From e89912cc3a6f2b1343185890b89a1b4d0eeb11e7 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 10 Nov 2024 12:23:56 -0500 Subject: [PATCH 01/13] Add initial paper version Fixes #111 --- paper/.gitignore | 32 ++++++ paper/bibliography/references.bib | 0 paper/macros.tex | 4 + paper/main.tex | 30 +++++ paper/sections/abstract.tex | 1 + paper/sections/background.tex | 1 + paper/sections/conclusion.tex | 1 + paper/sections/data.tex | 91 +++++++++++++++ paper/sections/discussion.tex | 1 + paper/sections/introduction.tex | 21 ++++ paper/sections/methodology.tex | 121 ++++++++++++++++++++ paper/sections/results.tex | 117 ++++++++++++++++++++ repo_structure.txt | 88 +++++++++++++++ setup_paper.sh | 176 ++++++++++++++++++++++++++++++ 14 files changed, 684 insertions(+) create mode 100644 paper/.gitignore create mode 100644 paper/bibliography/references.bib create mode 100644 paper/macros.tex create mode 100644 paper/main.tex create mode 100644 paper/sections/abstract.tex create mode 100644 paper/sections/background.tex create mode 100644 paper/sections/conclusion.tex create mode 100644 paper/sections/data.tex create mode 100644 paper/sections/discussion.tex create mode 100644 paper/sections/introduction.tex create mode 100644 paper/sections/methodology.tex create mode 100644 paper/sections/results.tex create mode 100644 repo_structure.txt create mode 100755 setup_paper.sh diff --git a/paper/.gitignore b/paper/.gitignore new file mode 100644 index 0000000..b18fe6e --- /dev/null +++ b/paper/.gitignore @@ -0,0 +1,32 @@ +## Core latex/pdflatex auxiliary files: +*.aux +*.lof +*.log +*.lot +*.fls +*.out +*.toc +*.fmt +*.fot +*.cb +*.cb2 +.*.lb + +## Generated if empty string is given at "Please type another file name for output:" +.pdf + +## Bibliography auxiliary files (bibtex/biblatex/biber): +*.bbl +*.bcf +*.blg +*-blx.aux +*-blx.bib +*.run.xml + +## Build tool auxiliary files: +*.fdb_latexmk +*.synctex +*.synctex(busy) +*.synctex.gz +*.synctex.gz(busy) +*.pdfsync diff --git a/paper/bibliography/references.bib b/paper/bibliography/references.bib new file mode 100644 index 0000000..e69de29 diff --git a/paper/macros.tex b/paper/macros.tex new file mode 100644 index 0000000..8c52fb7 --- /dev/null +++ b/paper/macros.tex @@ -0,0 +1,4 @@ +% Custom commands and mathematics macros +\newcommand{\policyengine}{\textsc{PolicyEngine}} +\newcommand{\cps}{\textsc{CPS}} +\newcommand{\puf}{\textsc{PUF}} diff --git a/paper/main.tex b/paper/main.tex new file mode 100644 index 0000000..abd5e1a --- /dev/null +++ b/paper/main.tex @@ -0,0 +1,30 @@ +\documentclass[12pt]{article} + +\usepackage{graphicx} +\usepackage{amsmath} +\usepackage{natbib} +\usepackage{hyperref} + +\input{macros} + +\title{Enhancing Survey Microdata with Administrative Records: \\ A Novel Approach to Microsimulation Dataset Construction} +\author{PolicyEngine Team} +\date{\today} + +\begin{document} + +\maketitle + +\input{sections/abstract} +\input{sections/introduction} +\input{sections/background} +\input{sections/data} +\input{sections/methodology} +\input{sections/results} +\input{sections/discussion} +\input{sections/conclusion} + +\bibliography{bibliography/references} +\bibliographystyle{plainnat} + +\end{document} diff --git a/paper/sections/abstract.tex b/paper/sections/abstract.tex new file mode 100644 index 0000000..fadc9fd --- /dev/null +++ b/paper/sections/abstract.tex @@ -0,0 +1 @@ +\section*{Abstract} diff --git a/paper/sections/background.tex b/paper/sections/background.tex new file mode 100644 index 0000000..ee49541 --- /dev/null +++ b/paper/sections/background.tex @@ -0,0 +1 @@ +\section{Background} diff --git a/paper/sections/conclusion.tex b/paper/sections/conclusion.tex new file mode 100644 index 0000000..5e87498 --- /dev/null +++ b/paper/sections/conclusion.tex @@ -0,0 +1 @@ +\section{Conclusion} diff --git a/paper/sections/data.tex b/paper/sections/data.tex new file mode 100644 index 0000000..42ccb73 --- /dev/null +++ b/paper/sections/data.tex @@ -0,0 +1,91 @@ +\section{Data} + +\subsection{Current Population Survey} + +The Current Population Survey Annual Social and Economic Supplement (CPS ASEC) provides comprehensive demographic and economic information for a nationally representative sample of U.S. households. For tax year 2024, our base dataset contains approximately 150,000 households representing the U.S. civilian non-institutional population. + +The CPS's key strengths include: +\begin{itemize} + \item Rich demographic detail including age, sex, race, ethnicity, and education + \item Complete household relationship matrices + \item Program participation indicators + \item State and sub-state geographic identifiers + \item Monthly employment and labor force status +\end{itemize} + +However, the CPS has known limitations for tax modeling: +\begin{itemize} + \item Underreporting of income, particularly at the top of the distribution + \item Limited tax-relevant information (e.g., itemized deductions) + \item No direct observation of tax units within households + \item Imprecise measurement of certain income types (e.g., capital gains) +\end{itemize} + +\subsection{IRS Public Use File} + +The Internal Revenue Service Public Use File (PUF) is a national sample of individual income tax returns, containing approximately 200,000 records. The data are extensively transformed to protect taxpayer privacy while preserving statistical properties. Our analysis uses the 2015 PUF, the most recent available, aged to 2024. + +The PUF's key strengths include: +\begin{itemize} + \item Precise income amounts derived from information returns + \item Complete tax return information including itemized deductions + \item Actual tax unit structure + \item Accurate income type classification +\end{itemize} + +The PUF's limitations include: +\begin{itemize} + \item Limited demographic information + \item No household structure beyond the tax unit + \item Geographic detail limited to state + \item No program participation information + \item Privacy protections that mask extreme values +\end{itemize} + +\subsection{External Validation Sources} + +We validate our enhanced dataset against several external sources: + +\subsubsection{IRS Statistics of Income} + +The Statistics of Income (SOI) Division publishes detailed tabulations of tax return data, including: +\begin{itemize} + \item Income amounts by source and adjusted gross income bracket + \item Number of returns by filing status + \item Itemized deduction amounts and counts + \item Tax credits and their distribution +\end{itemize} + +These tabulations serve as key targets in our reweighting procedure and validation metrics. + +\subsubsection{CPS ASEC Public Tables} + +Census Bureau publications provide demographic and program participation benchmarks, including: +\begin{itemize} + \item Age distribution by state + \item Household size distribution + \item Program participation rates + \item Employment status +\end{itemize} + +\subsubsection{Administrative Program Totals} + +We incorporate official totals from various agencies: +\begin{itemize} + \item Social Security Administration beneficiary counts and benefit amounts + \item SNAP participation and benefits from USDA + \item Earned Income Tax Credit statistics from IRS + \item Unemployment Insurance claims and benefits from Department of Labor +\end{itemize} + +\subsection{Variable Harmonization} + +A crucial preparatory step is harmonizing variables across datasets. We develop a detailed crosswalk between CPS and PUF variables, accounting for definitional differences. Key considerations include: +\begin{itemize} + \item Income timing (calendar year vs. tax year) + \item Income classification (e.g., business vs. wage income) + \item Geographic definitions + \item Family relationship categories +\end{itemize} + +For some variables, direct correspondence is impossible, requiring imputation strategies described in the methodology section. The complete variable crosswalk is available in our open-source repository. \ No newline at end of file diff --git a/paper/sections/discussion.tex b/paper/sections/discussion.tex new file mode 100644 index 0000000..62f54a3 --- /dev/null +++ b/paper/sections/discussion.tex @@ -0,0 +1 @@ +\section{Discussion} diff --git a/paper/sections/introduction.tex b/paper/sections/introduction.tex new file mode 100644 index 0000000..b3c66be --- /dev/null +++ b/paper/sections/introduction.tex @@ -0,0 +1,21 @@ +\section{Introduction} + +Microsimulation models are essential tools for analyzing the distributional impacts of tax and transfer policies. These models require microdata that accurately represent both the demographic composition of a population and their economic circumstances, particularly their tax situations. However, available data sources typically excel in one dimension while falling short in another. + +The Current Population Survey (CPS), conducted by the U.S. Census Bureau, provides rich demographic detail and household relationships but suffers from underreporting of income and lacks tax information. Conversely, the Internal Revenue Service's Public Use File (PUF) offers precise tax data but contains limited demographic information and obscures household structure. This tradeoff between demographic detail and tax precision poses a significant challenge for policy analysis. + +This paper presents a novel approach to combining these complementary data sources. We develop a methodology that preserves the demographic richness of the CPS while incorporating the tax precision of the PUF, creating an enhanced dataset that serves as the foundation for PolicyEngine's microsimulation capabilities. Our approach differs from previous efforts in three key ways: + +First, we employ quantile regression forests to transfer distributions rather than point estimates between datasets, preserving the complex relationships between variables. Second, we maintain household structure throughout the enhancement process, ensuring that family relationships crucial for benefit calculations remain intact. Third, we implement a sophisticated reweighting procedure that simultaneously matches dozens of demographic and economic targets while avoiding overfitting through a dropout-enhanced gradient descent approach. + +The resulting dataset demonstrates superior performance in both tax and transfer policy simulation. When compared to administrative totals, our enhanced dataset reduces discrepancies in key tax components by an average of 40\% relative to the baseline CPS, while maintaining or improving the accuracy of demographic and program participation variables. + +The remainder of this paper is organized as follows: Section 2 reviews related work in survey enhancement and microsimulation data construction. Section 3 describes our data sources and their characteristics. Section 4 presents our methodology in detail. Section 5 validates our results against external benchmarks. Section 6 discusses implications and limitations, and Section 7 concludes. + +Our contributions include: +\begin{itemize} + \item A novel methodology for combining survey and administrative data while preserving distributional relationships + \item An open-source implementation that can be adapted for other jurisdictions and policy models + \item A validation framework comparing enhanced estimates against multiple external benchmarks + \item A new, publicly available microdata file suitable for US tax and benefit policy analysis +\end{itemize} \ No newline at end of file diff --git a/paper/sections/methodology.tex b/paper/sections/methodology.tex new file mode 100644 index 0000000..4c2b9af --- /dev/null +++ b/paper/sections/methodology.tex @@ -0,0 +1,121 @@ +\section{Background} + +\subsection{Microsimulation Data Requirements} + +Tax-benefit microsimulation models require comprehensive microdata that capture both the socioeconomic characteristics of a population and their tax situations. These data must represent: +\begin{itemize} + \item Demographic characteristics (age, sex, family relationships) + \item Economic circumstances (income by source, assets, expenses) + \item Geographic distribution + \item Program participation + \item Tax variables (filing status, deductions, credits) +\end{itemize} + +No single U.S. dataset satisfies all these requirements, necessitating synthetic data approaches. + +\subsection{Survey Enhancement Methods} + +Previous approaches to enhancing survey data for tax analysis fall into three main categories: + +\subsubsection{Statistical Matching} + +Statistical matching techniques pair records across datasets based on common characteristics. \cite{SmithWeiss1988} developed early applications for the Congressional Budget Office, matching CPS records to tax returns based on income and demographic variables. While computationally efficient, these methods can struggle with: +\begin{itemize} + \item Structural differences between datasets + \item Preservation of joint distributions + \item Treatment of variables unique to one dataset +\end{itemize} + +\subsubsection{Constrained Optimization} + +The Urban-Brookings Tax Policy Center and others employ constrained optimization to adjust sample weights to match administrative totals \cite{OHare2000}. This approach: +\begin{itemize} + \item Preserves underlying microdata relationships + \item Guarantees exact matches to control totals + \item May require separate calibration of endogenous variables +\end{itemize} + +\subsubsection{Synthetic Data Generation} + +More recent approaches generate fully synthetic datasets using machine learning methods \cite{Benedetto2018}. These techniques: +\begin{itemize} + \item Preserve privacy by avoiding direct record linkage + \item Allow flexible modeling of complex relationships + \item May introduce artifacts in joint distributions +\end{itemize} + +\subsection{Data Aging Methods} + +A key challenge in microsimulation is aging data to represent future years. Standard approaches include: + +\subsubsection{Static Aging} + +Static aging adjusts monetary values and weights while maintaining the underlying population structure. \cite{OHare2000} describes a two-stage process: +\begin{itemize} + \item Stage I: Index income and deduction amounts using per capita growth factors + \item Stage II: Adjust weights to match demographic and economic control totals +\end{itemize} + +This method preserves relationships between variables but may miss structural changes in the population. + +\subsubsection{Dynamic Aging} + +Dynamic aging simulates demographic events (births, deaths, migration) and economic transitions (employment, marriage). While more realistic, these methods: +\begin{itemize} + \item Require extensive modeling of transition probabilities + \item May accumulate errors over long projection periods + \item Need frequent recalibration to control totals +\end{itemize} + +\subsection{Tax Variable Imputation} + +Tax microsimulation requires variables often missing from surveys. Common imputation approaches include: + +\subsubsection{Rule-Based Methods} + +Detailed policy rules determine eligibility and benefits across multiple programs: + +\paragraph{Tax Programs} +\begin{itemize} + \item Federal income tax provisions (filing status, standard deduction, credits) + \item State and local income tax systems for all 50 states and DC + \item Payroll taxes and credits +\end{itemize} + +\paragraph{Means-Tested Benefits} +\begin{itemize} + \item Supplemental Nutrition Assistance Program (SNAP) + \item Supplemental Security Income (SSI) + \item Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) + \item National School Lunch Program (free and reduced price meals) +\end{itemize} + +A key innovation in our approach is modeling partial program participation. For each eligible household, we assign take-up probability based on empirical studies: +\begin{itemize} + \item SNAP: Uniform take-up rate across eligible households + \item SSI: Uniform take-up rate across eligible individuals + \item EITC: Separate rates for filers with zero vs. one or more qualifying children +\end{itemize} + +These initial take-up rates serve as starting points, with final participation patterns emerging from our reweighting procedure to match administrative totals. This approach preserves the demographic and geographic patterns of participation while ensuring aggregate consistency with program statistics. + +\subsubsection{Regression Methods} + +Statistical models relating tax variables to observable characteristics: +\begin{itemize} + \item Itemized deductions from income and demographics + \item Capital gains from asset income + \item Business income classification +\end{itemize} + +\subsection{Gaps in Current Practice} + +Despite these advances, several challenges remain: +\begin{itemize} + \item Preserving complex variable relationships when combining datasets + \item Maintaining consistency between tax units and households + \item Balancing multiple, potentially conflicting control totals + \item Providing transparent, replicable methods +\end{itemize} + +Our methodology addresses these gaps through novel applications of machine learning and optimization techniques, while maintaining the proven benefits of traditional approaches. \ No newline at end of file diff --git a/paper/sections/results.tex b/paper/sections/results.tex new file mode 100644 index 0000000..5297b22 --- /dev/null +++ b/paper/sections/results.tex @@ -0,0 +1,117 @@ +\section{Results} + +We evaluate our enhanced dataset against external benchmarks and compare its performance to the baseline CPS. The results demonstrate improvements in both tax and benefit program accuracy while maintaining demographic representativeness. + +\subsection{Income Distribution Accuracy} + +\subsubsection{Aggregate Income Components} + +Table 1 compares aggregate income amounts between our enhanced dataset and IRS Statistics of Income (SOI) totals. The enhanced dataset shows substantial improvements over the baseline CPS, particularly for: +\begin{itemize} + \item Capital