The study compared the trade flows of the top 50 nations with the greatest export level for 2019 and 2020. The relations were discovered using Gephi 0.9.2, a network analysis application. Gephi 0.9.2 is an open source network analysis application that may be used to uncover complicated relations. Gephi is more adaptable and has better visualization capabilities than other existing network software packages.
The nations export data were collected from the Trademap (trademap.org) website. The collected data were modified to the network structure and loaded into the Gephi application. The Gephi Program received a total of 8,992 data in 2019 and 8,933 data in 2020. It has been discovered that certain countries’ data contains errors or insufficient information. Such issues have frequently been discovered in developing or small countries. These data are limited in quantity and have been detected and cleaned.
Each nation is a "node" in the graphs, while export flows between countries are defined as "edges." Because each nations export flow is not reciprocal, the connections are weighted and directed. Directional-weighted graphs were constructed by using the export quantities as connection weights. Weighted graphs can provide more precise and through information.
Figure 1 depicts graph of nations based on total export quantities. The geo-based network was spatialized using Geo-Layout, a geographic layout method. Each nations’ geographic coordinates (latitude and longitude) were gathered and placed into the Gephi application. Node sizes and colors are also formed according to export sizes (the big and bright pink node has the most export amount). The nations having the greatest export levels, are China, America, and Germany, in that order. At first, it can be observed that America has a strong commercial flow with China, Canada, and Mexico, China has strong links with Hong Kong, Japan, and Korea, and Germany has strong relations with both China and America. Since the diagram for 2019 is not different from the diagram shown, it has not been redrawn. This algorithm allows us to see all countries according to the coordinates. This technique, however, does not allow us to obtain precise information. Starting from this algorithm, we can see that Germany, China and America carry out a very large part of the export volume of the whole World.
In Fig. 2, the nodes are positioned according to the Fruchterman-Reingold algorithm. The continents are used to categorize countries. Because the network has a large number of nodes and edges, the Fruchterman-Reingold layout is utilized, which distributes the nodes equally across the display and prevents the edges from overlapping. The Fruchterman-Reingold algorithm follows the force distribution law. This method groups and puts the highly linked nodes in the center of the graph, while scattering the less connected nodes about the network. The size of nodes varies depending on the amount of export. The fact that the nodes in the picture are grouped together in the center indicates that the export flow is between Asian and European nations. The low level of economic development of the countries in the African continent is also reflected in the graphs. South Africa is the only country on the African continent with the greatest export level. Chile, Brazil and Argentina from the South American continent are countries with high export levels. Although the export levels of the countries are different between 2019 and 2020, it is understood that the positions of the countries that are related to each other have not changed. With this algorithm, we can see the countries that have an intense relationship, and we can also distinguish the countries with low export volumes and those with little relationship. Although in large networks, it is not possible to see the one-to-one relationships of the nodes, it is convenient in terms of seeing the whole and positioning the nodes according to the power-law distribution.
In big networks, "filtering" is required to improve intelligibility and assure the emergence of active nodes. The Gephi program's filtering feature was utilized in Fig. 3. The average number of degrees was calculated for both graph and the connections below the mean were removed from the graphs. As a result, densely connected nodes evolved. Germany, USA and China meet a very large part of the export amount in the world. In terms of global commerce, it can be seen that Asian and European nations have more intense links with each other. European nations with high GDP and sophisticated economies are positioned to be concentrated in the center. Country with few connections are situated along the network's edges, which also reveals the economic status of the countries. When we compare 2019 and 2020, it is seen that although the export flow between countries decreased in 2020, the countries that steer the world economy did not change. The locomotive of Europe is Germany, followed by the Netherlands, France, Italy and England, respectively. It is an active participant in the trade network of North and South American nations such as America, Mexico, Canada, Argentina, Chile, and Brazil. The People's Republic of China has the largest export value among Asian countries and all countries in the globe. With the emergence of the Covid-19 virus in the People's Republic of China in 2019, it has developed into a pandemic that will engulf the entire planet by 2020. Despite this, China continues to be the country with the highest volume of exports. The deepening of the linkages between nations indicates a high level of commerce movement. As a result, Germany, along with many other nations in Europe and, notably, America and China; we can see the intensive commercial flows of the People's Republic of China to Hong Kong and the United States, as well as the United States to Mexico and Canada. Despite the fact that the analysis includes the commercial movements of 53 nations in South and North America, it is clear that the whole continent is exported through 6 countries. Geographic distance is obviously quite important in all of these movements. It is assumed that trade volumes between neighboring nations are increased as well. The countries' stronger neighborly ties and cheaper logistical costs are two of the most important factors for this.
Community analysis is used to decompose clusters of highly connected nodes into several relatively independent modules (group, cluster, or community). Modularity is designed to measure the density of connections within communities as compared to links between communities (Zhigao et al. 2018). By means of modularity, information can be obtained about the level of regionalization in world trade.
In our study, the modularity optimization approach is used by the Gephi software. The determination of community structures gives a clue to us whether there is also regionalization. The Circle Pack Layout method was used to model community structures. This algorithm allows to determine what is wanted to be shown in the graph with hierarchical order.
