Research on Global Grain Trade Network Pattern and Its Driving Factors

Jian Duan, Changle Nie, Yingying Wang, Dan Yan and Weiwei Xiong
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Jian Duan: College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
Changle Nie: College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
Yingying Wang: College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
Dan Yan: College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
Weiwei Xiong: College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China

Sustainability, 2021, vol. 14, issue 1, 1-17

Abstract: Trading systems are essential in promoting global food security. With the growing proportion of global food consumption obtained through international trade, the global food trade pattern has become increasingly complex over recent years. This study constructed a weighted global grain network using the trade data of 196 countries in 2000 and 2018 to explore the structure and evolution based on the complex network theory. We established that the global grain network was scale-free. There was significant heterogeneity among nodes, and the heterogeneity of the out-degree was greater than that of the in-degree. The global grain network has a significant core-periphery structure, with the United States, Japan, Mexico, Egypt, South Korea, and Colombia as the core countries. Thereafter, by applying the quadratic assignment procedure model to explore the driving factors of the global grain network, we established that geographical distance had a positive impact on the food trade patterns in 2000 and 2018. This differs from the classical gravity model theory. Furthermore, grain trade had significant “boundary effects”; economic gaps, resource endowment, and regional free trade agreements had a positive impact on the evolution of the grain trade network, whereas cultural similarity and political differences had a negative impact on the grain trade network pattern.

Keywords: grain trade network; influencing factors; the QAP model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (4)

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