Journal of Conflict Resolution

Volume 51 Number 4
August 2007 531-567
Ó 2007 Sage Publications
Demobilization 10.1177/0022002707302790
http://jcr.sagepub.com

and Reintegration hosted at

http://online.sagepub.com

Macartan Humphreys
Department of Political Science
Columbia University, New York
Jeremy M. Weinstein
Department of Political Science
Stanford University, California

Since 1989, international efforts to end protracted conflicts have included sustained
investments in the disarmament, demobilization, and reintegration (DDR) of comba-
tants. Yet while policy analysts have debated the factors that contribute to successful
DDR programs and scholars have reasoned about the macro conditions that facilitate
successful peace building, little is known about the factors that account for successful
reintegration at the micro level. Using a new dataset of ex-combatants in Sierra
Leone, this article analyzes the individual-level determinants of demobilization and
reintegration. Past participation in an abusive military faction is the strongest predic-
tor of difficulty in achieving social reintegration. On economic and political reintegra-
tion, we find that wealthier and more educated combatants face greater difficulties.
Ideologues, men, and younger fighters are the most likely to retain strong ties to their
factions. Most important, we find little evidence at the micro level that internationally
funded programs facilitate demobilization and reintegration.

Keywords: rebellion; civil war; post-conflict reconstruction; international peacekeeping;

reintegration

S ince 1989, multidimensional peacekeeping operations led by the United

Nations have been central to efforts to end protracted conflicts in Africa, Latin
America, and Asia. Moving beyond traditional monitoring and peacekeeping, multi-
dimensional operations have sought to lay the foundation for stable, self-
sustaining transitions by building confidence among warring parties through social
and economic cooperation, rebuilding infrastructure, and reforming institutions so
as to reduce incentives for future violence. Among the most ambitious of these
tasks has been the attempt to dissolve militant organizations and return former
fighters to their communities (Berdal 1996).
These efforts to demobilize armed factions and reintegrate individual fighters
into civilian life—the latter two of the three elements of disarmament, demobiliza-
tion, and reintegration (DDR) programs—have emerged as a critical component of
these interventions. Formal programs to facilitate DDR date back to the operations

531

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
532 Journal of Conflict Resolution

of the UN Observer Group in Central America (ONUCA) in 1989. Since then,

DDR has figured prominently as part of UN operations in El Salvador, Cambodia,
Mozambique, Angola, Liberia, Sierra Leone, Guatemala, Tajikistan, and Burundi
to name a few. By 2000, when the secretary general was asked to report to the
Security Council on the role of the DDR efforts, he felt confident enough to con-
clude that ‘‘a process of disarmament, demobilization, and reintegration has repeat-
edly proved to be vital to stability in a post-conflict situation’’ (United Nations
2000, 1; italics added).
But despite the confidence of policy makers in the impact of DDR programs,
there have been few systematic efforts to evaluate the determinants of successful
reintegration by ex-combatants after conflict. The literature is chock-full of
‘‘lessons-learned’’ assessments that attempt to parse the factors that account for the
success (or failure) of a given DDR program (World Bank 1993).1 Surprisingly,
this debate has typically been carried out without an appropriate source of variation
in the key explanatory variables. At the macro level, studies of DDR have typically
not engaged in a comparison of outcomes in countries that did and those that did
not receive interventions. At the micro level, strikingly few rigorous attempts have
been made to identify factors that might explain why some individuals and not
others are able to successfully reintegrate after conflict. In particular, no studies
have systematically compared the reintegration success of those that have and have
not participated in demobilization and reintegration programs.
In practice, identifying the effects of DDR programs on peace building is diffi-
cult at the macro level. These programs rarely take place in isolation. They typi-
cally are complemented by other military, social, and economic interventions. An
academic consensus appears to be emerging that these multidimensional peace-
keeping operations improve the prospects for peace, democracy, and improved eco-
nomic performance in the aftermath of conflict (Walter 1997; Doyle and Sambanis
2000, 2006). Moreover, outside intervention seems to be particularly valuable
when hostilities are deep and local capacities have been destroyed (Doyle and Sam-
banis 2000, 2006). But the multidimensional character of these interventions makes

Authors’ Note: This research draws on a large survey led by the authors together with the Post-conflict
Reintegration Initiative for Development and Empowerment (PRIDE) in Freetown, Sierra Leone. Finan-
cial support came from the Earth Institute at Columbia University and logistical support from the Demo-
bilization and Reintegration office at the United Nations Mission in Sierra Leone (UNAMSIL). We are
particularly grateful to Alison Giffen and Richard Haselwood for their extensive work on this project; to
Christina Cacioppo and Daniel Butler for research assistance; to Allan Quee, Patrick Amara, and Lawr-
ence Sessay, our partners in the field at PRIDE; and to Desmond Molloy at UNAMSIL. We also thank
Christopher Blattman, David Cunningham, James Fearon, Kristian Gleditsch, David Laitin, Andrew
Mack, James Pugel, Jonah Schulhofer-Wohl, Stephen Stedman, Michael Tomz, Barbara Walter,
Jonathan Wand, and participants in seminars at the University of California, San Diego; Yale University;
and the annual meeting of the American Political Science Association. Replication materials are avail-
able at http://www.columbia.edu/ ∼ mh2245/papers1/jcr2007 and http://jcr.sagepub.com/cgi/content/
full/51/4/531/DC1/.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 533

it difficult to discern the individual contribution of specific programs to overall suc-

cess. There are, quite simply, too few cases and too many confounding variables.
In this article, we instead turn to micro-level data, drawing on a survey of 1,043
combatants from the five warring factions in Sierra Leone’s civil war. We employ
measures of variation in the reintegration experience across former combatants to
answer the question, what determines the ability of ex-combatants to reintegrate into
society? Our primary goal is to identify the impact of international attempts to facil-
itate reintegration, but we also explore how the ability of ex-combatants to reinte-
grate depends on their personal characteristics and on their experience of conflict.
We address these questions using a second-best approach. We emphasize that a
first-best approach exists, using a method of randomized intervention, in which (for
example) the order in which individuals undergo DDR processes is partly rando-
mized. This method provides enormous power for understanding the impact of
external interventions, but we know of no attempt to use the principle of randomi-
zation to evaluate DDR efforts in any postconflict country. In the absence of rando-
mization, we use data from ex-post survey work to identify the correlates of
successful reintegration and look for evidence of the impact of DDR programs.
To preview our results, we find surprising heterogeneity across outcome measures.
The breaking of ties between combatants and their factions, for example, is not asso-
ciated with more successful reintegration into the economy, the community, or politi-
cal life. Different processes appear to underlie distinct facets of social, economic, and
political integration. Moreover, a number of individual-level determinants of success-
ful reintegration stand out. Past participation in an abusive military faction is the
strongest predictor of difficulty in achieving social reintegration. For economic and
political integration, we find that individuals from wealthier and more educated back-
grounds report greater difficulties. Higher ranking combatants, we find, appear to be
less trusting of democratic politics. On our measure of the disestablishment of military
factions, the evidence suggests that ideologues are more likely to remain connected to
their units, as are male fighters and younger ex-combatants.
Our examination of DDR programs produces little evidence in support of claims
that these effectively break down factional structures and facilitate reintegration.
Combatants not exposed to the DDR program appear to reintegrate just as success-
fully as those that participated. In the absence of a randomized trial, however, there
are a number of reasons why we might fail to identify effects even if they exist—
chief among these are spillover, selection, and sampling biases. We examine each
of these sources of bias in turn. Based on the data available to us, our analysis sug-
gests that the nonfinding cannot be easily attributed to selection or sampling
effects. There is, however, some evidence for one of our four outcome measures
that spillover effects may render our ability to identify program effects particularly
difficult. Nonetheless, to discount this prima facie evidence that the DDR program
in Sierra Leone had no impact, policy makers will need to employ more robust stra-
tegies for demonstrating the efficacy of demobilization and reintegration efforts.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
534 Journal of Conflict Resolution

The second section of this article situates demobilization and reintegration in

the context of existing theoretical arguments about the determinants of successful
peace building. We then explore the question of whether the impact of macro-level
programs should be observable at the micro level, drawing out implications from
our discussion that then shape our empirical strategy. A fourth section introduces
the case of Sierra Leone and describes our research method. The fifth, sixth, and
seventh sections describe our empirical strategy for studying the determinants of
reintegration success and identifying the effects of international programs. A con-
cluding section discusses the relevance of our results for broader discussions of
DDR and postconflict strategy.

Hypotheses on Demobilization and Reintegration

Policy makers have long supported demobilization and reintegration because of

the perceived impact such programs have on the risk of a return to conflict. Spear
(2002) emphasizes the importance of dissolving armed factions: ‘‘Peace requires
breaking the command and control structures operating over rebel fighters . . . thus
making it more difficult for them to return to organized rebellion’’ (p. 141). Eco-
nomic dimensions of postconflict reintegration receive priority from the Interna-
tional Peace Academy (2002), which argues that ‘‘ex-combatants must be able to
earn a livelihood through legitimate means’’ (p. 5). Given that a higher risk of con-
flict is associated with an absence of income-earning opportunities for young men
(Collier and Hoeffler 2004), risk-reducing demobilization and reintegration pro-
grams seek to create economic opportunities for combatants.
The United Nations (2000) points to the need to ‘‘convert combatants who pur-
sue their objectives through force to civilians who pursue their objectives through
other means’’ (p. 11). Generating confidence in a democratic alternative to militar-
ized politics is a ‘‘critical test of the peace process’’ (United Nations 2000, 11).
Finally, civil society organizations often underscore the need for reconciliation in
the aftermath of conflict. To the extent that ex-combatants gain acceptance from
family members, friends, and neighbors through formal or informal processes of
reconciliation, communities are in a better position to reintegrate former soldiers
and facilitate their reinsertion into civilian life.
Policy makers thus recognize a range of distinct channels through which the
successful demobilization and reintegration of ex-combatants contributes to suc-
cessful peace building. The basic hypothesis we examine here is whether DDR pro-
grams facilitate this demobilization and reintegration, returning the many benefits
attributed to such programs by policy makers. We look for evidence that participa-
tion in demobilization and reintegration programs dissolves the factional networks
linking ex-combatants to one another, improves income-earning opportunities
available to former fighters, generates increased confidence in the democratic pro-
cess, and facilitates reconciliation with family, friends, and community members.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 535

Our core hypothesis, though motivated by policy debates about appropriate post-
conflict strategies, is consistent as well with theoretical work in the literature on
civil war termination. Two underlying mechanisms can be identified that link DDR
programs to the successful dissolution of warring factions in the aftermath of civil
war. The first draws on the logic of the security dilemma (Walter 1997; Roe 1999).
Even if all parties favor the dissolution of their military factions, in an institution-
ally weak environment, mutual mistrust may result in an unwillingness to take the
first step toward demobilization. By offering assurances that warring factions will
be protected, terms will be fulfilled, and promises will be kept, a credible third-party
guarantee provides one solution to this dilemma. By providing an environment in
which formerly warring parties can learn of each other’s intentions, DDR programs
can provide fighters with the security and confidence needed to disengage from their
factions and return to civilian life.
The second draws on research that examines the role of ‘‘spoilers’’ (Stedman
1997). Spoilers—individuals that use violence to undermine peace efforts—may
seek to maintain the structures of armed factions to bargain for more favorable
returns and thereby exacerbate security dilemmas. Yet as argued by Stedman, posi-
tive measures may exist to address the grievances of factions that stand in the way
of peace. Through the provision of training and transfers of monetary compensation,
DDR programs may alter the relative benefits and costs of engagement with a peace
process and remove the incentives for spoilers to maintain organizational structures.
These theoretical considerations suggest individual-level features that might
render reintegration more difficult in some circumstances:

Hypothesis 1: Individuals that distrust the intentions of other groups should be

less likely to reintegrate.
Hypothesis 2: Individuals that are dissatisfied with the terms of the peace should
be less likely to reintegrate.