In 2019, four trade communities developed, with five more to follow in 2020. Figure 3 shows the locations of these clusters. Each color symbolizes a different group. Within the community, each country is closely interconnected, although there are fewer regular linkages across communities. 4 trading communities emerged in 2019 and 5 in 2020. It contains Germany (purple color), which has the biggest community in 2019 and is surrounded by 75 nations. America (yellow color) and 46 nations around the tiniest settlement. Communities will continue to be led by China, Germany, and the United States in 2019 and 2020. The community headed by India, Saudi Arabia, and the United Arab Emirates in 2019 has shifted in 2020. India exited this commercial community and maintained its commercial activities in the cluster led by China (blue color). In addition, South African-led communities organized and developed a new business cluster in 2020. Although the economic structure of developed nations is solid and trade flows have not changed, we may claim that the Covid pandemic has established new trade avenues for poor countries. Indonesia and the Republic of Congo expanded their exports in 2020 and joined the China-based business community. It is interesting that there are two distinct business groupings in one society, where China is the focal point. In 2020, the number of nations in the trade flow, with China as the core, has risen. The Covid-19 virus emerged in China in 2019 and spread around the world in 2020. We may predict by 2020, China will have gained greater expertise in dealing with the outbreak and will have expanded its business flows in this area. As a result, by 2020, China will have initiated or expanded trade relations with a variety of nations. The number of nations with whom it has formed strong connections has not changed much in 2019 and 2020 under Germany's leadership. According to Wei and Liu (2012)’s analysis, Turkey is placed in the community in which Russia is the center. In 2019 and 2020, it takes place in a Germany-based community. This indicates that Turkey's commerce with European nations is improving, and that Russia is likewise emerging from a central commercial community. Although the number and communities of nations in trade flows focused on America, China, and Saudi Arabia have changed in 2020, there is essentially no change in the community structure in which Germany is the center, indicating that the countries with whom Germany deals have not changed. Usually, this is Europe and Russia. Despite the pandemic, the formation of such a picture indicates that Germany's economy is quite robust, and that it has strong relationships with the nations with which it has business dealings.
Iapadre and Tajoli (2014) talk about the existence of two opposite situations about regionalization in their study. First; the increase in regionalization in world trade. The second is the decrease in regionalization as a result of the decrease in transportation and communication costs, the increase in the number of commercial partners of the countries and the long-distance trade. Although both ideas require a detailed analysis, the first conclusion that can be drawn according to our study is the existence of regionalization.
Topological Characteristics of the World Trade Network
Table-1 depicts the World Trade method in terms of network analysis. According to the total quantity of exports, there is no notable change in the top 20 nations in 2019 and 2020. For both years, the top five nations with the largest export volume are China, America, Germany, Japan, and the Netherlands. Russia will have the greatest shift in export volume in 2020, with a 25% decline. France, England, and India have 17 percent each, while America and Canada have 15 percent each. While export volumes declined in several countries in 2020, the UAE with a 6% rise, Taipei with a 5% increase, China with a 4% increase, and Hong Kong with a 3% increase were among the nations that saw an increase.
One of the most significant network topological aspects is the degree distribution. It is a crucial criteria in large-scale networks for describing and understanding the network's distinctive structure. The degree distribution is a network criteria that specifies how many nodes in a network have particular degree. The force distribution (in this study) is the frequency distribution of the export volume values of the nations in the analyzed network. Figure 4 depicts the distribution of nodes based on export volumes. As a result, the World Trade network is seen to suit the scale-free distribution. Few nations have a large export volume, whereas many countries have a low export volume, based on the distribution of forces. In this situation, the nodes have a heterogeneous structure as opposed to a homogeneous structure.
With this distribution, we can see that the world is still far from being fully connected, but in some subregional components we can see interconnected nodes. This distribution is in parallel with the Benedictis and Tajoli (2011) study.
Centrality statistics are very useful in comparing the roles of nodes within the network. In this study, degree, closeness, and betweenness centralities were evaluated as centrality statics. Since each centrality criterion has different interpretations in a network, it should not be interpreted through a single centrality value. The measures of centrality that have been used attempt to assess how influential a country is within the international trading system as a whole. Since there are different interpretations of each centrality criterion in a network, it is not necessary to make sense of the nodes over a single centrality value, so different criteria have been evaluated.
A degree is the number of neighbors to which a node is linked. Degree centrality is defined by Freeman (1978) as the number of links that connect a node to other nodes. The simplest measure of centrality that measures the importance of nodes in a network is degree centrality. Degree centrality determines if a node is essential. The higher a node's degree, the more important it is in the network. Degree centrality in international trade is the number of countries a country exports to and imports. Therefore, the impact on the international trade network can be determined by degree centrality (Zhigao et al. 2018). According to Table 1, Germany, the Netherlands and the UK are the countries with the highest degree in 2019 and 2020. Although China has the largest export volume, degree centrality is low. This suggests that it has a greater volume of trade flows with certain countries.
