POWER AND INFLUENCE: THE EFFECTS OF EMBEDDEDNESS ON

COOPERATIVE STRATEGIC DECISION MAKING

by

Debbie de Lange

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Rotman School of Management University of Toronto

© Copyright Debbie de Lange 2008

POWER AND INFLUENCE: THE EFFECTS OF EMBEDDEDNESS ON

COOPERATIVE STRATEGIC DECISION MAKING

Debbie de Lange Doctor of Philosophy, 2008

Rotman School of Management University of Toronto

ABSTRACT

This dissertation investigates whether and why social structure influences cooperative

organizational strategic decision making in an international relations context, and in particular,

similar voting in the United Nations General Assembly (UNGA). The economic and

institutional embeddedness of organizations which are operationalized using network concepts

are posited as and found to be influences. Additionally, nested institutional embeddedness is

investigated in an inter-organizational setting. Based on a sensitivity analysis, nested

organizational embeddedness can potentially have both negative and positive effects. Multiple

issues and network methodology combined with an enormous and varied data set offer a wide-

range of future research opportunities.

More specifically, trade, military alliances, diplomatic visits, and two-mode

International Government Organizational (IGO) networks affect voting behaviour in the

UNGA due to power and influence relationships that demand or encourage organizational level

reciprocity, either as vote buying in backroom bargaining situations or for compliance reasons;

maintaining the nation’s good reputation is of importance in international relations. Each type

of inter-organizational network involves an interesting theoretical twist that makes it worth

researching and while theory testing is the primary objective, outcomes include practical

implications for negotiators.

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Finally, an advantageous data set offers an excellent context for unique and successful

testing of embeddedness view concepts in tighter causal relationships compared to other

studies that observe performance rather than decision outcomes. Moreover, the methodological

approach is a demonstration of how to deal with a multi-faceted econometric challenge.

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Dedication

This work is dedicated to my deceased father, Jan H. de Lange, my inspiration and role model.

He was a modern man ahead of his time, happy he had daughters. My belief in myself is a

credit to him and I consider my life an extension of his that was cut short too early. I strive for

excellence, righteousness, and decency in his remembrance.

Secondarily, I dedicate this thesis to the faculty at the Queen’s School of Business in Kingston,

Ontario over the time that I was there for my MBA 2000-2001. It was the many virtuous people

at Queen’s who inspired me to enter a doctoral program.

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Acknowledgements

I am grateful to my thesis committee: Professors Terry Amburgey, Dan Trefler, and Mihnea

Moldoveneau for supporting me on this multi-disciplinary project. Their support has been

crucial. I would also like to thank Professor Dan Trefler for generously funding this research.

Also, I would like to acknowledge those whose doors were open to me for questions and who

offered advice and support: Professors Joanne Oxley, Ken Corts, Avi Goldfarb, and Uli

Menzefrike.

Lastly, I would like to give credit to three incredibly bright and helpful research assistants:

Grace Chan, Kasia Trzaski, and David Wright.

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TABLE OF CONTENTS

CHAPTER ONE: INTRODUCTION .........................................................................................1

1.1 Summary of Theory and Key Findings...........................................................................2

1.2 Key Contributions ............................................................................................................5

1.3 Structure of Dissertation Chapters .................................................................................8

CHAPTER TWO: THEORETICAL BACKGROUND – EMBEDDEDNESS VIEW .....10

2.1 Embeddedness Perspective ............................................................................................12

CHAPTER THREE: EMBEDDEDNESS LEADS TO POWER AND INFLUENCE.......23

3.1 Study Motivation ............................................................................................................25

3.2 Types of Embeddedness and Underlying Mechanisms ................................................28

3.3 The United Nations General Assembly .........................................................................30

3.4 Level of Analysis ............................................................................................................36

CHAPTER FOUR: RESEARCH HYPOTHESES................................................................41

4.1 Economic Embeddedness – Position in the Trade Network ........................................42

4.2 Institutional Embeddedness ..........................................................................................48

4.3 Nested Embeddedness....................................................................................................57

CHAPTER FIVE: METHODS ...............................................................................................62

5.1 Data Sources ..................................................................................................................63

5.2 Views of the Data – Mapped Networks .........................................................................66

5.3 Dependent Variable .......................................................................................................82

5.4 Independent Variables ...................................................................................................82

5.5 Control Variables...........................................................................................................84

5.6 Model Specification .......................................................................................................87

CHAPTER SIX: RESULTS ....................................................................................................92

6.1 Economic Embeddedness ..............................................................................................93

6.2 Institutional Embeddedness ..........................................................................................94

CHAPTER SEVEN: ADDITIONAL ANALYSIS.................................................................97

7.1 Causality.........................................................................................................................98

7.2 Cross-Sectional Correlation ..........................................................................................99

7.3 Influential Observations ................................................................................................99

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7.4 Repercussions of Shocks .............................................................................................113

CHAPTER EIGHT: DISCUSSION AND CONCLUSIONS..............................................139

8.1 Discussion and Contributions .....................................................................................140

8.2 Limitations and Future Research ...............................................................................149

REFERENCES .......................................................................................................................152

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LIST OF TABLES

Table1: Basic Statistics...........................................................................................................162

Table 2: Variance Decomposition – Full Sample.................................................................162

Table 3: Main Model Table of Results..................................................................................164

Table 4: Main Model Table of Results..................................................................................165

Table 18: Basic Statistics - Sensitivity Analysis using Full Sample (Results with Deletions)..................................................................................................................................................201

Table 19: Sensitivity Analysis using Full Sample (Results with Deletions).......................201

Table 20: Sensitivity Analysis using Full Sample (Results with Deletions).......................203

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LIST OF FIGURES

Figure 1: Trade Networks......................................................................................................167 Figure 2: Alliance Networks of All UNGA Countries .........................................................171 Figure 3: Diplomatic Visits Networks 1990 - 2000 ..............................................................175 Figure 4: General IGO 2-Mode Networks (Country Subset) .............................................181 Figure 5: Political Military IGO 2-Mode Networks (Country Subset)..............................184 Figure 6: Economic IGO 2-Mode Networks (Country Subset) ..........................................190 Figure 7: Social Cultural IGO 2-Mode Networks (Country Subset) .................................195

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APPENDICES

Appendix 1: List of COW Country Codes ..........................................................................205 Appendix 2: List of IGO Codes .............................................................................................210 Appendix 3: Calculations to Support IGO Network Analysis Discussion ........................221 Appendix 4: Tables and Figures for Additional Analysis...................................................222

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CHAPTER ONE: INTRODUCTION

1

1.1 Summary of Theory and Key Findings

Overall, this study makes a contribution to the theory of organizations from an

embeddedness perspective. Embeddedness examines how social relations shape behavior such

that social structure affects actions (Sacks and Uzzi, 2000; Gulati, 1998; Granovetter, 1985). It

is a contextual view that can illuminate some of the external social structural factors that

influence cooperative strategic group decisions at the inter-organizational level, reflected in

voting behavior. Decisions are intermediate steps toward actions and are affected by strong

network ties because they allow the transfer of more complex and tacit information (Uzzi,

1996; Hansen, 1999). Another attribute of strong ties is that they have a greater requirement

for reciprocity than do weak ties (Hansen, 1999). Given this view, reciprocity is tested for in

various types of inter-organizational networks.

If members of the UNGA are in trade, alliance, diplomatic visits, or IGO networks, will

they vote similarly and why would one partner of a dyad pair vote in alignment with the other?

The dependent variable in this study is “Similar Voting” by dyads of countries (countries are

viewed as organizations in this study) in the United Nations General Assembly and different

network centrality measures are used as independent variables to test for power and influence

by one dyadic partner over or on another, depending on the network as will be described.

In the case of trade networks, organizational level actors are economically embedded.

When embeddedness places economic transactions into a social context such that economic

transactions can lead to long term inter-organizational relationships that are based on

reciprocity, trust and reputation, actors are said to be economically embedded (Granovetter,

1985). Differential power dictates the reciprocal behavior of a country that would like to trade

or is already trading with another. It is in a weaker position in the trade network than its

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prominently positioned partner; voting in accordance with the stronger partner is one way that

it can equalize the desirable trading relationship; it also becomes more prominent as it builds

more trading ties. Since trade is governed by binding contracts and is simultaneous (there is

little delay between contract performance and payment), then this is an interdependent

relationship and weaker positioned parties are motivated to reciprocate by voting similarly.

Relative trade in-degree centrality is used to measure the relative network prominence of

countries in a dyad where higher in-degree centrality means a state has more countries

importing into it (more inward directed ties) than does its dyadic partner. Results from the

analysis show that as countries increasingly differ in their network in-degree centrality

positions in the trade network, representing their increasing interdependence, they are more

likely to vote together.

Inter-organizational alliance networks are also built from ties created by binding

agreements; however, there is a simultaneity problem. Mutual dependence characterizes this

situation. It is a case where actors require each other to perform, but their performance is

sequential such that one actor may benefit more than the other; in fact, if one actor completes

his obligations to the agreement, the other actor can defect by not reciprocating. This type of

relationship is riskier than one of interdependence, like trade, because of the sequential nature

of the actions and derived benefits (Molm, 1994; Axelrod, 1997). Analogous network measures

are used for alliances as those used for trade, except that they are non-directional. Trade has

directional ties – goods move in one direction or another, depending on which partner is

importing and which is exporting. The partner with higher trade in-degree centrality is the one

with more inward ties (more importing countries) and is more prominent. On the other hand,

relative alliance degree centrality is used in non-directional alliance networks because military

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alliance agreements do not generally presuppose a direction of action – each partner is

supposed to support the other in time of need. Analysis using this variable shows that as

relative alliance degree centrality increases, similar voting decreases (a negative relationship).

This unexpected result says that position in the alliance network affects voting, but not in the

predicted manner. The negative estimate on the “relative” (subtractive) measure suggests that

similarity in degree centrality results in cooperative voting i.e., countries with fewer allies vote

together. A deeper investigation is required to explain this result and presents a future research

opportunity.

Diplomatic visits create a directed network of organizational level actors visiting

others. The results using visits centrality measures imply that prominence in the network

encourages cooperative voting. Two countries that receive many visits tend to vote similarly,

whereas those countries working hard to influence by making visits are less likely to vote

together. Perhaps, the prominent countries are influential because they are in receipt of more

information and are known for this - they are known to be wise. In contrast, those making

many visits are not as successful at being influential.

IGO Connectedness – the number of common memberships in IGOs that two countries

in a dyad have – is another independent variable that is influential on voting, not because of

relative power positions, but because of influence through shared information and common

views, even homophily. IGOs are like clubs or groups and the members are interdependent

because they must all cooperate in order to meet their goals (Molm, 1994) - and, therefore, the

actions of groups tend to be simultaneous1 (Molm, 1994; Axelrod, 1997). The results indicate

that IGO connectedness positively affects similar voting. Moreover, this study also classifies

1 An exception to this is free-riding; this occurs when it is possible for a subset of the group to complete the tasks while some members defect and still reap the benefits (Albanese and van Fleet, 1985).

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the IGOs by type (Ingram, Robinson, and Busch, 2005) and it is found that connectedness of

all types: economic, political military, social cultural, and the general category of IGOs

positively affects similar voting. Consistent with the types of issues with which the UNGA

usually deals, political military IGO connectedness has the strongest effect on similar voting.

Lastly, this study considers the effects of nested embeddedness on similar voting, a case

where, for example, two countries that have many mutual memberships in IGOs also have

similar political views or cultural heritage. They are influential on each other because they are

homophilous in many ways. When nesting of embeddedness is tested through the interaction

variables, it tends to unexpectedly negatively affect similar voting; so, this represents a future

research opportunity. However, additional analysis indicates that nesting may operate

positively when influential observations are removed. Under these circumstances, results show

that when countries have high IGO connectedness and the same culture, they vote similarly.

Also, when countries of the same culture or political orientation have high connectedness in

general IGOs, they vote similarly. Consequently, nested embeddedness effects may vary

depending on the set of observations and results are equivocal.

Evidently, embeddedness in various types of networks affects decision making at the

inter-organizational level. Moreover, the results provide persuasive evidence for not only

reciprocal behavior due to relative power and influence, but also for vote buying and backroom

bargaining.

1.2 Key Contributions

The main goal of this study is to make a contribution to organizational theory by

investigating in a tighter causal linkage: 1) the effects of what I will refer to as observable

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forms of embeddedness, economic and institutional embeddedness at the inter-organizational

level, on cooperative strategic decision making and to compare the strength of economic

embeddedness to various types of institutional embeddedness - and 2) to investigate the effects

of nested embeddedness where cultural and political embeddedness, less easily observable

forms, are expected to strengthen the institutional embeddedness.

Trade is a form of the economic embeddedness of organizations, countries in this case,

and three forms of institutional embeddedness are considered: 1) military alliances, 2) common

IGO memberships, and 3) direct country-to-country visits. Drawing on the embeddedness view

and other complementary work that underpins it at the inter-organizational level, I will

emphasize the two main forces: 1) differential power and reciprocity and 2) influence through

homophily and information sharing that underlie the embeddedness that affects cooperative

decision making. Understanding these dynamics is not only of academic interest.

Organizations, whether firms or governments of countries are interested in discovering what

factors and why they affect decisions so as to discern how they may improve their bargaining

positions.

Although this dissertation uses the international arena, specifically the UNGA and

various international networks, as an empirical testing ground that seems to be at a very macro

level in comparison to firm level activity, it has implications for firms at the inter-

organizational level both directly and analogously. More directly, firms face the forces of

globalization and need to strategize with inclusion of an understanding of the world wide

linkages that result in interdependencies amongst not only firms that may themselves be MNEs

(multi-national enterprises) but, also a constellation of actors that includes multilateral

international organizations and nation states or governments (Koka, Prescott, and Madhavan,

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1999; Rugman and Verbeke, 1998). Organizations such as the UN, the WTO, international

industry groups, and other international organizations influence a wide range of countries’

national policies, often promoting world wide policies and standards that firms are pressured to

adopt or adhere to such as trade policy, ISO 9000 quality standards, environmental policy, and

international accounting standards just to name a few examples (Koka, Prescott, and

Madhavan, 1999; Guler, Guillen, and Macpherson, 2002; Rugman and Verbeke, 1998;

Reynolds, Spiller, and Salmonson, 1977). Sometimes, government sponsorship results because

an industry is perceived as strategic in terms of national competitiveness (Sakakibara, 2002).

This dissertation identifies some of the mechanisms of social structural influence on the

decision making in various international forums; firms are in an advantageous position if they

can make sense of or anticipate the complicated reciprocal relationships and negotiations that

affect the policies that will affect them. They may even be able to identify ways to become

involved to their advantage in the backroom bargaining and influence mechanisms.

From a perspective of analogy, the network power and influence mechanisms

empirically tested for in the context of the UNGA can be generalized to firm level settings such

as those of industry associations and alliance constellations. Additionally, standards setting

bodies may sometimes be akin to industry associations or alliances when they are composed of

firms having similar concerns. For example, the HDTV standard has been driven by firm

competition with government intervention because of beliefs regarding national

competitiveness (Eisenmann, 2003)2. These are situations in which firms attempt to cooperate

with each other even though outside these settings, they are not equal perhaps in an economic

(profitability, revenue, market share) sense or other manner i.e., size in terms of number of

2 However, like the CSA (Canadian Standards Association), a standard setting body may strive to appear independent from the firms and products it certifies due to public concerns such as health and safety so, in these cases they are less likely to be analogous forums.

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employees, brand recognition, etc. Thus, they are contexts of network governance, like the

UNGA, because no overarching authority exists. Since they are not equal outside these forums

the use of leverage or practicing norms of reciprocity, acting as opinion leaders, or joining

several of the same industry organizations are analogous behaviors found to be effective in the

international realm of this study.

Generally, Gulati, Nohria, and Zaheer (2000) discuss many types of strategic networks

to emphasize that various types of network positions of firms can influence their intra-industry

success. One example they use in the automobile industry is “industry blocks” such as those of

US firms like GM, Chrysler, and Ford. Each US firm forms alliance blocks with Japanese and

Korean firms but, excludes the other US firms (Gulati, Nohria, and Zaheer, 2000). A type of

reciprocity occurs within the blocks but, not outside of the blocks (Gulati, Nohria, and Zaheer,

2000). Additionally, Das and Teng (2002) write about alliance constellations where multi-firm

alliances such as Sematech require norms of reciprocity, social sanctions, and macroculture in

order to operate effectively; thus a network type of governance exists as it also does in the

UNGA, to be described. My dissertation offers additional network mechanisms to the list such

that relative power positions in firm networks that require reciprocity, opinion leaders, and

membership in industry organizations may provide advantages.

1.3 Structure of Dissertation Chapters This dissertation is organized as follows. Chapter 2 is a literature review of the

embeddedness perspective. Chapter 3 connects the literature to my main questions by

explaining how my thesis addresses gaps in the literature. As part of this explanation, the

setting is introduced and a rationale for the level of analysis is provided because of its

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connection with the setting. Chapter 4 progresses to the nine testable hypotheses which

operationalize the research questions. Chapter 5 is the methodology section and Chapter 6

reports the results of the testing. Chapter 7 is additional analysis that takes a more fine grained

approach than the model of Chapter 5 so as to delve deeper into the data. It also includes

analysis of the effects of some historical shocks. Chapter 8 concludes this document with

discussion, contributions, limitations and possible paths for future research.

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CHAPTER TWO: THEORETICAL BACKGROUND – EMBEDDEDNESS VIEW

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In organizational strategic decision making, decisions by groups of actors are not

always as transparent as we would idealistically like them to be because of external influences

that occur prior to the formal meetings during which the decisions are officially made (Baehr

and Gordenker, 2005). The embeddedness of the organizational actors in their external

networks affects their decisions. Several forums, like meetings of boards of directors, union

negotiations, government parliaments, and world-wide organizations experience this

phenomenon affecting decision outcomes and, ultimately, organizations’ policies and actions

(Kuziemko and Werker, 2006; Davis, 1991; Davis and Greve, 1997). For example, directors of

firms are often members of clubs; at these clubs, they may have discussions and dealings. They

may, in fact, be members of many clubs and through these social networks, they may gain

information that they believe is valuable enough to affect their decisions, consciously or

subconsciously (McPherson, Smith-Lovin, and Cook, 2001). Thus, embeddedness effects are

highly consequential to all of our organizations.

Often, the hidden dealings amongst actors are referred to as backroom bargaining and

part of this can be vote buying, which affects decision outcomes (Kuziemko and Werker,

2006). Backroom bargaining is taken for granted but, it has been extremely difficult to find

evidence of it via academic studies because the dealings are not recorded and not observable.

This study uses network methodology to uncover the embeddedness effects on actors who

make important strategic decisions. Although many do not consider embeddedness a theory, it

is a well-recognized perspective that I will review in the next section. I would suggest that it is

a view that may become a theory as more contributions to it are made.

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2.1 Embeddedness Perspective

Embeddedness examines how social relations shape economic behavior such that social

structure affects economic action (Sacks and Uzzi, 2000; Granovetter, 1985). Decisions are

intermediate steps toward actions. Consequently, it is a contextual view that can illuminate

some of the external social structural factors that influence cooperative strategic group

decisions, reflected in voting behavior. Within the strategy literature, many scholars agree that

a more contextualized concept of strategy will better reflect the reality of how strategy is

devised (Baum and Dutton, 1996; Pfeffer, 1987). Moreover, embeddedness is evolving into a

theory that offers an alternative to transaction cost economics, a main difference being that

actors do not act atomistically outside of a social context (Granovetter, 1985) and with wide

ranging implications as will be further explicated. However, a recent review of the

embeddedness literature compares Granovetter’s (1985) embeddedness view to that of earlier

work by Polanyi [1944 (2001)] (Krippner and Alvarez, 2007). It suggests the two scholars’

contextualized approaches are different, such that Granovetter takes an exterior view whereas

Polanyi takes an interior view, as will be explained further and discussed in this section

(Krippner and Alvarez, 2007). Before explaining why this view is contextual and linking it to

decision making, I will provide more description including the theoretical environment, levels

of analysis, actor rationality and governance, some unique concepts, and implications.

Embeddedness theory presumes that the environment challenges actors to survive and

demonstrate their comparative advantage (Jones, Hesterley, and Borgatti, 1997). The

environment includes a variety of actors who are interlinked such that there is little distinction

between the environment and organizations. Organizations gain benefits from each other -

from their interconnectedness - and from their positions with respect to each other in the

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network; the network is both a conduit and a form of social capital (Burt, 1992; Gulati and

Gargiulo, 1999). Embeddedness is much more a theory of interdependence than dependence

such that networks of actors are the environment. Also, there is a dark side to embeddedness -

actors can be overly embedded, constraining instrumental action (Gulati and Gargiulo,1999); it

can decrease diversity and non-redundant information (Uzzi, 1997).

From this perspective, actions and outcomes within the environment are affected by

actors’ dyadic relations and by the structure of the overall network of relations that includes

trust and information sharing (Jones, Hesterley, and Borgatti, 1997). Gulati (1995) provides an

example when he discusses the antecedents for alliances. He says that trust is a “type of

expectation that alleviates the fear that one’s exchange partner will act opportunistically”

(Bradach and Eccles, 1989:104); the idea is akin to Simmel’s notion of mutual faithfulness in

social relationships (Simmel, 1978: 379). Gulati (1995) explains that knowledge and deterrent

based trust are built through repeated alliances between firms3. Alliances may begin as equity

ties, but over time, future alliances will not require hostages and administrative governance;

moreover, trust offers more flexibility such that alliances are less constrained.

This theory works at three levels of analysis: interpersonal, inter-unit, and inter-

organizational (Brass, Greve, and Tsai, 2004). When behavioral assumptions are at the inter-

personal, inter-unit, and inter-organizational levels, individuals are not the focus; rather, the

focus is on relational ties. For example, firms act collectively rational and therefore reciprocate

and build trust through different processes (Doney, Cannon and Mullen, 1998; Gulati, 1995).

3 Gulati (1995) compares knowledge-based and deterrent-based trust. Knowledge-based trust is based on repeated interactions and relationships whereas deterrence-based trust is based on deterrent sanctions and loss of reputation. Sanctions are imposed if the partner does not act reliably and in good faith; on utilitarian grounds, it is to the firm’s benefit to behave in a trustworthy manner. Most firms are embedded in a social network of prior alliances so the negative consequences exist; a problem for research is that trust is taken-for-granted and is difficult to observe.

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Also, actors are expertly rational because they are connected to others who have information;

the network offers them both information (access, timing and referrals) and control benefits

(tertius gaudens, entrepreneurial, motivation) (Burt, 1992). Sometimes cohesive relationships

are sought after and other times structural autonomy is desired – there is a debate in the field as

to which is the most beneficial and under what circumstances but, progress has been made

(Burt, 1992; Brass, Greve, Tsai, 2004).

As mentioned, this view assumes that actors are collectively rational which has

consequences for network governance. Actors with a collectively rational perspective work to

achieve pareto-improved solutions and to maintain their relationships through reciprocity

(Sacks and Uzzi, 2000). Moreover, network forms of governance are a response to exchange

conditions of asset specificity, demand uncertainty (generated by seasonality), task complexity,

and high frequency of interactions4 – these exchange conditions lead to structural

embeddedness of transactions. Accordingly, social mechanisms are incorporated into

governance; they are used for coordinating and safeguarding exchanges and they include:

restricted access, macro-culture, collective sanctions, and reputation (Jones, Hesterley, and

Borgatti, 1997).

When theories of governance are discussed, transaction cost economics (TCE)

inevitably comes up. Embeddedness either challenges or extends TCE, depending on one’s

preference. Due to the linkages amongst actors in the embeddedness view, as mentioned,

4 Frequency of interactions: 1) facilitates the transfer of tacit knowledge; 2) establishes the conditions for relational and structural embeddedness which provide the basis for social mechanisms to adapt, coordinate, and safeguard exchanges effectively; and, 3) provides cost efficiency in using governance structures (Williamson, 1985); frequency allows human asset specificity to develop from learning by doing (Williamson, 1991) (Jones, Hesterley, and Borgatti, 1997).

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pareto-improved solutions are sought such that the TCE assumption of opportunism

(Williamson, 1975) becomes inappropriate because this behavior is punished in networks

through the ability to sanction members who abuse their network partners (Uzzi, 1997; Jones,

Hesterley and Borgatti, 1997). Moreover, greater exchange intensity in a close business

relationship, in the embeddedness approach, motivates expectations of providing better

services than what is set out in the contract (Uzzi, 1996) but, this is not necessarily so in TCE.

In TCE, when a transaction becomes a small-numbers bargaining situation, then the buyer or

seller opportunistically takes above-market rents or shirks, based on his or her self-interest

(Ghoshal and Moran, 1996). The embeddedness view interjects that reciprocity rather than

calculativeness becomes important in the network not only out of fear of sanctions but also due

to the value of reputation (Sacks and Uzzi, 2000). Generally speaking, human intentions are

variable in the embeddedness view rather than a constant as assumed in the TCE approach so

embeddedness allows for a greater range of possibilities and therefore, greater realism.

The field of economic sociology, through the use of embeddedness conceptualization,

has managed to rebuff other theories’ simplifying assumptions such as those of TCE that

under-socialize economic systems through concepts of the atomistic, self interested actor who

is a born deal-maker working in a self-regulating market that exists within an autonomous

economy (Krippner and Alvarez, 2007; Granovetter, 1985; Smith [1776(1979)]). However, in

order for the embeddedness view to mature and become a theory, it must be able to define

itself without sole reliance on explicating how it stands out against other theories and,

according to a review by Krippner and Alvarez (2007), it is this problem that embeddedness

has to solve such that it can generate a positive research program.

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In attempts to move the view forward toward becoming a theory, the review points to a

particular problem that there is an “external” Granovetterian view and an “internal” Polanyian

view that seem difficult to reconcile (Krippner and Alvarez, 2007). The “external” view is one

that addresses the relational bases of social action in economic contexts and the “internal” view

is one of the integration of the economy into broader social systems (Madhavan, 2008;

Krippner and Alvarez, 2007). Although the review recognizes the different levels of analysis

that Granovetter and Polanyi work at – Granovetter being micro and meso whereas Polanyi is

macro - this is not the main differentiation that the authors choose to stress and on this point, I

tend to agree with them (Krippner and Alvarez, 2007). However, while I see that Polanyi’s

view may be appropriately called “interior”, I do not think that Granovetter’s is properly

labeled “exterior”. Moreover, I disagree that the two scholars are describing different points of

view; they are reconcilable and this is so because I disagree that Granovetter intends to express

a view of social relations as shaping economics from the outside (Krippner and Alvarez, 2007).

First, I’ll explain why Granovetter’s view is not “external” and then link the Polanyian and

Granovetterian views.

Granovetter views social relations as intertwined with economic transactions, rather

than as external to them and I will use his own words as solid evidence of this.

“In a general way, there is evidence all around us of the extent to which

business relations are mixed up with social ones. The trade associations

deplored by Adam Smith remain of great importance. It is well known

that many firms, small and large, are linked by interlocking directorates

so that relationships among directors of firms are many and densely knit.

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That business relations spill over into sociability and vice versa, especially

among business elites, is one of the best-documented facts in the

sociological study of business…” (Granovetter, 1985).

His discussion carries further evidence including mentions of trust, cooperation,

antagonism, and the list continues (Granovetter, 1985). These are aspects of relationships that

are intertwined in the economic relationships, not forces from outside. I would like to take his

last comment from the above quotation a step further and suggest that business relations are a

type of social relation simply because it is humans or human organizations that are involved in

them. It is impossible to separate any type of action or systems that humans engage in with

each other from the social world. We separate love from friendship relationships; similarly,

economic relationships are just another type of tie. Bringing in network concepts that will be

explained in more detail later, it is the tie contents or the type of tie that joins actors and that

defines the relationship type, is it not (Wasserman and Faust, 1994; Burt, 1992)? The nodes of

these networks are always humans at the individual level or human created entities at the

organizational level. In this dissertation, nation states and IGOs are the nodes; nation states are

geographic areas carved up based on political boundaries that humans have determined having

governments that are run by humans. IGOs are human organizations. Regardless of whether the

nation states are engaging in trade via their firms – economic transactions are the ties – or

alliances – institutional ties – the relationships are sociological because humans must be

involved in them in some manner.

Thus, I now bring the Granovetterian and Polanyian views together. From a macro

view, economics is a human system; thus, as Krippner and Alvarez (2007) describe Polanyi’s

17

view, economics is interior to the social world. However, precisely because economics is a

social system, sociology is intertwined in the economic world at the micro and meso levels. So,

the two views are part of each other and do not make sense without each other because they are

the same, only coming from different directions; one is top-down (macro), the other bottom-up

(micro and meso).

The “solution” to the “problem” that economic sociology purportedly is confronted

with, according to Krippner and Alvarez 92007), stares us in the face. The reason this debate

arises – a debate that assumes that somehow one system created by humans may be separated

from others and these others need to define themselves i.e., in this case, economics is separated

from sociology and needs to define its agenda vis-à-vis economics, according to Krippner and

Alvarez (2007) - is because of our need to compartmentalize the world; we break it down into

parts so that we may analyze the parts in feeble attempts to understand the parts and perhaps,

the whole. The world is interconnected and, as theorists in the social sciences, we have to

recognize that our theories will often emphasize one aspect of the human world over others

because of our limited ability to understand the world all at once in its entirety at any particular

time. The view that there is a division between sociology and economics is socially constructed

– it is only in our minds and then because of the macroculture that is created, it becomes a self-

fulfilling prophecy that we allow to become institutionalized. The solution is that we simply

have to use our minds and make choices to change the lens that we don’t notice exists – after

you’ve been wearing glasses for a while, you forget they’re sitting on your nose. Look in the

mirror and see that you’re wearing glasses. Don’t like the frames? The lenses are no longer

clear enough? Then, get a better pair of glasses; but, of course you will not likely ever have

perfect vision, even with the correction.

18

Additionally, some aspect of the human world may be emphasized because powerful

elites prefer that we choose a particular focus (Granovetter, 1985; 484). In recent times and

perhaps since Adam Smith, many believe in a taken-for-granted ideology that to act self-

interested, even opportunistically, is human nature; however, this is not new to the field of

strategy, whether in military or business strategy - to “divide and conquer” is an old tactic –

essentially, to place our own needs over all others fragments us; it is convenient for our

employers since we work harder for the reason that we do it for ourselves, but, this ultimately

benefits our employers since we are not paid what our labour is worth or else no profit could be

made. This ideology replaces older belief systems that were also formerly not to be questioned.

Human history is rife with powerful elites inculcating the masses with various belief systems

that facilitate their control over them. Religions are examples and the belief in “the market” is

no different. To some, I am a heretic to write of such a view but, so were others in olden times

who challenged the dominant myths. Our maintenance of academic freedom and freedom of

speech is our recourse in the face of ideology; ideology is not truth and therefore, has no place

in academia; however, it seeps in.

Next, after this commentary about the wider problem that the embeddedness view

faces, according to Krippner and Alvarez (2007), I will return to the description of the

embeddedness view as it stands today. Later, I will revisit the contribution that my research

makes in regards to this debate in economic sociology aside from this commentary.

Other unique concepts of embeddedness include: 1) structural homophily, which refers

to actors that are similar in centrality, and 2) structural differentiation, an emergent systemic

property that captures the extent to which actors come to occupy an identifiable set of network

positions, each characterized by a relational profile (Gulati and Gargiulo, 1999).

19

Some implications are that this theory 1) blurs the boundaries of the firm, 2) recognizes

a new constellation of forces as crucial to economic success i.e., government, higher education,

skilled labour pool, research institutes and, results in 3) the spread of technologically advanced,

smaller units of enterprise – expansion occurs through various cooperative interorganizational

relationships (Powell, 1990).

Now, I will address the question that asks why embeddedness is a contextual view and

how context matters to decision making. The view is supported by the body of knowledge of

social networks and operationalized using network methods. Embeddedness can not only be

relational, reciprocal, positional and structural, but also nested. Relational embeddedness refers

to the dyadic tie between two actors and can be unidirectional or bi-directional (Jones,

Hesterley, and Borgatti, 1997). Reciprocal embeddedness refers to the idea that actors re-shape

networks and create new ties (Dacin, Ventresca and Beal, 1999). It refers to the active process

of network building (Gulati, 1998; Dacin et al., 1999).

Structural and positional embeddedness have been discussed and seem somewhat

confused. Essentially, they are notions of the effects of networks on actors beyond the dyad.

Gulati and Gargiulo (1999) describe positional embeddedness using concepts that describe or

compare actors’ positions in the networks, such as equivalence and centrality; whereas,

structural embeddedness is used in reference to indirect ties (Gulati and Gargiulo, 1999).

Indirect ties impact the network through common third party effects which lead to concern for

reputation and opportunities through referrals (Gulati and Gargiulo, 1999). However, Jones,

Hesterley, and Borgatti (1997) discuss structural embeddedness in reference to the network’s

overall structure or architecture; it influences behavior through social control and it provides

the foundation for social mechanisms such as restricted access (knowing with whom to

20

exchange and who to avoid), macroculture (the common values, norms and beliefs shared

across firms), collective sanctions (to condemn or ostracize perpetrators), and reputation

(Jones, Hesterley, and Borgatti, 1997). Furthermore, Zukin and DiMaggio (1990: 18) use the

term “structural embeddedness” more loosely, relying on Granovetter’s conceptualization: it is

“a contextualization of economic exchange in patterns of ongoing interpersonal relations”

(Granovetter, 1985). For the sake of simplicity and to avoid confusion, I am going to call

embeddedness that considers network structure beyond the dyad “structural embeddedness”

because positions in the network only exist as a result of structure.

Finally, nested embeddedness, rather than a network property is an embeddedness

concept that has not been well-defined in the literature since it has not been much explored

(Dacin et al.,1999). Dacin et al. (1999) describe nested embeddedness as different types of

embeddedness that moderate one another. A more recent effort has suggested that the

definition is “an individual being in a nested structure of institutional layers…” (Kenney and

Goe, 2004). However, this definition is very specific to the paper and Dacin et al.’s is very

general. The interpretation in this paper will be the more general one since nestedness does not

imply a particular level of analysis – only that one type of embeddedness may create a context

affecting another type of embeddedness, thus the concept of a moderating influence is

consistent.

Context affects decision making because of actors’ ties. Ties are the important linkages

ultimately building the network context so it is important to explain some characteristics of

ties. A tie establishes a relation between a pair of actors (Wasserman and Faust, 1994) and ties

may have different strengths. Tie strength is consequential for influencing strategic decisions

because they involve highly complex situations where incomplete information is the rule

21

(Cyert and March, 1963; Simon, 1986). Weak ties tend to be valuable for exploration and

discovery (Granovetter, 1973; Burt, 1992); they are best used for discovering new information

that is not complicated or tacit (Hansen, 1999). In contrast, strong ties tend to be influential in

strategic decision making because of the imperative for the transfer of more complex and tacit

information (Uzzi, 1996; Hansen, 1999); strong ties enable actors to convey fine-grained

information and facilitate joint problem solving (Uzzi, 1996). They also have a greater

requirement for reciprocity (Hansen, 1999).

Finally, ties which create context affect decision making because they change the type

of actor rationality that can be assumed. Actors are collectively and expertly rational rather

than boundedly rational (Simon, 1997; Sacks and Uzzi, 2000; Williamson, 1975) or fully

rational (Axelrod, 1997) when they make decisions. While decision makers use heuristics and

are boundedly rational in information processing theory and transaction cost economics (TCE)

because of their isolated nature, they are expertly rational in the embeddedness view because

they have ties that reduce the boundedness through improved information transfer and

evenness of information distribution within the network (Sacks and Uzzi, 2000; March, 1999).

When a board of directors or the UN General Assembly makes a strategic decision, the actors

of these organizations cast their votes not only with the information that they have heard at the

formal meeting, but also with all the information that they have derived from their networks of

strong ties.

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CHAPTER THREE: EMBEDDEDNESS LEADS TO POWER AND INFLUENCE

23

Embeddedness has many types that arise in many forms and therefore, it affects

decisions for different reasons through different mechanisms and combinations thereof. In this

paper, I examine differential power and influence mechanisms as the main external drivers

related to embeddedness that affect decision making; analogously, Burt (1992) refers to the

beneficial effects of structural embeddedness in terms of control and information. Here, I am

interested in economic, institutional, political ideological and cultural types of embeddedness.

The terminology “political embeddedness” has been used to refer to ties between economic

actors and non-market institutions (Zukin and DiMaggio, 1990: 20), but I am using it as a short

form for political ideological embeddedness. The linkages are based on shared political

ideology rather than institutional ties.

By forms of embeddedness, I mean that economic embeddedness could occur because

of trade or foreign aid ties, for some examples. Institutional embeddedness could take the form

of alliances or common IGO memberships. These forms will be further discussed and the

differing mechanisms will be tied to the forms in the later development of the theory and

hypotheses.

First, I explain what motivates this study. Also, I explain the types of embeddedness of

interest in this paper and outline some underlying mechanisms that result in social structure

affecting cooperative decision making. Next, I will describe the setting and clarify the level of

analysis; the paper deals with an organ of an IGO (the UNGA) within which there are actors

who represent governments of states, debatably organizations themselves.

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3.1 Study Motivation

Why do state actors vote cooperatively in the United Nations General Assembly (UNGA)? In

the networks and social psychology literature, external social structural influences on group decision

making and cooperation have been of interest (Mizruchi and Potts, 1998; Kameda, Ohtsubo,

Takezawa, 1997). However, there are substantial theoretical and empirical gaps in examination of how

embeddedness affects strategic decision making at levels above that of the individual in the

management literature and this is especially true in international settings (Granovetter, 1985; Pfeffer,

1987). The area of international relations, that studies strategic voting, has not applied embeddedness

or network analytic approaches which together can uncover these more difficult-to-observe social

structural effects (Voeten, 2000, 2001, 2005; Rosenthal and Voeten, 2004; McCarty and Poole, 1995;

Weisberg, 1978). This cross-disciplinary approach of applying organizational theory to an

international relations setting is relatively unique (except for Ingram, Robinson and Busch, 2005;

Hafner-Burton and Montgomery, 2006); even though international organizations, such as the UNGA,

are excellent contexts for organizational theory testing. Consequently, the focus of this paper is to

contribute to organizational theory and development of the embeddedness view. More specifically, it

studies embeddedness effects on cooperative strategic decision making within an organization

composed of actors representing governments of states, arguably an inter-organizational setting.

Scholars suggest that further development of the embeddedness view, a developing

theory that considers economic action in the context of social structure, would include

investigating the content of ties, the intensity of relationships, and reciprocal embeddedness

(Dacin et al., 1999). This study will consider economic and institutional types of reciprocal

embeddedness which involve ties having different contents and different timing between

building the tie and acting on it. Economic embeddedness involves the immediate exchange of

25

goods and services for monetary compensation amongst trading partners. In contrast, the ties of

institutional embeddedness are built on common interests and include international alliance

agreements, IGO memberships, and visits by one country to another. These ties have

disconnects between the initiation or existence of the tie and action based on having the tie;

they are less reliable. In any of these cases, the intensity of the ties may be considered.

Moreover, little study has been done of the linkages and cross-level mechanisms of

embeddedness including the presence of multiple mechanisms of embeddedness or a nested

view (Dacin et al., 1999). This study can test nested embeddedness because of the political and

cultural embeddedness within the international setting that moderates the institutional

embeddedness.

Two main mechanisms creating reciprocal embeddedness are considered, each relying

on different types of tie contents that lead to different relationship dynamics. One is power

oriented, positing that relationships of interdependence and dependence lead to relationships

involving reciprocity that occurs outside of these relationships e.g., issue-linkage (Axelrod,

1997; Axelrod and Keohane, 1985; Molm, 1994). This mechanism operates in the case of

economic embeddedness. The other is influence oriented based on either similarities amongst

actors, referred to as homophylic influence, and / or information sharing. Actors sharing

similarities, such as similar social positions, will share enough commonalities that they will

develop similar world views, primarily through information sharing (Winquist and Larson,

1998). So, this latter mechanism is not based on the leverage that one actor has over another;

rather, it emphasizes information sharing that builds common views and leads to similar

decisions. This is the case more often for institutional embeddedness.

26

The international setting offers an environment conducive for testing the nested effects

of cultural and political embeddedness. The similarity of states on these dimensions is expected

to facilitate communication pathways such that they can develop common understandings more

easily because they already have a basis for it; it is the effect of homophily, but tested at the

inter-organizational or inter-governmental level rather than the oft-examined individual level

(McPherson, Lynn Smith-Lovin, and Cook, 2001; Burt, 2000; Brass, 1984). Furthermore,

because of the smoother relationships, they will tend to interact more, further enhancing and

building a shared set of views that result in similar voting (Axelrod, 1997; Borgatti and Foster,

2003). Cultural and political embeddedness facilitate the relational embeddedness of

institutional networks through improved communication leading to shared views that

ultimately build stronger ties that influence decision making (Hansen, 1999).

Also, the decisions of the UNGA are accurately recorded public information in the

form of roll call votes so rather than testing the effects of embeddedness on survival or

performance outcomes as is often the case (Schilling and Phelps, 2005; Galaskiewicz, Hager

and Larson, 2004; Uzzi, 1996; Burt, 1992; Baum and Oliver, 1991; Minor, Amburgey and

Stearns, 1990), the effects can be tested on the decisions, a closer link in the chain of causality

i.e., embeddedness affects the decisions actors make which lead to actions and ultimately,

performance outcomes. Social structure governs many intervening processes that regulate key

performance outcomes (Uzzi, 1996).

The context of this study is useful for investigating all of the aspects under study here at

a level beyond that of the individual. Since UNGA countries have several types of networks

with different contents of network ties and varying network positions, the four types of

embeddedness and their underlying mechanisms may be investigated here.

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3.2 Types of Embeddedness and Underlying Mechanisms

Economic and institutional embeddedness are more easily observable than the other

types mentioned and may involve mechanisms of either differential power or influence through

homophily and information sharing, depending on the forms they take. In contrast, political

ideological and cultural embeddedness are difficult to observe with certainty and are based on

value homophily which tends to lead to influence rather than differential power. Value

homophily (McPherson, Smith-Lovin, and Cook, 2001) is the general term for the mechanism

underlying these two latter types of embeddedness. Value homophily is “based on values,

attitudes and beliefs…” and “…includes a variety of internal states presumed to shape our

orientation toward the future behavior.” (McPherson et al., 2001; 419).

When embeddedness places economic transactions into a social context such that

economic transactions can lead to long term relationships that are based on reciprocity, trust

and reputation, actors are said to be economically embedded (Granovetter, 1985). Institutional

embeddedness occurs when actors are tied through any type of institution5. Notice that these

types of embeddedness, economic and institutional, have observable ties. Ongoing trading

relationships can be traced monetarily. Institutional linkages are recorded or reported in

documents. For example, military alliances are formally documented, IGOs have membership

lists, and visits between countries are reported by the press (King, 2006).

One mechanism that may underlie embeddedness is based on differential power. Tied

actors may act independently, dependently, in a mutually dependent fashion or

5 Many views of what an institution is exist. Two definitions are provided here. “Institutions are composed of cultural-cognitive, normative, and regulative elements that, together with associated activities and resources, provide stability and meaning to social life.” (Scott, 2001;48) Alternatively, an institution is, “a complex of status-role relationships which is concerned with a particular area of activity within any specified social system (total or partial).” (Kaplan, 1960; 179)

28

interdependently. Dependence and mutual dependence imply power imbalances caused by

inherent strengths and weaknesses as well as the timing of reciprocity. One actor may have

more to gain from the relationship than the other because it is inherently weaker in some

manner i.e., in a military alliance, the weaker actor may have poor combative capabilities or

low technology equipment and thus, be dependent. Mutual dependence is a case where actors

require each other to perform, but their performance is sequential such that one actor may

benefit more than the other; in fact, if one actor completes his obligations to the agreement, the

other actor can defect by not reciprocating. This type of relationship is riskier than one of

interdependence because of the sequential nature of the actions and derived benefits (Molm,

1994; Axelrod, 1997). Interdependence is less risky because it implies a group-like dynamic. In

the case of groups, members must all cooperate in order to reap benefits (Molm, 1994) - and,

therefore, the actions of groups tend to be simultaneous6. Interdependence in groups tends to

involve more equal levels of reliance and simultaneity in the sense that no benefits go to any

actor in the group until all actors have made their expected contributions (Molm, 1994;

Axelrod, 1997). In fact, interdependence and the resultant cooperation build social capital; the

interdependence maintains the structure of the relationships (Walker, Kogut, and Shan, 1997).

Other types of embeddedness operate through homophily and information sharing. Few

studies have applied the concept of homophily at the inter-organizational level. One study by

Li and Berta (2002) found that “investment banks conform to the homophily phenomenon…”

(Li and Berta, 2002). Political and cultural embeddedness occur when states align themselves

based on shared values, or value homophily. In international relations, the idea that homophily

creates shared views is put into different terminology. For example, according to Huntington

6 An exception to this is free-riding; this occurs when it is possible for a subset of the group to complete the tasks while some members defect and still reap the benefits (Albanese and van Fleet, 1985).

29

(1996), the post-cold world may be multipolar based on cultural differences. During the Cold

War, the world was generally accepted as bipolar based on political differences – there existed

a duel between democracy and communism (Russett and Oneal, 2001; 60, 92, 111). The value

homophily joins the states like a macroculture and no where is it officially recorded that this is

the reason for their alignment. Zukin and DiMaggio (1990:14-22) offer a definition of cultural

embeddedness as “the role of shared collective understandings in shaping economic strategies

and goals”. Alternatively, it has been defined more generally as, “the ways shared

understandings and meanings come to give form to organization activity, structures and

process.” (Dacin et al., 1999; 328)

What has been presented is an overview of the relevant concepts of the embeddedness

view, explanations of the different types of embeddedness, and the differing mechanisms that

may underlie each type of embeddedness. In developing the theory and hypotheses, I will

discuss why certain mechanisms accompany the different forms of the different types of

embeddedness to explain why they will affect decision making. The mechanisms generate

differential power and/or influence which explains why the embeddedness has an effect on

decision making. First, a description of the setting is provided because the choice of forms of

embeddedness is dependent on it.

3.3 The United Nations General Assembly

The UNGA, post-Cold War 1990-2000, offers a unique setting for investigating social

structural factors that affect cooperative decision making at the inter-organizational level. A

lack of transparency in explanations for UNGA decisions elicits questions about what factors,

including external social structural ones, may affect the vote outcomes. Studying the

30

networked relationships may reveal some of the underlying reasoning for the UNGA vote

outcomes in a post-cold war world that has no clear divisions or hierarchy to instruct it. Before

explaining this and the boundaries imposed by this setting, I will provide some information

about the UNGA.

The UN General Assembly that began in 1945 with 50 country members, today after

over 60 years, has 191 members who hold regular sessions. Its membership is geographically

distributed as follows: Western Europe 25 (13%); Eastern Europe 22 (12%); Americas 35

(18%); Africa 52 (27%); Asia 43 (23%); Australia and Pacific 14 (7%) (Baehr and Gordenker,

2005). It is a "parliament of nations" that holds its session from September to December of

each year. When necessary, it may resume its session or hold a special or emergency session

on subjects of particular concern. It meets to consider the world's most serious problems and

has an agenda of more than 150 items including: conflicts in the Middle East, economic

development, protection of the world environment and support of human rights (Baehr and

Gordenker, 2005). Below, is a list of the topics addressed over the 1990-2000 period, roughly

categorized for simplicity. For each topic, the number of times the issue was voted on is listed

under “Vote Frequency”. For example, votes on issues related to the Middle East were most

frequent – 237 times.

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1990-2000 UNGA Topics

UNGA Issue Areas Vote Frequency

Middle East 237 Disarmament 166 UN Principles 137 Human Rights 95 Peace Keeping 29 Africa 21 Outer Space 19 Caribbean 15 Economic 15 UN Internal Issues 15 Science & Technology 14 Eastern Europe 9 Antarctica 6 Europe 5 Asia 3 Terrorism 2 Environment 1

Its resolutions, or decisions, are approved by majority votes, consensus, acclamation, or

adoption without a vote (Baehr and Gordenker, 2005). Each Member State has one vote and

while most votes are decided by a simple majority, decisions on issues regarding international

peace and security, admitting new members, and the UN budget require a two-thirds majority.

Countries may vote “yes”, “no” or “abstain”. A majority is calculated based only on votes cast

as either “yes” or “no” votes. States are not legally required to act on resolutions, but the

recommendations of the UNGA are an important indication of world opinion and represent an

international moral authority (Baehr and Gordenker, 2005).

Some issues and points about the UNGA procedures provide further background. First,

there is debate about whether there should be weighted voting so as to be more democratic i.e.,

according to country size of population or other size criteria, because a small country’s vote is

equal to a large country’s vote (Baehr and Gordenker, 2005). Also, diplomacy takes place

outside of the discussions in the assembly i.e., debating in the assembly is called “conference

diplomacy” or “parliamentary diplomacy”, to the extent that decisions are often made outside

32

the council chambers, away from public scrutiny, sometimes in group meetings of various

compositions i.e., regional (Baehr and Gordenker, 2005). Some governments try to build

support for their views in these forums to gain a majority vote in the Assembly (Baehr and

Gordenker, 2005). Although the process is not transparent to the public, it is necessary

behavior for governments to negotiate and find compromise (Baehr and Gordenker, 2005).

Given that the behind-the-scenes negotiation is a well-known phenomenon amongst

UNGA members, I may refer to vote buying or reciprocity that in a wider sense is the

reciprocal embeddedness of these nations. While in many settings vote buying is generally

accepted, it has not been extensively tested empirically because the bargaining is unobservable

(Wiseman, 2004). Whether the behavior is either an overt trading of favours (vote buying) or

the more subtle return of a favour based on an implicit obligation both are types of cooperative

behavior or reciprocity difficult to observe and disentangle. This study seeks to find evidence

of difficult-to-observe cooperative behavior but it cannot disentangle the types.

Reciprocity enmeshes countries into a complicated interweaving of embedded

relationships of mutual dependence and interdependence. When a state votes, it makes

tradeoffs regarding these relationships – pleasing one state over another could have trade,

military and many other types of consequences, potentially strengthening and weakening these

ties at the same time or even changing its position and ultimately its power and influence in an

array of types of networks. While decisions are ultimately self-interested and moderated by

various types of embeddedness, they are made with the limited information that state officials

have and can process – the international context is complicated and there is no such thing as

complete information. Information is uneven in networks of nations, depending on their

positions in them and the strength of their relationships. Even so, the decisions that the UNGA

33

countries make have consequences for themselves and the states embroiled in the issues. For

example, UN Secretary-General Kofi Annan recently proposed a global counter-terrorism

strategy to the General Assembly and faced difficulties in finding consensus on it – not only

does this proposal seek to deter terrorism, but a major piece of it is related to defending the

human rights of all, including those suspected of terrorism; many states regard human rights to

be a US priority and will resist these principles partially because they don’t practice them

(Voeten, 2004; UN website).

Although the UNGA makes impactful decisions, there are no binding laws or formal

hierarchical governance structures for transnational institutions and their member states. States

can choose to join the UN and may not abide by its rules; for example members may not pay

their dues and arrears (Halifax Summit, 1995; Baehr and Gordenker, 2005). Therefore,

international institutions only exist within a network form of governance. Consequently, it is

social mechanisms, such as restricted access, macroculture, collective sanctions, and reputation

(Jones, Hesterley and Borgatti, 1997) as well as economic sanctions that regulate states’

behavior.

Moreover, the world seems more chaotic, post-cold war. During the Cold War, while

there was a strong non-alignment movement, many of these countries were aligned on some

level with one of the great powers. The alignment was so polarized that voting reflected this

and other influences of interest here were strongly affected by these loyalties (Voeten, 2000).

For example, US allies traded with each other and joined similar institutions so both economic

and institutional embeddedness were determined by the Cold War allegiances (Russett and

Oneal, 2001). After the Cold War, the landscape is not as clear and while there are a number of

theories in terms of how the powers are aligning (Voeten, 2000), these ties may not be so

34

strong as to override the effects of interest in this study. Voting will be affected by other

factors as well.

The boundaries for this investigation relate to group size and frequency of convening.

In the UNGA, members have some social proximity and recognize their mutual memberships

in some of the same networks which build external linkages amongst them. For example,

countries are members of IGOs or they may be trading partners; they may interact simply

because they are geographical neighbours impacting each other through their local actions or

sharing common concerns. They also expect to engage in future votes on a relatively regular

and frequent basis i.e., at least more than once per year. Thus, social structure amongst the

UNGA members exists. An analogous situation, of frequent study, has been roll-call voting in

the US Congress (Platt, Poole and Rosenthal, 1992; Poole, 1988). In this setting, influential

social structure exists amongst congressmen who represent their states. It has been found that

congressmen influence each others’ voting by talking to each other (Weisberg, 1978). This

setting is contrasted with a relatively disconnected electorate or crowds of people who could

also be considered groups; however, these types of groups do not purposely meet to vote or

affect decisions on a regular basis. These types of large groups are composed of actors too

disconnected to recognize social network consequences; they act much more autonomously

than do actors in settings such as the UNGA or the US Congress.

Furthermore, there is a strong membership aspect that creates social structure by

promoting norms of behavior (Axelrod, 1997). The UNGA has formal requirements for

membership such that a state must be peace-loving, it must accept the obligations contained in

the Charter and the judgment of the organization, it must have the ability and willingness to

35

carry out its obligations, and applicants to join the GA must be recommended by the Security

Council (Baehr and Gordenker, 2005).

Another important differentiation is one between groups and networks, especially

because they are both important social constructs used here. The UNGA is a large group of

member states each playing roles in many types of networks that, for example, may be based

on trade or common IGO memberships. A social network is the set of actors and the ties

among them where the ties may be any relationship between the actors (Wasserman and Faust,

1994). Networks can have subgroups and groups where a subgroup is any subset of actors,

including all the ties among them and a group is “…the collection of all actors on which ties

are to be measured” (Wasserman and Faust, 1994; 19); furthermore, the actors of a group

should comprise a bounded set (Wasserman and Faust, 1994). The 191 countries of the UNGA

are a bounded set. Some relevant network concepts of groups include cliques and structures of

affiliations (Wasserman and Faust, 1994). In networks, the basic unit is the dyad for direct

exchange and a triad for indirect exchange (Molm, 1994). It is anticipated that characteristics

of the UNGA member states’ networks may partially explain the voting outcomes of this

group.

3.4 Level of Analysis

The field of strategy has as its underpinnings varied academic roots stemming from

sociology, political science, economics and psychology. While this multitude of views offers

opportunities for interesting academic integration, it also presents challenges when definitions

of various constructs differ. Even within the areas, there are continuing debates and so, some of

this controversy inevitably finds itself here in the level of analysis. The setting for theory

36

testing in this paper is the United Nations General Assembly (UNGA). The United Nations is

an international governmental organization (IGO) that uses the UNGA as its primary decision

making body. The decision makers or “actors” who vote within the UNGA are individuals who

represent each member country or more specifically, the current government of each of the

member countries. A more detailed description of the UNGA is outlined later.

Two major inquiries arise that are related to the level of analysis and will be addressed.

First, is the UNGA an organization and what type of organization is it? How does it compare to

popular organizations of strategy study, such as firms? Second, what are these government

actors within the UNGA – are governments to be considered organizations? Given this

examination, what is the level analysis in this study?

From a sociological standpoint, Scott (2003) says that, “the level of analysis is

determined by the nature of the dependent variable – that is, by whether the phenomenon to be

explained is the behaviour of individuals, of organizations, or of systems of organizations.”

(Scott, 2003; 16) Moreover, he admits that distinguishing amongst levels of analysis is to some

extent subjective (Scott, 2003; 17). In this study, the dependent variable is the percentage of

similar voting between each dyad of member countries in the UNGA in a year. What is the

nature of this dependent variable?

I propose that that the UNGA is an organization and the actors within it are not acting

as individuals – they are representatives of organizations or of systems of organizations. I

expect that the latter choice is slightly controversial, according to the literature as will be

explained. Definitions of an organization and a system of organizations begin this exploration.

Parsons (1956) provided his early perspective:

37

“An organization is defined as a social system oriented to the attainment of a relatively specific type of goal, which contributes to a major function of a more comprehensive system, usually the society.” (Parsons, 1956)

He provides examples of organizations such as a government department or bureau, a

university, a hospital or a business firm (Parsons, 1956; 64). Since the UNGA is a principle

organ of the UN, a body that is like a world government, then the UNGA qualifies as an

“organization”.

However, how would Parsons classify the actors within the UNGA? The representative

actors are not acting at the individual level because within the UNGA, they represent

governments of nations. Depending on the context, self-interest and the interests of

governments will be represented by individuals to differing degrees. UNGA member

representatives have little leeway but to perform within the boundaries of their institutional

roles because of the government bureaucracy that leaves them little or no individual discretion.

Votes cast by UNGA members do not affect the individuals’ statuses; they affect the states’

statuses (Voeten, 2000). In fact, when UNGA votes are cast, the UNGA member countries are

the recognized voters, not the individuals who represent the countries - it is China that voted

“yes” or Russia that abstained. Therefore, this paper takes the view that governments of states

influence each other and it is the embeddedness of states in various types of relationships that

is influential on vote outcomes. However, Parsons says that a nation is not an organization

(Parsons a, 1956; 64); he doesn’t specifically address what a government is but, implies that it

is a system of organizations since a part of a government is an organization.

Scott (2003) provides more modern definitions of organizations from several scholars

representing rational and natural systems views. Parsons’ definition emphasizes specific goals,

38

but Scott (2003) suggests Parson’s overall view is a natural systems perspective, to be further

explicated, incorporating informal aspects of organizations (Scott, 2003; 72-75). Since

governments pursue many disparate goals the rational systems definition works against their

classification as organizations. Scott summarizes this view:

“Organizations are collectivities oriented to the pursuit of relatively specific goals and exhibiting relatively highly formalized social structures.” (Scott, 2003; 27)

From a natural systems perspective,

“Organizations are collectivities whose participants are pursuing multiple interests, both disparate and common, but who recognize the value of perpetuating the organization as an important resource. The informal structure of relations that develops among participants is more influential in guiding the behavior of participants than is the formal structure.”(Scott, 2003; 28)

This view allows for the pursuit of multiple goals and does not emphasize formal

structure. Thus, it is more open to the possibility that an entire government could be an

organization. Traditionally, we think of a corporation like General Motors as an organization

(Scott, 2003; 93). Is GM an organization or a system of organizations given that is sells a

variety of automotive vehicles and at the same time runs a huge financial services business?

Global corporations, studied as organizations are so vast and multifaceted that it is hard to

differentiate them from governments on the bases of existing organizational definitions, even

within the realm of sociology and organizational studies. In fact, Giddens, an organizations and

management scholar quoted by Whittington,

“…actually proposes the state as a generic form of organization; a collectivity in which knowledge about the conditions of systems reproduction is reflexively used to influence, shape or modify that system reproduction.” (Whittington, 1992; 695)

39

Moreover, in the Academy of Management Review, an organizational studies journal,

Kelley (1976) quotes Perrucci, Robert, and Pilisuk (1970): “People will readily admit that

governments are organizations. The converse – that organizations are governments – is equally

true, but rarely considered.” (Kelley, 1976; 66)

Finally, outside the realm of sociology, management and organizational studies,

political scientists generally accept that governments are organizations (Marshall and Jaggers,

2002). Moreover, some consider the UN, as a whole, an organization even though it acts like a

world government (Ingram, Robinson, Busch, 2005).

Clearly, the boundaries of what constitutes an organization are somewhat ambiguous

and part of the confusion may relate to the times. When Talcott Parsons defined organizations,

firms were smaller entities, not as global. The size and multi-functionality of organizations

today leaves old definitions obsolete. The area needs a revised definition. However, until that

happens, it remains acceptable, at least to a set of scholars, to consider governments as

organizations. The point here is to examine embeddedness at a level beyond the individual.

Whether this study is considered inter-organizational or between systems of organizations may

remain at debate. However, I have sufficient support to take an inter-organizational view until

a modern, widely accepted definition contradicts my position.

40

CHAPTER FOUR: RESEARCH HYPOTHESES

41

The theory links mechanisms of power and influence to the various forms of the types

of embeddedness examined, economic, institutional, political and cultural, to predict effects on

decision making, where votes are the observable result of the strategic decisions. The form of

economic embeddedness employed is trade ties and three forms of institutional embeddedness

are used: formalized military alliances, common international governmental organization

(IGO) memberships, and direct country-country visits. Political and cultural embeddedness are

moderators for the institutional embeddedness that is IGO membership. Each form utilizes

different mechanisms or a different combination of mechanisms. Predictions relate the

mechanisms to their expected impact on similar dyadic voting. Power and influence

mechanisms are operationalized in the hypotheses using network concepts such as degree

centrality and connectedness.

4.1 Economic Embeddedness – Position in the Trade Network

Economic embeddedness is expected to have a strong influence on voting in the

UNGA; here, economic embeddedness will be synonymous with international trade. Since

economic ties are monetary exchanges rather than exchanges of information, although

information may accompany the transactions, differential power rather than homophylic

influence is expected to matter more in influencing voting. In-degree centrality in the trade

network will reflect economic prowess and prominence because this measure is in direct

proportion to the number of trade partners a country has (Brass and Burkhardt, 1993). This is a

network positional measure and not a market power measure. Market power is controlled for in

the analysis with a dyadic trade intensity measure since it may be alternatively posited that

countries vote cooperatively because they do a lot of business with each other and in this way,

42

have strong ties that may be increasing in strength (Granovetter, 1973, 1983). Aligned voting is

however, first and foremost, a social political behavior, and will therefore be more highly

influenced by prominence, a dominant social position in an economic network, rather than

market power, a measure of economic influence.

Other network measures such as eigenvector centrality, closeness centrality, betweeness

centrality, or the Bonacich power index are not preferred to in-degree centrality for this theory.

Eigenvector and closeness centrality are measures that consider “the distance of an actor to all

others in the network by focusing on the geodesic distance from each actor to all others.”

(Hanneman, 2001; 65). In other words, the belief is that an actor is more central if it is closer to

more other actors in the network. Eigenvector centrality is a more sophisticated measure of this

than is closeness centrality since it recognizes the difference between an actor having a few

close ties and another having many slightly more distant ties, the latter actor being more central

(Hanneman, 2001; 68). Although these measures are more global in nature than is in-degree

centrality, they do not relate well to the reality of trade – which is that no relationships in trade

exist except with direct trade partners (direct ties). A reputation of prominence exists amongst

the entire global trading community, but this reputation is built on a state’s relationships with

its direct ties only i.e., a state’s distance along network paths to other states has no meaning in

the world of trade because trade is governed by contracts.

Betweeness centrality does not offer any advantage over in-degree centrality for

measuring power or prominence either. Sometimes goods travel through certain countries

prior to reaching others because of various domestic trade laws. Consequently, the country may

be between others as a “pass through” country or a broker between countries. However, if such

43

a country is powerful, then it will be brokering between many countries and consequently, it

will have many trade partners; this will be captured by in-degree centrality.

Finally, the Bonacich centrality measure suggests that if a country has more weak

trading partners, it is in a more powerful position. The dependence of the weak trading partners

makes them more vulnerable and therefore, more compliant and reciprocating. Weak trading

partners are defined as those having few direct contacts. This measure most convincingly

challenges the use of in-degree centrality. However, if power and prominence in trade circles is

affected by reputation with one’s direct trading partners, then being a powerful overlord rather

than a dutiful trading partner who exchanges with many other strong countries would not have

a positive affect on reputation, power or prominence in the network in a global sense. Trading

countries are attracted to prominent countries that have a reputation for fair trading practices.

Moreover, the concept of in-degree centrality does not exclude dependent relationships - the

type of relationship on which the Bonacich measure capitalizes.

Traditional measures of trade are not relevant for this study because they do not

consider overall or relative position in the trade network. Operationalizations of dyadic trade

interdependence either: 1) consider whether a given trade relationship is valuable relative to

others; for example, by measuring the concentration of trade share one country has with

another compared to that which it has with its other direct trading partners (Barbieri, 1995,

1996, 1998b; Gartzke and Li, 2003) or 2) they consider to what extent a given trade

relationship is valuable relative to a state’s overall economic performance (Oneal and Russett,

1997, 1999 a, b; Gartzke and Li, 2003). Rather, states in central positions, attracting many

trade partners, are prominent, powerful, and influential. In-degree centrality is the reflective

44

network concept that increases with the number of inwardly directed trade ties (Krackhardt,

1990).

“Centrality, the extent to which a given individual is connected to others in a network, is the structural property most often associated with instrumental out-comes, including power (Brass, 1984), influence in decision making (Friedkin, 1993), and innovation (Ibarra, 1993).” (Sparrowe, Liden, Kraimer, 2001; 316).

Prominent trading countries have greater relative network power which translates into

power on the world stage. Less prominent trading countries desire the benefits of association

with the more powerful countries through trade links and will repay or entice the prominent

states through other means, including aligned voting. In fact, some analogous evidence is that

in UN General Assembly voting, Latin American countries will bandwagon on issues of

interest to the United States because they receive foreign aid from the US (Voeten, 2000).

Furthermore, a history of having a good reputation as a reciprocating trading partner is

important in international relationships – reciprocation is a norm (Axelrod, 1997). If states

have successful ongoing trading relationships, then their reputations as good trading partners

are beneficial to them because other states are encouraged to trade with them – this is an effect

of structural embeddedness (Uzzi, 1996). These third parties who become new trading partners

or who increase their trade volume gain confidence through their vicarious learning that they

can trust their trade partners to reciprocate as expected. It is also, therefore, damaging to a

state’s reputation if trade relationships break down with trade partners.

The more basic rationale for trade to lead to similar voting is related to the kind of

relationship it entails. It is one of mutual dependence transformed to interdependence through

binding negotiated agreements that are continuously activated through frequent trade activity.

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As earlier discussed, there are two forms of relations, those that are based on: 1) mutual

dependence and 2) interdependence; these correspond to two types of social structure,

respectively: 1) exchange and networks and 2) groups (Molm, 1994). In the case of exchange

and networks (which includes trade), the mutual dependence is riskier than the interdependence

found in groups. Although trading relationships do not create “groups”, the negotiated binding

agreements created through a joint decision process prior to any actual exchange taking place

mitigates the risk of mutual dependence; the binding agreements directly change the structure

of the interactions, introducing interdependence. Also, trade tends to be ongoing and the

simultaneity of the transactions mitigates the risk of defection by one party.

However, while trade agreements set up this cooperation as far as the actual trade of

goods and services goes, outside of the agreement, parties are in different power/wealth

positions. Consequently, while the trade agreement sets up a false equality between unequal

powers, the weaker power is obliged to reciprocate for this benefit outside of the trade

relationship. Otherwise, the more powerful trading partner can change its trading terms, reduce

trade or not trade at all. Also, the less prominent partner’s reputation may be tarnished, as

explained earlier. In the case of trade dependence, dependent countries may reciprocate with

their powerful benefactor country by voting in accordance with it in the UNGA.

Reciprocity will occur when the relationship is consequential to the smaller trading

partner. Consequently, the level of reciprocity, reflected in similar voting, will vary depending

on the two countries’ relative in-degree centrality; in a pair, there may be two who are

powerful, two who are both weak, two who hold some medium level difference in prominence

and two who are vastly different in prominence. In the first two cases, the relative in-degree

centrality will be small with increasing values in the third and fourth cases. First, two countries

46

with high in-degree centrality both wish to trade with each other, but because they are

relatively equal players, they are quite independent and do not need to reciprocate outside the

trade relationship, whether it is to attract or keep the other partner. Second, two countries with

low in-degree centrality have little to gain from each other. Third, countries with comparable

in-degree centralities, but with some differential will be noticeable to each other. The less

central country will vote in alignment with the more prominent one. Even if one country is

more central, they are relatively important to each other. The final case occurs when countries’

relative trade in-degree centrality is very large. The less central country’s vote is still as

valuable to the other country as any other vote and the less powerful country would like to gain

the benefits associated with being tied to a prominent country. In summary, a country will vote

with those others who may offer needed benefits - reciprocal behavior occurs. Consequently, as

the relative trade in-degree centrality of a dyad of countries in the UNGA trade network

increases, the more likely they are to vote similarly.

H1a: As country-dyad relative trade in-degree centrality increases, so does similar voting.

Total trade in-degree centrality is another approach to test the same concept above.

When the two countries’ values added together are small, then they are inconsequential to each

other and will not vote together. When the two countries’ values added together are at the high

end of the scale then they are independent of each other, both being powerful. However, in the

middle of this range, countries will differ in their in-degree centrality and may be more

interdependent such that they will vote similarly. Once again, an inverted U-shaped

relationship is expected. This measure does not clearly delineate the case where the countries

47

are vastly different in in-degree centrality, as does the relative in-degree centrality measure.

However, total trade in-degree centrality is used as an additional measure because if a different

force, homophily, is working then a U-shape curve rather than an inverted U-shape curve will

occur. Consequently, as the total trade in-degree centrality of a dyad of countries in the UNGA

trade network increases, the more likely are they to vote similarly. However, this happens at a

decreasing rate such that at higher levels of total in-degree centrality, there will be a reversal

and the two countries will be less likely to vote similarly.

H1b: The relationship between country-dyad total trade in-degree centrality and

similar voting will form an inverted U-shaped curve.

Notice that these hypotheses are testing position in the trade network; the countries do

not have to be trade partners, they may be potential trade partners.

4.2 Institutional Embeddedness

Institutional embeddedness occurs when states are tied through international

institutions. With so many possible types of institutional ties only a limited set of these is

examined here as having an influence on voting: formal military alliances, common IGO

memberships, and country visits. Homophylic influence and information sharing tend to

dominate over power interdependence relationships as cooperative mechanisms.

It is posited that institutional embeddedness will have a weaker effect compared to

economic embeddedness on similar voting because binding agreements and simultaneity are

not present in institutional contexts to build interdependence. In the case of IGOs, membership

does not involve a dyadic exchange and states are not tightly bound by their membership

agreements. For example, a trade partner will notice and may punish the other partner who

48

defects. In contrast, it may go completely unnoticed that an IGO member has not abided by all

the rules or norms of a particular IGO. Even if it is noticed, consequences are unlikely

(Axelrod, 1997; 7). Similarly, when countries visit each other, no obligations may arise and

while information may be shared, there is no guarantee of agreement on the related issues.

Finally, while alliances seem to carry some stronger obligations, the actions are disconnected

with the initial agreements so power differentials are a lesser effect on voting behavior.

4.2.1 Alliance Position in the Network – Power or Power Differentials. Military alliances

attempt to build cooperation through contracts, as does trade, except there is a disconnect

between the agreement formation and the related action “…a treaty is just a scrap of paper”

(Russett and Oneal, 2001; 104); the action(s) may never occur because the mutual dependence

is not successfully transformed into interdependence (Molm, 1994). For this reason, the

mechanism through which trade influences voting may not operate to the same extent in the

case of this type of institutional embeddedness. Similar to trade, alliances in this study are

formal upfront binding agreements devised through joint decision processes. However, there is

a simultaneity problem in that there is a second, rather distant stage to this kind of cooperation

(Molm, 1994; Heckathorn, 1985). Parties to these types of agreements can and will sometimes

defect when it comes time to support their alliance partner. This sequential dependence is risky

because the likelihood of reciprocity within the alliance is lower. Consequently, it is common

to build trade and other contingent agreements into alliances to increase the likelihood of

cooperation (Larson, 1998; Long and Leeds, forthcoming). Building these sequentially

dependent transactions into situations of mutual dependence is another approach, along with

binding agreements, for enforcing cooperation. By building in the sequential contingencies,

49

allowing for retaliation on one level when partners defect on another level, the structure of the

relationship is changed. However, due to the difference in timing and power differences

between nations involved in these kinds of agreements, this enforcement approach is limited.

In general, institutional embeddedness often offers the chance to influence through

information pooling prior to group discussions. Influence pre-discussion occurs amongst

countries in similar powerful positions in the alliance network because they are homophylic.

Shared information disseminated prior to discussions is believed more influential than

unshared private information (even though it could be valuable) brought out in the meeting

situation because actors will focus on the more familiar information (Winquist and Larson,

1998). Given actors’ limited abilities to process the incomplete information in complex

situations (Axelrod, 1997), the most salient information will sway their decisions and therefore

their votes (Ocasio, 1997; Dutton and Jackson, 1987; Cyert and March, 1992). Security

information, discussed amongst alliance partners, is highly relevant to the issues that the

UNGA discusses, making this type of influence pre-discussion potentially impactful on voting.

However, it is the more powerful countries who will be more involved in this discussion since

they have the capabilities to defend themselves and protect others (Russett and Oneal, 2001;

89) – these actors are attractive alliance partners and will therefore have high degree centrality

in the alliance network.

Also, powerful countries seek to maintain their power and weak countries seek

protection. The powerful tend to maintain their positions by taking similar positions on various

issues that might mutually affect them. For this and the latter reason, high degree centrality

actors will tend to vote similarly.

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Other network measures such as eigenvector centrality, closeness centrality, betweeness

centrality, or the Bonacich power index are not preferred to degree centrality for this theory.

Eigenvector and closeness centrality are measures that consider “the distance of an actor to all

others in the network by focusing on the geodesic distance from each actor to all others.”

(Hanneman, 2001; 65). In other words, the belief is that an actor is more central if it is closer to

more other actors in the network. Eigenvector centrality is a more sophisticated measure of this

than is closeness centrality since it recognizes the difference between an actor having a few

close ties and another having many slightly more distant ties, the latter actor being more central

(Hanneman, 2001; 68). Although these measures are more global in nature than is degree

centrality, they do not relate well to the alliance network because they are direct tie agreements

shakier than those of trade. Like trade, a reputation for prominence exists amongst the world

community and this reputation is built on a state’s relationships with its direct ties only;

however, the time that lapses between the promises and unknown future actions of alliances

creates weaker ties; consequently, a state’s distance along network paths to other states has no

meaning in the world of alliances because the direct paths are already relatively weak.

Betweeness centrality does not offer any advantage over degree centrality for

measuring power or prominence either; although it could mean that a country is in a beneficial

position in terms of having access to many countries’ security secrets and knowledge, multiple

connections is already captured by degree centrality.

Finally, the Bonacich centrality measure suggests that if a country has more weak

alliance partners, it is in a more powerful position. The dependence of the weak trading

partners makes them more vulnerable and therefore, more compliant and reciprocating. Weak

alliance partners are defined as those having few direct contacts. This measure most

51

convincingly challenges the use of degree centrality. However, if power and prominence in the

alliance network is affected by reputation with one’s direct partners, then similar to the trade

situation, countries that offer protection without taking advantage of their position will have a

better reputation than those that act heinously. Also, even the most powerful countries gain

more by allying with other powerful countries, rather than weaklings, because of the military

capabilities that they can potentially contribute. Moreover, as mentioned earlier, the concept of

degree centrality does not exclude dependent relationships - the type of relationship on which

the Bonacich measure capitalizes.

H2a: As total alliance degree centrality increases between two countries in a dyad, they will be more likely to vote similarly.

In contrast, weak countries are peripheral because they lack military capabilities and

tend to be dependent on the powerful (Russett and Oneal, 2001; 89); they will have lower

degree centrality in the alliance network. When two countries in a voting dyad are both low in

alliance degree centrality, their relative degree centrality will be low and they will not take

notice of each other; they have no motivation to vote together. However, two powerful

countries will also have low relative degree centrality, but should vote together, as stated in

hypothesis H2a. Consequently, at low levels of relative degree centrality, the measure is not a

good predictor of similar voting because of the commingling of pairs of small and large

countries in the dyads. As the countries differ more in degree centrality, it is more likely the

case that a weak country will vote in alignment with a strong country, seeking its potential

military support. For example, the US protects others through NATO and expects cooperation

52

on other issues in return (Larson, 1998; 121). So, the measure of relative degree centrality, at

higher levels, may be useful for teasing out this dependence relationship.

H2b: At higher levels of relative alliance degree centrality countries in a dyad will tend to vote similarly.

4.2.2 Country-Country Visits – Degree centrality: Information Exchange and

Prominence. State visits are considered a type of institutional embeddedness - institutions are

meeting with each other. When countries visit each other they send their representatives; the

representatives are not making personal level visits. States strengthen their ties with each other

through these relationship-building interactions. These meetings are more directed for this

purpose than IGO forums because states are choosing to make direct efforts at building

particular relationships with each other. Information sharing under these circumstances is quite

specific and therefore influential because each is interested in the purpose of the visit. Also,

prominence in the visits network means that the actor has several visitors and is influential.

Out-degree centrality is reflective of the former concept and in-degree centrality is reflective of

the latter concept.

When two countries both have high out-degree centrality in the visits network, they are

both reaching out to many others, converting their private information into public information

prior to decision making. The shared information will influence voting in their favour. Also, they

are more likely influencing each other directly or through the third parties they mutually

encounter who diffuse the information, reciprocally influencing each other towards shared world

views. Moreover, those who they visit may subsequently visit others, diffusing the information to

third parties, further increasing the influence pre-discussion. In contrast, a dyad pair neither of

53

whom visits many others, while homophylic in the social structural sense, will not tend to vote

similarly since there is little chance that they can influence any other actors, including each

other. Thus, information sharing is more so the operative mechanism than is homophily.

However, when both actors are actively making efforts to influence, they have a likeness that

will lead to a cooperative outcome.

H3a: As the total out-degree centrality of a country-dyad in the country-country visits network increases, the more likely are the dyadic countries to vote similarly.

Prominence is also related to information sharing in the visits network because a

prominent actor receives more visitors, it is also known to hold more information. Such an

actor also has more opportunities to disperse its views and those views may hold more weight

because they are sought out. Another measure of centrality, in-degree centrality, measures this

prominence and actors will vote with those others who they notice as having this type of

prominence because they are recognized as well-informed.

H3b: As the total in-degree centrality of a country-dyad in the country-country visits network increases, the more likely are the dyadic countries to vote similarly.

As in the previous cases, network measures such as eigenvector centrality, closeness

centrality, betweeness centrality, or the Bonacich power index are not preferred to in-degree or

out-degree centrality for this theory. First, none of these alternative centrality measures allow

for the directionality that in/out-degree centrality offer. Eigenvector and closeness centrality

are measures that do not fit the main diplomatic visits situation that this study intends to

capture – one of direct relationship-building through direct interaction, as mentioned earlier.

54

However, either one could be used to capture secondary effects of the information spreading as

a result of direct interactions. Therefore, one of them could replace out-degree centrality except

for the problem that neither is directional; H3a would not be distinguished from H3b that uses

in-degree centrality. It might be interesting to use Eigenvector centrality in future research after

the effects of in/out-degree centrality are explored and understood.

For similar reasons, as explained earlier for trade and alliance networks, betweeness

and Bonacich centralities do not offer advantages over the in/out-degree centrality measures -

all types of ties are captured by the in- and out-degree measures and they are all of

consequence here.

4.2.3 IGO memberships – Influence. IGO memberships foster shared views and thus

cooperation through a homophylic mechanism; states have similar interests, however, they may

disagree on issues within these forums. Furthermore, if two states are in many of the same

IGOs, they are highly connected by common interests. Also, states that participate widely in

IGOs can repeatedly share their views and thereby turn private information strategically into

influential public information. Finally, group membership can potentially build a sense of

solidarity, affinity or cohesion that is transferable to other forums (Ingram, Robinson, Busch,

2005; 830-831; Gartzke, 2000; Friedkin, 1993; Burt, 1987).

Shared views are fostered amongst countries having common memberships for three

reasons. First, IGOs offer forums for exchanging information. If there is substantial

participation, then these forums are places where discussion reinforces these shared views that

will ultimately be reflected in similar voting on related issues; group membership influences

attitudes and behaviors (Rice, Grant, Schmitz, and Torobin, 1990). Common IGO membership

55

is an indication of value homophily (McPherson, Smith-Lovin, and Cook, 2001). Second,

where participants are not very active, common memberships still signal to states that they

share similar interests since they are at least symbolically supporting the same causes. Third,

the fact that an IGO exists makes the issues it represents salient. The salience of the issues and

views of the IGO contribute to the shared information that is so influential in decision making.

Also, countries that take part in more memberships have more opportunities to

disseminate their views. Consequently, they are more influential because they have

disseminated information that furthers their interests. They have turned their private

information into shared information prior to the voting or even some of the specific backroom

bargaining pertaining to the current UNGA issues. The shared information will be influential in

these informal discussions because it is in the backs of the minds of the negotiators, whether

consciously or subconsciously, and will be discussed more, consequently, having more

influence on the voting (Winquist and Larson, 1998; Larson, Christensen, Franz, Abbott,

1998).

Another aspect of IGOs that builds cooperation amongst members is that they are

interdependent groups. Without the participation of all members, it is difficult for the IGO to

further its cause. Although there will be free-riding and some members will be more active

than others, the IGO is more like a group than a situation of mutual exchange (Molm, 1994).

Members of groups may transfer their cooperative behavior to other forums such as the UNGA

out of familiarity with those who they have cooperated with on other causes; past cooperation

may reinforce itself to build a sense of solidarity (Ingram, Robinson, Busch, 2005; 830-831;

Gartzke, 2000). This is the only type of embeddedness in this paper that potentially involves a

group mechanism underlying the cooperation. This group mechanism will be stronger / weaker

56

depending on the cohesiveness of the IGO group where a tighter group will be more

cooperative. One study draws a causal connection between membership in structured IGOs,

those with stronger organizational capabilities, with higher trade levels (Ingram, Robinson,

Busch, 2005).

These are all reasons why it is expected that more common IGO memberships, or

higher connectedness, will have a positive influence on similar voting. The level of

connectedness is a tie strength measure because the expectation is that more connections via

memberships will build stronger relationships through the increased interactions based on

common interests (Ingram, Robinson, Busch, 2005). Ingram et al. (2005) found that higher

IGO connectedness leads to trade, but this is a social/institutional phenomenon influencing an

economic decision. What is posited here is one social/institutional phenomenon influencing

another social behavior – cooperation in voting. Also, the IGOs are considered together and

split into types using the same coding as Ingram et al. (2005) to test whether some types of

issues unite the membership more than do others. This includes economic, social cultural, and

political military categories, with the remainder falling into a general IGO category.

H4: As the IGO connectedness between two countries in a dyad increases, they will tend to vote similarly more often.

4.3 Nested Embeddedness

Strengthening the institutional embeddedness are other types of embeddedness that are

collectively internalized in the minds of people within states: cultural and political

embeddedness are nested in institutional embeddedness. Similarity on cultural and political

dimensions, otherwise known more generally as value homophily, strengthens the existing

57

links between the states; the states are therefore culturally and politically embedded. Although

occasionally the two concepts of culture and ideology are conflated, there is solid support for

keeping the two concepts quite separate (Williams, 1996). Huntington (1996) has generalized

culture at the state level, having made cultural assignments to the post-cold war globe of states

and suggested that this cultural embeddedness exists. Moreover, scholars such as Hofstede

(1983) have recognized that culture resides at the country level and have developed cultural

dimensions for states. Additionally, to identify political ideology at an international level,

political scientists use a common scoring method and refer to the “regime type” of the state.

The poles of this post-cold war multi-polar world are rooted in cliques of states having

similar cultures (Huntington, 1996) and this is what is meant by the cultural embeddedness of

states. States band together because of this type of value homophily, similar national culture.

National culture is a shared-meaning system wherein members of the same culture interpret

and evaluate events in a similar way (Erez and Earley, 1993: 20). An alternative

complementary definition is that it is “a system of values and norms that are shared among a

group of people and that when taken together constitute a design for living” (Doney, Cannon

and Mullen, 1998). The polarization is no longer between the East and West i.e., communism

versus democracy and free markets. Instead, the polarity is based on indigenization

(Huntington, 1996). Some argue that it is not multipolar; it is a uni-multipolar world (Voeten,

2000); however, this could be a transitional stage such that the world will become multipolar

(Huntington, 1999). Huntington’s civilizations include: Western, Latin America, African,

Islamic, Sinic, Hindu, Orthodox, Buddhist, and Japanese.

An important clarification is that this study it is not concerned with the direct

connection between culture and voting as a means to identify one of the dimensions on which

58

the world might be divided, as is Voeten’s study (Voeten, 2000). Instead, I look at how culture

moderates an institutional embeddedness mechanism that is expected to lead to cooperation. It

is not a “Clash of Nations” that this study is testing (Voeten, 2000, 2004; Huntington, 1996;

Russett and Oneal, 2001). Instead, it is considering culture as a basis for homophily that builds

common understanding and shared views that lead nations to vote similarly when they are

already embedded institutionally. As mentioned earlier, states may share and express common

interests by being members of IGOs, but within these organizations, there may be widely

varying opinions on the issues. More bases for similarity may be required to actually bring

states to consensus within these forums.

Like common culture, similar political ideology is a basis for value homophily. Kritzer

(1978) defines a political ideology as “a system of beliefs centered upon a small number of

central principles.” Within the West’s system of politics, ideology tends to be left-centre-right.

However, at an international level, this is too simplified so political scientists use a scoring

method devised by the Polity IV Project to identify the regime type of a nation, what I will

refer to as the political ideology of the state. According to the Polity IV Project, a “polity” is a

“ political or governmental organization; a society or institution with an organized government;

state; body politic.” Each polity or nation is assigned a democracy and an autocracy score and

the subtraction of these gives a rating from +10 to -10 such that a high positive value is

assigned to a democracy and a large negative score goes to an autocracy.

In contrast to the multipolar view which is more culturally based, the uni-multipolar

view is one where the US and its allies are challenged by rising powers such as China and

India; these countries object to Western views (Voeten, 2000). This is a result more of a

combination of economic development and political ideology than of culture according to the

59

study (Voeten, 2000). It is found that even when Western countries are excluded from the

analysis, states that are wealthy and democratic tend to vote in the UN General Assembly in

accordance with the US (Voeten, 2000). Consequently, in this study, political embeddedness is

also considered a moderator that will strengthen the effects of institutional embeddedness on

voting. Shared views are reinforced and thus improved communication ultimately builds

stronger ties (Axelrod, 1997; McPherson, Smith-Lovin, and Cook, 2001).

Nested embeddedness is the descriptive concept for this phenomenon - cultural or

political embeddedness will strengthen existing institutional embeddedness. Greater similarity

amongst nations in cultural and political orientations builds these types of embeddedness that

will lead to more interactions, consequently, building the institutional embeddedness. The

mutually reinforcing relationships will lead to strong ties that have a stronger influence on

voting (Granovetter, 1973).

Value homophily is the mechanism underlying the nested embeddedness effects

(McPherson, Smith-Lovin, and Cook, 2001); one kind will predominate depending on the

balance of power that dictates how people identify with each other. The balance of power

makes some issues more salient than others. For example, during the Cold War, the prevailing

identification was with political ideology because the opposing philosophies were democracy

versus communism. After the Cold War, indigenization led people to focus on cultural

homophily (Huntington, 1996). However, others may disagree that political homophily has lost

its salience to cultural homophily, as discussed (Voeten, 2000; Russett and Oneal, 2001).

Therefore, both types of embeddedness will act after the Cold War since political ideology is

still salient.

60

Similar world views encapsulated in political ideology or cultural similarity results in

states’ engagement with each other more within the networks to which they mutually belong.

This is going to be most evident within institutionally embedded groups. States who are

members of the same IGOs have common interests, often beyond economic considerations; the

cultural and political similarities will strengthen the bonds and thus they will communicate

more and more often vote similarly. For example, an IGO may have many members and within

these forums there will be cliques – the cliques may be formed because of common ways of

viewing the issues the IGO is concerned with and culture and/or political ideology may guide

the various positions on these issues. To demonstrate this influence, I will test the effects of

interactions between different types of IGO connectedness, as mentioned earlier, and cultural

and political embeddedness on voting.

H5a) As the IGO connectedness between two countries in a dyad increases, they will tend to vote similarly; this relationship is strengthened by cultural embeddedness.

H5b) As the IGO connectedness between two countries in a dyad increases, they will tend to vote similarly; this relationship is strengthened by political embeddedness.

61

CHAPTER FIVE: METHODS

62

5.1 Data Sources

The following is a list of the publicly available data sources that were drawn upon to

construct the data set.

United Nations General Assembly voting data. The United Nations General Assembly country

roll call voting data comes from Erik Voeten’s web site. He has extended the dataset beginning

in 1946 to 2002; it builds on several other scholars’ datasets as his site acknowledges. For each

resolution each year, the votes for each country are recorded as 1 = yes, 2 = abstain, 3 = no, 8 =

absent, and 9 = not a member. I reduce the list so as to use a post-cold war time period, 1990-

2000. I remove countries that were not members in a given year and for each year keep only

the countries that voted at least once in the year. Also, following a similar approach used by

Voeten (2000), I eliminate votes with less than 2.5 percent of the voters on the “No” side. This

disagreement rate is calculated as: [Number of “No” votes/(Number of total votes, including

abstentions)]. The purpose of this is to ensure enough variance in the data and the result of this

is to find 18% of the votes abstentions, 67% “yes” votes and the remaining 15% “no” votes.

Using this data is advantageous for testing theory because decision making data in

firms is hard, if not impossible to collect. Even in the case of recorded decisions by a board of

directors, firms are unlikely to offer this information for public research. Consequently, the UN

data offers a reliable and long term source of decision making data. Also, rather than studying

performance outcomes, decisions are nearer consequences to the effects that the embeddedness

view describes.

Trade and GDP data. The Expanded Trade and GDP Data are created by Gleditsch (2001).

Gleditsch (2001) has used International Monetary Fund (1997) trade data as a base and

63

imputed missing data through various means, using additional sources to make it more

complete. The trade data is in millions of current year USD. Dyadic total trade and directional

trade are available. Imports are used.

GDP is “the most common indicator of a state’s resources or economic wealth”

(Gleditsch, 2001). GDP per capita data also comes from the Expanded Trade and GDP Data

by Gleditsch (2001). The expanded GDP data is based on the Penn World Tables (Summers

and Heston, 1991). It is provided in both constant US dollars (base 1985) and nominal figures

in USD at current international prices. Constant USD is used.

Alliance data. Military alliance data comes from version 3.0 of the Correlates of War (COW)

Formal Interstate Alliance Data Set, 1816-2000 created by Gibler and Sarkees (forthcoming).

The second data set is used in which it records a single record per dyad year. Therefore, if two

countries have many documented alliances, the alliance with the highest level of commitment

is selected (defense = 1; neutrality or non-aggression = 2; and entente = 3). I recoded the

alliances such that they exist (1) or do not exist (0); the level of commitment is not used. Also,

the dataset is adjusted so as to create a non-directed dyad data set. All alliances in the data set

are supported by written ratified agreements.

IGO data. The list of International Governmental Organizations that each COW system

member belongs to from 1990-2000 comes from the COW web page. Version 2.1 created by

Pevehouse, Nordstrom and Warnke is used. An IGO is one when its membership includes at

least three members from the COW-defined state system, it holds regular plenary sessions at

64

least once every ten years, and has a permanent secretariat and headquarters. The coding of the

IGOs, generously provided by the authors of Ingram, Robinson, Busch (2005), is such that they

are divided according to whether they are economic (EIGOs), social/cultural (SCIGOs),

political/military (PMIGOs) or general IGOs. Some finer grained coding was also done but, not

used in this paper.

Country visits data. The country visits data comes from Gary King’s web site at

http://gking.harvard.edu/. His data set entitled, “10 Million International Dyadic Events”

includes the event “Travel to Meet” and this data is extracted at the country level from 1990 to

2000.

Political ideology data. The Polity IV data was downloaded from the site

http://www.cidcm.umd.edu/inscr/polity/ (Marshall and Jaggers, 2002). Polity coding of

national regime characteristics code the authority patterns of the effective polity, where a polity

is defined as a ‘political or governmental organization; a society or institution with an

organized government; state; body politic.’ The dataset provides a score for all member

countries of the international system, as defined by the Correlates of War project from 1800-

2002, ranging from -10 to 10 where -10 is strongly autocratic and a +10 is strongly democratic.

The scores do not apply to groups within states operating outside the main political arena

within the state.

Culture data. Culture is coded according to Huntington’s (1996) Map 1.3, “The World of

Civilizations: Post-1990”. The world is divided according to nine cultures: Western, Latin

65

America, African, Islamic, Sinic, Hindu, Orthodox, Buddhist, and Japanese. This coding has

been used in other studies (Voeten, 2000; Russett and Oneal, 2001)7.

Contiguity data. The Correlates of War project offers version 3.0 of direct contiguity amongst

nations with coding depending on whether the boundaries are by land or water (Stinnett, D.

M., Tir, J., Schafer, P., Diehl, P. F. and Gochman , C.). This is an important control since

geographic proximity affects trade and the tendency to ally or be involved in military disputes

(Ingram, Robinson and Busch, 2005; Russett and Oneal, 2001).

5.2 Views of the Data – Mapped Networks

Four main types of networks are created from the data sets: trade, alliance, diplomatic

visits, and IGO networks. The IGO networks are subdivided into categories according to

coding by Ingram, Robinson, and Busch (2005) so that there are General, Political Military,

Economic, and Social Cultural IGO networks. For example, the Political Military IGO

networks include country memberships in IGOs that are oriented to political and/or military

issues. Here, I will explain each type of network and discuss some the network diagrams in

Figures 1-7.

While the statistical model, described later in this chapter, uses all the network related

data available in the data sets for all the UNGA countries each year, the network diagrams for

the trade and IGO networks use a subset of countries because of the density of the networks;

the problem is that the diagrams are not informative when all the countries are shown with all

7 Hofstede’s dimensions (Hofstede, 2001, 2003) are not used for culture variables because of the limited number of countries dimensionalized. The study would become one of voting amongst Hofstede’s countries rather than voting in the UNGA – it’s too limiting.

66

their connections in one picture. However, alliance and diplomatic visits network diagrams

show all the UNGA countries in a given year.

Even when using subsets, an important observation when viewing these networks is

that it is difficult to draw many conclusions about the relationships unless there are obvious

outliers or patterns – the statistical model, described later in this chapter, is an improved

approach to researching the relationships. This discussion of the network diagrams helps only

to familiarize the reader with the data and the networks.

5.2.1 Trade Networks. See Figure 1 for a view of a subset of trade networks – 12 European

Union countries represented as both importers and exporters. Trade is directional and in the

diagrams, arrows point from the exporter to the importer. Notice that most countries

reciprocally import and export (For example in 1995, Italy imports $6431M USD into Belgium

while Belgium imports about a third more into Italy, $9506M USD); consequently, it is not

obvious which ones have higher in-degree centrality than others – they are about the same on

this measure amongst themselves. When the entire set of UNGA countries is included, in-

degree centralities change and are more varied. Another attribute of trade networks, already

alluded to, is that they are “valued” in terms of millions of dollars USD; this is the dollar

amount of trade that is being imported from one country to another in the year.

{Insert Figure 1 about here}

European Union countries are used to illustrate the trade networks because one of the

main goals of the EU is to develop a free-trade zone; it’s an attempt to be a single market that

67

allows free movement of people, goods, services, and capital. Also, the countries chosen for

this illustration are chosen by date – earlier and later members - because we can look into the

data to see whether those countries who were candidates for accession over the period of this

study voted in the UNGA in accordance with prominent trading countries already in the EU.

Twelve of the twenty-seven EU countries were chosen: 6 original members (France, Germany,

Italy, Luxembourg, Belgium, and the Netherlands – represented by yellow circles on the

network diagrams in Figure 1), 3 members that joined in 1995 (Finland, Sweden, and Austria –

represented by blue circles on the network diagrams in Figure 1), and 3 members that joined in

2004 (Poland, Malta, and Hungary – represented by pink circles on the network diagram in

Figure 1). Those members that joined in 2004 would have been under consideration and

thinking about their own candidacy in the 1990s because accession is a long process. European

integration requires a state to satisfy economic and political conditions known as the

Copenhagen criteria (they are a result of the Copenhagen summit in June 1993). These criteria

include a secular, democratic government, rule of law, and corresponding institutions and

freedoms. A key point is that the EU Treaty dictates that each current member state and also

the European Parliament have to agree to accept a new member. Consequently, countries

having membership in this trade club are powerful with respect to candidate countries success

in attaining EU membership, including the trade and other benefits that go with it. Perhaps,

these candidate countries will vote similarly in the UNGA with prominent EU members to

appease them?

Taking a look at the 1990 trade diagram in Figure 1, even prior to accession, EU

countries are trading with candidate countries. In 1990, only the yellow circle countries are EU

68

countries and all others are candidates. Notice that even tiny Malta trades with all the other

countries. In 1995 and 1999 diagrams Poland, Malta, and Hungary are not EU countries yet.

I will perform an illustrative experiment to see whether similar voting and EU

membership candidacy (membership in a trading block with prominent trading countries) trend

in the same direction. I will examine voting patterns between France, a very prominent

country of the EU, and the other six EU candidates (See the table and matching chart below).

Similar Voting with France by EU Candidate and Experimental Comparison Countries

% SIMILAR VOTING PROMINENT EU COUNTRY

EU CANDIDATE COUNTRY OR EXPERIMENTAL COUNTRY

1990 1995 1999

France (FRN 220) 1957 Original Member

Finland (FIN 375) 1995 Accession 30% 41% 75%

France (FRN 220) 1957 Original Member

Sweden (SWD 380) 1995 Accession 30% 34% 63%

France (FRN 220) 1957 Original Member

Austria (AUS 305) 1995 Accession 15% 34% 81%

France (FRN 220) 1957 Original Member

Malta (MLT 338) 2004 Accession 5% 34% 81%

France (FRN 220) 1957 Original Member

Hungary (HUN 310) 2004 Accession 21% 45% 81%

France (FRN 220) 1957 Original Member

Poland (POL 290) 2004 Accession 25% 46% 94%

France (FRN 220) 1957 Original Member

Russia (RUS 365) Not an EU candidate and not in the network diagrams

5% 34% 50%

France (FRN 220) 1957 Original Member

Italy (ITA 325) 1957 Original Member 80% 45% 88%

France (FRN 220) 1957 Original Member

Egypt (EGY 651) Not an EU candidate and not in the network diagrams

5% 11% 13%

69

Country Patterns of Voting with France in the UNGA

Finland (FIN 375)

Sweden (SWD 380)

Austria (AUS 305)

Malta (MLT 338) Hungary (HUN 310)

Poland (POL 290)

Russia (RUS 365)

Italy (ITA 325)Italy (ITA 325) 1957 Original

Member

Egypt (EGY 651)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1990 1995 1999

Year

% S

imila

r Vot

ing

An obvious trend is that EU candidate countries vote more similarly with France over

time. For comparison, three other countries, Russia, Italy, and Egypt – all countries not vying

for EU membership, are included in the table and chart (Italy is already a member, but is part

of the trading block). France is not influential on Russia and Egypt in regards to EU candidacy;

notice that correspondingly, the voting pattern shows similar voting to be lower in comparison

with the candidate countries and does not rise as dramatically over time. Italy is already in the

EU and while it votes with France, does not show the same pattern of increasing vote similarity

over time.

As emphasized earlier, this experiment is only meant to be illustrative since it merely

presents correlational information about a limited set of the data; many other factors other than

France being a prominent country within a trade network may be at play in these countries’

70

similar voting patterns – perhaps, being in the same geographic region plays a role, for

example? This is why the statistical model (presented later in this chapter) is an improvement

over this kind of rudimentary exploration.

5.2.2 Alliance Networks. See Figure 2 for a set of alliance networks that include all UNGA

countries. From 1990 to 2000, beginning with 1990, only years in which a significant network

change occurs are shown. In my networks, an alliance exists or it does not and it is non-

directional because each agreement is bilateral. So, either I can have bidirectional arrows

between countries that are officially allies according to binding agreements or, as I have done, I

can remove the arrows so the diagrams are cleaner to view. What are interesting about these

diagrams are the obvious clusters of allies. Also, over time, some bonds are broken and

created, sometimes joining or disconnecting clusters.

{Insert Figure 2 about here}

In 1990 there is an African cluster (many West African countries) joined to what I will

refer to as a Middle Eastern cluster by Mauritania, an Islamic country in North West Africa. In

all the years except 1999, it is the broker between these clusters. In 1999, the clusters are

completely separate. Mauritania used to be a French colony and became independent in 1960.

While previously under military rule, it legalized political parties in 1991 so, over most of the

period of this study it is governed by civilians. Another point about these two clusters is that

from 1992 onwards, they are isolated from other alliance clusters. Previous to 1992, Russia has

alliances with a couple of countries in the Islamic cluster, Iraq and Syria. Russia is the broker

71

between the Islamic and European clusters, but from 1992 onwards, Russia is part of the

European cluster. Moreover, there are other smaller groups of countries that Russia links with

prior to 1992, but afterwards, they are set out to sea in dyads, triads and a quartet – many are

sets of African countries, formerly British, French, and Portuguese colonies; also, there is an

East Asian group made up of India, Pakistan, and Bangladesh. Prior to 1992, India was

connected to Russia.

The European cluster is predictably joined to the Latin/South American cluster by

Canada and the USA. Also, Japan, the Philippines, Australia, and South Korea (beginning in

1992) are each alone connected to the USA. These latter connections last until the end of the

study period.

However, in 1992 we see that South Korea while connected to the US is also connected

to North Korea. North Korea’s only other connection is with China and China is alone

connected to North Korea. By 1994, North Korea has disconnected from South Korea to form a

lonely dyad with China. In the 1999 network notice that China has new connections with some

of the former Russian states.

In 1994, the world appears polarized, having two major clusters: a “Western” cluster

(Europe and the Americas with some notable exceptions like Australia, Japan, and South

Korea) and a tenuous African/Middle Eastern cluster. An Indian triad (India, Bangladesh, and

Pakistan) is also significant because of the huge population and political economy it represents.

This configuration remains for most of the study period except that in 1995, the new Russia is

connected to a cluster that is made up of many of its former states, now independently

recognized in the UN such as Moldova, Armenia, Ukraine, Belarus, Tajikistan, etc.

72

Given these observations regarding the alliance networks, some rudimentary analysis

tying similar voting to the cluster patterns, tie changes, and in-degree centrality is done (See

the table below). For example, since the US and Canada tie the European and American

clusters together, through so many connections on both sides, I will presuppose that their high

centrality makes them prominent and powerful such that other countries will tend to vote in

accordance with them. However, I will just choose one of them, the US. I can sample countries

in the “Western” cluster, in the “Afro-Mid East” cluster, and countries more isolated in triads

or in the quartet. A comparison will be China – does a country seemingly powerful on the

world stage yet with few official alliances command a great deal of influence in the UNGA?

Voting Similarity of a Prominent Country in the Alliance Networks with Other Less Prominent Countries

Prominent/ powerful country

Western (W), Afro-Mid East (AM), or Isolate (I) Country

1990 1992 1994 1999 2000US Brazil (W- A) 140 5% 13% 21% 19% 0%US Peru (W- A) 135 0% 19% 21% 6% 0%US Denmark (W - E) 390 45% 69% 86% 81% 79%US Netherlands (W - E) 210 90% 88% 86% 81% 79%US Nigeria (AM - A) 475 5% 6% 7% 19% 7%US Ghana (AM - A) 452 0% 7% 7% 6% 7%US Syria (AM - M) 652 0% 0% 0% 0% 0%US Jordan (AM - M) 663 5% 6% 7% 6% 0%US India (I - I) 750 0% 6% 7% 6% 0%US Pakistan (I- I) 770 5% 6% 7% 13% 0%US Angola (I - Q) 540 0% 6% 11% 0% 0%US Kenya (I - D) 501 6% 21% 7% 6% 7%

China Brazil (W- A) 140 85% 75% 69% 38% 57%China Peru (W- A) 135 85% 63% 77% 50% 43%China Denmark (W - E) 390 5% 63% 8% 6% 7%China Netherlands (W - E) 210 0% 0% 8% 6% 7%China Nigeria (AM - A) 475 90% 69% 77% 38% 64%China Ghana (AM - A) 452 90% 79% 85% 50% 71%China Syria (AM - M) 652 90% 93% 92% 77% 86%China Jordan (AM - M) 663 90% 69% 77% 56% 85%China India (I - I) 750 90% 75% 92% 69% 79%China Pakistan (I- I) 770 90% 75% 92% 69% 79%China Angola (I - Q) 540 94% 81% 78% 63% 69%China Kenya (I - D) 501 83% 64% 77% 56% 71%

% Similar Voting

73

Legend for codes used in the table above (The codes refer to a country’s position in the alliance network, as discussed earlier.): (W- A) Western country in the American cluster (W - E) Western country in the European cluster (AM – A) Afro-Mid East country in the African cluster (AM – M) Afro-Mid East country in the Middle East cluster (I – I) Isolate country – relatively isolated; from the Indian triad (I – Q) Isolate country – relatively isolated; from the quartet (I - T) Isolate country – relatively isolated; from a triad

UNGA Country Voting Similarity with the US

Brazil (W- A) 140Peru (W- A) 135

Denmark (W - E) 390Netherlands (W - E)

210

Nigeria (AM - A) 475Ghana (AM - A) 452

Syria (AM - M) 652

Jordan (AM - M) 663

India (I - I) 750

Pakistan (I- I) 770Angola (I - Q) 540

Kenya (I - D) 501

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

1990 1992 1994 1999 2000

Year

% S

imila

r Vot

es

The results suggest that it is the countries of the West, particularly the Europeans, who

tend to vote more similarly with the US. Denmark and the Netherlands have NATO (North

Atlantic Treaty Organization) in common with the US which may make them closer allies than

74

the South Americans in this exploration, Peru and Brazil. Historical relationships are an

obvious possible factor. Other countries tend to vote differently from the US.

A chart is unnecessary to show the voting pattern with China since it is essentially the

opposite of what is found for the US (See also the percentages in the table.). The NATO allies,

Denmark and the Netherlands rarely vote in accordance with China whereas the other countries

frequently vote similarly with China. It seems that alliance agreements do not necessarily

predict allied voting in the UNGA.

5.2.3 Diplomatic Visits Networks. See Figure 3 for network diagrams from 1990 to 2000 of

diplomatic visits by all UNGA countries in each year. The networks are directional and non-

valued. The arrows are in the direction of the visit – the visitor goes to see the visited country

so the head of the arrow points towards the visited country. Although the networks are

different each year, the high degree centrality countries appear to be the USA and the United

Kingdom (UKG), arrows are in both directions so they have both in-degree and out-degree

centralities. The cases of other countries vary from year to year. For example, in 1990 and

1991 Iraq seems to have many visitors, but this dies down in 1992. Another example is China

who is quite active in most years except for in 1998. The overall pattern of the network is

consistent – it has one large major cluster, not tightly connected and around it, some smaller

isolated groupings of countries and these groupings vary from year to year.

{Insert Figure 3 about here}

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The statistical model will use in- and out-degree centrality, but for a rough exploration,

I will choose the UK (UKG 200) as a high degree centrality country and test to see whether

countries, connected or not, vote in accordance with it in the UNGA. I will choose two

countries that are directly connected (C) and two that are not (NC) for each year 1990, 1995,

and 2000, in the table below.

SIMILAR VOTING IN THE DIPLOMATIC VISITS NETWORK

% SIMILAR VOTING HIGH CENTRALITY COUNTRY IN DIPLOMATIC

VISITS NETWORK

CONNECTED (C) OR NOT

CONNECTED (NC) UNGA COUNTRY IN

DIPLOMATIC VISITS NETWORK

1990 1995 2000

UK Argentina (C) 160 5%

UK Iraq (C) 645 5%

UK Poland (NC) 290 25%

UK Egypt (NC) 651 5%

UK Kuwait (C) 690 7%

UK Pakistan (C) 770 7%

UK Norway (NC) 385 43%

UK Italy (NC) 325 40%

UK Russia (C) 365 21%

UK Australia (C) 900 86%

UK Cuba (NC) 40 7%

UK Finland (NC) 375 93%

The table of information above provides inconclusive patterns – a statistical model is

required to search for the patterns sought after. Although it appears that countries not

76

connected tend to vote with the UK, a more prevalent (and likely pattern) is that European

countries tend to vote with the UK. For example, in 1990, Poland, a country vying for EU

membership votes with the UK more often than the others, none of them being European. In

1995, while the countries not connected to the UK, Norway and Italy, vote more similarly, they

are both European countries whereas the connected countries, Kuwait and Pakistan, are not.

