International Trade Network Research Articles

Investigating the Effect of International Sanctions on Iran's Trade Structure: A Network Theory Approach

After World War II, countries have used sanctions against target countries instead of military attacks to force them to change their policies. Particularly, following Iran revolution, Iran was sanctioned by the U.S. and its alliances which the strictest sanctions were imposed on Iran in 2012. From the Joint Comprehensive Plan of Action on, most of the levied sanctions were supposed to be lifted in 2016 by the United Nations which could alleviate the effect of sanctions on Iran economy. With regards to the effect of sanctions on countries economy, it is crucial to study the sanctions effectiveness on Iran economy. Therefore, this study compares the structural economic position of Iran in the international trade network in 2012 and 2016. Also, it investigates the effectiveness of international sanctions on Iran's structural position in the international trade network. Results show that Iran is not only a periphery in the international trade network, but also its structural position in the international trade network is not affected significantly by the sanctions.

Model-based clustering of time-evolving networks through temporal exponential-family random graph models

Dynamic networks are a general language for describing time-evolving complex systems, and discrete time network models provide an emerging statistical technique for various applications. It is a fundamental research question to detect a set of nodes sharing similar connectivity patterns in time-evolving networks. Our work is primarily motivated by detecting groups based on interesting features of the time-evolving networks (e.g., stability). In this work, we propose a model-based clustering framework for time-evolving networks based on discrete time exponential-family random graph models, which simultaneously allows both modeling and detecting group structure. To choose the number of groups, we use the conditional likelihood to construct an effective model selection criterion. Furthermore, we propose an efficient variational expectation–maximization (EM) algorithm to find approximate maximum likelihood estimates of network parameters and mixing proportions. The power of our method is demonstrated in simulation studies and empirical applications to international trade networks and the collaboration networks of a large research university.

Varieties of regional integration, international trade networks and risk

AbstractThis paper explores the relationship between regional integration models and trade network structures. The paper further investigates the implications of these trade relationship configurations on risk. The exploratory study models the European Union and the Association of Southeast Asian Nations as networks: countries in each region are nodes connected by lines representing commodity trade flows. The study compares the evolution of the two regional networks over a 25‐year period (1990–2014). The results confirm that different regional integration models lead to differently structured economic relationships. The findings show that loosely coupled networks are vulnerable to the failure of a few connected nodes (concentrated vulnerability), but that tightly coupled regional networks are vulnerable to the failure of any of its nodes (random vulnerability). These unique differences in vulnerability have critical implications for managing economic risk.

The Nature of Volatility Spillovers Across the International Capital Markets

This paper studies the nature of volatility spillovers across countries from the perspective of network theory and by relying on data of US-listed ETFs. I use a Lasso-related technique to estimate the International Volatility Network (IVN) where the nodes correspond to large-cap international stock markets while the links account for significant volatility lead-lags. Also included in the analysis is the International Trade Network (ITN), whose links measure bilateral export-import flows thus, capturing fundamental interconnections between countries. I find that the IVN and the ITN resemble each other closely pointing out that volatility does not disseminate randomly but tends to spread across fundamentally related economies. I also note that the amount of lagged volatility reactions in the IVN is consistent with the notion of gradual diffusion of information across investors who are subject to limited attention and home bias. I formally test this hypothesis by using as a direct proxy of investors’ attention the aggregate search frequency in Google. The empirical results support this intuition indicating that higher volatility surprises in key foreign markets predict higher domestic attention upon those markets in subsequent days. Once domestic attention is captured by such external shocks, it is contemporaneously transformed into higher domestic volatility.

The Influence of Global Trade in National Food Systems: A Dynamic Network Analysis, 1986–2013 (OR16-04-19)

ObjectivesThe objective of the study was to assess the evolution in international food trade relationships among countries during the period from 1986 until 2013, according to income groups, in order to analyze the contribution of global commerce to nutrition patterns in diverse populations worldwide. MethodsLongitudinal study based on the analysis of food trade among countries, using datasets publicly available from the Food and Agriculture Organization (FAO). The information on food items imported from each country to another partner country were converted into calories per capita per year, considering only edible parts, allowing the aggregation of annual food trade flows for identification of contributions from international food systems to each country, categorized according to income level classification of the World Bank. The dataset was organized in pairs of countries linked by food commerce to build the graph of food trade network for each year and to provide information on the dynamics of the network during the period analyzed, considering that countries were nodes and food trade connections were edges. ResultsThe dynamics of the global trade indicates intense growth in calories obtained from commerce among countries (from ∼1124 kcal per capita per day in 1986 to ∼2826 kcal in 2013), especially in low (+142.6%) and lower-middle income countries (+277.9%). However, major part of the calories traded among countries were concentrated among high income countries (from ∼2260 kcal per capita per day in 1986 to ∼4651 kcal in 2013). The evolution of international food trade network showed increase in graph density (from 0.164 in 1986 to 0.325 in 2013), and degree (from 25,365 in 1986 to 56,503 in 2013); nevertheless, there was relatively stable modularity (from 0.254 in 1986 to 0.325 in 2013), indicating intensification of calories traded among countries in the period, within similar patterns of commercial networks. ConclusionsInformation on networks connections along the period analyzed allowed indicating the role of global commerce on food situation of countries worldwide. There was increasing influence of global trade in national food systems during the last three decades; though inequalities remain regarding contributions from countries in different income levels. Funding SourcesNone. Supporting Tables, Images and/or Graphs▪▪▪▪

