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Application of Stochastic Frontier Gravity Model for Determining Seafood Export
Milad Aminizadeh1 , Hosein Mohammadi * 2, Alireza Karbasi1 , Hamed Rafiee3
1- Ferdowsi University of Mashhad
2- Associate Professor of Agricultural Economics, Agricultural Economics Department, Ferdowsi University of Mashhad, Iran. , hoseinmohammadi@um.ac.ir
3- Tehran University
Abstract:   (511 Views)
The efficiency estimation of industry-specific exports plays an important role in identifying export potentials and appropriate marketing strategies. This paper aimed to investigate the main determinants of Iran’s seafood exports to its 32 trading partners from 2001 to 2018, using the stochastic frontier gravity model. Moreover, this paper analyzed the efficiency and export potential of Iran’s seafood to its trading partners. The findings confirmed the consistency of stochastic frontier analysis for Iran’s seafood exports. The results indicated that the GDP of Iran and its trading partners had significant positive effects. In contrast, the bilateral exchange rate, common border, common religion, distance, economic crisis, and sanctions had significant negative effects on Iran’s seafood exports. In addition, the results of export efficiency revealed that Iran has great export potential to its trading partners, particularly neighboring countries. People's awareness of the benefits of seafood in neighboring countries with low consumption can increase their demand and increase Iran's exports to these countries. Considering the high export potential in neighboring countries with high religious and cultural similarities, it is suggested that Iran should strengthen its food trade relations with neighboring countries such as Iraq, Turkmenistan, Lebanon, Kuwait, the United Arab Emirates, and Afghanistan.

 
Keywords: export efficiency, export potential, seafood exports, stochastic frontier gravity model.
Full-Text [PDF 706 kb]   (332 Downloads)    
Article Type: Original Research | Subject: Agricultural Economics/Agriculture Marketing and Supply Chains
Received: 2023/06/10 | Accepted: 2024/01/2 | Published: 2024/03/31
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.