From c1c636dfa4428eda3978be0977c4db892cbb3ad1 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 12:02:00 -0500 Subject: [PATCH 01/32] Scaffold the APPAM 2026 roundtable deck Co-Authored-By: Claude Opus 5.5 --- lib/slideshows.ts | 2 ++ slideshows/appam-2026/config.ts | 16 ++++++++++++++++ slideshows/appam-2026/slides/CoverSlide.tsx | 5 +++++ slideshows/appam-2026/slides/EndSlide.tsx | 5 +++++ 4 files changed, 28 insertions(+) create mode 100644 slideshows/appam-2026/config.ts create mode 100644 slideshows/appam-2026/slides/CoverSlide.tsx create mode 100644 slideshows/appam-2026/slides/EndSlide.tsx diff --git a/lib/slideshows.ts b/lib/slideshows.ts index 193aba57..d58709fb 100644 --- a/lib/slideshows.ts +++ b/lib/slideshows.ts @@ -31,8 +31,10 @@ import { scottishGovernment2026Config } from "@/slideshows/scottish-government-2 import { goodGrowthFoundation2026Config } from "@/slideshows/good-growth-foundation-2026/config"; import { autumnBudget2026Config } from "@/slideshows/autumn-budget-2026/config"; import { prop40Webinar2026Config } from "@/slideshows/prop40-webinar-2026/config"; +import { appam2026Config } from "@/slideshows/appam-2026/config"; export const slideshows: SlideshowConfig[] = [ + appam2026Config, prop40Webinar2026Config, autumnBudget2026Config, goodGrowthFoundation2026Config, diff --git a/slideshows/appam-2026/config.ts b/slideshows/appam-2026/config.ts new file mode 100644 index 00000000..66abc1a7 --- /dev/null +++ b/slideshows/appam-2026/config.ts @@ -0,0 +1,16 @@ +import { SlideshowConfig } from '@/lib/types'; +import { speakers } from '@/lib/speakers'; +import CoverSlide from './slides/CoverSlide'; +import EndSlide from './slides/EndSlide'; + +export const appam2026Config: SlideshowConfig = { + id: 'appam-2026', + title: 'PolicyEngine for state policy research', + description: + 'Max Ghenis on the APPAM 2026 roundtable "State Policy Impacts on Family Resources: Tools to Improve Policy Analysis".', + date: '2026-11-05', + location: 'APPAM 2026, Boston', + footerText: 'PolicyEngine for state policy research · APPAM · November 5, 2026', + speakers: [speakers['max-ghenis']], + slides: [CoverSlide, EndSlide], +}; diff --git a/slideshows/appam-2026/slides/CoverSlide.tsx b/slideshows/appam-2026/slides/CoverSlide.tsx new file mode 100644 index 00000000..2e51c0c1 --- /dev/null +++ b/slideshows/appam-2026/slides/CoverSlide.tsx @@ -0,0 +1,5 @@ +import CoverSlideTemplate from '@/components/layout/CoverSlide'; + +export default function CoverSlide() { + return ; +} diff --git a/slideshows/appam-2026/slides/EndSlide.tsx b/slideshows/appam-2026/slides/EndSlide.tsx new file mode 100644 index 00000000..15d654f6 --- /dev/null +++ b/slideshows/appam-2026/slides/EndSlide.tsx @@ -0,0 +1,5 @@ +import EndSlideTemplate from '@/components/layout/EndSlide'; + +export default function EndSlide() { + return ; +} From b66f2cc721e2be8ba873689d38b457b09ca83caf Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 12:58:27 -0500 Subject: [PATCH 02/32] Compute the APPAM deck's family, microdata and Axiom results Adds the one-family household, its 51-state earnings sweep, the property and differential tests, and a real Axiom engine run of Georgia SNAP 3612. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/analysis/.gitignore | 2 + .../appam-2026/analysis/axiom_ga_3612.py | 106 ++++++++ .../appam-2026/analysis/compute_family.py | 51 ++++ slideshows/appam-2026/analysis/household.py | 158 +++++++++++ .../appam-2026/analysis/largest_counties.json | 257 ++++++++++++++++++ .../appam-2026/analysis/largest_counties.py | 63 +++++ .../analysis/massachusetts_reform.py | 82 ++++++ .../appam-2026/analysis/sample_families.py | 38 +++ .../analysis/simulated_instrument.py | 96 +++++++ slideshows/appam-2026/analysis/summarize.py | 144 ++++++++++ .../appam-2026/analysis/test_invariants.py | 95 +++++++ slideshows/appam-2026/data/axiom_ga_3612.json | 219 +++++++++++++++ slideshows/appam-2026/data/charts.json | 1 + slideshows/appam-2026/data/family.json | 1 + 14 files changed, 1313 insertions(+) create mode 100644 slideshows/appam-2026/analysis/.gitignore create mode 100644 slideshows/appam-2026/analysis/axiom_ga_3612.py create mode 100644 slideshows/appam-2026/analysis/compute_family.py create mode 100644 slideshows/appam-2026/analysis/household.py create mode 100644 slideshows/appam-2026/analysis/largest_counties.json create mode 100644 slideshows/appam-2026/analysis/largest_counties.py create mode 100644 slideshows/appam-2026/analysis/massachusetts_reform.py create mode 100644 slideshows/appam-2026/analysis/sample_families.py create mode 100644 slideshows/appam-2026/analysis/simulated_instrument.py create mode 100644 slideshows/appam-2026/analysis/summarize.py create mode 100644 slideshows/appam-2026/analysis/test_invariants.py create mode 100644 slideshows/appam-2026/data/axiom_ga_3612.json create mode 100644 slideshows/appam-2026/data/charts.json create mode 100644 slideshows/appam-2026/data/family.json diff --git a/slideshows/appam-2026/analysis/.gitignore b/slideshows/appam-2026/analysis/.gitignore new file mode 100644 index 00000000..8944671b --- /dev/null +++ b/slideshows/appam-2026/analysis/.gitignore @@ -0,0 +1,2 @@ +.hypothesis/ +__pycache__/ diff --git a/slideshows/appam-2026/analysis/axiom_ga_3612.py b/slideshows/appam-2026/analysis/axiom_ga_3612.py new file mode 100644 index 00000000..340faa05 --- /dev/null +++ b/slideshows/appam-2026/analysis/axiom_ga_3612.py @@ -0,0 +1,106 @@ +"""Run Georgia's SNAP earned income deduction on the Axiom rules engine. + +Compile first, from a checkout of TheAxiomFoundation/rulespec-us: + + axiom-rules-engine compile \ + --program /path/to/rulespec-us/us-ga/policies/dfcs/snap/3612.yaml \ + --rulespec-root /path/to/rulespec-us \ + --output ga3612.compiled.json + +Then: python axiom_ga_3612.py ga3612.compiled.json +Writes data/axiom_ga_3612.json. The cases cover the $40,000 family in Georgia, +the same family when it reports earnings late, and a month before the rule's +December 2019 effective date. +""" + +import json +import subprocess +import sys +from pathlib import Path + +MODULE = "us-ga:policies/dfcs/snap/3612" +RULES = ( + "earned_income_deduction_rate", + "earned_income_deduction_allowed", + "earned_income_deduction", +) +MONTHLY_EARNINGS = "3333.33" +CASES = ( + ("reported on time", "2026-06", False), + ("reported late", "2026-06", True), + ("before the effective date", "2019-11", False), +) + + +def month_bounds(month): + year, number = map(int, month.split("-")) + end = 30 if number in (4, 6, 9, 11) else 31 + return f"{month}-01", f"{month}-{end:02d}" + + +def request(month, late): + start, end = month_bounds(month) + interval = {"start": start, "end": end} + return { + "mode": "explain", + "dataset": { + "inputs": [ + { + "name": f"{MODULE}#input.total_countable_gross_earned_income", + "entity": "Household", + "entity_id": "family", + "interval": interval, + "value": {"kind": "decimal", "value": MONTHLY_EARNINGS}, + }, + { + "name": f"{MODULE}#input.assistance_unit_failed_to_report_earned_income_timely", + "entity": "Household", + "entity_id": "family", + "interval": interval, + "value": {"kind": "bool", "value": late}, + }, + ], + "relations": [], + }, + "queries": [ + { + "entity_id": "family", + "period": {"period_kind": "month", "start": start, "end": end}, + "outputs": [f"{MODULE}#{rule}" for rule in RULES], + } + ], + } + + +def main(engine, artifact): + version = subprocess.run( + [engine, "capabilities"], capture_output=True, text=True, check=True + ).stdout + results = [] + for label, month, late in CASES: + run = subprocess.run( + [engine, "run-compiled", "--artifact", artifact], + input=json.dumps(request(month, late)), + capture_output=True, + text=True, + ) + results.append( + { + "case": label, + "month": month, + "exit_code": run.returncode, + "stdout": json.loads(run.stdout) if run.stdout.strip() else None, + "stderr": run.stderr.strip(), + } + ) + payload = { + "engine": json.loads(version), + "monthly_earnings": MONTHLY_EARNINGS, + "results": results, + } + path = Path(__file__).parent.parent / "data" / "axiom_ga_3612.json" + path.write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(sys.argv[1], sys.argv[2]) diff --git a/slideshows/appam-2026/analysis/compute_family.py b/slideshows/appam-2026/analysis/compute_family.py new file mode 100644 index 00000000..f304ec9c --- /dev/null +++ b/slideshows/appam-2026/analysis/compute_family.py @@ -0,0 +1,51 @@ +"""Run the family through all 51 jurisdictions at every earnings level. + +Writes data/family.json: one row per state with each net-resources line on a +$500 grid from $0 to $120,000. The deck's charts read this file. +""" + +import json +import sys +from importlib.metadata import version +from multiprocessing import Pool +from pathlib import Path + +import household as hh + +EARNINGS_MIN = 0 +EARNINGS_MAX = 120_000 +EARNINGS_STEP = 500 +WORKERS = 4 + + +def run_state(state): + count = (EARNINGS_MAX - EARNINGS_MIN) // EARNINGS_STEP + 1 + axes = hh.earnings_axis(EARNINGS_MIN, EARNINGS_MAX, count) + sim = hh.simulate(state, axes=axes) + parts = hh.net_resources_parts(sim) + return state, {key: [round(float(x), 2) for x in value] for key, value in parts.items()} + + +def main(): + with Pool(WORKERS) as pool: + states = dict(pool.map(run_state, hh.STATES)) + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": sys.argv[1], + "year": hh.YEAR, + "family": { + "parent_age": hh.PARENT_AGE, + "child_ages": list(hh.CHILD_AGES), + "rent_per_month": hh.RENT_PER_MONTH, + "child_care_per_month": hh.CHILD_CARE_PER_MONTH, + }, + "earnings": list(range(EARNINGS_MIN, EARNINGS_MAX + 1, EARNINGS_STEP)), + "counties": {state: hh.COUNTIES[state]["county"] for state in hh.STATES}, + "states": states, + } + path = Path(__file__).parent.parent / "data" / "family.json" + path.write_text(json.dumps(payload, separators=(",", ":")) + "\n") + + +if __name__ == "__main__": + main() diff --git a/slideshows/appam-2026/analysis/household.py b/slideshows/appam-2026/analysis/household.py new file mode 100644 index 00000000..d46c0844 --- /dev/null +++ b/slideshows/appam-2026/analysis/household.py @@ -0,0 +1,158 @@ +"""The one family every chart in this deck follows. + +A single parent works full time all year, rents, and buys full-time care for +two children. Only the state and, on the earnings charts, the earnings change. + +Net resources here means earnings, plus benefits, minus taxes net of credits, +minus what the family pays for child care. The child care line comes from +childcare_expenses, which cannot fall below zero, so a subsidy that exceeds the +bill never adds cash. spm_unit_net_income and household_net_income each treat +child care and a few cash payments differently, so test_invariants.py reconciles +net resources with both. +""" + +import json +from pathlib import Path + +from policyengine_us import Simulation + +YEAR = 2026 +RENT_PER_MONTH = 1_200 +CHILD_CARE_PER_MONTH = 1_500 +PARENT_AGE = 30 +CHILD_AGES = (1, 4) + +STATES = [ + "AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "DC", "FL", "GA", "HI", + "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD", "MA", "MI", "MN", + "MS", "MO", "MT", "NE", "NV", "NH", "NJ", "NM", "NY", "NC", "ND", "OH", + "OK", "OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VT", "VA", "WA", + "WV", "WI", "WY", +] # fmt: skip + +COUNTIES = json.loads( + Path(__file__).with_name("largest_counties.json").read_text() +) + +# Colorado, Washington and West Virginia pay subsidies that the model also +# lists under spm_unit_benefits. Every state's subsidy shows up here through +# childcare_expenses, so these three leave the benefits line. +SUBSIDIES_IN_SPM_BENEFITS = ( + "co_ccap_subsidy", + "wa_child_care_subsidies", + "wv_child_care_subsidies", +) + +# Alaska's dividend reaches household_market_income and New Jersey's ANCHOR +# payment reaches household_benefits, but neither reaches the SPM unit totals. +CASH_OUTSIDE_SPM_TOTALS = ("ak_permanent_fund_dividend", "nj_property_tax_relief") + + +def situation(state, earnings, axes=None, year=YEAR): + """The family as a PolicyEngine situation, for one state.""" + parent = { + "age": {year: PARENT_AGE}, + "employment_income": {year: earnings}, + "weekly_hours_worked_before_lsr": {year: 40}, + "weeks_worked": {year: 52}, + "pre_subsidy_rent": {year: RENT_PER_MONTH * 12}, + } + people = {"parent": parent} + for i, age in enumerate(CHILD_AGES): + people[f"child{i + 1}"] = { + "age": {year: age}, + "childcare_days_per_week": {year: 5}, + "childcare_hours_per_day": {year: 8}, + "childcare_attending_days_per_month": {year: 21}, + } + members = list(people) + marital_units = {"parent_unit": {"members": ["parent"]}} + for name in members[1:]: + marital_units[f"{name}_unit"] = {"members": [name]} + spm_unit = { + "members": members, + "spm_unit_pre_subsidy_childcare_expenses": { + year: CHILD_CARE_PER_MONTH * 12 + }, + "has_heating_cooling_expense": {year: True}, + # Both default to a value that misdescribes a parent in a regular job: + # the activity test defaults to not met, and Wisconsin Works places + # every family in a Community Service Job. + "meets_ccdf_activity_test": {year: True}, + } + if state == "WI": + spm_unit["wi_works_placement"] = { + f"{year}-{month:02d}": "NONE" for month in range(1, 13) + } + result = { + "people": people, + "tax_units": {"tax_unit": {"members": members}}, + "families": {"family": {"members": members}}, + "marital_units": marital_units, + "spm_units": {"spm_unit": spm_unit}, + "households": { + "household": { + "members": members, + "state_name": {year: state}, + "county": {year: COUNTIES[state]["enum"]}, + } + }, + } + if axes is not None: + result["axes"] = axes + return result + + +def earnings_axis(minimum, maximum, count, year=YEAR): + """A PolicyEngine axis that sweeps the parent's earnings.""" + return [ + [ + { + "name": "employment_income", + "min": minimum, + "max": maximum, + "count": count, + "period": year, + } + ] + ] + + +def simulate(state, earnings=0, axes=None, year=YEAR): + return Simulation(situation=situation(state, earnings, axes, year)) + + +def net_resources_parts(sim, year=YEAR): + """Every line of net resources, as one array per line.""" + + def get(name): + return sim.calculate(name, year, map_to="spm_unit").astype(float) + + bill = get("spm_unit_pre_subsidy_childcare_expenses") + paid = get("childcare_expenses") + snap, wic, tanf = get("snap"), get("wic"), get("tanf") + subsidies_in_benefits = sum(get(name) for name in SUBSIDIES_IN_SPM_BENEFITS) + parts = { + "earnings": get("spm_unit_market_income"), + "snap": snap, + "wic": wic, + "tanf": tanf, + "child_care_subsidy": bill - paid, + "other_benefits": ( + get("spm_unit_benefits") + + get("acp") + + get("ebb") + - snap + - wic + - tanf + - subsidies_in_benefits + + sum(get(name) for name in CASH_OUTSIDE_SPM_TOTALS) + ), + "federal_taxes": -( + get("spm_unit_federal_tax") + get("spm_unit_payroll_tax") + ), + "state_taxes": -(get("spm_unit_state_tax") + get("spm_unit_local_tax")), + "child_care_bill": -bill, + } + parts["net_resources"] = sum(parts.values()) + return parts diff --git a/slideshows/appam-2026/analysis/largest_counties.json b/slideshows/appam-2026/analysis/largest_counties.json new file mode 100644 index 00000000..d5d0bf51 --- /dev/null +++ b/slideshows/appam-2026/analysis/largest_counties.json @@ -0,0 +1,257 @@ +{ + "AK": { + "county": "Anchorage Municipality", + "enum": "ANCHORAGE_MUNICIPALITY_AK", + "population": 292545 + }, + "AL": { + "county": "Jefferson County", + "enum": "JEFFERSON_COUNTY_AL", + "population": 672550 + }, + "AR": { + "county": "Pulaski County", + "enum": "PULASKI_COUNTY_AR", + "population": 397931 + }, + "AZ": { + "county": "Maricopa County", + "enum": "MARICOPA_COUNTY_AZ", + "population": 4367186 + }, + "CA": { + "county": "Los Angeles County", + "enum": "LOS_ANGELES_COUNTY_CA", + "population": 10019635 + }, + "CO": { + "county": "El Paso County", + "enum": "EL_PASO_COUNTY_CO", + "population": 722736 + }, + "CT": { + "county": "Fairfield County", + "enum": "FAIRFIELD_COUNTY_CT", + "population": 956446 + }, + "DC": { + "county": "District of Columbia", + "enum": "DISTRICT_OF_COLUMBIA_DC", + "population": 683154 + }, + "DE": { + "county": "New Castle County", + "enum": "NEW_CASTLE_COUNTY_DE", + "population": 567769 + }, + "FL": { + "county": "Miami-Dade County", + "enum": "MIAMI_DADE_COUNTY_FL", + "population": 2690113 + }, + "GA": { + "county": "Fulton County", + "enum": "FULTON_COUNTY_GA", + "population": 1054286 + }, + "HI": { + "county": "Honolulu County", + "enum": "HONOLULU_COUNTY_HI", + "population": 1015167 + }, + "IA": { + "county": "Polk County", + "enum": "POLK_COUNTY_IA", + "population": 488871 + }, + "ID": { + "county": "Ada County", + "enum": "ADA_COUNTY_ID", + "population": 485246 + }, + "IL": { + "county": "Cook County", + "enum": "COOK_COUNTY_IL", + "population": 5265398 + }, + "IN": { + "county": "Marion County", + "enum": "MARION_COUNTY_IN", + "population": 969542 + }, + "KS": { + "county": "Johnson County", + "enum": "JOHNSON_COUNTY_KS", + "population": 605154 + }, + "KY": { + "county": "Jefferson County", + "enum": "JEFFERSON_COUNTY_KY", + "population": 780449 + }, + "LA": { + "county": "East Baton Rouge Parish", + "enum": "EAST_BATON_ROUGE_PARISH_LA", + "population": 455447 + }, + "MA": { + "county": "Middlesex County", + "enum": "MIDDLESEX_COUNTY_MA", + "population": 1623411 + }, + "MD": { + "county": "Montgomery County", + "enum": "MONTGOMERY_COUNTY_MD", + "population": 1057201 + }, + "ME": { + "county": "Cumberland County", + "enum": "CUMBERLAND_COUNTY_ME", + "population": 300776 + }, + "MI": { + "county": "Wayne County", + "enum": "WAYNE_COUNTY_MI", + "population": 1789781 + }, + "MN": { + "county": "Hennepin County", + "enum": "HENNEPIN_COUNTY_MN", + "population": 1270283 + }, + "MO": { + "county": "St. Louis County", + "enum": "ST_LOUIS_COUNTY_MO", + "population": 1001982 + }, + "MS": { + "county": "Hinds County", + "enum": "HINDS_COUNTY_MS", + "population": 230772 + }, + "MT": { + "county": "Yellowstone County", + "enum": "YELLOWSTONE_COUNTY_MT", + "population": 163593 + }, + "NC": { + "county": "Wake County", + "enum": "WAKE_COUNTY_NC", + "population": 1112883 + }, + "ND": { + "county": "Cass County", + "enum": "CASS_COUNTY_ND", + "population": 182992 + }, + "NE": { + "county": "Douglas County", + "enum": "DOUGLAS_COUNTY_NE", + "population": 578771 + }, + "NH": { + "county": "Hillsborough County", + "enum": "HILLSBOROUGH_COUNTY_NH", + "population": 420504 + }, + "NJ": { + "county": "Bergen County", + "enum": "BERGEN_COUNTY_NJ", + "population": 952979 + }, + "NM": { + "county": "Bernalillo County", + "enum": "BERNALILLO_COUNTY_NM", + "population": 674919 + }, + "NV": { + "county": "Clark County", + "enum": "CLARK_COUNTY_NV", + "population": 2231147 + }, + "NY": { + "county": "Kings County", + "enum": "KINGS_COUNTY_NY", + "population": 2712360 + }, + "OH": { + "county": "Franklin County", + "enum": "FRANKLIN_COUNTY_OH", + "population": 1313598 + }, + "OK": { + "county": "Oklahoma County", + "enum": "OKLAHOMA_COUNTY_OK", + "population": 790938 + }, + "OR": { + "county": "Multnomah County", + "enum": "MULTNOMAH_COUNTY_OR", + "population": 810011 + }, + "PA": { + "county": "Philadelphia County", + "enum": "PHILADELPHIA_COUNTY_PA", + "population": 1596865 + }, + "RI": { + "county": "Providence County", + "enum": "PROVIDENCE_COUNTY_RI", + "population": 656672 + }, + "SC": { + "county": "Greenville County", + "enum": "GREENVILLE_COUNTY_SC", + "population": 519178 + }, + "SD": { + "county": "Minnehaha County", + "enum": "MINNEHAHA_COUNTY_SD", + "population": 194884 + }, + "TN": { + "county": "Shelby County", + "enum": "SHELBY_COUNTY_TN", + "population": 929178 + }, + "TX": { + "county": "Harris County", + "enum": "HARRIS_COUNTY_TX", + "population": 4697957 + }, + "UT": { + "county": "Salt Lake County", + "enum": "SALT_LAKE_COUNTY_UT", + "population": 1173331 + }, + "VA": { + "county": "Fairfax County", + "enum": "FAIRFAX_COUNTY_VA", + "population": 1146825 + }, + "VT": { + "county": "Chittenden County", + "enum": "CHITTENDEN_COUNTY_VT", + "population": 167523 + }, + "WA": { + "county": "King County", + "enum": "KING_COUNTY_WA", + "population": 2240876 + }, + "WI": { + "county": "Milwaukee County", + "enum": "MILWAUKEE_COUNTY_WI", + "population": 939123 + }, + "WV": { + "county": "Kanawha County", + "enum": "KANAWHA_COUNTY_WV", + "population": 181953 + }, + "WY": { + "county": "Laramie County", + "enum": "LARAMIE_COUNTY_WY", + "population": 99905 + } +} diff --git a/slideshows/appam-2026/analysis/largest_counties.py b/slideshows/appam-2026/analysis/largest_counties.py new file mode 100644 index 00000000..eb300520 --- /dev/null +++ b/slideshows/appam-2026/analysis/largest_counties.py @@ -0,0 +1,63 @@ +"""Each state's most populous county, for placing the family. + +Reads the 2017-2021 American Community Survey, the last vintage that reports +Connecticut's eight counties, which PolicyEngine's County variable still uses. +Run with CENSUS_API_KEY set. Writes largest_counties.json next to this file. +""" + +import json +import os +import re +import urllib.request +from pathlib import Path + +from policyengine_us.variables.household.demographic.geographic.county.county_enum import ( + County, +) + +URL = ( + "https://api.census.gov/data/2021/acs/acs5" + "?get=NAME,B01001_001E&for=county:*&key={key}" +) +STATE_FIPS_TO_CODE = { + "01": "AL", "02": "AK", "04": "AZ", "05": "AR", "06": "CA", "08": "CO", + "09": "CT", "10": "DE", "11": "DC", "12": "FL", "13": "GA", "15": "HI", + "16": "ID", "17": "IL", "18": "IN", "19": "IA", "20": "KS", "21": "KY", + "22": "LA", "23": "ME", "24": "MD", "25": "MA", "26": "MI", "27": "MN", + "28": "MS", "29": "MO", "30": "MT", "31": "NE", "32": "NV", "33": "NH", + "34": "NJ", "35": "NM", "36": "NY", "37": "NC", "38": "ND", "39": "OH", + "40": "OK", "41": "OR", "42": "PA", "44": "RI", "45": "SC", "46": "SD", + "47": "TN", "48": "TX", "49": "UT", "50": "VT", "51": "VA", "53": "WA", + "54": "WV", "55": "WI", "56": "WY", +} # fmt: skip + + +def county_enum_name(county, state): + name = county.upper().replace("'", "").replace(".", "") + return re.sub(r"[^A-Z0-9]+", "_", name).strip("_") + "_" + state + + +def main(): + url = URL.format(key=os.environ["CENSUS_API_KEY"]) + with urllib.request.urlopen(url, timeout=120) as response: + rows = json.load(response) + largest = {} + for name, population, state_fips, _ in rows[1:]: + state = STATE_FIPS_TO_CODE.get(state_fips) + if state is None: + continue + county = name.rsplit(", ", 1)[0] + population = int(population) + if state not in largest or population > largest[state]["population"]: + largest[state] = {"county": county, "population": population} + valid = {member.name for member in County} + for state, entry in largest.items(): + entry["enum"] = county_enum_name(entry["county"], state) + assert entry["enum"] in valid, entry + assert len(largest) == 51 + path = Path(__file__).with_name("largest_counties.json") + path.write_text(json.dumps(largest, indent=1, sort_keys=True) + "\n") + + +if __name__ == "__main__": + main() diff --git a/slideshows/appam-2026/analysis/massachusetts_reform.py b/slideshows/appam-2026/analysis/massachusetts_reform.py new file mode 100644 index 00000000..fdc84a34 --- /dev/null +++ b/slideshows/appam-2026/analysis/massachusetts_reform.py @@ -0,0 +1,82 @@ +"""Double the Massachusetts child and family tax credit and score it. + +Runs current law and the reform on Populace US for 2026 and reports Massachusetts +only. The reform assumes no behavioural responses. +""" + +import json +import sys +from importlib.metadata import version +from pathlib import Path + +import numpy as np +from policyengine_core.reforms import Reform +from policyengine_us import Microsimulation + +YEAR = 2026 +PARAMETER = "gov.states.ma.tax.income.credits.child_and_family.amount" +CURRENT = 440 +REFORMED = 880 +CHILD_AGE_LIMIT = 18 + + +def massachusetts_results(sim): + in_state = sim.calculate("state_code_str", YEAR, map_to="person").values == "MA" + weight = sim.calculate("person_weight", YEAR).values + age = sim.calculate("age", YEAR).values + poor = sim.calculate("spm_unit_is_in_spm_poverty", YEAR, map_to="person").values + deep = sim.calculate("spm_unit_is_in_deep_spm_poverty", YEAR, map_to="person").values + household_in_state = ( + sim.calculate("state_code_str", YEAR, map_to="household").values == "MA" + ) + children = in_state & (age < CHILD_AGE_LIMIT) + return { + "children": float(weight[children].sum()), + "people": float(weight[in_state].sum()), + "child_poverty": float(np.average(poor[children], weights=weight[children])), + "child_deep_poverty": float( + np.average(deep[children], weights=weight[children]) + ), + "poverty": float(np.average(poor[in_state], weights=weight[in_state])), + "household_weight": sim.calculate("household_weight", YEAR).values[ + household_in_state + ], + "household_net_income": sim.calculate( + "household_net_income", YEAR, map_to="household" + ).values[household_in_state], + "household_records": int(household_in_state.sum()), + } + + +def main(commit): + baseline = massachusetts_results(Microsimulation()) + reform = Reform.from_dict( + {PARAMETER: {f"{YEAR}-01-01.2100-12-31": REFORMED}}, country_id="us" + ) + reformed = massachusetts_results(Microsimulation(reform=reform)) + gains = reformed["household_net_income"] - baseline["household_net_income"] + weights = baseline["household_weight"] + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": commit, + "dataset": "Populace US", + "year": YEAR, + "parameter": PARAMETER, + "current": CURRENT, + "reformed": REFORMED, + "cost": float(np.sum(gains * weights)), + "households_gaining_share": float( + np.sum(weights[gains > 1]) / np.sum(weights) + ), + "any_household_loses": bool((gains < -1).any()), + "household_records": baseline["household_records"], + } + for key in ("children", "people", "child_poverty", "child_deep_poverty", "poverty"): + payload[f"baseline_{key}"] = baseline[key] + payload[f"reformed_{key}"] = reformed[key] + target = Path(__file__).parent.parent / "data" / "massachusetts_reform.json" + target.write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/slideshows/appam-2026/analysis/sample_families.py b/slideshows/appam-2026/analysis/sample_families.py new file mode 100644 index 00000000..c5f68a70 --- /dev/null +++ b/slideshows/appam-2026/analysis/sample_families.py @@ -0,0 +1,38 @@ +"""Draw a fixed sample of families from Populace US. + +Takes every person in a weighted random sample of households with children and +writes them as one flat file. simulated_instrument.py then treats that file the +way a researcher would treat a survey extract. +""" + +import sys + +import numpy as np +from policyengine_us import Microsimulation + +SAMPLE_SIZE = 3_000 +SEED = 2026 +YEAR = 2024 + + +def main(path): + sim = Microsimulation() + weights = sim.calculate("household_weight", YEAR).values.astype(float) + children = sim.calculate("is_child", YEAR, map_to="household").values + households = sim.calculate("household_id", YEAR).values + weights = np.where(children > 0, weights, 0) + rng = np.random.default_rng(SEED) + chosen = rng.choice( + households, size=SAMPLE_SIZE, replace=False, p=weights / weights.sum() + ) + frame = sim.to_input_dataframe() + sample = frame.loc[frame[f"person_household_id__{YEAR}"].isin(chosen)] + columns = [c for c in sample.columns if c.endswith(f"__{YEAR}")] + sample = sample[columns].reset_index(drop=True) + sample[f"person_id__{YEAR}"] = sample.index + sample.to_pickle(path) + print(sample.shape) + + +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/slideshows/appam-2026/analysis/simulated_instrument.py b/slideshows/appam-2026/analysis/simulated_instrument.py new file mode 100644 index 00000000..ad4c519d --- /dev/null +++ b/slideshows/appam-2026/analysis/simulated_instrument.py @@ -0,0 +1,96 @@ +"""Run one fixed sample of families through every state's rules. + +Each run keeps the same people, the same incomes and the same weights, and +changes only the state and the tax year. The variation in the results therefore +comes from the rules alone, which is the logic of a simulated instrument. + +families.pkl stands in for a survey extract: one row per person, with columns +named variable__year. sample_families.py builds it from Populace US. +""" + +import json +import sys +from importlib.metadata import version +from itertools import product +from multiprocessing import Pool +from pathlib import Path + +import numpy as np +import pandas as pd +from policyengine_core.data import Dataset +from policyengine_us import Microsimulation + +SAMPLE_YEAR = 2024 +YEARS = (2022, 2026) +WORKERS = 4 +STATE_FIPS = { + "AL": 1, "AK": 2, "AZ": 4, "AR": 5, "CA": 6, "CO": 8, "CT": 9, "DE": 10, + "DC": 11, "FL": 12, "GA": 13, "HI": 15, "ID": 16, "IL": 17, "IN": 18, + "IA": 19, "KS": 20, "KY": 21, "LA": 22, "ME": 23, "MD": 24, "MA": 25, + "MI": 26, "MN": 27, "MS": 28, "MO": 29, "MT": 30, "NE": 31, "NV": 32, + "NH": 33, "NJ": 34, "NM": 35, "NY": 36, "NC": 37, "ND": 38, "OH": 39, + "OK": 40, "OR": 41, "PA": 42, "RI": 44, "SC": 45, "SD": 46, "TN": 47, + "TX": 48, "UT": 49, "VT": 50, "VA": 51, "WA": 53, "WV": 54, "WI": 55, + "WY": 56, +} # fmt: skip + +families = None + + +def load(path): + global families + families = pd.read_pickle(path) + + +def run(state, year): + # A worker's exception that fails to unpickle hangs the whole pool, so each + # state and year reports its own failure as text. + try: + return simulate(state, year) + except Exception as error: + return {"state": state, "year": year, "error": repr(error)} + + +def simulate(state, year): + people = families.rename( + columns=lambda name: name.replace(f"__{SAMPLE_YEAR}", f"__{year}") + ) + people[f"state_fips__{year}"] = STATE_FIPS[state] + sim = Microsimulation(dataset=Dataset.from_dataframe(people, year)) + + def mean(name): + values = sim.calculate(name, year, map_to="household").values + return values.astype(float) + + weights = sim.calculate("household_weight", year).values + snap_eligible = sim.calculate("is_snap_eligible", f"{year}-01").values + spm_weights = sim.calculate("spm_unit_weight", year).values + return { + "state": state, + "year": year, + "state_income_tax": float(np.average(mean("state_income_tax"), weights=weights)), + "state_refundable_credits": float( + np.average(mean("state_refundable_credits"), weights=weights) + ), + "snap_eligible_share": float(np.average(snap_eligible, weights=spm_weights)), + "earnings_check": float(np.average(mean("employment_income"), weights=weights)), + } + + +def main(path, commit): + with Pool(WORKERS, initializer=load, initargs=(path,)) as pool: + rows = pool.starmap(run, product(STATE_FIPS, YEARS)) + sample = pd.read_pickle(path) + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": commit, + "households": int(sample[f"person_household_id__{SAMPLE_YEAR}"].nunique()), + "people": len(sample), + "rows": rows, + } + target = Path(__file__).parent.parent / "data" / "simulated_instrument.json" + target.write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(sys.argv[1], sys.argv[2]) diff --git a/slideshows/appam-2026/analysis/summarize.py b/slideshows/appam-2026/analysis/summarize.py new file mode 100644 index 00000000..d16e64d8 --- /dev/null +++ b/slideshows/appam-2026/analysis/summarize.py @@ -0,0 +1,144 @@ +"""Cut data/family.json down to what the deck's charts draw. + +Writes data/charts.json. The earnings, the sweep range and the cliff states +sit here so one edit changes every chart and the numbers beside them. +""" + +import json +from pathlib import Path +from statistics import median, pstdev + +HEADLINE_EARNINGS = 40_000 +SPREAD_EARNINGS = (30_000, 40_000, 50_000, 60_000) +SWEEP_MIN = 15_000 +SWEEP_MAX = 100_000 +CLIFF_STATES = ("IA", "MN", "MD") +EMTR_EARNINGS = (30_000, 60_000) +SPREAD_LINES = ("child_care_subsidy", "snap", "state_taxes", "federal_taxes") + +STATE_NAMES = { + "AL": "Alabama", "AK": "Alaska", "AZ": "Arizona", "AR": "Arkansas", + "CA": "California", "CO": "Colorado", "CT": "Connecticut", "DE": "Delaware", + "DC": "District of Columbia", "FL": "Florida", "GA": "Georgia", "HI": "Hawaii", + "ID": "Idaho", "IL": "Illinois", "IN": "Indiana", "IA": "Iowa", "KS": "Kansas", + "KY": "Kentucky", "LA": "Louisiana", "ME": "Maine", "MD": "Maryland", + "MA": "Massachusetts", "MI": "Michigan", "MN": "Minnesota", "MS": "Mississippi", + "MO": "Missouri", "MT": "Montana", "NE": "Nebraska", "NV": "Nevada", + "NH": "New Hampshire", "NJ": "New Jersey", "NM": "New Mexico", "NY": "New York", + "NC": "North Carolina", "ND": "North Dakota", "OH": "Ohio", "OK": "Oklahoma", + "OR": "Oregon", "PA": "Pennsylvania", "RI": "Rhode Island", "SC": "South Carolina", + "SD": "South Dakota", "TN": "Tennessee", "TX": "Texas", "UT": "Utah", + "VT": "Vermont", "VA": "Virginia", "WA": "Washington", "WV": "West Virginia", + "WI": "Wisconsin", "WY": "Wyoming", +} # fmt: skip + + +def rounded(value): + return int(round(value)) + + +def main(): + root = Path(__file__).parent.parent / "data" + family = json.loads((root / "family.json").read_text()) + earnings, states = family["earnings"], family["states"] + at = {value: earnings.index(value) for value in earnings} + i = at[HEADLINE_EARNINGS] + + cross_section = [] + for code, lines in states.items(): + row = {key: rounded(lines[key][i]) for key in lines} + row["code"] = code + row["name"] = STATE_NAMES[code] + row["county"] = family["counties"][code] + row["base"] = rounded( + lines["earnings"][i] + + lines["child_care_bill"][i] + + lines["federal_taxes"][i] + + lines["state_taxes"][i] + ) + cross_section.append(row) + cross_section.sort(key=lambda row: row["net_resources"]) + + spreads = [] + for value in SPREAD_EARNINGS: + ranked = sorted( + (lines["net_resources"][at[value]], code) for code, lines in states.items() + ) + spreads.append( + { + "earnings": value, + "low": rounded(ranked[0][0]), + "low_state": ranked[0][1], + "high": rounded(ranked[-1][0]), + "high_state": ranked[-1][1], + "spread": rounded(ranked[-1][0] - ranked[0][0]), + } + ) + + lines_by_program = [] + for key in SPREAD_LINES: + ranked = sorted((row[key], row["code"]) for row in cross_section) + lines_by_program.append( + { + "key": key, + "values": [{"code": code, "value": value} for value, code in ranked], + "standard_deviation": rounded(pstdev(value for value, _ in ranked)), + } + ) + + start, stop = at[SWEEP_MIN], at[SWEEP_MAX] + sweep_earnings = earnings[start : stop + 1] + sweep = { + code: [rounded(x) for x in lines["net_resources"][start : stop + 1]] + for code, lines in states.items() + } + + cliffs = [] + for code in CLIFF_STATES: + net = states[code]["net_resources"] + drops = [ + (net[j + 1] - net[j], earnings[j], earnings[j + 1]) + for j in range(start, stop) + ] + drop, before, after = min(drops) + cliffs.append( + { + "code": code, + "name": STATE_NAMES[code], + "before": before, + "after": after, + "drop": rounded(-drop), + "net_before": rounded(net[at[before]]), + "net_after": rounded(net[at[after]]), + } + ) + + emtr = [] + for value in EMTR_EARNINGS: + j = at[value] + step = earnings[j + 1] - earnings[j] + rates = [ + 1 - (lines["net_resources"][j + 1] - lines["net_resources"][j]) / step + for lines in states.values() + ] + emtr.append({"earnings": value, "median": round(median(rates), 3)}) + + payload = { + "policyengine_us_version": family["policyengine_us_version"], + "policyengine_us_commit": family["policyengine_us_commit"], + "year": family["year"], + "family": family["family"], + "headline_earnings": HEADLINE_EARNINGS, + "cross_section": cross_section, + "spreads": spreads, + "lines_by_program": lines_by_program, + "sweep_earnings": sweep_earnings, + "sweep": sweep, + "cliffs": cliffs, + "emtr": emtr, + } + (root / "charts.json").write_text(json.dumps(payload, separators=(",", ":")) + "\n") + + +if __name__ == "__main__": + main() diff --git a/slideshows/appam-2026/analysis/test_invariants.py b/slideshows/appam-2026/analysis/test_invariants.py new file mode 100644 index 00000000..4f85918b --- /dev/null +++ b/slideshows/appam-2026/analysis/test_invariants.py @@ -0,0 +1,95 @@ +"""Properties the family's numbers must hold at every input. + +Run from this folder with pytest. The cases come from Hypothesis, so they cover +states and earnings nobody picked by hand. +""" + +import numpy as np +from hypothesis import given, settings +from hypothesis import strategies as st + +import household as hh + +MAX_EXAMPLES = 40 +# The model stores money as 32-bit floats, so totals drift by fractions of a cent. +TOLERANCE = 0.02 +states = st.sampled_from(hh.STATES) +earnings = st.integers(min_value=0, max_value=150_000) + + +def line(sim, name): + return float(sim.calculate(name, hh.YEAR, map_to="spm_unit")[0]) + + +@settings(max_examples=MAX_EXAMPLES, deadline=None) +@given(states, earnings) +def test_lines_add_up_to_net_resources(state, income): + parts = hh.net_resources_parts(hh.simulate(state, income)) + total = parts.pop("net_resources") + assert abs(sum(parts.values())[0] - total[0]) < TOLERANCE + + +@settings(max_examples=MAX_EXAMPLES, deadline=None) +@given(states, earnings) +def test_subsidy_stays_between_zero_and_the_bill(state, income): + parts = hh.net_resources_parts(hh.simulate(state, income)) + subsidy = parts["child_care_subsidy"][0] + assert -TOLERANCE <= subsidy <= -parts["child_care_bill"][0] + TOLERANCE + + +@settings(max_examples=MAX_EXAMPLES, deadline=None) +@given(states, earnings) +def test_reconciles_with_spm_net_income(state, income): + """Differential test against the model's own SPM total. + + spm_unit_net_income subtracts the capped sum of work costs and the full + child care bill, plus medical costs and child support, and counts three + states' subsidies as benefits. Net resources subtracts only the part of the + bill the family pays, leaves those other costs out, and adds two state + payments the SPM totals miss. + """ + sim = hh.simulate(state, income) + net = hh.net_resources_parts(sim)["net_resources"][0] + subsidies_in_benefits = sum(line(sim, n) for n in hh.SUBSIDIES_IN_SPM_BENEFITS) + expected_gap = ( + sum(line(sim, name) for name in hh.CASH_OUTSIDE_SPM_TOTALS) + - subsidies_in_benefits + - line(sim, "childcare_expenses") + + line(sim, "spm_unit_capped_work_childcare_expenses") + + line(sim, "spm_unit_medical_out_of_pocket_expenses") + + line(sim, "child_support_expense") + ) + assert abs(net - line(sim, "spm_unit_net_income") - expected_gap) < TOLERANCE + + +@settings(max_examples=MAX_EXAMPLES, deadline=None) +@given(states, earnings) +def test_reconciles_with_household_net_income(state, income): + """Differential test against the model's household total. + + household_net_income ignores child care bills and leaves out Alaska's energy + relief. It also counts the subsidy as a benefit in some states and not in + others, so the remaining gap must equal either zero or the whole subsidy. + """ + sim = hh.simulate(state, income) + parts = hh.net_resources_parts(sim) + before_child_care = parts["net_resources"][0] + line(sim, "childcare_expenses") + gap = ( + line(sim, "household_net_income") + + line(sim, "ak_energy_relief") + - before_child_care + ) + subsidy = parts["child_care_subsidy"][0] + assert min(abs(gap), abs(gap - subsidy)) < TOLERANCE + + +@settings(max_examples=10, deadline=None) +@given(states, earnings) +def test_axis_and_single_runs_agree(state, income): + """The earnings grid and a one-household run share every rule.""" + grid = round(income / 500) * 500 + swept = hh.simulate(state, axes=hh.earnings_axis(grid, grid + 500, 2)) + single = hh.simulate(state, grid) + a = hh.net_resources_parts(swept)["net_resources"][0] + b = hh.net_resources_parts(single)["net_resources"][0] + assert np.isclose(a, b, atol=TOLERANCE) 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diff --git a/slideshows/appam-2026/data/family.json b/slideshows/appam-2026/data/family.json new file mode 100644 index 00000000..a1225487 --- /dev/null +++ b/slideshows/appam-2026/data/family.json @@ -0,0 +1 @@ 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From e2fe775c14f983ee6b63ae3b6a54c8899cced467 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 13:12:56 -0500 Subject: [PATCH 03/32] Build the APPAM deck's slides, charts and invariants Adds the family, program-line and earnings charts, the Texas subsidy run, the tools and scope slides, the Axiom slide, and speaker notes. Reconciles net resources against household_net_income as well as spm_unit_net_income. 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Thursday, November 5, 2026, 10:15 to 11:45am ET, Wellesley room, Boston. Cynthia Osborne moderates. Tara Watson and Linda Giannarelli also present. Each presenter has 10 to 12 minutes. + +Twelve slides in 11 minutes. Every figure on a slide traces to `EVIDENCE.md`. + +## Before presenting + +- Open the deck once on the presentation laptop. It needs no live connection after it loads. +- Keep the PDF backup on the laptop in case the room's network fails. +- Check the slide order against what Cynthia, Tara and Linda present first, and cut slide 9's table to one sentence if they have already covered it. + +## 1. PolicyEngine for state policy research (0:00, 15 seconds) + +Thanks, Cynthia, for putting this session together, and thanks to Tara and Linda for sharing the table. + +## 2. PolicyEngine computes taxes and benefits for a family or a population (0:15, 1 minute) + +PolicyEngine is a nonprofit that provides free, open source software to compute the impact of public policy. Our US model calculates federal and state income taxes and credits and the main benefit programs for one household or a whole population. + +Researchers use it three ways, and the next few slides take them in order: compare families, measure their own sample, and estimate reforms. + +## 3. One family ends up with $27,711 in Ohio and $54,345 in Alaska (1:15, 1 minute 30 seconds) + +Here is one family under each state's 2026 rules. One parent works full time and earns $40,000, has two young children, and pays $1,500 a month for child care. We put them in each state's largest county and treat them as a new applicant for every program. + +The gray base holds earnings after taxes and credits, minus the full child care bill. Everything above it comes from benefits. Ohio and Nevada turn this family down for child care help because both set a lower income limit for new applicants than for families already enrolled. + +$40,000 works out to $19.23 an hour, which exceeds every state's minimum wage on the Department of Labor's July 2026 table. The gap between the top and bottom state moves with earnings: $16,404 at $30,000 and $21,670 at $60,000. + +## 4. Child care subsidies move net resources most, then SNAP and state taxes (2:45, 1 minute 15 seconds) + +Each mark shows one state on one line of the family's budget, all on the same scale. Federal taxes and credits barely move. State policy moves the rest. + +These lines interact. Run Texas with and without its subsidy: the family gets $15,791 in child care help and loses $5,598 of SNAP, because SNAP deducts what the family pays for care. Net resources rise by $10,193. + +## 5. Cliffs cost this family up to $15,343, at earnings that vary by state (4:00, 1 minute) + +Each gray line follows one state as earnings rise. In Iowa, Minnesota and Maryland, a $500 raise takes the family past the child care income limit, and the family loses between $13,182 and $15,343. Every large drop here comes from the subsidy ending. + +Away from the cliffs, the median state's total effective marginal tax rate falls from 63.6% at $30,000 of earnings to 24.6% at $60,000. + +## 6. Parameters record when each value took effect and cite their sources (5:00, 45 seconds) + +This file holds one real parameter: the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. Each value carries its effective date, and each citation sits next to the values. Ask the model for any date and it reads the value in force on that date. + +## 9. Research questions and the tools that answer them (9:00, 1 minute) + +We all work on the same landscape from different angles. The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. + +PolicyEngine adds four things: you can change any parameter or write a new rule, run your own microdata, read and edit every line of code, and project current law forward through 2035. + +## 10. What these results leave out (10:00, 45 seconds) + +Child care subsidies reach all 51 jurisdictions from 2025, and we copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so the model leaves minimum wage and paid leave effects to tools like the Policy Impact Calculator. Hours stay fixed unless you set a labor supply response. + +## 11. The Axiom Foundation encodes rules with their source text and dates (10:45, 1 minute) + +I also lead the Axiom Foundation, which encodes rules as open, machine-readable files. Here is one Georgia SNAP rule. Georgia allows a 20% earned income deduction. Axiom's file records the manual's own words and the effective date beside each value. + +We ran this rule through the Axiom engine. A family earning $3,333.33 a month who reports on time gets a $666.67 deduction, and the same family reporting late gets nothing. Ask for November 2019, a month before the rule took effect, and the engine refuses. + +The engine reports `parameter earned_income_deduction_rate has no value for key 0 at 2019-11-01`. + +Axiom has encoded some state family-program rules, and that coverage remains partial. + +## 12. Thank you (11:45) + +The QR code opens this deck, with the code behind each chart. diff --git a/slideshows/appam-2026/analysis/interaction.py b/slideshows/appam-2026/analysis/interaction.py new file mode 100644 index 00000000..dadbd581 --- /dev/null +++ b/slideshows/appam-2026/analysis/interaction.py @@ -0,0 +1,43 @@ +"""Measure how a child care subsidy changes the family's other lines. + +Runs the family twice in one state and changes nothing but the subsidy, which +the second run fixes at zero as an input. +""" + +import json +import sys +from importlib.metadata import version +from pathlib import Path + +from policyengine_us import Simulation + +import household as hh + +STATE = "TX" +EARNINGS = 40_000 + + +def run(without_subsidy): + situation = hh.situation(STATE, EARNINGS) + if without_subsidy: + situation["spm_units"]["spm_unit"]["child_care_subsidies"] = {hh.YEAR: 0} + parts = hh.net_resources_parts(Simulation(situation=situation)) + return {key: round(float(value[0]), 2) for key, value in parts.items()} + + +def main(commit): + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": commit, + "year": hh.YEAR, + "earnings": EARNINGS, + "state": STATE, + "with_subsidy": run(without_subsidy=False), + "without_subsidy": run(without_subsidy=True), + } + path = Path(__file__).parent.parent / "data" / "interaction.json" + path.write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/slideshows/appam-2026/analysis/simulated_instrument.py b/slideshows/appam-2026/analysis/simulated_instrument.py index ad4c519d..a7ca6b8b 100644 --- a/slideshows/appam-2026/analysis/simulated_instrument.py +++ b/slideshows/appam-2026/analysis/simulated_instrument.py @@ -1,8 +1,7 @@ """Run one fixed sample of families through every state's rules. -Each run keeps the same people, the same incomes and the same weights, and -changes only the state and the tax year. The variation in the results therefore -comes from the rules alone, which is the logic of a simulated instrument. +Each run keeps the same people and the same incomes and changes only the state, +so the differences between runs come from each state's rules. families.pkl stands in for a survey extract: one row per person, with columns named variable__year. sample_families.py builds it from Populace US. @@ -11,18 +10,22 @@ import json import sys from importlib.metadata import version -from itertools import product from multiprocessing import Pool from pathlib import Path -import numpy as np import pandas as pd from policyengine_core.data import Dataset from policyengine_us import Microsimulation SAMPLE_YEAR = 2024 -YEARS = (2022, 2026) WORKERS = 4 +CHILD_AGE_LIMIT = 19 +# A family keeps its state only, so a county from one state never reaches another +# state's county-level rules. +FINER_GEOGRAPHY = ( + "county_fips", "congressional_district_geoid", "block_geoid", "tract_geoid", + "cbsa_code", "place_fips", +) # fmt: skip STATE_FIPS = { "AL": 1, "AK": 2, "AZ": 4, "AR": 5, "CA": 6, "CO": 8, "CT": 9, "DE": 10, "DC": 11, "FL": 12, "GA": 13, "HI": 15, "ID": 16, "IL": 17, "IN": 18, @@ -39,51 +42,54 @@ def load(path): global families - families = pd.read_pickle(path) + families = pd.read_pickle(path).drop( + columns=[f"{name}__{SAMPLE_YEAR}" for name in FINER_GEOGRAPHY], errors="ignore" + ) -def run(state, year): +def run(state): # A worker's exception that fails to unpickle hangs the whole pool, so each - # state and year reports its own failure as text. + # state reports its own failure as text. try: - return simulate(state, year) + return simulate(state) except Exception as error: - return {"state": state, "year": year, "error": repr(error)} + return {"state": state, "error": repr(error)} -def simulate(state, year): - people = families.rename( - columns=lambda name: name.replace(f"__{SAMPLE_YEAR}", f"__{year}") - ) - people[f"state_fips__{year}"] = STATE_FIPS[state] - sim = Microsimulation(dataset=Dataset.from_dataframe(people, year)) +def simulate(state): + people = families.copy() + people[f"state_fips__{SAMPLE_YEAR}"] = STATE_FIPS[state] + sim = Microsimulation(dataset=Dataset.from_dataframe(people, SAMPLE_YEAR)) + month = f"{SAMPLE_YEAR}-01" - def mean(name): - values = sim.calculate(name, year, map_to="household").values - return values.astype(float) + # sample_families.py draws households in proportion to their weights, so each + # household in the sample counts once. + def household_mean(name): + return float(sim.calculate(name, SAMPLE_YEAR, map_to="household").values.mean()) - weights = sim.calculate("household_weight", year).values - snap_eligible = sim.calculate("is_snap_eligible", f"{year}-01").values - spm_weights = sim.calculate("spm_unit_weight", year).values + children = sim.calculate("age", SAMPLE_YEAR).values < CHILD_AGE_LIMIT + covered = ( + sim.calculate("is_medicaid_eligible", SAMPLE_YEAR).values + | sim.calculate("is_chip_eligible", SAMPLE_YEAR).values + ) return { "state": state, - "year": year, - "state_income_tax": float(np.average(mean("state_income_tax"), weights=weights)), - "state_refundable_credits": float( - np.average(mean("state_refundable_credits"), weights=weights) - ), - "snap_eligible_share": float(np.average(snap_eligible, weights=spm_weights)), - "earnings_check": float(np.average(mean("employment_income"), weights=weights)), + "snap_eligible_share": float(sim.calculate("is_snap_eligible", month).values.mean()), + "child_medicaid_chip_share": float(covered[children].mean()), + "state_income_tax": household_mean("state_income_tax"), + "state_refundable_credits": household_mean("state_refundable_credits"), + "earnings_check": household_mean("employment_income"), } def main(path, commit): with Pool(WORKERS, initializer=load, initargs=(path,)) as pool: - rows = pool.starmap(run, product(STATE_FIPS, YEARS)) + rows = pool.map(run, STATE_FIPS) sample = pd.read_pickle(path) payload = { "policyengine_us_version": version("policyengine-us"), "policyengine_us_commit": commit, + "year": SAMPLE_YEAR, "households": int(sample[f"person_household_id__{SAMPLE_YEAR}"].nunique()), "people": len(sample), "rows": rows, diff --git a/slideshows/appam-2026/analysis/summarize.py b/slideshows/appam-2026/analysis/summarize.py index d16e64d8..87a4fe28 100644 --- a/slideshows/appam-2026/analysis/summarize.py +++ b/slideshows/appam-2026/analysis/summarize.py @@ -101,6 +101,10 @@ def main(): for j in range(start, stop) ] drop, before, after = min(drops) + changes = { + key: rounded(line[at[after]] - line[at[before]]) + for key, line in states[code].items() + } cliffs.append( { "code": code, @@ -110,6 +114,7 @@ def main(): "drop": rounded(-drop), "net_before": rounded(net[at[before]]), "net_after": rounded(net[at[after]]), + "changes": {k: v for k, v in changes.items() if abs(v) >= 100}, } ) diff --git a/slideshows/appam-2026/config.ts b/slideshows/appam-2026/config.ts index 66abc1a7..1aea58c1 100644 --- a/slideshows/appam-2026/config.ts +++ b/slideshows/appam-2026/config.ts @@ -1,7 +1,15 @@ import { SlideshowConfig } from '@/lib/types'; import { speakers } from '@/lib/speakers'; +import AxiomSlide from './slides/AxiomSlide'; +import CliffsSlide from './slides/CliffsSlide'; import CoverSlide from './slides/CoverSlide'; import EndSlide from './slides/EndSlide'; +import OneFamilySlide from './slides/OneFamilySlide'; +import ParametersSlide from './slides/ParametersSlide'; +import ScopeSlide from './slides/ScopeSlide'; +import ToolsSlide from './slides/ToolsSlide'; +import WhatItDoesSlide from './slides/WhatItDoesSlide'; +import WhichLinesSlide from './slides/WhichLinesSlide'; export const appam2026Config: SlideshowConfig = { id: 'appam-2026', @@ -12,5 +20,16 @@ export const appam2026Config: SlideshowConfig = { location: 'APPAM 2026, Boston', footerText: 'PolicyEngine for state policy research · APPAM · November 5, 2026', speakers: [speakers['max-ghenis']], - slides: [CoverSlide, EndSlide], + slides: [ + CoverSlide, + WhatItDoesSlide, + OneFamilySlide, + WhichLinesSlide, + CliffsSlide, + ParametersSlide, + ToolsSlide, + ScopeSlide, + AxiomSlide, + EndSlide, + ], }; diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts new file mode 100644 index 00000000..ab0342dc --- /dev/null +++ b/slideshows/appam-2026/content.test.ts @@ -0,0 +1,138 @@ +import { readFileSync } from 'node:fs'; +import { join } from 'node:path'; +import { describe, expect, it } from 'vitest'; +import { + axiomRuns, + childCareBill, + cliffs, + crossSection, + headlineEarnings, + highest, + interaction, + lowest, + programLines, + spreads, + sweep, + sweepEarnings, +} from './content'; +import { SERIES } from './slides/FamilyChart'; + +const family = JSON.parse(readFileSync(join(__dirname, 'data/family.json'), 'utf8')); + +const lineKeys = [ + 'earnings', + 'snap', + 'wic', + 'tanf', + 'child_care_subsidy', + 'other_benefits', + 'federal_taxes', + 'state_taxes', + 'child_care_bill', +] as const; + +// Each of the nine rounded lines can sit up to 50 cents from its true value. +const ROUNDING = 5; + +describe('family cross section', () => { + it('covers every state once, sorted by net resources', () => { + expect(new Set(crossSection.map((row) => row.code)).size).toBe(51); + const net = crossSection.map((row) => row.net_resources); + expect(net).toEqual([...net].sort((a, b) => a - b)); + expect(lowest.net_resources).toBe(net[0]); + expect(highest.net_resources).toBe(net[50]); + }); + + it('adds its lines up to net resources in every state', () => { + for (const row of crossSection) { + const total = lineKeys.reduce((sum, key) => sum + row[key], 0); + expect(Math.abs(total - row.net_resources)).toBeLessThanOrEqual(ROUNDING); + } + }); + + it('stacks every chart segment to the height of net resources', () => { + for (const row of crossSection) { + const stack = SERIES.reduce((sum, series) => sum + series.value(row), 0); + expect(Math.abs(stack - row.net_resources)).toBeLessThanOrEqual(ROUNDING); + } + }); + + it('keeps each subsidy between zero and the bill', () => { + for (const row of crossSection) { + expect(row.child_care_subsidy).toBeGreaterThanOrEqual(0); + expect(row.child_care_subsidy).toBeLessThanOrEqual(childCareBill); + } + }); + + it('matches the source sweep at the headline earnings', () => { + const index = family.earnings.indexOf(headlineEarnings); + for (const row of crossSection) { + const source = family.states[row.code]; + for (const key of [...lineKeys, 'net_resources'] as const) { + expect(Math.abs(row[key] - source[key][index])).toBeLessThanOrEqual(0.5); + } + } + }); +}); + +describe('derived chart data', () => { + it('takes each spread from the highest and lowest state in the source', () => { + for (const spread of spreads) { + const index = family.earnings.indexOf(spread.earnings); + const net = Object.values<{ net_resources: number[] }>(family.states).map( + (state) => state.net_resources[index], + ); + expect(Math.abs(spread.low - Math.min(...net))).toBeLessThanOrEqual(0.5); + expect(Math.abs(spread.high - Math.max(...net))).toBeLessThanOrEqual(0.5); + } + }); + + it('keeps a value for every state at every sweep point', () => { + expect(Object.keys(sweep)).toHaveLength(51); + for (const values of Object.values(sweep)) { + expect(values).toHaveLength(sweepEarnings.length); + } + }); + + it('reports each cliff as the largest fall in that state, one step wide', () => { + for (const cliff of cliffs) { + const series = sweep[cliff.code as keyof typeof sweep]; + const falls = series.slice(1).map((value, i) => series[i] - value); + expect(cliff.after - cliff.before).toBe(500); + expect(Math.abs(Math.max(...falls) - cliff.drop)).toBeLessThanOrEqual(1); + expect(cliff.changes.child_care_subsidy).toBeLessThan(0); + } + }); + + it('sorts every line of the rug plot and covers 51 states', () => { + for (const line of programLines) { + const values = line.values.map((entry) => entry.value); + expect(values).toHaveLength(51); + expect(values).toEqual([...values].sort((a, b) => a - b)); + } + }); +}); + +describe('Texas interaction', () => { + it('explains the change in net resources with the subsidy and SNAP', () => { + const total = interaction.subsidy + interaction.snapChange; + expect(Math.abs(total - interaction.netChange)).toBeLessThanOrEqual(1); + }); + + it('shrinks SNAP as the subsidy grows', () => { + expect(interaction.subsidy).toBeGreaterThan(0); + expect(interaction.snapChange).toBeLessThan(0); + }); +}); + +describe('Axiom run', () => { + it('pays 20% of earnings on time and nothing when reported late', () => { + expect(axiomRuns.onTime).toBeCloseTo(axiomRuns.monthlyEarnings * 0.2, 6); + expect(axiomRuns.late).toBe(0); + }); + + it('refuses a month before the effective date', () => { + expect(axiomRuns.beforeEffectiveDate.exit_code).not.toBe(0); + expect(axiomRuns.beforeEffectiveDate.stderr).toContain('2019-11-01'); + }); +}); diff --git a/slideshows/appam-2026/content.ts b/slideshows/appam-2026/content.ts new file mode 100644 index 00000000..cb1391f0 --- /dev/null +++ b/slideshows/appam-2026/content.ts @@ -0,0 +1,75 @@ +import axiomRun from './data/axiom_ga_3612.json'; +import charts from './data/charts.json'; +import interactionRun from './data/interaction.json'; + +export type StateRow = (typeof charts.cross_section)[number]; + +export const crossSection = charts.cross_section; +export const lowest = crossSection[0]; +export const median = crossSection[Math.floor(crossSection.length / 2)]; +export const highest = crossSection[crossSection.length - 1]; + +export const headlineEarnings = charts.headline_earnings; +export const family = charts.family; +export const childCareBill = family.child_care_per_month * 12; +export const modelVersion = charts.policyengine_us_version; +export const modelCommit = charts.policyengine_us_commit.slice(0, 7); + +export const spreads = charts.spreads; +export const programLines = charts.lines_by_program; +export const sweepEarnings = charts.sweep_earnings; +export const sweep = charts.sweep; +export const cliffs = charts.cliffs; +export const medianRates = charts.emtr; + +export const largestCliff = cliffs.reduce((a, b) => (b.drop > a.drop ? b : a)); + +export function spreadAt(earnings: number) { + const row = spreads.find((spread) => spread.earnings === earnings); + if (!row) throw new Error(`No spread at ${earnings}`); + return row; +} + +export function medianRateAt(earnings: number) { + const row = medianRates.find((rate) => rate.earnings === earnings); + if (!row) throw new Error(`No rate at ${earnings}`); + return row.median; +} + +const withSubsidy = interactionRun.with_subsidy; +const withoutSubsidy = interactionRun.without_subsidy; + +export const interaction = { + subsidy: withSubsidy.child_care_subsidy, + snapChange: withSubsidy.snap - withoutSubsidy.snap, + netChange: withSubsidy.net_resources - withoutSubsidy.net_resources, +}; + +export const axiomEngineVersion = axiomRun.engine.engine_version; + +const DEDUCTION_KEY = 'us-ga:policies/dfcs/snap/3612#earned_income_deduction'; + +function axiomDeduction(index: number) { + const outputs = axiomRun.results[index].stdout?.results[0].outputs; + const output = outputs?.[DEDUCTION_KEY as keyof typeof outputs] as + | { value: { value: string } } + | undefined; + if (!output) throw new Error(`No deduction in Axiom result ${index}`); + return Number(output.value.value); +} + +export const axiomRuns = { + monthlyEarnings: Number(axiomRun.monthly_earnings), + onTime: axiomDeduction(0), + late: axiomDeduction(1), + beforeEffectiveDate: axiomRun.results[2], +}; + +export function usd(value: number) { + const sign = value < 0 ? '−' : ''; + return `${sign}$${Math.abs(Math.round(value)).toLocaleString('en-US')}`; +} + +export function percent(share: number) { + return `${(share * 100).toFixed(1)}%`; +} diff --git a/slideshows/appam-2026/data/charts.json b/slideshows/appam-2026/data/charts.json index 1e9a3846..4eb889cc 100644 --- a/slideshows/appam-2026/data/charts.json +++ b/slideshows/appam-2026/data/charts.json @@ -1 +1 @@ -{"policyengine_us_version":"2.90.2","policyengine_us_commit":"469ad12cb957a1b3eaedf9e6dd3e4c69194bba26","year":2026,"family":{"parent_age":30,"child_ages":[1,4],"rent_per_month":1200,"child_care_per_month":1500},"headline_earnings":40000,"cross_section":[{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":0,"other_benefits":0,"federal_taxes":4263,"state_taxes":0,"child_care_bill":-18000,"net_resources":27711,"code":"OH","name":"Ohio","county":"Franklin County","base":26263},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":4925,"other_benefits":0,"federal_taxes":4263,"state_taxes":-1148,"child_care_bill":-18000,"net_resources":31487,"code":"IN","name":"Indiana","county":"Marion County","base":25115},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":6662,"other_benefits":0,"federal_taxes":4263,"state_taxes":-322,"child_care_bill":-18000,"net_resources":34051,"code":"OK","name":"Oklahoma","county":"Oklahoma County","base":25941},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":7500,"other_benefits":0,"federal_taxes":4263,"state_taxes":0,"child_care_bill":-18000,"net_resources":35211,"code":"SD","name":"South Dakota","county":"Minnehaha County","base":26263},{"earnings":40000,"snap":8739,"wic":1447,"tanf":0,"child_care_subsidy":0,"other_benefits":0,"federal_taxes":4263,"state_taxes":0,"child_care_bill":-18000,"net_resources":36450,"code":"NV","name":"Nevada","county":"Clark County","base":26263},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":14568,"other_benefits":0,"federal_taxes":4263,"state_taxes":-1557,"child_care_bill":-18000,"net_resources":40722,"code":"AL","name":"Alabama","county":"Jefferson County","base":24706},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":13720,"other_benefits":0,"federal_taxes":4263,"state_taxes":-228,"child_care_bill":-18000,"net_resources":41202,"code":"MS","name":"Mississippi","county":"Hinds County","base":26035},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":14220,"other_benefits":0,"federal_taxes":4263,"state_taxes":0,"child_care_bill":-18000,"net_resources":41931,"code":"MO","name":"Missouri","county":"St. Louis County","base":26263},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":15716,"other_benefits":0,"federal_taxes":4263,"state_taxes":-802,"child_care_bill":-18000,"net_resources":42625,"code":"AR","name":"Arkansas","county":"Pulaski County","base":25462},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":15244,"other_benefits":0,"federal_taxes":4263,"state_taxes":0,"child_care_bill":-18000,"net_resources":42955,"code":"GA","name":"Georgia","county":"Fulton County","base":26263},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":15687,"other_benefits":0,"federal_taxes":4263,"state_taxes":0,"child_care_bill":-18000,"net_resources":43398,"code":"WY","name":"Wyoming","county":"Laramie County","base":26263},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":16039,"other_benefits":0,"federal_taxes":4263,"state_taxes":0,"child_care_bill":-18000,"net_resources":43750,"code":"TN","name":"Tennessee","county":"Shelby County","base":26263},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":16200,"other_benefits":0,"federal_taxes":4263,"state_taxes":232,"child_care_bill":-18000,"net_resources":44142,"code":"ID","name":"Idaho","county":"Ada County","base":26495},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":16848,"other_benefits":0,"federal_taxes":4104,"state_taxes":-40,"child_care_bill":-18000,"net_resources":44359,"code":"KS","name":"Kansas","county":"Johnson County","base":26064},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":16824,"other_benefits":0,"federal_taxes":4113,"state_taxes":0,"child_care_bill":-18000,"net_resources":44385,"code":"UT","name":"Utah","county":"Salt Lake County","base":26113},{"earnings":40000,"snap":2841,"wic":1447,"tanf":0,"child_care_subsidy":15984,"other_benefits":0,"federal_taxes":4263,"state_taxes":-2013,"child_care_bill":-18000,"net_resources":44522,"code":"KY","name":"Kentucky","county":"Jefferson County","base":24250},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":18000,"other_benefits":0,"federal_taxes":3678,"state_taxes":0,"child_care_bill":-18000,"net_resources":45126,"code":"SC","name":"South Carolina","county":"Greenville County","base":25678},{"earnings":40000,"snap":4539,"wic":1447,"tanf":0,"child_care_subsidy":14004,"other_benefits":0,"federal_taxes":4263,"state_taxes":-660,"child_care_bill":-18000,"net_resources":45593,"code":"NC","name":"North Carolina","county":"Wake County","base":25603},{"earnings":40000,"snap":2793,"wic":1447,"tanf":0,"child_care_subsidy":16000,"other_benefits":0,"federal_taxes":4263,"state_taxes":-479,"child_care_bill":-18000,"net_resources":46024,"code":"VA","name":"Virginia","county":"Fairfax 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diff --git a/slideshows/appam-2026/data/interaction.json b/slideshows/appam-2026/data/interaction.json new file mode 100644 index 00000000..bf8d7786 --- /dev/null +++ b/slideshows/appam-2026/data/interaction.json @@ -0,0 +1,31 @@ +{ + "policyengine_us_version": "2.90.2", + "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "year": 2026, + "earnings": 40000, + "state": "TX", + "with_subsidy": { + "earnings": 40000.0, + "snap": 3141.0, + "wic": 1447.29, + "tanf": 0.0, + "child_care_subsidy": 15790.89, + "other_benefits": -0.0, + "federal_taxes": 4263.23, + "state_taxes": -0.0, + "child_care_bill": -18000.0, + "net_resources": 46642.42 + }, + "without_subsidy": { + "earnings": 40000.0, + "snap": 8739.0, + "wic": 1447.29, + "tanf": 0.0, + "child_care_subsidy": 0.0, + "other_benefits": -0.0, + "federal_taxes": 4263.23, + "state_taxes": -0.0, + "child_care_bill": -18000.0, + "net_resources": 36449.53 + } +} diff --git a/slideshows/appam-2026/data/massachusetts_reform.json b/slideshows/appam-2026/data/massachusetts_reform.json new file mode 100644 index 00000000..d3afd23e --- /dev/null +++ b/slideshows/appam-2026/data/massachusetts_reform.json @@ -0,0 +1,23 @@ +{ + "policyengine_us_version": "2.90.2", + "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "dataset": "Populace US", + "year": 2026, + "parameter": "gov.states.ma.tax.income.credits.child_and_family.amount", + "current": 440, + "reformed": 880, + "cost": 554611328.0, + "households_gaining_share": 0.2980068325996399, + "any_household_loses": false, + "household_records": 1522, + "baseline_children": 1494064.0, + "reformed_children": 1494064.0, + "baseline_people": 7249867.0, + "reformed_people": 7249867.0, + "baseline_child_poverty": 0.22329546523064753, + "reformed_child_poverty": 0.22329546523064753, + "baseline_child_deep_poverty": 0.03721871532325723, + "reformed_child_deep_poverty": 0.03721871532325723, + "baseline_poverty": 0.1962823513703525, + "reformed_poverty": 0.1962823513703525 +} diff --git a/slideshows/appam-2026/data/simulated_instrument.json b/slideshows/appam-2026/data/simulated_instrument.json new file mode 100644 index 00000000..8a4de0a5 --- /dev/null +++ b/slideshows/appam-2026/data/simulated_instrument.json @@ -0,0 +1,800 @@ +{ + "policyengine_us_version": "2.90.2", + "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "households": 3000, + "people": 12307, + "rows": [ + { + "state": "AL", + "year": 2022, + "state_income_tax": 4485.809173763909, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.21581993685323644, + "earnings_check": 100949.40977942833 + }, + { + "state": "AL", + "year": 2026, + "state_income_tax": 4588.0528968895105, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.27273015634498454, + "earnings_check": 100949.40977942833 + }, + { + "state": "AK", + "year": 2022, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.21139554382680845, + "earnings_check": 100949.40977942833 + }, + { + "state": "AK", + "year": 2026, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.4778980389115625, + "earnings_check": 100949.40977942833 + }, + { + "state": "AZ", + "year": 2022, + "state_income_tax": 2708.1628614486253, + "state_refundable_credits": 22.014940747364953, + "snap_eligible_share": 0.30940357910552757, + "earnings_check": 100949.40977942833 + }, + { + "state": "AZ", + "year": 2026, + "state_income_tax": 2217.419111177293, + "state_refundable_credits": 22.014940747364953, + "snap_eligible_share": 0.3621446172062773, + "earnings_check": 100949.40977942833 + }, + { + "state": "AR", + "year": 2022, + "state_income_tax": 4620.748735330941, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.15129322245795396, + "earnings_check": 100949.40977942833 + }, + { + "state": "AR", + "year": 2026, + "state_income_tax": 3694.750731688466, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.20043331332552852, + "earnings_check": 100949.40977942833 + }, + { + "state": "CA", + "year": 2022, + "state_income_tax": 5632.303612215754, + "state_refundable_credits": 292.8980186497598, + "snap_eligible_share": 0.30666169296258833, + "earnings_check": 100949.40977942833 + }, + { + "state": "CA", + "year": 2026, + "state_income_tax": 5117.564771444724, + "state_refundable_credits": 386.65159029200817, + "snap_eligible_share": 0.3563302663294956, + "earnings_check": 100949.40977942833 + }, + { + "state": "CO", + "year": 2022, + "state_income_tax": 3523.1796229432753, + "state_refundable_credits": 1012.3661921374396, + "snap_eligible_share": 0.21812456374664588, + "earnings_check": 100949.40977942833 + }, + { + "state": "CO", + "year": 2026, + "state_income_tax": 3646.888359844286, + "state_refundable_credits": 777.4938287318408, + "snap_eligible_share": 0.38460441596669925, + "earnings_check": 100949.40977942833 + }, + { + "state": "CT", + "year": 2022, + "state_income_tax": 6191.306203911877, + "state_refundable_credits": 380.2945731849094, + "snap_eligible_share": 0.3060862058461477, + "earnings_check": 100949.40977942833 + }, + { + "state": "CT", + "year": 2026, + "state_income_tax": 5441.162527788743, + "state_refundable_credits": 811.0613478962448, + "snap_eligible_share": 0.38568555722417264, + "earnings_check": 100949.40977942833 + }, + { + "state": "DE", + "year": 2022, + "state_income_tax": 6026.0429877915785, + "state_refundable_credits": 26.418675118731436, + "snap_eligible_share": 0.33832520536218874, + "earnings_check": 100949.40977942833 + }, + { + "state": "DE", + "year": 2026, + "state_income_tax": 5938.622126399449, + "state_refundable_credits": 31.82599280892689, + "snap_eligible_share": 0.39273527451488177, + "earnings_check": 100949.40977942833 + }, + { + "state": "DC", + "year": 2022, + "state_income_tax": 6322.3064479615205, + "state_refundable_credits": 1152.2984257337944, + "snap_eligible_share": 0.3277394358379021, + "earnings_check": 100949.40977942833 + }, + { + "state": "DC", + "year": 2026, + "state_income_tax": 3890.370956594866, + "state_refundable_credits": 3377.910985171372, + "snap_eligible_share": 0.38538110921179825, + "earnings_check": 100949.40977942833 + }, + { + "state": "FL", + "year": 2022, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.3277394358379021, + "earnings_check": 100949.40977942833 + }, + { + "state": "FL", + "year": 2026, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.38538110921179825, + "earnings_check": 100949.40977942833 + }, + { + "state": "GA", + "year": 2022, + "state_income_tax": 5817.729666686229, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.21525523111791572, + "earnings_check": 100949.40977942833 + }, + { + "state": "GA", + "year": 2026, + "state_income_tax": 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"state_income_tax": 3974.200512178105, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.3799383176802808, + "earnings_check": 100949.40977942833 + }, + { + "state": "LA", + "year": 2022, + "state_income_tax": 4448.268055684163, + "state_refundable_credits": 71.43804312983836, + "snap_eligible_share": 0.1989171401710658, + "earnings_check": 100949.40977942833 + }, + { + "state": "LA", + "year": 2026, + "state_income_tax": 3074.3212486448824, + "state_refundable_credits": 101.39428807519445, + "snap_eligible_share": 0.2837271056109273, + "earnings_check": 100949.40977942833 + }, + { + "state": "ME", + "year": 2022, + "state_income_tax": 5797.531974237218, + "state_refundable_credits": 574.3618716903577, + "snap_eligible_share": 0.31603446870410645, + "earnings_check": 100949.40977942833 + }, + { + "state": "ME", + "year": 2026, + "state_income_tax": 4903.80782286542, + "state_refundable_credits": 1437.8998119553125, + "snap_eligible_share": 0.3864177478096877, + 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100949.40977942833 + }, + { + "state": "ND", + "year": 2022, + "state_income_tax": 1337.963316294039, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.22874327115648482, + "earnings_check": 100949.40977942833 + }, + { + "state": "ND", + "year": 2026, + "state_income_tax": 992.9676854652907, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.2842769191641272, + "earnings_check": 100949.40977942833 + }, + { + "state": "OH", + "year": 2022, + "state_income_tax": 3094.952500215858, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.23704117548500492, + "earnings_check": 100949.40977942833 + }, + { + "state": "OH", + "year": 2026, + "state_income_tax": 2426.738831640137, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.2934743048199835, + "earnings_check": 100949.40977942833 + }, + { + "state": "OK", + "year": 2022, + "state_income_tax": 4730.512231803788, + "state_refundable_credits": 121.29740854024446, + "snap_eligible_share": 0.21606547622444, + "earnings_check": 100949.40977942833 + }, + { + "state": "OK", + "year": 2026, + "state_income_tax": 4406.290115354363, + "state_refundable_credits": 119.28496479180392, + "snap_eligible_share": 0.2737057241360581, + "earnings_check": 100949.40977942833 + }, + { + "state": "OR", + "year": 2022, + "state_income_tax": 8993.94353225364, + "state_refundable_credits": 288.37526274843856, + "snap_eligible_share": 0.339426691042413, + "earnings_check": 100949.40977942833 + }, + { + "state": "OR", + "year": 2026, + "state_income_tax": 8504.107829862658, + "state_refundable_credits": 503.35995090296785, + "snap_eligible_share": 0.39451352567621695, + "earnings_check": 100949.40977942833 + }, + { + "state": "PA", + "year": 2022, + "state_income_tax": 3898.91368596247, + "state_refundable_credits": 39.74424265651905, + "snap_eligible_share": 0.27535808408970897, + "earnings_check": 100949.40977942833 + }, + { + "state": "PA", + "year": 2026, + "state_income_tax": 3566.0684599164274, + "state_refundable_credits": 371.68801944851504, + "snap_eligible_share": 0.38568555722417264, + "earnings_check": 100949.40977942833 + }, + { + "state": "RI", + "year": 2022, + "state_income_tax": 4885.042172762693, + "state_refundable_credits": 199.0382490936301, + "snap_eligible_share": 0.24148167945906787, + "earnings_check": 100949.40977942833 + }, + { + "state": "RI", + "year": 2026, + "state_income_tax": 4384.383618069928, + "state_refundable_credits": 296.90106179762347, + "snap_eligible_share": 0.2835125732097495, + "earnings_check": 100949.40977942833 + }, + { + "state": "SC", + "year": 2022, + "state_income_tax": 5322.846464823121, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.21566004666108401, + "earnings_check": 100949.40977942833 + }, + { + "state": "SC", + "year": 2026, + "state_income_tax": 4417.362349975072, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.2729946614125347, + "earnings_check": 100949.40977942833 + }, + { + "state": "SD", + "year": 2022, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.16404254921703593, + "earnings_check": 100949.40977942833 + }, + { + "state": "SD", + "year": 2026, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.2144472854659317, + "earnings_check": 100949.40977942833 + }, + { + "state": "TN", + "year": 2022, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.15162120343507343, + "earnings_check": 100949.40977942833 + }, + { + "state": "TN", + "year": 2026, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.200761294302648, + "earnings_check": 100949.40977942833 + }, + { + "state": "TX", + "year": 2022, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.2094491696899193, + "earnings_check": 100949.40977942833 + }, + { + "state": "TX", + "year": 2026, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.25723534351167343, + "earnings_check": 100949.40977942833 + }, + { + "state": "UT", + "year": 2022, + "state_income_tax": 5532.289728994569, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.1588720142004796, + "earnings_check": 100949.40977942833 + }, + { + "state": "UT", + "year": 2026, + "state_income_tax": 4708.6602211037425, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.2098431709896105, + "earnings_check": 100949.40977942833 + }, + { + "state": "VT", + "year": 2022, + "state_income_tax": 5043.193078765479, + "state_refundable_credits": 985.822716085479, + "snap_eligible_share": 0.3063831940379679, + "earnings_check": 100949.40977942833 + }, + { + "state": "VT", + "year": 2026, + "state_income_tax": 4273.9083079636985, + "state_refundable_credits": 1250.5662945717318, + "snap_eligible_share": 0.3584635670996409, + "earnings_check": 100949.40977942833 + }, + { + "state": "VA", + "year": 2022, + "state_income_tax": 5363.322960245165, + "state_refundable_credits": 132.9170335416518, + "snap_eligible_share": 0.32890257197708067, + "earnings_check": 100949.40977942833 + }, + { + "state": "VA", + "year": 2026, + "state_income_tax": 5458.702028677792, + "state_refundable_credits": 280.8603820749461, + "snap_eligible_share": 0.3861198837140311, + "earnings_check": 100949.40977942833 + }, + { + "state": "WA", + "year": 2022, + "state_income_tax": -249.59040533265065, + "state_refundable_credits": 321.54962005780027, + "snap_eligible_share": 0.34024389149519463, + "earnings_check": 100949.40977942833 + }, + { + "state": "WA", + "year": 2026, + "state_income_tax": -361.76437979979227, + "state_refundable_credits": 424.90865651005464, + "snap_eligible_share": 0.39420907766384256, + "earnings_check": 100949.40977942833 + }, + { + "state": "WV", + "year": 2022, + "state_income_tax": 6999.711920840588, + "state_refundable_credits": 23.75518500808772, + "snap_eligible_share": 0.3277394358379021, + "earnings_check": 100949.40977942833 + }, + { + "state": "WV", + "year": 2026, + "state_income_tax": 4774.416912946368, + "state_refundable_credits": 31.909431978687966, + "snap_eligible_share": 0.38568555722417264, + "earnings_check": 100949.40977942833 + }, + { + "state": "WI", + "year": 2022, + "error": "ParameterNotFoundError(\"The parameter 'gov.states.wi.dcf.shares.rates.licensed_group.hourly.SOLANO_COUNTY_CA' was not found in the 2022-01-01 tax and benefit system.\")" + }, + { + "state": "WI", + "year": 2026, + "error": "ParameterNotFoundError(\"The parameter 'gov.states.wi.dcf.shares.rates.licensed_group.hourly.SOLANO_COUNTY_CA' was not found in the 2026-01-01 tax and benefit system.\")" + }, + { + "state": "WY", + "year": 2022, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.16087302337648024, + "earnings_check": 100949.40977942833 + }, + { + "state": "WY", + "year": 2026, + "state_income_tax": 0.0, + "state_refundable_credits": 0.0, + "snap_eligible_share": 0.21206257040649928, + "earnings_check": 100949.40977942833 + } + ] +} diff --git a/slideshows/appam-2026/slides/AxiomSlide.tsx b/slideshows/appam-2026/slides/AxiomSlide.tsx new file mode 100644 index 00000000..8a3a0c15 --- /dev/null +++ b/slideshows/appam-2026/slides/AxiomSlide.tsx @@ -0,0 +1,68 @@ +import CodeBlock from '@/components/content/CodeBlock'; +import Frame from './Frame'; +import { axiomEngineVersion, axiomRuns, usd } from '../content'; + +const RULESPEC = `- name: earned_income_deduction_rate + kind: parameter + metadata: + proof: + atoms: + - kind: amount + source: + corpus_citation_path: + us-ga/manual/dfcs/snap/3612 + excerpt: "20% earned income deduction" + - kind: effective_period + source: + excerpt: "Effective Date: December 2019" + versions: + - effective_from: '2019-12-01' + formula: 0.20`; + +const earnings = axiomRuns.monthlyEarnings.toLocaleString('en-US', { + style: 'currency', + currency: 'USD', +}); + +const runs = [ + { + family: `Earns ${earnings} a month and reports on time`, + result: `Deduction of ${usd(axiomRuns.onTime)} a month`, + }, + { family: 'Same family, reports late', result: `Deduction of ${usd(axiomRuns.late)}` }, + { family: 'Same family, November 2019', result: 'Engine refuses: no value at 2019-11-01' }, +]; + +export default function AxiomSlide() { + return ( + +
+ + 
+            {RULESPEC}
+
+ +
+

