Child Soldiers in Armed Conflict

A quantitative study concerning the effect of education on child soldier recruitment.

Julia Carlbäcker

Peace and Conflict Studies

Bachelor Thesis, 15hp, Fall of 2017

Supervisor: Ralph Sundberg

Department of Peace and Conflict Research

Uppsala University, Sweden

1

Table of Context

List of figures and tables ............................................................................................................ 2

1. Introduction ............................................................................................................................ 3

1.1 Introduction ...................................................................................................................... 3

1.2 Background to Child Soldering ........................................................................................ 4

1.3 Previous Research ............................................................................................................ 5

1.4 Research gap and contribution ......................................................................................... 7

2.Theoretical Framework ........................................................................................................... 8

3. Research Design ................................................................................................................... 11

3.1 Method and Data ............................................................................................................ 11

3.2 Operationalization of variables ...................................................................................... 13

3.2.1 Dependent variable .................................................................................................. 13

3.2.2 Independent variable ............................................................................................... 14

3.2.3 Multicollinearity and Index variables ..................................................................... 16

3.3 Source criticism .............................................................................................................. 19

3.4 Scope conditions ............................................................................................................ 20

4. Results and Analysis ............................................................................................................ 20

4.1 Descriptive Statistics ...................................................................................................... 21

4.2 Results ............................................................................................................................ 23

4.3 Analysis .......................................................................................................................... 26

4.4 Alternative explanations ................................................................................................. 30

5. Conclusion ............................................................................................................................ 31

6. List of References ................................................................................................................. 33

Appendix I. Codebook and operationalization of variables ..................................................... 36

Appendix II. Robustness checks .............................................................................................. 37

2

List of figures and tables

Figure 1. Theoretical claim ...................................................................................................... 11

Figure 2. Distribution of Index variables ................................................................................. 22

Figure 3. Predicted Probability (%) of Independent variables ................................................. 26

Table 1. VIF test ....................................................................................................................... 17

Table 2. Descriptive statistics ................................................................................................... 21

Table 3. Results from the logit regression ................................................................................ 25

3

1. Introduction

1.1 Introduction

The use of child soldiers in armed conflict is violating international law and is, according to the

UN security council, one of six grave violations against children in armed conflict (OSRSG-

CAAC, n.d.). Still, children are recruited in armed conflicts all around the world. Child soldiers

are present in some rebel groups but not in others and in some countries’ military forces but not

in neighboring countries’ forces. This variation imposes a puzzle for the international

community, NGOs and academic researchers. If we could understand under which

circumstances children are recruited in armed conflicts, we could have a better chance of

developing effective policy implications in order to protect children from the risk of being

recruited or associated with armed struggle.

Previous research concerning the causes of child soldering has found different

explanations for the puzzle. Although, in qualitative studies, educational opportunities have

been highlighted as a main important factor decreasing the recruitment of children. Several

qualitative studies have stressed the importance of the combination of quality of the education

provided as well as access and availability of education (Brett and Specht, 2004; Cohn and

Goodwin-Gill, 1994; Machel, 1996; Maclure and Denov, 2006). Despite these findings in

qualitative research, quantitative research has only looked at access to education and its

importance for child soldier recruitment and therefore missed out on a crucial aspect of the

quality of the education in the analysis of the effect of education (Tynes and Early, 2015; Vargas

and Restrepo-Jaramillo, 2016). According to my knowledge, no quantitative study has been

conducted which matches the theoretical argumentation from qualitative studies and therefore,

I argue, that the research area could be considered inadequate.1 A research gap is therefore

identified with the lack of an adequate quantitative study in the area of the effect of education

on child soldier recruitment.

In order to fill this research gap, I will conduct a quantitative research which includes

both aspects of quality of the education itself and access to education, in order to evaluate

education's effect on child soldier recruitment. From now on, I will use the term quality of

education as a concept including both quality of the education itself and access and availability

factors. I will build on existing theories from qualitative research in the field with the aim of

testing the validity of these theories in a quantitative study and to test education’s effect on

child soldier recruitment in a more adequate way concerning the theories developed in previous

1 This argument is also supported by Ames, 2010.

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qualitative studies. By doing so, I will try to answer the research question: How does quality of

education effect the likelihood of child soldier recruitment? If a relationship could be observed

on a global level between the quality of education and child soldier recruitment, it would be an

important contribution to the policy work protecting children from involvement in armed

conflict. I argue that increased quality of education decreases the risk of child soldier

recruitment in armed groups because increased quality of education leads to increased

educational opportunities which keep children occupied and they are therefore less vulnerable

for or attracted to recruitment in armed groups.

The structure of this research paper will proceed as follows. First, I will summarize

previous research on the causes of child soldering and thereafter look more closely on the

previous research concerning education. Second, I will explain my theory and methodological

choices that I have made in order to fill the research gap in the best way possible. Following

the methodological section, I will show the results of my systematic research and provide an

analysis. The results of the research conducted for this paper, concerning quality of education’s

effect on child recruitment, cannot confirm the relationship between the variables. In order to

establish the true effect of education on child soldier recruitment, more research is needed. This

will be discussed at the end of the paper where I will reflect on my methodological choices and

their impact on the results as well as discuss alternative explanations and areas of further

research.

1.2 Background to Child Soldering

International rules against child soldiers are present in the 1948 Universal Declaration of

Human Rights and in the additional protocols to the Geneva Convention which were adopted

in 1977, prohibiting military recruitment of children below 15 (Child Soldiers International,

n.d.). Rules prohibiting child soldier recruitment is also present in the 1989 Convention of the

right of the Child (Singer 2010:94). Although, it was not until 1996 that child soldiers received

special attention and the UN asked for a comprehensive report of children’s situation in armed

conflict. The report which was conducted by Graça Machel, shed new light on child soldiers

and has been an important study in the field of children and armed conflict. The report led to

the establishment of the Cape Town Principles in 1997, a meeting where experts were brought

together to develop strategies to prevent child recruitment (UNICEF, 1997). In 1999 the UN

security council adopted the first resolution on children and armed conflict and identified six

grave violations that were priority for the Council (OSRSG-CAAC, n.d.). Building on the Cape

Town Principles, the Paris commitments brought the issue up on the agenda again in 2007 and

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governments agreed to act against recruitment of children in armed forces and rebel groups

(Gates and Reich, 2010:3; UN, 2007). Even though the issue of child soldering has gained more

international attention since the end of the cold war, children are still recruited on all sides in

armed conflicts and even an increase has been observed (Singer 2010:93). It is impossible to

know how many children that are a part of modern warfare but the globalization and technical

advancement of small arms could be one factor explaining the observed increase (Gates and

Reich, 2010: 11).

1.3 Previous Research

Previous research concerning the causes of child soldering has mainly been conducted by

NGOs, civil society organizations and advocacy groups. Relatively little academic attention has

been brought to the research field (Gates and Reich, 2010: VII,14). The research of advocacy

groups is important but often lacks the time and money to conduct statistical analysis and

theoretical models needed in order to reach a more precise understanding of why children join

or are recruited into armed groups (Ames, 2010). Although, children’s situation in armed

conflict has gained more attention recently and the research field is growing.

