با همکاری انجمن اقتصاد کشاورزی ایران

نوع مقاله : مقالات پژوهشی

نویسندگان

گروه اقتصاد، ترویج و آموزش کشاورزی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

با توجه به اهمیت نهاده‌های کشاورزی به ویژه انرژی در بازار قیمت محصولات کشاورزی، در این مقاله رابطه مصرف انرژی و تورم بخش کشاورزی ایران در قالب مدل غیرخطی مارکوف-سوئیچینگ با استفاده از داده‌های فصلی طی دوره زمانی 1395-1370 مورد تجزیه و تحلیل قرار گرفته است. یکی از دلایل مهم استفاده از مدل مارکوف-سوئیچینگ، غیرخطی بودن سری زمانی قیمت محصولات کشاورزی و وجود نوسانات مختلف طی سال‌های مورد مطالعه است. به طور کلی نتایج مدل نشان می‌دهد مصرف انرژی و تورم بخش کشاورزی رابطه‌ای غیرخطی و نامتقارن دارند و تورم کشاورزی در دو رژیم مختلف، رفتار متفاوتی بر جای می‌گذارند. شواهد تجربی نشان می‌دهد که شوک ناشی از مصرف انرژی در هر دو رژیم اول (نرخ رشد تورم پایین) و رژیم دوم (نرخ رشد تورم بالا) تاثیر معنادار منفی بر تورم کشاورزی دارد، در حالی‌که تأثیر مصرف انرژی بر تورم کشاورزی در رژیم نرخ رشد تورم بالا کمتر از رژیم نرخ رشد تورم پایین است. همچنین براساس نتایج حاصل از براورد مدل مارکوف-سوئیچینگ، احتمال ماندگاری در رژیم دوم، 93 درصد و احتمال گذار از این رژیم به رژیم اول 7 درصد است و نرخ تورم کشاورزی در دو رژیم مورد نظر وابسته به دوره قرارگیری آن‌ها بوده است که برای سیاست گذاری اقتصاد در حوزه کشاورزی حائز اهمیت می‌باشد. در نتیجه، برنامه‌ریزان و سیاستگذاران باید به این عدم تقارن در نرخ تورم کشاورزی توجه داشته باشند تا با استفاده از ابزارهای سیاست گذاری مناسب، ثبات قیمت در بخش کشاورزی را تا حد ممکن افزایش دهند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Studying Nonlinear Relationship between Energy Consumption and Inflation in Agricultural Sector

نویسندگان [English]

  • N. Naraghi
  • R. Moghaddasi
  • A. Mohamadinejad

Department of Agricultural Economics, Extension & Education, Science and Research Branch, Islamic Azad University, Tehran, Iran

چکیده [English]

Introduction: Today, the food-energy nexus is a vital issue. Energy in the food production chain is an essential feature of agricultural development and a critical factor in achieving food security. Energy use in the agricultural sector has increased to respond to the growing demand of the population, as well the limited supply of cultivated lands, and the desire for high standards of living. Therefore, the agricultural sector is heavily dependent on energy that affects agricultural prices. Agricultural price fluctuations are one of the most critical challenges for policymakers. The rapid rise in food prices has a significant negative impact on social welfare, especially the poor in developing countries, which is an issue that is more critical in developing countries than in developed countries. According to the Food and Agriculture Organization (FAO) report in 2018, the food world price index increased from 89.6 to 229.9 during the period from 2002 to 2011. Our literature review shows a distinct lack of research on modeling and analyzing the linkage between agricultural input price shock, especially energy and agricultural commodity prices in Iran.
Materials and Methods: The Markov Switching model is a popular non-linear time-series model that involves multiple equations and can characterize the time-series behaviors in different regimes. This model is suitable for describing correlated data that exhibit distinct dynamic patterns during different periods. So, considering the sensitivity of food security and the impact of agricultural input, the main objective of this paper is to develop an econometric model to gain reliable insight into the impact of energy consumption on agricultural inflation, using the Markov Switching approach. To estimate this equation, we will run a MS-AR model, some preliminary tests, such as unit root test and stability test, are employed to ensure the reliability of MS-AR estimation results.
Results and Discussion: Due to use of time series data, it is necessary to check the stationary status of variables. We performed a common non-linear unit root test (Kapetanios, Shin and Shell (KSS), Zivot and Andrews, Lee and Strazicich). These results reveal that we can significantly reject the null hypothesis of unit root for API, PPI, FPI, and EC, implying that all four variables considered in this study are stationary with structural breaks at levels. The Markov-Switching model has the various types that each of these is a particular component of the regime-dependent equation. Therefore, to choose the best type, the Akaike information criterion was used, and the model with the minimum value was selected as the optimal one. After model estimation and selection, the LR test indicated that the hypothesis of linearity could be rejected in favor of a Markov switching model. According to this model, the period of the Markov switching model estimation is classified into two regimes. Approximately, all the estimated coefficients of the MSIAH (2) - AR (5) model are found to be significant at the conventional level.
Conclusion: The estimation results are consistent with theoretical foundations illustrating the importance of input prices and energy consumption on agricultural commodity prices. As with most experimental studies reviewed, this study has also shown energy consumption has a negative impact on agricultural commodity prices. In other words, it can be contended that during the study period, agricultural input prices have been influential factors on agricultural commodity prices. The findings revealed that the low inflation rate and high inflation rate regimes are stable and that only extreme events can switch regimes. The results of the MS model showed that the effect of input prices on agricultural inflation is different in regimes. In the case of energy, the impact of energy consumption on agricultural commodity prices in the high inflation rate regime is less than the low inflation rate regime because the elimination of energy subsidies policy has been applied in the second regime (high inflation rate). Thus, the results indicate the asymmetric impact of energy consumption shocks on agricultural commodity prices. The effect of agricultural input prices on agricultural commodity prices indicates that Iranian agriculture is significantly affected by changes in input prices. In this study, changes in input prices were caused by various shocks, such as the elimination of energy subsidies and drought. Therefore, it can be concluded that the elimination of energy subsidies and drought were, directly and indirectly, able to affect agricultural inflations through the price of inputs. In conclusion, planners and policymakers must pay attention to this asymmetry in agricultural commodity prices volatility to increase the price stability in agriculture as much as possible by appropriate policy tools.
 