+ Georgia’s Division of Family and Children Services allows a 20% earned income + deduction in section 3612 of its SNAP manual. Axiom encodes that rule in RuleSpec and + records the manual’s words and date beside each value. +

+

+ Axiom Rules Engine {axiomEngineVersion}, running the rule +

+
+ {runs.map((run) => ( +
+
+ {run.family} +
+
{run.result}
+
+ ))} +
+
+
+ + ); +} diff --git a/slideshows/appam-2026/slides/CliffsSlide.tsx b/slideshows/appam-2026/slides/CliffsSlide.tsx new file mode 100644 index 00000000..5fed8f22 --- /dev/null +++ b/slideshows/appam-2026/slides/CliffsSlide.tsx @@ -0,0 +1,50 @@ +import EarningsChart from './EarningsChart'; +import Frame from './Frame'; +import { cliffs, largestCliff, medianRateAt, percent, usd } from '../content'; + +export default function CliffsSlide() { + return ( + +
+
+

+ Net resources by earnings, one line per state, 2026 +

+
+ +
+
+
+
+

+ A $500 raise crosses the subsidy limit +

+
    + {cliffs.map((cliff) => ( +
  • + + {cliff.name}, {usd(cliff.after)} + + {usd(-cliff.drop)} +
    • + ))} +
+
+
+

+ Total effective marginal tax rate, median state +

+
    + {[30_000, 60_000].map((earnings) => ( +
  • + At {usd(earnings)} + {percent(medianRateAt(earnings))} +
    • + ))} +
+
+
+
+ + ); +} diff --git a/slideshows/appam-2026/slides/CoverSlide.tsx b/slideshows/appam-2026/slides/CoverSlide.tsx index 2e51c0c1..9bfe8fae 100644 --- a/slideshows/appam-2026/slides/CoverSlide.tsx +++ b/slideshows/appam-2026/slides/CoverSlide.tsx @@ -1,5 +1,13 @@ import CoverSlideTemplate from '@/components/layout/CoverSlide'; +import { speakers } from '@/lib/speakers'; export default function CoverSlide() { - return ; + return ( + + ); } diff --git a/slideshows/appam-2026/slides/EarningsChart.tsx b/slideshows/appam-2026/slides/EarningsChart.tsx new file mode 100644 index 00000000..25593464 --- /dev/null +++ b/slideshows/appam-2026/slides/EarningsChart.tsx @@ -0,0 +1,103 @@ +import { cliffs, sweep, sweepEarnings, usd } from '../content'; + +const WIDTH = 1200; +const HEIGHT = 600; +const LEFT = 76; +const RIGHT = 24; +const TOP = 14; +const BOTTOM = 40; +const Y_MIN = 20_000; +const Y_MAX = 90_000; +const X_TICKS = [20_000, 40_000, 60_000, 80_000, 100_000]; +const Y_TICKS = [20_000, 40_000, 60_000, 80_000]; +// A cliff past this point labels to its left, away from the chart's edge. +const LAST_LABEL_ON_RIGHT = 90_000; + +const SERIES_COLORS: Record = { + IA: 'var(--color-teal-600)', + MN: 'var(--pe-amber)', + MD: 'var(--chart-4)', +}; + +const xMin = sweepEarnings[0]; +const xMax = sweepEarnings[sweepEarnings.length - 1]; +const x = (value: number) => LEFT + ((value - xMin) / (xMax - xMin)) * (WIDTH - LEFT - RIGHT); +const y = (value: number) => + TOP + (1 - (value - Y_MIN) / (Y_MAX - Y_MIN)) * (HEIGHT - TOP - BOTTOM); + +function linePath(values: number[]) { + return values + .map((value, i) => `${i === 0 ? 'M' : 'L'}${x(sweepEarnings[i]).toFixed(1)},${y(value).toFixed(1)}`) + .join(''); +} + +export default function EarningsChart() { + const states = sweep as Record; + const highlighted = new Set(cliffs.map((cliff) => cliff.code)); + return ( + + {Y_TICKS.map((tick) => ( + + + + ${tick / 1000}K + + + ))} + {X_TICKS.map((tick) => ( + + ${tick / 1000}K + + ))} + {Object.entries(states) + .filter(([code]) => !highlighted.has(code)) + .map(([code, values]) => ( + + {code} + + ))} + {cliffs.map((cliff) => { + const values = states[cliff.code]; + return ( + + + {`${cliff.name}: loses ${usd(cliff.drop)} at ${usd(cliff.after)}`} + + LAST_LABEL_ON_RIGHT ? -10 : 10)} + y={y(cliff.net_after) + 30} + textAnchor={cliff.after > LAST_LABEL_ON_RIGHT ? 'end' : 'start'} + fontSize="23" + fontWeight="600" + fill="var(--color-gray-800)" + stroke="white" + strokeWidth="5" + paintOrder="stroke" + > + {cliff.name} + + + ); + })} + + ); +} diff --git a/slideshows/appam-2026/slides/EndSlide.tsx b/slideshows/appam-2026/slides/EndSlide.tsx index 15d654f6..5796a630 100644 --- a/slideshows/appam-2026/slides/EndSlide.tsx +++ b/slideshows/appam-2026/slides/EndSlide.tsx @@ -1,5 +1,19 @@ import EndSlideTemplate from '@/components/layout/EndSlide'; +const links = [ + { label: 'policyengine.org', url: 'https://policyengine.org' }, + { label: 'pip install policyengine-us', url: 'https://github.com/PolicyEngine/policyengine-us' }, + { label: 'max@policyengine.org', url: 'mailto:max@policyengine.org' }, +]; + export default function EndSlide() { - return ; + return ( + + ); } diff --git a/slideshows/appam-2026/slides/FamilyChart.tsx b/slideshows/appam-2026/slides/FamilyChart.tsx new file mode 100644 index 00000000..d08fab45 --- /dev/null +++ b/slideshows/appam-2026/slides/FamilyChart.tsx @@ -0,0 +1,141 @@ +import { childCareBill, crossSection, highest, lowest, median, StateRow, usd } from '../content'; + +const WIDTH = 1790; +const HEIGHT = 540; +const LEFT = 76; +const TOP = 44; +const BOTTOM = 34; +const AXIS_MAX = 60_000; +const BAR_WIDTH = 24; +const SURFACE_GAP = 2; +const CORNER = 4; +const TICKS = [0, 20_000, 40_000, 60_000]; +// Lifts the lowest state's label clear of its neighbours, which rise to about $36,000. +const LOW_CALLOUT_LEVEL = 38_000; + +const plotHeight = HEIGHT - TOP - BOTTOM; +const band = (WIDTH - LEFT) / crossSection.length; +const baseline = TOP + plotHeight; +const toHeight = (value: number) => (value / AXIS_MAX) * plotHeight; + +export const SERIES = [ + { + key: 'base', + label: `Earnings after taxes and credits, minus the ${usd(childCareBill)} child care bill`, + color: 'var(--color-gray-300)', + value: (row: StateRow) => row.base, + }, + { + key: 'subsidy', + label: 'Child care subsidy', + color: 'var(--color-teal-600)', + value: (row: StateRow) => row.child_care_subsidy, + }, + { + key: 'snap', + label: 'SNAP', + color: 'var(--pe-amber)', + value: (row: StateRow) => row.snap, + }, + { + key: 'other', + label: 'WIC and state payments', + color: 'var(--chart-4)', + value: (row: StateRow) => row.wic + row.tanf + row.other_benefits, + }, +]; + +function roundedTop(x: number, y: number, width: number, height: number) { + const r = Math.min(CORNER, height); + return `M${x},${y + height}V${y + r}Q${x},${y} ${x + r},${y}H${x + width - r}Q${x + width},${y} ${x + width},${y + r}V${y + height}Z`; +} + +function describe(row: StateRow) { + const lines = SERIES.map((series) => `${series.label}: ${usd(series.value(row))}`); + return `${row.name}, ${row.county}: ${usd(row.net_resources)}\n${lines.join('\n')}`; +} + +function Column({ row, index }: { row: StateRow; index: number }) { + const x = LEFT + index * band + (band - BAR_WIDTH) / 2; + const segments = SERIES.map((series) => ({ ...series, amount: series.value(row) })).filter( + (segment) => segment.amount > 0, + ); + let top = baseline; + return ( + + {describe(row)} + + {segments.map((segment, i) => { + const height = toHeight(segment.amount); + top -= height; + const drawn = Math.max(height - SURFACE_GAP, 1); + const y = top + SURFACE_GAP; + return i === segments.length - 1 ? ( + + ) : ( + + ); + })} + + {row.code} + + + ); +} + +function Callout({ + row, + anchor, + note, + raisedTo, +}: { + row: StateRow; + anchor: 'start' | 'middle' | 'end'; + note?: string; + raisedTo?: number; +}) { + const index = crossSection.indexOf(row); + const center = LEFT + index * band + band / 2; + const x = anchor === 'start' ? center - BAR_WIDTH / 2 : anchor === 'end' ? center + BAR_WIDTH / 2 : center; + const barTop = baseline - toHeight(row.net_resources); + const y = baseline - toHeight(raisedTo ?? row.net_resources) - 12; + return ( + + {raisedTo && ( + + )} + + {note ? `${note}, ${row.name}` : row.name} {usd(row.net_resources)} + + + ); +} + +export default function FamilyChart() { + return ( + + {TICKS.map((tick) => { + const y = baseline - toHeight(tick); + return ( + + + + {tick === 0 ? '$0' : `$${tick / 1000}K`} + + + ); + })} + {crossSection.map((row, index) => ( + + ))} + + + + + ); +} diff --git a/slideshows/appam-2026/slides/Frame.tsx b/slideshows/appam-2026/slides/Frame.tsx new file mode 100644 index 00000000..776c7b71 --- /dev/null +++ b/slideshows/appam-2026/slides/Frame.tsx @@ -0,0 +1,27 @@ +import { ReactNode } from 'react'; +import Slide from '@/components/core/Slide'; +import SlideHeader from '@/components/layout/SlideHeader'; + +/** Sizes scale with the window so a slide fits a laptop and a projector alike. */ +export default function Frame({ title, children }: { title: string; children: ReactNode }) { + return ( + +
+ +

+ {title} +

+ +
+ {children} +
+
+ + ); +} diff --git a/slideshows/appam-2026/slides/LineRugs.tsx b/slideshows/appam-2026/slides/LineRugs.tsx new file mode 100644 index 00000000..06be9578 --- /dev/null +++ b/slideshows/appam-2026/slides/LineRugs.tsx @@ -0,0 +1,95 @@ +import { programLines, usd } from '../content'; + +const WIDTH = 1240; +const HEIGHT = 560; +const LEFT = 330; +const RIGHT = 190; +const AXIS_MIN = -3_000; +const AXIS_MAX = 19_000; +const ROW_TOP = 8; +const ROW_PITCH = 124; +const TICK_HEIGHT = 84; +const TICK_WIDTH = 4; +const AXIS_TICKS = [0, 5_000, 10_000, 15_000]; +const MAX_NAMED_STATES = 3; + +const LABELS: Record = { + child_care_subsidy: ['Child care', 'subsidy'], + snap: ['SNAP'], + state_taxes: ['State credits', 'minus state taxes'], + federal_taxes: ['Federal credits', 'minus federal taxes'], +}; + +const x = (value: number) => + LEFT + ((value - AXIS_MIN) / (AXIS_MAX - AXIS_MIN)) * (WIDTH - LEFT - RIGHT); + +function endLabel(values: { code: string; value: number }[], value: number) { + const tied = values.filter((entry) => entry.value === value); + const who = + tied.length > MAX_NAMED_STATES + ? `${tied.length} states` + : tied.map((entry) => entry.code).join(', '); + return `${who} ${usd(value)}`; +} + +export default function LineRugs() { + const axisY = ROW_TOP + ROW_PITCH * programLines.length; + return ( + + {AXIS_TICKS.map((tick) => ( + + + + {tick === 0 ? '$0' : `$${tick / 1000}K`} + + + ))} + {programLines.map((line, row) => { + const top = ROW_TOP + row * ROW_PITCH; + const low = line.values[0]; + const high = line.values[line.values.length - 1]; + const labelLines = LABELS[line.key]; + return ( + + {labelLines.map((text, i) => ( + + {text} + + ))} + {line.values.map((entry) => ( + + {`${entry.code}: ${usd(entry.value)}`} + + ))} + + {endLabel(line.values, low.value)} + + + {endLabel(line.values, high.value)} + + + ); + })} + + ); +} diff --git a/slideshows/appam-2026/slides/OneFamilySlide.tsx b/slideshows/appam-2026/slides/OneFamilySlide.tsx new file mode 100644 index 00000000..1359d910 --- /dev/null +++ b/slideshows/appam-2026/slides/OneFamilySlide.tsx @@ -0,0 +1,52 @@ +import FamilyChart, { SERIES } from './FamilyChart'; +import Frame from './Frame'; +import { + family, + headlineEarnings, + highest, + lowest, + modelCommit, + modelVersion, + spreadAt, + usd, +} from '../content'; + +export default function OneFamilySlide() { + return ( + +
+
+ {SERIES.map((series) => ( + + + {series.label} + + ))} +
+
+ +
+
+

+ One parent, {family.parent_age}, earns {usd(headlineEarnings)} working full time and has + children aged {family.child_ages[0]} and {family.child_ages[1]},{' '} + {usd(family.rent_per_month)} monthly rent and a {usd(family.child_care_per_month)}{' '} + monthly child care bill. They apply for each program as a new applicant, in each + state’s largest county, under 2026 rules. PolicyEngine US {modelVersion} ( + {modelCommit}). +

+

+ Net resources add earnings and benefits, then subtract taxes net of credits and the + child care the family pays. They leave out health coverage. The gap between the highest + and lowest state runs {usd(spreadAt(30_000).spread)} at $30,000 of earnings and{' '} + {usd(spreadAt(60_000).spread)} at $60,000. South Dakota’s subsidy covers five + months. +

+
+
+ + ); +} diff --git a/slideshows/appam-2026/slides/ParametersSlide.tsx b/slideshows/appam-2026/slides/ParametersSlide.tsx new file mode 100644 index 00000000..f767bdec --- /dev/null +++ b/slideshows/appam-2026/slides/ParametersSlide.tsx @@ -0,0 +1,39 @@ +import CodeBlock from '@/components/content/CodeBlock'; +import Frame from './Frame'; + +const YAML = `description: Massachusetts provides this amount for each + dependent in its child and family tax credit. +values: + 2021-01-01: 180 + 2023-01-01: 310 + 2024-01-01: 440 +metadata: + reference: + - title: Massachusetts General Laws Part I Title IX + Chapter 62 Section 6 (y) (ii) (B) + href: https://malegislature.gov/Laws/GeneralLaws/... + - title: Massachusetts Session Laws Chapter 50 of the + Acts of 2023 Section 22 & Section 45 + href: https://malegislature.gov/Laws/SessionLaws/...`; + +export default function ParametersSlide() { + return ( + +
+ + 
+            {YAML}
+
+ +
+

+ The Massachusetts Legislature raised this credit to $310 for 2023 and $440 for 2024, and + lifted its two-dependent cap. +

+

Ask for any date and the model reads the value in force on that date.

+

Open the file and check each value against the citation beside it.

+
+
+ + ); +} diff --git a/slideshows/appam-2026/slides/ScopeSlide.tsx b/slideshows/appam-2026/slides/ScopeSlide.tsx new file mode 100644 index 00000000..9456d8a9 --- /dev/null +++ b/slideshows/appam-2026/slides/ScopeSlide.tsx @@ -0,0 +1,41 @@ +import Frame from './Frame'; + +const coverage = [ + 'Child care subsidies cover 46 jurisdictions through 2023, 48 in 2024 and all 51 from 2025.', + 'We copy each parameter’s earliest value back to 2015, so validate historical runs separately.', + 'The model applies Wisconsin’s current child care schedules to earlier years.', + 'South Dakota’s child care program starts in the model in August 2026.', +]; + +const mechanisms = [ + 'Earnings enter as an input, so minimum wage and paid leave laws leave a family’s income unchanged.', + 'Hours and earnings stay fixed when a reform changes rates or benefits, unless you set a labor supply response.', + 'The Medicaid model leaves out medically needy groups other than aged, blind and disabled people, and some spenddown rules.', + 'The family here claims all it qualifies for. Population runs use the take-up flags in Populace US.', +]; + +function Column({ title, items }: { title: string; items: string[] }) { + return ( +
+

+ {title} +

+
    + {items.map((item) => ( +
  • {item}
    • + ))} +
+
+ ); +} + +export default function ScopeSlide() { + return ( + +
+ + +
+ + ); +} diff --git a/slideshows/appam-2026/slides/ToolsSlide.tsx b/slideshows/appam-2026/slides/ToolsSlide.tsx new file mode 100644 index 00000000..360e500b --- /dev/null +++ b/slideshows/appam-2026/slides/ToolsSlide.tsx @@ -0,0 +1,63 @@ +import Frame from './Frame'; + +const rows = [ + { + question: 'What did each state’s rules say, year by year?', + tools: 'Welfare Rules Database and CCDF Policies Database (Urban), Policy Tracking System (PN-3)', + }, + { + question: 'What could a family receive across programs and states?', + tools: 'State Safety Net Interactive, 2001–2024 (Brookings), Policy Impact Calculator (PN-3), PolicyEngine', + }, + { + question: 'Who qualifies and takes part, state by state?', + tools: 'State of the Safety Net, from ATTIS and the American Community Survey (Urban)', + }, + { + question: 'What would a change do?', + tools: 'Policy Impact Calculator (PN-3), State of the Safety Net what-ifs (Urban), PolicyEngine', + }, +]; + +const additions = [ + { title: 'Reforms', body: 'Change any parameter, or write new rules in Python.' }, + { title: 'Microdata', body: 'Run your own survey or administrative file.' }, + { title: 'Open code', body: 'Read and edit every rule.' }, + { title: 'Current law forward', body: 'Project through 2035.' }, +]; + +export default function ToolsSlide() { + return ( + +
+ + + + + + + + + {rows.map((row) => ( + + + + + ))} + +
QuestionTools
{row.question}{row.tools}
+
+

What PolicyEngine adds

+
+ {additions.map((addition) => ( +
+

{addition.title}

+

{addition.body}

+
+ ))} +
+
+
+ + ); +} diff --git a/slideshows/appam-2026/slides/WhatItDoesSlide.tsx b/slideshows/appam-2026/slides/WhatItDoesSlide.tsx new file mode 100644 index 00000000..6bfdfe82 --- /dev/null +++ b/slideshows/appam-2026/slides/WhatItDoesSlide.tsx @@ -0,0 +1,45 @@ +import Frame from './Frame'; + +const uses = [ + { + title: 'Compare families', + body: 'Net resources, effective marginal tax rates and benefit cliffs, across states and recent years.', + }, + { + title: 'Measure your own sample', + body: 'Simulated eligibility and benefits for the households in your survey or administrative data.', + }, + { + title: 'Estimate reforms', + body: 'How proposed reforms would change poverty and household incomes in each state.', + }, +]; + +export default function WhatItDoesSlide() { + return ( + +
+

+ PolicyEngine: a nonprofit that provides free, open source software to compute the impact + of public policy. +

+
+ {uses.map((use) => ( +
+

+ {use.title} +

+

{use.body}

+
+ ))} +
+

+ The US model covers federal and state income taxes and credits, SNAP, SSI, TANF, WIC, + school meals, child care subsidies, Medicaid and CHIP eligibility, and ACA premium tax + credits. It includes every state income tax and the TANF and child care subsidy rules of + all 50 states and DC. +

+
+ + ); +} diff --git a/slideshows/appam-2026/slides/WhichLinesSlide.tsx b/slideshows/appam-2026/slides/WhichLinesSlide.tsx new file mode 100644 index 00000000..646014c9 --- /dev/null +++ b/slideshows/appam-2026/slides/WhichLinesSlide.tsx @@ -0,0 +1,51 @@ +import Frame from './Frame'; +import LineRugs from './LineRugs'; +import { headlineEarnings, interaction, usd } from '../content'; + +const rows = [ + { label: 'Child care subsidy', value: interaction.subsidy }, + { label: 'SNAP', value: interaction.snapChange }, + { label: 'Net resources', value: interaction.netChange }, +]; + +function signed(value: number) { + return value > 0 ? `+${usd(value)}` : usd(value); +} + +export default function WhichLinesSlide() { + return ( + +
+
+

+ Each mark shows one state, for the family earning {usd(headlineEarnings)}. +

+
+ +
+
+
+

+ Texas, with and without the subsidy +

+
+ {rows.map((row, i) => ( +
+
{row.label}
+
+ {signed(row.value)} +
+
+ ))} +
+

+ SNAP deducts what the family pays for care, so the subsidy shrinks that deduction. +

+
+
+ + ); +} From b74f4f324a7ab3c471ca387a95a13f34f929da50 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 14:11:32 -0500 Subject: [PATCH 04/32] Add the APPAM deck's microdata slide and evidence ledger Runs one fixed sample through every state's rules, keeps household roles in the flat file, and restores a space the JSX compiler dropped on the first chart. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 78 ++ .../appam-2026/analysis/sample_families.py | 3 +- slideshows/appam-2026/config.ts | 2 + slideshows/appam-2026/content.test.ts | 27 + slideshows/appam-2026/content.ts | 19 + .../appam-2026/data/simulated_instrument.json | 845 +++++------------- .../appam-2026/slides/InstrumentRugs.tsx | 73 ++ .../appam-2026/slides/MicrodataSlide.tsx | 43 + .../appam-2026/slides/OneFamilySlide.tsx | 4 +- 9 files changed, 477 insertions(+), 617 deletions(-) create mode 100644 slideshows/appam-2026/EVIDENCE.md create mode 100644 slideshows/appam-2026/slides/InstrumentRugs.tsx create mode 100644 slideshows/appam-2026/slides/MicrodataSlide.tsx diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md new file mode 100644 index 00000000..495ae4de --- /dev/null +++ b/slideshows/appam-2026/EVIDENCE.md @@ -0,0 +1,78 @@ +# Evidence ledger for the APPAM 2026 deck + +Each claim on a slide traces to a computed result, to code read at a named commit, or to a public source read while building this deck. Messaging documents do not count as evidence here. + +- **PolicyEngine US:** version 2.90.2, commit `469ad12cb957a1b3eaedf9e6dd3e4c69194bba26` (main, November 1, 2026). +- **Axiom Rules Engine:** version 0.2.2, commit `e0bda6475f30ac487b7be544f5b9742c716e4bba`. +- **rulespec-us:** commit `66d891b9df601c35c2edbf7e669529a649141d31`. + +## Computed results + +| Slide | Claim | Source | +| --- | --- | --- | +| 3 | $27,711 in Ohio, $54,345 in Alaska, median Delaware $46,920 | `analysis/compute_family.py` → `data/family.json` → `analysis/summarize.py` → `data/charts.json` | +| 3 | Gap of $16,404 at $30,000 and $21,670 at $60,000 | `data/charts.json`, `spreads` | +| 4 | Each state's value on four lines | `data/charts.json`, `lines_by_program` | +| 4 | Texas: $15,791 subsidy, −$5,598 SNAP, +$10,193 net resources | `analysis/interaction.py` → `data/interaction.json` | +| 5 | Iowa −$14,967 at $44,000, Minnesota −$13,182 at $57,000, Maryland −$15,343 at $97,500 | `data/charts.json`, `cliffs`, which also records each line's change | +| 5 | Median state's total effective marginal tax rate: 63.6% at $30,000, 24.6% at $60,000 | `data/charts.json`, `emtr`: one minus the change in net resources over a $500 raise | +| 11 | $666.666 deduction on time, $0 late, refusal for November 2019 | `analysis/axiom_ga_3612.py` → `data/axiom_ga_3612.json`, a real engine run | + +## The family + +`analysis/household.py` defines the family. + +- One parent, 30, with children aged 1 and 4. The parent works 40 hours for 52 weeks. Rent runs $1,200 a month and child care $1,500 a month. +- `analysis/largest_counties.py` picks each state's largest county from the 2017 to 2021 American Community Survey. +- `meets_ccdf_activity_test` starts true, because a parent in a regular job meets it and the input defaults to false. +- `wi_works_placement` starts as `NONE`, because the input defaults to a Community Service Job. +- $40,000 over 2,080 hours comes to $19.23 an hour. The highest basic state rate on the Department of Labor's table, updated July 1, 2026, belongs to DC at $18.40 (dol.gov/agencies/whd/minimum-wage/state). Local minimums can run higher, and the table leaves them out. + +Net resources add market income, SNAP, WIC, TANF, the child care subsidy and other benefits, then subtract federal and state taxes net of credits and the child care bill. + +- `analysis/test_invariants.py` reconciles the total with `spm_unit_net_income` and with `household_net_income` over random states and earnings. +- A separate pass checked the household reconciliation at all 241 earnings points in all 51 states, and it holds to within two cents. + +The reconciliation shows how the model's totals differ, and these differences belong in any state comparison that uses them. + +- `spm_unit_market_income` leaves out the Alaska Permanent Fund Dividend, which `household_market_income` includes. +- `spm_unit_benefits` leaves out New Jersey's ANCHOR payment, which `household_benefits` includes. +- `household_benefits` leaves out Alaska's energy relief payment, which `spm_unit_benefits` includes. +- `household_benefits` counts the child care subsidy in some states and not others. `spm_unit_benefits` counts it for Colorado, Washington and West Virginia alone. + +Net resources count all four payments in every state. + +## Why states land where they do + +- **Ohio:** `oh_ccap_fpg` sets the initial limit at 145% of the guideline, and enrolled families stay eligible to 300%. The $40,000 new applicant falls between them. +- **Nevada:** the intake limit sits at 41% of state median income, about $34,674, and the renewal limit at 49%. +- **Oklahoma:** the model uses the default one-star rate, $555 a month. +- **South Dakota:** `sd_cca` starts in August 2026, so $7,500 equals five months at $1,500. +- **Provider tier:** every state's default sits at its base or lowest tier. +- **Alaska:** other payments total $3,600, from a $1,000 dividend per person and $200 of energy relief per person. + +## Mechanisms + +| Slide | Claim | Code at `469ad12` | +| --- | --- | --- | +| 2 | Program list, every state income tax, TANF and child care rules of all 51 jurisdictions | `STATE_TANF_VARIABLES` holds 51 entries; `child_care_subsidy_programs` holds 51 from 2025 | +| 4 | SNAP deducts what the family pays for care | `snap_dependent_care_deduction` reads post-subsidy `childcare_expenses` | +| 6 | Massachusetts credit values, dates and citations | `parameters/gov/states/ma/tax/income/credits/child_and_family/amount.yaml` and `dependent_cap.yaml` | +| 10 | 46 jurisdictions through 2023, 48 in 2024, 51 from 2025 | `gov.hhs.ccdf.child_care_subsidy_programs`; missing DC, NC, OR, TN and TX through 2023, and DC, TN and TX in 2024 | +| 10 | Copy back to 2015 | `backdate_parameters(first_instant="2015-01-01")` in `system.py`, `FIRST_MODELED_YEAR = 2015` in `tools/parameters.py` | +| 10 | Wisconsin's schedules | Shares dates start 2025-10-01, and the model returns subsidies of $14,717 in 2021 and $15,483 in 2023 | +| 10 | Earnings enter as inputs | The minimum wage appears only in eligibility tests; `pfml_benefits` holds benefits received | +| 10 | Labor supply | Elasticities default to zero | +| 10 | Medicaid limits | `is_medically_needy_for_medicaid` documentation | +| 10 | Take-up | `takes_up_snap_if_eligible` defaults to true, and Populace US carries `takes_up_*` flags | + +## The other tools + +Slide 9 uses each tool's own description from Cynthia Osborne's session description of October 16, 2026, which covers the Policy Tracking System, the Policy Impact Calculator, the State Safety Net Interactive, the Welfare Rules Database, the CCDF Policies Database, and State of the Safety Net with ATTIS. + +## Axiom + +- The rule file sits at `us-ga/policies/dfcs/snap/3612.yaml` in `TheAxiomFoundation/rulespec-us`, published under CC BY 4.0. +- `axiom-rules-engine` carries an MIT license. +- Georgia's public manual at pamms.dhs.ga.gov/dfcs/snap/3612 confirms the rule text and the December 2019 effective date. +- The deck makes no claim about how much of any program Axiom covers. diff --git a/slideshows/appam-2026/analysis/sample_families.py b/slideshows/appam-2026/analysis/sample_families.py index c5f68a70..1f050cac 100644 --- a/slideshows/appam-2026/analysis/sample_families.py +++ b/slideshows/appam-2026/analysis/sample_families.py @@ -27,7 +27,8 @@ def main(path): ) frame = sim.to_input_dataframe() sample = frame.loc[frame[f"person_household_id__{YEAR}"].isin(chosen)] - columns = [c for c in sample.columns if c.endswith(f"__{YEAR}")] + # Household roles such as is_household_head hold for all time, not one year. + columns = [c for c in sample.columns if c.endswith((f"__{YEAR}", "__ETERNITY"))] sample = sample[columns].reset_index(drop=True) sample[f"person_id__{YEAR}"] = sample.index sample.to_pickle(path) diff --git a/slideshows/appam-2026/config.ts b/slideshows/appam-2026/config.ts index 1aea58c1..a75c455c 100644 --- a/slideshows/appam-2026/config.ts +++ b/slideshows/appam-2026/config.ts @@ -4,6 +4,7 @@ import AxiomSlide from './slides/AxiomSlide'; import CliffsSlide from './slides/CliffsSlide'; import CoverSlide from './slides/CoverSlide'; import EndSlide from './slides/EndSlide'; +import MicrodataSlide from './slides/MicrodataSlide'; import OneFamilySlide from './slides/OneFamilySlide'; import ParametersSlide from './slides/ParametersSlide'; import ScopeSlide from './slides/ScopeSlide'; @@ -27,6 +28,7 @@ export const appam2026Config: SlideshowConfig = { WhichLinesSlide, CliffsSlide, ParametersSlide, + MicrodataSlide, ToolsSlide, ScopeSlide, AxiomSlide, diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index ab0342dc..27682119 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -8,6 +8,7 @@ import { crossSection, headlineEarnings, highest, + instrument, interaction, lowest, programLines, @@ -136,3 +137,29 @@ describe('Axiom run', () => { expect(axiomRuns.beforeEffectiveDate.stderr).toContain('2019-11-01'); }); }); + +describe('fixed-sample microdata run', () => { + const STATE_COUNT = 51; + const CENT = 0.01; + + it('reports every state, with no failed run', () => { + const codes = instrument.rows.map((row) => row.state); + expect(new Set(codes).size).toBe(STATE_COUNT); + for (const row of instrument.rows) expect(row).not.toHaveProperty('error'); + }); + + it('keeps the same earnings in every state, because only the state changes', () => { + const earnings = instrument.rows.map((row) => row.earnings_check); + expect(Math.max(...earnings) - Math.min(...earnings)).toBeLessThan(CENT); + }); + + it('holds each share between zero and one, sorted for the rug plot', () => { + for (const line of instrument.lines) { + const values = line.values.map((entry) => entry.value); + expect(values).toHaveLength(STATE_COUNT); + expect(values).toEqual([...values].sort((a, b) => a - b)); + expect(values[0]).toBeGreaterThanOrEqual(0); + expect(values[STATE_COUNT - 1]).toBeLessThanOrEqual(1); + } + }); +}); diff --git a/slideshows/appam-2026/content.ts b/slideshows/appam-2026/content.ts index cb1391f0..8515e56f 100644 --- a/slideshows/appam-2026/content.ts +++ b/slideshows/appam-2026/content.ts @@ -1,5 +1,6 @@ import axiomRun from './data/axiom_ga_3612.json'; import charts from './data/charts.json'; +import instrumentRun from './data/simulated_instrument.json'; import interactionRun from './data/interaction.json'; export type StateRow = (typeof charts.cross_section)[number]; @@ -45,6 +46,24 @@ export const interaction = { netChange: withSubsidy.net_resources - withoutSubsidy.net_resources, }; +function instrumentLine(key: 'snap_eligible_share' | 'child_medicaid_chip_share', label: string) { + const values = instrumentRun.rows + .map((row) => ({ code: row.state, value: row[key] })) + .sort((a, b) => a.value - b.value); + return { key, label, values }; +} + +export const instrument = { + year: instrumentRun.year, + households: instrumentRun.households, + people: instrumentRun.people, + rows: instrumentRun.rows, + lines: [ + instrumentLine('snap_eligible_share', 'SNAP eligibility'), + instrumentLine('child_medicaid_chip_share', 'Child Medicaid or CHIP eligibility'), + ], +}; + export const axiomEngineVersion = axiomRun.engine.engine_version; const DEDUCTION_KEY = 'us-ga:policies/dfcs/snap/3612#earned_income_deduction'; diff --git a/slideshows/appam-2026/data/simulated_instrument.json b/slideshows/appam-2026/data/simulated_instrument.json index 8a4de0a5..4aa6cfa1 100644 --- a/slideshows/appam-2026/data/simulated_instrument.json +++ b/slideshows/appam-2026/data/simulated_instrument.json @@ -1,800 +1,417 @@ { "policyengine_us_version": "2.90.2", "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "year": 2024, "households": 3000, "people": 12307, "rows": [ { "state": "AL", - "year": 2022, - "state_income_tax": 4485.809173763909, + "snap_eligible_share": 0.24787442773054283, + "child_medicaid_chip_share": 0.4631506613983852, + "state_income_tax": 4642.204943516523, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.21581993685323644, - "earnings_check": 100949.40977942833 - }, - { - "state": "AL", - "year": 2026, - "state_income_tax": 4588.0528968895105, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.27273015634498454, - "earnings_check": 100949.40977942833 - }, - { - "state": "AK", - "year": 2022, - "state_income_tax": 0.0, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.21139554382680845, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "AK", - "year": 2026, + "snap_eligible_share": 0.23250490516677566, + "child_medicaid_chip_share": 0.5119395292904999, "state_income_tax": 0.0, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.4778980389115625, - "earnings_check": 100949.40977942833 - }, - { - "state": "AZ", - "year": 2022, - "state_income_tax": 2708.1628614486253, - "state_refundable_credits": 22.014940747364953, - "snap_eligible_share": 0.30940357910552757, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "AZ", - "year": 2026, - "state_income_tax": 2217.419111177293, - "state_refundable_credits": 22.014940747364953, - "snap_eligible_share": 0.3621446172062773, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.3384565075212557, + "child_medicaid_chip_share": 0.3989005325545439, + "state_income_tax": 2397.5109981310766, + "state_refundable_credits": 22.110333333333333, + "earnings_check": 100935.56711598643 }, { "state": "AR", - "year": 2022, - "state_income_tax": 4620.748735330941, + "snap_eligible_share": 0.17593198168737736, + "child_medicaid_chip_share": 0.41470537708297545, + "state_income_tax": 3993.160017771403, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.15129322245795396, - "earnings_check": 100949.40977942833 - }, - { - "state": "AR", - "year": 2026, - "state_income_tax": 3694.750731688466, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.20043331332552852, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "CA", - "year": 2022, - "state_income_tax": 5632.303612215754, - "state_refundable_credits": 292.8980186497598, - "snap_eligible_share": 0.30666169296258833, - "earnings_check": 100949.40977942833 - }, - { - "state": "CA", - "year": 2026, - "state_income_tax": 5117.564771444724, - "state_refundable_credits": 386.65159029200817, - "snap_eligible_share": 0.3563302663294956, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.34238064094179205, + "child_medicaid_chip_share": 0.5627898986428449, + "state_income_tax": 5662.352503046731, + "state_refundable_credits": 330.14634842966, + "earnings_check": 100935.56711598643 }, { "state": "CO", - "year": 2022, - "state_income_tax": 3523.1796229432753, - "state_refundable_credits": 1012.3661921374396, - "snap_eligible_share": 0.21812456374664588, - "earnings_check": 100949.40977942833 - }, - { - "state": "CO", - "year": 2026, - "state_income_tax": 3646.888359844286, - "state_refundable_credits": 777.4938287318408, - "snap_eligible_share": 0.38460441596669925, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.25277959450621323, + "child_medicaid_chip_share": 0.4452843154097234, + "state_income_tax": 801.2072039235433, + "state_refundable_credits": 3671.9969935913086, + "earnings_check": 100935.56711598643 }, { "state": "CT", - "year": 2022, - "state_income_tax": 6191.306203911877, - "state_refundable_credits": 380.2945731849094, - "snap_eligible_share": 0.3060862058461477, - "earnings_check": 100949.40977942833 - }, - { - "state": "CT", - "year": 2026, - "state_income_tax": 5441.162527788743, - "state_refundable_credits": 811.0613478962448, - "snap_eligible_share": 0.38568555722417264, - "earnings_check": 100949.40977942833 - }, - { - "state": "DE", - "year": 2022, - "state_income_tax": 6026.0429877915785, - "state_refundable_credits": 26.418675118731436, - "snap_eligible_share": 0.33832520536218874, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.3616742969260955, + "child_medicaid_chip_share": 0.5205291187081258, + "state_income_tax": 5876.206705067555, + "state_refundable_credits": 621.6451496944427, + "earnings_check": 100935.56711598643 }, { "state": "DE", - "year": 2026, - "state_income_tax": 5938.622126399449, - "state_refundable_credits": 31.82599280892689, - "snap_eligible_share": 0.39273527451488177, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.3731196860693264, + "child_medicaid_chip_share": 0.4083490809139323, + "state_income_tax": 6186.524370158315, + "state_refundable_credits": 29.73923017076651, + "earnings_check": 100935.56711598643 }, { "state": "DC", - "year": 2022, - "state_income_tax": 6322.3064479615205, - "state_refundable_credits": 1152.2984257337944, - "snap_eligible_share": 0.3277394358379021, - "earnings_check": 100949.40977942833 - 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"state_income_tax": 4142.077972152309, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.3800681961577649, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "ND", - "year": 2022, - "state_income_tax": 1337.963316294039, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.22874327115648482, - "earnings_check": 100949.40977942833 - }, - { - "state": "ND", - "year": 2026, - "state_income_tax": 992.9676854652907, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.2842769191641272, - "earnings_check": 100949.40977942833 - }, - { - "state": "OH", - "year": 2022, - "state_income_tax": 3094.952500215858, + "snap_eligible_share": 0.26324395029431, + "child_medicaid_chip_share": 0.4631506613983852, + "state_income_tax": 1108.540477870961, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.23704117548500492, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "OH", - "year": 2026, - "state_income_tax": 2426.738831640137, + "snap_eligible_share": 0.27174623937213865, + "child_medicaid_chip_share": 0.471740250816011, + "state_income_tax": 2838.545349334717, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.2934743048199835, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "OK", - "year": 2022, - "state_income_tax": 4730.512231803788, - "state_refundable_credits": 121.29740854024446, - "snap_eligible_share": 0.21606547622444, - "earnings_check": 100949.40977942833 - }, - { - "state": "OK", - "year": 2026, - "state_income_tax": 4406.290115354363, - "state_refundable_credits": 119.28496479180392, - "snap_eligible_share": 0.2737057241360581, - "earnings_check": 100949.40977942833 - }, - { - "state": "OR", - "year": 2022, - "state_income_tax": 8993.94353225364, - "state_refundable_credits": 288.37526274843856, - "snap_eligible_share": 0.339426691042413, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.249836494440811, + "child_medicaid_chip_share": 0.47122487545095343, + "state_income_tax": 4869.359526581327, + "state_refundable_credits": 117.1888083165884, + "earnings_check": 100935.56711598643 }, { "state": "OR", - "year": 2026, - "state_income_tax": 8504.107829862658, - "state_refundable_credits": 503.35995090296785, - "snap_eligible_share": 0.39451352567621695, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.3737737083060824, + "child_medicaid_chip_share": 0.4521559869438241, + "state_income_tax": 8990.778425834496, + "state_refundable_credits": 372.98409737151115, + "earnings_check": 100935.56711598643 }, { "state": "PA", - "year": 2022, - "state_income_tax": 3898.91368596247, - "state_refundable_credits": 39.74424265651905, - "snap_eligible_share": 0.27535808408970897, - "earnings_check": 100949.40977942833 - }, - { - "state": "PA", - "year": 2026, - "state_income_tax": 3566.0684599164274, - "state_refundable_credits": 371.68801944851504, - "snap_eligible_share": 0.38568555722417264, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.3616742969260955, + "child_medicaid_chip_share": 0.4636660367634427, + "state_income_tax": 3921.0559583371482, + "state_refundable_credits": 125.49070748287936, + "earnings_check": 100935.56711598643 }, { "state": "RI", - "year": 2022, - "state_income_tax": 4885.042172762693, - "state_refundable_credits": 199.0382490936301, - "snap_eligible_share": 0.24148167945906787, - "earnings_check": 100949.40977942833 - }, - { - "state": "RI", - "year": 2026, - "state_income_tax": 4384.383618069928, - "state_refundable_credits": 296.90106179762347, - "snap_eligible_share": 0.2835125732097495, - "earnings_check": 100949.40977942833 - }, - { - "state": "SC", - "year": 2022, - "state_income_tax": 5322.846464823121, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.21566004666108401, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.2710922171353826, + "child_medicaid_chip_share": 0.5557464353203917, + "state_income_tax": 4737.612254342874, + "state_refundable_credits": 262.03886022925377, + "earnings_check": 100935.56711598643 }, { "state": "SC", - "year": 2026, - "state_income_tax": 4417.362349975072, + "snap_eligible_share": 0.2475474166121648, + "child_medicaid_chip_share": 0.4746607112180038, + "state_income_tax": 5041.379117711544, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.2729946614125347, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "SD", - "year": 2022, - "state_income_tax": 0.0, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.16404254921703593, - "earnings_check": 100949.40977942833 - }, - { - "state": "SD", - "year": 2026, - "state_income_tax": 0.0, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.2144472854659317, - "earnings_check": 100949.40977942833 - }, - { - "state": "TN", - "year": 2022, + "snap_eligible_share": 0.18606932635709614, + "child_medicaid_chip_share": 0.4473458168699536, "state_income_tax": 0.0, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.15162120343507343, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "TN", - "year": 2026, + "snap_eligible_share": 0.17560497056899935, + "child_medicaid_chip_share": 0.5021473973544065, "state_income_tax": 0.0, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.200761294302648, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "TX", - "year": 2022, + "snap_eligible_share": 0.23610202746893394, + "child_medicaid_chip_share": 0.4019927847448892, "state_income_tax": 0.0, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.2094491696899193, - "earnings_check": 100949.40977942833 - }, - { - "state": "TX", - "year": 2026, - "state_income_tax": 0.0, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.25723534351167343, - "earnings_check": 100949.40977942833 - }, - { - "state": "UT", - "year": 2022, - "state_income_tax": 5532.289728994569, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.1588720142004796, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "UT", - "year": 2026, - "state_income_tax": 4708.6602211037425, + "snap_eligible_share": 0.18181818181818182, + "child_medicaid_chip_share": 0.39821336540113383, + "state_income_tax": 5122.588540344238, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.2098431709896105, - "earnings_check": 100949.40977942833 - }, - { - "state": "VT", - "year": 2022, - "state_income_tax": 5043.193078765479, - "state_refundable_credits": 985.822716085479, - "snap_eligible_share": 0.3063831940379679, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 }, { "state": "VT", - "year": 2026, - "state_income_tax": 4273.9083079636985, - "state_refundable_credits": 1250.5662945717318, - "snap_eligible_share": 0.3584635670996409, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.3351863963374755, + "child_medicaid_chip_share": 0.610719807593197, + "state_income_tax": 4839.219672122956, + "state_refundable_credits": 1087.1772877934775, + "earnings_check": 100935.56711598643 }, { "state": "VA", - "year": 2022, - "state_income_tax": 5363.322960245165, - "state_refundable_credits": 132.9170335416518, - "snap_eligible_share": 0.32890257197708067, - "earnings_check": 100949.40977942833 - }, - { - "state": "VA", - "year": 2026, - "state_income_tax": 5458.702028677792, - "state_refundable_credits": 280.8603820749461, - "snap_eligible_share": 0.3861198837140311, - "earnings_check": 100949.40977942833 - }, - { - "state": "WA", - "year": 2022, - "state_income_tax": -249.59040533265065, - "state_refundable_credits": 321.54962005780027, - "snap_eligible_share": 0.34024389149519463, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.36265533028122954, + "child_medicaid_chip_share": 0.41058237416251503, + "state_income_tax": 5455.676749258995, + "state_refundable_credits": 170.28072769069672, + "earnings_check": 100935.56711598643 }, { "state": "WA", - "year": 2026, - "state_income_tax": -361.76437979979227, - "state_refundable_credits": 424.90865651005464, - "snap_eligible_share": 0.39420907766384256, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.3734466971877044, + "child_medicaid_chip_share": 0.5279161656072839, + "state_income_tax": -288.07269490559895, + "state_refundable_credits": 382.141, + "earnings_check": 100935.56711598643 }, { "state": "WV", - "year": 2022, - "state_income_tax": 6999.711920840588, - "state_refundable_credits": 23.75518500808772, - "snap_eligible_share": 0.3277394358379021, - "earnings_check": 100949.40977942833 - }, - { - "state": "WV", - "year": 2026, - "state_income_tax": 4774.416912946368, - "state_refundable_credits": 31.909431978687966, - "snap_eligible_share": 0.38568555722417264, - "earnings_check": 100949.40977942833 - }, - { - "state": "WI", - "year": 2022, - "error": "ParameterNotFoundError(\"The parameter 'gov.states.wi.dcf.shares.rates.licensed_group.hourly.SOLANO_COUNTY_CA' was not found in the 2022-01-01 tax and benefit system.\")" + "snap_eligible_share": 0.3616742969260955, + "child_medicaid_chip_share": 0.4526713623088816, + "state_income_tax": 5585.146787513733, + "state_refundable_credits": 31.535528930664064, + "earnings_check": 100935.56711598643 }, { "state": "WI", - "year": 2026, - "error": "ParameterNotFoundError(\"The parameter 'gov.states.wi.dcf.shares.rates.licensed_group.hourly.SOLANO_COUNTY_CA' was not found in the 2026-01-01 tax and benefit system.\")" - }, - { - "state": "WY", - "year": 2022, - "state_income_tax": 0.0, - "state_refundable_credits": 0.0, - "snap_eligible_share": 0.16087302337648024, - "earnings_check": 100949.40977942833 + "snap_eligible_share": 0.36102027468933945, + "child_medicaid_chip_share": 0.4810170073870469, + "state_income_tax": 5270.688319202065, + "state_refundable_credits": 262.09629645669463, + "earnings_check": 100935.56711598643 }, { "state": "WY", - "year": 2026, + "snap_eligible_share": 0.18378024852844996, + "child_medicaid_chip_share": 0.4631506613983852, "state_income_tax": 0.0, "state_refundable_credits": 0.0, - "snap_eligible_share": 0.21206257040649928, - "earnings_check": 100949.40977942833 + "earnings_check": 100935.56711598643 } ] } diff --git a/slideshows/appam-2026/slides/InstrumentRugs.tsx b/slideshows/appam-2026/slides/InstrumentRugs.tsx new file mode 100644 index 00000000..37b9bec9 --- /dev/null +++ b/slideshows/appam-2026/slides/InstrumentRugs.tsx @@ -0,0 +1,73 @@ +import { instrument, percent } from '../content'; + +const WIDTH = 900; +const HEIGHT = 390; +const LEFT = 12; +const RIGHT = 12; +const AXIS_MAX = 0.8; +const ROW_TOP = 40; +const ROW_PITCH = 160; +const TICK_HEIGHT = 72; +const TICK_WIDTH = 4; +const LABEL_RISE = 14; +const AXIS_TICKS = [0, 0.2, 0.4, 0.6, 0.8]; + +const x = (share: number) => LEFT + (share / AXIS_MAX) * (WIDTH - LEFT - RIGHT); + +export default function InstrumentRugs() { + const axisY = ROW_TOP + ROW_PITCH * instrument.lines.length - (ROW_PITCH - TICK_HEIGHT) + 8; + return ( + + {AXIS_TICKS.map((tick, i) => ( + + + + {`${tick * 100}%`} + + + ))} + {instrument.lines.map((line, row) => { + const top = ROW_TOP + row * ROW_PITCH; + const low = line.values[0]; + const high = line.values[line.values.length - 1]; + return ( + + + {line.label} + + {line.values.map((entry) => ( + + {`${entry.code}: ${percent(entry.value)}`} + + ))} + + {`${low.code} ${percent(low.value)}`} + + + {`${high.code} ${percent(high.value)}`} + + + ); + })} + + ); +} diff --git a/slideshows/appam-2026/slides/MicrodataSlide.tsx b/slideshows/appam-2026/slides/MicrodataSlide.tsx new file mode 100644 index 00000000..226b725b --- /dev/null +++ b/slideshows/appam-2026/slides/MicrodataSlide.tsx @@ -0,0 +1,43 @@ +import CodeBlock from '@/components/content/CodeBlock'; +import { instrument } from '../content'; +import Frame from './Frame'; +import InstrumentRugs from './InstrumentRugs'; + +const PYTHON = `people = pd.read_csv("your_survey.csv") + +for fips in STATE_FIPS.values(): + people["state_fips__2024"] = fips + data = Dataset.from_dataframe(people, 2024) + sim = Microsimulation(dataset=data) + snap = sim.calculate("is_snap_eligible", "2024-01")`; + +export default function MicrodataSlide() { + return ( + +
+
+

Hold your sample fixed and change the state. Differences between runs come from each state’s rules.