The available research of causes of child soldiers can be summarized into three different

categories. One group focuses on strategic explanations for child soldier recruitment and the

second group argues that child soldiering is a consequence of the conflict setting. The last

branch of research highlights the importance of structural conditions and their effect on the

likelihood of child soldier recruitment.

First, the strategic explanations for why children are recruited in armed conflict include

lower cost of recruitment, tactical benefits and the easiness of manipulation (Beber and

Blattman, 2013; Maclure and Denov, 2006; Singer, 2006). Children are easy to recruit and

therefore a cheap way for armed groups to generate force according to Singer (2006). The use

of children in battle might also offer tactical benefits for rebel groups because it can induce

moral dilemmas for the opposite side (ibid.). Beber and Blattman (2013) studied rebel

recruitment in Uganda and found that children are attractive recruits even if their strength and

fighting abilities were less than adults because children can be manipulated at a lower cost, are

cheaper to indoctrinate and easier to misinform (ibid:68-69). Maclure and Denov (2006) studied

boy soldiers in Sierra Leone and found that when the conflict disrupts societal norms, children

are easily manipulated and socialized into new norms of violent behavior. In the case of Sierra

Leone, alcohol and hallucinatory drugs and the availability of small arms also facilitated the

manipulation of children (ibid:124–131).

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In contrast, Tynes and Early (2015) argue that child soldiers are recruited as a

consequence of the conflict setting. The authors argue that when disputants become more

desperate, child recruitments are more likely. The authors highlight the importance of looking

at conflict duration and intensity as well as conditions that effect the nature of the conflict, such

as level of militarization and presence of democratic institutions. Tynes and Early conclude that

duration and intensity are significant factors explaining child soldier recruitment due to

increased desperation from the fighting parties (Tynes and Early, 2015:107–108). According to

the findings of the research, democratic governance decreases the likelihood of child soldier

employment (ibid.). Tynes and Early (2015:85) explain this correlation by arguing that more

democratic regimes follow the norms of human rights to a greater extent than authoritarian and

repressive regimes.

The last and most comprehensive part of causes of child soldier research provides

analysis on a number of structural explanations for when children are recruited in armed

conflict, including existence of IDP (Internally displaced people) camps, poverty, employment

opportunities and education (Achvarina and Reich, 2005; Brett and Specht, 2004; Cohn and

Goodwin-Gill, 1994; Machel, 1996). First, Achvarina and Reich look at the relationship

between IDP camps in a country and the existence of child soldiers but do not find a strong

relationship (Achvarina and Reich, 2005). Other structural factors which have gained more

attention are the effect of poverty and employment opportunities. Although several studies

conclude that poverty is an important factor, it fails to explain variation because many

communities are poor, especially during conflict, but not all of them recruit children (Achvarina

and Reich, 2006; Brett and Specht, 2004; Vargas and Restrepo-Jaramillo, 2016). Brett and

Specht, (2004) argue that poverty creates a general vulnerability for recruitment and they find

that most child soldiers come from impoverished backgrounds. However, poverty cannot be the

only factor because many poor children do not become child soldiers (ibid.).

Instead, several authors emphasis that access to education is a more crucial factor

effecting child soldier recruitment (Brett and Specht, 2004; Cohn and Goodwin-Gill, 1994;

Machel, 1996; Maclure and Denov, 2006; Vargas and Restrepo-Jaramillo, 2016). If children

are not involved in education or work, they need to find another activity to secure their

economic survival or to find something to do with their time. In that case, according to Brett

and Specht (2004:126) they could be more prone to join an armed group as an alternative

economic activity. However, even if the authors find a strong relationship between education

and child soldiers, they argue that their research fails to systematically explain the variation

between variables because of the qualitative nature of their research (Brett and Specht,

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2004:147). The report by Machel to the UN in 1996 also shows that when educational

opportunities are limited, recruits tend to become younger and younger (Machel, 1996:17).

Improved economic status, access to basic health and education decrease the possibility of

armed groups to talk children into running away and join the armed struggle. Good education

opportunities might then prepare them to resist the offers from armed groups (Vargas-Barón

2010:203). Inadequate education can, on the other hand, make rebel groups seem like a viable

option (Cohn and Goodwin-Gill, 1994: 34).

1.4 Research gap and contribution

The importance of education has been mentioned in several previous studies, however, the

majority of them are qualitative studies based on interviews with former child soldiers (Brett

and Specht, 2004; Cohn and Goodwin-Gill, 1994; Machel, 1996; Maclure and Denov, 2006).

Despite the importance showed in qualitative research, only two quantitative articles address

the issue of education and both of them focus only on access to education. According to Brett

and Specht, access to education is important but also the quality of the education itself; “It is,

however, not only the real, effective access to school that is important, but also the quality of

the educational experience, and its relevance to perceived future or even present economic

prospects” (Brett and Specht, 2004: 131). Therefore, I argue that the quantitative research that

has been conducted in the field has measured education in an inadequate way to match the

theories developed by previous qualitative research.

The two previous quantitative studies which address education, have been conducted by

Vargas and Restrepo-Jaramillo (2016) and Tynes and Early (2015). Vargas and Restrepo-

Jaramillo (2016) conducted a quantitative study in Colombia and focus on poverty’s effect on

child soldier recruitment. The authors include education as a variable and measure it by school

attendance rates and average year of schooling but lack theoretical motivation for the

relationship as well as fail to include the important aspect of the quality of the education itself.

The study is also limited to Colombia and lack inclusion of regional and global variance. The

only global quantitative analysis which has included education as a variable, has been

conducted by Tynes and Early (2015). However, in their article, education is only measured as

expected year of schooling which is a measurement concerning how many years of schooling a

child could expect to receive. This variable does not include present enrolment rates which

indicates access and availability to education or the quality of the education provided. These

factors have been highlighted by theories in previous qualitative research and argued to have a

strong relationship with children´s educational opportunities and in turn their level of exposed

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vulnerability for recruitment (Brett and Specht, 2004; Cohn and Goodwin-Gill, 1994; Maclure

and Denov, 2006).

Concerning the importance of educational opportunities found in previous qualitative

research, and the scarce and inadequate way in which quantitative research has followed up the

theories developed in the field, I argue that a research gap is identified. There is a lack of a

comprehensive quantitative analysis of education’s effect on child soldier recruitment,

including both availability aspects as well as aspects of quality of the education itself. To fill

this research gap, I will conduct a quantitative research on education’s effect on child soldier

recruitment and develop the measurements of education further in order to include both aspects

of education. By doing this research, I will try to answer the research question: How does quality

of education effect the likelihood of child soldier recruitment? But why would quality of

education matter for child soldier recruitment? This will be discussed in the following section.

2.Theoretical Framework

In this section an elaboration of the theoretical explanation for the relationship between the

variables will be provided. Theoretical concepts used in this research will be specified and the

section will conclude with the theoretical claim for this paper followed by a flow chart

explaining the causal relationship between the variables and this paper’s testable implication in

the form of a hypothesis.

2.1 Theory

In order to fill the research gap, I will expand the measurements of education to correspond to

the theory provided in this section. In this paper, I argue that increased quality of education

decreases the risk of child soldier recruitment in armed groups because increased quality of

education leads to increased educational opportunities which keep children motivated and

occupied. Children are therefore less likely to drop out and join the armed struggle as well as

less vulnerable for forced recruitment.

