Introduction:
The world today is as interconnected as it has ever been. Economic, social, and political change in one country send shock waves across the world. Because of this, international relations and diplomacy are constantly in use and tested. The most prevalent form of these international relations—besides financial markets—are treaties. For centuries treaties have shaped and manipulated borders, security, and trade. Do certain countries influence treaty making and international relations more than others? Do these specific power brokers enhance national security and make the world a safer place? These are the questions I hope to answer in the project below. With that in mind, I hypothesize that the “Major World Powers” (which I will define below)—like the United States, Russia, France, Germany, and the United Kingdom—will have a greater connection via treaties to the rest of the world. Because I expect a high volume from these countries, I also hypothesize that in the years of greater treaty making, we can expect future years of peace.
The data set used to explore this question came from the Correlates of War Formal Interstate Alliance Dataset (created by University of Alabama Professor of Political Science, Douglas Gibler). Specifically, I used version 4.3—the Directed-Dyad Format—which made it so countries listed as state_name1 (state #1) made promises or directed the treaty toward the country listed as state_name2 (state #2). I chose this data set so that I could make it a directed network. Furthermore, I manipulated the dataset so that the columns listing state #1 and state #2 were the first two in the data, making it easier to create an igraph. Thus, I removed the version ID (which according to Professor Gibler—who I reached out to—are an arbitrarily assigned grouping of numbers), and the state name codes.
I have attached the variable dictionary for better understanding, but I will also briefly summarize the variables I used here. First, dyad_st_year, represents the year in which the alliance went into effect according to the Correlates of War system. Next, the types of treaties are listed in 0 and 1 form, with a 1 indicating the type of treaty it is. There is overlap, meaning a defense treaty can also count as a nonaggression treaty for example. The dictionary describes a left_censor treaty as an alliance that existed prior to 1/1/1816—the furthest back the dataset goes. A right_censor treaty is a directed dyad that is still considered in effect as of 12/31/2012. The final three types of treaties are defense, which says state #1 signed a defense pact to protect state #2, a neutrality treaty, which indicates that state #1 agreed to remain neutral toward state #2, a nonaggression treaty, which says that state #1 agrees to not attack state #2, and lastly an entente treaty, which says that state #1 agreed to consult with state #2 in the event of a crisis.
Using this information, I will first explore the volume and types of connections, then conduct a network analysis to see how countries interact with each other.
alliance <- read.csv("~/Downloads/version4.1_csv/alliance_v4.1_by_directedNEW.csv", header=TRUE)
library(igraph)
library(ggplot2)
g=graph.data.frame(alliance,directed=TRUE)
As I explained above, the dataset I chose above allowed me to make this igrpah into a directed network. Thus, after loading in the data (which I manipulated ahead of time), I created igrpah "g" to be used for later network analysis.
dim(alliance)
head(alliance)
TPY<-table(alliance$state_name1, alliance$dyad_st_year)
TreatyperYear<-apply(TPY, MARGIN=2, FUN=sum)
head(TreatyperYear)
barplot(TreatyperYear)
To begin, I created a plot of the total number of treaties per year. This is a good jumping off point to best see how the number of treaties fluctuates over time, with some years hitting around 400 different treaties and others close to zero. Furthermore, although this is a simple graphic, it is useful for knowing which years or decades to explore in greater detail later on. It appears that diplomacy has increased over time, and that the post-World War II era serves as a defining moment that the world got smaller and more connected via treaties.
This graphic is a good starting point, however, not every treaty is equal in terms of relevance or power. To best see how these treaties are truly operating with each other, it is necessary to look at the treaties induced (meaning they are state #1) by the major world powers. Using Professor James Morrow's, "The Political Determinants of International Trade: The Major Powers, 1907-1990" list of major powers I selected the US, Russia, UK, France, and Germany. I removed Italy from my list of Major powers given its current lack of international involvement, which aligns with additional scholarly research (see works cited). Additionlly, the eigenvector and betweeness centrality scores below confirm that these countries are indeed at the center of international diplomacy. Although, I am making certain assumptions with my selection, I beleive that current and historical rhetoric would agree that these are the countries that have been at the center of the world for the longest time.