+ + 
+              {PYTHON}
+
+ +

+ The TAXSIM-compatible interface runs TAXSIM-format files through PolicyEngine for tax years 2021 onward, and + through TAXSIM35 for earlier years. +

+
+
+
+ +
+

+ Each mark shows one state. The same {instrument.households.toLocaleString('en-US')} households with children + from Populace US, under each state’s {instrument.year} rules. SNAP in January, as a share of SPM units. + Medicaid or CHIP as a share of children under 19. +

+
+
+ + ); +} diff --git a/slideshows/appam-2026/slides/OneFamilySlide.tsx b/slideshows/appam-2026/slides/OneFamilySlide.tsx index 1359d910..59875b4d 100644 --- a/slideshows/appam-2026/slides/OneFamilySlide.tsx +++ b/slideshows/appam-2026/slides/OneFamilySlide.tsx @@ -42,8 +42,8 @@ export default function OneFamilySlide() { Net resources add earnings and benefits, then subtract taxes net of credits and the child care the family pays. They leave out health coverage. The gap between the highest and lowest state runs {usd(spreadAt(30_000).spread)} at $30,000 of earnings and{' '} - {usd(spreadAt(60_000).spread)} at $60,000. South Dakota’s subsidy covers five - months. + {usd(spreadAt(60_000).spread)} + {' at $60,000. South Dakota’s subsidy covers five months.'}

From 1a34f2543c3b14709a56a0b38922cea2fee3a85f Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 14:37:30 -0500 Subject: [PATCH 05/32] Add the APPAM deck's reform slide and state baselines The reform slide scores a doubled Massachusetts child and family credit on the state's own records. The scope slide reports the state baseline check against Census, which is why the deck prints no state poverty rate from Populace US. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 27 ++ slideshows/appam-2026/SPEAKER-NOTES.md | 20 +- .../analysis/massachusetts_reform.py | 61 +-- .../appam-2026/analysis/state_baseline.py | 115 +++++ slideshows/appam-2026/config.ts | 2 + slideshows/appam-2026/content.test.ts | 42 ++ slideshows/appam-2026/content.ts | 35 ++ .../appam-2026/data/census_child_spm.json | 260 +++++++++++ .../appam-2026/data/massachusetts_reform.json | 18 +- .../appam-2026/data/state_baseline.json | 417 ++++++++++++++++++ slideshows/appam-2026/slides/FamilyChart.tsx | 7 +- slideshows/appam-2026/slides/ReformSlide.tsx | 60 +++ slideshows/appam-2026/slides/ScopeSlide.tsx | 6 +- 13 files changed, 1029 insertions(+), 41 deletions(-) create mode 100644 slideshows/appam-2026/analysis/state_baseline.py create mode 100644 slideshows/appam-2026/data/census_child_spm.json create mode 100644 slideshows/appam-2026/data/state_baseline.json create mode 100644 slideshows/appam-2026/slides/ReformSlide.tsx diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index 495ae4de..3f245c18 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -16,8 +16,35 @@ Each claim on a slide traces to a computed result, to code read at a named commi | 4 | Texas: $15,791 subsidy, −$5,598 SNAP, +$10,193 net resources | `analysis/interaction.py` → `data/interaction.json` | | 5 | Iowa −$14,967 at $44,000, Minnesota −$13,182 at $57,000, Maryland −$15,343 at $97,500 | `data/charts.json`, `cliffs`, which also records each line's change | | 5 | Median state's total effective marginal tax rate: 63.6% at $30,000, 24.6% at $60,000 | `data/charts.json`, `emtr`: one minus the change in net resources over a $500 raise | +| 7 | SNAP eligibility 17.6% in Tennessee to 37.4% in Oregon; child Medicaid or CHIP 38.8% in Idaho to 65.7% in Hawaii | `analysis/sample_families.py` → `analysis/simulated_instrument.py` → `data/simulated_instrument.json` | +| 8 | $555 million cost, 29.8% of households gain, none lose, $167 million off the SPM poverty gap | `analysis/massachusetts_reform.py` → `data/massachusetts_reform.json` | +| 8 | 1,522 Massachusetts households, effective sample of about 255, no record crosses the threshold | `data/massachusetts_reform.json`: `household_records`, `effective_households` (Kish), `people_crossing_threshold` | +| 10 | Effective sample of about 185 households in the median state; child poverty rates differ from Census's by 4.8 points on average | `analysis/state_baseline.py` → `data/state_baseline.json`, compared with `data/census_child_spm.json` | | 11 | $666.666 deduction on time, $0 late, refusal for November 2019 | `analysis/axiom_ga_3612.py` → `data/axiom_ga_3612.json`, a real engine run | +Both code blocks ran as printed: slide 7's from a CSV of the fixed sample reproduced Tennessee and Oregon, and slide 8's on the Massachusetts records reproduced the cost. `analysis/massachusetts_reform.py` runs on the Massachusetts records alone, and a national run returned the same cost, gaining share and poverty rates. + +## The fixed sample + +`analysis/sample_families.py` draws 3,000 households with children from Populace US, with probability in proportion to household weight, so each household then counts once. `analysis/simulated_instrument.py` sets one state on the whole file and reruns it. Mean employment income stays the same in all 51 runs, and `content.test.ts` checks that. + +## Massachusetts poverty + +The deck leaves out Massachusetts's poverty rates on purpose. + +- Under the reform, 492 SPM unit records gain and 102 of them sit below the threshold. The closest of those falls $1,373 short and gains $440, so no record crosses and the rate does not change. +- 1,279 of the state's 1,684 SPM unit records carry weights below one, which leaves an effective sample of about 255 households. +- The model's Massachusetts child rate comes to 20.2% for 2025 and 22.3% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). +- The national comparison on the webinar deck's `BaselineVsCensusSlide` runs 16.6% in the model against 13.4% from Census, for children in 2025. + +## State baselines + +`analysis/state_baseline.py` cuts Populace US to one state at a time and runs 2025 child SPM poverty for all 51 jurisdictions. + +- The model's rates differ from Census's 2023 to 2025 averages by 4.8 points on average and exceed them by 3.2 points on net. In 38 of 51 jurisdictions the difference exceeds Census's margin of error. +- Effective samples run from 99 households in Maine to 965 in California, with a median of 185. +- The deck prints no state poverty rate from Populace US for these reasons. + ## The family `analysis/household.py` defines the family. diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index 9884bc6c..e01d0cb1 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -44,6 +44,22 @@ Away from the cliffs, the median state's total effective marginal tax rate falls This file holds one real parameter: the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. Each value carries its effective date, and each citation sits next to the values. Ask the model for any date and it reads the value in force on that date. +## 7. Build simulated eligibility measures on your own microdata (5:45, 1 minute 45 seconds) + +Many of you build simulated instruments, so here is how that looks. Read in your survey or administrative file, set the state, and run it. Keep the people and their incomes fixed, change only the state, and any difference between runs comes from that state's rules. + +We ran 3,000 households with children from Populace US, our default microdata, through each state's 2024 rules. The same families pass SNAP's tests at rates from 17.6% in Tennessee to 37.4% in Oregon. Their children qualify for Medicaid or CHIP at rates from 38.8% in Idaho to 65.7% in Hawaii. + +If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface takes the same files. It runs tax years 2021 onward through PolicyEngine and sends earlier years to TAXSIM35. + +## 8. Doubling the Massachusetts child and family credit would cost $555 million (7:30, 1 minute 30 seconds) + +Take the parameter from a moment ago and double it, from $440 to $880 per dependent. In 2026 that costs $555 million. 29.8% of Massachusetts households gain, none lose, and the SPM poverty gap shrinks by $167 million. + +The poverty rate holds still in this run. Populace US holds 1,522 Massachusetts households, an effective sample of about 255, and no record crosses the threshold. For a small reform in one state, read the poverty gap, which moves with every dollar. + +The Child Poverty Impact Dashboard applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state. + ## 9. Research questions and the tools that answer them (9:00, 1 minute) We all work on the same landscape from different angles. The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. @@ -54,9 +70,11 @@ PolicyEngine adds four things: you can change any parameter or write a new rule, Child care subsidies reach all 51 jurisdictions from 2025, and we copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so the model leaves minimum wage and paid leave effects to tools like the Policy Impact Calculator. Hours stay fixed unless you set a labor supply response. +Our default microdata carries thin state samples, about 185 effective households in the median state, and its child poverty rates differ from Census's by 4.8 points on average. For state poverty levels, bring your own microdata. + ## 11. The Axiom Foundation encodes rules with their source text and dates (10:45, 1 minute) -I also lead the Axiom Foundation, which encodes rules as open, machine-readable files. Here is one Georgia SNAP rule. Georgia allows a 20% earned income deduction. Axiom's file records the manual's own words and the effective date beside each value. +I also lead the Axiom Foundation. We publish open, machine-readable encodings of rules, starting with tax and benefit policy. Here is one Georgia SNAP rule. Georgia allows a 20% earned income deduction. Axiom's file records the manual's own words and the effective date beside each value. We ran this rule through the Axiom engine. A family earning $3,333.33 a month who reports on time gets a $666.67 deduction, and the same family reporting late gets nothing. Ask for November 2019, a month before the rule took effect, and the engine refuses. diff --git a/slideshows/appam-2026/analysis/massachusetts_reform.py b/slideshows/appam-2026/analysis/massachusetts_reform.py index fdc84a34..65afb0b6 100644 --- a/slideshows/appam-2026/analysis/massachusetts_reform.py +++ b/slideshows/appam-2026/analysis/massachusetts_reform.py @@ -1,7 +1,11 @@ """Double the Massachusetts child and family tax credit and score it. -Runs current law and the reform on Populace US for 2026 and reports Massachusetts -only. The reform assumes no behavioural responses. +Runs current law and the reform on the Massachusetts records in Populace US for +2026. The reform assumes no behavioral responses. + +Populace US carries few well-weighted records for any one state, so a small reform +can leave a state's poverty rate unchanged because no record crosses the threshold. +The poverty gap moves with every dollar, so the slide reports that alongside cost. """ import json @@ -10,50 +14,50 @@ from pathlib import Path import numpy as np +from policyengine_core.data import Dataset from policyengine_core.reforms import Reform from policyengine_us import Microsimulation +from state_baseline import DATA_YEAR, load, state_file YEAR = 2026 +MASSACHUSETTS_FIPS = 25 PARAMETER = "gov.states.ma.tax.income.credits.child_and_family.amount" CURRENT = 440 REFORMED = 880 CHILD_AGE_LIMIT = 18 -def massachusetts_results(sim): - in_state = sim.calculate("state_code_str", YEAR, map_to="person").values == "MA" +def results(sim): weight = sim.calculate("person_weight", YEAR).values - age = sim.calculate("age", YEAR).values poor = sim.calculate("spm_unit_is_in_spm_poverty", YEAR, map_to="person").values - deep = sim.calculate("spm_unit_is_in_deep_spm_poverty", YEAR, map_to="person").values - household_in_state = ( - sim.calculate("state_code_str", YEAR, map_to="household").values == "MA" - ) - children = in_state & (age < CHILD_AGE_LIMIT) + children = sim.calculate("age", YEAR).values < CHILD_AGE_LIMIT + gap = sim.calculate("poverty_gap", YEAR).values return { "children": float(weight[children].sum()), - "people": float(weight[in_state].sum()), + "people": float(weight.sum()), "child_poverty": float(np.average(poor[children], weights=weight[children])), - "child_deep_poverty": float( - np.average(deep[children], weights=weight[children]) - ), - "poverty": float(np.average(poor[in_state], weights=weight[in_state])), - "household_weight": sim.calculate("household_weight", YEAR).values[ - household_in_state - ], - "household_net_income": sim.calculate( - "household_net_income", YEAR, map_to="household" - ).values[household_in_state], - "household_records": int(household_in_state.sum()), + "poverty": float(np.average(poor, weights=weight)), + "poverty_gap": float(np.sum(gap * sim.calculate("spm_unit_weight", YEAR).values)), + "poor_records": poor, + "household_weight": sim.calculate("household_weight", YEAR).values, + "household_net_income": sim.calculate("household_net_income", YEAR).values, } def main(commit): - baseline = massachusetts_results(Microsimulation()) + load() + people = state_file(MASSACHUSETTS_FIPS) + baseline = results( + Microsimulation(dataset=Dataset.from_dataframe(people, DATA_YEAR)) + ) reform = Reform.from_dict( {PARAMETER: {f"{YEAR}-01-01.2100-12-31": REFORMED}}, country_id="us" ) - reformed = massachusetts_results(Microsimulation(reform=reform)) + reformed = results( + Microsimulation( + dataset=Dataset.from_dataframe(people, DATA_YEAR), reform=reform + ) + ) gains = reformed["household_net_income"] - baseline["household_net_income"] weights = baseline["household_weight"] payload = { @@ -69,9 +73,14 @@ def main(commit): np.sum(weights[gains > 1]) / np.sum(weights) ), "any_household_loses": bool((gains < -1).any()), - "household_records": baseline["household_records"], + "household_records": len(weights), + # Kish's effective sample size, which falls as weights concentrate. + "effective_households": float(weights.sum() ** 2 / (weights**2).sum()), + "people_crossing_threshold": int( + np.sum(baseline["poor_records"] != reformed["poor_records"]) + ), } - for key in ("children", "people", "child_poverty", "child_deep_poverty", "poverty"): + for key in ("children", "people", "child_poverty", "poverty", "poverty_gap"): payload[f"baseline_{key}"] = baseline[key] payload[f"reformed_{key}"] = reformed[key] target = Path(__file__).parent.parent / "data" / "massachusetts_reform.json" diff --git a/slideshows/appam-2026/analysis/state_baseline.py b/slideshows/appam-2026/analysis/state_baseline.py new file mode 100644 index 00000000..46f74f1d --- /dev/null +++ b/slideshows/appam-2026/analysis/state_baseline.py @@ -0,0 +1,115 @@ +"""Compare each state's baseline child poverty with Census and measure its sample. + +Cuts the pinned Populace US file to one state at a time, so each run stays small. +A Massachusetts file cut this way reproduces the national run's reform cost and +poverty rates. Census publishes a 2023 to 2025 average, and this run reports 2025. +""" + +import json +import sys +from importlib.metadata import version +from multiprocessing import Pool +from pathlib import Path + +import numpy as np +import pandas as pd +from huggingface_hub import hf_hub_download +from policyengine_core.data import Dataset +from policyengine_us import CountryTaxBenefitSystem, Microsimulation +from policyengine_us.system import DEFAULT_DATASET + +YEAR = 2025 +DATA_YEAR = 2024 +WORKERS = 3 +CHILD_AGE_LIMIT = 18 +GROUPS = ("tax_unit", "spm_unit", "family", "marital_unit") + +tables = None +known = None + + +def load(): + global tables, known + known = set(CountryTaxBenefitSystem().variables) + path, tag = DEFAULT_DATASET.removeprefix("hf://datasets/").rsplit("@", 1) + owner, repo, filename = path.split("/") + local = hf_hub_download( + f"{owner}/{repo}", filename, repo_type="dataset", revision=tag + ) + with pd.HDFStore(local, "r") as store: + tables = { + name: store.select(name) for name in ("household", "person", *GROUPS) + } + + +def state_file(fips): + """One state's people as a flat file, with group columns on each member's row.""" + households = tables["household"] + households = households[households["state_fips"] == fips] + people = tables["person"] + people = people[ + people["person_household_id"].isin(households["household_id"]) + ].reset_index(drop=True) + columns = { + name: people[name].values + for name in people.columns + if name in known or name == "person_id" or name.startswith("person_") + } + + def add_group(table, group): + ids = f"{group}_id" + membership = people[f"person_{group}_id"].values + rows = table.set_index(ids).index.get_indexer(membership) + for name in table.columns: + if name in known: + columns[name] = table[name].values[rows] + columns[ids] = membership + + add_group(households, "household") + for group in GROUPS: + add_group(tables[group], group) + return pd.DataFrame({f"{k}__{DATA_YEAR}": v for k, v in columns.items()}) + + +def run(fips): + sim = Microsimulation(dataset=Dataset.from_dataframe(state_file(fips), DATA_YEAR)) + age = sim.calculate("age", YEAR).values + weight = sim.calculate("person_weight", YEAR).values.astype(float) + poor = sim.calculate("spm_unit_is_in_spm_poverty", YEAR, map_to="person").values + households = sim.calculate("household_weight", YEAR).values.astype(float) + children = age < CHILD_AGE_LIMIT + return { + "state": str(sim.calculate("state_code_str", YEAR).values[0]), + "records": len(households), + # Kish's effective sample size, which falls as weights concentrate. + "effective_households": float(households.sum() ** 2 / (households**2).sum()), + "child_spm_percent": float( + 100 * np.average(poor[children], weights=weight[children]) + ), + } + + +def main(commit): + data = Path(__file__).parent.parent / "data" + census = json.loads((data / "census_child_spm.json").read_text()) + published = {row["state"]: row for row in census["rows"]} + load() + fips = sorted(int(value) for value in tables["household"]["state_fips"].unique()) + with Pool(WORKERS, initializer=load) as pool: + rows = pool.map(run, fips) + for row in rows: + row["census_percent"] = published[row["state"]]["rate"] + row["census_margin"] = published[row["state"]]["moe"] + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": commit, + "dataset": "Populace US", + "year": YEAR, + "census_source": census["source"], + "rows": rows, + } + (data / "state_baseline.json").write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/slideshows/appam-2026/config.ts b/slideshows/appam-2026/config.ts index a75c455c..f688d19c 100644 --- a/slideshows/appam-2026/config.ts +++ b/slideshows/appam-2026/config.ts @@ -7,6 +7,7 @@ import EndSlide from './slides/EndSlide'; import MicrodataSlide from './slides/MicrodataSlide'; import OneFamilySlide from './slides/OneFamilySlide'; import ParametersSlide from './slides/ParametersSlide'; +import ReformSlide from './slides/ReformSlide'; import ScopeSlide from './slides/ScopeSlide'; import ToolsSlide from './slides/ToolsSlide'; import WhatItDoesSlide from './slides/WhatItDoesSlide'; @@ -29,6 +30,7 @@ export const appam2026Config: SlideshowConfig = { CliffsSlide, ParametersSlide, MicrodataSlide, + ReformSlide, ToolsSlide, ScopeSlide, AxiomSlide, diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index 27682119..0721a8e1 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -11,8 +11,10 @@ import { instrument, interaction, lowest, + massachusetts, programLines, spreads, + stateBaseline, sweep, sweepEarnings, } from './content'; @@ -163,3 +165,43 @@ describe('fixed-sample microdata run', () => { } }); }); + +describe('Massachusetts reform', () => { + it('costs money, helps some households and hurts none', () => { + expect(massachusetts.cost).toBeGreaterThan(0); + expect(massachusetts.gainingShare).toBeGreaterThan(0); + expect(massachusetts.gainingShare).toBeLessThan(1); + expect(massachusetts.anyHouseholdLoses).toBe(false); + }); + + it('closes no more of the poverty gap than it spends', () => { + expect(massachusetts.gapReduction).toBeGreaterThan(0); + expect(massachusetts.gapReduction).toBeLessThanOrEqual(massachusetts.cost); + }); + + it('leaves the poverty rate alone only because no record crosses, as the slide says', () => { + expect(massachusetts.peopleCrossingThreshold).toBe(0); + expect(massachusetts.childPoverty[0]).toBe(massachusetts.childPoverty[1]); + }); + + it('keeps the effective sample below the record count', () => { + expect(massachusetts.effectiveHouseholds).toBeLessThan(massachusetts.records); + }); +}); + +describe('state baselines', () => { + it('covers every state, with rates in range and effective samples below records', () => { + expect(new Set(stateBaseline.rows.map((row) => row.state)).size).toBe(51); + for (const row of stateBaseline.rows) { + expect(row.child_spm_percent).toBeGreaterThanOrEqual(0); + expect(row.child_spm_percent).toBeLessThanOrEqual(100); + expect(row.effective_households).toBeLessThanOrEqual(row.records); + expect(row.census_margin).toBeGreaterThan(0); + } + }); + + it('counts each state beyond Census’s margin at most once', () => { + expect(stateBaseline.beyondCensusMargin).toBeLessThanOrEqual(stateBaseline.rows.length); + expect(stateBaseline.meanCensusGap).toBeGreaterThan(0); + }); +}); diff --git a/slideshows/appam-2026/content.ts b/slideshows/appam-2026/content.ts index 8515e56f..406c899d 100644 --- a/slideshows/appam-2026/content.ts +++ b/slideshows/appam-2026/content.ts @@ -2,6 +2,8 @@ import axiomRun from './data/axiom_ga_3612.json'; import charts from './data/charts.json'; import instrumentRun from './data/simulated_instrument.json'; import interactionRun from './data/interaction.json'; +import reformRun from './data/massachusetts_reform.json'; +import stateBaselineRun from './data/state_baseline.json'; export type StateRow = (typeof charts.cross_section)[number]; @@ -64,6 +66,35 @@ export const instrument = { ], }; +export const massachusetts = { + current: reformRun.current, + reformed: reformRun.reformed, + cost: reformRun.cost, + gainingShare: reformRun.households_gaining_share, + anyHouseholdLoses: reformRun.any_household_loses, + gapReduction: reformRun.baseline_poverty_gap - reformRun.reformed_poverty_gap, + records: reformRun.household_records, + effectiveHouseholds: Math.round(reformRun.effective_households), + peopleCrossingThreshold: reformRun.people_crossing_threshold, + childPoverty: [reformRun.baseline_child_poverty, reformRun.reformed_child_poverty], +}; + +const effectiveSamples = stateBaselineRun.rows + .map((row) => row.effective_households) + .sort((a, b) => a - b); +const censusGaps = stateBaselineRun.rows.map((row) => + Math.abs(row.child_spm_percent - row.census_percent), +); + +export const stateBaseline = { + rows: stateBaselineRun.rows, + medianEffective: Math.round(effectiveSamples[Math.floor(effectiveSamples.length / 2)]), + meanCensusGap: censusGaps.reduce((sum, gap) => sum + gap, 0) / censusGaps.length, + beyondCensusMargin: stateBaselineRun.rows.filter( + (row, i) => censusGaps[i] > row.census_margin, + ).length, +}; + export const axiomEngineVersion = axiomRun.engine.engine_version; const DEDUCTION_KEY = 'us-ga:policies/dfcs/snap/3612#earned_income_deduction'; @@ -89,6 +120,10 @@ export function usd(value: number) { return `${sign}$${Math.abs(Math.round(value)).toLocaleString('en-US')}`; } +export function millions(value: number) { + return `$${Math.round(value / 1_000_000)} million`; +} + export function percent(share: number) { return `${(share * 100).toFixed(1)}%`; } diff --git a/slideshows/appam-2026/data/census_child_spm.json b/slideshows/appam-2026/data/census_child_spm.json new file mode 100644 index 00000000..472b1ec7 --- /dev/null +++ b/slideshows/appam-2026/data/census_child_spm.json @@ -0,0 +1,260 @@ +{ + "source": "U.S. Census Bureau, Poverty in the United States: 2025 (P60-290), Table 18: SPM percent and margin of error for people under 18, three-year average 2023 to 2025", + "rows": [ + { + "state": "AK", + "rate": 10.5, + "moe": 2.1 + }, + { + "state": "AL", + "rate": 15.5, + "moe": 2.4 + }, + { + "state": "AR", + "rate": 13.3, + "moe": 2.7 + }, + { + "state": "AZ", + "rate": 13.3, + "moe": 2.3 + }, + { + "state": "CA", + "rate": 17.9, + "moe": 1.0 + }, + { + "state": "CO", + "rate": 7.7, + "moe": 1.8 + }, + { + "state": "CT", + "rate": 12.8, + "moe": 3.2 + }, + { + "state": "DC", + "rate": 20.3, + "moe": 3.7 + }, + { + "state": "DE", + "rate": 8.7, + "moe": 2.6 + }, + { + "state": "FL", + "rate": 18.4, + "moe": 1.8 + }, + { + "state": "GA", + "rate": 15.1, + "moe": 2.2 + }, + { + "state": "HI", + "rate": 13.0, + "moe": 2.6 + }, + { + "state": "IA", + "rate": 8.2, + "moe": 1.9 + }, + { + "state": "ID", + "rate": 6.5, + "moe": 1.3 + }, + { + "state": "IL", + "rate": 12.4, + "moe": 1.6 + }, + { + "state": "IN", + "rate": 10.6, + "moe": 2.2 + }, + { + "state": "KS", + "rate": 9.7, + "moe": 2.5 + }, + { + "state": "KY", + "rate": 13.3, + "moe": 2.9 + }, + { + "state": "LA", + "rate": 20.7, + "moe": 3.0 + }, + { + "state": "MA", + "rate": 12.8, + "moe": 2.0 + }, + { + "state": "MD", + "rate": 10.7, + "moe": 2.5 + }, + { + "state": "ME", + "rate": 6.5, + "moe": 2.3 + }, + { + "state": "MI", + "rate": 10.3, + "moe": 1.9 + }, + { + "state": "MN", + "rate": 6.9, + "moe": 1.9 + }, + { + "state": "MO", + "rate": 9.7, + "moe": 2.3 + }, + { + "state": "MS", + "rate": 19.1, + "moe": 2.6 + }, + { + "state": "MT", + "rate": 9.3, + "moe": 2.3 + }, + { + "state": "NC", + "rate": 14.4, + "moe": 2.2 + }, + { + "state": "ND", + "rate": 10.3, + "moe": 2.1 + }, + { + "state": "NE", + "rate": 7.1, + "moe": 1.9 + }, + { + "state": "NH", + "rate": 7.5, + "moe": 2.4 + }, + { + "state": "NJ", + "rate": 13.3, + "moe": 1.9 + }, + { + "state": "NM", + "rate": 10.9, + "moe": 2.1 + }, + { + "state": "NV", + "rate": 15.0, + "moe": 2.5 + }, + { + "state": "NY", + "rate": 16.2, + "moe": 1.9 + }, + { + "state": "OH", + "rate": 12.3, + "moe": 2.1 + }, + { + "state": "OK", + "rate": 9.9, + "moe": 2.0 + }, + { + "state": "OR", + "rate": 11.0, + "moe": 2.4 + }, + { + "state": "PA", + "rate": 11.1, + "moe": 2.0 + }, + { + "state": "RI", + "rate": 8.4, + "moe": 2.3 + }, + { + "state": "SC", + "rate": 11.1, + "moe": 2.1 + }, + { + "state": "SD", + "rate": 7.8, + "moe": 2.2 + }, + { + "state": "TN", + "rate": 10.0, + "moe": 1.7 + }, + { + "state": "TX", + "rate": 16.1, + "moe": 1.3 + }, + { + "state": "UT", + "rate": 7.3, + "moe": 1.6 + }, + { + "state": "VA", + "rate": 12.1, + "moe": 2.4 + }, + { + "state": "VT", + "rate": 8.3, + "moe": 2.5 + }, + { + "state": "WA", + "rate": 9.3, + "moe": 1.9 + }, + { + "state": "WI", + "rate": 7.3, + "moe": 1.7 + }, + { + "state": "WV", + "rate": 11.3, + "moe": 2.4 + }, + { + "state": "WY", + "rate": 6.5, + "moe": 1.4 + } + ] +} \ No newline at end of file diff --git a/slideshows/appam-2026/data/massachusetts_reform.json b/slideshows/appam-2026/data/massachusetts_reform.json index d3afd23e..57bcf93d 100644 --- a/slideshows/appam-2026/data/massachusetts_reform.json +++ b/slideshows/appam-2026/data/massachusetts_reform.json @@ -10,14 +10,16 @@ "households_gaining_share": 0.2980068325996399, "any_household_loses": false, "household_records": 1522, + "effective_households": 254.60922241210938, + "people_crossing_threshold": 0, "baseline_children": 1494064.0, "reformed_children": 1494064.0, - "baseline_people": 7249867.0, - "reformed_people": 7249867.0, - "baseline_child_poverty": 0.22329546523064753, - "reformed_child_poverty": 0.22329546523064753, - "baseline_child_deep_poverty": 0.03721871532325723, - "reformed_child_deep_poverty": 0.03721871532325723, - "baseline_poverty": 0.1962823513703525, - "reformed_poverty": 0.1962823513703525 + "baseline_people": 7249868.0, + "reformed_people": 7249868.0, + "baseline_child_poverty": 0.22329546499381753, + "reformed_child_poverty": 0.22329546499381753, + "baseline_poverty": 0.196282351430889, + "reformed_poverty": 0.196282351430889, + "baseline_poverty_gap": 11493076992.0, + "reformed_poverty_gap": 11326015488.0 } diff --git a/slideshows/appam-2026/data/state_baseline.json b/slideshows/appam-2026/data/state_baseline.json new file mode 100644 index 00000000..c0927771 --- /dev/null +++ b/slideshows/appam-2026/data/state_baseline.json @@ -0,0 +1,417 @@ +{ + "policyengine_us_version": "2.90.2", + "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "dataset": "Populace US", + "year": 2025, + "census_source": "U.S. Census Bureau, Poverty in the United States: 2025 (P60-290), Table 18: SPM percent and margin of error for people under 18, three-year average 2023 to 2025", + "rows": [ + { + "state": "AL", + "records": 1061, + "effective_households": 236.55107295398253, + "child_spm_percent": 16.98715636912008, + "census_percent": 15.5, + "census_margin": 2.4 + }, + { + "state": "AK", + "records": 826, + "effective_households": 133.0980287754569, + "child_spm_percent": 11.644426986408602, + "census_percent": 10.5, + "census_margin": 2.1 + }, + { + "state": "AZ", + "records": 1164, + "effective_households": 189.9592179909452, + "child_spm_percent": 16.951186258024865, + "census_percent": 13.3, + "census_margin": 2.3 + }, + { + "state": "AR", + "records": 952, + "effective_households": 205.80839413217132, + "child_spm_percent": 8.12845772011748, + "census_percent": 13.3, + "census_margin": 2.7 + }, + { + "state": "CA", + "records": 2659, + "effective_households": 964.8602098319376, + "child_spm_percent": 24.41650645326989, + "census_percent": 17.9, + "census_margin": 1.0 + }, + { + "state": "CO", + "records": 1164, + "effective_households": 145.44069860124233, + "child_spm_percent": 10.352503836811634, + "census_percent": 7.7, + "census_margin": 1.8 + }, + { + "state": "CT", + "records": 908, + "effective_households": 135.72407262331754, + "child_spm_percent": 13.371307682492237, + "census_percent": 12.8, + "census_margin": 3.2 + }, + { + "state": "DE", + "records": 1068, + "effective_households": 138.95841498485564, + "child_spm_percent": 22.429186288025384, + "census_percent": 8.7, + "census_margin": 2.6 + }, + { + "state": "DC", + "records": 551, + "effective_households": 175.55582420499337, + "child_spm_percent": 34.365303438733925, + "census_percent": 20.3, + "census_margin": 3.7 + }, + { + "state": "FL", + "records": 1642, + "effective_households": 561.268792979642, + "child_spm_percent": 28.15649890233655, + "census_percent": 18.4, + "census_margin": 1.8 + }, + { + "state": "GA", + "records": 1215, + "effective_households": 256.7233149102064, + "child_spm_percent": 18.428986738455304, + "census_percent": 15.1, + "census_margin": 2.2 + }, + { + "state": "HI", + "records": 838, + "effective_households": 197.42767357826924, + "child_spm_percent": 7.019040438905304, + "census_percent": 13.0, + "census_margin": 2.6 + }, + { + "state": "ID", + "records": 746, + "effective_households": 185.13141304399971, + "child_spm_percent": 6.957827284586571, + "census_percent": 6.5, + "census_margin": 1.3 + }, + { + "state": "IL", + "records": 1668, + "effective_households": 276.7964053720248, + "child_spm_percent": 14.678691391590323, + "census_percent": 12.4, + "census_margin": 1.6 + }, + { + "state": "IN", + "records": 1141, + "effective_households": 177.01966623060613, + "child_spm_percent": 12.73434830167166, + "census_percent": 10.6, + "census_margin": 2.2 + }, + { + "state": "IA", + "records": 973, + "effective_households": 138.47468952101661, + "child_spm_percent": 8.80898267607782, + "census_percent": 8.2, + "census_margin": 1.9 + }, + { + "state": "KS", + "records": 921, + "effective_households": 148.60541081180202, + "child_spm_percent": 9.659862214627816, + "census_percent": 9.7, + "census_margin": 2.5 + }, + { + "state": "KY", + "records": 776, + "effective_households": 129.5204444710968, + "child_spm_percent": 18.5371736654285, + "census_percent": 13.3, + "census_margin": 2.9 + }, + { + "state": "LA", + "records": 1000, + "effective_households": 260.9008626497421, + "child_spm_percent": 12.087426626104243, + "census_percent": 20.7, + "census_margin": 3.0 + }, + { + "state": "ME", + "records": 785, + "effective_households": 98.94461653737365, + "child_spm_percent": 12.449100684445808, + "census_percent": 6.5, + "census_margin": 2.3 + }, + { + "state": "MD", + "records": 1091, + "effective_households": 142.97248422508488, + "child_spm_percent": 23.16543402488221, + "census_percent": 10.7, + "census_margin": 2.5 + }, + { + "state": "MA", + "records": 1522, + "effective_households": 254.60923432676174, + "child_spm_percent": 20.202673816573878, + "census_percent": 12.8, + "census_margin": 2.0 + }, + { + "state": "MI", + "records": 1478, + "effective_households": 244.58680980543457, + "child_spm_percent": 14.518361699859508, + "census_percent": 10.3, + "census_margin": 1.9 + }, + { + "state": "MN", + "records": 1166, + "effective_households": 151.4108590065156, + "child_spm_percent": 8.431428192061507, + "census_percent": 6.9, + "census_margin": 1.9 + }, + { + "state": "MS", + "records": 882, + "effective_households": 217.18543375057635, + "child_spm_percent": 12.942211004413902, + "census_percent": 19.1, + "census_margin": 2.6 + }, + { + "state": "MO", + "records": 1013, + "effective_households": 158.23194095106248, + "child_spm_percent": 18.479794697686096, + "census_percent": 9.7, + "census_margin": 2.3 + }, + { + "state": "MT", + "records": 744, + "effective_households": 189.76250281214456, + "child_spm_percent": 2.5309436062772113, + "census_percent": 9.3, + "census_margin": 2.3 + }, + { + "state": "NE", + "records": 884, + "effective_households": 145.9510957065784, + "child_spm_percent": 10.711871752578398, + "census_percent": 7.1, + "census_margin": 1.9 + }, + { + "state": "NV", + "records": 952, + "effective_households": 153.2144593141471, + "child_spm_percent": 19.070065323872694, + "census_percent": 15.0, + "census_margin": 2.5 + }, + { + "state": "NH", + "records": 840, + "effective_households": 140.8689123605408, + "child_spm_percent": 10.516178219256554, + "census_percent": 7.5, + "census_margin": 2.4 + }, + { + "state": "NJ", + "records": 1651, + "effective_households": 214.92794398401983, + "child_spm_percent": 10.924964312878116, + "census_percent": 13.3, + "census_margin": 1.9 + }, + { + "state": "NM", + "records": 902, + "effective_households": 197.2115814203346, + "child_spm_percent": 10.15133671821333, + "census_percent": 10.9, + "census_margin": 2.1 + }, + { + "state": "NY", + "records": 2166, + "effective_households": 431.1950455090738, + "child_spm_percent": 17.727116992316713, + "census_percent": 16.2, + "census_margin": 1.9 + }, + { + "state": "NC", + "records": 1337, + "effective_households": 278.97265851969934, + "child_spm_percent": 18.100974612766848, + "census_percent": 14.4, + "census_margin": 2.2 + }, + { + "state": "ND", + "records": 682, + "effective_households": 154.69431784251822, + "child_spm_percent": 11.13047533590008, + "census_percent": 10.3, + "census_margin": 2.1 + }, + { + "state": "OH", + "records": 1531, + "effective_households": 259.8938711270293, + "child_spm_percent": 9.813281022726176, + "census_percent": 12.3, + "census_margin": 2.1 + }, + { + "state": "OK", + "records": 918, + "effective_households": 184.88950295988414, + "child_spm_percent": 17.21439734214549, + "census_percent": 9.9, + "census_margin": 2.0 + }, + { + "state": "OR", + "records": 1152, + "effective_households": 174.20405203877138, + "child_spm_percent": 16.2870253593211, + "census_percent": 11.0, + "census_margin": 2.4 + }, + { + "state": "PA", + "records": 1999, + "effective_households": 313.940721037965, + "child_spm_percent": 14.135315201475251, + "census_percent": 11.1, + "census_margin": 2.0 + }, + { + "state": "RI", + "records": 804, + "effective_households": 115.36121530553025, + "child_spm_percent": 17.87488315880047, + "census_percent": 8.4, + "census_margin": 2.3 + }, + { + "state": "SC", + "records": 955, + "effective_households": 198.5735582254525, + "child_spm_percent": 9.884637976608813, + "census_percent": 11.1, + "census_margin": 2.1 + }, + { + "state": "SD", + "records": 866, + "effective_households": 140.02483747681345, + "child_spm_percent": 11.33898407217388, + "census_percent": 7.8, + "census_margin": 2.2 + }, + { + "state": "TN", + "records": 1082, + "effective_households": 213.46111672978205, + "child_spm_percent": 23.237084061349254, + "census_percent": 10.0, + "census_margin": 1.7 + }, + { + "state": "TX", + "records": 1875, + "effective_households": 671.2372652446592, + "child_spm_percent": 19.24437100079317, + "census_percent": 16.1, + "census_margin": 1.3 + }, + { + "state": "UT", + "records": 861, + "effective_households": 182.57153738948298, + "child_spm_percent": 13.211297432608319, + "census_percent": 7.3, + "census_margin": 1.6 + }, + { + "state": "VT", + "records": 496, + "effective_households": 155.57089637922203, + "child_spm_percent": 17.678699918725112, + "census_percent": 8.3, + "census_margin": 2.5 + }, + { + "state": "VA", + "records": 1394, + "effective_households": 215.49254142849716, + "child_spm_percent": 18.12834306934188, + "census_percent": 12.1, + "census_margin": 2.4 + }, + { + "state": "WA", + "records": 1305, + "effective_households": 217.79313567185002, + "child_spm_percent": 14.206930139564697, + "census_percent": 9.3, + "census_margin": 1.9 + }, + { + "state": "WV", + "records": 845, + "effective_households": 220.3992065891528, + "child_spm_percent": 17.93170551281414, + "census_percent": 11.3, + "census_margin": 2.4 + }, + { + "state": "WI", + "records": 1232, + "effective_households": 168.63884893953457, + "child_spm_percent": 7.034634564819478, + "census_percent": 7.3, + "census_margin": 1.7 + }, + { + "state": "WY", + "records": 557, + "effective_households": 157.08532758317563, + "child_spm_percent": 8.787559212429837, + "census_percent": 6.5, + "census_margin": 1.4 + } + ] +} diff --git a/slideshows/appam-2026/slides/FamilyChart.tsx b/slideshows/appam-2026/slides/FamilyChart.tsx index d08fab45..e03a42ab 100644 --- a/slideshows/appam-2026/slides/FamilyChart.tsx +++ b/slideshows/appam-2026/slides/FamilyChart.tsx @@ -60,15 +60,14 @@ function Column({ row, index }: { row: StateRow; index: number }) { const segments = SERIES.map((series) => ({ ...series, amount: series.value(row) })).filter( (segment) => segment.amount > 0, ); - let top = baseline; + const heights = segments.map((segment) => toHeight(segment.amount)); return ( {describe(row)} {segments.map((segment, i) => { - const height = toHeight(segment.amount); - top -= height; - const drawn = Math.max(height - SURFACE_GAP, 1); + const top = baseline - heights.slice(0, i + 1).reduce((sum, height) => sum + height, 0); + const drawn = Math.max(heights[i] - SURFACE_GAP, 1); const y = top + SURFACE_GAP; return i === segments.length - 1 ? ( diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx new file mode 100644 index 00000000..ef7ab362 --- /dev/null +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -0,0 +1,60 @@ +import CodeBlock from '@/components/content/CodeBlock'; +import { massachusetts, millions, percent } from '../content'; +import Frame from './Frame'; + +const PYTHON = `reform = Reform.from_dict({ + "gov.states.ma.tax.income.credits" + ".child_and_family.amount": { + "2026-01-01.2100-12-31": 880 + } +}, country_id="us") + +baseline = Microsimulation() +reformed = Microsimulation(reform=reform)`; + +const results = [ + { value: percent(massachusetts.gainingShare), label: 'of households gain, and none lose' }, + { value: millions(massachusetts.gapReduction), label: 'off the SPM poverty gap' }, +]; + +export default function ReformSlide() { + return ( + +
+
+
+

Change any parameter, then compare the two runs for any state.

+ + 
+                {PYTHON}
+
+ +
+
+ {results.map((result) => ( +
+

+ {result.value} +

+

{result.label}

+
+ ))} +
+
+

+ The{' '} + + Child Poverty Impact Dashboard + {' '} + applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state. +

+

+ Populace US, 2026, assuming no behavioral responses. The file holds{' '} + {massachusetts.records.toLocaleString('en-US')} Massachusetts households, an effective sample of about{' '} + {massachusetts.effectiveHouseholds}, and no record crosses the SPM threshold under this reform. The poverty + gap moves with every dollar, so it suits small reforms. +

+
+ + ); +} diff --git a/slideshows/appam-2026/slides/ScopeSlide.tsx b/slideshows/appam-2026/slides/ScopeSlide.tsx index 9456d8a9..53f9d469 100644 --- a/slideshows/appam-2026/slides/ScopeSlide.tsx +++ b/slideshows/appam-2026/slides/ScopeSlide.tsx @@ -1,3 +1,4 @@ +import { stateBaseline } from '../content'; import Frame from './Frame'; const coverage = [ @@ -5,6 +6,7 @@ const coverage = [ 'We copy each parameter’s earliest value back to 2015, so validate historical runs separately.', 'The model applies Wisconsin’s current child care schedules to earlier years.', 'South Dakota’s child care program starts in the model in August 2026.', + `Populace US holds an effective sample of about ${stateBaseline.medianEffective} households in the median state, and its child poverty rates differ from Census’s by ${stateBaseline.meanCensusGap.toFixed(1)} points on average. Bring your own microdata for state levels.`, ]; const mechanisms = [ @@ -20,7 +22,7 @@ function Column({ title, items }: { title: string; items: string[] }) {

{title}

-
    +
      {items.map((item) => (
    • {item}
      • ))} @@ -32,7 +34,7 @@ function Column({ title, items }: { title: string; items: string[] }) { export default function ScopeSlide() { return ( -
      +
      From 100305f549b43b5d9b30acc69f24e712df9b4253 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 14:40:41 -0500 Subject: [PATCH 06/32] Apply the voice gate across the APPAM deck and notes Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 2 +- slideshows/appam-2026/SPEAKER-NOTES.md | 30 +++++++++---------- .../analysis/massachusetts_reform.py | 3 +- slideshows/appam-2026/slides/CliffsSlide.tsx | 2 +- slideshows/appam-2026/slides/CoverSlide.tsx | 2 +- slideshows/appam-2026/slides/EndSlide.tsx | 4 +++ .../appam-2026/slides/MicrodataSlide.tsx | 2 +- .../appam-2026/slides/OneFamilySlide.tsx | 6 ++-- slideshows/appam-2026/slides/ReformSlide.tsx | 2 +- slideshows/appam-2026/slides/ScopeSlide.tsx | 4 +-- .../appam-2026/slides/WhatItDoesSlide.tsx | 2 +- .../appam-2026/slides/WhichLinesSlide.tsx | 4 +-- 12 files changed, 33 insertions(+), 30 deletions(-) diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index 3f245c18..a86c11a1 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -83,7 +83,7 @@ Net resources count all four payments in every state. | Slide | Claim | Code at `469ad12` | | --- | --- | --- | | 2 | Program list, every state income tax, TANF and child care rules of all 51 jurisdictions | `STATE_TANF_VARIABLES` holds 51 entries; `child_care_subsidy_programs` holds 51 from 2025 | -| 4 | SNAP deducts what the family pays for care | `snap_dependent_care_deduction` reads post-subsidy `childcare_expenses` | +| 4 | Texas deducts what the family pays for care when it counts SNAP income | `snap_dependent_care_deduction` reads post-subsidy `childcare_expenses` | | 6 | Massachusetts credit values, dates and citations | `parameters/gov/states/ma/tax/income/credits/child_and_family/amount.yaml` and `dependent_cap.yaml` | | 10 | 46 jurisdictions through 2023, 48 in 2024, 51 from 2025 | `gov.hhs.ccdf.child_care_subsidy_programs`; missing DC, NC, OR, TN and TX through 2023, and DC, TN and TX in 2024 | | 10 | Copy back to 2015 | `backdate_parameters(first_instant="2015-01-01")` in `system.py`, `FIRST_MODELED_YEAR = 2015` in `tools/parameters.py` | diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index e01d0cb1..a45923f1 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -2,7 +2,7 @@ State Policy Impacts on Family Resources: Tools to Improve Policy Analysis. Thursday, November 5, 2026, 10:15 to 11:45am ET, Wellesley room, Boston. Cynthia Osborne moderates. Tara Watson and Linda Giannarelli also present. Each presenter has 10 to 12 minutes. -Twelve slides in 11 minutes. Every figure on a slide traces to `EVIDENCE.md`. +Twelve slides in just under 12 minutes. Every figure on a slide traces to `EVIDENCE.md`. ## Before presenting @@ -18,7 +18,7 @@ Thanks, Cynthia, for putting this session together, and thanks to Tara and Linda PolicyEngine is a nonprofit that provides free, open source software to compute the impact of public policy. Our US model calculates federal and state income taxes and credits and the main benefit programs for one household or a whole population. -Researchers use it three ways, and the next few slides take them in order: compare families, measure their own sample, and estimate reforms. +Researchers use it three ways: they compare families, measure their own samples and estimate reforms. ## 3. One family ends up with $27,711 in Ohio and $54,345 in Alaska (1:15, 1 minute 30 seconds) @@ -28,13 +28,13 @@ The gray base holds earnings after taxes and credits, minus the full child care $40,000 works out to $19.23 an hour, which exceeds every state's minimum wage on the Department of Labor's July 2026 table. The gap between the top and bottom state moves with earnings: $16,404 at $30,000 and $21,670 at $60,000. -## 4. Child care subsidies move net resources most, then SNAP and state taxes (2:45, 1 minute 15 seconds) +## 4. States differ most on child care subsidies, then SNAP and state taxes (2:45, 1 minute 15 seconds) Each mark shows one state on one line of the family's budget, all on the same scale. Federal taxes and credits barely move. State policy moves the rest. -These lines interact. Run Texas with and without its subsidy: the family gets $15,791 in child care help and loses $5,598 of SNAP, because SNAP deducts what the family pays for care. Net resources rise by $10,193. +These lines interact. Run Texas with and without its subsidy: the family gets $15,791 in child care help and loses $5,598 of SNAP, because Texas deducts what the family pays for care when it counts SNAP income. Net resources rise by $10,193. -## 5. Cliffs cost this family up to $15,343, at earnings that vary by state (4:00, 1 minute) +## 5. This family loses up to $15,343 at a benefit cliff, at earnings that vary by state (4:00, 1 minute) Each gray line follows one state as earnings rise. In Iowa, Minnesota and Maryland, a $500 raise takes the family past the child care income limit, and the family loses between $13,182 and $15,343. Every large drop here comes from the subsidy ending. @@ -42,11 +42,11 @@ Away from the cliffs, the median state's total effective marginal tax rate falls ## 6. Parameters record when each value took effect and cite their sources (5:00, 45 seconds) -This file holds one real parameter: the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. Each value carries its effective date, and each citation sits next to the values. Ask the model for any date and it reads the value in force on that date. +This file holds one parameter, the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. Each value carries its effective date, and each citation sits next to the values. Ask the model for any date and it reads the value in force on that date. ## 7. Build simulated eligibility measures on your own microdata (5:45, 1 minute 45 seconds) -Many of you build simulated instruments, so here is how that looks. Read in your survey or administrative file, set the state, and run it. Keep the people and their incomes fixed, change only the state, and any difference between runs comes from that state's rules. +Here is a simulated instrument in six lines. Read in your survey or administrative file, set the state and run it. Keep the people and their incomes fixed, change only the state, and any difference between runs comes from that state's rules. We ran 3,000 households with children from Populace US, our default microdata, through each state's 2024 rules. The same families pass SNAP's tests at rates from 17.6% in Tennessee to 37.4% in Oregon. Their children qualify for Medicaid or CHIP at rates from 38.8% in Idaho to 65.7% in Hawaii. @@ -54,23 +54,23 @@ If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface takes t ## 8. Doubling the Massachusetts child and family credit would cost $555 million (7:30, 1 minute 30 seconds) -Take the parameter from a moment ago and double it, from $440 to $880 per dependent. In 2026 that costs $555 million. 29.8% of Massachusetts households gain, none lose, and the SPM poverty gap shrinks by $167 million. +Take the parameter from a moment ago and double it, from $440 to $880 per dependent. That costs $555 million in 2026. Of Massachusetts households, 29.8% gain and none lose, and the SPM poverty gap shrinks by $167 million. -The poverty rate holds still in this run. Populace US holds 1,522 Massachusetts households, an effective sample of about 255, and no record crosses the threshold. For a small reform in one state, read the poverty gap, which moves with every dollar. +The poverty rate holds still in this run. Populace US holds 1,522 Massachusetts households, an effective sample of about 255, and no record crosses the threshold. For a small reform in one state, read the poverty gap: it moves with each dollar that reaches a family below the threshold. The Child Poverty Impact Dashboard applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state. ## 9. Research questions and the tools that answer them (9:00, 1 minute) -We all work on the same landscape from different angles. The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. +The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. PolicyEngine adds four things: you can change any parameter or write a new rule, run your own microdata, read and edit every line of code, and project current law forward through 2035. ## 10. What these results leave out (10:00, 45 seconds) -Child care subsidies reach all 51 jurisdictions from 2025, and we copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so the model leaves minimum wage and paid leave effects to tools like the Policy Impact Calculator. Hours stay fixed unless you set a labor supply response. +We model child care subsidies in all 51 jurisdictions from 2025, and we copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so the model leaves minimum wage and paid leave effects to tools like the Policy Impact Calculator. Hours stay fixed unless you set a labor supply response. -Our default microdata carries thin state samples, about 185 effective households in the median state, and its child poverty rates differ from Census's by 4.8 points on average. For state poverty levels, bring your own microdata. +Our default microdata carries thin state samples, about 185 effective households in the median state, and its state child poverty rates differ from Census's by 4.8 points on average. For state poverty levels, bring your own microdata. ## 11. The Axiom Foundation encodes rules with their source text and dates (10:45, 1 minute) @@ -78,10 +78,8 @@ I also lead the Axiom Foundation. We publish open, machine-readable encodings of We ran this rule through the Axiom engine. A family earning $3,333.33 a month who reports on time gets a $666.67 deduction, and the same family reporting late gets nothing. Ask for November 2019, a month before the rule took effect, and the engine refuses. -The engine reports `parameter earned_income_deduction_rate has no value for key 0 at 2019-11-01`. - -Axiom has encoded some state family-program rules, and that coverage remains partial. +Axiom has encoded some state family-program rules, and many remain. ## 12. Thank you (11:45) -The QR code opens this deck, with the code behind each chart. +The QR code opens this deck, and the GitHub link holds the code behind each chart. diff --git a/slideshows/appam-2026/analysis/massachusetts_reform.py b/slideshows/appam-2026/analysis/massachusetts_reform.py index 65afb0b6..c33b0185 100644 --- a/slideshows/appam-2026/analysis/massachusetts_reform.py +++ b/slideshows/appam-2026/analysis/massachusetts_reform.py @@ -5,7 +5,8 @@ Populace US carries few well-weighted records for any one state, so a small reform can leave a state's poverty rate unchanged because no record crosses the threshold. -The poverty gap moves with every dollar, so the slide reports that alongside cost. +The poverty gap moves with each dollar that reaches a family below the threshold, +so the slide reports that alongside cost. """ import json diff --git a/slideshows/appam-2026/slides/CliffsSlide.tsx b/slideshows/appam-2026/slides/CliffsSlide.tsx index 5fed8f22..51d1b5a2 100644 --- a/slideshows/appam-2026/slides/CliffsSlide.tsx +++ b/slideshows/appam-2026/slides/CliffsSlide.tsx @@ -4,7 +4,7 @@ import { cliffs, largestCliff, medianRateAt, percent, usd } from '../content'; export default function CliffsSlide() { return ( - +

      diff --git a/slideshows/appam-2026/slides/CoverSlide.tsx b/slideshows/appam-2026/slides/CoverSlide.tsx index 9bfe8fae..f9de54df 100644 --- a/slideshows/appam-2026/slides/CoverSlide.tsx +++ b/slideshows/appam-2026/slides/CoverSlide.tsx @@ -5,7 +5,7 @@ export default function CoverSlide() { return ( diff --git a/slideshows/appam-2026/slides/EndSlide.tsx b/slideshows/appam-2026/slides/EndSlide.tsx index 5796a630..cafed757 100644 --- a/slideshows/appam-2026/slides/EndSlide.tsx +++ b/slideshows/appam-2026/slides/EndSlide.tsx @@ -3,6 +3,10 @@ import EndSlideTemplate from '@/components/layout/EndSlide'; const links = [ { label: 'policyengine.org', url: 'https://policyengine.org' }, { label: 'pip install policyengine-us', url: 'https://github.com/PolicyEngine/policyengine-us' }, + { + label: 'github.com/PolicyEngine/policyengine-slides', + url: 'https://github.com/PolicyEngine/policyengine-slides/tree/main/slideshows/appam-2026', + }, { label: 'max@policyengine.org', url: 'mailto:max@policyengine.org' }, ]; diff --git a/slideshows/appam-2026/slides/MicrodataSlide.tsx b/slideshows/appam-2026/slides/MicrodataSlide.tsx index 226b725b..c0f93cf4 100644 --- a/slideshows/appam-2026/slides/MicrodataSlide.tsx +++ b/slideshows/appam-2026/slides/MicrodataSlide.tsx @@ -33,7 +33,7 @@ export default function MicrodataSlide() {

      Each mark shows one state. The same {instrument.households.toLocaleString('en-US')} households with children - from Populace US, under each state’s {instrument.year} rules. SNAP in January, as a share of SPM units. + from Populace US, our default microdata, under each state’s {instrument.year} rules. SNAP in January, as a share of SPM units. Medicaid or CHIP as a share of children under 19.

      diff --git a/slideshows/appam-2026/slides/OneFamilySlide.tsx b/slideshows/appam-2026/slides/OneFamilySlide.tsx index 59875b4d..ed0f3dd5 100644 --- a/slideshows/appam-2026/slides/OneFamilySlide.tsx +++ b/slideshows/appam-2026/slides/OneFamilySlide.tsx @@ -39,9 +39,9 @@ export default function OneFamilySlide() { {modelCommit}).

      - Net resources add earnings and benefits, then subtract taxes net of credits and the - child care the family pays. They leave out health coverage. The gap between the highest - and lowest state runs {usd(spreadAt(30_000).spread)} at $30,000 of earnings and{' '} + We add earnings and benefits, then subtract taxes net of credits and the child care the + family pays, to get net resources. We leave out health coverage. The gap between the + highest and lowest state runs {usd(spreadAt(30_000).spread)} at $30,000 of earnings and{' '} {usd(spreadAt(60_000).spread)} {' at $60,000. South Dakota’s subsidy covers five months.'}

      diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx index ef7ab362..bf7f6869 100644 --- a/slideshows/appam-2026/slides/ReformSlide.tsx +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -52,7 +52,7 @@ export default function ReformSlide() { Populace US, 2026, assuming no behavioral responses. The file holds{' '} {massachusetts.records.toLocaleString('en-US')} Massachusetts households, an effective sample of about{' '} {massachusetts.effectiveHouseholds}, and no record crosses the SPM threshold under this reform. The poverty - gap moves with every dollar, so it suits small reforms. + gap moves with each dollar that reaches a family below the threshold.

      diff --git a/slideshows/appam-2026/slides/ScopeSlide.tsx b/slideshows/appam-2026/slides/ScopeSlide.tsx index 53f9d469..2e1147f6 100644 --- a/slideshows/appam-2026/slides/ScopeSlide.tsx +++ b/slideshows/appam-2026/slides/ScopeSlide.tsx @@ -2,11 +2,11 @@ import { stateBaseline } from '../content'; import Frame from './Frame'; const coverage = [ - 'Child care subsidies cover 46 jurisdictions through 2023, 48 in 2024 and all 51 from 2025.', + 'We model child care subsidies in 46 jurisdictions through 2023, 48 in 2024 and all 51 from 2025.', 'We copy each parameter’s earliest value back to 2015, so validate historical runs separately.', 'The model applies Wisconsin’s current child care schedules to earlier years.', 'South Dakota’s child care program starts in the model in August 2026.', - `Populace US holds an effective sample of about ${stateBaseline.medianEffective} households in the median state, and its child poverty rates differ from Census’s by ${stateBaseline.meanCensusGap.toFixed(1)} points on average. Bring your own microdata for state levels.`, + `Populace US holds an effective sample of about ${stateBaseline.medianEffective} households in the median state, and its state child poverty rates differ from Census’s by ${stateBaseline.meanCensusGap.toFixed(1)} points on average. Bring your own microdata for state levels.`, ]; const mechanisms = [ diff --git a/slideshows/appam-2026/slides/WhatItDoesSlide.tsx b/slideshows/appam-2026/slides/WhatItDoesSlide.tsx index 6bfdfe82..c304cca6 100644 --- a/slideshows/appam-2026/slides/WhatItDoesSlide.tsx +++ b/slideshows/appam-2026/slides/WhatItDoesSlide.tsx @@ -20,7 +20,7 @@ export default function WhatItDoesSlide() {

      - PolicyEngine: a nonprofit that provides free, open source software to compute the impact + PolicyEngine is a nonprofit that provides free, open source software to compute the impact of public policy.

      diff --git a/slideshows/appam-2026/slides/WhichLinesSlide.tsx b/slideshows/appam-2026/slides/WhichLinesSlide.tsx index 646014c9..810163dc 100644 --- a/slideshows/appam-2026/slides/WhichLinesSlide.tsx +++ b/slideshows/appam-2026/slides/WhichLinesSlide.tsx @@ -14,7 +14,7 @@ function signed(value: number) { export default function WhichLinesSlide() { return ( - +

      @@ -42,7 +42,7 @@ export default function WhichLinesSlide() { ))}

      - SNAP deducts what the family pays for care, so the subsidy shrinks that deduction. + Texas deducts what the family pays for care when it counts SNAP income, so the subsidy shrinks that deduction.

      From 66d2599d7b40b845bec1f1d7269698dc4e455458 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 14:41:48 -0500 Subject: [PATCH 07/32] Format the APPAM analysis scripts Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/analysis/compute_family.py | 4 +++- slideshows/appam-2026/analysis/household.py | 12 +++--------- .../appam-2026/analysis/massachusetts_reform.py | 8 ++++---- .../appam-2026/analysis/simulated_instrument.py | 4 +++- slideshows/appam-2026/analysis/state_baseline.py | 4 +--- slideshows/appam-2026/slides/MicrodataSlide.tsx | 4 ++-- 6 files changed, 16 insertions(+), 20 deletions(-) diff --git a/slideshows/appam-2026/analysis/compute_family.py b/slideshows/appam-2026/analysis/compute_family.py index f304ec9c..d91c0b1d 100644 --- a/slideshows/appam-2026/analysis/compute_family.py +++ b/slideshows/appam-2026/analysis/compute_family.py @@ -23,7 +23,9 @@ def run_state(state): axes = hh.earnings_axis(EARNINGS_MIN, EARNINGS_MAX, count) sim = hh.simulate(state, axes=axes) parts = hh.net_resources_parts(sim) - return state, {key: [round(float(x), 2) for x in value] for key, value in parts.items()} + return state, { + key: [round(float(x), 2) for x in value] for key, value in parts.items() + } def main(): diff --git a/slideshows/appam-2026/analysis/household.py b/slideshows/appam-2026/analysis/household.py index d46c0844..39731854 100644 --- a/slideshows/appam-2026/analysis/household.py +++ b/slideshows/appam-2026/analysis/household.py @@ -30,9 +30,7 @@ "WV", "WI", "WY", ] # fmt: skip -COUNTIES = json.loads( - Path(__file__).with_name("largest_counties.json").read_text() -) +COUNTIES = json.loads(Path(__file__).with_name("largest_counties.json").read_text()) # Colorado, Washington and West Virginia pay subsidies that the model also # lists under spm_unit_benefits. Every state's subsidy shows up here through @@ -71,9 +69,7 @@ def situation(state, earnings, axes=None, year=YEAR): marital_units[f"{name}_unit"] = {"members": [name]} spm_unit = { "members": members, - "spm_unit_pre_subsidy_childcare_expenses": { - year: CHILD_CARE_PER_MONTH * 12 - }, + "spm_unit_pre_subsidy_childcare_expenses": {year: CHILD_CARE_PER_MONTH * 12}, "has_heating_cooling_expense": {year: True}, # Both default to a value that misdescribes a parent in a regular job: # the activity test defaults to not met, and Wisconsin Works places @@ -148,9 +144,7 @@ def get(name): - subsidies_in_benefits + sum(get(name) for name in CASH_OUTSIDE_SPM_TOTALS) ), - "federal_taxes": -( - get("spm_unit_federal_tax") + get("spm_unit_payroll_tax") - ), + "federal_taxes": -(get("spm_unit_federal_tax") + get("spm_unit_payroll_tax")), "state_taxes": -(get("spm_unit_state_tax") + get("spm_unit_local_tax")), "child_care_bill": -bill, } diff --git a/slideshows/appam-2026/analysis/massachusetts_reform.py b/slideshows/appam-2026/analysis/massachusetts_reform.py index c33b0185..dbbbe002 100644 --- a/slideshows/appam-2026/analysis/massachusetts_reform.py +++ b/slideshows/appam-2026/analysis/massachusetts_reform.py @@ -38,7 +38,9 @@ def results(sim): "people": float(weight.sum()), "child_poverty": float(np.average(poor[children], weights=weight[children])), "poverty": float(np.average(poor, weights=weight)), - "poverty_gap": float(np.sum(gap * sim.calculate("spm_unit_weight", YEAR).values)), + "poverty_gap": float( + np.sum(gap * sim.calculate("spm_unit_weight", YEAR).values) + ), "poor_records": poor, "household_weight": sim.calculate("household_weight", YEAR).values, "household_net_income": sim.calculate("household_net_income", YEAR).values, @@ -70,9 +72,7 @@ def main(commit): "current": CURRENT, "reformed": REFORMED, "cost": float(np.sum(gains * weights)), - "households_gaining_share": float( - np.sum(weights[gains > 1]) / np.sum(weights) - ), + "households_gaining_share": float(np.sum(weights[gains > 1]) / np.sum(weights)), "any_household_loses": bool((gains < -1).any()), "household_records": len(weights), # Kish's effective sample size, which falls as weights concentrate. diff --git a/slideshows/appam-2026/analysis/simulated_instrument.py b/slideshows/appam-2026/analysis/simulated_instrument.py index a7ca6b8b..e35401f1 100644 --- a/slideshows/appam-2026/analysis/simulated_instrument.py +++ b/slideshows/appam-2026/analysis/simulated_instrument.py @@ -74,7 +74,9 @@ def household_mean(name): ) return { "state": state, - "snap_eligible_share": float(sim.calculate("is_snap_eligible", month).values.mean()), + "snap_eligible_share": float( + sim.calculate("is_snap_eligible", month).values.mean() + ), "child_medicaid_chip_share": float(covered[children].mean()), "state_income_tax": household_mean("state_income_tax"), "state_refundable_credits": household_mean("state_refundable_credits"), diff --git a/slideshows/appam-2026/analysis/state_baseline.py b/slideshows/appam-2026/analysis/state_baseline.py index 46f74f1d..5531d03a 100644 --- a/slideshows/appam-2026/analysis/state_baseline.py +++ b/slideshows/appam-2026/analysis/state_baseline.py @@ -37,9 +37,7 @@ def load(): f"{owner}/{repo}", filename, repo_type="dataset", revision=tag ) with pd.HDFStore(local, "r") as store: - tables = { - name: store.select(name) for name in ("household", "person", *GROUPS) - } + tables = {name: store.select(name) for name in ("household", "person", *GROUPS)} def state_file(fips): diff --git a/slideshows/appam-2026/slides/MicrodataSlide.tsx b/slideshows/appam-2026/slides/MicrodataSlide.tsx index c0f93cf4..9005ec18 100644 --- a/slideshows/appam-2026/slides/MicrodataSlide.tsx +++ b/slideshows/appam-2026/slides/MicrodataSlide.tsx @@ -33,8 +33,8 @@ export default function MicrodataSlide() {

      Each mark shows one state. The same {instrument.households.toLocaleString('en-US')} households with children - from Populace US, our default microdata, under each state’s {instrument.year} rules. SNAP in January, as a share of SPM units. - Medicaid or CHIP as a share of children under 19. + from Populace US, our default microdata, under each state’s {instrument.year} rules. SNAP in January, as a + share of SPM units. Medicaid or CHIP as a share of children under 19.

      From d50751cbaecf011666e8a49b274394264633cc40 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 14:44:29 -0500 Subject: [PATCH 08/32] Lay out the APPAM closing slide so the logo clears the title Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/slides/EndSlide.tsx | 44 +++++++++++++++-------- 1 file changed, 29 insertions(+), 15 deletions(-) diff --git a/slideshows/appam-2026/slides/EndSlide.tsx b/slideshows/appam-2026/slides/EndSlide.tsx index cafed757..0b7fc779 100644 --- a/slideshows/appam-2026/slides/EndSlide.tsx +++ b/slideshows/appam-2026/slides/EndSlide.tsx @@ -1,23 +1,37 @@ -import EndSlideTemplate from '@/components/layout/EndSlide'; +import BasePathImage from '@/components/core/BasePathImage'; +import Slide from '@/components/core/Slide'; const links = [ - { label: 'policyengine.org', url: 'https://policyengine.org' }, - { label: 'pip install policyengine-us', url: 'https://github.com/PolicyEngine/policyengine-us' }, - { - label: 'github.com/PolicyEngine/policyengine-slides', - url: 'https://github.com/PolicyEngine/policyengine-slides/tree/main/slideshows/appam-2026', - }, - { label: 'max@policyengine.org', url: 'mailto:max@policyengine.org' }, + 'policyengine.org', + 'pip install policyengine-us', + 'github.com/PolicyEngine/policyengine-slides', + 'max@policyengine.org', ]; +const DECK_URL = 'policyengine.org/slides/appam-2026'; + export default function EndSlide() { return ( - + +
      +
      +

      Thank you

      +

      Questions?

