Beyond the Norm: Social Network Analysis of International Trade Disputes Among the

Members of the World Trade Organization (WTO)

Baldwin Wallace University

Spring 2013

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Abstract

Social Network Analysis (SNA) is a unique research framework centered on the

relationships among the interacting units, and the patterns of these relationships as the key to

understanding the dynamics of a given structure. This study explores the potential of the SNA

methodology in informing our understanding of the structure of international trade disputes by

comparing the results obtained through SNA tools to the results established by the traditional

statistical tools employed by economics researchers. A dataset of 454 World Trade Organization

(WTO) trade disputes over the period from 1995-2012 are analyzed using NodeXL, a network

analysis software. The paper discusses potential benefits and applications of network theory

with regards to international trade disputes.

Key words: Social Network Analysis (SNA), Graph Theory, WTO, International Trade Disputes, Agents, Structure, Dispute Settlement (DS) System.

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Table of Contents

Reform vs. Regression……………………………………………………………………….…....3

Understanding the WTO and DS Mechanism………………………………………………….…4

Overview of the Literature………………….…………………………………………………..…5

Theoretical Analysis……………...…...…………………………………………………………..8

Empirical Analysis……………………………………………………………………………....10

Discussion and Conclusion…………………………………………...………………………….20

Data Appendix…………………………………………………………………………………...22

References………………………………………………………………………………………..23

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Reform vs. Regression

Classical economic models and theory are gradually evolving from the premise of fully

forecasting the behavior of economic agents through the paradigm of rationality to its current

incarnation, which proposes an array of explanations for apparently complex issues and systems.

Behavioral economics and its recent outgrowth, neuroeconomics, are evidence of an upcoming

reform of traditional economic models. With the ever – changing nature of economic problems,

primarily due to increased global politico – economic interdependence, there is a need for novel

dynamic research tools capable of capturing the multidimensionality of today’s questions.

Network analysis should belong to the tool set of researchers and academics aspiring to explore

the complex domains of economics.

Developed within the social and behavioral sciences, social network analysis has at its

heart the branch of mathematics called graph theory1 (Scott and Carrington quoting Harary and

Norman 1953; Harary et al. 1956; Harary 1969). Graph theory analyses the formal properties of

graphs, which are system of points and lines between pairs of points. The concept of the graph

can be extended to take account of the direction of a line, so as to represent asymmetric

relationships such as friendship choices made or alternatively flow of influence or resources.

SNA is a specific application of graph theory in which individuals and other social actors such as

groups, organizations and so on, are represented by the points and their social relationships are

represented by the lines (Scott and Carrington 2011). Most social scientists recognize the power

of the network approach in exploring standard social and behavioral science research questions

by defining the various aspects of the political, economic, or social structural environment.

1 Graph theory is a set of axioms and deductions that originated in Euler’s mathematical investigation of the seven bridges of Köningsberg. He explored the problem of whether it is possible to walk through the city crossing each bridge just once in order to visit each of the islands that made up the city. Converting this problem into a model of points and lines, the points representing the islands and the lines representing the bridges he showed that the task is impossible.

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Analyzing relationships and understanding their patterns calls for a set of methods and

analytical concepts that are different from those of traditional statistics, for instance regression.

While numeric variables, statistics and abstract mathematical models are useful in informing

social science researchers, they do fall short when it comes to assessing the bigger picture. Social

Network Analysis is more of a “paradigm”, rather than simply a theory or method. In other

words, SNA is more of a way of conceptualizing and analyzing the quantitative and qualitative

aspects of complex system structures. This project does not aim to discard regression or similar

traditional data analysis tools. The goal of this paper is to explore the potential of the SNA

methodology in informing our understanding of the structure of international trade disputes by

comparing the results obtained through SNA tools to the results established by the traditional

statistical tools employed by economics researchers.

Understanding the WTO and DS Mechanism

Established in January 1995, the WTO is the result of the Uruguay Round and previous

agreements under the General Agreement on Trade and Tariffs (GATT). The nature of the WTO

as an international body is defined by the interactions and the relations among its 159 members,

of which 117 are developing countries (WTO2). The main purpose of the WTO is to provide a

forum for negotiating agreements aimed at reducing obstacles to international trade by ensuring a

level playing field for all. Over the past 60 years, the GATT and the WTO have helped to create

a prosperous international trading system, which in turn has fostered global economic growth.

