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).

Office of Research RAS – June 21, 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

Office of Research RAS – June 21, 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

Office of Research RAS – June 21, 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

Office of Research RAS – June 21, 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

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

Office of Research RAS – June 21, 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.

Office of Research RAS – June 21, 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)

Office of Research RAS – June 21, 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.

Office of Research RAS – June 21, Filer parameters user screen

Office of Research RAS – June 21, 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

Office of Research RAS – June 21, 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

Office of Research RAS – June 21, 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

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

Office of Research RAS – June 21, 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)

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

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

Office of Research RAS – June 21, 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