      +
      + {links.map((link) => ( +

      + {link} +

      + ))} +
      +
      +
      +
      + +
      +

      {DECK_URL}

      +
      +
      +       ); } From e986abf0a66d5860a83dda5f3360f962434030b3 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 14:49:15 -0500 Subject: [PATCH 09/32] Add the fullscreen step to the APPAM presenting checklist Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/SPEAKER-NOTES.md | 1 + 1 file changed, 1 insertion(+) diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index a45923f1..b92bcbe0 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -7,6 +7,7 @@ Twelve slides in just under 12 minutes. Every figure on a slide traces to `EVIDE ## Before presenting - Open the deck once on the presentation laptop. It needs no live connection after it loads. +- Press F for fullscreen, and the page control leaves the footer. - Keep the PDF backup on the laptop in case the room's network fails. - Check the slide order against what Cynthia, Tara and Linda present first, and cut slide 9's table to one sentence if they have already covered it. From c51ff2de17e8f548980fb4adfe2767d8599393a2 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 16:50:03 -0500 Subject: [PATCH 10/32] Resolve the APPAM deck's first review Slide 2's reform line now reads "Who would gain or lose if lawmakers adopted a proposed reform.", so it no longer promises state poverty results that slides 8 and 10 warn against. d2291 decides the same sentence for the paragraph to Cynthia. The fixed sample now draws with replacement in proportion to weight and gives each draw fresh ids. The old draw without replacement took 72% of the weight in 3,000 of 23,407 records, so it was not proportional. The script reads the pinned file's tables, which matches the old route to every digit on the same households and needs no national simulation. Slide 5 ranks all 51 states, slide 8 prints the Massachusetts baseline its gap rests on, and slide 10 gives the Census gap's direction and an effective sample of households with children. Slide 4 credits Congress for the SNAP deduction, and slide 11 dates the encoded section. Tests that could not fail went away. New ones check the 51-state cliff ranking, the subsidy attribution, marginal rates from the sweep, the state each run read, the national comparison, the Census source, and the typed figures in the notes and ledger. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 40 +- slideshows/appam-2026/SPEAKER-NOTES.md | 52 +- .../analysis/massachusetts_reform.py | 18 +- .../appam-2026/analysis/sample_families.py | 81 ++- .../analysis/simulated_instrument.py | 17 +- .../appam-2026/analysis/state_baseline.py | 21 +- slideshows/appam-2026/analysis/summarize.py | 14 +- .../appam-2026/analysis/test_invariants.py | 5 +- .../analysis/test_sample_families.py | 54 ++ slideshows/appam-2026/content.test.ts | 156 +++++- slideshows/appam-2026/content.ts | 31 +- slideshows/appam-2026/data/charts.json | 2 +- .../data/massachusetts_national_check.json | 10 + .../appam-2026/data/massachusetts_reform.json | 3 +- .../appam-2026/data/simulated_instrument.json | 513 ++++++++++-------- .../appam-2026/data/state_baseline.json | 51 ++ slideshows/appam-2026/slides/AxiomSlide.tsx | 21 +- slideshows/appam-2026/slides/CliffsSlide.tsx | 5 +- .../appam-2026/slides/EarningsChart.tsx | 4 +- .../appam-2026/slides/MicrodataSlide.tsx | 9 +- .../appam-2026/slides/OneFamilySlide.tsx | 2 +- .../appam-2026/slides/ParametersSlide.tsx | 4 +- slideshows/appam-2026/slides/ReformSlide.tsx | 6 +- slideshows/appam-2026/slides/ScopeSlide.tsx | 5 +- .../appam-2026/slides/WhatItDoesSlide.tsx | 2 +- .../appam-2026/slides/WhichLinesSlide.tsx | 2 +- 26 files changed, 760 insertions(+), 368 deletions(-) create mode 100644 slideshows/appam-2026/analysis/test_sample_families.py create mode 100644 slideshows/appam-2026/data/massachusetts_national_check.json diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index a86c11a1..7b312c89 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -1,6 +1,6 @@ # Evidence ledger for the APPAM 2026 deck -Each claim on a slide traces to a computed result, to code read at a named commit, or to a public source read while building this deck. Messaging documents do not count as evidence here. +Each claim on a slide traces to a computed result, to code read at a named commit, or to a public source read while building this deck. - **PolicyEngine US:** version 2.90.2, commit `469ad12cb957a1b3eaedf9e6dd3e4c69194bba26` (main, November 1, 2026). - **Axiom Rules Engine:** version 0.2.2, commit `e0bda6475f30ac487b7be544f5b9742c716e4bba`. @@ -14,26 +14,25 @@ Each claim on a slide traces to a computed result, to code read at a named commi | 3 | Gap of $16,404 at $30,000 and $21,670 at $60,000 | `data/charts.json`, `spreads` | | 4 | Each state's value on four lines | `data/charts.json`, `lines_by_program` | | 4 | Texas: $15,791 subsidy, −$5,598 SNAP, +$10,193 net resources | `analysis/interaction.py` → `data/interaction.json` | -| 5 | Iowa −$14,967 at $44,000, Minnesota −$13,182 at $57,000, Maryland −$15,343 at $97,500 | `data/charts.json`, `cliffs`, which also records each line's change | +| 5 | Maryland −$15,343 at $97,500, Iowa −$14,967 at $44,000, Idaho −$14,511 at $48,000, the three largest drops of the 51 | `data/charts.json`, `cliff_ranking`, which records each line's change in every state | +| 5 | In 46 of 51 states, the largest drop comes from losing the subsidy | `cliff_ranking`: TANF drives Massachusetts and New Hampshire, and WIC drives Maine, New Mexico and Vermont | | 5 | Median state's total effective marginal tax rate: 63.6% at $30,000, 24.6% at $60,000 | `data/charts.json`, `emtr`: one minus the change in net resources over a $500 raise | -| 7 | SNAP eligibility 17.6% in Tennessee to 37.4% in Oregon; child Medicaid or CHIP 38.8% in Idaho to 65.7% in Hawaii | `analysis/sample_families.py` → `analysis/simulated_instrument.py` → `data/simulated_instrument.json` | -| 8 | $555 million cost, 29.8% of households gain, none lose, $167 million off the SPM poverty gap | `analysis/massachusetts_reform.py` → `data/massachusetts_reform.json` | +| 7 | SNAP eligibility 17.5% in Arkansas and Tennessee to 38.3% in Maryland, among SPM units with children; child Medicaid or CHIP 39.8% in Idaho to 66.1% in Hawaii | `analysis/sample_families.py` → `analysis/simulated_instrument.py` → `data/simulated_instrument.json` | +| 8 | State cost, 29.8% of households gain, none lose, $167 million off the SPM poverty gap | `analysis/massachusetts_reform.py` → `data/massachusetts_reform.json`: `state_cost` holds the fall in `state_income_tax`, and `household_gain` holds the change in `household_net_income` | | 8 | 1,522 Massachusetts households, effective sample of about 255, no record crosses the threshold | `data/massachusetts_reform.json`: `household_records`, `effective_households` (Kish), `people_crossing_threshold` | -| 10 | Effective sample of about 185 households in the median state; child poverty rates differ from Census's by 4.8 points on average | `analysis/state_baseline.py` → `data/state_baseline.json`, compared with `data/census_child_spm.json` | +| 10 | Effective sample in the median state; child poverty rates exceed Census's in 40 of 51 jurisdictions and differ by 4.8 points on average | `analysis/state_baseline.py` → `data/state_baseline.json`, compared with `data/census_child_spm.json` | | 11 | $666.666 deduction on time, $0 late, refusal for November 2019 | `analysis/axiom_ga_3612.py` → `data/axiom_ga_3612.json`, a real engine run | -Both code blocks ran as printed: slide 7's from a CSV of the fixed sample reproduced Tennessee and Oregon, and slide 8's on the Massachusetts records reproduced the cost. `analysis/massachusetts_reform.py` runs on the Massachusetts records alone, and a national run returned the same cost, gaining share and poverty rates. +Slide 7's loop ran from a CSV of the fixed sample, with county and finer geography dropped and a `STATE_FIPS` dictionary defined above it, and reproduced Arkansas and Maryland to every digit. Slide 8's reform dictionary ran on the Massachusetts records, and `analysis/massachusetts_reform.py` runs on those records alone. A national run of the same reform returned the same household gain, gaining share and poverty rates (`data/massachusetts_national_check.json`). ## The fixed sample -`analysis/sample_families.py` draws 3,000 households with children from Populace US, with probability in proportion to household weight, so each household then counts once. `analysis/simulated_instrument.py` sets one state on the whole file and reruns it. Mean employment income stays the same in all 51 runs, and `content.test.ts` checks that. +`analysis/sample_families.py` makes 3,000 draws from the Populace US households with children, with replacement and in proportion to household weight. A household drawn twice enters the file twice with fresh ids, so each entry counts once, and the draws reach 2,075 distinct households. The script reads the pinned file's tables and keeps input variables only. On the same households, that route matches `to_input_dataframe` to every digit on child Medicaid or CHIP eligibility, state income tax, state refundable credits and earnings in Tennessee, Oregon and Hawaii. `analysis/simulated_instrument.py` drops county and finer geography, sets one state on the whole file and reruns it. Each run records the state the model read, and mean employment income stays the same in all 51 runs. `content.test.ts` checks both. ## Massachusetts poverty -The deck leaves out Massachusetts's poverty rates on purpose. - -- Under the reform, 492 SPM unit records gain and 102 of them sit below the threshold. The closest of those falls $1,373 short and gains $440, so no record crosses and the rate does not change. -- 1,279 of the state's 1,684 SPM unit records carry weights below one, which leaves an effective sample of about 255 households. +- No record crosses the threshold under the reform, so the rate does not change. +- The file's 1,522 Massachusetts household records carry an effective sample of about 255. - The model's Massachusetts child rate comes to 20.2% for 2025 and 22.3% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). - The national comparison on the webinar deck's `BaselineVsCensusSlide` runs 16.6% in the model against 13.4% from Census, for children in 2025. @@ -41,9 +40,8 @@ The deck leaves out Massachusetts's poverty rates on purpose. `analysis/state_baseline.py` cuts Populace US to one state at a time and runs 2025 child SPM poverty for all 51 jurisdictions. -- The model's rates differ from Census's 2023 to 2025 averages by 4.8 points on average and exceed them by 3.2 points on net. In 38 of 51 jurisdictions the difference exceeds Census's margin of error. -- Effective samples run from 99 households in Maine to 965 in California, with a median of 185. -- The deck prints no state poverty rate from Populace US for these reasons. +- The model's rates exceed Census's 2023 to 2025 averages in 40 of 51 jurisdictions. They differ by 4.8 points on average and exceed Census's by 3.2 points on net. In 38 of 51 jurisdictions the difference exceeds Census's margin of error. +- Effective samples of households with children run from 33 in Maine to 390 in California, with a median of 75. Across all households they run from 99 in Maine to 965 in California, with a median of 185. ## The family @@ -54,13 +52,13 @@ The deck leaves out Massachusetts's poverty rates on purpose. - `meets_ccdf_activity_test` starts true, because a parent in a regular job meets it and the input defaults to false. - `wi_works_placement` starts as `NONE`, because the input defaults to a Community Service Job. - $40,000 over 2,080 hours comes to $19.23 an hour. The highest basic state rate on the Department of Labor's table, updated July 1, 2026, belongs to DC at $18.40 (dol.gov/agencies/whd/minimum-wage/state). Local minimums can run higher, and the table leaves them out. +- The sweeps vary earnings and hold hours at 40 a week. $30,000 over 2,080 hours comes to $14.42 an hour, below DC's basic rate and some other states', so read the $30,000 points as part-time work at those states' wages. Net resources add market income, SNAP, WIC, TANF, the child care subsidy and other benefits, then subtract federal and state taxes net of credits and the child care bill. -- `analysis/test_invariants.py` reconciles the total with `spm_unit_net_income` and with `household_net_income` over random states and earnings. -- A separate pass checked the household reconciliation at all 241 earnings points in all 51 states, and it holds to within two cents. +`analysis/test_invariants.py` reconciles the total with `spm_unit_net_income` and with `household_net_income` over random states and earnings. -The reconciliation shows how the model's totals differ, and these differences belong in any state comparison that uses them. +Any state comparison built on these totals must account for four differences: - `spm_unit_market_income` leaves out the Alaska Permanent Fund Dividend, which `household_market_income` includes. - `spm_unit_benefits` leaves out New Jersey's ANCHOR payment, which `household_benefits` includes. @@ -83,9 +81,11 @@ Net resources count all four payments in every state. | Slide | Claim | Code at `469ad12` | | --- | --- | --- | | 2 | Program list, every state income tax, TANF and child care rules of all 51 jurisdictions | `STATE_TANF_VARIABLES` holds 51 entries; `child_care_subsidy_programs` holds 51 from 2025 | -| 4 | Texas deducts what the family pays for care when it counts SNAP income | `snap_dependent_care_deduction` reads post-subsidy `childcare_expenses` | +| 4 | Congress lets SNAP households deduct what they pay for care | `snap_dependent_care_deduction`, citing 7 U.S.C. 2014(e)(3), reads post-subsidy `childcare_expenses` | +| 7 | The TAXSIM-compatible interface runs 2021 onward through PolicyEngine and earlier years through TAXSIM35 | `StitchedRunner.PE_MIN_YEAR = 2021` in `policyengine_taxsim/runners/stitched_runner.py` | +| 9 | Project through 2035 | The PolicyEngine API's US metadata lists years 2022 to 2035 | | 6 | Massachusetts credit values, dates and citations | `parameters/gov/states/ma/tax/income/credits/child_and_family/amount.yaml` and `dependent_cap.yaml` | -| 10 | 46 jurisdictions through 2023, 48 in 2024, 51 from 2025 | `gov.hhs.ccdf.child_care_subsidy_programs`; missing DC, NC, OR, TN and TX through 2023, and DC, TN and TX in 2024 | +| 10 | 46 jurisdictions through 2023, 48 in 2024, 50 in 2025, 51 from August 2026 | `gov.hhs.ccdf.child_care_subsidy_programs` lists 51 from 2025, missing DC, NC, OR, TN and TX through 2023, and DC, TN and TX in 2024. `sd_cca` returns zero until `uses_weekly_rates` turns on in August 2026 | | 10 | Copy back to 2015 | `backdate_parameters(first_instant="2015-01-01")` in `system.py`, `FIRST_MODELED_YEAR = 2015` in `tools/parameters.py` | | 10 | Wisconsin's schedules | Shares dates start 2025-10-01, and the model returns subsidies of $14,717 in 2021 and $15,483 in 2023 | | 10 | Earnings enter as inputs | The minimum wage appears only in eligibility tests; `pfml_benefits` holds benefits received | @@ -95,7 +95,7 @@ Net resources count all four payments in every state. ## The other tools -Slide 9 uses each tool's own description from Cynthia Osborne's session description of October 16, 2026, which covers the Policy Tracking System, the Policy Impact Calculator, the State Safety Net Interactive, the Welfare Rules Database, the CCDF Policies Database, and State of the Safety Net with ATTIS. +Slide 9 uses each tool's description from the session abstract, which covers the Policy Tracking System, the Policy Impact Calculator, the State Safety Net Interactive, the Welfare Rules Database, the CCDF Policies Database, and State of the Safety Net with ATTIS. ## Axiom diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index b92bcbe0..af7a8dc7 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -2,7 +2,7 @@ State Policy Impacts on Family Resources: Tools to Improve Policy Analysis. Thursday, November 5, 2026, 10:15 to 11:45am ET, Wellesley room, Boston. Cynthia Osborne moderates. Tara Watson and Linda Giannarelli also present. Each presenter has 10 to 12 minutes. -Twelve slides in just under 12 minutes. Every figure on a slide traces to `EVIDENCE.md`. +Twelve slides in about 11 minutes. ## Before presenting @@ -21,66 +21,64 @@ PolicyEngine is a nonprofit that provides free, open source software to compute Researchers use it three ways: they compare families, measure their own samples and estimate reforms. -## 3. One family ends up with $27,711 in Ohio and $54,345 in Alaska (1:15, 1 minute 30 seconds) +## 3. One family ends up with $27,711 in Ohio and $54,345 in Alaska (1:15, 1 minute 15 seconds) -Here is one family under each state's 2026 rules. One parent works full time and earns $40,000, has two young children, and pays $1,500 a month for child care. We put them in each state's largest county and treat them as a new applicant for every program. +Take one family under each state's 2026 rules. One parent works full time and earns $40,000, has two young children, and pays $1,500 a month for child care. We put them in each state's largest county and treat them as a new applicant for every program. The gray base holds earnings after taxes and credits, minus the full child care bill. Everything above it comes from benefits. Ohio and Nevada turn this family down for child care help because both set a lower income limit for new applicants than for families already enrolled. $40,000 works out to $19.23 an hour, which exceeds every state's minimum wage on the Department of Labor's July 2026 table. The gap between the top and bottom state moves with earnings: $16,404 at $30,000 and $21,670 at $60,000. -## 4. States differ most on child care subsidies, then SNAP and state taxes (2:45, 1 minute 15 seconds) +## 4. States differ most on child care subsidies, then SNAP and state taxes (2:30, 1 minute 15 seconds) -Each mark shows one state on one line of the family's budget, all on the same scale. Federal taxes and credits barely move. State policy moves the rest. +Each mark shows one state on one line of the family's budget. Federal taxes and credits barely move across states, and states drive the rest through child care subsidies, SNAP options and their own taxes. -These lines interact. Run Texas with and without its subsidy: the family gets $15,791 in child care help and loses $5,598 of SNAP, because Texas deducts what the family pays for care when it counts SNAP income. Net resources rise by $10,193. +These lines interact. Run Texas with and without its subsidy: the family gets $15,791 in child care help and loses $5,598 of SNAP, because Congress lets SNAP households deduct what they pay for care and the subsidy shrinks that deduction. Net resources rise by $10,193. -## 5. This family loses up to $15,343 at a benefit cliff, at earnings that vary by state (4:00, 1 minute) +## 5. This family loses up to $15,343 at a benefit cliff, at earnings that vary by state (3:45, 1 minute) -Each gray line follows one state as earnings rise. In Iowa, Minnesota and Maryland, a $500 raise takes the family past the child care income limit, and the family loses between $13,182 and $15,343. Every large drop here comes from the subsidy ending. +Each gray line follows one state as earnings rise. In Maryland, Iowa and Idaho, a $500 raise takes the family past the child care income limit, and the family loses between $14,511 and $15,343. In 46 of 51 states, the family's largest drop comes from losing the subsidy. -Away from the cliffs, the median state's total effective marginal tax rate falls from 63.6% at $30,000 of earnings to 24.6% at $60,000. +Across states, the median total effective marginal tax rate falls from 63.6% at $30,000 of earnings to 24.6% at $60,000. -## 6. Parameters record when each value took effect and cite their sources (5:00, 45 seconds) +## 6. Parameters record when each value took effect and cite their sources (4:45, 45 seconds) -This file holds one parameter, the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. Each value carries its effective date, and each citation sits next to the values. Ask the model for any date and it reads the value in force on that date. +This file holds one parameter, the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. Each value carries its effective date, and the file cites the session law and tax forms behind them. Ask the model for any date and it reads the value in force on that date. -## 7. Build simulated eligibility measures on your own microdata (5:45, 1 minute 45 seconds) +## 7. Build simulated eligibility measures on your own microdata (5:30, 1 minute 30 seconds) -Here is a simulated instrument in six lines. Read in your survey or administrative file, set the state and run it. Keep the people and their incomes fixed, change only the state, and any difference between runs comes from that state's rules. +These lines build a simulated instrument. Read in your survey or administrative file, set the state and run it. Keep the people and their incomes fixed, change only the state, and any difference between runs comes from that state's rules. -We ran 3,000 households with children from Populace US, our default microdata, through each state's 2024 rules. The same families pass SNAP's tests at rates from 17.6% in Tennessee to 37.4% in Oregon. Their children qualify for Medicaid or CHIP at rates from 38.8% in Idaho to 65.7% in Hawaii. +We drew 3,000 households with children from Populace US, our default microdata, and ran them through each state's 2024 rules. The same families pass SNAP's tests at rates from 17.5% in Arkansas and Tennessee to 38.3% in Maryland. Their children qualify for Medicaid or CHIP at rates from 39.8% in Idaho to 66.1% in Hawaii. If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface takes the same files. It runs tax years 2021 onward through PolicyEngine and sends earlier years to TAXSIM35. -## 8. Doubling the Massachusetts child and family credit would cost $555 million (7:30, 1 minute 30 seconds) +## 8. Doubling the Massachusetts child and family credit would cost $555 million (7:00, 1 minute 30 seconds) -Take the parameter from a moment ago and double it, from $440 to $880 per dependent. That costs $555 million in 2026. Of Massachusetts households, 29.8% gain and none lose, and the SPM poverty gap shrinks by $167 million. +Take the parameter from a moment ago and double it, from $440 to $880 per dependent. That costs the state $555 million in 2026. Of Massachusetts households, 29.8% gain and none lose, and the SPM poverty gap shrinks by $167 million. -The poverty rate holds still in this run. Populace US holds 1,522 Massachusetts households, an effective sample of about 255, and no record crosses the threshold. For a small reform in one state, read the poverty gap: it moves with each dollar that reaches a family below the threshold. +The poverty rate holds still in this run, because no record crosses the threshold. Populace US holds 1,522 Massachusetts households, an effective sample of about 255, and it puts 20.2% of the state's children below the threshold in 2025, against Census's 12.8% for 2023 to 2025. For a small reform in one state, read the poverty gap: it moves with each dollar that reaches a family below the threshold, and it starts from that higher baseline. The Child Poverty Impact Dashboard applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state. -## 9. Research questions and the tools that answer them (9:00, 1 minute) +## 9. Research questions and the tools that answer them (8:30, 45 seconds) The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. PolicyEngine adds four things: you can change any parameter or write a new rule, run your own microdata, read and edit every line of code, and project current law forward through 2035. -## 10. What these results leave out (10:00, 45 seconds) +## 10. What these results leave out (9:15, 45 seconds) -We model child care subsidies in all 51 jurisdictions from 2025, and we copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so the model leaves minimum wage and paid leave effects to tools like the Policy Impact Calculator. Hours stay fixed unless you set a labor supply response. +We model child care subsidies in all 51 jurisdictions from August 2026, and we copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so a change in minimum wage or paid leave law leaves a family's income unchanged. Hours stay fixed unless you set a labor supply response. -Our default microdata carries thin state samples, about 185 effective households in the median state, and its state child poverty rates differ from Census's by 4.8 points on average. For state poverty levels, bring your own microdata. +Our default microdata carries thin state samples, about 75 effective households with children in the median state, and its child poverty rates exceed Census's 2023 to 2025 averages in 40 of 51 jurisdictions. For state poverty levels, bring your own microdata. -## 11. The Axiom Foundation encodes rules with their source text and dates (10:45, 1 minute) +## 11. The Axiom Foundation encodes rules with their source text and dates (10:00, 1 minute) -I also lead the Axiom Foundation. We publish open, machine-readable encodings of rules, starting with tax and benefit policy. Here is one Georgia SNAP rule. Georgia allows a 20% earned income deduction. Axiom's file records the manual's own words and the effective date beside each value. +I also lead the Axiom Foundation. We publish open, machine-readable encodings of rules, starting with tax and benefit policy. Georgia's Division of Family and Children Services applies a 20% earned income deduction in section 3612 of its SNAP manual, and Axiom's file records the manual's words and effective date beside each value. -We ran this rule through the Axiom engine. A family earning $3,333.33 a month who reports on time gets a $666.67 deduction, and the same family reporting late gets nothing. Ask for November 2019, a month before the rule took effect, and the engine refuses. +We ran this rule through the Axiom engine. A family earning $3,333.33 a month who reports on time gets a $666.67 deduction, and the same family reporting late gets nothing. Ask for November 2019, before the manual section Axiom encoded took effect, and the engine refuses. -Axiom has encoded some state family-program rules, and many remain. - -## 12. Thank you (11:45) +## 12. Thank you (11:00) The QR code opens this deck, and the GitHub link holds the code behind each chart. diff --git a/slideshows/appam-2026/analysis/massachusetts_reform.py b/slideshows/appam-2026/analysis/massachusetts_reform.py index dbbbe002..19d6de2a 100644 --- a/slideshows/appam-2026/analysis/massachusetts_reform.py +++ b/slideshows/appam-2026/analysis/massachusetts_reform.py @@ -6,7 +6,8 @@ Populace US carries few well-weighted records for any one state, so a small reform can leave a state's poverty rate unchanged because no record crosses the threshold. The poverty gap moves with each dollar that reaches a family below the threshold, -so the slide reports that alongside cost. +so the slide reports that alongside cost. Cost means the fall in state income tax, +and household_gain holds the change in household net income beside it. """ import json @@ -18,7 +19,8 @@ from policyengine_core.data import Dataset from policyengine_core.reforms import Reform from policyengine_us import Microsimulation -from state_baseline import DATA_YEAR, load, state_file + +from state_baseline import DATA_YEAR, kish, load, state_file YEAR = 2026 MASSACHUSETTS_FIPS = 25 @@ -41,6 +43,12 @@ def results(sim): "poverty_gap": float( np.sum(gap * sim.calculate("spm_unit_weight", YEAR).values) ), + "state_income_tax": float( + np.sum( + sim.calculate("state_income_tax", YEAR).values + * sim.calculate("tax_unit_weight", YEAR).values + ) + ), "poor_records": poor, "household_weight": sim.calculate("household_weight", YEAR).values, "household_net_income": sim.calculate("household_net_income", YEAR).values, @@ -71,12 +79,12 @@ def main(commit): "parameter": PARAMETER, "current": CURRENT, "reformed": REFORMED, - "cost": float(np.sum(gains * weights)), + "state_cost": baseline["state_income_tax"] - reformed["state_income_tax"], + "household_gain": float(np.sum(gains * weights)), "households_gaining_share": float(np.sum(weights[gains > 1]) / np.sum(weights)), "any_household_loses": bool((gains < -1).any()), "household_records": len(weights), - # Kish's effective sample size, which falls as weights concentrate. - "effective_households": float(weights.sum() ** 2 / (weights**2).sum()), + "effective_households": kish(weights), "people_crossing_threshold": int( np.sum(baseline["poor_records"] != reformed["poor_records"]) ), diff --git a/slideshows/appam-2026/analysis/sample_families.py b/slideshows/appam-2026/analysis/sample_families.py index 1f050cac..669db07c 100644 --- a/slideshows/appam-2026/analysis/sample_families.py +++ b/slideshows/appam-2026/analysis/sample_families.py @@ -1,38 +1,83 @@ """Draw a fixed sample of families from Populace US. -Takes every person in a weighted random sample of households with children and -writes them as one flat file. simulated_instrument.py then treats that file the -way a researcher would treat a survey extract. +Draws households with children in proportion to their weights, with replacement, +and writes every person in each draw to one flat file. A household drawn twice +enters twice with fresh ids, so each entry in the file counts once. +simulated_instrument.py then treats that file the way a researcher would treat +a survey extract. """ import sys import numpy as np -from policyengine_us import Microsimulation +import pandas as pd SAMPLE_SIZE = 3_000 SEED = 2026 YEAR = 2024 +CHILD_AGE_LIMIT = 18 +SOCIAL_SECURITY = ("retirement", "disability", "survivors", "dependents") +GROUPS = ("household", "tax_unit", "spm_unit", "family", "marital_unit") + + +def expand(frame, draws): + """One copy of each drawn household's people, with ids unique to each draw.""" + by_household = dict(list(frame.groupby(f"person_household_id__{YEAR}"))) + sample = pd.concat( + [by_household[household].assign(draw=i) for i, household in enumerate(draws)], + ignore_index=True, + ) + for group in GROUPS: + ids = sample.groupby(["draw", f"person_{group}_id__{YEAR}"]).ngroup() + sample[f"person_{group}_id__{YEAR}"] = ids + sample[f"{group}_id__{YEAR}"] = ids + sample[f"person_id__{YEAR}"] = sample.index + return sample.drop(columns="draw") + + +def inputs_only(frame, inputs): + """Keep what a survey extract would hold, in the names the model reads.""" + # The file stores these benefits under names the model now computes, and the + # model reads stored amounts through the reported variables. + frame = frame.rename( + columns={ + f"social_security_{kind}__{YEAR}": f"social_security_{kind}_reported__{YEAR}" + for kind in SOCIAL_SECURITY + } + ) + columns = [c for c in frame.columns if c in inputs or c.endswith(f"_id__{YEAR}")] + return frame[columns].copy() def main(path): - sim = Microsimulation() - weights = sim.calculate("household_weight", YEAR).values.astype(float) - children = sim.calculate("is_child", YEAR, map_to="household").values - households = sim.calculate("household_id", YEAR).values - weights = np.where(children > 0, weights, 0) + from policyengine_us import CountryTaxBenefitSystem + + import state_baseline as sb + + sb.load() + people = sb.tables["person"] + households = sb.tables["household"] + has_child = people.loc[people["age"] < CHILD_AGE_LIMIT, "person_household_id"] + households = households[households["household_id"].isin(has_child)] + weights = households["household_weight"].values.astype(float) rng = np.random.default_rng(SEED) - chosen = rng.choice( - households, size=SAMPLE_SIZE, replace=False, p=weights / weights.sum() + draws = rng.choice( + households["household_id"].values, + size=SAMPLE_SIZE, + replace=True, + p=weights / weights.sum(), ) - frame = sim.to_input_dataframe() - sample = frame.loc[frame[f"person_household_id__{YEAR}"].isin(chosen)] - # Household roles such as is_household_head hold for all time, not one year. - columns = [c for c in sample.columns if c.endswith((f"__{YEAR}", "__ETERNITY"))] - sample = sample[columns].reset_index(drop=True) - sample[f"person_id__{YEAR}"] = sample.index + # A researcher's file holds inputs, so the model recomputes everything else + # under each state's rules. + variables = CountryTaxBenefitSystem().variables + inputs = { + f"{name}__{YEAR}" + for name, variable in variables.items() + if variable.is_input_variable() + } + sample = expand(inputs_only(sb.flat_file(households), inputs), draws) sample.to_pickle(path) - print(sample.shape) + print(sample.shape, len(set(draws))) if __name__ == "__main__": diff --git a/slideshows/appam-2026/analysis/simulated_instrument.py b/slideshows/appam-2026/analysis/simulated_instrument.py index e35401f1..0aead5a2 100644 --- a/slideshows/appam-2026/analysis/simulated_instrument.py +++ b/slideshows/appam-2026/analysis/simulated_instrument.py @@ -23,8 +23,8 @@ # A family keeps its state only, so a county from one state never reaches another # state's county-level rules. FINER_GEOGRAPHY = ( - "county_fips", "congressional_district_geoid", "block_geoid", "tract_geoid", - "cbsa_code", "place_fips", + "county", "county_fips", "congressional_district_geoid", "block_geoid", + "tract_geoid", "cbsa_code", "place_fips", ) # fmt: skip STATE_FIPS = { "AL": 1, "AK": 2, "AZ": 4, "AR": 5, "CA": 6, "CO": 8, "CT": 9, "DE": 10, @@ -62,21 +62,24 @@ def simulate(state): sim = Microsimulation(dataset=Dataset.from_dataframe(people, SAMPLE_YEAR)) month = f"{SAMPLE_YEAR}-01" - # sample_families.py draws households in proportion to their weights, so each - # household in the sample counts once. + # sample_families.py draws households in proportion to their weights, with + # replacement, so each household in the file counts once. def household_mean(name): return float(sim.calculate(name, SAMPLE_YEAR, map_to="household").values.mean()) children = sim.calculate("age", SAMPLE_YEAR).values < CHILD_AGE_LIMIT + units_with_children = ( + sim.calculate("is_child", SAMPLE_YEAR, map_to="spm_unit").values > 0 + ) + snap = sim.calculate("is_snap_eligible", month).values covered = ( sim.calculate("is_medicaid_eligible", SAMPLE_YEAR).values | sim.calculate("is_chip_eligible", SAMPLE_YEAR).values ) return { "state": state, - "snap_eligible_share": float( - sim.calculate("is_snap_eligible", month).values.mean() - ), + "state_check": str(sim.calculate("state_code_str", SAMPLE_YEAR).values[0]), + "snap_eligible_share": float(snap[units_with_children].mean()), "child_medicaid_chip_share": float(covered[children].mean()), "state_income_tax": household_mean("state_income_tax"), "state_refundable_credits": household_mean("state_refundable_credits"), diff --git a/slideshows/appam-2026/analysis/state_baseline.py b/slideshows/appam-2026/analysis/state_baseline.py index 5531d03a..6a0d0895 100644 --- a/slideshows/appam-2026/analysis/state_baseline.py +++ b/slideshows/appam-2026/analysis/state_baseline.py @@ -28,6 +28,11 @@ known = None +def kish(weights): + """Kish's effective sample size, which falls as weights concentrate.""" + return float(weights.sum() ** 2 / (weights**2).sum()) + + def load(): global tables, known known = set(CountryTaxBenefitSystem().variables) @@ -40,10 +45,8 @@ def load(): tables = {name: store.select(name) for name in ("household", "person", *GROUPS)} -def state_file(fips): - """One state's people as a flat file, with group columns on each member's row.""" - households = tables["household"] - households = households[households["state_fips"] == fips] +def flat_file(households): + """These households' people, with group columns on each member's row.""" people = tables["person"] people = people[ people["person_household_id"].isin(households["household_id"]) @@ -69,18 +72,24 @@ def add_group(table, group): return pd.DataFrame({f"{k}__{DATA_YEAR}": v for k, v in columns.items()}) +def state_file(fips): + households = tables["household"] + return flat_file(households[households["state_fips"] == fips]) + + def run(fips): sim = Microsimulation(dataset=Dataset.from_dataframe(state_file(fips), DATA_YEAR)) age = sim.calculate("age", YEAR).values weight = sim.calculate("person_weight", YEAR).values.astype(float) poor = sim.calculate("spm_unit_is_in_spm_poverty", YEAR, map_to="person").values households = sim.calculate("household_weight", YEAR).values.astype(float) + with_children = sim.calculate("is_child", YEAR, map_to="household").values > 0 children = age < CHILD_AGE_LIMIT return { "state": str(sim.calculate("state_code_str", YEAR).values[0]), "records": len(households), - # Kish's effective sample size, which falls as weights concentrate. - "effective_households": float(households.sum() ** 2 / (households**2).sum()), + "effective_households": kish(households), + "effective_households_with_children": kish(households[with_children]), "child_spm_percent": float( 100 * np.average(poor[children], weights=weight[children]) ), diff --git a/slideshows/appam-2026/analysis/summarize.py b/slideshows/appam-2026/analysis/summarize.py index 87a4fe28..646193c1 100644 --- a/slideshows/appam-2026/analysis/summarize.py +++ b/slideshows/appam-2026/analysis/summarize.py @@ -1,6 +1,6 @@ """Cut data/family.json down to what the deck's charts draw. -Writes data/charts.json. The earnings, the sweep range and the cliff states +Writes data/charts.json. The earnings, the sweep range and the cliff count sit here so one edit changes every chart and the numbers beside them. """ @@ -12,7 +12,7 @@ SPREAD_EARNINGS = (30_000, 40_000, 50_000, 60_000) SWEEP_MIN = 15_000 SWEEP_MAX = 100_000 -CLIFF_STATES = ("IA", "MN", "MD") +CLIFF_COUNT = 3 EMTR_EARNINGS = (30_000, 60_000) SPREAD_LINES = ("child_care_subsidy", "snap", "state_taxes", "federal_taxes") @@ -93,8 +93,8 @@ def main(): for code, lines in states.items() } - cliffs = [] - for code in CLIFF_STATES: + ranking = [] + for code in states: net = states[code]["net_resources"] drops = [ (net[j + 1] - net[j], earnings[j], earnings[j + 1]) @@ -105,7 +105,7 @@ def main(): key: rounded(line[at[after]] - line[at[before]]) for key, line in states[code].items() } - cliffs.append( + ranking.append( { "code": code, "name": STATE_NAMES[code], @@ -117,6 +117,7 @@ def main(): "changes": {k: v for k, v in changes.items() if abs(v) >= 100}, } ) + ranking.sort(key=lambda row: row["drop"], reverse=True) emtr = [] for value in EMTR_EARNINGS: @@ -139,7 +140,8 @@ def main(): "lines_by_program": lines_by_program, "sweep_earnings": sweep_earnings, "sweep": sweep, - "cliffs": cliffs, + "cliffs": ranking[:CLIFF_COUNT], + "cliff_ranking": ranking, "emtr": emtr, } (root / "charts.json").write_text(json.dumps(payload, separators=(",", ":")) + "\n") diff --git a/slideshows/appam-2026/analysis/test_invariants.py b/slideshows/appam-2026/analysis/test_invariants.py index 4f85918b..5b800cae 100644 --- a/slideshows/appam-2026/analysis/test_invariants.py +++ b/slideshows/appam-2026/analysis/test_invariants.py @@ -31,10 +31,9 @@ def test_lines_add_up_to_net_resources(state, income): @settings(max_examples=MAX_EXAMPLES, deadline=None) @given(states, earnings) -def test_subsidy_stays_between_zero_and_the_bill(state, income): +def test_family_never_pays_more_than_the_bill(state, income): parts = hh.net_resources_parts(hh.simulate(state, income)) - subsidy = parts["child_care_subsidy"][0] - assert -TOLERANCE <= subsidy <= -parts["child_care_bill"][0] + TOLERANCE + assert parts["child_care_subsidy"][0] >= -TOLERANCE @settings(max_examples=MAX_EXAMPLES, deadline=None) diff --git a/slideshows/appam-2026/analysis/test_sample_families.py b/slideshows/appam-2026/analysis/test_sample_families.py new file mode 100644 index 00000000..ccb651ba --- /dev/null +++ b/slideshows/appam-2026/analysis/test_sample_families.py @@ -0,0 +1,54 @@ +"""The fixed sample must keep each drawn household whole and count each draw once.""" + +import pandas as pd +from hypothesis import given +from hypothesis import strategies as st + +import sample_families as sf + +YEAR = sf.YEAR +HOUSEHOLD = f"person_household_id__{YEAR}" +SPM_UNIT = f"person_spm_unit_id__{YEAR}" + + +def frame(): + """Five households, the third with two SPM units.""" + sizes = {10: 2, 20: 3, 30: 4, 40: 1, 50: 2} + rows = [] + for household, size in sizes.items(): + for member in range(size): + unit = household + (member % 2 if household == 30 else 0) + ids = { + "household": household, + "tax_unit": unit, + "spm_unit": unit, + "family": household, + "marital_unit": household + member, + } + row = {f"person_id__{YEAR}": household * 10 + member} + for group, value in ids.items(): + row[f"person_{group}_id__{YEAR}"] = value + row[f"{group}_id__{YEAR}"] = value + rows.append(row) + return pd.DataFrame(rows), sizes + + +@given(st.lists(st.sampled_from([10, 20, 30, 40, 50]), min_size=1, max_size=30)) +def test_each_draw_becomes_its_own_household(draws): + people, sizes = frame() + sample = sf.expand(people, draws) + assert sample[HOUSEHOLD].nunique() == len(draws) + assert len(sample) == sum(sizes[household] for household in draws) + assert sample[f"person_id__{YEAR}"].is_unique + for group in sf.GROUPS: + assert sample[f"{group}_id__{YEAR}"].equals( + sample[f"person_{group}_id__{YEAR}"] + ) + + +@given(st.lists(st.sampled_from([30]), min_size=1, max_size=10)) +def test_a_repeated_household_keeps_its_units_apart(draws): + people, _ = frame() + sample = sf.expand(people, draws) + assert sample[SPM_UNIT].nunique() == 2 * len(draws) + assert sample.groupby(SPM_UNIT)[HOUSEHOLD].nunique().max() == 1 diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index 0721a8e1..cf5ad16e 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -4,23 +4,40 @@ import { describe, expect, it } from 'vitest'; import { axiomRuns, childCareBill, + cliffRanking, cliffs, crossSection, headlineEarnings, highest, instrument, interaction, + largestCliff, lowest, massachusetts, + medianRateAt, + millions, + percent, programLines, + spreadAt, spreads, stateBaseline, + subsidyDrivenCliffs, sweep, sweepEarnings, + usd, + usdCents, } from './content'; +import nationalCheck from './data/massachusetts_national_check.json'; import { SERIES } from './slides/FamilyChart'; -const family = JSON.parse(readFileSync(join(__dirname, 'data/family.json'), 'utf8')); +const read = (path: string) => readFileSync(join(__dirname, path), 'utf8'); +const family = JSON.parse(read('data/family.json')); +const census = JSON.parse(read('data/census_child_spm.json')); +const notes = read('SPEAKER-NOTES.md'); +const ledger = read('EVIDENCE.md'); +const webinarMap = read('../cpid-webinar-2026/slides/StateChildPovertyMapSlide.tsx'); + +const NET_OF_THE_LOSS = ['earnings', 'net_resources', 'child_care_bill']; const lineKeys = [ 'earnings', @@ -97,13 +114,42 @@ describe('derived chart data', () => { } }); - it('reports each cliff as the largest fall in that state, one step wide', () => { - for (const cliff of cliffs) { + it('ranks all 51 states by their largest one-step fall in the sweep', () => { + expect(cliffRanking).toHaveLength(51); + for (const cliff of cliffRanking) { const series = sweep[cliff.code as keyof typeof sweep]; const falls = series.slice(1).map((value, i) => series[i] - value); expect(cliff.after - cliff.before).toBe(500); expect(Math.abs(Math.max(...falls) - cliff.drop)).toBeLessThanOrEqual(1); - expect(cliff.changes.child_care_subsidy).toBeLessThan(0); + } + const drops = cliffRanking.map((cliff) => cliff.drop); + expect(drops).toEqual([...drops].sort((a, b) => b - a)); + expect(largestCliff.drop).toBe(drops[0]); + expect(cliffs).toEqual(cliffRanking.slice(0, 3)); + }); + + it('counts the states where losing the subsidy costs the family most', () => { + const lossAt = (lines: Record, before: number, after: number) => { + const i = family.earnings.indexOf(before); + const j = family.earnings.indexOf(after); + const entries = Object.entries(lines).filter(([key]) => !NET_OF_THE_LOSS.includes(key)); + return entries.map(([key, line]) => [key, line[j] - line[i]] as const); + }; + const driven = cliffRanking.filter((cliff) => { + const changes = lossAt(family.states[cliff.code], cliff.before, cliff.after); + return changes.reduce((a, b) => (b[1] < a[1] ? b : a))[0] === 'child_care_subsidy'; + }); + expect(subsidyDrivenCliffs).toBe(driven.length); + for (const cliff of cliffs) expect(driven).toContainEqual(cliff); + }); + + it('recomputes each median marginal rate from the sweep', () => { + for (const earnings of [30_000, 60_000]) { + const i = family.earnings.indexOf(earnings); + const rates = Object.values<{ net_resources: number[] }>(family.states) + .map((state) => 1 - (state.net_resources[i + 1] - state.net_resources[i]) / 500) + .sort((a, b) => a - b); + expect(medianRateAt(earnings)).toBeCloseTo(rates[25], 3); } }); @@ -148,6 +194,18 @@ describe('fixed-sample microdata run', () => { const codes = instrument.rows.map((row) => row.state); expect(new Set(codes).size).toBe(STATE_COUNT); for (const row of instrument.rows) expect(row).not.toHaveProperty('error'); + expect(instrument.households).toBe(3_000); + }); + + it('runs each state under that state’s own rules', () => { + const tax = (code: string) => { + const row = instrument.rows.find((candidate) => candidate.state === code); + if (!row) throw new Error(`No run for ${code}`); + return row.state_income_tax; + }; + for (const row of instrument.rows) expect(row.state_check).toBe(row.state); + for (const code of ['AK', 'FL', 'TX']) expect(tax(code)).toBe(0); + for (const code of ['AL', 'CA']) expect(tax(code)).toBeGreaterThan(0); }); it('keeps the same earnings in every state, because only the state changes', () => { @@ -184,8 +242,16 @@ describe('Massachusetts reform', () => { expect(massachusetts.childPoverty[0]).toBe(massachusetts.childPoverty[1]); }); - it('keeps the effective sample below the record count', () => { - expect(massachusetts.effectiveHouseholds).toBeLessThan(massachusetts.records); + it('scores the same on the state cut as on a national run', () => { + const run = JSON.parse(read('data/massachusetts_reform.json')); + expect(run.household_gain).toBe(nationalCheck.household_gain); + expect(run.households_gaining_share).toBeCloseTo(nationalCheck.households_gaining_share, 6); + expect(run.baseline_child_poverty).toBeCloseTo(nationalCheck.baseline_child_poverty, 6); + expect(run.baseline_poverty).toBeCloseTo(nationalCheck.baseline_poverty, 6); + }); + + it('costs the state what households gain', () => { + expect(Math.abs(massachusetts.cost - massachusetts.householdGain)).toBeLessThan(1e-4 * massachusetts.cost); }); }); @@ -195,13 +261,83 @@ describe('state baselines', () => { for (const row of stateBaseline.rows) { expect(row.child_spm_percent).toBeGreaterThanOrEqual(0); expect(row.child_spm_percent).toBeLessThanOrEqual(100); - expect(row.effective_households).toBeLessThanOrEqual(row.records); + expect(row.effective_households_with_children).toBeLessThanOrEqual(row.effective_households); expect(row.census_margin).toBeGreaterThan(0); } }); - it('counts each state beyond Census’s margin at most once', () => { - expect(stateBaseline.beyondCensusMargin).toBeLessThanOrEqual(stateBaseline.rows.length); - expect(stateBaseline.meanCensusGap).toBeGreaterThan(0); + it('copies Census’s rates from the table the webinar deck maps', () => { + expect(census.rows).toHaveLength(51); + for (const row of census.rows) { + expect(webinarMap).toContain(`st: '${row.state}', rate: ${row.rate.toFixed(1)}, moe: ${row.moe.toFixed(1)}`); + } + }); + + it('keeps the direction of the gap with its size', () => { + const differences = stateBaseline.rows.map((row) => row.child_spm_percent - row.census_percent); + expect(differences.filter((value) => value > 0)).toHaveLength(stateBaseline.modelExceedsCensus); + const net = differences.reduce((sum, value) => sum + value, 0) / differences.length; + expect(net).toBeGreaterThan(0); + expect(net).toBeLessThanOrEqual(stateBaseline.meanCensusGap); + }); +}); + +describe('typed figures', () => { + const name = (code: string) => { + const row = crossSection.find((candidate) => candidate.code === code); + if (!row) throw new Error(`No state ${code}`); + return row.name; + }; + const endpoints = instrument.lines.flatMap((line) => { + const first = line.values[0]; + const last = line.values[line.values.length - 1]; + return [ + `${percent(first.value)} in ${name(first.code)}`, + `${percent(last.value)} in ${name(last.code)}`, + ]; + }); + const shared = [ + usd(highest.net_resources), + usd(lowest.net_resources), + usd(spreadAt(30_000).spread), + usd(spreadAt(60_000).spread), + usd(interaction.subsidy), + usd(-interaction.snapChange), + usd(interaction.netChange), + usd(largestCliff.drop), + `${subsidyDrivenCliffs} of 51 states`, + percent(medianRateAt(30_000)), + percent(medianRateAt(60_000)), + percent(massachusetts.gainingShare), + millions(massachusetts.gapReduction), + `${massachusetts.modelChildRate.toFixed(1)}%`, + `${massachusetts.censusChildRate.toFixed(1)}%`, + `${stateBaseline.modelExceedsCensus} of 51 jurisdictions`, + ...endpoints, + ]; + + it('match the data in the speaker notes', () => { + for (const figure of [ + ...shared, + usd(cliffs[2].drop), + millions(massachusetts.cost), + `about ${massachusetts.effectiveHouseholds}`, + `about ${stateBaseline.medianEffective} effective households`, + usdCents(axiomRuns.onTime), + usdCents(axiomRuns.monthlyEarnings), + ]) { + expect(notes).toContain(figure); + } + }); + + it('match the data in the evidence ledger', () => { + for (const figure of [ + ...shared, + `${stateBaseline.beyondCensusMargin} of 51 jurisdictions`, + `median of ${stateBaseline.medianEffective}`, + `${stateBaseline.meanCensusGap.toFixed(1)} points`, + ]) { + expect(ledger).toContain(figure); + } }); }); diff --git a/slideshows/appam-2026/content.ts b/slideshows/appam-2026/content.ts index 406c899d..97e2c203 100644 --- a/slideshows/appam-2026/content.ts +++ b/slideshows/appam-2026/content.ts @@ -25,7 +25,19 @@ export const sweep = charts.sweep; export const cliffs = charts.cliffs; export const medianRates = charts.emtr; -export const largestCliff = cliffs.reduce((a, b) => (b.drop > a.drop ? b : a)); +export const cliffRanking = charts.cliff_ranking; +export const largestCliff = cliffRanking[0]; + +function largestLoss(changes: Record) { + const losses = Object.entries(changes).filter( + ([key]) => !['earnings', 'net_resources', 'child_care_bill'].includes(key), + ); + return losses.reduce((a, b) => ((b[1] ?? 0) < (a[1] ?? 0) ? b : a))[0]; +} + +export const subsidyDrivenCliffs = cliffRanking.filter( + (cliff) => largestLoss(cliff.changes) === 'child_care_subsidy', +).length; export function spreadAt(earnings: number) { const row = spreads.find((spread) => spread.earnings === earnings); @@ -66,10 +78,14 @@ export const instrument = { ], }; +const massachusettsBaseline = stateBaselineRun.rows.find((row) => row.state === 'MA'); +if (!massachusettsBaseline) throw new Error('No Massachusetts baseline'); + export const massachusetts = { current: reformRun.current, reformed: reformRun.reformed, - cost: reformRun.cost, + cost: reformRun.state_cost, + householdGain: reformRun.household_gain, gainingShare: reformRun.households_gaining_share, anyHouseholdLoses: reformRun.any_household_loses, gapReduction: reformRun.baseline_poverty_gap - reformRun.reformed_poverty_gap, @@ -77,10 +93,12 @@ export const massachusetts = { effectiveHouseholds: Math.round(reformRun.effective_households), peopleCrossingThreshold: reformRun.people_crossing_threshold, childPoverty: [reformRun.baseline_child_poverty, reformRun.reformed_child_poverty], + modelChildRate: massachusettsBaseline.child_spm_percent, + censusChildRate: massachusettsBaseline.census_percent, }; const effectiveSamples = stateBaselineRun.rows - .map((row) => row.effective_households) + .map((row) => row.effective_households_with_children) .sort((a, b) => a - b); const censusGaps = stateBaselineRun.rows.map((row) => Math.abs(row.child_spm_percent - row.census_percent), @@ -93,6 +111,9 @@ export const stateBaseline = { beyondCensusMargin: stateBaselineRun.rows.filter( (row, i) => censusGaps[i] > row.census_margin, ).length, + modelExceedsCensus: stateBaselineRun.rows.filter( + (row) => row.child_spm_percent > row.census_percent, + ).length, }; export const axiomEngineVersion = axiomRun.engine.engine_version; @@ -120,6 +141,10 @@ export function usd(value: number) { return `${sign}$${Math.abs(Math.round(value)).toLocaleString('en-US')}`; } +export function usdCents(value: number) { + return value.toLocaleString('en-US', { style: 'currency', currency: 'USD' }); +} + export function millions(value: number) { return `$${Math.round(value / 1_000_000)} million`; } diff --git a/slideshows/appam-2026/data/charts.json b/slideshows/appam-2026/data/charts.json index 4eb889cc..c2056cd2 100644 --- a/slideshows/appam-2026/data/charts.json +++ b/slideshows/appam-2026/data/charts.json @@ -1 +1 @@ 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"state_check": "WY", + "snap_eligible_share": 0.18369384359400998, + "child_medicaid_chip_share": 0.46949239446248503, "state_income_tax": 0.0, "state_refundable_credits": 0.0, - "earnings_check": 100935.56711598643 + "earnings_check": 98673.01644589161 } ] } diff --git a/slideshows/appam-2026/data/state_baseline.json b/slideshows/appam-2026/data/state_baseline.json index c0927771..fd2004b9 100644 --- a/slideshows/appam-2026/data/state_baseline.json +++ b/slideshows/appam-2026/data/state_baseline.json @@ -9,6 +9,7 @@ "state": "AL", "records": 1061, "effective_households": 236.55107295398253, + "effective_households_with_children": 93.65080621535844, "child_spm_percent": 16.98715636912008, "census_percent": 15.5, "census_margin": 2.4 @@ -17,6 +18,7 @@ "state": "AK", "records": 826, "effective_households": 133.0980287754569, + "effective_households_with_children": 63.74883692498441, "child_spm_percent": 11.644426986408602, "census_percent": 10.5, "census_margin": 2.1 @@ -25,6 +27,7 @@ "state": "AZ", "records": 1164, "effective_households": 189.9592179909452, + "effective_households_with_children": 73.65186948063935, "child_spm_percent": 16.951186258024865, "census_percent": 13.3, "census_margin": 2.3 @@ -33,6 +36,7 @@ "state": "AR", "records": 952, "effective_households": 205.80839413217132, + "effective_households_with_children": 95.40156579624595, "child_spm_percent": 8.12845772011748, "census_percent": 13.3, "census_margin": 2.7 @@ -41,6 +45,7 @@ "state": "CA", "records": 2659, "effective_households": 964.8602098319376, + "effective_households_with_children": 389.73613737342515, "child_spm_percent": 24.41650645326989, "census_percent": 17.9, "census_margin": 1.0 @@ -49,6 +54,7 @@ "state": "CO", "records": 1164, "effective_households": 145.44069860124233, + "effective_households_with_children": 61.08269909168172, "child_spm_percent": 10.352503836811634, "census_percent": 7.7, "census_margin": 1.8 @@ -57,6 +63,7 @@ "state": "CT", "records": 908, "effective_households": 135.72407262331754, + "effective_households_with_children": 57.00506226538202, "child_spm_percent": 13.371307682492237, "census_percent": 12.8, "census_margin": 3.2 @@ -65,6 +72,7 @@ "state": "DE", "records": 1068, "effective_households": 138.95841498485564, + "effective_households_with_children": 49.220539244465236, "child_spm_percent": 22.429186288025384, "census_percent": 8.7, "census_margin": 2.6 @@ -73,6 +81,7 @@ "state": "DC", "records": 551, "effective_households": 175.55582420499337, + "effective_households_with_children": 48.284698236390824, "child_spm_percent": 34.365303438733925, "census_percent": 20.3, "census_margin": 3.7 @@ -81,6 +90,7 @@ "state": "FL", "records": 1642, "effective_households": 561.268792979642, + "effective_households_with_children": 204.65572062992027, "child_spm_percent": 28.15649890233655, "census_percent": 18.4, "census_margin": 1.8 @@ -89,6 +99,7 @@ "state": "GA", "records": 1215, "effective_households": 256.7233149102064, + "effective_households_with_children": 107.40193461203204, "child_spm_percent": 18.428986738455304, "census_percent": 15.1, "census_margin": 2.2 @@ -97,6 +108,7 @@ "state": "HI", "records": 838, "effective_households": 197.42767357826924, + "effective_households_with_children": 73.86813540998027, "child_spm_percent": 7.019040438905304, "census_percent": 13.0, "census_margin": 2.6 @@ -105,6 +117,7 @@ "state": "ID", "records": 746, "effective_households": 185.13141304399971, + "effective_households_with_children": 92.46790807341822, "child_spm_percent": 6.957827284586571, "census_percent": 6.5, "census_margin": 1.3 @@ -113,6 +126,7 @@ "state": "IL", "records": 1668, "effective_households": 276.7964053720248, + "effective_households_with_children": 105.04091641138457, "child_spm_percent": 14.678691391590323, "census_percent": 12.4, "census_margin": 1.6 @@ -121,6 +135,7 @@ "state": "IN", "records": 1141, "effective_households": 177.01966623060613, + "effective_households_with_children": 85.7977093328415, "child_spm_percent": 12.73434830167166, "census_percent": 10.6, "census_margin": 2.2 @@ -129,6 +144,7 @@ "state": "IA", "records": 973, "effective_households": 138.47468952101661, + "effective_households_with_children": 55.523208923018444, "child_spm_percent": 8.80898267607782, "census_percent": 8.2, "census_margin": 1.9 @@ -137,6 +153,7 @@ "state": "KS", "records": 921, "effective_households": 148.60541081180202, + "effective_households_with_children": 60.130820508314436, "child_spm_percent": 9.659862214627816, "census_percent": 9.7, "census_margin": 2.5 @@ -145,6 +162,7 @@ "state": "KY", "records": 776, "effective_households": 129.5204444710968, + "effective_households_with_children": 55.594811366477, "child_spm_percent": 18.5371736654285, "census_percent": 13.3, "census_margin": 2.9 @@ -153,6 +171,7 @@ "state": "LA", "records": 1000, "effective_households": 260.9008626497421, + "effective_households_with_children": 106.16514519454792, "child_spm_percent": 12.087426626104243, "census_percent": 20.7, "census_margin": 3.0 @@ -161,6 +180,7 @@ "state": "ME", "records": 785, "effective_households": 98.94461653737365, + "effective_households_with_children": 33.11521830422318, "child_spm_percent": 12.449100684445808, "census_percent": 6.5, "census_margin": 2.3 @@ -169,6 +189,7 @@ "state": "MD", "records": 1091, "effective_households": 142.97248422508488, + "effective_households_with_children": 55.030632992280125, "child_spm_percent": 23.16543402488221, "census_percent": 10.7, "census_margin": 2.5 @@ -177,6 +198,7 @@ "state": "MA", "records": 1522, "effective_households": 254.60923432676174, + "effective_households_with_children": 97.9541889651262, "child_spm_percent": 20.202673816573878, "census_percent": 12.8, "census_margin": 2.0 @@ -185,6 +207,7 @@ "state": "MI", "records": 1478, "effective_households": 244.58680980543457, + "effective_households_with_children": 95.45588822002858, "child_spm_percent": 14.518361699859508, "census_percent": 10.3, "census_margin": 1.9 @@ -193,6 +216,7 @@ "state": "MN", "records": 1166, "effective_households": 151.4108590065156, + "effective_households_with_children": 57.413064194300695, "child_spm_percent": 8.431428192061507, "census_percent": 6.9, "census_margin": 1.9 @@ -201,6 +225,7 @@ "state": "MS", "records": 882, "effective_households": 217.18543375057635, + "effective_households_with_children": 94.98469220801712, "child_spm_percent": 12.942211004413902, "census_percent": 19.1, "census_margin": 2.6 @@ -209,6 +234,7 @@ "state": "MO", "records": 1013, "effective_households": 158.23194095106248, + "effective_households_with_children": 70.0086423984445, "child_spm_percent": 18.479794697686096, "census_percent": 9.7, "census_margin": 2.3 @@ -217,6 +243,7 @@ "state": "MT", "records": 744, "effective_households": 189.76250281214456, + "effective_households_with_children": 74.0593002880217, "child_spm_percent": 2.5309436062772113, "census_percent": 9.3, "census_margin": 2.3 @@ -225,6 +252,7 @@ "state": "NE", "records": 884, "effective_households": 145.9510957065784, + "effective_households_with_children": 67.67826104471393, "child_spm_percent": 10.711871752578398, "census_percent": 7.1, "census_margin": 1.9 @@ -233,6 +261,7 @@ "state": "NV", "records": 952, "effective_households": 153.2144593141471, + "effective_households_with_children": 53.81897222709485, "child_spm_percent": 19.070065323872694, "census_percent": 15.0, "census_margin": 2.5 @@ -241,6 +270,7 @@ "state": "NH", "records": 840, "effective_households": 140.8689123605408, + "effective_households_with_children": 50.91685592377852, "child_spm_percent": 10.516178219256554, "census_percent": 7.5, "census_margin": 2.4 @@ -249,6 +279,7 @@ "state": "NJ", "records": 1651, "effective_households": 214.92794398401983, + "effective_households_with_children": 90.63484170002751, "child_spm_percent": 10.924964312878116, "census_percent": 13.3, "census_margin": 1.9 @@ -257,6 +288,7 @@ "state": "NM", "records": 902, "effective_households": 197.2115814203346, + "effective_households_with_children": 74.88789080295213, "child_spm_percent": 10.15133671821333, "census_percent": 10.9, "census_margin": 2.1 @@ -265,6 +297,7 @@ "state": "NY", "records": 2166, "effective_households": 431.1950455090738, + "effective_households_with_children": 161.4744380520421, "child_spm_percent": 17.727116992316713, "census_percent": 16.2, "census_margin": 1.9 @@ -273,6 +306,7 @@ "state": "NC", "records": 1337, "effective_households": 278.97265851969934, + "effective_households_with_children": 107.21798776187008, "child_spm_percent": 18.100974612766848, "census_percent": 14.4, "census_margin": 2.2 @@ -281,6 +315,7 @@ "state": "ND", "records": 682, "effective_households": 154.69431784251822, + "effective_households_with_children": 60.78103301460022, "child_spm_percent": 11.13047533590008, "census_percent": 10.3, "census_margin": 2.1 @@ -289,6 +324,7 @@ "state": "OH", "records": 1531, "effective_households": 259.8938711270293, + "effective_households_with_children": 94.1812931984067, "child_spm_percent": 9.813281022726176, "census_percent": 12.3, "census_margin": 2.1 @@ -297,6 +333,7 @@ "state": "OK", "records": 918, "effective_households": 184.88950295988414, + "effective_households_with_children": 75.74235124943412, "child_spm_percent": 17.21439734214549, "census_percent": 9.9, "census_margin": 2.0 @@ -305,6 +342,7 @@ "state": "OR", "records": 1152, "effective_households": 174.20405203877138, + "effective_households_with_children": 61.38530820629173, "child_spm_percent": 16.2870253593211, "census_percent": 11.0, "census_margin": 2.4 @@ -313,6 +351,7 @@ "state": "PA", "records": 1999, "effective_households": 313.940721037965, + "effective_households_with_children": 119.95047604838705, "child_spm_percent": 14.135315201475251, "census_percent": 11.1, "census_margin": 2.0 @@ -321,6 +360,7 @@ "state": "RI", "records": 804, "effective_households": 115.36121530553025, + "effective_households_with_children": 43.9686753685404, "child_spm_percent": 17.87488315880047, "census_percent": 8.4, "census_margin": 2.3 @@ -329,6 +369,7 @@ "state": "SC", "records": 955, "effective_households": 198.5735582254525, + "effective_households_with_children": 82.27387120944789, "child_spm_percent": 9.884637976608813, "census_percent": 11.1, "census_margin": 2.1 @@ -337,6 +378,7 @@ "state": "SD", "records": 866, "effective_households": 140.02483747681345, + "effective_households_with_children": 59.908755866285595, "child_spm_percent": 11.33898407217388, "census_percent": 7.8, "census_margin": 2.2 @@ -345,6 +387,7 @@ "state": "TN", "records": 1082, "effective_households": 213.46111672978205, + "effective_households_with_children": 98.0549475333965, "child_spm_percent": 23.237084061349254, "census_percent": 10.0, "census_margin": 1.7 @@ -353,6 +396,7 @@ "state": "TX", "records": 1875, "effective_households": 671.2372652446592, + "effective_households_with_children": 296.2185580859504, "child_spm_percent": 19.24437100079317, "census_percent": 16.1, "census_margin": 1.3 @@ -361,6 +405,7 @@ "state": "UT", "records": 861, "effective_households": 182.57153738948298, + "effective_households_with_children": 96.24997869494076, "child_spm_percent": 13.211297432608319, "census_percent": 7.3, "census_margin": 1.6 @@ -369,6 +414,7 @@ "state": "VT", "records": 496, "effective_households": 155.57089637922203, + "effective_households_with_children": 51.40700838972123, "child_spm_percent": 17.678699918725112, "census_percent": 8.3, "census_margin": 2.5 @@ -377,6 +423,7 @@ "state": "VA", "records": 1394, "effective_households": 215.49254142849716, + "effective_households_with_children": 92.96168387134998, "child_spm_percent": 18.12834306934188, "census_percent": 12.1, "census_margin": 2.4 @@ -385,6 +432,7 @@ "state": "WA", "records": 1305, "effective_households": 217.79313567185002, + "effective_households_with_children": 84.69243217289157, "child_spm_percent": 14.206930139564697, "census_percent": 9.3, "census_margin": 1.9 @@ -393,6 +441,7 @@ "state": "WV", "records": 845, "effective_households": 220.3992065891528, + "effective_households_with_children": 76.6798644841193, "child_spm_percent": 17.93170551281414, "census_percent": 11.3, "census_margin": 2.4 @@ -401,6 +450,7 @@ "state": "WI", "records": 1232, "effective_households": 168.63884893953457, + "effective_households_with_children": 63.911239375027826, "child_spm_percent": 7.034634564819478, "census_percent": 7.3, "census_margin": 1.7 @@ -409,6 +459,7 @@ "state": "WY", "records": 557, "effective_households": 157.08532758317563, + "effective_households_with_children": 68.56604831202455, "child_spm_percent": 8.787559212429837, "census_percent": 6.5, "census_margin": 1.4 diff --git a/slideshows/appam-2026/slides/AxiomSlide.tsx b/slideshows/appam-2026/slides/AxiomSlide.tsx index 8a3a0c15..66184219 100644 --- a/slideshows/appam-2026/slides/AxiomSlide.tsx +++ b/slideshows/appam-2026/slides/AxiomSlide.tsx @@ -1,6 +1,6 @@ import CodeBlock from '@/components/content/CodeBlock'; import Frame from './Frame'; -import { axiomEngineVersion, axiomRuns, usd } from '../content'; +import { axiomEngineVersion, axiomRuns, usd, usdCents } from '../content'; const RULESPEC = `- name: earned_income_deduction_rate kind: parameter @@ -19,34 +19,29 @@ const RULESPEC = `- name: earned_income_deduction_rate - effective_from: '2019-12-01' formula: 0.20`; -const earnings = axiomRuns.monthlyEarnings.toLocaleString('en-US', { - style: 'currency', - currency: 'USD', -}); - const runs = [ { - family: `Earns ${earnings} a month and reports on time`, - result: `Deduction of ${usd(axiomRuns.onTime)} a month`, + family: `Earns ${usdCents(axiomRuns.monthlyEarnings)} a month and reports on time`, + result: `Deduction of ${usdCents(axiomRuns.onTime)} a month`, }, { family: 'Same family, reports late', result: `Deduction of ${usd(axiomRuns.late)}` }, - { family: 'Same family, November 2019', result: 'Engine refuses: no value at 2019-11-01' }, + { family: 'Same family, November 2019', result: 'Engine refuses: the encoded section starts December 2019' }, ]; export default function AxiomSlide() { return (
      - + 
                   {RULESPEC}

      - Georgia’s Division of Family and Children Services allows a 20% earned income - deduction in section 3612 of its SNAP manual. Axiom encodes that rule in RuleSpec and - records the manual’s words and date beside each value. + Georgia’s Division of Family and Children Services applies a 20% earned income + deduction in section 3612 of its SNAP manual. Axiom encodes that rule and records the + manual’s words and date beside each value.

