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The Effect of Local Peacekeeping on Different Forms of Violence

During the Bosnian Civil War1

Tim DertwinkelUniversity of Essex & University of Oldenburg2

December 2009

- draft version -

Abstract

This paper examines the geographic link between subnational peacekeeping and the

location of civil war events during the Bosnian civil war 1992-1995. Drawing on the geo-

coded PKOLED and ACLED datasets, which break internal peacekeeping and conflict

down to the local level, I take a disaggregated approach to the study of the success or

failure of peacekeeping in terms of deterring or attracting violent conflict. Peacekeeping

events are further distinguished between monitoring and enforcement types, and violence

during the Bosnian civil war is categorized either one-sided against civilians or two-sided,

military related. Local level economic and population data is taken into account, as well

as local ethnic fractionalization and terrain. Whether variables identified earlier as robust

predictors in the study of civil war onset have an effect on both the presence of

peacekeeping and violence in Bosnia is investigated at the municipality level for the

whole conflict period. This is done first with the help of GIS generated kernel density

maps, and second with negative binomial regressions using event counts as dependent

variables. While controlling for potential spatial dependencies, I find that the amount of

peacekeeping and conflict events is spatially related. However, peacekeeping seems to

attract violence rather than deterring it.

1Paper prepared for the conference on ”Localized Effects and Impacts of Peacekeeping in Civil War” at theDepartment of Government, University of Essex, 11-12 December 2009.2Ph.D. Student, Department of Government, University of Essex, UK & Teaching Fellow, Chair forComparative Politics, University of Oldenburg, Germany. I would like to thank Stefano Costalli and Francesco N. Moro for data support.



Tim Dertwinkel Local peacekeeping during the Bosnian civil war

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1. Introduction

Since the end of the Cold War, UN peacekeeping operations (PKO) have increased in

absolute number, size of military and civilian personnel deployed on the ground and

financial budget approved (e.g. Fortna 2008; Heldt and Wallensteen 2006). After the

disastrous UN experiences in Somalia, Rwanda and Bosnia in the 1990s, mandates of

current missions are ever more complex and go well beyond monitoring of state borders

or overseeing ceasefire agreements in conflict areas as in the early days of the UN. A

shift away from “traditional” UN peacekeeping towards a “wider” peacekeeping

conception that incorporates peace enforcement activities - which do not rest any longer

on the “holy trinity” ideal of consent, impartiality and minimal use of force - has evolved

over time (Bellamy 2004). Aiming more fundamentally on long-term state-building

processes and on bringing good governance to conflict ridden societies, by military force

if necessary, is a reply to bitter lessons learned from earlier missions (e.g. Durch 1997).

This shift towards a more robust mandate ranks high on the new UN peacekeeping

agenda (United Nations 1996; see also Boutros-Ghali 1992).3

Whereas the number of UN PKO has risen after 1989, the number of intrastate

conflicts has dropped ever since (Gleditsch et. al 2002). It follows as a natural question

whether these two global macro-trends are causally linked, or more precisely, what the

rate of success for UN PKO is in terms of bringing sustainable peace to a country. Thisquestion has been tried to answer mainly from a large-N, macro-quantitative perspective,

treating the conflict and peacekeeping dynamics on the ground as “black boxes”, or by

detailed, qualitative case narratives that are in turn limited in their external validity

(Dorussen 2007).

3 However, “traditional” UN peacekeeping missions are still in place, such as UNTSO in Israel since 1948or UNFICYP in Cypress since 1964. There are also clear examples of UN “peace enforcement” missions

prior to the end of the Cold War, e.g. the famous ONUC mission in Congo 1960-1964 The main differencebetween “traditional” peacekeeping (or a “Westphalian” understanding of peacekeeping) and “peaceenforcement” (“post-Westphalian” perspective) lies in the different chapters of the UN charter behind thedecision when and how to engage, as well as in the different forms of violent conflict, interstate or intrastatewar (Bellamy 2004). In addition, Bellamy (2004) introduces “managing transition”, “wider peacekeeping”and “peace-support” operations of the UN, but admits that in practice, these categories are not fullymutually exclusive and tend to blend into each other once the mission proceeds over time or expandsgeographically. Thus, here I mainly use the terms peacekeeping and peace enforcement, limited to UNmissions. For information on mission type, geographic scope, mandate, personnel or budget, see the UnitedNations Department of Peacekeeping Operations website at http://www.un.org/en/peacekeeping/dpko.shtml.




3

This paper follows a different approach to look at the success of peacekeeping

operations. By making use of newly available, geo-coded datasets for peacekeeping

(PKOLED) and violent conflict (ACLED), I break internal peacekeeping and conflict

events down to the local level to study success or failure of the UN in terms of deterring

or attracting violent conflict. This spatially disaggregated research design is applied to the

“extreme case” of the Bosnian civil war 1992-1995 to study local, within-case variation

of peacekeeping and conflict at the municipality level. The PKOLED dataset (Dorussen

2007) allows to distinguish between a monitoring/observing (or traditional peacekeeping)

and active (or peace enforcement) role of the UN during the conflict. ACLED (Raleigh

and Hegre 2005) allows to categorize violent events occurring during the Bosnian

conflict as either one-sided (violence against civilians) or two-sided (military battle).