However, the theoretical logic suggests also (consistent with the perspective of pol-
icy makers) that DDR programs should be effective in facilitating reintegration.
Moreover, the logic suggests that these programs should be particularly salient
among individuals for whom distrust and dissatisfaction would otherwise impede
reintegration. This reasoning leads to the following three further hypotheses:

Hypothesis 3: Participation in DDR programs will lead to more successful

demobilization and reintegration among ex-combatants.
Hypothesis 4: The impact of DDR programs should be most pronounced among
individuals who distrust the intentions of other groups.
Hypothesis 5: The impact of DDR programs should be most pronounced among
individuals who are dissatisfied with the terms of the peace.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
536 Journal of Conflict Resolution

Beyond these hypotheses regarding the impact of DDR programs, we also

explore a series of individual- and group-level characteristics that might condi-
tion successful demobilization and reintegration. These control variables are of
considerable independent interest in that our study provides a first opportunity to
identify some of the empirical correlates of reintegration.
Recent research in social psychology and anthropology suggests some plausible
determinants of reintegration success. A rich empirical literature has examined the
impact on reintegration of exposure to violence on the part of noncombatants (see,
for example, Dyregrov, Gjestad, and Raundalen 2002; Husain et al. 1998). Some
single-country studies in Sierra Leone have examined the reintegration prospects
of particular subgroups of combatants, notably youth (Richards et al. 2003) and
women and girls (Mazurana et al. 2002). But beyond these studies, our review of
the literature on demobilization and reintegration yields little in the way of sys-
tematic theories about the conditions under which some combatants but not others
will give up their arms and reintegrate into civilian life. While scholars have
devoted much attention to the study of how organizations form, considerably less
effort has been directed at their dissolution. Thus, given the rudimentary state of
our knowledge, our examination of these factors is exploratory: we seek to docu-
ment the extent to which a set of demographic and social factors can account for
successful reintegration across individual combatants.
In the analysis that follows, we consider, without stating explicit hypotheses, the
relationship between reintegration success and eleven individual, group, and com-
munity characteristics: five measures of an individual’s demographic background
(age, gender, ethnicity, education, and wealth), four measures of an individual’s
experience of the conflict (whether the individual was abducted, whether he or she
was a political supporter of the group, whether he or she was an officer, and a mea-
sure of the abusiveness of the unit in which an individual fought) and, finally, two
measures of community characteristics (an indicator of wealth and an index of the
degree to which the community suffered from violence during the war).

Macro Processes and Micro Effects

Before turning to our empirical analysis, we are confronted by an inferential

challenge. In shifting from the macro to the micro, we make an implicit assumption
that the impact of a DDR program in a given country can be discerned by compar-
ing reintegration outcomes across individuals that did and did not participate in a
program. This assumption makes sense if the macro effects of DDR programs work
through the positive impact such interventions have on individual combatants, as
enumerated in the previous section. To the extent that DDR is designed explicitly
to break down ties within factions and facilitate economic, political, and social
reintegration, these effects should, in principle, be visible at the individual level.
We emphasize, however, that if DDR programs work instead at a more aggregate

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 537

level—for example, by enabling factions to commit to a peace process through the

reassurance DDR programs offer of each side’s intentions—the impact of the inter-
vention cannot be assessed through this micro-level approach.
Empirically, the strategy of comparing outcomes across treated and untreated
ex-combatants to identify the efficacy of a national-level process is complicated by
three distinct effects: spillover effects, selection effects, and sampling effects. We
review these three potential confounds here, as they shape our empirical strategy,
and then return to an analysis of the role they play in Sierra Leone in the discussion
of our results.

Spillover Effects
The core challenge posed by spillover effects can be illustrated with the follow-
ing example. Assume that of N individuals, T receive the treatment (with a true
average treatment effect on the treated normalized to 1). Whenever some fraction
of individuals (T=N) receives the treatment directly, another fraction, d, of the
untreated individuals also receives the treatment indirectly (or alternatively,
assume that all the untreated individuals receive a partial treatment of strength d).
In this case, a method that uses differences in reintegration between the treated and
untreated but ignores spillovers estimates the total treatment effect as
T × ð1 − dÞ:

However, taking account of spillovers, the true total treatment effect is

T + ðN − TÞd:

This approach underestimates the overall impact of DDR in two ways. On one
hand, the difference between the treated and the untreated is an underestimate of
the impact of the program on the treated. In addition, the estimate fails to
capture the benefits that spill over to nonparticipants as returns to the program.
Plausibly, the spillover parameter, d, is a function of the share of individuals trea-
ted. If spillover effects are increasing in the number treated, then applying the
reasoning above, it becomes increasingly difficult to identify treatment effects as
the number of individuals that participate in the program increases.

Selection Effects
A second source of bias can arise if there are systematic differences between
those that participated in DDR programs and those that did not (other than the fact
that they were exposed to the treatment). If selection effects are present, any differ-
ences in reintegration success (or the lack of a difference) between those that
entered and those that did not could be a result of the selection mechanism rather
than the impact (or nonimpact) of the treatment.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
538 Journal of Conflict Resolution

Table 1
Program Effects
Reintegrated Did Not Reintegrate

Treated A B
Untreated C D

Sampling Effects
A final source of bias might arise if individuals that did not take part in the DDR
program and did not successfully reintegrate were also less likely to appear in our
sample of ex-combatants. To see the logic, let the cell entries in Table 1 represent the
number of individuals belonging to each combination of reintegrated/nonreintegrated
and treated/untreated.
In the absence of selection effects, an estimate of the true average treatment
effect is given by
A C
b= − :
A+B C+D
If, however, a share, a, of those that did not reintegrate and did not enter the pro-
gram were missing from our sample, then our estimate of the average treatment
effect would be
A C
b0 = − ;
A + B C + aD
which is less than b. In other words, a sampling frame that systematically missed
individuals that did not reintegrate and did not enter the program would result in an
underestimation of the program effect. Conversely, if some share, a, of individuals
that took part in the program and failed to reintegrate also failed to enter our
sample, then the estimate of the average treatment effect is given by
A C
b00 = − ;
A + aB C + D
which clearly overestimates program effect. We consider these two possibilities in
the discussion of our findings.

Surveying DDR in Sierra Leone

In January 2002, when the government of Sierra Leone declared its more than
decade-long war officially over, the international community showered it with plau-
dits for a successful disarmament, demobilization, and reintegration program that
paved the way for a stable postwar political order. This turn of events was unex-
pected for a country that experienced a brutal civil war, which captured international

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 539

attention, a stop-and-start peace-building effort lasting more than four years, and the
persistent negative spillover effects of violence in neighboring Liberia.
The war in Sierra Leone began in 1991 when a small group of combatants—
calling themselves the Revolutionary United Front (RUF)—entered Sierra Leone
from neighboring Liberia, backed by Charles Taylor, to fight the Sierra Leone
Army (SLA). Over the course of ten years of fighting, three additional factions
emerged: the Civil Defense Forces (CDF), a militia group that joined forces with
President Kabbah’s government when the country returned to civilian rule in 1996;
the Armed Forces Revolutionary Council (AFRC), a group of soldiers that over-
threw Kabbah in a coup in 1997; and the West Side Boys (WSB), incorporating
elements of all the factions in yet another militia group. During the war, Sierra
Leone experienced violence of horrific proportions. Tens of thousands of civilians
were killed, and hundreds of thousands were displaced from their homes. After
attempts at power sharing failed, the war was brought to a close with the capture of
the leader of the RUF, an intervention by what was (at the time) the largest UN
mission in the world, and robust military action by third parties, notably Guinea
and the United Kingdom.
Given the ups and downs of the war itself, it should come as no surprise that the
DDR process faced innumerable hiccups in its implementation (Comninos, Stav-
rou, and Stewart 2002). Boutros Boutros-Ghali called for a demobilization and
reintegration effort in Sierra Leone as early as 1995 (Agence France Press 1995)
and a DDR program was written into the terms of the 1996 peace agreement. How-
ever, the first sustained efforts to demobilize fighters began only in 1998. Kabbah’s
government led this process after it was returned to power by the Nigerians. But it
was wholly unsuccessful, since only 3,000 ex-combatants registered for disarma-
ment and demobilization (Molloy 2004). A second phase began in 1999 after the
Lomé Accord was signed, and it continued until 2000 when the war broke out
anew. During this period, slightly fewer than 20,000 combatants turned up to be
demobilized. Although demobilization continued during negotiations, the bulk of
demobilization took place after the United Nations Mission in Sierra Leone was
beefed up, following the British intervention in 2001 and 2002. In the third and
final phase, close to 50,000 combatants disarmed. This brought the total caseload
to approximately 76,000 fighters (Molloy 2004).
The disarmament process was conducted at reception centers distributed
around the country. It included five phases: the assembly of combatants, collec-
tion of personal information, the verification and collection of weapons, the certi-
fication of eligibility for benefits, and transportation to a demobilization center.
Once disarmed, combatants were prepared to return to civilian life in demobiliza-
tion sites where they received basic necessities, reinsertion allowances, counsel-
ing, and eventually transportation to a local community where they elected to live
permanently. In the community, combatants benefited from training programs
(largely vocational skills, including auto repair, furniture-making, etc.) designed

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
540 Journal of Conflict Resolution

to ease their reentry into the local economy. Moving more than 76,000 soldiers
through this process is from an operational standpoint an accomplishment in
itself. Our data suggest that program implementation was successful in other ways
as well. Rates of participation were nearly equal across the five major factions
and we found little evidence that an individual’s political affiliation correlated
with his or her ultimate satisfaction with the program. Complaints about the pro-
gram centered mainly on its administrative efficiency and bureaucratic design—
common criticisms of UN-sponsored programs—but there is no evidence that the
process was manipulated to favor any one group to the exclusion of any other.
To assess the extent to which combatants have been able to reintegrate and iden-
tify the relative importance of participation in the DDR program, we gathered sys-
tematic data on a sample of ex-combatants, some of whom participated in the
formal DDR effort and others who remained outside of it (Humphreys and Wein-
stein 2004). The survey was conducted between June and August 2003, slightly
more than a year after the war came to an end. The study targeted a sample of
1,000 ex-combatants; a total of 1,043 surveys of ex-combatants were completed.
The main method for gathering information was through the administration of a
closed-ended questionnaire by an enumerator in the respondent’s local language.
Interviews were conducted at training program sites and in community centers
around the country.2
To ensure as unbiased a sample as possible, the survey employed a number of
levels of randomization. First, surveys were enumerated in forty-five chiefdoms or
urban localities that were randomly selected using estimates of the population of ex-
combatants residing in the chiefdoms provided to us by the National Commission on
Demobilization, Disarmament, and Reintegration (NCDDR 2002), the National Sta-
tistics Office, and estimates of experts in Sierra Leone.3 We note that the fact that the
sampling frame depended in part on NCDDR estimates implies that it is possible that
areas in which NCDDR was most inactive were underrepresented in our sample.
Within each enumeration unit, sites were also randomly selected, with both urban
and rural areas represented. However, because of the relatively small share of comba-
tants to noncombatants and the absence of lists, standard sampling methods could
not be used to generate a perfectly random sample of ex-combatants. Instead, enu-
merators worked through both official (United Nations and government) contacts
and local community leaders to identify a pool of ex-combatants at least two or three
times as large as the target number from which the actual subjects were randomly
selected. In most instances, chiefs and DDR staff asked a number of ex-combatants
to meet at a public location, and teams selected candidates randomly from that pool
(by choosing every third person or selecting numbers from a hat). While this method
worked well, there is no guarantee that the lists generated in this process are statisti-
cally representative of the population of fighters in each chiefdom.
The survey elicited a detailed profile of each of the combatants, including their
socioeconomic backgrounds, their experience of the war itself, their involvement

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 541

in the DDR process, and the realities they have faced in the postwar period. The
data are rich and textured, in spite of the survey’s closed-ended format. It allows
for a careful analysis of the determinants of reintegration success, which we under-
take in this article. But it also provides data useful for systematic examination of
the strategies of the warring factions and the determinants of levels of violence,
which are reported elsewhere (Humphreys and Weinstein 2006).