This argument is built upon previous qualitative research where opportunities have been

highlighted as one of the most important mechanism in explaining the causal relationship

between the variables (Brett and Specht, 2004; Maclure and Denov, 2006). The most

comprehensive analysis of the causal chain has been developed by Brett and Specht (2004). In

sum, the authors argue that lack of educational opportunities lead to that children search for

alternative opportunities and are therefore more attracted to employment in armed groups as a

valid alternative to education or more vulnerable for forced recruitment. The authors highlight

two main important factors that effects educational opportunities. First, motivation for

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education is one important factor that increases children’s willingness to stay in school or to

search for alternative opportunities (Brett and Specht, 2004). Lack of motivation can include

poor quality of the education provided or lack of relevance (ibid.). If education is inadequate or

unlikely to lead to employment, going to school might seem pointless and children are therefore

more likely to drop out (ibid.). Second, availability and access to education is crucial.

According to Brett and Specht, availability can consist of economic availability, meaning that

poor children might not have the opportunity to attend school or access to a physical school.

Access and availability also includes the equality and inclusion of education, for example equal

access and availability between gender (ibid.).

Further, the authors argue that if some or all of these factors are lacking, children may

not be in school and have nothing to do with their time. The need of activity, status, or income

might influence the choice of joining the rebellion or armed groups voluntarily during armed

conflict. Or, they are more vulnerable for forced recruitment because they have no place to be,

are looking for other things to do and are exposed to different violent groups in society (Brett

and Specht, 2004:44). Former child soldiers have emphasised this relationship and the

importance of opportunity in the causal explanation; The main cause of going there was

unemployment, I think. I had nothing to do here so I went there. If you have some business or

you are studying, then you do not think about taking part in Jihad. Aziz, Pakistan (Brett and

Specht, 2004:23).

Brett and Specht explain the relationship between the variables by arguing that both the

quality of the education itself and the access to it is important for children’s educational

opportunities. Therefore, I argue that the measurement of school life expectancy which has been

used in previous quantitative studies is not a sufficient measurement in order to evaluate the

importance of education for child recruitment. To contribute to the research field, the definition

of the concept of quality of education developed in this paper aims to include a broader meaning

of education, so that it would correspond to factors that affect children´s educational

opportunities. Hence, I believe that this definition captures the phenomenon of interest and

together with the description in the operationalization section, the concept is also possible to

measure and compare across different cases.

To fully understand the theoretical argumentation in this paper the concept of child

soldiers need to be specified. Several different assumptions exist regarding the concept of child

soldiers and child soldier recruitment and it is therefore crucial to address these in order to

explain how the concepts are used in this research paper. The definition of a child soldier varies

but this paper will use the most common definition which is stated in the Paris principles from

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2007. In the Paris Principles, a child soldier refers to “a person below 18 years of age who is or

who has been recruited or used by any armed force or armed group in any capacity” (UN,

2007: 7). This definition changed after 2002 and increased the age from 15 to 18 years old

(Child Soldiers International, n.d.). Even if the term soldier is used it can be misleading because

children often work as spies, cooks, porters or sex slaves and are not necessarily wearing a gun

in order to count as a child soldier (Gates and Reich, 2010: 3; UN, 2007: 7). In recent years a

new term has been developed in the international community to include this variation in the

concept. The more accurate term is Children Associated with Armed Forces and Groups (War

Child, 2015). However, with this knowledge this research paper will still refer to the term Child

Soldier due to the frequent use of the term by most official reports and academic research. This

definition is also used by the dataset for this research on child soldier existence (Haer and

Böhmelt, 2016: 160) which is why I have chosen to use the same definition for my research.

Another important aspect of the definition of child soldier recruitment is the difference

between forced and voluntary recruitment. The international community has previously focused

mainly on forced recruitment of young children. Although, in reality we see a high degree of

children and adolescents who voluntarily join armed groups (Brett and Specht, 2004). Little

research has focused on voluntary recruitment due to lack of valid information concerning why

children join armed groups (Ames, 2010). Brett and Specht aim to fill the research gap and try

to capture the concept of voluntary recruitment but at the same time they problematize it. How

voluntary is voluntary recruitment? The authors mean that even if children answer that they

voluntarily joined the armed group, they might not have had different options: “I didn’t choose

this situation. You know that we are in a country at war, and then you don’t have much choice.

You can run away or fight”, Christine DRC (Brett and Specht, 2004:10). Although, even if

considered difficult to measure, the concept of voluntary recruitment needs to be considered

and therefore I choose to include a causal explanation for both voluntary and forced recruitment

in my analysis. The data on child soldiers has not separated these two categories and therefore

an assumption is made that both forced and voluntary recruited children are present in the data.

So, in this research paper, the term recruitment refers to “compulsory, forced and voluntary

conscription or enlistment of children into any kind of armed forces or groups” (UN, 2007: 7).

Building on the theories developed in previous research with a focus on the theory

developed by Brett and Specht and upon the conceptual definitions specified, I argue that

quality of education, including both motivational factors and access and availability factors, are

crucial in explaining child soldier recruitment. Decreased quality of education might on one

hand lead to decreased motivation to complete the studies, or on the other hand, lead to

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decreased availability or access to education. Both of these factors might in turn lead to a search

for alternative opportunities which might increase the attractiveness of joining armed groups.

As well as increasing the vulnerability for forced recruitments due to increased levels of out of

school children with low levels of educational background. At the end, this might lead to

increased likelihood of child soldiers in armed groups. An illustration over the causal

relationship can be observed in Figure 1.

Figure 1. Theoretical claim

Through this research, I will test the following hypothesis: Decreased quality of education

increases the likelihood of child soldier recruitment in armed conflict. How the research will

be conducted and details concerning the method used to test the hypothesis, will be explained

in the next section.

3. Research Design

The following section will explain the methodological choices made in this research and the

data used to conduct the analysis followed by an elaboration of the operationalization of the

variables. Finally, a section discussing source criticism will be provided.

3.1 Method and Data

The aim of this paper is to provide a comprehensive quantitative analysis of quality of education

and the relationship to child soldier recruitment. This will be conducted in order to fill the

previously mentioned research gap by expanding on the analysis of education’s effect on child

recruitment. In order to do so, I will build my research on the Child Soldier dataset provided by

Haer and Böhmelt (2016)2. The data includes information concerning rebel and government

recruitment of children between 1989-2003 and consequently, I will use the same time period

for my analysis. However, my research will be limited to recruitment by rebel groups because

2 The original dataset is available at: https://www.polver.uni-konstanz.de/data/

Decreased

quality of

education

Search for

alternative

opportunities

Decreased

availability

and access

Decreased

motivation

Increase

attractiveness to

join armed groups

Increased physical

and mental

vulnerability of

forced recruitment

Increased

likelihood

of child

soldiers in

armed

groups

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the authors have argued that the government data is unreliable, and they advise not to use the

data in any research (Haer, 2017, personal communication [email], 20 November). This

limitation does not affect my study except for decreased variation in the dataset and a limited

scope. The level of analysis is dyad-year and the dataset provides the most detailed information

available in the field. This analysis is limited to the available observations from the Haer and

Böhmelt dataset. However, the sample provided gives a broad understanding of the whole

population of cases and the spread over regions increases the generalizability of the sample.