The article proceeds as follows. After a review of the macro-quantitative literature

on factors related to the success of UN peacekeeping in section 2, I draw on the

theoretical frame developed by Doyle and Sambanis (2000; 2005) to develop hypotheses

around the spatial relationship between local peacekeeping activities and local ongoing

conflict in section three. Section four than tests these hypotheses empirically, first with

the help of kernel density maps generated using GIS and second by running negative

binomial regressions on peacekeeping and conflict event counts as dependent variables.

Section five concludes.

2. Quantitative Studies of Peacekeeping Success

In the following, I limit myself to empirical work on peacekeeping that employs a

quantitative design. The number of large-N studies regarding humanitarian intervention

in general and UN PKO in particular is rather limited (Gilligan and Stedman 2003).

Studies of success or failure of the UN are dominated by detailed case studies aiming to

typologies such missions in a meaningful, often chronological way (e.g. Bellamy 2004),

to compare older, traditional peacekeeping with current peace-enforcement efforts.

However, this is almost never done in a systematic design over a larger number of

missions, limiting the reach and external validity of the qualitative literature (Dorussen

2007).




4

A first, systematic classification of peacekeeping operations is offered by Diehl,

Druckman and Wall (1998). The authors use a taxonomy according to function of the

mission with the aim of a more rigorous theoretical understanding of peacekeeping.

Borrowing from conflict management and resolution literature, different peacekeeping

functions are identified and structured along two main dimensions by using

multidimensional scaling. Dimension I consists of primary vs. third-party roles and

dimension II was identified as integrative vs. distributive processes during all

peacekeeping operations. Thus, four ideal type combinations of primary-third-party roles

and distributive-integrative processes can then be thought of in terms of their emphasis

on one or another of these quadrants in a two-dimensional space. According to the

authors, if dimensions in terms of the mandate overlap, the success of the UN mission

decreases.

Gilligan and Stedman (2003) point out that very little systematic research has

been done on where and when the UN deploys peacekeepers. By using a data set of all

post–Cold War civil wars and survival analysis to predict the duration until the UN

intervenes, the authors are able to show a selection bias toward intervening into civil

conflicts in Europe and the Western Hemisphere. The strongest predictor for UN

intervention is the number of deaths, but one has to disaggregate this finding regionally:

the UN acts faster when the deaths occur in Europe rather than Africa, and faster in

Africa when compared to Asia. Furthermore, the UN responds to civil wars in weaker

states, measured by the size of the country’s army, at a much higher rate than it does to

civil wars in stronger states.

Fortna (2004, 2008) finds that peacekeepers tend to be sent to more difficult cases

of civil conflict, controlling for factors that might affect both the likelihood of

peacekeepers being sent and the difficulty of maintaining peace. Fortna looks at both UN

peacekeeping and peacekeeping by other organizations or groups of states, and explores

the effects of different types of peacekeeping. She finds that in sum, the UN rarely goes

where war has ended in a decisive outcome, but rather to places where both sides have

the capacity to disrupt the peace. Interestingly, peacekeeping seems not more likely in

settings where a treaty has already been signed. However, peace lasts longer when

peacekeepers are still present in a country than when they are absent.




5

Heldt and Wallensteen (2006) are looking at peacekeeping operations carried out

by UN and non-UN actors in the period 1948–2004. They report that at the aggregate

country level, there is a strong relationship over time between the number of ongoing

peacekeeping operations carried out by UN and non-UN actors, especially in cases of

civil wars. UN peacekeeping operations last in general longer than non-UN actors’

operations, but only in interstate wars. Across world regions, however, there is a great

deal of variation in these regards, although there is only limited evidence for a

regionalization trend in intrastate peacekeeping, depending on the measure used.4

In a recent study, Hultman (2009) asks whether peacekeeping is an effective tool

in managing violence against civilians in intrastate conflicts. Thus, this is one of the rare

studies that disaggregates types of violence during a civil war, although exclusively

focusing on one-sided violence and not on military battles. She points out that after the

end of the Cold War, peacekeeping missions are often sent to ongoing conflicts, where

the warring parties have not yet managed to settle their dispute. The main theoretical

argument is that when a third party such as the UN enters a conflict, this alters the local

power balance on the ground and warring parties might choose to turn against civilian

targets as a last resort. Hultman points out that civilians are much harder for a

peacekeeping force to protect. A quantitative assessment of all intrastate armed conflicts

from 1989-2006 shows that the presence of peacekeepers is associated with higher levels

of violence, especially concerning violence committed by rebel groups when compared to

the violence committed by the government. Hence, Hultman concludes when dealing

with civil conflicts, it is important for peacekeeping missions to consider the possible

intensification effect of an intervention on the conflict situation.