      Axiom Rules Engine {axiomEngineVersion}, running the rule diff --git a/slideshows/appam-2026/slides/CliffsSlide.tsx b/slideshows/appam-2026/slides/CliffsSlide.tsx index 51d1b5a2..e8f0aa61 100644 --- a/slideshows/appam-2026/slides/CliffsSlide.tsx +++ b/slideshows/appam-2026/slides/CliffsSlide.tsx @@ -1,6 +1,6 @@ import EarningsChart from './EarningsChart'; import Frame from './Frame'; -import { cliffs, largestCliff, medianRateAt, percent, usd } from '../content'; +import { cliffs, largestCliff, medianRateAt, percent, subsidyDrivenCliffs, usd } from '../content'; export default function CliffsSlide() { return ( @@ -29,6 +29,9 @@ export default function CliffsSlide() { ))}

    +

    + In {subsidyDrivenCliffs} of 51 states, the family’s largest drop comes from losing the subsidy. +

    diff --git a/slideshows/appam-2026/slides/EarningsChart.tsx b/slideshows/appam-2026/slides/EarningsChart.tsx index 25593464..ffe73603 100644 --- a/slideshows/appam-2026/slides/EarningsChart.tsx +++ b/slideshows/appam-2026/slides/EarningsChart.tsx @@ -15,7 +15,7 @@ const LAST_LABEL_ON_RIGHT = 90_000; const SERIES_COLORS: Record = { IA: 'var(--color-teal-600)', - MN: 'var(--pe-amber)', + ID: 'var(--pe-amber)', MD: 'var(--chart-4)', }; @@ -38,7 +38,7 @@ export default function EarningsChart() { cliff.name).join(", ")} highlighted`} className="h-full w-full" > {Y_TICKS.map((tick) => ( diff --git a/slideshows/appam-2026/slides/MicrodataSlide.tsx b/slideshows/appam-2026/slides/MicrodataSlide.tsx index 9005ec18..6e295506 100644 --- a/slideshows/appam-2026/slides/MicrodataSlide.tsx +++ b/slideshows/appam-2026/slides/MicrodataSlide.tsx @@ -4,8 +4,9 @@ import Frame from './Frame'; import InstrumentRugs from './InstrumentRugs'; const PYTHON = `people = pd.read_csv("your_survey.csv") +# Keep state alone: drop county and finer geography. -for fips in STATE_FIPS.values(): +for fips in STATE_FIPS.values(): # all 51 people["state_fips__2024"] = fips data = Dataset.from_dataframe(people, 2024) sim = Microsimulation(dataset=data) @@ -32,9 +33,9 @@ export default function MicrodataSlide() {

    - Each mark shows one state. The same {instrument.households.toLocaleString('en-US')} households with children - from Populace US, our default microdata, under each state’s {instrument.year} rules. SNAP in January, as a - share of SPM units. Medicaid or CHIP as a share of children under 19. + Each mark shows one state. The same {instrument.households.toLocaleString('en-US')} households with children, + drawn by weight from Populace US, our default microdata, under each state’s {instrument.year} rules. SNAP in + January, as a share of SPM units with children. Medicaid or CHIP as a share of children under 19.

    diff --git a/slideshows/appam-2026/slides/OneFamilySlide.tsx b/slideshows/appam-2026/slides/OneFamilySlide.tsx index ed0f3dd5..5d460919 100644 --- a/slideshows/appam-2026/slides/OneFamilySlide.tsx +++ b/slideshows/appam-2026/slides/OneFamilySlide.tsx @@ -43,7 +43,7 @@ export default function OneFamilySlide() { family pays, to get net resources. We leave out health coverage. The gap between the highest and lowest state runs {usd(spreadAt(30_000).spread)} at $30,000 of earnings and{' '} {usd(spreadAt(60_000).spread)} - {' at $60,000. South Dakota’s subsidy covers five months.'} + {' at $60,000. The model starts South Dakota’s program in August 2026, so its subsidy covers five months.'}

    diff --git a/slideshows/appam-2026/slides/ParametersSlide.tsx b/slideshows/appam-2026/slides/ParametersSlide.tsx index f767bdec..a3e74b90 100644 --- a/slideshows/appam-2026/slides/ParametersSlide.tsx +++ b/slideshows/appam-2026/slides/ParametersSlide.tsx @@ -20,7 +20,7 @@ export default function ParametersSlide() { return (
    - + 
                 {YAML}
    @@ -31,7 +31,7 @@ export default function ParametersSlide() { lifted its two-dependent cap.

    Ask for any date and the model reads the value in force on that date.

    -

    Open the file and check each value against the citation beside it.

    +

    Open the file and check each value against the laws and forms it cites.

    diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx index bf7f6869..1706092e 100644 --- a/slideshows/appam-2026/slides/ReformSlide.tsx +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -51,8 +51,10 @@ export default function ReformSlide() {

    Populace US, 2026, assuming no behavioral responses. The file holds{' '} {massachusetts.records.toLocaleString('en-US')} Massachusetts households, an effective sample of about{' '} - {massachusetts.effectiveHouseholds}, and no record crosses the SPM threshold under this reform. The poverty - gap moves with each dollar that reaches a family below the threshold. + {massachusetts.effectiveHouseholds}, and puts {massachusetts.modelChildRate.toFixed(1)}% of the state’s + children below the SPM threshold in 2025, against Census’s {massachusetts.censusChildRate.toFixed(1)}% for + 2023 to 2025. No record crosses the threshold under this reform, so the rate holds still, while the gap moves + with each dollar that reaches a family below it.

    diff --git a/slideshows/appam-2026/slides/ScopeSlide.tsx b/slideshows/appam-2026/slides/ScopeSlide.tsx index 2e1147f6..30c11bf2 100644 --- a/slideshows/appam-2026/slides/ScopeSlide.tsx +++ b/slideshows/appam-2026/slides/ScopeSlide.tsx @@ -2,11 +2,10 @@ import { stateBaseline } from '../content'; import Frame from './Frame'; const coverage = [ - 'We model child care subsidies in 46 jurisdictions through 2023, 48 in 2024 and all 51 from 2025.', + 'We model child care subsidies in 46 jurisdictions through 2023, 48 in 2024, 50 in 2025 and all 51 from August 2026.', 'We copy each parameter’s earliest value back to 2015, so validate historical runs separately.', 'The model applies Wisconsin’s current child care schedules to earlier years.', - 'South Dakota’s child care program starts in the model in August 2026.', - `Populace US holds an effective sample of about ${stateBaseline.medianEffective} households in the median state, and its state child poverty rates differ from Census’s by ${stateBaseline.meanCensusGap.toFixed(1)} points on average. Bring your own microdata for state levels.`, + `Populace US holds an effective sample of about ${stateBaseline.medianEffective} households with children in the median state. Its child poverty rates exceed Census’s 2023 to 2025 averages in ${stateBaseline.modelExceedsCensus} of 51 jurisdictions and differ by ${stateBaseline.meanCensusGap.toFixed(1)} points on average. Bring your own microdata for state levels.`, ]; const mechanisms = [ diff --git a/slideshows/appam-2026/slides/WhatItDoesSlide.tsx b/slideshows/appam-2026/slides/WhatItDoesSlide.tsx index c304cca6..96cb93be 100644 --- a/slideshows/appam-2026/slides/WhatItDoesSlide.tsx +++ b/slideshows/appam-2026/slides/WhatItDoesSlide.tsx @@ -11,7 +11,7 @@ const uses = [ }, { title: 'Estimate reforms', - body: 'How proposed reforms would change poverty and household incomes in each state.', + body: 'Who would gain or lose if lawmakers adopted a proposed reform.', }, ]; diff --git a/slideshows/appam-2026/slides/WhichLinesSlide.tsx b/slideshows/appam-2026/slides/WhichLinesSlide.tsx index 810163dc..62180e3c 100644 --- a/slideshows/appam-2026/slides/WhichLinesSlide.tsx +++ b/slideshows/appam-2026/slides/WhichLinesSlide.tsx @@ -42,7 +42,7 @@ export default function WhichLinesSlide() { ))}

    - Texas deducts what the family pays for care when it counts SNAP income, so the subsidy shrinks that deduction. + Congress lets SNAP households deduct what they pay for care, so the subsidy shrinks that deduction.

    From b344caec5c1913b6fff80d2faba6ef8f14dc06ad Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 16:53:12 -0500 Subject: [PATCH 11/32] Tighten the APPAM copy after the voice pass Slide 7's caption and the ledger drop reduced passives, slide 8 gives the model its own verb, and the notes' longest spoken sentences split where they held two thoughts. The ledger's $30,000 line now matches the fixed hours in the sweep. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 4 ++-- slideshows/appam-2026/SPEAKER-NOTES.md | 12 ++++++------ slideshows/appam-2026/slides/MicrodataSlide.tsx | 7 ++++--- slideshows/appam-2026/slides/ReformSlide.tsx | 2 +- 4 files changed, 13 insertions(+), 12 deletions(-) diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index 7b312c89..b9a033ec 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -52,13 +52,13 @@ Slide 7's loop ran from a CSV of the fixed sample, with county and finer geograp - `meets_ccdf_activity_test` starts true, because a parent in a regular job meets it and the input defaults to false. - `wi_works_placement` starts as `NONE`, because the input defaults to a Community Service Job. - $40,000 over 2,080 hours comes to $19.23 an hour. The highest basic state rate on the Department of Labor's table, updated July 1, 2026, belongs to DC at $18.40 (dol.gov/agencies/whd/minimum-wage/state). Local minimums can run higher, and the table leaves them out. -- The sweeps vary earnings and hold hours at 40 a week. $30,000 over 2,080 hours comes to $14.42 an hour, below DC's basic rate and some other states', so read the $30,000 points as part-time work at those states' wages. +- The sweeps vary earnings and hold hours at 40 a week, so at $30,000 the family earns $14.42 an hour, less than DC's basic rate. Net resources add market income, SNAP, WIC, TANF, the child care subsidy and other benefits, then subtract federal and state taxes net of credits and the child care bill. `analysis/test_invariants.py` reconciles the total with `spm_unit_net_income` and with `household_net_income` over random states and earnings. -Any state comparison built on these totals must account for four differences: +Any state comparison that uses these totals must account for four differences: - `spm_unit_market_income` leaves out the Alaska Permanent Fund Dividend, which `household_market_income` includes. - `spm_unit_benefits` leaves out New Jersey's ANCHOR payment, which `household_benefits` includes. diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index af7a8dc7..37913603 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -33,7 +33,7 @@ $40,000 works out to $19.23 an hour, which exceeds every state's minimum wage on Each mark shows one state on one line of the family's budget. Federal taxes and credits barely move across states, and states drive the rest through child care subsidies, SNAP options and their own taxes. -These lines interact. Run Texas with and without its subsidy: the family gets $15,791 in child care help and loses $5,598 of SNAP, because Congress lets SNAP households deduct what they pay for care and the subsidy shrinks that deduction. Net resources rise by $10,193. +These lines interact when you run Texas with and without its subsidy: the family gets $15,791 in child care help and loses $5,598 of SNAP. Congress lets SNAP households deduct what they pay for care, so the subsidy shrinks that deduction. Net resources rise by $10,193. ## 5. This family loses up to $15,343 at a benefit cliff, at earnings that vary by state (3:45, 1 minute) @@ -57,7 +57,7 @@ If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface takes t Take the parameter from a moment ago and double it, from $440 to $880 per dependent. That costs the state $555 million in 2026. Of Massachusetts households, 29.8% gain and none lose, and the SPM poverty gap shrinks by $167 million. -The poverty rate holds still in this run, because no record crosses the threshold. Populace US holds 1,522 Massachusetts households, an effective sample of about 255, and it puts 20.2% of the state's children below the threshold in 2025, against Census's 12.8% for 2023 to 2025. For a small reform in one state, read the poverty gap: it moves with each dollar that reaches a family below the threshold, and it starts from that higher baseline. +The poverty rate holds still in this run, because no record crosses the threshold. Populace US holds 1,522 Massachusetts households, an effective sample of about 255. The model puts 20.2% of the state's children below the threshold in 2025, against Census's 12.8% for 2023 to 2025. For a small reform in one state, read the poverty gap: it moves with each dollar that reaches a family below the threshold, and it starts from that higher baseline. The Child Poverty Impact Dashboard applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state. @@ -65,17 +65,17 @@ The Child Poverty Impact Dashboard applies the model to child tax credit, EITC, The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. -PolicyEngine adds four things: you can change any parameter or write a new rule, run your own microdata, read and edit every line of code, and project current law forward through 2035. +With PolicyEngine, you can change any parameter or write a new rule, run your own microdata, read and edit every line of code, and project current law forward through 2035. ## 10. What these results leave out (9:15, 45 seconds) -We model child care subsidies in all 51 jurisdictions from August 2026, and we copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so a change in minimum wage or paid leave law leaves a family's income unchanged. Hours stay fixed unless you set a labor supply response. +We model child care subsidies in all 51 jurisdictions from August 2026. We copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so a change in minimum wage or paid leave law leaves a family's income unchanged. Hours stay fixed unless you set a labor supply response. -Our default microdata carries thin state samples, about 75 effective households with children in the median state, and its child poverty rates exceed Census's 2023 to 2025 averages in 40 of 51 jurisdictions. For state poverty levels, bring your own microdata. +Our default microdata carries thin state samples, about 75 effective households with children in the median state. Its child poverty rates exceed Census's 2023 to 2025 averages in 40 of 51 jurisdictions, so for state poverty levels, bring your own microdata. ## 11. The Axiom Foundation encodes rules with their source text and dates (10:00, 1 minute) -I also lead the Axiom Foundation. We publish open, machine-readable encodings of rules, starting with tax and benefit policy. Georgia's Division of Family and Children Services applies a 20% earned income deduction in section 3612 of its SNAP manual, and Axiom's file records the manual's words and effective date beside each value. +I also lead the Axiom Foundation. We publish open, machine-readable encodings of rules, starting with tax and benefit policy. Georgia's Division of Family and Children Services applies a 20% earned income deduction in section 3612 of its SNAP manual. Axiom's file records the manual's words and effective date beside each value. We ran this rule through the Axiom engine. A family earning $3,333.33 a month who reports on time gets a $666.67 deduction, and the same family reporting late gets nothing. Ask for November 2019, before the manual section Axiom encoded took effect, and the engine refuses. diff --git a/slideshows/appam-2026/slides/MicrodataSlide.tsx b/slideshows/appam-2026/slides/MicrodataSlide.tsx index 6e295506..58050717 100644 --- a/slideshows/appam-2026/slides/MicrodataSlide.tsx +++ b/slideshows/appam-2026/slides/MicrodataSlide.tsx @@ -33,9 +33,10 @@ export default function MicrodataSlide() {

    - Each mark shows one state. The same {instrument.households.toLocaleString('en-US')} households with children, - drawn by weight from Populace US, our default microdata, under each state’s {instrument.year} rules. SNAP in - January, as a share of SPM units with children. Medicaid or CHIP as a share of children under 19. + Each mark shows one state. We draw {instrument.households.toLocaleString('en-US')} households with children by + weight from Populace US, our default microdata, and run the same families under each state’s{' '} + {instrument.year} rules. SNAP in January, as a share of SPM units with children. Medicaid or CHIP as a share + of children under 19.

    diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx index 1706092e..13e4ca1e 100644 --- a/slideshows/appam-2026/slides/ReformSlide.tsx +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -51,7 +51,7 @@ export default function ReformSlide() {

    Populace US, 2026, assuming no behavioral responses. The file holds{' '} {massachusetts.records.toLocaleString('en-US')} Massachusetts households, an effective sample of about{' '} - {massachusetts.effectiveHouseholds}, and puts {massachusetts.modelChildRate.toFixed(1)}% of the state’s + {massachusetts.effectiveHouseholds}. The model puts {massachusetts.modelChildRate.toFixed(1)}% of the state’s children below the SPM threshold in 2025, against Census’s {massachusetts.censusChildRate.toFixed(1)}% for 2023 to 2025. No record crosses the threshold under this reform, so the rate holds still, while the gap moves with each dollar that reaches a family below it. From 1199c42d8a194278cfbf8f61611a975b6ba2a5ab Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 20:06:52 -0500 Subject: [PATCH 12/32] Run the APPAM state slides on the state data file Slides 8 and 10 described the national default file, but policyengine.py routes state regions to the Populace US local-area file. Rerun the Massachusetts reform and the 51-state baseline on that file, keep the default-file runs in the ledger, and move the notes, slide copy and tests to the new figures. Fix ruff findings in the analysis scripts and load the QR code eagerly. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 26 +- slideshows/appam-2026/SPEAKER-NOTES.md | 8 +- .../appam-2026/analysis/axiom_ga_3612.py | 3 +- slideshows/appam-2026/analysis/interaction.py | 3 +- .../analysis/massachusetts_reform.py | 27 +- .../appam-2026/analysis/sample_families.py | 3 +- .../analysis/simulated_instrument.py | 2 +- .../appam-2026/analysis/state_baseline.py | 98 ++- slideshows/appam-2026/analysis/summarize.py | 2 +- .../appam-2026/analysis/test_invariants.py | 3 +- .../analysis/test_sample_families.py | 3 +- slideshows/appam-2026/content.test.ts | 27 +- slideshows/appam-2026/content.ts | 1 + .../appam-2026/data/massachusetts_reform.json | 35 +- .../data/massachusetts_reform_default.json | 26 + .../appam-2026/data/state_baseline.json | 680 +++++++++--------- .../data/state_baseline_default.json | 468 ++++++++++++ slideshows/appam-2026/slides/EndSlide.tsx | 2 +- slideshows/appam-2026/slides/ReformSlide.tsx | 13 +- slideshows/appam-2026/slides/ScopeSlide.tsx | 2 +- 20 files changed, 1001 insertions(+), 431 deletions(-) create mode 100644 slideshows/appam-2026/data/massachusetts_reform_default.json create mode 100644 slideshows/appam-2026/data/state_baseline_default.json diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index b9a033ec..d525e9ac 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -18,12 +18,12 @@ Each claim on a slide traces to a computed result, to code read at a named commi | 5 | In 46 of 51 states, the largest drop comes from losing the subsidy | `cliff_ranking`: TANF drives Massachusetts and New Hampshire, and WIC drives Maine, New Mexico and Vermont | | 5 | Median state's total effective marginal tax rate: 63.6% at $30,000, 24.6% at $60,000 | `data/charts.json`, `emtr`: one minus the change in net resources over a $500 raise | | 7 | SNAP eligibility 17.5% in Arkansas and Tennessee to 38.3% in Maryland, among SPM units with children; child Medicaid or CHIP 39.8% in Idaho to 66.1% in Hawaii | `analysis/sample_families.py` → `analysis/simulated_instrument.py` → `data/simulated_instrument.json` | -| 8 | State cost, 29.8% of households gain, none lose, $167 million off the SPM poverty gap | `analysis/massachusetts_reform.py` → `data/massachusetts_reform.json`: `state_cost` holds the fall in `state_income_tax`, and `household_gain` holds the change in `household_net_income` | -| 8 | 1,522 Massachusetts households, effective sample of about 255, no record crosses the threshold | `data/massachusetts_reform.json`: `household_records`, `effective_households` (Kish), `people_crossing_threshold` | -| 10 | Effective sample in the median state; child poverty rates exceed Census's in 40 of 51 jurisdictions and differ by 4.8 points on average | `analysis/state_baseline.py` → `data/state_baseline.json`, compared with `data/census_child_spm.json` | +| 8 | State cost, 29.4% of households gain, none lose, $169 million off the SPM poverty gap | `analysis/massachusetts_reform.py` → `data/massachusetts_reform.json`: `state_cost` holds the fall in `state_income_tax`, and `household_gain` holds the change in `household_net_income` | +| 8 | 36,491 Massachusetts households, effective sample of about 395, 2026 child rate falls 0.2 points | `data/massachusetts_reform.json`: `household_records`, `effective_households` (Kish), `baseline_child_poverty`, `reformed_child_poverty` | +| 10 | Effective sample in the median state; child poverty rates miss Census's by 3.2 points on average, and 29 of 51 jurisdictions fall outside Census's margin | `analysis/state_baseline.py` → `data/state_baseline.json`, compared with `data/census_child_spm.json` | | 11 | $666.666 deduction on time, $0 late, refusal for November 2019 | `analysis/axiom_ga_3612.py` → `data/axiom_ga_3612.json`, a real engine run | -Slide 7's loop ran from a CSV of the fixed sample, with county and finer geography dropped and a `STATE_FIPS` dictionary defined above it, and reproduced Arkansas and Maryland to every digit. Slide 8's reform dictionary ran on the Massachusetts records, and `analysis/massachusetts_reform.py` runs on those records alone. A national run of the same reform returned the same household gain, gaining share and poverty rates (`data/massachusetts_national_check.json`). +Slide 7's loop ran from a CSV of the fixed sample, with county and finer geography dropped and a `STATE_FIPS` dictionary defined above it, and reproduced Arkansas and Maryland to every digit. Slide 8's reform dictionary ran on the Massachusetts records of the local-area file, and `analysis/massachusetts_reform.py` runs on those records alone. On the default file, a Massachusetts cut and a national run returned the same household gain, gaining share and poverty rates (`data/massachusetts_reform_default.json`, `data/massachusetts_national_check.json`). ## The fixed sample @@ -31,17 +31,22 @@ Slide 7's loop ran from a CSV of the fixed sample, with county and finer geograp ## Massachusetts poverty -- No record crosses the threshold under the reform, so the rate does not change. -- The file's 1,522 Massachusetts household records carry an effective sample of about 255. -- The model's Massachusetts child rate comes to 20.2% for 2025 and 22.3% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). +- 101 person records cross the threshold under the reform, and the 2026 child rate falls 0.2 points, from 23.1% to 22.9%. +- The file's 36,491 Massachusetts household records carry an effective sample of about 395, or 173 among households with children. Its 1,522 records from the Current Population Survey and tax data hold 67% of household weight. +- The model's Massachusetts child rate comes to 21.4% for 2025 and 23.1% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). That difference ranks third among the 51 jurisdictions, after Delaware and Louisiana. +- The same reform on the default file costs $555 million, with 29.8% of households gaining and $167 million off the poverty gap (`data/massachusetts_reform_default.json`). +- The same reform on the same records at policyengine-us 2.2.1, the version `policyengine.py` 6.11.1 pins, costs $657 million. The credit's formula changed between those versions: 2.2.1 counts a disabled spouse as a qualifying individual, and 2.90.2 counts a spouse incapable of self-care. - The national comparison on the webinar deck's `BaselineVsCensusSlide` runs 16.6% in the model against 13.4% from Census, for children in 2025. ## State baselines -`analysis/state_baseline.py` cuts Populace US to one state at a time and runs 2025 child SPM poverty for all 51 jurisdictions. +`analysis/state_baseline.py` runs 2025 child SPM poverty for all 51 jurisdictions, one state at a time, on the local-area Populace US file at revision `populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z`. `policyengine.py` 6.11.1 selects that file for state regions (`region_datasets` in `src/policyengine/data/bundle/manifest.json`, and `docs/regions.md`), and the file here matches the manifest's SHA-256. -- The model's rates exceed Census's 2023 to 2025 averages in 40 of 51 jurisdictions. They differ by 4.8 points on average and exceed Census's by 3.2 points on net. In 38 of 51 jurisdictions the difference exceeds Census's margin of error. -- Effective samples of households with children run from 33 in Maine to 390 in California, with a median of 75. Across all households they run from 99 in Maine to 965 in California, with a median of 185. +- The model's rates differ from Census's 2023 to 2025 averages by 3.2 points on average and exceed Census's by 0.9 points on net. They exceed Census's in 30 of 51 jurisdictions. In 29 of 51 jurisdictions the difference exceeds Census's margin of error. +- Effective samples of households with children run from 58 in Maine to 499 in California, with a median of 125. Across all households they run from 193 in Maryland to 1,146 in California, with a median of 280. +- `policyengine.py` leaves ACS group quarters out of SPM measures, and these runs keep them. Leaving them out moves no state's rate by more than 0.02 points and moves Massachusetts's by 0.01. +- PolicyEngine/microcosm#1445 ran the same file at the same model version, and its 51 state rates match these to six decimal places. +- The default file reads higher and thinner: a 4.8-point average difference, 40 of 51 above Census, 38 of 51 beyond Census's margin, and a median of 75 effective households with children (`data/state_baseline_default.json`). ## The family @@ -102,4 +107,3 @@ Slide 9 uses each tool's description from the session abstract, which covers the - The rule file sits at `us-ga/policies/dfcs/snap/3612.yaml` in `TheAxiomFoundation/rulespec-us`, published under CC BY 4.0. - `axiom-rules-engine` carries an MIT license. - Georgia's public manual at pamms.dhs.ga.gov/dfcs/snap/3612 confirms the rule text and the December 2019 effective date. -- The deck makes no claim about how much of any program Axiom covers. diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index 37913603..275a0676 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -53,11 +53,11 @@ We drew 3,000 households with children from Populace US, our default microdata, If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface takes the same files. It runs tax years 2021 onward through PolicyEngine and sends earlier years to TAXSIM35. -## 8. Doubling the Massachusetts child and family credit would cost $555 million (7:00, 1 minute 30 seconds) +## 8. Doubling the Massachusetts child and family credit would cost $577 million (7:00, 1 minute 30 seconds) -Take the parameter from a moment ago and double it, from $440 to $880 per dependent. That costs the state $555 million in 2026. Of Massachusetts households, 29.8% gain and none lose, and the SPM poverty gap shrinks by $167 million. +Take the parameter from a moment ago and double it, from $440 to $880 per dependent. That costs the state $577 million in 2026. Of Massachusetts households, 29.4% gain and none lose, and the SPM poverty gap shrinks by $169 million. -The poverty rate holds still in this run, because no record crosses the threshold. Populace US holds 1,522 Massachusetts households, an effective sample of about 255. The model puts 20.2% of the state's children below the threshold in 2025, against Census's 12.8% for 2023 to 2025. For a small reform in one state, read the poverty gap: it moves with each dollar that reaches a family below the threshold, and it starts from that higher baseline. +We ran this on our local-area file, the one policyengine.py uses for state regions. It holds 36,491 Massachusetts households, an effective sample of about 395. The model puts 21.4% of the state's children below the threshold in 2025, against Census's 12.8% for 2023 to 2025. The reform lowers the 2026 child rate by 0.2 points, from a baseline that runs high. The Child Poverty Impact Dashboard applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state. @@ -71,7 +71,7 @@ With PolicyEngine, you can change any parameter or write a new rule, run your ow We model child care subsidies in all 51 jurisdictions from August 2026. We copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so a change in minimum wage or paid leave law leaves a family's income unchanged. Hours stay fixed unless you set a labor supply response. -Our default microdata carries thin state samples, about 75 effective households with children in the median state. Its child poverty rates exceed Census's 2023 to 2025 averages in 40 of 51 jurisdictions, so for state poverty levels, bring your own microdata. +Our local-area file carries thin state samples, about 125 effective households with children in the median state. Its child poverty rates miss Census's 2023 to 2025 averages by 3.2 points on average, and 29 of 51 jurisdictions fall outside Census's margin. For state poverty levels, bring your own microdata. ## 11. The Axiom Foundation encodes rules with their source text and dates (10:00, 1 minute) diff --git a/slideshows/appam-2026/analysis/axiom_ga_3612.py b/slideshows/appam-2026/analysis/axiom_ga_3612.py index 340faa05..4223151c 100644 --- a/slideshows/appam-2026/analysis/axiom_ga_3612.py +++ b/slideshows/appam-2026/analysis/axiom_ga_3612.py @@ -33,7 +33,7 @@ def month_bounds(month): - year, number = map(int, month.split("-")) + number = int(month.split("-")[1]) end = 30 if number in (4, 6, 9, 11) else 31 return f"{month}-01", f"{month}-{end:02d}" @@ -83,6 +83,7 @@ def main(engine, artifact): input=json.dumps(request(month, late)), capture_output=True, text=True, + check=False, ) results.append( { diff --git a/slideshows/appam-2026/analysis/interaction.py b/slideshows/appam-2026/analysis/interaction.py index dadbd581..326abaaf 100644 --- a/slideshows/appam-2026/analysis/interaction.py +++ b/slideshows/appam-2026/analysis/interaction.py @@ -9,9 +9,8 @@ from importlib.metadata import version from pathlib import Path -from policyengine_us import Simulation - import household as hh +from policyengine_us import Simulation STATE = "TX" EARNINGS = 40_000 diff --git a/slideshows/appam-2026/analysis/massachusetts_reform.py b/slideshows/appam-2026/analysis/massachusetts_reform.py index 19d6de2a..87422c90 100644 --- a/slideshows/appam-2026/analysis/massachusetts_reform.py +++ b/slideshows/appam-2026/analysis/massachusetts_reform.py @@ -1,12 +1,8 @@ """Double the Massachusetts child and family tax credit and score it. -Runs current law and the reform on the Massachusetts records in Populace US for -2026. The reform assumes no behavioral responses. - -Populace US carries few well-weighted records for any one state, so a small reform -can leave a state's poverty rate unchanged because no record crosses the threshold. -The poverty gap moves with each dollar that reaches a family below the threshold, -so the slide reports that alongside cost. Cost means the fall in state income tax, +Runs current law and the reform on the Massachusetts records in the local-area +Populace US file, which policyengine.py uses for state regions, for 2026. The +reform assumes no behavioral responses. Cost means the fall in state income tax, and household_gain holds the change in household net income beside it. """ @@ -19,8 +15,7 @@ from policyengine_core.data import Dataset from policyengine_core.reforms import Reform from policyengine_us import Microsimulation - -from state_baseline import DATA_YEAR, kish, load, state_file +from state_baseline import DATA_YEAR, STATE_DATASET, kish, load_state, state_file YEAR = 2026 MASSACHUSETTS_FIPS = 25 @@ -50,13 +45,16 @@ def results(sim): ) ), "poor_records": poor, + "household_children": sim.calculate( + "is_child", YEAR, map_to="household" + ).values, "household_weight": sim.calculate("household_weight", YEAR).values, "household_net_income": sim.calculate("household_net_income", YEAR).values, } -def main(commit): - load() +def main(commit, folder): + load_state(MASSACHUSETTS_FIPS, Path(folder)) people = state_file(MASSACHUSETTS_FIPS) baseline = results( Microsimulation(dataset=Dataset.from_dataframe(people, DATA_YEAR)) @@ -74,7 +72,7 @@ def main(commit): payload = { "policyengine_us_version": version("policyengine-us"), "policyengine_us_commit": commit, - "dataset": "Populace US", + "dataset": STATE_DATASET, "year": YEAR, "parameter": PARAMETER, "current": CURRENT, @@ -85,6 +83,9 @@ def main(commit): "any_household_loses": bool((gains < -1).any()), "household_records": len(weights), "effective_households": kish(weights), + "effective_households_with_children": kish( + weights[baseline["household_children"] > 0] + ), "people_crossing_threshold": int( np.sum(baseline["poor_records"] != reformed["poor_records"]) ), @@ -97,4 +98,4 @@ def main(commit): if __name__ == "__main__": - main(sys.argv[1]) + main(sys.argv[1], sys.argv[2]) diff --git a/slideshows/appam-2026/analysis/sample_families.py b/slideshows/appam-2026/analysis/sample_families.py index 669db07c..8a93e439 100644 --- a/slideshows/appam-2026/analysis/sample_families.py +++ b/slideshows/appam-2026/analysis/sample_families.py @@ -50,9 +50,8 @@ def inputs_only(frame, inputs): def main(path): - from policyengine_us import CountryTaxBenefitSystem - import state_baseline as sb + from policyengine_us import CountryTaxBenefitSystem sb.load() people = sb.tables["person"] diff --git a/slideshows/appam-2026/analysis/simulated_instrument.py b/slideshows/appam-2026/analysis/simulated_instrument.py index 0aead5a2..4cca30ac 100644 --- a/slideshows/appam-2026/analysis/simulated_instrument.py +++ b/slideshows/appam-2026/analysis/simulated_instrument.py @@ -52,7 +52,7 @@ def run(state): # state reports its own failure as text. try: return simulate(state) - except Exception as error: + except Exception as error: # noqa: BLE001 return {"state": state, "error": repr(error)} diff --git a/slideshows/appam-2026/analysis/state_baseline.py b/slideshows/appam-2026/analysis/state_baseline.py index 6a0d0895..c2de21c9 100644 --- a/slideshows/appam-2026/analysis/state_baseline.py +++ b/slideshows/appam-2026/analysis/state_baseline.py @@ -1,8 +1,9 @@ """Compare each state's baseline child poverty with Census and measure its sample. -Cuts the pinned Populace US file to one state at a time, so each run stays small. -A Massachusetts file cut this way reproduces the national run's reform cost and -poverty rates. Census publishes a 2023 to 2025 average, and this run reports 2025. +Runs the local-area Populace US file, which policyengine.py uses for state regions, +one state at a time so each run stays small. A Massachusetts file cut from the +default file this way reproduces the national run's reform cost and poverty rates. +Census publishes a 2023 to 2025 average, and this run reports 2025. """ import json @@ -18,11 +19,16 @@ from policyengine_us import CountryTaxBenefitSystem, Microsimulation from policyengine_us.system import DEFAULT_DATASET +STATE_DATASET = ( + "hf://datasets/policyengine/populace-us/populace_us_2024_acs_local.h5" + "@populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z" +) YEAR = 2025 DATA_YEAR = 2024 WORKERS = 3 CHILD_AGE_LIMIT = 18 GROUPS = ("tax_unit", "spm_unit", "family", "marital_unit") +TABLES = ("household", "person", *GROUPS) tables = None known = None @@ -33,16 +39,59 @@ def kish(weights): return float(weights.sum() ** 2 / (weights**2).sum()) -def load(): - global tables, known - known = set(CountryTaxBenefitSystem().variables) - path, tag = DEFAULT_DATASET.removeprefix("hf://datasets/").rsplit("@", 1) +def download(dataset): + path, tag = dataset.removeprefix("hf://datasets/").rsplit("@", 1) owner, repo, filename = path.split("/") - local = hf_hub_download( + return hf_hub_download( f"{owner}/{repo}", filename, repo_type="dataset", revision=tag ) - with pd.HDFStore(local, "r") as store: - tables = {name: store.select(name) for name in ("household", "person", *GROUPS)} + + +def load(): + """The default file's tables, for draws that span the country.""" + global tables, known + known = set(CountryTaxBenefitSystem().variables) + with pd.HDFStore(download(DEFAULT_DATASET), "r") as store: + tables = {name: store.select(name) for name in TABLES} + + +def write_by_state(table, fips, name, folder): + for state, rows in table.groupby(fips.values, sort=False): + rows.to_pickle( + folder / f"{int(state):02d}-{name}.pkl.gz", + compression={"method": "gzip", "compresslevel": 1}, + ) + + +def split_by_state(folder): + """Split the local-area file by state, holding one table in memory at a time.""" + folder.mkdir(parents=True, exist_ok=True) + with pd.HDFStore(download(STATE_DATASET), "r") as store: + households = store.select("household") + states = households.set_index("household_id")["state_fips"] + write_by_state(households, households["state_fips"], "household", folder) + del households + people = store.select("person") + fips = people["person_household_id"].map(states) + write_by_state(people, fips, "person", folder) + members = { + group: pd.Series(fips.values, index=people[f"person_{group}_id"].values) + for group in GROUPS + } + del people + for group in GROUPS: + state_of = members.pop(group).groupby(level=0).first() + table = store.select(group) + write_by_state(table, table[f"{group}_id"].map(state_of), group, folder) + del table + + +def load_state(fips, folder): + global tables, known + known = known or set(CountryTaxBenefitSystem().variables) + tables = { + name: pd.read_pickle(folder / f"{fips:02d}-{name}.pkl.gz") for name in TABLES + } def flat_file(households): @@ -69,7 +118,12 @@ def add_group(table, group): add_group(households, "household") for group in GROUPS: add_group(tables[group], group) - return pd.DataFrame({f"{k}__{DATA_YEAR}": v for k, v in columns.items()}) + frame = pd.DataFrame({f"{k}__{DATA_YEAR}": v for k, v in columns.items()}) + # The local-area file leaves parent links blank; -1 means no parent. + for name in frame.columns[frame.columns.str.startswith("parent_")]: + if name.endswith(f"_id__{DATA_YEAR}"): + frame[name] = frame[name].fillna(-1).astype(np.int64) + return frame def state_file(fips): @@ -77,7 +131,9 @@ def state_file(fips): return flat_file(households[households["state_fips"] == fips]) -def run(fips): +def run(job): + fips, folder = job + load_state(fips, folder) sim = Microsimulation(dataset=Dataset.from_dataframe(state_file(fips), DATA_YEAR)) age = sim.calculate("age", YEAR).values weight = sim.calculate("person_weight", YEAR).values.astype(float) @@ -96,21 +152,25 @@ def run(fips): } -def main(commit): +def main(commit, folder): data = Path(__file__).parent.parent / "data" census = json.loads((data / "census_child_spm.json").read_text()) published = {row["state"]: row for row in census["rows"]} - load() - fips = sorted(int(value) for value in tables["household"]["state_fips"].unique()) - with Pool(WORKERS, initializer=load) as pool: - rows = pool.map(run, fips) + folder = Path(folder) + if not any(folder.glob("*-household.pkl.gz")): + split_by_state(folder) + fips = sorted(int(path.name[:2]) for path in folder.glob("*-household.pkl.gz")) + # Smallest first, and a fresh worker every few states, so memory stays flat. + fips.sort(key=lambda state: (folder / f"{state:02d}-person.pkl.gz").stat().st_size) + with Pool(WORKERS, maxtasksperchild=4) as pool: + rows = pool.map(run, [(state, folder) for state in fips], chunksize=1) for row in rows: row["census_percent"] = published[row["state"]]["rate"] row["census_margin"] = published[row["state"]]["moe"] payload = { "policyengine_us_version": version("policyengine-us"), "policyengine_us_commit": commit, - "dataset": "Populace US", + "dataset": STATE_DATASET, "year": YEAR, "census_source": census["source"], "rows": rows, @@ -119,4 +179,4 @@ def main(commit): if __name__ == "__main__": - main(sys.argv[1]) + main(sys.argv[1], sys.argv[2]) diff --git a/slideshows/appam-2026/analysis/summarize.py b/slideshows/appam-2026/analysis/summarize.py index 646193c1..41af0ce6 100644 --- a/slideshows/appam-2026/analysis/summarize.py +++ b/slideshows/appam-2026/analysis/summarize.py @@ -34,7 +34,7 @@ def rounded(value): - return int(round(value)) + return round(value) def main(): diff --git a/slideshows/appam-2026/analysis/test_invariants.py b/slideshows/appam-2026/analysis/test_invariants.py index 5b800cae..c5975ecb 100644 --- a/slideshows/appam-2026/analysis/test_invariants.py +++ b/slideshows/appam-2026/analysis/test_invariants.py @@ -4,12 +4,11 @@ states and earnings nobody picked by hand. """ +import household as hh import numpy as np from hypothesis import given, settings from hypothesis import strategies as st -import household as hh - MAX_EXAMPLES = 40 # The model stores money as 32-bit floats, so totals drift by fractions of a cent. TOLERANCE = 0.02 diff --git a/slideshows/appam-2026/analysis/test_sample_families.py b/slideshows/appam-2026/analysis/test_sample_families.py index ccb651ba..f59b1125 100644 --- a/slideshows/appam-2026/analysis/test_sample_families.py +++ b/slideshows/appam-2026/analysis/test_sample_families.py @@ -1,11 +1,10 @@ """The fixed sample must keep each drawn household whole and count each draw once.""" import pandas as pd +import sample_families as sf from hypothesis import given from hypothesis import strategies as st -import sample_families as sf - YEAR = sf.YEAR HOUSEHOLD = f"person_household_id__{YEAR}" SPM_UNIT = f"person_spm_unit_id__{YEAR}" diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index cf5ad16e..b2274a31 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -16,6 +16,7 @@ import { massachusetts, medianRateAt, millions, + modelVersion, percent, programLines, spreadAt, @@ -237,13 +238,22 @@ describe('Massachusetts reform', () => { expect(massachusetts.gapReduction).toBeLessThanOrEqual(massachusetts.cost); }); - it('leaves the poverty rate alone only because no record crosses, as the slide says', () => { - expect(massachusetts.peopleCrossingThreshold).toBe(0); - expect(massachusetts.childPoverty[0]).toBe(massachusetts.childPoverty[1]); + it('lowers the child rate because records cross the threshold', () => { + expect(massachusetts.peopleCrossingThreshold).toBeGreaterThan(0); + expect(massachusetts.childRateChange).toBeGreaterThan(0); }); - it('scores the same on the state cut as on a national run', () => { + it('runs on the file policyengine.py uses for state regions', () => { + const baselines = JSON.parse(read('data/state_baseline.json')); const run = JSON.parse(read('data/massachusetts_reform.json')); + expect(run.dataset).toContain('populace_us_2024_acs_local.h5'); + expect(baselines.dataset).toBe(run.dataset); + expect(run.policyengine_us_version).toBe(modelVersion); + expect(baselines.policyengine_us_version).toBe(modelVersion); + }); + + it('scores the same on a default-file state cut as on a national run', () => { + const run = JSON.parse(read('data/massachusetts_reform_default.json')); expect(run.household_gain).toBe(nationalCheck.household_gain); expect(run.households_gaining_share).toBeCloseTo(nationalCheck.households_gaining_share, 6); expect(run.baseline_child_poverty).toBeCloseTo(nationalCheck.baseline_child_poverty, 6); @@ -312,7 +322,10 @@ describe('typed figures', () => { millions(massachusetts.gapReduction), `${massachusetts.modelChildRate.toFixed(1)}%`, `${massachusetts.censusChildRate.toFixed(1)}%`, - `${stateBaseline.modelExceedsCensus} of 51 jurisdictions`, + `${stateBaseline.beyondCensusMargin} of 51 jurisdictions`, + `${stateBaseline.meanCensusGap.toFixed(1)} points`, + `${massachusetts.childRateChange.toFixed(1)} points`, + `about ${massachusetts.effectiveHouseholds}`, ...endpoints, ]; @@ -321,7 +334,6 @@ describe('typed figures', () => { ...shared, usd(cliffs[2].drop), millions(massachusetts.cost), - `about ${massachusetts.effectiveHouseholds}`, `about ${stateBaseline.medianEffective} effective households`, usdCents(axiomRuns.onTime), usdCents(axiomRuns.monthlyEarnings), @@ -333,9 +345,8 @@ describe('typed figures', () => { it('match the data in the evidence ledger', () => { for (const figure of [ ...shared, - `${stateBaseline.beyondCensusMargin} of 51 jurisdictions`, + `${stateBaseline.modelExceedsCensus} of 51 jurisdictions`, `median of ${stateBaseline.medianEffective}`, - `${stateBaseline.meanCensusGap.toFixed(1)} points`, ]) { expect(ledger).toContain(figure); } diff --git a/slideshows/appam-2026/content.ts b/slideshows/appam-2026/content.ts index 97e2c203..b3735177 100644 --- a/slideshows/appam-2026/content.ts +++ b/slideshows/appam-2026/content.ts @@ -93,6 +93,7 @@ export const massachusetts = { effectiveHouseholds: Math.round(reformRun.effective_households), peopleCrossingThreshold: reformRun.people_crossing_threshold, childPoverty: [reformRun.baseline_child_poverty, reformRun.reformed_child_poverty], + childRateChange: 100 * (reformRun.baseline_child_poverty - reformRun.reformed_child_poverty), modelChildRate: massachusettsBaseline.child_spm_percent, censusChildRate: massachusettsBaseline.census_percent, }; diff --git a/slideshows/appam-2026/data/massachusetts_reform.json b/slideshows/appam-2026/data/massachusetts_reform.json index ba56163c..89a550f4 100644 --- a/slideshows/appam-2026/data/massachusetts_reform.json +++ b/slideshows/appam-2026/data/massachusetts_reform.json @@ -1,26 +1,27 @@ { "policyengine_us_version": "2.90.2", "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", - "dataset": "Populace US", + "dataset": "hf://datasets/policyengine/populace-us/populace_us_2024_acs_local.h5@populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z", "year": 2026, "parameter": "gov.states.ma.tax.income.credits.child_and_family.amount", "current": 440, "reformed": 880, - "state_cost": 554612736.0, - "household_gain": 554611328.0, - "households_gaining_share": 0.2980068325996399, + "state_cost": 576530432.0, + "household_gain": 576530752.0, + "households_gaining_share": 0.2943657338619232, "any_household_loses": false, - "household_records": 1522, - "effective_households": 254.60922241210938, - "people_crossing_threshold": 0, - "baseline_children": 1494064.0, - "reformed_children": 1494064.0, - "baseline_people": 7249868.0, - "reformed_people": 7249868.0, - "baseline_child_poverty": 0.22329546499381753, - "reformed_child_poverty": 0.22329546499381753, - "baseline_poverty": 0.196282351430889, - "reformed_poverty": 0.196282351430889, - "baseline_poverty_gap": 11493076992.0, - "reformed_poverty_gap": 11326015488.0 + "household_records": 36491, + "effective_households": 395.481689453125, + "effective_households_with_children": 172.8724365234375, + "people_crossing_threshold": 101, + "baseline_children": 1581027.25, + "reformed_children": 1581027.25, + "baseline_people": 7140549.0, + "reformed_people": 7140549.0, + "baseline_child_poverty": 0.23087971973876323, + "reformed_child_poverty": 0.2290957998948957, + "baseline_poverty": 0.18810227848166503, + "reformed_poverty": 0.1873483402334525, + "baseline_poverty_gap": 11775758336.0, + "reformed_poverty_gap": 11606638592.0 } diff --git a/slideshows/appam-2026/data/massachusetts_reform_default.json b/slideshows/appam-2026/data/massachusetts_reform_default.json new file mode 100644 index 00000000..ba56163c --- /dev/null +++ b/slideshows/appam-2026/data/massachusetts_reform_default.json @@ -0,0 +1,26 @@ +{ + "policyengine_us_version": "2.90.2", + "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "dataset": "Populace US", + "year": 2026, + "parameter": "gov.states.ma.tax.income.credits.child_and_family.amount", + "current": 440, + "reformed": 880, + "state_cost": 554612736.0, + "household_gain": 554611328.0, + "households_gaining_share": 0.2980068325996399, + "any_household_loses": false, + "household_records": 1522, + "effective_households": 254.60922241210938, + "people_crossing_threshold": 0, + "baseline_children": 1494064.0, + "reformed_children": 1494064.0, + "baseline_people": 7249868.0, + "reformed_people": 7249868.0, + "baseline_child_poverty": 0.22329546499381753, + "reformed_child_poverty": 0.22329546499381753, + "baseline_poverty": 0.196282351430889, + "reformed_poverty": 0.196282351430889, + "baseline_poverty_gap": 11493076992.0, + "reformed_poverty_gap": 11326015488.0 +} diff --git a/slideshows/appam-2026/data/state_baseline.json b/slideshows/appam-2026/data/state_baseline.json index fd2004b9..ea55c5ef 100644 --- a/slideshows/appam-2026/data/state_baseline.json +++ b/slideshows/appam-2026/data/state_baseline.json @@ -1,468 +1,468 @@ { "policyengine_us_version": "2.90.2", "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", - "dataset": "Populace US", + "dataset": "hf://datasets/policyengine/populace-us/populace_us_2024_acs_local.h5@populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z", "year": 2025, "census_source": "U.S. Census Bureau, Poverty in the United States: 2025 (P60-290), Table 18: SPM percent and margin of error for people under 18, three-year average 2023 to 2025", "rows": [ { - "state": "AL", - "records": 1061, - "effective_households": 236.55107295398253, - "effective_households_with_children": 93.65080621535844, - "child_spm_percent": 16.98715636912008, - "census_percent": 15.5, - "census_margin": 2.4 + "state": "WY", + "records": 3357, + "effective_households": 222.66302203609837, + "effective_households_with_children": 95.08935436578841, + "child_spm_percent": 7.4014381737328545, + "census_percent": 6.5, + "census_margin": 1.4 + }, + { + "state": "DC", + "records": 4221, + "effective_households": 231.7543763826635, + "effective_households_with_children": 71.67743149904001, + "child_spm_percent": 21.17762070511618, + "census_percent": 20.3, + "census_margin": 3.7 + }, + { + "state": "VT", + "records": 3888, + "effective_households": 242.87469995513473, + "effective_households_with_children": 78.86132723000058, + "child_spm_percent": 10.308498340888855, + "census_percent": 8.3, + "census_margin": 2.5 }, { "state": "AK", - "records": 826, - "effective_households": 133.0980287754569, - "effective_households_with_children": 63.74883692498441, - "child_spm_percent": 11.644426986408602, + "records": 3899, + "effective_households": 279.50358570875954, + "effective_households_with_children": 113.14721899003231, + "child_spm_percent": 12.280018902222128, "census_percent": 10.5, "census_margin": 2.1 }, { - "state": "AZ", - "records": 1164, - "effective_households": 189.9592179909452, - "effective_households_with_children": 73.65186948063935, - "child_spm_percent": 16.951186258024865, - "census_percent": 13.3, - "census_margin": 2.3 - }, - { - "state": "AR", - "records": 952, - "effective_households": 205.80839413217132, - "effective_households_with_children": 95.40156579624595, - "child_spm_percent": 8.12845772011748, - "census_percent": 13.3, - "census_margin": 2.7 + "state": "ND", + "records": 4718, + "effective_households": 243.2073828465999, + "effective_households_with_children": 92.35480678921647, + "child_spm_percent": 10.773326006109645, + "census_percent": 10.3, + "census_margin": 2.1 }, { - "state": "CA", - "records": 2659, - "effective_households": 964.8602098319376, - "effective_households_with_children": 389.73613737342515, - "child_spm_percent": 24.41650645326989, - "census_percent": 17.9, - "census_margin": 1.0 + "state": "SD", + "records": 5270, + "effective_households": 214.49903121980063, + "effective_households_with_children": 68.97439795321789, + "child_spm_percent": 6.733403277773982, + "census_percent": 7.8, + "census_margin": 2.2 }, { - "state": "CO", - "records": 1164, - "effective_households": 145.44069860124233, - 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"469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "dataset": "Populace US", + "year": 2025, + "census_source": "U.S. Census Bureau, Poverty in the United States: 2025 (P60-290), Table 18: SPM percent and margin of error for people under 18, three-year average 2023 to 2025", + "rows": [ + { + "state": "AL", + "records": 1061, + "effective_households": 236.55107295398253, + "effective_households_with_children": 93.65080621535844, + "child_spm_percent": 16.98715636912008, + "census_percent": 15.5, + "census_margin": 2.4 + }, + { + "state": "AK", + "records": 826, + "effective_households": 133.0980287754569, + "effective_households_with_children": 63.74883692498441, + "child_spm_percent": 11.644426986408602, + "census_percent": 10.5, + "census_margin": 2.1 + }, + { + "state": "AZ", + "records": 1164, + "effective_households": 189.9592179909452, + "effective_households_with_children": 73.65186948063935, + "child_spm_percent": 16.951186258024865, + "census_percent": 13.3, + "census_margin": 2.3 + }, + { + "state": "AR", + "records": 952, + "effective_households": 205.80839413217132, + "effective_households_with_children": 95.40156579624595, + "child_spm_percent": 8.12845772011748, + "census_percent": 13.3, + "census_margin": 2.7 + }, + { + "state": "CA", + "records": 2659, + "effective_households": 964.8602098319376, + "effective_households_with_children": 389.73613737342515, + "child_spm_percent": 24.41650645326989, + "census_percent": 17.9, + "census_margin": 1.0 + }, + { + "state": "CO", + "records": 1164, + "effective_households": 145.44069860124233, + "effective_households_with_children": 61.08269909168172, + "child_spm_percent": 10.352503836811634, + "census_percent": 7.7, + "census_margin": 1.8 + }, + { + "state": "CT", + "records": 908, + "effective_households": 135.72407262331754, + "effective_households_with_children": 57.00506226538202, + "child_spm_percent": 13.371307682492237, + "census_percent": 12.8, + "census_margin": 3.2 + }, + { + "state": "DE", + "records": 1068, + "effective_households": 138.95841498485564, + "effective_households_with_children": 49.220539244465236, + "child_spm_percent": 22.429186288025384, + "census_percent": 8.7, + "census_margin": 2.6 + }, + { + "state": "DC", + "records": 551, + "effective_households": 175.55582420499337, + "effective_households_with_children": 48.284698236390824, + "child_spm_percent": 34.365303438733925, + "census_percent": 20.3, + "census_margin": 3.7 + }, + { + "state": "FL", + "records": 1642, + "effective_households": 561.268792979642, + "effective_households_with_children": 204.65572062992027, + "child_spm_percent": 28.15649890233655, + "census_percent": 18.4, + "census_margin": 1.8 + }, + { + "state": "GA", + "records": 1215, + "effective_households": 256.7233149102064, + "effective_households_with_children": 107.40193461203204, + "child_spm_percent": 18.428986738455304, + "census_percent": 15.1, + "census_margin": 2.2 + }, + { + "state": "HI", + "records": 838, + "effective_households": 197.42767357826924, + "effective_households_with_children": 73.86813540998027, + "child_spm_percent": 7.019040438905304, + "census_percent": 13.0, + "census_margin": 2.6 + }, + { + "state": "ID", + "records": 746, + "effective_households": 185.13141304399971, + "effective_households_with_children": 92.46790807341822, + "child_spm_percent": 6.957827284586571, + "census_percent": 6.5, + "census_margin": 1.3 + }, + { + "state": "IL", + "records": 1668, + "effective_households": 276.7964053720248, + "effective_households_with_children": 105.04091641138457, + "child_spm_percent": 14.678691391590323, + "census_percent": 12.4, + "census_margin": 1.6 + }, + { + "state": "IN", + "records": 1141, + "effective_households": 177.01966623060613, + "effective_households_with_children": 85.7977093328415, + "child_spm_percent": 12.73434830167166, + "census_percent": 10.6, + "census_margin": 2.2 + }, + { + "state": "IA", + "records": 973, + "effective_households": 138.47468952101661, + "effective_households_with_children": 55.523208923018444, + "child_spm_percent": 8.80898267607782, + "census_percent": 8.2, + "census_margin": 1.9 + }, + { + "state": "KS", + "records": 921, + "effective_households": 148.60541081180202, + "effective_households_with_children": 60.130820508314436, + "child_spm_percent": 9.659862214627816, + "census_percent": 9.7, + "census_margin": 2.5 + }, + { + "state": "KY", + "records": 776, + "effective_households": 129.5204444710968, + "effective_households_with_children": 55.594811366477, + "child_spm_percent": 18.5371736654285, + "census_percent": 13.3, + "census_margin": 2.9 + }, + { + "state": "LA", + "records": 1000, + "effective_households": 260.9008626497421, + "effective_households_with_children": 106.16514519454792, + "child_spm_percent": 12.087426626104243, + "census_percent": 20.7, + "census_margin": 3.0 + }, + { + "state": "ME", + "records": 785, + "effective_households": 98.94461653737365, + "effective_households_with_children": 33.11521830422318, + "child_spm_percent": 12.449100684445808, + "census_percent": 6.5, + "census_margin": 2.3 + }, + { + "state": "MD", + "records": 1091, + "effective_households": 142.97248422508488, + "effective_households_with_children": 55.030632992280125, + "child_spm_percent": 23.16543402488221, + "census_percent": 10.7, + "census_margin": 2.5 + }, + { + "state": "MA", + "records": 1522, + "effective_households": 254.60923432676174, + "effective_households_with_children": 97.9541889651262, + "child_spm_percent": 20.202673816573878, + "census_percent": 12.8, + "census_margin": 2.0 + }, + { + "state": "MI", + "records": 1478, + "effective_households": 244.58680980543457, + "effective_households_with_children": 95.45588822002858, + "child_spm_percent": 14.518361699859508, + "census_percent": 10.3, + "census_margin": 1.9 + }, + { + "state": "MN", + "records": 1166, + "effective_households": 151.4108590065156, + "effective_households_with_children": 57.413064194300695, + "child_spm_percent": 8.431428192061507, + "census_percent": 6.9, + "census_margin": 1.9 + }, + { + "state": "MS", + "records": 882, + "effective_households": 217.18543375057635, + "effective_households_with_children": 94.98469220801712, + "child_spm_percent": 12.942211004413902, + "census_percent": 19.1, + "census_margin": 2.6 + }, + { + "state": "MO", + "records": 1013, + "effective_households": 158.23194095106248, + "effective_households_with_children": 70.0086423984445, + "child_spm_percent": 18.479794697686096, + "census_percent": 9.7, + "census_margin": 2.3 + }, + { + "state": "MT", + "records": 744, + "effective_households": 189.76250281214456, + "effective_households_with_children": 74.0593002880217, + "child_spm_percent": 2.5309436062772113, + "census_percent": 9.3, + "census_margin": 2.3 + }, + { + "state": "NE", + "records": 884, + "effective_households": 145.9510957065784, + "effective_households_with_children": 67.67826104471393, + "child_spm_percent": 10.711871752578398, + "census_percent": 7.1, + "census_margin": 1.9 + }, + { + "state": "NV", + "records": 952, + "effective_households": 153.2144593141471, + "effective_households_with_children": 53.81897222709485, + "child_spm_percent": 19.070065323872694, + "census_percent": 15.0, + "census_margin": 2.5 + }, + { + "state": "NH", + "records": 840, + "effective_households": 140.8689123605408, + "effective_households_with_children": 50.91685592377852, + "child_spm_percent": 10.516178219256554, + "census_percent": 7.5, + "census_margin": 2.4 + }, + { + "state": "NJ", + "records": 1651, + "effective_households": 214.92794398401983, + "effective_households_with_children": 90.63484170002751, + "child_spm_percent": 10.924964312878116, + "census_percent": 13.3, + "census_margin": 1.9 + }, + { + "state": "NM", + "records": 902, + "effective_households": 197.2115814203346, + "effective_households_with_children": 74.88789080295213, + "child_spm_percent": 10.15133671821333, + "census_percent": 10.9, + "census_margin": 2.1 + }, + { + "state": "NY", + "records": 2166, + "effective_households": 431.1950455090738, + "effective_households_with_children": 161.4744380520421, + "child_spm_percent": 17.727116992316713, + "census_percent": 16.2, + "census_margin": 1.9 + }, + { + "state": "NC", + "records": 1337, + "effective_households": 278.97265851969934, + "effective_households_with_children": 107.21798776187008, + "child_spm_percent": 18.100974612766848, + "census_percent": 14.4, + "census_margin": 2.2 + }, + { + "state": "ND", + "records": 682, + "effective_households": 154.69431784251822, + "effective_households_with_children": 60.78103301460022, + "child_spm_percent": 11.13047533590008, + "census_percent": 10.3, + "census_margin": 2.1 + }, + { + "state": "OH", + "records": 1531, + "effective_households": 259.8938711270293, + "effective_households_with_children": 94.1812931984067, + "child_spm_percent": 9.813281022726176, + "census_percent": 12.3, + "census_margin": 2.1 + }, + { + "state": "OK", + "records": 918, + "effective_households": 184.88950295988414, + "effective_households_with_children": 75.74235124943412, + "child_spm_percent": 17.21439734214549, + "census_percent": 9.9, + "census_margin": 2.0 + }, + { + "state": "OR", + "records": 1152, + "effective_households": 174.20405203877138, + "effective_households_with_children": 61.38530820629173, + "child_spm_percent": 16.2870253593211, + "census_percent": 11.0, + "census_margin": 2.4 + }, + { + "state": "PA", + "records": 1999, + "effective_households": 313.940721037965, + "effective_households_with_children": 119.95047604838705, + "child_spm_percent": 14.135315201475251, + "census_percent": 11.1, + "census_margin": 2.0 + }, + { + "state": "RI", + "records": 804, + "effective_households": 115.36121530553025, + "effective_households_with_children": 43.9686753685404, + "child_spm_percent": 17.87488315880047, + "census_percent": 8.4, + "census_margin": 2.3 + }, + { + "state": "SC", + "records": 955, + "effective_households": 198.5735582254525, + "effective_households_with_children": 82.27387120944789, + "child_spm_percent": 9.884637976608813, + "census_percent": 11.1, + "census_margin": 2.1 + }, + { + "state": "SD", + "records": 866, + "effective_households": 140.02483747681345, + "effective_households_with_children": 59.908755866285595, + "child_spm_percent": 11.33898407217388, + "census_percent": 7.8, + "census_margin": 2.2 + }, + { + "state": "TN", + "records": 1082, + "effective_households": 213.46111672978205, + "effective_households_with_children": 98.0549475333965, + "child_spm_percent": 23.237084061349254, + "census_percent": 10.0, + "census_margin": 1.7 + }, + { + "state": "TX", + "records": 1875, + "effective_households": 671.2372652446592, + "effective_households_with_children": 296.2185580859504, + "child_spm_percent": 19.24437100079317, + "census_percent": 16.1, + "census_margin": 1.3 + }, + { + "state": "UT", + "records": 861, + "effective_households": 182.57153738948298, + "effective_households_with_children": 96.24997869494076, + "child_spm_percent": 13.211297432608319, + "census_percent": 7.3, + "census_margin": 1.6 + }, + { + "state": "VT", + "records": 496, + "effective_households": 155.57089637922203, + "effective_households_with_children": 51.40700838972123, + "child_spm_percent": 17.678699918725112, + "census_percent": 8.3, + "census_margin": 2.5 + }, + { + "state": "VA", + "records": 1394, + "effective_households": 215.49254142849716, + "effective_households_with_children": 92.96168387134998, + "child_spm_percent": 18.12834306934188, + "census_percent": 12.1, + "census_margin": 2.4 + }, + { + "state": "WA", + "records": 1305, + "effective_households": 217.79313567185002, + "effective_households_with_children": 84.69243217289157, + "child_spm_percent": 14.206930139564697, + "census_percent": 9.3, + "census_margin": 1.9 + }, + { + "state": "WV", + "records": 845, + "effective_households": 220.3992065891528, + "effective_households_with_children": 76.6798644841193, + "child_spm_percent": 17.93170551281414, + "census_percent": 11.3, + "census_margin": 2.4 + }, + { + "state": "WI", + "records": 1232, + "effective_households": 168.63884893953457, + "effective_households_with_children": 63.911239375027826, + "child_spm_percent": 7.034634564819478, + "census_percent": 7.3, + "census_margin": 1.7 + }, + { + "state": "WY", + "records": 557, + "effective_households": 157.08532758317563, + "effective_households_with_children": 68.56604831202455, + "child_spm_percent": 8.787559212429837, + "census_percent": 6.5, + "census_margin": 1.4 + } + ] +} diff --git a/slideshows/appam-2026/slides/EndSlide.tsx b/slideshows/appam-2026/slides/EndSlide.tsx index 0b7fc779..7e62c73b 100644 --- a/slideshows/appam-2026/slides/EndSlide.tsx +++ b/slideshows/appam-2026/slides/EndSlide.tsx @@ -27,7 +27,7 @@ export default function EndSlide() {

    - +

    {DECK_URL}

    diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx index 13e4ca1e..3272038f 100644 --- a/slideshows/appam-2026/slides/ReformSlide.tsx +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -1,5 +1,5 @@ import CodeBlock from '@/components/content/CodeBlock'; -import { massachusetts, millions, percent } from '../content'; +import { massachusetts, millions, modelVersion, percent } from '../content'; import Frame from './Frame'; const PYTHON = `reform = Reform.from_dict({ @@ -9,8 +9,9 @@ const PYTHON = `reform = Reform.from_dict({ } }, country_id="us") -baseline = Microsimulation() -reformed = Microsimulation(reform=reform)`; +# massachusetts: the state's rows in our local-area file +baseline = Microsimulation(dataset=massachusetts) +reformed = Microsimulation(dataset=massachusetts, reform=reform)`; const results = [ { value: percent(massachusetts.gainingShare), label: 'of households gain, and none lose' }, @@ -49,12 +50,12 @@ export default function ReformSlide() { applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state.