The total number of observations is 781 and the proportion of observation in each region is as

follows: Africa 37%, Asia 29%, Middle East 18%, Latin America 10% and Europe 6%.

Another available global dataset on child soldier recruitment is conducted by Tynes and

Early (2015). They analyze child soldier existence or no existence in rebel groups and

government forces, but are not specifying the result towards specific rebel groups. I have chosen

to build my analysis on the dataset by Haer and Böhmelt due to more specified information and

increased number of countries included in the dataset. Consequently, this choice increases the

number of observations for my sample with the intention to create more generalizable results

which hopefully correspond well to the overall population.

Concerning the independent variable, I will conduct my own dataset with variables on

relevant aspects of education. Expanded discussion about this can be found in the

operationalization section below. The data for the educational variables will be taken from

UNESCO Institute of Statistics (UIS, n.d.). This source is chosen because UNESCO provides

the most comprehensive data on education and is the main source of educational statistics used

by other international actors, for example the World Bank’s educational dataset (see World

Bank, n.d.). The created education dataset will then correspond to the same time period as the

information concerning child soldiers.

However, some adjustments to the original data have been made. The theory of the paper

emphasis the importance of available education for the children in risk of recruitment.

Therefore, a dyad between USA and Al-Quida in 2002 was deleted from the dataset because

the location of the conflict were USA and child soldier recruitment was observed in Al-Quida.

I argue that this dyad is outside of the theory of this paper because with my method of analyzing,

the child soldier observation would be analyzed together with educational statistics from USA

which would not be an accurate way of measuring according to my theory. Dyads where the

conflict is carried out in another country and where the exact origin of the rebel group is difficult

to trace should therefore not be included in the analysis. This is because in these cases, as the

case of Al-Quida, accurate educational statistics could not be obtained and matched to the

13

corresponding dyad and therefore the research would lack validity. In this paper, this dyad-year

was the only one that matched this criterion and therefore was the only dyad excluded from the

analysis.

3.2 Operationalization of variables

3.2.1 Dependent variable

The dependent variable of this analysis is child soldier recruitment in armed groups. According

to the definition provided by the Paris principles and by Haer and Böhmelt (2016) a child soldier

is defined as a person below 18 years of age which has been recruited or used by an armed

group. In their dataset, each dyad-year which contained child soldiers was assigned a code of 0

if no information could confirm child soldier usage in the armed group in that specific year and

1 if child soldier usage could be confirmed. Due to the dichotomous character of my dependent

variable I will conduct a binominal logit regression analysis in order to test my hypothesis.

The definition of the dependent variable is consistent with the international

understanding of the definition of a child soldier and the operationalization follows the same

definition. Child soldier recruitment is coded if the group used child soldiers in their armed

group regardless if the children were forcibly recruited or joined voluntarily or what task they

performed within the fighting group. This operationalization corresponds to the definition

explained in this paper which means that the operationalization of the dependent variable is

measuring what it is supposed to measure and therefore is valid to a great extent.

However, before 2002 child soldiers were defined as children below 15 years old. The data

traces information back to 1989 and uses several international organizations as sources for child

soldier recruitment. There is a valid chance that these organizations used the previous definition

of child soldiers before 2002 since that was the international standard at the time. This means

that the information concerning these cases is only measuring children below 15 years of age.

The coding for cases before 2002 might therefore be built on sources which have only reported

if a group used children below 15 and are therefore not corresponding to the definition provided

by Haer and Böhmelt as well as the definition used in this paper. However, child soldiers are

still measured both before and after 2002 even if children between 15-18 years old might not

have been included in the earlier period. Therefore, I argue that this is not a problem for the

results of the analysis, but this difference is important to acknowledge.

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According reliability of the operationalization, I argue that the operationalization is

reliable in the sense that the same data is available for download which makes it easy to test my

results or run a different analysis on the same data as I have used for this research.3

3.2.2 Independent variable

The theory in this paper, which is built from previous qualitative studies, focuses on educational

opportunities and highlights both motivational factors and the availability and access to

education. Previous quantitative research has not included both categories and therefore failed

to correspond to the theory concerning relevant and available educational opportunities.

Therefore, to match the theory, the independent variable used in this paper, quality of education,

is a variable which aims to include several different variables which in turn captures both

motivational factors as well as access and availability. There are no international standards on

how to measure the expanded concept of quality of education, and it is done in a variety of ways

dependent on what it is supposed to measure. Although, UNESCO, as a major actor of

educational policies at the international stage, published a report in 2015 on achievements and

challenges concerning the goal of education for all as an analysis as a preparation for the

Sustainable Development Goals (UNESCO, 2015). The report addresses important factors

which affect the availability, quality and access of education which corresponds closely to the

definition of quality of education used for this paper. I have chosen measurements that

correspond to the phenomenon explained in the theory and at the same time have been used in

international organizations to measure similar phenomena.

In order to operationalize quality of education to correspond to the theory I have chosen

to divide the concept of quality of education into two subgroups: Quality and Relevance of

education and Access and Availability. Thereafter, I have included three measurements in each

group which covers different aspects of the two subgroups. I will operationalize as follow:

Quality and Relevance of education will be measured by 1. Governments’ expenditure on

education as a percentage of total expenditure, 2. Pupil/teacher ratio and 3. Adult literacy rate

in the country. I have chosen these measurements because they capture important aspects of the

theory by effecting the motivation for education, as explained by Brett and Specht (2004) and

these are also used in UNESCO´s report concerning quality of education (UNESCO, 2015). In

that report government prioritization in education, measured by the governments’ expenditure

on education as a percentage of total government expenditure is used as well as adult literacy

3 The dataset for child soldier recruitment is available at: https://www.polver.uni-konstanz.de/data/

15

rates. UNESCO also highlights the importance of teachers in achieving quality of education

and the pupil/teacher ratio is used as a measure of quality (UNESCO, 2015), which is why I

included it as a variable. Access and Availability will be operationalized by 1. Net enrolment

rates, 2. School life expectancy and 3. Gender Parity Index of enrolment rates4. This choice is

also built on theory where school life expectancy has been used as a measurement of access

(Tynes and Early, 2015). This operationalization also corresponds nicely to UNESCO´s report

which used net enrolment rates as a measurement of access and availability of universal

education as well as inequality rates measured by the parity index in enrolment rates (UNESCO,

2015: xii, 80-83).

This operationalization is more inclusive in the understanding of the diversity of the

concept of education, compared to previous research in the field. I argue that these variables

correspond more precisely to the theory and the causal relationship provided. If the theory

points to the right relationship, increased levels of quality and relevance as well as access,

availability and equality will correspond to decreased likelihood of child soldier recruitment.

According to the theory, this is because these factors increase the educational opportunities and

decrease the search for alternative opportunities.

The unit of analysis for my research is dyad-year and the education statistics, available

for country level, will be matched to the location of the conflict in the child soldier dataset. By

doing so, accurate statistics on the quality of education provided in the country where the

conflict is ongoing will be used. When information on education about specific years is missing,

the closest prior value to the missing one will be used in order to receive the most accurate data

available which relates to the opportunities provided for the children in the country. When

conflict breaks out, educational statistics are often less available. However, the quality of

education available before the outbreak of the conflict still provides an understanding of the

level of quality of the education available for the exposed children. Increased intensity also

affects both the quality of the education available as well as child soldier recruitment and will

therefore be controlled for in my analysis in order to receive better accuracy of the educational

data.