The above mentioned studies all use mission or country level as units of analysis

and suffer from overaggregation of key variables (Dorussen 2007) as well as proposed

but untested causal micro-mechanisms. A similar point has recently been made in the

macro-quantitative literature on onset, duration and termination of civil war (e.g. Collier

and Sambanis 2005; Sambanis 2004; Ross 2004). Further elaboration on proposed causal

relationships by relying on disaggregated data and research designs more explicitly seems

also most promising for quantitative studies of the success or failure of UN peacekeeping.

4 That is, the number of deployed peacekeepers or the number of ongoing peacekeeping operations.




6

Such a research strategy is followed here. Next, I present a holistic theoretical

frame for peacekeeping success in intrastate conflict based on Doyle and Sambanis

(2000; 2005), applied to the question of the spatial relationship between different

peacekeeping event categories (traditional vs. enforcement) and conflict events (two-

sided vs. one-sided) during the Bosnian civil war.

3. Local Peacekeeping and Structural Conditions for Violence in Bosnia

The conflict in Bosnia and Herzegovina from April 1992 to December 1995 generated a

vast amount of theoretical explanations why it occurred5 and how its violence dynamics

unfolded on the ground (e.g. Melander 2007; Kalyvas and Sambanis 2005).6 Melander

(2007) as well as Kalyvas and Sambanis (2005) present detailed case narratives and

descriptive statistics of mainly ethnic cleansing dynamics from a local perspective. The

effect of peacekeeping is mainly left out of these studies. The main reason for this seems

that quantitative data on the conflict in Bosnia and the different parties involved has only

recently become available (see ACLED and PKOLED data sets described below).

In August 1991, localized war broke out in Croatia between Serbs and Croats in

the ethnically mixed communities of Krajina and Slavonia, soon after the Croatian

declaration of independence. Later on, the war spilled over to neighbouring Bosnia and

violence organized along ethnic lines between Croats, Serbs and Bosnian Muslimscontinued on a high scale until end of 1995. Troops of the United Nations Protection

Force (UNPROFOR) were deployed quite early in the conflict, initially in Croatia, and

UN protected areas were established later. With the conflict unfolding, the mandate of the

UN peacekeepers changed dramatically, trying to adapt to the changing situation on the

ground more or less on an ad hoc basis. The result was a disaster for “second-generation”

or wider peace operations as a whole (Bellamy 2004). The initial, traditional

peacekeeping tasks like monitoring and observing of ceasefires were accompanied by

tasks to facilitate the delivery of international humanitarian aid and substituted for more

and more peace-enforcement activities that involved active fighting to protect and defend

5 See Slack and Doyon 2001 for a theoretical overview and a focus on changing local ethnic populationdynamics as additional reason.6 I am not discussing the history of the conflict or theoretical explanations here in detail. For this, see e.g.Glenny (1999) or Woodward (1995).




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UN “safe areas” created around major cities. Table 1 offers a detailed overview on how

the mandate of UNPROFOR got more robust the longer the conflict lasted.

[Table 1 about here]

Within Bosnia, Croats, Serbs and Bosnian Muslims were widely dispersed geographically,

with several small areas of locally concentrated ethnic groups. Kalyvas and Sambanis

(2005) point out that violence seemed geographically concentrated in areas that were of

economic or strategic importance to the factions, while not distinguishing further for

different kinds of violence. It seems plausible to assume that areas of economic or

strategic value play a strong role for military considerations. Ethnic cleansing or one-

sided violence against civilian has been reported to happen mainly in the Bosnia-Muslim

dominated larger cities, actively supported by the military who shelled urban strongholds

from the rural surroundings. Anecdotic evidence suggests that paramilitary organizations

were “ethnically cleansing” those cities after shelling by the regular military artillery

forces and were responsible for most of this type of one-sided violence (Kalyvas and

Sambanis 2005)

However, a “Clausewitzian” war between regular, some of them emerging armies

was mainly fought in the countryside, quickly turning to a trench war with rather fixed

frontlines. Hence, Kalyvas (2005) has labelled the Bosnian civil war as “symmetric non-

conventional”, containing a mix of regular and irregular forces of roughly equal strength

mainly fighting over territorial control. In general, Kalyvas (2005) stresses the strategic

use of different types of violence in order to shape the behaviour of the civilian

population during civil war. Other scholars have asked why civilians and non-combatants

are sometimes the primary targets during fighting (Azam and Hoeffler 2002), or if one-

sided violence against civilians is a function of the military balance between rebels and

the government (Eck and Hultmann 2007).

Nevertheless, armed conflict is usually treated as violent contestation between

government and rebels only, and based on military, economic or demographic power

balances between them. Far less attention has been paid to actors that intervene from the

outside, such as the UN, and that those actors alter that balance by their actions on the




8

ground depending on their geographic location. Dorussen (2007) takes this as a starting

point to disaggregate the ongoing MONUC mission in Congo the same way recent

studies of civil war have proposed disaggregation of key variables used in that literature

(Buhaug and Lujala 2005; Buhaug and Gates 2002). The aim is to provide a more

detailed, temporally and geographically specific evaluation of the UN to control conflict

at a local level. Thus, a peacekeeping operations location and event dataset PKOLLED

(see below) is introduced. Based on Heldt (2002), Dorussen (2007) discusses four main

categories of conditions for successful intrastate peacekeeping. These four categories are

the sources of the conflict itself, field executive, mission and mandate.