Finally, in 2000, a connected country and a not connected country vote similarly with the UK.

However, Australia, the connected country, is a former British colony with strong ties to

Britain. Also, Finland is a European country. Whereas, Russia and Cuba are not European

countries (Russia is only partially European) and do not vote similarly with the UK.

5.2.4 IGO Networks. The density of the IGO networks requires that, for network diagrams,

a subset of the UNGA countries is used (The statistical model uses all the UNGA countries.).

The same subset of countries used in an additional analysis in Chapter 7 is used to illustrate the

two-mode IGO networks and they are: Czech Republic (or Czechoslovakia), Bosnia

Herzegovina (or Yugoslavia), Djibouti, Turkey, United Arab Emirates, and Japan. The

reasoning for choosing these countries for the additional analysis is explained in Chapter 7.

Essentially, they are outlier countries in regards to their network relationships. However, they

will also be interesting to look at in the IGO networks because of their uniqueness. Moreover,

if some of these countries share common interests, they may reveal these interests by joining

the same IGOs and this may be correlated with voting similarly in the UNGA. Again, a rough

exploratory analysis will be presented.

See Figures 4, 5, 6 and 7 for the General, Political Military, Economic, and Social

Cultural two-mode IGO networks. They are two-mode networks because the countries and the

77

IGOs are both shown – countries are depicted as red dots connected to the blue squares, the

IGOs to which the countries belong. No direction is indicated on the diagrams because an IGO

never belongs to a country; if arrows were to be shown, they would all point from the countries

to the IGOs, so they would merely clutter the diagrams. The IGOs are numbered because some

of the names are very long and acronyms sometimes do not apply (See the list of IGOs in

Appendix 2 so as to correspond the number with the name of the IGO.)

{Insert Figures 4, 5, 6, and 7 here)

The years of networks, shown in each figure, are chosen based on changes in the

networks. Some examples of changes are noted here. In the case of the General IGOs, 1990,

1995, and 2000 are years with some changes. Djibouti joins 3100 (International Seabed

Authority) and Turkey appears to have left 1730 (European Organization for the Safety of Air

Navigation (EUROCONTROL) in 1995. Note that by this time, Czechoslovakia (CZE) has

become the Czech Republic (CZR). Also, Djibouti joins 4050 (Regional African Satellite

Communications Organization) and 50 (African Civil Service Observatory) by the year 2000.

Yugoslavia has joined a number of IGOs such as 3100 (International Seabed Authority), and

1440 (Danube Commission) in 2000. Overall, Djibouti, United Arab Emirates, and Japan

share more common memberships. However, their average similar voting over three years is

47%, compared to an average of all the countries in the subset of 52% (See Appendix 3 for

more details of the calculations). Also, Japan, the Czech Republic, and Turkey may share

common interests, based on their memberships. They do vote together more often than all the

other countries in the subset together, on average over three years – 65% of the time.

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Referring to Figure 5, the set of Political Military IGO diagrams, from 1990 to 1992,

the only changes are that Czechoslovakia has left 4460 (Warsaw Treaty Organization) and

Yugoslavia has left 1080 (Central European Initiative (CEI)), having no memberships. Japan

also has no IGO memberships of this type in any year. 1993 sees no changes and in 1994, BOS

has memberships in 1080 (Central European Initiative (CEI)) and 3850 (Organization of the

Islamic Conference). Moreover, the Czech Republic joins 1390 (Council of Europe) and 1550

(Euro Atlantic Partnership Council). In 1999, it adds 3700 (North Atlantic Treaty

Organization). In 2000, Yugoslavia rejoins 1080 (Central European Initiative (CEI)) and this is

its only membership. Judging from the IGO memberships, overall, the United Arab Emirates

and Djibouti share common interests – this bears out at 91% similar voting on average over

three years (See Appendix 3 for details) and Turkey and the Czech Republic are a pair sharing

common interests; they vote similarly 71% of the time.

The Economic IGO networks in Figure 6 include the years 1990, 1993, 1995, 1997, and

2000. Taking an overall look at the diagrams, the countries are well connected with each other

through IGOs and Japan takes the lead in terms of membership in the most Economic IGOs,

which contrasts dramatically against its lack of membership in any Political Military IGOs,

likely related to its role in WWII. Some of the IGOs that seem to be central amongst all the

countries across most years (although not every country is necessarily a member of all of these)

are: 3000 (International Pepper Community), 2750 (International Finance Corporation), 4420

(United Nations Industrial Development Organization), 1840 (Food and Agricultural

Organization), 4570 (World Tourism Organization), 1160 (Common Fund for Commodities).

The similarity of voting by Japan with the other countries is 51% (See Appendix 3), close to

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the 52% average of all the countries so it is hard to say whether the IGO network might be

influential in particular for Japan.

Also, Japan, the Czech Republic, and Turkey seem to share many common

memberships: 3200 (International Union for the Protection of Industrial Property), 3750

(Organization for Economic Cooperation and Development), 3540 (Multilateral Investment

Guarantee Agency), 3940 (Permanent Court of Arbitration), 3210 (International Union for the

Protection of Literary and Artistic Works), 810 (Bank for International Settlement), 3260

(International Wheat Council), 2680 (International Cotton Advisory Committee), 580 (Asian

Development Bank) for some examples. A question then, is will the average of the percentage

of similar voting be higher amongst the latter three compared to all the countries combined? In

fact, it is – 65% similar voting compared to 52%.

The Social Cultural IGO networks in Figure 7 include the years 1990, 1993, 1994,

1996, 1999, and 2000 and once again, Japan is highly connected. The United Arab Emirates

seems to increase its memberships in the late 1990s, rivaling Japan. All the countries in the

subset are highly connected to each other and the main IGOs that connect them include: 3170

(International Telecommunications Satellite Organization), 2640 (International Committee of

Military Medicine and Pharmacy), 1650 (European Customs Union Study Group), 3530

(Multilateral Fund for the Implementation of the Montreal Protocol), 2930 (International

Office of Epizootics), 2500 (International Civil Aviation Organization), 2780 (International

Hydrographic Organization), 2290 (Intergovernmental Oceanographic Commission), 4430

(Universal Postal Union), 4530 (World Meteorological Organization), 4410 (United Nations

Educational, Scientific, and Cultural Organization), 4550 (World Health Organization), 2830

(International Labor Organization), 2760 (International Fund for Agricultural Development),

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2780 (International Hydrographic Organization), and 2290 (Intergovernmental Oceanographic

Commission). Do these countries also vote similarly with each other? Also, since the United

Arab Emirates has increased its memberships, will its percentage of similar voting with Japan

increase over the years, since they presumably become more connected?

In answer to these questions, earlier it was mentioned that the countries tend to vote

with each other 52% of the time. Moreover, the possible Japan-United Arab Emirates trend

may not exist – while similar voting does increase, it is not a steadily rising trend line to the

end of the study period i.e., 5% in 1990, up to 56% in 1995, and then a drop to 18% in 2000.

Also, these rates of similar voting are well below the total country similar voting averages i.e.,

39% in 1990, up to 74% in 1995, and down to 47% in 2000.

In summary, this preliminary exploration familiarizes the reader with the networked

data and provides hints that some correlations between voting and networked relationships may

exist. The statistical model to be described next, with its dependent and independent variables,

will provide improved insight.

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5.3 Dependent Variable

The same dependent variable is used in all hypothesis testing. It is a country-dyad

measure of vote similarity on each UNGA resolution that is then aggregated over each year. It

is aggregated to provide a measure of the tendency of two countries to vote cooperatively.

Consequently, it is a continuous variable, the percentage of vote similarity between country

dyads over a year, named “Similar votes”.

Similar votes = (Number of same votes by two countries over the year) (Number of times both countries voted on the same resolutions in the year) 5.4 Independent Variables

The independent variables of interest are as follows:

Relative trade in-degree centrality. In-degree centrality is a power measure related to

prominence or prestige (Hanneman, 2001), receptivity or popularity (Wasserman and Faust,

2005). When many other actors send direct ties to another actor, it indicates their importance.

The in-degree of a node is the number of nodes adjacent to it or the number of arcs terminating

at it (Wasserman and Faust, 2005). Countries with higher trade in-degree centrality offer

market access to large attractive markets. Relative in-degree centrality is the absolute value of

the difference between the two countries’ normed in-degree centralities. The difference

between the centralities becomes larger as the countries’ differ more in their prominence. A

less prominent country may defer to a more prominent country by, for example, cooperatively

voting so as to potentially gain access to the prominent country’s markets.

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Total trade in-degree centrality. This is the sum of the normed trade in-degree centralities of

the two nations in the dyad. Trade imports are directional and each country that imports into

another is a tie directed toward that other country. At low values, two countries are similar in

that they both have low prominence whereas at high values, two countries are similar because

they are both prominent. This measure can tease out whether it is relative power, homophily or

just similarity in low/high prominence that leads to similar voting (a U-shaped curve would

suggest homophily, an inverted U-shaped curve would suggest relative power, etc.).

Total alliance degree centrality. This is the sum of the normed alliance degree centralities of

the two nations in the dyad. Alliances are considered non-directional or reciprocal. A country

having more alliances has higher degree centrality. This measure tests similar concepts in the

alliance network as does the analogous trade measure.

Relative alliance degree centrality. This is the absolute value of the difference in the normed

alliance degree centralities of the countries in the dyad. It is a non-directional measure. This

measure tests similar concepts in the alliance network as does the analogous trade measure.

Visits total out-degree centrality. This is the total of the normed out-degree centralities of the

countries in the dyad. Out-degree centrality increases with the number of visits the country

makes to other nations in the year; it is a directional measure. Some countries may be

successful at being influential by sharing their information widely through country visits.

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Visits total in-degree centrality. This is the total of the normed in-degree centralities of the

countries in the dyad. In-degree centrality increases with the number of visits the country

receives from other nations in the year; it is a directional measure. Some countries may be

influential because of their prominence in terms of receipt of many visitors. These visitors may

arrive for a variety of reasons. As a result of its popularity, the visited country may be known

to hold more information and/or act as an opinion leader. In the face of uncertainty/ambiguity,

other countries may choose to imitate by voting similarly.

IGO connectedness. This is a measure duplicated from Ingram, Robinson and Busch (2005).

Symmetric country-country matrices report the number of common EIGO, SCIGO,

Military/Political IGO and General IGO memberships that country dyads have. A routine in

UCInet converts the country-IGO membership affiliation matrices by taking the cross product

with its transpose (Borgatti, Everett and Freeman, 2002). The COW data coded differing levels

of membership including observer status. The codes were converted to be binomial and

observer status was included as membership in this case since it indicates some level of

participation, common interest or receipt of information. Countries that share many of the same

memberships may have more opportunities to communicate with each other and develop

common views, leading to similar decisions.

5.5 Control Variables Control variables used in the models include: Trade intensity. Dyadic trade intensity is just the sum of the total trade in millions of current

year USD between each pair of countries for the year. This variable is to control for the case

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that countries vote cooperatively because they are trade partners and/or are increasing their

volume of trade with each other, rather than because of their positions in the trade network.

Allies. This is an indicator variable that is 1 if the country dyad has an alliance agreement and 0

otherwise. This variable controls for the case that countries in a dyad vote together because

they are allies rather than because of their positions in the alliance network.

Similar polity score. Each country has a polity score from 10 to -10. First, the values for each

of the two countries are subtracted and the absolute value is taken such that a smaller value

means they are similar. However, so that the variable increases as the countries become more

similar the absolute value is subtracted from 1. i.e., 1 – abs( score 1 – score 2). This creates a

continuous variable that increases as the two countries are more similar in regime type or

political ideology. Similar regime type is believed to be a predictor for similar voting (Voeten,

2000 ). This variable is also a main effect, utilized in interactions, in some models.

Similar wealth. For each dyad, the countries’ GDP/capita (in constant 1985 USD) are

subtracted, the absolute value taken and then subtracted from 1 so as to have an increasing

value as countries become more similar in wealth i.e., 1- abs(GDP/capita1 – GDP/capita2).

This value will be larger for two countries in a dyad that are both wealthy or both poor. Wealth

is a motivation for similar voting (Voeten, 2000).

Combined wealth. For each dyad the countries’ GDP/capitas are added. Therefore, if both

countries are poor it will be low, it will be medium if both countries each have medium values

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of wealth or if one is rich and the other poor, and it will be high if both countries are wealthy.

Wealthy countries are believed to vote similarly (Voeten, 2000).

Contiguity. The COW data codes countries based on various geographic characteristics that

make them near to each other. For this study, these codes were converted to an indicator

variable that is 1 if the COW data assigned a contiguity code of 1 or greater and 0 otherwise.

Proximity may be a motivation to vote similarly – countries close geographically may

experience some similar conditions/issues and therefore, have similar concerns (Ingram,

Robinson and Busch, 2005).

Same culture – Huntington. Each country’s culture was coded according to Huntington’s map

Fig. 1.3 (Huntington, 1996). He assigns each country a culture: Western, Islamic, Sinic, Latin

American, Orthodox, African, Japanese and Buddhist. When it was not clear from the map

what culture Huntington had assigned, the CIA World Fact Book provided the required

statistics to assign the country a culture code. A comparison of the countries in each dyad

resulted in the creation of an indicator variable, 1 if the countries have the same culture and 0

otherwise. This variable is also a main effect, utilized in interactions, in some models.

Countries having similar cultures may hold common views on issues and therefore, vote

similarly. This type of value homophily could be a moderator when countries also share high

connectedness through many common memberships in IGOs.

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5.6 Model Specification

The data consists of T=11 years of data with N = 162 968 country dyads, where N is much

larger than T, and a different NT number of country dyads for each year (N1990 = 11 781, N1991

= 13 366, N1992 = 14 861; N1993 = 15 357,. N1994 = 14 530, N1995 = 15 557; N1996 = 16 037,

N1997 = 15 219, N1998 = 15 500, N1999 = 14 518; N2000 = 16 242), so it is an unbalanced panel

data set (See Table 1). Also, this data has several issues to deal with including

heteroscedasticity, serial correlation and cross-sectional correlation of the errors. Variance

decomposition (See Table 2) shows both longitudinal and cross-sectional variation but, to

consider the time element in the data, a longitudinal model is chosen rather than pooling the

data, an approach that would incorrectly assume all the events contained in the data happen at

the same time; this is a necessary trade-off due to technical limitations. Other possible

problems are addressed for completeness. Moreover, extensive additional analysis (See

Chapter 7.) is conducted to investigate influential observations and the potential for an omitted

third variable.

{Insert Table 1 and 2 about here}

The heteroscedasticity, serial correlation, and cross-sectional correlation result in a

variance-covariance matrix of the errors that does not have constant variance along the

diagonal and zero off-diagonal covariances, resulting in biased standard errors. Plots of the

residuals and a Breusch-Pagan / Cook-Weisberg test for heteroscedasticity confirm that this is

a problem. Additionally, a Durbin Watson statistic test shows that there is serial correlation.

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Heteroscedasticity and serial correlation are reduced by calculating robust (White)

standard errors and using FGLS to transform the variance-covariance matrix. In particular, the

Prais-Winston method is the FGLS approach used to address first order autocorrelation. It

iteratively calculates a new sample correlation coefficient estimate and transforms the

variance-covariance matrix. First, it finds an FGLS estimator using an estimate of the

correlation coefficient, computes a set of residuals, and obtains a revised value of the estimate

for the correlation coefficient which is used to transform the matrix, and estimate with OLS

(Wooldridge, 2006; 426).

Although a differencing approach rather than fixed effects is often suggested in the case

of serial correlation (Wooldridge, 2006; 491), it is not used in the main model because it would

alter the research question to whether a change in the IVs of interest leads to a change in the

DV and this is not the question of this paper. Another problem with differencing is that some

variables with low variation are dropped. A Hausman test and a Breusch and Pagan Lagrangian

multiplier test for random effects were done both of which confirmed that a fixed effects model

would be preferred over a random effects model – the independent variables are not orthogonal

to the unobserved variables in the error term. Although it would be nice to control for some

unobservable effects including those related to country-dyad and country effects that are not

modeled, they cannot be modeled using dummies (as fixed effects) because the number of

variables becomes too large given the number of observations for each dyad in the data set8.

8 Using an approach that pools the data may allow for the incorporation of one type of fixed effects but this approach is not chosen because the time variation in the data is not recognized. Variance decomposition shows that many variables of interest have predominantly cross-sectional variation, but some have time variation as well and it was considered desirable to present a single main model (See Table 2 for the variance decomposition). Moreover, to specify the model correctly, the lagged DV is required and it cannot be included in a pooled approach.

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A particular type of serial correlation is related to the potential for a dyad to vote

similarly over time. If this is the case, then the replication downward biases standard errors.

This is specifically dealt with in the model by clustering on country dyads, relaxing the

assumption of independence of the errors of the same country dyad over time when calculating

the variance-covariance matrix of the residuals.

Moreover, two potential difficulties resulting in cross-sectional correlation arise

because of the dyadic nature of the dependent variable. One concern is transitivity in the

dependent variable; however, being an aggregated percentage alleviates this concern to some

extent as will be explained. If country A and B both vote “yes” and country A and C also vote

the same way then C and B must also have voted “yes”. This transitive nature of voting results

in a kind of replication of observations and, therefore, cross-sectional correlation in the error

terms, leading to low standard errors and overly optimistic results in terms of significance

levels. However, this dyadic DV is a percentage aggregated over the year for the same dyads

on multiple votes. If this percentage is 90-100% very often, then the concern is still valid.

However, the mean of similar voting is about 53%, thus, on average (See Table 1), A and B

vote the same way 53% of the time and A and C vote, on average, the same way at the same

rate, but potentially on different votes, therefore, it is hard to predict whether B and C are

voting the same way on those same votes. If one is still concerned, the model has a few aspects

that may help to alleviate the problem including the use of robust standard errors and GLS.

The second cross-sectional issue is that a country in a dyad may have a consistent

voting bias, voting in a particular way regularly. Thus once again, standard errors are

downward biased because of the replication. This problem would occur over time as well.

Differencing was used in a sensitivity analysis (not shown) to test for the extent of the problem

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serially and it was not found to be a substantial issue. This also tested the cross-sectional

problem since the consistent bias of country A values is removed in variables for both dyads

ABt and ACt by subtracting ABt-1 values from ABt values and ACt-1 values from ACt values.

Multi-collinearity presents a potential although not serious problem (See Table 1). The

IGO connectedness variables are correlated with each other. Also, the relative trade and total

trade in-degree centrality measures are correlated and the two wealth measures (combined and

similar wealth) are correlated with each other. Wealth measures and trade centrality measures

are somewhat correlated. The large sample size mitigates this potential problem (Wooldridge,

2006; 104).

Misspecification is avoided by using appropriate controls from theory from Russett and

Oneal (2001), Voeten(2000), and Ingram, Robinson and Busch (2005). In all cases, F tests

provide evidence that the models presented are significant and coefficients of determination are

in the 80% range. Another potential issue is the bounded dependent variable. Percentage

similar voting ranges from 0-100%; however, when such a “variable takes on many different

values, a special econometric model is rarely necessary.” (Wooldridge, 2006; 582) This

dependent variable has a mean of 53% +/- 34% and takes on a full range of values so special

methods are probably not required in this case.

In general, endogeneity is not an expected problem for network theoretic reasons. The

direction of causality is theoretically determined. For example, it makes little sense that similar

dyadic voting causes a player to have a position in a network. This kind of decision, to alter a

voting pattern in accordance with one other country, would simultaneously change

relationships with many others, potentially creating unknown havoc (Ingram, Robinson and

Busch, 2005).

The general form of the model is as follows where are the country-dyads and

are the years 1990-2000:

Ni ,...,1=

Tt ,...,1=

90

ituitxtiyity ito ++−+= 11, βαβ

oβ is a vector of constants

ity is a vector of continuous dependent variables

1, −tiy is a vector of lagged dependent variables

itα are parameters of the lagged dependent variable are 1 x k1 vectors of covariates itx

1β is a k1 x 1 vector of parameters to be estimated

is a vector of error terms itu

In summary, the model is estimated using FGLS (Prais-Winston) which corrects for

auto-correlation. It uses robust (White) standard errors and clusters on country-dyad.

Moreover, it has a lagged dependent variable.

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CHAPTER SIX: RESULTS

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6.1 Economic Embeddedness Referring to Table 3, Models 1a, 1b, and 1c progressively add variables: Model 1a is a

trade model, Model 1b includes alliance variables, and Model 1c adds the diplomatic visits

variables. The coefficients in all Models 1a, 1b and 1c on Relative Trade In-degree Centrality

and Relative Trade In-degree Centrality Squared are positive and of similar magnitude,

suggesting only a very slightly upward sloping curve, essentially a straight line, since the

coefficient on the squared term is very small. This result is support for H1a and, consequently,

an important concept is supported. As countries increasingly differ in their network centrality

positions in the trade network, representing their increasing interdependence, they are more

likely to vote together.

In the case of Total Trade In-degree Centrality, consistent results arise, both in sign and

magnitude in the three Models 1a, 1b, and 1c. However, a downward sloping curve is the result

suggesting that only the downward side of the inverted U shape that was predicted in H1b

results. As pairs of countries become more central in the trade network, they are less likely to

vote together. This is supportive of the idea that they do not need to reciprocate with each other

– they are already powerful. Whereas, less powerful countries and countries that differ in

power or are of medium prominence in the trade network do tend to reciprocate with each

other.

Notice that, as expected, dyadic trade intensity as a measure of market power plays a

much lesser role, although it is significant. It has a tiny effect (that wavers between being

positive and negative in the models) compared to the positional measures; this is evidence that

a social political behavior such as voting is effected by prominence, a social popularity

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measure, rather than the strength of ties, a more economically oriented measure in this study

because it is based on the volume of trade in monetary terms.

{Insert Table 3 about here}

6.2 Institutional Embeddedness

Table 3, Model 1b, introduces Relative and Total Alliance degree Centrality with their

respective squared terms. Model 1c introduces the Visits variables. All the alliance degree

parameters are significant, negative and of similar magnitudes from model to model.

Therefore, both relative and total alliance degree centrality are downward sloping curves,

falsifying H2a and H2b’s predictions of positive slopes. However, position in the alliance

network does matter since the alliance variables affect voting, just not in the predicted manner.

The “relative” measure suggests that similarity in centrality results in cooperative voting and

the “total” measure narrows this down to: similarity in low centrality in the network results in

cooperative voting i.e., countries with fewer allies vote together. A deeper investigation is

required to explain this result.

The visits centrality measures, added to Model 1c, are both significant and, together,

suggest that prominence in the visits network encourages cooperative voting – the Visits Total

In-degree variable has a positive coefficient whereas the Out-degree variable has a significant

negative coefficient. Two countries that are receiving many visits tend to vote similarly,

whereas those countries working hard to influence by making visits are less likely to vote

together. Perhaps, the prominent countries are influential because they are in receipt of more

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information and are known for this - they are known to be wise. In contrast, those making

many visits are not as successful at being influential.

See Table 4 for analysis of the effects of IGO connectedness, including different types

of IGO connectedness and nested embeddedness, on Percentage of Similar Voting. Model 2a

and 2b both show that IGO connectedness positively affects voting so H4 is supported.

However, when cultural or political embeddedness effects are interacted with IGO

connectedness either no effect, as in the former case, or a negative effect, as in the latter case

occurs. Similar politics may cause disruptions within an IGO forum. Note that instead, in all

models, Similar Polity Score has a positive direct effect on Percentage of Similar Voting.

Model 3a breaks down the various types of IGOs such that connectedness in economic

IGOs, political military IGOs, general IGOs, and social cultural IGOs positively affects the

percentage of similar voting. Notice that political military IGO connectedness has the largest

effect. This corresponds with the type of topics with which the UNGA is most often concerned.

When nesting of embeddedness is tested through the interaction variables in Model 3b,

it’s found that cultural nesting weakly and negatively affects similar voting when paired with

social cultural IGO connectedness. Also, political nesting negatively affects voting when

paired with economic and political military IGO connectedness. Thus, H5a and H5b are not

supported. If nested embeddedness has negative effects on cooperation then this offers a future

research opportunity since current theory does not explain it.

A final comparison, discussed earlier, is to consider whether economic or institutional

embeddedness effects are more impactful on decision making. The estimates on measures

utilizing centralities are comparable since normed centralities are used. From an absolute value

perspective, the alliance estimates have larger magnitudes than trade and visits estimates. In

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model 1c, Relative Trade In-degree Centrality has only a slightly larger effect than Visits Total

In-degree. The effect size seems to depend on the form of embeddedness – therefore, it’s

preferable not to draw conclusions by generalizing to the classifications, economic versus

institutional types of embeddedness.

Once again, the control variable trade intensity is significant but, with a tiny effect

compared to the institutional embeddedness variables. This provides further evidence that

market power represented by an economic dyadic strength of ties trade measure is not an

important effect compared to more social political oriented variables on voting, a highly social

political behavior itself.

{Insert Table 4 about here}

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CHAPTER SEVEN: ADDITIONAL ANALYSIS

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Further analysis that delves deeply into the data is presented to address various issues

and lend assurance to the results. Endogeneity, cross-sectional correlation, and influential

observations are some of the issues. Traditionally, endogeneity has been dealt with differently,

using lagged independent variables of interest. More recently tested theory using instruments

predicts causality is correctly represented in the network model so this issue is believed to be

adequately addressed (Ingram, Robinson and Busch, 2005). However, cross-sectional

correlation, stemming from the use of a dyadic dependent variable, is a more difficult problem

- if the approach used is not considered satisfactory then QAP (quadratic assignment

procedure) may be applied and the QAP results compared with these results; however, QAP’s

requirement for a more structured data set interferes with the ability to compare the results9.

Although QAP will not be used, a sensitivity analysis using differencing was done, as

mentioned earlier and the problem was not found to be consequential. Also, to investigate

whether the main model is reasonably representing the main trends, an in-depth cross-sectional

analysis is included that considers influential observations. Finally, a rough look at how sudden

historical events affect some of the international relationships, events represented by the

variables used in this study, is done in Section 7.4 entitled, “Repercussions of Shocks”.

7.1 Causality

The incorporation of lagged independent variables is an approach used to deal with

potential endogeneity (Russett and Oneal, 2001) but it is not used in this paper. The approach

considers the timing of events and, therefore the direction of causality – whether similar voting

is an outcome of the effects of the various independent variables as hypothesized or whether

9 QAP requires a square matrix so many observations have to be dropped. The results are less comparable when using very different data sets.

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some of the independent variables are outcomes of similar voting. A problem with this

approach is that the panel data is annual and to examine a situation where sharing information

is influential, the recency of the information, in order to be influential, may need to be less than

a year. Consequently, lagging by a year is too distant in time.

7.2 Cross-Sectional Correlation

In case the cross-sectional correlation remains a concern, an approach for handling non-

independent observations is QAP which allows for improved hypothesis testing because it

permutes the data set so that it corresponds with a null hypothesis that takes into account the

cross-sectional correlation amongst observations (Simpson, 2001). Thus, the coefficients’

significance is based on a comparison against the null hypothesis.

To perform this QAP testing, the data set has to be cut down so that each year is the

same square matrix – each year must contain the same dyads as all other years such that several

countries are no longer included. This systematic sampling approach could alter the results so

that they are not comparable with the results presented here i.e., after the Cold War, several

Eastern block countries became independent and members of the UN. No longer part of

Russia, their votes may be quite different.

7.3 Influential Observations

According to Belsley et al., influential observations or subsets of observations may

occur as a result of incorrectly recorded or observed data10. Alternatively, the outliers and

10 The data used in this study originates from experts so searching for incorrect data is not the focus of this analysis.

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leverage11 points that are influential may be legitimately occurring extreme observations and

this is considered a potentially beneficial situation, improving estimation efficiency (Belsley et

al, 1980; 6). However, the advice is to isolate these influential observations, whether beneficial

or not and determine the extent to which the estimates depend on them. Each cross-section may

be affected by influential observations that are particular to a year and not representative of the

general trends that this paper seeks to uncover. Also, reviewing the data in this manner may

suggest a better specification for the model or strengthen the case that this model is well

specified.

The main concern to be investigated is that the model estimates are representative of

the majority and not a subset of the data. Belsley et al. (1980; 39) advocates that single-row

analysis is not sufficient for this investigation. While multiple-row analysis is acceptable

additional analysis, a substitute for it can be a combination of partial-regression leverage plots

and stepwise multiple-row methods (Belsley et al., 1980; 39). This latter approach is preferred

for large data sets because of the problem of masking (Belsley et al., 1980; 39). Plots will be

included in this analysis; however, stepwise multiple-row methods are not technologically

feasible at this time12. After potentially influential observations are identified, I will delete

them in both the small and then the large samples, presenting the results only as a sensitivity

analysis.

For this analysis, I have taken a small sample because of the enormity of the data set.

To determine the small sample, I examined leverage-versus-residual-squared plots and partial- 11 Large residuals may be influential outliers. However, another type of influential observation may have a very small residual, yet be an isolated point. The regression line may run through the isolated point, thus the residual is small but, the point’s leverage is high. Points that have large residuals or are high in leverage are not necessarily influential (Stata Reference N-R (Release 8), p361). 12 An alternative to a stepwise approach is an all-possible-subsets regression (Belsley et al., 1980; 34). The code has been written by a Stata user, but not tried out yet. It may be a possible future approach; however, given the size of even the small sample, it is reasoned that visual techniques (i.e., plots) are most effective for finding groups of influential observations.

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regression leverage plots (for each variable of interest) of all the observations for each year.

Since many observations were clustered densely, I could identify those country dyads that

stood out. After creating extensive lists of these dyads, I chose six countries that were

frequently noticed as outlying and out of these, chose at least a couple of large countries in

terms of GDP per capita and a couple of small countries on the same basis. The small sample

data set includes all dyads for all years 1990 to 2000 that include one of the six countries:

Japan (740), United Arab Emirates (696), Turkey (640), Czech Republic (316), Djibouti (522)

and Bosnia Herzegovina (346). While Japan and Turkey are large, the others, especially

Djibouti, are small.

As an initial step, these data were modeled as was the entire sample for comparison

purposes. The basic statistics and models are shown in Tables 5, 6, and 7. They are in

Appendix 4.

{Insert Tables 5, 6, and 7 from Appendix 4 about here}

Comparing the small sample results in Table 6 with the large sample results in Table 3,

prior to making any deletions, the main conclusion is that this smaller sample is indeed made

up of outliers that would have the effect of amplifying the original results, with some

exceptions. Starting with the trade variables, the Relative Trade In-degree Centrality

coefficient and its squared term have the opposite sign – they are negative in the small sample

whereas they are positive in the large sample - suggesting a downward instead of an upward

slope – this is the main exception to the magnification problem mentioned. Consequently, these

outliers could be reducing the upward trend found in the main large sample model. The other

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trade, alliance, and visits variables’ coefficients have the same signs but, are larger – they

magnify the results of the large sample model. Looking at the control variables, it is noted that

the Same Polity Score coefficient is not quite double in magnitude and the Same Culture

variable is very similar in magnitude. Also, all the variables of interest are significant as in the

main model except Visits Total Out-degree. One other strange result in the small sample is the

small magnitude in Model 1c of the coefficient for Relative Alliance Degree Centrality. The

coefficient’s magnitude is extremely small compared to the large sample coefficient and

compared to the Model 1b small sample coefficient.

Comparing Table 4 – the large sample results for IGO connectedness and nested

embeddedness tests – with Table 7, the small sample equivalent prior to deletions, what is

evident are changes in the variables that are significant and once again, magnification. IGO

Connectedness has a larger effect on its own in the small sample Model 2a. In Model 2b, the

nested effect of political ideology with IGO connectedness now has a positive and larger effect.

In Models 3a and 3b, that breakdown the type of IGO connectedness into Economic, General,

Political Military, and Social Cultural classifications and then add nested tests, many variables

lose significance. Economic and Political Military IGO connectedness lose significance and the

effects of Social Cultural IGO connectedness are much larger. All the nested effects of culture

are not significant – in the large sample model the interaction of Culture and Social Cultural

IGO connectedness is significant. Moreover, the nested effects of political ideology all change

in significance – with Economic IGOs it is now not significant, with General IGOs it is

significant and larger, with Political Military IGOs it loses significance, and with Social

Cultural IGOs it becomes significant and larger (negative). Changes occur with the control

variables in significance and size. Most notable is that amongst all these changes, the Same

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Culture – Huntington coefficient stays very similar to that of the large sample. Culture has an

effect on similar voting even using an otherwise strangely behaving smaller sample.

A conclusion from this analytical comparison is that the potential magnification effects

and other oddities of this sample suggest that it is worthwhile performing a diagnostic analysis

according to Belsley et al. (1980). After the diagnosis is completed, Belsley et al. (1980; 9)

suggests that if some observations or a set of them are found to be erroneous and highly

influential, then deletion, down-weighting, or model reformulation are possible options.

However, very often, no action is taken because the observations are beneficial, serving as

valid evidence (Belsley et al., 1980; 9, 16).

7.3.1 Single-Row Analysis. Single-row analysis includes row deletion in the set of

observations to discover the effects on the residuals, estimated coefficients, the predicted

(fitted) values, and the covariance structure of the coefficients. Also, the diagonal values of the

hat-matrix, if large, are indicative of leverage points (Belsley et al., 1980).

Residuals:

A listing of the magnitudes of the studentized residuals helps to identify outliers

(Belsley et al., 1980; 19) (See Tables 8A to 8K in Appendix 4). Studentized residuals use the

root mean square error of a regression that omits the ith observation – the deleted row.

RSTUDENT = i

ii his

ee

−=

1)(*

{Insert Tables 8A to 8K in Appendix 4 about here}

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Since RSTUDENT is less dependent on sample size, an absolute cutoff for extreme

values is a value in excess of two. The largest of these residuals for each year are listed in a

summary table at the end of this section (Table 13 in Appendix 4). While there are several

residuals that surpass a value of two, some that are high include C20C740 (Canada and

Japan), C437C522 (Ivory Coast and Djibouti), C346C640 (Bosnia & Herzegovina and

Turkey), C20C346 (Canada and Bosnia & Herzegovina), C346C900 (Bosnia &

Herzegovina and Australia). Although these are outliers, they may not be influential so

further investigation as follows is required.

Leverage Points:

The hi are the diagonal elements of the least-squares projection matrix (called the “hat

matrix”):

TT XXXXH 1)( −=

that determines the predicted values (Belsley et al., 1980):

(0 <= hi <= 1) HyXby ==^

The influence on the yi of the fitted value is contained in hi. Moreover, the diagonal

elements of H are related to the distance between xi and . X-outliers will have large hi values

and this is useful information for detecting multivariate outliers (Belsley et al., 1980; 17). The

ith observation is a leverage point when hi > 2p/n where p is the number of independent

^

iy

−x

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variables and n is the sample size (Belsley et al., 1980; 17). This is a size-adjusted cutoff since

an absolute cutoff is inappropriate (Belsley et al., 1980; 28). These leverage points are listed in

Table 9 in Appendix 4.

{Insert Table 9 inclusive of Tables 9A to 9K in Appendix 4 about here}

The largest are listed in a summary table for cross-comparison. Dyads that frequently occur

over the years are: C2C740 (USA and Japan) and C2C522 (USA and Djibouti).

Coefficient Sensitivity:

The change in the coefficients when an observation is deleted is computed as follows:

(Belsley et al., 1980; 13)

DFBETA = b – b(i) = [(XTX)-1xiT ei ] /(1 – hi)

Where hi = xi(XTX)-1xiT

A large sample size decreases the chance of this measure being large when deleting one

observation. This chance is reduced in direct proportion to sample size unless the measure is

scaled in which case it is reduced in proportion to the square root of n. Consequently, absolute

cutoffs are inappropriate and instead, a size-adjusted cutoff: (2/ sqrt of n) is suggested (Belsley

et al., 1980; 28) and is used here as an initial way to list the potential deviant observations. A

more limiting cut-off rule is when the observation changes the coefficient by a standard error

105

or more. This rule may be used to reduce the list of observations of which to take notice. An

additional consideration is the length of the list of DFBETAs that surpass the cutoff.

Coefficients with a longer list have more extreme behavior (Belsley et al., 1980; 49).

{Insert Tables 10A to 10C in Appendix 4 about here}

For example, reviewing the dfbetas for Relative Trade In-degree Centrality over the years, the

estimate seems to be affected by C2C740 (USA and Japan), C344C346 (Croatia and Bosnia

& Herzegovina), C2C522 (USA and Djibouti), C346C438 (Bosnia & Herzegovina and

Guinea), C20C522 (Canada and Djibouti), C437C522 (Ivory Coast and Djibouti),

C433C522 (Senegal and Djibouti), and C344C640 (Croatia and Turkey).

While this detailed analysis could be performed for each variable, a wider view is that

three dyads repeatedly show up over the years across the estimates for the centrality measures

(i.e., trade, alliance, visits in Tables 10A and 10B in Appendix 4): C2C740 (USA and Japan),

C2C522 (USA and Djibouti), and C20C522 (Canada and Djibouti). Note that these

observations are also points of high leverage. However, they’re not frequently amongst the

largest residuals (C2C522 is an outlier in 1990 and C20C740 is an outlier in 1994).

The estimates for the IGO connectedness variables (See Table 10C – IGO, Econ,

General, Political Military, and Social Cultural connectedness), are frequently affected by:

C2C740 (USA and Japan), C52C740 (Trinidad & Tobago and Japan), C52C522 (Trinidad

& Tobago and Djibouti), C52C696 (Trinidad & Tobago and United Arab Emirates), and

C437C522 ( Ivory Coast and Djibouti). Once again, C2C740 (USA and Japan) is listed and

is a high leverage point. C52C522 (Trinidad & Tobago and Djibouti) and C52C696

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(Trinidad & Tobago and United Arab Emirates) have large residuals in 1991. C437C522

(Ivory Coast and Djibouti) has a large residual in 1997, 1998, 1999 and 2000. DFBETAs

reveal the dyad C2C740 and the country Djibouti (C522) as potentially influential.

Covariance Matrix Sensitivity:

The COVRATIO is another measure of influence of the ith observation. It considers the

effect on the variance-covariance matrix of the estimates – it is a ratio of the matrix with the ith

observation to that without the ith observation (Belsley, 1980; 22). Therefore, it is “a measure

of the effect of the ith observation on the efficiency of coefficient estimation.” (Belsley et al.,

1980; 48). Extreme values of COVRATIO lie outside the range 1 +/- 3(p/n) (Belsley, 1980;

23). This is a size-adjusted cutoff since an absolute cutoff is inappropriate (Belsley et al., 1980;

28).

Referring to Table 11 in Appendix 4, the list of COVRATIOs for each year, it becomes

apparent that many of the same dyads seemingly influential on the estimates (DFBETAs) also

stand out in the COVRATIO tables, suggesting that these observations are also affecting the

efficiency of the estimates. C2C740, C2C522, C20C740 have been noticed through the other

diagnostics. However, C350C640 (Greece and Turkey) and C344C346 (Croatia and Bosnia

& Herzegovina) frequently arise over the years in the COVRATIO table. Belsley et al. (1980;

48) point out that COVRATIO is a comprehensive diagnostic because this measure combines

the issues of high leverage and large residuals. For example, some country dyad observations

may not reveal themselves through the other measures as having especially high leverage or

large residuals, alone, but may reveal themselves as having both combined. These latter dyads

are examples of this.

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{Insert Table 11 in Appendix 4 about here}

Change in Fit:

The change in fit is simply the difference between the estimated fit with and without the

observation in question (Belsley et al., 1980; 15). When the ith row is deleted, it can

be found by calculating:

)(^^

iyy ii −

DFITS = [ hi / 1- hi]1/2 x [ ei/ s(i) ih−1 ]

The latter part of the expression is the studentized residuals and this is a scaled measure

(Belsley et al., 1980; 15). Since this measure depends on sample size, the size adjusted cutoff is

2/ sqrt(p/n) and an absolute cut-off, which could be defined as an excess of two standard

errors, is not recommended.

{Insert Table 12 in Appendix 4 about here}

How the fit is affected by deletion of observations is most notable for dyads C2C522

(USA and Djibouti), C375C740 (Finland and Japan), C20C740 (Canada and Japan),

C365C740 (Russia and Japan), C200C740 (United Kingdom and Japan), C437C740 (Ivory

Coast and Japan), and C435C740 (Mauritania and Japan). Both Canada and the USA

coupled with Japan have arisen in other diagnostic measures. The DFITS emphasize Japan as

a unique country affecting the regressions.

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Summary of Findings from Single-Row Analysis:

Reviewing the tables for each year, a summary of the outstanding observations is

produced for each diagnostic measure and each year, taking the top three when there are many

so as to make the analysis manageable. The DFBETAs are not listed since they are specific to

each variable; however, the previous wider analysis of DFBETAs is taken into account in the

table. If a dyad was identified as one affecting the estimates in that discussion and is also listed

under one of the headings in the summary chart (see Table 13 in Appendix 4), then the dyad is

shown in bold type. In general, when a dyad comes up as potentially influential according to

more than one diagnostic measure then it is bolded in Table 13 in Appendix 4.

{Insert Table 13 in Appendix 4 about here}

These single observations may be influential on their own or as part of a group.

Consequently, the next step is to review partial-regression leverage plots and combine the

findings from this section to derive a final list of influential observations and groups of

observations.

Partial-Regression Leverage Plots:

Analysis of plots for independent variables follows so as to visually identify influential

outliers and leverage points. Also, groups of influential observations and possibly masked

observations may be identified through the use of visual information. See the plots in Figures 8

to 22 in Appendix 4. Plots of residuals are included for each variable of interest for each year.

Observations and groups of observations that appear potentially influential have been circled

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and listed below each diagram. Very often, a group of dyads that appears influential has a

country repeatedly in many or all of the dyads of a group. Potentially, that country is influential

and has been listed as such under the plot. The dyads and countries are summarized in Table

14.

{Insert Table 14 about here}

Comparing the single-row analysis and plots analysis, it becomes apparent that many of

the same observations in Table 13 arise in Table 14. To discover whether the observations in

Tables 13 and 14 are impactful on the results, a sensitivity analysis is executed by running the

small sample main model without the influential observations, listed in the two tables, and then

comparing the results with the original small sample results (Tables 6 and 7).

{Insert Tables 15, 16 and 17 in Appendix 4 about here}

See Tables 15, 16, and 17 for the basic statistics and model results using the small

sample with deleted influential observations. Here, a brief comparison is made between the

small sample models with and without deletions. The model with the trade, alliance and visits

variables (Table 16) changes significantly without the influential observations, having positive

Relative Trade In-degree Centrality effects, although, not significant. Also, the Total Trade In-

degree Centrality Squared effects become much smaller in magnitude. Many of the alliance

coefficients become much larger in magnitude, remaining negative; however, the Relative

Alliance Degree Centrality Squared effect becomes non-significant. While the Visits Total

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Out-degree effect becomes significant and slightly larger, the Visits Total In-degree effect

loses significance in the model with deletions. In Table 17, the models including IGO and

nested variables, there are also changes. In Model 2a with deletions, IGO connectedness

becomes insignificant and in Model 2b it becomes negative and still not significant. The

interaction of Same Culture with IGO connectedness becomes positive, but remains not

significant. The interaction with Polity Score is similar but, is slightly larger. It is significant as

it was without the deletions. When the IGOs are put into their classifications, Economic and

General IGO Connectedness, as main effects, become significant and larger in Model 3b.

Social Cultural IGO Connectedness becomes smaller in both Models 3a and 3b. The nested

effects of Same Culture remain non significant, but smaller in magnitude, some of them

becoming positive. The nested effect of political orientation becomes significant and larger

with Economic IGO Connectedness but, with Social Cultural IGO Connectedness it loses

significance.

Since the deleted observations are influential on the small sample, then it is a possibility

that they also have an effect on the large sample. Consequently, a similar sensitivity analysis is

executed comparing the full sample main model results with the original full sample results

(Tables 3 and 4).

{Insert Tables 18, 19 and 20 about here}

See Tables 18, 19, and 20 for results using the large sample with the same deletions

made in the small sample. A comparison of the large sample results with and without deletions

– see Table 19 having the trade, alliance, and visits variables – finds that the original results are

111

not highly affected by the “influential observations” since all the variables of interest remain

significant and have the same signs. Some of the magnitudes change. The result for Relative

Trade In-degree Centrality is strengthened because the magnitude in Model 1a is almost three

times that in the original model without deletions and it is double in Models 1b and 1c – the

upward slope is greater. The coefficients for Total Trade In-degree Centrality and its squared

term are slightly larger. Relative Alliance Degree Centrality is slightly larger and its squared

term is almost the same. Total Alliance Degree Centrality and its squared term are both

smaller. Visits Total Out-degree remains almost the same and Visits Total In-degree is slightly

smaller. Control variables change in magnitude slightly – Similar Polity Score is smaller and

Same Culture is larger in Model 1a and becomes smaller in the other two models. The

conclusions drawn in answer to the hypotheses from this model with deletions would not be

different from those using the original model results as a basis.

The sensitivity analysis of Table 20, similar to the above but using IGO and nested

variables, arrives at a different conclusion in regards to the effects of the influential

observations. The deleted observations are influential in the large sample and lead to different

conclusions, being more supportive in the case of positive nested embeddedness effects and

less supportive of positive main effects. For example in Model 2a of Table 20, IGO

Connectedness is negative. In Model 2b the nested effect of Same Culture is positive and

significant and that of Same Polity Score is non-significant. When investigating the main

effects of IGO Connectedness in its different classifications, Model 3a still says that Economic

IGO Connectedness has a positive effect, but it is larger. General IGO Connectedness now has

a negative effect, Political Military IGO Connectedness becomes non-significant, and Social

Cultural IGO Connectedness becomes negative; a strange twist overall.

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In Model 3b, the nested effect of Same Culture with both Economic IGO

Connectedness and Political Military IGO Connectedness is now significant and larger, but still

negative. The interaction of Same Culture with General IGO Connectedness is positive, larger

and significant while that with Social Cultural IGO Connectedness is non-significant and

smaller. The Same Polity Score interaction with Economic IGO Connectedness is similar to the

original model, but the interaction with General IGOs becomes significant, positive, and

slightly larger. The interaction with Political Military IGOs becomes non-significant and

positive. The effects of the control variables, Similar Polity Score and Same Culture, are

smaller and Same Culture in non-significant in Model 2b.

Table 20 offers different conclusions: as a main effect, only Economic IGO

Connectedness has a positive effect on similar voting and the nested effects of culture and

political ideology are positive in General IGOs. Same Culture interacted with IGO

Connectedness also has a significant, positive effect. Consequently, nested embeddedness

gains more support in this model and the main effect of IGO Connectedness is positive when

the IGOs are economic. This sensitivity analysis suggests that nested embeddedness can have

positive effects and that the particular main effects that are significant and positive are affected

by influential observations. However, both models, with and without the deletions, lead to the

conclusions that IGO connectedness and nested embeddedness affect similar voting, some of

the main ideas of this dissertation.

7.4 Repercussions of Shocks

This section represents rough additional analysis that examines the effects of historical

events that may be considered shocks. A “shock” is a sudden unexpected event that is believed

113

to be significant enough that it could have repercussions for the country that experiences it.

The shock can be a positive or negative event and therefore, have positive or negative

repercussions on the focal country. The repercussions could affect a variety of domestic and

international aspects. Based on the variables available for use in this study’s dataset, the

impacts of shocks on certain relational (dyadic) aspects are chosen. For example, the variable

“allies” is an indicator variable that has the value of 1 if two countries in a dyad are allies and

has the value 0 if they are not allies. If a focal country becomes weaker because of a negative

shock, it may lose allies because these old allies no longer believe that the focal country is a

capable partner. This is a consequence or repercussion that the focal country experiences

because of the negative shock. While the thesis hypothesizes and tests relational effects on a

relational outcome variable, this section’s analysis investigates whether sudden world events

might affect this outcome variable.

The approach used here to obtain some indication that a shock is consequential or not is

to compare basic statistics and correlations before and after the year of the shock within the

time frame of the data set 1990-2000. This is very rough analysis that has several limitations.

One limitation is that it cannot be considered causal, only correlational. Regressions are not

used because not enough control variables for each particular shock case are available; also, to

produce good causal analysis it is anticipated that an instrumental variable approach would be

required.

Moreover, basic statistics can mask changes because the statistics are aggregated

measures. For example, the mean of the dyadic similar voting variable (percentage of similar

voting of the focal country with other countries in the data set) may not change before and after

the shock, but a change may still have occurred as a result of the shock. The countries that

114

were voting similarly together may have changed i.e., Israel and the US were voting together

before the shock and the USSR was not voting with Irsael, but after the shock the USSR votes

with Israel and the US does not. Therefore, the percentage of similar voting remains the same

but, the relationships have changed and this could have enormous consequences for world

politics.

Another limitation of this approach relates to the time frame used and this limitation

varies depending on when, over the time period 1990- 2000, the shock occurs. If the shock

occurs late in the study period, then the full or long-term extent of the shock consequences may

not be measured because of right truncation – the data is not available for a long enough time

after the shock occurs. Also, a shock may have huge immediate effects, the long-term is not

strongly affected or the shock may not have an effect right away – it may take some time to

have a measurable effect and this is not picked up. On the other hand, if the shock occurs early

in the study period and all the observations in the years after the shock occurs are included in

the statistics, the shock effects may be muddied or reduced because the effects wear-off after a

while. A possible approach when this latter problem exists is to make an educated guess

regarding the time frame of the shock consequences. For example, decide that the shock

repercussions are probably relatively immediate and only include two years of data in the after-

shock statistics. In this analysis, the shocks investigated are considered important historical

events that are expected to have long term consequences so, all the available data is used.

Another possible problem is that a country could have more than one shock in a year

and it is difficult to determine which one or whether both have possible repercussions. For

example, on January 1, 1994, the date that NAFTA went into effect in Mexico, the Zapatista

army suddenly revolted and attempted to bring down the government. While NAFTA would

115

have been anticipated, it was a turning point in the history of Mexico’s trading relationships

that took effect at a particular point in time, perhaps a positive shock. In contrast, the

Zapatista’s attack was a surprise negative shock. One shock could dominate while one

dampens the other or may have no effect. The basic statistics cannot disentangle this kind of

complication.

With these limitations in mind, the following table lists the historical events

investigated that are considered shocks. Each event is classified as being one that is expected to

make the focal country stronger or weaker and therefore, to have correspondingly positive or

negative ramifications. After this table, analysis and discussion for each country’s shock is

provided.

Focal Country (and COW code) Shock Event

Year of Shock

Expected Consequences

(positive or negative)

Type of shock

South Africa 560

Nelson Mandela is elected president of South Africa in 1994. His election led to the end of apartheid in South Africa.

1994 positive political

Israel 666

The Oslo Accords are signed in August 1993. This is Israel’s right to exist agreement between Palestine and Israel (US President Bill Clinton is present).

1993 positive political

Canada 20

The Quebec referendum results in a decision that Quebec does not separate from Canada (provincial

1995 positive political

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referendum).

Thailand 800

The Asian financial crisis starts in Thailand in July 1997. Thailand’s currency collapses (the Thai baht).

1997 negative economic

Poland 290

A political crisis started in 1995 with the election of Józef Oleksy - the new Prime Minister is accused to have been a Russian spy and lasts for only one year in office.

1995 negative political

Honduras 91

Hurricane Mitch in 1998 is believed to have destroyed 50 years of progress in the country. Honduras experienced a Category 5 hurricane that caused damage estimated at $4B.

1998 negative geographic

Mexico 70

On Jan 1, 1994, the Zapatista Army tried to start a revolution the same day that NAFTA went into effect.

1994 negative (but this negative effect may be washed out by NAFTA as a positive event)

political (NAFTA is economic)

South Africa

The following tables show basic statistics and correlations using the UNGA data from

1990 to 2000 when South Africa votes. The “similarvotes” variable is dyadic so it is the

percentage of similar voting between South Africa and another country. Notice that the “year”

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variable minimum is 1994. South Africa does not vote in this data set until 1994 which

corresponds with UN history and Nelson’s Mandela’s victory as president in South Africa. His

victory led to the end of apartheid. South Africa was virtually expelled by the world

community and the UN in 1974 because of its apartheid policy and was reinstated in 1994 (UN

website).

Basic Statistics

Variable Obs Mean SD Min Maxyear 1224 1997.01 1.999 1994 2000similarvotes 1224 0.56 0.234 0 1tradeptners 788 0.44 0.497 0 1tradeintensity 453 158.66 527.941 0 4317allies 1224 0.00 0.070 0 1visitors 1224 0.01 0.103 0 1SamePolity 1019 -5.78 6.389 -18 1huntsameculture 1222 0.15 0.362 0 1SameWealth 1217 -6154.27 4823.731 -36447 -0.33contiguity 1224 0.03 0.180 0 1igoconnect 1224 30.76 12.293 0 66

South Africa tends to vote similarly with other countries at a rate of 56% which does

not highly differ from the overall mean of the data set, 53%. Also, notice that South

Africa is highly connected with some countries in IGOs – up to 66 connections in common

(See the igoconnect variable which is a count of the number of IGO memberships South Africa

has in common with the other country in the dyad.).

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Correlations & Significance of Correlations with the Percentage of Similar Voting, “similarvotes”

similarvotes

similarvotes 1.000

tradeptners -0.2108*0.000

tradeintensity -0.2924*0.000

allies 0.0390.172

visitors -0.0140.636

SamePolity -0.2638*0.000

huntsameculture 0.2230*0.000

SameWealth 0.3436*0.000

contiguity 0.1466*0.000

igoconnect -0.1349*0.000

The correlations suggest that South Africa votes weakly with those of similar culture

and wealth (GDP/capita) since although, significant, the correlations are only 22% and 34%

respectively. No strong correlations exist.

Consequently, the analysis of this positive discontinuity in South Africa’s history

shows that now, it is accepted by the world community and it is actively participating.

Although it has trade partners, it has no allies and few diplomatic visitors. Therefore, while

it might be important internationally economically, it probably has little diplomatic influence.

119

Israel

The following tables show a similar set of statistics for Israel as were shown for South

Africa except that before and after statistics can be compared. This is because Israel voted in

the UNGA both before and after the 1993 Oslo Accords were signed. The signing of the Oslo

Accords is a positive shock for Israel because the document is an agreement that Palestine

recognizes the right of Israel to exist and vice versa (Mideastweb, 2008).

Basic Statistics Before 1993 beginning in 1990 – Before Peace Accords

Variable Obs Mean SD Min Maxyear 488 1991.04 0.816 1990 1992similarvotes 488 0.25 0.214 0 0.875tradeptners 283 0.70 0.458 0 1tradeintensity 204 129.95 473.055 0 3903allies 488 0.00 0.000 0 0visitors 488 0.00 0.064 0 1SamePolity 409 -7.02 6.896 -18 1huntsameculture 488 0.25 0.435 0 1SameWealth 483 -9302.55 3615.725 -14687.8 -143.32contiguity 488 0.03 0.167 0 1igoconnect 488 27.23 11.691 0 54

Basic Statistics After and Including 1993 up to the year 2000 – After Peace Accords

Variable Obs Mean SD Min Maxyear 1400 1996.51 2.293 1993 2000similarvotes 1400 0.25 0.261 0 0.9375tradeptners 914 0.47 0.500 0 1tradeintensity 619 204.50 946.813 0 12900allies 1400 0.00 0.000 0 0visitors 1233 0.03 0.157 0 1SamePolity 1168 -5.96 6.501 -19 1huntsameculture 1398 0.25 0.430 0 1SameWealth 1392 -10586.17 4476.146 -27034.8 -6.55contiguity 1400 0.04 0.196 0 1igoconnect 1400 31.66 13.259 0 73

Comparing the basic statistics before and after the Oslo Accords, similarvotes’ mean

does not change, although its maximum increases. However, the standard deviation does not

120

change much. Israel votes similarly with other countries at a much lower rate than the overall

mean of 53%. The Oslo Accords do not seem to have much of an effect in this regard. Mean

trade intensity increases quite a bit but, this is in concurrence with having fewer trade

partners. Also, Israel has more in common with more countries if IGO connectedness is an

indicator of this. Israel’s maximum IGO connectedness with another country increases from 54

to 73.

Correlations Before and After Oslo Accords

similarvotes before similarvotes after

similarvotes 1.000 1.000

tradeptners 0.1718* 0.4011*0.004 0.000

tradeintensity 0.4074* 0.2387*0.000 0.000

allies . .1.000 1.000

visitors 0.065 0.0480.155 0.092

SamePolity 0.5265* 0.5134*0.000 0.000

huntsameculture -0.3004* -0.3388*0.000 0.000

SameWealth 0.3731* 0.4545*0.000 0.000

contiguity -0.0933* -0.0659*0.039 0.014

igoconnect 0.3711* 0.2144*0.000 0.000

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Very noticeable is that the correlation between the number of trade partners and similar

voting increases from 17% to 40%. However, trade intensity correlations decrease. So, while

being a trade partner may become more related to similar voting, the amount of trade

conducted becomes less related to similar voting. Also, IGO connectedness becomes less

correlated with similar voting.

This analysis suggests that trade relationships are affected over this time period but it is

hard to tie these changes to the Oslo Accords in a logical manner. It seems that something

negative is happening because Israel has fewer trade partners; however, these relationships are

becoming more tied to reciprocal behavior in the form of similar voting. Also, while more is

being traded with fewer partners, the amount of trade is not as tied to reciprocal behavior.

More investigation is required, but perhaps Israel is becoming more dependent on fewer trade

partners.

Canada

From an international standpoint, the 1995 Quebec referendum outcome that decided

Quebec would not separate may have served to confirm the stability of Canada as a nation and

thus, have positive impacts on international relations. However, commingled in the analysis of

the effects of this positive shock may also be effects of NAFTA, the North American Free

Trade Agreement amongst three signatories – Canada, the US, and Mexico, effective in 1994.

From an international perspective, a trade block represents cohesiveness amongst the North

American nations and thus, would be expected to have positive repercussions in world affairs.

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Basic Statistics Prior to the Referendum (Before 1995)

Variable Obs Mean SD Min Maxyear 833 1992.06 1.399 1990 1994similarvotes 833 0.28 0.282 0 1tradeptners 830 0.71 0.455 0 1tradeintensity 826 871.99 8394.464 0 131956allies 833 0.22 0.417 0 1visitors 660 0.01 0.087 0 1SamePolity 701 -7.19 7.153 -19 1huntsameculture 833 0.22 0.414 0 1SameWealth 826 -15148.77 6396.389 -22040 -39.19contiguity 833 0.01 0.077 0 1igoconnect 833 33.26 14.777 0 73

Basic Statistics After the Referendum (1995 up to 2000)

Variable Obs Mean SD Min Maxyear 1055 1997.50 1.714 1995 2000similarvotes 1055 0.35 0.323 0 1tradeptners 1044 0.82 0.384 0 1tradeintensity 1039 1378.91 13882.330 0 229191allies 1055 0.23 0.418 0 1visitors 1055 0.01 0.087 0 1SamePolity 876 -6.39 6.758 -19 1huntsameculture 1053 0.24 0.425 0 1SameWealth 1049 -17146.40 7266.678 -26622.2 -50.27contiguity 1055 0.01 0.075 0 1igoconnect 1055 37.90 15.598 0 86

Of note, is that the mean of similar voting for Canada increases from 28% to 35% after

the Quebec referendum. Also, the mean number of trade partners and trade intensity both

increase. A more stable country may be attractive to trading partners because supply is stable.

However, NAFTA, being a trade agreement, may also explain these changes.

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Correlations Before and After the Quebec Referendum of 1995

similarvotes before similarvotes aftersimilarvotes 1.000 1.000

tradeptners 0.1735* -0.0190.000 0.539

tradeintensity 0.1422* 0.1028*0.000 0.001

allies 0.2328* 0.2018*0.000 0.000

visitors 0.011 0.0490.785 0.114

SamePolity 0.5982* 0.5098*0.000 0.000

huntsameculture 0.7178* 0.6391*0.000 0.000

SameWealth 0.6194* 0.5554*0.000 0.000

contiguity 0.1170* 0.0874*0.001 0.005

igoconnect 0.3904* 0.2672*0.000 0.000

The correlations with similar voting show that the number of trade partners is

significantly correlated prior to the referendum and not so afterwards. Also, most other

significant variables such as culture, same wealth, and contiguity are more highly correlated

with similar voting before 1995 compared to afterwards. Canada has contiguity primarily with

the US.

The patterns are more suggestive of NAFTA effects than referendum effects. Once

NAFTA is in place, Canada has less reason to vote in accordance with its North American

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neighbours. Trade increases later. If the referendum had an effect, other countries would be

inclined to vote with Canada in the later period so the correlations would have increased.

Thailand

The Asian economic crisis started in Thailand and spread like a disease across Asia

(Karunatilleka, 1999). Consequently, it severely economically damaged Thailand and is the

first negative shock to be discussed.

Basic Statistics for Thailand Prior to the Financial Crisis of 1997

Variable Obs Mean SD Min Maxyear 1188 1993.09 1.985 1990 1996similarvotes 1188 0.65 0.338 0 1tradeptners 808 0.57 0.496 0 1tradeintensity 559 225.82 1077.864 0 11854allies 1188 0.00 0.000 0 0visitors 1014 0.00 0.031 0 1SamePolity 996 -6.06 5.817 -18 1huntsameculture 1188 0.03 0.178 0 1SameWealth 1179 -5389.36 4829.164 -28049.5 0.74contiguity 1188 0.03 0.178 0 1igoconnect 1188 27.61 11.009 0 56

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Basic Statistics for Thailand After the Financial Crisis of 1997 (including 1997)

Variable Obs Mean SD Min Maxyear 700 1998.51 1.124 1997 2000similarvotes 700 0.59 0.335 0 1tradeptners 389 0.56 0.498 0 1tradeintensity 265 421.47 1857.859 0 17161allies 700 0.00 0.000 0 0visitors 700 0.01 0.075 0 1SamePolity 581 -5.70 6.322 -18 1huntsameculture 698 0.03 0.175 0 1SameWealth 696 -6140.78 5507.339 -37131.4 -4.46contiguity 700 0.04 0.189 0 1igoconnect 700 34.30 13.572 0 77

The main observations are that mean similar voting decreases in the later period and

mean trade intensity increases. Also, Thailand has a large increase in common IGO

connections with other countries, the maximum rising from 56 to 77. The increased trade may

be in exports since imports are reportedly too expensive (Karunatilleka, 1999). Also, in the

course of repairing the economic damage, Thailand may reach out for help by joining IGOs

that may offer support, as the IMF did. This statement makes the assumption that it is Thailand

that is joining more IGOs rather than its partners doing so. More investigation is required to

support these plausible explanations.

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Correlations Before and After Thailand’s Financial Crisis of 1997

similarvotes before similarvotes after

similarvotes 1.000 1.000

tradeptners -0.2032* -0.2255*0.000 0.000

tradeintensity -0.2344* -0.2297*0.000 0.000

allies . .1.000 1.000

visitors 0.027 -0.0240.387 0.531

SamePolity -0.3370* -0.3784*0.000 0.000

huntsameculture 0.1133* 0.1306*0.000 0.001

SameWealth 0.3966* 0.4036*0.000 0.000

contiguity 0.0973* 0.1195*0.001 0.002

igoconnect -0.0950* -0.0590.001 0.118

Many correlations with similar voting are negative both before and after the crisis. In

general, Thailand’s voting is not influenced by many factors in this study. Voting with

countries of the same wealth status is a possible connection because of the 40% correlation.

Voting with other countries in the same region (contiguity) and culture are also possible

connections; however, the correlations are not high.

This negative shock does not have major negative effects on the variables or produce

obvious patterns in this study. The IMF bailed out Thailand in August, 1997 with $17.2B

(Karunatilleka, 1999). Perhaps, this helped Thailand to bounce back quickly from the shock in

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terms of the international relationships represented by the variables. In early 1998,

markets stabilized but, in May, 1998 the Asian financial crisis went into a second phase that

also affected areas outside of Asia such as Russia, Brazil and the West (Karunatilleka, 1999).

However, no reflection of this second phase of trouble is found in the basic statistics or

correlations for Thailand.

Poland

Poland has a difficult history of political instability and although over the study period

it is democratic, the leadership is in question after the new prime minister, Józef Oleksy, is

accused of having been a Russian spy and lasts only one year in office (Perlez, 1996; Oljasz).

The political debacle is a negative shock to which the international community may respond.

Basic Statistics for Poland Prior to the Political Crisis of 1995

Variable Obs Mean SD Min Maxyear 833 1992.06 1.399 1990 1994similarvotes 833 0.27 0.286 0 1tradeptners 813 0.53 0.499 0 1tradeintensity 809 98.87 433.194 0 6277allies 833 0.02 0.153 0 1visitors 658 0.00 0.055 0 1SamePolity 701 -5.93 5.914 -17 1huntsameculture 833 0.22 0.414 0 1SameWealth 826 -5420.41 4417.958 -25423 -9.21contiguity 833 0.04 0.206 0 1igoconnect 833 28.09 13.134 0 61

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Basic Statistics for Poland After the Political Crisis of 1995 (including 1995)

Variable Obs Mean SD Min Maxyear 1055 1997.50 1.714 1995 2000similarvotes 1055 0.36 0.339 0 1tradeptners 1051 0.72 0.451 0 1tradeintensity 1049 190.68 819.532 0 10966allies 1055 0.05 0.215 0 1visitors 1055 0.01 0.075 0 1SamePolity 876 -5.78 6.369 -18 1huntsameculture 1053 0.24 0.425 0 1SameWealth 1049 -6516.86 4504.321 -34771.5 -21.38contiguity 1055 0.06 0.242 0 1igoconnect 1055 35.62 16.005 0 87

The mean of vote similarity with Poland is higher after this negative crisis. Also, it has

more trade partners and trade intensity is much higher in the later period. IGO connectedness

with other countries also increases.

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Correlations Before and After Poland’s Political Crisis of 1995

similarvotes before similarvotes aftersimilarvotes 1.000 1.000

tradeptners 0.3721* 0.0844*0.000 0.006

tradeintensity 0.2886* 0.2749*0.000 0.000

allies 0.1797* 0.3620*0.000 0.000

visitors 0.011 0.1012*0.771 0.001

SamePolity 0.4818* 0.4802*0.000 0.000

huntsameculture 0.6766* 0.6530*0.000 0.000

SameWealth -0.3782* -0.2640*0.000 0.000

contiguity 0.3391* 0.3052*0.000 0.000

igoconnect 0.4896* 0.3903*0.000 0.000

Some noticeable changes in correlations are evident. The trade partners variable has a

much lower correlation with similar voting, changing from 37% to 8%, whereas the trade

intensity variable correlation does not change. The allies variable correlation increases by more

than two-fold and the visitors correlation increases and becomes significant in the later period.

Also, similar culture, same polity type, and contiguity are relatively highly correlated with

similar voting in both periods.

Overall, it seems that the long term improving situation of Poland overrides the

negativity of the short term political crisis. Poland seems to choose to vote with visitors, allies,

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and those with whom it is similar rather than on the basis of being trade partners. Referring to

the “Power and Influence” themes of the thesis, it appears that Poland responds to forces of

influence rather than power. This is a case where a pattern can be surmised from the statistics;

however, it is not the pattern anticipated since it is likely the long term trend rather than the

short term shock that is consequential.

Honduras

Unique to this negative shock is that it is a natural disaster in the form of Hurricane

Mitch of 1998, rather than a political or economic shock. However, the damage of the

hurricane to Honduras had economic ramifications; the country required costly reconstruction

since it was believed to have destroyed 50 years of progress (NCDC, 2006).

Basic Statistics for Honduras Prior to the Hurricane of 1998

Variable Obs Mean SD Min Maxyear 1360 1993.58 2.268 1990 1997similarvotes 1360 0.64 0.262 0 1tradeptners 1348 0.26 0.440 0 1tradeintensity 1343 10.98 108.310 0 2439allies 1360 0.17 0.372 0 1visitors 1185 0.00 0.000 0 0SamePolity 1140 -5.36 4.960 -15 1huntsamecu~e 1360 0.17 0.379 0 1SameWealth 1350 -5690.77 6764.407 -35742.2 0.47contiguity 1360 0.04 0.186 0 1igoconnect 1360 26.02 11.169 0 59

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Basic Statistics for Honduras After the Hurricane of 1998 (including 1998)

Variable Obs Mean SD Min Maxyear 526 1999.01 0.823 1998 2000similarvotes 526 0.54 0.307 0 1tradeptners 517 0.40 0.490 0 1tradeintensity 510 24.80 221.922 0 3174allies 526 0.13 0.332 0 1visitors 526 0.00 0.000 0 0SamePolity 435 -4.85 5.040 -16 1huntsameculture 524 0.17 0.376 0 1SameWealth 523 -6577.40 7815.213 -41938.5 -7.29contiguity 526 0.04 0.196 0 1igoconnect 526 33.87 14.514 0 88

While the mean of similar voting decreased in the later period, the trade variable means

increased. Perhaps, after a natural disaster, imports are required as part of reconstruction. Thus,

Honduras has more trade partners and greater volume of trade after Hurricane Mitch.

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Correlations Before and After the Hurricane in 1998

similarvotes before similarvotes aftersimilarvotes 1.000 1.000

tradeptners -0.2018* -0.2144*0.000 0.000

tradeintensity -0.1197* -0.1000*0.000 0.024

allies 0.2127* 0.0770.000 0.078

visitors . .1.000 1.000

SamePolity -0.2598* -0.2432*0.000 0.000

huntsamecultu 0.2811* 0.2637*0.000 0.000

SameWealth 0.4707* 0.4439*0.000 0.000

contiguity 0.1387* 0.1294*0.000 0.003

igoconnect 0.1058* 0.1368*0.000 0.002

The trade variables are negatively correlated with similar voting. However, Honduras

seems to vote similarly with other countries of the same wealth, same culture, and those that

are neighbours or connected via IGOs. Similar wealth is the most highly correlated variable in

the 40% range.

Overall, this negative shock has potentially caused Honduras to resort to increased trade

for reconstruction. However, it has not influenced its international behavior otherwise because

correlations with similar voting have not changed much.

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Mexico

The case of Mexico could be an opposite one to that of Canada. It has a negative

political shock at the same time that NAFTA takes effect. The Zapatista army is determined

to bring down the Mexican government; although it does not succeed, the cause continues.

This contrasts with Canada’s situation. Rather than a violent revolt, it has a civilized

referendum that results in peace. The topic of Quebec separation is no longer on the political

table irregardless of whether it is still of interest around Quebecois family dinner tables. In the

Canadian case, it was surmised that NAFTA effects dominated over the positive shock. Let’s

see what happens in the case of Mexico’s negative shock.

Basic Statistics for Mexico Prior to the Zapatista Uprising of 1994

Variable Obs Mean SD Min Maxyear 663 1991.56 1.113 1990 1993similarvotes 663 0.66 0.335 0 1tradeptners 660 0.46 0.499 0 1tradeintensity 657 303.59 2742.847 0 40745allies 663 0.15 0.354 0 1visitors 491 0.00 0.045 0 1SamePolity 557 -5.92 2.702 -9 1huntsameculture 663 0.18 0.384 0 1SameWealth 657 -5724.38 3926.723 -23973.4 -9.96contiguity 663 0.02 0.149 0 1igoconnect 663 29.67 13.312 0 64

Basic Statistics for Mexico After the Zapatista Uprising of 1994 (including 1994)

Variable Obs Mean SD Min Maxyear 1225 1997.01 1.999 1994 2000similarvotes 1225 0.64 0.302 0 1tradeptners 1211 0.53 0.499 0 1tradeintensity 1202 686.84 7011.966 0 135080allies 1225 0.16 0.366 0 1visitors 1225 0.00 0.070 0 1SamePolity 1020 -5.01 4.405 -17 1huntsameculture 1223 0.17 0.374 0 1SameWealth 1218 -6280.77 4642.032 -35226.1 0.63contiguity 1225 0.03 0.167 0 1igoconnect 1225 35.87 15.840 0 99

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Mexico’s similar voting mean does not change much. The country votes similarly more

often than the average, in the mid 60% range rather than 53%. Very notable is that the mean,

standard deviation, and maximum of trade intensity increase dramatically. IGO connectedness

increases and the number of trade partners slightly increases.

Correlations Before and After the 1994 Zapatista Uprising

similarvotes before similarvotes after

similarvotes 1.000 1.000

tradeptners -0.3433* -0.1570*0.000 0.000

tradeintensity -0.1744* -0.1492*0.000 0.000

allies 0.1170* 0.2541*0.003 0.000

visitors -0.063 -0.0702*0.165 0.014

SamePolity 0.3635* -0.2047*0.000 0.000

huntsameculture 0.1960* 0.3191*0.000 0.000

SameWealth 0.3066* 0.3215*0.000 0.000

contiguity 0.014 0.0260.720 0.372

igoconnect -0.1411* -0.0290.000 0.313

After the shock, the trade partners correlation with similar voting increases, but

remains negative. The allies correlation increases by more than double. Also, the similar polity

correlation changes from 36% to -20%. So, while politics plays much less of a role in

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influencing similar voting, culture plays a larger role, noting that the same culture correlation

increases from about 20% to 32%. Similar wealth has a constant positive correlation and IGO

connectedness is no longer a significant correlation in the later period.

Overall, it seems that NAFTA has increased trade for Mexico, but it is not the

economic change that influences international decision making. A shift from politics to culture

is a theme and a choice of allies over other types of connections via IGOs is made. Mexico’s

case is different from that of Canada’s but, it is not clear that it is the Zapatistas who influence

that change. The Zapatistas do represent a cultural force; also, their cause is moved forward

through military rather than diplomatic means. Perhaps, this combination of cultural and

military orientation influences the psyches of the Mexican people and is ultimately reflected in

its national decision making. More investigation is required to understand Mexico’s case.

However, it is likely that influence mechanisms rather than power mechanisms are at play

since institutional rather than economic forces dominate in the correlation table.

Conclusion – Shock Repercussions

The following table briefly summarizes the results of the analysis above in an

additional column called “Results of Analysis”, keeping in mind the limitations mentioned

prior to the analysis

Focal Country (and COW code) Shock Event

Year of Shock

Expected Consequences

(positive or negative)

Type of shock

Results of Analysis

South Africa 560

Nelson Mandela is elected president of South Africa in 1994. His election led

1994 positive political South Africa reinstates its participation on the world stage; prior to 1994, it was

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to the end of apartheid in South Africa.

not allowed to be active due to apartheid.

Israel 666

The Oslo Accords are signed in August 1993. This is Israel’s right to exist agreement between Palestine and Israel (Signed in Washington DC with President Bill Clinton present).

1993 positive political Israel seems more dependent on fewer trade partners – the agreement may have negative ramifications or have no effect.

Canada 20

The Quebec referendum results in a decision that Quebec does not separate from Canada (provincial referendum).

1995 positive political NAFTA dominates as a positive trade effect and no positive impact of the referendum is reflected in the basic statistics.

Thailand 800

The Asian financial crisis starts in Thailand in July 1997. Thailand’s currency collapses (the Thai baht)

1997 negative economic Thailand bounces back quickly due to an IMF $17.2B bailout in August 1997– the shock does not affect the variables.

Poland 290

A political crisis started in 1995 with the election of Józef Oleksy - the new Prime Minister is accused to have been a Russian spy and lasts only one year in office.

1995 negative political Poland’s long term positive progress dominates over the short term negative shock.

Honduras 91

Hurricane Mitch 1998 is believed to have destroyed 50 years of progress in the country. Honduras

1998 negative geographic Trade increased likely due to reconstruction.

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experienced a Category 5 Hurricane that caused damage of approximately $4B (NCDC, 2006).

Mexico 70

On Jan 1, 1994 the Zapatista Army tried to start a revolution the same day that NAFTA went into effect.

1994 negative (but this negative effect may be washed out by NAFTA as a positive event)

political (NAFTA is economic)

Indeterminate – not likely NAFTA.

After reviewing the results, it is found that long term progress or changes rather than

short term shocks take precedence in the international relationships represented by the

variables of this study. Also, the changes that make a difference are those that are important to

the world rather than to a domestic situation. While trade levels fluctuate when a natural

disaster occurs, this does not tend to affect similar voting in the UNGA. Poland and Thailand

both have short term negative domestic shocks, but both these countries have long term

positive outlooks, so no reaction is found in the basic statistics. South Africa’s transition away

from apartheid is a shock that leads to a long term change in its international relations so it is

relevant. Also, the Quebec referendum in Canada, although a positive sign for the future of

Canada, is not a dominate effect in the statistics – this is internal politics that is not of great

international concern. Instead, NAFTA affects the statistics for Canada because it represents a

long term international change in the countries’ international relations. As mentioned earlier,

this analysis has several limitations and a great deal more investigation is required. However,

given the enormity of a deeper investigation, it is reserved for future research.

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CHAPTER EIGHT: DISCUSSION AND CONCLUSIONS

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8.1 Discussion and Contributions

8.1.1 Discussion of Results. In general, the results of this analysis make a

contribution to organizational theory by testing and finding empirically that social structure has

effects on cooperative strategic decision making, a tighter causal linkage compared to other

studies that seek effects on performance outcomes instead. Two particular social structural

mechanisms are successfully tested for: 1) interdependence resulting in power imbalances in

organizational relationships and 2) influence through information sharing between

organizations that may be encouraged by homophily. The first mechanism corresponds to

situations of organizations’ economic embeddedness and the second mechanism corresponds to

institutional embeddedness.

As expected, economic embeddedness effects are related to the power imbalances and

institutional embeddedness effects are related to information sharing and homophily. However,

institutional embeddedness effects, relating to military alliances were not as well predicted –

still, homophily rather than interdependence may be the driving force. Moreover, when

institutional embeddedness is examined using country visits, centrality is not a sufficient

predictor as hypothesized – it makes a large difference whether an actor has high in- or out-

degree centrality; prominence (in-degree centrality) in this type of network is important

whereas making an effort to visit does not influence voting positively (out-degree centrality).

This type of prominence is related to being knowledgeable and therefore, being perceived to

have valuable information to share. Finally, IGO connectedness is influential – members of

many of the same “clubs” will tend to vote cooperatively. However, having other

characteristics in common in addition to the memberships does not tend to positively influence

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voting. Instead, this nested embeddedness can have a negative effect and only potentially in

some situations does the nested embeddedness increase the likelihood of similar voting.

The power positions states have in their external networks, especially those that are

economic, make them influential through relationships of interdependence. As the gap in in-

degree centrality in the trade network widens, states tend to vote together. The state with less

trade network prominence is likely reciprocating with the state that is more prominent; the

lesser state desires the trading relationship. Additionally, states that are both prominent in the

trading network do not reciprocate by voting similarly – likely, they are each quite

independent, having many trading partners and markets sufficiently attractive that they do not

have to offer additional incentives. Instead, states that are less central in the trade network are

cooperative voters.

Unexpectedly, it is less central actors in alliance networks who vote together. Their

cooperative behavior may be a result of their similar vulnerability and need to make “friends”.

In contrast, countries highly central in this type of network are not motivated to reciprocate by

voting similarly since they are relatively secure – multiple alliances provides them with a

security network such that if one partner does not come through when required, other

alternative allies may be called upon. Also, their centrality may reflect military strength –

others seek them out as allies because they can provide protection. Consequently, they have no

need to defer to others through cooperative voting. In general, the literature on inter-

organizational alliances says that alliances tend to fail for a multitude of possible reasons

including rivalry, complexity, and lack of trust (Gulati, 1995b; Gulati, 1998; Park and Ungson,

2001). These past findings may explain the negative effects of the alliance variables on the

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cooperative dependent variable in this study. Additional support for this view is provided by

the dyadic control “allies” that is negative and significant in all models.

The outcome of the tests for centrality in the visits network suggests prominence

influences voting. The motivation to vote similarly with prominent others could be based both

on power and informational influence. A country receiving many visitors may be able to

bestow benefits on those visiting, thus they are powerful in some manner aside from the

information they possess. However, controls for wealth were incorporated. More likely, they

are influential because of the information they are known to possess – receiving more

information from many others and potentially being the state that others go to for advice. These

governments are viewed as wise so in the face of uncertainty and a highly complex

environment, others are encouraged to imitate their decisions.

Moreover, multiple IGO connections lead to cooperation. Homophily in terms of

interests is a mechanism for cohesion and information sharing. All the different types of IGO

connectedness have this positive effect: economic, political military, social cultural, and

general IGO connectedness promote cooperative voting.

An additional interesting outcome of this study is related to a control variable – dyadic

trade intensity, a monetary measure of market power. Its effect provides a contrast to the

effects of the social network measures for two reasons. First, it is a strong tie effect comparison

with positional measures of prominence in the trade network. Position, a wider network

concept, dominates over a dyadic relationship as an effect on voting in an economic network.

Second, the trade intensity covariate is purely economic being a monetary measure whereas

other variables are not. For example, the trade variables of interest are positional and IGO

connectedness, while also a strength of ties measure, is not based on monetary transactions.

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Consequently, one may carefully surmise that social political influences, whether positional or

dyadic, have greater effects on other social behaviors, such as voting, than do purely economic

influences.

When nested embeddedness is examined, it is only when influential observations are

removed that it becomes a positive factor affecting cooperation; otherwise, it either has no

effect or has a negative effect. Consequently, more investigation is suggested since the results

of this study are equivocal. Nesting may be disruptive to relationships within IGOs, regardless

of whether it is political or cultural embeddedness that is nested within the institutional

embeddedness of IGO connectedness. Perhaps this is evidence of the dark side of

embeddedness called “over embeddedness” (Gulati and Gargiulo,1999; Uzzi, 1997)?

Understanding these negative dynamics needs more in-depth theoretical exploration. However,

the results of all models suggest that cultural and political types of embeddedness have direct

positive influences on cooperative voting – nesting is not necessary for this type of homophily

to be influential.

8.1.2. Contributions. From an academic perspective this study conjoins two theoretical

areas, the embeddedness view, stemming from strategy and organizations theory, and

international relations. Also, it applies novel network approaches to uncover interesting

bargaining relationships, usually not observable. Furthermore, many of the embeddedness

view’s concepts are supported in a tighter causal link, empirically lending credence to this

fledgling theory. It also makes contributions in accordance with a recent review by Krippner

and Alvarez (2007). Moreover, some empirical challenges arise thus, this study is an example

of how to approach them. Finally, practical implications fall out from the theory.

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By applying network approaches to an area just newly discovering the opportunities for

its application, international relations, it provides empirical support for the embeddedness

view. The study of a developing view from organizational theory – embeddedness – in an

international relations context is novel, but the approach is gaining interest by scholars who are

interested in working in an interdisciplinary fashion (Ingram, Robinson, and Busch, 2005;

Hafner-Burton and Montgomery, 2006).

Also, this study has used network concepts of increasing interest, such as centrality.

We live in an inter-connected world so both the developing theory offered by the

embeddedness view together with the network methodology can uncover relationships in ways

that other theory and methods cannot. In this case, I find evidence of network positional power

and influence in a variety of types of networked relationships that lead to reciprocity, possibly

construed as backroom bargaining and vote buying.

Most importantly, this investigation offers greater credibility to the embeddedness view

through a contribution of empirical evidence supportive of its concepts. It explores

embeddedness at a level above the individual, arguably at the inter-organizational level and

shows that it does operate at this level. Also, it studies the nesting of embeddedness and shows

that it possibly works in both positive and negative ways; it is such a rare investigation that

there is not much literature available against which to compare what has been done here.

Homophily is influential but the evidence suggests that being too similar and, as a possible

consequence, too cohesive, may cause ruptures in relationships and blocks to cooperation.

These findings are motivational for interesting future research – why would having so much in

common sometimes lead to uncooperative behavior?

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Moreover, a tighter causal relationship has been demonstrated – embeddedness does

have an impact on decision making, the first step towards actions and outcomes. It is very rare

that such a close link can be studied because decisions are not usually publicly reported, only

performance measures are such as profit in firm studies. Once again, the international relations

context is beneficial for theoretical purposes because the international arena offers large public

data sets over long periods of time.

An earlier discussion of Krippner and Alvarez’s (2007) review addressed a tension

between scholars’ views of embeddedness such as those of Granovetter (1985) and Polanyi

[1944(2001)]. However, I believe that I reconciled the tension since I demonstrated, through

his own words, that Granovetter’s (1985) view is not “external” and rather, that he intertwines

sociological behavior in with economic systems. Also, I suggested that the division between

economics and sociology is perceived and not real such that sociology does not have to

continue this debate about how to define itself vis-à-vis economics; I will add to this now by

mentioning how my dissertation research demonstrates that embeddedness has a positive

research program.

If one were desirous of showing embeddedness effects in a realm not including

economic systems such that embeddedness has a positive research program without defining

itself solely in opposition to various economic assumptions (Granovetter, 1985; Krippner and

Alvarez, 2007), the entire section about institutional embeddedness provides examples. The

results that show both diplomatic visits and IGO networks influence decision making in the

UNGA. These are sociological networks, not based on economic ties, that affect other non-

economic behavior – voting in the UNGA. So, embeddedness has effects without consideration

of economic systems.

145

Moreover, by reviewing the results of the economic embeddedness section – how trade

networks affect voting – one finds that it is position in the network (relative centrality), not the

value of the transactions since this is controlled for, that influences similar decision making.

Consequently, this is support for both the Granovetterian and Polanyian views that I reconciled

in an earlier discourse. Granovetter (1985) wants to make the point that social structure is

important in economic systems, and this is exactly what is shown. Moreover, in regards to

Krippner and Alvarez’s (2007) concern about Polanyi’s concept of “disembeddedness”, the

results of my study show that through the reciprocal behavior, the economic realm of trade

networks is tied to the socio-political realm of UNGA voting. Thus, economic systems are not

disembedded or disentangled from the rest of the world (Krippner and Alvarez, 2007; Callon,

1998); from Polanyi’s interior perspective, economic systems are part of the sociological world

(Krippner and Alvarez, 2007).

Now, towards advancing the empirical advantages of the study, I reiterate that the

context is an opportune setting for discovering embeddedness effects amongst organizations

because of the public availability of unbiased information on large networks that are not

confined to a single culture or geographical area. Embeddedness studies in international

settings may be more generalizable for this reason. Most studies attempt to use the context of

the firm to test embeddedness, but this has enormous limitations due to problems of data and

information availability. Moreover, researchers that use firm data often do not share it publicly

for various reasons so results are not replicable by other researchers as they are when public

data are used. The use of public data lends credibility to the results presented here and thus, to

the support found for the embeddedness view. The cultural and political characteristics of

146

states has allowed for a rare study of nested embeddedness and value homophily at the inter-

organizational level 13.

The methodological contribution occurs on many fronts; while the data set represents

an excellent context for testing the research questions, it also presents many technical

challenges. These challenges require modeling trade-offs and a subsequent extensive in-depth

additional analysis to ensure that the patterns found using the main model are not spurious.

Consequently, this thesis presents an excellent example of how to empirically approach very

complicated circumstances that are rarely attempted by researchers. I will not reiterate Chapter

5 that explains many of the challenges and how they are addressed; rather, I will highlight

some examples as illustrations.

An example of a modeling trade-off in this study is that a longitudinal model is chosen

to recognize the timing of events even though cross sectional variation seems to dominate

longitudinal variation. The decision to use a lagged dependent variable imposed the necessity

of a longitudinal model – it’s a valid choice since results show that previous voting choices

affect future voting choices. Repetitive behavior by organizations resulting in routines has

ample previous empirical support (Gulati, 1995b; Amburgey and Minor, 1992; Amburgey,

Kelley, and Barnett, 1993). Additional analysis extensively examines cross-sections to

compensate for this choice.

Another challenge is the possibility of omitted variables. Omitted third variables in the

residual that are correlated with independent variables are a concern because this could result

in biased estimates (Wooldridge, 95-99; 2006). The use of theory to choose variables and an

in-depth search for patterns in cross-sections address this possibility. Moreover, removing

13 For those who disagree it is an inter-organizational study, the study is at a level beyond that of the individual, amongst systems of organizations.

147

influential observations from the large sample did not uncover different results that would

suggest omitted variables and misspecification.

Overall, the model results are quite sound and while there is some sensitivity in the

IGO connectedness and nested embedded results, it does not nullify the support for the

hypotheses; in some ways it is supportive. The methodological approaches used in this

dissertation represent a good example of how to deal with a combination of difficult challenges

using a very large data set.

From a practical standpoint, this dissertation uncovers some of the influences on an

otherwise secretive and opaque strategic decision making process that changes world events

and history. Related to this, it addresses some more specific controversial questions – is

national culture influential? – yes. Is political ideology still of importance in a post-Cold War

world? – yes, in some instances.

Negotiators who seek to be influential on behalf of their organizations may appreciate

that this study offers suggestions for dealing with highly complex situations. For example, if it

is not possible to set up a power interdependence relationship that creates a beneficial

reciprocal obligation, alternatively, an actor may be prominent as an opinion leader or active in

many related external organizations that offer forums for sharing information and views.

Contexts such as alliance constellations, industry associations, and standards setting bodies

may be network governance situations like that of the UNGA, as discussed earlier, in which the

aforementioned mechanisms may be operable.

From an international relations stand point, this study sheds light on how network

aspects influence governments’ or organizations’ decisions. While economics is a suspected

influencer, this study provides evidence of it – literally, the trade-offs are part of the game;

148

relationships of interdependence set up reciprocated action. Moreover, states’ use of diplomacy

is not ineffectual; even controlling for wealth and political ideology, country visits and

memberships in IGOs are influential – information sharing is impactful.

8.2 Limitations and Future Research

The limitations of this study relate to the connection between the evidence of the

behaviors and the mechanisms believed to explain them. Is it really power interdependence,

homophily or information sharing that explains the independent variables’ relationships to

cooperative voting behavior? This is a relatively disconnected empirical examination that uses

theory and electronic data from a very high level view. While the explanations are plausible,

future research could seek to further investigate this plausibility by using additional,

complementary research methods.

The very rough analysis of the effects of shocks in this thesis is a possible beginning,

but as pointed out earlier it could become a very fine grained analysis. Also, initial findings are

that long term trends dominate short term shocks in affecting the types of international

relations investigated here. Additional analysis along these lines is possible future research.

Also, the results of a wide cross section of interviews could tighten the linkages;

however, this type of approach could be insurmountably difficult – ignoring the costs of

interviewing diplomats and other country representatives – gaining access and even after

gaining access, obtaining unbiased information may be impossible because of the political

ramifications to country representatives who talk about their secret discussions. Consequently,

this study’s approach, although seemingly disconnected, may be one of a very few

149

possibilities. In general, the field of international relations has utilized this approach (Russett

and Oneal, 2001; Ingram, Robinson, and Busch, 2005) to make similar types of connections.

Is this study confined to strategic decision making at the inter-organizational level in

international relations settings or even only to the UNGA? The answer to this question of

generalizability is quite subjective; however, the author’s opinion is that it is not likely

confined to international institutions and voting situations. As implied earlier, organizations

that seek to be influential on various other organizations’ decisions can learn from this study. It

is generalizable to strategic decision making at the organizational level, whatever type of

organization it is, and makes a contribution to the embeddedness view at the inter-

organizational level.

Future research could ask more specific questions and address some of the questions

that arose out of the results mentioned earlier. More fine-grained analysis could study certain

countries’ power relationships and specific issues. For example, do social structural positions

in certain types of networks affect decisions on certain types of issues more than others? In this

study, the type of IGO connectedness made a difference to its level of influence on cooperative

voting – the political military IGO members were more likely dealing with issues similar to

those of the UNGA and this type of connectedness had a larger positive influence on similar

voting. Also, why does nested embeddedness in IGOs often result in non-cooperation? This

potentially destructive side to closeness needs further investigation.

Moreover, this setting could offer an excellent opportunity to explore the international

networks from a broader view point – how have the various networks – alliances, trade, and

country visits - changed over time and what influences the shape of these changes (Borgatti

and Foster, 2003;1000)? This kind of analysis may uncover historical processes, unthinkably

150

difficult to discover using any other methods. The interconnectedness can now be investigated

with advanced network analysis methods. New methods and theory applied to unconventional

contexts can provide exciting new insights and open up opportunities for future investigation

not previously considered.

Many more opportunities open up with the exploitation of the field of network

methodology; for example, this study examines relative dyadic centrality, but group centrality

and clustering the data (or examining cliques) are additional approaches to studying the

massive international data sets i.e., studying the G8 and other groups of nations (Borgatti and

Foster, 2003; 1001; Borgatti and Everett, 1992). This is out-of-scope for this study since the

research question focuses on individual organizational actor power and influence on a bilateral

basis (A dyadic dependent variable is used which corresponds to real situations of negotiations,

back room bargaining, and vote buying or reciprocal behavior including compliance.) not

group level power and influence. Delving deeper into two-mode networks, like IGO

memberships, studying the evolution of networks, and core and periphery structures are more

opportunities (Carrington, Scott and Wasserman, 2005). An example research question is,

“How do patterns in trade networks change as trade pacts are made and broken?”. Network

methodology is an ever expanding area offering creative approaches to research.

