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

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.