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Multi-Agent Simulation of Taxpayer Reporting Compliance. Kim M. Bloomquist U.S. Internal Revenue Service. Disclaimer The views expressed here are those of the author and should not be interpreted as those of the U.S. Internal Revenue Service (IRS). Outline.

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Multi agent simulation of taxpayer reporting compliance

Multi-Agent Simulation of Taxpayer Reporting Compliance

Kim M. Bloomquist

U.S. Internal Revenue Service


Disclaimer

The views expressed here are those of the author and should not be interpreted as those of the U.S. Internal Revenue Service (IRS).


Outline
Outline

  • Brief introduction to agent-based simulation

  • Model description


What is agent based simulation aka agent based modeling or abm
What is Agent-Based Simulation?(aka Agent-Based Modeling or ABM)

  • Computational simulation that models macro-level phenomena (e.g., changes in stock market prices) as the outcome of individual decisions made under bounded rationality and experience derived from local interactions with other agents. Close links to the field of behavioral economics.

    • Bounded rationality

      • Imperfect information, use of heuristics (e.g., gambler’s fallacy)

    • Types of agent interactions

      • Taxpayer and IRS (filing, service, enforcement)

      • Taxpayer and Tax Preparer (filing, tax advice)

      • Taxpayer and Employer (form & timing of compensation, withholding)

      • Taxpayer and Other Taxpayers (sharing tax experiences)


What are agents
What are Agents?

  • Virtual representations of physical objects (e.g., a taxpayer, a small business, a tax preparer, an IRS auditor).

  • Possess attributes and behaviors

    • Attributes: income, filing status, tax liability

    • Behaviors: sensitivity to IRS enforcement actions, influence of prior IRS actions on current behavior (learning)


When to use an abm
When to Use an ABM?

  • When agents are heterogeneous

    • Not all taxpayers have the same opportunity to evade

  • When agent interaction cannot be ignored

    • Impact of tax preparers on compliance

    • Role of IRS service and enforcement

  • When agents exhibit strategic behavior

    • Taxpayers playing the “audit lottery”

  • When geography is important

    • Tax schemes targeted to certain types of filers

    • Spatial correlation of compliance behavior


Research motivation
Research Motivation

  • Reducing the tax gap an IRS priority

    • Definition: amount of true tax liability not paid voluntarily and timely from individuals and corporations

    • Gross tax gap in TY 2001 = $345B (16% of total tax liability), $290 net tax gap

    • Decrease burden on compliant taxpayers

    • Lower Federal budget deficits

  • But analysis tools lacking. Why?

    • Complexity of tax system

      • Multiple income sources

      • Multiple institutions (tax preparers, employers, tax agency)

    • Complex nature of taxpayer behavior

      • Bounded rationality, Heterogeneity, Social norms, Group influences

    • Analytical methods unable to incorporate greater realism

  • Proposed solution: agent-based simulation


Design goals
Design Goals

  • Realistic portrayal of tax reporting and compliance system

    • Detailed tax return data

    • Institutions (e.g., tax agency, paid preparers, employers)

    • Enforcement tools (e.g., audits, 3rd party information reporting)

  • Allow external verification and validation (V&V)

    • Important for model acceptance (Axtell and Epstein 1994)

    • Requires open access to code and data

  • Protect taxpayer confidentiality

    • Precludes use of taxpayer data

    • Conflicts with above two goals


Approach
Approach

  • Model an entire community (testbed region)

    • Incorporate formal and informal taxpayer networks

      • Tax preparer – client

      • Employee – employer

      • Filer reference groups (work and residential)

    • Select a community with economic and demographic characteristics similar to nation but small enough to model on a laptop: selected region had 84,912 filers in TY 2001

  • Use artificial taxpayers

    • Facilitate external V&V

    • Protects taxpayer data confidentiality

    • Substitute data from the Statistics of Income (SOI) Public Use File (PUF) for tax return data

    • Impute line item misreporting from TY 2001 NRP data


Implementation
Implementation

  • Java JDK 1.6

    • javax.swing for user interface

    • java.sql for data retrieval and artificial data creation

    • javax.xml to save and load model scenarios

  • RePast Simphony

    • Random number generation (MersenneTwister RNG)

    • Chart generation (jFreeChart)



Dataset construction summary
Dataset Construction: Summary

  • Validation details provided in paper

  • 29,703 PUF cases used to represent 84,912 original tax returns

  • 66 of 74 original tax returns matched

  • Dataset contains only PUF cases that can be shared for independent model validation

  • Additional protection available by providing a random sample of the artificial dataset


Ircm architecture agent hierarchy

*

*

*

*

*

*

*

*

*

21 Zones

84,912 Filers

3,321 Employers

2,129 Tax Preparers

*

Preparer

Employers

Filer

Zone

Preparers

Employer

TaxAgency

Audit

Filers

Region

IRCM Architecture(Agent Hierarchy)


