Simulating Approaches to Improve Tax Payments and Reduce Tax Evasion Behavior: An Agent-Based Model

Document Type : Research Paper

Authors

1 Department of Economics, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran

2 Department of Economics, Faculty of Administrative Sciences and Economics, University of Isfahan

Abstract

Through investigating the phenomenon of tax evasion, tax experts are facing the constant challenge of designing and implementing policies to reduce tax evasion due to its hidden nature part. Agent-based models are one of the powerful tools for the behavioral simulation of tax evasion. By creating a virtual laboratory environment with agent-based models, researchers can examine the impact of different policies on people's behavior. In this research, people's behavior is modeled based on their risk-taking degree using a factor-based model; In such a way that the degree of risk-taking in this field is affected by three components of the social factors, the audit-penalty system and the amount of benefit from the public goods they received. The results emphasize the importance of paying attention to social factors, political factors, and government efficiency to reduce tax evasion behavior and increase the total amount of tax payment. The simulation result indicates that among the two audit-penalty policy combinations, a high audit and low penalty is a more suitable policy than a low audit and high penalty, and it causes the number of tax evaders to decrease, as well as the total amount of tax payment increase. Another result is that the government should pay more attention to distribution efficiency to reduce tax evasion behavior and allocation efficiency to increase total tax payments.
 JEL Classification: C63, H26, H41, L78
 

Keywords

Main Subjects

References

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Journal of Economic Research (Tahghighat- E- Eghtesadi)
Volume 58, Issue 4 - Serial Number 145
December 2023
Pages 663-694

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History

  • Receive Date: 25 April 2023
  • Revise Date: 24 November 2023
  • Accept Date: 05 December 2023

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