Mohammadi, A., Rajabi, A. (2012). An Optimal Tax Relief Policy with Aligning Markov Chain and Dynamic Programming Approach. Iranian Journal of Economic Studies, 1(1), 55-74. doi: 10.22099/ijes.2012.588
Ali Mohammadi; Ahmad Rajabi. "An Optimal Tax Relief Policy with Aligning Markov Chain and Dynamic Programming Approach". Iranian Journal of Economic Studies, 1, 1, 2012, 55-74. doi: 10.22099/ijes.2012.588
Mohammadi, A., Rajabi, A. (2012). 'An Optimal Tax Relief Policy with Aligning Markov Chain and Dynamic Programming Approach', Iranian Journal of Economic Studies, 1(1), pp. 55-74. doi: 10.22099/ijes.2012.588
Mohammadi, A., Rajabi, A. An Optimal Tax Relief Policy with Aligning Markov Chain and Dynamic Programming Approach. Iranian Journal of Economic Studies, 2012; 1(1): 55-74. doi: 10.22099/ijes.2012.588
An Optimal Tax Relief Policy with Aligning Markov Chain and Dynamic Programming Approach
Abstract In this paper, Markov chain and dynamic programming were used to represent a suitable pattern for tax relief and tax evasion decrease based on tax earnings in Iran from 2005 to 2009. Results, by applying this model, showed that tax evasion were 6714 billion Rials**. With 4% relief to tax payers and by calculating present value of the received tax, it was reduced to 3108 billion Rials. With regard to transition periods of tax-receipt and its discount index, the present value of future tax cash flows, during the future period, were 16034 billion Rials. Also, by means of dynamic programming, the break–even-point of tax relief index was determined. Consequently, if the discount rate were less than 22%, the optimal policy to increase tax revenue would be not to give discount and if it were more than 22%, the favorable policy would be to offer discount to tax payers.
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