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gh-policyengine-policyengin…/skills/policyengine-us-skill/SKILL.md
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policyengine-us PolicyEngine-US tax and benefit microsimulation patterns, situation creation, and common workflows

PolicyEngine-US

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

When you see results in PolicyEngine, these are 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 and Setup

# Install PolicyEngine-US
pip install policyengine-us

# Or with uv (recommended)
uv pip install policyengine-us

Quick Start

from policyengine_us import Simulation

# Create a household
situation = {
    "people": {
        "you": {
            "age": {2024: 30},
            "employment_income": {2024: 50000}
        }
    },
    "families": {"family": {"members": ["you"]}},
    "marital_units": {"marital_unit": {"members": ["you"]}},
    "tax_units": {"tax_unit": {"members": ["you"]}},
    "spm_units": {"spm_unit": {"members": ["you"]}},
    "households": {
        "household": {
            "members": ["you"],
            "state_name": {2024: "CA"}
        }
    }
}

# Calculate taxes and benefits
sim = Simulation(situation=situation)
income_tax = sim.calculate("income_tax", 2024)[0]
eitc = sim.calculate("eitc", 2024)[0]

print(f"Income tax: ${income_tax:,.0f}")
print(f"EITC: ${eitc:,.0f}")

Web App to Python

Web app URL:

policyengine.org/us/household?household=12345

Equivalent Python (conceptually): The household ID represents a situation dictionary. To replicate in Python, you'd create a similar situation.

When to Use This Skill

  • Creating household situations for tax/benefit calculations
  • Running microsimulations with PolicyEngine-US
  • Analyzing policy reforms and their impacts
  • Building tools that use PolicyEngine-US (calculators, analysis notebooks)
  • Debugging PolicyEngine-US calculations

For Contributors 💻

Repository

Location: PolicyEngine/policyengine-us

To see current implementation:

git clone https://github.com/PolicyEngine/policyengine-us
cd policyengine-us

# Explore structure
tree policyengine_us/

Key directories:

ls policyengine_us/
# - variables/   - Tax and benefit calculations
# - parameters/  - Policy rules (YAML)
# - reforms/     - Pre-defined reforms
# - tests/       - Test cases

Core Concepts

1. Situation Dictionary Structure

PolicyEngine requires a nested dictionary defining household composition and characteristics:

situation = {
    "people": {
        "person_id": {
            "age": {2024: 35},
            "employment_income": {2024: 50000},
            # ... other person attributes
        }
    },
    "families": {
        "family_id": {"members": ["person_id", ...]}
    },
    "marital_units": {
        "marital_unit_id": {"members": ["person_id", ...]}
    },
    "tax_units": {
        "tax_unit_id": {"members": ["person_id", ...]}
    },
    "spm_units": {
        "spm_unit_id": {"members": ["person_id", ...]}
    },
    "households": {
        "household_id": {
            "members": ["person_id", ...],
            "state_name": {2024: "CA"}
        }
    }
}

Key Rules:

  • All entities must have consistent member lists
  • Use year keys for all values: {2024: value}
  • State must be two-letter code (e.g., "CA", "NY", "TX")
  • All monetary values in dollars (not cents)

2. Creating Simulations

from policyengine_us import Simulation

# Create simulation from situation
simulation = Simulation(situation=situation)

# Calculate variables
income_tax = simulation.calculate("income_tax", 2024)
eitc = simulation.calculate("eitc", 2024)
household_net_income = simulation.calculate("household_net_income", 2024)

Common Variables:

Income:

  • employment_income - W-2 wages
  • self_employment_income - 1099/business income
  • qualified_dividend_income - Qualified dividends
  • capital_gains - Capital gains
  • interest_income - Interest income
  • social_security - Social Security benefits
  • pension_income - Pension/retirement income

Deductions:

  • charitable_cash_donations - Cash charitable giving
  • real_estate_taxes - State and local property taxes
  • mortgage_interest - Mortgage interest deduction
  • medical_expense - Medical and dental expenses
  • casualty_loss - Casualty and theft losses

Tax Outputs:

  • income_tax - Total federal income tax
  • payroll_tax - FICA taxes
  • state_income_tax - State income tax
  • household_tax - Total taxes (federal + state + local)

Benefits:

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

Summary:

  • household_net_income - Income minus taxes plus benefits

3. Using Axes for Parameter Sweeps

To vary a parameter across multiple values:

situation = {
    # ... normal situation setup ...
    "axes": [[{
        "name": "employment_income",
        "count": 1001,
        "min": 0,
        "max": 200000,
        "period": 2024
    }]]
}

simulation = Simulation(situation=situation)
# Now calculate() returns arrays of 1001 values
incomes = simulation.calculate("employment_income", 2024)  # Array of 1001 values
taxes = simulation.calculate("income_tax", 2024)  # Array of 1001 values

Important: Remove axes before creating single-point simulations:

situation_single = situation.copy()
situation_single.pop("axes", None)
simulation = Simulation(situation=situation_single)

4. Policy Reforms

from policyengine_us import Simulation

# Define a reform (modifies parameters)
reform = {
    "gov.irs.credits.ctc.amount.base_amount": {
        "2024-01-01.2100-12-31": 5000  # Increase CTC to $5000
    }
}

