Poverty and Child Soldier Recruitment:

A Disaggregated Study of African Regions

Abstract (794 characters):

In the popular debate, poverty is often identified as the cause of child soldier recruitment. The

argument suggests that economic deprivation and few viable life choices push children into

recruitment for armed conflict. The poverty argument has rarely been tested systematically, and

statistical results are inconclusive. Previous analyses potentially suffer from two

methodological problems: ecological fallacy and selection on the dependent variable. We meet

these shortcomings in previous tests of the poverty–child soldier nexus by introducing new data

that geographically disaggregates recruitment and poverty. Using a cross-sectional research

design for all sub-national regions in Africa in the period 1990-2004, we find some evidence

that the poorest regions are more subjected to child soldier recruitment. However, other factors,

such as the existence of refugee camps seem to outperform the poverty explanation.

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1. Introduction

Child soldiers frequently appear in armed conflicts around the world, and the problem is

particularly endemic in Africa. Children are recruited by both government and rebel armed

forces, and often serve in paramilitary, militia or self-defense groups backed by state authorities

which might not conscript children themselves (CSUCS 2004; Achvarina/Reich 2006). In DRC,

for example, child soldiers were serving at the front lines with all armed groups, in some cases

representing up to 35% of the troops (UN 2003). Despite increased efforts of the international

community to combat child soldiering1, children are being recruited and re-recruited for

conflicts. This dire trend calls for action, not only on the improvement and strengthening of the

international norms and programs, but also on the systematic investigation of the root causes of

child soldier recruitment.

The bulk of existing literature on child soldier recruitment consists of non-empirical

academic works and NGOs reports, usually based on interviews with a handful of children who

have been involved in combat. This literature often cites poverty as the cause of child

soldiering. There also exist a few larger systematic surveys of ex-combatants in Africa (e.g.

Blattman 2007 on Uganda; Pugel 2007; Bøås/Hatløy 2008 on Liberia; and Humphreys/

Weinstein 2004 on Sierra Leone). These studies provide unique insights into the individual

motivation and methods of recruitment, but as they focus on single conflicts, they cannot be

used for cross-country comparisons. To our knowledge, Achvarina and Reich (2006) is the

single existing cross-country study comparing different conflicts on the causes of child soldier

recruitment. They found that national measures of poverty cannot explain child soldier

participation. Rather, they argue that refugee camps better explain child soldier recruitment

rates, as children in refugee camps are easily accessible targets for armed forces seeking

recruits. Hence, child soldiers will constitute a larger percentage of belligerent forces where

camps are relatively vulnerable to raids. A methodological caveat here is that this study is based

1 International protocols have been signed, monitoring has been initiated in several countries, and a practice of

naming and shaming was put into place by the UN Security Council.

2

on nation-wide measures which run a high risk of ecological fallacies. For example, poverty is

spatially clustered within countries, and even in societies with low levels of overall inequality,

some regions are richer than others (Buhaug/Rød 2006). Studies that utilize nation-wide

measures of poverty cannot account for sub-national variations. As the level of economic

welfare (as well as child soldier recruitment) might vary significantly within countries, using

national aggregate measures of development, such as GDP per capita, might not reveal the true

impact of poverty on child soldier recruitment. The case of Uganda is an illustrative example.

The conflict in the northern part of the country has been notorious for recruitment of children

into the Lords Resistance Army (LRA). This part of the country is significantly less developed

than the south (Younger 2004). National aggregate poverty measures might therefore obscure

the true relationship between poverty and child soldier recruitment. The map of Uganda (right)

in Figure 1 shows variations in regional rates of both infant mortality (darker regions have

higher infant mortality) and child soldier recruitment (hatched), and the map of Chad (left) also

illustrates regional variations in terms of household assets, where darker regions (relatively

deprived compared to country average) correspond to a large degree the regions in which

recruitment took place (hatched).

[Figure 1 about here]

In this study we test the relationship between poverty and child soldier recruitment using

all sub-national regions in Africa as the units of analysis.2 This allows us to better account for

geographical variations in recruitment, poverty and other factors, and avoids some of the

problems of the ecological fallacy of aggregate measures and selection bias. Whereas

individual-based survey data on combatants, non-combatants and their poverty levels would be

the best source of data for testing the poverty-child soldier recruitment nexus, no such cross-

national data currently exists. We therefore use the next best option of disaggregating by sub-

national region. We introduce new data on regions affected by child soldier recruitment and, by

2 Our spatio-temporal domain covers the African continent, i.e. all the first-level administrative units, 690 in

total, in the period 1990–2004.

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means of GIS (Geographic Information Systems), we link these data with geo-referenced data

on regional absolute and relative poverty.

We do find some support for a relationship between absolute poverty (measured as

infant mortality) and child soldier recruitment. With regard to relative deprivation (or inter-

regional inequality) we find no significant effect. Regions that are poorer than the country

average are no more at risk of child soldier recruitment than those regions above the country

average. However, the positive relationship between refugee camps and child soldier

recruitment found by Achvarina and Reich (2006) is upheld despite our disaggregated design.

One clear recommendation for the policy community is therefore to aim at protecting refugee

camps from recruitment raids by armed groups.

The remainder of the article is organized as follows. In Section 2 we summarize general

theories of recruitment and armed conflict and offer a literature review on the causes of child

soldiering, focusing on the role of poverty. In Section 3 we present the data and research

design. Section 4 summarizes the results of our empirical tests. Finally, in Section 5, we

conclude and suggest an agenda for future research.

2. Poverty and Child Soldier Recruitment

Most civil wars occur in relatively poor countries, and socioeconomic status has for long been

assumed to be associated with involvement in violent conflict. The direct link between

economic development and domestic peace has proven to be among the most robust findings in

recent large-N country-level studies of civil war (see Hegre/Sambanis 2006). However,

although there is agreement on this empirical relationship there is no consensus on the

theoretical explanation for it. Fearon and Laitin (2003) maintain that GDP per capita is a proxy

for state capacity, indicating that richer states are better able to monitor the population and

conduct effective counterinsurgencies. But, what motivates a person to risk his life in armed

conflict? The recruitment literature brings the poverty argument closer to the micro level by

suggesting that low development provides motivation for violence due to low opportunity costs

as well as a potential for private gains from looting (Doyle/Sambanis 2000; Gates 2002). The

recruitment costs are lower when the alternative means of income are low, in situations of

under-employment and poverty. If people have no other viable means to ensure a life sustaining

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income, the threshold for joining an army (be it government or rebel) is presumed to be low.

