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Drugs in the Context of Armed Conflicts:
A Path to Destruction or Means to an End?
Candidate number: GYVZ3
Word Count: 9389
Dissertation submitted in part-fulfilment of the Masters Course in Security
Studies, UCL, September 2015.

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ABSTRACT
The article investigates how drug production affects armed conflicts duration and
outcomes. The author argues that illicit substances represent a very viable source of
money for insurgencies. It is suggested that drug money helps rebels to overcome
the power asymmetry problems by enhancing rebels’ capabilities and improving their
relative rebel strength in relation to the government. This has two effects – firstly,
the conflicts where drugs are involved are longer. Secondly, when rebels get
stronger they pose a more significant threat to the government which should be
incentivized to strike a deal with them. By examining armed conflicts between 1946
and 2003 with statistical methods, the author shows that the drug production in the
area of armed conflicts makes conflicts considerably longer. The second test
investigates whether the drug production improves insurgents’ chances in achieving
a negotiated settlement. It comes to the conclusion that there is no relationship
between drug production and the armed conflict type of termination.

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TABLE OF CONTENTS
TITLE PAGE ..................................................................... Error! Bookmark not defined.
ABSTRACT ..............................................................................................................2
TABLE OF CONTENTS .............................................................................................3
1. INTRODUCTION............................................................................................4
2. LITERATURE REVIEW ...................................................................................7
2.1. MACRO-LEVEL THEORETICAL CONSIDERATIONS ..........................................................8
2.2. MICRO-LEVEL RESEARCH..................................................................................................11
3. THEORY BUILDING.....................................................................................14
3.1. DRUGS – CHARATERISTICS AND PROFITABILITY........................................................14
3.2. BARGAINING FAILURES IN CIVIL WAR ..........................................................................16
3.3. RELATIVE REBEL STRENGTH............................................................................................18
3.4. RELATIVE REBEL STRENGTH – EXAMPLE OF TALIBAN................................................19
4. METHODOLOGY ..........................................................................................21
4.1. DATASETS PRESENTATION ..............................................................................................22
4.2. DEPENDENT VARIABLES ...................................................................................................24
4.3. INDEPENDENT VARIABLES ...............................................................................................25
4.4. CONTROL VARIABLES........................................................................................................26
5. THE LINEAR REGRESSION MODEL .............................................................27
6. THE MULTINOMIAL LOGISTIC REGRESSION MODEL .................................32
7. DISCUSSION AND LIMITATIONS................................................................36
8. CONCLUSION..............................................................................................37
9. BIBLIOGRAPHY ..........................................................................................41
9.1. BOOKS..................................................................................................................................41
9.2. JOURNALS............................................................................................................................41
9.3. ELECTRONIC SOURCES .....................................................................................................48
10. APPENDICES...............................................................................................54

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1. INTRODUCTION
“I've been in prison for 20 years, but you will never win this war when there is so
much money to be made. Never." (Jhon “Popeye” Velásquez in Gutsch and Moreno
2013)
The illegal drug trade is a global phenomenon that knows any boundaries. According
to the United Nations report (2012) the global drug trade in 2003 was estimated at
320 billion US dollars – new estimations have not been produced since then but “if
the drug trade were a country, it would have the 19th largest economy in the world”
(Branson 2012). The fact that illicit substances are a much-demanded commodity
did not go unnoticed by rebel groups who gradually got involved in this shady
business to financially support armed conflicts around the world. Intrastate conflicts
also known as civil wars represent a pressing issue for politicians, world leaders as
well as researchers and political scientist around the world. Scholars have noticed
that in the past 25 years the number of armed conflicts, in general, is in decline
(Pinker and Mack 2014). However, among different kinds of conflict the intrastate
wars are by far the most common type (see Figure 1 in Appendices). A lot of the
existing literature has focused on the onset and duration but what affects the
outcomes of civil wars, which shape the social dynamics once the conflict is finished,
remains yet to be thoroughly researched.
The onset, duration and type of termination of each conflict depends on a large
number of factors. Scholarship has discussed, among other issues, the influence of

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ethnicity (Wucherpfennig et al. 2012, Cederman et al. 2013, Wegenast and Basedau
2013), relative rebel capabilities (Clayton 2013), state capacity (de Rouen and Sobek
2004), refugee migration (Salehyan 2008) or geographical location (Buhaug and
Gleditsch 2006) on intrastate wars. One of the core aspects that have attracted a
substantial amount of attention is the role of natural resources. In the history of
armed conflicts, natural resources represent a major source of the cash inflow, and
started to play an even more significant role with the end of the Cold War.
During the Cold War era, when the world was divided into two blocks, just an
affiliation with one of the superpowers was a sufficient reason to receive funding for
proxy wars from either the US or Soviet government. After the dissolution of the
USSR in 1991, which is considered as the official end of the Cold War, this source of
revenue for insurgencies ran dry. Insurgents have continuously become more reliant
on other forms of funding, the most obvious being natural resources which can be
easily extracted and help rebels to secure funding for their cause, such as
gemstones, oil and drugs (marijuana, cocaine and opium) in particular. Drugs can be
characterized as lootable, illegal and renewable substances, which make them a
highly profitable commodity for belligerents. In this regard, it is hardly surprising
that many rebel groups get involved in drug production and to a certain level also
embroiled in organized crime. But what effects do drugs have on the duration and
outcomes of armed conflicts?
Quite a large number of academic papers (for example Fearon 2004, Buhaug and
Lujala 2005, Ross 2006, Buhaug, Gates and Lujala 2009, Lujala 2010) look into the

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relationship between illegal substances and armed conflicts but an unequivocal
perspective regarding their effect does not exist in the academic community. The
way drugs influence conflicts remain contested, therefore in my study I intend to
cast some light on this disputed matter. What is particularly missing in the on-going
debate is how drugs affect the type of termination of armed conflicts. I argue that
drug production indeed alters the dynamics of armed conflicts by enhancing rebel
capabilities, with two substantially distinct consequences. Firstly, with the money
gained from the illegal drug trade rebels can afford better equipment and motivate
combatants to stay and fight – in other words, drugs make the weaker party (usually
the rebel forces) stronger, which in return allows them to escape defeat – the
conflict drags on for much longer which is shown by my statistical model. Secondly,
the drug money helps insurgents improve their capabilities relative to the
government, which is more important than the absolute strength of the group.
Subsequently, it makes the threat they pose to the government more genuine. This
should incentivize governments to reach some kind of agreement with rebels, which
is tested with multinomial logistic regression.
This thesis is structured in the following manner: the section 2 focuses on the
existing scholarly literature on natural resources and the way they affect armed
conflicts. It is subdivided into two parts – the first one reviews the macro (global)
perspective and includes articles about natural resources, drugs and civil wars
duration and outcomes. The third section examines literature which concentrates on
the connection between drugs and conflict on a micro-level. After summarizing the
existing knowledge I introduce the gap in the literature regarding the drug