Negotiating and compiling international agreements does not guarantee the success of the

organization. The signatories have to comply with the obligations of the agreement, and be held

accountable if they do not comply. In the cases when the signatories do not respect the

negotiated agreements, trade disputes arise. Disputes need to be settled in a timely and structured

2 WTO refers to the actual World Trade Organization Website.

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manner in order to prevent unresolved international conflicts from disrupting economic growth

and to maintain a fair game based on common rules as opposed to power struggles between the

stronger and weaker players. As a result, the WTO Dispute Settlement (DS) system is considered

one of the major achievements of the Uruguay Round.

The DS system is critical for resolving trade quarrels and ensuring that trade flows

smoothly. A dispute arises when a member government believes that another member

government is violating an agreement or commitment it has made upon being awarded

membership in the WTO. These agreements are compiled by the member governments and are

the outcome. Nonetheless, disputes arise and the ultimate responsibility for settling them lies

with member governments through the DS System.

Overview of the Literature

There is abundant research in the literature on the SNA model and its applications, as

well as the DS system and trade disputes within the WTO. Some of the applications of the SNA

methodology, just to mention a few, include topics such as occupational mobility (White,

Boorman and Breiger 1976), the impact of urbanization on individual well-being (Fischer 1979),

the world political and economic system (Snyder and Kick 1979; Nemeth and Smith 1985) and

so on. There is no evidence of direct applications of the SNA perspective in specifically

analyzing the DS system or the dynamics of the WTO disputes. The majority of the research

works around this topic consist of traditional statistical models such as regression or simple

descriptive statistics. Alternatively, this study assesses the dichotomy between the numbers and

relationships among economic agents by applying SNA to the WTO Dispute Settlement (DS)

system trade dispute cases.

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Disputes are defined in various ways by different scholars. This study adopts the dispute

definition provided by Horn, Johannesson and Mavroidis (2011), who describe a dispute as a

bilateral disagreement between two WTO members. For instance, if two members complain

against another member, each of them is counted as having a dispute with the other member.

According to this definition, the WTO Dispute Settlement Data Set (Ver. 3.0) indicates that there

have been 426 WTO disputes during the period from January 1, 1995 to August 11, 2011 (Horn,

Johannesson and Mavroidis 2011). The average number of disputes per year was 20 during

2001-2010, much lower compared to the period during 1995-2000 when it was 36.5 disputes per

year. Horn and his collaborators simply display the data without any attempting to explain why

certain patterns emerge, what are the reasons for the trends in the data and how does this speak to

the function of the DS system and WTO’s role.

International trade disputes and the motivations of WTO members to engage in such

disputes have been thoroughly covered in the literature review. Some studies emphasize the

relationship between political systems and variables with trade disputes. Existing studies support

the assumption that democracies experience fewer trade conflicts or be more cooperative in

resolving disputes (Reinhardt 1999). Another hypothesis states that the increased number of

disputes being filed in an indication of the efficacy of the trade dispute settlement system.

Reinhart tested both hypotheses using data on dispute initiation within all GATT/WTO bilateral

trade relations from 1948 through 1998. Based on results obtained through multivariate

regression Reinhardt came to the conclusion democracies experience more trade conflict, and

democratic countries are less likely to solve trade disputes cooperatively.

From its genesis, the WTO was intended to be an apolitical body, but that is certainly not

the case. Given the political variables that have been driving dispute initiation and more

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importantly their settlement, the question rises as to whether or not smaller members of the WTO

are able to use the DS system on an equal basis with the more powerful members (Guzman and

Simmons 2005). Guzman and Simmons analyzed the relationship between the political power as

well as the wealth of the WTO members and their ability to fully participate in the DS system.

According to their “capacity hypothesis”, low income states lack the financial, human, and

institutional capital to fully take advantage of the DS system; therefore they tend to complain

about behavior by high income states. The inability of the low income WTO members to fully

participate in the DS system might be the factor driving the increase in the number of disputes.