کلیدواژه‌ها [English]

  • Agricultural Prices
  • Inflation
  • Energy consumption
  • Non-linearity
  • Markov - Switching Autoregressive model
1- Abbott P.C., Hurt C., and Tyner W.E. 2008. What’s Driving Food Prices? Issue Report. Farm Foundation, IL, USA. https://www.farmfoundation.org/wp-content/uploads/2018/09/IR-2011-Final-FoodPrices_web.pdf
2- Abdulaziz R.A., Rahim K.A., and Adamu P. 2016. Oil and food prices co-integration nexus for Indonesia: A non-linear autoregressive distributed lag analysis. International Journal of Energy Economics and Policy 6(1): 82-87.
3- Al-Maadid A., Caporale G.M., Spagnolo F., and Spagnolo N. 2017. Spillovers between food and energy prices and structural breaks. Journal of International Economics 150: 1–18.
4- Brock W.A., Dechert W.D., Scheinkman J.A., and LeBaron B. 1996. A test for independence based on the correlation dimension. Econometric Reviews 15(3): 197-235.
5- Cabrera B.L., and Schulz F. 2016. Volatility linkages between energy and agricultural commodity prices. Energy Economics 54: 190–203.
6- CAEEDAC. 2000. A descriptive analysis of energy consumption in agriculture and food sector in Canada. Available at: http://www.usask.ca/agriculture/caedac/pubs/processing.pdf
7- CBI, 2018. Central Bank of the Islamic Republic of Iran, Economic analysis report.
8- Gardebroek C., and Hernandez M. 2013. Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets. Energy Economics 40: 119-129.
9- Gilbert C.L. 2010. How to understand high food prices. Journal of Agricultural Economics 61: 398–425.
10- Grassberger P., and Procaccia I. 1983. Measuring the strangeness of strange attractors. Physica D 9: 189-208.
11- Hamilton J.D. 1989. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica 57(2): 357–384.
12- Hatirli S.A., Ozkan B., and Fert K. 2005. An econometric analysis of energy input- output in Turkish agriculture. Renewable and Sustainable Energy Reviews 9: 608-623.
13- Ibrahim M.H. 2015. Oil and food prices in Malaysia: a nonlinear ARDL analysis. Agricultural and Food Economics 3(2):1-14.
14- Javdan A., Pishbahar A., Haghighat J., and Mohammadrezaei R. 2017.  Comparison of Linear and Non-Linear Models in Assessing the Global Food Price Pass-Through into Domestic Food Price in Iran. Agricultural Economics 10(4): 101-118. (In Persian with English abstract)
15- Jiranyakul, K. 2015. Oil Price Shocks and Domestic Inflation in Thailand. Munich Personal RePEc Archive No. 62797.
16- Kazeroni A., Asgharpur H., Mohammadpoor S., and Bahari S. 2012. The Asymmetric Effects of Real Exchange Rate Fluctuations on the Economic Growth of Iran: Markov – Switching Approach. Economic Journal 12(7and8): 5-26. (In Persian)
17- Kennedy S. 2000. Energy use in American agriculture. Available at: http://web.mit.edu/10.391J/www/proceedings/Agri-culture_Kennedy2000.pdf
18- Koirala K.H., and Mehlhorn J.E. 2015. Energy prices and agricultural commodity prices: testing correlation using copulas method. Energy 81: 430–436.
19- Krolzig H. 1997. Markov-Switching Vector Autoregressions: Modelling, Statistical Inference, and Application to Business Cycle Analysis. Springer, Berlin.
20- Mawejje J. 2016. Food prices, energy and climate shocks in Uganda. Agricultural and Food Economics 4(1): 1–18.
21- McFarlane L. 2016. Agricultural commodity prices and oil prices: mutual causation. Outlook on Agriculture 45(2): 87–93.
