Document Type : Research Article
Authors
1 Department of Agricultural Economics, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Irans
2 Department of Agricultural Economics, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
Abstract
Introduction
The economy of countries are always exposed to shocks, including the Covid-19 pandemic, which causes many problems. The Covid-19 pandemic had various effects and consequences in different sectors, including the agricultural sector. The decline in income and production, coupled with the loss of customers due to health quarantines and border closures, severely impacted farmers businesses and created many problems for activists of various sectors of the agriculture. One of the most important effects of the Covid-19 pandemic is the decline in global economic growth. This has led to increased unemployment, decreased purchasing power among the population, and consequently, a decrease in demand. According to the impact of the covid-19 pandemic on food demand resulting from disruptions in the supply chain and income shocks, this research aims to investigate the existence of a structural break in the preferences of Iranian consumers for livestock products (red meat, chicken, eggs, and milk) using the Quadratic Almost Ideal Demand System (QAIDS) and the switching regression framework developed by Ohtani & Katayama (1986) during the period from Spring 2015 to Winter 2022.
Materials and Methods
Nonparametric and parametric approaches are utilized to investigate structural break in consumer preferences. This research employs parametric approaches and the Quadratic Almost Ideal Demand System to assess the structural break. The switching regression framework proposed by Ohtani and Katayama (1986) is utilized to model structural changes in preferences. In fact, a time transition function is incorporated into the demand system. Based on the characteristics of demand in the literature of structural changes, the Bewley likelihood-ratio test is applied to select an appropriate model. To evaluate the structural break and calculate the price and income elasticities, the price and per capita consumption data of livestock products are required, and in this research, seasonal time series data for the period of spring 2015 to winter 2022 have been used. The information related to the price of livestock products was obtained from the Joint Stock Company of the Support of Livestock Affairs. To obtain the per capita consumption, first, the information on the amount of production of red meat, chicken, milk, and egg are received from the joint stock company for livestock affairs. Then, by summing the amount of production and the amount of import of red meat, chicken, milk and eggs and deducting the amount of export from the said amount and dividing it by the population of the country, the amount of consumption per capita are calculated. The amount of export and import of red meat, chicken, milk and eggs is taken from the export and import report of the Ministry of Agriculture (Jihad), which is published monthly.
Results and Discussion
To estimate the system equations, one equation was removed, and the remaining equations were solved and estimated based on the removed equation. Accordingly, the equation related to milk was removed and the QAIDS with 33 parameters and three equations including those related to red meat, chicken and egg were estimated using the maximum likelihood estimator non-linearly. The results show the Based on the statistics of log-likelihood and DW the existence of a sudden structural break as a result of the Covid-19 pandemic. Comparing the Bewley likelihood-ratio test statistics calculated for an Non-Restricted QAIDS (with structural break) and a Restricted QAIDS (without structural break) with a critical χ^2 value with degrees of freedom of nine at the probability level of 5% indicates that the Non-Restricted QAIDS is selected as the appropriate functional form. Also, the results show that after the Covid-19 epidemic, the own price elasticity of red meat and chicken has increased significantly. Considering the high elasticity of the price of red meat, chicken and eggs after the Covid-19 epidemic, it is suggested that the government utilize price tools such as electronic coupon system to support consumers.
Conclusion
Due to the high cross-elasticity coefficients of demand for red meat, chicken and eggs after the Covid-19 pandemic, it can be expected that a change in the price of one of the red meat, chicken and egg products will significantly change the demand for the other product. Therefore, in case of a price increase in one of the products, it is suggested to consider special discounts for other products to support the consumers.
Keywords
- Change of preference
- Quadratic almost ideal demand system
- Structural break JEL Classification: Q11
- D1
- D12
Main Subjects
©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)
- Banks, J., Blundell, R., & Lewbel, A. (1997). Quadratic engel curves and consumer cemand, Review of Economics and Statistic, 79, 527-539. https://doi.org/10.1162/003465397557015
- Bewley, R. (1986). Allocation models: specification, estimation, and applications. Ballinger, Cambridge.
- Blanciforti, L.A., Green, R.D., & King, G.A., (1986). S. consumer behavior over thepostwarperiod: An almost ideal demand system analysis. Giannini Foundation Monograph 40, Davis, CA.
- Bopape, E.L. (2006). The influence of demand model selection on household welfare estimates: An application to South African food expenditures, Thesis (PHD), Michigan State University, USA. 177p.
- Burki, A. (1997). Estimating consumer preferences for food, using time series data of Pakistan, The Pakistan Development Review, Pakistan Institute of Development Economics, 36(2), 131-153. https://doi.org/10.30541/v36i2pp .131-153
- Chang, Y.Y.C., Wu, P.L., & Chiou, W.B. (2021). Thoughts of social distancing experiences affect food intake and hypothetical binge eating: Implications for people in home quarantine during COVID-19. Social Science & Medicine, 284, 1-5. 114218. https://doi.org/10.1016/j.socscimed.2021.114218
- Chavas, J.P. (1983). Structural change in the demand for meat. American Journal of Agricultural Economic, 65(1), 148–153. https://doi.org/10.2307/1240351
- Dadvarkhani, F., & Mousavi, S. (2022). The analysis of the effect of COVID-19 on the rural economy. Human Geography Research, 54(1), 391-413. (In Persian). https://doi.org/10.22059/jhgr.2022. 336178.1008431
- Dutt, P., & Padmanabhan, V. (2011). Crisis and consumption smoothing. Marketing Science, 30(3), 491–512. https://doi.org/10.1287/mksc.1100.0630
- Eales, J.S., Unnevehr, L.J., (1988). Demand for beef and chicken products: Separability and structural change. American Journal of Agricultural Economic, 70(3), 521–532. https://doi.org/10.2307/1241490
- (2020). The impact of COVID-19 on food security and nutrition. Food and Agriculture Organization of the United Nations.
