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The Logic of Sub-national Deployment of UN Peacekeepers
Andrea Ruggeri (Amsterdam)
Ismene Gizelis (Essex)
Han Dorussen (Essex)
Working Paper-Please do not cite without authors’ permission
Word count: 7.580
Paper to be presented at the ECPR General Conference, Reykjavik 2011. Previously
presented at 1st EPSA meeting, Dublin, 16-18 June 2011. Previously presented at
Seminario DSSP, Milano Statale, Italy, 26 May 2011. Project supported by funding
of the Folke Bernadotte Academy, Sweden.
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Abstract
The deployment of United Nations peacekeeping operations (PKO) to civil wars
improves the likelihood of (a stable) peace. Importantly, this finding is not driven by
selection bias; to the contrary, peacekeepers are mainly deployed to civil wars that
have proven to be difficult to end. Ecological bias, however, can undermine both
findings: it is still possible that PKO are deployed mainly in no-conflict areas at sub-
national level and that their contribution to peace is therefore less substantial. The
following questions are thus germane: where are UN peacekeepers deployed within a
country? Are deployment decision based on an ‘instrumental’ logic or rather on
‘convenience’? A theoretical framework is presented which explores the implications
of these different logics of deployment. Their respective relevance is evaluated using
geographically disaggregated data on UN PKO deployment in eight African countries
between 1989 and 2005. The analysis of geo-referenced event data suggests that UN
peacekeepers go where the conflict started but also tend to be deployed in conflict
areas closer to the capital.
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Introduction
In 2008, UN peacekeepers in the Congo came under attack for failing to protect
civilians against attacks, looting and mass rape by rebels, militia and the DRC army.
The news coverage emphasized that these things had been allowed to happen even
though a UN base was only 20 miles away.1 At the time, the UN peacekeeping
mission in the DRC, MONUC, was the largest mission deployed by the UN with a
broad mandate. The case illustrates not only that it matters where peacekeepers are
deployed within a country, but also what they are willing to do. Are they deployed in
conflict or in relative peaceful areas? Moreover, does the deployment follow an
‘instrumental’ logic—where peacekeepers actively attempt to resolve the conflict and
to protect civilians) or is it based on ‘convenience’—suggesting that peacekeepers are
mainly deployed to relatively safe areas?
The literature provides strong evidence that UN peacekeeping is focused on
‘difficult’ conflicts (Gilligan and Stedman 2003; Fortna 2004; 2008; Hultman 2010).
Peacekeepers are predominantly deployed to countries where the task of building a
stable peace is rendered particularly difficult as democracy and stable institutions are
in short supply and the legacy of war includes a large number of civilian causalities.
Recent evaluations of the effectiveness of UN peacekeeping recognize that the UN
tends to intervene as a last resort in hard cases, making it more challenging to
generate successful outcomes (Gilligan and Stedman 2003; Hultman 2010; Beardsley
and Schmidt n.d.). Yet so far, the literature has focused primarily on the aggregate
characteristics of conflicts, such as conflict history and national capabilities. There
has only been limited attention to the local implementation and impact of UN policies
and practices.2 A first question to answer is whether UN forces are deployed to areas
where actual fighting takes place, or whether they remain primarily in the capital and
other urban areas staying away from the most conflict prone areas?
1 The Guardian, 8 September 2010, “UN has failed Congo mass rape victims, says investigator”, http://www.guardian.co.uk/world/2010/sep/08/congo-mass-rape-500-khare 2 This applies particularly to statistical analyses. Ethnographic research has argued for some time that it is important to consider local conditions; e.g., Pouligny (2006) and Autesserre (2010)
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The restrictions on the use of force imposed on UN peacekeepers and the
often confusing rules of engagement, illustrated by missions like MONUC in the
Congo (Findlay 2002), have led observers to question whether UN missions are
actually deployed in order to engage troops in areas where most of the fighting occurs
or whether they tend to limit their activities to relatively safe areas that experience
lower levels of conflict. Accordingly, we suggest that there are two competing logics
of peacekeeping deployment: an instrumental logic and convenience logic. The
instrumental logic implies that peacekeepers are deployed where they can contribute
effectively to the resolution of conflict; in other words, to areas where the conflict
started and where there is a population to protect. The logic of convenience suggests
that peacekeepers are sent wherever deployment is feasible; that is, to areas that are
relatively safe with an infrastructure that allows for easy deployment and extraction
of forces. The convenience logic suggests that the UN—and the individual countries
contributing peacekeeping forces—are more risk adverse than under the instrumental
logic. The instrumental logic also points towards operations that are more costly to
deploy needing more resources to maintain lines of communication and to safeguard
the peacekeepers. Arguably, however, deployment following the instrumental logic
should be more effective in maintaining the peace and protecting civilians.