    - Populace US, 2026, assuming no behavioral responses. The file holds{' '} + Populace US local-area file and PolicyEngine US {modelVersion}, 2026, assuming no behavioral responses. The + file holds{' '} {massachusetts.records.toLocaleString('en-US')} Massachusetts households, an effective sample of about{' '} {massachusetts.effectiveHouseholds}. The model puts {massachusetts.modelChildRate.toFixed(1)}% of the state’s children below the SPM threshold in 2025, against Census’s {massachusetts.censusChildRate.toFixed(1)}% for - 2023 to 2025. No record crosses the threshold under this reform, so the rate holds still, while the gap moves - with each dollar that reaches a family below it. + 2023 to 2025. The reform lowers the 2026 child rate by {massachusetts.childRateChange.toFixed(1)} points.

    diff --git a/slideshows/appam-2026/slides/ScopeSlide.tsx b/slideshows/appam-2026/slides/ScopeSlide.tsx index 30c11bf2..37a6e9e5 100644 --- a/slideshows/appam-2026/slides/ScopeSlide.tsx +++ b/slideshows/appam-2026/slides/ScopeSlide.tsx @@ -5,7 +5,7 @@ const coverage = [ 'We model child care subsidies in 46 jurisdictions through 2023, 48 in 2024, 50 in 2025 and all 51 from August 2026.', 'We copy each parameter’s earliest value back to 2015, so validate historical runs separately.', 'The model applies Wisconsin’s current child care schedules to earlier years.', - `Populace US holds an effective sample of about ${stateBaseline.medianEffective} households with children in the median state. Its child poverty rates exceed Census’s 2023 to 2025 averages in ${stateBaseline.modelExceedsCensus} of 51 jurisdictions and differ by ${stateBaseline.meanCensusGap.toFixed(1)} points on average. Bring your own microdata for state levels.`, + `Our local-area file holds an effective sample of about ${stateBaseline.medianEffective} households with children in the median state. Its child poverty rates miss Census’s 2023 to 2025 averages by ${stateBaseline.meanCensusGap.toFixed(1)} points on average, and ${stateBaseline.beyondCensusMargin} of 51 jurisdictions fall outside Census’s margin. Bring your own microdata for state levels.`, ]; const mechanisms = [ From a25172c17dfea28b0376c034af7888cac098c18a Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 1 Nov 2026 20:10:22 -0500 Subject: [PATCH 13/32] State the Massachusetts formula change precisely in the APPAM ledger Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index d525e9ac..9d6db746 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -35,7 +35,7 @@ Slide 7's loop ran from a CSV of the fixed sample, with county and finer geograp - The file's 36,491 Massachusetts household records carry an effective sample of about 395, or 173 among households with children. Its 1,522 records from the Current Population Survey and tax data hold 67% of household weight. - The model's Massachusetts child rate comes to 21.4% for 2025 and 23.1% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). That difference ranks third among the 51 jurisdictions, after Delaware and Louisiana. - The same reform on the default file costs $555 million, with 29.8% of households gaining and $167 million off the poverty gap (`data/massachusetts_reform_default.json`). -- The same reform on the same records at policyengine-us 2.2.1, the version `policyengine.py` 6.11.1 pins, costs $657 million. The credit's formula changed between those versions: 2.2.1 counts a disabled spouse as a qualifying individual, and 2.90.2 counts a spouse incapable of self-care. +- The same reform on the same records at policyengine-us 2.2.1, the version `policyengine.py` 6.11.1 pins, costs $657 million. The credit's formula changed between those versions. On a joint return, 2.2.1 counts a filer with a disability as a qualifying individual, and 2.90.2 counts a filer incapable of self-care. 2.90.2 also counts a dependent incapable of self-care at any age. This run does not separate how much of the difference those changes explain. - The national comparison on the webinar deck's `BaselineVsCensusSlide` runs 16.6% in the model against 13.4% from Census, for children in 2025. ## State baselines From 99ee5b354d1274b9001b5da701f99564f6eedb1f Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Mon, 2 Nov 2026 18:19:49 -0500 Subject: [PATCH 14/32] Resolve round 2 review findings in the APPAM deck Correctness: - Read blank parent links as 0, the value PolicyEngine US treats as an unknown parent, and rerun all 51 state baselines and the Massachusetts reform. California moves 0.56 points, Delaware 0.006, Missouri 0.00001; no slide figure changes. - Count state payroll taxes and paid leave contributions on the state line. Rerun the family sweeps; the rug's end labels change. - Say that we take earnings and paid leave benefits as inputs on slide 10. - Say "any state" and "wherever a state taxes income" for the Child Poverty Impact Dashboard's reforms, from its option list at 977661f. - Add committed sources for every ledger figure: the 2.2.1 Massachusetts run, the fixed sample's column audit, the slide code check, Wisconsin's schedules, and the group-quarters column. Wisconsin's ledger figures now match the committed run. Voice: give fiscal claims, deductions and held-fixed inputs their actors, take "we estimate" on PolicyEngine's figures, introduce the local-area file and SPM once, and rewrite the ledger's concept subjects and passives. Tests: property tests for parent-link fill, effective samples and group quarters; a state-line payroll test; typed-figure checks across the ledger. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 94 ++++---- slideshows/appam-2026/SPEAKER-NOTES.md | 36 +-- .../analysis/audit_sample_columns.py | 84 +++++++ .../appam-2026/analysis/check_slide_code.py | 81 +++++++ slideshows/appam-2026/analysis/household.py | 12 +- .../appam-2026/analysis/sample_families.py | 15 +- .../appam-2026/analysis/state_baseline.py | 27 ++- .../appam-2026/analysis/test_invariants.py | 14 ++ .../analysis/test_sample_families.py | 4 +- .../analysis/test_state_baseline.py | 79 ++++++ .../analysis/wisconsin_schedules.py | 37 +++ slideshows/appam-2026/content.test.ts | 154 +++++++++++- slideshows/appam-2026/data/charts.json | 2 +- slideshows/appam-2026/data/family.json | 2 +- .../data/massachusetts_reform_221.json | 27 +++ .../appam-2026/data/microcosm_1445_rates.csv | 52 ++++ .../appam-2026/data/sample_columns.json | 226 ++++++++++++++++++ .../appam-2026/data/slide_code_check.json | 9 + .../appam-2026/data/state_baseline.json | 59 ++++- .../appam-2026/data/wisconsin_schedules.json | 11 + slideshows/appam-2026/slides/AxiomSlide.tsx | 4 +- slideshows/appam-2026/slides/CliffsSlide.tsx | 4 +- .../appam-2026/slides/EarningsChart.tsx | 2 +- .../appam-2026/slides/MicrodataSlide.tsx | 9 +- .../appam-2026/slides/OneFamilySlide.tsx | 2 +- .../appam-2026/slides/ParametersSlide.tsx | 2 +- slideshows/appam-2026/slides/ReformSlide.tsx | 25 +- slideshows/appam-2026/slides/ScopeSlide.tsx | 12 +- .../appam-2026/slides/WhatItDoesSlide.tsx | 8 +- .../appam-2026/slides/WhichLinesSlide.tsx | 2 +- 30 files changed, 984 insertions(+), 111 deletions(-) create mode 100644 slideshows/appam-2026/analysis/audit_sample_columns.py create mode 100644 slideshows/appam-2026/analysis/check_slide_code.py create mode 100644 slideshows/appam-2026/analysis/test_state_baseline.py create mode 100644 slideshows/appam-2026/analysis/wisconsin_schedules.py create mode 100644 slideshows/appam-2026/data/massachusetts_reform_221.json create mode 100644 slideshows/appam-2026/data/microcosm_1445_rates.csv create mode 100644 slideshows/appam-2026/data/sample_columns.json create mode 100644 slideshows/appam-2026/data/slide_code_check.json create mode 100644 slideshows/appam-2026/data/wisconsin_schedules.json diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index 9d6db746..f6e3f6dd 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -1,8 +1,10 @@ # Evidence ledger for the APPAM 2026 deck -Each claim on a slide traces to a computed result, to code read at a named commit, or to a public source read while building this deck. +Each claim on a slide traces to a computed result, to code read at a named commit, or to a public source. - **PolicyEngine US:** version 2.90.2, commit `469ad12cb957a1b3eaedf9e6dd3e4c69194bba26` (main, November 1, 2026). +- **policyengine-taxsim:** commit `9c987d8f23955dd1564af43dee3fac4054c0dbf2`. +- **child-poverty-impact-dashboard:** commit `977661f20bfecc75d0f5f8f75b1041684c9fa106`. - **Axiom Rules Engine:** version 0.2.2, commit `e0bda6475f30ac487b7be544f5b9742c716e4bba`. - **rulespec-us:** commit `66d891b9df601c35c2edbf7e669529a649141d31`. @@ -15,38 +17,42 @@ Each claim on a slide traces to a computed result, to code read at a named commi | 4 | Each state's value on four lines | `data/charts.json`, `lines_by_program` | | 4 | Texas: $15,791 subsidy, −$5,598 SNAP, +$10,193 net resources | `analysis/interaction.py` → `data/interaction.json` | | 5 | Maryland −$15,343 at $97,500, Iowa −$14,967 at $44,000, Idaho −$14,511 at $48,000, the three largest drops of the 51 | `data/charts.json`, `cliff_ranking`, which records each line's change in every state | -| 5 | In 46 of 51 states, the largest drop comes from losing the subsidy | `cliff_ranking`: TANF drives Massachusetts and New Hampshire, and WIC drives Maine, New Mexico and Vermont | +| 5 | In 46 of 51 jurisdictions, the largest drop comes from losing the subsidy | `cliff_ranking`. The family loses TANF at the largest drop in Massachusetts and New Hampshire, and WIC in Maine, New Mexico and Vermont. | | 5 | Median state's total effective marginal tax rate: 63.6% at $30,000, 24.6% at $60,000 | `data/charts.json`, `emtr`: one minus the change in net resources over a $500 raise | | 7 | SNAP eligibility 17.5% in Arkansas and Tennessee to 38.3% in Maryland, among SPM units with children; child Medicaid or CHIP 39.8% in Idaho to 66.1% in Hawaii | `analysis/sample_families.py` → `analysis/simulated_instrument.py` → `data/simulated_instrument.json` | -| 8 | State cost, 29.4% of households gain, none lose, $169 million off the SPM poverty gap | `analysis/massachusetts_reform.py` → `data/massachusetts_reform.json`: `state_cost` holds the fall in `state_income_tax`, and `household_gain` holds the change in `household_net_income` | -| 8 | 36,491 Massachusetts households, effective sample of about 395, 2026 child rate falls 0.2 points | `data/massachusetts_reform.json`: `household_records`, `effective_households` (Kish), `baseline_child_poverty`, `reformed_child_poverty` | -| 10 | Effective sample in the median state; child poverty rates miss Census's by 3.2 points on average, and 29 of 51 jurisdictions fall outside Census's margin | `analysis/state_baseline.py` → `data/state_baseline.json`, compared with `data/census_child_spm.json` | -| 11 | $666.666 deduction on time, $0 late, refusal for November 2019 | `analysis/axiom_ga_3612.py` → `data/axiom_ga_3612.json`, a real engine run | +| 8 | State cost, 29.4% of households would gain, none would lose, $169 million off the SPM poverty gap | `analysis/massachusetts_reform.py` → `data/massachusetts_reform.json`: `state_cost` holds the fall in `state_income_tax`, and `household_gain` holds the change in `household_net_income` | +| 8 | 36,491 Massachusetts households, effective sample of about 395, 2026 child poverty rate falls 0.2 points | `data/massachusetts_reform.json`: `household_records`, `effective_households` (Kish), `baseline_child_poverty`, `reformed_child_poverty` | +| 10 | Effective sample in the median state; child poverty estimates miss Census's by 3.2 points on average, and 29 of 51 jurisdictions fall outside Census's margin | `analysis/state_baseline.py` → `data/state_baseline.json`, compared with `data/census_child_spm.json` | +| 11 | $666.67 deduction on time, $0 late, refusal for November 2019 | `analysis/axiom_ga_3612.py` → `data/axiom_ga_3612.json` | -Slide 7's loop ran from a CSV of the fixed sample, with county and finer geography dropped and a `STATE_FIPS` dictionary defined above it, and reproduced Arkansas and Maryland to every digit. Slide 8's reform dictionary ran on the Massachusetts records of the local-area file, and `analysis/massachusetts_reform.py` runs on those records alone. On the default file, a Massachusetts cut and a national run returned the same household gain, gaining share and poverty rates (`data/massachusetts_reform_default.json`, `data/massachusetts_national_check.json`). +We dropped county and finer geography from the fixed-sample CSV and defined the `STATE_FIPS` dictionary before running slide 7's loop. The loop reproduced Arkansas and Maryland to every digit. We then ran slide 8's reform dictionary on the Massachusetts rows of Populace US's local-area file, and it returned the state cost in `data/massachusetts_reform.json` (`analysis/check_slide_code.py` → `data/slide_code_check.json`). On Populace US's default file, a Massachusetts cut and a national run returned the same household gain, gaining share and poverty rates (`data/massachusetts_reform_default.json`, `data/massachusetts_national_check.json`). ## The fixed sample -`analysis/sample_families.py` makes 3,000 draws from the Populace US households with children, with replacement and in proportion to household weight. A household drawn twice enters the file twice with fresh ids, so each entry counts once, and the draws reach 2,075 distinct households. The script reads the pinned file's tables and keeps input variables only. On the same households, that route matches `to_input_dataframe` to every digit on child Medicaid or CHIP eligibility, state income tax, state refundable credits and earnings in Tennessee, Oregon and Hawaii. `analysis/simulated_instrument.py` drops county and finer geography, sets one state on the whole file and reruns it. Each run records the state the model read, and mean employment income stays the same in all 51 runs. `content.test.ts` checks both. +`analysis/sample_families.py` makes 3,000 draws from the Populace US households with someone under 18, with replacement and in proportion to household weight. When the script draws a household twice, it gives each entry a fresh ID and counts it once. The sample includes 2,075 distinct households. Slide 7's Medicaid or CHIP share covers children under 19, so an 18-year-old counts only when the household also holds someone younger. + +The script keeps input variables and IDs. It renames the four stored Social Security amounts to the variables the model reads as reported, and it leaves out `has_marketplace_health_coverage`, `is_spm_independent_minor_role` and 179 names that PolicyEngine US 2.90.2 does not define. When we restored those two variables, Tennessee, Oregon and Hawaii returned the same SNAP and child Medicaid or CHIP shares to every digit (`analysis/audit_sample_columns.py` → `data/sample_columns.json`). + +`analysis/simulated_instrument.py` drops county and finer geography, sets one state on the whole file and reruns it. Each run records the state the model read, and mean employment income stays the same in all 51 runs. `content.test.ts` checks both. ## Massachusetts poverty -- 101 person records cross the threshold under the reform, and the 2026 child rate falls 0.2 points, from 23.1% to 22.9%. -- The file's 36,491 Massachusetts household records carry an effective sample of about 395, or 173 among households with children. Its 1,522 records from the Current Population Survey and tax data hold 67% of household weight. -- The model's Massachusetts child rate comes to 21.4% for 2025 and 23.1% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). That difference ranks third among the 51 jurisdictions, after Delaware and Louisiana. -- The same reform on the default file costs $555 million, with 29.8% of households gaining and $167 million off the poverty gap (`data/massachusetts_reform_default.json`). -- The same reform on the same records at policyengine-us 2.2.1, the version `policyengine.py` 6.11.1 pins, costs $657 million. The credit's formula changed between those versions. On a joint return, 2.2.1 counts a filer with a disability as a qualifying individual, and 2.90.2 counts a filer incapable of self-care. 2.90.2 also counts a dependent incapable of self-care at any age. This run does not separate how much of the difference those changes explain. -- The national comparison on the webinar deck's `BaselineVsCensusSlide` runs 16.6% in the model against 13.4% from Census, for children in 2025. +- We estimate that 101 person records cross the threshold under the doubled credit and that the 2026 child poverty rate falls 0.2 points, from 23.1% to 22.9%. +- The file's 36,491 Massachusetts household records hold an effective sample of about 395, or 173 among households with children. Its 1,522 records from the Current Population Survey and tax data hold 67% of household weight. +- We estimate a Massachusetts child poverty rate of 21.4% for 2025 and 23.1% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). That difference ranks third among the 51 jurisdictions, after Delaware and Louisiana. +- On the default file, we estimate that Massachusetts would forgo $555 million, 29.8% of households would gain and the poverty gap would shrink by $167 million (`data/massachusetts_reform_default.json`). +- On the same records at policyengine-us 2.2.1, the version `policyengine.py` 6.11.1 pins, we estimate that Massachusetts would forgo $657 million, 34.1% of households would gain and the child poverty rate would fall 0.5 points, from 22.2% to 21.7% (`data/massachusetts_reform_221.json`). We changed the credit's formula between those versions. On a joint return, 2.2.1 counts a filer with a disability as a qualifying individual, and 2.90.2 counts a filer incapable of self-care. 2.90.2 also counts a dependent incapable of self-care at any age. We did not separate how much of the difference those changes explain. +- Nationally, we estimate 16.6% of children below the threshold in 2025, and Census reports 13.4% (`BaselineVsCensusSlide` in `slideshows/cpid-webinar-2026`). ## State baselines -`analysis/state_baseline.py` runs 2025 child SPM poverty for all 51 jurisdictions, one state at a time, on the local-area Populace US file at revision `populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z`. `policyengine.py` 6.11.1 selects that file for state regions (`region_datasets` in `src/policyengine/data/bundle/manifest.json`, and `docs/regions.md`), and the file here matches the manifest's SHA-256. +`analysis/state_baseline.py` runs 2025 child SPM poverty for all 51 jurisdictions, one state at a time, on Populace US's local-area file at revision `populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z`. `policyengine.py` 6.11.1 selects that file for state regions (`region_datasets` in `src/policyengine/data/bundle/manifest.json`, and `docs/regions.md`), and the file here matches the manifest's SHA-256. -- The model's rates differ from Census's 2023 to 2025 averages by 3.2 points on average and exceed Census's by 0.9 points on net. They exceed Census's in 30 of 51 jurisdictions. In 29 of 51 jurisdictions the difference exceeds Census's margin of error. +- Our rates differ from Census's 2023 to 2025 averages by 3.2 points on average and exceed Census's by 0.9 points on net. They exceed Census's in 30 of 51 jurisdictions. In 29 of 51 jurisdictions the difference exceeds Census's margin of error. - Effective samples of households with children run from 58 in Maine to 499 in California, with a median of 125. Across all households they run from 193 in Maryland to 1,146 in California, with a median of 280. -- `policyengine.py` leaves ACS group quarters out of SPM measures, and these runs keep them. Leaving them out moves no state's rate by more than 0.02 points and moves Massachusetts's by 0.01. -- PolicyEngine/microcosm#1445 ran the same file at the same model version, and its 51 state rates match these to six decimal places. -- The default file reads higher and thinner: a 4.8-point average difference, 40 of 51 above Census, 38 of 51 beyond Census's margin, and a median of 75 effective households with children (`data/state_baseline_default.json`). +- `policyengine.py` leaves ACS group quarters out of SPM measures, and these runs keep them. Leaving them out moves no state's rate by more than 0.023 points (Connecticut) and moves Massachusetts's by 0.01 (`child_spm_percent_without_group_quarters` in `data/state_baseline.json`). +- Our rates match those in PolicyEngine/microcosm#1445 to within 0.000001 points in 48 of 51 jurisdictions (`data/microcosm_1445_rates.csv`), using the same file and model version. That run gives blank parent links the id −1, and ours gives them 0, the model's value for an unknown parent. California moves by 0.56 points, Delaware by 0.006 and Missouri by 0.00001. +- On the default file, our rates differ from Census's by 4.8 points on average. They exceed Census's in 40 of 51 jurisdictions and fall outside its margin in 38. The median effective sample includes 75 households with children (`data/state_baseline_default.json`). ## The family @@ -54,31 +60,31 @@ Slide 7's loop ran from a CSV of the fixed sample, with county and finer geograp - One parent, 30, with children aged 1 and 4. The parent works 40 hours for 52 weeks. Rent runs $1,200 a month and child care $1,500 a month. - `analysis/largest_counties.py` picks each state's largest county from the 2017 to 2021 American Community Survey. -- `meets_ccdf_activity_test` starts true, because a parent in a regular job meets it and the input defaults to false. -- `wi_works_placement` starts as `NONE`, because the input defaults to a Community Service Job. -- $40,000 over 2,080 hours comes to $19.23 an hour. The highest basic state rate on the Department of Labor's table, updated July 1, 2026, belongs to DC at $18.40 (dol.gov/agencies/whd/minimum-wage/state). Local minimums can run higher, and the table leaves them out. -- The sweeps vary earnings and hold hours at 40 a week, so at $30,000 the family earns $14.42 an hour, less than DC's basic rate. +- We set `meets_ccdf_activity_test` to true, because a parent in a regular job meets it and the input defaults to false. +- We set `wi_works_placement` to `NONE`, because the input defaults to a Community Service Job. +- $40,000 over 2,080 hours comes to $19.23 an hour. On the Department of Labor's table, updated July 1, 2026, DC sets the highest basic rate, at $18.40 (dol.gov/agencies/whd/minimum-wage/state). The Department of Labor excludes local minimums, which can run higher. +- The sweeps vary earnings and hold hours at 40 a week, so at $30,000 the family earns $14.42 an hour, less than the basic rate in 17 states and DC on that table. -Net resources add market income, SNAP, WIC, TANF, the child care subsidy and other benefits, then subtract federal and state taxes net of credits and the child care bill. +We compute net resources as market income plus SNAP, WIC, TANF, the child care subsidy and other benefits, minus federal and state taxes net of credits and the child care bill. We count state payroll taxes and paid leave contributions on the state line. `analysis/test_invariants.py` reconciles the total with `spm_unit_net_income` and with `household_net_income` over random states and earnings. -Any state comparison that uses these totals must account for four differences: +When comparing states using these totals, account for four differences: - `spm_unit_market_income` leaves out the Alaska Permanent Fund Dividend, which `household_market_income` includes. - `spm_unit_benefits` leaves out New Jersey's ANCHOR payment, which `household_benefits` includes. - `household_benefits` leaves out Alaska's energy relief payment, which `spm_unit_benefits` includes. - `household_benefits` counts the child care subsidy in some states and not others. `spm_unit_benefits` counts it for Colorado, Washington and West Virginia alone. -Net resources count all four payments in every state. +We count all four payments in net resources in every state. ## Why states land where they do -- **Ohio:** `oh_ccap_fpg` sets the initial limit at 145% of the guideline, and enrolled families stay eligible to 300%. The $40,000 new applicant falls between them. -- **Nevada:** the intake limit sits at 41% of state median income, about $34,674, and the renewal limit at 49%. -- **Oklahoma:** the model uses the default one-star rate, $555 a month. -- **South Dakota:** `sd_cca` starts in August 2026, so $7,500 equals five months at $1,500. -- **Provider tier:** every state's default sits at its base or lowest tier. +- **Ohio:** Ohio sets the initial limit at 145% of the guideline (`oh_ccap_fpg`), and enrolled families stay eligible to 300%. The $40,000 new applicant falls between them. +- **Nevada:** Nevada sets its intake limit at 41% of state median income, about $34,674, and its renewal limit at 49%. +- **Oklahoma:** we use the default one-star rate, $555 a month. +- **South Dakota:** we model `sd_cca` from August 2026, so $7,500 equals five months at $1,500. +- **Provider tier:** we leave each state at its default, its base or lowest tier. - **Alaska:** other payments total $3,600, from a $1,000 dividend per person and $200 of energy relief per person. ## Mechanisms @@ -86,24 +92,32 @@ Net resources count all four payments in every state. | Slide | Claim | Code at `469ad12` | | --- | --- | --- | | 2 | Program list, every state income tax, TANF and child care rules of all 51 jurisdictions | `STATE_TANF_VARIABLES` holds 51 entries; `child_care_subsidy_programs` holds 51 from 2025 | -| 4 | Congress lets SNAP households deduct what they pay for care | `snap_dependent_care_deduction`, citing 7 U.S.C. 2014(e)(3), reads post-subsidy `childcare_expenses` | -| 7 | The TAXSIM-compatible interface runs 2021 onward through PolicyEngine and earlier years through TAXSIM35 | `StitchedRunner.PE_MIN_YEAR = 2021` in `policyengine_taxsim/runners/stitched_runner.py` | -| 9 | Project through 2035 | The PolicyEngine API's US metadata lists years 2022 to 2035 | +| 4 | Congress lets SNAP households deduct their care costs | `snap_dependent_care_deduction`, citing 7 U.S.C. 2014(e)(3), reads post-subsidy `childcare_expenses` | | 6 | Massachusetts credit values, dates and citations | `parameters/gov/states/ma/tax/income/credits/child_and_family/amount.yaml` and `dependent_cap.yaml` | +| 7 | Our TAXSIM-compatible interface runs 2021 onward through PolicyEngine and earlier years through TAXSIM35 | `StitchedRunner.PE_MIN_YEAR = 2021` in `policyengine_taxsim/runners/stitched_runner.py` at the policyengine-taxsim commit above | +| 8 | Child tax credit, EITC and SNAP reforms in any state, and state tax credit reforms wherever a state taxes income | `getReformOptionsForState` in `frontend/lib/state-programs.ts` at the dashboard commit above returns federal child tax credit and EITC options and SNAP options in all 51 jurisdictions, and state EITC options in 43: the 42 with an income tax, and Washington | +| 9 | Project through 2035 | The PolicyEngine API's US metadata lists years 2022 to 2035 | | 10 | 46 jurisdictions through 2023, 48 in 2024, 50 in 2025, 51 from August 2026 | `gov.hhs.ccdf.child_care_subsidy_programs` lists 51 from 2025, missing DC, NC, OR, TN and TX through 2023, and DC, TN and TX in 2024. `sd_cca` returns zero until `uses_weekly_rates` turns on in August 2026 | | 10 | Copy back to 2015 | `backdate_parameters(first_instant="2015-01-01")` in `system.py`, `FIRST_MODELED_YEAR = 2015` in `tools/parameters.py` | -| 10 | Wisconsin's schedules | Shares dates start 2025-10-01, and the model returns subsidies of $14,717 in 2021 and $15,483 in 2023 | -| 10 | Earnings enter as inputs | The minimum wage appears only in eligibility tests; `pfml_benefits` holds benefits received | +| 10 | Wisconsin's schedules | Wisconsin Shares' rate parameters start on 2025-10-01, and the model returns subsidies of $13,258 in 2021 and $14,024 in 2023 (`analysis/wisconsin_schedules.py` → `data/wisconsin_schedules.json`) | +| 10 | We take earnings and paid leave benefits as inputs | The minimum wage appears only in eligibility tests. `pfml_benefits` holds benefits received, and the model computes employee contributions through `employee_state_payroll_tax` | | 10 | Labor supply | Elasticities default to zero | | 10 | Medicaid limits | `is_medically_needy_for_medicaid` documentation | | 10 | Take-up | `takes_up_snap_if_eligible` defaults to true, and Populace US carries `takes_up_*` flags | ## The other tools -Slide 9 uses each tool's description from the session abstract, which covers the Policy Tracking System, the Policy Impact Calculator, the State Safety Net Interactive, the Welfare Rules Database, the CCDF Policies Database, and State of the Safety Net with ATTIS. +We use the session abstract's descriptions of the following tools: + +- Policy Tracking System +- Policy Impact Calculator +- State Safety Net Interactive +- Welfare Rules Database +- CCDF Policies Database +- State of the Safety Net with ATTIS ## Axiom -- The rule file sits at `us-ga/policies/dfcs/snap/3612.yaml` in `TheAxiomFoundation/rulespec-us`, published under CC BY 4.0. -- `axiom-rules-engine` carries an MIT license. -- Georgia's public manual at pamms.dhs.ga.gov/dfcs/snap/3612 confirms the rule text and the December 2019 effective date. +- Axiom publishes `us-ga/policies/dfcs/snap/3612.yaml` in `TheAxiomFoundation/rulespec-us` under CC BY 4.0. +- Axiom publishes `axiom-rules-engine` under an MIT license. +- Georgia's Division of Family and Children Services publishes the rule text and December 2019 effective date in its public SNAP manual (pamms.dhs.ga.gov/dfcs/snap/3612). diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index 275a0676..c349cf35 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -17,7 +17,7 @@ Thanks, Cynthia, for putting this session together, and thanks to Tara and Linda ## 2. PolicyEngine computes taxes and benefits for a family or a population (0:15, 1 minute) -PolicyEngine is a nonprofit that provides free, open source software to compute the impact of public policy. Our US model calculates federal and state income taxes and credits and the main benefit programs for one household or a whole population. +PolicyEngine is a nonprofit that provides free, open source software to compute the impact of public policy. We calculate federal and state income taxes and credits and the main benefit programs for one household or a whole population. Researchers use it three ways: they compare families, measure their own samples and estimate reforms. @@ -25,60 +25,60 @@ Researchers use it three ways: they compare families, measure their own samples Take one family under each state's 2026 rules. One parent works full time and earns $40,000, has two young children, and pays $1,500 a month for child care. We put them in each state's largest county and treat them as a new applicant for every program. -The gray base holds earnings after taxes and credits, minus the full child care bill. Everything above it comes from benefits. Ohio and Nevada turn this family down for child care help because both set a lower income limit for new applicants than for families already enrolled. +The gray base shows earnings after taxes and credits, minus the full child care bill, and benefits sit above it. Ohio and Nevada turn this family down for child care help because both set a lower income limit for new applicants than for families already enrolled. -$40,000 works out to $19.23 an hour, which exceeds every state's minimum wage on the Department of Labor's July 2026 table. The gap between the top and bottom state moves with earnings: $16,404 at $30,000 and $21,670 at $60,000. +The gap between the top and bottom state moves with earnings: $16,404 at $30,000 and $21,670 at $60,000. ## 4. States differ most on child care subsidies, then SNAP and state taxes (2:30, 1 minute 15 seconds) -Each mark shows one state on one line of the family's budget. Federal taxes and credits barely move across states, and states drive the rest through child care subsidies, SNAP options and their own taxes. +Each mark shows one state on one line of the family's budget. Federal taxes and credits move least across states, and states drive the rest through child care subsidies, SNAP options and their own taxes. -These lines interact when you run Texas with and without its subsidy: the family gets $15,791 in child care help and loses $5,598 of SNAP. Congress lets SNAP households deduct what they pay for care, so the subsidy shrinks that deduction. Net resources rise by $10,193. +Run Texas with and without its subsidy, and the lines interact: the family gets $15,791 in child care help and loses $5,598 of SNAP. Congress lets SNAP households deduct their care costs, so this family deducts less when Texas pays for care. The family ends up $10,193 ahead. ## 5. This family loses up to $15,343 at a benefit cliff, at earnings that vary by state (3:45, 1 minute) -Each gray line follows one state as earnings rise. In Maryland, Iowa and Idaho, a $500 raise takes the family past the child care income limit, and the family loses between $14,511 and $15,343. In 46 of 51 states, the family's largest drop comes from losing the subsidy. +Each gray line follows one state as earnings rise. In Maryland, Iowa and Idaho, a $500 raise takes the family past the child care income limit, and the family loses between $14,511 and $15,343. In 46 of 51 jurisdictions, the family's largest drop comes from losing the subsidy. Across states, the median total effective marginal tax rate falls from 63.6% at $30,000 of earnings to 24.6% at $60,000. ## 6. Parameters record when each value took effect and cite their sources (4:45, 45 seconds) -This file holds one parameter, the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. Each value carries its effective date, and the file cites the session law and tax forms behind them. Ask the model for any date and it reads the value in force on that date. +Here's one parameter, the amount per dependent in the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. We record each value's effective date and cite the session law and tax forms behind it. Ask for any date and you get the value in force on that date. ## 7. Build simulated eligibility measures on your own microdata (5:30, 1 minute 30 seconds) -These lines build a simulated instrument. Read in your survey or administrative file, set the state and run it. Keep the people and their incomes fixed, change only the state, and any difference between runs comes from that state's rules. +To build a simulated instrument, read your survey or administrative file, set the state and run it. Keep the people and their incomes fixed, change only the state, and any difference between runs comes from that state's rules. We drew 3,000 households with children from Populace US, our default microdata, and ran them through each state's 2024 rules. The same families pass SNAP's tests at rates from 17.5% in Arkansas and Tennessee to 38.3% in Maryland. Their children qualify for Medicaid or CHIP at rates from 39.8% in Idaho to 66.1% in Hawaii. If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface takes the same files. It runs tax years 2021 onward through PolicyEngine and sends earlier years to TAXSIM35. -## 8. Doubling the Massachusetts child and family credit would cost $577 million (7:00, 1 minute 30 seconds) +## 8. We estimate Massachusetts would forgo $577 million if lawmakers doubled its child and family credit (7:00, 1 minute 30 seconds) -Take the parameter from a moment ago and double it, from $440 to $880 per dependent. That costs the state $577 million in 2026. Of Massachusetts households, 29.4% gain and none lose, and the SPM poverty gap shrinks by $169 million. +Take the parameter from a moment ago and double it, from $440 to $880 per dependent. We estimate Massachusetts would forgo $577 million in 2026. Of Massachusetts households, 29.4% would gain and none would lose, and under the Supplemental Poverty Measure, or SPM, the poverty gap would shrink by $169 million. -We ran this on our local-area file, the one policyengine.py uses for state regions. It holds 36,491 Massachusetts households, an effective sample of about 395. The model puts 21.4% of the state's children below the threshold in 2025, against Census's 12.8% for 2023 to 2025. The reform lowers the 2026 child rate by 0.2 points, from a baseline that runs high. +We ran this on the Populace US local-area file, which we use for state and district results. It holds 36,491 Massachusetts households, an effective sample of about 395. We estimate 21.4% of the state's children below the threshold in 2025, and Census reports 12.8% for 2023 to 2025. We estimate that the 2026 child poverty rate would fall by 0.2 points under the doubled credit, from that high baseline. -The Child Poverty Impact Dashboard applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state. +On our Child Poverty Impact Dashboard, you can run child tax credit, EITC and SNAP reforms in any state, and state tax credit reforms wherever a state taxes income. ## 9. Research questions and the tools that answer them (8:30, 45 seconds) The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. -With PolicyEngine, you can change any parameter or write a new rule, run your own microdata, read and edit every line of code, and project current law forward through 2035. +With PolicyEngine, you can change any parameter, write new rules and run your own microdata. You can read and edit every line of code, and project current law through 2035. ## 10. What these results leave out (9:15, 45 seconds) -We model child care subsidies in all 51 jurisdictions from August 2026. We copy early parameter values back to 2015, so validate any historical run before you use it. Earnings enter as an input, so a change in minimum wage or paid leave law leaves a family's income unchanged. Hours stay fixed unless you set a labor supply response. +We model child care subsidies in all 51 jurisdictions from August 2026. We copy early parameter values back to 2015, so validate any historical run before you use it. We take earnings and paid leave benefits as inputs and hold them fixed when you change minimum wage or paid leave law. We hold hours fixed unless you set a labor supply response. -Our local-area file carries thin state samples, about 125 effective households with children in the median state. Its child poverty rates miss Census's 2023 to 2025 averages by 3.2 points on average, and 29 of 51 jurisdictions fall outside Census's margin. For state poverty levels, bring your own microdata. +Our local-area file holds thin state samples, about 125 effective households with children in the median state. Our child poverty estimates on that file miss Census's 2023 to 2025 averages by 3.2 points on average, and 29 of 51 jurisdictions fall outside Census's margin. For state child poverty rates, bring your own microdata. ## 11. The Axiom Foundation encodes rules with their source text and dates (10:00, 1 minute) -I also lead the Axiom Foundation. We publish open, machine-readable encodings of rules, starting with tax and benefit policy. Georgia's Division of Family and Children Services applies a 20% earned income deduction in section 3612 of its SNAP manual. Axiom's file records the manual's words and effective date beside each value. +I also lead the Axiom Foundation. We publish open, machine-readable encodings of rules, starting with tax and benefit policy. Georgia's Division of Family and Children Services applies a 20% earned income deduction in section 3612 of its SNAP manual. We record the manual's words and effective date beside each value. -We ran this rule through the Axiom engine. A family earning $3,333.33 a month who reports on time gets a $666.67 deduction, and the same family reporting late gets nothing. Ask for November 2019, before the manual section Axiom encoded took effect, and the engine refuses. +We ran this rule through the Axiom engine. A family earning about $40,000 a year who reports on time gets a $666.67 monthly deduction, and the same family reporting late gets nothing. Ask for November 2019, before the manual section we encoded took effect, and the engine refuses. ## 12. Thank you (11:00) -The QR code opens this deck, and the GitHub link holds the code behind each chart. +Scan the QR code to open this deck, and follow the GitHub link to read the code behind each chart. diff --git a/slideshows/appam-2026/analysis/audit_sample_columns.py b/slideshows/appam-2026/analysis/audit_sample_columns.py new file mode 100644 index 00000000..3f0dab50 --- /dev/null +++ b/slideshows/appam-2026/analysis/audit_sample_columns.py @@ -0,0 +1,84 @@ +"""List the stored columns the fixed sample leaves out, and test whether they matter. + +sample_families.py keeps input variables and ids, so the model computes everything +else. This script sorts the rest of the default file's columns, rebuilds the sample +with the computed variables' stored values, and reruns the states it names. Slide 7's +two shares must not move. +""" + +import json +import sys +from importlib.metadata import version +from pathlib import Path + +import pandas as pd +import sample_families as sf +import simulated_instrument as si +import state_baseline as sb +from policyengine_us import CountryTaxBenefitSystem + +YEAR = sf.YEAR +STATES = ("TN", "OR", "HI") +SHARES = ("snap_eligible_share", "child_medicaid_chip_share") + + +def is_id(name): + return name == "person_id" or name.startswith("person_") or name.endswith("_id") + + +def rerun(sample): + si.families = sample.drop( + columns=[f"{name}__{YEAR}" for name in si.FINER_GEOGRAPHY], errors="ignore" + ) + rows = {state: si.simulate(state) for state in STATES} + return {state: {key: row[key] for key in SHARES} for state, row in rows.items()} + + +def main(commit, sample_path): + variables = CountryTaxBenefitSystem().variables + inputs = { + f"{name}__{YEAR}" + for name, variable in variables.items() + if variable.is_input_variable() + } + sample, draws = sf.build(inputs) + # Strings reload under whichever storage this environment prefers. + pd.testing.assert_frame_equal( + sample, + pd.read_pickle(sample_path), + check_dtype=False, + check_column_type=False, + ) + stored = {name for table in sb.tables.values() for name in table.columns} + social_security = {f"social_security_{kind}" for kind in sf.SOCIAL_SECURITY} + computed = sorted( + name + for name in stored - social_security + if name in variables and not variables[name].is_input_variable() + if not is_id(name) + ) + undefined = sorted( + name for name in stored if name not in variables and not is_id(name) + ) + restored, _ = sf.build(inputs | {f"{name}__{YEAR}" for name in computed}) + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": commit, + "draws": len(draws), + "distinct_households": len(set(draws)), + "has_parent_links": any( + name in sb.tables["person"].columns + for name in ("parent_1_id", "parent_2_id") + ), + "renamed_to_reported": sorted(social_security & stored), + "computed_by_the_model": computed, + "undefined_in_this_version": undefined, + "inputs_only": rerun(sample), + "with_stored_values": rerun(restored), + } + target = Path(__file__).parent.parent / "data" / "sample_columns.json" + target.write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(sys.argv[1], sys.argv[2]) diff --git a/slideshows/appam-2026/analysis/check_slide_code.py b/slideshows/appam-2026/analysis/check_slide_code.py new file mode 100644 index 00000000..dcf15c75 --- /dev/null +++ b/slideshows/appam-2026/analysis/check_slide_code.py @@ -0,0 +1,81 @@ +"""Run the code on slides 7 and 8 as the slides print it. + +Slide 7's loop reads a CSV of the fixed sample. Slide 8's reform dictionary runs on +the Massachusetts rows of the local-area file. content.test.ts compares the results +with the deck's own runs. +""" + +import json +import sys +import tempfile +from importlib.metadata import version +from pathlib import Path + +import pandas as pd +from policyengine_core.data import Dataset +from policyengine_core.reforms import Reform +from policyengine_us import Microsimulation +from simulated_instrument import FINER_GEOGRAPHY, SAMPLE_YEAR +from simulated_instrument import STATE_FIPS as ALL_STATES +from state_baseline import DATA_YEAR, load_state, state_file + +STATE_FIPS = {code: ALL_STATES[code] for code in ("AR", "MD")} +MASSACHUSETTS_FIPS = 25 + + +def slide_7(sample_path): + survey = pd.read_pickle(sample_path).drop( + columns=[f"{name}__{SAMPLE_YEAR}" for name in FINER_GEOGRAPHY], errors="ignore" + ) + shares = {} + with tempfile.TemporaryDirectory() as folder: + csv = Path(folder) / "your_survey.csv" + survey.to_csv(csv, index=False) + people = pd.read_csv(csv) + for code, fips in STATE_FIPS.items(): + people["state_fips__2024"] = fips + data = Dataset.from_dataframe(people, 2024) + sim = Microsimulation(dataset=data) + snap = sim.calculate("is_snap_eligible", "2024-01") + units = sim.calculate("is_child", 2024, map_to="spm_unit").values > 0 + shares[code] = float(snap.values[units].mean()) + return shares + + +def state_income_tax(sim): + return float( + ( + sim.calculate("state_income_tax", 2026).values + * sim.calculate("tax_unit_weight", 2026).values + ).sum() + ) + + +def slide_8(folder): + load_state(MASSACHUSETTS_FIPS, Path(folder)) + massachusetts = Dataset.from_dataframe(state_file(MASSACHUSETTS_FIPS), DATA_YEAR) + reform = Reform.from_dict({ + "gov.states.ma.tax.income.credits" + ".child_and_family.amount": { + "2026-01-01.2100-12-31": 880 + } + }, country_id="us") # fmt: skip + + baseline = Microsimulation(dataset=massachusetts) + reformed = Microsimulation(dataset=massachusetts, reform=reform) + return state_income_tax(baseline) - state_income_tax(reformed) + + +def main(commit, sample_path, folder): + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": commit, + "slide_7_snap_eligible_share": slide_7(sample_path), + "slide_8_state_cost": slide_8(folder), + } + target = Path(__file__).parent.parent / "data" / "slide_code_check.json" + target.write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(*sys.argv[1:4]) diff --git a/slideshows/appam-2026/analysis/household.py b/slideshows/appam-2026/analysis/household.py index 39731854..96fa1993 100644 --- a/slideshows/appam-2026/analysis/household.py +++ b/slideshows/appam-2026/analysis/household.py @@ -128,6 +128,8 @@ def get(name): paid = get("childcare_expenses") snap, wic, tanf = get("snap"), get("wic"), get("tanf") subsidies_in_benefits = sum(get(name) for name in SUBSIDIES_IN_SPM_BENEFITS) + # spm_unit_payroll_tax includes state payroll taxes and paid leave contributions. + state_payroll_tax = get("employee_state_payroll_tax") parts = { "earnings": get("spm_unit_market_income"), "snap": snap, @@ -144,8 +146,14 @@ def get(name): - subsidies_in_benefits + sum(get(name) for name in CASH_OUTSIDE_SPM_TOTALS) ), - "federal_taxes": -(get("spm_unit_federal_tax") + get("spm_unit_payroll_tax")), - "state_taxes": -(get("spm_unit_state_tax") + get("spm_unit_local_tax")), + "federal_taxes": -( + get("spm_unit_federal_tax") + + get("spm_unit_payroll_tax") + - state_payroll_tax + ), + "state_taxes": -( + get("spm_unit_state_tax") + get("spm_unit_local_tax") + state_payroll_tax + ), "child_care_bill": -bill, } parts["net_resources"] = sum(parts.values()) diff --git a/slideshows/appam-2026/analysis/sample_families.py b/slideshows/appam-2026/analysis/sample_families.py index 8a93e439..2ead96ba 100644 --- a/slideshows/appam-2026/analysis/sample_families.py +++ b/slideshows/appam-2026/analysis/sample_families.py @@ -49,11 +49,12 @@ def inputs_only(frame, inputs): return frame[columns].copy() -def main(path): +def build(keep): + """Draw the households with children, keeping these columns of their people.""" import state_baseline as sb - from policyengine_us import CountryTaxBenefitSystem - sb.load() + if sb.tables is None: + sb.load() people = sb.tables["person"] households = sb.tables["household"] has_child = people.loc[people["age"] < CHILD_AGE_LIMIT, "person_household_id"] @@ -66,6 +67,12 @@ def main(path): replace=True, p=weights / weights.sum(), ) + return expand(inputs_only(sb.flat_file(households), keep), draws), draws + + +def main(path): + from policyengine_us import CountryTaxBenefitSystem + # A researcher's file holds inputs, so the model recomputes everything else # under each state's rules. variables = CountryTaxBenefitSystem().variables @@ -74,7 +81,7 @@ def main(path): for name, variable in variables.items() if variable.is_input_variable() } - sample = expand(inputs_only(sb.flat_file(households), inputs), draws) + sample, draws = build(inputs) sample.to_pickle(path) print(sample.shape, len(set(draws))) diff --git a/slideshows/appam-2026/analysis/state_baseline.py b/slideshows/appam-2026/analysis/state_baseline.py index c2de21c9..183d3049 100644 --- a/slideshows/appam-2026/analysis/state_baseline.py +++ b/slideshows/appam-2026/analysis/state_baseline.py @@ -2,7 +2,7 @@ Runs the local-area Populace US file, which policyengine.py uses for state regions, one state at a time so each run stays small. A Massachusetts file cut from the -default file this way reproduces the national run's reform cost and poverty rates. +default file this way reproduces the national run's household gain and poverty rates. Census publishes a 2023 to 2025 average, and this run reports 2025. """ @@ -119,10 +119,10 @@ def add_group(table, group): for group in GROUPS: add_group(tables[group], group) frame = pd.DataFrame({f"{k}__{DATA_YEAR}": v for k, v in columns.items()}) - # The local-area file leaves parent links blank; -1 means no parent. + # The local-area file leaves some parent links blank; the model reads 0 as unknown. for name in frame.columns[frame.columns.str.startswith("parent_")]: if name.endswith(f"_id__{DATA_YEAR}"): - frame[name] = frame[name].fillna(-1).astype(np.int64) + frame[name] = frame[name].fillna(0).astype(np.int64) return frame @@ -131,6 +131,17 @@ def state_file(fips): return flat_file(households[households["state_fips"] == fips]) +def in_group_quarters(people, households): + """ACS group-quarters residents, whom policyengine.py leaves out of SPM measures.""" + record_type = people["person_household_id"].map( + households.set_index("household_id")["TYPEHUGQ"] + ) + relationship = people["RELSHIPP"] + return (record_type.eq(2) & relationship.eq(37)) | ( + record_type.eq(3) & relationship.eq(38) + ) + + def run(job): fips, folder = job load_state(fips, folder) @@ -141,6 +152,13 @@ def run(job): households = sim.calculate("household_weight", YEAR).values.astype(float) with_children = sim.calculate("is_child", YEAR, map_to="household").values > 0 children = age < CHILD_AGE_LIMIT + people = tables["person"] + if not np.array_equal( + sim.calculate("household_id", YEAR, map_to="person").values, + people["person_household_id"].values, + ): + raise ValueError("The simulation reordered this state's people") + housed = children & ~in_group_quarters(people, tables["household"]).values return { "state": str(sim.calculate("state_code_str", YEAR).values[0]), "records": len(households), @@ -149,6 +167,9 @@ def run(job): "child_spm_percent": float( 100 * np.average(poor[children], weights=weight[children]) ), + "child_spm_percent_without_group_quarters": float( + 100 * np.average(poor[housed], weights=weight[housed]) + ), } diff --git a/slideshows/appam-2026/analysis/test_invariants.py b/slideshows/appam-2026/analysis/test_invariants.py index c5975ecb..7bacc281 100644 --- a/slideshows/appam-2026/analysis/test_invariants.py +++ b/slideshows/appam-2026/analysis/test_invariants.py @@ -35,6 +35,20 @@ def test_family_never_pays_more_than_the_bill(state, income): assert parts["child_care_subsidy"][0] >= -TOLERANCE +@settings(max_examples=MAX_EXAMPLES, deadline=None) +@given(states, earnings) +def test_state_payroll_taxes_sit_on_the_state_line(state, income): + sim = hh.simulate(state, income) + parts = hh.net_resources_parts(sim) + state_payroll_tax = line(sim, "employee_state_payroll_tax") + federal = line(sim, "spm_unit_federal_tax") + line(sim, "spm_unit_payroll_tax") + state_and_local = line(sim, "spm_unit_state_tax") + line(sim, "spm_unit_local_tax") + assert abs(parts["federal_taxes"][0] + federal - state_payroll_tax) < TOLERANCE + assert ( + abs(parts["state_taxes"][0] + state_and_local + state_payroll_tax) < TOLERANCE + ) + + @settings(max_examples=MAX_EXAMPLES, deadline=None) @given(states, earnings) def test_reconciles_with_spm_net_income(state, income): diff --git a/slideshows/appam-2026/analysis/test_sample_families.py b/slideshows/appam-2026/analysis/test_sample_families.py index f59b1125..d9c4e466 100644 --- a/slideshows/appam-2026/analysis/test_sample_families.py +++ b/slideshows/appam-2026/analysis/test_sample_families.py @@ -2,7 +2,7 @@ import pandas as pd import sample_families as sf -from hypothesis import given +from hypothesis import given, settings from hypothesis import strategies as st YEAR = sf.YEAR @@ -32,6 +32,7 @@ def frame(): return pd.DataFrame(rows), sizes +@settings(deadline=None) @given(st.lists(st.sampled_from([10, 20, 30, 40, 50]), min_size=1, max_size=30)) def test_each_draw_becomes_its_own_household(draws): people, sizes = frame() @@ -45,6 +46,7 @@ def test_each_draw_becomes_its_own_household(draws): ) +@settings(deadline=None) @given(st.lists(st.sampled_from([30]), min_size=1, max_size=10)) def test_a_repeated_household_keeps_its_units_apart(draws): people, _ = frame() diff --git a/slideshows/appam-2026/analysis/test_state_baseline.py b/slideshows/appam-2026/analysis/test_state_baseline.py new file mode 100644 index 00000000..a1337bad --- /dev/null +++ b/slideshows/appam-2026/analysis/test_state_baseline.py @@ -0,0 +1,79 @@ +"""State runs must read parent links, weights and group quarters as the model does.""" + +import numpy as np +import pandas as pd +import state_baseline as sb +from hypothesis import given, settings +from hypothesis import strategies as st + +YEAR = sb.DATA_YEAR +PARENT_LINKS = (f"parent_1_id__{YEAR}", f"parent_2_id__{YEAR}") +parent_ids = st.one_of(st.none(), st.integers(min_value=1, max_value=10_000)) + + +def tables_with(links): + """One household whose people hold these parent links.""" + people = pd.DataFrame( + { + "person_id": range(1, len(links) + 1), + "person_household_id": 1, + "parent_1_id": pd.array(links, dtype="Float64").to_numpy( + dtype=float, na_value=np.nan + ), + "parent_2_id": np.nan, + } + ) + for group in sb.GROUPS: + people[f"person_{group}_id"] = 1 + tables = { + group: pd.DataFrame({f"{group}_id": [1], f"{group}_weight": [1.0]}) + for group in ("household", *sb.GROUPS) + } + tables["household"]["state_fips"] = 25 + tables["person"] = people + return tables + + +@settings(deadline=None) +@given(st.lists(parent_ids, min_size=1, max_size=8)) +def test_blank_links_become_unknown_and_others_stay(links): + sb.tables = tables_with(links) + sb.known = {"parent_1_id", "parent_2_id", "state_fips"} + frame = sb.state_file(25) + expected = [0 if link is None else link for link in links] + assert frame[PARENT_LINKS[0]].tolist() == expected + assert (frame[PARENT_LINKS[1]] == 0).all() + for name in PARENT_LINKS: + assert frame[name].dtype == np.int64 + assert (frame[name] >= 0).all() + + +@settings(deadline=None) +@given(st.lists(st.floats(min_value=0.01, max_value=1e5), min_size=1, max_size=50)) +def test_the_effective_sample_stays_between_one_and_the_record_count(weights): + effective = sb.kish(np.array(weights)) + assert 1 - 1e-9 <= effective <= len(weights) + 1e-9 + + +@settings(deadline=None) +@given(st.floats(min_value=0.01, max_value=1e5), st.integers(1, 50)) +def test_equal_weights_keep_every_record(weight, count): + assert abs(sb.kish(np.full(count, weight)) - count) < 1e-9 + + +def test_group_quarters_take_the_record_type_and_the_relationship(): + households = pd.DataFrame({"household_id": [1, 2, 3], "TYPEHUGQ": [1, 2, 3]}) + people = pd.DataFrame( + { + "person_household_id": [1, 1, 2, 2, 3, 3], + "RELSHIPP": [37, 38, 37, 38, 37, 38], + } + ) + assert sb.in_group_quarters(people, households).tolist() == [ + False, + False, + True, + False, + False, + True, + ] diff --git a/slideshows/appam-2026/analysis/wisconsin_schedules.py b/slideshows/appam-2026/analysis/wisconsin_schedules.py new file mode 100644 index 00000000..0e26716e --- /dev/null +++ b/slideshows/appam-2026/analysis/wisconsin_schedules.py @@ -0,0 +1,37 @@ +"""Run the family in Wisconsin in years before its current child care schedules. + +Wisconsin Shares' rate parameters start on 2025-10-01, and the model copies each +parameter's earliest value back to 2015, so earlier years return a subsidy too. +""" + +import json +import sys +from importlib.metadata import version +from pathlib import Path + +import household as hh + +STATE = "WI" +EARNINGS = 40_000 +YEARS = (2021, 2023, hh.YEAR) + + +def subsidy(year): + parts = hh.net_resources_parts(hh.simulate(STATE, EARNINGS, year=year), year) + return round(float(parts["child_care_subsidy"][0]), 2) + + +def main(commit): + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": commit, + "state": STATE, + "earnings": EARNINGS, + "child_care_subsidy": {str(year): subsidy(year) for year in YEARS}, + } + path = Path(__file__).parent.parent / "data" / "wisconsin_schedules.json" + path.write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index b2274a31..03091b44 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -34,6 +34,13 @@ import { SERIES } from './slides/FamilyChart'; const read = (path: string) => readFileSync(join(__dirname, path), 'utf8'); const family = JSON.parse(read('data/family.json')); const census = JSON.parse(read('data/census_child_spm.json')); +const reformRun = JSON.parse(read('data/massachusetts_reform.json')); +const defaultReform = JSON.parse(read('data/massachusetts_reform_default.json')); +const pinnedReform = JSON.parse(read('data/massachusetts_reform_221.json')); +const defaultBaseline = JSON.parse(read('data/state_baseline_default.json')); +const sampleColumns = JSON.parse(read('data/sample_columns.json')); +const slideCode = JSON.parse(read('data/slide_code_check.json')); +const wisconsin = JSON.parse(read('data/wisconsin_schedules.json')); const notes = read('SPEAKER-NOTES.md'); const ledger = read('EVIDENCE.md'); const webinarMap = read('../cpid-webinar-2026/slides/StateChildPovertyMapSlide.tsx'); @@ -55,6 +62,37 @@ const lineKeys = [ // Each of the nine rounded lines can sit up to 50 cents from its true value. const ROUNDING = 5; +type BaselineRow = (typeof stateBaseline.rows)[number]; + +const medianOf = (values: number[]) => + [...values].sort((a, b) => a - b)[Math.floor(values.length / 2)]; + +function baselineSummary(rows: BaselineRow[]) { + const gaps = rows.map((row) => row.child_spm_percent - row.census_percent); + return { + meanGap: gaps.reduce((sum, gap) => sum + Math.abs(gap), 0) / rows.length, + exceedsCensus: gaps.filter((gap) => gap > 0).length, + beyondMargin: rows.filter((row, i) => Math.abs(gaps[i]) > row.census_margin).length, + medianWithChildren: medianOf(rows.map((row) => row.effective_households_with_children)), + medianAll: medianOf(rows.map((row) => row.effective_households)), + }; +} + +function extreme(rows: BaselineRow[], key: 'effective_households' | 'effective_households_with_children') { + const sorted = [...rows].sort((a, b) => a[key] - b[key]); + return [sorted[0], sorted[sorted.length - 1]].map((row) => ({ + state: row.state, + value: Math.round(row[key]).toLocaleString('en-US'), + })); +} + +const groupQuartersMoves = stateBaseline.rows + .map((row) => ({ + state: row.state, + move: Math.abs(row.child_spm_percent - row.child_spm_percent_without_group_quarters), + })) + .sort((a, b) => b.move - a.move); + describe('family cross section', () => { it('covers every state once, sorted by net resources', () => { expect(new Set(crossSection.map((row) => row.code)).size).toBe(51); @@ -185,6 +223,11 @@ describe('Axiom run', () => { expect(axiomRuns.beforeEffectiveDate.exit_code).not.toBe(0); expect(axiomRuns.beforeEffectiveDate.stderr).toContain('2019-11-01'); }); + + it('runs on monthly pay that the notes round to the family’s $40,000', () => { + expect(Math.round(axiomRuns.monthlyEarnings * 12)).toBe(headlineEarnings); + expect(notes).toContain(`about ${usd(headlineEarnings)} a year`); + }); }); describe('fixed-sample microdata run', () => { @@ -223,6 +266,31 @@ describe('fixed-sample microdata run', () => { expect(values[STATE_COUNT - 1]).toBeLessThanOrEqual(1); } }); + + it('runs under the model version the slide prints', () => { + const run = JSON.parse(read('data/simulated_instrument.json')); + expect(run.policyengine_us_version).toBe(modelVersion); + }); + + it('matches what slide 7’s loop returns when run as printed', () => { + expect(slideCode.policyengine_us_version).toBe(modelVersion); + const states = Object.entries(slideCode.slide_7_snap_eligible_share); + expect(states.length).toBeGreaterThan(0); + for (const [code, share] of states) { + const row = instrument.rows.find((candidate) => candidate.state === code); + expect(share).toBe(row?.snap_eligible_share); + } + }); + + it('returns the same shares with the stored values it leaves out', () => { + expect(sampleColumns.with_stored_values).toEqual(sampleColumns.inputs_only); + expect(sampleColumns.draws).toBe(instrument.households); + for (const [code, shares] of Object.entries>(sampleColumns.inputs_only)) { + const row = instrument.rows.find((candidate) => candidate.state === code); + expect(shares.snap_eligible_share).toBe(row?.snap_eligible_share); + expect(shares.child_medicaid_chip_share).toBe(row?.child_medicaid_chip_share); + } + }); }); describe('Massachusetts reform', () => { @@ -263,6 +331,17 @@ describe('Massachusetts reform', () => { it('costs the state what households gain', () => { expect(Math.abs(massachusetts.cost - massachusetts.householdGain)).toBeLessThan(1e-4 * massachusetts.cost); }); + + it('matches what slide 8’s dictionary returns when run as printed', () => { + expect(slideCode.slide_8_state_cost).toBe(massachusetts.cost); + }); + + it('compares the same records at the version policyengine.py pins', () => { + expect(pinnedReform.policyengine_us_version).toBe('2.2.1'); + expect(pinnedReform.dataset).toBe(reformRun.dataset); + expect(pinnedReform.household_records).toBe(reformRun.household_records); + expect(pinnedReform.effective_households).toBe(reformRun.effective_households); + }); }); describe('state baselines', () => { @@ -271,11 +350,39 @@ describe('state baselines', () => { for (const row of stateBaseline.rows) { expect(row.child_spm_percent).toBeGreaterThanOrEqual(0); expect(row.child_spm_percent).toBeLessThanOrEqual(100); - expect(row.effective_households_with_children).toBeLessThanOrEqual(row.effective_households); + expect(row.effective_households).toBeLessThanOrEqual(row.records); + expect(row.effective_households_with_children).toBeLessThanOrEqual(row.records); expect(row.census_margin).toBeGreaterThan(0); } }); + it('matches microcosm#1445 outside the states its parent-link fill moves', () => { + const rates = read('data/microcosm_1445_rates.csv') + .trim() + .split('\n') + .slice(1) + .map((line) => line.split(',')); + expect(rates).toHaveLength(51); + const moves = new Map(); + for (const [state, rate] of rates) { + const row = stateBaseline.rows.find((candidate) => candidate.state === state); + if (!row) throw new Error(`No baseline for ${state}`); + const move = Math.abs(row.child_spm_percent - Number(rate)); + if (move >= 1e-6) moves.set(state, move); + } + expect([...moves.keys()].sort()).toEqual(['CA', 'DE', 'MO']); + expect(ledger).toContain(`to within 0.000001 points in ${51 - moves.size} of 51 jurisdictions`); + expect(ledger).toContain(`California moves by ${moves.get('CA')?.toFixed(2)} points`); + expect(ledger).toContain(`Delaware by ${moves.get('DE')?.toFixed(3)}`); + expect(ledger).toContain(`Missouri by ${moves.get('MO')?.toFixed(5)}`); + }); + + it('moves every rate by little when it leaves out group quarters', () => { + expect(groupQuartersMoves).toHaveLength(51); + expect(groupQuartersMoves[0].move).toBeLessThan(0.05); + expect(groupQuartersMoves[0].move).toBeGreaterThan(0); + }); + it('copies Census’s rates from the table the webinar deck maps', () => { expect(census.rows).toHaveLength(51); for (const row of census.rows) { @@ -315,7 +422,7 @@ describe('typed figures', () => { usd(-interaction.snapChange), usd(interaction.netChange), usd(largestCliff.drop), - `${subsidyDrivenCliffs} of 51 states`, + `${subsidyDrivenCliffs} of 51 jurisdictions`, percent(medianRateAt(30_000)), percent(medianRateAt(60_000)), percent(massachusetts.gainingShare), @@ -336,19 +443,60 @@ describe('typed figures', () => { millions(massachusetts.cost), `about ${stateBaseline.medianEffective} effective households`, usdCents(axiomRuns.onTime), - usdCents(axiomRuns.monthlyEarnings), ]) { expect(notes).toContain(figure); } }); it('match the data in the evidence ledger', () => { + const local = baselineSummary(stateBaseline.rows); + const onDefault = baselineSummary(defaultBaseline.rows); + const [fewestWithChildren, mostWithChildren] = extreme( + stateBaseline.rows, + 'effective_households_with_children', + ); + const [fewestAll, mostAll] = extreme(stateBaseline.rows, 'effective_households'); + const pinnedChildRates = [pinnedReform.baseline_child_poverty, pinnedReform.reformed_child_poverty]; + const massachusettsMove = groupQuartersMoves.find((row) => row.state === 'MA'); for (const figure of [ ...shared, `${stateBaseline.modelExceedsCensus} of 51 jurisdictions`, `median of ${stateBaseline.medianEffective}`, + massachusetts.records.toLocaleString('en-US'), + `from ${percent(massachusetts.childPoverty[0])} to ${percent(massachusetts.childPoverty[1])}`, + `or ${Math.round(reformRun.effective_households_with_children)} among households with children`, + `${massachusetts.peopleCrossingThreshold} person records`, + millions(defaultReform.state_cost), + percent(defaultReform.households_gaining_share), + millions(defaultReform.baseline_poverty_gap - defaultReform.reformed_poverty_gap), + sampleColumns.distinct_households.toLocaleString('en-US'), + ...sampleColumns.computed_by_the_model, + `${sampleColumns.undefined_in_this_version.length} names`, + `from ${fewestWithChildren.value} in ${name(fewestWithChildren.state)} to ${mostWithChildren.value} in ${name(mostWithChildren.state)}`, + `from ${fewestAll.value} in ${name(fewestAll.state)} to ${mostAll.value} in ${name(mostAll.state)}`, + `median of ${Math.round(local.medianAll)}`, + `${onDefault.meanGap.toFixed(1)} points`, + `${onDefault.exceedsCensus} of 51 jurisdictions`, + `its margin in ${onDefault.beyondMargin}`, + `includes ${Math.round(onDefault.medianWithChildren)} households`, + millions(pinnedReform.state_cost), + percent(pinnedReform.households_gaining_share), + `${(100 * (pinnedChildRates[0] - pinnedChildRates[1])).toFixed(1)} points`, + `from ${percent(pinnedChildRates[0])} to ${percent(pinnedChildRates[1])}`, + usd(wisconsin.child_care_subsidy['2021']), + usd(wisconsin.child_care_subsidy['2023']), + `${groupQuartersMoves[0].move.toFixed(3)} points (${name(groupQuartersMoves[0].state)})`, + `Massachusetts's by ${massachusettsMove?.move.toFixed(2)}`, ]) { expect(ledger).toContain(figure); } }); + + it('take Wisconsin’s current subsidy from the family run', () => { + const index = family.earnings.indexOf(wisconsin.earnings); + expect(wisconsin.child_care_subsidy[String(reformRun.year)]).toBeCloseTo( + family.states.WI.child_care_subsidy[index], + 2, + ); + }); }); diff --git a/slideshows/appam-2026/data/charts.json b/slideshows/appam-2026/data/charts.json index c2056cd2..2c5c34da 100644 --- a/slideshows/appam-2026/data/charts.json +++ b/slideshows/appam-2026/data/charts.json @@ -1 +1 @@ -{"policyengine_us_version":"2.90.2","policyengine_us_commit":"469ad12cb957a1b3eaedf9e6dd3e4c69194bba26","year":2026,"family":{"parent_age":30,"child_ages":[1,4],"rent_per_month":1200,"child_care_per_month":1500},"headline_earnings":40000,"cross_section":[{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":0,"other_benefits":0,"federal_taxes":4263,"state_taxes":0,"child_care_bill":-18000,"net_resources":27711,"code":"OH","name":"Ohio","county":"Franklin County","base":26263},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":4925,"other_benefits":0,"federal_taxes":4263,"state_taxes":-1148,"child_care_bill":-18000,"net_resources":31487,"code":"IN","name":"Indiana","county":"Marion County","base":25115},{"earnings":40000,"snap":0,"wic":1447,"tanf":0,"child_care_subsidy":6662,"other_benefits":0,"federal_taxes":4263,"state_taxes":-322,"child_care_bill":-18000,"net_resources":34051,"code":"OK","name":"Oklahoma","county":"Oklahoma 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"EIT_CRED", + "FEDTAX_AC", + "FEDTAX_BC", + "FRSE_VAL", + "HRSWK", + "H_TENURE", + "IHSFLG", + "INT_VAL", + "I_ERNVAL", + "I_SEVAL", + "LKWEEKS", + "MARG_TAX", + "MCARE", + "MIL", + "MOOP", + "NOW_CAID", + "NOW_CHAMPVA", + "NOW_COV", + "NOW_DIR", + "NOW_GRP", + "NOW_IHSFLG", + "NOW_MCAID", + "NOW_MCARE", + "NOW_MIL", + "NOW_MRK", + "NOW_MRKS", + "NOW_MRKUN", + "NOW_NONM", + "NOW_OTHMT", + "NOW_PCHIP", + "NOW_PRIV", + "NOW_PUB", + "NOW_VACARE", + "OI_OFF", + "OI_VAL", + "PAW_VAL", + "PEAFEVER", + "PECOHAB", + "PEDISDRS", + "PEDISEAR", + "PEDISEYE", + "PEDISOUT", + "PEDISPHY", + "PEDISREM", + "PEINUSYR", + "PEIO1COW", + "PEIOOCC", + "PEMCPREM", + "PENATVTY", + "PEN_SC1", + "PEN_SC2", + "PEPAR1", + "PEPAR2", + "PERIDNUM", + "PF_SEQ", + "PHIP_VAL", + "PH_SEQ", + "PMED_VAL", + "PNSN_VAL", + "POCCU2", + "POTC_VAL", + "PRCITSHP", + "PRDTHSP", + "PRDTRACE", + "PTOTVAL", + "P_SEQ", + "RESNSS1", + "RESNSS2", + "RESNSSI1", + "RESNSSI2", + "RETCB_VAL", + "RNT_VAL", + "SEMP_VAL", + "SPM_ACTC", + "SPM_BBSUBVAL", + "SPM_CAPHOUSESUB", + "SPM_CAPWKCCXPNS", + "SPM_CHILDCAREXPNS", + "SPM_CHILDSUPPD", + "SPM_EITC", + "SPM_ENGVAL", + "SPM_EQUIVSCALE", + "SPM_FAMTYPE", + "SPM_FEDTAX", + "SPM_FEDTAXBC", + "SPM_FICA", + "SPM_GEOADJ", + "SPM_HAGE", + "SPM_HHISP", + "SPM_HMARITALSTATUS", + "SPM_HRACE", + "SPM_ID", + "SPM_MEDXPNS", + "SPM_NUMADULTS", + "SPM_NUMKIDS", + "SPM_NUMPER", + "SPM_POOR", + "SPM_POVTHRESHOLD", + "SPM_RESOURCES", + "SPM_SCHLUNCH", + "SPM_SNAPSUB", + "SPM_STTAX", + "SPM_TENMORTSTATUS", + "SPM_TOTVAL", + "SPM_WCOHABIT", + "SPM_WEIGHT", + "SPM_WFOSTER22", + "SPM_WICVAL", + "SPM_WKXPNS", + "SPM_WNEWHEAD", + "SPM_WNEWPARENT", + "SPM_WUI_LT15", + "SSI_VAL", + "SSI_YN", + "SS_VAL", + "SS_YN", + "STATETAX_A", + "STATETAX_B", + "TAX_ID", + "TAX_INC", + "UC_VAL", + "VET_VAL", + "WC_VAL", + "WICYN", + "WKSWORK", + "WSAL_VAL", + "family_support_channel", + "family_support_clone_index", + "filing_status_input", + "first_home_mortgage_interest", + "household_support_channel", + "household_support_clone_index", + "marital_unit_support_channel", + "marital_unit_support_clone_index", + "medicare_part_b_premiums", + "second_home_mortgage_interest", + "source_year", + "spm_unit_support_channel", + "spm_unit_support_clone_index", + "tax_unit_support_channel", + "tax_unit_support_clone_index", + "would_claim_wic" + ], + "inputs_only": { + "TN": { + "snap_eligible_share": 0.17470881863560733, + "child_medicaid_chip_share": 0.5088019142027004 + }, + "OR": { + "snap_eligible_share": 0.38236272878535776, + "child_medicaid_chip_share": 0.46316868911297215 + }, + "HI": { + "snap_eligible_share": 0.31314475873544095, + "child_medicaid_chip_share": 0.6605708425910101 + } + }, + "with_stored_values": { + "TN": { + "snap_eligible_share": 0.17470881863560733, + "child_medicaid_chip_share": 0.5088019142027004 + }, + "OR": { + "snap_eligible_share": 0.38236272878535776, + "child_medicaid_chip_share": 0.46316868911297215 + }, + "HI": { + "snap_eligible_share": 0.31314475873544095, + "child_medicaid_chip_share": 0.6605708425910101 + } + } +} diff --git a/slideshows/appam-2026/data/slide_code_check.json b/slideshows/appam-2026/data/slide_code_check.json new file mode 100644 index 00000000..43388317 --- /dev/null +++ b/slideshows/appam-2026/data/slide_code_check.json @@ -0,0 +1,9 @@ +{ + "policyengine_us_version": "2.90.2", + "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "slide_7_snap_eligible_share": { + "AR": 0.17470881863560733, + "MD": 0.3826955074875208 + }, + "slide_8_state_cost": 576530432.0 +} diff --git a/slideshows/appam-2026/data/state_baseline.json b/slideshows/appam-2026/data/state_baseline.json index ea55c5ef..af137415 100644 --- a/slideshows/appam-2026/data/state_baseline.json +++ b/slideshows/appam-2026/data/state_baseline.json @@ -11,6 +11,7 @@ "effective_households": 222.66302203609837, "effective_households_with_children": 95.08935436578841, "child_spm_percent": 7.4014381737328545, + "child_spm_percent_without_group_quarters": 7.399965215910136, "census_percent": 6.5, "census_margin": 1.4 }, @@ -20,6 +21,7 @@ "effective_households": 231.7543763826635, "effective_households_with_children": 71.67743149904001, "child_spm_percent": 21.17762070511618, + "child_spm_percent_without_group_quarters": 21.161994126148613, "census_percent": 20.3, "census_margin": 3.7 }, @@ -29,6 +31,7 @@ "effective_households": 242.87469995513473, "effective_households_with_children": 78.86132723000058, "child_spm_percent": 10.308498340888855, + "child_spm_percent_without_group_quarters": 10.30400278510904, "census_percent": 8.3, "census_margin": 2.5 }, @@ -38,6 +41,7 @@ "effective_households": 279.50358570875954, "effective_households_with_children": 113.14721899003231, "child_spm_percent": 12.280018902222128, + "child_spm_percent_without_group_quarters": 12.273308593880808, "census_percent": 10.5, "census_margin": 2.1 }, @@ -47,6 +51,7 @@ "effective_households": 243.2073828465999, "effective_households_with_children": 92.35480678921647, "child_spm_percent": 10.773326006109645, + "child_spm_percent_without_group_quarters": 10.772662526054305, "census_percent": 10.3, "census_margin": 2.1 }, @@ -56,6 +61,7 @@ "effective_households": 214.49903121980063, "effective_households_with_children": 68.97439795321789, "child_spm_percent": 6.733403277773982, + "child_spm_percent_without_group_quarters": 6.72180287994076, "census_percent": 7.8, "census_margin": 2.2 }, @@ -65,6 +71,7 @@ "effective_households": 198.95878966876862, "effective_households_with_children": 69.35172258276282, "child_spm_percent": 13.603513442810936, + "child_spm_percent_without_group_quarters": 13.586037852883864, "census_percent": 8.4, "census_margin": 2.3 }, @@ -74,6 +81,7 @@ "effective_households": 315.1155458835466, "effective_households_with_children": 108.12545021234872, "child_spm_percent": 4.762273171782485, + "child_spm_percent_without_group_quarters": 4.753659493954277, "census_percent": 9.3, "census_margin": 2.3 }, @@ -82,7 +90,8 @@ "records": 5587, "effective_households": 254.35206313965872, "effective_households_with_children": 83.45466272959187, - "child_spm_percent": 22.21455593128497, + "child_spm_percent": 22.22042866679022, + "child_spm_percent_without_group_quarters": 22.22006721597605, "census_percent": 8.7, "census_margin": 2.6 }, @@ -92,6 +101,7 @@ "effective_households": 197.9040438366882, "effective_households_with_children": 58.44779451046045, "child_spm_percent": 7.341376635830637, + "child_spm_percent_without_group_quarters": 7.332242067006129, "census_percent": 6.5, "census_margin": 2.3 }, @@ -101,6 +111,7 @@ "effective_households": 216.9642807519042, "effective_households_with_children": 89.64596796651924, "child_spm_percent": 7.341353758564617, + "child_spm_percent_without_group_quarters": 7.340743328897833, "census_percent": 7.5, "census_margin": 2.4 }, @@ -110,6 +121,7 @@ "effective_households": 274.3920751676354, "effective_households_with_children": 125.46968589203908, "child_spm_percent": 5.857583743619603, + "child_spm_percent_without_group_quarters": 5.852749190399006, "census_percent": 13.0, "census_margin": 2.6 }, @@ -119,6 +131,7 @@ "effective_households": 347.14153197328847, "effective_households_with_children": 144.66816777888795, "child_spm_percent": 16.406165846886925, + "child_spm_percent_without_group_quarters": 16.40255054202996, "census_percent": 11.3, "census_margin": 2.4 }, @@ -128,6 +141,7 @@ "effective_households": 287.7337812179193, "effective_households_with_children": 129.2966436533776, "child_spm_percent": 6.8832216762626315, + "child_spm_percent_without_group_quarters": 6.878907716798353, "census_percent": 6.5, "census_margin": 1.3 }, @@ -137,6 +151,7 @@ "effective_households": 302.87445558594817, "effective_households_with_children": 115.72319399909837, "child_spm_percent": 14.011671453444666, + "child_spm_percent_without_group_quarters": 14.011670540609058, "census_percent": 10.9, "census_margin": 2.1 }, @@ -146,6 +161,7 @@ "effective_households": 247.9668823005608, "effective_households_with_children": 103.04276195762745, "child_spm_percent": 9.449340077906502, + "child_spm_percent_without_group_quarters": 9.434467949582562, "census_percent": 7.1, "census_margin": 1.9 }, @@ -155,6 +171,7 @@ "effective_households": 308.35744412601315, "effective_households_with_children": 141.8313878229509, "child_spm_percent": 13.834987349236789, + "child_spm_percent_without_group_quarters": 13.826648288931894, "census_percent": 19.1, "census_margin": 2.6 }, @@ -164,6 +181,7 @@ "effective_households": 245.36573083046912, "effective_households_with_children": 109.55463448989998, "child_spm_percent": 11.586384534741017, + "child_spm_percent_without_group_quarters": 11.582035350820192, "census_percent": 9.7, "census_margin": 2.5 }, @@ -173,6 +191,7 @@ "effective_households": 319.0109800411504, "effective_households_with_children": 156.52023169048948, "child_spm_percent": 6.865179158322447, + "child_spm_percent_without_group_quarters": 6.86094296535344, "census_percent": 13.3, "census_margin": 2.7 }, @@ -182,6 +201,7 @@ "effective_households": 245.19717686543063, "effective_households_with_children": 104.95003344226164, "child_spm_percent": 13.468276185314497, + "child_spm_percent_without_group_quarters": 13.465143855052226, "census_percent": 15.0, "census_margin": 2.5 }, @@ -191,6 +211,7 @@ "effective_households": 202.5543700226553, "effective_households_with_children": 100.29098754401335, "child_spm_percent": 5.3913265972729825, + "child_spm_percent_without_group_quarters": 5.388031095767471, "census_percent": 8.2, "census_margin": 1.9 }, @@ -200,6 +221,7 @@ "effective_households": 260.4524410781915, "effective_households_with_children": 131.02563595790164, "child_spm_percent": 8.396840445914263, + "child_spm_percent_without_group_quarters": 8.377243371859025, "census_percent": 7.3, "census_margin": 1.6 }, @@ -209,6 +231,7 @@ "effective_households": 237.28258514537356, "effective_households_with_children": 113.45987662681664, "child_spm_percent": 13.832832136918386, + "child_spm_percent_without_group_quarters": 13.828970045139782, "census_percent": 9.9, "census_margin": 2.0 }, @@ -218,6 +241,7 @@ "effective_households": 233.62979579983772, "effective_households_with_children": 94.02037861059644, "child_spm_percent": 12.639491351259352, + "child_spm_percent_without_group_quarters": 12.61674735882832, "census_percent": 12.8, "census_margin": 3.2 }, @@ -227,6 +251,7 @@ "effective_households": 322.756036976999, "effective_households_with_children": 148.31075514652943, "child_spm_percent": 11.730880622678743, + "child_spm_percent_without_group_quarters": 11.727659365004971, "census_percent": 20.7, "census_margin": 3.0 }, @@ -236,6 +261,7 @@ "effective_households": 199.3026999394935, "effective_households_with_children": 119.74320157951851, "child_spm_percent": 9.689846346880431, + "child_spm_percent_without_group_quarters": 9.686387194749264, "census_percent": 13.3, "census_margin": 2.9 }, @@ -245,6 +271,7 @@ "effective_households": 289.2575929970926, "effective_households_with_children": 117.20929118464336, "child_spm_percent": 14.960178605148503, + "child_spm_percent_without_group_quarters": 14.940375128375202, "census_percent": 11.0, "census_margin": 2.4 }, @@ -254,6 +281,7 @@ "effective_households": 301.06996981561286, "effective_households_with_children": 130.9816707611151, "child_spm_percent": 15.097029269703253, + "child_spm_percent_without_group_quarters": 15.087479414509811, "census_percent": 15.5, "census_margin": 2.4 }, @@ -263,6 +291,7 @@ "effective_households": 330.35466281690645, "effective_households_with_children": 149.81653406864606, "child_spm_percent": 10.81009163434252, + "child_spm_percent_without_group_quarters": 10.80620862545528, "census_percent": 11.1, "census_margin": 2.1 }, @@ -272,6 +301,7 @@ "effective_households": 259.0326497612957, "effective_households_with_children": 89.18071871598167, "child_spm_percent": 5.148035850741653, + "child_spm_percent_without_group_quarters": 5.142540152778085, "census_percent": 6.9, "census_margin": 1.9 }, @@ -281,6 +311,7 @@ "effective_households": 233.46103574250773, "effective_households_with_children": 98.9444451706557, "child_spm_percent": 9.444477858941154, + "child_spm_percent_without_group_quarters": 9.442395696291662, "census_percent": 7.7, "census_margin": 1.8 }, @@ -290,6 +321,7 @@ "effective_households": 193.3548885878453, "effective_households_with_children": 83.22800020967402, "child_spm_percent": 17.594849698931508, + "child_spm_percent_without_group_quarters": 17.591571799844882, "census_percent": 10.7, "census_margin": 2.5 }, @@ -298,7 +330,8 @@ "records": 31207, "effective_households": 263.16307250816317, "effective_households_with_children": 125.86567963802055, - "child_spm_percent": 16.712114849204532, + "child_spm_percent": 16.71212178070164, + "child_spm_percent_without_group_quarters": 16.700558889187654, "census_percent": 9.7, "census_margin": 2.3 }, @@ -308,6 +341,7 @@ "effective_households": 252.84197451677665, "effective_households_with_children": 110.02488630565745, "child_spm_percent": 5.88827454088196, + "child_spm_percent_without_group_quarters": 5.886188338316105, "census_percent": 7.3, "census_margin": 1.7 }, @@ -317,6 +351,7 @@ "effective_households": 289.2074238745529, "effective_households_with_children": 136.70455254696972, "child_spm_percent": 9.22344812166144, + "child_spm_percent_without_group_quarters": 9.219202294181942, "census_percent": 10.6, "census_margin": 2.2 }, @@ -326,6 +361,7 @@ "effective_households": 287.92206247924884, "effective_households_with_children": 158.62081412638236, "child_spm_percent": 16.563429876852265, + "child_spm_percent_without_group_quarters": 16.552500639384817, "census_percent": 10.0, "census_margin": 1.7 }, @@ -335,6 +371,7 @@ "effective_households": 262.9414980706346, "effective_households_with_children": 98.36566291045744, "child_spm_percent": 17.22742595902717, + "child_spm_percent_without_group_quarters": 17.223583211906, "census_percent": 13.3, "census_margin": 2.3 }, @@ -344,6 +381,7 @@ "effective_households": 395.48166288201764, "effective_households_with_children": 172.87246757134372, "child_spm_percent": 21.445486995867174, + "child_spm_percent_without_group_quarters": 21.434572164325164, "census_percent": 12.8, "census_margin": 2.0 }, @@ -353,6 +391,7 @@ "effective_households": 409.39850280262266, "effective_households_with_children": 148.14817213554065, "child_spm_percent": 14.191340913009428, + "child_spm_percent_without_group_quarters": 14.18834846196926, "census_percent": 9.3, "census_margin": 1.9 }, @@ -362,6 +401,7 @@ "effective_households": 296.03311241598743, "effective_households_with_children": 170.963951373496, "child_spm_percent": 15.480344609122307, + "child_spm_percent_without_group_quarters": 15.477395885595769, "census_percent": 12.1, "census_margin": 2.4 }, @@ -371,6 +411,7 @@ "effective_households": 278.16811583823, "effective_households_with_children": 138.65921384127614, "child_spm_percent": 9.557043380838682, + "child_spm_percent_without_group_quarters": 9.554059947303633, "census_percent": 13.3, "census_margin": 1.9 }, @@ -380,6 +421,7 @@ "effective_households": 349.83766926816173, "effective_households_with_children": 144.12349295086753, "child_spm_percent": 9.51832100478657, + "child_spm_percent_without_group_quarters": 9.513278002882778, "census_percent": 10.3, "census_margin": 1.9 }, @@ -389,6 +431,7 @@ "effective_households": 299.387800395794, "effective_households_with_children": 157.51964240048426, "child_spm_percent": 17.43337031195012, + "child_spm_percent_without_group_quarters": 17.42939481091213, "census_percent": 15.1, "census_margin": 2.2 }, @@ -398,6 +441,7 @@ "effective_households": 386.33715619659654, "effective_households_with_children": 154.7532883194728, "child_spm_percent": 11.995112136195859, + "child_spm_percent_without_group_quarters": 11.992276996238957, "census_percent": 14.4, "census_margin": 2.2 }, @@ -407,6 +451,7 @@ "effective_households": 387.1320638247037, "effective_households_with_children": 173.96855548601457, "child_spm_percent": 9.090849497593641, + "child_spm_percent_without_group_quarters": 9.086693235075417, "census_percent": 12.3, "census_margin": 2.1 }, @@ -416,6 +461,7 @@ "effective_households": 370.91268375511726, "effective_households_with_children": 144.0786867988308, "child_spm_percent": 13.732748385262012, + "child_spm_percent_without_group_quarters": 13.729450996073497, "census_percent": 12.4, "census_margin": 1.6 }, @@ -425,6 +471,7 @@ "effective_households": 482.7471954369402, "effective_households_with_children": 174.15590817246073, "child_spm_percent": 13.904691983293437, + "child_spm_percent_without_group_quarters": 13.899552660285266, "census_percent": 11.1, "census_margin": 2.0 }, @@ -434,6 +481,7 @@ "effective_households": 508.0764111895174, "effective_households_with_children": 218.03588719853272, "child_spm_percent": 16.438761323718364, + "child_spm_percent_without_group_quarters": 16.430627342596928, "census_percent": 16.2, "census_margin": 1.9 }, @@ -442,7 +490,8 @@ "records": 99746, "effective_households": 559.4696854992734, "effective_households_with_children": 288.55072069948665, - "child_spm_percent": 22.716581838784254, + "child_spm_percent": 22.716581843023185, + "child_spm_percent_without_group_quarters": 22.714369851827524, "census_percent": 18.4, "census_margin": 1.8 }, @@ -452,6 +501,7 @@ "effective_households": 788.2417528592817, "effective_households_with_children": 439.36194246023825, "child_spm_percent": 14.561294779369085, + "child_spm_percent_without_group_quarters": 14.554985311431567, "census_percent": 16.1, "census_margin": 1.3 }, @@ -460,7 +510,8 @@ "records": 162907, "effective_households": 1145.7926619500272, "effective_households_with_children": 498.65642401725273, - "child_spm_percent": 19.812992231545795, + "child_spm_percent": 20.374148222691655, + "child_spm_percent_without_group_quarters": 20.36801664597805, "census_percent": 17.9, "census_margin": 1.0 } diff --git a/slideshows/appam-2026/data/wisconsin_schedules.json b/slideshows/appam-2026/data/wisconsin_schedules.json new file mode 100644 index 00000000..b975aa3b --- /dev/null +++ b/slideshows/appam-2026/data/wisconsin_schedules.json @@ -0,0 +1,11 @@ +{ + "policyengine_us_version": "2.90.2", + "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "state": "WI", + "earnings": 40000, + "child_care_subsidy": { + "2021": 13257.6, + "2023": 14023.68, + "2026": 14424.96 + } +} diff --git a/slideshows/appam-2026/slides/AxiomSlide.tsx b/slideshows/appam-2026/slides/AxiomSlide.tsx index 66184219..78d690b6 100644 --- a/slideshows/appam-2026/slides/AxiomSlide.tsx +++ b/slideshows/appam-2026/slides/AxiomSlide.tsx @@ -25,14 +25,14 @@ const runs = [ result: `Deduction of ${usdCents(axiomRuns.onTime)} a month`, }, { family: 'Same family, reports late', result: `Deduction of ${usd(axiomRuns.late)}` }, - { family: 'Same family, November 2019', result: 'Engine refuses: the encoded section starts December 2019' }, + { family: 'Ask for November 2019', result: 'Engine refuses: the encoded section starts December 2019' }, ]; export default function AxiomSlide() { return (
    - + 
                 {RULESPEC}
    diff --git a/slideshows/appam-2026/slides/CliffsSlide.tsx b/slideshows/appam-2026/slides/CliffsSlide.tsx index e8f0aa61..3df28c63 100644 --- a/slideshows/appam-2026/slides/CliffsSlide.tsx +++ b/slideshows/appam-2026/slides/CliffsSlide.tsx @@ -17,7 +17,7 @@ export default function CliffsSlide() {