Sources of the conflict are based on the variables that recent large-N macro-

quantitative studies of civil war onset or duration propose to be important (e.g. Fearon

2004; Collier and Hoeffler 2004; Fearon and Laitin 2003). Typically, these are population

size of a country, level of economic development (GDP), natural resource endowments or

income distributions, ethnic fractionalization and finally variables that reflect how much

percentage of a country is covered by rough terrain that could benefit potential insurgents

in their fight against a weak state authority.

Field executive, mission and mandate are related to the peacekeeping mission

more directly, e.g. its size in terms of military and civilian personnel, its duration,

deployment timing related to the conflict, its composition in terms of international troop

providers, its internal command structure and finally its role in terms of more traditional

peacekeeping tasks such as monitoring versus wider peace-enforcement duties. As

Dorussen (2007) points out, geographical variation within a country affects nearly all the

conditions for successful peacekeeping mentioned above, as well as the underlying

structural factors prominently associated with the onset or duration of civil war.

In an influential article and follow-up book project, Doyle and Sambanis (2000;

2005) develop a theoretical frame, the “peacekeeping triangle”, to analyze success or

failure of UN missions after civil war. Their idea to conceptualize the success of

peacebuilding along three main categories comes close to the arguments proposed by

Held (2002) above. Doyle and Sambanis argue that:




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Although peacebuilding strategies must be designed to address particular conflicts,broad parameters that fit most conflicts can be identified. Strategies shouldaddress the local roots of hostility, the local capacities for change, and the (net)specific degree of international commitment available to assist sustainable peace.One can conceive of these as the three dimensions of a triangle whose area is the

"political space"-or effective capacity-for building peace (Doyle and Sambanis2000: 779).

The authors would like their model to be seen in a holistic way:

The triangle is a metaphor for the peacebuilding space after civil war. Availablespace is determined by the interaction of the triangle's three sides: LocalCapacities (LC), International Capacities (IC), and Hostility (H) level. The greaterlocal and international capacities and the smaller the hostility level, the greater thespace for peace (Doyle and Sambanis 2000: 782).

And later in the same article:

In sum, there should be a relation between the depth of hostility, the number andcharacter of the factions, and the level of economic development, on the one hand,and the extent of international assistance and effective authority, whethermonitoring or enforcement, needed to build peace, on the other (Doyle andSambanis 2000: 782).

In essence, the authors argue that the probability of successful peace-building after civil

war is a function of a country's local capacities, the available international assistance,

and the depth of war-related hostility. In their macro-quantitative empirical analysis of

124 post-World War Two civil wars, level of hostility is proxied by the number of deaths

and displacements, the type of conflict (ethnic or not), the number of hostile factions, the

level of ethnic fractionalization, and the outcomes of the war. International capacities are

provided by the strength and mandate of peace operations and by the amount of economic

assistance. Local county capacities are measured by a set of socioeconomic variables

such as per capita GDP, energy consumption, and natural resource dependence. They find

that UN peacekeeping is positively correlated with democratization processes after civil

war and multilateral enforcement operations are usually successful in ending the violence.

UN missions are most effective in the first few years after the end of war, and economic

development is the best way to decrease the risk of new fighting in the long run.




10

However, it remains unclear why such a perspective of the success of UN

involvement should only be applicable for situationsafter conflict. Thus, for this study, I

am adopting the theoretical frame of Doyle and Sambanis (2000; 2005), applying it to the

local level of one single conflict and testing which structural dimensions of the conflict or

of the peacekeeping mission have an effect on the success of mitigating violence. Based

on the discussion of the literature above, I propose the following hypotheses on the

geographic relationship between the local presence of peacekeeping and conflict events

in Bosnia 1992-1995.

Traditional peacekeeping, that is monitoring and observing based on the “holy

trinity” ideal of consent, impartiality and minimal use of force and thus on a

“Westphalian” idea how to promote peace (Bellamy 2004), should focus mainly on

military activities between regular forces. Thus, peacekeepers under such a mandate

should geographically be linked to conflict areas were regular troops are concentrated or

where the military frontlines are located. On the other hand, peace-enforcement or wider

peacekeeping mandates involve active fighting, for example by defending UN protected

areas against attacking forces, or by trying to stop massacres against civilians or attempts

of ethnic cleansing. Thus, peacekeepers under an active enforcement mandate should

geographically be linked to conflict areas were violence against civilians is most likely or

where such violence is concentrated.

To address the above mentioned spatial relationships between disaggregated types

of peacekeeping and conflict events, one has to control for other factors usually assumed

to affect both dependent variables. Therefore, I expect that higher population density,

higher levels of income, higher levels of ethnic fractionalization and rough terrain should

increase the presence of violence as well as peacekeeping events of all types.

4. Research Design

In the following, I describe the general setup of the study and report the empirical results.

