# Abstract

We present a methodology for creating enhanced microsimulation datasets by combining the Current
Population Survey (CPS) with the IRS Public Use File (PUF). Our approach uses quantile regression
forests to impute 67 tax variables from the PUF onto CPS records, preserving distributional
characteristics while maintaining household composition and member relationships. The imputation
process alone does not guarantee consistency with official statistics, necessitating a reweighting
step to align the combined dataset with known population totals and administrative benchmarks. We
apply a reweighting algorithm that calibrates the dataset to 2,813 targets from the IRS Statistics
of Income, Census population projections, Congressional Budget Office benefit program estimates,
Treasury expenditure data, Joint Committee on Taxation tax expenditure estimates, healthcare
spending patterns, and other benefit program costs. The reweighting employs dropout-regularized
gradient descent optimization to ensure consistency with administrative benchmarks. The dataset
maintains the CPS's demographic detail and geographic granularity while incorporating tax reporting
data from administrative sources. We release the enhanced dataset, source code, and documentation to
support policy analysis.