Apart from providing a theoretical framework outlining different logics of
peacekeeping deployment, we present disaggregated data on UN deployment in eight
African countries to empirically evaluate their relevance. Ultimately, these findings
are building blocks to study the local effectiveness of peacekeeping accounting for
potential selection bias. The findings so far suggest that deployment largely follows
an instrumental logic, but that peacekeepers tend to be pragmatic as well mixing
instrumental and convenience logics as shown by the deployment of contingents to
conflict areas close to national capitals.
The next section briefly discusses what we know about where the UN chooses
to intervene and the characteristics of these conflicts. We then expand on why it is
important to look at disaggregated information in the study of peacekeeping
operations. Section four discusses the contrasting logics of UN peacekeeping
deployment. Section five presents the empirical analysis
.
5
Where Do UN Peacekeepers Go?
A popular view in the media and among many academics (Gibbs 1997; Anderson
2000; Carter 2007) is UN peacekeeping missions are largely deployed to conflicts
where the national interests of key Security Council members is at stake, where in
particular the role of the United States is emphasized. Others, like Jacobsen (1996),
argue that media attention or the so-called CNN effect is more important to
understand when the UN chooses to intervene. In one of the first systematic studies of
possibly bias in UN peacekeeping, Gilligan and Stedman (2003) find that conflict
severity, measured in terms of causalities, is the key factors making intervention more
likely. Their finding suggests that humanitarian and security concerns are the main
motivating force for UN operations, even if there is a regional bias in favor of Europe
and the Western hemisphere (Gilligan and Stedman 2003, 38). Fortna (2004; 2008)
and de Jonge Oudraat (2007) similarly argue that the UN tends to intervene in more
severe conflicts.
Hultman (2010) shows that the UN overwhelmingly intervenes in conflicts
where there are no clear policy interests of the permanent Security Council members,
especially following the end of the Cold War. Beardsley and Schmidt (n.d.) examine
210 cases of international crises from 1945-2002, and also provide a more nuanced
and comprehensive analysis of the politics of UN involvement. They find that
although the overlap or conflict of national interests of the five permanent members
of the Security Council indeed influences and constraints the ability of the UN to act
in international crises, the severity of conflicts is a more important predictor of UN
intervention. The UN seems to abide mainly by the principle of the responsibility to
protect civilians. The need of the UN to maintain its legitimacy may partially explain
these decisions; in other words, it is in the self-interest of the UN to focus on the
hardest cases when it chooses to intervene.
It is noteworthy that all these studies exclusively focus on the aggregate
characteristics of the countries and conflicts, such as (under)development, severity of
the conflict, number of causalities, and duration, to explain UN intervention. In fact,
most of the studies base their analyses on the data and the models developed by
Doyle and Sambanis (2000). For example, Gilligan and Stedman (2003) use as
explanatory variables the regime of the target country and any ties to the Security
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Council permanent members, the size of the government army, whether the conflict
contests control over the government or territory, and the severity of conflict
measured by the number of casualties. Hultman (2010) focuses on whether civilians
are targeted using the Uppsala one-sided violence data to isolate the impact of the
conflict on civilians rather than including total levels of casualties.
There are, however, obvious limitations to treating each mission as a single
observation since it ignores significant variation over time and space in terms of the
mandates and operational capacity and activities of the UN, but also developments of
the conflict where the battlefront may shift and even alliance may be forged or
broken. For instance, although both the United Nations Observer Mission in Liberia
(UNOMIL) and the United Nations Mission in Liberia (UNMIL) included peace-
building in their mandates, UNOMIL was just a small observer mission with limited
capabilities, while UNMIL was activated based on Chapter VII of the UN Charter and
had a military strength of 15,000 troops. From a theoretical and methodological
perspective, it remains important to consider whether the aggregate characteristics of
the missions and the conflict adequate explain the overall performance of the UN
(Diehl and Druckman 2010).
It is entirely possible that the consensus that the UN selects hard cases based
on aggregate data suffers from ecological fallacy. Yes, the UN may intervene in more
violent or difficult conflicts, but once in the country, the peacekeeping forces may
still be predominantly located in areas with reliable infrastructure, e.g., around their
headquarters or major cities, rather than being deployed to areas where the actual
fighting takes place but with limited infrastructure. Without a credible local presence,
peacekeeping forces can become largely irrelevant to the process of enforcing and
maintaining peace, even if they are located in the ‘right’ country. Moreover, any
reputation of peacekeepers as being ‘soft targets’, ‘lazy’ or simply conflict avoiding
casts doubts on their ability to engage with possible spoilers of peace, either militias
or rebel groups. The loss of reputation for UN troops can encourage such groups to
either directly challenge the peacekeeping forces—for instance, the Serb forces took
hostage and used as human shields 400 peacekeepers in 1996 in Bosnia—or to
commit atrocities in areas that are under the UN supervision, as in the case of
Kiwanja in Congo (Human Rights Watch 2008). Such actions clearly erode local
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support for UN involvement and the overall credibility of the organization to operate
as a competent peacekeeping and peace-building force.