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production and outcomes of armed conflicts. I build on that in the fourth part which
is divided into three sub-sections: drug business, bargaining failures in civil wars and
relative rebel strength with a practical example of Afghanistan’s Taliban. In the fifth,
methodological section, I present used datasets, variables and test my hypotheses
with a linear regression model and multinomial logistic regression model. Finally, I
discuss my results and conclude by summarizing the main findings of my research.
2. LITERATURE REVIEW
Intrastate wars have been in the focus of researchers for a quite some time. With
the end of the Second World War, civil conflicts became more common than
interstate wars – Fearon and Laitin (2003) showed that ‘classic’ wars amid two or
more countries between 1945 and 1999 accounted for around 3 million lives
whereas civil wars’ death toll reached more than 15 million lives in the same time
period. Lost lives are only one side of the story – conflict is costly in general and
dramatically affects the dynamics of the society. Parties of the conflict usually
remain to live side by side within the same state even after the war is finished which
distinguishes civil wars from interstate wars and makes a compromise more difficult
to achieve (Licklider 1995). Some scholars view conflicts as the second best option
saying that conflicts are essentially a bargaining failure where negotiations break
down due to the lack/misinterpretation of information, indivisibility issues or
commitment problems (Fearon 1995). In the subsequent sections, I will look into
the work of scholars who focused on the role of natural resources (and drugs in
particular) in armed conflicts both from a global and a local level.

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2.1. MACRO-LEVEL THEORETICAL CONSIDERATIONS
Political scientists for many years considered religious, nationalist, and/or political
grievances to be the primary causes of civil wars. Scholars such as Frances Stewart
(2002) and David Keen (2012) have been keen proponents of this “traditional”
school of thought, despite the emergence of an opposing view. A group of
researchers with Collier and Hoeffler (1998) at the forefront supports the argument
that roots of armed conflicts lie rather in the concept of greed. They argue that all
societies have groups with overstated grievances, but civil wars do not occur in all of
them. In their quantitative analysis, they constructed two competing models – one
that inspects inequality, political oppression, and ethno-religious fractionalization,
while the second one focuses on the sources of finance of civil wars. They found
little evidence for social and political variables to be the determinants for the
outburst of a civil conflict. On the contrary, economic variables proved to be more
illustrative factors explaining civil wars, suggesting that the wealth from natural
resources increases the motivation of insurgents to accumulate private gain (Collier
and Hoeffler 2002). Nonetheless, some authors have criticized the ‘greed’ theory as
being simplified, since “combatants’ incentives for self-enrichment and/or
opportunities for insurgent mobilization created by access to natural and financial
resources were neither the primary nor the sole cause of the separatist and non-
separatist conflicts analysed” (Ballentine and Nitzschke 2003, p.1). Others claim that
“the greed and grievance models are not mutually exclusive, but they point to
differing rebel motivations for starting and continuing the war” (DeRouen and Sobek
2004, p. 305).

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Nonetheless, it is evident that natural resources play a contested role in armed
conflicts. They can be separated into two distinguishable categories – lootable and
non-lootable. The latter, such as minerals, off-shore oil, gas and primary diamonds
are hard to extract and makes it very complicated for rebels to capitalize on them.
On the other hand, drugs together with secondary diamonds are considered to be
lootable resources, which mean they “can be harvested by simple methods by
individuals or small groups, do not require investment in expensive equipment, and
can easily be smuggled” (Lujala, Gleditsch and Gilmore 2005, p. 539). Fearon (2004)
identified five types of civil wars whose duration is significantly shorter or longer
than most others – civil wars arising from coups, anti-colonial wars and wars in the
post-Soviet region were quite short-lived, while conflicts about land, natural
resources or wars where rebels have access to some kind of contraband (diamonds,
coca, opium…) tend to last longer. This is consistent with Ross’ (2004) findings who
suggested that lootable resources (in his case gemstones, drugs, and timber) may
prolong conflicts. Cornell (2007) supported this claim by stating that Afghanistan
(heroin), Colombia (cocaine, heroin), Peru (cocaine) and Myanmar (heroin) as four
countries which suffer with long-lasting conflicts (see Figure 2 in Appendices).
Fearon’s (2004) aforementioned research grouped contraband/lootable resources in
one category, which does not help in determining what effects each resource has.
Contemporary research tends to disaggregate natural resources labelled as lootable
resources (contraband) into three distinguishable categories – secondary diamonds,
drugs and oil (which shows a different effect on depending whether it is on-shore or
off-shore oil production). A group of researchers contested the relationship between

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drugs and conflict duration – Buhaug and Luajala (2005) in their paper came to the
conclusion that gems and coca leaves prolong armed conflicts, but marijuana
production does not have the same effect. However, they were cautious about the
results, because only a few countries were coded as drug producers. Interestingly,
they found a staggering difference between the production of primary and
secondary diamonds and its effect on ethnic wars – the production of secondary
diamonds increases the occurrence of ethnic wars by 200% because they are much
easier to extract than primary diamonds.
Buhaug, Gates and Lujala (2009) confirmed these findings in their paper, saying that
gems and petroleum production are associated with the conflict duration, but drugs
show no systematic relationship in connection to the length of a conflict. In a
subsequent research Lujala (2010) demonstrated that the rebels’ access to
gemstones or hydrocarbons doubles the conflict duration and that the mere
presence of oil fields or gems is sufficient cause for protracting the conflict. Drugs
cultivation, however, is not associated with the length of the conflict. Nonetheless,
natural resources have also a different effect – there is strong evidence that natural
resources are linked to a conflict reoccurrence through different mechanisms
because they are an extremely valuable commodity worth fighting for (Rustad and
Binningsbø 2012). LeBillon (2001) adds to the discussion that spatial distribution of
resources determines whether insurgents are able to benefit from them or not.
Nevertheless, every war has an end – the violence will continue until one side is
defeated or parties of the conflict find a negotiated agreement. One of the most
common classifications distinguishes government victory, rebel victory and some

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kind of settlement (Mason, Weingarten and Fett 1999). This division was later
improved by Kreutz (2010), who expanded the list to seven different kinds of
termination. This distinction proved to be more explanatory and also will be used
later on in my analysis. Scholars found out that decisive victories tend to be more
stable due to the fact that the defeated party of the conflict is usually eliminated or
radically deprived of power, whereas settlements are less enduring than landslide
victories (Licklider 1995). As noted in DeRouen and Sobek (2004) the type of
outcome determines the post-conflict dynamics of the society – truce might leave
grievances unresolved, treaties could lead to a long-lasting peace, rebel victories will
possibly establish new governments, and triumphs of the incumbent establishment
will lead to diminishing the insurgents’ cause. An influential piece written by
Cunningham, Gleditsch and Salehyan (2009) looked at civil war outcomes from a
slightly different angle – they moved beyond the aggregating country-level approach
to a clear dyadic level where they observe interactions between individual rebel
groups and government forces. Their main argument is that the “outcome and
duration of civil wars is a function of the balance of military capabilities between
states and rebels as well as incentives to find peaceful settlements” (Cunningham,
Gleditsch and Salehyan 2009, p. 572). When rebels are strong they are more likely
to fight a shorter war as well as gain concessions from the government.
2.2. MICRO-LEVEL RESEARCH
All the above-mentioned research looked at the link between natural resources and
armed conflicts at the macro level. But there is also a large body of research which
concentrates on micro foundations of this matter. Angrist and Kugler (2008) focused