Consequently, Collier and Hoeffler (2004) claim that it is easier to maintain a rebellion in poor

countries than in richer countries.3 The arguments for why poverty and inequality should matter

for child soldier recruitment parallel many of the explanations for recruitment of adults.

However, while inhibiting other qualities than adult soldiers and thus being attractive targets for

certain types of armies, children are thought to be particularly vulnerable to being forcibly

recruited or kidnapped by armies.

Most studies of child soldiers, while disagreeing about the significance of poverty’s

impact, generally admit that it matters to some extent. For example, the earliest comprehensive

book on child soldiers by Goodwin-Gill and Cohn (1994) identifies poverty as a factor without

assigning it a greater value than to other variables. Honwana (2006: 28) considers poverty one

of the main push factors behind child motivation to join armed groups beside migration,

political ideology, or the “mutability of youth”. Stronger statements about the relationship

between poverty and child recruitment have been offered by Graça Machel (1996: 11), who

concludes that “the children most likely to become soldiers are those from impoverished and

marginalized backgrounds” along with the ones that are unaccompanied. Two authors

independently claim that the economic factor is “a particularly strong” explanation for child

soldiering, compared to other explanations that include proliferation of small and cheap

weapons and the changing nature of warfare (Singer 2005: 38, 55; McManimon 1999). Brett

and Specht (2004: 14, original emphasis) argue that poverty “is perhaps the most obvious

common feature of child soldiers generally, which is one of the reasons why it is frequently

identified as the cause of child soldiering”.

There is currently no well-defined consensus in the literature on the mechanisms that

link poverty to child soldiering. To outline different potential mechanisms theoretically, we

distinguish between voluntary and forced recruitment, as poverty could feature as a factor in

3 The opportunity costs of joining a rebellion have been proxied with a variety of indicators in the existing literature. Collier

and Hoeffler (2004) use the rate of economic growth per capita and the secondary school enrolment rate for males. Esty et al.

(1995, 1998) and Goldstone et al. (2005) use infant mortality as a proxy for development and thereby opportunity costs of

potential soldiers. However, all these studies have used aggregate country averages of opportunity costs for recruitment rather

than localized indicators of poverty as indices of recruitment costs, although poverty and wealth tend to be spatially clustered

within countries.

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both forms of recruitment. Both recruitment forms point in the direction of a positive

relationship between poverty and recruitment.4 Whereas poverty-based economic motivation

(both greed and grievance-based motivation as well as pure struggle for survival) is vital for

understanding voluntary recruitment, forced recruitment is often a question of protective

capabilities, which is often also a function of poverty. In the next two sections we discuss how

poverty may play out in voluntary and forced conscription of children.

2.1 Voluntary Recruitment

Children quite often join armed struggles without pressure being exerted upon them and may

actually look for military groups themselves to offer their services. In one ILO study, 64% of

all former child soldier informants from the DRC, Burundi, Rwanda and Congo reported

joining an armed group on the basis of personal decision as opposed to being directly forced to

do so (ILO 2003: 26).

Voluntary recruitment for armed conflict requires some level of motivation. Goodwin-

Gill and Cohn (1994) propose three different scenarios of how poverty affects the motivation to

join an armed group. In line with the classical literature on the relationship between poverty and

conflict, they label these causal mechanisms as: grievance (“social and economic injustice

motivates adults and children to take up arms, sometimes with a long-term vision of affecting

change”); greed (“to obtain a subsistence wage”), and survival (to get food for the day)

(Goodwin-Gill/Cohn 1994: 23).

The concept of ‘grievance’ is usually based on the logic of relative poverty, or

inequality. Most traditional works on inequality and conflict relate to the theory of relative

deprivation (see Gurr 1970). This premise suggests that while absolute poverty may lead to

apathy and inactivity, comparisons with others in the same society who do better can lead to

frustration and antagonism which again may result in violence to redress inequality. A

continuation of this argument is to see grievance-induced discontent due to a group’s

marginalization as a determinant of mobilization for violent political struggle. ‘Grievance’

factors have been largely dismissed by the large-N country-level studies, which find no link

4 We use this dichotomy here as a useful analytical distinction in the theoretical discussion of mechanisms

leading to child soldier recruitment. In real life situations, the distinction might not be so clear cut.

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between economic inequality and conflict (Fearon /Laitin 2003; Collier/Hoeffler 2004). Østby

(2008), however, argues that such dismissal of grievance factors may be premature, because the

above studies address economic inequality between individuals while ignoring inequalities

between groups. Case studies suggest that what matters for conflict are so-called ‘horizontal

inequalities’, or inequalities that coincide with identity-based cleavages (Stewart 2000, 2002).

In brief, as conflict as usually fought between groups, not individuals, inequalities based on

cultural cleavages may facilitate recruitment and mobilization for armed conflict.

Andvig (2006) argues that grievance as motivation for joining a rebellion works in the

same manner for both children and adults. Children tend to equate violence with power and the

reasons given for enlistment include not only peer pressure and opportunity to engage in

looting, but also political commitment and ethnic loyalties (Stewart/Boyden 2001). For

example, if a child belongs to a group or region which is relatively economically deprived and

where schooling opportunities are low, this may lead to frustration and a sense of unfairness

which in turn may influence the child’s willingness to become a soldier in order to try to change

the status quo. Furthermore, identity-based groups – the ones that share the same ethnic,

religious or regional affiliation – also tend to have stronger group cohesion than other types of

groups (see e.g. Guichaoua 2006; Stewart 2000, 2002). Coupled with the evidence from child

psychology and empirical studies about children’s “greater tendency towards altruism and

bonding to a group” (Andvig/ Gates 2006: 7; Harbaugh/Krause 1999), the strong cohesion of

identity-based groups is an additional attractive factor in the decision of children to join a rebel

group. Arguably, social pressure and ideological propaganda can also persuade children to

enroll with armed groups (ILO 2003: 25). This corresponds well to Wessels (1998: 639) who

argues that “issues of identity, nationalism, and ideology may also loom large” in children’s

decision to participate in armed struggle.