151

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Table 1: Basic Statistics – Main Model

Table 2: Variance Decomposition – Full Sample

TABLE 2 VARIANCE DECOMPOSITION – FULL SAMPLE

Variable Mean Std. Dev. Min Max Observations Similar votes overall 0.527062 0.340564 0 1 N = 162968

between 0.305527 0 1 n = 17673 within 0.152815 -0.36183 1.394522 T-bar = 9.2213

Relative trade in-degree centrality

overall 27.95478 26.82057 0 96.532 N = 160780

between 25.31017 0 93.855 n = 17485 within 8.562676 -22.1925 88.07803 T-bar = 9.19531

Total trade in-degree centrality

overall 43.79061 39.48683 0 192.308 N = 160780

between 36.95279 0 183.9376 n = 17485 within 12.30246 -8.88083 123.9406 T-bar = 9.19531

Total alliance degree centrality

overall 12.73386 5.652426 0.59 39.622 N = 65865

between 5.585184 0.6 34.0668 n = 8531

162

within 1.386259 3.454606 27.47631 T-bar = 7.72067

Relative alliance degree centrality

overall 4.399772 3.52138 0 19.497 N = 65865

between 3.423672 0 19.208 n = 8531 within 0.98004 -4.89348 17.66337 T-bar = 7.72067

Visits total out-degree

overall 1.483267 3.403031 0 46.297 N = 162968

between 2.875584 0 35.49809 n = 17673 within 1.675104 -12.7682 20.98472 T-bar = 9.2213

Visits total in-degree

overall 1.482493 2.320283 0 29.63 N = 162968

between 1.767604 0 16.9576 n = 17673 within 1.532585 -6.71523 18.77369 T-bar = 9.2213

IGO connectedness

overall 26.17297 14.56595 0 132 N = 162968

between 13.91135 0 107.9091 n = 17673 within 5.118058 -10.5543 59.35479 T-bar = 9.2213

Econ IGO connectedness

overall 8.732653 5.339605 0 42 N = 162968

between 5.189212 0 39.18182 n = 17673 within 1.560045 -3.26735 16.73265 T-bar = 9.2213

General IGO connectedness

overall 5.522372 4.064667 0 45 N = 162968

between 2.969378 0 20.81818 n = 17673 within 2.824857 -3.38672 30.97692 T-bar = 9.2213

Political Military IGO connectedness

overall 0.520679 0.931603 0 5 N = 162968

between 0.899341 0 5 n = 17673 within 0.19251 -2.2571 2.338861 T-bar = 9.2213

Social Cultural IGO connectedness

overall 11.39727 6.116717 0 47 N = 162968

between 5.995831 0 44.36364 n = 17673 within 1.732625 -2.69364 19.30636 T-bar = 9.2213

163

Table 3: Main Model Table of Results

TABLE 3 Prais-Winston (FGLS) Estimation - with Robust Standard Errors -

of Percentage Similar Votes By Country Dyads, 1990-2000

Model 1a Model 1b Model 1c

Lagged Similar votes 0.825* (0.004)

0.826* (0.004)

0.827* (0.004)

Relative Trade In-degree Centrality / 1000

4.717* (0.596)

6.193* (0.614)

6.166* (0.604)

Relative Trade In-degree Centrality Squared / 1000

0.030* (0.006)

0.041* (0.007)

0.037* (0.006)

Total Trade In-degree Centrality / 1000

-11.866* (0.550)

-13.560* (0.565)

-13.530* (0.561)

Total Trade In-degree Centrality Squared / 1000

-0.074* (0.004)

-0.086* (0.004)

-0.085* (0.004)

Relative Alliance Degree Centrality / 1000

-31.932* (5.440)

-37.000* (5.210)

Relative Alliance Degree Centrality Squared / 1000

-2.856* (0.403)

-3.380* (0.400)

Total Alliance Degree Centrality / 1000

-147.568* (8.213)

-149.113* (7.968)

Total Alliance Degree Centrality Squared / 1000

-4.976* (0.256)

-5.120* (0.258)

Visits Total Out-degree / 1000

-1.869* (0.374)

Visits Total In-degree / 1000

6.963* (0.591)

Trade Intensity / 1000 0.002* (0.001)

-0.002* (0.001)

-0.002* (0.001)

Allies -0.026* (0.004)

-0.016* (0.003)

-0.013* (0.003)

Contiguity 0.009* (0.004)

0.012* (0.004)

0.012* (0.004)

164

Combined Wealth / 1000 0.001* (0.000)

-0.001* (0.000)

-0.001* (0.000)

Similar Wealth / 1000 0.003* (0.000)

0.003* (0.000)

0.003* (0.000)

Similar Polity Score / 1000

0.966* (0.000)

1.273* (0.156)

1.240* (0.156)

Same Culture - Huntington

0.045* (0.003)

0.043* (0.003)

0.042* (0.003)

Constant 0.119*

(0.004) 0.117* (0.004)

0.117* (0.004)

Observations 22101 22101 22101 No. of dyads 4415 4415 4415 R-Squared 0.8278 0.8309 0.8317 * p < 0.05, two-tailed test ** p < 0.05, one-tailed test Notes: standard errors in parentheses

Table 4: Main Model Table of Results

TABLE 4 Prais-Winston (FGLS) Estimation - with Robust Standard Errors -

of Similar Votes By Country Dyads, 1990-2000

Model 2a Model 2b Model 3a Model 3b Lagged Similar Votes 0.836*

(.004) 0.836* (.004)

0.835* (.004)

0.832* (.004)

IGO Connectedness / 1000 0.815* (0.289)

1.058* (0.346)

Econ IGO Connectedness / 1000

8.883* (2.165)

19.166* (2.915)

Gen IGO Connectedness / 1000 0.864* (0.333)

.895** (0.468)

Political Military IGO Connectedness / 1000

56.395* (14.631)

61.790* (19.960)

Social Cultural IGO Connectedness / 1000

9.570* (2.015)

7.850* (2.539)

Same Culture X IGO Connectedness / 1000

-0.487 (0.449)

Same Culture X Econ IGO Connectedness / 1000

-1.604 (3.815)

Same Culture X Gen IGO Connectedness / 1000

-0.666 (0.523)

165

Same Culture X Political Military IGO Connectedness / 1000

-16.300 (20.271)

Same Culture X Social Cultural IGO Connectedness / 1000

-6.011** (3.465)

Same Polity Score X IGO Connectedness / 1000

-0.052** (0.028)

Same Polity Score X Econ IGO Connectedness / 1000

-1.550* (0.215)

Same Polity Score X Gen IGO Connectedness / 1000

-0.064 (0.052)

Same Polity Score X Political Military IGO Connectedness / 1000

-1.114* (0.542)

Same Polity Score X Social Cultural IGO Connectedness / 1000

-0.000 (0.161)

Trade Intensity / 1000 -0.003* (0.001)

-0.003* (0.001)

-0.003* (0.001)

0.003* (0.001)

Allies -0.031* (0.004)

-0.031* (0.004)

-0.031* (0.004)

-0.031* (0.004)

Contiguity 0.006 (0.004)

0.006 (0.004)

0.005 (0.004)

0.006 (0.004)

Combined Wealth / 1000 -0.001* (0.000)

-0.001* (0.000)

-0.000* (0.000)

-0.001* (0.000)

Similar Wealth / 1000 0.003* (0.000)

0.003* (0.000)

0.003* (0.000)

0.003* (0.000)

Similar Polity Score 0.001* (0.000)

0.001* (0.000)

0.001* (0.000)

0.001* (0.000)

Same Culture - Huntington 0.043* (0.003)

0.044* (0.003)

0.047* (0.003)

0.052* (0.003)

Constant 0.103* (0.004)

0.103* (0.004)

0.099* (0.004)

0.100* (0.004)

Observations 22101 22101 22101 22101 No. of dyads 4415 4415 4415 4415 R-Squared 0.83 0.83 0.83 0.83

166

* p < 0.05, two-tailed test ** p < 0.05, one-tailed test

Figures Figure 1: Trade Networks Due to the density of the trade networks, a country subset was used. Only 12 of the 27 European Union members were chosen. Correlates of War (COW) country codes are next to the country names of the countries listed below. Also, the countries are listed by the date that they joined the EU. In the diagrams, original EU members, having joined in 1957 are yellow circles, those that joined in 1995 are blue circles, and those that joined in 2004 are pink circles. For a complete list of the COW UNGA countries, see Appendix 1. European Union Country Subset:

1957 1995 2004 Belgium (BEL) Austria (AUS) Hungary (HUN) France (FRN) Finland (FIN) Malta (MLT)

Germany (GFR) Sweden (SWD) Poland (POL) Italy (ITA)

Luxembourg (LUX)

Netherlands (NTH)

167

1990 Trade Subnetwork – 12 EU Countries

168

1995 Trade Subnetwork – 12 EU Countries

169

1999 Trade Subnetwork – 12 EU Countries

170

Figure 2: Alliance Networks of All UNGA Countries 1990 Alliance Network

171

1992 Alliance Network

1994 Alliance Network

172

1995 Alliance Network

1996 Alliance Network

173

1999 Alliance Network

2000 Alliance Network

174

Figure 3: Diplomatic Visits Networks 1990 - 2000 1990 Diplomatic Visits

1991 Diplomatic Visits

175

1992 Diplomatic Visits

176

1993 Diplomatic Visits

1994 Diplomatic Visits

177

1995 Diplomatic Visits

1996 Diplomatic Visits

178

1997 Diplomatic Visits

1998 Diplomatic Visits

179

1999 Diplomatic Visits

2000 Diplomatic Visits

180

Figure 4: General IGO 2-Mode Networks (Country Subset) Due to the density of the IGO networks, a country subset was used. Correlates of War (COW) country codes are next to the country names of the countries listed in the subset. In earlier years, Bosnia Herzegovina was part of Yugoslavia and the Czech Republic was Czechoslovakia. For a complete list of the COW UNGA countries, see Appendix 1. Country Subset: Czech Republic CZR (or Czechoslovakia CZE) Bosnia Herzegovina BOS (or Yugoslavia YUG) Djibouti DJI Turkey TUR United Arab Emirates UAE Japan JPN 1990 General IGOs

181

1995 General IGOs

182

2000 General IGOs

183

Figure 5: Political Military IGO 2-Mode Networks (Country Subset) 1990 Political Military IGOs

184

1992 Political Military IGOs

185

1993 Political Military IGOs

186

1994 Political Military IGOs

187

1999 Political Military IGOs

188

2000 Political Military IGOs

189

Figure 6: Economic IGO 2-Mode Networks (Country Subset) 1990 Economic IGOs

190

1993 Economic IGOs

191

1995 Economic IGOs

192

1997 Economic IGOs

s

193

2000 Economic IGOs

194

Figure 7: Social Cultural IGO 2-Mode Networks (Country Subset) 1990 Social Cultural IGOs

195

1993 Social Cultural IGOs

196

1994 Social Cultural IGOs

197

1996 Social Cultural IGOs

198

1999 Social Cultural IGOs

199

2000 Social Cultural IGOs

200

Table 18: Basic Statistics - Sensitivity Analysis using Full Sample (Results with Deletions)

Observations Mean SD Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 181 Similar Votes (percentage) 155105 0.53 0.34 0.00 1.002 Relative trade indegree centrality 20124 0.22 3.31 -20.49 22.10 0.083 Total trade indegree centrality 20124 0.41 5.03 -32.05 32.29 0.04 0.184 Relative alliance degree centrality 20124 -0.01 0.42 -3.16 6.52 -0.01 -0.03 -0.015 Total alliance degree centrality 20124 -0.01 0.37 -2.37 5.39 0.02 0.04 0.06 -0.176 Visits total outdegree centrality 20124 -0.08 2.42 -17.13 16.68 0.03 0.04 -0.02 0.05 0.117 Visits total indegree centrality 20124 -0.10 2.20 -14.97 11.62 0.04 -0.06 0.00 0.00 -0.01 0.108 IGO connectedness 18711 1.02 3.18 -16.68 26.72 0.08 0.04 0.10 -0.02 0.07 0.01 -0.069 Economic IGO connectedness 44932 0.19 0.92 -3.75 6.40 0.05 0.06 0.00 -0.04 0.06 0.01 0.09 0.1510 General IGO connectedness 44932 0.47 3.00 -8.29 23.98 0.02 0.01 0.02 0.01 0.04 0.04 -0.05 0.81 -0.0511 Political Military IGO connectedness 44932 0.01 0.16 -1.05 2.01 0.06 -0.01 -0.04 0.00 0.00 0.00 -0.03 0.00 -0.07 -0.0512 Social cultural IGO connectedness 44932 0.35 0.84 -3.25 4.88 0.00 -0.05 0.14 0.00 -0.03 -0.06 -0.03 0.20 -0.32 -0.08 -0.1213 Trade intensity 33826 -10.56 274.00 -7835.81 20377.73 0.00 0.00 -0.03 0.01 0.02 0.04 -0.06 0.03 0.01 0.03 -0.01 0.0314 Allies 155105 0.05 0.22 0.00 1.00 0.17 -0.01 -0.02 -0.01 0.07 0.04 -0.01 0.00 -0.02 -0.01 0.03 0.01 0.0215 Contiguity 155105 0.03 0.16 0.00 1.00 0.11 -0.01 -0.02 0.00 0.03 0.00 0.00 0.09 0.00 0.05 0.08 0.02 0.09 0.2616 Combined wealth 152973 14677.50 10414.93 705.54 73481.93 -0.31 -0.14 -0.15 0.00 0.03 0.00 -0.03 -0.12 -0.10 -0.03 0.03 -0.17 0.01 0.03 0.0017 Similar wealth 152973 -7645.31 7100.82 -43707.18 0.99 0.36 0.10 0.10 0.00 0.01 -0.01 0.03 0.13 0.09 0.04 0.00 0.15 0.01 0.07 0.08 -0.6618 Similar Polity Score 107317 -6.76 5.98 -19.00 1.00 0.18 0.01 -0.05 0.01 0.01 0.01 0.01 0.09 0.01 0.06 0.05 0.03 0.02 0.17 0.08 -0.04 0.1419 Same Culture - Huntington 154766 0.18 0.38 0.00 1.00 0.26 0.01 -0.02 0.00 0.01 0.01 0.00 0.08 -0.01 0.04 0.10 0.03 0.02 0.36 0.23 0.05 0.10 0.21

TABLE 18Full Sample Sensitivity Analysis: Basic Statistics (Results with Deletions)

Note: An instrumental variable method in Henderson (1999) was used to deal with serial correlation. These basic statistics are for the transformed variables. Table 19: Sensitivity Analysis using Full Sample (Results with Deletions)

TABLE 19 Prais-Winston (FGLS) Estimation - with Robust Standard Errors -

of Percentage Similar Votes By Country Dyads, 1990-2000

Model 1a Model 1b Model 1c

Lagged Similar votes 0.853* (0.004)

0.852* (0.004)

0.855* (0.004)

Relative Trade In-degree Centrality / 1000

11.927* (0.919)

12.786* (0.931)

12.772* (0.909)

Relative Trade In-degree Centrality Squared / 1000

0.122* (0.010)

0.128* (0.011)

0.124* (0.010)

Total Trade In-degree Centrality / 1000

-13.424* (0.761)

-14.593* (0.758)

-14.402* (0.758)

Total Trade In-degree Centrality Squared / 1000

-0.095* (0.005)

-0.105* (0.005)

-0.103* (0.005)

Relative Alliance Degree Centrality / 1000

-36.510* (5.326)

-38.298* (5.186)

201

Relative Alliance Degree Centrality Squared / 1000

-2.864* (0.421)

-3.108* (0.418)

Total Alliance Degree Centrality / 1000

-81.813* (8.011)

-81.989* (7.902)

Total Alliance Degree Centrality Squared / 1000

-3.422* (0.257)

-3.472* (0.259)

Visits Total Out-degree / 1000

-1.863* (0.543)

Visits Total In-degree / 1000

4.694* (0.554)

Trade Intensity / 1000 -0.0104* (0.003)

-0.012* (0.002)

-0.010* (0.002)

Allies 22.060* (0.004)

0.012* (0.004)

0.013* (0.004)

Contiguity -4.260 (0.004)

-0.354 (0.004)

-0.001 (0.004)

Combined Wealth / 1000 -0.001* (0.000)

-0.001* (0.000)

-0.001* (0.000)

Similar Wealth / 1000 0.002* (0.000)

0.002* (0.000)

0.002* (0.000)

Similar Polity Score / 1000

0.194 (0.000)

0.490* (0.167)

0.456* (0.166)

Same Culture - Huntington

6.081* (0.000)

0.010* (0.003)

0.009* (0.003)

Constant 0.118*

(0.004) 0.120* (0.004)

0.118* (0.004)

Observations 14 971 14 971 14 971 No. of dyads 3 768 3 768 3 768 R-Squared 0.8588 0.8610 0.8623 * p < 0.05, two-tailed test ** p < 0.05, one-tailed test Notes: standard errors in parentheses

202

Table 20: Sensitivity Analysis using Full Sample (Results with Deletions)

TABLE 20 Prais-Winston (FGLS) Estimation - with Robust Standard Errors -

of Similar Votes By Country Dyads, 1990-2000

Model 2a Model 2b Model 3a Model 3b Lagged Similar Votes 0.868*

(0.003) 0.868* (0.003)

0.849* (0.002)

0.847* (0.002)

IGO Connectedness / 1000 -1.917*

(0.295) -2.320* (0.300)

Econ IGO Connectedness / 1000

18.168* (1.054)

17.987* (1.036)

Gen IGO Connectedness / 1000 -3.472* (0.188)

-3.637* (0.188)

Political Military IGO Connectedness / 1000

3.268 (5.551)

10.015 (6.367)

Social Cultural IGO Connectedness / 1000

-4.537* (1.049)

-4.246* (1.484)

Same Culture X IGO Connectedness / 1000

3.922* (0.506)

Same Culture X Econ IGO Connectedness / 1000

-16.088* (2.484)

Same Culture X Gen IGO Connectedness / 1000

4.235* (0.433)

Same Culture X Political Military IGO Connectedness / 1000

-60.302* (11.100)

Same Culture X Social Cultural IGO Connectedness / 1000

-1.384 (2.579)

Same Polity Score X IGO Connectedness / 1000

-0.020 (0.030)

Same Polity Score X Econ IGO Connectedness / 1000

-1.058* (0.123)

203

Same Polity Score X Gen IGO Connectedness / 1000

0.123* (0.034)

Same Polity Score X Political Military IGO Connectedness / 1000

1.436 (1.175)

Same Polity Score X Social Cultural IGO Connectedness / 1000

0.099 (0.191)

Trade Intensity / 1000 -0.018* (0.003)

-0.018* (0.003)

-0.019* (0.004)

-.019* (0.004)

Allies 0.011* (0.004)

0.012* (0.004)

0.016* (0.003)

0.011* (0.003)

Contiguity 0.009** (0.005)

0.007* (0.005)

0.002 (0.004)

0.002 (0.004)

Combined Wealth / 1000 -0.000* (0.000)

-0.000* (0.000)

-0.000* (0.000)

-0.001* (0.000)

Similar Wealth / 1000 0.001* (0.000)

0.001* (0.000)

0.002* (0.000)

0.001* (0.000)

Similar Polity Score 0.000* (0.000)

0.001* (0.000)

0.001* (0.111)

.001* (0.000)

Same Culture - Huntington 0.012* (0.003)

0.002 (0.004)

0.010* (0.002)

0.012* (0.003)

Constant 0.106* (0.003)

0.110* (0.003)

0.121* (0.002)

0.123* (0.003)

Observations 14 268 14 268 33 826 33 826 No. of dyads 3 323 3 323 8059 8059 R-Squared 0.8543 0.8545 0.8267 0.8264 * p < 0.05, two-tailed test ** p < 0.05, one-tailed test

204

Appendix 1: List of COW Country Codes

STATE ABBREVIATION

STATE NUMBER

STATE NAME

USA 2 United States of America CAN 20 Canada BHM 31 Bahamas CUB 40 Cuba HAI 41 Haiti DOM 42 Dominican Republic JAM 51 Jamaica TRI 52 Trinidad and Tobago BAR 53 Barbados DMA 54 Dominica GRN 55 Grenada SLU 56 St. Lucia SVG 57 St. Vincent and the Grenadines AAB 58 Antigua & Barbuda SKN 60 St. Kitts and Nevis MEX 70 Mexico BLZ 80 Belize GUA 90 Guatemala HON 91 Honduras SAL 92 El Salvador NIC 93 Nicaragua COS 94 Costa Rica PAN 95 Panama COL 100 Colombia VEN 101 Venezuela GUY 110 Guyana SUR 115 Suriname ECU 130 Ecuador PER 135 Peru BRA 140 Brazil BOL 145 Bolivia PAR 150 Paraguay CHL 155 Chile ARG 160 Argentina URU 165 Uruguay UKG 200 United Kingdom IRE 205 Ireland NTH 210 Netherlands BEL 211 Belgium LUX 212 Luxembourg FRN 220 France MNC 221 Monaco LIE 223 Liechtenstein SWZ 225 Switzerland SPN 230 Spain AND 232 Andorra POR 235 Portugal

205

HAN 240 Hanover BAV 245 Bavaria GMY 255 Germany GFR 260 German Federal Republic GDR 265 German Democratic Republic BAD 267 Baden SAX 269 Saxony WRT 271 Wuerttemburg HSE 273 Hesse Electoral HSG 275 Hesse Grand Ducal MEC 280 Mecklenburg Schwerin POL 290 Poland AUH 300 Austria-Hungary AUS 305 Austria HUN 310 Hungary CZE 315 Czechoslovakia CZR 316 Czech Republic SLO 317 Slovakia ITA 325 Italy PAP 327 Papal States SIC 329 Two Sicilies SNM 331 San Marino MOD 332 Modena PMA 335 Parma TUS 337 Tuscany MLT 338 Malta ALB 339 Albania MAC 343 Macedonia CRO 344 Croatia YUG 345 Yugoslavia BOS 346 Bosnia and Herzegovina SLV 349 Slovenia GRC 350 Greece CYP 352 Cyprus BUL 355 Bulgaria MLD 359 Moldova ROM 360 Romania RUS 365 Russia EST 366 Estonia LAT 367 Latvia LAT 367 Latvia LIT 368 Lithuania UKR 369 Ukraine BLR 370 Belarus ARM 371 Armenia GRG 372 Georgia AZE 373 Azerbaijan FIN 375 Finland SWD 380 Sweden NOR 385 Norway DEN 390 Denmark ICE 395 Iceland

206

CAP 402 Cape Verde STP 403 Sao Tome and Principe GNB 404 Guinea-Bissau EQG 411 Equatorial Guinea GAM 420 Gambia MLI 432 Mali SEN 433 Senegal BEN 434 Benin MAA 435 Mauritania NIR 436 Niger CDI 437 Ivory Coast GUI 438 Guinea BFO 439 Burkina Faso LBR 450 Liberia SIE 451 Sierra Leone GHA 452 Ghana TOG 461 Togo CAO 471 Cameroon NIG 475 Nigeria GAB 481 Gabon CEN 482 Central African Republic CHA 483 Chad CON 484 Congo DRC 490 Democratic Republic of the Congo UGA 500 Uganda KEN 501 Kenya TAZ 510 Tanzania ZAN 511 Zanzibar BUI 516 Burundi RWA 517 Rwanda SOM 520 Somalia DJI 522 Djibouti ETH 530 Ethiopia ERI 531 Eritrea ANG 540 Angola MZM 541 Mozambique ZAM 551 Zambia ZIM 552 Zimbabwe MAW 553 Malawi SAF 560 South Africa NAM 565 Namibia LES 570 Lesotho BOT 571 Botswana SWA 572 Swaziland MAG 580 Madagascar COM 581 Comoros MAS 590 Mauritius SEY 591 Seychelles MOR 600 Morocco ALG 615 Algeria TUN 616 Tunisia LIB 620 Libya

207

SUD 625 Sudan IRN 630 Iran TUR 640 Turkey IRQ 645 Iraq EGY 651 Egypt SYR 652 Syria LEB 660 Lebanon JOR 663 Jordan ISR 666 Israel SAU 670 Saudi Arabia YAR 678 Yemen Arab Republic YEM 679 Yemen YPR 680 Yemen People's Republic KUW 690 Kuwait BAH 692 Bahrain QAT 694 Qatar UAE 696 United Arab Emirates OMA 698 Oman AFG 700 Afghanistan TKM 701 Turkmenistan TAJ 702 Tajikistan KYR 703 Kyrgyzstan UZB 704 Uzbekistan KZK 705 Kazakhstan CHN 710 China MON 712 Mongolia TAW 713 Taiwan KOR 730 Korea PRK 731 North Korea ROK 732 South Korea JPN 740 Japan JPN 740 Japan IND 750 India BHU 760 Bhutan PAK 770 Pakistan BNG 771 Bangladesh MYA 775 Myanmar SRI 780 Sri Lanka MAD 781 Maldives NEP 790 Nepal THI 800 Thailand CAM 811 Cambodia LAO 812 Laos DRV 816 Vietnam RVN 817 Republic of Vietnam MAL 820 Malaysia SIN 830 Singapore BRU 835 Brunei PHI 840 Philippines INS 850 Indonesia ETM 860 East Timor AUL 900 Australia

208

PNG 910 Papua New Guinea NEW 920 New Zealand VAN 935 Vanuatu SOL 940 Solomon Islands KIR 946 Kiribati TUV 947 Tuvalu FIJ 950 Fiji TON 955 Tonga NAU 970 Nauru MSI 983 Marshall Islands PAL 986 Palau FSM 987 Federated States of Micronesia WSM 990 Samoa

209

Appendix 2: List of IGO Codes

Source: Ingram, Robinson, and Busch (2005)

Code IGO_NAME 10 ACP-EEC Joint Assembly 20 Administrative Centre of Social Security for Rhine Boatmen 30 African and Malagasy Council for Higher Education 40 African and Malagasy Sugar Council 50 African Civil Service Observatory 60 African Cultural Institute 70 African Development Bank 80 African Export Import Bank 90 African Foundation for Research and Development

100 African Fund for Guarantee and Economic Cooperation 110 African Groundnut Council 115 African Intellectual Property Organization 120 African Malagasy Coffee Organization 130 African Oil Palm Development Association 140 African Organization of Cartography and Remote Sensing 150 African Petroleum Producers Association 155 African Postal and Telecommunications Union (APTU) 160 African Postal Union 170 African Regional Industrial Property Organization 180 African Reinsurance Corporation 190 African School of Architecture and Town Planning 200 African Standing Conference on Bibliographic Control 210 African Timber Organization 220 African, Caribbean and Pacific Group of States 230 Afro-Asian Rural Reconstruction Organization 240 Afro-Malagasy Industrial Property Office 250 Afro-Malagasy Postal and Telecommunications Union 260 Afro-Malagasy Union 270 Agency for Cultural and Technical Cooperation 280 Agency for Prohibition of nuclear weapons in LA and Caribbean (OPANAL) 290 Agency Safety Aerial Navigation in Africa

300 Agreement for Cooperation in Dealing with Pollution of the North Sea (Bonn Agreement)

310 Amazonian Cooperation Council 320 American Committee on Dependent Territories 330 Andean Common Market 340 Andean Parliament 350 Anglo-American Caribbean Commission 360 ANZUS Council 370 Arab Authority for Agricultural Investment and Development 380 Arab Bank for Economic Development in Africa 390 Arab Center for Medical Literature 400 Arab Cooperation Council

210

410 Arab Federation for Technical Education 420 Arab Fund for Economic and Social Development 430 Arab Gulf Programme for United Nations Development Organizations 440 Arab Industrial Development and Mining Organization 450 Arab Investment Company 460 Arab Labor Organization 470 Arab Maghreb Union 490 Arab Monetary Fund 500 Arab Organization for Agricultural Development 510 Arab Organization for Mineral Resources 520 Arab Postal Union 530 Arctic Council 540 Asia-Europe Foundation 550 Asian and Pacific Coconut Community 560 Asian and Pacific Council 570 Asian Clearing Union 580 Asian Development Bank 590 Asian Industrial Development Council 600 Asian Productivity Organization (APO) 610 Asian Reinsurance Corporation 620 Asian Vegetable Research and Development Center 630 Asian-African Legal Consultative Committee 640 Asian-Pacific Postal Union 650 Asia-Pacific Econ Cooperation (APEC) 660 Asia-Pacific Institute for Broadcasting Development 670 Asia-Pacific Telecommunity 680 Association Between the EEC and Partner States of East African Community 690 Association of African Central Banks 700 Association of African Tax Administrators 710 Association of African Trade Promotion Organizations 720 Association of Caribbean States 730 Association of Iron Ore Exporting Countries 740 Association of Natural Rubber Producing Countries 750 ASEAN 760 Association of Supervisors of Banks of Latin America and the Caribbean 770 Assoc Tin Producing Countries 780 Baltic Council 790 Baltic Environmental Forum 800 Baltic Peacekeeping Battalion 810 Bank for International Settlement 820 Banque Internationale d'information sur les Etats Francophones 830 Benelux Economic and Social Advisory Council 840 Benelux Economic Union 850 Bionet International-Global Network of Biosystematics 860 Board of Nordic Development Projects 870 Commonwealth Science Council 871 CAB International 872 Caribbean Commission 880 Caribbean Community 890 Caribbean Development Bank

211

900 Caribbean Examination Council 910 Caribbean Financial Action Task Force 912 Caribbean Free Trade Association 913 Caribbean Organisation 920 Caribbean Postal Union 940 Central African Customs and Economic Union 950 Central American Coffee Board 970 Central American Energy Commission 980 Central American Institute of Public Administration 990 Central American Integration System

1000 Central American Monetary Stabilization Fund 1010 Central American Research Institute Industry 1020 Central and Eastern European Privatization Network 1030 Central Asian Economic Community 1040 Central Bureau for the International 1:1,000,000 Map of the World 1050 Central Commission Navigation of the Rhine 1060 Central Compensation Office of the Maghreb 1070 Central European Free Trade Association 1080 Central European Initiative (CEI) 1090 Central Office for International Railway Transport 1095 Central Pan American Bureau of Eugenics and Homiculture 1100 Central Treaty Organization 1110 Center for Marketing Information and Advisory Services for Fishery 1120 Cocoa Producers' Alliance 1130 Comite Regional de Sanidad Vegetal del Cono Sur 1140 Commission for International Financial Control in Macedonia 1145 Comm Tech Coop in Africa South of Sahara 1150 Commission of the Chad Basin 1160 Common Fund for Commodities 1170 Common Market for Eastern and Southern Africa 1180 Commonwealth Advisory Aeronautical Research Council 1190 Commonwealth Agricultural Bureaux International 1200 Commonwealth Air Transport Council 1210 Commonwealth Economic Committee 1220 Commonwealth Education Liaison Committee 1230 Commonwealth of Independent States 1240 Commonwealth Secretariat 1250 Commonwealth Telecomm Board 1260 Communaute economique et monetaire d'Afrique centrale 1270 Community of Portuguese-Speaking Countries 1280 Concerted Action for African Development 1290 Conference interafricaine des marchs d'assurances 1300 Conference des ministres de la jeunesse et des sports des pays 1310 Conference of African Ministers Responsible for Sustainable Development 1320 Conference of Heads of State of Equatorial Africa 1330 Conference of Ministers of Agriculture of West and Central Africa 1340 Conference of Posts and Telecommunications Administrations of Central Africa 1350 Conferencia de Autoridades Cinematogrificas de Iberoamerica 1360 Convenio Andres Bello de integraci n educativa, cientfica y cultural 1370 Council for Mutual Economic Assitance (COMECON)

212

1380 Council Tech Coop South/South-East Asia 1390 Council of Europe 1400 Council of Ministers for Asian Economic Cooperation 1410 Council of Ministers of Health of the Arab States of the Gulf 1420 Council of the Baltic Sea States (CBSS) 1430 Council of the Entente 1440 Danube Commission 1450 Desert Locust Control Organization for East Africa 1460 Development Bank of the Great Lakes States 1470 East African Common Market 1475 East African Common Services Organization 1480 East African Development Bank 1486 East Caribbean Common Market 1489 East Caribbean Currency Authority 1490 Eastern Caribbean Central Bank 1500 Economic Community of Central African States (CEEAC) 1510 Economic Community of the Great Lakes Countries (CEPGL) 1520 Economic Community of the West African States (ECOWAS)

1530 Economic Cooperation Organization (ECO)/Prev--Regional Cooperaton for Development (1965-1984)

1535 Empire Marketing Board 1540 Eurasian Patent Organization 1550 Euro Atlantic Partnership Council 1560 European and Mediterranean Plant Protection Organization 1565 European Atomic Energy Commission 1570 European Bank for Reconstruction and Development 1580 European Central Bank 1585 European Coal and Steel Community (ECSC) 1590 European Collaboration on Measurement Standards 1600 European Commission for Control of Foot-and-Mouth Disease 1610 European Commission for Control of the Danube 1620 European Company for the Chemical Processing of Irradiated Fuels 1630 European Company for the Financing of Railroad Rolling Stock (EUROFIMA) 1640 European Conference of Postal and Telecommunications Administrations 1645 European Food Code Council 1650 European Customs Union Study Group 1653 European Economic Community (EEC) 1660 European Foundation for the Improvement of Living and Working Conditions 1670 European Free Trade Association (EFTA) 1680 European Institute of Public Administration 1690 European Investment Bank 1700 European Molecular Biology Conference 1710 European Molecular Biology Lab 1715 European Monetary Institute 1720 European Organization for Nuclear Research 1730 European Organization for the Safety of Air Navigation (EUROCONTROL) 1740 European Patent Office 1750 European Payments Union 1760 European Postal Financial Services Commission 1770 European Productivity Agency 1780 European Southern Observatory

213

1790 European Space Agency 1800 European Space Research Organization 1810 European Space Vehicle Launcher Development Org 1820 European Training Foundation 1830 European Union 1840 Food and Agricultural Organization 1850 Far Eastern Commission

1860 Fund for Development of the Indigenous Peoples of Latin America and the Carribean

1870 Gambia River Basin Development Organization 1880 General Agreement on Tariffs and Trade

1890 Permanent Secretariat of the General Treaty on Central American Economic Integration

1900 Global Environment Facility 1910 Group of Fifteen (G-15) 1920 Group of Latin American and Caribbean Sugar Exporting Countries 1930 Group of Schengen 1940 Group of Temperate Southern Hemisphere Countries on the Environment 1950 Group of Three 1970 Group on the Balkan Agreement on Cooperation on Tourism 1980 Guidance Committee for Road Safety in the Nordic Countries 1990 Gulf Cooperation Council 2000 Gulf Organization for Industrial Consulting 2010 Hague Conference on Private International Law 2020 Organization of Ibero-American States for Education, Science and Culture 2030 Imperial Defense Committee 2033 Imperial Institute of Entomology 2036 Imperial Mycological Institute 2040 Commonwealth War Graves Commission 2050 Indian Ocean Commission 2060 Indo-Pacific Fisheries Council 2070 Institute of Nutrition of Central America and Panama 2080 Inter-African Committee on Statistics 2090 Inter-African Phyto-Sanitary Council 2100 Inter-Allied Reparation Agency 2105 Inter-Allied Rhineland High Commission 2110 Inter-American Children's Institute 2120 Inter-American Coffee Board 2130 Inter-American Commission of Women 2140 Inter-American Conference on Social Security 2150 Inter-American Defense Board 2160 Inter-American Development Bank 2170 Inter-American Federation of Cotton 2175 Inter-American High Commission 2180 Inter-American Indian Institute 2190 Inter-American Institute for Cooperation on Agriculture 2200 Inter-American Investment Corporation 2203 Inter-American Radio Office 2206 Inter-American Trademark Bureau 2210 Inter-American Tropical Tuna Commission 2220 Inter-Arab Investment Guarantee Corporation

214

2230 Inter-Governmental Authority on Development (IGAD) 2240 Intergovernmental Bureau for Informatics 2250 International Organization for Migration (IOM) 2260 Intergovernmental Committee of the River Plate Basin Countries 2270 Intergovernmental Copyright Committee 2280 Intergovernmental Council for Copper Exporting Countries 2285 Intergovernmental Group of Twenty-Four on International Monetary Matters 2290 Intergovernmental Oceanographic Commission

2300 Intergovernmental Org Marketing Information and Technical Advisory for Fishery Products in the Asia and Pacific Region

2310 Intergovernmental TV and Radio Corporation 2320 Interim Committee for Coordination of Investigations of the Lower Mekong Basin 2325 International Advisory Committee for Long Distance Telephony 2330 International African Migratory Locust Organization 2340 International Arbitration Tribunal at San Jose 2345 International Association for Public Baths and Cleanliness 2350 International Association of Seismology 2360 International Association of Supreme Administrative Jurisdictions 2370 International Atomic Energy Agency 2380 International Authority for the Ruhr 2390 International Bank for Economic Cooperation 2400 International Bank Reconstruction and Development 2410 International Bauxite Association 2430 International Bureau for Information and Enquiries regarding Relief to Foreigners 2440 International Bureau for the Protection of the Moselle against Pollution 2450 International Bureau for the Protection of the Rhine Against Pollution 2455 International Bureau of Commercial Statistics 2460 International Bureau of Education 2470 International Bureau of Weights and Measures

2480 International Center for the Study of the Preservation and Restoration of Cultural Property

2490 International Central American Office 2495 International Chemistry Office 2500 International Civil Aviation Organization 2510 International Civil Defense Organization 2520 International Cocoa Organization 2530 International Coffee Organization

2540 International Commission for the Decennial Revision of the Nomenclature of the Causes of Death

2550 International Commission for the Hydrology of the Rhine Basin 2560 International Commission for the Navigation of the Congo 2570 International Commission Northwest Atlantic Fisheries 2575 International Commission of the Oder 2580 International Commission for the Scientific Exploration of the Mediterranean Sea 2590 International Commission for the Southeast Atlantic Fisheries 2600 International Commission of Agricultural Industries 2610 International Commission of the Cape Spartel Light in Tanger 2620 International Commission on Civil Status 2630 International Commission on the Teaching of Mathematics 2640 International Committee of Military Medicine and Pharmacy 2650 International Conference for Promoting Technical Unification on the Railways

215

2660 International Copper Study Group 2670 International Coral Reef Initiative 2680 International Cotton Advisory Committee 2690 International Council for the Exploration of the Sea 2700 International Criminal Police Organization 2705 International Elbe Commission 2710 International Energy Agency (IEA) 2720 International Exchange Service 2730 International Exhibitions Bureau 2740 International Finance Commission at Athens 2750 International Finance Corporation 2760 International Fund for Agricultural Development 2770 International Fund for Saving the Aral Sea 2780 International Hydrographic Organization 2790 International Institute for the Unification of Private Law 2800 International Institute of Agriculture 2805 International Institute of Commerce 2810 International Institute for Refrigeration 2820 International Jute Organization 2830 International Labor Organization 2840 International Lead and Zinc Study Group 2850 International Maritime Bureau Against the Slave Trade 2860 International Maritime Organization 2870 International Mobile Satellite Organization 2880 International Monetary Fund 2890 International Moselle Company 2900 International Natural Rubber Organization 2910 International Nickel Study Group 2920 International North Pacific Fisheries Commission 2930 International Office of Epizootics 2940 International Office of Public Hygiene 2950 International Oil Pollution Compensation Fund 2960 International Olive Oil Council 2970 International Organization of Legal Metrology 2972 International Patent Institute 2980 International Pedagogical Institute 2990 International Penal and Penitentiary Commission 3000 International Pepper Community 3010 International Physiological Laboratories on Monte-Rosa 3020 International Plant Genetic Resources Institute 3030 International Prize Court 3040 International Red Locust Control Service 3050 International Refugee Organization 3060 International Regional Organization Against Plant And Animal Diseases 3070 International Relief Union 3080 International Rice Commission 3090 International Rubber Study Group 3100 International Seabed Authority 3110 International Secretariat for the Unification of Pharmacological Terms 3130 International Sugar Council

216

3140 International Tea Promotion Association 3150 International Technical Committee of Legal Experts on Air Questions 3160 International Telecommunications Union 3170 International Telecommunications Satellite Organization 3175 International Telegraph Consultative Committee 3180 International Tin Council 3190 International Tropical Timber Organization 3200 International Union for the Protection of Industrial Property 3210 International Union for the Protection of Literary and Artistic Works 3220 International Union for the Publication of Customs Tariffs 3230 Pruth International Union for the Control of Navigation on the Danube 3240 International Vine and Wine Office 3250 International Whaling Commission 3260 International Wheat Council 3270 International Wool Study Group 3280 Interoceanmetal Joint Organization 3290 Inter-state Bank

3300 Inter-state Organization for Advanced Technicians of Hydraulics and Rural Equipment

3310 Inter-state School of Hyraulic and Rural Engineering for Senior Technicians 3320 Islamic Development Bank 3330 Joint Administration of the Turkic Culture and Arts 3340 Joint Anti-Locust and Anti Aviarian Organization 3350 Joint Institute for Nuclear Research 3360 Joint Nordic Organization for Lappish Culture and Reindeer Husbandry 3370 Latin American Center for Physics 3380 Latin American Civial Aviation Commission 3390 Latin American Economic System (LAES) 3400 Latin American Institute of Educational Communication 3410 Latin American Energy Organization 3420 Latin American Fisheries Development Organization 3428 Latin American Free Trade Association 3430 Latin American Integration Association 3440 Latin Union 3450 League of Arab States 3460 League of Nations 3470 Liptako-Gourma Integrated Development Authority 3480 Mano River Union 3490 Mediterranean Water Network 3500 Middle East - Mediterranean Travel and Tourism Association 3510 Ministerial Conference of West and Central African States on Maritime 3520 Multi-Country Posts and Telecommunications Training Center, Blantyre 3530 Multilateral Fund for the Implementation of the Montreal Protocol 3540 Multilateral Investment Guarantee Agency 3550 Multinational Force and Observers 3560 Network of Aquaculture Centers in Asia-Pacific 3570 Niger Basin Authority 3580 Non-Aligned Movement 3585 Nordic Children's Film Council 3590 Nordic Council 3600 Nordic Council for Reindeer Research

217

3610 Nordic Council Tax Research 3620 Nordic Council of Ministers 3630 Nordic Development Fund 3640 Nordic Economic Research Council 3650 Nordic Investment Bank 3660 Nordic Telecommunications Satellite Council 3670 North American Free Trade Agreement 3680 North American Plant Protection Organization 3690 North Atlantic Salmon Conservation Organization 3700 North Atlantic Treaty Organization 3710 North Pacific Fur Seal Commission 3720 North Pacific Marine Sciences Organization 3730 Northeast Atlantic Fisheries Commission 3740 Observatoire economique et statstique d'Afrique subsaharienne 3750 Organization for Economic Cooperation and Development 3760 Organization for African Unity 3762 Organization for European Economic Cooperation 3770 Organization for Security and Cooperation in Europe 3780 Organization for Cooperation of Railways 3790 Organization for the Management and Development of the Kagera River Basin 3800 Organization of Arab Petroleum Exporting Countries 3810 Organization of Black Sea Economic Cooperation 3812 Organization of Central American States 3820 Organization of Coordination for the Control of Endemic Diseases in Central Africa 3830 Organization of Eastern Carribean States 3840 Organization of Petroleum Exporting Countries 3850 Organization of the Islamic Conference 3855 Oslo Commission 3860 OSPAR Commission 3870 Pacific Cable Board 3880 Pan American Institute of Geography and History 3890 Pan-American Health Organization 3900 Organization of American States 3910 Paris Commission 3920 Partners in Population and Development - A South - South Initiative 3925 Permanent Association of Pan American Highway Congresses 3930 Permanent South Pacific Commission 3940 Permanent Court of Arbitration 3950 World Road Association

3960 Permanent International Bureau of Analytical Chemistry of Human and Animal Food

3965 Permanent International Commission of Studies on Sanitary Equipment 3970 Permanent Interstate Committee on Drought Control Sahel

3980 Permanent Secretariat of the South American Agreement on Narcotic Drugs and Psychotropic Substances

4000 Pole Europeen de developpement 4010 Port Management Association of Eastern and Southern Africa

4020 Postal Union of the Americas, Spain, Portugal (formerly Pan-American Postal Union and the Postal Union of the Americas and Spain)

4030 Preferential Trade Area for Eastern and Southern African States 4040 Radiotelegraph Union

218

4050 Regional African Satellite Communications Organization 4060 Regional Commonwealth in the Field of Communications 4070 Regional Cooperation Agreement for the Promotion of Nuclear Science 4075 Sterling Area Statistical Committee 4080 Regional Council for Adult Education and Literacy in Africa 4085 Reparation Commission 4090 Reserve internationale maritime en mediterranee occidentale 4100 Rio Group 4110 Scientific Council for Africa South of the Sahara 4120 Secretariat of the Commission for East African Cooperation 4130 Senegal River Development Organization 4140 Societe Arabe des mines de l'Inchiri 4150 South and West Asia Postal Union 4160 South Asia Cooperative Environment Programme 4170 South Asian Association for Regional Cooperation 4180 South Investment, Trade and Technological Data Exchange Center 4190 South Pacific Commission 4200 South Pacific Forum 4210 South-East Asia Treaty Organization 4220 Southeast Asian Ministers of Education Organization 4230 Southern Africa Regional Tourism Council 4240 Southern African Customs Union 4250 Southern African Development Community 4251 Southern African Development Coordination Conference 4260 Southern Common Market 4270 Special Arab Aid Fund for Africa 4280 Suez Canal Administration 4290 Sugar Union 4300 Superior Council of Health 4310 Trade and Investment Council 4320 Tripartite Commission for the Restitution of Monetary Gold 4325 Tripartite Commission on the Working Conditions of Rhine Boatmen 4330 Tropical Agriculture Research and Training Center 4340 Union economique et monetaire Ouest Africaine 4350 Union for the International Use of Carriages and Vans 4360 International Union for the Protection of New Varieties of Plants 4365 Union monetaire de l'Afrique centrale 4370 Union of Banana Exporting Countries 4380 United Arab Shipping Company 4390 United Kingdom--Dominion Wool Disposals 4400 United Nations 4410 United Nations Educational, Scientific, and Cultural Organization 4420 United Nations Industrial Development Organization 4430 Universal Postal Union 4440 University of the South Pacific 4450 Vision and Strategies Around the Baltic Sea 2010 4460 Warsaw Treaty Organization

4470 Wassenaar Arrangement on Export Controls for Conventional Arms and Dual-Use Goods and Technologies

4480 West Africa Rice Development Association 4485 West African Economic Community

219

4490 West African Examinations Council 4503 West African Monetary Union 4510 Western European Union 4520 West-Nordic Foundation 4530 World Meteorological Organization 4540 Working Community of the Danube Countries 4550 World Health Organization 4560 World Intellectual Property Organization 4570 World Tourism Organization 4580 World Trade Organization

220

Appendix 3: Calculations to Support IGO Network Analysis Discussion

Coun

try S

ubse

t: Cz

ech R

epub

lic (o

r Cze

chos

lovak

ia), B

osnia

Her

zego

vina (

or Y

ugos

lavia)

, Dijib

outi,

Turke

y, Un

ited A

rab E

mira

tes, a

nd Ja

pan.

1990

1995

2000

Aver

age O

ver

the Y

ears

DJI, U

AE,

JPN

JPN,

CZR

, CZ

E, T

URUA

E, D

JITU

R,

CZR,

CZE

JPN

w/all

ot

hers

JPN,

CZE

, CZ

R, T

URUA

E, JP

N 19

90UA

E, JP

N 19

95UA

E, JP

N 20

001

JPN

UAE

740

696

5%56

%18

%26

%26

%26

%5%

56%

18%

2JP

NTU

R74

064

050

%76

%36

%54

%54

%54

%54

%3

JPN

DJI

740

522

5%56

%14

%25

%25

%25

%4

JPN

BOS

740

346

N/A

95%

43%

69%

69%

5JP

NYU

G74

034

55%

N/A

100%

53%

53%

6JP

NCZ

E74

031

560

%N/

AN/

A60

%60

%60

%60

%7

JPN

CZR

740

316

N/A

77%

64%

71%

71%

71%

71%

8UA

ETU

R69

664

030

%54

%11

%32

%9

UAE

DJI

696

522

100%

100%

73%

91%

91%

91%

10UA

EBO

S69

634

6N/

A75

%0%

38%

11UA

EYU

G69

634

510

0%N/

A33

%67

%12

UAE

CZE

696

315

5%N/

AN/

A5%

13UA

ECZ

R69

631

6N/

A56

%9%

33%

14TU

RDJ

I64

052

232

%52

%27

%37

%15

TUR

BOS

640

346

N/A

95%

86%

91%

16TU

RYU

G64

034

530

%N/

A10

0%65

%17

TUR

CZE

640

315

55%

N/A

N/A

55%

55%

55%

55%

18TU

RCZ

R64

031

6N/

A93

%82

%88

%88

%88

%88

%19

DJI

BOS

522

346

N/A

75%

14%

45%

20DJ

IYU

G52

234

510

0%N/

A33

%67

%21

DJI

CZE

522

315

5%N/

AN/

A5%

22DJ

ICZ

R52

231

6N/

A56

%14

%35

%23

BOS

YUG

346

345

N/A

N/A

N/A

N/A

24BO

SCZ

E34

631

5N/

AN/

AN/

AN/

A25

BOS

CZR

346

316

N/A

90%

86%

88%

26YU

GCZ

E34

531

55%

N/A

N/A

5%27

YUG

CZR

345

316

N/A

N/A

100%

100%

Aver

age o

f % S

imila

r Vot

ing

for E

ach

Yea

Votin

g An

alysis

to S

uppo

rt IG

O Ne

twor

k Disc

ussio

n

r39

%74

%47

%52

%47

%65

%91

%71

%51

%65

%5%

56%

18%

Econ

omic

IGOs

Socia

l Cul

tura

l IGOs

Coun

try V

otin

g Pa

irsCo

untry

Cod

esGe

nera

l IGOs

%Sim

ilar V

otin

g fo

r Eac

h Pa

irPo

litica

l Milit

ary I

GOs

221

Appendix 4: Tables and Figures for Additional Analysis Table 5: Basic Statistics for Small Sample Analysis

Observations Mean SD Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 181 Similar Votes (percentage) 10026 0.46 0.35 0.00 1.002 Relative trade indegree centrality 1709 0.18 4.57 -22.23 21.89 0.043 Total trade indegree centrality 1709 0.66 6.51 -33.98 32.91 -0.11 0.144 Relative alliance indegree centrality 1709 0.00005 0.43 -3.15 4.49 -0.02 -0.06 -0.015 Total alliance indegree centrality 1709 0.005 0.44 -1.95 4.81 0.02 0.05 0.13 -0.316 Visits total outdegree centrality 1709 -0.11 2.95 -14.20 15.99 0.01 0.02 0.17 0.02 0.197 Visits total indegree centrality 1709 -0.15 2.77 -9.81 11.31 0.12 0.00 -0.23 0.02 -0.03 0.058 IGO connectedness 2870 0.58 2.16 -13.23 16.85 0.07 0.04 0.05 0.02 -0.06 0.03 -0.099 Economic IGO connectedness 2870 0.11 0.73 -3.92 5.12 0.04 0.10 -0.11 0.03 -0.11 0.00 0.11 0.31

10 General IGO connectedness 2870 0.13 1.58 -6.46 14.82 0.03 0.03 -0.01 0.01 -0.01 0.01 -0.08 0.65 -0.1111 Political Military IGO connectedness 2870 0.00001 0.08 -0.42 1.02 0.04 -0.01 -0.02 -0.02 0.03 -0.01 -0.01 -0.03 -0.11 0.0512 Social cultural IGO connectedness 2870 0.17 0.65 -2.16 4.02 0.03 -0.11 0.25 0.00 0.03 0.03 -0.17 0.22 -0.45 -0.10 -0.1413 Trade intensity 1709 18.20 698.40 -9606.90 14974.97 0.01 0.00 -0.01 -0.01 0.01 0.05 0.04 0.02 -0.04 0.04 0.00 0.0214 Allies 1709 0.01 0.19 -1.00 1.02 0.00 0.02 0.04 0.08 -0.18 -0.04 -0.16 0.09 0.04 0.11 -0.10 0.00 0.0015 Contiguity 10026 0.03 0.17 0.00 1.00 0.05 0.01 0.00 0.01 0.07 0.00 -0.01 0.05 -0.01 0.02 0.09 0.06 0.00 0.0116 Combined wealth 9960 19986.30 12118.95 2238.16 68833.40 -0.05 -0.06 -0.04 0.02 -0.07 -0.06 0.06 -0.08 -0.17 0.04 -0.03 -0.05 0.11 0.03 0.0017 Similar wealth 9960 -10375.72 8167.00 -41885.00 0.99 0.03 0.00 0.00 0.00 0.01 0.05 -0.04 0.07 0.05 0.01 0.06 0.05 0.02 -0.01 0.07 -0.5218 Similar Polity Score 8355 -7.09 6.32 -19.00 1.00 0.28 0.00 -0.01 0.06 -0.10 -0.04 -0.02 0.15 0.00 0.13 0.00 0.10 0.08 0.05 0.07 -0.09 0.1619 Same Culture - Huntington 10014 0.18 0.38 0.00 1.00 0.26 -0.01 -0.01 0.00 0.02 0.02 -0.01 -0.17 -0.09 -0.05 0.03 -0.12 0.00 -0.01 0.17 -0.07 0.07 0.14

TABLE 5Small Sample Basic Statistics

Table 6: Main Model using Small Sample

TABLE 6

Small Sample Prais-Winston (FGLS) Estimation - with Robust Standard Errors - of Percentage Similar Votes By Country Dyads, 1990-2000

Model 1a Model 1b Model 1c

Lagged Similar votes 0.828* (0.014)

.821* (0.014)

0.811* (0.015)

Relative Trade In-degree Centrality / 1000

-4.672* (1.985)

-2.958 (2.039)

-5.154* (2.070)

Relative Trade In-degree Centrality Squared / 1000

-0.058* (0.020)

-0.048* (0.021)

-0.075* (0.021)

Total Trade In-degree Centrality / 1000

-13.378* (2.142)

-16.706* (2.281)

-16.044* (2.346)

Total Trade In-degree Centrality Squared / 1000

-0.070* (0.016)

-0.094* (0.017)

-0.098* (0.018)

Relative Alliance Degree Centrality / 1000

-86.670* (20.109)

-0.090* (18.186)

Relative Alliance Degree Centrality Squared / 1000

-5.898* (1.125)

-6.399* (1.076)

Total Alliance Degree Centrality / 1000

-154.868* (31.671)

-181.963* (28.129)

Total Alliance Degree Centrality Squared / 1000

-4.365* (0.777)

-5.349* (0.735)

222

Visits Total Out-degree / 1000 -2.436 (1.535)

Visits Total In-degree / 1000 16.349* (2.211)

Trade Intensity / 1000 -0.002

(0.002) -0.002 (0.002)

-0.004** (0.002)

Allies -0.035* (0.011)

-0.042* (0.013)

-0.006 (0.012)

Contiguity 0.028 (0.032)

0.052** (0.028)

0.053** (0.029)

Combined Wealth / 1000 0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

Similar Wealth / 1000 -0.001 (0.001)

-0.001 (0.000)

-0.001 (0.001)

Similar Polity Score / 1000 1.509* (0.581)

1.917* (0.589)

2.034* (0.620)

Same Culture - Huntington .035* (0.012)

0.036* (0.013)

0.042* (0.013)

Constant 0.085*

(0.011) 0.082* (0.011)

0.088* (0.011)

Observations 1709 1709 1709 No. of dyads 414 414 414 R-Squared 0.77 0.77 0.76 * p < 0.05, two-tailed test ** p < 0.05, one-tailed test Notes: standard errors in parentheses

223

Table 7: Main Model using Small Sample

TABLE 7 Small Sample Prais-Winston (FGLS) Estimation - with Robust Standard Errors -

of Similar Votes By Country Dyads, 1990-2000

Model 2a Model 2b Model 3a Model 3b Lagged Similar Votes 0.841*

(0.013) 0.841* (0.013)

0.856* (0.011)

0.866* (0.011)

IGO Connectedness / 1000 4.692* (1.364)

0.791 (2.173)

Econ IGO Connectedness / 1000

6.783 (6.848)

-4.025 (14.360)

Gen IGO Connectedness / 1000

6.487* (1.477)

-0.943 (2.850)

Political Military IGO Connectedness / 1000

29.560 (52.000)

8.582 (75.581)

Social Cultural IGO Connectedness / 1000

66.594* (9.925)

70.969* (14.169)

Same Culture X IGO Connectedness / 1000

-3.104 (4.432)

Same Culture X Econ IGO Connectedness / 1000

-5.495 (39.761)

Same Culture X Gen IGO Connectedness / 1000

-0.221 (5.712)

Same Culture X Political Military IGO Connectedness / 1000

-79.105 (89.718)

Same Culture X Social Cultural IGO Connectedness / 1000

-54.434 (35.686)

224

Same Polity Score X IGO Connectedness / 1000

0.405* (0.172)

Same Polity Score X Econ IGO Connectedness / 1000

1.761 (1.072)

Same Polity Score X Gen IGO Connectedness / 1000

1.325* (0.221)

Same Polity Score X Political Military IGO Connectedness / 1000

-0.827 (1.940)

Same Polity Score X Social Cultural IGO Connectedness / 1000

-2.371* (0.789)

Trade Intensity / 1000 -0.003 (0.002)

-0.003 (0.002)

-0.004* (0.002)

-0.004* (0.002)

Allies -0.042* (0.012)

-0.050* (0.021)

-0.057* (0.014)

-0.081* (0.025)

Contiguity 0.024 (0.028)

0.026 (0.028)

0.011 (0.027)

0.019 (0.026)

Combined Wealth / 1000 0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

Similar Wealth / 1000 -0.001 (0.000)

-0.007 (0.000)

-0.001** (0.000)

-0.001 (0.000)

Similar Polity Score 0.001* (0.001)

0.001* (0.001)

0.000 (0.001)

0.000 (0.001)

Same Culture - Huntington 0.038* (0.012)

0.040* (0.013)

0.047* (0.011)

0.061* (0.016)

Constant 0.064* (0.009)

0.066* (0.009)

0.041* (0.009)

0.033* (0.009)

Observations 1709 1709 1709 1709 No. of dyads 414 414 414 414 R-Squared 0.79 0.79 0.82 0.84 * p < 0.05, two-tailed test ** p < 0.05, one-tailed test Notes: standard errors in parentheses

225

Table 8: Largest Studentized Residuals for the Years 1990-2000

Table 8A

NO. YEAR COUNTRY-

DYAD

RSTUDENT

1 1990 C375C740 2.09111* 2 1990 C140C696 2.006152* 3 1990 C140C522 1.870195 4 1990 C100C696 1.739847 5 1990 C2C522 1.671841 6 1990 C101C696 1.599178 7 1990 C145C696 1.575809 8 1990 C100C522 1.570064 9 1990 C434C696 1.552297 10 1990 C93C696 1.511681 Table 8B

NO. YEAR COUNTRY-DYAD

RSTUDENT

1 1991 C52C522 2.403487* 2 1991 C52C696 2.3831* 3 1991 C437C740 2.249914* 4 1991 C90C696 1.885193 5 1991 C90C522 1.866324 6 1991 C91C696 1.670603 7 1991 C51C522 1.659518 8 1991 C91C522 1.569627 9 1991 C51C696 1.543641 10 1991 C437C640 1.502873 Table 8C

NO. YEAR COUNTRY-DYAD

RSTUDENT

1 1992 C140C696 2.004416* 2 1992 C100C696 2.002795* 3 1992 C101C696 1.992614 4 1992 C235C740 1.931364 5 1992 C350C740 1.916829 6 1992 C344C740 1.898648 7 1992 C200C740 1.784819 8 1992 C160C740 1.744205 9 1992 C130C696 1.740119 10 1992 C230C740 1.731815

226

Table 8D

NO. YEAR COUNTRY-DYAD

RSTUDENT

1 1993 C346C640 2.610248* 2 1993 C100C696 2.087482 * 3 1993 C344C740 1.919983 4 1993 C325C740 1.848424 5 1993 C140C696 1.793621 6 1993 C101C696 1.719976 7 1993 C434C696 1.692457 8 1993 C436C696 1.62747 9 1993 C200C740 1.618461 10 1993 C434C522 1.601437

Table 8E

NO. YEAR COUNTRY-DYAD

RSTUDENT

1 1994 C346C365 2.753114* 2 1994 C140C522 2.216248* 3 1994 C100C696 2.136003* 4 1994 C20C740 2.11112* 5 1994 C385C740 2.064654* 6 1994 C344C740 1.972961 7 1994 C346C710 1.870294 8 1994 C433C696 1.845799 9 1994 C438C696 1.808788 10 1994 C140C346 1.774645 Table 8F

NO. YEAR COUNTRY-DYAD

RSTUDENT

1 1995 C230C640 1.732655 2 1995 C200C740 1.677902 3 1995 C20C740 1.670541 4 1995 C230C346 1.634548 5 1995 C346C732 1.601598 6 1995 C325C740 1.551753 7 1995 C210C740 1.549938 8 1995 C230C740 1.510826 9 1995 C434C522 1.456643 10 1995 C404C522 1.403698 Table 8G

NO. YEAR COUNTRY-DYAD

RSTUDENT

1 1996 C437C522 2.428518* 2 1996 C350C740 2.363423* 3 1996 C437C696 2.302361* 4 1996 C434C522 2.202692*

227

5 1996 C20C740 2.041158* 6 1996 C100C696 1.99285 7 1996 C160C346 1.970493 8 1996 C346C702 1.950911 9 1996 C434C696 1.886744 10 1996 C200C740 1.668766 Table 8H

NO. YEAR COUNTRY-DYAD

RSTUDENT

1 1997 C20C740 4.050669* 2 1997 C437C522 3.095243* 3 1997 C100C696 2.944769* 4 1997 C437C696 2.616979* 5 1997 C359C316 2.391098* 6 1997 C704C316 2.385951* 7 1997 C95C696 2.199283* 8 1997 C200C740 2.086976* 9 1997 C70C696 2.037645* 10 1997 C371C316 2.011364*

Table 8I NO. YEAR COUNTRY-

DYAD RSTUDENT

1 1998 C437C522 2.654206* 2 1998 C20C740 2.641532* 3 1998 C434C522 2.568628* 4 1998 C140C522 2.567199* 5 1998 C437C696 2.469832* 6 1998 C140C696 2.326787* 7 1998 C359C316 2.141541* 8 1998 C70C696 2.096317* 9 1998 C439C522 1.990948 10 1998 C434C696 1.981391 Table 8J

NO. YEAR COUNTRY-DYAD

RSTUDENT

1 1999 C346C316 2.537569* 2 1999 C290C346 2.52538* 3 1999 C346C900 2.508071* 4 1999 C235C346 2.466562* 5 1999 C350C316 2.434277* 6 1999 C230C346 2.383904* 7 1999 C346C640 2.38208* 8 1999 C437C522 2.268991* 9 1999 C346C359 2.242766* 10 1999 C200C346 2.19573*

228

11 1999 C91C696 2.15083* 12 1999 C91C522 2.112019* 13 1999 C42C696 2.10799* 14 1999 C325C346 2.070684* 15 1999 C70C696 2.034061* 16 1999 C42C522 1.970959 17 1999 C51C696 1.967405

18 1999 C346C385 1.915289

18 1999 C344C640 1.909368 20 1999 C310C346 1.899034

Table 8K

NO. YEAR COUNTRY-

DYAD

RSTUDENT

1 2000 C346C640 3.221442* 2 2000 C20C346 3.015492* 3 2000 C346C900 3.000346* 4 2000 C2C346 2.780937* 5 2000 C420C522 2.686388* 6 2000 C290C346 2.603601* 7 2000 C346C359 2.5918* 8 2000 C200C346 2.582164* 9 2000 C344C640 2.485444* 10 2000 C434C522 2.481102* 11 2000 C310C346 2.376302* 12 2000 C437C522 2.348294* 13 2000 C346C316 2.266818* 14 2000 C230C346 2.257758* 15 2000 C235C346 2.205454* 16 2000 C359C640 2.136217* 17 2000 C325C346 2.120241* 18 2000 C211C346 2.104045* 19 2000 C210C346 2.013794* 20 2000 C200C740 1.908363

Table 9: Largest Leverage Points for the Years 1990-2000 Table 9A

NO. YEAR COUNTRY-DYAD

HI

1 1990 C2C740 .9478069* 2 1990 C2C522 .415369* 3 1990 C435C522 .3650011* 4 1990 C435C740 .3623204* 5 1990 C350C640 .3523166*

229

6 1990 C355C640 .3475516* 7 1990 C360C640 .3401245* 8 1990 C2C696 .3376245* 9 1990 C2C640 .3259905* 10 1990 C375C740 .2450962* 11 1990 C20C522 .2345088* 12 1990 C375C522 .2191071* 13 1990 C435C696 .1964895* 14 1990 C20C740 .1875774* 15 1990 C260C740 .1562115 16 1990 C260C522 .1177643 17 1990 C100C640 .1175696 18 1990 C260C640 .1149239 19 1990 C290C640 .1116148 20 1990 C200C640 .1088951 Cut-off for1990 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/200 = 0.18

Table 9B

NO. YEAR COUNTRY-DYAD

HI

1 1991 C2C740 .9802142* 2 1991 C20C740 .5459399* 3 1991 C2C522 .5060998 4 1991 C20C522 .4298392* 5 1991 C2C696 .4049228* 6 1991 C2C640 .3572372 7 1991 C20C640 .3127947 8 1991 C20C696 .3050278 9 1991 C438C640 .2533216 10 1991 C439C640 .2518331 11 1991 C439C696 .2468156 12 1991 C438C696 .2460473 13 1991 C439C522 .2448261 14 1991 C439C740 .2419524 15 1991 C438C522 .2412024 16 1991 C437C740 .2220165 17 1991 C438C740 .1948086 18 1991 C41C522 .1807393 19 1991 C437C640 .1796786 20 1991 C41C696 .1764047 Cut-off for1991 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/97 = 0.37

230

Table 9C

NO. YEAR COUNTRY-DYAD

HI

1 1992 C2C740 .9740801* 2 1992 C344C346 .32074 * 3 1992 C2C522 .2935697* 4 1992 C365C740 .29311* 5 1992 C435C522 .2927456* 6 1992 C350C640 .2898287 * 7 1992 C365C640 .289031* 8 1992 C2C346 .2720842* 9 1992 C435C696 .2152003* 10 1992 C346C435 .2113466 * 11 1992 C2C696 .18932* 12 1992 C346C710 .1871835* 13 1992 C435C740 .1789109* 14 1992 C2C640 .1771377* 15 1992 C325C346 .1547997* 16 1992 C200C346 .1492348* 17 1992 C20C522 .1351927* 18 1992 C200C640 .1291621 19 1992 C325C522 .1269311 20 1992 C220C346 .1251894

Cut-off for1992 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/280 = 0.13 Table 9D

NO. YEAR COUNTRY-DYAD

HI

1 1993 C2C740 .9826204 2 1993 C344C346 .3221843 3 1993 C2C522 .3200295 4 1993 C365C640 .293952 5 1993 C365C740 .2922408 6 1993 C435C522 .2900931 7 1993 C350C640 .2885062 8 1993 C2C346 .2796343 9 1993 C200C346 .2274207 10 1993 C2C696 .2201488 11 1993 C200C640 .210305 12 1993 C2C640 .2071243 13 1993 C346C435 .205733 14 1993 C435C696 .1977233 15 1993 C200C740 .1765255 16 1993 C200C522 .1685424 17 1993 C435C740 .1605575 18 1993 C325C346 .1244064

231

19 1993 C435C740 .1605575 20 1993 C325C346 .1244064 Cut-off for1993 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/279 = 0.13 Table 9E

NO. YEAR COUNTRY-DYAD

HI

1 1994 C2C740 .9877061 2 1994 C344C346 .3273364 3 1994 C365C740 .3238558 4 1994 C2C522 .3107282 5 1994 C350C640 .3036598 6 1994 C435C522 .3007015 7 1994 C365C640 .2955173 8 1994 C435C696 .2454806 9 1994 C2C696 .2178223 10 1994 C435C740 .1911794 11 1994 C365C522 .1875133 12 1994 C2C346 .1854001 13 1994 C2C640 .1850792 14 1994 C346C435 .1835018 15 1994 C346C365 .1677595 16 1994 C200C640 .1396717 17 1994 C200C522 .1383746 18 1994 C20C522 .1289104 19 1994 C346C698 .1255573 20 1994 C435C640 .1236275 Cut-off for1994 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/263 = 0.14

Table 9F

NO. YEAR COUNTRY-DYAD

HI

1 1995 C2C740 .9806916 2 1995 C2C522 .2707913 3 1995 C435C522 .2680047 4 1995 C2C346 .2632029 5 1995 C344C346 .2503825 6 1995 C200C346 .214582 7 1995 C200C640 .1974572 8 1995 C2C696 .1955818 9 1995 C346C435 .195375 10 1995 C435C696 .1864041 11 1995 C350C640 .1796868 12 1995 C2C640 .176773 13 1995 C435C740 .1735524 14 1995 C373C640 .1723727

232

15 1995 C200C522 .1717591 16 1995 C365C740 .170763 17 1995 C200C740 .1617202 18 1995 C200C696 .1608671 19 1995 C365C640 .1557082 20 1995 C371C640 .1547216 21 1995 C369C640 .1509853 22 1995 C372C640 .1505253 23 1995 C20C522 .1371958 24 1995 C325C346 .1222038 Cut-off for1995 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/263 = 0.14 Table 9G

NO. YEAR COUNTRY-DYAD

HI

1 1996 C2C740 .978914 2 1996 C2C346 .2661002 3 1996 C435C522 .261744 4 1996 C200C346 .2556957 5 1996 C2C522 .255257 6 1996 C344C346 .236081 7 1996 C200C640 .2349477 8 1996 C200C640 .2349477 9 1996 C200C740 .2088927 10 1996 C200C696 .1979722 11 1996 C346C435 .1940278 12 1996 C2C696 .1914098 13 1996 C435C696 .1825818 14 1996 C435C740 .1771293 15 1996 C350C640 .1723837 16 1996 C2C640 .1687885 17 1996 C373C640 .1678897 18 1996 C365C740 .1593352 19 1996 C369C640 .1592173 20 1996 C371C640 .15747 21 1996 C372C640 .1501363 22 1996 C365C640 .1416718 23 1996 C220C346 .1353962 24 1996 C20C522 .1255134 Cut-off for1996 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/305 = 0.12 Table 9H

NO. YEAR COUNTRY-DYAD

HI

1 1997 C2C740 .9816024* 2 1997 C290C316 .5421101* 3 1997 C350C640 .5421101*

233

4 1997 C2C522 .4263605* 5 1997 C20C740 .4244366* 6 1997 C20C522 .3336013* 7 1997 C2C316 .2762467* 8 1997 C140C316 .2533543* 9 1997 C2C640 .2294359* 10 1997 C2C696 .2199597* 11 1997 C200C316 .2149129* 12 1997 C310C316 .1971155* 13 1997 C900C316 .1859504 14 1997 C369C316 .1750651 15 1997 C439C696 .1665806 16 1997 C439C522 .1654753 17 1997 C350C316 .161377 18 1997 C438C522 .1596354 19 1997 C200C740 .1566028 20 1997 C438C696 .1504552

Cut-off for1997 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/194 = 0.19

Table 9I

NO. YEAR COUNTRY-DYAD

HI

1 1998 C2C740 .9790164 2 1998 C435C740 .3924177 3 1998 C2C522 .3371256 4 1998 C200C316 .2986547 5 1998 C140C316 .2699217 6 1998 C435C522 .2493061 7 1998 C20C522 .2230694 8 1998 C2C316 .2106822 9 1998 C2C640 .2038972 10 1998 C20C740 .2019541 11 1998 C200C640 .1980608 12 1998 C200C740 .1932331 13 1998 C2C696 .1903159 14 1998 C200C522 .187217 15 1998 C200C696 .1854182 16 1998 C373C640 .176094 17 1998 C350C640 .1748399 18 1998 C290C316 .1747947 19 1998 C365C740 .1567008 20 1998 C390C316 .1566534 21 1998 C371C640 .1516089 22 1998 C369C640 .1438223 23 1998 C372C640 .1423612 24 1998 C690C316 .1415177 25 1998 C365C640 .138508 26 1998 C310C316 .1352013 27 1998 C220C316 .1348542 28 1998 C435C696 .1284295

234

Cut-off for1998 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/305 = 0.12 Table 9J

NO. YEAR COUNTRY-DYAD

HI

1 1999 C2C740 .9810216* 2 1999 C344C346 .3812374* 3 1999 C290C316 .3755838* 4 1999 C350C640 .3654148* 5 1999 C2C522 .2811799* 6 1999 C2C346 .2727847* 7 1999 C200C316 .201398* 8 1999 C200C740 .1988062* 9 1999 C20C522 .1907846* 10 1999 C200C696 .177548* 11 1999 C200C522 .1734869* 12 1999 C200C640 .1653048* 13 1999 C200C346 .1613434* 14 1999 C20C740 .1530321* 15 1999 C2C316 .1433078* 16 1999 C20C346 .1412854* 17 1999 C2C696 .1360597* 18 1999 C20C696 .118397 19 1999 C2C640 .1095097 20 1999 C346C371 .1071512

Cut-off for1999 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/ 286 = 0.13 Table 9K

NO. YEAR COUNTRY-DYAD

HI

1 2000 C2C740 .9810551 2 2000 C2C522 .2529073 3 2000 C2C346 .2498037 4 2000 C344C346 .225743 5 2000 C200C316 .1694364 6 2000 C435C522 .1682243 7 2000 C2C316 .1591713 8 2000 C200C740 .1589669 9 2000 C290C316 .1570455 10 2000 C200C640 .1527476 11 2000 C373C640 .1517559 12 2000 C2C696 .1454278 13 2000 C346C435 .1438317 14 2000 C20C522 .1423792

235

15 2000 C2C640 .1421813 16 2000 C350C640 .1410579 17 2000 C365C740 .1379589 18 2000 C369C640 .1345222 19 2000 C200C696 .1338168 20 2000 C365C640 .1317011 21 2000 C371C640 .1279693 22 2000 C200C522 .1265938 23 2000 C372C640 .1248094 24 2000 C435C740 .123805 25 2000 C20C740 .122397 26 2000 C200C346 .1158234 27 2000 C20C346 .1034211 28 2000 C20C316 .0990865 29 2000 C435C696 .0975986 30 2000 C346C522 .0887498

Cut-off for 2000 Data = 2p/n = 2 * (#of variables including constant)/ sample size (see Belsely p45) = (2 * 18)/403 = 0.09

Table 10: DFBETAS (Greater than the cutoff calculated for each year as |DFBETA| > 2/ squrt(n)) Cutoffs:

1990: abs(dfor) > 2/sqrt(200) = 0.14 n = 200 1991: abs(dfor) > 2/sqrt(97) = 0.20 n = 97 1992: abs(dfor) > 2/sqrt(280) = 0.12 n = 280 1993: abs(dfor) > 2/sqrt(279) = 0.12 n = 279 1994: abs(dfor) > 2/sqrt(263) = 0.12 n = 263 1995: abs(dfor) > 2/sqrt(300) = 0.12 n = 300 1996: abs(dfor) > 2/sqrt(305) = 0.11 n = 305 1997: abs(dfor) > 2/sqrt(194) = 0.14 n = 194 1998: abs(dfor) > 2/sqrt(305) = 0.11 n = 305 1999: abs(dfor) > 2/sqrt(286) = 0.12 n = 286 2000: abs(dfor) > 2/sqrt(403) = 0.10 n = 403

Table 10A

YEAR C DYAD

B2 RELATIVE

TRADE IN-DEGREE CENTALITY

C DYAD

B3 RELATIVE TRADE IN-

DEGREE CENTRALIT

Y SQUARED

C DYAD

B4 TOTAL

TRADE IN-DEGREE

CENTRALITY

C DYAD

B5 TOTAL TRADE

IN-DEGREE CENTRALITY

SQUARED

C DYAD B6 RELATIVE ALLIANCE

DEGREE CENTRALI

TY

1990 C140C696

.2763482 C2C522

.5327674 C2C522

-.1628255 C2C522

.2602711 C435C740

.5781446

C20C696

.2681947 C145C696

.1415255 C100C696

.1626955 C20C522

.394368

C435 .1924986 C2C740 .2914524

236

C696 C150

C740 .1804776 C2C640 .2477754

C435C522

.484328 C435C522

.1523623

C100C522

.1421098

1991 C2C7

40 2.125291 C2C7

40 -2.255105 C2C6

40 .2679942 C2C6

40 .337468 C2C740 .2749

C437C740

-.3430018 C91C740

.2402627 C52C696

.2664108 C20C522

.2450

C20C696

-.2653326 C90C696

.209092 C20C740

-.7349

C230C640

-.2071131 C20C696

-.211147

C355C696

-.2248717

C52C740

-.2962286

1992 C344

C346 .3523565 C2C7

40 .8182794 C2C7

40 .2474734 C2C3

46 .478453 C2C740 .2834

C2C522

.2397993 C2C522

-.2267015 C2C522

-.2131486 C20C522

.4466108 C434C640

.2263

C2C740

-.4641504 C20C522

-.2547005 C2C346

-.2707878 C200C346

.2747756 C436C640

.2201

C2C346

-.3573577 C20C522

-.4565848 C2C740

-.3427062 C20C522

.2197

C346C698

-.2077

C435C522

-.2260

C2C346 -.3571 1993 C344

C346 .3718843 C2C7

40 .7460876 C2C7

40 .1961927 C2C3

46 .3493043 C432C6

40 .2683111

C150C346

.3208858 C325C740

.2634104 C100C696

.1544717 C2C522

.3011244 C2C522 .2539315

C2C522

.2967519 C385C696

.1629815 C140C696

.1495655 C20C522

.2260567 C436C640

.2498931

C346C438

.2885088 C346C350

.1520354 C110C640

.1425028 C433C640

.1551078 C346C640

.2096559

C42C346

.1659209 C435C522

.1368476 C433C696

.1331113 C434C522

.14286 C2C740 .1670052

C20C522

.1376682 C230C740

.1341029 C70C696

.1198283 C160C522

.1425987 C438C640

.1629701

C2C346

.1262942 C220C740

.1279372 C346C438

.133735 C434C640

.16296

C346C640

.1212691 C420C640

.161089

C20C522

.1571219

C344C522

.1479228

1994 C346

C365 .2956117 C200

C522 .2880387 C346

C365 .3341194 C2C7

40 1.016065 C435C7

40 .3426399

237

C437C522

.197339 C220C522

.2192511 C346C710

.2327562 C435C522

.191813 C436C640

.276564

C346C710

.1741085 C420C740

.1999495 C344C740

.2297327 C385C696

.1532366 C344C522

.2731603

C437C740

.1481397 C435C740

.187998 C140C522

.2039596 C344C346

.1486201 C432C640

.2588525

C100C740

.1462842 C140C740

.1631099 C346C694

.1920509 C434C640

.2324836

C435C522

.1450153 C439C740

.1601034 C385C740

.1547954 C420C640

.1760167

C20C522

.1447866 C140C640

.1369409 C41C522

.1419801 C439C640

.1737485

C433C522

.1417243 C160C522

.1363872 C346C645

.1342328 C435C696

.1437435

C90C640

.1392046 C438C696

.1286175 C344C640

.133102 C346C698

.1364337

C52C640

.1365073 C437C640

.1296382

1995 C346

C420 .489949 C2C7

40 .5162908 C346

C701 .3444571 C230

C346 .3604158 C372C6

96 .4756998

C2C522

.4001844 C346C350

.2566935 C346C703

.316858 C2C522

.3344072 C420C640

.2423384

C346C372

.2762286 C211C346

.2500598 C211C346

.2778664 C346C372

.2515204 C346C540

.2236085

C420C696

.1486527 C220C346

.195647 C220C346

.2210385 C346C420

.2170985 C2C522 .2168534

C211C522

.1340272 C325C740

.1737228 C2C740

.1896125 C325C346

.2099237 C2C740 .1272778

C372C696

.1300986 C160C696

.1551283 C372C522

.1606012 C20C346

.1909276

C230C346

.1210209 C230C740

.1498643 C420C522

.1584245 C210C346

.1743873

C344C640

.1173483 C344C346

.127245 C346C371

.1234166 C350C522

.1458582

C346C371

.1194429 C420C740

.1195871

1996 C2C5

22 817727 C2C7

40 .7015554 C2C7

40 .2274544 C20C

522 .2419704 C20C52

2 .3219382

C20C522

.2419426 C385C696

.2280976 C371C522

.1861712 C2C522

.2391176 C371C696

.3203453

C346C434

.1682988 C435C522

.1996921 C437C696

.1631489 C2C346

.204388 C373C696

.2803995

C371C696

.1632197 C230C740

.1695725 C350C740

.1558434 C434C522

.1667469 C370C696

.24888

C344C640

.1523217 C220C640

.1624494 C344C740

.143161 C404C522

.1649854 C372C696

.246842

C437C522

.151345 C390C696

.1558257 C2C696

.1366342 C160C522

.1335034 C2C740 .2221666

C346C438

.1457708 C220C740

.1491095 C200C740

.1310834 C437C740

.1290466 C2C522 .1808428

C404C740

.1381768 C346C365

.1442603 C369C740

.1277231 C385C522

.1272606 C346C702

.1485491

C346C350

.1207486 C325C740

.1401578 C359C522

.1270936 C373C696

.1229208 C433C640

.1417708

C433C522

.1177786 C210C740

.1225573 C372C522

.1253746 C371C640

.1150762 C359C696

.1344689

238

1997 C200

C316 .2461939 C2C7

40 .9589529 C41C

640 .2831571 C200

C316 .2488328 C20C52

2 1.136243

C439C640

.1888701 C369C316

.3957254 C91C640

C91C640 C437C522

.2016812 C2C522 .5130962

C100C696

.1860832 C365C316

.2853341 C371C316

C371C316 C20C740

.1603807 C200C740

.2591045

C437C522

.1802604 C200C740

.1642971 C900C316

C900C316 C439C640

.158441 C100C696

.1692842

C437C696

.1583478 C350C316

.1470763 C92C640

C92C640 C2C522

.1579515

C100C316

.1567785 C20C696

.1498365

C70C696

.1544662

C70C522

.1509589

C438C640

.1461903

1998 C140

C316 .2194801 C2C7

40 .4699449 C390

C316 .2252521 C435

C522 .4788838 C20C52

2 .6417948

C435C522

.2044606 C369C316

.2578199 C310C316

.1631561 C435C696

.3188738 C2C522 .4914027

C404C640

.1793334 C365C316

.2336121 C359C316

.1359804 C140C316

.3134808 C435C522

.2859009

C435C696

.1580375 C140C522

.2003564 C2C696

.1849862 C371C696

.1585269

C404C740

.1500497 C404C522

.1985269 C2C522

.1404551 C359C696

.1573275

C437C522

.143341 C390C316

.1847725 C370C740

.1533212

C2C522

.129468 C140C696

.1840035 C140C696

.1253492

C350C316

.1787983 C372C696

.1167165

C310C316

.1741066

C200C640

.1390837

1999 C2C5

22 .3751581 C350

C316 .1937113 C346

C359 .3268043 C2C5

22 .3130756 C2C740 .2084624

C290C346

.2480967 C150C346

.1918464 C2C740

.1874602 C20C522

.2157568 C346C640

.2020648

C200C346

.2307104 C346C365

.1652186 C2C316

.1564667 C346C370

.212006 C230C346

.1814162

C437C522

.2019336 C2C740

.1603863 C359C316

.122459 C350C316

.1962038 C325C346

1713615

C346C316

.1898291 C110C346

.1576104 C346C640

.1933272 C20C522

.163864

C20C522

.1875869 C200C640

.1499972 C346C437

.1493477 C220C346

.1479797

C235C346

.1868441 C230C640

.1404388 C290C346

.1415332

C230C346

.1783958 C325C640

.1400242 C235C346

.1288505

239

C310C346

.1531612 C432C316

.1383386 C432C316

.1219181

C344C640

.1455639 C220C640

.1381254 C200C346

.1201583

2000 C2C5

22 .4872 C2C7

40 .3333278 C346

C359 .3563945 C2C5

22 .298972 C20C52

2 .3762345

C2C346

.2984611 C150C346

.235936 C2C316

.1584109 C435C522

.2328188 C2C522 .2383153

C20C522

.2920882 C369C316

.2329289 C20C316

.11595 C346C640

.2271379 C346C640

.2112737

C20C346

.2710187 C435C522

.2084923 C310C346

.1060252 C346C370

.2262878 C290C346

.1489377

C290C346

.2616467 C346C369

.1710013 C369C316

.1896428 C230C346

.1443763

C200C346

.2084244 C346C359

.1308436 C420C522

.165989 C325C346

.1331099

C310C346

.1976494 C365C316

.1273646 C20C522

.1600359 C310C346

.1227392

C230C346

.189015 C110C346

.1127513 C435C696

.1595033 C210C346

.1225228

C346C316

.1885123 C346C370

.111924 C434C522

.1327667 C235C346

.1223981

C235C346

.1823516 C346C365

.1117248 C346C316

.1214529

Table 10B

YEAR C DYAD B7 RELATIV

E ALLIANC

E DEGREE

CENTRALITY

SQUARED

C DYAD B8 TOTAL

ALLIANCE

DEGREE CENTRAL

ITY

C DYAD B9 TOTAL

ALLIANCE

DEGREE CENTRAL

ITY SQUARED

C DYAD B10 VISITS TOTAL

IN-DEGREE

C DYAD B11 VISITS TOTAL

OUTDEGREE

1990 C375C740

.5589307 C20C522 .3494829 C375C740

.8002346 C100C696

.4523218 C101C696

.3355522

C2C522 .3156261 C435C522

.3032263 C2C522 .5929342 C100C522

.4028553 C93C696 .3196335

C375C522

.2744991 C93C696 .2636876 C2C740 .4435416 C2C640 .2523479 C101C522

.2913743

C101C696

.2482366 C100C522

.2086998 C93C740 .2177665 C93C522 .2790455

C93C522 .1589276 C290C696

.1894136 C2C522 .2052748 C2C522 .2595167

C140C696

.1552871 C260C740

.1892857 C101C740

.1904502 C100C740

.1846195

C101C522

.1532896 C315C696

.1722738 C290C522

.1796977 C355C696

.1655216

C355C522

.1517327 C160C696

.1632306 C2C640 .1585348

C145C696

.1592479 C355C522

.1496276

C315C522

.1550782 C140C696

.1443129

240

1991 C20C740 1.0651 C20C522 .6080 C437C74

0 .3899 C20C640 .3510945 C20C522 .3993618

C437C740

.2376 C70C696 .2111 C2C740 -.2684 C437C640

.3308853 C20C696 .26531

C20C522 -.3007 C437C640

-.2161 C2C640 .1854295

C20C740 -.3757 C70C696 .1525483 C2C740 -.4084 C70C522 .151552 C437C74

0 -.6235

1992 C2C346 .4076638 C435C52

2 .6607085 C2C740 .6338654 C346C65

2 .2086692 C2C740 .488622

C346C698

.3236384 C20C522 .3602005 C344C346

-.2119102 C346C710

-.3333161 C2C346 .3548103

C346C522

.2703331 C2C346 .2329966 C20C522 .-591028 C20C696 -.3585334 C2C640 .3136823

C346C435

.2462277 C344C740

-.2192322 C435C522

-.8648595 C20C522 -.4295161 C200C740

.2760106

C435C740

-.2064649 C2C740 -.6296253 C20C696 .2679915

C344C522

-.2794085 C20C522 .2523934

C344C696

.2519158

C200C640

.2464894

C344C740

.2147597

C160C740

.2087144

C2C522 .2704568 1993 C346C43

5 .3403252 C435C52

2 .6087396 C2C740 .3300849 C346C71

0 .3456059 C2C740 1.215852

C2C346 .3091008 C20C522 .2156804 C432C640

.1938084 C346C640

.3357347 C200C740

.5516452

C346C698

.2486125 C344C346

.1728923 C325C740

.1775643 C2C346 .3049201 C200C640

.2535843

C346C522

.2060698 C2C522 .1670241 C438C640

.1704036 C346C670

.2110129 C344C740

.1795953

C20C740 .1923734 C2C346 .1430796 C344C740

.1676509 C346C698

.2014124 C325C696

.147167

C349C740

.1633094 C101C696

.1387678 C436C640

.1593986 C2C640 .1984606

C344C740

.1257583 C349C696

.1382226 C439C640

.1567009 C160C740

.1578302

C200C640

.1380769 C349C740

.1543074 C100C696

.143975

C435C640

.1300711 C433C640

.1326949 C70C696 .1433126

C346C900

.128808 C404C640

.1293832

1994 C346C43

5 .3215041 C435C52

2 .4850857 C344C74

0 .3186619 C346C71

0 .3579172 C346C36

5 .7592347

241

C20C740 .3080973 C20C522 .3085361 C420C640

.2221356 C346C750

.2654094 C365C522

.4111942

C365C522

.1909815 C435C640

.2305345 C2C740 .2191306 C2C346 .2241468 C385C522

.1853942

C2C346 .1756644 C346C900

.2102207 C385C740

.2127586 C20C522 .1701914 C200C522

.184462

C420C740

.1600888 C2C346 .1485495 C41C640 .197565 C385C740

.1541513 C230C696

.1677172

C344C740

.1573151 C439C640

.1957039 C344C522

.1486447 C346C690

.1577682

C346C710

.1559187 C436C640

.1926539 C433C696

.1301659 C346C900

.1490691

C432C640

.1761135 C140C346

.1236389 C344C740

.1469107

C435C696

.1554018 C2C740 .1354991

C390C740

.1458763 C140C640

.1283614

1995 C20C740 .3148264 C2C522 .3134677 C2C740 .6006236 C420C74

0 .3463026 C2C740 .5794491

C346C701

.2934038 C435C522

.3051037 C372C696

.2597789 C420C640

.2613341 C200C740

.5144464

C346C703

.2228522 C420C740

.2401004 C420C640

.2313093 C211C346

.1779687 C200C640

.3947738

C346C435

.2166376 C346C540

.2117481 C346C701

.1709495 C372C522

.173204 C211C522

.272902

C20C346 .1813002 C346C350

.1966499 C90C640 .1692589 C211C522

.1630006 C211C346

.2418813

C420C740

.1812236 C344C346

.1925401 C346C732

.1560941 C350C522

.1513304 C211C696

.2336258

C346C372

.1340681 C160C522

.1798582 C346C740

.1534209 C350C640

.1329988 C200C522

.1790785

C346C541

.1585848 C385C740

.1513865 C437C696

.1192057 C346C540

.1312184

C350C740

.1314838 C346C703

.1455153 C346C701

.1198803

C230C640

.1206509 C390C740

.1370059 C346C703

.1198165

1996 C20C740 .3786426 C435C52

2 .7171864 C2C740 .7706045 C140C69

6 .2190907 C2C740 .7725024

C346C435

.3116009 C2C522 .3548602 C344C740

.2135838 C2C640 .1993504 C200C740

.66589

C2C346 .2048826 C20C522 .3541622 C350C740

.2122777 C100C696

.1794034 C200C640

.5648838

C435C522

.1355804 C437C696

.151126 C346C740

C346C740

C369C740

.1602788 C210C696

.1422689

C344C740

.1283078 C434C696

.1413109 C371C696

C371C696

C2C346 .1588052 C20C696 .1382554

C435C696

.1278342 C435C696

.1341752 C372C696

C372C696

C70C696 .1531372 C230C696

.1165454

C437C740

.1322318 C346C900

C346C900

C385C522

.1303131

C200C640

.1322181 C346C732

C346C732

C437C696

.1203586

C325C740

C325C740

C211C74 C211C74

242

0 0 1997 C20C740 2.905419 C20C522 1.014526 C20C740 .5207887 C20C522 .2945659 C2C740 1.484423 C437C52

2 .5193339 C2C522 .7116122 C70C522 .2662294 C70C696 .2935707 C200C74

0 .5355216

C2C740 .2974576 C438C740

.2793113 C52C522 .1868844 C698C316

.2324352 C20C522 .3001958

C359C316

.1713044 C439C740

.2330451 C95C522 .1868377 C437C696

.2115754 C200C640

.2084984

C369C316

.1679838 C100C696

.2124434 C95C640 .1778217 C70C522 .1699011 C200C316

.1758922

C95C696 .1748585 C51C522 .1770379 C100C696

.1653149 C200C522

.1746324

C704C316

.1628076 C91C640 .1577014 C438C740

.1448386 C625C316

.1496157

C200C522

.1573276 C2C740 .1572132 C290C740

.1445594

C92C640 .1518984 C91C522 .1505977 1998 C435C74

0 1.056933 C435C52

2 1.227775 C2C740 .388256 C140C52

2 .4469079 C2C740 1.238928

C20C740 1.026167 C20C522 .5747809 C435C740

.2466534 C140C696

.4105278 C200C640

.4319049

C2C740 .3882211 C2C522 .5630062 C20C740 .2402194 C140C740

.2601221 C200C740

.3922476

C437C522

.2254778 C140C696

.2392823 C390C740

.1676608 C435C522

.2087413 C20C522 .272031

C435C696

.1617039 C436C740

.1691552 C210C740

.1396428 C200C696

.14518 C435C522

.177264

C434C522

.1268026 C200C640

.1571281 C385C740

.1263502 C690C316

.1412863 C20C696 .1407689

C20C316 .1237807 C70C696 .1379704 C150C640

.1158789 C350C316

.1309908 C20C316 .1175109

C350C316

.1211824 C404C740

.1359662 C435C696

.1257124

C432C740

.1293113 C140C316

.1251528

C140C640

.1234106

1999 C20C346 .4344882 C2C522 .8953375 C42C522 .1950745 C346C64

0 .3078994 C200C34

6 .8293403

C20C740 .1699913 C20C522 .552752 C346C900

.1799639 C325C640

.2345158 C200C640

.6632445

C290C640

.1562081 C200C640

.2930736 C91C522 .1760382 C344C640

.2306965 C2C740 .6148111

C640C316

.1549564 C91C696 .2136934 C101C522

.1716632 C325C346

.1863863 C200C740

.3482492

C235C640

.1477545 C41C696 .2088597 C90C522 .1521197 C230C640

.1829405 C20C522 .2279371

C310C640

.1430173 C2C316 .1719215 C230C346

.1460937 C2C740 .1738156

C230C640

.1261636 C42C696 .1564354 C346C316

.1416983 C210C640

.1403606

C350C316

.1243386 C51C696 .1557292 C325C346

.1407618 C20C640 .1326926

243

C2C696 .1322638 C51C522 .1388025 C346C48

1 .1230876 C235C34

6 .1374576

2000 C2C346 1.025347 C2C522 .5237977 C346C90

0 .297578 C2C346 .784834 C2C740 1.171326

C20C346 .7106741 C20C522 .4589345 C20C346 .1672534 C346C640

.2030418 C200C346

.6849797

C20C740 .4212789 C435C522

.4221386 C2C740 .1614064 C211C346

.1824528 C200C740

.6372091

C346C435

.2326174 C359C640

.1985318 C210C346

.1402792 C220C346

.1813724 C200C640

.3002941

C435C696

.1676241 C20C696 .1790133 C325C346

.1398036 C290C346

.1729061 C2C522 .1510655

C346C900

.1439717 C200C640

.1396155 C346C390

.1382018 C437C696

.1552862 C435C696

.1507633

C434C522

.1394695 C435C696

.1140372 C230C346

.1375114 C346C350

.1396432 C20C696 .1504172

C437C522

.1206773 C20C316 .1135121 C346C385

.1374712 C437C522

.1377548 C20C522 .1483229

C359C640

.1185701 C346C481

.10769 C235C346

.1331663 C346C696

.1206574

C359C316

.1060576 C369C316

.1002703 C211C346

.1324403 C101C522

.1200172

Table 10C YEAR C

DYAD B12 IGO

CONNECT

C DYAD

B13 ECON IGO CONNECT

C DYAD

B14 GENERAL

IGO CONNECT

C DYAD

B15 PM IGO

CONNECT

C DYAD

B16 SC IGO

CONNECT

1990 C260C640

.3121341 C2C740 .3170017 C439C740

.2330181 C434C522

.3003643 C92C740

.1790669

C92C740

.2608494 C390C740

.1786425 C130C740

.1749632 C339C640

.2272533 C260C522

.1730951

C260C696

.2604953 C210C740

.1564499 C52C740

.1728713 C52C522 .213926 C52C696

.1707983

C339C740

.1637783 C385C740

.14778 C339C696

.1664181 C51C522 .1541402 C260C696

.1456424

C260C522

.1602394 C380C740

.1414886 C90C740

.1657842 C110C522

.1490243 C435C696

.1327636

C92C640

.1388761 C140C522

.1376051 C439C640

.1366574 C434C696

.1428249 C200C522

.1316284

C375C740

.1351319 C420C640

.1314263

1991 C2C740 .4074586 C2C740 1.353648 C52C74

0 .4701034 C52C522 .4974404 C339C

696 .3703283

C52C740

.2764951 C42C640 .219294 C90C740

.2977208 C339C522

.3602492 C339C522

.3070848

C51C640

.2362123 C437C740

.185259 C439C740

.2830413 C51C522 .325487 C52C696

.2955212

C52C640

.2165338 C91C740

.2472607 C339C696

.2717422

C352C740

.2118145 C52C640

.231898 C438C522

.2327653

C437C522

.2059911 C439C522

.1958181

244

1992 C2C740 .5266738 C2C740 .6886932 C439C7

40 .2495856 C52C522 .2495977 C52C6

96 .1918982

C92C740

.1800646 C375C740

.1831263 C52C740

.2482723 C420C522

.2100099 C90C696

.1707234

C344C346

.171655 C380C740

.1746606 C346C482

.2050423 C436C522

.1717497 C130C696

.16395

C346C482

.1583901 C390C740

.1330095 C436C740

.1791476 C432C522

.1695907 C135C696

.152783

C92C640

.1250878 C230C740

.1270978 C52C640

.1666148 C110C522

.158085 C40C696

.1516284

C344C696

.115765 C350C740

.1178335 C344C346

.1578414 C346C360

.1300134 C155C696

.1385619

C436C522

.1044706 C385C740

.1102248 C439C640

.1539098 C346C355

.1139701 C101C696

.1129879

C350C740

.1035268 C344C696

.1099669 C352C696

.1487976 C433C522

.0992826 C346C360

.1114125

C130C696

.1010153 C145C640

.1029202 C91C740

.1444193 C435C696

.107468

C352C740

.1024796 C435C740

.1363487 C150C696

.1068238

1993 C2C740 .2947485 C2C740 .4842595 C438C6

40 .2102216 C52C522 .1939245 C40C6

96 .2010918

C344C346

.2547664 C375C740

.2043054 C52C740

.1862101 C110C522

.1661518 C52C696

.1892013

C434C522

.1305588 C380C740

.1729365 C439C640

.1813 C346C359

.1447298 C90C696

.1868005

C436C522

.1196755 C140C522

.147133 C20C316

.1806395 C434C522

.1383709 C130C696

.1736467

C360C740

.1172402 C310C740

.127406 C436C640

.1638885 C346C360

.135597 C135C696

.1706934

C343C346

.1162601 C2C696 .1214145 C434C740

.1477461 C346C371

.134432 C155C696

.1394754

C432C522

.1074 C390C740

.1091198 C435C640

.1458558 C346C355

.1201072 C110C696

.1300441

C436C316

.1064473 C325C740

.1066187 C432C740

.133206 C343C346

.1158025 C101C696

.1281915

C432C3 .0995676 C385C74 .1008283 C90C74 .1192894 C51C522 .1098732 C40C5 .1205268 C352C3

16 .0947798 C290C74

0 .1005919 C438C7

40 .1181694 C344C34

6 .1093394 C42C6

96 .1180747

1994 C2C740 .1927769 C2C740 1.264504 C90C74

0 .2463944 C434C52

2 .2364845 C52C6

96 .2252616

C910C316

.1606555 C380C740

.2713842 C438C640

.2421699 C434C696

.1537246 C90C696

.1772166

C434C522

.1273231 C101C640

.1634132 C439C640

.2363399 C110C522

.148685 C130C696

.1679543

C750C316

.0945251 C385C740

.1518519 C435C640

.1929578 C52C522 .1467744 C135C696

.1544193

C770C316

.0944889 C305C740

.1487622 C436C640

.1764467 C41C522 .1425719 C352C696

.1494044

C570C316

.0930113 C20C740 .1321229 C432C640

.1674675 C51C522 .1267933 C165C696

.1424679

C800C316

.0915024 C90C640 .1220797 C90C640

.1641036 C346C360

.0864832 C91C696

.1310564

C436C316

.0910216 C375C740

.1169751 C317C640

.1529977 C910C316

.0861745 C150C696

.1214752

C432C3 .0853535 C200C74 .1166754 C434C7 .152663 C40C6 .1211588

245

16 0 40 96 C140C52

2 .1163405 C432C7

40 .1523771 C110C

696 .1096906

1995 C346C4

11 .1548609 C380C74

0 .2180424 C90C64

0 .1983989 C434C52

2 .1693007 C2C74

0 .2961909

C541C316

.1418692 C2C696 .1963212 C90C740

.1895244 C346C372

.164375 C200C740

.2161661

C565C316

.1344348 C325C740

.1711056 C2C740 .153134 C437C522

.1417724 C200C346

.1638304

C703C316

.1265692 C385C740

.1667122 C220C740

.13128 C41C522 .1284061 C200C522

.1594487

C900C316

.1243854 C220C696

.1604463 C439C640

.1264167 C346C350

.1238907 C200C316

.1474357

C411C316

.1049468 C390C740

.146145 C346C366

.1241871 C420C640

.1165649 C437C696

.144053

C411C640

.1049089 C210C740

.1431153 C346C703

.1226917 C52C522 .1073623 C40C696

.1398033

C540C316

.1014919 C375C740

.1395359 C435C640

.120501 C436C522

.1052853 C211C346

.1303816

C701C316

.0980798 C20C740 .136727 C346C368

.1192985 C435C522

.1039264 C2C346

.1257914

C420C316

.0912308 C160C696

.1293885 C205C346

.1131571 C90C640 .0998307 C435C696

.1236368

1996 C910C3

16 .1908221 C2C740 .4608731 C439C6

40 .1483207 C434C52

2 .4469225 C95C6

96 .1780963

C950C316

.1422271 C350C740

.1714123 C432C740

.1424852 C437C522

.3489519 C52C696

.155035

C434C522

.1193673 C375C740

.1375815 C373C640

.1420675 C51C522 .2489972 C130C696

.1488096

C350C740

.1038138 C380C740

.1271974 C438C640

.1309283 C110C522

.2436307 C437C696

.1480368

C800C316

.0989581 C404C696

.1250617 C434C740

.114624 C41C522 .2319374 C101C696

.1307027

C940C316

.0978594 C305C740

.1076134 C94C522

.1129535 C52C522 .2179813 C90C696

.1204734

C2C740 .0972187 C325C740

.1057255 C432C640

.1127147 C42C522 .1891012 C110C696

.1199751

C344C640

.085622 C94C740 .1006757 C439C740

.1102069 C404C522

.1813506 C135C696

.1133476

C101C740

.0925828 C438C740

.1085042 C433C522

.1361908 C145C522

.1108839

C211C740

.0890966 C435C740

.1077015 C438C522

.1338984 C95C522

.1036936

1997 C51C52

2 .2056639 C2C740 .7855902 C439C6

40 .3023914 C437C52

2 .4681279 C95C6

96 .3195132

C41C522

.1859041 C70C522 .2060298 C70C696

.2323162 C52C522 .4471186 C52C696

.318463

C52C522

.1782059 C305C316

.1820385 C438C640

.2091968 C51C522 .3924555 C90C696

.2339771

C91C522

.1558806 C90C522 .1698828 C70C522

.2028075 C41C522 .3863893 C437C696

.2174546

C95C522

.1530999 C344C696

.1603869 C40C522

.1491171 C42C522 .3454622 C51C696

.201852

C42C52 .1478456 C210C74 .1566535 C344C6 .1428073 C666C31 .1257271 C42C6 .1922335

246

2 0 40 6 96 C90C52

2 .1318027 C200C74

0 .1298885 C344C7

40 .1355452 C91C6

96 .155958

C70C522

.1288722 C355C740

.1242465 C40C696

.1534265

C438C522

.1287472 C439C696

.132525

C437C522

.1260144

1998 C2C740 .1575296 C2C740 .5366855 C317C6

40 .1956193 C434C52

2 .2651196 C52C6

96 .2552581

C434C522

.1370921 C140C522

.1679293 C435C522

.1690985 C437C522

.2131229 C130C696

.2031998

C910C316

.1186332 C375C740

.1496922 C140C522

.1446662 C110C522

.21247 C40C696

.2017453

C950C316

.09944 C210C740

.1369771 C70C696

.1438735 C41C522 .1711034 C95C696

.1825354

C305C316

.1136273 C438C640

.1396331 C110C696

.1600633 C42C696

.173013

C385C740

.1122239 C94C522

.1380362 C52C522 .1590838 C404C640

.1604965

C325C740

.1104602 C435C640

.1235275 C42C522 .1498693 C165C696

.1455067

C211C740

.1074518 C70C522

.1148405 C51C522 .144516 C90C696

.1380027

C2C696 .1003769 C373C640

.1147173 C433C640

.131177 C437C696

.1257507

C92C522 .0987127 C165C522

.1082658 C434C696

.1183758 C110C696

.1229837

1999 C910C3

16 .1683771 C346C34

9 .1521037 C346C3

67 .2578475 C437C52

2 .4573263 C52C6

96 .3513529

C950C316

.1548271 C2C316 .1394809 C346C368

.1890532 C42C522 .3610696 C42C696

.3459672

C235C640

.1413481 C210C740

.1380907 C317C346

.1558405 C439C522

.3235249 C346C830

.2232938

C230C640

.1359278 C439C522

.1236866 C235C346

.1549224 C41C522 .2721794 C41C696

.2231891

C290C640

.1316849 C40C740 .1123941 C437C740

.1511842 C51C522 .2322329 C40C696

.2166458

C310C640

.1300827 C346C900

.1110402 C346C366

.1471914 C52C522 .2233264 C90C696

.2157354

C344C640

.1278903 C346C359

.1075433 C230C346

.1446466 C339C346

.2098165 C42C640

.1955115

C325C640

.1160352 C91C696 .1064089 C290C346

.1317853 C346C365

.178136 C437C696

.1918678

C305C346

.1063632 C346C375

.1300302 C438C522

.1725368 C40C522

.1716982

C344C640

.1295747 C346C349

.1223965 C91C696

.1504774

2000 C2C740 .1519878 C2C740 .2874712 C2C696 .1560958 C310C34

6 .2489288 C434C

522 .3450066

C344C640

.1107799 C346C367

.2270873 C40C522

.1536798 C344C346

.2389508 C110C522

.3107888

C349C640

.0954674 C310C346

.2101157 C370C522

.1332418 C346C367

.2329236 C420C522

.2665869

247

C910C316

.0927622 C346C366

.1930966 C339C696

.1157792 C2C740 .2089159 C437C522

.2352982

C305C640

.0921655 C346C368

.1549332 C52C740

.0974512 C346C366

.202368 C42C522

.22298

C420C522

.0913985 C344C346

.1507688 C385C696

.0970598 C346C368

.1825721 C52C522

.2149963

C950C316

.0864696 C346C349

.1489959 C20C696

.095555 C346C349

.1802276 C339C346

.2118015

C346C375

.1484337 C346C696

.087991 C290C346

.1507153 C51C522

.1965763

C346C355

.1382949 C2C522 .0877789 C346C355

.1472295 C41C522

.1877276

C290C346

.13153 C346C690

.087408 C317C346

.1449092 C305C346

.1324205

Table 11: COVRATIO (Report values greater than the cutoff |covratio – 1| >= 3*k/n for each year)

NO. YEAR COUNTRY-DYAD

COVRATIO

1 1990 C2C740 9.361046 2 1990 C360C640 1.653506 3 1990 C350C640 1.638907 4 1990 C355C640 1.627283 5 1990 C435C522 1.606972 6 1990 C2C696 1.559187 7 1990 C435C740 1.493049 8 1990 C2C522 1.464391 9 1990 C2C640 1.386451 10 1990 C375C522 1.339451 11 1990 C20C740 1.337219 1 1991 C2C740 37.55047 2 1991 C2C522 2.39541 3 1991 C20C740 2.030807 4 1991 C2C696 1.960459 5 1991 C20C522 1.74687 6 1991 C2C640 1.673999 7 1991 C20C640 1.60216 8 1991 C20C696 1.59802 9 1991 C438C640 1.578048 10 1991 C439C696 1.568865 11 1991 C439C522 1.566509 12 1991 C439C640 1.560352 13 1991 C52C740 .3969231 1 1992 C2C740 12.71438 2 1992 C350C640 1.50173 3 1992 C365C640 1.484591 4 1992 C2C522 1.407352 5 1992 C435C696 1.332512 6 1992 C346C435 1.325823 7 1992 C2C696 1.311285 8 1992 C435C740 1.274877

248

9 1992 C346C710 1.263806 10 1992 C325C346 1.263437 11 1992 C2C346 1.254762 12 1992 C365C740 1.241759 13 1992 C220C346 1.224013 14 1992 C20C640 1.217015 15 1992 C325C640 1.206237 16 1992 C210C346 1.200905 1 1993 C2C740 15.88371 2 1993 C350C640 1.505528 3 1993 C365C640 1.39341 4 1993 C200C346 1.381596 5 1993 C2C522 1.335522 6 1993 C2C346 1.328151 7 1993 C435C696 1.323216 8 1993 C200C640 1.312979 9 1993 C2C640 1.310854 10 1993 C365C740 1.288343 11 1993 C200C522 1.287904 12 1993 C346C435 1.28433 13 1993 C435C740 1.277477 14 1993 C2C696 1.255865 15 1993 C325C346 1.250857 16 1993 C200C696 1.234654 17 1993 C220C346 1.220456 18 1993 C346C365 1.208309 19 1993 C325C522 1.202503 1 1994 C2C740 70.41751 2 1994 C365C740 1.57628 3 1994 C2C522 1.542673 4 1994 C350C640 1.541141 5 1994 C344C346 1.498693 6 1994 C365C640 1.463743 7 1994 C435C696 1.38769 8 1994 C2C696 1.36847 9 1994 C2C640 1.320715 10 1994 C435C522 1.299651 11 1994 C346C435 1.252578 12 1994 C2C346 1.250867 13 1994 C200C640 1.250312 14 1994 C365C696 1.221321 15 1994 C200C740 1.210041 16 1994 C365C522 1.207082 17 1994 C160C522 .7833917 18 1994 C346C365 .7457306 1 1995 C2C740 27.32503 2 1995 C2C346 1.425995 3 1995 C200C346 1.353594 4 1995 C435C522 1.320644 5 1995 C344C346 1.307065 6 1995 C346C435 1.299582 7 1995 C2C640 1.290624

249

8 1995 C435C740 1.285805 9 1995 C365C740 1.272928 10 1995 C200C696 1.267301 11 1995 C435C696 1.267134 12 1995 C200C522 1.258102 13 1995 C373C640 1.256786 14 1995 C369C640 1.243432 15 1995 C200C640 1.224948 16 1995 C365C640 1.198845 17 1995 C372C640 1.191557 18 1995 C20C522 1.187563 19 1995 C325C522 1.186637 20 1995 C346C365 1.185052 21 1995 C344C522 1.180901 22 1995 C346C372 .8171435 23 1995 C90C640 .788469 24 1995 C350C696 .7636238 25 1995 C420C640 .6860104 26 1995 C420C740 .6700529 27 1995 C160C696 .6195773 28 1995 C372C696 .5881849 29 1995 C160C522 .5861977 30 1995 C372C522 .5559105 31 1995 C346C420 .5498338 32 1995 C346C703 .5441235 33 1995 C346C540 .5213901 34 1995 C346C701 .5182896 1 1996 C2C740 16.26139 2 1996 C200C346 1.426322 3 1996 C2C346 1.404126 4 1996 C344C346 1.380131 5 1996 C200C522 1.358639 6 1996 C435C740 1.2924 7 1996 C200C696 1.281628 8 1996 C346C435 1.27099 9 1996 C369C640 1.244444 10 1996 C200C640 1.242957 11 1996 C372C640 1.235883 12 1996 C220C346 1.231549 13 1996 C2C640 1.222427 14 1996 C365C740 1.208781 15 1996 C365C640 1.19979 16 1996 C435C696 1.189629 17 1996 C230C346 1.186933 18 1996 C210C346 1.186604 19 1996 C325C346 1.185264 20 1996 C434C522 .8126379 21 1996 C437C696 .7994991 22 1996 C350C740 .7826847 23 1996 C437C522 .7615468 1 1997 C2C740 3.097126 2 1997 C290C316 2.419092

250

3 1997 C350C640 2.419092 4 1997 C2C316 1.526667 5 1997 C140C316 1.483976 6 1997 C2C640 1.433707 7 1997 C310C316 1.364317 8 1997 C2C696 1.326031 9 1997 C200C316 1.316595 10 1997 C220C316 1.28151 11 1997 C95C696 .7187035 12 1997 C359C316 .6493334 13 1997 C704C316 .6439037 14 1997 C437C696 .6020375 15 1997 C20C522 .5779015 16 1997 C100C696 .4892189 17 1997 C437C522 .4694449 18 1997 C20C740 .3835806 1 1998 C2C740 16.89458 2 1998 C435C740 1.524155 3 1998 C200C316 1.512621 4 1998 C140C316 1.420458 5 1998 C2C316 1.345917 6 1998 C2C640 1.324093 7 1998 C200C522 1.287672 8 1998 C200C696 1.26536 9 1998 C350C640 1.257639 10 1998 C369C640 1.242257 11 1998 C371C640 1.241928 12 1998 C200C640 1.23202 13 1998 C200C740 1.229623 14 1998 C690C316 1.229334 15 1998 C220C316 1.229309 16 1998 C290C316 1.222838 17 1998 C390C316 1.219089 18 1998 C310C316 1.198256 19 1998 C160C316 1.184832 20 1998 C325C316 1.18371 21 1998 C365C740 1.183596 22 1998 C140C696 .8213941 23 1998 C140C522 .7737011 24 1998 C434C522 .7377889 25 1998 C437C522 .718344 26 1998 C435C522 .62852 1 1999 C2C740 40.67496 2 1999 C344C346 1.692771 3 1999 C350C640 1.677389 4 1999 C290C316 1.663414 5 1999 C2C346 1.464976 6 1999 C200C316 1.326318 7 1999 C200C696 1.294941 8 1999 C200C522 1.287781 9 1999 C200C740 1.265457 10 1999 C20C740 1.22352

251

11 1999 C346C371 1.190572 12 1999 C437C522 .8040501 13 1999 C350C316 .7930512 14 1999 C346C640 .7772579 15 1999 C230C346 .7747526 16 1999 C346C900 .7642865 17 1999 C235C346 .7494251 18 1999 C290C346 .7393245 19 1999 C346C316 .7317931 1 2000 C2C740 27.26156 2 2000 C344C346 1.347369 3 2000 C200C316 1.258865 4 2000 C290C316 1.225319 5 2000 C350C640 1.208893 6 2000 C200C696 1.208171 7 2000 C365C640 1.203848 8 2000 C369C640 1.20265 9 2000 C365C740 1.199712 10 2000 C200C522 1.198762 11 2000 C435C740 1.195777 12 2000 C346C435 1.190073 13 2000 C371C640 1.187912 14 2000 C2C640 1.18764 15 2000 C372C640 1.183929 16 2000 C200C640 1.182767 17 2000 C344C522 1.145907 18 2000 C140C316 1.143258 19 2000 C346C522 1.142897 20 2000 C346C371 1.142777 21 2000 C235C346 .8585008 22 2000 C230C346 .8532851 23 2000 C346C316 .8470057 24 2000 C437C522 .8366997 25 2000 C310C346 .8274372 26 2000 C346C359 .8157562 27 2000 C434C522 .8110564 28 2000 C344C640 .8039102 29 2000 C290C346 .7908249 30 2000 C420C522 .7903564 31 2000 C20C346 .766982 32 2000 C346C900 .7197441 33 2000 C346C640 .6639344

Cutoff calculation: |covratio – 1| >= 3*k/n Cutoffs: 1990: 1+/- 3*k/n = 1 +/- 3*18/200 = greater than 1.27 or less than 0.73 1991: 1 +/- 3*18/97 = greater than 1.56 or less than 0.44 1992: 1 +/- 3*18/280 = greater than 1.19 or less than 0.81 1993: 1 +/- 3*18/279 = greater than 1.19 or less than 0.81

252

1994: 1 +/- 3*18/263 = greater than 1.21 or less than 0.80 1995: 1 +/- 3*18/300 = greater than 1.18 or less than 0.80 1996: 1 +/- 3*18/305 = greater than 1.18 or less than 0.82 1997: 1 +/- 3*18/194 = greater than 1.28 or less than 0.72 1998: 1+/- 3*18/305 = greater than 1.18 or less than 0.82 1999: 1 +/- 3*18/286 = greater than 1.19 or less than 0.81 2000: 1 +/- 3*18/403 = greater than 1.13 or less than 0.87

Table 12: DFITS

(Report values greater than the cutoff of 2*sqrt(k/n) for each year where k is the no. of variables including the constant and n is the sample size.)

