Incorporating Indirect Effects in Audit Case Selection: An Agent-Based Approach

1. 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

2. 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).

3. RAS – June 21, 2012 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

4. RAS – June 21, 2012 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

5. RAS – June 21, 2012 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

6. RAS – June 21, 2012 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

7. RAS – June 21, 2012 Individual Reporting compliance Model (IRCM): agent architecture TaxAgency Employer Region Zone Filer Preparer * * * * * * * 21 Zones 84,912 Filers 3,321 Employers 2,129 Tax Preparers

8. RAS – June 21, 2012 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.

9. RAS – June 21, 2012 Filer response to a tax audit(Rule-based reporting regime) At time step t ak = { perfect, increase, decrease, no change } in reporting compliance Formally, a Markov Decision Process (MDP)

10. RAS – June 21, 2012 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.

11. RAS – June 21, 2012 Filer parameters user screen

12. RAS – June 21, 2012 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

13. RAS – June 21, 2012 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

14. RAS – June 21, 2012 Case study Compare the impact on reporting compliance of 5 different audit strategies Pure random CMY 100/0 – Constrained Maximum Yield with 100% maximum coverage rate and no minimum coverage CMY 10/0 – 10% maximum coverage rate, no minimum coverage CMY 1/0 – 1% maximum coverage rate, no minimum coverage CMY 10/5 – 10% maximum coverage rate and a minimum of five audits in each audit class

15. RAS – June 21, 2012 Targeted random audit classes

16. RAS – June 21, 2012 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)

17. RAS – June 21, 2012 Time Series of Tax NMP for 5 Alternative Audit Selection Strategies

18. RAS – June 21, 2012 Comparison of Alternative Audit Case Selection Strategies

19. RAS – June 21, 2012 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