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Industry upgrading: recommendations of new products based on world trade network

  • Zhang, Wen-Yao Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China - Department of Physics, University of Fribourg, Switzerland
  • Chen, Bo-Lun Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
  • Kong, Yi-Xiu Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China - Department of Physics, University of Fribourg, Switzerland
  • Shi, Gui-Yuan Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China - Department of Physics, University of Fribourg, Switzerland
  • Zhang, Yi-Cheng Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China - Department of Physics, University of Fribourg, Switzerland
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    09.01.2019
Published in:
  • Entropy. - 2019, vol. 21, no. 1, p. 39
economic complexity
proximity
industry upgrading
English GDP is a classic indicator of the extent of national economic development. Research based on the World Trade Network has found that a country’s GDP depends largely on the products it exports. In order to increase the competitiveness of a country and further increase its GDP, a crucial issue is finding the right direction to upgrade the industry so that the country can enhance its competitiveness. The proximity indicator measures the similarity between products and can be used to predict the probability that a country will develop a new industry. On the other hand, the Fitness–Complexity algorithm can help to find the important products and developing countries. In this paper, we find that the maximum of the proximity between a certain product and a country’s existing products is highly correlated with the probability that the country exports this new product in the next year. In addition, we find that the more products that are related to a certain product, the higher probability of the emergence of the new product. Finally, we combine the proximity indicator and the Fitness–Complexity algorithm and then attempt to provide a recommendation list of new products that can help developing countries to upgrade their industry. A few examples are given in the end.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Economics
License
License undefined
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Persistent URL
https://folia.unifr.ch/unifr/documents/307710
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