RAND's Interdisciplinary Behavioral and Social Science Agent-Based Model of Income Tax Evasion

Technical Report

by Raffaele Vardavas, Pavan Katkar, Andrew M. Parker, Gursel Rafig oglu Aliyev, Marlon Graf, Krishna B. Kumar

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Income tax evasion is a problem that poses considerable challenges for tax authorities and governments at the local, state, federal levels, as well as internationally. Its causes and implications are both economic and social, and therefore it is of enormous importance in policy design. We built an agent-based computational simulation model of income tax evasion. Within the simulation, individuals' compliance behavior changes through an adaptation process based on their past experiences with audits and tax evasion penalties, their perception of the fairness in taxation rates and social interactions with people in their social networks. To inform the model we have conducted a survey on a nationally representative sample on the perceptions of tax fairness. The specific purpose of our survey was to guide the model construction, test our model assumptions, as well as inform behavioral parameter values and the calibration procedure. In addition, the survey provides novel insights into the social dynamics of risk and fairness perceptions, including how they are influenced by perceptions and experiences of social network contacts and in community at large.

Here, we present technical details that describe our model and how it was informed by our survey. In our first two sections we provide a brief introduction to the problem and an overview of past agent-based models of tax compliance. Sections 3 to 11 provide a description of our agent based model following the overview, design concepts, and details protocol [69, 70]. Section 12 to 15 provides description of our survey and focuses only on the analyses that helped inform the model and in particular the behavioral mechanisms and parameter values. Sections 17 to 20 described model verification, validation and calibration. Sections 21 and 22 describes results from possible intervention policy. Finally, Sections 23 and 24 provide a discussion of our results and describe limitations and future work.

Table of Contents

  • Chapter One


  • Chapter Two

    Understanding social drivers of tax evasion

  • Chapter Three

    Overview of ABMs and Past Models of Tax Compliance

  • Chapter Four


  • Chapter Five

    Entities, State variables, and Scales

  • Chapter Six

    Model Overview and Scheduling

  • Chapter Seven

    Specifications of the Taxpayer's Behavioral Model Components

  • Chapter Eight

    Specifications and Initialization of the IRS Model Components

  • Chapter Nine

    Specifications of the Governing State (GS) Components

  • Chapter Ten

    Design concepts

  • Chapter Eleven

    Population Heterogeneities and Initialization

  • Chapter Twelve

    Input data

  • Chapter Thirteen

    Overview to the ALP Survey on Tax Compliance

  • Chapter Fourteen

    Using the ALP Survey to inform the model parameters

  • Chapter Fifteen

    Using the ALP Survey to inform the network of social interactions

  • Chapter Sixteen

    Using the ALP Survey to inform the taxpayer's behavioral attributes

  • Chapter Seventeen

    Model Verification and Validation

  • Chapter Eighteen

    Model Outputs for Analyses and Calibration

  • Chapter Nineteen

    Uncertainty and Sensitivity Analyses

  • Chapter Twenty

    Model Calibration

  • Chapter Twenty-One

    Simple intervention and "what if" scenarios

  • Chapter Twenty-Two

    Combining fiscal and deterrence intervention policies

  • Chapter Twenty-Three

    Summary and Discussion

  • Chapter Twenty-Four


  • Appendix A

    Experience Discounting and the EWMA process

  • Appendix B

    Eliciting social influence parameters for tax compliance behavior using the results from Alm et al. 2009

  • Appendix C

    Program Evaluation and Review Technique (PERT) distribution

  • Appendix D

    Survey Questions of the American Life PanelWell Being 456

  • Appendix E

    Fitting data to an S-Curve

  • Appendix F

    Estimating Tax Rate, Audit Rate, Penalty Rate Elasticities and the S-Curve Parameters

  • Appendix G

    Media Effect Factor on the Motivation to Comply

  • Appendix H

    Assortativity odds ratio measure

  • Appendix I

    Testing the model dynamics for stationarity

  • Appendix J

    CART and Random Forest methods

  • Appendix K

    Code and Data Repository

  • Appendix L

    Interactive Tool

Research conducted by

The research described in this report was conducted by RAND Education and Labor.

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