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on coca production in Colombia and discovered that growing areas experience
higher rates of violent deaths. They suggest that “coca supports rural insurgents and
paramilitary forces, thereby sustaining Colombia’s civil conflict” (Angrist and Kugler
2008, p. 27), which is in compliance with Collier’s and Hoeffler’s (2002) assumption
that economic viability might be a systematic explanation of insurgency. Drug
production does also affect conflicts even more directly – Hecker and Haer (2015)
suggested in their research about violent behaviour during armed conflicts that
drugs and alcohol consumption increases the probability of violence in the conflict
environment. They drew this conclusion from 224 interviews with former combatants
in the DRC.
The abundance of easily extracted resources is often seen as an advantage, but it is
not always the case. As noted in Weinstein (2005) the presence of financial support
(gems, drugs or other natural resources) makes it easy for rebel leaders to attract
recruits in the short term to join the insurgency under the pretext of prompt financial
gains. But this kind of rebels is usually not committed to the long-term goals of the
rebellion – they are often not willing to invest time and energy without getting paid,
which in return might reflect the success rate of insurgencies. In the case of
shortage of economic endowments it is more difficult to keep the rebellion alive
since leaders are forced to build armies around credible promises about incentives
which will be provided in the future if the rebellion is successful. Participation in
rebellions was later thoroughly researched by Humphreys and Weinstein (2008) who
discovered that financial motivation from natural resources plays an important role in
the recruitment process of both rebel groups and counterinsurgents. They

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demonstrated it on the case of Sierra Leone, which is famous for its diamond
industry, arguing that participation in the conflict was mainly predicted by economic
factors and, to a lesser degree, by social pressure.
Another piece of this puzzle from the micro point of view which is important for this
thesis was put together by Lind, Moene and Willumsen (2014) who studied opium
trade in Afghanistan, and how conflict affected its production. They found out that
besides infrastructure destruction, war weakens law enforcement which in turn helps
the development of illicit business such as opium (heroin) production over more
traditional (but less profitable), such as wheat. In a similar vein, Snyder (2006)
argues that poor implementation of law shows the actual weakness of a state that is
incapable of governing its territory properly which might be exploited by rebels. This
finding was later supported by Fearon and Laitin (2003, p. 75-76). They emphasized
the importance of state’s capacity, saying that “financially, organizationally, and
politically weak central governments render insurgency more feasible and attractive
due to weak local policing or inept and corrupt counterinsurgency practices.”
The fact that conflicts do not always only have negative effects was pointed out by
Keen (2000, p.22), who understood conflict as “an alternative system of profit,
power, and even protection.” This coincides with Cornell (2007), who noticed that
conflicts work as an opportunity for rebels to turn to criminal behaviour. Cornell
(2005) coined this emerging collaboration between criminal and rebel organizations
the so-called “crime-rebellion nexus.” This concept was influenced by Makarenko’s

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(2004) piece on the growing convergence of terrorist and crime organizations (see
Figure 3 in Appendices).
I have summarized previous research that looks into the issue of natural resources
and armed conflicts both from the perspective of duration and types of termination.
The role natural resources play in intrastate conflicts remains questioned, but the
existing literature almost solely focuses on the onset and duration and neglects the
effect illicit substances might have on outcomes of armed conflicts. Only Ohmura
(2012) attempted to cover this subject, but his essay remains yet to be finished. This
situation is rather surprising since drugs represent one of the “deadliest” resources:
the infamous Mexican drug war alone claimed more than 138 000 lives (Gomez
2015) not to mention the financial benefits which are involved in the global drug
business.
3. THEORY BUILDING
3.1. DRUGS – CHARATERISTICS AND PROFITABILITY
The production and trafficking of illicit drugs remain a serious problem that attracts
worldwide attention, mainly because of its connection to criminal groups which
capitalize on the fact that these substances are illegal. The illegality of drugs makes
them, ironically, very attractive to supply and when governments almost everywhere
around the world restrict the supply chain, the prices go up. Cornell (2007) laid out
several characteristics which make drugs (marijuana, coca leaves/cocaine and
poppy/heroine) so attractive for terrorist groups, insurgents and criminal

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organizations worldwide. Firstly, they are lootable which does not require any special
tools or skills to extract them. Secondly, they are renewable like any other plant;
therefore they guarantee a steady flow of income. Poppy is an annual plant,
marijuana can be harvested once or twice a year and coca leaves can be harvested
2-6 times a year depending on the climatic conditions (DEA 1994). Thirdly, drugs are
illegal which effectively excludes (at least officially) all governmental officials from
participation in the drug business. Lastly and most importantly – drugs are extremely
profitable.
For example, the cocaine business was in 2009 estimated approximately at $100
billion (UNODC 2012) but the product gets more expensive with the distance (see
Figure 5 in Appendices). A kilo of cocaine in Colombia might cost around $2000, but
the same amount of cocaine might cost a hundred times more in Australia (Stewart
2013). Although the financial benefits of the drug trade are obvious not all groups
have decided to get involved because illegal drugs are almost globally considered
immoral. Asal, Deloughery and Phillips (2012, p. 201) came with an interesting study
where they argue that “the organizational decision to sell drugs represents a violent
rejection of the political order.” However, it is conditioned by alleged need and
opportunity. They found a relationship between subnational ethnic political
organizations using violence/organizations being targeted by the state and their
involvement in the drug business. This characteristic fits many rebel organizations
which obviously rejected the state authority thus the illegality of drug business is not
an issue for them. Nevertheless, the money generated from the drug production
represents a very valuable prize worth fighting for. Buhaug (2006) characterizes

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rebel groups as political entities which seek to mobilize and maintain adequate
power to challenge the government and its monopoly of force in the whole state or
in a particular region. In order to achieve that, rebel groups face two strategic
issues. Firstly, they need to attract an ample number of combatants to represent an
efficacious challenge to the government (Gates 2002). Secondly, insurgents must
not only attract people, they also must keep them involved for a longer period of
time to achieve the goals of the rebellion (Wucherpfennig et al. 2012). To put it
differently, it is essential for the rebels to find a way to guarantee that combatants
do not give up the fight. This is when the drug money comes into a play. From the
preceding discussion I derive this hypothesis:
Hypothesis 1: Conflicts which take place in areas with drug production will last
longer than conflicts where drugs are not involved.
3.2. BARGAINING FAILURES IN CIVIL WAR
Theoretically speaking, conflicting parties should always prefer a negotiated
settlement over the war because conflict is costly (Fearon 1995); yet conflicts still
occur around the world. The contemporary scholarship (among others Schelling
1960, Powell 2002) has written extensively about this subject and emphasized the
role of three core issues which explain why states go to war. Firstly, opposing parties
tend to misrepresent private information about their own capabilities to wage a
successful war. The information problem in intrastate conflicts is even more serious
than in interstate wars, because this information will be hard to obtain due to the
anti-state nature of rebel organizations, as well as inaccurate and likely unreliable.

