Fuzzy Regulation Model of the Interaction Between the State and Economic Subject Within the Shadow Economy and Tax Field

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1306)


In this article, economic system is reviewed as an active system; and evaluation and reduction of the process emergence rate of shadow economy via optimization methods is being analyzed within economic relations between the state as a tax authority and economic subject as a taxpayer. Profitability of economic activity as a main influencing factor, expenses for staying in the shadow, contribution of tax and revenue for general utility are evaluated through fuzzy approach. Selecting profitability as a main factor is related with the process of not declaring the real level of profitability to tax authority by economic subject.


State and economic subject Shadow economy Optimization Proitability Tax evasion Mathematical model Fuzzy approach 


  1. 1.
    Schneider, F., Buehn, A.: Estimating the Size of the Shadow Economy: Methods, Problems and Open Questions, Discussion Paper No. 9820 (2016).
  2. 2.
    Zadeh, L.: Fuzzy sets. Inform. Control 8, 338–353 (1965).
  3. 3.
    Schneider, F.: Estimating the size of the danish shadow economy using the currency demand approach: an attempt. Scand. J. Econ. 88(4), 643 (1986).
  4. 4.
    Torgler, B., Schneider, F., Schaltegger, C.A.: Local autonomy, tax morale, and the shadow economy. Publ. Choice 144, 293–321 (2010). Scholar
  5. 5.
    Buehn, A., Schneider, F.: Corruption and the Shadow Economy: A Structural Equation Model Approach. IZA Discussion Paper No. 4182.
  6. 6.
    Lackó, M.: The hidden economies of visegrad countries in international comparison: a household electricity approach. In: Halpern, L., Wyplosz, Ch. (eds.) Towards a Market Economy, Hungary. Cambridge University Press, Cambridge (Mass.) (1998)Google Scholar
  7. 7.
    Aigner, D.J., Schneider, F., Ghosh, D.: Me and my shadow: estimating the size of the U.S. hidden economy from time series data. Dyn. Econ. Model. 297–334 (1998).
  8. 8.
    Medina, L., Schneider, F.: Shadow Economies Around the World: What Did We Learn Over the Last 20 Years? IMF Working Paper (2018). ISBN/ISSN: 9781484338636/1018-5941Google Scholar
  9. 9.
    Musayev, A.F., Madatova, S.G., Rustamov, S.S.: Evaluation of the impact of the tax legislation reforms on the tax potential by fuzzy inference method. In: 12th International Conference on Applied Fuzzy System Soft Computing (2016). Procedia Comput. Sci., 102, 507-514.
  10. 10.
    Musayev, A., Madatova, S., Rustamov, S.: Mamdani-type fuzzy inference system for evaluation of tax potential. In: Zadeh, L.A., Yager, R.R., Shahbazova, S.N., Reformat, M.Z., Kreinovich, V. (eds.) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. SFSC, vol. 361, pp. 511–523. Springer, Cham (2018). Scholar
  11. 11.
    Rustamov, S., Musayev, A., Madatova, S.: Evaluation of the impact of state’s administrative efforts on tax potential using sugeno-type fuzzy inference method. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F.M. (eds.) ICAFS 2018. AISC, vol. 896, pp. 352–360. Springer, Cham (2019). Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Institute of Economics, ANASBakuAzerbaijan
  2. 2.Azerbaijan UniversityBakuAzerbaijan
  3. 3.Training Center of Ministry of Taxes of the Republic of AzerbaijanBakuAzerbaijan
  4. 4.Baku State UniversityBakuAzerbaijan
  5. 5.ADA UniversityBakuAzerbaijan
  6. 6.Institute of Control Systems, ANASBakuAzerbaijan
  7. 7.Institute of Law and Human Rights, ANASBakuAzerbaijan
  8. 8.The Academy of Public Administration under the President of the Republic of AzerbaijanBakuAzerbaijan
  9. 9.Azerbaijan State Oil and Industry UniversityBakuAzerbaijan

Personalised recommendations