TAX DECLARATION RATES VIA AUDITS: A PREDICTION USING MARKOV MODEL

Yıl 2017, Cilt: 6 Sayı: 2, 97 - 111, 30.06.2017

Ersin Kiral Can Mavruk

https://doi.org/10.17261/Pressacademia.2017.499

Öz

Purpose- This
article predicts tax declaration rates by Markov chain model.

Methodology- Four Markov
models are constructed for the declaration rates of three tax revenues.
Declaration probabilities for the year 2017 are estimated by constructing
probability matrices of transitions between classes described for every model.
Declaration rates are predicted by the product of the initial probability
matrix and transition probability matrix. Limiting matrices of predictions are
found. The best Markov model was found by estimating the sum of mean square
errors for every model. 

Findings- Main results
of this study are i) transitions of tax declarations are declining in higher
states and improving in lower states, ii) in its best model corporate tax
declaration rate is predicted to be between 47% and 64.5% in 2017 with a
probability of 78% and would be stable in the same interval at a probability of
60% in 2038, iii) in the long run income tax would decrease ₺20,3 billion with
a probability of 42% and Value Added Tax would decrease ₺26,2 billion with a
probability of 40% and iv) expected declaration rates of income tax, corporate
tax and value added tax in 2017 are 52.3%, 61.6% and 62.8%, respectively.

Conclusion- Income tax
and Value Added Tax payoffs may substantially decrease from 2015 to 2038 and
2023 respectively. This may cause a revenue deficiency to Turkish Revenue
Administration. Therefore İncome tax and Value Added Tax audits should be
increased. Even though tax revenues increase over time, the declaration rates
show a decreasing to stationary or increasing to stationary behavior. 

Anahtar Kelimeler

Tax declaration rates, tax auditing, tax administration, transition probabilities, Markov analysis

Kaynakça

• Allingham, M.G. & Sandmo, A. 1972, “Income Tax Evasion: A Theoretical Analysis”, Journal of Public Economics, vol.1, pp. 323-338.
• Usher, M.B. 1979,”Markovian Approaches to Ecological Succession”, Journal of Animal Ecology, vol.48, no.2, pp. 413-426
• Serfozo, R. 2009, “Basics of Applied Stochastic Processes, Probability and its Applications”, Springer-Verlag, Berlin Heidelberg, 2
• Yeh, H.W., Chan, W., Symanski, E. & Davis, B.R. 2010, “Estimating Transition Probabilities for Ignorable Intermittent Missing Data in a Discrete-Time Markov Chain”, Communications in Statistics- Simulation and Computation, vol.39, no.2, pp. 433-448.
• Baasch, A., Tischew, S. & Bruelheide, H. 2010, ”Twelve years of succession on sandy substrates in a post-mining landscape: A Markov chain analysis”, Ecological Applications, vol. 20, no. 4, pp. 1136-1147.
• Grimshaw, S.D. & Alexander, W.P. 2011, “Markov Chain Models for Deliquency: Transition Matrix Estimation and Forecasting”, Applied Stochastic Models in Business and Industry, vol. 27, pp. 267-279.
• Lipták, K. 2011, “The Application Of Markov Chain Model To The Description Of Hungarian Labor Market Processes”, Zarządzanie Publiczne, vol. 4, no. 16, pp. 133-149.
• Büyüktatlı, F., İşbilir, S. & Çetin, E.İ. 2013, “Markov Analizi ile Yıllık Ödeneklere Bağlı Bir Tahmin Uygulaması”, Uluslararası Alanya İşletme Fakültesi Dergisi, vol. 5, pp. 1-8
• Lukić, P.,Gocić, M. & Trajković, S. 2013, “Prediction of Annual Precipitation on the Territory Of South Serbia Using Markov Chains”, Bulletin of the Faculty of Forestry, vol.108, pp. 81-92.
• Bluman, A.G. 2014, “Elemantary Statistics”, McGraw Hill Education, New York.
• Cavers, M.S. & Vasudevan, K. 2015, “Brief Communication: Earthquake Sequencing: Analysis of Time Series Constructed from the Markov Chain Model”, Nonlinear Process Geophysics, vol. 22, pp. 589-599
• Lazri, M., Ameur, S., Brucker, J.M., Lahdir, M. & Sehad, M. 2015, “Analysis of Drought Areas in Northern Algeria Using Markov Chains”, J. Earth Syst. Sci., vol. 124, no. 1, pp. 61–70.
• Kıral, E. & Mavruk, C. 2016, “Prediction of Central Government Budget Revenues Using Markov Model”, Journal of Cukurova University Social Sci. Inst., vol. 25, no. 2, pp. 41-56
• Mavruk, C. & Kıral, E. 2017, “Payment Strategies in Corporate Tax: A Mathematical Analysis”, Manuscript to be submitted for publication.
• TRA Activity Reports. 2000-2015, http://www.gib.gov.tr/kurumsal/stratejik-yonetim/faaliyet-raporlari, 5.1.2016