    - A $500 raise crosses the subsidy limit + The family crosses the subsidy limit after a $500 raise

      {cliffs.map((cliff) => ( @@ -30,7 +30,7 @@ export default function CliffsSlide() { ))}

    - In {subsidyDrivenCliffs} of 51 states, the family’s largest drop comes from losing the subsidy. + In {subsidyDrivenCliffs} of 51 jurisdictions, the family’s largest drop comes from losing the subsidy.

    diff --git a/slideshows/appam-2026/slides/EarningsChart.tsx b/slideshows/appam-2026/slides/EarningsChart.tsx index ffe73603..99fdddc7 100644 --- a/slideshows/appam-2026/slides/EarningsChart.tsx +++ b/slideshows/appam-2026/slides/EarningsChart.tsx @@ -80,7 +80,7 @@ export default function EarningsChart() { strokeLinejoin="round" strokeLinecap="round" > - {`${cliff.name}: loses ${usd(cliff.drop)} at ${usd(cliff.after)}`} + {`Family in ${cliff.name} loses ${usd(cliff.drop)} at ${usd(cliff.after)} of earnings`} LAST_LABEL_ON_RIGHT ? -10 : 10)} diff --git a/slideshows/appam-2026/slides/MicrodataSlide.tsx b/slideshows/appam-2026/slides/MicrodataSlide.tsx index 58050717..8536cbef 100644 --- a/slideshows/appam-2026/slides/MicrodataSlide.tsx +++ b/slideshows/appam-2026/slides/MicrodataSlide.tsx @@ -1,5 +1,5 @@ import CodeBlock from '@/components/content/CodeBlock'; -import { instrument } from '../content'; +import { instrument, modelVersion } from '../content'; import Frame from './Frame'; import InstrumentRugs from './InstrumentRugs'; @@ -24,7 +24,7 @@ export default function MicrodataSlide() {

    - The TAXSIM-compatible interface runs TAXSIM-format files through PolicyEngine for tax years 2021 onward, and + Our TAXSIM-compatible interface runs TAXSIM-format files through PolicyEngine for tax years 2021 onward, and through TAXSIM35 for earlier years.

    @@ -35,8 +35,9 @@ export default function MicrodataSlide() {

    Each mark shows one state. We draw {instrument.households.toLocaleString('en-US')} households with children by weight from Populace US, our default microdata, and run the same families under each state’s{' '} - {instrument.year} rules. SNAP in January, as a share of SPM units with children. Medicaid or CHIP as a share - of children under 19. + {instrument.year} rules, using PolicyEngine US {modelVersion}. January SNAP eligibility, as a share of + Supplemental Poverty Measure (SPM) units with children. Medicaid or CHIP eligibility, as a share of children + under 19.

    diff --git a/slideshows/appam-2026/slides/OneFamilySlide.tsx b/slideshows/appam-2026/slides/OneFamilySlide.tsx index 5d460919..2013debc 100644 --- a/slideshows/appam-2026/slides/OneFamilySlide.tsx +++ b/slideshows/appam-2026/slides/OneFamilySlide.tsx @@ -43,7 +43,7 @@ export default function OneFamilySlide() { family pays, to get net resources. We leave out health coverage. The gap between the highest and lowest state runs {usd(spreadAt(30_000).spread)} at $30,000 of earnings and{' '} {usd(spreadAt(60_000).spread)} - {' at $60,000. The model starts South Dakota’s program in August 2026, so its subsidy covers five months.'} + {' at $60,000. We model South Dakota’s program from August 2026, so its subsidy covers five months.'}

    diff --git a/slideshows/appam-2026/slides/ParametersSlide.tsx b/slideshows/appam-2026/slides/ParametersSlide.tsx index a3e74b90..103095bc 100644 --- a/slideshows/appam-2026/slides/ParametersSlide.tsx +++ b/slideshows/appam-2026/slides/ParametersSlide.tsx @@ -30,7 +30,7 @@ export default function ParametersSlide() { The Massachusetts Legislature raised this credit to $310 for 2023 and $440 for 2024, and lifted its two-dependent cap.

    -

    Ask for any date and the model reads the value in force on that date.

    +

    Ask for any date and you get the value in force on that date.

    Open the file and check each value against the laws and forms it cites.

    diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx index 3272038f..983ddb0d 100644 --- a/slideshows/appam-2026/slides/ReformSlide.tsx +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -9,18 +9,18 @@ const PYTHON = `reform = Reform.from_dict({ } }, country_id="us") -# massachusetts: the state's rows in our local-area file +# Use Massachusetts rows from the local-area file baseline = Microsimulation(dataset=massachusetts) reformed = Microsimulation(dataset=massachusetts, reform=reform)`; const results = [ - { value: percent(massachusetts.gainingShare), label: 'of households gain, and none lose' }, + { value: percent(massachusetts.gainingShare), label: 'of households would gain, and none would lose' }, { value: millions(massachusetts.gapReduction), label: 'off the SPM poverty gap' }, ]; export default function ReformSlide() { return ( - +
    @@ -43,19 +43,20 @@ export default function ReformSlide() {

    - The{' '} + On our{' '} Child Poverty Impact Dashboard - {' '} - applies the model to child tax credit, EITC, SNAP and state tax credit reforms in every state. + + , you can run child tax credit, EITC and SNAP reforms in any state, and state tax credit reforms wherever a + state taxes income.

    - Populace US local-area file and PolicyEngine US {modelVersion}, 2026, assuming no behavioral responses. The - file holds{' '} - {massachusetts.records.toLocaleString('en-US')} Massachusetts households, an effective sample of about{' '} - {massachusetts.effectiveHouseholds}. The model puts {massachusetts.modelChildRate.toFixed(1)}% of the state’s - children below the SPM threshold in 2025, against Census’s {massachusetts.censusChildRate.toFixed(1)}% for - 2023 to 2025. The reform lowers the 2026 child rate by {massachusetts.childRateChange.toFixed(1)} points. + We use Populace US’s local-area file with PolicyEngine US {modelVersion} for 2026 state estimates, assuming no + behavioral responses. The file holds {massachusetts.records.toLocaleString('en-US')} Massachusetts households, + an effective sample of about {massachusetts.effectiveHouseholds}. We estimate{' '} + {massachusetts.modelChildRate.toFixed(1)}% of the state’s children below the SPM threshold in 2025, and Census + reports {massachusetts.censusChildRate.toFixed(1)}% for 2023 to 2025. We estimate that the 2026 child poverty + rate would fall by {massachusetts.childRateChange.toFixed(1)} points under the doubled credit.

    diff --git a/slideshows/appam-2026/slides/ScopeSlide.tsx b/slideshows/appam-2026/slides/ScopeSlide.tsx index 37a6e9e5..69817028 100644 --- a/slideshows/appam-2026/slides/ScopeSlide.tsx +++ b/slideshows/appam-2026/slides/ScopeSlide.tsx @@ -4,15 +4,15 @@ import Frame from './Frame'; const coverage = [ 'We model child care subsidies in 46 jurisdictions through 2023, 48 in 2024, 50 in 2025 and all 51 from August 2026.', 'We copy each parameter’s earliest value back to 2015, so validate historical runs separately.', - 'The model applies Wisconsin’s current child care schedules to earlier years.', - `Our local-area file holds an effective sample of about ${stateBaseline.medianEffective} households with children in the median state. Its child poverty rates miss Census’s 2023 to 2025 averages by ${stateBaseline.meanCensusGap.toFixed(1)} points on average, and ${stateBaseline.beyondCensusMargin} of 51 jurisdictions fall outside Census’s margin. Bring your own microdata for state levels.`, + 'We apply Wisconsin’s current child care schedules to earlier years.', + `Our local-area file holds an effective sample of about ${stateBaseline.medianEffective} households with children in the median state. Our child poverty estimates on that file miss Census’s 2023 to 2025 averages by ${stateBaseline.meanCensusGap.toFixed(1)} points on average, and ${stateBaseline.beyondCensusMargin} of 51 jurisdictions fall outside Census’s margin. For state child poverty rates, bring your own microdata.`, ]; const mechanisms = [ - 'Earnings enter as an input, so minimum wage and paid leave laws leave a family’s income unchanged.', - 'Hours and earnings stay fixed when a reform changes rates or benefits, unless you set a labor supply response.', - 'The Medicaid model leaves out medically needy groups other than aged, blind and disabled people, and some spenddown rules.', - 'The family here claims all it qualifies for. Population runs use the take-up flags in Populace US.', + 'We take earnings and paid leave benefits as inputs and hold them fixed when you change minimum wage or paid leave law.', + 'When you change rates or benefits, we hold hours and earnings fixed unless you set a labor supply response.', + 'We model medically needy Medicaid for aged, blind and disabled people, and leave out other medically needy groups and some spenddown rules.', + 'The family here claims all it qualifies for. We apply the take-up flags in Populace US to population runs.', ]; function Column({ title, items }: { title: string; items: string[] }) { diff --git a/slideshows/appam-2026/slides/WhatItDoesSlide.tsx b/slideshows/appam-2026/slides/WhatItDoesSlide.tsx index 96cb93be..697b9449 100644 --- a/slideshows/appam-2026/slides/WhatItDoesSlide.tsx +++ b/slideshows/appam-2026/slides/WhatItDoesSlide.tsx @@ -34,10 +34,10 @@ export default function WhatItDoesSlide() { ))}

    - The US model covers federal and state income taxes and credits, SNAP, SSI, TANF, WIC, - school meals, child care subsidies, Medicaid and CHIP eligibility, and ACA premium tax - credits. It includes every state income tax and the TANF and child care subsidy rules of - all 50 states and DC. + We model federal and state income taxes and credits, SNAP, SSI, TANF, WIC, school meals, + child care subsidies, Medicaid and CHIP eligibility, and ACA premium tax credits. We + include every state income tax and the TANF and child care subsidy rules of all 50 states + and DC.

    diff --git a/slideshows/appam-2026/slides/WhichLinesSlide.tsx b/slideshows/appam-2026/slides/WhichLinesSlide.tsx index 62180e3c..6f47a2fe 100644 --- a/slideshows/appam-2026/slides/WhichLinesSlide.tsx +++ b/slideshows/appam-2026/slides/WhichLinesSlide.tsx @@ -42,7 +42,7 @@ export default function WhichLinesSlide() { ))}

    - Congress lets SNAP households deduct what they pay for care, so the subsidy shrinks that deduction. + Congress lets SNAP households deduct their care costs, so this family deducts less when Texas pays for care.

    From d4fa4b26b28e1cb0ce137d3423adced36bc51a3b Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Mon, 2 Nov 2026 18:35:01 -0500 Subject: [PATCH 15/32] Fit slide 8 and add standard errors for its Massachusetts figures Slide 8 overflowed at 1280x720 after the round 2 title and footer. Shorten the dashboard sentence to "state by state", drop a blank code line and tighten the gaps. A sibling session found state reform effects too thin to quote to the tenth, so compute household-clustered standard errors for the slide's figures, say "about 0.2 points" on the slide and in the notes, and record the errors in the ledger. Property tests cover the estimator, and content.test.ts ties its output to the slide's run. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 3 +- slideshows/appam-2026/SPEAKER-NOTES.md | 4 +- .../analysis/massachusetts_precision.py | 105 ++++++++++++++++++ .../analysis/test_massachusetts_precision.py | 48 ++++++++ slideshows/appam-2026/content.test.ts | 17 +++ .../data/massachusetts_precision.json | 16 +++ slideshows/appam-2026/slides/ReformSlide.tsx | 10 +- 7 files changed, 194 insertions(+), 9 deletions(-) create mode 100644 slideshows/appam-2026/analysis/massachusetts_precision.py create mode 100644 slideshows/appam-2026/analysis/test_massachusetts_precision.py create mode 100644 slideshows/appam-2026/data/massachusetts_precision.json diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index f6e3f6dd..6db57395 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -38,6 +38,7 @@ The script keeps input variables and IDs. It renames the four stored Social Secu ## Massachusetts poverty - We estimate that 101 person records cross the threshold under the doubled credit and that the 2026 child poverty rate falls 0.2 points, from 23.1% to 22.9%. +- That fall rests on 55 child records in 24 households. Holding weights fixed, we estimate household-clustered standard errors of 0.05 points on the fall, 3.8 points on the 2026 rate, $34 million on the fall in the poverty gap and $54 million on the state cost (`analysis/massachusetts_precision.py` → `data/massachusetts_precision.json`). They leave out what calibration adds. - The file's 36,491 Massachusetts household records hold an effective sample of about 395, or 173 among households with children. Its 1,522 records from the Current Population Survey and tax data hold 67% of household weight. - We estimate a Massachusetts child poverty rate of 21.4% for 2025 and 23.1% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). That difference ranks third among the 51 jurisdictions, after Delaware and Louisiana. - On the default file, we estimate that Massachusetts would forgo $555 million, 29.8% of households would gain and the poverty gap would shrink by $167 million (`data/massachusetts_reform_default.json`). @@ -95,7 +96,7 @@ We count all four payments in net resources in every state. | 4 | Congress lets SNAP households deduct their care costs | `snap_dependent_care_deduction`, citing 7 U.S.C. 2014(e)(3), reads post-subsidy `childcare_expenses` | | 6 | Massachusetts credit values, dates and citations | `parameters/gov/states/ma/tax/income/credits/child_and_family/amount.yaml` and `dependent_cap.yaml` | | 7 | Our TAXSIM-compatible interface runs 2021 onward through PolicyEngine and earlier years through TAXSIM35 | `StitchedRunner.PE_MIN_YEAR = 2021` in `policyengine_taxsim/runners/stitched_runner.py` at the policyengine-taxsim commit above | -| 8 | Child tax credit, EITC and SNAP reforms in any state, and state tax credit reforms wherever a state taxes income | `getReformOptionsForState` in `frontend/lib/state-programs.ts` at the dashboard commit above returns federal child tax credit and EITC options and SNAP options in all 51 jurisdictions, and state EITC options in 43: the 42 with an income tax, and Washington | +| 8 | Child tax credit, EITC, SNAP and state tax credit reforms, state by state | `getReformOptionsForState` in `frontend/lib/state-programs.ts` at the dashboard commit above returns federal child tax credit and EITC options and SNAP options in all 51 jurisdictions, and state EITC options in 43: the 42 with an income tax, and Washington | | 9 | Project through 2035 | The PolicyEngine API's US metadata lists years 2022 to 2035 | | 10 | 46 jurisdictions through 2023, 48 in 2024, 50 in 2025, 51 from August 2026 | `gov.hhs.ccdf.child_care_subsidy_programs` lists 51 from 2025, missing DC, NC, OR, TN and TX through 2023, and DC, TN and TX in 2024. `sd_cca` returns zero until `uses_weekly_rates` turns on in August 2026 | | 10 | Copy back to 2015 | `backdate_parameters(first_instant="2015-01-01")` in `system.py`, `FIRST_MODELED_YEAR = 2015` in `tools/parameters.py` | diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index c349cf35..355d2108 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -57,9 +57,9 @@ If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface takes t Take the parameter from a moment ago and double it, from $440 to $880 per dependent. We estimate Massachusetts would forgo $577 million in 2026. Of Massachusetts households, 29.4% would gain and none would lose, and under the Supplemental Poverty Measure, or SPM, the poverty gap would shrink by $169 million. -We ran this on the Populace US local-area file, which we use for state and district results. It holds 36,491 Massachusetts households, an effective sample of about 395. We estimate 21.4% of the state's children below the threshold in 2025, and Census reports 12.8% for 2023 to 2025. We estimate that the 2026 child poverty rate would fall by 0.2 points under the doubled credit, from that high baseline. +We ran this on the Populace US local-area file, which we use for state and district results. It holds 36,491 Massachusetts households, an effective sample of about 395. We estimate 21.4% of the state's children below the threshold in 2025, and Census reports 12.8% for 2023 to 2025. We estimate that the 2026 child poverty rate would fall by about 0.2 points under the doubled credit, from that high baseline. -On our Child Poverty Impact Dashboard, you can run child tax credit, EITC and SNAP reforms in any state, and state tax credit reforms wherever a state taxes income. +On our Child Poverty Impact Dashboard, you can run child tax credit, EITC, SNAP and state tax credit reforms state by state. ## 9. Research questions and the tools that answer them (8:30, 45 seconds) diff --git a/slideshows/appam-2026/analysis/massachusetts_precision.py b/slideshows/appam-2026/analysis/massachusetts_precision.py new file mode 100644 index 00000000..df07c87f --- /dev/null +++ b/slideshows/appam-2026/analysis/massachusetts_precision.py @@ -0,0 +1,105 @@ +"""Standard errors for slide 8's Massachusetts figures, clustered by household. + +Linearizes each figure over the local-area file's Massachusetts households. It treats +the weights as fixed, so it leaves out what calibration adds, and it leaves out errors +that every record shares, such as a data-wide error in rent. +""" + +import json +import sys +from importlib.metadata import version +from pathlib import Path + +import numpy as np +from massachusetts_reform import MASSACHUSETTS_FIPS, PARAMETER, REFORMED, YEAR +from policyengine_core.data import Dataset +from policyengine_core.reforms import Reform +from policyengine_us import Microsimulation +from state_baseline import DATA_YEAR, STATE_DATASET, load_state, state_file + +CHILD_AGE_LIMIT = 18 + + +def total_se(values, clusters): + """Standard error of a weighted total. The values already carry their weights.""" + _, index = np.unique(clusters, return_inverse=True) + totals = np.bincount(index, weights=values) + count = len(totals) + return float(np.sqrt(count / (count - 1) * np.sum((totals - totals.mean()) ** 2))) + + +def ratio_se(numerator, denominator, clusters): + """A ratio of weighted totals and its linearized standard error.""" + ratio = numerator.sum() / denominator.sum() + residuals = (numerator - ratio * denominator) / denominator.sum() + return float(ratio), total_se(residuals, clusters) + + +def read(sim): + return { + "poor": sim.calculate( + "spm_unit_is_in_spm_poverty", YEAR, map_to="person" + ).values.astype(float), + "gap": sim.calculate("poverty_gap", YEAR).values, + "tax": sim.calculate("state_income_tax", YEAR).values, + } + + +def main(commit, folder): + load_state(MASSACHUSETTS_FIPS, Path(folder)) + people = state_file(MASSACHUSETTS_FIPS) + sim = Microsimulation(dataset=Dataset.from_dataframe(people, DATA_YEAR)) + reform = Reform.from_dict( + {PARAMETER: {f"{YEAR}-01-01.2100-12-31": REFORMED}}, country_id="us" + ) + baseline = read(sim) + reformed = read( + Microsimulation( + dataset=Dataset.from_dataframe(people, DATA_YEAR), reform=reform + ) + ) + + def households(entity): + return sim.calculate("household_id", YEAR, map_to=entity).values + + is_child = sim.calculate("age", YEAR).values < CHILD_AGE_LIMIT + children = sim.calculate("person_weight", YEAR).values * is_child + child_rate, child_rate_se = ratio_se( + children * baseline["poor"], children, households("person") + ) + fall, fall_se = ratio_se( + children * (baseline["poor"] - reformed["poor"]), + children, + households("person"), + ) + crossing = is_child & (baseline["poor"] != reformed["poor"]) + gap_fall = (baseline["gap"] - reformed["gap"]) * sim.calculate( + "spm_unit_weight", YEAR + ).values + cost = (baseline["tax"] - reformed["tax"]) * sim.calculate( + "tax_unit_weight", YEAR + ).values + payload = { + "policyengine_us_version": version("policyengine-us"), + "policyengine_us_commit": commit, + "dataset": STATE_DATASET, + "year": YEAR, + "child_rate_percent": 100 * child_rate, + "child_rate_se_points": 100 * child_rate_se, + "child_rate_fall_points": 100 * fall, + "child_rate_fall_se_points": 100 * fall_se, + "child_records_crossing": int(crossing.sum()), + "households_with_a_child_crossing": len( + set(households("person")[crossing].tolist()) + ), + "poverty_gap_fall": float(gap_fall.sum()), + "poverty_gap_fall_se": total_se(gap_fall, households("spm_unit")), + "state_cost": float(cost.sum()), + "state_cost_se": total_se(cost, households("tax_unit")), + } + target = Path(__file__).parent.parent / "data" / "massachusetts_precision.json" + target.write_text(json.dumps(payload, indent=1) + "\n") + + +if __name__ == "__main__": + main(sys.argv[1], sys.argv[2]) diff --git a/slideshows/appam-2026/analysis/test_massachusetts_precision.py b/slideshows/appam-2026/analysis/test_massachusetts_precision.py new file mode 100644 index 00000000..73373a71 --- /dev/null +++ b/slideshows/appam-2026/analysis/test_massachusetts_precision.py @@ -0,0 +1,48 @@ +"""Standard errors must respect the sample's households.""" + +import massachusetts_precision as mp +import numpy as np +from hypothesis import given, settings +from hypothesis import strategies as st + +values = st.lists( + st.floats(min_value=-1e6, max_value=1e6), min_size=2, max_size=40 +).map(np.array) +positive = st.floats(min_value=0.01, max_value=1e6) + + +@settings(deadline=None) +@given(values) +def test_a_total_needs_no_negative_variance_and_scales_with_its_values(sample): + clusters = np.arange(len(sample)) + se = mp.total_se(sample, clusters) + assert se >= 0 + assert abs(mp.total_se(2 * sample, clusters) - 2 * se) <= 1e-6 * max(se, 1) + + +@settings(deadline=None) +@given(positive, st.integers(2, 40)) +def test_identical_households_leave_a_total_no_sampling_error(value, count): + assert mp.total_se(np.full(count, value), np.arange(count)) < 1e-6 * value + + +def test_a_household_counts_once_however_its_people_split_its_value(): + values = np.array([3.0, 1.0, 5.0]) + split = np.array([1.0, 2.0, 1.0, 5.0]) + assert mp.total_se(values, np.arange(3)) == mp.total_se( + split, np.array([0, 0, 1, 2]) + ) + + +@settings(deadline=None) +@given(values, positive) +def test_a_ratio_keeps_its_value_and_error_when_both_sides_scale(numerator, scale): + denominator = np.abs(numerator) + 1 + clusters = np.arange(len(numerator)) + ratio, se = mp.ratio_se(numerator, denominator, clusters) + scaled_ratio, scaled_se = mp.ratio_se( + scale * numerator, scale * denominator, clusters + ) + assert abs(ratio - numerator.sum() / denominator.sum()) < 1e-9 * max(abs(ratio), 1) + assert abs(scaled_ratio - ratio) < 1e-6 * max(abs(ratio), 1) + assert abs(scaled_se - se) < 1e-6 * max(se, 1) diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index 03091b44..288ff56a 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -37,6 +37,7 @@ const census = JSON.parse(read('data/census_child_spm.json')); const reformRun = JSON.parse(read('data/massachusetts_reform.json')); const defaultReform = JSON.parse(read('data/massachusetts_reform_default.json')); const pinnedReform = JSON.parse(read('data/massachusetts_reform_221.json')); +const precision = JSON.parse(read('data/massachusetts_precision.json')); const defaultBaseline = JSON.parse(read('data/state_baseline_default.json')); const sampleColumns = JSON.parse(read('data/sample_columns.json')); const slideCode = JSON.parse(read('data/slide_code_check.json')); @@ -342,6 +343,17 @@ describe('Massachusetts reform', () => { expect(pinnedReform.household_records).toBe(reformRun.household_records); expect(pinnedReform.effective_households).toBe(reformRun.effective_households); }); + + it('takes its standard errors from the same run as the slide', () => { + expect(precision.dataset).toBe(reformRun.dataset); + expect(precision.policyengine_us_version).toBe(modelVersion); + expect(precision.child_rate_percent).toBeCloseTo(100 * reformRun.baseline_child_poverty, 4); + expect(precision.child_rate_fall_points).toBeCloseTo(massachusetts.childRateChange, 4); + expect(Math.abs(precision.poverty_gap_fall - massachusetts.gapReduction)).toBeLessThan(1e-4 * massachusetts.gapReduction); + expect(Math.abs(precision.state_cost - massachusetts.cost)).toBeLessThan(1e-4 * massachusetts.cost); + expect(precision.child_records_crossing).toBeLessThanOrEqual(massachusetts.peopleCrossingThreshold); + expect(precision.households_with_a_child_crossing).toBeLessThanOrEqual(precision.child_records_crossing); + }); }); describe('state baselines', () => { @@ -466,6 +478,11 @@ describe('typed figures', () => { `from ${percent(massachusetts.childPoverty[0])} to ${percent(massachusetts.childPoverty[1])}`, `or ${Math.round(reformRun.effective_households_with_children)} among households with children`, `${massachusetts.peopleCrossingThreshold} person records`, + `${precision.child_records_crossing} child records in ${precision.households_with_a_child_crossing} households`, + `${precision.child_rate_fall_se_points.toFixed(2)} points on the fall`, + `${precision.child_rate_se_points.toFixed(1)} points on the 2026 rate`, + `${millions(precision.poverty_gap_fall_se)} on the fall in the poverty gap`, + `${millions(precision.state_cost_se)} on the state cost`, millions(defaultReform.state_cost), percent(defaultReform.households_gaining_share), millions(defaultReform.baseline_poverty_gap - defaultReform.reformed_poverty_gap), diff --git a/slideshows/appam-2026/data/massachusetts_precision.json b/slideshows/appam-2026/data/massachusetts_precision.json new file mode 100644 index 00000000..0a906d1c --- /dev/null +++ b/slideshows/appam-2026/data/massachusetts_precision.json @@ -0,0 +1,16 @@ +{ + "policyengine_us_version": "2.90.2", + "policyengine_us_commit": "469ad12cb957a1b3eaedf9e6dd3e4c69194bba26", + "dataset": "hf://datasets/policyengine/populace-us/populace_us_2024_acs_local.h5@populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z", + "year": 2026, + "child_rate_percent": 23.087971123178423, + "child_rate_se_points": 3.8397518194167084, + "child_rate_fall_points": 0.1783919778137313, + "child_rate_fall_se_points": 0.04991007799002909, + "child_records_crossing": 55, + "households_with_a_child_crossing": 24, + "poverty_gap_fall": 169120064.0, + "poverty_gap_fall_se": 34033868.964105144, + "state_cost": 576530752.0, + "state_cost_se": 53979332.877338916 +} diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx index 983ddb0d..8ba119b3 100644 --- a/slideshows/appam-2026/slides/ReformSlide.tsx +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -8,7 +8,6 @@ const PYTHON = `reform = Reform.from_dict({ "2026-01-01.2100-12-31": 880 } }, country_id="us") - # Use Massachusetts rows from the local-area file baseline = Microsimulation(dataset=massachusetts) reformed = Microsimulation(dataset=massachusetts, reform=reform)`; @@ -21,10 +20,10 @@ const results = [ export default function ReformSlide() { return ( -
    +
    -

    Change any parameter, then compare the two runs for any state.

    +

    Change any parameter, then compare the two runs for any state.

                     {PYTHON}
@@ -47,8 +46,7 @@ export default function ReformSlide() {
           
             Child Poverty Impact Dashboard
           
-          , you can run child tax credit, EITC and SNAP reforms in any state, and state tax credit reforms wherever a
-          state taxes income.
+          , you can run child tax credit, EITC, SNAP and state tax credit reforms state by state.
         
    
         
    
           We use Populace US’s local-area file with PolicyEngine US {modelVersion} for 2026 state estimates, assuming no
@@ -56,7 +54,7 @@ export default function ReformSlide() {
           an effective sample of about {massachusetts.effectiveHouseholds}. We estimate{' '}
           {massachusetts.modelChildRate.toFixed(1)}% of the state’s children below the SPM threshold in 2025, and Census
           reports {massachusetts.censusChildRate.toFixed(1)}% for 2023 to 2025. We estimate that the 2026 child poverty
-          rate would fall by {massachusetts.childRateChange.toFixed(1)} points under the doubled credit.
+          rate would fall by about {massachusetts.childRateChange.toFixed(1)} points under the doubled credit.
    From 3009ecc406387c933e3d690cf8257e86cb63d81d Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Mon, 2 Nov 2026 18:41:02 -0500 Subject: [PATCH 16/32] Join a staccato run in the notes and drop a reduced passive from the ledger Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 2 +- slideshows/appam-2026/SPEAKER-NOTES.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index 6db57395..5ed3515c 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -1,6 +1,6 @@ # Evidence ledger for the APPAM 2026 deck -Each claim on a slide traces to a computed result, to code read at a named commit, or to a public source. +Each claim on a slide traces to a computed result, to code at a named commit, or to a public source. - **PolicyEngine US:** version 2.90.2, commit `469ad12cb957a1b3eaedf9e6dd3e4c69194bba26` (main, November 1, 2026). - **policyengine-taxsim:** commit `9c987d8f23955dd1564af43dee3fac4054c0dbf2`. diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index 355d2108..43362b3b 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -65,7 +65,7 @@ On our Child Poverty Impact Dashboard, you can run child tax credit, EITC, SNAP The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. -With PolicyEngine, you can change any parameter, write new rules and run your own microdata. You can read and edit every line of code, and project current law through 2035. +With PolicyEngine, you can change any parameter, write new rules, run your own microdata, read and edit every line of code, and project current law through 2035. ## 10. What these results leave out (9:15, 45 seconds) From df344138036c09bbc04bca6906f9fb67afd12ed2 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Mon, 2 Nov 2026 19:00:44 -0500 Subject: [PATCH 17/32] Make every slide read from the PDF alone Cynthia's team may run the PDF in the room, so a reader gets no hover, no link and no live page. - Slide 8 prints the dashboard's address. It sat behind a link, and the PDF has none. - Slide 6 says where the parameter file lives, and links to it. - Slide 4's end labels count DC apart: "5 states and DC" replaces "6 states" on the two lines where DC ties, and a test holds it. Slides 4 and 7 say each mark shows one of 51 jurisdictions. - Slide 5's Maryland label moves above Maryland's peak. Idaho's line ran through it, and only a tooltip told them apart. - Slide 12's links reach 4.5:1 contrast on the gradient and link on the live page. - The notes' checklist now starts from the PDF on the room's laptop, with the live deck as the fallback. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/SPEAKER-NOTES.md | 8 +++--- slideshows/appam-2026/content.test.ts | 10 ++++++++ .../appam-2026/slides/EarningsChart.tsx | 11 ++++---- slideshows/appam-2026/slides/EndSlide.tsx | 25 +++++++++++++------ slideshows/appam-2026/slides/LineRugs.tsx | 14 +++++------ .../appam-2026/slides/MicrodataSlide.tsx | 2 +- .../appam-2026/slides/ParametersSlide.tsx | 11 +++++++- slideshows/appam-2026/slides/ReformSlide.tsx | 4 +-- .../appam-2026/slides/WhichLinesSlide.tsx | 2 +- 9 files changed, 58 insertions(+), 29 deletions(-) diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index 43362b3b..599e6b2f 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -6,10 +6,10 @@ Twelve slides in about 11 minutes. ## Before presenting -- Open the deck once on the presentation laptop. It needs no live connection after it loads. -- Press F for fullscreen, and the page control leaves the footer. -- Keep the PDF backup on the laptop in case the room's network fails. -- Check the slide order against what Cynthia, Tara and Linda present first, and cut slide 9's table to one sentence if they have already covered it. +- Send the PDF to Cynthia's team by Wednesday evening, November 4, for the room's laptop. +- Keep these notes on your phone. +- If the room's laptop fails, open policyengine.org/slides/appam-2026 on yours and press F for fullscreen. +- Check the slide order against what Cynthia, Tara and Linda present first. If they have already covered slide 9's table, give it one sentence. ## 1. PolicyEngine for state policy research (0:00, 15 seconds) diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index 288ff56a..6044c20b 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -30,6 +30,7 @@ import { } from './content'; import nationalCheck from './data/massachusetts_national_check.json'; import { SERIES } from './slides/FamilyChart'; +import { endLabel } from './slides/LineRugs'; const read = (path: string) => readFileSync(join(__dirname, path), 'utf8'); const family = JSON.parse(read('data/family.json')); @@ -200,6 +201,15 @@ describe('derived chart data', () => { expect(values).toEqual([...values].sort((a, b) => a - b)); } }); + + it('counts DC apart from the states in the rug plot labels', () => { + const line = (key: string) => programLines.find((entry) => entry.key === key)!.values; + expect(endLabel(line('child_care_subsidy'), 18_000)).toBe('5 states and DC $18,000'); + expect(endLabel(line('child_care_subsidy'), 0)).toBe('NV, OH $0'); + expect(endLabel(line('federal_taxes'), 3_678)).toBe('5 states and DC $3,678'); + expect(endLabel(line('federal_taxes'), 4_263)).toBe('27 states $4,263'); + expect(endLabel(line('snap'), 0)).toBe('15 states $0'); + }); }); describe('Texas interaction', () => { diff --git a/slideshows/appam-2026/slides/EarningsChart.tsx b/slideshows/appam-2026/slides/EarningsChart.tsx index 99fdddc7..ea2f0c04 100644 --- a/slideshows/appam-2026/slides/EarningsChart.tsx +++ b/slideshows/appam-2026/slides/EarningsChart.tsx @@ -10,8 +10,8 @@ const Y_MIN = 20_000; const Y_MAX = 90_000; const X_TICKS = [20_000, 40_000, 60_000, 80_000, 100_000]; const Y_TICKS = [20_000, 40_000, 60_000, 80_000]; -// A cliff past this point labels to its left, away from the chart's edge. -const LAST_LABEL_ON_RIGHT = 90_000; +// A cliff past this point labels above its peak, away from the chart's edge and the lines below it. +const LAST_LABEL_BELOW = 90_000; const SERIES_COLORS: Record = { IA: 'var(--color-teal-600)', @@ -70,6 +70,7 @@ export default function EarningsChart() { ))} {cliffs.map((cliff) => { const values = states[cliff.code]; + const below = cliff.after <= LAST_LABEL_BELOW; return ( {`Family in ${cliff.name} loses ${usd(cliff.drop)} at ${usd(cliff.after)} of earnings`} LAST_LABEL_ON_RIGHT ? -10 : 10)} - y={y(cliff.net_after) + 30} - textAnchor={cliff.after > LAST_LABEL_ON_RIGHT ? 'end' : 'start'} + x={below ? x(cliff.after) + 10 : x(cliff.before) - 4} + y={below ? y(cliff.net_after) + 30 : y(cliff.net_before) - 14} + textAnchor={below ? 'start' : 'end'} fontSize="23" fontWeight="600" fill="var(--color-gray-800)" diff --git a/slideshows/appam-2026/slides/EndSlide.tsx b/slideshows/appam-2026/slides/EndSlide.tsx index 7e62c73b..2ec6e3cc 100644 --- a/slideshows/appam-2026/slides/EndSlide.tsx +++ b/slideshows/appam-2026/slides/EndSlide.tsx @@ -2,10 +2,13 @@ import BasePathImage from '@/components/core/BasePathImage'; import Slide from '@/components/core/Slide'; const links = [ - 'policyengine.org', - 'pip install policyengine-us', - 'github.com/PolicyEngine/policyengine-slides', - 'max@policyengine.org', + { label: 'policyengine.org', href: 'https://www.policyengine.org' }, + { label: 'pip install policyengine-us' }, + { + label: 'github.com/PolicyEngine/policyengine-slides', + href: 'https://github.com/PolicyEngine/policyengine-slides/tree/main/slideshows/appam-2026', + }, + { label: 'max@policyengine.org', href: 'mailto:max@policyengine.org' }, ]; const DECK_URL = 'policyengine.org/slides/appam-2026'; @@ -17,10 +20,16 @@ export default function EndSlide() {

    Thank you

    Questions?

    -
    - {links.map((link) => ( -

    - {link} +

    + {links.map(({ label, href }) => ( +

    + {href ? ( + + {label} + + ) : ( + label + )}

    ))}
    diff --git a/slideshows/appam-2026/slides/LineRugs.tsx b/slideshows/appam-2026/slides/LineRugs.tsx index 06be9578..7870faf2 100644 --- a/slideshows/appam-2026/slides/LineRugs.tsx +++ b/slideshows/appam-2026/slides/LineRugs.tsx @@ -3,7 +3,7 @@ import { programLines, usd } from '../content'; const WIDTH = 1240; const HEIGHT = 560; const LEFT = 330; -const RIGHT = 190; +const RIGHT = 240; const AXIS_MIN = -3_000; const AXIS_MAX = 19_000; const ROW_TOP = 8; @@ -23,13 +23,13 @@ const LABELS: Record = { const x = (value: number) => LEFT + ((value - AXIS_MIN) / (AXIS_MAX - AXIS_MIN)) * (WIDTH - LEFT - RIGHT); -function endLabel(values: { code: string; value: number }[], value: number) { +export function endLabel(values: { code: string; value: number }[], value: number) { const tied = values.filter((entry) => entry.value === value); - const who = - tied.length > MAX_NAMED_STATES - ? `${tied.length} states` - : tied.map((entry) => entry.code).join(', '); - return `${who} ${usd(value)}`; + if (tied.length <= MAX_NAMED_STATES) { + return `${tied.map((entry) => entry.code).join(', ')} ${usd(value)}`; + } + const states = tied.filter((entry) => entry.code !== 'DC').length; + return `${states} states${states < tied.length ? ' and DC' : ''} ${usd(value)}`; } export default function LineRugs() { diff --git a/slideshows/appam-2026/slides/MicrodataSlide.tsx b/slideshows/appam-2026/slides/MicrodataSlide.tsx index 8536cbef..4a142c35 100644 --- a/slideshows/appam-2026/slides/MicrodataSlide.tsx +++ b/slideshows/appam-2026/slides/MicrodataSlide.tsx @@ -33,7 +33,7 @@ export default function MicrodataSlide() {

    - Each mark shows one state. We draw {instrument.households.toLocaleString('en-US')} households with children by + Each mark shows one of 51 jurisdictions. We draw {instrument.households.toLocaleString('en-US')} households with children by weight from Populace US, our default microdata, and run the same families under each state’s{' '} {instrument.year} rules, using PolicyEngine US {modelVersion}. January SNAP eligibility, as a share of Supplemental Poverty Measure (SPM) units with children. Medicaid or CHIP eligibility, as a share of children diff --git a/slideshows/appam-2026/slides/ParametersSlide.tsx b/slideshows/appam-2026/slides/ParametersSlide.tsx index 103095bc..aa69126c 100644 --- a/slideshows/appam-2026/slides/ParametersSlide.tsx +++ b/slideshows/appam-2026/slides/ParametersSlide.tsx @@ -31,7 +31,16 @@ export default function ParametersSlide() { lifted its two-dependent cap.

    Ask for any date and you get the value in force on that date.

    -

    Open the file and check each value against the laws and forms it cites.

    +

    + Read this file and the rest of our parameters at{' '} + + github.com/PolicyEngine/policyengine-us + + . +

    diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx index 8ba119b3..f03343cf 100644 --- a/slideshows/appam-2026/slides/ReformSlide.tsx +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -42,9 +42,9 @@ export default function ReformSlide() {

    - On our{' '} + At{' '} - Child Poverty Impact Dashboard + policyengine.org/us/child-poverty-impact-dashboard , you can run child tax credit, EITC, SNAP and state tax credit reforms state by state.

    diff --git a/slideshows/appam-2026/slides/WhichLinesSlide.tsx b/slideshows/appam-2026/slides/WhichLinesSlide.tsx index 6f47a2fe..076afde4 100644 --- a/slideshows/appam-2026/slides/WhichLinesSlide.tsx +++ b/slideshows/appam-2026/slides/WhichLinesSlide.tsx @@ -18,7 +18,7 @@ export default function WhichLinesSlide() {

    - Each mark shows one state, for the family earning {usd(headlineEarnings)}. + Each mark shows one of 51 jurisdictions, for the family earning {usd(headlineEarnings)}.

    From 0129c2a13da940af61ef1c50e643045b34384bc6 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Mon, 2 Nov 2026 19:05:32 -0500 Subject: [PATCH 18/32] Trim the PDF to 16:9 and link the addresses it prints `bun run export` leaves a white strip 0.42 points tall under each image, which shows as a thin line beneath the dark footer on a projector. Each page holds one image, so its printed addresses can't be clicked or copied either. `analysis/finish_pdf.py` trims each page to the image and adds a link over each address, at positions measured on the preview at 1440x810. It stops if the export doesn't have the 12 pages and page size it expects. Tests check the trim, the rectangles' flip about the page top, and that the PDF's targets match the slides'. The global `a` rule in `app/globals.css` overrode the slides' `text-pe-teal` and `underline` classes, so slides 6 and 8 now underline their addresses inline. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/analysis/finish_pdf.py | 99 +++++++++++++++++++ .../appam-2026/analysis/test_finish_pdf.py | 79 +++++++++++++++ slideshows/appam-2026/content.test.ts | 27 +++++ slideshows/appam-2026/slides/EndSlide.tsx | 8 +- .../appam-2026/slides/ParametersSlide.tsx | 2 +- slideshows/appam-2026/slides/ReformSlide.tsx | 2 +- 6 files changed, 208 insertions(+), 9 deletions(-) create mode 100644 slideshows/appam-2026/analysis/finish_pdf.py create mode 100644 slideshows/appam-2026/analysis/test_finish_pdf.py diff --git a/slideshows/appam-2026/analysis/finish_pdf.py b/slideshows/appam-2026/analysis/finish_pdf.py new file mode 100644 index 00000000..32fd03e3 --- /dev/null +++ b/slideshows/appam-2026/analysis/finish_pdf.py @@ -0,0 +1,99 @@ +# /// script +# requires-python = ">=3.11" +# dependencies = ["pypdf>=6"] +# /// +"""Finish the exported deck for the room's laptop. + +`bun run export appam-2026 --size 1440x810 --scale 2` prints each slide as one +image and leaves a white strip under it, 0.42 points tall, because Chrome rounds the +page height up to 607.92 points. This script trims every page to the image and puts +clickable links over the addresses the slides print. + +Usage: uv run finish_pdf.py + +Measure LINKS again if a slide's layout changes: open ?slide=N&export=1 at 1440x810 +and read each element's getBoundingClientRect. +""" + +import sys + +from pypdf import PdfReader, PdfWriter +from pypdf.annotations import Link +from pypdf.generic import RectangleObject + +PAGE_WIDTH = 1080 +IMAGE_HEIGHT = 607.5 +SLIDES = 12 +POINTS_PER_PIXEL = PAGE_WIDTH / 1440 + +DECK = "https://policyengine.org/slides/appam-2026" + +# Slide number, then left, top, right and bottom in pixels at 1440x810, then the target. +LINKS = [ + ( + 6, + (864.16, 556.32, 1251.13, 579.82), + "https://github.com/PolicyEngine/policyengine-us/blob/main/policyengine_us/parameters/gov/states/ma/tax/income/credits/child_and_family/amount.yaml", + ), + ( + 8, + (87.32, 601.16, 545.53, 623.16), + "https://www.policyengine.org/us/child-poverty-impact-dashboard", + ), + (12, (331.52, 434.0, 472.9, 456.0), "https://www.policyengine.org"), + ( + 12, + (331.52, 506.0, 714.55, 528.0), + "https://github.com/PolicyEngine/policyengine-slides/tree/main/slideshows/appam-2026", + ), + (12, (331.52, 542.0, 525.98, 564.0), "mailto:max@policyengine.org"), + (12, (853.52, 319.0, 1049.52, 515.0), DECK), + (12, (794.55, 527.0, 1108.48, 555.0), DECK), +] + + +def link_rectangle(pixels, page_top): + """Convert a rectangle in screen pixels, measured from the top, to PDF points.""" + left, top, right, bottom = pixels + return ( + left * POINTS_PER_PIXEL, + page_top - bottom * POINTS_PER_PIXEL, + right * POINTS_PER_PIXEL, + page_top - top * POINTS_PER_PIXEL, + ) + + +def finish(source, destination): + reader = PdfReader(source) + if len(reader.pages) != SLIDES: + raise ValueError(f"expected {SLIDES} pages, found {len(reader.pages)}") + writer = PdfWriter(clone_from=reader) + tops = [] + for page in writer.pages: + box = page.mediabox + if float(box.width) != PAGE_WIDTH: + raise ValueError( + f"expected a page {PAGE_WIDTH} points wide, found {box.width}" + ) + strip = float(box.height) - IMAGE_HEIGHT + if not 0 <= strip < 1: + raise ValueError(f"expected a strip under one point, found {strip}") + # The image sits at the top of the page, so the strip comes off the bottom. + top = float(box.top) + trimmed = RectangleObject((box.left, top - IMAGE_HEIGHT, box.right, top)) + page.mediabox = trimmed + page.cropbox = trimmed + page.trimbox = trimmed + page.bleedbox = trimmed + page.artbox = trimmed + tops.append(top) + for slide, pixels, target in LINKS: + annotation = Link( + rect=link_rectangle(pixels, tops[slide - 1]), url=target, border=[0, 0, 0] + ) + writer.add_annotation(page_number=slide - 1, annotation=annotation) + writer.write(destination) + + +if __name__ == "__main__": + finish(sys.argv[1], sys.argv[2]) diff --git a/slideshows/appam-2026/analysis/test_finish_pdf.py b/slideshows/appam-2026/analysis/test_finish_pdf.py new file mode 100644 index 00000000..bbec9d22 --- /dev/null +++ b/slideshows/appam-2026/analysis/test_finish_pdf.py @@ -0,0 +1,79 @@ +"""The finished PDF must fill a 16:9 screen and keep each link on its printed address.""" + +import finish_pdf as fp +import pytest +from hypothesis import given +from hypothesis import strategies as st +from pypdf import PdfReader, PdfWriter + + +def exported(tmp_path, pages=fp.SLIDES, height=607.92, width=fp.PAGE_WIDTH): + path = tmp_path / "exported.pdf" + writer = PdfWriter() + for _ in range(pages): + writer.add_blank_page(width=width, height=height) + writer.write(path) + return path + + +def finished(tmp_path, **kwargs): + source = exported(tmp_path, **kwargs) + destination = tmp_path / "finished.pdf" + fp.finish(source, destination) + return PdfReader(destination) + + +def test_every_page_trims_to_16_by_9_from_the_bottom(tmp_path): + reader = finished(tmp_path) + assert len(reader.pages) == fp.SLIDES + for page in reader.pages: + for box in ( + page.mediabox, + page.cropbox, + page.trimbox, + page.bleedbox, + page.artbox, + ): + assert float(box.width) / float(box.height) == pytest.approx(16 / 9) + assert float(box.top) == pytest.approx(607.92) + + +def test_each_link_lands_on_its_slide_inside_the_page(tmp_path): + reader = finished(tmp_path) + found = [] + for number, page in enumerate(reader.pages, start=1): + for annotation in page.get("/Annots", []): + annotation = annotation.get_object() + left, bottom, right, top = (float(v) for v in annotation["/Rect"]) + assert page.mediabox.left <= left < right <= page.mediabox.right + assert page.mediabox.bottom <= bottom < top <= page.mediabox.top + found.append((number, annotation["/A"]["/URI"])) + assert found == [(slide, target) for slide, _, target in fp.LINKS] + + +def test_links_use_public_addresses(): + for _, _, target in fp.LINKS: + assert "vercel" not in target + assert target.startswith(("https://", "mailto:")) + + +@pytest.mark.parametrize( + "kwargs", + [{"pages": 11}, {"height": 612.0}, {"height": 600.0}, {"width": 1440}], +) +def test_an_unexpected_export_stops_the_script(tmp_path, kwargs): + with pytest.raises(ValueError): + finished(tmp_path, **kwargs) + + +coordinates = st.floats(min_value=0, max_value=810, allow_nan=False) + + +@given(coordinates, coordinates, st.floats(min_value=1, max_value=100)) +def test_a_rectangle_keeps_its_height_and_flips_about_the_page_top(top, left, tall): + page_top = 607.92 + bottom = top + tall + x0, y0, x1, y1 = fp.link_rectangle((left, top, left + 10, bottom), page_top) + assert y1 == pytest.approx(page_top - top * fp.POINTS_PER_PIXEL) + assert y1 - y0 == pytest.approx(tall * fp.POINTS_PER_PIXEL) + assert x1 - x0 == pytest.approx(10 * fp.POINTS_PER_PIXEL) diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index 6044c20b..82a0eb10 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -212,6 +212,33 @@ describe('derived chart data', () => { }); }); +describe('links', () => { + const finishPdf = read('analysis/finish_pdf.py'); + + it('sends the PDF where each slide sends the live page', () => { + for (const slide of ['ParametersSlide', 'ReformSlide', 'EndSlide']) { + const targets = [...read(`slides/${slide}.tsx`).matchAll(/href[=:]\s*["']([^"']+)["']/g)].map((match) => match[1]); + expect(targets.length).toBeGreaterThan(0); + for (const target of targets) { + expect(finishPdf).toContain(target); + } + } + }); + + it('prints each address the PDF links to, on the slide that links to it', () => { + const printed: Record = { + ParametersSlide: ['github.com/PolicyEngine/policyengine-us'], + ReformSlide: ['policyengine.org/us/child-poverty-impact-dashboard'], + EndSlide: ['policyengine.org', 'github.com/PolicyEngine/policyengine-slides', 'max@policyengine.org', 'policyengine.org/slides/appam-2026'], + }; + for (const [slide, addresses] of Object.entries(printed)) { + for (const address of addresses) { + expect(read(`slides/${slide}.tsx`)).toContain(address); + } + } + }); +}); + describe('Texas interaction', () => { it('explains the change in net resources with the subsidy and SNAP', () => { const total = interaction.subsidy + interaction.snapChange; diff --git a/slideshows/appam-2026/slides/EndSlide.tsx b/slideshows/appam-2026/slides/EndSlide.tsx index 2ec6e3cc..8bc5f07f 100644 --- a/slideshows/appam-2026/slides/EndSlide.tsx +++ b/slideshows/appam-2026/slides/EndSlide.tsx @@ -23,13 +23,7 @@ export default function EndSlide() {
    {links.map(({ label, href }) => (

    - {href ? ( - - {label} - - ) : ( - label - )} + {href ? {label} : label}

    ))}
    diff --git a/slideshows/appam-2026/slides/ParametersSlide.tsx b/slideshows/appam-2026/slides/ParametersSlide.tsx index aa69126c..60f912b1 100644 --- a/slideshows/appam-2026/slides/ParametersSlide.tsx +++ b/slideshows/appam-2026/slides/ParametersSlide.tsx @@ -34,7 +34,7 @@ export default function ParametersSlide() {

