policyengine 4.6.0

A package to conduct policy analysis using PolicyEngine tax-benefit models.

PolicyEngine.py

A Python package for tax-benefit microsimulation analysis. Run policy simulations, analyse distributional impacts, and visualise results across the UK and US.

Quick start

Household calculator

import policyengine as pe

# UK: single adult earning £50,000
uk = pe.uk.calculate_household(
    people=[{"age": 35, "employment_income": 50_000}],
    year=2026,
)
print(uk.person[0].income_tax)                   # income tax
print(uk.household.hbai_household_net_income)    # net income

# US: single filer in California, with a reform
us = pe.us.calculate_household(
    people=[{"age": 35, "employment_income": 60_000}],
    tax_unit={"filing_status": "SINGLE"},
    household={"state_code": "CA"},
    year=2026,
    reform={"gov.irs.credits.ctc.amount.adult_dependent": 1000},
)
print(us.tax_unit.income_tax, us.household.household_net_income)

Population analysis

import policyengine as pe
from policyengine.core import Simulation
from policyengine.outputs.aggregate import Aggregate, AggregateType

datasets = pe.uk.ensure_datasets(
    datasets=["hf://policyengine/policyengine-uk-data/enhanced_frs_2023_24.h5"],
    years=[2026],
    data_folder="./data",
)
dataset = datasets["enhanced_frs_2023_24_2026"]

simulation = Simulation(dataset=dataset, tax_benefit_model_version=pe.uk.model)
simulation.run()

agg = Aggregate(
    simulation=simulation,
    variable="universal_credit",
    aggregate_type=AggregateType.SUM,
    entity="benunit",
)
agg.run()
print(f"Total UC spending: £{agg.result / 1e9:.1f}bn")

For baseline-vs-reform comparisons, see pe.uk.economic_impact_analysis and its US counterpart.

Documentation

Core concepts:

• Core concepts: Architecture, datasets, simulations, outputs
• UK tax-benefit model: Entities, parameters, examples
• US tax-benefit model: Entities, parameters, examples

Examples:

• examples/income_distribution_us.py: Analyse benefit distribution by decile
• examples/employment_income_variation_uk.py: Model employment income phase-outs
• examples/policy_change_uk.py: Analyse policy reform impacts
• examples/paper_repro_uk.py: Reproduce the UK reform analysis used in the JOSS paper draft

Installation

As a library

pip install policyengine

This installs both UK and US country models. To install only one:

pip install policyengine[uk]    # UK model only
pip install policyengine[us]    # US model only

For development

git clone https://github.com/PolicyEngine/policyengine.py.git
cd policyengine.py
uv pip install -e .[dev]        # install with dev dependencies (pytest, ruff, mypy, etc.)

Development

Running configurations

Configuration	Install	Use case
Library user	pip install policyengine	Using the package in your own code
UK only	pip install policyengine[uk]	Only need UK simulations
US only	pip install policyengine[us]	Only need US simulations
Developer	uv pip install -e .[dev]	Contributing to the package

Common commands

make format           # ruff format
make test             # pytest with coverage
make docs             # build static Quarto HTML docs
make docs-serve       # preview the docs locally
make clean            # remove caches, build artifacts, .h5 files

Testing

Tests require a HUGGING_FACE_TOKEN environment variable for downloading datasets:

export HUGGING_FACE_TOKEN=hf_...
make test

To run a specific test:

pytest tests/test_models.py -v
pytest tests/test_parametric_reforms.py -k "test_uk" -v

Linting and type checking

ruff format .                    # format code
ruff check .                     # lint
mypy src/policyengine            # type check (informational — not yet enforced in CI)

CI pipeline

PRs trigger the following checks:

Check	Status	Command
Lint + format	Required	ruff check . + ruff format --check .
Tests (Python 3.13)	Required	make test
Tests (Python 3.14)	Required	make test
Mypy	Informational	mypy src/policyengine
Docs build	Required	Jupyter Book build

Versioning and releases

This project uses towncrier for changelog management. When making a PR, add a changelog fragment:

# Fragment types: breaking, added, changed, fixed, removed
echo "Description of change" > changelog.d/my-change.added

On merge, the versioning workflow bumps the version, builds the changelog, and creates a GitHub Release.

Paper reproduction

Use the pinned interpreter and the UK extra to run the checked-in paper repro:

uv run --python 3.14 --extra uk python examples/paper_repro_uk.py

On first run this will create ./data/enhanced_frs_2023_24_year_2026.h5.

Features

• Multi-country support: UK and US tax-benefit systems
• Representative microdata: Load FRS, CPS, or create custom scenarios
• Policy reforms: Parametric reforms with date-bound parameter values
• Distributional analysis: Aggregate statistics by income decile, demographics
• Entity mapping: Automatic mapping between person, household, tax unit levels
• Visualisation: PolicyEngine-branded charts with Plotly

Key concepts

Datasets

Datasets contain microdata at entity level (person, household, tax unit). Load representative data or create custom scenarios:

from policyengine.tax_benefit_models.uk import PolicyEngineUKDataset

dataset = PolicyEngineUKDataset(
    name="Representative data",
    filepath="./data/frs_2023_24_year_2026.h5",
    year=2026,
)
dataset.load()

Simulations

Simulations apply tax-benefit models to datasets:

import policyengine as pe
from policyengine.core import Simulation

simulation = Simulation(
    dataset=dataset,
    tax_benefit_model_version=pe.uk.model,
)
simulation.run()

# Access calculated variables
output = simulation.output_dataset.data
print(output.household[["household_net_income", "household_benefits"]])

Outputs

Extract insights with aggregate statistics:

from policyengine.outputs.aggregate import Aggregate, AggregateType

# Mean income in top decile
agg = Aggregate(
    simulation=simulation,
    variable="household_net_income",
    aggregate_type=AggregateType.MEAN,
    filter_variable="household_net_income",
    quantile=10,
    quantile_eq=10,
)
agg.run()
print(f"Top decile mean income: £{agg.result:,.0f}")

Policy reforms

Apply parametric reforms:

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

parameter = Parameter(
    name="gov.hmrc.income_tax.allowances.personal_allowance.amount",
    tax_benefit_model_version=pe.uk.model,
    data_type=float,
)

policy = Policy(
    name="Increase personal allowance",
    parameter_values=[
        ParameterValue(
            parameter=parameter,
            start_date=datetime.date(2026, 1, 1),
            end_date=datetime.date(2026, 12, 31),
            value=15000,
        )
    ],
)

# Run reform simulation
reform_sim = Simulation(
    dataset=dataset,
    tax_benefit_model_version=pe.uk.model,
    policy=policy,
)
reform_sim.run()

Country models

UK

Three entity levels:

• Person: Individual with income and demographics
• Benunit: Benefit unit (single person or couple with children)
• Household: Residence unit

Key benefits: Universal Credit, Child Benefit, Pension Credit Key taxes: Income tax, National Insurance

US

Six entity levels:

• Person: Individual
• Tax unit: Federal tax filing unit
• SPM unit: Supplemental Poverty Measure unit
• Family: Census family definition
• Marital unit: Married couple or single person
• Household: Residence unit

Key benefits: SNAP, TANF, EITC, CTC, SSI, Social Security Key taxes: Federal income tax, payroll tax

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

AGPL-3.0