More recently a number of studies have begun exploring the local conditions
and impact of peacekeeping. Dorussen and Raleigh (2009) look at the spatial
variation of conflict events and the deployment of peacekeeping forces in the
Democratic Republic of Congo (DRC). Autesserre (2008, 2010) and Pouligny (2006)
use ethnographic methods and find that the failure of the conflict resolution and
peacekeeping strategies is rooted at local level. Mvukiyehe and Samii (2009, 2010)
survey households in Ivory Coast and Liberia and report mixed findings on
peacekeeping’s deployment and effectiveness. Finally, Costalli (2011) studies sub-
national variation in the presence of UN peacekeepers in Bosnia and highlights that
UN tends to be active where there was high level of violence against civilians.
However, as far as we know, our study is the first to attempt a quantitative
comparison in order to evaluate the sub-national deployment of peacekeepers.
The Loss-of-Strength Gradient and Peacekeeping
Concerns about potential ecological fallacy are also motivating disaggregated civil
war studies. After an empirical wave of quantitative methods focusing on national
and country structural characteristics (Collier and Hoeffler 2003, Fearon and Laitin
2004), the study of civil war has made increasingly use of data that are actor, time and
space specific. The ‘disaggregation approach’ moves theoretically the analysis from
structure to actors, and empirically collects information at a more detailed level. An
underpinning assumption is that the appropriate level of aggregation is dependent on
the purported causal mechanisms and research questions (Buhaug and Lujala 2005).
For instance, Kalyvas (2006) hypothesizes that local grievances motivate violent
collective action which suggests that any empirical implications should be tested at
the micro level as well (Tarrow 2007; Kalyvas 2008). Disaggregated research on civil
wars highlights the importance of location and the ability of the state to project force
(Buhaug 2010), as well as local political and economic grievances (Buhaug et al.
2011; Cederman, Gleditsch and Weidmann 2011). Arguably, these findings are
immediately relevant for the study of peacekeeping as well; if the conditions for
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conflict are local, the conditions for peace are likely to be local as well.3 Here, we
contend that the physical and social geography of a country which has experienced
civil war should also affect the logic of deployment of peacekeepers.
The limited capacity of government to project force in outlying areas, the
effects of the so-called loss-of-strength gradient, is of particular relevance for
peacekeeping as well. Civil wars often erupt in the periphery of countries (Buhaug
and Lujala 2005; Buhaug and Gates 2002), where particular or localized factors such
as borders with neighboring countries, the presence of natural resources, such as
diamonds and minerals, and population density may interact with specific political
and social factors, such as powerful ethnic minorities that are excluded from the
political process (Buhaug, Cederman and Rød 2008). Buhaug, Gates and Lujala
(2009) that remote areas along the border and regions where valuable resources are
located have a higher probability of experiencing prolonged civil wars. Using the
Armed Conflict Location and Events Dataset (ACLED), Raleigh and Hegre (2009)
find, however, that the location of the conflict in the periphery of the country only
moderately increases the probability of conflict. Further, any effect is conditional on
population size and its concentration in the remote areas prone to conflict; for
instance, the Eastern provinces in the Democratic Republic of Congo.
Apart from local conditions that drive the probability of onset and the duration
of conflicts, the dynamics of the conflict also affects the military capabilities of both
government and the rebel forces. Geographical distance presents opportunities for
minorities to mobilize and organize insurgencies, in particular in territorial disputes
with separatist goals (Buhaug 2006; Weidmann 2009). In large countries,
geographical factors such as mountainous terrain and long distances from the capital
can limit the ability of governments to extend their reach into peripheral areas. At the
same time, political instability and insurgencies in the periphery of a large country do
not necessarily constitute a major threat to the stability of the political regime, as long
as the government can exert effective control and extraction of resources to maintain
3 An alternative view would be that even though civil wars have their origins in local conditions or grievances, conflict resolution only or primarily requires a central agreement on power-sharing or possibly partition. The increasingly dominant view is however that the high failure rate of peace agreement is indicative of their failure to account for local conditions.
