IRS Compliance: Voluntary Filing Rate Measure and Taxpayer Burden

The compliance burden paper analyzes IRS data to develop a model for individual taxpayer compliance, aiming to improve accuracy and detail for tax policy analysis.

### Estimating Compliance Burden and Improving Voluntary Filing Rate Measurement
* 📊 The compliance burden of individual taxpayers is estimated to be **$92 billion annually**, with the burden increasing as **economic income increases**.
* The compliance burden is highest for the lowest income quintile as a **share of pre-tax income**.
* 📏 Compliance costs are influenced by **economic activity**, **tax preparation method**, and the **complexity of the tax return**.
* 🏛 Reform options to reduce compliance costs include a **revenue-neutral repeal of itemized deductions** and the **alternative minimum tax (AMT)**.
* 📈 Simplifying the tax code can help lower **compliance costs** and mitigate the burden for **taxpayers**.
* 📃 The voluntary filing rate is traditionally measured using IRS tax return data for the **numerator** and census data for the **denominator**.
* ❓ Due to discrepancies in estimating the required population size, a new methodology based solely on **IRS data** is proposed to measure the **voluntary filing rate**.
* 📉 The new methodology results in a **lower voluntary filing rate estimate**, highlighting the importance of using **consistent data sources for the numerator and denominator**.

### Understanding Tax Compliance Through Innovative Research
* 📊 "The Tax Policy Center's recent conference featured a diverse set of research topics, including **taxpayer behavior**, **tax administration challenges**, and the **use of external data sources**."
* 🧠 "The research presented at the conference provided valuable insights into **understanding tax compliance behaviors and outcomes**, as well as **addressing operational challenges in tax administration**."
* 🔎 "Several studies focused on advancing the estimation of **critical dimensions of tax compliance**, such as the **number of non-filers and required filers in the population**."
* 📈 "The conference highlighted the importance of **exploring new research questions and approaches** to guide future studies and **improve tax compliance behaviors and operations**."
* 🏛 "The Tax Policy Center, the Urban Institute, and the program committee played key roles in **organizing the conference and ensuring its success**."
* 👏 "The presenters, moderators, discussants, and audience members all contributed to the **rich discussions and constructive evaluations** of the research papers presented."
* 📅 "The conference attendees expressed their appreciation for the **insightful research presentations and discussions**, and look forward to the **next year's event**."

7th Annual IRS-TPC Joint Research Conference on Tax Administration - Part 4

Urban Institute

Using Non-Tax Data for Predicting Taxpayer Compliance

IRS researchers are exploring the use of credit bureau data to improve predictive models for taxpayer compliance with employment taxes.

The study involves a sizable and wide-ranging credit bureau dataset, matched with IRS data on tax compliance.

The research includes variable reduction, factor analysis, and building predictive models using both IRS and external data, with the goal of improving the goodness of fit for the models.

In-Depth Analysis of IRS and Credit Bureau Data

The IRS data showed a stable model with an AUC of 0.

The analysis focused specifically on employment tax and found that oversampling detected cases led to a significant change in the sample design.

The credit bureau data provided short-term and long-term credit scores, along with 200 more variables, offering potential for further exploration and analysis.

Risk Profiling Analysis Using Credit Scores

Short-term credit scores show little separation between detected cases and others, while long-term credit scores provide better distinction.

Combining short-term and long-term credit scores creates a risk matrix with three important buckets: medium risk, slow recovery, and high risk.

Further subset analysis reveals the ability to detect slow recovery and high-risk categories, as well as the challenges faced by newer businesses with low credit scores.

Improving Predictions of Delinquent Employment Tax

The IRS may be struggling to predict delinquent employment tax, and the potential return from better prediction is unclear.

Credit score data's effectiveness in predicting delinquent employment tax is uncertain, and using underlying data may be more promising.

The two papers have conflicting findings on the correlation between credit bureau data and delinquency, suggesting the need for further research and collaboration.

Estimating the Effects of Tax Reform on Compliance Burden

The paper focuses on the compliance cost of taxation, particularly in filing individual taxes, and how the IRS model was used to predict burden costs using public data.

The study found that compliance costs increase with economic activity and complexity of the tax return, with the highest burden being on the top income earners.

One proposed reform to lower compliance costs is a revenue-neutral repeal of itemized deductions, which would benefit the lowest income group the most.

Reform Options for Tax Deductions and AMT

Three reform options are being considered, including a change to itemized deductions, maintaining the mortgage interest and charitable giving deduction, and appealing the AMT while reducing the state and local tax deduction.

These options have varying impacts on different income percentiles, with the top 1 percent mainly benefiting from the proposed changes.

The estimation shows a $92 billion resource cost to taxation, with the highest burden on the lowest income quintile.

Simplifying the tax code is suggested to lower burden costs for taxpayers.

Compliance costs are increasing with ECI, and the next steps involve calibrating the model with the IRS and releasing documentation for findings replication.

The study presents a new methodology for the voluntary filing rate measure based on IRS data, showing discrepancies in the previous measure using census data.

The revised measure indicates a lower voluntary filing rate, raising concerns about the previous approach's accuracy.

Challenges in Estimating Tax Filing Rates

The study struggled to accurately estimate the number of required tax filers, leading to unexpected results in the voluntary filing rate.

The method of determining required filers involved complex imputation processes and random imputation to match census data, particularly challenging for non-filers.

Despite the difficulties, the study revealed a stable percentage of non-filers in the required population over the last five years, with a decrease in the share of late filers.

Tax Filing Requirements and Non-Filers Analysis

Taxpayers may be required to file due to self-employment or dependency, and being slightly over the threshold may not lead to IRS action.

There is a noticeable dip in the filing rate during middle age, especially for single taxpayers.

Late filers tend to have higher gross income and tax levels, with a larger share of refund returns.

The study aims to improve the accuracy of the filing rate measure and plans to explore non-filing drivers using combined IRS and census data.

Challenges in Combining Census and IRS Data for Non-Filer Analysis

The CPS samples US households but excludes those in institutions and overseas Americans, leading to population differences with IRS data.

There are concerns about the imputation of spouses and dependents for non-filers in the administrative data, potentially leading to an overestimation of income.

The lower voluntary filing rate of middle-aged taxpayers in the administrative data raises questions and calls for a closer exploration of the factors driving this behavior.

The focus is on determining the usefulness of credit bureau scores and addressing the issue of non-compliance.

It is acknowledged that the existing modeling framework for predicting compliance costs needs to be adjusted for non-filers.

There are challenges in matching the populations of different data sets and a need to reevaluate the burden of the Alternative Minimum Tax (AMT).

Discussion on Improving Census Data Accuracy

Matching CPS to 1040 before creating the denominator is crucial, especially for lower-income populations.

Reweighting CPS weights based on the probability of receiving identifiers can significantly impact the results.

The use of out-of-sample testing and variable selection is important for reliable model building and testing.

The Complexity of Tax Planning and Burden of Taxpayers

Tax planning costs are often challenging to directly associate with specific outcomes, such as avoiding the Alternative Minimum Tax (AMT) or deciding between state and local income tax versus sales tax.

The burden that taxpayers face is not fully reflected in their tax return and is difficult to observe and estimate accurately.

Surveys attempt to capture the burden of tax planning, but allocating this burden to the proper place is a significant challenge.