NO. YEAR COUNTRY-

DYAD

DFITS

1 1990 C2C522 1.409195 2 1990 C375C740 1.191515 3 1990 C2C640 .9280981 1 1991 C20C740 1.334944 2 1991 C437C740 1.201914 1 1992 C365C740 1.097374 2 1992 C2C346 .9317086 3 1992 C2C640 .6901401 4 1992 C200C740 .5938839 5 1992 C200C640 .5174155 1 1993 C365C740 .9833166 2 1993 C2C346 .7983263 3 1993 C200C740 .7493436 4 1993 C365C640 .717559 5 1993 C325C740 .5792865 1 1994 C346C365 1.23607 2 1994 C20C740 .7336162 3 1994 C385C740 .6626205 4 1994 C346C710 .6469026 5 1994 C344C740 .6069874 6 1994 C365C522 .5402957 7 1994 C140C522 .5305759 8 1994 C438C696 .5259438 1 1995 C200C740 .7369778 2 1995 C20C740 .6056584 3 1995 C200C640 .5584592 4 1995 C230C640 .537305 5 1995 C230C346 .5288893 6 1995 C371C640 .4966408 1 1996 C200C740 .8575103 2 1996 C200C640 .7441956 3 1996 C20C740 .7293912 4 1996 C371C640 .5158872

253

5 1996 C437C696 .4881366 1 1997 C20C740 3.478457 2 1997 C437C522 1.007102 3 1997 C200C740 .8992928 4 1997 C437C696 .7601001 5 1997 C100C696 .6872721 1 1998 C20C740 1.328827 2 1998 C435C740 1.199468 3 1998 C140C522 .7909402 4 1998 C140C696 .6625962 5 1998 C350C316 .6232403 6 1998 C437C522 .5653647 7 1998 C200C640 .5426901 8 1998 C434C522 .5422906 9 1998 C200C740 .5197328 1 1999 C200C346 .9630802 2 1999 C200C640 .82055 3 1999 C350C316 | .6868681 4 1999 C346C359 .6641625 5 1999 C346C900 .6357926 6 1999 C20C346 .604198 7 1999 C91C522 .5731235 8 1999 C91C696 .5192119 9 1999 C42C522 .5079096 1 2000 C2C346 1.604734 2 2000 C20C346 1.024162 3 2000 C200C346 .9345713 4 2000 C200C740 .8296746 5 2000 C20C740 .6594655 6 2000 C346C359 .6538658 7 2000 C420C522 .627084 8 2000 C346C900 .6170704 9 2000 C346C640 .4979397 10 2000 C290C346 .4804728 11 2000 C369C316 .4447082 12 2000 C434C522 .4332465 13 2000 C211C346 .423481

Table 13: Summary Table of Single-Row Analysis Results

YEAR RSTUDENT LEVERAGE COVRATIOS DFITS

1990 C375C740 C2C740 C2C740 C2C522

C140C696 C2C522 C360C640 C375C740

C435C522 C350C640 C2C640

254

1991 C52C522 C2C740 C2C740 C20C740

C52C696 C20C740 C2C522 C437C740

C437C740 C2C522 C20C740

1992 C140C696 C2C740 C2C740 C365C740

C100C696 C344C346 C350C640 C2C346

C2C522 C365C640 C2C640

1993 C346C640 C2C740 C2C740 C365C740

C100C696 C344C346 C350C640 C2C346

C2C522 C365C640 C200C740

1994 C346C365 C2C740 C2C740 C346C365

C140C522 C344C346 C365C740 C20C740

C100C696 C365C740 C2C522 C385C740

1995 C2C740 C2C740 C200C740

C2C522 C2C346 C20C740

C435C522 C200C346 C200C640

1996 C437C522 C2C740 C2C740 C200C740

C350C740 C2C346 C200C346 C200C640

255

C437C696 C435C522 C2C346 C20C740

1997 C20C740 C2C740 C2C740 C20C740

C437C522 C290C316 C290C316 C437C522

C100C696 C350C640 C350C640 C200C740

1998 C437C522 C2C740 C2C740 C20C740

C20C740 C435C740 C435C740 C435C740

C434C522 C2C522 C200C316 C140C522

1999 C346C316 C2C740 C2C740 C200C346

C290C346 C344C346 C344C346 C200C640

C346C900 C290C316 C350C640 C350C316

2000 C346C640 C2C740 C2C740 C2C346

C20C346 C2C522 C344C346 C20C346

C346C900 C2C346 C200C316 C200C346

256

Figure 8: Partial-regression leverage plots for small sample analysis - Relative Trade In-degree Centrality 1990 Relative Trade In-degree Centrality

C2C740

C145C740

C433C740

C434C740

C91C740

C325C522C355C640

C101C696

C404C696

C52C640

C155C522

C42C640

C100C640

C360C740

C385C522

C390C740

C350C740

C360C522

C325C696

C155C740

C375C740

C42C740

C92C522

C355C740

C235C522

C260C696

C355C522

C70C640

C93C522

C350C640

C160C640

C432C696

C42C696

C375C640

C310C740C42C522

C385C696

C355C696

C433C640

C433C696

C41C640

C434C640

C434C696

C350C522C130C640

C140C740

C165C640

C436C696

C290C522

C437C696

C437C740

C90C696

C420C640C90C740

C438C740

C91C696

C439C640

C325C740

C439C740

C140C696

C51C640C101C740

C375C696

C310C522

C70C522

C260C522

C70C740C94C640C230C696

C315C740C91C522C2C696

C20C740

C211C522

C92C640

C260C740

C210C740

C93C640

C93C696

C93C740

C90C522

C94C740

C350C696

C95C696

C290C696

C95C740

C135C740

C130C740

C92C696

C101C640C145C640

C200C696

C140C522

C290C740

C315C696

C155C640

C155C696

C375C522

C20C522

C438C640

C160C696

C390C522

C135C640

C360C696

C200C522

C95C522

C211C740

C200C740

C51C740

C20C696C390C696

C210C522

C437C522

C210C696

C140C640C360C640

C437C640

C211C696

C101C522

C150C640

C220C696

C135C522

C235C696

C439C696

C100C740

C100C522

C92C740

C310C696

C220C522

C135C696

C100C696

C70C696C160C522

C2C522

C230C522

C91C640

C385C740

C95C640

C235C740

C315C522

C438C696

C220C740

C230C740

C90C640

C404C522

C420C740C404C640

C150C696

C41C696C41C522

C435C640

C150C522

C420C696

C432C640

C432C522C436C522

C436C640

C165C522

C51C522C51C696

C160C740

C145C696

C439C522

C52C522

C41C740

C52C740

C150C740

C130C522

C436C740

C145C522

C404C740C432C740

C438C522

C433C522

C130C696C434C522

C165C740

C94C522C94C696

C165C696

C420C522

C385C640

C435C522

C315C640C390C640

C435C740

C290C640

C435C696

C52C696

C310C640

C325C640C20C640

C2C640

C235C640C260C640

C230C640

C200C640

C210C640C211C640

C220C640

-.50

.5e(

sim

ilarv

otes

| X

)

-2.000e-14 -1.000e-14 0 1.000e-14 2.000e-14e( reltradeindegnv | X )

coef = 0, se = .0014317, t = 0

Dyads: Countries: C2C740 C640

257

1991 Relative Trade In-degree Centrality

C2C740C210C522

C20C696

C439C640C211C640

C2C640

C52C640C355C696

C90C522

C220C640

C42C522

C230C522

C310C522

C52C696

C90C640C350C522

C90C740

C91C522

C41C640

C91C640

C42C640

C437C696

C2C696

C437C740

C230C740

C438C640

C315C640C438C522

C52C740

C70C522C437C640

C437C522

C200C640

C315C696

C91C740

C70C640

C41C740

C41C696

C51C740

C41C522C310C640

C90C696

C355C640

C355C522

C20C522

C350C696

C350C640C439C696

C325C740

C325C696

C325C640

C325C522

C315C740

C230C640

C51C640

C315C522

C310C740

C310C696

C220C740

C439C740

C42C740

C42C696

C52C522

C2C522

C290C740

C211C696

C210C696

C290C522

C235C740

C200C740

C439C522

C235C522C438C740

C70C696

C235C640

C230C696

C355C740

C220C696

C51C696

C220C522

C211C740

C290C696

C91C696

C211C522

C210C740C350C740

C210C640

C51C522

C20C740C438C696C20C640

C290C640

C235C696

C200C696

C70C740

C200C522

C95C522

-.50

.5e(

sim

ilarv

otes

| X

)

-1.500e-14 -1.000e-14 -5.000e-15 0e( reltradeindegnv | X )

coef = 0, se = .00146691, t = 0

Dyads: Countries: C2C740

258

1992 Relative Trade In-degree Centrality

C346C432

C110C346

C150C346

C2C522

C346C435C91C346

C346C434

C346C439C41C346C346C420C145C346

C344C346

C346C436

C2C696C346C438

C346C349C93C346

C41C696

C325C696

C325C740

C51C346

C346C404

C165C346

C404C696

C95C346

C52C346

C346C450

C220C696

C94C346

C42C346C200C696

C41C522

C110C696

C135C346

C200C740

C439C696

C150C696

C92C346

C325C522

C220C740C91C696

C2C640

C325C640

C436C740

C90C346

C210C696C420C740

C346C433

C110C522

C436C696C210C740

C435C522C404C740

C420C696

C145C696

C220C522

C404C522

C150C522

C434C696

C110C740

C200C522

C200C640

C432C740

C91C522

C93C696

C435C696C220C640

C432C696

C150C740

C436C640

C439C740

C110C640

C130C346

C91C740

C51C696

C165C696

C420C640

C434C740

C52C696

C95C696

C230C696

C41C740

C135C696C346C696C438C696

C145C522

C211C696

C145C740

C404C640C150C640

C230C740

C346C740

C211C740

C210C522C42C696

C20C522

C439C522

C94C696C210C640

C100C346C349C740

C91C640

C434C640

C41C640

C346C900

C2C346C93C522

C325C346

C346C437

C145C640

C160C346

C432C640

C90C696

C435C740

C92C696

C165C522

C52C522

C51C740

C165C740

C436C522

C51C522

C346C694

C95C522

C20C696

C346C481

C420C522

C135C522

C93C740

C52C740

C101C346

C439C640

C346C690

C344C696

C70C346

C95C740

C94C740

C94C522

C434C522

C42C522

C200C346

C346C698

C220C346

C346C692

C346C540

C155C346

C2C740

C210C346

C346C616

C365C740

C165C640

C52C640

C346C620

C346C490

C346C530

C51C640

C346C452

C93C640

C438C740

C346C660

C346C732

C432C522C346C615

C349C696

C211C522C346C516

C346C461

C346C385

C160C696

C230C522

C90C522

C437C696

C95C640

C346C484

C346C483C42C740

C390C696

C211C640

C344C740

C346C600

C130C696

C92C522C346C663

C100C696

C230C640C346C771C346C840

C390C740

C346C475

C346C482

C94C640

C346C551

C70C696

C346C510

C346C517

C346C750

C235C696

C101C696

C346C390

C346C451

C346C640

C435C640

C438C522C346C522

C235C740

C385C696

C346C651

C155C696

C92C740

C20C740

C346C501

C385C740

C349C640

C433C696

C42C640

C346C541

C346C770

C230C346

C346C625

C140C696

C346C670

C211C346

C160C522

C346C645C90C740

C92C640

C100C522C130C522

C135C740

C438C640

C70C522

C130C740

C101C522

C100C740

C346C731

C365C640

C350C640

C344C522

C235C346

C155C522

C20C346

C160C740

C140C346

C350C696C90C640

C101C740

C350C740

C433C740

C135C640

C390C522

C140C522

C155C740

C130C640

C346C365

C100C640

C390C640

C349C522

C344C640C346C652

C20C640C160C640

C385C522

C140C740

C101C640

C437C522

C235C522

C385C640

C235C640

C155C640

C346C710C365C696

C346C350C140C640

C437C740

C433C522

C70C740

C433C640

C70C640

C437C640

C350C522

C365C522

-1-.5

0.5

e( s

imila

rvot

es |

X )

-15 -10 -5 0 5 10e( reltradeindegnv | X )

coef = -.01226368, se = .00314928, t = -3.89

Dyads: Countries: C200C740 C740 C346C482

259

1993 Relative Trade In-degree Centrality

C344C346

C150C346C346C438

C346C349

C435C522

C346C434C145C346C93C346C52C346C91C346C51C346C92C346

C42C346

C346C435C94C346

C346C433C90C346C95C346C135C346

C165C346

C101C346C130C346

C346C437

C346C439C200C346

C155C346

C110C346C346C432C70C346C41C346

C346C385

C435C696

C160C346

C346C436

C346C390

C100C346

C346C420

C52C696

C150C696

C346C404

C349C740C110C696

C20C696

C346C365

C20C346

C140C346

C165C696C235C346

C2C696C200C696

C346C350

C110C522

C385C696

C41C696

C385C740

C101C696C200C740

C95C696

C145C696C92C696C94C696C51C696

C150C522

C390C696

C390C740

C41C522

C2C346C42C696

C91C696C90C696

C93C696

C135C696

C52C740

C344C740

C145C522

C150C740

C420C740

C155C696C404C696

C211C346

C93C522

C52C522

C91C522

C160C696

C51C522C92C522C200C640

C110C740

C20C740

C130C696

C432C740

C165C740

C420C696

C42C522

C439C696

C94C522

C210C346

C404C740

C90C522

C95C522

C70C696

C135C522

C432C696

C230C346

C165C522

C436C740

C438C696

C349C696

C434C696

C439C740

C220C346

C101C522

C94C740C434C740

C436C696

C145C740C2C522

C438C740C101C740

C95C740

C20C522

C51C740

C150C640C2C740

C92C740

C346C740

C130C522

C385C640

C344C696C110C640

C42C740C91C740

C100C696

C93C740

C390C640

C52C640

C41C740

C420C640

C325C346

C90C740

C155C522

C135C740C155C740

C432C640

C160C740

C130C740

C211C696C404C640C434C640

C165C640

C433C696

C95C640

C140C696

C437C696

C220C696C145C640

C94C640

C404C522

C51C640

C211C740

C346C900C350C696

C435C740C92C640

C20C640

C433C740

C436C640

C70C522

C350C740

C235C696

C439C640C101C640

C235C740

C235C640

C210C696

C420C522C439C522

C160C522

C346C732

C438C640

C42C640

C210C740

C91C640

C93C640C350C640

C432C522

C41C640

C100C522

C346C690

C90C640

C434C522

C70C740

C438C522C346C696

C135C640

C437C740

C436C522C220C740C349C640

C130C640

C344C640

C346C840

C155C640

C346C663C346C475

C100C740

C346C481

C346C694C2C640C346C625

C365C740C140C522

C346C652C346C731C346C660

C435C640

C325C696

C346C750

C433C640

C230C696

C211C640

C346C710

C325C740

C140C740

C230C740

C346C484

C346C616C346C770C160C640C346C620

C346C540C346C501

C346C771C346C516

C70C640C346C645C437C640

C433C522

C346C482

C346C517C437C522C346C600C210C640

C346C551C346C541C346C483

C346C452

C346C692C230C640C346C510

C100C640C344C522

C346C451C346C615

C365C696

C220C640

C346C490

C346C640

C346C651

C200C522

C346C698C346C670

C346C530C140C640C349C522

C346C522C346C461C325C640

C385C522C390C522

C350C522C235C522

C365C640

C365C522C220C522C211C522C210C522C230C522C325C522

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-30 -20 -10 0 10 20e( reltradeindegnv | X )

coef = -.00560286, se = .00204159, t = -2.74

Dyads: Countries: C344C346 C522 C346C640

260

1994 Relative Trade In-degree Centrality

C200C522C220C522

C2C522

C325C522

C70C740

C437C740C135C740

C435C696C155C740

C433C640

C210C522

C94C640

C52C640C95C640

C344C640

C145C696

C90C640

C165C640

C101C740

C91C640C346C652

C93C696

C211C522

C160C740

C110C346C346C435

C51C640C100C740

C346C436

C92C640

C150C696

C130C740

C434C696

C346C432

C346C731

C346C510C346C439

C346C420

C230C522

C130C640

C346C771

C41C346

C135C640

C346C452

C438C640

C145C346

C2C696

C432C696

C437C640

C346C840

C344C740

C101C640

C346C475C110C696

C346C501

C346C620

C436C696

C93C346

C438C696

C435C640

C346C434C346C651

C51C696

C155C640

C200C696

C92C696

C346C540

C346C615

C70C640

C165C740

C150C640

C365C740

C346C670

C94C696

C346C484

C52C696

C220C696

C20C522

C346C625

C150C346

C325C696

C91C696

C346C461

C95C740

C90C696

C346C481

C200C346C2C346

C346C698

C346C551

C346C660C41C522

C346C600C346C616

C346C690

C346C663

C93C640C210C696C435C522C346C692

C165C696

C346C438

C346C696

C2C640

C100C640

C95C696

C52C740

C346C451

C200C640

C145C640

C220C346

C41C696

C433C740

C2C740C436C522C51C346

C385C740

C346C572

C325C346

C211C696

C140C522

C90C740

C92C346

C220C640

C160C640

C94C740

C439C696

C433C696

C346C770

C350C640C325C640C91C740

C432C522

C350C740

C20C696C346C694

C91C346

C110C522C20C640

C94C346C420C696

C210C640

C20C740

C346C516

C434C640

C346C541

C235C640

C210C346

C344C346

C90C346

C346C482

C390C740

C390C522

C92C740

C346C645

C51C740

C365C640

C130C696

C439C522

C235C740

C420C640

C211C640C420C522

C230C640

C439C640

C101C696

C52C346

C346C522C145C522C235C522

C110C640

C155C696

C140C740

C346C517

C230C696

C434C522

C95C346C385C640

C432C640

C344C696

C211C346

C93C522

C346C640

C390C640

C435C740

C438C740

C165C346C220C740

C200C740

C390C696C350C522

C346C560

C325C740

C436C640

C210C740

C346C433

C70C696

C150C522

C211C740

C230C346

C130C346

C385C696

C150C740

C100C696

C160C696

C346C732C20C346

C135C696

C230C740

C385C522

C51C522

C346C750

C140C640C346C900

C92C522

C235C696

C101C346

C94C522

C365C522

C41C640

C91C522

C350C696

C155C346

C140C696

C93C740

C438C522

C140C346C100C522

C52C522C95C522C90C522

C130C522

C100C346

C365C696C165C522

C145C740

C135C346

C155C522

C346C437

C101C522

C433C522

C70C346

C346C710

C160C522

C437C696

C160C346

C346C350

C434C740

C420C740

C235C346

C70C522C135C522

C439C740

C110C740

C346C390

C346C365

C344C522

C346C740

C432C740

C437C522

C346C385

C436C740

C41C740

-.4-.2

0.2

.4.6

e( s

imila

rvot

es |

X )

-20 -10 0 10 20e( reltradeindegnv | X )

coef = .00410884, se = .0019875, t = 2.07

Dyads: Countries: C346C365

261

1995 Relative Trade In-degree Centrality

C346C436C346C438C346C434C346C432C110C346C346C439C41C346

C150C346C52C346C93C346

C346C373

C346C433C165C346C145C346C51C346C92C346C94C346C91C346

C346C371

C90C346

C344C346

C200C346C95C346

C346C435

C346C385

C346C372

C100C346

C160C346

C346C420

C101C346C346C359

C435C522

C346C390

C130C346

C346C404C155C346

C346C437C135C346C70C346

C385C696

C52C696

C385C740

C165C696

C372C696

C346C370C110C696C150C696

C371C696

C200C696

C200C740

C390C696

C110C522

C390C740

C41C696

C160C696

C359C696

C94C696C51C696

C200C640C93C696C92C696C385C640

C41C522

C145C696C150C522

C404C740

C372C640

C211C346

C95C696

C346C900

C420C740

C52C522C93C522

C435C696C52C740

C371C640C90C696

C220C346

C165C522

C346C350

C145C522

C165C740

C101C696

C436C740

C100C696

C51C522C434C696C210C346C91C696C20C346

C372C740

C92C522

C438C740

C94C522

C150C740C390C640

C110C740C432C740C434C740C150C640C110C640C370C696C434C640C52C640

C371C740C91C522

C155C696

C20C696

C420C696

C90C522

C165C640

C95C522

C439C740

C373C696

C404C696

C350C640C2C696

C2C346C371C522

C140C346

C41C740

C41C640

C130C696

C160C740

C94C740

C235C346C100C522

C51C740

C70C696

C160C522

C359C740

C94C640

C372C522

C93C740

C359C640

C51C640

C404C640C93C640

C230C346

C145C740C92C740

C2C740

C101C522

C145C640C92C640

C438C696

C95C740

C420C640

C346C369

C436C696

C95C640

C373C740

C135C696

C433C740

C220C696

C439C696

C359C522

C130C522

C436C640

C432C696

C101C740

C346C365

C160C640C90C740

C100C740

C325C346

C211C696C90C640

C100C640C438C640

C91C740

C101C640

C432C640C211C740

C91C640

C155C522

C350C696

C20C522C350C740

C20C740

C155C740

C2C522

C135C522

C439C640C435C740

C373C640

C70C522

C210C696

C434C522C346C740C370C740

C370C640

C220C740

C210C740C433C696

C346C732

C130C740

C155C640C130C640

C211C640

C420C522

C70C740

C404C522

C373C522

C220C640C433C640C70C640

C437C696

C370C522C365C696

C210C640

C369C696

C346C850

C20C640C135C740

C135C640

C346C481C346C690

C436C522C438C522

C346C694C437C740

C439C522

C437C640

C432C522

C435C640

C346C692

C344C740

C200C522

C2C640

C140C696

C385C522C325C696

C235C640

C230C696

C346C501

C325C740

C346C696

C346C616

C230C740

C346C840C346C572

C230C640

C365C740

C369C640

C235C696

C346C484C346C560C346C771C365C640

C235C740

C346C620

C433C522

C346C483C346C615

C346C551

C346C541

C346C510

C390C522

C325C640C346C652

C369C740

C346C600C346C452

C346C731

C346C703

C140C522

C346C461C346C475C346C704

C346C451

C437C522

C346C750

C346C540

C346C651

C346C698

C346C702

C346C660C344C696

C140C740C346C710

C365C522

C346C701

C140C640C369C522

C350C522

C346C705

C346C625C346C670C346C522

C344C640

C220C522

C346C770C346C663

C211C522

C210C522C235C522C344C522

C230C522

C325C522

-1-.5

0.5

e( s

imila

rvot

es |

X )

-20 -10 0 10 20e( reltradeindegnv | X )

coef = -.00336298, se = .00182688, t = -1.84

Dyads: Countries: C346

C522

262

1996 Relative Trade In-degree Centrality

C346C438

C346C435

C344C346

C110C346

C346C434

C145C346

C41C346

C150C346

C92C346

C346C385

C52C346C93C346

C346C433

C91C346

C346C439

C94C346C135C346

C346C373

C90C346C51C346

C42C346

C371C696

C346C371

C435C522

C165C346

C155C346C346C372

C346C390

C200C346

C346C432

C95C346

C160C346

C372C696

C346C436C346C359

C385C696

C346C437

C130C346

C101C346

C385C740

C370C696

C110C696C52C696

C359C696

C100C346

C150C696

C20C346

C390C696

C110C522

C346C370

C390C740

C346C420

C385C640

C145C696

C92C696

C372C640

C41C696

C70C346

C235C346

C200C740

C200C696

C346C450

C165C696

C145C522

C94C696

C155C696

C160C696

C150C522

C41C522

C211C346

C93C696C135C696

C434C640

C435C696

C404C740

C92C522

C420C696

C346C900

C90C696

C346C404

C91C696

C325C346

C51C696

C52C522

C110C640

C434C696

C93C522

C371C640

C52C740

C371C522

C373C696

C42C696

C390C640

C200C640

C210C346

C150C640

C95C696

C439C696

C52C640

C436C696

C91C522

C20C696

C359C640

C145C640

C20C522

C404C640C432C740

C94C522

C110C740

C435C740

C135C522

C346C350

C90C522

C404C696C51C522

C92C640

C150C740

C372C740

C145C740

C20C740

C41C640

C101C696

C220C346

C42C522

C420C740

C346C698

C94C640

C438C696

C2C740

C165C740

C165C522

C155C522

C93C640

C432C640C434C740

C94C740

C92C740

C439C740

C41C740

C346C740

C130C696C95C522

C438C740

C350C640C372C522

C90C640

C2C346

C160C522

C346C365

C165C640

C230C346

C51C640

C155C640

C436C740

C359C740

C155C740

C93C740

C420C640

C432C696

C42C640

C160C740

C437C696

C51C740

C91C640

C95C640

C371C740

C90C740

C439C640

C346C732

C160C640

C95C740

C438C640

C130C522

C346C694

C344C740

C101C522

C100C696

C365C696

C359C522

C436C640

C211C696

C42C740

C211C740

C91C740

C220C696

C70C696

C101C640

C370C522

C135C640

C350C740

C370C640

C325C696

C325C740C210C740

C346C481

C210C696

C235C640

C420C522

C101C740C130C640

C140C346

C135C740

C433C696

C346C690

C434C522

C235C696

C235C740

C211C640

C100C522

C130C740

C433C740

C439C522

C436C522

C435C640

C2C696

C325C640

C344C696

C346C369

C373C740

C437C640

C346C696

C370C740C404C522

C2C522

C346C692

C346C560

C220C740

C70C522

C210C640

C100C640

C433C640

C20C640

C373C522

C373C640

C346C840

C385C522

C365C640C346C572

C369C696

C438C522

C100C740

C346C640C346C540

C346C551

C346C484

C220C640

C346C660

C432C522

C346C483C346C771

C365C740

C437C522

C346C510C70C640

C230C696

C230C740

C437C740

C346C522

C230C640

C346C541C346C731

C70C740

C365C522

C390C522

C200C522

C344C640

C346C620

C346C501

C346C452

C346C703

C346C616

C346C770

C369C640

C346C461

C346C750C346C451C344C522

C346C625

C346C704

C433C522

C346C710

C346C652

C346C702

C140C696

C346C475C346C663

C346C615

C346C850

C346C705C2C640

C350C522

C346C600

C346C670

C369C740

C140C522

C235C522

C140C640

C346C651

C211C522

C140C740

C369C522

C325C522

C220C522C210C522C230C522

-.50

.5e(

sim

ilarv

otes

| X

)

-20 -10 0 10 20e( reltradeindegnv | X )

coef = -.00455133, se = .00174316, t = -2.61

Dyads: Countries: C160C346 C522

263

1997 Relative Trade In-degree Centrality

C439C740

C438C740

C350C316

C370C316

C41C740

C365C316

C91C740

C52C740

C52C696

C437C740

C51C740

C41C696

C437C316

C51C696

C42C740

C135C316

C41C522

C90C740

C91C696

C95C696

C51C522

C42C696

C52C522

C155C316

C373C316

C90C696C369C316

C698C316

C91C522

C100C316C52C640

C41C640

C433C316C385C316C390C316

C70C316

C235C316

C165C316C101C316

C95C522

C51C640

C694C316

C42C522

C200C316

C91C640

C692C316

C439C316

C160C316

C438C316

C625C316

C90C522

C95C316

C92C640

C371C316

C20C316

C211C316

C437C640

C310C316

C436C316

C95C640

C2C316

C130C316

C100C740

C435C316

C42C640

C572C316

C70C740

C372C316

C52C316

C290C316

C2C740

C90C640

C210C316

C731C316

C94C316C42C316

C310C640C90C316

C325C316C92C316

C710C316

C310C696

C220C316

C696C316

C620C316C670C316

C701C316

C310C740

C200C696

C100C696

C740C316

C484C316C230C316

C434C316

C359C316

C640C316

C20C696

C690C316

C91C316

C211C696

C2C696

C850C316

C540C316

C51C316

C20C740

C704C316

C439C640

C200C740

C600C316

C211C740

C235C696

C732C316

C705C316C140C316

C70C696

C350C640C150C316C93C316

C616C316C310C522

C438C640

C110C316

C235C740

C652C316C235C640

C450C316

C350C696C210C696

C475C316

C615C316

C483C316

C350C740

C200C640

C210C740

C510C316

C325C696

C437C696

C522C316

C702C316

C703C316

C20C640

C660C316

C211C640

C481C316

C437C522

C551C316

C438C696

C325C740

C2C640

C840C316C461C316

C404C316C432C316

C220C740

C438C522

C100C640

C145C316

C70C522

C41C316

C220C696

C451C316C235C522

C70C640

C210C640

C230C696

C560C316

C439C696

C452C316

C200C522

C350C522

C290C640

C541C316

C439C522

C325C640C501C316

C230C740C290C740

C750C316

C211C522

C290C696

C651C316

C230C640C220C640

C663C316

C770C316C210C522

C290C522

C325C522

C771C316

C230C522

C900C316

C20C522

C2C522

C220C522

-.50

.5e(

sim

ilarv

otes

| X

)

-60 -40 -20 0 20 40e( reltradeindegnv | X )

coef = -.00388418, se = .00090739, t = -4.28

Dyads: Countries: C2C522 C740 C20C522 C522 C350C316 C439C740 C438C740

264

1998 Relative Trade In-degree Centrality

C350C316

C404C740

C160C316

C420C740

C365C316

C370C316

C436C740

C435C740

C140C316C369C316

C2C316

C437C316

C439C740C150C740

C150C696

C70C316C155C316

C110C740

C371C740

C135C316

C110C696

C371C696

C438C740

C41C696

C371C522C100C316C371C640

C52C696

C41C740

C150C640

C41C522C110C522

C150C522C235C316

C432C740

C145C696

C110C640

C372C740

C101C316

C310C316

C372C696

C210C316

C165C316

C211C316

C434C522

C145C522

C690C316

C290C316

C372C640

C373C316

C51C696C93C696

C41C640

C434C696

C20C316

C372C522

C2C696

C145C740

C93C522

C433C316C91C740

C434C740C385C316C91C696

C230C316

C359C696

C359C522C404C640

C145C640

C93C740C130C316C433C740

C439C316

C51C522

C95C316

C42C696

C52C640

C220C316

C91C522

C92C696

C51C740

C52C522

C434C640

C359C740

C165C696

C390C316

C420C640C92C740

C51C640

C94C316C93C640

C325C316

C94C696C370C740

C91C640

C42C740

C438C316

C404C522

C90C316

C90C696

C95C696

C435C316

C52C316

C437C740

C436C640

C42C522

C2C740

C92C522

C92C316C42C316

C359C640

C694C316

C670C316

C42C640

C200C316

C370C640C436C316

C92C640

C90C740

C70C740

C70C696

C372C316C93C316

C90C522

C437C640

C94C522

C165C740C210C696

C94C640C95C740

C359C316

C101C696

C94C740

C435C640

C130C696

C211C696C90C640

C439C640

C95C522

C95C640

C51C316

C155C696

C420C316C135C740

C20C696

C91C316

C385C696

C434C316

C165C640

C350C696

C432C522

C101C740

C165C522

C692C316C70C640

C432C696

C130C522

C155C740

C373C640C698C316

C390C696C150C316

C130C740C145C316C652C316

C438C640C696C316

C420C522

C135C640

C432C316C371C316

C101C640

C70C522

C130C640

C2C640

C110C316

C420C696

C235C696

C100C696C101C522

C20C740

C350C640

C41C316

C732C316

C481C316

C210C740

C100C740

C432C640

C450C316

C310C696

C220C696

C572C316

C325C696

C710C316

C211C740

C435C696

C135C696

C230C696

C436C522

C155C640C160C696

C385C740C350C740

C310C640

C731C316C620C316

C155C522

C436C696C651C316C701C316C404C316

C160C740C439C522

C438C522C438C696

C100C522

C850C316

C365C740

C100C640C200C696

C704C316

C439C696

C437C522

C484C316

C390C740

C740C316

C350C522

C290C696

C437C696

C600C316

C135C522

C433C640

C235C640

C616C316

C705C316

C663C316

C140C696

C370C696

C235C740

C290C640

C510C316

C435C522

C369C696

C310C522

C615C316

C540C316

C210C640

C625C316

C522C316

C211C640

C365C640C310C740

C365C696

C475C316

C325C740C140C740

C483C316

C703C316

C369C740

C369C522

C660C316

C370C522

C230C740

C2C522

C20C640C235C522

C461C316C551C316C385C640C290C522

C230C640

C433C522

C560C316C369C640

C140C640

C290C740

C210C522

C433C696

C140C522

C160C640

C451C316

C373C522C211C522

C750C316

C200C740

C160C522

C452C316

C373C696

C390C640C840C316C365C522

C220C640

C482C316C770C316

C541C316

C325C640

C385C522

C230C522

C200C640

C390C522C325C522C220C522

C20C522

C200C522

C900C316

-1-.5

0.5

e( s

imila

rvot

es |

X )

-60 -40 -20 0 20 40e( reltradeindegnv | X )

coef = -.00095141, se = .00072211, t = -1.32

Dyads: Countries: C350C316 C435C522 C900C316

265

1999 Relative Trade In-degree Centrality

C110C346C150C346

C346C432

C41C346

C346C371

C346C439C346C438

C145C346

C2C522

C365C316C369C316C93C346C346C372

C91C346

C310C316

C220C522

C346C434

C2C640

C220C640

C210C522C325C522

C20C522

C346C404

C160C316

C210C640C325C640

C350C316

C92C346

C385C316C51C346

C230C522C200C522

C235C316

C211C316

C346C359

C20C640

C52C346

C230C640

C346C373

C200C640

C140C316

C42C346

C211C522

C90C346

C346C900

C346C696C346C690

C346C740

C211C640

C94C346

C220C696C230C696C346C433

C41C696

C390C316

C290C316C290C696C370C316C346C694C325C696C20C316

C2C696C100C316

C95C346

C346C732

C165C346

C41C740C135C316C235C522

C439C740

C350C522

C230C740

C220C740

C346C698C210C696

C437C316

C290C740

C439C696C346C670

C235C640

C346C692C625C316

C438C740

C452C316

C325C740

C475C316

C91C696

C702C316C200C696

C70C316

C155C316

C350C696

C704C316

C438C696C290C522

C510C316

C451C316

C461C316

C551C316C483C316C540C316C484C316C731C316C501C316

C346C481C541C316C652C316

C770C316

C710C316C651C316

C101C316C310C696

C600C316

C701C316C235C696C130C316

C750C316

C290C640

C522C316

C346C640

C210C316

C663C316

C572C316

C850C316

C210C740

C615C316

C2C740

C51C696

C696C316

C130C346

C346C850

C640C316

C840C316

C660C316

C346C616C690C316

C344C346

C346C437

C437C696

C91C740

C20C696C346C620C346C510

C346C565

C346C710

C346C475

C346C750

C346C452C346C701

C346C541

C325C316

C41C640C350C740

C346C483C565C316

C346C572C346C652

C90C696

C616C316C346C551C346C840

C346C731

C230C316C200C740

C346C501C346C660

C346C651

C620C316

C346C540C346C615

C705C316

C346C484C346C770C346C625C101C346C346C451C346C600

C346C461

C42C696

C346C663C310C522

C346C702

C437C740

C433C316

C310C740C235C740

C694C316

C346C705C560C316C70C346C481C316

C310C640

C155C346

C211C696

C41C522

C51C740

C346C522C740C316

C20C740

C350C640

C92C740

C95C316

C439C640

C670C316

C90C740

C70C696

C100C346C135C346

C900C316

C2C316

C698C316

C42C740

C373C316C90C316

C165C316

C438C640

C94C316

C692C316

C52C696

C220C316

C344C696

C359C316

C91C640C42C316

C52C522

C732C316

C344C740

C434C316

C346C385

C211C740

C346C370C404C316

C344C316

C51C316

C346C390

C70C740C439C316C93C316

C92C316C437C640

C70C522

C91C522

C439C522

C20C346C210C346

C91C316

C211C346

C51C640

C2C346

C51C522

C438C522

C325C346

C92C640

C42C522

C101C522

C52C740

C52C640

C432C316

C438C316

C90C640

C90C522

C372C316

C52C316

C220C346

C371C316

C145C316

C42C640

C41C316

C160C346

C344C522

C346C316

C70C640

C230C346C235C346

C344C640

C110C316

C310C346

C437C522

C200C316

C150C316

C346C350

C290C346

C200C346

C140C346

C346C365

C346C369

-.50

.51

e( s

imila

rvot

es |

X )

-20 -10 0 10e( reltradeindegnv | X )

coef = .0079358, se = .00291649, t = 2.72

Dyads: Countries: C2C522 C20C522

266

2000 Relative Trade In-degree Centrality

C150C346

C110C346C346C420C365C316C41C346C346C432

C369C316

C346C371C346C439C346C438

C2C522

C346C435C145C346

C220C522

C200C522C140C316C160C316

C210C522C325C522

C93C346

C91C346

C20C522

C230C522

C346C372

C390C522C385C522

C290C316

C346C434

C140C696C310C316

C92C346

C420C696

C211C522

C140C522C150C696

C235C316

C110C696

C51C346

C346C359

C350C316

C140C740

C41C696

C385C316C211C316

C52C346

C369C696

C346C696

C100C316C230C696

C140C640

C390C316C150C740

C432C696

C200C696

C235C522

C346C373C371C696

C200C740

C110C740C346C690

C230C740

C150C522C220C696

C346C694

C350C522

C420C740

C439C696

C369C740

C42C346

C370C316

C346C900

C200C640

C150C640

C220C640

C290C696C325C696

C110C522

C220C740

C369C640

C110C640C702C316C290C522

C432C740

C625C316

C2C640

C452C316C90C346

C20C316

C145C696

C135C316

C325C740

C461C316

C475C316

C451C316C703C316

C346C740C438C696C365C696

C346C698

C712C316

C371C740

C290C740

C522C316

C210C640

C2C696

C41C740

C94C346

C325C640C93C696

C369C522

C91C696

C346C692C704C316

C365C522

C371C640C420C640C551C316C210C696

C600C316

C651C316C346C433

C155C316

C365C740C483C316

C346C616

C615C316C346C670

C210C740

C750C316

C652C316

C346C705C701C316C540C316

C230C640

C365C640

C70C316

C663C316

C346C620

C41C640

C850C316

C41C522

C432C640

C346C732C437C316

C346C615C501C316C731C316C840C316C484C316

C346C702C165C346C145C740C346C461C346C625

C346C703

C346C640

C346C651C101C316C565C316C346C451C616C316C350C696

C420C522

C439C740

C373C696C346C660C346C452

C434C696

C346C600

C130C316

C346C701C660C316C210C316

C20C640C235C696

C620C316C346C652

C705C316C390C640

C359C696C95C346

C346C704

C93C740

C710C316C371C522

C346C475

C346C663C372C696C770C316

C310C522

C900C316

C91C740

C20C696

C92C696

C51C696

C696C316C346C481

C350C740

C160C522

C438C740

C346C551C572C316C346C483

C385C640

C235C740

C390C696C310C696

C560C316C145C640C325C316C346C540C346C560

C2C740C346C501

C432C522

C346C731C346C565C346C484C346C710

C434C740

C690C316

C90C696

C385C696

C740C316C135C696

C93C640

C145C522

C346C850C91C640C346C712C439C640

C390C740

C433C696C130C696C310C740

C359C740C346C522

C230C316C694C316C95C316

C346C572

C42C696

C346C840

C160C696

C130C346

C100C696

C211C640

C211C696

C435C740

C385C740C370C696

C698C316

C372C740

C435C640C346C770

C51C740

C433C316C438C640C346C750

C20C740

C481C316

C437C696C93C522C91C522

C160C640

C372C640

C94C696

C92C740

C211C740C439C522

C90C316C220C316

C95C696

C433C740

C101C346

C52C522C101C696

C94C316

C434C640C670C316

C372C522

C732C316C692C316

C438C522

C165C316

C92C522

C359C640

C90C740

C373C740

C42C316

C155C522

C51C522

C92C640C51C640C70C346

C359C316

C165C522

C42C740

C70C522C70C696

C373C640

C160C740

C130C740

C42C522

C100C522

C100C740

C350C640

C155C346

C435C696C2C316

C94C740

C370C522

C135C346

C437C740

C51C316

C155C696C94C522C135C522C165C696

C101C522

C52C696

C346C437

C90C522

C373C316

C200C316

C95C522

C90C640

C95C740C359C522

C135C740C433C640C93C316

C235C640

C52C640C92C316

C130C522

C130C640

C434C316C91C316

C434C522

C101C740

C42C640

C100C640

C373C522

C344C316

C70C740

C155C640C145C316C94C640C135C640C165C640C70C640

C346C370

C370C740

C372C316

C95C640C165C740C52C740

C437C640

C52C316C100C346

C155C740C101C640

C290C640

C344C696

C370C640

C432C316

C371C316

C640C316

C433C522

C110C316C41C316C439C316

C346C385C346C390

C150C316

C210C346

C344C346

C438C316

C344C740

C437C522C325C346

C420C316

C220C346C211C346C344C640C200C346

C344C522

C230C346

C435C316

C435C522

C20C346

C310C640

C160C346

C235C346C346C316C310C346

C346C350

C2C346

C346C365

C290C346

C140C346

C346C369

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-20 -10 0 10e( reltradeindegnv | X )

coef = .01535905, se = .00252347, t = 6.09

Dyads: Countries: C20C522 C2C522

267

Figure 9: Partial-regression leverage plots for small sample analysis - Relative Trade In-degree Centrality Squared 1992 Relative Trade In-degree Centrality Squared

C346C350

C346C365

C140C346

C235C346

C70C640

C437C640

C70C740

C385C640

C20C346

C433C640

C437C740

C346C390C346C385

C160C740

C235C640C160C640

C390C640

C385C740

C155C346

C385C696

C211C346

C160C346C155C640

C70C346

C101C640

C433C740

C101C346

C135C640

C200C346

C155C740

C101C740

C350C740

C160C696

C2C346

C230C346

C344C640

C130C522

C130C640C350C696

C160C522C100C640C438C640

C433C522

C346C437C140C640

C390C740

C346C710

C90C640

C20C640C135C740

C155C522C100C522

C390C696

C344C740

C346C652

C101C522

C130C740

C350C640

C100C740

C155C696

C101C696

C140C740

C100C346C70C522

C350C522

C92C640

C90C740C365C522

C365C696

C70C696

C140C522C130C696

C100C696

C130C346C92C522

C92C740

C210C346

C140C696

C20C740

C42C640

C437C522

C220C346

C433C696

C438C740

C349C696C94C640

C20C696

C90C522

C349C740

C52C740

C95C640C2C740

C346C731

C435C522

C349C640

C94C522

C42C740

C165C640

C52C640

C235C740

C385C522C42C522

C435C640C235C696C349C522

C92C696

C52C696

C346C645

C95C740

C165C740

C51C522

C51C640

C95C522

C94C740

C165C522

C52C522

C165C696

C346C670

C94C696

C93C640

C346C640

C90C696

C439C640

C135C522

C346C433

C346C732

C346C770

C437C696

C95C696

C93C522

C235C522

C435C696

C93C740C390C522

C51C740

C438C522

C42C696

C344C522

C90C346

C346C541

C346C740C346C501

C51C696

C346C625C346C663

C92C346

C346C692

C145C522

C211C640

C346C771

C432C522

C346C651

C365C640

C344C696

C365C740

C346C698

C346C750

C435C740

C432C640

C346C840C346C690

C135C696

C230C640C346C517

C41C640

C325C346

C145C640

C135C346

C150C640

C434C640

C346C551

C346C615

C346C482

C346C451

C346C510

C346C484

C91C640

C346C660

C434C522

C404C640

C346C600C346C475C346C483

C420C522

C41C740

C439C740

C145C740

C346C516

C93C696

C346C522

C346C900

C94C346

C150C740C434C740

C346C694C346C620

C42C346

C346C461

C91C522C438C696

C436C522

C91C740

C346C616

C20C522

C346C540

C420C640

C150C522C346C452

C346C530

C346C490

C145C696

C52C346

C346C481

C110C640

C436C640

C95C346

C211C740

C165C346

C432C696

C211C696

C432C740

C346C349

C110C522

C51C346

C150C696

C110C740C344C346

C434C696

C404C740

C439C522

C420C696

C91C696C346C696

C210C640

C200C640

C436C696

C93C346

C420C740

C2C640

C230C740

C346C438

C436C740

C230C696

C110C696

C2C696

C439C696C145C346

C346C404

C404C522

C346C450

C210C740

C220C640C41C522

C211C522C210C696

C200C740

C230C522

C346C436

C200C696

C346C420

C346C434

C220C740

C91C346C41C346

C150C346

C346C439C404C696C41C696

C220C696

C346C435C346C432

C110C346C210C522

C200C522

C325C640

C2C522

C220C522

C325C740

C325C696

C325C522

-.50

.5e(

sim

ilarv

otes

| X

)

-1000 0 1000 2000e( reltradeindegnvsqd | X )

coef = .00005566, se = .0000321, t = 1.73

Dyads: Countries: C346C350 C325C740 C325C522 C220C696 C325C522

268

1994 Relative Trade In-degree Centrality Squared

C140C740

C346C385

C420C740

C230C740

C439C740C435C740C140C640

C140C696

C95C522

C160C346

C165C522

C100C346

C52C522

C100C696

C346C740

C94C522C346C350

C41C740

C101C522

C130C522

C235C346

C70C346

C51C522

C70C522

C346C710

C346C437

C92C522

C432C740

C91C522

C346C390

C436C740

C101C346C155C522

C90C522

C438C740

C140C346

C155C346

C110C740

C220C740

C434C740

C433C522

C130C346

C350C696

C100C522

C437C696

C211C740

C346C750

C160C522

C150C522

C135C522

C344C522

C130C696

C93C740

C135C346

C325C740

C235C696

C135C696

C165C346

C145C522

C437C522

C210C740

C160C696

C70C696

C93C522

C95C346

C145C740

C434C522

C200C740

C365C696

C420C640

C101C696

C235C740

C346C900C433C740

C346C365

C20C346

C439C640

C155C696C230C640

C432C522

C41C640

C52C346

C346C560

C436C522

C438C522

C150C740

C346C640C94C346

C110C522

C385C640

C20C640

C390C640

C211C640

C350C740C230C346

C90C740

C91C740

C437C640

C365C640

C20C740

C346C732

C95C696

C435C640

C346C522

C92C740

C160C640

C346C645

C2C640

C346C433C235C640

C385C522C230C696

C41C522

C51C740

C51C346

C91C346

C41C696

C100C640

C210C640

C90C346

C385C696

C92C346

C350C522

C436C640

C350C640

C165C696

C110C640

C91C696

C433C696

C432C640

C220C640C325C640

C94C696

C439C522

C390C740

C211C346

C346C770

C346C698

C434C640

C346C517

C94C740

C365C522

C344C696C70C640

C420C522

C51C696

C346C694

C365C740

C135C640C390C696

C90C696

C95C740C235C522

C200C640

C52C696

C93C640

C92C696

C346C670

C346C692

C346C482

C346C600

C385C740

C145C640

C150C346

C438C640

C346C541

C436C696

C2C740

C344C740

C346C572

C346C690

C346C696

C52C740

C346C451

C437C740

C210C346

C346C438

C20C696

C432C696C439C696C420C696

C346C625

C110C696C346C616

C346C516

C2C346

C344C346

C100C740

C390C522

C155C640C346C551

C145C346

C211C696

C130C640

C93C346C346C651

C101C640

C140C522

C130C740C325C346C165C740

C346C615

C346C620

C150C640

C220C346

C346C663

C41C346

C346C434

C135C740

C346C475

C160C740

C435C522

C434C696

C346C461

C346C435

C346C660

C150C696

C346C484

C93C696

C346C840C346C481

C210C696

C145C696

C433C640

C101C740

C344C640

C346C432

C438C696

C346C420

C90C640

C346C501

C346C731

C325C696

C92C640C91C640

C346C510

C200C346

C51C640

C346C540

C220C696

C346C436

C70C740C346C652

C346C439C110C346C346C771

C165C640

C346C452

C95C640

C155C740

C200C696

C2C696

C20C522

C94C640

C52C640

C230C522

C435C696

C211C522C210C522C325C522

C2C522

C220C522C200C522

-.4-.2

0.2

.4.6

e( s

imila

rvot

es |

X )

-1000 0 1000 2000 3000e( reltradeindegnvsqd | X )

coef = -.00009121, se = .00002368, t = -3.85

Dyads: Countries: C2C522 C522

269

1999 Relative Trade In-degree Centrality Squared

C346C365

C150C316C346C369

C200C346

C372C316

C52C316C110C316

C371C316

C52C740

C346C350

C52C640

C344C740

C41C316

C438C316

C42C640

C145C316

C344C640

C70C640

C140C346

C310C346

C290C346

C52C696

C90C640

C439C316

C432C316

C92C640

C346C316

C91C316

C51C640

C200C316

C92C316

C235C346

C160C346

C437C640

C90C522

C93C316

C42C522

C51C316

C373C316

C70C740

C51C522

C346C370

C359C316

C42C316

C211C346C101C522

C434C316

C91C522

C52C522

C344C316

C165C316

C230C346

C94C316C91C640

C437C522

C70C696

C70C522

C42C740

C344C696C90C316

C2C346

C438C640

C325C346C346C385

C100C346

C90C740

C439C640C437C740

C92C740

C220C346

C211C740

C210C346C346C390

C95C316

C42C696

C51C740

C155C346

C135C346

C404C316

C20C346

C344C346

C70C346

C90C696

C346C437

C344C522

C433C316

C51C696

C211C696

C439C522

C670C316

C438C522

C101C346C692C316

C732C316

C437C696

C346C710

C91C740

C900C316

C41C522

C740C316C130C346

C41C640

C2C316

C694C316

C310C640

C438C740

C690C316

C91C696

C698C316

C220C316C310C740

C439C740

C350C640

C696C316C101C316

C235C740

C346C731

C130C316

C438C696

C20C740

C350C740

C200C740

C2C740C439C696C346C750C155C316

C70C316

C165C346

C310C696

C640C316

C620C316

C346C850

C20C696

C437C316

C235C696

C95C346

C346C640

C710C316C346C461C346C620

C346C663

C350C696

C346C452

C325C316

C346C615

C346C840C346C572C346C705C346C770

C41C740C346C652C346C651C346C565

C481C316

C346C701

C346C484C200C696

C346C481

C210C740C705C316

C616C316C346C501

C346C540

C346C475

C346C732

C100C316

C230C316

C346C600

C135C316C615C316C370C316C346C541

C346C451

C346C740

C346C483C346C551C560C316

C346C433C346C660C346C510C346C670

C346C625

C346C616C210C316

C346C702

C660C316

C663C316

C41C696

C94C346

C701C316

C346C522

C651C316C652C316C210C696

C325C740

C290C640

C346C900

C2C696

C235C640

C346C692C600C316

C90C346

C704C316

C310C522

C850C316

C42C346

C731C316

C565C316C461C316C325C696

C290C740

C346C694

C452C316

C52C346

C346C690

C750C316

C346C698

C346C373

C572C316

C230C740

C625C316C702C316

C220C740

C840C316

C475C316C522C316

C346C696

C451C316

C211C640

C484C316C390C316

C290C696

C346C359

C770C316C540C316

C20C316C230C696C551C316C220C696

C51C346

C501C316C483C316

C541C316

C510C316

C92C346

C290C316C350C522

C290C522

C235C522

C211C316

C346C434

C200C640

C385C316C140C316

C346C372

C235C316

C346C404

C350C316

C91C346

C230C640

C211C522

C20C640

C160C316

C93C346

C325C640C210C640

C20C522

C310C316C2C640

C200C522

C220C640

C365C316

C346C439

C2C522

C346C438C230C522

C145C346

C369C316

C325C522C210C522

C346C371

C220C522

C41C346

C346C432

C150C346C110C346

-.50

.51

e( s

imila

rvot

es |

X )

-1000 0 1000 2000e( reltradeindegnvsqd | X )

coef = -.00009876, se = .0000276, t = -3.58

Dyads: Countries: C20C522 C522 C2C522

270

2000 Relative Trade In-degree Centrality Squared

C2C346C290C346

C140C346

C346C369

C310C346

C435C316

C346C316

C160C346

C346C365

C435C522

C235C346

C20C346

C344C346

C346C350

C200C346C211C346

C438C316

C344C740

C420C316

C230C346C344C640

C220C346

C52C316

C310C640

C439C316C41C316

C344C522

C150C316

C325C346

C2C316

C346C385C346C390

C110C316

C371C316

C210C346

C344C696C52C740

C372C316

C437C522

C200C316

C432C316C165C740C100C346

C52C696

C370C640

C370C740

C155C740C145C316C165C640C95C640

C165C696

C373C316C94C640C92C316

C101C640

C70C740

C437C640

C91C316

C70C640C434C316C52C640C135C640

C155C696

C93C316C155C640

C42C640

C344C316

C101C740

C640C316

C51C316

C346C370

C346C437

C433C522

C95C740

C70C696C130C522

C155C346

C94C522C90C522

C95C522

C165C316C135C740

C434C522

C359C522

C42C316C220C316C90C640

C359C316

C130C640

C373C740

C94C740

C42C522

C437C740C100C640

C135C346

C101C522

C70C346C42C740

C51C522

C165C522

C435C696

C373C522C94C316

C290C640

C95C696C94C696

C92C640C51C640C130C740

C135C522C101C696

C90C316

C92C522

C70C522

C100C740

C90C740

C373C640

C52C522C100C522

C435C740

C372C640C155C522

C101C346

C370C522

C372C522

C92C740

C160C740

C42C696

C359C640

C91C522C93C522C372C740

C95C316

C346C750

C433C640C433C316

C434C640C230C316C346C770

C433C740

C235C640C350C640

C437C696

C51C740

C670C316

C90C696

C692C316

C130C346

C346C840

C145C522C100C696

C346C572C211C696

C130C696

C325C316

C160C696C211C740

C435C640C346C710

C359C740

C370C696

C346C850

C92C696

C51C696

C346C712

C438C522

C372C696

C438C740C434C740

C346C484

C346C522

C91C640C346C560C438C640

C93C640

C698C316

C346C565

C135C696

C2C740

C385C696

C346C731

C732C316

C439C522

C385C740

C145C640C160C640

C20C740

C694C316C690C316C210C316

C359C696C346C501C101C316C346C540C91C740

C130C316C346C481

C433C696

C346C663

C439C740

C95C346C70C316C390C696C93C740

C371C522

C439C640C346C483

C346C475C437C316

C434C696C390C740

C346C652

C346C704C155C316

C346C551C481C316

C20C696

C310C696

C346C660

C310C740

C165C346C211C640C346C732

C346C640

C346C701

C145C740C696C316

C41C522C740C316

C41C640C346C600

C346C670C346C651

C432C522

C346C452C346C703

C346C625C346C451C346C620

C91C696C93C696

C346C702

C160C522C346C461C346C615

C346C740

C371C640C235C696C41C740

C235C740

C135C316

C145C696

C370C316C350C696C346C433C346C692

C20C316

C350C740

C346C616

C2C696

C371C740

C94C346

C373C696

C385C640

C346C698

C420C522

C710C316C705C316C572C316C620C316C660C316C770C316C560C316

C310C522

C432C740

C346C705

C346C900

C90C346

C432C640

C110C522

C420C740

C210C696

C390C640

C616C316C110C640

C420C640C20C640

C365C740C150C640

C371C696

C663C316

C900C316

C346C694C346C690

C365C640

C210C740

C438C696

C42C346

C652C316

C701C316

C150C522C390C316

C110C740C484C316

C365C696

C150C740

C731C316C615C316C565C316C100C316C651C316

C350C316

C600C316C346C373

C501C316C704C316

C365C522

C540C316

C346C696

C850C316C840C316

C211C316

C2C640

C230C640

C52C346

C325C696C750C316C483C316

C290C740

C439C696

C385C316

C325C740

C290C696C220C696

C432C696

C703C316

C522C316C551C316

C41C696

C200C696

C220C740C150C696C110C696

C369C522

C210C640

C325C640

C451C316C625C316

C475C316

C461C316C702C316C369C640

C200C740

C452C316C712C316

C200C640

C235C316

C290C522

C369C740

C51C346

C220C640

C230C696C350C522

C230C740C369C696

C92C346

C310C316

C235C522

C290C316

C346C359

C420C696

C346C434

C140C640C140C740C160C316

C211C522

C140C696C140C522

C346C372

C140C316C91C346

C93C346

C385C522C390C522

C20C522

C145C346C346C435

C346C438

C230C522C210C522

C2C522

C325C522

C346C439C200C522

C220C522

C369C316

C365C316C346C432

C346C371C41C346 C346C420C110C346

C150C346

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-1000 0 1000 2000e( reltradeindegnvsqd | X )

coef = -.00015154, se = .00002421, t = -6.26

Dyads: Countries: C20C522 C522 C2C522

271

Figure 10: Partial-regression leverage plots for small sample analysis - Total Trade In-degree Centrality Note: Total trade In-degree centrality is dropped in 1990 and 1991 so there are no graphs. 1992 Total Trade In-degree Centrality

C325C522

C346C522

C211C522C349C522

C365C522

C346C670

C230C522

C346C625

C344C522

C210C522

C220C522

C346C651

C346C710

C346C645

C346C652C346C694C346C600

C346C698

C346C620C346C616C346C451C346C475

C346C461

C346C452

C346C696

C346C660

C346C541

C346C615

C346C770

C346C482

C346C483

C346C510

C346C731

C346C501

C346C750C346C551

C346C484

C346C517C346C663

C346C516C346C490

C346C692

C350C522

C346C690

C346C530

C346C840

C365C640

C435C740

C346C540

C235C522

C434C522

C200C522

C346C640

C346C481

C390C522

C325C640

C346C771

C325C696

C436C522

C385C522

C346C732

C420C522

C404C522

C346C900

C432C522

C365C696

C344C696

C439C522

C41C740

C349C696

C437C522C20C640

C435C640

C325C740

C220C640

C433C522

C41C640

C344C640C438C522

C404C740

C110C740

C211C640C349C640

C230C696

C91C740

C350C640

C211C696

C93C740

C439C740

C145C740

C434C696

C110C640

C230C640

C135C740

C92C740

C91C640

C150C740

C436C740

C220C740

C210C640

C42C740

C420C740

C90C740

C130C740

C20C740

C93C640

C51C740

C145C640

C432C740

C140C740

C436C696

C210C696

C220C696

C365C740

C434C740

C70C740C135C640

C420C696

C92C640

C433C740

C150C640

C438C740

C230C740

C95C740

C94C740C404C696

C432C696

C42C640

C51C640

C90C640C130C640C100C740

C437C740

C70C640C140C640

C346C740

C211C740

C404C640

C434C640

C439C696

C350C696

C435C696

C155C740

C94C640

C95C640

C437C696

C101C740

C165C740

C110C696

C165C640

C41C696

C100C640C439C640C235C696

C437C640

C91C696

C145C696

C155C640C52C740

C52C640

C101C640

C433C696C210C740C93C696

C436C640

C110C522

C92C696

C2C740

C420C640

C438C696

C150C696

C51C696

C130C696

C432C640

C91C522

C41C522C145C522

C20C696

C2C640

C150C522

C42C696

C235C640

C140C696

C200C696

C140C522

C200C640

C433C640

C92C522

C438C640

C94C696C51C522

C93C522

C130C522

C350C740

C90C696

C95C696

C390C640C94C522

C95C522

C100C696

C42C522

C165C522

C344C740

C135C696

C235C740

C100C522

C52C522C155C522

C70C522C90C522

C160C740C101C522

C385C640

C165C696

C155C696

C160C640

C70C696

C390C696

C135C522

C101C696C200C740

C52C696

C385C696

C346C450

C349C740

C20C522

C390C740

C160C522

C385C740

C346C404

C160C696

C2C696

C346C435C41C346C346C436C346C420

C110C346

C346C439

C435C522

C91C346

C150C346

C145C346C93C346

C51C346

C346C432C92C346

C94C346

C2C522C95C346C20C346C165C346C346C434C42C346

C344C346

C325C346

C130C346

C52C346

C135C346

C90C346

C346C438

C100C346C101C346

C346C433

C155C346

C211C346

C70C346C220C346

C140C346

C230C346

C346C437

C346C349C210C346

C160C346

C346C390

C235C346

C346C385

C346C350

C2C346C200C346

C346C365

-1-.5

0.5

e( s

imila

rvot

es |

X )

-20 -10 0 10 20 30e( totaltradeindegnv | X )

coef = .00156524, se = .00157765, t = .99

Dyads: Countries: C346C482

272

1993 Total Trade In-degree Centrality

C346C698C346C670

C346C522

C346C645

C346C710

C349C522

C346C692

C346C640

C346C651C346C615C346C461

C435C740C365C522

C346C600C346C652C346C451

C41C740

C346C620

C344C522

C346C694C346C750

C346C530

C435C640

C344C640

C346C625

C41C640

C346C660C346C616C346C770C346C663

C365C640

C346C510

C325C522

C346C541C346C731C346C551

C346C482

C230C522

C346C517

C436C522

C346C696

C346C484

C346C771C346C475C346C483C346C690

C346C452C346C490C346C501

C2C640

C350C522

C346C840

C70C740C211C522

C434C522

C385C522

C70C640

C346C540

C210C522C235C522

C420C522

C437C740

C346C516

C432C522

C346C900

C346C481C349C696

C220C522C437C640C404C740

C93C740C390C522

C439C740

C346C732

C135C740

C349C640

C438C740C346C740

C160C740

C93C640

C160C640

C90C740

C350C640

C135C640C433C740C100C740C100C640

C433C522

C404C640

C91C740

C90C640C365C696

C42C740

C439C640

C110C740

C438C640

C91C640

C92C740

C437C522

C344C696

C42C640

C110C640

C325C640

C130C740

C145C740C130C640

C433C640C436C740C145C640

C230C640

C150C740

C220C640

C344C740

C51C740

C95C740

C94C640

C51C640

C92C640

C439C522

C155C740C140C740

C365C740

C438C522

C200C522C20C640

C404C522

C436C696

C434C740C94C740C150C640C165C640

C95C640

C155C640C140C640

C211C640

C432C740

C101C740

C52C640

C385C640

C434C640

C165C740

C52C740

C230C696C420C740

C436C640

C235C640C210C640

C325C696

C434C696

C101C640

C100C522

C160C522C350C696

C420C696

C432C696

C390C640

C2C740

C432C640

C100C696

C2C696

C93C522

C211C696C235C696

C230C740

C420C640

C220C740

C160C696

C325C740

C385C696

C130C522C93C696

C210C696

C94C522C145C522

C350C740

C51C522

C165C522

C130C696

C155C522C145C696

C95C522C220C696

C140C522

C92C522C91C522C94C696

C20C740

C51C696

C70C522

C42C522

C235C740C433C696C211C740

C52C522

C110C522

C435C696

C390C696

C150C522

C385C740

C91C696C92C696

C95C696

C101C522

C165C696

C42C696

C140C696

C210C740

C155C696C110C696C437C696

C90C522

C70C696

C150C696

C200C640

C52C696

C135C522

C349C740C439C696C90C696

C101C696

C390C740C438C696C404C696

C135C696

C41C522

C41C696

C200C696

C20C696

C200C740

C346C404

C2C522

C346C420C346C436C41C346C110C346

C20C522

C346C432

C435C522

C2C346

C346C439C346C435

C100C346C160C346

C93C346C130C346C94C346C145C346C51C346

C165C346

C155C346C95C346

C140C346

C92C346C91C346

C70C346

C42C346

C344C346

C52C346

C150C346

C325C346C230C346

C101C346

C220C346C90C346

C20C346

C346C437

C135C346

C211C346

C346C434

C346C385

C210C346C235C346

C346C350

C346C438

C346C433

C346C390

C346C349C346C365

C200C346

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-20 -10 0 10 20 30e( totaltradeindegnv | X )

coef = .00055163, se = .00161617, t = .34

Dyads: Countries: C346C640

273

1994 Total Trade In-degree Centrality

C325C740C200C740

C210C740

C220C740

C346C694

C41C522C346C522

C420C522C110C522

C439C522

C211C740

C434C522

C230C740C436C522

C346C698

C432C522

C346C692C145C522

C346C482

C346C517

C346C541

C93C522

C346C690

C150C522

C346C645

C41C696

C346C572

C346C451

C325C640C346C696

C140C740

C346C663

C346C660

C346C516C346C481

C420C696

C200C640

C110C696

C346C625

C439C696

C210C640C325C346C346C461

C434C696

C365C740

C220C640C210C346

C92C522

C438C522

C346C551C52C522C346C484

C20C740

C51C522

C200C346

C436C696

C93C696C145C696

C432C696

C346C501C94C522C211C640C90C522

C220C346

C211C346

C91C522

C344C522

C346C420

C346C452C346C439C165C522

C235C740

C2C640C346C731C346C540

C41C346

C20C346

C325C696

C150C696

C91C696

C346C436

C346C900

C200C696

C95C522

C346C432C110C346

C350C740

C346C510

C92C696

C346C652

C346C771

C346C620

C51C696

C346C390

C2C346C365C640

C438C696

C210C696

C346C670

C20C640

C346C435

C90C696

C346C475

C390C740

C437C696

C346C434

C140C696

C101C522C346C615C346C616

C325C522

C94C696

C220C696

C210C522

C346C732

C433C522C135C696

C390C522

C344C696

C155C522

C130C696

C346C385

C130C522

C435C522

C145C346

C385C522

C150C346

C140C640

C230C346C93C346

C436C740C230C696

C211C696

C135C522

C20C522C432C740

C346C840C211C522

C70C522

C346C600

C439C740

C160C522

C433C696

C100C696

C346C438

C420C740

C95C696C230C640

C346C651

C52C696

C434C740C100C740

C200C522

C110C740

C101C696

C52C346

C437C522

C41C740C350C640

C390C640

C41C640

C93C740

C165C696

C145C740

C435C740

C155C696C92C346C220C522

C346C740

C235C696

C365C696

C110C640

C235C346C165C346

C91C740

C51C346

C70C696C100C522

C350C696

C94C346

C130C740

C346C640

C434C640

C350C522

C346C770

C135C740C235C522

C51C740

C438C740

C437C740

C160C696

C92C740

C91C346

C90C346

C150C740

C385C740

C433C740

C160C740

C93C640

C160C346

C94C740

C145C640

C346C433

C20C696

C101C346

C346C560

C230C522

C101C740

C95C346

C155C346

C2C740

C435C696

C344C346

C90C740

C436C640

C155C740C160C640

C365C522

C439C640C420C640

C140C346

C95C740

C432C640

C135C346C70C346

C2C522

C130C346

C385C640

C344C740

C70C740

C165C740C52C740

C100C346

C150C640C91C640

C2C696

C390C696

C100C640

C346C350

C92C640

C51C640

C140C522

C435C640

C130C640

C155C640

C52C640

C165C640

C235C640

C135C640

C346C437

C101C640C437C640

C94C640

C70C640

C90C640

C344C640C346C750

C385C696

C438C640

C95C640

C346C365

C433C640

C346C710

-.50

.5e(

sim

ilarv

otes

| X

)

-20 -10 0 10e( totaltradeindegnv | X )

coef = -.02141122, se = .00216766, t = -9.88

Dyads: Countries: C200C740 C740 C325C740 C210C740

274

1995 Total Trade In-degree Centrality

C346C663C346C522C346C670C344C522

C346C698C346C770C346C625

C346C701

C346C710C346C651

C346C705

C346C652

C346C660

C344C640

C346C704

C346C702

C346C696

C346C750C346C600

C346C703

C344C696

C346C451

C346C475C346C731

C346C620C235C522C346C615

C346C540

C346C461

C2C640

C346C452C346C694C325C522

C346C560

C346C616C230C522C346C692

C346C541

C346C551

C435C640

C346C690C346C483C346C771C346C484C346C572

C365C522C435C740

C346C850

C346C840

C346C510

C365C740C346C501

C432C522C436C522C344C740C433C522C404C522

C210C522

C437C522

C365C640

C2C696

C346C740

C135C740C346C481C140C740

C369C522

C438C522

C350C522

C439C522

C220C522

C235C696C435C696

C365C696

C437C740

C346C732

C369C740

C211C522

C434C522

C20C640

C420C522

C70C740C235C640

C140C640C325C696

C230C696

C135C640

C155C740

C235C740

C325C640

C230C640

C90C740

C437C640

C140C696

C373C740

C91C740

C70C640C390C522

C130C740

C432C696C436C696

C20C740

C373C522

C140C522

C200C522C373C640

C370C740

C41C740C93C740C92C740

C359C522

C230C740

C145C740C100C740

C325C740C433C696

C110C740C101C740C385C522C155C640C439C640

C404C696

C51C740

C433C640

C369C640

C437C696

C420C640

C370C522

C150C740C95C740C94C740

C220C640

C439C740

C160C740

C90C640

C433C740C130C640C346C900

C438C696

C372C522

C420C740

C210C696C369C696C91C640

C371C740

C371C522C20C696

C439C696

C438C640

C359C740C135C696C155C696

C2C740

C350C696

C145C640

C210C640

C52C740

C370C640

C372C740

C41C640

C438C740

C93C640

C70C696

C92C640

C155C522

C51C640C100C640

C165C740

C95C640C94C640

C135C522

C101C640

C434C696

C110C640

C220C696

C130C696

C432C640

C420C696

C220C740

C70C522

C404C640C436C640

C434C740C150C640

C160C640

C432C740

C130C522

C211C696

C91C696C145C696

C404C740

C93C696

C436C740

C51C696

C165C640

C95C696C94C696

C359C640

C52C640

C101C696C41C696

C210C740

C211C640

C92C696

C350C740

C100C696C145C522C91C522C93C522C95C522C90C696C94C522C51C522

C350C640

C101C522C41C522

C110C696

C92C522

C100C522

C150C696

C2C522

C371C640

C373C696

C90C522

C160C696

C165C696

C434C640

C52C696C110C522

C160C522

C150C522

C359C696

C165C522

C211C740

C372C640

C390C696

C52C522

C200C696

C390C640

C370C696

C200C640

C385C696

C346C365

C371C696

C372C696

C435C522

C385C640

C2C346

C390C740C385C740C200C740

C20C522

C346C369C140C346

C346C404C346C435C344C346

C346C420

C20C346C235C346

C155C346C135C346

C325C346

C70C346

C230C346

C130C346C346C359

C346C437C145C346C91C346C346C370

C346C350

C93C346C95C346C94C346C51C346C101C346

C346C372

C41C346C92C346

C346C371

C100C346

C220C346C90C346

C110C346

C160C346

C150C346C165C346C52C346

C210C346C346C373

C346C432C346C436

C211C346

C346C434C346C439C346C433C346C438

C346C390

C200C346

C346C385

-1-.5

0.5

e( s

imila

rvot

es |

X )

-20 -10 0 10 20e( totaltradeindegnv | X )

coef = .00144143, se = .00149765, t = .96

Dyads: Countries: Several possible dyads C346

275

1996 Total Trade In-degree Centrality

C346C651C346C670C346C663

C346C600

C346C652

C346C710

C346C704

C346C625

C346C702

C346C475

C346C705

C346C615C346C620

C346C522

C2C640

C346C850

C346C703

C346C616

C346C451C346C750

C432C522

C346C640

C346C461C346C452C346C692

C346C770

C404C522

C435C640C346C694C346C660C346C696

C373C740

C344C640

C200C522

C370C740

C346C698

C369C522

C346C731

C371C740

C346C501

C346C560

C346C572

C346C541

C346C690

C437C740

C434C522

C433C522

C346C771

C70C740

C346C484

C346C840

C235C522C436C740

C2C696

C350C522

C438C522

C230C522

C140C740

C420C740

C369C740

C439C740

C435C740

C344C522C135C740C346C483

C435C696

C346C540C346C510

C346C551

C365C740

C439C522

C20C640

C433C740C100C740C373C640

C325C522

C436C522C420C522

C359C522

C438C740

C211C522

C91C740

C210C522

C150C740C70C640

C41C740C110C740

C93C740C140C640C92C740

C42C740

C344C696

C90C740

C220C522

C437C522

C130C740C346C481

C372C740

C370C640

C436C640

C101C740

C359C740

C372C522

C51C740C420C640C145C740

C439C640

C135C640

C346C740C346C732

C165C740

C390C522

C160C740

C200C696

C432C696

C385C522

C140C696

C404C740

C155C740

C371C640

C373C522

C434C740

C94C740

C140C522

C95C740C100C640

C200C640

C365C522

C432C740

C346C900

C433C640

C52C740

C404C696

C437C640

C365C640

C344C740

C91C640

C150C640

C369C640

C438C640

C235C640

C130C640

C235C696C230C696

C165C640

C41C640C110C640

C230C640

C94C640C51C640C145C640

C93C640

C220C640

C95C640C92C640C42C640

C350C640

C90C640

C100C696

C20C696

C434C696

C433C696C70C696

C101C640

C100C522

C165C696

C325C696

C130C696C130C522

C200C740C70C522

C371C522C370C522

C93C522

C211C696

C145C522

C94C696

C2C740

C51C696C51C522C438C696

C210C696

C165C522C145C696C93C696

C94C522

C150C696

C235C740

C160C640

C95C696

C325C640

C150C522C42C696

C155C640

C42C522

C350C740

C20C740

C90C696C90C522

C52C640

C95C522C41C522C230C740

C404C640

C41C696C220C740C211C640

C91C522C101C696

C369C696

C91C696

C210C640

C101C522

C92C696

C92C522

C432C640

C359C640C439C696C110C522

C155C696

C110C696C52C696

C390C696

C372C640

C325C740

C385C696

C436C696

C160C696

C155C522

C420C696

C211C740

C52C522

C365C696

C210C740

C220C696

C390C640

C160C522

C385C640

C346C404

C434C640

C135C696C135C522

C390C740

C437C696

C385C740

C435C522

C359C696C346C420

C346C450

C372C696C2C522C373C696

C346C369

C346C436

C371C696

C140C346

C370C696C346C432

C346C365C346C359C346C439

C2C346

C100C346

C346C371

C130C346

C346C370

C70C346

C93C346C145C346C51C346

C165C346

C94C346

C20C522

C150C346

C42C346

C90C346C41C346

C95C346

C110C346

C346C372

C91C346C346C435

C101C346

C92C346

C346C373

C346C350

C155C346

C52C346

C346C437

C344C346

C160C346

C200C346

C346C434

C135C346

C235C346

C230C346

C20C346

C346C433

C220C346

C346C438

C325C346C211C346C210C346C346C390C346C385

-.50

.51

e( s

imila

rvot

es |

X )

-20 -10 0 10 20 30e( totaltradeindegnv | X )

coef = .00190706, se = .00130531, t = 1.46

Dyads: Countries: C20C522 C346

276

1997 Total Trade In-degree Centrality

C438C740C52C740C439C740

C52C522

C51C740

C52C696

C698C316

C52C640

C91C740

C51C522

C91C522

C41C740

C41C522

C42C740

C51C696

C51C640

C95C522

C92C640

C42C522

C694C316

C91C640

C91C696

C90C740

C95C696

C95C640

C90C522

C41C696

C41C640

C42C696

C42C640

C310C640

C350C640C640C316

C900C316C90C696

C90C640

C211C640

C438C640

C692C316

C625C316

C211C740

C235C640

C439C640

C438C696

C437C740

C438C522C732C316

C620C316

C740C316

C210C640

C385C316

C616C316

C439C522

C210C740

C439C696

C350C316

C705C316

C390C316

C310C740

C200C640

C100C740C660C316

C20C740

C20C640C615C316

C211C696

C235C740

C701C316

C200C740

C437C640

C600C316

C350C740

C696C316

C840C316

C704C316

C703C316

C100C696

C100C640

C210C696

C437C522C437C696

C211C316

C20C696

C572C316

C690C316

C230C640

C310C696

C325C640

C540C316

C20C522

C70C740

C475C316

C452C316

C235C696

C702C316

C235C316

C461C316C200C696C451C316

C325C740

C484C316

C2C740

C220C640

C165C316

C52C316

C210C316

C670C316C310C522

C150C316

C522C316C310C316

C230C740

C350C696

C483C316C551C316

C220C740

C541C316C155C316

C70C522

C510C316

C211C522

C450C316

C200C316C372C316

C70C696

C404C316

C95C316

C70C640C20C316

C371C316

C110C316C235C522

C290C640

C750C316

C325C696

C438C316

C370C316

C101C316

C92C316C210C522

C220C696

C436C316

C350C522C94C316

C560C316

C51C316

C731C316

C42C316

C230C696

C135C316

C359C316

C373C316

C439C316

C200C522C230C316

C90C316C434C316

C325C316C91C316

C100C316

C93C316

C652C316C433C316C130C316C770C316

C220C316

C435C316

C437C316

C850C316

C290C316

C290C740

C710C316

C365C316

C2C640

C230C522C325C522

C663C316

C220C522

C70C316C432C316

C41C316C145C316

C2C696C290C696

C481C316

C2C522

C290C522C771C316

C501C316

C651C316

C369C316

C2C316 C160C316

C140C316

-.4-.2

0.2

.4.6

e( s

imila

rvot

es |

X )

-50 0 50 100e( totaltradeindegnv | X )

coef = -.00523055, se = .00072749, t = -7.19

Dyads: Countries: C20C522 C2C522 C140C316

277

1998 Total Trade In-degree Centrality

C435C522

C150C640

C435C696

C404C740

C110C640C371C696

C310C640

C150C696

C41C640

C404C640

C110C696

C372C696

C235C640

C110C522

C371C640

C41C522

C150C522

C150C740

C52C696

C371C522C52C640

C434C640

C41C696

C420C740

C145C640

C290C640

C110C740

C371C740

C372C640

C359C640

C51C640

C230C640

C404C522

C41C740C385C640

C91C640C436C740

C211C640

C432C740C420C640

C145C522

C372C740C145C696

C92C640C42C640C359C696

C350C640C210C640

C91C522C372C522

C385C696

C93C640

C51C696

C434C696

C438C740

C420C696

C91C696

C145C740

C435C740

C436C640

C51C522

C432C640

C165C696

C325C640

C92C696

C434C522

C95C640

C42C696

C94C640

C434C740

C93C522

C390C640

C420C522

C90C640

C51C740

C359C522

C165C640

C439C740

C385C740

C52C522C93C696

C165C740

C436C696C220C640

C438C640C211C696

C92C522

C91C740

C210C696

C359C740

C390C696

C42C522

C42C740

C370C696

C92C740

C95C696C436C522C94C696

C101C640C20C640

C90C696

C20C696

C70C640

C93C740

C211C740

C370C640

C439C640

C20C740

C90C522C155C696

C210C740C432C696

C94C740

C70C696

C390C740

C95C740

C438C696

C130C640C155C640C373C696

C90C740

C732C316

C220C696C692C316

C325C696

C101C696

C433C740

C94C522C432C522

C437C640

C439C696C95C522

C155C740

C696C316

C694C316

C690C316

C135C640

C235C696

C100C640

C438C522

C435C640

C130C696

C200C640

C325C740

C439C522

C101C740

C437C696

C70C740

C230C696

C135C696

C740C316

C235C740

C433C640

C165C522

C130C522

C350C696

C373C522C160C696

C437C522

C670C316C130C740

C100C696

C230C740

C135C522C370C740C350C740C101C522

C698C316

C70C522

C310C696C373C640

C2C740

C350C316

C433C696

C160C740

C370C522C135C740

C160C640

C616C316C100C740

C2C640

C310C740

C437C740

C2C696

C620C316

C155C522

C481C316

C100C522C433C522

C660C316C200C696

C560C316C705C316C369C696C615C316

C900C316

C365C696

C600C316C701C316C663C316

C651C316C369C640

C290C696

C652C316C452C316C840C316

C703C316

C200C740

C704C316C475C316

C451C316C461C316C750C316C710C316

C365C640C290C740

C369C740

C572C316C625C316

C770C316C850C316

C160C522

C482C316C541C316C369C522

C235C316C484C316

C483C316

C540C316C551C316

C510C316

C310C522

C731C316

C140C640

C365C522

C522C316

C310C316

C450C316C404C316

C20C522

C230C316

C235C522

C365C740

C385C316

C211C316

C52C316

C140C696

C350C522

C210C316

C150C316

C371C316

C290C522C372C316

C420C316

C325C316

C110C316

C140C740

C432C316

C230C522

C390C316

C165C316C385C522

C436C316

C211C522

C438C316

C41C316

C140C522

C220C316

C210C522

C434C316

C145C316

C359C316

C51C316

C20C316

C439C316

C325C522

C42C316C155C316C92C316

C220C522C390C522C95C316

C94C316C91C316

C373C316

C433C316

C70C316

C290C316

C93C316C90C316

C160C316

C101C316

C370C316

C2C522

C435C316

C200C316

C130C316C100C316

C437C316

C135C316

C200C522

C369C316

C2C316

C365C316

C140C316

-.50

.51

e( s

imila

rvot

es |

X )

-50 0 50 100e( totaltradeindegnv | X )

coef = -.00274674, se = .00048813, t = -5.63

Dyads: Countries: C2C522 C316 C435C522

278

1999 Total Trade In-degree Centrality

C346C522

C140C316

C2C316

C346C698

C220C316C346C625

C20C316

C346C702C346C600C346C701

C346C652C290C316

C369C316

C346C651

C230C316

C346C616

C346C660

C346C692

C325C316C200C316

C210C316

C346C620C346C484C346C663C346C615

C346C475C346C451

C346C541C346C572C346C540C346C770C346C840

C346C694

C346C670C346C551C346C565

C346C483C346C461C230C522C346C510

C290C522

C346C640

C346C452

C346C501

C350C316

C344C522

C346C750

C365C316

C210C522C346C696

C346C850

C220C522

C390C316

C325C522

C572C316C346C731

C770C316

C235C522

C160C316

C346C690

C346C705C310C522C501C316C350C522

C346C481

C522C316

C438C522

C437C522

C698C316

C439C522

C483C316C41C740C510C316C484C316

C2C346

C20C346

C200C522

C41C522

C540C316

C211C522

C41C640C731C316C481C316

C140C346

C551C316

C346C732

C20C696C346C710

C220C346

C541C316C840C316

C235C316

C41C696

C565C316

C385C316

C211C316

C91C522

C20C740

C2C696

C625C316

C230C346C346C900

C451C316

C704C316

C2C740C600C316C702C316

C51C522

C701C316

C705C316

C652C316

C560C316

C461C316

C20C640

C210C346

C325C346

C750C316

C850C316

C370C316C135C316C220C696

C692C316

C91C696

C230C696

C91C740

C651C316C616C316

C90C522

C346C740

C475C316

C350C640

C42C522

C290C346

C210C696

C660C316

C51C696

C220C740

C91C640

C452C316C290C696

C438C740

C325C696C620C316

C200C346

C2C640

C92C740C70C316

C439C740

C615C316

C344C640

C663C316C155C316

C310C316

C694C316

C346C390

C670C316

C52C522

C51C640

C51C740

C90C696

C344C696

C100C316

C42C696

C92C640C710C316

C101C522

C235C696C90C740

C732C316

C42C740C101C316

C220C640

C52C696

C230C740

C90C640

C42C640

C70C522

C235C346

C290C740C200C696

C350C696

C210C740

C437C316

C230C640

C346C404C211C696C310C696C696C316

C290C640

C130C316

C160C346

C70C740

C325C740

C52C640

C52C740

C70C696

C20C522

C211C346

C690C316

C438C640

C346C385

C437C640

C439C640

C346C316

C210C640

C310C346

C70C640

C325C640

C433C316

C235C740

C95C316

C235C640

C165C316

C200C740

C2C522

C404C316

C310C640

C350C740

C211C740

C438C696

C310C740C740C316

C346C369

C437C696

C90C316

C640C316

C94C316

C373C316

C42C316

C437C740

C200C640

C41C316C92C316

C211C640

C51C316

C150C346C110C346C344C316

C439C696

C93C316

C130C346

C52C316C438C316C439C316C91C316

C155C346

C145C346

C110C316

C434C316

C100C346C101C346C70C346

C93C346

C145C316

C135C346

C150C316

C41C346

C95C346C51C346

C94C346C90C346

C432C316

C91C346

C344C740

C346C350

C42C346

C165C346C92C346

C359C316

C372C316C52C346

C371C316

C346C365

C900C316

C346C373

C346C432

C344C346

C346C434C346C438C346C439C346C433

C346C437

C346C370

C346C371

C346C359

C346C372

-.50

.51

e( s

imila

rvot

es |

X )

-20 -10 0 10 20e( totaltradeindegnv | X )

coef = .00480462, se = .00257437, t = 1.87

Dyads: Countries: C2C316 C346C370 C346C359

279

2000 Total Trade In-degree Centrality

C435C696

C420C696C150C696

C110C696

C150C640

C41C696

C110C640

C150C346

C150C740

C110C346

C346C696

C371C696C439C696

C346C690C110C740

C432C696C438C696

C346C705

C346C694C346C698

C41C346

C41C640

C373C696C346C660C420C640

C145C696

C432C640

C346C692C346C663

C346C616

C346C371C346C702

C91C696

C346C703C390C640C385C640

C93C696

C346C420C210C640

C346C670C372C696C145C640C150C522

C420C522

C220C640C371C740

C346C615C346C651

C325C640

C371C640

C211C640

C41C740C91C640C346C620

C92C696

C346C600

C346C640

C110C522

C346C701

C346C652

C93C640

C145C740

C20C640

C230C640

C346C625

C346C704

C52C696

C51C696

C346C522

C92C640

C371C522

C432C522

C438C640C359C696C439C640

C434C696

C93C740C52C640C91C740

C42C696

C235C640

C346C451C346C461C51C640

C346C475

C420C740

C346C452

C346C900

C52C740

C90C696

C432C740

C41C522

C640C316

C359C640

C438C522

C370C696C94C696

C165C696

C439C522

C42C640

C51C740

C92C740

C346C432

C2C640

C290C640

C435C640

C372C740

C346C732

C390C696C385C696

C310C640C372C640

C95C696

C210C696C90C640

C145C346

C433C696

C434C640

C135C696

C94C640C165C640C325C696

C130C696

C211C696

C220C696C346C710

C350C640

C42C740C94C740

C70C696

C165C740

C346C435

C95C640

C155C696

C435C522

C359C740

C200C640

C230C696C346C484

C372C522

C346C740

C90C740

C346C439C346C438

C101C696

C91C346

C20C696

C433C640C346C481C346C551

C95C740

C70C640

C346C372C93C346

C438C740

C130C640C346C483

C439C740

C346C565

C100C696

C155C640

C390C740

C346C540

C385C740

C145C522

C435C740

C900C316

C437C696

C235C696

C101C640

C160C696C210C740

C350C696

C140C696C365C696

C91C522

C346C501

C325C740

C434C740

C346C731

C434C522

C211C740

C155C740

C93C522

C696C316C344C696C100C640

C220C740

C346C560C346C850

C346C770

C20C740

C346C572C690C316

C359C522C70C740

C346C712C290C696C160C640

C230C740

C346C840

C373C640

C130C740

C140C640

C310C696

C369C696

C373C522

C92C522

C346C750C92C346

C135C640

C2C696

C101C740

C52C522

C390C522C385C522

C694C316C210C522

C344C640

C373C740

C51C522

C235C740

C100C740

C433C522

C732C316

C350C740

C346C359

C160C740

C51C346

C325C522

C200C696

C433C740

C437C640C140C740

C698C316

C211C522

C740C316

C705C316C660C316

C220C522

C42C522

C692C316C365C640

C135C740

C663C316

C230C522C2C740

C52C346C370C640

C616C316

C90C522

C310C740

C702C316

C290C740

C703C316

C94C522

C165C522

C670C316C346C434

C369C740

C615C316

C369C640

C651C316

C475C316

C95C522

C437C522

C235C522

C620C316

C42C346C600C316

C350C522

C344C522

C701C316

C652C316

C346C373

C365C740

C130C522

C625C316C452C316

C704C316C560C316C712C316

C522C316

C370C740

C437C740

C70C522

C565C316

C90C346

C200C740

C155C522

C370C522

C135C522

C451C316C461C316C290C522C840C316C750C316C310C522

C101C522

C94C346

C850C316C369C522

C344C740

C100C522

C165C346

C160C522C551C316C481C316

C200C522

C140C522

C365C522

C710C316

C484C316

C310C316

C95C346C483C316C385C316C235C316

C540C316

C211C316

C501C316

C20C522

C390C316

C731C316C770C316C572C316C130C346

C350C316

C346C433C135C346

C344C346

C101C346

C70C346

C20C316

C2C522

C155C346

C290C316

C210C316

C346C370

C365C316

C325C316

C100C346

C346C437C230C316

C150C316

C160C316

C220C316

C110C316C52C316

C155C316

C369C316

C165C316C70C316C145C316C94C316C371C316

C95C316

C100C316

C51C316

C93C316

C160C346

C42C316C92C316

C91C316

C90C316

C41C316

C372C316

C101C316C130C316

C2C316

C359C316

C346C365C432C316

C135C316

C140C316

C346C385

C370C316

C346C390

C420C316

C211C346

C434C316C433C316

C346C350

C310C346

C200C316

C344C316

C437C316

C438C316

C346C369

C346C316

C20C346

C439C316

C210C346C235C346

C373C316

C325C346

C220C346

C140C346

C230C346

C290C346

C435C316

C2C346C200C346

-.50

.51

e( s

imila

rvot

es |

X )

-50 0 50 100e( totaltradeindegnv | X )

coef = .00052046, se = .00040904, t = 1.27

Dyads: Countries: C20C522 C346 C2C522

280

Figure 11: Partial-regression leverage plots for small sample analysis – Total Trade In-degree Centrality Squared 1994 Total Trade In-degree Centrality Squared

C385C696

C346C710

C390C696

C346C365

C2C696

C344C346

C435C696C365C522

C52C740

C346C750

C2C522

C20C696

C165C740

C41C740

C385C740

C150C740

C235C640

C95C640

C346C437

C385C640

C435C522

C150C640

C2C740

C140C522

C346C740

C365C696

C94C640

C135C346

C52C640

C346C433

C110C740

C437C522

C346C560

C94C740

C145C740

C436C640

C436C740

C95C740

C70C346

C432C640

C155C640C165C640

C92C640

C90C640

C160C696

C344C696

C432C740

C101C640

C344C640

C51C640

C90C346

C51C740C92C740

C434C740

C155C346

C344C740

C438C640

C41C640

C433C640

C230C522

C90C740

C100C346

C145C640

C346C350

C93C740

C130C346

C70C696

C70C640

C346C770C220C522

C110C640

C235C522

C155C740

C350C522

C200C522

C101C346

C350C696

C95C346

C346C640C155C696

C160C346

C91C346

C130C640

C434C640

C165C696

C235C696

C93C640

C52C696

C211C696

C101C740

C439C740

C346C840

C135C522

C91C640

C390C640

C420C740

C438C740

C346C438

C346C732

C100C522

C70C522

C439C640

C346C651

C100C640

C92C346

C220C696

C420C640C160C522

C385C522C20C522

C390C522

C51C346

C94C346

C70C740

C165C346

C101C696C140C346C390C740C160C740

C91C740

C346C600C346C616

C211C522C52C346

C135C640

C160C640

C350C640

C433C522

C155C522

C210C696

C438C696C433C696

C344C522

C346C900C435C640

C235C346

C437C696

C433C740

C210C522

C346C615C95C696

C200C696

C150C346C93C346

C130C740

C135C696

C346C771

C325C522

C230C696C435C740

C346C434

C346C540

C90C696

C346C385

C130C522

C101C522C145C346

C346C670

C346C620

C346C475

C100C696

C346C510C346C452

C94C696

C325C696C437C640

C110C346

C230C640

C346C432

C346C652

C130C696

C346C501

C150C696

C346C390

C92C696

C41C346C346C484

C346C435

C346C439C346C436

C95C522C90C522

C438C522

C20C640C346C516

C346C731

C420C696

C100C740

C135C740

C51C696

C346C420C346C481

C230C346

C346C660

C2C346

C346C551C346C461

C439C696

C346C663

C165C522

C91C522

C91C696C140C696

C350C740C365C640

C346C696

C94C522

C346C690

C2C640C52C522

C346C572

C20C346

C432C696

C92C522

C437C740

C51C522

C20C740

C346C625

C346C541

C93C696

C346C645C346C517

C145C696

C346C482

C346C451

C436C696C235C740

C110C696

C434C696

C220C346

C140C640

C200C346

C346C692

C211C346C211C640

C41C696

C346C698

C150C522

C210C346C220C640

C365C740

C93C522

C200C640C210C640C325C346

C346C694

C420C522

C145C522

C439C522

C434C522

C346C522

C325C640C432C522C436C522

C110C522

C211C740

C41C522

C230C740

C140C740

C210C740

C220C740

C200C740C325C740

-.50

.5e(

sim

ilarv

otes

| X

)

-2000 -1000 0 1000 2000 3000e( totaltradeindegnvsqd | X )

coef = .00014301, se = .00001678, t = 8.52

Dyads: Countries: C385C696 C740

281

1999 Total Trade In-degree Centrality Squared

C346C372C346C371

C346C359

C346C438C346C439

C346C370

C346C433C346C434

C346C432

C346C437

C900C316C344C346

C346C373

C439C696

C52C346

C41C346

C344C740C92C346

C91C346

C438C696

C371C316

C110C346C42C346

C150C346

C372C316C165C346

C145C346C93C346

C51C346

C346C365

C90C346C94C346

C95C346

C359C316

C150C316

C135C346

C2C522

C432C316

C211C640

C346C350C437C696

C145C316

C70C346C101C346

C110C316

C437C740

C155C346

C200C640

C100C346

C211C740

C52C316

C740C316C130C346

C434C316

C310C740

C91C316

C93C316

C20C522

C350C740

C200C740

C640C316

C51C316

C344C316

C438C316

C70C696

C52C740

C438C640

C92C316

C235C740

C41C316

C439C640

C235C640C310C640

C439C316

C42C316C94C316C690C316

C404C316

C210C640

C346C404C70C640

C52C696

C52C640C90C316C211C696

C325C640

C696C316

C70C740C165C316C310C696

C373C316

C70C522

C200C696

C42C696

C346C369C350C696

C95C316

C90C696

C42C740

C325C740

C90C740

C210C740

C52C522

C437C640C235C696

C290C740

C344C696

C101C522

C42C640

C51C740

C90C640

C51C696C346C385C230C640

C92C640

C230C740

C290C640

C92C740

C438C740

C220C640

C433C316

C91C696

C346C316

C346C740

C211C346

C439C740

C310C346

C732C316

C51C640C694C316C130C316

C42C522

C290C696

C91C640

C91C740

C325C696

C90C522

C160C346

C210C696

C2C640

C663C316

C310C316

C615C316

C220C740

C346C900

C670C316

C660C316C101C316

C220C696

C344C640C41C696

C235C346

C51C522

C230C696C620C316

C437C316

C350C640

C452C316C616C316C710C316

C475C316C155C316

C100C316

C20C640

C692C316

C91C522

C346C390

C70C316

C651C316

C705C316

C2C696

C560C316

C850C316

C702C316

C750C316

C461C316

C2C740C600C316

C652C316

C20C740

C701C316

C451C316

C704C316

C200C346

C41C522

C346C732

C625C316C385C316

C135C316C20C696

C290C346

C211C316

C370C316C565C316

C210C346

C41C640

C41C740C840C316

C438C522C235C316

C325C346

C346C705

C541C316C346C690C211C522

C439C522

C346C710C346C481C346C696

C551C316C200C522

C230C346

C481C316C698C316C540C316C522C316C350C522C510C316

C437C522

C484C316C483C316C235C522C310C522C731C316C346C731

C220C346

C346C850

C220C522C346C694C210C522

C501C316C346C501C325C522

C20C346

C346C510C346C750C346C483

C2C346

C770C316C346C565C346C452

C346C551C390C316

C346C461

C160C316

C346C640

C346C670

C344C522C572C316C140C346C346C840C346C540C346C770C346C451

C346C572C346C541C230C522

C290C522C346C692C346C615C346C663

C346C475

C346C660

C346C484C346C620C346C616

C365C316

C350C316

C346C651

C346C702

C346C652

C346C600C346C701

C210C316

C325C316

C346C625C200C316

C290C316

C230C316

C346C698

C369C316

C20C316

C346C522C220C316

C2C316

C140C316

-.50

.51

e( s

imila

rvot

es |

X )

-4000 -2000 0 2000 4000e( totaltradeindegnvsqd | X )

coef = -.0000227, se = .00001462, t = -1.55

Dyads: Countries: C346C359 C140C316 C2C316

282

Figure 12: Partial-regression leverage plots for small sample analysis - Relative Alliance Degree Centrality 1990 Relative Alliance Degree Centrality

C20C522

C93C640C101C640

C93C696

C2C522

C101C696

C435C522

C375C740

C200C696C140C640

C220C696

C200C640

C41C640

C130C640

C91C640

C94C640

C404C696

C52C640C51C640

C93C522

C220C640

C140C696

C165C640C42C640

C150C696

C290C522

C436C696C432C696C439C696

C150C640

C155C640

C90C640

C90C696

C230C696

C41C696

C315C522

C438C696C91C696

C260C740

C350C640

C101C522

C42C696

C210C696C211C696

C325C696

C290C696

C70C696C200C740

C130C696

C95C640

C92C640

C433C696

C94C696

C135C640

C51C696

C315C696

C155C696

C165C696

C52C696

C93C740

C70C640

C360C522

C230C640

C210C640

C350C696

C420C696

C211C640

C420C640

C325C640

C101C740

C92C696

C420C740

C235C696

C310C522C385C696

C95C696C220C740

C135C696

C390C696

C145C640

C437C696

C200C522

C434C696

C220C522

C434C740

C140C522

C385C640

C150C522

C390C640

C433C640

C433C740

C235C640

C315C640

C360C696

C160C640

C404C640

C436C640C432C640

C90C522

C439C640

C404C740C436C740C432C740

C290C640

C439C740

C360C640

C41C522

C2C740

C91C522

C438C640

C70C522

C42C522

C438C740

C145C696

C230C740

C140C740

C310C696

C52C522

C94C522

C130C522

C165C522C437C740

C210C740

C51C522

C155C522C160C696

C325C740

C260C696

C211C740

C434C640

C355C522C41C740

C92C522

C230C522C94C740C210C522C211C522C51C740

C130C740

C91C740

C315C740

C165C740

C325C522

C95C522

C42C740

C135C522

C52C740

C290C740

C350C740

C155C740C150C740C90C740

C310C640

C404C522

C235C740

C437C640

C436C522

C385C740

C432C522C439C522

C390C740C260C640C438C522

C355C640

C385C522

C100C640

C350C522

C390C522

C95C740

C92C740

C135C740C235C522

C375C640

C70C740

C355C696

C433C522

C145C522C160C522

C435C640

C100C696

C420C522C310C740

C375C696

C360C740

C145C740

C20C696

C435C696

C437C522

C434C522

C160C740

C20C640

C260C522

C2C696

C20C740

C2C640

C100C522

C355C740

C100C740

C375C522

C435C740-.50

.5e(

sim

ilarv

otes

| X

)

-10 -5 0 5 10e( relallydegcent | X )

coef = -.0044344, se = .00642233, t = -.69

Dyads: Countries: C20C522 C435C740 C2C522

283

1991 Relative Alliance Degree Centrality

C2C522

C20C522

C41C522C41C696

C439C696C220C696

C438C696

C355C522

C220C640

C350C640C200C696

C211C696

C200C640C235C640

C325C696

C350C696

C211C640

C230C640

C70C696

C235C696

C325C640

C439C740C438C740C230C696

C51C640

C70C522

C220C740

C91C522

C52C640

C95C522

C91C640

C42C640

C200C740

C42C522

C91C696

C437C696

C42C696

C210C696

C310C522

C211C740

C52C696

C325C740

C210C640

C315C522

C51C522

C350C740

C51C696C90C522C90C696

C230C740C235C740

C52C522

C70C640C2C740

C437C740

C210C740

C290C522

C90C640C355C640

C41C640

C51C740

C52C740

C355C696

C315C640

C439C640

C91C740

C438C640

C42C740

C310C640C439C522

C315C696

C2C696

C220C522

C438C522

C290C640

C70C740

C310C696C350C522

C2C640

C290C696

C355C740

C90C740

C200C522

C235C522

C41C740

C211C522C230C522C325C522

C437C640

C437C522

C310C740C315C740

C210C522

C290C740

C20C696

C20C640C20C740

-.50

.5e(

sim

ilarv

otes

| X

)

-10 -5 0 5 10e( relallydegcent | X )

coef = -.0164216, se = .00918747, t = -1.79

Dyads: Countries: C2C522 C20 C20C696 C20C640 C20C740

284

1992 Relative Alliance Degree Centrality

C2C522

C20C522

C435C522

C434C640

C346C349

C41C522C41C696

C200C346

C436C640

C420C640C432C640

C135C522

C200C740

C349C740

C135C696

C160C522

C70C522C200C640

C346C740C346C365

C90C522

C110C522

C437C640

C91C522C110C696

C150C522

C90C696C93C522

C91C696

C437C696

C160C696

C42C522

C93C696

C70C696

C95C522

C42C696

C433C640

C92C522

C145C522

C150C696

C52C522

C439C640C404C640

C346C433

C145C696

C165C522

C51C522

C101C522

C92C696

C94C522

C155C522

C346C434

C100C522

C438C640

C346C438

C130C522

C95C696

C346C432

C344C740

C140C522

C404C696C51C696

C130C696

C94C696C346C439

C200C696

C100C696

C439C696C346C530

C52C696

C101C696

C155C696

C165C696

C140C696

C235C346

C434C696

C346C490C346C437

C438C696

C210C346

C346C385C346C390

C346C481

C346C420

C220C696

C346C540

C346C771

C346C900

C346C436

C346C516

C160C640

C365C740

C436C740

C420C740

C346C501

C145C640

C230C346C220C346C346C450

C346C732

C211C346

C325C346

C432C740

C433C696C346C404

C110C640

C346C840

C434C740

C52C640

C51C640

C91C640

C346C517

C150C640

C93C640

C2C696

C130C640

C165C640C94C640

C346C510

C95C640

C346C551C210C640

C100C640

C346C731

C235C740

C346C541C235C640

C92C640

C346C710

C42C640

C346C770

C101C640

C432C696C210C740

C385C640

C155C640

C436C696

C390C640

C420C696

C230C740

C350C740

C346C483

C140C640

C220C640

C346C750

C325C640

C90C640

C230C640C220C740

C346C484

C346C350

C211C640

C346C482

C325C740

C385C740

C235C696

C390C740

C344C346

C433C740

C211C740

C439C740C404C740C2C740

C135C640

C41C640

C349C640

C70C640

C350C696

C438C740

C210C696C230C696

C385C696

C437C740

C390C696

C350C640

C346C640

C325C696

C211C696

C346C452

C160C346

C344C640

C70C346C346C461

C135C346

C90C346

C365C640C91C346

C150C346

C346C475

C110C346

C93C346

C42C346

C346C451

C95C346

C92C346C145C346

C52C346

C41C346

C165C346

C51C346

C94C346

C101C346C2C346C346C663

C155C346C100C346

C130C346

C20C696

C346C660

C140C346C346C690

C145C740

C51C740

C160C740

C346C615

C94C740

C110C740

C346C616

C91C740

C165C740C150C740

C346C692

C200C522

C130C740

C93C740

C95C740

C344C696

C100C740

C2C640

C346C620C42C740

C92C740

C346C600

C52C740

C346C652

C437C522

C155C740

C101C740C220C522

C346C696

C140C740

C346C645

C346C651

C90C740

C346C625C346C694

C404C522C346C670

C70C740

C439C522

C365C696

C135C740

C41C740

C438C522C434C522

C433C522

C349C696

C210C522C235C522

C20C346

C432C522

C385C522C390C522

C436C522C420C522

C350C522

C230C522C325C522

C211C522

C435C696

C20C740

C20C640

C435C640

C346C435

C346C698

C344C522

C435C740

C346C522

C365C522

C349C522

-.50

.5e(

sim

ilarv

otes

| X

)

-15 -10 -5 0 5 10e( relallydegcent | X )

coef = -.01511989, se = .00474017, t = -3.19

Dyads: Countries: C2C522

285

1993 Relative Alliance Degree Centrality

C2C522

C20C522

C435C522

C200C346

C41C696

C200C740

C41C522

C434C640

C346C349C200C640

C420C640

C436C640

C346C365

C432C640

C135C696

C70C696

C70C522C90C696

C135C522

C349C740C110C696

C200C696

C91C696C437C696

C90C522C93C696C42C696

C346C433

C150C696

C92C696

C101C696

C110C522

C101C522

C145C696

C91C522

C42C522

C150C522

C93C522

C140C696

C51C696

C95C696

C92C522

C130C696C140C522

C437C640

C346C438

C95C522

C433C640

C52C522C404C696C94C696

C155C696

C346C434

C346C740

C145C522

C155C522

C51C522

C52C696

C160C522

C130C522

C100C696

C439C696

C165C522

C94C522

C165C696

C346C516

C346C530

C160C696

C439C640

C346C490

C438C696

C346C710

C100C522

C404C640C346C437

C344C740

C438C640

C346C481

C346C439

C235C346

C346C540

C211C346

C346C501

C346C390

C346C840

C434C696

C346C385

C346C900

C230C346

C220C696

C433C696

C346C432

C210C346

C346C517

C420C740

C346C732C346C771

C101C640

C365C740

C220C346

C436C740C432C740

C346C404C346C510C346C731C346C541C346C551

C325C346

C145C640

C346C436

C110C640

C346C770

C140C640

C51C640

C150C640C92C640

C346C420

C95C640

C434C740C91C640

C346C750

C93C640

C130C640

C52C640

C42C640

C346C483

C155C640

C235C740

C94C640

C344C346

C165C640

C346C482

C235C640

C346C484

C230C740

C100C640

C432C696C350C740

C211C640

C346C350

C390C640

C160C640

C385C640

C420C696

C230C640

C90C640

C436C696C211C740

C235C696

C210C640C220C640

C135C640

C210C740C220C740

C325C640

C2C740

C350C696

C390C740C385C740

C230C696

C325C740

C70C640

C433C740

C346C475

C346C452

C70C346

C211C696C2C696C439C740

C135C346

C390C696

C404C740

C385C696C20C696

C349C640

C438C740

C346C461

C210C696

C41C640C350C640

C90C346

C325C696

C437C740

C101C346C346C451

C91C346

C200C522

C42C346C150C346

C93C346

C344C640C365C640

C92C346

C140C346

C346C663C95C346

C346C640

C52C346C145C346

C155C346C51C346C346C660

C160C346

C130C346

C165C346

C94C346

C344C696

C100C346

C346C616C41C346C346C625

C110C346

C145C740C94C740

C346C690

C51C740

C101C740

C130C740

C437C522C346C620

C140C740C165C740

C346C600

C110C740

C150C740

C92C740

C95C740

C346C615

C91C740

C52C740

C346C645C100C740

C93C740

C42C740

C346C652C346C696

C365C696C155C740

C220C522

C346C651C346C694

C2C346

C90C740

C404C522C346C692

C160C740

C439C522

C135C740

C438C522

C346C670

C70C740

C20C346

C434C522

C433C522

C2C640

C41C740C435C696

C235C522

C349C696

C211C522

C20C740

C390C522

C385C522

C20C640

C230C522

C350C522

C432C522

C210C522

C420C522

C325C522

C436C522

C346C435

C435C640C344C522

C365C522

C346C522

C346C698

C435C740C349C522

-.50

.51

e( s

imila

rvot

es |

X )

-15 -10 -5 0 5 10e( relallydegcent | X )

coef = -.02041146, se = .00530747, t = -3.85

Dyads: Countries: C2C522

286

1994 Relative Alliance Degree Centrality

C2C522

C20C522

C435C522

C346C710

C434C640

C436C640C432C640

C346C516

C346C365

C135C522

C346C740

C135C696

C90C522

C70C522

C346C540

C437C696C90C696

C41C696

C41C640

C140C522

C346C750

C346C517

C70C696

C346C840

C41C522C433C640

C346C560

C346C481

C346C501

C160C522

C436C740

C140C696

C200C740C155C522

C346C551

C420C696C91C696

C91C522

C346C482

C220C696

C346C510

C437C640

C365C740

C432C740

C110C696

C346C541

C346C771C93C696

C346C770

C100C522

C101C522

C346C484C95C522

C93C522

C140C640

C92C522

C439C640

C200C640C346C732

C439C696C130C522

C110C522

C92C696

C434C740

C150C522

C346C900

C155C696

C145C640

C95C696

C110C640

C150C696

C155C640

C100C696

C130C696

C346C731

C160C696

C438C696

C93C640

C101C696

C52C522C51C522

C420C640

C346C433

C438C640

C95C640

C165C522

C51C696

C346C572

C150C640

C145C696

C94C522C220C640

C51C640

C92C640

C101C640

C145C522C130C640

C344C740C433C696

C230C740

C100C640

C91C640

C200C346

C160C640

C94C696C230C640

C52C640

C220C740

C52C696

C90C640

C94C640

C70C640

C165C696

C165C640

C346C452

C346C461

C220C346

C2C696

C350C740

C211C640

C346C437

C235C740

C325C640

C200C696

C210C640

C211C740

C385C640

C230C346

C210C740C325C740

C390C640

C235C640

C433C740

C365C640

C385C740

C346C438

C135C640

C434C696

C346C439

C211C346

C230C696

C346C434

C390C740

C325C346C210C346C2C740

C346C451

C439C740

C346C475

C41C740C346C385

C346C432

C346C420C346C390

C220C522

C235C346

C346C436

C346C640

C420C740

C432C696

C350C696C235C696

C438C740C211C696

C436C696

C325C696C346C660

C350C640

C210C696

C346C616

C346C663C346C350

C437C522

C437C740C145C740C385C696

C346C651

C390C696

C346C690C346C645

C51C740

C95C740

C94C740

C110C740

C365C696

C346C615

C93C740

C346C600

C20C696

C346C620C150C740C130C740C92C740

C140C740

C165C740

C200C522

C344C346

C91C740

C346C625

C52C740

C135C346C344C640

C100C740

C346C670

C90C346

C155C740

C90C740

C346C692

C346C652

C70C346

C2C640

C344C696

C101C740C346C696C420C522

C41C346

C140C346

C438C522

C346C694

C70C740

C230C522

C439C522

C433C522C160C740

C2C346

C211C522

C160C346

C155C346

C91C346

C325C522C210C522

C93C346

C95C346

C92C346

C101C346C110C346

C100C346

C150C346

C135C740

C385C522

C130C346

C390C522C235C522C350C522

C51C346C52C346

C145C346

C165C346

C94C346C434C522C435C696

C20C740

C20C640

C432C522

C365C522

C436C522C20C346

C435C640C344C522

C346C698C346C522

C346C435

C435C740-.4-.2

0.2

.4.6

e( s

imila

rvot

es |

X )

-15 -10 -5 0 5 10e( relallydegcent | X )

coef = -.01069394, se = .0038254, t = -2.8

Dyads: Countries: C2C522 C435C740

287

1995 Relative Alliance Degree Centrality

C2C522

C20C522C373C696

C370C696C371C696C434C640

C372C696

C435C522

C365C696C369C696C359C696

C436C640

C437C696

C432C640

C135C696

C404C640

C420C696

C135C522C359C640

C346C438C346C433

C90C696C439C696C70C522

C70C696

C90C522C110C696C91C696C200C346C41C696

C346C439

C92C696

C160C522

C150C696

C100C696

C346C510

C110C522C93C696

C346C481C346C434

C433C640

C346C385

C346C437

C91C522

C438C640

C433C696C346C390

C150C522

C100C522

C325C346C438C696C210C346

C437C640

C155C522

C346C540

C92C522

C200C740

C41C522

C230C346

C346C432

C160C696

C211C346

C145C696C140C696C155C696

C140C522C101C522C101C696C51C696C130C696C52C522

C346C740

C235C346C95C696

C346C436

C93C522C346C501

C220C346

C220C696

C369C640

C95C522

C346C732

C93C640

C346C710C52C696

C165C522C434C696C51C522C94C696

C155C640

C370C640

C346C483

C130C522C145C522

C346C404

C200C640

C372C640

C165C696

C41C640

C94C522

C140C640C101C640C145C640C439C640

C160C640

C52C640C51C640

C346C484C346C771

C92C640C130C640C150C640C100C640

C346C420

C95C640C110C640

C346C850

C91C640

C371C640

C346C541

C346C551

C346C900C165C640C346C770

C365C640C346C840C325C640

C346C370

C385C640

C94C640

C346C731C390C640C210C640

C420C640

C346C560

C404C696C230C640

C211C640

C230C740

C436C740

C235C740

C346C572

C346C350

C70C640

C235C640

C220C640

C210C740

C365C522

C325C740

C346C373

C350C740

C432C740

C344C740C432C696

C90C640

C200C696

C211C740

C346C750

C346C371

C2C740C434C740

C436C696

C220C740C235C696

C346C365

C385C740

C404C740

C346C452

C390C740

C230C696

C373C522

C350C696

C135C640

C346C372

C344C346C135C346C346C461

C370C522C325C696

C346C369

C210C696

C211C696

C385C696C390C696

C346C359C70C346

C90C346C350C640

C344C696C371C522

C346C475

C346C660

C359C740C346C451

C160C346

C372C522

C433C740C110C346C438C740C346C663

C91C346C346C616C150C346C100C346C346C615C155C346

C369C740

C92C346

C369C522C41C346

C140C346

C101C346

C372C740

C346C696C52C346C93C346

C373C640

C20C696

C346C690

C359C522

C95C346C165C346

C346C703

C51C346C130C346

C437C522

C145C346

C371C740

C2C696

C94C346

C439C740C346C620C437C740C346C600C346C651C95C740

C344C640

C420C740

C346C694C94C740

C346C702

C145C740

C420C522

C51C740

C346C625C165C740C130C740

C346C670C346C692

C220C522

C93C740

C140C740

C370C740

C346C705

C52C740C41C740C155C740C346C652

C346C701

C101C740

C439C522

C100C740C92C740C150C740C346C704C91C740

C110C740

C160C740

C365C740

C70C740

C20C346

C2C346

C373C740

C90C740

C438C522

C433C522

C435C696

C200C522C135C740

C434C522

C385C522C390C522C325C522C210C522

C230C522

C211C522

C2C640

C20C640

C235C522

C350C522

C20C740C404C522

C346C435

C432C522C436C522

C344C522

C435C640

C346C698C346C522

C435C740

-1-.5

0.5

e( s

imila

rvot

es |

X )

