The Urbanization of Insurgency: Shifts in the
Geography of Conflict
by
Nicholas T. Calluzzo
S.B. Political Science
Massachusetts Institute of Technology, 2009
SUBMITTED TO THE DEPARTMENT OF POLITICAL SCIENCE IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTERS OF SCIENCE IN POLITICAL SCIENCE
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
ARCHiES
JUNE 2010
© 2010 Nicholas T. Calluzzo. All rights reserved.
The author hereby grants to MIT permission to reproduce
and to distribute publicly paper and electronic
copies of this thesis document in whole or in part in any
mediunrnow known or hereafter created.
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Signature of Author:
Department 16f Political Science
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Certified by:
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Thesis Supervisor
Accepted by:
Roger Petersen
Professor of Political Science
Chairman, Graduate Program Committee
The Urbanization of Insurgency: Shifts in the Geography
of Conflict
by
Nicholas T. Calluzzo
SUBMITTED TO THE DEPARTMENT OF POLITICAL SCIENCE
ON MAY 26, 2010 IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
MASTER 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 Petersen
Title: Associate Professor of Political Science
Table of Contents
Acknowledgments
Chapter I: Introduction
Chapter II: Existing Literature
1. State Factors
2. The Means of Insurgency
3. Conflict Characteristics:Challenging the Conventional Wisdom
Chapter III: Urban Insurgency - A Working Theory
1. The Evolution of Modern Insurgency Doctrine
2. UrbanInsurgency
3. Manipulating Outcomes
4. Drawbacks of UrbanInsurgency
Chapter IV: Hypotheses
Chapter V: Testing the Theory
1. Research Scope
2. Building a Dataset
3. Coding Insurgency Type
4. Research Design
Chapter VI: Results
1. The ChangingNature of Insurgencies
2. Summary Statistics and Insurgency Profiles
3. PredictingInsurgency Types
4. PredictingInsurgency Outcomes
5. IntermediateDependent Variable Testing
Chapter VII: Discussion
1. Limitations
2. Shifts in the Geography of Conflict
3. Corroboratingand Challenging the Existing Literature
4. Reevaluating the Importance of Urbanization
5. The Peril of UrbanInsurgencies
6. Rough Terrainand Rural Insurgencies
Chapter VIII: Conclusion
Bibliography
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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. Chris
Wendt and Professors Roger Petersen and Gabe Lenz all helped see this thesis from conception to
completion, providing feedback and advice at critical stages. . In fact, this thesis grew out of a single
comment - "what about urbanization?" - I scribbled in the margin of a paper I read solely on the
suggestion of Professor Lenz. As my graduate thesis advisor, Professor Petersen provided continued
advice and encouragement.
Additionally, I would like to thank my friends and family for their patience and support. They have been
incredibly 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 importantfactor in war, but not the decisive
factor; 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.
Destructionof the rebelforces and occupation of the geographicterrain led us nowhere as long as we did
not 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).
theorist Mao Zedong points to the importance of people over materiel.
Classical insurgency
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.
World Urban Population
Total World Population
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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
6 2nd
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: AWorking Theory
Chuikov Effects
X
Mechanization
X
Urban Strategy
-
Concealment
I COIN
-
Casualties/
T Indiscriminate Violenoe
X
Preign CoupierCOIN
Defeat
I COIN Legitimacy
Urbanization
Target Rich
Environment
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 available
roads only magnifies these problems [of reduced soldier-civilian interaction]. Rather than
exercising control, mechanized forces are actually providing only "presence" since their greatest
asset, 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 sacrifice
depth 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 UrbanInsurgency
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.
problems are expected to be aggravated in urban insurgencies.
Furthermore, identification
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
autonomy, or subversion of existing authorities -
independence, greater
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 total
28
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 "/TerrainFactors
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 was
31
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 Frenchsympathizing 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.
ResearchDesign
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 IVDV 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
Start Year
End Year
Winner
Insurgency Type
Insurgency Type
(collapsed)
Urbanization
Mountainous
Forest Cover
102
102
102
102
1972.716
1981.186
0.9411765
0.745098
15.47363
16.65237
0.7938318
0.8287683
1945
1948
0
0
2002
2005
2
2
102
102
102
102
0.50
31.01402
0.5024692
19.93077
19.37879
0
3.96
1
90.09
0
81
21.8695
Terrain Roughness
102
Total Population
GDP (per capita)
Electricity Consumption
102
102
25.68413
81263.57
2809.276
0.2
3.1
75.076
418.424
97.2
101.3
532607
14142.85
(per capita)
102
2.202544
-7.732835
2.329489
7.1676
0.8322746
1.938008
7.543545
7.486537
1
0
-4.755993
1
-10
30
2
2.89783
31
10
ih~n uistic Diver it
External Support
Power
Duration
Regime
Type
Mechanization
(Vehicles/Soldier)
Helicopters
Foreign Occupier
Casualties
Yearly Casualty Rate
Per Capita Yearly
Casualty Rate
102
102
102
102
102
25.64529
44.01399
29262.31
2758.491
-1.084982
8.176471
0.98039
-1.040024
9.470588
-0.578431
102
2.617647
1.143593
1
4
102
0.3039216
0.4622205
0
1
102
102
102
0.2058824
49.52596
8.181673
0.4063417
135.8912
30.75499
0
0.25
0.0318182
1
1200
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
also partitions the coded dataset by insurgency type in order to illuminate the varying characteristics of
the three insurgency types.