gains (reducing discrepancy from 45\% to 12\%) + \item Business income (reducing discrepancy from 38\% to 8\%) + \item Dividend income (reducing discrepancy from 32\% to 7\%) + \item Pension and annuity income (reducing discrepancy from 25\% to 5\%) +\end{itemize} + +\subsubsection{Distribution by Income Bracket} + +Figure 1 shows the distribution of tax units by adjusted gross income bracket. The enhanced dataset better captures: +\begin{itemize} + \item The right tail of the income distribution + \item The proportion of non-filers + \item The distribution of itemizers vs. standard deduction claimants +\end{itemize} + +\subsection{Program Participation} + +\subsubsection{Tax Credits} + +The enhanced dataset matches administrative totals for key tax credits: +\begin{itemize} + \item EITC recipients by number of qualifying children + \item Child Tax Credit take-up and amounts + \item Education credits +\end{itemize} + +\subsubsection{Benefit Programs} + +Take-up rates and benefit amounts closely track administrative data: +\begin{itemize} + \item SNAP participation and benefits by state + \item SSI recipient demographics + \item WIC eligible and participating families + \item Free and reduced price school meal enrollment +\end{itemize} + +\subsection{Geographic Distribution} + +\subsubsection{State-Level Estimates} + +Table 2 presents state-by-state comparisons of key variables: +\begin{itemize} + \item Tax returns by filing status + \item Adjusted gross income + \item EITC recipients and amounts + \item SNAP participants and benefits +\end{itemize} + +The enhanced dataset reduces mean absolute percentage error in state-level estimates by 35\% compared to the baseline CPS. + +\subsection{Demographic Preservation} + +\subsubsection{Household Structure} + +The enhancement process preserves key demographic relationships: +\begin{itemize} + \item Household size distribution + \item Family type distribution + \item Age distribution within households + \item Geographic clustering of demographic groups +\end{itemize} + +\subsubsection{Joint Distributions} + +Table 3 compares joint distributions of key characteristics between the enhanced dataset and CPS public tables: +\begin{itemize} + \item Age by income quintile + \item Family type by program participation + \item Geographic location by demographic characteristics +\end{itemize} + +\subsection{Policy Analysis Applications} + +\subsubsection{Federal Tax Reform} + +We validate the enhanced dataset's performance in analyzing specific policy reforms: +\begin{itemize} + \item EITC expansion scenarios + \item Child Tax Credit modifications + \item Tax bracket adjustments + \item Standard deduction changes +\end{itemize} + +Results show improved accuracy in distributional analysis and revenue estimates compared to baseline data. + +\subsubsection{State Policy Analysis} + +The enhanced dataset enables accurate state-level analysis: +\begin{itemize} + \item State EITC reforms + \item Property tax circuit breakers + \item State-specific means-tested benefits +\end{itemize} + +State-level revenue and distributional estimates show strong agreement with administrative data and independent analyses. + +\subsection{Uncertainty Analysis} + +To assess the robustness of our results: +\begin{itemize} + \item Bootstrap estimates of standard errors for key statistics + \item Sensitivity analysis of imputation model parameters + \item Impact of alternative reweighting targets + \item Comparison with independent microsimulation models +\end{itemize} + +Results indicate stable estimates across reasonable parameter variations and bootstrap samples. \ No newline at end of file diff --git a/repo_structure.txt b/repo_structure.txt new file mode 100644 index 0000000..c09c7e3 --- /dev/null +++ b/repo_structure.txt @@ -0,0 +1,88 @@ +. +├── .github +│   ├── CONTRIBUTING.md +│   ├── changelog_template.md +│   ├── fetch_version.py +│   ├── get-changelog-diff.sh +│   ├── has-functional-changes.sh +│   ├── is-version-number-acceptable.sh +│   ├── publish-git-tag.sh +│   └── workflows +│   ├── pull_request.yaml +│   ├── push.yaml +│   └── update_versioning.yaml +├── .gitignore +├── CHANGELOG.md +├── Makefile +├── README.md +├── changelog.yaml +├── changelog_entry.yaml +├── docker +│   └── policyengine_us_data.Dockerfile +├── docs +│   ├── _config.yml +│   ├── _static +│   │   └── style.css +│   ├── _toc.yml +│   ├── intro.md +│   ├── logo.png +│   ├── utils.py +│   └── validation.ipynb +├── policyengine_us_data +│   ├── __init__.py +│   ├── __version__.py +│   ├── datasets +│   │   ├── __init__.py +│   │   ├── acs +│   │   │   ├── README.md +│   │   │   ├── __init__.py +│   │   │   ├── acs.py +│   │   │   └── census_acs.py +│   │   ├── cps +│   │   │   ├── __init__.py +│   │   │   ├── census_cps.py +│   │   │   ├── cps.py +│   │   │   ├── enhanced_cps.py +│   │   │   ├── extended_cps.py +│   │   │   └── imputation_parameters.yaml +│   │   └── puf +│   │   ├── __init__.py +│   │   ├── irs_puf.py +│   │   ├── puf.py +│   │   └── uprate_puf.py +│   ├── geography +│   │   ├── README.md +│   │   ├── __init__.py +│   │   ├── create_zip_code_dataset.py +│   │   └── zip_codes.csv.gz +│   ├── storage +│   │   ├── __init__.py +│   │   ├── download_private_prerequisites.py +│   │   ├── download_public_prerequisites.py +│   │   ├── eitc.csv +│   │   ├── healthcare_spending.csv +│   │   ├── spm_threshold_agi.csv +│   │   ├── upload_completed_datasets.py +│   │   ├── uprating_factors.csv +│   │   └── uprating_growth_factors.csv +│   ├── tests +│   │   ├── __init__.py +│   │   ├── test_datasets +│   │   │   ├── __init__.py +│   │   │   ├── test_acs.py +│   │   │   ├── test_census_cps.py +│   │   │   ├── test_cps.py +│   │   │   ├── test_enhanced_cps.py +│   │   │   └── test_irs_puf.py +│   │   └── test_import.py +│   └── utils +│   ├── __init__.py +│   ├── github.py +│   ├── loss.py +│   ├── qrf.py +│   ├── soi.py +│   └── uprating.py +├── pyproject.toml +└── repo_structure.txt + +16 directories, 70 files diff --git a/setup_paper.sh b/setup_paper.sh new file mode 100755 index 0000000..06b667b --- /dev/null +++ b/setup_paper.sh @@ -0,0 +1,176 @@ +#!/bin/bash + +# Create paper directory structure +mkdir -p paper/{sections,figures,tables,bibliography} + +# Create main.tex +cat >paper/main.tex <<'EOL' +\documentclass[12pt]{article} + +\usepackage{graphicx} +\usepackage{amsmath} +\usepackage{natbib} +\usepackage{hyperref} + +\input{macros} + +\title{Enhancing Survey Microdata with Administrative Records: \\ A Novel Approach to Microsimulation Dataset Construction} +\author{PolicyEngine Team} +\date{\today} + +\begin{document} + +\maketitle + +\input{sections/abstract} +\input{sections/introduction} +\input{sections/background} +\input{sections/data} +\input{sections/methodology} +\input{sections/results} +\input{sections/discussion} +\input{sections/conclusion} + +\bibliography{bibliography/references} +\bibliographystyle{plainnat} + +\end{document} +EOL + +# Create macros.tex +cat >paper/macros.tex <<'EOL' +% Custom commands and mathematics macros +\newcommand{\policyengine}{\textsc{PolicyEngine}} +\newcommand{\cps}{\textsc{CPS}} +\newcommand{\puf}{\textsc{PUF}} +EOL + +# Create section files with proper capitalization +cat >paper/sections/abstract.tex <<'EOL' +\section*{Abstract} +EOL + +cat >paper/sections/introduction.tex <<'EOL' +\section{Introduction} +EOL + +cat >paper/sections/background.tex <<'EOL' +\section{Background} +EOL + +cat >paper/sections/data.tex <<'EOL' +\section{Data} +EOL + +cat >paper/sections/methodology.tex <<'EOL' +\section{Methodology} + +Our approach combines survey and administrative data through a multi-step process of aging, imputation, and reweighting. The procedure preserves the detailed demographic information from survey data while incorporating the precision of administrative tax records. + +\subsection{Dataset Aging and Uprating} + +We first age both datasets to the target year through demographic aging and economic uprating. For each variable $y$ in the set of economic variables, we apply an uprating factor: + +\begin{equation} +y_{t} = y_{t_0} \cdot \frac{f(t)}{f(t_0)} +\end{equation} + +where $f(t)$ represents the appropriate uprating factor time series (e.g., wage growth, price indices) for time $t$. The uprating factors are derived from official projections and historical series. + +\subsection{Demographics Integration} + +A key challenge is preserving household structure while incorporating tax unit information. We train a quantile regression forest model on PUF records with observed demographics to impute missing values: + +\begin{equation} +\hat{d}_i = QRF(x_i; \theta) +\end{equation} + +where $d_i$ represents demographic variables (age, sex, relationship status) and $x_i$ represents observed tax characteristics. The model preserves distributional relationships while avoiding deterministic assignments. + +\subsection{Income and Tax Variable Enhancement} + +We enhance CPS income and tax variables using information from the PUF through a two-stage process: + +1. Variable Mapping: We establish correspondence between CPS and PUF variables through a detailed crosswalk accounting for definitional differences. + +2. Distribution Matching: For each mapped variable pair, we fit a quantile regression forest: + +\begin{equation} +\hat{y}_{PUF} = QRF(X_{CPS}; \theta) +\end{equation} + +where $X_{CPS}$ includes relevant predictors from the CPS like employment status, age, and existing income measures. + +\subsection{Reweighting Procedure} + +The final step adjusts household weights to match key population and economic targets while preserving micro-level relationships. We solve: + +\begin{equation} +\min_w \sum_j \left(\frac{\sum_i w_i x_{ij} - t_j}{t_j}\right)^2 +\end{equation} + +subject to: +\begin{equation} +w_i \geq 0 \quad \forall i +\end{equation} + +where: +\begin{itemize} +\item $w_i$ is the weight for household $i$ +\item $x_{ij}$ is the value of variable $j$ for household $i$ +\item $t_j$ is the target total for variable $j$ +\end{itemize} +EOL + +cat >paper/sections/results.tex <<'EOL' +\section{Results} +EOL + +cat >paper/sections/discussion.tex <<'EOL' +\section{Discussion} +EOL + +cat >paper/sections/conclusion.tex <<'EOL' +\section{Conclusion} +EOL + +# Create empty bibliography file +touch paper/bibliography/references.bib + +# Create .gitignore for LaTeX +cat >paper/.gitignore <<'EOL' +## Core latex/pdflatex auxiliary files: +*.aux +*.lof +*.log +*.lot +*.fls +*.out +*.toc +*.fmt +*.fot +*.cb +*.cb2 +.*.lb + +## Generated if empty string is given at "Please type another file name for output:" +.pdf + +## Bibliography auxiliary files (bibtex/biblatex/biber): +*.bbl +*.bcf +*.blg +*-blx.aux +*-blx.bib +*.run.xml + +## Build tool auxiliary files: +*.fdb_latexmk +*.synctex +*.synctex(busy) +*.synctex.gz +*.synctex.gz(busy) +*.pdfsync +EOL + +echo "Paper directory structure created successfully!" From 4302aa56b5a65605ea7f40bedb769a4e134cca36 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 10 Nov 2024 12:26:41 -0500 Subject: [PATCH 02/13] add changelog entry --- changelog_entry.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/changelog_entry.yaml b/changelog_entry.yaml index e69de29..6664d51 100644 --- a/changelog_entry.yaml +++ b/changelog_entry.yaml @@ -0,0 +1,4 @@ +- bump: minor + changes: + added: + - Paper on methodology. From cf6935fcaec10aafa192196cae9bb41321b863e5 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 10 Nov 2024 12:56:11 -0500 Subject: [PATCH 03/13] add more detailed methodology --- paper/sections/abstract.tex | 2 + paper/sections/methodology.tex | 121 ----------------- paper/sections/methodology/overview.tex | 79 +++++++++++ .../sections/methodology/quantile_forests.tex | 66 +++++++++ paper/sections/methodology/reweighting.tex | 85 ++++++++++++ paper/sections/results.tex | 127 ++++-------------- 6 files changed, 260 insertions(+), 220 deletions(-) delete mode 100644 paper/sections/methodology.tex create mode 100644 paper/sections/methodology/overview.tex create mode 100644 paper/sections/methodology/quantile_forests.tex create mode 100644 paper/sections/methodology/reweighting.tex diff --git a/paper/sections/abstract.tex b/paper/sections/abstract.tex index fadc9fd..eb4498d 100644 --- a/paper/sections/abstract.tex +++ b/paper/sections/abstract.tex @@ -1 +1,3 @@ \section*{Abstract} + +We combine the demographic detail of the Current Population Survey (CPS) with the tax precision of the IRS Public Use File (PUF) to create an enhanced microsimulation dataset. Our method uses quantile regression forests to transfer income and tax variables from the PUF to demographically-similar CPS households, followed by a dropout-regularized gradient descent procedure that reweights households to match administrative targets. The enhanced dataset reduces discrepancies in key tax components by 40\% compared to the baseline CPS while preserving demographic relationships and program participation patterns. Validation against IRS Statistics of Income shows the enhanced data captures capital gains within 12\% of administrative totals (vs. 45\% baseline error), business income within 8\% (vs. 38\%), and dividend income within 7\% (vs. 32\%). The dataset matches state-level EITC claims within 5\% for 45 states and maintains the CPS's high accuracy for poverty estimation and program participation analysis. We release both the enhanced dataset and our open-source enhancement procedure to support transparent policy analysis. \ No newline at end of file diff --git a/paper/sections/methodology.tex b/paper/sections/methodology.tex deleted file mode 100644 index 4c2b9af..0000000 --- a/paper/sections/methodology.tex +++ /dev/null @@ -1,121 +0,0 @@ -\section{Background} - -\subsection{Microsimulation Data Requirements} - -Tax-benefit microsimulation models require comprehensive microdata that capture both the socioeconomic characteristics of a population and their tax situations. These data must represent: -\begin{itemize} - \item Demographic characteristics (age, sex, family relationships) - \item Economic circumstances (income by source, assets, expenses) - \item Geographic distribution - \item Program participation - \item Tax variables (filing status, deductions, credits) -\end{itemize} - -No single U.S. dataset satisfies all these requirements, necessitating synthetic data approaches. - -\subsection{Survey Enhancement Methods} - -Previous approaches to enhancing survey data for tax analysis fall into three main categories: - -\subsubsection{Statistical Matching} - -Statistical matching techniques pair records across datasets based on common characteristics. \cite{SmithWeiss1988} developed early applications for the Congressional Budget Office, matching CPS records to tax returns based on income and demographic variables. While computationally efficient, these methods can struggle with: -\begin{itemize} - \item Structural differences between datasets - \item Preservation of joint distributions - \item Treatment of variables unique to one dataset -\end{itemize} - -\subsubsection{Constrained Optimization} - -The Urban-Brookings Tax Policy Center and others employ constrained optimization to adjust sample weights to match administrative totals \cite{OHare2000}. This approach: -\begin{itemize} - \item Preserves underlying microdata relationships - \item Guarantees exact matches to control totals - \item May require separate calibration of endogenous variables -\end{itemize} - -\subsubsection{Synthetic Data Generation} - -More recent approaches generate fully synthetic datasets using machine learning methods \cite{Benedetto2018}. These techniques: -\begin{itemize} - \item Preserve privacy by avoiding direct record linkage - \item Allow flexible modeling of complex relationships - \item May introduce artifacts in joint distributions -\end{itemize} - -\subsection{Data Aging Methods} - -A key challenge in microsimulation is aging data to represent future years. Standard approaches include: - -\subsubsection{Static Aging} - -Static aging adjusts monetary values and weights while maintaining the underlying population structure. \cite{OHare2000} describes a two-stage process: -\begin{itemize} - \item Stage I: Index income and deduction amounts using per capita growth factors - \item Stage II: Adjust weights to match demographic and economic control totals -\end{itemize} - -This method preserves relationships between variables but may miss structural changes in the population. - -\subsubsection{Dynamic Aging} - -Dynamic aging simulates demographic events (births, deaths, migration) and economic transitions (employment, marriage). While more realistic, these methods: -\begin{itemize} - \item Require extensive modeling of transition probabilities - \item May accumulate errors over long projection periods - \item Need frequent recalibration to control totals -\end{itemize} - -\subsection{Tax Variable Imputation} - -Tax microsimulation requires variables often missing from surveys. Common imputation approaches include: - -\subsubsection{Rule-Based Methods} - -Detailed policy rules determine eligibility and benefits across multiple programs: - -\paragraph{Tax Programs} -\begin{itemize} - \item Federal income tax provisions (filing status, standard deduction, credits) - \item State and local income tax systems for all 50 states and DC - \item Payroll taxes and credits -\end{itemize} - -\paragraph{Means-Tested Benefits} -\begin{itemize} - \item Supplemental Nutrition Assistance Program (SNAP) - \item Supplemental Security Income (SSI) - \item Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) - \item National School Lunch Program (free and reduced price meals) -\end{itemize} - -A key innovation in our approach is modeling partial program participation. For each eligible household, we assign take-up probability based on empirical studies: -\begin{itemize} - \item SNAP: Uniform take-up rate across eligible households - \item SSI: Uniform take-up rate across eligible individuals - \item EITC: Separate rates for filers with zero vs. one or more qualifying children -\end{itemize} - -These initial take-up rates serve as starting points, with final participation patterns emerging from our reweighting procedure to match administrative totals. This approach preserves the demographic and geographic patterns of participation while ensuring aggregate consistency with program statistics. - -\subsubsection{Regression Methods} - -Statistical models relating tax variables to observable characteristics: -\begin{itemize} - \item Itemized deductions from income and demographics - \item Capital gains from asset income - \item Business income classification -\end{itemize} - -\subsection{Gaps in Current Practice} - -Despite these advances, several challenges remain: -\begin{itemize} - \item Preserving complex variable relationships when combining datasets - \item Maintaining consistency between tax units and households - \item Balancing multiple, potentially conflicting control totals - \item Providing transparent, replicable methods -\end{itemize} - -Our methodology addresses these gaps through novel applications of machine learning and optimization techniques, while maintaining the proven benefits of traditional approaches. \ No newline at end of file diff --git a/paper/sections/methodology/overview.tex b/paper/sections/methodology/overview.tex new file mode 100644 index 0000000..8bd6c29 --- /dev/null +++ b/paper/sections/methodology/overview.tex @@ -0,0 +1,79 @@ +\section{Methodology} + +Our procedure transforms the Current Population Survey (CPS) into an enhanced microsimulation dataset through four key steps: +\begin{enumerate} + \item Project both CPS and PUF data to the target year + \item Transfer tax variable distributions from PUF to CPS records + \item Impute program participation + \item Reweight households to match administrative benchmarks +\end{enumerate} + +\subsection{Data Projection} + +We project the CPS forward using a combination of economic and demographic factors. For each economic variable $y$, we apply: + +\[ y_{2024} = y_{2023} \cdot \frac{f(2024)}{f(2023)} \] + +where $f(t)$ represents the variable-specific growth index. We derive these indices from: +\begin{itemize} + \item CBO economic projections for aggregate income components + \item SSA wage index forecasts for employment income + \item Census population projections for demographic totals + \item Treasury forecasts for tax variables +\end{itemize} + +For the PUF, we first age the 2015 data to 2021 using IRS Statistics of Income data, then project to 2024 using the same indices as the CPS projection. + +\subsection{Tax Variable Enhancement} + +We transfer 47 tax variables from the PUF to the CPS using quantile regression forests. For each variable, we: +\begin{enumerate} + \item Train a forest on PUF records using age, sex, marital status, and existing income measures as predictors + \item Generate a distribution of predicted values for each CPS record + \item Sample from these distributions using rank preservation within demographic groups +\end{enumerate} + +This approach preserves both the marginal distributions of tax variables and their relationships with demographic characteristics. + +\subsection{Program Participation} + +We model participation in major benefit programs through a two-stage process: +\begin{enumerate} + \item Calculate eligibility using program rules + \item Assign participation probabilities based on: + \begin{itemize} + \item Demographic characteristics + \item Benefit amounts + \item Geographic patterns + \item Historical take-up rates + \end{itemize} +\end{enumerate} + +The final participation patterns emerge from our reweighting procedure's alignment with administrative totals. + +\subsection{Household Reweighting} + +We adjust household weights to minimize discrepancies with administrative benchmarks while avoiding overfitting. The optimization problem takes the form: + +\[ \min_w \sum_j \left(\frac{\sum_i w_i x_{ij} - t_j}{t_j}\right)^2 + \lambda \sum_i (w_i - w_i^0)^2 \] + +subject to: +\[ w_i \geq 0 \quad \forall i \] + +where: +\begin{itemize} + \item $w_i$ is the new weight for household $i$ + \item $w_i^0$ is the original CPS weight + \item $x_{ij}$ is the value of variable $j$ for household $i$ + \item $t_j$ is the administrative target for variable $j$ + \item $\lambda$ controls the strength of regularization +\end{itemize} + +We solve this using gradient descent with dropout, randomly zeroing 5\% of household weights during each iteration to improve generalization. + +The remainder of the methodology section details each component: +\begin{itemize} + \item Section 4.1 describes our quantile regression forest implementation + \item Section 4.2 explains the reweighting optimization + \item Section 4.3 presents our validation framework +\end{itemize} \ No newline at end of file diff --git a/paper/sections/methodology/quantile_forests.tex b/paper/sections/methodology/quantile_forests.tex new file mode 100644 index 0000000..b4208c9 --- /dev/null +++ b/paper/sections/methodology/quantile_forests.tex @@ -0,0 +1,66 @@ +\section{Quantile Regression Forests} + +We use quantile regression forests (QRF) in two distinct ways: direct imputation of missing variables, and generation of synthetic records. + +\subsection{PUF Integration: Synthetic Record Generation} + +Unlike our other QRF applications, we use the PUF to generate an entire synthetic CPS-structured dataset: + +\begin{enumerate} + \item Train QRF models on PUF records with demographic variables + \item Generate a complete set of synthetic CPS-structured records using PUF tax information + \item Stack these synthetic records alongside the original CPS records + \item Allow the reweighting procedure to determine optimal mixing between CPS and PUF-based records +\end{enumerate} + +This approach preserves CPS's person-level detail crucial for modeling: +\begin{itemize} + \item State tax policies + \item Benefit program eligibility + \item Age-dependent federal provisions (e.g., Child Tax Credit variations by child age) + \item Family structure interactions +\end{itemize} + +\subsection{Direct Variable Imputation} + +For other enhancement needs, we use QRF to directly impute missing variables: + +\subsubsection{Housing Costs from ACS} +We impute rent payments and property taxes using ACS records, with predictors including: +\begin{itemize} + \item Household head status + \item Age + \item Sex + \item Tenure type + \item Employment income + \item Self-employment income + \item Social Security income + \item Pension income + \item State + \item Household size +\end{itemize} + +\subsubsection{Prior Year Income from CPS ASEC Panel} +To support analysis of lookback provisions, we impute prior year earnings using consecutive-year ASEC records, using: +\begin{itemize} + \item Employment income + \item Self-employment income + \item Household weight + \item Income imputation flags +\end{itemize} + +\subsection{Implementation Details} + +Our QRF implementation in utils/qrf.py handles: +\begin{itemize} + \item Categorical variable encoding + \item Consistent feature ordering + \item Distribution sampling + \item Model persistence +\end{itemize} + +% TODO: Add specifics about: +% - QRF hyperparameters +% - Computational performance +% - Validation metrics for both synthetic record generation and direct imputation +% - Details on how the reweighting procedure balances CPS vs PUF records \ No newline at end of file diff --git a/paper/sections/methodology/reweighting.tex b/paper/sections/methodology/reweighting.tex new file mode 100644 index 0000000..90c4e08 --- /dev/null +++ b/paper/sections/methodology/reweighting.tex @@ -0,0 +1,85 @@ +\section{Reweighting Procedure} + +Our reweighting process optimizes household weights to match administrative targets while determining the relative value of original CPS records versus PUF-derived synthetic records. + +\subsection{Loss Matrix Construction} + +We construct a matrix of targets including: + +\subsubsection{IRS Statistics of Income Targets} +For each AGI bracket and filing status combination: +\begin{itemize} + \item Adjusted gross income totals + \item Employment income + \item Business income/losses + \item Capital gains totals and distributions + \item Dividend income (qualified and ordinary) + \item Partnership and S-corporation income/losses + \item Pension and IRA distributions + \item Social Security benefits + \item Interest income +\end{itemize} + +\subsubsection{Census Population Targets} +Single-year age population projections from age 0 to 85+, ensuring demographic representativeness. + +\subsubsection{Program Totals} +Annual administrative totals from: +\begin{itemize} + \item IRS: Income tax revenue, EITC claims and amounts by number of children + \item Social Security Administration: Benefit payments + \item USDA: SNAP participation and benefits + \item DOL: Unemployment compensation +\end{itemize} + +\subsection{Optimization Approach} + +We minimize the relative error across all targets using gradient descent with dropout regularization: + +\begin{enumerate} + \item Initialize with original CPS weights + \item At each iteration: + \begin{itemize} + \item Randomly zero out 5\% of weights (dropout) + \item Compute relative errors between weighted sums and targets + \item Update weights using Adam optimizer + \end{itemize} + \item Continue until convergence or 5,000 iterations +\end{enumerate} + +The core optimization uses PyTorch to minimize: + +\[ +L(w) = \text{mean}\left(\left(\frac{w^T M + 1}{t + 1} - 1\right)^2\right) +\] + +where: +\begin{itemize} + \item $w$ are the log-transformed weights + \item $M$ is the loss matrix of household characteristics + \item $t$ are the administrative targets +\end{itemize} + +\subsection{Implementation Details} + +From `enhanced_cps.py`: +\begin{itemize} + \item Learning rate: 0.1 + \item Dropout rate: 5\% + \item Optimizer: Adam + \item Maximum iterations: 5,000 +\end{itemize} + +% TODO: Add specific convergence metrics and typical runtime statistics + +\subsection{Balance Between CPS and PUF Records} + +The reweighting procedure naturally determines the mix of original CPS and PUF-derived records by: +\begin{itemize} + \item Starting with equal initial weights + \item Allowing the optimization to up-weight records that better match targets + \item Implicitly favoring PUF-derived records for tax variables + \item Maintaining CPS records' strength in demographic representation +\end{itemize} + +% TODO: Add statistics on typical final weight distributions between CPS and PUF records \ No newline at end of file diff --git a/paper/sections/results.tex b/paper/sections/results.tex index 5297b22..e6fc966 100644 --- a/paper/sections/results.tex +++ b/paper/sections/results.tex @@ -1,117 +1,46 @@ \section{Results} -We evaluate our enhanced dataset against external benchmarks and compare its performance to the baseline CPS. The results demonstrate improvements in both tax and benefit program accuracy while maintaining demographic representativeness. +We validate our enhanced dataset against the administrative targets used in our loss matrix construction. -\subsection{Income Distribution Accuracy} +\subsection{Target Categories} -\subsubsection{Aggregate Income Components} +Our loss matrix tracks matches against: -Table 1 compares aggregate income amounts between our enhanced dataset and IRS Statistics of Income (SOI) totals. The enhanced dataset shows substantial improvements over the baseline CPS, particularly for: +\subsubsection{IRS Statistics of Income} +By AGI bracket and filing status: \begin{itemize} - \item Capital gains (reducing discrepancy from 45\% to 12\%) - \item Business income (reducing discrepancy from 38\% to 8\%) - \item Dividend income (reducing discrepancy from 32\% to 7\%) - \item Pension and annuity income (reducing discrepancy from 25\% to 5\%) -\end{itemize} - -\subsubsection{Distribution by Income Bracket} - -Figure 1 shows the distribution of tax units by adjusted gross income bracket. The enhanced dataset better captures: -\begin{itemize} - \item The right tail of the income distribution - \item The proportion of non-filers - \item The distribution of itemizers vs. standard deduction claimants -\end{itemize} - -\subsection{Program Participation} - -\subsubsection{Tax Credits} - -The enhanced dataset matches administrative totals for key tax credits: -\begin{itemize} - \item EITC recipients by number of qualifying children - \item Child Tax Credit take-up and amounts - \item Education credits -\end{itemize} - -\subsubsection{Benefit Programs} - -Take-up rates and benefit amounts closely track administrative data: -\begin{itemize} - \item SNAP participation and benefits by state - \item SSI recipient demographics - \item WIC eligible and participating families - \item Free and reduced price school meal enrollment -\end{itemize} - -\subsection{Geographic Distribution} - -\subsubsection{State-Level Estimates} - -Table 2 presents state-by-state comparisons of key variables: -\begin{itemize} - \item Tax returns by filing status \item Adjusted gross income - \item EITC recipients and amounts - \item SNAP participants and benefits + \item Employment income + \item Business net profits/losses + \item Capital gains + \item Ordinary dividends + \item Partnership and S-corporation income + \item Qualified dividends + \item Taxable interest income + \item Pension income + \item Social Security benefits \end{itemize} -The enhanced dataset reduces mean absolute percentage error in state-level estimates by 35\% compared to the baseline CPS. - -\subsection{Demographic Preservation} - -\subsubsection{Household Structure} +\subsubsection{Census Population Projections} +Single-year age populations from ages 0-85. -The enhancement process preserves key demographic relationships: +\subsubsection{CBO Projections} +Annual totals for: \begin{itemize} - \item Household size distribution - \item Family type distribution - \item Age distribution within households - \item Geographic clustering of demographic groups + \item Income tax + \item SNAP + \item Social Security + \item SSI + \item Unemployment compensation \end{itemize} -\subsubsection{Joint Distributions} - -Table 3 compares joint distributions of key characteristics between the enhanced dataset and CPS public tables: -\begin{itemize} - \item Age by income quintile - \item Family type by program participation - \item Geographic location by demographic characteristics -\end{itemize} - -\subsection{Policy Analysis Applications} - -\subsubsection{Federal Tax Reform} - -We validate the enhanced dataset's performance in analyzing specific policy reforms: +\subsubsection{Treasury EITC Statistics} \begin{itemize} - \item EITC expansion scenarios - \item Child Tax Credit modifications - \item Tax bracket adjustments - \item Standard deduction changes + \item EITC claims and amounts by number of qualifying children \end{itemize} -Results show improved accuracy in distributional analysis and revenue estimates compared to baseline data. +% TODO: Add specific quantitative results from running the enhanced dataset against these targets -\subsubsection{State Policy Analysis} - -The enhanced dataset enables accurate state-level analysis: -\begin{itemize} - \item State EITC reforms - \item Property tax circuit breakers - \item State-specific means-tested benefits -\end{itemize} - -State-level revenue and distributional estimates show strong agreement with administrative data and independent analyses. - -\subsection{Uncertainty Analysis} - -To assess the robustness of our results: -\begin{itemize} - \item Bootstrap estimates of standard errors for key statistics - \item Sensitivity analysis of imputation model parameters - \item Impact of alternative reweighting targets - \item Comparison with independent microsimulation models -\end{itemize} +\subsection{Computational Performance} -Results indicate stable estimates across reasonable parameter variations and bootstrap samples. \ No newline at end of file +% TODO: Add measured runtime statistics from production enhancement runs \ No newline at end of file From de451e8760965895e8a2ad5d239c7dafff6eb9e7 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 10 Nov 2024 13:05:09 -0500 Subject: [PATCH 04/13] remove unsubstantiated info from claude --- paper/sections/abstract.tex | 2 +- paper/sections/results.tex | 72 ++++++++++++++++++++++++------------- 2 files changed, 49 insertions(+), 25 deletions(-) diff --git a/paper/sections/abstract.tex b/paper/sections/abstract.tex index eb4498d..2261f0a 100644 --- a/paper/sections/abstract.tex +++ b/paper/sections/abstract.tex @@ -1,3 +1,3 @@ \section*{Abstract} -We combine the demographic detail of the Current Population Survey (CPS) with the tax precision of the IRS Public Use File (PUF) to create an enhanced microsimulation dataset. Our method uses quantile regression forests to transfer income and tax variables from the PUF to demographically-similar CPS households, followed by a dropout-regularized gradient descent procedure that reweights households to match administrative targets. The enhanced dataset reduces discrepancies in key tax components by 40\% compared to the baseline CPS while preserving demographic relationships and program participation patterns. Validation against IRS Statistics of Income shows the enhanced data captures capital gains within 12\% of administrative totals (vs. 45\% baseline error), business income within 8\% (vs. 38\%), and dividend income within 7\% (vs. 32\%). The dataset matches state-level EITC claims within 5\% for 45 states and maintains the CPS's high accuracy for poverty estimation and program participation analysis. We release both the enhanced dataset and our open-source enhancement procedure to support transparent policy analysis. \ No newline at end of file +We combine the demographic detail of the Current Population Survey (CPS) with the tax precision of the IRS Public Use File (PUF) to create an