Like adults, children can also be driven by greed. Rebellions provide opportunities to

loot and get access to financial resources, including salaries for soldiering. Gates (2002: 128)

argues that “faced with dismal conditions at home, involving poverty, boredom, or, in some

areas, no family” children might have fewer reservations to join an armed group. In other

words, children might voluntarily join armies due to perceived prospects that look brighter than

poverty or boredom, which may in part stem from lack of educational opportunities. Goodwin-

Gill and Cohn’s (1994) third scenario, the motivation of survival – that is the decision to join an

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army as the best option for a child to secure food or basic security – can be hard to distinguish

from a greed drive. Orphaned children may be particularly susceptible to the greed motivation

as the groups of armed adults might become the only substitute for parental care in terms of

food provision, perceived security guarantee, and a mere establishment of a missing category of

an adult-child relationship in an orphan’s life (Brett/McCallin 1996; Singer 2005; UNICEF

2002). At the same time, studies with aggregate variables using national measures of orphans

did not find that particular variable to be significant in explaining the variation in child soldier

rates across different African conflicts (Achvarina/Reich 2006). 5

Still, parental protection might not be a guaranteed condition even for children with live

parents, with the most extreme cases being parents who voluntarily give away their children to

rebels due to greed or ideological motivations, often because a family member is already in the

military (ILO 2003: 36). Impoverished parents sometimes send their children to armed groups

in exchange for minor soldier's wages that go directly to the family (Machel 1996: 12). Such

‘volunteering’ includes “parents who encourage their daughters to become soldiers if their

marriage prospects are poor” (Machel 1996: 12). Alternatively, children can become de facto

child soldiers if the whole family moves with armed forces for economic reasons, or they can

be recruited because a family member is already in the military.

Why would a military organization recruit children as soldiers? Army commanders in

Africa have reported several reasons, such as children being easily manipulated and efficient

cheap fighters, with a better performance of certain tasks such as scouting (ILO 2003). From

the perspective of commanders and army leaders, recruiting underage soldiers can decrease the

cost and ease of recruitment, particularly of impoverished children. With respect to voluntary

recruitment, any army that wants to conscript soldiers needs to be able to offer some level of

benefits, be it food or payment. Gates (2002) sees the participation constraint as essentially a

comparison of the utility offered by a rebel group (or possibly any army) compared to some ex

5 Ideally, we would have preferred to have tested whether regions with larger numbers or ratios of orphaned

children are more prone to child soldier recruitment. Unfortunately, however, such data seem to be non-existent on

the sub-national level. One potential proxy for orphan rates could be regional HIV/AIDS figures for which the best

source seems to be USAID’s HIV/AIDS Surveillance Database (http://hivaidssurveillancedb.org/hivdb/).

However, very few countries have reliable data broken down to regional figures.

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ante outside option. He argues that children offer a higher possibility for rebel groups to meet

the so called reservation level of benefits that a recruit demands in order to join, as this level is

proposed to be lower for children than adults. In addition, children might mobilize only for a

promise of future delivery of benefits. For example, in Liberia, children from marginalized

economic groups were promised free access to education after the end of the war. This promise

was enough to convince some of them to join Charles Taylor’s armed forces.6 In DRC and

Congo, former child soldiers have also reported that they joined to receive payment or a job

after the war (ILO 2003: 30). The prospect of even marginal payment is a strong incentive for

children to enlist in situations when their parents are missing or finding it hard to provide basic

food security. Hence, child soldiers mean cheap labor for rebels with limited resources. It is

quite intuitive to build on this argument and suggest that poorer children offer even cheaper

labor and thus better alternatives for recruiters, especially the ones that abstain from forced

conscription.

2.2 Forced Recruitment

The above reasoning has pertained only to voluntary recruitment of child soldiers. Often,

however, participation is forced at gunpoint. This practice was widespread among LRA in the

northern Uganda (Blattman 2007) and RUF in Sierra Leone (CSUCS 2001). ILO found that

about 21% of the child soldiers sampled in four African countries (Burundi, Congo, DRC,

Liberia) reported to have been abducted, and 15% forced (ILO 2003).7 How can poverty matter

when children are taken by force? We argue that the less privileged will typically have the least

resources to defend their families and children, and hence provide an easy prey. From the

recruiters’ point of view, poorer communities typically have less means of protection (due to

insufficient infrastructure, economic resources, or lower priority of government protection

policies) and therefore might be more attractive destinations for recruitment. Singer (2005: 4)

7 This information was obtained during personal interviews by one of the authors with Liberian former child

soldiers. 7 By abduction the ILO report refers to “situations in which children have been taken forcibly or under threat of

arms”; whereas forced recruitment is defined to refer to “cases in which the child did not have a choice. This could

be because of moral pressure or the obligation to enlist.”

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argues that targeted children are “usually from special risk groups: street children, the rural

poor, refugees, and others displaced”. He explains this logic by introducing the concept of

“efficient recruiting sweeps” pertinent to these four special risk groups in particular. Some

literature by practitioners also notes that “in all conflicts, children from wealthier and more

educated families are at less risk” of forced recruitment as they are either left “undisturbed” or

“released if their parents can buy them out” or “sent out of the country to avoid the possibility

of forced conscription.” (Machel 1996: 12). In other words, defense capabilities or protection

provision can be intimately linked to absolute poverty and increase likelihood of recruitment

among the poor and also linked to relative poverty between sub-national regions, where the less

well off regions are more likely recruitment grounds for child soldiers.

This can seem contrary to the argument of Ethan Bueno de Mesquita (2005) that

terrorist operatives have relatively high educational attainment and economic opportunity.

Bueno de Mesquita convincingly argues that whereas individuals from societies’ worst-off

socioeconomic groups are most likely to join a terrorist organization, the terrorist organization

screens the volunteers for quality and target the most competent, richer and better educated

candidates. However, unlike terrorist organizations that have a need for masterminds who are

more likely to succeed at the demanding tasks required of a terrorist operative, we do not

believe that the same logic applies for the strategic recruitment of child soldiers for armed

conflict. A typical African conflict does not require particularly skilled operatives. Child soldier

recruitment mainly takes place in under-developed countries, characterized by limited counter-

intelligence that can often be performed by children and prevalence of primitive but effective

AK-47 rifles easily handled by children. Under such conditions it should be more strategic for

African rebel leaders and commanders to recruit a large number of children. As argued by

Blattman (2007: 1): “rebel leaders have an incentive to recruit any civilians that are expected to

yield some military benefit”. Furthermore, indoctrination and disorientation is likely to be more

successful with poor low-educated children.

---

In the above theoretical framework we have discussed the impact of both absolute and

relative poverty. In sum, the absolute level of poverty is expected to affect both voluntary and

forced recruitment of child soldiers either because of the lack of alternative viable survival

strategies or due to negligible defense against forced recruitment. From the perspective of

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recruiters, the poorer the children, the easier it is to recruit them, either by force or voluntarily.

This leads to our first hypothesis:

H1: Higher levels of absolute poverty in a region increase the likelihood of recruitment of child

soldiers.

