Incorporating Indirect Effects in
Audit Case Selection: An AgentBased 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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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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

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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
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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Individual Reporting compliance Model
(IRCM): agent architecture
Region
Employer
*
Zone
*
*
TaxAgency
*
*
Filer
*
Preparer
*
21 Zones
84,912 Filers
3,321 Employers
2,129 Tax Preparers
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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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Filer response to a tax audit
(Rule-based reporting regime)
Filer
Audited (s1)
At time step t
Not Audited (s0)
Compliant (s1, 0)
Noncompliant (s1, 1)
Reduce reporting
compliance on items with little
or no information reporting
Randomly select action
ak | (s1, 0)
Randomly select action
ak | (s1, 1)
If amount <= de minimis
threshold, report $0
ak = { perfect, increase, decrease, no change } in reporting compliance
Formally, a Markov Decision Process (MDP)
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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
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.
 If
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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
Business
Income
Preparation
Type
Unit
Category
Mode
Taxable Income > 0
1
Standard
SB/SE
TPI<100K
Self
2
Standard
SB/SE
TPI<100K
Paid
3
Standard
SB/SE
TPI>=100K
Self
4
Standard
SB/SE
TPI>=100K
Paid
5
Standard
W&I
TPI<100K
Self
6
Standard
W&I
TPI<100K
Paid
7
Standard
W&I
TPI>=100K
Self
8
Standard
W&I
TPI>=100K
Paid
9
Itemized
SB/SE
TPI<100K
Self
10
Itemized
SB/SE
TPI<100K
Paid
11
Itemized
SB/SE
TPI>=100K
Self
12
Itemized
SB/SE
TPI>=100K
Paid
13
Itemized
W&I
TPI<100K
Self
14
Itemized
W&I
TPI<100K
Paid
15
Itemized
W&I
TPI>=100K
Self
16
Itemized
W&I
TPI>=100K
Paid
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Reported Taxable Income = 0
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Random
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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Time Series of Tax NMP for 5
Alternative Audit Selection Strategies
15.6%
15.4%
NMP (Tax)
15.2%
15.0%
14.8%
14.6%
14.4%
14.2%
14.0%
1
50
99
148
197
246
Time Step
CMY 100/0
CMY 10/0
CMY 1/0
CMY 10/5
Random
295
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Comparison of Alternative Audit
Case Selection Strategies
Audit Results ($1000)
Scenario
Random
CMY 100/0
CMY 10/0
CMY 1/0
CMY 10/5
Total Change
$252
$2,991 $2,739
$2,469 $2,217
$513
$262
$2,459 $2,207
Misreported Tax ($1000)
Total Reduction
$95,114
$91,017
$4,097
$91,522
$3,593
$94,195
$919
$89,789
$5,325
Deterrence
Multiplier
1.5
1.6
3.5
2.4
No
Change
Rate
76.4%
36.9%
38.4%
65.2%
42.9%
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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