Another study conducted by Davey (2005) explores the operation of the WTO’s DS

system from 1995 until 2004, utilizing the major users of the system including the United States,

the European Communities (EC), Canada, Japan, Brazil and India. Bilateral relations such as that

between the United States and the EC are given particular consideration. The goal was to

evaluate the success of the DS system in settling disputes in a timely fashion, though mutually

agreed solutions and the actual implementation of the panel’s reports (Davey 2005). Davey

concludes that since its inception in 1995 the system has successfully dealt with dispute

settlement at the consultation and implementation stage, but the lengthy period taken to achieve

these results demonstrates a lack of od time efficiency as well as promptness of the DS system.

The political factors involved in the dispute settlement process and the differences in power of

the WTO members might be playing a role in the lack of promptness.

A gap exists in the literature when it comes to SNA applications to trade disagreements.

Very few studies have applied network theory to international trade, and almost none have

considered trade disputes. Bhattacharya, Mukherjee, Saramäki, Kaski, and Manna (2008)

acknowledged the fact that the International Trade Network (ITN) system of mutual trading

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between countries in the world can be viewed as an interesting example of a real-world example.

What makes this study compelling is the combination of graph theory, network measurements

and standard statistical methods to explore the ITN dataset in depth. In the existing literature,

there is no comparable study concerning the WTO DS system and network theory applications.

In sum, the WTO and DS mechanism are complex systems based on relationships that resemble

a web-like structure. SNA exploration of the trade dispute relationships has the potential to yield

interesting and novel results by offering a new perspective supplementing the findings yielded by

traditional research methods.

Theoretical Analysis and Rationale

SNA was leveraged as an alternative model for looking at the structure and the

complexity of the relationships among the WTO members engaged in trade disputes. The WTO

trade dispute network is characterized by well-defined properties emerging from the economic

and political framework of the organization itself. As a result, a number of issues can be assessed

using network theory to survey the characteristics of trade dispute networks and how they have

changed over time. Various factors play a role in shaping the properties of the international trade

network disputes.

Economic variables are heavily emphasized when it comes to trade disputes due to the

assumptions and implications of international trade models. Political variables, essential in

capturing the dynamics of trade disputes, are often times left out of the equation. The new

enhanced WTO regulations and negotiated agreements among member states are thought to have

improved trade disputes, even though it is difficult to find measurable proof that this is indeed

the case. Unfortunately, the lack of a large enough data sample makes it challenging to test the

significance of political factors as applied to the WTO dispute mechanism.

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Grinols and Perrelli (2002) assessed the significance of the political variables in trade

disputes in order to determine whether the WTO has been successful in ameliorating disputation.

Using data from the United States’ trade dispute record they found that political factors are

influential in the initiation of trade disputes and in the length of adjudication, which exhibited a

number of regularities. The study looked at American legislation regarding international trade,

and decided to use US section 301 trade dispute data which spanned a period of 25 years.

Domestic legislation and governmental organizations, such as the US Trade Representative, are

among the political factors that hold a considerable weight responsible for the “success” or

“failure’ of the WTO regulations.

Additional political influences from the U.S.’s standpoint include the presidential life

cycle, and the country against which the dispute is being filed. Relative trade, bargaining power

and membership in intergovernmental organizations (IGOs) impact trade disputes in a number of

ways (Grinols and Perrelli 2002; Ingram, Robinson and Busch 2005). When actors like the

European Union (EU) are involved, the dispute cases are expected to last longer due to the

extended layers of bureaucracy involved in the dispute resolution. Large countries, which higher

stakes in the game, are often more willing to devote longer periods of time and more resources to

negotiations.

Strength of political ties between nations is another aspect impacting the dynamics of

dispute initiation. For instance, in discussing the trade relations between US and Taiwan are

important to consider the asymmetries political present and their evolution over time (Grinols

and Perrelli 2002). Despite economic theory suggesting that countries should pursue liberal

trade policies and exchange goods and services on the basis of their comparative advantage, in

practice most states actively intervene in international trade Hoekman and Kostecki (2001).

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While the principles of GATT and WTO have helped governments to liberalize trade by resisting

pressures for protectionist measures, and led to greater worldwide economic integration through

international trade. In return, the gamut of political variables has generated an immense level of

complexity in the trade dispute network which is hardly captured by conventional statistical

tools.