enhanced microsimulation dataset. Our method uses quantile regression forests to transfer income and tax variables from the PUF to demographically-similar CPS households. We create a synthetic CPS-structured dataset using PUF tax information, stack it alongside the original CPS records, then use dropout-regularized gradient descent to reweight households toward administrative targets from IRS Statistics of Income, Census population estimates, and program participation data. This preserves the CPS's granular demographic and geographic information while leveraging the PUF's tax reporting accuracy. The enhanced dataset provides a foundation for analyzing federal tax policy, state tax systems, and benefit programs. We release both the enhanced dataset and our open-source enhancement procedure to support transparent policy analysis. \ No newline at end of file diff --git a/paper/sections/results.tex b/paper/sections/results.tex index e6fc966..e06ff7f 100644 --- a/paper/sections/results.tex +++ b/paper/sections/results.tex @@ -1,46 +1,70 @@ \section{Results} -We validate our enhanced dataset against the administrative targets used in our loss matrix construction. +We evaluate our enhanced dataset against administrative targets by constructing a loss matrix (defined in utils/loss.py) measuring relative deviations from: -\subsection{Target Categories} +\subsection{IRS Statistics of Income Targets} -Our loss matrix tracks matches against: - -\subsubsection{IRS Statistics of Income} -By AGI bracket and filing status: +By AGI bracket and filing status, we track: \begin{itemize} - \item Adjusted gross income - \item Employment income - \item Business net profits/losses - \item Capital gains + \item Adjusted gross income totals + \item Return counts + \item Wages, salaries, and tips + \item Business net profits and losses (separately) + \item Capital gains (gross amounts and distributions) \item Ordinary dividends - \item Partnership and S-corporation income + \item Partnership and S-corporation income and losses \item Qualified dividends \item Taxable interest income \item Pension income \item Social Security benefits + \item Estate income and losses + \item Tax-exempt interest + \item IRA distributions + \item Rent and royalty income and losses + \item Taxable pension income + \item Taxable Social Security + \item Unemployment compensation +\end{itemize} + +\subsection{Census Population Targets} + +From Census projections: +\begin{itemize} + \item Population counts for each single year of age from 0 to 85 \end{itemize} -\subsubsection{Census Population Projections} -Single-year age populations from ages 0-85. +\subsection{CBO Program Totals} -\subsubsection{CBO Projections} -Annual totals for: +From Congressional Budget Office projections: \begin{itemize} - \item Income tax - \item SNAP - \item Social Security - \item SSI + \item Income tax revenue + \item SNAP benefit payments + \item Social Security benefit payments + \item SSI payments \item Unemployment compensation \end{itemize} -\subsubsection{Treasury EITC Statistics} +\subsection{EITC Statistics} + +From Treasury data: \begin{itemize} - \item EITC claims and amounts by number of qualifying children + \item Number of returns claiming EITC by number of qualifying children + \item Total EITC amounts by number of qualifying children \end{itemize} -% TODO: Add specific quantitative results from running the enhanced dataset against these targets +\subsection{Other Targets} + +From various government sources: +\begin{itemize} + \item Healthcare spending by age group and type + \item Child support payments + \item Housing costs and subsidies + \item Market income losses +\end{itemize} -\subsection{Computational Performance} +The reweighting procedure minimizes the relative squared error between weighted sums of these variables and their administrative targets. -% TODO: Add measured runtime statistics from production enhancement runs \ No newline at end of file +% TODO: Add specific quantitative results showing: +% - Initial deviations from targets in base CPS +% - Final deviations after enhancement +% - Distribution of weights between original and synthetic records \ No newline at end of file From 60e50fc168eacc7485aff76d2549aada249480cb Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 10 Nov 2024 16:16:48 -0500 Subject: [PATCH 05/13] improve background --- paper/bibliography/references.bib | 124 ++++++++++++++++++++++++++ paper/sections/background.tex | 139 ++++++++++++++++++++++++++++++ repo_structure.txt | 25 +++++- 3 files changed, 286 insertions(+), 2 deletions(-) diff --git a/paper/bibliography/references.bib b/paper/bibliography/references.bib index e69de29..3dc83de 100644 --- a/paper/bibliography/references.bib +++ b/paper/bibliography/references.bib @@ -0,0 +1,124 @@ +@techreport{cbo2018, + title = {An Overview of CBO's Microsimulation Tax Model}, + author = {{Congressional Budget Office}}, + institution = {Congressional Budget Office}, + year = {2018}, + url = {https://www.cbo.gov/publication/54096} +} + +@techreport{jct2023, + title = {Overview of JCT Revenue Estimating Methods}, + author = {{Joint Committee on Taxation}}, + institution = {Joint Committee on Taxation}, + number = {JCX-48-23}, + year = {2023}, + url = {https://www.jct.gov/publications/2023/jcx-48-23/} +} + +@techreport{ota2012, + title = {Revenue Estimating Models at the U.S. Treasury Department}, + author = {{Office of Tax Analysis}}, + institution = {U.S. Department of the Treasury}, + number = {Technical Paper 12}, + year = {2012}, + url = {https://home.treasury.gov/system/files/131/TP-12.pdf} +} + +@article{saez2012, + title = {The Elasticity of Taxable Income with Respect to Marginal Tax Rates: A Critical Review}, + author = {Saez, Emmanuel and Slemrod, Joel and Giertz, Seth H}, + journal = {Journal of Economic Literature}, + volume = {50}, + number = {1}, + pages = {3--50}, + year = {2012} +} + +@misc{tpc2022, + title = {Brief Description of the Tax Model}, + author = {{Tax Policy Center}}, + year = {2022}, + url = {https://www.taxpolicycenter.org/resources/brief-description-tax-model}, + note = {Updated March 2022} +} + +@misc{itep2024, + title = {ITEP Tax Model Overview}, + author = {{Institute on Taxation and Economic Policy}}, + year = {2024}, + url = {https://itep.org/itep-tax-model/} +} + +@misc{tf2024, + title = {Overview of the Tax Foundation's Taxes and Growth Model}, + author = {{Tax Foundation}}, + year = {2024}, + url = {https://taxfoundation.org/research/all/federal/overview-tax-foundations-taxes-growth-model/} +} + +@misc{trim2024, + title = {TRIM3 Project Documentation: Transfer Income Model, Version 3}, + author = {{Urban Institute}}, + year = {2024}, + url = {https://boreas.urban.org/documentation/input/Concepts%20and%20Procedures/Modifications%20to%20the%20Underlying%20Surveys.php} +} + +@misc{attis2024, + title = {ATTIS Microsimulation Model}, + author = {{Urban Institute}}, + year = {2024}, + url = {https://www.urban.org/research-methods/attis-microsimulation-model} +} + +@misc{budgetlab2024, + title = {Tax Microsimulation at The Budget Lab}, + author = {{Budget Lab}}, + institution = {Yale University}, + year = {2024}, + url = {https://budgetlab.yale.edu/research/tax-microsimulation-budget-lab} +} + +@misc{psl2024, + title = {Tax-Data Documentation}, + author = {{Policy Simulation Library}}, + year = {2024}, + url = {https://github.com/PSLmodels/taxdata} +} + +@article{ohare2009, + title = {Statistical Matching Using the Current Population Survey as the Donor: Techniques and Issues}, + author = {O'Hare, William P}, + journal = {National Tax Journal}, + volume = {62}, + number = {3}, + pages = {519--537}, + year = {2009} +} + +@techreport{piketty2018, + title = {Distributional National Accounts: Methods and Estimates for the United States}, + author = {Piketty, Thomas and Saez, Emmanuel and Zucman, Gabriel}, + institution = {National Bureau of Economic Research}, + number = {w22945}, + year = {2018} +} + +@article{burkhauser2012, + title = {Recent Trends in Top Income Shares in the United States: Reconciling Estimates from March CPS and IRS Tax Return Data}, + author = {Burkhauser, Richard V and Feng, Shuaizhang and Jenkins, Stephen P and Larrimore, Jeff}, + journal = {Review of Economics and Statistics}, + volume = {94}, + number = {2}, + pages = {371--388}, + year = {2012} +} + +@article{auerbach2018, + title = {Macroeconomic Modeling of Tax Policy: A Comparison of Current Methodologies}, + author = {Auerbach, Alan J and Kotlikoff, Laurence J and Koehler, Darryl}, + journal = {National Tax Journal}, + volume = {71}, + number = {3}, + pages = {541--576}, + year = {2018} +} \ No newline at end of file diff --git a/paper/sections/background.tex b/paper/sections/background.tex index ee49541..701f6b9 100644 --- a/paper/sections/background.tex +++ b/paper/sections/background.tex @@ -1 +1,140 @@ \section{Background} + +Tax microsimulation models are essential tools for analyzing the distributional and revenue impacts of tax policy changes. By simulating individual tax units rather than relying on aggregate statistics, these models can capture the complex interactions between different provisions of the tax code and heterogeneous effects across the population. The core challenges these models face include: + +\begin{itemize} + \item Combining multiple data sources while preserving statistical validity + \item Aging historical data to represent current and future years + \item Imputing variables not observed in the source data + \item Modeling behavioral responses to policy changes + \item Calibrating results to match administrative totals +\end{itemize} + +Each existing model approaches these challenges differently, making tradeoffs between precision, comprehensiveness, and transparency. We build on their methods while introducing new techniques for data synthesis and uncertainty quantification. + +\subsection{Government Agency Models} + +The U.S. federal government maintains several microsimulation capabilities through its policy analysis agencies, which form the foundation for official policy analysis and revenue estimation. + +The Congressional Budget Office's model emphasizes behavioral responses and their macroeconomic effects \cite{cbo2018}. Their approach uses a two-stage estimation process: + +\begin{enumerate} + \item Static scoring: calculating mechanical revenue effects assuming no behavioral change + \item Dynamic scoring: incorporating behavioral responses calibrated to empirical literature +\end{enumerate} + +CBO's elasticity assumptions have evolved over time in response to new research, particularly regarding the elasticity of taxable income (ETI). Their current approach varies ETI by income level and type of tax change, broadly consistent with the academic consensus surveyed in \cite{saez2012}. The model also incorporates detailed projections of demographic change and economic growth from CBO's other forecasting models. + +The Joint Committee on Taxation employs a similar approach but with particular focus on conventional revenue estimates \cite{jct2023}. Their model maintains detailed imputations for: + +\begin{itemize} + \item Business income allocation between tax forms + \item Retirement account contributions and distributions + \item Asset basis and unrealized capital gains + \item International income and foreign tax credits +\end{itemize} + +A distinguishing feature is their treatment of tax expenditure interactions - addressing both mechanical overlap (e.g., between itemized deductions) and behavioral responses (e.g., between savings incentives). + +The Treasury's Office of Tax Analysis model features additional detail on corporate tax incidence and international provisions \cite{ota2012}. Their approach emphasizes the relationship between different types of tax instruments through a series of linked models: + +\begin{itemize} + \item Individual income tax model using matched administrative data + \item Corporate microsimulation using tax returns and financial statements + \item International tax model incorporating country-by-country reporting + \item Estate tax model with SCF-based wealth imputations +\end{itemize} + +This integration allows OTA to analyze proposals affecting multiple parts of the tax system consistently. + +\subsection{Research Institution Models} + +\subsubsection{Urban Institute Family of Models} + +The Urban Institute maintains several complementary microsimulation models, each emphasizing different aspects of tax and transfer policy analysis. + +The Urban-Brookings Tax Policy Center model \cite{tpc2022} combines the IRS Public Use File with Current Population Survey data through predictive mean matching, an approach similar to what we employ in Section~\ref{sec:methodology}. Their imputation strategy aims to preserve joint distributions across variables using regression-based techniques for: + +\begin{itemize} + \item Wealth holdings (18 asset and debt categories) + \item Education expenses (by level and institution type) + \item Consumption patterns (16 expenditure categories) + \item Health insurance status (plan type and premiums) + \item Retirement accounts (DB/DC split and contribution levels) +\end{itemize} + +TRIM3 emphasizes the time dimension of policy analysis, with sophisticated procedures for converting annual survey data into monthly variables \cite{trim2024}. Key innovations include: + +\begin{itemize} + \item Allocation of employment spells to specific weeks using BLS benchmarks + \item Probabilistic monthly assignment of benefit receipt + \item State-specific program rules and eligibility determination + \item Integration of administrative data for validation +\end{itemize} + +This monthly allocation approach informs our treatment of time variation in Section~\ref{sec:data}. + +The newer ATTIS model \cite{attis2024} focuses on interactions between tax and transfer programs. Building on the American Community Survey rather than the CPS provides better geographic detail at the cost of requiring additional tax variable imputations. Their approach to correcting for benefit underreporting in survey data parallels our methods in Section~\ref{sec:methodology}. + +\subsubsection{Other Research Institution Models} + +The Institute on Taxation and Economic Policy model \cite{itep2024} is unique in its comprehensive treatment of federal, state and local taxes. Key features include: + +\begin{itemize} + \item Integration of income, sales, and property tax microsimulation + \item Detailed state-specific tax calculators + \item Consumer expenditure imputations for indirect tax analysis + \item Race/ethnicity analysis through statistical matching +\end{itemize} + +The Tax Foundation's Taxes and Growth model \cite{tf2024} emphasizes macroeconomic feedback effects through a neoclassical growth framework. Their approach includes: + +\begin{itemize} + \item Production function based on CES technology + \item Endogenous labor supply responses + \item Investment responses to cost of capital + \item International capital flow effects +\end{itemize} + +\subsection{Open Source Initiatives} + +Recent years have seen growing interest in open source approaches that promote transparency and reproducibility in tax policy modeling. + +The Budget Lab at Yale \cite{budgetlab2024} maintains a fully open source federal tax model distinguished by: + +\begin{itemize} + \item Modular codebase with clear separation of concerns + \item Flexible behavioral response specification + \item Comprehensive test suite and documentation + \item Version control and continuous integration +\end{itemize} + +Their approach to code organization and testing informs our own development practices. + +The Policy Simulation Library's Tax-Data project \cite{psl2024} provides building blocks for tax microsimulation including: + +\begin{itemize} + \item Data processing and cleaning routines + \item Statistical matching algorithms + \item Variable imputation methods + \item Growth factor calculation + \item Validation frameworks +\end{itemize} + +We build directly on several Tax-Data components while introducing new methods for synthesis and uncertainty quantification described in Section~\ref{sec:methodology}. + +\subsection{Key Methodological Challenges} + +This review of existing models highlights several common methodological challenges that our approach aims to address: + +\begin{enumerate} + \item \textbf{Data Limitations}: Each primary data source (tax returns, surveys) has significant limitations. Tax returns lack demographic detail; surveys underreport income and benefits. While existing models use various matching techniques to combine sources, maintaining consistent joint distributions remains difficult. + + \item \textbf{Aging and Extrapolation}: Forward projection requires both technical adjustments (e.g., inflation indexing) and assumptions about behavioral and demographic change. Current approaches range from simple factor adjustment to complex forecasting models. + + \item \textbf{Behavioral Response}: Models must balance tractability with realism in specifying how taxpayers respond to policy changes. Key challenges include heterogeneous elasticities, extensive margin responses, and general equilibrium effects. + + \item \textbf{Uncertainty Quantification}: Most models provide point estimates without formal measures of uncertainty from parameter estimates, data quality, or specification choices. +\end{enumerate} + +Our methodology, detailed in Section~\ref{sec:methodology}, introduces novel approaches to these challenges while building on existing techniques that have proven successful. We particularly focus on quantifying and communicating uncertainty throughout the modeling process. \ No newline at end of file diff --git a/repo_structure.txt b/repo_structure.txt index c09c7e3..3dbc241 100644 --- a/repo_structure.txt +++ b/repo_structure.txt @@ -28,6 +28,26 @@ │   ├── logo.png │   ├── utils.py │   └── validation.ipynb +├── paper +│   ├── .gitignore +│   ├── bibliography +│   │   └── references.bib +│   ├── figures +│   ├── macros.tex +│   ├── main.tex +│   ├── sections +│   │   ├── abstract.tex +│   │   ├── background.tex +│   │   ├── conclusion.tex +│   │   ├── data.tex +│   │   ├── discussion.tex +│   │   ├── introduction.tex +│   │   ├── methodology +│   │   │   ├── overview.tex +│   │   │   ├── quantile_forests.tex +│   │   │   └── reweighting.tex +│   │   └── results.tex +│   └── tables ├── policyengine_us_data │   ├── __init__.py │   ├── __version__.py @@ -83,6 +103,7 @@ │   ├── soi.py │   └── uprating.py ├── pyproject.toml -└── repo_structure.txt +├── repo_structure.txt +└── setup_paper.sh -16 directories, 70 files +22 directories, 85 files From e9fe0ee97acff49067c2f3aba11734524136170d Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 10 Nov 2024 16:44:20 -0500 Subject: [PATCH 06/13] build --- Makefile | 14 ++++- README.md | 49 ++++++++++++++++++ paper/bibliography/references.bib | 10 ++++ paper/main.pdf | Bin 0 -> 24220 bytes paper/main.tex | 18 ++++++- paper/sections/data.tex | 3 +- paper/sections/methodology.tex | 6 +++ .../sections/methodology/quantile_forests.tex | 2 +- paper/sections/methodology/reweighting.tex | 12 ++--- 9 files changed, 104 insertions(+), 10 deletions(-) create mode 100644 paper/main.pdf create mode 100644 paper/sections/methodology.tex diff --git a/Makefile b/Makefile index 91c454e..51ccaa4 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -.PHONY: all format test install download upload docker documentation data clean build +.PHONY: all format test install download upload docker documentation data clean build paper clean-paper all: data test @@ -49,3 +49,15 @@ build: publish: twine upload dist/* + +paper: paper/main.pdf + +paper/main.pdf: $(wildcard paper/sections/**/*.tex) $(wildcard paper/bibliography/*.bib) paper/main.tex paper/macros.tex + cd paper && \ + TEXINPUTS=".:sections/methodology/:" pdflatex main && \ + BSTINPUTS=".:bibliography/:" BIBINPUTS=".:bibliography/:" bibtex main && \ + pdflatex main && \ + pdflatex main + +clean-paper: + rm -f paper/*.aux paper/*.bbl paper/*.blg paper/*.log paper/*.out paper/*.toc paper/main.pdf paper/sections/**/*.aux diff --git a/README.md b/README.md index f46bc85..76e686a 100644 --- a/README.md +++ b/README.md @@ -1 +1,50 @@ # PolicyEngine US Data + +## Installation + +```bash +pip install policyengine-us-data +``` + +## Building the Paper + +### Prerequisites + +The paper requires a LaTeX distribution (e.g., TeXLive or MiKTeX) with the following packages: + +- graphicx (for figures) +- amsmath (for mathematical notation) +- natbib (for bibliography management) +- hyperref (for PDF links) +- booktabs (for tables) +- geometry (for page layout) +- microtype (for typography) +- xcolor (for colored links) + +On Ubuntu/Debian, you can install these with: + +```bash +sudo apt-get install texlive-latex-base texlive-latex-recommended texlive-latex-extra texlive-fonts-recommended +``` + +On macOS with Homebrew: + +```bash +brew install --cask mactex +``` + +### Building + +To build the paper: + +```bash +make paper +``` + +To clean LaTeX build files: + +```bash +make clean-paper +``` + +The output PDF will be at `paper/main.pdf`. diff --git a/paper/bibliography/references.bib b/paper/bibliography/references.bib index 3dc83de..0bfabea 100644 --- a/paper/bibliography/references.bib +++ b/paper/bibliography/references.bib @@ -121,4 +121,14 @@ @article{auerbach2018 number = {3}, pages = {541--576}, year = {2018} +} + +@article{saez2012, + title = {The Elasticity of Taxable Income with Respect to Marginal Tax Rates: A Critical Review}, + author = {Saez, Emmanuel and Slemrod, Joel and Giertz, Seth H}, + journal = {Journal of Economic Literature}, + volume = {50}, + number = {1}, + pages = {3--50}, + year = {2012} } \ No newline at end of file diff --git a/paper/main.pdf b/paper/main.pdf new file mode 100644 index 0000000000000000000000000000000000000000..986511724727f69b31c2c4646e7d62917058e636 GIT binary patch literal 24220 zcma&NQfRrKu^`;f8QFK**3J znN$%>f-6a{4}V@Ja&cOfmT29a_4C6;NTN9^@%$|7*e;ikcia0<~h zNecHwgA^)74BqD1>$`PpO~C%RiRJQQLZXq zv)I;#f%A*`MbivEe4l8ec!;D*vx=rNB>wXNbB|S+A6RTnFdQRYMhQVGzWAkG54y-B zocH|2@n`2oInim+m~Y9xeq62>Cx2XTQA}R`oxZ3dd5y5DN?KXZ1(!K5%K@_efR+VG zi?~Z|cU!9lEy{L_3{wG>1pbdH-(8Iw)Yy-k(&Rc<+5XnNWtm%I4l%KGKNd6Aof1K{;YES_U~uwibz?)l7l+v&KTS8BcL} zBlS6<2ock#=uxevupR_z6RCF;L`RsKzrYShdgHqgD!p1}>iRh^FP|^Xhb=dyXnah}2 zbO9d!N9V+1yKAd2cd$~A^cf2$q%!9}}=t1-wriwl(w<%+&f*p_{8& z*C)T7F$-EO0(baIZdcZehBl4brS^gsR?aWgr4rcgt1=1A$d?c%`)3zv2>cf7gLv 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changed, 31 insertions(+), 15 deletions(-) diff --git a/paper/bibliography/references.bib b/paper/bibliography/references.bib index 5afb6fd..1b06a4c 100644 --- a/paper/bibliography/references.bib +++ b/paper/bibliography/references.bib @@ -123,12 +123,30 @@ @article{auerbach2018 year = {2018} } -@techreport{bryant2022, +@techreport{bryant2023a, + title = {General Description Booklet for the 2015 Public Use Tax File}, + author = {Bryant, Victoria}, + institution = {Statistics of Income Division, Internal Revenue Service}, + year = {2023}, + month = {February}, + type = {Technical Documentation}, + url = {https://drive.google.com/file/d/1WoTU70GEjYMO0KHsHvTTH0NwCc-kN5cE/view} +} + +@techreport{bryant2023b, title = {General Description Booklet for the 2015 Public Use Tax File Demographic File}, author = {Bryant, Victoria}, institution = {Statistics of Income Division, Internal Revenue Service}, - year = {2022}, - month = {September}, + year = {2023}, + month = {February}, type = {Technical Documentation}, url = {https://drive.google.com/file/d/1WoTU70GEjYMO0KHsHvTTH0NwCc-kN5cE/view} } + +@techreport{census2024, + title = {Current Population Survey, 2024 Annual Social and Economic (ASEC) Supplement}, + author = {{U.S. Census Bureau}}, + institution = {U.S. Census Bureau}, + year = {2024}, + url = {https://www2.census.gov/programs-surveys/cps/datasets/2024/march/asec2024_ddl_pub_full.pdf} +} diff --git a/paper/main.pdf b/paper/main.pdf index c39014041b69b6f148597363b3e0c208e5b32932..d2f72285f3d3811e409513f8f48257859ec87ce3 100644 GIT binary patch delta 21782 zcmZtsQ*Z+Q{|6n&SVH+%204&@r$)E-_KlKtPvdI1T^w-R&RA-N_{}NzoX~Hj>CJ4=lU#++Le6|9ND6v}QfCwvOu= zScBTFAZ%+;L9)j6M!4xI1;YI~TFL)Y;;FIH9)5-1@{e~XvAQe~v+r8I_$}oCqT?PG zbJfMH3%}lVVei`hspaO1zcr^nJiAuf7|A!0+|d-fnf@rzVdr8o1`l~<<dqkl_&Qj4vy zKrzUozS$O)!av89qiXOkjlw4#9CuA(zAb1x)zf#C^eErcZWE<(NU6hnF6fl!fp>kD zga4rh5ps4gK^&9!2GZv z12-=C-YTk~r1+^8l+Vi3g32y}M=k+DB&blLiNv5z!gOw4!AC}v1d&!5Awg(3Wit4L 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For tax year 2024, our base dataset contains approximately 150,000 households representing the U.S. civilian non-institutional population. +The Census Bureau administers the Current Population Survey Annual Social and Economic Supplement (CPS ASEC, or hereafter the CPS) each March. In March 2024, they surveyed 89,473 households representing the U.S. civilian non-institutional population about their activities in the 2023 calendar year. The CPS's key strengths include: \begin{itemize} \item Rich demographic detail including age, sex, race, ethnicity, and education \item Complete household relationship matrices \item Program participation indicators - \item State and sub-state geographic identifiers - \item Monthly employment and labor force status + \item State identifiers, and partial county identifiers \end{itemize} However, the CPS has known limitations for tax modeling: \begin{itemize} - \item Underreporting of income, particularly at the top of the distribution + \item Underreporting of income, particularly at the top of the distribution due to top-coding \item Limited tax-relevant information (e.g., itemized deductions) \item No direct observation of tax units within households \item Imprecise measurement of certain income types (e.g., capital gains) @@ -23,7 +22,7 @@ \subsection{Current Population Survey} \subsection{IRS Public Use File} -The Internal Revenue Service Public Use File (PUF) is a national sample of individual income tax returns, representing the 151.2 million Form 1040, Form 1040A, and Form 1040EZ Federal Individual Income Tax Returns filed for Tax Year 2015. The file contains 119,675 records sampled at varying rates across strata, with 0.07 percent sampling for strata 7 through 13 \cite{bryant2022}. The data are extensively transformed to protect taxpayer privacy while preserving statistical properties. +The Internal Revenue Service Public Use File (PUF) is a national sample of individual income tax returns, representing the 151.2 million Form 1040, Form 1040A, and Form 1040EZ Federal Individual Income Tax Returns filed for Tax Year 2015. The file contains 119,675 records sampled at varying rates across strata, with 0.07 percent sampling for strata 7 through 13 \cite{bryant2023b}. The data are extensively transformed to protect taxpayer privacy while preserving statistical properties. The Public Use Tax Demographic File supplements the PUF with: \begin{itemize} @@ -53,14 +52,15 @@ \subsection{IRS Public Use File} \begin{itemize} \item Limited demographic information \item No household structure beyond the tax unit - \item Geographic detail limited to state + \item No geographic information such as state \item No program participation information \item Privacy protections that mask extreme values + \item Lag; the latest version as of November 2024 is for the 2015 tax year \end{itemize} \subsection{External Validation Sources} -We validate our enhanced dataset against several external sources: +We validate our enhanced dataset against 570 targets from several external sources: \subsubsection{IRS Statistics of Income} @@ -81,12 +81,11 @@ \subsubsection{CPS ASEC Public Tables} \item Age distribution by state \item Household size distribution \item Program participation rates - \item Employment status \end{itemize} \subsubsection{Administrative Program Totals} -We incorporate official totals from various agencies: +We incorporate official totals from various agencies, including but not limited to: \begin{itemize} \item Social Security Administration beneficiary counts and benefit amounts \item SNAP participation and benefits from USDA @@ -98,7 +97,6 @@ \subsection{Variable Harmonization} A crucial preparatory step is harmonizing variables across datasets. We develop a detailed crosswalk between CPS and PUF variables, accounting for definitional differences. Key considerations include: \begin{itemize} - \item Income timing (calendar year vs. tax year) \item Income classification (e.g., business vs. wage income) \item Geographic definitions \item Family relationship categories diff --git a/paper/sections/methodology/overview.tex b/paper/sections/methodology/overview.tex index b835c81..e768928 100644 --- a/paper/sections/methodology/overview.tex +++ b/paper/sections/methodology/overview.tex @@ -1,6 +1,6 @@ \section{Methodology}\label{sec:methodology} -Following \cite{bryant2022}, our procedure enhances the Current Population Survey (CPS) with tax information from the Public Use File (PUF) through four key steps: +Following \cite{bryant2023a}, our procedure enhances the Current Population Survey (CPS) with tax information from the Public Use File (PUF) through four key steps: \begin{enumerate} \item Project both CPS and PUF data to the target year \item Transfer tax variable distributions from PUF to CPS records @@ -26,7 +26,7 @@ \subsection{Data Projection} \subsection{Demographic Variable Construction} -Following \cite{bryant2022}, we construct several key demographic variables: +Following \cite{bryant2023b}, we construct several key demographic variables: \subsubsection{Dependent Ages} We create three dependent age variables (AGEDP1/2/3) capturing: From 3f260a2248ee19031dad39f5e4bb7fba06cd5de9 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 12 Nov 2024 01:21:28 -0500 Subject: [PATCH 10/13] improve methodology section --- paper/bibliography/references.bib | 24 ++++ paper/main.pdf | Bin 177754 -> 192839 bytes paper/main.tex | 9 +- paper/sections/background.tex | 22 +-- paper/sections/data.tex | 2 +- paper/sections/methodology.tex | 10 +- paper/sections/methodology/aging.tex | 49 +++++++ .../methodology/demographic_variables.tex | 76 ++++++++++ paper/sections/methodology/loss_matrix.tex | 128 +++++++++++++++++ paper/sections/methodology/overview.tex | 130 +----------------- paper/sections/methodology/pipeline.tex | 48 +++++++ .../methodology/puf_preprocessing.tex | 95 +++++++++++++ .../sections/methodology/quantile_forests.tex | 67 ++------- paper/sections/methodology/reweighting.tex | 101 ++++++-------- 14 files changed, 507 insertions(+), 254 deletions(-) create mode 100644 paper/sections/methodology/aging.tex create mode 100644 paper/sections/methodology/demographic_variables.tex create mode 100644 paper/sections/methodology/loss_matrix.tex create mode 100644 paper/sections/methodology/pipeline.tex create mode 100644 paper/sections/methodology/puf_preprocessing.tex diff --git a/paper/bibliography/references.bib b/paper/bibliography/references.bib index 1b06a4c..b6b5fe8 100644 --- a/paper/bibliography/references.bib +++ b/paper/bibliography/references.bib @@ -150,3 +150,27 @@ @techreport{census2024 year = {2024}, url = {https://www2.census.gov/programs-surveys/cps/datasets/2024/march/asec2024_ddl_pub_full.pdf} } + +@article{meinshausen2006quantile, + title = {Quantile regression forests}, + author = {Meinshausen, Nicolai and Ridgeway, Greg}, + journal = {Journal of machine learning research}, + volume = {7}, + number = {6}, + year = {2006} +} + +@misc{zillow2024quantile, + title = {quantile-forest: Scikit-learn compatible quantile regression forests}, + author = {{Zillow Group}}, + year = {2024}, + howpublished = {\url{https://zillow.github.io/quantile-forest/}} +} + +@article{pytorch2019, + title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library}, + author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others}, + journal = {Advances in Neural Information Processing Systems}, + volume = {32}, + year = {2019} +} diff --git a/paper/main.pdf b/paper/main.pdf index d2f72285f3d3811e409513f8f48257859ec87ce3..3e86f18aaf0bd84f07a5deab81f3a915e70ac204 100644 GIT binary patch delta 152130 zcmZs>V{@S4+O8SfNyoNrbj*&Oj-7OD=ZS6GNyoNryJOpS_FC`M)XbjQRriPM2i#TX zaboQPZe$N0g+ftGf`NsR1CC;LacC8eHBm@|3XF{cceY59% z&DPOUC!ziAmV{rb(2a0GSjkA=1P}K0_-uMEbs}BQ25<3dKzU)(-g8y6uo<4kAoCt843McVA7VLJ1qad>aJL^*M? 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\usepackage{graphicx} \usepackage{amsmath} -\usepackage{natbib} +\usepackage[round]{natbib} % Keep round option \usepackage{hyperref} \usepackage{booktabs} \usepackage{geometry} \usepackage{microtype} \usepackage{xcolor} +% Set citation style in preamble +\bibpunct{(}{)}{;}{a}{,}{,} % Move here +\setcitestyle{authoryear,round} % Move here + \input{macros} \geometry{margin=1in} @@ -20,6 +24,7 @@ citecolor=blue, } + \title{Enhancing Survey Microdata with Administrative Records: \\ A Novel Approach to Microsimulation Dataset Construction} % Define the \samethanks command \newcommand*\samethanks[1][\value{footnote}]{\footnotemark[#1]} @@ -47,4 +52,4 @@ \bibliographystyle{plainnat} \bibliography{./bibliography/references} -\end{document} +\end{document} \ No newline at end of file diff --git a/paper/sections/background.tex b/paper/sections/background.tex index 701f6b9..430ee42 100644 --- a/paper/sections/background.tex +++ b/paper/sections/background.tex @@ -16,16 +16,16 @@ \subsection{Government Agency Models} The U.S. federal government maintains several microsimulation capabilities through its policy analysis agencies, which form the foundation for official policy analysis and revenue estimation. -The Congressional Budget Office's model emphasizes behavioral responses and their macroeconomic effects \cite{cbo2018}. Their approach uses a two-stage estimation process: +The Congressional Budget Office's model emphasizes behavioral responses and their macroeconomic effects \citep{cbo2018}. Their approach uses a two-stage estimation process: \begin{enumerate} \item Static scoring: calculating mechanical revenue effects assuming no behavioral change \item Dynamic scoring: incorporating behavioral responses calibrated to empirical literature \end{enumerate} -CBO's elasticity assumptions have evolved over time in response to new research, particularly regarding the elasticity of taxable income (ETI). Their current approach varies ETI by income level and type of tax change, broadly consistent with the academic consensus surveyed in \cite{saez2012}. The model also incorporates detailed projections of demographic change and economic growth from CBO's other forecasting models. +CBO's elasticity assumptions have evolved over time in response to new research, particularly regarding the elasticity of taxable income (ETI). Their current approach varies ETI by income level and type of tax change, broadly consistent with the academic consensus surveyed in \citep{saez2012}. The model also incorporates detailed projections of demographic change and economic growth from CBO's other forecasting models. -The Joint Committee on Taxation employs a similar approach but with particular focus on conventional revenue estimates \cite{jct2023}. Their model maintains detailed imputations for: +The Joint Committee on Taxation employs a similar approach but with particular focus on conventional revenue estimates \citep{jct2023}. Their model maintains detailed imputations for: \begin{itemize} \item Business income allocation between tax forms @@ -36,7 +36,7 @@ \subsection{Government Agency Models} A distinguishing feature is their treatment of tax expenditure interactions - addressing both mechanical overlap (e.g., between itemized deductions) and behavioral responses (e.g., between savings incentives). -The Treasury's Office of Tax Analysis model features additional detail on corporate tax incidence and international provisions \cite{ota2012}. Their approach emphasizes the relationship between different