Agricultural and Rural Economics

Agricultural and Rural Economics

Regional Analysis of Food Price Anomaly in Iran

Document Type : Original Article

Authors
1 Assistant Professor of Agricultural Economics, Institute of Planning Research, Agricultural Economics and Rural Development, Tehran, Iran
2 Assistant Professor of Agricultural Economics, Agricultural Planning, Economic and Rural Development Research Institute (APERDRI). Tehran. Iran
3 Assistant Professor of Agricultural Economics
Abstract
Introduction: Food price stability is the main concern of most governments around the world, because any increase in food prices at the micro level affects the purchasing power of citizens and at the macro level, through the inflation channel, it affects many macroeconomic indicators.
Materials and Methods: With this approach, this study aimed at conducting a regional analysis of food price anomaly in Iran. For this purpose, first, the Indicator of Food Price Anomaly (IFPA) was calculated for different provinces during the period of 2018-2022. Furthermore, the K-means clustering technique was employed to comprehend the price discrepancy in the regions, to pinpoint the underlying cause of the variation among the provinces, to recognize the provinces indicating similar patterns, and to create a visual representation of the food price anomaly across Iran's provinces. 
Results and Discussion: The study findings indicated that throughout the examined years, the provinces of Qazvin, Zanjan, and Mazandaran showed the highest anomaly in food prices, respectively. In addition, Sistan and Baluchistan, Hormozgan, South Khorasan and East Azerbaijan provinces had the least anomaly in food prices during the mentioned period. Then, the provinces were classified into six clusters. In all the clusters, there was a direct relationship between 
expenditure and anomaly, and in the provinces where the real expenditure for buying food or the demand for food was higher, the price anomaly was also higher. The amount of household spending on food in the provinces located in the first cluster including Sistan and Baluchistan, South Khorasan, North Khorasan, Kerman and Hormozgan was lower than other provinces. Since these were among the deprived provinces of the country, households had lower consumption demand due to the lower income level and therefore, the food price anomaly in these provinces was lower than other provinces. On the other hand, in the provinces located in the third cluster, including Tehran, Mazandaran and Alborz, the real household expenditure on food was higher than other provinces. Therefore, in these provinces, although 25 percent of household expenses were spent on food, the amount of food expenses was numerically higher than other provinces. Thus, the consumption demand and consequently, the food price anomaly was higher in these provinces, compared to other provinces. Finally, based on the study results, it was observed that in the second, fifth and sixth clusters, the export or smuggling of food and agricultural goods from some border provinces was the main cause of the anomaly of food prices in these clusters.
Conclusion and Suggestions: According to the findings, it is suggested that the government revise its market regulation policies and pay special attention to the deprived provinces. In addition, in order to prevent the increased food prices in the border provinces, the phenomenon of smuggling should be controlled in the short term through control measures to prevent the unofficial exit of goods and in the long term by making food prices realistic.
Keywords