By my operationalization I have tried to include the most relevant variables which affect

children’s motivation for and access to education. These are selected upon previous research

on education´s effect on child soldier recruitment (Brett and Specht, 2004; Maclure and Denov,

4 In order to be measured together with the other variables, Gender parity index have been changed to include

only inequality between gender and not if it favourable for boys or girls. Consequently, the variable can take a

value between 0-1 were 1 is equality.

16

2006; Vargas and Restrepo-Jaramillo, 2016) as well as what have been highlighted by policy

makers in the international community (UNESCO, 2015; UNICEF, 2016). Therefore, upon my

knowledge and understanding, the operationalization is valid because it is measuring what is

important for the theory in this research in a way that is accepted and commonly used in large

international organizations. A full explanation of the variables used and the sources for them

can be found in Appendix I.

However, even though the variables are closely selected upon these criteria and

analyzed to be important both by scholars and policy makers, they are not perfect. Due to

inconsistency in measurements of quality of education, alternative variables may be of

importance in order to fully grasp the concept of quality of education. Education can be

measured in a variety of ways and the indicators are available in several different formats,

measuring slightly different aspects of a concept. For example, enrolment rates can be measured

by net enrolment rate, adjusted net enrolment rate or gross enrolment rate which measures

slightly different aspects of enrolment rates. I have, upon the best of my knowledge and building

on previous research and reports from policy makers, tried to include the most relevant

indicators. Although, this might need to be developed in future studies.

The operationalization is reliable due to available information on UNESCO´s website

on the precise variables and indicators used for measuring the independent variable in the

dataset for this paper. If my dataset on education will be used in future research, one will have

access to the exact same figures on education for country and year which increases the reliability

of the data.

3.2.3 Multicollinearity and Index variables

To measure education in a more inclusive way and not only in a different way, calls for a need

of several independent variables which might relate to each other due to the fact that they are

measuring different aspects of education. Before conducting the regression, a need to conclude

if multicollinearity exists among the independent variables, or if they might be run together in

the regression, is crucial. If a multicollinearity problem exists among the variables this might

in turn affect the results (Kellstedt and Whitten, 2009:238-244). One approach to identify

collinearity is to use a test of variance inflation factors (vif), where a higher value indicates

higher collinearity (beckmw, 2013). The results from the test in this paper can be observed in

Table 1. A value between 1-5 generally indicates low multicollinearity and a value between 5-

10 generally indicates high multicollinearity (beckmw, 2013). If a variable takes a value over

10 the regression coefficient would be poorly estimated due to multicollinearity (ibid.).

17

Table 1. VIF test

In Table 1, one can observe that both enrolment and school life expectancy have high

multicollinearity with other variables and literacy rate almost reaches the same level. Although,

none of the variables reach a level over 10. In order to follow the line of the theory and to

answer the research question presented in this paper, I argue that all variables are needed in

order to explain the variation in education. Therefore, I will keep all six variables but introduce

them separately in the regression models due to the risk of high multicollinearity.

According to the theory, these variables are interacting and capturing different important

aspects of education and the relationship between the total sum of all variables measured against

the dependent variable would therefore still be interesting to observe. To do so and at the same

time avoiding the risk of multicollinearity, I will create three index variables. All independent

variables are standardized in the way that the mean takes a value of 0 and the standard deviation

takes a value of 1. By doing so, all variables could be compared even if they initially were

measured in different scales. For example, variables measured in percentage could be compared

to variables measured in quantity. These standardized values could thereafter be added together

and divided by the number of variables to receive an average value of the variables included. I

will create a variable of an average of the total of all variables as well as an index variable

including the three variables concerning access and another one with the variables concerning

quality of the education itself. By doing so, the total score of all education variables could be

measured against the dependent variable. The importance of the two subgroups, access and

motivational factors could also be measured in the same manner, to evaluate if one subgroup is

more related to child recruitment than the other.

3.2.4 Control Variables

As discussed in the previous research section, several different explanations exist regarding

what causes child soldier recruitment. In order to evaluate the relationship between education’s

effect on recruitment of children, three other important variables from previous research have

been chosen to be included as control variables in this study; poverty, democratic governance

and intensity.

In previous research, poverty has been discussed to be a key factor of causes of child

soldering (Achvarina and Reich, 2005; Brett and Specht, 2004; Vargas and Restrepo-Jaramillo,

2016). However, even if poverty might have an impact, many poor countries do not recruit

Enrolment GPI in

enrolment

Literacy rate Expenditure

on education

Teacher/pupil

ratio

School life

expectancy

7.10 3.93 4.99 1.09 1.33 8.07

18

children and therefore the variable has failed to explain the variation (Achvarina and Reich,

2006: 5). Poverty is included as a control variable due to the attention that poverty has gained

in previous research and because it might have an effect on both quality of education and child

soldier recruitment. I have followed the same operationalization of the control variables as

Tynes and Early (2015), because their article is a well-established and well cited article in the

research field and they have all three variables included in their study. Poverty has been

calculated using GDP per capita for each country and year using statistics from the World Bank

(World Bank, 2017). Achvarina and Reich (2005) have measured poverty in a more inclusive

way including several different measurements which gasp more of the concept of poverty.

However, in the scope of this article, the inclusive approach is not possible to include and

therefore I will use the operationalization conducted by Tunes and Early.

Second, level of democratic governance will be measured by the Polity IV index as done

by Tynes and Early5. Even if other variables are available in order to measure governance, the

polity scale is used in order for this article to be more comparative to other research regarding

level of democracy as well as correspond to the work by Tynes and Early (2015). The authors

find correlation between the level of democratic governance and recruitment of children. Level

of democratic governance might also have an impact on quality of education because

governments’ policies are directly affecting the countries’ educational standards. Therefore, the

variable is included as a control variable in this research.

The third and last variable is intensity and will be measured by battle related deaths per

year in a country using data from UCDP, Uppsala Conflict Data Program6. Tynes and Early

(2015) argue that high intensity increases the desperation of the combatant groups and therefore

increases the likelihood of child soldier recruitment. Intensity might also affect education

opportunities due to increased closure and detriment of schools (GCPEA, 2017) and is therefore

important to include as a variable in order to control for the effect of intensity on child

recruitment. Tynes and Early coded battle related death as a dichotomous variable using 0 for

conflicts with 25-999 battle deaths per year and 1 for those conflicts reaching over 1000 (Tynes

and Early, 2015: 94). In this research I have decided to use the actual numbers of deaths per

year to include more variation in the variable. However, intensity data has been aggregated to

country and year level and not at dyad level. This is because of coding difficulties due to

inconsequent use of coding id and dyad names which made it difficult to merge intensity with

the rest of the data in the limited scope of this research. I also argue that the level of intensity

5 The Polity IV is available for download at: http://www.systemicpeace.org/inscrdata.html 6 Data available at: http://ucdp.uu.se/downloads/

19

in the whole country is important because it might influence the feeling of desperation in several

dyads which make the coding relevant in my case. Although, this might be done differently in

future research to test the significance of the actual intensity level of the specific dyads.

