Post on 30-Dec-2015
description
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).
RAS – June 21, 2012 3
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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Office ofResearch
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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Office ofResearch
Filer parameters user screen
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Office ofResearch
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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Office ofResearch
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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Office ofResearch
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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Office ofResearch
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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Office ofResearch
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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Office ofResearch
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