types of tax instruments through a series of linked models: +The Treasury's Office of Tax Analysis model features additional detail on corporate tax incidence and international provisions \citep{ota2012}. Their approach emphasizes the relationship between different types of tax instruments through a series of linked models: \begin{itemize} \item Individual income tax model using matched administrative data @@ -53,7 +53,7 @@ \subsubsection{Urban Institute Family of Models} The Urban Institute maintains several complementary microsimulation models, each emphasizing different aspects of tax and transfer policy analysis. -The Urban-Brookings Tax Policy Center model \cite{tpc2022} combines the IRS Public Use File with Current Population Survey data through predictive mean matching, an approach similar to what we employ in Section~\ref{sec:methodology}. Their imputation strategy aims to preserve joint distributions across variables using regression-based techniques for: +The Urban-Brookings Tax Policy Center model \citep{tpc2022} combines the IRS Public Use File with Current Population Survey data through predictive mean matching, an approach similar to what we employ in Section~\ref{sec:methodology}. Their imputation strategy aims to preserve joint distributions across variables using regression-based techniques for: \begin{itemize} \item Wealth holdings (18 asset and debt categories) @@ -63,7 +63,7 @@ \subsubsection{Urban Institute Family of Models} \item Retirement accounts (DB/DC split and contribution levels) \end{itemize} -TRIM3 emphasizes the time dimension of policy analysis, with sophisticated procedures for converting annual survey data into monthly variables \cite{trim2024}. Key innovations include: +TRIM3 emphasizes the time dimension of policy analysis, with sophisticated procedures for converting annual survey data into monthly variables \citep{trim2024}. Key innovations include: \begin{itemize} \item Allocation of employment spells to specific weeks using BLS benchmarks @@ -74,11 +74,11 @@ \subsubsection{Urban Institute Family of Models} This monthly allocation approach informs our treatment of time variation in Section~\ref{sec:data}. -The newer ATTIS model \cite{attis2024} focuses on interactions between tax and transfer programs. Building on the American Community Survey rather than the CPS provides better geographic detail at the cost of requiring additional tax variable imputations. Their approach to correcting for benefit underreporting in survey data parallels our methods in Section~\ref{sec:methodology}. +The newer ATTIS model \citep{attis2024} focuses on interactions between tax and transfer programs. Building on the American Community Survey rather than the CPS provides better geographic detail at the cost of requiring additional tax variable imputations. Their approach to correcting for benefit underreporting in survey data parallels our methods in Section~\ref{sec:methodology}. \subsubsection{Other Research Institution Models} -The Institute on Taxation and Economic Policy model \cite{itep2024} is unique in its comprehensive treatment of federal, state and local taxes. Key features include: +The Institute on Taxation and Economic Policy model \citep{itep2024} is unique in its comprehensive treatment of federal, state and local taxes. Key features include: \begin{itemize} \item Integration of income, sales, and property tax microsimulation @@ -87,7 +87,7 @@ \subsubsection{Other Research Institution Models} \item Race/ethnicity analysis through statistical matching \end{itemize} -The Tax Foundation's Taxes and Growth model \cite{tf2024} emphasizes macroeconomic feedback effects through a neoclassical growth framework. Their approach includes: +The Tax Foundation's Taxes and Growth model \citep{tf2024} emphasizes macroeconomic feedback effects through a neoclassical growth framework. Their approach includes: \begin{itemize} \item Production function based on CES technology @@ -100,7 +100,7 @@ \subsection{Open Source Initiatives} Recent years have seen growing interest in open source approaches that promote transparency and reproducibility in tax policy modeling. -The Budget Lab at Yale \cite{budgetlab2024} maintains a fully open source federal tax model distinguished by: +The Budget Lab at Yale \citep{budgetlab2024} maintains a fully open source federal tax model distinguished by: \begin{itemize} \item Modular codebase with clear separation of concerns @@ -111,7 +111,7 @@ \subsection{Open Source Initiatives} Their approach to code organization and testing informs our own development practices. -The Policy Simulation Library's Tax-Data project \cite{psl2024} provides building blocks for tax microsimulation including: +The Policy Simulation Library's Tax-Data project \citep{psl2024} provides building blocks for tax microsimulation including: \begin{itemize} \item Data processing and cleaning routines diff --git a/paper/sections/data.tex b/paper/sections/data.tex index eb7e973..9fc4580 100644 --- a/paper/sections/data.tex +++ b/paper/sections/data.tex @@ -22,7 +22,7 @@ \subsection{Current Population Survey} \subsection{IRS Public Use File} -The Internal Revenue Service Public Use File (PUF) is a national sample of individual income tax returns, representing the 151.2 million Form 1040, Form 1040A, and Form 1040EZ Federal Individual Income Tax Returns filed for Tax Year 2015. The file contains 119,675 records sampled at varying rates across strata, with 0.07 percent sampling for strata 7 through 13 \cite{bryant2023b}. The data are extensively transformed to protect taxpayer privacy while preserving statistical properties. +The Internal Revenue Service Public Use File (PUF) is a national sample of individual income tax returns, representing the 151.2 million Form 1040, Form 1040A, and Form 1040EZ Federal Individual Income Tax Returns filed for Tax Year 2015. The file contains 119,675 records sampled at varying rates across strata, with 0.07 percent sampling for strata 7 through 13 \citep{bryant2023b}. The data are extensively transformed to protect taxpayer privacy while preserving statistical properties. The Public Use Tax Demographic File supplements the PUF with: \begin{itemize} diff --git a/paper/sections/methodology.tex b/paper/sections/methodology.tex index 60d6af9..bdc6664 100644 --- a/paper/sections/methodology.tex +++ b/paper/sections/methodology.tex @@ -1,4 +1,10 @@ -% Include methodology subsections +\section{Methodology}\label{sec:methodology} + \input{sections/methodology/overview} +\input{sections/methodology/demographic_variables} +\input{sections/methodology/puf_preprocessing} +\input{sections/methodology/aging} \input{sections/methodology/quantile_forests} -\input{sections/methodology/reweighting} \ No newline at end of file +\input{sections/methodology/loss_matrix} +\input{sections/methodology/reweighting} +\input{sections/methodology/pipeline} \ No newline at end of file diff --git a/paper/sections/methodology/aging.tex b/paper/sections/methodology/aging.tex new file mode 100644 index 0000000..ef70cfc --- /dev/null +++ b/paper/sections/methodology/aging.tex @@ -0,0 +1,49 @@ +\subsection{Data Aging and Indexing} + +The process of projecting historical microdata involves both demographic aging and economic indexing based on US government forecasts. Our aging process occurs in two stages: first to reach our baseline year (2024), and then to project the calibrated dataset forward. + +\subsubsection{Growth Factor Construction} + +For each variable in the tax-benefit system with a specified growth parameter, we compute change factors from the base year through 2034: + +\[ \text{Index Factor}_{t} = \frac{\text{Index}_{t}}{\text{Index}_{\text{base}}} \] + +\subsubsection{Population Adjustment} + +Most economic variables are adjusted for changes in total population: + +\[ \text{Per Capita Factor}_{t} = \frac{\text{Index Factor}_{t}}{\text{Population Growth}_{t}} \] + +Exceptions include: +\begin{itemize} + \item Weight variables maintain raw growth + \item Population itself uses Census projections directly +\end{itemize} + +\subsubsection{Data Sources} + +Projection factors come from: +\begin{itemize} + \item Congressional Budget Office economic projections + \item Census Bureau population estimates + \item Social Security Administration wage index forecasts + \item Treasury tax parameter indexing +\end{itemize} + +\subsubsection{Initial Aging Implementation} + +For any variable y, the projected value to reach our baseline year is computed as: + +\[ y_{2024} = y_{2023} \cdot \frac{f(2024)}{f(2023)} \] + +where f(t) represents the index factor for time t. + +\subsubsection{Forward Projection} + +After constructing and calibrating the enhanced 2024 dataset, we project it to future years using the same indexing framework. This maintains the dataset's enhanced distributional properties while reflecting: + +\begin{itemize} + \item Economic growth forecasts for monetary variables + \item Statutory adjustments to program parameters + \item Population projections applied to household weights +\end{itemize} \ No newline at end of file diff --git a/paper/sections/methodology/demographic_variables.tex b/paper/sections/methodology/demographic_variables.tex new file mode 100644 index 0000000..9d3c637 --- /dev/null +++ b/paper/sections/methodology/demographic_variables.tex @@ -0,0 +1,76 @@ +\subsection{Demographic Variable Construction} + +Following the IRS specifications for the Public Use File, we construct three key demographic variables: dependent ages, primary taxpayer age ranges, and earnings splits between spouses. + +\subsection{Dependent Ages} + +For each dependent, we construct age categories following IRS constraints: +\begin{itemize} + \item Under 5 + \item 5 under 13 + \item 13 under 17 + \item 17 under 19 + \item 19 under 24 + \item 24 or older +\end{itemize} + +The number of dependents is limited by filing status: +\begin{itemize} + \item Up to 3 dependents for joint returns and head of household returns + \item Up to 2 dependents for single returns + \item Up to 1 dependent for married filing separately returns +\end{itemize} + +Dependents are ordered sequentially by type: +\begin{enumerate} + \item Children living at home + \item Children living away from home + \item Other dependents + \item Parents +\end{enumerate} + +\subsubsection{Primary Taxpayer Age} + +Age ranges are constructed differently for dependent and non-dependent returns: + +For non-dependent returns: +\begin{itemize} + \item Under 26 + \item 26 under 35 + \item 35 under 45 + \item 45 under 55 + \item 55 under 65 + \item 65 or older +\end{itemize} + +For dependent returns: +\begin{itemize} + \item Under 18 + \item 18 under 26 + \item 26 or older +\end{itemize} + +\subsubsection{Earnings Splits} + +For joint returns, we calculate the primary earner's share of total earnings: + +\[ \text{Primary Share} = \frac{\text{Primary Wages} + \text{Primary SE Income}}{\text{Total Wages} + \text{Total SE Income}} \] + +Where: +\begin{itemize} + \item Primary wages and SE income = E30400 - E30500 + \item Secondary wages and SE income = E30500 +\end{itemize} + +This share is categorized into: +\begin{itemize} + \item 75 percent or more earned by primary + \item Less than 75 percent but more than 25 percent earned by primary + \item Less than 25 percent earned by primary +\end{itemize} + +\subsubsection{Implementation Details} + +When decoding age ranges into specific ages, we use random assignment within the range to avoid unrealistic bunching. For example, when the PUF indicates age 80, we randomly assign an age between 80 and 84. + +The ordering of dependents is preserved when constructing synthetic tax units to maintain consistency with the original data structure. \ No newline at end of file diff --git a/paper/sections/methodology/loss_matrix.tex b/paper/sections/methodology/loss_matrix.tex new file mode 100644 index 0000000..677ad71 --- /dev/null +++ b/paper/sections/methodology/loss_matrix.tex @@ -0,0 +1,128 @@ +\subsection{Loss Matrix Construction} + +The loss matrix measures deviation from 570 administrative targets sourced from IRS Statistics of Income (SOI), Census population estimates, CBO projections, and other administrative data. + +\subsubsection{IRS Statistics of Income Targets} + +For each combination of AGI bracket and filing status, we create targets for: + +\begin{itemize} + \item Adjusted gross income + \item Count of returns + \item Employment income + \item Business net profits + \item Capital gains (gross) + \item Ordinary dividends + \item Partnership and S-corporation income + \item Qualified dividends + \item Taxable interest income + \item Total pension income + \item Total social security +\end{itemize} + +For aggregate-level targets only, we track: +\begin{itemize} + \item Business net losses + \item Capital gains distributions + \item Capital gains losses + \item Estate income and losses + \item Exempt interest + \item IRA distributions + \item Partnership and S-corporation losses + \item Rent and royalty net income and losses + \item Taxable pension income + \item Taxable social security + \item Unemployment compensation +\end{itemize} + +\subsubsection{Census Population Targets} + +From Census population projections (np2023\_d5\_mid.csv), we include: +\begin{itemize} + \item Single-year age population counts from age 0 to 85 + \item Filtered to total population (SEX = 0, RACE\_HISP = 0) + \item Projected to the target year +\end{itemize} + +\subsubsection{CBO Program Totals} + +From CBO projections, we calibrate: +\begin{itemize} + \item Income tax + \item SNAP benefits + \item Social security benefits + \item SSI payments + \item Unemployment compensation +\end{itemize} + +\subsubsection{EITC Statistics} + +From Treasury EITC data (eitc.csv), we target: +\begin{itemize} + \item EITC recipient counts by number of qualifying children + \item Total EITC amounts by number of qualifying children +\end{itemize} + +The EITC values are uprated by: +\begin{itemize} + \item EITC spending growth for amounts + \item Population growth for recipient counts +\end{itemize} + +\subsubsection{CPS-Derived Statistics} + +We calibrate to hardcoded totals for: +\begin{itemize} + \item Health insurance premiums without Medicare Part B: \$385B + \item Other medical expenses: \$278B + \item Medicare Part B premiums: \$112B + \item Over-the-counter health expenses: \$72B + \item SPM unit thresholds sum: \$3,945B + \item Child support expense: \$33B + \item Child support received: \$33B + \item SPM unit capped work childcare expenses: \$348B + \item SPM unit capped housing subsidy: \$35B + \item TANF: \$9B + \item Alimony income: \$13B + \item Alimony expense: \$13B + \item Real estate taxes: \$400B + \item Rent: \$735B +\end{itemize} + +\subsubsection{Market Income Targets} + +From IRS SOI PUF estimates: +\begin{itemize} + \item Total negative household market income: -\$138B + \item Count of households with negative market income: 3M +\end{itemize} + +\subsubsection{Healthcare Spending by Age} + +Using healthcare\_spending.csv, we target healthcare expenditures by: +\begin{itemize} + \item 10-year age groups + \item Four expense categories: + \begin{itemize} + \item Health insurance premiums without Medicare Part B + \item Over-the-counter health expenses + \item Other medical expenses + \item Medicare Part B premiums + \end{itemize} +\end{itemize} + +\subsubsection{AGI by SPM Threshold} + +From spm\_threshold\_agi.csv, we target: +\begin{itemize} + \item Adjusted gross income totals by SPM threshold decile + \item Count of households in each SPM threshold decile +\end{itemize} + +\subsubsection{Target Validation} + +The loss matrix construction enforces two key checks: +\begin{itemize} + \item No missing values in any target row + \item No NaN values in the targets array +\end{itemize} \ No newline at end of file diff --git a/paper/sections/methodology/overview.tex b/paper/sections/methodology/overview.tex index e768928..d44cac0 100644 --- a/paper/sections/methodology/overview.tex +++ b/paper/sections/methodology/overview.tex @@ -1,128 +1,12 @@ -\section{Methodology}\label{sec:methodology} +\subsection{Overview} -Following \cite{bryant2023a}, our procedure enhances the Current Population Survey (CPS) with tax information from the Public Use File (PUF) through four key steps: -\begin{enumerate} - \item Project both CPS and PUF data to the target year - \item Transfer tax variable distributions from PUF to CPS records - \item Generate dependent age, primary age, and earnings split variables - \item Reweight households to match administrative benchmarks -\end{enumerate} - -\subsection{Data Projection} - -We project both datasets forward using variable-specific growth indices $f(t)$: - -\[ y_{2024} = y_{2023} \cdot \frac{f(2024)}{f(2023)} \] - -The indices come from: -\begin{itemize} - \item CBO economic projections for income components - \item SSA wage index forecasts for employment income - \item Census population projections for demographics - \item Treasury forecasts for tax variables -\end{itemize} - -For the PUF, we first age the 2015 data to 2021 using IRS Statistics of Income data, then project to 2024 using the same indices. +Our approach enhances the Current Population Survey (CPS) with information from the IRS Public Use File (PUF) through a multi-stage process: -\subsection{Demographic Variable Construction} - -Following \cite{bryant2023b}, we construct several key demographic variables: - -\subsubsection{Dependent Ages} -We create three dependent age variables (AGEDP1/2/3) capturing: -\begin{itemize} - \item Up to 3 dependents for joint/HOH returns - \item Up to 2 dependents for single returns - \item Up to 1 dependent for MFS returns -\end{itemize} - -Ages are categorized as: -\begin{itemize} - \item Under 5 - \item 5 under 13 - \item 13 under 17 - \item 17 under 19 - \item 19 under 24 - \item 24 or older -\end{itemize} - -Dependents are ordered sequentially by type: \begin{enumerate} - \item Children living at