3.3 Source criticism

The validity and reliability of the data have been discussed but several aspects and shortcomings

concerning the sources of the data are necessary to highlight and specify at this point. Due to

the limitation of the scope of this research, I have been unable to collect new data and therefore

I had to rely on available data in the field. As mentioned before, quantitative studies of child

soldiers are limited, and data availability is therefore restricted which have limited the choice

of data used in this research. Hence, even if data has been available it is not perfect and therefore

some faults need to be brought up in order to increase the transparency of the research as well

as highlight some source criticism that might have an impact on the research outcome.

Concerning the data on child soldiers, it has been conducted from Hear and Böhmelt

(2016). The authors used both advocacy groups, news reports and academic articles in order to

receive information concerning child soldier usage in rebel groups (Haer and Böhmelt, 2016,

Appendix II7). However, these sources could include biases. For example, organizations might

have incentives to exaggerate the facts on child soldiers to receive attention to their organization

and current mission. On the other hand, rebel groups, when asked by reporters, academics or

staff from advocacy groups, might have incentives to downplay the use of children in order to

avoid punishment (ibid.). The sources used when conducting the data might have had potential

biases and the data could therefore suffer of reliability issues. However, this is a well-

established methodological problem in studies of child soldering due to great fear in post-

conflict societies to be honest about the facts, both from former child soldiers, rebel leaders and

governments (Gates and Reich, 2010).

The process of choosing cases to conduct data from might also suffer from potential

bias in the case of the data from Hear and Böhmelt. There is a vast majority of incidents

reported, compared to non-incidents. 666 observations include child soldier usage while only

115 include non-usage in the original dataset (including also 153 NA:s). This might be

representing the overall population, however, the variation in the dependent variable decreases,

which might influence the analysis in the research. There is also no information concerning

why these countries where chosen and in turn no discussion of sample representation is

provided.

7 The Appendix II was received from Haer upon email request and were not available online.

20

There are also some important facts to correct from the article of Haer and Böhmelt. In

their description of the data they state that the data includes dyads between 1989-2010.

However, in the dataset, only dyads between 1989-2003 are included. The time span is therefore

shortened with almost 1/3 of what was thought to be available, which also limits the

representation and variation for my research. Although, the dataset accomplishes to include a

variety of cases from different regions, including South America, Africa, Asia and Europe.

Concerning the independent variable, UNESCO builds its data on a majority of

governmental data which could sometimes suffer from bias. However, they also conduct

household surveys which might widen the picture and together it might be quite close to reality

(UIS, n.d.). A problem occurs under circumstances when this information is difficult to reach.

For example, during conflict. Data as close as possible to the actual date has been used, but it

might be invalid due to that the data might in reality be very different during the conflict period.

This is important to have in mind, but the use of the educational data is done in the best way

possible according to my knowledge as well as corresponding to previous research.

3.4 Scope conditions

According to Haer, the data on government use of child soldiers is unreliable (Haer, 2017,

personal communication [email], 20 November), and therefore it was not included in the

research. The results of the research are therefore limited to rebel use of child soldiers which in

turn then might not explain the use of children in government forces. The results of this paper

might be applicable even for those cases, but the scope of the research is limited to rebel groups.

The data includes countries from South America, Africa, Europe, Asia and the Middle East and

therefore I argue that the scope of the analysis is not restricted to any substantial geographical

limitations. However, the data collected for this research spans from 1989-2003 and a limitation

in the temporal dimension exists. As mentioned in the introduction, child soldier usage has

changed after the cold war and because of the limited time frame I argue that the research is

mostly applicable to armed conflicts after the end of the cold war. However, the theory of this

research does not have to be restricted to these circumstances even if the result of the analysis

might be restricted. If the theory is applicable to a broader domain should be tested in further

research.

4. Results and Analysis

In this section, the results from the logit regressions will be presented followed by a discussion

of the main findings of the research. Further, an analysis will be conducted concerning the

implications for theory as well as methodological choices which might influence the outcome

21

of the regressions. At the end of the section, shortcomings of the research will be presented and

discussed as well as alternative explanations and areas for further research.

4.1 Descriptive Statistics

Before presenting the main findings of the analysis a section on descriptive statistics will be

provided in order to increase the understanding of the data and the variables used for the

research.

Table 2. Descriptive statistics ============================================================

Statistic N Mean St. Dev. Min Max

------------------------------------------------------------------

DEPENDENT VARIABLE:

Child Soldiers 781 0.9 0.4 0 1

INDEPENDENT VARIABLES:

Enrolment rate 742 71.3 23.8 15.0 99.9

Equality 742 0.8 0.2 0.4 1.0

Literacy rate 714 63.4 24.7 10.9 99.7

Expenditure 727 13.8 4.7 2.9 41.4

Teacher 773 -32.2 14.3 -90.4 -12.3

School Life 764 7.6 2.9 1.6 13.0

INDEX ON EDUCATION:

Total 779 -0.1 0.7 -1.5 1.3

Access 764 -0.05 0.9 -2.3 1.4

Quality & Relevance 779 -0.01 0.6 -1.9 1.7

CONTROL VARIABLES:

Intensity 741 1,566.2 3,807.0 25 49,698

GDP per capita 700 2,602.9 5,365.1 65.0 27,759.3

Democracy 781 0.8 7.5 -10 10

R have been used to generate the statistical results.

The dichotomous character of the dependent variable limits the variable to values of 0 or 1. In

the descriptive statistics in Table 2 one can observe that the mean of the variable is 0.9. This

indicates that a clear majority of the observations take value 1 (child soldier recruitment)

compared to 0 (no recruitment) and consequently gives the analysis a limited variation in the

dependent variable. More precisely 14,7% of the observations take value 0 in the dependent

variable and 85,2% take value 1.

However, by consulting the descriptive statistic table an interesting variation can be

observed among the independent variables. Among the six independent variables on education,

a great difference between minimum and maximum can be seen. This indicates that the

observations in the sample range from very low levels of education with 15% enrolled and an

22

average of 1.6 years of schooling, to very high, with 99.9% enrolment and up to 13 years of

schooling. This gives the analysis an important variation in the independent variable which

increases the possibilities to test the hypothesis. The mean of the different independent variables

is fairly centered between the min and the max which indicates that the values are fairly

normally distributed around the mean. Further, it might be so that not so many extreme outliers

effect the results. Some exceptions to this statement concerns few observations in enrolment

with low values which differs from the mean and some cases with very high levels of

expenditure on education and teacher/pupil ratio8 which differs substantially from the mean of

the variable. Concerning the control variables, the mean of Intensity is 1,566 and the maximum

value is almost 50.000. This indicates that in the sample used for this analysis, few cases with

very high levels of intensity are present when the average of intensity among the observations

have lower intensity levels.

The variation in the three different index variables on education is small due to the

standardized values for all independent variables which the index is built upon. The limited

variation in the values makes the values in the descriptive statistics more difficult to analyze by

only looking at the numbers.

Figure 2. Distribution of Index variables

In order to familiarize oneself with the index variables and to facilitate the analysis of the

descriptive statistics, a boxplot showing the variation in the index variables can be observed in

Figure 2. By looking at the figure, the index variables are more visually explained. One can

observe that in the Quality variable several outliers are present with very low levels of quality

of education.

