policyengine-us

name: policyengine-us description: | ALWAYS LOAD THIS SKILL FIRST before writing any PolicyEngine-US code. Contains the correct API patterns for household calculations and population simulations using the new policyengine package. Covers US federal and state taxes/benefits. Triggers: "what would", "how much would a", "benefit be", "eligible for", "qualify for", "single parent", "married couple", "family of", "household of", "if they earn", "earning $", "making $", "calculate benefits", "calculate taxes", "benefit for a", "what would I get", "what is the maximum", "what is the rate", "poverty line", "income limit", "benefit amount", "maximum benefit", "compare states", "TANF", "SNAP", "EITC", "CTC", "SSI", "WIC", "Section 8", "Medicaid", "ACA", "child tax credit", "earned income", "supplemental security", "housing voucher", "microsimulation", "population", "reform", "policy impact", "budgetary", "decile".

PolicyEngine-US

IMPORTANT: Always use the current year (2026) in calculations, not 2024 or 2025.

PolicyEngine-US models the US federal and state tax and benefit system.

For Users

What is PolicyEngine-US?

PolicyEngine-US is the "calculator" for US taxes and benefits. When you use policyengine.org/us, PolicyEngine-US runs behind the scenes.

What it models:

Federal taxes:

• Income tax (with standard/itemized deductions)
• Payroll tax (Social Security, Medicare)
• Capital gains tax

Federal benefits:

• Earned Income Tax Credit (EITC)
• Child Tax Credit (CTC)
• SNAP (food stamps)
• WIC, ACA premium tax credits
• Social Security, SSI, TANF

State programs (varies by state):

• State income tax (all 50 states + DC)
• State EITC, CTC
• State-specific benefits

See full list: https://policyengine.org/us/parameters

Understanding Variables

Income variables:

• employment_income - W-2 wages
• self_employment_income - 1099 income
• qualified_dividend_income - Dividends
• capital_gains - Capital gains

Tax variables:

• income_tax - Federal income tax
• state_income_tax - State income tax
• payroll_tax - FICA taxes

Benefit variables:

• eitc - Earned Income Tax Credit
• ctc - Child Tax Credit
• snap - SNAP benefits

Summary variables:

• household_net_income - Income after taxes and benefits
• household_tax - Total taxes
• household_benefits - Total benefits

For Analysts

Installation

uv pip install policyengine

Two Modes of Analysis

1. Household Calculations - Single household, quick answers
2. Population Simulations - Microsimulation, policy analysis at scale

1. Household Calculations

Use calculate_household_impact() with USHouseholdInput for quick calculations.

Basic Pattern

from policyengine.tax_benefit_models.us import (
    USHouseholdInput,
    calculate_household_impact,
)

household = USHouseholdInput(
    people=[
        {"age": 35, "employment_income": 50_000, "is_tax_unit_head": True},
    ],
    household={"state_code_str": "CA"},
    year=2026,
)
result = calculate_household_impact(household)

print(f"Income tax: ${result.tax_unit[0]['income_tax']:,.0f}")
print(f"Net income: ${result.household['household_net_income']:,.0f}")

US Entity Structure (6 entities)

The US has more entities than the UK due to different program structures:

• person - Individual people
• marital_unit - Married couples
• family - Family unit
• spm_unit - SPM unit (for SNAP, TANF, poverty measures)
• tax_unit - Tax filing unit (for income tax, EITC, CTC)
• household - Physical household

Single Filer

household = USHouseholdInput(
    people=[
        {"age": 30, "employment_income": 60_000, "is_tax_unit_head": True},
    ],
    household={"state_code_str": "CA"},
    year=2026,
)
result = calculate_household_impact(household)

Married Couple with Children

household = USHouseholdInput(
    people=[
        {"age": 35, "employment_income": 80_000, "is_tax_unit_head": True},
        {"age": 33, "employment_income": 40_000, "is_tax_unit_spouse": True},
        {"age": 8, "is_tax_unit_dependent": True},
        {"age": 5, "is_tax_unit_dependent": True},
    ],
    tax_unit={"filing_status": "JOINT"},
    household={"state_code_str": "NY"},
    year=2026,
)
result = calculate_household_impact(household)

print(f"EITC: ${result.tax_unit[0]['eitc']:,.0f}")
print(f"CTC: ${result.tax_unit[0]['ctc']:,.0f}")
print(f"SNAP: ${result.spm_unit[0]['snap']:,.0f}")

Accessing Results

# Person-level
employment_income = result.person[0]['employment_income']

# Tax unit level (income tax, credits)
income_tax = result.tax_unit[0]['income_tax']
eitc = result.tax_unit[0]['eitc']
ctc = result.tax_unit[0]['ctc']

# SPM unit level (means-tested benefits)
snap = result.spm_unit[0]['snap']
tanf = result.spm_unit[0]['tanf']

# Household level
net_income = result.household['household_net_income']

2. Population Simulations

Use Simulation with datasets for population-level analysis.

