\subsection{Overview}

Our approach enhances the Current Population Survey (CPS) with information from the IRS Public Use File (PUF) through a multi-stage process. This design is motivated by empirical evidence from \citet{woodruff2023survey} showing that combining synthetic data generation with weight optimization achieves substantially better results than either technique alone or traditional enhancement methods. Their comprehensive benchmarking demonstrated an 88\% reduction in survey error through this combined approach, with improvements that generalized across multiple validation metrics.

\begin{enumerate}
    \item Train quantile regression forests on PUF tax records to learn distributions of tax-related variables
    \item Generate a synthetic dataset that combines PUF tax precision with CPS-like demographic detail
    \item Stack these synthetic records alongside the original CPS records
    \item Run the PolicyEngine US tax-benefit model on the stacked dataset to generate tax and benefit amounts
    \item Optimize household weights to match administrative benchmarks while determining the optimal mix of original and synthetic records
\end{enumerate}

This method preserves the CPS's demographic richness and household relationships while incorporating the PUF's precise tax information. Each component is detailed in the following sections.