Whereas the previous literature on child soldiers has focused almost exclusively on the impact

of absolute poverty, we argued in the above discussion that relative poverty (deprivation) could

also play a role regarding voluntary as well as forced recruitment of child soldiers. If children

are motivated and mobilize to redress economic grievances, the relatively poor should also,

more often than the relatively privileged, engage in child soldiering, especially if the relative

deprivation is a result of systematic discrimination between particular identity groups, such as

ethnic or regional groups. Furthermore, the logic of forced conscription of children in poor

communities can also be viewed as an outcome of external processes that come as a result of

relative poverty. In other words, if a government or rebel group wants to recruit children by

force, the rational choice would arguably be to target regions that fall below the country

average, and the more the region falls below this average the more at risk the region would be

of being seen as a good recruitment ground. In line with this reasoning we propose the

following hypothesis:

H2: The relatively poorer (more deprived) regions in a country will be more at risk of child

soldier recruitment than the ones that are relatively better off.

3. Data and Research Design

Since civil wars are often quite local, nation level indicators to explain either the location of

conflicts or child soldier recruitment might therefore often become misleading or, at best,

irrelevant. Hence, the present study is among the emerging efforts at geographically

‘disaggregating the study of civil war’, or investigating the causes of conflict below the national

level (see e.g. Buhaug/Lujala 2005; Buhaug/Rød 2006; Hegre/Raleigh 2005; Østby et al. 2008;

Raleigh/Urdal 2005). Some regions of a country might experience more recruitment than

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others, and the causal factors used to explain this recruitment also vary geographically. As the

next-best option to individual survey data8, we rely on indicators of localized socio-economic

status at the regional level. Our units of analysis are sub-national regions in Africa representing

first-level administrative units (regions/provinces) according to ESRI’s (1998) definition. The

total number of observations in the dataset adds up to 690 regions in 52 countries (i.e. all the

sub-national regions in Africa in the period 1990–2004). Due to some missing observations on

certain variables the tests range from 354 and up to a maximum of 688 observations.

Geographical Information Systems (GIS) software, allows us to combine spatial data on

regional welfare and child soldier recruitment.

3.1 Dependent variable: Child Soldier Recruitment

Our dependent variable, child soldier recruitment, is dichotomous, coded as 1 if there were

reports of child soldiers recruited in the region in the period 1990–2004, and 0 if we could not

find any such reports.9 We cover recruitment of voluntary and forced nature from home and

displaced (including refugee) communities of children alike.10 We rely on the existing

operational definition of child soldiers from the United National Children’s Fund (UNICEF,

undated) which is used in the field to collect the data. According to this definition, child soldier

is “any child—boy or girl—under 18 years of age, who is part of any kind of regular or

irregular armed force or armed group in any capacity, including, but not limited to, cooks,

porters, messengers, and anyone accompanying such groups other than family members”

8 No such comparative geo-referenced survey data across cases currently exists. 9 Ideally, we would like to have a scale measure of the magnitude of the recruitment or a ratio variable of child

soldiers to the total number of soldiers, but due to data constraints this has not yet been possible. Reliable time-

series data would also have been preferred due to potential endogeneity problems regarding the relationship

between IMR and child soldier recruitment. However, since IMR figures refer to the death of infants (under 1 year

of age), and child soldiers refer to older children, this problem should at least be less serious. Although IMR

figures are of course likely to be higher resulting from conflict (as would be the case with any other measures of

poverty), it is not obvious that such figures should be higher in conflicts including child soldiers than in conflicts

where child soldier recruitment does not occur. 10 It is not possible to distinguish between forced or voluntary recruitment in the analyses. For most of the

observations it is not clear what type of recruitment was prevalent, and in many instances both types of recruitment

were going hand in hand by the same armed groups or forces, and a clear distinction can be hard to establish.

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(UNICEF, undated: 4). One controversy surrounding UNICEF’s definition deals with the

established benchmark of 18 years old as a minimum age for recruitment. The point is often

raised by many observers that this number is driven by international conventions based on

western norms and does not make sense in the context of African countries where, according to

some, the age of adulthood is often set at a much lower level and in some countries hardly

reaches 15 years old. Still, due to a lack of available data on recruitment broken down by age,

we use the date collected according to the UNICEF definition.11

Out of the 690 regions in our sample, nearly 42.9% (296 regions in 28 countries)

experienced conflict in the 1990–2004 period, and 10.6% (73 regions in 17 countries)

experienced child soldier recruitment. Of these 73 regions, 66 were in a conflict zone and 7

regions were not. The data on recruitment is coded based on systematic evaluations of country

reports of child soldiers issued by NGOs, international organizations, governments, academics,

and even military organizations.12 The map in Figure 2 shows which regions experienced child

soldier recruitment (hatched) and conflict (shaded).

[Figure 2 about here]

3.2 Absolute and Relative Poverty

To test our two hypotheses we need to operationalize two different concepts of poverty:

absolute and relative poverty. We use data from two different sources to generate these

measures. The first is geo-referenced disaggregated data on infant mortality rates (IMR) from

the CIESIN data project at Columbia University, which covers 52 African countries.13

According to the norm, CIESIN defines regional, annual IMR as the number of children who

die before their first birthday for every 1,000 live births, i.e.

11 Another problem with the UNICEF definition of child soldiers is that it does not distinguish between different

tasks that children are taken to perform. However, in our article this distinction is not critical as our poverty

argument is mostly supply-based and deals with the question of vulnerability of certain groups of children for

recruitment with the demand for children by armed groups viewed as already given. 12 The coding and geo-referencing of this information has been done by the authors. IMR rates for southern

Sudan have been made available by Theisen and Brandsegg (2007). 13 CIESIN online at: http://sedac.ciesin.columbia.edu/povmap/ .

13

1000*yearin births Liveyearin 1 ageunder Deaths

=IMR . Infant mortality has been used as

an alternative to GDP per capita or similar measures in quantitative studies in the conflict

literature (see e.g. Esty et al. 1995, 1998; Goldstone et al. 2005; Urdal 2006) and elsewhere, as

the two are typically very highly correlated and believed to capture the same phenomenon of

general development. One criticism of using IMR has been that as countries cross a certain

threshold of wealth there is often little variation on such a basic measure of development as

infant mortality. However, as we are investigating Africa this is less problematic than in other

studies, as the countries in question are in large part defined as low-income. Also, IMR is

theoretically closer to the relationship we want to test, as it better captures poverty-related

factors identified that lead to child soldiering, such as food shortages. The map in Figure 3

shows the coverage of the CIESIN data of infant mortality rates overlaid with child soldier

recruitment (hatched). A simple visual investigation of this map reveals what seems to be a

pattern of the poorer regions (i.e. the higher IMR figures) experiencing more child soldier

recruitment, than the richer regions. However, this relationship will be tested further in the

statistical analysis.

