NSF Award
1519116
This interdisciplinary research project will advance basic understanding of how tax compliance behavior emerges by focusing on how perceptions of risk and tax fairness form, how these perceptions spread through a social network, and the interplay caused by the complex feedback loops among individual behaviors and population-level outcomes. The project also will address how audit, penalty, and taxation policies, and changes in those policies, influence perceptions and ultimately reduce tax evasion. The project will provide new insights regarding how tax compliance behavior is modified by the perceived aggregated compliance and on how behavior and the perceived aggregated compliance depend on social network structure over which perceptions of taxation spreads. The project will demonstrate the utility of the use of agent-based modeling for research on interactions between individuals and governments as well as how data from the American Life Panel collected by Rand Corporation can be employed to address problems like this. The analysis also will have practical utility, because it will shed light on why certain countries with high taxation levels, such as Scandinavian nations, can maintain high levels of tax compliance while other countries with lower taxation levels may have high levels of tax evasion as is the case in Greece and Italy. Project findings therefore should help nations like the United States reduce the occurrence of tax evasion. <br/><br/>Income tax evasion is a problem that poses considerable challenges for tax authorities and governments at the local, state, and federal levels. Its causes and implications are both economic and social. The role of tax evasion-related perceptions and how social networks influence those perceptions remain poorly understood, however. The investigators will build an agent-based computational simulation model of income tax evasion. Within the simulation, the compliance behavior of individuals will change 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. In conjunction, the investigators will conduct a national survey on the perceptions of tax fairness. The survey will gather individual-level data that will inform the simulation model's behavioral mechanisms. These mechanisms influence the propensity to evade, and the survey provides an empirical basis for choosing model-specific parameter values. Model assumptions will further be informed using sensitivity analyses. The simulation model will be validated and calibrated to reproduce U.S. national levels of income tax compliance for different tax brackets. In addition to the U.S., the model also will be calibrated using data sets and compliance levels for Greece and Denmark. Once calibrated, the model will be used to understand how tax evasion behaviors evolve differently depending on various starting assumptions, such as how social networks are structured or what fiscal policies are in place and the conditions for the system to produce tipping point dynamics. The model then will be used to identify fiscal policies that are most effective in minimize tax evasion and recovering compliance. The investigators will employ robust decision making, a method for improving policy decision making, to rank policies based both on their potential performance, as well as how robust this performance is to the key sources of uncertainty. This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.