- Fatahi Ardakani, A., Rezvani, M., Bostan, Y., & Sakhi, F. (2023). Analysis of preferences of bread consumption by urban households (Demand system approach). Journal of Agricultural Science and Technology, 25(5), 1015-1031. (In Persian). https://doi.org/22034/jast.25.5.1015
- Geoffrey, M.P., Capps, O., & Clauson, A. (2005). Demand for non-alcoholic beverages: evidence from the ACNielsen home scan panel. The American Agricultural Economics, 44, 159-170.
- Henchion, M., Hayes, M., Mullen, A.M., Fenelon, M., & Tiwari, B. (2017). Future protein supply and demand: Strategies and factors influencing a sustainable equilibrium. Foods, 6(7), 1-21. https://doi.org/10.3390/foods6070053
- Holland, A. (2012). The Arab spring and world food prices. American Security Project, https://www.jstor.org/stable/resrep05961
- Hovhannisyan, V., & Gould, B.W. (2014). Structural change in urban Chinese food preferences. Agricultural Economics (United Kingdom),45(2), 159-166. https://doi.org/10.1111/agec.12038
- Hovhannisyan, V., & Gould, B.W. (2011). Quantifying the structure of food demand in China: An econometric approach. Agricultural Economics, 42, 1–17. https://doi.org/10.1111/j.1574-0862.2011.00548.x
- Hovhannisyan, V., Kondaridze, M., Bastian, C.H., & Shanoyan, V. (2020). Empirical evidence of changing food demand and consumer preferences in Russia. Journal of Agricultural and Applied Economics, 52, 480–501. https://doi.org/10.1017/aae.2020.13
- Immordino, G., Jappelli, T., Oliviero, T., & Zazzaro, A. (2022). Fear of COVID-19 contagion and consumption: Evidence from a survey of Italian households. Health Economics, 31(3), 496–507. https://doi.org/10.1002/hec.4464
- Jia, P., Liu, L., Xie, X., Yuan, C., Chen, H., Guo, B., Zhou, J., & Yang, S. (2021). Changes in dietary patterns among youths in China during COVID-19 epidemic: The COVID-19 impact on lifestyle change survey (COINLICS). Appetite, 158, 1-9. 158, 105015. https://doi.org/1016/j.appet.2020.105015
- Kimsanova, B., Sanaev, G., & Herzfeld, T. (2023). Dynamics of food demand during political instability: Evidence from Kyrgyzstan. Agricultural Economics, 55(1), 41-53. https://doi.org/10.1111/agec.12810
- Lu Hsu, J. (2001). Gradual structural changes of meat consumption in Taiwan. Journal of International Food & Agribusiness Marketing, 11(4), 33-50. https://doi.org/10.1300/J047v11n04_03.
- Mas-Collell, A., Whinston, M.D., & Green, J. (1995). Microeconomic theory. Oxford University Press.
- Ministry of Agriculture- Jahad. Agricultural Statistics Yearbook, Volume 2, Various Years. https://www.maj.ir/
- Ohtani, K., & Katayama, S. (1986). A gradual switching regression model with autocorrelated errors. Economics Letters, 21(2), 169–172. https://doi.org/10.1016/0165-1765(86)90059-5
- Okrent A.M., & Alston, J.A. (2011). Demand for Food in the United States. Giannini Foundation Monograph 48. Davis, CA.
- Pangarkar, A., & Shukla, P. (2023). Conspicuous and inconspicuous consumption of luxury goods in a digital world: insights, implications, and future research directions. International Journal of Advertising, 42(7), 1226–1238. https://doi.org/10.1080/02650487.2023.2246260
- Rezvani, M., Pendar, M., & Vafaei, E. (2023). Assessing the effect of economic sanctions on the demand analysis of tea, sugar and sugar baskets of Iranian urban households. Economic Policies and Research, 2(3), 87-113. (In Persian). https://doi.org/22034/jepr.2024.140566.1084
- Roll, S., Chun, Y., Kondratjeva, O., Despard, M., Schwartz-Tayri T.M., & Grinstein-Weiss, M. (2022). Household spending patterns and hardships during COVID-19: a comparative study of the U.S. and Israel. Journal of Family and Economic Issues, 43, 261-281. https://org/10.1007/s10834-021-09814-z
- Salem, A.A., Zamani, R., & Faghihi, N.S. (2019). The effect of socio-economic variables on bread demand using AIDS model. Economics Research, 19(74), 81-110. (In Persian).
- Shahiki Tash, M.N., & Darvishi, B. (2012). Investigation of allocation system in urban household budget (Differential System Demand Approach). Journal of Economics and Modelling, 3(9), 94-121. (In Persian).
- Sim, K., Chua, H.C., Vieta, E., & Fernandez, G.(2020). The anatomy of panic buying related to the current COVID-19 pandemic. Psychiatry Research, 288, 113015. https://org/10.1016/j.psychres.2020.113015
- Su, C.W., Dai, K., Ullah, S., & Andlib, Z. (2022). COVID-19 pandemic and unemployment dynamics in European economies. Economic Research-Ekonomoska Istraživanja, 35(1), 1752-1764. https://doi.org/10. 1080/1331677X.2021.1912627
- Vaidheeswaran, S., & Karmugilan, M.K. (2021). Consumer buying behaviour on healthcare products and medical devices during COVID-19 pandemic period-a new spotlight. Natural Volatiles & Essential Oils (NVEO), 8(5), 9861-9872.
- Varian, H.R. (1982). The nonparametric approach to demand analysis. Econometrica, 50(4), 945–973. https://doi.org/10.2307/1912771
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