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---
title: Territorial Conflict
subtitle: POSC 3610 -- International Conflict
author: Steven V. Miller
institute: Department of Political Science
titlegraphic: /Dropbox/teaching/clemson-academic.png
date:
fontsize: 10pt
output:
beamer_presentation:
template: ~/Dropbox/miscelanea/svm-r-markdown-templates/svm-latex-beamer.tex
latex_engine: xelatex
dev: cairo_pdf
fig_caption: true
slide_level: 3
make149: true
mainfont: "Open Sans"
titlefont: "Titillium Web"
---
```{r setup, include=FALSE, cache=F, message=F, warning=F, results="hide"}
knitr::opts_chunk$set(cache=TRUE, warning=F)
knitr::opts_chunk$set(fig.path='figs/')
knitr::opts_chunk$set(cache.path='cache/')
knitr::opts_chunk$set(
fig.process = function(x) {
x2 = sub('-\\d+([.][a-z]+)$', '\\1', x)
if (file.rename(x, x2)) x2 else x
}
)
options(knitr.kable.NA = '')
```
```{r loadstuff, include=FALSE}
knitr::opts_chunk$set(cache=TRUE)
options(knitr.kable.NA = '')
library(tidyverse)
library(janitor)
library(knitr)
library(stevemisc)
```
```{r loaddata, cache=T, eval=T, echo=F, message=F, error=F, warning=F}
Data <- read.csv("~/Dropbox/projects/mid-project/gml-mid-data/2.03/gml-ndy-2.03.csv") %>% tbl_df()
Cont <- read.csv("~/Dropbox/data/cow/contiguity/3.2/contdird.csv") %>% tbl_df()
Majors <- read.csv("~/Dropbox/data/cow/states/majors2016.csv") %>% tbl_df()
Contcd <- read.csv("~/Dropbox/data/cow/coldepcont/contcold.csv") %>% tbl_df()
ICOWactive <- read.csv("~/Dropbox/data/icow/icow-provisional-1.01/200199.csv") %>% tbl_df()
Contcd %>%
rename(ccode1 = statelno, ccode2 = statehno) %>%
select(ccode1, ccode2, year, land, sea, total) %>%
left_join(Data, .) -> Data
Cont %>%
rename(ccode1 = state1no, ccode2 = state2no) %>%
select(ccode1, ccode2, year, conttype) %>%
left_join(Data, .) -> Data
Data %>%
mutate(conttype = ifelse(is.na(conttype), 6, conttype),
land = ifelse(is.na(land), 0, land),
sea = ifelse(is.na(sea), 0, sea),
total = ifelse(is.na( total), 0, total),
contig = ifelse(conttype == 6 & total == 0, 0, 1),
# Code regions...
region1 = NA,
region1 = ifelse(ccode1 < 200, "Americas", region1),
region1 = ifelse(ccode1 >= 200 & ccode1 < 400, "Europe", region1),
region1 = ifelse(ccode1 >= 400 & ccode1 < 600, "Africa", region1),
region1 = ifelse(ccode1 >= 600 & ccode1 < 700, "ME/NA", region1),
region1 = ifelse(ccode1 >= 700 & ccode1 < 900, "Asia", region1),
region1 = ifelse(ccode1 >= 900, "Oceania", region1),
region2 = NA,
region2 = ifelse(ccode2 < 200, "Americas", region2),
region2 = ifelse(ccode2 >= 200 & ccode2 < 400, "Europe", region2),
region2 = ifelse(ccode2 >= 400 & ccode2 < 600, "Africa", region2),
region2 = ifelse(ccode2 >= 600 & ccode2 < 700, "ME/NA", region2),
region2 = ifelse(ccode2 >= 700 & ccode2 < 900, "Asia", region2),
region2 = ifelse(ccode2 >= 900, "Oceania", region2),
# Code majors...
maj1 = NA,
maj1 = ifelse(ccode1 == 2 & year >= 1898, 1, maj1),
maj1 = ifelse(ccode1 == 200, 1, maj1),
maj1 = ifelse(ccode1 == 220 & year <= 1940, 1, maj1),
maj1 = ifelse(ccode1 == 220 & year >= 1945, 1, maj1),
maj1 = ifelse(ccode1 == 255 & year <= 1918, 1, maj1),
maj1 = ifelse(ccode1 == 255 & (year >= 1925 & year <= 1945), 1, maj1),
maj1 = ifelse(ccode1 == 255 & year >= 1991, 1, maj1),
maj1 = ifelse(ccode1 == 300 & year <= 1918, 1, maj1),
maj1 = ifelse(ccode1 == 325 & (year >= 1860 & year <= 1943), 1, maj1),
maj1 = ifelse(ccode1 == 365 & year <= 1917, 1, maj1),
maj1 = ifelse(ccode1 == 365 & year >= 1922, 1, maj1),
maj1 = ifelse(ccode1 == 710 & year >= 1950, 1, maj1),
maj1 = ifelse(ccode1 == 740 & (year >= 1895 & year <= 1945), 1, maj1),
maj1 = ifelse(ccode1 == 740 & year >= 1991, 1, maj1),
maj1 = ifelse(is.na(maj1), 0, maj1),
maj2 = NA,
maj2 = ifelse(ccode2 == 2 & year >= 1898, 1, maj2),
maj2 = ifelse(ccode2 == 200, 1, maj2),
maj2 = ifelse(ccode2 == 220 & year <= 1940, 1, maj2),
maj2 = ifelse(ccode2 == 220 & year >= 1945, 1, maj2),
maj2 = ifelse(ccode2 == 255 & year <= 1918, 1, maj2),
maj2 = ifelse(ccode2 == 255 & (year >= 1925 & year <= 1945), 1, maj2),
maj2 = ifelse(ccode2 == 255 & year >= 1991, 1, maj2),
maj2 = ifelse(ccode2 == 300 & year <= 1918, 1, maj2),
maj2 = ifelse(ccode2 == 325 & (year >= 1860 & year <= 1943), 1, maj2),
maj2 = ifelse(ccode2 == 365 & year <= 1917, 1, maj2),
maj2 = ifelse(ccode2 == 365 & year >= 1922, 1, maj2),
maj2 = ifelse(ccode2 == 710 & year >= 1950, 1, maj2),
maj2 = ifelse(ccode2 == 740 & (year >= 1895 & year <= 1945), 1, maj2),
maj2 = ifelse(ccode2 == 740 & year >= 1991, 1, maj2),
maj2 = ifelse(is.na(maj2), 0, maj2)) -> Data
Data %>%
mutate(region = NA,
region = ifelse(region1 == "Americas" & region2 == "Americas", "Americas", region),
region = ifelse(region1 == "Europe" & region2 == "Europe", "Europe", region),
region = ifelse(region1 == "Africa" & region2 == "Africa", "Africa", region),
region = ifelse(region1 == "ME/NA" & region2 == "ME/NA", "ME/NA", region),
region = ifelse(region1 == "Asia" & region2 == "Asia", "Asia", region),
region = ifelse(region1 == "Oceania" & region2 == "Oceania", "Oceania", region),
prd = NA,
prd = ifelse(contig == 1 | (maj1 == 1 | maj2 == 1), 1, 0)) -> Data
```
# Introduction
### Goal for Today
*Discuss the war-proneness of disputed territory.*
### Dangerous Dyads
| **Factor** | **Rank** | **Relationship** |
|:----------|:-------:|:-------------------:|
| Contiguity | 1 | + |
| Major power in the dyad | 2 | + |
| Shared alliance | 3 | - |
| Joint Militarization | 4 | + |
| Joint democracy | 5 | - |
| Jointly advanced economies | 6 | - |
| Power preponderance | 7 | - |
Table: Bremer's (1992) "Dangerous Dyads"
### Why Are Contiguous States Prone to Conflict?
1. Opportunity
2. Interactions/Willingness
3. Territory
### The "Correlates" of War
We knew very little of the issues states contest in war by the 1990s.
- Diehl (1992): data limitations, structural realism, the "black box" of the state.
However, we understand as politics as "who gets what, when, and how" (per Harold Laswell).
- In IR, we had no real understanding of the "what."
- Distribution problems pervade *all* levels of politics.
But, it seems issues must be underlying inter-state conflict.
### The "Correlates" of War
We'll talk about democratic peace soon but contiguity is the strongest correlate of war.
- We just weren't sure how "important" it was.
### Contiguity
Land-contiguous states and those separated by less than 150 miles of water are disproportionately responsible for inter-state conflict.
- Wallensteen (1981): 93% of contiguous dyads have at least one MID in their history.
- 64% have at least one war.
- Richardson (1960): Of 200 wars between 1480 and 1941, half were dyadic.
- 75% were fought involving no more than three states.
### The Problem of Contiguity
If war were *just* about the balance of power, these findings wouldn't make sense.
- The balance of power concerns everyone.
- We should expect more World War Is and Thirty Years' Wars.
However, it makes sense that certain issues would lead two neighbors to conflict and usually be of concern to no one else.
- That said, we really had no good explanation for this important correlate of war.
# Vasquez (1995)
### Vasquez (1995)
Vasquez asks the simple question of *why* neighbors fight.
- If there's no theory underlying the contiguity-war relationship, the correlation is trivial.
- It may be a strong statistical finding, but offer no leverage over the problem of war and peace.
Vasquez argues that the contiguity issue can be explained by reference to territorial issues.
## Assessing Competing Explanations
### Assessing Competing Explanations
Vasquez identifies three theories linking contiguity to war.
1. Proximity-as-opportunity
2. Proximity-as-interaction
3. Territoriality
### The Problem of Opportunity
Proximity-as-opportunity suggests neighbors fight because they can.
- We've yet to observe war in the Nigeria-Mongolia dyad, for example.
- Bolivia has fought Paraguay lots of times, but never Botswana.
What this is really predicting is the projection of great power status.
- Non-contiguous states should fight when they are powerful enough to send the military to great distances.
- This would work well in the case of the U.S.
### The Problem of Opportunity
Beyond that, "proximity-as-opportunity" has limited explanatory value.
- It basically explains a (rare) outcome with what amounts to a constant.
i.e. you're almost always going to have the same neighbors.
- Cases like the partition of Poland are exceptional events.
We're left with arguing about "necessity" for cause of the sampling frame.
### The Problem of Interaction
"Proximity-as-interaction" suggests states fight over points of interest.
- Neighbors would have more points of interest as they interact more.
- The more sources for disagreement, the more likely they militarize.
### The Problem of Interaction
However, this link is questionable.
- We do not have to accept the premises.
- More interaction may create more opportunity for cooperation.
This argument is incurably underspecified.
## Territoriality
### Territoriality
Vasquez argues neighbors fight because they disagree about the distribution of territory among them.
- Contiguity is a raw proxy for territorial disputes.
### Territoriality
His argument draws upon a variety of sources.
- Primitive anthropology: land is important to survival and fecundity.
- Evolutionary psychology: aggression in defense of territory is a learned response.
- Sociobiology: we are "soft-wired" to violence toward that end.
This aggregates to the level of the state in the international system.
### Other Arguments for Territory's Importance
1. Tangible value
- "Strategic value" largely falls here too.
2. Intangible value
3. Reputation concerns
## The Evidence
### The Evidence
Vasquez' evidence is largely circumstantial, given data limitations.
- Vasquez reproduces a chart from his 1993 book.
- The data itself comes from Holsti's (1991) classification of wars.
Table I in Vasquez charts wars by temporal domain and by issue type.
### Classifying Wars
- Direct territorial issue: war over territory, boundaries, "strategic territory", or irredenta issues.
- Indirect territorial issue: war over liberation, state creation, secession, unification, consolidation, empire, dynastic succession
Any war not classified as these is "none of the above."
### The Temporal Domains
In case you were curious about the rationale for the cut-offs:
- 1648: Peace of Westphalia, end of Thirty Years War.
- We typically think of this as the creation of the modern "state" system as we know it.
- Significantly neutered the Church as organizing principle in politics.
- 1714: Peace of Utrecht, end of War of the Spanish Succession
- Among other things: Spain cedes Spanish Netherlands, Naples, Milan and Sardinia to the Austrian Habsburgs. Gibraltar to Britain.
- First "real-time" discussion of "balance of power" occurs during this war and peace negotiation.
- 1814: Treaty of Paris, end of Napoleonic Wars.
- Congress of Vienna
- Rise of Britain as world's superpower.
- Rise of Prussia as a great power
- Effective end of Spanish/Portuguese empires.
I should not have to explain significance of 1918 and 1945 to you...
