The Application of Artificial Neural Network Method to Investigate the Effect of Unemployment on Tax Evasion
Keywords:
Tax Evasion, Artificial Neural Network Method, Sensitivity Analysis.
Abstract
Although tax is an important component of government revenue which is used to finance government expenditures, there are many causes that lead people to avoid or evade from paying tax. Among the causes of tax evasion, main factors of them are low income of taxpayers, high tax burdens; increase the size of government, trade openness, high inflation and unemployment. This study postulates that unemployment has a high effect on tax evasion. The present study applied the Sensivity Analysis with Artificial Neural Network (ANN) methodology to investigate the effect of unemployment on tax evasion and also to determine the relative importance of unemployment among other causes of tax evasion for Malaysian data from 1963-2012. Results reveal that there is a positive relationship between unemployment and the extent of tax evasion and unemployment has a high effect on tax evasion among other causes of tax evasion.
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References
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Economics Studies of Iran, 35 (2), 89-114.
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Applications, 15, 405-411.
[31] Tanzi, V. (1982). The Underground Economy in the United States and Abroad. Lexington Books, Lexington,
MA.
[32] Wong, T. C., Law, K. M. Y., Yau, H. K. & Ngan, S. C. 2011a. Analyzing supply chain operation models with
the PC-algorithm and the neural network. Expert Systems with Applications, 38, 7526-7534.
[33] Wong, T. C., Ngan, S.-C., Chan, F. T. S. & Chong, A. Y.-L. 2012. A two-stage analysis of the influences of
employee alignment on effecting business–IT alignment. Decision Support Systems, 53, 490-498.
[34] Wong, T. C., Wong, S. Y. & Chin, K. S. 2011b. A neural network-based approach of quantifying relative
importance among various determinants toward organizational innovation. Expert Systems with Applications,
38, 13064-13072.
[35] Yoon, Y., Guimaraes, T. & Swales, G. 1994. Integrating artificial neural networks with rule-based expert
systems. Decision Support Systems, 11, 497-507.
[36] Weigend, A.S., Huberman, B.A. & Rumelhart, D.E. 1992. Predicting sunspots and exchange rates with
connectionist networks In: Casdagli, M., Eubank, S. (Eds.), Nonlinear Modeling and Forecasting.
[37] Zarra Nezhad, M., Feghh Majidi, A. & Rezaei, R. 2009. Forecasting Exchange Rate with Artificial Neural
Network (ANN) and Autoregressive Integrated Moving Average Process, ARIMA. Quarterly Journal of
Economics Review. 5(4):107-130.
Contest. European Economic Review, 1-17.
[2] Busato F., Chiarini, B. & Marchetti E. 2010. The relationship between Tax Shocks, Sunspots and Tax Evasion.
The Open Economics Journal, 3: 14-24.
[3] Caballe, J. & Panade , J. (2004). Inflation, Tax Evasion, and the Distribution of Consumption, Journal of
Macroeconomics, 26,567–595.
[4] Campbell, JY. Andrew, w l. & Mackinlay, A. C. (1997). Econometric of Financial Market. Princeton University
press.
[5] Cebula, R.J. & Saadatmand, Y. (2005). Income Tax Evasion Determinants: New Evidence, Journal of American
Academy of Business, Cambridge, 7(2), 124.
[6] Cranea, S.E. &Nourzad, F. (1986). Inflation and tax evasion: an empirical analysis. The Review of Economics
and Statistics, 68(2), 217-223.
[7] Dell Anno, R. (2007). The Shadow Economy in Portugal: An analysis with the MIMIC Approach. Journal of
Applied Economics, (2).
[8] Embaye, A.B. (2007). Tax evasion and government spending in developing country and the Underground
Economy in South Africa. A Dissertation Submitted for the degree of Doctor of Philosophy. Economic report
book yearly, from 1963-1980.
[9] Fishlow, A. & Friedman, J. (1994). Tax evasion, Inflation and Stabilization. Journal of Development
Economics, 43, 105-123.
[10] Fishburn, G. (1981). Tax Evasion and Inflation. Australian Economic Papers, 20: 325–332.
[11] Fatt, C. K. & Ling, L.M (2008).Tax Practitioners Perception on Tax Audit and Tax Evasion: Survey Evidence
in Malaysia, Proceedings of the Eighth International Business Research Conference, Dubai, EAU, 27-28 March
2008, World Business Institute.
[12] Garson, G. D. 1991. Interpreting neural-network connection weights. AI Expert, 6, 46-51.
[13] Gevrey, M., Dimopoulos, I. & Lek, S. 2003. Review and comparison of methods to study the contribution of
variables in artificial neural network models. Ecological Modelling, 160, 249-264.
[14] Gademe, R & Moshiri, S. 2002. Modeling and forecasting economic growth by using neural network method in
Iran. Iranian Journal of Economic Researches, 12:97-125.