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political power and control over the majority of the territory (Buhaug 2010). Weaker
and smaller states, such as Liberia, have a limited ability to ‘ignore’ rebellions. When
large segments of the population challenge the legitimacy of the government and the
government faces challenges much closer to the capital, the survival of the
government is clearly at stake (Buhaug 2010). The most contested areas of conflict
tend to be regions where the government’s reach is limited because of diminishing
national strength—modeled by way of the loss-of-strength gradient (LSG). The reach
of the government is also influenced by the capabilities of the rebel forces, as well as
the geographical and economic characteristics of different regions within the borders
of a state, such as mountainous terrain and limited infrastructure (Lemke 1995;
Buhaug 2010).
The concept of LSG and the spatial dimension of conflict are not new to the
study of international relations or conflict research (see Boulding 1962; Bueno de
Mesquita 1981; Lemke 1995; Diehl 1991; Starr 2005). Boulding’s seminal study
(1962) outlines how the power of actors decays the further away they move from their
center, where the loss of power due to distance is not measured in absolute terms but
relative to the capabilities and the loss of power of the opponent. In other words,
power declines the further away from the center an actor is and the actors diminishing
ability to fight an opponent (Starr 2005, 390). Other factors, such as the topography
of the terrain and social and cultural cleavages in a population further influence the
decay of power from the center, in particular in civil wars (Lemke 1995; Buhaug
2010).
These insights have clear implications for effective peacekeeping and –
building. If local conditions drive conflict, peacekeepers should address these local
concerns which in practice means operating in areas where central governments have
limited reach because of the loss-of-strength gradient. This suggests that
peacekeepers would be deployed predominantly in peripheral or border areas. If (like
the central government), the deployment of peacekeepers is organized from the
capital, peacekeepers are also be affected by the loss-of-strength gradient and other
topographical features. However, it is important to first examine the underlying logic
of deployment.
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Instrumental versus Convenience Logic of Deployment
If, as stressed in both quantitative and qualitative research (Buhaug and Lujala, 2005;
Buhaug and Gates, 2002; Autesserre, 2010), the causes of civil war are local, the
PKO mission or country is an inappropriate unit of analysis for the study of
peacekeeping and peace-building. Geographical variation in conflict and social and
economic condition should to some degree influence the type of missions deployed
and the location of the peacekeeper troops. In many civil wars where the governments
are relatively weak and unable to provide public goods, such as safety, law and order,
infrastructure, multi-dimensional peacekeeping missions provide basic state functions
for the local populations (Dorussen and Gizelis 2010). In multi-dimensional
peacekeeping operations peacekeepers operate in large parts of the country, including
areas where the government has no or only limited control (Dorussen and Raleigh
2009).
The argument so far outlines an instrumental logic of deployment of
peacekeepers to conflict areas and where the population is ‘at risk’. If peacekeepers
want to effectively resolve a conflict, they have to operate in areas where the central
government is unable (or possibly unwilling) to address local grievances. The
instrumental logic of peacekeeping is not just an idealistic versus a pragmatic
categorization. The instrumental logic assumes that the peacekeepers are willing to
take greater risk and that the deployment is also more costly in terms of logistics. At
the same time, the deployment is tailored to be effective: peacekeepers go where the
job needs to be done. Based on this argument we expect that peacekeepers are present
in areas where conflict occurs. In these conflict areas the central government is weak
relative to the rebels, and peacekeepers become responsible to provide public goods
and governance—first of all security and humanitarian aid—to the local population.
A first testable hypothesis is therefore:
H 1 Peacekeepers are deployed to conflict areas
An alternative logic of deployment is not based on efficacy but on feasibility or
convenience: peacekeepers go where the conditions for deployment are clearly met.
The convenience logic suggests that the UN and peacekeepers are more risk and cost
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adverse. They prefer to be deployed in areas that are easily accessible with a good (or
at least usable) infrastructure and lines of communication. It is, moreover, not only
easier to deploy troops to these areas, it is also more straightforward to protect
peacekeepers who are on the ground and if necessary to extract troops. Importantly,
these ‘self-imposed’ constraints on where troops can be stationed do not exclusively
or even necessarily reflect a overly risk-adverse culture at the UN, or a disregard for
local conditions (as argued by Autesserre 2008; 2010). Instead, countries that are
willing to contribute to UN missions insist that deployment confirms to national rules
of deployment as well as the existence of a realistic exit strategy. Accordingly, at the
sub-national level peacekeepers would select deployment areas based on logistical
constraints: distance from the capital, roughness of the terrain, lack of infrastructure,
such as low road density, should discourage the deployment of UN peacekeepers.