-10 -5 0 5 10e( relallydegcent | X )

coef = -.00554994, se = .00438722, t = -1.27

Dyads: Countries: C2C522 C435C740

288

1996 Relative Alliance Degree Centrality

C346C435 C435C740

C2C346

C370C696

C371C696C20C522

C373C696C372C696

C434C640C344C522

C359C696

C20C740

C20C346

C369C696

C346C522

C2C522

C346C698

C432C640

C346C481

C404C640

C346C501

C437C640

C346C840

C346C740

C346C731

C438C640

C346C551

C346C510C346C771

C344C740

C433C640

C346C540C346C483

C346C732

C346C572

C346C770

C346C560

C346C484

C439C640

C420C640

C346C541

C436C640C220C696

C365C696

C437C696

C346C850

C346C900

C359C640

C346C750C385C640C390C640C372C640C211C640C210C640C235C640

C420C696

C325C640C436C696C369C640

C230C640C200C640C439C696

C344C346

C350C640

C434C696

C135C522

C385C696

C135C696

C390C696

C438C696

C346C438

C135C346

C346C433

C433C696

C435C640

C210C696C211C696

C437C522

C325C696

C370C640

C110C522C91C522

C160C696

C385C740C70C522C70C696

C200C696

C390C740

C110C696C92C522

C235C696C230C696

C41C522

C160C522

C2C740

C150C522C155C696

C91C696

C371C522

C92C696

C150C696

C52C696

C155C522

C371C640

C110C346

C90C522

C93C522

C435C522

C346C434

C42C522

C91C346

C41C696

C70C346

C101C522

C52C522C101C696

C100C522

C346C385

C145C522C404C696

C92C346

C346C437C41C346C346C390

C90C696

C100C696

C42C696

C51C522

C160C346

C211C740C95C522

C370C522

C150C346C93C696

C346C439

C130C522

C346C450

C95C696C325C740C94C522C145C696C210C740

C165C696C155C346

C51C696

C93C346

C90C346C42C346C211C346

C165C522

C101C346

C94C696

C52C346C210C346

C130C696

C100C346

C325C346C145C346

C350C740

C235C346

C235C740

C432C696

C51C346

C95C346C230C346

C155C640

C230C740

C130C346

C140C522C140C696

C200C346

C101C640

C52C640

C94C346

C346C436C346C432

C90C640

C365C640

C42C640

C165C346C346C710

C160C640

C420C522

C93C640

C436C522

C92C640C95C640C150C640C145C640C51C640C52C740

C165C740

C41C640

C200C740

C140C346

C94C640C130C640

C439C522

C165C640C110C640

C94C740C100C640C346C420

C220C640

C91C640

C346C404

C140C640

C155C740

C145C740C51C740

C160C740

C434C522

C130C740C101C740

C346C373

C90C740

C42C740C220C346

C365C522C95C740

C93C740

C385C522

C92C740

C373C522

C150C740

C390C522

C438C522

C373C640

C70C640

C41C740

C432C740

C110C740

C433C522

C372C522

C100C740C211C522C350C522C210C522C434C740

C220C740

C91C740

C325C522

C135C640

C235C522

C346C350

C404C740

C230C522

C140C740

C346C452C346C461C200C522

C2C696C359C522C20C696

C346C451

C70C740

C220C522

C346C640

C346C372

C404C522

C135C740

C369C522

C346C365

C346C371

C346C475C346C660

C438C740C344C696

C359C740

C344C640

C346C359

C432C522

C346C370

C433C740

C372C740

C346C616

C369C740

C346C615

C346C703

C346C369

C439C740

C346C663

C420C740C436C740

C346C690C346C620

C437C740

C365C740

C346C702

C346C600C346C692C346C696C346C625

C2C640

C346C694C346C651

C346C704

C346C652C346C670

C371C740

C346C705C373C740

C370C740

C20C640

C435C696

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-6 -4 -2 0 2e( relallydegcent | X )

coef = .03023822, se = .01466833, t = 2.06

Dyads: Countries: C346C435 C2C346 C435C740

289

1997 Relative Alliance Degree Centrality

C2C522

C20C522

C200C640

C200C740

C625C316

C439C696

C404C316

C438C640

C220C696

C200C696

C437C696

C359C316

C452C316C434C316

C200C316

C432C316

C438C696

C451C316

C439C640

C660C316

C372C316

C615C316

C616C316

C450C316C461C316

C325C640

C70C522

C95C522

C900C316

C91C522

C369C316

C600C316

C210C640

C663C316C620C316

C211C640

C42C522

C437C640

C703C316

C90C522

C52C522

C91C696

C475C316

C438C316

C220C640

C95C640C92C640

C51C522

C100C640

C41C522

C433C316

C705C316

C91C640

C702C316

C95C696

C365C316

C652C316C390C316

C90C640

C100C696

C325C696

C42C640

C41C640

C230C640

C325C740

C90C696

C651C316

C42C696

C41C696

C325C316

C70C696

C310C696

C439C316

C51C640

C290C696

C710C316

C704C316

C690C316

C70C640

C235C640

C51C696

C350C696

C385C316

C701C316

C696C316C235C696

C640C316

C52C640C95C316

C436C316

C371C316

C230C696

C350C640

C692C316

C310C740

C750C316C51C316

C230C740

C93C316

C210C740

C694C316

C94C316

C52C316

C145C316C150C316

C211C696

C350C740

C210C696

C210C316

C52C696C235C740

C373C316

C211C316

C740C316

C310C640

C211C740

C110C316

C41C316

C220C740

C42C316

C90C316C165C316

C290C640

C290C740

C92C316C91C316

C130C316

C437C316

C310C316

C2C740

C230C316

C101C316

C220C316

C155C316

C670C316

C235C316

C100C316

C370C316

C438C740

C290C316C560C316

C70C316

C135C316

C541C316

C350C316

C840C316

C439C740

C140C316

C2C696

C437C740C770C316

C732C316

C220C522

C160C316C200C522C551C316

C771C316

C540C316

C510C316C483C316

C20C696

C2C640

C850C316

C52C740

C484C316

C51C740

C90C740

C42C740

C325C522

C100C740

C439C522

C91C740

C41C740

C501C316

C70C740

C210C522

C731C316

C211C522

C437C522

C438C522C572C316

C2C316C230C522

C235C522C522C316

C350C522

C481C316

C20C640

C698C316

C310C522

C290C522

C20C740

C20C316C435C316

-.4-.2

0.2

.4.6

e( s

imila

rvot

es |

X )

-10 -5 0 5e( relallydegcent | X )

coef = -.01944469, se = .00696394, t = -2.79

Dyads: Countries: C2C522 C522 C20C522 C437C522 C20C740

290

1998 Relative Alliance Degree Centrality

C435C740

C20C740

C370C696

C20C522

C373C696C372C696

C371C696

C434C640

C359C696

C369C696C432C316C434C316

C220C696

C452C316

C2C522

C359C640

C451C316

C432C640

C404C316C450C316C475C316

C437C640

C461C316

C438C316C433C316C200C640

C615C316

C359C316

C616C316

C439C316C420C316

C404C640

C369C316

C371C316

C372C316C436C316

C200C696C438C640

C369C640

C433C640

C437C316

C437C522

C371C640

C372C640

C435C316C600C316C620C316

C437C696

C660C316

C365C696

C290C640

C310C640

C350C316

C439C640

C420C640

C235C640

C663C316C385C640

C230C640

C436C640

C211C640C200C316

C370C640C390C640

C435C640

C210C640C325C640

C385C696

C651C316

C325C696

C670C316

C211C696C390C696C235C696C230C696C696C316

C210C696C625C316

C2C696

C290C696C310C696

C692C316C350C696

C436C522

C690C316C373C316C652C316C701C316

C370C316

C165C740

C420C522

C434C522

C704C316

C694C316C94C740C703C316

C350C640C436C696C705C316

C51C740

C95C740

C145C740C101C740

C439C522

C420C696

C434C696

C370C522

C235C316

C135C696

C130C740

C200C522C42C740C385C316

C310C316C230C316

C150C740

C90C740C325C316C211C316

C150C640

C155C740C93C740

C41C740

C2C640

C41C640

C92C740

C92C640C101C640

C439C696

C110C740

C100C640C110C640

C390C316C70C640

C135C522

C438C522

C210C316

C91C740

C160C740

C42C640

C100C740

C91C640

C433C522

C404C522

C90C640C160C640

C150C696

C2C740

C155C640C93C640

C365C316

C145C640C51C640C95C640

C52C316

C70C696

C130C640

C438C696C110C696

C165C316C52C640

C433C696

C140C640

C91C696C94C640

C94C316

C140C740

C365C640

C165C640

C150C522

C70C740

C41C696

C145C316C51C316

C92C696C101C696

C160C696C95C316

C101C316

C100C696

C110C522

C130C316C91C522

C155C696

C42C696

C155C316C42C316

C90C696

C90C316

C150C316

C41C522

C70C522

C93C696

C93C316C41C316

C145C696C51C696

C95C696

C92C316

C435C522

C92C522

C160C316

C52C696

C220C640

C560C316

C130C696

C135C640

C135C740

C70C316

C110C316

C165C696

C100C316C290C522C371C522

C101C522C100C522

C310C522

C140C696

C94C696

C91C316

C140C316C42C522C90C522C93C522

C372C522

C385C740

C522C316

C710C316

C145C522

C432C522

C235C522C325C522

C51C522

C740C316

C2C316

C160C522C230C522C211C522

C385C522

C390C740

C698C316

C155C522

C130C522

C350C522

C373C522

C95C522

C235C740C732C316

C359C522

C310C740C290C316

C373C640

C94C522

C200C740

C211C740

C432C740

C432C696

C52C522

C350C740

C365C522

C434C740

C140C522

C390C522C210C522

C325C740

C165C522

C482C316C369C522

C750C316

C230C740C290C740

C770C316

C541C316C483C316C135C316C840C316

C359C740

C220C522

C210C740

C20C696

C369C740C372C740

C371C740

C404C740

C900C316

C551C316

C438C740C433C740

C220C316

C510C316C850C316

C439C740C420C740C540C316

C484C316

C436C740

C365C740

C437C740

C481C316

C20C640

C435C696

C370C740

C572C316C731C316

C20C316

-1-.5

0.5

e( s

imila

rvot

es |

X )

-6 -4 -2 0 2e( relallydegcent | X )

coef = .06661832, se = .0160382, t = 4.15

Dyads: Countries: C485C740 C522 C20C740 C20C522 C2C522 C435C522

291

1999 Relative Alliance Degree Centrality

C2C522

C20C522

C200C640C359C316

C371C316

C372C316

C211C640C52C522C70C522

C41C522C101C522

C91C522C42C522

C90C522

C344C740

C439C696

C51C522

C640C316C310C640C235C640

C346C740

C200C696C438C696

C346C438

C210C640C325C640

C432C316

C437C696

C373C316

C290C640

C434C316

C346C439C346C433

C200C316

C310C316

C230C640

C346C900

C346C432

C346C434

C310C696

C663C316

C660C316C346C437

C404C316

C615C316

C220C640

C41C696

C211C316

C385C316

C696C316C690C316

C438C316

C91C696

C438C640

C616C316

C346C481

C290C696C620C316

C145C316

C150C316

C439C316

C91C316

C90C696

C350C696

C346C732

C600C316

C235C316

C702C316

C42C696

C346C501

C93C316

C235C696C651C316

C439C640

C625C316

C437C640

C344C346

C433C316

C51C696

C200C740

C51C316

C70C696

C110C316C91C640

C694C316

C346C731

C211C696

C346C404C346C510

C94C316C92C316

C652C316

C52C316

C92C640C90C316

C390C316

C42C316

C52C696

C437C740

C346C483C370C316

C52C640

C350C316

C90C640C475C316

C438C740

C705C316

C42C640

C704C316

C437C316

C346C372

C41C316

C95C316

C701C316C51C640

C346C840C692C316C346C770

C165C316

C451C316

C70C640

C346C371

C452C316

C130C316

C670C316C346C551C346C565

C439C740

C461C316

C900C316

C220C696C346C541C346C572C346C850C346C540

C230C696C346C750

C369C316

C346C359

C41C640C210C316

C101C316

C210C696

C2C740

C346C710C325C696

C346C373

C100C316

C365C316

C155C316

C325C316

C346C484

C230C316

C70C316

C200C346

C346C451

C346C461

C310C740

C346C370

C350C640

C135C316

C740C316C346C452

C346C475

C350C740C235C740C290C740

C211C740

C220C316

C710C316

C200C522

C344C696

C346C385C211C346

C346C696

C160C316

C346C690

C551C316

C732C316

C346C316

C310C346

C850C316C541C316

C346C350

C210C740

C750C316

C565C316

C344C640

C230C740

C560C316

C235C346

C346C390

C484C316C540C316

C346C694C346C660

C344C316

C325C740

C41C346

C346C663

C346C365

C840C316

C346C615C290C316

C346C616

C346C640

C346C620

C220C522

C346C692

C510C316

C150C346

C290C346

C51C740

C483C316

C110C346

C91C346

C91C740

C210C346

C52C740

C90C740

C346C670

C346C369

C346C705

C42C740

C52C346

C92C346

C92C740C211C522

C346C651

C346C600C220C740

C325C346

C93C346

C135C346

C42C346C145C346

C70C740

C230C346

C346C702C51C346

C90C346

C346C625C165C346

C94C346

C95C346

C346C652C70C346

C522C316

C101C346

C501C316

C346C701

C310C522

C2C640

C770C316

C155C346

C235C522C731C316

C350C522

C140C316

C130C346

C100C346

C41C740

C220C346

C20C640

C481C316

C210C522

C572C316C290C522

C325C522

C438C522

C230C522

C439C522

C437C522

C160C346

C698C316

C2C696C20C696

C20C316

C2C316

C140C346C344C522

C346C698

C346C522

C20C740

C20C346

C2C346

-.4-.2

0.2

.4.6

e( s

imila

rvot

es |

X )

-10 -5 0 5 10e( relallydegcent | X )

coef = -.01210235, se = .00555311, t = -2.18

Dyads: Countries: C20C740 C522 C20C522 C2C522 C20C346

292

2000 Relative Alliance Degree Centrality

C2C346

C346C435

C435C740

C20C740

C346C522

C20C346

C344C522

C20C522

C373C696

C370C696

C344C740

C346C501C346C481

C346C740C371C696

C346C840C372C696C359C696

C359C640

C346C551

C359C316

C346C483

C346C770

C369C696

C346C540C346C560C433C316

C369C316

C371C316C220C696C434C640

C372C316

C432C316

C346C750C346C572C432C640C434C316

C615C316

C346C850C433C640C346C565

C616C316

C437C640C200C696C600C316C346C732

C437C316

C620C316C346C712

C200C316C452C316C475C316

C346C900

C2C522

C438C316

C290C696C438C640C310C696

C451C316C346C731

C660C316

C690C316C371C640C696C316

C369C640C372C640

C200C640

C439C316C235C696C439C640C350C696C461C316C692C316

C230C696

C663C316

C694C316C373C316

C670C316C370C640

C420C316

C325C696C420C640

C385C696

C651C316

C390C696

C625C316

C420C696

C210C696C370C316

C211C696

C652C316

C310C316

C350C316

C390C316C230C316C210C316

C346C484

C385C316

C439C696

C325C316

C235C316C702C316

C704C316

C211C316

C701C316

C438C522

C437C522C434C522

C438C696

C432C522

C703C316

C310C640C640C316

C290C640

C385C640

C235C640

C390C640C210C640

C230C640

C211C640

C325C640

C439C522C433C522

C705C316

C420C522

C437C696C434C696

C373C640

C135C522

C346C698

C432C696

C433C740

C200C522C145C740

C101C522

C2C740

C432C740

C145C316

C130C522

C94C740

C41C696C41C522

C110C522

C94C316

C51C740C434C740

C346C710

C51C316

C93C740

C92C522C91C522

C101C640

C165C740

C95C740

C100C522

C346C451

C150C522

C93C316

C433C696

C95C316

C90C740C165C316

C91C640

C52C740C101C740

C90C316C101C316C42C740C91C740C52C316

C93C522

C346C438

C42C522

C90C522

C70C522

C290C316

C92C640C42C316

C145C522

C160C522

C91C316

C93C640

C365C522

C155C740C150C640

C92C740

C346C452

C100C640

C155C316

C135C696

C145C640C140C316C150C740

C130C640C135C346

C95C522

C92C316

C51C522

C90C640

C155C522

C140C522

C100C740

C110C640

C70C316C100C316C346C461C130C740

C42C640

C150C316C110C740C130C316

C52C522

C165C522

C94C522

C51C640C70C640

C110C316

C160C316

C350C640

C160C640

C160C740

C710C316

C437C740

C346C439C140C740

C95C640C155C640

C435C316

C140C640

C900C316

C70C740

C365C316

C435C640

C94C640

C712C316

C52C640C165C640

C41C346

C346C437

C372C522

C346C432C346C475

C371C522

C346C420

C310C522

C438C740

C346C434

C522C316

C290C522

C385C522C235C522C390C522C210C522C850C316C325C522C230C522C211C522C350C522

C101C346

C130C696

C220C316

C101C696

C110C696

C359C522

C91C696

C346C433C130C346C560C316C135C640C750C316

C439C740

C92C696C310C740C93C696

C135C740C100C346

C150C696

C100C696

C135C316

C740C316C145C696C220C640C90C696

C220C522

C110C346

C369C522

C435C522

C160C346C92C346

C200C740

C565C316

C91C346C41C640

C42C696

C140C346

C200C346

C70C346C41C740

C290C740C51C696

C90C346C42C346

C235C740

C150C346

C93C346

C41C316C420C740

C350C740

C155C346

C346C365

C70C696C95C696

C344C640

C145C346

C732C316

C365C696

C95C346

C140C696

C51C346

C359C740

C160C696

C840C316

C370C522

C94C696

C165C346C94C346

C155C696

C52C346

C230C740C390C740C165C696C385C740

C373C522C484C316

C2C696

C52C696

C551C316

C344C316

C365C640

C371C740

C369C740

C325C740

C372C740

C365C740C346C615

C211C740

C346C702

C346C660

C344C346

C346C616

C483C316

C210C740

C346C703

C540C316

C346C640

C346C663

C346C385C346C390

C290C346

C210C346C235C346C346C316C310C346C230C346C325C346

C220C740

C346C600

C346C620

C211C346

C2C316

C346C651C346C705

C346C372

C220C346

C346C373

C346C690

C346C371

C346C625

C346C696

C346C652

C346C350

C346C370

C731C316C501C316

C346C359

C346C369

C346C692

C346C694

C346C701C346C704

C346C670C2C640

C770C316

C481C316

C20C696

C373C740

C698C316

C572C316

C370C740C344C696C20C316

C435C696

C20C640

-.50

.51

e( s

imila

rvot

es |

X )

-6 -4 -2 0 2e( relallydegcent | X )

coef = .02414604, se = .01219971, t = 1.98

Dyads: Countries: C2C346 C20C522 C2C522

293

Figure 13: Partial-regression leverage plots for small sample analysis - Relative Alliance Degree Centrality Squared 1996 Relative Alliance Degree Centrality Squared

C435C696C346C670C20C640

C373C740

C371C740

C346C705

C370C740

C346C696C346C694C346C651C346C652

C346C369

C346C690C346C600C346C692

C346C704

C346C663C346C620

C437C740

C346C625

C436C740C420C740C439C740

C369C740

C346C370

C346C615

C346C702

C346C359

C346C371

C346C616

C346C372

C365C740C359C740

C433C740

C372C740C346C703

C438C740

C346C365

C344C696

C346C475C346C660

C346C350C200C740

C200C346C220C346

C2C640

C404C740

C346C640

C434C740

C346C451

C432C740

C220C740

C344C640

C369C522

C346C461C346C452C135C640

C346C373

C365C522

C70C640

C435C522

C346C404

C91C640C140C640

C346C420

C325C346

C359C522C373C522

C41C640C110C640

C211C346

C100C640

C210C346

C20C696

C230C346

C165C640C346C385

C145C640

C140C522

C93C640C130C640

C346C432

C235C346

C346C436C51C640

C372C522

C150C640

C220C522

C94C640C346C390

C220C640

C92C640C160C640C42C640C90C640

C165C522C140C696

C95C640C346C439C346C450C52C640

C2C696

C101C640

C145C696C93C696

C373C640

C155C640

C130C696

C346C437C70C740

C94C522C51C696

C346C434

C94C696C100C522

C130C522C95C522C41C696C52C522

C42C696

C51C522

C100C696

C90C696C365C640

C101C522C230C740

C155C522

C95C696

C160C522

C70C522

C91C696C145C522

C135C740C150C522

C432C696C42C522C90C522C432C522C93C522

C165C696

C235C740

C150C696C346C710

C350C740

C92C696C110C696

C140C740

C200C522

C101C696C92C522C41C522C70C696C210C740C404C696C91C522C325C740C404C522C110C522

C346C433C95C740C91C740

C52C696

C155C696

C100C740C346C438

C370C522

C41C740

C211C740

C110C740

C160C696

C93C740C150C740C92C740

C371C522

C135C696

C90C740

C42C740

C200C696

C135C522

C145C740C130C740C51C740

C140C346

C433C696

C433C522

C101C740

C438C522

C165C346

C438C696C385C740

C94C740

C160C740

C155C740

C325C522C2C740

C390C740

C211C522C210C522

C371C640

C230C522

C94C346

C439C696

C385C522

C439C522

C235C522

C165C740

C235C696

C100C346

C130C346

C95C346

C52C346

C390C522

C51C346C101C346

C350C522

C52C740

C155C346

C160C346

C70C346

C436C696C145C346

C436C522

C230C696

C150C346

C42C346C420C696C90C346

C434C696

C420C522

C93C346

C434C522

C92C346

C370C640C41C346

C91C346

C110C346

C200C640

C325C696C211C696C210C696

C369C640

C135C346

C344C346

C385C696C390C696

C372C640C325C640

C437C696C437C522

C350C640

C359C640

C210C640C211C640C230C640

C385C640

C235C640

C390C640

C365C696

C346C900

C346C750C346C850

C2C522

C220C696

C346C541

C436C640C420C640C439C640

C346C732

C346C484

C346C560

C346C483C346C540C346C510

C433C640

C346C551

C346C770

C346C572

C435C640

C438C640

C346C771

C346C740

C404C640

C344C740

C437C640

C432C640

C346C731C346C840C346C501

C369C696

C346C481

C373C696

C359C696

C20C522

C434C640

C372C696C371C696

C370C696

C20C346

C20C740

C346C698C346C522

C344C522

C2C346

C435C740 C346C435

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-20 0 20 40 60 80e( relallydegcentsqd | X )

coef = -.00400388, se = .00107484, t = -3.73

Dyads: Countries: Several C346

294

1997 Relative Alliance Degree Centrality Squared

C200C740

C20C316

C731C316C572C316

C220C740

C220C316C484C316

C20C640

C850C316

C540C316

C52C522

C95C522

C210C740

C437C740C481C316

C501C316C483C316

C900C316

C510C316

C51C522

C220C640

C90C522

C42C522C325C740

C70C522

C551C316C135C316

C230C740

C220C522

C439C740

C211C740

C91C522

C20C696

C41C522

C95C696

C52C696

C438C740

C840C316C110C316

C100C696

C91C316

C51C696

C350C740C90C696

C541C316

C42C696C235C740

C150C316

C290C740

C770C316C92C316

C771C316

C310C740

C41C316C93C316

C70C696

C740C316

C732C316

C100C316

C42C316

C90C316C70C316

C130C316

C91C696

C95C316

C51C316

C101C316C155C316

C560C316

C145C316

C2C522

C710C316

C95C640

C52C640

C41C696

C52C316

C94C316

C140C316

C165C316C750C316C100C640

C51C640

C70C640

C90C640

C42C640

C91C640

C160C316

C438C696

C200C522

C92C640

C290C316

C200C316

C698C316

C702C316

C41C640

C210C522C325C522

C439C696

C211C522

C2C740

C230C522

C705C316

C365C316

C390C316

C703C316

C210C316

C385C316

C325C316

C235C522C350C522C211C316

C522C316

C230C316

C200C696

C200C640

C625C316

C310C316

C310C522

C235C316

C694C316

C704C316

C701C316

C290C522

C350C640

C692C316

C652C316

C325C640

C230C696

C2C316

C670C316C325C696C210C696

C210C640

C211C696

C350C696C235C696

C211C640

C230C640

C696C316C310C696

C371C316

C2C696C690C316

C2C640

C290C696

C373C316

C370C316

C235C640

C651C316

C640C316

C310C640

C437C696

C290C640

C660C316

C663C316

C70C740

C439C640

C600C316

C350C316

C620C316

C475C316

C438C640

C436C316

C100C740

C372C316

C437C316

C615C316

C616C316

C439C316

C438C522

C461C316

C359C316

C91C740

C90C740

C42C740C451C316C450C316C438C316

C437C640

C439C522

C433C316

C51C740

C369C316

C41C740

C452C316

C52C740

C20C522

C404C316

C220C696

C434C316C432C316

C437C522

C435C316

C20C740

-.4-.2

0.2

.4.6

e( s

imila

rvot

es |

X )

-20 0 20 40 60e( relallydegcentsqd | X )

coef = -.00515864, se = .00182426, t = -2.83

Dyads: Countries: C20C740

295

1998 Relative Alliance Degree Centrality Squared

C20C316

C731C316

C370C740

C572C316C437C740

C436C740

C439C740C420C740

C200C740

C900C316

C481C316C220C316

C435C696

C484C316C850C316

C540C316C433C740

C510C316

C438C740C20C640

C365C740

C435C522

C404C740

C210C740

C140C522

C551C316

C432C696

C371C740

C369C740C372C740

C165C522

C290C740

C359C740

C140C696

C52C522C230C740

C160C522C434C740

C155C522

C94C522

C432C740

C135C316

C325C740

C483C316

C350C740C95C522

C840C316

C211C740

C310C740

C541C316

C130C522

C770C316C365C522

C220C522

C101C522

C373C640

C750C316

C51C522

C94C696

C482C316

C100C522

C130C696

C93C696

C70C522

C93C522C42C522C90C522C145C522C235C740C433C696

C145C696

C51C696

C95C696

C220C640

C140C316

C90C696

C165C696C92C522

C740C316

C438C696

C52C696

C20C696

C42C696

C91C316

C390C740C100C696

C373C522

C101C696

C110C316

C92C696C385C740

C41C522

C41C696

C155C696

C439C696

C93C316

C135C640

C41C316

C91C696

C732C316

C91C522

C110C522

C150C522

C70C316

C140C640

C290C316

C150C316

C70C696

C160C696

C92C316

C710C316

C560C316

C100C316

C110C696

C90C316

C42C316C145C316C369C522

C150C696

C420C696

C130C316

C51C316

C165C640

C101C316

C436C696C160C316

C95C316

C135C522

C155C316

C365C640

C94C640

C130C640C93C640C52C640C94C316

C160C640C145C640C51C640C155C640

C359C522

C95C640

C135C696

C70C640

C91C640

C90C640C52C316

C42C640

C165C316

C100C640C110C640

C372C522

C101C640C92C640C41C640

C210C522C390C522

C2C316

C365C316

C150C640

C371C522C2C740

C434C696

C210C316

C385C522

C211C522

C390C316

C325C522

C350C522

C211C316

C385C316

C325C316

C230C522

C690C316

C703C316

C705C316

C140C740

C235C522C522C316

C310C316C230C316

C135C740

C698C316

C70C740

C652C316

C432C522

C290C522

C310C522C694C316C290C696C350C696

C235C316

C370C522C704C316

C200C522

C310C696

C701C316

C350C640C200C316

C625C316

C2C522

C373C316C2C640

C696C316C235C696C230C696

C210C696

C670C316

C325C640C651C316C370C316

C2C696

C692C316C100C740

C211C696C390C696

C91C740

C325C696

C210C640

C93C740

C160C740

C437C696

C110C740

C433C522

C92C740

C385C696

C404C522

C365C696

C390C640

C41C740

C438C522

C90C740

C130C740

C150C740

C155C740

C436C640

C42C740C101C740C663C316C145C740

C211C640

C439C640C385C640C51C740

C95C740

C439C522

C420C640C200C696

C370C640

C94C740

C230C640

C660C316C165C740

C420C522

C433C640

C235C640

C436C522

C200C640

C438C640

C371C640

C369C640

C290C640

C372C640

C310C640

C404C640

C620C316C600C316

C371C316

C372C316

C350C316

C359C316

C369C316

C434C522

C436C316C615C316

C616C316

C437C316C439C316

C420C316

C432C640

C461C316C475C316

C359C640

C450C316

C433C316C437C640C438C316

C404C316C451C316

C452C316

C435C640

C220C696

C437C522

C373C696

C432C316

C434C316

C369C696

C359C696C371C696

C372C696

C434C640

C20C522

C435C316

C370C696

C20C740

C435C740

-.50

.5e(

sim

ilarv

otes

| X

)

-50 0 50 100e( relallydegcentsqd | X )

coef = -.00671143, se = .00135149, t = -4.97

Dyads: Countries: C20C740 C522 C435C740 C740 C20C522 C435C522

296

2000 Relative Alliance Degree Centrality Squared

C435C696

C346C670

C20C640C346C690

C373C740

C370C740

C346C692

C20C316

C770C316C346C663

C346C371

C200C740

C698C316C346C694

C220C740

C346C359

C346C652

C344C696

C346C660C572C316

C346C701

C346C696

C346C704

C346C705

C220C346

C346C372

C346C640

C346C350

C346C625C2C640

C346C373

C346C369

C346C620

C200C346

C346C651

C346C600

C346C370

C481C316

C211C346

C731C316

C365C640

C220C640

C501C316

C41C640

C371C740

C290C346

C346C702

C346C616

C369C740C160C696

C365C696

C346C615

C52C696C150C696C145C696C94C696C210C740

C346C385C372C740

C165C696

C346C390

C346C703

C325C346C210C346

C51C696

C310C346C346C316C235C346C230C346

C93C696C140C696

C540C316

C110C696C95C696C211C740

C155C696

C42C696C91C696C90C696

C420C740

C483C316C484C316

C92C696C70C696

C359C740C365C316

C220C316

C2C316

C325C740

C135C316C135C640

C290C740C100C696

C41C316

C20C696

C101C696C130C696C350C740

C220C522

C230C740

C551C316

C439C740

C346C420C346C475C160C316

C110C640C160C640

C346C432

C373C522

C52C640

C150C640C145C640

C344C316

C94C640C165C640

C385C740C390C740

C51C640

C438C740

C235C740C310C740

C93C640

C140C640

C437C740C95C640

C344C640

C155C640C750C316C840C316C346C437

C732C316

C740C316

C42C640C91C640C90C640

C2C696

C435C522

C346C433C346C434C710C316

C130C640C70C640C92C640

C140C316

C370C522

C369C522

C41C696

C100C316C565C316C130C316

C346C439

C100C640

C135C696C150C346

C101C640C160C522C70C316C101C316

C110C316

C52C522

C365C522

C850C316

C560C316

C150C522

C110C346

C165C522

C94C522

C145C522

C145C346

C92C316

C155C316

C365C740

C91C316C150C316

C51C522

C346C438

C359C522

C90C316

C42C316

C140C522

C41C740

C433C696

C371C522

C93C522C432C696

C110C522

C95C522

C346C451

C95C316

C155C522

C93C316

C52C346

C93C346

C437C696

C346C710

C432C740

C41C522C42C522

C712C316C51C346

C900C316

C346C461

C90C522

C51C316

C91C522

C372C522

C165C316

C200C522

C94C346

C70C522

C346C452

C92C522

C94C316

C91C346C165C346

C52C316

C145C316

C434C740

C344C346C100C522

C92C346

C42C346

C95C346

C290C316C90C346

C101C522

C350C640

C346C365

C130C522C434C696

C211C522

C41C346

C155C346

C135C740C350C522C160C346

C373C640C433C740

C70C346

C135C522

C290C522C2C740C210C522C325C522

C705C316

C230C522

C390C522

C130C346

C385C522

C101C346

C100C346

C200C640

C110C740C235C522

C438C696

C150C740

C70C740

C522C316C310C522

C140C346

C2C522

C140C740C160C740C439C696

C435C640C702C316

C91C740

C420C696

C130C740C92C740

C703C316

C211C640C663C316

C93C740

C100C740C42C740

C420C640C90C740C145C740

C101C740

C325C640

C51C740

C652C316

C210C640

C290C640

C211C316

C155C740

C95C740

C230C640

C660C316

C701C316C704C316C135C346

C390C640

C52C740

C94C740

C385C640C200C316

C165C740

C235C640

C439C640C370C316C625C316

C640C316

C350C316

C310C640

C310C316

C235C316

C370C640C651C316

C435C316

C670C316C385C316C200C696C390C316

C346C484C371C640C438C640

C369C640

C373C316

C210C316C325C316

C372C640

C230C316C690C316C451C316

C475C316

C461C316

C211C696

C420C316

C692C316C437C640

C350C696

C437C316

C290C696

C439C316C694C316C325C696C210C696C230C696

C390C696C385C696

C369C316

C346C698C235C696C696C316C432C640

C420C522

C310C696

C452C316

C438C316C433C640

C620C316

C600C316

C346C900

C439C522

C434C640

C346C731

C433C522C432C522

C437C522C359C640

C369C696

C616C316C220C696

C438C522

C615C316

C372C316C346C732

C371C316

C434C316C432C316

C434C522

C433C316

C359C316

C346C712C359C696

C371C696

C372C696

C346C850C346C565

C346C770C346C750C346C572C346C540C346C560C346C483C346C551

C346C740

C373C696

C346C840

C370C696

C346C481C346C501

C20C522

C344C740 C346C522

C344C522

C20C740

C20C346

C435C740

C346C435

C2C346

-.50

.51

e( s

imila

rvot

es |

X )

-20 0 20 40 60 80e( relallydegcentsqd | X )

coef = -.00184993, se = .00093565, t = -1.98

Dyads: Countries: C2C522 C346 C20C522 Others

Figure 14: Partial-regression leverage plots for small sample analysis - Total Alliance Degree Centrality

297

1990 Total Alliance Degree Centrality

C375C740

C435C522

C260C740

C437C740

C20C522

C433C740C438C740C439C740

C100C522

C432C740C434C740C436C740

C100C740

C404C740

C2C522

C420C740

C355C522

C100C640

C375C696

C375C640

C235C740C160C522

C100C696

C160C740

C390C740

C360C522

C350C740

C385C740

C145C522

C310C522

C260C640

C437C640

C145C740

C325C740

C210C740C211C740

C230C740

C435C640

C260C696

C434C640

C355C640

C70C740

C235C522

C355C740

C433C640

C20C640

C135C522

C438C640C160C640

C437C696C70C522

C350C522

C235C640

C135C740

C439C640

C95C522

C432C640C436C640

C355C696

C92C522

C155C522

C95C740

C390C522

C404C640

C145C640

C155C740

C92C740

C385C522

C435C696

C130C522C51C522

C160C696

C315C522

C420C640

C434C696

C42C522C165C522

C94C522

C390C640

C52C522

C220C740

C52C740

C91C522

C130C740

C90C522

C290C522

C310C640C385C640

C51C740

C433C696

C165C740

C42C740

C90C740

C325C522

C20C696

C94C740

C41C522

C91C740C230C522

C145C696

C438C696

C235C696

C437C522

C211C522

C150C740

C210C522

C140C522

C325C640

C150C522

C140C740

C41C740

C420C696

C390C696

C200C740

C260C522

C70C640

C350C640C439C696C432C696

C230C640

C436C696

C350C696

C2C640

C385C696

C360C640

C211C640C210C640

C135C640

C404C696

C360C740

C95C640

C310C740

C434C522

C92C640

C155C640C325C696

C433C522

C310C696

C211C696C210C696

C70C696

C360C696

C51C640C130C640

C135C696

C42C640

C90C640

C165C640

C438C522

C52C640C230C696C94C640C2C740

C315C640

C2C696C420C522

C290C640

C91C640

C95C696

C220C522

C439C522

C155C696

C432C522C436C522

C150C640

C92C696

C41C640

C404C522C140C640

C52C696C51C696C130C696

C165C696

C220C640

C94C696C375C522

C42C696C315C740C290C740

C90C696

C91C696

C200C522

C41C696

C140C696

C20C740

C101C522

C150C696

C101C740C220C696

C200C640

C93C522

C315C696C435C740C290C696

C93C740

C200C696C101C640C93C640

C101C696C93C696

-.50

.51

e( s

imila

rvot

es |

X )

-6 -4 -2 0 2 4e( totallydegcent | X )

coef = .04777122, se = .01573035, t = 3.04

Dyads: Countries: C20C522 C435C522 C375C740 1991 Total Alliance Degree Centrality

298

C437C740C437C640

C438C740C439C740

C41C740C438C640C439C640

C70C740

C230C740

C200C740

C220C740C325C740C235C740

C350C740

C90C740

C41C640

C230C522

C210C740C315C740

C235C522

C211C740C200C522

C325C522

C42C740

C91C740

C350C522

C437C522

C290C740

C230C640

C230C696C70C640

C210C522C220C522

C211C522

C235C640

C235C696

C325C640

C51C740

C200C640

C325C696

C200C696C350C640

C350C696

C90C640

C220C640

C220C696

C210C640

C310C740

C210C696

C355C740

C211C696

C211C640C437C696

C315C640

C52C740

C42C640

C91C640

C51C522

C315C696

C315C522

C51C640

C290C640

C2C740

C51C696

C438C522C439C522

C42C522

C91C522

C290C696

C290C522

C310C640

C95C522

C42C696

C310C696

C91C696C90C522

C355C640

C310C522

C52C522

C70C522

C90C696

C52C640

C52C696

C438C696

C439C696

C355C696

C70C696

C20C740

C355C522

C41C522C41C696

C20C696

C20C640C2C696

C2C640

C20C522

C2C522

0.5

e( s

imila

rvot

es |

X )

-.5

-20 -10 0 10 20e( totallydegcent | X )

coef = .01514607, se = .00534685, t = 2.83

Dyads: Countries: C2C522 C20C522 1992 Total Alliance Degree Centrality

299

C349C522

C365C522

C437C740

C349C696

C365C696

C344C640

C346C840

C346C750

C346C517

C438C740

C344C740

C346C510

C346C551

C346C771

C346C451

C439C740C404C740

C41C740C230C522

C346C484

C135C740

C346C600C346C770

C235C522

C346C483C346C620C346C475C346C615

C346C516

C210C522C346C365

C346C481C230C640

C346C541

C390C522

C235C640

C346C616

C230C346

C344C522

C385C522

C346C640

C346C461

C346C501

C435C740C433C740

C346C900C346C350

C437C640

C346C731C346C522

C210C640

C346C625

C438C640C230C696

C346C530

C346C490

C235C346C210C346

C350C522

C346C540C390C640

C235C696

C70C740

C346C692

C346C452

C385C640

C420C522

C325C522

C436C522C432C522

C346C651

C210C696

C90C740

C439C640C404C640

C346C740

C390C696

C230C740

C349C640C346C390

C385C696

C140C740

C346C645C325C346

C235C740

C325C640

C344C696

C346C385

C210C740C140C346

C350C696

C346C696

C390C740

C155C740

C211C522

C325C696

C385C740

C101C740

C434C740

C435C640

C346C694C42C740

C433C640

C160C740

C420C696

C346C732

C346C698C436C696C432C696

C346C437C93C740

C346C663

C100C740

C432C740

C346C670C434C522

C211C640

C350C740

C155C346

C346C349

C100C346

C41C640

C349C740

C325C740C346C404C101C346

C211C346C91C740

C135C640

C346C660

C365C640

C95C740

C200C522C346C436

C110C740

C420C740

C436C740C211C696

C346C690

C365C740

C346C420

C150C740

C433C522

C94C346C220C640C220C346

C52C740

C344C346

C51C346

C94C740

C160C346

C145C740C2C740C92C740

C200C346C200C640

C51C740

C165C346C165C740

C346C652

C130C740

C211C740

C52C346C220C740

C20C640C346C450

C434C696

C438C522

C95C346C220C522

C130C346

C145C346

C200C696

C70C640

C346C710

C346C439C346C432

C42C346C346C438C41C346

C20C346C346C434

C432C640

C90C640

C350C640

C439C522

C93C346

C433C696

C200C740C140C696

C140C640

C90C346

C420C640C436C640

C135C346C437C522

C150C346

C20C740

C110C346

C91C346C92C346C404C522

C220C696

C100C696C155C696

C155C640

C438C696

C101C696

C434C640

C101C640C42C640

C160C640C94C696C346C433

C93C640

C51C696

C100C640

C165C696

C439C696

C435C696C346C435C70C346

C160C696

C52C696

C91C640C94C640

C95C640C95C696

C145C696

C51C640

C437C696

C110C640

C404C696

C165C640

C130C696

C150C640

C42C696

C140C522

C52C640

C145C640

C92C640

C93C696

C130C640

C150C696

C100C522

C90C696C2C346

C155C522

C91C696

C2C640

C20C696

C101C522

C92C696C110C696

C94C522C51C522

C165C522

C160C522

C52C522

C145C522

C95C522

C130C522

C42C522

C70C696C93C522

C150C522

C135C696C90C522C91C522C92C522

C110C522

C2C696

C70C522

C135C522C41C696C41C522

C346C482

C435C522

C20C522

C2C522

0-.5

.5e(

sim

ilarv

otes

| X

)-1

-10 0 10 20 30e( totallydegcent | X )

coef = .01414983, se = .00296048, t = 4.78

Dyads: Countries: C2C522 C522 C435C522 C20C522 C346C482 1993 Total Alliance Degree Centrality

300

C349C522

C365C522

C437C740

C349C696

C346C731

C365C696

C346C770

C404C740

C346C840

C438C740C439C740

C346C517C346C510C346C771C346C541C346C551C346C451C346C652

C346C501

C346C475

C433C740

C346C482

C346C750

C346C484

C210C522

C346C600

C344C740

C346C625

C344C640

C346C483

C235C522

C41C740

C346C620C346C732

C350C522C435C740

C346C481

C230C522C325C522

C346C900

C210C640

C346C350C346C540

C235C640

C420C522

C211C522C390C522

C346C615

C210C696

C346C516

C135C740

C432C522

C346C522

C235C696

C230C640C325C640C346C651

C346C452

C346C616

C346C694

C350C696

C346C640

C344C522

C210C346

C325C696

C349C640

C230C696

C211C640C346C365

C235C346C346C740C90C740

C390C640

C436C522C210C740

C437C640

C404C640

C438C640

C346C490

C439C640

C235C740

C211C696

C346C692

C140C740

C390C696

C434C740

C420C696

C346C420

C230C346

C385C522

C325C346

C346C690

C70C740

C350C740C432C696

C346C696

C325C740

C230C740C434C522

C346C645

C346C404

C346C670

C346C663

C433C640C432C740

C211C346

C346C390

C346C432

C344C696

C42C740

C20C640

C91C740C200C522

C346C349

C220C640

C211C740

C433C522

C346C461

C346C530

C390C740C140C346

C155C740

C385C640

C436C696

C420C740

C349C740

C101C740

C92C740

C346C437

C365C740

C110C740

C346C660

C220C740

C385C696

C346C698

C346C436

C130C740

C435C640

C95C740

C220C522C150C740

C200C640C346C439

C130C346

C20C346

C365C640

C94C346

C434C696

C165C346C51C740

C20C740

C220C346

C145C740

C93C740

C155C346

C110C346

C200C696C346C385

C436C740

C100C740

C346C710

C52C740

C51C346C101C346

C94C740

C145C346

C165C740

C433C696C385C740

C346C434

C100C346

C95C346C52C346

C41C640C2C740

C200C346

C346C438

C438C522

C200C740

C350C640

C92C346

C220C696

C439C522

C160C740

C41C346

C344C346

C42C346

C91C346

C150C346

C404C522

C346C433

C135C640

C90C346

C140C696

C437C522

C432C640

C438C696

C93C346

C439C696

C346C435

C420C640

C90C640

C135C346

C160C346

C20C696

C130C696

C404C696C94C696

C140C640

C434C640

C165C696

C155C696

C70C640

C435C696

C101C696

C51C696

C145C696

C100C696

C437C696

C95C696

C436C640

C52C696

C42C640C91C640C130C640C94C640C165C640

C92C696

C155C640C92C640

C101C640C51C640

C42C696

C91C696

C110C640C145C640

C140C522

C100C640

C150C696

C95C640

C52C640

C150C640

C110C696

C70C346

C93C640

C130C522C90C696

C94C522

C165C522

C155C522C93C696

C51C522

C101C522

C145C522

C160C696

C100C522

C135C696

C95C522

C52C522C160C640

C92C522

C2C640

C42C522

C91C522

C150C522

C110C522C90C522C2C346

C93C522

C70C696

C135C522

C160C522

C41C696

C70C522

C2C696

C20C522

C41C522

C435C522

C2C522

.50

1e(

sim

ilarv

otes

| X

)-.5

-10 0 10 20 30e( totallydegcent | X )

coef = .02031374, se = .0033623, t = 6.04

Dyads: Countries: C2C522 1994 Total Alliance Degree Centrality

301

C365C696

C365C522

C346C365

C420C740

C439C740

C390C696

C346C572C346C350

C344C346

C344C696

C344C640

C210C696

C346C541

C438C740C346C694C325C696

C346C390

C344C740

C344C522

C346C731C210C346C235C346

C200C696

C325C346

C346C482

C385C696

C346C771

C346C900

C155C346

C200C346

C346C451

C346C740

C346C640

C346C732

C346C484

C346C517

C346C501

C346C481

C346C615

C346C696

C235C696

C165C346

C94C346

C20C346

C346C436

C350C696

C346C420

C100C346

C346C625

C130C346

C435C740

C346C475

C51C346C52C346

C346C432

C211C696

C346C600

C346C770C346C560

C145C346C346C434C346C438

C101C346

C346C620

C420C640

C390C640

C346C385

C346C522C390C522

C160C346C346C439

C235C640

C346C663C210C522

C92C346

C346C551C346C840

C325C522

C346C692

C211C346

C150C346

C436C696C91C346

C235C522C350C522

C210C640

C200C522

C432C740

C439C640

C346C461

C434C740

C325C640

C436C740

C93C346

C346C437

C110C346

C432C696

C110C740

C346C616

C346C510

C346C652

C436C522C200C640C95C346C346C452C432C522

C20C640

C140C346C390C740

C346C660

C438C640

C230C346

C346C516

C385C640

C20C696

C150C740

C135C346

C93C740

C220C346

C433C740

C346C540

C346C645

C346C670

C385C522

C41C346

C211C522

C230C696

C434C522C90C346C346C435

C437C740

C434C696

C346C433

C145C740

C70C346

C211C640C2C740

C346C651

C41C740

C92C740

C90C740

C52C740

C346C698

C210C740

C220C696

C346C710

C20C740

C325C740

C91C740

C385C740

C135C740

C51C740

C155C740

C200C740C230C522

C346C750

C165C740

C346C690

C230C640

C435C640

C94C740

C439C696C235C740C435C696C350C740

C160C740

C220C640

C438C522

C155C696

C365C640

C420C696

C438C696C433C522

C101C740

C432C640

C165C696

C436C640

C211C740

C439C522

C433C696

C52C696

C160C696

C70C740

C365C740

C130C740

C220C522

C437C640

C420C522

C94C696

C95C740

C434C640

C101C696

C433C640

C100C696

C100C740

C350C640

C130C696

C220C740

C51C696

C110C640

C437C522

C150C696C145C696

C92C696C110C696

C135C640

C150C640

C93C640

C95C696

C145C640

C437C696

C2C346

C91C696

C230C740C155C640

C93C696

C140C696

C140C740

C94C640

C165C640

C92C640

C90C640C51C640

C41C696

C52C640C41C640

C70C696

C155C522

C135C696

C91C640

C160C640

C100C640C101C640

C130C640

C90C696

C100C522

C165C522

C70C640

C94C522

C130C522

C52C522

C101C522

C2C696C51C522

C160C522

C145C522

C2C640

C95C640

C92C522

C150C522

C91C522

C93C522

C140C522

C110C522

C95C522

C140C640

C135C522C70C522

C90C522

C41C522

C20C522

C435C522

C2C522

0.5

e( s

imila

rvot

es |

X )

-.5

-10 0 10 20 30e( totallydegcent | X )

coef = .01011658, se = .00239896, t = 4.22

Dyads: Countries: C2C522 1995 Total Alliance Degree Centrality

302

C420C740C420C640

C439C740

C373C740

C437C740C433C740C438C740

C370C740

C200C522

C344C640

C211C522C350C522

C439C640C435C740

C346C572

C344C740

C373C640C346C840

C436C522

C346C900

C385C522C390C522

C432C522

C346C731

C210C522C437C640

C200C640

C346C350

C346C771

C371C740

C346C541

C346C551

C433C640

C346C750

C346C501

C211C640

C230C522

C346C850

C346C704C346C560

C438C640C404C740

C346C451C346C484

C90C740C350C640

C200C740

C434C740

C135C740

C200C346

C200C696

C404C522

C372C740

C235C522

C346C732

C346C483C346C475

C325C522

C211C346

C211C696

C211C740

C385C640

C432C740

C390C640

C434C522

C346C600

C210C640

C350C696

C346C692

C369C740

C350C740

C346C481C346C620

C346C652

C435C640

C359C740

C436C740

C346C702

C70C740

C385C696C390C696

C385C740

C346C385

C346C705

C390C740

C346C701

C346C390

C91C740C100C740

C370C640

C230C640

C210C346

C210C740

C210C696

C346C740

C220C522

C346C510

C160C740

C436C696

C220C740

C110C740

C220C640

C92C740C346C625

C346C359C235C640

C346C420

C325C640

C101C740

C432C696

C41C740

C150C740

C346C436C346C461

C230C346

C346C694

C433C522

C140C740C130C740C230C696

C230C740

C346C651C346C770C346C616C93C740C346C432C346C710

C346C703

C346C615

C346C540

C165C346C130C346

C438C522

C95C740C346C452C94C346

C220C346

C346C369

C235C346

C346C404

C325C346

C404C640

C51C740C145C740

C325C696

C325C740

C346C522C346C434

C95C346

C52C740C235C696

C145C346

C235C740

C165C740C51C346

C346C372

C94C740

C404C696

C101C346C346C439C155C740

C346C373

C52C346

C100C346

C346C437

C359C522C432C640

C93C346

C434C696

C140C346

C344C346

C346C690

C371C640

C20C640C92C346

C346C371

C90C640C160C346

C436C640C434C640

C346C670C41C346

C135C640

C220C696

C365C740

C369C522

C91C346C150C346

C344C522

C439C522

C2C740C346C698

C20C740

C372C522

C20C346

C372C640

C155C346C110C346C346C370C346C433

C359C640

C369C640

C70C640C346C438C91C640

C433C696

C100C640C346C660C165C640C94C640C130C640C371C522

C420C522

C90C346

C438C696

C110C640C95C640

C160C640

C165C696

C92C640C346C696C51C640C145C640

C70C346

C94C696C130C696

C101C640C41C640C150C640C370C522C52C640

C346C435

C95C696

C140C640

C437C522

C93C640C359C696

C51C696C145C696

C165C522C130C522C101C696C94C522C52C696

C346C663

C100C696

C140C696

C95C522C93C696

C145C522C51C522

C369C696C344C696

C160C696

C101C522

C92C696

C20C696

C52C522C439C696C100C522

C155C640C135C346

C93C522C346C365

C372C696

C41C696

C140C522

C150C696

C91C696C92C522

C160C522

C155C696

C41C522

C110C696

C365C640

C373C522

C91C522

C150C522

C371C696

C420C696

C155C522C110C522

C2C640

C90C696

C370C696

C70C696

C437C696

C2C346

C90C522

C70C522

C365C522C435C696

C135C696

C373C696

C135C522

C365C696

C2C696

C20C522

C435C522

C2C522

0-.5

.5e(

sim

ilarv

otes

| X

)-1

-10 0 10 20e( totallydegcent | X )

coef = .01095377, se = .00303322, t = 3.61

Dyads: Countries: C435C522 C2C522 C420C740 C420C640 1996 Total Alliance Degree Centrality

303

C344C640

C437C740

C370C740

C373C740

C436C740

C371C740

C344C740

C420C740

C346C704

C439C740

C346C705

C433C740C230C522

C346C731C346C750

C346C652C346C625

C346C640

C210C522

C346C692

C346C572

C346C702

C438C740

C346C770

C346C350

C346C541

C211C522

C346C840

C325C522

C346C451

C346C475

C346C600C235C522

C346C771

C350C522

C346C620

C346C484

C346C670

C346C560

C230C346

C135C740

C230C696

C230C640

C346C461C346C452

C346C703

C436C640

C346C651

C346C501

C373C640C390C522

C346C850

C230C740

C420C640

C370C640

C346C900C346C732

C210C346

C346C694

C432C522

C210C640

C346C616

C346C369

C220C522

C220C740

C437C640

C369C740

C346C483

C210C696

C346C540

C220C640

C439C640

C346C615

C346C481

C210C740

C346C510C211C346

C346C551

C433C640

C325C346C220C346

C211C640

C235C696

C325C640

C235C346

C211C696

C346C370

C325C696

C235C640

C346C710

C344C696C385C522

C346C690

C346C740

C372C740

C70C740C346C660

C140C740

C434C740

C346C696

C235C740C211C740C325C740

C350C740

C365C740

C404C740

C404C522

C359C740

C435C640C346C359

C438C640

C346C404

C432C740

C91C740C346C390

C100C740C346C420

C344C522C390C640C20C640

C432C696

C101C740

C344C346

C390C696

C346C365

C160C740

C346C522C346C371

C130C740

C42C740

C140C346

C433C522

C155C740

C390C740

C435C740

C92C740C346C663

C110C740

C350C640

C346C372

C95C740

C434C522

C90C740

C41C740

C346C432

C346C436

C93C740

C346C437

C371C640

C346C385C385C640C130C346

C51C740C135C640

C385C696

C220C696

C404C696C438C522

C95C346

C101C346

C94C346

C369C522

C150C740

C52C740C145C740

C385C740

C94C740

C51C346

C346C698

C346C373

C155C346

C42C346

C2C740

C165C346C52C346

C90C346

C100C346C346C439

C346C434

C145C346C165C740

C433C696

C200C522

C160C346

C93C346

C434C696

C404C640

C346C450

C70C640C140C640

C359C522

C92C346

C432C640

C91C346

C41C346

C140C696C438C696

C130C640

C20C346

C91C640

C100C640C110C346

C439C522C130C696

C365C640

C95C640

C346C433

C94C640

C436C522

C101C640C160C640

C20C740

C70C346

C150C346

C51C640

C420C522

C372C522

C95C696C94C696

C101C696

C20C696

C155C640C42C640

C51C696

C92C640C165C640C110C640

C437C522

C359C640

C90C640C41C640C52C640

C200C346

C145C640

C200C640

C346C438

C42C696

C200C696

C93C640

C155C696

C200C740

C434C640

C90C696C165C696C369C640

C100C696

C52C696C145C696

C135C346

C93C696

C369C696

C160C696

C2C640

C150C640C365C522

C439C696

C92C696

C372C640

C91C696C41C696

C436C696

C373C522

C140C522

C420C696

C435C696C365C696

C437C696

C359C696

C130C522C110C696

C150C696

C70C696C95C522

C101C522C94C522C51C522

C155C522

C42C522C165C522

C370C522

C52C522C90C522

C100C522

C135C696

C372C696

C145C522

C160C522

C93C522C92C522

C346C435

C91C522C41C522

C371C522

C373C696

C110C522

C2C696

C70C522

C150C522

C2C346C135C522

C370C696

C371C696C20C522

C435C522 C2C522

.50

1e(

sim

ilarv

otes

| X

)-.5

-10 0 10 20e( totallydegcent | X )

coef = .01792804, se = .00326591, t = 5.49

Dyads: Countries: C435C522 C2C522 1997 Total Alliance Degree Centrality

304

C731C316C572C316

C481C316

C850C316

C437C740

C501C316C484C316

C540C316

C710C316C483C316C510C316

C551C316C437C316

C41C740

C91C740C771C316

C370C316

C840C316

C770C316

C70C740C42C740

C560C316

C90C740

C732C316

C51C740

C350C316

C437C640

C436C316

C541C316

C100C740

C439C316

C52C740

C433C316

C373C316

C290C740C310C740

C670C316C750C316C438C316

C740C316

C439C640

C235C316

C235C740

C652C316

C350C740

C310C640C310C316C475C316

C371C316

C692C316

C325C740C220C740C211C740

C290C640

C365C316

C200C740

C701C316

C230C740

C704C316

C210C740

C438C640

C651C316C435C316C625C316

C230C316C290C316

C20C740

C135C316

C694C316

C640C316

C200C316C220C316

C620C316C600C316C450C316C310C522C461C316

C325C316C235C640

C160C316

C369C316

C698C316

C290C522C211C316

C210C316

C310C696

C434C316C451C316

C702C316

C438C522

C696C316

C390C316

C690C316C70C316C200C696

C705C316

C432C316C522C316

C235C696

C230C640

C385C316

C325C696C350C696C663C316C616C316

C2C316

C2C740C615C316

C140C316

C350C522

C211C696

C290C696C20C316

C703C316C235C522

C452C316

C200C640

C210C696

C350C640

C100C316

C404C316C230C696

C325C640

C439C522C372C316C900C316

C437C522

C211C640C155C316

C41C640

C51C640

C220C640

C52C640

C101C316

C210C640

C660C316

C130C316

C52C696

C95C640

C230C522

C91C640

C359C316

C91C316C41C696

C51C696

C200C522

C42C640

C92C316

C90C640

C90C316

C42C316

C92C640

C95C316

C20C640

C2C640

C95C696

C325C522

C165C316

C438C696

C110C316

C42C696

C41C316C211C522

C90C696

C93C316

C70C640

C94C316

C51C316

C150C316C210C522

C52C316C220C696

C91C696

C145C316

C100C640C2C696

C439C696

C20C696

C437C696

C100C696

C70C696

C220C522

C41C522

C51C522C91C522

C52C522

C90C522

C95C522

C42C522

C2C522

C20C522

C439C740C438C740

C70C522

.50

1e(

sim

ilarv

otes

| X

)-.5

-10 0 10 20e( totallydegcent | X )

coef = .023428, se = .00387358, t = 6.05

Dyads: Countries: C70C522 C522 C2C522 C20C522 C439C740 C438C740 1998 Total Alliance Degree Centrality

305

C731C316C572C316

C481C316

C436C740

C420C740

C850C316C484C316

C439C740

C437C740

C370C740

C540C316

C510C316

C404C740

C438C740C433C740

C710C316

C551C316C483C316

C840C316C770C316

C436C640

C541C316

C732C316

C432C740

C482C316

C420C640

C371C740

C434C740

C750C316

C372C740

C560C316

C439C640

C437C316C437C640

C135C740C670C316

C370C316

C359C740

C436C316C652C316C370C640

C350C316

C310C640

C290C640C432C522

C439C316C373C316

C404C640

C150C740

C434C522

C110C740

C420C316C310C316

C692C316

C438C640

C701C316C704C316

C91C740

C369C740

C433C640C41C740

C235C316

C373C640

C350C740

C70C740

C290C740

C210C740

C92C740

C93C740

C211C740

C694C316

C433C316

C620C316C625C316

C740C316

C651C316

C310C740C235C740C365C740C290C316

C438C316C235C640

C42C740

C230C316

C230C740

C325C740

C90C740

C145C740

C200C740

C600C316C100C740

C140C740

C390C740

C51C740C101C740

C404C522C435C740

C160C740

C155C740C220C316

C385C740

C95C740

C461C316

C690C316C350C640

C450C316C475C316C130C740

C230C640

C705C316

C94C740

C404C316

C165C740C310C522

C210C316C211C316C290C522C703C316C616C316

C615C316

C200C316

C325C316C663C316C135C316

C359C522

C451C316

C432C640

C435C640

C432C696C438C522

C135C640

C434C640

C365C316C433C522

C434C696

C900C316

C350C522

C698C316

C371C522C350C696

C210C640C211C640

C210C696

C220C640

C200C640

C325C640

C452C316

C522C316C211C696

C439C522

C390C316

C696C316C310C696

C660C316

C145C640C235C696C94C640

C420C522

C390C696C51C640

C150C640

C110C640C230C696C385C316

C434C316

C235C522C200C696C325C696C93C640C372C522C290C696

C435C316

C140C316

C432C316

C2C740C52C640

C95C640C91C640C385C696

C70C316C369C522C390C640

C130C640

C41C640

C369C316

C436C522

C42C640

C160C316

C359C640

C90C640

C140C640

C372C316C385C640C70C640C101C640C230C522C92C640C165C640

C371C640C100C316

C371C316

C372C640C20C316

C91C316C155C316

C437C522

C359C316

C100C640C101C316C92C316C150C316

C110C316

C93C316

C52C696C438C696

C365C640

C90C316

C155C640

C42C316C41C316C130C316

C20C640

C145C696

C210C522

C94C696

C211C522C95C316

C433C696

C165C696

C51C696

C200C522C325C522

C93C696

C165C316

C51C316

C95C696

C94C316C145C316

C130C696C41C696C439C696

C2C316

C42C696

C52C316C220C696

C90C696C145C522

C140C696

C93C522

C150C696

C110C696

C420C696

C20C740

C101C696C92C696

C160C640

C369C640

C359C696

C155C696

C390C522

C51C522

C91C696

C41C522

C2C640C436C696

C373C522

C70C696

C385C522C371C696

C100C696C94C522C130C522

C372C696

C20C696

C90C522

C160C696

C91C522

C110C522C42C522

C220C522

C369C696

C95C522C92C522

C150C522

C437C696

C365C522

C52C522

C435C696

C140C522

C365C696

C101C522

C165C522

C2C696

C100C522

C370C522

C70C522C135C696C155C522

C135C522

C160C522C373C696

C370C696

C20C522C2C522

C435C522

.50

1e(

sim

ilarv

otes

| X

)-.5

-10 0 10 20e( totallydegcent | X )

coef = .02375852, se = .00328809, t = 7.23

Dyads: Countries: C435C522 C522 C20C522 C2C522 1999 Total Alliance Degree Centrality

306

C210C346C346C390

C230C346

C325C346

C501C316

C220C346

C840C316

C346C385

C770C316C572C316

C481C316

C211C346

C230C740

C510C316C483C316

C290C346C235C346

C541C316

C210C740

C565C316

C560C316

C325C740C290C740C220C740

C731C316

C750C316

C346C316

C235C740

C540C316

C850C316

C551C316

C310C346

C350C740

C211C740

C344C316

C740C316

C346C350

C310C740

C484C316

C346C369

C140C346

C732C316

C346C840

C200C346

C346C501

C346C572C346C770

C346C541C346C565

C346C510C346C483

C20C346

C344C696

C346C481

C346C540C346C551C346C484

C344C740

C346C750

C346C900

C346C732

C346C850

C900C316

C346C696

C160C346

C346C694

C346C451

C437C740

C346C475

C346C740

C346C692

C344C346

C346C731

C346C690

C346C625

C346C616

C346C600

C200C740

C346C660

C344C640

C346C452

C20C740

C346C461

C346C702

C346C652C346C651

C346C437

C346C701

C438C740

C346C670

C2C346

C346C433C346C615

C346C365

C346C370

C346C663

C439C740

C346C404C346C620

C346C640

C451C316C346C705

C346C434C475C316

C155C346

C344C522

C70C740

C70C346

C710C316

C346C438C210C316C346C432

C452C316C135C346

C461C316

C101C346C390C316

C346C710

C100C346

C220C316

C230C316

C346C439

C325C316

C404C316

C130C346C165C346

C346C373

C437C316

C433C316

C346C698C90C740C42C740

C95C346

C51C740

C92C740

C434C316

C369C316C94C346

C52C740

C346C522C52C346C385C316

C438C316

C432C316

C41C740

C90C346

C346C359

C42C346

C91C740

C230C696

C439C316

C346C372

C350C316

C51C346

C210C696

C211C316C92C346

C325C696

C93C346

C235C316

C290C696

C346C371

C290C316

C145C346

C140C316

C210C640

C91C346

C110C346

C41C346

C235C696

C230C640

C437C696C150C346C220C640

C325C640

C625C316

C211C696C220C696

C310C316

C350C696

C600C316

C696C316

C660C316

C616C316C694C316C310C696

C702C316

C692C316

C652C316

C704C316

C651C316

C705C316

C210C522C701C316

C211C640

C690C316C370C316

C200C316

C290C640

C230C522

C235C640

C663C316C615C316C325C522

C2C740C20C316

C160C316

C620C316

C670C316

C365C316

C437C640

C640C316

C438C696

C211C522

C310C640

C439C696

C359C316

C350C640

C373C316C290C522

C235C522C371C316

C372C316

C135C316

C200C696C155C316

C70C316

C350C522

C101C316C130C316

C522C316

C100C316C220C522

C698C316

C437C522

C200C640

C95C316

C310C522

C165C316

C20C640

C438C640

C90C316C94C316C42C316

C2C316

C51C316

C92C316

C93C316

C41C316

C439C640

C110C316C52C316C145C316

C20C696

C91C316

C150C316

C200C522

C438C522

C70C696

C439C522

C2C640

C70C640C2C696

C90C696

C42C696C51C696

C52C696

C91C696

C41C696

C52C640

C90C640

C42C640

C51C640

C92C640

C41C640

C91C640

C70C522

C101C522

C52C522C90C522

C42C522

C51C522

C91C522

C41C522

C20C522

C2C522

.4.2

0.6

e( s

imila

rvot

es |

X )

-.2-.4

-10 0 10 20e( totallydegcent | X )

coef = .00997911, se = .00326223, t = 3.06

Dyads: Countries: C20C522 C522 C2C522 2000 Total Alliance Degree Centrality

307

C344C346

C435C522

C20C522

C2C522

C344C740

C346C481

C346C840

C346C572C346C501C346C560

C346C732

C346C740

C346C900

C346C565C346C712C346C750

C346C540C346C731

C344C316

C346C483C346C551C346C850

C20C696

C20C316

C346C390C346C385C210C346C325C346C230C346

C481C316

C346C770

C235C346

C344C696

C211C346

C385C740

C346C316

C390C740C210C740

C346C350

C310C346

C572C316C346C484C501C316

C325C740C211C740

C20C346C290C346

C840C316

C230C740

C346C433

C235C740

C435C316

C732C316

C740C316C900C316

C540C316

C155C522

C20C640

C433C740

C560C316

C439C740

C483C316C731C316C565C316

C70C522

C165C522

C435C696

C310C740C140C522C52C522

C438C740

C551C316

C20C740

C344C640

C434C740C420C740

C346C434

C100C522C350C740C95C522C94C522

C2C316

C346C437

C370C740

C346C370

C135C522

C42C522C130C522

C346C369

C90C522

C770C316

C2C696

C365C522

C712C316

C160C522C437C740

C51C522

C346C461C850C316

C220C346

C92C522C290C740

C750C316

C101C522

C346C439

C432C740

C346C452

C346C710

C93C522C91C522

C346C438

C140C346

C346C696

C385C522

C365C740

C390C522C346C694

C145C522

C346C432

C210C522

C346C420

C41C522

C373C740

C325C522

C344C522C150C522

C484C316

C210C316

C110C522C370C522

C325C316

C235C522C230C522

C346C365

C346C451C522C316

C390C316

C346C475C290C316

C230C316C385C316

C220C740

C385C696C310C522C211C522C390C696

C155C346

C346C640

C346C373

C346C359

C373C522C210C696

C372C740

C70C346C100C346

C160C346C346C692

C369C740

C346C372

C346C600

C346C616C350C522C346C620

C165C346

C698C316

C325C696

C420C316

C359C740C346C701

C439C316

C155C740C435C640

C346C704

C135C740

C70C740

C369C522

C211C316

C346C651C290C522C95C346

C235C316

C359C522

C346C615C211C696

C433C522

C346C690

C52C346

C438C316C372C522

C135C346

C371C740

C52C740

C140C740C94C346

C130C346C165C740C346C625C230C696

C100C740

C435C740

C235C696C2C740

C434C316

C101C346

C350C640C42C346

C346C670

C90C346

C2C640

C346C371

C160C740

C433C316

C95C740

C696C316C130C740

C346C703

C310C316C433C696

C371C522

C200C346

C694C316C94C740C42C740C346C705

C434C522C2C346

C432C316C52C316C220C522

C439C522

C51C346

C155C696

C90C740

C101C740

C346C652

C310C696

C92C346

C92C740

C350C316

C346C698

C52C696

C438C522

C165C696

C165C316

C420C522

C220C316

C70C696

C41C316

C350C696C461C316

C346C522C155C316C710C316

C51C740

C370C316

C70C316

C135C316

C41C740

C140C696

C385C640C390C640

C432C522

C92C316C42C316

C434C696

C95C316C373C316

C346C435

C150C316C91C740

C93C346

C94C316

C91C346

C110C316

C100C696

C95C696

C93C740

C140C316

C210C640

C437C522

C90C316

C346C702

C94C696

C130C316

C51C316

C160C696

C452C316

C100C316

C91C316

C325C640

C437C316

C93C316

C365C696

C692C316

C110C740

C150C740

C42C696

C290C696

C235C640C145C346

C372C316

C230C640

C365C640

C373C640

C145C740

C640C316

C130C696

C101C316

C90C696C438C696C432C696

C145C316

C200C740

C439C696C135C696

C310C640C211C640

C101C696

C51C696

C371C316

C437C696

C92C696

C346C663C451C316C701C316C690C316C616C316

C359C316

C620C316

C150C346

C160C316

C41C346

C704C316C651C316

C110C346

C600C316

C346C660

C420C696

C93C696C91C696

C365C316

C625C316C703C316

C475C316C670C316C220C696C705C316C615C316

C372C640

C433C640

C145C696C290C640

C369C640

C439C640

C150C696C370C696C110C696

C438C640

C370C640

C420C640C155C640

C369C696

C652C316C135C640C372C696

C70C640C165C640

C434C640

C140C640

C359C696C52C640C371C640

C41C696C369C316

C100C640

C95C640

C702C316

C94C640C373C696

C437C640

C130C640

C42C640

C432C640

C160C640

C371C696C220C640

C90C640C101C640C51C640C92C640

C200C316C41C640C663C316C200C522C93C640

C91C640

C660C316C145C640C110C640C150C640

C200C696

C359C640

C200C640

.50

1e(

sim

ilarv

otes

| X

)-.5

-6 -4 -2 0 2 4e( totallydegcent | X )

coef = -.02295666, se = .00829728, t = -2.77

Dyads: Countries: C344C346 C522 C435C522 C20C522 C2C522 C359C640 C200C640

308

Figure 15: Partial-regression leverage plots for small sample analysis - Total Alliance Degree Centrality Squared 1990 Total Alliance Degree Centrality Squared

C93C696C101C696

C93C640C101C640

C200C696

C435C740

C375C522C200C640

C93C740

C315C696

C220C696

C290C696

C140C696

C101C740

C290C740

C220C640

C200C522

C93C522

C315C740C41C696C420C522

C101C522

C290C640

C91C696

C140C640

C94C696

C51C696

C20C740

C130C696

C315C640

C42C696

C150C696

C165C696

C90C696

C150C640

C230C696

C41C640

C52C696C155C696

C404C522

C91C640C90C640

C436C522C432C522C439C522

C433C522C92C696

C210C696

C42C640

C211C696

C434C522

C95C696

C260C522

C2C740C220C522

C135C696

C438C522

C94C640

C230C640

C325C696

C360C740

C51C640C130C640

C165C640

C210C640C211C640

C52C640

C360C696C310C696

C70C640

C155C640

C92C640

C325C640

C70C696

C310C740

C404C640

C350C696

C140C740

C95C640

C360C640

C135C640C436C640C432C640C439C640

C420C696C437C522

C235C696

C350C640

C150C740C41C740

C200C740

C438C640

C385C696

C230C522C91C740

C90C740

C390C696

C210C522C211C522

C310C640

C42C740

C145C696

C140C522

C385C640

C2C696

C420C640

C433C640

C235C640

C325C522

C390C640

C404C696

C130C740

C94C740

C436C696C432C696

C434C640

C51C740

C220C740

C439C696

C433C696

C434C696

C165C740

C92C740

C52C740

C437C640

C155C740

C438C696

C70C740

C41C522

C160C696

C95C740

C94C522

C135C740

C2C640

C145C640C260C696

C91C522

C51C522

C165C522

C150C522

C130C522

C42C522

C350C522

C52C522C90C522

C385C522

C235C522

C260C640

C155C522

C355C696

C390C522

C355C740

C160C640

C437C696

C20C696

C92C522

C315C522

C95C522

C230C740

C290C522

C135C522

C355C640

C211C740

C70C522

C210C740

C325C740

C145C740

C20C640

C435C640

C350C740

C435C696

C375C640

C160C740

C235C740C100C696

C375C696

C145C522

C385C740C390C740

C160C522

C100C640

C310C522C360C522

C404C740C436C740C432C740C439C740

C438C740

C434C740

C100C740C420C740

C433C740

C437C740

C100C522

C355C522

C260C740

C2C522

C20C522

C375C740

C435C522

-.50

.5e(

sim

ilarv

otes

| X

)

-100 0 100 200 300e( totallydegcentsqd | X )

coef = -.00070515, se = .00039151, t = -1.8

Dyads: Countries: C375C740 C522 C2C522 C20C522 C435C522

309

2000 Total Alliance Degree Centrality Squared

C200C640

C359C640

C200C696C110C640C145C640C150C640C660C316

C220C640

C91C640

C93C640C663C316

C41C640

C432C640C92C640

C101C640C51C640

C437C640C200C316

C90C640

C702C316

C130C640C160C640C42C640

C290C640

C434C640

C94C640

C100C640

C200C522

C371C696

C95C640

C652C316

C373C696

C140C640

C420C640C438C640C135C640

C370C640

C439C640C70C640C433C640C52C640C165C640

C625C316

C359C696C155C640

C703C316

C475C316

C615C316C371C640

C369C696

C705C316C372C696

C310C640

C41C696

C704C316

C600C316

C651C316C670C316

C346C660

C640C316

C369C316

C369C640C41C346C110C346

C451C316

C230C640

C620C316

C235C640

C211C640

C370C696

C701C316

C150C346

C616C316

C420C696

C346C663

C372C640

C110C696C325C640C145C696C150C696C145C346

C437C696

C220C696

C210C640

C145C740

C452C316C346C702

C439C696

C359C316

C432C696C91C696C93C696

C371C316

C438C696

C690C316C110C740

C150C740C390C640

C200C740

C692C316C385C640C91C346

C93C346

C93C740

C373C640

C290C696

C91C740

C461C316

C145C316

C372C316

C710C316

C365C316C92C696C101C696

C51C696C135C696

C41C740

C434C696

C130C696C90C696

C346C652

C365C640

C51C740

C93C316

C92C346

C91C316

C160C316

C101C316

C437C316

C346C705

C346C703

C42C696

C110C316

C373C316

C365C696

C51C346

C92C740C346C698C101C740C150C316

C90C740

C346C522

C51C316

C437C522

C90C316

C94C696

C100C696C432C522

C160C696

C130C316

C42C740C130C740

C95C696

C94C740

C90C346C433C696

C92C316C41C316

C140C696

C100C316

C101C346

C350C696

C432C316

C42C346

C346C625C94C316

C310C696C42C316

C95C740

C346C371

C95C316

C160C740C350C640

C200C346

C346C670C694C316C220C316

C346C435

C130C346

C420C522

C438C522

C100C740

C140C740

C135C346

C346C651

C94C346

C370C316C135C316

C350C316

C140C316

C346C615

C346C704

C310C316

C70C696C439C522

C95C346C696C316

C165C696

C70C316

C346C701

C135C740C433C316

C434C522

C434C316C165C740

C52C346

C165C316

C371C740

C52C696

C155C316

C155C696

C52C740C70C740

C346C620

C346C600

C438C316C235C696

C155C740

C165C346

C52C316C346C616

C359C740

C100C346

C230C696

C420C316C346C690C439C316

C160C346C2C740

C369C740

C346C372

C70C346

C346C359

C433C522

C346C692

C235C316C346C373

C155C346

C372C740

C211C696

C346C365

C435C740

C346C640

C211C316

C325C696C484C316

C698C316

C220C522

C371C522C290C522

C2C346

C346C475

C373C740

C210C696C346C451

C220C740

C140C346

C344C522C230C316

C390C696

C369C522C350C522

C385C696

C359C522C372C522

C2C640

C346C694

C385C316

C325C316

C390C316

C850C316C750C316

C435C640C210C316

C290C316C346C420

C310C522

C346C432

C712C316

C346C696

C770C316C346C452

C346C369

C365C740C373C522

C522C316

C230C522

C346C370

C346C438

C220C346

C235C522C346C461

C290C740

C211C522

C346C710C551C316

C370C740

C346C439

C432C740

C731C316C325C522

C483C316C565C316C540C316

C344C640

C437C740C210C522

C370C522

C560C316C346C437

C350C740

C390C522

C310C740

C385C522

C346C434

C434C740C420C740C438C740

C900C316

C840C316

C433C740

C439C740

C732C316

C740C316

C501C316

C290C346

C235C740

C20C740

C346C433C572C316

C230C740

C20C640

C310C346

C211C740

C346C350

C325C740

C110C522

C150C522

C435C696

C41C522

C20C346

C346C316

C145C522

C346C484

C210C740

C344C696

C481C316

C235C346

C91C522C93C522

C211C346

C390C740

C101C522

C230C346

C385C740

C92C522C51C522

C435C316

C365C522

C160C522

C325C346

C90C522

C130C522

C135C522

C42C522

C210C346

C2C316

C94C522

C346C390

C95C522C100C522

C140C522

C2C696

C346C385

C346C770

C70C522C52C522

C344C316

C165C522C155C522

C346C731C346C551C346C850C346C483C346C540

C346C750C346C712C346C565C346C560

C20C316

C346C900

C346C732

C346C501C346C572

C20C696

C346C740

C346C840

C346C481

C344C740

C344C346

C20C522C2C522

C435C522

-.50

.51

e( s

imila

rvot

es |

X )

-100 0 100 200e( totallydegcentsqd | X )

coef = .0012605, se = .00028995, t = 4.35

Dyads: Countries: C200C640 C522 C344C346 C435C522 C20C522 C2C522

310

Figure 16: Partial-regression leverage plots for small sample analysis - Visits Total In-Degree Centrality 1990 Visits Total In-Degree Centrality

C93C522C93C696

C93C740

C101C696C101C522

C101C740

C93C640C101C640

C200C696

C200C640

C220C696

C200C740

C290C522C315C522

C220C740C290C740

C220C640

C315C740

C200C522

C290C640

C290C696

C220C522

C315C640

C435C522

C315C696

C150C522

C140C696C140C522

C150C696C41C522

C140C740

C150C740

C41C696

C375C740

C404C522

C91C522

C41C740

C420C522

C350C640

C91C696

C90C522

C360C640

C436C522C94C522C432C522C94C696

C90C696

C42C522

C91C740

C439C522

C165C696C42C696

C52C696

C94C740

C210C696

C51C522

C165C522

C130C522

C90C740C211C696C230C696

C51C696

C165C740

C20C740

C130C696C52C522

C52C740

C42C740

C51C740

C140C640

C438C522

C130C740

C150C640

C230C640

C435C740

C20C522C41C640

C404C696

C375C522

C325C696

C210C640

C210C740C420C696

C420C740

C230C740

C211C740

C211C640

C433C522

C404C740

C155C696C155C522

C436C696

C155C740

C432C696

C436C740

C91C640C432C740

C434C522

C439C696

C310C522

C94C640

C360C522

C230C522

C90C640C439C740

C310C740

C434C740

C325C640

C325C740

C42C640C165C640

C210C522C51C640C130C640

C438C696

C211C522C52C640

C385C696

C435C696

C360C740

C438C740

C70C696C70C522

C390C696

C92C522

C433C696

C70C740

C433C740

C92C696

C2C740

C350C696

C325C522C155C640

C310C696

C385C740C95C522

C92C740

C385C640

C95C696

C434C696

C310C640

C360C696

C95C740

C420C640C404C640

C260C740

C390C740

C135C522

C390C640

C235C696

C437C522

C350C740

C135C696

C436C640

C375C696

C432C640

C235C640

C437C740

C135C740

C439C640

C350C522

C385C522C20C696

C70C640

C235C740

C92C640

C438C640

C390C522

C260C696C260C522

C95C640

C235C522

C433C640

C437C696

C135C640

C434C640C20C640

C260C640

C435C640

C355C640

C2C522

C145C522C145C696

C375C640

C145C740

C437C640

C160C696C160C522

C160C740

C355C522

C355C740

C145C640

C2C696

C355C696

C160C640

C2C640C100C522C100C696

C100C740

C100C640

-.50

.5e(

sim

ilarv

otes

| X

)

-4 -2 0 2 4e( visitstotindeg | X )

coef = -.04124323, se = .02203705, t = -1.87

Dyads: Countries: C100C640 C100 C100C740 C100C696 C100C522 Others

311

1991 Visits Total In-Degree Centrality

C41C522

C2C522

C70C522

C325C740

C355C522

C211C740

C220C740

C91C522

C230C740

C42C522

C20C522

C235C740C350C740

C95C522

C200C740

C51C522

C41C696

C438C740C439C740

C439C522C438C522

C315C522

C90C522

C438C696

C439C696

C210C740

C51C740

C52C522

C220C696C325C696

C310C522

C52C740

C70C696

C211C696

C42C740

C91C740

C437C522C230C696C235C696

C437C696

C350C696

C220C522

C235C522C350C522

C230C522

C437C740

C325C522C355C740C200C696

C70C740

C211C522

C42C696

C91C696

C235C640

C290C522

C350C640

C230C640

C220C640C325C640

C211C640C210C696

C51C696

C200C522

C52C696

C51C640

C2C740

C315C740

C42C640

C91C640

C41C740

C310C740

C90C696

C90C740

C200C640

C52C640C355C696

C210C522C355C640

C70C640

C210C640

C2C696

C315C696

C20C740

C290C740

C315C640C41C640

C90C640C310C696

C438C640C439C640

C310C640

C290C696

C2C640

C20C696

C290C640

C437C640

C20C640

-.50

.5e(

sim

ilarv

otes

| X

)

-5 0 5 10e( visitstotindeg | X )

coef = -.03449784, se = .01008237, t = -3.42

Dyads: Countries: None in particular

312

1992 Visits Total In-Degree Centrality

C210C522

C210C740

C230C522

C210C640

C230C740

C235C522

C230C640

C235C740

C390C522C210C696C385C522

C365C522

C390C740

C344C740

C235C640C210C346

C349C522

C385C740

C404C740

C349C740

C230C696C439C740

C390C640

C437C740

C325C522C438C740

C344C522C436C740

C420C740

C230C346

C325C740

C346C349C385C640

C432C740

C434C740

C434C522

C160C522

C140C522

C235C696

C160C740

C436C522

C235C346

C420C522

C41C740C140C740

C432C522

C346C432

C365C740

C346C390

C155C522

C437C522

C101C522

C346C434C165C522

C52C522

C325C640

C346C420

C150C522

C404C522

C135C740

C110C522

C145C522C346C439C94C522C51C522

C346C436

C100C522

C346C385

C390C696

C95C522

C91C522C439C522

C110C740

C346C771

C155C740

C91C740

C150C740C346C438

C93C522

C101C740C42C522

C90C740C365C696

C346C840C346C481

C93C740

C438C522C42C740C344C640

C346C404

C145C740

C90C522C95C740

C52C740C100C740

C346C517

C165C740

C385C696

C346C365

C51C740

C94C740C346C750

C41C522C346C551

C346C510

C346C516

C135C522

C344C346

C349C640

C346C530

C346C482

C346C490

C349C696

C346C484

C346C450C346C540

C346C437

C346C483

C150C346

C437C640

C110C346

C145C346C91C346C41C346

C160C346

C404C640

C165C346

C346C615

C51C346

C52C346

C435C740

C346C451

C94C346

C439C640

C434C640

C95C346

C346C475

C93C346

C325C696

C200C740

C435C522

C344C696

C200C522C346C461

C346C620

C346C452C42C346

C438C640

C346C600

C434C696

C346C616

C160C640

C100C346

C135C346

C41C640

C140C346C101C346

C436C640C420C640

C155C346

C160C696

C90C346

C432C640

C140C696

C436C696

C140C640

C420C696C432C696

C365C640

C135C640

C437C696

C110C640

C404C696

C2C740

C325C346

C91C640

C155C640

C150C640

C155C696

C101C640

C439C696

C101C696

C90C640

C110C696

C150C696

C145C696

C165C696

C51C696C346C435

C52C696

C93C640

C94C696

C165C640

C100C696

C91C696

C52C640

C42C640

C145C640

C95C696

C51C640

C95C640

C200C640

C94C640C100C640C350C522

C438C696

C346C740

C93C696

C42C696

C90C696

C350C740

C41C696

C135C696

C346C522

C220C522

C220C740

C433C740

C200C696

C435C640

C435C696

C346C900

C70C740

C220C640

C130C522

C70C522C92C522C200C346

C433C522

C346C640

C211C522

C346C770

C92C740

C350C696

C346C501

C130C740

C346C541

C346C692

C211C740

C346C433

C346C350

C346C696C346C663

C220C696

C350C640

C2C522

C92C346

C346C660

C211C640

C130C346C346C651C346C625

C433C640

C346C698

C70C640

C70C346C220C346

C130C696

C433C696

C92C640

C92C696

C130C640

C70C696

C211C696

C20C522

C2C640

C211C346

C20C740

C2C696

C346C732C20C640

C346C731C346C690

C2C346C346C694

C20C696

C346C645

C20C346

C346C670C346C652

C346C710

-1-.5

0.5

e( s

imila

rvot

es |

X )

-5 0 5 10e( visitstotindeg | X )

coef = .01892858, se = .00958395, t = 1.98

Dyads: Countries: C346C670 C346 C346C652 C346C710 1993 Visits Total In-Degree Centrality

313

C200C740

C365C522

C200C522

C235C740

C235C522

C200C640

C20C740C437C522C349C740

C20C522

C140C522C101C522C210C740

C349C522

C235C640

C435C522

C434C522C350C740

C140C740

C200C346

C101C740

C390C740

C420C740

C432C740

C434C740C350C522C365C696C210C522C200C696C433C740

C135C522

C390C522C235C346

C90C522C91C522

C437C740

C439C740

C42C522

C438C740

C404C522C110C522C346C365

C439C522

C404C740

C438C522

C92C522

C130C522

C145C522

C150C522C95C522

C155C522

C51C522

C433C522

C346C475

C94C522C346C349

C165C522

C52C522

C20C640

C235C696

C210C640C346C433

C432C522

C346C840

C130C740C94C740

C390C640

C344C522

C155C740

C420C522

C145C740C51C740

C165C740

C365C740

C95C740

C346C731

C92C740

C52C740

C91C740C42C740

C346C437

C346C438

C437C696

C346C434

C150C740

C110C740

C346C625

C140C346

C90C740C135C740

C101C346C346C732

C20C346

C346C439

C349C696

C140C696C230C740

C210C346

C20C696

C346C432

C344C740C101C696C211C740

C346C390

C434C696

C346C404C346C516C346C420

C346C540C350C696C346C501C210C696

C230C522

C346C517C346C771

C390C696

C346C510

C346C481

C346C541C346C551C346C770

C211C522

C135C346

C346C483C90C346

C346C482C346C484

C91C346

C42C346

C435C696

C92C346C130C346C346C652C145C346

C135C696

C150C346

C95C346C155C346C51C346

C404C696C439C696C438C696

C94C346

C90C696

C346C452

C433C696

C165C346

C52C346

C346C350

C91C696

C325C740

C42C696

C110C696

C230C640C92C696C155C696

C130C696

C344C346

C150C696

C110C346

C95C696

C145C696C51C696C346C451C94C696C211C640

C52C696

C165C696

C349C640

C344C696

C432C696

C41C522

C420C696

C436C740

C385C740

C140C640

C325C522

C435C740

C346C616

C434C640C101C640

C346C600

C385C522

C346C620

C230C346

C220C740

C93C522

C211C346

C100C522

C350C640

C437C640C230C696

C325C640

C346C435

C211C696C100C740

C436C522

C2C740

C385C640C346C663

C220C522

C346C750

C93C740

C130C640C155C640

C95C640

C145C640

C92C640

C51C640

C91C640

C420C640

C42C640

C432C640

C94C640C150C640

C110C640

C165C640

C52C640

C90C640C135C640

C325C346

C365C640

C346C900

C433C640

C346C436

C41C740

C346C385

C346C490

C439C640C438C640

C404C640

C220C640

C325C696

C385C696

C346C522C346C660

C344C640

C41C696

C70C522

C93C346

C100C346

C346C740

C220C346

C93C696C41C346

C100C696

C346C690

C220C696

C436C696

C70C740

C346C615C346C651

C160C522

C346C694C346C696C70C346

C160C740

C100C640

C436C640

C93C640

C70C696

C435C640

C346C530

C41C640

C160C346C346C461

C160C696

C346C640

C70C640

C346C692C160C640

C346C645

C346C698C346C670C346C710

.5

C2C522

C2C640C2C346

C2C696

01

e( s

imila

rvot

es |

X )

-.5-1

-5 0 5 10e( visitstotindeg | X )

coef = -.0246598, se = .0103575, t = -2.38

Dyads: Countries: C2C640 C2 C2C346 C2C696 C2C522 1994 Visits Total In-Degree Centrality

314

C365C522

C365C696

C346C365

C200C640

C200C522

C325C640

C325C522

C365C640

C210C640

C200C696

C344C522

C200C740

C210C522

C20C640

C344C696

C344C640

C200C346

C155C640

C325C696

C346C541

C155C696

C390C640

C210C696

C155C522C365C740

C390C522

C155C740

C434C522C346C771

C434C696

C325C346C325C740C346C663C210C346

C210C740

C155C346

C20C740

C434C640

C20C346

C20C522

C20C696

C344C740

C235C640

C235C522

C346C482

C346C615

C350C522C390C696

C110C640

C346C572

C346C501

C110C696

C93C640

C346C731C346C484

C346C694

C93C696

C346C451C145C640

C145C696

C439C740

C91C640

C420C740

C346C461

C436C740

C432C740

C346C439

C346C420

C434C740

C51C640

C110C522

C346C452

C92C640

C346C436

C235C696

C346C625

C346C481

C91C696

C150C640

C346C432

C346C390

C350C696

C130C640

C150C696

C94C640C93C522

C135C640

C346C475

C145C522

C51C696

C420C522

C92C696

C439C522

C438C740

C346C434

C346C660

C100C640

C436C522

C439C696C420C696

C432C522

C52C640

C346C522

C101C640

C130C696

C94C696

C436C696

C150C522

C165C640

C135C696C390C740

C432C696

C91C522

C110C740

C346C696C92C522

C346C438

C51C522

C93C740

C100C696

C145C740

C160C640

C110C346

C94C522

C91C740

C130C522C135C522

C346C620

C101C696C100C522

C438C522

C52C696

C130C740

C346C600

C135C740

C346C616C93C346

C438C696

C51C740

C100C740

C439C640

C145C346

C92C740C52C522

C420C640

C346C517

C436C640

C101C522

C165C696C235C740

C150C740

C432C640

C350C740

C165C522

C346C640

C94C740C346C732C235C346

C160C522

C435C740

C150C346

C160C696

C91C346

C101C740

C92C346

C51C346

C346C435

C438C640

C94C346

C52C740

C2C740

C130C346

C165C740

C135C346C160C740

C435C696

C100C346

C211C640C52C346

C101C346

C211C522

C90C640

C165C346C220C640

C160C346

C90C696

C220C522

C437C522

C437C696

C90C522

C435C640

C346C516

C437C640

C90C740

C346C551

C41C640

C41C696

C346C740C346C540

C346C510

C211C696

C385C640

C140C640

C90C346

C41C522

C385C522

C435C522

C346C900

C140C696

C437C740

C346C350

C433C740

C220C696C95C640

C140C522

C346C770

C346C840C220C740C211C346

C140C740

C41C740C346C652

C70C640

C211C740

C95C696C220C346

C41C346

C346C433

C346C692

C95C522

C70C696

C346C437C70C522

C70C740

C433C522

C433C696C140C346

C346C560

C95C740C385C696C433C640C344C346

C95C346

C230C640

C230C522

C70C346

C346C385

C350C640

C385C740

C346C651

C230C696

C346C698

C230C740

C230C346

C346C670

C346C645

C2C640

C346C690

C346C710

C346C750

C2C346

C2C696C2C522

0.5

e( s

imila

rvot

es |

X )

-.5

-5 0 5e( visitstotindeg | X )

coef = .00783732, se = .00928997, t = .84

Dyads: Countries: C365C522 C2 C365C690 C346C750 C2C346 C2C696 C2C522

315

1995 Visits Total In-Degree Centrality

C200C740

C200C522

C420C740

C200C640

C439C740C438C740

C373C740

C433C740C385C522

C200C346C385C640

C420C640

C385C740

C390C522

C404C740

C390C640

C350C640

C370C740

C350C522C211C522

C439C640C434C740

C390C740

C211C640

C432C740

C371C740

C373C640

C434C522

C436C740C211C740

C372C740

C370C640C371C640

C438C640

C346C385

C200C696

C434C640

C437C740

C350C740C433C640

C372C640

C359C740

C436C522

C346C390

C359C522C346C900

C90C740

C432C522C165C522

C437C640C100C740

C372C522

C211C346

C346C436

C404C522

C404C640

C110C740

C359C640

C165C740C346C432

C160C740

C165C640

C150C740

C371C522C435C740

C91C740

C210C522C385C696C92C740

C346C434C41C740

C94C522

C90C640

C52C522

C52C740

C432C640

C95C740C101C740

C100C522

C346C439

C51C522

C93C740

C210C640C145C522

C100C640

C436C640

C95C522

C51C740

C210C740

C145C740

C93C522C130C522

C110C640C94C640

C150C522

C94C740

C52C640

C101C522

C165C346C130C740

C41C522

C150C640C390C696

C160C522

C92C522

C91C640

C110C522

C160C640

C51C640C95C640C92C640C41C640C370C522C145C640

C438C522

C346C350

C91C522

C220C740

C346C438

C346C373

C346C420

C130C640

C93C640C101C640

C433C522

C346C433

C211C696

C94C346

C220C522

C52C346

C350C696

C220C640

C100C346C51C346C145C346C95C346C90C522C70C740

C346C404C93C346

C130C346

C439C522

C150C346C101C346C41C346

C210C346

C135C740

C160C346

C92C346C110C346

C420C522

C369C740

C435C640

C346C359

C434C696

C91C346

C346C372

C230C522C369C640

C70C640C135C640

C230C640

C90C346

C369C522

C346C732

C165C696

C346C371

C155C740

C220C346

C436C696

C346C572

C230C740

C359C696C373C522

C346C840

C346C437

C346C501C432C696

C372C696

C140C740C155C522

C210C696

C20C740

C52C696

C404C696

C325C522

C94C696

C235C522

C20C640C346C370

C100C696C70C522

C346C481

C346C771C325C640

C437C522

C51C696C95C696C145C696

C371C696

C155C640

C235C640

C140C522C346C551

C346C541

C2C740

C346C850

C130C696C101C696C93C696

C160C696

C150C696

C346C484

C325C740

C92C696C41C696

C230C346

C110C696C346C483

C140C640

C20C522

C235C740

C91C696

C346C435

C155C346

C370C696

C220C696

C438C696

C70C346

C135C522

C346C510

C346C451

C346C692

C433C696C344C740

C90C696C140C346

C20C346

C346C475C346C731

C344C640

C346C704

C325C346

C346C620

C235C346

C346C600

C346C616C346C369

C439C696

C346C560

C420C696

C346C461C230C696

C346C702

C135C346C346C452C346C750C346C615

C346C740

C346C703

C369C696

C155C696

C435C522

C346C652

C20C696C325C696

C373C696

C70C696

C235C696

C346C694

C140C696

C346C540

C346C705

C437C696

C346C690

C365C740

C346C701

C346C651C135C696

C344C346C346C625C346C522

C365C640

C344C522C435C696C346C660C365C522

C346C698

C346C710

C2C640C346C770C346C696

C2C522

C346C365

C344C696

C346C670

C2C346C365C696

C346C663

C2C696

-1-.5

0.5

e( s

imila

rvot

es |

X )

-5 0 5 10e( visitstotindeg | X )

coef = .01680245, se = .00585027, t = 2.87

Dyads: Countries: C346C365 C2C696 C2C522 Many possible others

316

1996 Visits Total In-Degree Centrality

C385C522C390C522

C200C522

C385C640C346C385

C200C740C344C640

C420C640

C439C640

C370C640

C436C640

C434C640C437C640

C385C740C346C900

C390C640

C371C640

C346C390

C200C640

C438C640C211C522

C344C740

C325C522C52C640

C404C640

C200C346

C420C740C439C740

C359C640

C435C740

C110C640

C436C740

C160C640

C210C522C432C640

C145C640

C371C740

C135C640C92C640C94C640

C390C740

C433C640

C155C640

C372C640

C220C522

C41C640

C438C740

C435C640

C52C522

C404C740

C235C522C350C522C51C640

C93C640C91C640

C90C640C95C640

C325C640

C370C740

C42C640

C211C640

C344C522

C373C740

C211C346

C220C740

C325C346

C160C522

C101C640

C372C522

C432C740

C372C740

C325C740

C130C640

C434C522

C155C522

C433C740

C210C640

C211C740

C434C740

C94C522C210C740

C437C740

C145C522

C110C740

C210C346

C373C640

C110C522

C230C522

C52C740C95C740

C346C698

C92C522

C359C522

C160C740

C220C640C95C522

C220C346

C51C522

C135C740

C359C740

C41C522

C52C346

C385C696

C92C740

C90C522

C93C522

C145C740C41C740

C155C740

C42C522

C404C522C101C522

C235C640

C235C346

C350C740

C235C740

C93C740C91C740

C94C740

C432C522

C90C740

C91C522

C160C346

C51C740

C130C522

C371C522

C42C740

C346C732

C155C346

C94C346

C101C740

C135C522

C346C434

C145C346

C346C435

C200C696

C370C522

C230C740

C92C346

C20C522

C346C438

C230C640

C110C346

C390C696

C438C522

C130C740

C230C346

C346C439

C439C522

C95C346

C51C346C165C640C41C346

C420C522

C90C346

C150C640

C93C346

C350C640

C42C346

C346C432

C101C346

C436C522

C346C350

C346C373

C437C522

C91C346

C346C436

C130C346

C346C692

C346C433

C433C522

C346C481

C20C740

C346C372

C346C437

C135C346

C372C696

C20C346

C346C371

C165C522

C346C420

C346C694

C359C696

C211C696C325C696

C346C359

C150C522

C346C370

C52C696

C434C696

C346C572

C373C522

C346C840

C220C696

C150C740

C346C404C100C640

C210C696

C145C696

C235C696

C110C696

C346C640

C94C696

C160C696

C92C696

C2C740

C155C696

C371C696

C41C696

C51C696

C93C696

C165C740

C404C696

C365C640C90C696

C165C346

C95C696

C20C640

C346C731C42C696

C432C696C91C696

C346C771

C150C346

C101C696

C346C484

C344C696

C130C696

C230C696

C344C346C346C541

C346C740

C370C696

C100C522

C365C522

C346C540

C135C696

C100C740

C346C483

C439C696C438C696

C346C551

C420C696C346C510

C346C704C346C560

C436C696

C346C625C346C660

C346C522

C437C696

C346C451

C433C696

C346C452

C365C740

C346C703

C373C696

C346C461

C346C702

C100C346

C346C705

C435C522

C70C640

C165C696

C150C696

C346C690C346C450

C20C696

C346C620C346C365

C70C740

C346C770C346C750

C346C501

C346C652

C365C696

C70C522

C100C696

C346C616

C140C640

C369C640

C346C475

C369C522

C369C740

C140C522

C70C346

C140C740

C346C696C435C696

C140C346

C346C369

C369C696

C70C696

C346C600C346C615

C140C696

C346C710C346C850

C346C663C346C651

C2C522

C346C670

C2C346

C2C640

C2C696

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5 10e( visitstotindeg | X )

coef = -.00311939, se = .00851732, t = -.37

Dyads: Countries: C2C522 C2 C2C696 1997 Visits Total In-Degree Centrality

317

C698C316

C625C316

C900C316

C694C316

C390C316

C20C522

C385C316

C200C740

C200C522

C52C522

C52C740

C620C316

C211C522

C616C316

C438C740

C705C316

C95C522

C211C740

C210C522

C692C316

C701C316

C660C316C439C740

C615C316

C200C316

C210C740

C840C316C600C316

C51C740

C350C640

C200C640

C165C316C211C316

C51C522C20C740

C704C316

C95C316

C155C316C91C740

C540C316

C52C316C42C740

C703C316

C572C316

C350C316

C42C522

C211C640

C210C316

C91C522

C52C640

C90C740

C150C316

C135C316

C732C316

C740C316C370C316

C702C316

C475C316

C90C522

C372C316

C20C316

C210C640

C452C316

C461C316C101C316C522C316

C484C316

C451C316

C235C740

C95C640

C371C316

C110C316C235C522C450C316C20C640

C483C316

C438C316C94C316C92C316

C310C740

C551C316

C350C740

C42C316

C52C696

C404C316

C541C316

C510C316

C100C316

C51C316

C437C740

C373C316

C436C316

C200C696C90C316

C51C640

C92C640

C359C316

C350C522

C230C740

C100C740

C95C696

C439C316

C130C316

C230C522C325C522C91C316

C310C522

C91C640

C434C316

C437C316

C433C316C220C522C93C316

C42C640

C325C740

C211C696

C435C316

C365C316

C235C316

C220C740

C640C316

C90C640

C51C696

C210C696

C235C640

C91C696C42C696

C41C740

C20C696

C2C740

C230C316

C750C316

C325C316

C731C316

C310C316

C90C696C438C522

C41C522

C220C316

C325C640

C230C640

C310C640

C696C316

C437C522

C438C640

C437C640

C100C640

C220C640

C439C522

C235C696C2C522

C439C640

C690C316

C310C696

C350C696

C100C696

C70C316C670C316

C290C316

C230C696C70C740

C41C640

C70C522

C438C696

C770C316

C325C696

C560C316

C437C696

C220C696

C652C316

C439C696

C145C316C41C316

C41C696

C432C316

C710C316C850C316

C369C316

C290C740

C70C640

C2C316

C2C640C290C522

C663C316

C70C696

C2C696

C290C640

C501C316

C771C316

C290C696

C481C316

C651C316

C140C316C160C316

.4.2

0.6

e( s

imila

rvot

es |

X )

-.2-.4

-4 -2 0 2 4e( visitstotindeg | X )

coef = -.00132552, se = .0119466, t = -.11

Dyads: Countries: C698C316 1998 Visits Total In-Degree Centrality

318

C200C696

C200C740

C200C522

C200C316C200C640

C560C316

C435C696

C435C522

C325C696C385C696

C370C696

C325C522

C437C522

C325C316

C385C522

C370C522

C325C740

C770C316

C698C316

C385C316

C385C740

C437C696

C230C696

C850C316

C160C696

C615C316

C230C522

C101C696

C150C316

C135C696

C325C640C840C316

C900C316

C369C696

C135C522

C235C696

C369C522

C230C316

C101C522

C235C522C160C522

C369C640

C696C316C522C316C310C522C541C316C452C316

C235C316

C373C696

C482C316C660C316

C310C696

C100C696

C230C740

C385C640

C155C696

C310C316

C373C522

C451C316

C100C522C433C522

C101C740C616C316

C160C740

C52C316

C155C522

C461C316

C436C522

C703C316C41C316C145C316

C705C316

C110C316

C551C316C101C316

C20C740

C150C696

C235C740

C51C316

C438C522

C165C696

C435C316

C150C522

C130C696C420C522

C371C316

C600C316

C130C522

C620C316C692C316

C483C316

C95C696

C740C316C230C640C432C316

C481C316

C155C740

C433C696

C404C316

C434C522

C90C696

C94C316

C165C316

C42C316

C310C740

C92C696

C290C316

C372C696C540C316

C95C522

C91C316

C90C522

C165C522

C94C696

C625C316C165C740

C42C696

C92C316C372C316

C92C522

C372C522

C95C316

C359C316

C436C696

C359C522

C100C740C90C316C701C316

C235C640C434C316

C359C696

C94C522

C42C522

C91C696

C91C522

C704C316

C52C696C438C696

C310C640

C130C740

C484C316

C130C316C94C740

C420C696

C371C522

C51C696C369C740

C95C740C371C696

C51C522C435C740

C155C316

C110C696C52C522

C434C696

C110C522

C145C696

C572C316

C41C696C145C522C41C522

C372C640

C731C316

C404C522

C90C740

C432C522

C20C696

C100C316C42C740

C420C316C438C316

C150C740C51C740C92C740

C135C740C160C316

C371C640

C365C740

C436C316

C220C696C145C740

C433C316

C432C696

C135C316

C369C316

C20C316

C91C740

C41C740

C211C696

C373C316

C20C522

C110C740C220C522

C359C740

C101C640

C160C640

C211C522

C372C740

C220C316

C390C696

C211C316

C437C316

C434C740

C211C740

C370C316

C370C740

C365C696

C373C640

C20C640C100C640

C371C740

C70C696

C365C522

C432C740C390C522C155C640

C2C740C290C522C663C316C433C740

C70C522

C750C316

C390C316C437C740

C290C696

C220C640

C390C740

C438C740C130C640

C165C640

C651C316

C365C640

C95C640

C150C640

C90C640

C211C640

C92C640C135C640

C94C640C42C640

C70C740C475C316

C710C316

C439C522

C359C640

C52C640C51C640

C91C640

C390C640

C404C740C145C640C41C640

C434C640

C70C316

C290C740

C510C316

C290C640

C436C740

C450C316C93C316

C110C640C420C740

C439C696

C370C640

C437C640

C435C640

C93C696C93C522

C432C640C433C640

C670C316

C350C640

C438C640

C93C740

C439C316

C365C316

C404C640

C70C640C436C640C420C640C210C696

C732C316

C350C316

C210C522

C210C740

C210C316

C350C696C350C522C694C316

C652C316

C439C740C93C640

C350C740

C210C640

C439

.5

C640

C2C696

C2C316

C140C696

C2C640

C2C522

C140C522

C140C740

C140C316

C140C640C690C316

01

e( s

imila

rvot

es |

X )