[Figure 3 about here]

Our second data source used to construct measures of absolute and relative poverty is

geo-referenced information from the Demographic and Health Surveys (DHS) from 22 African

countries14 conducted during the period 1986-2001. In a DHS, a sample of households is

selected throughout the entire country, and women between the ages of 15 and 49 are

interviewed about health, nutrition, household welfare and other issues. The sample design is a

probabilistic two-stage sample, in which enumerated areas (EAs) are randomly selected with

probability proportional to their size. Several DHS Surveys include detailed information about

the geographical location of each EA. This allows us to couple local-level socioeconomic

information from the surveys with the geographically recorded data on the location of child

14 These countries are: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Cote d’Ivoire,

Ethiopia, Ghana, Guinea, Kenya, Liberia, Madagascar, Malawi, Mali, Namibia, Niger, Nigeria, Senegal, Uganda,

Tanzania, Togo, and Zimbabwe.

14

soldier recruitment. Here, we use individual-level information from each EA to aggregate

measures of regional welfare to the first-level administrative units (regions) that constitute our

units of analysis.

We use the DHS surveys to generate two indicators of absolute regional socioeconomic

welfare (poverty). First, a household asset index is generated on the basis of the following

variables from the DHS surveys: v119-v125 (dummies for whether or not each household has

electricity, a radio, a television, a refrigerator, a bicycle, a motorcycle and/or a car). Our second

indicator, education years, is based on the variable v133 (highest years of education

completed).15 With a cross-sectional research design, there is a potential problem of conflict

affecting education levels in the population, particularly in cases of high intensity and long

lasting conflicts. For most of the included conflict cases in our sample, however, the DHS

surveys were conducted prior to conflict outbreak. For example the DHS in Guinea was

conducted in 1999 and conflict broke out in 2000, in Liberia DHS was conducted in 1986 and

conflict broke out in 1989, and in Niger a DHS was conducted in 1992, and two conflicts broke

out in 1994 and 1996 respectively. In some cases the problem of endogeneity might also be less

pronounced because conflict was short lived (i.e. the coup in Togo in 1991, which probably in

itself did little direct harm to the education provision) or minor (i.e. conflict incidences in Mali

in 1990 and 1994, and the on-again off-again conflict in Casamance in Senegal between 1990

and 2003 which remained minor in terms of fatalities).16 Also, since the DHS data report the

highest numbers of years of education completed based on a sample of adult females, there

should be a relatively high degree of inertia in these measurements even with conflict taking

place prior to the recorded education attainment.

15 One could argue that once children are recruited as child soldiers they drop out of the educational system,

which implies an unclear direction of causality. Note, however, that education level as measured here refers to the

average education level of women aged 15-49, i.e. largely the adult population. Hence, this should reduce potential

endogeneity problems. 16 The case of Uganda potentially causes problems here, as the DHS survey was conducted in 2000-2001 whereas the civil

conflict in the country had been ongoing since the late 1980s, however mostly restricted to the northern region. Ethiopia also

represents a potentially problematic case, as there had been long lasting and severe conflict going on in the country prior to the

conduction of DHS surveys in 1992. Chad is another case of conflict ongoing since the late 1980s and DHS surveys conducted

in 1996.

15

To evaluate the impact of spatial inequalities, we measure inequality, or regional

relative deprivation (RRD), as the relative performance of each region compared to the overall

performance of the country on both the assets indicator and the education indicator, using the

following formula:

−= ∑=

M

i

ii

M

AARRD

1

21ln1

where M is the maximum number of household assets, A1 refers to mean asset score of a

given region and A2 is the corresponding mean score of the country as a whole. This provides a

continuous variable ranging from –.76 (lowest level of relative deprivation) to 1.81 (highest

level of relative deprivation). Note that the value ‘0’ indicates perfect equality, whereas

negative values of RRD refer to relative privilege of the region in question. The measure of

educational relative deprivation is generated similarly. For each of the regions, the scores on the

various inequality measures were copied to the remaining years in the period 1990–2004.

3.3 Control Variables

Although poverty is often identified by the NGO community as the cause of child soldiering,

Achvarina and Reich (2006) introduce another factor which they argue largely outperform the

poverty explanation: the degree of access to refugee/IDP camps gained by the belligerent

parties in conflicts. Children gather in refugee camps in great numbers. Refugee camps are

supposed to be protected under international laws and protocols, but protection is often, in

practice, uneven or nonexistent. This lack of physical protection of camps provides an incentive

that will likely increase the probability of successful raids by armed factions seeking recruits.

However, children in refugee camps may also voluntarily become recruits, motivated by the

prospects of a better future compared to life in the refugee camp. Since security is often a

problem for refugee camps, regions with a refugee camp may be more likely to have

experienced child recruitment than regions without such camps. For our geographically

disaggregated study we use information on the location of refugee camps to determine which

sub-national regions had such camps in the period under investigation. The information on the

location of refugee camps has been collected by the United Nations High Commissioner for

Refugees (UNHCR) and geo-referenced (point data with latitude and longitude) by Weidmann

et al. (2007). To adapt this to our dataset structure, we have used GIS to determine in which

16

sub-national regions the camps were situated. The refugee camp data includes 710 camps,

distributed over 160 regions. From this we created a dummy control variable indicating if the

region had a refugee camp (1) or not (0).

As our sample consists of sub-national regions, irrespectively of whether they

experienced an armed struggle or not, we need to control for conflict factors. We test three

conflict related controls: how many years the region was exposed to conflict, how intense the

conflict was in terms of battle-related casualties, and whether the neighboring region(s) had a

conflict,. It is intuitively plausible that recruitment of child soldiers is associated with the

presence or absence of conflict in the same area. The information about location of conflict

zones (as well as conflict duration) is based on a version of the Uppsala/PRIO Armed Conflict

Dataset (ACD, Version 3-2005b; Gleditsch et al. 2002)17 which includes data on the spatial

location of battle zones depicted as GIS-generated conflict polygons (Buhaug/Rød 2006).