  • Baquedano, F. G. (2015). Developing an indicator of price anomalies as an early warning tool: a compound growth approach. Food and Agriculture Organization (FAO).
  • Barrett, C. B., & Dorosh, P. A. (1996). Farmers' welfare and changing food prices: nonparametric evidence from rice in Madagascar. American Journal of Agricultural Economics, 78(3), 656-669.
  • Bentley, A. R., Donovan, J., Sonder, K., Baudron, F., Lewis, J. M., Voss, R., ..., & Govaerts, B. (2022). Near-to long-term measures to stabilize global wheat supplies and food security. Nature Food, 3(7), 483-486.
  • Brown, L. R. (2008). Why ethanol production will drive world food prices even higher in 2008? Earth Policy Institute, Columbia.
  • Brown, M. E., & Kshirsagar, V. (2015). Weather and international price shocks on food prices in the developing world. Global Environmental Change, 35, 31-40.
  • Campbell, B. M., Vermeulen, S. J., Aggarwal, P. K., Corner-Dolloff, C., Girvetz, E., Loboguerrero, A. M., ..., & Wollenberg, E. (2016). Reducing risks to food security from climate change. Global Food Security, 11, 34-43.
  • Dadras Moghaddam, A., & Zibaei, M. (2009). Relation between macroeconomic and agriculture sector of iran (with emphasis on monetary policy). Iranian Journal of Economic Research, 13(39), 95-112. [In Persian]
  • Devereux, S., Béné, C., & Hoddinott, J. (2020). Conceptualising COVID-19’s impacts on household food security. Food Security, 12(4), 769-772.
  • Dietrich, S., Giuffrida, V., Martorano, B., & Schmerzeck, G. (2022). COVID‐19 policy responses, mobility, and food prices. American Journal of Agricultural Economics, 104(2), 569-588.
  • Ghahremanzadeh, M., Pishbahar, E., & Khalili Malekshah. S. (2016). The Effect of macroeconomic variables on food inflation in Iran: an ‎application of Structural Vector Error Correction Model (SVECM)‎. Iranian Journal of Agricultural Economics and Development Research, 47(4), 773-784. DOI: 10.22059/ijaedr.2016.61308. [In Persian]
  • Ghahremanzadeh, M., Samadpour, M., & Hosseinzad, J. (2022). The effects of agricultural trade openness on food price in Iran. Journal of Agricultural Economics and Development, 36(4), 363-376. DOI: 10.22067/jead.2022.73620.1098. [In Persian]
  • Ghetmiri, M., & Harati, J. (2005). An investigation of the impact of macroeconomic variables on food price index in Iran (1959-2000): an Auto-Regressive Distributed Lag (ARDL) approach. Iranian Journal of Economic Research, 7(23), 221-235. [In Persian]
  • Gilbert, C. L., Christiaensen, L., & Kaminski, J. (2017). Food price seasonality in Africa: measurement and extent. Food Policy, 67, 119-132.
  • Gombkötő, N. (2014). Causes and potential solutions of global food price increase. Societal Innovations for Global Growth, 1(3), 45-62.
  • Javdan, E., Raheli, H., & Naghadi, R. (2015). Analysis of factors affecting food price in Iran with emphasis on oil shocks. Agricultural Economics Research, 7(26), 179-195. [In Persian]
  • Javdan, E., Pishbahar, E., Haghighat, J., & Mohammad-Rezaei, 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. DOI: 10.22034/iaes.2017.22713. [In Persian]
  • Kalkuhl, M., von Braun, J., & Torero, M. (2016). Volatile and extreme food prices, food security, and policy: an overview. In: Food price volatility and its implications for food security and policy, pp. 3-31.
  • Kaminski, J., Christiaensen, L., & Gilbert, C. L. (2014). The end of seasonality: new insights from Sub-Saharan Africa. World Bank Policy Research Paper, No. 6907.
  • Karbasi, A., & Piri, M. (2008). The relationship between the price level of agricultural products and inflation uncertainty in Iran: 1971-2005. Iranian Journal of Trade Studies (IJTS), 13(47), 111-140. [In Persian]
  • Kohansal, M. R., & Hezareh, R. (2017). The impacts of oil price shocks, exchange rate on food prices in urban areas of Iran. Agricultural Economics Research, 8(32), 171-190. [In Persian]
  • Laborde, D., Lakatos, C., & Martin, W. J. (2019). Poverty impact of food price shocks and policies. World Bank Policy Research Working Paper, No. 8724.
  • Layani, G., & Mehrjou, S. (2023). Asymmetric effects of exchange rate and oil price changes on food and agricultural product prices in Iran: application of NARDL approach. Journal of Agricultural Economics and Development, 37(1), 35-48. DOI: 10.22067/jead.2022.74721.1113. [In Persian]
  • Liu, Y. (2022). Analysis and prediction of college students’ mental health based on K-means clustering algorithm. Applied Mathematics and Nonlinear Sciences, 7(1), 501-512.
  • Luo, J. (2022). Application of K-means method based on SPSS in graphic design score analysis. The Third International Conference on Big Data and Social Sciences (ICBDSS 2022), pp. 453-459, Atlantis Press.
  • Minot, N. (2014). Food price volatility in Sub-Saharan Africa: Has it really increased?. Food Policy, 45, 45-56.
  • Nazlioglu, S., & Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: a panel cointegration and causality analysis. Energy Economics, 34(4), 1098-1104.
  • Parizan, V., & Torkamani, J. (2003). The effects of monetary policies and exchange rates on changes in relative agricultural prices. Iran Agricultural Economics Conference. [In Persian]
  • Pishbahar, E., & Javdan E. (2016). The impact of monetary shocks on food price in Iran. QJER, 15(4),127-142. [In Persian]
  • Pishbahar, E., Ghahremanzadeh, M., & Jafari Sani, M. (2014). The effect of monetary policy on food price index: an application of Factor Augmented Vector Autoregressive (FAVAR) approach. Journal of Agricultural Economics and Development, 27(4), 319-327. DOI: 10.22067/jead2.v1391i5.27052. [In Persian]
  • Roman, M., Górecka, A., & Domagała, J. (2020). The linkages between crude oil and food prices. Energies, 13(24), 6545.
  • Samal, A., Ummalla, M., & Goyari, P. (2022). The impact of macroeconomic factors on food price inflation: an evidence from India. Future Business Journal, 8(1), 1-14.
  • Shlens, J. (2003). A tutorial on principal component analysis, derivation, discussion and singular value decomposition (Version-I). University of California in San Diego. Available at https://arxiv.org/pdf/1404.1100.pdf.
  • Smith, L. I. (2002). A tutorial on principal components analysis.
  • Sujithan, K. A., Avouyi-Dovi, S., & Koliai, L. (2014). On the determinants of food price volatility. In: International Conference on Food Price Volatility: Causes and Challenges, February 2014, p. 50.
  • Sun, Y., Gao, P., Raza, S. A., Shah, N., & Sharif, A. (2023). The asymmetric effects of oil price shocks on the world food prices: fresh evidence from quantile-on-quantile regression approach. Energy, 270, 126812.
  • Timmer, C. P. (2017). Food security, structural transformation, markets and government policy. Asia and the Pacific Policy Studies, 4(1), 4-19.
  • Traore, F., & Diop, I. (2021). Measuring food price volatility. AGRODEP Technical Notes, No. 0019.
  • Ulussever, T., Ertuğrul, H. M., Kılıç Depren, S., Kartal, M. T., & Depren, Ö. (2023). Estimation of impacts of global factors on world food prices: a comparison of machine learning algorithms and time series econometric models. Foods, 12(4), 873.
  • UNCTAD (2022). Trade and development report 2022. United Nations Conference on Trade and Development (UNCTAD). SDG Pulse 2022.
  • Wei, T., Glomsrød, S., & Zhang, T. (2017). Extreme weather, food security and the capacity to adapt: the case of crops in China. Food Security, 9, 523-535.