Loading Data

from policyengine.tax_benefit_models.us import (
    us_latest,
    ensure_datasets,
)

datasets = ensure_datasets(
    data_folder="./data",
    years=[2026],
)
dataset = datasets["enhanced_cps_2024_2026"]

Running Simulations

from policyengine.core import Simulation

simulation = Simulation(
    dataset=dataset,
    tax_benefit_model_version=us_latest,
)
simulation.ensure()

output = simulation.output_dataset.data
total_eitc = output.tax_unit['eitc'].sum()
total_snap = output.spm_unit['snap'].sum()

Policy Reforms

Parametric Reforms

from policyengine.core import Policy, ParameterValue
from datetime import datetime

param = us_latest.get_parameter("gov.irs.credits.ctc.amount.base_amount")

policy = Policy(
    name="CTC $5000",
    parameter_values=[
        ParameterValue(
            parameter=param,
            value=5000,
            start_date=datetime(2026, 1, 1),
        )
    ],
)

reform_sim = Simulation(
    dataset=dataset,
    tax_benefit_model_version=us_latest,
    policy=policy,
)
reform_sim.ensure()

Simulation Modifier Reforms

def expand_eitc(sim):
    """Expand EITC phase-out threshold."""
    sim.tax_benefit_system.parameters.get_child(
        "gov.irs.credits.eitc.phase_out.start"
    ).update(period="year:2026:10", value=25000)
    sim.tax_benefit_system.reset_parameter_caches()

policy = Policy(
    name="Expand EITC",
    simulation_modifier=expand_eitc,
)

Parameter Lookup

For quick parameter lookups:

from policyengine_us import CountryTaxBenefitSystem

params = CountryTaxBenefitSystem().parameters

# CTC amount
ctc = params.gov.irs.credits.ctc.amount.base_amount("2026-01-01")

# SNAP max (use .children["N"] for indexed params)
snap_max = params.gov.usda.snap.income.max_allotment.children["4"]("2026-01-01")

# State TANF
dc_tanf = params.gov.states.dc.dhs.tanf.standard_payment.amount.children["3"]("2026-01-01")

Common Pitfalls

1. Don't Strip Weights

# WRONG
mean = output.tax_unit['eitc'].values.mean()

# CORRECT
mean = output.tax_unit['eitc'].mean()

2. Tax Unit Roles Required

# People need tax unit roles
{"age": 35, "is_tax_unit_head": True}
{"age": 33, "is_tax_unit_spouse": True}
{"age": 8, "is_tax_unit_dependent": True}

3. Filing Status for Couples

tax_unit={"filing_status": "JOINT"}  # or "SEPARATE", "HEAD_OF_HOUSEHOLD"

State-Specific Variables

State variables use {state_code}_{program} naming:

• ca_tanf, ny_tanf, dc_tanf - State TANF
• ca_eitc, ny_eitc - State EITC
• state_income_tax - Aggregate state tax

from policyengine_us import CountryTaxBenefitSystem
system = CountryTaxBenefitSystem()

# Find state variables
ca_vars = [v for v in system.variables if v.startswith("ca_")]

SNAP Deep-Dive: Monthly Eligibility and Cliff Analysis

SNAP benefits are calculated monthly (definition_period = MONTH). When sweeping annual income, the annual SNAP value is the sum of 12 monthly calculations. This creates subtle cliff behavior.

SNAP Eligibility Tests

• Gross income test: Monthly gross income ≤ 130% of monthly FPL
• Net income test: Monthly net income ≤ 100% of monthly FPL
• Categorical eligibility: Can override gross income test in some states

FPL Fiscal Year Change

The Federal Poverty Level updates in October (new fiscal year). This means:

• Jan-Sep uses one FPL threshold, Oct-Dec uses a higher threshold
• A household can fail the gross income test for 9 months but pass for 3 months
• This creates a "partial-year" cliff where annual SNAP drops to ~25% rather than zero

Example: Missouri 3-Person Household (2025)

$33,550/yr → $2,795.83/mo → Eligible all 12 months → $1,956/yr SNAP
$33,600/yr → $2,800.00/mo → 130% FPL = $2,797.17/mo (Jan-Sep)
                           → Fails 9 months, passes Oct-Dec → $527/yr SNAP
$34,700/yr → $2,891.67/mo → Exceeds even Oct-Dec threshold → $0/yr SNAP

SNAP Variable Hierarchy for Debugging

snap (annual sum of monthly allotments)
├── snap_normal_allotment = max(snap_min_allotment, snap_max_allotment - snap_expected_contribution)
│   ├── snap_max_allotment (household size and region)
│   ├── snap_expected_contribution = floor(snap_net_income) × 0.30
│   │   └── snap_net_income = max(0, snap_gross_income - snap_deductions)
│   │       ├── snap_gross_income = snap_earned_income + snap_unearned_income
│   │       └── snap_deductions = standard + earned_income(20%) + shelter + dependent_care + medical + child_support
│   └── snap_min_allotment (usually only for 1-2 person households)
├── is_snap_eligible
│   ├── meets_snap_gross_income_test (≤ 130% FPL, or categorical)
│   ├── meets_snap_net_income_test (≤ 100% FPL)
│   ├── meets_snap_asset_test
│   └── meets_snap_work_requirements
└── snap_emergency_allotment (COVID-era, now $0)

Using Trace Mode for Monthly SNAP Debugging

sim = Simulation(situation=situation)
sim.trace = True
result = sim.calculate('snap_normal_allotment', '2025-01')  # Check specific month!

for node in sim.tracer.trees:
    def print_tree(n, indent=0):
        val = n.value
        val_str = str(val[0]) if hasattr(val, '__len__') and len(val) == 1 else str(val)
        print('  ' * indent + f'{n.name} <{n.period}> = {val_str}')
        for child in n.children:
            print_tree(child, indent + 1)
    print_tree(node)

Common Benefit Cliff Causes

Key Gotcha: Simulation vs Microsimulation

# ✅ CORRECT for custom situations
from policyengine_us import Simulation
sim = Simulation(situation=situation)

# ❌ WRONG - Microsimulation expects a dataset, not a situation
from policyengine_us import Microsimulation
sim = Microsimulation(situation=situation)  # Raises ValueError

Additional Resources

• Documentation: https://policyengine.org/us/docs
• Variable Explorer: https://policyengine.org/us/variables
• Parameter Explorer: https://policyengine.org/us/parameters