This argument is summed up in the second hypothesis:
H 2 Peacekeepers are deployed in areas that are more easily accessible
The two deployment logics, instrumental and convenience, do not need to be
mutually excludable. In fact, UN deployment could be largely instrumental but still
constrained by considerations of convenience or feasibility. Accordingly, we not only
test which logic best predicts the actual deployment of peacekeepers but also use
multivariate analysis to consider their significance ceteris paribus.
Research Design
Spatially disaggregated geographic information (GIS) data on the location of conflict
events as well as the deployment of peacekeeping forces are needed to evaluate both
hypotheses. Our sample we includes UN missions to eight countries in Sub-Sahara
Africa: Angola, Burundi, Central African Republic, Democratic Republic of Congo,
Ivory Coast, Liberia, Sierra Leone, and Sudan. The geographic unit of analysis is a
grid cells of 100 km x 100 km size (for appropriateness of this level of resolution, see
Buhaug and Rød 2006). The analytical results pertain to the sub-national cross-
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section analysis, observing the point when the size of the UN missions was at its
largest.4 Overall, UN peacekeepers were deployed in 144 out of a total of 755 grids.
To test the hypotheses on the spatial location of peacekeeping forces, we look
at the probability that peacekeepers are deployed in a particular area (or grid) as a
function of the (previous) level of conflict in that area and the spatial conflict lag.
These deployment data are estimates by the authors based on UN information
provided in the reports of the Secretary General. The ‘precise’ location is defined by
the deployment of a permanent UN peacekeepers base. Technically, we use a logit
estimator for our sub-national cross section analysis with country clustered errors.
To evaluate the instrumental logic, the model includes conflict location,
population and minority presence in the area as independent variables. Conflict
locations are based on the ACD database (Gleditsch et al., 2002), which contains
records of every contestation between a state government and an organized
opposition group causing at least 25 battle-deaths per year. The data include numeric
information on the spatial location of the battle zones, where each conflict is assigned
a circular zone of conflict by means of a center point (latitude and longitude
coordinates) and a radius variable (see also Buhaug and Gates 2002). We follow
Buhaug and Rød’s approach (2006), which uses a refined version of the conflict
location data, where they relax the crude assumption of circular conflict zones and
rather use polygons generated through GIS. The (natural logarithm of) population
density per grid cell is from UNEP-GRID and adjusted for resolution. Minority
cultural identity in the area is based on the Buhaug and Rød (2006) dummy variable
which measures whether the majority of the population in each cell belongs to the
same language family as the majority of the population in the capital city.
Distance, roughness of terrain and infrastructure are proxies for the
convenience logic. Distance is the geographical distance of the centre of each cell
(centroid) from the international borders and the capital (log border distance and log
capital distance respectively). Roughness of terrain is measured by the logged
percentage per grid of the land that is covered by forest and mountains using data
4 The deployment of UN peacekeepers is the only variable with notable temporal variation. As expected, most geographic features do not vary over time by construction. We are still working on temporal and geographic detailed information of conflict intensity.
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from UNEP and the Food and Agricultural Organization (FAO). The log of the road
density within each grid, normalized by the country mean, is a proxy for
infrastructure (for more details on the construction of the variables, see Buhaug and
Rød, 2006). The road density variable is based on the road data from ESRI’s Digital
Chart of the World.
Finally, we control whether distance from diamond or petroleum deposits
influences the likelihood of UN deployment. All data , except our dependent variable,
come from Buhaug and Rød (2006) replication dataset, please refer to their article for
a more detailed discussion of the construction of the independent variables (Buhaug
and Rød 2006: 323-324).
Empirical Analysis
Descriptive Evidence We first present some basic descriptive evidence on the location
and the size of the peacekeeping forces based on four out of the eight African
countries included in the empirical analysis.5 In most cases, like Angola, Liberia, and
Sierra Leone, there is more than one peacekeeping mission, and there is considerable
temporal and spatial variation among these missions, reflecting their different
mandates. For instance, for Liberia we include in our analysis both the United
Nations Observer Mission in Liberia (UNOMIL, 1993- 1997) and the United Nations
Mission in Liberia (UNMIL, 2003-present).
Figures I to V plot the size of UN deployment in the periphery versus the size
of UN deployment in the capital. Figure I shows the various UN deployments in
Angola from 1991 until 1997, from the United Nations Angola Verification Mission I
(UNAVEM I) to the United Nations Observer Mission in Angola (MONUA).
[Figure I about here]
The solid line indicates the size of the UN deployment in the capital, whereas the
dotted line represents the size of the UN mission in the periphery. Figure I shows that
during UNAVEM I and II the UN peacekeepers were predominantly located in the
5 In the empirical analysis we include the following missions: MONUA, UNOMIL, UNMIL, ONUB, UNOMSIL, UNAMSIL, MONUC, United Nations Mission in the Sudan (UNMIS), United Nations Operation in Côte d'Ivoire (UNOCI), and United Nations Mission in the Central African Republic (MINURCA).