Major<-alliance[alliance$state_name1==c("United States of America", "Russia", "United Kingdom", "France", "Germany"),]
head(Major)
dim(Major)
years <- seq(1811, 2009, by = 1)
Major$decade = cut(Major$dyad_st_year, seq(from = 1810, to = 2010, by = 10), include.lowest=TRUE, labels=seq(1810,2000, by=10))
Major$decade
ggplot(Major, aes(decade))+geom_bar(aes(fill=state_name1), color="black")+facet_wrap(~state_name1) +
theme(axis.text.x=element_text(angle=90),legend.justification=c(1,0), legend.position=c(1,0),text = element_text(size=10))+
xlab("Years, 1810-2010") + ylab("Totsl Number of Treaties Signed")+
ggtitle("Major Powers Treaty Count Over Time")
After sub-setting for the 5 Major countries, our number of treaties is reduced from 6444 to 135, a much more manageable work size. Next, to better get a sense of trends we take the dyad_st_year numbers and place them into decades from 1810 to 2010.
To see how the number of treaties signed by the major powers has changed over time, above is a ggplot with the count of treaties for each country from 1810-2010. It appears that France and Germany, and UK have had fairly consistent history of international involvement. Meanwhile, the US and Russia have an increased role in the later part of the 20th century. The US comes into the world strong following World War I. After that and WWII, I believe that the use of nuclear weapons and the US and Russia nuclear umbrellas are the cause for the increase for both countries. Using the network analysis later in this project I will explore this idea further.
ggplot(Major, aes(state_name1)) + geom_bar(aes(fill = state_name1), position = "identity")+
theme(axis.text.x=element_text(angle=90),text = element_text(size=10))+
xlab("Major Powers") + ylab("Totsl Number of Treaties Signed By Type of Treaty")+
ggtitle("Major Powers Treaty Count and Type; 1810-2010")
head(Major)
Major$Type=rep("defense", 135)
head(Major)
Major$Type[Major$defense==0]<-"non-defense"
head(Major)
ggplot(Major, aes(decade)) + geom_bar(aes(fill = Type), position = "stack")+
theme(axis.text.x=element_text(angle=90),text = element_text(size=10))+
ggtitle("Major Powers Defense and Non-defense Treaty Count; 1810-2010")
To better see treaty trends over time, I created a new column that combines treaty types by defense or non-defense treaties (this includes all remaining treaties beside left_censor and right_censor). In doing this we can see that over time there is fairly strong balance between defense and non-defense treaties over time. The exceptions are what we would expect. Following WWII in the 1940s most treaties were defensive, and then in the years after WWII, when the world tried to move away from all out world, an increase in non-aggression and neutrality treaties occurred.
set.seed(1)
lout<-layout.fruchterman.reingold(g)
head(lout)
layout3 <- layout_with_fr(g, start.temp = 50)
head(layout3)
install.packages("ggplot2")
require(ggplot2)
library(ggplot2)
plot.igraph(g, layout=layout3, vertex.size=5, vertex.label.cex=.5, asp = 0)
After exploring trends over time, I turned my attention to the network connections of treaty making. There is not a lot of scholarly work out on this type of international relations studies, but the leading research involves Emilie Hafner-Burton’s “Network Analysis for International Relations”. According to this article, network analysis plays a vital role in investigating the connection between countries. Hafner-Burton concludes that using network analysis to identify power we can redefine “access, brokerage, and exit options”. This study affirms that the work below is part of a new wave of international relations, and that “network analysis has already been demonstrated in more precise description of international networks.”