-.5

-5 0 5 10e( visitstotindeg | X )

coef = -.02445031, se = .00842439, t = -2.9

Dyads: Countries: C690C316 C200 C140 C2 1999 Visits Total In-Degree Centrality

319

C200C522

C840C316

C200C696

C565C316C541C316C211C522

C200C316C346C451

C346C660

C200C740

C346C510C346C551C346C501

C900C316

C346C483C551C316

C200C346

C344C522

C346C702

C235C522C344C696C346C616C346C540C290C522

C346C696

C451C316

C660C316

C438C696

C211C696C510C316C522C316

C346C705C501C316C483C316

C346C565

C346C522

C702C316

C344C346

C346C770

C290C696C310C522

C41C522

C346C541C346C484

C235C696

C540C316

C616C316C346C694

C346C600

C70C522

C390C316C560C316C346C840C346C692

C696C316

C705C316

C290C316

C438C522

C41C696

C385C316

C404C316

C346C625

C346C572

C310C696

C101C522

C346C698

C211C316

C770C316

C484C316

C70C696

C346C390

C220C522

C600C316

C150C316C346C404

C346C385C91C522

C694C316

C42C522

C145C316

C211C740

C90C522

C625C316

C432C316

C211C346

C52C522

C692C316

C235C316

C572C316

C52C316

C210C522

C110C316

C51C522

C91C696

C698C316

C94C316C165C316

C371C316

C51C316

C42C696

C310C316

C93C316

C90C696

C235C740

C230C522C346C438C220C696C475C316C346C433

C91C316

C135C346

C359C316

C434C316

C235C346

C95C316

C52C696

C290C346

C42C316

C20C522

C51C696

C372C316

C90C316C92C316

C740C316

C346C369C439C696

C130C316

C290C740

C346C316

C155C316

C310C740

C346C461

C346C481

C210C696

C160C316C369C316

C437C696

C346C663

C41C346

C346C690

C346C900

C160C346

C101C316

C310C346

C41C316C346C615C230C696C350C522

C2C740

C70C316

C70C346

C346C370

C346C372C346C475

C346C432C346C371

C461C316

C346C434

C438C316

C20C696

C155C346

C663C316

C101C346

C150C346C110C346

C481C316

C344C316C346C373

C346C620C615C316C350C696

C437C522

C732C316

C346C651C210C316

C690C316

C220C316

C346C359

C92C346

C220C346

C91C346

C750C316C52C740

C42C346

C2C522

C90C346

C165C346

C210C740

C220C740C95C346C52C346

C433C316

C230C316

C130C346

C93C346

C210C346

C135C316

C51C346

C94C346C145C346

C346C701

C346C652

C620C316C651C316

C850C316

C20C316

C230C740

C51C740

C70C740

C200C640

C42C740C90C740

C230C346

C439C522

C350C316

C20C740

C92C740C325C522

C91C740

C20C346

C2C696C652C316C701C316

C704C316

C346C437

C350C740

C373C316

C344C740

C346C350

C452C316C370C316C325C696

C346C740

C346C452

C346C439

C100C316

C41C740

C2C316

C346C732

C438C740

C100C346

C211C640

C2C346

C346C850

C325C316

C346C731C346C670C346C750

C439C316

C235C640

C325C740

C290C640

C437C316

C325C346

C640C316

C346C365

C731C316

C310C640

C670C316

C140C346

C140C316

C70C640

C220C640

C92C640

C91C640

C42C640

C90C640

C437C740

C210C640

C52C640

C350C640C365C316

C344C640

C51C640

C230C640

C346C640

C20C640

C41C640

C439C740C438C640

C2C640

C325C640

.5

C437C640

C439C640

C346C710C710C316

01

e( s

imila

rvot

es |

X )

-.5

-5 0 5 10e( visitstotindeg | X )

coef = .01692723, se = .00811576, t = 2.09

Dyads: Countries: C437C640 C640 C439C640 2000 Visits Total In-Degree Centrality

320

C344C346

C344C522

C850C316

C20C740

C560C316C572C316C501C316C731C316

C101C740

C432C522

C540C316C483C316

C438C522

C434C522

C481C316

C310C316

C551C316

C452C316

C840C316

C140C740

C101C522

C438C740

C900C316

C344C740C235C316

C620C316C230C522

C433C522

C310C522

C712C316C565C316C615C316

C420C522

C432C740

C439C522

C522C316C235C522

C461C316C325C522C145C740C230C316

C20C316

C140C522

C420C740C438C316

C145C522

C135C740

C439C740

C210C522

C210C740

C346C850

C230C740

C325C316C625C316C704C316

C200C740

C325C740

C432C316

C41C740

C600C316

C698C316C346C560C701C316

C370C740

C370C522

C70C740C100C740C359C522

C373C740C210C316C651C316

C130C740

C200C522

C434C740C433C740

C390C522C101C316

C310C740

C616C316C390C316C385C316C371C522C703C316

C420C316C91C740

C235C740

C385C522C93C740C90C740

C740C316

C110C740C92C740C42C740C372C522C51C740

C439C316C95C740

C150C740

C155C740C94C740

C373C522

C20C522

C100C522

C93C522

C290C316

C130C522C91C522

C200C316C359C740C346C522

C51C522

C90C522C135C522

C371C740

C165C740C344C696

C41C522

C94C522C101C696C95C522

C372C740

C390C740C42C522

C435C740

C92C522

C344C316

C110C522

C385C740

C434C316C52C740

C150C522

C70C522

C694C316

C155C522

C346C900

C433C316C373C316C145C316

C165C522

C101C346C370C316

C346C432

C230C696

C230C640C145C346

C435C522

C346C840

C52C522

C310C640

C20C696

C346C712

C140C696

C41C316C346C501C346C572C325C696C310C696

C640C316

C235C696C696C316

C432C696

C371C316

C325C640C235C640

C346C731

C20C640

C135C316

C210C696

C359C316

C365C740

C346C438

C145C696

C346C540C346C483

C372C316

C346C551

C434C696

C140C316

C110C316

C346C565C130C316

C91C316

C210C640

C93C316C100C316

C346C452

C150C316C346C481

C438C696

C200C696

C390C696C92C316C90C316

C346C420

C51C316

C20C346

C42C316C385C696

C200C640C110C346

C95C316C70C316

C150C346

C94C316C359C696

C93C346C433C696C91C346C155C316C390C640C346C439C41C346

C346C434

C750C316

C100C696

C346C433

C371C696

C165C316

C385C640C51C346

C346C698

C372C696

C346C620

C130C696

C346C615C52C316

C93C696

C90C346

C350C316C90C696

C92C346

C51C696C94C696C95C696C91C696C155C696C70C696

C140C346

C42C696

C94C346C42C346

C130C346

C92C696C135C696

C346C461

C110C696

C165C696

C95C346

C150C696C346C740C370C696

C451C316

C439C696

C100C346

C346C373C420C696C52C696

C346C625C290C522

C346C704

C484C316

C165C346

C41C696

C135C346

C70C346

C52C346C346C701

C155C346C346C600

C435C696

C373C640

C371C640C346C651

C346C703

C369C522

C160C740C346C435

C372C640

C652C316C346C371

C310C346

C2C740

C346C359

C350C640

C350C522

C435C316

C373C696C346C616

C346C316

C346C372

C101C640

C235C346C230C346

C369C740

C344C640

C160C522

C290C740

C211C316

C325C346

C438C640

C346C370

C211C522

C732C316C365C522C140C640

C210C346

C432C640

C350C740

C702C316

C346C694

C692C316

C346C750

C211C740

C434C640C145C640C370C640C705C316

C346C390C346C385

C420C640

C200C346

C439C640

C346C696

C135C640

C290C640

C433C640

C41C640C160C316

C290C696

C359C640

C369C316

C369C696

C100C640C130C640

C220C740

C93C640

C51C640C90C640

C160C696

C94C640C95C640C350C696C91C640C220C522

C42C640C110C640

C70C640C92C640

C365C316

C155C640

C710C316C150C640C770C316

C165C640C220C316C211C696

C365C696

C52C640C346C451C346C484

C211C640C369C640

C346C652C160C346

C346C732

C437C522

C365C640

C220C696

C290C346

C346C702

C220C640

C346C640

C670C316

C346C365

C346C369

C435C640

C475C316C346C692

C437C740C346C705

C346C350

C211C346

C160C640C690C316C346C770C346C710

C437C316

C220C346C437C696

C346C475

C346C437C346C670

C660C316

C346C690

C437C640C663C316

C346C660C346C663

C2C316

C2C522

C2C696

C2C640

C2C346

.50

1e(

sim

ilarv

otes

| X

)-.5

-1

-2 0 2 4 6e( visitstotindeg | X )

coef = -.02199866, se = .00857886, t = -2.56

Dyads: Countries: C2C346 C2 Figure 17: Partial-regression leverage plots for small sample analysis - Visits Total Out-Degree Centrality

321

1990 Visits Total Out-Degree Centrality

C100C522

C100C640

C100C696

C100C740

C355C696C355C522

C160C522

C160C640

C160C696

C260C640

C260C740

C355C740

C437C696

C160C740

C390C522

C145C640

C235C522

C145C522C145C696

C385C522

C145C740

C20C640

C260C522

C437C522

C350C522

C437C640

C375C640

C260C696

C235C740

C355C640

C390C640

C235C640

C235C696

C390C740

C70C522

C350C740

C385C640

C434C696

C20C696C360C696

C325C522

C385C740

C390C696

C70C696

C350C696

C434C640

C435C696

C310C640

C375C696

C385C696C310C696C360C522

C437C740

C211C522

C433C696

C210C522

C438C696

C310C522C20C522

C70C640

C325C740

C325C640

C434C522

C230C522

C135C522

C439C696

C433C740

C435C640

C433C640C135C640

C155C522

C432C696

C70C740

C436C696

C135C696

C95C522

C155C640

C404C696

C210C740

C211C640

C325C696

C155C696

C435C522

C95C640

C135C740

C230C740

C210C640

C211C740

C310C740

C92C522C433C522C95C696

C438C522

C360C740

C52C522

C434C740

C438C740

C438C640

C2C740C420C740

C155C740C420C640

C92C696

C52C640

C92C640

C165C522

C130C522

C230C640

C95C740

C130C640

C90C522

C42C522

C51C522

C51C640

C165C640

C211C696

C439C522

C210C696

C439C740

C439C640

C130C696C90C696

C42C696

C230C696

C420C696

C52C696

C432C522C94C522

C51C696

C375C522C436C522

C20C740

C432C740

C432C640

C42C640

C436C740

C436C640C92C740

C94C640

C165C696

C130C740

C51C740

C91C522

C52C740

C165C740

C404C522

C91C696

C94C696

C375C740

C150C522

C90C640

C350C640

C91C640

C94C740

C404C740

C404C640C42C740

C150C696

C41C522C41C696

C41C640

C90C740

C91C740

C140C522

C360C640

C420C522

C140C640

C150C640C41C740

C140C696

C220C522

C140C740

C150C740

C315C696

C290C696

C220C740

C435C740C290C522

C315C522

C315C640

C220C640

C290C640

C220C696

C315C740

C2C640

C290C740

C200C522

C200C740

C2C696

C2C522

C200C640

C200C696

C101C522

C101C640

C101C696

C101C740

C93C522C93C696

C93C640

C93C740

-.50

.5e(

sim

ilarv

otes

| X

)

-4 -2 0 2 4e( visitstotaloutdeg | X )

coef = -.01031769, se = .01488286, t = -.69

Dyads: Countries: C100C640 C100 C100C740 C100C696 C100C522

322

1991 Visits Total Out-Degree Centrality

C20C640

C20C696

C20C740

C290C696

C210C522

C310C696C315C696

C325C522C230C522C211C522

C290C640

C310C640

C355C696

C235C522

C210C640

C437C696C437C522

C90C696

C350C522

C315C640

C437C640

C290C740

C210C696C230C696

C325C640

C70C696

C235C696

C230C640

C211C640

C52C696

C51C696

C350C696

C310C740C315C740

C42C696

C325C696C355C640

C91C696

C2C740

C235C640

C211C696C90C640

C41C696

C290C522

C350C640

C438C696

C70C640

C438C522

C439C696

C52C640

C439C522

C220C522

C200C522

C438C640

C355C740

C439C640

C90C740

C20C522

C315C522

C52C522

C210C740

C310C522

C41C640C230C740

C42C640

C70C740

C51C640

C91C640

C90C522

C70C522

C220C640C325C740C235C740

C41C740

C350C740

C52C740

C42C740

C51C740

C51C522

C91C740

C200C640

C220C696

C211C740

C42C522

C200C696

C355C522

C91C522

C95C522

C437C740

C41C522

C200C740

C220C740

C438C740C439C740

C2C640

C2C696

C2C522

-.50

.5e(

sim

ilarv

otes

| X

)

-10 0 10 20 30e( visitstotaloutdeg | X )

coef = -.0007841, se = .00503847, t = -.16

Dyads: Countries: C2C522 1992 Visits Total Out-Degree Centrality

323

C325C522

C211C522

C325C696

C346C696C346C694

C211C696

C41C696

C437C696

C344C696

C404C696

C20C346

C20C696

C346C670

C325C640

C439C696

C20C640C435C696

C70C696

C438C696

C230C522

C135C696

C230C696

C220C522

C346C710C365C696

C433C696

C325C346

C344C522

C210C522

C390C522

C211C640C211C346

C346C522

C220C696

C385C522

C20C740

C350C696

C346C651

C365C522

C90C696

C437C522

C20C522

C325C740

C210C696

C346C452

C92C696

C346C660

C346C616

C70C346

C434C696

C346C625

C350C522

C140C696

C110C696C91C696

C349C696

C404C522

C346C620

C93C696

C346C481

C346C461

C130C696

C42C696

C346C490

C346C540

C346C690

C346C530C346C435

C150C696

C390C696

C439C522

C346C600

C155C696

C346C450

C438C522C346C475

C432C696

C220C640

C101C696

C346C900

C349C522

C346C501

C385C696

C145C696

C436C696

C95C696

C420C696

C346C541C365C640

C100C696

C346C516

C346C483

C433C522

C41C522

C211C740C70C522

C346C645

C346C451

C51C696

C220C346

C346C484C52C696

C346C390

C94C696

C2C740

C230C346

C235C696

C230C640

C165C696

C346C770

C346C482

C346C385

C135C346C346C510

C346C551

C150C346

C350C640

C90C346

C110C346C235C522

C92C346

C346C652

C346C433C210C640C41C346C91C346

C52C346

C346C517

C210C346

C135C522

C390C640

C346C750

C346C840

C230C740

C346C615

C346C732

C220C740

C93C346

C42C346

C346C404C346C663

C95C346C165C346

C130C640

C130C346

C349C640

C346C439

C101C346

C140C640

C346C698C155C346

C434C522

C52C640

C346C692

C140C346

C385C640

C145C346

C346C731

C92C640

C344C346

C94C346

C145C640

C155C640

C101C640C51C346

C346C438

C346C434

C346C350C165C640

C346C436

C100C346

C100C640

C346C432C346C420

C51C640

C140C522

C94C640

C95C640

C432C522

C150C640

C110C640

C90C522

C436C522

C91C640

C420C522

C346C771

C93C640

C210C740

C92C522

C346C349

C42C640

C155C522

C101C522

C52C522

C350C740

C130C740

C150C522

C70C640

C346C640C140C740

C130C522

C110C522

C346C365C346C437C42C522

C91C522

C95C522C160C696

C165C522C235C346

C93C522

C90C640

C92C740

C51C740

C100C522

C434C640

C94C740

C145C740

C436C640

C420C640

C100C740

C165C740

C365C740

C155C740C94C522

C101C740

C390C740

C145C522

C432C640

C235C640

C52C740

C51C522

C435C522

C95C740

C150C740

C110C740

C385C740

C91C740C93C740

C42C740

C435C640

C70C740

C436C740

C420C740C160C346

C344C640

C135C640

C235C740

C432C740

C90C740C434C740

C433C640

C41C640C435C740

C160C640

C349C740

C160C522C433C740

C200C522

C135C740

C439C640

C437C640

C404C640

C346C740

C438C640

C41C740

C200C696

C439C740C404C740

C160C740

C438C740

C437C740

C344C740

C200C640

C200C740

C200C346

C2C640

C2C346

C2C522

C2C696

0.5

e( s

imila

rvot

es |

X )

-.5

-10 -5 0 5 10 15e( visitstotaloutdeg | X )

coef = -.02044727, se = .00408605, t = -5

Dyads: Countries: C2C696 C2C522 1993 Visits Total Out-Degree Centrality

324

C346C698C160C640

C346C670

C325C696

C325C522C325C346

C346C692C325C640C346C696

C160C696C100C640

C346C694C160C346

C385C522

C210C522

C346C640

C210C696

C346C461

C346C385

C385C696

C210C346C70C640

C100C696

C70C696

C385C640

C365C640

C210C640

C346C530C230C696

C211C522C93C640

C211C696C155C640

C41C696

C390C522

C211C346

C437C696

C220C346

C346C651

C350C696

C346C615

C346C390

C346C522

C350C640

C390C696

C220C522C230C522

C220C696

C2C740

C160C522

C70C346

C349C640

C165C640C130C640

C211C640

C52C640

C93C696

C220C640

C435C640

C100C346

C230C346

C346C690

C95C640C390C640

C325C740

C433C696C436C696

C94C640C42C640

C436C640

C41C640C92C640

C51C640

C365C696C90C640

C344C696

C91C640

C346C490

C438C696C155C696

C230C640

C135C640C349C696C140C640

C135C696

C145C640

C350C522

C160C740

C346C616

C90C696

C346C350C150C640

C439C696

C130C696

C346C620C404C696

C110C640

C42C696

C95C696C346C645C165C696

C91C696

C41C346C346C600C92C696

C346C660

C346C900

C94C696C155C346

C235C696

C434C696

C346C452

C52C696

C51C696

C433C640

C140C696

C93C346

C346C481

C349C522C437C640

C145C696

C346C435C435C696

C150C696

C432C696

C101C640C434C640

C70C522

C100C740

C346C451

C346C710

C20C346

C110C696C420C696

C432C640

C420C640

C344C640

C165C346

C100C522

C365C522

C437C522

C210C740

C135C346C52C346C346C516

C346C540

C346C483

C346C482C346C484

C346C436

C90C346

C344C522

C95C346

C235C522

C130C346

C20C640

C438C640

C346C510C346C541C346C551

C346C501

C346C517C346C771

C385C740

C110C346C94C346

C42C346

C346C770

C235C346

C140C346

C92C346

C220C740

C439C640

C433C522

C2C346

C436C522C230C740

C51C346C91C346

C438C522

C70C740

C150C346

C101C696

C404C640

C41C522

C211C740

C20C696

C235C640C2C640

C145C346

C346C437

C346C433

C346C750C155C522

C346C404

C439C522

C93C740

C346C732C404C522

C350C740

C346C349C346C365C346C420C93C522

C346C652

C155C740

C346C432

C390C740

C346C439C101C346C346C663

C434C522

C165C522C130C740

C135C522C52C522

C346C438

C165C740

C90C522

C346C434

C346C740

C432C522

C95C522C94C740

C130C522

C435C740C2C696

C95C740

C420C522

C52C740C51C740

C94C522C42C522

C42C740

C41C740

C140C522

C92C740C140C740

C92C522

C90C740

C436C740C145C740

C91C740C135C740

C51C522C91C522C346C840

C365C740

C346C731

C150C522C150C740

C145C522

C20C740

C110C740

C235C740

C344C346C20C522

C110C522

C101C522

C437C740

C433C740

C346C625

C434C740C101C740

C349C740

C432C740

C420C740

C346C475

C438C740C439C740C2C522

C404C740

C435C522

C344C740.5

C200C696

C200C522C200C346

C200C640

C200C740

01

e( s

imila

rvot

es |

X )

-.5-1

-5 0 5 10 15e( visitstotaloutdeg | X )

coef = .00344946, se = .00757913, t = .46

Dyads: Countries: C200C740 C200 C200C640 C200C696 C200C522 C200C346 1994 Visits Total Out-Degree Centrality

325

C346C690

C350C640

C230C346

C346C385

C211C346

C230C696

C346C670

C346C750

C346C698

C230C740

C230C522

C140C640

C346C645

C385C522

C70C346

C140C346C433C696

C140C696

C211C696C346C692

C230C640C437C696

C211C740

C70C696

C211C522

C385C696C344C346

C95C346

C140C740

C70C640

C346C350

C95C696

C211C640

C346C900

C220C346

C160C346C2C346

C95C640

C385C640

C346C651

C346C390C346C696

C433C522

C385C740

C235C346

C160C640C70C522C346C433

C346C437

C433C640

C210C346

C346C652

C438C696

C420C696

C325C346C20C346

C439C696

C165C346C52C346

C135C696

C101C346

C135C346

C160C696

C437C522

C95C522

C346C435

C100C346

C140C522

C346C732

C100C696

C235C696C435C640

C346C540

C437C640

C90C696

C90C346

C346C516C70C740

C346C694

C350C696

C100C640

C101C696

C101C640

C165C640

C346C770

C130C696

C346C522

C432C696

C94C346

C130C346

C95C740

C346C510

C436C696

C150C346

C346C551

C52C640

C91C696C165C696

C92C346

C2C740

C52C696

C346C616

C220C740

C92C696

C130C640

C346C660

C41C696

C94C696

C51C346

C420C522

C51C696

C438C522C150C696

C93C696

C346C620

C91C346

C439C522

C110C696

C220C696C135C640

C346C640

C160C522

C41C346C145C696

C346C560

C93C346C110C346C145C346

C94C640

C235C740

C51C640

C346C710

C346C600

C92C640C235C522C390C522C91C640

C346C481

C436C640

C90C640

C350C522

C432C640

C150C640

C52C522

C434C696

C350C740

C145C640C41C640

C165C522C325C740

C93C640C325C696C210C696

C210C740

C220C522

C110C640

C346C438C101C522

C220C640

C346C625

C432C522

C100C740

C346C452

C160C740

C2C640C436C522

C135C522

C435C696

C90C522C346C461

C390C696

C438C640

C150C522C437C740

C346C434

C346C439C210C522

C346C475

C2C696

C94C522

C346C432

C92C522

C100C522

C346C420

C346C451

C433C740

C325C522

C439C640

C346C436

C346C840

C41C522

C101C740

C344C696

C130C740C51C522

C110C522

C434C640

C420C640

C130C522

C165C740C210C640C325C640

C93C522

C91C522

C346C484

C346C482

C346C517

C346C572

C20C696

C20C740

C145C522

C346C501

C94C740

C52C740

C390C640

C135C740

C434C522

C390C740

C2C522C51C740

C235C640

C346C731

C20C522

C346C663C92C740C20C640C91C740

C346C615

C90C740

C150C740

C435C740

C145C740

C344C522

C93C740

C155C346

C346C740C41C740

C110C740

C155C640

C155C696

C435C522

C344C640

C434C740

C436C740

C432C740

C346C771

C346C541

C438C740

C365C640C200C346C155C522

C439C740

C420C740

C155C740

C200C740

C344C740

C200C696

C365C740

C200C522

C200C640

C365C522

C346C365

C365C696

0.5

e( s

imila

rvot

es |

X )

-.5

-5 0 5 10e( visitstotaloutdeg | X )

coef = -.01734874, se = .00599549, t = -2.89

Dyads: Countries: C346C365 C365 C365C522 C365C696 1995 Visits Total Out-Degree Centrality

326

C325C522

C325C346

C325C696

C325C640

C325C740

C235C696C235C522

C211C522

C235C346

C211C346

C140C696C20C346

C211C696

C344C696C369C696C210C522C346C670

C20C522

C235C740

C235C640

C437C696

C210C346

C211C640

C390C522

C140C346

C346C698

C140C522C346C390

C346C690

C20C696

C346C369

C346C696

C140C740C20C640

C344C522C210C696

C135C696

C211C740

C20C740

C140C640C346C694

C346C350

C210C640

C350C696

C369C522C346C660C346C770C390C696

C230C522

C350C522

C155C696C390C640

C435C696

C370C696

C210C740

C369C640C230C696

C230C346

C373C696

C437C522

C346C663

C346C705

C2C740C344C346C135C346

C350C640

C346C370

C433C696C369C740

C346C522C346C625

C135C522

C346C701

C160C696

C155C346

C346C540

C346C692

C101C696C439C696

C130C696

C385C522

C155C522C90C696C70C696

C346C615

C230C640

C350C740

C230C740

C370C522

C346C703

C346C385

C390C740

C346C560

C346C452

C438C696

C346C620C346C461

C160C346C420C696

C155C640

C346C481

C346C702C91C696

C160C522

C95C696

C346C600C346C750C346C510C155C740C346C437

C92C696

C373C522

C346C475C346C704

C365C696

C346C451

C101C346C346C483

C94C696

C70C346C52C696

C385C696

C101C522

C346C484

C435C522

C51C696

C346C541

C346C551

C150C696

C70C522

C433C522

C385C640C346C365

C359C696

C346C771C145C696

C372C696

C160C640

C371C696

C432C696

C130C346C93C696C346C840

C90C346

C346C572

C346C651

C41C696

C135C740

C439C522

C130C522

C135C640

C130C740

C101C640

C110C696C90C522

C436C696

C95C740C101C740

C404C696

C160C740

C100C696

C130C640

C434C696

C95C346

C52C346

C438C522C344C640

C365C522

C95C522C52C522

C346C616

C420C522C346C372

C92C346

C346C740

C346C435

C91C346C94C346C346C433

C346C732

C346C359

C344C740

C92C522

C70C740

C346C371

C91C522

C95C640

C94C522

C94C740C150C346

C385C740

C52C640

C370C640

C372C522

C51C346

C70C640

C370C740

C150C522

C437C640C437C740C52C740

C346C710

C346C373C346C731

C165C696C51C522C365C640

C92C640C51C740C92C740C91C640C359C522C94C640

C91C740C346C438C145C346C145C740

C365C740

C371C522

C346C404

C90C640

C432C522

C90C740

C51C640

C100C346

C145C522

C93C346

C145C640

C372C640

C100C522

C41C346C110C346

C346C501

C93C640

C93C522

C150C640C93C740

C150C740

C436C522

C346C420

C359C640

C359C740

C41C522

C110C522

C404C522C434C522

C346C439

C373C640

C435C640

C165C346

C432C640C41C640C433C640

C41C740C346C434

C436C640

C371C640

C100C740

C165C522

C346C432

C372C740

C100C640

C346C652

C110C640

C110C740C346C436

C435C740

C220C522

C165C740

C432C740C433C740

C434C640

C165C640C438C640

C220C346

C434C740

C371C740

C436C740C404C640

C373C740

C220C696

C439C640

C438C740

C404C740C220C640

C220C740C439C740

C420C640C420C740

C346C850

C346C900

C2C346

C2C522C2C696

C2C640

C200C522C200C346

C200C740

C200C696

C200C640

0-.5

.5e(

sim

ilarv

otes

| X

)-1

-5 0 5 10 15 20e( visitstotaloutdeg | X )

coef = -.02661239, se = .00405733, t = -6.56

Dyads: Countries: C2C522 C200 C2C696 others 1996 Visits Total Out-Degree Centrality

327

C230C696

C230C522

C140C696

C230C640

C210C522

C210C696

C210C640C140C740

C369C696

C230C346C140C346

C230C740

C140C522

C210C346

C210C740

C346C369

C369C522

C369C740

C140C640

C350C522

C344C522

C20C346

C20C640

C344C696

C20C740

C20C696

C70C696

C211C522

C211C696C390C522

C346C350

C390C696

C211C640

C346C850C390C640

C70C740

C437C696

C235C696

C100C696

C70C346

C350C740

C350C640

C235C522

C2C740

C211C346

C369C640

C346C651C346C670C346C390

C20C522

C433C696

C70C522

C344C346

C437C522

C235C640

C220C696

C211C740

C346C501

C220C522

C365C696

C100C740

C101C696

C220C640

C346C770

C100C346

C325C696

C325C522

C370C696

C346C750

C390C740

C346C600

C130C696

C344C740

C373C696

C346C615

C433C522

C70C640C235C346

C325C640

C346C365

C100C522

C437C740

C346C731

C220C740

C346C510

C235C740

C346C551

C346C483C346C541C155C696C346C771C220C346

C344C640

C346C437

C346C540

C95C696

C346C484

C165C696

C135C696

C101C346

C438C696

C101C740

C346C370

C365C522

C370C522

C346C616

C42C696

C435C696

C130C740C346C461

C346C451

C346C452C95C740

C325C346

C100C640

C130C346C346C481

C373C522

C346C705

C51C696C101C522

C90C696

C346C696

C432C696

C437C640

C434C696C346C702

C325C740

C155C346

C346C522

C359C696

C436C696

C370C740

C91C696

C155C740

C94C696

C346C704

C165C346

C346C703C438C522

C52C696

C165C740

C346C450

C95C346

C130C522

C346C660C433C740

C135C740

C372C696

C135C346

C346C732

C420C696

C346C433

C92C696

C346C690

C93C696C365C740

C155C522C42C740C150C696

C433C640

C165C522

C404C696C432C522

C42C346

C101C640

C434C522

C371C696

C373C740C95C522C436C522

C346C404

C160C696

C51C740

C90C740

C41C696C346C373C135C522

C51C346C90C346

C130C640

C145C696

C346C359

C91C740

C94C740

C52C346

C370C640

C346C420

C346C692

C94C346

C359C522

C52C740

C91C346

C42C522C420C522C346C372

C155C640

C346C436

C372C522

C346C475

C110C696

C404C522

C165C640

C359C740

C92C740

C51C522

C90C522

C346C432

C93C740C95C640

C92C346

C52C522

C150C740

C94C522

C434C740

C150C346

C135C640

C93C346

C438C740

C160C346

C365C640

C91C522

C346C371C346C435C346C840

C346C434C346C438

C371C522

C160C740C372C740

C346C572

C41C740C42C640C346C652

C438C640

C145C740

C435C740

C92C522

C41C346C346C620

C150C522C145C346

C385C522

C371C740

C432C740

C51C640

C90C640

C93C522

C373C640

C346C740

C160C522

C439C696

C385C696

C91C640

C436C740

C94C640C52C640

C435C522

C110C346C346C625C110C740

C432C640

C359C640

C434C640

C41C522

C385C640

C436C640

C145C522

C346C640

C420C740

C92C640C93C640

C150C640

C404C740

C160C640C420C640

C110C522C439C522

C41C640C435C640

C404C640

C145C640

C346C385C110C640

C372C640

C385C740C346C560

C346C439

C371C640

C439C740C439C640

C346C663

C346C900

C346C710

C2C346

C2C640

C2C696

C346C694

C346C698

C200C696

C200C522

C200C640

C200C740

C200C346

C2C522

.50

1e(

sim

ilarv

otes

| X

)-.5

-5 0 10 155 20e( visitstotaloutdeg | X )

coef = -.00530063, se = .00513983, t = -1.03

Dyads: Countries: C2C522 C200 1997 Visits Total Out-Degree Centrality

328

C290C696

C70C696

C481C316

C160C316

C771C316

C140C316

C437C696

C501C316

C230C696

C290C522

C439C696

C100C696

C350C696

C438C696

C210C696

C41C696

C290C640

C235C696C690C316C663C316

C770C316

C211C696

C230C522

C310C696

C145C316

C696C316

C41C316C210C522

C651C316

C90C696

C211C522

C42C696C91C696C230C640

C350C522

C210C640

C437C522

C70C640

C235C522

C51C696

C325C696

C435C316

C220C696

C211C640

C432C316C70C316C522C316

C52C696

C230C316

C2C740

C70C522

C20C696

C732C316C439C522

C210C316

C670C316C20C640C310C522

C235C640C325C522

C70C740

C438C522

C290C740

C541C316

C220C640

C211C316C220C522C93C316

C310C316

C560C316

C51C316

C640C316

C369C316

C385C316

C290C316

C110C316

C91C316

C20C316

C150C316C94C316

C90C316

C235C316

C551C316

C95C696

C42C316

C92C316

C130C316

C325C640

C310C640

C510C316

C100C640C52C316

C483C316

C220C316

C101C316

C100C316

C41C640

C165C316

C404C316

C155C316

C325C316

C484C316

C452C316

C359C316

C230C740

C350C316

C451C316C434C316

C850C316

C703C316

C702C316

C750C316

C100C740

C461C316

C572C316

C840C316C450C316

C660C316

C90C640

C372C316

C390C316

C210C740

C42C640

C900C316

C694C316

C350C640

C705C316

C41C740

C475C316

C41C522

C92C640

C350C740

C615C316

C91C640

C616C316

C235C740

C704C316

C438C316C652C316

C51C640

C433C316

C95C316

C439C316

C211C740

C600C316

C371C316

C436C316C701C316

C52C640

C373C316

C90C740

C20C740

C310C740C90C522C740C316

C540C316

C620C316C42C740

C42C522C220C740

C698C316

C51C740

C437C316

C731C316

C91C522

C52C740

C135C316

C52C522

C91C740

C51C522

C438C640

C437C640

C692C316

C439C640

C370C316

C325C740

C95C640

C95C522

C20C522

C437C740

C2C696

C2C640

C365C316

C438C740

C200C696

C439C740

C2C316

C200C522C710C316C200C640

C200C316

C2C522

C625C316

C200C740.4.2

0.6

e( s

imila

rvot

es |

X )

-.2-.4

-5 0 5 10e( visitstotaloutdeg | X )

coef = -.00479654, se = .0059185, t = -.81

Dyads: Countries: C2C522 C625C316 C200C740 1998 Visits Total Out-Degree Centrality

329

C690C316

C210C522

C210C640

C390C522C20C640

C390C640

C210C696

C210C316

C900C316

C20C316

C732C316

C390C316C350C522

C390C696C350C696

C20C696

C140C696

C210C740

C140C640

C435C696

C140C316

C211C522C2C740

C350C640

C385C522

C694C316C230C522

C350C316

C20C740

C220C640

C140C740

C220C522

C93C696

C435C640

C211C640

C160C640

C439C696

C290C696

C390C740

C235C522

C385C640

C365C696

C52C696

C160C696

C165C696

C230C696C165C640

C750C316C230C640

C155C696

C52C640C93C640C522C316

C350C740

C155C640

C435C316

C698C316

C290C522

C235C696

C220C316

C696C316C211C696

C93C316

C140C522

C510C316

C481C316

C365C316C95C696

C211C316

C94C696C110C696

C325C522

C42C696

C235C640

C92C696

C160C316

C100C696

C51C696

C165C316C52C316

C135C696C41C696

C325C640

C90C696

C385C316

C145C696

C155C316

C130C696

C91C696

C438C696

C310C696C385C696

C230C316C420C696

C95C640

C436C696

C290C640

C94C640C110C640

C42C640

C692C316

C365C640

C92C640

C840C316

C220C696

C432C696

C100C640

C51C640

C433C696

C310C522

C439C522

C41C640

C90C640C95C316

C365C522

C145C640

C20C522

C94C316C135C640

C572C316

C130C640

C91C640

C434C696

C42C316

C437C696

C100C316

C110C316

C92C316C51C316

C70C696

C90C316

C235C316

C145C316

C731C316C135C316C41C316

C130C316C439C640C93C740

C370C696

C541C316C540C316C91C316C551C316

C484C316

C160C522C372C696

C482C316C475C316

C160C740

C70C640

C705C316

C373C696

C450C316

C290C316

C325C696

C370C522

C165C522

C165C740

C155C740

C371C696

C310C640C52C522C230C740

C483C316

C439C316

C155C522

C660C316

C652C316C370C640C325C316

C359C696

C703C316C70C316

C369C696

C438C522C290C740

C620C316C616C316C701C316

C310C316

C435C740

C372C522

C404C522C365C740C420C522C211C740

C370C316

C372C316

C95C740

C436C522

C94C740

C704C316

C359C640

C100C740C42C740

C432C522

C600C316

C433C522

C110C740

C92C740C51C740

C235C740

C438C640

C371C316

C145C740

C93C522

C404C640

C625C316

C90C740

C41C740

C130C740

C369C316

C420C640

C135C740

C434C522

C373C522C432C640

C437C522

C359C316

C70C740

C150C696

C436C640

C369C522

C91C740

C371C522

C373C316

C372C640

C433C640

C101C696

C740C316

C434C640C452C316

C95C522

C461C316C451C316

C359C522

C385C740

C438C316

C371C640C439C740

C94C522

C373C640

C437C640C710C316

C372C740C100C522

C404C316

C370C740

C310C740C42C522

C420C316C432C316

C150C640

C434C316

C369C640C436C316

C92C522

C433C316

C101C640

C135C522

C110C522C371C740

C663C316

C437C316

C651C316

C51C522C90C522

C359C740

C101C316C150C316

C130C522

C369C740C70C522C145C522C41C522C325C740

C770C316

C91C522C438C740

C404C740

C420C740

C432C740

C434C740

C436C740

C435C522

C433C740C437C740

C101C740C150C740

C615C316

C101C522

C150C522

C850C316C560C316C670C316

C2C640C2C316

C200C522

C200C640

C200C696

C200C316

C200C740

C2C696

C2C522

0-.5

.5e(

sim

ilarv

otes

| X

)-1

-5 0 5 10 15e( visitstotaloutdeg | X )

coef = -.00246987, se = .00517502, t = -.48

Dyads: Countries: C2C696 C200 C2C522 1999 Visits Total Out-Degree Centrality

330

C20C640

C20C316

C325C640C210C640

C210C522C325C522C20C696

C20C346

C385C316

C230C640

C325C696

C390C316C210C316

C230C522

C325C316

C210C696C346C690C230C696

C346C696

C750C316

C140C316C346C698

C560C316

C20C740

C850C316

C344C696

C230C316

C210C346

C325C346

C840C316

C346C390

C350C316

C346C385

C346C694

C235C640

C350C696

C220C640

C565C316C350C522C235C522

C541C316

C211C640

C290C696C235C696

C235C316

C2C740

C211C522

C437C696

C160C316

C439C696C481C316

C20C522

C369C316

C690C316

C732C316

C501C316

C696C316

C211C316

C344C522

C230C346

C210C740

C640C316C41C696

C510C316C346C670

C438C696

C551C316

C325C740

C483C316C522C316C452C316

C310C696

C310C316

C290C640

C475C316

C220C522

C698C316C346C692C290C522C770C316C540C316

C346C522

C70C696

C572C316

C346C640

C350C640

C344C640

C310C640

C230C740

C484C316

C211C696C346C660

C310C522

C100C316

C346C616C155C316C140C346

C694C316

C42C696

C220C696

C90C696C52C696

C51C696

C52C640C346C481

C110C346

C220C316

C235C346

C70C316C70C640

C346C705

C165C316

C41C346

C346C651

C660C316

C346C702

C101C316

C702C316C290C316

C346C452

C130C316

C211C346

C346C701C160C346C344C316C705C316

C670C316

C616C316

C95C316

C52C316

C346C652

C94C316

C346C600

C346C350

C651C316C92C640

C41C640C52C346C42C640

C346C732

C346C316

C135C316C90C640

C346C475

C51C640C701C316

C704C316

C600C316

C220C346

C652C316C90C316C42C316

C346C850C692C316

C437C522

C51C316

C900C316

C346C625

C404C316

C155C346

C437C316

C625C316

C350C740

C346C551

C165C346

C437C640

C70C346

C92C316C145C316

C290C346

C346C510

C145C346

C346C565

C93C316

C346C540

C346C483C135C346

C235C740

C346C501

C110C316

C92C346

C438C522

C346C750

C434C316

C100C346C346C438

C290C740

C346C404

C432C316

C310C346

C346C484C101C346

C91C696

C451C316

C42C346

C439C522

C346C371

C93C346

C346C615C346C439

C438C640

C346C432C346C541C95C346C346C770

C94C346

C346C663C346C840C51C346

C439C640

C346C572

C346C372

C433C316

C90C346

C370C316

C150C346

C41C316

C359C316

C740C316C310C740

C663C316

C372C316

C615C316

C438C316

C371C316

C220C740

C439C316

C130C346

C346C369

C91C640

C346C370

C70C740C346C437

C211C740

C346C433

C346C451

C346C434

C52C740

C346C900

C70C522

C346C359

C52C522

C51C740

C150C316C42C740C90C740C92C740

C91C316

C41C740

C41C522

C461C316

C91C346

C101C522

C346C620

C344C346

C42C522

C710C316

C90C522C51C522

C346C373

C731C316

C346C461C620C316

C437C740

C438C740

C346C740

C439C740C91C740

C373C316

C344C740C365C316

C346C710

C91C522

C346C731

C2C640

C346C365C2C316

C2C696

C2C346

.5

C2C522

C200C640

C200C522C200C696

C200C316

C200C346

C200C740

01

e( s

imila

rvot

es |

X )

-.5

-10 0 10 20 30e( visitstotaloutdeg | X )

coef = -.00121443, se = .00340292, t = -.36

Dyads: Countries: C2C522 C200 2000 Visits Total Out-Degree Centrality

331

C346C690

C346C663

C211C346

C346C696

C732C316

C346C660

C346C732

C346C385

C20C346

C346C390

C437C696

C20C696

C20C640C211C522

C210C346

C211C696

C435C696

C346C692

C346C475C211C640

C346C694

C346C350

C325C346

C346C705C385C522

C437C640

C346C437

C20C522

C2C696

C390C522

C2C640

C20C316

C346C670

C663C316

C370C696

C344C640C2C346

C420C696

C385C696C344C696

C365C696

C390C696

C350C696

C385C640

C210C522

C346C640

C365C640C390C640C475C316

C2C522

C373C696

C439C696

C230C346

C346C702C211C316

C210C696

C346C365C346C481

C290C696

C41C696

C2C740C690C316

C290C346

C210C640

C325C522

C135C696

C235C346

C325C696C350C522

C437C316

C346C484

C2C316

C660C316

C840C316C160C640

C52C640

C346C770

C437C522

C369C696C344C316

C346C616C155C640

C712C316

C230C696C70C640

C346C369

C325C640

C369C640C385C316

C750C316

C211C740

C346C316

C365C316

C165C640

C390C316

C565C316

C220C346

C435C640

C235C696

C346C435C160C696C70C696

C52C696

C230C522

C155C696

C150C640

C140C640

C346C551C52C316C100C640C346C698

C135C346

C346C501

C110C696

C92C640C696C316

C150C696C165C696

C52C346

C140C696

C346C483

C100C696

C42C640

C346C565

C346C370

C130C696

C160C346

C92C696

C350C640

C290C522C210C316

C434C696

C95C640C346C540

C110C640

C346C451C165C316

C42C696

C90C640C155C346

C310C346

C346C522

C91C640C346C461

C346C703

C20C740

C365C522

C95C696

C130C640C235C522C70C346

C372C696C94C640

C433C696

C90C696

C310C696

C91C696

C155C316

C230C640

C51C640

C346C372

C165C346

C438C696

C93C640

C94C696

C346C651

C770C316

C369C316

C433C640

C51C696C900C316

C371C696C385C740

C346C600

C93C696

C346C840

C481C316C94C316

C41C346

C434C640

C70C316

C390C740C290C640

C325C316

C346C439C160C316

C350C740

C95C316

C346C572

C359C640

C484C316C140C346

C210C740

C522C316

C346C371

C150C346C350C316

C346C652

C235C640

C346C701C705C316

C551C316C369C522

C435C316

C110C346

C51C316

C100C346C220C522C150C316C42C316

C346C710

C346C420

C359C696

C344C522C92C346

C42C346

C95C346

C140C316

C501C316

C702C316

C90C316C130C346C135C640

C92C316C346C625

C692C316

C346C731C346C750

C372C640C220C640

C94C346

C220C696C346C434

C110C316

C346C900

C325C740

C100C316

C346C704

C93C316C346C712

C90C346

C346C359

C346C373C640C316

C483C316

C91C316

C371C640C694C316

C91C346

C433C316

C160C522

C52C522

C540C316

C51C346

C310C522

C290C740

C346C615

C230C316C130C316C346C433

C437C740

C41C640

C155C522

C670C316

C93C346C70C522C432C696C230C740

C346C620C434C316

C165C522C346C438

C372C316

C346C452

C372C522

C359C316

C439C640

C235C316C135C522

C371C316

C290C316

C572C316

C310C640

C235C740C344C346

C140C522

C560C316C370C640

C370C522

C420C640

C145C640

C432C640

C145C696C100C522

C150C522C371C522

C740C316

C451C316

C110C522

C135C316C616C316

C369C740C42C522

C95C522

C92C522

C461C316

C130C522

C365C740C731C316

C434C522

C439C522

C94C522

C145C316

C310C740

C41C316C52C740

C41C522

C703C316C220C316

C310C316C90C522

C433C522

C698C316

C359C522

C346C432

C165C740

C420C522

C51C522C91C522

C710C316

C155C740

C850C316

C344C740

C438C640

C70C740

C651C316

C439C316

C93C522

C435C522

C101C640C373C522

C373C640

C346C560

C346C740

C600C316

C160C740

C94C740

C101C696

C420C316

C145C346

C432C316

C220C740

C140C740

C95C740

C452C316C652C316

C701C316C370C316C625C316

C42C740

C51C740

C438C522

C704C316

C150C740

C346C850C90C740

C100C740

C433C740

C92C740C93C740

C101C346

C373C316C110C740

C372C740

C359C740

C432C522

C91C740C130C740

C434C740

C438C316

C371C740

C615C316C101C316

C435C740C620C316

C145C522

C135C740C145C740

C101C522

C41C740

C439C740C370C740C420C740C432C740

C373C740

C438C740

C101C740

C200C346

C200C522C200C696

C200C640

C200C316

C200C740

.50

1e(

sim

ilarv

otes

| X

)-.5

-5 0 5 10 15e( visitstotaloutdeg | X )

coef = .00150497, se = .00427256, t = .35

Dyads: Countries: C200

332

Figure 18: Partial-regression leverage plots for small sample analysis - IGO Connectedness 1990 IGO Connectedness

C260C740

C260C640

C260C696C92C740

C92C640

C92C696

C260C522

C92C522C339C696

C339C740

C315C640

C110C696C40C696C345C696

C40C522C345C522

C2C696

C339C522C404C696

C110C522

C339C640

C20C696C52C640

C355C640

C390C696C94C640

C315C740

C220C696

C380C696

C200C696

C437C696

C352C740

C70C522C150C696

C52C740

C438C696

C385C696

C150C522

C305C696

C51C640

C435C522

C420C740C51C740

C145C640

C375C696

C436C696

C94C740

C315C696

C211C696C210C696

C2C522

C95C640

C325C696

C315C522

C439C696C432C696

C70C696

C420C640C145C740C290C522

C437C522

C360C522

C91C640

C310C640

C130C640

C205C696

C2C640

C90C522

C355C522

C435C696C439C522

C52C696

C432C522

C310C696

C90C696

C404C522C20C640

C290C696

C360C640

C355C696C101C640

C91C696

C434C696

C436C522

C360C696

C438C522

C310C522

C42C696

C93C640C230C696

C42C522

C150C640C100C640

C91C522

C352C696

C305C522

C93C696

C94C696

C20C522

C93C522

C380C522

C90C640

C385C522

C375C522

C165C522

C220C522

C235C696

C205C522

C200C522

C160C522

C94C522

C91C740

C140C522C155C522

C390C522

C95C740

C150C740

C350C696

C165C640

C140C696C352C522

C135C640

C42C640C41C640

C95C522

C165C740

C210C522C325C522

C310C740

C101C522C145C522

C41C522

C145C696

C95C696C100C522

C350C522

C290C640

C211C522

C135C522

C230C522

C110C640

C110C740

C165C696C100C696C52C522

C235C522

C355C740

C140C640

C130C522

C160C696C51C696

C404C740

C433C696

C93C740

C205C740

C2C740

C100C740

C155C696

C42C740C41C740

C101C696

C41C696

C101C740

C385C640

C352C640

C434C522

C51C522

C130C740

C433C522

C155C640

C420C696

C130C696C135C696

C90C740C434C740

C404C640

C390C640C305C740

C436C740

C434C640

C155C740

C200C640

C360C740C420C522

C160C640

C350C640

C432C740C438C740

C40C640C220C640

C135C740

C205C640

C435C740

C235C640

C70C640

C350C740

C211C640

C385C740

C210C640

C375C740

C438C640

C230C640C290C740

C436C640

C390C740

C439C740

C325C640

C211C740C20C740

C235C740

C380C740

C160C740

C432C640

C345C740

C435C640

C375C640

C40C740

C439C640

C433C740

C200C740

C70C740

C325C740

C433C640

C140C740

C230C740

C437C640

C220C740C210C740

C380C640C305C640

C345C640

C437C740

-.50

.51

e( s

imila

rvot

es |

X )

-60 -40 -20 0 20e( igoconnect | X )

coef = -.00411774, se = .00223997, t = -1.84

Dyads: Countries: C260C740 C260 C260C640 C92C640 C260C696 C92C740

333

1991 IGO Connectedness

C40C522

C40C696C345C522C345C696

C41C522

C52C640

C52C740C51C640

C41C696

C437C696C2C696

C339C740

C352C740

C51C740

C315C740

C70C522

C437C522

C91C640

C20C696C315C640

C339C696

C70C696

C220C696

C339C522

C200C696

C305C696

C90C522

C315C522

C211C696

C315C696

C210C696

C2C522

C325C696

C90C696

C2C740

C438C696

C42C640C91C740

C52C696

C310C740

C355C522

C439C696

C290C522C90C640

C42C522

C205C696

C91C522

C42C696

C205C740

C439C522

C310C522

C91C696

C310C696C305C522C230C696C339C640

C438C522

C95C522

C42C740

C352C696

C355C696C20C522C220C522

C205C522

C352C522

C200C522

C235C696

C290C696

C355C740C305C740

C350C696C210C522C325C522C350C522

C52C522

C230C522C211C522C235C522

C90C740

C355C640

C352C640

C51C696C51C522

C310C640

C290C740

C20C640

C2C640

C205C640

C350C740C235C740C211C740C20C740

C290C640

C200C740

C438C740

C325C740

C70C640C439C740

C230C740

C40C640

C220C740

C70C740

C41C640

C210C740

C345C740C438C640C40C740

C41C740C305C640

C437C640

C200C640

C345C640

C439C640C220C640

C437C740

C211C640

C235C640C350C640

C230C640

C210C640

C325C640

-.50

.51

e( s

imila

rvot

es |

X )

-20 -10 0 10 20e( igoconnect | X )

coef = -.00546731, se = .0026839, t = -2.04

Dyads: Countries: None of mention

334

1992 IGO Connectedness

C344C346

C92C740

C2C696

C92C696

C92C640

C349C740C368C740

C344C696

C346C830C366C740

C370C740

C20C696C390C696

C367C740

C373C696

C385C696

C346C696

C305C696

C435C696

C92C346

C369C740

C349C696

C346C705

C371C740C359C740

C220C696

C346C482

C346C690C346C694

C380C696C211C696

C2C346

C370C696

C200C696C210C696

C366C696

C325C696

C369C696

C435C522

C346C365

C373C740

C346C370

C346C369

C346C359

C346C740

C346C698

C346C700

C367C696C371C640

C373C640

C346C540

C359C696

C70C346

C346C373

C346C352

C2C740

C92C522

C368C696C346C366

C346C581

C346C360

C346C371

C346C900

C346C660C346C530

C346C390

C346C350

C375C696C346C385C20C346

C346C692

C220C346

C346C481

C369C640

C346C703

C346C732

C40C696

C205C696

C404C696C346C940

C211C346C210C346

C346C450

C346C380

C90C346

C346C355

C346C670

C346C663

C371C696

C110C346

C305C346

C346C950

C368C640C200C346

C346C666

C346C375

C346C820

C367C640

C135C346

C325C346

C52C740

C346C920C346C367

C42C346

C344C522

C346C790

C2C640

C346C571

C373C522

C346C522

C339C696

C349C640

C346C565

C346C760C346C630

C40C346

C344C740

C346C812

C20C640

C346C490

C93C346

C370C640C366C640

C339C346

C346C702

C346C433C346C541

C160C346C346C516C230C346

C95C346

C205C346

C346C452

C352C740

C101C346

C438C696

C145C346C346C616

C346C701C70C696

C91C346C346C590C339C740

C346C780

C365C696C346C775C150C346

C346C615C40C522

C235C346

C52C346

C346C368C230C696

C315C346

C346C475C346C620

C2C522

C51C346

C315C696

C155C346

C346C645

C346C451

C439C696

C346C910

C346C461

C52C696

C346C438C346C850

C346C484

C346C501

C346C651

C110C740

C346C800C346C652

C140C346C315C740

C346C471

C100C346C346C731

C346C437

C346C552C165C346

C352C696

C346C771

C235C696

C346C483C346C625C346C600

C346C712

C346C435

C346C510

C130C346C41C346

C359C640

C94C346C346C434C346C349

C346C770

C346C570

C346C710C346C750C437C696

C369C522

C346C439C346C517C346C840

C52C640

C346C572C346C404

C346C640

C346C432

C41C696

C290C346C310C696

C346C551

C346C500

C346C420

C366C522C51C740

C370C522C310C346

C94C740

C346C436

C359C522

C350C696

C110C696

C339C522

C346C580

C150C696

C349C522

C70C522

C355C696C94C640

C94C696

C385C640

C346C553

C145C740

C367C522

C91C696

C135C696

C368C522

C371C522

C420C740

C165C696

C365C522

C205C740

C305C522

C160C696

C42C696

C110C640

C436C740

C95C696

C433C696

C135C522

C385C522

C51C640

C90C696

C420C640

C420C696

C315C522

C165C740

C93C696

C41C522C305C740

C339C640

C290C696

C150C740

C390C522C360C696C380C522

C140C696

C344C640

C20C522

C101C696

C145C640

C95C740

C390C640

C432C740

C155C696

C145C696

C200C522

C95C640

C210C522C375C522

C150C522

C325C522

C360C522

C432C696

C100C696

C220C522C91C740

C310C522C205C522

C438C522

C51C696

C436C640

C436C696

C91C640

C352C522

C355C522C290C522

C439C522C310C740

C350C640C165C522

C365C740

C211C522

C90C522

C404C522

C160C522

C385C740

C150C640

C230C522C350C522

C200C640

C155C522C434C696

C315C640

C94C522

C434C740

C437C522C42C522

C140C522

C235C522

C365C640C95C522

C432C640

C390C740

C101C522

C91C522

C404C740

C93C522

C130C696

C42C740

C52C522

C211C640

C100C740

C145C522

C101C640

C110C522

C100C640

C355C740

C433C522

C100C522

C90C740

C130C640C404C640C93C740

C211C740

C235C640

C130C522

C90C640

C220C640

C93C640

C165C640C42C640

C352C640C210C640

C20C740

C380C740

C325C640

C101C740

C420C522

C51C522C235C740

C355C640

C230C640

C130C740

C200C740

C432C522C375C740

C155C740

C140C640

C438C740

C434C640

C160C740C325C740

C435C740

C350C740C436C522

C438C640C70C740C439C740C155C640

C210C740C290C740C360C740

C160C640

C220C740

C435C640C439C640

C230C740

C70C640C40C740C140C740

C135C640

C434C522

C205C640

C40C640C41C740

C360C640

C433C740

C310C640

C433C640

C41C640C437C740C135C740C375C640C290C640

C437C640

C305C640C380C640

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-40 -20 0 20 40e( igoconnect | X )

coef = -.00261062, se = .0014866, t = -1.76

Dyads: Countries: C344C346 C92C740

335

1993 IGO Connectedness

C385C316C390C316

C325C316

C380C316

C352C316

C375C316

C317C316C350C316

C344C346

C349C316C343C740

C92C740

C365C316

C355C316

C370C316C369C316C368C316C366C316

C367C316

C343C316

C343C696

C92C316

C343C640

C360C316

C372C316

C92C640

C92C696

C359C316

C371C316

C420C316

C339C316

C436C316C432C316

C344C316

C434C316C346C316

C373C316

C433C316

C2C696

C92C346

C438C316

C346C482

C343C346

C437C316

C439C316C435C316

C349C740

C343C522

C404C316

C346C830

C92C522

C359C740

C368C740

C20C696

C52C316C373C696C385C696

C390C696

C370C740

C344C696

C366C740

C2C740

C349C696C305C696

C435C696

C359C696

C373C740

C366C696

C346C690

C200C696

C371C740

C346C696

C220C696

C371C640

C317C696

C370C696

C380C696

C435C522

C210C696

C372C740

C211C696C2C346

C372C640C369C740

C372C696

C346C694C346C698

C369C696

C325C696

C346C700

C367C740

C346C811

C368C696

C369C640

C317C740

C2C640

C20C640

C205C696

C346C581C346C366

C344C522

C346C760

C2C522

C359C640

C346C530

C367C696

C346C940

C371C696

C51C316

C40C522C40C696

C110C316C375C696

C346C516

C346C373

C346C740C373C522

C346C692C346C571C346C812

C70C346

C346C900

C346C703

C370C640

C346C790

C346C540

C346C950

C40C346C346C732C346C522

C372C522

C369C522

C346C731

C346C481

C346C385

C346C705

C346C390C20C346

C70C696

C94C316

C359C522

C135C346C110C346C90C346

C200C346

C373C640

C52C740C220C346

C346C660

C370C522

C346C368

C52C696

C210C346C211C346C317C522

C365C696

C366C640

C95C346

C346C380

C346C490

C346C670C346C920

C346C666

C346C590

C346C820C346C565C346C541

C305C346C346C375

C346C616

C346C910

C339C740

C95C316

C230C696

C346C663

C42C346

C160C346

C70C522

C346C712

C404C696

C366C522

C325C346C346C775

C368C640

C52C346

C310C696

C51C346

C371C522C349C522

C346C461

C346C572C101C346C346C517

C110C740C365C522C346C630

C93C346

C91C316

C346C365

C346C780

C235C696C205C346

C110C696

C346C452

C52C640

C41C696

C339C346C150C346

C346C370

C140C346

C230C346

C352C696

C150C316

C367C640

C346C615

C346C484

C346C451C346C367

C346C645

C346C570C41C346

C346C372C346C369

C368C522

C2C316

C305C522

C346C552

C349C640

C91C346C346C652

C346C471

C346C620C155C346

C346C359

C90C316

C235C346

C317C346C346C800

C346C433C346C483

C437C696

C385C522

C346C438C346C500

C346C710C100C346

C100C316C346C437C346C701

C346C510

C165C346

C344C740

C346C439C346C432C385C640

C150C696

C346C435C94C346C130C346

C130C316C355C696

C346C850

C346C371

C350C696C367C522

C346C551

C310C522

C94C696

C380C522C110C640

C346C436C346C750

C390C522

C352C740

C346C501

C20C522

C145C346

C91C696

C346C352

C346C349

C346C640

C160C696C165C316

C346C651

C200C522

C346C771C346C475C346C840C346C580C95C696

C165C696

C42C316C375C522C290C522

C150C522

C346C420

C210C522C94C740

C346C350

C346C600

C51C740

C346C434

C94C640

C165C522

C42C696

C346C770

C325C522

C438C696C41C522

C360C522C145C316C339C696

C352C522

C290C346

C93C316

C90C696C135C522

C346C360

C346C404C346C553C346C625

C436C740

C220C522

C101C316C310C346

C93C696

C205C522C290C696C355C522

C439C696

C160C522

C346C355

C140C696

C51C640C420C740

C94C522

C305C316

C90C522C155C522C145C696

C317C640

C95C640

C20C316

C155C696

C390C640

C350C522

C140C522

C230C522C211C522

C101C696

C432C740

C160C316C95C522

C135C696C51C696

C91C640

C91C522

C339C522

C101C522

C350C640

C235C522

C93C522

C140C316

C100C696

C145C522

C360C696

C42C522

C200C640

C205C316

C52C522

C145C740

C110C522

C95C740

C165C740

C150C740C404C740

C100C522

C91C740

C130C696C130C522

C145C640C155C316

C434C696

C150C640

C135C316

C437C522

C420C696C51C522

C211C640

C420C640

C305C740

C235C640

C90C640

C344C640

C434C740

C433C696

C70C316

C101C640

C310C740C205C740

C200C316

C432C696

C365C640

C436C640

C436C696

C93C640

C42C740C42C640C130C640C90C740

C339C640C220C640

C438C522

C210C640

C100C640

C325C640

C439C522

C41C316

C235C316

C355C640

C165C640

C438C740

C210C316C365C740C211C316

C404C640

C93C740

C100C740

C404C522

C230C640

C355C740

C432C640

C220C316

C435C740C434C640C140C640

C230C316

C352C640

C40C316

C101C740

C130C740C439C740C135C640

C433C522

C385C740

C155C640

C420C522

C160C640

C160C740C20C740

C390C740

C155C740

C432C522

C70C740

C434C522

C70C640C40C640

C438C640

C235C740

C436C522

C211C740

C290C740

C200C740

C41C740

C380C740

C41C640

C310C316

C290C316

C360C740

C40C740

C350C740

C435C640

C433C740

C439C640C140C740

C205C640

C360C640

C135C740

C325C740

C375C740

C210C740

C437C740C437C640

C230C740

C220C740

C310C640C290C640

C433C640

C375C640C305C640C380C640

-.50

.51

e( s

imila

rvot

es |

X )

-40 -20 0 20 40e( igoconnect | X )

coef = .00224381, se = .00127224, t = 1.76

Dyads: Countries: C352C316 C344C346

336

1994 IGO Connectedness

C740C316

C317C316

C344C346

C385C316C390C316

C900C316C380C316

C325C316

C352C316

C666C316C375C316

C920C316

C350C316

C590C316

C349C316C732C316

C560C316

C92C740C830C316

C343C740

C800C316

C355C316

C368C316

C640C316

C571C316

C370C316

C820C316

C365C316

C696C316

C369C316

C367C316

C910C316

C92C316

C366C316

C690C316

C840C316C565C316

C343C316

C712C316C92C640

C750C316C770C316

C360C316

C580C316

C359C316

C570C316

C343C640

C371C316

C372C316

C436C316

C940C316

C694C316

C950C316C780C316

C339C316

C432C316C771C316

C553C316C343C696

C434C316C541C316

C551C316

C484C316

C698C316C790C316C482C316

C92C696

C581C316C481C316C692C316

C660C316

C705C316

C616C316

C670C316C811C316C344C316C346C316

C663C316

C92C346

C516C316C700C316

C346C482

C433C316

C452C316C540C316C703C316C620C316

C373C316

C630C316C343C346C461C316

C615C316

C651C316C438C316C471C316C600C316C552C316C850C316

C346C830

C500C316C501C316

C701C316

C437C316C710C316C645C316C439C316

C435C316C522C316

C652C316

C343C522

C702C316

C572C316

C2C740

C531C316

C435C696

C812C316

C517C316

C510C316

C420C316C625C316

C451C316C760C316

C704C316

C475C316

C92C522

C349C740

C359C740

C371C640C435C522

C731C316

C2C696

C372C640

C344C696

C368C740

C373C696C52C316

C346C690C371C740

C370C740

C20C640

C346C696

C40C696

C346C811C2C640

C346C516

C372C696

C346C700

C20C696

C367C740

C317C696

C372C740

C2C346

C385C696C390C696

C366C740

C40C522

C359C696

C344C522

C373C740

C366C696

C317C740

C369C640

C346C940

C305C696C349C696

C370C696

C346C373

C200C696

C346C581

C346C694

C346C531

C346C950C346C540

C373C522

C2C522

C346C698

C220C696

C135C346

C359C640

C346C760C346C522C369C740

C371C696

C380C696

C346C790

C94C316

C346C740

C346C692C346C571

C346C732

C346C731C346C812

C90C346

C346C705C367C696

C420C696

C368C696

C346C703

C346C900

C205C696

C40C346

C211C696C210C696C370C640

C346C660

C373C640

C51C316

C346C366

C110C316C325C696

C20C346C346C820

C369C696

C95C316

C70C346

C437C696

C372C522C52C740C365C696

C346C385C346C390

C346C461

C346C666

C346C910

C93C346C346C481

C317C522

C346C560

C370C522

C346C920

C346C372

C375C696

C200C346

C346C663

C160C346

C346C616

C52C640

C52C346

C359C522

C346C590

C110C346C339C740

C346C630

C367C640

C346C367

C211C346C210C346

C346C452C346C565

C346C380

C346C368

C346C365

C346C670

C346C359C365C522

C51C346

C220C346C346C484

C101C346

C352C740

C346C451

C346C500

C52C696

C346C572

C438C696

C110C696

C140C346

C369C522

C95C346

C346C375

C346C702C346C433

C305C346

C346C780

C346C704

C346C541

C70C696

C346C370

C439C696

C346C438

C366C522

C346C517C346C471

C436C740

C346C701

C325C346C346C712

C346C420C346C771

C385C640

C41C346

C346C369C346C800

C310C696

C150C346

C100C316

C346C552

C371C522

C205C346

C346C615

C350C640

C94C640

C346C501C100C346

C305C522

C346C551

C94C740

C91C346

C51C740

C339C346

C385C522C317C346C349C522

C346C437C230C346C346C620

C110C740

C346C439C155C346

C165C346

C346C510C346C645

C230C696C150C316

C90C316

C130C346C346C710C346C371

C346C652

C346C750

C355C696

C135C522

C90C696

C94C346C346C475

C339C696

C235C346

C352C696

C165C316C130C316

C346C570

C200C640

C235C696

C346C553

C346C349

C390C522

C346C580C346C850

C380C522

C91C696

C368C640

C20C522

C368C522

C94C696C150C696

C346C651C346C436

C51C640

C310C522

C346C435

C70C522

C390C640

C200C522C290C346

C150C522

C91C316C160C696C290C522

C346C432C346C434

C135C696

C95C640

C375C522

C145C346C366C640

C432C740

C346C640

C145C316

C367C522

C346C600

C344C740

C2C316

C210C522

C346C840

C110C640

C165C696

C346C360

C90C522

C95C696

C325C522

C93C696C352C522

C310C346

C360C522C346C355

C346C625

C317C640

C160C522

C220C522

C350C696

C346C770

C290C696C41C316C205C522C93C316

C140C696

C101C316C346C350

C305C316

C349C640

C211C522C346C352

C155C522

C165C522

C140C316C95C740

C360C696C355C522

C155C696

C51C696

C145C696

C145C740

C350C522

C230C522

C93C522

C101C696

C211C640

C101C522

C100C696

C91C640

C94C522

C41C696

C140C522C110C522C95C522

C235C522C165C740

C339C522

C434C696

C145C522C91C522

C20C316C310C740C420C522

C150C740

C365C640

C437C522

C130C696

C100C522

C436C640

C205C740

C150C640

C90C640C434C740C145C640

C210C640

C433C696

C101C640

C130C522

C205C316

C52C522

C91C740

C160C316

C355C640C220C640

C155C316

C235C640

C130C640

C365C740

C230C640

C325C640

C41C640

C439C522

C100C640

C70C316C135C316

C436C696

C51C522C41C522

C352C640

C438C522

C93C640

C432C696

C355C740

C165C640

C200C316

C90C740

C434C640

C100C740

C41C740

C432C640

C140C640

C93C740

C420C740

C211C316

C130C740C438C740

C344C640C339C640

C305C740

C390C740

C435C740

C210C316C385C740

C155C640

C433C522

C135C640C439C740

C235C316

C220C316

C101C740

C160C640

C155C740

C200C740C160C740

C230C316C20C740

C70C640

C290C316

C380C740

C40C316

C235C740

C420C640

C434C522

C211C740

C290C740

C432C522

C438C640

C70C740C140C740

C436C522

C360C640

C350C740C360C740

C40C640

C310C316

C375C740

C433C740

C40C740

C435C640

C205C640

C135C740

C325C740C230C740C210C740

C439C640

C310C640

C437C640

C437C740

C220C740

C290C640

C433C640

C375C640

C305C640C380C640

-.50

.51

e( s

imila

rvot

es |

X )

-40 -20 0 20 40e( igoconnect | X )

coef = .00380864, se = .00083128, t = 4.58

Dyads: Countries: C910C316 1995 IGO Connectedness

337

C317C316C740C316C385C316C390C316

C900C316

C380C316

C352C316

C325C316C375C316

C666C316

C920C316

C350C316C590C316

C349C316C344C346C732C316

C560C316

C800C316

C92C740

C830C316

C355C316

C368C316

C343C740

C640C316

C571C316

C411C696

C367C316C820C316

C366C316

C910C316

C365C316

C92C316

C840C316

C369C316

C750C316

C92C640

C343C316

C565C316

C712C316

C343C640

C696C316

C770C316

C690C316

C360C316

C580C316

C372C316

C359C316

C570C316C940C316C436C316

C950C316

C780C316

C339C316

C432C316C553C316

C771C316C434C316C551C316C484C316

C541C316

C694C316

C790C316C404C316

C370C316

C698C316

C371C316

C481C316C343C696C411C522C660C316

C692C316

C92C696

C811C316

C616C316C670C316

C346C316

C346C411

C705C316

C663C316C615C316

C92C346

C438C316

C343C346

C700C316C516C316C433C316

C344C316

C490C316

C452C316

C411C740

C620C316

C703C316

C510C316

C540C316

C630C316C461C316

C651C316C471C316C483C316C500C316C850C316

C552C316C600C316

C501C316

C373C316

C411C316

C437C316C343C522C710C316C439C316

C701C316C411C640

C435C316C522C316C652C316C702C316

C475C316

C92C522

C531C316C517C316

C572C316C812C316C625C316

C420C316

C2C740

C760C316

C451C316C775C316

C704C316C346C830

C435C696

C731C316

C435C522

C2C696

C372C640

C370C696

C344C696

C371C640

C52C316C344C522

C349C740

C40C696

C20C640

C40C522

C373C696

C2C640

C359C740

C346C940

C346C700C346C516C20C696

C370C522

C368C740C366C740

C385C696

C346C690

C346C531

C366C696

C390C696

C346C950

C346C370

C369C640

C346C696

C371C696C305C696

C346C811

C200C696

C346C698

C373C522

C367C740

C346C540

C346C760

C372C696

C346C522

C372C740

C95C316

C220C696

C205C696

C371C740C346C571

C380C696

C51C316

C346C703

C420C696

C2C522

C346C694

C110C316

C346C790

C349C696C359C696C52C740

C346C731

C317C696

C211C696

C210C696

C437C696

C2C346

C346C366

C346C910

C52C640

C325C696

C346C702

C346C438C346C692

C90C346

C369C696C346C812C135C346

C94C316C346C660

C346C732

C436C740

C350C640

C346C820

C346C705

C346C490

C366C522C365C696C352C740

C367C696

C110C346

C346C461

C372C522

C40C346C369C740

C371C522

C346C371

C375C696

C404C740

C359C522C368C696

C346C900

C52C346

C439C696

C365C522

C346C510C346C663

C346C484

C369C522

C346C701

C346C775

C346C740

C346C572

C346C615C70C346

C346C373

C317C522

C346C541

C110C696C52C696

C370C740

C346C517

C346C630

C346C565

C346C483C346C481

C130C316C51C640

C346C920

C346C616

C346C385

C110C740C346C452

C346C390C70C696C135C522

C346C372C200C346

C20C346

C91C346

C230C696

C95C640

C385C640C346C359

C346C666

C352C696

C310C696

C110C640

C346C670

C346C404

C95C346

C160C346

C346C590

C346C367

C346C500C317C740

C51C740

C346C420

C346C369C346C433

C346C380

C359C640C346C451

C100C316

C93C346

C367C522

C346C712C100C346

C150C316

C51C346C346C560

C93C316

C305C522

C346C780

C438C696C366C640

C385C522

C101C346

C346C471C346C439

C140C346

C90C316

C346C704

C200C640

C355C696C368C522

C41C346C346C365

C70C522

C349C522

C339C740C346C771

C91C696

C346C620C310C522

C90C522C150C696

C211C346

C210C346

C160C696

C205C346

C235C696

C135C696

C165C316

C160C522

C90C696

C346C652C346C434

C130C346C150C346

C360C522

C346C552C346C501

C404C640

C346C553C346C800

C41C316

C367C640

C346C551

C380C522

C339C346

C220C346

C94C640

C346C437

C346C570C390C640

C390C522C290C522

C150C522C346C375

C91C316

C200C522

C20C522

C352C522

C145C316

C370C640

C436C640

C346C436

C355C522

C305C346

C346C710

C375C522

C317C346

C346C435

C101C316

C155C346

C95C740

C325C346

C346C368C95C696

C346C475

C94C696C230C346

C210C522

C205C522

C155C522C404C696C165C696

C325C522

C350C696

C165C346C94C346

C2C316

C140C696

C346C840C346C580C346C850C346C750

C373C740

C220C522

C101C522

C339C696

C432C740

C100C696

C94C740

C165C522

C420C522

C360C696

C346C355

C290C696C346C651

C305C316

C95C522

C339C522

C346C432

C155C696

C346C360

C235C346

C140C316

C101C696C346C349

C355C640

C100C522

C145C346

C110C522

C93C696C437C522

C230C522

C145C696C51C696C91C522

C346C600

C368C640

C350C522

C41C640

C94C522

C346C625

C145C522

C373C640

C140C522

C346C770

C91C640

C41C696C290C346

C211C522

C420C740

C434C740

C93C522C130C522C310C346

C235C522

C434C696

C93C640

C346C352

C346C350

C145C740

C101C640C165C740

C205C740

C20C316

C211C640

C365C640

C433C696

C100C640

C439C522C235C640

C150C640

C130C696

C130C640C145C640

C434C640

C155C316

C52C522C41C522

C90C640

C310C740

C352C640

C160C316

C210C640

C230C640

C220C640

C150C740C365C740

C325C640

C51C522C436C696

C205C316

C349C640C432C696

C165C640

C344C740

C355C740

C41C740C135C316C91C740

C432C640

C70C316

C317C640

C140C640C93C740C438C740

C438C522

C100C740

C305C740

C200C316

C90C740

C155C640

C130C740

C390C740C433C522

C290C316

C160C640

C135C640

C385C740

C211C316C200C740

C438C640

C435C740

C434C522

C339C640C210C316

C155C740

C20C740

C101C740

C70C640C439C740

C235C316

C380C740C404C522C436C522

C220C316

C432C522C360C640C290C740C230C316

C160C740

C40C316C40C640

C211C740

C140C740

C350C740

C344C640C235C740

C360C740C205C640C310C316C375C740

C40C740

C435C640

C70C740

C439C640

C325C740

C433C740

C230C740

C437C640

C210C740C310C640

C135C740

C290C640

C437C740

C220C740

C433C640

C375C640

C305C640C380C640

C420C640

0-.5

.5e(

sim

ilarv

otes

| X

)-1

-40 -20 0 20 40e( igoconnect | X )

coef = .00243836, se = .00069954, t = 3.49

Dyads: Countries: C420C740 C420C640 1996 IGO Connectedness

338

C317C316C740C316

C385C316C390C316

C900C316C380C316

C325C316

C352C316C375C316

C666C316

C920C316

C350C316C355C316C360C316

C590C316

C349C316

C732C316C344C346

C560C316

C800C316

C830C316

C92C740

C368C316C343C740C640C316

C571C316

C411C696

C367C316

C820C316

C366C316C369C316

C712C316C92C316

C750C316C840C316

C365C316C92C640

C343C316

C565C316

C343C640

C770C316C580C316

C690C316

C372C316

C570C316

C359C316

C696C316

C940C316

C780C316C432C316C553C316C771C316C434C316C541C316C484C316

C411C522

C790C316C404C316C581C316C694C316C698C316C451C316C660C316C481C316C343C696C692C316C452C316

C92C696

C339C316

C811C316C616C316

C346C411

C346C316

C670C316

C551C316

C705C316C92C346

C663C316C438C316C615C316C343C346C450C316

C344C316

C433C316

C490C316

C411C740

C703C316C510C316C620C316C540C316C630C316C483C316C461C316

C370C316

C651C316C471C316C343C522C500C316C552C316

C850C316C600C316

C411C316

C371C316

C501C316C522C316C373C316C437C316C710C316C439C316C411C640

C92C522

C516C316C701C316C435C316C420C316

C702C316

C475C316C652C316C517C316C436C316C531C316C572C316

C812C316C625C316C760C316C775C316C700C316

C704C316

C2C740

C346C830C370C696

C435C522

C731C316

C435C696

C370C522C371C696C344C522

C52C316

C371C522C344C696C2C696

C372C640

C349C740

C40C522

C368C740

C20C640

C40C696

C2C640

C373C522

C366C740

C346C690

C346C581C359C740

C367C740

C346C531C346C811C369C640

C373C696

C346C940

C346C760

C385C696

C346C522C2C522

C346C698

C346C950

C20C696C366C696C52C740

C390C696

C51C316

C52C640

C346C540C346C731C110C316C346C571

C95C316

C305C696

C372C740

C346C790C346C450C346C696C205C696

C350C640

C366C522C94C316

C346C516C372C522

C371C640

C352C740

C404C740

C365C522

C200C696C220C696

C40C346C90C316C346C694

C437C696

C372C696

C420C696

C346C692

C380C696

C135C346

C349C696

C2C346C317C522

C436C696

C317C696

C346C812

C346C703

C346C820C346C483C346C461

C210C696

C346C438C346C700C346C910

C346C551C42C316

C211C696

C369C522

C339C522

C135C522

C367C696

C369C740

C346C663

C346C660

C346C451

C52C346

C346C484

C346C490

C325C696

C359C696

C368C696C359C522

C110C346C346C775

C346C740

C346C702

C346C572C385C640

C130C316

C439C696

C346C510

C346C900

C365C696C51C640

C375C696

C367C522

C346C565

C346C481

C346C732

C385C522

C346C366

C339C696

C93C316

C346C705

C70C522

C346C615

C110C640

C369C696C70C346C368C522C349C522

C346C541

C346C517

C165C316

C91C346

C51C740C346C630C145C316C160C522C346C404C310C522C110C740C346C670

C160C346

C346C500C346C452C346C433

C52C696

C305C522

C150C316

C390C522C290C522

C346C920C150C522

C317C740

C346C420

C339C346

C200C522C346C780

C93C346C346C560

C20C522

C70C696

C346C373

C346C471

C352C522

C20C346C42C346

C346C439

C346C370

C90C346

C110C696

C101C346

C346C666C140C346C346C371

C205C522

C390C640

C100C316

C346C590

C346C771

C371C740

C210C522C346C616

C155C522

C346C620

C346C385

C404C640

C380C522

C366C640

C325C522

C346C552C346C390C346C501

C100C346

C310C696

C150C346

C346C553

C438C696

C91C316

C51C346C94C640

C220C522

C200C346C346C800C346C436C375C522

C165C522

C101C522

C346C437

C437C522

C352C696

C346C570

C346C704

C95C522

C2C316

C355C522

C90C640

C230C696

C95C346

C200C640

C41C316

C346C367

C101C316

C367C640

C346C710

C155C346

C110C522

C346C435C360C522

C100C522

C95C640

C346C434

C346C380

C91C696

C160C696

C346C372

C91C522

C135C696

C346C475C346C652

C130C346

C350C522C235C696

C305C316

C150C696

C432C740

C41C346

C140C522

C94C740

C145C522

C346C375

C165C346

C230C522

C420C522

C94C522

C346C365

C346C850

C42C640C346C840

C94C346

C436C522

C355C696

C359C640C205C346

C346C750C346C369

C140C316C211C522

C210C346

C434C740

C370C740

C211C346

C373C740

C235C522C346C432C165C740

C346C712

C145C740

C42C522C90C522

C42C696

C373C640

C346C359

C220C346

C95C740

C355C640

C439C522

C145C346

C404C696C165C696

C91C640

C95C696

C317C346

C346C625C346C651

C305C346C352C640

C140C696

C94C696

C93C696

C346C770

C93C522

C42C740

C130C522

C290C696

C325C346

C155C316

C100C696

C51C696

C368C640C155C696

C346C368C339C740

C93C640

C52C522

C20C316C230C346

C205C740

C346C580

C90C740

C145C696

C41C640

C41C696

C235C346

C41C522

C150C640

C346C349

C90C696

C145C640

C211C640

C101C640C434C640

C346C600

C160C316

C235C640

C150C740

C434C696

C346C352

C101C696

C290C346

C51C522

C100C640

C130C696

C433C696

C210C640

C360C696

C130C640

C310C740

C310C346C346C640

C346C350

C220C640

C365C640

C230C640

C91C740

C346C355

C165C640

C438C522

C436C740

C432C640

C390C740

C346C360

C370C640C325C640

C41C740C70C316C93C740

C205C316

C344C740

C385C740

C365C740

C433C522C349C640C432C696C317C640

C438C740

C305C740

C140C640

C355C740

C135C316C155C640

C130C740

C100C740

C420C740

C290C316C380C740

C135C640C155C740

C160C640

C200C316

C404C522

C20C740

C436C640

C434C522

C439C740

C360C640

C438C640

C432C522

C435C740C101C740C420C640

C200C740C211C316C360C740

C40C316

C160C740

C70C640C40C640C210C316C211C740C230C316C220C316C290C740C235C316

C350C740

C140C740

C235C740C375C740

C40C740C435C640

C205C640

C439C640

C135C740

C70C740

C344C640

C325C740C310C316C210C740C339C640

C433C740

C437C640

C230C740

C310C640

C437C740

C290C640

C220C740

C433C640

C305C640

C375C640

C380C640

C910C316

C950C316

.50

1e(

sim

ilarv

otes

| X

)-.5

-1

-40 -20 0 20 40e( igoconnect | X )

coef = .00392113, se = .00079256, t = 4.95

Dyads: Countries: C950C316 C316 C910C316 others 1997 IGO Connectedness

339

C350C316

C385C316C390C316C325C316

C640C316C740C316C317C316

C352C316

C666C316

C830C316

C590C316

C732C316

C900C316

C560C316

C800C316

C380C316

C571C316

C375C316

C696C316C690C316

C355C316C360C316

C920C316

C820C316

C712C316

C840C316C750C316

C369C316

C92C316

C365C316

C343C316

C565C316

C372C316

C570C316

C359C316

C580C316C770C316

C349C316

C92C640

C630C316C553C316C780C316

C339C316

C771C316

C694C316

C434C316C541C316C432C316C790C316C404C316C581C316

C698C316C660C316C481C316

C452C316C692C316

C368C316

C616C316C551C316C438C316C663C316

C705C316

C615C316

C450C316C451C316C670C316C433C316C490C316

C367C316

C2C740

C366C316

C703C316

C510C316C501C316

C910C316

C461C316C483C316

C370C316

C620C316

C540C316

C651C316C411C316

C471C316

C940C316

C439C316C500C316C850C316

C552C316

C600C316

C702C316

C371C316

C522C316

C373C316

C950C316

C710C316C437C316C484C316C516C316C435C316C475C316

C701C316

C436C316

C517C316

C531C316C652C316C812C316C625C316

C572C316

C775C316C700C316C760C316

C704C316

C2C696

C2C316C731C316

C40C696

C344C696

C2C522

C344C316

C2C640

C52C316

C52C740

C20C696

C20C316C205C696

C20C640

C437C696

C305C696

C344C522

C352C740

C220C696C200C696C210C696C211C696C325C696

C350C640

C40C522

C290C316C110C316C20C522C51C740

C339C740

C51C316

C352C640

C95C316

C305C522

C205C522

C91C696

C70C696C52C696

C230C696

C94C316

C352C696

C210C522C200C522C235C696C220C522

C42C696

C355C696C310C696C325C522C349C522

C52C640

C95C696

C211C522C90C316

C100C696

C350C696

C93C316C200C316

C42C316

C41C696

C352C522

C290C696C165C316

C51C696

C90C696

C205C740

C42C740C211C316

C91C740C355C640C210C316

C100C316

C150C316C91C316

C230C522C130C316C310C522

C145C316C90C740C41C316C51C640

C305C316C350C522

C41C740

C235C522

C220C316

C101C316

C70C522

C439C696

C290C522

C200C640

C438C740

C310C740

C140C316

C355C740

C155C316C355C522C91C640

C95C522

C100C740

C305C740

C230C316

C438C696

C235C316

C95C640

C41C640C160C316C290C640

C91C522

C310C640

C437C522

C344C740

C90C640

C210C640C211C640C42C640

C42C522

C52C522

C100C640

C339C696

C439C740

C90C522

C20C740

C70C316

C41C522

C220C640

C200C740

C70C740C310C316

C51C522

C135C316

C290C740

C325C640C235C640C211C740

C70C640

C350C740C230C640C235C740

C40C740

C210C740C205C640C205C316C325C740

C437C740

C40C316

C339C522

C230C740C40C640

C439C522

C437C640

C220C740

C438C522

C344C640

C305C640

C438C640

C339C640

C439C640

.50

1e(

sim

ilarv

otes

| X

)-.5

-60 -40 -20 0 20 40e( igoconnect | X )

coef = .00344265, se = .00095718, t = 3.6

Dyads: Countries: C350C316 1998 IGO Connectedness

340

C385C316

C390C316

C740C316

C325C316

C92C740

C900C316C732C316

C92C640

C690C316

C560C316C750C316

C92C696

C840C316

C2C696C698C316

C770C316

C696C316

C2C316

C435C696

C92C316C694C316

C92C522

C541C316

C2C640

C371C640C482C316C670C316

C435C522

C660C316

C481C316

C372C316C370C522

C2C522

C692C316

C371C316

C372C640

C663C316

C359C316

C365C316

C705C316C551C316C616C316

C522C316

C452C316

C371C740

C370C696C615C316C510C316C432C316

C369C316

C703C316

C373C522

C434C316

C475C316C651C316C850C316C451C316

C483C316

C359C740

C450C316

C540C316

C20C696

C620C316

C372C740

C652C316

C20C640

C20C316

C461C316

C52C640

C710C316

C371C522

C110C740

C2C740C600C316

C404C316

C110C640

C436C696

C435C316

C484C316C150C522

C372C522

C420C696

C701C316

C404C740

C373C696

C438C316

C572C316C51C740C625C316

C51C640

C704C316

C135C522

C359C522

C165C740

C41C640

C420C522

C731C316

C433C316

C150C696

C110C696

C42C740

C91C640

C439C696

C145C740C52C316

C150C740

C436C522

C365C522

C439C316

C437C696

C150C640

C110C522

C385C696C95C740

C420C316

C70C522

C94C740

C371C696

C370C316

C91C522

C432C740

C41C740

C91C740

C93C640

C42C640

C145C522

C390C696

C93C740C369C640

C372C696

C439C522C70C696

C41C522

C373C316

C210C696

C52C696

C436C316

C93C522

C438C696

C91C696C90C640

C41C696

C90C740

C369C522

C42C522C155C522

C437C522

C211C696

C42C696

C200C696

C94C640C385C522

C160C522C434C740

C369C740

C165C522

C404C522

C95C522

C350C640

C90C522

C95C640

C101C522

C404C640

C359C640

C210C522

C52C522

C220C696

C135C696C94C522

C140C522

C100C522

C93C696

C145C640C359C696

C390C522

C437C316

C200C522C310C522

C165C696

C110C316

C350C522

C373C640

C20C522

C220C522C165C640

C51C522C51C696

C95C696C130C522

C325C696

C145C696

C365C696

C101C640C160C696

C51C316

C370C740

C438C522

C70C640C211C522

C290C522

C436C740

C155C696

C100C640

C94C696

C90C696

C325C522

C140C696

C420C740

C155C740

C365C640

C390C740

C385C740

C130C640

C130C740

C235C522

C20C740

C433C696

C230C522C438C740C155C640

C42C316

C100C696C101C696

C432C640C350C696

C165C316

C434C640

C95C316

C310C740C290C316

C94C316

C433C522

C432C696

C93C316

C230C696

C434C696

C235C696

C100C740C101C740

C200C316

C70C740

C130C696

C369C696

C150C316

C365C740

C385C640

C140C640

C41C316C90C316

C160C640

C290C640

C310C696

C210C316

C310C640

C160C740

C432C522

C439C740C145C316

C435C740

C211C316

C91C316

C434C522

C211C740C140C740

C435C640

C135C640

C220C316

C390C640

C290C696

C370C640

C420C640

C200C640

C130C316

C436C640C101C316

C350C740C210C740

C200C740

C210C640

C100C316

C211C640C235C316C230C316C220C640

C438C640

C155C316

C140C316C235C740C290C740

C433C740

C135C740

C70C316

C160C316C235C640

C325C740

C230C640

C439C640

C230C740

C310C316C325C640

C437C740

C437C640

C135C316

C433C640

C350C316.5

-1-.5

0e(

sim

ilarv

otes

| X

)

-40 -20 0 20 40e( igoconnect | X )

coef = .00191833, se = .00102252, t = 1.88

Dyads: Countries: C350C316

341

1999 IGO Connectedness

C317C316C740C316

C666C316C352C316

C385C316

C350C316

C390C316C900C316C590C316

C380C316

C375C316

C325C316C732C316

C920C316

C349C316

C571C316

C560C316

C92C740C800C316

C355C316C640C316

C712C316

C360C316

C92C640

C368C316

C750C316C830C316C840C316

C369C316

C92C316

C850C316

C343C316

C820C316C565C316

C367C316C372C316

C365C316

C359C316

C910C316

C366C316

C580C316

C553C316

C339C316

C771C316C780C316C790C316C434C316

C371C316

C541C316C432C316C630C316C940C316

C950C316

C690C316C475C316C696C316

C660C316C404C316

C522C316

C811C316

C452C316C346C411

C344C316

C92C346

C551C316

C346C316

C481C316C616C316C438C316C694C316C698C316C663C316C451C316

C705C316

C437C316C490C316

C615C316C433C316C702C316

C692C316

C510C316

C581C316

C343C346

C501C316C483C316C461C316C540C316

C370C316C670C316

C620C316

C2C740

C471C316C439C316C500C316C651C316C600C316

C411C316

C552C316C770C316C710C316C484C316

C344C346

C373C316

C517C316

C531C316

C346C830

C812C316C701C316

C2C522

C625C316C652C316

C700C316C775C316C760C316C572C316

C704C316

C731C316C344C522

C40C696

C52C316C346C581C346C522

C205C522

C346C940

C20C522

C346C451

C41C522

C346C690C349C522

C52C740C2C696C305C522

C346C702C352C740C346C760

C200C522

C310C522

C210C522C346C811

C70C522

C346C731

C220C522C325C522C352C522C51C316

C52C640

C346C950

C290C522C355C522

C211C522

C205C696

C344C696

C346C696

C346C438C346C540C346C551

C101C522

C135C346

C346C437C346C404

C2C346

C42C316

C230C522C110C316C350C522C20C696

C346C660C346C630C346C483C346C461

C41C346

C235C522C346C490

C305C696C346C663

C346C700C94C316

C95C316

C91C522

C346C820C346C694

C165C316

C110C346

C90C522

C346C790

C90C316

C346C812C346C510C40C346

C346C775

C42C522

C93C316C346C698

C52C522

C346C501

C40C522

C339C740

C346C481

C339C522

C346C500C346C517C346C433

C91C316

C91C640C51C640C346C565

C346C900

C205C740C346C452C52C346C346C571C346C692

C437C696

C210C696C352C640C220C696

C346C740

C346C439

C305C316

C51C740

C346C572C211C696

C150C316

C20C346

C346C616C91C346C346C484

C41C696

C346C701

C346C471C346C780C200C696

C346C366

C101C346C93C346

C346C920

C346C475

C346C615

C346C552C130C316C325C696C439C696

C42C740

C346C553C346C910

C150C346C42C640C42C346

C145C316

C355C640

C346C620

C346C590

C100C316

C51C522

C205C346C346C732

C346C385

C346C670C346C541

C70C696

C90C640C438C696C70C346

C100C346

C160C346

C346C390C346C666

C2C316

C140C346C90C346C155C346

C346C652

C165C346

C346C434C346C432

C346C373

C101C316

C346C380

C346C580

C211C346

C42C696

C346C770

C91C740

C346C375

C346C370

C346C771

C200C346

C346C710C230C696

C155C316C51C346

C90C740

C130C346

C346C625

C210C346

C346C372C95C346

C305C346

C145C346

C91C696

C235C696

C52C696

C346C651

C220C346

C339C346

C140C316C160C316

C346C850

C346C349

C346C800C346C840C352C696C346C371C310C696

C346C712C94C346

C305C740C310C740

C346C705C346C600

C346C367

C325C346

C20C316

C346C368

C90C696

C437C522C230C346

C350C696

C317C346

C346C750

C346C365

C339C696

C41C316C41C640C344C740C20C740

C290C696

C235C346

C355C696C70C316

C51C696C346C640

C70C640

C290C346

C205C316

C438C740

C346C359

C346C369

C310C346

C346C352

C355C740

C41C740

C200C740

C438C522C346C350

C211C740C2C640

C135C316

C70C740

C20C640C439C522

C290C740

C346C355

C235C740C290C316

C346C360

C210C740

C437C640

C439C740

C205C640

C350C740C200C316

C339C640

C437C740

C350C640

C438C640

C325C740C40C640C230C740C210C316C211C316

C40C316

C344C640

C230C316

C220C316

C40C740

C235C316

C220C740

C439C640

C305C640

C310C640

C310C316

C290C640

C200C640C211C640C210C640

C235C640

C220C640

C230C640C325C640

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-40 -20 0 20 40e( igoconnect | X )

coef = .00371392, se = .00084665, t = 4.39

Dyads: Countries: C350C316

342

2000 IGO Connectedness

C385C316C390C316

C350C316

C325C316

C317C316C640C316C740C316C666C316C352C316

C900C316

C590C316

C375C316C380C316

C732C316

C920C316

C349C316

C92C740

C411C696

C343C740

C571C316

C560C316

C800C316

C365C316

C830C316

C355C316

C712C316

C360C316

C750C316

C368C316

C840C316

C92C640C369C316C92C316

C343C640

C850C316

C343C316

C820C316C433C316

C565C316

C367C316C359C316

C372C316C344C316

C366C316

C910C316

C580C316

C553C316

C339C316

C771C316C780C316C790C316C434C316

C371C316

C541C316C630C316C432C316C690C316C437C316C570C316C696C316C475C316

C950C316

C343C522C343C696C660C316C522C316C811C316C452C316

C92C522

C346C411C510C316

C92C696

C411C740

C551C316

C346C316

C616C316C481C316C694C316C438C316C92C346

C698C316C705C316

C451C316

C663C316

C490C316

C411C522

C615C316C581C316C702C316C516C316

C692C316

C344C346

C703C316C501C316C483C316C461C316C370C316C540C316

C343C346

C439C316

C670C316

C411C640C411C316

C620C316

C471C316

C500C316C517C316

C651C316

C552C316C600C316

C420C316

C652C316C710C316C770C316

C346C830

C484C316

C435C316

C373C316

C531C316C812C316C701C316C625C316

C700C316

C775C316C760C316C572C316C704C316

C2C522

C731C316

C370C696

C2C740

C2C316

C368C740

C366C522

C349C740

C20C316C346C522

C371C640C346C581C385C522C205C522

C367C740

C40C696

C390C522

C372C640

C346C451

C346C690

C369C522C317C522C52C316

C365C522

C305C522C380C522C367C522

C317C740

C20C522

C349C522

C2C346

C200C522C210C522

C371C522

C346C516C375C522

C372C522C310C522

C346C696

C346C531

C2C696

C346C731

C344C522

C352C522C346C950C325C522

C359C740

C366C740C150C522

C368C522

C352C740C160C522

C135C522

C370C522

C346C811

C140C522

C349C696

C290C522

C220C522

C70C522

C230C522

C205C696C355C522

C211C522

C20C640

C366C696

C350C522

C165C522

C359C522C235C522C135C346C369C640

C155C522

C420C696

C372C740

C20C696

C346C702

C346C820

C101C522

C360C522

C367C696C346C551C51C316

C346C760

C130C522

C346C694

C371C740

C346C461C373C696

C346C366

C100C522C94C522

C368C696

C91C522C20C346

C346C570

C52C740

C346C692

C52C522C110C522

C2C640

C95C522

C346C420C346C490C346C517C346C812C40C346

C346C660C346C698C346C510C305C696

C346C900

C346C630C435C696

C346C704

C346C775

C145C522C40C522

C346C438

C346C740

C346C540

C52C346

C110C346

C90C522

C52C640C110C316

C42C522

C95C316

C94C316

C317C696

C346C481C385C696

C346C385

C205C740

C390C696C346C501

C346C701

C346C703

C346C390

C90C316

C205C346

C93C522

C200C316

C346C790

C346C375

C346C500

C41C696

C93C316

C372C696

C346C920

C346C571

C41C522

C346C370

C369C740

C210C696C165C316C42C316

C346C483C41C346

C211C346

C355C640

C211C696

C346C380

C375C696

C346C367

C346C572

C346C663

C380C696C346C475C359C696

C346C565

C346C732

C339C522

C339C740

C346C910C346C553

C346C452

C371C696

C365C640

C200C346

C344C740

C210C346

C346C471

C91C640

C220C696

C344C696

C51C522

C200C696C91C316

C346C666

C346C541C346C670

C140C346C150C316

C325C696

C367C640

C346C620

C165C346C346C590

C352C640

C317C346

C366C640

C101C346C160C346

C346C349

C346C780C346C771C130C346C100C316

C70C696

C93C346

C110C640

C346C616

C346C435C310C696C100C346

C305C346

C346C373

C350C640

C370C740

C290C316C150C346

C346C437

C220C346

C369C696

C346C652C91C346C346C484C346C615

C359C640

C135C696

C325C346

C346C439C95C346

C210C316

C51C346C346C552

C94C346

C94C740

C42C346

C373C522

C145C740

C145C316C145C346

C346C368

C150C696

C70C346

C230C346

C438C696

C346C372

C346C710

C90C346

C346C850C230C696

C51C640

C110C696

C93C640C155C346

C346C432

C211C316

C346C560

C91C696

C305C316

C346C580C346C712

C346C705

C346C770C346C800

C365C740

C94C640

C439C696

C339C346C368C640

C355C696

C432C740

C140C696

C310C346

C91C740C110C740

C101C316

C95C740

C42C696

C365C696

C437C522

C352C696

C160C696

C346C651C70C316

C290C346

C155C316

C100C640C165C740C290C696

C51C740

C230C316

C310C740

C346C433

C346C840

C346C369

C346C434

C94C696

C350C696

C433C740

C235C696

C346C640

C385C740

C95C640

C346C365

C346C750

C235C346

C93C696

C346C371

C90C640

C95C696

C339C696

C220C316C160C316

C145C696

C150C640

C437C696C90C696

C346C625C130C316

C435C522

C355C740

C52C696

C140C316

C165C696

C390C740

C346C600

C434C740

C42C640

C360C696

C100C696

C155C696C130C696

C346C359

C346C352

C150C740

C370C640C145C640

C90C740

C42C740

C235C316C205C316

C70C640

C101C640C385C640

C373C740C305C740

C93C740

C51C696

C155C740

C434C696C346C350

C434C522

C373C640

C101C696

C165C640

C317C640

C420C740

C20C740C380C740

C360C640

C70C740

C346C355

C438C522

C290C640

C390C640C211C640

C360C740

C434C640

C310C640

C432C522

C160C640

C200C640

C420C522

C130C640

C346C360

C438C740

C155C640

C437C740C100C740

C290C740C375C740

C437C640

C432C640

C211C740

C140C640

C432C696

C160C740

C439C522

C310C316

C349C640

C200C740

C135C316

C350C740

C433C522

C41C640C41C316C140C740

C210C640

C344C640

C439C740

C435C740

C235C640C205C640

C101C740

C235C740C433C696C210C740

C420C640

C40C316

C220C640

C433C640

C130C740

C135C640

C230C640

C40C640C230C740C325C740

C40C740C41C740

C339C640C325C640

C438C640

C135C740

C375C640

C305C640

C380C640

C220C740C435C640

C439C640

-.50

.51

e( s

imila

rvot

es |

X )

-60 -40 -20 0 20 40e( igoconnect | X )

coef = .00151701, se = .00064511, t = 2.35

Dyads: Countries: C350C316

343

Figure 19: Partial-regression leverage plots for small sample analysis - Econ IGO Connectedness 1990 Econ IGO Connectedness

C40C696

C165C740

C155C640

C101C740

C2C696

C101C640

C315C640

C155C740

C220C696

C155C696

C390C696

C380C696

C385C696C94C740

C95C696

C160C640

C352C696

C40C640

C150C740C20C696

C2C740

C40C522

C110C522

C160C696

C433C696

C150C640

C315C740

C205C696

C110C696

C375C696

C211C696C325C696C210C696

C70C696

C437C696

C42C640

C230C696

C145C640

C165C696

C352C740

C235C640

C345C696

C235C696C165C640

C339C640

C350C696

C95C640

C130C640

C135C696

C305C696

C200C696C40C740C290C696

C95C740C70C640C90C640

C94C640

C355C740

C42C740

C205C640

C90C696

C352C640

C150C696C42C696

C439C522C432C522

C360C696

C100C640

C360C740

C160C740

C235C740

C439C696

C70C740

C355C696

C438C522

C145C696

C260C740

C432C696

C385C640

C145C740

C345C640

C93C740

C135C640C2C640

C100C696

C52C696

C404C522

C220C640

C436C522

C101C696

C339C740

C94C696

C211C640

C438C696

C355C640

C437C522

C310C696

C436C696

C290C640

C315C696

C52C522

C145C522C140C696

C140C640

C41C696

C339C696

C93C640

C51C522

C230C640

C41C640C130C740

C90C740

C360C522

C93C696

C375C640

C130C696

C325C640

C290C740

C52C640

C150C522

C110C740

C70C522

C93C522

C420C640

C433C522

C433C640

C404C696

C41C740

C420C696C41C522C165C522

C51C640

C305C640C380C640

C155C522

C339C522C94C522C95C522

C101C522C51C696C434C696

C352C522

C110C640

C437C640

C345C522

C434C522

C435C522

C210C640C205C522

C100C740

C420C522

C220C522

C355C522

C42C522

C91C522

C2C522

C20C640C360C640

C315C522C432C640

C310C522

C160C522C100C522C135C522

C20C522

C434C640

C420C740

C438C640

C290C522

C130C522

C90C522

C433C740

C390C640

C200C640

C350C522

C435C640

C92C740

C92C640

C211C522

C350C640

C91C696

C305C522

C91C640

C205C740

C260C640

C436C640C260C696C432C740C390C522C210C522C325C522

C230C522C235C522

C435C740C438C740

C92C696

C135C740C52C740

C380C522

C385C522

C375C522

C404C640

C51C740

C350C740C92C522

C439C640C200C522

C310C740

C345C740

C435C696

C310C640

C439C740

C260C522

C140C522

C436C740

C230C740

C404C740

C305C740

C434C740

C220C740

C91C740C140C740

C385C740C325C740C20C740

C211C740C200C740

C437C740

C375C740C380C740

C390C740C210C740

-.4-.2

0.2

.4.6

e( s

imila

rvot

es |

X )

-5 0 5 10e( econigoconnect | X )

coef = -.00958483, se = .00764517, t = -1.25

Dyads: Countries: C437C740 C740

344

1991 Econ IGO Connectedness

C40C696

C315C740

C315C640

C352C640C90C640

C42C640

C2C696C220C696

C437C696C70C640

C40C522

C352C740

C352C696

C235C640

C70C696

C42C740

C205C696C2C740C40C640

C2C640

C20C696

C205C640

C345C696

C211C696C210C696C325C696

C355C640

C41C696

C70C740

C305C696C230C696C235C696

C90C696

C350C696

C42C696

C200C696

C439C696C41C522

C52C696

C52C640

C220C640

C90C740

C290C640

C290C696

C350C640

C438C696

C325C640

C345C640C339C740C339C640

C211C640C40C740

C235C740C439C522

C315C696C355C696C438C640C210C640C310C696

C437C522

C355C740C438C522

C51C640

C230C640

C345C522

C52C522

C70C522

C91C522

C51C696

C20C640

C51C522

C41C640

C95C522

C315C522

C290C740

C352C522

C200C640

C437C640

C42C522

C305C640

C91C696

C439C640

C339C696

C220C522C438C740

C2C522C205C522

C41C740

C20C522C310C522

C90C522

C91C640C339C522

C52C740

C439C740

C205C740

C350C740

C355C522C290C522C211C522C350C522

C305C522

C51C740

C345C740C310C640

C210C522C325C522C230C522C235C522

C310C740

C305C740

C200C522

C91C740

C220C740

C230C740C20C740

C325C740C211C740

C437C740

C200C740C210C740

-.50

.51

e( s

imila

rvot

es |

X )

