Incorporating Indirect Effects in Audit Case Selection: An Agent-

Based Approach

Presentation for the IRS Research Conference

June 21, 2012

Kim M. Bloomquist – RAS:OR: Compliance Analysis & Modeling

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).

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Office ofResearch

Audit Case Selection

Traditional approach → max(direct effects) Recommended tax change Relatively easy to measure and document Used for resource allocation

Preferred approach → max(direct + indirect effects) Theoretically better measure of total compliance

impact Why not used?

No methodology currently exists to include indirect effects

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Types of Indirect Effects

Induced effects Changes in compliance behavior due to a change in tax

agency enforcement level E.g., probability of detection, penalty rate

Subsequent period effects Changes in compliance behavior due to a previous tax audit

Taxpayer evaluates tax agency’s effective detection/penalty rate (Gemmell and Ratto 2012)

Compliance may increase or decrease

Group effects Changes in compliance behavior due to knowledge of a

neighbor’s or co-worker’s tax audit Also may lead taxpayer to reassess effective detection/penalty rate,

but with less information than a first-hand audit experience

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Office ofResearch Why agent-based

modeling? Method assumes agents (e.g. taxpayers) have bounded

rationality, exhibit heterogeneity & learn from local interactions Bounded rationality

Overestimating audit probability (Forest and Kirchler 2010) Misinterpret concepts of probability

E.g. “bomb crater” effect, Kastlunger et al. (2009) Heterogeneity

Reporting compliance & third-party information (Black et al. 2012) Response to random audits (Gemmell and Ratto 2012)

Localized interactions Taxpayer reliance on commercial tax preparers (Bloomquist et al. 2007) Tax compliance and social networks (Alm et al. 2009; Fortin et al. 2007) IRS Oversight Board Survey (2012)

28% of respondents: Family or Friends a “very valuable” source for tax information

21% of respondents: Neighbors’ honesty in tax matters has a “great deal” of influence on own tax reporting compliance

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Office ofResearch Individual reporting compliance

model (IRCM): design considerations Model formal and informal networks

Tax preparer – client Employee – employer Filer reference groups (work and residential)

Validate using TY2001 NRP data Desire region w/ socioeconomic characteristics similar to U.S.

“Proof-of-concept”: minimize hardware requirements

Test bed region: county w/ 85,000 filers in TY2001 Protect taxpayer confidentiality Facilitate external model V&V testing

Solution: use “artificial” taxpayers Swap Master File tax returns for Public Use File (PUF) cases Sample with replacement

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Office ofResearch Individual Reporting compliance Model

(IRCM): agent architecture

*

*

*

* *

21 Zones

84,912 Filers

3,321 Employers

2,129 Tax Preparers

*

TaxAgency

Employer

Region

Zone

Filer

Preparer

*

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Reporting regimes

SOI - amounts reported by filer same as PUF data

Rule-based - amounts reported by filer based on user-specified parameters for:

Level of information reporting coverage Marginal compliance impact of withholding Prevalence of filers complying for noneconomic

(deontological) reasons De minimis threshold for reporting.

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Office ofResearchFiler response to a tax audit

(Rule-based reporting regime)

Filer

Audited (s1) Not Audited (s0)

Reduce reportingcompliance on items with little

or no information reporting

If amount <= de minimisthreshold, report $0

Compliant (s1, 0) Noncompliant (s1, 1)

Randomly select actionak | (s1, 1)

Randomly select actionak | (s1, 0)

At time step t

ak = { perfect, increase, decrease, no change } in reporting compliance

Formally, a Markov Decision Process (MDP)

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Office ofResearch Group influence on reporting

compliance

If option specified: A neighbor reference group of user-specified size N

is created for all filers If filer is an employee in a firm with 2 or more

employees, filer also has a co-worker reference group

Two available network types: Random (default) and Smallworld

If a member of taxpayer j’s reference group is audited, then j adjusts his reporting compliance based on user-specified probabilities for 4 responses (e.g., perfect, increase, decrease and no change). Also, a MDP.