Measuring Demobilization and Reintegration Success

To reflect the multiplicity of goals associated with a DDR program, we gener-

ated four measures intended to capture distinct dimensions of reintegration.
Our first measure, DELINKED, captures the extent to which linkages with fac-
tions are maintained by the individual, recording whether the individual has suc-
cessfully broken his factional ties. The measure combines an individual’s responses
to three questions.4 The first asks individuals who they spend their time with. The
second asks, were they to start a business, whom they would partner with. For each
of these questions, one of the options available to respondents was ‘‘Friends that I
met in my faction during the war.’’ The final question asks individuals about the
avenues they pursue when confronting ongoing, personal problems in the postwar
period. The variable DELINKED takes a value of 1 unless (1) individuals spend
most of their time with friends from their faction, (2) individuals reported that
friends from their faction would be their preferred business partners, or (3) indivi-
duals felt that recourse to members of their faction was among the most effective
ways to deal with their problems.
The second measure, EMPLOYED, indicates whether an individual has reinte-
grated into the workforce. The measure takes a value of 0 if the individual declared
his or her present occupation to be unemployed or doing ‘‘odd jobs.’’ For all other
responses, this measure takes a value of 1.5
The third measure, DEMOCRATIC, focuses on the confidence ex-combatants
express in the democratic system. The measure takes a value of 1 if an individual
believes that exercising voice by voting in elections or approaching government
officials, either locally or centrally, is the most effective way to deal with commu-
nity problems. Alternative choices included protests, complaints to nongovernmen-
tal organizations, reaching out to traditional leaders or factional authorities, or
taking up arms to fight.
Our final measure, ACCEPTED, records whether individuals reported facing
ongoing difficulties in gaining acceptance from their families and communities.
This measure takes a value of 1 if the individual reported no problems in gaining
acceptance from family members and neighbors in the postwar period; if problems
are reported, the measure takes a value of 0.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
542 Journal of Conflict Resolution

Table 2
Measures of Reintegration
Variable N Mean SD Mean | CDF Mean | RUF Difference

Delinked? 1,007 0.86 0.34 0.88 0.84 .04

(1.52)
Employed? 1,037 0.84 0.36 0.90 0.79 .11∗∗∗
(4.62)
Democratic? 1,009 0.62 0.49 0.61 0.66 −0.05
(1.57)
Accepted? 1,020 0.93 0.25 0.99 0.86 0.13∗∗∗
(8.50)

Note: CDF = Civil Defense Forces; RUF = Revolutionary United Front. T-statistics in parentheses.
∗
significant at 10 percent. ∗∗ significant at 5 percent, ∗∗∗ significant at 1 percent.

Summary statistics for these four measures are provided in Table 2. We see from
the table that there is considerable variation across these measures in the extent to
which individuals can be considered successfully reintegrated. In addition, Table 2
shows the average scores on each of these measures for each of the two major fac-
tions in Sierra Leone’s war—the RUF and the CDF.
We find that 86 percent of combatants in our sample have broken ties with their
factions, while 14 percent still consider faction members to be among their closest
friends, most likely business partners, or as a primary source of support in the event
of problems. The distribution of this measure is similar across the two major factions.
On employment, 84 percent of our sample report some form of permanent occu-
pation. The problem of unemployment is found disproportionately within the RUF
subsample; of these ex-combatants, 21 percent report having no present full time
occupation.
Turning to democratic politics, we find that 62 percent of combatants express
confidence in electoral politics or approaches to state/local officials as among the
most effective ways to respond to problems in their communities. The remainder
looks either to their old factions or more commonly to outside actors, typically the
international community, as a means to effect change. Uniquely, fighters from the
CDF are less likely to be reintegrated than RUF members by this measure, although
the difference is not statistically significant.
Finally, on the measure of acceptance, we record the highest levels of successful
reintegration, with 93 percent reporting no problems. While this measure supports
the idea that across individuals in Sierra Leone, reintegration has proceeded with
great success, the difficulties faced by 7 percent of respondents should not be under-
emphasized. If our sample were entirely representative of the ex-combatant popula-
tion, this figure of 7 percent would correspond to approximately 5,000 former
soldiers facing challenges in being accepted into civilian life. In fact, since our sam-
ple does not include those combatants that failed to reintegrate and elected instead

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 543

to continue fighting in Liberia or Côte d’Ivoire, this number plausibly underesti-

mates the number of nonreintegrated fighters. Note, finally, that these acceptance
rates differ markedly across factions. Nonacceptance rates are much higher within
the RUF than within the CDF; while CDF members almost universally report no
problems of acceptance, 14 percent of ex-RUF members report such problems.
Do these four indicators capture different facets of the same underlying phenom-
enon? Are individuals that are poorly integrated socially also less likely to find
jobs? Are these same individuals more or less likely to maintain ties with their fac-
tions? If they were capturing a single underlying process of reintegration, we might
expect to find that the measures are positively correlated with one another. Table 3
reports the pairwise correlations between these measures. Uniformly, we find that
the correlations among the measures are low. Our measure of factional ties is
weakly and typically negatively correlated with the other measures. Faith in
democracy as a solution to local problems is also poorly correlated with the other
dimensions of reintegration. The highest correlation is between the variables that
measure whether combatants were accepted by friends and family and whether they
found employment—the two measures most closely associated with membership
of the CDF—yet even this correlation is well below 10 percent. These four mea-
sures, it seems, capture very different types of experiences.
To determine more formally whether or not these measures capture an underly-
ing phenomenon of reintegration success, we estimate Cronbach’s (1951) alpha
measure of scale reliability. This measure, ranging from negative infinity to 1, pro-
vides an indication of the reliability of a scale generated from our four basic mea-
sures. Although there is no clear threshold for when a scale may be considered
reliable, Nunnaly (1978) suggests that a cutoff point of 0.7 be used. We generate a
reliability measure of .08, indicating that these measures of reintegration success
cannot reliably be considered indicators of the same underlying phenomenon.
Reintegration on one dimension is typically not a good predictor of reintegration
on another dimension. Although it would be simpler, both from the point of view
of analysis and for policy reasons, to generate a single measure of reintegration
success, the structure of our data suggests that this is not appropriate. Distinct pro-
cesses may underlie each of these measures of reintegration success.

Individual, Group, and Community

Correlates of Reintegration Success

We begin our analysis by considering the nonprogrammatic determinants of

reintegration success. Consistent with the individual-level logic underpinning a
security dilemma motivation for DDR programs, ceteris paribus, individuals that
mistrust the intentions of other fighters should be less likely to leave the security of
their units and reintegrate into civilian life. Consistent with the logic of the role of

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
544 Journal of Conflict Resolution

Table 3
Correlation Matrix of Measures of Reintegration
Delinked? Employed? Democratic? Accepted?

Delinked? −0.02 −0.02 0.06

Employed? −0.02 0.06 0.07
Democratic? −0.02 0.06 0.02
Accepted? 0.06 0.07 0.02

spoilers in undermining peace processes, individuals that are dissatisfied with the
terms of the peace have a greater incentive to hold out and disrupt a peace process
rather than returning quietly to civilian life. While these logics may operate at mul-
tiple levels—at the level of armies or of individual units—we examine here
whether evidence of these logics can be identified at the individual level.
To measure distrust, we asked respondents to describe their beliefs regarding
the sincerity of different groups with respect to the implementation of the terms
of the Lomé Accord: Did a given combatant believe that other fighters would
respect the terms of the agreement, or did they expect them to renege? Our measure
of distrust takes a value of 1 if an individual reported a belief that parties to the
Lomé agreement were likely to renege on the agreement. In total, slightly more
than 20 percent of combatants expressed such concerns (summary statistics for this
and all other independent variables are provided in the appendix).
To measure dissatisfaction, we asked individual fighters which factions they
believed received the best and worst deals from the Lomé negotiations. Approximately
30 percent of combatants claimed that their own faction got the worst deal; these com-
batants we classify as dissatisfied. Strikingly, this dissatisfaction measure does not cor-
relate with factional affiliation. Thirty percent of RUF fighters felt that the RUF got
the worst deal, while 32 percent of CDF fighters claimed that the CDF did worst.
These two measures are used to examine hypotheses 1 and 2 for each of our four
measures of reintegration success. Table 4 reports the estimated marginal effect of
each measure based on a probit analysis with fixed effects for each faction and
clustering of errors by region.
The results suggest that distrust is a significant predictor of reintegration success
on one of the four dimensions. Individuals that distrust the intentions of the other
side are significantly less likely to place their trust in democratic processes to
resolve their concerns. Distrusting individuals also appear less likely to have bro-
ken ties with their factions, although the result is on the margins of statistical sig-
nificance. There is also a negative relationship between distrust and measures of
employment and acceptance, although these relationships are statistically weak.
Dissatisfaction accounts for variation on one of four dimensions of reintegration
success: it is associated with higher unemployment rates. Individuals that believed

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 545

Table 4
Nonprogram Determinants of Reintegration Success
(1) (2) (3) (4)
Delinked? Employed? Democratic? Accepted?