Applying network theory to the WTO trade dispute data set innovatively captures the

impact of non – economic variables, such as political influences. Network theory allows for a

better visual representation of the complex structure of trade disputes, without leading to a

prescribed outcome. The resulting networks generated by using SNA methodology can be

interpreted based on the context of the project and the specific purpose of the research. SNA has

the potential to uncover a set of network properties that capture the nature of the relationships

between the members of the WTO in terms of international trade disputes oblivious to alternative

data analysis models.

Empirical Analysis

In building and analyzing dispute network maps this study used the software application

NodeXL, a powerful and easy-to-use interactive network visualization and analysis tool.

NodeXL uses Microsoft Excel as the platform for representing generic graph data, performing

advanced network analysis and visual exploration of networks. The tool supports multiple social

network data providers that import graph data into the Excel spreadsheet (Hansen, D.,

Shneiderman, and Smith 2010). While being very user friendly, the program comes with a set of

data analysis tools that provide several network measurements in addition to various graphical

layouts and structural iterations.

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WTO trade dispute data from January 1995 – December 2012, which encompasses the

current lifespan of the organization, were used to compile a database of 454 dispute cases.

Specific attributes recorded for the each dispute cases include complainant, respondent, case

number, year, subject, and third parties. Complainant refers to the country who initiates the

dispute, while the respondent is the country to which the dispute is directed. The case number is

designated by the WTO system, it consists of the letters DS followed by an orderly number.

Subject of a dispute consists of a WTO/GATT agreement, a product or a service about which

two or more countries are arguing about. Third parties are countries other than the ones officially

involved in the dispute that request to join the consultation process due to the ties that can gave

to one of the parties involved, or because they are effected by the potential outcome of the

dispute, and thus decide to take a side.

Following Horn, Johannesson, and Mavroidis (2011) model the countries included in the

dataset were divided into five different groups3, to facilitate the representation of the differences

among WTO members in terms of their participation in the DS system. Namely the five groups

were identified as G2, IND, DEV, LDC, and BIC. G2 consists of the European Union (EU) and

the United States, with the EU being taken as the EU-15 before the enlargement. The authors

emphasize that the number of countries considered as part of the EU does not make a significant

difference quantitatively, because most EU countries have been almost inactive as individual

members of the WTO. IND includes other industrialized countries, DEV accounts for all the

developing countries that are not classified as least developed countries (LDC), and BIC stands

for Brazil, India, and China. While the LDC lists of countries is the same as the one prepared by

the United Nations, the classification of IND and DEV countries is quite different. Countries

classified under IND are OECD Members, the non-OECD Members among the 12 countries that

3 An updated list of the exact countries included in each group is included in the Appendix.

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most recently became members of the EU, which refers to countries currently at an advanced stage of

their accession negotiations, and countries that are not OECD Members but have a very high per

capita income, for instance Singapore. The significant number of cases in which Brazil, India and

China have participated, in addition to their overall participation in WTO, is the main reason for

classifying the three countries as their own group, the BIC. The rest of the countries that are not IND

or BIC were accounted for in the DEV group. When presenting the updated descriptive statistics

from Horn, Johannesson, and Mavroidis (2011), these same groups will be used.

Building on the work conducted by Horn, Johannesson, and Mavroidis (2011), the

descriptive statistics on WTO

trade disputes were updated to

include 2012 data. The bar graph

of the total number of disputes,

extended to include the period

from 1995 until 2012, shows

various fluctuations in the number

of disputes. Starting in 2004, a

consistent pattern of dispute case

trends emerges. After 2004, one

year the number of new dispute

increases, and the following year

it diminishes considerably. The greatest magnitude in the up – down trend took place between

2011 and 2012. Reading the graph enables the researcher to get a good idea of the number of

disputes and their change over time, while no information can be inferred regarding the countries

involved. Are new countries joining the trade dispute “family”? Are previous countries

Figure 1: Updated overview of the number of WTO disputes initiated each year from 1995 to 2012.

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included in trade disputes intensifying their complaints? How are the relative roles of central

countries within the organization changing over time? Crucial to understanding a body such as

the WTO, an organization of high strategic importance to the global economy, and a very

complex interdependent structure, the aforementioned questions cannot be answered by a bar

graph.