# Create simulation with reform
simulation = Simulation(situation=situation, reform=reform)

Common Patterns

Pattern 1: Single Household Calculation

from policyengine_us import Simulation

situation = {
    "people": {
        "parent": {
            "age": {2024: 35},
            "employment_income": {2024: 60000}
        },
        "child": {
            "age": {2024: 5}
        }
    },
    "families": {"family": {"members": ["parent", "child"]}},
    "marital_units": {"marital_unit": {"members": ["parent"]}},
    "tax_units": {"tax_unit": {"members": ["parent", "child"]}},
    "spm_units": {"spm_unit": {"members": ["parent", "child"]}},
    "households": {
        "household": {
            "members": ["parent", "child"],
            "state_name": {2024: "NY"}
        }
    }
}

sim = Simulation(situation=situation)
income_tax = sim.calculate("income_tax", 2024)[0]
ctc = sim.calculate("ctc", 2024)[0]

Pattern 2: Marginal Tax Rate Analysis

# Create baseline with axes varying income
situation_with_axes = {
    # ... situation setup ...
    "axes": [[{
        "name": "employment_income",
        "count": 1001,
        "min": 0,
        "max": 200000,
        "period": 2024
    }]]
}

sim = Simulation(situation=situation_with_axes)
incomes = sim.calculate("employment_income", 2024)
taxes = sim.calculate("income_tax", 2024)

# Calculate marginal tax rate
import numpy as np
mtr = np.gradient(taxes) / np.gradient(incomes)

Pattern 3: Charitable Donation Impact

# Baseline (no donation)
situation_baseline = create_situation(income=100000, donation=0)
sim_baseline = Simulation(situation=situation_baseline)
tax_baseline = sim_baseline.calculate("income_tax", 2024)[0]

# With donation
situation_donation = create_situation(income=100000, donation=5000)
sim_donation = Simulation(situation=situation_donation)
tax_donation = sim_donation.calculate("income_tax", 2024)[0]

# Tax savings from donation
tax_savings = tax_baseline - tax_donation
effective_discount = tax_savings / 5000  # e.g., 0.24 = 24% discount

Pattern 4: State Comparison

states = ["CA", "NY", "TX", "FL"]
results = {}

for state in states:
    situation = create_situation(state=state, income=75000)
    sim = Simulation(situation=situation)
    results[state] = {
        "state_income_tax": sim.calculate("state_income_tax", 2024)[0],
        "total_tax": sim.calculate("household_tax", 2024)[0]
    }

Helper Scripts

This skill includes helper scripts in the scripts/ directory:

from policyengine_skills.situation_helpers import (
    create_single_filer,
    create_married_couple,
    create_family_with_children,
    add_itemized_deductions
)

# Quick situation creation
situation = create_single_filer(
    income=50000,
    state="CA",
    age=30
)

# Add deductions
situation = add_itemized_deductions(
    situation,
    charitable_donations=5000,
    mortgage_interest=10000,
    real_estate_taxes=8000
)

Common Pitfalls and Solutions

Pitfall 1: Member Lists Out of Sync

Problem: Different entities have different members

# WRONG
"tax_units": {"tax_unit": {"members": ["parent"]}},
"households": {"household": {"members": ["parent", "child"]}}

Solution: Keep all entity member lists consistent:

# CORRECT
all_members = ["parent", "child"]
"families": {"family": {"members": all_members}},
"tax_units": {"tax_unit": {"members": all_members}},
"households": {"household": {"members": all_members}}

Pitfall 2: Forgetting Year Keys

Problem: "age": 35 instead of "age": {2024: 35}

Solution: Always use year dictionary:

"age": {2024: 35},
"employment_income": {2024: 50000}

Pitfall 3: Net Taxes vs Gross Taxes

Problem: Forgetting to subtract benefits from taxes

Solution: Use proper calculation:

# Net taxes (what household actually pays)
net_tax = sim.calculate("household_tax", 2024) - \
          sim.calculate("household_benefits", 2024)

Pitfall 4: Axes Persistence

Problem: Axes remain in situation when creating single-point simulation

Solution: Remove axes before single-point simulation:

situation_single = situation.copy()
situation_single.pop("axes", None)

Pitfall 5: State-Specific Variables

Problem: Using NYC-specific variables without in_nyc: True

Solution: Set NYC flag for NY residents in NYC:

"households": {
    "household": {
        "state_name": {2024: "NY"},
        "in_nyc": {2024: True}  # Required for NYC taxes
    }
}

NYC Handling

For New York City residents:

situation = {
    # ... people setup ...
    "households": {
        "household": {
            "members": ["person"],
            "state_name": {2024: "NY"},
            "in_nyc": {2024: True}  # Enable NYC tax calculations
        }
    }
}

Version Compatibility

  • Always use policyengine-us>=1.155.0 for 2024 calculations
  • Check version: import policyengine_us; print(policyengine_us.__version__)
  • Different years may require different package versions

Debugging Tips

  1. Enable tracing:

    simulation.trace = True
result = simulation.calculate("variable_name", 2024)

  2. Check intermediate calculations:

    agi = simulation.calculate("adjusted_gross_income", 2024)
taxable_income = simulation.calculate("taxable_income", 2024)

  3. Verify situation structure:

    import json
print(json.dumps(situation, indent=2))

  4. Test with PolicyEngine web app:

    • Go to policyengine.org/us/household
    • Enter same inputs
    • Compare results

Additional Resources

Examples Directory

See examples/ for complete working examples:

  • single_filer.yaml - Single person household
  • married_couple.yaml - Married filing jointly
  • family_with_children.yaml - Family with dependents
  • itemized_deductions.yaml - Using itemized deductions
  • donation_sweep.yaml - Analyzing donation impacts with axes
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