Linguistic summarization of fuzzy social and economic networks: an application on the international trade network

Although plenty of techniques such as link prediction, clustering, and position analysis have been proposed to analyze social and economic networks and patterns of social and economic relationships in various fields, few studies have addressed the transformation of social and economic network data into the knowledge in the form of linguistic summaries. In this study, we propose, for the first time in the literature, iteration, reciprocal and branching-based linguistic summary forms taking into account both the attributes (features) of social and economic actors and the relations between them. We then develop methods for evaluating the degree of truth of the suggested linguistic summary forms by leveraging generalized quantifiers, specifically semi-fuzzy and polyadic quantifiers. The advantages and applicability of the proposed linguistic summary forms are illustrated on the international trade network.

Tariffs, Domestic Import Substitution and Trade Diversion in Input-Output Production Networks: How to Deal with Brexit

This paper challenges and complements existing studies on the economic impact of Brexit providing a discussion of the UK's decision to leave the EU and how it will affect international trade networks and value-added. Using the World Input-Output Database, we develop a multi-sector inter-country model that allows us to identify the channels through which the economic effects of Brexit would propagate. The inclusion of global value chains and indirect Brexit effects in the model leads to estimates that diverge with the results of the main literature. Indeed, our findings suggest that Brexit could be risky and costly not only for the UK but also for many EU countries. Furthermore, building on the Dietzenbacher and Lahr (2013) method of hypothetical expansion, we develop a second model and present the first empirical analysis on the consequences of domestic import substitution and trade diversion policies in Input-Output schemes. We found that allowing sectors and countries to partly substitute foreign products, leads to significantly lower losses for both macro-regions. In the second model, the UK and EU27 would lose, at worst, the 0.05 and 0.5 percent of value-added, respectively.

Enhanced Gravity Model of Trade: Reconciling Macroeconomic and Network Models

The structure of the International Trade Network (ITN), whose nodes and links represent world countries and their trade relations respectively, affects key economic processes worldwide, including globalization, economic integration, industrial production, and the propagation of shocks and instabilities. Characterizing the ITN via a simple yet accurate model is an open problem. The traditional Gravity Model successfully reproduces the volume of trade between connected countries, using macroeconomic properties such as GDP, geographic distance, and possibly other factors. However, it predicts a network with complete or homogeneous topology, thus failing to reproduce the highly heterogeneous structure of the ITN. On the other hand, recent maximum-entropy network models successfully reproduce the complex topology of the ITN, but provide no information about trade volumes. Here we integrate these two currently incompatible approaches via the introduction of an Enhanced Gravity Model (EGM) of trade. The EGM is the simplest model combining the Gravity Model with the network approach within a maximum-entropy framework. Via a unified and principled mechanism that is transparent enough to be generalized to any economic network, the EGM provides a new econometric framework wherein trade probabilities and trade volumes can be separately controlled by any combination of dyadic and country-specific macroeconomic variables. The model successfully reproduces both the global topology and the local link weights of the ITN, parsimoniously reconciling the conflicting approaches. It also indicates that the probability that any two countries trade a certain volume should follow a geometric or exponential distribution with an additional point mass at zero volume.

Spatial Distribution of the International Food Prices: Unexpected Heterogeneity and Randomness

Global food prices are typically analysed in a time-series framework. We complement this approach by focusing on the spatial price dispersion of the country-pair bilateral trade in the international food trade network (IFTN), for ten relevant commodities. The main purposes are to verify if the Law of One Price (LOP) holds and to investigate the emergence of randomness in the price-formation mechanism.We distinguish between the “internal” variance, which indicates the magnitude of price discrimination, and the “external” variance, that is a measure of price dispersion. We find that, for some commodities, spatial price dispersion is remarkable and persistent over time (i.e., failure of the LOP) and that there exists a strict correlation between price spikes and peaks in spatial price variability.We test whether the price distribution can be replicated through a stochastic process of extraction. Surprisingly, the actual distribution of prices, for several commodities, is well described by a random distribution. Then, the process of data aggregation is not neutral because the information at the micro-level scale might be lost at the macro-scale, due to the complexity of the IFTN. Finally, we discuss some possible economic explanations of these outcomes and the main methodological, environmental, and policy consequences.

