32,0.2,1544,633.0
+VA,10.85,1.19,10.6,1.17,0.24,0.12,2380,8726.0
+WA,9.85,0.96,9.71,0.94,0.14,0.13,2752,7975.0
+WV,12.23,1.35,11.15,1.19,1.08,0.4,2547,1737.0
+WI,7.81,1.07,7.6,1.12,0.21,0.17,1754,5923.0
+WY,9.5,1.08,9.07,1.07,0.43,0.27,1876,568.0

analysis/asec-2026-anchored-thresholds/census_table17_state_3yr.csv

@@ -0,0 +1,52 @@
+state,name,official_plus_pct,official_plus_moe,spm_pct,spm_moe
+AL,Alabama,15.2,1.6,14.4,1.3
+AK,Alaska,10,1.1,10.4,1.3
+AZ,Arizona,10.7,1.1,13.7,1.2
+AR,Arkansas,12.7,1.5,12.8,1.4
+CA,California,10.7,0.5,17.8,0.6
+CO,Colorado,8.6,1.5,10.8,1.5
+CT,Connecticut,8.6,1.4,10.4,1.4
+DE,Delaware,7.3,1.2,8.9,1.3
+DC,District of Columbia,12.1,1.4,15.7,1.5
+FL,Florida,11.3,0.7,16.4,0.9
+GA,Georgia,12.3,1.1,14,1.2
+HI,Hawaii,8.5,1.3,11.7,1.3
+ID,Idaho,8.4,0.9,9.4,1
+IL,Illinois,10.4,0.8,11.6,0.9
+IN,Indiana,9.3,1.1,10.3,1.2
+IA,Iowa,8.1,0.9,8.3,1.1
+KS,Kansas,8.1,1.2,8.1,1
+KY,Kentucky,13.7,1.6,13.2,1.6
+LA,Louisiana,19.6,1.5,19,1.4
+ME,Maine,7.5,1.1,6.4,1.1
+MD,Maryland,7.2,1.1,10.9,1.4
+MA,Massachusetts,9.1,1,12.5,1
+MI,Michigan,11.5,0.9,10.6,1.2
+MN,Minnesota,7.4,1,7.9,1
+MS,Mississippi,17.6,1.2,16.8,1.4
+MO,Missouri,9.3,1.4,9.4,1.3
+MT,Montana,8.6,1.3,9.5,1.6
+NE,Nebraska,7.3,1.2,7.5,1
+NV,Nevada,11.8,1.2,15.3,1.5
+NH,New Hampshire,5.4,0.9,8,1.3
+NJ,New Jersey,8.9,1,12.1,1
+NM,New Mexico,16,2.6,13.2,1.5
+NY,New York,10.9,0.8,15.1,0.9
+NC,North Carolina,13.1,1.3,13.5,1.2
+ND,North Dakota,8.6,1.1,8.9,1.1
+OH,Ohio,10.8,1,9.9,1
+OK,Oklahoma,12.6,1.3,11.1,1.3
+OR,Oregon,9.2,0.9,11,1
+PA,Pennsylvania,9.3,0.9,10.7,1
+RI,Rhode Island,9.4,1.3,10.6,1.4
+SC,South Carolina,11,1.1,11.1,1.2
+SD,South Dakota,8.6,1.3,8,1.1
+TN,Tennessee,10.7,1.2,11.1,1.1
+TX,Texas,11.7,0.6,14.4,0.6
+UT,Utah,6.9,1,9.9,1.1
+VT,Vermont,7.3,1,8.9,1.2
+VA,Virginia,8.7,1.2,10.6,1.2
+WA,Washington,8.8,1.2,10.5,1.3
+WV,West Virginia,13.5,1.5,12.1,1.6
+WI,Wisconsin,8.8,1.5,7.9,1.2
+WY,Wyoming,8.6,1.1,8.7,1.1

analysis/asec-2026-anchored-thresholds/replicate_and_anchor.py

@@ -0,0 +1,28 @@
+"""Replicate P60-290 SPM/official toplines from the 2026 CPS ASEC public-use person file
+and recompute 2025 SPM poverty with national thresholds anchored to 2024 (x CPI-U).
+Weights: SPM_WEIGHT/100 for SPM, MARSUPWT/100 for official. Tenure codes verified from
+the implied base threshold: 1 owner w/ mortgage 41,323; 2 owner w/o 34,326; 3 renter 41,701.
+Anchoring scales only the national base by tenure (equivalence scale and geo adjustment
+stay at 2025 values). 2024 bases = BLS corrected series (39,231 / 32,879 / 39,220).
+CPI-U factor = Census's own 2025/2024 two-adult-two-child OPM threshold ratio.
+"""
+import sys, json, pandas as pd, numpy as np
+cpi = float(sys.argv[1]) if len(sys.argv) > 1 else 32649/31812
+d = pd.read_csv('pppub26.csv', usecols=['A_AGE','MARSUPWT','SPM_WEIGHT','SPM_POOR','SPM_RESOURCES','SPM_POVTHRESHOLD','SPM_TENMORTSTATUS','PERLIS','POV_UNIV'])
+mw = d.MARSUPWT/100; w = mw  # person weight for person-level poverty rates (SPM_WEIGHT variant checked: within 0.05pp)
+grp = np.select([d.A_AGE<18, d.A_AGE>=65], ['children','65+'], '18-64')
+t25 = {1:41322.707, 2:34325.998, 3:41700.556}; t24 = {1:39231.0, 2:32879.0, 3:39220.0}
+fac = d.SPM_TENMORTSTATUS.map({k:(t24[k]*cpi)/t25[k] for k in t25})
+poor_anch = d.SPM_RESOURCES < d.SPM_POVTHRESHOLD*fac
+def rates(flag, wt):
+    return {g: round(100*(wt[m]*flag[m]).sum()/wt[m].sum(), 2) for g,m in
+            [('all', np.ones(len(d),bool)), ('children', grp=='children'), ('18-64', grp=='18-64'), ('65+', grp=='65+')]}
+out = {"cpi_factor": round(cpi,5), "anchor_factors": {k: round((t24[k]*cpi)/t25[k],4) for k in t25},
+       "spm_published_replication": rates(d.SPM_POOR==1, w),
+       "official_replication": {g: round(100*(mw[m]*(d.PERLIS[m]==1)).sum()/mw[m].sum(),2) for g,m in
+            [('all', d.POV_UNIV==1), ('children', (grp=='children')&(d.POV_UNIV==1)), ('18-64', (grp=='18-64')&(d.POV_UNIV==1)), ('65+', (grp=='65+')&(d.POV_UNIV==1))]},
+       "spm_anchored_2024_thresholds": rates(poor_anch, w),
+       "by_tenure": {name: {"published": round(100*(w[m]*(d.SPM_POOR[m]==1)).sum()/w[m].sum(),2), "anchored": round(100*(w[m]*poor_anch[m]).sum()/w[m].sum(),2)}
+                     for name,m in [('owner_with_mortgage', d.SPM_TENMORTSTATUS==1), ('owner_without_mortgage', d.SPM_TENMORTSTATUS==2), ('renter', d.SPM_TENMORTSTATUS==3)]}}
+out["threshold_effect_pp"] = {g: round(out["spm_published_replication"][g]-out["spm_anchored_2024_thresholds"][g],2) for g in out["spm_published_replication"]}
+print(json.dumps(out, indent=1))