بررسی عوامل مؤثر بر قیمت آرد و نان در صورت اجرای سیاست آزادسازی قیمت‏ها در این حوزه

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار گروه اقتصاد کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران.

2 استاد گروه اقتصاد کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

3 دانشیار اقتصاد کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران.

4 دانش‏ آموخته کارشناسی ارشد اقتصاد کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

5 کارشناس سازمان امور مالیاتی استان خراسان رضوی، مشهد، ایران

چکیده

با در نظر گرفتن این واقعیت که اجرای سیاست آزادسازی قیمت در حوزه آرد و نان از مهم‏ترین موضوعاتی است که باید در اجرای قانون هدفمندی یارانه‏ ها مورد توجه قرار گیرد، هدف مطالعة حاضر شناسایی و اولویت‏بندی مهم‏ترین عوامل مؤثر بر قیمت آرد و نان در صورت اجرای سیاست آزادسازی قیمت در این حوزه بود. بدین منظور، ضمن استفاده از دو روش تحلیل سلسله‏ مراتبی و تحلیل سلسله ‏مراتبی فازی، نتایج دو روش با هم مقایسه شد. اطلاعات مورد نیاز در سال 1392 از طریق مصاحبه حضوری با هفت نفر از کارشناسان و خبرگان فعال در حوزه آرد و نان و تکمیل پرسشنامه گردآوری شد. نتایج مطالعه حاکی از نامعتبر بودن نتایج روش تحلیل سلسله‏ مراتبی فازی و تأیید اعتبار نتایج روش تحلیل سلسله ‏مراتبی بود. علاوه بر این، نتایج روش تحلیل سلسله‏ مراتبی نشان داد که مهم‏ترین عوامل مؤثر بر قیمت آرد، قیمت گندم داخلی و پس از آن، قیمت جهانی گندم و میزان صادرات است؛ همچنین، قیمت آرد، دستمزد نیروی کار و هزینه سوخت به ‏ترتیب مهم‏ترین عوامل مؤثر در قیمت نان ارزیابی شدند. از این‏رو، با توجه به تأثیرپذیری شدید قیمت نان از قیمت گندم پس از آزادسازی قیمت‏های آرد و نان، ایجاد ثبات در بازار گندم از طریق عملیات تنظیم بازار و در این راه، بازبینی جدی در قانون خرید تضمینی گندم اجتناب ‏ناپذیر می‏ نماید.

کلیدواژه‌ها


عنوان مقاله [English]

An Investigation on Influencing Factors of Bread and Flour Prices under the Assumption of Concerned Free Market Price Policy in Iran

نویسندگان [English]

  • L. Abolhassani 1
  • N. Shahnoushi 2
  • A. Dourandish 3
  • H. Taherpour 4
  • A. Ghaffari 5
1 Associate Professor, Department of Agricultural Economics, Ferdowsi University of Mashhad, Mashhad, Iran.
2 Professor of Agricultural Economics, Department of Agricultural Economics, Ferdowsi University of Mashhad, Mashhad, Iran
3 Associate Professor of Agricultural Economics, Ferdowsi University of Mashhad, Mashhad, Iran
4 MSc. Graduate in Agricultural Economics, Ferdowsi University of Mashhad, Mashhad, Iran.
5 Expert of Tax Administration of Khorasan Razavi Province, Mashhad, Iran.
چکیده [English]

Given the importance of pursuing a policy of free-market prices in bread and flour markets within Iran’s subsidies reform, this study aimed at recognizing and prioritizing the most important factors affecting the bread and flour prices under such an assumed situation using AHP and FAHP methods and comparing the results. The required data were gathered in 2013 through questionnaires and interviews with seven experts who had been active in government offices as well as the bread and flour producers. Unlike the FAHP results, the AHP results were proven to be valid. Furthermore, the AHP results indicated that the most important contributing factors of flour prices would be domestic wheat prices, international wheat prices plus export volume, respectively; in addition, bread prices would be influenced firstly by flour prices and then by wages and fuel costs, respectively. Therefore, since following the implementation of free-market price policy in bread and flour markets, the bread prices might be significantly influenced by the wheat ‎prices, it would be inevitable to build up a stable wheat market through a market regulation mechanism and to make a careful reconsideration ‎of wheat guaranteed prices in this respect. ‎

کلیدواژه‌ها [English]