8 In the statistics the variable of teacher/pupil ratio takes a negative value in order to follow the same scale as the

other variables, a very low number indicates more children per teacher

23

4.2 Results

This research was conducted with the aim of providing a quantitative analysis of education’s

effect on child soldier recruitment by providing a more inclusive measurement of the

independent variable. To do so, a logit regression was conducted with several different

independent variables, effecting both quality of the education itself and access to education.

The purpose of this paper is to provide an answer to how quality of education effect child soldier

recruitment. With the background from the descriptive statistics this section will present the

results of the logit regression made in this analysis.

In Table 3 the results from the logit regression between the dependent variable and the

different independent variables as well as the index variables can be observed. The regression

is made with a dichotomous dependent variable which make the interpretations of the

coefficients slightly different from a linear regression model. A positive coefficient indicates

that an increase in the independent variable relates to an increased possibility of 1 in the

dependent variable. A negative coefficient indicates a relationship where an increase in the

independent variable is associated with a decreased likelihood of 1 (Bjerling and Ohlsson,

2010). However, much more is difficult to say by only looking at the coefficient because the

relationship between the variables is not linear due to the dichotomous character of the

dependent variable (ibid.). In order to analyze the results in a more concrete way, a predicted

probability analysis has been conducted. The results from the predicted probability analysis are

presented in Figure 3. This shows the predicted probability of child recruitment when each

independent variable increases from its minimum value, to its mean and finally up to the

variables’ maximum value. By doing a predicted probability analysis it is easier to observe what

the relationship between the variables mean and not just consulting the value of the coefficient

(Bjerling and Ohlsson, 2010).

Although, some important things can be observed by looking directly at Table 3. In

model 1-6 all independent variables are introduced alone together with the control variables.

One can observe that when the independent variables are introduced separately, all the different

educational variables are statistically significant, according to the p-value. The p-value ranges

from 0-1. The lower p-value, the greater confidence we have in that the relationship between

the variables is systematic and not found by random change (Kellstedt and Whitten, 2009:147-

150). A larger sample size might, for example, increase our confidence in the relationship due

to a more accurate representation of the population (ibid.). A p-value of

24

In model 7 and 8 all education variables are measured together, first without the control

variables and then with the control variables. When analyzed together the variables have less

significance. One reason for this might be multicollinearity between the variables, a risk that

was observed in the VIF test. In model 9 one can observe the relationship between the

standardized total value of education, through the index variable, and child soldier recruitment.

In model 10 the standardized value for all variables related to access are introduced and in

model 11 the standardized value of all quality variables can be observed. All three index

variables are also statistically significant. However, even if the results are statistically

significant it does not prove the relationship to be strong or if the relationship is causal

(Kellstedt and Whitten, 2009:147-150). Consult the analysis section for further discussion.

When examining the results of each model an unexpected relationship can be observed.

The correlation coefficients for the majority of the independent variables are positive. In all

independent variables except expenditure on education, increased levels are therefore,

according to the table, correlated with an increased likelihood of child soldier recruitment.

These results points to the opposite relationship compared to the theory of this paper and

previous studies in the field. The same relationship can be observed in the index variables. An

increase in the total score of quality of education is related to an increased risk of child soldier

recruitment. Expenditure on education is the only variable which has a negative effect on child

soldier recruitment. Consulting the predicted probability in Figure 3, one can see that when

expenditure moves from its mean to its maximum value, the risk of child recruitment decreases

from 86% to 57% while the other variables indicates an increased change when the independent

variable moves from the mean value to the maximum value.

The only other variable which has a negative effect throughout all the models is GDP

per capita. GDP per capita is statistically significant and an increase is associated with a

decreased risk of child soldier recruitment, supporting the results from previous research

(Achvarina and Reich, 2006; Brett and Specht, 2004; Vargas and Restrepo-Jaramillo, 2016). It

is also the only control variable which is statistically significant.

Table 3. Results from the logit regression ==========================================================================================================================================

Dependent variable: Child Soldier Recruitment

-----------------------------------------------------------------------------------------------------------------------

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

------------------------------------------------------------------------------------------------------------------------------------------

Enrolment 0.023*** 0.031*** 0.014

(0.007) (0.012) (0.016)

School Life 0.183*** -0.120 0.143

(0.062) (0.107) (0.141)

Equality 2.472*** 1.670 2.544

(0.860) (1.447) (1.592)

Expenditure -0.056** -0.058*** -0.050**

(0.022) (0.021) (0.024)

Literacy rate 0.012** -0.015 -0.011

(0.006) (0.010) (0.011)

Teacher 0.029*** 0.011 0.025**

(0.009) (0.008) (0.010)

Index Total 0.865***

(0.257)

Index Access 0.582***

(0.182)

Index Quality 0.574**

(0.258)

Intensity 0.00002 0.00002 0.00001 0.00001 0.00003 0.00000 0.00001 0.00002 0.00002 0.00002

(0.00003) (0.00004) (0.00003) (0.00004) (0.00004) (0.00003) (0.00004) (0.00003) (0.00004) (0.00004)

GDP per capita -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001***

(0.00002) (0.00002) (0.00002) (0.00002) (0.00002) (0.00002) (0.00003) (0.00002) (0.00002) (0.00002)

Democracy -0.007 -0.006 0.022 0.023 0.024 0.008 -0.019 -0.003 -0.005 0.011

(0.022) (0.020) (0.018) (0.018) (0.018) (0.019) (0.026) (0.020) (0.020) (0.019)

Constant 0.377 0.720* -0.095 2.787*** 1.249*** 2.921*** 1.062 0.106 2.097*** 2.115*** 2.009***

(0.480) (0.426) (0.698) (0.370) (0.326) (0.339) (0.974) (1.039) (0.154) (0.156) (0.146)

------------------------------------------------------------------------------------------------------------------------------------------

Observations 637 659 637 647 648 663 651 602 667 659 667

Log Likelihood -252.231 -256.456 -253.583 -254.286 -256.278 -261.318 -273.809 -229.673 -261.703 -255.770 -264.978

Akaike Inf. Crit. 514.461 522.912 517.165 518.572 522.555 532.635 561.619 479.346 533.405 521.541 539.957

Note: *p

26

Figure 3. Predicted Probability (%) of Independent variables

4.3 Analysis

This section will provide an analysis of the results presented in the previous section as well as

a discussion about shortcomings of the research and areas for future research. The research

question of this paper is: How does quality of education effect the likelihood of child soldier

recruitment? And the expected relationship, if the theory is true, is summarized in the

hypothesis of the paper: Increased quality of education decreases the likelihood of child soldier

recruitment in armed conflict.

However, the results of the analysis cannot confirm the hypothesis. According to the

results the opposite relationship could be observed; increased quality of education increases the

likelihood of child soldier recruitment. All at a statistically significant level. Some scholars

have argued that education might actually have the opposite effect on child recruitment because

schools might in some cases work as a recruiting ground and teachers might encourage students

to join (Brett and Specht, 2004: 19; Vargas-Barón 2010:215). Nevertheless, this theory has little

support empirically and even if the results might point to that relationship, I argue that there are

several shortcomings with the data and the methodological choices that first need to be taken

into consideration before giving too much value to the outcome of the regressions. I argue that

Min Mean Max

Access 63,48424 86,45861 93,52014

Index Total 64,37896 86,06383 95,10961

Quality 67,8013 86,34425 94,5399

School Life 68,35869 86,53935 94,56615

Enrollment 62,73749 86,19795 92,4077

GPI 64,16148 85,53982 100

Expenditure 92,11999 86,37179 57,08451

Literacy 77,34331 86,31775 90,60155

Teacher 95,6057 99,00377 91,7471

0

10

20

30

40

50

60

70

80

90

100

27

a disconfirmation of the hypothesis cannot be made before addressing these shortcomings.

