November 2, 2021

Synthetic Public Use File of Administrative Tax Data

Methodology, Utility, and Privacy Implications

cbowen@urban.org www.clairemckaybowen.com @ClaireMKBowen

Claire McKay Bowen, Ph.D

Principal Research Associate
Our Project Team

 Andrés Felipe Barrientos – Assistant Professor, Dept. of Statistics, Florida State University

 Claire Bowen – Principal Research Associate, Urban Institute

 Len Burman – Institute Fellow, Tax Policy Center, Urban Institute 

 John Czajka – Senior Fellow, Mathematica Policy Research

 Lillian Hunter – Research Assistant, Tax Policy Center, Urban Institute

 Surachai Khitatrakun – Senior Research Methodologist, Tax Policy Center, Urban Institute

 Graham MacDonald – Associate Vice President, Technology & Data Science, Urban Institute

 Rob McClelland – Senior Fellow, Tax Policy Center, Urban Institute 

 Joshua Snoke – Statistician, RAND Corp.

 Silke Taylor – Lead Software Engineer, Technology & Data Science, Urban Institute

 Aaron R. Williams – Senior Data Scientist, Income and Benefits Policy Center, Urban Institute

 Doug Wissoker – Senior Fellow, Statistical Methods Group, Urban Institute

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Overview

1. Motivation: Why synthesize taxpayer data?

2. Background: What are non-filer data?

3. Methodology: How did we generate the synthetic data? How 

did we evaluate the quality and privacy?

4. Future: What are our next steps?

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Motivation


Background
Master File: A massive tax database of about 145 million unedited 

tax returns for 2012, but cannot be used in its current state due to:

 Size

 Timing of completion (e.g., late filers)

 Item content

 Potential data inconsistences

INSOLE: Stratified sample with weights to represent the U.S. 

taxpayer population.

Administrative Tax Data

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What do we mean by non-filer data?

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Any U.S. resident who did not file a federal tax return, had no obligation to file, 

and was not claimed as a dependent in 2012 but had income reported to the IRS 

on at least one information return for the 2012 tax year:

 10-in-9,999 sample of the confidential data

 Some variables are excluded and others are top-coded and/or recoded

 About 26,000 records

 19 variables after data preprocessing
Methodology
Produce a fully synthetic data file with the same record layout as 

IRS Administrative Data that:

 Protects the confidentiality of tax return information

 May be used for statistically valid analysis for certain 

research purposes

 May be used as a “training dataset” to develop programs to 

run on confidential data

What are our goals?

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Produce a fully synthetic data file with the same record layout as 

IRS Administrative Data that:

 Works well for tax microsimulation

 Avoids logical inconsistencies

 Avoids disclosure risk

What are our goals?

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How did we generate the synthetic data?

 Applying synthetic data generation, 

a data privacy and confidentiality 

method that is widely accepted across 

many disciplines.

 Train on Classification and Regression 

Trees (CART) models on the 

confidential data

 Sequentially synthesize variables in 

order of linear correlation with wages 

(highest to lowest) with previously 

synthesized variables as predictors.

Step Outcome Predictors

1 Sex Random sampling with 

replacement

2 Age Sampled Sex

3 Wages Sampled Sex, Synthetic Age

4 Withholding Sampled Sex, Synthetic Age, 

Synthetic Wages

... ... ...

n nth variable

Sampled Sex, Synthetic Age, 

Synthetic Wages, ..., n-1

st

synthetic variable
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 Number of Unique-Uniques

 Row-Wise Squared Inverse Frequency

 l-Diversity of Final Nodes in the CART Algorithm

How did we measure our disclosure risks?
Results
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Means
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Standard Deviations
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Standard Deviations
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Linear Correlations
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Tax Calculator
The Future
1. Additional non-filer data 

releases

The Future
1. Additional non-filer data 

releases

2. Synthetic IRS PUF

The Future
1. Additional non-filer data 

releases

2. Synthetic IRS PUF

3. library(tidysynthesis)

The Future
1. Additional non-filer data 

releases

2. Synthetic IRS PUF

3. library(tidysynthesis)

4. Validation server

The Future

Data

User

Data

User

Data

User

Synthetic 

Data AND/OR

Interface 

for 

Queries
Contact Me

@ClaireMKBowen

cbowen@urban.org

www.clairemckaybowen.com

/in/bowenclaire