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Secondly, settlements might be difficult to reach when disputants are not able to
reach an agreement about the division of stakes at the game. This indivisibility issue
was for example described by Hassner (2003) in the case of Jerusalem or by Toft
(2003) in the case of Kosovo, where a simple division is hindered by the sacredness
of these places. Finally, if parties of the conflict cannot credibly commit to upholding
a deal, then one of the parties might come to the conclusion that an absolute
military triumph is a viable option and subsequently go to war (Fearon 2004). Again,
this gets complicated in an intrastate conflict due to the usual power asymmetry
between rebels and government which incentivizes the stronger party (usually the
government) to renege on a deal. Additionally, settlements almost always make the
rebels weaker because, agreements usually involve a clause about demobilization
and/or cession of power over a certain territory back to the central government. This
state of affairs might feed the rebels’ sense of vulnerability, and they may therefore
be less willing to keep their promises (Walter 2009).
The power asymmetry mentioned by Fearon (2004) and its implications have
become a centerpiece of one branch of contemporary civil war research focusing on
relative rebel capabilities. Insurgents almost in all cases lack military capabilities
comparable with state forces and also usually lack the legitimacy held by the state.
This issue can be overcome through a negotiating process which will improve the
status of insurgents – when governments acknowledge rebel organizations and offer
them a seat at the negotiating table; they basically promote the rebels’ status to that
of political figures (Clayton 2013). This shift benefits rebels who can then move

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closer to their political goals which would be incomparably more complicated to
achieve only through military means (Melin and Svensson 2009).
Governments generally have fewer reasons to enter the negotiation process because
they usually possess all the necessary advantages (stronger army, legal power,
political power, easier access to financial sources etc.) to refuse insurgents’ demands
and rather incline towards a decisive military solution. But governments’ willingness
to negotiate will change “once they anticipate that they have a little chance to settle
the situation themselves” (Melin and Svensson 2009, p.251). There are possible
negative reputation effects associated with negotiations – the start of a negotiation
might be considered as a weakness and be exploited by other insurgent groups. On
the other hand negotiations might be instrumental in escaping a costly conflict.
There is one condition which makes negotiations more likely to happen – the relative
strength of a rebel movement.
3.3. RELATIVE REBEL STRENGTH
Scholars (Fearon 1995, Cunningham, Gleditsch and Salehyan 2009) have argued
that the relative strength of a movement in relation to its opponent is more
important than the absolute strength of an army. To show this I will use the
example of the Korean People’s Army (KPA). The KPA has the fifth biggest standing
army (in absolute numbers) comprising of more than one million soldiers (Blair
2013). This says nothing about its actual military strength. According to the Global
Firepower List (2015), which takes into consideration various factors determining
potential military strength, North Korea is on the 36th position, because its

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equipment cannot match hi-tech weaponry of the leaders of this list in spite of the
fact that the KPA outnumbers most of the countries on the list.
In the context of rebellions Cunningham, Gleditsch and Salehyan (2009) argue that
relative rebel strength has two distinct factors: 1) offensive power to inflict damage
to the government in the centre and 2) defensive strength that helps rebels to
endure the government’s attacks in insurgents’ power base (usually rural areas).
They add that defensive capabilities, unlike offensive power, do not incentivize state
officials to try to reach a settlement, because rebels hidden in safe havens do not
pose a threat to the government. A good example to put all the aforementioned
theory into practice would be Afghanistan’s insurgency group Taliban.
3.4. RELATIVE REBEL STRENGTH – EXAMPLE OF TALIBAN
When the US forces invaded Afghanistan after the 9/11 attacks, they ousted Taliban
from power with the help of the Northern Alliance. The coalition was partially
successful in reducing the numbers of Taliban’s fighters and crippling their offensive
power. Yet they did not succeed in uprooting the movement completely, because
insurgents retreated to remote regions on the Afghan-Pakistani border where the
central government had (and still has) almost non-existent power. Taliban’s
defensive capabilities proved to be quite high, and the coalition forces were not able
to inflict a decisive blow. Gradually Taliban started to regain confidence and in 2003-
2004 a new phase of insurgency began (Gall 2004). Their offensive power was
getting stronger, which went hand in hand with Taliban’s growing presence in the
regions infamous for large-scale poppy cultivation (Tiefer 2015), while the NATO-led

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coalition casualties’ toll grew almost every day. At the beginning of 2009 the first
soldiers of the expected American surge moved to Afghanistan to fight the Taliban,
which did not display any signs of defeat. Actually, it was quite the opposite – the
estimates of Taliban forces grew from 36 000 in 2010 to almost 60 000 in 2014
(Waldman 2014). What also grew was their influence around the country as well as
their fighting experience which increased their relative strength in relation to the
government forces.
Clayton (2013) argues that relatively strong rebels are more likely to start a
negotiation because they are able to mobilize a significant number of combatants,
challenge the government, threaten the regime and inflict some serious damage
while being able to endure the government’s counter-insurgency methods. Some
negotiations did happen indeed – in 2010 Karzai met some of the Taliban
commanders to end the war but without success (Filkins 2010). Similar efforts were
also made in 2013 in Doha (Graham-Harrison 2013) and quite recently in Pakistan
(Khan 2015). The negotiations have not been very fruitful due to the extremely
complex situation in Afghanistan (Taliban leadership struggle, the influence of the
Pakistani intelligence service ISI and tribalism to name a few) but the mere fact that
negotiations are happening is positive.
To summarize the laid out theory, I argue that drugs, as an exceptionally valuable
commodity, enhance rebels’ capabilities. They can use the money on equipment,
guns, and as an impetus for new fighters to join their cause. These advantages
coming from the drug money make the rebels stronger – they are able to overcome

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the power asymmetry and credibly threaten the government as well as inflict serious
damage, therefore governments should have more incentives to grant them
concessions in a form of a peace agreement or a ceasefire agreement. From this
discussion above I derive the second hypothesis:
Hypothesis 2: Rebels fighting in an area with established drug production are more
likely to obtain concessions from the central government.
4. METHODOLOGY
In order to test the aforementioned hypotheses, I have decided to use a quantitative
method and the statistical program STATA. Kellstedt and Whitten (2013, p. 4) say
that “hypothesis testing is a process in which scientists evaluate systematically
collected evidence to make a judgement of whether the evidence favours their
hypothesis or favours the corresponding null hypothesis”. In the area of social
sciences quantitative analysis is a popular method used to determine a relationship
between a dependent variable and one or more independent variables (Niño-
Zarazúa 2012). I will use two tests – a multiple linear regression to prove a
connection between drug production and the length of armed conflicts. The second
one will be a multinomial logistic regression which will provide more detailed
description of the dynamics between drug production and outcomes of armed
conflicts.

