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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

Collier, P. and Hoeffler, A. (1998). On economic causes of civil war. Oxford Economic

Papers, 50(4), pp.563-573.

Collier, P. and Hoeffler, A. (2004). Greed and grievance in civil war. Oxford Economic

Papers, 56(4), pp.563-595.

43

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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

Figure 3 – Crime-Terror Continuum (Source: Makarenko 2004)

Figure 4 – The major cocaine smuggling routes (Source: UNODC 2008)

56

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