Distrust −0.030 −0.020 −0.157 −0.014

[1.59] [0.96] [3.18]∗∗∗ [1.02]
Dissatisfaction −0.010 −0.044 0.039 −0.009
[0.51] [2.44]∗∗ [0.86] [0.83]
Age 0.002 0.000 0.002 0.000
[1.78]∗ [0.28] [1.10] [0.35]
Male −0.089 −0.012 −0.004 −0.002
[3.42]∗∗∗ [0.23] [0.06] [0.20]
Mende 0.004 0.014 −0.006 0.014
[0.22] [0.64] [0.13] [1.93]∗
Educated 0.005 −0.026 0.001 −0.001
[0.40] [2.00]∗∗ [0.06] [0.09]
Poor −0.001 0.036 0.089 0.022
[0.03] [1.57] [2.55]∗∗ [1.92]∗
Abducted −0.000 −0.039 0.193 −0.000
[0.01] [1.29] [4.39]∗∗∗ [0.02]
Political support −0.055 0.008 0.180 −0.028
[2.22]∗∗ [0.35] [3.53]∗∗∗ [2.59]∗∗∗
Officer −0.045 −0.021 −0.096 0.003
[1.40] [0.54] [2.10]∗∗ [0.20]
Abusiveness 0.009 −0.064 −0.217 −0.087
[0.08] [1.34] [1.51] [2.66]∗∗∗
District wealth 0.049 −0.102 −0.109 0.005
[1.40] [1.99]∗∗ [2.09]∗∗ [0.33]
Community suffering −0.056 −0.065 0.556 −0.082
[0.43] [0.30] [1.94]∗ [2.87]∗∗∗
SLA −0.017 −0.004 −0.073 0.032
[0.21] [0.09] [0.56] [1.93]∗
AFRC 0.062 0.083 0.271 0.011
[1.20] [1.86]∗ [2.47]∗∗ [0.71]
CDF 0.068 0.057 0.027 0.082
[1.25] [1.65]∗ [0.35] [4.20]∗∗∗
WSB 0.067 −0.024 0.085 0.018
[0.70] [0.29] [0.68] [0.97]
Observations 900 924 903 913
Pseudo R2 0.03 0.07 0.07 0.23

Note: Robust z statistics in brackets. Marginal coefficient estimates (at mean values for the explanatory
variables) from probit analyses reported. Faction fixed effects are included in all specifications. Revolu-
tionary United Front (RUF) is the omitted category. All models allow errors to be clustered geographi-
cally at the chiefdom level. SLA = Sierra Leone Army; AFRC = Armed Forces Revolutionary
Council; CDF = Civil Defense Forces; WSB = West Side Boys.
∗
significant at 10 percent. ∗∗ significant at 5 percent, ∗∗∗ significant at 1 percent.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
546 Journal of Conflict Resolution

their group did badly from the political allocation of resources at Lomé have also
fared badly in the postconflict economic environment. Consistent with the logic
described in the analysis of spoilers, it may be that these individuals are slower to
reintegrate economically because they are holding out for economic benefits from
the political processes. It could also be, however, that this correlation reflects a mun-
dane reporting bias: individuals that have failed to find employment may simply
assess the political benefits received in the peace agreement more harshly.
Table 4 also reports the results of our empirical investigation of additional corre-
lates of reintegration success. Two characteristics thought to be major factors in
the reintegration process, age and gender, exhibit weak effects across dimensions.
Consistent with the prevailing view that reintegration is harder for younger fighters,
we find that older ex-combatants are more likely to have broken ties to their factions.
But it turns out that younger combatants are no less likely to be accepted by their
communities, to place their faith in democratic processes, or to have found gainful
employment. We emphasize again that these results are conditional on faction fixed
effects: there is, for example, a strong bivariate relationship between age and accep-
tance, with younger participants likely to have greater problems in reintegrating; but
this relationship is not significant once we take account of fixed effects.6
Perhaps, surprisingly, given the extensive focus on the difficulties faced by
female ex-combatants, we find significant differences between male and female ex-
fighters on only one dimension. Female ex-combatants are more likely to have
broken ties to their factions. The difference is substantively large and significant at
the 99 percent level. While the qualitative literature has focused on the difficulties
women face in the reintegration, our evidence suggests that gender has no measur-
able impact on most outcomes, except for the fact that men appear less willing to
sever their ties to other combatants. Again, we emphasize that the results reported
here condition on fixed effects: women are (on average) considerably more likely
to report problems gaining acceptance, but since women are more likely to be
members of the RUF, this relationship, though strong in a bivariate analysis, disap-
pears when we account for faction fixed effects.7
Members of the Mende ethnic group—more strongly associated with the leader-
ship of the CDF faction and the current ruling government—exhibit somewhat
higher levels of acceptance (and this, conditional on CDF membership), although
this fails to reach significance at conventional levels. Overall postconflict reintegra-
tion success does not appear to be strongly structured along ethnic lines.
The effects of (prewar) poverty and to a lesser extent, education, appear to be
consistent across the indicators with less well educated and poorer individuals typi-
cally having more success in reintegrating. Poverty, measured at the individual
level (using a dummy variable capturing whether the walls of the prewar home
were constructed of mud and sticks), is associated with a higher likelihood of adopt-
ing democratic norms, gaining acceptance by community members, and (although
not significant) finding employment. Strikingly, more educated ex-combatants

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 547

(our education measure takes a value of 0 for no education, 1 for at least some pri-
mary, and 2 for at least some secondary education) were less likely to find employ-
ment in postconflict Sierra Leone. We find no relationship, however, between an
individual’s socioeconomic status and the likelihood that they break ties with their
factions.
In addition, we include a series of measures reflective of an individual’s perso-
nal experience of the war. These measures include whether fighters were abducted
into a faction, whether they joined because they supported the political causes of
the faction, and whether they served as officers. Each of these variables is measured
using a single question administered during the survey.
We find that although there is a strong, negative bivariate relationship (not
reported) between whether an individual was abducted and his or her progress in
gaining acceptance, the relationships are weaker once we condition on faction
effects. We find a relationship between abductee status and reintegration rates on
only one indicator: abductees were considerably more likely to turn to government
for support rather than to rely on traditional, factional, or international sources of
support. The relationship between political motivations for participation and our
indicators of reintegration appears particularly complex. If individuals joined
because they supported the cause of the group, they face more difficulty gaining
acceptance in the postwar period and are more likely to remain attached to their
factions. Strong believers, across factions, have a harder time readjusting to civilian
life. Surprisingly, however, these individuals also appear to place the greatest faith
in the electoral process.
Disturbingly, across most measures, higher ranking officers in the various mili-
tary factions encounter more severe problems in reintegration. While these rela-
tionships are generally not significant, we do find a strong rejection of democratic
processes among higher ranking officers.
The final measure of the individual’s experience of the war captures a charac-
teristic of the units in which they fought. Substantial differences exist in Sierra
Leone across the fighting factions, but for the purposes of this analysis, we focus
on one key group characteristic that is likely to affect an individual’s prospects
in the postwar period: the extent to which a unit was highly abusive toward civi-
lian populations. To the extent that individuals committed heinous crimes
against noncombatants, one might expect that they would face a more difficult
process of gaining acceptance by community members and resettling into a non-
military way of life. Our measure used answers to eight related questions given
by respondents who fought in the same area, for the same faction, during the
same period of the war. The weights derived from a factor analysis were then
used to create a single measure, abusiveness, which ranges from 0 to 1.8 Con-
trolling for faction-level fixed effects, this measure is strongly and negatively
associated with an individual’s reported ease in gaining acceptance. Individuals
from nonabusive units exhibit acceptance levels nearly one standard deviation

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
548 Journal of Conflict Resolution

higher than those from highly abusive units. The size of the coefficient is large
and in a bivariate setting, accounts for about 9 percent of total variation in
acceptance. This may be the result of the psychosocial impact of the conflict on
individual fighters (Blattman 2006) or reflect the unwillingness of host commu-
nity members to accept a returning fighter. We cannot distinguish between these
explanations, although we emphasize that the result is independent of our esti-
mate of the degree of abuse to which host communities were exposed. Although
the effects are not significant, individuals from abusive units also have a more
difficult time gaining employment and are less likely to place their faith in demo-
cratic processes.
Table 4 also reports results on two community characteristics that may shape
the reintegration prospects of individual fighters. The first is an indicator of district
wealth using data from the Sierra Leone Central Statistics Office. The index—
which ranges from 0 to 1—uses factor analysis to combine measures of typical
(imputed) rent payments in each district and an index of food poverty. Both use
information gathered just as the war came to an end but before the survey was com-
pleted. The results suggest that on two of four measures, individuals who settle in
wealthier locations face more difficulty reintegrating. They find it more difficult to
find employment, and they are less likely to have faith in democratic processes.
Plausibly, fighters relocated to wealthier districts, such as Freetown and the dia-
mond mining areas, to improve their employment opportunities but with little
success.
Finally, we develop a measure of how host communities experienced the war. A
number of our respondents described to us how membership in a faction affected
their experience in the postwar period not because of their personal characteristics
but because of the reputation of the faction in the area where they lived. To esti-
mate these effects, we calculate a measure of community suffering. This variable
captures the average level of abusiveness of combatants who were operational dur-
ing the course of the war in each of the chiefdoms. In computing these averages,
we utilize the index of abusiveness for all fighters who declared themselves active
in a chiefdom at any time during the war (even if these fighters did not subse-
quently attempt to reintegrate in those areas). We find that the degree of abuse of
local communities during the war is powerfully related to the level of acceptance
of ex-combatants. There is a weak positive relationship, however, with the degree
of acceptance of democratic principles.
Overall, the results from Table 4 suggest that very different processes appear to
underlie the different dimensions of reintegration. For each dimension of reintegra-
tion, we can identify a number of explanatory variables that emerge as relevant, but
these are rarely the same variables across indicators and in some cases, the effects
appear to work in different directions. Some differences remain across factions in
the reintegration success of ex-combatants even after accounting for individual,
group, and community correlates; specifically, fighters in the CDF are more likely

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 549

to be economically and socially reintegrated than those in the RUF. While theory
provides us with few strong priors about the direction and magnitude of the impact
of these various individual, group, and community characteristics, accounting for
these effects is important as we turn now to examine our core hypothesis on the
effectiveness of DDR programs.

The Impact of DDR Programs on Reintegration

To assess the effectiveness of the DDR program in facilitating reintegration, we

use two measures of the treatment. Our first measure emphasizes participation in the
program—87 percent of our sample joined the DDR program, while 13 percent
elected to reintegrate on their own. This estimate of the rate of nonparticipation is
somewhat higher than the official estimate, which suggests a total ex-combatant
population (within Sierra Leone) of 84,200 (FAO 2004) and thus a participation rate
of 76;000=84;200 = 90 percent. In addition, we collected a second measure that cap-
tures whether an individual completed DDR. By this measure, 46 percent of our
sample completed the DDR program at the time of our survey, and 54 percent had
not. Combining both measures allows us to distinguish between the effects of initial
and complete exposure to the DDR program.9
The results of our analyses of program effects are presented in Table 5. The
table shows the marginal effect of exposure to each type of intervention on the like-
lihood of reintegration for each of the four indicators. In all cases, the models con-
trol for the collection of independent variables used in Table 4 along with faction
fixed effects.
The most striking result is the lack of evidence that international programs are
returning the benefits attributed to them, as observed at the individual level. Evi-
dence from Sierra Leone does not support the hypothesis that participation in a
DDR program increases the degree to which combatants are accepted by their
families and communities. Nor is there a relationship between participation in these
programs and democratic attitudes, the likelihood that an individual breaks ties with
his or her faction, or the likelihood that he or she returns home. The only significant
results work in the ‘‘wrong’’ direction: individuals that have entered DDR programs
are less likely to place their faith in governmental structures, and those leaving
DDR programs are significantly less likely to be employed. Further analysis of the
data indicates that the negative employment result does not arise from the difference
between those that completed the program and those that never entered but between
those that completed and those that entered but did not complete. The most likely
interpretation is that individuals consider themselves to have an occupation over the
period in which they are active participants in the program. Other than the short-
term occupation provided during training, however, we find no effect of exposure to
DDR programming on the employment prospects of ex-combatants.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 550
Table 5
Impact of DDR Program on Reintegration
(1) (2) (3) (4) (5) (6) (7) (8)

Program Variable Enter DDR Completed DDR

Dependent Variable Delinked? Employed? Democratic? Accepted? Delinked? Employed? Democratic? Accepted?