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capital, whereas during UNAVEM III and MONUA the UN deployed much larger
number of peacekeepers in the periphery than in the capital.
In contrast, in the case of the United Nations Operation in Burundi (ONUB),
UN deployment was from the beginning present both in the periphery and the capital,
with most forces located in the periphery of the country (see Figure II).
[Figure II about here]
In the case of MONUC, however, we see a time lag before UN peacekeepers are
deployed in the periphery and away from the capital of Kinshasa. MONUC was
initially a very small mission of observers, whose role was to report on the
compliance of the local actors with the peace accords. After Resolution 1291 was
adopted by the UN Security Council, MONUC’s scope and size changed, with a
deployment size reaching 18,407 uniformed personnel at the highest peak of the
mission in 2007 (see Figure III).
[Figure III about here]
Figures IV and V show the rather startling differences between the two UN
PKO missions in Liberia (UNOMIL and UNMIL). UNOMIL (see Figure IV) was not
only very small in size compared to UNOMIL (see Figure V), but also predominantly
deployed to the capital. Contrary to UNOMIL, the UNMIL deployment in the
periphery was three times the size of the peacekeeping force in the capital Monrovia
(see Figure V).
[Figures IV and V about here]
The figures provide a number of important insights regarding the relationship
between UN size and the spatial spread of UN forces within a country. Not
unexpectedly, larger missions with a broader mandate are more widely deployed
within the country, while smaller, observer missions tend to be deployed in the
capital. However, the figures cannot provide any information as to whether the
peacekeeping forces are actually located in the areas of the periphery that have
experienced conflict. To do so, we map the location and the relative size of UN
PKOs, using the PKOLED data, and the location of conflict events prior and during
the UN PKO presence, using the ACLED data (Raleigh et al. 2010).
15
Map 1 shows the spatial distribution of UN forces in Angola in 1997 (blue
circles) as well as the spatial distribution of the conflict events one year prior to the
UN mission. The size of the blue circles represents the size of the UN forces (see
legend, Map 1) in each location.
[Map 1 about here]
Map 2 shows the deployment of the ONUB in Burundi, as well as the conflict
events 1 year prior to the UN deployment (red dots) and during the UN mission
(green dots). The UN deployment in DRC is depicted in Map 3, showing that most of
the UN forces are clearly located in the Kivu provinces. Similar to Map 2, the red
dots stand for conflict events 1 year prior to the UN deployment, while the green dots
represent conflict events during the UN mission.
[Maps 2 and 3 about here]
The three maps suggest significant spatial variation in the deployment of UN
forces within countries. Moreover, it is clear from these maps that when the UN
missions have a relatively large size in terms of personnel, they are deployed in the
areas that predominantly have experienced conflict. Thus, a cursory look into some
of the UN missions seems to indicate that the UN not only selects the hard cases, but
also goes where the actual fighting takes place.
Inferential Evidence Table 1 reports the bivariate relations between where conflict
has started in a particular grid area and the deployment of UN peacekeepers. There
appears to be a moderate relation between conflict area and deployment of UN
peacekeepers. Peacekeepers were deployed in a fifth of the areas (21%) that have
experienced conflict outbreaks compared to 16% of the remaining areas.
[Table 1 about here]
Table 2 compares the two deployment logics on the basis of multivariate logit
models. In model 1, the significance of conflict area and population density support
the instrument logic explanation. The spatial lag of conflict suggests that
peacekeepers even though are deployed in conflict areas, they are not deployed in the
neighboring areas of the conflict. Minority domination does not make the likelihood
of peacekeepers deployment in the area more likely.
[Table 3 about here]
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Model 2 suggests that in support of convenience logic of deployment the loss of
strength gradient, or distance, and infrastructure matters. Peacekeepers tend to be
deployed closer to the capital, and higher road density corresponds to higher
likelihood of deployment. Somewhat puzzling, we also find that peacekeepers tend to
be deployed in are where accessibility is low, namely to mountainous and forested
areas.
To compare the explanatory power of two possibly different logics of
peacekeeping deployment: instrumental versus convenience, we do not want focus
exclusively of the statistical significance of our variable but also on their relative
predictive power. Figure VI plots the Receiver Operator Characteristic (ROC) curves
derived from models 1 and 2 (Table 2). ROC plots illustrate the relationship between
the rate of false positives—defined as the number of incorrectly predicted UN
deployment divided by the total number of cases where deployment did not occur—
and the rate of true positives—the number of correctly predicted UN deployment
divided by the total number of cases where UN deployment did occur—over the
entire range of possible thresholds (Cleves 2002; Ward et al 2010). The stronger
models have a larger ROC area and the lowest false positive coupled with the highest
true positive rate. The area under the ROC curve is therefore often used to generate a
single statistic that summarizes the model’s overall predictive power (Fawcett 2006).