Exploring dependence structures in the international arms trade network: A network autocorrelation approach

In this article, we analyse dependence structures among international trade flows of major conventional weapons from 1952 to 2016. We employ a Network Disturbance Model commonly used in inferential network analysis and spatial econometrics. The dependence structure is represented by pre-defined weight matrices that allow for correlating flows from the network of international arms exchange. Three dependence structures are proposed, representing sender-, receiver- and sender–receiver-related dependencies. The appropriateness of the presumed structures is comparatively assessed using the Akaike Information Criterion (AIC). It turns out that the dependence structure among the arms trade flows is complex and can be represented best by a specification that relates each arms trade flow to all exports and imports of the sending and the receiving state. Controlling for exogenous variables, we find that the trade volume increases with the GDP of the sending and the receiving state while the impact of geographical distance, regime dissimilarity and formal alliance membership is rather small.

Structure and change of international trade network of intermediate goods: From the perspective of trade costs

Structure and change of international trade network of intermediate goods: From the perspective of trade costs

Umkämpfte Wirtschaften: Kaffeehandel, europäische Einwanderung und die zentralamerikanischen Ökonomien im Ersten Weltkrieg

El artículo analiza el impacto de la Primera Guerra Mundial en aquellas economías centroamericanas, caracterizadas, desde el siglo XIX, por su dependencia de las exportaciones de café y plátano. Dado el importante papel que los migrantes europeos en Centroamérica tenían en la actividad económica, el artículo aborda también los cambios en las redes comerciales fraguadas por estos actores. En este contexto, los años del conflicto armado trajeron distintos conflictos para las economías centroamericanas: Primero, la competencia económica entre los Estados Unidos y los países europeos por el control de la región. Segundo, el intento de la diplomacia de las potencias en guerra por utilizar a los migrantes europeos para sus propios fines políticos, lo que propició tensiones de distinto orden . Tercero, la utilización de la guerra por parte de los gobiernos centroamericanos como moneda de cambio para beneficiar sus propios intereses económicos. Esto a pesar de que sus posibilidades de éxito en ello fueran escasas, dado que no producían materias primas de valor estratégico para la guerra. Aunado a todo esto, el deterioro de los precios del café y de la depreciación de las monedas de los países centroamericanos afectaron la actividad económica de la región en su conjunto. Asimismo, después de que la Gran Bretaña y los Estados Unidos elaboraran listas negras de empresas consideradas como enemigas entre 1915 y 1917, se obligó a estas a definir de qué lado habrían de posicionarse en el contexto beligerante. Al final, los Estados Unidos impusieron la expropiación de empresas alemanas en Guatemala y Honduras. Aunque la mayoría de ellas fueron devueltas a sus antiguos dueños después de la guerra, el fin de esta no atenuó la competencia entre los Estados Unidos y Alemania. Ambos países siguieron disputándosela influencia económica en la región durante los años posteriores.

Quantifying the Robustness of Countries’ Competitiveness by Network-Based Methods

In economic researches, much effort was devoted to the problem of how to increase the economics of countries. However, the development of a country may fluctuate a lot due to international and domestic problems. Thus, we should also evaluate the robustness of countries against unexpected economic recessions. In this paper, we use perturbation to quantify the robustness of countries using two renowned algorithms: method of reflections (MR) and fitness-complexity method (FCM). The robustness characterizes the stability of countries’ competitiveness against economic recessions. The experiments in the international trade networks show that FCM could characterize the robustness better than MR. High fitness countries of FCM have strong robustness against economic crises, which enlarges the application fields of FCM. Additionally, we simulate the trade conflict between USA and China. The simulation results show that China suffers much in the trade conflict, while USA loses very little and has strong robustness in this conflict.

Analysis of the international polysilicon trade network

In the photovoltaic (PV) industrial chain, polysilicon is the most vital product affecting the scale of the entire industry. Polysilicon trade holds the highest profit margin, and the technological developments among countries is unbalanced, promoting international trade among countries. Therefore, analysis of the international polysilicon trade is crucial for understanding the development of the global PV industry. In this paper, we use a social network analysis (SNA) method to study the structure of the global polysilicon trade network and each country’s characteristics in the network based on international trade data from 2006 to 2016. Our results show that the network is very concentrated and shows an obvious core-periphery structure. The United States of America (USA), China, Germany and Japan are the most powerful countries in the trade network. We also find that the development of polysilicon trade can represent progress of the PV industry.