I start with the rational for using subnational administrative units of analysis in

combination with spatially disaggregated data for the variables under scrutiny. Next, in a

more exploratory way and as a first visual inspection of the data, I present a series of so-

called “hot spot” maps based on kernel density calculations for different aggregation




11

levels of the two dependent variables, ACLED and PKOLED events. A negative

binomial regression analysis follows, assessing the influence of the location of

peacekeeping on the location of conflict during the Bosnian civil war and vice versa,

while controlling for the set of independent variables usually applied in quantitative

studies of civil war.

Level of analysis

Focus on within-country variation requires to “scale down” the level and units of analysis

from the national to the subnational level to capture the uneven spatial distribution of

conflict and peacekeeping (Snyder 2001). Recent disaggregated studies of civil war have

followed mainly two different approaches (see Buhaug et. al 2009). First, and as done

here, one could rely on first- or second-order administrative entities (such as provinces,

municipalities or districts) of a country (e.g. Dorussen 2007). Advantages are that these

units and their boundaries have a political or economic “meaning” attached to them,

especially if we factor in income level, ethnic demographics or relative location in terms

of closeness to Croatia or Serbia. The design of political institutions to mitigate potential

conflicts such as ethno-federal state structures or territorial autonomy arrangements all

build on such territorial attachments.

Second, grid cells of a certain size, technically generated via GIS and overlaid on

countries, could be used as alternative (Buhaug et. al 2009; Hegre, Østby and Raleigh

2009; Buhaug and Rød 2006). The main reason is that this typically leads to a much

larger N included in the statistical analysis.7 However, the exact number and size of the

grids follows a trial and error process and appears highly artificial, although their

standardized size allows for global comparison.8 As I am not interested here in comparing

across countries or over time but rather in variation within one case, I have chosen the

more intuitive way of collecting data for all variables at the level of the municipality.

Before the war broke out in 1992, there were 110 of these in Bosnia.

7 This approach almost always results in a huge number of units that have zero observations. Thus, “zeroinflation” complicates the statistical analysis further and has to be corrected for.8 In principal, grid cells could be aggregated to match the boundaries of larger and probably more plausibleadministrative sub-units, but then the question remains why such political units have not been chosen in thefirst place.




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Data Structure and dependent variables

The PKOLED data is described in detail in Dorussen (2007). It records the locality as

well as timing of peacekeeping events at the highest level of geographic precision,

normally the village or city. When coding is finished, PKOLED will include all UN

peacekeeping missions to intrastate conflicts from 1989 to December 2005. The unique

strength is that PKOLED is compatible with other spatially disaggregated events based

datasets on civil war such as ACLED (see below), and that both can be merged and

analysed using GIS as done here. Peacekeeping events are coded from official UN

Secretary-General Reports, and geographic coordinates are added to the location of each

event. In this paper, I limit myself to study the geographic variation of peacekeeping and

conflict events in a cross-sectional way, leaving aside the time dimension. Therefore, I do

not make use of the monthly information included in PKOLED. Dorussen (2007) has

shown how an analysis of temporally specific peacekeeping and conflict events could

look like. He introduces yearly and quarterly time lags in the statistical analysis for the

case of the ongoing MONUC mission and finds that the number of peacekeeping has a

negative effect on future conflict events. It is planned to do this in a follow-up paper for

Bosnia as well. However, the small number of events could be a problem of a fully

dynamic version, as well as the nature of the independent variables that do not vary over

time.

I am making use of the fact that a peacekeeping event is coded either as active

participation of the UN peacekeepers (“peace-enforcement”, code 1) or as

monitoring/observing event type (“traditional peace-keeping”, code 2). After dropping all

events that do not have spatial coordinates and those that are coded at a lower geo-

precision than municipality (e.g., at the country level), I obtained 119 active and 97

monitoring events, summing up to 216 UN peacekeeping events for the whole conflict

period in Bosnia. This leads to three types of peacekeeping as dependent variables,

overall number of event counts, active number and monitoring number.

The Armed Conflict Location and Event Dataset (ACLED) (Raleigh and Hegre

2005) provides reported conflict events by political actors, date and geographic

coordinates. ACLED codes the exact location and specific information on individual

battle events, the transfer of military control from the government to the rebel groups and




13

vice versa, the location of rebel group strongholds and the spatial occurrence of one-sided

violence. In the current version, it covers mainly conflict countries in Western and

Central Africa from 1960 through 2004, plus a preliminary set of Balkan countries. The

pre-release for Bosnia contains 540 overall conflict events. 147 events (or 27% of total)

are coded as one-sided. I have collapsed the data into two categories, one-sided violence

against civilians (event category 7 in ACLED) and two-sided violence between the

military factions (event category 1-3), dropping the remaining types. For the Bosnian

case only five events do not belong to the two broader categories, so I do not loose much

information. Because of the preliminary and un-official nature of the data I re-checked

each event and recoded the obvious mistakes due to the description and comments given

in the data and by comparing to similar event codings. I dropped those events with

unclear or no longitude-latitude values, as well as those at a spatial precision level lower

than municipality. This results in a total of 133 one-sided and 358 two-sided events for

the Bosnian municipalities over the whole conflict period. Thus, overall conflict events

are disaggregated to get one-sided and two-sided counts as further dependent variables.