DDR −0.038 0.024 −0.050 −0.008 −0.011 −0.095 −0.017 −0.003

[1.09] [0.65] [1.65]∗ [0.69] [0.42] [4.05]∗∗∗ [0.61] [0.34]
Distrust −0.029 −0.020 −0.157 −0.013 −0.030 −0.017 −0.157 −0.013
[1.57] [0.96] [3.20]∗∗∗ [0.99] [1.57] [0.80] [3.17]∗∗∗ [1.02]
Dissatisfaction −0.009 −0.045 0.039 −0.009 −0.009 −0.037 0.040 −0.009
[0.43] [2.51]∗∗ [0.85] [0.79] [0.48] [2.09]∗∗ [0.88] [0.82]
Age 0.002 0.000 0.002 0.000 0.002 0.001 0.002 0.000
[1.82]∗ [0.30] [1.08] [0.40] [1.80]∗ [0.64] [1.12] [0.37]
Male −0.089 −0.010 −0.015 −0.001 −0.089 −0.016 −0.005 −0.002
[3.44]∗∗∗ [0.19] [0.21] [0.14] [3.43]∗∗∗ [0.30] [0.07] [0.20]
Mende 0.002 0.016 −0.011 0.014 0.004 0.012 −0.006 0.014
[0.08] [0.71] [0.26] [1.88]∗ [0.19] [0.57] [0.15] [1.94]∗
Educated 0.005 −0.026 0.002 −0.000 0.005 −0.025 0.001 −0.001
[0.42] [1.98]∗∗ [0.08] [0.06] [0.41] [1.96]∗ [0.06] [0.09]

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

Poor 0.001 0.036 0.089 0.023 −0.000 0.040 0.091 0.022
[0.02] [1.56] [2.59]∗∗∗ [2.04]∗∗ [0.00] [1.74]∗ [2.63]∗∗∗ [1.96]∗∗
Abducted 0.002 −0.040 0.196 0.000 −0.000 −0.033 0.193 −0.000

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
[0.05] [1.29] [4.47]∗∗∗ [0.03] [0.01] [1.02] [4.38]∗∗∗ [0.02]
Political support −0.052 0.007 0.183 −0.026 −0.055 0.014 0.180 −0.028
[2.08]∗∗ [0.27] [3.66]∗∗∗ [2.51]∗∗ [2.18]∗∗ [0.60] [3.57]∗∗∗ [2.57]∗∗
 Officer −0.043 −0.022 −0.093 0.003 −0.044 −0.011 −0.094 0.003
[1.35] [0.56] [2.01]∗∗ [0.22] [1.31] [0.30] [1.98]∗∗ [0.20]
Abusiveness 0.001 −0.059 −0.229 −0.087 0.008 −0.066 −0.217 −0.087
[0.01] [1.17] [1.61] [2.64]∗∗∗ [0.08] [1.39] [1.50] [2.66]∗∗∗
District wealth 0.038 −0.095 −0.124 0.002 0.047 −0.119 −0.111 0.004
[1.04] [1.69]∗ [2.43]∗∗ [0.15] [1.35] [2.62]∗∗∗ [2.12]∗∗ [0.27]
Community suffering −0.050 −0.079 0.593 −0.084 −0.060 −0.084 0.551 −0.083
[0.38] [0.36] [1.95]∗ [2.92]∗∗∗ [0.45] [0.42] [1.94]∗ [2.86]∗∗∗
Observations 899 922 902 911 900 923 903 912
Pseudo R2 0.04 0.07 0.07 0.23 0.03 0.09 0.07 0.23

Note: Robust z statistics in brackets. Faction fixed effects are included, and the model allows errors to be clustered geographically at the chiefdom level.
DDR = disarmament, demobilization, and reintegration.
∗
significant at 10 percent. ∗∗ significant at 5 percent, ∗∗∗ significant at 1 percent.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
551
 552
Table 6
Impact of DDR Program on Reintegration by Type
(1) (2) (3) (4) (5) (6) (7) (8)

Program Variable Enter DDR Completed DDR

Dependent Variable Delinked? Employed? Democratic? Accepted? Delinked? Employed? Democratic? Accepted?

DDR −0.003 0.066 −0.068 −0.009 0.023 −0.088 −0.043 0.001

[0.08] [1.62] [1.47] [0.70] [0.71] [2.90]∗∗∗ [1.23] [0.13]
Distrust 0.051 −0.035 −0.099 −0.017 0.004 −0.028 −0.196 0.013
[0.51] [0.41] [0.87] [0.66] [0.11] [0.88] [3.17]∗∗∗ [0.72]
Distrust × DDR −0.110 0.012 −0.062 0.004 −0.075 0.018 0.077 −0.073
[0.80] [0.14] [0.51] [0.15] [1.29] [0.44] [1.24] [1.79]∗
Dissatisfaction 0.089 0.084 −0.059 −0.009 0.021 −0.019 0.025 −0.016
[1.00] [1.72]∗ [0.56] [0.32] [0.85] [0.63] [0.41] [0.86]
Dissatisfaction × DDR −0.136 −0.180 0.108 0.000 −0.069 −0.032 0.032 0.011
[1.17] [2.59]∗∗∗ [1.00] [0.00] [1.50] [0.79] [0.50] [0.55]
Age 0.002 0.000 0.002 0.000 0.002 0.001 0.002 0.000
[1.76]∗ [0.22] [1.09] [0.40] [1.77]∗ [0.62] [1.15] [0.30]
Male −0.087 −0.009 −0.014 −0.001 −0.088 −0.016 −0.006 −0.002
[3.48]∗∗∗ [0.18] [0.21] [0.14] [3.46]∗∗∗ [0.30] [0.10] [0.25]
Mende −0.000 0.016 −0.011 0.014 0.006 0.012 −0.008 0.015
[0.01] [0.72] [0.25] [1.89]∗ [0.31] [0.57] [0.18] [2.02]∗∗

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

Educated 0.005 −0.026 0.002 −0.000 0.005 −0.025 0.002 −0.001
[0.41] [2.01]∗∗ [0.08] [0.05] [0.41] [1.98]∗∗ [0.09] [0.16]
Poor 0.002 0.037 0.088 0.023 −0.001 0.039 0.091 0.023

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
[0.05] [1.58] [2.57]∗∗ [2.00]∗∗ [0.04] [1.73]∗ [2.64]∗∗∗ [2.15]∗∗
Abducted −0.002 −0.041 0.196 0.000 −0.001 −0.032 0.193 −0.001
[0.04] [1.34] [4.47]∗∗∗ [0.03] [0.02] [1.01] [4.39]∗∗∗ [0.06]
Political support −0.052 0.008 0.182 −0.026 −0.056 0.013 0.182 −0.025
[2.02]∗∗ [0.32] [3.68]∗∗∗ [2.67]∗∗∗ [2.21]∗∗ [0.56] [3.62]∗∗∗ [2.29]∗∗
 Officer −0.038 −0.015 −0.098 0.003 −0.041 −0.011 −0.096 0.003
[1.19] [0.38] [2.08]∗∗ [0.21] [1.25] [0.29] [2.03]∗∗ [0.28]
Abusiveness −0.001 −0.068 −0.220 −0.087 0.016 −0.067 −0.223 −0.086
[0.01] [1.38] [1.57] [2.70]∗∗∗ [0.16] [1.41] [1.54] [2.59]∗∗∗
District wealth 0.034 −0.098 −0.123 0.002 0.046 −0.120 −0.111 0.006
[0.92] [1.84]∗ [2.37]∗∗ [0.15] [1.31] [2.66]∗∗∗ [2.10]∗∗ [0.39]
Community suffering −0.048 −0.072 0.587 −0.084 −0.055 −0.079 0.550 −0.089
[0.37] [0.33] [1.92]∗ [2.88]∗∗∗ [0.42] [0.40] [1.95]∗ [3.18]∗∗∗
Observations 899 922 902 911 900 923 903 912
Pseudo R2 0.04 0.07 0.07 0.23 0.04 0.09 0.07 0.24

Note: Robust z statistics in brackets. Faction fixed effects are included and the model allows errors to be clustered geographically at the chiefdom level.
DDR = disarmament, demobilization, and reintegration.
∗
significant at 10 percent. ∗∗ significant at 5 percent, ∗∗∗ significant at 1 percent.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
553
554 Journal of Conflict Resolution

Our discussion of the theoretical motivations for DDR suggested that the program
might be particularly effective for some types of combatants. By providing a secure
environment in which to engage with members of rival groups, we hypothesized that
the DDR process would be especially beneficial for individuals that distrust the moti-
vations of other groups. By providing material benefits, we anticipated that the pro-
gram would have greater impact on combatants that felt the political process yielded a
poor deal for their group. To examine these claims, we enter interaction terms into
our specification in Table 6. These interaction effects allow us to observe whether the
marginal effect of the program is greater for these populations.
Universally, we find that it is not. With one exception, the interaction terms are
not significant, and in half of the cases, the estimated coefficients are negative
(including the one significant finding). In our basic specification, we not only find
no impact of the program in general, but we also fail to find effects among those
populations for whom theory would predict the strongest effect.10
So far, there is little evidence of a relationship between participation in the DDR
program and the degree to which ex-combatants have reintegrated in Sierra Leone.
While the multidimensional peacekeeping operations in Sierra Leone may have
been effective at the macro level, we cannot identify an impact for the DDR com-
ponent of these programs at the micro level. As discussed previously, we must be
cautious in interpreting these findings as evidence that the DDR process had no
impact. It is possible that spillover effects, selection effects, and sample bias may
undermine our ability to properly identify the causal impact of the program. We
discuss each of these possibilities in turn.

Spillover Effects
Consider first the challenge posed by spillover effects. Arguably, the fact that
nearly 90 percent of combatants in Sierra Leone participated in the DDR program
may generate positive spillovers in communities that ease the reintegration of
others, even if they did not participate in the program. A number of distinct mechan-
isms could underpin such spillover effects. For example, when a program seeks to
separate ex-combatants from their factions, success in breaking any individual’s ties
to the network may undermine the network as a whole. A similar dynamic might
take place with respect to acceptance by families and communities. In principle,
combatants that did not take part in DDR programs may find their relationships with
community members improved precisely because those combatants that did take
part are successfully reintegrating with family and community members.
We test explicitly for such positive spillover effects by generating a measure of
the percentage of soldiers in a given chiefdom that participated in the demobiliza-
tion program.11 While such geographically structured spillovers are not the only
possible type of spillover (in particular, spillovers may occur between individuals
that are structurally linked but geographically separate within an organization),

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Table 7
Spillover Effects
(1) (2) (3) (4) (5) (6) (7) (8)

Dependent Variable Delinked? Employed? Democratic? Accepted? Delinked? Employed? Democratic? Accepted?