[15] Hoiden, K. & Peel, D.A. &Thompson, J.L. (1990). Economics Forecasting: An Introduction. Cambridge
University Press.
[16] Jain, A.K. (1987). Tax Avoidance and Tax Evasion: The Indian Case, Modern Asian Studies. Cambridge
University Press, 21(2), 233-255.
[17] Jaffar Harun, R. & Abu Bakar, M.J & Mohd Tahir, I. (2011). Ethics on Tax Evasion. International Business and
Management, 2 (1): 122-128.
[18] Kasipillai, J. & Aripin, N. & Amran, N.F.(2003).The Influence of Education on Tax Avoidance and Tax
Evasion , EJournal of Tax Research, 1 (2):134.
[19] Kasipillai, J. & Baldry, J. & DSPrasadaRao, (2000). Estimating the Size and Determinants of Hidden Income
and Tax Evasion in Malaysia. Asian Review of Accounting, 8 (2): 25 – 42.
[20] Kohzadi, N. & Boyd, M.S. & Kaastra, I. & Kermenshahi, B.S. & and Scuse, D. (1995)."Neural Network for
Forecasting: An Introduction ", Canadian journal of Agricultural Economics, 43: 463- 474.
[21] Howes, P. & Crook, N. 1999. Using input parameter influences to support the decisions of feedforward neural
networks. Neurocomputing, 24, 191-206.
[22] Lapedes, A., Farber, R., 1987. Nonlinear signal processing using neural networks: prediction and system
modeling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, NM.
[23] Miskam, M., Md Noor, R. & Omar, N. & Aziz. R. A. 2013. Determinants of Tax Evasion on Imported
Vehicles. International Conference on Economics and Business Research. Procedia Economics and Finance 7:
205 – 212.
[24] Olden, J. D. & Jackson, D. A. 2002. Illuminating the ―black box‖: a randomization approach for understanding
variable contributions in artificial neural networks. Ecological Modelling, 154, 135-150.
[25] Olden, J. D., Joy, M. K. & Death, R. G. 2004. An accurate comparison of methods for quantifying variable
importance in artificial neural networks using simulated data. Ecological Modelling, 178, 389-397.
[26] Özesmi, S. L. & Özesmi, U. 1999. An artificial neural network approach to spatial habitat modelling with
interspecific interaction. Ecological Modelling, 116, 15-31.
[27] Tsaur, S.-H., Chiu, Y.-C. & Huang, C.-H. 2002. Determinants of guest loyalty to international tourist hotels—a
neural network approach. Tourism Management, 23, 397-405.
[28] Richardson, G. (2008). The Relationship Between Culture and Tax Evasion Across Countries: Additional
Evidence and Extensions. Journal of International Accounting, Auditing and Taxation, 17: 67– 78.
[29] Sameti, M.A. &Sameti, M.O. & Dalaeemillan, A. (2009). Underground Economy in Iran. International
Economics Studies of Iran, 35 (2), 89-114.
[30] Sung, A. H. 1998. Ranking importance of input parameters of neural networks. Expert Systems with
Applications, 15, 405-411.
[31] Tanzi, V. (1982). The Underground Economy in the United States and Abroad. Lexington Books, Lexington,
MA.
[32] Wong, T. C., Law, K. M. Y., Yau, H. K. & Ngan, S. C. 2011a. Analyzing supply chain operation models with
the PC-algorithm and the neural network. Expert Systems with Applications, 38, 7526-7534.
[33] Wong, T. C., Ngan, S.-C., Chan, F. T. S. & Chong, A. Y.-L. 2012. A two-stage analysis of the influences of
employee alignment on effecting business–IT alignment. Decision Support Systems, 53, 490-498.
[34] Wong, T. C., Wong, S. Y. & Chin, K. S. 2011b. A neural network-based approach of quantifying relative
importance among various determinants toward organizational innovation. Expert Systems with Applications,
38, 13064-13072.
[35] Yoon, Y., Guimaraes, T. & Swales, G. 1994. Integrating artificial neural networks with rule-based expert
systems. Decision Support Systems, 11, 497-507.
[36] Weigend, A.S., Huberman, B.A. & Rumelhart, D.E. 1992. Predicting sunspots and exchange rates with
connectionist networks In: Casdagli, M., Eubank, S. (Eds.), Nonlinear Modeling and Forecasting.
[37] Zarra Nezhad, M., Feghh Majidi, A. & Rezaei, R. 2009. Forecasting Exchange Rate with Artificial Neural
Network (ANN) and Autoregressive Integrated Moving Average Process, ARIMA. Quarterly Journal of
Economics Review. 5(4):107-130.
Published
2015-10-22
How to Cite
Tabandeh, R., & Tamadonnejad, D. A. (2015). The Application of Artificial Neural Network Method to Investigate the Effect of Unemployment on Tax Evasion. Journal of Research in Business, Economics and Management, 4(3), 393-402. Retrieved from http://scitecresearch.com/journals/index.php/jrbem/article/view/400
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