The first network created is of all the original directed data. It is very messy given the number of nodes and edges. To better understand if this is expected in later networks, I then created a histogram of node degree. It appears that most countries have less than 100 treaties, with only a handful of countries playing a larger role on the international stage.
deg<- degree(g, mode="all")
hist(deg, breaks=50, main="Histogram of node degree")
bins <- unique(quantile(deg, seq(0,1, length.out = 5)))
bins
which(deg >150)
eigenCent <- evcent(g)$vector
sort(eigenCent, decreasing = TRUE)[1:10]
sort(eigenCent, decreasing = FALSE)[1:10]
bt<-betweenness(g)
sort(bt, decreasing = TRUE)[1:10]
sort(bt, decreasing = FALSE)[1:10]
cor(eigenCent, bt)
which(eigenCent >.7 & bt > 1800)
gmajor=graph.data.frame(Major,directed=TRUE)
eigenCentmajor <- evcent(gmajor)$vector
sort(eigenCentmajor, decreasing = TRUE)[1:10]
sort(eigenCentmajor, decreasing = FALSE)[1:10]
btmajor<-betweenness(gmajor)
sort(btmajor, decreasing = TRUE)[1:10]
sort(btmajor, decreasing = FALSE)[1:10]
cor(eigenCentmajor, btmajor)
Above is the eigenvector and betweeness scores for both the total data and for the Major countries subset. Later in the project I will use the eigenvector score between it is a simpler measurer (0 to 1 scale), and because the correlation between the two scores for the major countries is .86, meaning there is heavy carryover into which countries are at the center of that network.
colVals <-rep("grey80", length(V(gmajor)$name))
colVals[which(V(gmajor)$name=="United States of America")] <-"blue"
colVals[which(V(gmajor)$name=="Russia")] <-"red"
colVals[which(V(gmajor)$name=="France")] <-"purple"
colVals[which(V(gmajor)$name=="Germany")] <-"pink"
colVals[which(V(gmajor)$name=="United Kingdom")] <-"green"
V(gmajor)$color<-colVals
plot.igraph(gmajor, layout=layout3, vertex.size=eigenCentmajor, vertex.label.cex=.5, asp = 0, vertex.color=colVals)
Hmajor<-subgraph.edges(gmajor, E(gmajor))
Hmajor
new_lout <- layout.fruchterman.reingold(Hmajor)
E(Hmajor)$color <- as.factor(E(Hmajor)$right_censor)
plot.igraph(Hmajor, layout = new_lout, vertex.size = 5, vertex.label.cex = 0.6)
Hmajor<-subgraph.edges(gmajor, E(gmajor))
Hmajor
new_lout <- layout.fruchterman.reingold(Hmajor)
E(Hmajor)$color <- as.factor(E(Hmajor)$defense)
plot.igraph(Hmajor, layout = new_lout, vertex.size = eigenCentmajor*20, vertex.label.cex = 0.6)
#blue is a defense treaty
The network above is the final result of looking at all the treaties made by the major powers in the Correlates of War index. The blue edges indicate a defense treaty, while a yellow edge show non-defense treaties. The size of the major powers is based on their eigenvector score, multiplied by 20 for better visualization. Based on the network, it is clear that France has the most connectedness of the other major powers, and that they are connected with many of the European countries clustered in the center. That is also an important thing to notice, how many of the connections are based on geographic region. The best example are the connections for Russia, and many other eastern bloc countries. This network demonstrates the complexity of international relations and how certain countries are more active brokers and defenders than others.