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Filer parameters user screen

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Tax agency

Conducts taxpayer audits Performs automated verification checks

by matching income on tax returns against information documents

Issues Automated Underreporter (AUR) notices to filers with an estimated tax discrepancy AUR program assumed to correct inadvertent

errors only, no additional compliance impact

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Types of tax audits

Pure random (default) Targeted random

Fixed Constrained Maximum Yield (CMY)

a “greedy” type optimization algorithm Identifies the lowest and highest yielding

audit classes Increases (by 1) the number of high yield

audits and decreases (by 1) the number of low yield audits each simulation time step

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Case study

Compare the impact on reporting compliance of 5 different audit strategies

1. Pure random

2. CMY 100/0 – Constrained Maximum Yield with 100% maximum coverage rate and no minimum coverage

3. CMY 10/0 – 10% maximum coverage rate, no minimum coverage

4. CMY 1/0 – 1% maximum coverage rate, no minimum coverage

5. CMY 10/5 – 10% maximum coverage rate and a minimum of five audits in each audit class

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Targeted random audit classes

Audit Class

Deduction Type

Business Unit

Income Category

Preparation Mode

1 Standard SB/SE TPI<100K Self2 Standard SB/SE TPI<100K Paid3 Standard SB/SE TPI>=100K Self4 Standard SB/SE TPI>=100K Paid5 Standard W&I TPI<100K Self6 Standard W&I TPI<100K Paid7 Standard W&I TPI>=100K Self8 Standard W&I TPI>=100K Paid9 Itemized SB/SE TPI<100K Self10 Itemized SB/SE TPI<100K Paid11 Itemized SB/SE TPI>=100K Self12 Itemized SB/SE TPI>=100K Paid13 Itemized W&I TPI<100K Self14 Itemized W&I TPI<100K Paid15 Itemized W&I TPI>=100K Self16 Itemized W&I TPI>=100K Paid17 Reported Taxable Income = 018 Random

Taxable Income > 0

Notes: Standard = standard deduction, Itemized = itemized deduction, SB/SE = Small Business / Self-Employed, W&I = Wage and Investment, TPI = Total Positive Income, Self = self preparer, Paid = paid preparer

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Case study: assumptions Rule-based reporting parameters

% of filers who perceive misreporting can succeed on items with No information reporting (IR) (99%) Some IR (48%) Substantial IR (10%)

Marginal compliance impact of withholding (75%) Percentage of deontological filers (25%) De minimis reporting threshold on items with no IR ($1,000)

Subsequent period effects Response is perfect, increase, decrease, no change Filer is found compliant: (0.0, 0.0, 0.50, 0.50) Filers is found noncompliant: (0.0, 0.50, 0.25, 0.25)

Group effects Response is perfect (0.0), increase (0.25), decrease (0.25), no

change (0.50)

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Office ofResearch Time Series of Tax NMP for 5

Alternative Audit Selection Strategies

14.0%

14.2%

14.4%

14.6%

14.8%

15.0%

15.2%

15.4%

15.6%

1 50 99 148 197 246 295

Time Step

NM

P (

Tax

)

CMY 100/0 CMY 10/0 CMY 1/0 CMY 10/5 Random

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Office ofResearch Comparison of Alternative Audit

Case Selection Strategies

Scenario Total Change Total ReductionRandom $252 $95,114 76.4%CMY 100/0 $2,991 $2,739 $91,017 $4,097 1.5 36.9%CMY 10/0 $2,469 $2,217 $91,522 $3,593 1.6 38.4%CMY 1/0 $513 $262 $94,195 $919 3.5 65.2%CMY 10/5 $2,459 $2,207 $89,789 $5,325 2.4 42.9%

No Change

Rate

Audit Results ($1000) Misreported Tax ($1000) Deterrence Multiplier

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Summary and Future Research

Goal of paper: Demonstrate the feasibility of using ABMS to model the indirect effects of audits A community-based approach enables formal and informal

network relationships to be modeled explicitly IRCM can be used in “what if” analyses to determine the impact

on taxpayer reporting compliance of: Changes in information reporting coverage on income line items Changes in employment relationships (employee vs. IC) Changes in paid preparer compliance

Usefulness of ABMS depends on quality of data on taxpayer behavior Future IRS research should address behavioral issues

Impact of IRS Service and Enforcement on taxpayer behavior and subsequent compliance