Vaibe
ul
(104133
Start Year
End Year
Winner
Insurgency Type
Insurgency Type
Mixed
Cdeua
(10)
1971.985
1972.72
1979.609
1981.19
0.992481
0.75
).9412
).7451
0.50
0.5
(1)
26)
MiedUra
Urba
51)(25
1966.471
1976.118
1.078431
0
0
1977.654
1986.846
0.730769
1
1
1978.961
1986.255
0.803922
1.490196
1
1980.32
1985.64
0.88
2
1
(collapsed)
30.07791
20.56273
26.70406
46.95967
26832.95
2877.964
-1.28404
31.014
18.086
25.645
44.014
29262.3
2758.5
-1.0850
21.44039
34.78808
40.58765
46.6192
16.99783
25.94776
19.17337
12.128
Linguistic Diversity
External Support
Power
Duration
8.12782
1.015038
-1.16533
8.62406
8.1765
0.9804
-1.0400
9.4706
Regime Type
-0.76336
-0.5784
Urbanization
Mountainous
Forest Cover
Terrain Roughness
Total Population
GDP (per capita)
Electricity Consumption
26.62
26.43846
24.67059
22.832
43.61783
52.38622
43.84396
34.96
43488.64
14454.16
15035.98
15641.08
1905.673
2956.107
3611.308
4292.717
-1.87189
-0.3984
-0.29807
-0.19374
9.843137
7.961538
6.509804
5
1.098039
-1.05011
10.64706
-1.64706
2.254902
0.215686
0.196078
71.56553
12.46744
0.001171
1.115385
-1.03726
10.19231
-1.07692
2.923077
0.461539
0.230769
44.61108
5.116151
0.000525
0.862745
-1.02994
8.294118
0.490196
2.980392
0.392157
0.215686
27.48639
3.90E+00
0.000711
0.6
-1.02232
6.32
2.12
3.04
0.32
0.2
9.67672
2.62684
0.000905
(per capita)
Mechanization
2.556391
2.6176
Helicopters
0.240602
0.3040
Foreign Occupier
Casualties
Yearly Casualty Rate
Per Capita Yearly
0.203008
44.20952
7.438254
0.000908
0.2059
49.526
8.1817
0.0009413
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
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
When traditional rough terrain is
seem to support an important assumption about urban insurgencies:
lacking, the urban environment plausibly provides insurgents with an alternative.
Insurgency Profiles
Rural
Mixed
Urban
Urbanization
Low
Medium
High
Forest Cover
High
Medium
Low
Mountainous
Medium
High
Low
Total Population
High
Low
Medium
GDP (per capita)
Electricity Consumption
(per capita)
Low
Low
High
Low
Medium
High
Linguistic Diversity
High
Medium
Low
3. PredictingInsurgency 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.
Urbanization and Insurgency Type
oe
ase
Urban-
mom
*
**e
Mixed -
Rural -
wso
"
20
*m.
m
e
*e
60
40
Conflict 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
-0.0375
(0.1588)
-0.1510
(0.1410)
-0.1640
(0.2577)
0.0931
GDP per capita
(0.2410)
0.7382***
Urbanization
(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)
Occupier
Power
Mechanization
Regime
Total Military Personnel
Start Year
-0.3 152**
(0.1410)
-cons
n=102
R2=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. PredictingInsurgency Outcomes
i type
Rural
Mixed
Urban
Total
Insurgent
15(29.4)
12(46.2)
8(32.0)
35(34.3)
Winner
Draw
17(33.3)
9(34.6)
12(48.0)
38(37.3)
Incumbent
19(37.3)
5(19.2)
5(20.0)
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.
Mixed
Rural
-
12
10
a
20a
-
-~
- -
a Loss
Loss
-
-
-
N Draw
4
a 0
Da
-------
10
0-25
25-50
50-75
2
20
* Total
0 Total
75-100
~~ ~
0-25
Urbanization (%)
25-50
50-75
75-100
Urbanization (%)
Mixed/Urban
Urban
25
15 -r
20
10
5
--
----
0 Draw
is Win
0
0-25
15
Eloss
25-50
50-75
Urbanization {%)
IS
=Loss.