baltic<-alliance[alliance$state_name1==c("Russia","Hungary","Iraq","Syria","Palestine",
"Egypt","Turkey","Ukraine","Georgia"),]
head(baltic)
dim(baltic)
gbaltic=graph.data.frame(baltic,directed=TRUE)
Hbaltic<-subgraph.edges(gbaltic, E(gbaltic))
new_loutbaltic <- layout.fruchterman.reingold(Hbaltic)
E(Hbaltic)$color <- as.factor(E(Hbaltic)$nonaggression)
#blue is nonagression
plot.igraph(Hbaltic, layout = new_loutbaltic, vertex.size = eigenCentmajor*20, vertex.label.cex = 0.6)
Another example of how networks can help demonstrate a story of international is the connections of the countries in the Baltic region. A recent study from the Center for Strategic & International Studies titled, “The Geostrategic Importance of the Black Sea Region: A Brief History” explains that “an overview of the region with a view that the past is prologue to the region’s future as a restive powers reanimate empirical political and military strategies in a modern context.” The article explains the numerous wars, treaties, and border disputes between the constantly changing nations of the Black Sea and Baltic region. Although the dataset does not acknowledge every nation that once existed in the region (for example, there is no Ottoman empire), the network below demonstrates what the study explains: there has been a historically cantankerous and ever-changing power dynamic in the Baltic region. The authors conclude that it was this historical background that lead to Russia’s annexation of Crimea in 2014, and I would expect if I could do network analysis’ over different time periods, it would confirm that annexation as a possible end result.
Further another network of international diplomacy that must be explored, is the nuclear arms race (see next network). The network below is of defense treaties signed by the US, Russia, UK, France, and China (the 5 current nuclear power states) after 1950. This semester I took the course: The Politics of Nuclear Proliferation and Non-Proliferation. In this course we studied the importance of hegemonic influence on non-proliferation in the world. Professor Rebecca Gibbons explains that the two most powerful nuclear powers, the US and Russia, used their economic and political influence to encourage other countries to not pursue nuclear arsenals. Although the Nuclear Nonproliferation Treaty did not take effect until 1968, and does not require the US or Russia to defend non-nuclear nations, this network shows that there is a hegemonic influence that covers different regions of the world.
#Nuclear Umbrella
post1950<-alliance[alliance$dyad_st_year>1950,]
head(post1950)
nukes1<-post1950[post1950$state_name1==c("Russia","United States of America","France",
"United Kingdom","China"),]
nukes<-nukes1[nukes1$defense==1,]
head(nukes)
dim(nukes)
gnukes=graph.data.frame(nukes,directed=TRUE)
eigenCentnukes <- evcent(gnukes)$vector
sort(eigenCentnukes, decreasing = TRUE)[1:10]
sort(eigenCentnukes, decreasing = FALSE)[1:10]
Hnukes<-subgraph.edges(gnukes, E(gnukes))
new_loutnukes <- layout.fruchterman.reingold(Hnukes)
E(Hnukes)$color <- as.factor(E(Hnukes)$defense)
#blue is defense
plot.igraph(Hnukes, layout = new_loutnukes, vertex.size = eigenCentnukes*20, vertex.label.cex = 0.6)
Conclusion:
The exploration above fits my hypothesis and my expectations that there are more significant power brokers in international relations. The major powers I selected demonstrated that they are usually in the center of diplomacy over time, and that there are major trends in security following times of heavy international connections.
In addition, my project concludes what Hafner-Burton began to study, network analysis can be an excellent form of understanding international relations. Network analysis helps identify the major players, and their peripheral connections to best untangled competing interests on the world stage. Furthermore, these examples show that network analysis can be a useful tool at any level, and that in the future should be used to better understand power brokerage and diplomatic manipulation.
Works Cited:
Bosworth, Richard JB. Italy the Least of the Great Powers: Italian Foreign Policy before the First World War. Cambridge University Press, 2005.
Morrow, James D., Randolph M. Siverson, and Tressa E. Tabares. "The political determinants of international trade: the major powers, 1907–1990." American Political Science Review 92, no. 03 (1998): 649-661.
Hafner-Burton, Emilie M., Miles Kahler, and Alexander H. Montgomery. "Network analysis for international relations." International Organization(2009): 559-592.
"The Geostrategic Importance of the Black Sea Region: A Brief History." The Geostrategic Importance of the Black Sea Region: A Brief History | Center for Strategic and International Studies. May 01, 2017. Accessed May 14, 2017. https://www.csis.org/analysis/geostrategic-importance-black-sea-region-brief-history.
Gibbons, Rebecca Davis. "American hegemony and the politics of the nuclear nonproliferation regime." (2016).