22- Mehdiloo A., Sadeghi H., and Assari Arani A. 2015. Estimation of Non-Linearity Effect of Rent-Seeking Opportunities on Economic Growth in Iran: Using Markov-Switching Model. Economic Growth and Development Research 5(18): 11-30. (In Persian with English abstract)
23- Meyer D.F., Sanusi K.A., and Hassan A. 2018. Analysis of the asymmetric impacts of oil prices on food prices in oil-exporting developing countries. Journal of International Studies 11(3): 82-94.
24- Ministry of Agriculture Jihad, 2018. Ministry of Agriculture Jihad of the Islamic Republic of Iran, Agricultural Statistics Report.
25- MOE, 2016. Ministry of Energy, Energy Balance Sheets of the Country.
26- Nwoko I.C., Aye G.C., and Asogwa B.C. 2016. Effect of oil price on Nigeria's food price volatility. Cogent Food and Agriculture 2 (1): 1146057.
27- Olasunkanmi O.S., and Oladele K.S. 2018. Oil price shock and agricultural commodity prices in Nigeria: A non-linear autoregressive distributed lag (NARDL) approach. African Journal of Economic Review 6(2): 74-91.
28- Pejman N. 2012. Investigating the transfer of prices from farm to retail in saffron market: A case study of Fars province. PhD Thesis, Islamic Azad University, Marvdasht Branch, Faculty of Agriculture.
29- Pindyck R.S., and Rotemberg J.J. 1990. The excess co-movement of commodity prices. Economic Journal 100: 1173–1189.
30- Shehu A., Shsfii A.S., and Yau N.A. 2019. Asymmetric Effect of Oil Shocks on Food Prices in Nigeria: A Non-Linear Autoregressive Distributed Lags Analysis. International Journal of Energy Economics and Policy 9(3):128-134.
31- Singh J.M. 2000. On farm energy use pattern in different cropping systems in Haryana, India. [Ph.D. Thesis.], Ger-many, International Institute of Management, University of Flensburg.
32- Taghizadeh-Hesary F., Rasoulinezhad E., and Yoshino N. 2019. Energy and food Security: Linkages through Price Volatility. Energy Policy 128: 796–806.
33- Taki M., Ghsemi Mobtaker H., and Monjezi N. 2012. Energy input-output modeling and economical analyze for corn grain production in Iran. Elixir Agriculture Journal 52: 11500-11505.
34- Tarazkar M.H., and Sheikhzeinoddin A. 2019. The impacts of asymmetric oil shocks on agricultural commodity price: Application of nonlinear autoregressive distributed lags (NARDL) approach. Agricultural Economics Research 11(1): 81-100. (In Persian with English abstract)
35- Ueda T., and Kunimitsu Y. 2020. Interregional price linkages of fossil-energy and food sectors: evidence from an international input– output analysis using the GTAP database. Asia-Pacific Journal of Regional Science 4: 55–72.
36- Vahidi Z., Shaghaghi Shahri V., and PahlavanZade F. 2016. The Symmetric and asymmetric effects of oil shocks on the agricultural and industry value added. Quarterly Journal of the Macro and Strategic Policies 2(8): 77-92. (In Persian with English abstract)
37- Wang W., Van Gelder P.H.A.J.M., and Vrijling J.K. 2005. Trend and stationarity analysis for stream flow processes of rivers in Western Europe in the 20th century, In Proceedings: IWA International Conference on Water Economics, Statistics, and Finance Rethymno Greece: 8-10.
38- Zhang Z., Lohr L., Escalante C., and Wetzstein M. 2010. Food versus fuel: what do prices tell us? Energy Policy 38: 445–451.
CAPTCHA Image