    Read this file and the rest of our parameters at{' '} github.com/PolicyEngine/policyengine-us diff --git a/slideshows/appam-2026/slides/ReformSlide.tsx b/slideshows/appam-2026/slides/ReformSlide.tsx index f03343cf..33914144 100644 --- a/slideshows/appam-2026/slides/ReformSlide.tsx +++ b/slideshows/appam-2026/slides/ReformSlide.tsx @@ -43,7 +43,7 @@ export default function ReformSlide() {

    At{' '} - + policyengine.org/us/child-poverty-impact-dashboard , you can run child tax credit, EITC, SNAP and state tax credit reforms state by state. From 7ae1a4d03c270163f8ecc066263ba1b2169d131f Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 3 Nov 2026 01:54:13 -0500 Subject: [PATCH 19/32] Resolve the round 3 review of the APPAM deck Slides: - Slide 3: name the $18,000 child care bill and Alaska's payments, and enlarge the legend and state codes. - Slide 4: label the state line "State and local, credits minus taxes". - Slide 5: name the median rate's $500 step and the 51 jurisdictions. - Slide 6: cite the 2023 Form 1 and the 2023 session law, and drop the any-date sentence that slide 10 contradicted. - Slide 7: link the TAXSIM interface and name both states tied at the low end. - Slide 10: count subsidies from August 2026 only and drop the Wisconsin line. - Slide 11: rerun the Axiom engine at $2,500 a month, where PolicyEngine US gives the family Georgia SNAP, drop the late-reporting row and link axiom.org. Checks: - finish_pdf.py stops when a link lands off its address in the export, refuses a finished file, keeps the export's header and names the deck and its author. - standard_errors.py holds the clustered error formulas, tested against worked values and a delete-one-household jackknife. - content.test.ts maps each PDF link to its slide through config.ts. Co-Authored-By: Claude Opus 5.5 --- slideshows/appam-2026/EVIDENCE.md | 49 +++++---- slideshows/appam-2026/SPEAKER-NOTES.md | 42 ++++---- .../appam-2026/analysis/axiom_ga_3612.py | 21 ++-- slideshows/appam-2026/analysis/finish_pdf.py | 75 +++++++++++-- slideshows/appam-2026/analysis/household.py | 5 +- .../analysis/massachusetts_precision.py | 17 +-- .../appam-2026/analysis/standard_errors.py | 18 ++++ .../appam-2026/analysis/test_finish_pdf.py | 102 ++++++++++++++---- .../analysis/test_massachusetts_precision.py | 48 --------- .../analysis/test_standard_errors.py | 74 +++++++++++++ slideshows/appam-2026/content.test.ts | 77 +++++++++---- slideshows/appam-2026/content.ts | 15 ++- slideshows/appam-2026/data/axiom_ga_3612.json | 99 +---------------- slideshows/appam-2026/slides/AxiomSlide.tsx | 14 ++- slideshows/appam-2026/slides/CliffsSlide.tsx | 4 +- slideshows/appam-2026/slides/FamilyChart.tsx | 2 +- .../appam-2026/slides/InstrumentRugs.tsx | 5 +- slideshows/appam-2026/slides/LineRugs.tsx | 12 ++- .../appam-2026/slides/MicrodataSlide.tsx | 16 +-- .../appam-2026/slides/OneFamilySlide.tsx | 16 +-- .../appam-2026/slides/ParametersSlide.tsx | 9 +- slideshows/appam-2026/slides/ReformSlide.tsx | 4 +- slideshows/appam-2026/slides/ScopeSlide.tsx | 11 +- 23 files changed, 427 insertions(+), 308 deletions(-) create mode 100644 slideshows/appam-2026/analysis/standard_errors.py delete mode 100644 slideshows/appam-2026/analysis/test_massachusetts_precision.py create mode 100644 slideshows/appam-2026/analysis/test_standard_errors.py diff --git a/slideshows/appam-2026/EVIDENCE.md b/slideshows/appam-2026/EVIDENCE.md index 5ed3515c..e32d90a4 100644 --- a/slideshows/appam-2026/EVIDENCE.md +++ b/slideshows/appam-2026/EVIDENCE.md @@ -18,41 +18,41 @@ Each claim on a slide traces to a computed result, to code at a named commit, or | 4 | Texas: $15,791 subsidy, −$5,598 SNAP, +$10,193 net resources | `analysis/interaction.py` → `data/interaction.json` | | 5 | Maryland −$15,343 at $97,500, Iowa −$14,967 at $44,000, Idaho −$14,511 at $48,000, the three largest drops of the 51 | `data/charts.json`, `cliff_ranking`, which records each line's change in every state | | 5 | In 46 of 51 jurisdictions, the largest drop comes from losing the subsidy | `cliff_ranking`. The family loses TANF at the largest drop in Massachusetts and New Hampshire, and WIC in Maine, New Mexico and Vermont. | -| 5 | Median state's total effective marginal tax rate: 63.6% at $30,000, 24.6% at $60,000 | `data/charts.json`, `emtr`: one minus the change in net resources over a $500 raise | +| 5 | Median total effective marginal tax rate on a $500 raise, across 51 jurisdictions: 63.6% at $30,000, 24.6% at $60,000 | `data/charts.json`, `emtr`: one minus the change in net resources over a $500 raise | | 7 | SNAP eligibility 17.5% in Arkansas and Tennessee to 38.3% in Maryland, among SPM units with children; child Medicaid or CHIP 39.8% in Idaho to 66.1% in Hawaii | `analysis/sample_families.py` → `analysis/simulated_instrument.py` → `data/simulated_instrument.json` | | 8 | State cost, 29.4% of households would gain, none would lose, $169 million off the SPM poverty gap | `analysis/massachusetts_reform.py` → `data/massachusetts_reform.json`: `state_cost` holds the fall in `state_income_tax`, and `household_gain` holds the change in `household_net_income` | | 8 | 36,491 Massachusetts households, effective sample of about 395, 2026 child poverty rate falls 0.2 points | `data/massachusetts_reform.json`: `household_records`, `effective_households` (Kish), `baseline_child_poverty`, `reformed_child_poverty` | | 10 | Effective sample in the median state; child poverty estimates miss Census's by 3.2 points on average, and 29 of 51 jurisdictions fall outside Census's margin | `analysis/state_baseline.py` → `data/state_baseline.json`, compared with `data/census_child_spm.json` | -| 11 | $666.67 deduction on time, $0 late, refusal for November 2019 | `analysis/axiom_ga_3612.py` → `data/axiom_ga_3612.json` | +| 11 | $500.00 deduction on $2,500.00 of monthly earnings, refusal for November 2019 | `analysis/axiom_ga_3612.py` → `data/axiom_ga_3612.json`. At $30,000 a year, we estimate $6,363 of Georgia SNAP for slide 3's family (`data/family.json`) | -We dropped county and finer geography from the fixed-sample CSV and defined the `STATE_FIPS` dictionary before running slide 7's loop. The loop reproduced Arkansas and Maryland to every digit. We then ran slide 8's reform dictionary on the Massachusetts rows of Populace US's local-area file, and it returned the state cost in `data/massachusetts_reform.json` (`analysis/check_slide_code.py` → `data/slide_code_check.json`). On Populace US's default file, a Massachusetts cut and a national run returned the same household gain, gaining share and poverty rates (`data/massachusetts_reform_default.json`, `data/massachusetts_national_check.json`). +We dropped county and finer geography from the fixed-sample CSV and defined the `STATE_FIPS` dictionary before running slide 7's loop. The loop reproduced Arkansas and Maryland to every digit. We then ran slide 8's reform dictionary on the Massachusetts rows of Populace US's local-area file. It returned the state cost in `data/massachusetts_reform.json` (`analysis/check_slide_code.py` → `data/slide_code_check.json`). On Populace US's default file, a Massachusetts cut and a national run returned the same household gain, gaining share and poverty rates (`data/massachusetts_reform_default.json`, `data/massachusetts_national_check.json`). ## The fixed sample -`analysis/sample_families.py` makes 3,000 draws from the Populace US households with someone under 18, with replacement and in proportion to household weight. When the script draws a household twice, it gives each entry a fresh ID and counts it once. The sample includes 2,075 distinct households. Slide 7's Medicaid or CHIP share covers children under 19, so an 18-year-old counts only when the household also holds someone younger. +`analysis/sample_families.py` makes 3,000 draws from the Populace US households with someone under 18, with replacement and in proportion to household weight. When the script draws a household twice, it writes two copies under fresh IDs, and each copy counts once. The sample includes 2,075 distinct households. Slide 7's Medicaid or CHIP share covers children under 19, so an 18-year-old counts only when the household also holds someone younger. -The script keeps input variables and IDs. It renames the four stored Social Security amounts to the variables the model reads as reported, and it leaves out `has_marketplace_health_coverage`, `is_spm_independent_minor_role` and 179 names that PolicyEngine US 2.90.2 does not define. When we restored those two variables, Tennessee, Oregon and Hawaii returned the same SNAP and child Medicaid or CHIP shares to every digit (`analysis/audit_sample_columns.py` → `data/sample_columns.json`). +The script keeps input variables and IDs. It renames the four stored Social Security amounts (retirement, disability, survivors and dependents) to the `_reported` inputs the model reads. It leaves out `has_marketplace_health_coverage`, `is_spm_independent_minor_role` and 179 names that PolicyEngine US 2.90.2 does not define. When we restored those two variables, the runs for Tennessee, Oregon and Hawaii returned the same SNAP and child Medicaid or CHIP shares to every digit (`analysis/audit_sample_columns.py` → `data/sample_columns.json`). `analysis/simulated_instrument.py` drops county and finer geography, sets one state on the whole file and reruns it. Each run records the state the model read, and mean employment income stays the same in all 51 runs. `content.test.ts` checks both. ## Massachusetts poverty -- We estimate that 101 person records cross the threshold under the doubled credit and that the 2026 child poverty rate falls 0.2 points, from 23.1% to 22.9%. -- That fall rests on 55 child records in 24 households. Holding weights fixed, we estimate household-clustered standard errors of 0.05 points on the fall, 3.8 points on the 2026 rate, $34 million on the fall in the poverty gap and $54 million on the state cost (`analysis/massachusetts_precision.py` → `data/massachusetts_precision.json`). They leave out what calibration adds. -- The file's 36,491 Massachusetts household records hold an effective sample of about 395, or 173 among households with children. Its 1,522 records from the Current Population Survey and tax data hold 67% of household weight. +- Under the doubled credit, we estimate that 101 person records cross the threshold. The 2026 child poverty rate falls 0.2 points, from 23.1% to 22.9%. +- That fall rests on 55 child records in 24 households. We estimate household-clustered standard errors of 0.05 points on the fall, 3.8 points on the 2026 rate, $34 million on the fall in the poverty gap and $54 million on the state cost (`analysis/massachusetts_precision.py` → `data/massachusetts_precision.json`). We treat the calibrated weights as known and don't capture errors that every record shares, such as a data-wide error in rent. +- The file's 36,491 Massachusetts household records hold an effective sample of about 395, or 173 among households with children. - We estimate a Massachusetts child poverty rate of 21.4% for 2025 and 23.1% for 2026. Census reports 12.8%, with a margin of error of 2.0 points, for the 2023 to 2025 average (P60-290 Table 18, as used in `slideshows/cpid-webinar-2026`). That difference ranks third among the 51 jurisdictions, after Delaware and Louisiana. -- On the default file, we estimate that Massachusetts would forgo $555 million, 29.8% of households would gain and the poverty gap would shrink by $167 million (`data/massachusetts_reform_default.json`). -- On the same records at policyengine-us 2.2.1, the version `policyengine.py` 6.11.1 pins, we estimate that Massachusetts would forgo $657 million, 34.1% of households would gain and the child poverty rate would fall 0.5 points, from 22.2% to 21.7% (`data/massachusetts_reform_221.json`). We changed the credit's formula between those versions. On a joint return, 2.2.1 counts a filer with a disability as a qualifying individual, and 2.90.2 counts a filer incapable of self-care. 2.90.2 also counts a dependent incapable of self-care at any age. We did not separate how much of the difference those changes explain. -- Nationally, we estimate 16.6% of children below the threshold in 2025, and Census reports 13.4% (`BaselineVsCensusSlide` in `slideshows/cpid-webinar-2026`). +- On the default file, we estimate that Massachusetts would forgo $555 million and 29.8% of households would gain. The poverty gap would shrink by $167 million (`data/massachusetts_reform_default.json`). +- On the same records at policyengine-us 2.2.1, the version `policyengine.py` 6.11.1 pins, we estimate that Massachusetts would forgo $657 million and 34.1% of households would gain. The child poverty rate would fall 0.5 points, from 22.2% to 21.7% (`data/massachusetts_reform_221.json`). We changed how the model computes the credit between those versions. On a joint return, 2.2.1 counts a filer with a disability as a qualifying individual, and 2.90.2 counts a filer incapable of self-care. 2.90.2 also counts a dependent incapable of self-care at any age. We did not separate how much of the difference those changes explain. +- Nationally, we estimate that 16.6% of children lived below the threshold in 2025, and Census reports 13.4% (`BaselineVsCensusSlide` in `slideshows/cpid-webinar-2026`). ## State baselines -`analysis/state_baseline.py` runs 2025 child SPM poverty for all 51 jurisdictions, one state at a time, on Populace US's local-area file at revision `populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z`. `policyengine.py` 6.11.1 selects that file for state regions (`region_datasets` in `src/policyengine/data/bundle/manifest.json`, and `docs/regions.md`), and the file here matches the manifest's SHA-256. +`analysis/state_baseline.py` computes 2025 child SPM poverty rates for all 51 jurisdictions, one state at a time, on Populace US's local-area file at revision `populace-us-2024-buildo-acs-local-766b7c7-20261017T030317Z`. `policyengine.py` 6.11.1 selects that file for state regions (`region_datasets` in `src/policyengine/data/bundle/manifest.json`, and `docs/regions.md`), and the file here matches the manifest's SHA-256. - Our rates differ from Census's 2023 to 2025 averages by 3.2 points on average and exceed Census's by 0.9 points on net. They exceed Census's in 30 of 51 jurisdictions. In 29 of 51 jurisdictions the difference exceeds Census's margin of error. - Effective samples of households with children run from 58 in Maine to 499 in California, with a median of 125. Across all households they run from 193 in Maryland to 1,146 in California, with a median of 280. -- `policyengine.py` leaves ACS group quarters out of SPM measures, and these runs keep them. Leaving them out moves no state's rate by more than 0.023 points (Connecticut) and moves Massachusetts's by 0.01 (`child_spm_percent_without_group_quarters` in `data/state_baseline.json`). -- Our rates match those in PolicyEngine/microcosm#1445 to within 0.000001 points in 48 of 51 jurisdictions (`data/microcosm_1445_rates.csv`), using the same file and model version. That run gives blank parent links the id −1, and ours gives them 0, the model's value for an unknown parent. California moves by 0.56 points, Delaware by 0.006 and Missouri by 0.00001. +- `policyengine.py` leaves ACS group quarters out of SPM measures, and we keep them. Leaving them out moves no state's rate by more than 0.023 points (Connecticut) and moves Massachusetts's by 0.01 (`child_spm_percent_without_group_quarters` in `data/state_baseline.json`). +- On the same file and model version, our rates match `acs_local` in PolicyEngine/microcosm#1445 at 247f1a88 (`docs/evidence/us-state-child-spm-2026-11/outputs/acs_eval.csv`) to within 0.000001 points in 48 of 51 jurisdictions (`data/microcosm_1445_rates.csv`). That run gives blank parent links the id −1, and ours gives them 0, the model's value for an unknown parent. California moves by 0.56 points, Delaware by 0.006 and Missouri by 0.00001. - On the default file, our rates differ from Census's by 4.8 points on average. They exceed Census's in 40 of 51 jurisdictions and fall outside its margin in 38. The median effective sample includes 75 households with children (`data/state_baseline_default.json`). ## The family @@ -62,11 +62,11 @@ The script keeps input variables and IDs. It renames the four stored Social Secu - One parent, 30, with children aged 1 and 4. The parent works 40 hours for 52 weeks. Rent runs $1,200 a month and child care $1,500 a month. - `analysis/largest_counties.py` picks each state's largest county from the 2017 to 2021 American Community Survey. - We set `meets_ccdf_activity_test` to true, because a parent in a regular job meets it and the input defaults to false. -- We set `wi_works_placement` to `NONE`, because the input defaults to a Community Service Job. +- We set `wi_works_placement` to `NONE`. Otherwise we place a family with no child under 1 and no third-trimester pregnancy in a Community Service Job. - $40,000 over 2,080 hours comes to $19.23 an hour. On the Department of Labor's table, updated July 1, 2026, DC sets the highest basic rate, at $18.40 (dol.gov/agencies/whd/minimum-wage/state). The Department of Labor excludes local minimums, which can run higher. -- The sweeps vary earnings and hold hours at 40 a week, so at $30,000 the family earns $14.42 an hour, less than the basic rate in 17 states and DC on that table. +- We vary earnings in the sweeps and hold hours at 40 a week. At $30,000, the family earns $14.42 an hour, less than the basic rate in 17 states and DC on that table. -We compute net resources as market income plus SNAP, WIC, TANF, the child care subsidy and other benefits, minus federal and state taxes net of credits and the child care bill. We count state payroll taxes and paid leave contributions on the state line. +We compute net resources as market income plus SNAP, WIC, TANF, the child care subsidy and other benefits. From that we subtract federal, state and local taxes net of credits, and the $18,000 child care bill. We count local taxes, state payroll taxes and paid leave contributions on the state line. `analysis/test_invariants.py` reconciles the total with `spm_unit_net_income` and with `household_net_income` over random states and earnings. @@ -81,12 +81,12 @@ We count all four payments in net resources in every state. ## Why states land where they do -- **Ohio:** Ohio sets the initial limit at 145% of the guideline (`oh_ccap_fpg`), and enrolled families stay eligible to 300%. The $40,000 new applicant falls between them. +- **Ohio:** Ohio sets the initial limit at 145% of the federal poverty guideline (`oh_ccap_fpg`), and enrolled families stay eligible to 300%. The $40,000 new applicant falls between them. - **Nevada:** Nevada sets its intake limit at 41% of state median income, about $34,674, and its renewal limit at 49%. -- **Oklahoma:** we use the default one-star rate, $555 a month. +- **Oklahoma:** at its default one-star center rates, Oklahoma pays $555 a month after the family's copay. - **South Dakota:** we model `sd_cca` from August 2026, so $7,500 equals five months at $1,500. - **Provider tier:** we leave each state at its default, its base or lowest tier. -- **Alaska:** other payments total $3,600, from a $1,000 dividend per person and $200 of energy relief per person. +- **Alaska:** Alaska pays each person a $1,000 dividend and $200 of energy relief, $3,600 for this family (`gov.states.ak.dor.permanent_fund_dividend` and `energy_relief`). ## Mechanisms @@ -96,15 +96,14 @@ We count all four payments in net resources in every state. | 4 | Congress lets SNAP households deduct their care costs | `snap_dependent_care_deduction`, citing 7 U.S.C. 2014(e)(3), reads post-subsidy `childcare_expenses` | | 6 | Massachusetts credit values, dates and citations | `parameters/gov/states/ma/tax/income/credits/child_and_family/amount.yaml` and `dependent_cap.yaml` | | 7 | Our TAXSIM-compatible interface runs 2021 onward through PolicyEngine and earlier years through TAXSIM35 | `StitchedRunner.PE_MIN_YEAR = 2021` in `policyengine_taxsim/runners/stitched_runner.py` at the policyengine-taxsim commit above | -| 8 | Child tax credit, EITC, SNAP and state tax credit reforms, state by state | `getReformOptionsForState` in `frontend/lib/state-programs.ts` at the dashboard commit above returns federal child tax credit and EITC options and SNAP options in all 51 jurisdictions, and state EITC options in 43: the 42 with an income tax, and Washington | +| 8 | Child tax credit, EITC, SNAP and state tax credit reforms, state by state | `getReformOptionsForState` in `frontend/lib/state-programs.ts` at the dashboard commit above returns federal child tax credit and EITC options and SNAP options in all 51 jurisdictions, state EITC options in 43 (the 42 with an income tax, and Washington) and state child tax credit options in 17 for 2026 | | 9 | Project through 2035 | The PolicyEngine API's US metadata lists years 2022 to 2035 | -| 10 | 46 jurisdictions through 2023, 48 in 2024, 50 in 2025, 51 from August 2026 | `gov.hhs.ccdf.child_care_subsidy_programs` lists 51 from 2025, missing DC, NC, OR, TN and TX through 2023, and DC, TN and TX in 2024. `sd_cca` returns zero until `uses_weekly_rates` turns on in August 2026 | -| 10 | Copy back to 2015 | `backdate_parameters(first_instant="2015-01-01")` in `system.py`, `FIRST_MODELED_YEAR = 2015` in `tools/parameters.py` | -| 10 | Wisconsin's schedules | Wisconsin Shares' rate parameters start on 2025-10-01, and the model returns subsidies of $13,258 in 2021 and $14,024 in 2023 (`analysis/wisconsin_schedules.py` → `data/wisconsin_schedules.json`) | +| 10 | All 51 jurisdictions from August 2026 | `gov.hhs.ccdf.child_care_subsidy_programs` lists 51 from 2025. We model Illinois's program from July 2025, Florida's from October 2025, and Arkansas's and Ohio's from November 2025 (each program's `in_effect`), and South Dakota's from August 2026, when `uses_weekly_rates` turns on | +| 10 | Copy back to 2015 | `system.py` calls `backdate_parameters(self.parameters, first_instant=f"{FIRST_MODELED_YEAR}-01-01")`, and `tools/parameters.py` sets `FIRST_MODELED_YEAR = 2015`. Wisconsin Shares' rate parameters start on 2025-10-01, so we compute subsidies of $13,258 in 2021 and $14,024 in 2023 (`analysis/wisconsin_schedules.py` → `data/wisconsin_schedules.json`) | | 10 | We take earnings and paid leave benefits as inputs | The minimum wage appears only in eligibility tests. `pfml_benefits` holds benefits received, and the model computes employee contributions through `employee_state_payroll_tax` | | 10 | Labor supply | Elasticities default to zero | | 10 | Medicaid limits | `is_medically_needy_for_medicaid` documentation | -| 10 | Take-up | `takes_up_snap_if_eligible` defaults to true, and Populace US carries `takes_up_*` flags | +| 10 | Take-up | All 18 `takes_up_*` variables default to true, and Populace US carries `takes_up_*` flags | ## The other tools diff --git a/slideshows/appam-2026/SPEAKER-NOTES.md b/slideshows/appam-2026/SPEAKER-NOTES.md index 599e6b2f..29a87e85 100644 --- a/slideshows/appam-2026/SPEAKER-NOTES.md +++ b/slideshows/appam-2026/SPEAKER-NOTES.md @@ -7,9 +7,9 @@ Twelve slides in about 11 minutes. ## Before presenting - Send the PDF to Cynthia's team by Wednesday evening, November 4, for the room's laptop. -- Keep these notes on your phone. -- If the room's laptop fails, open policyengine.org/slides/appam-2026 on yours and press F for fullscreen. -- Check the slide order against what Cynthia, Tara and Linda present first. If they have already covered slide 9's table, give it one sentence. +- Before Thursday, scan slide 12's QR code and check that it opens this deck. +- Keep these notes on your phone and the PDF on your laptop. If the room's laptop fails, present from yours. +- If Cynthia, Tara or Linda cover slide 9's tools before you speak, give that slide one sentence. ## 1. PolicyEngine for state policy research (0:00, 15 seconds) @@ -19,13 +19,13 @@ Thanks, Cynthia, for putting this session together, and thanks to Tara and Linda PolicyEngine is a nonprofit that provides free, open source software to compute the impact of public policy. We calculate federal and state income taxes and credits and the main benefit programs for one household or a whole population. -Researchers use it three ways: they compare families, measure their own samples and estimate reforms. +With it, researchers can compare families, measure their own samples and estimate reforms. ## 3. One family ends up with $27,711 in Ohio and $54,345 in Alaska (1:15, 1 minute 15 seconds) Take one family under each state's 2026 rules. One parent works full time and earns $40,000, has two young children, and pays $1,500 a month for child care. We put them in each state's largest county and treat them as a new applicant for every program. -The gray base shows earnings after taxes and credits, minus the full child care bill, and benefits sit above it. Ohio and Nevada turn this family down for child care help because both set a lower income limit for new applicants than for families already enrolled. +The gray base shows earnings after taxes and credits, minus the full child care bill, and benefits sit above it. Ohio and Nevada turn this family down for child care help. Both set a lower income limit for new applicants than for families already enrolled. Alaska pays each person a $1,000 dividend and $200 of energy relief, $3,600 for this family. The gap between the top and bottom state moves with earnings: $16,404 at $30,000 and $21,670 at $60,000. @@ -33,52 +33,52 @@ The gap between the top and bottom state moves with earnings: $16,404 at $30,000 Each mark shows one state on one line of the family's budget. Federal taxes and credits move least across states, and states drive the rest through child care subsidies, SNAP options and their own taxes. -Run Texas with and without its subsidy, and the lines interact: the family gets $15,791 in child care help and loses $5,598 of SNAP. Congress lets SNAP households deduct their care costs, so this family deducts less when Texas pays for care. The family ends up $10,193 ahead. +With Texas's subsidy, the family gets $15,791 in child care help and loses $5,598 of SNAP. Congress lets SNAP households deduct their care costs, so this family deducts less when Texas pays for care. The family ends up $10,193 ahead. ## 5. This family loses up to $15,343 at a benefit cliff, at earnings that vary by state (3:45, 1 minute) -Each gray line follows one state as earnings rise. In Maryland, Iowa and Idaho, a $500 raise takes the family past the child care income limit, and the family loses between $14,511 and $15,343. In 46 of 51 jurisdictions, the family's largest drop comes from losing the subsidy. +Each gray line follows one state as earnings rise. In Maryland, Iowa and Idaho, the family crosses the child care income limit after a $500 raise. It then loses between $14,511 and $15,343. In 46 of 51 jurisdictions, the family's largest drop comes from losing the subsidy. Across states, the median total effective marginal tax rate falls from 63.6% at $30,000 of earnings to 24.6% at $60,000. -## 6. Parameters record when each value took effect and cite their sources (4:45, 45 seconds) +## 6. We record when each value took effect and cite its sources (4:45, 45 seconds) -Here's one parameter, the amount per dependent in the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. We record each value's effective date and cite the session law and tax forms behind it. Ask for any date and you get the value in force on that date. +Look at one parameter, the amount per dependent in the Massachusetts child and family credit. The Legislature raised it to $310 for 2023 and $440 for 2024. We record each value's effective date and cite the session law and tax forms behind it. ## 7. Build simulated eligibility measures on your own microdata (5:30, 1 minute 30 seconds) -To build a simulated instrument, read your survey or administrative file, set the state and run it. Keep the people and their incomes fixed, change only the state, and any difference between runs comes from that state's rules. +To build a simulated eligibility measure, read your survey or administrative file, set the state and run it. Keep the people and their incomes fixed and change only the state. Any difference between runs then comes from the rules in that state. We drew 3,000 households with children from Populace US, our default microdata, and ran them through each state's 2024 rules. The same families pass SNAP's tests at rates from 17.5% in Arkansas and Tennessee to 38.3% in Maryland. Their children qualify for Medicaid or CHIP at rates from 39.8% in Idaho to 66.1% in Hawaii. -If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface takes the same files. It runs tax years 2021 onward through PolicyEngine and sends earlier years to TAXSIM35. +If your pipeline already runs on TAXSIM, our TAXSIM-compatible interface at policyengine.org/us/taxsim takes the same files. It runs tax years 2021 onward through PolicyEngine and sends earlier years to TAXSIM35. ## 8. We estimate Massachusetts would forgo $577 million if lawmakers doubled its child and family credit (7:00, 1 minute 30 seconds) -Take the parameter from a moment ago and double it, from $440 to $880 per dependent. We estimate Massachusetts would forgo $577 million in 2026. Of Massachusetts households, 29.4% would gain and none would lose, and under the Supplemental Poverty Measure, or SPM, the poverty gap would shrink by $169 million. +Take the parameter from a moment ago and double it, from $440 to $880 per dependent. We estimate Massachusetts would forgo $577 million in 2026. Of Massachusetts households, 29.4% would gain and none would lose. Under the Supplemental Poverty Measure, or SPM, the poverty gap would shrink by $169 million. -We ran this on the Populace US local-area file, which we use for state and district results. It holds 36,491 Massachusetts households, an effective sample of about 395. We estimate 21.4% of the state's children below the threshold in 2025, and Census reports 12.8% for 2023 to 2025. We estimate that the 2026 child poverty rate would fall by about 0.2 points under the doubled credit, from that high baseline. +We ran this on the Populace US local-area file, which we use for state and district results. It holds 36,491 Massachusetts households, an effective sample of about 395. We estimate that 21.4% of the state's children lived below the SPM threshold in 2025, and Census reports 12.8% for 2023 to 2025. We estimate that the 2026 child poverty rate would fall by about 0.2 points under the doubled credit. On our Child Poverty Impact Dashboard, you can run child tax credit, EITC, SNAP and state tax credit reforms state by state. ## 9. Research questions and the tools that answer them (8:30, 45 seconds) -The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families, and the Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. +The Welfare Rules Database, the CCDF Policies Database and the Policy Tracking System record what states chose. The State Safety Net Interactive and the Policy Impact Calculator show what those choices mean for families. The Interactive reaches back to 2001. State of the Safety Net shows who qualifies and takes part. -With PolicyEngine, you can change any parameter, write new rules, run your own microdata, read and edit every line of code, and project current law through 2035. +With PolicyEngine, you can change any parameter, write new rules and run your own microdata. You can read and edit every line of code and project current law through 2035. -## 10. What these results leave out (9:15, 45 seconds) +## 10. What we leave out (9:15, 1 minute) -We model child care subsidies in all 51 jurisdictions from August 2026. We copy early parameter values back to 2015, so validate any historical run before you use it. We take earnings and paid leave benefits as inputs and hold them fixed when you change minimum wage or paid leave law. We hold hours fixed unless you set a labor supply response. +We model child care subsidies in all 51 jurisdictions from August 2026. We copy each parameter's earliest value back to 2015, so check historical runs against each year's rules. We take earnings and paid leave benefits as inputs and hold them fixed when you change minimum wage or paid leave law. We hold hours fixed unless you set a labor supply response. -Our local-area file holds thin state samples, about 125 effective households with children in the median state. Our child poverty estimates on that file miss Census's 2023 to 2025 averages by 3.2 points on average, and 29 of 51 jurisdictions fall outside Census's margin. For state child poverty rates, bring your own microdata. +Our local-area file holds thin state samples, about 125 effective households with children in the median state. Our child poverty estimates on that file miss Census's 2023 to 2025 averages by 3.2 points on average. In 29 of 51 jurisdictions, they fall outside Census's margin of error. For state child poverty rates, bring your own microdata. -## 11. The Axiom Foundation encodes rules with their source text and dates (10:00, 1 minute) +## 11. The Axiom Foundation encodes rules with their source text and dates (10:15, 45 seconds) I also lead the Axiom Foundation. We publish open, machine-readable encodings of rules, starting with tax and benefit policy. Georgia's Division of Family and Children Services applies a 20% earned income deduction in section 3612 of its SNAP manual. We record the manual's words and effective date beside each value. -We ran this rule through the Axiom engine. A family earning about $40,000 a year who reports on time gets a $666.67 monthly deduction, and the same family reporting late gets nothing. Ask for November 2019, before the manual section we encoded took effect, and the engine refuses. +When we run this rule through the Axiom engine, a family earning $30,000 a year gets a $500 monthly deduction. Ask for November 2019, before the manual section we encoded took effect, and the engine refuses. ## 12. Thank you (11:00) -Scan the QR code to open this deck, and follow the GitHub link to read the code behind each chart. +Scan the QR code to open this deck. From its last slide, you can open the code behind each chart. diff --git a/slideshows/appam-2026/analysis/axiom_ga_3612.py b/slideshows/appam-2026/analysis/axiom_ga_3612.py index 4223151c..7cce670f 100644 --- a/slideshows/appam-2026/analysis/axiom_ga_3612.py +++ b/slideshows/appam-2026/analysis/axiom_ga_3612.py @@ -8,9 +8,9 @@ --output ga3612.compiled.json Then: python axiom_ga_3612.py ga3612.compiled.json -Writes data/axiom_ga_3612.json. The cases cover the $40,000 family in Georgia, -the same family when it reports earnings late, and a month before the rule's -December 2019 effective date. +Writes data/axiom_ga_3612.json. The cases cover slide 3's family at $30,000 a +year in Georgia, where PolicyEngine US gives it SNAP, and a month before the +rule's December 2019 effective date. """ import json @@ -24,11 +24,10 @@ "earned_income_deduction_allowed", "earned_income_deduction", ) -MONTHLY_EARNINGS = "3333.33" +MONTHLY_EARNINGS = "2500.00" CASES = ( - ("reported on time", "2026-06", False), - ("reported late", "2026-06", True), - ("before the effective date", "2019-11", False), + ("June 2026", "2026-06"), + ("before the effective date", "2019-11"), ) @@ -38,7 +37,7 @@ def month_bounds(month): return f"{month}-01", f"{month}-{end:02d}" -def request(month, late): +def request(month): start, end = month_bounds(month) interval = {"start": start, "end": end} return { @@ -57,7 +56,7 @@ def request(month, late): "entity": "Household", "entity_id": "family", "interval": interval, - "value": {"kind": "bool", "value": late}, + "value": {"kind": "bool", "value": False}, }, ], "relations": [], @@ -77,10 +76,10 @@ def main(engine, artifact): [engine, "capabilities"], capture_output=True, text=True, check=True ).stdout results = [] - for label, month, late in CASES: + for label, month in CASES: run = subprocess.run( [engine, "run-compiled", "--artifact", artifact], - input=json.dumps(request(month, late)), + input=json.dumps(request(month)), capture_output=True, text=True, check=False, diff --git a/slideshows/appam-2026/analysis/finish_pdf.py b/slideshows/appam-2026/analysis/finish_pdf.py index 32fd03e3..aa9a1476 100644 --- a/slideshows/appam-2026/analysis/finish_pdf.py +++ b/slideshows/appam-2026/analysis/finish_pdf.py @@ -1,18 +1,20 @@ # /// script # requires-python = ">=3.11" -# dependencies = ["pypdf>=6"] +# dependencies = ["pillow", "pypdf>=6"] # /// """Finish the exported deck for the room's laptop. -`bun run export appam-2026 --size 1440x810 --scale 2` prints each slide as one -image and leaves a white strip under it, 0.42 points tall, because Chrome rounds the -page height up to 607.92 points. This script trims every page to the image and puts -clickable links over the addresses the slides print. +`bun run export appam-2026 --url --size 1440x810 --scale 2` prints +each slide as one 607.5-point image on a 607.92-point page, so a 0.42-point white +strip sits under each image. This script trims every page to the image and puts +clickable links over the addresses the slides print. It stops if a link would land +off its address, and it names the deck and its author in the file. Usage: uv run finish_pdf.py -Measure LINKS again if a slide's layout changes: open ?slide=N&export=1 at 1440x810 -and read each element's getBoundingClientRect. +Measure LINKS again if a slide's layout changes. The viewer counts from 0, so open +slide n at ?slide=&export=1 at 1440x810 and read each element's +getBoundingClientRect. """ import sys @@ -27,6 +29,7 @@ POINTS_PER_PIXEL = PAGE_WIDTH / 1440 DECK = "https://policyengine.org/slides/appam-2026" +METADATA = {"/Title": "PolicyEngine for state policy research", "/Author": "Max Ghenis"} # Slide number, then left, top, right and bottom in pixels at 1440x810, then the target. LINKS = [ @@ -51,6 +54,14 @@ (12, (794.55, 527.0, 1108.48, 555.0), DECK), ] +# In pixels at 1440x810: how far inside its rectangle a link's ink may start or end +# across and down, and the blank space it needs on its left, as before a word. +SIDE = 3 +END = 8 +GAP = 4 +# Gray levels that separate ink from the slide behind it. +INK = 64 + def link_rectangle(pixels, page_top): """Convert a rectangle in screen pixels, measured from the top, to PDF points.""" @@ -63,13 +74,51 @@ def link_rectangle(pixels, page_top): ) +def off_its_address(picture, pixels): + """Whether a link's rectangle misses the ink of the address it should cover.""" + scale = picture.width / 1440 + left, top, right, bottom = pixels + + def crop(box): + return picture.crop(tuple(round(value * scale) for value in box)) + + # The slide behind the link: the commonest gray in a ring around its rectangle. + surround = crop((left - 4 * GAP, top - 4 * GAP, right + 4 * GAP, bottom + 4 * GAP)) + ring = [ + around - within + for around, within in zip(surround.histogram(), crop(pixels).histogram()) + ] + background = ring.index(max(ring)) + + def ink(box): + mask = crop(box).point( + lambda value: 255 if abs(value - background) > INK else 0 + ) + return mask.getbbox() + + inside = ink(pixels) + if inside is None: + return True + first, highest, last, lowest = (value / scale for value in inside) + return ( + first > SIDE + or right - left - last > SIDE + or highest > END + or bottom - top - lowest > END + or ink((left - GAP, top, left - 1, bottom)) is not None + ) + + def finish(source, destination): reader = PdfReader(source) if len(reader.pages) != SLIDES: raise ValueError(f"expected {SLIDES} pages, found {len(reader.pages)}") writer = PdfWriter(clone_from=reader) + writer.pdf_header = reader.pdf_header tops = [] - for page in writer.pages: + for number, page in enumerate(writer.pages, start=1): + if "/Annots" in page: + raise ValueError(f"page {number} already has links; finish the export") box = page.mediabox if float(box.width) != PAGE_WIDTH: raise ValueError( @@ -87,11 +136,21 @@ def finish(source, destination): page.bleedbox = trimmed page.artbox = trimmed tops.append(top) + stranded = [] + for slide, pixels, target in LINKS: + images = reader.pages[slide - 1].images + if len(images) != 1: + raise ValueError(f"expected one image on page {slide}, found {len(images)}") + if off_its_address(images[0].image.convert("L"), pixels): + stranded.append(f"slide {slide}: {target}") + if stranded: + raise ValueError("measure LINKS again for " + "; ".join(stranded)) for slide, pixels, target in LINKS: annotation = Link( rect=link_rectangle(pixels, tops[slide - 1]), url=target, border=[0, 0, 0] ) writer.add_annotation(page_number=slide - 1, annotation=annotation) + writer.add_metadata(METADATA) writer.write(destination) diff --git a/slideshows/appam-2026/analysis/household.py b/slideshows/appam-2026/analysis/household.py index 96fa1993..b8ffb8b0 100644 --- a/slideshows/appam-2026/analysis/household.py +++ b/slideshows/appam-2026/analysis/household.py @@ -72,8 +72,9 @@ def situation(state, earnings, axes=None, year=YEAR): "spm_unit_pre_subsidy_childcare_expenses": {year: CHILD_CARE_PER_MONTH * 12}, "has_heating_cooling_expense": {year: True}, # Both default to a value that misdescribes a parent in a regular job: - # the activity test defaults to not met, and Wisconsin Works places - # every family in a Community Service Job. + # the activity test defaults to not met, and the Wisconsin Works + # placement puts a family with no child under 1 and no third-trimester + # pregnancy in a Community Service Job. "meets_ccdf_activity_test": {year: True}, } if state == "WI": diff --git a/slideshows/appam-2026/analysis/massachusetts_precision.py b/slideshows/appam-2026/analysis/massachusetts_precision.py index df07c87f..270c8eb9 100644 --- a/slideshows/appam-2026/analysis/massachusetts_precision.py +++ b/slideshows/appam-2026/analysis/massachusetts_precision.py @@ -10,31 +10,16 @@ from importlib.metadata import version from pathlib import Path -import numpy as np from massachusetts_reform import MASSACHUSETTS_FIPS, PARAMETER, REFORMED, YEAR from policyengine_core.data import Dataset from policyengine_core.reforms import Reform from policyengine_us import Microsimulation +from standard_errors import ratio_se, total_se from state_baseline import DATA_YEAR, STATE_DATASET, load_state, state_file CHILD_AGE_LIMIT = 18 -def total_se(values, clusters): - """Standard error of a weighted total. The values already carry their weights.""" - _, index = np.unique(clusters, return_inverse=True) - totals = np.bincount(index, weights=values) - count = len(totals) - return float(np.sqrt(count / (count - 1) * np.sum((totals - totals.mean()) ** 2))) - - -def ratio_se(numerator, denominator, clusters): - """A ratio of weighted totals and its linearized standard error.""" - ratio = numerator.sum() / denominator.sum() - residuals = (numerator - ratio * denominator) / denominator.sum() - return float(ratio), total_se(residuals, clusters) - - def read(sim): return { "poor": sim.calculate( diff --git a/slideshows/appam-2026/analysis/standard_errors.py b/slideshows/appam-2026/analysis/standard_errors.py new file mode 100644 index 00000000..2eb7980a --- /dev/null +++ b/slideshows/appam-2026/analysis/standard_errors.py @@ -0,0 +1,18 @@ +"""Standard errors of weighted totals and ratios, clustered by household.""" + +import numpy as np + + +def total_se(values, clusters): + """Standard error of a weighted total. The values already carry their weights.""" + _, index = np.unique(clusters, return_inverse=True) + totals = np.bincount(index, weights=values) + count = len(totals) + return float(np.sqrt(count / (count - 1) * np.sum((totals - totals.mean()) ** 2))) + + +def ratio_se(numerator, denominator, clusters): + """A ratio of weighted totals and its linearized standard error.""" + ratio = numerator.sum() / denominator.sum() + residuals = (numerator - ratio * denominator) / denominator.sum() + return float(ratio), total_se(residuals, clusters) diff --git a/slideshows/appam-2026/analysis/test_finish_pdf.py b/slideshows/appam-2026/analysis/test_finish_pdf.py index bbec9d22..bed972e6 100644 --- a/slideshows/appam-2026/analysis/test_finish_pdf.py +++ b/slideshows/appam-2026/analysis/test_finish_pdf.py @@ -1,17 +1,51 @@ -"""The finished PDF must fill a 16:9 screen and keep each link on its printed address.""" +"""finish_pdf must trim every page box to 16:9, put each link on its address inside +its page, and stop on an unexpected export.""" import finish_pdf as fp import pytest from hypothesis import given from hypothesis import strategies as st +from PIL import Image, ImageDraw from pypdf import PdfReader, PdfWriter - - -def exported(tmp_path, pages=fp.SLIDES, height=607.92, width=fp.PAGE_WIDTH): +from pypdf.generic import NameObject, RectangleObject + +BOXES = ("/MediaBox", "/CropBox", "/TrimBox", "/BleedBox", "/ArtBox") + + +def exported( + tmp_path, + pages=fp.SLIDES, + height=607.92, + width=fp.PAGE_WIDTH, + across=0, + down=0, + past=0, +): + """Pages like Chrome's: one 1440x810 image hung from the top of a taller page, + with ink under each link. Slide 8's ink moves `across` pixels right and `down` + pixels down, and runs `past` pixels beyond each side, as a sentence with no space + around the address would.""" + images = [] + for number in range(1, pages + 1): + image = Image.new("RGB", (1440, 810), "white") + draw = ImageDraw.Draw(image) + for slide, (left, top, right, bottom), _ in fp.LINKS: + if slide != number: + continue + if slide == 8: + left, right = left + across - past, right + across + past + top, bottom = top + down, bottom + down + draw.rectangle((left, top, right, bottom), fill="black") + images.append(image) + drawn = tmp_path / "drawn.pdf" + images[0].save(drawn, save_all=True, append_images=images[1:], resolution=96) + writer = PdfWriter(clone_from=PdfReader(drawn)) + writer.pdf_header = "%PDF-1.4" + for page in writer.pages: + box = RectangleObject((0, fp.IMAGE_HEIGHT - height, width, fp.IMAGE_HEIGHT)) + for name in BOXES: + page[NameObject(name)] = box path = tmp_path / "exported.pdf" - writer = PdfWriter() - for _ in range(pages): - writer.add_blank_page(width=width, height=height) writer.write(path) return path @@ -23,19 +57,14 @@ def finished(tmp_path, **kwargs): return PdfReader(destination) -def test_every_page_trims_to_16_by_9_from_the_bottom(tmp_path): +def test_every_page_box_trims_to_16_by_9_from_the_bottom(tmp_path): reader = finished(tmp_path) assert len(reader.pages) == fp.SLIDES for page in reader.pages: - for box in ( - page.mediabox, - page.cropbox, - page.trimbox, - page.bleedbox, - page.artbox, - ): - assert float(box.width) / float(box.height) == pytest.approx(16 / 9) - assert float(box.top) == pytest.approx(607.92) + for name in BOXES: + left, bottom, right, top = (float(value) for value in page[name]) + assert (right - left) / (top - bottom) == pytest.approx(16 / 9) + assert top == pytest.approx(fp.IMAGE_HEIGHT) def test_each_link_lands_on_its_slide_inside_the_page(tmp_path): @@ -51,6 +80,13 @@ def test_each_link_lands_on_its_slide_inside_the_page(tmp_path): assert found == [(slide, target) for slide, _, target in fp.LINKS] +def test_the_file_names_the_deck_and_its_author_and_keeps_the_export_header(tmp_path): + reader = finished(tmp_path) + assert reader.metadata["/Title"] == "PolicyEngine for state policy research" + assert reader.metadata["/Author"] == "Max Ghenis" + assert reader.pdf_header == PdfReader(tmp_path / "exported.pdf").pdf_header + + def test_links_use_public_addresses(): for _, _, target in fp.LINKS: assert "vercel" not in target @@ -59,21 +95,43 @@ def test_links_use_public_addresses(): @pytest.mark.parametrize( "kwargs", - [{"pages": 11}, {"height": 612.0}, {"height": 600.0}, {"width": 1440}], + [ + {"pages": 11}, + {"pages": 13}, + {"height": 612.0}, + {"height": 600.0}, + {"width": 1440}, + ], ) def test_an_unexpected_export_stops_the_script(tmp_path, kwargs): with pytest.raises(ValueError): finished(tmp_path, **kwargs) +@pytest.mark.parametrize( + "drawn", [{"across": -12}, {"across": 12}, {"down": 12}, {"past": 20}] +) +def test_a_link_off_its_address_stops_the_script(tmp_path, drawn): + with pytest.raises(ValueError, match="measure LINKS again"): + finished(tmp_path, **drawn) + + +def test_a_finished_file_cannot_be_finished_again(tmp_path): + finished(tmp_path) + with pytest.raises(ValueError, match="already has links"): + fp.finish(tmp_path / "finished.pdf", tmp_path / "twice.pdf") + + coordinates = st.floats(min_value=0, max_value=810, allow_nan=False) @given(coordinates, coordinates, st.floats(min_value=1, max_value=100)) -def test_a_rectangle_keeps_its_height_and_flips_about_the_page_top(top, left, tall): +def test_a_rectangle_keeps_its_size_and_flips_about_the_page_top(top, left, tall): + assert fp.POINTS_PER_PIXEL == 0.75 page_top = 607.92 bottom = top + tall x0, y0, x1, y1 = fp.link_rectangle((left, top, left + 10, bottom), page_top) - assert y1 == pytest.approx(page_top - top * fp.POINTS_PER_PIXEL) - assert y1 - y0 == pytest.approx(tall * fp.POINTS_PER_PIXEL) - assert x1 - x0 == pytest.approx(10 * fp.POINTS_PER_PIXEL) + assert x0 == pytest.approx(left * 0.75) + assert y1 == pytest.approx(page_top - top * 0.75) + assert y1 - y0 == pytest.approx(tall * 0.75) + assert x1 - x0 == pytest.approx(10 * 0.75) diff --git a/slideshows/appam-2026/analysis/test_massachusetts_precision.py b/slideshows/appam-2026/analysis/test_massachusetts_precision.py deleted file mode 100644 index 73373a71..00000000 --- a/slideshows/appam-2026/analysis/test_massachusetts_precision.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Standard errors must respect the sample's households.""" - -import massachusetts_precision as mp -import numpy as np -from hypothesis import given, settings -from hypothesis import strategies as st - -values = st.lists( - st.floats(min_value=-1e6, max_value=1e6), min_size=2, max_size=40 -).map(np.array) -positive = st.floats(min_value=0.01, max_value=1e6) - - -@settings(deadline=None) -@given(values) -def test_a_total_needs_no_negative_variance_and_scales_with_its_values(sample): - clusters = np.arange(len(sample)) - se = mp.total_se(sample, clusters) - assert se >= 0 - assert abs(mp.total_se(2 * sample, clusters) - 2 * se) <= 1e-6 * max(se, 1) - - -@settings(deadline=None) -@given(positive, st.integers(2, 40)) -def test_identical_households_leave_a_total_no_sampling_error(value, count): - assert mp.total_se(np.full(count, value), np.arange(count)) < 1e-6 * value - - -def test_a_household_counts_once_however_its_people_split_its_value(): - values = np.array([3.0, 1.0, 5.0]) - split = np.array([1.0, 2.0, 1.0, 5.0]) - assert mp.total_se(values, np.arange(3)) == mp.total_se( - split, np.array([0, 0, 1, 2]) - ) - - -@settings(deadline=None) -@given(values, positive) -def test_a_ratio_keeps_its_value_and_error_when_both_sides_scale(numerator, scale): - denominator = np.abs(numerator) + 1 - clusters = np.arange(len(numerator)) - ratio, se = mp.ratio_se(numerator, denominator, clusters) - scaled_ratio, scaled_se = mp.ratio_se( - scale * numerator, scale * denominator, clusters - ) - assert abs(ratio - numerator.sum() / denominator.sum()) < 1e-9 * max(abs(ratio), 1) - assert abs(scaled_ratio - ratio) < 1e-6 * max(abs(ratio), 1) - assert abs(scaled_se - se) < 1e-6 * max(se, 1) diff --git a/slideshows/appam-2026/analysis/test_standard_errors.py b/slideshows/appam-2026/analysis/test_standard_errors.py new file mode 100644 index 00000000..61963ea8 --- /dev/null +++ b/slideshows/appam-2026/analysis/test_standard_errors.py @@ -0,0 +1,74 @@ +"""Standard errors must respect the sample's households.""" + +import numpy as np +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st +from standard_errors import ratio_se, total_se + +values = st.lists( + st.floats(min_value=-1e6, max_value=1e6), min_size=2, max_size=40 +).map(np.array) +positive = st.floats(min_value=0.01, max_value=1e6) + + +def test_a_total_and_a_ratio_return_their_values_worked_by_hand(): + # Totals 1 and 3 around a mean of 2, scaled by 2/1: the square root of 4. + assert total_se(np.array([1.0, 3.0]), np.array([0, 1])) == pytest.approx( + 2.0, rel=1e-12 + ) + # Residuals of 1/6, -1/12 and -1/12 square to 1/24, and 3/2 of that is 1/16. + ratio, se = ratio_se( + np.array([2.0, 0.0, 1.0]), np.array([2.0, 1.0, 3.0]), np.array([0, 1, 2]) + ) + assert ratio == pytest.approx(0.5, rel=1e-12) + assert se == pytest.approx(0.25, rel=1e-12) + + +def test_a_ratio_error_agrees_with_a_jackknife_that_drops_one_household_at_a_time(): + rng = np.random.default_rng(5) + count = 300 + sizes = rng.integers(1, 5, size=count) + clusters = np.repeat(np.arange(count), sizes) + weights = np.repeat(rng.uniform(50, 500, size=count), sizes) + poor = np.repeat(rng.random(count) < 0.2, sizes) + numerator, denominator = weights * poor, weights + _, se = ratio_se(numerator, denominator, clusters) + numerators = np.bincount(clusters, weights=numerator) + denominators = np.bincount(clusters, weights=denominator) + dropped = (numerators.sum() - numerators) / (denominators.sum() - denominators) + jackknife = np.sqrt((count - 1) / count * np.sum((dropped - dropped.mean()) ** 2)) + assert se == pytest.approx(jackknife, rel=0.02) + + +@settings(deadline=None) +@given(values) +def test_a_total_needs_no_negative_variance_and_scales_with_its_values(sample): + clusters = np.arange(len(sample)) + se = total_se(sample, clusters) + assert se >= 0 + assert abs(total_se(2 * sample, clusters) - 2 * se) <= 1e-6 * max(se, 1) + + +@settings(deadline=None) +@given(positive, st.integers(2, 40)) +def test_identical_households_leave_a_total_no_sampling_error(value, count): + assert total_se(np.full(count, value), np.arange(count)) < 1e-6 * value + + +def test_a_household_counts_once_however_its_people_split_its_value(): + values = np.array([3.0, 1.0, 5.0]) + split = np.array([1.0, 2.0, 1.0, 5.0]) + assert total_se(values, np.arange(3)) == total_se(split, np.array([0, 0, 1, 2])) + + +@settings(deadline=None) +@given(values, positive) +def test_a_ratio_keeps_its_value_and_error_when_both_sides_scale(numerator, scale): + denominator = np.abs(numerator) + 1 + clusters = np.arange(len(numerator)) + ratio, se = ratio_se(numerator, denominator, clusters) + scaled_ratio, scaled_se = ratio_se(scale * numerator, scale * denominator, clusters) + assert abs(ratio - numerator.sum() / denominator.sum()) < 1e-9 * max(abs(ratio), 1) + assert abs(scaled_ratio - ratio) < 1e-6 * max(abs(ratio), 1) + assert abs(scaled_se - se) < 1e-6 * max(se, 1) diff --git a/slideshows/appam-2026/content.test.ts b/slideshows/appam-2026/content.test.ts index 82a0eb10..83edbf50 100644 --- a/slideshows/appam-2026/content.test.ts +++ b/slideshows/appam-2026/content.test.ts @@ -2,6 +2,7 @@ import { readFileSync } from 'node:fs'; import { join } from 'node:path'; import { describe, expect, it } from 'vitest'; import { + alaska, axiomRuns, childCareBill, cliffRanking, @@ -26,7 +27,6 @@ import { sweep, sweepEarnings, usd, - usdCents, } from './content'; import nationalCheck from './data/massachusetts_national_check.json'; import { SERIES } from './slides/FamilyChart'; @@ -210,30 +210,70 @@ describe('derived chart data', () => { expect(endLabel(line('federal_taxes'), 4_263)).toBe('27 states $4,263'); expect(endLabel(line('snap'), 0)).toBe('15 states $0'); }); + + it('names both states that tie at the low end of slide 7’s SNAP strip', () => { + const [snap, medicaid] = instrument.lines.map((line) => line.values); + const ends = (values: typeof snap) => [values[0].value, values[values.length - 1].value].map((value) => endLabel(values, value, percent)); + expect(ends(snap)).toEqual(['AR, TN 17.5%', 'MD 38.3%']); + expect(ends(medicaid)).toEqual(['ID 39.8%', 'HI 66.1%']); + }); + + it('pays Alaska’s family the dividend and energy relief for each person', () => { + const people = 1 + family.family.child_ages.length; + expect(alaska.payments).toBe(people * (alaska.dividend + alaska.energyRelief)); + for (const text of [notes, ledger]) { + for (const amount of [alaska.dividend, alaska.energyRelief, alaska.payments]) expect(text).toContain(usd(amount)); + } + }); }); describe('links', () => { const finishPdf = read('analysis/finish_pdf.py'); + const HREF = /href[=:]\s*["']([^"']+)["']/g; + const order = read('config.ts') + .match(/slides:\s*\[([^\]]*)\]/)![1] + .split(',') + .map((name) => name.trim()) + .filter(Boolean); + const slideSource = (name: string) => read(`slides/${name}.tsx`); + const deck = finishPdf.match(/^DECK = "([^"]+)"$/m)![1]; + const deckAddress = deck.replace(/^https:\/\//, ''); + const table = finishPdf.match(/^LINKS = \[([\s\S]*?)^\]/m)![1]; + const entries = [...table.matchAll(/\(\s*(\d+),\s*\([\d.,\s]+\),\s*(?:"([^"]+)"|DECK),?\s*\)/g)].map((match) => ({ + page: Number(match[1]), + target: match[2] ?? deck, + })); - it('sends the PDF where each slide sends the live page', () => { - for (const slide of ['ParametersSlide', 'ReformSlide', 'EndSlide']) { - const targets = [...read(`slides/${slide}.tsx`).matchAll(/href[=:]\s*["']([^"']+)["']/g)].map((match) => match[1]); - expect(targets.length).toBeGreaterThan(0); - for (const target of targets) { - expect(finishPdf).toContain(target); - } - } + it('reads every entry in the PDF link table', () => { + expect(Number(finishPdf.match(/^SLIDES = (\d+)$/m)![1])).toBe(order.length); + expect(entries).toHaveLength(table.match(/"(?:https|mailto):|\bDECK\b/g)!.length); + }); + + it('links each target on the page of the slide that sends the live page there, and nowhere else', () => { + const expected = order.flatMap((name, i) => { + const source = slideSource(name); + const targets = [...source.matchAll(HREF)].map((match) => match[1]); + if (source.includes(`'${deckAddress}'`)) targets.push(deck); + return targets.map((target) => `${i + 1} ${target}`); + }); + const actual = entries.map(({ page, target }) => `${page} ${target}`); + expect(new Set(actual)).toEqual(new Set(expected)); }); it('prints each address the PDF links to, on the slide that links to it', () => { const printed: Record = { ParametersSlide: ['github.com/PolicyEngine/policyengine-us'], + MicrodataSlide: ['policyengine.org/us/taxsim'], ReformSlide: ['policyengine.org/us/child-poverty-impact-dashboard'], - EndSlide: ['policyengine.org', 'github.com/PolicyEngine/policyengine-slides', 'max@policyengine.org', 'policyengine.org/slides/appam-2026'], + AxiomSlide: ['axiom.org'], + EndSlide: ['policyengine.org', 'github.com/PolicyEngine/policyengine-slides', 'max@policyengine.org', deckAddress], }; for (const [slide, addresses] of Object.entries(printed)) { + // Drop each link target, so an address counts only where the slide prints it. + const text = slideSource(slide).replace(HREF, ''); for (const address of addresses) { - expect(read(`slides/${slide}.tsx`)).toContain(address); + const escaped = address.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); + expect(text).toMatch(new RegExp(`(? { }); describe('Axiom run', () => { - it('pays 20% of earnings on time and nothing when reported late', () => { - expect(axiomRuns.onTime).toBeCloseTo(axiomRuns.monthlyEarnings * 0.2, 6); - expect(axiomRuns.late).toBe(0); + it('deducts 20% of earnings', () => { + expect(axiomRuns.deduction).toBeCloseTo(axiomRuns.monthlyEarnings * 0.2, 6); }); it('refuses a month before the effective date', () => { @@ -262,9 +301,11 @@ describe('Axiom run', () => { expect(axiomRuns.beforeEffectiveDate.stderr).toContain('2019-11-01'); }); - it('runs on monthly pay that the notes round to the family’s $40,000', () => { - expect(Math.round(axiomRuns.monthlyEarnings * 12)).toBe(headlineEarnings); - expect(notes).toContain(`about ${usd(headlineEarnings)} a year`); + it('runs slide 3’s family at earnings where PolicyEngine US gives it Georgia SNAP', () => { + const annual = axiomRuns.monthlyEarnings * 12; + expect(spreads.map((spread) => spread.earnings)).toContain(annual); + expect(family.states.GA.snap[family.earnings.indexOf(annual)]).toBeGreaterThan(0); + expect(notes).toContain(`earning ${usd(annual)} a year`); }); }); @@ -491,7 +532,7 @@ describe('typed figures', () => { usd(cliffs[2].drop), millions(massachusetts.cost), `about ${stateBaseline.medianEffective} effective households`, - usdCents(axiomRuns.onTime), + usd(axiomRuns.deduction), ]) { expect(notes).toContain(figure); } diff --git a/slideshows/appam-2026/content.ts b/slideshows/appam-2026/content.ts index b3735177..f02afabf 100644 --- a/slideshows/appam-2026/content.ts +++ b/slideshows/appam-2026/content.ts @@ -12,6 +12,16 @@ export const lowest = crossSection[0]; export const median = crossSection[Math.floor(crossSection.length / 2)]; export const highest = crossSection[crossSection.length - 1]; +const alaskaRow = crossSection.find((row) => row.code === 'AK'); +if (!alaskaRow) throw new Error('No Alaska row'); + +// gov.states.ak.dor.permanent_fund_dividend and energy_relief for 2026, per person. +export const alaska = { + dividend: 1_000, + energyRelief: 200, + payments: alaskaRow.other_benefits, +}; + export const headlineEarnings = charts.headline_earnings; export const family = charts.family; export const childCareBill = family.child_care_per_month * 12; @@ -132,9 +142,8 @@ function axiomDeduction(index: number) { export const axiomRuns = { monthlyEarnings: Number(axiomRun.monthly_earnings), - onTime: axiomDeduction(0), - late: axiomDeduction(1), - beforeEffectiveDate: axiomRun.results[2], + deduction: axiomDeduction(0), + beforeEffectiveDate: axiomRun.results[1], }; export function usd(value: number) { diff --git a/slideshows/appam-2026/data/axiom_ga_3612.json b/slideshows/appam-2026/data/axiom_ga_3612.json index 77973b17..9e964a1b 100644 --- a/slideshows/appam-2026/data/axiom_ga_3612.json +++ b/slideshows/appam-2026/data/axiom_ga_3612.json @@ -16,10 +16,10 @@ 2 ] }, - "monthly_earnings": "3333.33", + "monthly_earnings": "2500.00", "results": [ { - "case": "reported on time", + "case": "June 2026", "month": "2026-06", "exit_code": 0, "stdout": { @@ -46,7 +46,7 @@ "unit": "USD", "value": { "kind": "decimal", - "value": "666.666" + "value": "500" } }, "us-ga:policies/dfcs/snap/3612#earned_income_deduction_allowed": { @@ -78,7 +78,7 @@ "unit": "USD", "value": { "kind": "decimal", - "value": "666.666" + "value": "500" }, "source": "3612 Requirements; Basic Considerations", "executed_expression": "(if us-ga:policies/dfcs/snap/3612#earned_income_deduction_allowed then (us-ga:policies/dfcs/snap/3612#earned_income_deduction_rate[0] * input(\"total_countable_gross_earned_income\")) else 0)", @@ -117,97 +117,6 @@ }, "stderr": "" }, - { - "case": "reported late", - "month": "2026-06", - "exit_code": 0, - "stdout": { - "metadata": { - "relation_binding": "strict", - "requested_mode": "explain", - "actual_mode": "explain", - "fallback_reason": null - }, - "results": [ - { - "entity_id": "family", - "period": { - "period_kind": "month", - "start": "2026-06-01", - "end": "2026-06-30" - }, - "outputs": { - "us-ga:policies/dfcs/snap/3612#earned_income_deduction": { - "kind": "scalar", - "name": "earned_income_deduction", - "id": "us-ga:policies/dfcs/snap/3612#earned_income_deduction", - "dtype": "decimal", - "unit": "USD", - "value": { - "kind": "decimal", - "value": "0" - } - }, - "us-ga:policies/dfcs/snap/3612#earned_income_deduction_allowed": { - "kind": "judgment", - "name": "earned_income_deduction_allowed", - "id": "us-ga:policies/dfcs/snap/3612#earned_income_deduction_allowed", - "unit": null, - "outcome": "not_holds" - }, - "us-ga:policies/dfcs/snap/3612#earned_income_deduction_rate": { - "kind": "scalar", - "name": "earned_income_deduction_rate", - "id": "us-ga:policies/dfcs/snap/3612#earned_income_deduction_rate", - "dtype": "decimal", - "unit": null, - "value": { - "kind": "decimal", - "value": "0.2" - } - } - }, - "trace": { - "us-ga:policies/dfcs/snap/3612#earned_income_deduction": { - "kind": "scalar", - "name": "earned_income_deduction", - "id": "us-ga:policies/dfcs/snap/3612#earned_income_deduction", - "entity_id": "family", - "dtype": "decimal", - "unit": "USD", - "value": { - "kind": "decimal", - "value": "0" - }, - "source": "3612 Requirements; Basic Considerations", - "executed_expression": "(if us-ga:policies/dfcs/snap/3612#earned_income_deduction_allowed then (us-ga:policies/dfcs/snap/3612#earned_income_deduction_rate[0] * input(\"total_countable_gross_earned_income\")) else 0)", - "dependencies": [ - "us-ga:policies/dfcs/snap/3612#earned_income_deduction_allowed" - ], - "not_evaluated_dependencies": [ - { - "dependency": "us-ga:policies/dfcs/snap/3612#earned_income_deduction_rate", - "reason": "branch_not_selected" - } - ] - }, - "us-ga:policies/dfcs/snap/3612#earned_income_deduction_allowed": { - "kind": "judgment", - "name": "earned_income_deduction_allowed", - "id": "us-ga:policies/dfcs/snap/3612#earned_income_deduction_allowed", - "entity_id": "family", - "unit": null, - "outcome": "not_holds", - "source": "3612 Requirements; Basic Considerations", - "executed_expression": "((input(\"total_countable_gross_earned_income\") > 0) and not ((input(\"assistance_unit_failed_to_report_earned_income_timely\") == true)))", - "dependencies": [] - } - } - } - ] - }, - "stderr": "" - }, { "case": "before the effective date", "month": "2019-11", diff --git a/slideshows/appam-2026/slides/AxiomSlide.tsx b/slideshows/appam-2026/slides/AxiomSlide.tsx index 78d690b6..4b038222 100644 --- a/slideshows/appam-2026/slides/AxiomSlide.tsx +++ b/slideshows/appam-2026/slides/AxiomSlide.tsx @@ -1,6 +1,6 @@ import CodeBlock from '@/components/content/CodeBlock'; import Frame from './Frame'; -import { axiomEngineVersion, axiomRuns, usd, usdCents } from '../content'; +import { axiomEngineVersion, axiomRuns, usdCents } from '../content'; const RULESPEC = `- name: earned_income_deduction_rate kind: parameter @@ -21,10 +21,9 @@ const RULESPEC = `- name: earned_income_deduction_rate const runs = [ { - family: `Earns ${usdCents(axiomRuns.monthlyEarnings)} a month and reports on time`, - result: `Deduction of ${usdCents(axiomRuns.onTime)} a month`, + family: `Earns ${usdCents(axiomRuns.monthlyEarnings)} a month`, + result: `Deduction of ${usdCents(axiomRuns.deduction)} a month`, }, - { family: 'Same family, reports late', result: `Deduction of ${usd(axiomRuns.late)}` }, { family: 'Ask for November 2019', result: 'Engine refuses: the encoded section starts December 2019' }, ]; @@ -56,6 +55,13 @@ export default function AxiomSlide() {

    ))} +

    + Explore Axiom’s encodings at{' '} + + axiom.org + + . +

    diff --git a/slideshows/appam-2026/slides/CliffsSlide.tsx b/slideshows/appam-2026/slides/CliffsSlide.tsx index 3df28c63..8c469caa 100644 --- a/slideshows/appam-2026/slides/CliffsSlide.tsx +++ b/slideshows/appam-2026/slides/CliffsSlide.tsx @@ -8,7 +8,7 @@ export default function CliffsSlide() {

    - Net resources by earnings, one line per state, 2026 + Net resources by earnings, one line per state and DC, 2026

    @@ -35,7 +35,7 @@ export default function CliffsSlide() {

    - Total effective marginal tax rate, median state + Median total effective marginal tax rate on a $500 raise, across 51 jurisdictions