-10 -5 0 5 10e( econigoconnect | X )

coef = -.00479132, se = .00868039, t = -.55

Dyads: Countries: None of mention

345

1992 Econ IGO Connectedness

C40C696

C315C640

C315C740

C366C640

C101C640

C352C640

C366C740

C352C696

C101C740

C155C640C100C640C2C696

C344C696

C2C740C94C740

C366C696C352C740

C165C740

C40C522

C95C640

C349C740

C165C640C349C640

C155C696

C145C640

C160C640

C390C696C220C696C385C696

C135C696

C94C640C367C640C380C696C40C640C349C696C365C696

C20C696

C160C696

C368C640C130C640

C41C522C95C696C369C740

C367C740

C346C366

C235C696

C366C522

C369C696

C42C640C205C696C70C640C346C373C211C696

C373C522

C210C696

C437C696

C325C696C200C696

C368C740

C93C640

C433C696

C165C696

C90C640

C346C830

C90C696

C375C696

C373C696

C70C696

C150C640

C41C696

C344C522

C404C522

C230C696

C438C522C235C640

C42C696

C315C696C367C696

C290C640

C145C740

C110C522

C346C530

C110C640

C344C640

C339C740

C95C740

C350C696C339C640

C220C640

C155C740

C371C740

C437C522C290C696

C150C696

C373C740C205C640

C346C367

C346C690

C360C696C305C696

C110C740

C93C740C433C640

C438C696

C150C740

C365C640C355C640C2C640

C70C740

C110C696

C439C522C346C700C346C571

C160C740

C346C540C369C522

C346C790

C94C696C346C371C90C346

C339C346

C110C346

C40C740

C315C346

C145C696

C360C522

C346C541

C346C692C70C522

C432C640

C42C740

C52C640

C346C940

C92C740

C346C522C371C522

C101C696

C420C640

C135C522C346C812

C52C696

C346C365

C346C369

C385C640C346C910

C92C640

C346C775C346C950

C346C698C404C696

C211C640

C140C640

C370C740

C346C581

C145C522

C52C522

C346C475C371C696

C100C696

C346C630

C346C696

C145C346

C355C740

C339C522

C355C696

C360C740C439C696

C359C522

C365C522

C339C696C368C522

C40C346

C51C640

C433C522C51C522C346C663

C346C368

C95C346C42C522

C315C522

C91C522C93C522

C210C640C375C640C435C522

C165C522C42C346C135C346C346C450

C346C705

C93C346

C93C696

C94C522

C370C696C369C640

C2C346C2C522

C432C522C346C652

C368C696

C344C740C155C522

C310C696

C436C640

C140C696

C95C522

C346C760

C346C370

C325C640

C150C522

C220C522

C346C451

C367C522

C90C522

C370C640

C70C346

C135C640

C346C780

C310C522

C346C694

C346C482

C346C732

C346C359

C205C522

C352C522

C130C740

C346C501

C92C346

C346C516C346C360

C346C670

C349C522C290C346

C130C696

C346C771

C420C696

C346C355

C434C640

C360C640C346C433

C150C346

C346C615

C51C346C346C452

C346C850

C305C346C325C346

C100C740

C91C696

C433C740

C101C346

C346C660

C230C640

C20C522

C346C349

C346C500

C346C352

C370C522

C359C740

C346C350

C346C702

C310C346

C346C770

C346C565C346C750

C160C522C220C346

C91C346C346C420

C346C490

C355C522C290C522

C346C436C346C800C100C346

C91C640

C130C346

C51C696

C346C461

C346C820

C438C640

C346C390C346C385C20C346C211C346

C346C510

C210C346C346C380

C101C522

C350C640

C200C346C346C375C346C640

C346C703

C20C640C94C346

C346C471C371C640

C436C522

C230C346C205C346

C346C572

C41C640

C346C625

C235C346

C346C483C41C346C346C616C346C438

C359C696

C346C484

C92C696

C100C522C130C522

C200C640C434C522C305C640

C211C522C350C522

C432C696

C380C640C310C640

C346C439C346C731C346C481

C346C645

C90C740

C373C640

C346C651

C346C552C346C712

C160C346

C436C696

C432C740

C346C551

C346C620C420C522C346C600

C205C740

C346C710

C305C522C344C346

C92C522C155C346

C140C346

C436C740

C346C666

C346C840

C346C740C346C900

C52C346

C434C696

C346C920

C346C435

C346C434

C346C404

C346C437

C165C346

C359C640

C346C570C390C640

C346C517

C346C553

C390C522

C346C432C290C740

C346C701

C210C522C325C522C230C522C235C522

C437C640

C346C580

C385C522C380C522

C51C740

C235C740

C375C522C200C522C420C740C435C640

C365C740C346C590

C438C740

C140C522

C52C740

C41C740C404C640

C310C740C435C696

C439C640C435C740

C434C740C404C740

C350C740

C91C740

C305C740

C140C740C439C740

C135C740

C385C740

C220C740

C230C740

C200C740

C390C740C380C740

C20C740

C437C740

C211C740

C375C740

C325C740C210C740

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-10 -5 0 5 10e( econigoconnect | X )

coef = .00848603, se = .00517699, t = 1.64

Dyads: Countries: C740

346

1993 Econ IGO Connectedness

C40C696

C101C640

C160C316

C352C696

C94C740

C235C316

C2C696

C165C740

C2C740

C155C696

C40C640C155C640

C385C696

C165C316C390C696C101C740C94C316

C220C696

C42C640

C101C316C160C696C305C316

C40C522

C380C696

C135C696

C95C640

C339C740

C437C696

C165C640

C205C316

C150C640

C90C640

C95C696

C365C696

C90C696

C344C696

C210C316C211C316

C20C696

C94C640

C150C696

C205C696

C155C316

C165C696

C42C696

C352C640

C230C316

C220C316C2C316

C100C316C235C696C100C640

C160C640

C150C740

C346C940

C41C522C200C316

C210C696C211C696

C317C696

C325C696

C110C640C130C640

C366C640

C372C740

C369C740

C150C316

C375C696

C110C316

C110C696

C205C640C95C316

C42C740C200C696

C93C316

C42C316

C346C366

C40C316

C230C696

C94C696

C52C696

C366C696

C372C696

C2C640

C70C696

C317C740

C360C696

C95C346

C41C696

C346C522

C339C640

C350C696

C305C696

C90C316

C369C696

C352C740

C437C522C95C740

C371C740

C346C830

C349C640

C90C346C346C692

C110C522

C346C630

C70C640

C346C373

C130C696

C349C740

C110C346

C93C640C346C811C310C316

C346C530

C40C346

C110C740C349C696

C317C640C346C541

C433C696

C101C696

C140C640

C93C740

C355C640

C352C316

C220C640

C346C690

C373C696

C350C316C235C640

C365C640

C100C696

C51C316

C130C316

C339C346

C346C775C373C522C42C346

C135C640

C346C700

C93C346

C385C640

C290C696C372C522C70C316

C360C522

C371C522

C346C540

C346C663C360C640

C346C571

C52C640

C346C696

C140C316C346C910

C438C522

C20C316

C135C346

C404C522

C346C790

C317C346

C346C581

C438C696

C373C740

C346C950

C93C696

C390C316C385C316

C325C316

C52C316

C380C316

C339C696

C370C740

C145C522

C375C316

C439C522

C344C640C366C740

C346C771

C371C696

C346C670C346C652C346C850

C355C316

C346C732

C349C316

C91C696

C346C500

C346C780C346C812C368C640

C155C740

C52C522

C145C696

C346C625

C370C316

C135C316

C439C696

C51C640

C346C698

C290C640

C370C696

C346C770

C434C640

C91C346

C369C316

C40C740

C51C522

C92C740

C344C522

C70C522

C355C696

C150C522C160C740

C343C740C346C433

C365C316

C51C696

C70C346

C51C346

C359C522

C150C346

C92C640

C91C316

C343C640

C101C346

C346C365

C92C316

C90C740

C346C565C346C694

C91C522

C93C522

C343C316

C346C750C42C522

C220C522

C325C640C346C451

C346C800

C140C696

C432C522C100C346

C317C522

C130C346

C339C522

C165C522

C368C316

C346C452

C359C316

C420C316

C344C346

C359C740

C435C522

C371C316

C155C522

C436C316

C94C522

C432C640

C2C346

C360C740

C95C522

C346C615

C360C316

C94C346

C346C660

C368C740

C135C522

C346C703

C369C522

C91C640

C290C346C305C346

C372C316

C352C522

C432C316C325C346

C346C367

C344C740

C346C475C346C651C339C316

C372C640

C434C316

C370C522C2C522

C346C349

C210C640

C20C522

C70C740

C346C701

C367C316

C220C346C145C316

C346C369

C310C696

C438C640

C346C600C41C640

C369C640

C130C740

C310C346

C90C522

C346C368

C305C640

C346C385C346C390C20C346

C346C510

C433C522

C210C346C211C346

C366C316

C346C616

C346C380

C346C731C375C640

C434C696

C346C501C346C375

C317C316

C346C372

C160C346C346C760

C346C820

C205C346

C346C516

C434C522

C230C346

C346C482

C235C346

C346C436

C310C522

C432C740

C346C740

C355C522

C370C640

C346C666

C211C640C230C640

C92C346C155C346

C346C900

C145C640C367C696

C346C438

C160C522

C290C316

C350C640

C346C461

C365C522

C366C522

C346C840C346C920

C346C640

C346C552

C436C640

C404C696

C346C572C343C696

C346C360

C346C316

C346C551

C165C346

C20C640

C101C522

C346C620

C211C522C346C645

C436C522

C355C740

C346C434C346C483

C371C640

C346C471

C346C404

C346C484C433C640

C433C316

C100C522

C346C370

C200C346

C92C696

C130C522

C41C346

C346C439

C368C696

C436C740C346C481

C100C740

C349C522

C346C371

C359C696

C41C316

C346C490

C373C316

C346C355

C346C705

C346C710

C346C437

C290C522

C140C346

C343C346

C346C352

C205C522C346C350

C52C346C343C522

C432C696

C350C522

C346C359

C390C640

C438C316

C145C346

C368C522

C346C712

C436C696

C346C553

C92C522

C305C522

C344C316

C346C435

C438C740

C380C640

C420C696

C439C316C433C740C435C316

C41C740

C420C640

C437C316C404C316

C346C590

C367C640

C437C640C200C640

C346C420

C145C740

C367C522

C367C740

C346C432C373C640

C435C640

C390C522

C346C517

C210C522C325C522

C359C640

C52C740C51C740

C346C580

C230C522C235C522

C439C640

C346C570

C385C522C435C740

C380C522C375C522

C235C740

C420C522

C435C696

C439C740

C310C640

C434C740

C200C522

C205C740

C140C522

C91C740

C290C740

C135C740

C365C740C305C740

C404C740

C420C740C404C640C140C740

C310C740

C350C740

C20C740

C437C740

C220C740

C230C740C385C740

C211C740C200C740

C390C740

C380C740

C325C740C210C740

C375C740

-.50

.51

e( s

imila

rvot

es |

X )

-10 -5 0 5 10 15e( econigoconnect | X )

coef = .00576449, se = .00528719, t = 1.09

Dyads: Countries: C740

347

1994 Econ IGO Connectedness

C40C696

C94C740C2C696

C352C696

C101C640

C94C316C385C696C390C696C165C740

C155C696

C220C696C155C640

C2C740

C380C696

C40C522

C160C696C20C696C344C696

C235C316

C205C696

C369C740

C165C316

C90C640

C95C346C205C316

C165C696C135C696C95C696

C346C940

C349C740

C305C316

C95C640

C101C740

C165C640C94C640

C90C696

C373C696C211C696C210C696C40C640C235C696

C210C316

C101C316C95C316C325C696C349C696

C346C541

C352C640

C220C316

C373C740C230C316

C437C696

C375C696

C371C740

C352C740

C317C696

C95C740

C200C696

C346C698C160C316

C346C700

C369C696

C346C692

C365C696

C100C640

C90C346

C52C696

C100C316

C130C640C230C696

C40C346

C371C522

C346C663

C305C696

C346C531C94C696

C346C690C2C316

C346C581

C150C696

C346C630

C370C740

C360C522

C346C811C346C373

C346C571

C373C522

C346C670C346C830C346C696

C372C696C350C696

C372C740

C200C316

C346C522C339C740

C344C346C346C652

C346C780C740C316

C366C696

C90C316

C346C850C346C540

C360C696

C211C316C346C694

C235C640

C317C346

C346C790C135C346C346C770

C346C625

C70C696C130C696

C155C316C352C316

C110C522C666C316

C350C316C110C696

C385C640

C70C316

C160C640C346C732C220C346

C140C316

C93C346

C101C696

C346C812

C346C910C346C433

C100C696

C359C740

C590C316

C40C316

C371C696

C317C740C346C452

C437C522

C150C740

C346C615

C346C565

C52C640

C390C316C385C316

C41C640

C732C316

C290C696

C91C696

C900C316

C90C740

C369C522

C325C316

C344C522

C433C696

C145C696

C380C316

C346C500

C145C522

C101C346C2C346

C41C522

C349C640

C130C740

C375C316

C359C522

C339C346

C93C740

C317C640

C370C696

C920C316C346C651C346C365C346C800

C346C660

C346C950

C344C740

C100C346

C2C640

C140C640

C360C740

C346C750

C70C640

C346C600

C130C346C349C316

C305C346

C94C346C155C346

C205C640C346C385C346C390

C93C316C325C346

C560C316

C155C740C325C640

C160C740

C52C522

C346C703

C211C346C210C346

C165C522

C346C380

C360C640C346C436C346C616

C346C552

C346C481

C52C316

C346C840

C800C316

C346C375C830C316

C355C316

C346C451

C372C522

C20C346

C91C522C94C522

C310C316C205C346

C51C522C70C346

C438C522

C230C346

C339C696

C344C640

C220C522C92C740C2C522

C40C740

C346C771

C640C316

C571C316

C438C696

C359C696C235C346

C317C522

C346C352C365C640C346C740

C343C740

C346C900

C135C640

C155C522

C370C316

C20C522

C93C522

C372C640

C820C316C355C522

C317C316

C432C640

C220C640

C160C346

C365C316

C346C645

C435C522C355C696

C346C475

C355C640C346C620C346C666

C51C696

C346C471C346C920

C346C702

C95C522

C369C316

C41C346

C150C522

C366C522

C432C740

C371C640C346C435

C370C522

C91C346C150C346C346C501C346C560

C41C316

C346C434

C346C437

C696C316C92C316

C41C696

C92C640

C690C316

C840C316C373C640

C346C710

C565C316

C439C696

C343C316C343C640

C434C640

C346C439

C368C316

C135C522

C439C522

C346C572

C712C316

C346C369

C369C640C346C820C200C346

C140C696

C165C346

C360C316

C349C522

C346C360

C750C316C770C316

C130C316

C346C712C432C522

C367C696

C359C316

C352C522

C580C316

C290C346

C372C316

C570C316

C346C367

C346C349C371C316

C145C346

C438C640

C346C640

C310C522

C346C551

C346C432

C90C522

C436C316

C694C316

C150C640C780C316C145C316

C346C370

C367C316

C310C346

C346C461C346C731

C339C316

C370C640C150C316

C910C316

C366C316

C346C350

C346C438

C698C316

C346C570

C41C740

C432C316C771C316

C110C740C310C696

C93C696

C346C701

C553C316C692C316C434C316

C160C522

C346C371

C346C366

C484C316C368C696

C481C316

C541C316

C551C316

C420C696

C368C522

C390C640C210C640

C790C316

C346C484

C51C346

C581C316

C20C316

C482C316C346C372

C346C704

C346C760

C660C316

C343C696C365C522

C350C640

C140C346

C101C522

C670C316

C346C516

C135C316

C51C640

C705C316C366C640

C950C316

C940C316

C70C522C92C696

C616C316

C346C510

C145C640

C100C522

C145C740

C339C522

C811C316

C93C640

C359C640

C434C696C130C522

C51C316

C230C640

C346C553

C346C590

C92C346

C346C482

C663C316

C346C316

C433C740

C100C740C433C640

C346C517

C339C640C433C522

C346C355C290C522

C432C696

C620C316

C367C522

C346C705

C305C522C516C316C700C316

C433C316

C436C740

C703C316

C436C640

C452C316C630C316C540C316

C205C522C110C640

C438C740

C373C316

C368C740

C434C522

C91C316C110C316

C110C346

C70C740C350C522C461C316C211C522

C615C316

C344C316

C367C740

C651C316

C52C346C366C740

C438C316

C436C522

C355C740

C600C316

C435C740

C20C640

C435C640

C235C740

C850C316C471C316

C552C316C52C740C420C522C343C522C500C316

C701C316

C367C640

C343C346C91C640C645C316C501C316C710C316C437C316

C435C696

C439C316

C92C522

C652C316

C435C316C522C316

C572C316

C702C316

C51C740C812C316C531C316

C346C580

C517C316

C510C316

C420C316

C704C316

C625C316

C760C316C451C316C346C359

C475C316

C436C696

C211C640

C375C640

C390C522

C200C640

C210C522C385C522C325C522C437C640C731C316

C290C316

C346C420

C368C640

C380C522

C380C640

C290C640

C375C522C230C522C346C368C235C522C439C740

C205C740

C305C640

C200C522

C310C740

C140C522

C140C740C91C740C135C740

C310C640

C439C640

C290C740

C420C640

C434C740

C305C740

C350C740C365C740

C420C740

C20C740

C230C740C390C740

C437C740

C220C740

C385C740

C380C740

C200C740C211C740C375C740C210C740C325C740

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5 10e( econigoconnect | X )

coef = -.00542803, se = .00486614, t = -1.12

Dyads: Countries: C740 1995 Econ IGO Connectedness

348

C40C696

C155C640C40C640C101C640

C352C696

C94C740

C155C696

C95C640

C40C522

C94C316C95C316

C2C696

C100C640

C165C740

C135C696

C352C640

C2C740C385C696C390C696

C95C696

C95C740

C437C696

C41C640

C220C696

C380C696

C165C640C101C316

C165C696C346C940

C360C640

C205C696

C101C740

C160C696

C235C696

C94C640

C95C346

C20C696C369C696

C160C640

C94C696

C130C640

C370C696

C344C696C365C696

C90C696

C90C640

C305C316

C150C696

C346C531

C101C696

C160C316

C235C640

C100C316C165C316

C211C696

C210C696

C346C698

C325C696C317C696C360C696

C346C541

C375C696C352C740

C434C640

C360C522

C52C696C41C522

C385C640

C40C740

C230C696C41C740

C373C696

C366C696C346C700

C433C696

C200C696

C355C640

C41C316

C360C740

C70C696

C205C640

C350C696

C346C663

C130C696

C235C316

C100C696

C155C316

C110C696

C346C630

C373C522

C346C775

C110C522

C346C694

C359C522

C437C522

C370C522

C305C696

C740C316

C91C696C145C696

C352C316

C346C910

C344C522C40C316C350C316C93C316C70C640

C346C652

C346C702

C290C696C590C316

C666C316

C346C670

C140C640

C210C316C346C522

C93C740

C230C316

C355C522

C372C522

C436C640

C369C740C346C830

C2C316C220C316C420C696

C366C522C369C522

C95C522

C346C571

C145C522

C346C950

C367C696

C346C540

C346C373

C130C316

C346C370

C433C640C2C640

C93C640C90C346

C150C522

C732C316

C560C316

C346C696

C349C696

C310C316

C52C640C800C316

C325C316C390C316C385C316C380C316

C900C316

C420C522

C375C316

C920C316

C371C522

C349C316

C40C346

C355C316C640C316C91C522

C571C316C92C640

C372C696

C317C316

C140C316C155C740

C90C316

C52C522C51C522

C145C316

C41C696C343C640C439C696

C750C316

C165C522

C368C522

C346C565

C70C316

C94C522

C211C316

C712C316

C92C740C205C316

C290C640

C840C316

C438C696

C438C640

C580C316

C438C522

C436C740

C51C696

C368C696

C92C316

C150C640

C370C740

C770C316

C355C696

C343C740

C350C640

C570C316

C369C316C41C346C346C692

C436C316

C150C740

C343C316

C346C780

C565C316

C135C640

C359C316

C346C800

C51C640

C368C316

C411C696C411C522

C365C316

C325C640C346C690

C160C740

C439C522

C820C316

C360C316

C432C316

C346C850

C553C316

C372C316

C339C740

C145C640

C150C316

C317C346C346C560C130C346

C200C316

C346C625

C155C522

C346C490

C101C522

C771C316C910C316

C346C366

C780C316C434C316

C317C740

C339C316

C93C522

C551C316

C541C316

C432C522

C346C770

C140C696

C371C696

C365C640

C367C316

C484C316C346C615C346C436

C346C372

C110C740

C130C522

C790C316

C220C640

C346C368C135C522

C404C316

C346C840

C830C316

C346C411

C346C790

C346C367

C317C522

C346C433

C220C522

C371C740

C366C316

C434C522

C940C316

C371C316

C305C640

C317C640

C110C640

C346C365

C367C522

C93C696

C346C452

C346C385C346C390

C346C811

C92C346C368C640C346C380C90C522

C346C703

C205C346

C349C740

C339C346

C339C696

C368C740C370C316C436C522

C20C522

C432C640

C2C522

C100C740

C346C701

C950C316

C660C316C310C696

C93C346

C346C732

C346C371C352C522C344C346

C346C620

C811C316

C310C522

C220C346

C346C369C346C500C130C740

C373C740C434C696

C380C640

C375C640

C346C316

C346C731

C90C740

C433C522C100C346

C366C640

C355C740

C346C660

C349C640

C481C316

C432C696

C700C316C438C316C516C316C346C651

C490C316

C690C316

C369C640C339C522C663C316

C101C346

C616C316C346C434C433C316C359C696

C365C522C452C316C433C740

C70C522C100C522

C343C346

C615C316

C437C640

C696C316

C510C316C346C600

C91C346

C346C483

C705C316

C343C696

C210C640C346C750

C540C316

C135C346

C346C900

C346C712

C461C316

C346C920

C703C316

C346C451C359C740

C200C346

C698C316

C94C346

C367C740

C692C316C346C616

C92C696

C343C522C52C316

C20C640C155C346

C51C316

C411C640

C372C740

C404C522C694C316

C367C640

C346C771C346C481C346C704

C290C316

C92C522

C346C552

C630C316C471C316

C344C640

C483C316

C305C346C325C346

C500C316

C346C572

C91C316C620C316

C670C316

C390C640

C411C740

C438C740C651C316

C230C640

C2C346

C70C346

C211C522

C501C316

C411C316

C346C352C310C640

C439C316C91C640C552C316

C211C346

C210C346C346C375

C435C522

C372C640

C230C346

C373C316

C346C471

C235C346

C437C316

C432C740C850C316

C600C316

C290C522C435C316

C702C316

C346C484C346C360

C475C316

C710C316

C531C316C517C316C160C522

C346C355C346C501

C522C316

C436C696

C349C522C110C316

C346C705

C812C316C625C316

C420C316

C344C316

C346C437

C451C316C775C316

C370C640

C701C316

C344C740

C52C740

C435C640

C652C316

C211C640

C760C316

C20C346

C160C346

C572C316

C150C346C346C359

C205C522

C346C710C346C812

C350C522

C346C820

C704C316

C346C510

C20C316

C346C760

C346C435C305C522C339C640C346C438

C731C316

C290C346

C346C666

C346C349

C145C346C346C432

C346C740

C346C517

C346C551C310C346

C346C439C135C316

C346C350

C165C346

C140C740C145C740

C359C640C371C640

C346C461

C420C640

C366C740

C346C475C110C346

C435C740

C51C346

C346C420

C51C740

C140C346C346C570

C434C740C70C740

C439C640

C200C640

C52C346

C346C516C310C740

C390C522

C210C522C325C522C230C522C235C522

C346C553

C385C522C380C522

C375C522

C346C590

C140C522C435C696

C346C404

C439C740

C404C696

C235C740

C205C740

C200C522

C346C580

C373C640

C290C740

C350C740

C91C740

C305C740C20C740

C230C740

C390C740

C404C740

C404C640

C420C740

C135C740C365C740

C437C740

C385C740C380C740

C375C740C325C740C210C740

C220C740C200C740

C211C740

0-.5

.5e(

sim

ilarv

otes

| X

)-1

-10 -5 0 5 10e( econigoconnect | X )

coef = -.00044365, se = .00441004, t = -.1

Dyads: Countries: C740 1996 Econ IGO Connectednesss

349

C101C640C155C640

C40C640

C40C696

C42C640

C101C316

C40C522

C235C316

C352C640

C94C740

C100C640

C95C696

C160C640

C101C740

C385C640C94C640

C352C696

C165C640

C305C316

C2C740C42C740C95C640C165C740

C210C316

C40C740

C360C640

C220C316

C359C522

C160C316

C360C740

C200C316

C155C696

C90C640

C94C316

C205C640

C344C696C40C316

C211C316C235C640

C230C316

C205C316

C369C740

C2C316

C366C696

C95C316C360C522

C2C696

C42C316

C165C316

C352C740

C110C522

C155C316C380C640

C135C640

C93C316

C135C696

C370C522C95C740C346C694C346C698

C130C640

C165C696C42C696

C373C522

C41C640

C372C522C366C522

C93C640

C150C522

C344C640

C369C696

C370C696

C385C696C220C696C390C696

C90C316

C160C696

C94C696C95C522

C317C640

C380C696C371C522

C41C522

C369C522

C433C640

C344C522

C165C522

C346C630

C52C522

C145C522

C305C640

C101C696

C346C531

C94C522

C205C696C235C696

C437C696C51C522

C437C522

C346C940

C368C522

C90C696

C70C640

C372C696

C130C696

C317C696

C346C652

C355C640

C91C522

C310C316

C40C346

C100C316

C740C316

C365C696

C2C640

C367C696

C20C696

C346C775

C200C696

C290C640

C325C640

C352C316

C438C640

C150C696

C346C581

C2C522

C350C316

C373C696

C666C316

C368C640

C360C696

C590C316

C110C696

C346C670C346C830C346C663

C160C740

C433C696

C155C522

C438C522C366C640

C155C740C92C740

C434C640

C101C522

C317C522

C145C640

C560C316C352C522

C800C316

C411C696

C355C522

C385C316

C220C522

C390C316C373C740

C150C316C712C316

C343C740

C346C522

C325C316

C70C696

C900C316

C140C640

C640C316

C732C316C380C316

C355C316

C210C696C750C316

C571C316

C211C696

C360C316

C344C740

C325C696

C432C522

C580C316

C375C316

C375C696C840C316

C367C522

C51C640

C91C640

C770C316

C920C316

C369C316

C570C316

C230C696

C20C522

C92C316C92C640C220C640

C359C316

C310C522

C349C316

C100C696

C432C316C553C316

C343C640

C343C316

C565C316C368C696

C42C522

C150C640

C367C640

C771C316

C339C522

C365C316

C346C541

C434C316C541C316

C372C316

C411C522

C339C740

C140C316

C52C696

C780C316

C346C366

C368C316

C484C316C790C316

C820C316C404C316C290C696

C359C640

C369C640C93C740

C910C316

C830C316C130C316

C581C316

C390C640

C451C316C145C316

C135C522

C346C700C70C316

C370C740

C346C450

C51C696

C95C346C359C740

C52C640

C349C640

C346C690

C367C316

C145C696

C365C640C359C696C41C316

C940C316

C317C740

C372C740

C436C522C346C571

C452C316

C346C411C366C316

C811C316

C439C522

C660C316

C551C316

C70C522

C310C640

C375C640C346C811

C339C696C350C640

C420C522

C92C346

C339C316

C110C640

C346C316

C343C696C450C316

C346C373

C950C316

C372C640

C490C316

C365C522

C438C316

C346C702

C91C696C92C696

C433C316C346C692C690C316C510C316

C210C640

C663C316

C150C740C437C640C346C910C346C565C346C800

C696C316

C317C316

C616C316C346C615

C411C740

C100C522

C540C316C483C316C461C316

C481C316C615C316

C346C490

C433C522

C346C731

C41C740

C703C316

C705C316C404C522

C90C522

C20C316

C349C696

C93C696

C346C560

C211C522

C42C346

C90C346C20C640C317C346

C93C522

C471C316C500C316C339C346

C130C522

C343C522

C343C346

C346C368

C51C316C349C522

C346C385

C433C740

C411C316C346C390

C371C740

C630C316C501C316C411C640

C698C316C439C316C694C316

C305C696

C516C316

C290C522C651C316

C368C740

C346C372

C92C522

C552C316

C305C522

C620C316

C371C316

C522C316

C373C316

C371C696

C437C316

C370C316

C438C740

C370C640

C346C380

C435C316C692C316

C420C316C91C316

C702C316

C475C316C517C316C346C850

C436C316C531C316

C436C696

C850C316C600C316

C205C522

C346C712

C346C780

C710C316

C205C346

C346C625

C420C696

C812C316C625C316C775C316

C346C616

C700C316C670C316

C135C316

C110C740C90C740

C346C770

C760C316

C355C740

C160C522

C350C522

C701C316

C41C696

C367C740

C652C316

C572C316

C704C316

C349C740

C355C696

C731C316C110C316

C346C950C211C640

C344C316

C346C367

C346C840

C346C365

C434C522

C230C640

C346C790C220C346

C290C316C93C346

C346C660

C346C540C346C620

C346C451C200C346

C346C370

C346C433

C140C696

C101C346

C436C640C346C696

C438C696

C200C640

C366C740

C305C346

C325C346

C346C452

C310C740

C52C316

C130C346

C310C696C390C522

C346C500

C346C750

C94C346

C210C522

C210C346

C380C522

C211C346

C325C522

C346C640

C375C522C435C640

C439C696

C385C522C346C359C346C436

C346C369

C230C522

C432C640

C230C346

C346C771

C155C346

C346C760

C235C522

C235C346

C436C740

C91C346

C346C481

C135C346

C346C483

C339C640

C145C740C51C740C346C510

C140C522

C346C703

C346C705

C130C740

C432C696

C346C600

C434C696

C2C346

C51C346

C346C360

C346C572

C346C740

C346C349

C346C900

C346C352

C200C522

C346C471C346C375

C160C346

C100C740

C290C346

C346C552

C346C350

C346C501

C346C438

C346C920

C310C346

C371C640

C100C346C346C437

C150C346

C346C484

C346C820

C346C371

C52C740

C346C710C110C346C41C346

C420C640

C165C346

C346C551

C346C704

C145C346

C346C651

C439C640

C346C580

C346C812

C20C346

C346C355C346C435

C432C740

C346C434

C346C475

C434C740C346C432

C373C640

C346C590

C140C346

C70C740C439C740

C346C732

C70C346C135C740

C52C346

C435C740

C91C740C346C461C346C439

C344C346C346C666C290C740

C346C553C346C570

C435C522

C140C740C346C404

C404C640

C346C420C346C517

C346C516

C205C740

C404C696

C404C740

C235C740

C305C740

C420C740

C350C740

C390C740C20C740C435C696C385C740

C365C740

C380C740

C437C740

C200C740C230C740C211C740

C375C740

C220C740

C210C740C325C740

.50

1e(

sim

ilarv

otes

| X

)-.5

-5 0 5 10e( econigoconnect | X )

coef = -.00144399, se = .00521751, t = -.28

Dyads: Countries: C437C740 1997 Econ IGO Connectednesss

350

C40C696

C40C640

C95C696

C352C696

C344C696

C42C640

C305C316

C42C740C2C696C101C316

C205C640C160C316C2C740

C95C316

C40C740

C95C640

C235C640

C220C696C200C696C205C696

C100C640

C352C740

C165C316

C42C696

C352C640

C205C316

C42C316C155C316

C20C696C235C696

C740C316

C90C696

C40C522C437C696

C94C316C93C316C235C316

C90C640

C210C696C211C696C325C696C40C316

C210C316

C344C640

C52C522

C830C316C230C696C90C316

C150C316

C352C316

C666C316

C51C696

C220C316

C145C316

C290C696

C900C316

C2C640

C52C696

C350C696

C200C316C41C640C590C316

C380C316

C100C316

C339C740C230C316

C375C316

C70C640

C732C316

C344C740

C100C696

C696C316C211C316C305C640

C140C316

C690C316

C310C316

C70C696

C305C696

C438C640

C920C316C2C316

C560C316

C800C316

C571C316

C220C640C2C522

C41C316

C355C316C91C696

C51C316

C51C640

C360C316

C349C316

C820C316

C41C740

C310C740

C712C316

C840C316

C369C316

C92C316

C750C316

C437C522

C365C316

C694C316

C344C522

C343C316

C438C740C565C316

C372C316

C570C316

C359C316

C135C316

C51C522

C325C640C770C316C580C316C630C316C780C316

C698C316

C339C696

C553C316

C339C316

C771C316C434C316C541C316C432C316

C130C316

C91C522

C790C316

C205C522

C404C316

C92C640

C310C696

C581C316

C368C316

C660C316

C438C522

C41C522

C481C316C692C316

C91C316

C95C522

C367C316

C452C316C616C316C91C640C52C640

C366C316

C41C696

C220C522

C551C316

C910C316

C663C316

C705C316

C670C316C438C316

C615C316C210C640C450C316C110C316C451C316C90C740C433C316C490C316

C355C696C703C316

C940C316C510C316

C438C696

C501C316

C370C316

C620C316C461C316C483C316

C950C316

C540C316

C355C640

C230C640

C51C740C651C316

C70C316

C411C316

C471C316

C42C522

C439C316

C439C522C500C316C850C316C20C522

C552C316

C600C316

C371C316

C702C316

C200C640

C439C696

C522C316

C373C316

C710C316C437C316

C352C522

C484C316C516C316C211C640

C385C316

C701C316C310C640C435C316

C52C316

C475C316C652C316C436C316

C517C316

C531C316

C812C316

C572C316

C625C316C760C316

C390C316

C775C316C349C522C700C316

C704C316

C305C522C200C522

C731C316

C355C740

C344C316

C20C640

C350C316

C290C640C325C316

C52C740

C211C522

C310C522

C437C640

C350C640

C91C740

C290C522C350C522

C317C316C640C316

C355C522

C210C522C339C640C325C522

C339C522

C100C740

C70C522

C90C522

C230C522

C20C316

C305C740

C235C522

C235C740C205C740C290C740

C290C316C439C740C70C740

C439C640

C350C740

.5

C20C740C200C740

C437C740

C211C740C230C740C220C740

C210C740C325C740

01

e( s

imila

rvot

es |

X )

-.5

-5 0 5 10e( econigoconnect | X )

coef = -.00378927, se = .00805877, t = -.47

Dyads: Countries: C740 1998 Econ IGO Connectedness

351

C740C316

C900C316

C92C740C385C316

C390C316

C732C316

C350C316

C325C316

C92C640

C560C316

C690C316

C2C696

C750C316C840C316

C92C696

C2C316

C698C316

C770C316

C696C316

C2C640

C694C316C541C316C482C316C92C316

C481C316

C670C316C370C522C371C640

C2C522

C660C316

C371C740

C92C522

C692C316

C372C640

C551C316C372C316C663C316

C370C696C371C316

C510C316

C359C316

C705C316

C616C316C452C316

C522C316C373C522

C850C316

C20C696

C2C740

C615C316

C365C316C372C740

C703C316C540C316

C432C316

C483C316C475C316

C434C316

C651C316

C359C740C369C316

C451C316

C165C740C620C316

C710C316

C450C316

C436C696

C42C740

C652C316C484C316

C52C640

C461C316C93C740

C20C640

C371C522C600C316

C95C740

C372C522

C150C522

C572C316C404C316

C435C696

C373C696

C420C696

C420C522

C42C640C385C696

C435C522

C701C316

C110C740

C359C522

C435C316C94C740C150C740C110C640C93C640C51C640C20C316

C390C696C41C640

C731C316

C438C316

C51C740

C369C640

C625C316

C110C696

C704C316

C369C740

C150C696

C200C696

C436C522

C110C522

C437C696

C41C740

C42C696

C95C640

C70C696

C150C640

C210C696

C91C640

C404C522

C90C640

C95C696

C433C316

C145C740

C439C696

C404C740

C155C640

C41C522

C439C522

C101C640

C145C522

C94C640C211C696

C165C640

C439C316

C52C696

C91C522C220C696

C135C522

C420C316

C372C696

C369C522

C437C522

C365C522

C432C740

C91C740

C370C316

C371C696

C155C696C52C522

C135C696

C100C640

C90C740

C165C696C70C522C370C740

C350C640

C41C696C438C696C42C522

C436C316

C373C316

C52C316

C365C640

C220C522

C438C522C155C522

C91C696

C155C740

C160C696

C165C522

C70C640

C325C696C385C522

C365C696

C310C522

C95C522

C210C522C200C522C130C640C145C640

C385C640

C93C696C93C522

C434C740

C359C640

C211C522

C51C696

C390C522

C145C696

C101C740C350C522

C94C522

C160C640

C90C696

C200C316

C433C696

C436C740

C51C522

C160C522

C101C522

C310C740

C93C316

C94C696

C373C640

C90C522

C290C522

C359C696

C437C316

C95C316C235C696C165C316C51C316

C100C522

C20C522

C160C740

C369C696

C325C522

C42C316

C130C696

C438C740

C210C316

C350C696

C100C696

C434C640

C230C696

C290C316

C433C522

C110C316

C432C522

C94C316

C211C316

C101C696C130C522

C150C316

C140C640

C135C640

C404C640

C140C522C140C696

C220C316

C145C316

C235C522C230C522

C130C740

C432C640

C235C316

C434C696

C432C696

C390C640

C41C316

C310C696

C420C740C290C696C100C740C90C316

C101C316

C290C640

C390C740

C310C640

C70C740

C200C640

C155C316

C235C640

C435C640

C230C316

C434C522

C91C316C439C740C438C640

C160C316

C130C316

C436C640

C220C640

C435C740

C420C640

C210C640

C140C316

C370C640

C20C740

C433C740C100C316

C211C640

C140C740C385C740C365C740

C310C316

C290C740C235C740

C70C316

C350C740

C230C640

C135C740C325C640

C433C640C439C640

C200C740

C211C740

C135C316

C437C640

C210C740

C230C740C325C740

C437C740

-1-.5

0.5

e( s

imila

rvot

es |

X )

-20 -10 0 10 20e( econigoconnect | X )

coef = .0063726, se = .00273795, t = 2.33

Dyads: Countries: None of mention 1999 Econ IGO Connectedness

352

C235C640

C40C640

C40C696

C235C316

C211C316

C344C640

C310C316C220C316C352C696

C205C640

C230C316C42C640

C210C316C200C316

C220C640

C290C640

C211C640

C52C522

C40C522

C305C316C346C630

C325C640C230C640

C2C316C346C694

C2C740C155C316

C310C640

C210C640

C344C696

C2C640

C42C696

C93C316C220C696

C160C316

C346C652C2C696

C41C522

C165C316

C40C316

C200C640

C205C316

C40C740

C90C640

C101C316

C346C581

C42C522

C70C640

C235C696

C20C640

C346C940C305C640

C91C522

C20C696

C51C522C70C696C437C522

C344C522

C95C316

C352C740

C40C346

C150C316

C42C740

C41C640C346C700C42C316C346C670C346C770

C344C740

C210C696

C346C522

C211C696C200C696C325C696

C2C522

C349C522C290C696

C305C696

C41C696

C346C696

C100C316C145C316C230C696

C355C640

C346C615

C20C522

C346C663

C346C616

C590C316

C350C316

C90C696C352C316

C140C316C135C316C350C696

C666C316

C346C780

C438C522C437C696C732C316

C20C316C310C522

C92C640

C346C698

C94C316C346C625C352C640C352C522

C220C522

C740C316

C346C620

C571C316C91C640

C560C316

C41C316C205C696

C211C522

C800C316C346C433

C346C660

C290C316

C95C346

C339C740

C355C522C346C411C346C775

C640C316C355C316

C355C696C205C522

C52C640

C360C316

C90C316

C437C640

C712C316

C750C316

C820C316C840C316

C369C316

C92C316

C339C522

C343C316

C290C522

C365C316

C850C316

C346C702

C565C316

C372C316

C92C346

C359C316

C346C850C346C540

C350C640

C339C316

C630C316C580C316

C553C316C780C316C771C316

C371C316

C790C316C434C316C541C316C432C316

C346C731C346C541

C92C740C475C316

C90C522

C346C690

C70C522

C660C316

C101C522

C52C696

C522C316C51C640

C811C316C404C316

C438C640

C310C696

C93C346

C452C316

C481C316

C830C316

C616C316

C692C316

C346C316

C346C452

C551C316

C705C316

C438C316

C663C316

C339C346

C615C316

C437C316

C451C316C698C316

C433C316

C490C316

C670C316

C702C316C346C600

C510C316

C581C316C370C316

C501C316

C690C316

C483C316C461C316C70C316

C620C316C346C771C540C316

C91C316

C694C316C346C500

C346C481

C350C522

C651C316C600C316

C696C316C471C316C439C316

C500C316C411C316

C552C316

C346C840C110C316

C710C316C770C316

C346C565C484C316

C373C316

C310C740

C701C316C517C316

C531C316

C652C316C812C316C346C790

C625C316

C130C316C572C316

C700C316C775C316C760C316C346C800

C200C522

C42C346

C439C522

C704C316

C317C346

C155C346

C305C522

C349C316

C346C490

C731C316C135C346

C346C510C346C501

C346C910

C346C369

C920C316

C51C316C346C471C346C651

C346C640

C346C475

C325C316

C346C760

C51C696

C375C316

C346C692

C380C316

C368C316

C346C434

C200C346

C346C432

C346C373C900C316

C346C830

C390C316

C52C316

C211C346

C346C380

C385C316

C367C316C366C316

C100C346

C91C346

C346C390

C910C316

C346C385

C339C696

C339C640

C950C316

C346C950

C940C316

C343C346

C94C346

C346C438C346C437

C41C346

C344C316

C346C451

C220C346

C346C811

C325C346

C346C439

C355C740C346C552

C235C522C230C522

C346C920

C346C580

C205C346

C150C346

C290C346

C110C346

C90C346

C101C346C145C346

C325C522C41C740

C130C346C51C346

C210C522C346C365

C346C701

C235C346

C346C551

C230C346

C165C346

C346C483C346C461

C2C346C346C590

C346C820

C91C696

C52C346

C346C710

C305C346

C210C346

C346C484C346C750

C346C370

C346C366

C20C346

C439C696

C346C368

C346C352C160C346

C346C705

C346C712

C90C740

C346C360

C346C371C346C572

C346C372

C438C740

C346C666

C346C375

C346C367

C310C346

C346C553

C346C740

C140C346

C346C900

C346C812

C305C740

C346C359

C438C696

C346C349

C439C640

C346C404

C70C346

C346C517

C346C350C346C732

C346C571C290C740

C344C346

C317C316

C346C355

C51C740

C205C740

C91C740

C52C740

C70C740C439C740

C235C740

C20C740

C350C740C200C740

C230C740

C220C740

C325C740

C210C740C211C740

C437C740

.50

1e(

sim

ilarv

otes

| X

)-.5

-4 -2 0 2 4 6e( econigoconnect | X )

coef = -.01274086, se = .00980097, t = -1.3

Dyads: Countries: C740

353

2000 Econ IGO Connectedness

C40C696

C305C316C155C640

C40C640

C95C696

C42C640C101C640C135C640

C205C640

C165C640C211C316C352C696

C344C640

C155C696

C95C640

C155C316

C41C696

C90C640C359C522

C220C696C165C740

C368C522

C135C696

C370C522

C346C630

C2C740

C205C316C434C640

C130C640C165C316

C40C522

C365C696

C2C696

C235C696

C42C696

C220C316

C235C316

C346C652

C349C640

C165C696

C100C640

C352C640C41C640

C101C316

C135C316

C160C316

C110C522

C41C316C360C522

C2C316

C360C640C41C522

C145C522

C370C696C235C640

C369C696

C52C522

C210C316

C438C640C346C581

C160C640C52C640

C20C522C95C316C42C740

C140C640C230C316

C160C696

C437C640C93C316C42C316C366C522

C437C696

C380C640C360C740

C150C316

C52C696

C20C696

C51C640

C90C696

C2C522

C93C640C369C522

C100C696

C130C696

C40C740

C371C522C740C316

C145C316

C305C696

C666C316C352C316

C150C640

C101C696

C590C316C310C316

C385C696C390C696

C150C522

C210C696

C200C316

C211C696C317C696C375C696

C380C696C70C640

C352C740

C140C316

C200C696C101C740

C732C316

C95C740

C325C696C290C316

C349C522C344C696

C900C316

C432C522

C380C316

C91C522

C375C316

C346C540

C920C316

C42C522

C70C696C349C316

C40C316

C155C522

C40C346C230C696

C317C316

C95C522

C94C316

C571C316

C560C316

C110C696

C220C522

C800C316

C150C740

C150C696

C355C640

C365C640C91C640

C366C640

C110C640C93C740

C90C316

C211C522

C110C316

C92C640C365C316

C373C696

C437C522

C346C616

C350C696

C205C696

C346C615

C290C696

C355C316

C346C780

C343C640

C92C740

C712C316

C366C696

C360C316

C41C740

C94C696

C750C316

C52C316

C411C696

C91C316

C93C696

C368C640

C343C740

C368C316

C100C316C840C316

C346C770

C369C316

C145C696

C94C640

C360C696

C92C316C372C522

C346C670C433C640

C344C740

C850C316C433C316

C343C316

C373C522

C435C640

C820C316

C367C522

C565C316

C367C316C51C696C135C522C359C316

C339C740

C385C640C317C522

C344C316

C372C316

C344C522

C910C316

C580C316

C553C316

C366C316

C830C316

C369C640

C372C696

C771C316

C339C316

C780C316C790C316

C310C522

C145C640C438C522

C434C316C541C316

C371C316

C432C316

C346C660C205C522

C630C316

C355C522

C343C522

C2C640

C130C522

C130C316C390C640

C371C696

C290C522

C317C640

C101C522

C352C522

C359C696

C100C522

C346C411

C110C740

C92C522

C51C316

C437C316C570C316

C165C522

C439C696

C346C696

C475C316

C94C522

C346C663

C346C775

C90C522

C155C740

C41C346

C950C316

C439C522

C346C522

C93C522

C346C790

C346C452

C52C740

C90C740C92C346

C220C640

C660C316C522C316C811C316

C411C522

C51C522

C339C522

C452C316

C432C640

C355C696

C359C640

C346C565

C346C439

C510C316

C305C640

C433C696

C346C694

C375C640

C346C437

C371C740

C690C316

C346C620

C551C316

C346C316

C368C740

C346C552

C372C640

C91C346C696C316C616C316C481C316C438C316

C343C346

C451C316

C325C640

C663C316

C705C316

C490C316

C343C696C581C316

C434C696

C615C316C702C316

C439C640

C365C522

C95C346C367C696C346C434

C411C640C516C316

C346C531

C434C522

C698C316

C70C316

C501C316C703C316

C92C696

C94C740C346C702C483C316C461C316

C692C316

C135C346C346C731

C346C625

C694C316

C346C840

C346C600

C411C740

C155C346

C310C740

C370C316

C438C740

C540C316C439C316

C346C690

C346C438

C411C316C346C490

C160C522

C670C316C620C316

C471C316

C70C522

C385C522

C390C522

C500C316C517C316

C51C740

C552C316

C651C316

C305C522C380C522

C600C316

C200C522

C346C760

C432C740

C438C696

C350C316

C420C316

C432C696

C346C501

C359C740

C652C316

C346C500C346C483

C350C522

C770C316C710C316

C310C696C484C316C370C740

C20C316

C346C481

C435C316

C373C316

C531C316

C367C640

C210C640

C434C740

C812C316C625C316

C346C471C701C316

C700C316

C775C316

C433C522

C91C696

C230C640

C346C541

C140C696

C760C316

C372C740

C385C316

C572C316

C390C316

C420C696

C325C316

C346C698

C704C316C130C346C346C910

C349C740

C349C696C371C640

C339C696

C346C484

C93C346C101C346C731C316

C100C346

C150C346

C346C432C346C850

C290C640C369C740

C42C346C90C346

C346C651

C346C580

C310C640

C346C811

C346C920

C373C740

C346C433C346C800

C367C740

C346C950

C346C590

C368C696

C339C640

C346C385C346C390

C317C740

C205C346

C640C316

C211C346

C346C380

C200C640

C346C551

C200C346

C211C640

C420C640

C220C346

C346C510

C346C830

C366C740

C210C522C110C346C375C522C433C740

C420C522

C325C522

C350C640

C235C346

C230C522C235C522

C370C640

C346C475

C439C740C346C692C435C740

C346C771

C346C352

C2C346

C346C435

C346C572C346C373C52C346

C346C516

C160C740

C100C740

C346C710C51C346

C145C346

C160C346C165C346

C20C346

C235C740

C346C900

C346C705

C346C740

C94C346C20C640

C346C666

C346C560

C346C640

C346C369

C346C461

C346C750

C346C371

C210C346C140C522

C305C346

C325C346

C346C820

C230C346

C346C365

C346C570

C91C740

C346C517C130C740

C346C703C346C812

C355C740

C205C740C346C701

C346C451

C346C360

C346C553C346C571

C346C370

C346C350

C339C346

C140C346

C317C346

C135C740

C346C732C346C372

C70C346

C346C368C346C375

C290C346

C145C740

C305C740

C346C359

C346C420

C346C704

C70C740

C346C366

C290C740

C373C640

C346C349

C346C712

C420C740

C435C522

C346C355

C346C367

C350C740

C140C740

C20C740

C437C740

C310C346

C380C740

C435C696

C344C346

C390C740

C365C740

C211C740

C220C740

C385C740C200C740C210C740

C375C740

C325C740C230C740

-.50

.51

e( s

imila

rvot

es |

X )

-10 -5 0 5 10e( econigoconnect | X )

coef = .00414047, se = .00555266, t = .75

Dyads: Countries: C40C696

354

Figure 20: Partial-regression leverage plots for small sample analysis - General IGO Connectedness 1990 General IGO Connectedness

C435C696

C439C740 C90C740C52C740

C435C522

C130C740

C435C740C434C740C150C740

C91C740

C439C640

C260C640

C325C740

C135C740C432C740

C42C740

C52C640

C360C640

C52C696

C20C740

C345C522

C305C740

C90C640

C150C640

C91C640

C434C522C211C740C439C696

C434C640

C436C740C404C740C438C740

C355C640

C437C522

C260C696

C135C640

C435C640

C434C696

C315C522

C305C696

C41C740

C150C696

C165C740

C130C696

C110C740

C210C740

C42C640

C310C640

C220C740

C235C740C437C696

C2C640

C51C740C438C640

C436C522C404C522

C165C640

C95C740

C439C522

C150C522

C92C640

C432C522

C130C640

C110C640

C205C696

C92C522

C315C640

C51C640

C90C696

C437C740

C51C522

C110C696

C110C522C390C740

C41C696C135C696

C436C640

C135C522C91C696

C101C696

C130C522C100C522C52C522

C93C522C70C696

C380C740

C51C696C160C522

C390C696

C41C522

C375C696

C92C740

C260C740

C41C640

C95C522

C211C696

C93C740C101C740

C145C522

C230C740

C155C522

C380C696

C210C696

C165C696

C260C522

C101C522

C385C696

C325C696

C390C640

C92C696

C230C640

C385C740

C220C696

C432C696

C211C522

C42C696

C420C740

C90C522

C433C522

C40C522

C220C522

C352C522

C404C640

C145C696

C235C522

C91C522

C20C696

C438C522

C95C696C42C522

C2C696

C385C640

C230C522

C345C696

C140C740

C350C522

C438C696C404C696C375C740

C230C696

C20C522

C70C522

C432C640C360C522

C94C696C94C522C436C696

C325C522

C100C696

C210C522

C94C740

C100C740

C2C740

C140C640

C160C696

C205C522

C165C522

C205C740

C235C696

C93C696

C390C522

C350C696

C140C522

C155C640

C352C696

C375C522

C385C522

C350C740

C380C522

C40C696

C200C740

C305C522

C355C696

C155C696

C70C740

C20C640

C94C640

C140C696

C290C522

C205C640

C200C522

C437C640

C339C740

C40C740C355C522

C101C640

C360C696C155C740

C200C696C310C522

C310C696C160C740

C40C640C325C640

C310C740

C339C640

C420C522

C95C640

C433C696

C315C696

C339C522

C345C640

C145C740

C200C640

C160C640

C2C522

C210C640

C352C740

C290C640

C350C640

C345C740C352C640C93C640

C433C740

C290C696

C355C740

C145C640

C420C696

C420C640

C360C740

C70C640

C290C740

C235C640

C211C640

C220C640

C315C740

C339C696

C100C640

C305C640C380C640

C433C640

C375C640

-.50

.5e(

sim

ilarv

otes

| X

)

-6 -4 -2 0 2 4e( genigoconnect | X )

coef = -.00311973, se = .01752664, t = -.18

Dyads: Countries: C435C696

355

1991 General IGO Connectedness

C439C740

C90C740

C52C740

C439C640

C437C522C90C640C42C740

C52C640C91C740

C52C696

C438C740

C439C522C325C740

C42C640

C345C522

C20C740

C439C696

C305C740

C438C640C41C740

C91C640

C437C696

C211C740

C51C522

C52C522

C305C696

C438C522C220C740C235C740

C2C640

C51C740

C210C740

C91C696

C41C696

C90C696

C41C522

C205C696

C41C640

C205C640

C70C696

C211C522C220C522

C51C640

C51C696

C437C740

C438C696C42C522C42C696

C235C522C230C522

C211C696C210C696

C350C522

C325C696

C91C522

C325C522

C220C696

C210C522

C350C740C95C522

C230C640

C205C522

C345C696

C40C522

C310C640

C315C522

C40C696

C352C522

C230C740

C230C696

C339C740

C20C522

C2C740

C2C696C235C696C350C696

C70C740

C90C522

C20C696

C352C696

C40C640

C200C522

C70C522

C205C740

C355C696

C200C740

C325C640C305C522

C437C640

C40C740

C352C740

C290C522

C310C696

C355C522

C355C640

C200C696C310C522C200C640

C345C640

C210C640C70C640

C310C740

C20C640

C2C522

C355C740

C290C696

C345C740C305C640

C290C740

C315C696

C235C640

C220C640C315C640C290C640

C352C640

C350C640

C211C640

C339C522

C339C640C315C740

C339C696

-.50

.51

e( s

imila

rvot

es |

X )

-4 -2 0 2 4e( genigoconnect | X )

coef = -.02710084, se = .01829985, t = -1.48

Dyads: Countries: C52C696 C352C640 C339C522 C339C696 1992 General IGO Connectedness

356

C435C696

C439C740C52C740

C439C640

C435C522

C434C740C344C346C90C740

C150C740

C91C740C52C640C435C740C432C740

C150C640

C325C740

C436C740

C135C740

C439C696

C52C696

C92C640

C438C640C90C640

C20C740

C371C640C438C740

C346C482

C92C346

C91C640C435C640

C305C740

C211C740C42C740

C368C740C92C522C150C696

C92C740C434C640

C135C640

C359C740

C51C740C436C640

C235C740

C110C740

C110C640

C210C740

C359C522

C370C522

C92C696

C220C740

C368C640C359C640

C130C696

C438C696

C369C640

C437C522C42C640

C346C590

C41C740

C359C696C305C696

C434C696

C439C522

C346C703

C150C522C437C696

C90C696

C51C640

C339C740

C404C740

C346C701C135C522

C368C696

C91C696

C420C740

C373C640

C41C522C346C702

C436C522

C434C522C370C740

C346C581C346C700

C90C522

C346C731C390C740

C41C696

C165C740

C130C740

C95C740C371C740

C432C640

C346C760C432C522C367C740

C437C740

C315C522

C41C640

C130C522C135C696C100C522C93C522

C110C696

C91C522

C432C696C101C522C436C696

C160C522

C346C940

C346C433

C101C696

C346C452

C380C740C40C522C346C484

C346C461

C349C740

C70C346

C346C438

C346C471C130C640C20C522

C95C522

C375C740

C205C696

C346C483C346C552

C140C640

C373C740

C346C710C2C640

C346C651

C211C522

C155C522

C346C616C346C439C346C600

C211C696

C346C840

C346C551

C346C820C385C740

C390C696C210C696

C390C640

C367C522

C346C663

C325C696

C145C522

C385C696

C346C620

C135C346

C70C522

C371C522C140C346C346C780

C42C696

C220C522

C51C696

C155C346

C346C553

C346C660C346C501

C160C346

C346C771C346C850C346C770

C346C750

C165C696

C220C696C346C490

C371C696

C375C696

C165C346

C140C522

C369C522

C352C522

C346C615

C52C346

C150C346

C380C696

C349C522

C433C522

C346C420

C165C640

C130C346

C346C500

C90C346C346C436C346C572

C346C437

C100C346

C375C522

C438C522C369C740

C368C522

C230C740C346C800

C94C346

C360C522

C346C435

C40C696

C101C346

C346C434

C346C570

C350C740

C380C522

C346C450

C346C517

C346C950

C346C475

C346C666

C346C630

C404C522

C2C696

C346C732C346C481

C346C404

C346C432

C94C696

C385C522

C42C522

C20C696

C235C522

C346C920

C346C652

C94C640

C40C346

C346C580

C70C696

C91C346

C367C640

C346C516

C230C522

C51C522

C346C540C95C346

C110C522

C94C740

C94C522

C230C696

C346C565C346C712C20C640

C346C625

C305C522C346C900

C41C346

C404C640

C346C510

C350C522

C339C640

C165C522

C385C640

C339C522

C370C696

C145C696C52C522

C346C645

C325C522

C404C696

C290C522C210C522

C370C640

C355C522

C140C696

C93C346

C346C740

C346C910

C346C571

C42C346

C390C522

C93C696

C140C740

C350C696

C420C640

C346C451

C205C740

C346C530

C51C346

C365C522

C344C740

C160C696C95C640

C367C696

C110C346

C2C740

C205C522

C235C696

C365C640

C346C522C346C830C346C812C346C790

C200C522

C365C696

C95C696C346C541

C310C522

C145C346C346C692C365C740

C100C696

C346C775

C346C690

C200C740

C346C670C346C696

C200C696C355C696

C205C640

C310C740

C373C522

C155C696

C346C698

C200C640

C350C640C373C696

C346C694

C310C696

C366C740

C346C370

C344C522

C369C696

C349C640C349C696

C433C696

C325C640

C346C640

C346C359C2C522C339C696C346C705

C355C640

C145C740

C344C640C230C640

C437C640

C93C740C235C346

C290C346C366C522

C205C346C230C346

C420C522

C344C696C310C346

C100C740

C346C375C200C346C220C346C346C380C210C346C211C346C366C696

C346C349

C366C640

C20C346C346C385

C339C346

C346C390

C420C696

C352C696

C346C373

C160C740

C235C640

C290C696C325C346

C360C640

C305C346

C160C640

C346C368C2C346

C101C640

C145C640

C155C640

C346C367C315C346C346C355C346C366

C70C740

C352C740C360C696

C346C360

C346C369C355C740

C346C365C346C350

C40C640

C346C352

C155C740C315C696

C290C740

C220C640

C101C740

C346C371

C93C640

C210C640

C70C640

C310C640

C40C740

C211C640

C433C740

C380C640C305C640

C352C640

C360C740

C100C640

C375C640

C315C740

C315C640

C433C640

C290C640

.50

1e(

sim

ilarv

otes

| X

)-.5

-1

-6 -4 -2 0 2 4e( genigoconnect | X )

coef = -.03528389, se = .01215948, t = -2.9

Dyads: Countries: C435C696

357

1993 General IGO Connectedness

C435C696

C52C740

C90C740

C439C640

C438C640

C438C740

C435C522

C439C740

C434C740

C52C640

C439C696

C90C640

C42C740

C435C640

C91C740

C135C740C435C740

C150C740

C432C740

C135C640

C52C696

C42C640

C325C740

C434C640

C436C640

C439C522

C235C740

C91C640C150C640

C20C740C317C316

C436C740

C305C740

C438C696

C110C640

C220C740

C110C740

C436C522C211C740C432C522

C165C740

C437C522

C437C696

C346C703C432C640

C90C696

C41C740

C350C316

C51C740

C130C696

C352C316

C210C740

C305C696

C434C696

C344C346

C369C640

C51C640

C346C552

C438C522

C434C522C349C316

C130C740

C346C551

C95C740C433C522C404C522

C359C740

C52C316

C346C565

C165C640

C346C700

C346C581

C135C696

C130C640

C432C696

C375C316

C91C696

C343C640C41C640

C436C696

C365C316

C150C696

C92C640

C380C316

C369C316

C355C316

C325C316

C370C316

C94C740

C346C500

C42C696

C390C316C385C316

C110C696

C359C522C91C316

C385C696

C373C640

C390C696

C101C696

C346C482C205C696

C368C316

C210C696

C343C316

C211C696

C92C346

C41C696

C369C740

C325C696

C92C316

C205C640

C41C522

C366C316

C165C696C372C316

C40C696C360C316

C339C740

C220C696

C346C760C367C316

C380C696

C94C640

C368C740

C390C740C346C572

C346C483

C437C740

C359C316

C371C640C371C316

C346C439

C375C696

C51C696

C92C522

C343C522

C339C316

C135C522

C385C740

C420C316

C94C696

C436C316

C90C522

C343C346C2C696

C359C640

C40C522

C432C316

C434C316

C344C316

C346C359

C339C640

C365C696

C205C740

C346C590

C346C436

C20C696

C346C571

C160C696

C93C522

C343C740

C91C522

C365C640

C346C316

C346C516

C380C740

C2C640C373C316

C346C940

C420C740

C375C740

C92C740

C130C522

C368C640C350C740

C230C696

C346C517

C420C640C433C316

C404C740C438C316

C346C452

C346C570

C371C522

C100C522

C135C316C360C522

C230C740

C359C696C90C316

C101C522

C346C712

C145C522

C110C316

C95C522C135C346C95C640

C390C640

C437C316

C346C501

C70C346

C435C316C439C316

C160C522

C155C522

C346C780C346C840C91C346

C346C433

C404C316

C350C696

C433C696

C20C522

C346C812

C235C696

C372C640

C100C696

C70C696

C90C346C346C432

C346C750

C155C696

C211C522

C343C696C93C346

C220C522C42C346

C346C663

C150C346

C130C346

C95C696

C404C696

C404C640

C346C651

C93C696

C155C346

C94C316

C346C600C385C640

C92C696

C150C316

C100C346

C145C696

C365C740

C110C522

C371C740

C346C800C94C346

C70C522

C346C771

C160C346C101C346

C346C770

C165C346

C346C850

C51C522

C352C522

C140C640

C346C625

C140C696

C346C461

C346C484

C95C346C346C732

C346C630

C369C522

C42C316C368C522C93C740C370C522

C346C471C346C437

C355C522

C346C710

C150C522

C437C640

C373C740

C165C316

C346C369

C346C540

C145C346

C346C666

C52C522

C200C696

C346C660

C373C522

C94C522

C346C553

C360C640

C367C740

C344C740

C346C920

C346C438

C155C640

C346C510

C344C640

C140C346C160C740

C325C640C346C616

C346C731C160C640

C346C950

C165C522

C51C316

C42C522C110C346

C346C434

C101C640

C420C522C346C404

C368C696

C51C346

C140C522

C352C696

C375C522

C346C541

C346C820C355C640

C339C522

C52C346

C235C522

C372C740

C346C900

C366C740

C370C740

C346C775

C346C620

C346C371

C40C346

C346C475

C349C740

C373C696

C2C740

C346C615

C380C522

C350C522

C369C696

C230C522

C346C490

C346C910C346C740

C20C640

C100C740

C200C740

C346C701

C355C696C365C522

C230C640C346C652

C290C522C385C522

C205C522

C305C522

C145C740

C346C370C346C811

C367C640

C325C522

C350C640

C210C522

C346C580C41C346

C346C530

C390C522

C366C640

C310C522

C346C435

C339C696

C346C790

C344C522

C360C696

C420C696C310C740

C70C740

C346C420

C101C740

C371C696

C310C696

C346C670

C346C481

C346C373

C40C640

C346C705

C352C740

C346C645

C346C368

C346C451

C367C522

C346C692

C346C360

C41C316

C366C696

C155C740

C366C522

C346C522

C200C640

C200C522

C372C522

C290C346

C145C640C290C696

C235C640

C346C355

C372C696C235C346C140C740C310C346

C317C522

C346C365C317C346

C230C346

C370C640

C205C346

C346C696

C344C696

C349C522

C95C316C346C830C346C349

C346C375

C70C640

C349C640

C130C316

C220C346

C346C367

C220C640

C346C350

C346C380

C355C740

C370C696C2C522

C211C346C210C346

C93C640

C367C696

C339C346

C346C352C310C640

C20C346

C346C372

C346C390C346C385

C346C640

C380C640

C305C316

C346C690

C325C346C305C346

C346C694

C349C696

C310C316

C290C740

C40C740

C160C316

C210C640

C200C346C145C316

C360C740

C346C698

C70C316C352C640

C375C640C346C366

C433C740C2C346C100C640C155C316

C317C740

C235C316

C290C316

C433C640

C305C640C211C640

C205C316

C317C696

C290C640

C317C640

C93C316

C101C316C100C316

C230C316

C40C316

C211C316C210C316

C220C316

C140C316

C20C316

C200C316C2C316

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5e( genigoconnect | X )

coef = -.0203375, se = .01034669, t = -1.97

Dyads: Countries: C435C696 1994 General IGO Connectedness

358

C435C696

C90C740

C52C740C91C740

C439C640C438C640

C435C640

C438C740C435C522C439C740C435C740

C135C740

C434C740C432C740

C439C696

C90C640

C150C740C110C740

C52C640

C91C640C41C740

C135C640

C439C522

C235C740C325C740

C52C696

C436C522

C165C740

C20C740

C436C640

C434C640

C436C740

C432C640

C110C640

C220C740

C373C640

C346C552

C438C696

C91C316C51C740

C346C581C346C700

C317C316C432C696

C95C740

C150C640C130C740C94C740

C346C570

C130C696

C346C551C211C740

C434C522

C90C696

C52C316

C91C696

C437C696

C210C740

C434C696

C346C439

C305C696

C346C432

C436C696

C365C640C346C500

C305C740

C346C436

C41C640

C432C522

C368C740

C51C640

C346C760

C150C696

C339C740C346C712

C346C572

C346C517

C110C696

C373C740

C135C696

C135C316

C346C452

C346C840

C346C560

C346C703

C346C590

C165C640

C346C780

C130C640C385C696

C350C316

C390C696

C437C740

C732C316

C352C316

C346C702

C155C346

C205C696

C346C571C349C316

C346C433

C666C316

C373C696

C346C651

C90C316

C371C522

C346C600C368C640C346C663

C165C696

C360C522

C101C696

C346C770

C692C316

C371C640C920C316C346C850

C590C316

C375C316

C205C640

C698C316C211C696C210C696

C346C625C670C316

C95C346

C380C316

C2C640

C359C740

C325C316

C325C696

C740C316C900C316

C830C316

C385C316

C380C696

C390C316

C690C316

C220C696

C350C740

C694C316

C437C522

C51C696

C94C696

C375C696

C41C522

C696C316

C371C740

C820C316

C91C346

C20C522C481C316

C346C437

C94C640C110C316

C346C710

C560C316

C365C740

C145C346C365C316

C346C471

C369C640

C346C501

C135C522

C90C522

C40C696C355C316

C150C346

C220C522

C135C346

C433C522

C438C522

C346C812C346C750

C93C740

C145C696C130C522

C2C696

C70C346

C800C316

C366C316C346C565

C93C522

C130C346

C100C522

C160C696

C368C316

C370C316

C368C522

C343C640

C41C696

C346C660

C90C346

C20C696

C640C316

C100C346

C571C316C92C640

C94C346

C101C522

C370C740C165C346C346C800C346C435C344C316

C344C740

C346C616C101C346C160C346

C367C316

C369C316

C94C316

C346C434

C95C522C145C522

C349C740

C346C940C230C740

C375C740

C346C531

C620C316

C705C316

C355C522

C346C620

C160C522C230C696

C155C522

C616C316

C346C481C346C615

C205C740

C950C316C420C740

C420C640

C346C732

C373C522

C565C316

C372C316C343C316

C360C316

C346C666C92C316

C660C316

C344C346

C630C316

C346C652C346C630

C92C346

C910C316

C346C920

C615C316

C145C740

C346C482

C701C316

C385C740

C343C522C343C346

C92C522

C369C740C366C740

C645C316

C840C316C663C316C780C316

C390C740

C600C316

C346C541

C325C640

C165C316

C651C316

C339C316

C346C900

C652C316

C359C640

C100C696

C850C316

C155C696

C940C316

C359C316C110C522

C350C696C572C316C235C696

C346C740

C346C516

C371C316

C367C740

C703C316C150C316C51C316

C770C316C750C316

C359C696

C160C740C346C670

C712C316

C373C316

C552C316

C346C484

C438C316C581C316

C484C316

C343C740

C710C316

C570C316

C211C522

C346C316C811C316

C704C316

C771C316

C92C740

C95C696

C790C316

C95C640

C346C461

C434C316

C93C696

C580C316C436C316

C522C316C437C316

C482C316

C433C316

C541C316

C551C316

C432C316

C360C640

C760C316

C452C316

C553C316

C140C346

C540C316C812C316C435C316

C346C371

C471C316

C346C510

C346C553

C343C696C501C316C516C316C700C316

C346C438

C461C316

C93C346

C420C316C625C316

C41C346C702C316

C439C316

C92C696

C451C316C500C316

C380C740

C475C316

C346C540

C359C522

C531C316

C731C316

C370C522C517C316

C368C696

C510C316

C372C640C346C370C346C771

C346C820

C346C645

C140C696

C40C346

C100C740

C155C640

C365C696C437C640

C339C522

C346C475

C339C640

C375C522C2C740

C235C522

C390C640C235C640

C380C522C41C316

C372C740

C230C522

C350C522C385C522C200C696C305C522

C101C740

C350C640

C205C522

C385C640

C110C346

C325C522C210C522

C140C640

C305C316

C352C696

C390C522

C346C692

C346C704

C346C580

C20C640

C51C346

C145C640

C346C698

C346C731

C52C346

C371C696

C346C811C346C694

C346C950

C367C522

C160C640

C365C522

C344C522

C420C696

C290C522

C367C640C346C790

C91C522

C346C696C346C910

C346C701

C366C522

C230C640

C155C740

C339C696

C355C696

C101C640

C369C522

C346C373

C366C696

C310C522

C346C451

C346C365

C40C522

C310C740

C349C522

C40C640

C355C640

C433C696

C200C522C349C696

C51C522

C20C346

C346C368

C346C359

C420C522

C360C740C344C640

C360C696C310C696

C52C522

C346C420

C130C316

C200C740C346C690

C2C522

C95C316

C140C740C372C522

C70C522

C200C640

C145C316

C346C369

C352C740

C70C696

C367C696C370C640C317C522

C70C740

C346C522

C370C696

C94C522

C344C696C290C696

C380C640

C160C316

C310C316

C165C522C352C522

C220C640

C366C640

C346C830

C372C696

C369C696

C310C640

C346C352C290C740

C235C316C220C346

C40C740

C140C522

C355C740

C2C346

C346C372

C346C367

C93C640

C349C640

C346C360

C150C522

C375C640

C235C346C210C640

C346C350

C230C346

C205C346

C317C346

C346C375C346C380

C155C316

C210C346C211C346C205C316C346C390C346C385C346C705C325C346C305C346

C346C355

C352C640

C339C346

C346C640

C290C346C310C346

C346C349C200C346

C317C740C230C316

C70C316C70C640

C100C640

C290C316C220C316

C210C316

C211C640

C305C640

C93C316

C290C640

C433C640

C346C366

C101C316

C100C316

C317C696

C433C740C40C316

C211C316C2C316

C140C316

C20C316

C200C316

C317C640

.50

1e(

sim

ilarv

otes

| X

)-.5

-6 -4 -2 0 2 4e( genigoconnect | X )

coef = -.00094254, se = .0085523, t = -.11

Dyads: Countries: C435C696 1995 General IGO Connectedness

359

C435C696

C90C740

C439C640

C52C740

C435C640

C435C522

C439C740

C135C740C435C740

C434C740

C150C740

C432C740

C91C740

C90C640

C41C740

C325C740

C130C740

C52C640

C439C696

C438C640

C438C740C110C740

C235C740

C135C640

C439C522

C20C740

C220C740

C436C522

C432C640

C150C640

C211C740

C110C640

C436C640

C434C640

C52C696

C373C640

C165C740

C51C740

C436C740

C346C570

C372C640

C210C740C359C740

C91C640

C346C439

C438C696C432C696

C346C483

C339C740

C52C316

C434C522

C372C740C346C760

C346C517

C434C696

C346C552

C305C740

C432C522

C95C740

C91C696

C346C572

C130C696C90C696

C437C740

C317C316C436C696

C51C640C135C316

C437C696

C346C703

C41C640

C371C522C91C316

C373C740

C368C740C346C571

C420C740

C305C696

C90C316

C130C640

C346C812C346C702

C346C700

C346C551C371C640

C165C640

C420C640

C150C696C91C346

C110C696

C230C740

C360C522

C346C435

C350C740

C375C740C370C740

C346C432

C135C696

C110C316

C437C522C371C740

C732C316

C692C316C350C316C404C740

C90C522

C135C522

C150C316C205C640C346C434C346C500C670C316C359C696

C100C522

C352C316

C349C316

C666C316

C590C316C698C316

C2C640

C346C516

C93C522

C211C522

C365C740

C433C522

C41C522

C20C522

C130C522

C920C316

C346C436C205C696C346C461

C370C522

C385C740C51C696

C694C316

C220C522

C346C553

C93C740

C375C316

C481C316C438C522

C820C316

C145C522

C145C740

C101C522

C339C640

C101C696C205C740

C359C522

C690C316

C830C316

C165C696

C740C316C346C484

C155C522

C380C316

C343C640C325C316

C404C640C346C531

C92C640C346C438

C390C740

C900C316

C560C316

C95C522

C385C696C696C316

C41C696

C800C316

C365C316

C390C696

C355C316

C355C522

C390C316C380C740C385C316C359C640

C411C522

C346C411

C366C316

C346C940

C620C316

C92C346

C640C316

C211C696

C92C522

C210C696C571C316C343C522

C145C696

C366C740C346C590

C346C452

C705C316C346C712

C325C696

C344C316

C370C316

C616C316

C369C640

C346C541

C51C316

C368C316

C365C640

C367C316C630C316

C343C346

C346C731C369C316

C360C316

C660C316

C343C316

C615C316

C372C316

C565C316

C369C740

C701C316C411C640

C92C316

C325C640

C950C316

C375C696C41C316

C380C696

C346C780

C373C696

C346C840

C652C316C663C316C145C640C850C316

C110C522

C651C316

C346C540

C346C433C600C316

C155C346

C572C316

C220C696

C840C316C780C316

C346C600C346C651

C360C640

C339C316

C710C316

C372C696

C438C316C552C316

C910C316

C165C316

C359C316

C703C316

C704C316

C371C316

C160C522

C750C316

C373C316

C770C316

C346C316

C522C316

C811C316

C484C316C437C316C940C316

C760C316

C712C316

C230C696C346C770

C771C316C570C316

C404C696

C433C316C812C316

C160C740

C790C316C452C316

C540C316

C434C316

C501C316C435C316C471C316

C411C316

C625C316C580C316

C436C316

C420C316

C432C316C551C316

C346C850

C541C316

C702C316

C404C316C461C316

C110C346

C483C316C475C316

C346C625C553C316

C437C640

C731C316C439C316C451C316C700C316C500C316C775C316C516C316

C531C316C517C316

C404C522

C40C696

C510C316

C490C316

C235C522

C93C696

C375C522

C100C696

C339C522

C343C740

C346C404C92C740C346C437C380C522C20C696

C145C346C346C710C346C663C343C696

C310C740

C230C522

C346C501C346C471

C350C522

C92C696

C390C640C135C346

C373C522C95C640C100C740

C350C696

C385C522

C150C346C52C346

C346C420

C339C696

C368C640

C346C750C305C522

C70C346

C155C696

C325C522

C2C696

C210C522

C235C696

C95C346C346C790

C205C522

C411C740

C140C696

C346C692

C165C346

C420C696

C140C640

C95C696

C346C490

C411C696

C94C346C100C346

C346C660

C160C346

C235C640

C390C522C346C616

C101C346C41C346

C365C522

C90C346

C290C522

C130C346

C155C640

C346C565

C160C696

C346C481

C367C740

C346C615C371C696

C346C950

C346C701

C91C522

C367C522

C346C372

C230C640

C344C522

C346C560

C346C666

C346C620C365C696C101C740

C346C910

C420C522

C370C640

C346C732

C349C522C310C522

C372C522

C369C522C317C522C145C316

C349C740

C350C640

C346C510C346C698

C368C522

C160C640

C346C800

C155C740C366C522

C200C522

C140C346

C346C920

C346C652

C346C740

C2C740C346C630C346C475

C94C696

C200C740

C200C696

C94C740

C310C640C93C346

C40C522

C346C900

C101C640

C344C346

C346C771

C355C696

C51C522C346C371

C346C820

C352C696

C368C696

C346C670

C70C522

C366C696

C200C640

C40C346

C52C522

C70C740

C346C580

C140C740C349C696C370C696

C433C696C346C373

C130C316

C346C370C346C522C360C740

C305C316

C355C640C346C775

C40C640

C2C522

C150C522C51C346

C94C522

C310C696

C290C740

C360C696

C346C811

C140C522C165C522

C366C640

C352C740

C346C359

C220C640

C352C522

C95C316

C346C704C310C316

C346C451

C160C316

C290C696

C70C696

C235C316

C20C640

C93C640

C355C740

C94C640

C344C740

C385C640C346C694C346C696C375C640C210C640C369C696

C40C740

C20C346

C94C316

C205C316

C155C316

C346C367

C317C740

C367C696

C346C369C346C690

C211C640

C367C640

C380C640

C346C352

C220C346

C346C360

C317C696C344C696

C346C830

C70C316

C70C640

C2C346

C230C316

C235C346

C346C365

C346C350C220C316

C230C346

C210C316

C352C640

C346C375

C100C640

C317C346C344C640C210C346

C211C346

C93C316

C205C346

C290C316C305C640

C346C380

C325C346C305C346C349C640C339C346C290C346C346C390C346C385

C346C355C346C705C290C640C310C346

C346C368

C433C640

C433C740

C211C316

C346C349

C100C316C101C316

C200C346

C40C316

C140C316

C2C316

C20C316C317C640

C200C316

C346C3660

-.5.5

e( s

imila

rvot

es |

X )

-1

-6 -4 -2 0 2 4e( genigoconnect | X )

coef = .01018937, se = .00718578, t = 1.42

Dyads: Countries: C435C696 1996 General IGO Connectedness

360

C435C696

C435C522C90C740

C52C740

C439C640

C435C740C439C740C435C640

C135C740

C434C740C432C740

C150C740

C110C740

C135C640

C438C740

C130C740C41C740

C439C696

C325C740

C90C640

C373C640

C42C740

C438C640

C52C640

C439C522

C436C522

C150C640

C51C740

C220C740

C359C740

C110C640

C52C696

C432C640

C211C740C235C740

C436C640

C20C740

C434C640

C346C570

C436C740C91C740C346C439

C438C696

C434C522

C42C640C135C316

C372C640

C432C696

C165C740

C359C640

C346C483C437C740

C346C581C210C740

C52C316

C434C696

C346C517

C41C316

C95C740

C371C640

C305C696

C41C640

C346C702

C51C640

C42C316

C91C696

C346C552

C432C522

C359C696

C346C572C346C760

C91C316

C110C696

C420C740

C436C696

C373C740

C130C696

C90C696

C437C696

C317C316

C130C640C90C316

C368C740

C365C740

C346C438

C110C316C346C812

C346C435C346C434

C346C551

C305C740

C346C432

C150C696

C101C696

C420C640C205C696

C346C571C165C640

C385C696

C346C700

C91C346

C150C316

C404C640

C346C600

C346C590

C390C696C210C696C404C740

C692C316

C91C640

C359C522

C211C696C371C522

C366C740

C346C516

C437C522

C346C436C346C500

C325C696

C732C316

C698C316

C433C522

C670C316

C375C740

C372C740

C135C696

C230C740

C350C316

C352C316

C666C316

C375C696

C694C316

C42C696C370C740

C346C553C590C316

C346C461

C373C696

C41C522

C368C640

C346C616C380C696

C438C522

C211C522

C690C316

C165C696C371C740

C51C316

C349C316

C696C316

C220C696

C95C640

C41C696

C830C316

C110C346

C93C522

C346C484

C346C651

C51C696C130C522

C740C316

C90C522

C920C316

C346C703

C339C740

C145C740C481C316C820C316

C135C522

C100C522

C404C696

C375C316

C360C522C93C740

C20C696

C130C316

C145C696

C220C522

C380C316C344C346

C346C541

C325C316

C230C696

C20C522

C2C640C900C316

C350C740

C145C640

C390C316

C145C316C346C452C560C316

C385C316

C620C316

C346C433C800C316

C346C540

C365C316

C346C420

C343C640

C346C940

C346C660

C346C404C355C522

C705C316

C92C640C370C640

C640C316

C370C316

C616C316

C101C522

C2C696

C155C346

C571C316C630C316

C100C740

C355C316

C349C740

C344C316

C346C531

C404C522

C92C522

C155C522

C346C840

C370C522

C95C316C343C522C346C770

C200C696

C346C780C701C316

C366C316

C205C740

C660C316

C92C346

C615C316

C346C411

C385C740

C145C522

C140C640

C411C522

C360C316C100C696

C346C850

C652C316

C343C316

C346C625C850C316

C372C316

C600C316C70C346

C565C316C346C615

C651C316

C235C522

C572C316

C369C316

C92C316

C663C316

C411C640

C95C522C110C522

C140C696

C368C316

C346C731

C339C640

C367C316

C235C696

C346C490

C145C346

C710C316C346C437

C230C522

C704C316

C365C640C552C316C41C346

C52C346

C346C710

C339C316

C780C316

C93C696

C950C316

C390C740

C438C316

C703C316

C840C316

C371C316

C160C522

C346C501

C165C316

C369C640C343C346C346C471C522C316

C760C316C373C316

C150C346

C135C346C346C580

C100C346C437C316

C346C316

C375C522

C359C316

C346C732

C95C346

C811C316

C380C740

C346C663

C95C696

C812C316

C325C522C750C316C350C522

C411C316

C346C510C435C316

C770C316C380C522

C433C316

C501C316C625C316

C42C522

C484C316

C540C316C731C316C471C316

C581C316C210C522

C346C666

C420C316C420C696

C702C316

C475C316

C205C640

C165C346

C771C316C452C316

C775C316C700C316

C339C696

C439C316C517C316C436C316C531C316

C450C316

C500C316C343C696C346C692C790C316C483C316C461C316C346C750C570C316

C516C316

C155C696C343C740

C434C316

C910C316

C551C316

C92C696

C712C316C437C640

C451C316

C346C481

C160C346

C385C522C404C316C541C316

C40C696

C510C316

C390C522C432C316

C160C696

C130C346

C580C316

C553C316

C92C740C420C522C490C316

C940C316C371C696

C94C346

C205C522

C346C790

C339C522

C346C652

C101C346

C346C950C390C640

C346C620

C200C740

C346C630C305C522

C365C696

C366C640

C346C920

C346C359

C411C740

C367C740C346C373

C90C346

C42C346C346C712C230C640C200C640

C101C740

C346C565C346C910C140C740

C411C696C140C346

C346C475

C200C522

C346C450

C373C522

C346C900

C101C640

C346C560

C155C640

C290C522

C365C522

C346C800

C346C740

C366C696

C2C740

C349C696C346C820

C51C346

C372C696C355C696

C155C316

C235C640

C346C771

C155C740

C310C740

C70C740C346C698C349C522

C350C640

C94C696

C367C522C310C696C352C696

C346C371

C369C740

C310C522

C91C522

C93C346

C368C696

C344C522

C317C522

C51C522

C369C522C372C522C346C670C70C316C368C522C370C696C366C522

C346C704

C325C640

C433C696

C94C316

C52C522

C160C740

C94C740

C346C522C360C696

C346C370

C70C522

C346C372

C346C696

C290C696

C160C316

C305C316

C140C522C70C696

C40C346

C352C740C367C640

C346C451C346C775C346C694

C2C522

C40C522

C220C640C310C640

C346C811

C290C740C346C367

C94C522

C93C640C20C346

C150C522

C352C522

C165C522

C94C640

C290C316C20C640

C93C316C160C640

C100C316

C40C640

C355C640

C369C696

C210C640

C230C316

C220C346

C360C640

C101C316

C310C316

C346C640C140C316

C349C640

C346C360

C235C346

C346C690

C367C696

C346C369

C230C346

C344C696

C385C640C211C640

C40C740

C317C346

C346C350

C70C640C211C346C210C346C375C640

C325C346

C305C346C346C352

C346C365

C2C346

C317C696

C290C346

C310C346

C40C316

C205C316

C205C346C344C740

C346C355

C346C380

C339C346

C346C349C346C705

C346C390C346C385C346C375

C360C740

C352C640

C346C368

C346C830

C355C740

C100C640

C200C346

C317C740

C433C640

C220C316C235C316C210C316

C433C740

C380C640

C346C366C20C316

C344C640

C211C316C2C316

C305C640

C290C640

C200C316

C317C640

.50

1e(

sim

ilarv

otes

| X

)-.5

-6 -4 -2 0 2 4e( genigoconnect | X )

coef = .00944423, se = .00874342, t = 1.08

Dyads: Countries: C435C696

361

1997 General IGO Connectedness

C439C640

C439C740C42C740C52C740C42C640

C438C640

C90C740C438C740

C350C316

C52C640C41C740

C42C316

C439C696

C51C740

C640C316

C52C696

C90C640C290C316

C41C316

C439C522

C325C316

C437C740C200C640

C51C640

C390C316C385C316

C52C316

C91C316C41C640

C438C696

C210C740

C317C316

C325C740

C91C696

C91C740

C305C696

C90C696

C110C316

C220C740

C135C316

C41C522

C51C316C130C316C205C696

C437C696

C350C640C310C640

C211C740

C692C316

C437C640

C51C696

C670C316

C42C696

C698C316

C20C740

C91C640

C90C522

C210C696

C694C316

C211C696

C2C640

C42C522

C732C316

C325C696

C352C316

C666C316

C590C316

C90C316

C690C316

C481C316

C95C522

C235C740

C200C696C220C696

C696C316C101C316C820C316C211C522

C370C316

C620C316

C205C640

C310C316

C41C696

C220C522C830C316C20C696

C705C316

C616C316C165C316

C701C316C365C316

C235C522C230C696

C210C640C652C316

C740C316

C92C640C560C316

C95C696

C350C522

C437C522

C850C316C615C316

C572C316

C800C316

C230C522

C651C316

C660C316

C600C316

C339C740

C630C316

C325C640C355C522

C704C316

C571C316C710C316C663C316

C150C316

C305C316

C372C316

C552C316

C355C316

C305C740

C51C522

C349C316

C760C316

C371C316

C2C696

C411C316

C290C522

C343C316

C703C316

C522C316

C373C316

C350C696

C437C316C484C316

C565C316

C360C316

C235C696C438C316

C369C316

C92C316C731C316C812C316

C200C740

C325C522

C2C740C625C316

C100C696

C20C522C435C316C475C316C775C316

C702C316

C700C316C471C316

C438C522

C210C522

C436C316

C517C316

C531C316

C780C316

C540C316

C40C696

C920C316

C339C316

C840C316

C433C316C439C316C500C316

C501C316C516C316

C200C522

C344C316

C461C316C483C316

C339C522

C145C316

C305C522C450C316

C359C316

C581C316C451C316C452C316

C551C316C205C522C510C316

C52C522

C770C316C790C316C750C316C771C316

C490C316

C375C316

C339C640

C380C316

C434C316C404C316C570C316C541C316C432C316

C344C522

C366C316

C553C316C155C316C712C316

C580C316

C900C316

C235C640

C95C640

C220C640

C367C316C368C316

C70C740

C350C740

C950C316C339C696

C40C640

C94C316

C205C740

C91C522

C95C316

C310C522

C290C640C230C740C230C316C235C316

C100C740

C352C696

C230C640

C70C316C940C316

C349C522

C910C316

C220C316C160C316C210C316

C40C522

C290C696

C355C696

C70C522

C211C316C93C316C70C640C211C640

C344C696

C352C522C2C522

C140C316

C100C316

C40C316

C70C696

C40C740C20C640

C344C740

C352C640C310C740

C2C316C20C316

C310C696

C200C316

C352C740C290C740C355C640

C100C640C205C316

C344C640

C305C640

C355C740

-.50

.51

e( s

imila

rvot

es |

X )

-4 -2 0 2 4e( genigoconnect | X )

coef = -.00798242, se = .0160308, t = -.5

Dyads: Countries: C439C640 C438C640

362

1998 General IGO Connectedness

C350C316

C390C316

C385C316

C325C316

C740C316

C435C696

C732C316C900C316

C435C522

C92C640

C92C740

C690C316

C560C316C750C316

C698C316

C840C316

C2C696C696C316

C92C696

C694C316C92C316

C770C316C2C640

C371C640

C92C522

C670C316C541C316

C660C316C482C316C692C316C372C316

C372C640

C481C316

C370C522C663C316

C365C316

C371C316

C2C522

C359C316

C52C640

C705C316

C616C316

C2C316

C110C640

C110C740

C522C316

C615C316C452C316C551C316

C369C316

C703C316

C432C316C510C316

C359C740C434C316

C51C740

C651C316

C371C740

C52C316

C475C316C620C316

C451C316C850C316C652C316C450C316C483C316

C20C696

C540C316

C370C696C710C316

C432C740

C600C316C461C316

C372C740

C404C316

C373C522C51C640

C701C316

C371C522

C484C316

C435C316

C373C696C2C740

C436C696

C42C740

C438C316

C436C522C135C522

C625C316C572C316C704C316

C439C696

C90C740

C52C696

C439C522

C437C696

C42C640

C165C740C20C640C41C640

C41C740

C110C696

C434C740

C359C522

C404C740

C433C316

C90C640

C110C522

C370C316

C365C522C731C316

C438C696

C439C316

C110C316

C372C522

C91C640C20C316

C91C740

C150C640

C150C740

C420C316

C390C696C91C696C385C696

C150C696

C210C696

C41C522

C420C696

C373C640

C130C740

C373C316

C93C522

C160C522

C436C316

C211C696

C20C522

C42C522C155C522

C51C316

C220C522

C101C522

C437C522

C371C696

C100C522

C210C522

C359C640

C200C696

C90C522

C150C522

C145C522

C42C316

C95C740

C70C522

C200C522

C95C522C135C696

C438C740C220C696C211C522C390C522

C385C522

C140C522

C41C316

C130C522

C350C522

C42C696

C140C696

C41C696

C91C522

C210C740

C437C316

C325C696C130C640C145C740C369C522

C372C696

C350C640

C436C740

C420C522

C432C640

C369C640

C211C740

C325C522

C90C696C93C696

C359C696

C93C740

C404C640

C365C696

C51C696

C165C696

C165C640

C434C640

C20C740

C435C640

C290C316

C91C316C290C522

C140C640

C145C696

C94C740

C95C640

C230C522C439C740C235C522

C52C522

C130C696C51C522

C101C696

C70C696

C160C696C93C640

C165C522

C432C696

C310C522

C200C740

C420C740

C94C640

C390C740

C135C640

C385C740

C100C696

C434C696

C90C316C130C316

C145C640

C94C522C369C740

C434C522

C438C522

C95C696C435C740

C101C640

C155C696

C350C696

C230C696

C94C316

C165C316

C404C522

C370C740

C365C740

C95C316

C200C640

C390C640

C94C696

C310C640

C150C316

C433C522

C100C740

C438C640C235C696

C145C316

C365C640

C101C740C70C640C70C740

C432C522C350C740

C93C316

C436C640

C135C740

C325C740

C370C640

C290C640

C155C640

C210C640

C140C740

C155C740

C439C640

C235C740C433C696

C100C640

C310C740

C385C640

C369C696

C101C316

C437C740

C290C696C420C640

C210C316

C310C696

C200C316C220C316

C211C316

C220C640

C230C740

C155C316

C160C640

C140C316

C160C740

C100C316C70C316

C211C640C310C316C230C316C235C316

C290C740

C135C316

C325C640C235C640C230C640C160C316

C437C640

C433C740C433C640

-1-.5

0.5

e( s

imila

rvot

es |

X )

-10 -5 0 5 10e( genigoconnect | X )

coef = .01037571, se = .00469761, t = 2.21

Dyads: Countries: C435C696 C435C522 C350C316 1999 General IGO Connectedness

363

C52C740

C439C522

C439C640C51C740

C438C640

C437C740

C438C740C439C740

C439C696C90C740C41C740

C438C696

C52C696

C42C740

C290C316C220C740

C91C740

C346C483

C210C740C52C640C41C316

C52C316

C91C696

C110C316C235C740

C438C522

C346C702

C437C696

C346C571

C346C432C346C517

C211C522C220C522

C42C316

C130C316

C91C316

C346C572

C90C696

C200C740C211C740C51C640

C51C696C41C696

C42C640

C346C439

C325C740

C339C696

C317C316

C437C522

C346C438

C90C640

C346C552C135C316C346C700

C210C522

C42C522

C325C522

C346C760

C51C316

C339C640

C346C500C346C812

C305C316C230C522C235C522

C41C522

C41C640

C346C461

C339C740

C20C522C305C522

C42C696

C200C522

C346C484

C20C740

C346C551

C350C522

C437C640

C339C522

C205C522

C91C346C230C740

C346C553C346C701C346C581C52C346C305C696C110C346

C70C740

C101C522

C350C740

C90C522

C90C316

C346C625C310C316C346C663

C346C404

C51C522

C346C373

C346C434

C91C640C346C371C145C316C346C910C205C696

C346C600

C205C640

C346C698

C346C790C696C316C220C696C346C490

C346C590

C346C541

C690C316

C350C696C694C316C230C696

C165C346

C830C316

C325C696

C52C522

C290C522

C346C652C211C696C210C696C731C316C200C696

C704C316

C572C316C698C316C652C316

C385C316

C701C316C760C316

C346C692

C775C316C700C316C625C316C812C316

C390C316

C346C705

C373C316

C900C316

C710C316C531C316

C517C316

C670C316

C484C316C770C316C552C316C600C316

C620C316

C355C522C651C316

C411C316

C370C316C692C316

C344C316

C101C346

C500C316

C439C316C471C316

C380C316

C346C616

C375C316

C51C346

C92C740

C130C346

C540C316C349C522

C150C346C90C346

C325C316

C145C346

C461C316C483C316C501C316

C705C316

C581C316C615C316

C346C615C481C316C510C316

C616C316C702C316C663C316

C433C316C437C316

C94C316

C950C316

C346C580C438C316

C490C316

C451C316

C346C316

C940C316

C346C950C551C316

C366C316

C920C316

C660C316

C452C316

C346C651

C367C316

C811C316

C522C316C235C696

C910C316

C404C316

C740C316

C475C316

C368C316

C630C316

C349C316

C41C346

C343C346

C371C316

C780C316

C339C316

C432C316C541C316C434C316C346C437C820C316

C790C316C771C316

C365C316

C372C316

C553C316

C580C316

C40C696

C305C740

C565C316

C359C316

C343C316

C850C316

C92C316

C369C316

C840C316

C640C316C360C316

C750C316

C355C316

C712C316

C732C316

C800C316

C150C316

C666C316

C42C346

C560C316

C571C316

C344C522

C352C316

C92C346

C95C316

C346C940

C346C565

C350C316

C590C316

C2C522

C346C359

C346C850C346C411

C346C712

C205C740

C92C640

C346C731

C346C660

C290C696

C101C316C310C522

C346C433

C346C732

C20C696

C346C900

C344C346

C346C740

C165C316C346C522

C305C346

C346C800C70C346

C346C770

C2C696

C346C481

C346C666

C155C316C155C346

C346C771

C346C820

C2C740

C355C696

C160C346

C346C696

C346C840

C200C640

C70C316

C91C522

C70C522C346C920

C346C620

C93C346

C352C522

C2C640

C346C750

C346C452

C346C710C346C451C346C540

C350C640

C346C670

C210C640

C95C346

C290C740

C346C372

C310C640

C344C740

C70C696

C93C316

C346C811

C94C346

C346C475

C310C696

C346C630

C140C316

C339C346

C346C780C100C316

C352C696C100C346C140C346

C40C522

C310C740

C346C471

C135C346

C346C501C346C510

C205C346

C346C694C40C740

C344C696

C205C316

C346C370

C346C349

C220C316C346C775

C346C690

C352C640

C160C316

C20C346

C325C640

C211C316

C325C346

C210C316

C235C640

C235C316

C220C346

C230C316C40C640

C40C316

C346C360

C70C640

C346C355

C310C346

C346C640

C2C346

C352C740

C220C640

C210C346C290C640

C40C346

C346C350

C346C368

C230C346C346C366C290C346C235C346

C346C385

C346C830

C346C390

C346C352C346C380

C211C346C200C346

C317C346

C20C316

C230C640

C346C375

C2C316

C344C640

C346C365

C305C640

C200C316

C346C369C355C740

C211C640C20C640

C346C367

C355C640

-.50

.51

e( s

imila

rvot

es |

X )

-4 -2 0 2 4e( genigoconnect | X )

coef = .0692873, se = .01054118, t = 6.57

Dyads: Countries: None of mention 2000 General IGO Connectedness

364

C435C522

C365C740

C220C740

C211C740C210C740

C344C346

C200C740

C435C696

C20C740

C325C740C230C740

C375C740

C390C740C385C740

C437C740

C640C316

C339C346

C346C420

C346C375

C310C346

C346C355

C346C367

C346C704

C346C451

C350C316

C385C316C390C316C325C316

C346C701

C20C346

C346C666

C290C346

C346C740

C210C346

C346C370

C325C346

C346C366

C230C522C420C740

C346C349

C420C696

C230C346

C367C740

C325C522C210C522

C305C346

C346C461

C420C522

C346C900

C346C830

C346C732

C346C712

C70C346

C375C522C411C740

C731C316

C92C696

C775C316

C700C316

C625C316C531C316C812C316C760C316

C435C316

C343C696C704C316C420C316C517C316C484C316C500C316C770C316

C471C316

C572C316C439C316C373C316C461C316C483C316C540C316C516C316

C490C316

C552C316

C501C316C701C316

C451C316

C710C316C702C316C652C316C600C316

C581C316C551C316C510C316C411C316

C651C316C703C316

C411C696

C452C316C438C316

C346C316

C475C316C811C316C522C316

C92C740C663C316

C343C740

C570C316C432C316

C615C316C437C316

C541C316

C620C316

C434C316

C553C316

C580C316C790C316C771C316

C371C316

C433C316

C359C316

C370C316C616C316

C780C316

C339C316

C411C640C696C316

C712C316C705C316

C660C316

C750C316C690C316

C481C316

C850C316

C694C316C840C316C565C316

C372C316

C830C316

C630C316

C92C316

C92C346

C411C522

C369C316

C343C316

C360C316

C670C316

C92C522

C343C522C698C316C346C411

C355C316

C692C316

C346C771C380C522

C346C372

C346C475

C346C705

C200C522

C346C553

C346C690

C235C522

C160C346

C346C517

C433C522C369C740

C140C522

C346C800

C346C812

C346C820

C346C850

C346C703

C339C696

C346C380

C346C371

C346C481C346C698

C372C740

C373C640

C346C359

C346C516

C359C740

C385C522

C390C522

C367C640

C346C432

C346C570

C434C522

C317C346

C346C438C346C483

C211C346

C368C696

C305C522

C346C750

C346C640

C346C435C346C433

C346C350C346C360C346C385

C346C710

C205C346

C346C373

C220C346

C140C346

C346C390

C346C368

C346C369

C346C560C110C346C346C365

C346C920

C346C692

C368C740

C346C571C346C522

C200C346

C800C316

C365C316

C820C316

C560C316

C571C316

C92C640

C343C640

C910C316C950C316

C343C346

C740C316

C732C316

C666C316

C367C316

C352C316C344C316

C368C316

C317C316

C590C316

C366C316

C900C316

C346C531

C375C316C380C316C920C316

C349C316

C165C346

C100C346C135C740C70C316

C346C910

C290C640

C346C551

C91C740

C339C640

C290C740

C432C740

C432C696C160C522

C70C522

C150C346

C375C640

C435C640C350C740

C380C740

C210C640

C439C522

C2C740

C346C500

C51C346C317C522

C290C316

C370C640

C434C696

C20C640C346C590

C160C740

C371C640

C101C346

C305C740

C235C346

C52C346

C434C740

C390C640C42C346

C346C510

C346C694

C346C760C435C740

C235C740C145C346

C346C352

C310C522C372C640

C52C740C369C640C346C580

C433C740

C94C740

C91C696

C211C640

C350C522

C373C740

C51C740

C438C740

C51C522

C371C740

C205C740

C20C316

C220C522C346C660

C346C490C346C434

C346C572

C437C522

C210C316

C438C522C438C696

C211C522C439C740

C439C696

C367C696

C94C346C90C346

C145C740

C339C740C352C740

C100C740

C140C696

C305C640

C346C625C346C620

C355C740

C140C740

C346C696

C230C640

C230C316

C359C640

C350C640C310C640C325C640

C317C640

C70C740

C346C702

C349C740

C346C950

C365C522

C135C346

C346C651C205C522

C346C541

C317C740

C346C663C110C740

C344C740

C346C439

C366C740C2C346

C346C811

C359C696

C200C640

C346C484C346C471

C373C696

C130C346C130C740C346C501

C150C640C346C731C90C740

C367C522

C346C670C346C770

C344C522

C371C696

C100C522

C91C640

C91C316C95C346

C433C696

C93C346

C370C696

C20C522

C155C346

C346C615

C372C522

C51C316

C346C600

C135C522

C91C346

C373C522

C93C522

C41C522

C346C552

C366C640

C339C522

C94C522

C346C565

C165C522

C52C316C346C540C100C316C366C522

C130C522

C346C437

C51C696

C346C616

C355C522

C352C522C41C346C366C696

C110C696

C355C696

C432C522

C346C790C346C775

C2C522

C94C316

C432C640

C346C452

C310C696

C42C522

C110C522

C110C640

C290C522

C420C640C370C740

C368C640

C360C696

C101C522

C90C316

C90C522

C346C581

C94C640C372C696C346C840

C369C522

C371C522

C100C696

C42C696

C52C640

C91C522

C145C316

C90C696C155C522C145C696

C130C316C110C316C95C316

C437C696

C369C696

C130C696C93C696

C52C522

C93C640

C150C696

C40C346C346C780C290C696

C93C316

C101C696

C349C522

C95C522

C145C640

C349C696

C51C640

C94C696

C150C316

C150C522

C42C316

C325C696

C344C696

C145C522C370C522

C359C522

C160C696C41C696

C317C696

C346C630

C365C696

C70C696

C210C696C346C652C375C696

C360C522C368C522

C305C696

C135C696

C40C316C380C696

C20C696C390C696

C40C522

C211C696C350C696C385C696C200C696

C205C696

C230C696

C95C696

C42C740

C200C316C70C640

C220C316C93C740

C150C740

C52C696

C165C316C434C640

C42C640

C95C640

C95C740

C140C640

C160C316C165C640

C165C740

C205C640

C140C316

C40C740

C211C316C135C316

C2C316C101C740

C310C316C101C316

C2C696

C41C316

C433C640

C165C696

C438C640

C437C640

C160C640

C155C316C235C316C385C640

C235C640

C349C640

C130C640C205C316

C352C640

C310C740

C360C640

C360C740

C365C640

C2C640C439C640

C220C640C101C640

C380C640

C155C740C100C640

C41C640

C41C740

C90C640

C355C640

C344C640

C235C696C220C696

C155C696C40C696

C352C696

.5

C40C640

C305C316

C135C640

C155C640

01

e( s

imila

rvot

es |

X )

-.5

-1.000e-14 -5.000e-15 0 5.000e-15 1.000e-14 1.500e-14e( genigoconnect | X )

coef = 0, se = .00484699, t = 0

Dyads: Countries: C640 Figure 21: Partial-regression leverage plots for small sample analysis - PM IGO Connectedness

365

1990 PM IGO Connectedness

C339C522

C339C640

C290C640

C339C696

C315C640

C260C640

C433C640

C360C640

C420C640

C355C640

C404C640

C436C640C432C640C439C640C435C640C438C640

C310C640

C435C696

C100C640C93C640

C404C696

C420C696

C140C522

C436C696

C90C522

C91C522

C438C696

C42C522

C92C522

C41C522

C145C640

C437C640

C140C640

C41C640

C432C696

C91C640

C93C522

C135C522C130C522

C290C522

C51C640

C433C696

C70C640

C95C640

C100C522

C92C640

C345C640

C145C522

C360C522

C94C640

C94C522

C70C522

C95C522

C160C640

C439C696

C150C522

C135C640

C160C522

C355C522

C155C522C345C522

C110C640

C130C640

C165C522

C101C522

C310C522

C375C740

C140C740

C20C640

C315C522

C101C640

C40C522

C42C640

C390C740

C155C640

C380C740

C165C640

C437C740

C210C740

C90C640C52C640C40C640

C385C740C310C740C350C740

C150C640C345C740C433C740

C352C522

C200C740C211C740C290C740C230C740

C91C740

C51C740

C375C522

C205C740

C385C522

C420C740

C380C522

C100C740

C325C740C360C740

C305C522

C135C740C145C740C260C522

C434C740C160C740

C305C740C355C740

C220C740C20C740

C315C740

C436C740

C2C640

C404C740C41C740

C92C740C93C740

C438C740C20C522C352C740

C339C740

C52C740C70C740

C91C696

C432C740

C235C740

C155C740

C92C696

C435C740

C95C740

C2C522

C94C740

C93C696

C130C740

C40C740C110C740C439C740C315C696C290C696

C140C696

C41C696

C360C696

C51C696

C101C740

C42C740

C100C696

C90C740

C345C696

C310C696

C94C696

C110C696

C437C696

C145C696

C2C740

C42C696

C130C696

C355C696

C101C696C90C696

C165C740

C70C696C135C696

C150C696

C160C696

C40C696

C155C696

C350C696

C95C696

C150C740

C165C696

C235C696C200C696C230C696

C260C696

C352C696C52C696

C205C696

C325C696C210C696C211C696

C375C696

C20C696

C305C696

C220C696C385C696

C380C696

C390C696

C260C740

C2C696

C235C640

C200C640

C230C640

C210C640C211C640

C220C640

C434C640

C325C640

C375C640

C305C640C390C640C380C640

C235C522

C385C640

C350C522

C205C640

C230C522C200C522

C205C522

C325C522C210C522

C350C640

C390C522C211C522C220C522

C434C696

C352C640

C110C522C51C522

C52C522

C435C522

C420C522C433C522C438C522C404C522C436C522

C432C522C439C522

C437C522

C434C522

-.50

.5e(

sim

ilarv

otes

| X

)

-2 0 2 4e( pmigoconnect | X )

coef = .06946261, se = .03245644, t = 2.14

Dyads: Countries: C339C640 C434C522 1991 PM IGO Connectedness

366

C339C640

C339C522C339C696

C345C640C437C640C355C640

C70C640

C315C640

C290C740

C41C640C40C640

C90C522

C355C740

C345C740

C290C522

C91C522

C315C740

C355C522C310C522

C95C522

C51C640

C230C740

C70C522

C437C740C310C740

C42C640

C40C740

C70C740

C91C640

C40C522C350C740

C200C740

C315C522

C352C522

C438C640

C235C740

C90C640

C210C740C205C740

C41C740

C290C696C305C522

C315C696

C220C740

C439C640

C345C522

C352C740

C20C522

C290C640

C2C522

C51C740

C211C740

C41C696

C325C740C339C740

C438C740

C52C640

C42C696

C355C696

C42C740

C51C696

C91C740

C310C696

C20C740

C437C696

C91C696C90C696

C40C696

C352C640

C70C696C438C696C345C696

C305C740C305C640

C350C696

C90C740

C235C696

C352C696

C439C696

C439C740

C230C696C2C740C200C696C310C640

C20C640

C205C696

C52C740

C325C696C210C696C211C696

C205C640

C220C696

C52C696

C20C696

C41C522

C42C522

C305C696C2C696

C2C640C350C522C235C522C230C522

C350C640

C205C522C200C522

C235C640

C325C522C210C522C211C522

C211C640

C220C522

C220C640C210C640

C325C640

C200C640

C230C640

C439C522

C51C522

C52C522

.5

C438C522

C437C522

01

e( s

imila

rvot

es |

X )

-.5

-2 -1 0 1 2 3e( pmigoconnect | X )

coef = .06842436, se = .03915692, t = 1.75

Dyads: Countries: C339C696 C522 C339C522 C437C522 C438C522 C439C522 1992 PM IGO Connectedness

367

C346C359C346C355

C346C371

C346C370

C346C360

C435C696

C346C350

C346C369

C346C705

C346C365

C346C352

C439C640C435C640C438C640

C41C640

C20C640C2C640

C135C640C437C640

C135C740

C439C740

C404C640C344C346

C433C640C40C640

C90C640

C41C740

C432C640

C436C640

C435C740

C130C522

C145C522

C437C740

C42C640C150C640

C100C522C93C522C91C522

C438C740

C360C640

C140C640

C95C522

C339C522

C339C640

C94C522

C101C522

C150C522

C90C740

C70C640

C90C522

C155C522

C130C640

C165C640

C91C640

C436C696

C160C640

C155C640

C140C522

C160C522

C432C696

C420C640C315C522C359C522

C165C522

C360C522

C371C522

C130C696

C290C522C110C640C355C522

C40C740

C368C522C310C522

C135C522

C352C522

C42C740

C434C740

C367C522

C370C522C150C740

C365C640

C373C522

C433C740

C52C640

C101C640

C51C640

C404C740

C235C740

C130C740

C346C553

C95C640

C373C640

C439C696

C140C740

C344C640

C93C640

C432C740C91C740C349C522C365C522

C150C696

C346C731

C70C522

C420C696

C20C522

C346C436

C375C522

C346C420

C369C522

C433C696

C346C580

C94C640

C90C696

C325C740

C101C696

C51C696

C346C500

C346C750

C92C522C346C572

C346C551

C346C770

C52C696

C346C840

C346C710

C165C696

C359C640

C346C439

C70C740

C380C522

C145C696

C130C346C346C432

C436C740

C160C740C346C771

C438C696

C155C696

C346C760

C366C522

C350C740

C346C517C94C346

C346C652

C346C910

C346C600

C346C702

C346C552C346C712

C346C570

C346C701C346C404

C346C483

C210C740

C100C696

C346C484

C346C501

C346C471

C41C346C42C696

C346C850

C346C434

C91C696

C346C438C346C625

C346C437

C100C346C346C651C346C435

C93C740

C230C740

C346C510

C145C640

C346C475

C305C522

C368C640

C211C740

C40C522

C346C461

C94C696

C385C522

C346C800

C95C696

C100C640

C140C696

C160C696

C220C740

C165C740

C346C590

C95C740C346C451C140C346C346C780

C51C346

C375C740C346C940

C165C346

C145C346

C155C740

C346C645

C346C615

C346C775C150C346

C135C696C110C696

C290C740

C371C640

C346C620

C346C482

C91C346

C315C640

C346C452

C101C740

C155C346

C93C696

C95C346

C346C433

C346C640

C346C812

C346C541C93C346

C346C522C360C740

C350C696

C346C616C346C630

C346C703

C346C581

C369C640

C52C740

C346C516

C40C346

C101C346

C346C571C92C346C346C790

C52C346

C135C346C110C346C42C346

C290C696C355C696

C339C696

C20C740C346C565

C346C950

C380C740

C110C740

C346C663

C235C696C346C490C367C640

C90C346

C160C346

C305C740

C100C740

C346C700

C420C740

C230C696

C355C740

C390C740C365C740

C366C640

C346C820C437C696C346C450

C339C346

C352C696

C51C740

C230C640

C346C368C310C696

C339C740

C360C696C205C696

C41C696

C385C740

C235C640C94C740

C370C640

C344C522

C346C530

C365C696C346C540

C404C696C346C670

C368C696

C346C660

C375C696

C310C740

C346C367

C346C666

C2C740

C346C920C235C346

C346C732

C349C640

C346C692

C346C481

C70C346

C371C696

C145C740

C325C640

C70C696

C350C640

C205C740

C325C696

C359C696C205C346

C344C740

C210C696C230C346C2C522C305C696C211C696C346C373C380C696

C92C640

C346C366

C40C696

C346C698

C220C696C367C696C385C696C315C696

C373C696C373C740C200C640

C390C696C346C375C346C900C200C346

C220C640C390C640C210C640C359C740

C200C696C20C696

C200C740C370C696

C369C696

C346C380C210C346C349C696C366C696

C385C640

C211C346

C346C690

C346C740C220C346

C371C740

C352C740

C20C346C346C385

C211C640

C346C390

C369C740

C368C740C367C740C346C694

C315C740

C370C740

C92C696

C2C696

C366C740

C346C830

C346C696

C2C346

C349C740

C344C696C92C740C434C640

C205C640C42C522

C290C640

C310C640C355C640

C41C522

C235C522

C380C640

C350C522

C305C640

C230C522

C375C640

C434C696

C211C522C205C522C220C522C325C522C210C522C200C522

C352C640

C390C522C290C346C310C346C315C346

C346C349

C325C346346

C51C522

C110C522

C52C522

C435C522

C436C522C432C522C420C522

C433C522C439C522C404C522C438C522

C437C522

.5

C305C

C434C522

01

e( s

imila

rvot

es |

X )