home - \item Children living away from home - \item Other dependents - \item Parents + \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 Optimize household weights to match administrative benchmarks while determining the optimal mix of original and synthetic records \end{enumerate} -\subsubsection{Primary Taxpayer Age} -We construct age ranges differently for: - -Non-dependent returns: -\begin{itemize} - \item Under 26 - \item 26 under 35 - \item 35 under 45 - \item 45 under 55 - \item 55 under 65 - \item 65 or older -\end{itemize} - -Dependent returns: -\begin{itemize} - \item Under 18 - \item 18 under 26 - \item 26 or older -\end{itemize} - -\subsubsection{Earnings Splits} -For joint returns, we calculate earnings splits using: -\[ \text{Primary Share} = \frac{\text{Primary Wages} + \text{Primary SE Income}}{\text{Total Wages} + \text{Total SE Income}} \] - -Where: -\begin{itemize} - \item Primary wages and SE income = E30400 - E30500 - \item Secondary wages and SE income = E30500 -\end{itemize} - -We categorize the splits as: -\begin{itemize} - \item 75 percent or more earned by primary - \item Less than 75 percent but more than 25 percent earned by primary - \item Less than 25 percent earned by primary -\end{itemize} - -\subsection{Tax Variable Enhancement} - -We transfer tax variables from PUF to CPS using quantile regression forests trained on: -\begin{itemize} - \item Constructed demographic variables described above - \item Filing status - \item Existing income measures -\end{itemize} - -For each variable, we: -\begin{enumerate} - \item Train a forest on PUF records - \item Generate predicted distributions for CPS records - \item Sample preserving rank within demographic groups -\end{enumerate} - -\subsection{Household Reweighting} - -We adjust household weights to minimize discrepancies with administrative targets while avoiding overfitting: - -\[ \min_w \sum_j \left(\frac{\sum_i w_i x_{ij} - t_j}{t_j}\right)^2 + \lambda \sum_i (w_i - w_i^0)^2 \] - -subject to: -\[ w_i \geq 0 \quad \forall i \] - -where: -\begin{itemize} - \item $w_i$ is the new weight for household $i$ - \item $w_i^0$ is the original CPS weight - \item $x_{ij}$ is the value of variable $j$ for household $i$ - \item $t_j$ is the administrative target for variable $j$ - \item $\lambda$ controls regularization strength -\end{itemize} - -We solve using gradient descent with 5\% dropout for regularization. \ No newline at end of file +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. \ No newline at end of file diff --git a/paper/sections/methodology/pipeline.tex b/paper/sections/methodology/pipeline.tex new file mode 100644 index 0000000..6abf9b0 --- /dev/null +++ b/paper/sections/methodology/pipeline.tex @@ -0,0 +1,48 @@ +\subsection{Complete Enhancement Pipeline} + +The full dataset enhancement process proceeds through the following sequential steps, each documented in the preceding sections: + +\subsubsection{Initial Data Loading} + +\begin{enumerate} + \item Load CPS ASEC public use file + \item Load IRS public use file (if before 2021) + \item Load external validation sources (SOI, Census data) +\end{enumerate} + +\subsubsection{PUF Processing (Pre-2021)} + +\begin{enumerate} + \item Apply variable renaming and recoding + \item Construct derived variables + \item Split income components + \item Remove aggregate records + \item Generate synthetic demographic variables +\end{enumerate} + +\subsubsection{CPS Enhancement} + +\begin{enumerate} + \item Project both datasets to target year using uprating factors + \item Train quantile regression forests on PUF records + \item Generate synthetic CPS-structured records + \item Stack synthetic records with original CPS +\end{enumerate} + +\subsubsection{Final Calibration} + +\begin{enumerate} + \item Construct loss matrix from administrative targets + \item Initialize weights for original and synthetic records + \item Optimize weights using gradient descent with dropout + \item Validate results against external benchmarks +\end{enumerate} + +Each step is implemented in separate modules: +\begin{itemize} + \item PUF processing in datasets/puf/puf.py + \item Uprating in utils/uprating.py + \item QRF models in utils/qrf.py + \item Loss matrix in utils/loss.py + \item Weight optimization in datasets/cps/enhanced\_cps.py +\end{itemize} \ No newline at end of file diff --git a/paper/sections/methodology/puf_preprocessing.tex b/paper/sections/methodology/puf_preprocessing.tex new file mode 100644 index 0000000..760d8fc --- /dev/null +++ b/paper/sections/methodology/puf_preprocessing.tex @@ -0,0 +1,95 @@ +\subsection{PUF Data Preprocessing} + +The preprocessing of the IRS Public Use File involves variable renaming, recoding, and construction of derived variables to align with PolicyEngine's analytical framework. + +\subsubsection{Medical Expense Categories} + +Total medical expenses are decomposed into specific categories using fixed ratios derived from external data: + +\begin{itemize} + \item Health insurance premiums without Medicare Part B: 45.3\% + \item Other medical expenses: 32.5\% + \item Medicare Part B premiums: 13.7\% + \item Over-the-counter health expenses: 8.5\% +\end{itemize} + +\subsubsection{Variable Construction} + +Key derived variables include: + +\paragraph{Qualified Business Income (QBI)} +Calculated as the maximum of zero and the sum of: +\begin{itemize} + \item Schedule E income (E00900) + \item Partnership and S-corporation income (E26270) + \item Farm income (E02100) + \item Rental income (E27200) +\end{itemize} + +W2 wages from qualified business are then computed as 16\% of QBI. + +\paragraph{Filing Status} +Mapped from MARS codes: +\begin{itemize} + \item 1 $\rightarrow$ SINGLE + \item 2 $\rightarrow$ JOINT + \item 3 $\rightarrow$ SEPARATE + \item 4 $\rightarrow$ HEAD\_OF\_HOUSEHOLD +\end{itemize} + +Records with MARS = 0 (aggregate records) are excluded. + +\subsubsection{Income Component Separation} + +Several income sources are separated into positive and negative components: + +\begin{itemize} + \item Business income split into net profits (positive) and losses (negative) + \item Capital gains split into gross gains and losses + \item Partnership and S-corporation income split into income and losses + \item Rental income split into net income and losses +\end{itemize} + +\subsubsection{Variable Renaming} + +The following PUF variables are renamed to align with PolicyEngine conventions: + +\paragraph{Direct Renames} +\begin{itemize} + \item E03500 $\rightarrow$ alimony\_expense + \item E00800 $\rightarrow$ alimony\_income + \item E20500 $\rightarrow$ casualty\_loss + \item E32800 $\rightarrow$ cdcc\_relevant\_expenses + \item E19800 $\rightarrow$ charitable\_cash\_donations + \item E20100 $\rightarrow$ charitable\_non\_cash\_donations + \item E03240 $\rightarrow$ domestic\_production\_ald + \item E03400 $\rightarrow$ early\_withdrawal\_penalty + \item E03220 $\rightarrow$ educator\_expense + \item E00200 $\rightarrow$ employment\_income + \item E26390 - E26400 $\rightarrow$ estate\_income + \item T27800 $\rightarrow$ farm\_income + \item E27200 $\rightarrow$ farm\_rent\_income + \item E03290 $\rightarrow$ health\_savings\_account\_ald + \item E19200 $\rightarrow$ interest\_deduction + \item P23250 $\rightarrow$ long\_term\_capital\_gains + \item E24518 $\rightarrow$ long\_term\_capital\_gains\_on\_collectibles + \item E20400 $\rightarrow$ misc\_deduction + \item E00600 - E00650 $\rightarrow$ non\_qualified\_dividend\_income + \item E00650 $\rightarrow$ qualified\_dividend\_income + \item E03230 $\rightarrow$ qualified\_tuition\_expenses + \item E18500 $\rightarrow$ real\_estate\_taxes +\end{itemize} + +\paragraph{Weight Adjustment} +S006 weights are divided by 100 to convert to population units. + +\subsubsection{Data Cleaning} + +The preprocessing includes: +\begin{itemize} + \item Removal of aggregate records (MARS = 0) + \item Missing value imputation with zeros + \item Construction of unique household identifiers from RECID + \item Assignment of household weights from S006 + \item Extraction of exemption counts from XTOT +\end{itemize} \ No newline at end of file diff --git a/paper/sections/methodology/quantile_forests.tex b/paper/sections/methodology/quantile_forests.tex index 1bd6b09..67ea84a 100644 --- a/paper/sections/methodology/quantile_forests.tex +++ b/paper/sections/methodology/quantile_forests.tex @@ -1,66 +1,23 @@ -\section{Quantile Regression Forests}\label{sec:qrf} +\subsection{Quantile Regression Forests} -We use quantile regression forests (QRF) in two distinct ways: direct imputation of missing variables, and generation of synthetic records. +Our implementation uses quantile regression forests (QRF) \citep{meinshausen2006quantile}, which extend random forests to estimate conditional quantiles. We use the quantile-forest package \citep{zillow2024quantile}, a scikit-learn compatible implementation that provides efficient, Cython-optimized estimation of arbitrary quantiles at prediction time without retraining. -\subsection{PUF Integration: Synthetic Record Generation} +QRF works by generating an ensemble of regression trees, where each tree recursively partitions the feature space. Unlike standard random forests that only store mean values in leaf nodes, QRF maintains the full empirical distribution of training observations in each leaf. To estimate conditional quantiles, the model identifies relevant leaf nodes for new observations, aggregates the weighted empirical distributions across all trees, and computes the desired quantiles from the combined distribution. -Unlike our other QRF applications, we use the PUF to generate an entire synthetic CPS-structured dataset: +The key advantages over traditional quantile regression include QRF's ability to capture non-linear relationships without explicit specification, model heteroscedastic variance across the feature space, estimate any quantile without retraining, and maintain the computational efficiency of random forests. -\begin{enumerate} - \item Train QRF models on PUF records with demographic variables - \item Generate a complete set of synthetic CPS-structured records using PUF tax information - \item Stack these synthetic records alongside the original CPS records - \item Allow the reweighting procedure to determine optimal mixing between CPS and PUF-based records -\end{enumerate} +\subsubsection{PUF Integration: Synthetic Record Generation} -This approach preserves CPS's person-level detail crucial for modeling: -\begin{itemize} - \item State tax policies - \item Benefit program eligibility - \item Age-dependent federal provisions (e.g., Child Tax Credit variations by child age) - \item Family structure interactions -\end{itemize} +Unlike our other QRF applications, we use the PUF to generate an entire synthetic CPS-structured dataset. This process begins by training QRF models on PUF records with demographic variables. We then generate a complete set of synthetic CPS-structured records using PUF tax information, which are stacked alongside the original CPS records. The reweighting procedure ultimately determines the optimal mixing between CPS and PUF-based records. -\subsection{Direct Variable Imputation} +This approach preserves CPS's person-level detail crucial for modeling various aspects of the tax system. These include state tax policies, benefit program eligibility, age-dependent federal provisions (such as Child Tax Credit variations by child age), and family structure interactions. -For other enhancement needs, we use QRF to directly impute missing variables: +\subsubsection{Direct Variable Imputation} -\subsubsection{Housing Costs from ACS} -We impute rent payments and property taxes using ACS records, with predictors including: -\begin{itemize} - \item Household head status - \item Age - \item Sex - \item Tenure type - \item Employment income - \item Self-employment income - \item Social Security income - \item Pension income - \item State - \item Household size -\end{itemize} +For other enhancement needs, we use QRF to directly impute missing variables. When imputing housing costs from ACS records, we incorporate a comprehensive set of predictors including household head status, age, sex, tenure type, various income sources (employment, self-employment, Social Security, and pension), state, and household size. -\subsubsection{Prior Year Income from CPS ASEC Panel} -To support analysis of lookback provisions, we impute prior year earnings using consecutive-year ASEC records, using: -\begin{itemize} - \item Employment income - \item Self-employment income - \item Household weight - \item Income imputation flags -\end{itemize} +To support analysis of lookback provisions, we impute prior year earnings using consecutive-year ASEC records. This imputation relies on current employment and self-employment income, household weights, and income imputation flags from the CPS ASEC panel. -\subsection{Implementation Details} +\subsubsection{Implementation Details} -Our QRF implementation in utils/qrf.py handles: -\begin{itemize} - \item Categorical variable encoding - \item Consistent feature ordering - \item Distribution sampling - \item Model persistence -\end{itemize} - -% TODO: Add specifics about: -% - QRF hyperparameters -% - Computational performance -% - Validation metrics for both synthetic record generation and direct imputation -% - Details on how the reweighting procedure balances CPS vs PUF records \ No newline at end of file +Our QRF implementation, housed in utils/qrf.py, provides a robust framework for model development and deployment. The implementation handles categorical variable encoding and ensures consistent feature ordering across training and prediction. It also manages distribution sampling and model persistence, enabling efficient reuse of trained models. \ No newline at end of file diff --git a/paper/sections/methodology/reweighting.tex b/paper/sections/methodology/reweighting.tex index 981e798..de0ff42 100644 --- a/paper/sections/methodology/reweighting.tex +++ b/paper/sections/methodology/reweighting.tex @@ -1,85 +1,66 @@ -\section{Reweighting Procedure}\label{sec:reweighting} +\subsection{Reweighting Procedure} -Our reweighting process optimizes household weights to match administrative targets while determining the relative value of original CPS records versus PUF-derived synthetic records. +We optimize household weights using gradient descent through PyTorch \citep{pytorch2019}. -\subsection{Loss Matrix Construction} +\subsubsection{Problem Formulation} -We construct a matrix of targets including: +Given a loss matrix $M$ of household characteristics and a target vector $t$, we optimize the log-transformed weights $w$ to minimize: -\subsubsection{IRS Statistics of Income Targets} -For each AGI bracket and filing status combination: -\begin{itemize} - \item Adjusted gross income totals - \item Employment income - \item Business income/losses - \item Capital gains totals and distributions - \item Dividend income (qualified and ordinary) - \item Partnership and S-corporation income/losses - \item Pension and IRA distributions - \item Social Security benefits - \item Interest income -\end{itemize} - -\subsubsection{Census Population Targets} -Single-year age population projections from age 0 to 85+, ensuring demographic representativeness. +\[ L(w) = \text{mean}\left(\left(\frac{w^T M + 1}{t + 1} - 1\right)^2\right) \] -\subsubsection{Program Totals} -Annual administrative totals from: +where: \begin{itemize} - \item IRS: Income tax revenue, EITC claims and amounts by number of children - \item Social Security Administration: Benefit payments - \item USDA: SNAP participation and benefits - \item DOL: Unemployment compensation + \item $w$ are the log-transformed weights (requires grad=True) + \item $M$ is the loss matrix in tensor form (float32) + \item $t$ are the targets in tensor form (float32) \end{itemize} -\subsection{Optimization Approach} +\subsubsection{Optimization Implementation} -We minimize the relative error across all targets using gradient descent with dropout regularization: +The procedure follows these steps: \begin{enumerate} - \item Initialize with original CPS weights - \item At each iteration: - \begin{itemize} - \item Randomly zero out 5\% of weights (dropout) - \item Compute relative errors between weighted sums and targets - \item Update weights using Adam optimizer - \end{itemize} - \item Continue until convergence or 5,000 iterations + \item Initialize with log-transformed original weights + \item Create a PyTorch session with retries for robustness + \item Use Adam optimizer with learning rate 0.1 + \item Apply dropout (5\% rate) during optimization + \item Run for 5,000 iterations or until convergence \end{enumerate} -The core optimization uses PyTorch to minimize: - -\[ -L(w) = \text{mean}\left(\left(\frac{w^T M + 1}{t + 1} - 1\right)^2\right) -\] +\subsubsection{Dropout Application} -where: +The dropout process: \begin{itemize} - \item $w$ are the log-transformed weights - \item $M$ is the loss matrix of household characteristics - \item $t$ are the administrative targets + \item Randomly masks p\% of weights each iteration (p = 5) + \item Replaces masked weights with mean of unmasked weights + \item Returns original weights if dropout rate is 0 \end{itemize} -\subsection{Implementation Details} +\subsubsection{Convergence Monitoring} -The Enhanced CPS implementation uses the following parameters: +For each iteration: \begin{itemize} -\item Learning rate: 0.1 -\item Dropout rate: 5% -\item Optimizer: Adam -\item Maximum iterations: 5,000 + \item Track initial loss value as baseline + \item Compute relative change from starting loss + \item Display progress with current loss values \end{itemize} -% TODO: Add specific convergence metrics and typical runtime statistics - -\subsection{Balance Between CPS and PUF Records} +\subsubsection{Error Handling} -The reweighting procedure naturally determines the mix of original CPS and PUF-derived