Another way of looking at the entirety of trade disputes over this period of time, as

proposed by Horn and his collaborators, is to use a case count and weighted percentages.

After updating the statistics to include 2011 and 2012 data, the group that stands out with the

highest percentage number of total dispute cases is G2, which includes the US and the EU.

Country Status Complainant % Respondent % Total %

BIC 58 12% 65 13% 123 13%

DEV 112 23% 90 19% 202 21%

G2 190 39% 229 48% 419 43%

IND 121 25% 98 20% 219 23%

LDC 1 0% 0 0% 1 0%

Total 4824 100% 482 100% 964 100%

Table 1: General statistics of trade disputes from 1995-2012 by groups.

Second in the list are IND countries with 23% of the total dispute cases as indicated by Table 1.

Developing countries (DEV) follow with 21 % of the total cases, but that does not necessarily

speak to their relative power within the organization. One reason for the relatively high

percentage weight might be as simple as the fact that developing countries compose the largest

group in terms of members within the WTO. 117 out of the 154 members of the WTO are

4 The total number of disputes is higher than 454 because some cases in the previous calculations made by et. al. were double counted due to overlapping relationships among the designated groups

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developing countries (WTO). Therefore, if we were to compare the number of cases filed by

each individual member within these groups the disproportion would be obvious. Going back to

the G2 group, the idea is that the EU and US together hold the highest number of trade disputes

filed over the considered time period, but it is not obvious which of them has the highest number

of disputes in their individual records. This information could be obtained by breaking the

percentages down further, but even then nothing would be known about the fraction of the

disputes that the EU and U.S. have been filing against each other. The latter is a proxy of the

kind of relationship between the two major economic and political players of the WTO have.

Network theory provides an interesting and practical approach in answering the aforementioned

questions in a very elegant fashion.

Knowing what the general statistics and the metrics on the properties of the dispute

network are it is much easier to understand what network theory and specifically SNA has to

Figure 2: A general overview of the WTO trade dispute network.

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offer. First, let’s have a look at a general overview of the WTO trade dispute network. In Figure

2, each node (sphere) represents a WTO member state that has been involved in at least one trade

dispute. The node colored in red is the European Union (EU), while the blue node stands for the

United States (U.S.)5. As indicated by the network map the U.S and the E.U. are the central

players in the network with everything else revolving around them. This observation is consistent

with Horn, Johannesson, and Mavroidis’ (2011) results, but it does provide additional

information about the dynamics of the relationships between the two key players in the network.

Not only do the U.S and the E.U have the highest number of trade disputes with other members,

but they are also highly interconnected with each other. The two countries have been filing the

largest number of disputes against each other. The arrows connecting the E.U. and the U.S

indicate the aggregate number of disputes each country has, as a complainant or respondent.

Additionally, using NodeXl the network can be broken down6 in order to center on the

United States. A close up view of the U.S’ piece of the network allows for a better analysis of the

U.S-E.U. relationship. After having weighted7 the network, it is clear that the major trade dispute

adversary of the U.S is the EU followed by Canada, China, Brazil, Japan, and surprisingly South

Korea. The two headed thick arrow linking the U.S and the E.U indicates that this is a

bidirectional relationship. Comparing the size of the arrow head going into the U.S to the going

out, it is obvious that the one going into is bigger, which means that the E.U has been filing a

greater number of complaints against the U.S. Interestingly enough, the majority of the questions

raised when looking at the descriptive statistics presented by Horn can be easily answered by

looked at network graphs of the trade dispute structure. Network maps identify the difference 5 The rest of the nodes have not been labeled in order to keep the picture clean, and also for the purpose of the research at this point it is not necessary to overcomplicate the network.6 This NodeXL function yields a similar result to zooming in and out in order to control the amount of detail present in the network7 Weighting is a process during which the software associates a positive or negative value with each line indicating the magnitude of the relationship’s strength between the connected nodes

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between the highly central actors in the structure, and the marginalized countries that have only

participating in a few disputes. While before it was only obvious that the U.S. was a central

actor, SNA shows that the U.S. is an antagonistic member in the international trade dispute

network. Now the implication of political variables can be factored in to explain the relationship

between the U.S. and other major actors in the global economy, for instance the E.U., China and

Brazil as a representative of emerging economies. It can be inferred from this specific network

map that the nature of relationships among this group of countries revolving around the U.S. and

their dynamics shape the effects that trade has on the global economy.