-.5-1

-1 0 1 2 3e( pmigoconnect | X )

coef = .05533009, se = .02560355, t = 2.16

Dyads: Countries: C424C522 C522

368

1993 PM IGO Connectedness

C346C359C346C371C346C360C346C355

C346C369C346C370C346C372C346C705

C346C350

C346C365C346C352

C343C346C344C346

C41C640

C437C640

C344C316

C135C640

C439C640

C435C696

C20C640

C435C640

C367C316C366C316

C90C640

C438C640

C40C640C2C640

C42C640

C317C316C368C316

C145C522C130C522

C93C522

C20C316

C91C522

C100C522

C130C640

C346C551

C150C640

C90C522

C365C640

C91C640

C95C522

C135C740

C70C640

C439C740C360C522

C165C640C41C740C371C522C437C740

C94C522C135C522

C346C552

C140C640

C359C522

C101C522

C346C553

C155C640

C346C750

C349C316

C346C501

C346C840C346C570

C155C522

C110C640

C150C522C344C640

C145C346C346C580

C41C316

C355C522

C91C346

C346C712C435C740

C140C522

C346C572C346C517C346C510

C438C740

C130C346C160C640

C346C437C433C640

C93C346

C51C640

C346C710C346C565

C346C471C346C452

C94C346C346C780

C346C775

C93C640

C346C484

C41C346

C290C522C368C522

C100C346C165C522C346C541C2C316C346C760

C42C346

C346C461

C160C522

C432C640

C346C731C346C812

C101C640

C346C800

C367C522

C51C346

C346C940

C150C346C404C640C346C910C135C316

C94C640

C90C740

C436C640

C110C346

C52C640

C310C522

C95C640

C145C640C100C640

C346C516C90C346

C370C522

C346C540

C165C346

C155C346C95C346

C346C490

C346C790C346C571

C135C346

C140C346

C40C316

C101C346

C42C740

C369C640

C352C522

C434C740

C369C522

C375C316

C70C522

C372C522

C339C640

C371C640C40C346

C366C522

C372C640

C339C522

C40C522

C346C367

C346C950

C40C740C365C522C433C740

C346C590

C346C530

C346C368

C380C316

C92C522

C130C740

C346C811

C325C316

C52C346

C91C316

C160C346

C317C522

C235C740

C91C740C90C316C70C316

C373C640C359C640

C420C640

C42C316

C130C696

C70C346

C343C522

C349C522

C432C740

C346C482

C367C640

C150C740

C365C740

C404C740

C360C740

C375C522C93C740

C390C316C385C316

C140C740C235C346

C325C740

C344C522

C150C316

C346C732C230C740

C70C740

C92C346C350C740

C436C740

C110C316

C20C522C380C522

C220C740

C165C316

C51C696

C346C666

C160C740

C155C316

C90C696

C230C346

C210C740

C135C696

C346C366C375C740

C155C740

C290C740

C145C316

C42C696

C370C640

C101C740

C346C920

C91C696

C205C346

C100C696

C130C316C94C316

C145C696

C95C740

C160C316

C101C696

C165C740C305C522

C211C740

C100C740

C110C740

C150C696

C373C522

C404C316

C94C696

C93C316

C93C696

C110C696C155C696

C165C696

C435C316

C52C696

C385C522

C51C316

C437C316

C439C316

C95C316

C51C740

C350C640

C420C740

C95C696

C437C696C355C740

C360C696

C140C696

C380C740

C436C696

C346C375C94C740

C230C640

C140C316

C432C696

C52C740

C339C740

C438C316C145C740C101C316C160C696

C344C740

C390C740

C373C316

C433C316

C20C740

C41C696

C346C380

C52C316

C235C640

C100C316C350C696C290C696C346C900C355C696

C385C740

C200C346

C310C740

C211C346C220C346C210C346C346C316

C205C740

C305C740

C92C640

C343C640

C235C696

C439C696

C365C696

C325C640

C346C740C230C696C20C346

C352C696

C346C390C310C696C346C385

C433C696

C434C316C432C316

C371C696

C235C316

C373C740

C2C740

C420C696

C339C316

C70C696

C436C316

C40C696

C359C696C420C316C205C696

C369C740

C368C696

C371C316

C438C696C359C316

C220C640

C372C316

C375C696

C371C740

C360C316

C230C316C200C740

C372C740C359C740

C369C696C210C640

C2C522C92C316

C367C696

C343C316

C352C740C372C696C325C696

C200C640

C367C740

C390C640

C211C696C210C696C380C696C339C696C305C696

C366C696C370C696

C220C696

C369C316C370C316

C368C740

C355C316

C365C316

C366C740

C385C640C370C740C211C640C346C830

C390C696C385C696

C205C316

C200C696

C220C316

C2C346

C404C696

C349C696

C211C316

C20C696

C210C316

C317C740

C344C696

C317C696

C360C640

C92C696

C200C316C343C696

C349C740

C350C316

C434C640C352C316

C2C696

C92C740

C373C696

C343C740

C310C640

C355C640

C205C640C346C436

C346C500

C346C625

C290C640

C346C770C346C439C346C771C346C432C346C483C346C404C346C434C346C475C346C600C346C420C346C850C346C651

C346C703

C380C640C346C433C346C435

C346C438

C346C652C375C640C346C451C346C581

C346C701C346C630

C352C640

C346C663

C346C700

C346C615

C350C522

C346C645C235C522

C290C346

C305C640C346C522

C339C346

C346C620

C310C346

C346C640

C346C616

C230C522

C346C660

C346C373

C368C640

C205C522

C346C820C317C346

C434C696

C211C522

C346C349

C220C522

C346C670C290C316

C366C640

C346C481

C325C522C200C522C210C522

C346C692

C317C640

C310C316

C349C640

C390C522

C325C346

C346C698

C305C346

C346C694C346C690C346C696

C305C316

C51C522

C110C522

C42C522C41C522

C52C522

C435C522

C436C522C432C522

C437C522C433C522C420C522

C439C522C404C522C438C522

C434C522

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-1 0 1 2 3e( pmigoconnect | X )

coef = .12345264, se = .02331074, t = 5.3

Dyads: Countries: C434C522 C522

369

1994 PM IGO Connectedness

C346C371

C346C360

C346C359

C346C370C346C369

C346C355

C346C365

C346C372

C346C350

C346C352C346C705

C343C346

C344C346

C344C316

C437C640

C20C640C2C640

C20C316

C135C640C40C640

C940C316

C950C316

C365C640

C317C316

C910C316C90C640

C366C316

C367C316

C435C696C2C316

C145C522

C368C316

C41C640

C439C640

C93C522

C130C522

C435C640

C346C570

C155C640

C91C522

C91C640

C371C522

C130C640

C346C552

C100C522

C346C551

C360C522

C346C840

C438C640

C95C522

C150C640

C145C346C346C750

C346C712

C165C640

C344C640

C346C517

C90C522

C346C437C346C501C91C346

C346C553

C140C640

C346C780C346C710

C110C640

C135C740

C346C452

C101C522

C346C471

C355C522

C346C510C349C316C130C346

C369C640

C41C346

C94C522

C346C572C346C580

C160C640

C70C640

C135C522

C95C346

C155C522

C359C522

C94C346

C93C640C439C740C368C522C145C640C435C740

C101C640

C346C484C100C346C93C346C346C812C346C760C150C346

C135C316

C371C640

C51C640

C346C565

C346C531

C346C704

C367C522

C346C461

C346C800

C437C740

C155C346

C438C740

C95C640

C94C640C40C316

C100C640

C346C940

C290C522

C150C522

C372C640

C90C346C51C346

C140C522

C90C740

C110C346

C369C522

C346C790

C433C640

C346C910C346C540

C346C731C346C571

C101C346

C160C522

C165C522

C346C560

C52C640

C370C522C310C522

C165C346C135C346C40C346

C91C316

C920C316

C140C346

C366C522

C70C522

C372C522

C420C640

C432C640

C41C740

C373C640

C40C740

C375C316

C346C516C70C346

C365C522

C346C811

C90C316

C346C590

C360C740

C41C316C346C368

C346C367C91C740

C346C950C365C740C160C346C359C640

C40C522

C130C740

C380C316

C325C316

C349C522

C434C740

C52C346

C93C740C317C522

C235C740

C367C640

C70C316C433C740

C436C640

C160C316

C155C316

C352C522

C101C740

C150C740

C165C316

C900C316

C344C522C150C316

C339C640

C432C740

C420C740C145C316

C339C522

C130C696

C235C346

C92C522

C346C732

C370C640

C160C740

C130C316C165C740C110C316

C385C316C390C316

C93C316

C94C316

C343C522C155C740

C350C740

C375C522C346C666

C140C740

C350C640C346C366

C70C740

C95C740

C110C740

C230C346

C346C482C41C696

C51C316

C346C920

C290C740

C101C316C20C522

C145C696C91C696

C95C316

C380C522

C205C346

C230C740

C100C740

C94C740

C51C696

C90C696

C220C740

C325C740

C140C316C436C740

C101C696

C135C696

C100C696

C150C696

C92C346

C155C696

C305C522

C100C316

C145C740

C94C696C95C696C165C696

C52C696

C344C740

C51C740

C93C696

C235C640

C731C316

C339C740

C110C696

C52C316

C210C740

C346C375

C373C522

C375C740C355C740

C52C740

C220C346

C360C696

C385C522C475C316

C451C316

C437C696

C760C316

C625C316C420C316

C510C316

C704C316

C517C316

C160C696

C531C316

C140C696

C812C316

C702C316

C522C316C346C900

C572C316C435C316

C373C740

C439C316C652C316

C346C380

C350C696

C437C316

C211C740

C501C316C710C316C500C316

C210C346

C235C316

C211C346

C645C316

C701C316

C471C316

C552C316

C290C696

C230C640

C200C346

C850C316C235C696C600C316

C305C740

C346C740

C438C316C651C316

C310C740

C355C696C461C316

C352C696

C20C346C615C316

C20C740C369C740

C230C696C540C316

C373C316

C452C316

C380C740

C700C316

C433C316

C703C316C516C316

C205C740

C630C316

C371C696

C432C696

C343C640

C365C696

C325C640

C620C316

C346C390

C92C640

C230C316

C346C385C346C316

C310C696

C371C740

C70C696

C359C696

C663C316

C2C740

C368C696

C811C316

C40C696

C436C696

C369C696

C205C696

C390C740

C385C740C372C740

C616C316C375C696

C705C316C370C740

C482C316

C660C316

C670C316C581C316C367C696C790C316C551C316

C541C316C484C316C434C316C325C696

C553C316C366C696

C359C740

C771C316C352C740

C220C640

C432C316

C481C316

C366C740

C2C522C370C696

C367C740

C210C696C380C696C211C696

C339C316

C436C316C780C316

C368C740

C220C696C372C696

C371C316

C570C316

C349C696

C580C316

C692C316

C359C316

C305C696

C372C316

C770C316C750C316

C210C640C220C316

C360C316

C712C316

C205C316

C439C696

C343C316

C840C316C565C316

C92C316

C433C696

C390C696C385C696

C210C316

C2C346

C698C316

C369C316

C200C696

C346C830

C344C696

C317C740

C200C640C390C640

C20C696

C365C316

C370C316

C200C740

C820C316

C438C696

C571C316

C640C316

C385C640

C349C740

C211C640

C317C696

C355C316

C800C316C560C316

C211C316

C339C696

C360C640

C200C316

C694C316

C732C316

C92C696

C590C316

C2C696

C343C696C690C316

C350C316

C420C696

C373C696C352C316

C343C740

C830C316

C666C316

C92C740C696C316

C434C640

C355C640

C346C436

C346C625

C346C770

C290C640C346C432C310C640C346C439C346C434C346C500C346C435C346C600C205C640

C346C850C346C651C346C541C346C702

C740C316

C346C433

C352C640

C346C475

C346C652

C346C581C346C771

C380C640

C375C640C346C615C346C663

C346C451

C346C438C346C645C346C700

C346C703

C346C630C346C620

C346C420

C346C701

C346C616

C339C346C305C640C368C640

C346C660

C346C522

C350C522

C346C373C346C640

C235C522C290C346

C346C481C346C670

C310C346C230C522

C366C640

C317C346

C346C820

C205C522

C220C522

C349C640

C346C349

C434C696

C346C692

C211C522C325C522

C346C698

C210C522

C317C640

C200C522C390C522C325C346C305C346

C346C694

C305C316

C346C690C346C696

C41C522C110C522C51C522

C52C522

C290C316C310C316

C435C522

C437C522C436C522C432C522

C439C522

C433C522

C438C522C420C522

C434C522

-.50

.51

e( s

imila

rvot

es |

X )

-1 0 1 2 3e( pmigoconnect | X )

coef = .094584, se = .01952034, t = 4.85

Dyads: Countries: C434C522 C522 1995 PM IGO Connectedness

370

C346C372C346C350

C9

C346C359

C346C371C346C360

C346C369

C346C355C346C365

C346C370C346C352

C343C346C344C346

C344C316C317C316

C365C640

C437C640

C20C316

C950C316

C940C316C135C640

C373C640

C439C640C910C316C435C640C40C640

C435C696C366C316

C367C316

C20C640

C2C640

0C640

C368C316

C2C316

C93C522

C372C640

C145C522

C371C640C346C570

C130C522C91C522

C150C640C41C640

C346C552

C438C640

C346C551

C360C522C130C640

C95C522

C346C840

C100C522

C346C750

C91C640

C145C346

C346C517

C371C522

C346C553

C155C640

C90C522C346C712

C359C522

C349C316C91C346

C165C640

C135C740

C344C640C41C346C346C437

C346C572C346C501

C101C522

C355C522

C346C710

C145C640

C94C522

C346C780

C110C640C140C640

C420C640

C346C471C346C452

C346C580

C70C640

C346C812C130C346

C135C522

C439C740

C93C346

C435C740

C160C640

C346C484C94C346

C433C640

C155C522

C346C760

C346C775C346C731C95C346

C346C461

C150C522

C368C522

C101C640

C135C316

C346C531C432C640

C346C490

C365C740

C367C522

C437C740

C150C346

C51C640C290C522C369C522

C110C346

C346C565

C372C522

C100C346C346C790

C346C910

C140C522

C346C510

C93C640

C346C540

C346C940

C90C740

C346C571C155C346C51C346

C165C522

C920C316

C40C316C95C640C310C522

C90C346

C346C560C101C346C370C640C165C346

C100C640

C70C522

C52C640

C160C522

C375C316

C135C346

C140C346

C40C346

C94C640

C346C516

C366C522C365C522C41C740C370C522C438C740

C70C346

C346C367C436C640

C91C316

C339C522

C346C368

C317C522

C90C316

C346C811

C41C316C130C740

C40C740

C325C316

C380C316C346C950C346C590

C160C346

C349C522

C150C740

C420C740

C52C346

C91C740

C235C740

C360C740

C40C522

C900C316

C70C316

C150C316

C434C740

C350C640

C145C316

C160C316

C433C740C432C740C165C316

C390C316

C101C740C93C740

C130C696

C155C316

C92C522

C385C316C352C522

C160C740

C344C522

C235C346

C343C522C165C740C110C316C70C740C375C522

C373C740

C346C732

C130C316

C350C740C404C640

C411C640

C155C740C373C522

C346C666

C346C411

C20C522C110C740

C41C696

C51C316

C145C696

C100C740C93C316

C91C696

C140C740

C325C740

C220C740

C51C696

C380C522

C90C696

C339C740

C346C920

C101C316

C346C366

C94C316

C290C740C230C740

C101C696

C305C522

C92C346

C93C696

C411C522

C230C346

C95C316C140C316

C150C696

C235C316

C165C696

C100C316

C135C696

C436C740

C145C740

C52C696C95C696C155C696

C210C740

C100C696

C344C740

C95C740C51C740C731C316

C110C696

C52C316

C205C346

C211C740

C94C696

C375C740

C94C740

C235C640

C52C740C775C316C451C316

C704C316C370C740

C760C316C420C316

C385C522C625C316

C355C740

C432C696C346C375C140C696

C360C696C812C316

C517C316C531C316C220C346

C475C316

C702C316

C572C316

C437C696

C522C316C435C316

C310C740

C350C696

C652C316C439C316C437C316

C436C696

C411C316

C710C316C501C316

C701C316

C373C316

C160C696

C290C696

C305C740

C500C316C235C696

C483C316

C600C316C552C316C471C316C850C316

C346C900

C371C740

C230C316C369C740

C359C696

C20C740

C404C740

C355C696

C372C740

C210C346

C211C346

C651C316

C346C705

C346C740

C346C380C380C740

C461C316

C359C740

C540C316C200C346

C510C316

C20C346

C703C316

C630C316

C490C316

C365C696

C230C696

C452C316

C230C640C352C696

C368C696C433C316C516C316C620C316C700C316C310C696

C372C696

C343C640C70C696C205C740

C92C640C438C316

C346C316

C385C740

C663C316

C369C696C2C740C615C316

C390C740C325C640C705C316

C811C316

C375C696C371C696

C368C740

C616C316

C220C316

C346C390

C205C696

C367C696

C346C385C40C696

C670C316

C439C696C438C696

C205C316C325C696

C317C740

C371C316

C660C316C404C316

C210C316

C210C696

C211C696

C433C696

C367C740

C790C316

C380C696

C360C640C370C316

C366C696

C220C696

C481C316

C541C316

C551C316C349C696

C366C740

C484C316

C305C696

C2C522

C434C316

C692C316C339C696

C771C316

C553C316

C404C696

C432C316

C220C640

C339C316

C317C696

C780C316

C211C316

C436C316C352C740C570C316

C359C316

C580C316

C390C696

C770C316

C370C696C385C696

C372C316

C712C316

C360C316

C210C640

C200C696

C750C316

C565C316

C343C316

C840C316

C698C316C92C316

C369C316

C20C696

C2C346

C365C316

C200C316

C211C640

C820C316

C200C740C200C640

C411C740

C390C640C349C740

C571C316

C640C316C355C316

C344C696

C346C830

C420C696

C800C316

C560C316C694C316C385C640

C373C696

C92C696

C2C696

C343C696

C732C316

C590C316C690C316

C350C316

C355C640

C696C316C411C696

C343C740

C666C316

C92C740

C830C316

C352C316

C434C640

C369C640

C310C640C346C432C346C770C346C439C346C625C346C436C346C435

C290C640

C346C483C346C434C346C600

C352C640

C346C500

C346C541

C346C850C346C651

C346C702

C346C433C346C475

C346C420

C346C652C346C771C205C640C346C404

C740C316

C346C703

C346C451C346C704

C346C373

C346C663

C375C640

C346C700C346C800

C368C640

C346C438

C346C701

C346C630C346C620

C339C640C380C640C346C615

C359C640

C346C522C346C616

C339C346

C350C522

C235C522C346C660C305C640

C290C346

C346C481

C310C346

C346C670

C366C640

C230C522

C367C640

C317C346C317C640

C346C820C205C522

C211C522C220C522

C346C692

C349C640

C434C696

C346C349

C325C522C210C522

C200C522

C346C698

C390C522

C325C346C305C346

C346C694C346C690C346C696

C41C522

C110C522C51C522C52C522

C290C316

C310C316

C305C316

C435C522C436C522C432C522C437C522

C404C522C433C522C439C522

C438C522

C420C522

C434C5220

-.5.5

e( s

imila

rvot

es |

X )

-1

-1 0 1 2 3e( pmigoconnect | X )

coef = .04432443, se = .01689858, t = 2.62

Dyads: Countries: C420C522 C434C522 C346C350 C346C372 1996 PM IGO Connectedness

371

C346C371C346C372

C346C365

C346C350

C346C352

C343C346

C344C346C20C316

C344C316

C317C316

C940C316

C950C316

C371C640

C910C316

C2C316

C373C640

C367C316C366C316

C437C640

C368C316

C20C640C372C640

C135C640C40C640

C439C640

C435C696

C435C640

C2C640

C93C522

C346C580

C433C640

C346C570

C349C316

C346C712

C145C522C130C522

C41C640C346C750

C145C346

C70C640C346C553C41C346

C91C522

C130C640

C90C522

C346C840

C346C517

C346C552C150C640

C346C501

C346C437

C438C640

C359C522

C42C640C370C640

C140C640C346C452

C346C471C90C640

C91C346

C346C710C145C640

C360C522C436C640

C365C740C91C640C93C346

C130C346

C155C640

C346C490

C346C780C346C510

C346C551

C420C640

C100C522

C346C484

C346C572

C346C812C346C775

C95C522

C94C346

C101C640

C346C461

C90C346C51C346

C165C640

C355C522

C42C346

C94C522

C93C640C110C346

C95C346

C160C640C346C516

C110C640C920C316

C95C640

C150C346

C346C731

C100C640

C101C522

C100C346C346C450C346C540

C346C790

C135C522

C432C640

C346C565C135C316

C346C760

C346C910

C40C316

C346C368

C150C522

C346C531

C346C367

C369C522

C51C640

C140C522

C101C346

C346C940C375C316

C368C522

C94C640C135C346

C367C522

C140C346

C155C346C346C560

C155C522

C372C522C290C522

C165C346

C437C740

C346C359

C346C571C70C346C40C346

C346C811

C325C316

C70C522

C380C316

C135C740

C165C522

C346C360C371C522

C310C522C41C316

C346C950C365C522

C435C740C70C316C439C740C366C522

C346C590

C160C522C91C316

C900C316C346C366

C346C355

C160C346

C52C346

C235C346

C40C522

C52C640C346C369

C317C522

C339C522

C390C316

C433C740

C404C640

C145C316C155C316C160C316C150C316

C385C316

C42C316

C40C740

C349C522

C438C740

C350C640

C346C370

C230C346

C41C740

C370C522

C130C316

C101C316C130C740C93C316C90C316

C420C740C140C316C436C740

C373C522C100C316

C346C732

C101C740

C92C522

C352C522

C344C522

C110C316C165C316

C360C740

C90C740

C346C666

C93C740

C235C740

C235C316

C70C740

C346C705

C343C522C91C740C51C316

C346C411

C42C740

C95C316C140C740

C290C740

C150C740

C94C316C434C740

C346C920

C411C640

C432C740

C205C346

C230C316C160C740

C100C740

C130C696C350C740

C230C740

C220C346

C90C696

C155C740

C92C346C373C740

C411C522

C41C696

C339C740

C145C696

C346C375

C101C696

C211C346

C110C740

C91C696C93C696

C220C740

C346C380

C51C696

C42C696

C210C346

C355C740

C325C740

C20C522

C200C346

C145C740

C360C696

C344C740

C165C740

C95C696

C370C740C135C696C371C740

C110C696

C100C696

C150C696

C51C740

C731C316

C94C696

C375C740

C94C740

C432C696

C310C740C155C696

C52C316

C165C696

C700C316C775C316

C140C696

C220C316

C211C740

C359C696C625C316

C210C740C20C346

C346C900

C812C316C760C316C531C316

C95C740

C436C316C517C316C475C316

C702C316

C704C316

C420C316

C355C696

C516C316C435C316

C235C640

C290C696

C437C696

C439C316

C52C696

C346C390C572C316

C501C316C437C316

C411C316C373C316C500C316C522C316C652C316

C385C522

C346C740

C710C316

C371C316

C471C316

C210C316

C404C740

C346C385

C701C316

C359C740

C552C316

C369C740

C160C696

C205C316

C600C316C461C316C850C316C483C316

C211C316

C510C316C369C696

C230C640

C651C316C540C316

C372C740

C490C316

C380C740

C235C696

C70C696

C343C640C305C740

C200C316

C310C696

C365C696

C92C640

C703C316C52C740C433C316

C372C696

C433C696

C450C316C368C696

C551C316

C370C316

C630C316

C230C696

C20C740C346C316

C438C316C352C696C620C316

C2C740

C404C696

C452C316C367C696

C811C316C663C316

C438C696

C615C316

C439C696

C339C316

C451C316

C317C740C205C740C705C316C390C740

C404C316

C40C696

C581C316C366C696

C616C316C790C316

C385C740

C541C316

C366C740

C368C740C367C740

C434C316

C325C640

C484C316C553C316C660C316C432C316C771C316C317C696C349C696C375C696

C436C696C220C640

C570C316

C780C316

C670C316

C580C316

C481C316

C359C316

C339C696

C325C696C770C316

C2C346

C205C696

C352C740C200C740

C360C640

C371C696

C2C522

C712C316

C211C696

C372C316

C750C316

C420C696

C210C696C380C696

C220C696

C692C316

C840C316

C565C316

C210C640

C343C316

C92C316

C344C696

C211C640

C200C696C305C696

C369C316C349C740

C200C640

C373C696

C360C316

C365C316

C370C696

C355C316

C571C316

C390C696

C820C316C346C830

C640C316

C20C696C385C696

C411C740

C698C316

C365C640

C800C316

C560C316

C390C640

C355C640

C694C316

C92C696

C385C640C343C696

C590C316C732C316

C350C316

C2C696

C411C696

C369C640

C343C740

C690C316

C92C740

C352C316

C666C316

C696C316

C290C640C310C640

C352C640

C344C640

C346C625C346C432C346C770

C346C600

C434C640

C346C434C830C316

C346C439

C346C435C346C436C346C651C346C475

C346C702

C346C500

C346C541

C339C640

C346C850C346C433C346C652C346C483

C346C373

C346C420

C346C700C346C771

C359C640

C346C404

C346C704

C346C640

C346C438

C368C640

C346C616

C346C451

C346C703

C375C640

C346C581

C346C663C346C615C346C630

C740C316

C290C346

C380C640C346C620

C346C800

C346C660

C310C346C205C640

C346C522

C367C640

C305C640

C366C640

C317C346

C317C640

C346C481

C349C640

C350C522C235C522

C346C349

C346C820C346C670

C230C522

C346C692

C205C522C211C522C220C522

C434C696

C375C522

C325C346

C325C522C380C522C200C522

C305C346

C346C698

C210C522C305C522

C346C694

C390C522

C346C696

C346C690

C41C522C51C522C110C522

C42C522C52C522

C290C316

C339C346

C310C316

C305C316

C435C522

C432C522

C437C522

C404C522C433C522C439C522C438C522

C436C522C420C522

C434C522

.50

1e(

sim

ilarv

otes

| X

)-.5

-1 0 1 2 3e( pmigoconnect | X )

coef = .06203637, se = .01972801, t = 3.14

Dyads: Countries: C434C522 C522 C316

372

1997 PM IGO Connectedness

C20C316

C2C316

C317C316

C437C640

C90C522

C91C522

C40C640C355C522

C40C316

C70C522C95C522

C290C522

C344C316

C437C740C310C522

C355C640

C70C640C235C316C100C640C230C316

C91C640

C940C316

C950C316

C325C316

C135C316

C352C522

C70C316

C349C522C95C640

C41C640C220C316C290C640

C910C316

C230C740C200C316

C40C740C211C316

C290C740

C90C640

C51C640

C140C316

C367C316C366C316

C210C316

C390C316

C40C522

C310C640C220C740

C100C316

C355C740

C70C740C93C316

C385C316

C439C640

C368C316

C145C316

C640C316

C91C316

C339C522

C350C740

C344C740

C42C640

C344C522

C325C740

C439C740C130C316C150C316C41C316

C160C316

C155C316

C100C740

C731C316

C352C640C210C740

C52C640C235C740

C90C316

C700C316C775C316

C41C696

C211C740

C94C316C625C316

C101C316

C760C316C355C696

C110C316

C704C316

C812C316

C531C316

C517C316

C436C316C310C696C475C316C435C316

C572C316

C20C522C516C316

C51C316

C200C740

C95C316

C350C640C652C316C484C316C437C316

C702C316C310C740

C290C696

C373C316

C522C316C500C316C439C316

C371C316

C701C316

C710C316

C91C740

C165C316

C552C316

C51C696

C471C316

C100C696

C600C316

C70C696

C438C740

C438C640

C411C316C850C316

C349C316

C42C316

C540C316

C483C316C461C316C651C316C510C316

C90C696

C501C316

C91C696

C490C316

C703C316

C433C316C41C740C451C316

C350C316

C42C696

C450C316C620C316C350C696

C370C316

C551C316

C20C740

C339C740

C95C696

C438C316C90C740C663C316C52C316

C615C316

C452C316

C305C740

C920C316C705C316

C235C696

C52C696

C616C316

C205C740

C230C696

C2C740C2C522

C404C316C581C316

C92C640C20C640

C670C316

C790C316C660C316C51C740

C432C316C541C316

C352C696

C434C316C481C316C553C316

C771C316

C375C316

C339C316

C580C316

C380C316

C780C316

C692C316

C437C696

C344C640

C770C316C570C316

C359C316

C630C316

C42C740C712C316

C565C316

C372C316

C750C316

C343C316

C900C316

C325C696C840C316

C352C740

C92C316

C369C316C365C316

C211C696

C344C696

C698C316

C200C696C210C696

C360C316

C220C696C305C696

C355C316

C52C740

C820C316

C205C316

C339C696

C205C696C694C316

C20C696

C40C696

C571C316

C800C316

C560C316C2C640C305C640

C438C696

C690C316

C732C316

C590C316

C439C696

C696C316C290C316C235C522C350C522

C2C696C230C522

C666C316

C352C316

C230C640

C830C316C339C640C325C522

C235C640

C200C522

C205C640

C210C522C211C522

C205C522

C305C522C220C522

C325C640C211C640C220C640

C740C316

C210C640C200C640

C51C522C41C522

C310C316

C42C522

C52C522

C305C316

C437C522

C438C522C439C522

-.50

.51

e( s

imila

rvot

es |

X )

-1 0 1 2 3e( pmigoconnect | X )

coef = .09302815, se = .03323615, t = 2.8

Dyads: Countries: C438C522 C522 C439C522 C437C522

373

1998 PM IGO Connectedness

C20C316

C325C316C2C316

C385C316

C390C316

C435C740

C435C640C373C522

C900C316

C371C522

C359C522

C372C522C369C522C370C522

C91C522

C435C696

C145C522

C310C522

C135C522

C90C522

C130C522C92C522

C150C522

C93C522

C290C522

C100C522C94C522C95C522

C371C640

C373C640

C101C522

C372C640

C522C316

C365C522

C155C522C70C522

C165C522C235C316

C160C522

C310C640

C20C740

C435C316

C41C640

C230C316

C91C640C698C316C145C640

C290C640

C92C640

C150C640

C90C640C51C640C42C640

C130C640C100C640C101C640C432C640

C95C640C94C640

C135C640C20C640

C404C640

C433C640C438C640C93C640

C200C316

C420C640C436C640C52C640C155C640

C350C316

C140C522

C70C640

C165C640

C437C640

C439C640

C160C640

C370C640

C385C522

C220C316

C211C316C452C316

C451C316

C703C316C660C316

C461C316C615C316

C625C316

C770C316C705C316C840C316C600C316C616C316C541C316C560C316C704C316

C482C316C701C316C620C316C145C740C41C740C551C316C51C740

C91C740C365C740

C130C740

C90C740

C110C740

C94C740C42C740C92C740C150C740

C483C316

C95C740

C540C316C101C740C663C316C475C316C100C740C165C740

C850C316C210C316C484C316C731C316C750C316

C692C316

C651C316

C140C640

C135C740

C20C522

C155C740C572C316

C481C316

C93C740

C160C740

C510C316

C2C640

C373C696C2C740

C710C316C404C316C652C316

C436C696C420C696

C70C740

C20C696

C670C316

C370C696

C135C696C433C696

C110C316

C41C316

C438C696

C91C696

C732C316

C150C316

C41C696

C450C316C150C696

C371C696

C145C696C92C696C90C696

C432C696

C359C696

C51C696

C42C696C130C696

C100C696

C372C696

C437C696

C101C696C95C696C94C696

C369C696

C359C740

C439C696

C155C696C371C740C52C696

C145C316

C160C696

C165C696

C371C316

C369C740

C420C316

C372C740

C93C696

C432C316

C70C696

C91C316

C365C696

C436C316

C51C316C310C696

C370C740

C432C740

C434C740

C696C316C200C696C235C696

C140C740

C52C316

C310C740

C404C740

C372C316

C434C316

C694C316

C230C696

C740C316

C92C316C42C316C433C740C438C740C93C316

C290C696C325C696

C359C316

C420C740

C235C740

C436C740

C438C316

C437C740C90C316

C220C696

C290C740C230C740

C94C316

C385C696

C439C740

C373C316

C95C316C211C696

C325C740

C439C316

C350C696

C2C696

C130C316

C390C696

C433C316

C369C640

C165C316

C385C740

C140C696

C350C740

C101C316

C2C522

C211C740

C210C696

C200C740

C390C740

C235C522

C135C316

C365C640

C100C316C155C316

C230C522

C70C316

C350C640

C210C740

C437C316

C350C522

C110C640C200C522

C370C316

C325C522

C690C316

C359C640

C434C640C160C316C235C640

C369C316

C211C522

C230C640

C220C522

C365C316

C390C522C210C522

C200C640

C325C640

C140C316

C220C640C211C640

C385C640

C390C640

C110C696

C210C640

C434C696

C41C522C290C316C51C522C42C522

C310C316

C52C522

C435C522

C436C522C420C522C404C522

C433C522

C438C522C432C522

C437C522

C439C522

C110C522

C434C522

-1-.5

0.5

e( s

imila

rvot

es |

X )

-2 -1 0 1 2 3e( pmigoconnect | X )

coef = .10034989, se = .02125139, t = 4.72

Dyads: Countries: C434C522 C522 C435C522 1999 PM IGO Connectedness

374

C346C365

C346C371C346C352C346C372

C346C350

C40C640

C317C316

C70C640

C355C640

C42C640

C155C316C160C316

C40C316

C93C316

C90C640

C437C640

C40C740

C41C640

C101C316

C343C346

C40C522

C140C316C145C316

C352C640

C165C316C91C640

C355C740

C346C780

C40C696

C91C522

C100C316

C346C452C135C316

C40C346

C150C316

C346C510C95C316C70C316

C910C316

C346C501

C95C346

C940C316C346C471C346C490C346C840C310C740

C346C775C346C540

C94C316C135C346

C93C346C344C740

C950C316

C41C316C51C640

C368C316

C90C316C130C316C42C740

C100C346

C290C740

C42C316

C367C316

C346C367

C346C437C346C580

C344C522

C346C565

C155C346C94C346

C346C750

C41C346

C346C710

C42C346

C90C522

C346C940

C235C346

C346C790

C70C522

C355C522

C91C316

C346C368

C366C316C344C316

C346C811

C145C346

C346C366

C352C740

C101C522

C130C346C90C346

C91C346C51C346

C344C696

C230C346

C346C551

C200C346

C346C712C150C346C346C552

C220C346

C346C910C352C696

C211C346

C346C380

C290C522C339C740C101C346

C42C696

C346C731C310C522C346C484

C352C522C51C316

C437C696

C41C740C140C346

C52C640

C41C696

C346C390

C349C522

C70C696

C160C346

C70C346

C346C553

C355C696

C349C316C346C385

C346C950

C210C346

C110C316C165C346

C70C740C290C696

C90C740

C90C696

C346C375

C920C316

C346C920

C205C346

C235C740

C2C316

C346C369

C346C590C346C760

C346C666

C346C517C350C740C346C572C346C812C438C640

C325C316C346C732

C305C740

C375C316

C20C522

C52C346C380C316

C346C360

C20C316C235C696

C20C346

C438C740

C51C696

C91C740

C900C316

C350C696C310C696

C390C316

C346C830

C385C316

C344C640

C344C346

C346C740

C339C522

C346C571

C439C740

C52C316

C220C696

C346C900

C2C346

C51C740C230C696C439C640

C2C740

C2C522

C346C355

C346C411

C91C696

C230C740

C346C359

C346C705

C92C346

C52C696

C205C740C325C696C200C696

C346C370

C305C640C220C740

C92C640C20C740

C211C696C210C696C712C316

C553C316

C580C316C432C316C541C316C434C316C404C316

C475C316

C790C316C490C316C20C696C771C316C510C316

C750C316

C551C316

C359C316

C451C316C211C740C452C316C325C740

C437C740C461C316C483C316

C702C316

C811C316C501C316

C371C316

C500C316C522C316C439C316C433C316C840C316

C531C316

C540C316C581C316

C517C316C471C316

C2C696C437C316

C305C696

C850C316

C775C316

C780C316

C700C316

C339C316

C346C316

C200C740C625C316C812C316

C360C316

C731C316C484C316C438C316C411C316

C92C316

C369C316

C770C316

C565C316

C52C740

C760C316

C205C640

C355C316

C552C316

C343C316

C373C316

C663C316

C704C316

C372C316

C710C316

C800C316

C600C316

C630C316

C640C316

C572C316C651C316

C350C640C205C696

C615C316

C660C316

C560C316C652C316C571C316

C339C696

C701C316

C365C316C439C696

C616C316

C210C740

C705C316

C620C316

C235C316C20C640

C820C316

C370C316

C481C316

C2C640

C92C740

C590C316

C438C696

C350C316

C732C316

C352C316

C346C630

C211C316

C666C316

C670C316C230C316C692C316

C220C316C346C770

C698C316

C346C652C346C433C346C615C210C316

C740C316

C200C316

C346C640

C346C616

C694C316C346C625

C346C660

C346C850C346C600

C346C771

C346C663C690C316

C346C541C346C475

C346C620C830C316

C696C316

C317C346

C346C651

C290C346

C346C694

C346C670

C346C581C346C700

C346C434C346C800C346C522

C346C481

C346C500

C346C373

C346C432

C346C702

C310C346

C346C451C346C439

C235C640

C346C438C346C461

C325C346

C346C820C205C316C346C698

C346C483

C346C696

C339C640

C110C346

C346C692

C346C701

C346C349

C346C404C305C316C350C522

C305C346

C346C690

C220C522

C230C640

C220C640

C211C522

C290C640C325C640

C235C522C205C522

C211C640

C230C522C200C522C305C522

C310C640

C210C640

C325C522C210C522

C200C640

C438C522

C439C522C339C346

C41C522C52C522C51C522

C42C522

C290C316 C310C316

C437C522

.50

1e(

sim

ilarv

otes

| X

)-.5

-1

-1 0 1 2 3e( pmigoconnect | X )

coef = .0452257, se = .02378084, t = 1.9

Dyads: Countries: C439C522 C522 C438C522 C290C316 C310C316 C437C522

375

2000 PM IGO Connectedness

C325C316

C346C371

C390C316C385C316C346C372

C346C365

C346C350

C346C352

C640C316

C343C346

C20C316

C344C346

C371C640

C2C316

C317C316

C135C640

C350C316

C40C640C41C640

C373C640

C437C640C130C640

C910C316C950C316

C140C640

C365C740

C155C640

C101C640C42C640

C90C640

C346C580

C145C640C346C840C41C316

C346C437

C346C452

C346C552C346C750

C93C640

C150C640C91C346

C367C316

C346C484

C165C640

C346C780C346C471

C368C316

C95C640

C344C316

C100C640C41C346

C135C316

C346C540C160C640

C93C346

C346C490C346C501

C366C316

C346C712C51C640

C346C790

C93C522

C90C346C145C346

C346C710C130C346

C145C522

C346C775C346C553C40C316

C346C510

C70C640C359C522

C91C522

C91C640C51C346

C150C346

C346C565

C42C346

C346C551C439C640

C346C910C95C346C100C346

C94C640

C346C517

C90C522

C40C522

C435C640C360C522

C130C522

C346C572

C346C570C94C346

C346C812

C101C346

C346C760C346C560C40C346C41C740

C95C522

C371C522

C155C346

C346C811

C135C346

C100C522

C130C316

C438C640

C346C368

C346C531

C346C516

C135C522

C145C316

C70C346

C94C522

C150C522

C140C346

C101C522

C370C640

C346C731C140C316C135C740

C355C522

C433C640

C101C316C372C522C368C522

C91C316C93C316

C40C740

C369C522C165C346

C346C571C130C740C101C740

C370C522

C439C740

C155C316

C160C346

C346C367

C150C316

C155C522

C346C950

C235C346

C160C316C435C740

C349C316

C100C316

C360C740

C42C316

C432C640

C70C522

C165C522

C290C522C90C316C344C522

C140C522

C70C316

C52C640

C93C740

C346C590C367C522

C110C316

C420C640

C95C316

C52C346

C438C740

C346C366

C94C316

C365C522

C165C316

C51C316

C160C522

C920C316

C20C640

C434C740

C437C740C100C740

C235C316

C42C740

C310C522

C90C740

C346C360

C230C346

C150C740

C437C696

C366C522

C346C732C346C666

C433C740C432C740

C317C522C140C740

C130C696

C346C359

C51C740

C420C740

C2C640

C51C696

C93C696

C110C740

C101C696

C95C740C91C740

C346C920

C373C740

C90C696C145C696

C373C522

C95C696

C160C740

C100C696

C290C740

C360C696

C220C346C135C696C346C355

C349C522

C41C696

C165C740C352C522

C230C316C155C740

C355C740C235C740

C42C696

C339C522C346C369

C145C740

C200C346

C380C316

C91C696C375C316

C94C740

C339C740

C155C696

C369C696

C150C696

C94C696

C365C696

C70C740

C346C380

C350C740

C211C346

C220C316

C371C696

C359C696

C165C696

C900C316

C355C696

C346C705

C210C346

C310C740

C411C522

C369C740C371C740

C52C316

C211C316

C346C375

C220C740

C344C740

C52C696

C140C696

C290C696C346C411C92C346

C372C696

C40C696

C92C522

C205C346

C346C740

C160C696

C346C390

C230C740

C346C385

C359C740

C346C900

C70C696

C343C522

C411C640C344C696

C435C696

C372C740

C210C316

C360C640

C370C740

C20C346

C731C316

C20C522

C352C696C350C640

C235C696

C433C696

C346C370

C200C316C775C316

C700C316

C350C696

C531C316C625C316

C52C740

C812C316

C325C740

C760C316

C310C696

C435C316

C420C316C704C316C517C316C500C316C484C316

C2C740C770C316

C471C316

C432C696

C439C316C572C316

C367C696

C461C316C373C316C516C316C483C316C540C316

C317C696C490C316C368C696C501C316

C552C316

C451C316

C702C316C710C316C701C316

C366C696

C581C316

C385C522

C510C316C551C316C652C316C600C316

C411C316

C366C740

C230C696

C703C316C92C640

C651C316

C211C740C305C740

C452C316

C367C740

C343C640

C475C316

C375C740C380C740

C346C316

C438C316

C368C740C210C740

C811C316C522C316

C570C316C663C316

C439C696C317C740C352C740

C432C316C541C316

C437C316

C553C316

C580C316C434C316

C615C316

C790C316

C200C740

C771C316

C620C316

C433C316

C371C316

C359C316C355C640

C220C696

C370C696

C2C346

C712C316

C780C316

C339C316

C325C696

C616C316

C339C696

C370C316C660C316

C705C316

C365C640

C438C696

C750C316C200C696C850C316

C390C740

C840C316C565C316

C372C316

C481C316

C20C740

C92C316

C630C316

C346C830

C369C640

C369C316C380C696

C349C696

C360C316

C343C316

C375C696C211C696

C349C740

C210C696C373C696

C670C316

C385C740

C2C522

C355C316

C305C696

C420C696C205C740C692C316

C390C696

C352C640

C800C316C385C696

C365C316C560C316

C571C316C820C316

C235C640

C372C640

C290C640

C411C740C20C696

C310C640

C92C696

C698C316C205C696

C230C640

C343C696

C325C640

C694C316

C411C696

C220C640

C434C640C92C740

C343C740

C590C316

C2C696

C344C640

C732C316C210C640

C352C316

C690C316C346C625

C666C316

C346C434C696C316

C200C640

C346C770

C346C600

C346C439C346C433C346C432

C211C640

C346C652

C390C640

C346C483C346C651C346C541C346C615C346C435

C346C475

C346C850C346C500C346C771C346C663

C359C640

C385C640C346C373

C380C640

C346C616C830C316

C349C640

C346C640

C346C438

C346C702C110C640

C346C630C346C800C346C461

C317C640

C346C703

C375C640

C305C640

C346C620

C368C640

C346C660

C346C420C110C346

C740C316

C290C346

C346C704C346C701

C346C581C346C481

C367C640

C366C640

C346C451

C205C640

C346C522

C339C640

C346C670

C310C346C317C346

C346C820

C434C696

C346C692

C346C349

C350C522

C346C698

C235C522

C325C346

C110C696

C346C694

C230C522

C305C346

C220C522C211C522C325C522C200C522

C346C696

C380C522C305C522C375C522

C346C690

C210C522C205C522C390C522

C205C316

C290C316

C41C522C339C346C51C522C42C522

C310C316C305C316

C52C522C437C522

C435C522

C110C522

C439C522C433C522C432C522C438C522

C420C522C434C522

-.50

.51

e( s

imila

rvot

es |

X )

-1 0 1 2 3e( pmigoconnect | X )

coef = .08498365, se = .02041785, t = 4.16

Dyads: Countries: C434C522 C522 C435C522

376

Figure 22: Partial-regression leverage plots for small sample analysis - SC IGO Connectedness 1990 SC IGO Connectedness

C339C696

C92C740

C92C696

C260C740

C260C522

C375C740

C375C640

C260C696

C200C740

C260C640C92C640

C310C740C380C740

C200C522C420C640

C390C740C92C522

C2C522

C100C640

C433C640

C140C522

C339C522

C380C522

C385C522

C345C740

C375C522

C350C640

C385C740

C315C740

C339C740

C390C522

C380C640C305C640

C210C522

C305C522

C325C522C290C522

C140C740C200C640

C420C696

C93C640

C310C522

C437C740

C315C696

C355C522

C230C522

C220C640

C210C740

C211C640

C235C522C420C740

C290C740C404C696

C20C640C420C522

C51C740

C205C740

C433C740

C350C740

C145C640

C350C522C145C740

C20C522

C90C522

C70C522

C352C740

C205C522C210C640

C290C696

C211C522C404C640

C310C696

C91C696

C310C640

C290C640C211C740C360C740

C220C522

C42C522

C355C740

C91C522

C352C640

C165C522

C436C696

C360C522

C93C696

C235C640

C438C696

C339C640

C200C696

C140C696

C94C522

C100C740

C95C640

C404C740

C230C740

C160C522

C94C640

C352C522

C360C696

C345C696

C390C640

C91C740

C20C740

C433C522

C70C640

C100C522

C220C740

C51C640

C355C696

C325C640

C135C522C130C522

C94C696

C438C522

C101C522C155C522

C140C640

C95C522

C436C740

C150C522

C305C740

C51C696C93C522

C41C522

C91C640

C432C696

C315C522

C40C522

C100C696

C436C640

C52C522C345C522

C437C640

C41C640

C145C522

C432C640

C20C696

C110C696

C325C740

C385C640

C42C696

C110C640

C434C696

C404C522

C51C522

C145C696

C433C696

C436C522

C350C696C235C696C2C696

C110C522

C230C696

C437C522

C41C696

C434C522

C438C740

C432C522C439C522

C434C740C160C740C94C740

C40C696

C93C740

C325C696C210C696

C101C696

C211C696

C375C696

C352C696

C355C640

C101C640

C205C640

C52C740

C345C640

C230C640

C2C640

C70C696

C160C640

C90C696

C110C740

C220C696

C434C640

C438C640

C52C640

C385C696

C380C696

C390C696

C150C696

C41C740

C160C696

C70C740

C2C740

C360C640

C165C696

C315C640

C40C640

C40C740

C437C696

C130C696C155C696

C130C640

C135C740

C95C696

C305C696

C205C696

C155C740C432C740C435C640

C435C522

C439C696

C95C740

C135C640

C135C696

C165C640

C52C696

C439C640

C435C740

C155C640

C42C640

C90C640

C101C740

C235C740

C150C640

C435C696

C42C740

C130C740

C439C740

C165C740

C90C740C150C740

-.50

.5e(

sim

ilarv

otes

| X

)

-5 0 5e( scigoconnect | X )

coef = .00032651, se = .01187036, t = .03

Dyads: Countries: C339C696 C740 C92C696

377

1991 SC IGO Connectedness

C339C696 C339C522

C200C740C310C740

C200C522C2C522C315C696

C355C522C290C522

C210C740

C210C522C325C522

C305C522

C310C522C230C522

C51C740

C235C522

C339C640

C205C740

C230C740

C315C740

C90C522

C345C740

C70C522

C290C740

C350C522

C211C740

C310C640C20C640

C355C740

C205C522

C339C740

C20C522C211C522

C310C696

C437C740

C211C640C200C696

C42C522

C315C522

C20C740

C305C640

C51C640

C220C522

C290C696

C325C740

C290C640

C438C522

C345C696

C91C740

C200C640

C220C740

C352C740

C305C740

C355C696

C352C522

C41C522C345C522

C350C640C315C640

C91C640

C350C740C40C522C95C522

C91C522

C352C640

C20C696C220C640

C91C696

C437C522

C52C522

C2C696

C438C696

C439C522

C210C640

C51C696

C51C522

C52C740

C211C696C210C696C325C696C2C740

C437C640

C230C696

C41C696

C235C696C350C696

C42C696

C235C640

C40C696

C220C696

C355C640

C40C740

C352C696

C70C696

C230C640

C305C696C325C640

C90C696

C345C640

C52C640

C437C696C205C696

C41C640

C70C640

C235C740

C439C696

C438C740

C41C740

C52C696

C205C640

C70C740C2C640

C40C640

C438C640

C42C640

C42C740C439C740

C439C640

C90C740

C90C640

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5e( scigoconnect | X )

coef = .0019057, se = .01481756, t = .13

Dyads: Countries: C439C640 C640

378

1992 SC IGO Connectedness

C200C740

C305C346C325C346C2C346

C346C390

C390C740C380C740C375C740

C346C385

C346C352

C20C346C211C346C210C346C346C350C346C380C220C346

C346C349

C200C346C346C375

C346C360

C385C740

C310C346

C346C694

C230C346C205C346

C420C522

C315C346

C346C740

C210C740

C235C346C346C355C290C346

C346C365C346C900

C346C371

C346C369

C230C740C310C740

C346C368

C200C522

C346C696

C211C640

C290C740

C346C920

C360C740

C346C705

C346C640

C346C367C315C696

C390C522C346C666

C310C640C375C640

C346C481

C346C670

C211C740C305C640

C346C359

C380C640

C346C698

C210C522

C370C696

C325C522

C2C522

C92C696

C339C346C346C366C339C696

C346C370

C210C640

C365C740

C325C740

C420C696

C290C640

C437C740

C346C580

C346C645

C52C346

C346C437

C346C570

C346C517C346C432

C20C740

C165C346

C346C435

C346C434

C346C404

C373C696C346C565

C346C690

C140C740

C346C712

C160C346

C310C696

C346C660

C344C740

C230C522

C51C346

C385C522

C346C373

C205C740

C433C740C433C640

C346C732

C155C346C346C830C346C625C41C346C91C346

C346C510

C235C522

C20C640

C370C640

C140C346

C380C522C360C696

C350C640

C346C490

C200C640

C346C692C350C740

C346C620

C355C740C42C346

C346C820C315C740C101C346

C346C516C375C522

C100C740

C346C451

C305C522

C92C740

C93C346C346C616C94C346

C346C800C145C346

C373C640

C346C600

C346C450

C346C840

C404C696

C70C346

C100C346

C404C640C344C346

C350C522C344C696

C346C553

C220C740

C346C651

C205C522

C369C696

C346C590

C349C696

C346C572

C346C710

C130C346

C150C346

C346C615

C346C701

C370C740C346C552

C346C522C346C812

C346C775

C110C346

C200C696

C346C438C346C541C346C551

C390C640

C346C484

C346C790

C220C640

C346C439

C346C540

C404C740

C95C346

C346C850

C344C522

C305C740

C140C522

C346C471C367C696

C346C483C40C346

C290C696

C346C770

C346C750

C346C420C346C436

C346C461

C359C696

C346C500

C420C740

C355C696

C346C652

C346C780

C371C696

C135C346

C346C771

C346C433

C346C452

C230C640

C346C950

C346C630

C346C571

C346C501

C40C740

C346C530C433C522C346C663

C211C522

C366C696C352C740C346C703

C346C910

C346C731

C100C640

C359C740

C90C346

C52C522

C310C522

C346C475

C355C522

C368C696

C346C702

C290C522

C325C640

C145C740

C349C522

C434C522

C93C696

C93C640

C220C522

C140C696

C438C522

C20C696

C346C760

C365C522

C373C740C352C640

C155C740C359C640

C371C740

C51C740

C2C696

C373C522

C369C740

C366C740

C385C640C404C522

C339C522

C366C522

C346C940

C70C740

C315C640

C92C522

C100C696

C346C581C436C522

C51C696

C51C522C360C640

C349C740C344C640

C355C640C42C522

C436C696

C434C696

C346C700

C20C522

C432C696

C367C522

C235C640

C160C740

C349C640C211C696C210C696C325C696

C420C640

C432C522

C375C696

C369C640

C367C740

C230C696

C92C346

C368C522

C350C696

C437C640

C305C696

C92C640

C165C522

C352C522

C369C522

C70C696

C93C740C371C640

C433C696

C145C640

C94C696

C346C482

C390C696C385C696C380C696

C91C696C145C696

C235C696C220C696

C2C640C365C696

C110C522

C94C522

C70C640

C439C522

C70C522

C160C522

C352C696

C101C522

C371C522

C205C696

C2C740

C370C522

C100C522

C91C740

C365C640

C360C522C315C522

C367C640

C110C696C130C522C155C522

C339C640C435C522C95C522

C437C522

C368C740

C101C696

C95C696

C366C640

C101C740

C90C522C339C740

C235C740C42C696

C160C696

C51C640

C438C696

C205C640

C91C522C93C522

C150C522

C41C696

C165C696C145C522C135C522

C155C696

C434C740

C359C522

C436C740

C130C740

C52C740

C41C740

C110C740

C94C640

C40C640C135C740

C130C696

C95C740C160C640

C94C740

C432C640C91C640C140C640C438C740

C436C640

C435C740

C437C696

C439C696

C95C640

C41C522C40C522

C432C740

C368C640C155C640

C52C696

C434C640

C101C640

C41C640

C135C696C90C696

C435C640

C150C696

C110C640

C40C696

C435C696

C130C640

C42C740

C52C640

C165C740C165C640

C439C740

C438C640C90C740

C135C640

C439C640C42C640

C150C740C150C640

C90C640

-1-.5

0.5

1e(

sim

ilarv

otes

| X

)

-5 0 5 10e( scigoconnect | X )

coef = -.00246463, se = .00588514, t = -.42

Dyads: Countries: None of mention

379

1993 SC IGO Connectedness

C200C740

C375C740C380C740

C390C740C385C740

C20C316

C210C740

C200C522

C310C740

C2C346

C230C740C2C316

C140C316

C211C640

C200C346

C346C698C346C694

C317C640

C290C740

C200C316

C140C740

C390C522C367C696

C305C346

C92C696

C325C346

C210C522

C346C640

C343C696

C346C481

C325C522

C317C696C2C522

C211C740C346C420

C346C385

C290C316

C346C390C20C346

C349C522

C367C740

C385C522

C346C690C346C435C420C522C325C740

C210C346C211C346C230C522

C367C522C370C696

C370C640

C317C740

C346C380

C346C645

C346C696

C145C316

C200C640

C220C346

C93C316

C346C349

C380C522

C235C522C346C375C100C316

C140C522

C404C316

C346C352

C437C316

C346C580

C437C740

C435C316C349C696C375C522C439C316

C310C640

C367C640

C346C350

C205C346C438C316

C310C346

C368C696C433C740C230C346C205C522C420C740

C343C740

C92C740

C305C522C366C522

C290C640

C373C316

C344C316

C235C346

C346C705

C346C367

C433C316

C346C830

C350C522

C346C372

C290C346

C404C740

C346C316C404C640

C346C620

C145C740

C346C368

C210C640

C420C696

C317C522

C346C451

C346C740

C346C820

C41C346

C346C490

C52C346C350C740

C20C640C365C740C346C366

C433C640

C346C432

C310C696

C346C355

C305C640C220C316

C40C316

C210C316

C346C900

C211C316

C355C740

C339C346

C101C316

C346C670C346C404C317C346

C385C316

C346C434

C434C316

C390C316

C346C616

C359C696C290C522

C343C522

C92C522

C339C316

C432C316

C359C640C140C346

C346C438C346C701C344C522

C372C316

C436C316

C365C522

C366C740

C366C316

C145C640C371C696

C420C316

C360C316

C371C316

C359C316

C370C740

C310C522

C367C316

C325C316

C380C316C346C615

C343C346

C368C522

C346C360

C346C920

C343C316C92C316

C346C370

C346C660

C349C640C346C570C145C346

C230C316C346C692

C346C553

C350C640

C375C316C349C740

C346C475C205C316

C41C316

C368C316C365C316C346C365C20C740

C346C517

C369C316

C346C666

C346C731

C373C696

C375C640C346C710

C346C373

C370C316C220C740

C346C437

C92C346

C42C522

C380C640

C355C316

C404C696

C372C696

C205C740

C346C652

C366C696

C346C484C346C371C346C471

C344C346

C346C590

C70C316C130C316

C346C522

C372C522

C360C740

C346C712

C349C316

C211C522C346C482

C346C461C346C790

C373C740

C344C696

C371C640

C343C640

C165C346

C92C640

C390C640C373C640

C344C740

C372C640

C100C740

C370C522

C339C696

C346C510

C369C522

C52C522

C355C696

C160C346

C352C316

C317C316

C368C740

C346C600

C290C696

C350C316

C346C651C346C439

C155C346C51C346

C346C530

C355C522

C346C811

C346C483C95C316C230C640

C40C740

C346C516

C346C950

C165C522

C155C316

C369C696

C220C522C339C522

C346C436

C346C732C310C316

C220C640

C51C522

C420C640C51C316

C352C740

C346C369C346C850

C346C840

C346C770

C346C433

C346C910

C346C625

C305C316

C366C640

C94C522

C235C640

C352C522

C101C346

C20C522C160C522

C346C771

C94C346

C373C522

C140C696

C40C346C346C760C346C800

C70C522

C70C740

C70C346

C346C359

C359C740

C100C346C368C640

C100C640

C93C640

C346C501C200C696

C346C572

C101C522

C346C812

C346C540

C346C775

C150C522

C130C346

C346C663

C371C740

C150C346C155C740

C372C740

C100C522

C346C541C305C740

C51C740C346C630

C110C346

C325C640

C352C640

C130C522

C346C452

C346C750

C145C696

C91C346C346C780

C155C522

C110C522

C360C696

C434C522

C385C640

C93C696

C437C640

C95C522C135C346

C344C640

C90C522

C52C316

C235C316

C369C640

C433C522C355C640

C42C346

C70C640

C436C696C135C522

C346C581

C346C571

C432C696

C93C346C95C346C160C740

C346C551C90C346C346C552

C70C696

C346C700C346C500C346C703

C91C522

C359C522

C93C522

C91C316

C371C522

C51C696

C2C740

C135C316

C434C696

C145C522

C346C565C160C316

C100C696

C42C316C110C316C350C696C20C696C360C522

C438C522C2C640C404C522

C90C316

C150C316

C230C696

C91C740

C436C522

C93C740

C91C696

C433C696

C375C696

C369C740C360C640

C436C740

C235C696

C305C696C325C696C2C696

C211C696C210C696

C435C522

C346C940

C41C522

C140C640

C41C696

C432C522

C94C696

C51C640C94C316

C101C696

C52C740

C41C740

C365C640

C235C740

C380C696

C95C696

C165C316

C365C696C130C740C205C696C220C696

C352C696

C437C522C439C522

C390C696

C385C696

C110C696

C101C740

C434C740

C135C740

C40C522

C435C740

C339C640

C160C640

C41C640

C432C740

C438C696

C339C740

C432C640C436C640

C95C640

C160C696C91C640

C165C696

C95C740

C110C740

C40C640C94C640

C155C696

C42C696

C439C740

C130C696

C150C696

C155C640

C101C640

C435C696

C435C640

C434C640

C439C696

C205C640

C438C740

C135C696

C130C640

C52C696C90C696

C52C640

C437C696

C110C640

C439C640

C94C740

C165C640

C40C696

C90C740

C135C640

C438C640

C150C740

C150C640

C165C740C42C740

C42C640C90C640

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5 10e( scigoconnect | X )

coef = .00507797, se = .00694204, t = .73

Dyads: Countries: None of mention

380

1994 SC IGO Connectedness

C200C740

C380C740

C375C740C385C740C317C640

C390C740

C290C316C211C640

C20C316

C346C366

C290C640C210C740

C305C640

C211C740

C230C740C290C740

C140C522

C346C349

C433C740

C420C522C200C522C310C346

C420C740C290C346

C310C640C346C640C346C420

C346C705C200C346

C140C316

C437C740

C200C316

C325C740C365C740

C200C640

C433C640

C346C355C346C368C93C316

C310C740C317C740

C211C316

C305C346C325C346

C140C740

C366C640

C317C696

C355C740

C390C522

C339C346

C375C640

C210C640

C2C316

C210C522

C350C740

C731C316C325C522C510C316

C517C316

C475C316

C451C316C531C316

C40C316C625C316C420C316

C702C316

C346C385

C439C316

C346C390

C100C316

C760C316C230C522C500C316C812C316C435C316C346C367

C92C696

C501C316

C346C359

C220C740

C235C522

C522C316C211C346

C471C316

C210C346

C704C316

C346C380

C437C316

C461C316C346C372

C343C696

C346C375

C700C316C516C316

C710C316C540C316C93C640C452C316

C572C316C552C316C346C350C433C316

C205C346

C370C640

C438C316

C367C640

C652C316C367C696

C20C640

C230C346

C385C522

C380C640

C850C316

C373C316

C235C346

C600C316C645C316C651C316

C346C316

C703C316

C811C316

C380C522

C701C316

C482C316C553C316C551C316

C541C316

C790C316

C2C346

C375C522

C432C316

C70C522C150C522

C434C316

C305C740

C615C316

C52C346

C92C522

C581C316

C436C316C771C316

C343C522

C484C316C663C316

C346C580

C580C316C630C316

C310C696

C92C740

C343C740

C570C316C205C522

C352C522

C371C316

C770C316C750C316

C339C316C420C696

C343C346C317C346

C349C640

C712C316

C70C640

C940C316

C359C316

C346C360C780C316

C346C701

C420C640

C620C316C350C522C70C316C660C316C145C316

C346C482

C92C346

C616C316

C2C522

C70C740

C110C346C367C740

C840C316

C705C316

C101C316

C344C316

C372C316

C950C316

C366C740

C360C316

C317C522C290C522C92C316

C910C316

C343C316

C130C316C565C316

C370C696

C367C522

C52C522

C305C522

C346C830

C205C740

C211C522

C220C640

C365C522C310C316C369C316

C367C316C51C522C20C740

C481C316C696C316

C230C640C346C731

C372C522

C20C346

C346C369

C366C316

C92C640

C343C640

C370C316

C571C316

C694C316

C640C316

C51C346

C368C316

C349C522

C346C704C346C451

C800C316

C350C640

C220C346

C365C316

C690C316C346C820C670C316

C346C352

C355C316

C368C640

C100C640

C310C522

C165C522

C820C316

C433C522

C368C696C372C696C698C316

C346C516

C560C316

C346C690

C155C316

C390C640

C51C316

C346C438C346C522

C692C316

C830C316

C359C640

C94C522

C145C640

C355C640

C355C696C290C696C366C522

C339C696C339C522

C346C553

C140C346C346C510

C390C316C385C316

C110C316

C372C640C900C316

C70C696

C352C640

C220C316

C346C696C346C475

C325C316

C380C316

C100C740

C210C316

C346C645C740C316

C40C740

C346C694

C375C316

C344C522

C346C950C920C316

C590C316

C346C461

C339C640

C346C771

C346C484C230C316

C437C640C369C696

C732C316

C93C696

C145C740

C140C696

C666C316C368C740

C349C316

C371C696

C344C346

C359C696

C150C316

C370C522

C352C316

C344C640C346C373

C41C316

C350C316

C438C522

C369C522

C433C696

C366C696

C346C370

C346C740

C91C522

C346C900

C346C365

C434C522

C346C910

C41C346C346C371

C346C620

C346C481C346C434

C360C696

C346C920C346C790

C346C435

C369C640

C160C522

C346C666

C368C522

C344C696

C205C316C346C616

C101C522

C100C522

C352C740

C385C640

C346C590

C130C522

C371C640

C317C316

C346C698C165C346

C346C660

C346C811C346C692

C155C740

C360C740C160C346C372C740

C220C522C359C522C349C696

C437C522

C346C517

C436C696

C90C522

C95C316

C160C316

C135C522

C150C346C145C346

C110C522

C70C346C346C760

C346C702

C346C501

C41C696

C346C710

C346C615C346C471C346C437C91C346

C140C640

C93C346

C434C696

C155C522C359C740

C20C522

C346C540

C355C522

C95C522

C200C696C346C703

C325C640C432C522

C346C670

C51C740

C51C696

C346C732

C51C640

C41C522C346C560

C93C522

C344C740C94C346

C346C800C101C346

C100C346

C135C316

C360C640C346C432C160C640

C346C572C130C346

C346C712C2C640

C235C640

C365C696

C100C696

C40C346C346C750

C346C439

C346C652

C346C812C346C565

C346C600

C305C316

C160C740

C346C630

C346C651C370C740

C373C522

C145C696

C235C316C135C346

C436C522C346C433

C40C522

C145C522

C52C316C91C316C350C696

C434C740

C155C346

C2C740

C436C740

C432C696

C346C551

C346C436

C346C570

C349C740

C346C531

C346C840

C93C740

C346C625

C346C541C346C500C346C770C346C850

C371C740

C110C640C230C696

C439C522

C346C452C90C346C346C663

C101C696C110C696

C205C640

C373C640

C235C696

C436C640

C346C571

C150C640

C365C640

C90C316

C94C696

C369C740

C438C696

C373C696C91C640

C346C940

C375C696

C95C696

C40C640

C95C640

C20C696

C346C780C339C740

C91C696

C101C740

C325C696C435C522C210C696C211C696C165C316C360C522C305C696

C371C522

C373C740

C235C740

C91C740

C41C640

C434C640

C94C640C135C740

C150C696

C346C552

C110C740

C352C696

C101C640

C346C700C346C581

C160C696C205C696C380C696C220C696C2C696

C439C696

C130C696

C130C740C130C640

C155C696

C432C640

C41C740C52C740

C95C346

C94C316

C165C696

C439C640

C439C740

C437C696

C155C640

C135C696

C390C696C385C696

C432C740

C165C640

C52C640

C90C696

C435C740

C95C740

C135C640

C438C740C150C740

C52C696

C435C640

C40C696

C435C696

C438C640

C94C740C165C740

C90C740C90C640

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5e( scigoconnect | X )

coef = .01080072, se = .00566329, t = 1.91

Dyads: Countries: C200C740 C740 C438C640 C640

381

1995 SC IGO Connectedness

C200C740

C380C740C385C740

C375C740C390C740C346C349

C210C740C310C346C20C316

C290C346

C346C705

C346C366C305C346C325C346

C200C346

C317C640

C290C316

C230C740

C211C346

C210C346

C2C346

C346C375

C200C316

C230C346

C339C346C346C355C349C640

C346C350

C325C740

C200C522

C235C346C346C385C346C390

C365C740

C367C640C211C740

C346C380

C290C740

C211C640

C346C580

C140C522C205C346C20C346

C317C346

C346C368

C140C316

C404C640

C404C740

C200C640

C346C360C346C690C344C640

C437C740

C220C346C2C316

C433C740

C346C352

C51C346

C390C522C346C820C346C404C346C830

C344C346C20C640

C346C475C346C704

C210C522C325C522

C346C451C346C365C344C740

C220C740

C411C740

C140C346

C211C316

C230C522

C404C696C346C740C92C696

C305C640

C210C640

C385C522C343C696

C346C420

C235C522

C52C346

C366C640

C317C740C346C359

C380C522C346C696

C346C367

C731C316

C346C369

C290C640

C375C522

C346C771

C775C316C451C316

C704C316

C420C316C760C316

C625C316C93C316

C70C740C812C316

C517C316C531C316

C475C316

C702C316

C572C316

C346C510C411C696

C435C316C522C316C696C316

C346C666

C420C740

C652C316C439C316

C411C316

C437C316C501C316C710C316

C346C590C373C316

C701C316

C500C316C483C316C205C522

C2C522

C552C316C600C316

C471C316C850C316

C380C640

C366C740C310C740

C140C740

C205C740

C651C316C694C316C346C481

C461C316

C355C740

C70C316C510C316

C346C811

C540C316

C690C316

C40C316

C346C900

C703C316

C110C346

C490C316

C630C316C452C316

C343C740

C20C740

C433C316C516C316C700C316C310C696C620C316

C92C740

C367C740

C100C316

C344C316

C367C696

C346C516

C305C740

C352C522

C349C522

C438C316

C350C522

C346C316

C165C346

C663C316

C350C740

C615C316

C359C640

C305C522

C811C316

C705C316

C346C616

C349C740C370C640

C616C316C830C316

C160C346

C698C316C346C438C670C316

C346C920

C70C522

C411C640

C371C316

C404C316C660C316C790C316

C346C694C205C316

C541C316

C551C316

C370C316

C484C316C346C660

C220C316

C692C316

C434C316

C481C316C210C316

C344C696C553C316

C771C316

C404C522

C346C553

C432C316

C92C522

C375C640

C339C316

C343C522

C780C316C436C316

C290C522

C346C701

C433C640

C150C346

C940C316

C950C316

C570C316C580C316

C359C316

C145C346

C346C620

C770C316

C70C346

C420C522

C372C316

C317C696C712C316

C230C640

C360C316

C220C640

C750C316

C390C640

C565C316

C343C316

C840C316

C346C710C369C316C52C522

C92C316C230C316

C910C316

C385C316C390C316

C346C461

C365C316C343C346

C346C732

C366C316

C367C316

C346C437C93C346C346C501

C820C316

C900C316

C365C522

C346C471

C92C346C310C640C740C316

C571C316

C640C316C368C316

C371C640C380C316

C346C522C346C615

C135C346

C325C316

C355C316

C346C371

C369C640C371C696

C101C316

C160C522

C310C316

C800C316

C375C316C51C522C346C373

C346C411

C145C740C560C316C349C696

C94C346C101C346

C368C696C310C522C51C316C70C640

C920C316C346C432C346C800

C317C522

C343C640

C411C522

C100C346C346C435C346C692C92C640

C165C522

C732C316

C666C316

C355C696

C346C750

C368C640

C93C640

C346C600C352C316

C110C316

C346C484

C367C522

C40C346C346C651

C349C316

C433C522

C211C522

C590C316

C346C790C339C640C290C696

C346C670

C94C522

C350C640

C130C316C350C316C339C696

C155C346

C155C316C339C522

C346C370

C150C522

C385C640C346C731C346C560C346C812

C346C490

C346C433

C70C696

C346C551C346C712

C359C696

C140C696

C130C346

C346C434

C368C522C145C316

C346C372

C93C696

C317C316

C346C950

C346C500

C305C316

C346C652C346C850

C352C640

C346C565

C346C770C346C625C344C522

C373C640C366C522

C369C696C366C696

C41C346

C420C696

C100C740

C438C522C346C630

C352C740

C346C760

C346C775

C220C522

C369C522

C90C346

C346C517

C372C696

C370C696C346C452

C372C522

C420C640

C346C439

C200C696

C355C640C371C740

C346C698C346C436

C91C522

C346C910C368C740

C20C522

C91C346

C135C316

C100C522

C346C840

C52C316

C372C740

C91C316

C346C703

C100C640

C155C740

C235C316C51C740

C433C696

C346C780

C360C696

C346C570C346C572

C436C696

C91C740

C434C522

C346C540

C94C640

C160C316

C325C640

C437C640

C90C522

C145C640C40C740C373C522

C135C522

C346C663

C155C522

C372C640

C365C640C359C740C110C522C369C740

C346C483

C346C552

C434C696C101C522

C150C316

C437C522

C373C740C130C522

C94C316

C41C696

C370C740

C160C740

C373C696

C51C696

C2C640

C432C522C2C740

C93C522

C140C640C365C696

C360C740

C370C522

C350C696

C94C696

C95C316C91C640

C100C696

C235C740C346C702C435C522

C355C522

C95C522

C20C696C230C696

C145C696

C90C316C346C541

C145C522

C51C640

C305C696

C375C696

C346C571

C359C522C135C740C346C700

C432C696

C235C640

C325C696C371C522C41C316

C41C522

C210C696

C211C696C160C696

C93C740

C235C696

C436C522C439C522

C95C346

C40C522

C2C696

C339C740

C346C531

C165C316

C110C696

C434C740

C160C640

C438C696

C380C696

C220C696

C110C640C436C740

C95C696C101C696C346C940C91C696

C390C696C385C696C205C696

C101C740C438C740C439C740

C205C640

C432C740C360C522C52C740

C352C696

C432C640

C150C696

C110C740C94C740

C165C696

C130C696

C435C740

C439C696

C360C640

C95C640

C130C740C150C640

C155C696C90C696

C436C640

C101C640C130C640

C435C696

C52C640

C439C640

C135C696

C437C696C52C696

C40C640C135C640C165C640

C434C640C435C640C438C640

C95C740C150C740

C41C640C155C640

C40C696

C90C740

C41C740C165C740

C90C640

-1-.5

0.5

e( s

imila

rvot

es |

X )

-5 0 5e( scigoconnect | X )

coef = -.00049792, se = .00489063, t = -.1

Dyads: Countries: C200C740 C90C640

382

1996 SC IGO Connectedness

C200C740

C375C740C380C740

C230C740

C385C740C210C740

C346C349C346C375

C2C346

C390C740

C346C705

C310C346

C346C355

C290C346

C317C640

C200C346

C325C740C305C346

C325C346

C211C740

C339C346

C20C346C365C740C210C346C211C346

C20C316C346C352C290C740C344C346C346C366

C346C369C220C740

C346C350

C346C360

C230C346

C346C704C317C740

C346C640

C235C346

C346C830C346C696

C346C385C346C390

C346C690

C346C820

C355C740C346C380

C346C404C346C420

C205C346

C346C368C211C640

C433C740

C346C475

C317C346

C205C740

C290C316

C346C451

C350C740

C140C346

C140C522

C200C522

C404C696

C140C740

C346C666

C404C740C437C740C346C365

C220C346

C346C732

C52C346

C346C740

C20C640

C346C900

C290C640

C346C771C346C516C70C346

C305C740C346C920

C70C740

C346C371

C51C346

C346C481

C346C370C346C367

C310C696

C349C640

C210C640

C390C522

C160C346

C305C640

C346C811

C165C346

C404C640

C411C740

C210C522C380C522

C346C553

C325C522

C411C696

C420C740C140C316

C344C740

C375C522

C2C316

C317C696

C92C696

C344C640

C346C461C100C346

C20C740C150C346

C343C696

C346C580C200C640

C385C522

C145C346

C346C710

C346C510

C135C346C346C501

C346C471C41C346

C230C522C367C696

C310C740

C346C437

C92C740

C343C740

C346C620

C93C346

C367C740

C731C316C700C316C775C316C531C316C436C316

C235C522

C516C316C517C316

C375C640C200C316

C475C316C625C316

C702C316

C346C359

C420C316

C439C316C346C651C812C316C500C316C435C316C510C316C490C316

C346C703

C501C316

C371C696

C471C316

C411C316

C461C316C483C316

C760C316

C437C316

C420C522C373C316C540C316

C551C316

C522C316C450C316

C704C316

C433C316

C367C640

C371C316

C451C316C452C316C404C316

C710C316C552C316C572C316

C541C316C553C316

C346C316

C432C316

C703C316C811C316C790C316C434C316C581C316C940C316

C346C522

C652C316

C580C316

C438C316C771C316

C600C316

C484C316

C651C316C850C316C570C316C701C316

C712C316

C339C316

C305C522

C910C316

C770C316

C663C316

C359C316

C750C316

C101C346

C780C316C349C696

C615C316

C950C316

C630C316

C70C522

C346C950

C370C316

C355C696C2C522

C840C316

C94C346

C344C316

C110C346

C660C316C696C316C205C522

C346C484

C705C316

C411C640

C616C316C620C316

C372C316

C565C316

C92C316C130C346

C343C316

C369C316

C367C316C346C590

C346C790

C346C372

C830C316C349C522C690C316

C346C432

C385C316

C343C346

C368C316

C360C316

C352C522

C211C316

C346C434

C366C316

C346C750

C390C316

C346C435

C346C800C343C522

C92C522

C355C316

C900C316

C571C316

C349C740

C694C316

C640C316

C365C316

C346C438

C310C316

C290C696

C350C640

C481C316

C92C346

C350C522

C70C696

C740C316

C100C740

C800C316

C380C316

C346C660

C420C696

C355C640

C325C316

C560C316

C220C640

C100C316

C346C517

C380C640

C346C411

C346C692

C375C316

C290C522C820C316

C230C640

C346C433

C155C346

C698C316

C411C522

C92C640

C343C640

C670C316

C346C373

C920C316

C346C812

C365C522

C344C696

C317C316

C145C740C692C316

C339C640

C346C560

C346C670

C310C640

C368C696

C346C551

C369C696

C140C696C349C316

C346C910

C666C316

C590C316

C346C540

C433C640

C352C316

C366C740

C732C316C42C346

C90C346

C346C712

C346C565

C370C696

C404C522

C346C452C40C346

C350C316

C346C436C346C500

C346C775C160C522

C346C600C346C694

C346C450

C52C522

C346C850C346C615C346C625C317C522

C369C640

C310C522C346C770

C70C640

C42C522

C339C696

C70C316

C165C522

C367C522

C390C640C346C731

C433C696

C51C522

C346C439C93C316

C344C522

C205C316

C91C346

C360C696

C94C522

C150C522

C346C840

C210C316

C346C490

C346C780

C93C696

C220C316C366C640

C339C522

C346C616

C433C522C352C740

C346C698

C325C640C346C570

C235C740

C420C640C51C316

C346C572C346C760

C346C663

C93C640

C211C522C372C696

C368C522C366C522

C160C740

C346C552

C371C640

C95C346

C369C522C346C483C372C522

C365C640

C368C740

C352C640

C52C316

C434C696C41C696

C371C740

C385C640C365C696C110C316

C360C740

C91C522

C91C740C346C541

C370C640C155C740

C438C522

C40C316C346C630

C373C522

C346C652C100C640C145C316

C100C522

C372C740

C130C522

C130C316

C90C522

C235C316

C220C522

C100C696C94C696C93C522

C305C316

C135C522

C200C696

C434C522

C346C700

C436C696

C366C696

C437C640

C20C522

C145C696

C230C696C20C696

C369C740

C230C316

C94C316

C51C696

C437C522

C373C696

C155C522

C346C571

C101C522

C339C740

C435C522

C2C640

C370C740

C355C522

C360C640

C40C740C305C696C375C696

C160C696

C432C696

C110C522

C325C696C370C522

C346C531

C93C740

C346C940

C211C696C210C696

C2C740

C155C316

C235C696

C346C702

C94C640C140C640

C368C640

C438C696

C91C316

C2C696

C145C640

C40C522

C51C740

C160C316

C41C522

C436C740

C95C522

C373C640

C359C696

C235C640

C432C522

C150C316

C220C696

C165C316

C91C696

C101C316

C145C522

C380C696

C91C640

C371C522

C95C316

C390C696

C160C640

C385C696

C205C696

C372C640

C150C696

C432C640

C352C696

C432C740

C439C696

C434C740C94C740

C439C522

C436C522

C373C740

C346C581

C51C640

C110C696

C41C316

C359C640

C436C640

C165C696

C435C696

C437C696

C42C696

C135C316

C155C696

C90C316

C130C740C360C522

C135C696

C205C640

C52C740

C101C696

C359C740

C130C696

C90C696

C439C740

C135C740

C435C740

C110C640

C52C696

C52C640

C95C696

C434C640C110C740

C101C740

C40C696

C130C640

C42C316

C95C640

C359C522

C41C640

C439C640C438C740

C40C640

C95C740C41C740

C150C640C150C740

C435C640

C165C640

C90C740C155C640C101C640

C438C640

C165C740

C90C640C135C640C42C740C42C640

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5 10e( scigoconnect | X )

coef = .00383748, se = .00541363, t = .71

Dyads: Countries: None of mention

383

1997 SC IGO Connectedness

C230C740

C355C740C290C740C200C740

C20C316C305C640

C325C740C211C740C210C740

C220C740

C355C640

C20C640

C350C740

C344C640

C205C740

C211C640

C70C522

C310C696C70C740

C352C522

C20C740

C205C316

C100C740

C2C522

C310C740

C210C522C325C522

C2C316

C230C522

C437C740

C235C522

C355C696

C940C316

C910C316

C70C696

C200C316

C305C740

C349C522

C230C640

C305C522C200C522

C950C316

C100C640

C339C640C310C522

C344C740

C490C316

C290C640

C367C316C368C316

C580C316

C553C316C510C316

C432C316

C100C316

C541C316C404C316

C712C316

C434C316C350C522

C551C316C451C316C140C316

C352C740

C516C316C483C316

C570C316

C450C316C461C316

C790C316

C438C522

C452C316

C500C316C531C316

C517C316

C439C316C436C316C700C316

C771C316

C775C316C475C316C501C316C581C316

C731C316

C702C316

C770C316C471C316

C540C316C625C316C433C316

C339C522

C435C316

C750C316

C812C316C211C316

C359C316

C366C316

C484C316

C70C316

C344C316

C437C316C760C316C373C316

C522C316

C339C316

C371C316

C780C316

C411C316

C438C316

C840C316

C703C316C552C316

C704C316

C205C522C710C316

C211C522

C235C740

C565C316

C92C316

C369C316

C572C316

C663C316C600C316C290C522

C360C316

C651C316

C343C316

C850C316

C900C316

C437C522

C652C316

C372C316

C355C522C615C316

C355C316

C380C316

C339C696

C701C316

C20C522C660C316

C375C316

C630C316

C352C640

C70C640C92C640

C571C316

C920C316C705C316

C616C316

C800C316

C220C640

C620C316C93C316

C290C696

C349C316

C365C316C370C316

C560C316

C344C522

C210C640

C220C522

C51C522

C91C522

C40C316C481C316

C820C316

C42C522

C90C522

C740C316

C830C316C696C316

C210C316

C40C522

C690C316C590C316

C52C522

C220C316

C325C640

C666C316

C352C316

C694C316C350C640C230C316

C732C316

C670C316

C41C696

C317C316

C698C316C692C316C290C316C40C740

C344C696

C310C316

C94C316

C100C696

C385C316C390C316C325C316C339C740

C350C696

C91C740C235C316C145C316

C640C316

C95C522

C41C522

C437C640

C230C696

C95C316

C310C640C150C316C91C640C2C640C235C640

C439C522

C155C316C235C696

C160C316C2C740

C110C316C52C316

C20C696

C350C316

C325C696C90C316

C200C640

C95C640C130C316

C438C696

C211C696C210C696C51C316C2C696

C352C696

C305C696

C165C316

C305C316

C91C696

C51C696

C220C696C200C696

C91C316C135C316

C437C696

C42C696

C51C740

C439C696

C205C696

C40C696

C205C640C51C640C40C640

C95C696

C90C696

C439C740C41C640C41C316

C101C316

C52C740

C52C640

C90C740

C439C640

C52C696

C41C740

C438C740C90C640C42C316

C438C640

C42C640C42C740

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5 10e( scigoconnect | X )

coef = .01175835, se = .0094891, t = 1.24

Dyads: Countries: C42C640 C42C740

384

1998 SC IGO Connectedness

C350C316

C385C316

C390C316

C740C316

C325C316

C92C740

C92C640

C900C316C732C316

C690C316

C560C316

C92C696

C750C316C840C316

C435C522

C698C316

C435C696

C92C316

C92C522

C696C316

C770C316

C2C696

C694C316

C2C316

C541C316C372C316C371C640C482C316

C359C316

C365C316

C660C316

C2C640C371C316

C670C316

C2C522

C369C316

C663C316

C481C316

C692C316

C432C316

C616C316

C705C316

C434C316C370C696

C452C316

C372C640

C522C316

C551C316

C404C740

C615C316C370C522

C20C316

C703C316

C510C316

C20C640C373C522

C475C316

C371C740

C450C316

C451C316C651C316

C359C740C420C696

C483C316C620C316

C850C316C652C316

C540C316

C372C740C420C522

C404C316

C150C522

C435C316C461C316

C110C640

C20C696

C600C316

C710C316

C371C522

C438C316

C110C740

C436C696

C372C522C701C316

C484C316

C145C740C2C740C373C696

C110C522C433C316

C625C316

C52C640

C704C316C572C316

C371C696

C436C522C70C522

C439C316C370C316

C135C522

C51C740

C110C696

C404C640

C404C522

C150C696C420C316

C385C740

C365C522C91C522

C439C696

C91C640

C51C640C52C316C372C696

C42C522

C731C316

C70C696

C373C316

C437C522

C439C522

C91C740C41C522

C41C640

C94C740

C436C316

C150C640

C41C696

C369C640

C93C522

C93C640

C52C522

C437C696

C145C522

C140C522

C150C740

C359C522

C165C522

C91C696

C359C640C51C522C438C696

C350C640

C390C740

C210C522

C432C740C385C522

C160C522

C385C696

C437C316

C94C522

C359C696

C90C522

C420C740C210C696

C438C522

C95C740

C41C740

C94C640

C93C696

C145C640

C434C740

C101C522

C390C522

C155C522

C390C696C165C740

C95C522

C20C740

C369C522C93C740

C211C696

C369C740

C100C522

C350C522

C52C696

C110C316

C200C522

C42C696

C90C740

C130C522C94C696

C145C696

C310C522C325C522

C51C696

C42C740

C365C740C135C696

C20C522

C140C696

C220C696C70C640

C433C522

C95C640

C90C640C235C522C200C696

C165C696C211C740

C42C640

C370C740

C70C740

C160C696

C51C316C230C522

C434C696

C365C696

C211C522

C436C740

C100C640

C433C696

C325C696C220C522

C155C740

C290C522

C434C522

C365C640

C210C740

C373C640

C100C740

C310C696

C95C696

C90C696C310C740C155C696

C140C740C432C696

C290C316

C350C696

C100C696

C94C316C101C640

C350C740

C165C640

C290C640

C101C696C432C522

C130C740C369C696

C200C740

C165C316C95C316

C93C316

C230C696

C434C640

C432C640

C385C640C438C740

C90C316

C235C696

C200C316C211C640

C150C316C420C640

C160C740

C100C316C42C316C439C740

C370C640

C310C640C325C740

C210C316

C210C640

C435C740

C101C740

C160C640

C290C696C130C640C91C316

C290C740

C41C316

C230C740

C211C316

C130C696

C200C640

C145C316C390C640

C155C640

C433C740

C140C640C220C316

C130C316C70C316

C220C640

C140C316

C101C316

C235C740

C436C640

C230C316

C155C316

C230C640

C435C640

C160C316C135C740

C235C316C235C640

C437C740

C135C640

C325C640

C438C640

C310C316

C439C640C433C640

C437C640

C135C316

-1-.5

0.5

e( s

imila

rvot

es |

X )

-20 -10 0 10e( scigoconnect | X )

coef = .0017993, se = .00240851, t = .75

Dyads: Countries: C350C316

385

1999 SC IGO Connectedness

C325C740

C310C346

C230C740

C346C349

C346C367

C346C355

C290C346

C210C740

C346C375

C200C740

C317C346

C325C346

C211C740

C2C346

C339C346

C346C830

C346C366

C346C640

C346C368

C210C346

C346C820

C344C346

C355C740

C346C360

C230C346C305C346

C205C740

C235C346

C346C451

C346C690C346C369

C20C346

C350C740

C346C370

C346C385

C220C740

C346C390C346C350

C346C404

C346C380C20C740

C211C346C200C346

C140C346

C290C740C346C475

C346C732

C346C352

C220C346

C20C640

C70C346

C346C900

C346C740

C346C365

C205C346C346C666

C346C359

C20C316

C346C481

C160C346

C346C800

C355C640C211C640

C305C740

C346C771C346C811

C346C710C346C692C346C750

C346C920

C346C701C110C346

C830C316C70C740

C740C316

C696C316C690C316

C437C740C346C651

C346C696C346C620

C385C316

C305C640

C390C316C900C316

C694C316C346C705

C346C712

C666C316

C94C346C380C316

C92C740

C100C346

C352C316

C375C316

C135C346

C210C522

C698C316

C230C640

C325C316

C732C316

C350C316

C325C522

C590C316

C346C501C346C471C346C510

C692C316

C920C316C438C522

C346C461

C670C316C346C372

C820C316C230C522C571C316

C349C316

C560C316

C481C316C346C670

C365C316

C800C316

C640C316

C346C373

C235C522

C370C316

C705C316

C616C316

C372C316

C355C316

C630C316

C620C316

C346C434

C343C316

C346C660

C660C316

C369C316

C360C316

C92C316

C565C316C310C696

C615C316

C850C316C840C316

C346C522

C663C316

C339C316

C701C316

C780C316

C651C316

C352C740

C750C316

C600C316

C652C316

C371C316

C200C316

C710C316C438C316

C359C316

C346C850

C572C316

C346C316

C522C316C712C316

C771C316C811C316

C552C316

C790C316

C704C316

C350C640C373C316

C437C316

C366C316

C434C316

C411C316

C541C316C432C316

C433C316

C580C316

C553C316

C452C316C581C316C770C316C475C316C760C316

C484C316

C702C316

C346C433

C540C316

C501C316C551C316

C404C316

C471C316C483C316C461C316

C451C316

C812C316C439C316

C500C316

C344C316

C625C316C310C740

C510C316C517C316C490C316

C531C316

C731C316

C700C316C775C316

C367C316C368C316

C950C316

C92C640

C205C316

C910C316

C437C522

C940C316C305C522

C346C590

C200C522

C317C316

C346C775

C290C640

C40C346

C92C346C165C346

C51C522

C346C411C346C950

C101C346C346C553

C346C600

C150C346C155C346

C70C522

C346C541C350C522

C90C346

C343C346

C51C346C220C640

C130C346

C352C640

C41C346C145C346

C346C437C346C698

C346C580C346C770C346C840C346C540

C52C346C93C346

C2C316

C346C438

C346C452

C205C522

C346C694

C200C640

C346C439C352C522

C346C812

C70C316C346C616C346C615

C339C640C325C640

C235C740C42C346

C346C565

C346C571

C346C731

C355C696

C310C640

C95C346

C52C522

C346C780

C2C522

C346C517

C210C640

C346C500

C210C316C346C663C310C522

C205C696

C346C484C346C551C140C316

C100C316

C346C625

C439C522

C346C910

C20C696C346C790

C70C696

C230C316

C346C490

C346C483

C346C630

C344C740

C91C740

C42C522

C346C371

C41C522

C346C572C70C640C346C432

C2C696

C290C522

C339C696

C91C346C355C522

C290C696C210C696

C2C640

C211C696C325C696C200C696C344C696

C2C740

C230C696

C40C316C211C522

C349C522

C91C522

C220C522

C94C316

C350C696

C346C760

C310C316

C346C552

C101C522

C346C581C305C696C344C522

C344C640

C220C316

C90C522

C160C316

C346C652

C290C316C51C316

C346C940

C339C522

C438C696

C91C696

C346C702C90C316

C235C696

C51C696

C95C316

C150C316C346C700

C40C740

C211C316C91C640

C93C316C439C740

C235C316C52C316

C235C640

C40C522

C165C316

C20C522

C145C316C339C740

C101C316C51C740

C352C696C220C696

C130C316

C91C316

C439C696C51C640C155C316C110C316

C305C316

C437C640C52C740

C90C696C41C696

C90C740

C438C740

C205C640C135C316

C439C640

C437C696

C52C640C41C640

C42C316C41C740

C42C696C52C696

C40C640

C41C316C90C640

C40C696

C438C640

C42C740

C42C640

-.50

.51

e( s

imila

rvot

es |

X )

-5 0 5 10e( scigoconnect | X )

coef = -.02868404, se = .00633194, t = -4.53

Dyads: Countries: None of mention Note: 2000 SC IGO Connectedness was dropped so there is no plot.

386

Table 14: Summary of Influential Dyads and Countries from Plots Analysis VARIABLE YEAR INFLUENTIAL

DYAD INFLUENTIAL COUNTRY

Relative trade in-degree centrality

1990 C2C740 C640

1991 C2C740 1992 C200C740

C346C482

C740

1993 C344C346 C346C640

C522

1994 C346C365 1995 C346

C522 1996 C160C346 C522

1997 C2C522 C20C522 C350C316 C439C740 C438C740

C740 C522

1998 C350C316 C435C522 C900C316

1999 C2C522 C20C522

2000 C2C522 C20C522

Relative trade in-degree centrality squared

1992 C346C350 C325C740 C325C522 C220C696 C325C522

1994 C2C522 C522

1999 C2C522 C20C522

C522

2000 C2C522 C20C522

C522

Total trade in-degree centrality

1992 C346C482

1993 C346C640 1994 C200C740

C325C740 C210C740

C740

1995 C346 1996 C20C522 C346 1997 C2C522

C20C522

387

C140C316 1998 C2C522

C435C522 C316

1999 C2C316 C346C370 C346C359

2000 C2C522 C20C522

C346

Total trade in-degree centrality squared

1994 C385C696 C740

1999 C346C359 C140C316 C2C316

Relative alliance degree centrality

1990 C2C522 C20C522 C435C740

1991 C2C522 C20C696 C20C640 C20C740

C20

1992 C2C522 1993 C2C522 1994 C2C522

C435C740

1995 C2C522 C435C740

1996 C346C435 C2C346 C435C740

1997 C2C522 C20C522 C437C522 C20C740

C522

1998 C485C740 C20C740 C20C522 C2C522 C435C522

C522

1999 C20C740 C20C522 C2C522 C20C346

C522

2000 C20C522 C2C522 C2C346

Relative alliance degree centrality

1996 C346

388

squared 1997 C20C740 1998 C20C740

C20C522 C435C740 C435C522

C522 C740

2000 C20C522 C2C522

C346

Total alliance degree centrality

1990 C20C522 C435C522 C375C740

1991 C20C522 C2C522

1992 C20C522 C2C522 C435C522 C346C482

C522

1993 C2C522 1994 C2C522 1995 C2C522

C435C522 C420C740 C420C640

1996 C2C522 C435C522

1997 C20C522 C2C522 C70C522 C439C740 C439C740

C522

1998 C2C522 C435C522 C20C522

C522

1999 C20C522 C2C522

C522

2000 C344C346 C435C522 C20C522 C2C522 C359C640 C200C640

C522

Total alliance degree centrality squared

1990 C20C522 C2C522 C435C522 C375C740

C522

2000 C344C346 C435C522 C20C522 C2C522 C359C640 C200C640

C522

389

Visits total in-degree centrality

1990 C100C640 C100C740 C100C696 C100C522

C100

1991 n/a n/a 1992 C346C670

C346C652 C346C710

C346

1993 C2C640 C2C346 C2C696 C2C522

C2

1994 C365C522 C365C690 C346C750 C2C346 C2C696 C2C522

C2

1995 C346C365 C2C696 C2C522

1996 C2C696 C2C522

C2

1997 C698C316 1998 C690C316 C200

C140 C2

1999 C437C640 C439C640

C640

2000 C2C346 C2

Visits total out-degree centrality

1990 C100C640 C100C740 C100C696 C100C522

C100

1991 C2C522 1992 C2C696

C2C522

1993 C200C740 C200C640 C200C696 C200C522 C200C346

C200

1994 C346C365 C365C522 C365C696

C365

390

1995 C2C522 C2C696

C200

1996 C2C522

C200

1997 C2C522 C625C316 C200C740

1998 C2C696 C2C522

C200

1999 C2C522 C200 2000 C200 IGO Connectedness 1990 C260C740

C260C640 C92C640 C260C696 C92C740

C260

1991 n/a n/a 1992 C344C346

C92C740

1993 C352C316 C344C346

1994 C910C316 1995 C420C740

C420C640

1996 C950C316 C910C316

C316

1997 C350C316

1998 C350C316 1999 C350C316 2000 C350C316

Econ IGO Connectedness

1990 C437C740 C740

1991 n/a n/a 1992 C740 1993 C740 1994 C740 1995 C740 1996 C437C740 1997 C740 1998 n/a n/a 1999 C740 2000 C40C696 General IGO Connectedness

1990 C435C696

1991 C52C696 C352C640 C339C522 C339C696

391

1992 C435C696 1993 C435C696 1994 C435C696 1995 C435C696 1996 C435C696 1997 C439C640

C438C640

1998 C435C696 C435C522 C350C316

1999 n/a n/a 2000 C640

Political Military IGO Connectedness

1990 C339C640 C434C522

1991 C339C522 C339C696 C437C522 C438C522 C439C522

C522

1992 C424C522 C522 1993 C434C522 C522

1994 C434C522 C522 1995 C420C522

C434C522 C346C350 C346C372

1996 C434C522 C522 C316

1997 C437C522 C438C522 C439C522

C522

1998 C434C522 C435C522

C522

1999 C438C522 C439C522 C290C316 C310C316 C437C522

C522

2000 C434C522 C435C522

C522

Social Cultural IGO Connectedness

1990 C339C696 C92C696

C740

1991 C439C640 C640

1992 n/a n/a 1993 n/a n/a 1994 C200C740

C438C640 C740 C640

1995 C200C740 C90C640

1996 n/a n/a

392

1997 C42C640 C42C740

1998 C350C316 1999 n/a n/a 2000 n/a n/a Table 15: Basic Statistics - Sensitivity Analysis with Small Sample (Deletions)

Observations Mean SD Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 181 Similar Votes (percentage) 4818.00 0.46 0.34 0.00 1.002 Relative trade indegree centrality 781.00 0.17 4.04 -12.65 19.31 0.053 Total trade indegree centrality 781.00 0.21 5.34 -22.93 26.14 0.01 -0.104 Relative alliance degree centrality 781.00 0.01 0.41 -3.15 1.79 -0.06 -0.05 0.025 Total alliance degree centrality 781.00 -0.03 0.33 -1.95 2.89 0.00 0.10 0.16 -0.346 Visits total outdegree centrality 781.00 -0.31 2.11 -12.66 10.32 0.08 0.08 -0.05 0.11 0.047 Visits total indegree centrality 781.00 -0.30 2.59 -8.81 10.97 0.06 0.11 -0.32 -0.06 0.10 0.068 IGO connectedness 1404.00 0.46 2.23 -13.23 14.54 -0.02 0.05 0.07 -0.02 0.04 0.09 -0.169 Economic IGO connectedness 1404.00 -0.02 0.56 -2.69 5.12 -0.05 0.18 -0.05 -0.05 -0.01 0.03 0.03 0.2010 General IGO connectedness 1404.00 0.14 1.76 -4.81 14.82 -0.03 0.05 0.03 0.01 0.04 0.11 -0.07 0.70 -0.1211 Political Military IGO connectedness 1404.00 0.00 0.08 -0.13 1.00 0.04 -0.02 -0.01 0.02 0.01 0.01 -0.08 0.07 0.04 0.0412 Social cultural IGO connectedness 1404.00 0.21 0.66 -2.05 4.02 0.09 -0.16 0.18 0.02 -0.05 -0.12 -0.20 0.26 -0.42 -0.12 -0.1313 Trade intensity 781.00 -38.64 195.61 -1274.28 3019.14 0.02 0.05 0.00 -0.02 0.05 0.05 -0.02 0.07 0.07 0.07 -0.01 -0.0314 Allies 781.00 0.02 0.19 -0.97 1.00 0.02 -0.03 0.06 0.02 -0.23 0.00 -0.18 -0.07 0.07 -0.07 -0.04 0.01 0.0415 Contiguity 4818.00 0.03 0.18 0.00 1.00 0.08 0.02 0.00 -0.02 0.05 -0.05 -0.02 0.05 0.02 -0.01 0.18 0.04 0.03 0.0516 Combined wealth 4788.00 23354.12 11400.38 2368.29 68833.40 0.21 -0.03 0.03 -0.01 0.04 0.06 0.05 -0.03 0.02 0.10 -0.04 -0.29 -0.04 0.02 -0.0117 Similar wealth 4788.00 -11990.68 8054.37 -39045.03 0.99 -0.35 0.02 -0.09 -0.02 -0.05 -0.06 -0.05 0.06 -0.06 -0.01 0.08 0.17 -0.09 0.00 0.12 -0.3718 Similar Polity Score 3996.00 -7.36 6.49 -19.00 1.00 0.27 0.05 -0.06 0.02 -0.02 -0.06 -0.04 0.12 -0.07 0.15 0.02 0.08 0.00 0.09 0.10 -0.19 0.1119 Same Culture - Huntington 4812.00 0.20 0.40 0.00 1.00 0.28 -0.01 -0.12 -0.08 -0.02 0.02 0.03 -0.18 -0.03 -0.07 0.00 -0.18 0.03 0.02 0.22 0.03 -0.03 0.17

TABLE 15Small Sample Sensitivity Analysis: Basic Statistics

Note: An instrumental variable method in Henderson (1999) was used to deal with serial correlation. These basic statistics are for the transformed variables. Table 16: Small Sample Sensitivity Analysis (Results with Deletions)

TABLE 16 Small Sample Sensitivity Analysis: Prais-Winston (FGLS) Estimation - with Robust Standard Errors - of Percentage

Similar Votes By Country Dyads, 1990-2000 Model 1a Model 1b Model 1c

Lagged Similar votes 0.808* (0.027)

0.802* (0.027)

0.801* (0.027)

Relative Trade In-degree Centrality / 1000

2.026 (3.858)

3.940 (4.032)

1.1664 (4.280)

Relative Trade In-degree Centrality Squared / 1000

0.023 (0.040)

0.033 (0.045)

0.008 (0.048)

Total Trade In-degree Centrality / 1000

-20.650* (3.815)

-2.427* (3.981)

-21.114* (4.158)

Total Trade In-degree Centrality Squared / 1000

-0.125* (0.027)

-0.157* (0.028)

-0.142* (0.029)

Relative Alliance Degree Centrality / 1000

-159.994* (52.224)

-123.127* (52.566)

393

Relative Alliance Degree Centrality Squared / 1000

-12.786* (5.547)

-7.985 (5.592)

Total Alliance Degree Centrality / 1000

-257.308* (51.352)

-260.006* (49.080)

Total Alliance Degree Centrality Squared / 1000

-8.690* (1.859)

-8.6758* (1.678)

Visits Total Out-degree / 1000 8.995* (3.484)

Visits Total In-degree / 1000 3.457 (3.388)

Trade Intensity / 1000 -0.0286

(0.041) -0.017 (0.040)

-0.020 (0.042)

Allies 0.020 (0.022)

-0.029 (0.026)

-0.030 (0.026)

Contiguity -0.158* (0.049)

-0.145* (0.055)

-0.136* (0.059)

Combined Wealth / 1000 -0.001* (0.001)

-0.001* (0.001)

1.318* (0.001)

Similar Wealth / 1000 -0.001 (0.001)

-0.001 (0.000)

-0.001 (0.001)

Similar Polity Score / 1000 0.374 (1.165)

1.124 (1.187)

1.318 (1.202)

Same Culture - Huntington 0.037 (0.026)

0.036 (0.027)

0.034 (0.027)

Constant 0.114*

(0.019)

0.113* (0.019)

0.122* (0.020)

Observations 781 781 781 No. of dyads 326 326 326 R-Squared 0.711 0.716 0.720 * p < 0.05, two-tailed test ** p < 0.05, one-tailed test Notes: standard errors in parentheses

394

Table 17: Small Sample Sensitivity Analysis (Results with Deletions)

TABLE 17 Small Sample Sensitivity Analysis: Prais-Winston (FGLS) Estimation - with Robust Standard Errors -

of Similar Votes By Country Dyads, 1990-2000

Model 2a Model 2b Model 3a Model 3b Lagged Similar Votes 0.771*

(0.017) 0.771* (0.018)

0.762* (0.018)

0.766* (0.019)

IGO Connectedness / 1000 0.245 (1.988)

-4.270 (2.672)

Econ IGO Connectedness / 1000

3.804 (10.985)

-27.530* (15.666)

Gen IGO Connectedness / 1000

-0.597 (2.272)

-9.310* (3.518)

Political Military IGO Connectedness / 1000

0.076 (65.596)

123.205 (80.600)

Social Cultural IGO Connectedness / 1000

0.025* (9.862)

22.406** (12.189)

Same Culture X IGO Connectedness / 1000

1.5084 (4.507)

Same Culture X Econ IGO Connectedness / 1000

-4.165 (28.626)

Same Culture X Gen IGO Connectedness / 1000

0.003 (5.203)

Same Culture X Political Military IGO Connectedness / 1000

88.768 (63.930)

395

Same Culture X Social Cultural IGO Connectedness / 1000

-34.070 (28.129)

Same Polity Score X IGO Connectedness / 1000

0.5876* (0.186)

Same Polity Score X Econ IGO Connectedness / 1000

4.841* (1.228)

Same Polity Score X Gen IGO Connectedness / 1000

1.612* (0.254)

Same Polity Score X Political Military IGO Connectedness / 1000

-0.388 (5.789)

Same Polity Score X Social Cultural IGO Connectedness / 1000

-1.457 (0.978)

Trade Intensity / 1000 -0.0325 (0.029)

-0.029 (0.028)

-0.031 (0.029)

-0.0295 (0.029)

Allies 0.023** (0.012)

0.022 (0.020)

0.019 (0.013)

0.028 (0.022)

Contiguity -0.026 (0.024)

-0.026 (0.024)

-0.032 (0.026)

-0.026 (0.024)

Combined Wealth / 1000 -0.002* (0.000)

-0.002* (0.000)

-0.001* (0.000)

-0.001* (0.000)

Similar Wealth / 1000 -0.000 (0.001)

-0.000 (0.001)

-0.001 (0.001)

-0.001 (0.001)

Similar Polity Score 0.002* (0.001)

0.002* (0.001)

0.002* (0.001)

0.001 (0.001)

Same Culture - Huntington 0.045* (0.017)

0.040* (0.018)

0.055* (0.017)

0.052* (0.019)

Constant 0.172* (0.014)

0.173* (0.014)

0.157* (0.014)

0.1456* (0.014)

Observations 1404 1404 1404 1404 No. of dyads 478 478 478 478 R-Squared 0.689 0.689 0.695 0.705 * p < 0.05, two-tailed test ** p < 0.05, one-tailed test Notes: standard errors in parentheses

396