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4.1. DATASETS PRESENTATION
For my dissertation I used two datasets. The first one (Non-State Actor Data) was a
collaborative work of Cunningham, Gleditsch, and Salehyan (2009). It builds on the
Uppsala Armed Conflict Data (Gleditsch et. al 2002) about civil conflicts and expands
it not only with information about non-state actors involved in armed conflicts and
data about external dimensions of conflicts, but also with information about the type
of terminations, which is crucial for this dissertation. In order to answer my research
question it will be useful to define what kind of conflict I am interested in. Fearon
and Laitin (2003) define civil wars as a 1) fight between a state and non-state
group(s) that strive for control over a certain part of the state’s territory or topple
the government, 2) the intensity threshold must surpass 1000 casualties over the
battle period with 100 deaths per year and 3) at least 100 casualties on each side of
the conflict. In my research I will use the definition by the Uppsala Armed Conflict
Data (ACD), which categorizes only armed conflicts that are defined as “a contested
incompatibility which concerns government and/or territory where the use of armed
force between two parties, of which at least one is the government of a state,
results in at least 25 battle-related deaths” (Wallensteen and Sollenberg 2001, p.
643). The lower threshold used by the ACD allows me to analyse more observation
which will be helpful in my models.
Unlike the ACD, the Non-State Actor dataset (NSA) disaggregates conflicts into
government-insurgent dyads. It is not uncommon that a government fights multiple
rebel groups with different characteristics during a conflict at the same time, thus it
would be unfounded to aggregate them all together. The second dataset is based on

23. 23
the research of Buhaug, Gates and Lujala (2009) which provides information about
drug production in the area of an armed conflict. These scholars focus on the
cultivation of marijuana, opium and coca leaves (which are later modified to
cocaine). Thanks to the identification code of every conflict (confid) I was able to
merge both datasets together.
This modified dataset has a new binary variable (drugs) which will be central for my
analysis. The time dimension of my research is set from 1946 to 2003 – I have not
deliberately chosen these specific years, but I was limited mainly by the latter
dataset which contains information only up to the year 2003. After the merge, the
dataset consists of 2301 observations. Drug production was present in 569 dyads or,
24.73% of all observations, but the drugs were produced only in 18 countries out of
the total number of 92 (or 91, to be more precise, because the Soviet Union and
Russia are listed as two different countries), which is 16.56%. The discrepancy is
caused by the fact that was mentioned earlier – a government of one country could
be in an armed conflict with more than one rebel group which can be all engaged in
a drug production business. The majority of drug-cultivating countries is located in
Asia (8 out of 18) followed by the region of Central and South America (4) and Africa
(4). The two remaining countries are Georgia and Russia/The Soviet Union. This can
be explained by the climate conditions suitable for cultivation of these plants. For
example, more than 98 percent of the world’s coca production is located in the
Andean region of Colombia, Peru and Bolivia because coca plants require a specific
set of conditions to grow which can be found in this region (Moreno-Sanchez,
Kraybill and Thompson 2003).

24. 24
4.2. DEPENDENT VARIABLES
For the first test to show the connection between the conflict duration and drug
production, I have created a new variable nyears according to the variable dyadid
which is a unique value for each rebel-governmental dyad. It provided me with the
information of the duration of each of the 321 conflicts – encounters lasted from a
single day (the conflict in Tunisia in 1980 and in Guinea in 1970) to 43 years (the
conflict between the government of Burma and the Burmese Communist Party). The
average duration of an armed conflict is the mean of nyears, which is 5.84 years,
despite the fact that almost 47% of all conflicts lasted two years or less. When I ran
the histogram command, I observed that the distribution of years is skewed which
would later affect my regression model. In order to fix that, I generated a new
variable lognyears which will be the log of nyears and become more normally
distributed.
For the multinomial logistic regression I used typeoftermination as the dependent
variable. It indicates how a dyad conflict ended and can take on several values: -8)
conflict not terminated, 1) peace agreement, 2) ceasefire agreement with conflict
regulation, 3) ceasefire agreement, 4) victory, 5) no or low activity, 6) other and
6.1) dyad ended when groups combined to form a new group (ex. Guatemala). This
conflict termination division was taken from Kreutz (2010) and his project within the
Uppsala Conflict Data Program. For the purpose of this paper, I have decided to
recode values 2 and 3 as 2 because it represents some kind of ceasefire agreement
and it would be difficult to distinguish between these categories. The values 6/6.1
were recoded as missing because of the unclear type of termination.

25. 25
4.3. INDEPENDENT VARIABLES
Some string variables (containing non-numeric characters) that I will later use in my
tests had to be recoded because STATA cannot work with text. This applies to the
ordinal variable fightcap, which indicates fighting capacity relative to the government
– some groups might be small in numbers but fairly effective when it comes to
coordination and fighting the government. The data shows that the vast majority of
groups have a low fighting capacity and only 1.5% has a high fighting capacity. The
binary variable terrcont specifies whether insurgents control some part of the
territory or not. The territory control might give rebels an advantage because they
can retreat and regroup far from the reach of the government. The data shows that
almost 38% of rebel groups exercise some sort of territory control. Another
dichotomous variable is centcontrol measuring the clear central command of a rebel
group – academia argues that a clear central command is essential for effective
insurgencies (Heger, Jung and Wong 2008).
The variable rebpolwing shows the connection between insurgents and their political
wing – the existence of a political party is important in helping rebels to achieve
their goals through democratic means, but it doesn’t say anything about legality of a
said party. That is why I included a dummy variable lpw for indication of a legal
political wing which is present only in 11% of all observations. For example, rebels
with a legal political representation can be seen in Ireland where Sinn Fein is a legal
political wing of the Irish Republican Army, or in Angola, where FLEC has formed its
political wing FLEC-CSA to voice their demands (James 2011). Cunningham,
Gleditsch and Salehyan (2009) suggested that the sole existence of a legal political

26. 26
wing should make conflicts shorter due to other opportunities to voice the rebels’
concerns. The ordinal variable rebstrength shows the strength of the rebel forces
relative to the government forces because the absolute numbers of standing army
are not very explanatory on their own. It ranges from “much weaker” to “much
stronger” and the data shows that almost 90% of rebel groups are either much
weaker or weaker than the government forces. The dynamics of a war can be
altered by an external support, which is why I included the variables rebsuport and
rebextpart – the first one measures whether rebels obtain some kind of support from
external states, the latter shows support from external non-state actors such as
Irish-American supporters of the Provisional IRA (Duffy 2001). All of the variables
mentioned in this section had to be recoded in order to obtain clear results – all
values coded as “unclear” or “does not apply” were recoded as missing.
4.4. CONTROL VARIABLES
For my analysis, it is essential to include several so-called confounding/control
variables. My results might be affected if these are not included due to the lack of
internal validity caused by a confounding effect. Fearon (2004, p. 286) in his
influential paper “Why Do Some Civil Wars Last So Much Longer Than Others?” calls
them the ‘usual suspects.’ The first control variable is the ethnic and linguistic
fractionalization index, which measures the likelihood of two random people being
from different ethnic groups. It is included because in the past it was argued that
ethnic conflicts show different characteristics than other conflicts (Sambanis 2001).
Secondly, I controlled for GDP per capita as a proxy for state strength (Fearon and
Laitin 2003), which can be linked to military strength. Thirdly, I included a log of