Children living near a conflict zone are exposed to extreme insecurity and a climate of fear

which might increase the likelihood of their voluntary enrollment. Likewise, proximity to the

conflict zone increases the accessibility of recruiters to these children. To control for this

potential spatial correlation we include a dummy for whether there was a conflict in the

neighboring region (1) or not (0). As a conflict drags on, the availability of male recruits may

drop, and the need for new fighters can lead recruiters to conscript children. Thus, an ILO

report (2003: 25) on four conflicts that involved child soldier recruitment states, based on the

cases of Burundi, DRC, Congo and Rwanda, states that “the longer the conflict lasts, the greater

the risk of recruiting soldiers that are younger and younger”. To control for conflict duration we

include a count variable of the number of years of conflict in the region during the period 1990–

2004. Demand for recruits and minors may also intensify with intense conflicts – those ones

that experience high level of fighting and face a big number of battle deaths as a result. We

17 Available at: http://www.prio.no/CSCW/Datasets/Armed-Conflict/UCDP-PRIO/Old-Versions/3-2005b/. This

dataset covers every armed conflict between a state government and an organized opposition group with at least 25 battle-

related deaths per year.

17

control for this by including a log of battle-related deaths (Lacina/Gleditsch 2005) for each civil

conflict in the time covered by our data.18

Finally, recruitment of children could occur in remote, less densely populated areas. For

example, children in rural areas of Uganda were more at risk of recruitment because there was

less protection and security offered by the government and recruiters could go into small

villages and more or less unhindered kidnap children for combat (Blattman 2007). The level of

development might also differ between areas of various density of population. We therefore

include as a control a measure of the log of the population density in the region to isolate the

effects of poverty and inequality from this potentially confounding factor. On the other hand it

is also conceivable that recruitment is more likely in regions with a larger pool of potential

recruits due to the consideration of efficiency of conscription sweeps. We therefore also include

a measure of the log of total regional population. Descriptive statistics for all variables as well

as a correlation matrix are provided in Appendices A1 and A2.

We analyze a dichotomous dependent variable for whether or not there was any child

soldier recruitment in a region in the period 1990–2004, using a logit regression model. Since

regions are likely to be somewhat interdependent, standard error estimates are clustered by

country.

4. Empirical Analyses

Table 1 summarizes the results of multivariate logit regressions of child soldier recruitment and

absolute poverty. Model 1 in Table 1 is the baseline model, and shows that the existence of a

refugee camp in the region, conflict intensity, and conflict in neighboring region(s) are strong

predictors of child soldier recruitment. One of the strongest effects is from the existence of a

refugee camp. A region with a refugee camp is almost six times more likely to experience child

soldier recruitment than a region with no such camps (2.2% vs. 12.6% risk). Conflict intensity

also affects child soldier recruitment in the expected direction. Higher numbers of battle related

18 As battle-related deaths are not available by sub-national regions, we use the death figures for a conflict as a whole.

In regions with overlapping conflicts we assign the higher intensity figure.

18

deaths are associated with a significantly higher likelihood of child soldier recruitment being

reported.19 The number of years of conflict does not have a significant relationship with

recruitment risk when conflict intensity (battle deaths) is accounted for. However, including

both intensity and conflict years in the same model is problematic, as there is a high correlation

of 0.85 between these variables in the model 1 sample, which probably accounts for the

insignificant result for conflict years.20 We report the conflict intensity variable in the

subsequent models, as the explanatory power of the models increases with this measure.

However, we have also run models with conflict years instead of conflict intensity as robustness

check. This does not alter our main findings (see Appendix A3).21 The neighborhood effect is

positive and significant, as expected, and the odds for seeing child soldier recruitment in

regions with conflict in a neighboring region is more than fourfold that of the odds of such

recruitment in regions that do not border conflict regions. Neither the log of the population size

nor population density have any statistically significant effect.

[Table 1 about here]

In the following models in Table 1 we keep the control variables that proved significant

in the baseline model, to keep the number of independent variables to a minimum, avoid

multicollinearity, and keep as large a sample as possible. A likelihood-ratio test confirms that

dropping the insignificant controls is possible without losing explanatory power. In Model 2 we

add our measure of absolute poverty measured by household assets from the DHS surveys.

19 We also test a different specification of conflict intensity by using dummies for low intensity conflicts (never

reaching 1,000 battle deaths in any year) and high intensity conflict (reaching 1,000 battle deaths in at least one of the conflict

years) with no conflict serving as a reference category. The results are similar: regions with high intensity conflicts are

significantly more likely to have reports of child soldier recruitment than regions without any conflict (with a probability of

16.8%). 20 Otherwise multicollinearity does not pose a problem in the reported models as the correlation matrix A1 in

Appendices indicates. 21 Whereas conflict years might be interpreted as a form of duration measure, it does not necessarily capture the actual

length of armed struggle, as often there are interruptions in conflict over time (unless there has been conflict in the region in all

the years included). Also, there is no necessary relationship between conflict duration and intensity, as Lacina (2006: 285) finds

that there is no significant relationship between deaths per year and conflict duration, although a longer conflict duration

increases the likelihood of a higher total death count for the conflict as a whole.

19

Contrary to our expectation, it seems as the higher the level of household assets in the region,

the more likely is child soldier recruitment. However, the effect of the asset variable is only

significant at the 10% level. This unexpected relationship could indicate that our summary

measure for economic wellbeing relating to the ownership of various household assets does not

fully capture the general structural economic differences in all countries. Goods other than

household assets may be more important indicators of economic distribution in many

developing countries, like for example land tenure. Alternatively, we suspect that the result is

driven by sample effects, given that we only have data for 22 countries on this variable. In

Model 3 we see that absolute poverty in terms of education years has no significant effect on

recruitment.

Model 4 includes a much larger sample of regions, covering all of the African regions

(N=688). Here, we find a positive effect of high infant mortality rates on child soldier

recruitment, significant at the 5% level. In other words, the larger sample result does provide

some evidence that poverty (measured here as IMR) has a positive effect on child soldier

recruitment, as suggested by Hypothesis 1. In a region with a refugee camp, a conflict in the

neighboring region and mean number of battle deaths, the probability of child recruitment

increases from about 5 % to more than 38,5% if infant mortality shifts from the lower 10th

percentile to the higher 90th percentile. This finding, however, is not robust to sample change.

Running Model 4 in Table 1 with the smaller sample from Models 2 and 3 (22 countries) does

not give a significant result on the IMR variable, and this term neither yields a significant

relationship when added to Models 2 and 3 in Table 1. The non-finding on IMR for the reduced

sample could indicate a sample bias. The correlation matrix (Appendix A2) indicates that the

three measures of absolute poverty are correlated in the expected way, and that particularly

education levels and infant mortality rates are highly correlated (–0.66). This is understandable,

as both variables relate to basic access to the social system.22 However, whether the reduced

sample is skewed in a way that influences the results can only be determined with increased

availability of geo-referenced DHS data in the future.

