# Background

## The Microsimulation Landscape

Tax and benefit microsimulation models play a role in policy analysis by projecting the
distributional and revenue impacts of proposed reforms. Institutions maintaining these models
include government agencies like the Congressional Budget Office (CBO), Joint Committee on Taxation
(JCT), and Treasury's Office of Tax Analysis (OTA), as well as non-governmental organizations
including the Urban-Brookings Tax Policy Center (TPC), Tax Foundation, Penn Wharton Budget Model
(PWBM), Institute on Taxation and Economic Policy (ITEP), Yale Budget Lab, and the open-source
Policy Simulation Library (PSL). Each model serves specific institutional needs but faces common
data challenges.

The core challenges these models face stem from the tradeoff between data comprehensiveness and
accessibility. Administrative tax data provides income reporting but lacks the household context
that models need to analyze benefit programs and family-level impacts {cite:p}`sabelhaus2020`.
Survey data captures household relationships and program participation but suffers from income
underreporting that worsens at higher income levels {cite:p}`meyer2021`. The need to protect
taxpayer privacy limits data availability because administrators cannot publicly release microdata.

## Data Enhancement Approaches

Different microsimulation models use various approaches to enhance their underlying data:

Government models (CBO, JCT, Treasury) have access to confidential administrative data but cannot
share their enhanced microdata. Non-governmental models work with public data, leading to various
enhancement strategies. Some organizations use proprietary extracts of tax returns, while others
enhance survey data with various methods.

Our enhanced dataset provides an open-source methodology with state identifiers and calibration to
state-level targets. This enables analysis of federal-state tax interactions. Researchers can use
the dataset with PolicyEngine or other microsimulation models.

The open-source nature promotes methodological transparency. The modular design allows researchers
to substitute alternative imputation or calibration methods while maintaining the overall framework.
Regular updates as new CPS and administrative data become available ensure the dataset remains
current.