27. 27
population since larger countries seem to have somewhat longer civil wars (Fearon
2004). Finally, I used a dummy variable for democracy created on the basis of a
polity index which classifies countries that score six or more points as democracies.
5. THE LINEAR REGRESSION MODEL
In order to find a relationship between the drug production and the length of a
conflict, I run a linear regression test, where the variable nyears was the dependent
variable. The unit of analysis in this test is a conflict between a government and a
non-state actor. I included the drug production, the support of rebels by a foreign
government, the military support by transnational state actors, territory control by
rebels, fighting capabilities of rebels relative to the government, the rebels’ strength,
clear central command of insurgents and the indication of whether rebels have a
political wing or not as independent variables, while controlling for ethnic and
linguistic fractionalization, gross domestic product per capita, democracy and the
population size.
After the OLS regression, I checked the model for multicollinearity and none of the
variables scored a large VIF value – the mean VIF score is 1.38, thus none of the
variables are near perfect linear combinations of one another. I also ran Breusch-
Pagan/Cook-Weisberg as well as White's test, which proved the presence of
heteroskedasticity. I corrected my regression with a robustness check and found out
that the more complex model (Model 5 in Table 1) is not substantially more
explanatory than the parsimonious Model 1.

28. 28
The F-test values (0.0000) for all tests signify that all the models are statistically
significant. The R-squared value determines the proportion of variance in the
dependent variable that can be explained by the independent variables. In statistical
terms, this means that my models explain from 19 to 21% of the variability of the
dependent variable. My test shows (see Table 1) that drugs play a major part in the
duration of armed conflicts – a one unit increase in the drug production scale (which
means from “no production” to a “production” of illicit substances) leads to the
prolonging of a conflict by 5.74 years, thus I rejected the null hypothesis. This runs
contrary to the findings of Buhaug, Gates and Lujala (2009) and Lujala (2010) who
claimed that there is no systematic connection between drugs production and the
duration of conflicts. There are several theories which might cast some light on this
– the first one is in line with Collier and Hoeffler’s (2004) explanation of civil wars
which points out the economic opportunities created by armed conflicts. Fearon
(2004) in a similar vein argues that a dependable source of money helps rebels to
sustain rebellions.
In my model, the second factor prolonging conflict is rebels’ support by one or more
foreign governments, which is consistent with Cunningham (2010) – he argues that
when external states become involved in armed conflicts, they pursue an agenda
which is beneficial for them but not necessary for the recipient of the support. This
makes conflicts more complicated and difficult to resolve which results in the
extended duration of the conflict. The last variable positively correlated with conflict
duration is the dummy for democracy – if a country scores six or more points on the

29. 29
polity scale then it can expect conflicts to last 2.57 years longer. This is consistent
with findings of Cunningham, Gleditsch and Salehyan (2009, p. 586) who suggest
that “conflicts in democratic states tend to be less likely to end.” This could be
explained along the following lines. The established democratic mechanisms of
accountability and legitimacy may limit the repertoire of counterinsurgency tactics
available to democracies when compared to less democratic states. The potential
international backlash that could be expected from excessively violent
counterinsurgency tactics may encourage democracies to pursue less effective
strategies that are however in line with international norms.
On the contrary, my model shows two variables which seem to reduce the conflict
duration. The first one is a dummy variable determining an existence of rebels’ legal
political wing and the second one expresses rebels’ fighting capacity relative to the
government. A legal political wing, unlike the mere existence of a political wing (not
necessarily a legal one), is statistically significant and reduces the duration of a
conflict by more than 3 years. One can argue that legal political wings help rebels
voice their demands in a non-violent manner. Peaceful means are more respected in
democratic societies (democracy is a necessary requirement for the legality of any
political wing) than a military action. Another channel of pressurizing the
government can, therefore lead to a shorter civil war. Additionally, a one-unit
increase in the rebels’ fighting capacity leads to a decrease of 2. 33 years in the
conflict length. The indication of rebel’s fighting capacity has very little to do with
the absolute numbers of combatants, since even small groups can be very
experienced in the conduct of war and represent a serious threat to the government

30. 30
forces such as the Rwandan Patriotic Front during the Rwandan civil war of 1990-
1994 (Cunningham, Gleditsch and Salehyan 2013).
Subsequently, all the other variables show a positive relationship regarding the
duration of the conflict, although none of them are statistically significant. In spite of
a slightly lower R-squared value which explains around 20% of the variance, it is a
statistically significant model. But is the drug production at all helpful in improving
rebels’ chances of striking a deal with governments?

31. 31
Table 1 – Regression models of the conflict length
VARIABLES Model 1 Model 2 Model 3 Model 4 Model 5
Drug production 5.731*** 5.687*** 5.684*** 5.746*** 5.740***
(1.653) (1.665) (1.670) (1.662) (1.672)
Rebels supported
by a foreign
government
0.831*
(0.429)
0.838*
(0.427)
0.862**
(0.425)
0.934**
(0.432)
0.935**
(0.434)
Rebels supported
by a non-state actor
0.640
(0.521)
0.665
(0.525)
0.675
(0.528)
0.571
(0.508)
0.560
(0.492)
Rebel’s territory
control
1.165
(0.928)
1.028
(0.920)
0.970
(0.920)
1.284
(0.899)
1.287
(0.897)
Rebel’s fighting
capacity
-2.720***
(0.954)
-2.502**
(0.964)
-2.555***
(0.966)
-2.331**
(0.977)
-2.326**
(0.979)
Rebel’s clear central
command
1.222
(1.108)
1.121
(1.056)
1.133
(1.046)
1.262
(1.051)
1.296
(1.105)
Rebel’s political
wing
0.225
(0.402)
0.268
(0.404)
0.290
(0.418)
0.299
(0.417)
0.305
(0.414)
Rebel’s legal
political wing
-2.574**
(1.176)
-2.779**
(1.204)
-2.816**
(1.212)
-3.074**
(1.249)
-3.111**
(1.257)
Rebel’s strength
relative to the
government
0.544
(0.596)
0.700
(0.611)
0.728
(0.612)
0.730
(0.611)
0.742
(0.616)
Population (log) 0.373 0.364 0.222 0.229
(0.267) (0.267) (0.272) (0.267)
ELF index 0.689 0.773 0.872
(1.494) (1.469) (1.621)
Democracy
(dummy)
2.616*
(1.414)
2.573*
(1.417)
GDP (log) 0.0842
(0.499)
Constant 1.499 -2.453 -2.792 -2.760 -3.602
(2.613) (3.954) (3.989) (4.058) (6.064)
Observations 249 249 249 249 249
F- test 0.0000 0.0000 0.0000 0.0000 0.0000
R-squared 0.192 0.197 0.198 0.211 0.211
* p<0.05, ** p<0.01, *** p<0.001

