Bobby Castro

Which of the following Is a Positive Effect of Regional Trade Agreements

In empirical analysis, some unobserved characteristics of trade relations can be defined and optimized at the same time, and the analysis uses a trade purity indicator to describe the trading environment and countries` positions in regional and global trade relations. The gravitational model is one of the most effective empirical methods in the field of social sciences [14]. In particular, Isard and Tinbergen pioneered the application of the gravitational model to describe patterns of bilateral aggregated trade flows between countries [23, 24]. Their work has produced a vast empirical literature that seems to work well in modelling trade flows and studying the factors that influence them [14, 25, 26], as the variation in flows could be captured by the adjusted relationship [27]. The outer edges connect nodes from different communities, while the inner edges connect two nodes belonging to the same community. and are the sum of the external and internal degrees for all nodes; and are the sum of the external and internal weights for all nodes. Based on the results of the backbone network in 2007 and 2017, the E-I index (degrees) increased from 0.2711 to 0.1000 and the E-I index (weight) from -0.1019 to 0.0281. Relationships within the global trade network are more diverse, but the intensity of trade is focused on local communities. Based on the advanced gravity model, we can quantify the resilience of international trade for 198 firms (Figure 1). We assume that most of the trading resistance can be divided into two categories. The former has low expected barriers that are mainly related to natural factors such as geographical distance, and the other includes countries with relatively high artificial trade barriers such as trade restrictions, border blockades, cultural differences and political politics. It shows that most of the trade relations between the United States (red dots), China (green dots) and other countries fall into the first category; That is, most trade resistances are positively associated with geographical distance, so they are concentrated near the blue dotted line (Figures 1(a), 1(c) and 1(e)).

For the United States and China, few bilateral trade relations are affected by more artificial barriers. In a free trade agreement, all trade barriers between members are removed, which means that they can move goods and services freely among themselves. When dealing with non-Members, the trade policy of each Member shall always be in force. Figure 7 (Appendix) provides detailed information. The x-coordinate expresses the TPI between a particular country and other countries in the same Union, while the y-coordinate expresses the IPT between a particular country and other countries outside the Union. The size of the points is proportional to the net exit, measured as the absolute value of the difference between exports and imports; A red label means that the country had a trade deficit, while a blue label means a flow of trade surplus. Most points are below the red diagonal line “y = x”, which means that the IPRs of most EU countries are higher than those outside the EU. In addition, countries with significant trade flows, both within and outside the Union, have a higher TPI. RTAs are usually signed between neighboring countries, so their impact on regional trade is associated with geographical distance and other factors. The innovation of this paper is to examine and describe the business unit relationship of countries, separating other typical factors such as economic volume, geographical distance, and the overall increase in transportation and labor costs.

Unlike the existing literature that constantly increases observable variables to quantify trading costs [11-13], here we define synthesized trade resistance [14], break it down into natural and artificial factors, and propose a Trade Purity Indicator (TPI) to describe the real trading environment and relations between countries. The role of the RTA can be studied by comparing the IPT and its evolution within and outside a union. Here we apply the Expectation Maximization (EM) algorithm to optimize parameters and quantify the purity indicator for trading. Member States benefit from trade agreements, including the creation of new employment opportunities, lower unemployment rates and market expansion. Since trade agreements are usually accompanied by investment guarantees, investors wishing to invest in developing countries are protected from political risks. We have carried out a Community classification for the commercial backbone network, and Table 3 shows the modularity of the Community classification for the period 2007-2017. All are greater than 0.7, which means that the network structure has clustering properties. Here we take the years 2007 and 2017 as examples and show the partition of the community in Figure 5. Nodes that have the same color are assigned to the same community.

Obviously, there were structural changes between 2007 and 2017. After extracting the backbone network to classify the network into several communities, we apply the Leuven community detection algorithm [46] and evaluate the result with the index [47]: where the weight of the periphery is between the nodes and is the sum of the weights of the edges added to the nodes is the community to which the node belongs, and is 1 if and 0 otherwise. is the sum of the edge weights. Based on the trade resistances quantified during the period 2007-2017, we can build the basic network of world trade for each year and try to examine the community classification of the network. Companies in the Member States have a greater incentive to trade in new markets thanks to the policies contained in the agreements. Regional trade agreements are increasing in number and are changing in character. Fifty trade agreements were in force in 1990. In 2017, there were more than 280. In many trade agreements today, negotiations go beyond tariffs and cover several policy areas that affect trade and investment in goods and services, including cross-border rules such as competition policy, public procurement rules and intellectual property rights. RTAs covering tariffs and other border measures are “superficial” agreements; RTAs covering a wider range of policy areas, both inside and outside the border, are “deep” agreements. Compared to estimating exogenous parameters in existing research on the quantification of trade costs [15-18], the method in this article is more scientific and effective and could be expanded to discuss the impact of RTAs on a number of countries around the world.

The document is structured as follows: Section 2 briefly describes the data source and gravity model with synthesized trade resistance. Here, we establish a maximum probability function to simultaneously estimate unobserved parameters and quantify the trade purity indicator. Section 3 presents the results. Here we focus on six typical RTAs: Belt and Road (BRI), European Union (EU), North American Free Trade Agreement (NAFTA), Organization of the African Union (OAU), Caribbean Free Trade Area (CARIFTA) and Association of Southeast Asian Nations (ASEAN). We discuss the evolution of the TPI at the regional and global trade levels and analyse the impact of RTAs over the period 2007-2017. In addition, we discuss the development of commercial communities based on network methods. This shows that representative RTAs are the central structure of the international trade network, but that the role of trade unions has diminished and multilateral trade liberalization has accelerated over the past decade. Finally, Section 4 provides the conclusion and discussion. Similarity was higher in 2007; That is, the unions were more similar to the actual result of the accumulation of trade.

In 2017, the role of trade unions weakened. For most unions, the maximum agreement of members with communities is decreasing. Here, the EU and NAFTA remain exceptions, which are relatively similar to Communities 1 and 8. This could be due to their mature trading experience. In addition, the IPT of the EU and NAFTA remained stable, while the IPT of the other trade unions declined (Figure 4). The IPT focuses on trade-friendly relations with other countries, while the Communities also reflect the differences in trade relations between countries within and outside the Community. It is therefore reasonable and scientific to conclude that the EU and NAFTA have specific characteristics in both outcomes. Compared to 2007, the community structure of the “other” countries has become more decentralised.

In Figure 7 (Appendix A), we can see these two types of unions more clearly. The red labels indicate a trade deficit, while the blue labels indicate a trade surplus and the size of the spots represents the net outputs (exports minus imports). The EU and NAFTA (Figures 7(a) and 7(e)) with fewer members have higher economic development and IPTs. Therefore, the points are very concentrated. Other trade unions (BRI, OAU, CARIFTA and ASEAN) (Figures 7(b)–7(d) and 7(f)) are more unequal because the points are distributed from low to high-bit, and some member countries have a trade surplus while others have a trade deficit. In addition, it shows that countries with large trade flows within and outside their unions always have a higher CFI. Here, the trading resistance of each pair has a probability of belonging to the limited trading resistance group (natural barriers or category I). For each country, we define the trade purity indicator by adding the probability that its trade relations belong to category I, where the number of countries is equal to 1 if the trade relations between and to category I belong, and otherwise to 0. The TPI indicator provides a quantitative measure of the openness of a country`s business environment. If we assume that trade resistance is bilateral, then we can simply drift for each pair of countries according to the least squares method, where regional trade agreements refer to a treaty signed by two or more countries to promote the free movement of goods and services across the borders of its members. .