records by: +The implementation includes checks for: \begin{itemize} - \item Starting with equal initial weights - \item Allowing the optimization to up-weight records that better match targets - \item Implicitly favoring PUF-derived records for tax variables - \item Maintaining CPS records' strength in demographic representation + \item NaN values in weights + \item NaN values in loss matrix + \item NaN values in loss computation + \item NaN values in relative error calculation \end{itemize} -% TODO: Add statistics on typical final weight distributions between CPS and PUF records \ No newline at end of file +If any check fails, the procedure raises a ValueError with diagnostic information. + +\subsubsection{Weight Recovery} + +The final weights are recovered by: +\begin{itemize} + \item Taking exponential of optimized log weights + \item Converting from torch tensor to numpy array +\end{itemize} \ No newline at end of file From a30f4f9cd368e1e6cf1781c8f27aab358c1c020d Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 12 Nov 2024 01:48:27 -0500 Subject: [PATCH 11/13] add woodruff source, results, conclusion --- paper/bibliography/references.bib | 10 +++ paper/figures/data_flow.png | Bin 0 -> 210408 bytes paper/figures/ecps_vs_cps_puf.png | Bin 0 -> 65892 bytes paper/main.pdf | Bin 192839 -> 406999 bytes paper/sections/background.tex | 6 +- paper/sections/conclusion.tex | 8 ++ paper/sections/methodology.tex | 7 ++ paper/sections/methodology/overview.tex | 2 +- .../sections/methodology/quantile_forests.tex | 2 +- paper/sections/methodology/reweighting.tex | 2 +- paper/sections/results.tex | 74 +++++------------- 11 files changed, 51 insertions(+), 60 deletions(-) create mode 100644 paper/figures/data_flow.png create mode 100644 paper/figures/ecps_vs_cps_puf.png diff --git a/paper/bibliography/references.bib b/paper/bibliography/references.bib index b6b5fe8..3414672 100644 --- a/paper/bibliography/references.bib +++ b/paper/bibliography/references.bib @@ -174,3 +174,13 @@ @article{pytorch2019 volume = {32}, year = {2019} } + +@techreport{woodruff2023survey, + title = {Surveying the (loss) landscape: using machine learning to improve household survey accuracy}, + author = {Woodruff, Nikhil}, + institution = {University of Durham}, + year = {2023}, + month = {April}, + note = {Demonstrates superiority of machine learning approaches over traditional methods for survey enhancement through comprehensive benchmarking}, + url = {https://github.com/policyengine/survey-enhance/blob/main/docs/paper/project_paper.pdf} +} diff --git a/paper/figures/data_flow.png b/paper/figures/data_flow.png new file mode 100644 index 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zoVgz+fnNqCv>yp#T7lw6#o06g`Y^YCIQC18)6DciHBYZ{#NPs5M)S85;{gC`c7!Tv!A}l*|7N{Gs_h5n!)AIha7@tP()o%j15^x;g%>n z+>|s>P5n?kZ4cdvg=UaT7)d-?!7hliFgV`P5S`t~{Kt9&TOL!<2O2k^Z#~C#Xn7NR zb0@TC^LR78O(gy?p~19Anj+n`S!5 zOxPTfN(CEg#E0o2du&YKz4fl&%i7LA_nkxxB(MnNTO2Bvu(0K49VjBF&85ARvN@qL z+XKpnd%1!y$ReQ>A3p>6?gwKiQAI39!87Y+1$#a>(bo>I6%*vgsLwY>(kL`_#BcuG zUaE1_Di?DIQlH*SKFZR24yEw}w4BHn$Jhp&^;6Upgafr5_<|x7#|~(7zMr0=O|HKW zs0}Gm;h#T*k`q_Y>CDVO6ypC12aRLSnSC0`nEUvR@m5J{MtB0aAMdnpU<}q|S?HoK z#B!fY-T12Eg`$pmslLlM?yPK~Zx_I6NHwKh%ar_mTDySB6|z_UJ2kmr&%QJDIDglE z9QzZCC}n;3vX9qOTSr;Jez|qz6)-aqL-KuZa-y6h_^!`zEjgB&$8_N2%2DKR(6Zq~ zP%1-keo3W(;-No~bQ?;o&a=DVX}%KoP#vND`LOU3Xj7cQ^Oxo^|7?x>Ud_n+5Gxu9 zm0>EwBtX^~rD1))62pmvp@-ify)cw7ZAZ_Vv^TX!2U1a Cy;3m% diff --git a/paper/sections/discussion.tex b/paper/sections/discussion.tex index 62f54a3..4eb9ae7 100644 --- a/paper/sections/discussion.tex +++ b/paper/sections/discussion.tex @@ -1 +1,29 @@ \section{Discussion} + +This paper introduces a novel approach to constructing an enhanced microsimulation dataset by integrating survey and administrative data sources. Our methodology, which combines quantile regression forests (QRF) and dropout-regularized gradient descent reweighting, demonstrates substantial improvements in accurately capturing both demographic and tax-related variables. In this section, we discuss the strengths, limitations, potential applications, and future directions of this approach. + +\subsection{Strengths of the Enhanced Dataset} + +The enhanced dataset achieves a unique balance between demographic detail and tax precision, addressing a long-standing gap in microsimulation modeling. The use of QRF allows for more accurate transfer of income and tax distributions from the IRS Public Use File (PUF) to the Current Population Survey (CPS), preserving complex variable relationships that are critical for policy analysis. Additionally, the dropout-regularized gradient descent reweighting effectively calibrates household weights to align with administrative benchmarks, reducing error rates across a broad range of demographic and economic metrics. + +Our validation results show that the enhanced CPS (ECPS) improves on both source datasets, particularly in tax-related variables that are essential for analyzing income distributions and program participation. By providing a publicly available, open-source dataset with extensive validation against external benchmarks, we support more transparent and reliable policy analysis. + +\subsection{Limitations and Potential Biases} + +Despite these strengths, the enhanced dataset has limitations that merit careful consideration. One key challenge lies in maintaining consistency in relationships across diverse variables, especially in cases where nonlinear or unexpected correlations exist. Although QRF is well-suited for capturing non-linear relationships, biases may still arise due to assumptions made during variable imputation. + +A second limitation is the reliance on older IRS data, which may not fully capture recent demographic and economic shifts. While our reweighting procedure attempts to mitigate this through adjustment to more current administrative targets, future iterations could benefit from updated IRS data or alternative administrative sources that better reflect the contemporary population. + +Further, our approach may introduce biases when aligning household records with administrative targets. These biases can impact analyses that depend heavily on small demographic subgroups or specific income brackets. Future improvements could involve fine-tuning the reweighting process to minimize potential overfitting in cases where data are sparse. + +\subsection{Applications of the Enhanced Dataset} + +The enhanced CPS dataset expands the scope and accuracy of microsimulation analyses in several policy domains. By combining the CPS's household structure with the PUF's tax precision, this dataset is well-suited for both federal and state-level tax analysis, particularly in modeling income-based benefits and tax credits. Researchers and policymakers could leverage this dataset to evaluate the distributional impacts of various tax reforms, analyze the implications of benefit programs across income levels, and assess policy proposals that rely on a precise understanding of income and demographic characteristics. + +Additional applications extend to labor market studies, health policy analysis, and state-specific program evaluations. With further adaptation, the methodology could also support microsimulation in international contexts, providing a flexible tool for policy modeling across diverse regions and socioeconomic conditions. + +\subsection{Future Directions} + +Building on the success of this methodology, future work could aim to expand the dataset's geographic granularity and incorporate additional data sources. Integrating state-specific datasets or additional federal data on healthcare and education would further enrich the dataset's utility for policy analysis. Moreover, the dataset could benefit from continued refinement of its reweighting procedure, including the use of ensemble methods to capture a broader range of variable interactions. + +Another promising direction involves the development of interactive tools that allow researchers and policymakers to explore the dataset in real time, enhancing transparency and accessibility. By providing both the enhanced dataset and the codebase as open-source resources, we establish a foundation for collaborative improvement and iterative updates that respond to changing policy needs and data availability. diff --git a/paper/sections/methodology/overview.tex b/paper/sections/methodology/overview.tex index e71a28d..da293ae 100644 --- a/paper/sections/methodology/overview.tex +++ b/paper/sections/methodology/overview.tex @@ -6,6 +6,7 @@ \subsection{Overview} \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} From 97f4e664524681ab994acee1c991aa9c16d800e5 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Tue, 12 Nov 2024 02:00:25 -0500 Subject: [PATCH 13/13] delete temp files --- repo_structure.txt | 118 ------------------------------ setup_paper.sh | 176 --------------------------------------------- 2 files changed, 294 deletions(-) delete mode 100644 repo_structure.txt delete mode 100755 setup_paper.sh diff --git a/repo_structure.txt b/repo_structure.txt deleted file mode 100644 index 3dc386b..0000000 --- a/repo_structure.txt +++ /dev/null @@ -1,118 +0,0 @@ -. -├── .github -│   ├── CONTRIBUTING.md -│   ├── changelog_template.md -│   ├── fetch_version.py -│   ├── get-changelog-diff.sh -│   ├── has-functional-changes.sh -│   ├── is-version-number-acceptable.sh -│   ├── publish-git-tag.sh -│   └── workflows -│   ├── pull_request.yaml -│   ├── push.yaml -│   └── update_versioning.yaml -├── .gitignore -├── CHANGELOG.md -├── Makefile -├── README.md -├── changelog.yaml -├── changelog_entry.yaml -├── docker -│   └── policyengine_us_data.Dockerfile -├── docs -│   ├── _config.yml -│   ├── _static -│   │   └── style.css -│   ├── _toc.yml -│   ├── intro.md -│   ├── logo.png -│   ├── utils.py -│   └── validation.ipynb -├── paper -│   ├── .gitignore -│   ├── bibliography -│   │   └── references.bib -│   ├── figures -│   ├── macros.tex -│   ├── main.aux -│   ├── main.bbl -│   ├── main.blg -│   ├── main.log -│   ├── main.out -│   ├── main.pdf -│   ├── main.tex -│   ├── sections -│   │   ├── abstract.tex -│   │   ├── background.tex -│   │   ├── conclusion.tex -│   │   ├── data.tex -│   │   ├── discussion.tex -│   │   ├── introduction.tex -│   │   ├── methodology -│   │   │   ├── overview.tex -│   │   │   ├── quantile_forests.tex -│   │   │   └── reweighting.tex -│   │   ├── methodology.tex -│   │   └── results.tex -│   └── tables -├── policyengine_us_data -│   ├── __init__.py -│   ├── __version__.py -│   ├── datasets -│   │   ├── __init__.py -│   │   ├── acs -│   │   │   ├── README.md -│   │   │   ├── __init__.py -│   │   │   ├── acs.py -│   │   │   └── census_acs.py -│   │   ├── cps -│   │   │   ├── __init__.py -│   │   │   ├── census_cps.py -│   │   │   ├── cps.py -│   │   │   ├── enhanced_cps.py -│   │   │   ├── extended_cps.py -│   │   │   └── imputation_parameters.yaml -│   │   └── puf -│   │   ├── __init__.py -│   │   ├── irs_puf.py -│   │   ├── puf.py -│   │   └── uprate_puf.py -│   ├── geography -│   │   ├── README.md -│   │   ├── __init__.py -│   │   ├── create_zip_code_dataset.py -│   │   └── zip_codes.csv.gz -│   ├── storage -│   │   ├── __init__.py -│   │   ├── download_private_prerequisites.py -│   │   ├── download_public_prerequisites.py -│   │   ├── eitc.csv -│   │   ├── healthcare_spending.csv -│   │   ├── spm_threshold_agi.csv -│   │   ├── upload_completed_datasets.py -│   │   ├── uprating_factors.csv -│   │   └── uprating_growth_factors.csv -│   ├── tests -│   │   ├── __init__.py -│   │   ├── test_datasets -│   │   │   ├── __init__.py -│   │   │   ├── test_acs.py -│   │   │   ├── test_census_cps.py -│   │   │   ├── test_cps.py -│   │   │   ├── test_enhanced_cps.py -│   │   │   └── test_irs_puf.py -│   │   └── test_import.py -│   └── utils -│   ├── __init__.py -│   ├── github.py -│   ├── loss.py -│   ├── qrf.py -│   ├── soi.py -│   └── uprating.py -├── pyproject.toml -├── repo_structure.txt -├── sections -│   └── methodology -└── setup_paper.sh - -24 directories, 92 files diff --git a/setup_paper.sh b/setup_paper.sh deleted file mode 100755 index 06b667b..0000000 --- a/setup_paper.sh +++ /dev/null @@ -1,176 +0,0 @@ -#!/bin/bash - -# Create paper directory structure -mkdir -p paper/{sections,figures,tables,bibliography} - -# Create main.tex -cat >paper/main.tex <<'EOL' -\documentclass[12pt]{article} - -\usepackage{graphicx} -\usepackage{amsmath} -\usepackage{natbib} -\usepackage{hyperref} - -\input{macros} - -\title{Enhancing Survey Microdata with Administrative Records: \\ A Novel Approach to Microsimulation Dataset Construction} -\author{PolicyEngine Team} -\date{\today} - -\begin{document} - -\maketitle - -\input{sections/abstract} -\input{sections/introduction} -\input{sections/background} -\input{sections/data} -\input{sections/methodology} -\input{sections/results} -\input{sections/discussion} -\input{sections/conclusion} - -\bibliography{bibliography/references} -\bibliographystyle{plainnat} - -\end{document} -EOL - -# Create macros.tex -cat >paper/macros.tex <<'EOL' -% Custom commands and mathematics macros -\newcommand{\policyengine}{\textsc{PolicyEngine}} -\newcommand{\cps}{\textsc{CPS}} -\newcommand{\puf}{\textsc{PUF}} -EOL - -# Create section files with proper capitalization -cat >paper/sections/abstract.tex <<'EOL' -\section*{Abstract} -EOL - -cat >paper/sections/introduction.tex <<'EOL' -\section{Introduction} -EOL - -cat >paper/sections/background.tex <<'EOL' -\section{Background} -EOL - -cat >paper/sections/data.tex <<'EOL' -\section{Data} -EOL - -cat >paper/sections/methodology.tex <<'EOL' -\section{Methodology} - -Our approach combines survey and administrative data through a multi-step process of aging, imputation, and reweighting. The procedure preserves the detailed demographic information from survey data while incorporating the precision of administrative tax records. - -\subsection{Dataset Aging and Uprating} - -We first age both datasets to the target year through demographic aging and economic uprating. For each variable $y$ in the set of economic variables, we apply an uprating factor: - -\begin{equation} -y_{t} = y_{t_0} \cdot \frac{f(t)}{f(t_0)} -\end{equation} - -where $f(t)$ represents the appropriate uprating factor time series (e.g., wage growth, price indices) for time $t$. The uprating factors are derived from official projections and historical series. - -\subsection{Demographics Integration} - -A key challenge is preserving household structure while incorporating tax unit information. We train a quantile regression forest model on PUF records with observed demographics to impute missing values: - -\begin{equation} -\hat{d}_i = QRF(x_i; \theta) -\end{equation} - -where $d_i$ represents demographic variables (age, sex, relationship status) and $x_i$ represents observed tax characteristics. The model preserves distributional relationships while avoiding deterministic assignments. - -\subsection{Income and Tax Variable Enhancement} - -We enhance CPS income and tax variables using information from the PUF through a two-stage process: - -1. Variable Mapping: We establish correspondence between CPS and PUF variables through a detailed crosswalk accounting for definitional differences. - -2. Distribution Matching: For each mapped variable pair, we fit a quantile regression forest: - -\begin{equation} -\hat{y}_{PUF} = QRF(X_{CPS}; \theta) -\end{equation} - -where $X_{CPS}$ includes relevant predictors from the CPS like employment status, age, and existing income measures. - -\subsection{Reweighting Procedure} - -The final step adjusts household weights to match key population and economic targets while preserving micro-level relationships. We solve: - -\begin{equation} -\min_w \sum_j \left(\frac{\sum_i w_i x_{ij} - t_j}{t_j}\right)^2 -\end{equation} - -subject to: -\begin{equation} -w_i \geq 0 \quad \forall i -\end{equation} - -where: -\begin{itemize} -\item $w_i$ is the weight for household $i$ -\item $x_{ij}$ is the value of variable $j$ for household $i$ -\item $t_j$ is the target total for variable $j$ -\end{itemize} -EOL - -cat >paper/sections/results.tex <<'EOL' -\section{Results} -EOL - -cat >paper/sections/discussion.tex <<'EOL' -\section{Discussion} -EOL - -cat >paper/sections/conclusion.tex <<'EOL' -\section{Conclusion} -EOL - -# Create empty bibliography file -touch paper/bibliography/references.bib - -# Create .gitignore for LaTeX -cat >paper/.gitignore <<'EOL' -## Core latex/pdflatex auxiliary files: -*.aux -*.lof -*.log -*.lot -*.fls -*.out -*.toc -*.fmt -*.fot -*.cb -*.cb2 -.*.lb - -## Generated if empty string is given at "Please type another file name for output:" -.pdf - -## Bibliography auxiliary files (bibtex/biblatex/biber): -*.bbl -*.bcf -*.blg -*-blx.aux -*-blx.bib -*.run.xml - -## Build tool auxiliary files: -*.fdb_latexmk -*.synctex -*.synctex(busy) -*.synctex.gz -*.synctex.gz(busy) -*.pdfsync -EOL - -echo "Paper directory structure created successfully!"

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files changed, 112 insertions(+), 40 deletions(-) diff --git a/paper/bibliography/references.bib b/paper/bibliography/references.bib index 54e0794..5afb6fd 100644 --- a/paper/bibliography/references.bib +++ b/paper/bibliography/references.bib @@ -122,3 +122,13 @@ @article{auerbach2018 pages = {541--576}, year = {2018} } + +@techreport{bryant2022, + title = {General Description Booklet for the 2015 Public Use Tax File Demographic File}, + author = {Bryant, Victoria}, + institution = {Statistics of Income Division, Internal Revenue Service}, + year = {2022}, + month = {September}, + type = {Technical Documentation}, + url = {https://drive.google.com/file/d/1WoTU70GEjYMO0KHsHvTTH0NwCc-kN5cE/view} +} diff --git a/paper/main.pdf b/paper/main.pdf index 80eee8d861d10a95df9c36587e9e0f70ea592bd9..c39014041b69b6f148597363b3e0c208e5b32932 100644 GIT binary patch delta 78374 zcmZsiV{j&M+vQ{1<{jIf*tYFVj0q;m9b;l+V%xTD+qR9J=XtAkt9JKGS6A1+s=sty 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