Independent and control variables

My model of peacekeeping and conflict events does not attempt to present an inclusive

theory. Therefore, I identified a number of plausible independent and control variables

that might have an effect both on the location of violence during civil war within a

country and the location of peacekeeping. If the location of peacekeeping and violence

are related, then the same set of independent variables should apply to both. In order to

simplify the task of causal inference and to avoid adding laundry lists of “usual suspects”,

I restrict the number of these to a minimum. While the exact interpretations and causal

mechanisms vary in the literature, these variables have turned out to be robustly related to

the onset of civil war (Hegre and Sambanis 2006) and are also tested in studies of

violence during civil war (Kalyvas 2006; Kalyvas and Sambanis 2005) as well as

peacekeeping success after civil war (Doyle and Sambanis 2005; 2000).

Population is one of the rare variables that reaches significance in almost all

quantitative studies of civil war onset. Instead of using absolute population figures, which

could be quite misleading, population density is used instead. This also mitigates the




15

Geographic proximity to an international border should be controlled for. Hence I

include a dummy variable indicating whether a municipality borders with the Croatian or

Serbian borderline. To account for potential spatial dependence in the dependent

variables, meaning that the number of peacekeeping or conflict events could depend on

the number of such events in neighbouring units, I include a “spatial lag” dummy that

records whether there is a peacekeeping or conflict event ongoing in municipalities

within the first-order neighbourhood. The spatio-geographic distribution of each of the

four main independent variables population density, income per capita, local ELF, and

elevation level is shown in Figure 1 below by so-called choropleth maps using GIS, from

top left to bottom right. Darker colours represent higher values in these maps.

[Figure 1 about here]

Statistical Method

All six dependent variables are cumulative counts of peacekeeping or conflict events in

each municipality over the entire period of the Bosnian conflict, thus I am running

negative binomial regressions. Because it is very likely that event counts per municipality

are not independent of each other, a Poisson distribution is not appropriate. Hegre, Østby

and Raleigh (2009) follow a similar statistical strategy, using the cumulative number of

conflict events per grid cells for the civil war in Liberia as dependent variable. The

respective alpha coefficients of the negative binomial regressions are unequal to zero, so

the models are not better estimated using Poisson instead. I also tried zero-inflated

negative binomial regression alternatives to account for the large number of zero-count

observations and possible overdispersion within the positive-counts as Hegre, Østby and

Raleigh (2009) recommend, with no substantial improvements or differences.12

12 The decision for a simple negative binomial is based on the Vuong test not shown here. This might bedue to the fact that I am not following the grid-cell approach to spatial disaggregation. I end up withrelative small numbers of zeros out of the 110 Bosnian municipalities instead of several hundreds orthousands of “empty” grids.




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Empirical Results

Next, I present the empirical results, starting with an exploratory visualization of the

spatial distribution of the dependent variables. Starting with the ACLED dataset, a kernel

density map that was generated using GIS’ spatial analysis tool is shown in Figure 2.


The figure shows the kernel density maps for the three categories of the ACLED data that

I am using in this study, all ACLED events, one-sided violence and two-sided violence.

Thus, by eyeballing it becomes possible to investigate spatial relationships between the

different types of violence during the whole time period of the Bosnian civil war. The

kernel option in GIS employs a circular search neighbourhood around each point event

and calculates each cell value by adding the values of the overlapping neighbourhoods,

resulting in a smoother surface than e.g. using simple point density surfaces. In general,

density mapping measures the number of features in a study area based on some standard

unit of area, in this case the overall number of conflict (or peacekeeping) events per

square kilometre. In principal, a separate kernel density map for each year of the conflict

could be constructed, allowing to visualize temporal change or conflict spread.

For all density maps shown, the same ad-hoc bandwidth of 15 km has been used.

A more sophisticated way would be to use k-nearest neighbour distances between points

for the “optimal” bandwidth, reflecting the degree of clustering and dispersion in the

point pattern.13 The kernel density maps for the ACLED data reveal that there seems to

be a strong spatial correlation between the local occurrences of one-sided and two-sided

violence during the Bosnian civil war, giving support to the claim that violence against

civilians and battle-related violence seems to go hand in hand and is probably

coordinated by some third party. This relationship seems weaker in the central part of

Bosnia around Zenica, Doboj and around the Brcko district.

Figure 3 below shows similar kernel density maps for the location of the three

peacekeeping event categories, all peacekeeping events, UN monitoring role and UN

13 This is left for later inspections of the data, as well as running a Moran’s I-Test for spatial clustering ofthe dependent variables.




17

active role. There seems to be a difference between the location of peace-keeping

(monitoring) and peace-enforcement (active) activities, especially if we look at the north-

western part of Bosnia around Prijedor, close to the Croatian border. Comparing the

spatial location of all ACLED events with all PKOLED events, the density patterns seem

closely related.


In order to test for the spatial relationship between conflict and peacekeeping events more

systematically, I am presenting a series of negative binomial regressions. First, as

indicated in Table 2, I am including the four main independent variables plus the two

controls to see whether these variables can jointly predict the number of conflict events.

Peacekeeping is left out of the picture for the moment.