It seems that the instrumental logic (area under ROC curve equals 0.78) slightly better
predicts UN peacekeepers deployment than the convenience logic model (area under
Roc curve equals 0.73) at 95 % confidence intervals.
[Figure VI here]
Table 3 combines the instrumental and convenience logics. Model 1 in Table
3 includes only two explanatory variables: whether an area was the initial spot of the
conflict outbreak (Conflict Onset) and whether an area was surrounding the initial
conflict area (Spatial Conflict Lag). The first variable has a positive effect on the odds
that the peacekeepers will be deployed in a particular location, whereas the variable
spatial conflict lag reduces the probability that the PKOs will be deployed in a
particular geographical area.
[Table 3 about here]
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In Models 2-5 in Table 3 we include the main explanatory variables of
interest: Conflict Onset, Spatial Conflict Lag, as well as the distance from the borders
(log Border Distance) and distance from the capital (log Capital Distance) as control
variables. Peacekeepers tend to be deployed in areas where the conflict has started.
A sub-national grid including the conflict outbreak area has 186% greater odds of
peacekeeping deployment in that area. On the other hand, it is less likely that
peacekeepers will be deployed in areas surrounding of the initial conflict zones. In
the surrounding areas the odds of the UN peacekeeping deployment are reduced by
35%. The effect of distance from the borders does not differ from zero at the
standard statistical significance threshold. However, there is a higher chance that
peacekeepers will deploy in an area closer to the capital; an increase of one standard
deviation of capital distance leads to a 43% decrease in the odds that the peacekeepers
will be deployed.
In model 3 we control for the remoteness and the type of terrain of the
location where peacekeepers are deployed. Peacekeepers tend to deploy in more
populated areas; the probability of deployment increase by 166% for a standard
deviation increase in population. Mountain areas tend to have more peacekeeping
deployment (23% increase of odds for one standard deviation). However, road
density and forested land do not seem to affect the probability of peacekeeping
deployment.
In model 4 we control distance from resources. Distance from oil production
areas does not have a statistically significant effect on the probability of UN
deployment; whereas, even though on the edge of statistical significance, there is a
higher probability of UN forces in areas closer to diamond deposits. In model 5 we
also control for UN deployment in areas with minorities; yet, the presence of
linguistic minorities in an area does not increase the odds of peacekeeping
deployment.
Figure VII displays the probability of UN PKO deployment in areas that have
experience conflict onset controlling for distances from the capital. The figure shows
that peacekeepers are more likely deployed in areas where conflict has started, but
that at the same time the odds that the UN PKOs are deployed in a certain area
decreases the further an area is from the capital.
18
[Figure VII here]
Robustness Tests The Cook’s distance test did not identify any influential outliers that
could potential bias the results. Further, we have used a case-control logit design,
comparing UN peacekeepers’ deployment cells to a random sample of non-onset
observations (see King and Zeng 2001; Buhaug et al 2011). Since odds and odds
ratios are invariant to changes in the marginal totals, the estimated logit coefficients
for the covariates will not be influenced by the relative share of 1s and 0s in the
sample. Using a case-control design also helps to address the problem of spatial
correlation across nearby cells, since a smaller random comparison sample is unlikely
to include many nearby cells with less additional information (as opposed to the full
sample, where the number of close cells will be very high). Randomly resampling our
observation, with both exclusion of 10% and 30% of the zeros, did not change the
results.
Finally, in order to check for multicollinearity we have run the diagnostic test of
variance inflation factor (VIF). The explanatory variables are all above the tolerance
threshold (Allison 1999,141) and therefore multicollinearity of the explanatory
variables cannot drive the results.
Final Remarks
Where do peacekeepers go? We know that overall UN peacekeeping operations
choose the hard cases to intervene. However, a full answer to the question requires
looking beyond the country level and to use disaggregated information on UN
peacekeeping deployment. Do peacekeepers actually go to locations where conflict is
observed or do they tend to concentrate in the capital or areas that are far away from
the actual (sources of) conflict?
On the basis of geo-referenced deployment and conflict data, we can show
that the UN peacekeepers go where the conflict is located, but do not go to the
surrounding areas. A possible interpretation of this finding is that the UN
peacekeeping forces choose to deploy in areas where the conflict originated; possibly
to address the ‘source’ of the conflict or to compensate for the limited capabilities of
19
the central government. Yet, peacekeepers do not appear to be proactive and to
deploy in areas where diffusion of conflict is quite likely.
