# Introduction

Microsimulation models require high-quality microdata that accurately represent demographic
characteristics and economic outcomes. The ideal dataset would combine the demographic richness and
household structure of surveys with the income precision of administrative tax records. However,
publicly available datasets typically excel in one dimension while lacking in the other.

The Current Population Survey (CPS) Annual Social and Economic Supplement provides detailed
household demographics, family relationships, and program participation data for a representative
sample of US households. However, it suffers from well-documented income underreporting,
particularly at the top of the distribution. The IRS Public Use File (PUF) contains accurate tax
return information but lacks household structure, demographic detail, and state identifiers needed
for comprehensive policy analysis.

This paper presents a methodology for creating an Enhanced CPS dataset that combines the strengths
of both sources. Through an enhancement process: imputation followed by reweighting, we create a
dataset suitable for analyzing both tax and transfer policies at federal and state levels.

## Related Work

Researchers have developed several approaches to address the limitations of survey data for
microsimulation. Researchers have long used statistical matching techniques to combine datasets.
{cite:t}`radner1978` pioneered exact matching methods for combining survey and administrative data,
while {cite:t}`rodgers1984` developed statistical matching based on common variables. More recently,
{cite:t}`dorazio2006` provided a comprehensive framework for modern statistical matching methods.

Economic researchers address dataset limitations through various strategies. The Congressional
Budget Office combines CPS data with tax return information through statistical matching
{cite:p}`cbo2022`. The Tax Policy Center creates synthetic datasets by statistically matching the
CPS to a subset of tax returns {cite:p}`rohaly2005`. However, these approaches often sacrifice
either demographic detail or tax precision, which limits their utility for comprehensive policy
analysis.

Statistical agencies and researchers employ reweighting methods to align survey data with
administrative totals. The Luxembourg Income Study uses calibration to improve cross-national
comparability {cite:p}`gornick2013`. The Urban-Brookings Tax Policy Center employs reweighting in
their microsimulation model but relies on proprietary data that cannot be shared publicly
{cite:p}`khitatrakun2016`.

Our approach differs from previous efforts in three key ways. First, we employ quantile regression
forests to preserve distributional characteristics during imputation, improving upon traditional
hot-deck and regression-based methods that may distort variable relationships. We conduct robustness
checks comparing QRF performance to gradient boosting and neural network approaches, finding QRF
provides the best balance of accuracy and interpretability. Second, we calibrate to 2,813 targets
from multiple administrative sources, far exceeding the scope of previous calibration efforts which
typically use fewer than 100 targets. Third, we provide a fully open-source implementation enabling
reproducibility and collaborative improvement, addressing the transparency limitations of existing
proprietary models.

## Contributions

This paper makes three main contributions to the economic and public policy literature.
Methodologically, we demonstrate how quantile regression forests can effectively impute detailed tax
variables while preserving their joint distribution and relationship to demographics. This advances
the statistical matching literature by showing how modern machine learning methods can overcome
limitations of traditional hot-deck and parametric approaches. The preservation of distributional
characteristics is particularly important for tax policy analysis where outcomes often depend on
complex interactions between income sources and household characteristics.

Our empirical contribution involves creating and validating a publicly available enhanced dataset
that addresses longstanding data limitations in microsimulation modeling. By combining the
demographic richness of the CPS with the tax precision of the PUF, we enable analyses that were
previously infeasible with public data. The dataset's calibration to 2,813 administrative targets
ensures consistency with official statistics across multiple dimensions simultaneously.

From a practical perspective, we provide open-source tools and comprehensive documentation that
enable researchers to apply these methods, modify the approach, or build upon our work. This
transparency contrasts with existing proprietary models and supports reproducible research.
Government agencies could use our framework to enhance their own microsimulation capabilities, while
academic researchers gain access to data suitable for analyzing distributional impacts of tax and
transfer policies. The modular design allows incremental improvements as new data sources become
available.

We organize the remainder of this paper as follows. Section 2 describes our data sources including
the primary datasets and calibration targets. Section 3 details the enhancement methodology
including both the imputation and reweighting stages. Section 4 presents validation results
comparing performance across datasets. Section 5 discusses limitations, applications, and future
directions. Section 6 concludes with implications for policy analysis.
