National Economic Status Inference from the World Trade Network

Throughout the Tyrian oracles, Tyre is depicted in the midst of the sea. In Ezekiel 27, Tyre as the center of the world trade maintains her wealth through her trade and merchandise. Against their public transcript, Ezekiel highlights the center of Judah and the land of Israel in Ezek 27:17. In the trading list of Ezek 27:12–26a, Ezekiel omits Babylon. Rather, he locates Judah and the land of Israel in the center of the list. According to Mario Liverani’s analysis of the trading list, the list reflects King Josiah’s period. During the period of King Josiah, the economic status of the kingdom of Judah was developed. By evoking the restoration period by King Josiah, Ezekiel accentuates the reality that the center of the world is not Babylon, but Judah and the land of Israel.

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Community structure in the World Trade Network based on communicability distances

Journal of Economic Interaction and Coordination ◽

10.1007/s11403-020-00309-y ◽

2020 ◽

Author(s):

Paolo Bartesaghi ◽

Gian Paolo Clemente ◽

Rosanna Grassi

Keyword(s):

Community Structure ◽

Numerical Analysis ◽

World Trade ◽

Metric Spaces ◽

Trade Network ◽

Distance Threshold ◽

Alternative Clustering ◽

Mesoscale Structure ◽

Long Range Interactions ◽

The World

AbstractIn this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.

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A stochastic generative model of the World Trade Network

Scientific Reports ◽

10.1038/s41598-019-54979-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Javier García-Algarra ◽

Mary Luz Mouronte-López ◽

Javier Galeano

Keyword(s):

Stochastic Model ◽

World Trade ◽

Preferential Attachment ◽

Weighted Degree ◽

Trade Network ◽

Exchange Flows ◽

The World ◽

Generative Mechanisms ◽

Source Of Information ◽

Synthetic Networks

AbstractThe World Trade Network (WTN) is a network of exchange flows among countries whose topological and statistical properties are a valuable source of information. Degree and strength (weighted degree) are key magnitudes to understand its structure and generative mechanisms. In this work, we describe a stochastic model that yields synthetic networks that closely mimic the properties of annual empirical data. The model combines two popular mechanisms of network generation: preferential attachment and multiplicative process. Agreement between empirical and synthetic networks is checked using the available series from 1962 to 2017.

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Understanding the Evolution of the World Trade Network: An Analytic Network Process Framework

Asian Nations and Multinationals ◽

10.1007/978-3-030-00913-7_4 ◽

2018 ◽

pp. 47-65

Author(s):

Guoqin Zhao ◽

Sam Dzever

Keyword(s):

World Trade ◽

Analytic Network Process ◽

Trade Network ◽

Process Framework ◽

The World ◽

Network Process

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Post-Brexit Power of European Union From the World Trade Network Analysis

Business Information Systems ◽

10.52825/bis.v1i.48 ◽

2021 ◽

pp. 39-47

Author(s):

Justin Loye ◽

Katia Jaffrès-Runser ◽

Dima L. Shepelyansky

Keyword(s):

European Union ◽

World Trade ◽

Dominant Role ◽

Matrix Analysis ◽

The European Union ◽

Trade Network ◽

Google Matrix ◽

The World ◽

Leading Position

We develop the Google matrix analysis of the multiproduct world trade network obtained from the UN COMTRADE database in recent years. The comparison is done between this new approach and the usual Import-Export description of this world trade network. The Google matrix analysis takes into account the multiplicity of trade transactions thus highlighting in a better way the world influence of specific countries and products. It shows that after Brexit, the European Union of 27 countries has the leading position in the world trade network ranking, being ahead of USA and China. Our approach determines also a sensitivity of trade country balance to specific products showing the dominant role of machinery and mineral fuels in multiproduct exchanges. It also underlines the growing influence of Asian countries.

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Industry Upgrading: Recommendations of New Products Based on World Trade Network

Entropy ◽

10.3390/e21010039 ◽

2019 ◽

Vol 21 (1) ◽

pp. 39

Author(s):

Wen-Yao Zhang ◽

Bo-Lun Chen ◽

Yi-Xiu Kong ◽

Gui-Yuan Shi ◽

Yi-Cheng Zhang

Keyword(s):

Developing Countries ◽

World Trade ◽

New Products ◽

New Product ◽

Trade Network ◽

National Economic ◽

Highly Correlated ◽

The Right ◽

New Industry ◽

Complexity Algorithm

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.

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Developing Medium and Small Technological Enterprises in China

Global Information Technologies ◽

10.4018/978-1-59904-939-7.ch101 ◽

2008 ◽

pp. 1390-1408

Author(s):

Zhimin Huang ◽

Shuqin Cai

Keyword(s):

Information Technology ◽

World Trade ◽

Technological Development ◽

The United States ◽

Rapid Expansion ◽

Enterprise Management ◽

Essential Information ◽

Enterprise Informatization ◽

National Economic ◽

The World

The 20-year economic reform in China has bred a new group of medium- or small-sized enterprises whose businesses are largely based on scientific and technological development. After China joined the World Trade Organization, these enterprises have had to seek strategies of informatization to survive and develop when facing challenges of economic globalization and rapid expansion of information technology. This paper attempts to examine in detail the necessity and importance of informatization of these enterprises within the context of China’s national economic system, identify issues crucial to the informatization process, and, by relating to the useful experience of enterprise informatization in the United States, propose some practical strategies of adopting advanced means and procedures through integration of essential information technology into enterprise management and operations.

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Network Analysis of World Trade using the BACI-CEPII Dataset

Global Economy Journal ◽

10.1515/gej-2014-0032 ◽

2014 ◽

Vol 14 (03n04) ◽

pp. 287-343 ◽

Cited By ~ 44

Author(s):

Luca De Benedictis ◽

Silvia Nenci ◽

Gianluca Santoni ◽

Lucia Tajoli ◽

Claudio Vicarelli

Keyword(s):

International Trade ◽

Network Analysis ◽

World Trade ◽

Centrality Measures ◽

Trade Network ◽

Weighted Version ◽

Trade Data ◽

Hands On ◽

The World

In this paper we explore the BACI-CEPII database using Network Analysis. From the visualization of the World Trade Network, we define and describe its topology, both in its binary version and in its weighted version, by calculating and discussing a number of the commonly used network statistics. We finally discuss various specific topics that can be studied with Network Analysis and International Trade data, both at the aggregated and at the sectorial level. The analysis is carried out with multiple software (Stata, R and Pajek). The scripts to replicate part of the analysis are included in the appendix and can be used as a hands-on tutorial. Moreover, local and global centrality measures, based on the unweighted and the weighted version of the aggregated World Trade Network, have been calculated for each country (178 in total) and each year (from 1995 to 2010) and can be downloaded from the CEPII webpage.

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