خلاصة:
In this paper, we have tried to utilize a combination of qualitative and quantitative model for evaluating and prioritizing the efficiency of Iran’s provincial tax offices from 2011 to 2014. For this purpose, the tax offices in each province have been considered as a decision-making unit (DMU) that has several inputs and outputs. At first, the provinces were divided into less developed and more developed provinces, then the efficiency of them was calculated by Data Envelopment Analysis (DEA) model and related values to improve efficiency of inefficient provinces have been offered. By using AHP/DEA model, the provinces have been ranked. Offering the complete rankings of tax offices and utilizing the advantages of both quantitative and qualitative methods for prioritizing the decision making units are the major advantages of this model. The results show that among developed provinces, Isfahan in 2011 and 2014, and Markazi in 2012 and 2013 have the highest ranks. Also, among less developed provinces, West Azarbaijan in 2011, 2012 and 2013, and Kordestan in 2014 have the top ranks.
ملخص الجهاز:
"672542 Evaluating the Efficiency of Iran’s Provincial Tax Offices and Ranking Them by DEA/AHP Ali Mohamadi, Maryam Sadeghi, Payam Shojaei, Ameneh Rezaei Faculty of Economics, Management and Social Science, Management Department, Shiraz University, Shiraz, Iran (Received: February 17, 2017; Revised: September 4, 2017; Accepted: September 17, 2017) Abstract In this paper, we have tried to utilize a combination of qualitative and quantitative model for evaluating and prioritizing the efficiency of Iran’s provincial tax offices from 2011 to 2014.
The advantage of the DEA/AHP ranking model is that the comparative weight can be derived from inputs/outputs via AHP pairwise comparison (Tseng & Lee, 2009).
Conducting literature reviews shows that a quantitative method such as Data Envelopment Analysis (DEA) has been used to prioritize tax offices based on efficiency, while utilizing a quantitative and subjective integrated method can cover the disadvantage of a quantitative one.
So, outstanding aspect in this research is employing DEA and AHP integrated method in order to use advantages of both techniques for prioritizing provincial tax offices and extracting pragmatic and objective results.
Inputs including current cost and labor number of province tax office, were driven from Forsund and Edvardsen (2015), González and Rubio (2013), Katharaki and Tsakas (2010), Barros (2005), Moesen and Persoon (2002), Arab Mazar and Mousavi (2010).
Categorization of provinces East Azerbaijan- Isfahan- Razavi Khorasan- Khozestan -Zanjan- Semnan- Fars- Qazvin-Qom- Kerman-Gilan -Mazandaran-Markazi-Hamadan- Yazd West Azerbaijan- Ardabil- ILam- Chaharmahal Bakhtiari- South Khorasan- North Khorasan-Sistan and Baluchestan- Kurdistan- Kohgiluyeh and Boyer- Ahmad- Kermanshah-Golestan- Lorestan We utilized CCR input-oriented model for calculating provincial tax office efficiency with constant returns to scale based on the above categorization by DEA Frontier 2007 from 2011 to 2014."