Effects of Regional Trade Agreement to Local and Global Trade Purity Relationships

Abstract

In contrast to the rapid integration of the world economy, many regional trade agreements (RTAs) have also emerged since the early 1990s. This contradiction has encouraged scholars and policymakers to explore the true effects of RTAs, including both regional and global trade relationships. This paper defines synthesized trade resistance and decomposes it into natural and artificial factors. Here, we separate the influence of geographical distance, economic volume, and overall increase in transportation and labor costs and use the expectation maximization algorithm to optimize the parameters and quantify the trade purity indicator, which describes the true global trade environment and relationships among countries. This indicates that although global and most regional trade relations gradually deteriorated during the period 2007–2017, RTAs generate trade relations among members, especially contributing to the relative prosperity of European Union (EU) and North American Free Trade Agreement (NAFTA) countries. In addition, we apply the network to reflect the purity of the trade relations among countries. The effects of RTAs can be analyzed by comparing typical trade unions and trade communities, which are presented using an empirical network structure. This analysis shows that the community structure is quite consistent with some trade unions, and the representative RTAs constitute the core structure of international trade network. However, the role of trade unions has weakened, and multilateral trade liberalization has accelerated in the past decade. This means that more countries have recently tended to expand their trading partners outside of these unions rather than limit their trading activities to RTAs.

Research on Global Grain Trade Network Pattern and Its Driving Factors

“…The emergence of the "border puzzle" phenomenon makes more scholars regard the contiguity of land as a crucial factor in measuring trade costs. Kimura [40] and Lee [41], as well as Gani and Clemes [42] established that a common geographical boundary could not only reflect the geographical distance between economies, but also better capture their geospatial relationships [43]. Therefore, in addition to geographical distance, a common land border is considered a crucial factor in measuring trade cost, which is used as a proxy variable of geographical distance.…”

Quantitative analysis of trade networks: data and robustness

“…This smaller 234-by-154 sized data set is used to verify the robustness of studying the trade network based on trading partners, in part by examining the largest complete subset (154-by-154) and the effects of artificially deleting data from that subset. Accordingly, in order to deal with missing data, exports of country j to country i might be used as a proxy for the import of country i from country j and conversely in the export network [28,30]. Although this proxy is not precise, it provides an approximation to the missing data.…”

“…Also, in the export network, the a ij element shows the export value from country i to country j, derived from country j's imports where the export data are absent. This approach is not exact but provides an approximate way to impute the missing entries [28,30].…”

“…Therefore, one way to impute missing data and construct same-sized networks is to use data from the trading partners of the reporting countries, some of which may report trade with a country that did not itself report. Following [28,30], in this paper, exports of a country is used as a proxy for the import of another country from the first country in the import network and conversely in the export network.…”

An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks

“…Backbone extraction has a wide range of applications. One uses it for various types of networks, such as social [61][62][63][64][65][66][67][68][69] , biological 70 , brain [71][72][73][74] , gene [75][76][77][78][79] , metabolic [80][81][82][83][84] , food web 85,86 , environmental 87,88 , finance [89][90][91][92] , trade [93][94][95][96][97] , information [98][99][100][101] , political 102,103 , transportation [104][105][106][107][108] , and others [109][110]…”