Filer class relationships individual reporting compliance model ircm
Filer Class RelationshipsIndividual Reporting Compliance Model (IRCM)



Imputableelement
ImputableElement

  • An income or offset item that may be adjusted for misreporting

    • True income = reported income + misreported amt

    • Income items:

      • wages, taxable interest, dividends, state tax refunds, alimony, Schedule C, Schedule D, Form 4797, taxable IRA income, taxable pension income, Schedule E, Schedule F, unemployment compensation, gross social security, other income

    • Offset items:

      • adjustments (net adjustment for ½ of self-employment tax), deductions, exemptions, credits (net child tax credit)

      • Adjustments to certain credits (CTC, EITC, ACTC) that reflect a change in income are handled by the tax calculator. Other adjustments (e.g., qualifying child requirements) not made. This is a topic for future research.


Change Amount ($1000)

Change Amount ($1000)

(y)

(y)

x

Imputation of Pension Income


Filer response to an audit
Filer Response to an Audit

  • A baseline reporting rate is set for all income and offset line items with potential misreporting (19 items)

    • Assumed to be the filer’s preferred reporting behavior under bounded rationality and existing enforcement environment

    • Stochastic assignment of misreporting from empirical CDFs derived from TY 2001 NRP data

  • When audited, user specified probabilities determine filer’s compliance response (Perfect, More, Less, Same)

    • Size of ‘More’ & ‘Less’ response a random draw

    • Compliance response to an audit decays at a fixed rate (set by user) over time until preferred behavior is reached again (unless decay rate = 0)

    • Bi-directional constant rate of decay assumed

  • If activated, response to a neighbor’s or coworker’s audit modeled in same manner as a personal audit


Reference groups in itcm

Employer

Zone

Reference Groups in ITCM

Neighbors

Coworkers

Filer


Tax agency
Tax Agency

  • Audits tax returns

    • Fixed # of audits per time period (year)

    • Pure random or mixed random and non-random

    • If some non-random audits, tax agency tries to improve yield by shifting audits incrementally from the least to most productive strategy (17 strategies)

  • Performs automated checks of tax return data

    • Issues notices to taxpayers with discrepancies exceeding a user-specified threshold

      • No separate deterrence impact related to receipt of a notice

      • Area for future research

    • mapAs feature to map the reporting behavior of a given income item as if it were like another item


Tax preparers
Tax Preparers

  • Preparer-client networks in model reflect actual relationships in region

  • Misreporting imputed separately for self and paid prepared tax returns.

  • Misreporting on paid-prepared returns can be changed locally to explore contagion effects or universally to reflect a change in IRS paid preparer enforcement

  • Preparers can be deactivated (everyone self-prepares) to assess impact of preparers on compliance


Step 2

Step 3

Step 5

Step 10

Paid Preparer Scenario

Paid Preparer Illegal Tax Scheme Starting in Zone 11


Employers
Employers

  • Employer-employee networks in model reflect actual relationships in region

  • Workers’ employment status can be changed from employee to independent contractor

    • All or only some employers

    • Wage income becomes Schedule C income

    • Reporting characteristics of Schedule C income used

    • Income and self-employment tax calculated with tax calculator



Tax calculator
Tax Calculator

Total Tax on reported income & after offsets

SOI = $521.3 million

Tax calculator = $519.9 million

Difference = $1.4 million (0.27%)


Model results vs nrp data net misreporting percentage
Model Results vs. NRP Data(Net Misreporting Percentage)


Start

Read Data

Instantiate Agents

Time Loop

Tax Calculator Loop

Calculate Tax

Perform Audits

Issue AUR Notices

Collect Statistics

Process Stop Filers

Update Learning Behavior

Update Tax Agency Audit Targeting

Wrap Up

Generate Tables & Charts

End

Running a Simulation:Top-Level View


Hypothetical example

Hypothetical Example:

50% Increase and 50% Decrease in Number of Audits Performed


A=100% random audits, prob. audited taxpayer increases compliance=0.50 & decrease=0.50 ; B=A except 5% random audits, minimum 5 audits per audit class, maximum 10% coverage; C=B except 100% taxpayers increase compliance; D=C except prob. taxpayer increases compliance=0.50 if a reference group member is audited (prob. no change=0.50), reference group size=3.




Summary
Summary

  • ABM a natural approach to simulating taxpayer compliance

    • Greater complexity and realism vs. analytical models

    • Represent heterogeneous taxpayer behavior

    • Estimate direct and indirect effects on compliance

  • Community testbed design concept

    • Incorporate network relationships (preparers, employers, neighbors)

    • Investigate compliance impacts in silico before field testing (in testbed community)

    • Data from field tests used to improve models leading to more efficient use of tax agency resources and more compliance


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