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Leveraging the subject property recorded in the initial dataset allows for the weighting of

the products or services involved in disputes according to their frequency of occurrence. Trade

disputes in which the U.S. has been involved over time include a very diverse and extended

range of subjects. This additional piece of information can be used to validate the line of

arguments built based on the previous network maps. Also, being able to look at the product

space of the entirety of countries involved enables the researcher to understand the political

agenda of the member states in terms of protectionist measures. For example, the most common

product that the U.S has been disputing about is agriculture and food products, and comparing it

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to the previous network graph, this specific relationship is with the E.U. The size of the arrow is

the clue indicating that the trading partner for that specific product is the E. U. disagreement in

the agriculture sector are mainly due to the fact that the European union heavily subsidizes

agriculture products.

Discussion and Conclusions

In sum, three network maps were enough to construct a better understanding of the DS

system database and in understanding the fundamental nature of international trade disputes.

SNA traveled from a bird’s eye view of the WTO trade dispute network, identifying central and

emerging actors, and exploring the U.S.’s strategic ties to the rest of the members in the network

to pinpointing a specific product or industry as the corner stone of an entire web like structured

affected the world’s economy. These few sentences summarize the complete 20 some pages it

took to review previous work done in this area, and uncover properties that for decades have

been lost in regression equations and a multitude of numbers.

Network theory is a very strategic tool for exploration and the study of the international

trade disputes’ structure. The results obtained through SNA tools, were compared to the results

established by the traditional statistical tools prevailing in the literature review. While regression

and other more standard methods are very well suited to conventional economic questions,

network theory offers a different perspective for the exploration of complex phenomena in a

fairly simple way. Analyzing structures like the international trade dispute system, where

relationships are the underlying foundation of economic outcomes, inquires the use of

unconventional methods. Network maps can summarize vast amounts of information, and

organize it in a fashion that serves the purpose of examining specific issues. Patterns, trends,

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clusters, weights and the network metrics nicely summarize the overall dynamics of the WTO’s

DS system. The main advantage of SNA is the visual aspect of the application. By looking at the

completed map of trade disputes information that was not easily derived before, becomes self-

evident. To a certain extent, one does not have to think about the questions. With SNA the

questions and the answers are both embedded in the network structure.

While broad in scope, this study has significant implications for future research.

Additional research might be more centered on the U.S and how its role in the WTO has changed

over time. Other political variables, including presidential terms or the political relationships

between the countries engaging in international trade disputes, and separating government

initiated disputes from business initiated ones, would be useful to consider. An interesting

application would involve isolating the trade dispute data during, for instance President’s Bill J.

Clinton term and look at the policies he implemented and how they affected the structure of the

network.

Alternatively, interesting pairs of countries, such as U.S and China, or the U.S and the

E.U could hold the key to understanding what could make the DS system work better. The next

step is to move from observing patterns in the network into explaining their origin, existence and

potential evolution in the future as well as efficiency of the DS system. Depending on the end

result of this research track, strategic recommendations could be drawn with the objective of

reforming certain WTO mechanisms to properly handle external variables that have been

stagnating the world economy by disrupting the flow of free trade.

Being able to understand the complexity of the trade ties among countries not just as

numbers, statistics, or correlations, but as dynamic relationships that determine the success of the

WTO is an approach that the current research community has not leveraged almost at all. What

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has been presented so far is an attempt to recognize the novelty of network theory and the

infinite realms of possibilities for further explorations. Network theory is a novel tool that

provides new lenses for looking at old problems with the premise of not only uncovering the

characteristics of those problems, but potentially offering unique insight that might

consequentially lead to feasible solutions.

Time has come for economists to upgrade their toolset to adopt the emerging complexity

of contemporary economic issues and for economic models to shift towards a more constructivist

approach factoring in human behavior. Before striving to predict the trajectory of economic

systems or microsystems, economists have to preoccupy themselves with accurately representing

and understanding these systems.

Data Appendix

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Groupings based on Horn, Johannesson and Mavroidis, 2011: The WTO Dispute Settlement System 1995-2012: Some Descriptive Statistics

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