The results are interesting in several ways. Population density is significant in all three

models and shows the expected positive sign, meaning that conflict is more likely in

populated areas. The Local ELF Index also reaches significance and is positive related to

the amount of violence.14 If we disaggregate the data into one-sided and two-sided

violence, the picture changes. Now, ethnic fractionalization is only positively related to

two-sided violence or military battles, contrary to expectations that the ethnic makeup

should affect violence against civilians more than military actions. Whether a

municipality is a border unit has a significant effect on all violence types as well. What is

striking is that in none of the models I ran, income or elevation plays any significant role,

although in many instances the coefficients just slightly miss the significant levels. More

robustness test of different specifications of income and elevation are needed here.15

14 Note that amount of violence means amount of events here. I do not have data that accounts for intensityof the events in terms of how many people died per event.15 For example, a better measure for income could be to compute the average income for all municipalitiesand to re-run the models with a variable that captures the deviation of the average, to account for intra-statedifferences more closely. Remember that elevation here is not the average of the municipality, but relatedto the elevation of the largest city within the municipality, which could drive these non-findings.




18

Income per capita shows a negative sign almost in all twelve models run, with the slight

exception of model 2. What is also puzzling is that in none of the models, the variable

trying to capture spatial dependence reaches significance.

Next, I am interested in the question whether the variables that are usually

included to explain violent conflict could also explain the amount of peacekeeping. I am

regressing the same independent variables, this time on the three categories of the

PKOLED data. Results are shown in Table 3.


Now, only population density seems to matter, and not even for UN monitoring activities

(see Model 6). All other variables lack significance, although they are showing in most

instances the expected sign. The interpretation of Models 4-6 might be that the decision

of the UN where to go to locally is only weakly pre-determined by structural factors on

the ground that might have an impact for the occurrence of violence, such as the local

ethnic composition of a municipality or whether the entity is adjacent to an international

border.

Finally, I am re-running both sets of dependent variables, but this time controlling for the

respective type of peacekeeping or conflict (Models 7-9 and 10-12)


Starting with Models 7-9, after controlling for the three categories of peacekeeping

events, the main results of Models 1-3 are still valid. Population density, the local ELF

and the fact that a unit is a border unit are significant and show the same sign as before.

However, if we factor in the presence of peacekeeping, all three types of PKO have a

significant impact on the presence of violent conflict (Models 7-9). If we turn around the

table, as done in Table 5, the ACLED event count variable is a significant predictor for

the occurrence of PKOLED events (Model 10), whereas the other independent variables

included in Models 7-9 are still no good predictors except for population density.




19


This relationship also holds for the occurrence of one-sided violence and an active role of

the UN (peace-enforcement), which makes theoretical sense (Model 11). For two-sided

violence and UN monitoring activities (peace-keeping), the relationship is not significant,

although the significance threshold is just slightly missed. Models 10-12 show a positive

sign in terms of the relation between conflict and peacekeeping, indicating that more

conflict events lead to more peacekeeping reaction.

However, the bad news for the UN is that the opposite seems also to hold, as

shown in Models (7-9). All types of peacekeeping events have a significant positive

effect on the most plausible types of conflict, an active role of the UN increases instead

of decreases the number of one-sided violence against civilians (Model 8), as well as UN-

monitoring activities increase the number of two-sided military activities significantly

(Model 9). This devastating finding for the role of the UN is consistent with the

numerous stories about the failure and the “triumph of the lack of will” of the

international community to end the war in Bosnia, which was only achieved after massive

NATO air strikes against Serbian strongholds in 1995, and less through UN peacekeeping

or enforcement in previous periods of the war.

These results are clearly preliminary, and further robustness tests in terms how the

independent variables are measured are needed. More sophisticated spatial regression

approaches (Ward and Gleditsch 2008) could be used instead of dummy variables to

account more directly for spatial dependencies in the data.

5. Conclusion

This article is an attempt to further disaggregate peacekeeping and conflict research,

applied to a single case, but still using a quantitative frame. The fruitfulness of such a

research strategy should have become clear, especially when we start to accept that many

key variables used in political science are not evenly distributed within a country. This

information usually gets aggregated away in macro-quantitative comparisons. Further

disaggregated work on the temporal dynamics during the Bosnian civil war seems to be

promising as well.




20

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Appendix

Table 1: UNPROFOR´s changing mandate

Security CouncilResolution Date Purpose

713 25 Sept.91 Arms embargo against Yugoslavia743 21 Feb.92 Establishes UNPROFOR to monitor ceasefire in

UN protected areas in Croatia757 30 May 92 Sanctions on Serbia and Montenegro758 8 June 92 Change of UNPROFORS mandate to include