  • Bread Price
  • Flour Price
  • Fuzzy Analytical Hierarchy Process (FAHP) Model
  • AHP model
  1. Aazamzadeh Shooroki, M. and Khalilian, S. (2010). Study of monetary policies effect on food price in Iran. Agricultural Economics and Development(Agricultural Science and Technology), 24(2): 177-184. (Persian)
  2. Alexandridis, A. 2010. An analysis of factors affecting the price and volatility of coffee future returns. Middle Eastern Finance and Economics, 8: 114-122.
  3. Amadeh, H. (2010). Analysis of changes in chicken meat prices using ARDL model: a case study of Tehran province. Economic Research, 10(37): 295-326. (Persian)
  4. Amanpour, S., Razmgir, F., Damanbagh, S. and Hosseini Siahgoli, M. (2014). A comparative study of urban service distribution in Ahwaz County using FAHP. Geograpgy and Urban Planning (Zagros Perspective), 6(20): 137-159. (Persian)
  5. Bakhshoudeh, M. (2004). Estimating the seasonal price fluctuations of potatoes and onions. Iranian Journal of Agriculture Science, 35(2): 5110516. (Persian)
  6. Boroumandi, M., Khamehchian, M. and Niloudel,
  7. Bourke, I. J. 1979. Comparing the Box-Jenkins and econometrics techniques for forecasting beef prices. Review of Marketing and Agricultural Economics, 47(2): 95-106.
  8. Brandt, J. A. and Bessler, D. A. 1981. Composite forecasting: an application with US hog prices. American Journal of Agricultural Economics. 63: 135-140.
  9. Chang , D.Y., 1996.Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3): 649-655.
  10. Dadashian-Sarai, M., Dashti, Gh., Hayati, B. and Ghahremanzadeh, M. (2015). The combined use of AHP and TOPSIS technique for determining the weighted criteria and evaluation of agricultural sustainability (case study: selected counties of East Azarbaijan province). Agricultural Science and Sustainable Production, 25(1): 145-157. (Persian)
  11. Donohue, Kathleen G..2005. Freedom from Want: American Liberalism and the Idea of the Consumer. Johns Hopkins University Press.
  12. Fahimi-Fard, S.M., Keikhah, A.A. and Salarpour, M. (2009). Forecasting the prices of selected agricultural products in Iran using a combined method of NNARX. Agricultural Economics and Development (Agricultural Science and Technology), 23(2): 46-54. (Persian)
  13. Farajzadeh, Z. and Shahvali, A. (2009). Forecasting agricultural crops prices: case study of cotton, rice and saffron. Agricultural Economics and Development, 17(67): 43-71. (Persian)
  14. Ghetmiri, M.A. and Herati, J. (2005). An investigation of the impact of macroeconomic variables on food price index in iran (1959-2000), an auto-regressive distributed lag (ADL) approach. Iranian Journal of Economic Research, 7(23): 221-235. (Persian)
  15. Guilanpour, A. and Kohzadi, N. (1997). Forecasting the rice price in international market using autoregressive moving average (ARMA) model. Agricultural Economics and Development, 8: 189-200. (Persian)
  16. Kohzadi, N., Boyd, M. S., Kermanshahi, B. and Kaastra, L. 1996. A comparison of artificial neural networks and time series model for forecasting commodity price. Neurocomput, 10: 169-181.
  17. Mehrabi, A., Khakpour, A. and Asefpour Vakilian, M. (2014). Performance assessment of technical and vocational training centers of hamedan using balanced scorecard and analytic hierarchy process in 2013. Skill Training, 2(8):111-122. (Persian)
  18. Moemeni-Helali, H., Ahmadpour, A. and Poursaeed, A.R. (2015). Identifying the most appropriate cultivar for sustainability of rice production system using analytic hierarchy process (AHP). Journal of Crop Production and Processing (JCPP), 5(16): 163-173. (Persian)
  19. Moghaddasi, R. and Rahimi Badr, B. (2009). Power assessment of different econometry models for forecasting the wheat price. Economic Research, 9(35): 239-263. (Persian)
  20. Moghaddasi, R. and Zhaleh Rajabi, M. (2011). Hybrid modeling approach for prediction of agricultural products prices. Agricultural Economics and Development (Agricultural Science and Technology), 25(3): 355-364. (Persian)