32. 32
6. THE MULTINOMIAL LOGISTIC REGRESSION MODEL
This question will be answered with a multinomial logistic regression which “is used
to model nominal outcome variables, in which the log odds of the outcomes are
modelled as a linear combination of the predictor variables” (UCLA 2015). Unlike the
first model, where the unit of analysis was the conflict itself, in this regression the
unit of analysis is a dyad between a rebel organization and governmental forces. The
vast majority of the dyads (86%, 1980 dyads) were not terminated. The most
common type of termination was “low or no activity” (112 dyads) followed by
governmental victories (109 dyads). Rebels were able to reach a peace agreement in
57 cases and some kind of ceasefire agreement was achieved in 14 cases.
I ran a complex test where my dependent variable was a type of termination. I
included the drug production and several other independent and control variables
that could possibly affect the outcome of armed conflicts (see Table 2). Like other
academics (Cunningham, Gleditsch and Salehyan 2009), I have also decided to
evaluate the overall significance of each variable for the expected outcome since I
believe it has a higher informative value. The value for conflict continuation was
chosen as the baseline category so that I can observe what effects drug production
has on the different types of armed conflicts termination.
My research revolves around drug production in the area of an armed conflict. The
number of observations in my second model is 1970 with a p-value of 0.000, which
demonstrates that this model is statistically significant. After running the multinomial
logistic regression, I observed that the measure of drug production only has a

33. 33
negative effect in the case of government victory, indicating that drug production
diminishes chances of governments for a victorious outcome. On the other hand
peace agreements and especially ceasefire agreements are more likely to happen
when drug production is present in the area of an armed conflict. This finding is
consistent with my hypothesis suggesting that drug production improves rebels’
chances for a negotiated settlement. However, after testing for an overall effect of
each of the variables, I came to the conclusion that drug production is not
statistically significant and therefore I failed to reject the null hypothesis.
Subsequently, I cannot declare with any confidence that drug cultivation increases
the probability of concession from a government.
What speaks in favour of my argument are the coefficients of the “rebels’ strength
relative to the government” variable – if a rebel group were to increase their relative
rebel strength by one unit, the multinomial log-odds for peace agreement/ceasefire
agreement would be expected to increase by 0.56 and 0.75 unit respectively holding
all other variables equal. In substantive terms, this means that the stronger rebels
get, the more likely they are to strike a deal with the government. Unfortunately, the
variable is not statistically significant.
Other variable, rebel’s fighting capacity rated relative to the government proved to
be statistically significant in overall and very significant in the case of “no or low
activity.” My test showed that this variable only has a positive coefficient in cases of
government victory and ceasefire agreement, although in the ceasefire agreements
case it is very low. What is somewhat confusing is that at the same time the

34. 34
likelihood of government victory becomes more likely. This could mean that when
rebels’ fighting capacity is enhanced, they neither want to accept a peace agreement
nor end up in low activity warfare, but would rather fight. The length of conflict also
plays a significant role – all coefficients have negative values which mean that the
longer conflicts go on, the less likely they are to end in any kind of defined
termination. Rebels’ legal political wings also have a positive coefficient in all cases,
which implies that the existence of such a political body improves the chances for a
negotiated settlement.
Despite the fact that rebels’ support by a foreign government/non-state actor is not
statistically significant, they show the same direction of coefficients except for the
ceasefire agreement outcome. A possible explanation might be that both foreign
governments and non-state actors have their own agenda which makes the peace
agreement or government victory less likely.

35. 35
Table 2 – Multinomial Logistic Regression
Type of termination Peace
Agreement
Ceasefire
Agreement
Government
Victory
No or Low
Activity
Drug production 0.159 1.947** -0.262 0.379
(0.423) (0.867) (0.487) (0.311)
Rebels’ fighting capacity** -0.0732
(0.342)
0.0178
(1. 220)
0.475*
(0.271)
-0.944***
(0.332)
Length of conflict*** -1.099***
(0.207)
-1.910***
(0.357)
-1.828***
(0.186)
-1.860***
(0.152)
Rebels’ support by a
foreign government
-0.104
(0.166)
0.576
(0.491)
-0.0645
(0.135)
0.197
(0.135)
Rebels’ support by a non-
state actor
-0.104
(0.138)
-0.849**
(0.398)
-0.237
(0.181)
0.0197
(0.130)
Rebels’ territory control 0.0866
(0.400)
0.446
(0.612)
0.180
(0.290)
-0.310
(0.268)
Rebels’ strength relative to
the government
0.556
(0.378)
0.749
(0.975)
0.195
(0.322)
0.0141
(0.235)
Rebels’ legal political
wing***
0.936
(0.590)
0.554
(1.343)
1.296***
(0.387)
0.895*
(0.456)
0.694 -0.840 -1.515*** 1.046*ELF Index
(0.837) (2.057) (0.473) (0.605)
GDP (log) 0.0662 0.851** -0.197 0.0599
(0.300) (0.365) (0.162) (0.142)
Democratic country* -0.210 0.550 -1.113* -0.572*
(0.581) (0.759) (0.568) (0.292)
Population (log)* -0.278** -0.199 0.189** 0.153*
(0.126) (0.304) (0.0859) (0.0881)
Constant 0.130 -9.334** 0.128 -1.151
(3.186) (4.315) (1.478) (1.596)
Observations 1,970 1,970 1,970 1,970
Stars next to variables show overall statistical significance of the variable
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

36. 36
7. DISCUSSION AND LIMITATIONS
There are some concerns that have to be acknowledged. Firstly, it is the nature of
drug trade itself. Drugs are illegal substances; therefore it is complicated to find
reliable data. In some instances, the connection between drug production and rebel
organizations is unclear or simply unknown – one of the explanations might be the
different approaches to illicit substances. Different regions and cultures perceive
drugs differently and in some cases the drug production or usage is not acceptable
(Abbott and Chase 2008) which incentivizes insurgents to keep their involvement in
narcotics cultivation a secret.
Secondly, a major concern is that Buhaug, Gates and Lujala (2009) focused only on
countries or locations where drugs are produced but did not include countries which
act as a connecting link between producers and consumers. Countries in Central Asia
such as Tajikistan or Kyrgyzstan as well as Central American states are to a certain
extent affected by the growing drug trade.
Thirdly, what I found problematic is the fact that drug data provided us only with the
information about the existence of drug production in the area of an armed conflict.
However, it does not mention the extent of the production and what is the share of
revenues from the drug trade on the overall funding of rebels.
Fourthly, the dataset (Buhaug, Gates and Lujala 2009) from where I get the
information about drug production, is not updated – the latest entry is from 2003
which was more than ten years ago. According to the updated UCDP ACD dataset,