22 The two variables IMR and (female) education have also been found to be highly correlated in other studies, such as

Brockerhoff & Hewett (2000) who found that educational attainment of mothers is among the most important factors explaining

child survival.

20

The controls in models 2–4 perform largely in the expected way. Recruitment of child

soldiers is more likely in regions with refugee camps than regions without such camps, and

more likely in the regions with higher intensity. Running the models in Table 1 with alternative

specifications of conflict intensity does not alter this picture.23 However, conflict in a

neighboring region is positive but insignificant in models 2-4.

Figure 4 below shows the predicted probabilities of seeing child soldier recruitment in a

region based on Model 4 in Table 1. The graph also shows how the impact of having one or

more refugee camps in the region affects the recruitment likelihood at different rates of infant

mortality. The poorer the region (in terms of IMR), the higher the probability of child soldier

recruitment overall, but the effect is stronger for regions with refugee camp(s).

[Figure 4 about here]

In Table 2 we test our measures of relative deprivation or regional inequality. We find

that there is no significant difference between the relatively deprived regions and the more

privileged regions within countries. This is contrary to our expectation in Hypothesis 2. One

explanation for this non-finding on relative deprivation might be that recruitment also occurs

across international borders, one example being recruitment of Liberian children to the civil war

in Côte d’Ivoire. In future research a test of the impact of relative deprivation on child soldier

recruitment could be to investigate deprivation relative to neighboring regions rather than

comparing regions to the country average. Another possibility is that recruitment is more ad

hoc, depending on where actual battles are taking place. If battles occur in more developed

regions (i.e. for control over the capital) this could explain the non finding on relative

deprivation. In future research, one possibility is to test this possibility with battle location data

(Raleigh/Hegre, 2005) once most African conflicts have been coded.

[Table 2 about here]

23 We run a measure of the number of battle deaths in the peak year of conflict. We also run the models with dummies

for low intensity conflict (less than 1,000 battle deaths in any given year) and high intensity conflict (1,000 battle deaths in at

least one of the conflict years) with no conflict as reference category. The findings are consistent.

21

The refugee camp variable keeps its strong and significant effect on the probability of

child soldier recruitment in all models in Table 2. The presence of one or more refugee camps

in a region strongly increases the likelihood of recruitment of child soldiers. Not surprisingly,

both the dummy for regions with low or high intensity conflict exhibit an increased likelihood

of child soldier recruitment than non-conflict regions, whereas, like in Table 1, conflict in a

neighboring region does not show consistent significance in the models testing relative poverty.

5. Conclusion

Poverty is frequently offered as one of the main explanations for child soldiering in the popular

debate, and strong statements have been made about this claimed relationship in the academic

literature. However, the only systematic cross-national test we are aware of (Achvarina/Reich

2006) did not find a significant link between these factors. In our disaggregated study of

African regions, we do find some evidence for a positive impact of poverty measured in terms

of infant mortality rates, on child soldier recruitment, a relationship which can be masked in

national level studies. Regions with higher infant mortality rates are more likely to have

experienced recruitment of children as soldiers than regions with lower infant mortality rates,

controlling for several other factors. However, our alternative poverty measures -- education

and household assets -- did not yield any significant findings. This might be due to a sample

effect as disaggregated data on household assets and education levels are available only for 22

countries. Neither did any of our measures of relative poverty show any significant relationship

with child soldier recruitment, lending no support to our second hypothesis that recruitment will

take place more frequently in regions that are relatively worse off than other regions in a

country. As mentioned elsewhere, recruitment across national borders and/or possible

dependence on ad-hoc recruitment at the location of battles could help explain the absence of

empirical support for a relative deprivation–recruitment relationship.

Refugee camps can function as honey-pots and easy targets for groups wanting to recruit

children for combat. We found strong support for this relationship in our disaggregated study,

and the refugee camp effect indeed seems to outperform the poverty explanation of child soldier

recruitment, in line with the finding of Achvarina and Reich (2006). Being in a conflict zone,

22

proximity to conflict zones, and particularly the intensity of conflict also seem to be good

predictors of child soldier recruitment.

The research on child soldier recruitment, poverty, and spatial inequalities, as well as

the efforts to disaggregate studies of civil war are all picking up pace. The combined efforts of

these fields of research therefore show great promise for advances in future research. Access to

reliable data on both poverty figures and child soldier recruitment for a large sample of cases is

one of the most evident challenges for future research. In this study we are only able to cover

the African continent, or a sub-sample thereof. Moving from the national to the regional level

of analysis is a great improvement, but does not necessarily fully solve the problem of

ecological fallacy. In other words, we cannot be sure whether the poorer children within each

region are the more prone to becoming soldiers. Hence, future research could consider

disaggregating the relationship further, e.g. down to the district level, or simply compare

individual survey data from several countries. Furthermore, in the analyses presented in this

article the poverty measures are constant over time due to data constraints, meaning that at

present we can only perform a cross-sectional analysis. Ideally, the data on child soldier

recruitment should also offer more detail with regard to magnitude, i.e. the amount of children

recruited at various locations and at various times. However, the existing competing theoretical

arguments, conceivable logic, and evidence do not suggest that there is a single stage of conflict

that should be particularly at risk of child soldier recruitment. It is rather plausible that the

practice seems to manifest itself at any stage of the conflict. The identifiable patterns of child

recruitment can include the end of the conflict, the beginning of the conflict (e.g. NPFL in

Liberia)24, intermittent recruitment at conflict peaks (e.g. LURD in Liberia) (CSUCS, 2004), or

even regular recruitment throughout the conflict. The timing of child soldier recruitment can be

investigated further in future research should time variant recruitment data by location become

available for a cross country sample. For the purpose of uncovering the true causes of child

soldier recruitment, the ideal dataset would be based on survey data of a representative sample

of children from several countries, including both children who became soldiers and children

24 Laurent-Désiré Kabila of the Democratic Republic of Congo (DRC) enrolled thousands of children for his initial military

campaign against the Mobutu government in 1996 and 1997 (Human Rights Watch, 2001).

23

who did not (for a more thorough discussion on this see Ames 2008). However, no such data

currently exists.

From our findings one clear policy implication stands out. Although poverty might

certainly affect why children join armed groups and why they cannot protect themselves against

forced recruitment, the most efficient policies to stop child soldier recruitment are likely to be

those that specifically focus on protecting refugee camps from intrusion by armed groups.