DDR −0.029 0.005 −0.045 −0.015 −0.016 −0.098 0.081 −0.020

[0.97] [0.14] [1.14] [1.03] [0.14] [0.98] [0.66] [0.59]
Spillover −0.068 0.131 −0.034 0.062 −0.054 0.012 0.071 0.054
[0.55] [0.92] [0.31] [1.79]∗ [0.28] [0.07] [0.62] [0.86]
DDR × spillover −0.020 0.180 −0.171 0.012
[0.13] [1.11] [1.11] [0.18]
Distrust −0.029 −0.020 −0.157 −0.014 −0.029 −0.018 −0.159 −0.014
[1.59] [0.96] [3.21]∗∗∗ [1.09] [1.61] [0.88] [3.27]∗∗∗ [1.09]
Dissatisfaction −0.008 −0.046 0.039 −0.009 −0.008 −0.045 0.039 −0.009
[0.41] [2.54]∗∗ [0.85] [0.81] [0.41] [2.51]∗∗ [0.84] [0.83]
Observations 899 922 902 911 899 922 902 911
Pseudo R2 0.04 0.07 0.07 0.24 0.04 0.07 0.07 0.24

Note: Robust z statistics in brackets. Faction fixed effects are included and the model allows errors to be clustered geographically at the chiefdom level. Addi-

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

tional controls include all variables in previous multivariate models (Tables 4 and 5). DDR = disarmament, demobilization, and reintegration
∗
significant at 10 percent. ∗∗ significant at 5 percent. ∗∗∗ significant at 1 percent.

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
555
556 Journal of Conflict Resolution

many plausible spillover mechanisms have a geographic component—in particular,

spillover mechanisms that work through hypothesized impacts on host commu-
nities. Our measure of chiefdom exposure takes an average value of 0.87 and has a
standard deviation of 0.16. In Table 7, we show the marginal effect of our estimate
of the share of fighters in a combatant’s community that did enter these programs
on the likelihood of reintegration. In columns 1 through 4, the spillover term is
entered on its own; in columns 5 through 8, the term is entered interactively with
the program effect. This second specification allows us to examine directly the esti-
mated impact of DDR spillovers on the treated and the untreated. In addition, it
allows us to estimate the effect of treatment in the presence (and in the absence) of
spillovers. We condition again on fixed effects for each faction and all of the corre-
lates used in the analyses in Tables 4, 5, and 6.
On three of the four measures of reintegration success, we continue to find no
evidence that DDR programs, either directly or through spillover effects, increase
the likelihood of reintegration. Notably, we find a negative relationship on our mea-
sure of delinking, which is consistent with the negative relationship we find on the
direct effect, although it is not significant at conventional levels.
We do, however, find some evidence of spillover effects of participation in DDR
for acceptance. The relationship we identify, in model 4, is significant only at the 90
percent level but is substantively large. A hypothetical one-unit shift in the indepen-
dent variable is equivalent to a comparison of a situation with no DDR program in a
given chiefdom to one with universal coverage. Setting all other values at their
means and assuming the relationship we identify here obtains also in hypothetical
states of the world outside of our sample space,12 the model suggests that indivi-
duals that do not join the program have a 92 percent (95 percent CI: 67 to 99.7 per-
cent) chance of gaining acceptance if no individuals in their chiefdom join
programs and a 99 percent probability if all individuals enter the program (95 per-
cent CI: 95 to 99.9 percent). With 87 percent joining in a given chiefdom (our esti-
mated take-up rate in the population), an individual not joining has an estimated 98
percent probability of gaining acceptance (95 percent CI: 94 to 99.9 percent). In
other words, even though we find no evidence of direct effects, the estimated effect
of complete coverage is, in principle, large enough to overcome the problems faced
by ex-combatants in gaining acceptance. Note that in model 8, we do not find that
the spillover effect from the treated to the untreated is greater than that from the
treated to the treated (indeed it is slightly weaker). Moreover, the model fails to pro-
duce evidence for a positive direct effect of the program for any level of spillover.

Selection Effects
A second possibility is that we find no evidence of direct impacts at the indivi-
dual level because there is a selection effect in operation. That is, the population of
combatants who participated in DDR may be systematically different from those

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 557

that elected to reintegrate without external assistance. It may be that DDR took on
the very difficult cases—such as hardcore members of the RUF—while the rank
and file of the CDF (which was widely seen as victorious in the conflict) decided to
return home on their own. Such differences, if unobserved and not controlled for in
our models, might explain the nonresult. We emphasize, however, that precisely
the opposite argument may be made for the bias introduced by selection effects.
Plausibly, it is the difficult cases—those afraid of being identified by authorities or
those unwilling to cooperate with the government—that refused to enter DDR,
whereas those simply needing a means to reenter civilian life elected to participate.
If such a selection effect were in operation, we would find that participants fared
better than nonparticipants even if the program had no impact.
Whether our finding of no program effect can be attributed to selection then
depends on what form of selection was in operation. One of the advantages of a
survey approach is that we could ask individuals directly why they did or did not
enter, allowing for an open-ended response.
The answers are revealing. In many cases, the answers do not suggest an
obvious selection effect. In a number of cases, respondents reported that they had
wanted to enter, but happened to be traveling or sick at the time. Other answers
suggest that the selection effect is likely to work against finding evidence of an
impact of the program. Some that didn’t participate simply had other options; they
reported having communities and jobs waiting for them. One claimed that he was
‘‘not interested because of the delay and the waste of time.’’ Another explained that
he did not register ‘‘because my parents were willing to assist me.’’ Finally, some
responses suggest a selection effect that would bias the results toward finding an
effect of the program. Some refused to enter because of distrust or suspicion. ‘‘It
was a waste of time because they were lying,’’ one said. Another explained that
‘‘my husband threatened that the disarmament records were going to be used after
four to five years to punish all those who took part in the war so I gave my weapon
to another member of my unit to disarm.’’
Based on these open-ended responses, then, it is not clear that selection effects
work either to hide or magnify a program effect. Beyond our qualitative examina-
tion of the determinants of selection, however, there are statistical approaches we
can employ to explore the issue of selection more systematically. An optimal
approach is to employ an instrument, but finding a variable that explains participa-
tion but is otherwise unrelated to reintegration success is difficult.13 We concen-
trate here on another approach: using propensity matching estimators to compare
outcomes across individuals with a similar propensity to take part in the program.
Propensity matching indicators estimate, for each individual, a probability of
entering DDR based on all relevant available data. Based on these probabilities, the
method matches pairs of individuals that have the same estimated propensity of
joining, but one of whom did and the other of whom did not join. If our estimates
for the propensity of joining are accurate, then for any pair matched in this way, we

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
558 Journal of Conflict Resolution

can treat the difference in reintegration success for those that do join DDR and
those that do not as a result of the fact of joining. The method, however, is only as
good as our ability to predict joining probabilities, and selection effects may still
obtain if unobservable characteristics of individuals simultaneously determine their
decision (or ability) to enter the program and the likelihood of successfully
reintegrating.
We employ propensity matching on our sample of respondents, using as predic-
tors of joining DDR all of the explanatory variables in Tables 3 and 4 as well an
indicator of their location at the end of the war. Beyond capturing key demographic
features, this set includes conflict-relevant variables that we know in some cases to
be related to reintegration success but that could in principle explain participation
in the DDR program as well. As treatment variables, we examine both the decision
to join and the completion of the DDR program. In addition, we compute estimates
of the treatment effect for each of the outcome variables of interest.
In conducting each test, we generate information not just about the treatment
effect, but also about the determinants of selection into the treatment. For the case
of joining DDR, our explanatory variables account for approximately 24 percent of
the variation across ex-combatants. We find that officers were more likely to enter
the DDR program and more educated fighters were also more likely to participate.
Strikingly, there is no clear evidence of factional differences in participation; nor is
it the case that fighters from abusive units were any more or less likely to partici-
pate. Gender and age also have no impact. Much of the predictive power comes
from location fixed effects rather than the measures of individual level attributes.
We can account for only 11 percent of the variation in our completion measure.
Officers and members of the Mende tribe were more likely to have completed the
program. Older fighters were also more likely to have completed the programs, and
there was some variation across factions, with CDF fighters more likely to have
completed DDR relative to RUF fighters.
The results of our propensity matching analysis are given in Table 8. The table
shows that even when we condition on all selection effects identified by observable
characteristics, we continue to find no evidence that participation in DDR contribu-
ted to reintegration success on any dimension. To check that this nonresult is not
driven by the particular method of propensity matching we employ (nearest neigh-
bor with replacement), we also examined methods in which we match nearest
neighbors without replacement and in which we condition on cases with overlap-
ping support (not reported). In addition, we report results from matching based on
Mahalanobis distance (Cochran and Rubin 1973). In this approach, we match each
treated unit with the closest control unit where the distance is defined over our set
of determinants of participation in the DDR program. Again, we find that even
when accounting for selection effects in this way, we cannot recover evidence of
program effectiveness.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 559

Table 8
Selection into the DDR Program
Delinked? Employed? Democratic? Accepted?
Outcome Variable
Method A B A B A B A B

Treatment
Entered DDR −0.09 −0.04 −0.05 −0.01 −0.13 0.03 −0.01 −0.05
[1.51] [.83] [0.43] [0.13] [1.09] [.37] [0.21] [1.51]

Completed DDR −0.06 −0.05 −0.10 −0.10 −0.03 0.00 −0.04 0.02
[1.83] [1.45] [2.85] [2.96] [0.52] [0.10] [1.53] [0.61]

Note: Each cell reports the estimated treatment effect on the treated (ATT). Estimated t statistics
reported in brackets. Method A employs nearest neighbor propensity score matching with replacement;
Method B uses full Mahalanobis matching. All estimates are derived using the PSMATCH2 module for
STATA (Leuven and Sianesi. 2003). DDR = disarmament, demobilization, and reintegration.

Sampling Bias
Finally, we explore the possibility that the nonresult on program effects is driven
by an imperfection in our sample of respondents. In particular, it may be the case
that those ex-combatants that faced the greatest difficulty reintegrating were also
the least likely to be enumerated when our survey teams came to the selected chief-
doms. Indeed, if the hard-core fighters from Sierra Leone that migrated to take part
in the civil wars of neighboring countries did not demobilize and reintegrate and,
in addition, were absent from our sample, it is possible that a sampling bias
accounts for our nonfinding.
There are three important responses to this charge of sampling bias. We already
signaled the first: if fighters took part in the DDR programs and subsequently left
the country to fight in other wars, then the bias works in the opposite direction. If
this effect predominates, then our estimate in fact overestimates program effective-
ness. Relatively little is known about the fighters that left Sierra Leone to fight else-
where in the subregion. Perhaps the most careful study of these fighters has been
undertaken by Human Rights Watch in a report on West Africa’s regional warriors.
The report cites multiple instances of individuals that took part in the Sierra Leone
DDR process and later moved to fight in Liberia with, in some cases, recruitment
to the Liberia war linked to their participation in the Sierra Leone DDR process
(Human Rights Watch 2005, 22-24).
Second, it may be that for individuals that wished to take part in the DDR pro-
cess but were unable to participate, their lack of access to the program could have
actively contributed both to their failure to reintegrate and their absence from the
sample. Indeed, Human Rights Watch (2005) reports that, ‘‘the majority of those

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
560 Journal of Conflict Resolution

[regional warriors] interviewed had negative experiences with the DDR program in
Sierra Leone . . . the program’s failure to engage them contributed to their decision
to take up arms with another armed group’’ (p. 49). In this case, including these
individuals in the sample would lead to a more favorable measurement of the
impact of DDR but only because of the adverse effect of the program on the
untreated rather than its positive impact on the treated.
The third response relates to the fact that those populations not available to our
enumerators likely reflect in part those same samples that were not available for
the DDR program. If fighters left the country to pursue more lucrative soldiering
options elsewhere and if this fact explains why they did not take part in DDR pro-
grams, then what appears to be a sampling problem in fact masks a selection pro-
blem. It is the lack of reintegration that explains their failure to participate in the
program, not vice versa. Attributing their failure to reintegrate as evidence of the
program’s success is in this case a fallacy. Instead, our goal should be to estimate
the impact of the program on the relevant population of potential program partici-
pants. If the same individuals that select out of the population of potential benefi-
ciaries also select out of our sample, then in the absence of other selection
effects, our estimate is not a biased estimate of program impact on the relevant
population.
In short, sampling biases could have effects in either direction; the qualitative evi-
dence suggests, however, that the bias is likely either to result in an overestimation
of the positive effects or an underestimation of the adverse effects of the program on
the untreated. Together, these three considerations—spillover, selection, and sam-
pling—point to the complexity of interpreting simple two-sample comparisons in the
absence of a randomized intervention. We now turn to alternative designs that can
surmount these challenges.

Improving the Measurement of the Impact

of Humanitarian Intervention

In our study of program effects, we find little evidence that participation in the
DDR program increased the likelihood that combatants successfully reintegrated.
Our examination of the three major threats to the validity of our findings, however,
underscores just how difficult it is to identify a program’s causal effects in the
absence of an experimental design. We believe that it is not appropriate, based on
the results presented here, to conclude that the DDR program had no positive
micro-level impacts in Sierra Leone. Nonetheless, the nonfindings should be seen
as a wakeup call to advocates of these programs. Needed now is a method that is
better suited to identify causal impacts in the presence of the confounding effects
we have discussed. The best approach involves the development of monitoring and
evaluation systems that employ some form of randomized intervention.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 561

If scholars and policy analysts are to disentangle the effects of demobilization

programs from the range of other initiatives launched as part of multidimensional
peacekeeping operations, DDR programs must be designed in such a way that the
reintegration trajectories of participants can be usefully compared with those of
nonparticipants, when both groups are identical except for the treatment itself. With
an appropriate treatment and control group, selected by lottery, data collected after
the DDR program is completed (for the treatment group) can be used to answer the
relevant counterfactual, how successfully do ex-combatants reintegrate if they do
not participate in the program? Selection effects then no longer represent the threat
to valid measurement of program effects described above.
How can practitioners identify an appropriate control group for an experimental
study of DDR programs? It does not make sense in a postconflict environment to
simply deny treatment in the demobilization process to a group of combatants for
the purpose of evaluating the program’s impact. To overcome this problem, we
advocate an experimental approach that randomizes the timing of participation in
DDR programs. The core idea is that although all ex-combatants will eventually
participate in the program, the timing of their entry into the program will be deter-
mined by lottery, allowing for comparisons of program participants and nonpartici-
pants (that is, combatants that have not yet entered the program).
This approach to evaluating the effects of DDR is increasingly feasible as the
United Nations and its partners have moved beyond an individual-level program
model in which combatants register for cash payments and training to include more
community-focused forms of demobilization and reintegration in which assistance
is provided to communities with significant populations of ex-combatants. With
this new model, the rollout of UN program efforts is constrained by logistical con-
siderations and staffing capacity, necessitating some delays in the extension of pro-
grams to different communities. Any limit on the feasibility of providing a
treatment to the full sample of recipients at a single point in time provides the
opportunity for an experimental design. It is reasonable to consider making deci-
sions about which communities should receive the program first by lottery to
ensure fairness, although some localities might be excluded from the process of
random selection if their situation is particularly dire. With a sufficient number of
communities included in the lottery, identifying program effects by comparing par-
ticipants to nonparticipants is feasible. Moreover, by conducting such an evaluation
using a community-focused approach, both individual effects (such as those mea-
sured in this article) and community-level outcomes, such as levels of conflict, can
be assessed in the same design.
Beyond selection issues, a randomized design can also help to mitigate problems
posed by spillover effects and sampling bias. With DDR programs implemented
community by community and stratified by region within a country, one can use
the (exogenous) variation in the density of treatment communities in a given geo-
graphic region as the program is phased in, to empirically estimate the positive (or

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
562 Journal of Conflict Resolution

negative) externalities experienced by communities not yet included in the program

(following Miguel and Kremer 2004). Other nongeographic spillover effects may
also be studied with random sources of variation in treatment at the individual or
unit level. Of particular interest because of the potential they hold to shed light on
organizational cohesion and resilience are spillovers that occur through vertical
and horizontal organizational channels.14
An additional advantage of a randomized intervention is that the population for
which inferences can be made is well defined and identified. It is precisely that
population over which the randomization in the allocation of the treatment is
undertaken. Although individuals that are not available for program assignment
may fare worse systematically than program participants, the marginal impact of
the program on the relevant population can be estimated without bias. In addition,
although individuals that take part in the program may subsequently exit a sam-
pling frame, a randomized design with follow-up allows for this fact to be observed
and accounted for.
While the discussion above underscores how randomized intervention can be use-
ful for assessing the overall impact of DDR programs on individual and community-
level outcomes, the logic can also be extended to questions of program design.
Indeed, UN officials grapple consistently with questions that can be addressed in this
manner: how much power to provide in community-level initiatives to ex-combatants
versus community leaders; what role to reserve for women in the leadership and
management of community efforts; and what forms of assistance are likely to be
most beneficial for ex-combatant reintegration.

Conclusion

With the growing involvement of external actors in postconflict situations,

increasing attention is being dedicated to the challenges of peace building. The dis-
armament, demobilization, and reintegration of combatants is a central component
of efforts to reestablish legitimate governance and prevent the recurrence of con-
flict. Yet in spite of nearly a decade of involvement in demobilizing warring fac-
tions, there is little evidence about the factors that explain whether individuals can
successfully reintegrate after conflict and the precise causal impact of externally
funded programs to reintegrate combatants. Instead, the scant literature on demobi-
lization has focused attention on details of program design and implementation.
In a first attempt to correct for these shortcomings, we present the results of a
large N survey of combatants in Sierra Leone, which allowed us to track the pro-
gress of DDR participants and nonparticipants in the postwar period. Our findings
provide insights useful to practitioners of postconflict reconstruction. At the indivi-
dual level, we find that contrary to conventional wisdom, there is little evidence
that women or young people faced a significantly harder time gaining acceptance

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 563

into civilian life, finding employment, breaking ties to their factions, or adopting
the new democratic political system. Instead, aspects of an individual’s experience
of the conflict seem to exert more powerful effects. Higher ranking officers are con-
siderably less likely to exhibit faith in democratic processes, and the abusiveness of
the unit in which an individual fought is strongly associated with problems in gain-
ing acceptance, even controlling for unobserved attributes correlated with member-
ship in the different factions. The implication is that aspects of a combatant’s
wartime history should be taken into account more prominently in the design of
DDR programs.
Perhaps the most surprising result, however, is that we find little evidence that
UN operations were instrumental in facilitating DDR at the individual level. Non-
participants in DDR do just as well as those who entered the formal demobilization
program. Without a complete handle on spillover effects, selection effects, and
sample bias, however, we argue that these negative results should be treated with
caution. The results may suggest that other factors—measurable only at the country
level—may have been far more important for determining the path of reintegration
than the DDR programs that were implemented in Sierra Leone. In particular, the
fact that the war ended decisively with a major military intervention by the British
may be consequential for the high rates of reintegration success both among sol-
diers formally demobilized and those who returned home on their own. Alterna-
tively, it may be that the effects of DDR programs only become apparent after
longer periods of observation or that the impacts are more apparent at the level of
communities rather than individuals. Finally, it may be that genuine medium-term
program benefits existed but were not sufficiently large to overcome spillover,
selection, or sampling biases. In any event, our study suggests that the impact of
the program is not identifiable using the methods we have at our disposal. Policy
makers concerned with demonstrating the efficacy of DDR programs will need to
employ more robust strategies for identifying program effects, specifically rando-
mized intervention.
More generally, in the absence of sufficient cross-national variation to allow for
an assessment of the impact of individual components of multidimensional peace-
keeping operations, we advocate an approach that exploits within-country varia-
tion. The advantage of a micro-level approach is that it can increase our confidence
that the mechanisms attributed to work in a given case indeed function as believed.
By exploiting subnational variation, we can work out with greater confidence
whether a program is effective but also for whom a program is failing. A disadvan-
tage, however, is that the external validity of the results may reasonably be called
into question. If there is little evidence that DDR programs were effective in Sierra
Leone, this does not mean that DDR programs are never successful. While the
Sierra Leone case is an important case—regarded as a success story, elements of
the Sierra Leone model are being replicated in neighboring Liberia, in Burundi,
and now as far away as Haiti—it should still be seen as a single data point in a

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
564 Journal of Conflict Resolution

larger model that attempts to explain cross-national variation in program effective-

ness. Single-country experimental designs need to be complemented by attention to
country-specific factors that impact the trajectory of DDR programs. To understand
how DDR contributes to successful peace building, subnational studies represent
only the building blocks for a richer analysis of how external interventions affect
postwar trajectories.

Appendix
Summary Statistics (Independent Variables)
Variable N Mean SD Min Max

Distrust 976 0.19 0.39 0 1

Dissatisfied 978 0.31 0.46 0 1
Age 1,039 31 10 14 77
Male 1,035 0.89 0.31 0 1
Mende 1,037 0.53 0.50 0 1
Abducted 1,043 0.36 0.48 0 1
Political support 1,043 0.39 0.49 0 1
Educated 1,043 1.08 0.86 0 2
Poor 1,040 0.68 0.47 0 1
Officer 1,040 0.12 0.32 0 1
Abusiveness 1,024 0.20 0.21 0 1
District wealth 1,043 0.28 0.32 0 1
Community suffering 1,043 0.20 0.12 0.03 0.53
Entered DDR program 1,038 0.87 0.34 0 1
Completed DDR program 1,039 0.46 0.50 0 1
Share in chiefdom demobilized 1,043 0.87 0.17 0.18 1

Note: DDR = disarmament, demobilization, and reintegration.

