شبیه‌سازی راهکارهای بهبود پرداختهای‌مالیاتی و کاهش رفتار فرار‌مالیاتی در چارچوب مدلهای عامل‌محور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه اقتصاد، دانشکده امور اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران

2 گروه اقتصاد، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان

چکیده

‌ماهیت پنهانی‌بودن پدیده فرار‌ مالیاتی سبب شده است تا محققان و کارشناسان در مسیر مطالعه آن با چالش همیشگی طراحی و اجرایی کردن سیاست‌ها و مشوق‌های کاهش رفتار فرار‌ مالیاتی رو‌به‌رو باشند. یکی از ابزارهای قدرتمند در زمینه شبیه سازی رفتاری پدیده فرار‌مالیاتی، مدل‌های عامل ‌محور است. مدل‌های عامل ‌محور با ایجاد یک محیط آزمایشگاهی مجازی، این امکان را برای محققان فراهم می‌آورند که تأثیر سیاست‌های مختلف را بر رفتار مؤدیان مالیاتی مورد بررسی قرار دهند. در این پژوهش با استفاده از یک مدل عامل ‌محور، رفتار مؤدیان مالیاتی براساس درجه ریسک‌پذیری آنها مدل‌سازی شده است؛ به‌گونه‌ای که درجه ریسک‌پذیری افراد در زمینه انجام فرار‌ مالیاتی تحت تأثیر سه مؤلفه بستر اجتماعی، وضعیت نظام حسابرسی-جریمه و میزان بهره‌مندی از کالای‌ عمومی قرار داشته است. نتایج حاصل از این پژوهش بر اهمیت توجه به عوامل اجتماعی (جمعیتی)، سیاستی و کارایی دولت‌ها در جهت کاهش رفتار فرار‌ مالیاتی و افزایش میزان مجموع مالیات پرداختی تأکید دارد و خروجی‌های شبیه‌سازی نشان‌دهنده این مطلب است که از میان دو ترکیب سیاستی حسابرسی-جریمه، حسابرسی بالا و جریمه کم سیاست مناسب‌تری نسبت به‌حسابرسی پایین و جریمه زیاد می‌باشد و سبب می‌شود که از نظر آماری، تعداد فرارکنندگان مالیاتی در جامعه، کاهش و میزان مجموع مالیات پرداختی افزایش یابد. همچنین مشخص شده است که دولت برای کاهش رفتار فرار‌ مالیاتی بایستی به کارایی توزیعی و به‌منظور افزایش مجموع مالیات پرداختی، به کارایی تخصیصی توجه بیشتری کند.
طبقه‌بندی JEL:
C63, H26, H41, L78

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

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

نویسندگان [English]

  • Maede Mohammadi 1
  • Sasan Gharakhani 1
  • Majid Sameti 1
  • Hadi Amiri 2
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
چکیده [English]

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
 

کلیدواژه‌ها [English]

  • Tax evasion
  • Agent-based modeling
  • Public goods procurement
  • Allocation efficiency
  • Distribution efficiency

مراجع

  1. Allingham, M. G., & Sandmo, A. (1972). lIncome Tax Evasion: A Theoretical Analy’sis, Journal of Public Economics.
  2. Amiri, M. (2017). “Behavioral Economics and Tax Evasion”. Journal of Economic Research, 17(64): 95-130. doi: 22054/10/joer.7670/2017 (In Persian).
  3. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
  4. Bazart, C., Bonein, A., Hokamp, S., & Seibold, G. (2016). Behavioural economics and tax evasion: calibrating an agent-based econophysics model with experimental tax compliance data. Journal of Tax Administration, 2(1), 126–144.
  5. Becker, G. S. (1968). Crime and punishment: An economic approach. In The economic dimensions of crime (pp. 13–68). Springer.
  6. Bloomquist, K. M. (2004). Modeling taxpayers’ response to compliance improvement alternatives. Annual Conference of the North American Association for Computational Social and Organizational Sciences, Pittsburgh, PA, 27–29.
  7. Bloomquist, K. M. (2006). A comparison of agent-based models of income tax evasion. Social Science Computer Review, 24(4), 411–425. https://doi.org/1177/10/0894439306287021
  8. Cai, J., & Xiong, H. (2017). An agent-based simulation of cooperation in the use of irrigation systems. Complex Adaptive Systems Modeling, 5(1), 1–23.
  9. Chaman, T., Mohajeri, P., Arab MazarYazdi, A. (2019). “The Impact of Financial Development on Tax Evasion in Iran”. Journal of Economic Research, 19(72): 105-139. doi: 22054/10/joer.10156/2019 (In Persian).
  10. Davis, J. S., Hecht, G., & Perkins, J. D. (2003). Social behaviors, enforcement, and tax compliance dynamics. The Accounting Review, 78(1), 39–69.
  11. Dell’Anno, R. (2022). Measuring the unobservable: estimating informal economy by a structural equation modeling approach. International Tax and Public Finance, 1–31.
  12. Di Mauro, L. S., Pluchino, A., & Biondo, A. E. (2019). Tax evasion as a contagion game: evidences from an agent-based model. The European Physical Journal, 92(5), 1–12.
  13. Dubbelboer, J., Nikolic, I., Jenkins, K., & Hall, J. (2017). An agent-based model of flood risk and insurance. Journal of Artificial Societies and Social Simulation, 20(1).
  14. Erdős, P., & Rényi, A. (1960). On the evolution of random graphs. Math. Inst. Hung. Acad. Sci, 5(1), 17-60.
  15. Fuest, C., & Riedel, N. (2009). Tax evasion, tax avoidance and tax expenditures in developing countries: A review of the literature. Report prepared for the UK Department for International Development (DFID), 44.
  16. Gao, S., Zheng, R., & Hu, T. (2012). Can increases in the cigarette tax rate be linked to cigarette retail prices? Solving mysteries related to the cigarette pricing mechanism in China. Tobacco Control, 21(6), 560–562.
  17. Gharakhani, S., Amiri, H., Safari, B. (2023). Agent-based modeling of common-pool resource management through the emergence of self-governing institutions. Journal of Economic Research, 57(3): 533-561. doi: 22059/10/jte.348357/2023.1008717 (In Persian).
  18. Ghorbani, A., & Bravo, G. (2016). Managing the commons: a simple model of the emergence of institutions through collective action. International Journal of the Commons, 10(1).
  19. Hardin, R. (1995). [BOOK REVIEW] One for all, the logic of group conflict. Political Science Quarterly, 110(4), 668–669.
  20. Hashimzade, N., Myles, G. D., Page, F., & Rablen, M. D. (2015). The use of agent-based modelling to investigate tax compliance. Economics of Governance, 16(2), 143–164. https://doi.org/1007/10/s10101-014-0151-8
  21. Heckathorn, D. D. (1996). The dynamics and dilemmas of collective action. American Sociological Review, 250–277.
  22. Hokamp, S., & Pickhardt, M. (2010). Income tax evasion in a society of heterogeneous agents–Evidence from an agent-based model. International Economic Journal, 24(4), 541–553.
  23. Hokamp, S., Gulyás, L., Koehler, M., & Wijesinghe, S. (2018). Agent-Based Modeling and Tax Evasion: Theory and Application. In Agent-based Modeling of Tax Evasion (Issue February).

