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

Cliff	Cause	Typical magnitude
SNAP 130% FPL (partial year)	Gross income test fails 9 months, passes Oct-Dec	~75% of SNAP lost
SNAP 130% FPL (full)	Exceeds even Oct-Dec threshold	100% of SNAP lost
School meals (free → reduced)	Free school meals lost at ~130% FPL	~$250/child/yr
School meals (reduced → none)	Reduced-price meals lost at ~185% FPL	~$990/child/yr

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