Notes
1. A number of studies exist for the case of Sierra Leone, including Comninos, Stavrou, and Stewart
(2002), Ginifer (2003), Richards et al. (2003), and Stavrou et al. (2003).
2. An obvious concern with survey work is truth telling. Respondents may have strong incentives to
misrepresent the facts. In the training, a script was developed for enumerators to help allay these con-
cerns. It was also important that survey teams administer the survey in private in an effort to protect peo-
ple’s privacy, that anonymity was preserved throughout, and that questions of an incriminating nature
were not asked.
3. The data provided by the National Commission on Demobilization, Disarmament, and Reintegra-
tion (NCDDR) for the distribution of ex-combatants were incomplete. We have since received data made
available by the Food and Agricultural Organization that provide a more complete sample frame for ex-
combatants in Sierra Leone and which allow for the possibility to reweight our data ex-post. The core
results on programmatic effects and spillovers presented in this article, however, are invariant to ex-post
adjustments in sampling weights.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 565

4. To examine the precise wording of this question and all other questions used in the analysis, see
the survey instrument, available online at http://www.columbia.edu/ ∼ mh2245/SL.htm.
5. The employment variable is coded based on a question about the respondent’s occupation rather
than whether individuals have a job. When asked about their occupation, only 12.5 percent indicate that
they have no employment whatsoever. Twenty-three percent report farming as their primary occupation;
16 percent are artisans; approximately 5 percent are traders. Plausibly, if one asked most of these indivi-
duals whether they have a job, they would say no. Insofar as jobs are thought of as formal sector occupa-
tions, a broader definition of unemployment than the one we use—to include those in the informal sector
and the underemployed—might yield substantially different results.
6. This finding should be interpreted with caution. Human subjects’ concerns prevented us from
interviewing soldiers who were children at the end of the fighting. Nonetheless, our sample includes a
substantial proportion of individuals who joined the factions as children and were over eighteen when
the war came to an end.
7. Note, however, that if nonintegrated women are more likely to conceal their involvement in the
conflict and thus more likely to be absent from our sample, then we would overestimate reintegration
success among women ex-combatants.
8. The measures used to construct the index include three distinct types of questions. First, we
include questions that assess whether the environment was permissive of abuse using a measure of the
likelihood that an individual would be punished for stealing, amputating, and raping a civilian if these
were done without the express order of a commander. Second, we add questions about the ways in which
food was collected, including whether food was taken forcibly or through more contractual arrange-
ments. Finally, the index includes the respondents’ evaluation of actions undertaken by the group for the
benefit of civilian populations, including educational and ideological training. Two versions of the index
are constructed, one using individuals in the same chiefdom and same faction and one using individuals
in the same district and subfaction. The variable used here averages across these two indices. Note that
the indices combine negative sanctions (violence, forcible food collection) and positive benefits (secur-
ity, education). Although in some cases, the logics behind these positive and negative strategies may dif-
fer, results in previous work with this variable are robust to disaggregation. For more information on this
measure, see Humphreys and Weinstein (2006).
9. In a recent analysis of ex-combatant reintegration in Liberia, Pugel (2007) emphasizes a further
distinction among participants in the disarmament, demobilization, and reintegration (DDR) program.
He finds that on some outcome measures for Liberia, differences arise between those that have accepted
a reinsertion benefit and initiated a skills training program and those that have a reinsertion benefit but
have not started any training. In the Sierra Leone data, however, we continue to find no evidence of a
program impact of DDR when making these distinctions among program participants (results not
shown).
10. We look also for heterogeneous treatment effects across the five major factions. There is weak
evidence in a bivariate specification that Revolutionary United Front (RUF) combatants who participate
in DDR are more likely to express faith in the democratic process, but we cannot reject the null hypoth-
esis that the aggregate impact of entering DDR on democratic beliefs for the RUF is zero. We also find
some evidence that DDR participants from the RUF are more likely to be employed; this effect, how-
ever, is reversed when the completion of DDR is used as the treatment variable, reinforcing the fact that
employment reflects participation in skills training and nothing more permanent. In short, we find no evi-
dence that DDR was particularly effective for some armed groups and not for others.
11. In testing for spillovers, we focus on entrance into the DDR program as the treatment, although
the results are substantively similar if we use completion of the program instead. We note that better
tests for externalities can be used in settings with random assignment of the treatment and more geogra-
phically precise data. Under such conditions, exogenous variation in the density of treatment units across
space can be used to empirically identify spillover effects (see Miguel and Kremer [2004], for example).

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
566 Journal of Conflict Resolution

12. We emphasize that this is a strong assumption. In fact, this hypothetical comparison requires
making statements about a part of the space that we never observe: within our sample there are no chief-
doms with 0 percent demobilized. As noted by King and Zheng (2006), the comparison then depends
strongly on our assumption regarding the functional form of our model.
13. To employ instrumental variables estimation, we constructed an instrument based on the distance
between where an individual fought in the closing stages of the war and the closest DDR site. This
instrument is plausibly related to whether an individual joined DDR in terms of the costs of moving one-
self to a DDR site. We constructed a second instrument that records the distance between the nearest
DDR site and an ex-combatant’s preconflict home. While it is plausible that remoteness is not otherwise
related to acceptance, one could imagine arguments that suggest a violation of the exclusion restriction
for this instrument. Our results using both of these instruments, not reported here, do not provide new
evidence supporting a link between DDR and successful reintegration.
14. We caution that as noted in section 2 above, some forms of spillover that work through organiza-
tional structures may not be identified using our proposed randomization procedure. As an example, con-
sider a situation with strong structures of command and control in which commanders permit effective
demobilization only when all combatants have been admitted to a demobilization program. In such a
case, the effect of treatment on the treated may only be observable once the control group is also treated.

References
Agence France Press. 1995. Boutros-Ghali calls for democracy in Sierra Leone ahead of trip. November 24.
Berdal, Mats. 1996. Disarmament and demobilization after civil wars: Arms, soldiers, and the termina-
tion of conflict. Adelphi paper no. 303. Oxford, UK: Oxford University Press.
Blattman, Christopher. 2006. The consequences of child soldiering. Unpublished manuscript. University
of California, Berkeley.
Cochran, William G., and Donald B. Rubin. 1973. Controlling bias in observational studies: A review.
Sankhya, series A35:417-46.
Collier, Paul, and Anke Hoeffler. 2004. Greed and grievance in civil war. Oxford Economic Papers
56 (4): 563-95.
Comninos, Stelios, Aki Stavrou, and Brian Stewart. 2002. Assessment of the reintegration programmes
of the National Committee for Disarmament, Demobilisation, and Reintegration. Paper written and
disseminated in Freetown, Sierra Leone.
Cronbach, Lee J. 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16:297-334.
Doyle, Michael, and Nicholas Sambanis. 2000. International peacebuilding: A theoretical and quantita-
tive analysis. American Political Science Review 94 (4): 779-801.
———. 2006. Making war and building peace. Princeton, NJ: Princeton University Press.
Dyregrov, Atle, Rolf Gjestad, and Magne Raundalen. 2002. Children exposed to warfare: A longitudinal
study. Journal of Traumatic Stress 15 (1): 59-68.
Food and Agricultural Organization (FAO) Food and Security Atlas 2004 Sierra Leone. 2004. http://
www.daco-sl.org/encyclopedia/2_data/2_4_fao.htm (accessed on April 13, 2004).
Ginifer, Jeremy. 2003. Reintegration of ex-combatants. In Sierra Leone: Building the road to recovery,
edited by Sarah Meek, Thokozani Thusi, Jeremy Ginifer, and Patrick Coke, 39-52. Institute for
Security Studies monograph no. 80. Pretoria, South Africa: Institute for Security Studies.
Human Rights Watch. 2005. Youth, poverty and blood: The lethal legacy of West Africa’s regional war-
riors. New York: Human Rights Watch.
Humphreys, Macartan, and Jeremy Weinstein. 2004. What the fighters say. Center for Globalization and
Sustainable Development working paper, Columbia University, New York.
———. 2006. Handling and manhandling civilians in civil war. American Political Science Review 100
(3): 429-47.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
 Humphreys, Weinstein / Demobilization and Reintegration 567

Husain, Syed Arshad, Jyotsna Nair., William Holcomb, John Reid, Victor Vargas, and Satish S. Nair.
1998. Stress reactions of children and adolescents in war and siege conditions. American Journal of
Psychiatry 155 (12): 1718-9.
International Peace Academy. 2002. A framework for lasting disarmament, demobilization and reinte-
gration of former combatants in crisis situations. New York: International Peace Academy.
King, Charles. 1997. Ending civil wars. Adelphi paper no. 308. Oxford, UK: Oxford University Press.
King, Gary, and Langche Zheng. 2006. The dangers of extreme counterfactuals. Political Analysis 14 (2):
131-59.
Leuven, Edwin, and Barbara Sianesi. 2003. PSMATCH2: Stata module to perform full Mahalanobis and
propensity score matching, common support graphing, and covariate imbalance testing. Version
3.0.0. http://ideas.repec.org/c/boc/bocode/s432001.html (accessed in October 2006).
Mazurana, Dyan E., S. A. McKay, K. C. Carlson, and J. C. Kasper. 2002. Girls in fighting forces and
groups: Their recruitment, participation, demobilization, and reintegration. Peace and Conflict: Jour-
nal of Peace Psychology 8 (2): 97-123.
Miguel, Edward, and Michael Kremer. 2004. Worms: Identifying impacts on health and education in the
presence of treatment externalities. Econometrica 72 (1): 159-217.
Molloy, Desmond. 2004. The DDR process in Sierra Leone: An overview and lessons learned. Freetown,
Sierra Leone: United Nations Mission in Sierra Leone.
National Committee on Disarmament, Demobilization, and Reintegration. 2002. Statistical report. Free-
town, Sierra Leone: NCDDR.
Nunnaly, Jum C. 1978. Psychometric theory. New York: McGraw-Hill.
Pugel, James. 2007. What the fighters say: A survey of ex-combatants in Liberia. Monrovia, Liberia:
United Nations Development Programme.
Richards, Paul, Steve Archibald, Khadija Bah, and James Vincent. 2003. Where have all the young peo-
ple gone? Transitioning ex-combatants towards community reconstruction after the war in Sierra
Leone. Unpublished report. School of Oriental and African Studies, London.
Roe, Paul. 1999. The intrastate security dilemma: Ethnic conflict as a ‘‘Tragedy’’? Journal of Peace
Research 36 (2): 183-202.
Spear, Joanna. 2002. Disarmament and demobilization. In Ending civil wars: The implementation of
peace agreements, edited by Stephen John Stedman, Donald Rothchild, and Elizabeth M. Cousens,
141-82. Boulder, CO: Lynne Rienner.
Stavrou, Aki, James Vincent, Krijn Peters, Patrick Burton, and Samuel Johnson. 2003. Tracer study and
follow-up assessment of the reintegration component of Sierra Leone’s disarmament, demobilization,
and reintegration program. University of Ireland, Cork: Centre for Sustainable Livelihoods.
Stedman, Stephen. 1997. Spoiler problems in peace processes. International Security 22 (2): 5-53.
United Nations. 2000. The role of United Nations peacekeeping in disarmament, demobilization, and
reintegration: Report of the secretary general. Report no. S/2000/101, released February 11. http://
www.smallarmssurvey.org/files/portal/issueareas/measures/Measur_pdf/i_measur_pdf/UN_doc/s_
council/S_2000_101.pdf (accessed in October 2006).
Walter, Barbara. 1995. Designing transitions from civil war: Demobilization, democratization, and com-
mitments to peace. International Security 24 (1): 127-55.
———. 1997. The critical barrier to civil war settlement. International Organization 51 (3): 335-64.
World Bank. 1993. Demobilization and reintegration of military personnel in Africa: The evidence from
seven country case studies. Discussion paper, Africa Regional Series. Washington, DC: World Bank.

Downloaded from http://jcr.sagepub.com at COLUMBIA UNIV on August 20, 2007

© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.