Unfolding the Complexity of the Global Value Chain: Strength and Entropy in the Single-Layer, Multiplex, and Multi-Layer International Trade Networks.

The worldwide trade network has been widely studied through different data sets and network representations with a view to better understanding interactions among countries and products. Here we investigate international trade through the lenses of the single-layer, multiplex, and multi-layer networks. We discuss differences among the three network frameworks in terms of their relative advantages in capturing salient topological features of trade. We draw on the World Input-Output Database to build the three networks. We then uncover sources of heterogeneity in the way strength is allocated among countries and transactions by computing the strength distribution and entropy in each network. Additionally, we trace how entropy evolved, and show how the observed peaks can be associated with the onset of the global economic downturn. Findings suggest how more complex representations of trade, such as the multi-layer network, enable us to disambiguate the distinct roles of intra- and cross-industry transactions in driving the evolution of entropy at a more aggregate level. We discuss our results and the implications of our comparative analysis of networks for research on international trade and other empirical domains across the natural and social sciences.

Bilateral Trade Agreements and the Interconnectedness of Global Trade

Over the last decades, bilateral trade agreements (BTAs) have increased considerably in number and economic relevance. Notably, such agreements substantially affect global trade, since the reorganization of flows of goods and services has prominent impacts on the contracting countries' economies, but also on other parties that are (directly or indirectly) engaged in trade with these countries. Here, we empirically study the effect of BTAs on the input-output linkages between the contractual parties' national economic sectors by defining a new measure of Trade Interconnectedness (TI), which describes the relative importance of direct and indirect production linkages between the two countries in the international trade network. By analyzing its time evolution for each pair of trade agreement partners, we demonstrate that while most BTAs are succeeded by an increase in TI between the contractors, there are some notable exceptions. In particular, comparing the trade profiles of China and the United States (US), we find indications that both countries have been pursuing fundamentally different objectives and strategies related to the negotiation of BTAs.

Trade network reconstruction and simulation with changes in trade policy

The interdependent nature of the global economy has become stronger with increases in international trade and investment. We propose a new model to reconstruct the international trade network and associated cost network by maximizing entropy based on local information about inward and outward trade. We show that the trade network can be successfully reconstructed using the proposed model. In addition to this reconstruction, we simulated structural changes in the international trade network caused by changing trade tariffs in the context of the government’s trade policy. The simulation for the FOOD category shows that import of FOOD from the US to Japan increase drastically by halving the import cost. Meanwhile, the simulation for the MACHINERY category shows that exports from Japan to the US decrease drastically by doubling the export cost, while exports to the EU increased.

Automotive international trade networks: A comparative analysis over the last two decades

AbstractThis paper explores the structural changes in the international organization of automotive production since the early 1990s. We apply descriptive network measures to international trade data for the period 1993–2013 for three automotive component groups with different technological intensity, with the aim of understanding (1) how the automotive international trade network has changed since the beginning of the 1990s; (2) whether regionalization has increased over time; (3) how the role that rising powers play within and between regions interacts with regionalization patterns. Our findings suggest that the structure of trade has changed significantly over the last two decades in all components. The network for electric and electrical parts and engines has evolved to become more hierarchical, with a cohesive core tied to hangers-on in the periphery, while the opposite has happened for rubber and metal. Regionalization patterns also show important differences across components. Finally, we have found an apparent association between the strengthening of regionalization patterns and the role played by traditional players and rising powers.

The Middle-Income Trap and the Coping Strategies From Network-Based Perspectives.

When a developing country reaches a relatively average income level, it often stops growing further and its income does not improve. This is known as the middle-income trap. How to overcome this trap is a longstanding problem for developing countries, and has been studied in various research fields. In this work, we use the Fitness-Complexity method (FCM) to analyze the common characteristics of the countries that successfully get through the middle-income trap, and show the origin of the middle-income trap based on the international trade network. In the analysis, a novel method is proposed to characterize the interdependency between products. The results show that some middle-complexity products depend much on each other, which indicates that developing countries should focus on them simultaneously, implying high difficulty to escape the middle-income trap. To tackle the middle-income trap, developing countries should learn experiences from developed countries that share similar development history. we then design an effective method to evaluate the similarity between countries and recommend developed countries to a certain developing country. The effectiveness of our method is validated in the international trade network.

Enhancing Countries’ Fitness with Recommender Systems on the International Trade Network

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries’ data of the International Trade Network. A recommendation system is then used to identify the products that correspond to the production capacity of each individual country but are somehow overlooked by the country. Then, we simulate the evolution of the country’s fitness if it would have followed the recommendations. The result of this work is the combination of these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries.