The Urbanization of Insurgency: Shifts in theGeography of Conflict

by

Nicholas T. Calluzzo

S.B. Political ScienceMassachusetts Institute of Technology, 2009

SUBMITTED TO THE DEPARTMENT OF POLITICAL SCIENCE IN PARTIALFULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTERS OF SCIENCE IN POLITICAL SCIENCEAT THE

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

JUNE 2010

© 2010 Nicholas T. Calluzzo. All rights reserved.

The author hereby grants to MIT permission to reproduceand to distribute publicly paper and electronic

copies of this thesis document in whole or in part in anymediunrnow known or hereafter created.

ARCHiES

MASSACHUSETTS INSTfTUTEOF TECHNOLOGY

JUN 2 9 2010

LIBRARIES

Signature of Author:Department 16f Political Science

May 26, 2010

Certified by:Roger Petersen

Professor of Political ScienceThesis Supervisor

Roger PetersenProfessor of Political Science

Chairman, Graduate Program Committee

Accepted by:

The Urbanization of Insurgency: Shifts in the Geographyof Conflict

by

Nicholas T. Calluzzo

SUBMITTED TO THE DEPARTMENT OF POLITICAL SCIENCEON MAY 26, 2010 IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCE IN POLITICAL SCIENCE

ABSTRACT

The 20"' century witnessed the steady decline of the ability of states, particularly great powers, to defeat

insurgencies. During the same period, the world has become both more populous and more urban. As

people have taken to the cities, so too have insurgents increasingly made battlefields out of urban areas.

This study has sought to determine the impact of urbanization on insurgency outcomes using a post-war

dataset of insurgencies. It has predicted that urbanized insurgencies favor the insurgent by facilitating

concealment and cover, nullifying the relatively power differential enjoyed by states, and providing them

with an abundance of soft targets useful for undermining the counterinsurgent's legitimacy. Although

constrained by a number of data limitations, the results demonstrated that more urbanized insurgencies

were a significant challenge to counterinsurgents. By partitioning the dataset by insurgency type, the

study was able to determine unique predictors of conflict outcome for each type. Urbanized insurgencies

are particularly hard to defeat when the counterinsurgent is a foreign occupier, more democratic, and the

insurgency has external support. Rural insurgencies become more difficult to defeat the more

linguistically diverse the population. Furthermore, by increasing the number of conflict casualties, rural

insurgents can particularly benefit from rough terrain.

Thesis Advisor: Roger PetersenTitle: Associate Professor of Political Science

Table of Contents

Acknowledgments 5Chapter I: Introduction 6Chapter II: Existing Literature 9

1. State Factors 92. The Means of Insurgency 113. Conflict Characteristics: Challenging the Conventional Wisdom 13

Chapter III: Urban Insurgency - A Working Theory 171. The Evolution of Modern Insurgency Doctrine 172. Urban Insurgency 183. Manipulating Outcomes 224. Drawbacks of Urban Insurgency 24

Chapter IV: Hypotheses 25Chapter V: Testing the Theory 27

1. Research Scope 272. Building a Dataset 283. Coding Insurgency Type 324. Research Design 35

Chapter VI: Results 361. The Changing Nature of Insurgencies 362. Summary Statistics and Insurgency Profiles 373. Predicting Insurgency Types 404. Predicting Insurgency Outcomes 425. Intermediate Dependent Variable Testing 59

Chapter VII: Discussion 551. Limitations 552. Shifts in the Geography of Conflict 583. Corroborating and Challenging the Existing Literature 584. Reevaluating the Importance of Urbanization 615. The Peril of Urban Insurgencies 636. Rough Terrain and Rural Insurgencies 68

Chapter VIII: Conclusion 70Bibliography 73

Acknowledgments

My undergraduate thesis advisor Professor Fotini Christia was a constant source of motivation,inspiration, and criticism. Because of her guidance, I have completed something I am proud of. ChrisWendt and Professors Roger Petersen and Gabe Lenz all helped see this thesis from conception tocompletion, providing feedback and advice at critical stages. . In fact, this thesis grew out of a singlecomment - "what about urbanization?" - I scribbled in the margin of a paper I read solely on thesuggestion of Professor Lenz. As my graduate thesis advisor, Professor Petersen provided continuedadvice and encouragement.

Additionally, I would like to thank my friends and family for their patience and support. They have beenincredibly understanding these last few months, putting up with missed calls, inexcusably slow responses,and weekend nights spent in the library. And still, when I needed them, they were there.

Chapter I: Introduction

"We see not only weapons but also people. Weapons are an important factor in war, but not the decisivefactor; it is people, not things that are decisive"

- Mao Zedong

"The objective is the population. The population is at the same time the real terrain of the war.Destruction of the rebel forces and occupation of the geographic terrain led us nowhere as long as we didnot control and get the support of the population."

- David Galula

The 20 century witnessed the steady decline of the ability of states, particularly great powers, to

defeat insurgencies (Lyall, 2009). The United States currently finds itself conducting major

counterinsurgency campaigns in two countries and with the potential for further operations. As a result,

determining the underlying causes of this alarming trend would increase the ability of the United States to

pursue its foreign policy agenda. I argue that the dual trends of rising population and rising urbanization

have combined to create a formidable terrain obstacle, in the form of cities, in the path of the modem

counterinsurgent. I theorize that insurgents choosing to wage more urban insurgencies are more likely to

succeed, by complicating the identification problem, negating traditional relative power advantages, and

exploiting the presence of numerous targets through which they can undermine the legitimacy of the state.

States have steadily seen their ability to defeat insurgencies erode. Why has this been the case?

What can explain this puzzling variation over a time when states have, at least in purely conventional

terms, seen their raw military power inflated by technological advances? One possible explanation is

hinted at in the strategic writing on insurgency and counterinsurgency (COIN). Classical insurgency

theorist Mao Zedong points to the importance of people over materiel. Indeed, the most recent U.S.

COIN manual, FM 3-24, enshrines the notion of population control. Therefore, to better understand the

shifting trends in COIN outcomes, perhaps there is some value in looking at the shifting trends of

population dynamics. Even the most cursory analysis of demographic changes over the course of the 20*

century reveals two major transformations. The first is the explosive rise in total population numbers.

Global population more than tripled during the 1900 to 2000 period, from about 1.65 to nearly 6.0 billion

people. Total population is expected to increase another 2.5 billion between now (2008) and 2050. While

the world has become more populated, it has also become increasingly urban in nature. In 2008, for the

first time in history, more than half of the world's population lived in cities. By 2050, an estimated 69%

of all people in the world will live in an urban environment. Although total population will increase by

2.5 billion by 2050, urban population is expected to increase by 3.0 billion over the same period - greater

urbanization is being accompanied by rural depopulation. Furthermore, the vast majority of this urban

population increase will come from the developing world.

Total World Population World Urban Population__ _ _ __ _ _ __ _ _ __ _ .... ...... ............ .....

1- -t____________

-~~ - -wiltr~T{ as

. ........ 7 BilIlion_ _

6Billion

0 53 Billion

0 4----4-- -

to to ro CO o') C) 'r o1 0) V0) C) M) 0) 0) a C> C> C>0

.Source: US Census Bureau. International Database.July 2007 version,

3

0

15601)561960105 191 IISS iiift1906 1500 3 1906 00 20065)-0IU 0015 70M

Source: UN, Department of Econornirand Social Affairs, Population Divisioni(2003)

I propose that this is more than just a simple correlation of time-trends. Urban environments,

much like heavily forested or mountainous regions, act as terrain obstacles to impede counterinsurgents.

In the same way that a forest or a cave can conceal, human terrain conceals an insurgent by making him

indistinguishable from the noncombatant population. In the same way that hills, valleys, and trees

provide physical cover for ambushes and attacks, urban structures and population densities enable the

insurgent to strike quickly and retreat under the cover of civilians. This hypothesis was tested using

large-n quantitative analysis of insurgency outcomes during the 1945-2005 period. This paper is

organized as follows: first, I review the current literature on insurgencies and predictors of success, as

well as the conventional wisdom regarding urban environments. Next, I lay out a chain of causal

mechanisms connecting insurgency type to insurgency outcomes that synthesizes the recent literature on

urban insurgency and the established research on intrastate wars, classical insurgency, and asymmetric

conflicts. The third section distills the proposed theory into a set of testable hypotheses. The forth

section lays out a research design for testing these hypotheses. The fifth section presents results and

discusses their relevance within the existing literature, the proposed theoretical framework, and the

inherent limitations imposed by the data. A final section concludes and presents future avenues of

research.

Chapter II: Existing Literature

Although the study of intra-state conflicts has deep historical roots, it is only in the last decade or

so that it has re-emerged as a major field in international relations and comparative politics. The study of

these conflicts has also transcended purely qualitative analysis and only recently come to include a

significant statistical component. The current literature on unconventional wars focuses on three broad

categories of outcome determinants- state factors, means of insurgency, and conflict characteristics. In

dealing with the third, I present and challenge the conventional wisdom on urbanization and insurgency

outcomes.

1. State Factors

Given that the state is one of two principle actors in an unconventional war, it is not surprising

that a significant body of literature is devoted to the study of state attributes - no doubt due in part to the

relative ease of measurement. Conventionalist theory. places great weight on the importance of state

power in determining conflict outcomes. However, perhaps not surprisingly, numerous studies (Lyall,

2009; Mack, 1975; Arreguin-Toft, 2001) present and/or test theories that challenge the relevance of

traditional state power in unconventional wars. The ability to circumvent state power is, after all, the

hallmark of unconventional warfare. In the context of intra-state conflict, state power goes beyond raw

force capability and includes notions of bureaucratic and administrative competence (Fearon and Laitin,

2002). However, identifying proxy variables that accurately disaggregate these effects has been

problematic.

Indeed, rather than being merely non-predictive in unconventional wars, the literature has

suggested that conventional state power is in fact detrimental to the ability of states to defeat insurgencies

(Lyall, 2009). This line of argument has both a general and specific form. The general form contends

that states, in particular great powers, typically maintain standing conventional armies that are equipped,

organized, and guided by doctrine that prepares them for combating and defending against other

conventional armies. What their equipment, organization and doctrine does not prepare them for are

unconventional, asymmetric opponents (Cohen, 1984; Cassidy, 2000). An alternate general form has

been put forward by Mack (1975). Mack argues that the very power differential that benefits the stronger

actor in an asymmetric conflict is mitigated by a consequent "interest asymmetry" that bolsters the weak

actor. The strong actor, precisely because he faces a weak actor that cannot threaten his existence, is less

willing to absorb the costs of the conflict. The weak actor, bolstered by basic survival imperatives, is less

sensitive to the costs of conflict. He needs only to wait out the conflict as the political costs accrue

against the strong actor. Although this theory was presented primarily as an interstate asymmetric

dynamic, it mirrors an insurgency in which the state power is a foreign occupier. Indeed, Mack

specifically theorized that insurgencies, bolstered by nationalist goals of establishing a homeland, have

longer time horizons and a lower sensitivity to political costs than their opponents. As an additional note

on foreign occupiers, Morris (1996) identifies an alternative mechanism through which foreign

occupation might affect conflict dynamics. Soldiers in a foreign occupation force, far removed from their

framework of social norms, could be more predisposed to indiscriminate violence against civilians.

Higher levels of indiscriminate civilian violence undermine counterinsurgents since they distort

individual incentives for collaboration (Kalyvas, 2006). As a caveat, Morris' research pertains

specifically to rape, which might act in different ways than murder or collateral damage.

The specific form of the detrimental power argument, best articulated by Lyall (2009), argues for

an affect via the identification problem. By physically isolating counterinsurgent in vehicles and making

them less dependent on the local environment for supply, Lyall argues that mechanization prevents

modern militaries from integrating themselves into local information networks. As a result, mechanized

counterinsurgents are less able to solve the identification problem and credibly and efficiently separate the

insurgent from the noncombatant population. It is a relationship, if not a causal pathway, that is supported

by his empirics.

The importance of regime type is another point of theoretical contention. Popular theory,

reinforced by some comparative case work, (Merom, 2003) argues that domestic constructs inherent in

democracy provide the basis for defeat in small wars - particularly because democratic publics can

stomach neither the use of force nor the length of commitment necessary to achieve victory. The main

flaw in Merom's work is that he chooses his cases based on the dependent variable - only instances in

which democracies lost. Furthermore, Merom exclusively chooses instances of foreign occupation,

limiting the external validity of his results. Large-n studies have found no particular benefit for either

authoritarian or democratic regimes when fighting wars (Desch, 2002). Contending explanations argue

for a parabolic relationship between regime type and success. Consolidated regimes - either highly

authoritarian or highly democratic - are found to be more successful whereas transitional or mix regimes

are associated with defeat (Desch, 2002). Although Desch aggregates all conflict types, findings

correlating specifically counterinsurgent defeat with greater levels of democracy have been fairly limited

(Lyall, 2009).

2. The Means of Insurgency

On the other side of the equation, insurgent characteristics - or, the means of insurgency - are

frequently posited as being primary determinants of conflict outcomes. Essentially, anything that might

negate the power differential, broadly defined, enjoyed by the counterinsurgent has been posited as a

hindrance to the incumbent. For example, the politics of the Cold War are believed to have provided

insurgent groups with the material and financial support necessary to sustain insurgent warfare (Gleditsch,

2007). An alternate account of time-dependent effects looks at the widespread availability and diffusion

of increasingly powerful small arms (Kahaner, 2006), providing insurgents with the kind of military

power that was once exclusive only to state actors. This is a trend that would presumably have

dramatically accelerated after the collapse of the Soviet Union. Literature has also shown that the

presence of foreign sanctuaries (Gleditsch, 2007), diaspora funding, and international armed military

11

intervention (Lyall, 2009) can affect the chances of insurgent success. Finally, the presence of exploitable

commodities has been shown in some literature to increase the duration of conflicts (Ross, 2004);

however, it is unclear whether these resources are a means to an end, or an end in and of themselves.

Finally, the ability of insurgents to utilize the modem international media to rally both domestic and

international support as well as bring the war home to their opponents has been seen as increasing their

ability to undermine the will of incumbent actors (Bob 2005).

Much ink has been spilled over the relationship between ethnicity and intrastate war outbreak,

termination, and insurgency in particular. Indeed, for some authors, insurgency is only observable in the

absence of ethnically-driven conflict (Kaufman, 1996) - that there is no such thing as an ethnic

insurgency. The salience of ethnicity is typically seen as being a critical factor in determining whether an

intrastate conflict takes the form of an irregular war (i.e. an insurgency) or something approaching a quasi

inter-state war between rival ethnic groups. The key distinction between these two conflict typologies is

the ability of individuals to defect. Insurgency, for example, is traditionally portrayed as a battle over

noncombatant loyalties. However, in a situation where people identify primarily with an ethnic group, the

ability to defect is believed to be absolutely constrained since identities are "fixed since birth"

(Kaufmann, 1996). This produces conflicts that essentially evolve into battles over territory. However,

this "ethnic war model" has been met with severe criticism (Kalyvas and Kocher, 2007). Additionally, the

assumption that ethnicity has a deterministic effect on the possibility for defection is contentious

(Kalyvas, 2008). As soon as this assumption breaks down, and defection occurs- even if actual defection

is small - conflicts once again become battles over loyalties. Furthermore, ethnicity has been seen to

have surprisingly little impact in a variety of intrastate conflict processes. One would expect that

ethnicity would be one of the primary determinants of alliance formation in civil wars, for the same

reasons that combatant groups appear to first mobilize around ethnic groups. But in fact, civil war

alliance formation has been shown to be informed first and foremost by realist considerations of relative

power distribution. Only after these are satisfied do groups use ethnicity or other identity elements to

12

justify their decisions (Christia, 2008). Numerous studies have also failed to find a connection between

ethnicity and the nature of intrastate conflict outcomes (Mason and Fett, 1996; Licklider, 1995).

3. Conflict Characteristics: Challenging the Conventional Wisdom

Bridging the concepts of state attributes and the means of insurgency, recent research has applied

the notion of relative balances of power to conflict dynamics and outcome. Christia (2008) notes that

where relative power differentials are small - that is to say, both participants approach power parity -

conflicts tend to be longer. This is a fundamentally intuitive notion. The less dominance one actor has

over another, the less likely it is that it will be able to decisively and quickly defeat its opponent. Mason

and Fett (1996) link conflict duration to outcome by proposing a rational choice model of negotiated

settlement - via a "war weariness" or "hurting stalemate" mechanism. An additional conflict

characteristic, foreign intervention, has been argued as being decisive in determining the nature of conflict

outcomes - military vs. negotiated settlement. Intervention is typically seen as facilitating the negotiated

settlement of a conflict by allowing factions to overcome commitment problems (Walter, 1999) or

security dilemmas, or by propping up a losing faction and therefore forcing the stronger faction to make a

settlement (Kaufmann, 1996). Not all interventions are created equally, however. "Kin-state"

intervention, or more generally any type of overwhelmingly one-sided intervention, could lead to a

decisive military victory by dramatically altering the relative balance of power (Christia, 2008).

In war, terrain matters. The literature suggests that insurgency is no exception. Acting similar to

a "means of insurgency," strategic utilization of terrain can make up for quantitative and qualitative force

differentials. Beyond serving insurgencies as a force multiplier, terrain provides the added benefit of

facilitating a fundamental rebel imperative - hiding from government forces. It is precisely the numerical

and capabilities weaknesses of non-state actors that necessitates they spend the majority of their time

avoiding state forces and selectively picking their battles. As such, the conventional wisdom is that rough

terrain - forests and mountains - is beneficial to insurgents (Collier and Hoeffler, 2004; Fearon and Laitin,

13

2002). Although both of these works focus on conflict outbreak, the underlying logic is still the same.

Certain types of terrain are believed to facilitate insurgency - though, some of these findings have not

been borne out in empirical studies on conflict outcome (Lyall, 2009). Implicit, and in some cases

explicit, in this conventional wisdom is the related emphasis on rural terrain as being conducive to

insurgency. Indeed, Fearon and Laitin (2002) essentially use the terms insurgency and "rural guerrilla

warfare" interchangeably. Collier and Hoeffler (2004) predict that "low population density and low

urbanization may inhibit government ability," facilitating the survival of insurgent movements. Rural

environments are seen as crucial precisely because of what they are not: urban environments. Urban

environments are typically viewed as bastions of state power and control, as well as places where

anonymous denunciation is easier (Fearon and Laitin, 2003 p. 8). In her seminal work, Condit (1973)

found that urban insurgencies were the easiest for counterinsurgents to defeat. As a result, a rural base,

some distance from the centers of government power and not easily reachable by roads, is typically seen

as essentially to waging insurgency (Fearon and Laitin, 1999).

However, the conventional wisdom is increasingly being called in to question, not the least

because of the rise of observed urban insurgencies and the articulation of specific urban insurgent

strategies (Taw and Hoffman, 1994). The issue is not so much that the fundamental logic - rough terrain

favors insurgents - is flawed, but rather that the nature of urban terrain has evolved. The two

demographic shifts of the 20t century - rising population and increasing urbanization - would seem to

favor the rise of urban insurgency by "roughening" the urban environment (Taw and Hoffman, 1994).

Furthermore, although scholars like Fearon and Laitin (2002) make bold claims about the intrinsic

linkages between insurgency and rural environments, the theory isn't necessarily borne out by their

empirics. As mentioned above, these studies (Fearon and Laitin, 2002; Collier and Hoeffler, 2004) focus

on civil war outbreak. One could perhaps reasonably argue that even if their argument was valid - that

rural terrain facilitates insurgency, and civil war outbreak is more strongly correlated with the

opportunities for insurgency than with the level of grievance - the relationship might not necessarily carry

14

over to conflict outcome. In short, while a rural base might be necessary toforming an insurgency, it may

be neither necessary nor sufficient to winning an insurgency. Additionally, civil wars and insurgencies

are not one and he same. However, even if we assume that similar dynamics determine conflict outbreak

and outcome and operate similarly in insurgencies and civil wars collectively, Fearon and Laitin (2002)

do not conclusively confirm the importance of a rural environment. Instead, they merely define

insurgency as being rural, and then go about testing a number of other factors that relate to the

opportunity for insurgency - the closest being mountainous terrain as a proxy for rough terrain. There is

no direct test for population density, nor is there any attempt to relate population distributions with the

location of mountainous terrain - a shortcoming they admit. Of course, their significant finding for

mountainous terrain does not preclude significant findings for urbanization - conceptualized as a terrain

factor. Again, both concepts rely on the same underlying logic - rough terrain favors the insurgent.

Furthermore, Fearon and Laitin do statistically verify the importance of population, acknowledging that

larger populations facilitate concealment and recruitment - a somewhat odd point to make since the crux

of their argument is that insurgencies do not require a large number of fighters. But again, they look only

at absolute population, and not population density, or distributions of population density. While other

research (Fearon and Laitin, 1999) deals specifically with the question of rural bases, the dependent

variable of interest there is the outbreak of ethnic violence. Also, the dummy variable for "rural bases"

could be interpreted simply as a regional concentration variable. Considering one of the two ethnic

conflict types is secessionist, it is perhaps not surprising that Fearon find such a strong correlation for the

rural bases variable. Furthermore, none of the existing studies have allowed for a variable relationship

between urbanization and outcome conditional on insurgency type.

While the phenomenon of urban insurgency has been examined in case studies (Sorenson, 1965;

Miller, 2002; Marques, 2003; Fair, 2004), providing useful exercises in theory building and plausible

causal mechanisms, there exist no quantitative studies testing the effects of increasingly urban

insurgencies on conflict outcomes. Below, I present a comprehensive theory that examines the causal

links between urban environment and insurgency outcome.

Chapter III: Urban Insurgency - A Working Theory

1. The Evolution of Modern Insurgency Doctrine

The notion of urban insurgency, superficially, cuts against traditional conceptions of guerrilla

warfare and asymmetric conflict. While the conventional wisdom of rural insurgency has already been

touched upon above, that literature is merely a reflection of classical - rural - insurgency doctrine. Two

of the most popular revolutionary theorists of the 20* century, Mao Zedong and Che Guevara explicitly

refer to the importance of leading insurgency from the countryside. Mao's entire revolutionary

philosophy was based around avoiding cities, and instead building support among China's massive rural

peasant population before confronting the government in urban environments. In fact, it was the CCP's

near total failure to successfully foment revolution in urban settings that created the strategic vacuum for

Maoist rural doctrine to emerge. Guevara believed that a small vanguard of guerrillas could "focus"

revolutionary support and through military actions against the state create the conditions for insurrection,

rather than wait for those conditions to develop exogenously. However, one of the three tenets of his foco

theory was that, in the underdeveloped countries of Latin America, rural areas were the best battlefields

for revolution. In general, cities have typically been the final objective of an insurgency, not a continually

contested zone (Taw and Hoffman, 1994). However, matching the continued urbanization of the

developing world has been the emergence of urban insurgency doctrines. These doctrines originated first

in Latin America - one of the first developing regions touched by the recent explosion of urbanization.

Indeed, it was the failure of Guevara's rural foco doctrine in Bolivia, coupled with the failure of a number

of other rural Latin American insurgencies, which spurred the articulation of urban insurgency doctrines

by strategists such as Abraham Guill6n and Carlos Marighella. From these doctrines, and the

observations of their implementation, we can distill a theory of causality linking urban insurgency to

decreasing COIN success. It is not a new theory of insurgency, but rather one that incorporates the

peculiar benefits of the urban environment while detailing the ways in which cities can provide substitutes

for the traditional benefits of rural settings and traditional rough terrain.

2. Urban Insurgency

Examining the impact of urban insurgency on conflict outcomes requires the disaggregation of

two different proposed effects. The first of these is a base of power effect. Although the realization of

Maoist and Focoist strategy is a rural focus, the underpinning of both their arguments seems to be merely

the identification of and enmeshment with a base of power. Classical insurgency theory points to the

importance of the support of the population. The population furnishes the insurgency with fighters and

supplies, but it also facilitates concealment and cover. To quote Mao, the guerrilla swims in the sea of

people. These benefits are realized in a strategic and tactical context, respectively. Strategically,

population concealment manifests itself as the "identification problem," perhaps the most critical issue

facing the counterinsurgent. The counterinsurgent force must credibly separate the combatant from the

noncombatant population, apply force selectively, or risk defeat. In a strategic sense, the population

enables the insurgency to hide and therefore to survive. There is also a tactical identification problem that

operates during actual engagements between insurgent and counterinsurgent. In actual operations,

insurgents can quickly strike from and blend back into the population. Additionally, civilians serve as

human cover, shielding the insurgent from retaliation. In tactical settings, the population furnishes the

insurgent with physical cover. In China the center of power lay in the countryside, where the

overwhelming majority of the population resided. Yet, the demographic trends of the 20* century seem

to be altering the fundamental balance of power between rural and urban environments. Global

urbanization has resulted in hundreds of millions more people living in cities. Additionally, globalization

has produced vast new financial inequalities (Miller, 2002). So, on one level, the detrimental effect that

rising urbanization could have on COIN outcomes could simply be a result of the insurgent following the

population base - as the people go, so goes the insurgent.

However, a base of power shift from the rural to urban environment wouldn't necessarily explain

the decline in counterinsurgent win-rates, just a change in the insurgency type. To account for a decline

in the percentage of insurgencies won by the COIN force, there would have to be some reason why urban

insurgencies are more formidable than rural ones. I identify the following causal mechanisms to explain

why the urban battlefield facilitates the success of modem insurgency. Of course, the predicted negative

effects on COIN outcomes are conditional on the existence of an increasing urban base of power - these

effects should be amplified by higher levels of urbanization. If an urban insurgency is not paired with

higher urbanization, and vice versa for rural insurgencies, then the insurgent runs substantial risks. He is

potentially waging an insurgency without the benefits afforded by that insurgency type:

(1) Concealment - Mao expounds upon the need for the insurgent to swim like a fish among the

sea of people. The high population density of urban environments lends itself better to both

strategic and tactical concealment. All other things being equal, the urban sea is bigger

(although population migration has also comparatively "drained the sea" in the countryside).

Strategically, the increased difficulty of solving the identification problem hinders the ability

of the counterinsurgent to separate insurgent from population, leading to higher levels of

indiscriminate violence. Counterinsurgents are increasingly finding urban environments to be

as, if not more, inaccessible as traditional rough terrain and rural settings. The urban

environment also provides increased tactical concealment. Indeed, the insurgent can

manipulate the tactical concealment advantage presented by urban environments to

deliberately provoke and maximize civilian casualties. Higher levels of indiscriminate

violence undermine the legitimacy of the COIN campaign, producing feedback effects on

concealment.

(2) Chuikov Effect - Rather than continually state, "nullified power advantage due to urban

terrain constraints," I choose instead to pay homage to Soviet General Vasily Chuikov,

commander of the Soviet 62nd Army in the Battle of Stalingrad. General Chuikov exhorted

his troops to "hug the enemy" in order to force the Nazis to fight a mostly infantry based

battle, forgoing artillery and close air support to avoid massacring their own troops. The

materiel superiority of the German forces was thus nullified. Insurgents fighting in an urban

environment benefit from similar dynamics. First, the physical and human cover provided by

urban battlefields enables the insurgent to more easily close the distance between himself and

his counterinsurgent opponent - hugging the enemy with greater ease than a soldier in a

classical interstate urban warzone, where urban battlegrounds are typically depopulated and

adversaries were less sensitive to civilian casualties, and even more easily than a rural

insurgent. While the insurgent can undoubtedly use this ability to his advantage by exploiting

counterinsurgent fears of friendly fire, additional benefit comes from exploiting fears about

civilian casualties. As a result, the counterinsurgent is unable to effectively bring to bear the

materiel and technological advantages that its power advantage would imply, lest it risk

incurring legitimacy-undermining collateral damage. At the same time, forgoing a material

superiority risks potentially increasing counterinsurgent casualties. Urban environments

therefore effectively serve to mitigate what would otherwise be an overwhelming power

differential, altering the relative power distribution between counterinsurgent and insurgent to

the benefit of the insurgent.

The difficult choice facing the counterinsurgent can be seen in the Iraq example. In order to

bring the full military might of the US armed forces to bear, without inflicting massive

civilian casualties, counterinsurgent campaigns have been forced to telegraph their

operational objectives in order to deprive the insurgent of its human cover and concealment.

However, this completely eliminates the element of surprise and provides insurgents with an

opportunity to quit the battlefield and melt away with noncombatants should it so choose.

(3) Target/supply rich environment - The urban environment presents the insurgent with a highly

dense concentration of soft targets. These targets take the form of infrastructure ( civilian,

security, and government) as well as human targets (i.e. government officials, foreign

diplomats, etc). Since people prefer to collaborate with the political actor that best guarantees

their survival (Kalyvas, 2006), attacking these further undermines the legitimacy of the state

by exposing its inability to provide basic services and a secure environment, and to defend

itself. Furthermore, urban insurgents can utilize urban infrastructure to meet basic supply

needs, including the acquisition of funds.

Urban Insurgency: A Working Theory

Chuikov Effects

X I COIN Casualties/Mechanization - T Indiscriminate Violenoe

X X

Urban Strategy - Concealment Preign CoupierCOIN Defeat

Urbanization I COIN Legitimacy

Target RichEnvironment

From these principal causal mechanisms, I identify intermediary effects driving COIN outcome.

The theory in full is graphically outlined above. Concealment and "Chuikov" effects lead to increased

COIN casualties and indiscriminate violence while simultaneously facilitating the survivability of the

insurgent. Both of these effects are exacerbated by the deployment of highly mechanized COIN forces.

Lyall (2007) posits that, in general, mechanization precludes local information gathering and magnifies

the identification problem (strategic concealment):

"The fact that mechanized forces are ill-suited for certain types of terrain and are tied to availableroads only magnifies these problems [of reduced soldier-civilian interaction]. Rather thanexercising control, mechanized forces are actually providing only "presence" since their greatestasset, mobility, allows them to cover more ground without having to embed in a particular

location. This asset is nonetheless a liability: with fewer soldiers mechanized forces must sacrificedepth for breadth." (Lyall, 2009)

Additionally, a more highly mechanized army will be disproportionately impacted by Chuikov effects

since it has relatively more to lose by such dynamics. Furthermore, more mechanized, modernized

militaries are presumably relatively less capable of penetrating the tactical cover (both human and

physical) of the urban environment than they are of penetrating the tactical cover of rural environments.

As can be seen, this theory complements rather than challenges that of Lyall et al. Both predict a negative

mechanization effect. However, in differentiating between insurgency types, I predict a more negative

effect when the insurgency is more urban in nature. Higher levels of indiscriminate violence and the

increased incidence of soft target destruction serve to undermine support for and legitimacy of the COIN

actor. My model leaves open the possibility of feedback effects. As support for the COIN actor

decreases it is possible that the identification problem will be further magnified due to the increased

support - either willing or coerced - for insurgents. Furthermore, increased COIN casualties could serve

to undermine COIN legitimacy, since it reflects the inability of the incumbent to protect even its own

combatants (be they soldiers or policemen). By fighting in urban settings, insurgents are presumably able

to magnify the relative importance of costs. As Bob (2005) notes, insurgents play to both domestic and

international audiences. In an urban environment, it is likely easier for insurgents to reach these

audiences and to publicize their attacks. Urban casualties are also likely to carry with them a

psychological multiplier that inflates the political cost they carry for the counterinsurgent due to their

perception as strikes in the heart of government authority.

3. Manipulating Outcomes

To connect these intermediate effects (increased COIN and civilian casualties, and decreased

COIN support) to COIN outcomes, I rely on the literature of conflict termination - in particular, I build on

the rational choice model of Mason and Fett (1996). Mason and Fett posit the following expression for an

actor's (i.e. the counterinsurgent) expected utility of continuing conflict:

1,

E(Uc) = Pv(UV)+ (1 - Py)(UD) - C,

An actor's willingness to continue fighting towards victory can be changed by manipulating his costs, the

time over which these costs are accrued, or his probability of victory (assuming the utility he gains from

victory, defeat, or settlement remains constant). By raising the costs of conflict, Cj, urban insurgencies

decrease the expected utility of continuing conflict. From the civil wars literature we predict that

"conflict duration largely depends on the power differential between opposing coalitions" (Christia,

2008). The fundamental consequence of "Chuikov" effects and concealment is to nullify the raw power

asymmetry benefiting the counterinsurgent - shrinking the relative balance of power between insurgent

and counterinsurgent. As a result, ceteris paribus, urban insurgencies act to increase the time t, over

which the increased costs of conflict are to be incurred. Alternatively, in more extreme cases, urban

insurgencies could flip the power differential, to shortening the conflict in favor of the insurgents. A more

balanced relative power differential, presumably along with decreased legitimacy for the

counterinsurgent, also serves to lower its probability of victory, P,. The cumulative impact of all of these

effects is to lower the counterinsurgent's expected utility of continuing conflict, making settlement more

attractive or defeat unavoidable, but on the whole making outright military victory for the

counterinsurgent less likely. To quote Kissinger, "the guerrilla wins if he does not lose."

Although the Mason and Fett model remains simple for the sake of parsimony, it provides the

framework for merging additional variables of significance. Following the interest asymmetry arguments

of Mack (1975), I posits that foreign occupiers will be more sensitive to the costs of conflict and that their

political legitimacy will be more sensitive to noncombatant casualties due to the increasing nationalist

sentiment insurgents can rely upon. This first sensitivity can be conceptualized as multipliers on the cost

23

summation of the expected utility function. The, of local political legitimacy, can be conceptualized as a

higher DPvIDCNC, where CNC represents noncombatant casualties.

4. Drawbacks of Urban Insurgency

It is important to note that the urban environment is not without its peculiar set of disadvantages.

Decades of writing on insurgency strategy and predictors of insurgency outcomes have not been

completely invalidated. Urban environments are still potentially conducive to anonymous denunciation

(Fearon and Laitin, 2003 p. 8). Furthermore, security imperatives require urban insurgents to maintain a

higher degree of mobility as well as a more cellular structure that likely takes a toll on their operational

capabilities. By operating so close to incumbent security forces and forsaking the relative lawlessness of a

rural base, insurgents run a high risk of detection. The urban insurgent truly is hiding in plain sight. The

relative inability to remain in fixed territories and constant fear of discovery hinders the ability of urban

insurgents to train. Afghan urban insurgents fighting the Soviets were noted to be distinctly less

proficient marksmen than rural insurgents (Elkhamri et al, 2005). By more thoroughly enmeshing

themselves with the civilian population for concealment and cover, urban insurgents also run a greater

risk of provoking a popular backlash. Urban insurgents, like their counterinsurgent foes, are also

constrained in their use of weaponry and their ability to mass larger attacks. However, counterinsurgents

presumably suffer more from this constrain than the insurgents.

Chapter IV: Hypotheses

From my theoretical framework I derive a number of hypotheses. The first of these (Hypothesis

1) states simply that as the degree of urbanization of an insurgency increases, counterinsurgents will be

less likely to obtain victory. To test for a base of power argument, I hypothesize that, contingent on the

use of a mixed or urban strategy the level of urbanization will be negatively and significantly related to

the conflict's outcome (Hypothesis 2). Alternatively, I hypothesize that, conditional on the use of a rural

strategy, the level of urbanization will be positively and significantly related to the conflict's outcome

(Hypothesis 2a). The more urbanized a given conflict zone, the greater the concealment and cover

afforded. As a result, I predict that, conditional on the use of an urban insurgency, the level of

urbanization will be positively and significantly correlated with casualty levels (Hypothesis 3) and

conflict duration (Hypothesis 4). Linking intermediate DVs to conflict outcome, and applying the Mason

and Fett expected utility model, I predict that conflict duration (Hypothesis 5) and casualties (Hypothesis

6) will be negatively and significantly correlated with the likelihood of incumbent victory across all

insurgencies. However, I predict that the magnitude of the effect of casualties on outcome will greater in

urban insurgencies (Hypothesis 7). Chuikov effects hinder counterinsurgent fighting in urban

environments by negating some of its advantageous relative power differential. Consequently, I predict

that relative to rural insurgencies, the level of mechanization of a counterinsurgent will be more

negatively associated with conflict outcome (Hypothesis 8). The effect of mechanization is not expected

to be positive in rural insurgencies. Incumbents battling rural insurgencies are still expected to face

identification problems, but these should be mitigated by the greater ability to exercise force - and the

greater force differential afforded my mechanization - in rural settings. Furthermore, identification

problems are expected to be aggravated in urban insurgencies. Chuikov effects predict an observable

increase in overall casualties - be it through increased counterinsurgent or civilian deaths. As a result, I

predict that mechanization will have a larger effect on total casualties in mixed/urban insurgencies than in

rural insurgencies (Hypothesis 9). Although Chuikov effects should be variable with the level of

counterinsurgent mechanization and the level of urbanization, more complex relationships will not be

tested in the study. Finally, my theory predicts foreign occupiers, due to an interest asymmetry and a

consequent higher sensitivity to costs, will have a lower likelihood of defeating insurgencies (Hypothesis

10).

Chapter V: Testing the Theory

1. Research Scope

This study tested for determinants of insurgency outcomes during the 1945-2005 period. In doing

so, it followed the conventions set forth by Lyall (2009). An insurgency was defined as a "protracted

violent struggle by non-state actors to obtain their political objectives -often independence, greater

autonomy, or subversion of existing authorities - against the current political authority (the incumbent)."

What separates an insurgency from the universe of civil wars is the use of a guerrilla strategy. Lyall uses

two criteria to define a guerrilla strategy: (1) the deployment of small, mobile groups to inflict

punishment on the incumbent through hit-and-run strikes while avoiding direct battle when possible; (2)

attempts to win the allegiance of at least some portion of the noncombatant population. As a result, the

term insurgency implicitly assumes a relative power asymmetry between the incumbent and the non-state

actor. This eliminates the problems posed by including civil wars that are essentially stand-up fights

between conventionally armed and oriented military forces. Such cases are likely to have much different

explanatory factors, and my theory makes no attempt to explain the role urbanization plays in an

essentially symmetric conflict. It also implicitly eliminates the quasi-interstate wars of Kaufmann (1996)

- if they actually did exist. Guerrillas must compete with the incumbent for loyalties, whether through

coercion or cooperation. Finally, the scope of the study was limited to insurgencies that passed a 1000

battle deaths threshold, with at least 100 casualties incurred on either side.

This period was chosen primarily for data considerations. Many of the explanatory and control

variables in question have limited spread in the second half of the 20* century, let alone the 19t or 18*.

Furthermore, the difficulty of manually coding insurgency types made it impractical to extend the study

much further. Ideally, the dataset would cover the full 20* century, which would allow for a more

rigorous test of the change relationship between urbanization and insurgency outcomes.

2. Building a Dataset

The dataset used in the study was built off of the Correlates of Insurgency dataset compiled by

Lyall et al (2009). Utilizing that dataset's conflict list, case profiles were constructed by manually

appending the required independent and dependent variables from their original sources, and directly

from the Correlates of Insurgency dataset where necessary. The follow is a list of all dependent,

independent, and control variables included in the analysis, with brief descriptions and sources. Notably

missing from the list is a casualties-type variable for soft-targets. No such data exists for anywhere near

the necessary range of cases.

Dependent Variables:

Outcome (winner) - an ordinal variable of conflict outcome coded from the incumbents perspective and

taking values of zero (incumbent defeat), one (draw) or two (incumbent victory). A win occurs when the

insurgency is militarily defeated and its organization destroyed, or the war ends without any political

concessions granted to insurgent forces. A draw occurs when an incumbent is forced to concede to some,

but not all, insurgent demands, and neither side obtains its maximal aims. Concessions might include

greater political autonomy for a region (but not independence) or voluntary disarmament in exchange for

political integration. A loss occurs when the incumbent unilaterally concedes to all, or nearly all,

insurgent demands. Loss scenarios include the overthrow of the incumbent government, the granting of

political independence, or de facto independence. The key distinction between a draw and a win or a loss

is that neither side obtains its maximal objectives.

Sources: Lyall et al (2009)

Casualties (cas-acd, avcas, avcas-pop) - Measure of the number of civil and military casualties incurred

by the country in which the insurgency took place over the duration of the conflict, including the foreign

occupier if the incumbent actor was foreign. Data was acquired from the UCDP/PRIO Battle Deaths

Dataset. Due in part to UCDP/PRIO's lower casualty threshold for conflicts, conflict duration often

diverged from the Correlates of Dataset. Where conflict coding differed in time span, casualty data was

used only from years that were available. Furthermore, in cases of multiple interveners and differing time

spans, total casualties for the incumbent was estimated as the sum of the incumbent's proportion of total28

casualties in each desired conflict year. Casualties were measured as an absolute, yearly average, and a

per-capita yearly average. Furthermore, absolute casualties were measured in thousands of deaths.

Sources: Lacina and Gleditsch (2005)

Duration (dur) - measures the length of the conflict in years.

Sources: Lyall et al (2009)

Independent Variables

Insurgency Type (i_type/i-type2) - Ordinal coding of insurgency type. The variable takes a value of

zero if the insurgency was primarily rural, one if the insurgency contained urban and rural components of

near equal importance, and two if the insurgency was primarily urban. A collapsed measure took a value

of 0 if the insurgency was primarily rural, and 1 if the insurgency was either mixed or urban.

Sources: Various.

"Means of Insurgency "/Terrain Factors

Urbanization (curb2) - measured as a percentage (0-100) of the total population living in areas

classified as urban for a given conflict country. Classification is done according to criteria put forth by

each country. Data was primarily available from 1950-2005 in 5-year intervals. Where necessary, values

were interpolated using fitted trend-lines to fill in missing values between reported values and as far back

as 1944. The variable was lagged one year prior to conflict outbreak. Where available and appropriate,

regional values were used. However, the variable was by and large a state-level indicator, and so lacked

the desired level of specificity.

Sources: UNData, UN World Urbanization Prospects 2008, Gapminder.org, various.

Forest Cover (cjfor) - measured as the percent of land area in a given conflict country covered by forest.

The variable is a state-level indicator and approximates opportunity of insurgencies to conceal, cover, and

hide themselves from incumbent forces. It was also used as a component of the composite "Terrain

Roughness" variable, a straight sum of Forest Cover and Mountainous Terrain.

Sources: UN Food and Agriculture Organization, various.

Mountainous Terrain (c-mtn)- provides an indicator of the level of mountainous terrain in a given

conflict country. The variable is a state-level indicator and approximates opportunity of insurgencies to

conceal, cover, and hide themselves from incumbent forces. It was also used as a component of the

composite "Terrain Roughness" variable, a straight sum of Forest Cover and Mountainous Terrain.

Sources: Fearon et al (2002), based on a dataset created by geographer A.J. Gerard.

Foreign Support (support)- an ordinal variable measuring the existence of either foreign sanctuaries or

material economic and military aid for the insurgency. The variable took a value of two if both were

present, one if only one type was available, and zero if neither.

Sources: Lyall et al (2009)

"Incumbent Attributes"

Power (power) - measured using COW's Composite Index of National Capabilities (CINC) dataset. The

variable is a combination of six constituent variables (total population, urban population, iron and steel

production, energy consumption, military personnel, and military expenditure) recorded for any given

incumbent-year. It indicates the average of the state's share of the global total of each constituent. The

variable is lagged to one year prior to the outbreak of conflict and provides an approximation of total state

power. The variable is logged.

Sources: COW Ver3.02

Regime Type (st-pol) - measures the relative level of autocracy or democracy of the incumbent actor in

the year prior to the outbreak of conflict. The variable seeks to capture the relative advantages or

disadvantages of varying types of government.

Sources: Polity IV Dataset, Lyall et al (2009).

Mechanization (mech) - measures the prewar soldier-to-mechanized vehicle ratio of the incumbent's

military. The ratio is collapsed at 25 percent quartiles into a scaled index taking values from 1 (low

mechanization) to 4 (high mechanization). The variable approximates the force structure of the

incumbent, and so seeks to capture the effects of "information starvation."

Sources: Lyall et al (2009)

Helicopters (heli) - binary variable indicating whether or not the incumbent used 25 or more helicopters

in the conflict. The variable acts as a battlefield-level indicator of mechanization, roughly capturing the

actual use of mechanized forces in a given conflict, rather than the force structure of an incumbent's

entire military.

Sources: Lyall et al (2009)

Occupier (occ) - a binary variable indicating whether or not the incumbent was a foreign occupier. The

variable acts as a proxy for two interrelated effects. On the one hand, it captures the potential for

nationalist sentiment aroused against a foreign regime, and the difficulty this could pose for a

counterinsurgent. On the other hand, it roughly captures relative lack of importance of the conflict to the

incumbent. While foreign occupiers do not necessarily have less vested in an insurgency outcome, the

conflict likely does not invoke the same survival instinct that it would in an indigenous incumbent. Both

of these effects serve to capture an interest asymmetry between the incumbent and the insurgency.

Related, the variable could act to approximate the difficulty of an occupier to socially and politically

navigate a foreign population.

Sources: Lyall et al (2009)

Control Variables

GDP per capita (c-gdp) - measured in real PPP, representing the average purchasing power of an

individual in a given conflict country one year prior to the outbreak of a conflict. Where necessary,

values were interpolated using fitted trend-lines to fill in missing values back to 1944. The variable acts

as a rough proxy of either state power or the opportunity cost of insurgency.

Sources: Gapminder.org

Population (c-pop2) - measure (in thousands) as the total population living in a conflict zone - typically

the country in which the conflict took place. The variable was lagged to one year prior to the outbreak of

conflict. Where necessary, values were interpolated using fitted trend-lines to fill in missing values back

to 1944. Additionally, where available and appropriate, regional values were used. Total population was31

measured in thousands of people. Where possible, regional values were used, especially if the conflict

country was extremely populous.

Sources: UN World Population Prospects 2008, Gapminder.org, various.

Electricity Consumption (celec) - a logged measure of per capita electricity consumption by

inhabitants of the country in which the insurgency took place. The variable acts as a rough measure of

total development in a given country and an important control on the effect of urbanization. It is also a

possible proxy for soft-target abundance.

Sources: COW Ver3.02

Linguistic Diversity (numlang) - measures the number of languages spoken in a given conflict region

(i.e. Nagaland rather than India). The variable approximates the "roughness" of human terrain. It is

derived from Fearon and Laitin's (2003) national indicator.

Sources: Lyall et al (2009)

3. Coding Insurgency Type

Insurgencies were coded as rural, mixed, or urban. Unfortunately, such coding, to the extent

required, simply did not exist at the outset of this study. Of course, this should not come as much of a

surprise. To start with, data on insurgencies, let alone insurgency types is particularly hard to come by.

Only one true "insurgency" dataset exists - the Lyall et al (2009) "Correlates of Insurgency" dataset.

Furthermore, given that insurgency has historically been a rural phenomenon, there simply hasn't been a

need to specifically code for insurgency type. Until relatively recently, virtually all insurgencies were

rural. Fortunately, this endeavor was supported by a large body of case work on insurgencies stretching

back decades, as well as a smaller body of more recent work pertaining specifically to the advent of urban

insurgencies.

The criteria for differentiating between rural and urban insurgencies were purely geographic.

Coding did not require insurgents to explicitly declare adherence to urban insurgency doctrine, or to

32

necessarily practice all components of any insurgency strategy. As a result, rather loose definitions were

used. Urban insurgencies were defined as: insurgency primarily focused in urban areas with an

intermediate goal of reducing the ruling authorities will to resist (O'Neill, 1990). Rural insurgencies were

likewise loosing defined as: insurgency primarily focused in rural areas with an intermediate goal of

reducing the ruling authorities will to resist. Mixed insurgencies inhabited the amorphous region between

these two poles: insurgencies focused in both rural and urban areas with an intermediate goal of reducing

the ruling authorities will to resist.

In order to determine whether an insurgency was focused "primarily" in one setting or another,

cases were analyzed along four principle dimensions: Overall insurgent strategy, relative emphasis of

COIN activity, relative tactical emphasis of insurgent activity, and location of bases of support (both

materiel and moral). Where available, explicit statements on strategy by insurgents were used, and

strongly weighted in determining a coding decision. However, for obvious reasons, these statements were

never the sole source of a coding decision. In other instances, case authors did the work of laying out an

explicit insurgent strategy. Given that insurgents essentially "choose first," analyzing counterinsurgent

activity was a method of identifying insurgent strategy when such strategy was not explicitly known.

Beyond these two dimensions, specific tactical activities were looked for that would indicate urban or

rural components. Activities were drawn directly from the primary literature on insurgency doctrine. For

example, indicators of more urban insurgencies included instances of bombings, assassinations, armed

robberies or kidnappings, or attacks against urban infrastructure. Insurgencies were more likely to be

coded rural if the literature specifically mentioned attacks on outposts, supply lines or patrols, actions in

small villages or towns, or insurgent or counterinsurgent operations in traditional "rough terrain" (forests,

mountains, bush, etc). A final coding decision was reached after analyzing multiple sources, comparing

the relative prevalence of these two categories of activities, and balancing that assessment against

definitive statements on insurgent and counterinsurgent strategy, tactical emphasis, and locations of

support.

Coding by nature relied on a measure of subjectivity. The limited scope of the UCDP/PRIO

ACLED prevented the use of comprehensive event sets for all insurgencies. As a result, coding relied on

comparing the relative prevalence of events and activities emphasized by case authors - not necessarily a

random sample of all events. Given that few insurgencies are purely rural and few purely urban, this

introduces an element of bias. After all, even formative urban insurgency theorists Guillen and

Marighella advise balancing urban insurgency with some kind of rural campaign. In fact, of the two, only

Guillen advocates making the urban theater the primary component. Establishing a coding therefore

required determining the relative emphasis of the urban and rural components of an insurgency. For

example, even though Chechen guerrillas fought the Russians in the mountainous areas of Chechnya, the

decisive campaign of the conflict was the Battle of Grozny. Less murky is the case of the Punjab

insurgency (India v. Sikhs). Coded as an urban insurgency, the conflict was punctuated by two major

campaigns: Operation Bluestar and Operation Black Thunder. Both were urban clearing operation

conducted in the city of Amritsar. The Franco-Algerian War is a prime example of a mixed insurgency.

Although the Battle of Algiers had all the hallmarks of an urban insurgency campaign, the war also

contained a significant classical rural insurgency component, aimed at colonial farms and factories and at

establishing territorial control in regions with traditional rough terrain. UNITA's insurgency against the

MPLA-led Angolan government is another example of a mixed insurgency. Although the insurgency

followed a self-proclaimed Maoist doctrine, it maintained a constant operational presence in capital

Luanda and provincial capital Huambo (Huambo province is over 50% urbanized) essentially from the

outbreak of the conflict, and expanded to other regional capitals. UNITA commando units conducted

classic urban insurgency operations, including attacks on security forces, bombings of government

buildings, and assaults on infrastructure targets. Typical cases of rural insurgencies included the PLA's

war against KMT-led China, the Mau-Mau Insurgency in Kenya, and the Dhofar Rebellion. Frequently,

insurgencies were coded as rural even though they maintained some semblance of an urban component.

In their war for independence, the Cameroon UPC relied on large bases of ethnic support in Douala and

34

Yaounde to launch urban operations. However, the insurgency was primarily fought in the countryside,

consisting of sabotage of communications and transportation lines, attacks on plantations, and French-

sympathizing villages. The Mau-Mau Insurgency operated an "army" in Nairobi, but this urban

component was quickly crushed by counterinsurgency operations near the outset of the insurgency,

whereas the two components operating from traditional rough terrain - the reserves and bush - conducted

operations for the duration of the conflict.

4. Research Design

The analysis begins with an establishment of summary statistics, broken down across the whole

dataset and within insurgency type. This enables the establishment of general insurgency-type profiles.

A brief establishment of the bivariate relationship between insurgency type and insurgency outcomes is

then done. The multivariate analysis will consists of regressions run on the primary dependent variable

(outcome) using the principle independent variable (insurgency type), competing explanatory variables

(i.e. regime type, state power, foreign occupation, mechanization), and control variables (i.e. total

population, and start year). These regressions are run across the full dataset as well as partitioned datasets

conditioned on insurgency type (rural vs. mixed/urban). This allows for the determination of varying IV-

DV relationships across different insurgency types. A similar analysis, using multivariate regressions, is

then done for the intermediate dependent variables (duration and total casualties) in order to more fully

test the various hypotheses laid out above.

5. Chapter VI: Results

1. The Changing Nature of Insurgencies

Urbanization and the Rise in Urban Insurgencies

70 -

60 -.

som% ongoing insurgencies...-+-% Urban (Global)

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

As the world has become more urbanized, so too have insurgencies become increasingly urban in nature.

Over the past sixty years, the percentage of ongoing insurgencies with major or dominant urban

components has risen dramatically. This is clearly evident in the above graph. With the world certain to

become even more urban in the next forty years, we can only expect the current trend in the urbanization

of insurgencies to continue. Insurgencies have become more urban at the same time that states have had a

harder time prevailing. But is the relationship anything more than correlation? The following statistical

analysis has sought to discover trends both across all insurgencies, between different kinds of

insurgencies, and within insurgency types.

2. Summary Statistics and Insurgency Profiles

102 1972.716 15.47363 1945

End YearWinner

Insurgency TypeInsurgency Type

(collapsed)UrbanizationMountainous

102102102

1981.186 16.65237 1948 20050.9411765 0.7938318 0 20.745098 0.8287683 0 2

102 0.50 0.5024692102

102

Forest Cover 102Terrain Roughness 102

Total Population 102GDP (per capita) 102

Electricity Consumption(per capita) 102

i h~n uistic Diver it 102

31.01402

25.6452944.0139929262.312758.491

-1.084982

19.9307719.3787921.8695

25.68413

0 13.96 90.090 81

0.2 97.23.1 101.3

81263.57 75.076 5326072809.276 418.424 14142.85

2.202544 -7.732835 2.3294898.176471 7.1676 1 30

External Support

PowerDuration

Regime Type

Mechanization(Vehicles/Soldier)

HelicoptersForeign Occupier

CasualtiesYearly Casualty Rate

Per Capita YearlyCasualty Rate

102 0.98039 0.8322746 0 2102 -1.040024 1.938008 -4.755993 2.89783102 9.470588 7.543545 1 31102 -0.578431 7.486537 -10 10

102 2.617647 1.143593 1 4102 0.3039216 0.4622205 0 1

102 0.2058824 0.4063417 0 1102 49.52596 135.8912 0.25 1200102 8.181673 30.75499 0.0318182 300

102 0.0009413 0.0021748 1.01E-07 0.0139764

The above is a statistical summary of the variables of the dataset used in the following analysis.

However, it is important to remember that the dataset used is a truncation of the Lyall (2009) insurgency

dataset, containing only those cases for which insurgency type was coded. To bring to light any possible

bias introduced by the non-random coding, the following chart presents means for every variable of

analysis across the full post-War dataset, and the insurgency-coded dataset used in my analysis. The chart

Start Year 2002

also partitions the coded dataset by insurgency type in order to illuminate the varying characteristics of

the three insurgency types.

Vaibe ul Cdeua Mixed MiedUra Urba

(104133 (10) (1) 26) 51)(25

1971.985 1972.72 1966.471 1977.654 1978.961 1980.321979.609 10.992481

0.75

0.5

981.19).9412).7451

0.50(collapsed)

Urbanization 30.07791 31.014Mountainous 20.56273 18.086Forest Cover 26.70406 25.645

Terrain Roughness 46.95967 44.014Total Population 26832.95 29262.3GDP (per capita) 2877.964 2758.5

Electricity Consumption -1.28404 -1.0850(per capita)

Linguistic Diversity 8.12782 8.1765External Support 1.015038 0.9804

Power -1.16533 -1.0400Duration 8.62406 9.4706

Regime Type -0.76336 -0.5784Mechanization 2.556391 2.6176

Helicopters 0.240602 0.3040

Foreign Occupier 0.203008 0.2059Casualties 44.20952 49.526

Yearly Casualty Rate 7.438254 8.1817Per Capita Yearly 0.000908 0.0009413

1976.118 1986.8461.078431 0.730769

0 10 1

21.44039 34.7880816.99783 25.94776

26.62 26.43846

43.61783 52.3862243488.64 14454.161905.673 2956.107-1.87189 -0.3984

1986.255 1985.640.803922 0.881.490196 2

1 1

40.58765 46.619219.17337 12.12824.67059 22.83243.84396 34.9615035.98 15641.083611.308 4292.717-0.29807 -0.19374

9.843137 7.961538 6.509804 51.098039 1.115385 0.862745 0.6-1.05011 -1.03726 -1.02994 -1.0223210.64706 10.19231 8.294118 6.32-1.64706 -1.07692 0.490196 2.122.254902 2.923077 2.980392 3.04

0.215686 0.461539 0.392157 0.320.196078 0.230769 0.215686 0.271.56553 44.61108 27.48639 9.6767212.46744 5.116151 3.90E+00 2.626840.001171 0.000525 0.000711 0.000905

The biggest discrepancies between the full post-War Lyall dataset and the insurgency-type-coded

dataset are in duration, population, and casualties. The conflicts for which insurgency is coded tended to

be longer, be fought in countries with larger populations, and tended to be more deadly (even when

expressed as a yearly average). However, it is worth noting that in actual regressions the lack of

insurgency type coding, while the greatest limiting factor, is not the only one. Missing data was not

Start YearEnd YearWinner

Insurgency TypeInsurgency Type

restricted to solely a few conflicts, but rather spread throughout a number of cases, limiting the total

number that could be included in any multivariate regression, with or without insurgency type coding.

Looking now at the three major insurgency types, we can see substantial differentials across a

number of incumbent and conflict characteristics. Each insurgency type fits a general "conflict profile."

Of the three insurgency types, mixed insurgencies appear to be the hardest for incumbents to defeat,

followed by urban insurgencies and then rural insurgencies. However, they were not the most deadly,

either in absolute terms, as a yearly average, or a per capita yearly average. Rural insurgencies had the

most total casualties across all metrics. Interestingly, urban insurgencies, while being the least violent in

terms of total and yearly casualties, at first appear to be more deadly than rural insurgencies once the

figures are normalized for country population. In fact, by this metric they are only slightly less deadly

than rural insurgencies. However, the result is driven in large part by one conflict - Russo-Chechen I.

Removing this conflict drops the mean per capita yearly casualty rate from 0.0009049 to .000416 (lower

than all other insurgency types). Russo-Chechen I is one of the few regional conflicts that accurate

reflects the characteristics of the conflict zone, with variables measured at the regional level - including

total population. Given the high level of urbanization found in urban insurgencies, it is not surprising that

these conflict countries also had the highest average per capita electricity usage and the highest average

GDP per capita. Not surprisingly, incumbents facing urban insurgencies also tended to be more

mechanized, more powerful, and more democratic.

There does seem to be a clear trend between the level of urbanization and insurgency type.

Mixed insurgencies take place in more urban conflict zones than rural insurgencies, with urban

insurgencies taking place in the most urban conflict zones (on average). Forest cover follows a fairly

predictable trend moving from rural to urban insurgencies, with rural insurgencies taking place, on

average, in countries with the most forest cover, and urban insurgencies the least. However, the same is

not true of the relationship between mountainous terrain and insurgency type. While urban insurgencies

take place in conflict zones with the least amount of mountainous terrain, mixed insurgencies have higher

average levels of mountainous terrain than rural insurgencies. Overall though, insurgency characteristics

seem to support an important assumption about urban insurgencies: When traditional rough terrain is

lacking, the urban environment plausibly provides insurgents with an alternative.

Insurgency ProfilesRural Mixed Urban

Urbanization Low Medium High

Forest Cover High Medium Low

Mountainous Medium High Low

Total Population High Low Medium

GDP (per capita) Low Low HighElectricity Consumption

(per capita) Low Medium High

Linguistic Diversity High Medium Low

3. Predicting Insurgency Types

While the above results point to some clear discontinuities across insurgencies and specific

attributes that characterize each type, mean statistics by definition gloss over complex relationships. In

particular, interesting dynamics between urbanization and insurgency type require further examination.

Urban-

Mixed -

Rural -

Urbanization and Insurgency Typeoe ase mom * **e

wso " *m. m e *e

20 40 60Conflict Zone Urbanization (%)

100

Beyond the clear relationship between urbanization and insurgency type, the above scatter plot

reveals some interesting phenomena and important thresholds. First, the relationship between

urbanization level and rural insurgencies is even more striking than the average reveals. A full 62.7% of

all rural insurgencies (32 of 51) occur in conflict zones with urbanization levels less than 20%. Among

these insurgencies, the state averages a winner score of 0.75. However, in the 38.8% of rural insurgencies

taking place in conflict zones with urbanization levels above 20%, the state averages a winner score of

1.63. Clearly then, neither insurgency type nor the relative level of urbanization by themselves should be

able to tell the whole story about the importance of terrain. What will be critical is how the two variables

interact. Beyond this finding, the data makes visible the minimum urbanization levels in mixed and urban

insurgencies. The minimum urbanization value in a mixed insurgency is just 14.05%. The minimum for

urban insurgencies is also surprisingly low, at just 18.62%.

Forest Cover

Mountainous

Population

GDP per capita

Urbanization

Occupier

Power

Mechanization

Regime

Total Military Personnel

Start Year

-0.0375(0.1588)-0.1510(0.1410)-0.1640(0.2577)0.0931

(0.2410)0.7382***(0.2107)0.1037

(0.1144)-0.1403(0.2208)-0.0128(0.1113)0.2518**(0.1185)0.0289

(0.2894)0.5554***(0.1677)

-0.3 152**-cons (0.1410)

n=102R 2=0.4306

* - p<.10, ** - p<0.05, *** - p<0.01

Insurgents are more likely to choose more urban insurgencies when: the conflict zone is more

urban and the counterinsurgent is more democratic. Conversely, more rural insurgencies are more likely

to be waged when the conflict zone is more rural, poorer, and linguistically diverse, and the

counterinsurgent is autocratic. Forest cover is totally non-predictive of insurgency type, likely reflecting

the fact that urbanization and forest cover are not mutually exclusive - urban insurgencies might very well

be in countries with high levels of forest cover - and that mixed insurgencies utilize rough terrain.

Indicative of the time-series trend in insurgencies, Start Year is a highly significant predictor of

insurgency type. The results are nearly identical (not shown) whether the regression is run on the full

insurgency type variable, or the collapsed one. Interestingly, regime type loses some of its significance

when mixed and urban insurgencies are aggregated. Purely urban insurgencies seem to be doing a fair

amount of the statistical heavy lifting. On the whole, these results reinforce what the summary statistics

have already highlighted: that the relationship between traditional terrain factors and insurgency type is

not necessarily linear, and that each insurgency type has some core attributes. As an important note, none

of these findings indicate that insurgents are making appropriate or wise choices. In order to help

determine whether or not insurgents are making the right choices, we will need to look at predictors of

conflict outcome.

4. Predicting Insurgency Outcomes

Winneri type Insurgent Draw IncumbentRural 15(29.4) 17(33.3) 19(37.3)Mixed 12(46.2) 9(34.6) 5(19.2)Urban 8(32.0) 12(48.0) 5(20.0)Total 35(34.3) 38(37.3) 29(28.4)

Tabulating win/draw/loss statistics by insurgency type reveals a few interesting findings. Mixed

Insurgencies are clearly the most difficult for the incumbent to contend with, with the highest percentage

of outright losses and the lowest percentage of outright wins. Urban insurgencies are slightly more

difficult for counterinsurgents to deal with than rural insurgencies, with a slightly higher percentage of

outright insurgent wins. Interestingly, however, urban insurgencies have the highest percentage (48.00) of

draws of any insurgency type. The cross-tabs themselves don't lend any explanations, but the above

summary statistics suggest some hypotheses - for example, democracies might be more likely to end

conflicts with negotiated settlements. Alternatively, the percentage of draws as a fraction of insurgency

outcomes might be increasing over time. Since urban insurgencies are becoming more prevalent over

time, this time-effect could be the cause of the higher percentage. However, preliminary regression

results (not shown) indicate that while the greatest predictors of draws are conflict duration and start year,

insurgency type has a small and wholly insignificant effect.

Rural

20a -~ - - - - - - Loss

10 ------- Daa 0

0 Total0-25 25-50 50-75 75-100

Urbanization (%)

Urban15 -r

10 -- Eloss

5 ---- - 0 Draw

is Win0 - -

a Total0-25 25-50 50-75 75-100

Urbanization {%)

Mixed12 -

10a

4

220 ~~ ~

25

20

15

IS 10

5

0

a Loss

N Draw

* Total0-25 25-50 50-75 75-100

Urbanization (%)

Mixed/Urban

_____ =Loss.

- Draw

a Total0-25 25-50 50-75 75-100

Urbanization {%)

The series of graphs above helps to illustrate the complex relationship between urbanization,

insurgency type, and insurgency outcomes. Perhaps the most noticeable result is the one indicated in the

earlier scatter plot. The vast majority of rural insurgencies occur in conflict zones with very limited levels

of urbanization. Furthermore, as the regression results below will reinforce, increasing urbanization

benefits the counterinsurgent in rural insurgencies. The outright win rate at urbanization less than 25% is

only 24.2%. Above 25% urbanization, counterinsurgents have a 53.3% outright win rate.

Mixed insurgencies present an interesting case. While they do, on average, occur in more

urbanized settings than rural insurgencies, the plurality of mixed insurgencies (42.3%) take place in

countries that are less than 25% urban (more precisely, 42.3% of mixed insurgencies take place in

countries between 14.05-25% urban). Furthermore, counterinsurgents do relatively poorly against mixed

insurgencies when they take place in these less urbanized conflict zones, winning outright in only two of

nine insurgencies. That being said, counterinsurgents do not fare much better against more urbanized

mixed insurgencies, winning outright in just two of eight mixed insurgencies in the 25-50% urbanization

range, and two of eight in the 50-75% range. The data seems to be indicating that sustaining a major

urban component in a mixed insurgency does not require a high level of urbanization to be successful -

only 14.05%. This should not be altogether surprising since mixed insurgencies are waged in both cities

and the countryside. Given that, mixed insurgencies outcomes should presumably be influenced by other

factors, i.e. traditional terrain variables, in addition to the level of urbanization. The results indicate a

weakly linear, positive relationship between urbanization and outcome. However, it is possible that the

lack of data points, and consequent small number of collapse points, is masking a non-linear relationship.

Pure urban insurgencies are, as shown, concentrated in the more urban conflict zones. However,

even urban insurgencies occur primarily in the 25-50% urbanized range. Of course, insurgencies in

general appear to not take place in very urbanized conflict countries - the average urbanization rate across

the entire dataset is 31.01%. Counterinsurgents win zero of two urban insurgencies in the 0-25% range,

two of thirteen in the 25-50% range, two of seven in the 50-75% range, and one of three in the 75-100%

44

range. While counterinsurgents do best in very urbanized settings, winning outright in three of ten urban

insurgencies in conflict zones with greater than 50% urbanization, there is not enough data to conclude

the nature of the relationship - weakly positive or non-linear. The graphs also reiterate how common

draws are among urban insurgencies relative to other outcomes and other insurgency types. However,

this does not appear to be directly attributable to insurgency type.

Bivariate

Bivariate results- reveal a negative relationship between insurgency type and conflict outcome,

regardless of the insurgency or winner variable (the collapsed measure counts losses and draws the same)

used. In other words, the more urbanized the insurgency, the less likely incumbents are to prevail.

However, only the collapsed measure is statistically significant in the bivariate analysis, no doubt

reflecting the low win rate counterinsurgents have against mixed insurgencies. The results indicate that

by adopting urban elements, insurgents increase their chance of achieving concessions or victory by

15.7%. Of course, bivariate results are not very conclusive.

Insurgency Type -0.137* -0.157*(collapsed) (0.078) (0.088)

R = 0.0165, R2 = 0.0244,2 2R0.0302 R = 0.0309

Multivariate

Fl Mxd / R

Insurgency Type

Urbanization

Mountainous

Forest Cover

Total Population

GDP (per capita)Electricity Consumption

(per capita),

External Support

Linguistic Diversity

Power

Regime TypeMechanization

(per/veh)

Foreign OccupationCasualties

(total)

Duration

Start Year

cons

-0.2304**(0.1123)0.3780

(0.2395)-0.0231(0.1524)-0.1047(0.1696)0.1450

(0.3115)-0.2158(0.2604)

0.2666(0.2623)

-0.2653***(0.0929)-0.2336(0.1624)0.1592

(0.2277)-0.2344**(0.1162)-0.1450(0.1160)

-0.3316**(0.1254)

-0.9285**(0.3727)-0.0419(0.1517)-0.2964(0.2009)

0.8845***(0.1764)

n=102

R2=0.3771

0.6600*(0.3606)-0.2537(0.2408)-0.0971(0.2602)2.0134

(1.7078)-0.3040(0.3763)-0.1836(0.5632)-0.2047(0.1439)0.1844

(0.3665)0.2505

(0.3988)-0.0997(0.1892)0.0119

(0.1699)-0.4218*(0.2182)0.8611

(1.1867)0.0817

(0.2189)-0.2939(0.2928)0.5581

(0.3434)

n=51

R2 =0.3912

0.3900(0.3773)0.1457

(0.2759)-0.1892(0.2604)0.2995

(0.3293)-0.3267(0.4072)0.6581*(0.3349)-0.2288(0.1627)

-0.4609**(0.2091)-0.0590(0.3251)-0.3624*(0.1802)-0.2141(0.1637)-0.2863(0.2004)

-1.2532***(0.4148)-0.2583(0.2838)-0.6180*(0.3229)

1.0910***(0.2709)

n=51

R2 =0.5808

Multivariate regression analysis allowed for the creation of a more comprehensive model of

insurgency outcome. Furthermore, it enabled competing explanations to be compared for both statistical

significance and magnitude of impact. Most importantly, it enabled individual variable effects to be

isolated through the use of control variables. Multivariate analysis of the predictors of insurgency

outcome revealed a number of significant and substantive findings. Importantly, the results indicate that

insurgencies with larger urban components are harder for the counterinsurgent to defeat. The effect is

substantial: all other things being equal, a shift from a purely rural to a purely urban insurgency decreases

the chance of counterinsurgent victory by 23.04%. Furthermore, the result holds across a wide variety of

model specifications (not shown) that vary both the inclusion of various control models and the use of

different measures of the same variable (i.e. casualties). Additionally, it is important to note that this is

the non-collapsed measure. Although the result has a higher degree of statistical significance when

insurgency type is collapsed, the non-collapsed finding represents more robust support for the theory.

Effect of Insurgency Type on Conflict Outcomes

00 0

0 0 0

0 0p X

0

LO 0 0 0

00

-1 -.5 0 .e( L-type IX)

coef= -.2807509, se =.12721605, t =-2.21

Importantly, the significant impact of insurgency type on outcome is independent of the actual

level of urbanization in a given conflict country. The effect of urbanization, independent of insurgency

type, is large and positive - although statistically insignificant. In other words, the greater the level of

urbanization in the conflict zone, the more likely that the counterinsurgent is victorious. This is the first

clear indication in the results that higher levels of urbanization, in and of themselves, are not necessarily

good for the insurgent. Even when insurgency type is excluded from the regression, the positive

47

relationship remains. However, given that this regression is across all insurgency types, it should not be

surprising to see a non-negative relationship - the predictions relating urbanization to insurgency outcome

pertain specifically to urban or mixed insurgencies.

Traditional terrain factors (forest cover, mountains) are found to be non-predictive for insurgency

outcome, although they do at least act in the expected direction. However, these effects are, again,

independent of insurgency type. In contrast, linguistic diversity, perhaps acting as a proxy for human

terrain, has a larger magnitude (although is likewise statistically insignificant)- the greater the number of

languages spoken in a given conflict region, the lower the probability of counterinsurgent success.

Although non-significant, the multivariate regression results indicate that higher GDP per capita

decreases a counterinsurgent's probability of success, conversing higher energy consumption increases

the probability of counterinsurgency success. Finally, external support is highly significant and negative.

Insurgent groups that have the benefit of foreign aid or foreign sanctuaries are much more likely to avoid

defeat, giving tacit support to the idea that relative balances of power matter.

Incumbent characteristics provide few statistically significant findings, with the exceptions being

foreign occupation and regime type. Foreign occupation is the second largest and statistically significant

predictor of insurgency outcome, surpassing insurgency type, external support, and regime type.

Surprisingly, even taking into account this effect, regime type remains statistically significant. Increasing

democracy is shown to be a hindrance to counterinsurgents. On the other hand, mechanization is shown

to be a statistically insignificant, although negative, predictor of outcome. Likewise, overall

counterinsurgent power is non-predictive. Finally, increasing levels of (absolute) casualties are shown to

have the greatest (negative) impact on insurgency outcomes. The more deadly a conflict, the less likely

the counterinsurgent will prevail. Although not shown, the finding holds for the yearly average measure,

although not the per capita yearly average measure. All act in the same direction (although only when

the per capita yearly measure is run without outlier Russo-Chechen I). The result seems to indicate a

sensitivity of counterinsurgents to costs - at least as measured by fatalities. Given the large coefficient on

casualties, it is important to note that the model's predictions remain essentially identical if the potentially

endogenous variables (casualties and duration) are excluded, (result not shown).

The conditional regressions reveal a number of interesting and significant results. First,

increasing urbanization is a benefit to counterinsurgents not just in rural insurgencies but also in

mixed/urban ones, although insignificantly so in both regressions. Furthermore, the effect is larger, and

statistically significance in mixed/urban insurgencies. Traditional terrain roughness is a hindrance to the

counterinsurgent in both insurgency types, although not in a statistically significant manner. However,

forest cover has a greater impact in rural insurgencies, as might be expected. The effect of electricity

consumption on conflict outcome is the complete opposite. In mixed/urban insurgencies, development

hinders the counterinsurgent, whereas in rural insurgencies it helps - to a statistically significant degree.

If development is indeed acting as a proxy for soft target prevalence, then this would seem to support the

proposed theoretical framework. Oddly, external support is statistically significant in neither insurgency

type, even though it is highly significant across the full dataset. Linguistic diversity is statistically

significant, but only among rural insurgencies. Being a foreign occupier is a significant liability to

counterinsurgents in all insurgency types; however, the effect is only significant among mixed/urban

insurgencies. Indeed, foreign occupiers have not defeated an urban or mixed insurgency outright in the

entire post-war period. On the other hand, democracy appears to only be a hindrance in rural

insurgencies. The variable loses its statistical significance in mixed/urban insurgencies. Finally, the

statistically significant effect of casualties in the full regression appears to be driven entirely by rural

insurgencies. Whereas urban insurgencies predict a positive effect of casualties, the effect is highly

significant (p=0.005) and highly negative in rural insurgencies.

5. Intermediate Dependent Variable Testing

-0.0399 -0.0753 -0.0805Urbanization (0.0678) (0.0484) (0.1445)

0.0713* 0.0145 0.2066**Terrain Roughness (0.0397) (0.0281) (0.0902)

0.3590*** 0.0470 0.2917**Total Population (0.0802) (0.2122) (0.1234)

-0.0229 -0.0080 -0.0355GDP (per capita) (0.07196) (0.0495) (0.1589)

-0.0187 -0.0428 0.0554Linguistic Diversity (0.0454) (0.0439) (0.0802)

0.0236 0.0133 0.0559External Support (0.0263) (0.0190) (0.0583)

0.0135 0.0468 -0.0774Foreign Occupation (0.0341) (0.0279) (0.0685)

0.0325 0.0333 0.0591Power (0.0577) (0.0448) (0.1207)

0.0156 0.0090 0.0188Mechanization (0.0327) (0.0236) (0.0646)

-0.0685** -0.0596** -0.1382**Regime Type (0.0316) (0.0309) (0.0606)

-0.0207 0.0481 -0.1634Duration (0.0434) (0.0293) (0.1092)

-0.0661 0.0367 -0.1591Start Year (0.0511) (0.0367) (0.1041)

0.0409 0.0173 0.0756_cons (0.0493) (0.0391) (0.1054)

n=102 n=51 n=512= 2 2=R =0.3468 R =0.3898 R =0.4384 ..

Insurgency type was found to be a statistically significant predictor of insurgency outcome across

a variety of model specifications. In order to test possible causal mechanisms and associated hypotheses,

multivariate regressions were run against two intermediate dependent variables - total casualties and

conflict duration. The multivariate results make it clear that while urban insurgencies are not more deadly

than rural insurgencies, the relationship is clearly more complex than simple summary statistics would

indicate. Whereas the summary statistics indicated that more urban insurgencies were consistently less

deadly, the above results indicate that once other variables are controlled for, they are actually slightly

more deadly than more rural insurgencies, although the relationship is far from significant. Nevertheless,

is not altogether surprising that insurgency type does not predict total casualties in a statistically

significant manner. Insurgency type indicates nothing about the frequency or nature of violent

interactions. Battles in urban insurgencies might be more or less frequent or more or less deadly.

Furthermore, insurgency type indicates nothing about the size of the insurgency.

Perhaps the most surprising result of the full-dataset multivariate analysis is that conflict duration

is negatively correlated with total casualties. This could be indicating that very long insurgencies (i.e.

10+ years) are typically lower-intensity conflicts and so have a lower number of total casualties, even

though there is a longer period over which they can accrue. Conflict population has a (predictably)

statistically significant and highly positive effect on total casualties. In fact, all other variables pale in

comparison to the size of the effect exerted by total population on total casualties. Larger countries end

up being more deadly battlegrounds (in absolute terms). Regime type has a very significant effect on total

casualties - the more autocratic a regime, the bloodier the insurgency. However, it is unclear whether this

is due to more harsh COIN practices or to some sort of grievance-related explanation. Traditional terrain

factors also have a significant impact on overall casualties. State power and mechanization, although not

statistically significant, are both positively associated with total casualties. More powerful and

mechanized militaries would presumably be more deadly due to their force structure and likely overall

military doctrine. Foreign occupiers are also found to be more deadly counterinsurgents, but in a

statistically insignificant way.

The conditional analysis reveals some interesting and significant differences between the casualty

predictors for mixed/urban and rural insurgencies. As might be expected, the statistically significant,

positive, relationship between terrain roughness and casualties is restricted to rural insurgencies. Almost

certainly reflecting the geography of conflicts, terrain roughness has no effect on the number of casualties

in mixed/urban insurgencies, a testament to the strength of the relationship in rural insurgencies. In fact,

in rural insurgencies, terrain roughness is the second largest statistically significant predictor of total

casualties. This is just one of a number of divergences. Population distribution and the geography of

conflict are potentially impacting the conditional effect of total population on casualties - insurgencies in

more populous states are only more deadly when insurgency is rural. If an insurgency is primarily urban

in nature, it is irrelevant whether the country is populous or not.

Partitioning urban and rural insurgencies reveals a more complex relationship between duration

and total casualties. Among mixed/urban insurgencies, the relationship carries the expected positive

coefficient and is nearly significant (p=0.135). However, among rural insurgencies the relationship is

negative and highly significant. If the negative coefficient is capturing different conflict profiles - i.e.

low intensity vs. high intensity - the dichotomy appears to be restricted to rural insurgencies. Incumbent

power and mechanization have no significant relationship to total casualties in either urban or rural

insurgencies Foreign occupation is significant in neither regression, although it approaches significance

in mixed/urban insurgencies. Additionally, the effect is only positive in mixed/urban insurgencies. In

rural insurgencies, the total casualties are lower when the counterinsurgent is a foreign occupier. Finally,

the statistically relationship between regime type and casualties holds in both types of insurgencies,

although the effect is much stronger in rural insurgencies. Regime type is the only statistically significant

predictor of total casualties in mixed/urban insurgencies.

-0.2013 -0.0714 -0.4586**Urbanization (0.1669) (0.2671) (0.2085)

0.1656* 0.2870* 0.2541*Terrain Roughness (0.0958) (0.1481) (0.1262)

0.3340* 0.5132* 0.1304GDP (per capita) (0.1793) (0.2766) (0.2387)

-0.3418* -0.1350 -0.3597**Total Population (0.1944) (1.1778) (0.1712)

Electricity 0.0440 -0.0379 0.1934Consumption (0.1841) (0.4192) (0.1923)

0.1373 0.3711 0.1602Linguistic Diversity (0.1103) (0.2447) (0.1148)

0.0421 -0.0995* 0.2021**External Support (0.0645) (0.1045) (0.0791)

-0.1409 -0.1087 -0.2690**Foreign Occupation (0.0868) (0.1576) (0.1065)

0.2940* 0.3568 0.3613*Power (0.1560) (0.2954) (0.1826)

-0.0600 -0.0062 -0.1681*Regime Type (0.0794) (0.1304) (0.0943)

-0.0914 -0.0567 -0.0252Mechanization (0.0807) (0.1299) (0.0966)

0.0152 -0.1985 0.1010Start Year (0.1381) (0.2202) (0.1780)

0.0152 0.0530 -0.0225_cons (0.1381) (0.2618) (0.1555)

n=102 n=51 n=51

R2=0.2026 R2=0.3089 R2=0.4073

Analyzing the effect of insurgency type on conflict duration, as well as different predictors of

conflict duration by insurgency type, requires recognition of the limits of the dataset used. As indicated

earlier, the dataset used differed from the wider Correlates of Insurgency dataset in a few specific regards.

One of these is in duration. Coded cases were an average of 0.85 years longer than cases in the full

dataset, or 9.86% longer. As a result, analysis using duration as a dependent variable is somewhat non-

representative. Furthermore, given that conflict duration was not in any way a predictor of insurgency

outcome, it is hard to fully interpret these results. Nevertheless, duration is the strongest predictor of

negotiated settlements.

Across the full dataset, there were a number of statistically significant predictors of conflict

duration. Although insurgency type was entirely non-predictive, terrain roughness, GDP per capita, total

population, and state power were all significant predictors. Of these, the results for terrain roughness and

GDP per capita are intuitive. Rougher terrain should enable insurgents to compensate for the

asymmetries they face by enabling them to hide and fight more effectively. A closer effective power

balance should lead to a longer conflict. As a "means of insurgency" variable, more wealth would help

the insurgency sustain itself. At the same time, this violates the "opportunity cost" hypothesis. However,

a positive effect for state power is unexpected. A stronger state would presumably be able to end a

conflict more quickly. This effect is also independent of the effect of mechanization, which presumably

captures the more military-oriented components of state power.

Partitioned analysis revealed some highly unexpected results. Mixed/urban insurgencies have

just two significant predictors of duration - terrain roughness and GDP per capita. That terrain roughness

matters in both insurgency types is likely a consequence of the collapsed insurgency type measure being

used, which combines mixed and purely urban insurgencies. The statistical significance of GDP per

capita, which is a significant predictor of duration only in mixed/urban insurgencies (and the largest at

that), supports the notion that wealth sustains the urban insurgent. A number of variables were

statistically significant only in rural insurgencies, including the level of urbanization, total population,

external support, foreign occupation, state power, and regime type. Rural insurgencies fought in more

urban conflict zones tended to be significantly shorter, as did those fought in more populous states.

External support, on the other hand, extended the length of conflicts. Foreign counterinsurgents fought

shorter insurgencies. Finally, while the effect of regime type on duration was negative in both

regressions, the effect was only significant in rural insurgencies. Democracies fight shorter insurgencies.

As previous results show, this isn't because they win quickly.

Chapter VII: Discussion

Quantitative analysis shed a significant amount of light on the complex relationship between

terrain factors (broadly defined), insurgency type, counterinsurgent characteristics, and conflict outcomes.

As will be discussed below, the results force a reevaluation of both the existing literature and the

theoretical framework articulated in earlier sections. However, the results must be evaluated with an

understanding of the significant posed by the data used. Further refinement of the data, coupled with the

interesting findings discovered thus far, will allow for extensive future research.

1. Limitations

Interpreting the results of the above analysis requires recognition of the inherent limits imposed

by the data. As the earlier summary statistics indicate, although the insurgency-coded subset of the

dataset used in this analysis is fairly representative of the entire insurgency dataset, there are some key

differences. While it is unclear if the differences are significant, it must be again noted that the truncated

dataset contains conflicts that were longer, more violent (by all casualty measures), that were fought in

less populated countries. This could be introducing bias into the above results. The Lyall case list, used

as the basis for this dataset, could also be flawed.

Furthermore, the dataset lacked some important variables. Utilizing the physical and human

cover of cities is but one way that insurgents and states can manipulate a relative power differential.

While the dataset does code for external support of insurgents, it fails to account for external support for

the incumbent actor. External support is likely critical if the incumbent is presiding over a newly

independent state. Furthermore, it goes without saying that conducting an analysis predicated on power

differentials is inherently limited when it is only possible to directly measure the power of one of the

primary actors. Insurgent "power" was indirectly approximated by utilizing "means of insurgency"-type

variables, but the lack of any sort of CINC-for-insurgencies measure is constraining. The dataset also

55

lacked some other key means of insurgency variables, particularly the presence of exploitable

commodities. Given the importance of exploitable commodities in conflict duration literature, the

variable's exclusion was somewhat problematic for intermediate dependent variable testing. Other

omitted variables, like a coding for ethnic vs. non-ethnic conflicts and for overall ethnicity (i.e. ELF),

might be important predictors of, or at least control variables for, casualty rates. Finally, while this study

focused on insurgent strategy, the omission of a counterinsurgent strategy variable - however crude - is

less than ideal and requires a covering assumption that counterinsurgent strategy is not a determinant of

conflict outcome, casualties, or duration. Furthermore, some of the measures used might be invalid or

inaccurate. Besides urbanization, there might very well be some accuracy issues with the other terrain

variables - forest cover and mountains - when conflicts are regional and/or separatist (Kosovo,

Chechnya, Tibet). It is also unclear what the right level of analysis is for conflict zone characteristics for

conflicts that are regional or international in nature.

Casualty estimates present another opportunity for data refinement. Unfortunately, a valid

casualty measure may be impossible to come across. The theoretically framework predicts higher levels

of indiscriminate, i.e. civilian casualties, when insurgencies are urban and the urbanization level is higher.

Additionally, higher COIN casualties are expected. However, these higher levels are not in absolute

terms. As mentioned earlier, a hallmark of urban insurgencies is the urban cell. Large formations of

insurgents cannot be fielded, lest they risk detection and disruption. Engagements are therefore, by

necessity, on a smaller scale. What the theory is predicting are not higher absolute levels of

counterinsurgent and civilian casualties, but a higher percentage. Additionally, raw casualty figures make

it impossible to distinguish between high value and low value casualties. However, given the lack of

casualty specificity for most conflicts, as well as the inherent difficulty of distinguishing between civilian

and non-civilian casualties in a civil war, let alone high- and low-value targets, such precision might be

impossible. Furthermore, there are also accuracy concerns, particularly when trying to normalize

casualties for population size. Total state population statistics could be skewing the results if the relevant

56

level of analysis is not the state but the conflict zone. The Russo-Chechen I was an outlier in the casualty

analysis, likely because it had the most accurate value. The population figure used was specific to

Chechnya. Not surprisingly, the Russo-Chechen I conflict had the highest per capita casualty figure,

followed by the Polisario conflict. In this conflict, the population figure used was also specific to the

conflict zone (Western Sahara). However, it is unclear how actors register "costs" of conflicts, especially

the human casualty component of costs.

There might also be accuracy issues with the ordinal insurgency coding. While the difference

between a predominately rural insurgency and a predominately urban insurgency is fairly clear, mixed

insurgencies can be quite difficult to code. A number of rural insurgencies contained some urban

elements but were nevertheless coded as rural since their relative scale and importance (as emphasized in

literature) seemed small compared to the rural efforts. The same is true of some urban insurgencies.

More thorough case research could alleviate this bias. While very limited, the ACLED could be used in

future studies to check the relevant overlapping cases.

The study is also limited by an overall lack of cases. While this is not necessarily an issue when

regressions are run across the full dataset of 102 cases, it makes partitioned analysis difficult especially

when regressions have only 51 cases and ten independent variables. In order to draw more definitive

conclusions about predictors of dependent variables (outcome, duration, casualties) by insurgency type or

by counterinsurgent type (democracy vs. autocracy, foreign vs. indigenous), or to see how relationships

have changed over time, it will be necessary to code more cases. More cases will also allow for the

disaggregation of mixed and urban insurgencies. It seems clear that lumping the two types together is

obscuring the relationships between independent and dependent variables. With more cases it will be

easier to test hypotheses about the variable effects of terrain variables on conflict outcome as well as

variation in effects over time. The dataset also omits a number of more recent pure urban and mixed

insurgencies, some presumably due to their low casualty figures. More urban insurgencies will help

clarify the relationship between urbanization level and urban insurgency success.

Finally, the dataset fails to reliably account for a major strategic component of urban insurgencies

- the ability to undermine support for the incumbent government by attacking the high density of

relatively soft and undefended targets. None of the independent variables used in this analysis do a

particularly good job of approximating even the existence of these targets - although per capita electricity

consumption is a start - let alone their destruction. Although this effect is potentially captured as part of

the more encompassing insurgency coding, the inability to separate the various casual mechanisms of the

proposed theoretical framework limits the analysis.

2. Shifts in the Geography of Conflict

The geography of conflict has clearly changed, with insurgencies increasingly taking place in

urban settings. Throughout only the second half of the 20t century, the percentage of ongoing

insurgencies with a major or dominant urban component has risen substantially. Undoubtedly, a full 20h

Century dataset would make this dramatic rise in urban insurgencies even more pronounced. Urban

insurgency is an unavoidable reality. Furthermore, it is clear that urban insurgencies and rural

insurgencies differ across a number of dimensions and present different sorts of challenges for different

types of counterinsurgents. A recognition of and adaption to these crucial differences will plausibly

increase the ability of 21" Century counterinsurgents to succeed in future conflicts.

Although it is impossible from a large-n analysis to determine conscious choice, the regression

analysis does provide very clear indications of what sort of conflict and counterinsurgent characteristics

are associated with various types of insurgency. Urban insurgencies become much more likely when the

counterinsurgent is democratic and the conflict country is urban. The fact that urban insurgency becomes

more likely the more urban the conflict country is provides tacit proof that insurgents do follow the

population. Regardless, the mere fact that the United States will remain a democracy for the foreseeable

future, and that the world is getting increasingly urbanized, reinforces the importance and relevance of

understanding urban insurgencies. Urban insurgents seemingly seek to exploit the space for action

58

created by democratic societies. Where democracy is lacking, insurgents are more likely to be found in

the countryside. Of course, none of this is to say that insurgents are making the right choice. Insurgents

are not statistically more likely to pick a more urban insurgency type when the counterinsurgent is

foreign, even though foreign insurgents are more likely to lose mixed/urban insurgencies than rural ones.

On the other hand, insurgencies are much more likely to be urban if the counterinsurgent is more

democratic, even though democracies perform much worse in rural insurgencies than in mixed/urban

ones. And of course, there is the complex relationship between urbanization and outcome.

3. Corroborating and Challenging the Existing Literature

The quantitative results corroborate existing findings and theories on predictors of insurgency

outcome and, importantly, contradict others. Across the full dataset, there were five (excluding the time

control and constant) statistically significant predictors of outcome: insurgency type, external support,

regime type, foreign occupation, and total casualties. Two of these variables - external support, and

occupation - have been found to be statistically significant (Lyall, 2009) in prior large-n analyses. That

external support improves the chances of insurgent victory should not be surprising, since external

support directly addresses the critical power imbalance that by definition accompanies insurgencies. The

finding confirms those of Gleditsch (2007). The significant finding for foreign occupation also

corroborates Mack's (1975) interest asymmetry theory. Indeed, strongly confirming Hypothesis 10,

foreign occupation was the second largest predictor of insurgency outcome across the full dataset - less

important than total casualties but more important than insurgency type and external support. The fact

that the relationship also holds within both insurgency types (although insignificantly in rural

insurgencies) provides even stronger support. This particular finding provides some insight into the

strategic benefits of urban insurgencies. If we assume foreign occupation approximates an interest

asymmetry, and therefore an increased sensitivity to costs, then the fact that foreign occupation is only

significant in urban insurgencies would seem to indicate that the urban environment provides a better

59

venue for exploiting cost sensitivity. If foreign occupation instead correlates with an unwillingness to

engage in harsh COIN techniques, then cities would likewise appear to be the best location to exploit this

unwillingness. The above results indicate a significant democracy penalty. Even with foreign occupation

controlled for, democracies are systematically worse than autocracies at fighting insurgencies (p=0.032).

This agrees with some of the literature (Merom, 2003), which has suggested either a negative or non-

finding. However, Merom's work (2003) does not control for foreign occupation. In fact, all of his cases

are instances of foreign occupation. Importantly, neither incumbent power nor incumbent mechanization

level were found to be statistically significant predictors of insurgency outcome. Taken together, these

two non-findings would appear to require a qualification of the large body of theoretical work that views

great powers or conventional militaries as unable, doctrinally, to fight insurgencies (Cohen, 1984;

Cassidy, 2000). More powerful or conventional militaries are not any worse at fighting insurgencies; it is

simply the case that their increased power in no way improves their probability of success, something that

would likely be the case in a conventional war. The non-finding for mechanization runs counter to the

work of Lyall (2009). This is especially interesting given that the dataset used in this study is derived

from Lyall's own work. Mechanization was rarely ever a significant predictor even when critical

variables are removed, like the time control and, importantly, insurgency type, and typically only when

the helicopter dummy variable was used. With a lack of statistical significance when a time control is

included, Lyall appears to be merely identifying a collinearity of rising levels of mechanization and

decreasing counterinsurgent success. Finally, the fact that total casualty levels were the greatest predictor

of insurgency outcome provides support to the Mason and Fett (1996) expected utility model, and

confirms Hypothesis 6. Mason and Fett predict that the greater the level of total costs (incremental costs

summed over the duration of the conflict), the lower the expected utility of outright victory, and the

greater the likelihood of that participant accepting defeat or pursuing a negotiated settlement - in short,

settling for less than outright victory. Conceptualizing total casualties as a total cost for the incumbent,

this analysis found that higher costs predicted a lower chance of outright victory for the incumbent.

60

However, refuting Hypothesis 5, conflict duration was not significantly or negatively predictive of

conflict outcome. Nevertheless, it is possible that part of the effect of duration could be realized through

an increase in casualties. Although the linguistic diversity variable is probably more accurately a "human

terrain" proxy than a true ethnicity measure, the significant finding -albeit only in rural insurgencies -

supports the importance of ethnicity in predicting insurgency outcomes. Contrary to the findings of

Kaufmann (1996), ethnic insurgencies do appear to exist. Finally, the nearly statistically significant,

negative, year control variable could be acting as a proxy for any number of time-related trends and as a

result provide token support for Kahaner (2006), whose alternate explanation was not otherwise explicitly

incorporated into the model.

4. Reevaluating the Importance of Urbanization

The results provided evidence that ran counter to some key components of the theoretical

framework laid out above - namely, a negative linear relationship between urbanization and conflict

outcome, conditioned on insurgency type. On the contrary, counterinsurgents fighting urban insurgencies

were found to fair better when the conflict zone was highly urbanized - although the effect was

insignificant - refuting Hypothesis 2. This would have been expected - and was observed, confirming

Hypothesis 2a - for rural insurgencies by demonstrating that insurgents pay a price for choosing the

"wrong" insurgency type. The presence of a more urbanized population worked against urban

insurgents. However, this is not to say that urban insurgency itself is a bad choice for insurgents, as will

be addressed below. On the whole, this seems to invalidate, or at least require qualification of, a simple

base of power argument to explain the relevance of urban insurgency. Where urban insurgencies

succeeded, it was in spite of increasing urbanization.

Possible Urbanization-Outcome Relationshipwithin Urban Insurgencies

State VictoryObservable

State Defeat

Level of Urbanization

There are a number of possible explanations for this unexpected outcome. As has been touched

on above, the relationship could be non-linear. Some minimum level of urbanization is needed to sustain

the urban components of a mixed or purely urban insurgency. The results showed a sharp change in

insurgency type around an urbanization level of about 20%. Above this, the prevalence of purely rural

insurgency drops significantly. Once a minimum level is met, increasing urbanization might provide the

insurgent with more cover, more potential recruits, and more targets. However, at a certain point, higher

urbanization might begin to correlate with more capable state police or intelligence or lower individual

incentives for insurgency. In this vein, total urbanization might be irrelevant to sustaining an urban

insurgency as long as there is one city - i.e. a capital - large enough, and important enough, to sustain

one. On the other hand, it is possible that the half of the parabola where very low urbanization is also

associated with state victory simply isn't observable because insurgents, seeking to improve their chances

of victory, simply choose to wage rural insurgency in such conditions. After all, insurgency type is not

randomly assigned to insurgencies fought in a perfect range of conflict settings. Instead, insurgency type

is chosen with the objective of maximizing the insurgents' probability of success. The results showed a

strong, linear, relationship between increasing urbanization and an increasing probability of more

urbanized insurgency. Given this, what we might be observing is only the part of the spectrum to the

right of the "minimum" (where counterinsurgents fare worst), or at least a segment where the regression

62

produces an overall positive relationship. Alternatively, the lack of coded cases could be putting undue

emphasis on statistical outliers.

The urbanization metric being used could also be an invalid or inaccurate measure. Different

urban measures, like gender ratios, urban infrastructure, slum population, or the rate of urbanization,

might be better at capturing the benefits (human concealment/cover, physical cover, etc) an urban

environment presents to the insurgent. On the other hand, like many state-level indicators, country-wide

percent urbanization might just not be an accurate measure, particularly if a conflict is regional in nature.

If a successful urban or mixed insurgency takes place in a province more urbanized than the country

average, then the effect of urbanization would be biased in the positive direction. More generally, the

country-wide measure might not be accurately capturing where the revolutionary base of power actually

lies. If a conflict is very ethnic and ethnic geography falls on an urban/rural divide, then bias is

introduced. For example, if an ethnic insurgency's kin group is disproportionately rural, then high

urbanization could be correlated with incumbent success.

5. The Peril of Urban Insurgencies

Given all of this, insurgency type was still statistically significant in a variety of multivariate

regression models, independent of urbanization level. The effect was negative across all models - more

urban insurgencies were harder for counterinsurgents to defeat. This is the strongest support of the

theoretical framework to come from the data presented, and corroborates a large body of case literature

predicting the growing importance of urbanized insurgencies (Soreson, 1965; Taw and Hoffman, 1994;

Miller 2002; Marques, 2003; Fair, 2004). However, it must be noted that the effect is not strictly linear.

When mixed and urban insurgencies were aggregated, the effect was much more statistically significant.

As the summary statistics showed, mixed insurgencies did have the lowest counterinsurgent win rate

among all insurgency types. That being said, the finding is important. Whereas insurgency might

historically have been a rural phenomenon, in the post-war period more urban insurgencies have been

63

harder for counterinsurgents to defeat. This partly corroborates the earlier findings of Condit (1973), who

similarly found that mixed insurgencies were the hardest to defeat. However, her analysis was on a

smaller scale and, importantly, covered a different time period: one that mostly predated the explosion of

urbanized insurgencies. Contrary to what Condit found, it now seems that purely urban insurgencies are

harder for counterinsurgents to defeat than purely rural insurgencies. This finding also contradicts Fearon

and Laitin (1999), who emphasized the importance of a rural base (of power) some distance from the

centers of government power and not easily reachable by roads, as essential to insurgency. While such a

base no doubt contributes to the success of rural and mixed insurgencies, urban insurgents appear to do

just fine relying primarily on an urban base and an urban theater of operations.

Furthermore, the fact that more urbanized insurgencies are harder for counterinsurgents to defeat,

independent of urbanization level, lends greater support to the non-linear relationship outlined above.

While more urbanization might not always be good for the insurgent, at some level it would have to be.

The urban components of a mixed or purely urban insurgency presumably require some threshold to be

surpassed in order to be sustained. Since insurgencies with these components are more difficult to defeat,

a negative relationship between urbanization and insurgency outcome should theoretically exist, even if

the reasons stated above preclude its observation.

The ability to distinguish between insurgency types allowed for more detailed analysis. Not only

are there clear differences between insurgency types, as evidenced in the summary statistics, but the two

insurgency types have different statistically significant predictors of outcome. As was already mentioned,

the effect of foreign occupation and urbanization varied by insurgency type. Depending on the model

specifications, mixed/urban insurgencies had four statistically significant predictors (urbanization and

foreign occupation), whereas rural insurgencies had five (electricity consumption, linguistic diversity,

regime type, total casualties, and start year). Furthermore, various variable effects were reversed once the

regressions were conditioned on insurgency type - however the partitioned effects rarely ever approached

statistical significance. Importantly, any form of democracy penalty appeared to be primarily restricted to

64

rural insurgencies. In this sense, the conditional results do corroborate the findings of Merom (2003). On

the other hand, foreign counterinsurgents appear to struggle primarily in mixed or urban. These

conditional results corroborate Mack (1975). That terrain variables exerted negative effects on conflict

outcome across both insurgency types is likely evidence of one of two things: Either the pooling of mixed

and urban insurgencies distorts results, or terrain factors hinder counterinsurgents even if insurgencies are

primarily urban. The classification of "purely" urban insurgency is, after all, relative. Finally, the effect

of total casualties on conflict outcome was negative only in rural insurgencies, a finding that will be

elaborated on below.

While insurgency type was found to be a statistically significant predictor of conflict outcome,

the causal mechanisms remain unclear. That foreign occupiers are particularly hindered in mixed/urban

insurgencies is at least suggestive of possible explanations. These actors are potentially the ones most

sensitive to costs or most unwilling to adopt harsh tactics. Given the imperatives for individual security

and the need to constantly evade security forces, urban insurgencies likely require some level of societal

openness to survive and operate. Insurgents likely recognize the benefits of democracy, and choose

accordingly, evidenced by the fact that regime type is a very significant predictor of insurgency type.

However, while democracy might be necessary for establishing an urban insurgency, the results show that

democracies do not improve insurgents' chance of victory. Likewise foreign occupiers might be

particularly handicapped against urbanized insurgencies if those conflicts (a) provide better opportunities

for imposing costs on counterinsurgents than more rural insurgencies, or (b) it is easier to arouse

nationalist sentiment in an urbanized insurgency, or (c) the identification problem is harder to solve in

urban contexts foreigners, then it might explain the significance of foreign occupation. Of course, (b) and

(c) are both inherently cost-related explanations. Of course, the analysis provides no direct test of these

suppositions. Given the relatively small number of instances of foreign occupation in the truncated

dataset, it is impossible to run conditional regressions.

Increased development, a weak proxy for soft-targets, was associated with increased

counterinsurgent defeat in mixed/urban insurgencies (although not significantly) and increased success in

rural insurgencies. This weakly supports the proposed theoretical framework. Development presumably

doesn't matter as much, indeed benefits the counterinsurgent, when the insurgency is rural. First, in rural

insurgencies development/infrastructure targets are likely dispersed. Furthermore, in a rural insurgency

this smaller negative effect is likely balanced by a larger positive effect. Specific types of development,

i.e. roads, might specifically benefit the counterinsurgent in rural insurgencies by increasing power

projection in rough terrain and rural areas. However, in urbanized insurgencies, the infrastructure is part

of the terrain. Greater development also means more targets for the insurgents to attack - targets that,

while dispersed in rural theaters, are concentrated in urban ones. However, the observed effect is far from

significant. More specific development or soft target metrics might strengthen the evidence.

As has been articulated, duration analysis was somewhat limited by data considerations, and

insurgency type was found to have no significant effect on casualties. Recognizing the limitations of the

duration analysis, Hypothesis 4 was somewhat supported. In rural insurgencies, the more urbanized the

conflict zone, the shorter the insurgency. On the other hand, greater urbanization did not increase the

duration of mixed/urban insurgencies. Urbanized insurgencies are not more or less bloody than rural

insurgencies, once control variables were included. Within urbanized insurgencies, increasing

urbanization did not lead to higher total casualties, refuting Hypothesis 3. This would not be an issue if

Hypothesis 7 was satisfied: the magnitude of the effect of casualties on outcome will greater in urban

insurgencies. If urban insurgencies don't produce more casualties than rural insurgencies (as was found,

refuting Hypothesis 7), but each casualty matters more in an urban insurgency than a rural insurgency,

then it help explain why urban insurgencies are harder for incumbents to defeat. The difficulty of

urbanized insurgencies would lay in some intrinsic characteristic that magnified the importance of

casualties. Looking at predictors of casualties in mixed/urban insurgencies would then provide insight

into prescriptions. For example, we would be able to say that greater power leads to higher casualties in

66

mixed/urban insurgencies, and that each of those casualties has a greater effect on the outcome of the

insurgency than if it had been incurred in a rural insurgency. Actions taken to neutralize the relationship

between power and casualties might then increase the probability of incumbent victory.

However, total casualties itself had no effect on the outcome of mixed/urban insurgencies,

whereas and the variable was highly statistical significance in rural insurgencies. The eventual political

outcome of a rural insurgency appears to be more directly tied to the military campaign. This directly

contradicts Hypothesis 7. Does this mean that casualties and cost accumulation do not influence conflict

outcome in urban insurgencies? Potentially, but not necessarily. Urban insurgents might only need to

inflict casualties past a certain threshold in order to register as a legitimate threat to the state. As

mentioned earlier, urban insurgents might be more susceptible to a popular backlash if total casualties

increase substantially beyond this threshold. Alternatively, if different kinds of casualties register as

different "costs," then a metric that treated all casualties equally would misrepresent the connection

between casualties and outcomes. Urban insurgencies are known for the opportunities they provide to

strike at higher value government targets.

However, perhaps every casualty is roughly equal, but total casualties isn't a proper proxy for

costs. Total casualties does not account for the population of the conflict state. In very crude terms, if we

believe that cost-sensitivity is some function of how much a state has to "spend" - i.e. the state's total

population - then per capita measures would be appropriate than an absolute figure. Indeed, once the per

capita measure was used, casualty effects were found to be more statistically significant and much larger

in urban insurgencies - although still insignificant with p=O. 162. If this result were to hold in a larger-n

analysis, it would provide strong evidence of a cost-magnifying effect of urbanized insurgency.

Furthermore, when a population adjusted casualty measure is used as the dependent variable rather than

the total measure, insurgency type is a nearly significant predictor. It still remains to be determined what

type of casualty measure (absolute, per capita, yearly average, or some combination) best addresses the

hypothesized causal mechanisms.

6. Rough Terrain and Rural Insurgencies

Relating conflict characteristics, costs, and outcomes requires less explanation when rural

insurgencies are considered. That linguistic diversity was a statistically significant predictor of rural

insurgency outcomes, and casualties a highly significant predictor, provides some important insight.

First, the fact that the effect varies by insurgency type is fairly novel. Fearon and Laitin's (1999) work on

the outbreak of large scale ethnic conflict does point to the importance of having a rural base of power.

The significant finding would imply that a similar dynamic predicts outcomes. If we instead take

linguistic diversity to approximate "human terrain," then it is not altogether surprising to find the effect to

be more significant in rural insurgencies. The average level of linguistic diversity is nearly twice as high

in rural insurgencies as it is in mixed/urban insurgencies. If linguistic diversity only becomes a hindrance

after a certain point, for example, when seven or more languages are spoken in the conflict region, then

there would more likely be a significant relationship in rural insurgencies, where the average number of

languages spoken is 9.84. Drawing an analogy to a traditional terrain variable, more forest cover might

not help an insurgency if the range of likely levels is only 0-15%. However, if the range is 0-50%, then a

noticeable impact might be observable.

Intermediate DV testing enables a causal chain to be constructed between traditional terrain

factors and conflict outcomes. Although terrain factors were insignificant when directly regressed against

conflict outcome in rural insurgencies, increasing terrain roughness was significantly associated with

increasing casualties. Again, total casualties were a highly significant predictor of conflict outcome only

in rural insurgencies (p=0.005). In other words, although terrain factors do not significantly impact

conflict outcomes independent of total casualties, they do influence outcomes through their effect on

casualties. As earlier studies (Fearon and Laitin, 2003; Collier and Hoeffler, 2004) have found a

significant impact of terrain factors on conflict outbreak, we now see a significant impact of terrain

68

factors on conflict outcomes - but only in rural insurgencies. This partly contradicts the results of Lyall et

al (2009), who found no significant impact of rough terrain. That analysis restricted rough terrain to only

mountainous terrain, and did not disaggregate insurgency types. Rough terrain matters, but almost

exclusively in rural insurgencies. Increasing terrain roughness (primarily forest cover) allows insurgents

to wage more bloody rural insurgencies, presumably inflicting higher costs on the counterinsurgent. The

more bloody the rural insurgency, the more likely the incumbent will be defeated. In the case of rural

insurgencies, intuition appears to prevail.

Chapter VIII: Conclusion

The past century has seen a dramatic decline in the ability of states to defeat insurgencies. Over

the same period, the world has become both more populous and more urban. Accompanying these

demographic changes has been a dramatic shift in the geography of insurgency. As people have moved

from the countryside to cities they have taken their greed and grievances with them. Insurgencies are now

substantially more urban in nature. The study sought first to develop a theory explaining the difficulty of

urban insurgencies. It then attempted to clarify the importance of the geographic shift by incorporating

insurgency type as an independent variable in a large-n quantitative analysis - a first. The results thus far

indicate a complex, and in some instances causal, relationship between conflict factors, insurgency

choice, and insurgent success. Insurgencies with major or dominant urban components are significantly

harder for insurgents to defeat. The combination of increasing incidence of urbanized insurgencies and

the observed difficulty of defeating urbanized insurgencies relative to rural insurgencies might partially

explain the declining ability of states to defeat counterinsurgencies over the time period examined. The

choice of conflict geography (rural versus urban) produces interesting and at times statistically significant

differences in predictors of conflict outcomes. Geography does matter, both as an overall predictor of

insurgency outcomes, and as an important conditioning variable for analyzing the varied effects of IVs.

Given the increasing prevalence of urban insurgencies, counterinsurgents, particularly democratic and

foreign ones, would be wise to recognize and prepare for the peculiar challenges posed by these conflicts.

Although the basic premise of the theoretical framework - that urban insurgencies are now harder

than rural insurgencies to defeat - was confirmed, the analysis did not provide substantial support for the

causal mechanisms outlined. The results have forced a reevaluation of the specified theoretical

framework. The limitations addressed above provide the first steps towards future research, as do the new

questions raised by the statistical analysis. Fully coding the Lyall dataset for insurgency type, as well as

appending the dataset with entirely new cases of insurgency (primarily urban insurgencies, but also rural

ones), will increase the degrees of freedom for statistical analysis. More cases will also allow the dataset

to be partitioned along more variables than just insurgency type. Furthermore, mixed and urban

insurgencies will finally be able to be disaggregated, eliminating any distortion of effects due to pooling.

Furthermore, fully coding the Lyall dataset will eliminate any current disparities in conflict duration,

allowing for more meaningful intermediate dependent variable testing. Intermediate dependent variable

testing will be strengthened by the addition of further control variables, such as lootable commodities, a

better ethnicity measure, and an ethnic/religious vs. ideological dummy variable. Refinement of current

variables, such as urbanization, total population, casualties, and insurgency type will increase the

accuracy of the statistical analysis. The inclusion of better development indicators and specific soft-target

measures would enable of more encompassing analysis of the effects of urban insurgencies on conflict

outcome. Using the ACLED for insurgency coding allow for a more rigorous assessment of the relative

prevalence of events in urban and rural settings. This will allow for a much more continuous measure of

insurgency type. Furthermore, the dataset will also enable a rough breakdown of casualties by location.

This will be incredibly useful for evaluating the importance of casualties both by and within insurgency

types. Determining whether or not the realized cost of a casualty is contingent on the geographic location

in which it was incurred will be made much easier. Finally, a more dynamic democracy indicator will

also enable a better determination of a relationship between insurgency type and counterinsurgent

strategy, roughly speaking, and conflict outcomes. It will be easier to predict the effect of insurgency

type on changes in the level of authoritarianism/democracy, as well as the effect of these changes on

eventual conflict outcomes.

Future research will focus on parsing out potential non-linear relationships, particularly the

complicated relationship between urbanization level and insurgency outcome, as well as casualties and

insurgency duration. The above analysis has forced a reevaluation of theory specifications. Increasing

urbanization within urban insurgencies does not appear to improve insurgents' chance of success, either

by directly influencing outcome or by producing more casualties. All that seems to matter is that the

71

insurgency has urban components. This would seem to point to a non-linear or threshold relationship,

where a certain level of urbanization is needed to sustain those urban components and realize the

associated benefits. Future research will allow for more complex threshold testing using collapsed

independent variables - urbanization in particular - to more accurate describe relationships. Additionally,

extensive analysis remains to be done on conflict-terrain matching. The analysis in this study was limited

solely to urbanization level and did not incorporate traditional rough terrain measures into a

comprehensive measure in order to test the effect of proper "matching" on conflict outcomes. Finally

robustness tests, particularly outlier analysis, will provide additional support to the quantitative results

detailed above. Moving forward, the use of the case studies and qualitative analysis will help illuminate

causal mechanisms at work. Furthermore, case studies will allow the analysis to address the important

issue of soft-target exploitation, as well as analyze some of the more complex variables, like

indiscriminate casualties, overall popular support, and counterinsurgent strategy in order to further clarify

the importance of urban insurgencies. Case studies will also allow for a more detailed look at conflict

processes.

Bibliography

1. Ivan Arreguin-Toft, "How the Weak Win Wars: A Theory of Asymmetric Conflict."

International Security 26 1 (Summer 2001), 93-128.2. Clifford Bob, "The Marketing of Rebellion: Insurgents, Media and International Activism,"

Cambridge University Press (2005).3. Robert M. Cassidy, "Why Great Powers Fight Small Wars Badly," Military Review (September-

October 2000), pp. 41- 53.4. Fotini Christia, "The Closest of Enemies: Alliance Formation in the Afghan and Bosnian Civil

Wars (Dissertation)," Kennedy School of Government, (2008).5. Eliot A. Cohen, "Constraints on America's Conduct of Small Wars," International Security, Vol.

9, No. 2 (Autumn 1984), pp. 151-181.6. Paul Collier and Anke Hoeffler, "Greed and Grievance in Civil War", Oxford University Press

(2004), pp. 569-570.7. D. M. Condit, "Modem Revolutionary Warfare: An Analytical Overview," American Institutes

for Research (May 1973), pp. 1-136.8. Michael C. Desch, "Democracy and Victory: Why Regime Type Hardly Matters," International

Security, Vol. 27, No. 2 (Fall 2002), pp. 5-47.9. Mounir Elkhamri et al, "Urban Population Control in a Counterinsurgency," Foreign Military

Studies Office, Centerfor Army Lessons Learned, (20050, pp. 1-72.10. C. Christine Fair, "Urban Battlefields of South Asia," RAND Corporation Arroyo Center (2004),

pp. 1-150.11. James D. Fearon and David D. Laitin, "Ethnicity, Insurgency, Civil War," The American Political

Science Review, Vol. 97, No. 1. (February 2003), pp.7 5 -90 .12. James D. Fearon and David D. Laitin, "Weak States, Rough Terrain, and Large-Scale Ethnic

Violence Since 1945," delivered at the 1999 Annual Meetings of the American Political Science

Association, 2-5 September 1999.13. Kristian S. Gleditsch, "Transnational Dimensions of Civil War," Journal of Peace Research, Vol.

44, No. 3, (2007), pp. 293-309.14. Larry Kahaner, "AK-47: The Weapon that Changed the Face of War," New York: Wiley (2006).15. Stathis N. Kalyvas, "The Logic of Violence in Civil War," Cambridge University Press (May 1,

2006).16. Stathis N. Kalyvas, "Ethnic Defection in Civil War," Comparative Political Studies, Vol. 41, No.

8, (August 2008) pp. 1043-1068.17. Stathis N. Kalyvas and Matthew A Kocher, "Ethnic Cleavages and Irregular War: Iraq and

Vietnam," Politics & Society, Vol. 35 No. 2, (June 2007), pp. 183-223.18. Chaim Kaufmann, "Intervention in Ethnic and Ideological Civil Wars: Why One Can Be Done

and the Other Can't," Security Studies 6 no.1 (Autumn 1996), pp. 62-100.19. Bethany Ann Lacina and Nils Petter Gleditsch, "Monitoring Trends in Global Combat: A New

Dataset of Battle Deaths," European Journal of Population, Vol. 21 No. 2-3 (2005), pp 145-165.20. Roy Licklider, "The Consequences of Negotiated Settlements in Civil Wars, 1945-1993,"

American Political Science Review, Vol. 89, No. 3 (September 1995), pp. 681-690.

21. Jason Lyall, Isaiah Wilson III, "Rage Against the Machines: Mechanization and the Determinantsof Victory in Counterinsurgency Warfare," working paper, 2007.

22. Andrew J.R. Mack, "Why Big Nations Lose Small Wars: The Politics of Asymmetric Conflict,"World Politics, Vol. 27, No. 2 (January 1975), pp. 175-200.

23. Patrick D. Marques, "Guerrilla Warfare Tactics in Urban Environments (Masters Thesis," USArmy Command and General Staff College, (June 6, 2003).

24. T. David Mason and Patrick J. Fett, "How Civil Wars End: A Rational Choice Approach," TheJournal of Conflict Resolution, Vol. 40, No. 4 (Dec., 1996), pp. 546-568.

25. Gil Merom, "How Democracies Lose Small Wars: State, Society, and the Failure of Franc e inAlgeria, Israel in Lebanon, and the United States in Vietnam," Cambridge University Press(August 4, 2003).

26. Madeline Morris, "By Force of Arms: Rape, War, and Military Culture," Duke Law Journal 45,no. 4 (1996): 651-781.

27. Thomas E. Miller, "The Efficacy of Urban Insurgency in the Modem Era (Masters Thesis)," USArmy Command and General Staff College, (May 31, 2002).

28. Michael L. Ross, "What Do We Know About Natural Resources and Civil War?" Journal ofPeace Research, vol. 41, no. 3 ( 2004), pp. 337-356.

29. J. David Singer, "Reconstructing the Correlates of War Dataset on Material Capabilities of States,1816-1985," International Interactions, 14 (1987), pp. 115-32.

30. John L. Sorenson, "Urban Insurgency Cases," Defense Research Corporation (February 1965),pp. 1-144.

31. Jennifer Morrison Taw and Bruce Hoffman, "The Urbanization of Insurgency: The PotentialChallenge to U.S. Army Operations," RAND (1994), pp. 1-61.

32. Barbara F. Walter, "Designing Transitions from Civil War: Demobilization, Democratization, andCommitments to Peace," International Security, Vol. 24, No. 1 (Summer 1999), pp. 127-155.