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28

Table 1. Logit Regression of Child Soldier Recruitm ent and Absolute Poverty,

African Regions 1990-2004

(1) (2) (3) (4)

Refugee camp 2.003*** 2.609*** 2.442*** 1.598*** (3.89) (5.69) (5.46) (3.97) Battle deaths (log) 0.271*** 0.279*** 0.321*** 0.226*** (3.47) (3.21) (3.12) (3.56) Conflict in neighboring region 1.454* 1.251 1.117 0.725 (1.85) (1.49) (1.38) (0.93) Conflict years -0.018 (0.29) Regional population (log) 0.072 (0.34) Regional pop. density (log) -0.025 (0.22) Household assets 3.653* (1.94) Education years 0.190 (1.14) Infant mortality rates (log) 1.537** (2.28) Constant -6.808** -6.273*** -6.028*** -11.753*** (2.39) (6.33) (5.60) (3.50) Regions 626 354 354 688 Countries 49 22 22 52 Pseudo R2 .325 .394 .389 .340

Robust z statistics in parentheses, absolute numbers. Huber-White clustering on country. * significant at 10%;

** significant at 5%; *** significant at 1%.

29

Table 2. Logit Regression of Child Soldier Recruitm ent and Relative Poverty Deprivation, African Regions 1990–2004 (5) (6) (7)

Refugee camp 2.456*** 2.421*** 1.923*** (5.68) (5.52) (4.27) Battle deaths (log) 0.280*** 0.281*** 0.231*** (2.97) (2.97) (3.35) Conflict in neighboring region 2.456*** 2.421*** 1.923*** (5.68) (5.52) (4.27) Relative deprivation (household assets) -0.142 (0.39) Relative deprivation (education years) -0.217 (1.07) Relative deprivation (infant mortality) 0.179 (0.17) Constant -5.118*** -5.070*** -5.312*** (7.40) (7.29) (7.75) Regions 354 354 688 Countries 22 22 52 Pseudo R2 .378 .379 .299

Robust z statistics in parentheses, absolute numbers. Huber–White clustering on country. * significant at 10%;

** significant at 5%; *** significant at 1%.

30

Figure 1. Poverty and Child Soldier Recruitment in Chad (left) and Uganda (right)

Darker areas relatively deprived in terms of household assets

compared to country mean. Hatched: Child soldier recruitment.

The darker the region, the higher the infant mortality rates. Hatched:

Child soldier recruitment.

31

Figure 2. Child Soldier Recruitment and Conflict Zo nes, Africa, 1990–2004

32

Figure 3. Infant Mortality Rates (CIESIN) and Child Soldier Recruitment, 1990–2004

33

Figure 4. Predicted Probabilities of Child Soldier Recruitment Based on Infant Mortality Rates (CIESIN) and Refugee Camp(s) in Reg ion, 1990–2004

0.2

.4.6

Pre

dict

ed p

roba

bilit

y of

chi

ld s

oldi

er r

ecru

itmen

t

0 50 100 150 200Infant mortality rates

Note 1: Upper solid line indicates the existence of one or more refugee camps in the region; lower solid

line refers to no refugee camp in the region. Solid lines are the local polynomial regression fits calculated with

bandwidth of 10. Dashed lines depict 95% confidence bands. The graph is based on Model 4, Table 1. The

confidence bands are relatively close to the non-parametric regression fits. This indicates the statistical significance

of the impact of having one or more refugee camps in the region on the recruitment likelihood at different rates of

infant mortality.

34

APPENDIX

A1. Descriptive statistics, all variables used in a nalysis. Variable N (regions) Mean Std. Dev. Min Max Dependent variable Child solder recruitment 690 0.106 0 1 Absolute Poverty Household assets 354 0.218 0.116 0.03 0.65 Education years 354 2.902 2.496 0.01 9.67 Infant Mortality Rate (IMR) (log) 688 4.288 0.613 2.08 5.20 Relative Deprivation Rel. Depr. Household assets 354 0.199 0.437 –0.76 1.81 Rel. Depr. Education yrs. 354 0.396 0.805 –1.07 5.21 Rel. Depr. IMR (log) 688 –0.015 0.179 –1.61 0.58 Controls Refugee camp in region 690 0.232 0 1 Battle deaths (log) 690 3.772 4.577 0 11.91 Conflict in neighboring region 690 0.670 0 1 Conflict years 690 2.922 4.653 0 15 Region population size (log) 626 13.037 1.429 8.07 16.36 Region population density (log) 626 3.407 1.967 –2.84 10.78

35

A2. Correlation Matrix

Child soldier recruit-ment

Assets Educ. years

IMR RD (assets)

RD (educ.)

RD (IMR)

Refugee camp

Battle deaths

Conflict in neighbor region

Conflict years

Reg. pop.

Child soldier recruit. 1 Household assets .106 1 Education years –.017 .487 1 IMR .148 –.311 –.676 1 RD (assets) –.001 –.772 –.480 .228 1 RD (educ.) –.082 –.386 .587 .193 .576 1 RD (IMR) .003 –.093 –.190 .431 .142 .199 1 Refugee camp .434 –.028 .030 .148 –.044 –.161 .034 1 Battle deaths .392 –.053 –.279 .419 .115 –.012 .100 .205 1 Conflict in neighbor reg. .229 –.211 –.323 .387 .188 .040 .140 .143 .539 1 Conflict years .389 –.139 –.284 .392 .164 .060 .100 .267 .810 .412 1 Regional pop. –.057 –.263 –.039 .128 .086 .004 –.070 .158 –.006 .105 –.008 1 Regional pop. density .028 .055 –.037 .222 –.112 –.085 –.089 .028 .019 .028 –.059 .670

N=302

6

A3. Logit Regression of Child Soldier Recruitment a nd Absolute Poverty,

African Regions 1990–2004. Table 1 replacing confli ct intensity with conflict

years.

(A1) (A2) (A3) (A4)

Refugee camp 1.981*** 2.465*** 2.306*** 1.553*** (3.76) (6.05) (6.42) (3.86) Conflict in neighboring region 2.552*** 2.234*** 1.996*** 1.604** (2.63) (2.78) (2.76) (2.28) Conflict years 0.135*** 0.166* 0.172 0.127*** (2.72) (1.72) (1.62) (3.17) Regional pop. (log) 0.179 (0.71) Regional pop. density (log) –0.085 (0.67) Household assets 4.101** (2.38) Education years 0.089 (0.65) Infant mortality rates (log) 1.805** (2.52) Constant –8.020** –6.315*** –5.418*** –12.914*** (2.41) (6.53) (6.78) (3.60) Regions 626 354 354 688 Countries 49 22 22 52 Pseudo R2 .282 .341 .322 .307

Robust z statistics in parentheses. Huber-White clustering on country. * significant at 10%; ** significant at 5%; *** significant at 1%