The number of incoming connection to a node is referred to as the "indegree" in directed (asymmetric) networks, whereas the number of outbound connection from the node is referred to as the "outdegree." While Germany, the Netherlands, and the United Kingdom have the highest outdegree degree, certain nations, such as Austria and the United Arab Emirates, will not be among the top 20 in 2020. Even though Turkey's export volume fell in 2020, its outdegree centrality grew. This indicates that Turkey has begun to establish business with a broader range of countries.
Looking at the indegree centrality, economically weak nations rank first. Because the study only includes the 50 nations with the greatest export levels, the export data of many of the countries with high indegree centrality is missing. Greece and Bulgaria are the most noticeable European continent countries here. When the import and export quantities of both nations are reviewed, it is determined that imports exceed exports, resulting in a current account deficit. As a result of our research, it is possible to conclude that economic issues exist in many of the nations with a high indegree of centrality. Figure 2 shows that Greece and Bulgaria are placed away from the network center, towards the edges of the network which is validating our previous assumption.
Nodes with a high amount of indegree are referred to as "authority," whereas nodes with a high degree of output are referred to as "hubs." When determining authority and hubs, the HITS (Hyper-link Induced Topic Search) method is used. Kleinberg created this method for analyzing the network of web sites, and it is widely used in search engines, data and text mining. The HITS method is used by the Gephi application to do computations. Therefore, it is possible to calculate the authority and hub centrality. Nodes with high centrality of authority have a large number of connections from nodes with high centrality. Similarly, the high centrality of a node means that this node has outbound connections to many high-authority nodes (Newman 2010: 179). According to Table 1, the nations with the highest hub value in 2019, respectively, whereas Italy, the Netherlands, Germany, and India are the UK, the Netherlands, Germany, and Belgium in 2020. The hub value is proportional to the outdegree. When we look at the outdegrees we can see that there isn't much of a difference in the ranks. EU nations with a high hub value are also attracting attention. With their trade taxes and policies inside the EU territory, EU nations may gain from free trade. However, because countries outside the EU cannot profit from them, EU countries prefer to trade more with each other. (Deguchi et al. 2014) studies, while China has the world's largest hub value and the third largest authority value, in our study it is in the 19th place in terms of hub value in 2019 and is not even in the top 20 in 2020. This actually shows that China may switched to a different commercial strategy. It can also show that it has more commercial partnerships with certain countries. It shows that it has moved away from being the factory of the world in the year 2000 and has turned into a more world market.
Closeness centrality is an indicator of the distance of a node from other nodes (in terms of topological distance) and measures how easily a node can be reached by other nodes. It also means that an actor with high affinity has access to many other nodes and is therefore relatively independent of the control of others (Kilduff and Tsai 2003). The closeness centrality of a nation in the trade network relates to how much it is impacted by other countries and how much it is affected by other countries. As a result, after ranking first in 2019, Germany fell to second in 2020.
In betweenness centrality, the location of the node in the network is more essential than the number of nodes linked. Freeman (1977) defined it as the number of shortest paths between two nodes. If one actor is connected to other players in the network by a communication channel, that actor is the center. Actors having a high betweenness centrality may be able to influence how other actors communicate with one another. A more valuable network measure is betweenness centrality in trade network, which indicates how important a country is in terms of connecting other countries. Countries having a high betweenness centrality operate as a commercial bridge with other countries in the trade network. Betweenness centrality therefore quantifies the extent to which a certain node operates as an intermediate or gatekeeper in the network.
While Germany, Europe's locomotive, had the greatest value in 2019, it was surpassed by South Africa in 2020, with Germany taking second position. We can observe that European nations' centrality values are in the top 20 for both years. This might imply that European nations have diverse economic partners, as well as that there is a lot of commerce going on between them. The fact that Turkey is ranked among the top 20 nations in 2020 suggests that, in addition to its trade volume, it exports to other countries and serves as a bridge. A more significant organization measure is betweenness centrality, which demonstrates how significant a nation is as far as con-necting different nations. High betweenness centralities for the US, Turkey, and Germany demonstrate those nations are significant extensions between territorial business sectors.
The graphs of the betweenness centralities in Fig. 6 are drawn with the Openord algorithm. This layout s very useful to detect clusters. OpenOrd is basically Fruchterman-Reingold with an extra parameter (edge cut) that controls the greatest length of an edge during the optimization process (https://towardsdatascience.com). This layout is more suitable for large networks. The Openord algorithm has divided the graphs into 3 clusters. In 2019, the countries with high betweenness centrality in each cluster were Germany, UAE and Singapore, while in 2020 it was Germany, UAE and South Africa. Those countries are important bridges between regional markets.
If we evaluate the betweenness centrality over Table 1; While Germany, the locomotive of Europe, had the highest value in 2019, it was replaced by South Africa in 2020. We see that the centrality values of European countries are in the top 20 for both years. This may be an indication that European countries have trading partners in different regions. In the centrality of betweenness, Turkey's being among the top 20 countries in 2020 can mean that it exports to different countries and acts as a bridge.