37. 37
there were 381 cases of armed conflicts around the world between 2004 and 2014
(Pettersson and Wallensteen 2015). The results might be different if all the above-
mentioned issues were taken into consideration.
Finally, it is also important to take into consideration the variance in yield and
profitability of different drugs. It is worth mentioning again that since drugs are
illegal, we have only limited data at our disposal. For example the average yield from
a one-hectare field is 5.86 kilos of cocaine (Washington Office on Latin America
2012), 2 500 kilos of marijuana (UNODC 2008, p.97) and 42.5 kilos of heroin
(UNODC 2008, p. 40). The wholesale price of one gram of marijuana in the US in
2015 is around 11, 5 USD (Williams 2015), 30.5 USD for a gram of cocaine in 2006
(UNODC 2008, p. 82) and 87.7 USD for a gram of heroin (UNODC 2008, p.49). As
we can see (Figure 5 in Appendices) the yield and prices vary dramatically.
8. CONCLUSION
The aim of this thesis was set out to explore the effects of illegal drug production on
different aspects of armed conflicts. Not only are drugs connected to organized
crime and funding terrorism but in the past two decades, they have become a crucial
source of income for rebel groups worldwide due to their profitability, renewability
and lootability. These characteristics make them a convenient commodity that can
be easily transported to the big markets of Europe and the USA, while the money
goes to the rebel groups, who use them to enhance their power. In this thesis, my

38. 38
research has focused on two parts of armed conflicts that might be affected by drug
production – duration and the type of termination.
Although the majority of the general theoretical literature on the role of natural
resources and drugs points out that production of illicit substances in the area of an
armed conflict actually prolongs the conflict, yet some authors (Buhaug and Lujala
2005; Buhaug, Gates and Luajala 2009; Lujala 2010) remained doubtful about their
effect. The first section of this study has sought to shed some light on this matter. I
have argued that drug money enhances rebel capabilities which help rebels resist
government forces. My statistical model proved that conflicts where drugs are
produced are considerably longer than conflicts in areas without any drug
cultivation. This confirmed the findings of Fearon (2004), Ross (2004) and Cornell
(2007). The second part focused on the possible effect of drug cultivation on the
outcome of an armed conflict. Unexpectedly, the literature regarding this topic is
non-existent. This is rather surprising since drugs have represented a very viable
source of money for rebel organizations, terrorists and criminal enterprises around
the world. I have reasoned that since drug money improves the insurgent’s relative
strength in relation to the incumbent government, rebels who have access to this
source of income can overcome the power asymmetry as mentioned by Fearon
(2004) and become a credible threat to the regime. This enhanced leverage should
improve their chances for a negotiated settlement but my second model showed that
drug production does not have a statistically significant effect on the type of
termination.

39. 39
It follows that drug production does not help rebel groups to achieve their goals, as
I hypothesized in the beginning, yet I confirmed that the drug cultivation does
prolong conflicts. The perfect example can be seen in Colombia where rebels from
the Revolutionary Armed Forces of Colombia (FARC) alone make approximately $550
million per annum from illegal drug trade (Abrams 2014). The armed conflict
between the government, the FARC and the National Liberation Army (ELN) has
been going on for more than fifty years.
However, there are two main issues which must be borne in mind – firstly, the drugs
dataset is more than ten years old. Due to obvious reasons, (such as the illegality of
drug trade), it is complicated to obtain reliable data. Secondly, the dataset is
oblivious to the fact that drugs must be transported from producers to consumers.
This involves a lot of countries along the road and some of them have their own
insurgencies (Central America, the Western Africa) which take part in drug business
operations. Despite the fact that they do not produce the drugs, they help to
transport them and certainly get their own share of revenues. I believe this is also
the path for a future research which should include transport countries.
To get back to the quote from the beginning of this thesis which was uttered by a
man who was a former assassin for the infamous drug lord Pablo Escobar, the
connection between insurgents and drugs is only one part of the drug puzzle. If
there is a strong demand for illicit drugs, then there will also be a supply to satisfy
these needs. The United States tried to eradicate the drug problem since Nixon
declared the “War on Drugs” in 1971 but without any noticeable success (Vulliamy

40. 40
2011; Global Commission on Drug Policy 2011). The obvious and logical policy
implication would be to call for a decriminalization or legalization of drugs on a
global level, but this option is very controversial and does not attract a wide support.
Meanwhile, the bodies are piling up and bills for fighting rebels and gangs are yet to
be paid.

41. 41
9. BIBLIOGRAPHY
9.1. BOOKS
Buhaug, H. and Gleditsch, N. (2006). The Death of Distance? The Globalization of
Armed Conflict, in Kahler, M. and Walter B., eds, Territoriality and Conflict In an Era
of Globalization. New York: Cambridge University Press, pp.187–216.
James, W. (2011). Historical dictionary of Angola (2nd ed.). Lanham, Md.: Scarecrow
Press.
Keen, David. (2000). Incentives and disincentives for violence. In: Berdal, Mats and
Malone, David M., (eds.) Greed and Grievance: Economic Agendas in Civil Wars.
Lynne Reinner Publishers; International Development Research Centre, Boulder, CO,
USA; Ottawa, Ontario, Canada, pp. 19-42. ISBN 9781555878689.
Kellstedt, P. and Whitten, G. (2013). The fundamentals of political science research.
Cambridge: Cambridge University Press.
Schelling, T. (1960). The strategy of conflict. Cambridge: Harvard University Press.
9.2. JOURNALS
Angrist, J. and Kugler, A. (2008). Rural Windfall or a New Resource Curse? Coca,
Income, and Civil Conflict in Colombia. Review of Economics and Statistics, 90(2),
pp.191-215.

42. 42
Asal, V., Deloughery, K. and Phillips, B. (2012). When Politicians Sell Drugs:
Examining Why Middle East Ethnopolitical Organizations Are Involved in the Drug
Trade. Terrorism and Political Violence, 24(2), pp.199-212.
Buhaug, H. and Lujala, P. (2005). Accounting for scale: Measuring geography in
quantitative studies of civil war. Political Geography, 24(4), pp.399-418.
Buhaug, H. (2006). Relative Capability and Rebel Objective in Civil War. Journal of
Peace Research, 43(6), pp.691-708.
Buhaug, H., Gates, S. and Lujala, P. (2009). Geography, Rebel Capability, and the
Duration of Civil Conflict. Journal of Conflict Resolution, 53(4), pp.544-569.
Clayton, G. (2013). Relative rebel strength and the onset and outcome of civil war
mediation. Journal of Peace Research, 50(5), pp.609-622.
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Figure 1 – Armed Conflict Statistics (Source: Themnér and Wallensteen 2014)
10. APPENDICES
Figure 2 – Heroin Production and Trafficking Map (Source: DEA Museum 2015)

55. 55
Figure 3 – Crime-Terror Continuum (Source: Makarenko 2004)
Figure 4 – The major cocaine smuggling routes (Source: UNODC 2008)

56. 56
Figure 5 – Heroin Prices in the US (Source: UNODC 2008)