  21. Moghli, M., salehnasab, A., Faghihi, J., Dabehkar, A. and Sousani, J. (2015). Positioning of natural parks using analytic hierarchy process: a case study of Ghaleh-Gol Basin. Geography, 13(46): 253-269. (Persian)
  22. Mohammadi, Sh. and Pirmohammadiani, R. (2005). Behavioral scoring model using data mining approach and analytic hierarchy process. Computing and Information Technology, 4(3): 66-80. (Persian)
  23. Monroe, K. 1990. Pricing making profitable decision, McGraw- Hill International, Editions.
  24. Najafi, B., Zibaei, M., Sheikhi, M.H. and Tarazkar, M.H. (2007). Forecasting price of some crop products in fars province: application of Artificial Neural Network. Journal of Water and Soil Science (JWSS), 11(1): 501-512. (Persian)
  25. Nasiri, P. (2003). Long-term and short-term effects of macro variables on agricultural sector (1971-1999). Proceedings of the First Conference on Adriculture and National Development (Vol. 2). Agricultural Planning, Economics and Rural Development Research Institute (APERDRI), Tehran. (Persian)
  26. Rantala, S. H., Kola, J. and Niemi, J. 2010. Factors affecting world cereal prices: an econometric study. IAMA Symposium, Boston, 20 June.
  27. Sadigh Maroufi, Sh., Mohammadi, A., Mousavi, S. and Raadabadi, M. (2014). Study of most important factors influencing the effectiveness of teaching from viewpoint of graduate students: AHP model approach. Medical Education and Development, 9(3): 58-66. (Persian)
  28. Sadr-Mousavi, M., Abazarloo, Sh., Mousakhani, K. and Abazarloo, S. (2013). Opti,al positioning of urban solid waste burial using analytical hierarchy process (AHP): a case study of Zanjan County. Environmental Based Territorial Planning, 6(21): 65-88. (Persian)
  29. Safaeepour, M., Alizadeh, H. and Damanbagh, S. (2014). Measuring the priority level of macro goals of worn building texture in central Ahwaz County using fuzzy hierarchical analysis (FAHP) method. Geography and Development, 12(35): 67-82. (Persian)
  30. Shahnoushi, N. (2014). Investigating the effects of implementing the Targeted Subsidies Law on the chain of wheat, flour and bread. Research Project in Government Trading Corporation of Iran. (Persian)
  31. Shahnoushi, N. Firouz Zare, A. and Zhaleh Rajabi, M. (2008). Investigating the causes and determining the extent of bread wastes in Iran. Research Project in Ferdowsi University of Mashhad. (Persian)
  32. Sharifi, M., Akram, A., Rafiee, Sh. and Sabzehparvar, M. (2014). prioritization of strategic agricultural crops in Alborz province using the Fuzzy Delphi method and the Analytical Hierarchy Process (AHP). Agricultural Machinery, 4(1): 116-124. (Persian)
  33. Tabibi, S.K., Azarbaijani, K. and Biari, L. (2009). A comparison between Artificial Neural Networks and Time Series methods for forecasting chicken meat price in Iran. Iranian Economic Journal: Macroeconomics (IEJM), 9-1(32): 59-78. (Persian)
  34. Tabibi, S.K., Azarbaijani, K. and Biari, L. (2009). Forecasting egg price in Iran: a comparison between Artificial Neural Networks and ARCH methods. Agricultural Economics and Development, 17(65): 73-96. (Persian)
  35. Ya-Ali Jahromi, M., Mohammadi, H. and Farajzadeh, Z. (2009). Forecasting sugar beet price in Iran. Sugar Beet, 25(1): 97-111. (Persian)
  36. Yazdanbakhsh, S. (2011). Identification of factors affecting the production capacity of food and beverage industries and presentation of appropriate strategies their improvement: a case study of Khorasan Razavi province. MSc. Dissertation in Ferdowsi University of Mashhad, Iran. (Persian)
  37. Zibaei, M. (2003). Assessment of guaranteed purchasing plan for agricultural products in Fars province and formulation of new strategies. Research Project in Agriculture - Jahad Department of Fars Province, Shiraz, Iran. (Persian)
  38. Zibaei, M. and Najafi, B. (1993). Study of pricing policies on changes in farmers’ cropping pattern and income. Proceedings of Second Symposium on Agricultural Policy of Iran, Faculty of Agriculture, University of Shiraz. (Persian)