Investigating Drivers of the Price of Meat Types in Iran Using Panel-SVAR Model

Document Type : Research Article

Author

• R. Heydari

Agricultural Planning, Economics and Rural Development Research Institute (APERDRI), Tehran, Iran

https://doi.org/10.22067/jead.2022.72367.1080

Abstract

Introduction
Meat is one of the most important sources of animal protein and plays an important role in human nutrition. In addition, meat is one of the main commodities in the basket of Iranian households, so that it included about 20% of food costs in Iran. In recent years, fluctuations in meat prices have always been one of the main challenges of the meat market of Iran and every year the imbalance in its market reduces the welfare of consumers and causes damage to producers. In the current situation where the Iran foreign exchange resources are limited and prices of the meat market has many fluctuations, examining the price drivers of meat price in Iran from the perspective of microeconomics and especially the chain of vertical price transmission can be a good guide for policymakers and planners in adopting appropriate policies to control prices and domestic consumption of these products. The purpose of this study is to identify the drivers of the price of meat different types in Iran using the Panel-SVAR model in 30 provinces of the country during the years 2006-2019.
 
Materials and Methods
We use the panel SVAR methodology developed by Pedroni (2013) to analyze the implications of shocks on the price of meat types. Defining z_𝑖t ≡ (y_𝑖𝑡, x_𝑖𝑡, s_𝑖𝑡, m_𝑖𝑡)′, the heterogeneous panel SVAR model can be formulated as:
𝐵_𝑖 z_𝑖𝑡 = 𝐴_𝑖 (𝐿) z_𝑖𝑡−1 +   , 𝑖 = 1,…,, 𝑡 = 1, … , 𝑇  and  𝑢_it ∼ (0,Σ_𝑖)
Where 𝐵_𝑖 is the matrix of structural parameters, reflecting the instantaneous relations among model variables, 𝑧_𝑖𝑡 is the vector of endogenous variables, (𝐿) is a polynomial of lagged coefficients for ith province. 𝑢_𝑖𝑡 ≡ (𝑢_𝑦𝑖𝑡, 𝑢_x𝑖𝑡, 𝑢_s𝑖𝑡, 𝑢_m𝑖𝑡)′ is the vector of the structural shocks or innovations in 𝑧_𝑖𝑡, the variance-covariance matrix Σ_𝑖 is diagonal. Assuming 𝐵_𝑖 be an invertible matrix, pre-multiply both sides of Equation (1) by , we get the reduced form VAR model:
𝑧_it = Π_𝑖 (𝐿) 𝑧_it−1 + 𝜀it    where Π_𝑖 (𝐿) = 𝐴_𝑖 (𝐿), 𝜀_𝑖𝑡 =  𝑢𝑖𝑡 and 𝜀_it ∼ (0,Ω_𝑖)
Moreover, the variance-covariance matrix Ω𝑖 of the reduced form error 𝜀_𝑖𝑡 = (𝜀_𝑦𝑖𝑡, 𝜀_x𝑖𝑡, 𝜀_s𝑖𝑡, 𝜀_m𝑖𝑡)′  is full rank and no diagonal. The reason is that the errors are correlated between equations, implying that the innovations are not orthogonal. Traditionally, when this happens, innovations are correlated with each other and the matrix Ω𝑖 can be orthogonal zed by structural Cholesky decompositions. This method imposes an economic structure and allows the specific ordering of the panel SVAR variables. Finally, the contemporary matrix 𝐵_𝑖 is of the following form:
B_i =
 
Results and Discussion
The results of Pedroni and Cao co-integration test showed that the hypothesis of no co-integration among the variables could not be rejected. The optimal lags length for the Panel-VAR model, using the criterion of Schwartz-Bayesian, was determined as 2. The unit root test of the circle also showed that the estimated Panel-VAR model provides the stability condition. The results of Panel-VAR Granger causality test also showed that there is a direct or indirect causal relationship between all the studied variables. The results of estimating the "matrix of long-term response function in the Panel-SVAR model showed that all estimated coefficients are significant. The results of the Impulse Response Functions (IRFs) showed that the effect of shocks of the value added of the agricultural sector in the agricultural sector on meat price index, mutton price and beef price is negative and on chicken price is positive, while the shocks effect of the price index of imported inputs (corn, soybean meal and barley), livestock prices and meat prices are positive on meat price changes (chicken, sheep and beef). The maximum and minimum effect of these variables occurred between the first to the fifth period and their effect pattern is sinusoidal, afterwards shocks continue to be almost constant (or with low amplitude). This result shows that the effect of shocks of meat price stimuli in Iran is continuous and stable. The results of analysis of variance and historical decompositions also showed that the shocks related to the value added of the agricultural sector have the least effect and the shocks of the meat price variable have the greatest effect on meat price changes in Iran. The results of analysis of variance and historical analysis also showed that the shocks related to the value added variable of the agricultural sector have the least effect and the shocks of the meat price variable itself have the greatest effect on variation of meat prices in Iran. This result indicates that the impact of agricultural shocks on the meat price of is relatively weak, in contrast, the impact of shocks of the price transmission especially in the short term (beginning of periods) play a vital role.
Conclusion
In this study, impulses effect of four variables of the value added of agricultural sector, price index of imported inputs (corn, barley and soybean meal) and livestock price (live chicken, live sheep, live calf) was examined in four channels (equation) of price including meat price index (Total meat market), chicken, mutton and beef using the Panel-SVAR model in 30 provinces during the years 2006-2019. The findings of this study showed that the most important cause of price fluctuations in the Iran meat market is due to shocks to the vertical price transmission channel, especially in the short term. Therefore, preventing from price shocks in the Iran meat market will be one of the most important tools to create efficiency in the market of this product. Managing inflation expectations is a good way to reduce the price of meat in Iran. In addition, the use of appropriate protectionist policies throughout the meat production, distribution and consumption chain, such as monitoring the production, distribution and consumption stages; modify market structure instead of price control; timely provision of production inputs for producers; development of livestock inputs in the country; providing the supply of meat in the stock market; adequate and timely distribution to consumers; cash payments are offered to households and meat producers in the event of price shocks and explosions, is suggested.

Keywords

• Meat price
• Panel-SVAR model
• The value added of agricultural sector

20.1001.1.20084722.1401.36.3.3.4

Main Subjects

• Agricultural Economics

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