https://doi.org/1002/10/9781119155713.ch1

  1. Izadi, A., Sameti, M., Akbari, N. (2021). Estimation of Tax Evasion in Iran Using MIMIC Method (1976-2016). J Tax Res, 28 (48) :7-32 (In Persian).
  2. Kirchler, E. (2007). The economic psychology of tax behaviour. Cambridge University Press.
  3. Korobow, A., Johnson, C., & Axtell, R. (2007). An agent–based model of tax compliance with social networks. National Tax Journal, 60(3), 589–610.
  4. Lima, F. W. S., & Zaklan, G. (2008). A multi-agent-based approach to tax morale. International Journal of Modern Physics C, 19(12), 1797–1808.
  5. Matlabi, M., Alizade, M., Faraji Dizaji, S. (2018). Estimating Shadow Economy and Tax Evasion Considering Behavioral Factors. Quarterly Journal of Applied Economics Studies in Iran, 7(27): 141-167 (In Persian).
  6. Mittone, L., & Patelli, P. (2000). Imitative behaviour in tax evasion. In Economic simulations in swarm: Agent-based modelling and object-oriented programming (pp. 133–158). Springer.
  7. Nazarpoor, M., NaslMusavi, H., HoseiniShirvani, M. (2020). A Model for Tax Evasion Forecasting based on ID3 Algorithm and Bayesian Network. J Tax Res, 28(45): 59-87 (In Persian).
  8. Rapoport, A. (1974). Prisoner’s dilemma—recollections and observations. In Game Theory as a Theory of a Conflict Resolution (pp. 17–34). Springer.
  9. Rezaee siabidi, M. (2017). Ways to Deal with and Prevent Tax Evasion. Ghanonyar, 4(4): 145-159 (In Persian).
  10. Robbins, L. (1932). The nature and significance of economic science. The Philosophy of Economics: An Anthology, 1, 73–99.
  11. Schulz, M. (2003). Statistical physics and economics: concepts, tools, and applications (Vol. 184). Springer Science & Business Media.
  12. Slemrod, J. (2007). Cheating ourselves: The economics of tax evasion. Journal of Economic Perspectives, 21(1), 25–48.
  13. Slemrod, J., & Yitzhaki, S. (2002). Tax avoidance, evasion, and administration. In Handbook of public economics, 3, 1423–1470. Elsevier.
  14. Srinivasan, T. N. (1973). Tax evasion: A model.
  15. Stankevicius, E., & Leonas, L. (2015). Hybrid approach model for prevention of tax evasion and fraud. Procedia-Social and Behavioral Sciences, 213, 383–389.
  16. Vale, R. (2015). A model for tax evasion with some realistic properties. Available at SSRN 2601214.
  17. Van Dam, K. H., Nikolic, I., & Lukszo, Z. (Eds.). (2012). Agent-based modelling of socio-technical systems (Vol. 9). Springer Science & Business Media.
  18. Voss, T. (2001). Game-theoretical perspectives on the emergence of social norms. na.
  19. Warner, G., Wijesinghe, S., Marques, U., Badar, O., Rosen, J., Hemberg, E., & O’Reilly, U.-M. (2015). Modeling tax evasion with genetic algorithms. Economics of Governance, 16, 165–178.
  20. Wats, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440–442.
  21. YITZHAKlt, S. (1974). A note on income tax evasion: A theoretical analysis. Journal of Public Economics, 3, 201–202.
  22. Zaklan, G., Westerhoff, F., & Stauffer, D. (2009). Analysing tax evasion dynamics via the Ising model. Journal of Economic Interaction and Coordination, 4(1), 1–14.

فایل ها

سابقه مقاله

  • تاریخ دریافت: 05 اردیبهشت 1402
  • تاریخ بازنگری: 03 آذر 1402
  • تاریخ پذیرش: 14 آذر 1402

هم رسانی

ارجاع به این مقاله

آمار