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. Office of Research RAS – June 21, 2012 3 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. Office of Research RAS – June 21, 2012 4 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. Office of Research RAS – June 21, 2012 5 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. Office of Research RAS – June 21, 2012 6 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. Office of Research RAS – June 21, 2012 7 Individual Reporting compliance Model (IRCM): agent architecture * * * ** 21 Zones 84,912 Filers 3,321 Employers 2,129 Tax Preparers * TaxAgencyEmployerRegionZoneFilerPreparer *

8. Office of Research RAS – June 21, 2012 8 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. Office of Research RAS – June 21, 2012 9 Filer response to a tax audit (Rule-based reporting regime) Filer Audited (s1) Compliant (s1, 0) Randomly select action ak | (s1, 0) Noncompliant (s1, 1) Randomly select action ak | (s1, 1) Not Audited (s0) Reduce reporting compliance on items with little or no information reporting If amount <= de minimis threshold, report $0 At time step t a k = { perfect, increase, decrease, no change } in reporting compliance Formally, a Markov Decision Process (MDP)

10. Office of Research RAS – June 21, 2012 10 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. Office of Research RAS – June 21, 2012 11 Filer parameters user screen

12. Office of Research RAS – June 21, 2012 12 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. Office of Research RAS – June 21, 2012 13 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. Office of Research RAS – June 21, 2012 14 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

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

16. Office of Research RAS – June 21, 2012 16 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. Office of Research RAS – June 21, 2012 17 Time Series of Tax NMP for 5 Alternative Audit Selection Strategies

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

19. Office of Research RAS – June 21, 2012 19 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