_____
10
- Draw
5
0
75-100
a Total
0-25
25-50
50-75
Urbanization {%)
75-100
a Total
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*
(collapsed)
(0.078)
R = 0.0165,
2
R0.0302
-0.157*
(0.088)
R2 = 0.0244,
R 2 = 0.0309
Multivariate
Fl
Insurgency Type
Urbanization
Mountainous
Forest Cover
-0.2304**
(0.1123)
0.3780
(0.2395)
-0.0231
(0.1524)
-0.1047
(0.1696)
0.1450
Total Population
(0.3115)
GDP (per capita)
Electricity Consumption
(per capita),
(0.2604)
-0.2158
External Support
Linguistic Diversity
Power
Regime Type
Mechanization
(per/veh)
Foreign Occupation
Casualties
(total)
Duration
Start Year
cons
Mxd
/
R
0.6600*
(0.3606)
-0.2537
(0.2408)
-0.0971
(0.2602)
2.0134
(1.7078)
-0.3040
(0.3763)
0.3900
(0.3773)
0.1457
(0.2759)
-0.1892
(0.2604)
0.2995
(0.3293)
-0.3267
(0.4072)
-0.1836
0.6581*
(0.5632)
-0.2047
(0.1439)
(0.3349)
0.1844
(0.3665)
-0.4609**
(0.2091)
-0.0590
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
-0.2939
(0.2928)
0.5581
(0.3434)
n=51
(0.2709)
n=51
R 2=0.3771
R2 =0.3912
R2 =0.5808
0.2505
(0.3988)
-0.2288
(0.1627)
(0.3251)
-0.0997
(0.1892)
0.0119
(0.1699)
-0.4218*
-0.3624*
(0.1802)
(0.2182)
0.8611
(1.1867)
(0.2004)
-1.2532***
(0.4148)
0.0817
(0.2189)
(0.2838)
-0.2141
(0.1637)
-0.2863
-0.2583
-0.6180*
(0.3229)
1.0910***
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
0
0
LO
0
00
-1
-.5
e( L-type
IX)
0
.
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
-0.0805
(0.0678)
(0.0484)
(0.1445)
Total Population
0.0713*
(0.0397)
0.3590***
(0.0802)
0.0145
(0.0281)
0.0470
(0.2122)
0.2066**
(0.0902)
0.2917**
(0.1234)
GDP (per capita)
-0.0229
(0.07196)
-0.0080
(0.0495)
-0.0355
(0.1589)
Urbanization
Terrain Roughness
Linguistic Diversity
External Support
Foreign Occupation
Power
Mechanization
Regime Type
Duration
Start Year
_cons
-0.0187
-0.0428
0.0554
(0.0454)
0.0236
(0.0263)
0.0135
(0.0341)
0.0325
(0.0577)
0.0156
(0.0327)
-0.0685**
(0.0316)
-0.0207
(0.0434)
-0.0661
(0.0511)
0.0409
(0.0493)
(0.0439)
0.0133
(0.0190)
0.0468
(0.0279)
0.0333
(0.0448)
0.0090
(0.0236)
-0.0596**
(0.0309)
0.0481
(0.0293)
0.0367
(0.0367)
0.0173
(0.0391)
(0.0802)
0.0559
(0.0583)
-0.0774
(0.0685)
0.0591
(0.1207)
0.0188
(0.0646)
-0.1382**
(0.0606)
-0.1634
(0.1092)
-0.1591
(0.1041)
0.0756
(0.1054)
n=102
2=
2
R =0.3468
n=51
n=51
R =0.3898
R 2==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.
Urbanization
Terrain Roughness
-0.2013
(0.1669)
0.1656*
(0.0958)
-0.0714
(0.2671)
0.2870*
(0.1481)
-0.4586**
(0.2085)
0.2541*
(0.1262)
GDP (per capita)
Total Population
Electricity
Consumption
Linguistic Diversity
External Support
Foreign Occupation
Power
Regime Type
Mechanization
Start Year
_cons
0.3340*
(0.1793)
-0.3418*
(0.1944)
0.0440
(0.1841)
0.1373
(0.1103)
0.0421
(0.0645)
-0.1409
(0.0868)
0.2940*
(0.1560)
-0.0600
(0.0794)
-0.0914
(0.0807)
0.0152
(0.1381)
0.0152
0.5132*
(0.2766)
-0.1350
(1.1778)
-0.0379
(0.4192)
0.3711
(0.2447)
-0.0995*
(0.1045)
-0.1087
(0.1576)
0.3568
(0.2954)
-0.0062
(0.1304)
-0.0567
(0.1299)
-0.1985
(0.2202)
0.0530
0.1304
(0.2387)
-0.3597**
(0.1712)
0.1934
(0.1923)
0.1602
(0.1148)
0.2021**
(0.0791)
-0.2690**
(0.1065)
0.3613*
(0.1826)
-0.1681*
(0.0943)
-0.0252
(0.0966)
0.1010
(0.1780)
-0.0225
(0.2618)
(0.1381)
n=102
n=51
(0.1555)
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 nonrepresentative. 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.
insurgency is an unavoidable reality.
Urban
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.
Corroboratingand 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.
strategic benefits of urban insurgencies.
This particular finding provides some insight into the
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 nonfinding. 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.
succeeded, it was in spite of increasing urbanization.
Where urban insurgencies
Possible Urbanization-Outcome Relationship
within Urban Insurgencies
State Victory
Observable
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
If a conflict is very ethnic and ethnic geography falls on an urban/rural divide, then bias is
lies.
introduced. For example, if an ethnic insurgency's kin group is disproportionately rural, then high
urbanization could be correlated with incumbent success.
5.
The Perilof 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 conditionalresults 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.
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