Bosnia to monitor humanitarian aid delivery764 13 July 92 Empowers UNPROFOR to secure Sarajevo

airport770 13 Aug. 92 Demands access to all refugee and prisoner

camps776 14 Sept. 92 Enlarges UNPROFOR mandate to include

protection of convoys781 9 Oct. 92 Creates the no-fly zone over Bosnia787 16 Oct. 92 Deployment of observers to Bosnia’s borders to

enforce compliance with sanctions816 31 March 93 Right to enforce the no-fly zones819 16 April 93 Designates Srebrenica a “safe area” free from

armed attack824 6 May 93 Designates Sarejevo, Tuzla, Zepa, Gorazde and

Bihac as “safe areas”and strengthening ofUNPROFOR by 50 military observers

827 25 May 93 Creates the ICTY836 4 June 93 Gives UNPROFOR the task of deterring attacks

on the safe areas913 22 April 93 Gives UNPROFOR responsibility for collecting

heavy weapons around Gorazde998 16 June 95 Welcomes the creation and deployment of

NATO Rapid Reaction Force1035 21 Dec. 95 Authorizes deployment of IFOR

Source: adapted with slight changes from Bellamy (2004).




25

Figure 1: Spatial Distribution of Population, Income, LELF and Elevation 1991



Figure 2: Kernel density map of ACLED events. From top to bottom right: all

ACLED events, one-sided violence and two-sided violence. Darker colors represent

higher point density.




27

Figure 3: Kernel density map of PKOLED events. From top to bottom right: all

PKOLED events, UN monitoring role and UN active role. Darker colors represent

higher point density.




28

Table 2: Number of ACLED event types as dependent variables

(Model 1) (Model 2) (Model 3)All ACLED

events

One-sided

violence

Two-sided

violence

Log population density 0.396** 0.730*** 0.284* (0.137) (0.209) (0.140)

Log income per capita -0.976 0.0805 -1.340(0.818) (1.367) (0.800)

Local ELF 3.321*** 2.215 4.006*** (0.853) (1.340) (0.913)

Elevation 0.000 -0.000 -0.000(0.001) (0.001) (0.001)

Border unit 0.895** 1.251** 0.868** (0.302) (0.468) (0.304)

Neighbouring conflict -0.136 -0.0626 -0.177(1.236) (1.917) (1.199)

Constant 5.956 -5.563 8.970(6.894) (11.478) (6.696)

N 110 110 110Alpha 1.342 2.558 1.227Log likelihood -261.4 -132.7 -232.9

Table entries are coefficients, standard errors in parentheses.* p < 0.05, ** p < 0.01, *** p < 0.001 (two-sided tests).Negative binomial regression using STATA 9.2.




29

Table 3: Number of PKOLED event types as dependent variables

(Model 4) (Model 5) (Model 6)All PKOLED

events

UN active

role

UN monitoring

role

Log population density 0.787* 0.884* 0.557(0.324) (0.391) (0.330)

Log income per capita -1.513 -1.017 -1.544(1.665) (2.185) (1.613)

Local ELF 1.155 1.207 0.780(1.639) (2.146) (1.803)

Elevation 0.000 0.000 -0.000(0.001) (0.002) (0.001)

Border unit 0.345 0.313 0.505(0.545) (0.731) (0.537)

Neighbouring peacekeeping -0.063 -0.733 1.807(0.878) (1.093) (1.268)

Constant 8.930 3.920 8.071(13.586) (17.863) (13.138)

N 110 110 110Alpha 4.936 7.898 4.242Log likelihood -158.7 -104.1 -119.6





30

Table 4: Number of ACLED event types as dependent variables, controlling for

PKOLED event types

(Model 7) (Model 8) (Model 9)

All ACLEDevents

One-sidedviolence

Two-sidedviolence

Log population density 0.388** 0.788*** 0.252(0.125) (0.197) (0.133)


Local ELF 3.096*** 1.709 3.979*** (0.805) (1.311) (0.878)

Elevation -0.000 -0.000 -0.000(0.001) (0.001) (0.001)

Border unit 0.720* 1.136* 0.684* (0.294) (0.465) (0.304)

Neighbouring conflict -0.386 -0.414 -0.318(1.171) (1.854) (1.153)

All PKOLED events 0.0889**

(0.030)

UN active role 0.202* (0.083)

UN monitoring role 0.136* (0.058)

Constant 4.513 5.287 6.585(6.600) (6.258) (6.581)

N 110 110 110

Alpha 1.184 2.306 1.120Log likelihood -256.4 -129.2 -229.8





Table 5: Number of PKOLED event types as dependent variables, controlling for

ACLED event types

(Model 10) (Model 11) (Model 12)

All PKOLEDevents

UN activerole

UN monitoringrole

Log population density 0.863** 1.122*** 0.546(0.275) (0.340) (0.313)


Local ELF -0.697 -1.275 0.219(1.549) (2.042) (1.721)

Elevation 0.000 0.002 -0.000(0.001) (0.001) (0.001)

Border unit -0.239 -0.595 0.203(0.512) (0.736) (0.544)

Neighbouring peacekeeping 0.233 -0.347 1.723(0.859) (1.083) (1.278)

All ACLED events 0.113**

(0.037)

One-sided violence 0.396* (0.154)

Two-sided violence 0.0856(0.050)

Constant 0.933 1.760*** 2.573(12.086) (0.308) (13.034)

N 110 110 110

Alpha 3.781 5.814 3.885Log likelihood -152.6 -98.93 -117.9


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