Consequently, future research, building on lessons from this research, is needed in this area in

order to confirm or disconfirm the hypothesis of this paper.

First, the whole model needs to be analyzed. One reason for the unexpected relationship

presented in the results could be because of regional differences which could have a great

impact on the results. For example, European countries have in general higher levels of

education. By excluding Europe from the regression, one excludes several outliers. Then, by

choosing to only include countries in the same region in the regression it is possible to conduct

a regression including only a variation where educational differences are less extreme and other

socio-economic factors are more similar. This can be done in the form of a robustness check of

the main regression model. If one can observe different results in a regional regression one can

argue that the main model is less robust. If the robustness check shows the same results, it can

indicate that the model is robust or that other problems exist that might need to be taken into

consideration.

Consequently, to test the robustness of the regression I have conducted a robustness

check where only African countries have been included in the regression and another one with

only Asian countries. Otherwise, the regression has been carried out in the same way as the

main one, presented in Table 3. To do so, it is possible to find out if similar results are presented

even if limiting the regression to only a subset of the main sample. The results from the

robustness check with both African and Asian countries showed similar relationship between

the variables, which might indicate that outliers or regional differences are less important for

the outcome and that the main model might be robust. The results from the robustness check

can be observed in Appendix II. Due to the robustness check I argue that the robustness of the

main model might be less problematic and turn towards shortcomings in the different variables.

Concerning the independent variables, the data for education from UNESCO was only

available at country level and the unit of analysis of this research is dyad-year. This resulted in

a need of generalization: that different dyads in the same country had access to the same quality

of education. This, of course creates a problem, because regional differences exist inside one

country. Conflicts are often localized among different ethnic groups or regions inside the

country. For example, rural areas are often more likely to be involved than urban areas (Ames,

2010). By taking national data on quality of education, differences inside a country are not taken

into consideration which might be of importance for the results (Ames 2010:16). If education

statistics concerning the area of conflict could be obtained, one could analyze the difference

between conflicts regarding the level of education for the effected children compared to

28

recruitment in these areas. This change in data collection might influence the results of the

relationship that we observe in this paper.

Another shortcoming of the research concerning the collection of data for the

independent variable is the availability of data during conflict. Data on education is not only

difficult to obtain on micro level, it is also difficult to access when a country is in conflict. In

this research this resulted in the use of the closest value available. In some countries with long

conflicts the value on the independent variables might be far better than reality because the

value is taken from a point in time before the conflict started. In this paper, I argue that the

quality of education in a country before the outbreak of the conflict still might affect the

children’s opportunities, although, it might not be true in every case. In many conflicts, good

educational opportunities might be lost during years of conflict or due to very high intensity. In

order to more closely analyse education’s effect on children’s opportunities, more detailed

micro level data on education during periods of conflict need to be obtained. Therefore,

education data needs more micro level specification as well as more detailed time collection in

order to test the hypothesis of this paper.

The aim of this research was to include more measurements of education in order for

the research to be more inclusive and be able to more closely measure what the theory specified

as important. This in turn caused a problem of multicollinearity. In this paper, I solved the

problem by creating an index variable with standardized values of all independent variables.

This opened the possibility to use all the variables’ total relation to the outcome. However, in

the scope of this research, only the total sum of the standardized values could be measured.

With more time and knowledge, I argue that for this index variable to correspond with the real

effect, the variables should be weighted in order to be a more adequate measurement. All

variables are theorized to be of importance, but they might have different levels of importance

in relation to the outcome. For example, the theory emphasis opportunities for education and

both quality of education and access are theorized to be important. However, one can argue that

enrolment might have a more direct effect on opportunities than for example, expenditure on

education. One example from the data conducted for this research points towards this problem.

United Kingdom has one of the highest values on all educational variables except expenditure

on education. United Kingdom spends 12% of its budget on education while Azerbaijan spends

40%. Azerbaijan has lower values on all other variables but scores a higher total value due to

the high expenditure. This variation between the variables might also criticize the use of

expenditure on education as a measurement of quality. A government could, in theory, spend

more money on education when the education is poor, rather than for it to be a measurement of

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prioritization in education. This calls for a need to more closely observe which variables that

have the most important effect on child soldier recruitment. A weighted index variable would

be suggested in future research to measure the total effect of education on child recruitment.

Moving from the independent variable, some issues with the dependent variable need

to be taken into consideration. When doing the regressions, several different dyads in one

country had different results on the dependent variable but had the same score in the

independent variable. Due the overrepresentation of the value 1 (child soldier recruitment), this

might lead to the result that good quality of education actually shows a relationship to child

soldier usage. Because very few cases with non-recruitment exist in the dataset in order to show

results for when a country does not have child soldiers at all in any dyads. The only countries

in the dataset which had no child soldiers in any dyads were: Azerbaijan, Eritrea, Mali,

Moldova, Nicaragua, Niger, Romania; Senegal, Soviet Union and Trinidad and Tobago. So,

these were the only countries that could show any relationship between the quality of education

and a total absence of child soldiers. However, the quality of education differs substantially

between these cases, which might indicate that the theory is actually not true. Quality of

education might not affect the recruitment of child soldiers. Although, it is interesting to notice,

that the seven observations with the highest score on the index for total level of quality of

education do not recruit child soldiers. However, these cases might be too few to show any real

relationship between the variables.

Another important aspect of the dependent variable is the coding of 0 and 1. Could child

soldiering really be treated as a binary variable? Treating the variable as existence or not creates

a need of being able to confirm absence. This is problematic because it could be very difficult

to confirm absolute absence of child soldiers in rebel groups. This could be due to lack of valid

information and due to the fact that rebel groups do not face similar international pressure not

to use children in their forces. There are very few cases where children are not at all involved,

which is also present in the data for this research. A more accurate way of measuring child

soldiers should be by having the actual numbers of children involved in armed struggle. This

would create a better chance to look at education’s effect on the outcome because one could

observe if there is a difference in educational opportunities in countries which have few child

recruitments compared to dyads with higher proportion of children in the armed groups. Haer

and Böhmelt constructed a variable of 0, 1, 2 where 2 indicates if children constitute over 50%

of the force. Although, this might indicate a low presence of adults rather than a high presence

of children.

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According to the theory, children are more available for recruitment if they are not

attending school. An interesting relationship, developed from the previous argument, would

therefore be to look at the proportion of children that are recruited compared to the number of

children in school age. This would create a possibility to look at differences in quality of

education where a high proportion of school age children are recruited compared to a low

proportion which might indicate the proportion of children exposed to recruitment. Even if this

would create better opportunities to test the hypothesis of this paper, several researchers have

discussed that collecting this type of data is very difficult (Ames, 2010; Haer and Böhmelt,

2016; Achvarina and Reich, 2005).

In summary, I argue that increased education still might have a ne