Moreover, even though the PKO forces go to areas that have experienced
conflict within a country, they still shy away from conflict areas that are in the
periphery of the country or areas that are far from the capital. This suggests potential
selection bias in where UN forces are deployed within a country, even the country as
whole can be classified as a ‘hard case.’ Overall, it seems that UN peacekeepers
deployments is led by instrumental logic but mitigated by ‘convenience’.
Accordingly, future studies aiming to evaluate the sub-national effectiveness of
peacekeeping must take in account the selection bias that could affect possible
inference on the role of peacekeeping.
20
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Figures and Maps
Figure I: UN PKO deployments in Angola (1990-1999)
Figure II: UN PKO Deployment in Burundi
25
Figure III: UN deployment in DRC (MONUC)
Figure IV: UN Deployment in Liberia (UNOMIL)
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Figure V: UN Deployment in Liberia (UNMIL)
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Figure VI
Comparing Predictive Power Obs ROC area Std. Err. 95% Conf. Intervals Instrumental Logic 755 0.7834 0.0208 0.74265 0.82407 Convenience Logic 755 0.7319 0.0242 0.68452 0.77924 Ho: area(p1) = area(p2) Chi2(1) = 4.80 Prob>chi2 = 0.0284
28
Figure VII
Probabilty UN PKO Sub‐National Deployment Conflict Zones at Different Distances from Capital
0%
10%
20%
30%
40%
50%
60%
70%
120 km 178 km 345 km 531 km 608 km 962 km 1471 km
Conflict Areas and Distance from Capital
Percentile Distance Probability 95% Conf. Intervals
PKO deployment 5% 120 km 54% 42% 66% 10% 178 km 44% 35% 54% 25% 345 km 30% 24% 35% Mean 531 km 22% 18% 26% 50% 608 km 20% 16% 24% 75% 962 km 14% 10% 17% 95% 1471 km 10% 6% 12% Note: Confidence Intervals by Delta Method
29
Table 1: PKO Deployment and Conflict Zones Conflict Zone No Yes
No 232 379 PKO 84% 79% Deployed Yes 44 100
16% 21% LLratio X2=2.819 P=0.093
30
Table 2: Sub-National Deployment of UN Peacekeepers
(1) (2)
Instrumental Logic Convenience Logic
Conflict Onset Area 0.661**
(0.265)
Spatial Conflict Lag -1.677***
(0.420)
log Population 0.671***
(0.080)
Minority language -0.205
(0.219)
log Border Distance -0.114
(0.084)
log Capital Distance -0.879***
(0.127)
log Relative road density 1.619***
(0.604)
log Mountain 0.220***
(0.065)
log Forest 0.146**
(0.059)
Constant -3.028*** 2.253**
(0.545) (1.068)
Observations 755 755
LL -300.4 -325.6
df_m 4 5
χ2 135.0 84.51
Pseudo R2 0.183 0.115
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1
31
Table 3: PKO Deployment and Conflict (1) (2) (3) (4) (5)
Conflict Onset Area 0.903*** 1.051*** 0.629** 1.018*** 1.052*** (0.247) (0.254) (0.280) (0.280) (0.257) Spatial Conflict Lag -2.446*** -1.994*** -1.380*** -1.967*** -1.993*** (0.393) (0.410) (0.445) (0.474) (0.411) log Border Distance -0.132 -0.060 -0.102 -0.132 (0.082) (0.090) (0.086) (0.082) log Capital Distance -0.789*** -0.517*** -0.774*** -0.789*** (0.127) (0.141) (0.139) (0.129) log Population 0.579*** (0.093) log Relative road density -0.125 (0.620) log Mountain 0.142** (0.072) log Forest 0.038 (0.066) log Distance to petroleum 0.101 (0.116) log Distance to diamonds -0.162* (0.083) Minority language 0.005 (0.213) Constant 0.211 5.274*** 0.408 5.287*** 5.273*** (0.326) (0.882) (1.133) (1.068) (0.884) Observations 755 755 755 755 755 LL -347.1 -326.1 -291.4 -322.9 -326.1 χ2 41.54 83.56 152.9 89.92 83.56 Pseudo R2 0.0565 0.114 0.208 0.122 0.114 Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1
32
MAP 1: Conflict and Peacekeeping Events in Angola (1997)
33
MAP 2: Conflict and Peacekeeping Events in Burundi
34
MAP 3: Conflict and Peacekeeping Events in Democratic Republic of
Congo