###
```{r distribution-wars-1648-1990-holsti-vasquez, eval=T, echo=F, fig.height=8.5, fig.width=14, message = F}
tribble(
~period, ~Type, ~perc, ~count,
#-----, -------, -----, -----
"1648-1714", "Territory", 77, 17,
"1648-1714", "Territory + Territory-related", 86, 19,
"1648-1714", "Other Issue", 14, 3,
"1715-1814", "Territory", 72, 26,
"1715-1814", "Territory + Territory-related", 83, 4+26,
"1715-1814", "Other Issue", 17, 6,
"1815-1914", "Territory", 58, 18,
"1815-1914", "Territory + Territory-related", 84, 18+8,
"1815-1914", "Other Issue", 16, 5,
"1918-1941", "Territory", 73, 22,
"1918-1941", "Territory + Territory-related", 93, 28,
"1918-1941", "Other Issue", 7, 2,
"1945-[1990]", "Territory", 47, 27,
"1945-[1990]", "Territory + Territory-related", 79, 27+19,
"1945-[1990]", "Other Issue", 21, 12
) %>%
mutate(Type = forcats::fct_relevel(Type, "Territory", "Territory + Territory-related", "Other Issue"),
perc = perc/100) %>%
ggplot(.,aes(x=period, y=perc, fill=Type)) + theme_steve_web() +
geom_bar(stat="identity", position = "dodge",
alpha = I(0.8), color="black") +
xlab("Historical Period") + ylab("Percentage of All Wars") +
scale_y_continuous(labels = scales::percent) +
labs(title = "Percentage and Frequency of Wars By Issue Type, 1648-1990",
subtitle = "Most wars over time have been fought over territory or territory-related issues than other issue types.",
caption = "Data: Vasquez (1993) via Holsti (1991). Note: counts appear on top of the bars by issue-type.") +
geom_text(aes(label = count, group = Type), color="black",
position = position_dodge(width=.9), size=4,
vjust = -.5, family ="Open Sans")
```
### Figure 1 (i.e. Table I in Vasquez)
Figure 1 (i.e. Table I in Vasquez) has a straightforward interpretation.
- No fewer than 47% of wars were fought over direct territorial issues in any temporal domain.
- As many as 77% of wars were fought over direct territorial issues in one temporal domain.
- No fewer than 79% of wars were fought over combined direct and indirect territorial issues.
- As many as 93% of wars were fought over combined direct and indirect territorial issues.
In other words, states predominantly fight wars over territory.
###
```{r terr-mids-by-period, eval=T, echo=F, message=F, warning=F}
Data %>%
mutate(territ = ifelse(revtype11 == 1 | revtype12 == 1 |
revtype21 == 1 | revtype22 == 1, 1, 0)) %>%
filter(midonset == 1) %>%
mutate(era = NA,
era = ifelse(year < 1946, "1816-1945", era),
era = ifelse(year > 1945 & year < 2002, "1946-2001", era),
era = ifelse(year > 2001, "2002-2010", era)) %>%
group_by(era, territ) %>%
summarize(n = n()) %>% filter(!is.na(territ)) %>%
mutate(freq = round((n / sum(n))*100, 2),
freq = paste0(freq, "%")) %>% select(-n) %>% ungroup() %>% spread(era, freq) %>%
mutate(territ = ifelse(territ == 0, "No", "Yes")) %>% rename(`Territorial MID?` = territ) %>%
kable(., align=c("l","c","c", "c"),
caption="Territorial MIDs by Period (GML MID Data [v. 2.03])")
```
However, disputed territory seems to be less a focal point for MID onset over time.
###
\small
```{r icow-examples, eval=T, echo=F, message=F, warning=F}
ICOWactive %>%
filter(adjend == 201599) %>%
group_by(region) %>%
summarize(N = n()) %>%
mutate(Examples = ifelse(region == 1, "Seal Island, Guantanamo Bay, Belize, Falklands", NA),
Examples = ifelse(region == 2, "Gibraltar, Northern Cyprus, now: Crimea", Examples),
Examples = ifelse(region == 3, "Lete Island, Halaib Triangle, Mayotte", Examples),
Examples = ifelse(region == 4, "Golan Heights, Hatay, Shebaa Farms", Examples),
Examples = ifelse(region == 5, "Kashmir, Spratly/Paracel Islands, Liancourt Rocks, Kurils", Examples),
region = ifelse(region == 1, "Americas", region),
region = ifelse(region == 2, "Europe", region),
region = ifelse(region == 3, "Sub-Saharan Africa", region),
region = ifelse(region == 4, "Middle East/North Africa", region),
region = ifelse(region == 5, "Asia", region)) %>%
rename(Region = region) %>%
kable(., caption="Active Territorial Claims (as of 2015) in the World, by Region (Source: ICOW)",
align=c("l","c","l"))
```
\normalsize
# Conclusion
### Conclusion
- Contiguity is ultimately a rough proxy for disputed territory.
- Contiguity is an important correlate the extent to which it picks that up.
- Other arguments re: contiguity are ultimately underspecified.
- More wars are fought over territory than other specific issue.
- That said, disputes over territory are fortunately decreasing.
Disputed territory is a root cause of war, contingent on how issues are handled.
- We'll talk about power and alliances next.